js886io/Diplomovka
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
67061cf373
Diplomovka/aya_300k_lr_experiments/logs/lr_0p00008.log
Jakub 67061cf373 Add Aya 300k learning-rate study and benchmark results 
Document six completed LR runs as preparation for full Aya training on Perun. Record selection of LR 0.00010 by lowest final validation loss, selected-model lm-eval and partial SK-bench results, reproducible provenance, and dataset-access/context-truncation limitations. Preserve existing 30k and Titan material.
2026-10-25 00:33:24 +02:00

12452 lines
2.5 MiB
Raw Blame History

This file contains invisible Unicode characters

This file contains invisible Unicode characters that are indistinguishable to humans but may be processed differently by a computer. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

[INFO|2026-10-21 19:11:32] llamafactory.hparams.parser:651 >> Process rank: 0, world size: 1, device: cuda:0, distributed training: False, compute dtype: torch.bfloat16
Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.
[INFO|configuration_utils.py:765] 2026-10-21 19:11:32,966 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
[INFO|configuration_utils.py:841] 2026-10-21 19:11:32,975 >> Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
[INFO|configuration_utils.py:765] 2026-10-21 19:11:34,611 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
[INFO|configuration_utils.py:841] 2026-10-21 19:11:34,613 >> Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
[INFO|configuration_utils.py:765] 2026-10-21 19:11:34,751 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
[INFO|configuration_utils.py:841] 2026-10-21 19:11:34,752 >> Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
[INFO|2026-10-21 19:11:35] llamafactory.data.loader:144 >> Loading dataset aya_slovak_300k_fixed.jsonl...
training example:
input_ids:
[20548, 336, 349, 396, 13126, 369, 13966, 264, 3638, 28723, 12018, 264, 2899, 369, 6582, 1999, 2691, 274, 272, 2159, 28723, 13, 13, 27332, 3133, 3112, 28747, 13, 28762, 3673, 554, 493, 28747, 945, 29365, 18765, 298, 287, 4362, 462, 28729, 5011, 710, 15200, 28820, 9041, 28725, 1683, 28709, 28725, 446, 6909, 28797, 686, 494, 328, 282, 28725, 486, 6125, 28709, 2513, 28728, 5240, 28710, 29114, 724, 4071, 300, 21775, 29114, 264, 14628, 28879, 332, 311, 6309, 29114, 11175, 4566, 3907, 710, 15200, 28820, 1189, 28718, 28723, 4702, 29365, 18765, 18336, 1683, 28709, 6670, 28862, 28827, 2340, 28709, 24949, 15690, 284, 8341, 2129, 300, 534, 760, 7264, 28725, 23103, 287, 4362, 724, 15504, 28725, 446, 6909, 28820, 295, 626, 271, 5945, 28723, 13, 13, 27332, 12107, 28747, 13, 2238, 2, 13, 13]
inputs:
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
Odsudok: keďže to bola lekcia prezentácie, meno, ktoré zavolal, by malo potvrdiť prítomnosť a tiež urobiť krátku prezentáciu. Keďže jej meno začínalo prvým písmenom abecedy, Ana bola prvá, ktorá hovorila.
### Response:
ana</s>
label_ids:
[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 2238, 2, 13, 13]
labels:
ana</s>
eval example:
input_ids:
[20548, 336, 349, 396, 13126, 369, 13966, 264, 3638, 28723, 12018, 264, 2899, 369, 6582, 1999, 2691, 274, 272, 2159, 28723, 13, 13, 27332, 3133, 3112, 28747, 13, 28759, 377, 28827, 28873, 424, 11175, 4566, 4845, 9335, 783, 13470, 12577, 28889, 4321, 25933, 1879, 261, 28863, 532, 20659, 2390, 28747, 259, 23547, 18441, 1500, 17323, 676, 28879, 301, 3907, 13, 13, 27332, 12107, 28747, 13, 28798, 293, 8269, 28725, 8582, 2218, 11175, 4566, 4845, 4321, 25933, 28747, 14706, 17323, 9884, 4265, 28723, 5400, 28820, 637, 8866, 264, 268, 28863, 676, 28879, 21657, 332, 28879, 281, 3026, 712, 4845, 28723, 14706, 5666, 7747, 328, 1879, 15612, 667, 405, 28768, 264, 4321, 14029, 7264, 268, 28863, 668, 17760, 28723, 2, 13, 13]
inputs:
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
Napíšte krátky rozprávkový odsek na túto tému: Osoba X má manželku
### Response:
Jasné, tu je krátky odsek: Monte má ženu. Volá sa Maria a sú manželia už dva roky. Monte ju stretol na vysokej a odvtedy sú spolu.</s>
label_ids:
[-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 28798, 293, 8269, 28725, 8582, 2218, 11175, 4566, 4845, 4321, 25933, 28747, 14706, 17323, 9884, 4265, 28723, 5400, 28820, 637, 8866, 264, 268, 28863, 676, 28879, 21657, 332, 28879, 281, 3026, 712, 4845, 28723, 14706, 5666, 7747, 328, 1879, 15612, 667, 405, 28768, 264, 4321, 14029, 7264, 268, 28863, 668, 17760, 28723, 2, 13, 13]
labels:
Jasné, tu je krátky odsek: Monte má ženu. Volá sa Maria a sú manželia už dva roky. Monte ju stretol na vysokej a odvtedy sú spolu.</s>
[INFO|configuration_utils.py:765] 2026-10-21 19:11:36,353 >> loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
[INFO|configuration_utils.py:841] 2026-10-21 19:11:36,356 >> Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
[INFO|2026-10-21 19:11:36] llamafactory.model.model_utils.kv_cache:144 >> KV cache is disabled during training.
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/unsloth/__init__.py:165: UserWarning: WARNING: Unsloth should be imported before [trl, transformers, peft] to ensure all optimizations are applied. Your code may run slower or encounter memory issues without these optimizations.
Please restructure your imports with 'import unsloth' at the top of your file.
from ._gpu_init import *
🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.
🦥 Unsloth Zoo will now patch everything to make training faster!
loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
Unsloth: WARNING `trust_remote_code` is True.
Are you certain you want to do remote code execution?
==((====))== Unsloth 2026.6.1: Fast Mistral patching. Transformers: 5.5.0.
\\ /| NVIDIA RTX A4500. Num GPUs = 1. Max memory: 19.573 GB. Platform: Linux.
O^O/ \_/ \ Torch: 2.6.0+cu124. CUDA: 8.6. CUDA Toolkit: 12.4. Triton: 3.2.0
\ / Bfloat16 = TRUE. FA [Xformers = 0.0.29.post3. FA2 = False]
"-____-" Free license: http://github.com/unslothai/unsloth
Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!
loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
loading weights file model.safetensors from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/model.safetensors.index.json
Will use dtype=torch.bfloat16 as defined in model's config object
Generate config GenerationConfig {
"bos_token_id": 1,
"eos_token_id": 2,
"output_attentions": false,
"output_hidden_states": false,
"pad_token_id": 2,
"use_cache": true
}
Loading weights: 0%| | 0/291 [00:00<?, ?it/s] Loading weights: 2%|▏ | 6/291 [00:00<00:05, 52.16it/s] Loading weights: 8%|▊ | 22/291 [00:00<00:02, 108.97it/s] Loading weights: 13%|█▎ | 37/291 [00:00<00:02, 124.87it/s] Loading weights: 17%|█▋ | 50/291 [00:00<00:01, 126.14it/s] Loading weights: 23%|██▎ | 67/291 [00:00<00:01, 134.15it/s] Loading weights: 28%|██▊ | 81/291 [00:00<00:01, 131.74it/s] Loading weights: 33%|███▎ | 95/291 [00:00<00:01, 120.36it/s] Loading weights: 37%|███▋ | 108/291 [00:00<00:01, 122.05it/s] Loading weights: 42%|████▏ | 122/291 [00:00<00:01, 124.78it/s] Loading weights: 48%|████▊ | 139/291 [00:01<00:01, 133.30it/s] Loading weights: 53%|█████▎ | 153/291 [00:01<00:01, 127.06it/s] Loading weights: 57%|█████▋ | 166/291 [00:01<00:01, 116.79it/s] Loading weights: 61%|██████ | 178/291 [00:01<00:01, 105.72it/s] Loading weights: 65%|██████▍ | 189/291 [00:01<00:00, 103.34it/s] Loading weights: 69%|██████▉ | 202/291 [00:01<00:00, 107.49it/s] Loading weights: 73%|███████▎ | 213/291 [00:01<00:00, 103.00it/s] Loading weights: 79%|███████▊ | 229/291 [00:01<00:00, 114.84it/s] Loading weights: 83%|████████▎ | 241/291 [00:02<00:00, 113.63it/s] Loading weights: 88%|████████▊ | 256/291 [00:02<00:00, 123.10it/s] Loading weights: 92%|█████████▏| 269/291 [00:02<00:00, 120.34it/s] Loading weights: 97%|█████████▋| 283/291 [00:02<00:00, 122.89it/s] Loading weights: 100%|██████████| 291/291 [00:02<00:00, 118.84it/s]
loading configuration file generation_config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/generation_config.json
Generate config GenerationConfig {
"bos_token_id": 1,
"eos_token_id": 2
}
Could not locate the custom_generate/generate.py inside slovak-nlp/mistral-sk-7b.
loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
Unsloth: slovak-nlp/mistral-sk-7b had a bad pad_token (</s>). Using pad_token = <unk>.
/home/schwarc/LLaMA-Factory/src/llamafactory/model/model_utils/checkpointing.py:68: FutureWarning: `torch.cuda.amp.custom_fwd(args...)` is deprecated. Please use `torch.amp.custom_fwd(args..., device_type='cuda')` instead.
@torch.cuda.amp.custom_fwd
/home/schwarc/LLaMA-Factory/src/llamafactory/model/model_utils/checkpointing.py:85: FutureWarning: `torch.cuda.amp.custom_bwd(args...)` is deprecated. Please use `torch.amp.custom_bwd(args..., device_type='cuda')` instead.
@torch.cuda.amp.custom_bwd
[INFO|2026-10-21 19:11:53] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled.
[INFO|2026-10-21 19:11:53] llamafactory.model.adapter:144 >> Upcasting trainable params to float32.
[INFO|2026-10-21 19:11:53] llamafactory.model.adapter:144 >> Fine-tuning method: LoRA
Unsloth 2026.6.1 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.
[INFO|2026-10-21 19:11:56] llamafactory.model.loader:144 >> trainable params: 41,943,040 || all params: 7,283,675,136 || trainable%: 0.5758
The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'pad_token_id': 2}.
==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1
\\ /| Num examples = 299,000 | Num Epochs = 1 | Total steps = 18,688
O^O/ \_/ \ Batch size per device = 1 | Gradient accumulation steps = 16
\ / Data Parallel GPUs = 1 | Total batch size (1 x 16 x 1) = 16
"-____-" Trainable parameters = 41,943,040 of 7,283,675,136 (0.58% trained)
0%| | 0/18688 [00:00<?, ?it/s]`use_return_dict` is deprecated! Use `return_dict` instead!
Unsloth: Double buffering enabled (parallel H2D + compute) for backward pass.
Unsloth: Will smartly offload gradients to save VRAM!
0%| | 1/18688 [00:06<32:48:44, 6.32s/it] 0%| | 2/18688 [00:09<24:38:33, 4.75s/it] 0%| | 3/18688 [00:14<23:11:10, 4.47s/it] 0%| | 4/18688 [00:17<21:34:04, 4.16s/it] 0%| | 5/18688 [00:21<21:01:55, 4.05s/it] 0%| | 6/18688 [00:25<20:57:26, 4.04s/it] 0%| | 7/18688 [00:31<23:29:29, 4.53s/it] 0%| | 8/18688 [00:34<21:48:34, 4.20s/it] 0%| | 9/18688 [00:39<23:27:40, 4.52s/it] 0%| | 10/18688 [00:43<21:43:48, 4.19s/it] {'loss': '2.106', 'grad_norm': '5.752', 'learning_rate': '4.8e-07', 'epoch': '0.0005351', 'num_input_tokens_seen': 35216, 'train_runtime': '43.37', 'train_tokens_per_second': '811.9'}
0%| | 10/18688 [00:43<21:43:48, 4.19s/it] 0%| | 11/18688 [00:47<21:02:09, 4.05s/it] 0%| | 12/18688 [00:52<23:18:49, 4.49s/it] 0%| | 13/18688 [00:56<22:43:26, 4.38s/it] 0%| | 14/18688 [01:00<22:18:01, 4.30s/it] 0%| | 15/18688 [01:05<22:04:57, 4.26s/it] 0%| | 16/18688 [01:08<20:58:07, 4.04s/it] 0%| | 17/18688 [01:12<20:24:31, 3.94s/it] 0%| | 18/18688 [01:16<20:23:32, 3.93s/it] 0%| | 19/18688 [01:19<19:52:45, 3.83s/it] 0%| | 20/18688 [01:23<19:35:49, 3.78s/it] {'loss': '2.045', 'grad_norm': '3.803', 'learning_rate': '1.013e-06', 'epoch': '0.00107', 'num_input_tokens_seen': 66488, 'train_runtime': '83.43', 'train_tokens_per_second': '797'}
0%| | 20/18688 [01:23<19:35:49, 3.78s/it] 0%| | 21/18688 [01:27<20:33:26, 3.96s/it] 0%| | 22/18688 [01:31<20:32:15, 3.96s/it] 0%| | 23/18688 [01:35<20:38:36, 3.98s/it] 0%| | 24/18688 [01:39<20:36:06, 3.97s/it] 0%| | 25/18688 [01:43<20:43:10, 4.00s/it] 0%| | 26/18688 [01:47<20:55:25, 4.04s/it] 0%| | 27/18688 [01:51<20:43:23, 4.00s/it] 0%| | 28/18688 [01:55<20:10:52, 3.89s/it] 0%| | 29/18688 [01:59<20:35:44, 3.97s/it] 0%| | 30/18688 [02:03<20:34:15, 3.97s/it] {'loss': '1.997', 'grad_norm': '4.031', 'learning_rate': '1.547e-06', 'epoch': '0.001605', 'num_input_tokens_seen': 97280, 'train_runtime': '123.6', 'train_tokens_per_second': '787'}
0%| | 30/18688 [02:03<20:34:15, 3.97s/it] 0%| | 31/18688 [02:07<20:58:54, 4.05s/it] 0%| | 32/18688 [02:12<21:10:08, 4.08s/it] 0%| | 33/18688 [02:16<21:12:31, 4.09s/it] 0%| | 34/18688 [02:20<20:58:15, 4.05s/it] 0%| | 35/18688 [02:23<20:20:57, 3.93s/it] 0%| | 36/18688 [02:27<20:11:03, 3.90s/it] 0%| | 37/18688 [02:31<20:58:33, 4.05s/it] 0%| | 38/18688 [02:36<22:23:32, 4.32s/it] 0%| | 39/18688 [02:41<22:34:35, 4.36s/it] 0%| | 40/18688 [02:44<21:20:53, 4.12s/it] {'loss': '2.057', 'grad_norm': '7.137', 'learning_rate': '2.08e-06', 'epoch': '0.00214', 'num_input_tokens_seen': 129200, 'train_runtime': '164.9', 'train_tokens_per_second': '783.4'}
0%| | 40/18688 [02:44<21:20:53, 4.12s/it] 0%| | 41/18688 [02:48<20:40:50, 3.99s/it] 0%| | 42/18688 [02:52<20:40:17, 3.99s/it] 0%| | 43/18688 [02:56<20:31:12, 3.96s/it] 0%| | 44/18688 [03:00<21:21:41, 4.12s/it] 0%| | 45/18688 [03:05<22:00:33, 4.25s/it] 0%| | 46/18688 [03:10<22:53:45, 4.42s/it] 0%| | 47/18688 [03:14<21:49:52, 4.22s/it] 0%| | 48/18688 [03:18<22:03:17, 4.26s/it] 0%| | 49/18688 [03:22<21:43:56, 4.20s/it] 0%| | 50/18688 [03:27<23:15:49, 4.49s/it] {'loss': '1.766', 'grad_norm': '2.218', 'learning_rate': '2.613e-06', 'epoch': '0.002676', 'num_input_tokens_seen': 163208, 'train_runtime': '207.7', 'train_tokens_per_second': '785.8'}
0%| | 50/18688 [03:27<23:15:49, 4.49s/it] 0%| | 51/18688 [03:31<21:39:10, 4.18s/it] 0%| | 52/18688 [03:36<22:42:58, 4.39s/it] 0%| | 53/18688 [03:40<22:53:32, 4.42s/it] 0%| | 54/18688 [03:44<21:35:16, 4.17s/it] 0%| | 55/18688 [03:48<21:31:19, 4.16s/it] 0%| | 56/18688 [03:53<22:45:37, 4.40s/it] 0%| | 57/18688 [03:57<21:56:32, 4.24s/it] 0%| | 58/18688 [04:00<21:05:26, 4.08s/it] 0%| | 59/18688 [04:05<21:26:05, 4.14s/it] 0%| | 60/18688 [04:09<22:15:19, 4.30s/it] {'loss': '1.652', 'grad_norm': '2.05', 'learning_rate': '3.147e-06', 'epoch': '0.003211', 'num_input_tokens_seen': 197328, 'train_runtime': '249.7', 'train_tokens_per_second': '790.2'}
0%| | 60/18688 [04:09<22:15:19, 4.30s/it] 0%| | 61/18688 [04:14<22:19:40, 4.32s/it] 0%| | 62/18688 [04:18<22:21:42, 4.32s/it] 0%| | 63/18688 [04:23<24:01:47, 4.64s/it] 0%| | 64/18688 [04:27<22:24:59, 4.33s/it] 0%| | 65/18688 [04:32<23:50:58, 4.61s/it] 0%| | 66/18688 [04:36<22:27:20, 4.34s/it] 0%| | 67/18688 [04:39<21:02:59, 4.07s/it] 0%| | 68/18688 [04:44<21:31:58, 4.16s/it] 0%| | 69/18688 [04:48<21:39:32, 4.19s/it] 0%| | 70/18688 [04:52<21:37:30, 4.18s/it] {'loss': '1.567', 'grad_norm': '2.077', 'learning_rate': '3.68e-06', 'epoch': '0.003746', 'num_input_tokens_seen': 234064, 'train_runtime': '292.6', 'train_tokens_per_second': '799.9'}
0%| | 70/18688 [04:52<21:37:30, 4.18s/it] 0%| | 71/18688 [04:56<21:51:33, 4.23s/it] 0%| | 72/18688 [05:00<21:26:41, 4.15s/it] 0%| | 73/18688 [05:04<20:43:48, 4.01s/it] 0%| | 74/18688 [05:08<20:35:38, 3.98s/it] 0%| | 75/18688 [05:12<20:37:31, 3.99s/it] 0%| | 76/18688 [05:16<21:07:05, 4.08s/it] 0%| | 77/18688 [05:21<21:40:35, 4.19s/it] 0%| | 78/18688 [05:24<20:34:03, 3.98s/it] 0%| | 79/18688 [05:29<22:01:00, 4.26s/it] 0%| | 80/18688 [05:33<21:40:13, 4.19s/it] {'loss': '1.537', 'grad_norm': '2.069', 'learning_rate': '4.213e-06', 'epoch': '0.004281', 'num_input_tokens_seen': 266768, 'train_runtime': '333.7', 'train_tokens_per_second': '799.4'}
0%| | 80/18688 [05:33<21:40:13, 4.19s/it] 0%| | 81/18688 [05:38<22:10:06, 4.29s/it] 0%| | 82/18688 [05:42<22:03:52, 4.27s/it] 0%| | 83/18688 [05:45<20:50:24, 4.03s/it] 0%| | 84/18688 [05:49<20:25:31, 3.95s/it] 0%| | 85/18688 [05:53<20:34:39, 3.98s/it] 0%| | 86/18688 [05:58<21:16:08, 4.12s/it] 0%| | 87/18688 [06:01<20:16:56, 3.93s/it] 0%| | 88/18688 [06:06<22:12:29, 4.30s/it] 0%| | 89/18688 [06:11<22:39:35, 4.39s/it] 0%| | 90/18688 [06:15<21:53:35, 4.24s/it] {'loss': '1.427', 'grad_norm': '2.011', 'learning_rate': '4.747e-06', 'epoch': '0.004816', 'num_input_tokens_seen': 299720, 'train_runtime': '375.3', 'train_tokens_per_second': '798.6'}
0%| | 90/18688 [06:15<21:53:35, 4.24s/it] 0%| | 91/18688 [06:20<22:48:34, 4.42s/it] 0%| | 92/18688 [06:24<22:34:34, 4.37s/it] 0%| | 93/18688 [06:28<22:00:17, 4.26s/it] 1%| | 94/18688 [06:32<22:24:49, 4.34s/it] 1%| | 95/18688 [06:37<22:40:01, 4.39s/it] 1%| | 96/18688 [06:41<22:46:53, 4.41s/it] 1%| | 97/18688 [06:45<21:35:44, 4.18s/it] 1%| | 98/18688 [06:49<20:47:55, 4.03s/it] 1%| | 99/18688 [06:53<20:29:24, 3.97s/it] 1%| | 100/18688 [06:56<19:52:21, 3.85s/it] {'loss': '1.393', 'grad_norm': '2.031', 'learning_rate': '5.28e-06', 'epoch': '0.005351', 'num_input_tokens_seen': 333368, 'train_runtime': '416.6', 'train_tokens_per_second': '800.2'}
1%| | 100/18688 [06:56<19:52:21, 3.85s/it] 1%| | 101/18688 [07:02<22:21:07, 4.33s/it] 1%| | 102/18688 [07:06<23:06:55, 4.48s/it] 1%| | 103/18688 [07:10<21:51:12, 4.23s/it] 1%| | 104/18688 [07:14<22:06:58, 4.28s/it] 1%| | 105/18688 [07:19<23:14:08, 4.50s/it] 1%| | 106/18688 [07:24<23:53:18, 4.63s/it] 1%| | 107/18688 [07:28<22:35:27, 4.38s/it] 1%| | 108/18688 [07:32<22:13:09, 4.31s/it] 1%| | 109/18688 [07:36<22:03:10, 4.27s/it] 1%| | 110/18688 [07:40<20:57:05, 4.06s/it] {'loss': '1.325', 'grad_norm': '2.141', 'learning_rate': '5.813e-06', 'epoch': '0.005886', 'num_input_tokens_seen': 370256, 'train_runtime': '460.6', 'train_tokens_per_second': '803.9'}
1%| | 110/18688 [07:40<20:57:05, 4.06s/it] 1%| | 111/18688 [07:44<20:59:56, 4.07s/it] 1%| | 112/18688 [07:48<20:20:10, 3.94s/it] 1%| | 113/18688 [07:52<20:39:41, 4.00s/it] 1%| | 114/18688 [07:56<21:16:39, 4.12s/it] 1%| | 115/18688 [08:00<20:27:41, 3.97s/it] 1%| | 116/18688 [08:03<19:39:02, 3.81s/it] 1%| | 117/18688 [08:07<19:47:12, 3.84s/it] 1%| | 118/18688 [08:12<21:33:47, 4.18s/it] 1%| | 119/18688 [08:17<22:33:31, 4.37s/it] 1%| | 120/18688 [08:21<21:30:52, 4.17s/it] {'loss': '1.19', 'grad_norm': '2.029', 'learning_rate': '6.347e-06', 'epoch': '0.006421', 'num_input_tokens_seen': 402568, 'train_runtime': '501.3', 'train_tokens_per_second': '803.1'}
1%| | 120/18688 [08:21<21:30:52, 4.17s/it] 1%| | 121/18688 [08:25<21:27:47, 4.16s/it] 1%| | 122/18688 [08:28<20:18:39, 3.94s/it] 1%| | 123/18688 [08:32<19:34:43, 3.80s/it] 1%| | 124/18688 [08:35<19:03:40, 3.70s/it] 1%| | 125/18688 [08:40<20:26:13, 3.96s/it] 1%| | 126/18688 [08:43<19:40:16, 3.82s/it] 1%| | 127/18688 [08:47<20:10:51, 3.91s/it] 1%| | 128/18688 [08:52<20:26:52, 3.97s/it] 1%| | 129/18688 [08:56<21:35:40, 4.19s/it] 1%| | 130/18688 [09:02<23:15:57, 4.51s/it] {'loss': '1.185', 'grad_norm': '1.846', 'learning_rate': '6.88e-06', 'epoch': '0.006957', 'num_input_tokens_seen': 434592, 'train_runtime': '542.1', 'train_tokens_per_second': '801.8'}
1%| | 130/18688 [09:02<23:15:57, 4.51s/it] 1%| | 131/18688 [09:06<22:25:09, 4.35s/it] 1%| | 132/18688 [09:11<24:07:15, 4.68s/it] 1%| | 133/18688 [09:15<23:40:52, 4.59s/it] 1%| | 134/18688 [09:19<22:20:23, 4.33s/it] 1%| | 135/18688 [09:23<21:21:38, 4.14s/it] 1%| | 136/18688 [09:28<23:46:12, 4.61s/it] 1%| | 137/18688 [09:32<22:28:41, 4.36s/it] 1%| | 138/18688 [09:36<22:12:48, 4.31s/it] 1%| | 139/18688 [09:41<21:48:19, 4.23s/it] 1%| | 140/18688 [09:45<21:36:45, 4.19s/it] {'loss': '1.117', 'grad_norm': '1.675', 'learning_rate': '7.413e-06', 'epoch': '0.007492', 'num_input_tokens_seen': 471752, 'train_runtime': '585.1', 'train_tokens_per_second': '806.2'}
1%| | 140/18688 [09:45<21:36:45, 4.19s/it] 1%| | 141/18688 [09:48<20:46:04, 4.03s/it] 1%| | 142/18688 [09:53<21:39:46, 4.21s/it] 1%| | 143/18688 [09:57<21:10:09, 4.11s/it] 1%| | 144/18688 [10:01<20:42:41, 4.02s/it] 1%| | 145/18688 [10:04<19:53:35, 3.86s/it] 1%| | 146/18688 [10:09<21:02:41, 4.09s/it] 1%| | 147/18688 [10:13<21:31:29, 4.18s/it] 1%| | 148/18688 [10:17<20:46:10, 4.03s/it] 1%| | 149/18688 [10:20<19:52:16, 3.86s/it] 1%| | 150/18688 [10:24<19:54:37, 3.87s/it] {'loss': '1.116', 'grad_norm': '1.985', 'learning_rate': '7.947e-06', 'epoch': '0.008027', 'num_input_tokens_seen': 501528, 'train_runtime': '624.6', 'train_tokens_per_second': '802.9'}
1%| | 150/18688 [10:24<19:54:37, 3.87s/it] 1%| | 151/18688 [10:28<20:20:37, 3.95s/it] 1%| | 152/18688 [10:32<19:49:22, 3.85s/it] 1%| | 153/18688 [10:35<19:18:31, 3.75s/it] 1%| | 154/18688 [10:39<19:09:22, 3.72s/it] 1%| | 155/18688 [10:44<20:34:38, 4.00s/it] 1%| | 156/18688 [10:48<20:34:27, 4.00s/it] 1%| | 157/18688 [10:51<20:04:49, 3.90s/it] 1%| | 158/18688 [10:55<19:49:44, 3.85s/it] 1%| | 159/18688 [11:00<21:09:01, 4.11s/it] 1%| | 160/18688 [11:03<20:15:15, 3.94s/it] {'loss': '1.067', 'grad_norm': '1.902', 'learning_rate': '8.48e-06', 'epoch': '0.008562', 'num_input_tokens_seen': 532056, 'train_runtime': '663.8', 'train_tokens_per_second': '801.5'}
1%| | 160/18688 [11:03<20:15:15, 3.94s/it] 1%| | 161/18688 [11:08<20:51:36, 4.05s/it] 1%| | 162/18688 [11:11<20:09:26, 3.92s/it] 1%| | 163/18688 [11:16<20:56:07, 4.07s/it] 1%| | 164/18688 [11:20<21:00:37, 4.08s/it] 1%| | 165/18688 [11:25<22:38:58, 4.40s/it] 1%| | 166/18688 [11:29<22:07:28, 4.30s/it] 1%| | 167/18688 [11:33<20:56:48, 4.07s/it] 1%| | 168/18688 [11:36<20:41:11, 4.02s/it] 1%| | 169/18688 [11:40<20:00:19, 3.89s/it] 1%| | 170/18688 [11:44<19:36:06, 3.81s/it] {'loss': '1.114', 'grad_norm': '1.818', 'learning_rate': '9.013e-06', 'epoch': '0.009097', 'num_input_tokens_seen': 564304, 'train_runtime': '704.2', 'train_tokens_per_second': '801.4'}
1%| | 170/18688 [11:44<19:36:06, 3.81s/it] 1%| | 171/18688 [11:47<19:34:24, 3.81s/it] 1%| | 172/18688 [11:51<19:50:24, 3.86s/it] 1%| | 173/18688 [11:56<20:58:41, 4.08s/it] 1%| | 174/18688 [11:59<20:00:20, 3.89s/it] 1%| | 175/18688 [12:04<20:53:28, 4.06s/it] 1%| | 176/18688 [12:08<21:22:38, 4.16s/it] 1%| | 177/18688 [12:12<21:08:50, 4.11s/it] 1%| | 178/18688 [12:17<21:23:26, 4.16s/it] 1%| | 179/18688 [12:21<21:45:20, 4.23s/it] 1%| | 180/18688 [12:25<21:13:11, 4.13s/it] {'loss': '1.154', 'grad_norm': '1.617', 'learning_rate': '9.547e-06', 'epoch': '0.009632', 'num_input_tokens_seen': 595424, 'train_runtime': '745.4', 'train_tokens_per_second': '798.8'}
1%| | 180/18688 [12:25<21:13:11, 4.13s/it] 1%| | 181/18688 [12:28<20:20:15, 3.96s/it] 1%| | 182/18688 [12:32<19:39:17, 3.82s/it] 1%| | 183/18688 [12:37<21:05:49, 4.10s/it] 1%| | 184/18688 [12:40<20:30:51, 3.99s/it] 1%| | 185/18688 [12:44<20:13:45, 3.94s/it] 1%| | 186/18688 [12:48<20:12:38, 3.93s/it] 1%| | 187/18688 [12:52<20:35:46, 4.01s/it] 1%| | 188/18688 [12:56<20:46:48, 4.04s/it] 1%| | 189/18688 [13:00<20:31:03, 3.99s/it] 1%| | 190/18688 [13:05<21:32:22, 4.19s/it] {'loss': '1.064', 'grad_norm': '2.07', 'learning_rate': '1.008e-05', 'epoch': '0.01017', 'num_input_tokens_seen': 628248, 'train_runtime': '785.5', 'train_tokens_per_second': '799.8'}
1%| | 190/18688 [13:05<21:32:22, 4.19s/it] 1%| | 191/18688 [13:10<22:04:03, 4.29s/it] 1%| | 192/18688 [13:13<21:16:18, 4.14s/it] 1%| | 193/18688 [13:17<20:14:07, 3.94s/it] 1%| | 194/18688 [13:21<20:09:06, 3.92s/it] 1%| | 195/18688 [13:25<20:42:41, 4.03s/it] 1%| | 196/18688 [13:30<21:46:33, 4.24s/it] 1%| | 197/18688 [13:34<21:36:38, 4.21s/it] 1%| | 198/18688 [13:38<20:53:25, 4.07s/it] 1%| | 199/18688 [13:42<20:51:36, 4.06s/it] 1%| | 200/18688 [13:46<21:21:02, 4.16s/it] {'loss': '1.078', 'grad_norm': '1.505', 'learning_rate': '1.061e-05', 'epoch': '0.0107', 'num_input_tokens_seen': 661320, 'train_runtime': '826.5', 'train_tokens_per_second': '800.2'}
1%| | 200/18688 [13:46<21:21:02, 4.16s/it] 1%| | 201/18688 [13:50<20:59:06, 4.09s/it] 1%| | 202/18688 [13:54<20:43:00, 4.03s/it] 1%| | 203/18688 [13:58<21:29:15, 4.18s/it] 1%| | 204/18688 [14:03<21:29:03, 4.18s/it] 1%| | 205/18688 [14:07<21:37:37, 4.21s/it] 1%| | 206/18688 [14:11<21:56:30, 4.27s/it] 1%| | 207/18688 [14:16<22:08:16, 4.31s/it] 1%| | 208/18688 [14:19<21:23:29, 4.17s/it] 1%| | 209/18688 [14:25<23:06:07, 4.50s/it] 1%| | 210/18688 [14:28<21:45:49, 4.24s/it] {'loss': '1.185', 'grad_norm': '2.156', 'learning_rate': '1.115e-05', 'epoch': '0.01124', 'num_input_tokens_seen': 696656, 'train_runtime': '868.9', 'train_tokens_per_second': '801.8'}
1%| | 210/18688 [14:28<21:45:49, 4.24s/it] 1%| | 211/18688 [14:32<20:45:53, 4.05s/it] 1%| | 212/18688 [14:36<21:23:19, 4.17s/it] 1%| | 213/18688 [14:40<20:44:17, 4.04s/it] 1%| | 214/18688 [14:44<20:27:36, 3.99s/it] 1%| | 215/18688 [14:48<19:57:59, 3.89s/it] 1%| | 216/18688 [14:52<20:05:53, 3.92s/it] 1%| | 217/18688 [14:55<19:37:06, 3.82s/it] 1%| | 218/18688 [14:59<19:54:07, 3.88s/it] 1%| | 219/18688 [15:04<20:43:32, 4.04s/it] 1%| | 220/18688 [15:08<21:28:52, 4.19s/it] {'loss': '1.102', 'grad_norm': '1.031', 'learning_rate': '1.168e-05', 'epoch': '0.01177', 'num_input_tokens_seen': 727368, 'train_runtime': '908.7', 'train_tokens_per_second': '800.4'}
1%| | 220/18688 [15:08<21:28:52, 4.19s/it] 1%| | 221/18688 [15:12<21:35:04, 4.21s/it] 1%| | 222/18688 [15:17<21:40:05, 4.22s/it] 1%| | 223/18688 [15:21<21:32:39, 4.20s/it] 1%| | 224/18688 [15:25<21:07:48, 4.12s/it] 1%| | 225/18688 [15:29<21:58:13, 4.28s/it] 1%| | 226/18688 [15:33<21:32:29, 4.20s/it] 1%| | 227/18688 [15:38<21:19:57, 4.16s/it] 1%| | 228/18688 [15:42<22:29:18, 4.39s/it] 1%| | 229/18688 [15:46<21:37:38, 4.22s/it] 1%| | 230/18688 [15:50<21:17:49, 4.15s/it] {'loss': '1.083', 'grad_norm': '2.148', 'learning_rate': '1.221e-05', 'epoch': '0.01231', 'num_input_tokens_seen': 761544, 'train_runtime': '950.8', 'train_tokens_per_second': '800.9'}
1%| | 230/18688 [15:50<21:17:49, 4.15s/it] 1%| | 231/18688 [15:54<21:17:22, 4.15s/it] 1%| | 232/18688 [16:00<23:14:23, 4.53s/it] 1%| | 233/18688 [16:04<22:44:57, 4.44s/it] 1%|▏ | 234/18688 [16:09<23:16:11, 4.54s/it] 1%|▏ | 235/18688 [16:13<21:59:59, 4.29s/it] 1%|▏ | 236/18688 [16:18<23:48:12, 4.64s/it] 1%|▏ | 237/18688 [16:22<22:08:54, 4.32s/it] 1%|▏ | 238/18688 [16:26<21:55:22, 4.28s/it] 1%|▏ | 239/18688 [16:30<22:03:27, 4.30s/it] 1%|▏ | 240/18688 [16:34<20:50:45, 4.07s/it] {'loss': '1.088', 'grad_norm': '1.995', 'learning_rate': '1.275e-05', 'epoch': '0.01284', 'num_input_tokens_seen': 797960, 'train_runtime': '994.2', 'train_tokens_per_second': '802.6'}
1%|▏ | 240/18688 [16:34<20:50:45, 4.07s/it] 1%|▏ | 241/18688 [16:38<21:28:12, 4.19s/it] 1%|▏ | 242/18688 [16:43<21:56:56, 4.28s/it] 1%|▏ | 243/18688 [16:47<21:54:27, 4.28s/it] 1%|▏ | 244/18688 [16:51<22:04:18, 4.31s/it] 1%|▏ | 245/18688 [16:55<21:10:17, 4.13s/it] 1%|▏ | 246/18688 [16:59<20:54:51, 4.08s/it] 1%|▏ | 247/18688 [17:03<20:13:05, 3.95s/it] 1%|▏ | 248/18688 [17:06<19:54:49, 3.89s/it] 1%|▏ | 249/18688 [17:11<20:38:29, 4.03s/it] 1%|▏ | 250/18688 [17:15<20:37:44, 4.03s/it] {'loss': '1.096', 'grad_norm': '1.309', 'learning_rate': '1.328e-05', 'epoch': '0.01338', 'num_input_tokens_seen': 831064, 'train_runtime': '1035', 'train_tokens_per_second': '802.8'}
1%|▏ | 250/18688 [17:15<20:37:44, 4.03s/it] 1%|▏ | 251/18688 [17:19<20:38:24, 4.03s/it] 1%|▏ | 252/18688 [17:22<20:05:05, 3.92s/it] 1%|▏ | 253/18688 [17:26<19:50:17, 3.87s/it] 1%|▏ | 254/18688 [17:30<19:59:58, 3.91s/it] 1%|▏ | 255/18688 [17:34<20:15:40, 3.96s/it] 1%|▏ | 256/18688 [17:38<20:33:09, 4.01s/it] 1%|▏ | 257/18688 [17:43<21:09:30, 4.13s/it] 1%|▏ | 258/18688 [17:47<20:47:54, 4.06s/it] 1%|▏ | 259/18688 [17:51<20:41:24, 4.04s/it] 1%|▏ | 260/18688 [17:55<21:20:17, 4.17s/it] {'loss': '1.03', 'grad_norm': '1.875', 'learning_rate': '1.381e-05', 'epoch': '0.01391', 'num_input_tokens_seen': 862936, 'train_runtime': '1076', 'train_tokens_per_second': '802.2'}
1%|▏ | 260/18688 [17:55<21:20:17, 4.17s/it] 1%|▏ | 261/18688 [18:00<21:42:26, 4.24s/it] 1%|▏ | 262/18688 [18:04<22:18:31, 4.36s/it] 1%|▏ | 263/18688 [18:09<22:27:13, 4.39s/it] 1%|▏ | 264/18688 [18:13<21:58:40, 4.29s/it] 1%|▏ | 265/18688 [18:16<21:06:03, 4.12s/it] 1%|▏ | 266/18688 [18:20<20:26:28, 3.99s/it] 1%|▏ | 267/18688 [18:25<21:41:55, 4.24s/it] 1%|▏ | 268/18688 [18:29<20:54:28, 4.09s/it] 1%|▏ | 269/18688 [18:33<20:30:58, 4.01s/it] 1%|▏ | 270/18688 [18:36<20:02:44, 3.92s/it] {'loss': '1.027', 'grad_norm': '1.553', 'learning_rate': '1.435e-05', 'epoch': '0.01445', 'num_input_tokens_seen': 893208, 'train_runtime': '1117', 'train_tokens_per_second': '799.8'}
1%|▏ | 270/18688 [18:36<20:02:44, 3.92s/it] 1%|▏ | 271/18688 [18:40<19:44:56, 3.86s/it] 1%|▏ | 272/18688 [18:44<20:44:04, 4.05s/it] 1%|▏ | 273/18688 [18:48<20:24:31, 3.99s/it] 1%|▏ | 274/18688 [18:52<20:39:26, 4.04s/it] 1%|▏ | 275/18688 [18:56<20:13:19, 3.95s/it] 1%|▏ | 276/18688 [19:00<20:03:14, 3.92s/it] 1%|▏ | 277/18688 [19:04<19:42:12, 3.85s/it] 1%|▏ | 278/18688 [19:08<19:34:53, 3.83s/it] 1%|▏ | 279/18688 [19:13<21:42:16, 4.24s/it] 1%|▏ | 280/18688 [19:17<22:02:03, 4.31s/it] {'loss': '1.101', 'grad_norm': '1.558', 'learning_rate': '1.488e-05', 'epoch': '0.01498', 'num_input_tokens_seen': 925664, 'train_runtime': '1158', 'train_tokens_per_second': '799.6'}
1%|▏ | 280/18688 [19:17<22:02:03, 4.31s/it] 2%|▏ | 281/18688 [19:21<21:14:01, 4.15s/it] 2%|▏ | 282/18688 [19:26<22:07:18, 4.33s/it] 2%|▏ | 283/18688 [19:30<22:42:29, 4.44s/it] 2%|▏ | 284/18688 [19:35<22:33:17, 4.41s/it] 2%|▏ | 285/18688 [19:39<21:32:07, 4.21s/it] 2%|▏ | 286/18688 [19:43<22:30:34, 4.40s/it] 2%|▏ | 287/18688 [19:47<21:25:56, 4.19s/it] 2%|▏ | 288/18688 [19:51<21:42:03, 4.25s/it] 2%|▏ | 289/18688 [19:56<22:43:19, 4.45s/it] 2%|▏ | 290/18688 [20:00<21:48:52, 4.27s/it] {'loss': '1.09', 'grad_norm': '1.581', 'learning_rate': '1.541e-05', 'epoch': '0.01552', 'num_input_tokens_seen': 960824, 'train_runtime': '1201', 'train_tokens_per_second': '800.2'}
2%|▏ | 290/18688 [20:00<21:48:52, 4.27s/it] 2%|▏ | 291/18688 [20:04<21:27:53, 4.20s/it] 2%|▏ | 292/18688 [20:08<20:31:56, 4.02s/it] 2%|▏ | 293/18688 [20:11<19:47:10, 3.87s/it] 2%|▏ | 294/18688 [20:15<19:20:21, 3.79s/it] 2%|▏ | 295/18688 [20:19<19:14:06, 3.76s/it] 2%|▏ | 296/18688 [20:23<19:48:44, 3.88s/it] 2%|▏ | 297/18688 [20:27<20:09:12, 3.94s/it] 2%|▏ | 298/18688 [20:31<21:05:50, 4.13s/it] 2%|▏ | 299/18688 [20:36<21:37:30, 4.23s/it] 2%|▏ | 300/18688 [20:40<21:43:04, 4.25s/it] {'loss': '1.071', 'grad_norm': '1.703', 'learning_rate': '1.595e-05', 'epoch': '0.01605', 'num_input_tokens_seen': 993184, 'train_runtime': '1241', 'train_tokens_per_second': '800.5'}
2%|▏ | 300/18688 [20:40<21:43:04, 4.25s/it] 2%|▏ | 301/18688 [20:44<21:24:52, 4.19s/it] 2%|▏ | 302/18688 [20:49<22:06:34, 4.33s/it] 2%|▏ | 303/18688 [20:53<21:49:33, 4.27s/it] 2%|▏ | 304/18688 [20:57<20:50:22, 4.08s/it] 2%|▏ | 305/18688 [21:00<19:50:05, 3.88s/it] 2%|▏ | 306/18688 [21:04<19:47:39, 3.88s/it] 2%|▏ | 307/18688 [21:08<19:38:25, 3.85s/it] 2%|▏ | 308/18688 [21:12<20:11:34, 3.96s/it] 2%|▏ | 309/18688 [21:17<21:15:50, 4.17s/it] 2%|▏ | 310/18688 [21:21<20:50:48, 4.08s/it] {'loss': '1.069', 'grad_norm': '1.336', 'learning_rate': '1.648e-05', 'epoch': '0.01659', 'num_input_tokens_seen': 1022656, 'train_runtime': '1281', 'train_tokens_per_second': '798.3'}
2%|▏ | 310/18688 [21:21<20:50:48, 4.08s/it] 2%|▏ | 311/18688 [21:24<20:15:28, 3.97s/it] 2%|▏ | 312/18688 [21:28<19:27:13, 3.81s/it] 2%|▏ | 313/18688 [21:32<20:04:35, 3.93s/it] 2%|▏ | 314/18688 [21:36<19:39:45, 3.85s/it] 2%|▏ | 315/18688 [21:40<19:54:18, 3.90s/it] 2%|▏ | 316/18688 [21:44<20:31:48, 4.02s/it] 2%|▏ | 317/18688 [21:48<21:18:35, 4.18s/it] 2%|▏ | 318/18688 [21:52<20:49:04, 4.08s/it] 2%|▏ | 319/18688 [21:56<20:04:52, 3.94s/it] 2%|▏ | 320/18688 [22:00<20:32:38, 4.03s/it] {'loss': '1.038', 'grad_norm': '1.298', 'learning_rate': '1.701e-05', 'epoch': '0.01712', 'num_input_tokens_seen': 1053904, 'train_runtime': '1321', 'train_tokens_per_second': '798'}
2%|▏ | 320/18688 [22:00<20:32:38, 4.03s/it] 2%|▏ | 321/18688 [22:04<20:44:49, 4.07s/it] 2%|▏ | 322/18688 [22:08<20:47:07, 4.07s/it] 2%|▏ | 323/18688 [22:14<23:07:38, 4.53s/it] 2%|▏ | 324/18688 [22:18<22:14:44, 4.36s/it] 2%|▏ | 325/18688 [22:22<21:59:32, 4.31s/it] 2%|▏ | 326/18688 [22:26<21:21:03, 4.19s/it] 2%|▏ | 327/18688 [22:31<22:18:46, 4.37s/it] 2%|▏ | 328/18688 [22:35<22:04:46, 4.33s/it] 2%|▏ | 329/18688 [22:40<23:06:23, 4.53s/it] 2%|▏ | 330/18688 [22:44<21:44:28, 4.26s/it] {'loss': '1.091', 'grad_norm': '1.498', 'learning_rate': '1.755e-05', 'epoch': '0.01766', 'num_input_tokens_seen': 1090264, 'train_runtime': '1364', 'train_tokens_per_second': '799.2'}
2%|▏ | 330/18688 [22:44<21:44:28, 4.26s/it] 2%|▏ | 331/18688 [22:47<20:51:29, 4.09s/it] 2%|▏ | 332/18688 [22:51<20:14:50, 3.97s/it] 2%|▏ | 333/18688 [22:55<19:24:43, 3.81s/it] 2%|▏ | 334/18688 [22:58<19:20:52, 3.79s/it] 2%|▏ | 335/18688 [23:03<20:13:23, 3.97s/it] 2%|▏ | 336/18688 [23:06<19:34:08, 3.84s/it] 2%|▏ | 337/18688 [23:10<19:38:12, 3.85s/it] 2%|▏ | 338/18688 [23:14<20:25:49, 4.01s/it] 2%|▏ | 339/18688 [23:19<20:36:06, 4.04s/it] 2%|▏ | 340/18688 [23:23<21:55:41, 4.30s/it] {'loss': '0.9865', 'grad_norm': '1.89', 'learning_rate': '1.808e-05', 'epoch': '0.01819', 'num_input_tokens_seen': 1121696, 'train_runtime': '1404', 'train_tokens_per_second': '798.9'}
2%|▏ | 340/18688 [23:23<21:55:41, 4.30s/it] 2%|▏ | 341/18688 [23:27<20:47:52, 4.08s/it] 2%|▏ | 342/18688 [23:31<20:10:11, 3.96s/it] 2%|▏ | 343/18688 [23:35<21:01:54, 4.13s/it] 2%|▏ | 344/18688 [23:39<20:30:01, 4.02s/it] 2%|▏ | 345/18688 [23:43<20:14:35, 3.97s/it] 2%|▏ | 346/18688 [23:47<20:34:08, 4.04s/it] 2%|▏ | 347/18688 [23:52<21:10:28, 4.16s/it] 2%|▏ | 348/18688 [23:55<20:29:34, 4.02s/it] 2%|▏ | 349/18688 [24:00<21:23:29, 4.20s/it] 2%|▏ | 350/18688 [24:03<20:26:44, 4.01s/it] {'loss': '1.075', 'grad_norm': '1.646', 'learning_rate': '1.861e-05', 'epoch': '0.01873', 'num_input_tokens_seen': 1153216, 'train_runtime': '1444', 'train_tokens_per_second': '798.7'}
2%|▏ | 350/18688 [24:03<20:26:44, 4.01s/it] 2%|▏ | 351/18688 [24:07<20:33:50, 4.04s/it] 2%|▏ | 352/18688 [24:12<21:16:23, 4.18s/it] 2%|▏ | 353/18688 [24:16<21:27:52, 4.21s/it] 2%|▏ | 354/18688 [24:20<20:44:05, 4.07s/it] 2%|▏ | 355/18688 [24:24<19:57:39, 3.92s/it] 2%|▏ | 356/18688 [24:29<22:53:59, 4.50s/it] 2%|▏ | 357/18688 [24:34<22:27:25, 4.41s/it] 2%|▏ | 358/18688 [24:38<22:10:40, 4.36s/it] 2%|▏ | 359/18688 [24:42<21:48:21, 4.28s/it] 2%|▏ | 360/18688 [24:46<21:00:29, 4.13s/it] {'loss': '1.059', 'grad_norm': '1.43', 'learning_rate': '1.915e-05', 'epoch': '0.01926', 'num_input_tokens_seen': 1188456, 'train_runtime': '1486', 'train_tokens_per_second': '799.6'}
2%|▏ | 360/18688 [24:46<21:00:29, 4.13s/it] 2%|▏ | 361/18688 [24:49<20:02:39, 3.94s/it] 2%|▏ | 362/18688 [24:54<21:17:59, 4.18s/it] 2%|▏ | 363/18688 [24:58<21:10:02, 4.16s/it] 2%|▏ | 364/18688 [25:03<21:41:23, 4.26s/it] 2%|▏ | 365/18688 [25:07<22:18:04, 4.38s/it] 2%|▏ | 366/18688 [25:11<21:02:50, 4.14s/it] 2%|▏ | 367/18688 [25:15<21:40:54, 4.26s/it] 2%|▏ | 368/18688 [25:20<21:29:51, 4.22s/it] 2%|▏ | 369/18688 [25:24<21:10:56, 4.16s/it] 2%|▏ | 370/18688 [25:28<21:51:45, 4.30s/it] {'loss': '1.087', 'grad_norm': '1.512', 'learning_rate': '1.968e-05', 'epoch': '0.0198', 'num_input_tokens_seen': 1222640, 'train_runtime': '1529', 'train_tokens_per_second': '799.8'}
2%|▏ | 370/18688 [25:28<21:51:45, 4.30s/it] 2%|▏ | 371/18688 [25:32<20:48:28, 4.09s/it] 2%|▏ | 372/18688 [25:35<20:08:36, 3.96s/it] 2%|▏ | 373/18688 [25:39<19:52:37, 3.91s/it] 2%|▏ | 374/18688 [25:44<20:29:01, 4.03s/it] 2%|▏ | 375/18688 [25:48<20:31:50, 4.04s/it] 2%|▏ | 376/18688 [25:52<20:24:16, 4.01s/it] 2%|▏ | 377/18688 [25:56<20:41:12, 4.07s/it] 2%|▏ | 378/18688 [26:00<21:10:28, 4.16s/it] 2%|▏ | 379/18688 [26:04<20:15:25, 3.98s/it] 2%|▏ | 380/18688 [26:08<20:25:13, 4.02s/it] {'loss': '1.093', 'grad_norm': '1.641', 'learning_rate': '2.021e-05', 'epoch': '0.02033', 'num_input_tokens_seen': 1253888, 'train_runtime': '1568', 'train_tokens_per_second': '799.5'}
2%|▏ | 380/18688 [26:08<20:25:13, 4.02s/it] 2%|▏ | 381/18688 [26:11<19:53:04, 3.91s/it] 2%|▏ | 382/18688 [26:15<19:10:14, 3.77s/it] 2%|▏ | 383/18688 [26:19<19:59:11, 3.93s/it] 2%|▏ | 384/18688 [26:23<20:12:36, 3.97s/it] 2%|▏ | 385/18688 [26:27<20:27:27, 4.02s/it] 2%|▏ | 386/18688 [26:32<21:01:49, 4.14s/it] 2%|▏ | 387/18688 [26:36<21:34:00, 4.24s/it] 2%|▏ | 388/18688 [26:40<20:51:13, 4.10s/it] 2%|▏ | 389/18688 [26:44<20:08:49, 3.96s/it] 2%|▏ | 390/18688 [26:49<22:04:41, 4.34s/it] {'loss': '1.017', 'grad_norm': '1.292', 'learning_rate': '2.075e-05', 'epoch': '0.02087', 'num_input_tokens_seen': 1286896, 'train_runtime': '1609', 'train_tokens_per_second': '799.6'}
2%|▏ | 390/18688 [26:49<22:04:41, 4.34s/it] 2%|▏ | 391/18688 [26:53<21:33:01, 4.24s/it] 2%|▏ | 392/18688 [26:57<20:35:10, 4.05s/it] 2%|▏ | 393/18688 [27:00<19:54:23, 3.92s/it] 2%|▏ | 394/18688 [27:04<20:06:49, 3.96s/it] 2%|▏ | 395/18688 [27:09<21:14:33, 4.18s/it] 2%|▏ | 396/18688 [27:13<20:56:59, 4.12s/it] 2%|▏ | 397/18688 [27:17<20:39:50, 4.07s/it] 2%|▏ | 398/18688 [27:20<19:51:24, 3.91s/it] 2%|▏ | 399/18688 [27:25<20:45:39, 4.09s/it] 2%|▏ | 400/18688 [27:29<20:43:19, 4.08s/it] {'loss': '1.039', 'grad_norm': '2.286', 'learning_rate': '2.128e-05', 'epoch': '0.0214', 'num_input_tokens_seen': 1319712, 'train_runtime': '1649', 'train_tokens_per_second': '800.1'}
2%|▏ | 400/18688 [27:29<20:43:19, 4.08s/it] 2%|▏ | 401/18688 [27:34<21:42:01, 4.27s/it] 2%|▏ | 402/18688 [27:38<21:11:17, 4.17s/it] 2%|▏ | 403/18688 [27:42<21:01:39, 4.14s/it] 2%|▏ | 404/18688 [27:45<20:32:08, 4.04s/it] 2%|▏ | 405/18688 [27:49<20:20:12, 4.00s/it] 2%|▏ | 406/18688 [27:55<22:43:12, 4.47s/it] 2%|▏ | 407/18688 [27:59<21:52:02, 4.31s/it] 2%|▏ | 408/18688 [28:02<20:37:33, 4.06s/it] 2%|▏ | 409/18688 [28:07<21:13:48, 4.18s/it] 2%|▏ | 410/18688 [28:10<20:26:01, 4.02s/it] {'loss': '1.092', 'grad_norm': '1.323', 'learning_rate': '2.181e-05', 'epoch': '0.02194', 'num_input_tokens_seen': 1352936, 'train_runtime': '1691', 'train_tokens_per_second': '800.1'}
2%|▏ | 410/18688 [28:10<20:26:01, 4.02s/it] 2%|▏ | 411/18688 [28:15<20:30:55, 4.04s/it] 2%|▏ | 412/18688 [28:18<19:42:07, 3.88s/it] 2%|▏ | 413/18688 [28:22<20:14:19, 3.99s/it] 2%|▏ | 414/18688 [28:27<20:38:38, 4.07s/it] 2%|▏ | 415/18688 [28:31<20:29:15, 4.04s/it] 2%|▏ | 416/18688 [28:35<21:17:10, 4.19s/it] 2%|▏ | 417/18688 [28:39<20:55:00, 4.12s/it] 2%|▏ | 418/18688 [28:43<20:45:46, 4.09s/it] 2%|▏ | 419/18688 [28:47<20:52:00, 4.11s/it] 2%|▏ | 420/18688 [28:51<20:17:12, 4.00s/it] {'loss': '1.088', 'grad_norm': '1.866', 'learning_rate': '2.235e-05', 'epoch': '0.02247', 'num_input_tokens_seen': 1385008, 'train_runtime': '1731', 'train_tokens_per_second': '799.9'}
2%|▏ | 420/18688 [28:51<20:17:12, 4.00s/it] 2%|▏ | 421/18688 [28:56<21:13:34, 4.18s/it] 2%|▏ | 422/18688 [29:01<22:28:38, 4.43s/it] 2%|▏ | 423/18688 [29:04<21:23:31, 4.22s/it] 2%|▏ | 424/18688 [29:08<21:08:20, 4.17s/it] 2%|▏ | 425/18688 [29:12<20:44:34, 4.09s/it] 2%|▏ | 426/18688 [29:16<20:17:14, 4.00s/it] 2%|▏ | 427/18688 [29:20<20:30:29, 4.04s/it] 2%|▏ | 428/18688 [29:26<22:44:46, 4.48s/it] 2%|▏ | 429/18688 [29:30<22:26:58, 4.43s/it] 2%|▏ | 430/18688 [29:33<20:59:17, 4.14s/it] {'loss': '1.042', 'grad_norm': '1.622', 'learning_rate': '2.288e-05', 'epoch': '0.02301', 'num_input_tokens_seen': 1420952, 'train_runtime': '1774', 'train_tokens_per_second': '801'}
2%|▏ | 430/18688 [29:33<20:59:17, 4.14s/it] 2%|▏ | 431/18688 [29:37<20:31:00, 4.05s/it] 2%|▏ | 432/18688 [29:41<20:35:16, 4.06s/it] 2%|▏ | 433/18688 [29:45<20:37:24, 4.07s/it] 2%|▏ | 434/18688 [29:50<20:55:02, 4.13s/it] 2%|▏ | 435/18688 [29:54<21:10:26, 4.18s/it] 2%|▏ | 436/18688 [29:58<20:14:02, 3.99s/it] 2%|▏ | 437/18688 [30:02<20:44:18, 4.09s/it] 2%|▏ | 438/18688 [30:06<20:07:04, 3.97s/it] 2%|▏ | 439/18688 [30:09<19:38:47, 3.88s/it] 2%|▏ | 440/18688 [30:13<19:54:26, 3.93s/it] {'loss': '1.087', 'grad_norm': '1.404', 'learning_rate': '2.341e-05', 'epoch': '0.02355', 'num_input_tokens_seen': 1452280, 'train_runtime': '1814', 'train_tokens_per_second': '800.7'}
2%|▏ | 440/18688 [30:13<19:54:26, 3.93s/it] 2%|▏ | 441/18688 [30:17<19:50:25, 3.91s/it] 2%|▏ | 442/18688 [30:22<21:01:21, 4.15s/it] 2%|▏ | 443/18688 [30:26<20:48:49, 4.11s/it] 2%|▏ | 444/18688 [30:31<22:40:33, 4.47s/it] 2%|▏ | 445/18688 [30:36<23:17:53, 4.60s/it] 2%|▏ | 446/18688 [30:40<21:59:15, 4.34s/it] 2%|▏ | 447/18688 [30:43<20:44:01, 4.09s/it] 2%|▏ | 448/18688 [30:47<20:17:29, 4.00s/it] 2%|▏ | 449/18688 [30:52<21:12:16, 4.19s/it] 2%|▏ | 450/18688 [30:56<21:07:24, 4.17s/it] {'loss': '1.046', 'grad_norm': '1.635', 'learning_rate': '2.395e-05', 'epoch': '0.02408', 'num_input_tokens_seen': 1487680, 'train_runtime': '1856', 'train_tokens_per_second': '801.4'}
2%|▏ | 450/18688 [30:56<21:07:24, 4.17s/it] 2%|▏ | 451/18688 [30:59<20:03:58, 3.96s/it] 2%|▏ | 452/18688 [31:04<20:27:23, 4.04s/it] 2%|▏ | 453/18688 [31:07<20:11:27, 3.99s/it] 2%|▏ | 454/18688 [31:12<21:17:52, 4.20s/it] 2%|▏ | 455/18688 [31:18<23:18:16, 4.60s/it] 2%|▏ | 456/18688 [31:22<23:15:03, 4.59s/it] 2%|▏ | 457/18688 [31:26<21:43:43, 4.29s/it] 2%|▏ | 458/18688 [31:30<21:40:56, 4.28s/it] 2%|▏ | 459/18688 [31:34<20:36:53, 4.07s/it] 2%|▏ | 460/18688 [31:37<20:04:12, 3.96s/it] {'loss': '0.9943', 'grad_norm': '1.67', 'learning_rate': '2.448e-05', 'epoch': '0.02462', 'num_input_tokens_seen': 1521584, 'train_runtime': '1898', 'train_tokens_per_second': '801.7'}
2%|▏ | 460/18688 [31:37<20:04:12, 3.96s/it] 2%|▏ | 461/18688 [31:41<19:57:38, 3.94s/it] 2%|▏ | 462/18688 [31:45<19:36:31, 3.87s/it] 2%|▏ | 463/18688 [31:49<20:00:07, 3.95s/it] 2%|▏ | 464/18688 [31:53<19:48:13, 3.91s/it] 2%|▏ | 465/18688 [31:57<19:16:35, 3.81s/it] 2%|▏ | 466/18688 [32:01<19:52:02, 3.93s/it] 2%|▏ | 467/18688 [32:05<19:48:37, 3.91s/it] 3%|▎ | 468/18688 [32:09<21:04:58, 4.17s/it] 3%|▎ | 469/18688 [32:13<20:03:04, 3.96s/it] 3%|▎ | 470/18688 [32:18<21:40:28, 4.28s/it] {'loss': '1.053', 'grad_norm': '1.331', 'learning_rate': '2.501e-05', 'epoch': '0.02515', 'num_input_tokens_seen': 1553928, 'train_runtime': '1938', 'train_tokens_per_second': '801.7'}
3%|▎ | 470/18688 [32:18<21:40:28, 4.28s/it] 3%|▎ | 471/18688 [32:23<22:27:31, 4.44s/it] 3%|▎ | 472/18688 [32:27<22:20:15, 4.41s/it] 3%|▎ | 473/18688 [32:31<21:46:46, 4.30s/it] 3%|▎ | 474/18688 [32:35<21:11:03, 4.19s/it] 3%|▎ | 475/18688 [32:39<20:21:18, 4.02s/it] 3%|▎ | 476/18688 [32:43<20:37:29, 4.08s/it] 3%|▎ | 477/18688 [32:47<20:49:08, 4.12s/it] 3%|▎ | 478/18688 [32:51<20:14:03, 4.00s/it] 3%|▎ | 479/18688 [32:55<21:13:25, 4.20s/it] 3%|▎ | 480/18688 [32:59<20:47:45, 4.11s/it] {'loss': '1.126', 'grad_norm': '1.726', 'learning_rate': '2.555e-05', 'epoch': '0.02569', 'num_input_tokens_seen': 1588296, 'train_runtime': '1980', 'train_tokens_per_second': '802.2'}
3%|▎ | 480/18688 [32:59<20:47:45, 4.11s/it] 3%|▎ | 481/18688 [33:04<21:37:44, 4.28s/it] 3%|▎ | 482/18688 [33:08<21:01:46, 4.16s/it] 3%|▎ | 483/18688 [33:12<20:18:43, 4.02s/it] 3%|▎ | 484/18688 [33:16<20:14:57, 4.00s/it] 3%|▎ | 485/18688 [33:20<20:53:19, 4.13s/it] 3%|▎ | 486/18688 [33:23<19:51:35, 3.93s/it] 3%|▎ | 487/18688 [33:27<20:04:26, 3.97s/it] 3%|▎ | 488/18688 [33:32<20:15:33, 4.01s/it] 3%|▎ | 489/18688 [33:36<20:40:43, 4.09s/it] 3%|▎ | 490/18688 [33:40<20:59:45, 4.15s/it] {'loss': '1.024', 'grad_norm': '1.816', 'learning_rate': '2.608e-05', 'epoch': '0.02622', 'num_input_tokens_seen': 1620376, 'train_runtime': '2021', 'train_tokens_per_second': '801.9'}
3%|▎ | 490/18688 [33:40<20:59:45, 4.15s/it] 3%|▎ | 491/18688 [33:44<20:16:04, 4.01s/it] 3%|▎ | 492/18688 [33:48<20:32:22, 4.06s/it] 3%|▎ | 493/18688 [33:52<20:15:16, 4.01s/it] 3%|▎ | 494/18688 [33:56<20:35:47, 4.08s/it] 3%|▎ | 495/18688 [34:01<21:02:30, 4.16s/it] 3%|▎ | 496/18688 [34:04<20:11:55, 4.00s/it] 3%|▎ | 497/18688 [34:08<19:32:08, 3.87s/it] 3%|▎ | 498/18688 [34:12<20:14:55, 4.01s/it] 3%|▎ | 499/18688 [34:16<20:20:29, 4.03s/it] 3%|▎ | 500/18688 [34:21<22:24:46, 4.44s/it] {'loss': '1.029', 'grad_norm': '1.259', 'learning_rate': '2.661e-05', 'epoch': '0.02676', 'num_input_tokens_seen': 1653200, 'train_runtime': '2062', 'train_tokens_per_second': '801.8'}
3%|▎ | 500/18688 [34:21<22:24:46, 4.44s/it] 3%|▎ | 501/18688 [34:25<21:03:19, 4.17s/it] 3%|▎ | 502/18688 [34:29<20:26:27, 4.05s/it] 3%|▎ | 503/18688 [34:32<19:52:35, 3.93s/it] 3%|▎ | 504/18688 [34:36<19:51:19, 3.93s/it] 3%|▎ | 505/18688 [34:41<20:17:38, 4.02s/it] 3%|▎ | 506/18688 [34:45<21:33:23, 4.27s/it] 3%|▎ | 507/18688 [34:49<21:04:16, 4.17s/it] 3%|▎ | 508/18688 [34:53<20:24:09, 4.04s/it] 3%|▎ | 509/18688 [34:57<20:37:48, 4.09s/it] 3%|▎ | 510/18688 [35:01<19:59:05, 3.96s/it] {'loss': '1.08', 'grad_norm': '1.645', 'learning_rate': '2.715e-05', 'epoch': '0.02729', 'num_input_tokens_seen': 1682936, 'train_runtime': '2101', 'train_tokens_per_second': '800.8'}
3%|▎ | 510/18688 [35:01<19:59:05, 3.96s/it] 3%|▎ | 511/18688 [35:04<19:10:59, 3.80s/it] 3%|▎ | 512/18688 [35:10<22:03:16, 4.37s/it] 3%|▎ | 513/18688 [35:15<22:13:02, 4.40s/it] 3%|▎ | 514/18688 [35:19<21:37:24, 4.28s/it] 3%|▎ | 515/18688 [35:23<21:36:26, 4.28s/it] 3%|▎ | 516/18688 [35:27<21:34:56, 4.28s/it] 3%|▎ | 517/18688 [35:31<20:41:00, 4.10s/it] 3%|▎ | 518/18688 [35:35<20:54:06, 4.14s/it] 3%|▎ | 519/18688 [35:40<21:27:29, 4.25s/it] 3%|▎ | 520/18688 [35:44<21:31:28, 4.27s/it] {'loss': '1.028', 'grad_norm': '1.596', 'learning_rate': '2.768e-05', 'epoch': '0.02783', 'num_input_tokens_seen': 1718448, 'train_runtime': '2144', 'train_tokens_per_second': '801.4'}
3%|▎ | 520/18688 [35:44<21:31:28, 4.27s/it] 3%|▎ | 521/18688 [35:47<20:24:39, 4.04s/it] 3%|▎ | 522/18688 [35:52<21:24:03, 4.24s/it] 3%|▎ | 523/18688 [35:56<20:31:52, 4.07s/it] 3%|▎ | 524/18688 [36:00<20:26:40, 4.05s/it] 3%|▎ | 525/18688 [36:05<21:30:34, 4.26s/it] 3%|▎ | 526/18688 [36:08<20:38:59, 4.09s/it] 3%|▎ | 527/18688 [36:12<19:44:51, 3.91s/it] 3%|▎ | 528/18688 [36:16<19:47:48, 3.92s/it] 3%|▎ | 529/18688 [36:19<19:34:19, 3.88s/it] 3%|▎ | 530/18688 [36:24<20:37:19, 4.09s/it] {'loss': '0.9818', 'grad_norm': '1.325', 'learning_rate': '2.821e-05', 'epoch': '0.02836', 'num_input_tokens_seen': 1750696, 'train_runtime': '2185', 'train_tokens_per_second': '801.4'}
3%|▎ | 530/18688 [36:24<20:37:19, 4.09s/it] 3%|▎ | 531/18688 [36:27<19:37:23, 3.89s/it] 3%|▎ | 532/18688 [36:31<18:59:25, 3.77s/it] 3%|▎ | 533/18688 [36:35<18:52:21, 3.74s/it] 3%|▎ | 534/18688 [36:39<20:03:59, 3.98s/it] 3%|▎ | 535/18688 [36:43<20:18:52, 4.03s/it] 3%|▎ | 536/18688 [36:47<19:35:40, 3.89s/it] 3%|▎ | 537/18688 [36:52<20:52:15, 4.14s/it] 3%|▎ | 538/18688 [36:55<20:13:46, 4.01s/it] 3%|▎ | 539/18688 [36:59<19:25:43, 3.85s/it] 3%|▎ | 540/18688 [37:02<18:57:51, 3.76s/it] {'loss': '1.084', 'grad_norm': '1.345', 'learning_rate': '2.875e-05', 'epoch': '0.0289', 'num_input_tokens_seen': 1780496, 'train_runtime': '2223', 'train_tokens_per_second': '801'}
3%|▎ | 540/18688 [37:02<18:57:51, 3.76s/it] 3%|▎ | 541/18688 [37:07<19:59:41, 3.97s/it] 3%|▎ | 542/18688 [37:10<19:21:46, 3.84s/it] 3%|▎ | 543/18688 [37:14<18:58:27, 3.76s/it] 3%|▎ | 544/18688 [37:18<19:32:03, 3.88s/it] 3%|▎ | 545/18688 [37:23<21:39:07, 4.30s/it] 3%|▎ | 546/18688 [37:28<22:16:57, 4.42s/it] 3%|▎ | 547/18688 [37:32<21:07:21, 4.19s/it] 3%|▎ | 548/18688 [37:35<20:28:58, 4.06s/it] 3%|▎ | 549/18688 [37:39<19:42:26, 3.91s/it] 3%|▎ | 550/18688 [37:44<20:54:40, 4.15s/it] {'loss': '0.9857', 'grad_norm': '1.437', 'learning_rate': '2.928e-05', 'epoch': '0.02943', 'num_input_tokens_seen': 1814136, 'train_runtime': '2264', 'train_tokens_per_second': '801.2'}
3%|▎ | 550/18688 [37:44<20:54:40, 4.15s/it] 3%|▎ | 551/18688 [37:47<20:02:45, 3.98s/it] 3%|▎ | 552/18688 [37:51<20:15:15, 4.02s/it] 3%|▎ | 553/18688 [37:55<19:23:01, 3.85s/it] 3%|▎ | 554/18688 [37:59<19:52:22, 3.95s/it] 3%|▎ | 555/18688 [38:02<19:07:22, 3.80s/it] 3%|▎ | 556/18688 [38:07<19:42:07, 3.91s/it] 3%|▎ | 557/18688 [38:11<20:12:52, 4.01s/it] 3%|▎ | 558/18688 [38:15<20:07:41, 4.00s/it] 3%|▎ | 559/18688 [38:19<20:27:13, 4.06s/it] 3%|▎ | 560/18688 [38:23<19:31:53, 3.88s/it] {'loss': '0.9236', 'grad_norm': '1.365', 'learning_rate': '2.981e-05', 'epoch': '0.02997', 'num_input_tokens_seen': 1844456, 'train_runtime': '2303', 'train_tokens_per_second': '800.9'}
3%|▎ | 560/18688 [38:23<19:31:53, 3.88s/it] 3%|▎ | 561/18688 [38:27<20:15:51, 4.02s/it] 3%|▎ | 562/18688 [38:31<21:06:29, 4.19s/it] 3%|▎ | 563/18688 [38:35<19:59:13, 3.97s/it] 3%|▎ | 564/18688 [38:39<20:33:53, 4.08s/it] 3%|▎ | 565/18688 [38:44<21:02:49, 4.18s/it] 3%|▎ | 566/18688 [38:48<20:41:49, 4.11s/it] 3%|▎ | 567/18688 [38:51<19:51:42, 3.95s/it] 3%|▎ | 568/18688 [38:55<20:19:25, 4.04s/it] 3%|▎ | 569/18688 [38:59<19:47:07, 3.93s/it] 3%|▎ | 570/18688 [39:03<20:21:42, 4.05s/it] {'loss': '1.049', 'grad_norm': '1.308', 'learning_rate': '3.035e-05', 'epoch': '0.0305', 'num_input_tokens_seen': 1877632, 'train_runtime': '2344', 'train_tokens_per_second': '801.1'}
3%|▎ | 570/18688 [39:03<20:21:42, 4.05s/it] 3%|▎ | 571/18688 [39:08<21:08:14, 4.20s/it] 3%|▎ | 572/18688 [39:12<20:37:31, 4.10s/it] 3%|▎ | 573/18688 [39:16<21:24:05, 4.25s/it] 3%|▎ | 574/18688 [39:20<20:59:03, 4.17s/it] 3%|▎ | 575/18688 [39:24<20:04:48, 3.99s/it] 3%|▎ | 576/18688 [39:28<20:10:58, 4.01s/it] 3%|▎ | 577/18688 [39:33<21:06:18, 4.20s/it] 3%|▎ | 578/18688 [39:37<20:33:43, 4.09s/it] 3%|▎ | 579/18688 [39:40<19:33:02, 3.89s/it] 3%|▎ | 580/18688 [39:45<20:50:10, 4.14s/it] {'loss': '0.9685', 'grad_norm': '1.413', 'learning_rate': '3.088e-05', 'epoch': '0.03104', 'num_input_tokens_seen': 1911192, 'train_runtime': '2385', 'train_tokens_per_second': '801.3'}
3%|▎ | 580/18688 [39:45<20:50:10, 4.14s/it] 3%|▎ | 581/18688 [39:49<20:39:37, 4.11s/it] 3%|▎ | 582/18688 [39:52<19:51:51, 3.95s/it] 3%|▎ | 583/18688 [39:56<20:08:57, 4.01s/it] 3%|▎ | 584/18688 [40:01<20:19:33, 4.04s/it] 3%|▎ | 585/18688 [40:04<19:53:27, 3.96s/it] 3%|▎ | 586/18688 [40:09<20:40:52, 4.11s/it] 3%|▎ | 587/18688 [40:12<20:01:54, 3.98s/it] 3%|▎ | 588/18688 [40:17<20:24:19, 4.06s/it] 3%|▎ | 589/18688 [40:21<20:53:45, 4.16s/it] 3%|▎ | 590/18688 [40:25<20:18:27, 4.04s/it] {'loss': '1.008', 'grad_norm': '1.244', 'learning_rate': '3.141e-05', 'epoch': '0.03157', 'num_input_tokens_seen': 1943152, 'train_runtime': '2425', 'train_tokens_per_second': '801.2'}
3%|▎ | 590/18688 [40:25<20:18:27, 4.04s/it] 3%|▎ | 591/18688 [40:29<19:51:16, 3.95s/it] 3%|▎ | 592/18688 [40:33<21:01:13, 4.18s/it] 3%|▎ | 593/18688 [40:37<20:25:21, 4.06s/it] 3%|▎ | 594/18688 [40:41<20:01:52, 3.99s/it] 3%|▎ | 595/18688 [40:45<20:28:31, 4.07s/it] 3%|▎ | 596/18688 [40:49<20:34:17, 4.09s/it] 3%|▎ | 597/18688 [40:53<19:40:17, 3.91s/it] 3%|▎ | 598/18688 [40:57<19:25:56, 3.87s/it] 3%|▎ | 599/18688 [41:02<21:40:40, 4.31s/it] 3%|▎ | 600/18688 [41:06<21:27:38, 4.27s/it] {'loss': '1.011', 'grad_norm': '1.394', 'learning_rate': '3.195e-05', 'epoch': '0.03211', 'num_input_tokens_seen': 1976328, 'train_runtime': '2467', 'train_tokens_per_second': '801.2'}
3%|▎ | 600/18688 [41:06<21:27:38, 4.27s/it] 3%|▎ | 601/18688 [41:10<20:43:22, 4.12s/it] 3%|▎ | 602/18688 [41:14<20:23:50, 4.06s/it] 3%|▎ | 603/18688 [41:18<19:50:52, 3.95s/it] 3%|▎ | 604/18688 [41:22<19:59:45, 3.98s/it] 3%|▎ | 605/18688 [41:27<21:52:17, 4.35s/it] 3%|▎ | 606/18688 [41:31<21:19:42, 4.25s/it] 3%|▎ | 607/18688 [41:35<20:33:24, 4.09s/it] 3%|▎ | 608/18688 [41:39<20:24:36, 4.06s/it] 3%|▎ | 609/18688 [41:43<21:02:25, 4.19s/it] 3%|▎ | 610/18688 [41:47<20:34:10, 4.10s/it] {'loss': '0.9882', 'grad_norm': '1.48', 'learning_rate': '3.248e-05', 'epoch': '0.03264', 'num_input_tokens_seen': 2008480, 'train_runtime': '2507', 'train_tokens_per_second': '801'}
3%|▎ | 610/18688 [41:47<20:34:10, 4.10s/it] 3%|▎ | 611/18688 [41:51<20:37:15, 4.11s/it] 3%|▎ | 612/18688 [41:56<21:56:50, 4.37s/it] 3%|▎ | 613/18688 [42:00<21:40:29, 4.32s/it] 3%|▎ | 614/18688 [42:04<21:15:36, 4.23s/it] 3%|▎ | 615/18688 [42:08<20:13:29, 4.03s/it] 3%|▎ | 616/18688 [42:13<22:39:44, 4.51s/it] 3%|▎ | 617/18688 [42:18<23:17:44, 4.64s/it] 3%|▎ | 618/18688 [42:22<21:44:54, 4.33s/it] 3%|▎ | 619/18688 [42:26<21:25:39, 4.27s/it] 3%|▎ | 620/18688 [42:30<20:12:10, 4.03s/it] {'loss': '1.017', 'grad_norm': '1.724', 'learning_rate': '3.301e-05', 'epoch': '0.03318', 'num_input_tokens_seen': 2043576, 'train_runtime': '2550', 'train_tokens_per_second': '801.4'}
3%|▎ | 620/18688 [42:30<20:12:10, 4.03s/it] 3%|▎ | 621/18688 [42:34<20:39:56, 4.12s/it] 3%|▎ | 622/18688 [42:37<19:41:56, 3.93s/it] 3%|▎ | 623/18688 [42:42<20:35:43, 4.10s/it] 3%|▎ | 624/18688 [42:46<19:59:52, 3.99s/it] 3%|▎ | 625/18688 [42:49<19:41:31, 3.92s/it] 3%|▎ | 626/18688 [42:54<19:59:21, 3.98s/it] 3%|▎ | 627/18688 [42:58<20:14:07, 4.03s/it] 3%|▎ | 628/18688 [43:03<21:46:19, 4.34s/it] 3%|▎ | 629/18688 [43:06<20:26:23, 4.07s/it] 3%|▎ | 630/18688 [43:10<20:29:44, 4.09s/it] {'loss': '1.035', 'grad_norm': '1.314', 'learning_rate': '3.355e-05', 'epoch': '0.03371', 'num_input_tokens_seen': 2077600, 'train_runtime': '2591', 'train_tokens_per_second': '801.9'}
3%|▎ | 630/18688 [43:10<20:29:44, 4.09s/it] 3%|▎ | 631/18688 [43:15<20:59:55, 4.19s/it] 3%|▎ | 632/18688 [43:19<20:46:06, 4.14s/it] 3%|▎ | 633/18688 [43:23<20:42:08, 4.13s/it] 3%|▎ | 634/18688 [43:27<20:43:43, 4.13s/it] 3%|▎ | 635/18688 [43:31<20:34:21, 4.10s/it] 3%|▎ | 636/18688 [43:35<19:52:45, 3.96s/it] 3%|▎ | 637/18688 [43:39<20:30:23, 4.09s/it] 3%|▎ | 638/18688 [43:43<20:52:15, 4.16s/it] 3%|▎ | 639/18688 [43:47<20:28:22, 4.08s/it] 3%|▎ | 640/18688 [43:51<19:50:57, 3.96s/it] {'loss': '1.073', 'grad_norm': '1.409', 'learning_rate': '3.408e-05', 'epoch': '0.03425', 'num_input_tokens_seen': 2110816, 'train_runtime': '2631', 'train_tokens_per_second': '802.2'}
3%|▎ | 640/18688 [43:51<19:50:57, 3.96s/it] 3%|▎ | 641/18688 [43:56<21:35:12, 4.31s/it] 3%|▎ | 642/18688 [44:01<22:10:52, 4.42s/it] 3%|▎ | 643/18688 [44:05<21:22:39, 4.26s/it] 3%|▎ | 644/18688 [44:09<20:55:45, 4.18s/it] 3%|▎ | 645/18688 [44:13<21:12:24, 4.23s/it] 3%|▎ | 646/18688 [44:18<22:14:50, 4.44s/it] 3%|▎ | 647/18688 [44:21<20:57:15, 4.18s/it] 3%|▎ | 648/18688 [44:26<21:41:16, 4.33s/it] 3%|▎ | 649/18688 [44:30<21:13:49, 4.24s/it] 3%|▎ | 650/18688 [44:35<21:27:48, 4.28s/it] {'loss': '0.9871', 'grad_norm': '1.169', 'learning_rate': '3.461e-05', 'epoch': '0.03478', 'num_input_tokens_seen': 2146864, 'train_runtime': '2675', 'train_tokens_per_second': '802.5'}
3%|▎ | 650/18688 [44:35<21:27:48, 4.28s/it] 3%|▎ | 651/18688 [44:39<21:34:27, 4.31s/it] 3%|▎ | 652/18688 [44:44<22:16:23, 4.45s/it] 3%|▎ | 653/18688 [44:47<20:59:41, 4.19s/it] 3%|▎ | 654/18688 [44:51<20:33:03, 4.10s/it] 4%|▎ | 655/18688 [44:55<19:47:55, 3.95s/it] 4%|▎ | 656/18688 [44:59<20:37:56, 4.12s/it] 4%|▎ | 657/18688 [45:03<20:35:31, 4.11s/it] 4%|▎ | 658/18688 [45:07<19:36:05, 3.91s/it] 4%|▎ | 659/18688 [45:11<19:55:14, 3.98s/it] 4%|▎ | 660/18688 [45:15<20:19:07, 4.06s/it] {'loss': '1.066', 'grad_norm': '1.338', 'learning_rate': '3.515e-05', 'epoch': '0.03532', 'num_input_tokens_seen': 2179224, 'train_runtime': '2716', 'train_tokens_per_second': '802.4'}
4%|▎ | 660/18688 [45:15<20:19:07, 4.06s/it] 4%|▎ | 661/18688 [45:19<20:04:47, 4.01s/it] 4%|▎ | 662/18688 [45:24<20:47:23, 4.15s/it] 4%|▎ | 663/18688 [45:27<20:07:24, 4.02s/it] 4%|▎ | 664/18688 [45:33<22:20:30, 4.46s/it] 4%|▎ | 665/18688 [45:37<21:28:21, 4.29s/it] 4%|▎ | 666/18688 [45:40<20:24:32, 4.08s/it] 4%|▎ | 667/18688 [45:44<20:19:00, 4.06s/it] 4%|▎ | 668/18688 [45:48<19:44:37, 3.94s/it] 4%|▎ | 669/18688 [45:52<19:39:37, 3.93s/it] 4%|▎ | 670/18688 [45:56<20:21:39, 4.07s/it] {'loss': '1.077', 'grad_norm': '1.594', 'learning_rate': '3.568e-05', 'epoch': '0.03585', 'num_input_tokens_seen': 2211824, 'train_runtime': '2757', 'train_tokens_per_second': '802.3'}
4%|▎ | 670/18688 [45:56<20:21:39, 4.07s/it] 4%|▎ | 671/18688 [46:01<21:47:22, 4.35s/it] 4%|▎ | 672/18688 [46:06<22:13:43, 4.44s/it] 4%|▎ | 673/18688 [46:11<22:44:45, 4.55s/it] 4%|▎ | 674/18688 [46:15<22:35:29, 4.51s/it] 4%|▎ | 675/18688 [46:19<21:48:07, 4.36s/it] 4%|▎ | 676/18688 [46:23<21:30:33, 4.30s/it] 4%|▎ | 677/18688 [46:27<21:05:02, 4.21s/it] 4%|▎ | 678/18688 [46:31<20:50:28, 4.17s/it] 4%|▎ | 679/18688 [46:36<21:04:37, 4.21s/it] 4%|▎ | 680/18688 [46:40<20:40:28, 4.13s/it] {'loss': '1.057', 'grad_norm': '1.693', 'learning_rate': '3.621e-05', 'epoch': '0.03639', 'num_input_tokens_seen': 2246768, 'train_runtime': '2800', 'train_tokens_per_second': '802.4'}
4%|▎ | 680/18688 [46:40<20:40:28, 4.13s/it] 4%|▎ | 681/18688 [46:44<21:10:19, 4.23s/it] 4%|▎ | 682/18688 [46:48<20:58:49, 4.19s/it] 4%|▎ | 683/18688 [46:52<20:21:39, 4.07s/it] 4%|▎ | 684/18688 [46:56<20:43:21, 4.14s/it] 4%|▎ | 685/18688 [47:00<19:45:18, 3.95s/it] 4%|▎ | 686/18688 [47:03<19:11:12, 3.84s/it] 4%|▎ | 687/18688 [47:08<19:46:43, 3.96s/it] 4%|▎ | 688/18688 [47:12<20:38:08, 4.13s/it] 4%|▎ | 689/18688 [47:16<20:50:12, 4.17s/it] 4%|▎ | 690/18688 [47:21<21:07:29, 4.23s/it] {'loss': '1.032', 'grad_norm': '1.271', 'learning_rate': '3.675e-05', 'epoch': '0.03692', 'num_input_tokens_seen': 2280616, 'train_runtime': '2841', 'train_tokens_per_second': '802.7'}
4%|▎ | 690/18688 [47:21<21:07:29, 4.23s/it] 4%|▎ | 691/18688 [47:25<21:49:23, 4.37s/it] 4%|▎ | 692/18688 [47:30<21:45:36, 4.35s/it] 4%|▎ | 693/18688 [47:34<20:57:41, 4.19s/it] 4%|▎ | 694/18688 [47:37<20:23:49, 4.08s/it] 4%|▎ | 695/18688 [47:42<20:40:05, 4.14s/it] 4%|▎ | 696/18688 [47:46<21:33:30, 4.31s/it] 4%|▎ | 697/18688 [47:50<20:50:38, 4.17s/it] 4%|▎ | 698/18688 [47:54<20:10:07, 4.04s/it] 4%|▎ | 699/18688 [47:58<20:02:28, 4.01s/it] 4%|▎ | 700/18688 [48:02<20:29:43, 4.10s/it] {'loss': '1.007', 'grad_norm': '0.9936', 'learning_rate': '3.728e-05', 'epoch': '0.03746', 'num_input_tokens_seen': 2314848, 'train_runtime': '2883', 'train_tokens_per_second': '803'}
4%|▎ | 700/18688 [48:02<20:29:43, 4.10s/it] 4%|▍ | 701/18688 [48:06<20:28:57, 4.10s/it] 4%|▍ | 702/18688 [48:10<20:04:42, 4.02s/it] 4%|▍ | 703/18688 [48:14<20:23:23, 4.08s/it] 4%|▍ | 704/18688 [48:18<20:25:56, 4.09s/it] 4%|▍ | 705/18688 [48:22<19:40:32, 3.94s/it] 4%|▍ | 706/18688 [48:26<19:53:18, 3.98s/it] 4%|▍ | 707/18688 [48:31<20:28:14, 4.10s/it] 4%|▍ | 708/18688 [48:34<20:09:21, 4.04s/it] 4%|▍ | 709/18688 [48:39<20:27:42, 4.10s/it] 4%|▍ | 710/18688 [48:42<19:43:06, 3.95s/it] {'loss': '1.103', 'grad_norm': '1.329', 'learning_rate': '3.781e-05', 'epoch': '0.03799', 'num_input_tokens_seen': 2347584, 'train_runtime': '2923', 'train_tokens_per_second': '803.2'}
4%|▍ | 710/18688 [48:42<19:43:06, 3.95s/it] 4%|▍ | 711/18688 [48:46<19:13:57, 3.85s/it] 4%|▍ | 712/18688 [48:49<18:50:47, 3.77s/it] 4%|▍ | 713/18688 [48:53<19:12:53, 3.85s/it] 4%|▍ | 714/18688 [48:57<19:25:28, 3.89s/it] 4%|▍ | 715/18688 [49:01<19:33:32, 3.92s/it] 4%|▍ | 716/18688 [49:06<20:14:25, 4.05s/it] 4%|▍ | 717/18688 [49:10<20:47:51, 4.17s/it] 4%|▍ | 718/18688 [49:14<20:07:47, 4.03s/it] 4%|▍ | 719/18688 [49:18<19:37:07, 3.93s/it] 4%|▍ | 720/18688 [49:22<20:35:43, 4.13s/it] {'loss': '1.063', 'grad_norm': '1.141', 'learning_rate': '3.835e-05', 'epoch': '0.03853', 'num_input_tokens_seen': 2379992, 'train_runtime': '2963', 'train_tokens_per_second': '803.3'}
4%|▍ | 720/18688 [49:22<20:35:43, 4.13s/it] 4%|▍ | 721/18688 [49:26<20:09:23, 4.04s/it] 4%|▍ | 722/18688 [49:30<19:58:41, 4.00s/it] 4%|▍ | 723/18688 [49:34<20:21:15, 4.08s/it] 4%|▍ | 724/18688 [49:39<21:17:55, 4.27s/it] 4%|▍ | 725/18688 [49:44<21:48:48, 4.37s/it] 4%|▍ | 726/18688 [49:47<20:26:37, 4.10s/it] 4%|▍ | 727/18688 [49:51<20:16:52, 4.07s/it] 4%|▍ | 728/18688 [49:55<19:59:19, 4.01s/it] 4%|▍ | 729/18688 [49:59<20:21:25, 4.08s/it] 4%|▍ | 730/18688 [50:03<19:27:06, 3.90s/it] {'loss': '0.9936', 'grad_norm': '1.513', 'learning_rate': '3.888e-05', 'epoch': '0.03906', 'num_input_tokens_seen': 2413688, 'train_runtime': '3003', 'train_tokens_per_second': '803.7'}
4%|▍ | 730/18688 [50:03<19:27:06, 3.90s/it] 4%|▍ | 731/18688 [50:06<19:09:22, 3.84s/it] 4%|▍ | 732/18688 [50:12<21:10:47, 4.25s/it] 4%|▍ | 733/18688 [50:15<20:12:46, 4.05s/it] 4%|▍ | 734/18688 [50:19<20:37:58, 4.14s/it] 4%|▍ | 735/18688 [50:23<20:11:38, 4.05s/it] 4%|▍ | 736/18688 [50:27<19:38:37, 3.94s/it] 4%|▍ | 737/18688 [50:31<20:25:56, 4.10s/it] 4%|▍ | 738/18688 [50:35<19:26:18, 3.90s/it] 4%|▍ | 739/18688 [50:39<19:01:06, 3.81s/it] 4%|▍ | 740/18688 [50:43<19:22:27, 3.89s/it] {'loss': '1.016', 'grad_norm': '1.311', 'learning_rate': '3.941e-05', 'epoch': '0.0396', 'num_input_tokens_seen': 2446864, 'train_runtime': '3043', 'train_tokens_per_second': '804.1'}
4%|▍ | 740/18688 [50:43<19:22:27, 3.89s/it] 4%|▍ | 741/18688 [50:46<18:56:40, 3.80s/it] 4%|▍ | 742/18688 [50:50<18:31:13, 3.72s/it] 4%|▍ | 743/18688 [50:54<19:11:53, 3.85s/it] 4%|▍ | 744/18688 [50:57<18:33:05, 3.72s/it] 4%|▍ | 745/18688 [51:01<18:14:20, 3.66s/it] 4%|▍ | 746/18688 [51:04<18:06:14, 3.63s/it] 4%|▍ | 747/18688 [51:08<18:02:11, 3.62s/it] 4%|▍ | 748/18688 [51:12<18:09:55, 3.65s/it] 4%|▍ | 749/18688 [51:17<20:09:10, 4.04s/it] 4%|▍ | 750/18688 [51:21<20:27:43, 4.11s/it] {'loss': '0.9841', 'grad_norm': '1.102', 'learning_rate': '3.995e-05', 'epoch': '0.04013', 'num_input_tokens_seen': 2477248, 'train_runtime': '3081', 'train_tokens_per_second': '803.9'}
4%|▍ | 750/18688 [51:21<20:27:43, 4.11s/it] 4%|▍ | 751/18688 [51:25<19:50:07, 3.98s/it] 4%|▍ | 752/18688 [51:28<19:34:09, 3.93s/it] 4%|▍ | 753/18688 [51:32<19:42:16, 3.96s/it] 4%|▍ | 754/18688 [51:36<19:36:29, 3.94s/it] 4%|▍ | 755/18688 [51:41<20:47:02, 4.17s/it] 4%|▍ | 756/18688 [51:46<21:47:47, 4.38s/it] 4%|▍ | 757/18688 [51:49<20:34:48, 4.13s/it] 4%|▍ | 758/18688 [51:53<20:26:25, 4.10s/it] 4%|▍ | 759/18688 [51:58<20:43:49, 4.16s/it] 4%|▍ | 760/18688 [52:03<22:03:05, 4.43s/it] {'loss': '1.052', 'grad_norm': '1.43', 'learning_rate': '4.048e-05', 'epoch': '0.04067', 'num_input_tokens_seen': 2512440, 'train_runtime': '3123', 'train_tokens_per_second': '804.4'}
4%|▍ | 760/18688 [52:03<22:03:05, 4.43s/it] 4%|▍ | 761/18688 [52:08<22:48:36, 4.58s/it] 4%|▍ | 762/18688 [52:12<22:22:43, 4.49s/it] 4%|▍ | 763/18688 [52:16<22:14:44, 4.47s/it] 4%|▍ | 764/18688 [52:20<21:03:54, 4.23s/it] 4%|▍ | 765/18688 [52:24<19:50:05, 3.98s/it] 4%|▍ | 766/18688 [52:27<19:27:53, 3.91s/it] 4%|▍ | 767/18688 [52:31<19:08:14, 3.84s/it] 4%|▍ | 768/18688 [52:35<19:29:10, 3.91s/it] 4%|▍ | 769/18688 [52:39<19:31:30, 3.92s/it] 4%|▍ | 770/18688 [52:44<20:33:01, 4.13s/it] {'loss': '1.018', 'grad_norm': '1.268', 'learning_rate': '4.101e-05', 'epoch': '0.0412', 'num_input_tokens_seen': 2546856, 'train_runtime': '3164', 'train_tokens_per_second': '804.9'}
4%|▍ | 770/18688 [52:44<20:33:01, 4.13s/it] 4%|▍ | 771/18688 [52:47<20:07:46, 4.04s/it] 4%|▍ | 772/18688 [52:51<19:48:52, 3.98s/it] 4%|▍ | 773/18688 [52:55<19:45:20, 3.97s/it] 4%|▍ | 774/18688 [52:59<19:11:29, 3.86s/it] 4%|▍ | 775/18688 [53:02<18:34:26, 3.73s/it] 4%|▍ | 776/18688 [53:06<18:10:14, 3.65s/it] 4%|▍ | 777/18688 [53:10<19:34:43, 3.94s/it] 4%|▍ | 778/18688 [53:14<19:48:31, 3.98s/it] 4%|▍ | 779/18688 [53:19<20:16:25, 4.08s/it] 4%|▍ | 780/18688 [53:24<22:25:39, 4.51s/it] {'loss': '1.005', 'grad_norm': '1.342', 'learning_rate': '4.155e-05', 'epoch': '0.04174', 'num_input_tokens_seen': 2579896, 'train_runtime': '3205', 'train_tokens_per_second': '805'}
4%|▍ | 780/18688 [53:24<22:25:39, 4.51s/it] 4%|▍ | 781/18688 [53:28<22:03:16, 4.43s/it] 4%|▍ | 782/18688 [53:33<21:43:17, 4.37s/it] 4%|▍ | 783/18688 [53:36<20:51:07, 4.19s/it] 4%|▍ | 784/18688 [53:40<20:06:57, 4.04s/it] 4%|▍ | 785/18688 [53:44<20:14:58, 4.07s/it] 4%|▍ | 786/18688 [53:48<19:30:25, 3.92s/it] 4%|▍ | 787/18688 [53:52<19:49:54, 3.99s/it] 4%|▍ | 788/18688 [53:56<20:05:55, 4.04s/it] 4%|▍ | 789/18688 [54:00<20:03:01, 4.03s/it] 4%|▍ | 790/18688 [54:05<21:01:14, 4.23s/it] {'loss': '1.116', 'grad_norm': '1.28', 'learning_rate': '4.208e-05', 'epoch': '0.04227', 'num_input_tokens_seen': 2612736, 'train_runtime': '3245', 'train_tokens_per_second': '805.1'}
4%|▍ | 790/18688 [54:05<21:01:14, 4.23s/it] 4%|▍ | 791/18688 [54:09<21:04:39, 4.24s/it] 4%|▍ | 792/18688 [54:14<22:00:17, 4.43s/it] 4%|▍ | 793/18688 [54:18<22:03:12, 4.44s/it] 4%|▍ | 794/18688 [54:22<20:50:04, 4.19s/it] 4%|▍ | 795/18688 [54:26<21:01:14, 4.23s/it] 4%|▍ | 796/18688 [54:31<20:57:22, 4.22s/it] 4%|▍ | 797/18688 [54:35<20:35:29, 4.14s/it] 4%|▍ | 798/18688 [54:39<20:50:26, 4.19s/it] 4%|▍ | 799/18688 [54:43<21:19:27, 4.29s/it] 4%|▍ | 800/18688 [54:48<21:06:01, 4.25s/it] {'loss': '1.082', 'grad_norm': '1.303', 'learning_rate': '4.261e-05', 'epoch': '0.04281', 'num_input_tokens_seen': 2648032, 'train_runtime': '3288', 'train_tokens_per_second': '805.4'}
4%|▍ | 800/18688 [54:48<21:06:01, 4.25s/it] 4%|▍ | 801/18688 [54:51<19:55:50, 4.01s/it] 4%|▍ | 802/18688 [54:55<20:10:56, 4.06s/it] 4%|▍ | 803/18688 [54:59<20:06:42, 4.05s/it] 4%|▍ | 804/18688 [55:03<20:03:18, 4.04s/it] 4%|▍ | 805/18688 [55:08<20:53:36, 4.21s/it] 4%|▍ | 806/18688 [55:12<20:23:13, 4.10s/it] 4%|▍ | 807/18688 [55:17<21:43:40, 4.37s/it] 4%|▍ | 808/18688 [55:21<21:40:19, 4.36s/it] 4%|▍ | 809/18688 [55:25<21:11:07, 4.27s/it] 4%|▍ | 810/18688 [55:30<22:07:52, 4.46s/it] {'loss': '1.067', 'grad_norm': '0.9322', 'learning_rate': '4.315e-05', 'epoch': '0.04334', 'num_input_tokens_seen': 2683584, 'train_runtime': '3330', 'train_tokens_per_second': '805.8'}
4%|▍ | 810/18688 [55:30<22:07:52, 4.46s/it] 4%|▍ | 811/18688 [55:34<21:11:20, 4.27s/it] 4%|▍ | 812/18688 [55:39<22:10:00, 4.46s/it] 4%|▍ | 813/18688 [55:43<22:09:09, 4.46s/it] 4%|▍ | 814/18688 [55:47<20:42:45, 4.17s/it] 4%|▍ | 815/18688 [55:51<20:42:04, 4.17s/it] 4%|▍ | 816/18688 [55:55<20:06:25, 4.05s/it] 4%|▍ | 817/18688 [55:59<21:08:28, 4.26s/it] 4%|▍ | 818/18688 [56:04<21:57:24, 4.42s/it] 4%|▍ | 819/18688 [56:08<20:41:30, 4.17s/it] 4%|▍ | 820/18688 [56:11<20:03:58, 4.04s/it] {'loss': '1.016', 'grad_norm': '1.563', 'learning_rate': '4.368e-05', 'epoch': '0.04388', 'num_input_tokens_seen': 2716960, 'train_runtime': '3372', 'train_tokens_per_second': '805.7'}
4%|▍ | 820/18688 [56:11<20:03:58, 4.04s/it] 4%|▍ | 821/18688 [56:15<19:32:32, 3.94s/it] 4%|▍ | 822/18688 [56:20<20:26:49, 4.12s/it] 4%|▍ | 823/18688 [56:24<21:17:42, 4.29s/it] 4%|▍ | 824/18688 [56:28<20:28:12, 4.13s/it] 4%|▍ | 825/18688 [56:32<19:31:48, 3.94s/it] 4%|▍ | 826/18688 [56:36<20:20:18, 4.10s/it] 4%|▍ | 827/18688 [56:40<20:37:51, 4.16s/it] 4%|▍ | 828/18688 [56:44<20:00:43, 4.03s/it] 4%|▍ | 829/18688 [56:48<19:31:31, 3.94s/it] 4%|▍ | 830/18688 [56:52<19:04:45, 3.85s/it] {'loss': '0.9962', 'grad_norm': '1.449', 'learning_rate': '4.421e-05', 'epoch': '0.04441', 'num_input_tokens_seen': 2747024, 'train_runtime': '3412', 'train_tokens_per_second': '805.1'}
4%|▍ | 830/18688 [56:52<19:04:45, 3.85s/it] 4%|▍ | 831/18688 [56:55<18:39:08, 3.76s/it] 4%|▍ | 832/18688 [56:59<19:09:31, 3.86s/it] 4%|▍ | 833/18688 [57:03<19:24:45, 3.91s/it] 4%|▍ | 834/18688 [57:08<20:09:48, 4.07s/it] 4%|▍ | 835/18688 [57:12<20:42:21, 4.18s/it] 4%|▍ | 836/18688 [57:16<19:56:58, 4.02s/it] 4%|▍ | 837/18688 [57:20<20:37:13, 4.16s/it] 4%|▍ | 838/18688 [57:24<20:39:26, 4.17s/it] 4%|▍ | 839/18688 [57:29<21:00:01, 4.24s/it] 4%|▍ | 840/18688 [57:33<20:25:08, 4.12s/it] {'loss': '0.9979', 'grad_norm': '1.033', 'learning_rate': '4.475e-05', 'epoch': '0.04495', 'num_input_tokens_seen': 2780568, 'train_runtime': '3453', 'train_tokens_per_second': '805.2'}
4%|▍ | 840/18688 [57:33<20:25:08, 4.12s/it] 5%|▍ | 841/18688 [57:36<19:26:57, 3.92s/it] 5%|▍ | 842/18688 [57:40<19:45:26, 3.99s/it] 5%|▍ | 843/18688 [57:44<19:04:41, 3.85s/it] 5%|▍ | 844/18688 [57:48<19:32:28, 3.94s/it] 5%|▍ | 845/18688 [57:51<18:48:48, 3.80s/it] 5%|▍ | 846/18688 [57:57<20:55:16, 4.22s/it] 5%|▍ | 847/18688 [58:00<19:49:28, 4.00s/it] 5%|▍ | 848/18688 [58:04<20:25:55, 4.12s/it] 5%|▍ | 849/18688 [58:09<21:39:32, 4.37s/it] 5%|▍ | 850/18688 [58:14<21:55:20, 4.42s/it] {'loss': '1.018', 'grad_norm': '1.301', 'learning_rate': '4.528e-05', 'epoch': '0.04548', 'num_input_tokens_seen': 2816704, 'train_runtime': '3494', 'train_tokens_per_second': '806'}
5%|▍ | 850/18688 [58:14<21:55:20, 4.42s/it] 5%|▍ | 851/18688 [58:19<22:31:47, 4.55s/it] 5%|▍ | 852/18688 [58:23<21:24:03, 4.32s/it] 5%|▍ | 853/18688 [58:26<20:34:29, 4.15s/it] 5%|▍ | 854/18688 [58:31<21:13:16, 4.28s/it] 5%|▍ | 855/18688 [58:35<21:33:03, 4.35s/it] 5%|▍ | 856/18688 [58:40<21:18:49, 4.30s/it] 5%|▍ | 857/18688 [58:43<20:19:52, 4.10s/it] 5%|▍ | 858/18688 [58:48<20:47:07, 4.20s/it] 5%|▍ | 859/18688 [58:51<20:10:46, 4.07s/it] 5%|▍ | 860/18688 [58:56<20:23:53, 4.12s/it] {'loss': '1.036', 'grad_norm': '1.243', 'learning_rate': '4.581e-05', 'epoch': '0.04602', 'num_input_tokens_seen': 2852384, 'train_runtime': '3536', 'train_tokens_per_second': '806.6'}
5%|▍ | 860/18688 [58:56<20:23:53, 4.12s/it] 5%|▍ | 861/18688 [59:00<20:32:12, 4.15s/it] 5%|▍ | 862/18688 [59:04<19:54:15, 4.02s/it] 5%|▍ | 863/18688 [59:08<21:01:23, 4.25s/it] 5%|▍ | 864/18688 [59:13<21:18:41, 4.30s/it] 5%|▍ | 865/18688 [59:16<20:01:19, 4.04s/it] 5%|▍ | 866/18688 [59:21<20:28:19, 4.14s/it] 5%|▍ | 867/18688 [59:24<19:49:21, 4.00s/it] 5%|▍ | 868/18688 [59:28<20:01:07, 4.04s/it] 5%|▍ | 869/18688 [59:33<20:53:46, 4.22s/it] 5%|▍ | 870/18688 [59:38<22:20:50, 4.52s/it] {'loss': '1.06', 'grad_norm': '1.249', 'learning_rate': '4.635e-05', 'epoch': '0.04656', 'num_input_tokens_seen': 2887288, 'train_runtime': '3579', 'train_tokens_per_second': '806.8'}
5%|▍ | 870/18688 [59:38<22:20:50, 4.52s/it] 5%|▍ | 871/18688 [59:43<22:07:11, 4.47s/it] 5%|▍ | 872/18688 [59:47<21:39:50, 4.38s/it] 5%|▍ | 873/18688 [59:51<21:42:44, 4.39s/it] 5%|▍ | 874/18688 [59:55<20:39:53, 4.18s/it] 5%|▍ | 875/18688 [59:59<20:11:16, 4.08s/it] 5%|▍ | 876/18688 [1:00:02<19:23:08, 3.92s/it] 5%|▍ | 877/18688 [1:00:07<19:46:35, 4.00s/it] 5%|▍ | 878/18688 [1:00:10<19:20:01, 3.91s/it] 5%|▍ | 879/18688 [1:00:14<19:53:36, 4.02s/it] 5%|▍ | 880/18688 [1:00:18<19:29:12, 3.94s/it] {'loss': '1.074', 'grad_norm': '0.9434', 'learning_rate': '4.688e-05', 'epoch': '0.04709', 'num_input_tokens_seen': 2919984, 'train_runtime': '3619', 'train_tokens_per_second': '806.9'}
5%|▍ | 880/18688 [1:00:18<19:29:12, 3.94s/it] 5%|▍ | 881/18688 [1:00:23<20:33:16, 4.16s/it] 5%|▍ | 882/18688 [1:00:27<20:39:48, 4.18s/it] 5%|▍ | 883/18688 [1:00:31<20:24:20, 4.13s/it] 5%|▍ | 884/18688 [1:00:35<19:28:17, 3.94s/it] 5%|▍ | 885/18688 [1:00:39<20:02:55, 4.05s/it] 5%|▍ | 886/18688 [1:00:44<21:15:05, 4.30s/it] 5%|▍ | 887/18688 [1:00:48<21:29:50, 4.35s/it] 5%|▍ | 888/18688 [1:00:52<20:57:09, 4.24s/it] 5%|▍ | 889/18688 [1:00:57<21:26:36, 4.34s/it] 5%|▍ | 890/18688 [1:01:01<20:29:21, 4.14s/it] {'loss': '1.009', 'grad_norm': '1.134', 'learning_rate': '4.741e-05', 'epoch': '0.04763', 'num_input_tokens_seen': 2954488, 'train_runtime': '3661', 'train_tokens_per_second': '807'}
5%|▍ | 890/18688 [1:01:01<20:29:21, 4.14s/it] 5%|▍ | 891/18688 [1:01:04<19:33:40, 3.96s/it] 5%|▍ | 892/18688 [1:01:08<19:40:06, 3.98s/it] 5%|▍ | 893/18688 [1:01:12<19:47:16, 4.00s/it] 5%|▍ | 894/18688 [1:01:16<19:27:10, 3.94s/it] 5%|▍ | 895/18688 [1:01:20<19:41:40, 3.98s/it] 5%|▍ | 896/18688 [1:01:24<20:19:02, 4.11s/it] 5%|▍ | 897/18688 [1:01:29<20:56:31, 4.24s/it] 5%|▍ | 898/18688 [1:01:33<20:40:48, 4.18s/it] 5%|▍ | 899/18688 [1:01:38<21:08:50, 4.28s/it] 5%|▍ | 900/18688 [1:01:41<20:23:08, 4.13s/it] {'loss': '1.015', 'grad_norm': '1.119', 'learning_rate': '4.795e-05', 'epoch': '0.04816', 'num_input_tokens_seen': 2986632, 'train_runtime': '3702', 'train_tokens_per_second': '806.8'}
5%|▍ | 900/18688 [1:01:41<20:23:08, 4.13s/it] 5%|▍ | 901/18688 [1:01:45<20:28:48, 4.15s/it] 5%|▍ | 902/18688 [1:01:49<19:28:49, 3.94s/it] 5%|▍ | 903/18688 [1:01:53<20:08:56, 4.08s/it] 5%|▍ | 904/18688 [1:01:57<19:35:34, 3.97s/it] 5%|▍ | 905/18688 [1:02:02<20:48:01, 4.21s/it] 5%|▍ | 906/18688 [1:02:06<21:09:54, 4.28s/it] 5%|▍ | 907/18688 [1:02:10<20:45:15, 4.20s/it] 5%|▍ | 908/18688 [1:02:15<20:49:21, 4.22s/it] 5%|▍ | 909/18688 [1:02:19<20:54:46, 4.23s/it] 5%|▍ | 910/18688 [1:02:23<20:34:42, 4.17s/it] {'loss': '0.9315', 'grad_norm': '1.172', 'learning_rate': '4.848e-05', 'epoch': '0.0487', 'num_input_tokens_seen': 3021168, 'train_runtime': '3743', 'train_tokens_per_second': '807.1'}
5%|▍ | 910/18688 [1:02:23<20:34:42, 4.17s/it] 5%|▍ | 911/18688 [1:02:27<19:58:44, 4.05s/it] 5%|▍ | 912/18688 [1:02:30<19:14:59, 3.90s/it] 5%|▍ | 913/18688 [1:02:34<19:05:39, 3.87s/it] 5%|▍ | 914/18688 [1:02:38<18:41:39, 3.79s/it] 5%|▍ | 915/18688 [1:02:42<19:20:06, 3.92s/it] 5%|▍ | 916/18688 [1:02:46<20:13:18, 4.10s/it] 5%|▍ | 917/18688 [1:02:50<19:27:33, 3.94s/it] 5%|▍ | 918/18688 [1:02:54<20:25:48, 4.14s/it] 5%|▍ | 919/18688 [1:02:59<20:53:32, 4.23s/it] 5%|▍ | 920/18688 [1:03:03<21:22:16, 4.33s/it] {'loss': '1.038', 'grad_norm': '1.142', 'learning_rate': '4.901e-05', 'epoch': '0.04923', 'num_input_tokens_seen': 3053520, 'train_runtime': '3784', 'train_tokens_per_second': '807'}
5%|▍ | 920/18688 [1:03:03<21:22:16, 4.33s/it] 5%|▍ | 921/18688 [1:03:07<20:13:02, 4.10s/it] 5%|▍ | 922/18688 [1:03:11<20:12:11, 4.09s/it] 5%|▍ | 923/18688 [1:03:16<20:49:03, 4.22s/it] 5%|▍ | 924/18688 [1:03:19<20:18:10, 4.11s/it] 5%|▍ | 925/18688 [1:03:24<20:15:25, 4.11s/it] 5%|▍ | 926/18688 [1:03:28<20:11:23, 4.09s/it] 5%|▍ | 927/18688 [1:03:32<19:52:22, 4.03s/it] 5%|▍ | 928/18688 [1:03:35<19:12:47, 3.89s/it] 5%|▍ | 929/18688 [1:03:39<19:03:19, 3.86s/it] 5%|▍ | 930/18688 [1:03:43<19:49:28, 4.02s/it] {'loss': '0.967', 'grad_norm': '1.191', 'learning_rate': '4.955e-05', 'epoch': '0.04977', 'num_input_tokens_seen': 3085744, 'train_runtime': '3824', 'train_tokens_per_second': '807'}
5%|▍ | 930/18688 [1:03:43<19:49:28, 4.02s/it] 5%|▍ | 931/18688 [1:03:47<20:02:52, 4.06s/it] 5%|▍ | 932/18688 [1:03:52<20:06:58, 4.08s/it] 5%|▍ | 933/18688 [1:03:56<20:27:30, 4.15s/it] 5%|▍ | 934/18688 [1:04:01<21:36:51, 4.38s/it] 5%|▌ | 935/18688 [1:04:05<20:43:43, 4.20s/it] 5%|▌ | 936/18688 [1:04:09<21:20:13, 4.33s/it] 5%|▌ | 937/18688 [1:04:13<20:52:31, 4.23s/it] 5%|▌ | 938/18688 [1:04:17<20:00:43, 4.06s/it] 5%|▌ | 939/18688 [1:04:21<20:12:24, 4.10s/it] 5%|▌ | 940/18688 [1:04:25<20:17:50, 4.12s/it] {'loss': '1.016', 'grad_norm': '1.049', 'learning_rate': '5.008e-05', 'epoch': '0.0503', 'num_input_tokens_seen': 3119488, 'train_runtime': '3866', 'train_tokens_per_second': '807'}
5%|▌ | 940/18688 [1:04:25<20:17:50, 4.12s/it] 5%|▌ | 941/18688 [1:04:30<20:58:50, 4.26s/it] 5%|▌ | 942/18688 [1:04:33<20:04:37, 4.07s/it] 5%|▌ | 943/18688 [1:04:38<20:31:42, 4.16s/it] 5%|▌ | 944/18688 [1:04:42<20:20:51, 4.13s/it] 5%|▌ | 945/18688 [1:04:46<19:56:30, 4.05s/it] 5%|▌ | 946/18688 [1:04:50<20:51:21, 4.23s/it] 5%|▌ | 947/18688 [1:04:54<19:47:17, 4.02s/it] 5%|▌ | 948/18688 [1:04:58<19:17:46, 3.92s/it] 5%|▌ | 949/18688 [1:05:02<20:03:27, 4.07s/it] 5%|▌ | 950/18688 [1:05:06<20:08:40, 4.09s/it] {'loss': '1.021', 'grad_norm': '1.012', 'learning_rate': '5.061e-05', 'epoch': '0.05084', 'num_input_tokens_seen': 3153640, 'train_runtime': '3907', 'train_tokens_per_second': '807.3'}
5%|▌ | 950/18688 [1:05:06<20:08:40, 4.09s/it] 5%|▌ | 951/18688 [1:05:11<20:35:12, 4.18s/it] 5%|▌ | 952/18688 [1:05:15<20:34:51, 4.18s/it] 5%|▌ | 953/18688 [1:05:19<20:14:46, 4.11s/it] 5%|▌ | 954/18688 [1:05:23<20:12:49, 4.10s/it] 5%|▌ | 955/18688 [1:05:28<21:12:12, 4.30s/it] 5%|▌ | 956/18688 [1:05:32<21:19:44, 4.33s/it] 5%|▌ | 957/18688 [1:05:38<23:38:18, 4.80s/it] 5%|▌ | 958/18688 [1:05:42<22:28:16, 4.56s/it] 5%|▌ | 959/18688 [1:05:46<21:25:15, 4.35s/it] 5%|▌ | 960/18688 [1:05:49<20:29:08, 4.16s/it] {'loss': '1.063', 'grad_norm': '1.256', 'learning_rate': '5.115e-05', 'epoch': '0.05137', 'num_input_tokens_seen': 3189048, 'train_runtime': '3950', 'train_tokens_per_second': '807.4'}
5%|▌ | 960/18688 [1:05:49<20:29:08, 4.16s/it] 5%|▌ | 961/18688 [1:05:53<19:55:09, 4.05s/it] 5%|▌ | 962/18688 [1:05:57<19:35:42, 3.98s/it] 5%|▌ | 963/18688 [1:06:01<19:22:17, 3.93s/it] 5%|▌ | 964/18688 [1:06:04<18:41:56, 3.80s/it] 5%|▌ | 965/18688 [1:06:08<18:45:25, 3.81s/it] 5%|▌ | 966/18688 [1:06:12<19:31:14, 3.97s/it] 5%|▌ | 967/18688 [1:06:17<20:47:27, 4.22s/it] 5%|▌ | 968/18688 [1:06:22<21:12:07, 4.31s/it] 5%|▌ | 969/18688 [1:06:26<20:20:43, 4.13s/it] 5%|▌ | 970/18688 [1:06:30<20:56:45, 4.26s/it] {'loss': '0.9605', 'grad_norm': '1.122', 'learning_rate': '5.168e-05', 'epoch': '0.05191', 'num_input_tokens_seen': 3219536, 'train_runtime': '3991', 'train_tokens_per_second': '806.8'}
5%|▌ | 970/18688 [1:06:30<20:56:45, 4.26s/it] 5%|▌ | 971/18688 [1:06:34<20:43:48, 4.21s/it] 5%|▌ | 972/18688 [1:06:39<21:00:22, 4.27s/it] 5%|▌ | 973/18688 [1:06:42<20:01:01, 4.07s/it] 5%|▌ | 974/18688 [1:06:47<21:14:33, 4.32s/it] 5%|▌ | 975/18688 [1:06:52<21:49:16, 4.43s/it] 5%|▌ | 976/18688 [1:06:56<21:38:35, 4.40s/it] 5%|▌ | 977/18688 [1:07:01<21:50:25, 4.44s/it] 5%|▌ | 978/18688 [1:07:04<20:55:15, 4.25s/it] 5%|▌ | 979/18688 [1:07:09<21:38:25, 4.40s/it] 5%|▌ | 980/18688 [1:07:13<21:08:16, 4.30s/it] {'loss': '1.006', 'grad_norm': '1.028', 'learning_rate': '5.221e-05', 'epoch': '0.05244', 'num_input_tokens_seen': 3255952, 'train_runtime': '4034', 'train_tokens_per_second': '807.2'}
5%|▌ | 980/18688 [1:07:13<21:08:16, 4.30s/it] 5%|▌ | 981/18688 [1:07:17<20:04:05, 4.08s/it] 5%|▌ | 982/18688 [1:07:22<21:08:40, 4.30s/it] 5%|▌ | 983/18688 [1:07:26<21:56:43, 4.46s/it] 5%|▌ | 984/18688 [1:07:31<21:55:56, 4.46s/it] 5%|▌ | 985/18688 [1:07:35<21:33:33, 4.38s/it] 5%|▌ | 986/18688 [1:07:39<20:33:28, 4.18s/it] 5%|▌ | 987/18688 [1:07:43<20:30:57, 4.17s/it] 5%|▌ | 988/18688 [1:07:47<19:40:30, 4.00s/it] 5%|▌ | 989/18688 [1:07:50<19:30:22, 3.97s/it] 5%|▌ | 990/18688 [1:07:55<19:50:24, 4.04s/it] {'loss': '0.989', 'grad_norm': '1.026', 'learning_rate': '5.275e-05', 'epoch': '0.05298', 'num_input_tokens_seen': 3289808, 'train_runtime': '4075', 'train_tokens_per_second': '807.3'}
5%|▌ | 990/18688 [1:07:55<19:50:24, 4.04s/it] 5%|▌ | 991/18688 [1:07:58<19:30:52, 3.97s/it] 5%|▌ | 992/18688 [1:08:02<18:51:42, 3.84s/it] 5%|▌ | 993/18688 [1:08:07<20:13:45, 4.12s/it] 5%|▌ | 994/18688 [1:08:10<19:25:27, 3.95s/it] 5%|▌ | 995/18688 [1:08:15<20:43:17, 4.22s/it] 5%|▌ | 996/18688 [1:08:20<21:30:16, 4.38s/it] 5%|▌ | 997/18688 [1:08:24<21:35:53, 4.40s/it] 5%|▌ | 998/18688 [1:08:29<21:35:15, 4.39s/it] 5%|▌ | 999/18688 [1:08:33<21:10:49, 4.31s/it] 5%|▌ | 1000/18688 [1:08:37<21:27:37, 4.37s/it] {'loss': '0.9714', 'grad_norm': '1.205', 'learning_rate': '5.328e-05', 'epoch': '0.05351', 'num_input_tokens_seen': 3324984, 'train_runtime': '4118', 'train_tokens_per_second': '807.4'}
5%|▌ | 1000/18688 [1:08:37<21:27:37, 4.37s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:71: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:281: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 0/1000 [00:00<?, ?it/s]/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:172: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:202: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:254: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 3/1000 [00:00<01:01, 16.32it/s]
0%| | 5/1000 [00:00<01:04, 15.54it/s]
1%| | 7/1000 [00:00<01:03, 15.54it/s]
1%| | 9/1000 [00:00<01:04, 15.29it/s]
1%| | 11/1000 [00:00<01:03, 15.54it/s]
1%|▏ | 13/1000 [00:00<01:01, 15.96it/s]
2%|▏ | 15/1000 [00:00<01:04, 15.17it/s]
2%|▏ | 17/1000 [00:01<01:04, 15.24it/s]
2%|▏ | 19/1000 [00:01<01:38, 10.00it/s]
2%|▏ | 21/1000 [00:01<02:00, 8.13it/s]
2%|▏ | 23/1000 [00:01<01:45, 9.25it/s]
2%|▎ | 25/1000 [00:02<01:49, 8.94it/s]
3%|▎ | 27/1000 [00:02<01:38, 9.86it/s]
3%|▎ | 29/1000 [00:02<01:30, 10.72it/s]
3%|▎ | 31/1000 [00:02<01:36, 10.01it/s]
3%|▎ | 33/1000 [00:02<01:26, 11.13it/s]
4%|▎ | 35/1000 [00:02<01:20, 11.96it/s]
4%|▎ | 37/1000 [00:03<01:29, 10.79it/s]
4%|▍ | 39/1000 [00:03<01:23, 11.45it/s]
4%|▍ | 41/1000 [00:03<01:51, 8.61it/s]
4%|▍ | 43/1000 [00:03<01:36, 9.95it/s]
4%|▍ | 45/1000 [00:03<01:24, 11.26it/s]
5%|▍ | 47/1000 [00:04<01:17, 12.28it/s]
5%|▍ | 49/1000 [00:04<01:25, 11.11it/s]
5%|▌ | 51/1000 [00:04<01:23, 11.34it/s]
5%|▌ | 53/1000 [00:04<01:21, 11.65it/s]
6%|▌ | 55/1000 [00:04<01:19, 11.85it/s]
6%|▌ | 57/1000 [00:05<01:58, 7.96it/s]
6%|▌ | 59/1000 [00:05<01:42, 9.19it/s]
6%|▌ | 61/1000 [00:05<01:32, 10.10it/s]
6%|▋ | 63/1000 [00:05<01:24, 11.09it/s]
6%|▋ | 65/1000 [00:05<01:17, 12.01it/s]
7%|▋ | 67/1000 [00:05<01:14, 12.54it/s]
7%|▋ | 69/1000 [00:06<01:10, 13.21it/s]
7%|▋ | 71/1000 [00:06<01:07, 13.75it/s]
7%|▋ | 73/1000 [00:06<01:07, 13.76it/s]
8%|▊ | 75/1000 [00:06<01:06, 13.95it/s]
8%|▊ | 77/1000 [00:06<01:02, 14.72it/s]
8%|▊ | 79/1000 [00:06<01:01, 14.87it/s]
8%|▊ | 81/1000 [00:06<01:08, 13.34it/s]
8%|▊ | 83/1000 [00:07<01:04, 14.12it/s]
8%|▊ | 85/1000 [00:07<01:03, 14.50it/s]
9%|▊ | 87/1000 [00:07<01:03, 14.32it/s]
9%|▉ | 89/1000 [00:07<01:04, 14.19it/s]
9%|▉ | 91/1000 [00:07<01:02, 14.47it/s]
9%|▉ | 93/1000 [00:07<01:03, 14.24it/s]
10%|▉ | 95/1000 [00:07<01:03, 14.14it/s]
10%|▉ | 97/1000 [00:08<01:03, 14.14it/s]
10%|▉ | 99/1000 [00:08<01:02, 14.46it/s]
10%|█ | 101/1000 [00:08<01:31, 9.85it/s]
10%|█ | 103/1000 [00:08<01:22, 10.85it/s]
10%|█ | 105/1000 [00:09<02:10, 6.83it/s]
11%|█ | 106/1000 [00:09<02:07, 7.03it/s]
11%|█ | 108/1000 [00:09<01:44, 8.57it/s]
11%|█ | 110/1000 [00:09<01:27, 10.16it/s]
11%|█ | 112/1000 [00:09<01:18, 11.36it/s]
11%|█▏ | 114/1000 [00:09<01:13, 12.03it/s]
12%|█▏ | 116/1000 [00:10<01:09, 12.65it/s]
12%|█▏ | 118/1000 [00:10<01:05, 13.43it/s]
12%|█▏ | 120/1000 [00:10<01:08, 12.82it/s]
12%|█▏ | 122/1000 [00:10<01:22, 10.61it/s]
12%|█▏ | 124/1000 [00:10<01:30, 9.66it/s]
13%|█▎ | 126/1000 [00:10<01:22, 10.63it/s]
13%|█▎ | 128/1000 [00:11<01:15, 11.56it/s]
13%|█▎ | 130/1000 [00:11<01:11, 12.23it/s]
13%|█▎ | 132/1000 [00:11<01:05, 13.21it/s]
13%|█▎ | 134/1000 [00:11<01:09, 12.41it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.37it/s]
14%|█▍ | 138/1000 [00:11<01:04, 13.29it/s]
14%|█▍ | 140/1000 [00:12<01:25, 10.11it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.54it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.57it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.60it/s]
15%|█▍ | 148/1000 [00:12<01:14, 11.49it/s]
15%|█▌ | 150/1000 [00:13<01:07, 12.57it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.15it/s]
15%|█▌ | 154/1000 [00:13<01:03, 13.42it/s]
16%|█▌ | 156/1000 [00:13<01:03, 13.25it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.73it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.64it/s]
16%|█▌ | 162/1000 [00:13<01:02, 13.51it/s]
16%|█▋ | 164/1000 [00:14<01:01, 13.62it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.76it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.23it/s]
17%|█▋ | 170/1000 [00:14<01:19, 10.46it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.44it/s]
17%|█▋ | 174/1000 [00:15<01:30, 9.17it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.19it/s]
18%|█▊ | 178/1000 [00:15<01:34, 8.66it/s]
18%|█▊ | 180/1000 [00:15<01:33, 8.78it/s]
18%|█▊ | 182/1000 [00:16<01:35, 8.56it/s]
18%|█▊ | 184/1000 [00:16<01:23, 9.81it/s]
19%|█▊ | 186/1000 [00:16<01:37, 8.34it/s]
19%|█▉ | 188/1000 [00:16<01:23, 9.67it/s]
19%|█▉ | 190/1000 [00:16<01:15, 10.71it/s]
19%|█▉ | 192/1000 [00:16<01:11, 11.35it/s]
19%|█▉ | 194/1000 [00:17<01:16, 10.56it/s]
20%|█▉ | 196/1000 [00:17<01:29, 8.98it/s]
20%|█▉ | 198/1000 [00:17<01:18, 10.19it/s]
20%|██ | 200/1000 [00:17<01:15, 10.54it/s]
20%|██ | 202/1000 [00:17<01:09, 11.51it/s]
20%|██ | 204/1000 [00:18<01:03, 12.61it/s]
21%|██ | 206/1000 [00:18<01:01, 12.96it/s]
21%|██ | 208/1000 [00:18<01:01, 12.89it/s]
21%|██ | 210/1000 [00:18<00:58, 13.40it/s]
21%|██ | 212/1000 [00:18<00:59, 13.14it/s]
21%|██▏ | 214/1000 [00:18<00:57, 13.56it/s]
22%|██▏ | 216/1000 [00:18<00:57, 13.62it/s]
22%|██▏ | 218/1000 [00:19<00:55, 14.19it/s]
22%|██▏ | 220/1000 [00:19<01:29, 8.68it/s]
22%|██▏ | 222/1000 [00:19<01:26, 8.99it/s]
22%|██▏ | 224/1000 [00:19<01:21, 9.48it/s]
23%|██▎ | 226/1000 [00:20<01:13, 10.51it/s]
23%|██▎ | 228/1000 [00:20<01:07, 11.45it/s]
23%|██▎ | 230/1000 [00:20<01:04, 11.97it/s]
23%|██▎ | 232/1000 [00:20<01:02, 12.36it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.13it/s]
24%|██▎ | 236/1000 [00:20<00:55, 13.64it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.29it/s]
24%|██▍ | 240/1000 [00:20<00:53, 14.26it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.82it/s]
24%|██▍ | 244/1000 [00:21<00:50, 15.01it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.99it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.69it/s]
25%|██▌ | 250/1000 [00:21<00:55, 13.45it/s]
25%|██▌ | 252/1000 [00:21<01:01, 12.19it/s]
25%|██▌ | 254/1000 [00:22<00:56, 13.21it/s]
26%|██▌ | 256/1000 [00:22<00:54, 13.75it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.79it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.91it/s]
26%|██▌ | 262/1000 [00:22<00:56, 13.07it/s]
26%|██▋ | 264/1000 [00:22<00:53, 13.63it/s]
27%|██▋ | 266/1000 [00:22<00:52, 13.93it/s]
27%|██▋ | 268/1000 [00:23<00:50, 14.51it/s]
27%|██▋ | 270/1000 [00:23<00:55, 13.24it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.63it/s]
27%|██▋ | 274/1000 [00:23<01:01, 11.71it/s]
28%|██▊ | 276/1000 [00:23<00:56, 12.74it/s]
28%|██▊ | 278/1000 [00:23<00:55, 12.97it/s]
28%|██▊ | 280/1000 [00:24<00:53, 13.56it/s]
28%|██▊ | 282/1000 [00:24<00:49, 14.51it/s]
28%|██▊ | 284/1000 [00:24<00:49, 14.46it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.71it/s]
29%|██▉ | 288/1000 [00:24<00:47, 15.10it/s]
29%|██▉ | 290/1000 [00:24<00:48, 14.76it/s]
29%|██▉ | 292/1000 [00:24<00:50, 13.98it/s]
29%|██▉ | 294/1000 [00:24<00:52, 13.50it/s]
30%|██▉ | 296/1000 [00:25<00:53, 13.09it/s]
30%|██▉ | 298/1000 [00:25<00:53, 13.07it/s]
30%|███ | 300/1000 [00:25<00:52, 13.43it/s]
30%|███ | 302/1000 [00:25<00:50, 13.93it/s]
30%|███ | 304/1000 [00:25<00:53, 13.03it/s]
31%|███ | 306/1000 [00:25<00:49, 13.95it/s]
31%|███ | 308/1000 [00:26<00:49, 14.07it/s]
31%|███ | 310/1000 [00:26<00:48, 14.35it/s]
31%|███ | 312/1000 [00:26<00:47, 14.43it/s]
31%|███▏ | 314/1000 [00:26<00:45, 15.21it/s]
32%|███▏ | 316/1000 [00:26<00:44, 15.53it/s]
32%|███▏ | 318/1000 [00:26<00:42, 16.01it/s]
32%|███▏ | 320/1000 [00:27<01:12, 9.36it/s]
32%|███▏ | 322/1000 [00:27<01:03, 10.74it/s]
32%|███▏ | 324/1000 [00:27<00:58, 11.52it/s]
33%|███▎ | 326/1000 [00:27<00:58, 11.46it/s]
33%|███▎ | 328/1000 [00:27<00:58, 11.51it/s]
33%|███▎ | 330/1000 [00:27<00:53, 12.47it/s]
33%|███▎ | 332/1000 [00:27<00:52, 12.72it/s]
33%|███▎ | 334/1000 [00:28<00:50, 13.17it/s]
34%|███▎ | 336/1000 [00:28<00:48, 13.56it/s]
34%|███▍ | 338/1000 [00:28<01:05, 10.06it/s]
34%|███▍ | 340/1000 [00:28<00:59, 11.12it/s]
34%|███▍ | 342/1000 [00:28<00:53, 12.41it/s]
34%|███▍ | 344/1000 [00:28<00:50, 13.06it/s]
35%|███▍ | 346/1000 [00:29<00:47, 13.67it/s]
35%|███▍ | 348/1000 [00:29<00:46, 14.09it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.74it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.94it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.95it/s]
36%|███▌ | 356/1000 [00:29<00:42, 15.04it/s]
36%|███▌ | 358/1000 [00:29<00:43, 14.72it/s]
36%|███▌ | 360/1000 [00:29<00:42, 15.09it/s]
36%|███▌ | 362/1000 [00:30<00:47, 13.39it/s]
36%|███▋ | 364/1000 [00:30<00:45, 13.89it/s]
37%|███▋ | 366/1000 [00:30<00:52, 11.97it/s]
37%|███▋ | 368/1000 [00:30<00:51, 12.35it/s]
37%|███▋ | 370/1000 [00:30<00:47, 13.24it/s]
37%|███▋ | 372/1000 [00:30<00:46, 13.57it/s]
37%|███▋ | 374/1000 [00:31<00:54, 11.41it/s]
38%|███▊ | 376/1000 [00:31<00:56, 11.00it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.68it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.49it/s]
38%|███▊ | 382/1000 [00:31<00:48, 12.69it/s]
38%|███▊ | 384/1000 [00:31<00:45, 13.48it/s]
39%|███▊ | 386/1000 [00:32<00:44, 13.79it/s]
39%|███▉ | 388/1000 [00:32<00:44, 13.71it/s]
39%|███▉ | 390/1000 [00:32<00:44, 13.58it/s]
39%|███▉ | 392/1000 [00:32<00:43, 13.90it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.68it/s]
40%|███▉ | 396/1000 [00:32<00:43, 13.79it/s]
40%|███▉ | 398/1000 [00:32<00:47, 12.57it/s]
40%|████ | 400/1000 [00:33<00:45, 13.24it/s]
40%|████ | 402/1000 [00:33<00:43, 13.90it/s]
40%|████ | 404/1000 [00:33<00:41, 14.48it/s]
41%|████ | 406/1000 [00:33<00:39, 14.95it/s]
41%|████ | 408/1000 [00:33<00:47, 12.53it/s]
41%|████ | 410/1000 [00:33<00:56, 10.48it/s]
41%|████ | 412/1000 [00:34<00:50, 11.57it/s]
41%|████▏ | 414/1000 [00:34<01:07, 8.63it/s]
42%|████▏ | 416/1000 [00:34<01:23, 6.97it/s]
42%|████▏ | 418/1000 [00:35<01:10, 8.29it/s]
42%|████▏ | 420/1000 [00:35<01:00, 9.56it/s]
42%|████▏ | 422/1000 [00:35<00:52, 10.91it/s]
42%|████▏ | 424/1000 [00:35<01:14, 7.70it/s]
43%|████▎ | 426/1000 [00:35<01:04, 8.95it/s]
43%|████▎ | 428/1000 [00:35<00:56, 10.17it/s]
43%|████▎ | 430/1000 [00:36<01:02, 9.12it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.05it/s]
43%|████▎ | 434/1000 [00:36<01:08, 8.29it/s]
44%|████▎ | 436/1000 [00:36<01:00, 9.33it/s]
44%|████▍ | 438/1000 [00:37<01:02, 8.99it/s]
44%|████▍ | 440/1000 [00:37<00:58, 9.50it/s]
44%|████▍ | 442/1000 [00:37<00:52, 10.57it/s]
44%|████▍ | 444/1000 [00:37<00:48, 11.43it/s]
45%|████▍ | 446/1000 [00:37<00:45, 12.19it/s]
45%|████▍ | 448/1000 [00:38<00:50, 10.99it/s]
45%|████▌ | 450/1000 [00:38<00:46, 11.79it/s]
45%|████▌ | 452/1000 [00:38<00:42, 12.83it/s]
45%|████▌ | 454/1000 [00:38<00:44, 12.29it/s]
46%|████▌ | 456/1000 [00:38<00:42, 12.72it/s]
46%|████▌ | 458/1000 [00:38<00:39, 13.71it/s]
46%|████▌ | 460/1000 [00:38<00:39, 13.82it/s]
46%|████▌ | 462/1000 [00:39<00:37, 14.44it/s]
46%|████▋ | 464/1000 [00:39<00:35, 14.98it/s]
47%|████▋ | 466/1000 [00:39<00:35, 14.86it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.91it/s]
47%|████▋ | 470/1000 [00:39<00:52, 10.06it/s]
47%|████▋ | 472/1000 [00:39<00:51, 10.23it/s]
47%|████▋ | 474/1000 [00:40<00:45, 11.53it/s]
48%|████▊ | 476/1000 [00:40<00:43, 12.18it/s]
48%|████▊ | 478/1000 [00:40<00:43, 12.02it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.94it/s]
48%|████▊ | 482/1000 [00:40<00:39, 12.97it/s]
48%|████▊ | 484/1000 [00:40<00:39, 13.21it/s]
49%|████▊ | 486/1000 [00:40<00:38, 13.41it/s]
49%|████▉ | 488/1000 [00:41<00:39, 13.11it/s]
49%|████▉ | 490/1000 [00:41<00:48, 10.60it/s]
49%|████▉ | 492/1000 [00:41<00:42, 11.93it/s]
49%|████▉ | 494/1000 [00:41<00:39, 12.97it/s]
50%|████▉ | 496/1000 [00:41<00:36, 13.74it/s]
50%|████▉ | 498/1000 [00:41<00:35, 14.21it/s]
50%|█████ | 500/1000 [00:42<00:43, 11.45it/s]
50%|█████ | 502/1000 [00:42<00:41, 11.87it/s]
50%|█████ | 504/1000 [00:42<00:40, 12.29it/s]
51%|█████ | 506/1000 [00:42<00:39, 12.51it/s]
51%|█████ | 508/1000 [00:42<00:37, 13.19it/s]
51%|█████ | 510/1000 [00:43<00:48, 10.18it/s]
51%|█████ | 512/1000 [00:43<00:44, 10.99it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 11.90it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.49it/s]
52%|█████▏ | 518/1000 [00:43<00:37, 12.99it/s]
52%|█████▏ | 520/1000 [00:43<00:35, 13.39it/s]
52%|█████▏ | 522/1000 [00:43<00:33, 14.12it/s]
52%|█████▏ | 524/1000 [00:44<00:33, 14.14it/s]
53%|█████▎ | 526/1000 [00:44<00:33, 14.30it/s]
53%|█████▎ | 528/1000 [00:44<00:39, 11.95it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.81it/s]
53%|█████▎ | 532/1000 [00:44<00:33, 13.78it/s]
53%|█████▎ | 534/1000 [00:44<00:33, 14.12it/s]
54%|█████▎ | 536/1000 [00:44<00:32, 14.24it/s]
54%|█████▍ | 538/1000 [00:45<00:32, 14.01it/s]
54%|█████▍ | 540/1000 [00:45<00:31, 14.43it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.71it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.59it/s]
55%|█████▍ | 546/1000 [00:45<00:31, 14.23it/s]
55%|█████▍ | 548/1000 [00:45<00:32, 13.92it/s]
55%|█████▌ | 550/1000 [00:45<00:31, 14.16it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.31it/s]
55%|█████▌ | 554/1000 [00:46<00:31, 13.98it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.13it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.47it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 15.02it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 15.00it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.79it/s]
57%|█████▋ | 566/1000 [00:47<00:37, 11.71it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.26it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.40it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.87it/s]
57%|█████▋ | 574/1000 [00:47<00:30, 13.76it/s]
58%|█████▊ | 576/1000 [00:47<00:31, 13.64it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.67it/s]
58%|█████▊ | 580/1000 [00:48<00:32, 12.91it/s]
58%|█████▊ | 582/1000 [00:48<00:30, 13.67it/s]
58%|█████▊ | 584/1000 [00:48<00:29, 13.87it/s]
59%|█████▊ | 586/1000 [00:48<00:29, 14.26it/s]
59%|█████▉ | 588/1000 [00:48<00:27, 15.10it/s]
59%|█████▉ | 590/1000 [00:48<00:27, 15.08it/s]
59%|█████▉ | 592/1000 [00:48<00:27, 14.81it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.35it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.83it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 14.03it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.41it/s]
60%|██████ | 602/1000 [00:49<00:27, 14.55it/s]
60%|██████ | 604/1000 [00:49<00:27, 14.35it/s]
61%|██████ | 606/1000 [00:50<00:29, 13.55it/s]
61%|██████ | 608/1000 [00:50<00:29, 13.13it/s]
61%|██████ | 610/1000 [00:50<00:30, 12.82it/s]
61%|██████ | 612/1000 [00:50<00:30, 12.63it/s]
61%|██████▏ | 614/1000 [00:50<00:30, 12.60it/s]
62%|██████▏ | 616/1000 [00:50<00:28, 13.42it/s]
62%|██████▏ | 618/1000 [00:50<00:28, 13.30it/s]
62%|██████▏ | 620/1000 [00:51<00:40, 9.44it/s]
62%|██████▏ | 622/1000 [00:51<00:38, 9.71it/s]
62%|██████▏ | 624/1000 [00:51<00:33, 11.20it/s]
63%|██████▎ | 626/1000 [00:51<00:31, 11.92it/s]
63%|██████▎ | 628/1000 [00:51<00:28, 13.18it/s]
63%|██████▎ | 630/1000 [00:52<00:27, 13.70it/s]
63%|██████▎ | 632/1000 [00:52<00:29, 12.40it/s]
63%|██████▎ | 634/1000 [00:52<00:27, 13.14it/s]
64%|██████▎ | 636/1000 [00:52<00:33, 10.94it/s]
64%|██████▍ | 638/1000 [00:52<00:33, 10.67it/s]
64%|██████▍ | 640/1000 [00:52<00:31, 11.44it/s]
64%|██████▍ | 642/1000 [00:53<00:29, 12.32it/s]
64%|██████▍ | 644/1000 [00:53<00:28, 12.58it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.88it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.36it/s]
65%|██████▌ | 650/1000 [00:53<00:27, 12.56it/s]
65%|██████▌ | 652/1000 [00:53<00:29, 11.97it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.43it/s]
66%|██████▌ | 656/1000 [00:54<00:31, 10.78it/s]
66%|██████▌ | 658/1000 [00:54<00:28, 11.82it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.14it/s]
66%|██████▌ | 662/1000 [00:54<00:29, 11.27it/s]
66%|██████▋ | 664/1000 [00:54<00:28, 11.77it/s]
67%|██████▋ | 666/1000 [00:55<00:26, 12.80it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.31it/s]
67%|██████▋ | 670/1000 [00:55<00:29, 11.07it/s]
67%|██████▋ | 672/1000 [00:55<00:27, 11.85it/s]
67%|██████▋ | 674/1000 [00:55<00:26, 12.43it/s]
68%|██████▊ | 676/1000 [00:56<00:27, 11.72it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.22it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 12.99it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.12it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.29it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.40it/s]
69%|██████▉ | 688/1000 [00:56<00:22, 13.71it/s]
69%|██████▉ | 690/1000 [00:57<00:22, 13.79it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.26it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.35it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 14.09it/s]
70%|██████▉ | 698/1000 [00:57<00:20, 14.67it/s]
70%|███████ | 700/1000 [00:57<00:24, 12.21it/s]
70%|███████ | 702/1000 [00:57<00:22, 13.03it/s]
70%|███████ | 704/1000 [00:58<00:29, 9.87it/s]
71%|███████ | 706/1000 [00:58<00:27, 10.67it/s]
71%|███████ | 708/1000 [00:58<00:30, 9.63it/s]
71%|███████ | 710/1000 [00:58<00:26, 10.92it/s]
71%|███████ | 712/1000 [00:58<00:24, 11.90it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.97it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 13.07it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.58it/s]
72%|███████▏ | 720/1000 [00:59<00:20, 13.54it/s]
72%|███████▏ | 722/1000 [00:59<00:19, 13.99it/s]
72%|███████▏ | 724/1000 [00:59<00:19, 13.80it/s]
73%|███████▎ | 726/1000 [00:59<00:19, 13.99it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.91it/s]
73%|███████▎ | 730/1000 [01:00<00:32, 8.20it/s]
73%|███████▎ | 732/1000 [01:00<00:32, 8.32it/s]
73%|███████▎ | 734/1000 [01:00<00:30, 8.61it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.45it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.91it/s]
74%|███████▍ | 739/1000 [01:01<00:23, 11.02it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.70it/s]
74%|███████▍ | 743/1000 [01:01<00:20, 12.34it/s]
74%|███████▍ | 745/1000 [01:01<00:24, 10.20it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.57it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.75it/s]
75%|███████▌ | 751/1000 [01:02<00:20, 11.86it/s]
75%|███████▌ | 753/1000 [01:02<00:19, 12.62it/s]
76%|███████▌ | 755/1000 [01:02<00:18, 13.35it/s]
76%|███████▌ | 757/1000 [01:03<00:23, 10.28it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.31it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.31it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.26it/s]
76%|███████▋ | 765/1000 [01:03<00:27, 8.61it/s]
77%|███████▋ | 767/1000 [01:04<00:24, 9.56it/s]
77%|███████▋ | 769/1000 [01:04<00:21, 10.58it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.85it/s]
77%|███████▋ | 773/1000 [01:04<00:17, 12.63it/s]
78%|███████▊ | 775/1000 [01:04<00:17, 13.17it/s]
78%|███████▊ | 777/1000 [01:04<00:15, 14.14it/s]
78%|███████▊ | 779/1000 [01:04<00:21, 10.52it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.42it/s]
78%|███████▊ | 783/1000 [01:05<00:26, 8.31it/s]
78%|███████▊ | 784/1000 [01:05<00:30, 7.06it/s]
78%|███████▊ | 785/1000 [01:05<00:29, 7.39it/s]
79%|███████▊ | 786/1000 [01:06<00:30, 6.99it/s]
79%|███████▉ | 788/1000 [01:06<00:23, 8.83it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.16it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 11.31it/s]
79%|███████▉ | 794/1000 [01:06<00:17, 11.94it/s]
80%|███████▉ | 796/1000 [01:06<00:16, 12.38it/s]
80%|███████▉ | 798/1000 [01:06<00:15, 13.13it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.33it/s]
80%|████████ | 802/1000 [01:07<00:23, 8.25it/s]
80%|████████ | 804/1000 [01:07<00:23, 8.30it/s]
81%|████████ | 806/1000 [01:07<00:21, 8.98it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.38it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.50it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.72it/s]
81%|████████▏ | 814/1000 [01:08<00:14, 12.62it/s]
82%|████████▏ | 816/1000 [01:08<00:16, 11.04it/s]
82%|████████▏ | 818/1000 [01:08<00:15, 11.99it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.48it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 13.03it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.85it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.26it/s]
83%|████████▎ | 828/1000 [01:09<00:11, 14.52it/s]
83%|████████▎ | 830/1000 [01:09<00:12, 13.95it/s]
83%|████████▎ | 832/1000 [01:09<00:11, 14.38it/s]
83%|████████▎ | 834/1000 [01:09<00:11, 14.12it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.49it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.36it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.46it/s]
84%|████████▍ | 842/1000 [01:10<00:10, 14.46it/s]
84%|████████▍ | 844/1000 [01:10<00:11, 13.96it/s]
85%|████████▍ | 846/1000 [01:10<00:11, 13.92it/s]
85%|████████▍ | 848/1000 [01:10<00:10, 13.91it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 13.82it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.48it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.16it/s]
86%|████████▌ | 856/1000 [01:11<00:10, 14.04it/s]
86%|████████▌ | 858/1000 [01:11<00:09, 14.59it/s]
86%|████████▌ | 860/1000 [01:11<00:09, 15.36it/s]
86%|████████▌ | 862/1000 [01:11<00:10, 13.51it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 14.05it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.85it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.74it/s]
87%|████████▋ | 870/1000 [01:12<00:09, 14.16it/s]
87%|████████▋ | 872/1000 [01:12<00:08, 14.80it/s]
87%|████████▋ | 874/1000 [01:12<00:10, 12.48it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.06it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.95it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.60it/s]
88%|████████▊ | 882/1000 [01:13<00:10, 11.38it/s]
88%|████████▊ | 884/1000 [01:13<00:09, 12.11it/s]
89%|████████▊ | 886/1000 [01:13<00:08, 12.90it/s]
89%|████████▉ | 888/1000 [01:13<00:08, 13.89it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.34it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.11it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 13.70it/s]
90%|████████▉ | 896/1000 [01:14<00:07, 13.24it/s]
90%|████████▉ | 898/1000 [01:14<00:08, 12.48it/s]
90%|█████████ | 900/1000 [01:14<00:08, 11.24it/s]
90%|█████████ | 902/1000 [01:15<00:08, 12.17it/s]
90%|█████████ | 904/1000 [01:15<00:10, 9.43it/s]
91%|█████████ | 906/1000 [01:15<00:08, 10.49it/s]
91%|█████████ | 908/1000 [01:15<00:08, 11.41it/s]
91%|█████████ | 910/1000 [01:15<00:07, 11.76it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.01it/s]
91%|█████████▏| 914/1000 [01:16<00:12, 6.74it/s]
92%|█████████▏| 916/1000 [01:16<00:10, 8.07it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.35it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.77it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 10.24it/s]
92%|█████████▏| 924/1000 [01:17<00:06, 10.94it/s]
93%|█████████▎| 926/1000 [01:17<00:06, 11.79it/s]
93%|█████████▎| 928/1000 [01:17<00:06, 10.63it/s]
93%|█████████▎| 930/1000 [01:17<00:05, 11.84it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 12.43it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.93it/s]
94%|█████████▎| 936/1000 [01:18<00:04, 13.52it/s]
94%|█████████▍| 938/1000 [01:18<00:04, 14.37it/s]
94%|█████████▍| 940/1000 [01:18<00:04, 14.66it/s]
94%|█████████▍| 942/1000 [01:18<00:04, 12.32it/s]
94%|█████████▍| 944/1000 [01:18<00:04, 12.84it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.88it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 12.70it/s]
95%|█████████▌| 950/1000 [01:19<00:03, 13.50it/s]
95%|█████████▌| 952/1000 [01:19<00:03, 14.21it/s]
95%|█████████▌| 954/1000 [01:19<00:03, 14.30it/s]
96%|█████████▌| 956/1000 [01:19<00:03, 14.27it/s]
96%|█████████▌| 958/1000 [01:19<00:02, 14.82it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 13.14it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.77it/s]
96%|█████████▋| 964/1000 [01:20<00:03, 10.25it/s]
97%|█████████▋| 966/1000 [01:20<00:03, 10.73it/s]
97%|█████████▋| 968/1000 [01:20<00:02, 11.22it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 11.56it/s]
97%|█████████▋| 972/1000 [01:21<00:03, 9.21it/s]
97%|█████████▋| 974/1000 [01:21<00:02, 10.44it/s]
98%|█████████▊| 976/1000 [01:21<00:02, 11.29it/s]
98%|█████████▊| 978/1000 [01:21<00:02, 9.68it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.81it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.36it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 10.40it/s]
99%|█████████▊| 986/1000 [01:22<00:01, 10.66it/s]
99%|█████████▉| 988/1000 [01:22<00:01, 10.88it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 11.37it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 12.28it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 13.04it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 13.95it/s]
100%|█████████▉| 998/1000 [01:23<00:00, 14.11it/s]
100%|██████████| 1000/1000 [01:23<00:00, 14.76it/s]
{'eval_loss': '1.002', 'eval_runtime': '83.77', 'eval_samples_per_second': '11.94', 'eval_steps_per_second': '11.94', 'epoch': '0.05351', 'num_input_tokens_seen': 3324984}
5%|▌ | 1000/18688 [1:10:01<21:27:37, 4.37s/it]
100%|██████████| 1000/1000 [01:23<00:00, 14.76it/s]
 5%|▌ | 1001/18688 [1:10:05<144:27:39, 29.40s/it] 5%|▌ | 1002/18688 [1:10:09<107:15:55, 21.83s/it] 5%|▌ | 1003/18688 [1:10:14<82:15:33, 16.74s/it] 5%|▌ | 1004/18688 [1:10:19<63:56:28, 13.02s/it] 5%|▌ | 1005/18688 [1:10:23<51:12:35, 10.43s/it] 5%|▌ | 1006/18688 [1:10:27<42:09:37, 8.58s/it] 5%|▌ | 1007/18688 [1:10:31<35:30:40, 7.23s/it] 5%|▌ | 1008/18688 [1:10:35<30:18:23, 6.17s/it] 5%|▌ | 1009/18688 [1:10:39<27:41:34, 5.64s/it] 5%|▌ | 1010/18688 [1:10:45<27:44:21, 5.65s/it] {'loss': '1.036', 'grad_norm': '0.6418', 'learning_rate': '5.381e-05', 'epoch': '0.05405', 'num_input_tokens_seen': 3363320, 'train_runtime': '4246', 'train_tokens_per_second': '792.2'}
5%|▌ | 1010/18688 [1:10:45<27:44:21, 5.65s/it] 5%|▌ | 1011/18688 [1:10:49<24:45:50, 5.04s/it] 5%|▌ | 1012/18688 [1:10:53<23:43:00, 4.83s/it] 5%|▌ | 1013/18688 [1:10:58<23:39:45, 4.82s/it] 5%|▌ | 1014/18688 [1:11:03<23:46:47, 4.84s/it] 5%|▌ | 1015/18688 [1:11:08<24:09:17, 4.92s/it] 5%|▌ | 1016/18688 [1:11:12<22:58:28, 4.68s/it] 5%|▌ | 1017/18688 [1:11:16<21:33:01, 4.39s/it] 5%|▌ | 1018/18688 [1:11:20<21:10:53, 4.32s/it] 5%|▌ | 1019/18688 [1:11:25<22:17:23, 4.54s/it] 5%|▌ | 1020/18688 [1:11:29<21:23:50, 4.36s/it] {'loss': '1.029', 'grad_norm': '1.537', 'learning_rate': '5.435e-05', 'epoch': '0.05458', 'num_input_tokens_seen': 3400000, 'train_runtime': '4289', 'train_tokens_per_second': '792.7'}
5%|▌ | 1020/18688 [1:11:29<21:23:50, 4.36s/it] 5%|▌ | 1021/18688 [1:11:34<22:07:47, 4.51s/it] 5%|▌ | 1022/18688 [1:11:38<21:57:29, 4.47s/it] 5%|▌ | 1023/18688 [1:11:43<21:56:51, 4.47s/it] 5%|▌ | 1024/18688 [1:11:47<21:25:28, 4.37s/it] 5%|▌ | 1025/18688 [1:11:52<23:19:19, 4.75s/it] 5%|▌ | 1026/18688 [1:11:56<21:58:30, 4.48s/it] 5%|▌ | 1027/18688 [1:12:00<20:53:40, 4.26s/it] 6%|▌ | 1028/18688 [1:12:04<21:12:21, 4.32s/it] 6%|▌ | 1029/18688 [1:12:08<20:18:08, 4.14s/it] 6%|▌ | 1030/18688 [1:12:12<20:19:29, 4.14s/it] {'loss': '1.019', 'grad_norm': '1.315', 'learning_rate': '5.488e-05', 'epoch': '0.05512', 'num_input_tokens_seen': 3435320, 'train_runtime': '4333', 'train_tokens_per_second': '792.9'}
6%|▌ | 1030/18688 [1:12:12<20:19:29, 4.14s/it] 6%|▌ | 1031/18688 [1:12:16<20:17:22, 4.14s/it] 6%|▌ | 1032/18688 [1:12:21<20:25:58, 4.17s/it] 6%|▌ | 1033/18688 [1:12:25<20:20:24, 4.15s/it] 6%|▌ | 1034/18688 [1:12:30<21:29:15, 4.38s/it] 6%|▌ | 1035/18688 [1:12:34<21:10:55, 4.32s/it] 6%|▌ | 1036/18688 [1:12:38<21:06:35, 4.31s/it] 6%|▌ | 1037/18688 [1:12:42<21:18:39, 4.35s/it] 6%|▌ | 1038/18688 [1:12:46<20:31:18, 4.19s/it] 6%|▌ | 1039/18688 [1:12:50<19:28:30, 3.97s/it] 6%|▌ | 1040/18688 [1:12:54<19:35:12, 4.00s/it] {'loss': '0.9967', 'grad_norm': '1.17', 'learning_rate': '5.541e-05', 'epoch': '0.05565', 'num_input_tokens_seen': 3469792, 'train_runtime': '4374', 'train_tokens_per_second': '793.2'}
6%|▌ | 1040/18688 [1:12:54<19:35:12, 4.00s/it] 6%|▌ | 1041/18688 [1:12:58<19:44:35, 4.03s/it] 6%|▌ | 1042/18688 [1:13:01<19:01:29, 3.88s/it] 6%|▌ | 1043/18688 [1:13:06<19:54:36, 4.06s/it] 6%|▌ | 1044/18688 [1:13:10<19:51:20, 4.05s/it] 6%|▌ | 1045/18688 [1:13:14<20:01:17, 4.09s/it] 6%|▌ | 1046/18688 [1:13:19<21:26:05, 4.37s/it] 6%|▌ | 1047/18688 [1:13:23<20:18:03, 4.14s/it] 6%|▌ | 1048/18688 [1:13:27<20:20:13, 4.15s/it] 6%|▌ | 1049/18688 [1:13:31<19:55:51, 4.07s/it] 6%|▌ | 1050/18688 [1:13:35<19:35:02, 4.00s/it] {'loss': '1.002', 'grad_norm': '1.069', 'learning_rate': '5.595e-05', 'epoch': '0.05619', 'num_input_tokens_seen': 3502400, 'train_runtime': '4415', 'train_tokens_per_second': '793.3'}
6%|▌ | 1050/18688 [1:13:35<19:35:02, 4.00s/it] 6%|▌ | 1051/18688 [1:13:40<20:48:15, 4.25s/it] 6%|▌ | 1052/18688 [1:13:43<19:49:28, 4.05s/it] 6%|▌ | 1053/18688 [1:13:48<20:28:02, 4.18s/it] 6%|▌ | 1054/18688 [1:13:51<19:27:03, 3.97s/it] 6%|▌ | 1055/18688 [1:13:56<20:13:11, 4.13s/it] 6%|▌ | 1056/18688 [1:13:59<19:26:50, 3.97s/it] 6%|▌ | 1057/18688 [1:14:03<19:44:54, 4.03s/it] 6%|▌ | 1058/18688 [1:14:08<20:35:16, 4.20s/it] 6%|▌ | 1059/18688 [1:14:12<20:28:25, 4.18s/it] 6%|▌ | 1060/18688 [1:14:16<20:45:12, 4.24s/it] {'loss': '1.016', 'grad_norm': '1.126', 'learning_rate': '5.648e-05', 'epoch': '0.05672', 'num_input_tokens_seen': 3536816, 'train_runtime': '4457', 'train_tokens_per_second': '793.6'}
6%|▌ | 1060/18688 [1:14:16<20:45:12, 4.24s/it] 6%|▌ | 1061/18688 [1:14:20<20:26:12, 4.17s/it] 6%|▌ | 1062/18688 [1:14:25<20:35:13, 4.20s/it] 6%|▌ | 1063/18688 [1:14:29<20:11:35, 4.12s/it] 6%|▌ | 1064/18688 [1:14:33<21:04:48, 4.31s/it] 6%|▌ | 1065/18688 [1:14:37<20:07:19, 4.11s/it] 6%|▌ | 1066/18688 [1:14:41<19:56:26, 4.07s/it] 6%|▌ | 1067/18688 [1:14:45<20:04:08, 4.10s/it] 6%|▌ | 1068/18688 [1:14:49<19:34:08, 4.00s/it] 6%|▌ | 1069/18688 [1:14:54<20:30:02, 4.19s/it] 6%|▌ | 1070/18688 [1:14:58<20:40:23, 4.22s/it] {'loss': '1.01', 'grad_norm': '1.049', 'learning_rate': '5.701e-05', 'epoch': '0.05726', 'num_input_tokens_seen': 3571048, 'train_runtime': '4498', 'train_tokens_per_second': '793.8'}
6%|▌ | 1070/18688 [1:14:58<20:40:23, 4.22s/it] 6%|▌ | 1071/18688 [1:15:02<20:37:36, 4.22s/it] 6%|▌ | 1072/18688 [1:15:06<20:28:20, 4.18s/it] 6%|▌ | 1073/18688 [1:15:10<20:17:10, 4.15s/it] 6%|▌ | 1074/18688 [1:15:15<21:18:12, 4.35s/it] 6%|▌ | 1075/18688 [1:15:19<20:49:40, 4.26s/it] 6%|▌ | 1076/18688 [1:15:23<20:48:25, 4.25s/it] 6%|▌ | 1077/18688 [1:15:27<19:57:53, 4.08s/it] 6%|▌ | 1078/18688 [1:15:31<20:28:04, 4.18s/it] 6%|▌ | 1079/18688 [1:15:35<19:47:01, 4.04s/it] 6%|▌ | 1080/18688 [1:15:39<19:06:02, 3.91s/it] {'loss': '1.025', 'grad_norm': '1.336', 'learning_rate': '5.755e-05', 'epoch': '0.05779', 'num_input_tokens_seen': 3604416, 'train_runtime': '4539', 'train_tokens_per_second': '794'}
6%|▌ | 1080/18688 [1:15:39<19:06:02, 3.91s/it] 6%|▌ | 1081/18688 [1:15:43<19:14:38, 3.93s/it] 6%|▌ | 1082/18688 [1:15:47<20:22:07, 4.16s/it] 6%|▌ | 1083/18688 [1:15:51<19:47:26, 4.05s/it] 6%|▌ | 1084/18688 [1:15:55<19:48:20, 4.05s/it] 6%|▌ | 1085/18688 [1:15:59<18:54:21, 3.87s/it] 6%|▌ | 1086/18688 [1:16:03<19:58:33, 4.09s/it] 6%|▌ | 1087/18688 [1:16:08<20:52:50, 4.27s/it] 6%|▌ | 1088/18688 [1:16:12<20:45:41, 4.25s/it] 6%|▌ | 1089/18688 [1:16:17<21:00:09, 4.30s/it] 6%|▌ | 1090/18688 [1:16:21<21:25:31, 4.38s/it] {'loss': '1.021', 'grad_norm': '0.9795', 'learning_rate': '5.808e-05', 'epoch': '0.05833', 'num_input_tokens_seen': 3639504, 'train_runtime': '4582', 'train_tokens_per_second': '794.3'}
6%|▌ | 1090/18688 [1:16:21<21:25:31, 4.38s/it] 6%|▌ | 1091/18688 [1:16:25<20:39:53, 4.23s/it] 6%|▌ | 1092/18688 [1:16:29<20:40:58, 4.23s/it] 6%|▌ | 1093/18688 [1:16:34<20:58:50, 4.29s/it] 6%|▌ | 1094/18688 [1:16:38<20:49:57, 4.26s/it] 6%|▌ | 1095/18688 [1:16:42<20:49:04, 4.26s/it] 6%|▌ | 1096/18688 [1:16:47<20:55:05, 4.28s/it] 6%|▌ | 1097/18688 [1:16:50<19:55:07, 4.08s/it] 6%|▌ | 1098/18688 [1:16:54<19:54:48, 4.08s/it] 6%|▌ | 1099/18688 [1:16:58<19:12:14, 3.93s/it] 6%|▌ | 1100/18688 [1:17:02<20:08:54, 4.12s/it] {'loss': '1.06', 'grad_norm': '0.9098', 'learning_rate': '5.861e-05', 'epoch': '0.05886', 'num_input_tokens_seen': 3673560, 'train_runtime': '4623', 'train_tokens_per_second': '794.6'}
6%|▌ | 1100/18688 [1:17:02<20:08:54, 4.12s/it] 6%|▌ | 1101/18688 [1:17:07<20:51:24, 4.27s/it] 6%|▌ | 1102/18688 [1:17:11<20:08:17, 4.12s/it] 6%|▌ | 1103/18688 [1:17:15<19:40:33, 4.03s/it] 6%|▌ | 1104/18688 [1:17:19<19:46:09, 4.05s/it] 6%|▌ | 1105/18688 [1:17:23<19:26:19, 3.98s/it] 6%|▌ | 1106/18688 [1:17:26<19:13:10, 3.94s/it] 6%|▌ | 1107/18688 [1:17:31<19:55:04, 4.08s/it] 6%|▌ | 1108/18688 [1:17:35<19:35:45, 4.01s/it] 6%|▌ | 1109/18688 [1:17:39<19:29:43, 3.99s/it] 6%|▌ | 1110/18688 [1:17:43<20:24:34, 4.18s/it] {'loss': '1.001', 'grad_norm': '0.9746', 'learning_rate': '5.915e-05', 'epoch': '0.0594', 'num_input_tokens_seen': 3706976, 'train_runtime': '4664', 'train_tokens_per_second': '794.9'}
6%|▌ | 1110/18688 [1:17:43<20:24:34, 4.18s/it] 6%|▌ | 1111/18688 [1:17:48<21:17:04, 4.36s/it] 6%|▌ | 1112/18688 [1:17:52<21:07:01, 4.33s/it] 6%|▌ | 1113/18688 [1:17:56<20:07:29, 4.12s/it] 6%|▌ | 1114/18688 [1:17:59<19:24:09, 3.97s/it] 6%|▌ | 1115/18688 [1:18:04<19:48:07, 4.06s/it] 6%|▌ | 1116/18688 [1:18:08<20:32:34, 4.21s/it] 6%|▌ | 1117/18688 [1:18:12<20:08:42, 4.13s/it] 6%|▌ | 1118/18688 [1:18:16<20:19:34, 4.16s/it] 6%|▌ | 1119/18688 [1:18:21<20:08:52, 4.13s/it] 6%|▌ | 1120/18688 [1:18:25<21:02:21, 4.31s/it] {'loss': '0.9762', 'grad_norm': '0.9421', 'learning_rate': '5.968e-05', 'epoch': '0.05993', 'num_input_tokens_seen': 3740912, 'train_runtime': '4706', 'train_tokens_per_second': '795'}
6%|▌ | 1120/18688 [1:18:25<21:02:21, 4.31s/it] 6%|▌ | 1121/18688 [1:18:29<20:45:19, 4.25s/it] 6%|▌ | 1122/18688 [1:18:34<21:06:10, 4.32s/it] 6%|▌ | 1123/18688 [1:18:38<20:15:34, 4.15s/it] 6%|▌ | 1124/18688 [1:18:42<20:23:49, 4.18s/it] 6%|▌ | 1125/18688 [1:18:46<20:38:02, 4.23s/it] 6%|▌ | 1126/18688 [1:18:50<20:13:33, 4.15s/it] 6%|▌ | 1127/18688 [1:18:54<19:21:48, 3.97s/it] 6%|▌ | 1128/18688 [1:18:58<19:43:06, 4.04s/it] 6%|▌ | 1129/18688 [1:19:02<19:11:02, 3.93s/it] 6%|▌ | 1130/18688 [1:19:05<18:40:19, 3.83s/it] {'loss': '0.9312', 'grad_norm': '0.9444', 'learning_rate': '6.021e-05', 'epoch': '0.06047', 'num_input_tokens_seen': 3772344, 'train_runtime': '4746', 'train_tokens_per_second': '794.9'}
6%|▌ | 1130/18688 [1:19:05<18:40:19, 3.83s/it] 6%|▌ | 1131/18688 [1:19:10<20:26:54, 4.19s/it] 6%|▌ | 1132/18688 [1:19:14<19:33:34, 4.01s/it] 6%|▌ | 1133/18688 [1:19:18<19:43:09, 4.04s/it] 6%|▌ | 1134/18688 [1:19:22<19:17:59, 3.96s/it] 6%|▌ | 1135/18688 [1:19:26<19:11:11, 3.94s/it] 6%|▌ | 1136/18688 [1:19:29<18:53:23, 3.87s/it] 6%|▌ | 1137/18688 [1:19:33<18:33:03, 3.81s/it] 6%|▌ | 1138/18688 [1:19:39<21:16:10, 4.36s/it] 6%|▌ | 1139/18688 [1:19:43<21:16:04, 4.36s/it] 6%|▌ | 1140/18688 [1:19:47<20:08:26, 4.13s/it] {'loss': '0.9748', 'grad_norm': '1.078', 'learning_rate': '6.075e-05', 'epoch': '0.061', 'num_input_tokens_seen': 3806288, 'train_runtime': '4787', 'train_tokens_per_second': '795.1'}
6%|▌ | 1140/18688 [1:19:47<20:08:26, 4.13s/it] 6%|▌ | 1141/18688 [1:19:51<21:03:32, 4.32s/it] 6%|▌ | 1142/18688 [1:19:55<20:21:09, 4.18s/it] 6%|▌ | 1143/18688 [1:19:59<19:45:02, 4.05s/it] 6%|▌ | 1144/18688 [1:20:03<20:04:26, 4.12s/it] 6%|▌ | 1145/18688 [1:20:08<20:28:34, 4.20s/it] 6%|▌ | 1146/18688 [1:20:12<20:04:52, 4.12s/it] 6%|▌ | 1147/18688 [1:20:15<19:10:14, 3.93s/it] 6%|▌ | 1148/18688 [1:20:19<19:03:35, 3.91s/it] 6%|▌ | 1149/18688 [1:20:23<19:53:46, 4.08s/it] 6%|▌ | 1150/18688 [1:20:28<20:32:08, 4.22s/it] {'loss': '1.061', 'grad_norm': '1.164', 'learning_rate': '6.128e-05', 'epoch': '0.06154', 'num_input_tokens_seen': 3839712, 'train_runtime': '4828', 'train_tokens_per_second': '795.2'}
6%|▌ | 1150/18688 [1:20:28<20:32:08, 4.22s/it] 6%|▌ | 1151/18688 [1:20:32<20:30:06, 4.21s/it] 6%|▌ | 1152/18688 [1:20:36<20:22:22, 4.18s/it] 6%|▌ | 1153/18688 [1:20:41<20:46:39, 4.27s/it] 6%|▌ | 1154/18688 [1:20:44<20:01:33, 4.11s/it] 6%|▌ | 1155/18688 [1:20:48<19:24:06, 3.98s/it] 6%|▌ | 1156/18688 [1:20:54<21:43:30, 4.46s/it] 6%|▌ | 1157/18688 [1:20:57<20:43:31, 4.26s/it] 6%|▌ | 1158/18688 [1:21:02<20:35:01, 4.23s/it] 6%|▌ | 1159/18688 [1:21:07<22:15:32, 4.57s/it] 6%|▌ | 1160/18688 [1:21:11<21:34:12, 4.43s/it] {'loss': '1.072', 'grad_norm': '1.114', 'learning_rate': '6.181e-05', 'epoch': '0.06207', 'num_input_tokens_seen': 3876000, 'train_runtime': '4872', 'train_tokens_per_second': '795.6'}
6%|▌ | 1160/18688 [1:21:11<21:34:12, 4.43s/it] 6%|▌ | 1161/18688 [1:21:15<20:15:16, 4.16s/it] 6%|▌ | 1162/18688 [1:21:19<20:41:45, 4.25s/it] 6%|▌ | 1163/18688 [1:21:24<21:55:19, 4.50s/it] 6%|▌ | 1164/18688 [1:21:28<21:02:14, 4.32s/it] 6%|▌ | 1165/18688 [1:21:32<20:36:15, 4.23s/it] 6%|▌ | 1166/18688 [1:21:36<19:27:04, 4.00s/it] 6%|▌ | 1167/18688 [1:21:39<18:38:15, 3.83s/it] 6%|▋ | 1168/18688 [1:21:43<18:40:54, 3.84s/it] 6%|▋ | 1169/18688 [1:21:47<19:15:33, 3.96s/it] 6%|▋ | 1170/18688 [1:21:51<19:25:21, 3.99s/it] {'loss': '0.9805', 'grad_norm': '1.005', 'learning_rate': '6.235e-05', 'epoch': '0.06261', 'num_input_tokens_seen': 3908440, 'train_runtime': '4912', 'train_tokens_per_second': '795.7'}
6%|▋ | 1170/18688 [1:21:51<19:25:21, 3.99s/it] 6%|▋ | 1171/18688 [1:21:55<19:09:25, 3.94s/it] 6%|▋ | 1172/18688 [1:21:59<19:38:31, 4.04s/it] 6%|▋ | 1173/18688 [1:22:04<20:34:39, 4.23s/it] 6%|▋ | 1174/18688 [1:22:08<19:53:40, 4.09s/it] 6%|▋ | 1175/18688 [1:22:12<19:34:47, 4.02s/it] 6%|▋ | 1176/18688 [1:22:16<19:34:51, 4.03s/it] 6%|▋ | 1177/18688 [1:22:20<19:54:34, 4.09s/it] 6%|▋ | 1178/18688 [1:22:24<19:35:49, 4.03s/it] 6%|▋ | 1179/18688 [1:22:28<20:02:31, 4.12s/it] 6%|▋ | 1180/18688 [1:22:32<20:09:11, 4.14s/it] {'loss': '0.9827', 'grad_norm': '1.007', 'learning_rate': '6.288e-05', 'epoch': '0.06314', 'num_input_tokens_seen': 3941608, 'train_runtime': '4953', 'train_tokens_per_second': '795.8'}
6%|▋ | 1180/18688 [1:22:32<20:09:11, 4.14s/it] 6%|▋ | 1181/18688 [1:22:37<20:42:56, 4.26s/it] 6%|▋ | 1182/18688 [1:22:41<20:30:25, 4.22s/it] 6%|▋ | 1183/18688 [1:22:46<21:27:09, 4.41s/it] 6%|▋ | 1184/18688 [1:22:49<20:13:15, 4.16s/it] 6%|▋ | 1185/18688 [1:22:53<19:10:50, 3.95s/it] 6%|▋ | 1186/18688 [1:22:57<19:57:43, 4.11s/it] 6%|▋ | 1187/18688 [1:23:02<20:55:45, 4.31s/it] 6%|▋ | 1188/18688 [1:23:06<20:24:33, 4.20s/it] 6%|▋ | 1189/18688 [1:23:10<19:36:14, 4.03s/it] 6%|▋ | 1190/18688 [1:23:14<20:06:35, 4.14s/it] {'loss': '1.015', 'grad_norm': '1.198', 'learning_rate': '6.341e-05', 'epoch': '0.06368', 'num_input_tokens_seen': 3974776, 'train_runtime': '4995', 'train_tokens_per_second': '795.8'}
6%|▋ | 1190/18688 [1:23:14<20:06:35, 4.14s/it] 6%|▋ | 1191/18688 [1:23:18<20:10:17, 4.15s/it] 6%|▋ | 1192/18688 [1:23:22<20:00:16, 4.12s/it] 6%|▋ | 1193/18688 [1:23:27<20:20:37, 4.19s/it] 6%|▋ | 1194/18688 [1:23:30<19:31:40, 4.02s/it] 6%|▋ | 1195/18688 [1:23:34<19:12:16, 3.95s/it] 6%|▋ | 1196/18688 [1:23:38<19:48:20, 4.08s/it] 6%|▋ | 1197/18688 [1:23:43<20:01:39, 4.12s/it] 6%|▋ | 1198/18688 [1:23:46<19:33:59, 4.03s/it] 6%|▋ | 1199/18688 [1:23:50<19:16:54, 3.97s/it] 6%|▋ | 1200/18688 [1:23:54<19:33:29, 4.03s/it] {'loss': '0.9746', 'grad_norm': '1.047', 'learning_rate': '6.395e-05', 'epoch': '0.06421', 'num_input_tokens_seen': 4008136, 'train_runtime': '5035', 'train_tokens_per_second': '796.1'}
6%|▋ | 1200/18688 [1:23:54<19:33:29, 4.03s/it] 6%|▋ | 1201/18688 [1:23:58<19:00:15, 3.91s/it] 6%|▋ | 1202/18688 [1:24:02<19:24:24, 4.00s/it] 6%|▋ | 1203/18688 [1:24:07<20:31:53, 4.23s/it] 6%|▋ | 1204/18688 [1:24:12<21:29:59, 4.43s/it] 6%|▋ | 1205/18688 [1:24:16<20:22:18, 4.19s/it] 6%|▋ | 1206/18688 [1:24:19<19:55:21, 4.10s/it] 6%|▋ | 1207/18688 [1:24:24<20:56:21, 4.31s/it] 6%|▋ | 1208/18688 [1:24:28<20:23:57, 4.20s/it] 6%|▋ | 1209/18688 [1:24:33<20:58:05, 4.32s/it] 6%|▋ | 1210/18688 [1:24:37<20:42:52, 4.27s/it] {'loss': '1.055', 'grad_norm': '1.015', 'learning_rate': '6.448e-05', 'epoch': '0.06475', 'num_input_tokens_seen': 4042776, 'train_runtime': '5077', 'train_tokens_per_second': '796.2'}
6%|▋ | 1210/18688 [1:24:37<20:42:52, 4.27s/it] 6%|▋ | 1211/18688 [1:24:40<19:34:13, 4.03s/it] 6%|▋ | 1212/18688 [1:24:45<20:00:35, 4.12s/it] 6%|▋ | 1213/18688 [1:24:49<19:59:35, 4.12s/it] 6%|▋ | 1214/18688 [1:24:53<20:27:26, 4.21s/it] 7%|▋ | 1215/18688 [1:24:57<19:39:57, 4.05s/it] 7%|▋ | 1216/18688 [1:25:00<18:54:06, 3.89s/it] 7%|▋ | 1217/18688 [1:25:05<19:13:00, 3.96s/it] 7%|▋ | 1218/18688 [1:25:08<18:58:12, 3.91s/it] 7%|▋ | 1219/18688 [1:25:13<20:06:57, 4.15s/it] 7%|▋ | 1220/18688 [1:25:17<19:33:47, 4.03s/it] {'loss': '0.9922', 'grad_norm': '0.941', 'learning_rate': '6.501e-05', 'epoch': '0.06528', 'num_input_tokens_seen': 4074336, 'train_runtime': '5117', 'train_tokens_per_second': '796.2'}
7%|▋ | 1220/18688 [1:25:17<19:33:47, 4.03s/it] 7%|▋ | 1221/18688 [1:25:21<20:06:10, 4.14s/it] 7%|▋ | 1222/18688 [1:25:25<19:20:09, 3.99s/it] 7%|▋ | 1223/18688 [1:25:29<19:23:16, 4.00s/it] 7%|▋ | 1224/18688 [1:25:33<18:58:04, 3.91s/it] 7%|▋ | 1225/18688 [1:25:37<19:46:57, 4.08s/it] 7%|▋ | 1226/18688 [1:25:42<20:52:05, 4.30s/it] 7%|▋ | 1227/18688 [1:25:45<19:42:06, 4.06s/it] 7%|▋ | 1228/18688 [1:25:50<20:40:30, 4.26s/it] 7%|▋ | 1229/18688 [1:25:55<20:55:00, 4.31s/it] 7%|▋ | 1230/18688 [1:25:59<20:38:37, 4.26s/it] {'loss': '1.037', 'grad_norm': '1.289', 'learning_rate': '6.555e-05', 'epoch': '0.06582', 'num_input_tokens_seen': 4107808, 'train_runtime': '5159', 'train_tokens_per_second': '796.2'}
7%|▋ | 1230/18688 [1:25:59<20:38:37, 4.26s/it] 7%|▋ | 1231/18688 [1:26:03<21:07:28, 4.36s/it] 7%|▋ | 1232/18688 [1:26:08<20:56:35, 4.32s/it] 7%|▋ | 1233/18688 [1:26:12<20:32:38, 4.24s/it] 7%|▋ | 1234/18688 [1:26:16<20:16:58, 4.18s/it] 7%|▋ | 1235/18688 [1:26:20<20:18:31, 4.19s/it] 7%|▋ | 1236/18688 [1:26:24<20:23:45, 4.21s/it] 7%|▋ | 1237/18688 [1:26:28<19:24:11, 4.00s/it] 7%|▋ | 1238/18688 [1:26:31<18:57:24, 3.91s/it] 7%|▋ | 1239/18688 [1:26:35<19:20:56, 3.99s/it] 7%|▋ | 1240/18688 [1:26:39<19:18:09, 3.98s/it] {'loss': '0.9397', 'grad_norm': '0.8774', 'learning_rate': '6.608e-05', 'epoch': '0.06635', 'num_input_tokens_seen': 4141480, 'train_runtime': '5200', 'train_tokens_per_second': '796.4'}
7%|▋ | 1240/18688 [1:26:39<19:18:09, 3.98s/it] 7%|▋ | 1241/18688 [1:26:43<18:51:17, 3.89s/it] 7%|▋ | 1242/18688 [1:26:48<20:45:37, 4.28s/it] 7%|▋ | 1243/18688 [1:26:52<19:45:05, 4.08s/it] 7%|▋ | 1244/18688 [1:26:56<19:55:01, 4.11s/it] 7%|▋ | 1245/18688 [1:27:00<20:01:48, 4.13s/it] 7%|▋ | 1246/18688 [1:27:04<19:51:34, 4.10s/it] 7%|▋ | 1247/18688 [1:27:08<19:18:23, 3.99s/it] 7%|▋ | 1248/18688 [1:27:12<19:33:50, 4.04s/it] 7%|▋ | 1249/18688 [1:27:16<19:35:36, 4.04s/it] 7%|▋ | 1250/18688 [1:27:21<20:21:17, 4.20s/it] {'loss': '1.037', 'grad_norm': '0.8758', 'learning_rate': '6.661e-05', 'epoch': '0.06689', 'num_input_tokens_seen': 4175648, 'train_runtime': '5241', 'train_tokens_per_second': '796.7'}
7%|▋ | 1250/18688 [1:27:21<20:21:17, 4.20s/it] 7%|▋ | 1251/18688 [1:27:25<19:55:19, 4.11s/it] 7%|▋ | 1252/18688 [1:27:28<19:17:23, 3.98s/it] 7%|▋ | 1253/18688 [1:27:32<18:53:29, 3.90s/it] 7%|▋ | 1254/18688 [1:27:36<19:32:35, 4.04s/it] 7%|▋ | 1255/18688 [1:27:41<20:08:06, 4.16s/it] 7%|▋ | 1256/18688 [1:27:45<20:13:39, 4.18s/it] 7%|▋ | 1257/18688 [1:27:50<20:42:27, 4.28s/it] 7%|▋ | 1258/18688 [1:27:53<19:46:14, 4.08s/it] 7%|▋ | 1259/18688 [1:27:57<19:33:56, 4.04s/it] 7%|▋ | 1260/18688 [1:28:01<18:43:21, 3.87s/it] {'loss': '0.9796', 'grad_norm': '1.136', 'learning_rate': '6.715e-05', 'epoch': '0.06742', 'num_input_tokens_seen': 4207024, 'train_runtime': '5281', 'train_tokens_per_second': '796.6'}
7%|▋ | 1260/18688 [1:28:01<18:43:21, 3.87s/it] 7%|▋ | 1261/18688 [1:28:04<18:14:07, 3.77s/it] 7%|▋ | 1262/18688 [1:28:08<18:14:46, 3.77s/it] 7%|▋ | 1263/18688 [1:28:12<18:08:47, 3.75s/it] 7%|▋ | 1264/18688 [1:28:15<18:04:28, 3.73s/it] 7%|▋ | 1265/18688 [1:28:19<17:52:03, 3.69s/it] 7%|▋ | 1266/18688 [1:28:23<18:30:30, 3.82s/it] 7%|▋ | 1267/18688 [1:28:27<18:17:20, 3.78s/it] 7%|▋ | 1268/18688 [1:28:31<18:13:13, 3.77s/it] 7%|▋ | 1269/18688 [1:28:34<18:02:33, 3.73s/it] 7%|▋ | 1270/18688 [1:28:39<19:03:22, 3.94s/it] {'loss': '1.003', 'grad_norm': '1.011', 'learning_rate': '6.768e-05', 'epoch': '0.06796', 'num_input_tokens_seen': 4236392, 'train_runtime': '5319', 'train_tokens_per_second': '796.5'}
7%|▋ | 1270/18688 [1:28:39<19:03:22, 3.94s/it] 7%|▋ | 1271/18688 [1:28:43<19:24:21, 4.01s/it] 7%|▋ | 1272/18688 [1:28:46<18:45:28, 3.88s/it] 7%|▋ | 1273/18688 [1:28:50<18:27:32, 3.82s/it] 7%|▋ | 1274/18688 [1:28:54<18:29:03, 3.82s/it] 7%|▋ | 1275/18688 [1:28:58<19:14:04, 3.98s/it] 7%|▋ | 1276/18688 [1:29:02<19:04:08, 3.94s/it] 7%|▋ | 1277/18688 [1:29:06<18:48:41, 3.89s/it] 7%|▋ | 1278/18688 [1:29:09<18:18:49, 3.79s/it] 7%|▋ | 1279/18688 [1:29:13<18:46:39, 3.88s/it] 7%|▋ | 1280/18688 [1:29:17<18:10:32, 3.76s/it] {'loss': '0.9791', 'grad_norm': '1.064', 'learning_rate': '6.821e-05', 'epoch': '0.06849', 'num_input_tokens_seen': 4265784, 'train_runtime': '5357', 'train_tokens_per_second': '796.2'}
7%|▋ | 1280/18688 [1:29:17<18:10:32, 3.76s/it] 7%|▋ | 1281/18688 [1:29:21<18:19:57, 3.79s/it] 7%|▋ | 1282/18688 [1:29:25<18:19:27, 3.79s/it] 7%|▋ | 1283/18688 [1:29:29<19:16:05, 3.99s/it] 7%|▋ | 1284/18688 [1:29:33<18:38:44, 3.86s/it] 7%|▋ | 1285/18688 [1:29:37<19:32:56, 4.04s/it] 7%|▋ | 1286/18688 [1:29:41<19:58:26, 4.13s/it] 7%|▋ | 1287/18688 [1:29:45<19:04:15, 3.95s/it] 7%|▋ | 1288/18688 [1:29:48<18:30:58, 3.83s/it] 7%|▋ | 1289/18688 [1:29:53<18:59:42, 3.93s/it] 7%|▋ | 1290/18688 [1:29:57<19:51:46, 4.11s/it] {'loss': '1.036', 'grad_norm': '1.025', 'learning_rate': '6.875e-05', 'epoch': '0.06903', 'num_input_tokens_seen': 4298040, 'train_runtime': '5398', 'train_tokens_per_second': '796.3'}
7%|▋ | 1290/18688 [1:29:57<19:51:46, 4.11s/it] 7%|▋ | 1291/18688 [1:30:01<19:08:14, 3.96s/it] 7%|▋ | 1292/18688 [1:30:05<19:58:12, 4.13s/it] 7%|▋ | 1293/18688 [1:30:10<21:01:11, 4.35s/it] 7%|▋ | 1294/18688 [1:30:15<21:54:58, 4.54s/it] 7%|▋ | 1295/18688 [1:30:19<20:28:51, 4.24s/it] 7%|▋ | 1296/18688 [1:30:22<19:41:40, 4.08s/it] 7%|▋ | 1297/18688 [1:30:26<18:52:05, 3.91s/it] 7%|▋ | 1298/18688 [1:30:30<19:02:54, 3.94s/it] 7%|▋ | 1299/18688 [1:30:35<20:33:39, 4.26s/it] 7%|▋ | 1300/18688 [1:30:39<19:48:15, 4.10s/it] {'loss': '1.055', 'grad_norm': '0.8941', 'learning_rate': '6.928e-05', 'epoch': '0.06957', 'num_input_tokens_seen': 4332752, 'train_runtime': '5439', 'train_tokens_per_second': '796.6'}
7%|▋ | 1300/18688 [1:30:39<19:48:15, 4.10s/it] 7%|▋ | 1301/18688 [1:30:42<19:27:07, 4.03s/it] 7%|▋ | 1302/18688 [1:30:47<19:27:21, 4.03s/it] 7%|▋ | 1303/18688 [1:30:51<19:28:24, 4.03s/it] 7%|▋ | 1304/18688 [1:30:54<18:49:58, 3.90s/it] 7%|▋ | 1305/18688 [1:30:58<19:28:10, 4.03s/it] 7%|▋ | 1306/18688 [1:31:03<20:16:48, 4.20s/it] 7%|▋ | 1307/18688 [1:31:08<20:56:09, 4.34s/it] 7%|▋ | 1308/18688 [1:31:11<20:00:50, 4.15s/it] 7%|▋ | 1309/18688 [1:31:15<19:12:42, 3.98s/it] 7%|▋ | 1310/18688 [1:31:19<19:21:50, 4.01s/it] {'loss': '1.064', 'grad_norm': '0.9769', 'learning_rate': '6.981e-05', 'epoch': '0.0701', 'num_input_tokens_seen': 4365560, 'train_runtime': '5480', 'train_tokens_per_second': '796.7'}
7%|▋ | 1310/18688 [1:31:19<19:21:50, 4.01s/it] 7%|▋ | 1311/18688 [1:31:23<18:58:00, 3.93s/it] 7%|▋ | 1312/18688 [1:31:27<18:49:01, 3.90s/it] 7%|▋ | 1313/18688 [1:31:31<19:12:05, 3.98s/it] 7%|▋ | 1314/18688 [1:31:35<19:19:30, 4.00s/it] 7%|▋ | 1315/18688 [1:31:39<19:29:18, 4.04s/it] 7%|▋ | 1316/18688 [1:31:43<19:06:42, 3.96s/it] 7%|▋ | 1317/18688 [1:31:47<19:11:55, 3.98s/it] 7%|▋ | 1318/18688 [1:31:51<18:54:39, 3.92s/it] 7%|▋ | 1319/18688 [1:31:55<19:41:37, 4.08s/it] 7%|▋ | 1320/18688 [1:31:59<19:50:26, 4.11s/it] {'loss': '0.9859', 'grad_norm': '1.014', 'learning_rate': '7.035e-05', 'epoch': '0.07064', 'num_input_tokens_seen': 4398144, 'train_runtime': '5520', 'train_tokens_per_second': '796.8'}
7%|▋ | 1320/18688 [1:31:59<19:50:26, 4.11s/it] 7%|▋ | 1321/18688 [1:32:03<19:35:05, 4.06s/it] 7%|▋ | 1322/18688 [1:32:07<19:42:25, 4.09s/it] 7%|▋ | 1323/18688 [1:32:11<18:48:05, 3.90s/it] 7%|▋ | 1324/18688 [1:32:15<19:19:53, 4.01s/it] 7%|▋ | 1325/18688 [1:32:19<19:12:10, 3.98s/it] 7%|▋ | 1326/18688 [1:32:23<19:17:00, 4.00s/it] 7%|▋ | 1327/18688 [1:32:27<19:18:00, 4.00s/it] 7%|▋ | 1328/18688 [1:32:31<18:40:03, 3.87s/it] 7%|▋ | 1329/18688 [1:32:35<19:36:39, 4.07s/it] 7%|▋ | 1330/18688 [1:32:39<18:51:11, 3.91s/it] {'loss': '1.041', 'grad_norm': '1.142', 'learning_rate': '7.088e-05', 'epoch': '0.07117', 'num_input_tokens_seen': 4428848, 'train_runtime': '5559', 'train_tokens_per_second': '796.7'}
7%|▋ | 1330/18688 [1:32:39<18:51:11, 3.91s/it] 7%|▋ | 1331/18688 [1:32:43<19:37:26, 4.07s/it] 7%|▋ | 1332/18688 [1:32:47<19:20:45, 4.01s/it] 7%|▋ | 1333/18688 [1:32:51<18:41:02, 3.88s/it] 7%|▋ | 1334/18688 [1:32:54<18:21:40, 3.81s/it] 7%|▋ | 1335/18688 [1:32:59<19:50:35, 4.12s/it] 7%|▋ | 1336/18688 [1:33:04<21:41:33, 4.50s/it] 7%|▋ | 1337/18688 [1:33:08<20:11:22, 4.19s/it] 7%|▋ | 1338/18688 [1:33:12<20:24:06, 4.23s/it] 7%|▋ | 1339/18688 [1:33:16<19:17:06, 4.00s/it] 7%|▋ | 1340/18688 [1:33:19<18:59:25, 3.94s/it] {'loss': '1.023', 'grad_norm': '1.132', 'learning_rate': '7.141e-05', 'epoch': '0.07171', 'num_input_tokens_seen': 4462248, 'train_runtime': '5600', 'train_tokens_per_second': '796.8'}
7%|▋ | 1340/18688 [1:33:19<18:59:25, 3.94s/it] 7%|▋ | 1341/18688 [1:33:24<19:37:47, 4.07s/it] 7%|▋ | 1342/18688 [1:33:28<20:22:44, 4.23s/it] 7%|▋ | 1343/18688 [1:33:32<19:12:55, 3.99s/it] 7%|▋ | 1344/18688 [1:33:36<19:12:22, 3.99s/it] 7%|▋ | 1345/18688 [1:33:42<21:56:34, 4.55s/it] 7%|▋ | 1346/18688 [1:33:46<21:12:06, 4.40s/it] 7%|▋ | 1347/18688 [1:33:50<20:39:19, 4.29s/it] 7%|▋ | 1348/18688 [1:33:54<21:01:32, 4.37s/it] 7%|▋ | 1349/18688 [1:33:58<20:32:18, 4.26s/it] 7%|▋ | 1350/18688 [1:34:03<21:23:19, 4.44s/it] {'loss': '1.071', 'grad_norm': '1.197', 'learning_rate': '7.195e-05', 'epoch': '0.07224', 'num_input_tokens_seen': 4497528, 'train_runtime': '5644', 'train_tokens_per_second': '796.9'}
7%|▋ | 1350/18688 [1:34:03<21:23:19, 4.44s/it] 7%|▋ | 1351/18688 [1:34:08<21:34:22, 4.48s/it] 7%|▋ | 1352/18688 [1:34:12<21:27:31, 4.46s/it] 7%|▋ | 1353/18688 [1:34:17<21:54:38, 4.55s/it] 7%|▋ | 1354/18688 [1:34:21<21:06:34, 4.38s/it] 7%|▋ | 1355/18688 [1:34:26<22:01:17, 4.57s/it] 7%|▋ | 1356/18688 [1:34:30<21:32:35, 4.47s/it] 7%|▋ | 1357/18688 [1:34:35<21:43:33, 4.51s/it] 7%|▋ | 1358/18688 [1:34:39<21:02:22, 4.37s/it] 7%|▋ | 1359/18688 [1:34:43<20:30:03, 4.26s/it] 7%|▋ | 1360/18688 [1:34:47<19:41:05, 4.09s/it] {'loss': '1.033', 'grad_norm': '1.148', 'learning_rate': '7.248e-05', 'epoch': '0.07278', 'num_input_tokens_seen': 4534280, 'train_runtime': '5687', 'train_tokens_per_second': '797.3'}
7%|▋ | 1360/18688 [1:34:47<19:41:05, 4.09s/it] 7%|▋ | 1361/18688 [1:34:51<19:54:03, 4.13s/it] 7%|▋ | 1362/18688 [1:34:55<19:31:10, 4.06s/it] 7%|▋ | 1363/18688 [1:34:58<19:08:52, 3.98s/it] 7%|▋ | 1364/18688 [1:35:02<18:44:24, 3.89s/it] 7%|▋ | 1365/18688 [1:35:06<18:15:02, 3.79s/it] 7%|▋ | 1366/18688 [1:35:10<18:17:55, 3.80s/it] 7%|▋ | 1367/18688 [1:35:13<18:18:03, 3.80s/it] 7%|▋ | 1368/18688 [1:35:18<19:53:08, 4.13s/it] 7%|▋ | 1369/18688 [1:35:22<19:00:31, 3.95s/it] 7%|▋ | 1370/18688 [1:35:25<18:30:10, 3.85s/it] {'loss': '1.033', 'grad_norm': '0.9394', 'learning_rate': '7.301e-05', 'epoch': '0.07331', 'num_input_tokens_seen': 4565296, 'train_runtime': '5726', 'train_tokens_per_second': '797.3'}
7%|▋ | 1370/18688 [1:35:25<18:30:10, 3.85s/it] 7%|▋ | 1371/18688 [1:35:30<19:16:17, 4.01s/it] 7%|▋ | 1372/18688 [1:35:34<19:55:47, 4.14s/it] 7%|▋ | 1373/18688 [1:35:39<20:13:40, 4.21s/it] 7%|▋ | 1374/18688 [1:35:43<20:21:26, 4.23s/it] 7%|▋ | 1375/18688 [1:35:46<19:19:06, 4.02s/it] 7%|▋ | 1376/18688 [1:35:50<18:45:04, 3.90s/it] 7%|▋ | 1377/18688 [1:35:54<18:26:11, 3.83s/it] 7%|▋ | 1378/18688 [1:35:58<19:14:22, 4.00s/it] 7%|▋ | 1379/18688 [1:36:02<19:45:01, 4.11s/it] 7%|▋ | 1380/18688 [1:36:06<19:04:47, 3.97s/it] {'loss': '0.9616', 'grad_norm': '1.115', 'learning_rate': '7.355e-05', 'epoch': '0.07385', 'num_input_tokens_seen': 4596896, 'train_runtime': '5767', 'train_tokens_per_second': '797.2'}
7%|▋ | 1380/18688 [1:36:06<19:04:47, 3.97s/it] 7%|▋ | 1381/18688 [1:36:10<19:03:22, 3.96s/it] 7%|▋ | 1382/18688 [1:36:14<19:07:43, 3.98s/it] 7%|▋ | 1383/18688 [1:36:18<19:36:22, 4.08s/it] 7%|▋ | 1384/18688 [1:36:22<19:37:33, 4.08s/it] 7%|▋ | 1385/18688 [1:36:27<20:03:00, 4.17s/it] 7%|▋ | 1386/18688 [1:36:31<20:38:09, 4.29s/it] 7%|▋ | 1387/18688 [1:36:36<20:31:41, 4.27s/it] 7%|▋ | 1388/18688 [1:36:40<20:02:57, 4.17s/it] 7%|▋ | 1389/18688 [1:36:44<19:59:26, 4.16s/it] 7%|▋ | 1390/18688 [1:36:48<20:01:25, 4.17s/it] {'loss': '0.9851', 'grad_norm': '0.9138', 'learning_rate': '7.408e-05', 'epoch': '0.07438', 'num_input_tokens_seen': 4631296, 'train_runtime': '5808', 'train_tokens_per_second': '797.3'}
7%|▋ | 1390/18688 [1:36:48<20:01:25, 4.17s/it] 7%|▋ | 1391/18688 [1:36:52<19:53:47, 4.14s/it] 7%|▋ | 1392/18688 [1:36:57<21:22:02, 4.45s/it] 7%|▋ | 1393/18688 [1:37:01<20:50:39, 4.34s/it] 7%|▋ | 1394/18688 [1:37:05<20:41:08, 4.31s/it] 7%|▋ | 1395/18688 [1:37:11<21:45:07, 4.53s/it] 7%|▋ | 1396/18688 [1:37:15<21:50:44, 4.55s/it] 7%|▋ | 1397/18688 [1:37:19<20:14:47, 4.22s/it] 7%|▋ | 1398/18688 [1:37:23<20:41:29, 4.31s/it] 7%|▋ | 1399/18688 [1:37:27<20:44:00, 4.32s/it] 7%|▋ | 1400/18688 [1:37:32<21:09:01, 4.40s/it] {'loss': '1.053', 'grad_norm': '0.918', 'learning_rate': '7.461e-05', 'epoch': '0.07492', 'num_input_tokens_seen': 4668640, 'train_runtime': '5853', 'train_tokens_per_second': '797.7'}
7%|▋ | 1400/18688 [1:37:32<21:09:01, 4.40s/it] 7%|▋ | 1401/18688 [1:37:36<21:04:22, 4.39s/it] 8%|▊ | 1402/18688 [1:37:41<21:08:35, 4.40s/it] 8%|▊ | 1403/18688 [1:37:45<20:24:12, 4.25s/it] 8%|▊ | 1404/18688 [1:37:49<20:14:42, 4.22s/it] 8%|▊ | 1405/18688 [1:37:52<19:17:35, 4.02s/it] 8%|▊ | 1406/18688 [1:37:56<18:36:39, 3.88s/it] 8%|▊ | 1407/18688 [1:38:00<18:36:39, 3.88s/it] 8%|▊ | 1408/18688 [1:38:04<19:13:01, 4.00s/it] 8%|▊ | 1409/18688 [1:38:08<18:29:45, 3.85s/it] 8%|▊ | 1410/18688 [1:38:12<19:41:24, 4.10s/it] {'loss': '1.006', 'grad_norm': '1.061', 'learning_rate': '7.515e-05', 'epoch': '0.07545', 'num_input_tokens_seen': 4701160, 'train_runtime': '5893', 'train_tokens_per_second': '797.8'}
8%|▊ | 1410/18688 [1:38:12<19:41:24, 4.10s/it] 8%|▊ | 1411/18688 [1:38:16<19:29:14, 4.06s/it] 8%|▊ | 1412/18688 [1:38:21<19:49:18, 4.13s/it] 8%|▊ | 1413/18688 [1:38:25<20:19:20, 4.24s/it] 8%|▊ | 1414/18688 [1:38:31<22:20:23, 4.66s/it] 8%|▊ | 1415/18688 [1:38:35<21:30:56, 4.48s/it] 8%|▊ | 1416/18688 [1:38:38<20:17:29, 4.23s/it] 8%|▊ | 1417/18688 [1:38:43<21:10:03, 4.41s/it] 8%|▊ | 1418/18688 [1:38:47<20:32:14, 4.28s/it] 8%|▊ | 1419/18688 [1:38:51<19:42:25, 4.11s/it] 8%|▊ | 1420/18688 [1:38:55<19:19:52, 4.03s/it] {'loss': '1.016', 'grad_norm': '1.198', 'learning_rate': '7.568e-05', 'epoch': '0.07599', 'num_input_tokens_seen': 4735568, 'train_runtime': '5935', 'train_tokens_per_second': '797.9'}
8%|▊ | 1420/18688 [1:38:55<19:19:52, 4.03s/it] 8%|▊ | 1421/18688 [1:38:59<19:35:46, 4.09s/it] 8%|▊ | 1422/18688 [1:39:04<20:37:24, 4.30s/it] 8%|▊ | 1423/18688 [1:39:10<22:43:00, 4.74s/it] 8%|▊ | 1424/18688 [1:39:13<21:26:50, 4.47s/it] 8%|▊ | 1425/18688 [1:39:18<21:04:38, 4.40s/it] 8%|▊ | 1426/18688 [1:39:21<20:19:54, 4.24s/it] 8%|▊ | 1427/18688 [1:39:26<20:35:08, 4.29s/it] 8%|▊ | 1428/18688 [1:39:30<19:51:26, 4.14s/it] 8%|▊ | 1429/18688 [1:39:34<20:48:47, 4.34s/it] 8%|▊ | 1430/18688 [1:39:38<19:59:56, 4.17s/it] {'loss': '1.029', 'grad_norm': '1.11', 'learning_rate': '7.621e-05', 'epoch': '0.07652', 'num_input_tokens_seen': 4771024, 'train_runtime': '5979', 'train_tokens_per_second': '798'}
8%|▊ | 1430/18688 [1:39:38<19:59:56, 4.17s/it] 8%|▊ | 1431/18688 [1:39:42<18:59:36, 3.96s/it] 8%|▊ | 1432/18688 [1:39:46<19:33:55, 4.08s/it] 8%|▊ | 1433/18688 [1:39:50<19:16:09, 4.02s/it] 8%|▊ | 1434/18688 [1:39:54<18:49:09, 3.93s/it] 8%|▊ | 1435/18688 [1:39:58<19:15:12, 4.02s/it] 8%|▊ | 1436/18688 [1:40:01<18:32:42, 3.87s/it] 8%|▊ | 1437/18688 [1:40:06<19:05:20, 3.98s/it] 8%|▊ | 1438/18688 [1:40:09<18:24:49, 3.84s/it] 8%|▊ | 1439/18688 [1:40:13<18:06:58, 3.78s/it] 8%|▊ | 1440/18688 [1:40:17<18:18:44, 3.82s/it] {'loss': '0.9426', 'grad_norm': '1.018', 'learning_rate': '7.675e-05', 'epoch': '0.07706', 'num_input_tokens_seen': 4800712, 'train_runtime': '6017', 'train_tokens_per_second': '797.8'}
8%|▊ | 1440/18688 [1:40:17<18:18:44, 3.82s/it] 8%|▊ | 1441/18688 [1:40:21<19:07:18, 3.99s/it] 8%|▊ | 1442/18688 [1:40:25<19:22:01, 4.04s/it] 8%|▊ | 1443/18688 [1:40:31<21:02:49, 4.39s/it] 8%|▊ | 1444/18688 [1:40:34<20:13:58, 4.22s/it] 8%|▊ | 1445/18688 [1:40:39<20:23:06, 4.26s/it] 8%|▊ | 1446/18688 [1:40:42<19:26:21, 4.06s/it] 8%|▊ | 1447/18688 [1:40:46<19:38:52, 4.10s/it] 8%|▊ | 1448/18688 [1:40:51<19:50:54, 4.14s/it] 8%|▊ | 1449/18688 [1:40:55<19:43:05, 4.12s/it] 8%|▊ | 1450/18688 [1:40:59<19:30:25, 4.07s/it] {'loss': '1.035', 'grad_norm': '1.035', 'learning_rate': '7.728e-05', 'epoch': '0.07759', 'num_input_tokens_seen': 4835448, 'train_runtime': '6059', 'train_tokens_per_second': '798'}
8%|▊ | 1450/18688 [1:40:59<19:30:25, 4.07s/it] 8%|▊ | 1451/18688 [1:41:02<18:58:00, 3.96s/it] 8%|▊ | 1452/18688 [1:41:06<18:19:14, 3.83s/it] 8%|▊ | 1453/18688 [1:41:10<18:23:24, 3.84s/it] 8%|▊ | 1454/18688 [1:41:14<19:10:10, 4.00s/it] 8%|▊ | 1455/18688 [1:41:19<20:50:50, 4.36s/it] 8%|▊ | 1456/18688 [1:41:23<20:04:12, 4.19s/it] 8%|▊ | 1457/18688 [1:41:28<20:32:24, 4.29s/it] 8%|▊ | 1458/18688 [1:41:32<20:06:19, 4.20s/it] 8%|▊ | 1459/18688 [1:41:36<19:30:24, 4.08s/it] 8%|▊ | 1460/18688 [1:41:41<21:41:13, 4.53s/it] {'loss': '0.9855', 'grad_norm': '0.8965', 'learning_rate': '7.781e-05', 'epoch': '0.07813', 'num_input_tokens_seen': 4868640, 'train_runtime': '6102', 'train_tokens_per_second': '797.9'}
8%|▊ | 1460/18688 [1:41:41<21:41:13, 4.53s/it] 8%|▊ | 1461/18688 [1:41:45<21:23:54, 4.47s/it] 8%|▊ | 1462/18688 [1:41:50<21:24:55, 4.48s/it] 8%|▊ | 1463/18688 [1:41:53<20:04:52, 4.20s/it] 8%|▊ | 1464/18688 [1:41:59<21:22:51, 4.47s/it] 8%|▊ | 1465/18688 [1:42:03<21:31:08, 4.50s/it] 8%|▊ | 1466/18688 [1:42:07<21:11:29, 4.43s/it] 8%|▊ | 1467/18688 [1:42:12<22:01:46, 4.61s/it] 8%|▊ | 1468/18688 [1:42:17<21:30:42, 4.50s/it] 8%|▊ | 1469/18688 [1:42:21<21:03:40, 4.40s/it] 8%|▊ | 1470/18688 [1:42:25<20:44:29, 4.34s/it] {'loss': '1.046', 'grad_norm': '0.8798', 'learning_rate': '7.835e-05', 'epoch': '0.07866', 'num_input_tokens_seen': 4907240, 'train_runtime': '6146', 'train_tokens_per_second': '798.5'}
8%|▊ | 1470/18688 [1:42:25<20:44:29, 4.34s/it] 8%|▊ | 1471/18688 [1:42:30<21:00:16, 4.39s/it] 8%|▊ | 1472/18688 [1:42:33<19:59:12, 4.18s/it] 8%|▊ | 1473/18688 [1:42:38<20:32:00, 4.29s/it] 8%|▊ | 1474/18688 [1:42:43<21:07:38, 4.42s/it] 8%|▊ | 1475/18688 [1:42:47<20:45:01, 4.34s/it] 8%|▊ | 1476/18688 [1:42:50<19:44:52, 4.13s/it] 8%|▊ | 1477/18688 [1:42:54<19:46:02, 4.13s/it] 8%|▊ | 1478/18688 [1:42:58<19:05:41, 3.99s/it] 8%|▊ | 1479/18688 [1:43:02<18:32:37, 3.88s/it] 8%|▊ | 1480/18688 [1:43:07<19:49:10, 4.15s/it] {'loss': '0.9666', 'grad_norm': '0.9451', 'learning_rate': '7.888e-05', 'epoch': '0.0792', 'num_input_tokens_seen': 4940128, 'train_runtime': '6187', 'train_tokens_per_second': '798.5'}
8%|▊ | 1480/18688 [1:43:07<19:49:10, 4.15s/it] 8%|▊ | 1481/18688 [1:43:10<19:12:35, 4.02s/it] 8%|▊ | 1482/18688 [1:43:14<18:25:54, 3.86s/it] 8%|▊ | 1483/18688 [1:43:18<19:17:04, 4.04s/it] 8%|▊ | 1484/18688 [1:43:22<19:10:19, 4.01s/it] 8%|▊ | 1485/18688 [1:43:26<19:24:21, 4.06s/it] 8%|▊ | 1486/18688 [1:43:31<20:33:10, 4.30s/it] 8%|▊ | 1487/18688 [1:43:35<19:27:59, 4.07s/it] 8%|▊ | 1488/18688 [1:43:38<18:50:09, 3.94s/it] 8%|▊ | 1489/18688 [1:43:42<18:17:50, 3.83s/it] 8%|▊ | 1490/18688 [1:43:46<19:16:35, 4.04s/it] {'loss': '1.005', 'grad_norm': '1.108', 'learning_rate': '7.941e-05', 'epoch': '0.07973', 'num_input_tokens_seen': 4971208, 'train_runtime': '6227', 'train_tokens_per_second': '798.3'}
8%|▊ | 1490/18688 [1:43:46<19:16:35, 4.04s/it] 8%|▊ | 1491/18688 [1:43:51<20:14:31, 4.24s/it] 8%|▊ | 1492/18688 [1:43:56<20:46:18, 4.35s/it] 8%|▊ | 1493/18688 [1:44:00<20:25:16, 4.28s/it] 8%|▊ | 1494/18688 [1:44:04<20:08:44, 4.22s/it] 8%|▊ | 1495/18688 [1:44:08<19:18:49, 4.04s/it] 8%|▊ | 1496/18688 [1:44:12<19:36:52, 4.11s/it] 8%|▊ | 1497/18688 [1:44:17<20:35:55, 4.31s/it] 8%|▊ | 1498/18688 [1:44:20<19:29:27, 4.08s/it] 8%|▊ | 1499/18688 [1:44:24<19:38:48, 4.11s/it] 8%|▊ | 1500/18688 [1:44:29<20:00:51, 4.19s/it] {'loss': '1.032', 'grad_norm': '1.144', 'learning_rate': '7.995e-05', 'epoch': '0.08027', 'num_input_tokens_seen': 5004640, 'train_runtime': '6269', 'train_tokens_per_second': '798.3'}
8%|▊ | 1500/18688 [1:44:29<20:00:51, 4.19s/it] 8%|▊ | 1501/18688 [1:44:33<19:47:09, 4.14s/it] 8%|▊ | 1502/18688 [1:44:37<19:39:00, 4.12s/it] 8%|▊ | 1503/18688 [1:44:41<19:28:31, 4.08s/it] 8%|▊ | 1504/18688 [1:44:45<19:11:04, 4.02s/it] 8%|▊ | 1505/18688 [1:44:49<19:36:09, 4.11s/it] 8%|▊ | 1506/18688 [1:44:52<18:45:02, 3.93s/it] 8%|▊ | 1507/18688 [1:44:57<19:11:04, 4.02s/it] 8%|▊ | 1508/18688 [1:45:01<20:00:09, 4.19s/it] 8%|▊ | 1509/18688 [1:45:06<20:14:38, 4.24s/it] 8%|▊ | 1510/18688 [1:45:10<19:47:37, 4.15s/it] {'loss': '1.011', 'grad_norm': '5.907', 'learning_rate': '8e-05', 'epoch': '0.0808', 'num_input_tokens_seen': 5038496, 'train_runtime': '6310', 'train_tokens_per_second': '798.5'}
8%|▊ | 1510/18688 [1:45:10<19:47:37, 4.15s/it] 8%|▊ | 1511/18688 [1:45:13<19:23:00, 4.06s/it] 8%|▊ | 1512/18688 [1:45:18<19:47:12, 4.15s/it] 8%|▊ | 1513/18688 [1:45:22<19:24:21, 4.07s/it] 8%|▊ | 1514/18688 [1:45:26<19:53:28, 4.17s/it] 8%|▊ | 1515/18688 [1:45:31<21:27:34, 4.50s/it] 8%|▊ | 1516/18688 [1:45:35<20:25:41, 4.28s/it] 8%|▊ | 1517/18688 [1:45:39<19:48:08, 4.15s/it] 8%|▊ | 1518/18688 [1:45:43<19:43:41, 4.14s/it] 8%|▊ | 1519/18688 [1:45:48<20:21:23, 4.27s/it] 8%|▊ | 1520/18688 [1:45:52<20:12:54, 4.24s/it] {'loss': '0.9755', 'grad_norm': '1.026', 'learning_rate': '8e-05', 'epoch': '0.08134', 'num_input_tokens_seen': 5072456, 'train_runtime': '6352', 'train_tokens_per_second': '798.5'}
8%|▊ | 1520/18688 [1:45:52<20:12:54, 4.24s/it] 8%|▊ | 1521/18688 [1:45:56<20:34:01, 4.31s/it] 8%|▊ | 1522/18688 [1:46:01<20:51:55, 4.38s/it] 8%|▊ | 1523/18688 [1:46:06<21:35:16, 4.53s/it] 8%|▊ | 1524/18688 [1:46:10<20:33:06, 4.31s/it] 8%|▊ | 1525/18688 [1:46:14<20:11:10, 4.23s/it] 8%|▊ | 1526/18688 [1:46:18<20:25:34, 4.28s/it] 8%|▊ | 1527/18688 [1:46:23<20:47:49, 4.36s/it] 8%|▊ | 1528/18688 [1:46:26<19:52:49, 4.17s/it] 8%|▊ | 1529/18688 [1:46:32<21:27:58, 4.50s/it] 8%|▊ | 1530/18688 [1:46:36<21:29:10, 4.51s/it] {'loss': '0.9747', 'grad_norm': '1.065', 'learning_rate': '8e-05', 'epoch': '0.08187', 'num_input_tokens_seen': 5109856, 'train_runtime': '6397', 'train_tokens_per_second': '798.8'}
8%|▊ | 1530/18688 [1:46:36<21:29:10, 4.51s/it] 8%|▊ | 1531/18688 [1:46:40<21:03:47, 4.42s/it] 8%|▊ | 1532/18688 [1:46:45<22:08:10, 4.65s/it] 8%|▊ | 1533/18688 [1:46:50<22:13:10, 4.66s/it] 8%|▊ | 1534/18688 [1:46:56<23:51:33, 5.01s/it] 8%|▊ | 1535/18688 [1:47:00<22:11:22, 4.66s/it] 8%|▊ | 1536/18688 [1:47:04<21:53:47, 4.60s/it] 8%|▊ | 1537/18688 [1:47:10<22:50:07, 4.79s/it] 8%|▊ | 1538/18688 [1:47:13<21:23:42, 4.49s/it] 8%|▊ | 1539/18688 [1:47:17<20:10:07, 4.23s/it] 8%|▊ | 1540/18688 [1:47:21<20:33:24, 4.32s/it] {'loss': '0.9982', 'grad_norm': '1.007', 'learning_rate': '8e-05', 'epoch': '0.08241', 'num_input_tokens_seen': 5146272, 'train_runtime': '6442', 'train_tokens_per_second': '798.9'}
8%|▊ | 1540/18688 [1:47:21<20:33:24, 4.32s/it] 8%|▊ | 1541/18688 [1:47:25<19:59:41, 4.20s/it] 8%|▊ | 1542/18688 [1:47:30<20:01:14, 4.20s/it] 8%|▊ | 1543/18688 [1:47:34<19:42:59, 4.14s/it] 8%|▊ | 1544/18688 [1:47:37<19:04:13, 4.00s/it] 8%|▊ | 1545/18688 [1:47:41<18:31:37, 3.89s/it] 8%|▊ | 1546/18688 [1:47:45<18:18:18, 3.84s/it] 8%|▊ | 1547/18688 [1:47:48<18:14:57, 3.83s/it] 8%|▊ | 1548/18688 [1:47:53<19:48:29, 4.16s/it] 8%|▊ | 1549/18688 [1:47:58<19:51:26, 4.17s/it] 8%|▊ | 1550/18688 [1:48:01<19:12:28, 4.03s/it] {'loss': '0.9086', 'grad_norm': '0.9765', 'learning_rate': '8e-05', 'epoch': '0.08294', 'num_input_tokens_seen': 5178800, 'train_runtime': '6482', 'train_tokens_per_second': '799'}
8%|▊ | 1550/18688 [1:48:01<19:12:28, 4.03s/it] 8%|▊ | 1551/18688 [1:48:05<19:28:36, 4.09s/it] 8%|▊ | 1552/18688 [1:48:10<20:20:29, 4.27s/it] 8%|▊ | 1553/18688 [1:48:15<20:52:03, 4.38s/it] 8%|▊ | 1554/18688 [1:48:20<21:21:29, 4.49s/it] 8%|▊ | 1555/18688 [1:48:23<20:08:46, 4.23s/it] 8%|▊ | 1556/18688 [1:48:27<19:49:39, 4.17s/it] 8%|▊ | 1557/18688 [1:48:32<20:31:07, 4.31s/it] 8%|▊ | 1558/18688 [1:48:35<19:32:22, 4.11s/it] 8%|▊ | 1559/18688 [1:48:39<19:13:31, 4.04s/it] 8%|▊ | 1560/18688 [1:48:44<20:04:26, 4.22s/it] {'loss': '0.9448', 'grad_norm': '1.071', 'learning_rate': '8e-05', 'epoch': '0.08348', 'num_input_tokens_seen': 5214832, 'train_runtime': '6525', 'train_tokens_per_second': '799.3'}
8%|▊ | 1560/18688 [1:48:44<20:04:26, 4.22s/it] 8%|▊ | 1561/18688 [1:48:48<20:10:06, 4.24s/it] 8%|▊ | 1562/18688 [1:48:52<19:57:06, 4.19s/it] 8%|▊ | 1563/18688 [1:48:57<20:37:34, 4.34s/it] 8%|▊ | 1564/18688 [1:49:01<20:06:35, 4.23s/it] 8%|▊ | 1565/18688 [1:49:06<20:44:07, 4.36s/it] 8%|▊ | 1566/18688 [1:49:09<19:39:59, 4.14s/it] 8%|▊ | 1567/18688 [1:49:13<18:43:54, 3.94s/it] 8%|▊ | 1568/18688 [1:49:16<18:10:39, 3.82s/it] 8%|▊ | 1569/18688 [1:49:21<19:03:03, 4.01s/it] 8%|▊ | 1570/18688 [1:49:25<19:32:25, 4.11s/it] {'loss': '0.9903', 'grad_norm': '1.17', 'learning_rate': '8e-05', 'epoch': '0.08401', 'num_input_tokens_seen': 5248400, 'train_runtime': '6566', 'train_tokens_per_second': '799.4'}
8%|▊ | 1570/18688 [1:49:25<19:32:25, 4.11s/it] 8%|▊ | 1571/18688 [1:49:29<19:35:28, 4.12s/it] 8%|▊ | 1572/18688 [1:49:33<19:31:13, 4.11s/it] 8%|▊ | 1573/18688 [1:49:37<19:00:48, 4.00s/it] 8%|▊ | 1574/18688 [1:49:42<20:03:29, 4.22s/it] 8%|▊ | 1575/18688 [1:49:46<19:46:19, 4.16s/it] 8%|▊ | 1576/18688 [1:49:50<19:59:20, 4.21s/it] 8%|▊ | 1577/18688 [1:49:54<19:17:20, 4.06s/it] 8%|▊ | 1578/18688 [1:49:58<19:11:28, 4.04s/it] 8%|▊ | 1579/18688 [1:50:01<18:22:27, 3.87s/it] 8%|▊ | 1580/18688 [1:50:05<18:29:50, 3.89s/it] {'loss': '1.005', 'grad_norm': '0.8271', 'learning_rate': '8e-05', 'epoch': '0.08455', 'num_input_tokens_seen': 5280136, 'train_runtime': '6606', 'train_tokens_per_second': '799.3'}
8%|▊ | 1580/18688 [1:50:05<18:29:50, 3.89s/it] 8%|▊ | 1581/18688 [1:50:09<18:42:24, 3.94s/it] 8%|▊ | 1582/18688 [1:50:14<19:34:39, 4.12s/it] 8%|▊ | 1583/18688 [1:50:18<18:58:23, 3.99s/it] 8%|▊ | 1584/18688 [1:50:21<18:27:16, 3.88s/it] 8%|▊ | 1585/18688 [1:50:25<18:38:10, 3.92s/it] 8%|▊ | 1586/18688 [1:50:30<19:25:41, 4.09s/it] 8%|▊ | 1587/18688 [1:50:36<22:30:45, 4.74s/it] 8%|▊ | 1588/18688 [1:50:41<22:44:58, 4.79s/it] 9%|▊ | 1589/18688 [1:50:44<21:03:27, 4.43s/it] 9%|▊ | 1590/18688 [1:50:48<20:22:52, 4.29s/it] {'loss': '1.013', 'grad_norm': '1.082', 'learning_rate': '7.999e-05', 'epoch': '0.08508', 'num_input_tokens_seen': 5316072, 'train_runtime': '6649', 'train_tokens_per_second': '799.5'}
9%|▊ | 1590/18688 [1:50:48<20:22:52, 4.29s/it] 9%|▊ | 1591/18688 [1:50:52<20:02:54, 4.22s/it] 9%|▊ | 1592/18688 [1:50:56<19:33:04, 4.12s/it] 9%|▊ | 1593/18688 [1:51:00<18:52:36, 3.98s/it] 9%|▊ | 1594/18688 [1:51:04<18:17:04, 3.85s/it] 9%|▊ | 1595/18688 [1:51:07<17:46:40, 3.74s/it] 9%|▊ | 1596/18688 [1:51:11<17:27:34, 3.68s/it] 9%|▊ | 1597/18688 [1:51:15<18:58:33, 4.00s/it] 9%|▊ | 1598/18688 [1:51:19<18:23:16, 3.87s/it] 9%|▊ | 1599/18688 [1:51:23<18:42:06, 3.94s/it] 9%|▊ | 1600/18688 [1:51:27<18:16:46, 3.85s/it] {'loss': '0.9736', 'grad_norm': '1.025', 'learning_rate': '7.999e-05', 'epoch': '0.08562', 'num_input_tokens_seen': 5345640, 'train_runtime': '6687', 'train_tokens_per_second': '799.4'}
9%|▊ | 1600/18688 [1:51:27<18:16:46, 3.85s/it] 9%|▊ | 1601/18688 [1:51:31<18:19:35, 3.86s/it] 9%|▊ | 1602/18688 [1:51:35<19:31:12, 4.11s/it] 9%|▊ | 1603/18688 [1:51:41<22:08:55, 4.67s/it] 9%|▊ | 1604/18688 [1:51:46<21:54:54, 4.62s/it] 9%|▊ | 1605/18688 [1:51:49<20:47:07, 4.38s/it] 9%|▊ | 1606/18688 [1:51:53<20:11:42, 4.26s/it] 9%|▊ | 1607/18688 [1:51:57<19:21:55, 4.08s/it] 9%|▊ | 1608/18688 [1:52:01<19:08:58, 4.04s/it] 9%|▊ | 1609/18688 [1:52:05<19:41:26, 4.15s/it] 9%|▊ | 1610/18688 [1:52:10<19:56:39, 4.20s/it] {'loss': '1.031', 'grad_norm': '1.003', 'learning_rate': '7.999e-05', 'epoch': '0.08615', 'num_input_tokens_seen': 5380488, 'train_runtime': '6730', 'train_tokens_per_second': '799.4'}
9%|▊ | 1610/18688 [1:52:10<19:56:39, 4.20s/it] 9%|▊ | 1611/18688 [1:52:14<19:18:25, 4.07s/it] 9%|▊ | 1612/18688 [1:52:17<19:01:48, 4.01s/it] 9%|▊ | 1613/18688 [1:52:21<18:48:06, 3.96s/it] 9%|▊ | 1614/18688 [1:52:26<19:59:14, 4.21s/it] 9%|▊ | 1615/18688 [1:52:30<19:14:11, 4.06s/it] 9%|▊ | 1616/18688 [1:52:34<18:46:34, 3.96s/it] 9%|▊ | 1617/18688 [1:52:39<20:14:51, 4.27s/it] 9%|▊ | 1618/18688 [1:52:43<20:51:27, 4.40s/it] 9%|▊ | 1619/18688 [1:52:47<19:59:40, 4.22s/it] 9%|▊ | 1620/18688 [1:52:51<19:46:41, 4.17s/it] {'loss': '1.004', 'grad_norm': '0.9887', 'learning_rate': '7.999e-05', 'epoch': '0.08669', 'num_input_tokens_seen': 5414080, 'train_runtime': '6772', 'train_tokens_per_second': '799.5'}
9%|▊ | 1620/18688 [1:52:51<19:46:41, 4.17s/it] 9%|▊ | 1621/18688 [1:52:55<19:50:05, 4.18s/it] 9%|▊ | 1622/18688 [1:52:59<19:30:25, 4.11s/it] 9%|▊ | 1623/18688 [1:53:04<20:34:54, 4.34s/it] 9%|▊ | 1624/18688 [1:53:09<21:25:46, 4.52s/it] 9%|▊ | 1625/18688 [1:53:14<21:46:29, 4.59s/it] 9%|▊ | 1626/18688 [1:53:18<21:07:28, 4.46s/it] 9%|▊ | 1627/18688 [1:53:22<20:39:54, 4.36s/it] 9%|▊ | 1628/18688 [1:53:26<19:37:21, 4.14s/it] 9%|▊ | 1629/18688 [1:53:31<20:43:01, 4.37s/it] 9%|▊ | 1630/18688 [1:53:35<20:14:43, 4.27s/it] {'loss': '1.044', 'grad_norm': '1.166', 'learning_rate': '7.999e-05', 'epoch': '0.08722', 'num_input_tokens_seen': 5448424, 'train_runtime': '6815', 'train_tokens_per_second': '799.5'}
9%|▊ | 1630/18688 [1:53:35<20:14:43, 4.27s/it] 9%|▊ | 1631/18688 [1:53:39<20:17:48, 4.28s/it] 9%|▊ | 1632/18688 [1:53:44<21:22:03, 4.51s/it] 9%|▊ | 1633/18688 [1:53:49<21:33:32, 4.55s/it] 9%|▊ | 1634/18688 [1:53:53<20:55:31, 4.42s/it] 9%|▊ | 1635/18688 [1:53:58<21:36:17, 4.56s/it] 9%|▉ | 1636/18688 [1:54:02<20:49:21, 4.40s/it] 9%|▉ | 1637/18688 [1:54:05<19:45:06, 4.17s/it] 9%|▉ | 1638/18688 [1:54:10<19:59:44, 4.22s/it] 9%|▉ | 1639/18688 [1:54:13<19:24:48, 4.10s/it] 9%|▉ | 1640/18688 [1:54:17<19:17:50, 4.07s/it] {'loss': '1.049', 'grad_norm': '0.9587', 'learning_rate': '7.999e-05', 'epoch': '0.08776', 'num_input_tokens_seen': 5483208, 'train_runtime': '6858', 'train_tokens_per_second': '799.5'}
9%|▉ | 1640/18688 [1:54:17<19:17:50, 4.07s/it] 9%|▉ | 1641/18688 [1:54:21<19:10:43, 4.05s/it] 9%|▉ | 1642/18688 [1:54:25<19:02:31, 4.02s/it] 9%|▉ | 1643/18688 [1:54:30<19:29:43, 4.12s/it] 9%|▉ | 1644/18688 [1:54:34<18:58:52, 4.01s/it] 9%|▉ | 1645/18688 [1:54:38<19:14:28, 4.06s/it] 9%|▉ | 1646/18688 [1:54:41<18:33:17, 3.92s/it] 9%|▉ | 1647/18688 [1:54:45<18:33:34, 3.92s/it] 9%|▉ | 1648/18688 [1:54:50<19:29:10, 4.12s/it] 9%|▉ | 1649/18688 [1:54:53<18:33:37, 3.92s/it] 9%|▉ | 1650/18688 [1:54:57<18:41:34, 3.95s/it] {'loss': '0.9465', 'grad_norm': '1.515', 'learning_rate': '7.999e-05', 'epoch': '0.08829', 'num_input_tokens_seen': 5514240, 'train_runtime': '6898', 'train_tokens_per_second': '799.4'}
9%|▉ | 1650/18688 [1:54:57<18:41:34, 3.95s/it] 9%|▉ | 1651/18688 [1:55:02<19:18:00, 4.08s/it] 9%|▉ | 1652/18688 [1:55:06<19:22:23, 4.09s/it] 9%|▉ | 1653/18688 [1:55:11<20:39:34, 4.37s/it] 9%|▉ | 1654/18688 [1:55:15<20:29:17, 4.33s/it] 9%|▉ | 1655/18688 [1:55:19<20:21:58, 4.30s/it] 9%|▉ | 1656/18688 [1:55:25<22:16:49, 4.71s/it] 9%|▉ | 1657/18688 [1:55:29<21:38:08, 4.57s/it] 9%|▉ | 1658/18688 [1:55:34<21:36:53, 4.57s/it] 9%|▉ | 1659/18688 [1:55:38<21:42:09, 4.59s/it] 9%|▉ | 1660/18688 [1:55:42<20:34:10, 4.35s/it] {'loss': '1.002', 'grad_norm': '0.9099', 'learning_rate': '7.998e-05', 'epoch': '0.08883', 'num_input_tokens_seen': 5552472, 'train_runtime': '6943', 'train_tokens_per_second': '799.8'}
9%|▉ | 1660/18688 [1:55:42<20:34:10, 4.35s/it] 9%|▉ | 1661/18688 [1:55:46<20:29:10, 4.33s/it] 9%|▉ | 1662/18688 [1:55:51<20:19:07, 4.30s/it] 9%|▉ | 1663/18688 [1:55:55<19:43:40, 4.17s/it] 9%|▉ | 1664/18688 [1:55:58<18:51:03, 3.99s/it] 9%|▉ | 1665/18688 [1:56:02<18:15:26, 3.86s/it] 9%|▉ | 1666/18688 [1:56:06<19:36:13, 4.15s/it] 9%|▉ | 1667/18688 [1:56:10<19:20:32, 4.09s/it] 9%|▉ | 1668/18688 [1:56:15<20:30:12, 4.34s/it] 9%|▉ | 1669/18688 [1:56:20<20:39:46, 4.37s/it] 9%|▉ | 1670/18688 [1:56:24<20:08:01, 4.26s/it] {'loss': '0.9772', 'grad_norm': '0.9163', 'learning_rate': '7.998e-05', 'epoch': '0.08936', 'num_input_tokens_seen': 5586232, 'train_runtime': '6984', 'train_tokens_per_second': '799.8'}
9%|▉ | 1670/18688 [1:56:24<20:08:01, 4.26s/it] 9%|▉ | 1671/18688 [1:56:28<20:16:59, 4.29s/it] 9%|▉ | 1672/18688 [1:56:32<19:22:37, 4.10s/it] 9%|▉ | 1673/18688 [1:56:36<19:10:25, 4.06s/it] 9%|▉ | 1674/18688 [1:56:40<19:30:10, 4.13s/it] 9%|▉ | 1675/18688 [1:56:44<19:02:25, 4.03s/it] 9%|▉ | 1676/18688 [1:56:47<18:24:17, 3.89s/it] 9%|▉ | 1677/18688 [1:56:52<19:07:37, 4.05s/it] 9%|▉ | 1678/18688 [1:56:56<19:04:16, 4.04s/it] 9%|▉ | 1679/18688 [1:56:59<18:25:38, 3.90s/it] 9%|▉ | 1680/18688 [1:57:03<18:06:32, 3.83s/it] {'loss': '0.9574', 'grad_norm': '0.9786', 'learning_rate': '7.998e-05', 'epoch': '0.0899', 'num_input_tokens_seen': 5615848, 'train_runtime': '7024', 'train_tokens_per_second': '799.6'}
9%|▉ | 1680/18688 [1:57:03<18:06:32, 3.83s/it] 9%|▉ | 1681/18688 [1:57:07<18:26:32, 3.90s/it] 9%|▉ | 1682/18688 [1:57:12<19:00:04, 4.02s/it] 9%|▉ | 1683/18688 [1:57:16<20:12:58, 4.28s/it] 9%|▉ | 1684/18688 [1:57:21<20:00:11, 4.23s/it] 9%|▉ | 1685/18688 [1:57:24<19:18:21, 4.09s/it] 9%|▉ | 1686/18688 [1:57:29<19:35:23, 4.15s/it] 9%|▉ | 1687/18688 [1:57:33<19:25:03, 4.11s/it] 9%|▉ | 1688/18688 [1:57:36<18:43:53, 3.97s/it] 9%|▉ | 1689/18688 [1:57:41<19:14:02, 4.07s/it] 9%|▉ | 1690/18688 [1:57:45<19:36:19, 4.15s/it] {'loss': '1.028', 'grad_norm': '1.08', 'learning_rate': '7.998e-05', 'epoch': '0.09043', 'num_input_tokens_seen': 5649632, 'train_runtime': '7065', 'train_tokens_per_second': '799.6'}
9%|▉ | 1690/18688 [1:57:45<19:36:19, 4.15s/it] 9%|▉ | 1691/18688 [1:57:49<20:14:24, 4.29s/it] 9%|▉ | 1692/18688 [1:57:53<19:31:20, 4.14s/it] 9%|▉ | 1693/18688 [1:57:57<18:33:45, 3.93s/it] 9%|▉ | 1694/18688 [1:58:01<18:29:01, 3.92s/it] 9%|▉ | 1695/18688 [1:58:05<19:11:52, 4.07s/it] 9%|▉ | 1696/18688 [1:58:09<19:18:13, 4.09s/it] 9%|▉ | 1697/18688 [1:58:13<18:38:59, 3.95s/it] 9%|▉ | 1698/18688 [1:58:17<19:00:27, 4.03s/it] 9%|▉ | 1699/18688 [1:58:21<19:05:31, 4.05s/it] 9%|▉ | 1700/18688 [1:58:25<19:35:56, 4.15s/it] {'loss': '0.9836', 'grad_norm': '0.9106', 'learning_rate': '7.997e-05', 'epoch': '0.09097', 'num_input_tokens_seen': 5680272, 'train_runtime': '7106', 'train_tokens_per_second': '799.4'}
9%|▉ | 1700/18688 [1:58:25<19:35:56, 4.15s/it] 9%|▉ | 1701/18688 [1:58:29<18:49:39, 3.99s/it] 9%|▉ | 1702/18688 [1:58:33<19:21:10, 4.10s/it] 9%|▉ | 1703/18688 [1:58:38<19:26:40, 4.12s/it] 9%|▉ | 1704/18688 [1:58:41<18:52:23, 4.00s/it] 9%|▉ | 1705/18688 [1:58:45<18:26:08, 3.91s/it] 9%|▉ | 1706/18688 [1:58:50<19:54:23, 4.22s/it] 9%|▉ | 1707/18688 [1:58:55<20:21:30, 4.32s/it] 9%|▉ | 1708/18688 [1:58:58<19:44:53, 4.19s/it] 9%|▉ | 1709/18688 [1:59:02<18:59:10, 4.03s/it] 9%|▉ | 1710/18688 [1:59:06<19:30:40, 4.14s/it] {'loss': '0.9246', 'grad_norm': '1.158', 'learning_rate': '7.997e-05', 'epoch': '0.09151', 'num_input_tokens_seen': 5711896, 'train_runtime': '7147', 'train_tokens_per_second': '799.2'}
9%|▉ | 1710/18688 [1:59:06<19:30:40, 4.14s/it] 9%|▉ | 1711/18688 [1:59:11<19:46:50, 4.19s/it] 9%|▉ | 1712/18688 [1:59:15<20:09:57, 4.28s/it] 9%|▉ | 1713/18688 [1:59:19<19:47:34, 4.20s/it] 9%|▉ | 1714/18688 [1:59:24<20:04:54, 4.26s/it] 9%|▉ | 1715/18688 [1:59:28<20:00:23, 4.24s/it] 9%|▉ | 1716/18688 [1:59:32<19:29:56, 4.14s/it] 9%|▉ | 1717/18688 [1:59:36<19:29:50, 4.14s/it] 9%|▉ | 1718/18688 [1:59:40<20:05:43, 4.26s/it] 9%|▉ | 1719/18688 [1:59:44<19:30:14, 4.14s/it] 9%|▉ | 1720/18688 [1:59:48<18:41:58, 3.97s/it] {'loss': '1.039', 'grad_norm': '1.087', 'learning_rate': '7.997e-05', 'epoch': '0.09204', 'num_input_tokens_seen': 5744968, 'train_runtime': '7188', 'train_tokens_per_second': '799.2'}
9%|▉ | 1720/18688 [1:59:48<18:41:58, 3.97s/it] 9%|▉ | 1721/18688 [1:59:52<18:19:16, 3.89s/it] 9%|▉ | 1722/18688 [1:59:56<18:40:47, 3.96s/it] 9%|▉ | 1723/18688 [2:00:00<18:42:17, 3.97s/it] 9%|▉ | 1724/18688 [2:00:03<18:08:54, 3.85s/it] 9%|▉ | 1725/18688 [2:00:07<17:53:24, 3.80s/it] 9%|▉ | 1726/18688 [2:00:12<19:48:12, 4.20s/it] 9%|▉ | 1727/18688 [2:00:17<20:42:36, 4.40s/it] 9%|▉ | 1728/18688 [2:00:21<20:42:02, 4.39s/it] 9%|▉ | 1729/18688 [2:00:25<20:14:28, 4.30s/it] 9%|▉ | 1730/18688 [2:00:30<20:27:05, 4.34s/it] {'loss': '0.9872', 'grad_norm': '1.006', 'learning_rate': '7.996e-05', 'epoch': '0.09258', 'num_input_tokens_seen': 5778576, 'train_runtime': '7230', 'train_tokens_per_second': '799.2'}
9%|▉ | 1730/18688 [2:00:30<20:27:05, 4.34s/it] 9%|▉ | 1731/18688 [2:00:33<19:29:54, 4.14s/it] 9%|▉ | 1732/18688 [2:00:37<19:15:28, 4.09s/it] 9%|▉ | 1733/18688 [2:00:41<18:26:59, 3.92s/it] 9%|▉ | 1734/18688 [2:00:46<19:33:38, 4.15s/it] 9%|▉ | 1735/18688 [2:00:50<20:20:06, 4.32s/it] 9%|▉ | 1736/18688 [2:00:54<19:48:35, 4.21s/it] 9%|▉ | 1737/18688 [2:00:59<20:11:02, 4.29s/it] 9%|▉ | 1738/18688 [2:01:04<20:45:43, 4.41s/it] 9%|▉ | 1739/18688 [2:01:08<20:39:39, 4.39s/it] 9%|▉ | 1740/18688 [2:01:12<19:40:36, 4.18s/it] {'loss': '1.004', 'grad_norm': '1.138', 'learning_rate': '7.996e-05', 'epoch': '0.09311', 'num_input_tokens_seen': 5811808, 'train_runtime': '7272', 'train_tokens_per_second': '799.2'}
9%|▉ | 1740/18688 [2:01:12<19:40:36, 4.18s/it] 9%|▉ | 1741/18688 [2:01:16<19:41:06, 4.18s/it] 9%|▉ | 1742/18688 [2:01:19<18:41:27, 3.97s/it] 9%|▉ | 1743/18688 [2:01:23<18:28:17, 3.92s/it] 9%|▉ | 1744/18688 [2:01:27<17:54:08, 3.80s/it] 9%|▉ | 1745/18688 [2:01:31<18:24:50, 3.91s/it] 9%|▉ | 1746/18688 [2:01:34<18:01:43, 3.83s/it] 9%|▉ | 1747/18688 [2:01:38<17:57:21, 3.82s/it] 9%|▉ | 1748/18688 [2:01:42<18:22:49, 3.91s/it] 9%|▉ | 1749/18688 [2:01:46<18:30:05, 3.93s/it] 9%|▉ | 1750/18688 [2:01:51<19:38:47, 4.18s/it] {'loss': '0.9677', 'grad_norm': '0.9763', 'learning_rate': '7.996e-05', 'epoch': '0.09365', 'num_input_tokens_seen': 5842184, 'train_runtime': '7311', 'train_tokens_per_second': '799'}
9%|▉ | 1750/18688 [2:01:51<19:38:47, 4.18s/it] 9%|▉ | 1751/18688 [2:01:55<19:18:35, 4.10s/it] 9%|▉ | 1752/18688 [2:01:58<18:24:44, 3.91s/it] 9%|▉ | 1753/18688 [2:02:02<18:01:52, 3.83s/it] 9%|▉ | 1754/18688 [2:02:07<19:03:26, 4.05s/it] 9%|▉ | 1755/18688 [2:02:11<19:31:38, 4.15s/it] 9%|▉ | 1756/18688 [2:02:16<20:13:53, 4.30s/it] 9%|▉ | 1757/18688 [2:02:21<21:03:13, 4.48s/it] 9%|▉ | 1758/18688 [2:02:25<20:39:54, 4.39s/it] 9%|▉ | 1759/18688 [2:02:29<19:54:42, 4.23s/it] 9%|▉ | 1760/18688 [2:02:32<19:02:02, 4.05s/it] {'loss': '0.9722', 'grad_norm': '0.9908', 'learning_rate': '7.996e-05', 'epoch': '0.09418', 'num_input_tokens_seen': 5875024, 'train_runtime': '7353', 'train_tokens_per_second': '799'}
9%|▉ | 1760/18688 [2:02:32<19:02:02, 4.05s/it] 9%|▉ | 1761/18688 [2:02:36<19:16:19, 4.10s/it] 9%|▉ | 1762/18688 [2:02:41<20:23:59, 4.34s/it] 9%|▉ | 1763/18688 [2:02:45<19:29:01, 4.14s/it] 9%|▉ | 1764/18688 [2:02:49<18:41:16, 3.98s/it] 9%|▉ | 1765/18688 [2:02:53<18:51:35, 4.01s/it] 9%|▉ | 1766/18688 [2:02:56<18:24:58, 3.92s/it] 9%|▉ | 1767/18688 [2:03:00<18:19:36, 3.90s/it] 9%|▉ | 1768/18688 [2:03:05<20:02:21, 4.26s/it] 9%|▉ | 1769/18688 [2:03:09<19:05:04, 4.06s/it] 9%|▉ | 1770/18688 [2:03:13<18:52:02, 4.01s/it] {'loss': '1.004', 'grad_norm': '0.9364', 'learning_rate': '7.995e-05', 'epoch': '0.09472', 'num_input_tokens_seen': 5907784, 'train_runtime': '7393', 'train_tokens_per_second': '799.1'}
9%|▉ | 1770/18688 [2:03:13<18:52:02, 4.01s/it] 9%|▉ | 1771/18688 [2:03:17<18:55:12, 4.03s/it] 9%|▉ | 1772/18688 [2:03:21<19:02:33, 4.05s/it] 9%|▉ | 1773/18688 [2:03:25<18:49:06, 4.01s/it] 9%|▉ | 1774/18688 [2:03:29<19:19:22, 4.11s/it] 9%|▉ | 1775/18688 [2:03:33<19:09:44, 4.08s/it] 10%|▉ | 1776/18688 [2:03:37<18:31:28, 3.94s/it] 10%|▉ | 1777/18688 [2:03:41<18:51:59, 4.02s/it] 10%|▉ | 1778/18688 [2:03:45<18:29:31, 3.94s/it] 10%|▉ | 1779/18688 [2:03:49<19:11:39, 4.09s/it] 10%|▉ | 1780/18688 [2:03:53<19:04:02, 4.06s/it] {'loss': '1.015', 'grad_norm': '0.9941', 'learning_rate': '7.995e-05', 'epoch': '0.09525', 'num_input_tokens_seen': 5938800, 'train_runtime': '7434', 'train_tokens_per_second': '798.9'}
10%|▉ | 1780/18688 [2:03:53<19:04:02, 4.06s/it] 10%|▉ | 1781/18688 [2:03:58<19:25:45, 4.14s/it] 10%|▉ | 1782/18688 [2:04:02<19:42:32, 4.20s/it] 10%|▉ | 1783/18688 [2:04:07<20:43:44, 4.41s/it] 10%|▉ | 1784/18688 [2:04:12<21:51:23, 4.65s/it] 10%|▉ | 1785/18688 [2:04:16<21:21:22, 4.55s/it] 10%|▉ | 1786/18688 [2:04:20<19:49:15, 4.22s/it] 10%|▉ | 1787/18688 [2:04:24<19:22:01, 4.13s/it] 10%|▉ | 1788/18688 [2:04:28<19:39:40, 4.19s/it] 10%|▉ | 1789/18688 [2:04:32<19:20:59, 4.12s/it] 10%|▉ | 1790/18688 [2:04:36<19:16:12, 4.11s/it] {'loss': '0.9463', 'grad_norm': '0.9573', 'learning_rate': '7.994e-05', 'epoch': '0.09579', 'num_input_tokens_seen': 5975136, 'train_runtime': '7477', 'train_tokens_per_second': '799.2'}
10%|▉ | 1790/18688 [2:04:36<19:16:12, 4.11s/it] 10%|▉ | 1791/18688 [2:04:41<20:03:47, 4.27s/it] 10%|▉ | 1792/18688 [2:04:44<18:55:11, 4.03s/it] 10%|▉ | 1793/18688 [2:04:48<18:22:52, 3.92s/it] 10%|▉ | 1794/18688 [2:04:52<18:09:20, 3.87s/it] 10%|▉ | 1795/18688 [2:04:56<18:10:50, 3.87s/it] 10%|▉ | 1796/18688 [2:04:59<17:43:04, 3.78s/it] 10%|▉ | 1797/18688 [2:05:03<18:09:56, 3.87s/it] 10%|▉ | 1798/18688 [2:05:07<18:16:52, 3.90s/it] 10%|▉ | 1799/18688 [2:05:11<18:25:59, 3.93s/it] 10%|▉ | 1800/18688 [2:05:16<19:05:19, 4.07s/it] {'loss': '0.946', 'grad_norm': '1.116', 'learning_rate': '7.994e-05', 'epoch': '0.09632', 'num_input_tokens_seen': 6004904, 'train_runtime': '7516', 'train_tokens_per_second': '798.9'}
10%|▉ | 1800/18688 [2:05:16<19:05:19, 4.07s/it] 10%|▉ | 1801/18688 [2:05:20<19:37:12, 4.18s/it] 10%|▉ | 1802/18688 [2:05:24<19:25:38, 4.14s/it] 10%|▉ | 1803/18688 [2:05:28<19:28:38, 4.15s/it] 10%|▉ | 1804/18688 [2:05:32<19:33:32, 4.17s/it] 10%|▉ | 1805/18688 [2:05:37<19:52:06, 4.24s/it] 10%|▉ | 1806/18688 [2:05:41<19:47:13, 4.22s/it] 10%|▉ | 1807/18688 [2:05:45<19:06:17, 4.07s/it] 10%|▉ | 1808/18688 [2:05:48<18:42:54, 3.99s/it] 10%|▉ | 1809/18688 [2:05:53<19:54:30, 4.25s/it] 10%|▉ | 1810/18688 [2:05:58<19:55:08, 4.25s/it] {'loss': '0.9677', 'grad_norm': '1.134', 'learning_rate': '7.994e-05', 'epoch': '0.09686', 'num_input_tokens_seen': 6039224, 'train_runtime': '7558', 'train_tokens_per_second': '799'}
10%|▉ | 1810/18688 [2:05:58<19:55:08, 4.25s/it] 10%|▉ | 1811/18688 [2:06:02<20:47:43, 4.44s/it] 10%|▉ | 1812/18688 [2:06:07<21:34:23, 4.60s/it] 10%|▉ | 1813/18688 [2:06:12<20:53:43, 4.46s/it] 10%|▉ | 1814/18688 [2:06:15<19:44:17, 4.21s/it] 10%|▉ | 1815/18688 [2:06:20<20:03:17, 4.28s/it] 10%|▉ | 1816/18688 [2:06:24<20:08:03, 4.30s/it] 10%|▉ | 1817/18688 [2:06:28<19:36:06, 4.18s/it] 10%|▉ | 1818/18688 [2:06:32<19:06:18, 4.08s/it] 10%|▉ | 1819/18688 [2:06:35<18:34:49, 3.97s/it] 10%|▉ | 1820/18688 [2:06:39<17:59:24, 3.84s/it] {'loss': '1.014', 'grad_norm': '0.8916', 'learning_rate': '7.993e-05', 'epoch': '0.09739', 'num_input_tokens_seen': 6071920, 'train_runtime': '7599', 'train_tokens_per_second': '799'}
10%|▉ | 1820/18688 [2:06:39<17:59:24, 3.84s/it] 10%|▉ | 1821/18688 [2:06:43<18:05:00, 3.86s/it] 10%|▉ | 1822/18688 [2:06:46<17:41:31, 3.78s/it] 10%|▉ | 1823/18688 [2:06:51<18:43:16, 4.00s/it] 10%|▉ | 1824/18688 [2:06:55<18:43:10, 4.00s/it] 10%|▉ | 1825/18688 [2:06:59<19:15:11, 4.11s/it] 10%|▉ | 1826/18688 [2:07:03<18:45:01, 4.00s/it] 10%|▉ | 1827/18688 [2:07:07<18:48:07, 4.01s/it] 10%|▉ | 1828/18688 [2:07:12<19:57:24, 4.26s/it] 10%|▉ | 1829/18688 [2:07:16<20:08:37, 4.30s/it] 10%|▉ | 1830/18688 [2:07:21<21:02:47, 4.49s/it] {'loss': '1.05', 'grad_norm': '0.8445', 'learning_rate': '7.993e-05', 'epoch': '0.09793', 'num_input_tokens_seen': 6107064, 'train_runtime': '7642', 'train_tokens_per_second': '799.2'}
10%|▉ | 1830/18688 [2:07:21<21:02:47, 4.49s/it] 10%|▉ | 1831/18688 [2:07:26<21:00:43, 4.49s/it] 10%|▉ | 1832/18688 [2:07:29<19:40:33, 4.20s/it] 10%|▉ | 1833/18688 [2:07:33<19:29:11, 4.16s/it] 10%|▉ | 1834/18688 [2:07:37<19:22:18, 4.14s/it] 10%|▉ | 1835/18688 [2:07:42<19:55:49, 4.26s/it] 10%|▉ | 1836/18688 [2:07:47<20:31:02, 4.38s/it] 10%|▉ | 1837/18688 [2:07:50<19:40:49, 4.20s/it] 10%|▉ | 1838/18688 [2:07:54<18:55:14, 4.04s/it] 10%|▉ | 1839/18688 [2:07:59<19:26:02, 4.15s/it] 10%|▉ | 1840/18688 [2:08:04<21:28:46, 4.59s/it] {'loss': '0.9815', 'grad_norm': '0.9496', 'learning_rate': '7.992e-05', 'epoch': '0.09846', 'num_input_tokens_seen': 6144296, 'train_runtime': '7685', 'train_tokens_per_second': '799.6'}
10%|▉ | 1840/18688 [2:08:04<21:28:46, 4.59s/it] 10%|▉ | 1841/18688 [2:08:08<20:15:03, 4.33s/it] 10%|▉ | 1842/18688 [2:08:13<20:49:16, 4.45s/it] 10%|▉ | 1843/18688 [2:08:17<21:24:54, 4.58s/it] 10%|▉ | 1844/18688 [2:08:22<21:16:16, 4.55s/it] 10%|▉ | 1845/18688 [2:08:27<21:23:16, 4.57s/it] 10%|▉ | 1846/18688 [2:08:31<21:32:04, 4.60s/it] 10%|▉ | 1847/18688 [2:08:36<21:28:26, 4.59s/it] 10%|▉ | 1848/18688 [2:08:40<21:29:19, 4.59s/it] 10%|▉ | 1849/18688 [2:08:44<20:11:06, 4.32s/it] 10%|▉ | 1850/18688 [2:08:49<20:20:57, 4.35s/it] {'loss': '1.014', 'grad_norm': '1.038', 'learning_rate': '7.992e-05', 'epoch': '0.099', 'num_input_tokens_seen': 6179064, 'train_runtime': '7729', 'train_tokens_per_second': '799.5'}
10%|▉ | 1850/18688 [2:08:49<20:20:57, 4.35s/it] 10%|▉ | 1851/18688 [2:08:53<21:04:27, 4.51s/it] 10%|▉ | 1852/18688 [2:08:58<20:42:54, 4.43s/it] 10%|▉ | 1853/18688 [2:09:02<20:04:47, 4.29s/it] 10%|▉ | 1854/18688 [2:09:06<20:42:24, 4.43s/it] 10%|▉ | 1855/18688 [2:09:11<20:37:46, 4.41s/it] 10%|▉ | 1856/18688 [2:09:15<20:26:48, 4.37s/it] 10%|▉ | 1857/18688 [2:09:19<19:29:22, 4.17s/it] 10%|▉ | 1858/18688 [2:09:23<19:20:48, 4.14s/it] 10%|▉ | 1859/18688 [2:09:27<19:03:31, 4.08s/it] 10%|▉ | 1860/18688 [2:09:32<20:43:49, 4.43s/it] {'loss': '1.001', 'grad_norm': '0.8372', 'learning_rate': '7.991e-05', 'epoch': '0.09953', 'num_input_tokens_seen': 6213504, 'train_runtime': '7772', 'train_tokens_per_second': '799.4'}
10%|▉ | 1860/18688 [2:09:32<20:43:49, 4.43s/it] 10%|▉ | 1861/18688 [2:09:36<20:15:44, 4.33s/it] 10%|▉ | 1862/18688 [2:09:40<20:06:24, 4.30s/it] 10%|▉ | 1863/18688 [2:09:44<19:51:19, 4.25s/it] 10%|▉ | 1864/18688 [2:09:48<19:25:31, 4.16s/it] 10%|▉ | 1865/18688 [2:09:53<19:46:51, 4.23s/it] 10%|▉ | 1866/18688 [2:09:57<19:29:59, 4.17s/it] 10%|▉ | 1867/18688 [2:10:01<19:00:58, 4.07s/it] 10%|▉ | 1868/18688 [2:10:05<19:22:03, 4.15s/it] 10%|█ | 1869/18688 [2:10:10<19:58:15, 4.27s/it] 10%|█ | 1870/18688 [2:10:13<18:52:14, 4.04s/it] {'loss': '0.9855', 'grad_norm': '1.124', 'learning_rate': '7.991e-05', 'epoch': '0.1001', 'num_input_tokens_seen': 6248336, 'train_runtime': '7814', 'train_tokens_per_second': '799.7'}
10%|█ | 1870/18688 [2:10:13<18:52:14, 4.04s/it] 10%|█ | 1871/18688 [2:10:17<18:17:29, 3.92s/it] 10%|█ | 1872/18688 [2:10:20<18:06:33, 3.88s/it] 10%|█ | 1873/18688 [2:10:24<18:13:41, 3.90s/it] 10%|█ | 1874/18688 [2:10:29<18:50:04, 4.03s/it] 10%|█ | 1875/18688 [2:10:33<19:09:46, 4.10s/it] 10%|█ | 1876/18688 [2:10:38<20:47:24, 4.45s/it] 10%|█ | 1877/18688 [2:10:42<20:20:37, 4.36s/it] 10%|█ | 1878/18688 [2:10:47<20:39:52, 4.43s/it] 10%|█ | 1879/18688 [2:10:51<20:17:59, 4.35s/it] 10%|█ | 1880/18688 [2:10:55<18:58:16, 4.06s/it] {'loss': '0.9648', 'grad_norm': '1.094', 'learning_rate': '7.99e-05', 'epoch': '0.1006', 'num_input_tokens_seen': 6282464, 'train_runtime': '7855', 'train_tokens_per_second': '799.8'}
10%|█ | 1880/18688 [2:10:55<18:58:16, 4.06s/it] 10%|█ | 1881/18688 [2:10:59<19:12:29, 4.11s/it] 10%|█ | 1882/18688 [2:11:03<18:51:51, 4.04s/it] 10%|█ | 1883/18688 [2:11:08<21:10:55, 4.54s/it] 10%|█ | 1884/18688 [2:11:12<19:50:16, 4.25s/it] 10%|█ | 1885/18688 [2:11:16<19:32:21, 4.19s/it] 10%|█ | 1886/18688 [2:11:20<19:54:25, 4.27s/it] 10%|█ | 1887/18688 [2:11:25<20:48:04, 4.46s/it] 10%|█ | 1888/18688 [2:11:29<20:20:11, 4.36s/it] 10%|█ | 1889/18688 [2:11:33<19:31:13, 4.18s/it] 10%|█ | 1890/18688 [2:11:37<19:27:16, 4.17s/it] {'loss': '0.9589', 'grad_norm': '0.9728', 'learning_rate': '7.99e-05', 'epoch': '0.1011', 'num_input_tokens_seen': 6317720, 'train_runtime': '7898', 'train_tokens_per_second': '799.9'}
10%|█ | 1890/18688 [2:11:37<19:27:16, 4.17s/it] 10%|█ | 1891/18688 [2:11:41<18:28:59, 3.96s/it] 10%|█ | 1892/18688 [2:11:45<19:03:27, 4.08s/it] 10%|█ | 1893/18688 [2:11:49<19:13:21, 4.12s/it] 10%|█ | 1894/18688 [2:11:53<18:40:01, 4.00s/it] 10%|█ | 1895/18688 [2:11:58<19:15:38, 4.13s/it] 10%|█ | 1896/18688 [2:12:02<19:10:47, 4.11s/it] 10%|█ | 1897/18688 [2:12:06<19:31:28, 4.19s/it] 10%|█ | 1898/18688 [2:12:10<19:49:04, 4.25s/it] 10%|█ | 1899/18688 [2:12:15<20:50:33, 4.47s/it] 10%|█ | 1900/18688 [2:12:19<19:34:10, 4.20s/it] {'loss': '0.9301', 'grad_norm': '1.204', 'learning_rate': '7.989e-05', 'epoch': '0.1017', 'num_input_tokens_seen': 6351128, 'train_runtime': '7939', 'train_tokens_per_second': '799.9'}
10%|█ | 1900/18688 [2:12:19<19:34:10, 4.20s/it] 10%|█ | 1901/18688 [2:12:23<18:39:29, 4.00s/it] 10%|█ | 1902/18688 [2:12:27<19:08:55, 4.11s/it] 10%|█ | 1903/18688 [2:12:31<18:45:19, 4.02s/it] 10%|█ | 1904/18688 [2:12:35<18:47:48, 4.03s/it] 10%|█ | 1905/18688 [2:12:40<20:01:51, 4.30s/it] 10%|█ | 1906/18688 [2:12:44<19:46:38, 4.24s/it] 10%|█ | 1907/18688 [2:12:48<19:35:54, 4.20s/it] 10%|█ | 1908/18688 [2:12:52<18:59:35, 4.07s/it] 10%|█ | 1909/18688 [2:12:56<19:17:17, 4.14s/it] 10%|█ | 1910/18688 [2:13:01<20:11:07, 4.33s/it] {'loss': '1.007', 'grad_norm': '1.876', 'learning_rate': '7.989e-05', 'epoch': '0.1022', 'num_input_tokens_seen': 6385648, 'train_runtime': '7981', 'train_tokens_per_second': '800.1'}
10%|█ | 1910/18688 [2:13:01<20:11:07, 4.33s/it] 10%|█ | 1911/18688 [2:13:04<19:09:07, 4.11s/it] 10%|█ | 1912/18688 [2:13:08<18:16:17, 3.92s/it] 10%|█ | 1913/18688 [2:13:12<18:07:19, 3.89s/it] 10%|█ | 1914/18688 [2:13:15<17:48:06, 3.82s/it] 10%|█ | 1915/18688 [2:13:19<17:25:50, 3.74s/it] 10%|█ | 1916/18688 [2:13:22<17:00:30, 3.65s/it] 10%|█ | 1917/18688 [2:13:27<18:09:50, 3.90s/it] 10%|█ | 1918/18688 [2:13:31<17:58:04, 3.86s/it] 10%|█ | 1919/18688 [2:13:35<19:02:35, 4.09s/it] 10%|█ | 1920/18688 [2:13:39<18:35:19, 3.99s/it] {'loss': '0.9823', 'grad_norm': '1.068', 'learning_rate': '7.988e-05', 'epoch': '0.1027', 'num_input_tokens_seen': 6414704, 'train_runtime': '8019', 'train_tokens_per_second': '799.9'}
10%|█ | 1920/18688 [2:13:39<18:35:19, 3.99s/it] 10%|█ | 1921/18688 [2:13:43<19:06:30, 4.10s/it] 10%|█ | 1922/18688 [2:13:48<19:21:01, 4.15s/it] 10%|█ | 1923/18688 [2:13:52<20:03:55, 4.31s/it] 10%|█ | 1924/18688 [2:13:56<18:51:44, 4.05s/it] 10%|█ | 1925/18688 [2:14:00<19:27:52, 4.18s/it] 10%|█ | 1926/18688 [2:14:05<19:48:02, 4.25s/it] 10%|█ | 1927/18688 [2:14:08<19:07:19, 4.11s/it] 10%|█ | 1928/18688 [2:14:12<19:03:20, 4.09s/it] 10%|█ | 1929/18688 [2:14:17<19:50:37, 4.26s/it] 10%|█ | 1930/18688 [2:14:21<19:25:12, 4.17s/it] {'loss': '1.006', 'grad_norm': '1.018', 'learning_rate': '7.988e-05', 'epoch': '0.1033', 'num_input_tokens_seen': 6449208, 'train_runtime': '8062', 'train_tokens_per_second': '800'}
10%|█ | 1930/18688 [2:14:21<19:25:12, 4.17s/it] 10%|█ | 1931/18688 [2:14:24<18:27:35, 3.97s/it] 10%|█ | 1932/18688 [2:14:28<17:49:46, 3.83s/it] 10%|█ | 1933/18688 [2:14:31<17:18:48, 3.72s/it] 10%|█ | 1934/18688 [2:14:36<18:03:51, 3.88s/it] 10%|█ | 1935/18688 [2:14:41<20:25:07, 4.39s/it] 10%|█ | 1936/18688 [2:14:46<20:17:22, 4.36s/it] 10%|█ | 1937/18688 [2:14:49<19:17:45, 4.15s/it] 10%|█ | 1938/18688 [2:14:54<20:15:24, 4.35s/it] 10%|█ | 1939/18688 [2:14:58<19:05:03, 4.10s/it] 10%|█ | 1940/18688 [2:15:02<19:17:03, 4.15s/it] {'loss': '0.9984', 'grad_norm': '1.033', 'learning_rate': '7.987e-05', 'epoch': '0.1038', 'num_input_tokens_seen': 6482200, 'train_runtime': '8102', 'train_tokens_per_second': '800'}
10%|█ | 1940/18688 [2:15:02<19:17:03, 4.15s/it] 10%|█ | 1941/18688 [2:15:06<18:38:20, 4.01s/it] 10%|█ | 1942/18688 [2:15:09<18:11:05, 3.91s/it] 10%|█ | 1943/18688 [2:15:13<18:31:39, 3.98s/it] 10%|█ | 1944/18688 [2:15:17<17:58:10, 3.86s/it] 10%|█ | 1945/18688 [2:15:21<17:45:12, 3.82s/it] 10%|█ | 1946/18688 [2:15:25<17:55:51, 3.86s/it] 10%|█ | 1947/18688 [2:15:29<19:20:19, 4.16s/it] 10%|█ | 1948/18688 [2:15:34<20:12:02, 4.34s/it] 10%|█ | 1949/18688 [2:15:38<19:16:31, 4.15s/it] 10%|█ | 1950/18688 [2:15:42<18:44:19, 4.03s/it] {'loss': '1.048', 'grad_norm': '1.046', 'learning_rate': '7.987e-05', 'epoch': '0.1043', 'num_input_tokens_seen': 6514736, 'train_runtime': '8142', 'train_tokens_per_second': '800.1'}
10%|█ | 1950/18688 [2:15:42<18:44:19, 4.03s/it] 10%|█ | 1951/18688 [2:15:46<18:28:33, 3.97s/it] 10%|█ | 1952/18688 [2:15:50<18:40:50, 4.02s/it] 10%|█ | 1953/18688 [2:15:54<19:09:34, 4.12s/it] 10%|█ | 1954/18688 [2:15:58<19:16:33, 4.15s/it] 10%|█ | 1955/18688 [2:16:02<19:16:09, 4.15s/it] 10%|█ | 1956/18688 [2:16:07<19:29:04, 4.19s/it] 10%|█ | 1957/18688 [2:16:11<19:37:46, 4.22s/it] 10%|█ | 1958/18688 [2:16:16<20:25:50, 4.40s/it] 10%|█ | 1959/18688 [2:16:20<20:02:44, 4.31s/it] 10%|█ | 1960/18688 [2:16:24<19:12:00, 4.13s/it] {'loss': '1.016', 'grad_norm': '1.105', 'learning_rate': '7.986e-05', 'epoch': '0.1049', 'num_input_tokens_seen': 6548928, 'train_runtime': '8184', 'train_tokens_per_second': '800.2'}
10%|█ | 1960/18688 [2:16:24<19:12:00, 4.13s/it] 10%|█ | 1961/18688 [2:16:27<18:13:53, 3.92s/it] 10%|█ | 1962/18688 [2:16:32<20:22:09, 4.38s/it] 11%|█ | 1963/18688 [2:16:37<20:25:40, 4.40s/it] 11%|█ | 1964/18688 [2:16:42<21:26:46, 4.62s/it] 11%|█ | 1965/18688 [2:16:46<19:58:57, 4.30s/it] 11%|█ | 1966/18688 [2:16:50<19:40:48, 4.24s/it] 11%|█ | 1967/18688 [2:16:53<19:04:13, 4.11s/it] 11%|█ | 1968/18688 [2:16:58<19:17:39, 4.15s/it] 11%|█ | 1969/18688 [2:17:02<19:30:37, 4.20s/it] 11%|█ | 1970/18688 [2:17:06<19:01:56, 4.10s/it] {'loss': '0.9617', 'grad_norm': '0.8833', 'learning_rate': '7.985e-05', 'epoch': '0.1054', 'num_input_tokens_seen': 6583256, 'train_runtime': '8226', 'train_tokens_per_second': '800.3'}
11%|█ | 1970/18688 [2:17:06<19:01:56, 4.10s/it] 11%|█ | 1971/18688 [2:17:10<18:27:42, 3.98s/it] 11%|█ | 1972/18688 [2:17:14<18:57:48, 4.08s/it] 11%|█ | 1973/18688 [2:17:19<19:40:25, 4.24s/it] 11%|█ | 1974/18688 [2:17:23<19:57:42, 4.30s/it] 11%|█ | 1975/18688 [2:17:28<20:31:54, 4.42s/it] 11%|█ | 1976/18688 [2:17:31<19:34:40, 4.22s/it] 11%|█ | 1977/18688 [2:17:36<19:59:24, 4.31s/it] 11%|█ | 1978/18688 [2:17:40<18:58:02, 4.09s/it] 11%|█ | 1979/18688 [2:17:44<20:00:23, 4.31s/it] 11%|█ | 1980/18688 [2:17:49<20:55:46, 4.51s/it] {'loss': '1.025', 'grad_norm': '1.158', 'learning_rate': '7.985e-05', 'epoch': '0.106', 'num_input_tokens_seen': 6619632, 'train_runtime': '8270', 'train_tokens_per_second': '800.5'}
11%|█ | 1980/18688 [2:17:49<20:55:46, 4.51s/it] 11%|█ | 1981/18688 [2:17:54<20:48:45, 4.48s/it] 11%|█ | 1982/18688 [2:17:58<20:55:38, 4.51s/it] 11%|█ | 1983/18688 [2:18:02<20:05:54, 4.33s/it] 11%|█ | 1984/18688 [2:18:06<19:46:05, 4.26s/it] 11%|█ | 1985/18688 [2:18:10<19:35:28, 4.22s/it] 11%|█ | 1986/18688 [2:18:15<19:52:59, 4.29s/it] 11%|█ | 1987/18688 [2:18:18<18:44:45, 4.04s/it] 11%|█ | 1988/18688 [2:18:23<19:10:02, 4.13s/it] 11%|█ | 1989/18688 [2:18:28<20:11:19, 4.35s/it] 11%|█ | 1990/18688 [2:18:31<19:29:20, 4.20s/it] {'loss': '0.9753', 'grad_norm': '0.9585', 'learning_rate': '7.984e-05', 'epoch': '0.1065', 'num_input_tokens_seen': 6654592, 'train_runtime': '8312', 'train_tokens_per_second': '800.6'}
11%|█ | 1990/18688 [2:18:31<19:29:20, 4.20s/it] 11%|█ | 1991/18688 [2:18:36<20:36:16, 4.44s/it] 11%|█ | 1992/18688 [2:18:42<21:31:26, 4.64s/it] 11%|█ | 1993/18688 [2:18:46<20:38:29, 4.45s/it] 11%|█ | 1994/18688 [2:18:50<20:36:51, 4.45s/it] 11%|█ | 1995/18688 [2:18:55<21:40:44, 4.68s/it] 11%|█ | 1996/18688 [2:19:00<21:20:10, 4.60s/it] 11%|█ | 1997/18688 [2:19:03<20:12:31, 4.36s/it] 11%|█ | 1998/18688 [2:19:07<19:05:15, 4.12s/it] 11%|█ | 1999/18688 [2:19:11<18:36:28, 4.01s/it] 11%|█ | 2000/18688 [2:19:14<17:50:00, 3.85s/it] {'loss': '0.9933', 'grad_norm': '0.9852', 'learning_rate': '7.983e-05', 'epoch': '0.107', 'num_input_tokens_seen': 6689560, 'train_runtime': '8355', 'train_tokens_per_second': '800.7'}
11%|█ | 2000/18688 [2:19:14<17:50:00, 3.85s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
0%| | 3/1000 [00:00<00:38, 26.05it/s]
1%| | 6/1000 [00:00<00:54, 18.32it/s]
1%| | 8/1000 [00:00<00:56, 17.59it/s]
1%| | 10/1000 [00:00<00:59, 16.64it/s]
1%| | 12/1000 [00:00<01:00, 16.43it/s]
1%|▏ | 14/1000 [00:00<01:01, 15.99it/s]
2%|▏ | 16/1000 [00:00<01:02, 15.79it/s]
2%|▏ | 18/1000 [00:01<01:38, 10.01it/s]
2%|▏ | 20/1000 [00:01<01:32, 10.54it/s]
2%|▏ | 22/1000 [00:01<01:52, 8.72it/s]
2%|▏ | 24/1000 [00:01<01:39, 9.78it/s]
3%|▎ | 26/1000 [00:02<01:44, 9.29it/s]
3%|▎ | 28/1000 [00:02<01:36, 10.12it/s]
3%|▎ | 30/1000 [00:02<01:28, 11.00it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.14it/s]
3%|▎ | 34/1000 [00:02<01:25, 11.25it/s]
4%|▎ | 36/1000 [00:03<01:32, 10.43it/s]
4%|▍ | 38/1000 [00:03<01:24, 11.35it/s]
4%|▍ | 40/1000 [00:03<01:27, 10.96it/s]
4%|▍ | 42/1000 [00:03<01:46, 9.01it/s]
4%|▍ | 44/1000 [00:03<01:30, 10.52it/s]
5%|▍ | 46/1000 [00:03<01:21, 11.71it/s]
5%|▍ | 48/1000 [00:04<01:29, 10.66it/s]
5%|▌ | 50/1000 [00:04<01:21, 11.70it/s]
5%|▌ | 52/1000 [00:04<01:20, 11.75it/s]
5%|▌ | 54/1000 [00:04<01:19, 11.85it/s]
6%|▌ | 56/1000 [00:04<01:18, 11.99it/s]
6%|▌ | 58/1000 [00:05<01:57, 8.05it/s]
6%|▌ | 60/1000 [00:05<01:44, 9.02it/s]
6%|▌ | 62/1000 [00:05<01:35, 9.83it/s]
6%|▋ | 64/1000 [00:05<01:28, 10.60it/s]
7%|▋ | 66/1000 [00:05<01:24, 11.11it/s]
7%|▋ | 68/1000 [00:06<01:20, 11.52it/s]
7%|▋ | 70/1000 [00:06<01:17, 11.94it/s]
7%|▋ | 72/1000 [00:06<01:14, 12.38it/s]
7%|▋ | 74/1000 [00:06<01:14, 12.47it/s]
8%|▊ | 76/1000 [00:06<01:14, 12.47it/s]
8%|▊ | 78/1000 [00:06<01:14, 12.39it/s]
8%|▊ | 80/1000 [00:07<01:18, 11.68it/s]
8%|▊ | 82/1000 [00:07<01:16, 12.00it/s]
8%|▊ | 84/1000 [00:07<01:15, 12.21it/s]
9%|▊ | 86/1000 [00:07<01:14, 12.35it/s]
9%|▉ | 88/1000 [00:07<01:13, 12.44it/s]
9%|▉ | 90/1000 [00:07<01:12, 12.53it/s]
9%|▉ | 92/1000 [00:07<01:11, 12.68it/s]
9%|▉ | 94/1000 [00:08<01:11, 12.63it/s]
10%|▉ | 96/1000 [00:08<01:11, 12.64it/s]
10%|▉ | 98/1000 [00:08<01:11, 12.62it/s]
10%|█ | 100/1000 [00:08<01:39, 9.06it/s]
10%|█ | 102/1000 [00:08<01:31, 9.80it/s]
10%|█ | 104/1000 [00:09<02:02, 7.30it/s]
10%|█ | 105/1000 [00:09<02:11, 6.83it/s]
11%|█ | 106/1000 [00:09<02:06, 7.05it/s]
11%|█ | 108/1000 [00:09<01:46, 8.35it/s]
11%|█ | 110/1000 [00:10<01:34, 9.44it/s]
11%|█ | 112/1000 [00:10<01:26, 10.23it/s]
11%|█▏ | 114/1000 [00:10<01:21, 10.83it/s]
12%|█▏ | 116/1000 [00:10<01:17, 11.34it/s]
12%|█▏ | 118/1000 [00:10<01:15, 11.74it/s]
12%|█▏ | 120/1000 [00:10<01:16, 11.46it/s]
12%|█▏ | 122/1000 [00:11<01:29, 9.82it/s]
12%|█▏ | 124/1000 [00:11<01:37, 9.01it/s]
13%|█▎ | 126/1000 [00:11<01:27, 9.99it/s]
13%|█▎ | 128/1000 [00:11<01:19, 11.02it/s]
13%|█▎ | 130/1000 [00:11<01:13, 11.79it/s]
13%|█▎ | 132/1000 [00:11<01:07, 12.87it/s]
13%|█▎ | 134/1000 [00:12<01:11, 12.16it/s]
14%|█▎ | 136/1000 [00:12<01:05, 13.16it/s]
14%|█▍ | 138/1000 [00:12<01:05, 13.15it/s]
14%|█▍ | 140/1000 [00:12<01:25, 10.06it/s]
14%|█▍ | 142/1000 [00:13<01:40, 8.53it/s]
14%|█▍ | 144/1000 [00:13<01:29, 9.57it/s]
15%|█▍ | 146/1000 [00:13<01:20, 10.59it/s]
15%|█▍ | 148/1000 [00:13<01:14, 11.51it/s]
15%|█▌ | 150/1000 [00:13<01:07, 12.62it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.17it/s]
15%|█▌ | 154/1000 [00:13<01:03, 13.42it/s]
16%|█▌ | 156/1000 [00:14<01:03, 13.25it/s]
16%|█▌ | 158/1000 [00:14<01:01, 13.75it/s]
16%|█▌ | 160/1000 [00:14<01:01, 13.64it/s]
16%|█▌ | 162/1000 [00:14<01:01, 13.52it/s]
16%|█▋ | 164/1000 [00:14<01:01, 13.63it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.81it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.32it/s]
17%|█▋ | 170/1000 [00:15<01:19, 10.50it/s]
17%|█▋ | 172/1000 [00:15<01:12, 11.47it/s]
17%|█▋ | 174/1000 [00:15<01:29, 9.18it/s]
18%|█▊ | 176/1000 [00:16<01:54, 7.19it/s]
18%|█▊ | 178/1000 [00:16<01:34, 8.65it/s]
18%|█▊ | 180/1000 [00:16<01:33, 8.78it/s]
18%|█▊ | 182/1000 [00:16<01:35, 8.57it/s]
18%|█▊ | 184/1000 [00:16<01:23, 9.82it/s]
19%|█▊ | 186/1000 [00:17<01:37, 8.34it/s]
19%|█▉ | 188/1000 [00:17<01:23, 9.67it/s]
19%|█▉ | 190/1000 [00:17<01:15, 10.71it/s]
19%|█▉ | 192/1000 [00:17<01:11, 11.35it/s]
19%|█▉ | 194/1000 [00:17<01:16, 10.57it/s]
20%|█▉ | 196/1000 [00:18<01:29, 8.96it/s]
20%|█▉ | 198/1000 [00:18<01:18, 10.21it/s]
20%|██ | 200/1000 [00:18<01:15, 10.55it/s]
20%|██ | 202/1000 [00:18<01:09, 11.53it/s]
20%|██ | 204/1000 [00:18<01:02, 12.66it/s]
21%|██ | 206/1000 [00:18<00:59, 13.26it/s]
21%|██ | 208/1000 [00:18<00:58, 13.64it/s]
21%|██ | 210/1000 [00:19<00:54, 14.52it/s]
21%|██ | 212/1000 [00:19<00:53, 14.78it/s]
21%|██▏ | 214/1000 [00:19<00:53, 14.74it/s]
22%|██▏ | 216/1000 [00:19<00:54, 14.46it/s]
22%|██▏ | 218/1000 [00:19<00:52, 14.84it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.89it/s]
22%|██▏ | 222/1000 [00:20<01:24, 9.17it/s]
22%|██▏ | 224/1000 [00:20<01:20, 9.65it/s]
23%|██▎ | 226/1000 [00:20<01:12, 10.66it/s]
23%|██▎ | 228/1000 [00:20<01:06, 11.57it/s]
23%|██▎ | 230/1000 [00:20<01:03, 12.07it/s]
23%|██▎ | 232/1000 [00:20<01:01, 12.43it/s]
23%|██▎ | 234/1000 [00:21<00:57, 13.21it/s]
24%|██▎ | 236/1000 [00:21<00:55, 13.71it/s]
24%|██▍ | 238/1000 [00:21<00:53, 14.35it/s]
24%|██▍ | 240/1000 [00:21<00:53, 14.30it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.79it/s]
24%|██▍ | 244/1000 [00:21<00:50, 15.01it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.99it/s]
25%|██▍ | 248/1000 [00:22<00:59, 12.67it/s]
25%|██▌ | 250/1000 [00:22<00:55, 13.44it/s]
25%|██▌ | 252/1000 [00:22<01:01, 12.18it/s]
25%|██▌ | 254/1000 [00:22<00:56, 13.22it/s]
26%|██▌ | 256/1000 [00:22<00:53, 13.80it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.82it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.97it/s]
26%|██▌ | 262/1000 [00:23<00:56, 13.10it/s]
26%|██▋ | 264/1000 [00:23<00:53, 13.65it/s]
27%|██▋ | 266/1000 [00:23<00:52, 14.03it/s]
27%|██▋ | 268/1000 [00:23<00:50, 14.62it/s]
27%|██▋ | 270/1000 [00:23<00:54, 13.31it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.66it/s]
27%|██▋ | 274/1000 [00:24<01:01, 11.74it/s]
28%|██▊ | 276/1000 [00:24<00:56, 12.77it/s]
28%|██▊ | 278/1000 [00:24<00:55, 12.98it/s]
28%|██▊ | 280/1000 [00:24<00:53, 13.55it/s]
28%|██▊ | 282/1000 [00:24<00:49, 14.46it/s]
28%|██▊ | 284/1000 [00:24<00:49, 14.40it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.70it/s]
29%|██▉ | 288/1000 [00:25<00:47, 15.08it/s]
29%|██▉ | 290/1000 [00:25<00:45, 15.55it/s]
29%|██▉ | 292/1000 [00:25<00:45, 15.48it/s]
29%|██▉ | 294/1000 [00:25<00:45, 15.37it/s]
30%|██▉ | 296/1000 [00:25<00:45, 15.42it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.55it/s]
30%|███ | 300/1000 [00:25<00:46, 15.19it/s]
30%|███ | 302/1000 [00:25<00:45, 15.21it/s]
30%|███ | 304/1000 [00:26<00:50, 13.74it/s]
31%|███ | 306/1000 [00:26<00:47, 14.49it/s]
31%|███ | 308/1000 [00:26<00:47, 14.46it/s]
31%|███ | 310/1000 [00:26<00:47, 14.66it/s]
31%|███ | 312/1000 [00:26<00:46, 14.67it/s]
31%|███▏ | 314/1000 [00:26<00:44, 15.37it/s]
32%|███▏ | 316/1000 [00:26<00:43, 15.65it/s]
32%|███▏ | 318/1000 [00:26<00:42, 16.13it/s]
32%|███▏ | 320/1000 [00:27<01:12, 9.39it/s]
32%|███▏ | 322/1000 [00:27<01:03, 10.75it/s]
32%|███▏ | 324/1000 [00:27<00:58, 11.52it/s]
33%|███▎ | 326/1000 [00:27<00:58, 11.49it/s]
33%|███▎ | 328/1000 [00:28<00:58, 11.53it/s]
33%|███▎ | 330/1000 [00:28<00:53, 12.46it/s]
33%|███▎ | 332/1000 [00:28<00:52, 12.71it/s]
33%|███▎ | 334/1000 [00:28<00:50, 13.20it/s]
34%|███▎ | 336/1000 [00:28<00:48, 13.60it/s]
34%|███▍ | 338/1000 [00:28<01:05, 10.08it/s]
34%|███▍ | 340/1000 [00:29<00:59, 11.15it/s]
34%|███▍ | 342/1000 [00:29<00:52, 12.45it/s]
34%|███▍ | 344/1000 [00:29<00:50, 13.08it/s]
35%|███▍ | 346/1000 [00:29<00:47, 13.68it/s]
35%|███▍ | 348/1000 [00:29<00:46, 14.10it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.73it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.89it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.89it/s]
36%|███▌ | 356/1000 [00:30<00:43, 14.97it/s]
36%|███▌ | 358/1000 [00:30<00:43, 14.66it/s]
36%|███▌ | 360/1000 [00:30<00:42, 15.05it/s]
36%|███▌ | 362/1000 [00:30<00:47, 13.39it/s]
36%|███▋ | 364/1000 [00:30<00:45, 13.92it/s]
37%|███▋ | 366/1000 [00:30<00:52, 11.98it/s]
37%|███▋ | 368/1000 [00:31<00:51, 12.38it/s]
37%|███▋ | 370/1000 [00:31<00:47, 13.28it/s]
37%|███▋ | 372/1000 [00:31<00:46, 13.58it/s]
37%|███▋ | 374/1000 [00:31<00:54, 11.42it/s]
38%|███▊ | 376/1000 [00:31<00:56, 11.01it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.69it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.50it/s]
38%|███▊ | 382/1000 [00:32<00:48, 12.69it/s]
38%|███▊ | 384/1000 [00:32<00:45, 13.46it/s]
39%|███▊ | 386/1000 [00:32<00:44, 13.77it/s]
39%|███▉ | 388/1000 [00:32<00:44, 13.70it/s]
39%|███▉ | 390/1000 [00:32<00:44, 13.59it/s]
39%|███▉ | 392/1000 [00:32<00:43, 13.91it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.68it/s]
40%|███▉ | 396/1000 [00:33<00:43, 13.81it/s]
40%|███▉ | 398/1000 [00:33<00:47, 12.59it/s]
40%|████ | 400/1000 [00:33<00:45, 13.28it/s]
40%|████ | 402/1000 [00:33<00:42, 13.94it/s]
40%|████ | 404/1000 [00:33<00:40, 14.57it/s]
41%|████ | 406/1000 [00:33<00:39, 15.10it/s]
41%|████ | 408/1000 [00:34<00:46, 12.63it/s]
41%|████ | 410/1000 [00:34<00:56, 10.51it/s]
41%|████ | 412/1000 [00:34<00:50, 11.59it/s]
41%|████▏ | 414/1000 [00:34<01:07, 8.66it/s]
42%|████▏ | 416/1000 [00:35<01:23, 6.99it/s]
42%|████▏ | 418/1000 [00:35<01:10, 8.29it/s]
42%|████▏ | 420/1000 [00:35<01:00, 9.55it/s]
42%|████▏ | 422/1000 [00:35<00:53, 10.90it/s]
42%|████▏ | 424/1000 [00:36<01:14, 7.69it/s]
43%|████▎ | 426/1000 [00:36<01:04, 8.95it/s]
43%|████▎ | 428/1000 [00:36<00:56, 10.14it/s]
43%|████▎ | 430/1000 [00:36<01:02, 9.11it/s]
43%|████▎ | 432/1000 [00:37<01:20, 7.05it/s]
43%|████▎ | 434/1000 [00:37<01:08, 8.28it/s]
44%|████▎ | 436/1000 [00:37<01:00, 9.33it/s]
44%|████▍ | 438/1000 [00:37<01:02, 9.00it/s]
44%|████▍ | 440/1000 [00:37<00:58, 9.50it/s]
44%|████▍ | 442/1000 [00:37<00:52, 10.55it/s]
44%|████▍ | 444/1000 [00:38<00:48, 11.41it/s]
45%|████▍ | 446/1000 [00:38<00:45, 12.17it/s]
45%|████▍ | 448/1000 [00:38<00:50, 10.97it/s]
45%|████▌ | 450/1000 [00:38<00:46, 11.79it/s]
45%|████▌ | 452/1000 [00:38<00:42, 12.84it/s]
45%|████▌ | 454/1000 [00:38<00:44, 12.32it/s]
46%|████▌ | 456/1000 [00:38<00:42, 12.76it/s]
46%|████▌ | 458/1000 [00:39<00:39, 13.78it/s]
46%|████▌ | 460/1000 [00:39<00:38, 13.90it/s]
46%|████▌ | 462/1000 [00:39<00:37, 14.51it/s]
46%|████▋ | 464/1000 [00:39<00:35, 15.01it/s]
47%|████▋ | 466/1000 [00:39<00:35, 14.84it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.93it/s]
47%|████▋ | 470/1000 [00:40<00:52, 10.07it/s]
47%|████▋ | 472/1000 [00:40<00:51, 10.23it/s]
47%|████▋ | 474/1000 [00:40<00:45, 11.53it/s]
48%|████▊ | 476/1000 [00:40<00:43, 12.18it/s]
48%|████▊ | 478/1000 [00:40<00:43, 12.02it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.97it/s]
48%|████▊ | 482/1000 [00:41<00:38, 13.37it/s]
48%|████▊ | 484/1000 [00:41<00:37, 13.88it/s]
49%|████▊ | 486/1000 [00:41<00:36, 14.05it/s]
49%|████▉ | 488/1000 [00:41<00:35, 14.32it/s]
49%|████▉ | 490/1000 [00:41<00:45, 11.12it/s]
49%|████▉ | 492/1000 [00:41<00:40, 12.42it/s]
49%|████▉ | 494/1000 [00:41<00:37, 13.42it/s]
50%|████▉ | 496/1000 [00:42<00:35, 14.21it/s]
50%|████▉ | 498/1000 [00:42<00:34, 14.58it/s]
50%|█████ | 500/1000 [00:42<00:43, 11.60it/s]
50%|█████ | 502/1000 [00:42<00:41, 12.05it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.64it/s]
51%|█████ | 506/1000 [00:42<00:38, 12.81it/s]
51%|█████ | 508/1000 [00:42<00:35, 13.76it/s]
51%|█████ | 510/1000 [00:43<00:47, 10.41it/s]
51%|█████ | 512/1000 [00:43<00:43, 11.18it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 12.08it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.61it/s]
52%|█████▏ | 518/1000 [00:43<00:36, 13.13it/s]
52%|█████▏ | 520/1000 [00:43<00:35, 13.50it/s]
52%|█████▏ | 522/1000 [00:44<00:33, 14.25it/s]
52%|█████▏ | 524/1000 [00:44<00:33, 14.27it/s]
53%|█████▎ | 526/1000 [00:44<00:32, 14.41it/s]
53%|█████▎ | 528/1000 [00:44<00:39, 12.01it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.87it/s]
53%|█████▎ | 532/1000 [00:44<00:33, 13.83it/s]
53%|█████▎ | 534/1000 [00:45<00:32, 14.18it/s]
54%|█████▎ | 536/1000 [00:45<00:32, 14.30it/s]
54%|█████▍ | 538/1000 [00:45<00:32, 14.06it/s]
54%|█████▍ | 540/1000 [00:45<00:31, 14.51it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.76it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.61it/s]
55%|█████▍ | 546/1000 [00:45<00:31, 14.21it/s]
55%|█████▍ | 548/1000 [00:45<00:32, 13.89it/s]
55%|█████▌ | 550/1000 [00:46<00:31, 14.11it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.23it/s]
55%|█████▌ | 554/1000 [00:46<00:32, 13.90it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.09it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.48it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 15.00it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 14.60it/s]
56%|█████▋ | 564/1000 [00:47<00:43, 9.95it/s]
57%|█████▋ | 566/1000 [00:47<00:40, 10.80it/s]
57%|█████▋ | 568/1000 [00:47<00:37, 11.53it/s]
57%|█████▋ | 570/1000 [00:47<00:33, 12.69it/s]
57%|█████▋ | 572/1000 [00:47<00:35, 12.19it/s]
57%|█████▋ | 574/1000 [00:48<00:34, 12.22it/s]
58%|█████▊ | 576/1000 [00:48<00:34, 12.43it/s]
58%|█████▊ | 578/1000 [00:48<00:38, 10.99it/s]
58%|█████▊ | 580/1000 [00:48<00:35, 11.91it/s]
58%|█████▊ | 582/1000 [00:48<00:32, 12.88it/s]
58%|█████▊ | 584/1000 [00:48<00:31, 13.30it/s]
59%|█████▊ | 586/1000 [00:48<00:30, 13.79it/s]
59%|█████▉ | 588/1000 [00:49<00:27, 14.73it/s]
59%|█████▉ | 590/1000 [00:49<00:27, 14.79it/s]
59%|█████▉ | 592/1000 [00:49<00:27, 14.63it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.24it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.80it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 14.03it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.40it/s]
60%|██████ | 602/1000 [00:50<00:27, 14.54it/s]
60%|██████ | 604/1000 [00:50<00:26, 15.10it/s]
61%|██████ | 606/1000 [00:50<00:26, 14.85it/s]
61%|██████ | 608/1000 [00:50<00:25, 15.18it/s]
61%|██████ | 610/1000 [00:50<00:25, 15.02it/s]
61%|██████ | 612/1000 [00:50<00:25, 15.18it/s]
61%|██████▏ | 614/1000 [00:50<00:26, 14.42it/s]
62%|██████▏ | 616/1000 [00:51<00:25, 14.81it/s]
62%|██████▏ | 618/1000 [00:51<00:26, 14.18it/s]
62%|██████▏ | 620/1000 [00:51<00:38, 9.76it/s]
62%|██████▏ | 622/1000 [00:51<00:37, 9.97it/s]
62%|██████▏ | 624/1000 [00:51<00:33, 11.36it/s]
63%|██████▎ | 626/1000 [00:51<00:31, 12.05it/s]
63%|██████▎ | 628/1000 [00:52<00:27, 13.29it/s]
63%|██████▎ | 630/1000 [00:52<00:26, 13.79it/s]
63%|██████▎ | 632/1000 [00:52<00:29, 12.46it/s]
63%|██████▎ | 634/1000 [00:52<00:27, 13.19it/s]
64%|██████▎ | 636/1000 [00:52<00:33, 10.95it/s]
64%|██████▍ | 638/1000 [00:53<00:33, 10.69it/s]
64%|██████▍ | 640/1000 [00:53<00:31, 11.48it/s]
64%|██████▍ | 642/1000 [00:53<00:28, 12.36it/s]
64%|██████▍ | 644/1000 [00:53<00:28, 12.62it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.89it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.36it/s]
65%|██████▌ | 650/1000 [00:53<00:27, 12.57it/s]
65%|██████▌ | 652/1000 [00:54<00:29, 11.82it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.39it/s]
66%|██████▌ | 656/1000 [00:54<00:32, 10.75it/s]
66%|██████▌ | 658/1000 [00:54<00:28, 11.83it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.13it/s]
66%|██████▌ | 662/1000 [00:55<00:30, 11.25it/s]
66%|██████▋ | 664/1000 [00:55<00:28, 11.75it/s]
67%|██████▋ | 666/1000 [00:55<00:26, 12.78it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.09it/s]
67%|██████▋ | 670/1000 [00:55<00:30, 10.90it/s]
67%|██████▋ | 672/1000 [00:55<00:29, 11.31it/s]
67%|██████▋ | 674/1000 [00:56<00:28, 11.60it/s]
68%|██████▊ | 676/1000 [00:56<00:29, 11.12it/s]
68%|██████▊ | 678/1000 [00:56<00:27, 11.54it/s]
68%|██████▊ | 680/1000 [00:56<00:26, 11.95it/s]
68%|██████▊ | 682/1000 [00:56<00:27, 11.48it/s]
68%|██████▊ | 684/1000 [00:56<00:26, 11.74it/s]
69%|██████▊ | 686/1000 [00:57<00:26, 11.94it/s]
69%|██████▉ | 688/1000 [00:57<00:25, 12.06it/s]
69%|██████▉ | 690/1000 [00:57<00:25, 12.24it/s]
69%|██████▉ | 692/1000 [00:57<00:25, 12.26it/s]
69%|██████▉ | 694/1000 [00:57<00:24, 12.27it/s]
70%|██████▉ | 696/1000 [00:57<00:24, 12.46it/s]
70%|██████▉ | 698/1000 [00:58<00:22, 13.39it/s]
70%|███████ | 700/1000 [00:58<00:25, 11.61it/s]
70%|███████ | 702/1000 [00:58<00:23, 12.50it/s]
70%|███████ | 704/1000 [00:58<00:30, 9.64it/s]
71%|███████ | 706/1000 [00:58<00:27, 10.50it/s]
71%|███████ | 708/1000 [00:59<00:30, 9.54it/s]
71%|███████ | 710/1000 [00:59<00:26, 10.86it/s]
71%|███████ | 712/1000 [00:59<00:24, 11.86it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.96it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 13.04it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.55it/s]
72%|███████▏ | 720/1000 [00:59<00:20, 13.51it/s]
72%|███████▏ | 722/1000 [01:00<00:19, 13.97it/s]
72%|███████▏ | 724/1000 [01:00<00:20, 13.80it/s]
73%|███████▎ | 726/1000 [01:00<00:19, 14.01it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.95it/s]
73%|███████▎ | 730/1000 [01:00<00:32, 8.21it/s]
73%|███████▎ | 732/1000 [01:01<00:32, 8.34it/s]
73%|███████▎ | 734/1000 [01:01<00:30, 8.63it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.46it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.90it/s]
74%|███████▍ | 739/1000 [01:01<00:23, 11.00it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.67it/s]
74%|███████▍ | 743/1000 [01:02<00:20, 12.30it/s]
74%|███████▍ | 745/1000 [01:02<00:25, 10.18it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.57it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.75it/s]
75%|███████▌ | 751/1000 [01:02<00:20, 11.86it/s]
75%|███████▌ | 753/1000 [01:03<00:19, 12.64it/s]
76%|███████▌ | 755/1000 [01:03<00:18, 13.36it/s]
76%|███████▌ | 757/1000 [01:03<00:23, 10.31it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.34it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.33it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.28it/s]
76%|███████▋ | 765/1000 [01:04<00:27, 8.61it/s]
77%|███████▋ | 767/1000 [01:04<00:24, 9.57it/s]
77%|███████▋ | 769/1000 [01:04<00:21, 10.59it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.85it/s]
77%|███████▋ | 773/1000 [01:04<00:17, 12.67it/s]
78%|███████▊ | 775/1000 [01:05<00:17, 13.20it/s]
78%|███████▊ | 777/1000 [01:05<00:15, 14.21it/s]
78%|███████▊ | 779/1000 [01:05<00:20, 10.53it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.43it/s]
78%|███████▊ | 783/1000 [01:05<00:26, 8.33it/s]
78%|███████▊ | 784/1000 [01:06<00:30, 7.08it/s]
78%|███████▊ | 785/1000 [01:06<00:29, 7.38it/s]
79%|███████▊ | 786/1000 [01:06<00:30, 6.99it/s]
79%|███████▉ | 788/1000 [01:06<00:24, 8.83it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.15it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 11.28it/s]
79%|███████▉ | 794/1000 [01:07<00:17, 11.89it/s]
80%|███████▉ | 796/1000 [01:07<00:16, 12.33it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 13.10it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.30it/s]
80%|████████ | 802/1000 [01:07<00:24, 8.24it/s]
80%|████████ | 804/1000 [01:08<00:23, 8.28it/s]
81%|████████ | 806/1000 [01:08<00:21, 8.97it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.35it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.48it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.72it/s]
81%|████████▏ | 814/1000 [01:08<00:14, 12.62it/s]
82%|████████▏ | 816/1000 [01:09<00:16, 11.02it/s]
82%|████████▏ | 818/1000 [01:09<00:15, 11.44it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.11it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 12.74it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.61it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.06it/s]
83%|████████▎ | 828/1000 [01:10<00:11, 14.38it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.83it/s]
83%|████████▎ | 832/1000 [01:10<00:11, 14.25it/s]
83%|████████▎ | 834/1000 [01:10<00:11, 14.01it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.40it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.32it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.44it/s]
84%|████████▍ | 842/1000 [01:11<00:10, 14.44it/s]
84%|████████▍ | 844/1000 [01:11<00:11, 13.98it/s]
85%|████████▍ | 846/1000 [01:11<00:11, 13.92it/s]
85%|████████▍ | 848/1000 [01:11<00:11, 13.45it/s]
85%|████████▌ | 850/1000 [01:11<00:11, 13.46it/s]
85%|████████▌ | 852/1000 [01:11<00:11, 12.98it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 13.51it/s]
86%|████████▌ | 856/1000 [01:12<00:10, 13.19it/s]
86%|████████▌ | 858/1000 [01:12<00:10, 13.84it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 14.72it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.05it/s]
86%|████████▋ | 864/1000 [01:12<00:10, 13.14it/s]
87%|████████▋ | 866/1000 [01:12<00:10, 13.23it/s]
87%|████████▋ | 868/1000 [01:12<00:10, 12.79it/s]
87%|████████▋ | 870/1000 [01:13<00:10, 12.65it/s]
87%|████████▋ | 872/1000 [01:13<00:09, 13.34it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 11.75it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 10.67it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.61it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.30it/s]
88%|████████▊ | 882/1000 [01:14<00:10, 11.19it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 11.98it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.81it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.80it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.24it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.04it/s]
89%|████████▉ | 894/1000 [01:15<00:07, 14.19it/s]
90%|████████▉ | 896/1000 [01:15<00:07, 13.92it/s]
90%|████████▉ | 898/1000 [01:15<00:07, 13.35it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.27it/s]
90%|█████████ | 902/1000 [01:15<00:07, 13.00it/s]
90%|█████████ | 904/1000 [01:15<00:09, 9.81it/s]
91%|█████████ | 906/1000 [01:16<00:08, 10.87it/s]
91%|█████████ | 908/1000 [01:16<00:07, 11.86it/s]
91%|█████████ | 910/1000 [01:16<00:07, 12.11it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.29it/s]
91%|█████████▏| 914/1000 [01:17<00:12, 6.81it/s]
92%|█████████▏| 916/1000 [01:17<00:10, 8.14it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.38it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.70it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 10.16it/s]
92%|█████████▏| 924/1000 [01:18<00:06, 10.87it/s]
93%|█████████▎| 926/1000 [01:18<00:06, 11.72it/s]
93%|█████████▎| 928/1000 [01:18<00:06, 10.60it/s]
93%|█████████▎| 930/1000 [01:18<00:05, 11.82it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 12.43it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.92it/s]
94%|█████████▎| 936/1000 [01:18<00:04, 13.47it/s]
94%|█████████▍| 938/1000 [01:19<00:04, 14.37it/s]
94%|█████████▍| 940/1000 [01:19<00:04, 14.65it/s]
94%|█████████▍| 942/1000 [01:19<00:04, 12.31it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.83it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.88it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 12.70it/s]
95%|█████████▌| 950/1000 [01:20<00:03, 13.47it/s]
95%|█████████▌| 952/1000 [01:20<00:03, 14.19it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 14.29it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 14.27it/s]
96%|█████████▌| 958/1000 [01:20<00:02, 14.81it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 13.13it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.77it/s]
96%|█████████▋| 964/1000 [01:21<00:03, 10.24it/s]
97%|█████████▋| 966/1000 [01:21<00:02, 11.46it/s]
97%|█████████▋| 968/1000 [01:21<00:02, 12.12it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 12.74it/s]
97%|█████████▋| 972/1000 [01:21<00:02, 9.69it/s]
97%|█████████▋| 974/1000 [01:22<00:02, 10.88it/s]
98%|█████████▊| 976/1000 [01:22<00:02, 11.66it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.88it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.96it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.43it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 10.44it/s]
99%|█████████▊| 986/1000 [01:23<00:01, 10.69it/s]
99%|█████████▉| 988/1000 [01:23<00:01, 10.90it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 11.39it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 12.29it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 13.04it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 13.95it/s]
100%|█████████▉| 998/1000 [01:24<00:00, 14.11it/s]
100%|██████████| 1000/1000 [01:24<00:00, 14.77it/s]
{'eval_loss': '0.9672', 'eval_runtime': '84.22', 'eval_samples_per_second': '11.87', 'eval_steps_per_second': '11.87', 'epoch': '0.107', 'num_input_tokens_seen': 6689560}
11%|█ | 2000/18688 [2:20:38<17:50:00, 3.85s/it]
100%|██████████| 1000/1000 [01:24<00:00, 14.77it/s]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00008/checkpoint-2000
loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
11%|█ | 2001/18688 [2:20:45<138:41:56, 29.92s/it] 11%|█ | 2002/18688 [2:20:48<101:51:09, 21.97s/it] 11%|█ | 2003/18688 [2:20:53<77:11:51, 16.66s/it] 11%|█ | 2004/18688 [2:20:57<60:06:26, 12.97s/it] 11%|█ | 2005/18688 [2:21:01<47:52:10, 10.33s/it] 11%|█ | 2006/18688 [2:21:06<39:49:21, 8.59s/it] 11%|█ | 2007/18688 [2:21:11<34:38:44, 7.48s/it] 11%|█ | 2008/18688 [2:21:15<30:43:41, 6.63s/it] 11%|█ | 2009/18688 [2:21:19<26:28:39, 5.71s/it] 11%|█ | 2010/18688 [2:21:22<23:35:00, 5.09s/it] {'loss': '0.9243', 'grad_norm': '0.839', 'learning_rate': '7.983e-05', 'epoch': '0.1076', 'num_input_tokens_seen': 6727088, 'train_runtime': '8483', 'train_tokens_per_second': '793'}
11%|█ | 2010/18688 [2:21:22<23:35:00, 5.09s/it] 11%|█ | 2011/18688 [2:21:27<23:24:25, 5.05s/it] 11%|█ | 2012/18688 [2:21:32<22:07:42, 4.78s/it] 11%|█ | 2013/18688 [2:21:35<20:49:04, 4.49s/it] 11%|█ | 2014/18688 [2:21:39<19:25:48, 4.20s/it] 11%|█ | 2015/18688 [2:21:44<20:10:27, 4.36s/it] 11%|█ | 2016/18688 [2:21:47<19:01:59, 4.11s/it] 11%|█ | 2017/18688 [2:21:51<18:27:36, 3.99s/it] 11%|█ | 2018/18688 [2:21:55<18:10:28, 3.92s/it] 11%|█ | 2019/18688 [2:21:59<18:20:57, 3.96s/it] 11%|█ | 2020/18688 [2:22:03<18:15:45, 3.94s/it] {'loss': '0.9966', 'grad_norm': '0.9872', 'learning_rate': '7.982e-05', 'epoch': '0.1081', 'num_input_tokens_seen': 6760040, 'train_runtime': '8523', 'train_tokens_per_second': '793.1'}
11%|█ | 2020/18688 [2:22:03<18:15:45, 3.94s/it] 11%|█ | 2021/18688 [2:22:06<17:53:41, 3.87s/it] 11%|█ | 2022/18688 [2:22:11<18:52:27, 4.08s/it] 11%|█ | 2023/18688 [2:22:15<19:00:57, 4.11s/it] 11%|█ | 2024/18688 [2:22:20<20:11:05, 4.36s/it] 11%|█ | 2025/18688 [2:22:24<20:08:00, 4.35s/it] 11%|█ | 2026/18688 [2:22:28<19:41:06, 4.25s/it] 11%|█ | 2027/18688 [2:22:32<18:41:43, 4.04s/it] 11%|█ | 2028/18688 [2:22:36<18:09:10, 3.92s/it] 11%|█ | 2029/18688 [2:22:39<17:51:16, 3.86s/it] 11%|█ | 2030/18688 [2:22:43<17:57:06, 3.88s/it] {'loss': '1.01', 'grad_norm': '1.272', 'learning_rate': '7.981e-05', 'epoch': '0.1086', 'num_input_tokens_seen': 6791920, 'train_runtime': '8564', 'train_tokens_per_second': '793.1'}
11%|█ | 2030/18688 [2:22:43<17:57:06, 3.88s/it] 11%|█ | 2031/18688 [2:22:47<18:31:39, 4.00s/it] 11%|█ | 2032/18688 [2:22:51<18:32:35, 4.01s/it] 11%|█ | 2033/18688 [2:22:56<19:04:58, 4.12s/it] 11%|█ | 2034/18688 [2:23:00<18:36:23, 4.02s/it] 11%|█ | 2035/18688 [2:23:04<19:04:05, 4.12s/it] 11%|█ | 2036/18688 [2:23:09<19:52:27, 4.30s/it] 11%|█ | 2037/18688 [2:23:12<18:51:41, 4.08s/it] 11%|█ | 2038/18688 [2:23:16<18:55:55, 4.09s/it] 11%|█ | 2039/18688 [2:23:21<19:12:59, 4.16s/it] 11%|█ | 2040/18688 [2:23:25<19:03:46, 4.12s/it] {'loss': '1.008', 'grad_norm': '1.171', 'learning_rate': '7.981e-05', 'epoch': '0.1092', 'num_input_tokens_seen': 6827176, 'train_runtime': '8605', 'train_tokens_per_second': '793.4'}
11%|█ | 2040/18688 [2:23:25<19:03:46, 4.12s/it] 11%|█ | 2041/18688 [2:23:29<19:21:57, 4.19s/it] 11%|█ | 2042/18688 [2:23:33<18:23:32, 3.98s/it] 11%|█ | 2043/18688 [2:23:37<18:25:41, 3.99s/it] 11%|█ | 2044/18688 [2:23:42<20:18:17, 4.39s/it] 11%|█ | 2045/18688 [2:23:47<20:50:02, 4.51s/it] 11%|█ | 2046/18688 [2:23:50<19:43:37, 4.27s/it] 11%|█ | 2047/18688 [2:23:54<18:52:45, 4.08s/it] 11%|█ | 2048/18688 [2:23:58<18:43:24, 4.05s/it] 11%|█ | 2049/18688 [2:24:02<18:21:03, 3.97s/it] 11%|█ | 2050/18688 [2:24:06<18:15:02, 3.95s/it] {'loss': '0.9646', 'grad_norm': '0.8819', 'learning_rate': '7.98e-05', 'epoch': '0.1097', 'num_input_tokens_seen': 6859736, 'train_runtime': '8646', 'train_tokens_per_second': '793.4'}
11%|█ | 2050/18688 [2:24:06<18:15:02, 3.95s/it] 11%|█ | 2051/18688 [2:24:09<17:50:19, 3.86s/it] 11%|█ | 2052/18688 [2:24:14<19:11:49, 4.15s/it] 11%|█ | 2053/18688 [2:24:19<19:36:54, 4.24s/it] 11%|█ | 2054/18688 [2:24:23<19:39:24, 4.25s/it] 11%|█ | 2055/18688 [2:24:26<18:34:01, 4.02s/it] 11%|█ | 2056/18688 [2:24:31<18:46:46, 4.06s/it] 11%|█ | 2057/18688 [2:24:34<18:33:34, 4.02s/it] 11%|█ | 2058/18688 [2:24:38<18:17:17, 3.96s/it] 11%|█ | 2059/18688 [2:24:43<19:01:06, 4.12s/it] 11%|█ | 2060/18688 [2:24:47<19:25:24, 4.21s/it] {'loss': '0.9344', 'grad_norm': '0.7174', 'learning_rate': '7.979e-05', 'epoch': '0.1102', 'num_input_tokens_seen': 6893248, 'train_runtime': '8688', 'train_tokens_per_second': '793.4'}
11%|█ | 2060/18688 [2:24:47<19:25:24, 4.21s/it] 11%|█ | 2061/18688 [2:24:52<19:36:53, 4.25s/it] 11%|█ | 2062/18688 [2:24:56<19:38:14, 4.25s/it] 11%|█ | 2063/18688 [2:25:00<18:54:00, 4.09s/it] 11%|█ | 2064/18688 [2:25:04<19:31:52, 4.23s/it] 11%|█ | 2065/18688 [2:25:08<18:52:10, 4.09s/it] 11%|█ | 2066/18688 [2:25:12<19:16:17, 4.17s/it] 11%|█ | 2067/18688 [2:25:17<20:27:06, 4.43s/it] 11%|█ | 2068/18688 [2:25:21<19:42:00, 4.27s/it] 11%|█ | 2069/18688 [2:25:26<20:15:59, 4.39s/it] 11%|█ | 2070/18688 [2:25:30<19:20:59, 4.19s/it] {'loss': '1.012', 'grad_norm': '0.8976', 'learning_rate': '7.978e-05', 'epoch': '0.1108', 'num_input_tokens_seen': 6926840, 'train_runtime': '8730', 'train_tokens_per_second': '793.4'}
11%|█ | 2070/18688 [2:25:30<19:20:59, 4.19s/it] 11%|█ | 2071/18688 [2:25:35<20:39:43, 4.48s/it] 11%|█ | 2072/18688 [2:25:39<19:56:28, 4.32s/it] 11%|█ | 2073/18688 [2:25:44<21:37:54, 4.69s/it] 11%|█ | 2074/18688 [2:25:48<20:15:16, 4.39s/it] 11%|█ | 2075/18688 [2:25:52<19:53:56, 4.31s/it] 11%|█ | 2076/18688 [2:25:56<20:00:15, 4.34s/it] 11%|█ | 2077/18688 [2:26:00<19:06:55, 4.14s/it] 11%|█ | 2078/18688 [2:26:04<18:15:15, 3.96s/it] 11%|█ | 2079/18688 [2:26:07<17:49:42, 3.86s/it] 11%|█ | 2080/18688 [2:26:12<18:21:58, 3.98s/it] {'loss': '0.9554', 'grad_norm': '1.09', 'learning_rate': '7.978e-05', 'epoch': '0.1113', 'num_input_tokens_seen': 6960856, 'train_runtime': '8772', 'train_tokens_per_second': '793.5'}
11%|█ | 2080/18688 [2:26:12<18:21:58, 3.98s/it] 11%|█ | 2081/18688 [2:26:15<17:43:38, 3.84s/it] 11%|█ | 2082/18688 [2:26:19<17:49:17, 3.86s/it] 11%|█ | 2083/18688 [2:26:24<18:46:39, 4.07s/it] 11%|█ | 2084/18688 [2:26:28<19:00:48, 4.12s/it] 11%|█ | 2085/18688 [2:26:32<19:17:01, 4.18s/it] 11%|█ | 2086/18688 [2:26:36<19:20:56, 4.20s/it] 11%|█ | 2087/18688 [2:26:40<18:45:49, 4.07s/it] 11%|█ | 2088/18688 [2:26:44<19:05:22, 4.14s/it] 11%|█ | 2089/18688 [2:26:48<18:13:46, 3.95s/it] 11%|█ | 2090/18688 [2:26:52<18:30:28, 4.01s/it] {'loss': '0.9234', 'grad_norm': '0.9961', 'learning_rate': '7.977e-05', 'epoch': '0.1118', 'num_input_tokens_seen': 6993128, 'train_runtime': '8813', 'train_tokens_per_second': '793.5'}
11%|█ | 2090/18688 [2:26:52<18:30:28, 4.01s/it] 11%|█ | 2091/18688 [2:26:56<18:55:03, 4.10s/it] 11%|█ | 2092/18688 [2:27:01<19:24:26, 4.21s/it] 11%|█ | 2093/18688 [2:27:05<19:11:03, 4.16s/it] 11%|█ | 2094/18688 [2:27:09<19:20:07, 4.19s/it] 11%|█ | 2095/18688 [2:27:13<19:26:03, 4.22s/it] 11%|█ | 2096/18688 [2:27:18<19:52:49, 4.31s/it] 11%|█ | 2097/18688 [2:27:22<19:42:36, 4.28s/it] 11%|█ | 2098/18688 [2:27:27<20:45:49, 4.51s/it] 11%|█ | 2099/18688 [2:27:31<19:40:04, 4.27s/it] 11%|█ | 2100/18688 [2:27:34<18:33:48, 4.03s/it] {'loss': '0.9906', 'grad_norm': '1.089', 'learning_rate': '7.976e-05', 'epoch': '0.1124', 'num_input_tokens_seen': 7027808, 'train_runtime': '8855', 'train_tokens_per_second': '793.7'}
11%|█ | 2100/18688 [2:27:34<18:33:48, 4.03s/it] 11%|█ | 2101/18688 [2:27:39<19:42:22, 4.28s/it] 11%|█ | 2102/18688 [2:27:43<18:56:10, 4.11s/it] 11%|█▏ | 2103/18688 [2:27:47<19:13:17, 4.17s/it] 11%|█▏ | 2104/18688 [2:27:51<18:23:16, 3.99s/it] 11%|█▏ | 2105/18688 [2:27:55<18:44:52, 4.07s/it] 11%|█▏ | 2106/18688 [2:27:59<18:11:16, 3.95s/it] 11%|█▏ | 2107/18688 [2:28:03<18:09:01, 3.94s/it] 11%|█▏ | 2108/18688 [2:28:06<17:46:13, 3.86s/it] 11%|█▏ | 2109/18688 [2:28:10<18:02:20, 3.92s/it] 11%|█▏ | 2110/18688 [2:28:15<19:40:03, 4.27s/it] {'loss': '0.9151', 'grad_norm': '1.118', 'learning_rate': '7.975e-05', 'epoch': '0.1129', 'num_input_tokens_seen': 7060536, 'train_runtime': '8896', 'train_tokens_per_second': '793.7'}
11%|█▏ | 2110/18688 [2:28:15<19:40:03, 4.27s/it] 11%|█▏ | 2111/18688 [2:28:20<19:32:59, 4.25s/it] 11%|█▏ | 2112/18688 [2:28:24<18:59:43, 4.13s/it] 11%|█▏ | 2113/18688 [2:28:27<18:41:06, 4.06s/it] 11%|█▏ | 2114/18688 [2:28:32<19:30:19, 4.24s/it] 11%|█▏ | 2115/18688 [2:28:36<19:37:02, 4.26s/it] 11%|█▏ | 2116/18688 [2:28:40<18:39:17, 4.05s/it] 11%|█▏ | 2117/18688 [2:28:44<19:18:42, 4.20s/it] 11%|█▏ | 2118/18688 [2:28:49<19:35:34, 4.26s/it] 11%|█▏ | 2119/18688 [2:28:53<19:25:15, 4.22s/it] 11%|█▏ | 2120/18688 [2:28:57<19:33:28, 4.25s/it] {'loss': '1.044', 'grad_norm': '0.9283', 'learning_rate': '7.974e-05', 'epoch': '0.1134', 'num_input_tokens_seen': 7095224, 'train_runtime': '8938', 'train_tokens_per_second': '793.8'}
11%|█▏ | 2120/18688 [2:28:57<19:33:28, 4.25s/it] 11%|█▏ | 2121/18688 [2:29:02<19:35:29, 4.26s/it] 11%|█▏ | 2122/18688 [2:29:06<19:59:58, 4.35s/it] 11%|█▏ | 2123/18688 [2:29:11<20:05:37, 4.37s/it] 11%|█▏ | 2124/18688 [2:29:14<19:02:10, 4.14s/it] 11%|█▏ | 2125/18688 [2:29:18<18:42:07, 4.06s/it] 11%|█▏ | 2126/18688 [2:29:22<19:05:32, 4.15s/it] 11%|█▏ | 2127/18688 [2:29:26<18:28:12, 4.02s/it] 11%|█▏ | 2128/18688 [2:29:30<18:13:11, 3.96s/it] 11%|█▏ | 2129/18688 [2:29:35<19:43:24, 4.29s/it] 11%|█▏ | 2130/18688 [2:29:39<18:37:30, 4.05s/it] {'loss': '0.9962', 'grad_norm': '1.204', 'learning_rate': '7.974e-05', 'epoch': '0.114', 'num_input_tokens_seen': 7128176, 'train_runtime': '8979', 'train_tokens_per_second': '793.9'}
11%|█▏ | 2130/18688 [2:29:39<18:37:30, 4.05s/it] 11%|█▏ | 2131/18688 [2:29:42<18:21:39, 3.99s/it] 11%|█▏ | 2132/18688 [2:29:46<17:48:15, 3.87s/it] 11%|█▏ | 2133/18688 [2:29:51<19:09:46, 4.17s/it] 11%|█▏ | 2134/18688 [2:29:56<19:57:46, 4.34s/it] 11%|█▏ | 2135/18688 [2:29:59<19:22:34, 4.21s/it] 11%|█▏ | 2136/18688 [2:30:04<19:45:25, 4.30s/it] 11%|█▏ | 2137/18688 [2:30:08<19:19:57, 4.21s/it] 11%|█▏ | 2138/18688 [2:30:12<18:27:59, 4.02s/it] 11%|█▏ | 2139/18688 [2:30:15<17:57:03, 3.90s/it] 11%|█▏ | 2140/18688 [2:30:19<17:28:47, 3.80s/it] {'loss': '0.9815', 'grad_norm': '1.027', 'learning_rate': '7.973e-05', 'epoch': '0.1145', 'num_input_tokens_seen': 7160536, 'train_runtime': '9019', 'train_tokens_per_second': '793.9'}
11%|█▏ | 2140/18688 [2:30:19<17:28:47, 3.80s/it] 11%|█▏ | 2141/18688 [2:30:22<17:05:58, 3.72s/it] 11%|█▏ | 2142/18688 [2:30:26<17:11:30, 3.74s/it] 11%|█▏ | 2143/18688 [2:30:31<19:11:57, 4.18s/it] 11%|█▏ | 2144/18688 [2:30:35<18:31:04, 4.03s/it] 11%|█▏ | 2145/18688 [2:30:39<19:00:26, 4.14s/it] 11%|█▏ | 2146/18688 [2:30:43<18:25:05, 4.01s/it] 11%|█▏ | 2147/18688 [2:30:48<19:24:25, 4.22s/it] 11%|█▏ | 2148/18688 [2:30:52<19:12:51, 4.18s/it] 11%|█▏ | 2149/18688 [2:30:56<19:49:49, 4.32s/it] 12%|█▏ | 2150/18688 [2:31:00<19:09:40, 4.17s/it] {'loss': '0.9671', 'grad_norm': '1.083', 'learning_rate': '7.972e-05', 'epoch': '0.1151', 'num_input_tokens_seen': 7194240, 'train_runtime': '9061', 'train_tokens_per_second': '794'}
12%|█▏ | 2150/18688 [2:31:00<19:09:40, 4.17s/it] 12%|█▏ | 2151/18688 [2:31:04<18:36:33, 4.05s/it] 12%|█▏ | 2152/18688 [2:31:08<18:52:01, 4.11s/it] 12%|█▏ | 2153/18688 [2:31:12<18:54:42, 4.12s/it] 12%|█▏ | 2154/18688 [2:31:16<18:18:07, 3.98s/it] 12%|█▏ | 2155/18688 [2:31:20<17:57:15, 3.91s/it] 12%|█▏ | 2156/18688 [2:31:24<18:26:53, 4.02s/it] 12%|█▏ | 2157/18688 [2:31:28<18:29:14, 4.03s/it] 12%|█▏ | 2158/18688 [2:31:32<18:10:57, 3.96s/it] 12%|█▏ | 2159/18688 [2:31:36<17:58:06, 3.91s/it] 12%|█▏ | 2160/18688 [2:31:40<18:41:57, 4.07s/it] {'loss': '0.9866', 'grad_norm': '1.161', 'learning_rate': '7.971e-05', 'epoch': '0.1156', 'num_input_tokens_seen': 7224368, 'train_runtime': '9101', 'train_tokens_per_second': '793.8'}
12%|█▏ | 2160/18688 [2:31:40<18:41:57, 4.07s/it] 12%|█▏ | 2161/18688 [2:31:45<19:46:26, 4.31s/it] 12%|█▏ | 2162/18688 [2:31:49<18:35:09, 4.05s/it] 12%|█▏ | 2163/18688 [2:31:52<17:58:05, 3.91s/it] 12%|█▏ | 2164/18688 [2:31:57<18:43:17, 4.08s/it] 12%|█▏ | 2165/18688 [2:32:00<18:04:48, 3.94s/it] 12%|█▏ | 2166/18688 [2:32:05<18:42:18, 4.08s/it] 12%|█▏ | 2167/18688 [2:32:09<18:45:46, 4.09s/it] 12%|█▏ | 2168/18688 [2:32:13<19:05:26, 4.16s/it] 12%|█▏ | 2169/18688 [2:32:18<20:03:37, 4.37s/it] 12%|█▏ | 2170/18688 [2:32:22<19:33:36, 4.26s/it] {'loss': '0.978', 'grad_norm': '0.9029', 'learning_rate': '7.97e-05', 'epoch': '0.1161', 'num_input_tokens_seen': 7257872, 'train_runtime': '9142', 'train_tokens_per_second': '793.9'}
12%|█▏ | 2170/18688 [2:32:22<19:33:36, 4.26s/it] 12%|█▏ | 2171/18688 [2:32:25<18:35:11, 4.05s/it] 12%|█▏ | 2172/18688 [2:32:30<19:22:32, 4.22s/it] 12%|█▏ | 2173/18688 [2:32:34<19:22:31, 4.22s/it] 12%|█▏ | 2174/18688 [2:32:38<18:19:30, 3.99s/it] 12%|█▏ | 2175/18688 [2:32:42<18:18:23, 3.99s/it] 12%|█▏ | 2176/18688 [2:32:46<18:33:21, 4.05s/it] 12%|█▏ | 2177/18688 [2:32:50<17:55:01, 3.91s/it] 12%|█▏ | 2178/18688 [2:32:54<18:10:24, 3.96s/it] 12%|█▏ | 2179/18688 [2:32:57<17:49:32, 3.89s/it] 12%|█▏ | 2180/18688 [2:33:01<17:48:46, 3.88s/it] {'loss': '0.9345', 'grad_norm': '0.9974', 'learning_rate': '7.969e-05', 'epoch': '0.1167', 'num_input_tokens_seen': 7288792, 'train_runtime': '9182', 'train_tokens_per_second': '793.8'}
12%|█▏ | 2180/18688 [2:33:01<17:48:46, 3.88s/it] 12%|█▏ | 2181/18688 [2:33:05<17:48:01, 3.88s/it] 12%|█▏ | 2182/18688 [2:33:09<17:25:13, 3.80s/it] 12%|█▏ | 2183/18688 [2:33:13<17:49:15, 3.89s/it] 12%|█▏ | 2184/18688 [2:33:17<18:04:24, 3.94s/it] 12%|█▏ | 2185/18688 [2:33:21<18:12:45, 3.97s/it] 12%|█▏ | 2186/18688 [2:33:25<18:38:09, 4.07s/it] 12%|█▏ | 2187/18688 [2:33:30<19:26:49, 4.24s/it] 12%|█▏ | 2188/18688 [2:33:33<18:27:14, 4.03s/it] 12%|█▏ | 2189/18688 [2:33:38<18:53:24, 4.12s/it] 12%|█▏ | 2190/18688 [2:33:42<19:00:03, 4.15s/it] {'loss': '1.018', 'grad_norm': '1.267', 'learning_rate': '7.968e-05', 'epoch': '0.1172', 'num_input_tokens_seen': 7320992, 'train_runtime': '9222', 'train_tokens_per_second': '793.8'}
12%|█▏ | 2190/18688 [2:33:42<19:00:03, 4.15s/it] 12%|█▏ | 2191/18688 [2:33:46<18:40:54, 4.08s/it] 12%|█▏ | 2192/18688 [2:33:49<17:52:39, 3.90s/it] 12%|█▏ | 2193/18688 [2:33:55<19:53:05, 4.34s/it] 12%|█▏ | 2194/18688 [2:33:59<19:48:18, 4.32s/it] 12%|█▏ | 2195/18688 [2:34:03<19:15:42, 4.20s/it] 12%|█▏ | 2196/18688 [2:34:07<19:21:06, 4.22s/it] 12%|█▏ | 2197/18688 [2:34:11<18:44:12, 4.09s/it] 12%|█▏ | 2198/18688 [2:34:16<19:27:55, 4.25s/it] 12%|█▏ | 2199/18688 [2:34:20<19:28:06, 4.25s/it] 12%|█▏ | 2200/18688 [2:34:25<21:04:19, 4.60s/it] {'loss': '0.9902', 'grad_norm': '1.251', 'learning_rate': '7.967e-05', 'epoch': '0.1177', 'num_input_tokens_seen': 7357008, 'train_runtime': '9266', 'train_tokens_per_second': '794'}
12%|█▏ | 2200/18688 [2:34:25<21:04:19, 4.60s/it] 12%|█▏ | 2201/18688 [2:34:30<21:27:56, 4.69s/it] 12%|█▏ | 2202/18688 [2:34:35<21:07:28, 4.61s/it] 12%|█▏ | 2203/18688 [2:34:38<20:02:34, 4.38s/it] 12%|█▏ | 2204/18688 [2:34:42<19:36:18, 4.28s/it] 12%|█▏ | 2205/18688 [2:34:47<19:47:49, 4.32s/it] 12%|█▏ | 2206/18688 [2:34:51<20:00:55, 4.37s/it] 12%|█▏ | 2207/18688 [2:34:55<19:05:44, 4.17s/it] 12%|█▏ | 2208/18688 [2:34:59<18:33:24, 4.05s/it] 12%|█▏ | 2209/18688 [2:35:02<17:53:05, 3.91s/it] 12%|█▏ | 2210/18688 [2:35:06<17:57:52, 3.92s/it] {'loss': '1.048', 'grad_norm': '1.071', 'learning_rate': '7.966e-05', 'epoch': '0.1183', 'num_input_tokens_seen': 7390192, 'train_runtime': '9307', 'train_tokens_per_second': '794.1'}
12%|█▏ | 2210/18688 [2:35:06<17:57:52, 3.92s/it] 12%|█▏ | 2211/18688 [2:35:10<17:51:08, 3.90s/it] 12%|█▏ | 2212/18688 [2:35:15<18:30:21, 4.04s/it] 12%|█▏ | 2213/18688 [2:35:19<18:36:26, 4.07s/it] 12%|█▏ | 2214/18688 [2:35:23<18:18:04, 4.00s/it] 12%|█▏ | 2215/18688 [2:35:27<18:59:37, 4.15s/it] 12%|█▏ | 2216/18688 [2:35:31<18:39:22, 4.08s/it] 12%|█▏ | 2217/18688 [2:35:36<19:31:36, 4.27s/it] 12%|█▏ | 2218/18688 [2:35:40<19:25:24, 4.25s/it] 12%|█▏ | 2219/18688 [2:35:44<19:35:44, 4.28s/it] 12%|█▏ | 2220/18688 [2:35:48<18:45:17, 4.10s/it] {'loss': '0.9395', 'grad_norm': '0.9527', 'learning_rate': '7.966e-05', 'epoch': '0.1188', 'num_input_tokens_seen': 7422720, 'train_runtime': '9348', 'train_tokens_per_second': '794'}
12%|█▏ | 2220/18688 [2:35:48<18:45:17, 4.10s/it] 12%|█▏ | 2221/18688 [2:35:52<18:57:49, 4.15s/it] 12%|█▏ | 2222/18688 [2:35:56<18:09:01, 3.97s/it] 12%|█▏ | 2223/18688 [2:36:01<19:18:07, 4.22s/it] 12%|█▏ | 2224/18688 [2:36:05<20:19:03, 4.44s/it] 12%|█▏ | 2225/18688 [2:36:09<19:31:48, 4.27s/it] 12%|█▏ | 2226/18688 [2:36:14<19:44:10, 4.32s/it] 12%|█▏ | 2227/18688 [2:36:18<19:33:26, 4.28s/it] 12%|█▏ | 2228/18688 [2:36:23<20:15:08, 4.43s/it] 12%|█▏ | 2229/18688 [2:36:27<20:20:29, 4.45s/it] 12%|█▏ | 2230/18688 [2:36:32<20:20:47, 4.45s/it] {'loss': '0.9796', 'grad_norm': '0.9204', 'learning_rate': '7.965e-05', 'epoch': '0.1193', 'num_input_tokens_seen': 7459088, 'train_runtime': '9392', 'train_tokens_per_second': '794.2'}
12%|█▏ | 2230/18688 [2:36:32<20:20:47, 4.45s/it] 12%|█▏ | 2231/18688 [2:36:36<20:04:16, 4.39s/it] 12%|█▏ | 2232/18688 [2:36:40<19:17:16, 4.22s/it] 12%|█▏ | 2233/18688 [2:36:44<18:43:03, 4.10s/it] 12%|█▏ | 2234/18688 [2:36:47<18:10:09, 3.98s/it] 12%|█▏ | 2235/18688 [2:36:52<18:58:54, 4.15s/it] 12%|█▏ | 2236/18688 [2:36:56<18:35:31, 4.07s/it] 12%|█▏ | 2237/18688 [2:37:00<18:15:22, 4.00s/it] 12%|█▏ | 2238/18688 [2:37:04<18:24:39, 4.03s/it] 12%|█▏ | 2239/18688 [2:37:08<18:17:03, 4.00s/it] 12%|█▏ | 2240/18688 [2:37:12<18:25:49, 4.03s/it] {'loss': '0.9811', 'grad_norm': '1.165', 'learning_rate': '7.964e-05', 'epoch': '0.1199', 'num_input_tokens_seen': 7489936, 'train_runtime': '9432', 'train_tokens_per_second': '794.1'}
12%|█▏ | 2240/18688 [2:37:12<18:25:49, 4.03s/it] 12%|█▏ | 2241/18688 [2:37:15<18:01:17, 3.94s/it] 12%|█▏ | 2242/18688 [2:37:19<17:44:42, 3.88s/it] 12%|█▏ | 2243/18688 [2:37:26<21:11:04, 4.64s/it] 12%|█▏ | 2244/18688 [2:37:30<20:59:31, 4.60s/it] 12%|█▏ | 2245/18688 [2:37:34<19:46:53, 4.33s/it] 12%|█▏ | 2246/18688 [2:37:37<18:36:14, 4.07s/it] 12%|█▏ | 2247/18688 [2:37:41<18:39:57, 4.09s/it] 12%|█▏ | 2248/18688 [2:37:46<18:58:22, 4.15s/it] 12%|█▏ | 2249/18688 [2:37:50<18:57:35, 4.15s/it] 12%|█▏ | 2250/18688 [2:37:54<19:00:41, 4.16s/it] {'loss': '0.9448', 'grad_norm': '1.155', 'learning_rate': '7.963e-05', 'epoch': '0.1204', 'num_input_tokens_seen': 7523312, 'train_runtime': '9475', 'train_tokens_per_second': '794.1'}
12%|█▏ | 2250/18688 [2:37:54<19:00:41, 4.16s/it] 12%|█▏ | 2251/18688 [2:37:58<18:53:43, 4.14s/it] 12%|█▏ | 2252/18688 [2:38:03<19:31:19, 4.28s/it] 12%|█▏ | 2253/18688 [2:38:06<18:48:36, 4.12s/it] 12%|█▏ | 2254/18688 [2:38:12<21:05:03, 4.62s/it] 12%|█▏ | 2255/18688 [2:38:17<20:46:25, 4.55s/it] 12%|█▏ | 2256/18688 [2:38:20<19:15:28, 4.22s/it] 12%|█▏ | 2257/18688 [2:38:24<18:37:15, 4.08s/it] 12%|█▏ | 2258/18688 [2:38:29<19:43:49, 4.32s/it] 12%|█▏ | 2259/18688 [2:38:32<18:50:28, 4.13s/it] 12%|█▏ | 2260/18688 [2:38:37<19:43:01, 4.32s/it] {'loss': '1.092', 'grad_norm': '0.9496', 'learning_rate': '7.962e-05', 'epoch': '0.1209', 'num_input_tokens_seen': 7558424, 'train_runtime': '9518', 'train_tokens_per_second': '794.1'}
12%|█▏ | 2260/18688 [2:38:37<19:43:01, 4.32s/it] 12%|█▏ | 2261/18688 [2:38:42<20:13:59, 4.43s/it] 12%|█▏ | 2262/18688 [2:38:47<21:19:04, 4.67s/it] 12%|█▏ | 2263/18688 [2:38:51<19:37:18, 4.30s/it] 12%|█▏ | 2264/18688 [2:38:55<19:16:50, 4.23s/it] 12%|█▏ | 2265/18688 [2:38:58<18:39:25, 4.09s/it] 12%|█▏ | 2266/18688 [2:39:02<18:28:49, 4.05s/it] 12%|█▏ | 2267/18688 [2:39:06<18:16:44, 4.01s/it] 12%|█▏ | 2268/18688 [2:39:10<18:32:12, 4.06s/it] 12%|█▏ | 2269/18688 [2:39:15<19:15:21, 4.22s/it] 12%|█▏ | 2270/18688 [2:39:20<20:07:26, 4.41s/it] {'loss': '0.9578', 'grad_norm': '1.072', 'learning_rate': '7.961e-05', 'epoch': '0.1215', 'num_input_tokens_seen': 7591752, 'train_runtime': '9560', 'train_tokens_per_second': '794.1'}
12%|█▏ | 2270/18688 [2:39:20<20:07:26, 4.41s/it] 12%|█▏ | 2271/18688 [2:39:24<19:29:33, 4.27s/it] 12%|█▏ | 2272/18688 [2:39:27<18:36:59, 4.08s/it] 12%|█▏ | 2273/18688 [2:39:32<19:01:53, 4.17s/it] 12%|█▏ | 2274/18688 [2:39:36<19:19:24, 4.24s/it] 12%|█▏ | 2275/18688 [2:39:40<18:20:14, 4.02s/it] 12%|█▏ | 2276/18688 [2:39:43<17:57:49, 3.94s/it] 12%|█▏ | 2277/18688 [2:39:48<19:18:10, 4.23s/it] 12%|█▏ | 2278/18688 [2:39:53<19:17:51, 4.23s/it] 12%|█▏ | 2279/18688 [2:39:57<18:58:33, 4.16s/it] 12%|█▏ | 2280/18688 [2:40:01<18:37:32, 4.09s/it] {'loss': '0.9665', 'grad_norm': '0.8457', 'learning_rate': '7.96e-05', 'epoch': '0.122', 'num_input_tokens_seen': 7626000, 'train_runtime': '9601', 'train_tokens_per_second': '794.3'}
12%|█▏ | 2280/18688 [2:40:01<18:37:32, 4.09s/it] 12%|█▏ | 2281/18688 [2:40:05<19:41:00, 4.32s/it] 12%|█▏ | 2282/18688 [2:40:10<19:55:48, 4.37s/it] 12%|█▏ | 2283/18688 [2:40:14<19:31:26, 4.28s/it] 12%|█▏ | 2284/18688 [2:40:19<19:54:16, 4.37s/it] 12%|█▏ | 2285/18688 [2:40:23<19:39:49, 4.32s/it] 12%|█▏ | 2286/18688 [2:40:26<18:48:43, 4.13s/it] 12%|█▏ | 2287/18688 [2:40:30<18:04:49, 3.97s/it] 12%|█▏ | 2288/18688 [2:40:35<18:48:10, 4.13s/it] 12%|█▏ | 2289/18688 [2:40:39<18:38:34, 4.09s/it] 12%|█▏ | 2290/18688 [2:40:42<18:21:31, 4.03s/it] {'loss': '0.9998', 'grad_norm': '1.04', 'learning_rate': '7.958e-05', 'epoch': '0.1225', 'num_input_tokens_seen': 7659472, 'train_runtime': '9643', 'train_tokens_per_second': '794.3'}
12%|█▏ | 2290/18688 [2:40:42<18:21:31, 4.03s/it] 12%|█▏ | 2291/18688 [2:40:47<19:32:20, 4.29s/it] 12%|█▏ | 2292/18688 [2:40:53<20:50:20, 4.58s/it] 12%|█▏ | 2293/18688 [2:40:56<19:54:30, 4.37s/it] 12%|█▏ | 2294/18688 [2:41:01<20:36:31, 4.53s/it] 12%|█▏ | 2295/18688 [2:41:05<19:17:14, 4.24s/it] 12%|█▏ | 2296/18688 [2:41:09<19:35:07, 4.30s/it] 12%|█▏ | 2297/18688 [2:41:13<18:46:01, 4.12s/it] 12%|█▏ | 2298/18688 [2:41:17<18:37:08, 4.09s/it] 12%|█▏ | 2299/18688 [2:41:22<19:24:27, 4.26s/it] 12%|█▏ | 2300/18688 [2:41:27<20:09:37, 4.43s/it] {'loss': '1.063', 'grad_norm': '0.9062', 'learning_rate': '7.957e-05', 'epoch': '0.1231', 'num_input_tokens_seen': 7696560, 'train_runtime': '9687', 'train_tokens_per_second': '794.5'}
12%|█▏ | 2300/18688 [2:41:27<20:09:37, 4.43s/it] 12%|█▏ | 2301/18688 [2:41:30<18:59:37, 4.17s/it] 12%|█▏ | 2302/18688 [2:41:34<18:46:14, 4.12s/it] 12%|█▏ | 2303/18688 [2:41:38<19:00:37, 4.18s/it] 12%|█▏ | 2304/18688 [2:41:43<19:58:02, 4.39s/it] 12%|█▏ | 2305/18688 [2:41:47<19:06:27, 4.20s/it] 12%|█▏ | 2306/18688 [2:41:51<18:26:33, 4.05s/it] 12%|█▏ | 2307/18688 [2:41:55<18:27:55, 4.06s/it] 12%|█▏ | 2308/18688 [2:41:59<17:59:14, 3.95s/it] 12%|█▏ | 2309/18688 [2:42:03<19:07:36, 4.20s/it] 12%|█▏ | 2310/18688 [2:42:09<21:19:25, 4.69s/it] {'loss': '0.9913', 'grad_norm': '0.9393', 'learning_rate': '7.956e-05', 'epoch': '0.1236', 'num_input_tokens_seen': 7731888, 'train_runtime': '9730', 'train_tokens_per_second': '794.7'}
12%|█▏ | 2310/18688 [2:42:09<21:19:25, 4.69s/it] 12%|█▏ | 2311/18688 [2:42:13<20:43:35, 4.56s/it] 12%|█▏ | 2312/18688 [2:42:18<20:08:25, 4.43s/it] 12%|█▏ | 2313/18688 [2:42:21<18:53:36, 4.15s/it] 12%|█▏ | 2314/18688 [2:42:25<18:16:36, 4.02s/it] 12%|█▏ | 2315/18688 [2:42:29<19:05:03, 4.20s/it] 12%|█▏ | 2316/18688 [2:42:33<18:25:34, 4.05s/it] 12%|█▏ | 2317/18688 [2:42:37<17:58:00, 3.95s/it] 12%|█▏ | 2318/18688 [2:42:41<18:52:13, 4.15s/it] 12%|█▏ | 2319/18688 [2:42:45<18:42:22, 4.11s/it] 12%|█▏ | 2320/18688 [2:42:49<18:31:26, 4.07s/it] {'loss': '0.9068', 'grad_norm': '0.89', 'learning_rate': '7.955e-05', 'epoch': '0.1241', 'num_input_tokens_seen': 7763960, 'train_runtime': '9770', 'train_tokens_per_second': '794.7'}
12%|█▏ | 2320/18688 [2:42:49<18:31:26, 4.07s/it] 12%|█▏ | 2321/18688 [2:42:54<18:58:27, 4.17s/it] 12%|█▏ | 2322/18688 [2:42:59<19:44:13, 4.34s/it] 12%|█▏ | 2323/18688 [2:43:02<18:33:24, 4.08s/it] 12%|█▏ | 2324/18688 [2:43:06<18:58:09, 4.17s/it] 12%|█▏ | 2325/18688 [2:43:11<19:19:11, 4.25s/it] 12%|█▏ | 2326/18688 [2:43:16<19:53:51, 4.38s/it] 12%|█▏ | 2327/18688 [2:43:20<20:25:32, 4.49s/it] 12%|█▏ | 2328/18688 [2:43:25<20:50:23, 4.59s/it] 12%|█▏ | 2329/18688 [2:43:29<19:51:23, 4.37s/it] 12%|█▏ | 2330/18688 [2:43:32<18:41:40, 4.11s/it] {'loss': '0.9466', 'grad_norm': '0.9979', 'learning_rate': '7.954e-05', 'epoch': '0.1247', 'num_input_tokens_seen': 7800728, 'train_runtime': '9813', 'train_tokens_per_second': '794.9'}
12%|█▏ | 2330/18688 [2:43:32<18:41:40, 4.11s/it] 12%|█▏ | 2331/18688 [2:43:37<18:45:41, 4.13s/it] 12%|█▏ | 2332/18688 [2:43:41<19:05:40, 4.20s/it] 12%|█▏ | 2333/18688 [2:43:45<19:01:39, 4.19s/it] 12%|█▏ | 2334/18688 [2:43:50<20:05:23, 4.42s/it] 12%|█▏ | 2335/18688 [2:43:54<19:06:12, 4.21s/it] 12%|█▎ | 2336/18688 [2:43:58<19:31:50, 4.30s/it] 13%|█▎ | 2337/18688 [2:44:02<18:50:45, 4.15s/it] 13%|█▎ | 2338/18688 [2:44:07<20:01:26, 4.41s/it] 13%|█▎ | 2339/18688 [2:44:11<19:07:25, 4.21s/it] 13%|█▎ | 2340/18688 [2:44:15<18:40:13, 4.11s/it] {'loss': '0.9864', 'grad_norm': '0.944', 'learning_rate': '7.953e-05', 'epoch': '0.1252', 'num_input_tokens_seen': 7834536, 'train_runtime': '9855', 'train_tokens_per_second': '795'}
13%|█▎ | 2340/18688 [2:44:15<18:40:13, 4.11s/it] 13%|█▎ | 2341/18688 [2:44:19<18:19:51, 4.04s/it] 13%|█▎ | 2342/18688 [2:44:23<18:24:02, 4.05s/it] 13%|█▎ | 2343/18688 [2:44:27<18:31:28, 4.08s/it] 13%|█▎ | 2344/18688 [2:44:31<18:50:11, 4.15s/it] 13%|█▎ | 2345/18688 [2:44:35<18:15:28, 4.02s/it] 13%|█▎ | 2346/18688 [2:44:39<17:52:53, 3.94s/it] 13%|█▎ | 2347/18688 [2:44:42<17:30:23, 3.86s/it] 13%|█▎ | 2348/18688 [2:44:46<17:19:36, 3.82s/it] 13%|█▎ | 2349/18688 [2:44:50<17:14:36, 3.80s/it] 13%|█▎ | 2350/18688 [2:44:54<17:11:06, 3.79s/it] {'loss': '0.9432', 'grad_norm': '1.043', 'learning_rate': '7.952e-05', 'epoch': '0.1258', 'num_input_tokens_seen': 7863608, 'train_runtime': '9894', 'train_tokens_per_second': '794.8'}
13%|█▎ | 2350/18688 [2:44:54<17:11:06, 3.79s/it] 13%|█▎ | 2351/18688 [2:44:58<18:39:04, 4.11s/it] 13%|█▎ | 2352/18688 [2:45:02<17:54:54, 3.95s/it] 13%|█▎ | 2353/18688 [2:45:06<18:35:57, 4.10s/it] 13%|█▎ | 2354/18688 [2:45:11<18:35:02, 4.10s/it] 13%|█▎ | 2355/18688 [2:45:15<19:18:46, 4.26s/it] 13%|█▎ | 2356/18688 [2:45:19<18:56:13, 4.17s/it] 13%|█▎ | 2357/18688 [2:45:24<19:26:11, 4.28s/it] 13%|█▎ | 2358/18688 [2:45:28<18:55:30, 4.17s/it] 13%|█▎ | 2359/18688 [2:45:33<20:08:00, 4.44s/it] 13%|█▎ | 2360/18688 [2:45:36<19:11:40, 4.23s/it] {'loss': '1.035', 'grad_norm': '1.229', 'learning_rate': '7.951e-05', 'epoch': '0.1263', 'num_input_tokens_seen': 7898208, 'train_runtime': '9937', 'train_tokens_per_second': '794.8'}
13%|█▎ | 2360/18688 [2:45:36<19:11:40, 4.23s/it] 13%|█▎ | 2361/18688 [2:45:41<19:23:20, 4.28s/it] 13%|█▎ | 2362/18688 [2:45:45<19:13:51, 4.24s/it] 13%|█▎ | 2363/18688 [2:45:49<19:27:17, 4.29s/it] 13%|█▎ | 2364/18688 [2:45:53<19:03:54, 4.20s/it] 13%|█▎ | 2365/18688 [2:45:58<19:43:22, 4.35s/it] 13%|█▎ | 2366/18688 [2:46:02<19:00:54, 4.19s/it] 13%|█▎ | 2367/18688 [2:46:06<18:54:44, 4.17s/it] 13%|█▎ | 2368/18688 [2:46:10<19:02:35, 4.20s/it] 13%|█▎ | 2369/18688 [2:46:15<20:12:34, 4.46s/it] 13%|█▎ | 2370/18688 [2:46:19<19:17:15, 4.26s/it] {'loss': '0.9659', 'grad_norm': '1.108', 'learning_rate': '7.95e-05', 'epoch': '0.1268', 'num_input_tokens_seen': 7933504, 'train_runtime': '9980', 'train_tokens_per_second': '795'}
13%|█▎ | 2370/18688 [2:46:19<19:17:15, 4.26s/it] 13%|█▎ | 2371/18688 [2:46:24<19:51:48, 4.38s/it] 13%|█▎ | 2372/18688 [2:46:27<18:40:53, 4.12s/it] 13%|█▎ | 2373/18688 [2:46:31<18:30:57, 4.09s/it] 13%|█▎ | 2374/18688 [2:46:35<18:01:05, 3.98s/it] 13%|█▎ | 2375/18688 [2:46:39<17:52:06, 3.94s/it] 13%|█▎ | 2376/18688 [2:46:43<17:47:30, 3.93s/it] 13%|█▎ | 2377/18688 [2:46:46<17:18:32, 3.82s/it] 13%|█▎ | 2378/18688 [2:46:51<18:28:58, 4.08s/it] 13%|█▎ | 2379/18688 [2:46:55<18:38:43, 4.12s/it] 13%|█▎ | 2380/18688 [2:46:59<18:41:07, 4.12s/it] {'loss': '0.986', 'grad_norm': '0.9824', 'learning_rate': '7.948e-05', 'epoch': '0.1274', 'num_input_tokens_seen': 7965544, 'train_runtime': '1.002e+04', 'train_tokens_per_second': '795'}
13%|█▎ | 2380/18688 [2:46:59<18:41:07, 4.12s/it] 13%|█▎ | 2381/18688 [2:47:03<18:02:05, 3.98s/it] 13%|█▎ | 2382/18688 [2:47:07<17:24:11, 3.84s/it] 13%|█▎ | 2383/18688 [2:47:10<17:00:51, 3.76s/it] 13%|█▎ | 2384/18688 [2:47:14<17:35:59, 3.89s/it] 13%|█▎ | 2385/18688 [2:47:18<17:59:31, 3.97s/it] 13%|█▎ | 2386/18688 [2:47:23<18:56:57, 4.18s/it] 13%|█▎ | 2387/18688 [2:47:28<19:17:49, 4.26s/it] 13%|█▎ | 2388/18688 [2:47:31<18:35:51, 4.11s/it] 13%|█▎ | 2389/18688 [2:47:35<18:28:27, 4.08s/it] 13%|█▎ | 2390/18688 [2:47:40<18:46:25, 4.15s/it] {'loss': '0.9427', 'grad_norm': '1.249', 'learning_rate': '7.947e-05', 'epoch': '0.1279', 'num_input_tokens_seen': 7996976, 'train_runtime': '1.006e+04', 'train_tokens_per_second': '794.9'}
13%|█▎ | 2390/18688 [2:47:40<18:46:25, 4.15s/it] 13%|█▎ | 2391/18688 [2:47:43<17:51:20, 3.94s/it] 13%|█▎ | 2392/18688 [2:47:47<17:20:02, 3.83s/it] 13%|█▎ | 2393/18688 [2:47:50<16:47:54, 3.71s/it] 13%|█▎ | 2394/18688 [2:47:54<17:22:19, 3.84s/it] 13%|█▎ | 2395/18688 [2:47:59<18:13:40, 4.03s/it] 13%|█▎ | 2396/18688 [2:48:03<18:05:47, 4.00s/it] 13%|█▎ | 2397/18688 [2:48:06<17:47:31, 3.93s/it] 13%|█▎ | 2398/18688 [2:48:10<17:12:23, 3.80s/it] 13%|█▎ | 2399/18688 [2:48:15<18:26:05, 4.07s/it] 13%|█▎ | 2400/18688 [2:48:20<19:30:04, 4.31s/it] {'loss': '0.9114', 'grad_norm': '1.008', 'learning_rate': '7.946e-05', 'epoch': '0.1284', 'num_input_tokens_seen': 8028608, 'train_runtime': '1.01e+04', 'train_tokens_per_second': '794.9'}
13%|█▎ | 2400/18688 [2:48:20<19:30:04, 4.31s/it] 13%|█▎ | 2401/18688 [2:48:23<18:47:10, 4.15s/it] 13%|█▎ | 2402/18688 [2:48:28<19:13:34, 4.25s/it] 13%|█▎ | 2403/18688 [2:48:32<19:33:17, 4.32s/it] 13%|█▎ | 2404/18688 [2:48:37<20:30:10, 4.53s/it] 13%|█▎ | 2405/18688 [2:48:41<19:21:35, 4.28s/it] 13%|█▎ | 2406/18688 [2:48:45<19:37:56, 4.34s/it] 13%|█▎ | 2407/18688 [2:48:49<18:36:15, 4.11s/it] 13%|█▎ | 2408/18688 [2:48:54<19:21:00, 4.28s/it] 13%|█▎ | 2409/18688 [2:48:58<19:18:06, 4.27s/it] 13%|█▎ | 2410/18688 [2:49:02<19:19:06, 4.27s/it] {'loss': '0.9297', 'grad_norm': '1.166', 'learning_rate': '7.945e-05', 'epoch': '0.129', 'num_input_tokens_seen': 8061768, 'train_runtime': '1.014e+04', 'train_tokens_per_second': '794.8'}
13%|█▎ | 2410/18688 [2:49:02<19:19:06, 4.27s/it] 13%|█▎ | 2411/18688 [2:49:06<18:35:35, 4.11s/it] 13%|█▎ | 2412/18688 [2:49:10<18:49:14, 4.16s/it] 13%|█▎ | 2413/18688 [2:49:14<18:41:11, 4.13s/it] 13%|█▎ | 2414/18688 [2:49:18<17:51:42, 3.95s/it] 13%|█▎ | 2415/18688 [2:49:23<18:55:40, 4.19s/it] 13%|█▎ | 2416/18688 [2:49:26<18:26:07, 4.08s/it] 13%|█▎ | 2417/18688 [2:49:32<19:54:59, 4.41s/it] 13%|█▎ | 2418/18688 [2:49:35<18:54:32, 4.18s/it] 13%|█▎ | 2419/18688 [2:49:39<18:13:30, 4.03s/it] 13%|█▎ | 2420/18688 [2:49:44<19:43:45, 4.37s/it] {'loss': '1.009', 'grad_norm': '0.9373', 'learning_rate': '7.944e-05', 'epoch': '0.1295', 'num_input_tokens_seen': 8096248, 'train_runtime': '1.018e+04', 'train_tokens_per_second': '795'}
13%|█▎ | 2420/18688 [2:49:44<19:43:45, 4.37s/it] 13%|█▎ | 2421/18688 [2:49:48<18:45:41, 4.15s/it] 13%|█▎ | 2422/18688 [2:49:51<18:10:52, 4.02s/it] 13%|█▎ | 2423/18688 [2:49:55<17:54:17, 3.96s/it] 13%|█▎ | 2424/18688 [2:49:59<17:52:12, 3.96s/it] 13%|█▎ | 2425/18688 [2:50:04<18:19:55, 4.06s/it] 13%|█▎ | 2426/18688 [2:50:09<19:43:25, 4.37s/it] 13%|█▎ | 2427/18688 [2:50:12<18:31:03, 4.10s/it] 13%|█▎ | 2428/18688 [2:50:16<18:12:48, 4.03s/it] 13%|█▎ | 2429/18688 [2:50:19<17:23:57, 3.85s/it] 13%|█▎ | 2430/18688 [2:50:25<19:44:21, 4.37s/it] {'loss': '0.953', 'grad_norm': '1.102', 'learning_rate': '7.942e-05', 'epoch': '0.13', 'num_input_tokens_seen': 8128752, 'train_runtime': '1.023e+04', 'train_tokens_per_second': '795'}
13%|█▎ | 2430/18688 [2:50:25<19:44:21, 4.37s/it] 13%|█▎ | 2431/18688 [2:50:29<19:24:20, 4.30s/it] 13%|█▎ | 2432/18688 [2:50:33<19:06:14, 4.23s/it] 13%|█▎ | 2433/18688 [2:50:37<19:04:41, 4.23s/it] 13%|█▎ | 2434/18688 [2:50:42<18:57:00, 4.20s/it] 13%|█▎ | 2435/18688 [2:50:47<20:20:20, 4.51s/it] 13%|█▎ | 2436/18688 [2:50:50<19:10:37, 4.25s/it] 13%|█▎ | 2437/18688 [2:50:54<18:17:41, 4.05s/it] 13%|█▎ | 2438/18688 [2:50:58<18:35:54, 4.12s/it] 13%|█▎ | 2439/18688 [2:51:03<18:46:31, 4.16s/it] 13%|█▎ | 2440/18688 [2:51:07<19:01:43, 4.22s/it] {'loss': '1.026', 'grad_norm': '1.254', 'learning_rate': '7.941e-05', 'epoch': '0.1306', 'num_input_tokens_seen': 8161808, 'train_runtime': '1.027e+04', 'train_tokens_per_second': '794.9'}
13%|█▎ | 2440/18688 [2:51:07<19:01:43, 4.22s/it] 13%|█▎ | 2441/18688 [2:51:10<18:02:12, 4.00s/it] 13%|█▎ | 2442/18688 [2:51:15<18:16:18, 4.05s/it] 13%|█▎ | 2443/18688 [2:51:18<17:44:09, 3.93s/it] 13%|█▎ | 2444/18688 [2:51:22<17:51:17, 3.96s/it] 13%|█▎ | 2445/18688 [2:51:27<19:14:01, 4.26s/it] 13%|█▎ | 2446/18688 [2:51:31<18:22:38, 4.07s/it] 13%|█▎ | 2447/18688 [2:51:35<19:12:07, 4.26s/it] 13%|█▎ | 2448/18688 [2:51:40<19:01:33, 4.22s/it] 13%|█▎ | 2449/18688 [2:51:44<19:13:44, 4.26s/it] 13%|█▎ | 2450/18688 [2:51:48<19:05:40, 4.23s/it] {'loss': '0.9706', 'grad_norm': '1.098', 'learning_rate': '7.94e-05', 'epoch': '0.1311', 'num_input_tokens_seen': 8195656, 'train_runtime': '1.031e+04', 'train_tokens_per_second': '795'}
13%|█▎ | 2450/18688 [2:51:48<19:05:40, 4.23s/it] 13%|█▎ | 2451/18688 [2:51:53<19:57:21, 4.42s/it] 13%|█▎ | 2452/18688 [2:51:57<19:03:47, 4.23s/it] 13%|█▎ | 2453/18688 [2:52:00<18:02:59, 4.00s/it] 13%|█▎ | 2454/18688 [2:52:05<18:27:53, 4.09s/it] 13%|█▎ | 2455/18688 [2:52:09<18:45:06, 4.16s/it] 13%|█▎ | 2456/18688 [2:52:15<20:52:33, 4.63s/it] 13%|█▎ | 2457/18688 [2:52:19<20:19:48, 4.51s/it] 13%|█▎ | 2458/18688 [2:52:23<19:54:32, 4.42s/it] 13%|█▎ | 2459/18688 [2:52:27<19:12:54, 4.26s/it] 13%|█▎ | 2460/18688 [2:52:31<18:41:23, 4.15s/it] {'loss': '0.9592', 'grad_norm': '1.184', 'learning_rate': '7.939e-05', 'epoch': '0.1316', 'num_input_tokens_seen': 8231304, 'train_runtime': '1.035e+04', 'train_tokens_per_second': '795.2'}
13%|█▎ | 2460/18688 [2:52:31<18:41:23, 4.15s/it] 13%|█▎ | 2461/18688 [2:52:35<18:26:36, 4.09s/it] 13%|█▎ | 2462/18688 [2:52:39<18:02:32, 4.00s/it] 13%|█▎ | 2463/18688 [2:52:43<18:36:31, 4.13s/it] 13%|█▎ | 2464/18688 [2:52:47<18:36:35, 4.13s/it] 13%|█▎ | 2465/18688 [2:52:52<19:08:54, 4.25s/it] 13%|█▎ | 2466/18688 [2:52:55<18:36:05, 4.13s/it] 13%|█▎ | 2467/18688 [2:52:59<18:20:25, 4.07s/it] 13%|█▎ | 2468/18688 [2:53:03<17:57:16, 3.98s/it] 13%|█▎ | 2469/18688 [2:53:08<18:37:01, 4.13s/it] 13%|█▎ | 2470/18688 [2:53:12<18:11:24, 4.04s/it] {'loss': '0.9669', 'grad_norm': '1.025', 'learning_rate': '7.937e-05', 'epoch': '0.1322', 'num_input_tokens_seen': 8264552, 'train_runtime': '1.039e+04', 'train_tokens_per_second': '795.3'}
13%|█▎ | 2470/18688 [2:53:12<18:11:24, 4.04s/it] 13%|█▎ | 2471/18688 [2:53:16<18:25:41, 4.09s/it] 13%|█▎ | 2472/18688 [2:53:21<19:38:46, 4.36s/it] 13%|█▎ | 2473/18688 [2:53:25<20:01:20, 4.45s/it] 13%|█▎ | 2474/18688 [2:53:29<18:53:14, 4.19s/it] 13%|█▎ | 2475/18688 [2:53:33<18:05:08, 4.02s/it] 13%|█▎ | 2476/18688 [2:53:37<18:35:18, 4.13s/it] 13%|█▎ | 2477/18688 [2:53:41<19:03:32, 4.23s/it] 13%|█▎ | 2478/18688 [2:53:46<19:49:15, 4.40s/it] 13%|█▎ | 2479/18688 [2:53:51<20:03:33, 4.46s/it] 13%|█▎ | 2480/18688 [2:53:54<18:43:46, 4.16s/it] {'loss': '0.9868', 'grad_norm': '1.086', 'learning_rate': '7.936e-05', 'epoch': '0.1327', 'num_input_tokens_seen': 8300808, 'train_runtime': '1.043e+04', 'train_tokens_per_second': '795.5'}
13%|█▎ | 2480/18688 [2:53:54<18:43:46, 4.16s/it] 13%|█▎ | 2481/18688 [2:53:58<18:07:29, 4.03s/it] 13%|█▎ | 2482/18688 [2:54:02<18:15:14, 4.05s/it] 13%|█▎ | 2483/18688 [2:54:06<17:44:27, 3.94s/it] 13%|█▎ | 2484/18688 [2:54:10<17:27:22, 3.88s/it] 13%|█▎ | 2485/18688 [2:54:14<18:37:23, 4.14s/it] 13%|█▎ | 2486/18688 [2:54:18<18:16:34, 4.06s/it] 13%|█▎ | 2487/18688 [2:54:22<17:52:16, 3.97s/it] 13%|█▎ | 2488/18688 [2:54:26<18:37:00, 4.14s/it] 13%|█▎ | 2489/18688 [2:54:30<17:52:36, 3.97s/it] 13%|█▎ | 2490/18688 [2:54:34<17:20:10, 3.85s/it] {'loss': '0.9475', 'grad_norm': '1.103', 'learning_rate': '7.935e-05', 'epoch': '0.1332', 'num_input_tokens_seen': 8332048, 'train_runtime': '1.047e+04', 'train_tokens_per_second': '795.5'}
13%|█▎ | 2490/18688 [2:54:34<17:20:10, 3.85s/it] 13%|█▎ | 2491/18688 [2:54:38<17:36:55, 3.92s/it] 13%|█▎ | 2492/18688 [2:54:41<17:25:46, 3.87s/it] 13%|█▎ | 2493/18688 [2:54:46<17:53:50, 3.98s/it] 13%|█▎ | 2494/18688 [2:54:51<19:27:42, 4.33s/it] 13%|█▎ | 2495/18688 [2:54:55<19:23:08, 4.31s/it] 13%|█▎ | 2496/18688 [2:54:59<18:26:13, 4.10s/it] 13%|█▎ | 2497/18688 [2:55:02<17:47:53, 3.96s/it] 13%|█▎ | 2498/18688 [2:55:06<17:15:30, 3.84s/it] 13%|█▎ | 2499/18688 [2:55:09<16:47:32, 3.73s/it] 13%|█▎ | 2500/18688 [2:55:13<16:36:23, 3.69s/it] {'loss': '0.9819', 'grad_norm': '1.044', 'learning_rate': '7.934e-05', 'epoch': '0.1338', 'num_input_tokens_seen': 8362896, 'train_runtime': '1.051e+04', 'train_tokens_per_second': '795.4'}
13%|█▎ | 2500/18688 [2:55:13<16:36:23, 3.69s/it] 13%|█▎ | 2501/18688 [2:55:17<16:57:15, 3.77s/it] 13%|█▎ | 2502/18688 [2:55:20<16:28:02, 3.66s/it] 13%|█▎ | 2503/18688 [2:55:24<16:39:11, 3.70s/it] 13%|█▎ | 2504/18688 [2:55:29<17:39:07, 3.93s/it] 13%|█▎ | 2505/18688 [2:55:32<16:59:45, 3.78s/it] 13%|█▎ | 2506/18688 [2:55:36<17:19:04, 3.85s/it] 13%|█▎ | 2507/18688 [2:55:40<17:14:26, 3.84s/it] 13%|█▎ | 2508/18688 [2:55:44<18:01:55, 4.01s/it] 13%|█▎ | 2509/18688 [2:55:48<17:59:38, 4.00s/it] 13%|█▎ | 2510/18688 [2:55:53<18:52:57, 4.20s/it] {'loss': '0.9022', 'grad_norm': '1.141', 'learning_rate': '7.932e-05', 'epoch': '0.1343', 'num_input_tokens_seen': 8395088, 'train_runtime': '1.055e+04', 'train_tokens_per_second': '795.5'}
13%|█▎ | 2510/18688 [2:55:53<18:52:57, 4.20s/it] 13%|█▎ | 2511/18688 [2:55:58<19:28:39, 4.33s/it] 13%|█▎ | 2512/18688 [2:56:01<18:42:39, 4.16s/it] 13%|█▎ | 2513/18688 [2:56:05<18:44:51, 4.17s/it] 13%|█▎ | 2514/18688 [2:56:09<17:42:57, 3.94s/it] 13%|█▎ | 2515/18688 [2:56:14<19:08:18, 4.26s/it] 13%|█▎ | 2516/18688 [2:56:17<18:10:35, 4.05s/it] 13%|█▎ | 2517/18688 [2:56:21<18:06:42, 4.03s/it] 13%|█▎ | 2518/18688 [2:56:26<18:16:03, 4.07s/it] 13%|█▎ | 2519/18688 [2:56:30<18:30:18, 4.12s/it] 13%|█▎ | 2520/18688 [2:56:34<18:27:52, 4.11s/it] {'loss': '0.9305', 'grad_norm': '1.127', 'learning_rate': '7.931e-05', 'epoch': '0.1348', 'num_input_tokens_seen': 8428832, 'train_runtime': '1.059e+04', 'train_tokens_per_second': '795.6'}
13%|█▎ | 2520/18688 [2:56:34<18:27:52, 4.11s/it] 13%|█▎ | 2521/18688 [2:56:38<17:53:46, 3.99s/it] 13%|█▎ | 2522/18688 [2:56:42<18:07:55, 4.04s/it] 14%|█▎ | 2523/18688 [2:56:45<17:34:41, 3.91s/it] 14%|█▎ | 2524/18688 [2:56:50<18:14:59, 4.06s/it] 14%|█▎ | 2525/18688 [2:56:54<18:15:56, 4.07s/it] 14%|█▎ | 2526/18688 [2:56:59<19:19:11, 4.30s/it] 14%|█▎ | 2527/18688 [2:57:02<18:26:52, 4.11s/it] 14%|█▎ | 2528/18688 [2:57:07<18:44:51, 4.18s/it] 14%|█▎ | 2529/18688 [2:57:12<19:47:38, 4.41s/it] 14%|█▎ | 2530/18688 [2:57:15<18:32:07, 4.13s/it] {'loss': '0.9622', 'grad_norm': '1.118', 'learning_rate': '7.929e-05', 'epoch': '0.1354', 'num_input_tokens_seen': 8462960, 'train_runtime': '1.064e+04', 'train_tokens_per_second': '795.7'}
14%|█▎ | 2530/18688 [2:57:15<18:32:07, 4.13s/it] 14%|█▎ | 2531/18688 [2:57:19<17:58:35, 4.01s/it] 14%|█▎ | 2532/18688 [2:57:23<18:02:38, 4.02s/it] 14%|█▎ | 2533/18688 [2:57:27<18:06:35, 4.04s/it] 14%|█▎ | 2534/18688 [2:57:31<17:31:50, 3.91s/it] 14%|█▎ | 2535/18688 [2:57:35<17:51:50, 3.98s/it] 14%|█▎ | 2536/18688 [2:57:40<19:31:00, 4.35s/it] 14%|█▎ | 2537/18688 [2:57:45<20:24:53, 4.55s/it] 14%|█▎ | 2538/18688 [2:57:50<20:28:08, 4.56s/it] 14%|█▎ | 2539/18688 [2:57:55<21:26:59, 4.78s/it] 14%|█▎ | 2540/18688 [2:57:59<20:00:45, 4.46s/it] {'loss': '0.9664', 'grad_norm': '0.9759', 'learning_rate': '7.928e-05', 'epoch': '0.1359', 'num_input_tokens_seen': 8499904, 'train_runtime': '1.068e+04', 'train_tokens_per_second': '795.9'}
14%|█▎ | 2540/18688 [2:57:59<20:00:45, 4.46s/it] 14%|█▎ | 2541/18688 [2:58:03<20:32:49, 4.58s/it] 14%|█▎ | 2542/18688 [2:58:08<20:22:56, 4.54s/it] 14%|█▎ | 2543/18688 [2:58:12<20:15:35, 4.52s/it] 14%|█▎ | 2544/18688 [2:58:16<19:08:13, 4.27s/it] 14%|█▎ | 2545/18688 [2:58:20<18:04:16, 4.03s/it] 14%|█▎ | 2546/18688 [2:58:23<17:51:10, 3.98s/it] 14%|█▎ | 2547/18688 [2:58:27<17:53:17, 3.99s/it] 14%|█▎ | 2548/18688 [2:58:31<17:30:33, 3.91s/it] 14%|█▎ | 2549/18688 [2:58:35<17:32:56, 3.91s/it] 14%|█▎ | 2550/18688 [2:58:39<18:02:52, 4.03s/it] {'loss': '0.9949', 'grad_norm': '1.199', 'learning_rate': '7.927e-05', 'epoch': '0.1365', 'num_input_tokens_seen': 8533320, 'train_runtime': '1.072e+04', 'train_tokens_per_second': '796'}
14%|█▎ | 2550/18688 [2:58:39<18:02:52, 4.03s/it] 14%|█▎ | 2551/18688 [2:58:43<17:44:03, 3.96s/it] 14%|█▎ | 2552/18688 [2:58:48<18:38:19, 4.16s/it] 14%|█▎ | 2553/18688 [2:58:52<19:06:11, 4.26s/it] 14%|█▎ | 2554/18688 [2:58:57<19:43:02, 4.40s/it] 14%|█▎ | 2555/18688 [2:59:02<20:22:35, 4.55s/it] 14%|█▎ | 2556/18688 [2:59:06<20:18:57, 4.53s/it] 14%|█▎ | 2557/18688 [2:59:10<19:13:32, 4.29s/it] 14%|█▎ | 2558/18688 [2:59:14<18:37:07, 4.16s/it] 14%|█▎ | 2559/18688 [2:59:18<19:02:57, 4.25s/it] 14%|█▎ | 2560/18688 [2:59:22<18:48:12, 4.20s/it] {'loss': '0.9662', 'grad_norm': '1.027', 'learning_rate': '7.925e-05', 'epoch': '0.137', 'num_input_tokens_seen': 8569816, 'train_runtime': '1.076e+04', 'train_tokens_per_second': '796.2'}
14%|█▎ | 2560/18688 [2:59:22<18:48:12, 4.20s/it] 14%|█▎ | 2561/18688 [2:59:27<18:44:33, 4.18s/it] 14%|█▎ | 2562/18688 [2:59:31<18:41:48, 4.17s/it] 14%|█▎ | 2563/18688 [2:59:35<18:40:57, 4.17s/it] 14%|█▎ | 2564/18688 [2:59:39<18:14:41, 4.07s/it] 14%|█▎ | 2565/18688 [2:59:43<18:29:11, 4.13s/it] 14%|█▎ | 2566/18688 [2:59:47<17:43:21, 3.96s/it] 14%|█▎ | 2567/18688 [2:59:50<17:03:57, 3.81s/it] 14%|█▎ | 2568/18688 [2:59:54<16:58:09, 3.79s/it] 14%|█▎ | 2569/18688 [2:59:58<17:00:28, 3.80s/it] 14%|█▍ | 2570/18688 [3:00:01<16:32:27, 3.69s/it] {'loss': '0.9755', 'grad_norm': '1.074', 'learning_rate': '7.924e-05', 'epoch': '0.1375', 'num_input_tokens_seen': 8600752, 'train_runtime': '1.08e+04', 'train_tokens_per_second': '796.2'}
14%|█▍ | 2570/18688 [3:00:01<16:32:27, 3.69s/it] 14%|█▍ | 2571/18688 [3:00:06<18:30:52, 4.14s/it] 14%|█▍ | 2572/18688 [3:00:11<18:46:38, 4.19s/it] 14%|█▍ | 2573/18688 [3:00:15<18:44:13, 4.19s/it] 14%|█▍ | 2574/18688 [3:00:20<19:28:55, 4.35s/it] 14%|█▍ | 2575/18688 [3:00:24<19:50:33, 4.43s/it] 14%|█▍ | 2576/18688 [3:00:29<20:02:31, 4.48s/it] 14%|█▍ | 2577/18688 [3:00:33<20:19:01, 4.54s/it] 14%|█▍ | 2578/18688 [3:00:38<20:42:38, 4.63s/it] 14%|█▍ | 2579/18688 [3:00:42<19:34:39, 4.38s/it] 14%|█▍ | 2580/18688 [3:00:45<18:18:51, 4.09s/it] {'loss': '0.9799', 'grad_norm': '1.236', 'learning_rate': '7.922e-05', 'epoch': '0.1381', 'num_input_tokens_seen': 8638960, 'train_runtime': '1.085e+04', 'train_tokens_per_second': '796.5'}
14%|█▍ | 2580/18688 [3:00:45<18:18:51, 4.09s/it] 14%|█▍ | 2581/18688 [3:00:49<18:10:24, 4.06s/it] 14%|█▍ | 2582/18688 [3:00:54<19:18:42, 4.32s/it] 14%|█▍ | 2583/18688 [3:00:59<19:32:38, 4.37s/it] 14%|█▍ | 2584/18688 [3:01:02<18:27:03, 4.12s/it] 14%|█▍ | 2585/18688 [3:01:06<18:15:32, 4.08s/it] 14%|█▍ | 2586/18688 [3:01:10<17:42:12, 3.96s/it] 14%|█▍ | 2587/18688 [3:01:14<17:19:02, 3.87s/it] 14%|█▍ | 2588/18688 [3:01:18<17:22:47, 3.89s/it] 14%|█▍ | 2589/18688 [3:01:21<17:16:14, 3.86s/it] 14%|█▍ | 2590/18688 [3:01:25<17:03:14, 3.81s/it] {'loss': '1.036', 'grad_norm': '1.026', 'learning_rate': '7.921e-05', 'epoch': '0.1386', 'num_input_tokens_seen': 8671328, 'train_runtime': '1.089e+04', 'train_tokens_per_second': '796.6'}
14%|█▍ | 2590/18688 [3:01:25<17:03:14, 3.81s/it] 14%|█▍ | 2591/18688 [3:01:29<17:00:02, 3.80s/it] 14%|█▍ | 2592/18688 [3:01:32<16:41:23, 3.73s/it] 14%|█▍ | 2593/18688 [3:01:37<17:39:56, 3.95s/it] 14%|█▍ | 2594/18688 [3:01:41<17:37:31, 3.94s/it] 14%|█▍ | 2595/18688 [3:01:44<17:05:36, 3.82s/it] 14%|█▍ | 2596/18688 [3:01:49<18:38:55, 4.17s/it] 14%|█▍ | 2597/18688 [3:01:53<17:41:50, 3.96s/it] 14%|█▍ | 2598/18688 [3:01:57<18:15:24, 4.08s/it] 14%|█▍ | 2599/18688 [3:02:02<19:16:22, 4.31s/it] 14%|█▍ | 2600/18688 [3:02:06<18:14:29, 4.08s/it] {'loss': '0.9808', 'grad_norm': '1.018', 'learning_rate': '7.92e-05', 'epoch': '0.1391', 'num_input_tokens_seen': 8704528, 'train_runtime': '1.093e+04', 'train_tokens_per_second': '796.7'}
14%|█▍ | 2600/18688 [3:02:06<18:14:29, 4.08s/it] 14%|█▍ | 2601/18688 [3:02:09<17:26:06, 3.90s/it] 14%|█▍ | 2602/18688 [3:02:13<18:00:54, 4.03s/it] 14%|█▍ | 2603/18688 [3:02:18<18:15:49, 4.09s/it] 14%|█▍ | 2604/18688 [3:02:22<19:13:04, 4.30s/it] 14%|█▍ | 2605/18688 [3:02:27<19:15:18, 4.31s/it] 14%|█▍ | 2606/18688 [3:02:31<19:07:54, 4.28s/it] 14%|█▍ | 2607/18688 [3:02:35<18:56:32, 4.24s/it] 14%|█▍ | 2608/18688 [3:02:39<18:28:57, 4.14s/it] 14%|█▍ | 2609/18688 [3:02:43<18:20:14, 4.11s/it] 14%|█▍ | 2610/18688 [3:02:47<17:41:27, 3.96s/it] {'loss': '0.9629', 'grad_norm': '1.138', 'learning_rate': '7.918e-05', 'epoch': '0.1397', 'num_input_tokens_seen': 8737680, 'train_runtime': '1.097e+04', 'train_tokens_per_second': '796.7'}
14%|█▍ | 2610/18688 [3:02:47<17:41:27, 3.96s/it] 14%|█▍ | 2611/18688 [3:02:51<18:36:45, 4.17s/it] 14%|█▍ | 2612/18688 [3:02:55<18:23:25, 4.12s/it] 14%|█▍ | 2613/18688 [3:03:01<20:28:47, 4.59s/it] 14%|█▍ | 2614/18688 [3:03:06<20:24:17, 4.57s/it] 14%|█▍ | 2615/18688 [3:03:10<20:00:56, 4.48s/it] 14%|█▍ | 2616/18688 [3:03:13<18:49:30, 4.22s/it] 14%|█▍ | 2617/18688 [3:03:17<18:13:26, 4.08s/it] 14%|█▍ | 2618/18688 [3:03:21<18:11:01, 4.07s/it] 14%|█▍ | 2619/18688 [3:03:25<18:14:36, 4.09s/it] 14%|█▍ | 2620/18688 [3:03:29<17:35:19, 3.94s/it] {'loss': '0.9945', 'grad_norm': '1.174', 'learning_rate': '7.917e-05', 'epoch': '0.1402', 'num_input_tokens_seen': 8772632, 'train_runtime': '1.101e+04', 'train_tokens_per_second': '796.8'}
14%|█▍ | 2620/18688 [3:03:29<17:35:19, 3.94s/it] 14%|█▍ | 2621/18688 [3:03:33<18:11:21, 4.08s/it] 14%|█▍ | 2622/18688 [3:03:38<18:40:31, 4.18s/it] 14%|█▍ | 2623/18688 [3:03:41<17:58:00, 4.03s/it] 14%|█▍ | 2624/18688 [3:03:45<17:11:57, 3.85s/it] 14%|█▍ | 2625/18688 [3:03:49<17:30:10, 3.92s/it] 14%|█▍ | 2626/18688 [3:03:53<16:55:49, 3.79s/it] 14%|█▍ | 2627/18688 [3:03:57<17:58:56, 4.03s/it] 14%|█▍ | 2628/18688 [3:04:02<19:21:33, 4.34s/it] 14%|█▍ | 2629/18688 [3:04:06<19:02:21, 4.27s/it] 14%|█▍ | 2630/18688 [3:04:10<18:05:46, 4.06s/it] {'loss': '0.9707', 'grad_norm': '1.015', 'learning_rate': '7.915e-05', 'epoch': '0.1407', 'num_input_tokens_seen': 8806376, 'train_runtime': '1.105e+04', 'train_tokens_per_second': '796.9'}
14%|█▍ | 2630/18688 [3:04:10<18:05:46, 4.06s/it] 14%|█▍ | 2631/18688 [3:04:13<17:27:47, 3.92s/it] 14%|█▍ | 2632/18688 [3:04:18<18:02:48, 4.05s/it] 14%|█▍ | 2633/18688 [3:04:22<18:31:44, 4.15s/it] 14%|█▍ | 2634/18688 [3:04:26<17:59:21, 4.03s/it] 14%|█▍ | 2635/18688 [3:04:29<17:18:27, 3.88s/it] 14%|█▍ | 2636/18688 [3:04:33<17:00:08, 3.81s/it] 14%|█▍ | 2637/18688 [3:04:37<17:29:47, 3.92s/it] 14%|█▍ | 2638/18688 [3:04:41<17:26:11, 3.91s/it] 14%|█▍ | 2639/18688 [3:04:46<18:07:21, 4.07s/it] 14%|█▍ | 2640/18688 [3:04:50<18:14:54, 4.09s/it] {'loss': '0.9775', 'grad_norm': '1.05', 'learning_rate': '7.914e-05', 'epoch': '0.1413', 'num_input_tokens_seen': 8839120, 'train_runtime': '1.109e+04', 'train_tokens_per_second': '797'}
14%|█▍ | 2640/18688 [3:04:50<18:14:54, 4.09s/it] 14%|█▍ | 2641/18688 [3:04:54<18:48:24, 4.22s/it] 14%|█▍ | 2642/18688 [3:04:58<18:32:26, 4.16s/it] 14%|█▍ | 2643/18688 [3:05:03<19:19:26, 4.34s/it] 14%|█▍ | 2644/18688 [3:05:07<18:24:53, 4.13s/it] 14%|█▍ | 2645/18688 [3:05:11<18:40:53, 4.19s/it] 14%|█▍ | 2646/18688 [3:05:16<19:26:13, 4.36s/it] 14%|█▍ | 2647/18688 [3:05:19<18:35:34, 4.17s/it] 14%|█▍ | 2648/18688 [3:05:24<18:37:30, 4.18s/it] 14%|█▍ | 2649/18688 [3:05:28<19:06:36, 4.29s/it] 14%|█▍ | 2650/18688 [3:05:34<20:41:48, 4.65s/it] {'loss': '0.9323', 'grad_norm': '1.234', 'learning_rate': '7.912e-05', 'epoch': '0.1418', 'num_input_tokens_seen': 8876544, 'train_runtime': '1.113e+04', 'train_tokens_per_second': '797.2'}
14%|█▍ | 2650/18688 [3:05:34<20:41:48, 4.65s/it] 14%|█▍ | 2651/18688 [3:05:38<19:37:07, 4.40s/it] 14%|█▍ | 2652/18688 [3:05:42<19:02:13, 4.27s/it] 14%|█▍ | 2653/18688 [3:05:46<19:39:42, 4.41s/it] 14%|█▍ | 2654/18688 [3:05:51<20:21:17, 4.57s/it] 14%|█▍ | 2655/18688 [3:05:55<19:10:43, 4.31s/it] 14%|█▍ | 2656/18688 [3:05:59<18:58:46, 4.26s/it] 14%|█▍ | 2657/18688 [3:06:03<18:08:48, 4.08s/it] 14%|█▍ | 2658/18688 [3:06:07<18:20:39, 4.12s/it] 14%|█▍ | 2659/18688 [3:06:11<18:39:48, 4.19s/it] 14%|█▍ | 2660/18688 [3:06:15<18:07:50, 4.07s/it] {'loss': '0.9904', 'grad_norm': '1.004', 'learning_rate': '7.911e-05', 'epoch': '0.1423', 'num_input_tokens_seen': 8910464, 'train_runtime': '1.118e+04', 'train_tokens_per_second': '797.3'}
14%|█▍ | 2660/18688 [3:06:15<18:07:50, 4.07s/it] 14%|█▍ | 2661/18688 [3:06:19<18:34:23, 4.17s/it] 14%|█▍ | 2662/18688 [3:06:23<18:11:46, 4.09s/it] 14%|█▍ | 2663/18688 [3:06:27<17:53:17, 4.02s/it] 14%|█▍ | 2664/18688 [3:06:31<17:26:37, 3.92s/it] 14%|█▍ | 2665/18688 [3:06:35<17:22:08, 3.90s/it] 14%|█▍ | 2666/18688 [3:06:38<16:52:46, 3.79s/it] 14%|█▍ | 2667/18688 [3:06:43<18:16:52, 4.11s/it] 14%|█▍ | 2668/18688 [3:06:47<17:44:39, 3.99s/it] 14%|█▍ | 2669/18688 [3:06:51<17:31:14, 3.94s/it] 14%|█▍ | 2670/18688 [3:06:54<17:10:48, 3.86s/it] {'loss': '1.004', 'grad_norm': '0.9922', 'learning_rate': '7.909e-05', 'epoch': '0.1429', 'num_input_tokens_seen': 8940648, 'train_runtime': '1.121e+04', 'train_tokens_per_second': '797.2'}
14%|█▍ | 2670/18688 [3:06:54<17:10:48, 3.86s/it] 14%|█▍ | 2671/18688 [3:06:58<16:55:42, 3.80s/it] 14%|█▍ | 2672/18688 [3:07:02<17:36:15, 3.96s/it] 14%|█▍ | 2673/18688 [3:07:07<18:45:39, 4.22s/it] 14%|█▍ | 2674/18688 [3:07:11<17:55:30, 4.03s/it] 14%|█▍ | 2675/18688 [3:07:14<17:31:36, 3.94s/it] 14%|█▍ | 2676/18688 [3:07:19<18:28:55, 4.16s/it] 14%|█▍ | 2677/18688 [3:07:23<17:34:45, 3.95s/it] 14%|█▍ | 2678/18688 [3:07:27<18:06:36, 4.07s/it] 14%|█▍ | 2679/18688 [3:07:32<19:00:10, 4.27s/it] 14%|█▍ | 2680/18688 [3:07:36<18:58:48, 4.27s/it] {'loss': '0.9656', 'grad_norm': '0.9728', 'learning_rate': '7.907e-05', 'epoch': '0.1434', 'num_input_tokens_seen': 8974040, 'train_runtime': '1.126e+04', 'train_tokens_per_second': '797.2'}
14%|█▍ | 2680/18688 [3:07:36<18:58:48, 4.27s/it] 14%|█▍ | 2681/18688 [3:07:39<17:53:39, 4.02s/it] 14%|█▍ | 2682/18688 [3:07:43<17:42:41, 3.98s/it] 14%|█▍ | 2683/18688 [3:07:48<18:25:32, 4.14s/it] 14%|█▍ | 2684/18688 [3:07:53<19:42:00, 4.43s/it] 14%|█▍ | 2685/18688 [3:07:57<18:36:54, 4.19s/it] 14%|█▍ | 2686/18688 [3:08:02<20:02:38, 4.51s/it] 14%|█▍ | 2687/18688 [3:08:06<19:28:11, 4.38s/it] 14%|█▍ | 2688/18688 [3:08:11<19:51:20, 4.47s/it] 14%|█▍ | 2689/18688 [3:08:14<18:30:02, 4.16s/it] 14%|█▍ | 2690/18688 [3:08:18<18:39:05, 4.20s/it] {'loss': '0.9787', 'grad_norm': '1.158', 'learning_rate': '7.906e-05', 'epoch': '0.1439', 'num_input_tokens_seen': 9008864, 'train_runtime': '1.13e+04', 'train_tokens_per_second': '797.3'}
14%|█▍ | 2690/18688 [3:08:18<18:39:05, 4.20s/it] 14%|█▍ | 2691/18688 [3:08:22<18:29:15, 4.16s/it] 14%|█▍ | 2692/18688 [3:08:26<18:19:01, 4.12s/it] 14%|█▍ | 2693/18688 [3:08:30<17:55:04, 4.03s/it] 14%|█▍ | 2694/18688 [3:08:34<17:21:14, 3.91s/it] 14%|█▍ | 2695/18688 [3:08:38<18:16:11, 4.11s/it] 14%|█▍ | 2696/18688 [3:08:44<19:33:16, 4.40s/it] 14%|█▍ | 2697/18688 [3:08:47<18:58:14, 4.27s/it] 14%|█▍ | 2698/18688 [3:08:52<18:54:11, 4.26s/it] 14%|█▍ | 2699/18688 [3:08:56<19:06:03, 4.30s/it] 14%|█▍ | 2700/18688 [3:09:00<18:31:51, 4.17s/it] {'loss': '0.946', 'grad_norm': '1.2', 'learning_rate': '7.904e-05', 'epoch': '0.1445', 'num_input_tokens_seen': 9043752, 'train_runtime': '1.134e+04', 'train_tokens_per_second': '797.5'}
14%|█▍ | 2700/18688 [3:09:00<18:31:51, 4.17s/it] 14%|█▍ | 2701/18688 [3:09:04<17:48:00, 4.01s/it] 14%|█▍ | 2702/18688 [3:09:08<17:54:28, 4.03s/it] 14%|█▍ | 2703/18688 [3:09:12<18:21:06, 4.13s/it] 14%|█▍ | 2704/18688 [3:09:17<19:22:37, 4.36s/it] 14%|█▍ | 2705/18688 [3:09:21<19:24:49, 4.37s/it] 14%|█▍ | 2706/18688 [3:09:25<18:26:57, 4.16s/it] 14%|█▍ | 2707/18688 [3:09:29<17:56:27, 4.04s/it] 14%|█▍ | 2708/18688 [3:09:33<18:13:17, 4.11s/it] 14%|█▍ | 2709/18688 [3:09:37<18:01:49, 4.06s/it] 15%|█▍ | 2710/18688 [3:09:41<18:12:20, 4.10s/it] {'loss': '0.9314', 'grad_norm': '1.219', 'learning_rate': '7.903e-05', 'epoch': '0.145', 'num_input_tokens_seen': 9077560, 'train_runtime': '1.138e+04', 'train_tokens_per_second': '797.6'}
15%|█▍ | 2710/18688 [3:09:41<18:12:20, 4.10s/it] 15%|█▍ | 2711/18688 [3:09:45<18:16:17, 4.12s/it] 15%|█▍ | 2712/18688 [3:09:49<18:02:35, 4.07s/it] 15%|█▍ | 2713/18688 [3:09:53<17:54:03, 4.03s/it] 15%|█▍ | 2714/18688 [3:09:57<18:04:28, 4.07s/it] 15%|█▍ | 2715/18688 [3:10:02<18:29:44, 4.17s/it] 15%|█▍ | 2716/18688 [3:10:06<17:57:01, 4.05s/it] 15%|█▍ | 2717/18688 [3:10:10<18:30:36, 4.17s/it] 15%|█▍ | 2718/18688 [3:10:15<19:15:52, 4.34s/it] 15%|█▍ | 2719/18688 [3:10:19<18:35:14, 4.19s/it] 15%|█▍ | 2720/18688 [3:10:24<20:39:23, 4.66s/it] {'loss': '0.977', 'grad_norm': '1.116', 'learning_rate': '7.901e-05', 'epoch': '0.1456', 'num_input_tokens_seen': 9113984, 'train_runtime': '1.142e+04', 'train_tokens_per_second': '797.7'}
15%|█▍ | 2720/18688 [3:10:24<20:39:23, 4.66s/it] 15%|█▍ | 2721/18688 [3:10:28<19:20:52, 4.36s/it] 15%|█▍ | 2722/18688 [3:10:32<18:21:10, 4.14s/it] 15%|█▍ | 2723/18688 [3:10:35<17:37:00, 3.97s/it] 15%|█▍ | 2724/18688 [3:10:39<17:37:53, 3.98s/it] 15%|█▍ | 2725/18688 [3:10:44<18:11:09, 4.10s/it] 15%|█▍ | 2726/18688 [3:10:48<18:29:27, 4.17s/it] 15%|█▍ | 2727/18688 [3:10:52<18:32:02, 4.18s/it] 15%|█▍ | 2728/18688 [3:10:57<19:57:43, 4.50s/it] 15%|█▍ | 2729/18688 [3:11:02<19:46:34, 4.46s/it] 15%|█▍ | 2730/18688 [3:11:06<18:59:40, 4.29s/it] {'loss': '0.9698', 'grad_norm': '0.9089', 'learning_rate': '7.9e-05', 'epoch': '0.1461', 'num_input_tokens_seen': 9148144, 'train_runtime': '1.147e+04', 'train_tokens_per_second': '797.8'}
15%|█▍ | 2730/18688 [3:11:06<18:59:40, 4.29s/it] 15%|█▍ | 2731/18688 [3:11:10<18:36:35, 4.20s/it] 15%|█▍ | 2732/18688 [3:11:14<18:16:25, 4.12s/it] 15%|█▍ | 2733/18688 [3:11:17<17:48:42, 4.02s/it] 15%|█▍ | 2734/18688 [3:11:21<17:02:43, 3.85s/it] 15%|█▍ | 2735/18688 [3:11:25<17:57:11, 4.05s/it] 15%|█▍ | 2736/18688 [3:11:29<17:22:10, 3.92s/it] 15%|█▍ | 2737/18688 [3:11:33<17:01:30, 3.84s/it] 15%|█▍ | 2738/18688 [3:11:37<17:36:12, 3.97s/it] 15%|█▍ | 2739/18688 [3:11:42<19:13:02, 4.34s/it] 15%|█▍ | 2740/18688 [3:11:46<18:57:47, 4.28s/it] {'loss': '1.006', 'grad_norm': '1.04', 'learning_rate': '7.898e-05', 'epoch': '0.1466', 'num_input_tokens_seen': 9182056, 'train_runtime': '1.151e+04', 'train_tokens_per_second': '798'}
15%|█▍ | 2740/18688 [3:11:46<18:57:47, 4.28s/it] 15%|█▍ | 2741/18688 [3:11:50<18:44:34, 4.23s/it] 15%|█▍ | 2742/18688 [3:11:54<18:09:55, 4.10s/it] 15%|█▍ | 2743/18688 [3:11:58<17:30:47, 3.95s/it] 15%|█▍ | 2744/18688 [3:12:02<17:16:45, 3.90s/it] 15%|█▍ | 2745/18688 [3:12:05<16:40:54, 3.77s/it] 15%|█▍ | 2746/18688 [3:12:09<16:37:02, 3.75s/it] 15%|█▍ | 2747/18688 [3:12:13<17:38:45, 3.99s/it] 15%|█▍ | 2748/18688 [3:12:17<17:44:38, 4.01s/it] 15%|█▍ | 2749/18688 [3:12:22<18:01:28, 4.07s/it] 15%|█▍ | 2750/18688 [3:12:25<17:48:16, 4.02s/it] {'loss': '1.002', 'grad_norm': '1.074', 'learning_rate': '7.896e-05', 'epoch': '0.1472', 'num_input_tokens_seen': 9213128, 'train_runtime': '1.155e+04', 'train_tokens_per_second': '798'}
15%|█▍ | 2750/18688 [3:12:25<17:48:16, 4.02s/it] 15%|█▍ | 2751/18688 [3:12:29<17:39:53, 3.99s/it] 15%|█▍ | 2752/18688 [3:12:33<17:18:09, 3.91s/it] 15%|█▍ | 2753/18688 [3:12:37<17:44:19, 4.01s/it] 15%|█▍ | 2754/18688 [3:12:42<19:03:30, 4.31s/it] 15%|█▍ | 2755/18688 [3:12:47<19:05:07, 4.31s/it] 15%|█▍ | 2756/18688 [3:12:51<18:48:50, 4.25s/it] 15%|█▍ | 2757/18688 [3:12:55<18:24:45, 4.16s/it] 15%|█▍ | 2758/18688 [3:12:58<17:30:43, 3.96s/it] 15%|█▍ | 2759/18688 [3:13:02<17:40:32, 3.99s/it] 15%|█▍ | 2760/18688 [3:13:06<17:36:07, 3.98s/it] {'loss': '0.9861', 'grad_norm': '0.9809', 'learning_rate': '7.895e-05', 'epoch': '0.1477', 'num_input_tokens_seen': 9245840, 'train_runtime': '1.159e+04', 'train_tokens_per_second': '798'}
15%|█▍ | 2760/18688 [3:13:06<17:36:07, 3.98s/it] 15%|█▍ | 2761/18688 [3:13:10<17:48:44, 4.03s/it] 15%|█▍ | 2762/18688 [3:13:15<18:43:03, 4.23s/it] 15%|█▍ | 2763/18688 [3:13:20<19:35:31, 4.43s/it] 15%|█▍ | 2764/18688 [3:13:24<19:17:08, 4.36s/it] 15%|█▍ | 2765/18688 [3:13:28<18:24:16, 4.16s/it] 15%|█▍ | 2766/18688 [3:13:33<19:28:40, 4.40s/it] 15%|█▍ | 2767/18688 [3:13:38<19:59:04, 4.52s/it] 15%|█▍ | 2768/18688 [3:13:42<19:49:48, 4.48s/it] 15%|█▍ | 2769/18688 [3:13:45<18:26:43, 4.17s/it] 15%|█▍ | 2770/18688 [3:13:49<17:34:52, 3.98s/it] {'loss': '0.9904', 'grad_norm': '1.115', 'learning_rate': '7.893e-05', 'epoch': '0.1482', 'num_input_tokens_seen': 9281336, 'train_runtime': '1.163e+04', 'train_tokens_per_second': '798.1'}
15%|█▍ | 2770/18688 [3:13:49<17:34:52, 3.98s/it] 15%|█▍ | 2771/18688 [3:13:53<17:39:06, 3.99s/it] 15%|█▍ | 2772/18688 [3:13:57<17:54:56, 4.05s/it] 15%|█▍ | 2773/18688 [3:14:01<17:31:46, 3.97s/it] 15%|█▍ | 2774/18688 [3:14:05<18:18:41, 4.14s/it] 15%|█▍ | 2775/18688 [3:14:10<18:37:51, 4.21s/it] 15%|█▍ | 2776/18688 [3:14:13<17:45:42, 4.02s/it] 15%|█▍ | 2777/18688 [3:14:18<18:58:12, 4.29s/it] 15%|█▍ | 2778/18688 [3:14:23<19:14:50, 4.36s/it] 15%|█▍ | 2779/18688 [3:14:26<18:05:28, 4.09s/it] 15%|█▍ | 2780/18688 [3:14:30<17:53:23, 4.05s/it] {'loss': '0.9707', 'grad_norm': '1.125', 'learning_rate': '7.891e-05', 'epoch': '0.1488', 'num_input_tokens_seen': 9315912, 'train_runtime': '1.167e+04', 'train_tokens_per_second': '798.2'}
15%|█▍ | 2780/18688 [3:14:30<17:53:23, 4.05s/it] 15%|█▍ | 2781/18688 [3:14:35<18:39:14, 4.22s/it] 15%|█▍ | 2782/18688 [3:14:39<18:11:00, 4.12s/it] 15%|█▍ | 2783/18688 [3:14:43<18:48:34, 4.26s/it] 15%|█▍ | 2784/18688 [3:14:47<18:01:20, 4.08s/it] 15%|█▍ | 2785/18688 [3:14:51<17:36:50, 3.99s/it] 15%|█▍ | 2786/18688 [3:14:55<17:27:25, 3.95s/it] 15%|█▍ | 2787/18688 [3:14:59<17:31:01, 3.97s/it] 15%|█▍ | 2788/18688 [3:15:03<17:35:35, 3.98s/it] 15%|█▍ | 2789/18688 [3:15:06<16:54:02, 3.83s/it] 15%|█▍ | 2790/18688 [3:15:10<17:12:04, 3.90s/it] {'loss': '1.006', 'grad_norm': '1.041', 'learning_rate': '7.889e-05', 'epoch': '0.1493', 'num_input_tokens_seen': 9347640, 'train_runtime': '1.171e+04', 'train_tokens_per_second': '798.2'}
15%|█▍ | 2790/18688 [3:15:10<17:12:04, 3.90s/it] 15%|█▍ | 2791/18688 [3:15:15<18:29:47, 4.19s/it] 15%|█▍ | 2792/18688 [3:15:19<18:29:39, 4.19s/it] 15%|█▍ | 2793/18688 [3:15:24<18:45:04, 4.25s/it] 15%|█▍ | 2794/18688 [3:15:27<17:55:53, 4.06s/it] 15%|█▍ | 2795/18688 [3:15:32<18:19:57, 4.15s/it] 15%|█▍ | 2796/18688 [3:15:35<17:37:08, 3.99s/it] 15%|█▍ | 2797/18688 [3:15:39<17:19:13, 3.92s/it] 15%|█▍ | 2798/18688 [3:15:44<18:24:02, 4.17s/it] 15%|█▍ | 2799/18688 [3:15:49<19:32:48, 4.43s/it] 15%|█▍ | 2800/18688 [3:15:53<19:18:16, 4.37s/it] {'loss': '0.9696', 'grad_norm': '1.178', 'learning_rate': '7.888e-05', 'epoch': '0.1498', 'num_input_tokens_seen': 9383216, 'train_runtime': '1.175e+04', 'train_tokens_per_second': '798.3'}
15%|█▍ | 2800/18688 [3:15:53<19:18:16, 4.37s/it] 15%|█▍ | 2801/18688 [3:15:58<20:14:32, 4.59s/it] 15%|█▍ | 2802/18688 [3:16:02<18:47:52, 4.26s/it] 15%|█▍ | 2803/18688 [3:16:05<17:50:52, 4.04s/it] 15%|█▌ | 2804/18688 [3:16:09<17:29:58, 3.97s/it] 15%|█▌ | 2805/18688 [3:16:14<18:19:04, 4.15s/it] 15%|█▌ | 2806/18688 [3:16:19<19:26:58, 4.41s/it] 15%|█▌ | 2807/18688 [3:16:22<18:34:11, 4.21s/it] 15%|█▌ | 2808/18688 [3:16:26<18:00:34, 4.08s/it] 15%|█▌ | 2809/18688 [3:16:30<17:50:09, 4.04s/it] 15%|█▌ | 2810/18688 [3:16:34<17:10:49, 3.90s/it] {'loss': '0.9158', 'grad_norm': '1.124', 'learning_rate': '7.886e-05', 'epoch': '0.1504', 'num_input_tokens_seen': 9415184, 'train_runtime': '1.179e+04', 'train_tokens_per_second': '798.3'}
15%|█▌ | 2810/18688 [3:16:34<17:10:49, 3.90s/it] 15%|█▌ | 2811/18688 [3:16:38<18:20:28, 4.16s/it] 15%|█▌ | 2812/18688 [3:16:42<18:18:45, 4.15s/it] 15%|█▌ | 2813/18688 [3:16:46<17:23:35, 3.94s/it] 15%|█▌ | 2814/18688 [3:16:49<16:52:08, 3.83s/it] 15%|█▌ | 2815/18688 [3:16:53<16:24:22, 3.72s/it] 15%|█▌ | 2816/18688 [3:16:57<17:25:13, 3.95s/it] 15%|█▌ | 2817/18688 [3:17:01<16:53:04, 3.83s/it] 15%|█▌ | 2818/18688 [3:17:05<17:03:38, 3.87s/it] 15%|█▌ | 2819/18688 [3:17:09<16:47:34, 3.81s/it] 15%|█▌ | 2820/18688 [3:17:13<17:58:06, 4.08s/it] {'loss': '0.9393', 'grad_norm': '0.9795', 'learning_rate': '7.884e-05', 'epoch': '0.1509', 'num_input_tokens_seen': 9447952, 'train_runtime': '1.183e+04', 'train_tokens_per_second': '798.4'}
15%|█▌ | 2820/18688 [3:17:13<17:58:06, 4.08s/it] 15%|█▌ | 2821/18688 [3:17:18<18:15:34, 4.14s/it] 15%|█▌ | 2822/18688 [3:17:23<19:43:48, 4.48s/it] 15%|█▌ | 2823/18688 [3:17:27<18:38:47, 4.23s/it] 15%|█▌ | 2824/18688 [3:17:31<18:21:36, 4.17s/it] 15%|█▌ | 2825/18688 [3:17:35<18:59:39, 4.31s/it] 15%|█▌ | 2826/18688 [3:17:39<18:48:09, 4.27s/it] 15%|█▌ | 2827/18688 [3:17:44<19:01:11, 4.32s/it] 15%|█▌ | 2828/18688 [3:17:48<19:00:06, 4.31s/it] 15%|█▌ | 2829/18688 [3:17:52<18:03:11, 4.10s/it] 15%|█▌ | 2830/18688 [3:17:56<18:21:45, 4.17s/it] {'loss': '0.9796', 'grad_norm': '1.021', 'learning_rate': '7.883e-05', 'epoch': '0.1514', 'num_input_tokens_seen': 9485040, 'train_runtime': '1.188e+04', 'train_tokens_per_second': '798.6'}
15%|█▌ | 2830/18688 [3:17:56<18:21:45, 4.17s/it] 15%|█▌ | 2831/18688 [3:18:01<18:44:29, 4.25s/it] 15%|█▌ | 2832/18688 [3:18:05<18:45:09, 4.26s/it] 15%|█▌ | 2833/18688 [3:18:09<18:13:02, 4.14s/it] 15%|█▌ | 2834/18688 [3:18:13<18:27:16, 4.19s/it] 15%|█▌ | 2835/18688 [3:18:17<18:37:24, 4.23s/it] 15%|█▌ | 2836/18688 [3:18:21<18:26:47, 4.19s/it] 15%|█▌ | 2837/18688 [3:18:26<19:19:55, 4.39s/it] 15%|█▌ | 2838/18688 [3:18:31<20:11:30, 4.59s/it] 15%|█▌ | 2839/18688 [3:18:36<20:15:51, 4.60s/it] 15%|█▌ | 2840/18688 [3:18:40<20:05:51, 4.57s/it] {'loss': '1.017', 'grad_norm': '0.969', 'learning_rate': '7.881e-05', 'epoch': '0.152', 'num_input_tokens_seen': 9523104, 'train_runtime': '1.192e+04', 'train_tokens_per_second': '798.9'}
15%|█▌ | 2840/18688 [3:18:40<20:05:51, 4.57s/it] 15%|█▌ | 2841/18688 [3:18:44<18:42:54, 4.25s/it] 15%|█▌ | 2842/18688 [3:18:48<17:59:24, 4.09s/it] 15%|█▌ | 2843/18688 [3:18:52<17:56:33, 4.08s/it] 15%|█▌ | 2844/18688 [3:18:56<17:42:23, 4.02s/it] 15%|█▌ | 2845/18688 [3:18:59<17:20:29, 3.94s/it] 15%|█▌ | 2846/18688 [3:19:04<18:10:40, 4.13s/it] 15%|█▌ | 2847/18688 [3:19:08<18:05:06, 4.11s/it] 15%|█▌ | 2848/18688 [3:19:12<17:39:33, 4.01s/it] 15%|█▌ | 2849/18688 [3:19:16<18:31:44, 4.21s/it] 15%|█▌ | 2850/18688 [3:19:20<18:17:57, 4.16s/it] {'loss': '0.9252', 'grad_norm': '1.05', 'learning_rate': '7.879e-05', 'epoch': '0.1525', 'num_input_tokens_seen': 9555376, 'train_runtime': '1.196e+04', 'train_tokens_per_second': '798.9'}
15%|█▌ | 2850/18688 [3:19:20<18:17:57, 4.16s/it] 15%|█▌ | 2851/18688 [3:19:25<18:22:15, 4.18s/it] 15%|█▌ | 2852/18688 [3:19:29<18:06:56, 4.12s/it] 15%|█▌ | 2853/18688 [3:19:32<17:22:38, 3.95s/it] 15%|█▌ | 2854/18688 [3:19:37<18:10:34, 4.13s/it] 15%|█▌ | 2855/18688 [3:19:40<17:29:46, 3.98s/it] 15%|█▌ | 2856/18688 [3:19:45<17:52:12, 4.06s/it] 15%|█▌ | 2857/18688 [3:19:49<18:31:56, 4.21s/it] 15%|█▌ | 2858/18688 [3:19:53<18:13:43, 4.15s/it] 15%|█▌ | 2859/18688 [3:19:57<18:24:39, 4.19s/it] 15%|█▌ | 2860/18688 [3:20:02<18:15:35, 4.15s/it] {'loss': '0.9603', 'grad_norm': '0.93', 'learning_rate': '7.877e-05', 'epoch': '0.153', 'num_input_tokens_seen': 9589488, 'train_runtime': '1.2e+04', 'train_tokens_per_second': '799'}
15%|█▌ | 2860/18688 [3:20:02<18:15:35, 4.15s/it] 15%|█▌ | 2861/18688 [3:20:06<19:16:23, 4.38s/it] 15%|█▌ | 2862/18688 [3:20:12<20:22:34, 4.64s/it] 15%|█▌ | 2863/18688 [3:20:16<20:02:46, 4.56s/it] 15%|█▌ | 2864/18688 [3:20:20<19:06:56, 4.35s/it] 15%|█▌ | 2865/18688 [3:20:25<19:44:47, 4.49s/it] 15%|█▌ | 2866/18688 [3:20:29<19:37:54, 4.47s/it] 15%|█▌ | 2867/18688 [3:20:33<18:36:51, 4.24s/it] 15%|█▌ | 2868/18688 [3:20:37<18:35:47, 4.23s/it] 15%|█▌ | 2869/18688 [3:20:41<18:29:49, 4.21s/it] 15%|█▌ | 2870/18688 [3:20:45<18:10:47, 4.14s/it] {'loss': '1.001', 'grad_norm': '1.063', 'learning_rate': '7.875e-05', 'epoch': '0.1536', 'num_input_tokens_seen': 9626728, 'train_runtime': '1.205e+04', 'train_tokens_per_second': '799.2'}
15%|█▌ | 2870/18688 [3:20:45<18:10:47, 4.14s/it] 15%|█▌ | 2871/18688 [3:20:49<18:18:16, 4.17s/it] 15%|█▌ | 2872/18688 [3:20:53<17:52:24, 4.07s/it] 15%|█▌ | 2873/18688 [3:20:58<18:23:08, 4.19s/it] 15%|█▌ | 2874/18688 [3:21:01<17:41:56, 4.03s/it] 15%|█▌ | 2875/18688 [3:21:06<18:00:08, 4.10s/it] 15%|█▌ | 2876/18688 [3:21:10<17:47:21, 4.05s/it] 15%|█▌ | 2877/18688 [3:21:15<19:37:52, 4.47s/it] 15%|█▌ | 2878/18688 [3:21:20<20:05:45, 4.58s/it] 15%|█▌ | 2879/18688 [3:21:25<20:14:54, 4.61s/it] 15%|█▌ | 2880/18688 [3:21:28<18:43:34, 4.26s/it] {'loss': '0.9481', 'grad_norm': '1.305', 'learning_rate': '7.874e-05', 'epoch': '0.1541', 'num_input_tokens_seen': 9662024, 'train_runtime': '1.209e+04', 'train_tokens_per_second': '799.3'}
15%|█▌ | 2880/18688 [3:21:28<18:43:34, 4.26s/it] 15%|█▌ | 2881/18688 [3:21:32<17:42:03, 4.03s/it] 15%|█▌ | 2882/18688 [3:21:35<17:25:57, 3.97s/it] 15%|█▌ | 2883/18688 [3:21:39<17:18:05, 3.94s/it] 15%|█▌ | 2884/18688 [3:21:43<17:08:24, 3.90s/it] 15%|█▌ | 2885/18688 [3:21:47<17:03:02, 3.88s/it] 15%|█▌ | 2886/18688 [3:21:51<17:21:01, 3.95s/it] 15%|█▌ | 2887/18688 [3:21:55<16:56:20, 3.86s/it] 15%|█▌ | 2888/18688 [3:21:59<17:22:15, 3.96s/it] 15%|█▌ | 2889/18688 [3:22:04<19:10:22, 4.37s/it] 15%|█▌ | 2890/18688 [3:22:09<19:46:33, 4.51s/it] {'loss': '0.9719', 'grad_norm': '0.9056', 'learning_rate': '7.872e-05', 'epoch': '0.1546', 'num_input_tokens_seen': 9695896, 'train_runtime': '1.213e+04', 'train_tokens_per_second': '799.4'}
15%|█▌ | 2890/18688 [3:22:09<19:46:33, 4.51s/it] 15%|█▌ | 2891/18688 [3:22:13<19:11:50, 4.37s/it] 15%|█▌ | 2892/18688 [3:22:17<19:01:59, 4.34s/it] 15%|█▌ | 2893/18688 [3:22:21<18:11:48, 4.15s/it] 15%|█▌ | 2894/18688 [3:22:25<17:48:41, 4.06s/it] 15%|█▌ | 2895/18688 [3:22:29<17:53:29, 4.08s/it] 15%|█▌ | 2896/18688 [3:22:32<17:07:07, 3.90s/it] 16%|█▌ | 2897/18688 [3:22:37<17:19:47, 3.95s/it] 16%|█▌ | 2898/18688 [3:22:41<18:31:08, 4.22s/it] 16%|█▌ | 2899/18688 [3:22:45<18:07:59, 4.13s/it] 16%|█▌ | 2900/18688 [3:22:49<17:14:24, 3.93s/it] {'loss': '0.9823', 'grad_norm': '1.232', 'learning_rate': '7.87e-05', 'epoch': '0.1552', 'num_input_tokens_seen': 9727832, 'train_runtime': '1.217e+04', 'train_tokens_per_second': '799.4'}
16%|█▌ | 2900/18688 [3:22:49<17:14:24, 3.93s/it] 16%|█▌ | 2901/18688 [3:22:53<17:32:18, 4.00s/it] 16%|█▌ | 2902/18688 [3:22:57<17:24:20, 3.97s/it] 16%|█▌ | 2903/18688 [3:23:00<17:01:10, 3.88s/it] 16%|█▌ | 2904/18688 [3:23:04<16:47:39, 3.83s/it] 16%|█▌ | 2905/18688 [3:23:08<17:14:14, 3.93s/it] 16%|█▌ | 2906/18688 [3:23:12<17:17:47, 3.95s/it] 16%|█▌ | 2907/18688 [3:23:17<17:47:23, 4.06s/it] 16%|█▌ | 2908/18688 [3:23:20<17:02:22, 3.89s/it] 16%|█▌ | 2909/18688 [3:23:25<18:03:42, 4.12s/it] 16%|█▌ | 2910/18688 [3:23:29<18:05:31, 4.13s/it] {'loss': '0.9883', 'grad_norm': '1.398', 'learning_rate': '7.868e-05', 'epoch': '0.1557', 'num_input_tokens_seen': 9759024, 'train_runtime': '1.221e+04', 'train_tokens_per_second': '799.3'}
16%|█▌ | 2910/18688 [3:23:29<18:05:31, 4.13s/it] 16%|█▌ | 2911/18688 [3:23:33<18:15:58, 4.17s/it] 16%|█▌ | 2912/18688 [3:23:37<18:03:47, 4.12s/it] 16%|█▌ | 2913/18688 [3:23:41<17:31:49, 4.00s/it] 16%|█▌ | 2914/18688 [3:23:45<17:37:51, 4.02s/it] 16%|█▌ | 2915/18688 [3:23:49<18:02:42, 4.12s/it] 16%|█▌ | 2916/18688 [3:23:54<18:21:38, 4.19s/it] 16%|█▌ | 2917/18688 [3:23:59<19:42:53, 4.50s/it] 16%|█▌ | 2918/18688 [3:24:03<19:03:08, 4.35s/it] 16%|█▌ | 2919/18688 [3:24:07<18:22:59, 4.20s/it] 16%|█▌ | 2920/18688 [3:24:11<18:28:48, 4.22s/it] {'loss': '0.9821', 'grad_norm': '1.052', 'learning_rate': '7.866e-05', 'epoch': '0.1563', 'num_input_tokens_seen': 9794000, 'train_runtime': '1.225e+04', 'train_tokens_per_second': '799.4'}
16%|█▌ | 2920/18688 [3:24:11<18:28:48, 4.22s/it] 16%|█▌ | 2921/18688 [3:24:15<18:08:37, 4.14s/it] 16%|█▌ | 2922/18688 [3:24:19<18:04:44, 4.13s/it] 16%|█▌ | 2923/18688 [3:24:24<19:08:45, 4.37s/it] 16%|█▌ | 2924/18688 [3:24:29<19:37:07, 4.48s/it] 16%|█▌ | 2925/18688 [3:24:32<18:17:40, 4.18s/it] 16%|█▌ | 2926/18688 [3:24:36<17:27:23, 3.99s/it] 16%|█▌ | 2927/18688 [3:24:40<17:42:50, 4.05s/it] 16%|█▌ | 2928/18688 [3:24:44<17:28:24, 3.99s/it] 16%|█▌ | 2929/18688 [3:24:48<17:40:25, 4.04s/it] 16%|█▌ | 2930/18688 [3:24:53<18:31:24, 4.23s/it] {'loss': '1.019', 'grad_norm': '1.133', 'learning_rate': '7.864e-05', 'epoch': '0.1568', 'num_input_tokens_seen': 9827592, 'train_runtime': '1.229e+04', 'train_tokens_per_second': '799.4'}
16%|█▌ | 2930/18688 [3:24:53<18:31:24, 4.23s/it] 16%|█▌ | 2931/18688 [3:24:57<18:50:50, 4.31s/it] 16%|█▌ | 2932/18688 [3:25:01<18:02:12, 4.12s/it] 16%|█▌ | 2933/18688 [3:25:05<18:03:22, 4.13s/it] 16%|█▌ | 2934/18688 [3:25:09<17:29:41, 4.00s/it] 16%|█▌ | 2935/18688 [3:25:12<16:56:26, 3.87s/it] 16%|█▌ | 2936/18688 [3:25:16<16:54:15, 3.86s/it] 16%|█▌ | 2937/18688 [3:25:20<16:54:24, 3.86s/it] 16%|█▌ | 2938/18688 [3:25:24<16:44:21, 3.83s/it] 16%|█▌ | 2939/18688 [3:25:29<18:22:44, 4.20s/it] 16%|█▌ | 2940/18688 [3:25:33<18:23:52, 4.21s/it] {'loss': '0.9607', 'grad_norm': '1.265', 'learning_rate': '7.862e-05', 'epoch': '0.1573', 'num_input_tokens_seen': 9859472, 'train_runtime': '1.233e+04', 'train_tokens_per_second': '799.4'}
16%|█▌ | 2940/18688 [3:25:33<18:23:52, 4.21s/it] 16%|█▌ | 2941/18688 [3:25:37<17:28:59, 4.00s/it] 16%|█▌ | 2942/18688 [3:25:41<18:37:49, 4.26s/it] 16%|█▌ | 2943/18688 [3:25:45<18:05:31, 4.14s/it] 16%|█▌ | 2944/18688 [3:25:50<18:25:47, 4.21s/it] 16%|█▌ | 2945/18688 [3:25:53<17:44:39, 4.06s/it] 16%|█▌ | 2946/18688 [3:25:57<17:49:51, 4.08s/it] 16%|█▌ | 2947/18688 [3:26:02<18:21:27, 4.20s/it] 16%|█▌ | 2948/18688 [3:26:06<18:38:35, 4.26s/it] 16%|█▌ | 2949/18688 [3:26:10<17:39:15, 4.04s/it] 16%|█▌ | 2950/18688 [3:26:15<18:36:12, 4.26s/it] {'loss': '1.024', 'grad_norm': '1.031', 'learning_rate': '7.861e-05', 'epoch': '0.1579', 'num_input_tokens_seen': 9893912, 'train_runtime': '1.238e+04', 'train_tokens_per_second': '799.5'}
16%|█▌ | 2950/18688 [3:26:15<18:36:12, 4.26s/it] 16%|█▌ | 2951/18688 [3:26:19<18:21:43, 4.20s/it] 16%|█▌ | 2952/18688 [3:26:24<19:14:50, 4.40s/it] 16%|█▌ | 2953/18688 [3:26:27<18:17:18, 4.18s/it] 16%|█▌ | 2954/18688 [3:26:32<18:24:11, 4.21s/it] 16%|█▌ | 2955/18688 [3:26:36<18:08:42, 4.15s/it] 16%|█▌ | 2956/18688 [3:26:39<17:24:30, 3.98s/it] 16%|█▌ | 2957/18688 [3:26:44<17:59:17, 4.12s/it] 16%|█▌ | 2958/18688 [3:26:48<18:53:39, 4.32s/it] 16%|█▌ | 2959/18688 [3:26:53<18:45:04, 4.29s/it] 16%|█▌ | 2960/18688 [3:26:56<18:10:49, 4.16s/it] {'loss': '0.9868', 'grad_norm': '1.03', 'learning_rate': '7.859e-05', 'epoch': '0.1584', 'num_input_tokens_seen': 9928720, 'train_runtime': '1.242e+04', 'train_tokens_per_second': '799.6'}
16%|█▌ | 2960/18688 [3:26:56<18:10:49, 4.16s/it] 16%|█▌ | 2961/18688 [3:27:01<18:25:46, 4.22s/it] 16%|█▌ | 2962/18688 [3:27:05<18:24:02, 4.21s/it] 16%|█▌ | 2963/18688 [3:27:09<18:13:49, 4.17s/it] 16%|█▌ | 2964/18688 [3:27:13<18:16:09, 4.18s/it] 16%|█▌ | 2965/18688 [3:27:17<17:24:44, 3.99s/it] 16%|█▌ | 2966/18688 [3:27:21<18:05:55, 4.14s/it] 16%|█▌ | 2967/18688 [3:27:26<18:46:30, 4.30s/it] 16%|█▌ | 2968/18688 [3:27:30<17:45:25, 4.07s/it] 16%|█▌ | 2969/18688 [3:27:33<17:04:48, 3.91s/it] 16%|█▌ | 2970/18688 [3:27:37<16:41:56, 3.82s/it] {'loss': '1.03', 'grad_norm': '1.275', 'learning_rate': '7.857e-05', 'epoch': '0.1589', 'num_input_tokens_seen': 9960152, 'train_runtime': '1.246e+04', 'train_tokens_per_second': '799.6'}
16%|█▌ | 2970/18688 [3:27:37<16:41:56, 3.82s/it] 16%|█▌ | 2971/18688 [3:27:40<16:26:14, 3.76s/it] 16%|█▌ | 2972/18688 [3:27:45<17:53:40, 4.10s/it] 16%|█▌ | 2973/18688 [3:27:50<19:17:13, 4.42s/it] 16%|█▌ | 2974/18688 [3:27:54<18:26:51, 4.23s/it] 16%|█▌ | 2975/18688 [3:27:58<18:24:56, 4.22s/it] 16%|█▌ | 2976/18688 [3:28:02<17:32:08, 4.02s/it] 16%|█▌ | 2977/18688 [3:28:07<18:35:57, 4.26s/it] 16%|█▌ | 2978/18688 [3:28:10<17:48:31, 4.08s/it] 16%|█▌ | 2979/18688 [3:28:15<18:41:55, 4.29s/it] 16%|█▌ | 2980/18688 [3:28:19<18:46:42, 4.30s/it] {'loss': '0.9833', 'grad_norm': '1.088', 'learning_rate': '7.855e-05', 'epoch': '0.1595', 'num_input_tokens_seen': 9997408, 'train_runtime': '1.25e+04', 'train_tokens_per_second': '799.8'}
16%|█▌ | 2980/18688 [3:28:19<18:46:42, 4.30s/it] 16%|█▌ | 2981/18688 [3:28:25<20:07:48, 4.61s/it] 16%|█▌ | 2982/18688 [3:28:28<18:44:33, 4.30s/it] 16%|█▌ | 2983/18688 [3:28:32<18:26:19, 4.23s/it] 16%|█▌ | 2984/18688 [3:28:36<17:50:43, 4.09s/it] 16%|█▌ | 2985/18688 [3:28:40<17:17:35, 3.96s/it] 16%|█▌ | 2986/18688 [3:28:44<17:07:54, 3.93s/it] 16%|█▌ | 2987/18688 [3:28:48<17:24:51, 3.99s/it] 16%|█▌ | 2988/18688 [3:28:52<17:45:15, 4.07s/it] 16%|█▌ | 2989/18688 [3:28:56<17:03:24, 3.91s/it] 16%|█▌ | 2990/18688 [3:28:59<16:32:28, 3.79s/it] {'loss': '0.9671', 'grad_norm': '1.114', 'learning_rate': '7.853e-05', 'epoch': '0.16', 'num_input_tokens_seen': 10029264, 'train_runtime': '1.254e+04', 'train_tokens_per_second': '799.8'}
16%|█▌ | 2990/18688 [3:28:59<16:32:28, 3.79s/it] 16%|█▌ | 2991/18688 [3:29:03<16:50:36, 3.86s/it] 16%|█▌ | 2992/18688 [3:29:07<16:30:10, 3.79s/it] 16%|█▌ | 2993/18688 [3:29:11<16:46:19, 3.85s/it] 16%|█▌ | 2994/18688 [3:29:15<17:11:35, 3.94s/it] 16%|█▌ | 2995/18688 [3:29:19<17:26:14, 4.00s/it] 16%|█▌ | 2996/18688 [3:29:24<18:00:10, 4.13s/it] 16%|█▌ | 2997/18688 [3:29:27<17:26:19, 4.00s/it] 16%|█▌ | 2998/18688 [3:29:31<17:43:55, 4.07s/it] 16%|█▌ | 2999/18688 [3:29:36<18:10:08, 4.17s/it] 16%|█▌ | 3000/18688 [3:29:41<19:09:18, 4.40s/it] {'loss': '0.901', 'grad_norm': '1.042', 'learning_rate': '7.851e-05', 'epoch': '0.1605', 'num_input_tokens_seen': 10063432, 'train_runtime': '1.258e+04', 'train_tokens_per_second': '799.9'}
16%|█▌ | 3000/18688 [3:29:41<19:09:18, 4.40s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:71: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:281: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 0/1000 [00:00<?, ?it/s]/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:172: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:202: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:254: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 3/1000 [00:00<00:38, 25.93it/s]
1%| | 6/1000 [00:00<00:54, 18.23it/s]
1%| | 8/1000 [00:00<00:56, 17.56it/s]
1%| | 10/1000 [00:00<00:59, 16.64it/s]
1%| | 12/1000 [00:00<01:00, 16.44it/s]
1%|▏ | 14/1000 [00:00<01:01, 16.00it/s]
2%|▏ | 16/1000 [00:01<01:09, 14.21it/s]
2%|▏ | 18/1000 [00:01<01:42, 9.56it/s]
2%|▏ | 20/1000 [00:01<01:35, 10.21it/s]
2%|▏ | 22/1000 [00:01<01:54, 8.57it/s]
2%|▏ | 24/1000 [00:01<01:41, 9.62it/s]
3%|▎ | 26/1000 [00:02<01:46, 9.18it/s]
3%|▎ | 28/1000 [00:02<01:36, 10.02it/s]
3%|▎ | 30/1000 [00:02<01:28, 10.94it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.14it/s]
3%|▎ | 34/1000 [00:02<01:25, 11.24it/s]
4%|▎ | 36/1000 [00:03<01:32, 10.42it/s]
4%|▍ | 38/1000 [00:03<01:25, 11.31it/s]
4%|▍ | 40/1000 [00:03<01:27, 10.93it/s]
4%|▍ | 42/1000 [00:03<01:46, 9.02it/s]
4%|▍ | 44/1000 [00:03<01:30, 10.53it/s]
5%|▍ | 46/1000 [00:04<01:23, 11.43it/s]
5%|▍ | 48/1000 [00:04<01:32, 10.32it/s]
5%|▌ | 50/1000 [00:04<01:23, 11.43it/s]
5%|▌ | 52/1000 [00:04<01:20, 11.85it/s]
5%|▌ | 54/1000 [00:04<01:19, 11.96it/s]
6%|▌ | 56/1000 [00:04<01:18, 12.07it/s]
6%|▌ | 58/1000 [00:05<01:57, 8.05it/s]
6%|▌ | 60/1000 [00:05<01:45, 8.92it/s]
6%|▌ | 62/1000 [00:05<01:34, 9.96it/s]
6%|▋ | 64/1000 [00:05<01:24, 11.10it/s]
7%|▋ | 66/1000 [00:05<01:19, 11.76it/s]
7%|▋ | 68/1000 [00:06<01:14, 12.48it/s]
7%|▋ | 70/1000 [00:06<01:17, 12.04it/s]
7%|▋ | 72/1000 [00:06<01:12, 12.87it/s]
7%|▋ | 74/1000 [00:06<01:11, 12.98it/s]
8%|▊ | 76/1000 [00:06<01:07, 13.77it/s]
8%|▊ | 78/1000 [00:06<01:05, 14.15it/s]
8%|▊ | 80/1000 [00:06<01:09, 13.28it/s]
8%|▊ | 82/1000 [00:07<01:05, 13.97it/s]
8%|▊ | 84/1000 [00:07<01:03, 14.46it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.64it/s]
9%|▉ | 88/1000 [00:07<01:02, 14.48it/s]
9%|▉ | 90/1000 [00:07<01:02, 14.54it/s]
9%|▉ | 92/1000 [00:07<01:05, 13.90it/s]
9%|▉ | 94/1000 [00:07<01:05, 13.78it/s]
10%|▉ | 96/1000 [00:08<01:03, 14.17it/s]
10%|▉ | 98/1000 [00:08<01:03, 14.14it/s]
10%|█ | 100/1000 [00:08<01:32, 9.77it/s]
10%|█ | 102/1000 [00:08<01:23, 10.74it/s]
10%|█ | 104/1000 [00:09<01:54, 7.81it/s]
11%|█ | 106/1000 [00:09<02:02, 7.32it/s]
11%|█ | 108/1000 [00:09<01:44, 8.55it/s]
11%|█ | 110/1000 [00:09<01:28, 10.00it/s]
11%|█ | 112/1000 [00:09<01:19, 11.15it/s]
11%|█▏ | 114/1000 [00:09<01:14, 11.83it/s]
12%|█▏ | 116/1000 [00:10<01:10, 12.48it/s]
12%|█▏ | 118/1000 [00:10<01:10, 12.47it/s]
12%|█▏ | 120/1000 [00:10<01:12, 12.19it/s]
12%|█▏ | 122/1000 [00:10<01:25, 10.32it/s]
12%|█▏ | 124/1000 [00:10<01:31, 9.55it/s]
13%|█▎ | 126/1000 [00:11<01:23, 10.50it/s]
13%|█▎ | 128/1000 [00:11<01:16, 11.43it/s]
13%|█▎ | 130/1000 [00:11<01:11, 12.12it/s]
13%|█▎ | 132/1000 [00:11<01:06, 13.09it/s]
13%|█▎ | 134/1000 [00:11<01:10, 12.30it/s]
14%|█▎ | 136/1000 [00:11<01:05, 13.25it/s]
14%|█▍ | 138/1000 [00:11<01:05, 13.19it/s]
14%|█▍ | 140/1000 [00:12<01:25, 10.09it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.53it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.57it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.59it/s]
15%|█▍ | 148/1000 [00:13<01:14, 11.51it/s]
15%|█▌ | 150/1000 [00:13<01:07, 12.61it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.17it/s]
15%|█▌ | 154/1000 [00:13<01:02, 13.43it/s]
16%|█▌ | 156/1000 [00:13<01:03, 13.27it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.78it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.64it/s]
16%|█▌ | 162/1000 [00:13<01:01, 13.53it/s]
16%|█▋ | 164/1000 [00:14<01:02, 13.35it/s]
17%|█▋ | 166/1000 [00:14<01:01, 13.63it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.19it/s]
17%|█▋ | 170/1000 [00:14<01:19, 10.45it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.44it/s]
17%|█▋ | 174/1000 [00:15<01:30, 9.15it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.19it/s]
18%|█▊ | 178/1000 [00:15<01:34, 8.67it/s]
18%|█▊ | 180/1000 [00:15<01:33, 8.80it/s]
18%|█▊ | 182/1000 [00:16<01:35, 8.59it/s]
18%|█▊ | 184/1000 [00:16<01:23, 9.83it/s]
19%|█▊ | 186/1000 [00:16<01:37, 8.36it/s]
19%|█▉ | 188/1000 [00:16<01:23, 9.68it/s]
19%|█▉ | 190/1000 [00:16<01:15, 10.71it/s]
19%|█▉ | 192/1000 [00:17<01:11, 11.34it/s]
19%|█▉ | 194/1000 [00:17<01:16, 10.56it/s]
20%|█▉ | 196/1000 [00:17<01:29, 8.98it/s]
20%|█▉ | 198/1000 [00:17<01:18, 10.23it/s]
20%|██ | 200/1000 [00:17<01:15, 10.57it/s]
20%|██ | 202/1000 [00:18<01:09, 11.54it/s]
20%|██ | 204/1000 [00:18<01:02, 12.69it/s]
21%|██ | 206/1000 [00:18<00:59, 13.26it/s]
21%|██ | 208/1000 [00:18<00:58, 13.64it/s]
21%|██ | 210/1000 [00:18<00:54, 14.55it/s]
21%|██ | 212/1000 [00:18<00:53, 14.82it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.83it/s]
22%|██▏ | 216/1000 [00:18<00:53, 14.55it/s]
22%|██▏ | 218/1000 [00:19<00:52, 14.95it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.93it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.20it/s]
22%|██▏ | 224/1000 [00:19<01:20, 9.68it/s]
23%|██▎ | 226/1000 [00:20<01:12, 10.69it/s]
23%|██▎ | 228/1000 [00:20<01:06, 11.57it/s]
23%|██▎ | 230/1000 [00:20<01:03, 12.06it/s]
23%|██▎ | 232/1000 [00:20<01:01, 12.43it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.18it/s]
24%|██▎ | 236/1000 [00:20<00:55, 13.68it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.33it/s]
24%|██▍ | 240/1000 [00:21<00:53, 14.15it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.72it/s]
24%|██▍ | 244/1000 [00:21<00:50, 14.97it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.96it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.69it/s]
25%|██▌ | 250/1000 [00:21<00:55, 13.46it/s]
25%|██▌ | 252/1000 [00:21<01:01, 12.19it/s]
25%|██▌ | 254/1000 [00:22<00:58, 12.67it/s]
26%|██▌ | 256/1000 [00:22<00:56, 13.18it/s]
26%|██▌ | 258/1000 [00:22<00:57, 12.96it/s]
26%|██▌ | 260/1000 [00:22<01:01, 11.99it/s]
26%|██▌ | 262/1000 [00:22<00:59, 12.35it/s]
26%|██▋ | 264/1000 [00:22<00:57, 12.85it/s]
27%|██▋ | 266/1000 [00:22<00:55, 13.30it/s]
27%|██▋ | 268/1000 [00:23<00:52, 13.95it/s]
27%|██▋ | 270/1000 [00:23<00:56, 12.92it/s]
27%|██▋ | 272/1000 [00:23<01:03, 11.43it/s]
27%|██▋ | 274/1000 [00:23<01:02, 11.57it/s]
28%|██▊ | 276/1000 [00:23<00:57, 12.64it/s]
28%|██▊ | 278/1000 [00:23<00:55, 12.91it/s]
28%|██▊ | 280/1000 [00:24<00:53, 13.52it/s]
28%|██▊ | 282/1000 [00:24<00:49, 14.55it/s]
28%|██▊ | 284/1000 [00:24<00:49, 14.47it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.74it/s]
29%|██▉ | 288/1000 [00:24<00:47, 15.11it/s]
29%|██▉ | 290/1000 [00:24<00:45, 15.56it/s]
29%|██▉ | 292/1000 [00:24<00:45, 15.50it/s]
29%|██▉ | 294/1000 [00:24<00:45, 15.39it/s]
30%|██▉ | 296/1000 [00:25<00:45, 15.37it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.50it/s]
30%|███ | 300/1000 [00:25<00:46, 15.15it/s]
30%|███ | 302/1000 [00:25<00:46, 15.17it/s]
30%|███ | 304/1000 [00:25<00:50, 13.76it/s]
31%|███ | 306/1000 [00:25<00:47, 14.53it/s]
31%|███ | 308/1000 [00:25<00:47, 14.49it/s]
31%|███ | 310/1000 [00:26<00:47, 14.66it/s]
31%|███ | 312/1000 [00:26<00:47, 14.34it/s]
31%|███▏ | 314/1000 [00:26<00:45, 15.16it/s]
32%|███▏ | 316/1000 [00:26<00:44, 15.51it/s]
32%|███▏ | 318/1000 [00:26<00:42, 16.04it/s]
32%|███▏ | 320/1000 [00:27<01:12, 9.38it/s]
32%|███▏ | 322/1000 [00:27<01:03, 10.75it/s]
32%|███▏ | 324/1000 [00:27<00:58, 11.53it/s]
33%|███▎ | 326/1000 [00:27<00:58, 11.47it/s]
33%|███▎ | 328/1000 [00:27<00:58, 11.48it/s]
33%|███▎ | 330/1000 [00:27<00:54, 12.40it/s]
33%|███▎ | 332/1000 [00:27<00:52, 12.67it/s]
33%|███▎ | 334/1000 [00:28<00:50, 13.17it/s]
34%|███▎ | 336/1000 [00:28<00:48, 13.59it/s]
34%|███▍ | 338/1000 [00:28<01:05, 10.08it/s]
34%|███▍ | 340/1000 [00:28<00:59, 11.14it/s]
34%|███▍ | 342/1000 [00:28<00:52, 12.43it/s]
34%|███▍ | 344/1000 [00:28<00:50, 13.09it/s]
35%|███▍ | 346/1000 [00:29<00:47, 13.69it/s]
35%|███▍ | 348/1000 [00:29<00:46, 14.12it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.75it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.94it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.94it/s]
36%|███▌ | 356/1000 [00:29<00:42, 15.03it/s]
36%|███▌ | 358/1000 [00:29<00:43, 14.71it/s]
36%|███▌ | 360/1000 [00:29<00:42, 15.10it/s]
36%|███▌ | 362/1000 [00:30<00:47, 13.40it/s]
36%|███▋ | 364/1000 [00:30<00:45, 13.90it/s]
37%|███▋ | 366/1000 [00:30<00:52, 11.98it/s]
37%|███▋ | 368/1000 [00:30<00:51, 12.38it/s]
37%|███▋ | 370/1000 [00:30<00:49, 12.83it/s]
37%|███▋ | 372/1000 [00:30<00:49, 12.62it/s]
37%|███▋ | 374/1000 [00:31<00:57, 10.80it/s]
38%|███▊ | 376/1000 [00:31<01:00, 10.34it/s]
38%|███▊ | 378/1000 [00:31<00:57, 10.78it/s]
38%|███▊ | 380/1000 [00:31<00:55, 11.22it/s]
38%|███▊ | 382/1000 [00:31<00:52, 11.76it/s]
38%|███▊ | 384/1000 [00:32<00:50, 12.20it/s]
39%|███▊ | 386/1000 [00:32<00:47, 12.80it/s]
39%|███▉ | 388/1000 [00:32<00:47, 13.01it/s]
39%|███▉ | 390/1000 [00:32<00:46, 13.09it/s]
39%|███▉ | 392/1000 [00:32<00:44, 13.55it/s]
39%|███▉ | 394/1000 [00:32<00:45, 13.43it/s]
40%|███▉ | 396/1000 [00:32<00:44, 13.59it/s]
40%|███▉ | 398/1000 [00:33<00:48, 12.49it/s]
40%|████ | 400/1000 [00:33<00:45, 13.20it/s]
40%|████ | 402/1000 [00:33<00:43, 13.87it/s]
40%|████ | 404/1000 [00:33<00:41, 14.53it/s]
41%|████ | 406/1000 [00:33<00:39, 15.07it/s]
41%|████ | 408/1000 [00:33<00:46, 12.61it/s]
41%|████ | 410/1000 [00:34<00:56, 10.52it/s]
41%|████ | 412/1000 [00:34<00:50, 11.61it/s]
41%|████▏ | 414/1000 [00:34<01:07, 8.67it/s]
42%|████▏ | 416/1000 [00:34<01:23, 6.99it/s]
42%|████▏ | 418/1000 [00:35<01:10, 8.31it/s]
42%|████▏ | 420/1000 [00:35<01:00, 9.59it/s]
42%|████▏ | 422/1000 [00:35<00:52, 10.95it/s]
42%|████▏ | 424/1000 [00:35<01:14, 7.71it/s]
43%|████▎ | 426/1000 [00:35<01:03, 8.97it/s]
43%|████▎ | 428/1000 [00:36<00:56, 10.19it/s]
43%|████▎ | 430/1000 [00:36<01:02, 9.16it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.07it/s]
43%|████▎ | 434/1000 [00:36<01:08, 8.30it/s]
44%|████▎ | 436/1000 [00:37<01:00, 9.35it/s]
44%|████▍ | 438/1000 [00:37<01:02, 9.03it/s]
44%|████▍ | 440/1000 [00:37<00:58, 9.53it/s]
44%|████▍ | 442/1000 [00:37<00:52, 10.60it/s]
44%|████▍ | 444/1000 [00:37<00:48, 11.46it/s]
45%|████▍ | 446/1000 [00:37<00:45, 12.21it/s]
45%|████▍ | 448/1000 [00:38<00:50, 10.98it/s]
45%|████▌ | 450/1000 [00:38<00:46, 11.80it/s]
45%|████▌ | 452/1000 [00:38<00:42, 12.84it/s]
45%|████▌ | 454/1000 [00:38<00:44, 12.30it/s]
46%|████▌ | 456/1000 [00:38<00:42, 12.75it/s]
46%|████▌ | 458/1000 [00:38<00:39, 13.78it/s]
46%|████▌ | 460/1000 [00:38<00:38, 13.91it/s]
46%|████▌ | 462/1000 [00:39<00:37, 14.53it/s]
46%|████▋ | 464/1000 [00:39<00:35, 15.05it/s]
47%|████▋ | 466/1000 [00:39<00:35, 14.89it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.92it/s]
47%|████▋ | 470/1000 [00:39<00:52, 10.08it/s]
47%|████▋ | 472/1000 [00:40<00:51, 10.25it/s]
47%|████▋ | 474/1000 [00:40<00:45, 11.54it/s]
48%|████▊ | 476/1000 [00:40<00:43, 12.17it/s]
48%|████▊ | 478/1000 [00:40<00:43, 12.03it/s]
48%|████▊ | 480/1000 [00:40<00:40, 13.00it/s]
48%|████▊ | 482/1000 [00:40<00:38, 13.40it/s]
48%|████▊ | 484/1000 [00:40<00:37, 13.91it/s]
49%|████▊ | 486/1000 [00:41<00:36, 14.02it/s]
49%|████▉ | 488/1000 [00:41<00:35, 14.30it/s]
49%|████▉ | 490/1000 [00:41<00:45, 11.12it/s]
49%|████▉ | 492/1000 [00:41<00:40, 12.45it/s]
49%|████▉ | 494/1000 [00:41<00:37, 13.44it/s]
50%|████▉ | 496/1000 [00:41<00:35, 14.24it/s]
50%|████▉ | 498/1000 [00:41<00:34, 14.67it/s]
50%|█████ | 500/1000 [00:42<00:42, 11.65it/s]
50%|█████ | 502/1000 [00:42<00:41, 12.10it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.67it/s]
51%|█████ | 506/1000 [00:42<00:38, 12.81it/s]
51%|█████ | 508/1000 [00:42<00:35, 13.79it/s]
51%|█████ | 510/1000 [00:43<00:46, 10.44it/s]
51%|█████ | 512/1000 [00:43<00:43, 11.21it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 12.07it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.60it/s]
52%|█████▏ | 518/1000 [00:43<00:36, 13.09it/s]
52%|█████▏ | 520/1000 [00:43<00:35, 13.47it/s]
52%|█████▏ | 522/1000 [00:43<00:33, 14.23it/s]
52%|█████▏ | 524/1000 [00:43<00:33, 14.26it/s]
53%|█████▎ | 526/1000 [00:44<00:32, 14.41it/s]
53%|█████▎ | 528/1000 [00:44<00:39, 12.03it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.89it/s]
53%|█████▎ | 532/1000 [00:44<00:33, 13.86it/s]
53%|█████▎ | 534/1000 [00:44<00:32, 14.20it/s]
54%|█████▎ | 536/1000 [00:44<00:32, 14.31it/s]
54%|█████▍ | 538/1000 [00:45<00:32, 14.05it/s]
54%|█████▍ | 540/1000 [00:45<00:31, 14.47it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.75it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.65it/s]
55%|█████▍ | 546/1000 [00:45<00:31, 14.27it/s]
55%|█████▍ | 548/1000 [00:45<00:32, 13.94it/s]
55%|█████▌ | 550/1000 [00:45<00:31, 14.17it/s]
55%|█████▌ | 552/1000 [00:45<00:31, 14.33it/s]
55%|█████▌ | 554/1000 [00:46<00:31, 13.97it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.15it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.51it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 15.07it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 15.05it/s]
56%|█████▋ | 564/1000 [00:46<00:40, 10.82it/s]
57%|█████▋ | 566/1000 [00:47<00:36, 11.75it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.29it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.43it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.92it/s]
57%|█████▋ | 574/1000 [00:47<00:30, 13.82it/s]
58%|█████▊ | 576/1000 [00:47<00:30, 13.72it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.70it/s]
58%|█████▊ | 580/1000 [00:48<00:32, 12.95it/s]
58%|█████▊ | 582/1000 [00:48<00:30, 13.68it/s]
58%|█████▊ | 584/1000 [00:48<00:29, 13.89it/s]
59%|█████▊ | 586/1000 [00:48<00:29, 14.28it/s]
59%|█████▉ | 588/1000 [00:48<00:27, 15.04it/s]
59%|█████▉ | 590/1000 [00:48<00:27, 14.98it/s]
59%|█████▉ | 592/1000 [00:48<00:27, 14.75it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.32it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.87it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 14.13it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.45it/s]
60%|██████ | 602/1000 [00:49<00:27, 14.56it/s]
60%|██████ | 604/1000 [00:49<00:26, 15.14it/s]
61%|██████ | 606/1000 [00:49<00:26, 14.89it/s]
61%|██████ | 608/1000 [00:50<00:25, 15.22it/s]
61%|██████ | 610/1000 [00:50<00:25, 15.04it/s]
61%|██████ | 612/1000 [00:50<00:25, 15.19it/s]
61%|██████▏ | 614/1000 [00:50<00:26, 14.41it/s]
62%|██████▏ | 616/1000 [00:50<00:25, 14.81it/s]
62%|██████▏ | 618/1000 [00:50<00:26, 14.21it/s]
62%|██████▏ | 620/1000 [00:51<00:38, 9.77it/s]
62%|██████▏ | 622/1000 [00:51<00:37, 9.98it/s]
62%|██████▏ | 624/1000 [00:51<00:32, 11.44it/s]
63%|██████▎ | 626/1000 [00:51<00:30, 12.11it/s]
63%|██████▎ | 628/1000 [00:51<00:27, 13.38it/s]
63%|██████▎ | 630/1000 [00:51<00:26, 13.88it/s]
63%|██████▎ | 632/1000 [00:51<00:29, 12.50it/s]
63%|██████▎ | 634/1000 [00:52<00:27, 13.21it/s]
64%|██████▎ | 636/1000 [00:52<00:33, 10.96it/s]
64%|██████▍ | 638/1000 [00:52<00:33, 10.69it/s]
64%|██████▍ | 640/1000 [00:52<00:31, 11.48it/s]
64%|██████▍ | 642/1000 [00:52<00:28, 12.36it/s]
64%|██████▍ | 644/1000 [00:52<00:28, 12.60it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.88it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.37it/s]
65%|██████▌ | 650/1000 [00:53<00:28, 12.35it/s]
65%|██████▌ | 652/1000 [00:53<00:29, 11.72it/s]
65%|██████▌ | 654/1000 [00:54<00:39, 8.71it/s]
66%|██████▌ | 656/1000 [00:54<00:34, 10.08it/s]
66%|██████▌ | 658/1000 [00:54<00:30, 11.23it/s]
66%|██████▌ | 660/1000 [00:54<00:34, 9.77it/s]
66%|██████▌ | 662/1000 [00:54<00:30, 10.92it/s]
66%|██████▋ | 664/1000 [00:54<00:29, 11.47it/s]
67%|██████▋ | 666/1000 [00:54<00:26, 12.50it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.16it/s]
67%|██████▋ | 670/1000 [00:55<00:30, 11.00it/s]
67%|██████▋ | 672/1000 [00:55<00:27, 11.78it/s]
67%|██████▋ | 674/1000 [00:55<00:26, 12.41it/s]
68%|██████▊ | 676/1000 [00:55<00:27, 11.70it/s]
68%|██████▊ | 678/1000 [00:55<00:26, 12.20it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 12.99it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.12it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.32it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.40it/s]
69%|██████▉ | 688/1000 [00:56<00:22, 13.72it/s]
69%|██████▉ | 690/1000 [00:56<00:22, 13.80it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.28it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.36it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 14.10it/s]
70%|██████▉ | 698/1000 [00:57<00:20, 14.70it/s]
70%|███████ | 700/1000 [00:57<00:24, 12.22it/s]
70%|███████ | 702/1000 [00:57<00:22, 13.03it/s]
70%|███████ | 704/1000 [00:58<00:29, 9.89it/s]
71%|███████ | 706/1000 [00:58<00:27, 10.69it/s]
71%|███████ | 708/1000 [00:58<00:30, 9.64it/s]
71%|███████ | 710/1000 [00:58<00:26, 10.94it/s]
71%|███████ | 712/1000 [00:58<00:24, 11.91it/s]
71%|███████▏ | 714/1000 [00:58<00:21, 13.01it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 13.08it/s]
72%|███████▏ | 718/1000 [00:59<00:23, 12.04it/s]
72%|███████▏ | 720/1000 [00:59<00:21, 13.11it/s]
72%|███████▏ | 722/1000 [00:59<00:21, 12.85it/s]
72%|███████▏ | 724/1000 [00:59<00:21, 12.99it/s]
73%|███████▎ | 726/1000 [00:59<00:20, 13.43it/s]
73%|███████▎ | 728/1000 [01:00<00:24, 10.97it/s]
73%|███████▎ | 730/1000 [01:00<00:34, 7.89it/s]
73%|███████▎ | 732/1000 [01:00<00:33, 8.08it/s]
73%|███████▎ | 734/1000 [01:00<00:31, 8.41it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.28it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.75it/s]
74%|███████▍ | 739/1000 [01:01<00:23, 10.88it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.60it/s]
74%|███████▍ | 743/1000 [01:01<00:20, 12.26it/s]
74%|███████▍ | 745/1000 [01:01<00:25, 10.18it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.56it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.75it/s]
75%|███████▌ | 751/1000 [01:02<00:20, 11.87it/s]
75%|███████▌ | 753/1000 [01:02<00:19, 12.65it/s]
76%|███████▌ | 755/1000 [01:02<00:18, 13.35it/s]
76%|███████▌ | 757/1000 [01:02<00:23, 10.29it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.32it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.25it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.25it/s]
76%|███████▋ | 765/1000 [01:03<00:27, 8.61it/s]
77%|███████▋ | 767/1000 [01:03<00:24, 9.57it/s]
77%|███████▋ | 769/1000 [01:04<00:21, 10.58it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.84it/s]
77%|███████▋ | 773/1000 [01:04<00:17, 12.64it/s]
78%|███████▊ | 775/1000 [01:04<00:17, 12.92it/s]
78%|███████▊ | 777/1000 [01:04<00:16, 13.90it/s]
78%|███████▊ | 779/1000 [01:04<00:21, 10.42it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.36it/s]
78%|███████▊ | 783/1000 [01:05<00:26, 8.04it/s]
78%|███████▊ | 784/1000 [01:05<00:31, 6.90it/s]
78%|███████▊ | 785/1000 [01:05<00:29, 7.25it/s]
79%|███████▊ | 786/1000 [01:06<00:31, 6.89it/s]
79%|███████▉ | 788/1000 [01:06<00:25, 8.31it/s]
79%|███████▉ | 790/1000 [01:06<00:22, 9.33it/s]
79%|███████▉ | 792/1000 [01:06<00:20, 10.12it/s]
79%|███████▉ | 794/1000 [01:06<00:19, 10.66it/s]
80%|███████▉ | 796/1000 [01:06<00:18, 11.05it/s]
80%|███████▉ | 798/1000 [01:07<00:17, 11.44it/s]
80%|████████ | 800/1000 [01:07<00:17, 11.20it/s]
80%|████████ | 802/1000 [01:07<00:25, 7.89it/s]
80%|████████ | 803/1000 [01:07<00:26, 7.50it/s]
80%|████████ | 805/1000 [01:07<00:21, 8.94it/s]
81%|████████ | 807/1000 [01:08<00:20, 9.65it/s]
81%|████████ | 809/1000 [01:08<00:17, 10.75it/s]
81%|████████ | 811/1000 [01:08<00:17, 10.95it/s]
81%|████████▏ | 813/1000 [01:08<00:15, 12.18it/s]
82%|████████▏ | 815/1000 [01:08<00:14, 12.94it/s]
82%|████████▏ | 817/1000 [01:08<00:16, 11.19it/s]
82%|████████▏ | 819/1000 [01:09<00:15, 11.88it/s]
82%|████████▏ | 821/1000 [01:09<00:14, 12.58it/s]
82%|████████▏ | 823/1000 [01:09<00:13, 13.29it/s]
82%|████████▎ | 825/1000 [01:09<00:12, 14.15it/s]
83%|████████▎ | 827/1000 [01:09<00:12, 14.31it/s]
83%|████████▎ | 829/1000 [01:09<00:12, 14.15it/s]
83%|████████▎ | 831/1000 [01:09<00:11, 14.25it/s]
83%|████████▎ | 833/1000 [01:10<00:11, 14.04it/s]
84%|████████▎ | 835/1000 [01:10<00:11, 14.24it/s]
84%|████████▎ | 837/1000 [01:10<00:11, 13.88it/s]
84%|████████▍ | 839/1000 [01:10<00:11, 13.66it/s]
84%|████████▍ | 841/1000 [01:10<00:11, 14.11it/s]
84%|████████▍ | 843/1000 [01:10<00:11, 14.11it/s]
84%|████████▍ | 845/1000 [01:10<00:11, 13.84it/s]
85%|████████▍ | 847/1000 [01:11<00:11, 13.88it/s]
85%|████████▍ | 849/1000 [01:11<00:10, 13.99it/s]
85%|████████▌ | 851/1000 [01:11<00:10, 13.82it/s]
85%|████████▌ | 853/1000 [01:11<00:10, 13.83it/s]
86%|████████▌ | 855/1000 [01:11<00:10, 14.16it/s]
86%|████████▌ | 857/1000 [01:11<00:10, 14.29it/s]
86%|████████▌ | 859/1000 [01:11<00:09, 14.33it/s]
86%|████████▌ | 861/1000 [01:12<00:10, 13.02it/s]
86%|████████▋ | 863/1000 [01:12<00:10, 13.26it/s]
86%|████████▋ | 865/1000 [01:12<00:10, 13.26it/s]
87%|████████▋ | 867/1000 [01:12<00:09, 13.33it/s]
87%|████████▋ | 869/1000 [01:12<00:09, 13.40it/s]
87%|████████▋ | 871/1000 [01:12<00:09, 14.25it/s]
87%|████████▋ | 873/1000 [01:12<00:09, 13.42it/s]
88%|████████▊ | 875/1000 [01:13<00:09, 12.56it/s]
88%|████████▊ | 877/1000 [01:13<00:10, 11.34it/s]
88%|████████▊ | 879/1000 [01:13<00:10, 11.96it/s]
88%|████████▊ | 881/1000 [01:13<00:10, 10.84it/s]
88%|████████▊ | 883/1000 [01:13<00:09, 12.14it/s]
88%|████████▊ | 885/1000 [01:13<00:09, 12.49it/s]
89%|████████▊ | 887/1000 [01:14<00:08, 13.69it/s]
89%|████████▉ | 889/1000 [01:14<00:07, 14.41it/s]
89%|████████▉ | 891/1000 [01:14<00:07, 14.17it/s]
89%|████████▉ | 893/1000 [01:14<00:07, 14.34it/s]
90%|████████▉ | 895/1000 [01:14<00:07, 14.03it/s]
90%|████████▉ | 897/1000 [01:14<00:07, 14.36it/s]
90%|████████▉ | 899/1000 [01:14<00:08, 12.18it/s]
90%|█████████ | 901/1000 [01:15<00:07, 12.66it/s]
90%|█████████ | 903/1000 [01:15<00:07, 12.98it/s]
90%|█████████ | 905/1000 [01:15<00:09, 9.69it/s]
91%|█████████ | 907/1000 [01:15<00:08, 10.86it/s]
91%|█████████ | 909/1000 [01:15<00:07, 12.24it/s]
91%|█████████ | 911/1000 [01:16<00:07, 11.90it/s]
91%|█████████▏| 913/1000 [01:16<00:09, 9.40it/s]
92%|█████████▏| 915/1000 [01:16<00:11, 7.23it/s]
92%|█████████▏| 917/1000 [01:17<00:12, 6.90it/s]
92%|█████████▏| 919/1000 [01:17<00:09, 8.17it/s]
92%|█████████▏| 921/1000 [01:17<00:08, 9.74it/s]
92%|█████████▏| 923/1000 [01:17<00:07, 10.76it/s]
92%|█████████▎| 925/1000 [01:17<00:06, 11.54it/s]
93%|█████████▎| 927/1000 [01:17<00:06, 12.12it/s]
93%|█████████▎| 929/1000 [01:18<00:06, 10.95it/s]
93%|█████████▎| 931/1000 [01:18<00:05, 12.19it/s]
93%|█████████▎| 933/1000 [01:18<00:05, 12.43it/s]
94%|█████████▎| 935/1000 [01:18<00:04, 13.36it/s]
94%|█████████▎| 937/1000 [01:18<00:04, 13.84it/s]
94%|█████████▍| 939/1000 [01:18<00:04, 14.68it/s]
94%|█████████▍| 941/1000 [01:18<00:04, 14.69it/s]
94%|█████████▍| 943/1000 [01:19<00:04, 12.31it/s]
94%|█████████▍| 945/1000 [01:19<00:04, 12.58it/s]
95%|█████████▍| 947/1000 [01:19<00:04, 11.94it/s]
95%|█████████▍| 949/1000 [01:19<00:04, 12.75it/s]
95%|█████████▌| 951/1000 [01:19<00:03, 13.57it/s]
95%|█████████▌| 953/1000 [01:19<00:03, 14.09it/s]
96%|█████████▌| 955/1000 [01:19<00:03, 13.87it/s]
96%|█████████▌| 957/1000 [01:20<00:03, 14.31it/s]
96%|█████████▌| 959/1000 [01:20<00:03, 13.28it/s]
96%|█████████▌| 961/1000 [01:20<00:02, 13.36it/s]
96%|█████████▋| 963/1000 [01:20<00:02, 14.02it/s]
96%|█████████▋| 965/1000 [01:20<00:03, 10.30it/s]
97%|█████████▋| 967/1000 [01:20<00:02, 11.54it/s]
97%|█████████▋| 969/1000 [01:21<00:02, 12.00it/s]
97%|█████████▋| 971/1000 [01:21<00:02, 12.58it/s]
97%|█████████▋| 973/1000 [01:21<00:02, 9.88it/s]
98%|█████████▊| 975/1000 [01:21<00:02, 10.75it/s]
98%|█████████▊| 977/1000 [01:21<00:02, 9.56it/s]
98%|█████████▊| 979/1000 [01:22<00:02, 9.72it/s]
98%|█████████▊| 981/1000 [01:22<00:01, 10.03it/s]
98%|█████████▊| 983/1000 [01:22<00:01, 11.03it/s]
98%|█████████▊| 985/1000 [01:22<00:01, 10.36it/s]
99%|█████████▊| 987/1000 [01:22<00:01, 11.26it/s]
99%|█████████▉| 989/1000 [01:22<00:00, 11.21it/s]
99%|█████████▉| 991/1000 [01:23<00:00, 11.91it/s]
99%|█████████▉| 993/1000 [01:23<00:00, 12.55it/s]
100%|█████████▉| 995/1000 [01:23<00:00, 13.48it/s]
100%|█████████▉| 997/1000 [01:23<00:00, 14.07it/s]
100%|█████████▉| 999/1000 [01:23<00:00, 14.66it/s]
{'eval_loss': '0.9506', 'eval_runtime': '83.76', 'eval_samples_per_second': '11.94', 'eval_steps_per_second': '11.94', 'epoch': '0.1605', 'num_input_tokens_seen': 10063432}
16%|█▌ | 3000/18688 [3:31:05<19:09:18, 4.40s/it]
100%|██████████| 1000/1000 [01:23<00:00, 14.66it/s]
 16%|█▌ | 3001/18688 [3:31:09<128:20:08, 29.45s/it] 16%|█▌ | 3002/18688 [3:31:13<95:37:40, 21.95s/it] 16%|█▌ | 3003/18688 [3:31:17<71:32:57, 16.42s/it] 16%|█▌ | 3004/18688 [3:31:21<55:40:14, 12.78s/it] 16%|█▌ | 3005/18688 [3:31:25<44:24:20, 10.19s/it] 16%|█▌ | 3006/18688 [3:31:29<36:32:31, 8.39s/it] 16%|█▌ | 3007/18688 [3:31:33<30:12:11, 6.93s/it] 16%|█▌ | 3008/18688 [3:31:36<25:55:26, 5.95s/it] 16%|█▌ | 3009/18688 [3:31:40<22:39:46, 5.20s/it] 16%|█▌ | 3010/18688 [3:31:44<20:44:00, 4.76s/it] {'loss': '0.9734', 'grad_norm': '1.011', 'learning_rate': '7.849e-05', 'epoch': '0.1611', 'num_input_tokens_seen': 10094960, 'train_runtime': '1.27e+04', 'train_tokens_per_second': '794.6'}
16%|█▌ | 3010/18688 [3:31:44<20:44:00, 4.76s/it] 16%|█▌ | 3011/18688 [3:31:47<19:11:07, 4.41s/it] 16%|█▌ | 3012/18688 [3:31:53<20:50:40, 4.79s/it] 16%|█▌ | 3013/18688 [3:31:57<19:33:29, 4.49s/it] 16%|█▌ | 3014/18688 [3:32:01<18:55:26, 4.35s/it] 16%|█▌ | 3015/18688 [3:32:05<19:16:16, 4.43s/it] 16%|█▌ | 3016/18688 [3:32:09<18:09:23, 4.17s/it] 16%|█▌ | 3017/18688 [3:32:13<17:40:31, 4.06s/it] 16%|█▌ | 3018/18688 [3:32:17<17:47:15, 4.09s/it] 16%|█▌ | 3019/18688 [3:32:20<17:05:58, 3.93s/it] 16%|█▌ | 3020/18688 [3:32:24<16:48:40, 3.86s/it] {'loss': '0.9682', 'grad_norm': '0.953', 'learning_rate': '7.847e-05', 'epoch': '0.1616', 'num_input_tokens_seen': 10128312, 'train_runtime': '1.274e+04', 'train_tokens_per_second': '794.7'}
16%|█▌ | 3020/18688 [3:32:24<16:48:40, 3.86s/it] 16%|█▌ | 3021/18688 [3:32:28<17:25:42, 4.00s/it] 16%|█▌ | 3022/18688 [3:32:32<16:47:52, 3.86s/it] 16%|█▌ | 3023/18688 [3:32:35<16:16:20, 3.74s/it] 16%|█▌ | 3024/18688 [3:32:39<16:28:28, 3.79s/it] 16%|█▌ | 3025/18688 [3:32:44<17:45:45, 4.08s/it] 16%|█▌ | 3026/18688 [3:32:48<17:02:43, 3.92s/it] 16%|█▌ | 3027/18688 [3:32:52<17:16:53, 3.97s/it] 16%|█▌ | 3028/18688 [3:32:55<16:58:14, 3.90s/it] 16%|█▌ | 3029/18688 [3:32:59<16:48:12, 3.86s/it] 16%|█▌ | 3030/18688 [3:33:03<16:36:59, 3.82s/it] {'loss': '0.925', 'grad_norm': '0.9861', 'learning_rate': '7.845e-05', 'epoch': '0.1621', 'num_input_tokens_seen': 10158072, 'train_runtime': '1.278e+04', 'train_tokens_per_second': '794.6'}
16%|█▌ | 3030/18688 [3:33:03<16:36:59, 3.82s/it] 16%|█▌ | 3031/18688 [3:33:07<16:47:39, 3.86s/it] 16%|█▌ | 3032/18688 [3:33:11<16:42:32, 3.84s/it] 16%|█▌ | 3033/18688 [3:33:16<18:43:24, 4.31s/it] 16%|█▌ | 3034/18688 [3:33:20<18:01:19, 4.14s/it] 16%|█▌ | 3035/18688 [3:33:24<17:42:45, 4.07s/it] 16%|█▌ | 3036/18688 [3:33:28<17:42:24, 4.07s/it] 16%|█▋ | 3037/18688 [3:33:32<17:57:42, 4.13s/it] 16%|█▋ | 3038/18688 [3:33:36<17:40:42, 4.07s/it] 16%|█▋ | 3039/18688 [3:33:41<19:01:36, 4.38s/it] 16%|█▋ | 3040/18688 [3:33:45<18:47:46, 4.32s/it] {'loss': '0.9567', 'grad_norm': '1.08', 'learning_rate': '7.843e-05', 'epoch': '0.1627', 'num_input_tokens_seen': 10193360, 'train_runtime': '1.283e+04', 'train_tokens_per_second': '794.8'}
16%|█▋ | 3040/18688 [3:33:45<18:47:46, 4.32s/it] 16%|█▋ | 3041/18688 [3:33:49<17:48:09, 4.10s/it] 16%|█▋ | 3042/18688 [3:33:54<18:26:50, 4.24s/it] 16%|█▋ | 3043/18688 [3:33:59<19:27:41, 4.48s/it] 16%|█▋ | 3044/18688 [3:34:03<19:32:40, 4.50s/it] 16%|█▋ | 3045/18688 [3:34:07<19:03:25, 4.39s/it] 16%|█▋ | 3046/18688 [3:34:11<18:43:02, 4.31s/it] 16%|█▋ | 3047/18688 [3:34:15<17:45:00, 4.09s/it] 16%|█▋ | 3048/18688 [3:34:19<17:44:30, 4.08s/it] 16%|█▋ | 3049/18688 [3:34:23<17:11:29, 3.96s/it] 16%|█▋ | 3050/18688 [3:34:26<16:56:45, 3.90s/it] {'loss': '0.9537', 'grad_norm': '0.9587', 'learning_rate': '7.841e-05', 'epoch': '0.1632', 'num_input_tokens_seen': 10226736, 'train_runtime': '1.287e+04', 'train_tokens_per_second': '794.8'}
16%|█▋ | 3050/18688 [3:34:26<16:56:45, 3.90s/it] 16%|█▋ | 3051/18688 [3:34:30<17:08:53, 3.95s/it] 16%|█▋ | 3052/18688 [3:34:34<16:38:47, 3.83s/it] 16%|█▋ | 3053/18688 [3:34:38<16:13:18, 3.74s/it] 16%|█▋ | 3054/18688 [3:34:41<16:11:24, 3.73s/it] 16%|█▋ | 3055/18688 [3:34:45<16:41:27, 3.84s/it] 16%|█▋ | 3056/18688 [3:34:50<18:06:21, 4.17s/it] 16%|█▋ | 3057/18688 [3:34:55<18:37:10, 4.29s/it] 16%|█▋ | 3058/18688 [3:34:59<18:07:05, 4.17s/it] 16%|█▋ | 3059/18688 [3:35:03<18:00:14, 4.15s/it] 16%|█▋ | 3060/18688 [3:35:07<18:07:40, 4.18s/it] {'loss': '0.9719', 'grad_norm': '1.105', 'learning_rate': '7.839e-05', 'epoch': '0.1637', 'num_input_tokens_seen': 10259368, 'train_runtime': '1.291e+04', 'train_tokens_per_second': '794.8'}
16%|█▋ | 3060/18688 [3:35:07<18:07:40, 4.18s/it] 16%|█▋ | 3061/18688 [3:35:11<17:59:22, 4.14s/it] 16%|█▋ | 3062/18688 [3:35:15<17:36:08, 4.06s/it] 16%|█▋ | 3063/18688 [3:35:19<17:52:24, 4.12s/it] 16%|█▋ | 3064/18688 [3:35:23<17:34:13, 4.05s/it] 16%|█▋ | 3065/18688 [3:35:27<16:57:37, 3.91s/it] 16%|█▋ | 3066/18688 [3:35:31<17:46:16, 4.10s/it] 16%|█▋ | 3067/18688 [3:35:36<18:32:15, 4.27s/it] 16%|█▋ | 3068/18688 [3:35:40<17:46:38, 4.10s/it] 16%|█▋ | 3069/18688 [3:35:44<17:35:48, 4.06s/it] 16%|█▋ | 3070/18688 [3:35:47<17:14:03, 3.97s/it] {'loss': '0.9451', 'grad_norm': '1.194', 'learning_rate': '7.837e-05', 'epoch': '0.1643', 'num_input_tokens_seen': 10292208, 'train_runtime': '1.295e+04', 'train_tokens_per_second': '794.9'}
16%|█▋ | 3070/18688 [3:35:47<17:14:03, 3.97s/it] 16%|█▋ | 3071/18688 [3:35:51<16:47:45, 3.87s/it] 16%|█▋ | 3072/18688 [3:35:56<18:03:38, 4.16s/it] 16%|█▋ | 3073/18688 [3:35:59<17:11:25, 3.96s/it] 16%|█▋ | 3074/18688 [3:36:04<18:09:37, 4.19s/it] 16%|█▋ | 3075/18688 [3:36:08<18:00:37, 4.15s/it] 16%|█▋ | 3076/18688 [3:36:12<17:17:50, 3.99s/it] 16%|█▋ | 3077/18688 [3:36:16<17:35:02, 4.06s/it] 16%|█▋ | 3078/18688 [3:36:21<19:10:52, 4.42s/it] 16%|█▋ | 3079/18688 [3:36:26<20:05:47, 4.63s/it] 16%|█▋ | 3080/18688 [3:36:30<18:59:40, 4.38s/it] {'loss': '0.9457', 'grad_norm': '1.347', 'learning_rate': '7.835e-05', 'epoch': '0.1648', 'num_input_tokens_seen': 10327872, 'train_runtime': '1.299e+04', 'train_tokens_per_second': '795'}
16%|█▋ | 3080/18688 [3:36:30<18:59:40, 4.38s/it] 16%|█▋ | 3081/18688 [3:36:35<19:38:23, 4.53s/it] 16%|█▋ | 3082/18688 [3:36:39<18:21:16, 4.23s/it] 16%|█▋ | 3083/18688 [3:36:43<18:51:42, 4.35s/it] 17%|█▋ | 3084/18688 [3:36:48<18:56:26, 4.37s/it] 17%|█▋ | 3085/18688 [3:36:53<19:46:30, 4.56s/it] 17%|█▋ | 3086/18688 [3:36:57<19:45:41, 4.56s/it] 17%|█▋ | 3087/18688 [3:37:02<20:44:41, 4.79s/it] 17%|█▋ | 3088/18688 [3:37:07<20:05:49, 4.64s/it] 17%|█▋ | 3089/18688 [3:37:11<19:29:56, 4.50s/it] 17%|█▋ | 3090/18688 [3:37:15<18:27:57, 4.26s/it] {'loss': '0.987', 'grad_norm': '1.155', 'learning_rate': '7.832e-05', 'epoch': '0.1654', 'num_input_tokens_seen': 10365440, 'train_runtime': '1.304e+04', 'train_tokens_per_second': '795.2'}
17%|█▋ | 3090/18688 [3:37:15<18:27:57, 4.26s/it] 17%|█▋ | 3091/18688 [3:37:18<17:43:39, 4.09s/it] 17%|█▋ | 3092/18688 [3:37:22<17:18:22, 3.99s/it] 17%|█▋ | 3093/18688 [3:37:27<18:27:32, 4.26s/it] 17%|█▋ | 3094/18688 [3:37:31<18:21:37, 4.24s/it] 17%|█▋ | 3095/18688 [3:37:36<19:13:28, 4.44s/it] 17%|█▋ | 3096/18688 [3:37:40<18:47:27, 4.34s/it] 17%|█▋ | 3097/18688 [3:37:45<19:31:13, 4.51s/it] 17%|█▋ | 3098/18688 [3:37:49<18:52:09, 4.36s/it] 17%|█▋ | 3099/18688 [3:37:53<18:15:56, 4.22s/it] 17%|█▋ | 3100/18688 [3:37:57<18:10:38, 4.20s/it] {'loss': '1.004', 'grad_norm': '0.8701', 'learning_rate': '7.83e-05', 'epoch': '0.1659', 'num_input_tokens_seen': 10401096, 'train_runtime': '1.308e+04', 'train_tokens_per_second': '795.3'}
17%|█▋ | 3100/18688 [3:37:57<18:10:38, 4.20s/it] 17%|█▋ | 3101/18688 [3:38:01<17:56:50, 4.15s/it] 17%|█▋ | 3102/18688 [3:38:05<17:23:24, 4.02s/it] 17%|█▋ | 3103/18688 [3:38:09<17:36:49, 4.07s/it] 17%|█▋ | 3104/18688 [3:38:13<17:01:28, 3.93s/it] 17%|█▋ | 3105/18688 [3:38:17<17:02:52, 3.94s/it] 17%|█▋ | 3106/18688 [3:38:21<17:04:03, 3.94s/it] 17%|█▋ | 3107/18688 [3:38:24<16:42:29, 3.86s/it] 17%|█▋ | 3108/18688 [3:38:29<17:24:06, 4.02s/it] 17%|█▋ | 3109/18688 [3:38:32<16:52:54, 3.90s/it] 17%|█▋ | 3110/18688 [3:38:36<16:54:13, 3.91s/it] {'loss': '0.9903', 'grad_norm': '1.23', 'learning_rate': '7.828e-05', 'epoch': '0.1664', 'num_input_tokens_seen': 10432040, 'train_runtime': '1.312e+04', 'train_tokens_per_second': '795.3'}
17%|█▋ | 3110/18688 [3:38:36<16:54:13, 3.91s/it] 17%|█▋ | 3111/18688 [3:38:40<16:57:40, 3.92s/it] 17%|█▋ | 3112/18688 [3:38:44<16:42:02, 3.86s/it] 17%|█▋ | 3113/18688 [3:38:48<16:50:15, 3.89s/it] 17%|█▋ | 3114/18688 [3:38:51<16:27:44, 3.81s/it] 17%|█▋ | 3115/18688 [3:38:55<16:43:11, 3.87s/it] 17%|█▋ | 3116/18688 [3:39:00<18:02:23, 4.17s/it] 17%|█▋ | 3117/18688 [3:39:04<17:37:33, 4.08s/it] 17%|█▋ | 3118/18688 [3:39:08<16:55:42, 3.91s/it] 17%|█▋ | 3119/18688 [3:39:12<17:27:08, 4.04s/it] 17%|█▋ | 3120/18688 [3:39:17<18:20:58, 4.24s/it] {'loss': '0.9896', 'grad_norm': '1.174', 'learning_rate': '7.826e-05', 'epoch': '0.167', 'num_input_tokens_seen': 10464328, 'train_runtime': '1.316e+04', 'train_tokens_per_second': '795.3'}
17%|█▋ | 3120/18688 [3:39:17<18:20:58, 4.24s/it] 17%|█▋ | 3121/18688 [3:39:21<18:03:12, 4.18s/it] 17%|█▋ | 3122/18688 [3:39:25<18:00:46, 4.17s/it] 17%|█▋ | 3123/18688 [3:39:29<17:40:27, 4.09s/it] 17%|█▋ | 3124/18688 [3:39:32<16:56:02, 3.92s/it] 17%|█▋ | 3125/18688 [3:39:36<16:44:48, 3.87s/it] 17%|█▋ | 3126/18688 [3:39:40<17:17:05, 4.00s/it] 17%|█▋ | 3127/18688 [3:39:44<17:19:24, 4.01s/it] 17%|█▋ | 3128/18688 [3:39:48<16:43:28, 3.87s/it] 17%|█▋ | 3129/18688 [3:39:53<18:00:11, 4.17s/it] 17%|█▋ | 3130/18688 [3:39:58<19:03:06, 4.41s/it] {'loss': '0.9718', 'grad_norm': '0.9903', 'learning_rate': '7.824e-05', 'epoch': '0.1675', 'num_input_tokens_seen': 10498560, 'train_runtime': '1.32e+04', 'train_tokens_per_second': '795.4'}
17%|█▋ | 3130/18688 [3:39:58<19:03:06, 4.41s/it] 17%|█▋ | 3131/18688 [3:40:01<17:53:16, 4.14s/it] 17%|█▋ | 3132/18688 [3:40:05<17:42:02, 4.10s/it] 17%|█▋ | 3133/18688 [3:40:09<17:25:11, 4.03s/it] 17%|█▋ | 3134/18688 [3:40:14<18:16:44, 4.23s/it] 17%|█▋ | 3135/18688 [3:40:17<17:16:41, 4.00s/it] 17%|█▋ | 3136/18688 [3:40:21<17:03:26, 3.95s/it] 17%|█▋ | 3137/18688 [3:40:26<18:27:25, 4.27s/it] 17%|█▋ | 3138/18688 [3:40:31<18:36:34, 4.31s/it] 17%|█▋ | 3139/18688 [3:40:35<18:44:36, 4.34s/it] 17%|█▋ | 3140/18688 [3:40:39<18:03:20, 4.18s/it] {'loss': '0.8984', 'grad_norm': '1.139', 'learning_rate': '7.822e-05', 'epoch': '0.168', 'num_input_tokens_seen': 10532632, 'train_runtime': '1.324e+04', 'train_tokens_per_second': '795.6'}
17%|█▋ | 3140/18688 [3:40:39<18:03:20, 4.18s/it] 17%|█▋ | 3141/18688 [3:40:42<17:18:22, 4.01s/it] 17%|█▋ | 3142/18688 [3:40:46<16:39:47, 3.86s/it] 17%|█▋ | 3143/18688 [3:40:50<16:39:58, 3.86s/it] 17%|█▋ | 3144/18688 [3:40:54<16:57:22, 3.93s/it] 17%|█▋ | 3145/18688 [3:40:59<18:03:05, 4.18s/it] 17%|█▋ | 3146/18688 [3:41:02<17:10:38, 3.98s/it] 17%|█▋ | 3147/18688 [3:41:07<17:39:50, 4.09s/it] 17%|█▋ | 3148/18688 [3:41:11<17:59:57, 4.17s/it] 17%|█▋ | 3149/18688 [3:41:16<18:53:32, 4.38s/it] 17%|█▋ | 3150/18688 [3:41:20<18:34:31, 4.30s/it] {'loss': '1.005', 'grad_norm': '1.022', 'learning_rate': '7.82e-05', 'epoch': '0.1686', 'num_input_tokens_seen': 10566248, 'train_runtime': '1.328e+04', 'train_tokens_per_second': '795.6'}
17%|█▋ | 3150/18688 [3:41:20<18:34:31, 4.30s/it] 17%|█▋ | 3151/18688 [3:41:25<19:44:29, 4.57s/it] 17%|█▋ | 3152/18688 [3:41:29<19:17:47, 4.47s/it] 17%|█▋ | 3153/18688 [3:41:34<19:39:29, 4.56s/it] 17%|█▋ | 3154/18688 [3:41:38<18:24:30, 4.27s/it] 17%|█▋ | 3155/18688 [3:41:41<17:28:03, 4.05s/it] 17%|█▋ | 3156/18688 [3:41:45<17:29:30, 4.05s/it] 17%|█▋ | 3157/18688 [3:41:50<18:08:31, 4.21s/it] 17%|█▋ | 3158/18688 [3:41:54<18:03:37, 4.19s/it] 17%|█▋ | 3159/18688 [3:41:59<18:44:56, 4.35s/it] 17%|█▋ | 3160/18688 [3:42:03<18:59:32, 4.40s/it] {'loss': '0.9873', 'grad_norm': '1.149', 'learning_rate': '7.818e-05', 'epoch': '0.1691', 'num_input_tokens_seen': 10603952, 'train_runtime': '1.332e+04', 'train_tokens_per_second': '795.9'}
17%|█▋ | 3160/18688 [3:42:03<18:59:32, 4.40s/it] 17%|█▋ | 3161/18688 [3:42:07<18:45:45, 4.35s/it] 17%|█▋ | 3162/18688 [3:42:11<17:38:08, 4.09s/it] 17%|█▋ | 3163/18688 [3:42:15<17:13:29, 3.99s/it] 17%|█▋ | 3164/18688 [3:42:19<17:14:14, 4.00s/it] 17%|█▋ | 3165/18688 [3:42:22<16:39:11, 3.86s/it] 17%|█▋ | 3166/18688 [3:42:26<16:15:51, 3.77s/it] 17%|█▋ | 3167/18688 [3:42:29<15:55:51, 3.70s/it] 17%|█▋ | 3168/18688 [3:42:34<16:47:36, 3.90s/it] 17%|█▋ | 3169/18688 [3:42:39<18:01:57, 4.18s/it] 17%|█▋ | 3170/18688 [3:42:43<18:00:56, 4.18s/it] {'loss': '0.9435', 'grad_norm': '1.264', 'learning_rate': '7.815e-05', 'epoch': '0.1696', 'num_input_tokens_seen': 10636456, 'train_runtime': '1.336e+04', 'train_tokens_per_second': '795.9'}
17%|█▋ | 3170/18688 [3:42:43<18:00:56, 4.18s/it] 17%|█▋ | 3171/18688 [3:42:47<17:46:03, 4.12s/it] 17%|█▋ | 3172/18688 [3:42:52<18:41:06, 4.34s/it] 17%|█▋ | 3173/18688 [3:42:57<19:55:39, 4.62s/it] 17%|█▋ | 3174/18688 [3:43:01<19:02:58, 4.42s/it] 17%|█▋ | 3175/18688 [3:43:05<18:29:55, 4.29s/it] 17%|█▋ | 3176/18688 [3:43:09<18:52:20, 4.38s/it] 17%|█▋ | 3177/18688 [3:43:13<17:41:41, 4.11s/it] 17%|█▋ | 3178/18688 [3:43:16<16:56:54, 3.93s/it] 17%|█▋ | 3179/18688 [3:43:20<17:08:53, 3.98s/it] 17%|█▋ | 3180/18688 [3:43:25<17:45:20, 4.12s/it] {'loss': '0.971', 'grad_norm': '1.108', 'learning_rate': '7.813e-05', 'epoch': '0.1702', 'num_input_tokens_seen': 10671104, 'train_runtime': '1.341e+04', 'train_tokens_per_second': '796'}
17%|█▋ | 3180/18688 [3:43:25<17:45:20, 4.12s/it] 17%|█▋ | 3181/18688 [3:43:29<17:55:24, 4.16s/it] 17%|█▋ | 3182/18688 [3:43:33<17:43:25, 4.11s/it] 17%|█▋ | 3183/18688 [3:43:37<17:29:12, 4.06s/it] 17%|█▋ | 3184/18688 [3:43:41<17:10:25, 3.99s/it] 17%|█▋ | 3185/18688 [3:43:46<18:46:53, 4.36s/it] 17%|█▋ | 3186/18688 [3:43:51<18:49:56, 4.37s/it] 17%|█▋ | 3187/18688 [3:43:54<17:46:21, 4.13s/it] 17%|█▋ | 3188/18688 [3:43:59<18:09:13, 4.22s/it] 17%|█▋ | 3189/18688 [3:44:03<18:24:48, 4.28s/it] 17%|█▋ | 3190/18688 [3:44:08<18:46:42, 4.36s/it] {'loss': '0.9867', 'grad_norm': '0.9326', 'learning_rate': '7.811e-05', 'epoch': '0.1707', 'num_input_tokens_seen': 10707056, 'train_runtime': '1.345e+04', 'train_tokens_per_second': '796.2'}
17%|█▋ | 3190/18688 [3:44:08<18:46:42, 4.36s/it] 17%|█▋ | 3191/18688 [3:44:13<20:46:53, 4.83s/it] 17%|█▋ | 3192/18688 [3:44:17<19:21:18, 4.50s/it] 17%|█▋ | 3193/18688 [3:44:22<19:12:39, 4.46s/it] 17%|█▋ | 3194/18688 [3:44:25<18:21:19, 4.26s/it] 17%|█▋ | 3195/18688 [3:44:29<17:36:14, 4.09s/it] 17%|█▋ | 3196/18688 [3:44:33<16:47:59, 3.90s/it] 17%|█▋ | 3197/18688 [3:44:36<16:48:55, 3.91s/it] 17%|█▋ | 3198/18688 [3:44:40<16:27:44, 3.83s/it] 17%|█▋ | 3199/18688 [3:44:44<17:06:43, 3.98s/it] 17%|█▋ | 3200/18688 [3:44:48<17:00:16, 3.95s/it] {'loss': '1.052', 'grad_norm': '1.1', 'learning_rate': '7.809e-05', 'epoch': '0.1712', 'num_input_tokens_seen': 10740128, 'train_runtime': '1.349e+04', 'train_tokens_per_second': '796.2'}
17%|█▋ | 3200/18688 [3:44:48<17:00:16, 3.95s/it] 17%|█▋ | 3201/18688 [3:44:53<17:24:37, 4.05s/it] 17%|█▋ | 3202/18688 [3:44:56<17:12:04, 4.00s/it] 17%|█▋ | 3203/18688 [3:45:01<17:39:17, 4.10s/it] 17%|█▋ | 3204/18688 [3:45:06<18:43:27, 4.35s/it] 17%|█▋ | 3205/18688 [3:45:09<17:47:30, 4.14s/it] 17%|█▋ | 3206/18688 [3:45:13<17:18:09, 4.02s/it] 17%|█▋ | 3207/18688 [3:45:18<18:12:59, 4.24s/it] 17%|█▋ | 3208/18688 [3:45:22<17:57:21, 4.18s/it] 17%|█▋ | 3209/18688 [3:45:25<17:10:31, 3.99s/it] 17%|█▋ | 3210/18688 [3:45:30<18:09:48, 4.22s/it] {'loss': '0.9633', 'grad_norm': '1.028', 'learning_rate': '7.806e-05', 'epoch': '0.1718', 'num_input_tokens_seen': 10775008, 'train_runtime': '1.353e+04', 'train_tokens_per_second': '796.3'}
17%|█▋ | 3210/18688 [3:45:30<18:09:48, 4.22s/it] 17%|█▋ | 3211/18688 [3:45:34<17:35:29, 4.09s/it] 17%|█▋ | 3212/18688 [3:45:38<17:19:21, 4.03s/it] 17%|█▋ | 3213/18688 [3:45:42<17:01:31, 3.96s/it] 17%|█▋ | 3214/18688 [3:45:45<16:36:25, 3.86s/it] 17%|█▋ | 3215/18688 [3:45:49<16:02:50, 3.73s/it] 17%|█▋ | 3216/18688 [3:45:53<16:45:16, 3.90s/it] 17%|█▋ | 3217/18688 [3:45:57<16:47:28, 3.91s/it] 17%|█▋ | 3218/18688 [3:46:01<17:27:47, 4.06s/it] 17%|█▋ | 3219/18688 [3:46:07<18:54:40, 4.40s/it] 17%|█▋ | 3220/18688 [3:46:11<19:07:13, 4.45s/it] {'loss': '0.946', 'grad_norm': '0.9301', 'learning_rate': '7.804e-05', 'epoch': '0.1723', 'num_input_tokens_seen': 10809208, 'train_runtime': '1.357e+04', 'train_tokens_per_second': '796.5'}
17%|█▋ | 3220/18688 [3:46:11<19:07:13, 4.45s/it] 17%|█▋ | 3221/18688 [3:46:16<19:14:42, 4.48s/it] 17%|█▋ | 3222/18688 [3:46:20<18:37:31, 4.34s/it] 17%|█▋ | 3223/18688 [3:46:25<20:21:13, 4.74s/it] 17%|█▋ | 3224/18688 [3:46:30<19:49:23, 4.61s/it] 17%|█▋ | 3225/18688 [3:46:33<18:44:11, 4.36s/it] 17%|█▋ | 3226/18688 [3:46:37<17:47:56, 4.14s/it] 17%|█▋ | 3227/18688 [3:46:42<18:42:08, 4.35s/it] 17%|█▋ | 3228/18688 [3:46:46<18:16:02, 4.25s/it] 17%|█▋ | 3229/18688 [3:46:50<17:25:12, 4.06s/it] 17%|█▋ | 3230/18688 [3:46:55<18:43:24, 4.36s/it] {'loss': '0.9941', 'grad_norm': '0.9236', 'learning_rate': '7.802e-05', 'epoch': '0.1728', 'num_input_tokens_seen': 10845352, 'train_runtime': '1.362e+04', 'train_tokens_per_second': '796.6'}
17%|█▋ | 3230/18688 [3:46:55<18:43:24, 4.36s/it] 17%|█▋ | 3231/18688 [3:46:59<19:14:38, 4.48s/it] 17%|█▋ | 3232/18688 [3:47:03<18:06:20, 4.22s/it] 17%|█▋ | 3233/18688 [3:47:07<17:43:15, 4.13s/it] 17%|█▋ | 3234/18688 [3:47:11<17:24:44, 4.06s/it] 17%|█▋ | 3235/18688 [3:47:14<16:54:39, 3.94s/it] 17%|█▋ | 3236/18688 [3:47:20<19:34:11, 4.56s/it] 17%|█▋ | 3237/18688 [3:47:24<18:44:37, 4.37s/it] 17%|█▋ | 3238/18688 [3:47:29<19:28:14, 4.54s/it] 17%|█▋ | 3239/18688 [3:47:33<18:43:17, 4.36s/it] 17%|█▋ | 3240/18688 [3:47:37<18:15:54, 4.26s/it] {'loss': '0.9225', 'grad_norm': '1.193', 'learning_rate': '7.8e-05', 'epoch': '0.1734', 'num_input_tokens_seen': 10882248, 'train_runtime': '1.366e+04', 'train_tokens_per_second': '796.8'}
17%|█▋ | 3240/18688 [3:47:37<18:15:54, 4.26s/it] 17%|█▋ | 3241/18688 [3:47:41<17:32:46, 4.09s/it] 17%|█▋ | 3242/18688 [3:47:45<17:21:41, 4.05s/it] 17%|█▋ | 3243/18688 [3:47:49<17:08:58, 4.00s/it] 17%|█▋ | 3244/18688 [3:47:53<17:19:41, 4.04s/it] 17%|█▋ | 3245/18688 [3:47:57<17:21:22, 4.05s/it] 17%|█▋ | 3246/18688 [3:48:01<16:52:10, 3.93s/it] 17%|█▋ | 3247/18688 [3:48:04<16:38:59, 3.88s/it] 17%|█▋ | 3248/18688 [3:48:09<17:52:08, 4.17s/it] 17%|█▋ | 3249/18688 [3:48:13<17:03:48, 3.98s/it] 17%|█▋ | 3250/18688 [3:48:17<17:26:22, 4.07s/it] {'loss': '0.9847', 'grad_norm': '1.233', 'learning_rate': '7.797e-05', 'epoch': '0.1739', 'num_input_tokens_seen': 10913536, 'train_runtime': '1.37e+04', 'train_tokens_per_second': '796.7'}
17%|█▋ | 3250/18688 [3:48:17<17:26:22, 4.07s/it] 17%|█▋ | 3251/18688 [3:48:21<17:28:50, 4.08s/it] 17%|█▋ | 3252/18688 [3:48:25<17:27:21, 4.07s/it] 17%|█▋ | 3253/18688 [3:48:30<17:42:36, 4.13s/it] 17%|█▋ | 3254/18688 [3:48:33<17:01:21, 3.97s/it] 17%|█▋ | 3255/18688 [3:48:37<17:04:55, 3.98s/it] 17%|█▋ | 3256/18688 [3:48:41<16:51:53, 3.93s/it] 17%|█▋ | 3257/18688 [3:48:45<17:02:42, 3.98s/it] 17%|█▋ | 3258/18688 [3:48:50<18:05:31, 4.22s/it] 17%|█▋ | 3259/18688 [3:48:54<18:38:36, 4.35s/it] 17%|█▋ | 3260/18688 [3:48:59<18:20:07, 4.28s/it] {'loss': '0.9278', 'grad_norm': '0.9943', 'learning_rate': '7.795e-05', 'epoch': '0.1744', 'num_input_tokens_seen': 10946160, 'train_runtime': '1.374e+04', 'train_tokens_per_second': '796.7'}
17%|█▋ | 3260/18688 [3:48:59<18:20:07, 4.28s/it] 17%|█▋ | 3261/18688 [3:49:03<19:03:12, 4.45s/it] 17%|█▋ | 3262/18688 [3:49:08<18:45:46, 4.38s/it] 17%|█▋ | 3263/18688 [3:49:12<18:22:33, 4.29s/it] 17%|█▋ | 3264/18688 [3:49:15<17:17:48, 4.04s/it] 17%|█▋ | 3265/18688 [3:49:20<17:57:27, 4.19s/it] 17%|█▋ | 3266/18688 [3:49:23<17:16:23, 4.03s/it] 17%|█▋ | 3267/18688 [3:49:28<17:23:52, 4.06s/it] 17%|█▋ | 3268/18688 [3:49:33<18:44:55, 4.38s/it] 17%|█▋ | 3269/18688 [3:49:36<17:34:14, 4.10s/it] 17%|█▋ | 3270/18688 [3:49:40<17:31:23, 4.09s/it] {'loss': '0.9385', 'grad_norm': '0.9976', 'learning_rate': '7.793e-05', 'epoch': '0.175', 'num_input_tokens_seen': 10980520, 'train_runtime': '1.378e+04', 'train_tokens_per_second': '796.8'}
17%|█▋ | 3270/18688 [3:49:40<17:31:23, 4.09s/it] 18%|█▊ | 3271/18688 [3:49:44<17:47:36, 4.15s/it] 18%|█▊ | 3272/18688 [3:49:49<17:54:25, 4.18s/it] 18%|█▊ | 3273/18688 [3:49:53<17:49:45, 4.16s/it] 18%|█▊ | 3274/18688 [3:49:56<17:02:21, 3.98s/it] 18%|█▊ | 3275/18688 [3:50:01<17:54:45, 4.18s/it] 18%|█▊ | 3276/18688 [3:50:05<17:46:09, 4.15s/it] 18%|█▊ | 3277/18688 [3:50:09<17:47:45, 4.16s/it] 18%|█▊ | 3278/18688 [3:50:14<18:45:15, 4.38s/it] 18%|█▊ | 3279/18688 [3:50:19<19:02:02, 4.45s/it] 18%|█▊ | 3280/18688 [3:50:24<19:28:04, 4.55s/it] {'loss': '0.9401', 'grad_norm': '1.936', 'learning_rate': '7.79e-05', 'epoch': '0.1755', 'num_input_tokens_seen': 11016736, 'train_runtime': '1.382e+04', 'train_tokens_per_second': '796.9'}
18%|█▊ | 3280/18688 [3:50:24<19:28:04, 4.55s/it] 18%|█▊ | 3281/18688 [3:50:28<19:10:46, 4.48s/it] 18%|█▊ | 3282/18688 [3:50:32<18:53:52, 4.42s/it] 18%|█▊ | 3283/18688 [3:50:36<18:14:47, 4.26s/it] 18%|█▊ | 3284/18688 [3:50:40<17:51:48, 4.17s/it] 18%|█▊ | 3285/18688 [3:50:44<17:38:13, 4.12s/it] 18%|█▊ | 3286/18688 [3:50:48<17:48:12, 4.16s/it] 18%|█▊ | 3287/18688 [3:50:53<17:59:04, 4.20s/it] 18%|█▊ | 3288/18688 [3:50:57<18:40:47, 4.37s/it] 18%|█▊ | 3289/18688 [3:51:02<18:49:34, 4.40s/it] 18%|█▊ | 3290/18688 [3:51:06<18:10:07, 4.25s/it] {'loss': '1.012', 'grad_norm': '1.222', 'learning_rate': '7.788e-05', 'epoch': '0.1761', 'num_input_tokens_seen': 11051480, 'train_runtime': '1.387e+04', 'train_tokens_per_second': '797'}
18%|█▊ | 3290/18688 [3:51:06<18:10:07, 4.25s/it] 18%|█▊ | 3291/18688 [3:51:10<18:00:45, 4.21s/it] 18%|█▊ | 3292/18688 [3:51:14<17:33:12, 4.10s/it] 18%|█▊ | 3293/18688 [3:51:17<17:00:08, 3.98s/it] 18%|█▊ | 3294/18688 [3:51:22<17:14:37, 4.03s/it] 18%|█▊ | 3295/18688 [3:51:26<18:07:23, 4.24s/it] 18%|█▊ | 3296/18688 [3:51:30<17:26:22, 4.08s/it] 18%|█▊ | 3297/18688 [3:51:34<18:00:48, 4.21s/it] 18%|█▊ | 3298/18688 [3:51:38<17:17:25, 4.04s/it] 18%|█▊ | 3299/18688 [3:51:42<16:35:06, 3.88s/it] 18%|█▊ | 3300/18688 [3:51:45<16:06:53, 3.77s/it] {'loss': '0.9366', 'grad_norm': '1.006', 'learning_rate': '7.786e-05', 'epoch': '0.1766', 'num_input_tokens_seen': 11083184, 'train_runtime': '1.391e+04', 'train_tokens_per_second': '797'}
18%|█▊ | 3300/18688 [3:51:45<16:06:53, 3.77s/it] 18%|█▊ | 3301/18688 [3:51:49<16:36:32, 3.89s/it] 18%|█▊ | 3302/18688 [3:51:53<16:54:40, 3.96s/it] 18%|█▊ | 3303/18688 [3:51:58<17:57:37, 4.20s/it] 18%|█▊ | 3304/18688 [3:52:03<18:40:04, 4.37s/it] 18%|█▊ | 3305/18688 [3:52:07<18:04:36, 4.23s/it] 18%|█▊ | 3306/18688 [3:52:11<18:17:07, 4.28s/it] 18%|█▊ | 3307/18688 [3:52:15<17:25:13, 4.08s/it] 18%|█▊ | 3308/18688 [3:52:19<17:08:28, 4.01s/it] 18%|█▊ | 3309/18688 [3:52:22<16:26:18, 3.85s/it] 18%|█▊ | 3310/18688 [3:52:27<17:42:47, 4.15s/it] {'loss': '1.004', 'grad_norm': '1.268', 'learning_rate': '7.783e-05', 'epoch': '0.1771', 'num_input_tokens_seen': 11115968, 'train_runtime': '1.395e+04', 'train_tokens_per_second': '797'}
18%|█▊ | 3310/18688 [3:52:27<17:42:47, 4.15s/it] 18%|█▊ | 3311/18688 [3:52:31<17:31:07, 4.10s/it] 18%|█▊ | 3312/18688 [3:52:35<17:05:45, 4.00s/it] 18%|█▊ | 3313/18688 [3:52:40<18:22:53, 4.30s/it] 18%|█▊ | 3314/18688 [3:52:44<18:16:27, 4.28s/it] 18%|█▊ | 3315/18688 [3:52:48<18:19:52, 4.29s/it] 18%|█▊ | 3316/18688 [3:52:53<18:28:03, 4.32s/it] 18%|█▊ | 3317/18688 [3:52:57<17:54:34, 4.19s/it] 18%|█▊ | 3318/18688 [3:53:01<18:11:27, 4.26s/it] 18%|█▊ | 3319/18688 [3:53:05<18:25:32, 4.32s/it] 18%|█▊ | 3320/18688 [3:53:11<19:19:38, 4.53s/it] {'loss': '1.06', 'grad_norm': '1.637', 'learning_rate': '7.781e-05', 'epoch': '0.1777', 'num_input_tokens_seen': 11152640, 'train_runtime': '1.399e+04', 'train_tokens_per_second': '797.1'}
18%|█▊ | 3320/18688 [3:53:11<19:19:38, 4.53s/it] 18%|█▊ | 3321/18688 [3:53:15<18:49:17, 4.41s/it] 18%|█▊ | 3322/18688 [3:53:19<18:46:44, 4.40s/it] 18%|█▊ | 3323/18688 [3:53:24<19:32:56, 4.58s/it] 18%|█▊ | 3324/18688 [3:53:28<19:01:07, 4.46s/it] 18%|█▊ | 3325/18688 [3:53:32<18:13:53, 4.27s/it] 18%|█▊ | 3326/18688 [3:53:36<17:43:41, 4.15s/it] 18%|█▊ | 3327/18688 [3:53:40<17:46:16, 4.16s/it] 18%|█▊ | 3328/18688 [3:53:45<18:28:13, 4.33s/it] 18%|█▊ | 3329/18688 [3:53:49<17:59:26, 4.22s/it] 18%|█▊ | 3330/18688 [3:53:52<17:14:43, 4.04s/it] {'loss': '1.005', 'grad_norm': '1.338', 'learning_rate': '7.779e-05', 'epoch': '0.1782', 'num_input_tokens_seen': 11186944, 'train_runtime': '1.403e+04', 'train_tokens_per_second': '797.2'}
18%|█▊ | 3330/18688 [3:53:52<17:14:43, 4.04s/it] 18%|█▊ | 3331/18688 [3:53:57<17:48:23, 4.17s/it] 18%|█▊ | 3332/18688 [3:54:00<17:03:15, 4.00s/it] 18%|█▊ | 3333/18688 [3:54:04<16:30:55, 3.87s/it] 18%|█▊ | 3334/18688 [3:54:08<17:10:18, 4.03s/it] 18%|█▊ | 3335/18688 [3:54:12<16:59:18, 3.98s/it] 18%|█▊ | 3336/18688 [3:54:16<17:06:28, 4.01s/it] 18%|█▊ | 3337/18688 [3:54:21<17:15:32, 4.05s/it] 18%|█▊ | 3338/18688 [3:54:25<18:08:07, 4.25s/it] 18%|█▊ | 3339/18688 [3:54:29<17:39:54, 4.14s/it] 18%|█▊ | 3340/18688 [3:54:34<18:44:24, 4.40s/it] {'loss': '0.957', 'grad_norm': '1.168', 'learning_rate': '7.776e-05', 'epoch': '0.1787', 'num_input_tokens_seen': 11220456, 'train_runtime': '1.407e+04', 'train_tokens_per_second': '797.2'}
18%|█▊ | 3340/18688 [3:54:34<18:44:24, 4.40s/it] 18%|█▊ | 3341/18688 [3:54:38<17:44:17, 4.16s/it] 18%|█▊ | 3342/18688 [3:54:42<17:42:35, 4.15s/it] 18%|█▊ | 3343/18688 [3:54:45<16:49:21, 3.95s/it] 18%|█▊ | 3344/18688 [3:54:49<16:35:55, 3.89s/it] 18%|█▊ | 3345/18688 [3:54:54<17:19:01, 4.06s/it] 18%|█▊ | 3346/18688 [3:54:58<17:27:34, 4.10s/it] 18%|█▊ | 3347/18688 [3:55:02<17:14:02, 4.04s/it] 18%|█▊ | 3348/18688 [3:55:06<18:04:21, 4.24s/it] 18%|█▊ | 3349/18688 [3:55:11<18:02:35, 4.23s/it] 18%|█▊ | 3350/18688 [3:55:15<17:49:52, 4.19s/it] {'loss': '1.032', 'grad_norm': '1.107', 'learning_rate': '7.774e-05', 'epoch': '0.1793', 'num_input_tokens_seen': 11252456, 'train_runtime': '1.412e+04', 'train_tokens_per_second': '797.2'}
18%|█▊ | 3350/18688 [3:55:15<17:49:52, 4.19s/it] 18%|█▊ | 3351/18688 [3:55:19<17:48:08, 4.18s/it] 18%|█▊ | 3352/18688 [3:55:24<18:59:10, 4.46s/it] 18%|█▊ | 3353/18688 [3:55:28<18:34:59, 4.36s/it] 18%|█▊ | 3354/18688 [3:55:32<18:13:07, 4.28s/it] 18%|█▊ | 3355/18688 [3:55:36<18:12:32, 4.28s/it] 18%|█▊ | 3356/18688 [3:55:40<17:15:53, 4.05s/it] 18%|█▊ | 3357/18688 [3:55:44<17:23:51, 4.09s/it] 18%|█▊ | 3358/18688 [3:55:49<18:18:56, 4.30s/it] 18%|█▊ | 3359/18688 [3:55:52<17:19:26, 4.07s/it] 18%|█▊ | 3360/18688 [3:55:57<17:19:26, 4.07s/it] {'loss': '0.9643', 'grad_norm': '1.193', 'learning_rate': '7.771e-05', 'epoch': '0.1798', 'num_input_tokens_seen': 11285240, 'train_runtime': '1.416e+04', 'train_tokens_per_second': '797.1'}
18%|█▊ | 3360/18688 [3:55:57<17:19:26, 4.07s/it] 18%|█▊ | 3361/18688 [3:56:01<17:57:59, 4.22s/it] 18%|█▊ | 3362/18688 [3:56:05<17:18:16, 4.06s/it] 18%|█▊ | 3363/18688 [3:56:09<18:03:43, 4.24s/it] 18%|█▊ | 3364/18688 [3:56:13<17:17:28, 4.06s/it] 18%|█▊ | 3365/18688 [3:56:17<17:38:23, 4.14s/it] 18%|█▊ | 3366/18688 [3:56:21<17:02:27, 4.00s/it] 18%|█▊ | 3367/18688 [3:56:25<16:22:29, 3.85s/it] 18%|█▊ | 3368/18688 [3:56:28<16:08:34, 3.79s/it] 18%|█▊ | 3369/18688 [3:56:32<16:42:11, 3.93s/it] 18%|█▊ | 3370/18688 [3:56:37<17:39:28, 4.15s/it] {'loss': '0.9353', 'grad_norm': '0.9896', 'learning_rate': '7.769e-05', 'epoch': '0.1803', 'num_input_tokens_seen': 11316984, 'train_runtime': '1.42e+04', 'train_tokens_per_second': '797.1'}
18%|█▊ | 3370/18688 [3:56:37<17:39:28, 4.15s/it] 18%|█▊ | 3371/18688 [3:56:41<17:48:53, 4.19s/it] 18%|█▊ | 3372/18688 [3:56:46<18:38:36, 4.38s/it] 18%|█▊ | 3373/18688 [3:56:51<18:40:48, 4.39s/it] 18%|█▊ | 3374/18688 [3:56:55<19:00:04, 4.47s/it] 18%|█▊ | 3375/18688 [3:57:00<18:44:00, 4.40s/it] 18%|█▊ | 3376/18688 [3:57:03<17:46:54, 4.18s/it] 18%|█▊ | 3377/18688 [3:57:07<17:10:00, 4.04s/it] 18%|█▊ | 3378/18688 [3:57:11<17:18:50, 4.07s/it] 18%|█▊ | 3379/18688 [3:57:15<17:18:21, 4.07s/it] 18%|█▊ | 3380/18688 [3:57:20<18:01:16, 4.24s/it] {'loss': '0.9378', 'grad_norm': '1.795', 'learning_rate': '7.766e-05', 'epoch': '0.1809', 'num_input_tokens_seen': 11352040, 'train_runtime': '1.424e+04', 'train_tokens_per_second': '797.2'}
18%|█▊ | 3380/18688 [3:57:20<18:01:16, 4.24s/it] 18%|█▊ | 3381/18688 [3:57:24<18:01:26, 4.24s/it] 18%|█▊ | 3382/18688 [3:57:29<18:33:19, 4.36s/it] 18%|█▊ | 3383/18688 [3:57:32<17:33:24, 4.13s/it] 18%|█▊ | 3384/18688 [3:57:37<18:02:46, 4.25s/it] 18%|█▊ | 3385/18688 [3:57:41<18:30:25, 4.35s/it] 18%|█▊ | 3386/18688 [3:57:46<18:22:50, 4.32s/it] 18%|█▊ | 3387/18688 [3:57:50<18:27:43, 4.34s/it] 18%|█▊ | 3388/18688 [3:57:54<17:35:41, 4.14s/it] 18%|█▊ | 3389/18688 [3:57:57<16:56:18, 3.99s/it] 18%|█▊ | 3390/18688 [3:58:02<17:57:23, 4.23s/it] {'loss': '0.9928', 'grad_norm': '1.199', 'learning_rate': '7.764e-05', 'epoch': '0.1814', 'num_input_tokens_seen': 11386080, 'train_runtime': '1.428e+04', 'train_tokens_per_second': '797.2'}
18%|█▊ | 3390/18688 [3:58:02<17:57:23, 4.23s/it] 18%|█▊ | 3391/18688 [3:58:07<18:25:55, 4.34s/it] 18%|█▊ | 3392/18688 [3:58:11<18:36:20, 4.38s/it] 18%|█▊ | 3393/18688 [3:58:15<17:46:36, 4.18s/it] 18%|█▊ | 3394/18688 [3:58:19<17:15:50, 4.06s/it] 18%|█▊ | 3395/18688 [3:58:23<17:16:09, 4.07s/it] 18%|█▊ | 3396/18688 [3:58:26<16:44:45, 3.94s/it] 18%|█▊ | 3397/18688 [3:58:31<17:24:06, 4.10s/it] 18%|█▊ | 3398/18688 [3:58:34<16:32:20, 3.89s/it] 18%|█▊ | 3399/18688 [3:58:39<17:02:14, 4.01s/it] 18%|█▊ | 3400/18688 [3:58:43<17:02:44, 4.01s/it] {'loss': '0.9035', 'grad_norm': '1.174', 'learning_rate': '7.761e-05', 'epoch': '0.1819', 'num_input_tokens_seen': 11418256, 'train_runtime': '1.432e+04', 'train_tokens_per_second': '797.2'}
18%|█▊ | 3400/18688 [3:58:43<17:02:44, 4.01s/it] 18%|█▊ | 3401/18688 [3:58:47<17:12:06, 4.05s/it] 18%|█▊ | 3402/18688 [3:58:51<16:51:51, 3.97s/it] 18%|█▊ | 3403/18688 [3:58:54<16:27:21, 3.88s/it] 18%|█▊ | 3404/18688 [3:58:59<18:02:24, 4.25s/it] 18%|█▊ | 3405/18688 [3:59:04<18:12:21, 4.29s/it] 18%|█▊ | 3406/18688 [3:59:08<18:43:47, 4.41s/it] 18%|█▊ | 3407/18688 [3:59:13<18:45:41, 4.42s/it] 18%|█▊ | 3408/18688 [3:59:16<17:46:49, 4.19s/it] 18%|█▊ | 3409/18688 [3:59:21<18:00:30, 4.24s/it] 18%|█▊ | 3410/18688 [3:59:25<17:27:18, 4.11s/it] {'loss': '1.015', 'grad_norm': '0.9731', 'learning_rate': '7.759e-05', 'epoch': '0.1825', 'num_input_tokens_seen': 11451800, 'train_runtime': '1.437e+04', 'train_tokens_per_second': '797.2'}
18%|█▊ | 3410/18688 [3:59:25<17:27:18, 4.11s/it] 18%|█▊ | 3411/18688 [3:59:28<16:45:20, 3.95s/it] 18%|█▊ | 3412/18688 [3:59:32<16:07:03, 3.80s/it] 18%|█▊ | 3413/18688 [3:59:35<15:58:30, 3.76s/it] 18%|█▊ | 3414/18688 [3:59:40<16:37:04, 3.92s/it] 18%|█▊ | 3415/18688 [3:59:44<16:53:31, 3.98s/it] 18%|█▊ | 3416/18688 [3:59:47<16:29:54, 3.89s/it] 18%|█▊ | 3417/18688 [3:59:52<17:16:53, 4.07s/it] 18%|█▊ | 3418/18688 [3:59:56<16:46:08, 3.95s/it] 18%|█▊ | 3419/18688 [3:59:59<16:08:24, 3.81s/it] 18%|█▊ | 3420/18688 [4:00:03<16:29:03, 3.89s/it] {'loss': '0.9653', 'grad_norm': '1.316', 'learning_rate': '7.756e-05', 'epoch': '0.183', 'num_input_tokens_seen': 11482472, 'train_runtime': '1.44e+04', 'train_tokens_per_second': '797.2'}
18%|█▊ | 3420/18688 [4:00:03<16:29:03, 3.89s/it] 18%|█▊ | 3421/18688 [4:00:07<15:59:38, 3.77s/it] 18%|█▊ | 3422/18688 [4:00:11<16:47:09, 3.96s/it] 18%|█▊ | 3423/18688 [4:00:16<17:37:30, 4.16s/it] 18%|█▊ | 3424/18688 [4:00:20<17:17:48, 4.08s/it] 18%|█▊ | 3425/18688 [4:00:24<17:38:23, 4.16s/it] 18%|█▊ | 3426/18688 [4:00:28<17:04:39, 4.03s/it] 18%|█▊ | 3427/18688 [4:00:31<16:22:19, 3.86s/it] 18%|█▊ | 3428/18688 [4:00:35<16:20:40, 3.86s/it] 18%|█▊ | 3429/18688 [4:00:39<16:26:04, 3.88s/it] 18%|█▊ | 3430/18688 [4:00:44<17:34:26, 4.15s/it] {'loss': '0.9745', 'grad_norm': '1.235', 'learning_rate': '7.754e-05', 'epoch': '0.1835', 'num_input_tokens_seen': 11515112, 'train_runtime': '1.444e+04', 'train_tokens_per_second': '797.2'}
18%|█▊ | 3430/18688 [4:00:44<17:34:26, 4.15s/it] 18%|█▊ | 3431/18688 [4:00:48<17:25:57, 4.11s/it] 18%|█▊ | 3432/18688 [4:00:52<18:02:30, 4.26s/it] 18%|█▊ | 3433/18688 [4:00:57<18:25:17, 4.35s/it] 18%|█▊ | 3434/18688 [4:01:01<17:59:20, 4.25s/it] 18%|█▊ | 3435/18688 [4:01:05<17:37:08, 4.16s/it] 18%|█▊ | 3436/18688 [4:01:08<17:00:22, 4.01s/it] 18%|█▊ | 3437/18688 [4:01:13<17:24:53, 4.11s/it] 18%|█▊ | 3438/18688 [4:01:17<18:07:51, 4.28s/it] 18%|█▊ | 3439/18688 [4:01:21<17:07:26, 4.04s/it] 18%|█▊ | 3440/18688 [4:01:26<18:35:47, 4.39s/it] {'loss': '0.971', 'grad_norm': '0.8053', 'learning_rate': '7.751e-05', 'epoch': '0.1841', 'num_input_tokens_seen': 11549936, 'train_runtime': '1.449e+04', 'train_tokens_per_second': '797.3'}
18%|█▊ | 3440/18688 [4:01:26<18:35:47, 4.39s/it] 18%|█▊ | 3441/18688 [4:01:30<17:28:36, 4.13s/it] 18%|█▊ | 3442/18688 [4:01:35<18:31:28, 4.37s/it] 18%|█▊ | 3443/18688 [4:01:40<19:31:21, 4.61s/it] 18%|█▊ | 3444/18688 [4:01:43<18:01:55, 4.26s/it] 18%|█▊ | 3445/18688 [4:01:47<17:23:49, 4.11s/it] 18%|█▊ | 3446/18688 [4:01:52<18:14:46, 4.31s/it] 18%|█▊ | 3447/18688 [4:01:56<18:01:46, 4.26s/it] 18%|█▊ | 3448/18688 [4:01:59<17:02:29, 4.03s/it] 18%|█▊ | 3449/18688 [4:02:04<17:43:59, 4.19s/it] 18%|█▊ | 3450/18688 [4:02:08<17:40:00, 4.17s/it] {'loss': '0.9491', 'grad_norm': '1.103', 'learning_rate': '7.749e-05', 'epoch': '0.1846', 'num_input_tokens_seen': 11583232, 'train_runtime': '1.453e+04', 'train_tokens_per_second': '797.3'}
18%|█▊ | 3450/18688 [4:02:08<17:40:00, 4.17s/it] 18%|█▊ | 3451/18688 [4:02:13<18:32:46, 4.38s/it] 18%|█▊ | 3452/18688 [4:02:17<18:12:06, 4.30s/it] 18%|█▊ | 3453/18688 [4:02:21<18:08:10, 4.29s/it] 18%|█▊ | 3454/18688 [4:02:26<18:10:17, 4.29s/it] 18%|█▊ | 3455/18688 [4:02:29<17:27:46, 4.13s/it] 18%|█▊ | 3456/18688 [4:02:33<16:56:38, 4.00s/it] 18%|█▊ | 3457/18688 [4:02:38<17:40:28, 4.18s/it] 19%|█▊ | 3458/18688 [4:02:42<17:32:23, 4.15s/it] 19%|█▊ | 3459/18688 [4:02:46<17:54:52, 4.23s/it] 19%|█▊ | 3460/18688 [4:02:51<18:35:21, 4.39s/it] {'loss': '0.9237', 'grad_norm': '1.075', 'learning_rate': '7.746e-05', 'epoch': '0.1852', 'num_input_tokens_seen': 11618872, 'train_runtime': '1.457e+04', 'train_tokens_per_second': '797.4'}
19%|█▊ | 3460/18688 [4:02:51<18:35:21, 4.39s/it] 19%|█▊ | 3461/18688 [4:02:55<18:17:13, 4.32s/it] 19%|█▊ | 3462/18688 [4:02:59<17:22:29, 4.11s/it] 19%|█▊ | 3463/18688 [4:03:03<17:16:06, 4.08s/it] 19%|█▊ | 3464/18688 [4:03:06<16:31:46, 3.91s/it] 19%|█▊ | 3465/18688 [4:03:10<16:05:15, 3.80s/it] 19%|█▊ | 3466/18688 [4:03:15<17:15:48, 4.08s/it] 19%|█▊ | 3467/18688 [4:03:19<17:07:25, 4.05s/it] 19%|█▊ | 3468/18688 [4:03:22<16:37:58, 3.93s/it] 19%|█▊ | 3469/18688 [4:03:26<16:54:40, 4.00s/it] 19%|█▊ | 3470/18688 [4:03:30<16:57:40, 4.01s/it] {'loss': '0.8989', 'grad_norm': '0.9606', 'learning_rate': '7.744e-05', 'epoch': '0.1857', 'num_input_tokens_seen': 11650752, 'train_runtime': '1.461e+04', 'train_tokens_per_second': '797.4'}
19%|█▊ | 3470/18688 [4:03:30<16:57:40, 4.01s/it] 19%|█▊ | 3471/18688 [4:03:35<17:05:20, 4.04s/it] 19%|█▊ | 3472/18688 [4:03:38<16:19:39, 3.86s/it] 19%|█▊ | 3473/18688 [4:03:43<17:22:25, 4.11s/it] 19%|█▊ | 3474/18688 [4:03:47<17:04:26, 4.04s/it] 19%|█▊ | 3475/18688 [4:03:51<18:12:27, 4.31s/it] 19%|█▊ | 3476/18688 [4:03:56<18:32:43, 4.39s/it] 19%|█▊ | 3477/18688 [4:04:00<18:03:57, 4.28s/it] 19%|█▊ | 3478/18688 [4:04:04<17:04:09, 4.04s/it] 19%|█▊ | 3479/18688 [4:04:08<17:02:21, 4.03s/it] 19%|█▊ | 3480/18688 [4:04:12<16:58:27, 4.02s/it] {'loss': '0.9401', 'grad_norm': '1.025', 'learning_rate': '7.741e-05', 'epoch': '0.1862', 'num_input_tokens_seen': 11683496, 'train_runtime': '1.465e+04', 'train_tokens_per_second': '797.4'}
19%|█▊ | 3480/18688 [4:04:12<16:58:27, 4.02s/it] 19%|█▊ | 3481/18688 [4:04:15<16:44:40, 3.96s/it] 19%|█▊ | 3482/18688 [4:04:20<16:58:37, 4.02s/it] 19%|█▊ | 3483/18688 [4:04:23<16:17:39, 3.86s/it] 19%|█▊ | 3484/18688 [4:04:27<15:55:46, 3.77s/it] 19%|█▊ | 3485/18688 [4:04:31<16:27:25, 3.90s/it] 19%|█▊ | 3486/18688 [4:04:35<17:06:27, 4.05s/it] 19%|█▊ | 3487/18688 [4:04:40<18:13:02, 4.31s/it] 19%|█▊ | 3488/18688 [4:04:45<19:03:01, 4.51s/it] 19%|█▊ | 3489/18688 [4:04:49<18:24:59, 4.36s/it] 19%|█▊ | 3490/18688 [4:04:53<17:29:38, 4.14s/it] {'loss': '0.9509', 'grad_norm': '0.9471', 'learning_rate': '7.739e-05', 'epoch': '0.1868', 'num_input_tokens_seen': 11717152, 'train_runtime': '1.469e+04', 'train_tokens_per_second': '797.5'}
19%|█▊ | 3490/18688 [4:04:53<17:29:38, 4.14s/it] 19%|█▊ | 3491/18688 [4:04:57<17:45:15, 4.21s/it] 19%|█▊ | 3492/18688 [4:05:01<16:51:20, 3.99s/it] 19%|█▊ | 3493/18688 [4:05:05<16:47:15, 3.98s/it] 19%|█▊ | 3494/18688 [4:05:08<16:31:03, 3.91s/it] 19%|█▊ | 3495/18688 [4:05:12<16:04:38, 3.81s/it] 19%|█▊ | 3496/18688 [4:05:16<16:55:57, 4.01s/it] 19%|█▊ | 3497/18688 [4:05:21<17:26:59, 4.14s/it] 19%|█▊ | 3498/18688 [4:05:25<17:57:16, 4.26s/it] 19%|█▊ | 3499/18688 [4:05:29<16:55:00, 4.01s/it] 19%|█▊ | 3500/18688 [4:05:33<17:00:14, 4.03s/it] {'loss': '0.8724', 'grad_norm': '1.281', 'learning_rate': '7.736e-05', 'epoch': '0.1873', 'num_input_tokens_seen': 11748664, 'train_runtime': '1.473e+04', 'train_tokens_per_second': '797.4'}
19%|█▊ | 3500/18688 [4:05:33<17:00:14, 4.03s/it] 19%|█▊ | 3501/18688 [4:05:37<17:25:26, 4.13s/it] 19%|█▊ | 3502/18688 [4:05:42<18:04:35, 4.29s/it] 19%|█▊ | 3503/18688 [4:05:46<18:25:08, 4.37s/it] 19%|█▉ | 3504/18688 [4:05:51<18:35:44, 4.41s/it] 19%|█▉ | 3505/18688 [4:05:55<18:17:04, 4.34s/it] 19%|█▉ | 3506/18688 [4:06:00<18:50:54, 4.47s/it] 19%|█▉ | 3507/18688 [4:06:04<18:46:39, 4.45s/it] 19%|█▉ | 3508/18688 [4:06:08<18:32:31, 4.40s/it] 19%|█▉ | 3509/18688 [4:06:13<18:11:41, 4.32s/it] 19%|█▉ | 3510/18688 [4:06:18<19:50:04, 4.70s/it] {'loss': '0.9806', 'grad_norm': '0.9462', 'learning_rate': '7.733e-05', 'epoch': '0.1878', 'num_input_tokens_seen': 11788320, 'train_runtime': '1.478e+04', 'train_tokens_per_second': '797.7'}
19%|█▉ | 3510/18688 [4:06:18<19:50:04, 4.70s/it] 19%|█▉ | 3511/18688 [4:06:22<18:38:05, 4.42s/it] 19%|█▉ | 3512/18688 [4:06:26<18:19:03, 4.35s/it] 19%|█▉ | 3513/18688 [4:06:30<17:40:44, 4.19s/it] 19%|█▉ | 3514/18688 [4:06:34<17:05:03, 4.05s/it] 19%|█▉ | 3515/18688 [4:06:38<17:34:42, 4.17s/it] 19%|█▉ | 3516/18688 [4:06:42<17:33:29, 4.17s/it] 19%|█▉ | 3517/18688 [4:06:46<17:02:16, 4.04s/it] 19%|█▉ | 3518/18688 [4:06:50<16:27:52, 3.91s/it] 19%|█▉ | 3519/18688 [4:06:55<17:52:50, 4.24s/it] 19%|█▉ | 3520/18688 [4:06:58<17:11:39, 4.08s/it] {'loss': '0.9392', 'grad_norm': '0.8867', 'learning_rate': '7.731e-05', 'epoch': '0.1884', 'num_input_tokens_seen': 11819968, 'train_runtime': '1.482e+04', 'train_tokens_per_second': '797.6'}
19%|█▉ | 3520/18688 [4:06:58<17:11:39, 4.08s/it] 19%|█▉ | 3521/18688 [4:07:03<17:23:19, 4.13s/it] 19%|█▉ | 3522/18688 [4:07:07<17:21:56, 4.12s/it] 19%|█▉ | 3523/18688 [4:07:11<17:17:52, 4.11s/it] 19%|█▉ | 3524/18688 [4:07:15<17:52:14, 4.24s/it] 19%|█▉ | 3525/18688 [4:07:21<20:04:58, 4.77s/it] 19%|█▉ | 3526/18688 [4:07:25<18:56:38, 4.50s/it] 19%|█▉ | 3527/18688 [4:07:29<18:15:48, 4.34s/it] 19%|█▉ | 3528/18688 [4:07:35<20:04:39, 4.77s/it] 19%|█▉ | 3529/18688 [4:07:39<18:58:47, 4.51s/it] 19%|█▉ | 3530/18688 [4:07:42<17:43:46, 4.21s/it] {'loss': '0.9911', 'grad_norm': '1.276', 'learning_rate': '7.728e-05', 'epoch': '0.1889', 'num_input_tokens_seen': 11856992, 'train_runtime': '1.486e+04', 'train_tokens_per_second': '797.8'}
19%|█▉ | 3530/18688 [4:07:42<17:43:46, 4.21s/it] 19%|█▉ | 3531/18688 [4:07:47<17:43:03, 4.21s/it] 19%|█▉ | 3532/18688 [4:07:52<18:48:10, 4.47s/it] 19%|█▉ | 3533/18688 [4:07:56<18:10:53, 4.32s/it] 19%|█▉ | 3534/18688 [4:08:00<17:39:38, 4.20s/it] 19%|█▉ | 3535/18688 [4:08:04<17:39:43, 4.20s/it] 19%|█▉ | 3536/18688 [4:08:08<17:48:50, 4.23s/it] 19%|█▉ | 3537/18688 [4:08:13<18:27:53, 4.39s/it] 19%|█▉ | 3538/18688 [4:08:17<17:49:15, 4.23s/it] 19%|█▉ | 3539/18688 [4:08:21<17:48:43, 4.23s/it] 19%|█▉ | 3540/18688 [4:08:25<18:01:58, 4.29s/it] {'loss': '0.9498', 'grad_norm': '1.188', 'learning_rate': '7.725e-05', 'epoch': '0.1894', 'num_input_tokens_seen': 11892928, 'train_runtime': '1.491e+04', 'train_tokens_per_second': '797.9'}
19%|█▉ | 3540/18688 [4:08:25<18:01:58, 4.29s/it] 19%|█▉ | 3541/18688 [4:08:29<17:41:25, 4.20s/it] 19%|█▉ | 3542/18688 [4:08:33<17:12:34, 4.09s/it] 19%|█▉ | 3543/18688 [4:08:37<17:24:14, 4.14s/it] 19%|█▉ | 3544/18688 [4:08:41<16:57:27, 4.03s/it] 19%|█▉ | 3545/18688 [4:08:45<16:30:12, 3.92s/it] 19%|█▉ | 3546/18688 [4:08:49<17:05:04, 4.06s/it] 19%|█▉ | 3547/18688 [4:08:53<16:37:39, 3.95s/it] 19%|█▉ | 3548/18688 [4:08:57<16:44:55, 3.98s/it] 19%|█▉ | 3549/18688 [4:09:01<17:16:27, 4.11s/it] 19%|█▉ | 3550/18688 [4:09:06<18:23:52, 4.38s/it] {'loss': '0.9799', 'grad_norm': '1.296', 'learning_rate': '7.723e-05', 'epoch': '0.19', 'num_input_tokens_seen': 11925280, 'train_runtime': '1.495e+04', 'train_tokens_per_second': '797.8'}
19%|█▉ | 3550/18688 [4:09:06<18:23:52, 4.38s/it] 19%|█▉ | 3551/18688 [4:09:10<17:34:28, 4.18s/it] 19%|█▉ | 3552/18688 [4:09:14<17:04:12, 4.06s/it] 19%|█▉ | 3553/18688 [4:09:19<18:11:52, 4.33s/it] 19%|█▉ | 3554/18688 [4:09:23<18:24:18, 4.38s/it] 19%|█▉ | 3555/18688 [4:09:27<17:56:11, 4.27s/it] 19%|█▉ | 3556/18688 [4:09:32<17:52:26, 4.25s/it] 19%|█▉ | 3557/18688 [4:09:36<17:59:48, 4.28s/it] 19%|█▉ | 3558/18688 [4:09:40<17:17:21, 4.11s/it] 19%|█▉ | 3559/18688 [4:09:44<17:55:33, 4.27s/it] 19%|█▉ | 3560/18688 [4:09:50<19:45:32, 4.70s/it] {'loss': '0.9577', 'grad_norm': '1.104', 'learning_rate': '7.72e-05', 'epoch': '0.1905', 'num_input_tokens_seen': 11959592, 'train_runtime': '1.499e+04', 'train_tokens_per_second': '797.8'}
19%|█▉ | 3560/18688 [4:09:50<19:45:32, 4.70s/it] 19%|█▉ | 3561/18688 [4:09:55<20:35:23, 4.90s/it] 19%|█▉ | 3562/18688 [4:10:00<20:02:09, 4.77s/it] 19%|█▉ | 3563/18688 [4:10:04<19:13:48, 4.58s/it] 19%|█▉ | 3564/18688 [4:10:08<18:04:50, 4.30s/it] 19%|█▉ | 3565/18688 [4:10:11<17:09:39, 4.09s/it] 19%|█▉ | 3566/18688 [4:10:16<17:35:01, 4.19s/it] 19%|█▉ | 3567/18688 [4:10:19<17:04:18, 4.06s/it] 19%|█▉ | 3568/18688 [4:10:24<17:44:11, 4.22s/it] 19%|█▉ | 3569/18688 [4:10:28<17:36:00, 4.19s/it] 19%|█▉ | 3570/18688 [4:10:33<18:07:49, 4.32s/it] {'loss': '0.9874', 'grad_norm': '1.084', 'learning_rate': '7.717e-05', 'epoch': '0.191', 'num_input_tokens_seen': 11994864, 'train_runtime': '1.503e+04', 'train_tokens_per_second': '797.9'}
19%|█▉ | 3570/18688 [4:10:33<18:07:49, 4.32s/it] 19%|█▉ | 3571/18688 [4:10:36<17:18:51, 4.12s/it] 19%|█▉ | 3572/18688 [4:10:42<18:35:02, 4.43s/it] 19%|█▉ | 3573/18688 [4:10:47<19:33:11, 4.66s/it] 19%|█▉ | 3574/18688 [4:10:51<19:04:01, 4.54s/it] 19%|█▉ | 3575/18688 [4:10:55<17:58:18, 4.28s/it] 19%|█▉ | 3576/18688 [4:10:58<17:07:40, 4.08s/it] 19%|█▉ | 3577/18688 [4:11:02<16:21:55, 3.90s/it] 19%|█▉ | 3578/18688 [4:11:05<15:53:16, 3.79s/it] 19%|█▉ | 3579/18688 [4:11:09<16:01:28, 3.82s/it] 19%|█▉ | 3580/18688 [4:11:14<17:05:08, 4.07s/it] {'loss': '0.9532', 'grad_norm': '1.008', 'learning_rate': '7.715e-05', 'epoch': '0.1916', 'num_input_tokens_seen': 12028152, 'train_runtime': '1.507e+04', 'train_tokens_per_second': '797.9'}
19%|█▉ | 3580/18688 [4:11:14<17:05:08, 4.07s/it] 19%|█▉ | 3581/18688 [4:11:18<17:10:20, 4.09s/it] 19%|█▉ | 3582/18688 [4:11:22<17:25:01, 4.15s/it] 19%|█▉ | 3583/18688 [4:11:26<17:29:19, 4.17s/it] 19%|█▉ | 3584/18688 [4:11:30<16:36:11, 3.96s/it] 19%|█▉ | 3585/18688 [4:11:35<17:37:18, 4.20s/it] 19%|█▉ | 3586/18688 [4:11:39<17:40:16, 4.21s/it] 19%|█▉ | 3587/18688 [4:11:43<17:25:51, 4.16s/it] 19%|█▉ | 3588/18688 [4:11:47<16:47:16, 4.00s/it] 19%|█▉ | 3589/18688 [4:11:51<16:50:50, 4.02s/it] 19%|█▉ | 3590/18688 [4:11:55<17:13:36, 4.11s/it] {'loss': '0.9242', 'grad_norm': '1.175', 'learning_rate': '7.712e-05', 'epoch': '0.1921', 'num_input_tokens_seen': 12063056, 'train_runtime': '1.512e+04', 'train_tokens_per_second': '798.1'}
19%|█▉ | 3590/18688 [4:11:55<17:13:36, 4.11s/it] 19%|█▉ | 3591/18688 [4:11:59<16:46:08, 4.00s/it] 19%|█▉ | 3592/18688 [4:12:04<18:00:52, 4.30s/it] 19%|█▉ | 3593/18688 [4:12:07<17:22:43, 4.14s/it] 19%|█▉ | 3594/18688 [4:12:12<18:15:49, 4.36s/it] 19%|█▉ | 3595/18688 [4:12:17<18:47:44, 4.48s/it] 19%|█▉ | 3596/18688 [4:12:23<20:03:10, 4.78s/it] 19%|█▉ | 3597/18688 [4:12:26<18:22:04, 4.38s/it] 19%|█▉ | 3598/18688 [4:12:30<18:12:07, 4.34s/it] 19%|█▉ | 3599/18688 [4:12:35<18:51:24, 4.50s/it] 19%|█▉ | 3600/18688 [4:12:39<17:34:37, 4.19s/it] {'loss': '0.9237', 'grad_norm': '1.185', 'learning_rate': '7.709e-05', 'epoch': '0.1926', 'num_input_tokens_seen': 12098216, 'train_runtime': '1.516e+04', 'train_tokens_per_second': '798.1'}
19%|█▉ | 3600/18688 [4:12:39<17:34:37, 4.19s/it] 19%|█▉ | 3601/18688 [4:12:42<16:39:14, 3.97s/it] 19%|█▉ | 3602/18688 [4:12:46<16:32:54, 3.95s/it] 19%|█▉ | 3603/18688 [4:12:51<18:09:55, 4.34s/it] 19%|█▉ | 3604/18688 [4:12:55<17:25:44, 4.16s/it] 19%|█▉ | 3605/18688 [4:12:59<17:13:27, 4.11s/it] 19%|█▉ | 3606/18688 [4:13:03<17:37:40, 4.21s/it] 19%|█▉ | 3607/18688 [4:13:08<17:34:04, 4.19s/it] 19%|█▉ | 3608/18688 [4:13:11<17:09:04, 4.09s/it] 19%|█▉ | 3609/18688 [4:13:15<16:22:59, 3.91s/it] 19%|█▉ | 3610/18688 [4:13:19<16:32:46, 3.95s/it] {'loss': '0.904', 'grad_norm': '1.301', 'learning_rate': '7.706e-05', 'epoch': '0.1932', 'num_input_tokens_seen': 12131904, 'train_runtime': '1.52e+04', 'train_tokens_per_second': '798.2'}
19%|█▉ | 3610/18688 [4:13:19<16:32:46, 3.95s/it] 19%|█▉ | 3611/18688 [4:13:23<17:03:16, 4.07s/it] 19%|█▉ | 3612/18688 [4:13:27<16:47:20, 4.01s/it] 19%|█▉ | 3613/18688 [4:13:31<16:46:23, 4.01s/it] 19%|█▉ | 3614/18688 [4:13:35<16:04:17, 3.84s/it] 19%|█▉ | 3615/18688 [4:13:40<17:48:07, 4.25s/it] 19%|█▉ | 3616/18688 [4:13:44<17:07:55, 4.09s/it] 19%|█▉ | 3617/18688 [4:13:47<16:26:31, 3.93s/it] 19%|█▉ | 3618/18688 [4:13:52<17:25:04, 4.16s/it] 19%|█▉ | 3619/18688 [4:13:55<16:39:10, 3.98s/it] 19%|█▉ | 3620/18688 [4:13:59<16:45:00, 4.00s/it] {'loss': '0.9781', 'grad_norm': '1.026', 'learning_rate': '7.704e-05', 'epoch': '0.1937', 'num_input_tokens_seen': 12164544, 'train_runtime': '1.524e+04', 'train_tokens_per_second': '798.2'}
19%|█▉ | 3620/18688 [4:13:59<16:45:00, 4.00s/it] 19%|█▉ | 3621/18688 [4:14:03<16:21:37, 3.91s/it] 19%|█▉ | 3622/18688 [4:14:08<17:00:13, 4.06s/it] 19%|█▉ | 3623/18688 [4:14:11<16:15:18, 3.88s/it] 19%|█▉ | 3624/18688 [4:14:15<15:56:47, 3.81s/it] 19%|█▉ | 3625/18688 [4:14:19<16:59:13, 4.06s/it] 19%|█▉ | 3626/18688 [4:14:24<17:35:39, 4.21s/it] 19%|█▉ | 3627/18688 [4:14:28<16:58:31, 4.06s/it] 19%|█▉ | 3628/18688 [4:14:32<17:30:39, 4.19s/it] 19%|█▉ | 3629/18688 [4:14:37<18:26:40, 4.41s/it] 19%|█▉ | 3630/18688 [4:14:41<17:41:56, 4.23s/it] {'loss': '0.9213', 'grad_norm': '1.254', 'learning_rate': '7.701e-05', 'epoch': '0.1942', 'num_input_tokens_seen': 12198296, 'train_runtime': '1.528e+04', 'train_tokens_per_second': '798.3'}
19%|█▉ | 3630/18688 [4:14:41<17:41:56, 4.23s/it] 19%|█▉ | 3631/18688 [4:14:45<17:26:38, 4.17s/it] 19%|█▉ | 3632/18688 [4:14:49<17:05:43, 4.09s/it] 19%|█▉ | 3633/18688 [4:14:52<16:32:08, 3.95s/it] 19%|█▉ | 3634/18688 [4:14:56<16:43:30, 4.00s/it] 19%|█▉ | 3635/18688 [4:15:00<16:16:12, 3.89s/it] 19%|█▉ | 3636/18688 [4:15:04<16:28:22, 3.94s/it] 19%|█▉ | 3637/18688 [4:15:08<16:41:53, 3.99s/it] 19%|█▉ | 3638/18688 [4:15:14<18:57:22, 4.53s/it] 19%|█▉ | 3639/18688 [4:15:18<17:36:14, 4.21s/it] 19%|█▉ | 3640/18688 [4:15:22<17:36:20, 4.21s/it] {'loss': '0.99', 'grad_norm': '1.231', 'learning_rate': '7.698e-05', 'epoch': '0.1948', 'num_input_tokens_seen': 12232216, 'train_runtime': '1.532e+04', 'train_tokens_per_second': '798.3'}
19%|█▉ | 3640/18688 [4:15:22<17:36:20, 4.21s/it] 19%|█▉ | 3641/18688 [4:15:26<17:03:40, 4.08s/it] 19%|█▉ | 3642/18688 [4:15:30<18:05:45, 4.33s/it] 19%|█▉ | 3643/18688 [4:15:35<18:02:25, 4.32s/it] 19%|█▉ | 3644/18688 [4:15:38<17:03:36, 4.08s/it] 20%|█▉ | 3645/18688 [4:15:42<16:58:04, 4.06s/it] 20%|█▉ | 3646/18688 [4:15:46<17:09:16, 4.11s/it] 20%|█▉ | 3647/18688 [4:15:50<16:43:21, 4.00s/it] 20%|█▉ | 3648/18688 [4:15:54<16:13:28, 3.88s/it] 20%|█▉ | 3649/18688 [4:15:57<15:57:28, 3.82s/it] 20%|█▉ | 3650/18688 [4:16:01<16:07:04, 3.86s/it] {'loss': '0.9611', 'grad_norm': '1.087', 'learning_rate': '7.695e-05', 'epoch': '0.1953', 'num_input_tokens_seen': 12262912, 'train_runtime': '1.536e+04', 'train_tokens_per_second': '798.3'}
20%|█▉ | 3650/18688 [4:16:01<16:07:04, 3.86s/it] 20%|█▉ | 3651/18688 [4:16:06<17:20:41, 4.15s/it] 20%|█▉ | 3652/18688 [4:16:10<16:43:40, 4.01s/it] 20%|█▉ | 3653/18688 [4:16:15<17:41:14, 4.24s/it] 20%|█▉ | 3654/18688 [4:16:18<17:04:15, 4.09s/it] 20%|█▉ | 3655/18688 [4:16:22<16:46:50, 4.02s/it] 20%|█▉ | 3656/18688 [4:16:26<16:00:27, 3.83s/it] 20%|█▉ | 3657/18688 [4:16:29<15:29:21, 3.71s/it] 20%|█▉ | 3658/18688 [4:16:33<15:53:35, 3.81s/it] 20%|█▉ | 3659/18688 [4:16:38<17:09:20, 4.11s/it] 20%|█▉ | 3660/18688 [4:16:42<17:33:52, 4.21s/it] {'loss': '0.9516', 'grad_norm': '1.2', 'learning_rate': '7.693e-05', 'epoch': '0.1959', 'num_input_tokens_seen': 12295296, 'train_runtime': '1.54e+04', 'train_tokens_per_second': '798.2'}
20%|█▉ | 3660/18688 [4:16:42<17:33:52, 4.21s/it] 20%|█▉ | 3661/18688 [4:16:46<17:22:58, 4.16s/it] 20%|█▉ | 3662/18688 [4:16:51<17:30:19, 4.19s/it] 20%|█▉ | 3663/18688 [4:16:55<18:07:33, 4.34s/it] 20%|█▉ | 3664/18688 [4:17:00<18:08:52, 4.35s/it] 20%|█▉ | 3665/18688 [4:17:04<17:20:33, 4.16s/it] 20%|█▉ | 3666/18688 [4:17:07<16:30:40, 3.96s/it] 20%|█▉ | 3667/18688 [4:17:11<16:51:57, 4.04s/it] 20%|█▉ | 3668/18688 [4:17:16<18:01:08, 4.32s/it] 20%|█▉ | 3669/18688 [4:17:20<17:05:21, 4.10s/it] 20%|█▉ | 3670/18688 [4:17:24<17:13:37, 4.13s/it] {'loss': '0.9032', 'grad_norm': '1.011', 'learning_rate': '7.69e-05', 'epoch': '0.1964', 'num_input_tokens_seen': 12330624, 'train_runtime': '1.544e+04', 'train_tokens_per_second': '798.4'}
20%|█▉ | 3670/18688 [4:17:24<17:13:37, 4.13s/it] 20%|█▉ | 3671/18688 [4:17:28<17:29:06, 4.19s/it] 20%|█▉ | 3672/18688 [4:17:33<17:28:22, 4.19s/it] 20%|█▉ | 3673/18688 [4:17:36<16:48:44, 4.03s/it] 20%|█▉ | 3674/18688 [4:17:41<17:46:22, 4.26s/it] 20%|█▉ | 3675/18688 [4:17:45<17:13:48, 4.13s/it] 20%|█▉ | 3676/18688 [4:17:48<16:24:07, 3.93s/it] 20%|█▉ | 3677/18688 [4:17:53<16:52:12, 4.05s/it] 20%|█▉ | 3678/18688 [4:17:57<17:06:23, 4.10s/it] 20%|█▉ | 3679/18688 [4:18:01<16:45:43, 4.02s/it] 20%|█▉ | 3680/18688 [4:18:04<16:05:50, 3.86s/it] {'loss': '0.9891', 'grad_norm': '1.009', 'learning_rate': '7.687e-05', 'epoch': '0.1969', 'num_input_tokens_seen': 12362352, 'train_runtime': '1.548e+04', 'train_tokens_per_second': '798.4'}
20%|█▉ | 3680/18688 [4:18:04<16:05:50, 3.86s/it] 20%|█▉ | 3681/18688 [4:18:08<15:47:29, 3.79s/it] 20%|█▉ | 3682/18688 [4:18:12<16:22:33, 3.93s/it] 20%|█▉ | 3683/18688 [4:18:16<16:49:13, 4.04s/it] 20%|█▉ | 3684/18688 [4:18:21<17:05:12, 4.10s/it] 20%|█▉ | 3685/18688 [4:18:25<17:12:11, 4.13s/it] 20%|█▉ | 3686/18688 [4:18:29<17:58:03, 4.31s/it] 20%|█▉ | 3687/18688 [4:18:34<18:08:49, 4.35s/it] 20%|█▉ | 3688/18688 [4:18:38<18:17:28, 4.39s/it] 20%|█▉ | 3689/18688 [4:18:43<19:00:21, 4.56s/it] 20%|█▉ | 3690/18688 [4:18:47<18:03:54, 4.34s/it] {'loss': '0.9553', 'grad_norm': '1.098', 'learning_rate': '7.684e-05', 'epoch': '0.1975', 'num_input_tokens_seen': 12399136, 'train_runtime': '1.553e+04', 'train_tokens_per_second': '798.5'}
20%|█▉ | 3690/18688 [4:18:47<18:03:54, 4.34s/it] 20%|█▉ | 3691/18688 [4:18:51<18:00:22, 4.32s/it] 20%|█▉ | 3692/18688 [4:18:56<17:57:57, 4.31s/it] 20%|█▉ | 3693/18688 [4:19:00<17:23:31, 4.18s/it] 20%|█▉ | 3694/18688 [4:19:05<19:08:53, 4.60s/it] 20%|█▉ | 3695/18688 [4:19:10<19:03:39, 4.58s/it] 20%|█▉ | 3696/18688 [4:19:15<19:38:52, 4.72s/it] 20%|█▉ | 3697/18688 [4:19:18<18:12:15, 4.37s/it] 20%|█▉ | 3698/18688 [4:19:23<18:29:21, 4.44s/it] 20%|█▉ | 3699/18688 [4:19:27<17:43:27, 4.26s/it] 20%|█▉ | 3700/18688 [4:19:31<18:12:51, 4.37s/it] {'loss': '1', 'grad_norm': '1.085', 'learning_rate': '7.681e-05', 'epoch': '0.198', 'num_input_tokens_seen': 12436408, 'train_runtime': '1.557e+04', 'train_tokens_per_second': '798.6'}
20%|█▉ | 3700/18688 [4:19:31<18:12:51, 4.37s/it] 20%|█▉ | 3701/18688 [4:19:35<17:04:49, 4.10s/it] 20%|█▉ | 3702/18688 [4:19:39<16:30:28, 3.97s/it] 20%|█▉ | 3703/18688 [4:19:42<15:55:47, 3.83s/it] 20%|█▉ | 3704/18688 [4:19:47<17:46:45, 4.27s/it] 20%|█▉ | 3705/18688 [4:19:52<17:41:20, 4.25s/it] 20%|█▉ | 3706/18688 [4:19:56<18:02:45, 4.34s/it] 20%|█▉ | 3707/18688 [4:20:00<17:38:07, 4.24s/it] 20%|█▉ | 3708/18688 [4:20:04<16:50:02, 4.05s/it] 20%|█▉ | 3709/18688 [4:20:07<16:29:26, 3.96s/it] 20%|█▉ | 3710/18688 [4:20:11<15:58:31, 3.84s/it] {'loss': '0.9754', 'grad_norm': '1.195', 'learning_rate': '7.678e-05', 'epoch': '0.1985', 'num_input_tokens_seen': 12468512, 'train_runtime': '1.561e+04', 'train_tokens_per_second': '798.7'}
20%|█▉ | 3710/18688 [4:20:11<15:58:31, 3.84s/it] 20%|█▉ | 3711/18688 [4:20:15<15:37:01, 3.75s/it] 20%|█▉ | 3712/18688 [4:20:19<16:23:44, 3.94s/it] 20%|█▉ | 3713/18688 [4:20:23<16:03:45, 3.86s/it] 20%|█▉ | 3714/18688 [4:20:26<16:00:32, 3.85s/it] 20%|█▉ | 3715/18688 [4:20:31<16:22:31, 3.94s/it] 20%|█▉ | 3716/18688 [4:20:34<16:16:08, 3.91s/it] 20%|█▉ | 3717/18688 [4:20:39<16:56:14, 4.07s/it] 20%|█▉ | 3718/18688 [4:20:43<17:22:52, 4.18s/it] 20%|█▉ | 3719/18688 [4:20:47<17:15:17, 4.15s/it] 20%|█▉ | 3720/18688 [4:20:52<17:44:59, 4.27s/it] {'loss': '0.9692', 'grad_norm': '1.091', 'learning_rate': '7.675e-05', 'epoch': '0.1991', 'num_input_tokens_seen': 12501288, 'train_runtime': '1.565e+04', 'train_tokens_per_second': '798.7'}
20%|█▉ | 3720/18688 [4:20:52<17:44:59, 4.27s/it] 20%|█▉ | 3721/18688 [4:20:55<16:51:51, 4.06s/it] 20%|█▉ | 3722/18688 [4:20:59<16:21:30, 3.93s/it] 20%|█▉ | 3723/18688 [4:21:04<16:56:22, 4.08s/it] 20%|█▉ | 3724/18688 [4:21:09<18:07:26, 4.36s/it] 20%|█▉ | 3725/18688 [4:21:13<18:05:01, 4.35s/it] 20%|█▉ | 3726/18688 [4:21:17<18:14:52, 4.39s/it] 20%|█▉ | 3727/18688 [4:21:21<17:29:35, 4.21s/it] 20%|█▉ | 3728/18688 [4:21:25<16:50:33, 4.05s/it] 20%|█▉ | 3729/18688 [4:21:29<16:54:39, 4.07s/it] 20%|█▉ | 3730/18688 [4:21:32<16:10:36, 3.89s/it] {'loss': '0.9716', 'grad_norm': '1.186', 'learning_rate': '7.673e-05', 'epoch': '0.1996', 'num_input_tokens_seen': 12533360, 'train_runtime': '1.569e+04', 'train_tokens_per_second': '798.7'}
20%|█▉ | 3730/18688 [4:21:32<16:10:36, 3.89s/it] 20%|█▉ | 3731/18688 [4:21:36<15:56:53, 3.84s/it] 20%|█▉ | 3732/18688 [4:21:40<15:53:08, 3.82s/it] 20%|█▉ | 3733/18688 [4:21:44<15:37:37, 3.76s/it] 20%|█▉ | 3734/18688 [4:21:47<15:24:14, 3.71s/it] 20%|█▉ | 3735/18688 [4:21:51<15:43:35, 3.79s/it] 20%|█▉ | 3736/18688 [4:21:55<15:19:28, 3.69s/it] 20%|█▉ | 3737/18688 [4:21:58<15:26:41, 3.72s/it] 20%|██ | 3738/18688 [4:22:03<16:06:44, 3.88s/it] 20%|██ | 3739/18688 [4:22:07<17:11:15, 4.14s/it] 20%|██ | 3740/18688 [4:22:12<17:16:43, 4.16s/it] {'loss': '0.9721', 'grad_norm': '1.077', 'learning_rate': '7.67e-05', 'epoch': '0.2001', 'num_input_tokens_seen': 12563192, 'train_runtime': '1.573e+04', 'train_tokens_per_second': '798.6'}
20%|██ | 3740/18688 [4:22:12<17:16:43, 4.16s/it] 20%|██ | 3741/18688 [4:22:15<16:36:33, 4.00s/it] 20%|██ | 3742/18688 [4:22:19<16:11:03, 3.90s/it] 20%|██ | 3743/18688 [4:22:22<15:43:00, 3.79s/it] 20%|██ | 3744/18688 [4:22:26<15:48:23, 3.81s/it] 20%|██ | 3745/18688 [4:22:31<16:36:57, 4.00s/it] 20%|██ | 3746/18688 [4:22:35<16:39:42, 4.01s/it] 20%|██ | 3747/18688 [4:22:39<17:16:50, 4.16s/it] 20%|██ | 3748/18688 [4:22:44<17:29:11, 4.21s/it] 20%|██ | 3749/18688 [4:22:47<16:38:46, 4.01s/it] 20%|██ | 3750/18688 [4:22:52<17:14:17, 4.15s/it] {'loss': '0.8454', 'grad_norm': '0.8048', 'learning_rate': '7.667e-05', 'epoch': '0.2007', 'num_input_tokens_seen': 12595568, 'train_runtime': '1.577e+04', 'train_tokens_per_second': '798.6'}
20%|██ | 3750/18688 [4:22:52<17:14:17, 4.15s/it] 20%|██ | 3751/18688 [4:22:56<18:07:30, 4.37s/it] 20%|██ | 3752/18688 [4:23:01<17:57:55, 4.33s/it] 20%|██ | 3753/18688 [4:23:05<17:19:38, 4.18s/it] 20%|██ | 3754/18688 [4:23:09<17:08:52, 4.13s/it] 20%|██ | 3755/18688 [4:23:15<19:31:13, 4.71s/it] 20%|██ | 3756/18688 [4:23:18<17:59:31, 4.34s/it] 20%|██ | 3757/18688 [4:23:22<17:35:22, 4.24s/it] 20%|██ | 3758/18688 [4:23:27<18:15:46, 4.40s/it] 20%|██ | 3759/18688 [4:23:31<17:41:03, 4.26s/it] 20%|██ | 3760/18688 [4:23:34<16:47:15, 4.05s/it] {'loss': '0.9238', 'grad_norm': '1.069', 'learning_rate': '7.664e-05', 'epoch': '0.2012', 'num_input_tokens_seen': 12630368, 'train_runtime': '1.581e+04', 'train_tokens_per_second': '798.6'}
20%|██ | 3760/18688 [4:23:34<16:47:15, 4.05s/it] 20%|██ | 3761/18688 [4:23:39<17:17:41, 4.17s/it] 20%|██ | 3762/18688 [4:23:43<17:15:40, 4.16s/it] 20%|██ | 3763/18688 [4:23:47<17:08:17, 4.13s/it] 20%|██ | 3764/18688 [4:23:51<17:07:10, 4.13s/it] 20%|██ | 3765/18688 [4:23:56<17:43:10, 4.27s/it] 20%|██ | 3766/18688 [4:24:00<17:06:39, 4.13s/it] 20%|██ | 3767/18688 [4:24:04<17:05:36, 4.12s/it] 20%|██ | 3768/18688 [4:24:09<18:27:21, 4.45s/it] 20%|██ | 3769/18688 [4:24:13<18:28:47, 4.46s/it] 20%|██ | 3770/18688 [4:24:17<17:56:00, 4.33s/it] {'loss': '1.035', 'grad_norm': '1.342', 'learning_rate': '7.661e-05', 'epoch': '0.2017', 'num_input_tokens_seen': 12665824, 'train_runtime': '1.586e+04', 'train_tokens_per_second': '798.7'}
20%|██ | 3770/18688 [4:24:17<17:56:00, 4.33s/it] 20%|██ | 3771/18688 [4:24:22<17:43:27, 4.28s/it] 20%|██ | 3772/18688 [4:24:26<17:22:11, 4.19s/it] 20%|██ | 3773/18688 [4:24:30<17:20:08, 4.18s/it] 20%|██ | 3774/18688 [4:24:34<17:07:22, 4.13s/it] 20%|██ | 3775/18688 [4:24:39<17:58:45, 4.34s/it] 20%|██ | 3776/18688 [4:24:42<16:49:38, 4.06s/it] 20%|██ | 3777/18688 [4:24:46<16:29:01, 3.98s/it] 20%|██ | 3778/18688 [4:24:50<16:35:32, 4.01s/it] 20%|██ | 3779/18688 [4:24:53<16:02:20, 3.87s/it] 20%|██ | 3780/18688 [4:24:58<16:46:58, 4.05s/it] {'loss': '0.8858', 'grad_norm': '1.062', 'learning_rate': '7.658e-05', 'epoch': '0.2023', 'num_input_tokens_seen': 12697808, 'train_runtime': '1.59e+04', 'train_tokens_per_second': '798.7'}
20%|██ | 3780/18688 [4:24:58<16:46:58, 4.05s/it] 20%|██ | 3781/18688 [4:25:02<16:34:22, 4.00s/it] 20%|██ | 3782/18688 [4:25:06<16:44:34, 4.04s/it] 20%|██ | 3783/18688 [4:25:10<17:18:31, 4.18s/it] 20%|██ | 3784/18688 [4:25:15<17:58:00, 4.34s/it] 20%|██ | 3785/18688 [4:25:19<17:46:55, 4.30s/it] 20%|██ | 3786/18688 [4:25:24<17:50:59, 4.31s/it] 20%|██ | 3787/18688 [4:25:28<17:40:41, 4.27s/it] 20%|██ | 3788/18688 [4:25:32<17:43:53, 4.28s/it] 20%|██ | 3789/18688 [4:25:36<17:29:50, 4.23s/it] 20%|██ | 3790/18688 [4:25:40<17:22:56, 4.20s/it] {'loss': '1.026', 'grad_norm': '1.363', 'learning_rate': '7.655e-05', 'epoch': '0.2028', 'num_input_tokens_seen': 12733016, 'train_runtime': '1.594e+04', 'train_tokens_per_second': '798.8'}
20%|██ | 3790/18688 [4:25:40<17:22:56, 4.20s/it] 20%|██ | 3791/18688 [4:25:44<16:53:53, 4.08s/it] 20%|██ | 3792/18688 [4:25:48<16:38:28, 4.02s/it] 20%|██ | 3793/18688 [4:25:52<15:59:44, 3.87s/it] 20%|██ | 3794/18688 [4:25:55<15:58:47, 3.86s/it] 20%|██ | 3795/18688 [4:26:00<16:57:49, 4.10s/it] 20%|██ | 3796/18688 [4:26:04<16:12:19, 3.92s/it] 20%|██ | 3797/18688 [4:26:07<15:42:40, 3.80s/it] 20%|██ | 3798/18688 [4:26:11<16:08:45, 3.90s/it] 20%|██ | 3799/18688 [4:26:15<15:58:37, 3.86s/it] 20%|██ | 3800/18688 [4:26:19<15:53:08, 3.84s/it] {'loss': '0.9074', 'grad_norm': '1.18', 'learning_rate': '7.652e-05', 'epoch': '0.2033', 'num_input_tokens_seen': 12763008, 'train_runtime': '1.598e+04', 'train_tokens_per_second': '798.7'}
20%|██ | 3800/18688 [4:26:19<15:53:08, 3.84s/it] 20%|██ | 3801/18688 [4:26:24<17:29:32, 4.23s/it] 20%|██ | 3802/18688 [4:26:28<17:47:21, 4.30s/it] 20%|██ | 3803/18688 [4:26:33<18:37:41, 4.51s/it] 20%|██ | 3804/18688 [4:26:38<18:34:59, 4.49s/it] 20%|██ | 3805/18688 [4:26:42<18:08:13, 4.39s/it] 20%|██ | 3806/18688 [4:26:46<17:53:55, 4.33s/it] 20%|██ | 3807/18688 [4:26:51<18:02:26, 4.36s/it] 20%|██ | 3808/18688 [4:26:55<17:51:55, 4.32s/it] 20%|██ | 3809/18688 [4:26:59<17:37:33, 4.26s/it] 20%|██ | 3810/18688 [4:27:03<17:48:58, 4.31s/it] {'loss': '0.9819', 'grad_norm': '1.011', 'learning_rate': '7.649e-05', 'epoch': '0.2039', 'num_input_tokens_seen': 12799280, 'train_runtime': '1.602e+04', 'train_tokens_per_second': '798.8'}
20%|██ | 3810/18688 [4:27:03<17:48:58, 4.31s/it] 20%|██ | 3811/18688 [4:27:07<17:02:07, 4.12s/it] 20%|██ | 3812/18688 [4:27:11<16:38:06, 4.03s/it] 20%|██ | 3813/18688 [4:27:16<17:43:29, 4.29s/it] 20%|██ | 3814/18688 [4:27:20<17:33:32, 4.25s/it] 20%|██ | 3815/18688 [4:27:24<17:38:17, 4.27s/it] 20%|██ | 3816/18688 [4:27:28<17:12:12, 4.16s/it] 20%|██ | 3817/18688 [4:27:32<16:20:42, 3.96s/it] 20%|██ | 3818/18688 [4:27:35<16:01:02, 3.88s/it] 20%|██ | 3819/18688 [4:27:39<15:52:50, 3.84s/it] 20%|██ | 3820/18688 [4:27:43<16:03:36, 3.89s/it] {'loss': '0.9713', 'grad_norm': '1.027', 'learning_rate': '7.646e-05', 'epoch': '0.2044', 'num_input_tokens_seen': 12830240, 'train_runtime': '1.606e+04', 'train_tokens_per_second': '798.7'}
20%|██ | 3820/18688 [4:27:43<16:03:36, 3.89s/it] 20%|██ | 3821/18688 [4:27:47<16:20:05, 3.96s/it] 20%|██ | 3822/18688 [4:27:52<16:50:08, 4.08s/it] 20%|██ | 3823/18688 [4:27:55<16:33:38, 4.01s/it] 20%|██ | 3824/18688 [4:27:59<15:56:09, 3.86s/it] 20%|██ | 3825/18688 [4:28:03<15:38:26, 3.79s/it] 20%|██ | 3826/18688 [4:28:06<15:38:44, 3.79s/it] 20%|██ | 3827/18688 [4:28:11<16:24:31, 3.97s/it] 20%|██ | 3828/18688 [4:28:16<18:10:43, 4.40s/it] 20%|██ | 3829/18688 [4:28:20<18:04:29, 4.38s/it] 20%|██ | 3830/18688 [4:28:24<17:22:11, 4.21s/it] {'loss': '0.9277', 'grad_norm': '1.005', 'learning_rate': '7.643e-05', 'epoch': '0.2049', 'num_input_tokens_seen': 12862056, 'train_runtime': '1.61e+04', 'train_tokens_per_second': '798.6'}
20%|██ | 3830/18688 [4:28:24<17:22:11, 4.21s/it] 20%|██ | 3831/18688 [4:28:28<16:41:21, 4.04s/it] 21%|██ | 3832/18688 [4:28:33<17:30:44, 4.24s/it] 21%|██ | 3833/18688 [4:28:37<17:15:37, 4.18s/it] 21%|██ | 3834/18688 [4:28:42<18:26:19, 4.47s/it] 21%|██ | 3835/18688 [4:28:46<18:18:45, 4.44s/it] 21%|██ | 3836/18688 [4:28:50<17:21:31, 4.21s/it] 21%|██ | 3837/18688 [4:28:53<16:32:07, 4.01s/it] 21%|██ | 3838/18688 [4:28:58<16:49:03, 4.08s/it] 21%|██ | 3839/18688 [4:29:02<17:17:29, 4.19s/it] 21%|██ | 3840/18688 [4:29:06<16:41:13, 4.05s/it] {'loss': '0.937', 'grad_norm': '1.412', 'learning_rate': '7.64e-05', 'epoch': '0.2055', 'num_input_tokens_seen': 12895912, 'train_runtime': '1.615e+04', 'train_tokens_per_second': '798.7'}
21%|██ | 3840/18688 [4:29:06<16:41:13, 4.05s/it] 21%|██ | 3841/18688 [4:29:09<15:58:48, 3.87s/it] 21%|██ | 3842/18688 [4:29:13<16:13:17, 3.93s/it] 21%|██ | 3843/18688 [4:29:17<15:37:37, 3.79s/it] 21%|██ | 3844/18688 [4:29:23<18:56:49, 4.60s/it] 21%|██ | 3845/18688 [4:29:27<18:26:20, 4.47s/it] 21%|██ | 3846/18688 [4:29:32<18:08:07, 4.40s/it] 21%|██ | 3847/18688 [4:29:36<17:26:31, 4.23s/it] 21%|██ | 3848/18688 [4:29:40<17:25:14, 4.23s/it] 21%|██ | 3849/18688 [4:29:44<17:23:19, 4.22s/it] 21%|██ | 3850/18688 [4:29:49<18:09:45, 4.41s/it] {'loss': '0.9609', 'grad_norm': '0.9937', 'learning_rate': '7.637e-05', 'epoch': '0.206', 'num_input_tokens_seen': 12932544, 'train_runtime': '1.619e+04', 'train_tokens_per_second': '798.8'}
21%|██ | 3850/18688 [4:29:49<18:09:45, 4.41s/it] 21%|██ | 3851/18688 [4:29:53<17:17:47, 4.20s/it] 21%|██ | 3852/18688 [4:29:56<16:32:23, 4.01s/it] 21%|██ | 3853/18688 [4:30:00<16:22:25, 3.97s/it] 21%|██ | 3854/18688 [4:30:05<17:28:42, 4.24s/it] 21%|██ | 3855/18688 [4:30:08<16:44:10, 4.06s/it] 21%|██ | 3856/18688 [4:30:13<17:17:21, 4.20s/it] 21%|██ | 3857/18688 [4:30:17<17:38:21, 4.28s/it] 21%|██ | 3858/18688 [4:30:22<17:26:48, 4.24s/it] 21%|██ | 3859/18688 [4:30:25<16:52:17, 4.10s/it] 21%|██ | 3860/18688 [4:30:30<16:55:38, 4.11s/it] {'loss': '0.963', 'grad_norm': '1.126', 'learning_rate': '7.634e-05', 'epoch': '0.2066', 'num_input_tokens_seen': 12965064, 'train_runtime': '1.623e+04', 'train_tokens_per_second': '798.8'}
21%|██ | 3860/18688 [4:30:30<16:55:38, 4.11s/it] 21%|██ | 3861/18688 [4:30:35<18:35:45, 4.52s/it] 21%|██ | 3862/18688 [4:30:38<17:17:30, 4.20s/it] 21%|██ | 3863/18688 [4:30:43<17:26:22, 4.23s/it] 21%|██ | 3864/18688 [4:30:46<16:48:38, 4.08s/it] 21%|██ | 3865/18688 [4:30:51<17:52:19, 4.34s/it] 21%|██ | 3866/18688 [4:30:55<17:25:22, 4.23s/it] 21%|██ | 3867/18688 [4:30:59<17:00:15, 4.13s/it] 21%|██ | 3868/18688 [4:31:03<16:11:39, 3.93s/it] 21%|██ | 3869/18688 [4:31:07<16:41:03, 4.05s/it] 21%|██ | 3870/18688 [4:31:11<16:14:09, 3.94s/it] {'loss': '0.9822', 'grad_norm': '1.21', 'learning_rate': '7.631e-05', 'epoch': '0.2071', 'num_input_tokens_seen': 12999672, 'train_runtime': '1.627e+04', 'train_tokens_per_second': '798.9'}
21%|██ | 3870/18688 [4:31:11<16:14:09, 3.94s/it] 21%|██ | 3871/18688 [4:31:14<15:54:10, 3.86s/it] 21%|██ | 3872/18688 [4:31:18<15:38:55, 3.80s/it] 21%|██ | 3873/18688 [4:31:22<16:16:30, 3.95s/it] 21%|██ | 3874/18688 [4:31:27<16:29:53, 4.01s/it] 21%|██ | 3875/18688 [4:31:30<16:07:08, 3.92s/it] 21%|██ | 3876/18688 [4:31:35<17:33:50, 4.27s/it] 21%|██ | 3877/18688 [4:31:40<17:52:36, 4.35s/it] 21%|██ | 3878/18688 [4:31:44<18:03:08, 4.39s/it] 21%|██ | 3879/18688 [4:31:49<17:46:25, 4.32s/it] 21%|██ | 3880/18688 [4:31:52<17:02:31, 4.14s/it] {'loss': '0.9594', 'grad_norm': '1.645', 'learning_rate': '7.628e-05', 'epoch': '0.2076', 'num_input_tokens_seen': 13032520, 'train_runtime': '1.631e+04', 'train_tokens_per_second': '798.9'}
21%|██ | 3880/18688 [4:31:52<17:02:31, 4.14s/it] 21%|██ | 3881/18688 [4:31:56<16:48:48, 4.09s/it] 21%|██ | 3882/18688 [4:32:00<16:38:00, 4.04s/it] 21%|██ | 3883/18688 [4:32:04<16:24:52, 3.99s/it] 21%|██ | 3884/18688 [4:32:08<16:41:52, 4.06s/it] 21%|██ | 3885/18688 [4:32:12<16:32:59, 4.02s/it] 21%|██ | 3886/18688 [4:32:16<15:51:53, 3.86s/it] 21%|██ | 3887/18688 [4:32:20<16:13:33, 3.95s/it] 21%|██ | 3888/18688 [4:32:24<16:57:11, 4.12s/it] 21%|██ | 3889/18688 [4:32:28<16:53:45, 4.11s/it] 21%|██ | 3890/18688 [4:32:32<16:35:20, 4.04s/it] {'loss': '0.9148', 'grad_norm': '1.264', 'learning_rate': '7.625e-05', 'epoch': '0.2082', 'num_input_tokens_seen': 13063912, 'train_runtime': '1.635e+04', 'train_tokens_per_second': '798.9'}
21%|██ | 3890/18688 [4:32:32<16:35:20, 4.04s/it] 21%|██ | 3891/18688 [4:32:36<16:31:09, 4.02s/it] 21%|██ | 3892/18688 [4:32:42<18:04:43, 4.40s/it] 21%|██ | 3893/18688 [4:32:46<18:07:49, 4.41s/it] 21%|██ | 3894/18688 [4:32:50<17:04:10, 4.15s/it] 21%|██ | 3895/18688 [4:32:54<16:55:35, 4.12s/it] 21%|██ | 3896/18688 [4:32:58<17:44:36, 4.32s/it] 21%|██ | 3897/18688 [4:33:03<18:03:05, 4.39s/it] 21%|██ | 3898/18688 [4:33:07<17:09:02, 4.17s/it] 21%|██ | 3899/18688 [4:33:11<17:33:10, 4.27s/it] 21%|██ | 3900/18688 [4:33:15<17:05:53, 4.16s/it] {'loss': '0.9779', 'grad_norm': '1.095', 'learning_rate': '7.622e-05', 'epoch': '0.2087', 'num_input_tokens_seen': 13097456, 'train_runtime': '1.64e+04', 'train_tokens_per_second': '798.8'}
21%|██ | 3900/18688 [4:33:15<17:05:53, 4.16s/it] 21%|██ | 3901/18688 [4:33:20<18:08:50, 4.42s/it] 21%|██ | 3902/18688 [4:33:25<18:12:20, 4.43s/it] 21%|██ | 3903/18688 [4:33:29<17:54:46, 4.36s/it] 21%|██ | 3904/18688 [4:33:33<17:17:41, 4.21s/it] 21%|██ | 3905/18688 [4:33:36<16:30:51, 4.02s/it] 21%|██ | 3906/18688 [4:33:41<17:36:16, 4.29s/it] 21%|██ | 3907/18688 [4:33:46<18:53:00, 4.60s/it] 21%|██ | 3908/18688 [4:33:52<19:35:45, 4.77s/it] 21%|██ | 3909/18688 [4:33:56<18:42:07, 4.56s/it] 21%|██ | 3910/18688 [4:33:59<17:31:56, 4.27s/it] {'loss': '0.9994', 'grad_norm': '0.994', 'learning_rate': '7.618e-05', 'epoch': '0.2092', 'num_input_tokens_seen': 13135768, 'train_runtime': '1.644e+04', 'train_tokens_per_second': '799'}
21%|██ | 3910/18688 [4:33:59<17:31:56, 4.27s/it] 21%|██ | 3911/18688 [4:34:04<17:55:42, 4.37s/it] 21%|██ | 3912/18688 [4:34:07<16:58:48, 4.14s/it] 21%|██ | 3913/18688 [4:34:11<16:20:20, 3.98s/it] 21%|██ | 3914/18688 [4:34:16<17:19:24, 4.22s/it] 21%|██ | 3915/18688 [4:34:20<17:03:21, 4.16s/it] 21%|██ | 3916/18688 [4:34:23<16:25:42, 4.00s/it] 21%|██ | 3917/18688 [4:34:27<16:08:17, 3.93s/it] 21%|██ | 3918/18688 [4:34:31<15:40:03, 3.82s/it] 21%|██ | 3919/18688 [4:34:35<15:48:11, 3.85s/it] 21%|██ | 3920/18688 [4:34:39<16:01:21, 3.91s/it] {'loss': '0.9145', 'grad_norm': '1.253', 'learning_rate': '7.615e-05', 'epoch': '0.2098', 'num_input_tokens_seen': 13165712, 'train_runtime': '1.648e+04', 'train_tokens_per_second': '798.9'}
21%|██ | 3920/18688 [4:34:39<16:01:21, 3.91s/it] 21%|██ | 3921/18688 [4:34:43<16:16:22, 3.97s/it] 21%|██ | 3922/18688 [4:34:48<17:21:35, 4.23s/it] 21%|██ | 3923/18688 [4:34:52<17:09:16, 4.18s/it] 21%|██ | 3924/18688 [4:34:57<18:15:55, 4.45s/it] 21%|██ | 3925/18688 [4:35:01<17:37:58, 4.30s/it] 21%|██ | 3926/18688 [4:35:05<16:55:26, 4.13s/it] 21%|██ | 3927/18688 [4:35:09<17:04:19, 4.16s/it] 21%|██ | 3928/18688 [4:35:13<17:32:03, 4.28s/it] 21%|██ | 3929/18688 [4:35:17<17:22:38, 4.24s/it] 21%|██ | 3930/18688 [4:35:22<17:09:38, 4.19s/it] {'loss': '0.9922', 'grad_norm': '0.9586', 'learning_rate': '7.612e-05', 'epoch': '0.2103', 'num_input_tokens_seen': 13200840, 'train_runtime': '1.652e+04', 'train_tokens_per_second': '799'}
21%|██ | 3930/18688 [4:35:22<17:09:38, 4.19s/it] 21%|██ | 3931/18688 [4:35:26<17:21:24, 4.23s/it] 21%|██ | 3932/18688 [4:35:30<17:12:30, 4.20s/it] 21%|██ | 3933/18688 [4:35:34<16:36:34, 4.05s/it] 21%|██ | 3934/18688 [4:35:38<17:01:43, 4.16s/it] 21%|██ | 3935/18688 [4:35:42<17:01:44, 4.16s/it] 21%|██ | 3936/18688 [4:35:46<16:56:48, 4.14s/it] 21%|██ | 3937/18688 [4:35:51<17:00:33, 4.15s/it] 21%|██ | 3938/18688 [4:35:55<17:30:15, 4.27s/it] 21%|██ | 3939/18688 [4:35:59<16:31:32, 4.03s/it] 21%|██ | 3940/18688 [4:36:03<16:40:34, 4.07s/it] {'loss': '0.924', 'grad_norm': '1.061', 'learning_rate': '7.609e-05', 'epoch': '0.2108', 'num_input_tokens_seen': 13232512, 'train_runtime': '1.656e+04', 'train_tokens_per_second': '798.9'}
21%|██ | 3940/18688 [4:36:03<16:40:34, 4.07s/it] 21%|██ | 3941/18688 [4:36:07<16:45:11, 4.09s/it] 21%|██ | 3942/18688 [4:36:12<17:43:15, 4.33s/it] 21%|██ | 3943/18688 [4:36:15<16:47:07, 4.10s/it] 21%|██ | 3944/18688 [4:36:20<16:59:13, 4.15s/it] 21%|██ | 3945/18688 [4:36:24<17:00:33, 4.15s/it] 21%|██ | 3946/18688 [4:36:28<17:31:00, 4.28s/it] 21%|██ | 3947/18688 [4:36:32<16:42:36, 4.08s/it] 21%|██ | 3948/18688 [4:36:36<17:16:19, 4.22s/it] 21%|██ | 3949/18688 [4:36:40<16:28:50, 4.03s/it] 21%|██ | 3950/18688 [4:36:44<15:56:00, 3.89s/it] {'loss': '0.9406', 'grad_norm': '0.9407', 'learning_rate': '7.606e-05', 'epoch': '0.2114', 'num_input_tokens_seen': 13265784, 'train_runtime': '1.66e+04', 'train_tokens_per_second': '798.9'}
21%|██ | 3950/18688 [4:36:44<15:56:00, 3.89s/it] 21%|██ | 3951/18688 [4:36:47<15:44:20, 3.84s/it] 21%|██ | 3952/18688 [4:36:53<17:34:20, 4.29s/it] 21%|██ | 3953/18688 [4:36:57<17:10:22, 4.20s/it] 21%|██ | 3954/18688 [4:37:01<17:16:37, 4.22s/it] 21%|██ | 3955/18688 [4:37:05<17:38:13, 4.31s/it] 21%|██ | 3956/18688 [4:37:09<16:34:30, 4.05s/it] 21%|██ | 3957/18688 [4:37:12<16:01:04, 3.91s/it] 21%|██ | 3958/18688 [4:37:17<16:44:55, 4.09s/it] 21%|██ | 3959/18688 [4:37:21<16:48:56, 4.11s/it] 21%|██ | 3960/18688 [4:37:25<16:37:52, 4.07s/it] {'loss': '0.962', 'grad_norm': '1.225', 'learning_rate': '7.603e-05', 'epoch': '0.2119', 'num_input_tokens_seen': 13299808, 'train_runtime': '1.665e+04', 'train_tokens_per_second': '799'}
21%|██ | 3960/18688 [4:37:25<16:37:52, 4.07s/it] 21%|██ | 3961/18688 [4:37:30<17:18:51, 4.23s/it] 21%|██ | 3962/18688 [4:37:34<16:52:40, 4.13s/it] 21%|██ | 3963/18688 [4:37:38<17:24:11, 4.25s/it] 21%|██ | 3964/18688 [4:37:43<17:51:22, 4.37s/it] 21%|██ | 3965/18688 [4:37:47<17:26:23, 4.26s/it] 21%|██ | 3966/18688 [4:37:52<18:11:58, 4.45s/it] 21%|██ | 3967/18688 [4:37:55<17:08:54, 4.19s/it] 21%|██ | 3968/18688 [4:37:59<16:17:36, 3.98s/it] 21%|██ | 3969/18688 [4:38:03<16:27:49, 4.03s/it] 21%|██ | 3970/18688 [4:38:07<16:28:22, 4.03s/it] {'loss': '0.9074', 'grad_norm': '1.172', 'learning_rate': '7.6e-05', 'epoch': '0.2124', 'num_input_tokens_seen': 13334904, 'train_runtime': '1.669e+04', 'train_tokens_per_second': '799.1'}
21%|██ | 3970/18688 [4:38:07<16:28:22, 4.03s/it] 21%|██ | 3971/18688 [4:38:11<16:05:34, 3.94s/it] 21%|██▏ | 3972/18688 [4:38:16<18:04:45, 4.42s/it] 21%|██▏ | 3973/18688 [4:38:20<17:25:09, 4.26s/it] 21%|██▏ | 3974/18688 [4:38:24<16:42:11, 4.09s/it] 21%|██▏ | 3975/18688 [4:38:28<16:56:02, 4.14s/it] 21%|██▏ | 3976/18688 [4:38:32<16:04:59, 3.94s/it] 21%|██▏ | 3977/18688 [4:38:36<16:37:39, 4.07s/it] 21%|██▏ | 3978/18688 [4:38:40<16:18:53, 3.99s/it] 21%|██▏ | 3979/18688 [4:38:46<18:32:35, 4.54s/it] 21%|██▏ | 3980/18688 [4:38:49<17:40:20, 4.33s/it] {'loss': '0.9481', 'grad_norm': '1.255', 'learning_rate': '7.596e-05', 'epoch': '0.213', 'num_input_tokens_seen': 13370056, 'train_runtime': '1.673e+04', 'train_tokens_per_second': '799.2'}
21%|██▏ | 3980/18688 [4:38:49<17:40:20, 4.33s/it] 21%|██▏ | 3981/18688 [4:38:53<16:37:45, 4.07s/it] 21%|██▏ | 3982/18688 [4:38:57<16:18:16, 3.99s/it] 21%|██▏ | 3983/18688 [4:39:01<16:10:45, 3.96s/it] 21%|██▏ | 3984/18688 [4:39:04<16:02:04, 3.93s/it] 21%|██▏ | 3985/18688 [4:39:09<16:49:52, 4.12s/it] 21%|██▏ | 3986/18688 [4:39:13<16:49:34, 4.12s/it] 21%|██▏ | 3987/18688 [4:39:17<16:38:05, 4.07s/it] 21%|██▏ | 3988/18688 [4:39:22<17:15:43, 4.23s/it] 21%|██▏ | 3989/18688 [4:39:26<17:18:02, 4.24s/it] 21%|██▏ | 3990/18688 [4:39:29<16:31:25, 4.05s/it] {'loss': '1.034', 'grad_norm': '1.124', 'learning_rate': '7.593e-05', 'epoch': '0.2135', 'num_input_tokens_seen': 13401352, 'train_runtime': '1.677e+04', 'train_tokens_per_second': '799.1'}
21%|██▏ | 3990/18688 [4:39:29<16:31:25, 4.05s/it] 21%|██▏ | 3991/18688 [4:39:33<16:14:33, 3.98s/it] 21%|██▏ | 3992/18688 [4:39:37<16:08:07, 3.95s/it] 21%|██▏ | 3993/18688 [4:39:41<15:54:15, 3.90s/it] 21%|██▏ | 3994/18688 [4:39:45<15:48:00, 3.87s/it] 21%|██▏ | 3995/18688 [4:39:49<15:41:07, 3.84s/it] 21%|██▏ | 3996/18688 [4:39:54<17:07:44, 4.20s/it] 21%|██▏ | 3997/18688 [4:39:57<16:26:41, 4.03s/it] 21%|██▏ | 3998/18688 [4:40:02<17:15:29, 4.23s/it] 21%|██▏ | 3999/18688 [4:40:05<16:26:46, 4.03s/it] 21%|██▏ | 4000/18688 [4:40:10<17:00:24, 4.17s/it] {'loss': '0.9459', 'grad_norm': '0.9308', 'learning_rate': '7.59e-05', 'epoch': '0.214', 'num_input_tokens_seen': 13433360, 'train_runtime': '1.681e+04', 'train_tokens_per_second': '799.1'}
21%|██▏ | 4000/18688 [4:40:10<17:00:24, 4.17s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
0%| | 3/1000 [00:00<00:38, 25.66it/s]
1%| | 6/1000 [00:00<00:54, 18.19it/s]
1%| | 8/1000 [00:00<00:56, 17.44it/s]
1%| | 10/1000 [00:00<00:59, 16.55it/s]
1%| | 12/1000 [00:00<01:00, 16.36it/s]
1%|▏ | 14/1000 [00:00<01:01, 15.91it/s]
2%|▏ | 16/1000 [00:00<01:03, 15.57it/s]
2%|▏ | 18/1000 [00:01<01:38, 9.96it/s]
2%|▏ | 20/1000 [00:01<01:33, 10.52it/s]
2%|▏ | 22/1000 [00:01<01:52, 8.70it/s]
2%|▏ | 24/1000 [00:01<01:40, 9.74it/s]
3%|▎ | 26/1000 [00:02<01:45, 9.26it/s]
3%|▎ | 28/1000 [00:02<01:36, 10.10it/s]
3%|▎ | 30/1000 [00:02<01:28, 11.00it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.17it/s]
3%|▎ | 34/1000 [00:02<01:25, 11.28it/s]
4%|▎ | 36/1000 [00:03<01:32, 10.43it/s]
4%|▍ | 38/1000 [00:03<01:24, 11.33it/s]
4%|▍ | 40/1000 [00:03<01:27, 10.95it/s]
4%|▍ | 42/1000 [00:03<01:46, 9.03it/s]
4%|▍ | 44/1000 [00:03<01:30, 10.53it/s]
5%|▍ | 46/1000 [00:03<01:21, 11.70it/s]
5%|▍ | 48/1000 [00:04<01:29, 10.64it/s]
5%|▌ | 50/1000 [00:04<01:21, 11.68it/s]
5%|▌ | 52/1000 [00:04<01:17, 12.27it/s]
5%|▌ | 54/1000 [00:04<01:13, 12.88it/s]
6%|▌ | 56/1000 [00:04<01:10, 13.31it/s]
6%|▌ | 58/1000 [00:05<01:55, 8.13it/s]
6%|▌ | 60/1000 [00:05<01:41, 9.23it/s]
6%|▌ | 62/1000 [00:05<01:31, 10.22it/s]
6%|▋ | 64/1000 [00:05<01:22, 11.31it/s]
7%|▋ | 66/1000 [00:05<01:18, 11.90it/s]
7%|▋ | 68/1000 [00:05<01:14, 12.54it/s]
7%|▋ | 70/1000 [00:06<01:10, 13.22it/s]
7%|▋ | 72/1000 [00:06<01:07, 13.74it/s]
7%|▋ | 74/1000 [00:06<01:08, 13.57it/s]
8%|▊ | 76/1000 [00:06<01:05, 14.20it/s]
8%|▊ | 78/1000 [00:06<01:03, 14.41it/s]
8%|▊ | 80/1000 [00:06<01:08, 13.41it/s]
8%|▊ | 82/1000 [00:06<01:05, 14.04it/s]
8%|▊ | 84/1000 [00:07<01:03, 14.51it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.63it/s]
9%|▉ | 88/1000 [00:07<01:03, 14.47it/s]
9%|▉ | 90/1000 [00:07<01:02, 14.48it/s]
9%|▉ | 92/1000 [00:07<01:04, 14.14it/s]
9%|▉ | 94/1000 [00:07<01:04, 13.97it/s]
10%|▉ | 96/1000 [00:07<01:03, 14.32it/s]
10%|▉ | 98/1000 [00:08<01:03, 14.23it/s]
10%|█ | 100/1000 [00:08<01:31, 9.80it/s]
10%|█ | 102/1000 [00:08<01:23, 10.76it/s]
10%|█ | 104/1000 [00:08<01:54, 7.81it/s]
11%|█ | 106/1000 [00:09<02:02, 7.32it/s]
11%|█ | 108/1000 [00:09<01:43, 8.65it/s]
11%|█ | 110/1000 [00:09<01:28, 10.09it/s]
11%|█ | 112/1000 [00:09<01:19, 11.22it/s]
11%|█▏ | 114/1000 [00:09<01:14, 11.88it/s]
12%|█▏ | 116/1000 [00:09<01:10, 12.52it/s]
12%|█▏ | 118/1000 [00:10<01:06, 13.29it/s]
12%|█▏ | 120/1000 [00:10<01:09, 12.73it/s]
12%|█▏ | 122/1000 [00:10<01:23, 10.58it/s]
12%|█▏ | 124/1000 [00:10<01:30, 9.65it/s]
13%|█▎ | 126/1000 [00:10<01:22, 10.61it/s]
13%|█▎ | 128/1000 [00:11<01:15, 11.52it/s]
13%|█▎ | 130/1000 [00:11<01:11, 12.18it/s]
13%|█▎ | 132/1000 [00:11<01:06, 13.13it/s]
13%|█▎ | 134/1000 [00:11<01:10, 12.34it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.31it/s]
14%|█▍ | 138/1000 [00:11<01:05, 13.22it/s]
14%|█▍ | 140/1000 [00:12<01:25, 10.08it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.52it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.56it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.58it/s]
15%|█▍ | 148/1000 [00:12<01:14, 11.50it/s]
15%|█▌ | 150/1000 [00:12<01:07, 12.58it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.13it/s]
15%|█▌ | 154/1000 [00:13<01:03, 13.40it/s]
16%|█▌ | 156/1000 [00:13<01:03, 13.23it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.74it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.65it/s]
16%|█▌ | 162/1000 [00:13<01:01, 13.53it/s]
16%|█▋ | 164/1000 [00:13<01:01, 13.62it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.77it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.25it/s]
17%|█▋ | 170/1000 [00:14<01:19, 10.48it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.46it/s]
17%|█▋ | 174/1000 [00:14<01:30, 9.17it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.18it/s]
18%|█▊ | 178/1000 [00:15<01:35, 8.64it/s]
18%|█▊ | 180/1000 [00:15<01:33, 8.77it/s]
18%|█▊ | 182/1000 [00:16<01:35, 8.53it/s]
18%|█▊ | 184/1000 [00:16<01:23, 9.79it/s]
19%|█▊ | 186/1000 [00:16<01:37, 8.32it/s]
19%|█▉ | 188/1000 [00:16<01:24, 9.64it/s]
19%|█▉ | 190/1000 [00:16<01:16, 10.59it/s]
19%|█▉ | 192/1000 [00:16<01:13, 11.04it/s]
19%|█▉ | 194/1000 [00:17<01:19, 10.12it/s]
20%|█▉ | 196/1000 [00:17<01:33, 8.57it/s]
20%|█▉ | 198/1000 [00:17<01:25, 9.40it/s]
20%|██ | 200/1000 [00:17<01:20, 9.93it/s]
20%|██ | 202/1000 [00:17<01:15, 10.53it/s]
20%|██ | 204/1000 [00:18<01:12, 11.02it/s]
21%|██ | 206/1000 [00:18<01:09, 11.40it/s]
21%|██ | 208/1000 [00:18<01:07, 11.67it/s]
21%|██ | 210/1000 [00:18<01:05, 11.99it/s]
21%|██ | 212/1000 [00:18<01:05, 12.10it/s]
21%|██▏ | 214/1000 [00:18<01:04, 12.17it/s]
22%|██▏ | 216/1000 [00:19<01:04, 12.24it/s]
22%|██▏ | 218/1000 [00:19<01:03, 12.34it/s]
22%|██▏ | 220/1000 [00:19<01:34, 8.22it/s]
22%|██▏ | 222/1000 [00:19<01:29, 8.68it/s]
22%|██▏ | 224/1000 [00:20<01:24, 9.23it/s]
23%|██▎ | 226/1000 [00:20<01:15, 10.27it/s]
23%|██▎ | 228/1000 [00:20<01:08, 11.24it/s]
23%|██▎ | 230/1000 [00:20<01:05, 11.81it/s]
23%|██▎ | 232/1000 [00:20<01:02, 12.24it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.04it/s]
24%|██▎ | 236/1000 [00:20<00:56, 13.55it/s]
24%|██▍ | 238/1000 [00:21<00:53, 14.14it/s]
24%|██▍ | 240/1000 [00:21<00:53, 14.14it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.65it/s]
24%|██▍ | 244/1000 [00:21<00:50, 14.91it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.90it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.65it/s]
25%|██▌ | 250/1000 [00:21<00:56, 13.36it/s]
25%|██▌ | 252/1000 [00:22<01:01, 12.12it/s]
25%|██▌ | 254/1000 [00:22<00:56, 13.16it/s]
26%|██▌ | 256/1000 [00:22<00:54, 13.74it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.80it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.92it/s]
26%|██▌ | 262/1000 [00:22<00:56, 13.06it/s]
26%|██▋ | 264/1000 [00:22<00:54, 13.63it/s]
27%|██▋ | 266/1000 [00:23<00:52, 13.99it/s]
27%|██▋ | 268/1000 [00:23<00:50, 14.56it/s]
27%|██▋ | 270/1000 [00:23<00:54, 13.28it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.65it/s]
27%|██▋ | 274/1000 [00:23<01:01, 11.73it/s]
28%|██▊ | 276/1000 [00:23<00:56, 12.74it/s]
28%|██▊ | 278/1000 [00:24<00:55, 12.97it/s]
28%|██▊ | 280/1000 [00:24<00:53, 13.55it/s]
28%|██▊ | 282/1000 [00:24<00:49, 14.52it/s]
28%|██▊ | 284/1000 [00:24<00:49, 14.45it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.73it/s]
29%|██▉ | 288/1000 [00:24<00:47, 15.10it/s]
29%|██▉ | 290/1000 [00:24<00:45, 15.55it/s]
29%|██▉ | 292/1000 [00:24<00:45, 15.49it/s]
29%|██▉ | 294/1000 [00:25<00:45, 15.38it/s]
30%|██▉ | 296/1000 [00:25<00:45, 15.42it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.51it/s]
30%|███ | 300/1000 [00:25<00:46, 15.17it/s]
30%|███ | 302/1000 [00:25<00:45, 15.18it/s]
30%|███ | 304/1000 [00:25<00:50, 13.77it/s]
31%|███ | 306/1000 [00:25<00:47, 14.47it/s]
31%|███ | 308/1000 [00:26<00:47, 14.44it/s]
31%|███ | 310/1000 [00:26<00:47, 14.63it/s]
31%|███ | 312/1000 [00:26<00:47, 14.63it/s]
31%|███▏ | 314/1000 [00:26<00:45, 15.24it/s]
32%|███▏ | 316/1000 [00:26<00:44, 15.50it/s]
32%|███▏ | 318/1000 [00:26<00:42, 15.96it/s]
32%|███▏ | 320/1000 [00:27<01:12, 9.36it/s]
32%|███▏ | 322/1000 [00:27<01:03, 10.73it/s]
32%|███▏ | 324/1000 [00:27<00:58, 11.52it/s]
33%|███▎ | 326/1000 [00:27<00:58, 11.45it/s]
33%|███▎ | 328/1000 [00:27<00:58, 11.49it/s]
33%|███▎ | 330/1000 [00:27<00:53, 12.45it/s]
33%|███▎ | 332/1000 [00:27<00:52, 12.72it/s]
33%|███▎ | 334/1000 [00:28<00:50, 13.22it/s]
34%|███▎ | 336/1000 [00:28<00:48, 13.61it/s]
34%|███▍ | 338/1000 [00:28<01:05, 10.07it/s]
34%|███▍ | 340/1000 [00:28<01:00, 10.85it/s]
34%|███▍ | 342/1000 [00:28<00:54, 12.17it/s]
34%|███▍ | 344/1000 [00:28<00:50, 12.88it/s]
35%|███▍ | 346/1000 [00:29<00:48, 13.54it/s]
35%|███▍ | 348/1000 [00:29<00:46, 14.00it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.65it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.82it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.86it/s]
36%|███▌ | 356/1000 [00:29<00:43, 14.97it/s]
36%|███▌ | 358/1000 [00:29<00:43, 14.68it/s]
36%|███▌ | 360/1000 [00:30<00:42, 15.09it/s]
36%|███▌ | 362/1000 [00:30<00:47, 13.40it/s]
36%|███▋ | 364/1000 [00:30<00:46, 13.59it/s]
37%|███▋ | 366/1000 [00:30<00:53, 11.81it/s]
37%|███▋ | 368/1000 [00:30<00:51, 12.25it/s]
37%|███▋ | 370/1000 [00:30<00:47, 13.16it/s]
37%|███▋ | 372/1000 [00:30<00:46, 13.50it/s]
37%|███▋ | 374/1000 [00:31<00:55, 11.38it/s]
38%|███▊ | 376/1000 [00:31<00:57, 10.94it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.62it/s]
38%|███▊ | 380/1000 [00:31<00:50, 12.39it/s]
38%|███▊ | 382/1000 [00:31<00:49, 12.59it/s]
38%|███▊ | 384/1000 [00:32<00:46, 13.36it/s]
39%|███▊ | 386/1000 [00:32<00:44, 13.68it/s]
39%|███▉ | 388/1000 [00:32<00:44, 13.65it/s]
39%|███▉ | 390/1000 [00:32<00:45, 13.54it/s]
39%|███▉ | 392/1000 [00:32<00:43, 13.88it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.66it/s]
40%|███▉ | 396/1000 [00:32<00:43, 13.78it/s]
40%|███▉ | 398/1000 [00:33<00:47, 12.56it/s]
40%|████ | 400/1000 [00:33<00:45, 13.23it/s]
40%|████ | 402/1000 [00:33<00:43, 13.89it/s]
40%|████ | 404/1000 [00:33<00:41, 14.50it/s]
41%|████ | 406/1000 [00:33<00:39, 14.99it/s]
41%|████ | 408/1000 [00:33<00:47, 12.54it/s]
41%|████ | 410/1000 [00:34<00:56, 10.48it/s]
41%|████ | 412/1000 [00:34<00:50, 11.57it/s]
41%|████▏ | 414/1000 [00:34<01:07, 8.64it/s]
42%|████▏ | 416/1000 [00:34<01:23, 6.98it/s]
42%|████▏ | 418/1000 [00:35<01:10, 8.29it/s]
42%|████▏ | 420/1000 [00:35<01:00, 9.56it/s]
42%|████▏ | 422/1000 [00:35<00:52, 10.91it/s]
42%|████▏ | 424/1000 [00:35<01:14, 7.69it/s]
43%|████▎ | 426/1000 [00:35<01:04, 8.94it/s]
43%|████▎ | 428/1000 [00:36<00:56, 10.14it/s]
43%|████▎ | 430/1000 [00:36<01:02, 9.12it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.05it/s]
43%|████▎ | 434/1000 [00:36<01:08, 8.27it/s]
44%|████▎ | 436/1000 [00:37<01:00, 9.30it/s]
44%|████▍ | 438/1000 [00:37<01:02, 8.99it/s]
44%|████▍ | 440/1000 [00:37<00:58, 9.49it/s]
44%|████▍ | 442/1000 [00:37<00:52, 10.56it/s]
44%|████▍ | 444/1000 [00:37<00:48, 11.42it/s]
45%|████▍ | 446/1000 [00:37<00:45, 12.17it/s]
45%|████▍ | 448/1000 [00:38<00:50, 10.96it/s]
45%|████▌ | 450/1000 [00:38<00:46, 11.78it/s]
45%|████▌ | 452/1000 [00:38<00:42, 12.82it/s]
45%|████▌ | 454/1000 [00:38<00:44, 12.29it/s]
46%|████▌ | 456/1000 [00:38<00:42, 12.75it/s]
46%|████▌ | 458/1000 [00:38<00:39, 13.77it/s]
46%|████▌ | 460/1000 [00:38<00:38, 13.91it/s]
46%|████▌ | 462/1000 [00:39<00:37, 14.49it/s]
46%|████▋ | 464/1000 [00:39<00:35, 15.00it/s]
47%|████▋ | 466/1000 [00:39<00:36, 14.81it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.90it/s]
47%|████▋ | 470/1000 [00:39<00:52, 10.05it/s]
47%|████▋ | 472/1000 [00:40<00:51, 10.24it/s]
47%|████▋ | 474/1000 [00:40<00:45, 11.52it/s]
48%|████▊ | 476/1000 [00:40<00:43, 12.16it/s]
48%|████▊ | 478/1000 [00:40<00:43, 11.97it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.92it/s]
48%|████▊ | 482/1000 [00:40<00:38, 13.33it/s]
48%|████▊ | 484/1000 [00:40<00:37, 13.85it/s]
49%|████▊ | 486/1000 [00:41<00:36, 13.99it/s]
49%|████▉ | 488/1000 [00:41<00:37, 13.63it/s]
49%|████▉ | 490/1000 [00:41<00:47, 10.84it/s]
49%|████▉ | 492/1000 [00:41<00:41, 12.16it/s]
49%|████▉ | 494/1000 [00:41<00:38, 13.18it/s]
50%|████▉ | 496/1000 [00:41<00:36, 13.95it/s]
50%|████▉ | 498/1000 [00:41<00:34, 14.40it/s]
50%|█████ | 500/1000 [00:42<00:44, 11.15it/s]
50%|█████ | 502/1000 [00:42<00:43, 11.42it/s]
50%|█████ | 504/1000 [00:42<00:41, 11.86it/s]
51%|█████ | 506/1000 [00:42<00:42, 11.54it/s]
51%|█████ | 508/1000 [00:42<00:41, 11.95it/s]
51%|█████ | 510/1000 [00:43<00:51, 9.60it/s]
51%|█████ | 512/1000 [00:43<00:46, 10.45it/s]
51%|█████▏ | 514/1000 [00:43<00:42, 11.44it/s]
52%|█████▏ | 516/1000 [00:43<00:39, 12.11it/s]
52%|█████▏ | 518/1000 [00:43<00:38, 12.66it/s]
52%|█████▏ | 520/1000 [00:43<00:36, 13.14it/s]
52%|█████▏ | 522/1000 [00:44<00:34, 13.88it/s]
52%|█████▏ | 524/1000 [00:44<00:34, 14.00it/s]
53%|█████▎ | 526/1000 [00:44<00:33, 14.20it/s]
53%|█████▎ | 528/1000 [00:44<00:39, 11.95it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.79it/s]
53%|█████▎ | 532/1000 [00:44<00:35, 13.11it/s]
53%|█████▎ | 534/1000 [00:44<00:34, 13.64it/s]
54%|█████▎ | 536/1000 [00:45<00:33, 13.91it/s]
54%|█████▍ | 538/1000 [00:45<00:33, 13.78it/s]
54%|█████▍ | 540/1000 [00:45<00:32, 14.28it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.59it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.51it/s]
55%|█████▍ | 546/1000 [00:45<00:32, 14.14it/s]
55%|█████▍ | 548/1000 [00:45<00:32, 13.82it/s]
55%|█████▌ | 550/1000 [00:46<00:31, 14.07it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.23it/s]
55%|█████▌ | 554/1000 [00:46<00:32, 13.92it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.10it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.46it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 15.00it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 14.95it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.77it/s]
57%|█████▋ | 566/1000 [00:47<00:37, 11.69it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.23it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.25it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.77it/s]
57%|█████▋ | 574/1000 [00:47<00:31, 13.65it/s]
58%|█████▊ | 576/1000 [00:48<00:31, 13.58it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.63it/s]
58%|█████▊ | 580/1000 [00:48<00:32, 12.87it/s]
58%|█████▊ | 582/1000 [00:48<00:30, 13.61it/s]
58%|█████▊ | 584/1000 [00:48<00:30, 13.85it/s]
59%|█████▊ | 586/1000 [00:48<00:29, 14.22it/s]
59%|█████▉ | 588/1000 [00:48<00:27, 15.04it/s]
59%|█████▉ | 590/1000 [00:49<00:27, 15.03it/s]
59%|█████▉ | 592/1000 [00:49<00:27, 14.80it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.34it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.86it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 14.06it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.42it/s]
60%|██████ | 602/1000 [00:49<00:27, 14.54it/s]
60%|██████ | 604/1000 [00:49<00:26, 15.11it/s]
61%|██████ | 606/1000 [00:50<00:26, 14.86it/s]
61%|██████ | 608/1000 [00:50<00:25, 15.13it/s]
61%|██████ | 610/1000 [00:50<00:26, 14.98it/s]
61%|██████ | 612/1000 [00:50<00:25, 15.11it/s]
61%|██████▏ | 614/1000 [00:50<00:26, 14.37it/s]
62%|██████▏ | 616/1000 [00:50<00:26, 14.76it/s]
62%|██████▏ | 618/1000 [00:50<00:26, 14.17it/s]
62%|██████▏ | 620/1000 [00:51<00:38, 9.75it/s]
62%|██████▏ | 622/1000 [00:51<00:37, 9.96it/s]
62%|██████▏ | 624/1000 [00:51<00:33, 11.37it/s]
63%|██████▎ | 626/1000 [00:51<00:31, 12.05it/s]
63%|██████▎ | 628/1000 [00:51<00:28, 13.23it/s]
63%|██████▎ | 630/1000 [00:52<00:26, 13.76it/s]
63%|██████▎ | 632/1000 [00:52<00:29, 12.43it/s]
63%|██████▎ | 634/1000 [00:52<00:27, 13.16it/s]
64%|██████▎ | 636/1000 [00:52<00:33, 10.94it/s]
64%|██████▍ | 638/1000 [00:52<00:33, 10.67it/s]
64%|██████▍ | 640/1000 [00:52<00:31, 11.28it/s]
64%|██████▍ | 642/1000 [00:53<00:29, 11.99it/s]
64%|██████▍ | 644/1000 [00:53<00:28, 12.33it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.68it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.21it/s]
65%|██████▌ | 650/1000 [00:53<00:28, 12.48it/s]
65%|██████▌ | 652/1000 [00:53<00:29, 11.89it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.39it/s]
66%|██████▌ | 656/1000 [00:54<00:32, 10.74it/s]
66%|██████▌ | 658/1000 [00:54<00:28, 11.82it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.14it/s]
66%|██████▌ | 662/1000 [00:54<00:29, 11.27it/s]
66%|██████▋ | 664/1000 [00:55<00:28, 11.77it/s]
67%|██████▋ | 666/1000 [00:55<00:26, 12.80it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.32it/s]
67%|██████▋ | 670/1000 [00:55<00:29, 11.09it/s]
67%|██████▋ | 672/1000 [00:55<00:27, 11.86it/s]
67%|██████▋ | 674/1000 [00:55<00:26, 12.44it/s]
68%|██████▊ | 676/1000 [00:56<00:27, 11.71it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.20it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 12.99it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.13it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.32it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.40it/s]
69%|██████▉ | 688/1000 [00:56<00:22, 13.71it/s]
69%|██████▉ | 690/1000 [00:57<00:22, 13.78it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.24it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.34it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 14.09it/s]
70%|██████▉ | 698/1000 [00:57<00:22, 13.56it/s]
70%|███████ | 700/1000 [00:57<00:26, 11.53it/s]
70%|███████ | 702/1000 [00:58<00:25, 11.79it/s]
70%|███████ | 704/1000 [00:58<00:31, 9.27it/s]
71%|███████ | 706/1000 [00:58<00:29, 9.97it/s]
71%|███████ | 708/1000 [00:58<00:32, 9.07it/s]
71%|███████ | 710/1000 [00:58<00:29, 9.94it/s]
71%|███████ | 712/1000 [00:59<00:27, 10.57it/s]
71%|███████▏ | 714/1000 [00:59<00:25, 11.14it/s]
72%|███████▏ | 716/1000 [00:59<00:24, 11.48it/s]
72%|███████▏ | 718/1000 [00:59<00:25, 11.09it/s]
72%|███████▏ | 720/1000 [00:59<00:24, 11.54it/s]
72%|███████▏ | 722/1000 [00:59<00:23, 11.82it/s]
72%|███████▏ | 724/1000 [01:00<00:23, 11.95it/s]
73%|███████▎ | 726/1000 [01:00<00:22, 12.09it/s]
73%|███████▎ | 728/1000 [01:00<00:25, 10.50it/s]
73%|███████▎ | 730/1000 [01:00<00:35, 7.57it/s]
73%|███████▎ | 732/1000 [01:01<00:34, 7.78it/s]
73%|███████▎ | 734/1000 [01:01<00:33, 8.04it/s]
74%|███████▎ | 735/1000 [01:01<00:33, 8.00it/s]
74%|███████▎ | 737/1000 [01:01<00:28, 9.17it/s]
74%|███████▍ | 739/1000 [01:01<00:26, 10.01it/s]
74%|███████▍ | 741/1000 [01:02<00:24, 10.73it/s]
74%|███████▍ | 743/1000 [01:02<00:22, 11.54it/s]
74%|███████▍ | 745/1000 [01:02<00:25, 9.82it/s]
75%|███████▍ | 747/1000 [01:02<00:27, 9.31it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.51it/s]
75%|███████▌ | 751/1000 [01:02<00:21, 11.65it/s]
75%|███████▌ | 753/1000 [01:03<00:20, 12.21it/s]
76%|███████▌ | 755/1000 [01:03<00:18, 13.01it/s]
76%|███████▌ | 757/1000 [01:03<00:24, 10.12it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.17it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.19it/s]
76%|███████▋ | 763/1000 [01:04<00:23, 10.20it/s]
76%|███████▋ | 765/1000 [01:04<00:27, 8.57it/s]
77%|███████▋ | 767/1000 [01:04<00:24, 9.53it/s]
77%|███████▋ | 769/1000 [01:04<00:21, 10.55it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.83it/s]
77%|███████▋ | 773/1000 [01:04<00:17, 12.63it/s]
78%|███████▊ | 775/1000 [01:05<00:17, 13.16it/s]
78%|███████▊ | 777/1000 [01:05<00:15, 14.13it/s]
78%|███████▊ | 779/1000 [01:05<00:20, 10.53it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.42it/s]
78%|███████▊ | 783/1000 [01:06<00:26, 8.31it/s]
78%|███████▊ | 784/1000 [01:06<00:30, 7.07it/s]
78%|███████▊ | 785/1000 [01:06<00:29, 7.38it/s]
79%|███████▊ | 786/1000 [01:06<00:30, 6.99it/s]
79%|███████▉ | 788/1000 [01:06<00:23, 8.84it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.17it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 11.30it/s]
79%|███████▉ | 794/1000 [01:07<00:17, 11.92it/s]
80%|███████▉ | 796/1000 [01:07<00:16, 12.37it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 13.11it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.30it/s]
80%|████████ | 802/1000 [01:08<00:24, 8.25it/s]
80%|████████ | 804/1000 [01:08<00:23, 8.30it/s]
81%|████████ | 806/1000 [01:08<00:21, 8.98it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.35it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.47it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.66it/s]
81%|████████▏ | 814/1000 [01:09<00:14, 12.55it/s]
82%|████████▏ | 816/1000 [01:09<00:16, 11.02it/s]
82%|████████▏ | 818/1000 [01:09<00:15, 11.98it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.50it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 13.04it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.84it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.23it/s]
83%|████████▎ | 828/1000 [01:10<00:11, 14.49it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.93it/s]
83%|████████▎ | 832/1000 [01:10<00:11, 14.31it/s]
83%|████████▎ | 834/1000 [01:10<00:11, 14.03it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.41it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.33it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.44it/s]
84%|████████▍ | 842/1000 [01:11<00:10, 14.46it/s]
84%|████████▍ | 844/1000 [01:11<00:11, 13.98it/s]
85%|████████▍ | 846/1000 [01:11<00:11, 13.94it/s]
85%|████████▍ | 848/1000 [01:11<00:10, 13.90it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.09it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.69it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.23it/s]
86%|████████▌ | 856/1000 [01:12<00:10, 14.07it/s]
86%|████████▌ | 858/1000 [01:12<00:09, 14.55it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 15.32it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.49it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 14.02it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.86it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.72it/s]
87%|████████▋ | 870/1000 [01:13<00:09, 14.11it/s]
87%|████████▋ | 872/1000 [01:13<00:08, 14.73it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 12.48it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.08it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.95it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.57it/s]
88%|████████▊ | 882/1000 [01:14<00:10, 11.33it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 12.08it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.87it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.83it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.27it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.06it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 14.23it/s]
90%|████████▉ | 896/1000 [01:15<00:07, 13.97it/s]
90%|████████▉ | 898/1000 [01:15<00:07, 13.36it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.27it/s]
90%|█████████ | 902/1000 [01:15<00:07, 13.01it/s]
90%|█████████ | 904/1000 [01:15<00:09, 9.81it/s]
91%|█████████ | 906/1000 [01:15<00:08, 10.87it/s]
91%|█████████ | 908/1000 [01:16<00:07, 11.85it/s]
91%|█████████ | 910/1000 [01:16<00:07, 12.10it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.29it/s]
91%|█████████▏| 914/1000 [01:17<00:12, 6.81it/s]
92%|█████████▏| 916/1000 [01:17<00:10, 8.14it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.41it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.82it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 10.26it/s]
92%|█████████▏| 924/1000 [01:17<00:06, 10.96it/s]
93%|█████████▎| 926/1000 [01:18<00:06, 11.81it/s]
93%|█████████▎| 928/1000 [01:18<00:06, 10.64it/s]
93%|█████████▎| 930/1000 [01:18<00:05, 11.85it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 12.45it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.95it/s]
94%|█████████▎| 936/1000 [01:18<00:04, 13.48it/s]
94%|█████████▍| 938/1000 [01:18<00:04, 14.37it/s]
94%|█████████▍| 940/1000 [01:19<00:04, 14.67it/s]
94%|█████████▍| 942/1000 [01:19<00:04, 12.33it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.81it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.85it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 12.67it/s]
95%|█████████▌| 950/1000 [01:19<00:03, 13.46it/s]
95%|█████████▌| 952/1000 [01:19<00:03, 14.16it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 14.28it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 14.25it/s]
96%|█████████▌| 958/1000 [01:20<00:02, 14.75it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 13.12it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.74it/s]
96%|█████████▋| 964/1000 [01:21<00:03, 10.22it/s]
97%|█████████▋| 966/1000 [01:21<00:02, 11.42it/s]
97%|█████████▋| 968/1000 [01:21<00:02, 12.09it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 12.72it/s]
97%|█████████▋| 972/1000 [01:21<00:02, 9.71it/s]
97%|█████████▋| 974/1000 [01:21<00:02, 10.85it/s]
98%|█████████▊| 976/1000 [01:22<00:02, 11.60it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.83it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.91it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.42it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 10.44it/s]
99%|█████████▊| 986/1000 [01:23<00:01, 10.70it/s]
99%|█████████▉| 988/1000 [01:23<00:01, 10.91it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 11.40it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 12.31it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 13.05it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 13.95it/s]
100%|█████████▉| 998/1000 [01:23<00:00, 14.11it/s]
100%|██████████| 1000/1000 [01:24<00:00, 14.77it/s]
{'eval_loss': '0.9353', 'eval_runtime': '84.09', 'eval_samples_per_second': '11.89', 'eval_steps_per_second': '11.89', 'epoch': '0.214', 'num_input_tokens_seen': 13433360}
21%|██▏ | 4000/18688 [4:41:34<17:00:24, 4.17s/it]
100%|██████████| 1000/1000 [01:24<00:00, 14.77it/s]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00008/checkpoint-4000
loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
21%|██▏ | 4001/18688 [4:41:39<121:08:12, 29.69s/it] 21%|██▏ | 4002/18688 [4:41:43<89:53:36, 22.04s/it] 21%|██▏ | 4003/18688 [4:41:48<68:23:16, 16.77s/it] 21%|██▏ | 4004/18688 [4:41:52<53:07:03, 13.02s/it] 21%|██▏ | 4005/18688 [4:41:57<42:55:13, 10.52s/it] 21%|██▏ | 4006/18688 [4:42:01<35:44:54, 8.77s/it] 21%|██▏ | 4007/18688 [4:42:06<30:21:42, 7.45s/it] 21%|██▏ | 4008/18688 [4:42:09<25:29:21, 6.25s/it] 21%|██▏ | 4009/18688 [4:42:14<23:22:46, 5.73s/it] 21%|██▏ | 4010/18688 [4:42:17<20:36:36, 5.05s/it] {'loss': '0.9612', 'grad_norm': '1.171', 'learning_rate': '7.587e-05', 'epoch': '0.2146', 'num_input_tokens_seen': 13467800, 'train_runtime': '1.694e+04', 'train_tokens_per_second': '795.1'}
21%|██▏ | 4010/18688 [4:42:17<20:36:36, 5.05s/it] 21%|██▏ | 4011/18688 [4:42:22<19:45:37, 4.85s/it] 21%|██▏ | 4012/18688 [4:42:26<19:21:10, 4.75s/it] 21%|██▏ | 4013/18688 [4:42:31<18:53:11, 4.63s/it] 21%|██▏ | 4014/18688 [4:42:34<17:54:34, 4.39s/it] 21%|██▏ | 4015/18688 [4:42:39<18:08:43, 4.45s/it] 21%|██▏ | 4016/18688 [4:42:43<18:07:39, 4.45s/it] 21%|██▏ | 4017/18688 [4:42:47<17:20:01, 4.25s/it] 22%|██▏ | 4018/18688 [4:42:51<17:04:25, 4.19s/it] 22%|██▏ | 4019/18688 [4:42:56<17:14:21, 4.23s/it] 22%|██▏ | 4020/18688 [4:42:59<16:36:10, 4.07s/it] {'loss': '0.9612', 'grad_norm': '1.075', 'learning_rate': '7.583e-05', 'epoch': '0.2151', 'num_input_tokens_seen': 13502624, 'train_runtime': '1.698e+04', 'train_tokens_per_second': '795.2'}
22%|██▏ | 4020/18688 [4:42:59<16:36:10, 4.07s/it] 22%|██▏ | 4021/18688 [4:43:04<17:23:12, 4.27s/it] 22%|██▏ | 4022/18688 [4:43:08<17:32:09, 4.30s/it] 22%|██▏ | 4023/18688 [4:43:12<16:50:43, 4.14s/it] 22%|██▏ | 4024/18688 [4:43:17<17:31:20, 4.30s/it] 22%|██▏ | 4025/18688 [4:43:21<17:00:57, 4.18s/it] 22%|██▏ | 4026/18688 [4:43:25<17:01:12, 4.18s/it] 22%|██▏ | 4027/18688 [4:43:28<16:12:19, 3.98s/it] 22%|██▏ | 4028/18688 [4:43:32<16:04:47, 3.95s/it] 22%|██▏ | 4029/18688 [4:43:36<16:07:51, 3.96s/it] 22%|██▏ | 4030/18688 [4:43:40<15:56:18, 3.91s/it] {'loss': '1.031', 'grad_norm': '1.17', 'learning_rate': '7.58e-05', 'epoch': '0.2157', 'num_input_tokens_seen': 13535688, 'train_runtime': '1.702e+04', 'train_tokens_per_second': '795.3'}
22%|██▏ | 4030/18688 [4:43:40<15:56:18, 3.91s/it] 22%|██▏ | 4031/18688 [4:43:44<16:05:17, 3.95s/it] 22%|██▏ | 4032/18688 [4:43:49<16:53:50, 4.15s/it] 22%|██▏ | 4033/18688 [4:43:53<17:05:16, 4.20s/it] 22%|██▏ | 4034/18688 [4:43:57<16:28:22, 4.05s/it] 22%|██▏ | 4035/18688 [4:44:00<15:48:24, 3.88s/it] 22%|██▏ | 4036/18688 [4:44:04<15:44:45, 3.87s/it] 22%|██▏ | 4037/18688 [4:44:08<15:51:56, 3.90s/it] 22%|██▏ | 4038/18688 [4:44:12<15:50:37, 3.89s/it] 22%|██▏ | 4039/18688 [4:44:16<15:46:31, 3.88s/it] 22%|██▏ | 4040/18688 [4:44:20<15:53:39, 3.91s/it] {'loss': '0.9727', 'grad_norm': '1.231', 'learning_rate': '7.577e-05', 'epoch': '0.2162', 'num_input_tokens_seen': 13566128, 'train_runtime': '1.706e+04', 'train_tokens_per_second': '795.2'}
22%|██▏ | 4040/18688 [4:44:20<15:53:39, 3.91s/it] 22%|██▏ | 4041/18688 [4:44:23<15:36:24, 3.84s/it] 22%|██▏ | 4042/18688 [4:44:27<15:52:44, 3.90s/it] 22%|██▏ | 4043/18688 [4:44:32<16:10:56, 3.98s/it] 22%|██▏ | 4044/18688 [4:44:35<15:49:57, 3.89s/it] 22%|██▏ | 4045/18688 [4:44:39<16:00:14, 3.93s/it] 22%|██▏ | 4046/18688 [4:44:43<16:09:58, 3.97s/it] 22%|██▏ | 4047/18688 [4:44:47<15:43:11, 3.87s/it] 22%|██▏ | 4048/18688 [4:44:52<16:49:42, 4.14s/it] 22%|██▏ | 4049/18688 [4:44:57<18:07:09, 4.46s/it] 22%|██▏ | 4050/18688 [4:45:02<18:25:22, 4.53s/it] {'loss': '0.9259', 'grad_norm': '1.122', 'learning_rate': '7.574e-05', 'epoch': '0.2167', 'num_input_tokens_seen': 13599768, 'train_runtime': '1.71e+04', 'train_tokens_per_second': '795.2'}
22%|██▏ | 4050/18688 [4:45:02<18:25:22, 4.53s/it] 22%|██▏ | 4051/18688 [4:45:06<17:36:38, 4.33s/it] 22%|██▏ | 4052/18688 [4:45:10<17:58:37, 4.42s/it] 22%|██▏ | 4053/18688 [4:45:14<17:05:02, 4.20s/it] 22%|██▏ | 4054/18688 [4:45:18<16:28:48, 4.05s/it] 22%|██▏ | 4055/18688 [4:45:23<17:32:59, 4.32s/it] 22%|██▏ | 4056/18688 [4:45:26<16:40:32, 4.10s/it] 22%|██▏ | 4057/18688 [4:45:30<16:45:03, 4.12s/it] 22%|██▏ | 4058/18688 [4:45:34<16:01:25, 3.94s/it] 22%|██▏ | 4059/18688 [4:45:38<15:50:39, 3.90s/it] 22%|██▏ | 4060/18688 [4:45:41<15:17:03, 3.76s/it] {'loss': '0.8727', 'grad_norm': '1.254', 'learning_rate': '7.57e-05', 'epoch': '0.2173', 'num_input_tokens_seen': 13631920, 'train_runtime': '1.714e+04', 'train_tokens_per_second': '795.3'}
22%|██▏ | 4060/18688 [4:45:41<15:17:03, 3.76s/it] 22%|██▏ | 4061/18688 [4:45:45<15:35:37, 3.84s/it] 22%|██▏ | 4062/18688 [4:45:50<16:52:58, 4.16s/it] 22%|██▏ | 4063/18688 [4:45:55<17:23:29, 4.28s/it] 22%|██▏ | 4064/18688 [4:45:59<17:15:20, 4.25s/it] 22%|██▏ | 4065/18688 [4:46:03<17:24:02, 4.28s/it] 22%|██▏ | 4066/18688 [4:46:07<16:39:24, 4.10s/it] 22%|██▏ | 4067/18688 [4:46:12<17:36:45, 4.34s/it] 22%|██▏ | 4068/18688 [4:46:16<17:39:15, 4.35s/it] 22%|██▏ | 4069/18688 [4:46:20<17:11:44, 4.23s/it] 22%|██▏ | 4070/18688 [4:46:24<17:08:58, 4.22s/it] {'loss': '1.024', 'grad_norm': '1.095', 'learning_rate': '7.567e-05', 'epoch': '0.2178', 'num_input_tokens_seen': 13668632, 'train_runtime': '1.718e+04', 'train_tokens_per_second': '795.4'}
22%|██▏ | 4070/18688 [4:46:24<17:08:58, 4.22s/it] 22%|██▏ | 4071/18688 [4:46:28<16:23:30, 4.04s/it] 22%|██▏ | 4072/18688 [4:46:32<16:28:38, 4.06s/it] 22%|██▏ | 4073/18688 [4:46:36<17:04:38, 4.21s/it] 22%|██▏ | 4074/18688 [4:46:40<16:14:02, 4.00s/it] 22%|██▏ | 4075/18688 [4:46:44<16:25:45, 4.05s/it] 22%|██▏ | 4076/18688 [4:46:49<17:06:56, 4.22s/it] 22%|██▏ | 4077/18688 [4:46:53<16:40:32, 4.11s/it] 22%|██▏ | 4078/18688 [4:46:56<16:12:37, 3.99s/it] 22%|██▏ | 4079/18688 [4:47:00<15:50:19, 3.90s/it] 22%|██▏ | 4080/18688 [4:47:04<15:43:51, 3.88s/it] {'loss': '0.9709', 'grad_norm': '1.004', 'learning_rate': '7.564e-05', 'epoch': '0.2183', 'num_input_tokens_seen': 13699016, 'train_runtime': '1.722e+04', 'train_tokens_per_second': '795.3'}
22%|██▏ | 4080/18688 [4:47:04<15:43:51, 3.88s/it] 22%|██▏ | 4081/18688 [4:47:07<15:14:06, 3.75s/it] 22%|██▏ | 4082/18688 [4:47:12<16:20:35, 4.03s/it] 22%|██▏ | 4083/18688 [4:47:18<18:17:30, 4.51s/it] 22%|██▏ | 4084/18688 [4:47:21<17:24:52, 4.29s/it] 22%|██▏ | 4085/18688 [4:47:26<17:26:35, 4.30s/it] 22%|██▏ | 4086/18688 [4:47:30<17:37:16, 4.34s/it] 22%|██▏ | 4087/18688 [4:47:34<17:02:28, 4.20s/it] 22%|██▏ | 4088/18688 [4:47:38<16:13:17, 4.00s/it] 22%|██▏ | 4089/18688 [4:47:41<15:55:04, 3.93s/it] 22%|██▏ | 4090/18688 [4:47:46<16:40:35, 4.11s/it] {'loss': '0.9275', 'grad_norm': '1.038', 'learning_rate': '7.56e-05', 'epoch': '0.2189', 'num_input_tokens_seen': 13733208, 'train_runtime': '1.727e+04', 'train_tokens_per_second': '795.4'}
22%|██▏ | 4090/18688 [4:47:46<16:40:35, 4.11s/it] 22%|██▏ | 4091/18688 [4:47:50<17:14:51, 4.25s/it] 22%|██▏ | 4092/18688 [4:47:54<16:58:06, 4.19s/it] 22%|██▏ | 4093/18688 [4:47:59<17:50:49, 4.40s/it] 22%|██▏ | 4094/18688 [4:48:03<16:42:48, 4.12s/it] 22%|██▏ | 4095/18688 [4:48:07<16:44:25, 4.13s/it] 22%|██▏ | 4096/18688 [4:48:11<16:47:03, 4.14s/it] 22%|██▏ | 4097/18688 [4:48:15<16:03:54, 3.96s/it] 22%|██▏ | 4098/18688 [4:48:18<15:34:18, 3.84s/it] 22%|██▏ | 4099/18688 [4:48:22<15:59:09, 3.94s/it] 22%|██▏ | 4100/18688 [4:48:27<17:02:08, 4.20s/it] {'loss': '0.9133', 'grad_norm': '1.012', 'learning_rate': '7.557e-05', 'epoch': '0.2194', 'num_input_tokens_seen': 13767232, 'train_runtime': '1.731e+04', 'train_tokens_per_second': '795.4'}
22%|██▏ | 4100/18688 [4:48:27<17:02:08, 4.20s/it] 22%|██▏ | 4101/18688 [4:48:32<17:16:11, 4.26s/it] 22%|██▏ | 4102/18688 [4:48:36<17:38:09, 4.35s/it] 22%|██▏ | 4103/18688 [4:48:40<16:31:01, 4.08s/it] 22%|██▏ | 4104/18688 [4:48:44<16:36:44, 4.10s/it] 22%|██▏ | 4105/18688 [4:48:48<16:35:36, 4.10s/it] 22%|██▏ | 4106/18688 [4:48:53<18:07:39, 4.48s/it] 22%|██▏ | 4107/18688 [4:48:58<18:06:19, 4.47s/it] 22%|██▏ | 4108/18688 [4:49:02<17:38:01, 4.35s/it] 22%|██▏ | 4109/18688 [4:49:06<16:59:41, 4.20s/it] 22%|██▏ | 4110/18688 [4:49:10<17:32:53, 4.33s/it] {'loss': '0.9409', 'grad_norm': '1.045', 'learning_rate': '7.554e-05', 'epoch': '0.2199', 'num_input_tokens_seen': 13803928, 'train_runtime': '1.735e+04', 'train_tokens_per_second': '795.6'}
22%|██▏ | 4110/18688 [4:49:10<17:32:53, 4.33s/it] 22%|██▏ | 4111/18688 [4:49:15<18:10:59, 4.49s/it] 22%|██▏ | 4112/18688 [4:49:19<17:32:23, 4.33s/it] 22%|██▏ | 4113/18688 [4:49:23<17:00:37, 4.20s/it] 22%|██▏ | 4114/18688 [4:49:27<16:27:32, 4.07s/it] 22%|██▏ | 4115/18688 [4:49:31<17:13:32, 4.26s/it] 22%|██▏ | 4116/18688 [4:49:35<16:25:38, 4.06s/it] 22%|██▏ | 4117/18688 [4:49:39<16:26:27, 4.06s/it] 22%|██▏ | 4118/18688 [4:49:43<15:43:45, 3.89s/it] 22%|██▏ | 4119/18688 [4:49:47<16:20:32, 4.04s/it] 22%|██▏ | 4120/18688 [4:49:53<19:05:44, 4.72s/it] {'loss': '0.9775', 'grad_norm': '0.9868', 'learning_rate': '7.55e-05', 'epoch': '0.2205', 'num_input_tokens_seen': 13839704, 'train_runtime': '1.739e+04', 'train_tokens_per_second': '795.7'}
22%|██▏ | 4120/18688 [4:49:53<19:05:44, 4.72s/it] 22%|██▏ | 4121/18688 [4:49:58<18:36:11, 4.60s/it] 22%|██▏ | 4122/18688 [4:50:02<17:48:13, 4.40s/it] 22%|██▏ | 4123/18688 [4:50:06<17:57:44, 4.44s/it] 22%|██▏ | 4124/18688 [4:50:10<17:35:41, 4.35s/it] 22%|██▏ | 4125/18688 [4:50:14<16:58:48, 4.20s/it] 22%|██▏ | 4126/18688 [4:50:18<16:17:43, 4.03s/it] 22%|██▏ | 4127/18688 [4:50:22<16:46:38, 4.15s/it] 22%|██▏ | 4128/18688 [4:50:26<16:32:14, 4.09s/it] 22%|██▏ | 4129/18688 [4:50:30<16:41:43, 4.13s/it] 22%|██▏ | 4130/18688 [4:50:36<18:20:31, 4.54s/it] {'loss': '0.9887', 'grad_norm': '1.225', 'learning_rate': '7.547e-05', 'epoch': '0.221', 'num_input_tokens_seen': 13873808, 'train_runtime': '1.744e+04', 'train_tokens_per_second': '795.7'}
22%|██▏ | 4130/18688 [4:50:36<18:20:31, 4.54s/it] 22%|██▏ | 4131/18688 [4:50:40<17:29:42, 4.33s/it] 22%|██▏ | 4132/18688 [4:50:44<18:01:03, 4.46s/it] 22%|██▏ | 4133/18688 [4:50:48<16:54:29, 4.18s/it] 22%|██▏ | 4134/18688 [4:50:51<16:06:16, 3.98s/it] 22%|██▏ | 4135/18688 [4:50:56<16:16:06, 4.02s/it] 22%|██▏ | 4136/18688 [4:51:00<16:55:41, 4.19s/it] 22%|██▏ | 4137/18688 [4:51:04<17:03:08, 4.22s/it] 22%|██▏ | 4138/18688 [4:51:09<17:26:38, 4.32s/it] 22%|██▏ | 4139/18688 [4:51:13<17:28:00, 4.32s/it] 22%|██▏ | 4140/18688 [4:51:17<17:12:15, 4.26s/it] {'loss': '1.036', 'grad_norm': '1.181', 'learning_rate': '7.544e-05', 'epoch': '0.2215', 'num_input_tokens_seen': 13908144, 'train_runtime': '1.748e+04', 'train_tokens_per_second': '795.8'}
22%|██▏ | 4140/18688 [4:51:17<17:12:15, 4.26s/it] 22%|██▏ | 4141/18688 [4:51:21<16:57:49, 4.20s/it] 22%|██▏ | 4142/18688 [4:51:26<17:13:32, 4.26s/it] 22%|██▏ | 4143/18688 [4:51:30<17:02:15, 4.22s/it] 22%|██▏ | 4144/18688 [4:51:34<16:52:23, 4.18s/it] 22%|██▏ | 4145/18688 [4:51:38<16:40:35, 4.13s/it] 22%|██▏ | 4146/18688 [4:51:42<16:21:24, 4.05s/it] 22%|██▏ | 4147/18688 [4:51:46<16:48:51, 4.16s/it] 22%|██▏ | 4148/18688 [4:51:51<17:28:52, 4.33s/it] 22%|██▏ | 4149/18688 [4:51:55<16:31:06, 4.09s/it] 22%|██▏ | 4150/18688 [4:51:58<15:55:02, 3.94s/it] {'loss': '0.9195', 'grad_norm': '0.981', 'learning_rate': '7.54e-05', 'epoch': '0.2221', 'num_input_tokens_seen': 13941400, 'train_runtime': '1.752e+04', 'train_tokens_per_second': '795.8'}
22%|██▏ | 4150/18688 [4:51:58<15:55:02, 3.94s/it] 22%|██▏ | 4151/18688 [4:52:02<15:20:16, 3.80s/it] 22%|██▏ | 4152/18688 [4:52:06<15:42:28, 3.89s/it] 22%|██▏ | 4153/18688 [4:52:10<15:32:39, 3.85s/it] 22%|██▏ | 4154/18688 [4:52:13<15:21:02, 3.80s/it] 22%|██▏ | 4155/18688 [4:52:17<15:17:19, 3.79s/it] 22%|██▏ | 4156/18688 [4:52:21<15:38:03, 3.87s/it] 22%|██▏ | 4157/18688 [4:52:26<17:13:38, 4.27s/it] 22%|██▏ | 4158/18688 [4:52:30<17:11:42, 4.26s/it] 22%|██▏ | 4159/18688 [4:52:36<18:49:24, 4.66s/it] 22%|██▏ | 4160/18688 [4:52:40<18:20:38, 4.55s/it] {'loss': '1.008', 'grad_norm': '1.102', 'learning_rate': '7.537e-05', 'epoch': '0.2226', 'num_input_tokens_seen': 13975592, 'train_runtime': '1.756e+04', 'train_tokens_per_second': '795.8'}
22%|██▏ | 4160/18688 [4:52:40<18:20:38, 4.55s/it] 22%|██▏ | 4161/18688 [4:52:45<18:41:54, 4.63s/it] 22%|██▏ | 4162/18688 [4:52:49<18:06:25, 4.49s/it] 22%|██▏ | 4163/18688 [4:52:53<17:09:29, 4.25s/it] 22%|██▏ | 4164/18688 [4:52:56<16:10:28, 4.01s/it] 22%|██▏ | 4165/18688 [4:53:01<16:30:51, 4.09s/it] 22%|██▏ | 4166/18688 [4:53:05<16:42:10, 4.14s/it] 22%|██▏ | 4167/18688 [4:53:09<16:45:02, 4.15s/it] 22%|██▏ | 4168/18688 [4:53:13<16:52:06, 4.18s/it] 22%|██▏ | 4169/18688 [4:53:18<17:12:42, 4.27s/it] 22%|██▏ | 4170/18688 [4:53:22<17:11:08, 4.26s/it] {'loss': '0.9397', 'grad_norm': '1.057', 'learning_rate': '7.533e-05', 'epoch': '0.2231', 'num_input_tokens_seen': 14010624, 'train_runtime': '1.76e+04', 'train_tokens_per_second': '795.9'}
22%|██▏ | 4170/18688 [4:53:22<17:11:08, 4.26s/it] 22%|██▏ | 4171/18688 [4:53:26<16:49:46, 4.17s/it] 22%|██▏ | 4172/18688 [4:53:30<16:57:10, 4.20s/it] 22%|██▏ | 4173/18688 [4:53:35<16:47:30, 4.16s/it] 22%|██▏ | 4174/18688 [4:53:39<17:00:33, 4.22s/it] 22%|██▏ | 4175/18688 [4:53:43<16:21:44, 4.06s/it] 22%|██▏ | 4176/18688 [4:53:47<16:59:01, 4.21s/it] 22%|██▏ | 4177/18688 [4:53:51<16:35:22, 4.12s/it] 22%|██▏ | 4178/18688 [4:53:54<15:46:03, 3.91s/it] 22%|██▏ | 4179/18688 [4:53:59<16:18:54, 4.05s/it] 22%|██▏ | 4180/18688 [4:54:02<15:43:04, 3.90s/it] {'loss': '0.9343', 'grad_norm': '1.102', 'learning_rate': '7.53e-05', 'epoch': '0.2237', 'num_input_tokens_seen': 14042928, 'train_runtime': '1.764e+04', 'train_tokens_per_second': '796'}
22%|██▏ | 4180/18688 [4:54:02<15:43:04, 3.90s/it] 22%|██▏ | 4181/18688 [4:54:07<16:10:53, 4.02s/it] 22%|██▏ | 4182/18688 [4:54:11<16:04:01, 3.99s/it] 22%|██▏ | 4183/18688 [4:54:14<15:49:08, 3.93s/it] 22%|██▏ | 4184/18688 [4:54:20<17:19:08, 4.30s/it] 22%|██▏ | 4185/18688 [4:54:24<17:07:53, 4.25s/it] 22%|██▏ | 4186/18688 [4:54:28<17:46:29, 4.41s/it] 22%|██▏ | 4187/18688 [4:54:32<16:37:20, 4.13s/it] 22%|██▏ | 4188/18688 [4:54:36<16:07:02, 4.00s/it] 22%|██▏ | 4189/18688 [4:54:40<16:22:40, 4.07s/it] 22%|██▏ | 4190/18688 [4:54:43<15:41:01, 3.89s/it] {'loss': '0.9802', 'grad_norm': '1.497', 'learning_rate': '7.527e-05', 'epoch': '0.2242', 'num_input_tokens_seen': 14075792, 'train_runtime': '1.768e+04', 'train_tokens_per_second': '796'}
22%|██▏ | 4190/18688 [4:54:43<15:41:01, 3.89s/it] 22%|██▏ | 4191/18688 [4:54:48<16:03:07, 3.99s/it] 22%|██▏ | 4192/18688 [4:54:53<17:42:41, 4.40s/it] 22%|██▏ | 4193/18688 [4:54:56<16:32:42, 4.11s/it] 22%|██▏ | 4194/18688 [4:55:00<16:13:40, 4.03s/it] 22%|██▏ | 4195/18688 [4:55:04<16:06:06, 4.00s/it] 22%|██▏ | 4196/18688 [4:55:08<16:03:48, 3.99s/it] 22%|██▏ | 4197/18688 [4:55:12<15:42:48, 3.90s/it] 22%|██▏ | 4198/18688 [4:55:15<15:18:44, 3.80s/it] 22%|██▏ | 4199/18688 [4:55:19<15:41:46, 3.90s/it] 22%|██▏ | 4200/18688 [4:55:24<16:16:13, 4.04s/it] {'loss': '0.9614', 'grad_norm': '1.155', 'learning_rate': '7.523e-05', 'epoch': '0.2247', 'num_input_tokens_seen': 14106968, 'train_runtime': '1.772e+04', 'train_tokens_per_second': '795.9'}
22%|██▏ | 4200/18688 [4:55:24<16:16:13, 4.04s/it] 22%|██▏ | 4201/18688 [4:55:27<15:42:06, 3.90s/it] 22%|██▏ | 4202/18688 [4:55:31<15:14:50, 3.79s/it] 22%|██▏ | 4203/18688 [4:55:35<15:05:09, 3.75s/it] 22%|██▏ | 4204/18688 [4:55:38<15:11:46, 3.78s/it] 23%|██▎ | 4205/18688 [4:55:43<15:42:03, 3.90s/it] 23%|██▎ | 4206/18688 [4:55:47<16:14:50, 4.04s/it] 23%|██▎ | 4207/18688 [4:55:51<15:49:39, 3.93s/it] 23%|██▎ | 4208/18688 [4:55:55<16:32:01, 4.11s/it] 23%|██▎ | 4209/18688 [4:55:59<15:47:22, 3.93s/it] 23%|██▎ | 4210/18688 [4:56:04<16:55:20, 4.21s/it] {'loss': '0.9408', 'grad_norm': '0.9531', 'learning_rate': '7.52e-05', 'epoch': '0.2253', 'num_input_tokens_seen': 14138728, 'train_runtime': '1.776e+04', 'train_tokens_per_second': '795.9'}
23%|██▎ | 4210/18688 [4:56:04<16:55:20, 4.21s/it] 23%|██▎ | 4211/18688 [4:56:07<16:03:08, 3.99s/it] 23%|██▎ | 4212/18688 [4:56:12<16:46:43, 4.17s/it] 23%|██▎ | 4213/18688 [4:56:15<16:15:20, 4.04s/it] 23%|██▎ | 4214/18688 [4:56:19<16:15:28, 4.04s/it] 23%|██▎ | 4215/18688 [4:56:25<17:57:44, 4.47s/it] 23%|██▎ | 4216/18688 [4:56:29<17:12:25, 4.28s/it] 23%|██▎ | 4217/18688 [4:56:33<16:59:27, 4.23s/it] 23%|██▎ | 4218/18688 [4:56:38<17:44:15, 4.41s/it] 23%|██▎ | 4219/18688 [4:56:42<18:00:25, 4.48s/it] 23%|██▎ | 4220/18688 [4:56:46<17:07:00, 4.26s/it] {'loss': '0.963', 'grad_norm': '1.029', 'learning_rate': '7.516e-05', 'epoch': '0.2258', 'num_input_tokens_seen': 14173688, 'train_runtime': '1.781e+04', 'train_tokens_per_second': '796'}
23%|██▎ | 4220/18688 [4:56:46<17:07:00, 4.26s/it] 23%|██▎ | 4221/18688 [4:56:50<16:19:20, 4.06s/it] 23%|██▎ | 4222/18688 [4:56:54<17:08:35, 4.27s/it] 23%|██▎ | 4223/18688 [4:56:59<17:43:20, 4.41s/it] 23%|██▎ | 4224/18688 [4:57:04<18:28:56, 4.60s/it] 23%|██▎ | 4225/18688 [4:57:08<17:33:51, 4.37s/it] 23%|██▎ | 4226/18688 [4:57:12<16:39:56, 4.15s/it] 23%|██▎ | 4227/18688 [4:57:16<16:38:28, 4.14s/it] 23%|██▎ | 4228/18688 [4:57:20<16:48:06, 4.18s/it] 23%|██▎ | 4229/18688 [4:57:24<16:16:48, 4.05s/it] 23%|██▎ | 4230/18688 [4:57:28<15:51:20, 3.95s/it] {'loss': '0.9514', 'grad_norm': '1.05', 'learning_rate': '7.513e-05', 'epoch': '0.2264', 'num_input_tokens_seen': 14206008, 'train_runtime': '1.785e+04', 'train_tokens_per_second': '795.9'}
23%|██▎ | 4230/18688 [4:57:28<15:51:20, 3.95s/it] 23%|██▎ | 4231/18688 [4:57:32<17:04:10, 4.25s/it] 23%|██▎ | 4232/18688 [4:57:36<16:07:35, 4.02s/it] 23%|██▎ | 4233/18688 [4:57:40<16:00:57, 3.99s/it] 23%|██▎ | 4234/18688 [4:57:44<16:26:42, 4.10s/it] 23%|██▎ | 4235/18688 [4:57:48<16:20:42, 4.07s/it] 23%|██▎ | 4236/18688 [4:57:52<16:19:09, 4.07s/it] 23%|██▎ | 4237/18688 [4:57:57<17:21:06, 4.32s/it] 23%|██▎ | 4238/18688 [4:58:02<18:15:06, 4.55s/it] 23%|██▎ | 4239/18688 [4:58:07<18:07:02, 4.51s/it] 23%|██▎ | 4240/18688 [4:58:11<17:33:46, 4.38s/it] {'loss': '0.9163', 'grad_norm': '1.113', 'learning_rate': '7.509e-05', 'epoch': '0.2269', 'num_input_tokens_seen': 14241608, 'train_runtime': '1.789e+04', 'train_tokens_per_second': '796'}
23%|██▎ | 4240/18688 [4:58:11<17:33:46, 4.38s/it] 23%|██▎ | 4241/18688 [4:58:15<17:05:32, 4.26s/it] 23%|██▎ | 4242/18688 [4:58:19<16:35:58, 4.14s/it] 23%|██▎ | 4243/18688 [4:58:23<16:29:25, 4.11s/it] 23%|██▎ | 4244/18688 [4:58:27<17:10:52, 4.28s/it] 23%|██▎ | 4245/18688 [4:58:31<16:51:44, 4.20s/it] 23%|██▎ | 4246/18688 [4:58:35<16:46:41, 4.18s/it] 23%|██▎ | 4247/18688 [4:58:39<16:09:25, 4.03s/it] 23%|██▎ | 4248/18688 [4:58:44<16:49:09, 4.19s/it] 23%|██▎ | 4249/18688 [4:58:47<15:55:49, 3.97s/it] 23%|██▎ | 4250/18688 [4:58:51<15:35:09, 3.89s/it] {'loss': '0.9483', 'grad_norm': '1.111', 'learning_rate': '7.506e-05', 'epoch': '0.2274', 'num_input_tokens_seen': 14275344, 'train_runtime': '1.793e+04', 'train_tokens_per_second': '796.1'}
23%|██▎ | 4250/18688 [4:58:51<15:35:09, 3.89s/it] 23%|██▎ | 4251/18688 [4:58:56<16:59:15, 4.24s/it] 23%|██▎ | 4252/18688 [4:59:00<16:24:02, 4.09s/it] 23%|██▎ | 4253/18688 [4:59:04<16:43:36, 4.17s/it] 23%|██▎ | 4254/18688 [4:59:08<17:02:23, 4.25s/it] 23%|██▎ | 4255/18688 [4:59:13<16:59:14, 4.24s/it] 23%|██▎ | 4256/18688 [4:59:17<16:51:46, 4.21s/it] 23%|██▎ | 4257/18688 [4:59:22<18:08:15, 4.52s/it] 23%|██▎ | 4258/18688 [4:59:26<17:31:47, 4.37s/it] 23%|██▎ | 4259/18688 [4:59:30<16:46:20, 4.18s/it] 23%|██▎ | 4260/18688 [4:59:34<17:08:56, 4.28s/it] {'loss': '0.9841', 'grad_norm': '1.141', 'learning_rate': '7.502e-05', 'epoch': '0.228', 'num_input_tokens_seen': 14310808, 'train_runtime': '1.797e+04', 'train_tokens_per_second': '796.2'}
23%|██▎ | 4260/18688 [4:59:34<17:08:56, 4.28s/it] 23%|██▎ | 4261/18688 [4:59:39<17:04:06, 4.26s/it] 23%|██▎ | 4262/18688 [4:59:42<16:32:15, 4.13s/it] 23%|██▎ | 4263/18688 [4:59:46<16:27:35, 4.11s/it] 23%|██▎ | 4264/18688 [4:59:50<15:43:11, 3.92s/it] 23%|██▎ | 4265/18688 [4:59:54<16:00:46, 4.00s/it] 23%|██▎ | 4266/18688 [4:59:59<16:47:53, 4.19s/it] 23%|██▎ | 4267/18688 [5:00:03<17:19:29, 4.32s/it] 23%|██▎ | 4268/18688 [5:00:08<17:29:16, 4.37s/it] 23%|██▎ | 4269/18688 [5:00:13<18:11:35, 4.54s/it] 23%|██▎ | 4270/18688 [5:00:17<17:13:26, 4.30s/it] {'loss': '0.9645', 'grad_norm': '1.222', 'learning_rate': '7.499e-05', 'epoch': '0.2285', 'num_input_tokens_seen': 14345096, 'train_runtime': '1.802e+04', 'train_tokens_per_second': '796.2'}
23%|██▎ | 4270/18688 [5:00:17<17:13:26, 4.30s/it] 23%|██▎ | 4271/18688 [5:00:21<17:48:14, 4.45s/it] 23%|██▎ | 4272/18688 [5:00:25<17:11:26, 4.29s/it] 23%|██▎ | 4273/18688 [5:00:29<17:00:16, 4.25s/it] 23%|██▎ | 4274/18688 [5:00:33<16:15:15, 4.06s/it] 23%|██▎ | 4275/18688 [5:00:37<16:06:41, 4.02s/it] 23%|██▎ | 4276/18688 [5:00:41<15:34:25, 3.89s/it] 23%|██▎ | 4277/18688 [5:00:44<15:29:47, 3.87s/it] 23%|██▎ | 4278/18688 [5:00:48<15:49:01, 3.95s/it] 23%|██▎ | 4279/18688 [5:00:52<15:39:41, 3.91s/it] 23%|██▎ | 4280/18688 [5:00:56<15:56:47, 3.98s/it] {'loss': '0.9736', 'grad_norm': '1.181', 'learning_rate': '7.495e-05', 'epoch': '0.229', 'num_input_tokens_seen': 14376600, 'train_runtime': '1.806e+04', 'train_tokens_per_second': '796.2'}
23%|██▎ | 4280/18688 [5:00:56<15:56:47, 3.98s/it] 23%|██▎ | 4281/18688 [5:01:00<15:46:16, 3.94s/it] 23%|██▎ | 4282/18688 [5:01:04<15:35:54, 3.90s/it] 23%|██▎ | 4283/18688 [5:01:08<15:24:10, 3.85s/it] 23%|██▎ | 4284/18688 [5:01:12<15:34:58, 3.89s/it] 23%|██▎ | 4285/18688 [5:01:16<15:38:58, 3.91s/it] 23%|██▎ | 4286/18688 [5:01:20<15:44:45, 3.94s/it] 23%|██▎ | 4287/18688 [5:01:24<15:32:35, 3.89s/it] 23%|██▎ | 4288/18688 [5:01:27<15:12:15, 3.80s/it] 23%|██▎ | 4289/18688 [5:01:31<15:21:14, 3.84s/it] 23%|██▎ | 4290/18688 [5:01:35<15:06:04, 3.78s/it] {'loss': '0.9375', 'grad_norm': '1.143', 'learning_rate': '7.491e-05', 'epoch': '0.2296', 'num_input_tokens_seen': 14405560, 'train_runtime': '1.81e+04', 'train_tokens_per_second': '796.1'}
23%|██▎ | 4290/18688 [5:01:35<15:06:04, 3.78s/it] 23%|██▎ | 4291/18688 [5:01:39<16:01:23, 4.01s/it] 23%|██▎ | 4292/18688 [5:01:44<16:39:21, 4.17s/it] 23%|██▎ | 4293/18688 [5:01:47<16:05:07, 4.02s/it] 23%|██▎ | 4294/18688 [5:01:51<15:38:48, 3.91s/it] 23%|██▎ | 4295/18688 [5:01:55<15:20:10, 3.84s/it] 23%|██▎ | 4296/18688 [5:01:58<14:54:43, 3.73s/it] 23%|██▎ | 4297/18688 [5:02:02<14:47:47, 3.70s/it] 23%|██▎ | 4298/18688 [5:02:06<15:15:33, 3.82s/it] 23%|██▎ | 4299/18688 [5:02:10<15:21:13, 3.84s/it] 23%|██▎ | 4300/18688 [5:02:14<15:22:29, 3.85s/it] {'loss': '0.8932', 'grad_norm': '1.042', 'learning_rate': '7.488e-05', 'epoch': '0.2301', 'num_input_tokens_seen': 14436256, 'train_runtime': '1.813e+04', 'train_tokens_per_second': '796.1'}
23%|██▎ | 4300/18688 [5:02:14<15:22:29, 3.85s/it] 23%|██▎ | 4301/18688 [5:02:18<16:23:02, 4.10s/it] 23%|██▎ | 4302/18688 [5:02:23<16:29:51, 4.13s/it] 23%|██▎ | 4303/18688 [5:02:26<15:57:52, 4.00s/it] 23%|██▎ | 4304/18688 [5:02:32<17:40:35, 4.42s/it] 23%|██▎ | 4305/18688 [5:02:36<17:03:17, 4.27s/it] 23%|██▎ | 4306/18688 [5:02:41<17:45:54, 4.45s/it] 23%|██▎ | 4307/18688 [5:02:45<17:19:57, 4.34s/it] 23%|██▎ | 4308/18688 [5:02:49<17:33:45, 4.40s/it] 23%|██▎ | 4309/18688 [5:02:53<16:48:50, 4.21s/it] 23%|██▎ | 4310/18688 [5:02:57<16:18:55, 4.09s/it] {'loss': '0.9655', 'grad_norm': '0.9543', 'learning_rate': '7.484e-05', 'epoch': '0.2306', 'num_input_tokens_seen': 14470816, 'train_runtime': '1.818e+04', 'train_tokens_per_second': '796.1'}
23%|██▎ | 4310/18688 [5:02:57<16:18:55, 4.09s/it] 23%|██▎ | 4311/18688 [5:03:01<16:13:28, 4.06s/it] 23%|██▎ | 4312/18688 [5:03:04<15:30:16, 3.88s/it] 23%|██▎ | 4313/18688 [5:03:08<15:57:48, 4.00s/it] 23%|██▎ | 4314/18688 [5:03:13<16:16:09, 4.07s/it] 23%|██▎ | 4315/18688 [5:03:16<15:53:20, 3.98s/it] 23%|██▎ | 4316/18688 [5:03:21<16:25:45, 4.12s/it] 23%|██▎ | 4317/18688 [5:03:25<16:27:31, 4.12s/it] 23%|██▎ | 4318/18688 [5:03:29<15:55:41, 3.99s/it] 23%|██▎ | 4319/18688 [5:03:33<16:01:07, 4.01s/it] 23%|██▎ | 4320/18688 [5:03:38<17:10:33, 4.30s/it] {'loss': '0.9553', 'grad_norm': '1.114', 'learning_rate': '7.481e-05', 'epoch': '0.2312', 'num_input_tokens_seen': 14504184, 'train_runtime': '1.822e+04', 'train_tokens_per_second': '796.1'}
23%|██▎ | 4320/18688 [5:03:38<17:10:33, 4.30s/it] 23%|██▎ | 4321/18688 [5:03:42<17:31:41, 4.39s/it] 23%|██▎ | 4322/18688 [5:03:46<16:45:29, 4.20s/it] 23%|██▎ | 4323/18688 [5:03:50<16:22:18, 4.10s/it] 23%|██▎ | 4324/18688 [5:03:55<17:59:03, 4.51s/it] 23%|██▎ | 4325/18688 [5:04:00<17:42:00, 4.44s/it] 23%|██▎ | 4326/18688 [5:04:04<17:14:41, 4.32s/it] 23%|██▎ | 4327/18688 [5:04:08<17:04:37, 4.28s/it] 23%|██▎ | 4328/18688 [5:04:12<17:13:14, 4.32s/it] 23%|██▎ | 4329/18688 [5:04:16<16:14:40, 4.07s/it] 23%|██▎ | 4330/18688 [5:04:19<15:30:41, 3.89s/it] {'loss': '0.9339', 'grad_norm': '1.038', 'learning_rate': '7.477e-05', 'epoch': '0.2317', 'num_input_tokens_seen': 14538640, 'train_runtime': '1.826e+04', 'train_tokens_per_second': '796.2'}
23%|██▎ | 4330/18688 [5:04:19<15:30:41, 3.89s/it] 23%|██▎ | 4331/18688 [5:04:23<15:23:32, 3.86s/it] 23%|██▎ | 4332/18688 [5:04:27<15:37:01, 3.92s/it] 23%|██▎ | 4333/18688 [5:04:31<15:21:11, 3.85s/it] 23%|██▎ | 4334/18688 [5:04:35<15:51:18, 3.98s/it] 23%|██▎ | 4335/18688 [5:04:40<17:05:58, 4.29s/it] 23%|██▎ | 4336/18688 [5:04:44<16:38:38, 4.17s/it] 23%|██▎ | 4337/18688 [5:04:48<16:21:25, 4.10s/it] 23%|██▎ | 4338/18688 [5:04:52<16:20:37, 4.10s/it] 23%|██▎ | 4339/18688 [5:04:56<15:58:11, 4.01s/it] 23%|██▎ | 4340/18688 [5:05:00<15:50:30, 3.97s/it] {'loss': '0.9711', 'grad_norm': '1.86', 'learning_rate': '7.473e-05', 'epoch': '0.2322', 'num_input_tokens_seen': 14570032, 'train_runtime': '1.83e+04', 'train_tokens_per_second': '796.2'}
23%|██▎ | 4340/18688 [5:05:00<15:50:30, 3.97s/it] 23%|██▎ | 4341/18688 [5:05:04<16:06:15, 4.04s/it] 23%|██▎ | 4342/18688 [5:05:08<16:10:47, 4.06s/it] 23%|██▎ | 4343/18688 [5:05:12<15:46:08, 3.96s/it] 23%|██▎ | 4344/18688 [5:05:16<15:48:48, 3.97s/it] 23%|██▎ | 4345/18688 [5:05:20<15:57:47, 4.01s/it] 23%|██▎ | 4346/18688 [5:05:25<16:58:41, 4.26s/it] 23%|██▎ | 4347/18688 [5:05:29<16:55:46, 4.25s/it] 23%|██▎ | 4348/18688 [5:05:35<18:28:22, 4.64s/it] 23%|██▎ | 4349/18688 [5:05:39<17:45:46, 4.46s/it] 23%|██▎ | 4350/18688 [5:05:44<18:38:58, 4.68s/it] {'loss': '0.9422', 'grad_norm': '1.373', 'learning_rate': '7.47e-05', 'epoch': '0.2328', 'num_input_tokens_seen': 14605536, 'train_runtime': '1.834e+04', 'train_tokens_per_second': '796.2'}
23%|██▎ | 4350/18688 [5:05:44<18:38:58, 4.68s/it] 23%|██▎ | 4351/18688 [5:05:48<18:10:10, 4.56s/it] 23%|██▎ | 4352/18688 [5:05:53<18:27:12, 4.63s/it] 23%|██▎ | 4353/18688 [5:05:58<19:09:12, 4.81s/it] 23%|██▎ | 4354/18688 [5:06:03<18:49:04, 4.73s/it] 23%|██▎ | 4355/18688 [5:06:06<17:50:34, 4.48s/it] 23%|██▎ | 4356/18688 [5:06:11<17:26:20, 4.38s/it] 23%|██▎ | 4357/18688 [5:06:14<16:34:47, 4.16s/it] 23%|██▎ | 4358/18688 [5:06:18<15:50:22, 3.98s/it] 23%|██▎ | 4359/18688 [5:06:23<17:27:49, 4.39s/it] 23%|██▎ | 4360/18688 [5:06:27<16:21:56, 4.11s/it] {'loss': '0.971', 'grad_norm': '1.339', 'learning_rate': '7.466e-05', 'epoch': '0.2333', 'num_input_tokens_seen': 14641800, 'train_runtime': '1.839e+04', 'train_tokens_per_second': '796.3'}
23%|██▎ | 4360/18688 [5:06:27<16:21:56, 4.11s/it] 23%|██▎ | 4361/18688 [5:06:31<16:36:54, 4.17s/it] 23%|██▎ | 4362/18688 [5:06:35<16:34:06, 4.16s/it] 23%|██▎ | 4363/18688 [5:06:39<16:02:04, 4.03s/it] 23%|██▎ | 4364/18688 [5:06:43<15:48:31, 3.97s/it] 23%|██▎ | 4365/18688 [5:06:47<15:41:43, 3.94s/it] 23%|██▎ | 4366/18688 [5:06:50<15:11:14, 3.82s/it] 23%|██▎ | 4367/18688 [5:06:55<16:15:37, 4.09s/it] 23%|██▎ | 4368/18688 [5:07:00<17:13:55, 4.33s/it] 23%|██▎ | 4369/18688 [5:07:04<16:52:51, 4.24s/it] 23%|██▎ | 4370/18688 [5:07:07<16:03:36, 4.04s/it] {'loss': '0.9565', 'grad_norm': '1.104', 'learning_rate': '7.463e-05', 'epoch': '0.2338', 'num_input_tokens_seen': 14674688, 'train_runtime': '1.843e+04', 'train_tokens_per_second': '796.3'}
23%|██▎ | 4370/18688 [5:07:07<16:03:36, 4.04s/it] 23%|██▎ | 4371/18688 [5:07:12<17:16:48, 4.35s/it] 23%|██▎ | 4372/18688 [5:07:16<16:22:51, 4.12s/it] 23%|██▎ | 4373/18688 [5:07:21<16:58:52, 4.27s/it] 23%|██▎ | 4374/18688 [5:07:24<16:19:22, 4.11s/it] 23%|██▎ | 4375/18688 [5:07:28<15:55:17, 4.00s/it] 23%|██▎ | 4376/18688 [5:07:32<16:24:17, 4.13s/it] 23%|██▎ | 4377/18688 [5:07:36<15:38:24, 3.93s/it] 23%|██▎ | 4378/18688 [5:07:40<15:28:04, 3.89s/it] 23%|██▎ | 4379/18688 [5:07:44<15:23:37, 3.87s/it] 23%|██▎ | 4380/18688 [5:07:48<15:41:41, 3.95s/it] {'loss': '0.9511', 'grad_norm': '1.346', 'learning_rate': '7.459e-05', 'epoch': '0.2344', 'num_input_tokens_seen': 14705816, 'train_runtime': '1.847e+04', 'train_tokens_per_second': '796.3'}
23%|██▎ | 4380/18688 [5:07:48<15:41:41, 3.95s/it] 23%|██▎ | 4381/18688 [5:07:53<17:17:23, 4.35s/it] 23%|██▎ | 4382/18688 [5:07:57<16:56:14, 4.26s/it] 23%|██▎ | 4383/18688 [5:08:01<16:16:32, 4.10s/it] 23%|██▎ | 4384/18688 [5:08:06<17:14:28, 4.34s/it] 23%|██▎ | 4385/18688 [5:08:09<16:37:49, 4.19s/it] 23%|██▎ | 4386/18688 [5:08:14<17:22:12, 4.37s/it] 23%|██▎ | 4387/18688 [5:08:18<17:03:50, 4.30s/it] 23%|██▎ | 4388/18688 [5:08:23<17:34:32, 4.42s/it] 23%|██▎ | 4389/18688 [5:08:29<19:12:05, 4.83s/it] 23%|██▎ | 4390/18688 [5:08:33<17:56:33, 4.52s/it] {'loss': '0.9863', 'grad_norm': '1.199', 'learning_rate': '7.455e-05', 'epoch': '0.2349', 'num_input_tokens_seen': 14744160, 'train_runtime': '1.851e+04', 'train_tokens_per_second': '796.4'}
23%|██▎ | 4390/18688 [5:08:33<17:56:33, 4.52s/it] 23%|██▎ | 4391/18688 [5:08:37<17:44:32, 4.47s/it] 24%|██▎ | 4392/18688 [5:08:42<18:42:17, 4.71s/it] 24%|██▎ | 4393/18688 [5:08:47<18:21:25, 4.62s/it] 24%|██▎ | 4394/18688 [5:08:51<17:25:09, 4.39s/it] 24%|██▎ | 4395/18688 [5:08:55<17:57:01, 4.52s/it] 24%|██▎ | 4396/18688 [5:08:59<17:09:22, 4.32s/it] 24%|██▎ | 4397/18688 [5:09:03<16:52:32, 4.25s/it] 24%|██▎ | 4398/18688 [5:09:09<18:06:06, 4.56s/it] 24%|██▎ | 4399/18688 [5:09:13<17:16:22, 4.35s/it] 24%|██▎ | 4400/18688 [5:09:17<16:51:20, 4.25s/it] {'loss': '1.028', 'grad_norm': '1.061', 'learning_rate': '7.451e-05', 'epoch': '0.2355', 'num_input_tokens_seen': 14780304, 'train_runtime': '1.856e+04', 'train_tokens_per_second': '796.5'}
24%|██▎ | 4400/18688 [5:09:17<16:51:20, 4.25s/it] 24%|██▎ | 4401/18688 [5:09:21<16:45:16, 4.22s/it] 24%|██▎ | 4402/18688 [5:09:25<17:20:47, 4.37s/it] 24%|██▎ | 4403/18688 [5:09:29<16:34:12, 4.18s/it] 24%|██▎ | 4404/18688 [5:09:34<17:36:53, 4.44s/it] 24%|██▎ | 4405/18688 [5:09:38<17:16:08, 4.35s/it] 24%|██▎ | 4406/18688 [5:09:44<18:16:32, 4.61s/it] 24%|██▎ | 4407/18688 [5:09:49<19:02:00, 4.80s/it] 24%|██▎ | 4408/18688 [5:09:53<18:14:36, 4.60s/it] 24%|██▎ | 4409/18688 [5:09:57<17:31:11, 4.42s/it] 24%|██▎ | 4410/18688 [5:10:01<17:11:50, 4.34s/it] {'loss': '0.9978', 'grad_norm': '1.298', 'learning_rate': '7.448e-05', 'epoch': '0.236', 'num_input_tokens_seen': 14818472, 'train_runtime': '1.86e+04', 'train_tokens_per_second': '796.6'}
24%|██▎ | 4410/18688 [5:10:01<17:11:50, 4.34s/it] 24%|██▎ | 4411/18688 [5:10:06<18:03:22, 4.55s/it] 24%|██▎ | 4412/18688 [5:10:10<17:02:18, 4.30s/it] 24%|██▎ | 4413/18688 [5:10:13<16:02:12, 4.04s/it] 24%|██▎ | 4414/18688 [5:10:17<16:12:03, 4.09s/it] 24%|██▎ | 4415/18688 [5:10:21<15:55:41, 4.02s/it] 24%|██▎ | 4416/18688 [5:10:26<16:51:54, 4.25s/it] 24%|██▎ | 4417/18688 [5:10:30<16:11:07, 4.08s/it] 24%|██▎ | 4418/18688 [5:10:34<16:06:40, 4.06s/it] 24%|██▎ | 4419/18688 [5:10:38<15:53:19, 4.01s/it] 24%|██▎ | 4420/18688 [5:10:42<16:23:07, 4.13s/it] {'loss': '0.9307', 'grad_norm': '1.155', 'learning_rate': '7.444e-05', 'epoch': '0.2365', 'num_input_tokens_seen': 14850488, 'train_runtime': '1.864e+04', 'train_tokens_per_second': '796.6'}
24%|██▎ | 4420/18688 [5:10:42<16:23:07, 4.13s/it] 24%|██▎ | 4421/18688 [5:10:46<16:26:08, 4.15s/it] 24%|██▎ | 4422/18688 [5:10:51<17:09:32, 4.33s/it] 24%|██▎ | 4423/18688 [5:10:55<17:04:20, 4.31s/it] 24%|██▎ | 4424/18688 [5:11:00<17:20:46, 4.38s/it] 24%|██▎ | 4425/18688 [5:11:05<17:44:39, 4.48s/it] 24%|██▎ | 4426/18688 [5:11:10<18:36:33, 4.70s/it] 24%|██▎ | 4427/18688 [5:11:14<18:21:58, 4.64s/it] 24%|██▎ | 4428/18688 [5:11:18<16:57:37, 4.28s/it] 24%|██▎ | 4429/18688 [5:11:22<16:41:33, 4.21s/it] 24%|██▎ | 4430/18688 [5:11:26<16:08:16, 4.07s/it] {'loss': '1.032', 'grad_norm': '0.9846', 'learning_rate': '7.44e-05', 'epoch': '0.2371', 'num_input_tokens_seen': 14886120, 'train_runtime': '1.869e+04', 'train_tokens_per_second': '796.6'}
24%|██▎ | 4430/18688 [5:11:26<16:08:16, 4.07s/it] 24%|██▎ | 4431/18688 [5:11:29<15:34:10, 3.93s/it] 24%|██▎ | 4432/18688 [5:11:34<16:06:37, 4.07s/it] 24%|██▎ | 4433/18688 [5:11:37<15:55:13, 4.02s/it] 24%|██▎ | 4434/18688 [5:11:41<15:17:26, 3.86s/it] 24%|██▎ | 4435/18688 [5:11:45<15:58:06, 4.03s/it] 24%|██▎ | 4436/18688 [5:11:49<15:24:02, 3.89s/it] 24%|██▎ | 4437/18688 [5:11:53<15:52:49, 4.01s/it] 24%|██▎ | 4438/18688 [5:11:57<15:55:15, 4.02s/it] 24%|██▍ | 4439/18688 [5:12:01<15:14:09, 3.85s/it] 24%|██▍ | 4440/18688 [5:12:05<15:45:17, 3.98s/it] {'loss': '0.962', 'grad_norm': '1.255', 'learning_rate': '7.437e-05', 'epoch': '0.2376', 'num_input_tokens_seen': 14916592, 'train_runtime': '1.873e+04', 'train_tokens_per_second': '796.6'}
24%|██▍ | 4440/18688 [5:12:05<15:45:17, 3.98s/it] 24%|██▍ | 4441/18688 [5:12:10<16:53:29, 4.27s/it] 24%|██▍ | 4442/18688 [5:12:13<15:58:24, 4.04s/it] 24%|██▍ | 4443/18688 [5:12:19<17:42:23, 4.47s/it] 24%|██▍ | 4444/18688 [5:12:22<16:34:12, 4.19s/it] 24%|██▍ | 4445/18688 [5:12:27<16:30:24, 4.17s/it] 24%|██▍ | 4446/18688 [5:12:30<15:54:56, 4.02s/it] 24%|██▍ | 4447/18688 [5:12:34<15:26:20, 3.90s/it] 24%|██▍ | 4448/18688 [5:12:38<15:14:20, 3.85s/it] 24%|██▍ | 4449/18688 [5:12:42<15:31:19, 3.92s/it] 24%|██▍ | 4450/18688 [5:12:46<15:52:19, 4.01s/it] {'loss': '0.946', 'grad_norm': '1.145', 'learning_rate': '7.433e-05', 'epoch': '0.2381', 'num_input_tokens_seen': 14949064, 'train_runtime': '1.877e+04', 'train_tokens_per_second': '796.6'}
24%|██▍ | 4450/18688 [5:12:46<15:52:19, 4.01s/it] 24%|██▍ | 4451/18688 [5:12:50<16:22:09, 4.14s/it] 24%|██▍ | 4452/18688 [5:12:54<16:12:12, 4.10s/it] 24%|██▍ | 4453/18688 [5:12:59<16:28:13, 4.17s/it] 24%|██▍ | 4454/18688 [5:13:03<16:24:34, 4.15s/it] 24%|██▍ | 4455/18688 [5:13:08<17:47:59, 4.50s/it] 24%|██▍ | 4456/18688 [5:13:13<18:28:21, 4.67s/it] 24%|██▍ | 4457/18688 [5:13:17<17:31:30, 4.43s/it] 24%|██▍ | 4458/18688 [5:13:21<16:27:48, 4.17s/it] 24%|██▍ | 4459/18688 [5:13:24<15:47:14, 3.99s/it] 24%|██▍ | 4460/18688 [5:13:28<15:18:25, 3.87s/it] {'loss': '0.8994', 'grad_norm': '1.221', 'learning_rate': '7.429e-05', 'epoch': '0.2387', 'num_input_tokens_seen': 14984544, 'train_runtime': '1.881e+04', 'train_tokens_per_second': '796.7'}
24%|██▍ | 4460/18688 [5:13:28<15:18:25, 3.87s/it] 24%|██▍ | 4461/18688 [5:13:32<16:15:43, 4.11s/it] 24%|██▍ | 4462/18688 [5:13:36<15:52:53, 4.02s/it] 24%|██▍ | 4463/18688 [5:13:40<15:20:21, 3.88s/it] 24%|██▍ | 4464/18688 [5:13:43<15:04:26, 3.82s/it] 24%|██▍ | 4465/18688 [5:13:47<14:52:03, 3.76s/it] 24%|██▍ | 4466/18688 [5:13:51<15:17:07, 3.87s/it] 24%|██▍ | 4467/18688 [5:13:55<14:59:37, 3.80s/it] 24%|██▍ | 4468/18688 [5:14:00<16:28:48, 4.17s/it] 24%|██▍ | 4469/18688 [5:14:03<15:45:20, 3.99s/it] 24%|██▍ | 4470/18688 [5:14:08<16:45:34, 4.24s/it] {'loss': '0.9229', 'grad_norm': '1.195', 'learning_rate': '7.425e-05', 'epoch': '0.2392', 'num_input_tokens_seen': 15015072, 'train_runtime': '1.885e+04', 'train_tokens_per_second': '796.6'}
24%|██▍ | 4470/18688 [5:14:08<16:45:34, 4.24s/it] 24%|██▍ | 4471/18688 [5:14:13<17:22:41, 4.40s/it] 24%|██▍ | 4472/18688 [5:14:17<16:57:20, 4.29s/it] 24%|██▍ | 4473/18688 [5:14:22<17:34:39, 4.45s/it] 24%|██▍ | 4474/18688 [5:14:26<17:12:05, 4.36s/it] 24%|██▍ | 4475/18688 [5:14:30<17:10:51, 4.35s/it] 24%|██▍ | 4476/18688 [5:14:34<16:38:14, 4.21s/it] 24%|██▍ | 4477/18688 [5:14:39<17:47:48, 4.51s/it] 24%|██▍ | 4478/18688 [5:14:45<18:33:00, 4.70s/it] 24%|██▍ | 4479/18688 [5:14:48<17:31:20, 4.44s/it] 24%|██▍ | 4480/18688 [5:14:53<17:24:17, 4.41s/it] {'loss': '0.9678', 'grad_norm': '1.31', 'learning_rate': '7.422e-05', 'epoch': '0.2397', 'num_input_tokens_seen': 15052064, 'train_runtime': '1.889e+04', 'train_tokens_per_second': '796.7'}
24%|██▍ | 4480/18688 [5:14:53<17:24:17, 4.41s/it] 24%|██▍ | 4481/18688 [5:14:58<17:44:52, 4.50s/it] 24%|██▍ | 4482/18688 [5:15:02<17:17:50, 4.38s/it] 24%|██▍ | 4483/18688 [5:15:06<16:47:17, 4.25s/it] 24%|██▍ | 4484/18688 [5:15:09<16:06:48, 4.08s/it] 24%|██▍ | 4485/18688 [5:15:13<16:06:13, 4.08s/it] 24%|██▍ | 4486/18688 [5:15:18<16:12:37, 4.11s/it] 24%|██▍ | 4487/18688 [5:15:22<17:03:55, 4.33s/it] 24%|██▍ | 4488/18688 [5:15:27<17:29:54, 4.44s/it] 24%|██▍ | 4489/18688 [5:15:32<18:18:35, 4.64s/it] 24%|██▍ | 4490/18688 [5:15:36<17:18:56, 4.39s/it] {'loss': '0.9765', 'grad_norm': '0.9962', 'learning_rate': '7.418e-05', 'epoch': '0.2403', 'num_input_tokens_seen': 15086400, 'train_runtime': '1.894e+04', 'train_tokens_per_second': '796.7'}
24%|██▍ | 4490/18688 [5:15:36<17:18:56, 4.39s/it] 24%|██▍ | 4491/18688 [5:15:40<16:53:38, 4.28s/it] 24%|██▍ | 4492/18688 [5:15:44<16:07:06, 4.09s/it] 24%|██▍ | 4493/18688 [5:15:48<15:52:17, 4.03s/it] 24%|██▍ | 4494/18688 [5:15:53<17:40:11, 4.48s/it] 24%|██▍ | 4495/18688 [5:15:57<17:34:26, 4.46s/it] 24%|██▍ | 4496/18688 [5:16:01<16:37:08, 4.22s/it] 24%|██▍ | 4497/18688 [5:16:05<16:01:22, 4.06s/it] 24%|██▍ | 4498/18688 [5:16:09<16:02:26, 4.07s/it] 24%|██▍ | 4499/18688 [5:16:14<16:49:16, 4.27s/it] 24%|██▍ | 4500/18688 [5:16:18<16:56:10, 4.30s/it] {'loss': '0.9563', 'grad_norm': '1.564', 'learning_rate': '7.414e-05', 'epoch': '0.2408', 'num_input_tokens_seen': 15120512, 'train_runtime': '1.898e+04', 'train_tokens_per_second': '796.7'}
24%|██▍ | 4500/18688 [5:16:18<16:56:10, 4.30s/it] 24%|██▍ | 4501/18688 [5:16:23<17:36:28, 4.47s/it] 24%|██▍ | 4502/18688 [5:16:27<17:47:34, 4.52s/it] 24%|██▍ | 4503/18688 [5:16:31<16:54:15, 4.29s/it] 24%|██▍ | 4504/18688 [5:16:35<16:28:32, 4.18s/it] 24%|██▍ | 4505/18688 [5:16:39<15:56:07, 4.04s/it] 24%|██▍ | 4506/18688 [5:16:44<16:35:58, 4.21s/it] 24%|██▍ | 4507/18688 [5:16:48<17:16:47, 4.39s/it] 24%|██▍ | 4508/18688 [5:16:52<16:52:33, 4.28s/it] 24%|██▍ | 4509/18688 [5:16:56<16:15:09, 4.13s/it] 24%|██▍ | 4510/18688 [5:17:01<16:53:23, 4.29s/it] {'loss': '0.9874', 'grad_norm': '1.111', 'learning_rate': '7.41e-05', 'epoch': '0.2413', 'num_input_tokens_seen': 15155280, 'train_runtime': '1.902e+04', 'train_tokens_per_second': '796.8'}
24%|██▍ | 4510/18688 [5:17:01<16:53:23, 4.29s/it] 24%|██▍ | 4511/18688 [5:17:05<16:47:23, 4.26s/it] 24%|██▍ | 4512/18688 [5:17:10<17:18:17, 4.39s/it] 24%|██▍ | 4513/18688 [5:17:15<17:53:33, 4.54s/it] 24%|██▍ | 4514/18688 [5:17:19<18:04:17, 4.59s/it] 24%|██▍ | 4515/18688 [5:17:24<18:22:06, 4.67s/it] 24%|██▍ | 4516/18688 [5:17:28<17:01:35, 4.33s/it] 24%|██▍ | 4517/18688 [5:17:32<16:35:01, 4.21s/it] 24%|██▍ | 4518/18688 [5:17:35<16:01:37, 4.07s/it] 24%|██▍ | 4519/18688 [5:17:39<15:25:07, 3.92s/it] 24%|██▍ | 4520/18688 [5:17:43<15:32:22, 3.95s/it] {'loss': '0.9477', 'grad_norm': '1.356', 'learning_rate': '7.406e-05', 'epoch': '0.2419', 'num_input_tokens_seen': 15189120, 'train_runtime': '1.906e+04', 'train_tokens_per_second': '796.8'}
24%|██▍ | 4520/18688 [5:17:43<15:32:22, 3.95s/it] 24%|██▍ | 4521/18688 [5:17:48<16:37:07, 4.22s/it] 24%|██▍ | 4522/18688 [5:17:52<17:06:16, 4.35s/it] 24%|██▍ | 4523/18688 [5:17:56<16:13:34, 4.12s/it] 24%|██▍ | 4524/18688 [5:18:00<16:27:07, 4.18s/it] 24%|██▍ | 4525/18688 [5:18:04<15:54:24, 4.04s/it] 24%|██▍ | 4526/18688 [5:18:08<15:54:53, 4.05s/it] 24%|██▍ | 4527/18688 [5:18:12<16:03:51, 4.08s/it] 24%|██▍ | 4528/18688 [5:18:16<15:50:24, 4.03s/it] 24%|██▍ | 4529/18688 [5:18:21<16:34:02, 4.21s/it] 24%|██▍ | 4530/18688 [5:18:25<16:10:00, 4.11s/it] {'loss': '0.929', 'grad_norm': '1.559', 'learning_rate': '7.402e-05', 'epoch': '0.2424', 'num_input_tokens_seen': 15223632, 'train_runtime': '1.911e+04', 'train_tokens_per_second': '796.8'}
24%|██▍ | 4530/18688 [5:18:25<16:10:00, 4.11s/it] 24%|██▍ | 4531/18688 [5:18:29<16:34:00, 4.21s/it] 24%|██▍ | 4532/18688 [5:18:33<16:11:54, 4.12s/it] 24%|██▍ | 4533/18688 [5:18:37<16:18:56, 4.15s/it] 24%|██▍ | 4534/18688 [5:18:41<15:54:36, 4.05s/it] 24%|██▍ | 4535/18688 [5:18:45<15:41:09, 3.99s/it] 24%|██▍ | 4536/18688 [5:18:49<15:58:12, 4.06s/it] 24%|██▍ | 4537/18688 [5:18:53<16:09:07, 4.11s/it] 24%|██▍ | 4538/18688 [5:18:57<15:49:36, 4.03s/it] 24%|██▍ | 4539/18688 [5:19:01<16:01:10, 4.08s/it] 24%|██▍ | 4540/18688 [5:19:05<15:42:33, 4.00s/it] {'loss': '0.981', 'grad_norm': '1.062', 'learning_rate': '7.399e-05', 'epoch': '0.2429', 'num_input_tokens_seen': 15255144, 'train_runtime': '1.915e+04', 'train_tokens_per_second': '796.8'}
24%|██▍ | 4540/18688 [5:19:05<15:42:33, 4.00s/it] 24%|██▍ | 4541/18688 [5:19:09<15:56:34, 4.06s/it] 24%|██▍ | 4542/18688 [5:19:15<17:20:17, 4.41s/it] 24%|██▍ | 4543/18688 [5:19:19<16:57:13, 4.31s/it] 24%|██▍ | 4544/18688 [5:19:23<16:40:07, 4.24s/it] 24%|██▍ | 4545/18688 [5:19:27<16:09:33, 4.11s/it] 24%|██▍ | 4546/18688 [5:19:31<16:27:42, 4.19s/it] 24%|██▍ | 4547/18688 [5:19:35<15:45:19, 4.01s/it] 24%|██▍ | 4548/18688 [5:19:39<15:48:53, 4.03s/it] 24%|██▍ | 4549/18688 [5:19:43<15:55:20, 4.05s/it] 24%|██▍ | 4550/18688 [5:19:47<16:29:11, 4.20s/it] {'loss': '0.9774', 'grad_norm': '1.077', 'learning_rate': '7.395e-05', 'epoch': '0.2435', 'num_input_tokens_seen': 15289496, 'train_runtime': '1.919e+04', 'train_tokens_per_second': '796.8'}
24%|██▍ | 4550/18688 [5:19:47<16:29:11, 4.20s/it] 24%|██▍ | 4551/18688 [5:19:51<16:03:10, 4.09s/it] 24%|██▍ | 4552/18688 [5:19:56<16:22:21, 4.17s/it] 24%|██▍ | 4553/18688 [5:20:00<16:16:32, 4.15s/it] 24%|██▍ | 4554/18688 [5:20:04<16:41:35, 4.25s/it] 24%|██▍ | 4555/18688 [5:20:08<16:29:57, 4.20s/it] 24%|██▍ | 4556/18688 [5:20:12<16:25:07, 4.18s/it] 24%|██▍ | 4557/18688 [5:20:16<15:58:44, 4.07s/it] 24%|██▍ | 4558/18688 [5:20:20<15:55:58, 4.06s/it] 24%|██▍ | 4559/18688 [5:20:24<15:25:45, 3.93s/it] 24%|██▍ | 4560/18688 [5:20:28<15:23:06, 3.92s/it] {'loss': '0.9129', 'grad_norm': '1.084', 'learning_rate': '7.391e-05', 'epoch': '0.244', 'num_input_tokens_seen': 15321784, 'train_runtime': '1.923e+04', 'train_tokens_per_second': '796.8'}
24%|██▍ | 4560/18688 [5:20:28<15:23:06, 3.92s/it] 24%|██▍ | 4561/18688 [5:20:31<15:08:20, 3.86s/it] 24%|██▍ | 4562/18688 [5:20:35<15:25:31, 3.93s/it] 24%|██▍ | 4563/18688 [5:20:39<15:01:03, 3.83s/it] 24%|██▍ | 4564/18688 [5:20:43<15:11:24, 3.87s/it] 24%|██▍ | 4565/18688 [5:20:47<14:51:04, 3.79s/it] 24%|██▍ | 4566/18688 [5:20:50<14:40:31, 3.74s/it] 24%|██▍ | 4567/18688 [5:20:55<16:23:34, 4.18s/it] 24%|██▍ | 4568/18688 [5:21:00<16:19:38, 4.16s/it] 24%|██▍ | 4569/18688 [5:21:04<16:20:14, 4.17s/it] 24%|██▍ | 4570/18688 [5:21:07<15:31:54, 3.96s/it] {'loss': '0.9344', 'grad_norm': '1.219', 'learning_rate': '7.387e-05', 'epoch': '0.2445', 'num_input_tokens_seen': 15351872, 'train_runtime': '1.927e+04', 'train_tokens_per_second': '796.8'}
24%|██▍ | 4570/18688 [5:21:07<15:31:54, 3.96s/it] 24%|██▍ | 4571/18688 [5:21:11<15:46:02, 4.02s/it] 24%|██▍ | 4572/18688 [5:21:16<16:10:29, 4.13s/it] 24%|██▍ | 4573/18688 [5:21:20<15:55:40, 4.06s/it] 24%|██▍ | 4574/18688 [5:21:24<16:23:39, 4.18s/it] 24%|██▍ | 4575/18688 [5:21:28<15:41:20, 4.00s/it] 24%|██▍ | 4576/18688 [5:21:32<15:54:11, 4.06s/it] 24%|██▍ | 4577/18688 [5:21:36<16:10:20, 4.13s/it] 24%|██▍ | 4578/18688 [5:21:40<16:19:37, 4.17s/it] 25%|██▍ | 4579/18688 [5:21:44<16:03:38, 4.10s/it] 25%|██▍ | 4580/18688 [5:21:50<17:23:36, 4.44s/it] {'loss': '0.9683', 'grad_norm': '1.12', 'learning_rate': '7.383e-05', 'epoch': '0.2451', 'num_input_tokens_seen': 15386312, 'train_runtime': '1.931e+04', 'train_tokens_per_second': '796.8'}
25%|██▍ | 4580/18688 [5:21:50<17:23:36, 4.44s/it] 25%|██▍ | 4581/18688 [5:21:55<18:32:58, 4.73s/it] 25%|██▍ | 4582/18688 [5:22:00<19:13:36, 4.91s/it] 25%|██▍ | 4583/18688 [5:22:04<18:17:04, 4.67s/it] 25%|██▍ | 4584/18688 [5:22:09<18:05:37, 4.62s/it] 25%|██▍ | 4585/18688 [5:22:13<17:27:11, 4.46s/it] 25%|██▍ | 4586/18688 [5:22:17<17:19:21, 4.42s/it] 25%|██▍ | 4587/18688 [5:22:21<16:35:41, 4.24s/it] 25%|██▍ | 4588/18688 [5:22:25<16:04:52, 4.11s/it] 25%|██▍ | 4589/18688 [5:22:29<15:39:03, 4.00s/it] 25%|██▍ | 4590/18688 [5:22:32<15:03:38, 3.85s/it] {'loss': '0.9396', 'grad_norm': '1.043', 'learning_rate': '7.379e-05', 'epoch': '0.2456', 'num_input_tokens_seen': 15420224, 'train_runtime': '1.935e+04', 'train_tokens_per_second': '796.8'}
25%|██▍ | 4590/18688 [5:22:32<15:03:38, 3.85s/it] 25%|██▍ | 4591/18688 [5:22:37<15:43:38, 4.02s/it] 25%|██▍ | 4592/18688 [5:22:40<15:06:43, 3.86s/it] 25%|██▍ | 4593/18688 [5:22:45<16:07:16, 4.12s/it] 25%|██▍ | 4594/18688 [5:22:48<15:20:12, 3.92s/it] 25%|██▍ | 4595/18688 [5:22:52<15:13:57, 3.89s/it] 25%|██▍ | 4596/18688 [5:22:57<15:55:24, 4.07s/it] 25%|██▍ | 4597/18688 [5:23:00<15:24:52, 3.94s/it] 25%|██▍ | 4598/18688 [5:23:05<15:54:10, 4.06s/it] 25%|██▍ | 4599/18688 [5:23:08<15:15:34, 3.90s/it] 25%|██▍ | 4600/18688 [5:23:12<15:40:25, 4.01s/it] {'loss': '0.9443', 'grad_norm': '1.127', 'learning_rate': '7.375e-05', 'epoch': '0.2462', 'num_input_tokens_seen': 15452360, 'train_runtime': '1.939e+04', 'train_tokens_per_second': '796.8'}
25%|██▍ | 4600/18688 [5:23:12<15:40:25, 4.01s/it] 25%|██▍ | 4601/18688 [5:23:16<15:24:26, 3.94s/it] 25%|██▍ | 4602/18688 [5:23:20<15:48:59, 4.04s/it] 25%|██▍ | 4603/18688 [5:23:25<15:53:31, 4.06s/it] 25%|██▍ | 4604/18688 [5:23:29<16:41:51, 4.27s/it] 25%|██▍ | 4605/18688 [5:23:34<16:50:40, 4.31s/it] 25%|██▍ | 4606/18688 [5:23:38<16:22:16, 4.19s/it] 25%|██▍ | 4607/18688 [5:23:42<16:33:58, 4.24s/it] 25%|██▍ | 4608/18688 [5:23:46<16:33:59, 4.24s/it] 25%|██▍ | 4609/18688 [5:23:51<16:42:53, 4.27s/it] 25%|██▍ | 4610/18688 [5:23:55<16:58:36, 4.34s/it] {'loss': '1.029', 'grad_norm': '1.066', 'learning_rate': '7.371e-05', 'epoch': '0.2467', 'num_input_tokens_seen': 15488664, 'train_runtime': '1.944e+04', 'train_tokens_per_second': '796.9'}
25%|██▍ | 4610/18688 [5:23:55<16:58:36, 4.34s/it] 25%|██▍ | 4611/18688 [5:23:59<16:52:02, 4.31s/it] 25%|██▍ | 4612/18688 [5:24:04<17:29:52, 4.48s/it] 25%|██▍ | 4613/18688 [5:24:08<16:49:08, 4.30s/it] 25%|██▍ | 4614/18688 [5:24:12<15:51:05, 4.05s/it] 25%|██▍ | 4615/18688 [5:24:16<15:53:51, 4.07s/it] 25%|██▍ | 4616/18688 [5:24:20<16:18:50, 4.17s/it] 25%|██▍ | 4617/18688 [5:24:24<15:47:59, 4.04s/it] 25%|██▍ | 4618/18688 [5:24:28<15:39:03, 4.00s/it] 25%|██▍ | 4619/18688 [5:24:32<15:37:02, 4.00s/it] 25%|██▍ | 4620/18688 [5:24:36<15:43:58, 4.03s/it] {'loss': '0.9207', 'grad_norm': '1.262', 'learning_rate': '7.367e-05', 'epoch': '0.2472', 'num_input_tokens_seen': 15522336, 'train_runtime': '1.948e+04', 'train_tokens_per_second': '797'}
25%|██▍ | 4620/18688 [5:24:36<15:43:58, 4.03s/it] 25%|██▍ | 4621/18688 [5:24:40<16:13:13, 4.15s/it] 25%|██▍ | 4622/18688 [5:24:44<16:01:13, 4.10s/it] 25%|██▍ | 4623/18688 [5:24:48<15:56:27, 4.08s/it] 25%|██▍ | 4624/18688 [5:24:52<15:22:23, 3.94s/it] 25%|██▍ | 4625/18688 [5:24:56<16:09:59, 4.14s/it] 25%|██▍ | 4626/18688 [5:25:01<16:04:47, 4.12s/it] 25%|██▍ | 4627/18688 [5:25:04<15:19:17, 3.92s/it] 25%|██▍ | 4628/18688 [5:25:07<14:44:45, 3.78s/it] 25%|██▍ | 4629/18688 [5:25:12<15:48:07, 4.05s/it] 25%|██▍ | 4630/18688 [5:25:18<18:06:47, 4.64s/it] {'loss': '0.9547', 'grad_norm': '1.099', 'learning_rate': '7.363e-05', 'epoch': '0.2478', 'num_input_tokens_seen': 15557696, 'train_runtime': '1.952e+04', 'train_tokens_per_second': '797.1'}
25%|██▍ | 4630/18688 [5:25:18<18:06:47, 4.64s/it] 25%|██▍ | 4631/18688 [5:25:22<17:32:43, 4.49s/it] 25%|██▍ | 4632/18688 [5:25:26<16:52:17, 4.32s/it] 25%|██▍ | 4633/18688 [5:25:30<16:39:11, 4.27s/it] 25%|██▍ | 4634/18688 [5:25:34<16:23:02, 4.20s/it] 25%|██▍ | 4635/18688 [5:25:38<15:57:30, 4.09s/it] 25%|██▍ | 4636/18688 [5:25:42<15:40:15, 4.01s/it] 25%|██▍ | 4637/18688 [5:25:46<15:30:39, 3.97s/it] 25%|██▍ | 4638/18688 [5:25:50<15:02:45, 3.86s/it] 25%|██▍ | 4639/18688 [5:25:54<16:04:38, 4.12s/it] 25%|██▍ | 4640/18688 [5:25:58<16:06:16, 4.13s/it] {'loss': '0.9545', 'grad_norm': '0.968', 'learning_rate': '7.36e-05', 'epoch': '0.2483', 'num_input_tokens_seen': 15590776, 'train_runtime': '1.956e+04', 'train_tokens_per_second': '797.1'}
25%|██▍ | 4640/18688 [5:25:58<16:06:16, 4.13s/it] 25%|██▍ | 4641/18688 [5:26:02<15:56:13, 4.08s/it] 25%|██▍ | 4642/18688 [5:26:07<16:08:43, 4.14s/it] 25%|██▍ | 4643/18688 [5:26:10<15:32:21, 3.98s/it] 25%|██▍ | 4644/18688 [5:26:14<15:26:55, 3.96s/it] 25%|██▍ | 4645/18688 [5:26:18<14:52:39, 3.81s/it] 25%|██▍ | 4646/18688 [5:26:22<15:17:38, 3.92s/it] 25%|██▍ | 4647/18688 [5:26:26<15:05:21, 3.87s/it] 25%|██▍ | 4648/18688 [5:26:30<15:31:47, 3.98s/it] 25%|██▍ | 4649/18688 [5:26:33<14:55:02, 3.83s/it] 25%|██▍ | 4650/18688 [5:26:38<15:27:53, 3.97s/it] {'loss': '0.9511', 'grad_norm': '0.9562', 'learning_rate': '7.356e-05', 'epoch': '0.2488', 'num_input_tokens_seen': 15622168, 'train_runtime': '1.96e+04', 'train_tokens_per_second': '797.1'}
25%|██▍ | 4650/18688 [5:26:38<15:27:53, 3.97s/it] 25%|██▍ | 4651/18688 [5:26:41<15:03:56, 3.86s/it] 25%|██▍ | 4652/18688 [5:26:45<14:51:53, 3.81s/it] 25%|██▍ | 4653/18688 [5:26:49<14:47:43, 3.80s/it] 25%|██▍ | 4654/18688 [5:26:53<15:14:28, 3.91s/it] 25%|██▍ | 4655/18688 [5:26:56<14:53:03, 3.82s/it] 25%|██▍ | 4656/18688 [5:27:00<15:10:06, 3.89s/it] 25%|██▍ | 4657/18688 [5:27:05<16:01:45, 4.11s/it] 25%|██▍ | 4658/18688 [5:27:09<16:02:36, 4.12s/it] 25%|██▍ | 4659/18688 [5:27:14<16:50:08, 4.32s/it] 25%|██▍ | 4660/18688 [5:27:18<16:02:05, 4.12s/it] {'loss': '0.9131', 'grad_norm': '1.156', 'learning_rate': '7.352e-05', 'epoch': '0.2494', 'num_input_tokens_seen': 15654144, 'train_runtime': '1.964e+04', 'train_tokens_per_second': '797.1'}
25%|██▍ | 4660/18688 [5:27:18<16:02:05, 4.12s/it] 25%|██▍ | 4661/18688 [5:27:22<15:48:58, 4.06s/it] 25%|██▍ | 4662/18688 [5:27:26<15:39:52, 4.02s/it] 25%|██▍ | 4663/18688 [5:27:30<15:38:50, 4.02s/it] 25%|██▍ | 4664/18688 [5:27:34<15:59:26, 4.10s/it] 25%|██▍ | 4665/18688 [5:27:38<15:38:48, 4.02s/it] 25%|██▍ | 4666/18688 [5:27:42<15:48:12, 4.06s/it] 25%|██▍ | 4667/18688 [5:27:45<15:19:55, 3.94s/it] 25%|██▍ | 4668/18688 [5:27:49<14:56:00, 3.83s/it] 25%|██▍ | 4669/18688 [5:27:53<14:49:25, 3.81s/it] 25%|██▍ | 4670/18688 [5:27:57<15:10:15, 3.90s/it] {'loss': '0.9426', 'grad_norm': '1.08', 'learning_rate': '7.348e-05', 'epoch': '0.2499', 'num_input_tokens_seen': 15684704, 'train_runtime': '1.968e+04', 'train_tokens_per_second': '797.1'}
25%|██▍ | 4670/18688 [5:27:57<15:10:15, 3.90s/it] 25%|██▍ | 4671/18688 [5:28:02<16:00:00, 4.11s/it] 25%|██▌ | 4672/18688 [5:28:05<15:42:16, 4.03s/it] 25%|██▌ | 4673/18688 [5:28:09<15:06:22, 3.88s/it] 25%|██▌ | 4674/18688 [5:28:13<15:19:15, 3.94s/it] 25%|██▌ | 4675/18688 [5:28:17<15:25:04, 3.96s/it] 25%|██▌ | 4676/18688 [5:28:21<15:06:34, 3.88s/it] 25%|██▌ | 4677/18688 [5:28:26<16:20:44, 4.20s/it] 25%|██▌ | 4678/18688 [5:28:30<16:09:56, 4.15s/it] 25%|██▌ | 4679/18688 [5:28:33<15:32:38, 3.99s/it] 25%|██▌ | 4680/18688 [5:28:37<15:39:15, 4.02s/it] {'loss': '0.9611', 'grad_norm': '1.405', 'learning_rate': '7.344e-05', 'epoch': '0.2504', 'num_input_tokens_seen': 15717224, 'train_runtime': '1.972e+04', 'train_tokens_per_second': '797.1'}
25%|██▌ | 4680/18688 [5:28:37<15:39:15, 4.02s/it] 25%|██▌ | 4681/18688 [5:28:41<15:08:31, 3.89s/it] 25%|██▌ | 4682/18688 [5:28:44<14:42:15, 3.78s/it] 25%|██▌ | 4683/18688 [5:28:48<14:45:24, 3.79s/it] 25%|██▌ | 4684/18688 [5:28:52<14:51:55, 3.82s/it] 25%|██▌ | 4685/18688 [5:28:57<16:28:39, 4.24s/it] 25%|██▌ | 4686/18688 [5:29:02<16:29:02, 4.24s/it] 25%|██▌ | 4687/18688 [5:29:05<15:45:00, 4.05s/it] 25%|██▌ | 4688/18688 [5:29:10<16:47:37, 4.32s/it] 25%|██▌ | 4689/18688 [5:29:14<16:45:00, 4.31s/it] 25%|██▌ | 4690/18688 [5:29:18<15:46:05, 4.06s/it] {'loss': '0.9363', 'grad_norm': '1.054', 'learning_rate': '7.34e-05', 'epoch': '0.251', 'num_input_tokens_seen': 15749296, 'train_runtime': '1.976e+04', 'train_tokens_per_second': '797.1'}
25%|██▌ | 4690/18688 [5:29:18<15:46:05, 4.06s/it] 25%|██▌ | 4691/18688 [5:29:23<16:25:09, 4.22s/it] 25%|██▌ | 4692/18688 [5:29:26<15:41:47, 4.04s/it] 25%|██▌ | 4693/18688 [5:29:30<15:17:51, 3.94s/it] 25%|██▌ | 4694/18688 [5:29:34<15:23:15, 3.96s/it] 25%|██▌ | 4695/18688 [5:29:37<14:48:21, 3.81s/it] 25%|██▌ | 4696/18688 [5:29:41<14:31:24, 3.74s/it] 25%|██▌ | 4697/18688 [5:29:45<14:57:02, 3.85s/it] 25%|██▌ | 4698/18688 [5:29:51<17:36:01, 4.53s/it] 25%|██▌ | 4699/18688 [5:29:55<17:11:10, 4.42s/it] 25%|██▌ | 4700/18688 [5:30:00<16:58:36, 4.37s/it] {'loss': '0.871', 'grad_norm': '1.309', 'learning_rate': '7.335e-05', 'epoch': '0.2515', 'num_input_tokens_seen': 15783216, 'train_runtime': '1.98e+04', 'train_tokens_per_second': '797.1'}
25%|██▌ | 4700/18688 [5:30:00<16:58:36, 4.37s/it] 25%|██▌ | 4701/18688 [5:30:03<16:25:41, 4.23s/it] 25%|██▌ | 4702/18688 [5:30:08<16:48:47, 4.33s/it] 25%|██▌ | 4703/18688 [5:30:12<15:58:10, 4.11s/it] 25%|██▌ | 4704/18688 [5:30:16<15:57:01, 4.11s/it] 25%|██▌ | 4705/18688 [5:30:20<16:05:02, 4.14s/it] 25%|██▌ | 4706/18688 [5:30:25<16:42:00, 4.30s/it] 25%|██▌ | 4707/18688 [5:30:29<16:45:33, 4.32s/it] 25%|██▌ | 4708/18688 [5:30:32<15:45:20, 4.06s/it] 25%|██▌ | 4709/18688 [5:30:36<15:22:37, 3.96s/it] 25%|██▌ | 4710/18688 [5:30:40<15:42:23, 4.05s/it] {'loss': '0.9125', 'grad_norm': '1.108', 'learning_rate': '7.331e-05', 'epoch': '0.252', 'num_input_tokens_seen': 15817536, 'train_runtime': '1.984e+04', 'train_tokens_per_second': '797.2'}
25%|██▌ | 4710/18688 [5:30:40<15:42:23, 4.05s/it] 25%|██▌ | 4711/18688 [5:30:44<15:29:53, 3.99s/it] 25%|██▌ | 4712/18688 [5:30:49<16:29:43, 4.25s/it] 25%|██▌ | 4713/18688 [5:30:53<16:36:26, 4.28s/it] 25%|██▌ | 4714/18688 [5:30:58<16:46:53, 4.32s/it] 25%|██▌ | 4715/18688 [5:31:01<15:54:37, 4.10s/it] 25%|██▌ | 4716/18688 [5:31:05<15:16:24, 3.94s/it] 25%|██▌ | 4717/18688 [5:31:10<16:19:15, 4.21s/it] 25%|██▌ | 4718/18688 [5:31:14<16:12:03, 4.17s/it] 25%|██▌ | 4719/18688 [5:31:18<16:17:10, 4.20s/it] 25%|██▌ | 4720/18688 [5:31:22<16:08:12, 4.16s/it] {'loss': '0.8854', 'grad_norm': '1.112', 'learning_rate': '7.327e-05', 'epoch': '0.2526', 'num_input_tokens_seen': 15852056, 'train_runtime': '1.988e+04', 'train_tokens_per_second': '797.3'}
25%|██▌ | 4720/18688 [5:31:22<16:08:12, 4.16s/it] 25%|██▌ | 4721/18688 [5:31:26<15:53:31, 4.10s/it] 25%|██▌ | 4722/18688 [5:31:30<15:40:03, 4.04s/it] 25%|██▌ | 4723/18688 [5:31:34<15:02:31, 3.88s/it] 25%|██▌ | 4724/18688 [5:31:37<14:58:23, 3.86s/it] 25%|██▌ | 4725/18688 [5:31:41<14:41:44, 3.79s/it] 25%|██▌ | 4726/18688 [5:31:45<15:13:52, 3.93s/it] 25%|██▌ | 4727/18688 [5:31:50<16:16:24, 4.20s/it] 25%|██▌ | 4728/18688 [5:31:54<15:40:47, 4.04s/it] 25%|██▌ | 4729/18688 [5:31:57<15:08:49, 3.91s/it] 25%|██▌ | 4730/18688 [5:32:01<14:43:57, 3.80s/it] {'loss': '0.9644', 'grad_norm': '1.181', 'learning_rate': '7.323e-05', 'epoch': '0.2531', 'num_input_tokens_seen': 15882192, 'train_runtime': '1.992e+04', 'train_tokens_per_second': '797.2'}
25%|██▌ | 4730/18688 [5:32:01<14:43:57, 3.80s/it] 25%|██▌ | 4731/18688 [5:32:05<14:37:31, 3.77s/it] 25%|██▌ | 4732/18688 [5:32:09<15:24:45, 3.98s/it] 25%|██▌ | 4733/18688 [5:32:13<15:36:24, 4.03s/it] 25%|██▌ | 4734/18688 [5:32:17<15:44:01, 4.06s/it] 25%|██▌ | 4735/18688 [5:32:22<16:19:56, 4.21s/it] 25%|██▌ | 4736/18688 [5:32:26<16:14:06, 4.19s/it] 25%|██▌ | 4737/18688 [5:32:30<15:44:30, 4.06s/it] 25%|██▌ | 4738/18688 [5:32:34<16:13:06, 4.19s/it] 25%|██▌ | 4739/18688 [5:32:38<15:49:19, 4.08s/it] 25%|██▌ | 4740/18688 [5:32:42<15:50:20, 4.09s/it] {'loss': '0.9837', 'grad_norm': '0.8265', 'learning_rate': '7.319e-05', 'epoch': '0.2536', 'num_input_tokens_seen': 15916552, 'train_runtime': '1.996e+04', 'train_tokens_per_second': '797.3'}
25%|██▌ | 4740/18688 [5:32:42<15:50:20, 4.09s/it] 25%|██▌ | 4741/18688 [5:32:46<15:13:28, 3.93s/it] 25%|██▌ | 4742/18688 [5:32:49<14:42:27, 3.80s/it] 25%|██▌ | 4743/18688 [5:32:53<14:32:11, 3.75s/it] 25%|██▌ | 4744/18688 [5:32:57<14:34:54, 3.76s/it] 25%|██▌ | 4745/18688 [5:33:01<15:26:13, 3.99s/it] 25%|██▌ | 4746/18688 [5:33:05<15:21:04, 3.96s/it] 25%|██▌ | 4747/18688 [5:33:09<15:35:01, 4.02s/it] 25%|██▌ | 4748/18688 [5:33:15<17:45:32, 4.59s/it] 25%|██▌ | 4749/18688 [5:33:19<16:40:36, 4.31s/it] 25%|██▌ | 4750/18688 [5:33:23<16:47:36, 4.34s/it] {'loss': '0.9457', 'grad_norm': '1.145', 'learning_rate': '7.315e-05', 'epoch': '0.2542', 'num_input_tokens_seen': 15948256, 'train_runtime': '2e+04', 'train_tokens_per_second': '797.3'}
25%|██▌ | 4750/18688 [5:33:23<16:47:36, 4.34s/it] 25%|██▌ | 4751/18688 [5:33:28<16:38:32, 4.30s/it] 25%|██▌ | 4752/18688 [5:33:32<16:31:25, 4.27s/it] 25%|██▌ | 4753/18688 [5:33:36<16:30:21, 4.26s/it] 25%|██▌ | 4754/18688 [5:33:39<15:34:42, 4.02s/it] 25%|██▌ | 4755/18688 [5:33:44<15:40:48, 4.05s/it] 25%|██▌ | 4756/18688 [5:33:48<15:44:48, 4.07s/it] 25%|██▌ | 4757/18688 [5:33:53<16:38:43, 4.30s/it] 25%|██▌ | 4758/18688 [5:33:56<15:56:11, 4.12s/it] 25%|██▌ | 4759/18688 [5:34:00<15:38:48, 4.04s/it] 25%|██▌ | 4760/18688 [5:34:04<16:03:57, 4.15s/it] {'loss': '0.9352', 'grad_norm': '1.168', 'learning_rate': '7.311e-05', 'epoch': '0.2547', 'num_input_tokens_seen': 15982480, 'train_runtime': '2.004e+04', 'train_tokens_per_second': '797.3'}
25%|██▌ | 4760/18688 [5:34:04<16:03:57, 4.15s/it] 25%|██▌ | 4761/18688 [5:34:09<16:05:25, 4.16s/it] 25%|██▌ | 4762/18688 [5:34:13<16:32:58, 4.28s/it] 25%|██▌ | 4763/18688 [5:34:18<16:46:45, 4.34s/it] 25%|██▌ | 4764/18688 [5:34:22<16:23:30, 4.24s/it] 25%|██▌ | 4765/18688 [5:34:26<16:18:18, 4.22s/it] 26%|██▌ | 4766/18688 [5:34:31<16:50:05, 4.35s/it] 26%|██▌ | 4767/18688 [5:34:35<16:59:20, 4.39s/it] 26%|██▌ | 4768/18688 [5:34:39<16:40:21, 4.31s/it] 26%|██▌ | 4769/18688 [5:34:43<16:08:03, 4.17s/it] 26%|██▌ | 4770/18688 [5:34:47<15:23:03, 3.98s/it] {'loss': '0.9618', 'grad_norm': '1.282', 'learning_rate': '7.307e-05', 'epoch': '0.2553', 'num_input_tokens_seen': 16017888, 'train_runtime': '2.009e+04', 'train_tokens_per_second': '797.4'}
26%|██▌ | 4770/18688 [5:34:47<15:23:03, 3.98s/it] 26%|██▌ | 4771/18688 [5:34:50<14:52:56, 3.85s/it] 26%|██▌ | 4772/18688 [5:34:55<16:03:15, 4.15s/it] 26%|██▌ | 4773/18688 [5:35:00<16:57:45, 4.39s/it] 26%|██▌ | 4774/18688 [5:35:04<16:16:19, 4.21s/it] 26%|██▌ | 4775/18688 [5:35:07<15:45:23, 4.08s/it] 26%|██▌ | 4776/18688 [5:35:11<15:42:01, 4.06s/it] 26%|██▌ | 4777/18688 [5:35:16<16:41:21, 4.32s/it] 26%|██▌ | 4778/18688 [5:35:21<16:36:57, 4.30s/it] 26%|██▌ | 4779/18688 [5:35:25<16:48:59, 4.35s/it] 26%|██▌ | 4780/18688 [5:35:29<16:28:28, 4.26s/it] {'loss': '0.932', 'grad_norm': '0.9977', 'learning_rate': '7.303e-05', 'epoch': '0.2558', 'num_input_tokens_seen': 16053640, 'train_runtime': '2.013e+04', 'train_tokens_per_second': '797.5'}
26%|██▌ | 4780/18688 [5:35:29<16:28:28, 4.26s/it] 26%|██▌ | 4781/18688 [5:35:33<16:22:25, 4.24s/it] 26%|██▌ | 4782/18688 [5:35:38<16:54:53, 4.38s/it] 26%|██▌ | 4783/18688 [5:35:43<17:04:12, 4.42s/it] 26%|██▌ | 4784/18688 [5:35:48<17:53:56, 4.63s/it] 26%|██▌ | 4785/18688 [5:35:52<17:20:11, 4.49s/it] 26%|██▌ | 4786/18688 [5:35:56<17:02:19, 4.41s/it] 26%|██▌ | 4787/18688 [5:36:00<16:49:07, 4.36s/it] 26%|██▌ | 4788/18688 [5:36:04<16:19:36, 4.23s/it] 26%|██▌ | 4789/18688 [5:36:08<16:12:58, 4.20s/it] 26%|██▌ | 4790/18688 [5:36:12<15:30:29, 4.02s/it] {'loss': '0.8959', 'grad_norm': '0.9549', 'learning_rate': '7.299e-05', 'epoch': '0.2563', 'num_input_tokens_seen': 16089048, 'train_runtime': '2.017e+04', 'train_tokens_per_second': '797.6'}
26%|██▌ | 4790/18688 [5:36:12<15:30:29, 4.02s/it] 26%|██▌ | 4791/18688 [5:36:16<15:40:03, 4.06s/it] 26%|██▌ | 4792/18688 [5:36:21<16:06:03, 4.17s/it] 26%|██▌ | 4793/18688 [5:36:25<16:36:11, 4.30s/it] 26%|██▌ | 4794/18688 [5:36:30<16:44:23, 4.34s/it] 26%|██▌ | 4795/18688 [5:36:33<15:53:26, 4.12s/it] 26%|██▌ | 4796/18688 [5:36:37<16:05:32, 4.17s/it] 26%|██▌ | 4797/18688 [5:36:41<15:17:00, 3.96s/it] 26%|██▌ | 4798/18688 [5:36:45<15:04:53, 3.91s/it] 26%|██▌ | 4799/18688 [5:36:48<14:45:31, 3.83s/it] 26%|██▌ | 4800/18688 [5:36:52<14:45:26, 3.83s/it] {'loss': '0.9751', 'grad_norm': '1.158', 'learning_rate': '7.295e-05', 'epoch': '0.2569', 'num_input_tokens_seen': 16120568, 'train_runtime': '2.021e+04', 'train_tokens_per_second': '797.5'}
26%|██▌ | 4800/18688 [5:36:52<14:45:26, 3.83s/it] 26%|██▌ | 4801/18688 [5:36:56<14:29:21, 3.76s/it] 26%|██▌ | 4802/18688 [5:37:01<16:27:31, 4.27s/it] 26%|██▌ | 4803/18688 [5:37:05<15:38:05, 4.05s/it] 26%|██▌ | 4804/18688 [5:37:11<17:39:29, 4.58s/it] 26%|██▌ | 4805/18688 [5:37:15<17:54:52, 4.65s/it] 26%|██▌ | 4806/18688 [5:37:20<17:53:16, 4.64s/it] 26%|██▌ | 4807/18688 [5:37:24<16:48:57, 4.36s/it] 26%|██▌ | 4808/18688 [5:37:27<16:05:07, 4.17s/it] 26%|██▌ | 4809/18688 [5:37:31<15:41:02, 4.07s/it] 26%|██▌ | 4810/18688 [5:37:35<15:11:42, 3.94s/it] {'loss': '0.944', 'grad_norm': '1.19', 'learning_rate': '7.29e-05', 'epoch': '0.2574', 'num_input_tokens_seen': 16156104, 'train_runtime': '2.026e+04', 'train_tokens_per_second': '797.6'}
26%|██▌ | 4810/18688 [5:37:35<15:11:42, 3.94s/it] 26%|██▌ | 4811/18688 [5:37:39<15:19:20, 3.97s/it] 26%|██▌ | 4812/18688 [5:37:43<15:23:39, 3.99s/it] 26%|██▌ | 4813/18688 [5:37:48<16:03:33, 4.17s/it] 26%|██▌ | 4814/18688 [5:37:52<15:53:20, 4.12s/it] 26%|██▌ | 4815/18688 [5:37:55<15:29:01, 4.02s/it] 26%|██▌ | 4816/18688 [5:38:00<15:38:30, 4.06s/it] 26%|██▌ | 4817/18688 [5:38:03<15:28:32, 4.02s/it] 26%|██▌ | 4818/18688 [5:38:08<15:46:25, 4.09s/it] 26%|██▌ | 4819/18688 [5:38:12<16:08:18, 4.19s/it] 26%|██▌ | 4820/18688 [5:38:16<15:30:53, 4.03s/it] {'loss': '0.9509', 'grad_norm': '1.105', 'learning_rate': '7.286e-05', 'epoch': '0.2579', 'num_input_tokens_seen': 16188728, 'train_runtime': '2.03e+04', 'train_tokens_per_second': '797.6'}
26%|██▌ | 4820/18688 [5:38:16<15:30:53, 4.03s/it] 26%|██▌ | 4821/18688 [5:38:21<16:32:07, 4.29s/it] 26%|██▌ | 4822/18688 [5:38:26<17:09:12, 4.45s/it] 26%|██▌ | 4823/18688 [5:38:30<17:33:37, 4.56s/it] 26%|██▌ | 4824/18688 [5:38:34<16:18:57, 4.24s/it] 26%|██▌ | 4825/18688 [5:38:39<17:03:52, 4.43s/it] 26%|██▌ | 4826/18688 [5:38:43<17:06:28, 4.44s/it] 26%|██▌ | 4827/18688 [5:38:48<17:15:48, 4.48s/it] 26%|██▌ | 4828/18688 [5:38:51<16:21:48, 4.25s/it] 26%|██▌ | 4829/18688 [5:38:56<16:09:46, 4.20s/it] 26%|██▌ | 4830/18688 [5:39:01<17:11:09, 4.46s/it] {'loss': '0.9294', 'grad_norm': '1.06', 'learning_rate': '7.282e-05', 'epoch': '0.2585', 'num_input_tokens_seen': 16227520, 'train_runtime': '2.034e+04', 'train_tokens_per_second': '797.8'}
26%|██▌ | 4830/18688 [5:39:01<17:11:09, 4.46s/it] 26%|██▌ | 4831/18688 [5:39:05<16:44:12, 4.35s/it] 26%|██▌ | 4832/18688 [5:39:09<16:08:57, 4.20s/it] 26%|██▌ | 4833/18688 [5:39:13<16:59:06, 4.41s/it] 26%|██▌ | 4834/18688 [5:39:18<16:54:32, 4.39s/it] 26%|██▌ | 4835/18688 [5:39:22<16:28:34, 4.28s/it] 26%|██▌ | 4836/18688 [5:39:25<15:45:30, 4.10s/it] 26%|██▌ | 4837/18688 [5:39:30<15:40:50, 4.08s/it] 26%|██▌ | 4838/18688 [5:39:34<15:53:57, 4.13s/it] 26%|██▌ | 4839/18688 [5:39:38<15:46:49, 4.10s/it] 26%|██▌ | 4840/18688 [5:39:42<15:38:21, 4.07s/it] {'loss': '0.8729', 'grad_norm': '1.056', 'learning_rate': '7.278e-05', 'epoch': '0.259', 'num_input_tokens_seen': 16261304, 'train_runtime': '2.038e+04', 'train_tokens_per_second': '797.8'}
26%|██▌ | 4840/18688 [5:39:42<15:38:21, 4.07s/it] 26%|██▌ | 4841/18688 [5:39:46<15:37:21, 4.06s/it] 26%|██▌ | 4842/18688 [5:39:51<16:34:28, 4.31s/it] 26%|██▌ | 4843/18688 [5:39:55<16:43:47, 4.35s/it] 26%|██▌ | 4844/18688 [5:39:59<16:37:09, 4.32s/it] 26%|██▌ | 4845/18688 [5:40:03<16:16:27, 4.23s/it] 26%|██▌ | 4846/18688 [5:40:08<16:40:43, 4.34s/it] 26%|██▌ | 4847/18688 [5:40:13<16:57:24, 4.41s/it] 26%|██▌ | 4848/18688 [5:40:17<17:02:07, 4.43s/it] 26%|██▌ | 4849/18688 [5:40:21<16:09:33, 4.20s/it] 26%|██▌ | 4850/18688 [5:40:25<15:54:58, 4.14s/it] {'loss': '0.9843', 'grad_norm': '1.214', 'learning_rate': '7.274e-05', 'epoch': '0.2595', 'num_input_tokens_seen': 16297392, 'train_runtime': '2.043e+04', 'train_tokens_per_second': '797.9'}
26%|██▌ | 4850/18688 [5:40:25<15:54:58, 4.14s/it] 26%|██▌ | 4851/18688 [5:40:29<16:31:09, 4.30s/it] 26%|██▌ | 4852/18688 [5:40:33<15:45:16, 4.10s/it] 26%|██▌ | 4853/18688 [5:40:37<15:42:59, 4.09s/it] 26%|██▌ | 4854/18688 [5:40:41<15:19:48, 3.99s/it] 26%|██▌ | 4855/18688 [5:40:45<15:04:04, 3.92s/it] 26%|██▌ | 4856/18688 [5:40:49<15:12:03, 3.96s/it] 26%|██▌ | 4857/18688 [5:40:53<15:50:32, 4.12s/it] 26%|██▌ | 4858/18688 [5:40:57<15:35:30, 4.06s/it] 26%|██▌ | 4859/18688 [5:41:01<15:12:42, 3.96s/it] 26%|██▌ | 4860/18688 [5:41:05<15:24:25, 4.01s/it] {'loss': '0.9872', 'grad_norm': '1.535', 'learning_rate': '7.27e-05', 'epoch': '0.2601', 'num_input_tokens_seen': 16328984, 'train_runtime': '2.047e+04', 'train_tokens_per_second': '797.9'}
26%|██▌ | 4860/18688 [5:41:05<15:24:25, 4.01s/it] 26%|██▌ | 4861/18688 [5:41:09<15:32:59, 4.05s/it] 26%|██▌ | 4862/18688 [5:41:13<15:24:09, 4.01s/it] 26%|██▌ | 4863/18688 [5:41:17<14:46:45, 3.85s/it] 26%|██▌ | 4864/18688 [5:41:20<14:35:24, 3.80s/it] 26%|██▌ | 4865/18688 [5:41:24<14:51:08, 3.87s/it] 26%|██▌ | 4866/18688 [5:41:28<15:06:48, 3.94s/it] 26%|██▌ | 4867/18688 [5:41:33<15:28:25, 4.03s/it] 26%|██▌ | 4868/18688 [5:41:37<16:05:44, 4.19s/it] 26%|██▌ | 4869/18688 [5:41:41<16:06:16, 4.20s/it] 26%|██▌ | 4870/18688 [5:41:45<15:25:56, 4.02s/it] {'loss': '0.949', 'grad_norm': '1.153', 'learning_rate': '7.265e-05', 'epoch': '0.2606', 'num_input_tokens_seen': 16360632, 'train_runtime': '2.051e+04', 'train_tokens_per_second': '797.9'}
26%|██▌ | 4870/18688 [5:41:45<15:25:56, 4.02s/it] 26%|██▌ | 4871/18688 [5:41:49<15:03:43, 3.92s/it] 26%|██▌ | 4872/18688 [5:41:53<15:41:50, 4.09s/it] 26%|██▌ | 4873/18688 [5:41:57<16:00:45, 4.17s/it] 26%|██▌ | 4874/18688 [5:42:02<15:59:05, 4.17s/it] 26%|██▌ | 4875/18688 [5:42:06<16:08:08, 4.21s/it] 26%|██▌ | 4876/18688 [5:42:10<16:09:23, 4.21s/it] 26%|██▌ | 4877/18688 [5:42:14<16:01:47, 4.18s/it] 26%|██▌ | 4878/18688 [5:42:18<15:33:36, 4.06s/it] 26%|██▌ | 4879/18688 [5:42:22<15:39:04, 4.08s/it] 26%|██▌ | 4880/18688 [5:42:26<15:34:10, 4.06s/it] {'loss': '0.9791', 'grad_norm': '1.294', 'learning_rate': '7.261e-05', 'epoch': '0.2611', 'num_input_tokens_seen': 16394784, 'train_runtime': '2.055e+04', 'train_tokens_per_second': '797.9'}
26%|██▌ | 4880/18688 [5:42:26<15:34:10, 4.06s/it] 26%|██▌ | 4881/18688 [5:42:30<15:34:48, 4.06s/it] 26%|██▌ | 4882/18688 [5:42:35<15:56:22, 4.16s/it] 26%|██▌ | 4883/18688 [5:42:39<15:38:42, 4.08s/it] 26%|██▌ | 4884/18688 [5:42:43<15:39:31, 4.08s/it] 26%|██▌ | 4885/18688 [5:42:47<15:50:09, 4.13s/it] 26%|██▌ | 4886/18688 [5:42:51<15:49:16, 4.13s/it] 26%|██▌ | 4887/18688 [5:42:55<15:07:40, 3.95s/it] 26%|██▌ | 4888/18688 [5:42:58<14:56:39, 3.90s/it] 26%|██▌ | 4889/18688 [5:43:02<15:15:42, 3.98s/it] 26%|██▌ | 4890/18688 [5:43:07<15:56:53, 4.16s/it] {'loss': '0.9309', 'grad_norm': '1.534', 'learning_rate': '7.257e-05', 'epoch': '0.2617', 'num_input_tokens_seen': 16427848, 'train_runtime': '2.059e+04', 'train_tokens_per_second': '798'}
26%|██▌ | 4890/18688 [5:43:07<15:56:53, 4.16s/it] 26%|██▌ | 4891/18688 [5:43:11<15:27:41, 4.03s/it] 26%|██▌ | 4892/18688 [5:43:16<16:26:13, 4.29s/it] 26%|██▌ | 4893/18688 [5:43:19<15:29:41, 4.04s/it] 26%|██▌ | 4894/18688 [5:43:24<16:33:44, 4.32s/it] 26%|██▌ | 4895/18688 [5:43:28<16:12:28, 4.23s/it] 26%|██▌ | 4896/18688 [5:43:32<15:46:44, 4.12s/it] 26%|██▌ | 4897/18688 [5:43:38<18:16:50, 4.77s/it] 26%|██▌ | 4898/18688 [5:43:43<18:00:08, 4.70s/it] 26%|██▌ | 4899/18688 [5:43:47<16:57:45, 4.43s/it] 26%|██▌ | 4900/18688 [5:43:51<16:45:18, 4.37s/it] {'loss': '1.047', 'grad_norm': '1.052', 'learning_rate': '7.253e-05', 'epoch': '0.2622', 'num_input_tokens_seen': 16465344, 'train_runtime': '2.063e+04', 'train_tokens_per_second': '798.1'}
26%|██▌ | 4900/18688 [5:43:51<16:45:18, 4.37s/it] 26%|██▌ | 4901/18688 [5:43:55<16:14:11, 4.24s/it] 26%|██▌ | 4902/18688 [5:43:59<15:50:03, 4.13s/it] 26%|██▌ | 4903/18688 [5:44:03<16:02:39, 4.19s/it] 26%|██▌ | 4904/18688 [5:44:07<15:41:58, 4.10s/it] 26%|██▌ | 4905/18688 [5:44:11<15:28:25, 4.04s/it] 26%|██▋ | 4906/18688 [5:44:15<15:14:32, 3.98s/it] 26%|██▋ | 4907/18688 [5:44:19<15:16:34, 3.99s/it] 26%|██▋ | 4908/18688 [5:44:22<14:50:09, 3.88s/it] 26%|██▋ | 4909/18688 [5:44:26<14:30:39, 3.79s/it] 26%|██▋ | 4910/18688 [5:44:30<14:24:46, 3.77s/it] {'loss': '0.9335', 'grad_norm': '1.037', 'learning_rate': '7.248e-05', 'epoch': '0.2627', 'num_input_tokens_seen': 16495096, 'train_runtime': '2.067e+04', 'train_tokens_per_second': '798'}
26%|██▋ | 4910/18688 [5:44:30<14:24:46, 3.77s/it] 26%|██▋ | 4911/18688 [5:44:34<14:54:41, 3.90s/it] 26%|██▋ | 4912/18688 [5:44:38<15:52:43, 4.15s/it] 26%|██▋ | 4913/18688 [5:44:43<15:44:43, 4.11s/it] 26%|██▋ | 4914/18688 [5:44:46<15:20:16, 4.01s/it] 26%|██▋ | 4915/18688 [5:44:50<14:45:26, 3.86s/it] 26%|██▋ | 4916/18688 [5:44:53<14:30:52, 3.79s/it] 26%|██▋ | 4917/18688 [5:44:58<15:00:43, 3.92s/it] 26%|██▋ | 4918/18688 [5:45:03<16:43:53, 4.37s/it] 26%|██▋ | 4919/18688 [5:45:07<16:17:36, 4.26s/it] 26%|██▋ | 4920/18688 [5:45:11<16:06:12, 4.21s/it] {'loss': '1.036', 'grad_norm': '1.257', 'learning_rate': '7.244e-05', 'epoch': '0.2633', 'num_input_tokens_seen': 16528224, 'train_runtime': '2.071e+04', 'train_tokens_per_second': '798'}
26%|██▋ | 4920/18688 [5:45:11<16:06:12, 4.21s/it] 26%|██▋ | 4921/18688 [5:45:16<16:52:41, 4.41s/it] 26%|██▋ | 4922/18688 [5:45:22<18:17:05, 4.78s/it] 26%|██▋ | 4923/18688 [5:45:25<16:58:12, 4.44s/it] 26%|██▋ | 4924/18688 [5:45:30<17:16:30, 4.52s/it] 26%|██▋ | 4925/18688 [5:45:34<16:41:32, 4.37s/it] 26%|██▋ | 4926/18688 [5:45:38<15:52:29, 4.15s/it] 26%|██▋ | 4927/18688 [5:45:41<15:14:04, 3.99s/it] 26%|██▋ | 4928/18688 [5:45:45<14:53:39, 3.90s/it] 26%|██▋ | 4929/18688 [5:45:49<14:33:49, 3.81s/it] 26%|██▋ | 4930/18688 [5:45:53<14:55:27, 3.91s/it] {'loss': '0.9387', 'grad_norm': '1.009', 'learning_rate': '7.24e-05', 'epoch': '0.2638', 'num_input_tokens_seen': 16562256, 'train_runtime': '2.075e+04', 'train_tokens_per_second': '798.1'}
26%|██▋ | 4930/18688 [5:45:53<14:55:27, 3.91s/it] 26%|██▋ | 4931/18688 [5:45:57<15:06:46, 3.95s/it] 26%|██▋ | 4932/18688 [5:46:02<16:25:46, 4.30s/it] 26%|██▋ | 4933/18688 [5:46:06<15:50:17, 4.15s/it] 26%|██▋ | 4934/18688 [5:46:09<15:04:52, 3.95s/it] 26%|██▋ | 4935/18688 [5:46:13<15:16:30, 4.00s/it] 26%|██▋ | 4936/18688 [5:46:17<14:54:13, 3.90s/it] 26%|██▋ | 4937/18688 [5:46:21<14:41:50, 3.85s/it] 26%|██▋ | 4938/18688 [5:46:25<14:43:55, 3.86s/it] 26%|██▋ | 4939/18688 [5:46:29<14:54:35, 3.90s/it] 26%|██▋ | 4940/18688 [5:46:33<15:29:00, 4.05s/it] {'loss': '0.8786', 'grad_norm': '1.156', 'learning_rate': '7.235e-05', 'epoch': '0.2643', 'num_input_tokens_seen': 16593744, 'train_runtime': '2.079e+04', 'train_tokens_per_second': '798'}
26%|██▋ | 4940/18688 [5:46:33<15:29:00, 4.05s/it] 26%|██▋ | 4941/18688 [5:46:37<15:16:43, 4.00s/it] 26%|██▋ | 4942/18688 [5:46:41<15:06:22, 3.96s/it] 26%|██▋ | 4943/18688 [5:46:45<15:30:16, 4.06s/it] 26%|██▋ | 4944/18688 [5:46:49<15:42:25, 4.11s/it] 26%|██▋ | 4945/18688 [5:46:54<16:14:16, 4.25s/it] 26%|██▋ | 4946/18688 [5:46:58<15:49:44, 4.15s/it] 26%|██▋ | 4947/18688 [5:47:01<15:02:36, 3.94s/it] 26%|██▋ | 4948/18688 [5:47:05<15:14:18, 3.99s/it] 26%|██▋ | 4949/18688 [5:47:10<16:10:35, 4.24s/it] 26%|██▋ | 4950/18688 [5:47:15<16:40:11, 4.37s/it] {'loss': '0.9011', 'grad_norm': '1.103', 'learning_rate': '7.231e-05', 'epoch': '0.2649', 'num_input_tokens_seen': 16628392, 'train_runtime': '2.084e+04', 'train_tokens_per_second': '798.1'}
26%|██▋ | 4950/18688 [5:47:15<16:40:11, 4.37s/it] 26%|██▋ | 4951/18688 [5:47:20<17:06:22, 4.48s/it] 26%|██▋ | 4952/18688 [5:47:24<16:51:22, 4.42s/it] 27%|██▋ | 4953/18688 [5:47:28<16:41:55, 4.38s/it] 27%|██▋ | 4954/18688 [5:47:32<15:56:50, 4.18s/it] 27%|██▋ | 4955/18688 [5:47:36<15:22:40, 4.03s/it] 27%|██▋ | 4956/18688 [5:47:39<14:51:48, 3.90s/it] 27%|██▋ | 4957/18688 [5:47:43<14:32:03, 3.81s/it] 27%|██▋ | 4958/18688 [5:47:47<14:58:23, 3.93s/it] 27%|██▋ | 4959/18688 [5:47:51<14:46:26, 3.87s/it] 27%|██▋ | 4960/18688 [5:47:55<15:22:51, 4.03s/it] {'loss': '0.9262', 'grad_norm': '1.283', 'learning_rate': '7.227e-05', 'epoch': '0.2654', 'num_input_tokens_seen': 16659344, 'train_runtime': '2.088e+04', 'train_tokens_per_second': '798'}
27%|██▋ | 4960/18688 [5:47:55<15:22:51, 4.03s/it] 27%|██▋ | 4961/18688 [5:47:59<14:59:18, 3.93s/it] 27%|██▋ | 4962/18688 [5:48:02<14:35:37, 3.83s/it] 27%|██▋ | 4963/18688 [5:48:06<14:40:25, 3.85s/it] 27%|██▋ | 4964/18688 [5:48:11<15:59:41, 4.20s/it] 27%|██▋ | 4965/18688 [5:48:15<15:25:58, 4.05s/it] 27%|██▋ | 4966/18688 [5:48:19<14:59:00, 3.93s/it] 27%|██▋ | 4967/18688 [5:48:23<15:07:03, 3.97s/it] 27%|██▋ | 4968/18688 [5:48:27<15:02:11, 3.95s/it] 27%|██▋ | 4969/18688 [5:48:32<16:30:19, 4.33s/it] 27%|██▋ | 4970/18688 [5:48:37<17:58:52, 4.72s/it] {'loss': '0.9666', 'grad_norm': '0.9697', 'learning_rate': '7.223e-05', 'epoch': '0.266', 'num_input_tokens_seen': 16692968, 'train_runtime': '2.092e+04', 'train_tokens_per_second': '798'}
27%|██▋ | 4970/18688 [5:48:37<17:58:52, 4.72s/it] 27%|██▋ | 4971/18688 [5:48:42<17:54:03, 4.70s/it] 27%|██▋ | 4972/18688 [5:48:46<17:30:35, 4.60s/it] 27%|██▋ | 4973/18688 [5:48:51<17:32:57, 4.61s/it] 27%|██▋ | 4974/18688 [5:48:56<17:52:07, 4.69s/it] 27%|██▋ | 4975/18688 [5:49:00<16:43:31, 4.39s/it] 27%|██▋ | 4976/18688 [5:49:03<16:05:19, 4.22s/it] 27%|██▋ | 4977/18688 [5:49:07<15:12:59, 4.00s/it] 27%|██▋ | 4978/18688 [5:49:11<15:18:16, 4.02s/it] 27%|██▋ | 4979/18688 [5:49:15<14:47:56, 3.89s/it] 27%|██▋ | 4980/18688 [5:49:19<14:58:21, 3.93s/it] {'loss': '0.9493', 'grad_norm': '1.204', 'learning_rate': '7.218e-05', 'epoch': '0.2665', 'num_input_tokens_seen': 16727232, 'train_runtime': '2.096e+04', 'train_tokens_per_second': '798.1'}
27%|██▋ | 4980/18688 [5:49:19<14:58:21, 3.93s/it] 27%|██▋ | 4981/18688 [5:49:22<14:38:54, 3.85s/it] 27%|██▋ | 4982/18688 [5:49:26<14:33:59, 3.83s/it] 27%|██▋ | 4983/18688 [5:49:30<15:13:04, 4.00s/it] 27%|██▋ | 4984/18688 [5:49:34<15:16:44, 4.01s/it] 27%|██▋ | 4985/18688 [5:49:38<14:58:44, 3.94s/it] 27%|██▋ | 4986/18688 [5:49:43<15:59:36, 4.20s/it] 27%|██▋ | 4987/18688 [5:49:47<15:46:00, 4.14s/it] 27%|██▋ | 4988/18688 [5:49:51<15:00:55, 3.95s/it] 27%|██▋ | 4989/18688 [5:49:55<16:04:32, 4.22s/it] 27%|██▋ | 4990/18688 [5:49:59<15:51:16, 4.17s/it] {'loss': '0.9668', 'grad_norm': '1.03', 'learning_rate': '7.214e-05', 'epoch': '0.267', 'num_input_tokens_seen': 16760360, 'train_runtime': '2.1e+04', 'train_tokens_per_second': '798.1'}
27%|██▋ | 4990/18688 [5:49:59<15:51:16, 4.17s/it] 27%|██▋ | 4991/18688 [5:50:03<15:42:00, 4.13s/it] 27%|██▋ | 4992/18688 [5:50:08<15:50:12, 4.16s/it] 27%|██▋ | 4993/18688 [5:50:13<16:41:45, 4.39s/it] 27%|██▋ | 4994/18688 [5:50:16<15:43:44, 4.14s/it] 27%|██▋ | 4995/18688 [5:50:20<15:36:50, 4.11s/it] 27%|██▋ | 4996/18688 [5:50:24<15:36:50, 4.11s/it] 27%|██▋ | 4997/18688 [5:50:29<16:11:17, 4.26s/it] 27%|██▋ | 4998/18688 [5:50:33<15:32:35, 4.09s/it] 27%|██▋ | 4999/18688 [5:50:37<15:29:12, 4.07s/it] 27%|██▋ | 5000/18688 [5:50:41<15:50:50, 4.17s/it] {'loss': '0.9576', 'grad_norm': '1.157', 'learning_rate': '7.209e-05', 'epoch': '0.2676', 'num_input_tokens_seen': 16794000, 'train_runtime': '2.104e+04', 'train_tokens_per_second': '798.1'}
27%|██▋ | 5000/18688 [5:50:41<15:50:50, 4.17s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:71: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:281: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 0/1000 [00:00<?, ?it/s]/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:172: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:202: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:254: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 3/1000 [00:00<00:39, 25.38it/s]
1%| | 6/1000 [00:00<00:55, 17.96it/s]
1%| | 8/1000 [00:00<00:57, 17.18it/s]
1%| | 10/1000 [00:00<01:01, 16.20it/s]
1%| | 12/1000 [00:00<01:02, 15.77it/s]
1%|▏ | 14/1000 [00:00<01:06, 14.91it/s]
2%|▏ | 16/1000 [00:01<01:08, 14.45it/s]
2%|▏ | 18/1000 [00:01<01:42, 9.58it/s]
2%|▏ | 20/1000 [00:01<01:36, 10.20it/s]
2%|▏ | 22/1000 [00:01<01:54, 8.56it/s]
2%|▏ | 24/1000 [00:02<01:41, 9.63it/s]
3%|▎ | 26/1000 [00:02<01:45, 9.20it/s]
3%|▎ | 28/1000 [00:02<01:36, 10.04it/s]
3%|▎ | 30/1000 [00:02<01:28, 10.95it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.13it/s]
3%|▎ | 34/1000 [00:02<01:26, 11.23it/s]
4%|▎ | 36/1000 [00:03<01:32, 10.42it/s]
4%|▍ | 38/1000 [00:03<01:25, 11.31it/s]
4%|▍ | 40/1000 [00:03<01:27, 10.92it/s]
4%|▍ | 42/1000 [00:03<01:46, 9.01it/s]
4%|▍ | 44/1000 [00:03<01:31, 10.50it/s]
5%|▍ | 46/1000 [00:04<01:21, 11.69it/s]
5%|▍ | 48/1000 [00:04<01:29, 10.64it/s]
5%|▌ | 50/1000 [00:04<01:21, 11.67it/s]
5%|▌ | 52/1000 [00:04<01:15, 12.48it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.22it/s]
6%|▌ | 56/1000 [00:04<01:08, 13.84it/s]
6%|▌ | 58/1000 [00:05<01:48, 8.70it/s]
6%|▌ | 60/1000 [00:05<01:36, 9.74it/s]
6%|▌ | 62/1000 [00:05<01:27, 10.71it/s]
6%|▋ | 64/1000 [00:05<01:19, 11.79it/s]
7%|▋ | 66/1000 [00:05<01:15, 12.30it/s]
7%|▋ | 68/1000 [00:05<01:12, 12.88it/s]
7%|▋ | 70/1000 [00:06<01:08, 13.49it/s]
7%|▋ | 72/1000 [00:06<01:06, 13.92it/s]
7%|▋ | 74/1000 [00:06<01:07, 13.71it/s]
8%|▊ | 76/1000 [00:06<01:04, 14.27it/s]
8%|▊ | 78/1000 [00:06<01:03, 14.46it/s]
8%|▊ | 80/1000 [00:06<01:08, 13.42it/s]
8%|▊ | 82/1000 [00:06<01:05, 14.05it/s]
8%|▊ | 84/1000 [00:07<01:03, 14.53it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.67it/s]
9%|▉ | 88/1000 [00:07<01:02, 14.50it/s]
9%|▉ | 90/1000 [00:07<01:02, 14.53it/s]
9%|▉ | 92/1000 [00:07<01:03, 14.21it/s]
9%|▉ | 94/1000 [00:07<01:04, 14.01it/s]
10%|▉ | 96/1000 [00:07<01:03, 14.30it/s]
10%|▉ | 98/1000 [00:08<01:03, 14.23it/s]
10%|█ | 100/1000 [00:08<01:31, 9.80it/s]
10%|█ | 102/1000 [00:08<01:23, 10.76it/s]
10%|█ | 104/1000 [00:08<01:54, 7.80it/s]
11%|█ | 106/1000 [00:09<02:02, 7.31it/s]
11%|█ | 108/1000 [00:09<01:47, 8.30it/s]
11%|█ | 110/1000 [00:09<01:36, 9.23it/s]
11%|█ | 112/1000 [00:09<01:29, 9.96it/s]
11%|█▏ | 114/1000 [00:09<01:24, 10.52it/s]
12%|█▏ | 116/1000 [00:10<01:20, 10.98it/s]
12%|█▏ | 118/1000 [00:10<01:17, 11.37it/s]
12%|█▏ | 120/1000 [00:10<01:18, 11.20it/s]
12%|█▏ | 122/1000 [00:10<01:30, 9.69it/s]
12%|█▏ | 124/1000 [00:10<01:37, 8.98it/s]
13%|█▎ | 126/1000 [00:11<01:28, 9.90it/s]
13%|█▎ | 128/1000 [00:11<01:21, 10.64it/s]
13%|█▎ | 130/1000 [00:11<01:16, 11.33it/s]
13%|█▎ | 132/1000 [00:11<01:09, 12.45it/s]
13%|█▎ | 134/1000 [00:11<01:12, 11.90it/s]
14%|█▎ | 136/1000 [00:11<01:06, 12.96it/s]
14%|█▍ | 138/1000 [00:11<01:06, 13.01it/s]
14%|█▍ | 140/1000 [00:12<01:25, 10.01it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.50it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.53it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.56it/s]
15%|█▍ | 148/1000 [00:13<01:14, 11.47it/s]
15%|█▌ | 150/1000 [00:13<01:07, 12.57it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.11it/s]
15%|█▌ | 154/1000 [00:13<01:03, 13.38it/s]
16%|█▌ | 156/1000 [00:13<01:03, 13.24it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.77it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.64it/s]
16%|█▌ | 162/1000 [00:14<01:02, 13.51it/s]
16%|█▋ | 164/1000 [00:14<01:01, 13.61it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.78it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.28it/s]
17%|█▋ | 170/1000 [00:14<01:19, 10.48it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.46it/s]
17%|█▋ | 174/1000 [00:15<01:30, 9.17it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.19it/s]
18%|█▊ | 178/1000 [00:15<01:34, 8.66it/s]
18%|█▊ | 180/1000 [00:15<01:33, 8.79it/s]
18%|█▊ | 182/1000 [00:16<01:35, 8.56it/s]
18%|█▊ | 184/1000 [00:16<01:23, 9.79it/s]
19%|█▊ | 186/1000 [00:16<01:37, 8.35it/s]
19%|█▉ | 188/1000 [00:16<01:23, 9.67it/s]
19%|█▉ | 190/1000 [00:16<01:15, 10.71it/s]
19%|█▉ | 192/1000 [00:17<01:11, 11.35it/s]
19%|█▉ | 194/1000 [00:17<01:16, 10.56it/s]
20%|█▉ | 196/1000 [00:17<01:29, 8.98it/s]
20%|█▉ | 198/1000 [00:17<01:18, 10.22it/s]
20%|██ | 200/1000 [00:17<01:15, 10.57it/s]
20%|██ | 202/1000 [00:18<01:09, 11.52it/s]
20%|██ | 204/1000 [00:18<01:02, 12.65it/s]
21%|██ | 206/1000 [00:18<00:59, 13.25it/s]
21%|██ | 208/1000 [00:18<00:58, 13.64it/s]
21%|██ | 210/1000 [00:18<00:54, 14.51it/s]
21%|██ | 212/1000 [00:18<00:54, 14.46it/s]
21%|██▏ | 214/1000 [00:18<00:54, 14.55it/s]
22%|██▏ | 216/1000 [00:18<00:54, 14.35it/s]
22%|██▏ | 218/1000 [00:19<00:53, 14.69it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.87it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.15it/s]
22%|██▏ | 224/1000 [00:19<01:20, 9.64it/s]
23%|██▎ | 226/1000 [00:20<01:12, 10.63it/s]
23%|██▎ | 228/1000 [00:20<01:06, 11.54it/s]
23%|██▎ | 230/1000 [00:20<01:03, 12.05it/s]
23%|██▎ | 232/1000 [00:20<01:01, 12.42it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.13it/s]
24%|██▎ | 236/1000 [00:20<00:56, 13.61it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.25it/s]
24%|██▍ | 240/1000 [00:21<00:53, 14.24it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.80it/s]
24%|██▍ | 244/1000 [00:21<00:50, 15.00it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.97it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.69it/s]
25%|██▌ | 250/1000 [00:21<00:55, 13.45it/s]
25%|██▌ | 252/1000 [00:21<01:01, 12.17it/s]
25%|██▌ | 254/1000 [00:22<00:56, 13.20it/s]
26%|██▌ | 256/1000 [00:22<00:53, 13.79it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.82it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.94it/s]
26%|██▌ | 262/1000 [00:22<00:56, 13.08it/s]
26%|██▋ | 264/1000 [00:22<00:53, 13.65it/s]
27%|██▋ | 266/1000 [00:22<00:52, 13.99it/s]
27%|██▋ | 268/1000 [00:23<00:50, 14.54it/s]
27%|██▋ | 270/1000 [00:23<00:54, 13.28it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.64it/s]
27%|██▋ | 274/1000 [00:23<01:01, 11.73it/s]
28%|██▊ | 276/1000 [00:23<00:56, 12.75it/s]
28%|██▊ | 278/1000 [00:23<00:55, 12.98it/s]
28%|██▊ | 280/1000 [00:24<00:53, 13.54it/s]
28%|██▊ | 282/1000 [00:24<00:49, 14.51it/s]
28%|██▊ | 284/1000 [00:24<00:49, 14.44it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.70it/s]
29%|██▉ | 288/1000 [00:24<00:47, 15.00it/s]
29%|██▉ | 290/1000 [00:24<00:46, 15.40it/s]
29%|██▉ | 292/1000 [00:24<00:46, 15.35it/s]
29%|██▉ | 294/1000 [00:24<00:46, 15.27it/s]
30%|██▉ | 296/1000 [00:25<00:45, 15.33it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.45it/s]
30%|███ | 300/1000 [00:25<00:46, 15.12it/s]
30%|███ | 302/1000 [00:25<00:46, 15.16it/s]
30%|███ | 304/1000 [00:25<00:50, 13.76it/s]
31%|███ | 306/1000 [00:25<00:47, 14.51it/s]
31%|███ | 308/1000 [00:25<00:47, 14.49it/s]
31%|███ | 310/1000 [00:26<00:46, 14.69it/s]
31%|███ | 312/1000 [00:26<00:46, 14.66it/s]
31%|███▏ | 314/1000 [00:26<00:44, 15.37it/s]
32%|███▏ | 316/1000 [00:26<00:43, 15.64it/s]
32%|███▏ | 318/1000 [00:26<00:42, 16.08it/s]
32%|███▏ | 320/1000 [00:26<01:15, 9.06it/s]
32%|███▏ | 322/1000 [00:27<01:04, 10.43it/s]
32%|███▏ | 324/1000 [00:27<01:01, 10.98it/s]
33%|███▎ | 326/1000 [00:27<01:03, 10.57it/s]
33%|███▎ | 328/1000 [00:27<01:02, 10.81it/s]
33%|███▎ | 330/1000 [00:27<00:56, 11.82it/s]
33%|███▎ | 332/1000 [00:27<00:55, 12.00it/s]
33%|███▎ | 334/1000 [00:28<00:53, 12.44it/s]
34%|███▎ | 336/1000 [00:28<00:51, 12.80it/s]
34%|███▍ | 338/1000 [00:28<01:07, 9.79it/s]
34%|███▍ | 340/1000 [00:28<01:00, 10.91it/s]
34%|███▍ | 342/1000 [00:28<00:53, 12.19it/s]
34%|███▍ | 344/1000 [00:28<00:50, 12.89it/s]
35%|███▍ | 346/1000 [00:29<00:48, 13.52it/s]
35%|███▍ | 348/1000 [00:29<00:46, 13.94it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.54it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.75it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.81it/s]
36%|███▌ | 356/1000 [00:29<00:43, 14.91it/s]
36%|███▌ | 358/1000 [00:29<00:43, 14.61it/s]
36%|███▌ | 360/1000 [00:30<00:42, 14.98it/s]
36%|███▌ | 362/1000 [00:30<00:47, 13.32it/s]
36%|███▋ | 364/1000 [00:30<00:45, 13.84it/s]
37%|███▋ | 366/1000 [00:30<00:53, 11.95it/s]
37%|███▋ | 368/1000 [00:30<00:51, 12.34it/s]
37%|███▋ | 370/1000 [00:30<00:47, 13.21it/s]
37%|███▋ | 372/1000 [00:30<00:46, 13.55it/s]
37%|███▋ | 374/1000 [00:31<00:55, 11.26it/s]
38%|███▊ | 376/1000 [00:31<00:58, 10.66it/s]
38%|███▊ | 378/1000 [00:31<00:56, 11.10it/s]
38%|███▊ | 380/1000 [00:31<00:53, 11.52it/s]
38%|███▊ | 382/1000 [00:31<00:51, 12.02it/s]
38%|███▊ | 384/1000 [00:32<00:47, 12.92it/s]
39%|███▊ | 386/1000 [00:32<00:45, 13.36it/s]
39%|███▉ | 388/1000 [00:32<00:45, 13.41it/s]
39%|███▉ | 390/1000 [00:32<00:45, 13.35it/s]
39%|███▉ | 392/1000 [00:32<00:44, 13.72it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.54it/s]
40%|███▉ | 396/1000 [00:32<00:44, 13.72it/s]
40%|███▉ | 398/1000 [00:33<00:47, 12.56it/s]
40%|████ | 400/1000 [00:33<00:45, 13.21it/s]
40%|████ | 402/1000 [00:33<00:43, 13.86it/s]
40%|████ | 404/1000 [00:33<00:41, 14.50it/s]
41%|████ | 406/1000 [00:33<00:39, 15.02it/s]
41%|████ | 408/1000 [00:33<00:47, 12.56it/s]
41%|████ | 410/1000 [00:34<00:56, 10.49it/s]
41%|████ | 412/1000 [00:34<00:50, 11.58it/s]
41%|████▏ | 414/1000 [00:34<01:07, 8.66it/s]
42%|████▏ | 416/1000 [00:34<01:23, 6.98it/s]
42%|████▏ | 418/1000 [00:35<01:10, 8.28it/s]
42%|████▏ | 420/1000 [00:35<01:00, 9.51it/s]
42%|████▏ | 422/1000 [00:35<00:53, 10.85it/s]
42%|████▏ | 424/1000 [00:35<01:15, 7.67it/s]
43%|████▎ | 426/1000 [00:35<01:04, 8.93it/s]
43%|████▎ | 428/1000 [00:36<00:56, 10.13it/s]
43%|████▎ | 430/1000 [00:36<01:02, 9.12it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.06it/s]
43%|████▎ | 434/1000 [00:36<01:08, 8.28it/s]
44%|████▎ | 436/1000 [00:37<01:01, 9.23it/s]
44%|████▍ | 438/1000 [00:37<01:04, 8.68it/s]
44%|████▍ | 440/1000 [00:37<01:02, 9.00it/s]
44%|████▍ | 442/1000 [00:37<00:57, 9.78it/s]
44%|████▍ | 444/1000 [00:37<00:51, 10.74it/s]
45%|████▍ | 446/1000 [00:38<00:47, 11.61it/s]
45%|████▍ | 448/1000 [00:38<00:52, 10.57it/s]
45%|████▌ | 450/1000 [00:38<00:48, 11.40it/s]
45%|████▌ | 452/1000 [00:38<00:47, 11.57it/s]
45%|████▌ | 454/1000 [00:38<00:48, 11.23it/s]
46%|████▌ | 456/1000 [00:38<00:45, 11.92it/s]
46%|████▌ | 458/1000 [00:38<00:41, 13.07it/s]
46%|████▌ | 460/1000 [00:39<00:40, 13.40it/s]
46%|████▌ | 462/1000 [00:39<00:38, 14.07it/s]
46%|████▋ | 464/1000 [00:39<00:36, 14.64it/s]
47%|████▋ | 466/1000 [00:39<00:36, 14.54it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.77it/s]
47%|████▋ | 470/1000 [00:40<00:52, 10.03it/s]
47%|████▋ | 472/1000 [00:40<00:51, 10.22it/s]
47%|████▋ | 474/1000 [00:40<00:45, 11.52it/s]
48%|████▊ | 476/1000 [00:40<00:43, 12.16it/s]
48%|████▊ | 478/1000 [00:40<00:43, 12.00it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.91it/s]
48%|████▊ | 482/1000 [00:40<00:38, 13.34it/s]
48%|████▊ | 484/1000 [00:41<00:37, 13.85it/s]
49%|████▊ | 486/1000 [00:41<00:36, 14.02it/s]
49%|████▉ | 488/1000 [00:41<00:36, 14.22it/s]
49%|████▉ | 490/1000 [00:41<00:45, 11.09it/s]
49%|████▉ | 492/1000 [00:41<00:40, 12.40it/s]
49%|████▉ | 494/1000 [00:41<00:37, 13.40it/s]
50%|████▉ | 496/1000 [00:41<00:35, 14.21it/s]
50%|████▉ | 498/1000 [00:42<00:34, 14.64it/s]
50%|█████ | 500/1000 [00:42<00:42, 11.64it/s]
50%|█████ | 502/1000 [00:42<00:41, 12.08it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.67it/s]
51%|█████ | 506/1000 [00:42<00:38, 12.82it/s]
51%|█████ | 508/1000 [00:42<00:35, 13.79it/s]
51%|█████ | 510/1000 [00:43<00:47, 10.41it/s]
51%|█████ | 512/1000 [00:43<00:43, 11.18it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 12.05it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.62it/s]
52%|█████▏ | 518/1000 [00:43<00:36, 13.13it/s]
52%|█████▏ | 520/1000 [00:43<00:35, 13.50it/s]
52%|█████▏ | 522/1000 [00:44<00:33, 14.23it/s]
52%|█████▏ | 524/1000 [00:44<00:33, 14.24it/s]
53%|█████▎ | 526/1000 [00:44<00:32, 14.40it/s]
53%|█████▎ | 528/1000 [00:44<00:39, 11.99it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.86it/s]
53%|█████▎ | 532/1000 [00:44<00:33, 13.78it/s]
53%|█████▎ | 534/1000 [00:44<00:32, 14.17it/s]
54%|█████▎ | 536/1000 [00:45<00:32, 14.29it/s]
54%|█████▍ | 538/1000 [00:45<00:32, 14.03it/s]
54%|█████▍ | 540/1000 [00:45<00:31, 14.46it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.73it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.59it/s]
55%|█████▍ | 546/1000 [00:45<00:32, 14.13it/s]
55%|█████▍ | 548/1000 [00:45<00:33, 13.49it/s]
55%|█████▌ | 550/1000 [00:46<00:34, 13.12it/s]
55%|█████▌ | 552/1000 [00:46<00:34, 12.85it/s]
55%|█████▌ | 554/1000 [00:46<00:34, 12.77it/s]
56%|█████▌ | 556/1000 [00:46<00:35, 12.65it/s]
56%|█████▌ | 558/1000 [00:46<00:34, 12.64it/s]
56%|█████▌ | 560/1000 [00:46<00:34, 12.64it/s]
56%|█████▌ | 562/1000 [00:47<00:34, 12.57it/s]
56%|█████▋ | 564/1000 [00:47<00:45, 9.63it/s]
57%|█████▋ | 566/1000 [00:47<00:42, 10.27it/s]
57%|█████▋ | 568/1000 [00:47<00:40, 10.80it/s]
57%|█████▋ | 570/1000 [00:47<00:35, 11.95it/s]
57%|█████▋ | 572/1000 [00:47<00:35, 11.92it/s]
57%|█████▋ | 574/1000 [00:48<00:32, 13.01it/s]
58%|█████▊ | 576/1000 [00:48<00:32, 13.14it/s]
58%|█████▊ | 578/1000 [00:48<00:37, 11.37it/s]
58%|█████▊ | 580/1000 [00:48<00:33, 12.66it/s]
58%|█████▊ | 582/1000 [00:48<00:31, 13.45it/s]
58%|█████▊ | 584/1000 [00:48<00:30, 13.75it/s]
59%|█████▊ | 586/1000 [00:48<00:29, 14.16it/s]
59%|█████▉ | 588/1000 [00:49<00:27, 15.04it/s]
59%|█████▉ | 590/1000 [00:49<00:27, 14.99it/s]
59%|█████▉ | 592/1000 [00:49<00:27, 14.76it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.33it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.87it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 14.11it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.43it/s]
60%|██████ | 602/1000 [00:50<00:27, 14.59it/s]
60%|██████ | 604/1000 [00:50<00:26, 15.17it/s]
61%|██████ | 606/1000 [00:50<00:26, 14.92it/s]
61%|██████ | 608/1000 [00:50<00:25, 15.25it/s]
61%|██████ | 610/1000 [00:50<00:25, 15.07it/s]
61%|██████ | 612/1000 [00:50<00:25, 15.15it/s]
61%|██████▏ | 614/1000 [00:50<00:26, 14.39it/s]
62%|██████▏ | 616/1000 [00:51<00:25, 14.78it/s]
62%|██████▏ | 618/1000 [00:51<00:27, 14.11it/s]
62%|██████▏ | 620/1000 [00:51<00:39, 9.72it/s]
62%|██████▏ | 622/1000 [00:51<00:38, 9.94it/s]
62%|██████▏ | 624/1000 [00:51<00:32, 11.41it/s]
63%|██████▎ | 626/1000 [00:51<00:30, 12.10it/s]
63%|██████▎ | 628/1000 [00:52<00:27, 13.36it/s]
63%|██████▎ | 630/1000 [00:52<00:26, 13.84it/s]
63%|██████▎ | 632/1000 [00:52<00:29, 12.50it/s]
63%|██████▎ | 634/1000 [00:52<00:27, 13.21it/s]
64%|██████▎ | 636/1000 [00:52<00:33, 10.98it/s]
64%|██████▍ | 638/1000 [00:53<00:33, 10.70it/s]
64%|██████▍ | 640/1000 [00:53<00:31, 11.47it/s]
64%|██████▍ | 642/1000 [00:53<00:28, 12.35it/s]
64%|██████▍ | 644/1000 [00:53<00:28, 12.60it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.88it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.37it/s]
65%|██████▌ | 650/1000 [00:53<00:27, 12.61it/s]
65%|██████▌ | 652/1000 [00:54<00:28, 12.00it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.45it/s]
66%|██████▌ | 656/1000 [00:54<00:31, 10.81it/s]
66%|██████▌ | 658/1000 [00:54<00:28, 11.90it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.19it/s]
66%|██████▌ | 662/1000 [00:55<00:29, 11.31it/s]
66%|██████▋ | 664/1000 [00:55<00:28, 11.80it/s]
67%|██████▋ | 666/1000 [00:55<00:26, 12.84it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.35it/s]
67%|██████▋ | 670/1000 [00:55<00:29, 11.13it/s]
67%|██████▋ | 672/1000 [00:55<00:27, 11.89it/s]
67%|██████▋ | 674/1000 [00:56<00:26, 12.49it/s]
68%|██████▊ | 676/1000 [00:56<00:27, 11.74it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.04it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 12.84it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.05it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.28it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.40it/s]
69%|██████▉ | 688/1000 [00:57<00:22, 13.70it/s]
69%|██████▉ | 690/1000 [00:57<00:22, 13.78it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.23it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.32it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 14.09it/s]
70%|██████▉ | 698/1000 [00:57<00:20, 14.66it/s]
70%|███████ | 700/1000 [00:58<00:24, 12.23it/s]
70%|███████ | 702/1000 [00:58<00:22, 13.03it/s]
70%|███████ | 704/1000 [00:58<00:29, 9.88it/s]
71%|███████ | 706/1000 [00:58<00:27, 10.68it/s]
71%|███████ | 708/1000 [00:58<00:30, 9.63it/s]
71%|███████ | 710/1000 [00:58<00:26, 10.94it/s]
71%|███████ | 712/1000 [00:59<00:24, 11.93it/s]
71%|███████▏ | 714/1000 [00:59<00:21, 13.01it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 13.09it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.60it/s]
72%|███████▏ | 720/1000 [00:59<00:20, 13.56it/s]
72%|███████▏ | 722/1000 [00:59<00:19, 14.02it/s]
72%|███████▏ | 724/1000 [00:59<00:19, 13.80it/s]
73%|███████▎ | 726/1000 [01:00<00:19, 14.01it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.96it/s]
73%|███████▎ | 730/1000 [01:00<00:32, 8.21it/s]
73%|███████▎ | 732/1000 [01:00<00:32, 8.33it/s]
73%|███████▎ | 734/1000 [01:01<00:30, 8.63it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.47it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.91it/s]
74%|███████▍ | 739/1000 [01:01<00:23, 11.00it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.66it/s]
74%|███████▍ | 743/1000 [01:01<00:20, 12.30it/s]
74%|███████▍ | 745/1000 [01:02<00:25, 10.20it/s]
75%|███████▍ | 747/1000 [01:02<00:27, 9.25it/s]
75%|███████▍ | 749/1000 [01:02<00:24, 10.39it/s]
75%|███████▌ | 751/1000 [01:02<00:22, 10.97it/s]
75%|███████▌ | 753/1000 [01:02<00:20, 11.89it/s]
76%|███████▌ | 755/1000 [01:02<00:19, 12.69it/s]
76%|███████▌ | 757/1000 [01:03<00:24, 9.95it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.00it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.05it/s]
76%|███████▋ | 763/1000 [01:03<00:24, 9.87it/s]
76%|███████▋ | 765/1000 [01:04<00:28, 8.37it/s]
77%|███████▋ | 767/1000 [01:04<00:25, 9.26it/s]
77%|███████▋ | 769/1000 [01:04<00:22, 10.32it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.62it/s]
77%|███████▋ | 773/1000 [01:04<00:18, 12.43it/s]
78%|███████▊ | 775/1000 [01:04<00:17, 13.00it/s]
78%|███████▊ | 777/1000 [01:04<00:15, 14.01it/s]
78%|███████▊ | 779/1000 [01:05<00:21, 10.48it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.40it/s]
78%|███████▊ | 783/1000 [01:05<00:26, 8.30it/s]
78%|███████▊ | 784/1000 [01:06<00:30, 7.06it/s]
78%|███████▊ | 785/1000 [01:06<00:29, 7.39it/s]
79%|███████▊ | 786/1000 [01:06<00:30, 7.00it/s]
79%|███████▉ | 788/1000 [01:06<00:23, 8.84it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.16it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 11.30it/s]
79%|███████▉ | 794/1000 [01:06<00:17, 11.91it/s]
80%|███████▉ | 796/1000 [01:07<00:16, 12.34it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 13.11it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.30it/s]
80%|████████ | 802/1000 [01:07<00:24, 8.24it/s]
80%|████████ | 804/1000 [01:08<00:23, 8.30it/s]
81%|████████ | 806/1000 [01:08<00:21, 8.98it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.37it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.50it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.36it/s]
81%|████████▏ | 814/1000 [01:08<00:15, 12.34it/s]
82%|████████▏ | 816/1000 [01:09<00:16, 10.90it/s]
82%|████████▏ | 818/1000 [01:09<00:15, 11.86it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.29it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 12.88it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.68it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.11it/s]
83%|████████▎ | 828/1000 [01:09<00:11, 14.40it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.87it/s]
83%|████████▎ | 832/1000 [01:10<00:11, 14.30it/s]
83%|████████▎ | 834/1000 [01:10<00:11, 14.04it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.45it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.34it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.40it/s]
84%|████████▍ | 842/1000 [01:10<00:10, 14.38it/s]
84%|████████▍ | 844/1000 [01:11<00:11, 13.93it/s]
85%|████████▍ | 846/1000 [01:11<00:11, 13.92it/s]
85%|████████▍ | 848/1000 [01:11<00:10, 13.89it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.09it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.70it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.30it/s]
86%|████████▌ | 856/1000 [01:11<00:10, 14.13it/s]
86%|████████▌ | 858/1000 [01:11<00:09, 14.64it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 15.39it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.49it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 13.65it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.59it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.44it/s]
87%|████████▋ | 870/1000 [01:12<00:09, 13.95it/s]
87%|████████▋ | 872/1000 [01:12<00:08, 14.61it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 12.39it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.02it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.90it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.55it/s]
88%|████████▊ | 882/1000 [01:13<00:10, 11.38it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 12.12it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.92it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.91it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.34it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.12it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 14.25it/s]
90%|████████▉ | 896/1000 [01:14<00:07, 13.97it/s]
90%|████████▉ | 898/1000 [01:15<00:07, 13.38it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.26it/s]
90%|█████████ | 902/1000 [01:15<00:07, 13.01it/s]
90%|█████████ | 904/1000 [01:15<00:09, 9.81it/s]
91%|█████████ | 906/1000 [01:15<00:08, 10.89it/s]
91%|█████████ | 908/1000 [01:15<00:07, 11.86it/s]
91%|█████████ | 910/1000 [01:16<00:07, 12.10it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.27it/s]
91%|█████████▏| 914/1000 [01:16<00:12, 6.80it/s]
92%|█████████▏| 916/1000 [01:17<00:10, 8.13it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.39it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.77it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 10.20it/s]
92%|█████████▏| 924/1000 [01:17<00:06, 10.89it/s]
93%|█████████▎| 926/1000 [01:17<00:06, 11.75it/s]
93%|█████████▎| 928/1000 [01:18<00:06, 10.60it/s]
93%|█████████▎| 930/1000 [01:18<00:05, 11.82it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 12.43it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.92it/s]
94%|█████████▎| 936/1000 [01:18<00:04, 13.50it/s]
94%|█████████▍| 938/1000 [01:18<00:04, 14.36it/s]
94%|█████████▍| 940/1000 [01:18<00:04, 14.65it/s]
94%|█████████▍| 942/1000 [01:19<00:04, 12.31it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.81it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.88it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 12.70it/s]
95%|█████████▌| 950/1000 [01:19<00:03, 13.47it/s]
95%|█████████▌| 952/1000 [01:19<00:03, 14.19it/s]
95%|█████████▌| 954/1000 [01:19<00:03, 14.27it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 14.26it/s]
96%|█████████▌| 958/1000 [01:20<00:02, 14.82it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 12.98it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 12.91it/s]
96%|█████████▋| 964/1000 [01:20<00:03, 9.73it/s]
97%|█████████▋| 966/1000 [01:21<00:03, 10.40it/s]
97%|█████████▋| 968/1000 [01:21<00:02, 10.93it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 11.38it/s]
97%|█████████▋| 972/1000 [01:21<00:03, 9.11it/s]
97%|█████████▋| 974/1000 [01:21<00:02, 9.89it/s]
98%|█████████▊| 976/1000 [01:22<00:02, 10.64it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.32it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.51it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 9.82it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 9.97it/s]
99%|█████████▊| 986/1000 [01:23<00:01, 10.08it/s]
99%|█████████▉| 988/1000 [01:23<00:01, 10.36it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 10.86it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 11.41it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 11.66it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 11.99it/s]
100%|█████████▉| 998/1000 [01:24<00:00, 12.35it/s]
100%|██████████| 1000/1000 [01:24<00:00, 12.56it/s]
{'eval_loss': '0.9295', 'eval_runtime': '84.3', 'eval_samples_per_second': '11.86', 'eval_steps_per_second': '11.86', 'epoch': '0.2676', 'num_input_tokens_seen': 16794000}
27%|██▋ | 5000/18688 [5:52:05<15:50:50, 4.17s/it]
100%|██████████| 1000/1000 [01:24<00:00, 12.56it/s]
 27%|██▋ | 5001/18688 [5:52:09<111:48:35, 29.41s/it] 27%|██▋ | 5002/18688 [5:52:14<83:02:21, 21.84s/it] 27%|██▋ | 5003/18688 [5:52:17<62:25:11, 16.42s/it] 27%|██▋ | 5004/18688 [5:52:21<47:53:47, 12.60s/it] 27%|██▋ | 5005/18688 [5:52:25<37:41:53, 9.92s/it] 27%|██▋ | 5006/18688 [5:52:29<30:53:04, 8.13s/it] 27%|██▋ | 5007/18688 [5:52:33<26:48:02, 7.05s/it] 27%|██▋ | 5008/18688 [5:52:37<22:53:57, 6.03s/it] 27%|██▋ | 5009/18688 [5:52:41<20:34:25, 5.41s/it] 27%|██▋ | 5010/18688 [5:52:45<18:44:13, 4.93s/it] {'loss': '0.8888', 'grad_norm': '1.125', 'learning_rate': '7.205e-05', 'epoch': '0.2681', 'num_input_tokens_seen': 16824824, 'train_runtime': '2.117e+04', 'train_tokens_per_second': '794.9'}
27%|██▋ | 5010/18688 [5:52:45<18:44:13, 4.93s/it] 27%|██▋ | 5011/18688 [5:52:48<17:25:36, 4.59s/it] 27%|██▋ | 5012/18688 [5:52:53<17:04:08, 4.49s/it] 27%|██▋ | 5013/18688 [5:52:57<17:15:21, 4.54s/it] 27%|██▋ | 5014/18688 [5:53:02<17:25:09, 4.59s/it] 27%|██▋ | 5015/18688 [5:53:06<16:38:50, 4.38s/it] 27%|██▋ | 5016/18688 [5:53:10<16:33:00, 4.36s/it] 27%|██▋ | 5017/18688 [5:53:14<15:42:04, 4.13s/it] 27%|██▋ | 5018/18688 [5:53:17<15:07:35, 3.98s/it] 27%|██▋ | 5019/18688 [5:53:22<15:50:43, 4.17s/it] 27%|██▋ | 5020/18688 [5:53:26<15:08:15, 3.99s/it] {'loss': '0.9194', 'grad_norm': '1.184', 'learning_rate': '7.201e-05', 'epoch': '0.2686', 'num_input_tokens_seen': 16854992, 'train_runtime': '2.121e+04', 'train_tokens_per_second': '794.8'}
27%|██▋ | 5020/18688 [5:53:26<15:08:15, 3.99s/it] 27%|██▋ | 5021/18688 [5:53:30<15:17:45, 4.03s/it] 27%|██▋ | 5022/18688 [5:53:33<14:46:54, 3.89s/it] 27%|██▋ | 5023/18688 [5:53:37<14:28:18, 3.81s/it] 27%|██▋ | 5024/18688 [5:53:41<14:43:55, 3.88s/it] 27%|██▋ | 5025/18688 [5:53:46<16:20:34, 4.31s/it] 27%|██▋ | 5026/18688 [5:53:50<16:08:17, 4.25s/it] 27%|██▋ | 5027/18688 [5:53:55<16:08:19, 4.25s/it] 27%|██▋ | 5028/18688 [5:53:59<16:42:42, 4.40s/it] 27%|██▋ | 5029/18688 [5:54:04<17:28:20, 4.61s/it] 27%|██▋ | 5030/18688 [5:54:08<16:22:46, 4.32s/it] {'loss': '0.9333', 'grad_norm': '1.207', 'learning_rate': '7.196e-05', 'epoch': '0.2692', 'num_input_tokens_seen': 16890760, 'train_runtime': '2.125e+04', 'train_tokens_per_second': '794.9'}
27%|██▋ | 5030/18688 [5:54:08<16:22:46, 4.32s/it] 27%|██▋ | 5031/18688 [5:54:12<15:38:37, 4.12s/it] 27%|██▋ | 5032/18688 [5:54:15<14:54:02, 3.93s/it] 27%|██▋ | 5033/18688 [5:54:20<15:24:48, 4.06s/it] 27%|██▋ | 5034/18688 [5:54:24<16:02:14, 4.23s/it] 27%|██▋ | 5035/18688 [5:54:28<15:46:05, 4.16s/it] 27%|██▋ | 5036/18688 [5:54:32<15:15:28, 4.02s/it] 27%|██▋ | 5037/18688 [5:54:36<15:35:55, 4.11s/it] 27%|██▋ | 5038/18688 [5:54:42<17:12:30, 4.54s/it] 27%|██▋ | 5039/18688 [5:54:46<16:52:43, 4.45s/it] 27%|██▋ | 5040/18688 [5:54:50<16:17:42, 4.30s/it] {'loss': '1.048', 'grad_norm': '1.19', 'learning_rate': '7.192e-05', 'epoch': '0.2697', 'num_input_tokens_seen': 16926128, 'train_runtime': '2.129e+04', 'train_tokens_per_second': '795'}
27%|██▋ | 5040/18688 [5:54:50<16:17:42, 4.30s/it] 27%|██▋ | 5041/18688 [5:54:53<15:19:49, 4.04s/it] 27%|██▋ | 5042/18688 [5:54:58<15:23:40, 4.06s/it] 27%|██▋ | 5043/18688 [5:55:01<14:50:00, 3.91s/it] 27%|██▋ | 5044/18688 [5:55:05<14:44:29, 3.89s/it] 27%|██▋ | 5045/18688 [5:55:09<14:45:56, 3.90s/it] 27%|██▋ | 5046/18688 [5:55:13<14:52:47, 3.93s/it] 27%|██▋ | 5047/18688 [5:55:18<15:58:01, 4.21s/it] 27%|██▋ | 5048/18688 [5:55:22<15:45:38, 4.16s/it] 27%|██▋ | 5049/18688 [5:55:26<16:03:08, 4.24s/it] 27%|██▋ | 5050/18688 [5:55:31<16:29:12, 4.35s/it] {'loss': '0.9771', 'grad_norm': '0.9645', 'learning_rate': '7.188e-05', 'epoch': '0.2702', 'num_input_tokens_seen': 16959600, 'train_runtime': '2.133e+04', 'train_tokens_per_second': '795.1'}
27%|██▋ | 5050/18688 [5:55:31<16:29:12, 4.35s/it] 27%|██▋ | 5051/18688 [5:55:35<16:27:57, 4.35s/it] 27%|██▋ | 5052/18688 [5:55:40<16:28:49, 4.35s/it] 27%|██▋ | 5053/18688 [5:55:44<16:06:42, 4.25s/it] 27%|██▋ | 5054/18688 [5:55:48<16:21:04, 4.32s/it] 27%|██▋ | 5055/18688 [5:55:52<16:27:44, 4.35s/it] 27%|██▋ | 5056/18688 [5:55:57<16:27:56, 4.35s/it] 27%|██▋ | 5057/18688 [5:56:01<16:35:05, 4.38s/it] 27%|██▋ | 5058/18688 [5:56:05<15:44:07, 4.16s/it] 27%|██▋ | 5059/18688 [5:56:09<15:42:48, 4.15s/it] 27%|██▋ | 5060/18688 [5:56:13<15:21:18, 4.06s/it] {'loss': '1', 'grad_norm': '1.237', 'learning_rate': '7.183e-05', 'epoch': '0.2708', 'num_input_tokens_seen': 16991944, 'train_runtime': '2.137e+04', 'train_tokens_per_second': '795'}
27%|██▋ | 5060/18688 [5:56:13<15:21:18, 4.06s/it] 27%|██▋ | 5061/18688 [5:56:17<15:28:43, 4.09s/it] 27%|██▋ | 5062/18688 [5:56:22<16:24:51, 4.34s/it] 27%|██▋ | 5063/18688 [5:56:26<15:39:50, 4.14s/it] 27%|██▋ | 5064/18688 [5:56:30<15:56:18, 4.21s/it] 27%|██▋ | 5065/18688 [5:56:35<16:32:44, 4.37s/it] 27%|██▋ | 5066/18688 [5:56:39<16:16:05, 4.30s/it] 27%|██▋ | 5067/18688 [5:56:43<15:53:16, 4.20s/it] 27%|██▋ | 5068/18688 [5:56:47<15:40:47, 4.14s/it] 27%|██▋ | 5069/18688 [5:56:51<15:56:54, 4.22s/it] 27%|██▋ | 5070/18688 [5:56:55<15:18:00, 4.04s/it] {'loss': '0.9845', 'grad_norm': '1.138', 'learning_rate': '7.179e-05', 'epoch': '0.2713', 'num_input_tokens_seen': 17026992, 'train_runtime': '2.142e+04', 'train_tokens_per_second': '795.1'}
27%|██▋ | 5070/18688 [5:56:55<15:18:00, 4.04s/it] 27%|██▋ | 5071/18688 [5:56:59<15:31:54, 4.11s/it] 27%|██▋ | 5072/18688 [5:57:03<15:08:55, 4.01s/it] 27%|██▋ | 5073/18688 [5:57:08<15:58:41, 4.22s/it] 27%|██▋ | 5074/18688 [5:57:12<16:40:57, 4.41s/it] 27%|██▋ | 5075/18688 [5:57:16<15:47:02, 4.17s/it] 27%|██▋ | 5076/18688 [5:57:20<15:12:29, 4.02s/it] 27%|██▋ | 5077/18688 [5:57:24<14:58:44, 3.96s/it] 27%|██▋ | 5078/18688 [5:57:28<15:08:53, 4.01s/it] 27%|██▋ | 5079/18688 [5:57:31<14:43:05, 3.89s/it] 27%|██▋ | 5080/18688 [5:57:36<15:16:00, 4.04s/it] {'loss': '0.945', 'grad_norm': '1.657', 'learning_rate': '7.174e-05', 'epoch': '0.2718', 'num_input_tokens_seen': 17059280, 'train_runtime': '2.146e+04', 'train_tokens_per_second': '795.1'}
27%|██▋ | 5080/18688 [5:57:36<15:16:00, 4.04s/it] 27%|██▋ | 5081/18688 [5:57:41<16:40:08, 4.41s/it] 27%|██▋ | 5082/18688 [5:57:47<18:05:01, 4.78s/it] 27%|██▋ | 5083/18688 [5:57:51<17:50:44, 4.72s/it] 27%|██▋ | 5084/18688 [5:57:57<18:30:24, 4.90s/it] 27%|██▋ | 5085/18688 [5:58:00<17:06:52, 4.53s/it] 27%|██▋ | 5086/18688 [5:58:06<18:41:56, 4.95s/it] 27%|██▋ | 5087/18688 [5:58:10<17:21:59, 4.60s/it] 27%|██▋ | 5088/18688 [5:58:14<16:26:12, 4.35s/it] 27%|██▋ | 5089/18688 [5:58:17<15:43:56, 4.16s/it] 27%|██▋ | 5090/18688 [5:58:21<15:21:14, 4.06s/it] {'loss': '0.9677', 'grad_norm': '1.267', 'learning_rate': '7.17e-05', 'epoch': '0.2724', 'num_input_tokens_seen': 17099072, 'train_runtime': '2.15e+04', 'train_tokens_per_second': '795.2'}
27%|██▋ | 5090/18688 [5:58:21<15:21:14, 4.06s/it] 27%|██▋ | 5091/18688 [5:58:25<15:13:25, 4.03s/it] 27%|██▋ | 5092/18688 [5:58:30<15:40:06, 4.15s/it] 27%|██▋ | 5093/18688 [5:58:34<15:41:59, 4.16s/it] 27%|██▋ | 5094/18688 [5:58:38<15:29:10, 4.10s/it] 27%|██▋ | 5095/18688 [5:58:42<15:25:44, 4.09s/it] 27%|██▋ | 5096/18688 [5:58:46<15:23:42, 4.08s/it] 27%|██▋ | 5097/18688 [5:58:49<14:50:02, 3.93s/it] 27%|██▋ | 5098/18688 [5:58:53<14:39:52, 3.88s/it] 27%|██▋ | 5099/18688 [5:58:57<14:32:28, 3.85s/it] 27%|██▋ | 5100/18688 [5:59:02<15:45:02, 4.17s/it] {'loss': '0.9054', 'grad_norm': '1.352', 'learning_rate': '7.165e-05', 'epoch': '0.2729', 'num_input_tokens_seen': 17129952, 'train_runtime': '2.154e+04', 'train_tokens_per_second': '795.2'}
27%|██▋ | 5100/18688 [5:59:02<15:45:02, 4.17s/it] 27%|██▋ | 5101/18688 [5:59:06<15:45:23, 4.17s/it] 27%|██▋ | 5102/18688 [5:59:10<14:58:38, 3.97s/it] 27%|██▋ | 5103/18688 [5:59:14<15:53:43, 4.21s/it] 27%|██▋ | 5104/18688 [5:59:18<15:45:48, 4.18s/it] 27%|██▋ | 5105/18688 [5:59:23<15:45:34, 4.18s/it] 27%|██▋ | 5106/18688 [5:59:26<15:11:19, 4.03s/it] 27%|██▋ | 5107/18688 [5:59:30<15:13:42, 4.04s/it] 27%|██▋ | 5108/18688 [5:59:34<14:45:20, 3.91s/it] 27%|██▋ | 5109/18688 [5:59:38<14:49:35, 3.93s/it] 27%|██▋ | 5110/18688 [5:59:42<15:27:19, 4.10s/it] {'loss': '0.9196', 'grad_norm': '1.113', 'learning_rate': '7.161e-05', 'epoch': '0.2734', 'num_input_tokens_seen': 17163160, 'train_runtime': '2.158e+04', 'train_tokens_per_second': '795.2'}
27%|██▋ | 5110/18688 [5:59:42<15:27:19, 4.10s/it] 27%|██▋ | 5111/18688 [5:59:47<16:02:00, 4.25s/it] 27%|██▋ | 5112/18688 [5:59:52<16:47:28, 4.45s/it] 27%|██▋ | 5113/18688 [5:59:56<15:47:32, 4.19s/it] 27%|██▋ | 5114/18688 [6:00:00<15:50:51, 4.20s/it] 27%|██▋ | 5115/18688 [6:00:04<15:40:57, 4.16s/it] 27%|██▋ | 5116/18688 [6:00:08<15:17:04, 4.05s/it] 27%|██▋ | 5117/18688 [6:00:11<14:45:56, 3.92s/it] 27%|██▋ | 5118/18688 [6:00:16<15:13:08, 4.04s/it] 27%|██▋ | 5119/18688 [6:00:20<15:23:56, 4.09s/it] 27%|██▋ | 5120/18688 [6:00:24<15:26:25, 4.10s/it] {'loss': '0.9678', 'grad_norm': '1.164', 'learning_rate': '7.156e-05', 'epoch': '0.274', 'num_input_tokens_seen': 17196272, 'train_runtime': '2.162e+04', 'train_tokens_per_second': '795.2'}
27%|██▋ | 5120/18688 [6:00:24<15:26:25, 4.10s/it] 27%|██▋ | 5121/18688 [6:00:28<15:38:12, 4.15s/it] 27%|██▋ | 5122/18688 [6:00:33<15:58:31, 4.24s/it] 27%|██▋ | 5123/18688 [6:00:36<15:12:06, 4.03s/it] 27%|██▋ | 5124/18688 [6:00:41<15:34:36, 4.13s/it] 27%|██▋ | 5125/18688 [6:00:45<15:50:00, 4.20s/it] 27%|██▋ | 5126/18688 [6:00:48<15:01:24, 3.99s/it] 27%|██▋ | 5127/18688 [6:00:52<14:46:13, 3.92s/it] 27%|██▋ | 5128/18688 [6:00:56<14:55:55, 3.96s/it] 27%|██▋ | 5129/18688 [6:01:00<14:42:37, 3.91s/it] 27%|██▋ | 5130/18688 [6:01:04<14:28:44, 3.84s/it] {'loss': '0.9511', 'grad_norm': '1.071', 'learning_rate': '7.152e-05', 'epoch': '0.2745', 'num_input_tokens_seen': 17226720, 'train_runtime': '2.166e+04', 'train_tokens_per_second': '795.2'}
27%|██▋ | 5130/18688 [6:01:04<14:28:44, 3.84s/it] 27%|██▋ | 5131/18688 [6:01:08<14:44:07, 3.91s/it] 27%|██▋ | 5132/18688 [6:01:12<15:08:10, 4.02s/it] 27%|██▋ | 5133/18688 [6:01:16<15:10:24, 4.03s/it] 27%|██▋ | 5134/18688 [6:01:20<14:53:30, 3.96s/it] 27%|██▋ | 5135/18688 [6:01:25<16:01:52, 4.26s/it] 27%|██▋ | 5136/18688 [6:01:29<16:13:26, 4.31s/it] 27%|██▋ | 5137/18688 [6:01:33<15:43:43, 4.18s/it] 27%|██▋ | 5138/18688 [6:01:38<15:57:52, 4.24s/it] 27%|██▋ | 5139/18688 [6:01:42<16:00:07, 4.25s/it] 28%|██▊ | 5140/18688 [6:01:46<15:54:42, 4.23s/it] {'loss': '0.9706', 'grad_norm': '0.9849', 'learning_rate': '7.147e-05', 'epoch': '0.2751', 'num_input_tokens_seen': 17260824, 'train_runtime': '2.171e+04', 'train_tokens_per_second': '795.2'}
28%|██▊ | 5140/18688 [6:01:46<15:54:42, 4.23s/it] 28%|██▊ | 5141/18688 [6:01:50<15:56:17, 4.24s/it] 28%|██▊ | 5142/18688 [6:01:54<15:55:42, 4.23s/it] 28%|██▊ | 5143/18688 [6:01:59<15:48:59, 4.20s/it] 28%|██▊ | 5144/18688 [6:02:03<15:51:44, 4.22s/it] 28%|██▊ | 5145/18688 [6:02:07<15:50:32, 4.21s/it] 28%|██▊ | 5146/18688 [6:02:12<16:08:34, 4.29s/it] 28%|██▊ | 5147/18688 [6:02:17<17:21:59, 4.62s/it] 28%|██▊ | 5148/18688 [6:02:20<16:08:47, 4.29s/it] 28%|██▊ | 5149/18688 [6:02:25<16:28:05, 4.38s/it] 28%|██▊ | 5150/18688 [6:02:29<15:47:39, 4.20s/it] {'loss': '1.025', 'grad_norm': '1.273', 'learning_rate': '7.143e-05', 'epoch': '0.2756', 'num_input_tokens_seen': 17295792, 'train_runtime': '2.175e+04', 'train_tokens_per_second': '795.2'}
28%|██▊ | 5150/18688 [6:02:29<15:47:39, 4.20s/it] 28%|██▊ | 5151/18688 [6:02:33<15:27:38, 4.11s/it] 28%|██▊ | 5152/18688 [6:02:37<15:19:13, 4.07s/it] 28%|██▊ | 5153/18688 [6:02:41<15:49:22, 4.21s/it] 28%|██▊ | 5154/18688 [6:02:45<15:42:39, 4.18s/it] 28%|██▊ | 5155/18688 [6:02:49<14:57:19, 3.98s/it] 28%|██▊ | 5156/18688 [6:02:53<15:34:59, 4.15s/it] 28%|██▊ | 5157/18688 [6:02:57<15:11:02, 4.04s/it] 28%|██▊ | 5158/18688 [6:03:02<15:56:26, 4.24s/it] 28%|██▊ | 5159/18688 [6:03:06<15:21:13, 4.09s/it] 28%|██▊ | 5160/18688 [6:03:10<15:38:01, 4.16s/it] {'loss': '0.9877', 'grad_norm': '1.18', 'learning_rate': '7.138e-05', 'epoch': '0.2761', 'num_input_tokens_seen': 17328232, 'train_runtime': '2.179e+04', 'train_tokens_per_second': '795.2'}
28%|██▊ | 5160/18688 [6:03:10<15:38:01, 4.16s/it] 28%|██▊ | 5161/18688 [6:03:14<15:22:49, 4.09s/it] 28%|██▊ | 5162/18688 [6:03:18<15:51:23, 4.22s/it] 28%|██▊ | 5163/18688 [6:03:24<17:12:42, 4.58s/it] 28%|██▊ | 5164/18688 [6:03:29<17:26:41, 4.64s/it] 28%|██▊ | 5165/18688 [6:03:32<16:28:46, 4.39s/it] 28%|██▊ | 5166/18688 [6:03:36<16:03:57, 4.28s/it] 28%|██▊ | 5167/18688 [6:03:41<16:54:40, 4.50s/it] 28%|██▊ | 5168/18688 [6:03:45<15:53:16, 4.23s/it] 28%|██▊ | 5169/18688 [6:03:50<16:54:02, 4.50s/it] 28%|██▊ | 5170/18688 [6:03:57<19:09:53, 5.10s/it] {'loss': '0.9211', 'grad_norm': '1.078', 'learning_rate': '7.134e-05', 'epoch': '0.2767', 'num_input_tokens_seen': 17368832, 'train_runtime': '2.184e+04', 'train_tokens_per_second': '795.4'}
28%|██▊ | 5170/18688 [6:03:57<19:09:53, 5.10s/it] 28%|██▊ | 5171/18688 [6:04:01<18:15:22, 4.86s/it] 28%|██▊ | 5172/18688 [6:04:05<16:52:25, 4.49s/it] 28%|██▊ | 5173/18688 [6:04:10<17:24:19, 4.64s/it] 28%|██▊ | 5174/18688 [6:04:14<17:08:09, 4.56s/it] 28%|██▊ | 5175/18688 [6:04:19<17:20:51, 4.62s/it] 28%|██▊ | 5176/18688 [6:04:23<16:57:47, 4.52s/it] 28%|██▊ | 5177/18688 [6:04:27<15:52:40, 4.23s/it] 28%|██▊ | 5178/18688 [6:04:30<15:13:01, 4.05s/it] 28%|██▊ | 5179/18688 [6:04:34<15:16:50, 4.07s/it] 28%|██▊ | 5180/18688 [6:04:38<14:52:31, 3.96s/it] {'loss': '0.9354', 'grad_norm': '1.044', 'learning_rate': '7.129e-05', 'epoch': '0.2772', 'num_input_tokens_seen': 17402472, 'train_runtime': '2.188e+04', 'train_tokens_per_second': '795.4'}
28%|██▊ | 5180/18688 [6:04:38<14:52:31, 3.96s/it] 28%|██▊ | 5181/18688 [6:04:42<14:39:14, 3.91s/it] 28%|██▊ | 5182/18688 [6:04:46<14:49:56, 3.95s/it] 28%|██▊ | 5183/18688 [6:04:50<15:10:52, 4.05s/it] 28%|██▊ | 5184/18688 [6:04:54<15:21:52, 4.10s/it] 28%|██▊ | 5185/18688 [6:04:58<15:05:56, 4.03s/it] 28%|██▊ | 5186/18688 [6:05:02<14:51:27, 3.96s/it] 28%|██▊ | 5187/18688 [6:05:06<15:00:03, 4.00s/it] 28%|██▊ | 5188/18688 [6:05:11<15:27:03, 4.12s/it] 28%|██▊ | 5189/18688 [6:05:16<16:51:15, 4.49s/it] 28%|██▊ | 5190/18688 [6:05:20<16:43:39, 4.46s/it] {'loss': '0.9239', 'grad_norm': '1.208', 'learning_rate': '7.125e-05', 'epoch': '0.2777', 'num_input_tokens_seen': 17436336, 'train_runtime': '2.192e+04', 'train_tokens_per_second': '795.4'}
28%|██▊ | 5190/18688 [6:05:20<16:43:39, 4.46s/it] 28%|██▊ | 5191/18688 [6:05:24<15:55:13, 4.25s/it] 28%|██▊ | 5192/18688 [6:05:28<16:11:10, 4.32s/it] 28%|██▊ | 5193/18688 [6:05:33<16:17:21, 4.35s/it] 28%|██▊ | 5194/18688 [6:05:38<17:07:48, 4.57s/it] 28%|██▊ | 5195/18688 [6:05:42<16:08:01, 4.30s/it] 28%|██▊ | 5196/18688 [6:05:45<15:28:44, 4.13s/it] 28%|██▊ | 5197/18688 [6:05:50<15:32:02, 4.15s/it] 28%|██▊ | 5198/18688 [6:05:53<15:13:07, 4.06s/it] 28%|██▊ | 5199/18688 [6:05:58<15:32:30, 4.15s/it] 28%|██▊ | 5200/18688 [6:06:02<15:48:52, 4.22s/it] {'loss': '0.9701', 'grad_norm': '1.126', 'learning_rate': '7.12e-05', 'epoch': '0.2783', 'num_input_tokens_seen': 17468840, 'train_runtime': '2.196e+04', 'train_tokens_per_second': '795.4'}
28%|██▊ | 5200/18688 [6:06:02<15:48:52, 4.22s/it] 28%|██▊ | 5201/18688 [6:06:06<15:06:10, 4.03s/it] 28%|██▊ | 5202/18688 [6:06:10<14:49:33, 3.96s/it] 28%|██▊ | 5203/18688 [6:06:14<14:53:03, 3.97s/it] 28%|██▊ | 5204/18688 [6:06:19<16:00:15, 4.27s/it] 28%|██▊ | 5205/18688 [6:06:24<17:04:34, 4.56s/it] 28%|██▊ | 5206/18688 [6:06:28<16:21:11, 4.37s/it] 28%|██▊ | 5207/18688 [6:06:31<15:41:57, 4.19s/it] 28%|██▊ | 5208/18688 [6:06:35<15:00:14, 4.01s/it] 28%|██▊ | 5209/18688 [6:06:39<15:12:24, 4.06s/it] 28%|██▊ | 5210/18688 [6:06:43<15:17:48, 4.09s/it] {'loss': '1.001', 'grad_norm': '1.408', 'learning_rate': '7.116e-05', 'epoch': '0.2788', 'num_input_tokens_seen': 17501400, 'train_runtime': '2.2e+04', 'train_tokens_per_second': '795.4'}
28%|██▊ | 5210/18688 [6:06:43<15:17:48, 4.09s/it] 28%|██▊ | 5211/18688 [6:06:47<14:51:20, 3.97s/it] 28%|██▊ | 5212/18688 [6:06:52<15:34:46, 4.16s/it] 28%|██▊ | 5213/18688 [6:06:56<16:13:43, 4.34s/it] 28%|██▊ | 5214/18688 [6:07:01<16:36:15, 4.44s/it] 28%|██▊ | 5215/18688 [6:07:05<15:58:12, 4.27s/it] 28%|██▊ | 5216/18688 [6:07:09<15:59:06, 4.27s/it] 28%|██▊ | 5217/18688 [6:07:13<15:51:24, 4.24s/it] 28%|██▊ | 5218/18688 [6:07:18<15:54:29, 4.25s/it] 28%|██▊ | 5219/18688 [6:07:22<15:45:15, 4.21s/it] 28%|██▊ | 5220/18688 [6:07:25<15:08:27, 4.05s/it] {'loss': '0.9163', 'grad_norm': '1.385', 'learning_rate': '7.111e-05', 'epoch': '0.2793', 'num_input_tokens_seen': 17534272, 'train_runtime': '2.205e+04', 'train_tokens_per_second': '795.4'}
28%|██▊ | 5220/18688 [6:07:25<15:08:27, 4.05s/it] 28%|██▊ | 5221/18688 [6:07:31<16:21:00, 4.37s/it] 28%|██▊ | 5222/18688 [6:07:35<16:21:33, 4.37s/it] 28%|██▊ | 5223/18688 [6:07:39<16:23:52, 4.38s/it] 28%|██▊ | 5224/18688 [6:07:44<16:15:57, 4.35s/it] 28%|██▊ | 5225/18688 [6:07:48<15:53:24, 4.25s/it] 28%|██▊ | 5226/18688 [6:07:52<16:00:37, 4.28s/it] 28%|██▊ | 5227/18688 [6:07:58<17:45:59, 4.75s/it] 28%|██▊ | 5228/18688 [6:08:02<16:39:15, 4.45s/it] 28%|██▊ | 5229/18688 [6:08:06<16:49:49, 4.50s/it] 28%|██▊ | 5230/18688 [6:08:10<16:01:08, 4.29s/it] {'loss': '0.9868', 'grad_norm': '1.05', 'learning_rate': '7.106e-05', 'epoch': '0.2799', 'num_input_tokens_seen': 17572440, 'train_runtime': '2.209e+04', 'train_tokens_per_second': '795.5'}
28%|██▊ | 5230/18688 [6:08:10<16:01:08, 4.29s/it] 28%|██▊ | 5231/18688 [6:08:14<16:06:56, 4.31s/it] 28%|██▊ | 5232/18688 [6:08:19<16:09:02, 4.32s/it] 28%|██▊ | 5233/18688 [6:08:23<15:55:52, 4.26s/it] 28%|██▊ | 5234/18688 [6:08:28<16:21:21, 4.38s/it] 28%|██▊ | 5235/18688 [6:08:33<17:33:45, 4.70s/it] 28%|██▊ | 5236/18688 [6:08:37<16:58:13, 4.54s/it] 28%|██▊ | 5237/18688 [6:08:41<16:43:40, 4.48s/it] 28%|██▊ | 5238/18688 [6:08:46<17:14:15, 4.61s/it] 28%|██▊ | 5239/18688 [6:08:50<16:08:27, 4.32s/it] 28%|██▊ | 5240/18688 [6:08:54<15:24:57, 4.13s/it] {'loss': '0.9488', 'grad_norm': '1.4', 'learning_rate': '7.102e-05', 'epoch': '0.2804', 'num_input_tokens_seen': 17609416, 'train_runtime': '2.213e+04', 'train_tokens_per_second': '795.6'}
28%|██▊ | 5240/18688 [6:08:54<15:24:57, 4.13s/it] 28%|██▊ | 5241/18688 [6:08:57<14:56:24, 4.00s/it] 28%|██▊ | 5242/18688 [6:09:01<14:42:00, 3.94s/it] 28%|██▊ | 5243/18688 [6:09:05<14:41:23, 3.93s/it] 28%|██▊ | 5244/18688 [6:09:09<14:37:39, 3.92s/it] 28%|██▊ | 5245/18688 [6:09:13<14:30:23, 3.88s/it] 28%|██▊ | 5246/18688 [6:09:17<14:53:52, 3.99s/it] 28%|██▊ | 5247/18688 [6:09:21<14:32:30, 3.89s/it] 28%|██▊ | 5248/18688 [6:09:25<14:40:55, 3.93s/it] 28%|██▊ | 5249/18688 [6:09:29<15:33:18, 4.17s/it] 28%|██▊ | 5250/18688 [6:09:34<15:24:44, 4.13s/it] {'loss': '1.052', 'grad_norm': '1.26', 'learning_rate': '7.097e-05', 'epoch': '0.2809', 'num_input_tokens_seen': 17639536, 'train_runtime': '2.217e+04', 'train_tokens_per_second': '795.5'}
28%|██▊ | 5250/18688 [6:09:34<15:24:44, 4.13s/it] 28%|██▊ | 5251/18688 [6:09:38<15:54:12, 4.26s/it] 28%|██▊ | 5252/18688 [6:09:42<15:22:09, 4.12s/it] 28%|██▊ | 5253/18688 [6:09:46<15:04:21, 4.04s/it] 28%|██▊ | 5254/18688 [6:09:49<14:31:11, 3.89s/it] 28%|██▊ | 5255/18688 [6:09:53<14:07:51, 3.79s/it] 28%|██▊ | 5256/18688 [6:09:57<14:28:49, 3.88s/it] 28%|██▊ | 5257/18688 [6:10:00<14:06:34, 3.78s/it] 28%|██▊ | 5258/18688 [6:10:04<13:46:29, 3.69s/it] 28%|██▊ | 5259/18688 [6:10:08<14:23:11, 3.86s/it] 28%|██▊ | 5260/18688 [6:10:12<14:23:19, 3.86s/it] {'loss': '0.9162', 'grad_norm': '1.129', 'learning_rate': '7.092e-05', 'epoch': '0.2815', 'num_input_tokens_seen': 17669064, 'train_runtime': '2.221e+04', 'train_tokens_per_second': '795.5'}
28%|██▊ | 5260/18688 [6:10:12<14:23:19, 3.86s/it] 28%|██▊ | 5261/18688 [6:10:18<16:18:45, 4.37s/it] 28%|██▊ | 5262/18688 [6:10:21<15:28:42, 4.15s/it] 28%|██▊ | 5263/18688 [6:10:25<14:54:47, 4.00s/it] 28%|██▊ | 5264/18688 [6:10:29<15:19:11, 4.11s/it] 28%|██▊ | 5265/18688 [6:10:33<14:58:00, 4.01s/it] 28%|██▊ | 5266/18688 [6:10:37<14:53:54, 4.00s/it] 28%|██▊ | 5267/18688 [6:10:42<15:51:04, 4.25s/it] 28%|██▊ | 5268/18688 [6:10:47<16:46:50, 4.50s/it] 28%|██▊ | 5269/18688 [6:10:51<16:00:45, 4.30s/it] 28%|██▊ | 5270/18688 [6:10:55<15:50:00, 4.25s/it] {'loss': '0.9199', 'grad_norm': '1.369', 'learning_rate': '7.088e-05', 'epoch': '0.282', 'num_input_tokens_seen': 17705704, 'train_runtime': '2.226e+04', 'train_tokens_per_second': '795.6'}
28%|██▊ | 5270/18688 [6:10:55<15:50:00, 4.25s/it] 28%|██▊ | 5271/18688 [6:10:59<15:22:51, 4.13s/it] 28%|██▊ | 5272/18688 [6:11:02<14:39:31, 3.93s/it] 28%|██▊ | 5273/18688 [6:11:06<14:46:58, 3.97s/it] 28%|██▊ | 5274/18688 [6:11:11<15:50:23, 4.25s/it] 28%|██▊ | 5275/18688 [6:11:16<16:12:16, 4.35s/it] 28%|██▊ | 5276/18688 [6:11:19<15:31:02, 4.17s/it] 28%|██▊ | 5277/18688 [6:11:23<14:57:22, 4.01s/it] 28%|██▊ | 5278/18688 [6:11:27<15:04:56, 4.05s/it] 28%|██▊ | 5279/18688 [6:11:31<14:32:01, 3.90s/it] 28%|██▊ | 5280/18688 [6:11:35<15:00:03, 4.03s/it] {'loss': '1.011', 'grad_norm': '1.013', 'learning_rate': '7.083e-05', 'epoch': '0.2825', 'num_input_tokens_seen': 17737472, 'train_runtime': '2.23e+04', 'train_tokens_per_second': '795.6'}
28%|██▊ | 5280/18688 [6:11:35<15:00:03, 4.03s/it] 28%|██▊ | 5281/18688 [6:11:39<14:48:34, 3.98s/it] 28%|██▊ | 5282/18688 [6:11:44<15:42:23, 4.22s/it] 28%|██▊ | 5283/18688 [6:11:49<17:03:29, 4.58s/it] 28%|██▊ | 5284/18688 [6:11:53<16:18:34, 4.38s/it] 28%|██▊ | 5285/18688 [6:11:57<15:54:36, 4.27s/it] 28%|██▊ | 5286/18688 [6:12:02<16:04:50, 4.32s/it] 28%|██▊ | 5287/18688 [6:12:06<16:11:16, 4.35s/it] 28%|██▊ | 5288/18688 [6:12:10<16:20:37, 4.39s/it] 28%|██▊ | 5289/18688 [6:12:15<16:26:52, 4.42s/it] 28%|██▊ | 5290/18688 [6:12:19<16:03:14, 4.31s/it] {'loss': '0.9036', 'grad_norm': '0.9795', 'learning_rate': '7.078e-05', 'epoch': '0.2831', 'num_input_tokens_seen': 17773016, 'train_runtime': '2.234e+04', 'train_tokens_per_second': '795.6'}
28%|██▊ | 5290/18688 [6:12:19<16:03:14, 4.31s/it] 28%|██▊ | 5291/18688 [6:12:24<17:10:11, 4.61s/it] 28%|██▊ | 5292/18688 [6:12:28<16:17:41, 4.38s/it] 28%|██▊ | 5293/18688 [6:12:32<15:57:35, 4.29s/it] 28%|██▊ | 5294/18688 [6:12:36<15:47:42, 4.25s/it] 28%|██▊ | 5295/18688 [6:12:40<15:32:51, 4.18s/it] 28%|██▊ | 5296/18688 [6:12:46<16:43:16, 4.49s/it] 28%|██▊ | 5297/18688 [6:12:49<15:47:59, 4.25s/it] 28%|██▊ | 5298/18688 [6:12:53<15:07:26, 4.07s/it] 28%|██▊ | 5299/18688 [6:12:57<15:01:39, 4.04s/it] 28%|██▊ | 5300/18688 [6:13:01<14:47:44, 3.98s/it] {'loss': '0.9358', 'grad_norm': '1.336', 'learning_rate': '7.074e-05', 'epoch': '0.2836', 'num_input_tokens_seen': 17806056, 'train_runtime': '2.238e+04', 'train_tokens_per_second': '795.6'}
28%|██▊ | 5300/18688 [6:13:01<14:47:44, 3.98s/it] 28%|██▊ | 5301/18688 [6:13:04<14:24:28, 3.87s/it] 28%|██▊ | 5302/18688 [6:13:08<14:29:23, 3.90s/it] 28%|██▊ | 5303/18688 [6:13:13<15:32:34, 4.18s/it] 28%|██▊ | 5304/18688 [6:13:17<15:03:45, 4.05s/it] 28%|██▊ | 5305/18688 [6:13:21<14:46:41, 3.98s/it] 28%|██▊ | 5306/18688 [6:13:25<15:08:39, 4.07s/it] 28%|██▊ | 5307/18688 [6:13:29<15:06:46, 4.07s/it] 28%|██▊ | 5308/18688 [6:13:34<15:47:54, 4.25s/it] 28%|██▊ | 5309/18688 [6:13:37<14:59:55, 4.04s/it] 28%|██▊ | 5310/18688 [6:13:41<14:32:20, 3.91s/it] {'loss': '0.9303', 'grad_norm': '1.154', 'learning_rate': '7.069e-05', 'epoch': '0.2841', 'num_input_tokens_seen': 17837904, 'train_runtime': '2.242e+04', 'train_tokens_per_second': '795.6'}
28%|██▊ | 5310/18688 [6:13:41<14:32:20, 3.91s/it] 28%|██▊ | 5311/18688 [6:13:45<14:52:39, 4.00s/it] 28%|██▊ | 5312/18688 [6:13:50<15:15:33, 4.11s/it] 28%|██▊ | 5313/18688 [6:13:54<15:59:09, 4.30s/it] 28%|██▊ | 5314/18688 [6:13:58<15:29:08, 4.17s/it] 28%|██▊ | 5315/18688 [6:14:03<15:52:18, 4.27s/it] 28%|██▊ | 5316/18688 [6:14:07<16:15:48, 4.38s/it] 28%|██▊ | 5317/18688 [6:14:11<15:28:09, 4.16s/it] 28%|██▊ | 5318/18688 [6:14:15<15:19:14, 4.13s/it] 28%|██▊ | 5319/18688 [6:14:20<15:57:46, 4.30s/it] 28%|██▊ | 5320/18688 [6:14:24<15:48:40, 4.26s/it] {'loss': '1.018', 'grad_norm': '1.142', 'learning_rate': '7.064e-05', 'epoch': '0.2847', 'num_input_tokens_seen': 17873048, 'train_runtime': '2.246e+04', 'train_tokens_per_second': '795.6'}
28%|██▊ | 5320/18688 [6:14:24<15:48:40, 4.26s/it] 28%|██▊ | 5321/18688 [6:14:28<15:12:53, 4.10s/it] 28%|██▊ | 5322/18688 [6:14:32<15:07:55, 4.08s/it] 28%|██▊ | 5323/18688 [6:14:36<15:14:17, 4.10s/it] 28%|██▊ | 5324/18688 [6:14:40<15:03:52, 4.06s/it] 28%|██▊ | 5325/18688 [6:14:45<16:23:19, 4.42s/it] 28%|██▊ | 5326/18688 [6:14:48<15:20:23, 4.13s/it] 29%|██▊ | 5327/18688 [6:14:53<15:28:00, 4.17s/it] 29%|██▊ | 5328/18688 [6:14:57<15:04:26, 4.06s/it] 29%|██▊ | 5329/18688 [6:15:01<15:49:12, 4.26s/it] 29%|██▊ | 5330/18688 [6:15:06<16:46:49, 4.52s/it] {'loss': '0.9699', 'grad_norm': '5.371', 'learning_rate': '7.06e-05', 'epoch': '0.2852', 'num_input_tokens_seen': 17907056, 'train_runtime': '2.251e+04', 'train_tokens_per_second': '795.6'}
29%|██▊ | 5330/18688 [6:15:06<16:46:49, 4.52s/it] 29%|██▊ | 5331/18688 [6:15:10<16:03:15, 4.33s/it] 29%|██▊ | 5332/18688 [6:15:15<16:03:04, 4.33s/it] 29%|██▊ | 5333/18688 [6:15:18<15:28:58, 4.17s/it] 29%|██▊ | 5334/18688 [6:15:23<15:26:40, 4.16s/it] 29%|██▊ | 5335/18688 [6:15:26<14:41:54, 3.96s/it] 29%|██▊ | 5336/18688 [6:15:31<15:17:42, 4.12s/it] 29%|██▊ | 5337/18688 [6:15:35<15:30:45, 4.18s/it] 29%|██▊ | 5338/18688 [6:15:39<15:15:10, 4.11s/it] 29%|██▊ | 5339/18688 [6:15:44<15:57:03, 4.30s/it] 29%|██▊ | 5340/18688 [6:15:47<15:05:57, 4.07s/it] {'loss': '0.9831', 'grad_norm': '1.005', 'learning_rate': '7.055e-05', 'epoch': '0.2858', 'num_input_tokens_seen': 17939856, 'train_runtime': '2.255e+04', 'train_tokens_per_second': '795.6'}
29%|██▊ | 5340/18688 [6:15:47<15:05:57, 4.07s/it] 29%|██▊ | 5341/18688 [6:15:51<14:49:52, 4.00s/it] 29%|██▊ | 5342/18688 [6:15:55<14:58:13, 4.04s/it] 29%|██▊ | 5343/18688 [6:15:59<15:16:10, 4.12s/it] 29%|██▊ | 5344/18688 [6:16:03<14:40:25, 3.96s/it] 29%|██▊ | 5345/18688 [6:16:07<14:37:24, 3.95s/it] 29%|██▊ | 5346/18688 [6:16:12<16:18:11, 4.40s/it] 29%|██▊ | 5347/18688 [6:16:16<15:29:48, 4.18s/it] 29%|██▊ | 5348/18688 [6:16:21<16:17:08, 4.39s/it] 29%|██▊ | 5349/18688 [6:16:25<15:51:52, 4.28s/it] 29%|██▊ | 5350/18688 [6:16:29<15:34:16, 4.20s/it] {'loss': '0.9182', 'grad_norm': '1.105', 'learning_rate': '7.05e-05', 'epoch': '0.2863', 'num_input_tokens_seen': 17974912, 'train_runtime': '2.259e+04', 'train_tokens_per_second': '795.7'}
29%|██▊ | 5350/18688 [6:16:29<15:34:16, 4.20s/it] 29%|██▊ | 5351/18688 [6:16:33<15:20:51, 4.14s/it] 29%|██▊ | 5352/18688 [6:16:38<16:01:26, 4.33s/it] 29%|██▊ | 5353/18688 [6:16:43<17:07:38, 4.62s/it] 29%|██▊ | 5354/18688 [6:16:47<16:10:37, 4.37s/it] 29%|██▊ | 5355/18688 [6:16:52<16:39:17, 4.50s/it] 29%|██▊ | 5356/18688 [6:16:56<17:07:57, 4.63s/it] 29%|██▊ | 5357/18688 [6:17:01<16:48:50, 4.54s/it] 29%|██▊ | 5358/18688 [6:17:04<15:40:33, 4.23s/it] 29%|██▊ | 5359/18688 [6:17:08<15:21:40, 4.15s/it] 29%|██▊ | 5360/18688 [6:17:13<15:31:16, 4.19s/it] {'loss': '0.9863', 'grad_norm': '1.029', 'learning_rate': '7.046e-05', 'epoch': '0.2868', 'num_input_tokens_seen': 18011176, 'train_runtime': '2.263e+04', 'train_tokens_per_second': '795.8'}
29%|██▊ | 5360/18688 [6:17:13<15:31:16, 4.19s/it] 29%|██▊ | 5361/18688 [6:17:17<15:23:32, 4.16s/it] 29%|██▊ | 5362/18688 [6:17:20<14:50:07, 4.01s/it] 29%|██▊ | 5363/18688 [6:17:25<15:25:16, 4.17s/it] 29%|██▊ | 5364/18688 [6:17:29<14:54:26, 4.03s/it] 29%|██▊ | 5365/18688 [6:17:33<15:06:52, 4.08s/it] 29%|██▊ | 5366/18688 [6:17:37<15:40:51, 4.24s/it] 29%|██▊ | 5367/18688 [6:17:41<15:02:02, 4.06s/it] 29%|██▊ | 5368/18688 [6:17:45<14:37:47, 3.95s/it] 29%|██▊ | 5369/18688 [6:17:49<14:49:40, 4.01s/it] 29%|██▊ | 5370/18688 [6:17:53<15:10:25, 4.10s/it] {'loss': '0.8974', 'grad_norm': '1.121', 'learning_rate': '7.041e-05', 'epoch': '0.2874', 'num_input_tokens_seen': 18042736, 'train_runtime': '2.267e+04', 'train_tokens_per_second': '795.8'}
29%|██▊ | 5370/18688 [6:17:53<15:10:25, 4.10s/it] 29%|██▊ | 5371/18688 [6:17:59<16:38:49, 4.50s/it] 29%|██▊ | 5372/18688 [6:18:03<16:36:40, 4.49s/it] 29%|██▉ | 5373/18688 [6:18:07<15:44:42, 4.26s/it] 29%|██▉ | 5374/18688 [6:18:11<16:03:41, 4.34s/it] 29%|██▉ | 5375/18688 [6:18:15<15:03:58, 4.07s/it] 29%|██▉ | 5376/18688 [6:18:19<15:25:58, 4.17s/it] 29%|██▉ | 5377/18688 [6:18:25<16:56:06, 4.58s/it] 29%|██▉ | 5378/18688 [6:18:29<16:49:36, 4.55s/it] 29%|██▉ | 5379/18688 [6:18:34<16:42:44, 4.52s/it] 29%|██▉ | 5380/18688 [6:18:38<16:15:30, 4.40s/it] {'loss': '0.9843', 'grad_norm': '1.366', 'learning_rate': '7.036e-05', 'epoch': '0.2879', 'num_input_tokens_seen': 18081032, 'train_runtime': '2.272e+04', 'train_tokens_per_second': '795.9'}
29%|██▉ | 5380/18688 [6:18:38<16:15:30, 4.40s/it] 29%|██▉ | 5381/18688 [6:18:42<16:31:34, 4.47s/it] 29%|██▉ | 5382/18688 [6:18:47<16:14:28, 4.39s/it] 29%|██▉ | 5383/18688 [6:18:51<15:46:25, 4.27s/it] 29%|██▉ | 5384/18688 [6:18:55<16:16:18, 4.40s/it] 29%|██▉ | 5385/18688 [6:18:59<15:44:14, 4.26s/it] 29%|██▉ | 5386/18688 [6:19:04<16:46:08, 4.54s/it] 29%|██▉ | 5387/18688 [6:19:09<16:49:15, 4.55s/it] 29%|██▉ | 5388/18688 [6:19:13<16:26:42, 4.45s/it] 29%|██▉ | 5389/18688 [6:19:17<16:07:15, 4.36s/it] 29%|██▉ | 5390/18688 [6:19:22<16:30:02, 4.47s/it] {'loss': '0.9012', 'grad_norm': '0.9042', 'learning_rate': '7.031e-05', 'epoch': '0.2884', 'num_input_tokens_seen': 18118448, 'train_runtime': '2.276e+04', 'train_tokens_per_second': '796'}
29%|██▉ | 5390/18688 [6:19:22<16:30:02, 4.47s/it] 29%|██▉ | 5391/18688 [6:19:26<15:22:16, 4.16s/it] 29%|██▉ | 5392/18688 [6:19:30<15:29:33, 4.19s/it] 29%|██▉ | 5393/18688 [6:19:34<15:16:43, 4.14s/it] 29%|██▉ | 5394/18688 [6:19:38<15:11:38, 4.11s/it] 29%|██▉ | 5395/18688 [6:19:41<14:30:06, 3.93s/it] 29%|██▉ | 5396/18688 [6:19:46<14:56:18, 4.05s/it] 29%|██▉ | 5397/18688 [6:19:50<15:13:33, 4.12s/it] 29%|██▉ | 5398/18688 [6:19:54<15:01:14, 4.07s/it] 29%|██▉ | 5399/18688 [6:19:59<15:54:47, 4.31s/it] 29%|██▉ | 5400/18688 [6:20:03<16:16:24, 4.41s/it] {'loss': '0.9228', 'grad_norm': '1.119', 'learning_rate': '7.027e-05', 'epoch': '0.289', 'num_input_tokens_seen': 18152576, 'train_runtime': '2.28e+04', 'train_tokens_per_second': '796'}
29%|██▉ | 5400/18688 [6:20:03<16:16:24, 4.41s/it] 29%|██▉ | 5401/18688 [6:20:07<15:27:30, 4.19s/it] 29%|██▉ | 5402/18688 [6:20:11<15:32:02, 4.21s/it] 29%|██▉ | 5403/18688 [6:20:17<16:58:46, 4.60s/it] 29%|██▉ | 5404/18688 [6:20:22<17:14:22, 4.67s/it] 29%|██▉ | 5405/18688 [6:20:26<16:40:49, 4.52s/it] 29%|██▉ | 5406/18688 [6:20:31<17:21:17, 4.70s/it] 29%|██▉ | 5407/18688 [6:20:35<16:02:09, 4.35s/it] 29%|██▉ | 5408/18688 [6:20:38<15:26:29, 4.19s/it] 29%|██▉ | 5409/18688 [6:20:43<15:40:29, 4.25s/it] 29%|██▉ | 5410/18688 [6:20:47<15:21:43, 4.17s/it] {'loss': '0.9375', 'grad_norm': '1.332', 'learning_rate': '7.022e-05', 'epoch': '0.2895', 'num_input_tokens_seen': 18188768, 'train_runtime': '2.285e+04', 'train_tokens_per_second': '796.1'}
29%|██▉ | 5410/18688 [6:20:47<15:21:43, 4.17s/it] 29%|██▉ | 5411/18688 [6:20:51<15:47:29, 4.28s/it] 29%|██▉ | 5412/18688 [6:20:56<15:55:23, 4.32s/it] 29%|██▉ | 5413/18688 [6:21:00<15:55:10, 4.32s/it] 29%|██▉ | 5414/18688 [6:21:04<15:52:34, 4.31s/it] 29%|██▉ | 5415/18688 [6:21:08<15:07:09, 4.10s/it] 29%|██▉ | 5416/18688 [6:21:11<14:27:10, 3.92s/it] 29%|██▉ | 5417/18688 [6:21:15<14:28:22, 3.93s/it] 29%|██▉ | 5418/18688 [6:21:20<14:48:32, 4.02s/it] 29%|██▉ | 5419/18688 [6:21:23<14:22:43, 3.90s/it] 29%|██▉ | 5420/18688 [6:21:28<15:51:28, 4.30s/it] {'loss': '0.944', 'grad_norm': '1.058', 'learning_rate': '7.017e-05', 'epoch': '0.29', 'num_input_tokens_seen': 18221832, 'train_runtime': '2.289e+04', 'train_tokens_per_second': '796.1'}
29%|██▉ | 5420/18688 [6:21:28<15:51:28, 4.30s/it] 29%|██▉ | 5421/18688 [6:21:33<15:57:55, 4.33s/it] 29%|██▉ | 5422/18688 [6:21:37<15:27:57, 4.20s/it] 29%|██▉ | 5423/18688 [6:21:40<14:47:05, 4.01s/it] 29%|██▉ | 5424/18688 [6:21:44<14:36:12, 3.96s/it] 29%|██▉ | 5425/18688 [6:21:48<14:43:06, 4.00s/it] 29%|██▉ | 5426/18688 [6:21:53<15:13:45, 4.13s/it] 29%|██▉ | 5427/18688 [6:21:57<15:26:54, 4.19s/it] 29%|██▉ | 5428/18688 [6:22:01<14:59:37, 4.07s/it] 29%|██▉ | 5429/18688 [6:22:05<15:20:05, 4.16s/it] 29%|██▉ | 5430/18688 [6:22:09<15:14:57, 4.14s/it] {'loss': '0.9307', 'grad_norm': '1.314', 'learning_rate': '7.012e-05', 'epoch': '0.2906', 'num_input_tokens_seen': 18254664, 'train_runtime': '2.293e+04', 'train_tokens_per_second': '796.1'}
29%|██▉ | 5430/18688 [6:22:09<15:14:57, 4.14s/it] 29%|██▉ | 5431/18688 [6:22:13<14:36:31, 3.97s/it] 29%|██▉ | 5432/18688 [6:22:17<14:22:57, 3.91s/it] 29%|██▉ | 5433/18688 [6:22:21<14:48:23, 4.02s/it] 29%|██▉ | 5434/18688 [6:22:25<15:05:59, 4.10s/it] 29%|██▉ | 5435/18688 [6:22:29<14:25:44, 3.92s/it] 29%|██▉ | 5436/18688 [6:22:32<14:11:41, 3.86s/it] 29%|██▉ | 5437/18688 [6:22:37<14:39:40, 3.98s/it] 29%|██▉ | 5438/18688 [6:22:41<14:53:59, 4.05s/it] 29%|██▉ | 5439/18688 [6:22:45<14:54:57, 4.05s/it] 29%|██▉ | 5440/18688 [6:22:49<14:52:40, 4.04s/it] {'loss': '0.9215', 'grad_norm': '1.303', 'learning_rate': '7.007e-05', 'epoch': '0.2911', 'num_input_tokens_seen': 18284960, 'train_runtime': '2.297e+04', 'train_tokens_per_second': '796.1'}
29%|██▉ | 5440/18688 [6:22:49<14:52:40, 4.04s/it] 29%|██▉ | 5441/18688 [6:22:53<14:29:31, 3.94s/it] 29%|██▉ | 5442/18688 [6:22:56<13:58:08, 3.80s/it] 29%|██▉ | 5443/18688 [6:23:00<14:03:00, 3.82s/it] 29%|██▉ | 5444/18688 [6:23:04<14:44:52, 4.01s/it] 29%|██▉ | 5445/18688 [6:23:08<14:44:11, 4.01s/it] 29%|██▉ | 5446/18688 [6:23:13<15:02:49, 4.09s/it] 29%|██▉ | 5447/18688 [6:23:16<14:24:19, 3.92s/it] 29%|██▉ | 5448/18688 [6:23:20<13:58:07, 3.80s/it] 29%|██▉ | 5449/18688 [6:23:24<14:09:41, 3.85s/it] 29%|██▉ | 5450/18688 [6:23:28<14:09:36, 3.85s/it] {'loss': '0.8787', 'grad_norm': '1.117', 'learning_rate': '7.002e-05', 'epoch': '0.2916', 'num_input_tokens_seen': 18315640, 'train_runtime': '2.301e+04', 'train_tokens_per_second': '796.1'}
29%|██▉ | 5450/18688 [6:23:28<14:09:36, 3.85s/it] 29%|██▉ | 5451/18688 [6:23:31<13:51:40, 3.77s/it] 29%|██▉ | 5452/18688 [6:23:35<14:20:36, 3.90s/it] 29%|██▉ | 5453/18688 [6:23:39<14:06:03, 3.84s/it] 29%|██▉ | 5454/18688 [6:23:43<14:20:04, 3.90s/it] 29%|██▉ | 5455/18688 [6:23:47<14:22:09, 3.91s/it] 29%|██▉ | 5456/18688 [6:23:51<14:36:07, 3.97s/it] 29%|██▉ | 5457/18688 [6:23:56<16:03:40, 4.37s/it] 29%|██▉ | 5458/18688 [6:24:00<15:20:58, 4.18s/it] 29%|██▉ | 5459/18688 [6:24:04<14:51:58, 4.05s/it] 29%|██▉ | 5460/18688 [6:24:08<14:31:15, 3.95s/it] {'loss': '0.8981', 'grad_norm': '1.421', 'learning_rate': '6.998e-05', 'epoch': '0.2922', 'num_input_tokens_seen': 18346304, 'train_runtime': '2.305e+04', 'train_tokens_per_second': '796'}
29%|██▉ | 5460/18688 [6:24:08<14:31:15, 3.95s/it] 29%|██▉ | 5461/18688 [6:24:11<14:21:39, 3.91s/it] 29%|██▉ | 5462/18688 [6:24:16<15:10:29, 4.13s/it] 29%|██▉ | 5463/18688 [6:24:20<14:58:11, 4.07s/it] 29%|██▉ | 5464/18688 [6:24:25<15:25:56, 4.20s/it] 29%|██▉ | 5465/18688 [6:24:29<15:23:02, 4.19s/it] 29%|██▉ | 5466/18688 [6:24:33<15:39:53, 4.27s/it] 29%|██▉ | 5467/18688 [6:24:37<14:57:19, 4.07s/it] 29%|██▉ | 5468/18688 [6:24:40<14:36:29, 3.98s/it] 29%|██▉ | 5469/18688 [6:24:46<16:06:52, 4.39s/it] 29%|██▉ | 5470/18688 [6:24:50<15:19:42, 4.17s/it] {'loss': '0.9195', 'grad_norm': '1.172', 'learning_rate': '6.993e-05', 'epoch': '0.2927', 'num_input_tokens_seen': 18380496, 'train_runtime': '2.309e+04', 'train_tokens_per_second': '796'}
29%|██▉ | 5470/18688 [6:24:50<15:19:42, 4.17s/it] 29%|██▉ | 5471/18688 [6:24:54<15:35:46, 4.25s/it] 29%|██▉ | 5472/18688 [6:24:58<15:30:41, 4.23s/it] 29%|██▉ | 5473/18688 [6:25:02<14:57:00, 4.07s/it] 29%|██▉ | 5474/18688 [6:25:06<15:02:49, 4.10s/it] 29%|██▉ | 5475/18688 [6:25:10<14:34:29, 3.97s/it] 29%|██▉ | 5476/18688 [6:25:15<15:34:17, 4.24s/it] 29%|██▉ | 5477/18688 [6:25:20<16:32:30, 4.51s/it] 29%|██▉ | 5478/18688 [6:25:24<16:04:47, 4.38s/it] 29%|██▉ | 5479/18688 [6:25:28<16:22:05, 4.46s/it] 29%|██▉ | 5480/18688 [6:25:32<15:22:22, 4.19s/it] {'loss': '0.9214', 'grad_norm': '0.9897', 'learning_rate': '6.988e-05', 'epoch': '0.2932', 'num_input_tokens_seen': 18415344, 'train_runtime': '2.313e+04', 'train_tokens_per_second': '796.1'}
29%|██▉ | 5480/18688 [6:25:32<15:22:22, 4.19s/it] 29%|██▉ | 5481/18688 [6:25:36<14:59:25, 4.09s/it] 29%|██▉ | 5482/18688 [6:25:39<14:32:43, 3.97s/it] 29%|██▉ | 5483/18688 [6:25:43<14:10:16, 3.86s/it] 29%|██▉ | 5484/18688 [6:25:48<15:12:01, 4.14s/it] 29%|██▉ | 5485/18688 [6:25:52<15:10:52, 4.14s/it] 29%|██▉ | 5486/18688 [6:25:56<15:11:48, 4.14s/it] 29%|██▉ | 5487/18688 [6:26:00<14:45:45, 4.03s/it] 29%|██▉ | 5488/18688 [6:26:04<14:34:25, 3.97s/it] 29%|██▉ | 5489/18688 [6:26:07<14:13:31, 3.88s/it] 29%|██▉ | 5490/18688 [6:26:11<14:03:57, 3.84s/it] {'loss': '0.9936', 'grad_norm': '1.132', 'learning_rate': '6.983e-05', 'epoch': '0.2938', 'num_input_tokens_seen': 18445968, 'train_runtime': '2.317e+04', 'train_tokens_per_second': '796.1'}
29%|██▉ | 5490/18688 [6:26:11<14:03:57, 3.84s/it] 29%|██▉ | 5491/18688 [6:26:16<14:48:10, 4.04s/it] 29%|██▉ | 5492/18688 [6:26:20<15:09:56, 4.14s/it] 29%|██▉ | 5493/18688 [6:26:25<16:00:04, 4.37s/it] 29%|██▉ | 5494/18688 [6:26:29<16:11:02, 4.42s/it] 29%|██▉ | 5495/18688 [6:26:33<15:22:58, 4.20s/it] 29%|██▉ | 5496/18688 [6:26:37<14:33:22, 3.97s/it] 29%|██▉ | 5497/18688 [6:26:40<14:06:06, 3.85s/it] 29%|██▉ | 5498/18688 [6:26:45<14:57:27, 4.08s/it] 29%|██▉ | 5499/18688 [6:26:49<15:14:20, 4.16s/it] 29%|██▉ | 5500/18688 [6:26:53<14:42:57, 4.02s/it] {'loss': '0.9426', 'grad_norm': '1.262', 'learning_rate': '6.978e-05', 'epoch': '0.2943', 'num_input_tokens_seen': 18479888, 'train_runtime': '2.321e+04', 'train_tokens_per_second': '796.1'}
29%|██▉ | 5500/18688 [6:26:53<14:42:57, 4.02s/it] 29%|██▉ | 5501/18688 [6:26:57<14:48:29, 4.04s/it] 29%|██▉ | 5502/18688 [6:27:00<14:08:17, 3.86s/it] 29%|██▉ | 5503/18688 [6:27:05<14:50:09, 4.05s/it] 29%|██▉ | 5504/18688 [6:27:10<15:55:24, 4.35s/it] 29%|██▉ | 5505/18688 [6:27:13<14:57:26, 4.08s/it] 29%|██▉ | 5506/18688 [6:27:17<14:59:22, 4.09s/it] 29%|██▉ | 5507/18688 [6:27:22<15:10:37, 4.15s/it] 29%|██▉ | 5508/18688 [6:27:25<14:35:47, 3.99s/it] 29%|██▉ | 5509/18688 [6:27:30<15:18:20, 4.18s/it] 29%|██▉ | 5510/18688 [6:27:35<15:52:19, 4.34s/it] {'loss': '0.9554', 'grad_norm': '1.173', 'learning_rate': '6.973e-05', 'epoch': '0.2948', 'num_input_tokens_seen': 18515552, 'train_runtime': '2.326e+04', 'train_tokens_per_second': '796.2'}
29%|██▉ | 5510/18688 [6:27:35<15:52:19, 4.34s/it] 29%|██▉ | 5511/18688 [6:27:39<15:34:51, 4.26s/it] 29%|██▉ | 5512/18688 [6:27:43<15:23:59, 4.21s/it] 30%|██▉ | 5513/18688 [6:27:48<16:03:57, 4.39s/it] 30%|██▉ | 5514/18688 [6:27:51<15:17:09, 4.18s/it] 30%|██▉ | 5515/18688 [6:27:55<14:50:31, 4.06s/it] 30%|██▉ | 5516/18688 [6:27:59<14:10:11, 3.87s/it] 30%|██▉ | 5517/18688 [6:28:03<14:24:50, 3.94s/it] 30%|██▉ | 5518/18688 [6:28:06<14:06:33, 3.86s/it] 30%|██▉ | 5519/18688 [6:28:10<13:51:12, 3.79s/it] 30%|██▉ | 5520/18688 [6:28:14<14:07:41, 3.86s/it] {'loss': '0.8725', 'grad_norm': '1.21', 'learning_rate': '6.968e-05', 'epoch': '0.2954', 'num_input_tokens_seen': 18546288, 'train_runtime': '2.329e+04', 'train_tokens_per_second': '796.2'}
30%|██▉ | 5520/18688 [6:28:14<14:07:41, 3.86s/it] 30%|██▉ | 5521/18688 [6:28:18<13:45:03, 3.76s/it] 30%|██▉ | 5522/18688 [6:28:21<13:25:11, 3.67s/it] 30%|██▉ | 5523/18688 [6:28:25<13:19:02, 3.64s/it] 30%|██▉ | 5524/18688 [6:28:30<14:48:29, 4.05s/it] 30%|██▉ | 5525/18688 [6:28:34<14:59:43, 4.10s/it] 30%|██▉ | 5526/18688 [6:28:38<15:23:55, 4.21s/it] 30%|██▉ | 5527/18688 [6:28:43<15:40:40, 4.29s/it] 30%|██▉ | 5528/18688 [6:28:46<14:46:04, 4.04s/it] 30%|██▉ | 5529/18688 [6:28:50<14:32:27, 3.98s/it] 30%|██▉ | 5530/18688 [6:28:53<13:59:22, 3.83s/it] {'loss': '0.904', 'grad_norm': '1.17', 'learning_rate': '6.964e-05', 'epoch': '0.2959', 'num_input_tokens_seen': 18577384, 'train_runtime': '2.333e+04', 'train_tokens_per_second': '796.2'}
30%|██▉ | 5530/18688 [6:28:53<13:59:22, 3.83s/it] 30%|██▉ | 5531/18688 [6:28:57<14:07:32, 3.87s/it] 30%|██▉ | 5532/18688 [6:29:01<13:53:16, 3.80s/it] 30%|██▉ | 5533/18688 [6:29:05<14:28:09, 3.96s/it] 30%|██▉ | 5534/18688 [6:29:09<14:10:39, 3.88s/it] 30%|██▉ | 5535/18688 [6:29:13<13:45:12, 3.76s/it] 30%|██▉ | 5536/18688 [6:29:17<14:00:17, 3.83s/it] 30%|██▉ | 5537/18688 [6:29:21<14:21:33, 3.93s/it] 30%|██▉ | 5538/18688 [6:29:25<14:36:17, 4.00s/it] 30%|██▉ | 5539/18688 [6:29:30<15:26:06, 4.23s/it] 30%|██▉ | 5540/18688 [6:29:35<16:31:23, 4.52s/it] {'loss': '0.9182', 'grad_norm': '1.243', 'learning_rate': '6.959e-05', 'epoch': '0.2965', 'num_input_tokens_seen': 18611152, 'train_runtime': '2.338e+04', 'train_tokens_per_second': '796.2'}
30%|██▉ | 5540/18688 [6:29:35<16:31:23, 4.52s/it] 30%|██▉ | 5541/18688 [6:29:40<16:57:55, 4.65s/it] 30%|██▉ | 5542/18688 [6:29:44<16:09:38, 4.43s/it] 30%|██▉ | 5543/18688 [6:29:48<15:39:25, 4.29s/it] 30%|██▉ | 5544/18688 [6:29:52<15:25:08, 4.22s/it] 30%|██▉ | 5545/18688 [6:29:57<16:28:34, 4.51s/it] 30%|██▉ | 5546/18688 [6:30:01<16:14:18, 4.45s/it] 30%|██▉ | 5547/18688 [6:30:05<15:27:23, 4.23s/it] 30%|██▉ | 5548/18688 [6:30:10<15:56:35, 4.37s/it] 30%|██▉ | 5549/18688 [6:30:15<16:33:29, 4.54s/it] 30%|██▉ | 5550/18688 [6:30:19<16:01:52, 4.39s/it] {'loss': '0.9753', 'grad_norm': '1.073', 'learning_rate': '6.954e-05', 'epoch': '0.297', 'num_input_tokens_seen': 18648848, 'train_runtime': '2.342e+04', 'train_tokens_per_second': '796.3'}
30%|██▉ | 5550/18688 [6:30:19<16:01:52, 4.39s/it] 30%|██▉ | 5551/18688 [6:30:23<15:44:10, 4.31s/it] 30%|██▉ | 5552/18688 [6:30:27<15:04:40, 4.13s/it] 30%|██▉ | 5553/18688 [6:30:31<15:14:29, 4.18s/it] 30%|██▉ | 5554/18688 [6:30:35<14:51:38, 4.07s/it] 30%|██▉ | 5555/18688 [6:30:38<14:30:10, 3.98s/it] 30%|██▉ | 5556/18688 [6:30:42<14:34:07, 3.99s/it] 30%|██▉ | 5557/18688 [6:30:46<14:27:15, 3.96s/it] 30%|██▉ | 5558/18688 [6:30:50<14:07:27, 3.87s/it] 30%|██▉ | 5559/18688 [6:30:53<13:44:51, 3.77s/it] 30%|██▉ | 5560/18688 [6:30:58<14:07:29, 3.87s/it] {'loss': '0.8693', 'grad_norm': '1.017', 'learning_rate': '6.949e-05', 'epoch': '0.2975', 'num_input_tokens_seen': 18679568, 'train_runtime': '2.346e+04', 'train_tokens_per_second': '796.3'}
30%|██▉ | 5560/18688 [6:30:58<14:07:29, 3.87s/it] 30%|██▉ | 5561/18688 [6:31:01<13:48:37, 3.79s/it] 30%|██▉ | 5562/18688 [6:31:06<15:11:18, 4.17s/it] 30%|██▉ | 5563/18688 [6:31:11<15:24:29, 4.23s/it] 30%|██▉ | 5564/18688 [6:31:16<16:13:52, 4.45s/it] 30%|██▉ | 5565/18688 [6:31:20<16:03:58, 4.41s/it] 30%|██▉ | 5566/18688 [6:31:24<16:02:41, 4.40s/it] 30%|██▉ | 5567/18688 [6:31:28<15:44:05, 4.32s/it] 30%|██▉ | 5568/18688 [6:31:33<15:38:01, 4.29s/it] 30%|██▉ | 5569/18688 [6:31:36<15:08:07, 4.15s/it] 30%|██▉ | 5570/18688 [6:31:40<14:47:53, 4.06s/it] {'loss': '0.9457', 'grad_norm': '1.18', 'learning_rate': '6.944e-05', 'epoch': '0.2981', 'num_input_tokens_seen': 18714848, 'train_runtime': '2.35e+04', 'train_tokens_per_second': '796.3'}
30%|██▉ | 5570/18688 [6:31:40<14:47:53, 4.06s/it] 30%|██▉ | 5571/18688 [6:31:44<14:18:15, 3.93s/it] 30%|██▉ | 5572/18688 [6:31:48<14:26:01, 3.96s/it] 30%|██▉ | 5573/18688 [6:31:52<13:59:53, 3.84s/it] 30%|██▉ | 5574/18688 [6:31:57<15:29:00, 4.25s/it] 30%|██▉ | 5575/18688 [6:32:00<14:57:05, 4.10s/it] 30%|██▉ | 5576/18688 [6:32:04<14:32:58, 3.99s/it] 30%|██▉ | 5577/18688 [6:32:09<15:14:51, 4.19s/it] 30%|██▉ | 5578/18688 [6:32:13<15:42:32, 4.31s/it] 30%|██▉ | 5579/18688 [6:32:17<14:51:14, 4.08s/it] 30%|██▉ | 5580/18688 [6:32:21<14:32:27, 3.99s/it] {'loss': '0.9346', 'grad_norm': '1.167', 'learning_rate': '6.939e-05', 'epoch': '0.2986', 'num_input_tokens_seen': 18747136, 'train_runtime': '2.354e+04', 'train_tokens_per_second': '796.4'}
30%|██▉ | 5580/18688 [6:32:21<14:32:27, 3.99s/it] 30%|██▉ | 5581/18688 [6:32:25<14:23:10, 3.95s/it] 30%|██▉ | 5582/18688 [6:32:29<14:43:36, 4.05s/it] 30%|██▉ | 5583/18688 [6:32:32<14:08:53, 3.89s/it] 30%|██▉ | 5584/18688 [6:32:37<14:22:14, 3.95s/it] 30%|██▉ | 5585/18688 [6:32:40<14:05:37, 3.87s/it] 30%|██▉ | 5586/18688 [6:32:44<14:26:46, 3.97s/it] 30%|██▉ | 5587/18688 [6:32:48<13:54:37, 3.82s/it] 30%|██▉ | 5588/18688 [6:32:52<13:53:13, 3.82s/it] 30%|██▉ | 5589/18688 [6:32:56<14:54:47, 4.10s/it] 30%|██▉ | 5590/18688 [6:33:01<14:57:18, 4.11s/it] {'loss': '0.894', 'grad_norm': '0.9274', 'learning_rate': '6.934e-05', 'epoch': '0.2991', 'num_input_tokens_seen': 18779008, 'train_runtime': '2.358e+04', 'train_tokens_per_second': '796.4'}
30%|██▉ | 5590/18688 [6:33:01<14:57:18, 4.11s/it] 30%|██▉ | 5591/18688 [6:33:05<15:07:39, 4.16s/it] 30%|██▉ | 5592/18688 [6:33:08<14:32:47, 4.00s/it] 30%|██▉ | 5593/18688 [6:33:12<14:23:37, 3.96s/it] 30%|██▉ | 5594/18688 [6:33:16<14:20:39, 3.94s/it] 30%|██▉ | 5595/18688 [6:33:20<13:53:36, 3.82s/it] 30%|██▉ | 5596/18688 [6:33:24<14:29:06, 3.98s/it] 30%|██▉ | 5597/18688 [6:33:28<14:04:31, 3.87s/it] 30%|██▉ | 5598/18688 [6:33:32<13:57:33, 3.84s/it] 30%|██▉ | 5599/18688 [6:33:36<14:22:49, 3.96s/it] 30%|██▉ | 5600/18688 [6:33:40<14:25:44, 3.97s/it] {'loss': '0.8829', 'grad_norm': '1.432', 'learning_rate': '6.929e-05', 'epoch': '0.2997', 'num_input_tokens_seen': 18810528, 'train_runtime': '2.362e+04', 'train_tokens_per_second': '796.4'}
30%|██▉ | 5600/18688 [6:33:40<14:25:44, 3.97s/it] 30%|██▉ | 5601/18688 [6:33:44<14:42:19, 4.05s/it] 30%|██▉ | 5602/18688 [6:33:48<15:03:31, 4.14s/it] 30%|██▉ | 5603/18688 [6:33:53<15:46:53, 4.34s/it] 30%|██▉ | 5604/18688 [6:33:57<15:10:56, 4.18s/it] 30%|██▉ | 5605/18688 [6:34:01<14:47:42, 4.07s/it] 30%|██▉ | 5606/18688 [6:34:04<14:06:09, 3.88s/it] 30%|███ | 5607/18688 [6:34:08<13:59:49, 3.85s/it] 30%|███ | 5608/18688 [6:34:12<14:13:12, 3.91s/it] 30%|███ | 5609/18688 [6:34:16<13:44:57, 3.78s/it] 30%|███ | 5610/18688 [6:34:19<13:30:10, 3.72s/it] {'loss': '0.8809', 'grad_norm': '1.186', 'learning_rate': '6.924e-05', 'epoch': '0.3002', 'num_input_tokens_seen': 18841392, 'train_runtime': '2.366e+04', 'train_tokens_per_second': '796.4'}
30%|███ | 5610/18688 [6:34:19<13:30:10, 3.72s/it] 30%|███ | 5611/18688 [6:34:23<14:07:39, 3.89s/it] 30%|███ | 5612/18688 [6:34:28<14:32:16, 4.00s/it] 30%|███ | 5613/18688 [6:34:31<14:05:20, 3.88s/it] 30%|███ | 5614/18688 [6:34:35<13:59:02, 3.85s/it] 30%|███ | 5615/18688 [6:34:39<14:03:56, 3.87s/it] 30%|███ | 5616/18688 [6:34:43<13:54:34, 3.83s/it] 30%|███ | 5617/18688 [6:34:46<13:41:53, 3.77s/it] 30%|███ | 5618/18688 [6:34:51<14:09:47, 3.90s/it] 30%|███ | 5619/18688 [6:34:55<14:35:56, 4.02s/it] 30%|███ | 5620/18688 [6:35:00<15:39:38, 4.31s/it] {'loss': '0.9122', 'grad_norm': '1.018', 'learning_rate': '6.919e-05', 'epoch': '0.3007', 'num_input_tokens_seen': 18874376, 'train_runtime': '2.37e+04', 'train_tokens_per_second': '796.4'}
30%|███ | 5620/18688 [6:35:00<15:39:38, 4.31s/it] 30%|███ | 5621/18688 [6:35:04<15:09:51, 4.18s/it] 30%|███ | 5622/18688 [6:35:08<14:49:29, 4.08s/it] 30%|███ | 5623/18688 [6:35:12<15:08:20, 4.17s/it] 30%|███ | 5624/18688 [6:35:17<15:46:55, 4.35s/it] 30%|███ | 5625/18688 [6:35:21<16:05:59, 4.44s/it] 30%|███ | 5626/18688 [6:35:26<15:52:11, 4.37s/it] 30%|███ | 5627/18688 [6:35:29<15:12:24, 4.19s/it] 30%|███ | 5628/18688 [6:35:33<14:51:40, 4.10s/it] 30%|███ | 5629/18688 [6:35:38<15:11:46, 4.19s/it] 30%|███ | 5630/18688 [6:35:42<14:59:52, 4.13s/it] {'loss': '0.9796', 'grad_norm': '0.9459', 'learning_rate': '6.914e-05', 'epoch': '0.3013', 'num_input_tokens_seen': 18908680, 'train_runtime': '2.374e+04', 'train_tokens_per_second': '796.4'}
30%|███ | 5630/18688 [6:35:42<14:59:52, 4.13s/it] 30%|███ | 5631/18688 [6:35:46<15:23:48, 4.25s/it] 30%|███ | 5632/18688 [6:35:50<15:05:45, 4.16s/it] 30%|███ | 5633/18688 [6:35:55<15:36:07, 4.30s/it] 30%|███ | 5634/18688 [6:35:59<15:45:07, 4.34s/it] 30%|███ | 5635/18688 [6:36:03<15:41:07, 4.33s/it] 30%|███ | 5636/18688 [6:36:08<16:12:55, 4.47s/it] 30%|███ | 5637/18688 [6:36:12<15:54:22, 4.39s/it] 30%|███ | 5638/18688 [6:36:16<15:11:43, 4.19s/it] 30%|███ | 5639/18688 [6:36:20<14:35:10, 4.02s/it] 30%|███ | 5640/18688 [6:36:23<14:05:17, 3.89s/it] {'loss': '0.9622', 'grad_norm': '1.512', 'learning_rate': '6.909e-05', 'epoch': '0.3018', 'num_input_tokens_seen': 18941968, 'train_runtime': '2.378e+04', 'train_tokens_per_second': '796.4'}
30%|███ | 5640/18688 [6:36:23<14:05:17, 3.89s/it] 30%|███ | 5641/18688 [6:36:28<15:02:13, 4.15s/it] 30%|███ | 5642/18688 [6:36:32<14:24:41, 3.98s/it] 30%|███ | 5643/18688 [6:36:35<14:11:36, 3.92s/it] 30%|███ | 5644/18688 [6:36:40<14:17:58, 3.95s/it] 30%|███ | 5645/18688 [6:36:43<13:46:53, 3.80s/it] 30%|███ | 5646/18688 [6:36:47<14:12:49, 3.92s/it] 30%|███ | 5647/18688 [6:36:51<14:05:12, 3.89s/it] 30%|███ | 5648/18688 [6:36:56<14:54:45, 4.12s/it] 30%|███ | 5649/18688 [6:37:00<15:26:22, 4.26s/it] 30%|███ | 5650/18688 [6:37:04<14:46:55, 4.08s/it] {'loss': '0.9633', 'grad_norm': '1.241', 'learning_rate': '6.904e-05', 'epoch': '0.3023', 'num_input_tokens_seen': 18974096, 'train_runtime': '2.382e+04', 'train_tokens_per_second': '796.4'}
30%|███ | 5650/18688 [6:37:04<14:46:55, 4.08s/it] 30%|███ | 5651/18688 [6:37:08<14:25:21, 3.98s/it] 30%|███ | 5652/18688 [6:37:12<14:55:39, 4.12s/it] 30%|███ | 5653/18688 [6:37:16<14:47:14, 4.08s/it] 30%|███ | 5654/18688 [6:37:21<15:42:40, 4.34s/it] 30%|███ | 5655/18688 [6:37:25<15:19:02, 4.23s/it] 30%|███ | 5656/18688 [6:37:29<14:47:17, 4.09s/it] 30%|███ | 5657/18688 [6:37:32<14:25:13, 3.98s/it] 30%|███ | 5658/18688 [6:37:37<14:26:48, 3.99s/it] 30%|███ | 5659/18688 [6:37:40<14:17:40, 3.95s/it] 30%|███ | 5660/18688 [6:37:44<14:12:10, 3.92s/it] {'loss': '0.883', 'grad_norm': '1.082', 'learning_rate': '6.899e-05', 'epoch': '0.3029', 'num_input_tokens_seen': 19005576, 'train_runtime': '2.386e+04', 'train_tokens_per_second': '796.4'}
30%|███ | 5660/18688 [6:37:44<14:12:10, 3.92s/it] 30%|███ | 5661/18688 [6:37:49<14:36:27, 4.04s/it] 30%|███ | 5662/18688 [6:37:52<14:30:12, 4.01s/it] 30%|███ | 5663/18688 [6:37:58<15:39:54, 4.33s/it] 30%|███ | 5664/18688 [6:38:02<15:52:59, 4.39s/it] 30%|███ | 5665/18688 [6:38:06<15:07:08, 4.18s/it] 30%|███ | 5666/18688 [6:38:09<14:20:37, 3.97s/it] 30%|███ | 5667/18688 [6:38:13<14:11:01, 3.92s/it] 30%|███ | 5668/18688 [6:38:18<15:30:43, 4.29s/it] 30%|███ | 5669/18688 [6:38:23<15:46:41, 4.36s/it] 30%|███ | 5670/18688 [6:38:26<14:46:46, 4.09s/it] {'loss': '0.9215', 'grad_norm': '1.215', 'learning_rate': '6.894e-05', 'epoch': '0.3034', 'num_input_tokens_seen': 19039616, 'train_runtime': '2.391e+04', 'train_tokens_per_second': '796.4'}
30%|███ | 5670/18688 [6:38:26<14:46:46, 4.09s/it] 30%|███ | 5671/18688 [6:38:30<14:38:45, 4.05s/it] 30%|███ | 5672/18688 [6:38:34<14:04:10, 3.89s/it] 30%|███ | 5673/18688 [6:38:38<14:32:40, 4.02s/it] 30%|███ | 5674/18688 [6:38:42<14:33:09, 4.03s/it] 30%|███ | 5675/18688 [6:38:45<13:56:18, 3.86s/it] 30%|███ | 5676/18688 [6:38:49<13:55:02, 3.85s/it] 30%|███ | 5677/18688 [6:38:53<13:26:16, 3.72s/it] 30%|███ | 5678/18688 [6:38:57<13:53:19, 3.84s/it] 30%|███ | 5679/18688 [6:39:00<13:37:16, 3.77s/it] 30%|███ | 5680/18688 [6:39:04<13:24:26, 3.71s/it] {'loss': '0.8881', 'grad_norm': '1.228', 'learning_rate': '6.889e-05', 'epoch': '0.3039', 'num_input_tokens_seen': 19069448, 'train_runtime': '2.394e+04', 'train_tokens_per_second': '796.4'}
30%|███ | 5680/18688 [6:39:04<13:24:26, 3.71s/it] 30%|███ | 5681/18688 [6:39:08<13:29:17, 3.73s/it] 30%|███ | 5682/18688 [6:39:12<13:37:52, 3.77s/it] 30%|███ | 5683/18688 [6:39:16<13:52:25, 3.84s/it] 30%|███ | 5684/18688 [6:39:20<14:09:22, 3.92s/it] 30%|███ | 5685/18688 [6:39:25<15:26:09, 4.27s/it] 30%|███ | 5686/18688 [6:39:29<15:33:15, 4.31s/it] 30%|███ | 5687/18688 [6:39:33<14:56:40, 4.14s/it] 30%|███ | 5688/18688 [6:39:38<15:20:00, 4.25s/it] 30%|███ | 5689/18688 [6:39:41<14:50:25, 4.11s/it] 30%|███ | 5690/18688 [6:39:46<15:08:30, 4.19s/it] {'loss': '0.9736', 'grad_norm': '1.28', 'learning_rate': '6.884e-05', 'epoch': '0.3045', 'num_input_tokens_seen': 19103552, 'train_runtime': '2.399e+04', 'train_tokens_per_second': '796.4'}
30%|███ | 5690/18688 [6:39:46<15:08:30, 4.19s/it] 30%|███ | 5691/18688 [6:39:49<14:26:42, 4.00s/it] 30%|███ | 5692/18688 [6:39:54<15:00:04, 4.16s/it] 30%|███ | 5693/18688 [6:39:58<15:12:37, 4.21s/it] 30%|███ | 5694/18688 [6:40:04<17:07:02, 4.74s/it] 30%|███ | 5695/18688 [6:40:08<16:23:59, 4.54s/it] 30%|███ | 5696/18688 [6:40:12<15:52:41, 4.40s/it] 30%|███ | 5697/18688 [6:40:17<16:23:26, 4.54s/it] 30%|███ | 5698/18688 [6:40:21<15:20:36, 4.25s/it] 30%|███ | 5699/18688 [6:40:25<15:31:45, 4.30s/it] 31%|███ | 5700/18688 [6:40:29<14:53:01, 4.13s/it] {'loss': '0.9346', 'grad_norm': '1.148', 'learning_rate': '6.879e-05', 'epoch': '0.305', 'num_input_tokens_seen': 19140408, 'train_runtime': '2.403e+04', 'train_tokens_per_second': '796.5'}
31%|███ | 5700/18688 [6:40:29<14:53:01, 4.13s/it] 31%|███ | 5701/18688 [6:40:33<14:30:57, 4.02s/it] 31%|███ | 5702/18688 [6:40:36<14:11:40, 3.94s/it] 31%|███ | 5703/18688 [6:40:40<14:02:35, 3.89s/it] 31%|███ | 5704/18688 [6:40:44<14:29:37, 4.02s/it] 31%|███ | 5705/18688 [6:40:48<14:18:32, 3.97s/it] 31%|███ | 5706/18688 [6:40:52<14:05:52, 3.91s/it] 31%|███ | 5707/18688 [6:40:56<14:27:50, 4.01s/it] 31%|███ | 5708/18688 [6:41:00<13:53:17, 3.85s/it] 31%|███ | 5709/18688 [6:41:04<14:42:57, 4.08s/it] 31%|███ | 5710/18688 [6:41:08<14:26:16, 4.00s/it] {'loss': '0.9498', 'grad_norm': '1.14', 'learning_rate': '6.874e-05', 'epoch': '0.3056', 'num_input_tokens_seen': 19171408, 'train_runtime': '2.407e+04', 'train_tokens_per_second': '796.5'}
31%|███ | 5710/18688 [6:41:08<14:26:16, 4.00s/it] 31%|███ | 5711/18688 [6:41:12<14:15:25, 3.96s/it] 31%|███ | 5712/18688 [6:41:16<14:39:10, 4.07s/it] 31%|███ | 5713/18688 [6:41:21<14:46:05, 4.10s/it] 31%|███ | 5714/18688 [6:41:25<14:37:00, 4.06s/it] 31%|███ | 5715/18688 [6:41:28<14:13:18, 3.95s/it] 31%|███ | 5716/18688 [6:41:33<14:56:23, 4.15s/it] 31%|███ | 5717/18688 [6:41:37<14:59:01, 4.16s/it] 31%|███ | 5718/18688 [6:41:41<15:18:58, 4.25s/it] 31%|███ | 5719/18688 [6:41:46<15:49:17, 4.39s/it] 31%|███ | 5720/18688 [6:41:51<15:54:48, 4.42s/it] {'loss': '0.9618', 'grad_norm': '1.247', 'learning_rate': '6.868e-05', 'epoch': '0.3061', 'num_input_tokens_seen': 19207288, 'train_runtime': '2.411e+04', 'train_tokens_per_second': '796.6'}
31%|███ | 5720/18688 [6:41:51<15:54:48, 4.42s/it] 31%|███ | 5721/18688 [6:41:55<16:16:55, 4.52s/it] 31%|███ | 5722/18688 [6:42:01<17:10:25, 4.77s/it] 31%|███ | 5723/18688 [6:42:04<15:49:45, 4.40s/it] 31%|███ | 5724/18688 [6:42:09<16:02:23, 4.45s/it] 31%|███ | 5725/18688 [6:42:14<16:22:57, 4.55s/it] 31%|███ | 5726/18688 [6:42:18<16:12:01, 4.50s/it] 31%|███ | 5727/18688 [6:42:22<16:04:39, 4.47s/it] 31%|███ | 5728/18688 [6:42:26<15:03:26, 4.18s/it] 31%|███ | 5729/18688 [6:42:30<14:42:41, 4.09s/it] 31%|███ | 5730/18688 [6:42:34<14:17:50, 3.97s/it] {'loss': '0.9362', 'grad_norm': '1.239', 'learning_rate': '6.863e-05', 'epoch': '0.3066', 'num_input_tokens_seen': 19242856, 'train_runtime': '2.415e+04', 'train_tokens_per_second': '796.7'}
31%|███ | 5730/18688 [6:42:34<14:17:50, 3.97s/it] 31%|███ | 5731/18688 [6:42:38<14:51:18, 4.13s/it] 31%|███ | 5732/18688 [6:42:42<14:20:12, 3.98s/it] 31%|███ | 5733/18688 [6:42:45<13:54:59, 3.87s/it] 31%|███ | 5734/18688 [6:42:49<13:36:26, 3.78s/it] 31%|███ | 5735/18688 [6:42:54<14:57:57, 4.16s/it] 31%|███ | 5736/18688 [6:42:58<14:52:21, 4.13s/it] 31%|███ | 5737/18688 [6:43:02<14:59:34, 4.17s/it] 31%|███ | 5738/18688 [6:43:06<14:28:17, 4.02s/it] 31%|███ | 5739/18688 [6:43:10<14:45:04, 4.10s/it] 31%|███ | 5740/18688 [6:43:14<14:03:03, 3.91s/it] {'loss': '0.8846', 'grad_norm': '1.299', 'learning_rate': '6.858e-05', 'epoch': '0.3072', 'num_input_tokens_seen': 19275640, 'train_runtime': '2.419e+04', 'train_tokens_per_second': '796.7'}
31%|███ | 5740/18688 [6:43:14<14:03:03, 3.91s/it] 31%|███ | 5741/18688 [6:43:18<14:30:39, 4.03s/it] 31%|███ | 5742/18688 [6:43:22<14:06:47, 3.92s/it] 31%|███ | 5743/18688 [6:43:26<14:23:08, 4.00s/it] 31%|███ | 5744/18688 [6:43:30<14:16:32, 3.97s/it] 31%|███ | 5745/18688 [6:43:33<13:47:20, 3.84s/it] 31%|███ | 5746/18688 [6:43:37<14:01:24, 3.90s/it] 31%|███ | 5747/18688 [6:43:41<13:44:45, 3.82s/it] 31%|███ | 5748/18688 [6:43:45<14:22:54, 4.00s/it] 31%|███ | 5749/18688 [6:43:50<14:50:44, 4.13s/it] 31%|███ | 5750/18688 [6:43:54<14:36:10, 4.06s/it] {'loss': '0.9103', 'grad_norm': '1.079', 'learning_rate': '6.853e-05', 'epoch': '0.3077', 'num_input_tokens_seen': 19307320, 'train_runtime': '2.423e+04', 'train_tokens_per_second': '796.7'}
31%|███ | 5750/18688 [6:43:54<14:36:10, 4.06s/it] 31%|███ | 5751/18688 [6:43:57<14:10:50, 3.95s/it] 31%|███ | 5752/18688 [6:44:02<15:06:32, 4.20s/it] 31%|███ | 5753/18688 [6:44:06<14:19:00, 3.98s/it] 31%|███ | 5754/18688 [6:44:09<13:47:21, 3.84s/it] 31%|███ | 5755/18688 [6:44:13<14:09:21, 3.94s/it] 31%|███ | 5756/18688 [6:44:17<14:09:48, 3.94s/it] 31%|███ | 5757/18688 [6:44:21<13:43:27, 3.82s/it] 31%|███ | 5758/18688 [6:44:25<13:38:27, 3.80s/it] 31%|███ | 5759/18688 [6:44:28<13:38:03, 3.80s/it] 31%|███ | 5760/18688 [6:44:33<14:18:29, 3.98s/it] {'loss': '0.9037', 'grad_norm': '1.002', 'learning_rate': '6.848e-05', 'epoch': '0.3082', 'num_input_tokens_seen': 19338200, 'train_runtime': '2.427e+04', 'train_tokens_per_second': '796.7'}
31%|███ | 5760/18688 [6:44:33<14:18:29, 3.98s/it] 31%|███ | 5761/18688 [6:44:37<14:04:35, 3.92s/it] 31%|███ | 5762/18688 [6:44:41<14:23:47, 4.01s/it] 31%|███ | 5763/18688 [6:44:45<14:36:34, 4.07s/it] 31%|███ | 5764/18688 [6:44:49<15:05:38, 4.20s/it] 31%|███ | 5765/18688 [6:44:54<15:22:09, 4.28s/it] 31%|███ | 5766/18688 [6:44:58<14:36:28, 4.07s/it] 31%|███ | 5767/18688 [6:45:02<15:03:53, 4.20s/it] 31%|███ | 5768/18688 [6:45:07<15:52:00, 4.42s/it] 31%|███ | 5769/18688 [6:45:11<15:29:13, 4.32s/it] 31%|███ | 5770/18688 [6:45:15<15:31:15, 4.33s/it] {'loss': '0.9943', 'grad_norm': '1.136', 'learning_rate': '6.843e-05', 'epoch': '0.3088', 'num_input_tokens_seen': 19373864, 'train_runtime': '2.432e+04', 'train_tokens_per_second': '796.8'}
31%|███ | 5770/18688 [6:45:15<15:31:15, 4.33s/it] 31%|███ | 5771/18688 [6:45:20<15:44:32, 4.39s/it] 31%|███ | 5772/18688 [6:45:25<17:01:57, 4.75s/it] 31%|███ | 5773/18688 [6:45:30<16:23:25, 4.57s/it] 31%|███ | 5774/18688 [6:45:34<15:50:19, 4.42s/it] 31%|███ | 5775/18688 [6:45:38<15:39:38, 4.37s/it] 31%|███ | 5776/18688 [6:45:42<15:09:39, 4.23s/it] 31%|███ | 5777/18688 [6:45:46<15:16:08, 4.26s/it] 31%|███ | 5778/18688 [6:45:50<14:31:20, 4.05s/it] 31%|███ | 5779/18688 [6:45:53<14:05:15, 3.93s/it] 31%|███ | 5780/18688 [6:45:58<14:20:21, 4.00s/it] {'loss': '0.9078', 'grad_norm': '1.52', 'learning_rate': '6.838e-05', 'epoch': '0.3093', 'num_input_tokens_seen': 19409144, 'train_runtime': '2.436e+04', 'train_tokens_per_second': '796.8'}
31%|███ | 5780/18688 [6:45:58<14:20:21, 4.00s/it] 31%|███ | 5781/18688 [6:46:01<14:05:02, 3.93s/it] 31%|███ | 5782/18688 [6:46:05<14:17:41, 3.99s/it] 31%|███ | 5783/18688 [6:46:09<14:19:25, 4.00s/it] 31%|███ | 5784/18688 [6:46:14<15:08:34, 4.22s/it] 31%|███ | 5785/18688 [6:46:18<14:39:41, 4.09s/it] 31%|███ | 5786/18688 [6:46:22<14:45:10, 4.12s/it] 31%|███ | 5787/18688 [6:46:26<14:38:52, 4.09s/it] 31%|███ | 5788/18688 [6:46:31<15:29:02, 4.32s/it] 31%|███ | 5789/18688 [6:46:35<15:22:10, 4.29s/it] 31%|███ | 5790/18688 [6:46:40<15:21:52, 4.29s/it] {'loss': '0.9753', 'grad_norm': '1.298', 'learning_rate': '6.833e-05', 'epoch': '0.3098', 'num_input_tokens_seen': 19443104, 'train_runtime': '2.44e+04', 'train_tokens_per_second': '796.8'}
31%|███ | 5790/18688 [6:46:40<15:21:52, 4.29s/it] 31%|███ | 5791/18688 [6:46:44<15:22:11, 4.29s/it] 31%|███ | 5792/18688 [6:46:48<14:56:05, 4.17s/it] 31%|███ | 5793/18688 [6:46:52<15:05:22, 4.21s/it] 31%|███ | 5794/18688 [6:46:58<16:25:14, 4.58s/it] 31%|███ | 5795/18688 [6:47:01<15:19:16, 4.28s/it] 31%|███ | 5796/18688 [6:47:05<15:04:27, 4.21s/it] 31%|███ | 5797/18688 [6:47:09<14:51:06, 4.15s/it] 31%|███ | 5798/18688 [6:47:13<14:57:01, 4.18s/it] 31%|███ | 5799/18688 [6:47:17<14:24:33, 4.02s/it] 31%|███ | 5800/18688 [6:47:20<13:47:24, 3.85s/it] {'loss': '0.9504', 'grad_norm': '1.052', 'learning_rate': '6.827e-05', 'epoch': '0.3104', 'num_input_tokens_seen': 19475768, 'train_runtime': '2.444e+04', 'train_tokens_per_second': '796.8'}
31%|███ | 5800/18688 [6:47:20<13:47:24, 3.85s/it] 31%|███ | 5801/18688 [6:47:26<15:20:47, 4.29s/it] 31%|███ | 5802/18688 [6:47:30<15:33:24, 4.35s/it] 31%|███ | 5803/18688 [6:47:34<15:18:22, 4.28s/it] 31%|███ | 5804/18688 [6:47:38<14:53:12, 4.16s/it] 31%|███ | 5805/18688 [6:47:42<14:35:23, 4.08s/it] 31%|███ | 5806/18688 [6:47:47<14:54:05, 4.16s/it] 31%|███ | 5807/18688 [6:47:51<14:42:31, 4.11s/it] 31%|███ | 5808/18688 [6:47:54<13:58:31, 3.91s/it] 31%|███ | 5809/18688 [6:47:58<14:39:34, 4.10s/it] 31%|███ | 5810/18688 [6:48:02<14:10:20, 3.96s/it] {'loss': '0.911', 'grad_norm': '1.048', 'learning_rate': '6.822e-05', 'epoch': '0.3109', 'num_input_tokens_seen': 19510000, 'train_runtime': '2.448e+04', 'train_tokens_per_second': '796.9'}
31%|███ | 5810/18688 [6:48:02<14:10:20, 3.96s/it] 31%|███ | 5811/18688 [6:48:06<13:48:59, 3.86s/it] 31%|███ | 5812/18688 [6:48:11<14:47:43, 4.14s/it] 31%|███ | 5813/18688 [6:48:14<14:15:33, 3.99s/it] 31%|███ | 5814/18688 [6:48:18<13:49:54, 3.87s/it] 31%|███ | 5815/18688 [6:48:23<15:00:19, 4.20s/it] 31%|███ | 5816/18688 [6:48:27<14:53:54, 4.17s/it] 31%|███ | 5817/18688 [6:48:31<14:52:40, 4.16s/it] 31%|███ | 5818/18688 [6:48:35<14:51:33, 4.16s/it] 31%|███ | 5819/18688 [6:48:39<15:01:00, 4.20s/it] 31%|███ | 5820/18688 [6:48:44<15:07:29, 4.23s/it] {'loss': '0.9046', 'grad_norm': '1.03', 'learning_rate': '6.817e-05', 'epoch': '0.3114', 'num_input_tokens_seen': 19544144, 'train_runtime': '2.452e+04', 'train_tokens_per_second': '796.9'}
31%|███ | 5820/18688 [6:48:44<15:07:29, 4.23s/it] 31%|███ | 5821/18688 [6:48:48<14:53:37, 4.17s/it] 31%|███ | 5822/18688 [6:48:51<14:08:39, 3.96s/it] 31%|███ | 5823/18688 [6:48:56<14:47:28, 4.14s/it] 31%|███ | 5824/18688 [6:49:00<15:15:16, 4.27s/it] 31%|███ | 5825/18688 [6:49:04<14:34:27, 4.08s/it] 31%|███ | 5826/18688 [6:49:08<14:51:27, 4.16s/it] 31%|███ | 5827/18688 [6:49:12<14:07:42, 3.95s/it] 31%|███ | 5828/18688 [6:49:16<13:53:33, 3.89s/it] 31%|███ | 5829/18688 [6:49:19<13:52:07, 3.88s/it] 31%|███ | 5830/18688 [6:49:23<14:03:43, 3.94s/it] {'loss': '0.9252', 'grad_norm': '1.016', 'learning_rate': '6.812e-05', 'epoch': '0.312', 'num_input_tokens_seen': 19575072, 'train_runtime': '2.456e+04', 'train_tokens_per_second': '796.9'}
31%|███ | 5830/18688 [6:49:23<14:03:43, 3.94s/it] 31%|███ | 5831/18688 [6:49:28<14:24:10, 4.03s/it] 31%|███ | 5832/18688 [6:49:32<14:25:53, 4.04s/it] 31%|███ | 5833/18688 [6:49:36<14:33:29, 4.08s/it] 31%|███ | 5834/18688 [6:49:40<14:51:44, 4.16s/it] 31%|███ | 5835/18688 [6:49:44<14:15:29, 3.99s/it] 31%|███ | 5836/18688 [6:49:48<13:54:51, 3.90s/it] 31%|███ | 5837/18688 [6:49:52<14:21:54, 4.02s/it] 31%|███ | 5838/18688 [6:49:56<14:09:21, 3.97s/it] 31%|███ | 5839/18688 [6:50:00<14:42:40, 4.12s/it] 31%|███▏ | 5840/18688 [6:50:04<14:13:06, 3.98s/it] {'loss': '0.9936', 'grad_norm': '1.355', 'learning_rate': '6.807e-05', 'epoch': '0.3125', 'num_input_tokens_seen': 19607816, 'train_runtime': '2.46e+04', 'train_tokens_per_second': '796.9'}
31%|███▏ | 5840/18688 [6:50:04<14:13:06, 3.98s/it] 31%|███▏ | 5841/18688 [6:50:07<13:46:50, 3.86s/it] 31%|███▏ | 5842/18688 [6:50:12<14:18:25, 4.01s/it] 31%|███▏ | 5843/18688 [6:50:16<13:58:36, 3.92s/it] 31%|███▏ | 5844/18688 [6:50:19<13:45:59, 3.86s/it] 31%|███▏ | 5845/18688 [6:50:23<14:04:04, 3.94s/it] 31%|███▏ | 5846/18688 [6:50:27<13:34:15, 3.80s/it] 31%|███▏ | 5847/18688 [6:50:31<13:40:58, 3.84s/it] 31%|███▏ | 5848/18688 [6:50:35<14:06:29, 3.96s/it] 31%|███▏ | 5849/18688 [6:50:39<14:30:03, 4.07s/it] 31%|███▏ | 5850/18688 [6:50:43<14:08:45, 3.97s/it] {'loss': '0.9852', 'grad_norm': '1.315', 'learning_rate': '6.801e-05', 'epoch': '0.313', 'num_input_tokens_seen': 19639928, 'train_runtime': '2.464e+04', 'train_tokens_per_second': '797'}
31%|███▏ | 5850/18688 [6:50:43<14:08:45, 3.97s/it] 31%|███▏ | 5851/18688 [6:50:47<13:54:45, 3.90s/it] 31%|███▏ | 5852/18688 [6:50:51<14:05:37, 3.95s/it] 31%|███▏ | 5853/18688 [6:50:55<14:20:53, 4.02s/it] 31%|███▏ | 5854/18688 [6:51:00<15:36:54, 4.38s/it] 31%|███▏ | 5855/18688 [6:51:04<15:07:14, 4.24s/it] 31%|███▏ | 5856/18688 [6:51:08<15:03:38, 4.23s/it] 31%|███▏ | 5857/18688 [6:51:12<14:35:02, 4.09s/it] 31%|███▏ | 5858/18688 [6:51:17<15:29:53, 4.35s/it] 31%|███▏ | 5859/18688 [6:51:21<14:54:35, 4.18s/it] 31%|███▏ | 5860/18688 [6:51:25<14:54:13, 4.18s/it] {'loss': '0.9413', 'grad_norm': '1.341', 'learning_rate': '6.796e-05', 'epoch': '0.3136', 'num_input_tokens_seen': 19674232, 'train_runtime': '2.469e+04', 'train_tokens_per_second': '797'}
31%|███▏ | 5860/18688 [6:51:25<14:54:13, 4.18s/it] 31%|███▏ | 5861/18688 [6:51:29<14:38:11, 4.11s/it] 31%|███▏ | 5862/18688 [6:51:33<14:08:20, 3.97s/it] 31%|███▏ | 5863/18688 [6:51:37<14:24:13, 4.04s/it] 31%|███▏ | 5864/18688 [6:51:42<15:05:42, 4.24s/it] 31%|███▏ | 5865/18688 [6:51:46<15:07:33, 4.25s/it] 31%|███▏ | 5866/18688 [6:51:50<14:36:43, 4.10s/it] 31%|███▏ | 5867/18688 [6:51:54<14:31:16, 4.08s/it] 31%|███▏ | 5868/18688 [6:51:58<14:27:11, 4.06s/it] 31%|███▏ | 5869/18688 [6:52:02<14:54:47, 4.19s/it] 31%|███▏ | 5870/18688 [6:52:07<16:05:57, 4.52s/it] {'loss': '0.8988', 'grad_norm': '1.036', 'learning_rate': '6.791e-05', 'epoch': '0.3141', 'num_input_tokens_seen': 19708232, 'train_runtime': '2.473e+04', 'train_tokens_per_second': '797'}
31%|███▏ | 5870/18688 [6:52:07<16:05:57, 4.52s/it] 31%|███▏ | 5871/18688 [6:52:11<15:02:30, 4.22s/it] 31%|███▏ | 5872/18688 [6:52:15<14:50:10, 4.17s/it] 31%|███▏ | 5873/18688 [6:52:19<14:31:12, 4.08s/it] 31%|███▏ | 5874/18688 [6:52:24<15:37:06, 4.39s/it] 31%|███▏ | 5875/18688 [6:52:28<15:05:46, 4.24s/it] 31%|███▏ | 5876/18688 [6:52:32<14:33:32, 4.09s/it] 31%|███▏ | 5877/18688 [6:52:37<15:24:34, 4.33s/it] 31%|███▏ | 5878/18688 [6:52:40<14:48:38, 4.16s/it] 31%|███▏ | 5879/18688 [6:52:46<16:09:10, 4.54s/it] 31%|███▏ | 5880/18688 [6:52:51<16:39:15, 4.68s/it] {'loss': '0.9713', 'grad_norm': '0.9423', 'learning_rate': '6.786e-05', 'epoch': '0.3146', 'num_input_tokens_seen': 19744040, 'train_runtime': '2.477e+04', 'train_tokens_per_second': '797.1'}
31%|███▏ | 5880/18688 [6:52:51<16:39:15, 4.68s/it] 31%|███▏ | 5881/18688 [6:52:56<16:52:05, 4.74s/it] 31%|███▏ | 5882/18688 [6:52:59<15:37:10, 4.39s/it] 31%|███▏ | 5883/18688 [6:53:03<14:53:07, 4.18s/it] 31%|███▏ | 5884/18688 [6:53:07<14:44:36, 4.15s/it] 31%|███▏ | 5885/18688 [6:53:11<14:50:32, 4.17s/it] 31%|███▏ | 5886/18688 [6:53:15<14:29:23, 4.07s/it] 32%|███▏ | 5887/18688 [6:53:19<14:54:18, 4.19s/it] 32%|███▏ | 5888/18688 [6:53:23<14:35:46, 4.11s/it] 32%|███▏ | 5889/18688 [6:53:27<14:26:19, 4.06s/it] 32%|███▏ | 5890/18688 [6:53:31<14:01:15, 3.94s/it] {'loss': '0.9622', 'grad_norm': '1.39', 'learning_rate': '6.78e-05', 'epoch': '0.3152', 'num_input_tokens_seen': 19775696, 'train_runtime': '2.481e+04', 'train_tokens_per_second': '797'}
32%|███▏ | 5890/18688 [6:53:31<14:01:15, 3.94s/it] 32%|███▏ | 5891/18688 [6:53:35<13:51:33, 3.90s/it] 32%|███▏ | 5892/18688 [6:53:39<14:14:07, 4.00s/it] 32%|███▏ | 5893/18688 [6:53:43<14:08:33, 3.98s/it] 32%|███▏ | 5894/18688 [6:53:47<14:42:35, 4.14s/it] 32%|███▏ | 5895/18688 [6:53:52<14:52:19, 4.19s/it] 32%|███▏ | 5896/18688 [6:53:56<14:35:01, 4.10s/it] 32%|███▏ | 5897/18688 [6:54:00<14:20:05, 4.03s/it] 32%|███▏ | 5898/18688 [6:54:03<13:43:43, 3.86s/it] 32%|███▏ | 5899/18688 [6:54:08<14:29:13, 4.08s/it] 32%|███▏ | 5900/18688 [6:54:12<14:31:03, 4.09s/it] {'loss': '0.8914', 'grad_norm': '1.152', 'learning_rate': '6.775e-05', 'epoch': '0.3157', 'num_input_tokens_seen': 19808688, 'train_runtime': '2.485e+04', 'train_tokens_per_second': '797.1'}
32%|███▏ | 5900/18688 [6:54:12<14:31:03, 4.09s/it] 32%|███▏ | 5901/18688 [6:54:15<13:56:45, 3.93s/it] 32%|███▏ | 5902/18688 [6:54:19<13:40:21, 3.85s/it] 32%|███▏ | 5903/18688 [6:54:24<15:07:10, 4.26s/it] 32%|███▏ | 5904/18688 [6:54:28<14:39:16, 4.13s/it] 32%|███▏ | 5905/18688 [6:54:33<15:10:08, 4.27s/it] 32%|███▏ | 5906/18688 [6:54:37<14:56:43, 4.21s/it] 32%|███▏ | 5907/18688 [6:54:41<14:44:18, 4.15s/it] 32%|███▏ | 5908/18688 [6:54:44<14:20:59, 4.04s/it] 32%|███▏ | 5909/18688 [6:54:49<15:12:01, 4.28s/it] 32%|███▏ | 5910/18688 [6:54:53<14:22:04, 4.05s/it] {'loss': '0.9677', 'grad_norm': '1.117', 'learning_rate': '6.77e-05', 'epoch': '0.3163', 'num_input_tokens_seen': 19842304, 'train_runtime': '2.489e+04', 'train_tokens_per_second': '797.1'}
32%|███▏ | 5910/18688 [6:54:53<14:22:04, 4.05s/it] 32%|███▏ | 5911/18688 [6:54:57<14:26:43, 4.07s/it] 32%|███▏ | 5912/18688 [6:55:00<13:50:24, 3.90s/it] 32%|███▏ | 5913/18688 [6:55:04<13:54:50, 3.92s/it] 32%|███▏ | 5914/18688 [6:55:08<13:24:39, 3.78s/it] 32%|███▏ | 5915/18688 [6:55:12<13:49:25, 3.90s/it] 32%|███▏ | 5916/18688 [6:55:16<13:38:20, 3.84s/it] 32%|███▏ | 5917/18688 [6:55:21<14:52:17, 4.19s/it] 32%|███▏ | 5918/18688 [6:55:25<14:35:09, 4.11s/it] 32%|███▏ | 5919/18688 [6:55:29<14:22:21, 4.05s/it] 32%|███▏ | 5920/18688 [6:55:33<14:48:52, 4.18s/it] {'loss': '0.9798', 'grad_norm': '1.238', 'learning_rate': '6.765e-05', 'epoch': '0.3168', 'num_input_tokens_seen': 19875192, 'train_runtime': '2.493e+04', 'train_tokens_per_second': '797.1'}
32%|███▏ | 5920/18688 [6:55:33<14:48:52, 4.18s/it] 32%|███▏ | 5921/18688 [6:55:38<15:35:43, 4.40s/it] 32%|███▏ | 5922/18688 [6:55:42<14:43:25, 4.15s/it] 32%|███▏ | 5923/18688 [6:55:46<14:40:19, 4.14s/it] 32%|███▏ | 5924/18688 [6:55:50<14:27:39, 4.08s/it] 32%|███▏ | 5925/18688 [6:55:54<14:37:02, 4.12s/it] 32%|███▏ | 5926/18688 [6:55:58<14:31:17, 4.10s/it] 32%|███▏ | 5927/18688 [6:56:01<13:48:56, 3.90s/it] 32%|███▏ | 5928/18688 [6:56:06<15:04:05, 4.25s/it] 32%|███▏ | 5929/18688 [6:56:10<14:19:25, 4.04s/it] 32%|███▏ | 5930/18688 [6:56:15<15:47:25, 4.46s/it] {'loss': '0.966', 'grad_norm': '1.404', 'learning_rate': '6.759e-05', 'epoch': '0.3173', 'num_input_tokens_seen': 19910424, 'train_runtime': '2.498e+04', 'train_tokens_per_second': '797.2'}
32%|███▏ | 5930/18688 [6:56:15<15:47:25, 4.46s/it] 32%|███▏ | 5931/18688 [6:56:19<15:13:07, 4.29s/it] 32%|███▏ | 5932/18688 [6:56:23<14:42:28, 4.15s/it] 32%|███▏ | 5933/18688 [6:56:27<14:06:31, 3.98s/it] 32%|███▏ | 5934/18688 [6:56:31<14:00:15, 3.95s/it] 32%|███▏ | 5935/18688 [6:56:35<14:36:03, 4.12s/it] 32%|███▏ | 5936/18688 [6:56:40<15:19:40, 4.33s/it] 32%|███▏ | 5937/18688 [6:56:44<15:16:05, 4.31s/it] 32%|███▏ | 5938/18688 [6:56:49<15:25:04, 4.35s/it] 32%|███▏ | 5939/18688 [6:56:52<14:46:50, 4.17s/it] 32%|███▏ | 5940/18688 [6:56:56<14:01:49, 3.96s/it] {'loss': '0.908', 'grad_norm': '1.189', 'learning_rate': '6.754e-05', 'epoch': '0.3179', 'num_input_tokens_seen': 19942736, 'train_runtime': '2.502e+04', 'train_tokens_per_second': '797.2'}
32%|███▏ | 5940/18688 [6:56:56<14:01:49, 3.96s/it] 32%|███▏ | 5941/18688 [6:57:01<15:26:15, 4.36s/it] 32%|███▏ | 5942/18688 [6:57:05<14:42:35, 4.15s/it] 32%|███▏ | 5943/18688 [6:57:09<15:03:31, 4.25s/it] 32%|███▏ | 5944/18688 [6:57:14<16:07:10, 4.55s/it] 32%|███▏ | 5945/18688 [6:57:18<15:26:24, 4.36s/it] 32%|███▏ | 5946/18688 [6:57:23<15:11:58, 4.29s/it] 32%|███▏ | 5947/18688 [6:57:26<14:24:07, 4.07s/it] 32%|███▏ | 5948/18688 [6:57:30<14:12:14, 4.01s/it] 32%|███▏ | 5949/18688 [6:57:34<13:52:08, 3.92s/it] 32%|███▏ | 5950/18688 [6:57:37<13:41:10, 3.87s/it] {'loss': '0.9282', 'grad_norm': '1.075', 'learning_rate': '6.749e-05', 'epoch': '0.3184', 'num_input_tokens_seen': 19975128, 'train_runtime': '2.506e+04', 'train_tokens_per_second': '797.2'}
32%|███▏ | 5950/18688 [6:57:37<13:41:10, 3.87s/it] 32%|███▏ | 5951/18688 [6:57:41<13:24:37, 3.79s/it] 32%|███▏ | 5952/18688 [6:57:46<14:15:10, 4.03s/it] 32%|███▏ | 5953/18688 [6:57:49<13:43:35, 3.88s/it] 32%|███▏ | 5954/18688 [6:57:53<13:54:54, 3.93s/it] 32%|███▏ | 5955/18688 [6:57:58<14:20:43, 4.06s/it] 32%|███▏ | 5956/18688 [6:58:01<14:10:56, 4.01s/it] 32%|███▏ | 5957/18688 [6:58:05<13:44:45, 3.89s/it] 32%|███▏ | 5958/18688 [6:58:09<13:18:10, 3.76s/it] 32%|███▏ | 5959/18688 [6:58:12<13:11:00, 3.73s/it] 32%|███▏ | 5960/18688 [6:58:16<13:24:19, 3.79s/it] {'loss': '0.9516', 'grad_norm': '1.167', 'learning_rate': '6.743e-05', 'epoch': '0.3189', 'num_input_tokens_seen': 20004584, 'train_runtime': '2.51e+04', 'train_tokens_per_second': '797.1'}
32%|███▏ | 5960/18688 [6:58:16<13:24:19, 3.79s/it] 32%|███▏ | 5961/18688 [6:58:21<15:00:11, 4.24s/it] 32%|███▏ | 5962/18688 [6:58:26<14:50:32, 4.20s/it] 32%|███▏ | 5963/18688 [6:58:29<14:18:18, 4.05s/it] 32%|███▏ | 5964/18688 [6:58:33<13:55:09, 3.94s/it] 32%|███▏ | 5965/18688 [6:58:36<13:28:52, 3.81s/it] 32%|███▏ | 5966/18688 [6:58:40<13:24:40, 3.80s/it] 32%|███▏ | 5967/18688 [6:58:44<13:56:18, 3.94s/it] 32%|███▏ | 5968/18688 [6:58:49<14:50:07, 4.20s/it] 32%|███▏ | 5969/18688 [6:58:53<14:41:25, 4.16s/it] 32%|███▏ | 5970/18688 [6:58:58<15:00:33, 4.25s/it] {'loss': '0.9709', 'grad_norm': '1.439', 'learning_rate': '6.738e-05', 'epoch': '0.3195', 'num_input_tokens_seen': 20038552, 'train_runtime': '2.514e+04', 'train_tokens_per_second': '797.1'}
32%|███▏ | 5970/18688 [6:58:58<15:00:33, 4.25s/it] 32%|███▏ | 5971/18688 [6:59:02<15:20:24, 4.34s/it] 32%|███▏ | 5972/18688 [6:59:07<15:47:03, 4.47s/it] 32%|███▏ | 5973/18688 [6:59:12<15:55:45, 4.51s/it] 32%|███▏ | 5974/18688 [6:59:16<15:15:31, 4.32s/it] 32%|███▏ | 5975/18688 [6:59:20<15:04:40, 4.27s/it] 32%|███▏ | 5976/18688 [6:59:23<14:19:09, 4.06s/it] 32%|███▏ | 5977/18688 [6:59:27<14:18:27, 4.05s/it] 32%|███▏ | 5978/18688 [6:59:31<14:04:03, 3.98s/it] 32%|███▏ | 5979/18688 [6:59:36<14:51:25, 4.21s/it] 32%|███▏ | 5980/18688 [6:59:40<14:49:53, 4.20s/it] {'loss': '0.9618', 'grad_norm': '1.213', 'learning_rate': '6.733e-05', 'epoch': '0.32', 'num_input_tokens_seen': 20073584, 'train_runtime': '2.518e+04', 'train_tokens_per_second': '797.2'}
32%|███▏ | 5980/18688 [6:59:40<14:49:53, 4.20s/it] 32%|███▏ | 5981/18688 [6:59:44<14:03:37, 3.98s/it] 32%|███▏ | 5982/18688 [6:59:47<13:43:26, 3.89s/it] 32%|███▏ | 5983/18688 [6:59:51<13:56:32, 3.95s/it] 32%|███▏ | 5984/18688 [6:59:56<14:23:51, 4.08s/it] 32%|███▏ | 5985/18688 [7:00:01<15:33:19, 4.41s/it] 32%|███▏ | 5986/18688 [7:00:05<14:48:48, 4.20s/it] 32%|███▏ | 5987/18688 [7:00:09<14:47:20, 4.19s/it] 32%|███▏ | 5988/18688 [7:00:13<14:54:03, 4.22s/it] 32%|███▏ | 5989/18688 [7:00:17<15:00:39, 4.26s/it] 32%|███▏ | 5990/18688 [7:00:21<14:41:45, 4.17s/it] {'loss': '0.9087', 'grad_norm': '1.168', 'learning_rate': '6.727e-05', 'epoch': '0.3205', 'num_input_tokens_seen': 20106224, 'train_runtime': '2.522e+04', 'train_tokens_per_second': '797.2'}
32%|███▏ | 5990/18688 [7:00:21<14:41:45, 4.17s/it] 32%|███▏ | 5991/18688 [7:00:25<14:15:34, 4.04s/it] 32%|███▏ | 5992/18688 [7:00:29<14:16:18, 4.05s/it] 32%|███▏ | 5993/18688 [7:00:34<14:46:06, 4.19s/it] 32%|███▏ | 5994/18688 [7:00:38<15:12:07, 4.31s/it] 32%|███▏ | 5995/18688 [7:00:42<14:52:01, 4.22s/it] 32%|███▏ | 5996/18688 [7:00:47<15:09:39, 4.30s/it] 32%|███▏ | 5997/18688 [7:00:51<14:51:52, 4.22s/it] 32%|███▏ | 5998/18688 [7:00:55<14:50:33, 4.21s/it] 32%|███▏ | 5999/18688 [7:00:59<14:23:46, 4.08s/it] 32%|███▏ | 6000/18688 [7:01:03<14:35:21, 4.14s/it] {'loss': '0.9657', 'grad_norm': '0.9134', 'learning_rate': '6.722e-05', 'epoch': '0.3211', 'num_input_tokens_seen': 20140120, 'train_runtime': '2.526e+04', 'train_tokens_per_second': '797.2'}
32%|███▏ | 6000/18688 [7:01:03<14:35:21, 4.14s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
0%| | 3/1000 [00:00<00:38, 25.89it/s]
1%| | 6/1000 [00:00<00:54, 18.25it/s]
1%| | 8/1000 [00:00<00:56, 17.52it/s]
1%| | 10/1000 [00:00<00:59, 16.55it/s]
1%| | 12/1000 [00:00<01:00, 16.35it/s]
1%|▏ | 14/1000 [00:00<01:02, 15.88it/s]
2%|▏ | 16/1000 [00:00<01:02, 15.65it/s]
2%|▏ | 18/1000 [00:01<01:38, 9.98it/s]
2%|▏ | 20/1000 [00:01<01:33, 10.54it/s]
2%|▏ | 22/1000 [00:01<01:52, 8.71it/s]
2%|▏ | 24/1000 [00:01<01:40, 9.75it/s]
3%|▎ | 26/1000 [00:02<01:45, 9.27it/s]
3%|▎ | 28/1000 [00:02<01:36, 10.09it/s]
3%|▎ | 30/1000 [00:02<01:28, 10.99it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.16it/s]
3%|▎ | 34/1000 [00:02<01:25, 11.25it/s]
4%|▎ | 36/1000 [00:03<01:32, 10.42it/s]
4%|▍ | 38/1000 [00:03<01:24, 11.33it/s]
4%|▍ | 40/1000 [00:03<01:27, 10.93it/s]
4%|▍ | 42/1000 [00:03<01:46, 9.02it/s]
4%|▍ | 44/1000 [00:03<01:30, 10.52it/s]
5%|▍ | 46/1000 [00:03<01:21, 11.68it/s]
5%|▍ | 48/1000 [00:04<01:29, 10.64it/s]
5%|▌ | 50/1000 [00:04<01:21, 11.63it/s]
5%|▌ | 52/1000 [00:04<01:16, 12.47it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.22it/s]
6%|▌ | 56/1000 [00:04<01:08, 13.87it/s]
6%|▌ | 58/1000 [00:05<01:48, 8.71it/s]
6%|▌ | 60/1000 [00:05<01:36, 9.75it/s]
6%|▌ | 62/1000 [00:05<01:27, 10.71it/s]
6%|▋ | 64/1000 [00:05<01:19, 11.78it/s]
7%|▋ | 66/1000 [00:05<01:16, 12.28it/s]
7%|▋ | 68/1000 [00:05<01:14, 12.51it/s]
7%|▋ | 70/1000 [00:06<01:14, 12.41it/s]
7%|▋ | 72/1000 [00:06<01:14, 12.38it/s]
7%|▋ | 74/1000 [00:06<01:15, 12.27it/s]
8%|▊ | 76/1000 [00:06<01:15, 12.26it/s]
8%|▊ | 78/1000 [00:06<01:15, 12.29it/s]
8%|▊ | 80/1000 [00:06<01:18, 11.68it/s]
8%|▊ | 82/1000 [00:07<01:16, 12.07it/s]
8%|▊ | 84/1000 [00:07<01:14, 12.31it/s]
9%|▊ | 86/1000 [00:07<01:13, 12.43it/s]
9%|▉ | 88/1000 [00:07<01:14, 12.31it/s]
9%|▉ | 90/1000 [00:07<01:14, 12.27it/s]
9%|▉ | 92/1000 [00:07<01:12, 12.45it/s]
9%|▉ | 94/1000 [00:08<01:12, 12.54it/s]
10%|▉ | 96/1000 [00:08<01:12, 12.43it/s]
10%|▉ | 98/1000 [00:08<01:11, 12.60it/s]
10%|█ | 100/1000 [00:08<01:37, 9.23it/s]
10%|█ | 102/1000 [00:08<01:27, 10.27it/s]
10%|█ | 104/1000 [00:09<01:57, 7.60it/s]
10%|█ | 105/1000 [00:09<02:06, 7.05it/s]
11%|█ | 106/1000 [00:09<02:03, 7.25it/s]
11%|█ | 108/1000 [00:09<01:39, 8.95it/s]
11%|█ | 110/1000 [00:09<01:23, 10.62it/s]
11%|█ | 112/1000 [00:09<01:15, 11.78it/s]
11%|█▏ | 114/1000 [00:10<01:11, 12.36it/s]
12%|█▏ | 116/1000 [00:10<01:08, 12.92it/s]
12%|█▏ | 118/1000 [00:10<01:04, 13.58it/s]
12%|█▏ | 120/1000 [00:10<01:08, 12.88it/s]
12%|█▏ | 122/1000 [00:10<01:22, 10.61it/s]
12%|█▏ | 124/1000 [00:11<01:30, 9.67it/s]
13%|█▎ | 126/1000 [00:11<01:22, 10.63it/s]
13%|█▎ | 128/1000 [00:11<01:15, 11.55it/s]
13%|█▎ | 130/1000 [00:11<01:11, 12.22it/s]
13%|█▎ | 132/1000 [00:11<01:05, 13.18it/s]
13%|█▎ | 134/1000 [00:11<01:09, 12.38it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.34it/s]
14%|█▍ | 138/1000 [00:12<01:04, 13.27it/s]
14%|█▍ | 140/1000 [00:12<01:25, 10.09it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.54it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.56it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.59it/s]
15%|█▍ | 148/1000 [00:13<01:14, 11.48it/s]
15%|█▌ | 150/1000 [00:13<01:07, 12.56it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.11it/s]
15%|█▌ | 154/1000 [00:13<01:03, 13.38it/s]
16%|█▌ | 156/1000 [00:13<01:03, 13.23it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.75it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.65it/s]
16%|█▌ | 162/1000 [00:14<01:01, 13.53it/s]
16%|█▋ | 164/1000 [00:14<01:01, 13.61it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.75it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.27it/s]
17%|█▋ | 170/1000 [00:14<01:19, 10.48it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.46it/s]
17%|█▋ | 174/1000 [00:15<01:30, 9.16it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.17it/s]
18%|█▊ | 178/1000 [00:15<01:35, 8.62it/s]
18%|█▊ | 180/1000 [00:16<01:33, 8.77it/s]
18%|█▊ | 182/1000 [00:16<01:35, 8.57it/s]
18%|█▊ | 184/1000 [00:16<01:23, 9.80it/s]
19%|█▊ | 186/1000 [00:16<01:37, 8.33it/s]
19%|█▉ | 188/1000 [00:16<01:24, 9.66it/s]
19%|█▉ | 190/1000 [00:17<01:15, 10.70it/s]
19%|█▉ | 192/1000 [00:17<01:11, 11.34it/s]
19%|█▉ | 194/1000 [00:17<01:16, 10.54it/s]
20%|█▉ | 196/1000 [00:17<01:29, 8.96it/s]
20%|█▉ | 198/1000 [00:17<01:18, 10.19it/s]
20%|██ | 200/1000 [00:18<01:15, 10.54it/s]
20%|██ | 202/1000 [00:18<01:09, 11.49it/s]
20%|██ | 204/1000 [00:18<01:03, 12.63it/s]
21%|██ | 206/1000 [00:18<00:59, 13.24it/s]
21%|██ | 208/1000 [00:18<00:58, 13.62it/s]
21%|██ | 210/1000 [00:18<00:54, 14.43it/s]
21%|██ | 212/1000 [00:18<00:53, 14.63it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.68it/s]
22%|██▏ | 216/1000 [00:19<00:54, 14.44it/s]
22%|██▏ | 218/1000 [00:19<00:52, 14.83it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.88it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.16it/s]
22%|██▏ | 224/1000 [00:20<01:20, 9.64it/s]
23%|██▎ | 226/1000 [00:20<01:12, 10.61it/s]
23%|██▎ | 228/1000 [00:20<01:07, 11.51it/s]
23%|██▎ | 230/1000 [00:20<01:04, 12.02it/s]
23%|██▎ | 232/1000 [00:20<01:01, 12.40it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.16it/s]
24%|██▎ | 236/1000 [00:20<00:56, 13.58it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.17it/s]
24%|██▍ | 240/1000 [00:21<00:53, 14.16it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.67it/s]
24%|██▍ | 244/1000 [00:21<00:50, 14.89it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.88it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.62it/s]
25%|██▌ | 250/1000 [00:21<00:56, 13.37it/s]
25%|██▌ | 252/1000 [00:22<01:01, 12.13it/s]
25%|██▌ | 254/1000 [00:22<00:56, 13.17it/s]
26%|██▌ | 256/1000 [00:22<00:54, 13.76it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.80it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.91it/s]
26%|██▌ | 262/1000 [00:22<00:56, 13.02it/s]
26%|██▋ | 264/1000 [00:22<00:54, 13.59it/s]
27%|██▋ | 266/1000 [00:23<00:52, 13.96it/s]
27%|██▋ | 268/1000 [00:23<00:50, 14.46it/s]
27%|██▋ | 270/1000 [00:23<00:55, 13.21it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.59it/s]
27%|██▋ | 274/1000 [00:23<01:02, 11.69it/s]
28%|██▊ | 276/1000 [00:23<00:56, 12.72it/s]
28%|██▊ | 278/1000 [00:24<00:55, 12.96it/s]
28%|██▊ | 280/1000 [00:24<00:53, 13.53it/s]
28%|██▊ | 282/1000 [00:24<00:49, 14.50it/s]
28%|██▊ | 284/1000 [00:24<00:49, 14.42it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.71it/s]
29%|██▉ | 288/1000 [00:24<00:47, 15.06it/s]
29%|██▉ | 290/1000 [00:24<00:45, 15.51it/s]
29%|██▉ | 292/1000 [00:24<00:45, 15.46it/s]
29%|██▉ | 294/1000 [00:25<00:45, 15.36it/s]
30%|██▉ | 296/1000 [00:25<00:45, 15.37it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.47it/s]
30%|███ | 300/1000 [00:25<00:46, 15.13it/s]
30%|███ | 302/1000 [00:25<00:46, 15.16it/s]
30%|███ | 304/1000 [00:25<00:50, 13.74it/s]
31%|███ | 306/1000 [00:25<00:47, 14.49it/s]
31%|███ | 308/1000 [00:26<00:47, 14.46it/s]
31%|███ | 310/1000 [00:26<00:47, 14.65it/s]
31%|███ | 312/1000 [00:26<00:47, 14.61it/s]
31%|███▏ | 314/1000 [00:26<00:45, 15.19it/s]
32%|███▏ | 316/1000 [00:26<00:44, 15.49it/s]
32%|███▏ | 318/1000 [00:26<00:42, 15.97it/s]
32%|███▏ | 320/1000 [00:27<01:12, 9.35it/s]
32%|███▏ | 322/1000 [00:27<01:03, 10.73it/s]
32%|███▏ | 324/1000 [00:27<00:58, 11.48it/s]
33%|███▎ | 326/1000 [00:27<00:58, 11.43it/s]
33%|███▎ | 328/1000 [00:27<00:58, 11.48it/s]
33%|███▎ | 330/1000 [00:27<00:53, 12.44it/s]
33%|███▎ | 332/1000 [00:27<00:52, 12.67it/s]
33%|███▎ | 334/1000 [00:28<00:50, 13.15it/s]
34%|███▎ | 336/1000 [00:28<00:48, 13.56it/s]
34%|███▍ | 338/1000 [00:28<01:05, 10.06it/s]
34%|███▍ | 340/1000 [00:28<00:59, 11.11it/s]
34%|███▍ | 342/1000 [00:28<00:53, 12.32it/s]
34%|███▍ | 344/1000 [00:28<00:50, 12.98it/s]
35%|███▍ | 346/1000 [00:29<00:48, 13.59it/s]
35%|███▍ | 348/1000 [00:29<00:46, 14.03it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.65it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.84it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.87it/s]
36%|███▌ | 356/1000 [00:29<00:43, 14.95it/s]
36%|███▌ | 358/1000 [00:29<00:43, 14.66it/s]
36%|███▌ | 360/1000 [00:29<00:42, 15.04it/s]
36%|███▌ | 362/1000 [00:30<00:47, 13.35it/s]
36%|███▋ | 364/1000 [00:30<00:45, 13.88it/s]
37%|███▋ | 366/1000 [00:30<00:53, 11.95it/s]
37%|███▋ | 368/1000 [00:30<00:51, 12.36it/s]
37%|███▋ | 370/1000 [00:30<00:47, 13.24it/s]
37%|███▋ | 372/1000 [00:30<00:46, 13.57it/s]
37%|███▋ | 374/1000 [00:31<00:54, 11.40it/s]
38%|███▊ | 376/1000 [00:31<00:56, 10.97it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.64it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.43it/s]
38%|███▊ | 382/1000 [00:31<00:48, 12.62it/s]
38%|███▊ | 384/1000 [00:31<00:45, 13.40it/s]
39%|███▊ | 386/1000 [00:32<00:44, 13.71it/s]
39%|███▉ | 388/1000 [00:32<00:44, 13.66it/s]
39%|███▉ | 390/1000 [00:32<00:44, 13.56it/s]
39%|███▉ | 392/1000 [00:32<00:43, 13.88it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.65it/s]
40%|███▉ | 396/1000 [00:32<00:43, 13.76it/s]
40%|███▉ | 398/1000 [00:33<00:47, 12.57it/s]
40%|████ | 400/1000 [00:33<00:45, 13.23it/s]
40%|████ | 402/1000 [00:33<00:43, 13.86it/s]
40%|████ | 404/1000 [00:33<00:41, 14.47it/s]
41%|████ | 406/1000 [00:33<00:39, 14.97it/s]
41%|████ | 408/1000 [00:33<00:47, 12.53it/s]
41%|████ | 410/1000 [00:34<00:56, 10.46it/s]
41%|████ | 412/1000 [00:34<00:50, 11.54it/s]
41%|████▏ | 414/1000 [00:34<01:07, 8.63it/s]
42%|████▏ | 416/1000 [00:34<01:23, 6.96it/s]
42%|████▏ | 418/1000 [00:35<01:10, 8.28it/s]
42%|████▏ | 420/1000 [00:35<01:00, 9.54it/s]
42%|████▏ | 422/1000 [00:35<00:53, 10.88it/s]
42%|████▏ | 424/1000 [00:35<01:14, 7.68it/s]
43%|████▎ | 426/1000 [00:35<01:04, 8.95it/s]
43%|████▎ | 428/1000 [00:36<00:56, 10.16it/s]
43%|████▎ | 430/1000 [00:36<01:02, 9.12it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.05it/s]
43%|████▎ | 434/1000 [00:36<01:08, 8.27it/s]
44%|████▎ | 436/1000 [00:37<01:00, 9.31it/s]
44%|████▍ | 438/1000 [00:37<01:02, 8.97it/s]
44%|████▍ | 440/1000 [00:37<01:01, 9.17it/s]
44%|████▍ | 442/1000 [00:37<00:55, 10.13it/s]
44%|████▍ | 444/1000 [00:37<00:51, 10.71it/s]
45%|████▍ | 446/1000 [00:37<00:50, 11.07it/s]
45%|████▍ | 448/1000 [00:38<00:53, 10.31it/s]
45%|████▌ | 450/1000 [00:38<00:48, 11.26it/s]
45%|████▌ | 452/1000 [00:38<00:44, 12.33it/s]
45%|████▌ | 454/1000 [00:38<00:45, 11.89it/s]
46%|████▌ | 456/1000 [00:38<00:44, 12.31it/s]
46%|████▌ | 458/1000 [00:38<00:40, 13.36it/s]
46%|████▌ | 460/1000 [00:39<00:39, 13.60it/s]
46%|████▌ | 462/1000 [00:39<00:37, 14.24it/s]
46%|████▋ | 464/1000 [00:39<00:36, 14.80it/s]
47%|████▋ | 466/1000 [00:39<00:36, 14.70it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.82it/s]
47%|████▋ | 470/1000 [00:39<00:52, 10.01it/s]
47%|████▋ | 472/1000 [00:40<00:51, 10.19it/s]
47%|████▋ | 474/1000 [00:40<00:45, 11.48it/s]
48%|████▊ | 476/1000 [00:40<00:43, 12.14it/s]
48%|████▊ | 478/1000 [00:40<00:43, 12.00it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.93it/s]
48%|████▊ | 482/1000 [00:40<00:38, 13.33it/s]
48%|████▊ | 484/1000 [00:40<00:37, 13.84it/s]
49%|████▊ | 486/1000 [00:41<00:36, 14.01it/s]
49%|████▉ | 488/1000 [00:41<00:35, 14.26it/s]
49%|████▉ | 490/1000 [00:41<00:45, 11.13it/s]
49%|████▉ | 492/1000 [00:41<00:40, 12.39it/s]
49%|████▉ | 494/1000 [00:41<00:37, 13.38it/s]
50%|████▉ | 496/1000 [00:41<00:35, 14.17it/s]
50%|████▉ | 498/1000 [00:41<00:34, 14.59it/s]
50%|█████ | 500/1000 [00:42<00:43, 11.60it/s]
50%|█████ | 502/1000 [00:42<00:41, 12.03it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.61it/s]
51%|█████ | 506/1000 [00:42<00:38, 12.76it/s]
51%|█████ | 508/1000 [00:42<00:36, 13.62it/s]
51%|█████ | 510/1000 [00:43<00:47, 10.36it/s]
51%|█████ | 512/1000 [00:43<00:43, 11.13it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 11.99it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.56it/s]
52%|█████▏ | 518/1000 [00:43<00:36, 13.07it/s]
52%|█████▏ | 520/1000 [00:43<00:35, 13.45it/s]
52%|█████▏ | 522/1000 [00:43<00:34, 13.77it/s]
52%|█████▏ | 524/1000 [00:44<00:36, 13.19it/s]
53%|█████▎ | 526/1000 [00:44<00:36, 12.91it/s]
53%|█████▎ | 528/1000 [00:44<00:42, 11.00it/s]
53%|█████▎ | 530/1000 [00:44<00:41, 11.42it/s]
53%|█████▎ | 532/1000 [00:44<00:37, 12.40it/s]
53%|█████▎ | 534/1000 [00:44<00:35, 13.10it/s]
54%|█████▎ | 536/1000 [00:45<00:34, 13.52it/s]
54%|█████▍ | 538/1000 [00:45<00:34, 13.52it/s]
54%|█████▍ | 540/1000 [00:45<00:32, 14.09it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.46it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.35it/s]
55%|█████▍ | 546/1000 [00:45<00:32, 14.05it/s]
55%|█████▍ | 548/1000 [00:45<00:32, 13.80it/s]
55%|█████▌ | 550/1000 [00:46<00:31, 14.07it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.24it/s]
55%|█████▌ | 554/1000 [00:46<00:32, 13.90it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.09it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.47it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 15.02it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 14.98it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.79it/s]
57%|█████▋ | 566/1000 [00:47<00:37, 11.66it/s]
57%|█████▋ | 568/1000 [00:47<00:37, 11.51it/s]
57%|█████▋ | 570/1000 [00:47<00:33, 12.66it/s]
57%|█████▋ | 572/1000 [00:47<00:34, 12.40it/s]
57%|█████▋ | 574/1000 [00:47<00:31, 13.36it/s]
58%|█████▊ | 576/1000 [00:48<00:31, 13.37it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.51it/s]
58%|█████▊ | 580/1000 [00:48<00:32, 12.79it/s]
58%|█████▊ | 582/1000 [00:48<00:30, 13.53it/s]
58%|█████▊ | 584/1000 [00:48<00:30, 13.77it/s]
59%|█████▊ | 586/1000 [00:48<00:29, 14.15it/s]
59%|█████▉ | 588/1000 [00:48<00:27, 14.99it/s]
59%|█████▉ | 590/1000 [00:49<00:27, 14.99it/s]
59%|█████▉ | 592/1000 [00:49<00:27, 14.76it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.30it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.79it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 14.01it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.37it/s]
60%|██████ | 602/1000 [00:49<00:27, 14.52it/s]
60%|██████ | 604/1000 [00:50<00:26, 15.09it/s]
61%|██████ | 606/1000 [00:50<00:26, 14.84it/s]
61%|██████ | 608/1000 [00:50<00:25, 15.16it/s]
61%|██████ | 610/1000 [00:50<00:25, 15.00it/s]
61%|██████ | 612/1000 [00:50<00:25, 15.16it/s]
61%|██████▏ | 614/1000 [00:50<00:26, 14.40it/s]
62%|██████▏ | 616/1000 [00:50<00:25, 14.80it/s]
62%|██████▏ | 618/1000 [00:50<00:26, 14.21it/s]
62%|██████▏ | 620/1000 [00:51<00:38, 9.75it/s]
62%|██████▏ | 622/1000 [00:51<00:37, 9.97it/s]
62%|██████▏ | 624/1000 [00:51<00:32, 11.40it/s]
63%|██████▎ | 626/1000 [00:51<00:30, 12.07it/s]
63%|██████▎ | 628/1000 [00:51<00:27, 13.30it/s]
63%|██████▎ | 630/1000 [00:52<00:26, 13.79it/s]
63%|██████▎ | 632/1000 [00:52<00:29, 12.46it/s]
63%|██████▎ | 634/1000 [00:52<00:27, 13.18it/s]
64%|██████▎ | 636/1000 [00:52<00:33, 10.97it/s]
64%|██████▍ | 638/1000 [00:52<00:33, 10.66it/s]
64%|██████▍ | 640/1000 [00:52<00:31, 11.42it/s]
64%|██████▍ | 642/1000 [00:53<00:29, 12.31it/s]
64%|██████▍ | 644/1000 [00:53<00:28, 12.57it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.87it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.34it/s]
65%|██████▌ | 650/1000 [00:53<00:27, 12.55it/s]
65%|██████▌ | 652/1000 [00:53<00:29, 11.94it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.42it/s]
66%|██████▌ | 656/1000 [00:54<00:31, 10.76it/s]
66%|██████▌ | 658/1000 [00:54<00:28, 11.83it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.17it/s]
66%|██████▌ | 662/1000 [00:54<00:29, 11.29it/s]
66%|██████▋ | 664/1000 [00:55<00:28, 11.78it/s]
67%|██████▋ | 666/1000 [00:55<00:26, 12.81it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.31it/s]
67%|██████▋ | 670/1000 [00:55<00:29, 11.10it/s]
67%|██████▋ | 672/1000 [00:55<00:27, 11.87it/s]
67%|██████▋ | 674/1000 [00:55<00:26, 12.45it/s]
68%|██████▊ | 676/1000 [00:56<00:27, 11.72it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.21it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 12.98it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.13it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.33it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.40it/s]
69%|██████▉ | 688/1000 [00:56<00:22, 13.70it/s]
69%|██████▉ | 690/1000 [00:57<00:22, 13.78it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.24it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.05it/s]
70%|██████▉ | 696/1000 [00:57<00:22, 13.29it/s]
70%|██████▉ | 698/1000 [00:57<00:21, 13.94it/s]
70%|███████ | 700/1000 [00:57<00:27, 10.89it/s]
70%|███████ | 702/1000 [00:58<00:24, 11.96it/s]
70%|███████ | 704/1000 [00:58<00:31, 9.42it/s]
71%|███████ | 706/1000 [00:58<00:28, 10.19it/s]
71%|███████ | 708/1000 [00:58<00:31, 9.32it/s]
71%|███████ | 710/1000 [00:58<00:27, 10.62it/s]
71%|███████ | 712/1000 [00:59<00:24, 11.61it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.75it/s]
72%|███████▏ | 716/1000 [00:59<00:22, 12.90it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.42it/s]
72%|███████▏ | 720/1000 [00:59<00:20, 13.40it/s]
72%|███████▏ | 722/1000 [00:59<00:20, 13.83it/s]
72%|███████▏ | 724/1000 [00:59<00:20, 13.69it/s]
73%|███████▎ | 726/1000 [01:00<00:19, 13.92it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.91it/s]
73%|███████▎ | 730/1000 [01:00<00:32, 8.19it/s]
73%|███████▎ | 732/1000 [01:00<00:32, 8.31it/s]
73%|███████▎ | 734/1000 [01:01<00:30, 8.59it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.44it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.89it/s]
74%|███████▍ | 739/1000 [01:01<00:23, 10.99it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.68it/s]
74%|███████▍ | 743/1000 [01:01<00:20, 12.30it/s]
74%|███████▍ | 745/1000 [01:02<00:25, 10.19it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.57it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.75it/s]
75%|███████▌ | 751/1000 [01:02<00:21, 11.86it/s]
75%|███████▌ | 753/1000 [01:02<00:19, 12.59it/s]
76%|███████▌ | 755/1000 [01:02<00:18, 13.29it/s]
76%|███████▌ | 757/1000 [01:03<00:23, 10.26it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.30it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.23it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.19it/s]
76%|███████▋ | 765/1000 [01:04<00:27, 8.57it/s]
77%|███████▋ | 767/1000 [01:04<00:24, 9.53it/s]
77%|███████▋ | 769/1000 [01:04<00:22, 10.42it/s]
77%|███████▋ | 771/1000 [01:04<00:20, 11.02it/s]
77%|███████▋ | 773/1000 [01:04<00:19, 11.38it/s]
78%|███████▊ | 775/1000 [01:04<00:19, 11.61it/s]
78%|███████▊ | 777/1000 [01:04<00:18, 11.87it/s]
78%|███████▊ | 779/1000 [01:05<00:23, 9.30it/s]
78%|███████▊ | 781/1000 [01:05<00:25, 8.67it/s]
78%|███████▊ | 782/1000 [01:05<00:30, 7.23it/s]
78%|███████▊ | 784/1000 [01:06<00:30, 7.02it/s]
78%|███████▊ | 785/1000 [01:06<00:29, 7.32it/s]
79%|███████▊ | 786/1000 [01:06<00:30, 6.96it/s]
79%|███████▉ | 788/1000 [01:06<00:24, 8.75it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.07it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 11.21it/s]
79%|███████▉ | 794/1000 [01:06<00:17, 11.86it/s]
80%|███████▉ | 796/1000 [01:07<00:16, 12.33it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 13.08it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.26it/s]
80%|████████ | 802/1000 [01:07<00:24, 8.23it/s]
80%|████████ | 804/1000 [01:08<00:23, 8.28it/s]
81%|████████ | 806/1000 [01:08<00:21, 8.96it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.34it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.48it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.65it/s]
81%|████████▏ | 814/1000 [01:08<00:14, 12.54it/s]
82%|████████▏ | 816/1000 [01:09<00:16, 11.00it/s]
82%|████████▏ | 818/1000 [01:09<00:15, 11.95it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.46it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 13.02it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.82it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.23it/s]
83%|████████▎ | 828/1000 [01:09<00:11, 14.51it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.93it/s]
83%|████████▎ | 832/1000 [01:10<00:11, 14.31it/s]
83%|████████▎ | 834/1000 [01:10<00:11, 14.04it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.41it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.35it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.47it/s]
84%|████████▍ | 842/1000 [01:10<00:10, 14.44it/s]
84%|████████▍ | 844/1000 [01:11<00:11, 13.99it/s]
85%|████████▍ | 846/1000 [01:11<00:11, 13.93it/s]
85%|████████▍ | 848/1000 [01:11<00:10, 13.89it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.09it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.69it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.29it/s]
86%|████████▌ | 856/1000 [01:11<00:10, 14.13it/s]
86%|████████▌ | 858/1000 [01:11<00:09, 14.61it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 15.37it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.50it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 14.01it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.83it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.71it/s]
87%|████████▋ | 870/1000 [01:12<00:09, 14.04it/s]
87%|████████▋ | 872/1000 [01:12<00:08, 14.66it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 12.42it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.05it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.92it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.57it/s]
88%|████████▊ | 882/1000 [01:13<00:10, 11.34it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 12.05it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.79it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.76it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.23it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.01it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 14.17it/s]
90%|████████▉ | 896/1000 [01:14<00:07, 13.92it/s]
90%|████████▉ | 898/1000 [01:15<00:07, 13.32it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.27it/s]
90%|█████████ | 902/1000 [01:15<00:07, 13.02it/s]
90%|█████████ | 904/1000 [01:15<00:09, 9.80it/s]
91%|█████████ | 906/1000 [01:15<00:08, 10.87it/s]
91%|█████████ | 908/1000 [01:15<00:07, 11.84it/s]
91%|█████████ | 910/1000 [01:16<00:07, 12.09it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.28it/s]
91%|█████████▏| 914/1000 [01:16<00:12, 6.81it/s]
92%|█████████▏| 916/1000 [01:16<00:10, 8.13it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.39it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.79it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 10.24it/s]
92%|█████████▏| 924/1000 [01:17<00:06, 10.95it/s]
93%|█████████▎| 926/1000 [01:17<00:06, 11.80it/s]
93%|█████████▎| 928/1000 [01:18<00:06, 10.47it/s]
93%|█████████▎| 930/1000 [01:18<00:06, 11.50it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 12.07it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.18it/s]
94%|█████████▎| 936/1000 [01:18<00:05, 12.59it/s]
94%|█████████▍| 938/1000 [01:18<00:04, 13.52it/s]
94%|█████████▍| 940/1000 [01:18<00:04, 13.97it/s]
94%|█████████▍| 942/1000 [01:19<00:04, 11.91it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.44it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.58it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 12.44it/s]
95%|█████████▌| 950/1000 [01:19<00:03, 13.28it/s]
95%|█████████▌| 952/1000 [01:19<00:03, 14.03it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 14.18it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 14.18it/s]
96%|█████████▌| 958/1000 [01:20<00:02, 14.62it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 13.00it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.65it/s]
96%|█████████▋| 964/1000 [01:20<00:03, 10.20it/s]
97%|█████████▋| 966/1000 [01:21<00:02, 11.42it/s]
97%|█████████▋| 968/1000 [01:21<00:02, 12.09it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 12.71it/s]
97%|█████████▋| 972/1000 [01:21<00:02, 9.67it/s]
97%|█████████▋| 974/1000 [01:21<00:02, 10.85it/s]
98%|█████████▊| 976/1000 [01:21<00:02, 11.65it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.86it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.94it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.44it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 10.44it/s]
99%|█████████▊| 986/1000 [01:22<00:01, 10.68it/s]
99%|█████████▉| 988/1000 [01:23<00:01, 10.79it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 11.31it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 12.23it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 12.98it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 13.91it/s]
100%|█████████▉| 998/1000 [01:23<00:00, 14.09it/s]
100%|██████████| 1000/1000 [01:23<00:00, 14.74it/s]
{'eval_loss': '0.9265', 'eval_runtime': '84.01', 'eval_samples_per_second': '11.9', 'eval_steps_per_second': '11.9', 'epoch': '0.3211', 'num_input_tokens_seen': 20140120}
32%|███▏ | 6000/18688 [7:02:27<14:35:21, 4.14s/it]
100%|██████████| 1000/1000 [01:23<00:00, 14.74it/s]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00008/checkpoint-6000
loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
32%|███▏ | 6001/18688 [7:02:32<104:36:20, 29.68s/it] 32%|███▏ | 6002/18688 [7:02:36<77:04:52, 21.87s/it] 32%|███▏ | 6003/18688 [7:02:41<58:47:06, 16.68s/it] 32%|███▏ | 6004/18688 [7:02:45<45:48:48, 13.00s/it] 32%|███▏ | 6005/18688 [7:02:49<36:01:00, 10.22s/it] 32%|███▏ | 6006/18688 [7:02:53<30:08:30, 8.56s/it] 32%|███▏ | 6007/18688 [7:02:59<26:32:47, 7.54s/it] 32%|███▏ | 6008/18688 [7:03:03<23:40:15, 6.72s/it] 32%|███▏ | 6009/18688 [7:03:07<20:39:02, 5.86s/it] 32%|███▏ | 6010/18688 [7:03:12<19:00:11, 5.40s/it] {'loss': '0.9053', 'grad_norm': '1.045', 'learning_rate': '6.717e-05', 'epoch': '0.3216', 'num_input_tokens_seen': 20175880, 'train_runtime': '2.539e+04', 'train_tokens_per_second': '794.6'}
32%|███▏ | 6010/18688 [7:03:12<19:00:11, 5.40s/it] 32%|███▏ | 6011/18688 [7:03:16<17:40:25, 5.02s/it] 32%|███▏ | 6012/18688 [7:03:20<16:57:49, 4.82s/it] 32%|███▏ | 6013/18688 [7:03:24<16:00:39, 4.55s/it] 32%|███▏ | 6014/18688 [7:03:28<15:18:24, 4.35s/it] 32%|███▏ | 6015/18688 [7:03:31<14:26:17, 4.10s/it] 32%|███▏ | 6016/18688 [7:03:37<15:38:40, 4.44s/it] 32%|███▏ | 6017/18688 [7:03:41<15:14:41, 4.33s/it] 32%|███▏ | 6018/18688 [7:03:45<14:46:57, 4.20s/it] 32%|███▏ | 6019/18688 [7:03:48<14:16:55, 4.06s/it] 32%|███▏ | 6020/18688 [7:03:52<13:50:41, 3.93s/it] {'loss': '0.9221', 'grad_norm': '1.029', 'learning_rate': '6.711e-05', 'epoch': '0.3221', 'num_input_tokens_seen': 20208592, 'train_runtime': '2.543e+04', 'train_tokens_per_second': '794.6'}
32%|███▏ | 6020/18688 [7:03:52<13:50:41, 3.93s/it] 32%|███▏ | 6021/18688 [7:03:56<14:03:06, 3.99s/it] 32%|███▏ | 6022/18688 [7:04:01<15:00:35, 4.27s/it] 32%|███▏ | 6023/18688 [7:04:05<15:03:36, 4.28s/it] 32%|███▏ | 6024/18688 [7:04:10<15:37:37, 4.44s/it] 32%|███▏ | 6025/18688 [7:04:14<14:49:20, 4.21s/it] 32%|███▏ | 6026/18688 [7:04:17<14:11:07, 4.03s/it] 32%|███▏ | 6027/18688 [7:04:21<13:59:29, 3.98s/it] 32%|███▏ | 6028/18688 [7:04:26<15:09:22, 4.31s/it] 32%|███▏ | 6029/18688 [7:04:30<15:02:08, 4.28s/it] 32%|███▏ | 6030/18688 [7:04:34<14:41:11, 4.18s/it] {'loss': '0.9', 'grad_norm': '1.159', 'learning_rate': '6.706e-05', 'epoch': '0.3227', 'num_input_tokens_seen': 20243008, 'train_runtime': '2.547e+04', 'train_tokens_per_second': '794.6'}
32%|███▏ | 6030/18688 [7:04:34<14:41:11, 4.18s/it] 32%|███▏ | 6031/18688 [7:04:38<13:59:39, 3.98s/it] 32%|███▏ | 6032/18688 [7:04:41<13:27:08, 3.83s/it] 32%|███▏ | 6033/18688 [7:04:46<14:22:28, 4.09s/it] 32%|███▏ | 6034/18688 [7:04:50<13:51:49, 3.94s/it] 32%|███▏ | 6035/18688 [7:04:54<14:19:02, 4.07s/it] 32%|███▏ | 6036/18688 [7:04:58<14:37:51, 4.16s/it] 32%|███▏ | 6037/18688 [7:05:02<14:00:46, 3.99s/it] 32%|███▏ | 6038/18688 [7:05:06<14:09:10, 4.03s/it] 32%|███▏ | 6039/18688 [7:05:10<13:41:50, 3.90s/it] 32%|███▏ | 6040/18688 [7:05:14<13:49:40, 3.94s/it] {'loss': '0.9479', 'grad_norm': '1.007', 'learning_rate': '6.701e-05', 'epoch': '0.3232', 'num_input_tokens_seen': 20273568, 'train_runtime': '2.551e+04', 'train_tokens_per_second': '794.6'}
32%|███▏ | 6040/18688 [7:05:14<13:49:40, 3.94s/it] 32%|███▏ | 6041/18688 [7:05:18<14:10:08, 4.03s/it] 32%|███▏ | 6042/18688 [7:05:22<14:11:13, 4.04s/it] 32%|███▏ | 6043/18688 [7:05:26<13:57:08, 3.97s/it] 32%|███▏ | 6044/18688 [7:05:30<13:58:47, 3.98s/it] 32%|███▏ | 6045/18688 [7:05:35<14:44:23, 4.20s/it] 32%|███▏ | 6046/18688 [7:05:38<14:04:25, 4.01s/it] 32%|███▏ | 6047/18688 [7:05:42<13:34:00, 3.86s/it] 32%|███▏ | 6048/18688 [7:05:46<14:10:26, 4.04s/it] 32%|███▏ | 6049/18688 [7:05:50<13:50:09, 3.94s/it] 32%|███▏ | 6050/18688 [7:05:55<14:58:04, 4.26s/it] {'loss': '0.9075', 'grad_norm': '1.248', 'learning_rate': '6.695e-05', 'epoch': '0.3237', 'num_input_tokens_seen': 20306824, 'train_runtime': '2.556e+04', 'train_tokens_per_second': '794.6'}
32%|███▏ | 6050/18688 [7:05:55<14:58:04, 4.26s/it] 32%|███▏ | 6051/18688 [7:05:59<14:33:12, 4.15s/it] 32%|███▏ | 6052/18688 [7:06:03<14:47:34, 4.21s/it] 32%|███▏ | 6053/18688 [7:06:07<14:40:47, 4.18s/it] 32%|███▏ | 6054/18688 [7:06:11<14:10:44, 4.04s/it] 32%|███▏ | 6055/18688 [7:06:15<14:01:00, 3.99s/it] 32%|███▏ | 6056/18688 [7:06:19<14:16:02, 4.07s/it] 32%|███▏ | 6057/18688 [7:06:23<14:18:24, 4.08s/it] 32%|███▏ | 6058/18688 [7:06:27<14:03:41, 4.01s/it] 32%|███▏ | 6059/18688 [7:06:31<13:41:21, 3.90s/it] 32%|███▏ | 6060/18688 [7:06:35<13:37:24, 3.88s/it] {'loss': '0.9509', 'grad_norm': '1.171', 'learning_rate': '6.69e-05', 'epoch': '0.3243', 'num_input_tokens_seen': 20338936, 'train_runtime': '2.56e+04', 'train_tokens_per_second': '794.6'}
32%|███▏ | 6060/18688 [7:06:35<13:37:24, 3.88s/it] 32%|███▏ | 6061/18688 [7:06:38<13:11:06, 3.76s/it] 32%|███▏ | 6062/18688 [7:06:42<13:48:05, 3.94s/it] 32%|███▏ | 6063/18688 [7:06:46<13:44:44, 3.92s/it] 32%|███▏ | 6064/18688 [7:06:51<14:38:32, 4.18s/it] 32%|███▏ | 6065/18688 [7:06:55<14:12:26, 4.05s/it] 32%|███▏ | 6066/18688 [7:06:59<13:56:26, 3.98s/it] 32%|███▏ | 6067/18688 [7:07:03<13:56:50, 3.98s/it] 32%|███▏ | 6068/18688 [7:07:07<14:39:20, 4.18s/it] 32%|███▏ | 6069/18688 [7:07:11<14:09:16, 4.04s/it] 32%|███▏ | 6070/18688 [7:07:15<13:55:45, 3.97s/it] {'loss': '0.9751', 'grad_norm': '1.219', 'learning_rate': '6.684e-05', 'epoch': '0.3248', 'num_input_tokens_seen': 20371608, 'train_runtime': '2.564e+04', 'train_tokens_per_second': '794.7'}
32%|███▏ | 6070/18688 [7:07:15<13:55:45, 3.97s/it] 32%|███▏ | 6071/18688 [7:07:19<14:02:48, 4.01s/it] 32%|███▏ | 6072/18688 [7:07:22<13:38:42, 3.89s/it] 32%|███▏ | 6073/18688 [7:07:26<13:10:59, 3.76s/it] 33%|███▎ | 6074/18688 [7:07:30<13:33:51, 3.87s/it] 33%|███▎ | 6075/18688 [7:07:33<13:07:33, 3.75s/it] 33%|███▎ | 6076/18688 [7:07:37<13:23:25, 3.82s/it] 33%|███▎ | 6077/18688 [7:07:41<13:30:01, 3.85s/it] 33%|███▎ | 6078/18688 [7:07:45<13:35:09, 3.88s/it] 33%|███▎ | 6079/18688 [7:07:50<14:24:24, 4.11s/it] 33%|███▎ | 6080/18688 [7:07:54<14:16:15, 4.07s/it] {'loss': '0.8561', 'grad_norm': '1.083', 'learning_rate': '6.679e-05', 'epoch': '0.3254', 'num_input_tokens_seen': 20402328, 'train_runtime': '2.567e+04', 'train_tokens_per_second': '794.7'}
33%|███▎ | 6080/18688 [7:07:54<14:16:15, 4.07s/it] 33%|███▎ | 6081/18688 [7:07:59<15:03:54, 4.30s/it] 33%|███▎ | 6082/18688 [7:08:03<14:32:10, 4.15s/it] 33%|███▎ | 6083/18688 [7:08:07<15:04:56, 4.31s/it] 33%|███▎ | 6084/18688 [7:08:12<15:03:48, 4.30s/it] 33%|███▎ | 6085/18688 [7:08:15<14:21:28, 4.10s/it] 33%|███▎ | 6086/18688 [7:08:19<14:07:58, 4.04s/it] 33%|███▎ | 6087/18688 [7:08:23<13:39:01, 3.90s/it] 33%|███▎ | 6088/18688 [7:08:26<13:16:56, 3.79s/it] 33%|███▎ | 6089/18688 [7:08:30<13:08:46, 3.76s/it] 33%|███▎ | 6090/18688 [7:08:33<12:48:12, 3.66s/it] {'loss': '0.9777', 'grad_norm': '1.284', 'learning_rate': '6.674e-05', 'epoch': '0.3259', 'num_input_tokens_seen': 20433016, 'train_runtime': '2.571e+04', 'train_tokens_per_second': '794.6'}
33%|███▎ | 6090/18688 [7:08:33<12:48:12, 3.66s/it] 33%|███▎ | 6091/18688 [7:08:37<13:07:03, 3.75s/it] 33%|███▎ | 6092/18688 [7:08:41<12:50:36, 3.67s/it] 33%|███▎ | 6093/18688 [7:08:44<12:36:10, 3.60s/it] 33%|███▎ | 6094/18688 [7:08:48<12:47:07, 3.65s/it] 33%|███▎ | 6095/18688 [7:08:53<14:15:41, 4.08s/it] 33%|███▎ | 6096/18688 [7:08:58<14:39:58, 4.19s/it] 33%|███▎ | 6097/18688 [7:09:02<15:00:35, 4.29s/it] 33%|███▎ | 6098/18688 [7:09:07<15:21:38, 4.39s/it] 33%|███▎ | 6099/18688 [7:09:10<14:40:14, 4.20s/it] 33%|███▎ | 6100/18688 [7:09:14<14:00:55, 4.01s/it] {'loss': '0.8928', 'grad_norm': '1.091', 'learning_rate': '6.668e-05', 'epoch': '0.3264', 'num_input_tokens_seen': 20466352, 'train_runtime': '2.575e+04', 'train_tokens_per_second': '794.7'}
33%|███▎ | 6100/18688 [7:09:14<14:00:55, 4.01s/it] 33%|███▎ | 6101/18688 [7:09:18<14:15:25, 4.08s/it] 33%|███▎ | 6102/18688 [7:09:23<14:32:07, 4.16s/it] 33%|███▎ | 6103/18688 [7:09:26<13:56:40, 3.99s/it] 33%|███▎ | 6104/18688 [7:09:30<13:38:10, 3.90s/it] 33%|███▎ | 6105/18688 [7:09:34<13:33:44, 3.88s/it] 33%|███▎ | 6106/18688 [7:09:39<15:08:44, 4.33s/it] 33%|███▎ | 6107/18688 [7:09:44<15:24:44, 4.41s/it] 33%|███▎ | 6108/18688 [7:09:47<14:31:06, 4.15s/it] 33%|███▎ | 6109/18688 [7:09:51<14:18:45, 4.10s/it] 33%|███▎ | 6110/18688 [7:09:55<13:40:01, 3.91s/it] {'loss': '0.9737', 'grad_norm': '1.036', 'learning_rate': '6.663e-05', 'epoch': '0.327', 'num_input_tokens_seen': 20499760, 'train_runtime': '2.58e+04', 'train_tokens_per_second': '794.7'}
33%|███▎ | 6110/18688 [7:09:55<13:40:01, 3.91s/it] 33%|███▎ | 6111/18688 [7:09:59<13:40:50, 3.92s/it] 33%|███▎ | 6112/18688 [7:10:02<13:23:54, 3.84s/it] 33%|███▎ | 6113/18688 [7:10:08<15:03:00, 4.31s/it] 33%|███▎ | 6114/18688 [7:10:12<14:42:25, 4.21s/it] 33%|███▎ | 6115/18688 [7:10:16<14:38:12, 4.19s/it] 33%|███▎ | 6116/18688 [7:10:20<14:10:56, 4.06s/it] 33%|███▎ | 6117/18688 [7:10:24<14:39:48, 4.20s/it] 33%|███▎ | 6118/18688 [7:10:28<14:31:55, 4.16s/it] 33%|███▎ | 6119/18688 [7:10:32<14:17:16, 4.09s/it] 33%|███▎ | 6120/18688 [7:10:36<14:19:43, 4.10s/it] {'loss': '0.8801', 'grad_norm': '1.139', 'learning_rate': '6.657e-05', 'epoch': '0.3275', 'num_input_tokens_seen': 20533952, 'train_runtime': '2.584e+04', 'train_tokens_per_second': '794.8'}
33%|███▎ | 6120/18688 [7:10:36<14:19:43, 4.10s/it] 33%|███▎ | 6121/18688 [7:10:41<14:38:02, 4.19s/it] 33%|███▎ | 6122/18688 [7:10:44<14:19:03, 4.10s/it] 33%|███▎ | 6123/18688 [7:10:48<13:47:49, 3.95s/it] 33%|███▎ | 6124/18688 [7:10:53<14:33:24, 4.17s/it] 33%|███▎ | 6125/18688 [7:10:57<14:08:13, 4.05s/it] 33%|███▎ | 6126/18688 [7:11:00<13:43:33, 3.93s/it] 33%|███▎ | 6127/18688 [7:11:05<14:34:05, 4.18s/it] 33%|███▎ | 6128/18688 [7:11:09<14:57:26, 4.29s/it] 33%|███▎ | 6129/18688 [7:11:13<14:26:02, 4.14s/it] 33%|███▎ | 6130/18688 [7:11:17<13:41:33, 3.93s/it] {'loss': '0.9746', 'grad_norm': '1.458', 'learning_rate': '6.652e-05', 'epoch': '0.328', 'num_input_tokens_seen': 20565120, 'train_runtime': '2.588e+04', 'train_tokens_per_second': '794.7'}
33%|███▎ | 6130/18688 [7:11:17<13:41:33, 3.93s/it] 33%|███▎ | 6131/18688 [7:11:22<14:57:04, 4.29s/it] 33%|███▎ | 6132/18688 [7:11:26<15:16:26, 4.38s/it] 33%|███▎ | 6133/18688 [7:11:30<14:34:49, 4.18s/it] 33%|███▎ | 6134/18688 [7:11:34<13:49:24, 3.96s/it] 33%|███▎ | 6135/18688 [7:11:37<13:21:54, 3.83s/it] 33%|███▎ | 6136/18688 [7:11:41<13:16:03, 3.81s/it] 33%|███▎ | 6137/18688 [7:11:44<13:00:34, 3.73s/it] 33%|███▎ | 6138/18688 [7:11:49<14:10:38, 4.07s/it] 33%|███▎ | 6139/18688 [7:11:53<13:52:35, 3.98s/it] 33%|███▎ | 6140/18688 [7:11:57<14:05:51, 4.04s/it] {'loss': '0.9583', 'grad_norm': '1.188', 'learning_rate': '6.646e-05', 'epoch': '0.3286', 'num_input_tokens_seen': 20598680, 'train_runtime': '2.592e+04', 'train_tokens_per_second': '794.8'}
33%|███▎ | 6140/18688 [7:11:57<14:05:51, 4.04s/it] 33%|███▎ | 6141/18688 [7:12:01<13:52:28, 3.98s/it] 33%|███▎ | 6142/18688 [7:12:05<13:58:29, 4.01s/it] 33%|███▎ | 6143/18688 [7:12:09<13:41:18, 3.93s/it] 33%|███▎ | 6144/18688 [7:12:13<14:19:59, 4.11s/it] 33%|███▎ | 6145/18688 [7:12:17<14:01:47, 4.03s/it] 33%|███▎ | 6146/18688 [7:12:21<13:51:35, 3.98s/it] 33%|███▎ | 6147/18688 [7:12:25<14:02:21, 4.03s/it] 33%|███▎ | 6148/18688 [7:12:29<13:40:14, 3.92s/it] 33%|███▎ | 6149/18688 [7:12:33<13:18:52, 3.82s/it] 33%|███▎ | 6150/18688 [7:12:37<13:33:11, 3.89s/it] {'loss': '0.9696', 'grad_norm': '1.18', 'learning_rate': '6.641e-05', 'epoch': '0.3291', 'num_input_tokens_seen': 20629968, 'train_runtime': '2.596e+04', 'train_tokens_per_second': '794.8'}
33%|███▎ | 6150/18688 [7:12:37<13:33:11, 3.89s/it] 33%|███▎ | 6151/18688 [7:12:41<14:27:35, 4.15s/it] 33%|███▎ | 6152/18688 [7:12:45<13:47:51, 3.96s/it] 33%|███▎ | 6153/18688 [7:12:48<13:24:33, 3.85s/it] 33%|███▎ | 6154/18688 [7:12:52<13:22:49, 3.84s/it] 33%|███▎ | 6155/18688 [7:12:56<13:26:02, 3.86s/it] 33%|███▎ | 6156/18688 [7:13:00<13:31:56, 3.89s/it] 33%|███▎ | 6157/18688 [7:13:04<13:20:16, 3.83s/it] 33%|███▎ | 6158/18688 [7:13:08<14:11:15, 4.08s/it] 33%|███▎ | 6159/18688 [7:13:13<14:30:58, 4.17s/it] 33%|███▎ | 6160/18688 [7:13:17<14:16:05, 4.10s/it] {'loss': '1.002', 'grad_norm': '1.022', 'learning_rate': '6.635e-05', 'epoch': '0.3296', 'num_input_tokens_seen': 20661592, 'train_runtime': '2.6e+04', 'train_tokens_per_second': '794.8'}
33%|███▎ | 6160/18688 [7:13:17<14:16:05, 4.10s/it] 33%|███▎ | 6161/18688 [7:13:20<13:42:53, 3.94s/it] 33%|███▎ | 6162/18688 [7:13:24<13:20:23, 3.83s/it] 33%|███▎ | 6163/18688 [7:13:28<13:21:32, 3.84s/it] 33%|███▎ | 6164/18688 [7:13:32<13:49:20, 3.97s/it] 33%|███▎ | 6165/18688 [7:13:36<13:17:00, 3.82s/it] 33%|███▎ | 6166/18688 [7:13:39<13:21:29, 3.84s/it] 33%|███▎ | 6167/18688 [7:13:43<13:28:44, 3.88s/it] 33%|███▎ | 6168/18688 [7:13:48<13:47:37, 3.97s/it] 33%|███▎ | 6169/18688 [7:13:52<14:12:28, 4.09s/it] 33%|███▎ | 6170/18688 [7:13:56<14:30:27, 4.17s/it] {'loss': '0.8952', 'grad_norm': '1.081', 'learning_rate': '6.63e-05', 'epoch': '0.3302', 'num_input_tokens_seen': 20692840, 'train_runtime': '2.604e+04', 'train_tokens_per_second': '794.8'}
33%|███▎ | 6170/18688 [7:13:56<14:30:27, 4.17s/it] 33%|███▎ | 6171/18688 [7:14:01<14:30:28, 4.17s/it] 33%|███▎ | 6172/18688 [7:14:05<14:26:38, 4.15s/it] 33%|███▎ | 6173/18688 [7:14:09<14:28:35, 4.16s/it] 33%|███▎ | 6174/18688 [7:14:13<14:00:02, 4.03s/it] 33%|███▎ | 6175/18688 [7:14:16<13:39:42, 3.93s/it] 33%|███▎ | 6176/18688 [7:14:20<13:26:37, 3.87s/it] 33%|███▎ | 6177/18688 [7:14:24<13:30:04, 3.88s/it] 33%|███▎ | 6178/18688 [7:14:28<13:59:31, 4.03s/it] 33%|███▎ | 6179/18688 [7:14:33<14:26:55, 4.16s/it] 33%|███▎ | 6180/18688 [7:14:37<14:16:13, 4.11s/it] {'loss': '0.8937', 'grad_norm': '1.255', 'learning_rate': '6.624e-05', 'epoch': '0.3307', 'num_input_tokens_seen': 20726280, 'train_runtime': '2.608e+04', 'train_tokens_per_second': '794.8'}
33%|███▎ | 6180/18688 [7:14:37<14:16:13, 4.11s/it] 33%|███▎ | 6181/18688 [7:14:41<14:36:44, 4.21s/it] 33%|███▎ | 6182/18688 [7:14:45<13:55:51, 4.01s/it] 33%|███▎ | 6183/18688 [7:14:49<14:19:41, 4.12s/it] 33%|███▎ | 6184/18688 [7:14:53<14:25:26, 4.15s/it] 33%|███▎ | 6185/18688 [7:14:57<13:47:43, 3.97s/it] 33%|███▎ | 6186/18688 [7:15:01<13:47:48, 3.97s/it] 33%|███▎ | 6187/18688 [7:15:05<13:33:50, 3.91s/it] 33%|███▎ | 6188/18688 [7:15:09<13:48:54, 3.98s/it] 33%|███▎ | 6189/18688 [7:15:14<14:55:10, 4.30s/it] 33%|███▎ | 6190/18688 [7:15:17<14:09:51, 4.08s/it] {'loss': '0.9392', 'grad_norm': '1.162', 'learning_rate': '6.619e-05', 'epoch': '0.3312', 'num_input_tokens_seen': 20759248, 'train_runtime': '2.612e+04', 'train_tokens_per_second': '794.8'}
33%|███▎ | 6190/18688 [7:15:17<14:09:51, 4.08s/it] 33%|███▎ | 6191/18688 [7:15:22<14:44:41, 4.25s/it] 33%|███▎ | 6192/18688 [7:15:26<14:12:00, 4.09s/it] 33%|███▎ | 6193/18688 [7:15:31<15:19:35, 4.42s/it] 33%|███▎ | 6194/18688 [7:15:35<15:01:17, 4.33s/it] 33%|███▎ | 6195/18688 [7:15:40<15:35:27, 4.49s/it] 33%|███▎ | 6196/18688 [7:15:44<15:14:17, 4.39s/it] 33%|███▎ | 6197/18688 [7:15:49<15:26:43, 4.45s/it] 33%|███▎ | 6198/18688 [7:15:53<15:03:48, 4.34s/it] 33%|███▎ | 6199/18688 [7:15:57<15:04:29, 4.35s/it] 33%|███▎ | 6200/18688 [7:16:01<14:19:25, 4.13s/it] {'loss': '1.03', 'grad_norm': '1.395', 'learning_rate': '6.613e-05', 'epoch': '0.3318', 'num_input_tokens_seen': 20796600, 'train_runtime': '2.616e+04', 'train_tokens_per_second': '794.9'}
33%|███▎ | 6200/18688 [7:16:01<14:19:25, 4.13s/it] 33%|███▎ | 6201/18688 [7:16:05<14:21:02, 4.14s/it] 33%|███▎ | 6202/18688 [7:16:09<13:54:27, 4.01s/it] 33%|███▎ | 6203/18688 [7:16:12<13:35:28, 3.92s/it] 33%|███▎ | 6204/18688 [7:16:16<13:32:39, 3.91s/it] 33%|███▎ | 6205/18688 [7:16:20<13:36:51, 3.93s/it] 33%|███▎ | 6206/18688 [7:16:24<13:46:27, 3.97s/it] 33%|███▎ | 6207/18688 [7:16:29<14:21:34, 4.14s/it] 33%|███▎ | 6208/18688 [7:16:32<13:53:46, 4.01s/it] 33%|███▎ | 6209/18688 [7:16:36<13:18:16, 3.84s/it] 33%|███▎ | 6210/18688 [7:16:40<13:30:31, 3.90s/it] {'loss': '0.924', 'grad_norm': '1.069', 'learning_rate': '6.608e-05', 'epoch': '0.3323', 'num_input_tokens_seen': 20825928, 'train_runtime': '2.62e+04', 'train_tokens_per_second': '794.9'}
33%|███▎ | 6210/18688 [7:16:40<13:30:31, 3.90s/it] 33%|███▎ | 6211/18688 [7:16:44<13:40:06, 3.94s/it] 33%|███▎ | 6212/18688 [7:16:47<13:08:30, 3.79s/it] 33%|███▎ | 6213/18688 [7:16:51<13:08:41, 3.79s/it] 33%|███▎ | 6214/18688 [7:16:55<12:46:52, 3.69s/it] 33%|███▎ | 6215/18688 [7:16:59<13:36:29, 3.93s/it] 33%|███▎ | 6216/18688 [7:17:03<13:14:05, 3.82s/it] 33%|███▎ | 6217/18688 [7:17:07<14:00:21, 4.04s/it] 33%|███▎ | 6218/18688 [7:17:11<14:00:10, 4.04s/it] 33%|███▎ | 6219/18688 [7:17:15<13:25:53, 3.88s/it] 33%|███▎ | 6220/18688 [7:17:19<13:40:08, 3.95s/it] {'loss': '0.915', 'grad_norm': '1.165', 'learning_rate': '6.602e-05', 'epoch': '0.3328', 'num_input_tokens_seen': 20856744, 'train_runtime': '2.624e+04', 'train_tokens_per_second': '794.9'}
33%|███▎ | 6220/18688 [7:17:19<13:40:08, 3.95s/it] 33%|███▎ | 6221/18688 [7:17:23<13:22:35, 3.86s/it] 33%|███▎ | 6222/18688 [7:17:26<13:07:41, 3.79s/it] 33%|███▎ | 6223/18688 [7:17:30<12:50:55, 3.71s/it] 33%|███▎ | 6224/18688 [7:17:35<14:13:54, 4.11s/it] 33%|███▎ | 6225/18688 [7:17:39<14:15:20, 4.12s/it] 33%|███▎ | 6226/18688 [7:17:43<14:08:06, 4.08s/it] 33%|███▎ | 6227/18688 [7:17:47<14:01:44, 4.05s/it] 33%|███▎ | 6228/18688 [7:17:51<13:54:28, 4.02s/it] 33%|███▎ | 6229/18688 [7:17:55<13:56:57, 4.03s/it] 33%|███▎ | 6230/18688 [7:17:59<13:34:30, 3.92s/it] {'loss': '0.9274', 'grad_norm': '0.9783', 'learning_rate': '6.597e-05', 'epoch': '0.3334', 'num_input_tokens_seen': 20888192, 'train_runtime': '2.628e+04', 'train_tokens_per_second': '794.9'}
33%|███▎ | 6230/18688 [7:17:59<13:34:30, 3.92s/it] 33%|███▎ | 6231/18688 [7:18:04<14:43:56, 4.26s/it] 33%|███▎ | 6232/18688 [7:18:07<13:56:02, 4.03s/it] 33%|███▎ | 6233/18688 [7:18:11<13:58:17, 4.04s/it] 33%|███▎ | 6234/18688 [7:18:15<13:41:25, 3.96s/it] 33%|███▎ | 6235/18688 [7:18:19<14:08:02, 4.09s/it] 33%|███▎ | 6236/18688 [7:18:23<14:11:25, 4.10s/it] 33%|███▎ | 6237/18688 [7:18:27<13:51:31, 4.01s/it] 33%|███▎ | 6238/18688 [7:18:32<14:18:02, 4.14s/it] 33%|███▎ | 6239/18688 [7:18:35<13:53:50, 4.02s/it] 33%|███▎ | 6240/18688 [7:18:39<13:39:16, 3.95s/it] {'loss': '0.9221', 'grad_norm': '1.222', 'learning_rate': '6.591e-05', 'epoch': '0.3339', 'num_input_tokens_seen': 20921856, 'train_runtime': '2.632e+04', 'train_tokens_per_second': '794.9'}
33%|███▎ | 6240/18688 [7:18:39<13:39:16, 3.95s/it] 33%|███▎ | 6241/18688 [7:18:44<14:26:23, 4.18s/it] 33%|███▎ | 6242/18688 [7:18:48<14:00:05, 4.05s/it] 33%|███▎ | 6243/18688 [7:18:52<14:05:52, 4.08s/it] 33%|███▎ | 6244/18688 [7:18:56<13:47:27, 3.99s/it] 33%|███▎ | 6245/18688 [7:19:00<14:27:07, 4.18s/it] 33%|███▎ | 6246/18688 [7:19:04<14:23:48, 4.17s/it] 33%|███▎ | 6247/18688 [7:19:08<14:13:18, 4.12s/it] 33%|███▎ | 6248/18688 [7:19:12<13:57:51, 4.04s/it] 33%|███▎ | 6249/18688 [7:19:16<14:00:25, 4.05s/it] 33%|███▎ | 6250/18688 [7:19:20<13:22:12, 3.87s/it] {'loss': '0.9682', 'grad_norm': '1.149', 'learning_rate': '6.585e-05', 'epoch': '0.3344', 'num_input_tokens_seen': 20955112, 'train_runtime': '2.636e+04', 'train_tokens_per_second': '795'}
33%|███▎ | 6250/18688 [7:19:20<13:22:12, 3.87s/it] 33%|███▎ | 6251/18688 [7:19:23<12:53:40, 3.73s/it] 33%|███▎ | 6252/18688 [7:19:27<13:19:00, 3.85s/it] 33%|███▎ | 6253/18688 [7:19:31<13:10:32, 3.81s/it] 33%|███▎ | 6254/18688 [7:19:35<12:53:28, 3.73s/it] 33%|███▎ | 6255/18688 [7:19:38<12:41:18, 3.67s/it] 33%|███▎ | 6256/18688 [7:19:42<13:22:36, 3.87s/it] 33%|███▎ | 6257/18688 [7:19:47<13:42:42, 3.97s/it] 33%|███▎ | 6258/18688 [7:19:51<13:39:55, 3.96s/it] 33%|███▎ | 6259/18688 [7:19:54<13:32:36, 3.92s/it] 33%|███▎ | 6260/18688 [7:19:58<13:12:50, 3.83s/it] {'loss': '0.8963', 'grad_norm': '1.19', 'learning_rate': '6.58e-05', 'epoch': '0.335', 'num_input_tokens_seen': 20986040, 'train_runtime': '2.64e+04', 'train_tokens_per_second': '795'}
33%|███▎ | 6260/18688 [7:19:58<13:12:50, 3.83s/it] 34%|███▎ | 6261/18688 [7:20:02<13:24:28, 3.88s/it] 34%|███▎ | 6262/18688 [7:20:06<13:40:55, 3.96s/it] 34%|███▎ | 6263/18688 [7:20:11<14:30:54, 4.21s/it] 34%|███▎ | 6264/18688 [7:20:15<14:14:37, 4.13s/it] 34%|███▎ | 6265/18688 [7:20:19<14:06:39, 4.09s/it] 34%|███▎ | 6266/18688 [7:20:23<13:57:29, 4.05s/it] 34%|███▎ | 6267/18688 [7:20:27<14:25:24, 4.18s/it] 34%|███▎ | 6268/18688 [7:20:31<14:01:02, 4.06s/it] 34%|███▎ | 6269/18688 [7:20:35<14:00:23, 4.06s/it] 34%|███▎ | 6270/18688 [7:20:39<13:41:41, 3.97s/it] {'loss': '0.9842', 'grad_norm': '1.326', 'learning_rate': '6.574e-05', 'epoch': '0.3355', 'num_input_tokens_seen': 21020464, 'train_runtime': '2.644e+04', 'train_tokens_per_second': '795'}
34%|███▎ | 6270/18688 [7:20:39<13:41:41, 3.97s/it] 34%|███▎ | 6271/18688 [7:20:44<14:25:58, 4.18s/it] 34%|███▎ | 6272/18688 [7:20:48<14:49:25, 4.30s/it] 34%|███▎ | 6273/18688 [7:20:52<14:47:20, 4.29s/it] 34%|███▎ | 6274/18688 [7:20:56<14:05:24, 4.09s/it] 34%|███▎ | 6275/18688 [7:21:00<14:03:40, 4.08s/it] 34%|███▎ | 6276/18688 [7:21:04<14:09:57, 4.11s/it] 34%|███▎ | 6277/18688 [7:21:08<13:50:21, 4.01s/it] 34%|███▎ | 6278/18688 [7:21:12<13:43:32, 3.98s/it] 34%|███▎ | 6279/18688 [7:21:16<14:08:46, 4.10s/it] 34%|███▎ | 6280/18688 [7:21:21<15:05:22, 4.38s/it] {'loss': '0.9418', 'grad_norm': '0.963', 'learning_rate': '6.569e-05', 'epoch': '0.3361', 'num_input_tokens_seen': 21055680, 'train_runtime': '2.648e+04', 'train_tokens_per_second': '795.1'}
34%|███▎ | 6280/18688 [7:21:21<15:05:22, 4.38s/it] 34%|███▎ | 6281/18688 [7:21:25<14:23:30, 4.18s/it] 34%|███▎ | 6282/18688 [7:21:30<14:52:33, 4.32s/it] 34%|███▎ | 6283/18688 [7:21:34<14:23:59, 4.18s/it] 34%|███▎ | 6284/18688 [7:21:38<14:33:18, 4.22s/it] 34%|███▎ | 6285/18688 [7:21:42<14:34:40, 4.23s/it] 34%|███▎ | 6286/18688 [7:21:46<13:52:56, 4.03s/it] 34%|███▎ | 6287/18688 [7:21:49<13:37:46, 3.96s/it] 34%|███▎ | 6288/18688 [7:21:54<14:24:27, 4.18s/it] 34%|███▎ | 6289/18688 [7:21:58<13:38:23, 3.96s/it] 34%|███▎ | 6290/18688 [7:22:02<13:36:50, 3.95s/it] {'loss': '0.9351', 'grad_norm': '0.9923', 'learning_rate': '6.563e-05', 'epoch': '0.3366', 'num_input_tokens_seen': 21088384, 'train_runtime': '2.652e+04', 'train_tokens_per_second': '795.1'}
34%|███▎ | 6290/18688 [7:22:02<13:36:50, 3.95s/it] 34%|███▎ | 6291/18688 [7:22:05<13:25:04, 3.90s/it] 34%|███▎ | 6292/18688 [7:22:09<13:28:51, 3.92s/it] 34%|███▎ | 6293/18688 [7:22:13<13:39:16, 3.97s/it] 34%|███▎ | 6294/18688 [7:22:18<13:52:59, 4.03s/it] 34%|███▎ | 6295/18688 [7:22:22<13:56:11, 4.05s/it] 34%|███▎ | 6296/18688 [7:22:26<14:30:13, 4.21s/it] 34%|███▎ | 6297/18688 [7:22:30<13:42:12, 3.98s/it] 34%|███▎ | 6298/18688 [7:22:33<13:18:56, 3.87s/it] 34%|███▎ | 6299/18688 [7:22:37<13:15:11, 3.85s/it] 34%|███▎ | 6300/18688 [7:22:41<13:48:47, 4.01s/it] {'loss': '0.934', 'grad_norm': '0.9157', 'learning_rate': '6.557e-05', 'epoch': '0.3371', 'num_input_tokens_seen': 21120936, 'train_runtime': '2.656e+04', 'train_tokens_per_second': '795.2'}
34%|███▎ | 6300/18688 [7:22:41<13:48:47, 4.01s/it] 34%|███▎ | 6301/18688 [7:22:46<13:50:40, 4.02s/it] 34%|███▎ | 6302/18688 [7:22:50<13:55:06, 4.05s/it] 34%|███▎ | 6303/18688 [7:22:53<13:27:44, 3.91s/it] 34%|███▎ | 6304/18688 [7:22:57<13:24:45, 3.90s/it] 34%|███▎ | 6305/18688 [7:23:01<13:24:27, 3.90s/it] 34%|███▎ | 6306/18688 [7:23:05<13:16:57, 3.86s/it] 34%|███▎ | 6307/18688 [7:23:09<13:47:32, 4.01s/it] 34%|███▍ | 6308/18688 [7:23:14<14:25:28, 4.19s/it] 34%|███▍ | 6309/18688 [7:23:17<13:47:59, 4.01s/it] 34%|███▍ | 6310/18688 [7:23:22<14:20:03, 4.17s/it] {'loss': '0.9606', 'grad_norm': '1.145', 'learning_rate': '6.552e-05', 'epoch': '0.3377', 'num_input_tokens_seen': 21152248, 'train_runtime': '2.66e+04', 'train_tokens_per_second': '795.1'}
34%|███▍ | 6310/18688 [7:23:22<14:20:03, 4.17s/it] 34%|███▍ | 6311/18688 [7:23:26<14:14:42, 4.14s/it] 34%|███▍ | 6312/18688 [7:23:30<14:15:42, 4.15s/it] 34%|███▍ | 6313/18688 [7:23:34<14:24:29, 4.19s/it] 34%|███▍ | 6314/18688 [7:23:39<14:34:44, 4.24s/it] 34%|███▍ | 6315/18688 [7:23:43<14:17:53, 4.16s/it] 34%|███▍ | 6316/18688 [7:23:46<13:47:42, 4.01s/it] 34%|███▍ | 6317/18688 [7:23:51<13:59:05, 4.07s/it] 34%|███▍ | 6318/18688 [7:23:55<14:48:09, 4.31s/it] 34%|███▍ | 6319/18688 [7:24:00<14:51:04, 4.32s/it] 34%|███▍ | 6320/18688 [7:24:05<15:44:50, 4.58s/it] {'loss': '0.9267', 'grad_norm': '1.079', 'learning_rate': '6.546e-05', 'epoch': '0.3382', 'num_input_tokens_seen': 21188680, 'train_runtime': '2.665e+04', 'train_tokens_per_second': '795.2'}
34%|███▍ | 6320/18688 [7:24:05<15:44:50, 4.58s/it] 34%|███▍ | 6321/18688 [7:24:10<16:26:28, 4.79s/it] 34%|███▍ | 6322/18688 [7:24:14<15:19:21, 4.46s/it] 34%|███▍ | 6323/18688 [7:24:18<14:22:07, 4.18s/it] 34%|███▍ | 6324/18688 [7:24:22<14:10:33, 4.13s/it] 34%|███▍ | 6325/18688 [7:24:26<14:30:48, 4.23s/it] 34%|███▍ | 6326/18688 [7:24:30<14:28:43, 4.22s/it] 34%|███▍ | 6327/18688 [7:24:34<13:43:19, 4.00s/it] 34%|███▍ | 6328/18688 [7:24:39<14:47:14, 4.31s/it] 34%|███▍ | 6329/18688 [7:24:43<14:36:33, 4.26s/it] 34%|███▍ | 6330/18688 [7:24:47<14:39:28, 4.27s/it] {'loss': '0.9495', 'grad_norm': '1.43', 'learning_rate': '6.54e-05', 'epoch': '0.3387', 'num_input_tokens_seen': 21223512, 'train_runtime': '2.669e+04', 'train_tokens_per_second': '795.3'}
34%|███▍ | 6330/18688 [7:24:47<14:39:28, 4.27s/it] 34%|███▍ | 6331/18688 [7:24:53<15:48:40, 4.61s/it] 34%|███▍ | 6332/18688 [7:24:57<15:40:39, 4.57s/it] 34%|███▍ | 6333/18688 [7:25:01<14:47:22, 4.31s/it] 34%|███▍ | 6334/18688 [7:25:04<13:59:03, 4.08s/it] 34%|███▍ | 6335/18688 [7:25:08<13:29:37, 3.93s/it] 34%|███▍ | 6336/18688 [7:25:11<13:08:17, 3.83s/it] 34%|███▍ | 6337/18688 [7:25:17<14:28:30, 4.22s/it] 34%|███▍ | 6338/18688 [7:25:21<14:11:51, 4.14s/it] 34%|███▍ | 6339/18688 [7:25:25<14:25:51, 4.21s/it] 34%|███▍ | 6340/18688 [7:25:29<14:20:44, 4.18s/it] {'loss': '0.8988', 'grad_norm': '1.212', 'learning_rate': '6.535e-05', 'epoch': '0.3393', 'num_input_tokens_seen': 21257224, 'train_runtime': '2.673e+04', 'train_tokens_per_second': '795.3'}
34%|███▍ | 6340/18688 [7:25:29<14:20:44, 4.18s/it] 34%|███▍ | 6341/18688 [7:25:33<14:33:27, 4.24s/it] 34%|███▍ | 6342/18688 [7:25:37<13:54:17, 4.05s/it] 34%|███▍ | 6343/18688 [7:25:41<13:34:40, 3.96s/it] 34%|███▍ | 6344/18688 [7:25:45<13:41:00, 3.99s/it] 34%|███▍ | 6345/18688 [7:25:49<13:46:51, 4.02s/it] 34%|███▍ | 6346/18688 [7:25:53<14:15:13, 4.16s/it] 34%|███▍ | 6347/18688 [7:25:58<14:51:53, 4.34s/it] 34%|███▍ | 6348/18688 [7:26:02<13:58:21, 4.08s/it] 34%|███▍ | 6349/18688 [7:26:06<13:52:12, 4.05s/it] 34%|███▍ | 6350/18688 [7:26:10<14:03:23, 4.10s/it] {'loss': '0.8878', 'grad_norm': '1.029', 'learning_rate': '6.529e-05', 'epoch': '0.3398', 'num_input_tokens_seen': 21291256, 'train_runtime': '2.677e+04', 'train_tokens_per_second': '795.3'}
34%|███▍ | 6350/18688 [7:26:10<14:03:23, 4.10s/it] 34%|███▍ | 6351/18688 [7:26:14<13:47:06, 4.02s/it] 34%|███▍ | 6352/18688 [7:26:18<14:06:37, 4.12s/it] 34%|███▍ | 6353/18688 [7:26:22<13:40:04, 3.99s/it] 34%|███▍ | 6354/18688 [7:26:26<13:58:45, 4.08s/it] 34%|███▍ | 6355/18688 [7:26:30<14:05:21, 4.11s/it] 34%|███▍ | 6356/18688 [7:26:34<14:19:03, 4.18s/it] 34%|███▍ | 6357/18688 [7:26:38<13:43:18, 4.01s/it] 34%|███▍ | 6358/18688 [7:26:42<13:40:39, 3.99s/it] 34%|███▍ | 6359/18688 [7:26:47<14:12:20, 4.15s/it] 34%|███▍ | 6360/18688 [7:26:50<13:29:57, 3.94s/it] {'loss': '0.8849', 'grad_norm': '1.071', 'learning_rate': '6.523e-05', 'epoch': '0.3403', 'num_input_tokens_seen': 21322936, 'train_runtime': '2.681e+04', 'train_tokens_per_second': '795.3'}
34%|███▍ | 6360/18688 [7:26:50<13:29:57, 3.94s/it] 34%|███▍ | 6361/18688 [7:26:54<13:58:05, 4.08s/it] 34%|███▍ | 6362/18688 [7:26:58<13:42:15, 4.00s/it] 34%|███▍ | 6363/18688 [7:27:02<13:35:17, 3.97s/it] 34%|███▍ | 6364/18688 [7:27:07<14:07:11, 4.12s/it] 34%|███▍ | 6365/18688 [7:27:11<14:18:27, 4.18s/it] 34%|███▍ | 6366/18688 [7:27:16<14:45:54, 4.31s/it] 34%|███▍ | 6367/18688 [7:27:20<14:56:23, 4.37s/it] 34%|███▍ | 6368/18688 [7:27:25<15:06:12, 4.41s/it] 34%|███▍ | 6369/18688 [7:27:29<14:54:31, 4.36s/it] 34%|███▍ | 6370/18688 [7:27:33<15:02:34, 4.40s/it] {'loss': '0.9732', 'grad_norm': '1.318', 'learning_rate': '6.518e-05', 'epoch': '0.3409', 'num_input_tokens_seen': 21359280, 'train_runtime': '2.685e+04', 'train_tokens_per_second': '795.4'}
34%|███▍ | 6370/18688 [7:27:33<15:02:34, 4.40s/it] 34%|███▍ | 6371/18688 [7:27:38<14:52:22, 4.35s/it] 34%|███▍ | 6372/18688 [7:27:42<15:07:21, 4.42s/it] 34%|███▍ | 6373/18688 [7:27:46<14:08:41, 4.13s/it] 34%|███▍ | 6374/18688 [7:27:50<14:09:22, 4.14s/it] 34%|███▍ | 6375/18688 [7:27:54<13:57:22, 4.08s/it] 34%|███▍ | 6376/18688 [7:27:58<13:59:13, 4.09s/it] 34%|███▍ | 6377/18688 [7:28:02<14:13:27, 4.16s/it] 34%|███▍ | 6378/18688 [7:28:06<14:04:25, 4.12s/it] 34%|███▍ | 6379/18688 [7:28:11<14:46:22, 4.32s/it] 34%|███▍ | 6380/18688 [7:28:14<13:55:09, 4.07s/it] {'loss': '0.924', 'grad_norm': '1.114', 'learning_rate': '6.512e-05', 'epoch': '0.3414', 'num_input_tokens_seen': 21392696, 'train_runtime': '2.689e+04', 'train_tokens_per_second': '795.4'}
34%|███▍ | 6380/18688 [7:28:14<13:55:09, 4.07s/it] 34%|███▍ | 6381/18688 [7:28:18<13:20:06, 3.90s/it] 34%|███▍ | 6382/18688 [7:28:22<13:08:39, 3.85s/it] 34%|███▍ | 6383/18688 [7:28:27<14:21:56, 4.20s/it] 34%|███▍ | 6384/18688 [7:28:31<14:40:47, 4.30s/it] 34%|███▍ | 6385/18688 [7:28:36<14:58:08, 4.38s/it] 34%|███▍ | 6386/18688 [7:28:40<14:48:02, 4.33s/it] 34%|███▍ | 6387/18688 [7:28:44<14:02:28, 4.11s/it] 34%|███▍ | 6388/18688 [7:28:47<13:33:51, 3.97s/it] 34%|███▍ | 6389/18688 [7:28:51<13:29:20, 3.95s/it] 34%|███▍ | 6390/18688 [7:28:55<13:19:09, 3.90s/it] {'loss': '0.9483', 'grad_norm': '1.216', 'learning_rate': '6.506e-05', 'epoch': '0.3419', 'num_input_tokens_seen': 21424928, 'train_runtime': '2.694e+04', 'train_tokens_per_second': '795.4'}
34%|███▍ | 6390/18688 [7:28:55<13:19:09, 3.90s/it] 34%|███▍ | 6391/18688 [7:28:59<13:31:24, 3.96s/it] 34%|███▍ | 6392/18688 [7:29:03<13:36:31, 3.98s/it] 34%|███▍ | 6393/18688 [7:29:07<13:17:38, 3.89s/it] 34%|███▍ | 6394/18688 [7:29:11<13:16:16, 3.89s/it] 34%|███▍ | 6395/18688 [7:29:14<13:03:36, 3.82s/it] 34%|███▍ | 6396/18688 [7:29:18<13:04:12, 3.83s/it] 34%|███▍ | 6397/18688 [7:29:22<13:01:17, 3.81s/it] 34%|███▍ | 6398/18688 [7:29:27<13:56:11, 4.08s/it] 34%|███▍ | 6399/18688 [7:29:31<14:38:37, 4.29s/it] 34%|███▍ | 6400/18688 [7:29:36<14:36:14, 4.28s/it] {'loss': '0.9305', 'grad_norm': '1.063', 'learning_rate': '6.501e-05', 'epoch': '0.3425', 'num_input_tokens_seen': 21458152, 'train_runtime': '2.698e+04', 'train_tokens_per_second': '795.5'}
34%|███▍ | 6400/18688 [7:29:36<14:36:14, 4.28s/it] 34%|███▍ | 6401/18688 [7:29:40<14:22:26, 4.21s/it] 34%|███▍ | 6402/18688 [7:29:44<14:40:49, 4.30s/it] 34%|███▍ | 6403/18688 [7:29:48<14:37:14, 4.28s/it] 34%|███▍ | 6404/18688 [7:29:52<14:00:29, 4.11s/it] 34%|███▍ | 6405/18688 [7:29:57<14:35:39, 4.28s/it] 34%|███▍ | 6406/18688 [7:30:00<13:45:43, 4.03s/it] 34%|███▍ | 6407/18688 [7:30:04<13:24:02, 3.93s/it] 34%|███▍ | 6408/18688 [7:30:07<13:00:29, 3.81s/it] 34%|███▍ | 6409/18688 [7:30:11<12:37:54, 3.70s/it] 34%|███▍ | 6410/18688 [7:30:16<13:40:13, 4.01s/it] {'loss': '0.913', 'grad_norm': '1.158', 'learning_rate': '6.495e-05', 'epoch': '0.343', 'num_input_tokens_seen': 21490280, 'train_runtime': '2.702e+04', 'train_tokens_per_second': '795.5'}
34%|███▍ | 6410/18688 [7:30:16<13:40:13, 4.01s/it] 34%|███▍ | 6411/18688 [7:30:20<13:57:46, 4.09s/it] 34%|███▍ | 6412/18688 [7:30:24<13:58:53, 4.10s/it] 34%|███▍ | 6413/18688 [7:30:28<13:27:09, 3.95s/it] 34%|███▍ | 6414/18688 [7:30:31<13:19:08, 3.91s/it] 34%|███▍ | 6415/18688 [7:30:36<13:39:53, 4.01s/it] 34%|███▍ | 6416/18688 [7:30:40<13:37:19, 4.00s/it] 34%|███▍ | 6417/18688 [7:30:43<13:18:57, 3.91s/it] 34%|███▍ | 6418/18688 [7:30:48<13:48:56, 4.05s/it] 34%|███▍ | 6419/18688 [7:30:53<14:47:44, 4.34s/it] 34%|███▍ | 6420/18688 [7:30:58<15:23:44, 4.52s/it] {'loss': '0.9759', 'grad_norm': '1.14', 'learning_rate': '6.489e-05', 'epoch': '0.3435', 'num_input_tokens_seen': 21525752, 'train_runtime': '2.706e+04', 'train_tokens_per_second': '795.5'}
34%|███▍ | 6420/18688 [7:30:58<15:23:44, 4.52s/it] 34%|███▍ | 6421/18688 [7:31:02<14:44:03, 4.32s/it] 34%|███▍ | 6422/18688 [7:31:05<13:52:31, 4.07s/it] 34%|███▍ | 6423/18688 [7:31:09<13:19:59, 3.91s/it] 34%|███▍ | 6424/18688 [7:31:12<13:12:15, 3.88s/it] 34%|███▍ | 6425/18688 [7:31:16<13:26:12, 3.94s/it] 34%|███▍ | 6426/18688 [7:31:20<13:19:46, 3.91s/it] 34%|███▍ | 6427/18688 [7:31:24<13:16:51, 3.90s/it] 34%|███▍ | 6428/18688 [7:31:29<14:12:41, 4.17s/it] 34%|███▍ | 6429/18688 [7:31:33<13:41:56, 4.02s/it] 34%|███▍ | 6430/18688 [7:31:36<13:21:32, 3.92s/it] {'loss': '0.8842', 'grad_norm': '1.27', 'learning_rate': '6.484e-05', 'epoch': '0.3441', 'num_input_tokens_seen': 21556080, 'train_runtime': '2.71e+04', 'train_tokens_per_second': '795.5'}
34%|███▍ | 6430/18688 [7:31:36<13:21:32, 3.92s/it] 34%|███▍ | 6431/18688 [7:31:40<13:19:45, 3.91s/it] 34%|███▍ | 6432/18688 [7:31:44<13:35:03, 3.99s/it] 34%|███▍ | 6433/18688 [7:31:49<13:40:13, 4.02s/it] 34%|███▍ | 6434/18688 [7:31:53<13:46:56, 4.05s/it] 34%|███▍ | 6435/18688 [7:31:57<14:01:31, 4.12s/it] 34%|███▍ | 6436/18688 [7:32:01<13:58:07, 4.10s/it] 34%|███▍ | 6437/18688 [7:32:06<14:46:25, 4.34s/it] 34%|███▍ | 6438/18688 [7:32:10<14:54:10, 4.38s/it] 34%|███▍ | 6439/18688 [7:32:15<14:45:39, 4.34s/it] 34%|███▍ | 6440/18688 [7:32:18<14:17:06, 4.20s/it] {'loss': '0.9284', 'grad_norm': '1.236', 'learning_rate': '6.478e-05', 'epoch': '0.3446', 'num_input_tokens_seen': 21591096, 'train_runtime': '2.714e+04', 'train_tokens_per_second': '795.6'}
34%|███▍ | 6440/18688 [7:32:18<14:17:06, 4.20s/it] 34%|███▍ | 6441/18688 [7:32:23<14:50:28, 4.36s/it] 34%|███▍ | 6442/18688 [7:32:27<13:58:30, 4.11s/it] 34%|███▍ | 6443/18688 [7:32:30<13:29:56, 3.97s/it] 34%|███▍ | 6444/18688 [7:32:34<13:30:46, 3.97s/it] 34%|███▍ | 6445/18688 [7:32:39<14:31:49, 4.27s/it] 34%|███▍ | 6446/18688 [7:32:44<14:33:59, 4.28s/it] 34%|███▍ | 6447/18688 [7:32:48<14:23:13, 4.23s/it] 35%|███▍ | 6448/18688 [7:32:51<13:42:22, 4.03s/it] 35%|███▍ | 6449/18688 [7:32:55<13:18:50, 3.92s/it] 35%|███▍ | 6450/18688 [7:32:59<13:49:33, 4.07s/it] {'loss': '0.895', 'grad_norm': '1.168', 'learning_rate': '6.472e-05', 'epoch': '0.3452', 'num_input_tokens_seen': 21624784, 'train_runtime': '2.718e+04', 'train_tokens_per_second': '795.6'}
35%|███▍ | 6450/18688 [7:32:59<13:49:33, 4.07s/it] 35%|███▍ | 6451/18688 [7:33:04<14:29:17, 4.26s/it] 35%|███▍ | 6452/18688 [7:33:09<15:01:12, 4.42s/it] 35%|███▍ | 6453/18688 [7:33:13<14:12:47, 4.18s/it] 35%|███▍ | 6454/18688 [7:33:16<13:56:53, 4.10s/it] 35%|███▍ | 6455/18688 [7:33:20<13:26:00, 3.95s/it] 35%|███▍ | 6456/18688 [7:33:24<13:29:29, 3.97s/it] 35%|███▍ | 6457/18688 [7:33:28<13:08:37, 3.87s/it] 35%|███▍ | 6458/18688 [7:33:32<13:38:34, 4.02s/it] 35%|███▍ | 6459/18688 [7:33:36<13:43:50, 4.04s/it] 35%|███▍ | 6460/18688 [7:33:40<13:50:05, 4.07s/it] {'loss': '0.9558', 'grad_norm': '1.167', 'learning_rate': '6.466e-05', 'epoch': '0.3457', 'num_input_tokens_seen': 21657664, 'train_runtime': '2.722e+04', 'train_tokens_per_second': '795.6'}
35%|███▍ | 6460/18688 [7:33:40<13:50:05, 4.07s/it] 35%|███▍ | 6461/18688 [7:33:45<14:07:53, 4.16s/it] 35%|███▍ | 6462/18688 [7:33:49<14:46:59, 4.35s/it] 35%|███▍ | 6463/18688 [7:33:53<14:05:33, 4.15s/it] 35%|███▍ | 6464/18688 [7:33:57<13:42:05, 4.04s/it] 35%|███▍ | 6465/18688 [7:34:02<14:36:33, 4.30s/it] 35%|███▍ | 6466/18688 [7:34:06<14:07:55, 4.16s/it] 35%|███▍ | 6467/18688 [7:34:09<13:39:17, 4.02s/it] 35%|███▍ | 6468/18688 [7:34:14<14:16:11, 4.20s/it] 35%|███▍ | 6469/18688 [7:34:18<14:13:33, 4.19s/it] 35%|███▍ | 6470/18688 [7:34:22<13:58:52, 4.12s/it] {'loss': '0.9564', 'grad_norm': '1.17', 'learning_rate': '6.461e-05', 'epoch': '0.3462', 'num_input_tokens_seen': 21691824, 'train_runtime': '2.726e+04', 'train_tokens_per_second': '795.7'}
35%|███▍ | 6470/18688 [7:34:22<13:58:52, 4.12s/it] 35%|███▍ | 6471/18688 [7:34:26<14:00:54, 4.13s/it] 35%|███▍ | 6472/18688 [7:34:30<13:32:25, 3.99s/it] 35%|███▍ | 6473/18688 [7:34:34<13:51:00, 4.08s/it] 35%|███▍ | 6474/18688 [7:34:38<13:23:25, 3.95s/it] 35%|███▍ | 6475/18688 [7:34:42<13:25:29, 3.96s/it] 35%|███▍ | 6476/18688 [7:34:46<13:59:18, 4.12s/it] 35%|███▍ | 6477/18688 [7:34:50<13:48:20, 4.07s/it] 35%|███▍ | 6478/18688 [7:34:54<13:55:00, 4.10s/it] 35%|███▍ | 6479/18688 [7:34:59<14:18:56, 4.22s/it] 35%|███▍ | 6480/18688 [7:35:03<14:07:37, 4.17s/it] {'loss': '0.9046', 'grad_norm': '0.9904', 'learning_rate': '6.455e-05', 'epoch': '0.3468', 'num_input_tokens_seen': 21723792, 'train_runtime': '2.73e+04', 'train_tokens_per_second': '795.6'}
35%|███▍ | 6480/18688 [7:35:03<14:07:37, 4.17s/it] 35%|███▍ | 6481/18688 [7:35:07<13:37:55, 4.02s/it] 35%|███▍ | 6482/18688 [7:35:11<14:13:09, 4.19s/it] 35%|███▍ | 6483/18688 [7:35:15<14:11:34, 4.19s/it] 35%|███▍ | 6484/18688 [7:35:20<14:44:48, 4.35s/it] 35%|███▍ | 6485/18688 [7:35:24<14:07:49, 4.17s/it] 35%|███▍ | 6486/18688 [7:35:28<14:08:05, 4.17s/it] 35%|███▍ | 6487/18688 [7:35:33<14:32:12, 4.29s/it] 35%|███▍ | 6488/18688 [7:35:37<14:27:01, 4.26s/it] 35%|███▍ | 6489/18688 [7:35:41<14:19:49, 4.23s/it] 35%|███▍ | 6490/18688 [7:35:45<14:07:20, 4.17s/it] {'loss': '0.9372', 'grad_norm': '1.175', 'learning_rate': '6.449e-05', 'epoch': '0.3473', 'num_input_tokens_seen': 21759184, 'train_runtime': '2.735e+04', 'train_tokens_per_second': '795.7'}
35%|███▍ | 6490/18688 [7:35:45<14:07:20, 4.17s/it] 35%|███▍ | 6491/18688 [7:35:49<14:12:49, 4.20s/it] 35%|███▍ | 6492/18688 [7:35:53<13:38:49, 4.03s/it] 35%|███▍ | 6493/18688 [7:35:57<13:35:19, 4.01s/it] 35%|███▍ | 6494/18688 [7:36:01<13:21:41, 3.94s/it] 35%|███▍ | 6495/18688 [7:36:05<14:07:23, 4.17s/it] 35%|███▍ | 6496/18688 [7:36:09<13:47:44, 4.07s/it] 35%|███▍ | 6497/18688 [7:36:13<13:25:41, 3.97s/it] 35%|███▍ | 6498/18688 [7:36:17<13:18:33, 3.93s/it] 35%|███▍ | 6499/18688 [7:36:21<13:20:56, 3.94s/it] 35%|███▍ | 6500/18688 [7:36:25<13:33:42, 4.01s/it] {'loss': '0.9668', 'grad_norm': '1.261', 'learning_rate': '6.443e-05', 'epoch': '0.3478', 'num_input_tokens_seen': 21790976, 'train_runtime': '2.739e+04', 'train_tokens_per_second': '795.7'}
35%|███▍ | 6500/18688 [7:36:25<13:33:42, 4.01s/it] 35%|███▍ | 6501/18688 [7:36:29<14:00:27, 4.14s/it] 35%|███▍ | 6502/18688 [7:36:34<14:00:18, 4.14s/it] 35%|███▍ | 6503/18688 [7:36:38<14:14:49, 4.21s/it] 35%|███▍ | 6504/18688 [7:36:42<14:17:27, 4.22s/it] 35%|███▍ | 6505/18688 [7:36:46<14:20:27, 4.24s/it] 35%|███▍ | 6506/18688 [7:36:50<13:39:54, 4.04s/it] 35%|███▍ | 6507/18688 [7:36:54<13:55:28, 4.12s/it] 35%|███▍ | 6508/18688 [7:36:59<14:56:02, 4.41s/it] 35%|███▍ | 6509/18688 [7:37:03<14:19:23, 4.23s/it] 35%|███▍ | 6510/18688 [7:37:07<13:53:35, 4.11s/it] {'loss': '0.8703', 'grad_norm': '1.069', 'learning_rate': '6.437e-05', 'epoch': '0.3484', 'num_input_tokens_seen': 21825512, 'train_runtime': '2.743e+04', 'train_tokens_per_second': '795.8'}
35%|███▍ | 6510/18688 [7:37:07<13:53:35, 4.11s/it] 35%|███▍ | 6511/18688 [7:37:11<13:28:33, 3.98s/it] 35%|███▍ | 6512/18688 [7:37:14<13:11:31, 3.90s/it] 35%|███▍ | 6513/18688 [7:37:19<13:43:39, 4.06s/it] 35%|███▍ | 6514/18688 [7:37:23<14:02:56, 4.15s/it] 35%|███▍ | 6515/18688 [7:37:27<13:56:20, 4.12s/it] 35%|███▍ | 6516/18688 [7:37:32<14:30:39, 4.29s/it] 35%|███▍ | 6517/18688 [7:37:36<14:32:00, 4.30s/it] 35%|███▍ | 6518/18688 [7:37:40<13:40:51, 4.05s/it] 35%|███▍ | 6519/18688 [7:37:44<13:38:02, 4.03s/it] 35%|███▍ | 6520/18688 [7:37:48<13:45:20, 4.07s/it] {'loss': '0.8986', 'grad_norm': '1.098', 'learning_rate': '6.432e-05', 'epoch': '0.3489', 'num_input_tokens_seen': 21858352, 'train_runtime': '2.747e+04', 'train_tokens_per_second': '795.8'}
35%|███▍ | 6520/18688 [7:37:48<13:45:20, 4.07s/it] 35%|███▍ | 6521/18688 [7:37:51<13:16:05, 3.93s/it] 35%|███▍ | 6522/18688 [7:37:55<13:08:47, 3.89s/it] 35%|███▍ | 6523/18688 [7:38:00<13:44:03, 4.06s/it] 35%|███▍ | 6524/18688 [7:38:04<14:02:24, 4.16s/it] 35%|███▍ | 6525/18688 [7:38:09<14:16:14, 4.22s/it] 35%|███▍ | 6526/18688 [7:38:12<13:38:08, 4.04s/it] 35%|███▍ | 6527/18688 [7:38:16<13:41:25, 4.05s/it] 35%|███▍ | 6528/18688 [7:38:21<14:30:29, 4.30s/it] 35%|███▍ | 6529/18688 [7:38:25<14:21:47, 4.25s/it] 35%|███▍ | 6530/18688 [7:38:30<14:27:00, 4.28s/it] {'loss': '0.9821', 'grad_norm': '1.199', 'learning_rate': '6.426e-05', 'epoch': '0.3494', 'num_input_tokens_seen': 21891648, 'train_runtime': '2.751e+04', 'train_tokens_per_second': '795.8'}
35%|███▍ | 6530/18688 [7:38:30<14:27:00, 4.28s/it] 35%|███▍ | 6531/18688 [7:38:34<14:36:49, 4.33s/it] 35%|███▍ | 6532/18688 [7:38:38<14:22:56, 4.26s/it] 35%|███▍ | 6533/18688 [7:38:42<13:50:03, 4.10s/it] 35%|███▍ | 6534/18688 [7:38:46<13:36:26, 4.03s/it] 35%|███▍ | 6535/18688 [7:38:49<13:15:32, 3.93s/it] 35%|███▍ | 6536/18688 [7:38:53<12:46:23, 3.78s/it] 35%|███▍ | 6537/18688 [7:38:56<12:34:02, 3.72s/it] 35%|███▍ | 6538/18688 [7:39:01<12:59:48, 3.85s/it] 35%|███▍ | 6539/18688 [7:39:05<13:22:08, 3.96s/it] 35%|███▍ | 6540/18688 [7:39:09<13:31:54, 4.01s/it] {'loss': '0.8988', 'grad_norm': '1.288', 'learning_rate': '6.42e-05', 'epoch': '0.35', 'num_input_tokens_seen': 21922472, 'train_runtime': '2.755e+04', 'train_tokens_per_second': '795.8'}
35%|███▍ | 6540/18688 [7:39:09<13:31:54, 4.01s/it] 35%|███▌ | 6541/18688 [7:39:13<13:16:46, 3.94s/it] 35%|███▌ | 6542/18688 [7:39:16<13:05:56, 3.88s/it] 35%|███▌ | 6543/18688 [7:39:20<12:53:50, 3.82s/it] 35%|███▌ | 6544/18688 [7:39:25<13:33:24, 4.02s/it] 35%|███▌ | 6545/18688 [7:39:29<13:49:11, 4.10s/it] 35%|███▌ | 6546/18688 [7:39:33<13:36:49, 4.04s/it] 35%|███▌ | 6547/18688 [7:39:37<13:42:30, 4.06s/it] 35%|███▌ | 6548/18688 [7:39:41<13:26:17, 3.99s/it] 35%|███▌ | 6549/18688 [7:39:46<14:36:19, 4.33s/it] 35%|███▌ | 6550/18688 [7:39:49<13:47:01, 4.09s/it] {'loss': '0.9522', 'grad_norm': '1.134', 'learning_rate': '6.414e-05', 'epoch': '0.3505', 'num_input_tokens_seen': 21955056, 'train_runtime': '2.759e+04', 'train_tokens_per_second': '795.8'}
35%|███▌ | 6550/18688 [7:39:49<13:47:01, 4.09s/it] 35%|███▌ | 6551/18688 [7:39:54<14:24:45, 4.27s/it] 35%|███▌ | 6552/18688 [7:39:58<14:11:59, 4.21s/it] 35%|███▌ | 6553/18688 [7:40:02<14:13:24, 4.22s/it] 35%|███▌ | 6554/18688 [7:40:07<14:59:48, 4.45s/it] 35%|███▌ | 6555/18688 [7:40:12<14:42:49, 4.37s/it] 35%|███▌ | 6556/18688 [7:40:15<14:16:33, 4.24s/it] 35%|███▌ | 6557/18688 [7:40:19<13:39:51, 4.06s/it] 35%|███▌ | 6558/18688 [7:40:23<13:47:14, 4.09s/it] 35%|███▌ | 6559/18688 [7:40:28<14:25:48, 4.28s/it] 35%|███▌ | 6560/18688 [7:40:32<14:37:45, 4.34s/it] {'loss': '0.9068', 'grad_norm': '1.186', 'learning_rate': '6.408e-05', 'epoch': '0.351', 'num_input_tokens_seen': 21991416, 'train_runtime': '2.763e+04', 'train_tokens_per_second': '795.8'}
35%|███▌ | 6560/18688 [7:40:32<14:37:45, 4.34s/it] 35%|███▌ | 6561/18688 [7:40:37<14:31:04, 4.31s/it] 35%|███▌ | 6562/18688 [7:40:41<14:29:16, 4.30s/it] 35%|███▌ | 6563/18688 [7:40:45<14:07:33, 4.19s/it] 35%|███▌ | 6564/18688 [7:40:50<14:39:33, 4.35s/it] 35%|███▌ | 6565/18688 [7:40:54<14:58:13, 4.45s/it] 35%|███▌ | 6566/18688 [7:40:58<14:38:06, 4.35s/it] 35%|███▌ | 6567/18688 [7:41:03<14:21:50, 4.27s/it] 35%|███▌ | 6568/18688 [7:41:06<13:34:36, 4.03s/it] 35%|███▌ | 6569/18688 [7:41:10<13:13:27, 3.93s/it] 35%|███▌ | 6570/18688 [7:41:13<12:54:37, 3.84s/it] {'loss': '0.8686', 'grad_norm': '1.204', 'learning_rate': '6.403e-05', 'epoch': '0.3516', 'num_input_tokens_seen': 22024552, 'train_runtime': '2.767e+04', 'train_tokens_per_second': '795.9'}
35%|███▌ | 6570/18688 [7:41:13<12:54:37, 3.84s/it] 35%|███▌ | 6571/18688 [7:41:18<13:25:39, 3.99s/it] 35%|███▌ | 6572/18688 [7:41:21<12:53:29, 3.83s/it] 35%|███▌ | 6573/18688 [7:41:26<14:03:53, 4.18s/it] 35%|███▌ | 6574/18688 [7:41:31<14:23:54, 4.28s/it] 35%|███▌ | 6575/18688 [7:41:34<13:49:35, 4.11s/it] 35%|███▌ | 6576/18688 [7:41:39<14:06:55, 4.20s/it] 35%|███▌ | 6577/18688 [7:41:42<13:36:14, 4.04s/it] 35%|███▌ | 6578/18688 [7:41:48<14:54:28, 4.43s/it] 35%|███▌ | 6579/18688 [7:41:52<14:29:50, 4.31s/it] 35%|███▌ | 6580/18688 [7:41:55<13:46:28, 4.10s/it] {'loss': '0.9388', 'grad_norm': '1.289', 'learning_rate': '6.397e-05', 'epoch': '0.3521', 'num_input_tokens_seen': 22060344, 'train_runtime': '2.772e+04', 'train_tokens_per_second': '795.9'}
35%|███▌ | 6580/18688 [7:41:55<13:46:28, 4.10s/it] 35%|███▌ | 6581/18688 [7:41:59<13:21:18, 3.97s/it] 35%|███▌ | 6582/18688 [7:42:04<14:21:22, 4.27s/it] 35%|███▌ | 6583/18688 [7:42:08<14:30:58, 4.32s/it] 35%|███▌ | 6584/18688 [7:42:12<13:54:20, 4.14s/it] 35%|███▌ | 6585/18688 [7:42:17<14:10:34, 4.22s/it] 35%|███▌ | 6586/18688 [7:42:21<14:36:11, 4.34s/it] 35%|███▌ | 6587/18688 [7:42:26<14:35:25, 4.34s/it] 35%|███▌ | 6588/18688 [7:42:31<15:15:23, 4.54s/it] 35%|███▌ | 6589/18688 [7:42:34<14:24:28, 4.29s/it] 35%|███▌ | 6590/18688 [7:42:38<13:37:28, 4.05s/it] {'loss': '0.9969', 'grad_norm': '1.021', 'learning_rate': '6.391e-05', 'epoch': '0.3526', 'num_input_tokens_seen': 22095392, 'train_runtime': '2.776e+04', 'train_tokens_per_second': '796'}
35%|███▌ | 6590/18688 [7:42:38<13:37:28, 4.05s/it] 35%|███▌ | 6591/18688 [7:42:41<13:17:57, 3.96s/it] 35%|███▌ | 6592/18688 [7:42:46<13:28:06, 4.01s/it] 35%|███▌ | 6593/18688 [7:42:49<13:10:02, 3.92s/it] 35%|███▌ | 6594/18688 [7:42:53<13:18:31, 3.96s/it] 35%|███▌ | 6595/18688 [7:42:57<13:06:56, 3.90s/it] 35%|███▌ | 6596/18688 [7:43:01<12:47:29, 3.81s/it] 35%|███▌ | 6597/18688 [7:43:06<14:07:57, 4.21s/it] 35%|███▌ | 6598/18688 [7:43:10<13:50:05, 4.12s/it] 35%|███▌ | 6599/18688 [7:43:14<13:47:23, 4.11s/it] 35%|███▌ | 6600/18688 [7:43:17<13:10:46, 3.93s/it] {'loss': '0.9587', 'grad_norm': '1.247', 'learning_rate': '6.385e-05', 'epoch': '0.3532', 'num_input_tokens_seen': 22126984, 'train_runtime': '2.78e+04', 'train_tokens_per_second': '796'}
35%|███▌ | 6600/18688 [7:43:17<13:10:46, 3.93s/it] 35%|███▌ | 6601/18688 [7:43:22<13:26:47, 4.00s/it] 35%|███▌ | 6602/18688 [7:43:26<14:00:11, 4.17s/it] 35%|███▌ | 6603/18688 [7:43:30<13:33:18, 4.04s/it] 35%|███▌ | 6604/18688 [7:43:34<13:49:29, 4.12s/it] 35%|███▌ | 6605/18688 [7:43:40<15:37:30, 4.66s/it] 35%|███▌ | 6606/18688 [7:43:44<14:47:56, 4.41s/it] 35%|███▌ | 6607/18688 [7:43:48<14:40:36, 4.37s/it] 35%|███▌ | 6608/18688 [7:43:53<15:01:23, 4.48s/it] 35%|███▌ | 6609/18688 [7:43:57<14:19:53, 4.27s/it] 35%|███▌ | 6610/18688 [7:44:01<14:40:08, 4.37s/it] {'loss': '0.9561', 'grad_norm': '1.468', 'learning_rate': '6.379e-05', 'epoch': '0.3537', 'num_input_tokens_seen': 22163488, 'train_runtime': '2.784e+04', 'train_tokens_per_second': '796.1'}
35%|███▌ | 6610/18688 [7:44:01<14:40:08, 4.37s/it] 35%|███▌ | 6611/18688 [7:44:05<13:55:10, 4.15s/it] 35%|███▌ | 6612/18688 [7:44:09<13:41:04, 4.08s/it] 35%|███▌ | 6613/18688 [7:44:13<13:40:30, 4.08s/it] 35%|███▌ | 6614/18688 [7:44:16<13:10:36, 3.93s/it] 35%|███▌ | 6615/18688 [7:44:21<13:19:03, 3.97s/it] 35%|███▌ | 6616/18688 [7:44:25<13:56:01, 4.16s/it] 35%|███▌ | 6617/18688 [7:44:29<13:35:23, 4.05s/it] 35%|███▌ | 6618/18688 [7:44:33<13:59:54, 4.18s/it] 35%|███▌ | 6619/18688 [7:44:38<14:14:18, 4.25s/it] 35%|███▌ | 6620/18688 [7:44:42<14:34:23, 4.35s/it] {'loss': '0.9728', 'grad_norm': '1.089', 'learning_rate': '6.373e-05', 'epoch': '0.3542', 'num_input_tokens_seen': 22198272, 'train_runtime': '2.788e+04', 'train_tokens_per_second': '796.1'}
35%|███▌ | 6620/18688 [7:44:42<14:34:23, 4.35s/it] 35%|███▌ | 6621/18688 [7:44:47<14:43:22, 4.39s/it] 35%|███▌ | 6622/18688 [7:44:51<14:29:50, 4.33s/it] 35%|███▌ | 6623/18688 [7:44:55<13:58:58, 4.17s/it] 35%|███▌ | 6624/18688 [7:44:59<14:23:54, 4.30s/it] 35%|███▌ | 6625/18688 [7:45:04<14:54:23, 4.45s/it] 35%|███▌ | 6626/18688 [7:45:09<14:54:09, 4.45s/it] 35%|███▌ | 6627/18688 [7:45:12<14:07:46, 4.22s/it] 35%|███▌ | 6628/18688 [7:45:17<14:45:16, 4.40s/it] 35%|███▌ | 6629/18688 [7:45:22<15:31:16, 4.63s/it] 35%|███▌ | 6630/18688 [7:45:27<15:03:33, 4.50s/it] {'loss': '0.9584', 'grad_norm': '1.23', 'learning_rate': '6.367e-05', 'epoch': '0.3548', 'num_input_tokens_seen': 22236368, 'train_runtime': '2.793e+04', 'train_tokens_per_second': '796.2'}
35%|███▌ | 6630/18688 [7:45:27<15:03:33, 4.50s/it] 35%|███▌ | 6631/18688 [7:45:31<14:49:35, 4.43s/it] 35%|███▌ | 6632/18688 [7:45:35<14:34:02, 4.35s/it] 35%|███▌ | 6633/18688 [7:45:39<14:15:56, 4.26s/it] 35%|███▌ | 6634/18688 [7:45:43<14:14:29, 4.25s/it] 36%|███▌ | 6635/18688 [7:45:49<15:36:08, 4.66s/it] 36%|███▌ | 6636/18688 [7:45:54<15:34:50, 4.65s/it] 36%|███▌ | 6637/18688 [7:45:58<15:11:59, 4.54s/it] 36%|███▌ | 6638/18688 [7:46:02<14:32:11, 4.34s/it] 36%|███▌ | 6639/18688 [7:46:06<14:04:16, 4.20s/it] 36%|███▌ | 6640/18688 [7:46:10<14:17:54, 4.27s/it] {'loss': '0.9233', 'grad_norm': '1.071', 'learning_rate': '6.361e-05', 'epoch': '0.3553', 'num_input_tokens_seen': 22272800, 'train_runtime': '2.797e+04', 'train_tokens_per_second': '796.3'}
36%|███▌ | 6640/18688 [7:46:10<14:17:54, 4.27s/it] 36%|███▌ | 6641/18688 [7:46:14<13:36:44, 4.07s/it] 36%|███▌ | 6642/18688 [7:46:17<13:10:27, 3.94s/it] 36%|███▌ | 6643/18688 [7:46:22<13:59:28, 4.18s/it] 36%|███▌ | 6644/18688 [7:46:26<14:03:05, 4.20s/it] 36%|███▌ | 6645/18688 [7:46:30<13:31:32, 4.04s/it] 36%|███▌ | 6646/18688 [7:46:35<14:02:59, 4.20s/it] 36%|███▌ | 6647/18688 [7:46:39<14:21:06, 4.29s/it] 36%|███▌ | 6648/18688 [7:46:43<13:35:26, 4.06s/it] 36%|███▌ | 6649/18688 [7:46:47<14:23:46, 4.30s/it] 36%|███▌ | 6650/18688 [7:46:51<13:44:04, 4.11s/it] {'loss': '0.979', 'grad_norm': '1.387', 'learning_rate': '6.356e-05', 'epoch': '0.3559', 'num_input_tokens_seen': 22305800, 'train_runtime': '2.801e+04', 'train_tokens_per_second': '796.3'}
36%|███▌ | 6650/18688 [7:46:51<13:44:04, 4.11s/it] 36%|███▌ | 6651/18688 [7:46:55<13:15:39, 3.97s/it] 36%|███▌ | 6652/18688 [7:46:59<13:30:28, 4.04s/it] 36%|███▌ | 6653/18688 [7:47:03<13:28:17, 4.03s/it] 36%|███▌ | 6654/18688 [7:47:07<13:12:44, 3.95s/it] 36%|███▌ | 6655/18688 [7:47:11<13:58:29, 4.18s/it] 36%|███▌ | 6656/18688 [7:47:16<14:10:39, 4.24s/it] 36%|███▌ | 6657/18688 [7:47:20<14:26:12, 4.32s/it] 36%|███▌ | 6658/18688 [7:47:24<13:37:38, 4.08s/it] 36%|███▌ | 6659/18688 [7:47:28<13:37:54, 4.08s/it] 36%|███▌ | 6660/18688 [7:47:32<13:19:27, 3.99s/it] {'loss': '1.011', 'grad_norm': '1.37', 'learning_rate': '6.35e-05', 'epoch': '0.3564', 'num_input_tokens_seen': 22338624, 'train_runtime': '2.805e+04', 'train_tokens_per_second': '796.3'}
36%|███▌ | 6660/18688 [7:47:32<13:19:27, 3.99s/it] 36%|███▌ | 6661/18688 [7:47:36<13:41:37, 4.10s/it] 36%|███▌ | 6662/18688 [7:47:40<13:30:55, 4.05s/it] 36%|███▌ | 6663/18688 [7:47:45<14:20:54, 4.30s/it] 36%|███▌ | 6664/18688 [7:47:49<14:06:26, 4.22s/it] 36%|███▌ | 6665/18688 [7:47:53<14:09:37, 4.24s/it] 36%|███▌ | 6666/18688 [7:47:58<14:37:28, 4.38s/it] 36%|███▌ | 6667/18688 [7:48:02<14:17:41, 4.28s/it] 36%|███▌ | 6668/18688 [7:48:06<14:15:56, 4.27s/it] 36%|███▌ | 6669/18688 [7:48:11<14:21:29, 4.30s/it] 36%|███▌ | 6670/18688 [7:48:14<13:43:37, 4.11s/it] {'loss': '0.9851', 'grad_norm': '1.368', 'learning_rate': '6.344e-05', 'epoch': '0.3569', 'num_input_tokens_seen': 22374912, 'train_runtime': '2.809e+04', 'train_tokens_per_second': '796.4'}
36%|███▌ | 6670/18688 [7:48:14<13:43:37, 4.11s/it] 36%|███▌ | 6671/18688 [7:48:19<14:03:52, 4.21s/it] 36%|███▌ | 6672/18688 [7:48:23<14:17:46, 4.28s/it] 36%|███▌ | 6673/18688 [7:48:28<14:29:41, 4.34s/it] 36%|███▌ | 6674/18688 [7:48:31<14:01:14, 4.20s/it] 36%|███▌ | 6675/18688 [7:48:35<13:18:08, 3.99s/it] 36%|███▌ | 6676/18688 [7:48:38<12:46:13, 3.83s/it] 36%|███▌ | 6677/18688 [7:48:43<13:37:28, 4.08s/it] 36%|███▌ | 6678/18688 [7:48:47<13:12:45, 3.96s/it] 36%|███▌ | 6679/18688 [7:48:52<14:52:42, 4.46s/it] 36%|███▌ | 6680/18688 [7:48:57<15:16:33, 4.58s/it] {'loss': '0.974', 'grad_norm': '0.9754', 'learning_rate': '6.338e-05', 'epoch': '0.3575', 'num_input_tokens_seen': 22411704, 'train_runtime': '2.814e+04', 'train_tokens_per_second': '796.5'}
36%|███▌ | 6680/18688 [7:48:57<15:16:33, 4.58s/it] 36%|███▌ | 6681/18688 [7:49:02<15:43:34, 4.72s/it] 36%|███▌ | 6682/18688 [7:49:06<14:47:41, 4.44s/it] 36%|███▌ | 6683/18688 [7:49:10<14:24:05, 4.32s/it] 36%|███▌ | 6684/18688 [7:49:14<13:56:19, 4.18s/it] 36%|███▌ | 6685/18688 [7:49:18<13:23:17, 4.02s/it] 36%|███▌ | 6686/18688 [7:49:22<13:25:22, 4.03s/it] 36%|███▌ | 6687/18688 [7:49:25<13:01:55, 3.91s/it] 36%|███▌ | 6688/18688 [7:49:30<14:01:16, 4.21s/it] 36%|███▌ | 6689/18688 [7:49:35<14:16:50, 4.28s/it] 36%|███▌ | 6690/18688 [7:49:40<14:56:28, 4.48s/it] {'loss': '0.9415', 'grad_norm': '1.133', 'learning_rate': '6.332e-05', 'epoch': '0.358', 'num_input_tokens_seen': 22447064, 'train_runtime': '2.818e+04', 'train_tokens_per_second': '796.6'}
36%|███▌ | 6690/18688 [7:49:40<14:56:28, 4.48s/it] 36%|███▌ | 6691/18688 [7:49:43<14:10:02, 4.25s/it] 36%|███▌ | 6692/18688 [7:49:47<13:40:24, 4.10s/it] 36%|███▌ | 6693/18688 [7:49:52<14:23:24, 4.32s/it] 36%|███▌ | 6694/18688 [7:49:56<14:17:20, 4.29s/it] 36%|███▌ | 6695/18688 [7:50:00<13:51:14, 4.16s/it] 36%|███▌ | 6696/18688 [7:50:04<13:41:20, 4.11s/it] 36%|███▌ | 6697/18688 [7:50:07<13:07:37, 3.94s/it] 36%|███▌ | 6698/18688 [7:50:12<13:44:16, 4.12s/it] 36%|███▌ | 6699/18688 [7:50:17<14:48:47, 4.45s/it] 36%|███▌ | 6700/18688 [7:50:21<14:12:34, 4.27s/it] {'loss': '0.9276', 'grad_norm': '1.096', 'learning_rate': '6.326e-05', 'epoch': '0.3585', 'num_input_tokens_seen': 22482968, 'train_runtime': '2.822e+04', 'train_tokens_per_second': '796.7'}
36%|███▌ | 6700/18688 [7:50:21<14:12:34, 4.27s/it] 36%|███▌ | 6701/18688 [7:50:25<14:03:21, 4.22s/it] 36%|███▌ | 6702/18688 [7:50:30<14:20:48, 4.31s/it] 36%|███▌ | 6703/18688 [7:50:34<14:40:11, 4.41s/it] 36%|███▌ | 6704/18688 [7:50:38<13:43:33, 4.12s/it] 36%|███▌ | 6705/18688 [7:50:42<13:38:40, 4.10s/it] 36%|███▌ | 6706/18688 [7:50:46<13:32:36, 4.07s/it] 36%|███▌ | 6707/18688 [7:50:49<13:00:56, 3.91s/it] 36%|███▌ | 6708/18688 [7:50:53<12:48:57, 3.85s/it] 36%|███▌ | 6709/18688 [7:50:57<13:03:14, 3.92s/it] 36%|███▌ | 6710/18688 [7:51:01<12:40:47, 3.81s/it] {'loss': '0.9249', 'grad_norm': '1.436', 'learning_rate': '6.32e-05', 'epoch': '0.3591', 'num_input_tokens_seen': 22513312, 'train_runtime': '2.826e+04', 'train_tokens_per_second': '796.6'}
36%|███▌ | 6710/18688 [7:51:01<12:40:47, 3.81s/it] 36%|███▌ | 6711/18688 [7:51:04<12:18:53, 3.70s/it] 36%|███▌ | 6712/18688 [7:51:08<12:46:15, 3.84s/it] 36%|███▌ | 6713/18688 [7:51:12<12:24:13, 3.73s/it] 36%|███▌ | 6714/18688 [7:51:15<12:20:21, 3.71s/it] 36%|███▌ | 6715/18688 [7:51:20<12:40:30, 3.81s/it] 36%|███▌ | 6716/18688 [7:51:24<13:12:37, 3.97s/it] 36%|███▌ | 6717/18688 [7:51:28<12:52:36, 3.87s/it] 36%|███▌ | 6718/18688 [7:51:32<13:26:55, 4.04s/it] 36%|███▌ | 6719/18688 [7:51:36<13:15:33, 3.99s/it] 36%|███▌ | 6720/18688 [7:51:40<13:18:12, 4.00s/it] {'loss': '0.899', 'grad_norm': '0.9889', 'learning_rate': '6.314e-05', 'epoch': '0.3596', 'num_input_tokens_seen': 22544488, 'train_runtime': '2.83e+04', 'train_tokens_per_second': '796.6'}
36%|███▌ | 6720/18688 [7:51:40<13:18:12, 4.00s/it] 36%|███▌ | 6721/18688 [7:51:44<13:15:22, 3.99s/it] 36%|███▌ | 6722/18688 [7:51:48<13:22:41, 4.02s/it] 36%|███▌ | 6723/18688 [7:51:52<13:09:42, 3.96s/it] 36%|███▌ | 6724/18688 [7:51:55<12:54:55, 3.89s/it] 36%|███▌ | 6725/18688 [7:52:00<13:22:47, 4.03s/it] 36%|███▌ | 6726/18688 [7:52:05<14:29:25, 4.36s/it] 36%|███▌ | 6727/18688 [7:52:09<13:56:42, 4.20s/it] 36%|███▌ | 6728/18688 [7:52:14<14:48:18, 4.46s/it] 36%|███▌ | 6729/18688 [7:52:18<15:01:22, 4.52s/it] 36%|███▌ | 6730/18688 [7:52:22<14:23:00, 4.33s/it] {'loss': '0.925', 'grad_norm': '1.048', 'learning_rate': '6.308e-05', 'epoch': '0.3601', 'num_input_tokens_seen': 22580160, 'train_runtime': '2.834e+04', 'train_tokens_per_second': '796.7'}
36%|███▌ | 6730/18688 [7:52:22<14:23:00, 4.33s/it] 36%|███▌ | 6731/18688 [7:52:26<13:53:43, 4.18s/it] 36%|███▌ | 6732/18688 [7:52:30<13:12:26, 3.98s/it] 36%|███▌ | 6733/18688 [7:52:34<13:04:38, 3.94s/it] 36%|███▌ | 6734/18688 [7:52:38<13:19:20, 4.01s/it] 36%|███▌ | 6735/18688 [7:52:41<13:03:49, 3.93s/it] 36%|███▌ | 6736/18688 [7:52:45<13:01:03, 3.92s/it] 36%|███▌ | 6737/18688 [7:52:49<12:55:55, 3.90s/it] 36%|███▌ | 6738/18688 [7:52:53<12:30:52, 3.77s/it] 36%|███▌ | 6739/18688 [7:52:57<12:47:20, 3.85s/it] 36%|███▌ | 6740/18688 [7:53:01<12:50:59, 3.87s/it] {'loss': '0.8833', 'grad_norm': '1.129', 'learning_rate': '6.302e-05', 'epoch': '0.3607', 'num_input_tokens_seen': 22609760, 'train_runtime': '2.838e+04', 'train_tokens_per_second': '796.6'}
36%|███▌ | 6740/18688 [7:53:01<12:50:59, 3.87s/it] 36%|███▌ | 6741/18688 [7:53:05<12:59:17, 3.91s/it] 36%|███▌ | 6742/18688 [7:53:09<13:23:40, 4.04s/it] 36%|███▌ | 6743/18688 [7:53:13<13:18:48, 4.01s/it] 36%|███▌ | 6744/18688 [7:53:17<13:16:28, 4.00s/it] 36%|███▌ | 6745/18688 [7:53:21<13:06:27, 3.95s/it] 36%|███▌ | 6746/18688 [7:53:25<13:03:33, 3.94s/it] 36%|███▌ | 6747/18688 [7:53:28<12:54:36, 3.89s/it] 36%|███▌ | 6748/18688 [7:53:33<13:20:45, 4.02s/it] 36%|███▌ | 6749/18688 [7:53:36<12:52:10, 3.88s/it] 36%|███▌ | 6750/18688 [7:53:40<12:40:46, 3.82s/it] {'loss': '0.9813', 'grad_norm': '1.665', 'learning_rate': '6.296e-05', 'epoch': '0.3612', 'num_input_tokens_seen': 22640632, 'train_runtime': '2.842e+04', 'train_tokens_per_second': '796.6'}
36%|███▌ | 6750/18688 [7:53:40<12:40:46, 3.82s/it] 36%|███▌ | 6751/18688 [7:53:44<13:16:36, 4.00s/it] 36%|███▌ | 6752/18688 [7:53:48<13:02:25, 3.93s/it] 36%|███▌ | 6753/18688 [7:53:52<13:15:18, 4.00s/it] 36%|███▌ | 6754/18688 [7:53:56<13:01:54, 3.93s/it] 36%|███▌ | 6755/18688 [7:54:00<13:22:35, 4.04s/it] 36%|███▌ | 6756/18688 [7:54:04<13:19:49, 4.02s/it] 36%|███▌ | 6757/18688 [7:54:09<14:01:17, 4.23s/it] 36%|███▌ | 6758/18688 [7:54:13<13:28:25, 4.07s/it] 36%|███▌ | 6759/18688 [7:54:17<13:49:09, 4.17s/it] 36%|███▌ | 6760/18688 [7:54:21<13:53:43, 4.19s/it] {'loss': '1.044', 'grad_norm': '1.09', 'learning_rate': '6.29e-05', 'epoch': '0.3617', 'num_input_tokens_seen': 22674000, 'train_runtime': '2.846e+04', 'train_tokens_per_second': '796.6'}
36%|███▌ | 6760/18688 [7:54:21<13:53:43, 4.19s/it] 36%|███▌ | 6761/18688 [7:54:26<13:51:48, 4.18s/it] 36%|███▌ | 6762/18688 [7:54:31<14:49:15, 4.47s/it] 36%|███▌ | 6763/18688 [7:54:34<13:51:23, 4.18s/it] 36%|███▌ | 6764/18688 [7:54:39<14:19:21, 4.32s/it] 36%|███▌ | 6765/18688 [7:54:43<13:43:17, 4.14s/it] 36%|███▌ | 6766/18688 [7:54:46<13:12:00, 3.99s/it] 36%|███▌ | 6767/18688 [7:54:50<13:24:48, 4.05s/it] 36%|███▌ | 6768/18688 [7:54:54<13:18:02, 4.02s/it] 36%|███▌ | 6769/18688 [7:54:58<13:20:40, 4.03s/it] 36%|███▌ | 6770/18688 [7:55:04<14:41:50, 4.44s/it] {'loss': '0.947', 'grad_norm': '1.278', 'learning_rate': '6.284e-05', 'epoch': '0.3623', 'num_input_tokens_seen': 22710184, 'train_runtime': '2.85e+04', 'train_tokens_per_second': '796.7'}
36%|███▌ | 6770/18688 [7:55:04<14:41:50, 4.44s/it] 36%|███▌ | 6771/18688 [7:55:08<13:56:36, 4.21s/it] 36%|███▌ | 6772/18688 [7:55:12<14:12:32, 4.29s/it] 36%|███▌ | 6773/18688 [7:55:16<13:25:28, 4.06s/it] 36%|███▌ | 6774/18688 [7:55:19<13:05:16, 3.95s/it] 36%|███▋ | 6775/18688 [7:55:23<13:04:34, 3.95s/it] 36%|███▋ | 6776/18688 [7:55:28<13:28:47, 4.07s/it] 36%|███▋ | 6777/18688 [7:55:32<13:33:56, 4.10s/it] 36%|███▋ | 6778/18688 [7:55:36<13:48:27, 4.17s/it] 36%|███▋ | 6779/18688 [7:55:42<15:40:47, 4.74s/it] 36%|███▋ | 6780/18688 [7:55:46<14:38:54, 4.43s/it] {'loss': '0.9623', 'grad_norm': '1.338', 'learning_rate': '6.278e-05', 'epoch': '0.3628', 'num_input_tokens_seen': 22745592, 'train_runtime': '2.855e+04', 'train_tokens_per_second': '796.8'}
36%|███▋ | 6780/18688 [7:55:46<14:38:54, 4.43s/it] 36%|███▋ | 6781/18688 [7:55:51<14:57:25, 4.52s/it] 36%|███▋ | 6782/18688 [7:55:55<14:48:44, 4.48s/it] 36%|███▋ | 6783/18688 [7:55:59<14:11:30, 4.29s/it] 36%|███▋ | 6784/18688 [7:56:02<13:34:18, 4.10s/it] 36%|███▋ | 6785/18688 [7:56:06<13:27:01, 4.07s/it] 36%|███▋ | 6786/18688 [7:56:10<13:16:28, 4.02s/it] 36%|███▋ | 6787/18688 [7:56:14<13:14:59, 4.01s/it] 36%|███▋ | 6788/18688 [7:56:18<12:53:08, 3.90s/it] 36%|███▋ | 6789/18688 [7:56:23<13:33:51, 4.10s/it] 36%|███▋ | 6790/18688 [7:56:27<13:31:07, 4.09s/it] {'loss': '0.9496', 'grad_norm': '1.22', 'learning_rate': '6.272e-05', 'epoch': '0.3633', 'num_input_tokens_seen': 22778088, 'train_runtime': '2.859e+04', 'train_tokens_per_second': '796.8'}
36%|███▋ | 6790/18688 [7:56:27<13:31:07, 4.09s/it] 36%|███▋ | 6791/18688 [7:56:30<12:57:41, 3.92s/it] 36%|███▋ | 6792/18688 [7:56:35<13:47:20, 4.17s/it] 36%|███▋ | 6793/18688 [7:56:39<13:45:01, 4.16s/it] 36%|███▋ | 6794/18688 [7:56:43<13:50:20, 4.19s/it] 36%|███▋ | 6795/18688 [7:56:47<13:34:33, 4.11s/it] 36%|███▋ | 6796/18688 [7:56:51<12:57:10, 3.92s/it] 36%|███▋ | 6797/18688 [7:56:55<13:32:13, 4.10s/it] 36%|███▋ | 6798/18688 [7:56:59<12:59:58, 3.94s/it] 36%|███▋ | 6799/18688 [7:57:03<13:42:00, 4.15s/it] 36%|███▋ | 6800/18688 [7:57:08<13:55:37, 4.22s/it] {'loss': '0.9693', 'grad_norm': '1.104', 'learning_rate': '6.266e-05', 'epoch': '0.3639', 'num_input_tokens_seen': 22812664, 'train_runtime': '2.863e+04', 'train_tokens_per_second': '796.9'}
36%|███▋ | 6800/18688 [7:57:08<13:55:37, 4.22s/it] 36%|███▋ | 6801/18688 [7:57:12<14:22:29, 4.35s/it] 36%|███▋ | 6802/18688 [7:57:16<13:37:06, 4.12s/it] 36%|███▋ | 6803/18688 [7:57:20<13:42:14, 4.15s/it] 36%|███▋ | 6804/18688 [7:57:24<13:09:38, 3.99s/it] 36%|███▋ | 6805/18688 [7:57:28<13:06:43, 3.97s/it] 36%|███▋ | 6806/18688 [7:57:32<12:54:16, 3.91s/it] 36%|███▋ | 6807/18688 [7:57:36<13:07:15, 3.98s/it] 36%|███▋ | 6808/18688 [7:57:39<12:43:00, 3.85s/it] 36%|███▋ | 6809/18688 [7:57:43<12:40:29, 3.84s/it] 36%|███▋ | 6810/18688 [7:57:47<12:18:33, 3.73s/it] {'loss': '0.888', 'grad_norm': '1.235', 'learning_rate': '6.26e-05', 'epoch': '0.3644', 'num_input_tokens_seen': 22841872, 'train_runtime': '2.867e+04', 'train_tokens_per_second': '796.8'}
36%|███▋ | 6810/18688 [7:57:47<12:18:33, 3.73s/it] 36%|███▋ | 6811/18688 [7:57:51<13:02:28, 3.95s/it] 36%|███▋ | 6812/18688 [7:57:55<12:41:47, 3.85s/it] 36%|███▋ | 6813/18688 [7:58:00<14:16:04, 4.33s/it] 36%|███▋ | 6814/18688 [7:58:05<14:55:25, 4.52s/it] 36%|███▋ | 6815/18688 [7:58:09<13:52:08, 4.21s/it] 36%|███▋ | 6816/18688 [7:58:13<13:46:54, 4.18s/it] 36%|███▋ | 6817/18688 [7:58:17<13:52:45, 4.21s/it] 36%|███▋ | 6818/18688 [7:58:21<13:50:20, 4.20s/it] 36%|███▋ | 6819/18688 [7:58:26<14:10:43, 4.30s/it] 36%|███▋ | 6820/18688 [7:58:29<13:34:57, 4.12s/it] {'loss': '0.9236', 'grad_norm': '1.141', 'learning_rate': '6.254e-05', 'epoch': '0.3649', 'num_input_tokens_seen': 22878008, 'train_runtime': '2.871e+04', 'train_tokens_per_second': '796.9'}
36%|███▋ | 6820/18688 [7:58:29<13:34:57, 4.12s/it] 36%|███▋ | 6821/18688 [7:58:33<13:11:20, 4.00s/it] 37%|███▋ | 6822/18688 [7:58:38<13:50:00, 4.20s/it] 37%|███▋ | 6823/18688 [7:58:42<13:32:26, 4.11s/it] 37%|███▋ | 6824/18688 [7:58:46<13:20:25, 4.05s/it] 37%|███▋ | 6825/18688 [7:58:50<13:27:45, 4.09s/it] 37%|███▋ | 6826/18688 [7:58:53<12:54:06, 3.92s/it] 37%|███▋ | 6827/18688 [7:58:57<12:40:22, 3.85s/it] 37%|███▋ | 6828/18688 [7:59:01<12:38:39, 3.84s/it] 37%|███▋ | 6829/18688 [7:59:04<12:34:13, 3.82s/it] 37%|███▋ | 6830/18688 [7:59:09<13:14:15, 4.02s/it] {'loss': '0.9155', 'grad_norm': '1.069', 'learning_rate': '6.248e-05', 'epoch': '0.3655', 'num_input_tokens_seen': 22910608, 'train_runtime': '2.875e+04', 'train_tokens_per_second': '796.9'}
37%|███▋ | 6830/18688 [7:59:09<13:14:15, 4.02s/it] 37%|███▋ | 6831/18688 [7:59:13<13:38:25, 4.14s/it] 37%|███▋ | 6832/18688 [7:59:17<13:08:22, 3.99s/it] 37%|███▋ | 6833/18688 [7:59:21<13:21:51, 4.06s/it] 37%|███▋ | 6834/18688 [7:59:26<13:42:45, 4.16s/it] 37%|███▋ | 6835/18688 [7:59:30<13:36:49, 4.13s/it] 37%|███▋ | 6836/18688 [7:59:34<13:53:24, 4.22s/it] 37%|███▋ | 6837/18688 [7:59:38<13:19:26, 4.05s/it] 37%|███▋ | 6838/18688 [7:59:43<14:33:29, 4.42s/it] 37%|███▋ | 6839/18688 [7:59:48<14:45:54, 4.49s/it] 37%|███▋ | 6840/18688 [7:59:52<14:23:34, 4.37s/it] {'loss': '0.9275', 'grad_norm': '1.198', 'learning_rate': '6.242e-05', 'epoch': '0.366', 'num_input_tokens_seen': 22947848, 'train_runtime': '2.879e+04', 'train_tokens_per_second': '797'}
37%|███▋ | 6840/18688 [7:59:52<14:23:34, 4.37s/it] 37%|███▋ | 6841/18688 [7:59:56<13:47:41, 4.19s/it] 37%|███▋ | 6842/18688 [8:00:00<14:02:20, 4.27s/it] 37%|███▋ | 6843/18688 [8:00:05<14:56:27, 4.54s/it] 37%|███▋ | 6844/18688 [8:00:09<14:24:07, 4.38s/it] 37%|███▋ | 6845/18688 [8:00:13<14:01:57, 4.27s/it] 37%|███▋ | 6846/18688 [8:00:17<13:39:50, 4.15s/it] 37%|███▋ | 6847/18688 [8:00:21<13:05:21, 3.98s/it] 37%|███▋ | 6848/18688 [8:00:25<12:56:54, 3.94s/it] 37%|███▋ | 6849/18688 [8:00:29<13:04:54, 3.98s/it] 37%|███▋ | 6850/18688 [8:00:33<13:15:48, 4.03s/it] {'loss': '0.8863', 'grad_norm': '1.28', 'learning_rate': '6.236e-05', 'epoch': '0.3666', 'num_input_tokens_seen': 22979960, 'train_runtime': '2.883e+04', 'train_tokens_per_second': '797'}
37%|███▋ | 6850/18688 [8:00:33<13:15:48, 4.03s/it] 37%|███▋ | 6851/18688 [8:00:37<13:21:13, 4.06s/it] 37%|███▋ | 6852/18688 [8:00:41<13:01:21, 3.96s/it] 37%|███▋ | 6853/18688 [8:00:45<13:29:42, 4.11s/it] 37%|███▋ | 6854/18688 [8:00:49<13:11:54, 4.02s/it] 37%|███▋ | 6855/18688 [8:00:53<12:55:41, 3.93s/it] 37%|███▋ | 6856/18688 [8:00:57<13:17:21, 4.04s/it] 37%|███▋ | 6857/18688 [8:01:00<12:49:28, 3.90s/it] 37%|███▋ | 6858/18688 [8:01:04<12:23:30, 3.77s/it] 37%|███▋ | 6859/18688 [8:01:08<12:24:58, 3.78s/it] 37%|███▋ | 6860/18688 [8:01:12<12:42:07, 3.87s/it] {'loss': '0.9811', 'grad_norm': '1.267', 'learning_rate': '6.23e-05', 'epoch': '0.3671', 'num_input_tokens_seen': 23010608, 'train_runtime': '2.887e+04', 'train_tokens_per_second': '797'}
37%|███▋ | 6860/18688 [8:01:12<12:42:07, 3.87s/it] 37%|███▋ | 6861/18688 [8:01:16<13:12:53, 4.02s/it] 37%|███▋ | 6862/18688 [8:01:20<12:48:34, 3.90s/it] 37%|███▋ | 6863/18688 [8:01:24<13:02:42, 3.97s/it] 37%|███▋ | 6864/18688 [8:01:28<12:59:25, 3.96s/it] 37%|███▋ | 6865/18688 [8:01:33<13:41:09, 4.17s/it] 37%|███▋ | 6866/18688 [8:01:36<13:15:46, 4.04s/it] 37%|███▋ | 6867/18688 [8:01:41<13:41:48, 4.17s/it] 37%|███▋ | 6868/18688 [8:01:45<13:32:56, 4.13s/it] 37%|███▋ | 6869/18688 [8:01:48<12:55:10, 3.94s/it] 37%|███▋ | 6870/18688 [8:01:53<13:15:39, 4.04s/it] {'loss': '0.9471', 'grad_norm': '1.182', 'learning_rate': '6.224e-05', 'epoch': '0.3676', 'num_input_tokens_seen': 23044024, 'train_runtime': '2.891e+04', 'train_tokens_per_second': '797'}
37%|███▋ | 6870/18688 [8:01:53<13:15:39, 4.04s/it] 37%|███▋ | 6871/18688 [8:01:57<13:47:08, 4.20s/it] 37%|███▋ | 6872/18688 [8:02:01<13:12:04, 4.02s/it] 37%|███▋ | 6873/18688 [8:02:04<12:51:30, 3.92s/it] 37%|███▋ | 6874/18688 [8:02:09<13:46:59, 4.20s/it] 37%|███▋ | 6875/18688 [8:02:13<13:15:42, 4.04s/it] 37%|███▋ | 6876/18688 [8:02:17<13:34:03, 4.14s/it] 37%|███▋ | 6877/18688 [8:02:21<13:20:40, 4.07s/it] 37%|███▋ | 6878/18688 [8:02:25<13:30:29, 4.12s/it] 37%|███▋ | 6879/18688 [8:02:29<12:50:54, 3.92s/it] 37%|███▋ | 6880/18688 [8:02:33<13:06:30, 4.00s/it] {'loss': '0.9685', 'grad_norm': '1.354', 'learning_rate': '6.218e-05', 'epoch': '0.3682', 'num_input_tokens_seen': 23076920, 'train_runtime': '2.895e+04', 'train_tokens_per_second': '797'}
37%|███▋ | 6880/18688 [8:02:33<13:06:30, 4.00s/it] 37%|███▋ | 6881/18688 [8:02:37<12:46:19, 3.89s/it] 37%|███▋ | 6882/18688 [8:02:40<12:33:12, 3.83s/it] 37%|███▋ | 6883/18688 [8:02:44<12:18:35, 3.75s/it] 37%|███▋ | 6884/18688 [8:02:48<12:28:13, 3.80s/it] 37%|███▋ | 6885/18688 [8:02:52<12:27:31, 3.80s/it] 37%|███▋ | 6886/18688 [8:02:56<13:22:21, 4.08s/it] 37%|███▋ | 6887/18688 [8:03:00<12:58:10, 3.96s/it] 37%|███▋ | 6888/18688 [8:03:04<13:12:46, 4.03s/it] 37%|███▋ | 6889/18688 [8:03:08<13:10:06, 4.02s/it] 37%|███▋ | 6890/18688 [8:03:12<12:55:29, 3.94s/it] {'loss': '0.9286', 'grad_norm': '0.9948', 'learning_rate': '6.211e-05', 'epoch': '0.3687', 'num_input_tokens_seen': 23106328, 'train_runtime': '2.899e+04', 'train_tokens_per_second': '797'}
37%|███▋ | 6890/18688 [8:03:12<12:55:29, 3.94s/it] 37%|███▋ | 6891/18688 [8:03:17<14:13:46, 4.34s/it] 37%|███▋ | 6892/18688 [8:03:21<13:50:26, 4.22s/it] 37%|███▋ | 6893/18688 [8:03:26<13:56:03, 4.25s/it] 37%|███▋ | 6894/18688 [8:03:29<13:13:45, 4.04s/it] 37%|███▋ | 6895/18688 [8:03:33<12:46:28, 3.90s/it] 37%|███▋ | 6896/18688 [8:03:37<13:14:14, 4.04s/it] 37%|███▋ | 6897/18688 [8:03:41<13:11:43, 4.03s/it] 37%|███▋ | 6898/18688 [8:03:45<12:59:06, 3.96s/it] 37%|███▋ | 6899/18688 [8:03:49<13:05:26, 4.00s/it] 37%|███▋ | 6900/18688 [8:03:52<12:35:10, 3.84s/it] {'loss': '0.8947', 'grad_norm': '1.196', 'learning_rate': '6.205e-05', 'epoch': '0.3692', 'num_input_tokens_seen': 23139560, 'train_runtime': '2.903e+04', 'train_tokens_per_second': '797'}
37%|███▋ | 6900/18688 [8:03:52<12:35:10, 3.84s/it] 37%|███▋ | 6901/18688 [8:03:57<13:11:08, 4.03s/it] 37%|███▋ | 6902/18688 [8:04:01<12:49:35, 3.92s/it] 37%|███▋ | 6903/18688 [8:04:05<13:08:22, 4.01s/it] 37%|███▋ | 6904/18688 [8:04:09<13:30:36, 4.13s/it] 37%|███▋ | 6905/18688 [8:04:14<13:59:45, 4.28s/it] 37%|███▋ | 6906/18688 [8:04:18<13:54:36, 4.25s/it] 37%|███▋ | 6907/18688 [8:04:23<14:24:37, 4.40s/it] 37%|███▋ | 6908/18688 [8:04:27<14:26:12, 4.41s/it] 37%|███▋ | 6909/18688 [8:04:31<13:56:38, 4.26s/it] 37%|███▋ | 6910/18688 [8:04:35<13:52:29, 4.24s/it] {'loss': '0.9619', 'grad_norm': '1.193', 'learning_rate': '6.199e-05', 'epoch': '0.3698', 'num_input_tokens_seen': 23175688, 'train_runtime': '2.908e+04', 'train_tokens_per_second': '797.1'}
37%|███▋ | 6910/18688 [8:04:35<13:52:29, 4.24s/it] 37%|███▋ | 6911/18688 [8:04:40<14:15:08, 4.36s/it] 37%|███▋ | 6912/18688 [8:04:44<14:14:47, 4.36s/it] 37%|███▋ | 6913/18688 [8:04:48<13:36:47, 4.16s/it] 37%|███▋ | 6914/18688 [8:04:52<13:40:34, 4.18s/it] 37%|███▋ | 6915/18688 [8:04:57<14:04:13, 4.30s/it] 37%|███▋ | 6916/18688 [8:05:00<13:22:24, 4.09s/it] 37%|███▋ | 6917/18688 [8:05:04<13:12:50, 4.04s/it] 37%|███▋ | 6918/18688 [8:05:09<13:35:06, 4.16s/it] 37%|███▋ | 6919/18688 [8:05:13<13:13:01, 4.04s/it] 37%|███▋ | 6920/18688 [8:05:16<12:59:30, 3.97s/it] {'loss': '0.9504', 'grad_norm': '1.234', 'learning_rate': '6.193e-05', 'epoch': '0.3703', 'num_input_tokens_seen': 23209168, 'train_runtime': '2.912e+04', 'train_tokens_per_second': '797.1'}
37%|███▋ | 6920/18688 [8:05:16<12:59:30, 3.97s/it] 37%|███▋ | 6921/18688 [8:05:22<14:11:14, 4.34s/it] 37%|███▋ | 6922/18688 [8:05:25<13:44:30, 4.20s/it] 37%|███▋ | 6923/18688 [8:05:29<13:29:01, 4.13s/it] 37%|███▋ | 6924/18688 [8:05:34<13:35:37, 4.16s/it] 37%|███▋ | 6925/18688 [8:05:38<13:24:51, 4.11s/it] 37%|███▋ | 6926/18688 [8:05:42<13:55:28, 4.26s/it] 37%|███▋ | 6927/18688 [8:05:46<13:47:22, 4.22s/it] 37%|███▋ | 6928/18688 [8:05:50<13:18:31, 4.07s/it] 37%|███▋ | 6929/18688 [8:05:54<13:18:33, 4.07s/it] 37%|███▋ | 6930/18688 [8:05:59<13:38:04, 4.17s/it] {'loss': '0.8973', 'grad_norm': '0.9792', 'learning_rate': '6.187e-05', 'epoch': '0.3708', 'num_input_tokens_seen': 23243400, 'train_runtime': '2.916e+04', 'train_tokens_per_second': '797.1'}
37%|███▋ | 6930/18688 [8:05:59<13:38:04, 4.17s/it] 37%|███▋ | 6931/18688 [8:06:02<13:05:56, 4.01s/it] 37%|███▋ | 6932/18688 [8:06:06<13:01:52, 3.99s/it] 37%|███▋ | 6933/18688 [8:06:10<12:49:44, 3.93s/it] 37%|███▋ | 6934/18688 [8:06:14<12:48:48, 3.92s/it] 37%|███▋ | 6935/18688 [8:06:18<12:37:19, 3.87s/it] 37%|███▋ | 6936/18688 [8:06:21<12:22:14, 3.79s/it] 37%|███▋ | 6937/18688 [8:06:25<12:17:42, 3.77s/it] 37%|███▋ | 6938/18688 [8:06:29<13:00:59, 3.99s/it] 37%|███▋ | 6939/18688 [8:06:33<12:44:23, 3.90s/it] 37%|███▋ | 6940/18688 [8:06:37<12:37:20, 3.87s/it] {'loss': '0.9988', 'grad_norm': '1.061', 'learning_rate': '6.181e-05', 'epoch': '0.3714', 'num_input_tokens_seen': 23273432, 'train_runtime': '2.92e+04', 'train_tokens_per_second': '797.1'}
37%|███▋ | 6940/18688 [8:06:37<12:37:20, 3.87s/it] 37%|███▋ | 6941/18688 [8:06:41<13:11:09, 4.04s/it] 37%|███▋ | 6942/18688 [8:06:46<13:32:19, 4.15s/it] 37%|███▋ | 6943/18688 [8:06:50<14:00:39, 4.29s/it] 37%|███▋ | 6944/18688 [8:06:55<14:38:38, 4.49s/it] 37%|███▋ | 6945/18688 [8:06:59<14:10:18, 4.34s/it] 37%|███▋ | 6946/18688 [8:07:04<14:45:21, 4.52s/it] 37%|███▋ | 6947/18688 [8:07:08<14:27:11, 4.43s/it] 37%|███▋ | 6948/18688 [8:07:12<13:54:08, 4.26s/it] 37%|███▋ | 6949/18688 [8:07:17<14:11:09, 4.35s/it] 37%|███▋ | 6950/18688 [8:07:22<14:51:33, 4.56s/it] {'loss': '0.9481', 'grad_norm': '1.034', 'learning_rate': '6.175e-05', 'epoch': '0.3719', 'num_input_tokens_seen': 23313368, 'train_runtime': '2.924e+04', 'train_tokens_per_second': '797.2'}
37%|███▋ | 6950/18688 [8:07:22<14:51:33, 4.56s/it] 37%|███▋ | 6951/18688 [8:07:26<13:54:02, 4.26s/it] 37%|███▋ | 6952/18688 [8:07:29<13:25:27, 4.12s/it] 37%|███▋ | 6953/18688 [8:07:34<13:49:01, 4.24s/it] 37%|███▋ | 6954/18688 [8:07:39<14:52:39, 4.56s/it] 37%|███▋ | 6955/18688 [8:07:43<14:07:53, 4.34s/it] 37%|███▋ | 6956/18688 [8:07:46<13:18:05, 4.08s/it] 37%|███▋ | 6957/18688 [8:07:50<13:15:26, 4.07s/it] 37%|███▋ | 6958/18688 [8:07:55<13:48:00, 4.24s/it] 37%|███▋ | 6959/18688 [8:07:59<13:22:11, 4.10s/it] 37%|███▋ | 6960/18688 [8:08:02<12:43:03, 3.90s/it] {'loss': '0.9239', 'grad_norm': '1.43', 'learning_rate': '6.169e-05', 'epoch': '0.3724', 'num_input_tokens_seen': 23347136, 'train_runtime': '2.928e+04', 'train_tokens_per_second': '797.3'}
37%|███▋ | 6960/18688 [8:08:02<12:43:03, 3.90s/it] 37%|███▋ | 6961/18688 [8:08:07<13:14:04, 4.06s/it] 37%|███▋ | 6962/18688 [8:08:12<14:01:29, 4.31s/it] 37%|███▋ | 6963/18688 [8:08:16<14:07:47, 4.34s/it] 37%|███▋ | 6964/18688 [8:08:20<13:36:39, 4.18s/it] 37%|███▋ | 6965/18688 [8:08:24<13:12:17, 4.06s/it] 37%|███▋ | 6966/18688 [8:08:28<13:24:17, 4.12s/it] 37%|███▋ | 6967/18688 [8:08:33<14:05:04, 4.33s/it] 37%|███▋ | 6968/18688 [8:08:36<13:19:38, 4.09s/it] 37%|███▋ | 6969/18688 [8:08:40<13:24:37, 4.12s/it] 37%|███▋ | 6970/18688 [8:08:44<13:00:09, 3.99s/it] {'loss': '0.9176', 'grad_norm': '1.466', 'learning_rate': '6.163e-05', 'epoch': '0.373', 'num_input_tokens_seen': 23381240, 'train_runtime': '2.932e+04', 'train_tokens_per_second': '797.3'}
37%|███▋ | 6970/18688 [8:08:44<13:00:09, 3.99s/it] 37%|███▋ | 6971/18688 [8:08:48<13:07:02, 4.03s/it] 37%|███▋ | 6972/18688 [8:08:52<12:35:44, 3.87s/it] 37%|███▋ | 6973/18688 [8:08:56<12:56:47, 3.98s/it] 37%|███▋ | 6974/18688 [8:09:01<13:48:03, 4.24s/it] 37%|███▋ | 6975/18688 [8:09:04<13:00:17, 4.00s/it] 37%|███▋ | 6976/18688 [8:09:09<13:50:39, 4.26s/it] 37%|███▋ | 6977/18688 [8:09:14<14:08:10, 4.35s/it] 37%|███▋ | 6978/18688 [8:09:18<14:26:39, 4.44s/it] 37%|███▋ | 6979/18688 [8:09:22<14:05:18, 4.33s/it] 37%|███▋ | 6980/18688 [8:09:27<13:53:36, 4.27s/it] {'loss': '0.9049', 'grad_norm': '1.407', 'learning_rate': '6.156e-05', 'epoch': '0.3735', 'num_input_tokens_seen': 23417216, 'train_runtime': '2.937e+04', 'train_tokens_per_second': '797.4'}
37%|███▋ | 6980/18688 [8:09:27<13:53:36, 4.27s/it] 37%|███▋ | 6981/18688 [8:09:31<14:01:37, 4.31s/it] 37%|███▋ | 6982/18688 [8:09:35<13:37:28, 4.19s/it] 37%|███▋ | 6983/18688 [8:09:38<13:05:08, 4.02s/it] 37%|███▋ | 6984/18688 [8:09:43<13:27:08, 4.14s/it] 37%|███▋ | 6985/18688 [8:09:47<12:58:34, 3.99s/it] 37%|███▋ | 6986/18688 [8:09:51<13:04:09, 4.02s/it] 37%|███▋ | 6987/18688 [8:09:55<13:00:36, 4.00s/it] 37%|███▋ | 6988/18688 [8:09:59<13:17:23, 4.09s/it] 37%|███▋ | 6989/18688 [8:10:02<12:45:44, 3.93s/it] 37%|███▋ | 6990/18688 [8:10:06<12:45:22, 3.93s/it] {'loss': '0.9357', 'grad_norm': '1.638', 'learning_rate': '6.15e-05', 'epoch': '0.374', 'num_input_tokens_seen': 23448592, 'train_runtime': '2.941e+04', 'train_tokens_per_second': '797.4'}
37%|███▋ | 6990/18688 [8:10:06<12:45:22, 3.93s/it] 37%|███▋ | 6991/18688 [8:10:10<12:33:07, 3.86s/it] 37%|███▋ | 6992/18688 [8:10:15<13:21:59, 4.11s/it] 37%|███▋ | 6993/18688 [8:10:19<13:05:45, 4.03s/it] 37%|███▋ | 6994/18688 [8:10:22<12:36:32, 3.88s/it] 37%|███▋ | 6995/18688 [8:10:26<12:31:42, 3.86s/it] 37%|███▋ | 6996/18688 [8:10:30<12:14:49, 3.77s/it] 37%|███▋ | 6997/18688 [8:10:34<12:58:34, 4.00s/it] 37%|███▋ | 6998/18688 [8:10:38<12:45:19, 3.93s/it] 37%|███▋ | 6999/18688 [8:10:42<12:55:13, 3.98s/it] 37%|███▋ | 7000/18688 [8:10:46<13:14:28, 4.08s/it] {'loss': '0.8705', 'grad_norm': '1.38', 'learning_rate': '6.144e-05', 'epoch': '0.3746', 'num_input_tokens_seen': 23480632, 'train_runtime': '2.945e+04', 'train_tokens_per_second': '797.4'}
37%|███▋ | 7000/18688 [8:10:46<13:14:28, 4.08s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:71: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:281: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 0/1000 [00:00<?, ?it/s]/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:172: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:202: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:254: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 3/1000 [00:00<00:38, 25.59it/s]
1%| | 6/1000 [00:00<00:54, 18.07it/s]
1%| | 8/1000 [00:00<00:57, 17.34it/s]
1%| | 10/1000 [00:00<01:00, 16.47it/s]
1%| | 12/1000 [00:00<01:00, 16.29it/s]
1%|▏ | 14/1000 [00:00<01:02, 15.87it/s]
2%|▏ | 16/1000 [00:00<01:04, 15.29it/s]
2%|▏ | 18/1000 [00:01<01:39, 9.88it/s]
2%|▏ | 20/1000 [00:01<01:33, 10.45it/s]
2%|▏ | 22/1000 [00:01<01:52, 8.67it/s]
2%|▏ | 24/1000 [00:01<01:40, 9.73it/s]
3%|▎ | 26/1000 [00:02<01:45, 9.25it/s]
3%|▎ | 28/1000 [00:02<01:36, 10.09it/s]
3%|▎ | 30/1000 [00:02<01:28, 11.00it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.14it/s]
3%|▎ | 34/1000 [00:02<01:25, 11.24it/s]
4%|▎ | 36/1000 [00:03<01:32, 10.42it/s]
4%|▍ | 38/1000 [00:03<01:24, 11.34it/s]
4%|▍ | 40/1000 [00:03<01:28, 10.89it/s]
4%|▍ | 42/1000 [00:03<01:46, 9.00it/s]
4%|▍ | 44/1000 [00:03<01:31, 10.50it/s]
5%|▍ | 46/1000 [00:04<01:21, 11.65it/s]
5%|▍ | 48/1000 [00:04<01:29, 10.61it/s]
5%|▌ | 50/1000 [00:04<01:21, 11.61it/s]
5%|▌ | 52/1000 [00:04<01:16, 12.45it/s]
5%|▌ | 54/1000 [00:04<01:13, 12.84it/s]
6%|▌ | 56/1000 [00:04<01:14, 12.61it/s]
6%|▌ | 58/1000 [00:05<01:54, 8.20it/s]
6%|▌ | 60/1000 [00:05<01:43, 9.11it/s]
6%|▌ | 62/1000 [00:05<01:34, 9.88it/s]
6%|▋ | 64/1000 [00:05<01:29, 10.49it/s]
7%|▋ | 66/1000 [00:05<01:25, 10.90it/s]
7%|▋ | 68/1000 [00:06<01:22, 11.24it/s]
7%|▋ | 70/1000 [00:06<01:19, 11.64it/s]
7%|▋ | 72/1000 [00:06<01:16, 12.06it/s]
7%|▋ | 74/1000 [00:06<01:15, 12.32it/s]
8%|▊ | 76/1000 [00:06<01:13, 12.59it/s]
8%|▊ | 78/1000 [00:06<01:12, 12.80it/s]
8%|▊ | 80/1000 [00:07<01:17, 11.90it/s]
8%|▊ | 82/1000 [00:07<01:15, 12.09it/s]
8%|▊ | 84/1000 [00:07<01:14, 12.24it/s]
9%|▊ | 86/1000 [00:07<01:11, 12.81it/s]
9%|▉ | 88/1000 [00:07<01:09, 13.17it/s]
9%|▉ | 90/1000 [00:07<01:07, 13.57it/s]
9%|▉ | 92/1000 [00:07<01:07, 13.54it/s]
9%|▉ | 94/1000 [00:08<01:06, 13.54it/s]
10%|▉ | 96/1000 [00:08<01:04, 13.96it/s]
10%|▉ | 98/1000 [00:08<01:04, 13.96it/s]
10%|█ | 100/1000 [00:08<01:32, 9.73it/s]
10%|█ | 102/1000 [00:08<01:23, 10.71it/s]
10%|█ | 104/1000 [00:09<01:55, 7.78it/s]
11%|█ | 106/1000 [00:09<02:02, 7.30it/s]
11%|█ | 108/1000 [00:09<01:43, 8.63it/s]
11%|█ | 110/1000 [00:09<01:28, 10.08it/s]
11%|█ | 112/1000 [00:09<01:19, 11.21it/s]
11%|█▏ | 114/1000 [00:10<01:14, 11.87it/s]
12%|█▏ | 116/1000 [00:10<01:10, 12.50it/s]
12%|█▏ | 118/1000 [00:10<01:06, 13.29it/s]
12%|█▏ | 120/1000 [00:10<01:09, 12.74it/s]
12%|█▏ | 122/1000 [00:10<01:22, 10.58it/s]
12%|█▏ | 124/1000 [00:11<01:30, 9.66it/s]
13%|█▎ | 126/1000 [00:11<01:22, 10.62it/s]
13%|█▎ | 128/1000 [00:11<01:15, 11.53it/s]
13%|█▎ | 130/1000 [00:11<01:11, 12.21it/s]
13%|█▎ | 132/1000 [00:11<01:05, 13.17it/s]
13%|█▎ | 134/1000 [00:11<01:10, 12.37it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.32it/s]
14%|█▍ | 138/1000 [00:12<01:05, 13.25it/s]
14%|█▍ | 140/1000 [00:12<01:25, 10.11it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.54it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.57it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.60it/s]
15%|█▍ | 148/1000 [00:13<01:14, 11.50it/s]
15%|█▌ | 150/1000 [00:13<01:07, 12.60it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.15it/s]
15%|█▌ | 154/1000 [00:13<01:03, 13.41it/s]
16%|█▌ | 156/1000 [00:13<01:03, 13.25it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.75it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.66it/s]
16%|█▌ | 162/1000 [00:14<01:01, 13.52it/s]
16%|█▋ | 164/1000 [00:14<01:05, 12.83it/s]
17%|█▋ | 166/1000 [00:14<01:03, 13.20it/s]
17%|█▋ | 168/1000 [00:14<01:00, 13.86it/s]
17%|█▋ | 170/1000 [00:14<01:20, 10.31it/s]
17%|█▋ | 172/1000 [00:14<01:13, 11.33it/s]
17%|█▋ | 174/1000 [00:15<01:30, 9.09it/s]
18%|█▊ | 176/1000 [00:15<01:55, 7.14it/s]
18%|█▊ | 178/1000 [00:15<01:35, 8.61it/s]
18%|█▊ | 180/1000 [00:16<01:33, 8.75it/s]
18%|█▊ | 182/1000 [00:16<01:35, 8.54it/s]
18%|█▊ | 184/1000 [00:16<01:23, 9.78it/s]
19%|█▊ | 186/1000 [00:16<01:37, 8.31it/s]
19%|█▉ | 188/1000 [00:16<01:24, 9.64it/s]
19%|█▉ | 190/1000 [00:17<01:15, 10.67it/s]
19%|█▉ | 192/1000 [00:17<01:11, 11.32it/s]
19%|█▉ | 194/1000 [00:17<01:16, 10.54it/s]
20%|█▉ | 196/1000 [00:17<01:29, 8.97it/s]
20%|█▉ | 198/1000 [00:17<01:18, 10.20it/s]
20%|██ | 200/1000 [00:18<01:15, 10.54it/s]
20%|██ | 202/1000 [00:18<01:09, 11.49it/s]
20%|██ | 204/1000 [00:18<01:03, 12.58it/s]
21%|██ | 206/1000 [00:18<01:00, 13.20it/s]
21%|██ | 208/1000 [00:18<00:58, 13.59it/s]
21%|██ | 210/1000 [00:18<00:54, 14.45it/s]
21%|██ | 212/1000 [00:18<00:53, 14.71it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.74it/s]
22%|██▏ | 216/1000 [00:19<00:54, 14.50it/s]
22%|██▏ | 218/1000 [00:19<00:52, 14.89it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.90it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.17it/s]
22%|██▏ | 224/1000 [00:20<01:20, 9.65it/s]
23%|██▎ | 226/1000 [00:20<01:12, 10.65it/s]
23%|██▎ | 228/1000 [00:20<01:06, 11.55it/s]
23%|██▎ | 230/1000 [00:20<01:03, 12.06it/s]
23%|██▎ | 232/1000 [00:20<01:01, 12.41it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.17it/s]
24%|██▎ | 236/1000 [00:20<00:55, 13.65it/s]
24%|██▍ | 238/1000 [00:21<00:53, 14.26it/s]
24%|██▍ | 240/1000 [00:21<00:53, 14.23it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.70it/s]
24%|██▍ | 244/1000 [00:21<00:50, 14.93it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.85it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.63it/s]
25%|██▌ | 250/1000 [00:21<00:56, 13.39it/s]
25%|██▌ | 252/1000 [00:22<01:01, 12.15it/s]
25%|██▌ | 254/1000 [00:22<00:56, 13.17it/s]
26%|██▌ | 256/1000 [00:22<00:54, 13.74it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.77it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.88it/s]
26%|██▌ | 262/1000 [00:22<00:56, 13.03it/s]
26%|██▋ | 264/1000 [00:22<00:54, 13.60it/s]
27%|██▋ | 266/1000 [00:23<00:52, 13.98it/s]
27%|██▋ | 268/1000 [00:23<00:50, 14.55it/s]
27%|██▋ | 270/1000 [00:23<00:55, 13.27it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.64it/s]
27%|██▋ | 274/1000 [00:23<01:01, 11.72it/s]
28%|██▊ | 276/1000 [00:23<00:56, 12.74it/s]
28%|██▊ | 278/1000 [00:24<00:55, 12.95it/s]
28%|██▊ | 280/1000 [00:24<00:53, 13.49it/s]
28%|██▊ | 282/1000 [00:24<00:49, 14.45it/s]
28%|██▊ | 284/1000 [00:24<00:49, 14.40it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.69it/s]
29%|██▉ | 288/1000 [00:24<00:47, 15.03it/s]
29%|██▉ | 290/1000 [00:24<00:46, 15.42it/s]
29%|██▉ | 292/1000 [00:24<00:45, 15.40it/s]
29%|██▉ | 294/1000 [00:25<00:46, 15.30it/s]
30%|██▉ | 296/1000 [00:25<00:45, 15.37it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.50it/s]
30%|███ | 300/1000 [00:25<00:46, 15.15it/s]
30%|███ | 302/1000 [00:25<00:46, 15.15it/s]
30%|███ | 304/1000 [00:25<00:50, 13.74it/s]
31%|███ | 306/1000 [00:25<00:47, 14.49it/s]
31%|███ | 308/1000 [00:26<00:47, 14.47it/s]
31%|███ | 310/1000 [00:26<00:47, 14.66it/s]
31%|███ | 312/1000 [00:26<00:47, 14.64it/s]
31%|███▏ | 314/1000 [00:26<00:44, 15.28it/s]
32%|███▏ | 316/1000 [00:26<00:44, 15.52it/s]
32%|███▏ | 318/1000 [00:26<00:42, 15.87it/s]
32%|███▏ | 320/1000 [00:27<01:12, 9.33it/s]
32%|███▏ | 322/1000 [00:27<01:03, 10.69it/s]
32%|███▏ | 324/1000 [00:27<00:58, 11.47it/s]
33%|███▎ | 326/1000 [00:27<00:58, 11.44it/s]
33%|███▎ | 328/1000 [00:27<00:58, 11.49it/s]
33%|███▎ | 330/1000 [00:27<00:53, 12.43it/s]
33%|███▎ | 332/1000 [00:27<00:52, 12.70it/s]
33%|███▎ | 334/1000 [00:28<00:50, 13.19it/s]
34%|███▎ | 336/1000 [00:28<00:48, 13.58it/s]
34%|███▍ | 338/1000 [00:28<01:05, 10.06it/s]
34%|███▍ | 340/1000 [00:28<00:59, 11.13it/s]
34%|███▍ | 342/1000 [00:28<00:53, 12.40it/s]
34%|███▍ | 344/1000 [00:28<00:50, 13.07it/s]
35%|███▍ | 346/1000 [00:29<00:47, 13.68it/s]
35%|███▍ | 348/1000 [00:29<00:46, 14.11it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.76it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.95it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.94it/s]
36%|███▌ | 356/1000 [00:29<00:42, 15.00it/s]
36%|███▌ | 358/1000 [00:29<00:43, 14.70it/s]
36%|███▌ | 360/1000 [00:30<00:42, 15.07it/s]
36%|███▌ | 362/1000 [00:30<00:47, 13.37it/s]
36%|███▋ | 364/1000 [00:30<00:45, 13.89it/s]
37%|███▋ | 366/1000 [00:30<00:53, 11.96it/s]
37%|███▋ | 368/1000 [00:30<00:51, 12.36it/s]
37%|███▋ | 370/1000 [00:30<00:47, 13.22it/s]
37%|███▋ | 372/1000 [00:30<00:46, 13.55it/s]
37%|███▋ | 374/1000 [00:31<00:54, 11.39it/s]
38%|███▊ | 376/1000 [00:31<00:56, 10.97it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.66it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.49it/s]
38%|███▊ | 382/1000 [00:31<00:48, 12.66it/s]
38%|███▊ | 384/1000 [00:31<00:46, 13.35it/s]
39%|███▊ | 386/1000 [00:32<00:44, 13.66it/s]
39%|███▉ | 388/1000 [00:32<00:44, 13.62it/s]
39%|███▉ | 390/1000 [00:32<00:45, 13.53it/s]
39%|███▉ | 392/1000 [00:32<00:43, 13.86it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.64it/s]
40%|███▉ | 396/1000 [00:32<00:43, 13.78it/s]
40%|███▉ | 398/1000 [00:33<00:47, 12.58it/s]
40%|████ | 400/1000 [00:33<00:45, 13.24it/s]
40%|████ | 402/1000 [00:33<00:43, 13.88it/s]
40%|████ | 404/1000 [00:33<00:41, 14.52it/s]
41%|████ | 406/1000 [00:33<00:39, 15.04it/s]
41%|████ | 408/1000 [00:33<00:47, 12.58it/s]
41%|████ | 410/1000 [00:34<00:56, 10.49it/s]
41%|████ | 412/1000 [00:34<00:50, 11.56it/s]
41%|████▏ | 414/1000 [00:34<01:07, 8.64it/s]
42%|████▏ | 416/1000 [00:34<01:23, 6.97it/s]
42%|████▏ | 418/1000 [00:35<01:10, 8.28it/s]
42%|████▏ | 420/1000 [00:35<01:00, 9.55it/s]
42%|████▏ | 422/1000 [00:35<00:53, 10.90it/s]
42%|████▏ | 424/1000 [00:35<01:14, 7.68it/s]
43%|████▎ | 426/1000 [00:35<01:04, 8.94it/s]
43%|████▎ | 428/1000 [00:36<00:56, 10.15it/s]
43%|████▎ | 430/1000 [00:36<01:02, 9.12it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.05it/s]
43%|████▎ | 434/1000 [00:36<01:08, 8.27it/s]
44%|████▎ | 436/1000 [00:37<01:00, 9.32it/s]
44%|████▍ | 438/1000 [00:37<01:02, 8.98it/s]
44%|████▍ | 440/1000 [00:37<00:59, 9.48it/s]
44%|████▍ | 442/1000 [00:37<00:52, 10.54it/s]
44%|████▍ | 444/1000 [00:37<00:48, 11.40it/s]
45%|████▍ | 446/1000 [00:37<00:45, 12.17it/s]
45%|████▍ | 448/1000 [00:38<00:50, 10.97it/s]
45%|████▌ | 450/1000 [00:38<00:46, 11.79it/s]
45%|████▌ | 452/1000 [00:38<00:42, 12.81it/s]
45%|████▌ | 454/1000 [00:38<00:44, 12.29it/s]
46%|████▌ | 456/1000 [00:38<00:42, 12.73it/s]
46%|████▌ | 458/1000 [00:38<00:39, 13.75it/s]
46%|████▌ | 460/1000 [00:38<00:38, 13.86it/s]
46%|████▌ | 462/1000 [00:39<00:37, 14.47it/s]
46%|████▋ | 464/1000 [00:39<00:35, 14.98it/s]
47%|████▋ | 466/1000 [00:39<00:35, 14.84it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.93it/s]
47%|████▋ | 470/1000 [00:39<00:52, 10.04it/s]
47%|████▋ | 472/1000 [00:40<00:51, 10.21it/s]
47%|████▋ | 474/1000 [00:40<00:45, 11.50it/s]
48%|████▊ | 476/1000 [00:40<00:43, 12.16it/s]
48%|████▊ | 478/1000 [00:40<00:43, 12.02it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.96it/s]
48%|████▊ | 482/1000 [00:40<00:38, 13.34it/s]
48%|████▊ | 484/1000 [00:40<00:38, 13.55it/s]
49%|████▊ | 486/1000 [00:41<00:38, 13.23it/s]
49%|████▉ | 488/1000 [00:41<00:37, 13.65it/s]
49%|████▉ | 490/1000 [00:41<00:50, 10.00it/s]
49%|████▉ | 492/1000 [00:41<00:44, 11.39it/s]
49%|████▉ | 494/1000 [00:41<00:40, 12.51it/s]
50%|████▉ | 496/1000 [00:41<00:37, 13.39it/s]
50%|████▉ | 498/1000 [00:41<00:36, 13.92it/s]
50%|█████ | 500/1000 [00:42<00:44, 11.18it/s]
50%|█████ | 502/1000 [00:42<00:42, 11.66it/s]
50%|█████ | 504/1000 [00:42<00:40, 12.31it/s]
51%|█████ | 506/1000 [00:42<00:39, 12.56it/s]
51%|█████ | 508/1000 [00:42<00:36, 13.50it/s]
51%|█████ | 510/1000 [00:43<00:47, 10.32it/s]
51%|█████ | 512/1000 [00:43<00:43, 11.10it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 12.00it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.57it/s]
52%|█████▏ | 518/1000 [00:43<00:36, 13.09it/s]
52%|█████▏ | 520/1000 [00:43<00:35, 13.46it/s]
52%|█████▏ | 522/1000 [00:43<00:33, 14.20it/s]
52%|█████▏ | 524/1000 [00:44<00:33, 14.20it/s]
53%|█████▎ | 526/1000 [00:44<00:32, 14.37it/s]
53%|█████▎ | 528/1000 [00:44<00:39, 11.98it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.82it/s]
53%|█████▎ | 532/1000 [00:44<00:34, 13.72it/s]
53%|█████▎ | 534/1000 [00:44<00:33, 14.09it/s]
54%|█████▎ | 536/1000 [00:44<00:32, 14.22it/s]
54%|█████▍ | 538/1000 [00:45<00:33, 13.98it/s]
54%|█████▍ | 540/1000 [00:45<00:31, 14.40it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.67it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.57it/s]
55%|█████▍ | 546/1000 [00:45<00:31, 14.20it/s]
55%|█████▍ | 548/1000 [00:45<00:32, 13.90it/s]
55%|█████▌ | 550/1000 [00:46<00:34, 13.11it/s]
55%|█████▌ | 552/1000 [00:46<00:33, 13.52it/s]
55%|█████▌ | 554/1000 [00:46<00:33, 13.43it/s]
56%|█████▌ | 556/1000 [00:46<00:32, 13.75it/s]
56%|█████▌ | 558/1000 [00:46<00:31, 14.21it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 14.80it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 14.81it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.74it/s]
57%|█████▋ | 566/1000 [00:47<00:37, 11.64it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.19it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.30it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.77it/s]
57%|█████▋ | 574/1000 [00:47<00:31, 13.62it/s]
58%|█████▊ | 576/1000 [00:47<00:31, 13.56it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.61it/s]
58%|█████▊ | 580/1000 [00:48<00:32, 12.84it/s]
58%|█████▊ | 582/1000 [00:48<00:30, 13.62it/s]
58%|█████▊ | 584/1000 [00:48<00:30, 13.85it/s]
59%|█████▊ | 586/1000 [00:48<00:29, 14.22it/s]
59%|█████▉ | 588/1000 [00:48<00:27, 15.00it/s]
59%|█████▉ | 590/1000 [00:48<00:27, 14.97it/s]
59%|█████▉ | 592/1000 [00:49<00:27, 14.76it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.33it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.85it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 14.07it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.38it/s]
60%|██████ | 602/1000 [00:49<00:27, 14.49it/s]
60%|██████ | 604/1000 [00:49<00:26, 15.04it/s]
61%|██████ | 606/1000 [00:50<00:27, 14.33it/s]
61%|██████ | 608/1000 [00:50<00:28, 13.68it/s]
61%|██████ | 610/1000 [00:50<00:29, 13.21it/s]
61%|██████ | 612/1000 [00:50<00:30, 12.90it/s]
61%|██████▏ | 614/1000 [00:50<00:30, 12.64it/s]
62%|██████▏ | 616/1000 [00:50<00:30, 12.64it/s]
62%|██████▏ | 618/1000 [00:51<00:30, 12.61it/s]
62%|██████▏ | 620/1000 [00:51<00:41, 9.15it/s]
62%|██████▏ | 622/1000 [00:51<00:39, 9.51it/s]
62%|██████▏ | 624/1000 [00:51<00:34, 10.97it/s]
63%|██████▎ | 626/1000 [00:51<00:31, 11.74it/s]
63%|██████▎ | 628/1000 [00:51<00:28, 13.02it/s]
63%|██████▎ | 630/1000 [00:52<00:27, 13.60it/s]
63%|██████▎ | 632/1000 [00:52<00:29, 12.36it/s]
63%|██████▎ | 634/1000 [00:52<00:27, 13.10it/s]
64%|██████▎ | 636/1000 [00:52<00:33, 10.90it/s]
64%|██████▍ | 638/1000 [00:52<00:34, 10.64it/s]
64%|██████▍ | 640/1000 [00:53<00:31, 11.41it/s]
64%|██████▍ | 642/1000 [00:53<00:29, 12.29it/s]
64%|██████▍ | 644/1000 [00:53<00:28, 12.56it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.84it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.33it/s]
65%|██████▌ | 650/1000 [00:53<00:27, 12.57it/s]
65%|██████▌ | 652/1000 [00:53<00:29, 11.97it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.42it/s]
66%|██████▌ | 656/1000 [00:54<00:31, 10.75it/s]
66%|██████▌ | 658/1000 [00:54<00:28, 11.83it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.14it/s]
66%|██████▌ | 662/1000 [00:54<00:30, 11.26it/s]
66%|██████▋ | 664/1000 [00:55<00:28, 11.75it/s]
67%|██████▋ | 666/1000 [00:55<00:26, 12.78it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.33it/s]
67%|██████▋ | 670/1000 [00:55<00:30, 10.97it/s]
67%|██████▋ | 672/1000 [00:55<00:27, 11.77it/s]
67%|██████▋ | 674/1000 [00:55<00:26, 12.38it/s]
68%|██████▊ | 676/1000 [00:56<00:27, 11.69it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.19it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 12.97it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.12it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.32it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.38it/s]
69%|██████▉ | 688/1000 [00:57<00:22, 13.69it/s]
69%|██████▉ | 690/1000 [00:57<00:22, 13.77it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.19it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.30it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 14.05it/s]
70%|██████▉ | 698/1000 [00:57<00:20, 14.63it/s]
70%|███████ | 700/1000 [00:57<00:24, 12.21it/s]
70%|███████ | 702/1000 [00:58<00:22, 13.00it/s]
70%|███████ | 704/1000 [00:58<00:29, 9.87it/s]
71%|███████ | 706/1000 [00:58<00:27, 10.67it/s]
71%|███████ | 708/1000 [00:58<00:30, 9.62it/s]
71%|███████ | 710/1000 [00:58<00:26, 10.90it/s]
71%|███████ | 712/1000 [00:59<00:24, 11.87it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.97it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 13.06it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.56it/s]
72%|███████▏ | 720/1000 [00:59<00:20, 13.52it/s]
72%|███████▏ | 722/1000 [00:59<00:19, 13.93it/s]
72%|███████▏ | 724/1000 [00:59<00:20, 13.76it/s]
73%|███████▎ | 726/1000 [01:00<00:19, 13.98it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.92it/s]
73%|███████▎ | 730/1000 [01:00<00:32, 8.20it/s]
73%|███████▎ | 732/1000 [01:00<00:32, 8.32it/s]
73%|███████▎ | 734/1000 [01:01<00:30, 8.60it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.44it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.89it/s]
74%|███████▍ | 739/1000 [01:01<00:23, 11.01it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.69it/s]
74%|███████▍ | 743/1000 [01:01<00:20, 12.32it/s]
74%|███████▍ | 745/1000 [01:02<00:25, 10.18it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.57it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.74it/s]
75%|███████▌ | 751/1000 [01:02<00:21, 11.84it/s]
75%|███████▌ | 753/1000 [01:02<00:19, 12.61it/s]
76%|███████▌ | 755/1000 [01:02<00:18, 13.33it/s]
76%|███████▌ | 757/1000 [01:03<00:23, 10.29it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.32it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.31it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.25it/s]
76%|███████▋ | 765/1000 [01:04<00:27, 8.59it/s]
77%|███████▋ | 767/1000 [01:04<00:24, 9.53it/s]
77%|███████▋ | 769/1000 [01:04<00:21, 10.55it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.82it/s]
77%|███████▋ | 773/1000 [01:04<00:18, 12.61it/s]
78%|███████▊ | 775/1000 [01:04<00:17, 13.15it/s]
78%|███████▊ | 777/1000 [01:04<00:16, 13.82it/s]
78%|███████▊ | 779/1000 [01:05<00:21, 10.37it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.32it/s]
78%|███████▊ | 783/1000 [01:05<00:26, 8.26it/s]
78%|███████▊ | 784/1000 [01:05<00:30, 7.04it/s]
78%|███████▊ | 785/1000 [01:06<00:29, 7.35it/s]
79%|███████▊ | 786/1000 [01:06<00:30, 6.96it/s]
79%|███████▉ | 788/1000 [01:06<00:24, 8.80it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.14it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 11.28it/s]
79%|███████▉ | 794/1000 [01:06<00:17, 11.90it/s]
80%|███████▉ | 796/1000 [01:06<00:16, 12.35it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 13.10it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.28it/s]
80%|████████ | 802/1000 [01:07<00:24, 8.23it/s]
80%|████████ | 804/1000 [01:07<00:23, 8.30it/s]
81%|████████ | 806/1000 [01:08<00:21, 8.97it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.35it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.46it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.69it/s]
81%|████████▏ | 814/1000 [01:08<00:14, 12.58it/s]
82%|████████▏ | 816/1000 [01:08<00:16, 11.04it/s]
82%|████████▏ | 818/1000 [01:09<00:15, 11.97it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.47it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 13.02it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.82it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.22it/s]
83%|████████▎ | 828/1000 [01:09<00:11, 14.48it/s]
83%|████████▎ | 830/1000 [01:09<00:12, 13.74it/s]
83%|████████▎ | 832/1000 [01:10<00:12, 13.79it/s]
83%|████████▎ | 834/1000 [01:10<00:12, 13.50it/s]
84%|████████▎ | 836/1000 [01:10<00:12, 13.26it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 13.50it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 13.73it/s]
84%|████████▍ | 842/1000 [01:10<00:11, 13.82it/s]
84%|████████▍ | 844/1000 [01:10<00:11, 13.43it/s]
85%|████████▍ | 846/1000 [01:11<00:11, 13.46it/s]
85%|████████▍ | 848/1000 [01:11<00:11, 13.56it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 13.85it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.52it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.14it/s]
86%|████████▌ | 856/1000 [01:11<00:10, 14.02it/s]
86%|████████▌ | 858/1000 [01:11<00:09, 14.53it/s]
86%|████████▌ | 860/1000 [01:11<00:09, 15.31it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.46it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 13.99it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.81it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.70it/s]
87%|████████▋ | 870/1000 [01:12<00:09, 14.10it/s]
87%|████████▋ | 872/1000 [01:12<00:08, 14.69it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 12.42it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.04it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.93it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.58it/s]
88%|████████▊ | 882/1000 [01:13<00:10, 11.38it/s]
88%|████████▊ | 884/1000 [01:13<00:09, 12.12it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.84it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.76it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.15it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 13.94it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 14.11it/s]
90%|████████▉ | 896/1000 [01:14<00:07, 13.85it/s]
90%|████████▉ | 898/1000 [01:14<00:07, 13.21it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.20it/s]
90%|█████████ | 902/1000 [01:15<00:07, 12.94it/s]
90%|█████████ | 904/1000 [01:15<00:09, 9.78it/s]
91%|█████████ | 906/1000 [01:15<00:08, 10.84it/s]
91%|█████████ | 908/1000 [01:15<00:07, 11.81it/s]
91%|█████████ | 910/1000 [01:16<00:07, 12.08it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.29it/s]
91%|█████████▏| 914/1000 [01:16<00:12, 6.80it/s]
92%|█████████▏| 916/1000 [01:16<00:10, 8.12it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.39it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.79it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 10.24it/s]
92%|█████████▏| 924/1000 [01:17<00:06, 10.91it/s]
93%|█████████▎| 926/1000 [01:17<00:06, 11.77it/s]
93%|█████████▎| 928/1000 [01:18<00:06, 10.63it/s]
93%|█████████▎| 930/1000 [01:18<00:05, 11.81it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 12.40it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.45it/s]
94%|█████████▎| 936/1000 [01:18<00:05, 12.36it/s]
94%|█████████▍| 938/1000 [01:18<00:05, 12.33it/s]
94%|█████████▍| 940/1000 [01:18<00:04, 12.24it/s]
94%|█████████▍| 942/1000 [01:19<00:05, 10.72it/s]
94%|█████████▍| 944/1000 [01:19<00:05, 11.07it/s]
95%|█████████▍| 946/1000 [01:19<00:05, 10.69it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 11.16it/s]
95%|█████████▌| 950/1000 [01:19<00:04, 12.08it/s]
95%|█████████▌| 952/1000 [01:19<00:03, 13.05it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 13.45it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 13.66it/s]
96%|█████████▌| 958/1000 [01:20<00:02, 14.32it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 12.88it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.56it/s]
96%|█████████▋| 964/1000 [01:20<00:03, 10.15it/s]
97%|█████████▋| 966/1000 [01:21<00:02, 11.38it/s]
97%|█████████▋| 968/1000 [01:21<00:02, 12.05it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 12.67it/s]
97%|█████████▋| 972/1000 [01:21<00:02, 9.69it/s]
97%|█████████▋| 974/1000 [01:21<00:02, 10.89it/s]
98%|█████████▊| 976/1000 [01:21<00:02, 11.67it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.86it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.94it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.44it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 10.45it/s]
99%|█████████▊| 986/1000 [01:23<00:01, 10.70it/s]
99%|█████████▉| 988/1000 [01:23<00:01, 10.91it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 11.39it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 12.30it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 13.04it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 13.96it/s]
100%|█████████▉| 998/1000 [01:23<00:00, 14.12it/s]
100%|██████████| 1000/1000 [01:23<00:00, 14.78it/s]
{'eval_loss': '0.9184', 'eval_runtime': '84.04', 'eval_samples_per_second': '11.9', 'eval_steps_per_second': '11.9', 'epoch': '0.3746', 'num_input_tokens_seen': 23480632}
37%|███▋ | 7000/18688 [8:12:10<13:14:28, 4.08s/it]
100%|██████████| 1000/1000 [01:23<00:00, 14.78it/s]
 37%|███▋ | 7001/18688 [8:12:14<94:36:46, 29.14s/it] 37%|███▋ | 7002/18688 [8:12:17<69:41:39, 21.47s/it] 37%|███▋ | 7003/18688 [8:12:21<52:12:16, 16.08s/it] 37%|███▋ | 7004/18688 [8:12:25<40:10:44, 12.38s/it] 37%|███▋ | 7005/18688 [8:12:29<32:17:04, 9.95s/it] 37%|███▋ | 7006/18688 [8:12:33<26:20:01, 8.12s/it] 37%|███▋ | 7007/18688 [8:12:37<22:37:26, 6.97s/it] 38%|███▊ | 7008/18688 [8:12:41<19:54:29, 6.14s/it] 38%|███▊ | 7009/18688 [8:12:45<17:30:16, 5.40s/it] 38%|███▊ | 7010/18688 [8:12:48<15:43:25, 4.85s/it] {'loss': '0.9346', 'grad_norm': '1.181', 'learning_rate': '6.138e-05', 'epoch': '0.3751', 'num_input_tokens_seen': 23509808, 'train_runtime': '2.957e+04', 'train_tokens_per_second': '795.1'}
38%|███▊ | 7010/18688 [8:12:48<15:43:25, 4.85s/it] 38%|███▊ | 7011/18688 [8:12:53<15:00:41, 4.63s/it] 38%|███▊ | 7012/18688 [8:12:57<14:54:05, 4.59s/it] 38%|███▊ | 7013/18688 [8:13:01<14:00:09, 4.32s/it] 38%|███▊ | 7014/18688 [8:13:05<13:44:38, 4.24s/it] 38%|███▊ | 7015/18688 [8:13:09<13:41:55, 4.22s/it] 38%|███▊ | 7016/18688 [8:13:13<13:44:58, 4.24s/it] 38%|███▊ | 7017/18688 [8:13:17<13:32:51, 4.18s/it] 38%|███▊ | 7018/18688 [8:13:21<13:31:03, 4.17s/it] 38%|███▊ | 7019/18688 [8:13:25<12:52:53, 3.97s/it] 38%|███▊ | 7020/18688 [8:13:29<12:58:44, 4.00s/it] {'loss': '0.9115', 'grad_norm': '1.108', 'learning_rate': '6.132e-05', 'epoch': '0.3757', 'num_input_tokens_seen': 23542376, 'train_runtime': '2.961e+04', 'train_tokens_per_second': '795.1'}
38%|███▊ | 7020/18688 [8:13:29<12:58:44, 4.00s/it] 38%|███▊ | 7021/18688 [8:13:34<13:41:31, 4.22s/it] 38%|███▊ | 7022/18688 [8:13:38<13:55:42, 4.30s/it] 38%|███▊ | 7023/18688 [8:13:43<13:50:55, 4.27s/it] 38%|███▊ | 7024/18688 [8:13:47<13:51:39, 4.28s/it] 38%|███▊ | 7025/18688 [8:13:51<13:38:35, 4.21s/it] 38%|███▊ | 7026/18688 [8:13:55<13:07:09, 4.05s/it] 38%|███▊ | 7027/18688 [8:13:58<12:36:42, 3.89s/it] 38%|███▊ | 7028/18688 [8:14:02<12:28:47, 3.85s/it] 38%|███▊ | 7029/18688 [8:14:06<12:25:04, 3.83s/it] 38%|███▊ | 7030/18688 [8:14:09<12:11:45, 3.77s/it] {'loss': '0.9734', 'grad_norm': '1.213', 'learning_rate': '6.125e-05', 'epoch': '0.3762', 'num_input_tokens_seen': 23574728, 'train_runtime': '2.965e+04', 'train_tokens_per_second': '795.1'}
38%|███▊ | 7030/18688 [8:14:09<12:11:45, 3.77s/it] 38%|███▊ | 7031/18688 [8:14:13<11:59:59, 3.71s/it] 38%|███▊ | 7032/18688 [8:14:17<12:31:56, 3.87s/it] 38%|███▊ | 7033/18688 [8:14:22<13:26:30, 4.15s/it] 38%|███▊ | 7034/18688 [8:14:25<12:47:33, 3.95s/it] 38%|███▊ | 7035/18688 [8:14:29<12:46:50, 3.95s/it] 38%|███▊ | 7036/18688 [8:14:34<13:14:26, 4.09s/it] 38%|███▊ | 7037/18688 [8:14:38<13:25:32, 4.15s/it] 38%|███▊ | 7038/18688 [8:14:42<13:21:33, 4.13s/it] 38%|███▊ | 7039/18688 [8:14:47<14:00:26, 4.33s/it] 38%|███▊ | 7040/18688 [8:14:51<13:37:37, 4.21s/it] {'loss': '0.9238', 'grad_norm': '1.189', 'learning_rate': '6.119e-05', 'epoch': '0.3767', 'num_input_tokens_seen': 23608888, 'train_runtime': '2.969e+04', 'train_tokens_per_second': '795.1'}
38%|███▊ | 7040/18688 [8:14:51<13:37:37, 4.21s/it] 38%|███▊ | 7041/18688 [8:14:55<13:56:26, 4.31s/it] 38%|███▊ | 7042/18688 [8:14:59<13:42:57, 4.24s/it] 38%|███▊ | 7043/18688 [8:15:04<13:53:22, 4.29s/it] 38%|███▊ | 7044/18688 [8:15:07<13:12:37, 4.08s/it] 38%|███▊ | 7045/18688 [8:15:12<13:14:47, 4.10s/it] 38%|███▊ | 7046/18688 [8:15:15<12:54:04, 3.99s/it] 38%|███▊ | 7047/18688 [8:15:19<13:02:53, 4.04s/it] 38%|███▊ | 7048/18688 [8:15:24<13:49:46, 4.28s/it] 38%|███▊ | 7049/18688 [8:15:28<13:02:15, 4.03s/it] 38%|███▊ | 7050/18688 [8:15:32<13:13:35, 4.09s/it] {'loss': '0.9403', 'grad_norm': '1.085', 'learning_rate': '6.113e-05', 'epoch': '0.3773', 'num_input_tokens_seen': 23643440, 'train_runtime': '2.973e+04', 'train_tokens_per_second': '795.2'}
38%|███▊ | 7050/18688 [8:15:32<13:13:35, 4.09s/it] 38%|███▊ | 7051/18688 [8:15:36<13:11:18, 4.08s/it] 38%|███▊ | 7052/18688 [8:15:40<12:54:12, 3.99s/it] 38%|███▊ | 7053/18688 [8:15:44<13:08:34, 4.07s/it] 38%|███▊ | 7054/18688 [8:15:48<13:10:23, 4.08s/it] 38%|███▊ | 7055/18688 [8:15:52<13:26:29, 4.16s/it] 38%|███▊ | 7056/18688 [8:15:56<13:02:40, 4.04s/it] 38%|███▊ | 7057/18688 [8:16:00<12:46:36, 3.95s/it] 38%|███▊ | 7058/18688 [8:16:05<13:36:05, 4.21s/it] 38%|███▊ | 7059/18688 [8:16:08<13:05:02, 4.05s/it] 38%|███▊ | 7060/18688 [8:16:12<12:56:38, 4.01s/it] {'loss': '0.9174', 'grad_norm': '1.199', 'learning_rate': '6.107e-05', 'epoch': '0.3778', 'num_input_tokens_seen': 23676168, 'train_runtime': '2.977e+04', 'train_tokens_per_second': '795.2'}
38%|███▊ | 7060/18688 [8:16:12<12:56:38, 4.01s/it] 38%|███▊ | 7061/18688 [8:16:17<13:05:59, 4.06s/it] 38%|███▊ | 7062/18688 [8:16:21<13:33:59, 4.20s/it] 38%|███▊ | 7063/18688 [8:16:25<13:30:36, 4.18s/it] 38%|███▊ | 7064/18688 [8:16:29<13:27:52, 4.17s/it] 38%|███▊ | 7065/18688 [8:16:34<13:43:10, 4.25s/it] 38%|███▊ | 7066/18688 [8:16:38<13:10:39, 4.08s/it] 38%|███▊ | 7067/18688 [8:16:42<13:13:42, 4.10s/it] 38%|███▊ | 7068/18688 [8:16:46<13:29:11, 4.18s/it] 38%|███▊ | 7069/18688 [8:16:50<13:18:20, 4.12s/it] 38%|███▊ | 7070/18688 [8:16:53<12:40:38, 3.93s/it] {'loss': '0.9208', 'grad_norm': '1.212', 'learning_rate': '6.101e-05', 'epoch': '0.3783', 'num_input_tokens_seen': 23709624, 'train_runtime': '2.981e+04', 'train_tokens_per_second': '795.3'}
38%|███▊ | 7070/18688 [8:16:53<12:40:38, 3.93s/it] 38%|███▊ | 7071/18688 [8:16:58<13:08:16, 4.07s/it] 38%|███▊ | 7072/18688 [8:17:02<13:30:18, 4.19s/it] 38%|███▊ | 7073/18688 [8:17:07<13:35:03, 4.21s/it] 38%|███▊ | 7074/18688 [8:17:11<13:42:10, 4.25s/it] 38%|███▊ | 7075/18688 [8:17:14<12:56:46, 4.01s/it] 38%|███▊ | 7076/18688 [8:17:18<12:34:52, 3.90s/it] 38%|███▊ | 7077/18688 [8:17:22<12:56:40, 4.01s/it] 38%|███▊ | 7078/18688 [8:17:26<12:35:12, 3.90s/it] 38%|███▊ | 7079/18688 [8:17:30<12:54:58, 4.01s/it] 38%|███▊ | 7080/18688 [8:17:35<13:29:36, 4.18s/it] {'loss': '0.9894', 'grad_norm': '1.14', 'learning_rate': '6.094e-05', 'epoch': '0.3789', 'num_input_tokens_seen': 23743736, 'train_runtime': '2.986e+04', 'train_tokens_per_second': '795.3'}
38%|███▊ | 7080/18688 [8:17:35<13:29:36, 4.18s/it] 38%|███▊ | 7081/18688 [8:17:39<13:36:36, 4.22s/it] 38%|███▊ | 7082/18688 [8:17:43<13:42:06, 4.25s/it] 38%|███▊ | 7083/18688 [8:17:48<13:48:21, 4.28s/it] 38%|███▊ | 7084/18688 [8:17:53<14:52:09, 4.61s/it] 38%|███▊ | 7085/18688 [8:17:57<13:52:34, 4.31s/it] 38%|███▊ | 7086/18688 [8:18:01<13:28:21, 4.18s/it] 38%|███▊ | 7087/18688 [8:18:04<13:03:40, 4.05s/it] 38%|███▊ | 7088/18688 [8:18:08<12:45:35, 3.96s/it] 38%|███▊ | 7089/18688 [8:18:12<12:53:45, 4.00s/it] 38%|███▊ | 7090/18688 [8:18:17<13:18:03, 4.13s/it] {'loss': '0.8874', 'grad_norm': '0.9968', 'learning_rate': '6.088e-05', 'epoch': '0.3794', 'num_input_tokens_seen': 23778216, 'train_runtime': '2.99e+04', 'train_tokens_per_second': '795.3'}
38%|███▊ | 7090/18688 [8:18:17<13:18:03, 4.13s/it] 38%|███▊ | 7091/18688 [8:18:22<14:15:03, 4.42s/it] 38%|███▊ | 7092/18688 [8:18:25<13:20:45, 4.14s/it] 38%|███▊ | 7093/18688 [8:18:29<13:05:19, 4.06s/it] 38%|███▊ | 7094/18688 [8:18:33<12:35:19, 3.91s/it] 38%|███▊ | 7095/18688 [8:18:36<12:22:36, 3.84s/it] 38%|███▊ | 7096/18688 [8:18:41<12:43:01, 3.95s/it] 38%|███▊ | 7097/18688 [8:18:44<12:29:59, 3.88s/it] 38%|███▊ | 7098/18688 [8:18:48<12:27:48, 3.87s/it] 38%|███▊ | 7099/18688 [8:18:52<12:11:07, 3.79s/it] 38%|███▊ | 7100/18688 [8:18:56<12:50:32, 3.99s/it] {'loss': '0.9018', 'grad_norm': '1.157', 'learning_rate': '6.082e-05', 'epoch': '0.3799', 'num_input_tokens_seen': 23808680, 'train_runtime': '2.994e+04', 'train_tokens_per_second': '795.3'}
38%|███▊ | 7100/18688 [8:18:56<12:50:32, 3.99s/it] 38%|███▊ | 7101/18688 [8:19:00<12:30:24, 3.89s/it] 38%|███▊ | 7102/18688 [8:19:04<12:54:44, 4.01s/it] 38%|███▊ | 7103/18688 [8:19:08<12:36:39, 3.92s/it] 38%|███▊ | 7104/18688 [8:19:13<13:35:12, 4.22s/it] 38%|███▊ | 7105/18688 [8:19:17<13:45:03, 4.27s/it] 38%|███▊ | 7106/18688 [8:19:21<13:20:24, 4.15s/it] 38%|███▊ | 7107/18688 [8:19:25<12:51:31, 4.00s/it] 38%|███▊ | 7108/18688 [8:19:29<13:04:17, 4.06s/it] 38%|███▊ | 7109/18688 [8:19:33<13:13:16, 4.11s/it] 38%|███▊ | 7110/18688 [8:19:39<14:51:10, 4.62s/it] {'loss': '0.9614', 'grad_norm': '1.302', 'learning_rate': '6.076e-05', 'epoch': '0.3805', 'num_input_tokens_seen': 23845016, 'train_runtime': '2.998e+04', 'train_tokens_per_second': '795.4'}
38%|███▊ | 7110/18688 [8:19:39<14:51:10, 4.62s/it] 38%|███▊ | 7111/18688 [8:19:43<14:13:16, 4.42s/it] 38%|███▊ | 7112/18688 [8:19:47<14:10:54, 4.41s/it] 38%|███▊ | 7113/18688 [8:19:52<14:14:21, 4.43s/it] 38%|███▊ | 7114/18688 [8:19:56<14:22:18, 4.47s/it] 38%|███▊ | 7115/18688 [8:20:01<14:28:58, 4.51s/it] 38%|███▊ | 7116/18688 [8:20:05<13:46:26, 4.29s/it] 38%|███▊ | 7117/18688 [8:20:09<13:47:07, 4.29s/it] 38%|███▊ | 7118/18688 [8:20:14<14:56:07, 4.65s/it] 38%|███▊ | 7119/18688 [8:20:18<13:55:10, 4.33s/it] 38%|███▊ | 7120/18688 [8:20:22<13:08:23, 4.09s/it] {'loss': '0.9092', 'grad_norm': '1.255', 'learning_rate': '6.069e-05', 'epoch': '0.381', 'num_input_tokens_seen': 23880984, 'train_runtime': '3.002e+04', 'train_tokens_per_second': '795.4'}
38%|███▊ | 7120/18688 [8:20:22<13:08:23, 4.09s/it] 38%|███▊ | 7121/18688 [8:20:26<13:06:35, 4.08s/it] 38%|███▊ | 7122/18688 [8:20:30<13:17:53, 4.14s/it] 38%|███▊ | 7123/18688 [8:20:34<13:42:19, 4.27s/it] 38%|███▊ | 7124/18688 [8:20:40<14:44:24, 4.59s/it] 38%|███▊ | 7125/18688 [8:20:45<14:51:28, 4.63s/it] 38%|███▊ | 7126/18688 [8:20:48<13:53:58, 4.33s/it] 38%|███▊ | 7127/18688 [8:20:52<13:32:11, 4.22s/it] 38%|███▊ | 7128/18688 [8:20:56<13:36:03, 4.24s/it] 38%|███▊ | 7129/18688 [8:21:00<12:53:41, 4.02s/it] 38%|███▊ | 7130/18688 [8:21:03<12:22:01, 3.85s/it] {'loss': '0.9073', 'grad_norm': '1.285', 'learning_rate': '6.063e-05', 'epoch': '0.3815', 'num_input_tokens_seen': 23915912, 'train_runtime': '3.006e+04', 'train_tokens_per_second': '795.5'}
38%|███▊ | 7130/18688 [8:21:03<12:22:01, 3.85s/it] 38%|███▊ | 7131/18688 [8:21:07<12:33:42, 3.91s/it] 38%|███▊ | 7132/18688 [8:21:12<12:54:59, 4.02s/it] 38%|███▊ | 7133/18688 [8:21:16<13:03:25, 4.07s/it] 38%|███▊ | 7134/18688 [8:21:20<12:37:20, 3.93s/it] 38%|███▊ | 7135/18688 [8:21:23<12:25:55, 3.87s/it] 38%|███▊ | 7136/18688 [8:21:27<12:31:40, 3.90s/it] 38%|███▊ | 7137/18688 [8:21:31<12:33:24, 3.91s/it] 38%|███▊ | 7138/18688 [8:21:35<12:25:14, 3.87s/it] 38%|███▊ | 7139/18688 [8:21:40<13:15:37, 4.13s/it] 38%|███▊ | 7140/18688 [8:21:44<13:00:40, 4.06s/it] {'loss': '1.037', 'grad_norm': '1.378', 'learning_rate': '6.057e-05', 'epoch': '0.3821', 'num_input_tokens_seen': 23948352, 'train_runtime': '3.01e+04', 'train_tokens_per_second': '795.5'}
38%|███▊ | 7140/18688 [8:21:44<13:00:40, 4.06s/it] 38%|███▊ | 7141/18688 [8:21:48<13:00:44, 4.06s/it] 38%|███▊ | 7142/18688 [8:21:52<13:44:02, 4.28s/it] 38%|███▊ | 7143/18688 [8:21:56<12:56:04, 4.03s/it] 38%|███▊ | 7144/18688 [8:21:59<12:29:41, 3.90s/it] 38%|███▊ | 7145/18688 [8:22:04<12:57:41, 4.04s/it] 38%|███▊ | 7146/18688 [8:22:08<13:22:27, 4.17s/it] 38%|███▊ | 7147/18688 [8:22:13<13:33:48, 4.23s/it] 38%|███▊ | 7148/18688 [8:22:17<13:15:01, 4.13s/it] 38%|███▊ | 7149/18688 [8:22:21<13:12:41, 4.12s/it] 38%|███▊ | 7150/18688 [8:22:24<12:37:32, 3.94s/it] {'loss': '0.915', 'grad_norm': '1.504', 'learning_rate': '6.051e-05', 'epoch': '0.3826', 'num_input_tokens_seen': 23981296, 'train_runtime': '3.014e+04', 'train_tokens_per_second': '795.5'}
38%|███▊ | 7150/18688 [8:22:24<12:37:32, 3.94s/it] 38%|███▊ | 7151/18688 [8:22:28<12:48:00, 3.99s/it] 38%|███▊ | 7152/18688 [8:22:33<13:00:05, 4.06s/it] 38%|███▊ | 7153/18688 [8:22:36<12:32:39, 3.91s/it] 38%|███▊ | 7154/18688 [8:22:40<12:39:19, 3.95s/it] 38%|███▊ | 7155/18688 [8:22:44<12:14:10, 3.82s/it] 38%|███▊ | 7156/18688 [8:22:49<13:30:12, 4.22s/it] 38%|███▊ | 7157/18688 [8:22:54<14:51:31, 4.64s/it] 38%|███▊ | 7158/18688 [8:22:59<14:22:09, 4.49s/it] 38%|███▊ | 7159/18688 [8:23:03<14:15:34, 4.45s/it] 38%|███▊ | 7160/18688 [8:23:07<13:47:43, 4.31s/it] {'loss': '0.9372', 'grad_norm': '1.249', 'learning_rate': '6.044e-05', 'epoch': '0.3831', 'num_input_tokens_seen': 24016600, 'train_runtime': '3.019e+04', 'train_tokens_per_second': '795.6'}
38%|███▊ | 7160/18688 [8:23:07<13:47:43, 4.31s/it] 38%|███▊ | 7161/18688 [8:23:11<13:44:32, 4.29s/it] 38%|███▊ | 7162/18688 [8:23:15<13:44:00, 4.29s/it] 38%|███▊ | 7163/18688 [8:23:19<12:59:30, 4.06s/it] 38%|███▊ | 7164/18688 [8:23:24<14:06:17, 4.41s/it] 38%|███▊ | 7165/18688 [8:23:28<13:27:04, 4.20s/it] 38%|███▊ | 7166/18688 [8:23:32<13:07:54, 4.10s/it] 38%|███▊ | 7167/18688 [8:23:35<12:32:16, 3.92s/it] 38%|███▊ | 7168/18688 [8:23:39<12:33:01, 3.92s/it] 38%|███▊ | 7169/18688 [8:23:44<13:42:27, 4.28s/it] 38%|███▊ | 7170/18688 [8:23:48<13:07:22, 4.10s/it] {'loss': '0.9463', 'grad_norm': '1.143', 'learning_rate': '6.038e-05', 'epoch': '0.3837', 'num_input_tokens_seen': 24050728, 'train_runtime': '3.023e+04', 'train_tokens_per_second': '795.6'}
38%|███▊ | 7170/18688 [8:23:48<13:07:22, 4.10s/it] 38%|███▊ | 7171/18688 [8:23:52<13:21:21, 4.17s/it] 38%|███▊ | 7172/18688 [8:23:57<13:41:48, 4.28s/it] 38%|███▊ | 7173/18688 [8:24:02<14:25:03, 4.51s/it] 38%|███▊ | 7174/18688 [8:24:06<13:56:45, 4.36s/it] 38%|███▊ | 7175/18688 [8:24:10<13:40:34, 4.28s/it] 38%|███▊ | 7176/18688 [8:24:14<13:26:23, 4.20s/it] 38%|███▊ | 7177/18688 [8:24:19<13:54:03, 4.35s/it] 38%|███▊ | 7178/18688 [8:24:23<13:38:49, 4.27s/it] 38%|███▊ | 7179/18688 [8:24:26<13:03:37, 4.09s/it] 38%|███▊ | 7180/18688 [8:24:31<13:31:57, 4.23s/it] {'loss': '0.9119', 'grad_norm': '1.164', 'learning_rate': '6.032e-05', 'epoch': '0.3842', 'num_input_tokens_seen': 24088088, 'train_runtime': '3.027e+04', 'train_tokens_per_second': '795.7'}
38%|███▊ | 7180/18688 [8:24:31<13:31:57, 4.23s/it] 38%|███▊ | 7181/18688 [8:24:35<13:16:36, 4.15s/it] 38%|███▊ | 7182/18688 [8:24:39<12:50:56, 4.02s/it] 38%|███▊ | 7183/18688 [8:24:43<13:33:39, 4.24s/it] 38%|███▊ | 7184/18688 [8:24:48<13:39:18, 4.27s/it] 38%|███▊ | 7185/18688 [8:24:52<13:34:41, 4.25s/it] 38%|███▊ | 7186/18688 [8:24:56<13:32:20, 4.24s/it] 38%|███▊ | 7187/18688 [8:25:00<13:23:02, 4.19s/it] 38%|███▊ | 7188/18688 [8:25:04<12:52:57, 4.03s/it] 38%|███▊ | 7189/18688 [8:25:08<12:32:39, 3.93s/it] 38%|███▊ | 7190/18688 [8:25:12<12:41:48, 3.98s/it] {'loss': '0.995', 'grad_norm': '1.126', 'learning_rate': '6.025e-05', 'epoch': '0.3847', 'num_input_tokens_seen': 24121392, 'train_runtime': '3.031e+04', 'train_tokens_per_second': '795.8'}
38%|███▊ | 7190/18688 [8:25:12<12:41:48, 3.98s/it] 38%|███▊ | 7191/18688 [8:25:17<13:32:35, 4.24s/it] 38%|███▊ | 7192/18688 [8:25:21<13:49:50, 4.33s/it] 38%|███▊ | 7193/18688 [8:25:25<13:33:46, 4.25s/it] 38%|███▊ | 7194/18688 [8:25:30<13:42:54, 4.30s/it] 39%|███▊ | 7195/18688 [8:25:34<13:25:33, 4.21s/it] 39%|███▊ | 7196/18688 [8:25:37<13:08:11, 4.12s/it] 39%|███▊ | 7197/18688 [8:25:42<13:52:34, 4.35s/it] 39%|███▊ | 7198/18688 [8:25:47<14:07:32, 4.43s/it] 39%|███▊ | 7199/18688 [8:25:52<14:39:13, 4.59s/it] 39%|███▊ | 7200/18688 [8:25:56<14:22:41, 4.51s/it] {'loss': '0.9483', 'grad_norm': '1.211', 'learning_rate': '6.019e-05', 'epoch': '0.3853', 'num_input_tokens_seen': 24160816, 'train_runtime': '3.036e+04', 'train_tokens_per_second': '795.9'}
39%|███▊ | 7200/18688 [8:25:56<14:22:41, 4.51s/it] 39%|███▊ | 7201/18688 [8:26:00<13:47:38, 4.32s/it] 39%|███▊ | 7202/18688 [8:26:04<13:23:14, 4.20s/it] 39%|███▊ | 7203/18688 [8:26:08<12:46:41, 4.01s/it] 39%|███▊ | 7204/18688 [8:26:11<12:27:41, 3.91s/it] 39%|███▊ | 7205/18688 [8:26:15<12:16:43, 3.85s/it] 39%|███▊ | 7206/18688 [8:26:19<12:37:57, 3.96s/it] 39%|███▊ | 7207/18688 [8:26:23<12:44:34, 4.00s/it] 39%|███▊ | 7208/18688 [8:26:28<12:58:39, 4.07s/it] 39%|███▊ | 7209/18688 [8:26:32<13:18:17, 4.17s/it] 39%|███▊ | 7210/18688 [8:26:36<12:56:42, 4.06s/it] {'loss': '0.9555', 'grad_norm': '1.079', 'learning_rate': '6.013e-05', 'epoch': '0.3858', 'num_input_tokens_seen': 24192264, 'train_runtime': '3.04e+04', 'train_tokens_per_second': '795.9'}
39%|███▊ | 7210/18688 [8:26:36<12:56:42, 4.06s/it] 39%|███▊ | 7211/18688 [8:26:40<12:43:22, 3.99s/it] 39%|███▊ | 7212/18688 [8:26:44<12:54:39, 4.05s/it] 39%|███▊ | 7213/18688 [8:26:47<12:32:03, 3.93s/it] 39%|███▊ | 7214/18688 [8:26:52<12:44:47, 4.00s/it] 39%|███▊ | 7215/18688 [8:26:56<13:04:00, 4.10s/it] 39%|███▊ | 7216/18688 [8:27:00<12:54:10, 4.05s/it] 39%|███▊ | 7217/18688 [8:27:04<13:00:32, 4.08s/it] 39%|███▊ | 7218/18688 [8:27:08<12:33:30, 3.94s/it] 39%|███▊ | 7219/18688 [8:27:11<12:11:10, 3.83s/it] 39%|███▊ | 7220/18688 [8:27:16<12:51:04, 4.03s/it] {'loss': '0.8942', 'grad_norm': '1.063', 'learning_rate': '6.007e-05', 'epoch': '0.3864', 'num_input_tokens_seen': 24224664, 'train_runtime': '3.044e+04', 'train_tokens_per_second': '795.9'}
39%|███▊ | 7220/18688 [8:27:16<12:51:04, 4.03s/it] 39%|███▊ | 7221/18688 [8:27:20<12:50:53, 4.03s/it] 39%|███▊ | 7222/18688 [8:27:23<12:25:24, 3.90s/it] 39%|███▊ | 7223/18688 [8:27:27<12:11:53, 3.83s/it] 39%|███▊ | 7224/18688 [8:27:31<12:22:31, 3.89s/it] 39%|███▊ | 7225/18688 [8:27:35<12:32:40, 3.94s/it] 39%|███▊ | 7226/18688 [8:27:40<13:08:30, 4.13s/it] 39%|███▊ | 7227/18688 [8:27:44<13:18:28, 4.18s/it] 39%|███▊ | 7228/18688 [8:27:49<13:56:35, 4.38s/it] 39%|███▊ | 7229/18688 [8:27:53<13:54:05, 4.37s/it] 39%|███▊ | 7230/18688 [8:27:57<13:41:32, 4.30s/it] {'loss': '0.9195', 'grad_norm': '1.056', 'learning_rate': '6e-05', 'epoch': '0.3869', 'num_input_tokens_seen': 24258840, 'train_runtime': '3.048e+04', 'train_tokens_per_second': '796'}
39%|███▊ | 7230/18688 [8:27:57<13:41:32, 4.30s/it] 39%|███▊ | 7231/18688 [8:28:02<13:50:13, 4.35s/it] 39%|███▊ | 7232/18688 [8:28:06<13:33:45, 4.26s/it] 39%|███▊ | 7233/18688 [8:28:10<13:07:00, 4.12s/it] 39%|███▊ | 7234/18688 [8:28:14<13:45:24, 4.32s/it] 39%|███▊ | 7235/18688 [8:28:19<14:02:44, 4.42s/it] 39%|███▊ | 7236/18688 [8:28:23<13:39:56, 4.30s/it] 39%|███▊ | 7237/18688 [8:28:28<14:19:33, 4.50s/it] 39%|███▊ | 7238/18688 [8:28:32<14:14:38, 4.48s/it] 39%|███▊ | 7239/18688 [8:28:37<14:02:48, 4.42s/it] 39%|███▊ | 7240/18688 [8:28:41<13:35:15, 4.27s/it] {'loss': '0.9066', 'grad_norm': '1.217', 'learning_rate': '5.994e-05', 'epoch': '0.3874', 'num_input_tokens_seen': 24295616, 'train_runtime': '3.052e+04', 'train_tokens_per_second': '796'}
39%|███▊ | 7240/18688 [8:28:41<13:35:15, 4.27s/it] 39%|███▊ | 7241/18688 [8:28:45<13:29:32, 4.24s/it] 39%|███▉ | 7242/18688 [8:28:48<12:52:51, 4.05s/it] 39%|███▉ | 7243/18688 [8:28:53<13:15:49, 4.17s/it] 39%|███▉ | 7244/18688 [8:28:56<12:34:28, 3.96s/it] 39%|███▉ | 7245/18688 [8:29:00<12:34:12, 3.95s/it] 39%|███▉ | 7246/18688 [8:29:04<12:40:26, 3.99s/it] 39%|███▉ | 7247/18688 [8:29:08<12:14:56, 3.85s/it] 39%|███▉ | 7248/18688 [8:29:12<12:54:16, 4.06s/it] 39%|███▉ | 7249/18688 [8:29:17<13:00:25, 4.09s/it] 39%|███▉ | 7250/18688 [8:29:21<13:08:25, 4.14s/it] {'loss': '0.886', 'grad_norm': '1.257', 'learning_rate': '5.988e-05', 'epoch': '0.388', 'num_input_tokens_seen': 24327648, 'train_runtime': '3.056e+04', 'train_tokens_per_second': '796'}
39%|███▉ | 7250/18688 [8:29:21<13:08:25, 4.14s/it] 39%|███▉ | 7251/18688 [8:29:25<13:37:49, 4.29s/it] 39%|███▉ | 7252/18688 [8:29:29<13:08:54, 4.14s/it] 39%|███▉ | 7253/18688 [8:29:34<13:17:24, 4.18s/it] 39%|███▉ | 7254/18688 [8:29:37<13:00:57, 4.10s/it] 39%|███▉ | 7255/18688 [8:29:43<14:15:00, 4.49s/it] 39%|███▉ | 7256/18688 [8:29:47<14:10:38, 4.46s/it] 39%|███▉ | 7257/18688 [8:29:51<13:41:12, 4.31s/it] 39%|███▉ | 7258/18688 [8:29:57<14:37:24, 4.61s/it] 39%|███▉ | 7259/18688 [8:30:01<14:12:47, 4.48s/it] 39%|███▉ | 7260/18688 [8:30:05<14:04:29, 4.43s/it] {'loss': '0.9455', 'grad_norm': '1.274', 'learning_rate': '5.981e-05', 'epoch': '0.3885', 'num_input_tokens_seen': 24365472, 'train_runtime': '3.061e+04', 'train_tokens_per_second': '796.1'}
39%|███▉ | 7260/18688 [8:30:05<14:04:29, 4.43s/it] 39%|███▉ | 7261/18688 [8:30:09<13:30:08, 4.25s/it] 39%|███▉ | 7262/18688 [8:30:13<13:25:55, 4.23s/it] 39%|███▉ | 7263/18688 [8:30:17<13:19:09, 4.20s/it] 39%|███▉ | 7264/18688 [8:30:22<13:36:49, 4.29s/it] 39%|███▉ | 7265/18688 [8:30:25<12:51:11, 4.05s/it] 39%|███▉ | 7266/18688 [8:30:29<13:05:04, 4.12s/it] 39%|███▉ | 7267/18688 [8:30:34<13:08:07, 4.14s/it] 39%|███▉ | 7268/18688 [8:30:38<13:14:49, 4.18s/it] 39%|███▉ | 7269/18688 [8:30:42<13:03:40, 4.12s/it] 39%|███▉ | 7270/18688 [8:30:46<12:58:23, 4.09s/it] {'loss': '0.9381', 'grad_norm': '1.022', 'learning_rate': '5.975e-05', 'epoch': '0.389', 'num_input_tokens_seen': 24399032, 'train_runtime': '3.065e+04', 'train_tokens_per_second': '796.1'}
39%|███▉ | 7270/18688 [8:30:46<12:58:23, 4.09s/it] 39%|███▉ | 7271/18688 [8:30:50<13:04:22, 4.12s/it] 39%|███▉ | 7272/18688 [8:30:55<13:40:25, 4.31s/it] 39%|███▉ | 7273/18688 [8:30:59<13:29:51, 4.26s/it] 39%|███▉ | 7274/18688 [8:31:04<14:35:34, 4.60s/it] 39%|███▉ | 7275/18688 [8:31:08<13:55:17, 4.39s/it] 39%|███▉ | 7276/18688 [8:31:12<13:04:40, 4.13s/it] 39%|███▉ | 7277/18688 [8:31:16<13:20:47, 4.21s/it] 39%|███▉ | 7278/18688 [8:31:21<13:41:50, 4.32s/it] 39%|███▉ | 7279/18688 [8:31:26<14:12:09, 4.48s/it] 39%|███▉ | 7280/18688 [8:31:30<14:18:43, 4.52s/it] {'loss': '1.02', 'grad_norm': '1.023', 'learning_rate': '5.968e-05', 'epoch': '0.3896', 'num_input_tokens_seen': 24436864, 'train_runtime': '3.069e+04', 'train_tokens_per_second': '796.2'}
39%|███▉ | 7280/18688 [8:31:30<14:18:43, 4.52s/it] 39%|███▉ | 7281/18688 [8:31:35<14:58:45, 4.73s/it] 39%|███▉ | 7282/18688 [8:31:40<14:36:20, 4.61s/it] 39%|███▉ | 7283/18688 [8:31:44<14:09:09, 4.47s/it] 39%|███▉ | 7284/18688 [8:31:47<13:17:15, 4.19s/it] 39%|███▉ | 7285/18688 [8:31:52<13:18:06, 4.20s/it] 39%|███▉ | 7286/18688 [8:31:56<13:24:31, 4.23s/it] 39%|███▉ | 7287/18688 [8:32:00<12:57:16, 4.09s/it] 39%|███▉ | 7288/18688 [8:32:04<13:03:58, 4.13s/it] 39%|███▉ | 7289/18688 [8:32:09<13:29:30, 4.26s/it] 39%|███▉ | 7290/18688 [8:32:13<13:28:56, 4.26s/it] {'loss': '0.8667', 'grad_norm': '1.202', 'learning_rate': '5.962e-05', 'epoch': '0.3901', 'num_input_tokens_seen': 24472512, 'train_runtime': '3.073e+04', 'train_tokens_per_second': '796.3'}
39%|███▉ | 7290/18688 [8:32:13<13:28:56, 4.26s/it] 39%|███▉ | 7291/18688 [8:32:16<12:56:16, 4.09s/it] 39%|███▉ | 7292/18688 [8:32:21<13:43:37, 4.34s/it] 39%|███▉ | 7293/18688 [8:32:26<13:59:05, 4.42s/it] 39%|███▉ | 7294/18688 [8:32:30<13:16:57, 4.20s/it] 39%|███▉ | 7295/18688 [8:32:34<13:07:56, 4.15s/it] 39%|███▉ | 7296/18688 [8:32:38<12:51:38, 4.06s/it] 39%|███▉ | 7297/18688 [8:32:42<13:21:41, 4.22s/it] 39%|███▉ | 7298/18688 [8:32:46<12:51:42, 4.07s/it] 39%|███▉ | 7299/18688 [8:32:50<12:49:32, 4.05s/it] 39%|███▉ | 7300/18688 [8:32:54<12:40:08, 4.01s/it] {'loss': '0.9299', 'grad_norm': '1.051', 'learning_rate': '5.956e-05', 'epoch': '0.3906', 'num_input_tokens_seen': 24506688, 'train_runtime': '3.077e+04', 'train_tokens_per_second': '796.3'}
39%|███▉ | 7300/18688 [8:32:54<12:40:08, 4.01s/it] 39%|███▉ | 7301/18688 [8:32:57<12:20:36, 3.90s/it] 39%|███▉ | 7302/18688 [8:33:01<12:06:45, 3.83s/it] 39%|███▉ | 7303/18688 [8:33:05<11:46:38, 3.72s/it] 39%|███▉ | 7304/18688 [8:33:08<11:56:09, 3.77s/it] 39%|███▉ | 7305/18688 [8:33:13<12:15:34, 3.88s/it] 39%|███▉ | 7306/18688 [8:33:18<13:22:18, 4.23s/it] 39%|███▉ | 7307/18688 [8:33:23<14:02:37, 4.44s/it] 39%|███▉ | 7308/18688 [8:33:27<14:09:36, 4.48s/it] 39%|███▉ | 7309/18688 [8:33:31<13:56:19, 4.41s/it] 39%|███▉ | 7310/18688 [8:33:36<13:47:47, 4.37s/it] {'loss': '0.926', 'grad_norm': '1.241', 'learning_rate': '5.949e-05', 'epoch': '0.3912', 'num_input_tokens_seen': 24542768, 'train_runtime': '3.082e+04', 'train_tokens_per_second': '796.4'}
39%|███▉ | 7310/18688 [8:33:36<13:47:47, 4.37s/it] 39%|███▉ | 7311/18688 [8:33:39<13:02:54, 4.13s/it] 39%|███▉ | 7312/18688 [8:33:43<12:42:06, 4.02s/it] 39%|███▉ | 7313/18688 [8:33:47<12:42:11, 4.02s/it] 39%|███▉ | 7314/18688 [8:33:51<12:37:12, 3.99s/it] 39%|███▉ | 7315/18688 [8:33:55<12:12:11, 3.86s/it] 39%|███▉ | 7316/18688 [8:33:59<13:14:17, 4.19s/it] 39%|███▉ | 7317/18688 [8:34:04<13:23:48, 4.24s/it] 39%|███▉ | 7318/18688 [8:34:08<13:03:49, 4.14s/it] 39%|███▉ | 7319/18688 [8:34:12<12:54:17, 4.09s/it] 39%|███▉ | 7320/18688 [8:34:16<13:20:45, 4.23s/it] {'loss': '0.9755', 'grad_norm': '1.336', 'learning_rate': '5.943e-05', 'epoch': '0.3917', 'num_input_tokens_seen': 24574384, 'train_runtime': '3.086e+04', 'train_tokens_per_second': '796.4'}
39%|███▉ | 7320/18688 [8:34:16<13:20:45, 4.23s/it] 39%|███▉ | 7321/18688 [8:34:20<12:53:25, 4.08s/it] 39%|███▉ | 7322/18688 [8:34:24<12:54:05, 4.09s/it] 39%|███▉ | 7323/18688 [8:34:28<13:07:38, 4.16s/it] 39%|███▉ | 7324/18688 [8:34:33<13:04:49, 4.14s/it] 39%|███▉ | 7325/18688 [8:34:37<13:00:26, 4.12s/it] 39%|███▉ | 7326/18688 [8:34:40<12:24:28, 3.93s/it] 39%|███▉ | 7327/18688 [8:34:44<12:13:46, 3.88s/it] 39%|███▉ | 7328/18688 [8:34:48<12:29:38, 3.96s/it] 39%|███▉ | 7329/18688 [8:34:52<12:51:34, 4.08s/it] 39%|███▉ | 7330/18688 [8:34:56<12:27:58, 3.95s/it] {'loss': '0.9212', 'grad_norm': '1.212', 'learning_rate': '5.937e-05', 'epoch': '0.3922', 'num_input_tokens_seen': 24606744, 'train_runtime': '3.09e+04', 'train_tokens_per_second': '796.4'}
39%|███▉ | 7330/18688 [8:34:56<12:27:58, 3.95s/it] 39%|███▉ | 7331/18688 [8:35:00<12:41:18, 4.02s/it] 39%|███▉ | 7332/18688 [8:35:04<12:16:03, 3.89s/it] 39%|███▉ | 7333/18688 [8:35:07<11:53:00, 3.77s/it] 39%|███▉ | 7334/18688 [8:35:11<11:42:57, 3.71s/it] 39%|███▉ | 7335/18688 [8:35:15<12:26:55, 3.95s/it] 39%|███▉ | 7336/18688 [8:35:20<12:44:58, 4.04s/it] 39%|███▉ | 7337/18688 [8:35:24<12:58:23, 4.11s/it] 39%|███▉ | 7338/18688 [8:35:28<12:46:22, 4.05s/it] 39%|███▉ | 7339/18688 [8:35:33<13:30:45, 4.29s/it] 39%|███▉ | 7340/18688 [8:35:36<12:45:30, 4.05s/it] {'loss': '0.9324', 'grad_norm': '1.145', 'learning_rate': '5.93e-05', 'epoch': '0.3928', 'num_input_tokens_seen': 24638920, 'train_runtime': '3.094e+04', 'train_tokens_per_second': '796.4'}
39%|███▉ | 7340/18688 [8:35:36<12:45:30, 4.05s/it] 39%|███▉ | 7341/18688 [8:35:40<12:14:04, 3.88s/it] 39%|███▉ | 7342/18688 [8:35:43<12:09:29, 3.86s/it] 39%|███▉ | 7343/18688 [8:35:47<11:57:29, 3.79s/it] 39%|███▉ | 7344/18688 [8:35:51<12:12:48, 3.88s/it] 39%|███▉ | 7345/18688 [8:35:55<12:07:31, 3.85s/it] 39%|███▉ | 7346/18688 [8:35:58<11:46:27, 3.74s/it] 39%|███▉ | 7347/18688 [8:36:03<12:11:05, 3.87s/it] 39%|███▉ | 7348/18688 [8:36:07<12:51:17, 4.08s/it] 39%|███▉ | 7349/18688 [8:36:11<12:36:14, 4.00s/it] 39%|███▉ | 7350/18688 [8:36:15<13:00:03, 4.13s/it] {'loss': '0.8739', 'grad_norm': '1.286', 'learning_rate': '5.924e-05', 'epoch': '0.3933', 'num_input_tokens_seen': 24669840, 'train_runtime': '3.098e+04', 'train_tokens_per_second': '796.4'}
39%|███▉ | 7350/18688 [8:36:15<13:00:03, 4.13s/it] 39%|███▉ | 7351/18688 [8:36:19<12:42:08, 4.03s/it] 39%|███▉ | 7352/18688 [8:36:23<12:46:37, 4.06s/it] 39%|███▉ | 7353/18688 [8:36:27<12:50:09, 4.08s/it] 39%|███▉ | 7354/18688 [8:36:32<13:36:04, 4.32s/it] 39%|███▉ | 7355/18688 [8:36:38<14:39:18, 4.66s/it] 39%|███▉ | 7356/18688 [8:36:41<13:32:41, 4.30s/it] 39%|███▉ | 7357/18688 [8:36:45<13:24:56, 4.26s/it] 39%|███▉ | 7358/18688 [8:36:50<14:06:02, 4.48s/it] 39%|███▉ | 7359/18688 [8:36:55<14:19:31, 4.55s/it] 39%|███▉ | 7360/18688 [8:37:00<14:37:43, 4.65s/it] {'loss': '0.9089', 'grad_norm': '1.215', 'learning_rate': '5.917e-05', 'epoch': '0.3938', 'num_input_tokens_seen': 24709056, 'train_runtime': '3.102e+04', 'train_tokens_per_second': '796.5'}
39%|███▉ | 7360/18688 [8:37:00<14:37:43, 4.65s/it] 39%|███▉ | 7361/18688 [8:37:04<14:02:58, 4.47s/it] 39%|███▉ | 7362/18688 [8:37:09<14:18:24, 4.55s/it] 39%|███▉ | 7363/18688 [8:37:15<15:29:06, 4.92s/it] 39%|███▉ | 7364/18688 [8:37:18<14:19:06, 4.55s/it] 39%|███▉ | 7365/18688 [8:37:22<13:29:28, 4.29s/it] 39%|███▉ | 7366/18688 [8:37:25<12:43:48, 4.05s/it] 39%|███▉ | 7367/18688 [8:37:29<12:30:51, 3.98s/it] 39%|███▉ | 7368/18688 [8:37:34<12:52:05, 4.09s/it] 39%|███▉ | 7369/18688 [8:37:38<12:51:17, 4.09s/it] 39%|███▉ | 7370/18688 [8:37:42<13:20:50, 4.25s/it] {'loss': '0.8815', 'grad_norm': '0.8864', 'learning_rate': '5.911e-05', 'epoch': '0.3944', 'num_input_tokens_seen': 24743888, 'train_runtime': '3.106e+04', 'train_tokens_per_second': '796.6'}
39%|███▉ | 7370/18688 [8:37:42<13:20:50, 4.25s/it] 39%|███▉ | 7371/18688 [8:37:46<12:59:25, 4.13s/it] 39%|███▉ | 7372/18688 [8:37:50<13:04:08, 4.16s/it] 39%|███▉ | 7373/18688 [8:37:54<12:43:03, 4.05s/it] 39%|███▉ | 7374/18688 [8:37:58<12:37:08, 4.02s/it] 39%|███▉ | 7375/18688 [8:38:03<13:38:07, 4.34s/it] 39%|███▉ | 7376/18688 [8:38:07<13:26:29, 4.28s/it] 39%|███▉ | 7377/18688 [8:38:12<13:28:06, 4.29s/it] 39%|███▉ | 7378/18688 [8:38:15<12:58:18, 4.13s/it] 39%|███▉ | 7379/18688 [8:38:19<12:51:35, 4.09s/it] 39%|███▉ | 7380/18688 [8:38:23<12:27:13, 3.96s/it] {'loss': '0.9041', 'grad_norm': '1.365', 'learning_rate': '5.905e-05', 'epoch': '0.3949', 'num_input_tokens_seen': 24775520, 'train_runtime': '3.11e+04', 'train_tokens_per_second': '796.5'}
39%|███▉ | 7380/18688 [8:38:23<12:27:13, 3.96s/it] 39%|███▉ | 7381/18688 [8:38:28<12:55:04, 4.11s/it] 40%|███▉ | 7382/18688 [8:38:33<13:58:39, 4.45s/it] 40%|███▉ | 7383/18688 [8:38:37<13:46:57, 4.39s/it] 40%|███▉ | 7384/18688 [8:38:41<13:06:48, 4.18s/it] 40%|███▉ | 7385/18688 [8:38:45<13:14:50, 4.22s/it] 40%|███▉ | 7386/18688 [8:38:49<13:03:34, 4.16s/it] 40%|███▉ | 7387/18688 [8:38:53<12:39:46, 4.03s/it] 40%|███▉ | 7388/18688 [8:38:57<12:29:16, 3.98s/it] 40%|███▉ | 7389/18688 [8:39:01<13:05:17, 4.17s/it] 40%|███▉ | 7390/18688 [8:39:05<12:25:36, 3.96s/it] {'loss': '0.9674', 'grad_norm': '1.339', 'learning_rate': '5.898e-05', 'epoch': '0.3955', 'num_input_tokens_seen': 24809368, 'train_runtime': '3.115e+04', 'train_tokens_per_second': '796.6'}
40%|███▉ | 7390/18688 [8:39:05<12:25:36, 3.96s/it] 40%|███▉ | 7391/18688 [8:39:10<13:19:05, 4.24s/it] 40%|███▉ | 7392/18688 [8:39:13<12:43:43, 4.06s/it] 40%|███▉ | 7393/18688 [8:39:17<12:48:44, 4.08s/it] 40%|███▉ | 7394/18688 [8:39:22<13:00:18, 4.15s/it] 40%|███▉ | 7395/18688 [8:39:26<13:16:05, 4.23s/it] 40%|███▉ | 7396/18688 [8:39:30<12:41:24, 4.05s/it] 40%|███▉ | 7397/18688 [8:39:34<13:01:59, 4.16s/it] 40%|███▉ | 7398/18688 [8:39:38<12:41:51, 4.05s/it] 40%|███▉ | 7399/18688 [8:39:41<12:11:38, 3.89s/it] 40%|███▉ | 7400/18688 [8:39:46<12:38:35, 4.03s/it] {'loss': '0.9226', 'grad_norm': '1.111', 'learning_rate': '5.892e-05', 'epoch': '0.396', 'num_input_tokens_seen': 24842808, 'train_runtime': '3.119e+04', 'train_tokens_per_second': '796.6'}
40%|███▉ | 7400/18688 [8:39:46<12:38:35, 4.03s/it] 40%|███▉ | 7401/18688 [8:39:49<12:10:27, 3.88s/it] 40%|███▉ | 7402/18688 [8:39:53<12:25:20, 3.96s/it] 40%|███▉ | 7403/18688 [8:39:58<12:44:35, 4.07s/it] 40%|███▉ | 7404/18688 [8:40:02<12:56:03, 4.13s/it] 40%|███▉ | 7405/18688 [8:40:06<12:44:39, 4.07s/it] 40%|███▉ | 7406/18688 [8:40:10<12:21:16, 3.94s/it] 40%|███▉ | 7407/18688 [8:40:13<12:03:59, 3.85s/it] 40%|███▉ | 7408/18688 [8:40:17<12:00:33, 3.83s/it] 40%|███▉ | 7409/18688 [8:40:21<12:15:12, 3.91s/it] 40%|███▉ | 7410/18688 [8:40:26<13:12:23, 4.22s/it] {'loss': '0.8764', 'grad_norm': '1.121', 'learning_rate': '5.885e-05', 'epoch': '0.3965', 'num_input_tokens_seen': 24874784, 'train_runtime': '3.123e+04', 'train_tokens_per_second': '796.6'}
40%|███▉ | 7410/18688 [8:40:26<13:12:23, 4.22s/it] 40%|███▉ | 7411/18688 [8:40:30<13:14:22, 4.23s/it] 40%|███▉ | 7412/18688 [8:40:34<12:49:54, 4.10s/it] 40%|███▉ | 7413/18688 [8:40:38<12:17:22, 3.92s/it] 40%|███▉ | 7414/18688 [8:40:43<13:28:32, 4.30s/it] 40%|███▉ | 7415/18688 [8:40:46<12:41:04, 4.05s/it] 40%|███▉ | 7416/18688 [8:40:50<12:07:59, 3.88s/it] 40%|███▉ | 7417/18688 [8:40:54<12:29:38, 3.99s/it] 40%|███▉ | 7418/18688 [8:40:58<12:33:26, 4.01s/it] 40%|███▉ | 7419/18688 [8:41:02<12:31:02, 4.00s/it] 40%|███▉ | 7420/18688 [8:41:06<12:07:35, 3.87s/it] {'loss': '0.8737', 'grad_norm': '1.182', 'learning_rate': '5.879e-05', 'epoch': '0.3971', 'num_input_tokens_seen': 24906192, 'train_runtime': '3.127e+04', 'train_tokens_per_second': '796.6'}
40%|███▉ | 7420/18688 [8:41:06<12:07:35, 3.87s/it] 40%|███▉ | 7421/18688 [8:41:09<11:48:29, 3.77s/it] 40%|███▉ | 7422/18688 [8:41:13<11:39:29, 3.73s/it] 40%|███▉ | 7423/18688 [8:41:17<12:06:29, 3.87s/it] 40%|███▉ | 7424/18688 [8:41:22<12:51:17, 4.11s/it] 40%|███▉ | 7425/18688 [8:41:26<12:54:56, 4.13s/it] 40%|███▉ | 7426/18688 [8:41:30<13:12:43, 4.22s/it] 40%|███▉ | 7427/18688 [8:41:35<13:26:24, 4.30s/it] 40%|███▉ | 7428/18688 [8:41:40<14:14:18, 4.55s/it] 40%|███▉ | 7429/18688 [8:41:44<13:45:30, 4.40s/it] 40%|███▉ | 7430/18688 [8:41:48<13:23:02, 4.28s/it] {'loss': '0.9839', 'grad_norm': '1.151', 'learning_rate': '5.872e-05', 'epoch': '0.3976', 'num_input_tokens_seen': 24941200, 'train_runtime': '3.131e+04', 'train_tokens_per_second': '796.6'}
40%|███▉ | 7430/18688 [8:41:48<13:23:02, 4.28s/it] 40%|███▉ | 7431/18688 [8:41:52<13:14:24, 4.23s/it] 40%|███▉ | 7432/18688 [8:41:56<12:52:37, 4.12s/it] 40%|███▉ | 7433/18688 [8:42:00<12:33:05, 4.01s/it] 40%|███▉ | 7434/18688 [8:42:04<12:22:50, 3.96s/it] 40%|███▉ | 7435/18688 [8:42:08<12:57:33, 4.15s/it] 40%|███▉ | 7436/18688 [8:42:12<12:45:09, 4.08s/it] 40%|███▉ | 7437/18688 [8:42:16<12:59:38, 4.16s/it] 40%|███▉ | 7438/18688 [8:42:21<13:06:06, 4.19s/it] 40%|███▉ | 7439/18688 [8:42:25<13:02:48, 4.18s/it] 40%|███▉ | 7440/18688 [8:42:29<13:03:00, 4.18s/it] {'loss': '0.9551', 'grad_norm': '1.377', 'learning_rate': '5.866e-05', 'epoch': '0.3981', 'num_input_tokens_seen': 24975024, 'train_runtime': '3.135e+04', 'train_tokens_per_second': '796.7'}
40%|███▉ | 7440/18688 [8:42:29<13:03:00, 4.18s/it] 40%|███▉ | 7441/18688 [8:42:33<13:22:20, 4.28s/it] 40%|███▉ | 7442/18688 [8:42:38<13:44:30, 4.40s/it] 40%|███▉ | 7443/18688 [8:42:42<13:17:47, 4.26s/it] 40%|███▉ | 7444/18688 [8:42:46<13:07:12, 4.20s/it] 40%|███▉ | 7445/18688 [8:42:50<12:33:28, 4.02s/it] 40%|███▉ | 7446/18688 [8:42:54<12:58:49, 4.16s/it] 40%|███▉ | 7447/18688 [8:42:59<13:36:58, 4.36s/it] 40%|███▉ | 7448/18688 [8:43:03<13:02:49, 4.18s/it] 40%|███▉ | 7449/18688 [8:43:07<12:47:11, 4.10s/it] 40%|███▉ | 7450/18688 [8:43:10<12:25:55, 3.98s/it] {'loss': '0.9438', 'grad_norm': '1.24', 'learning_rate': '5.859e-05', 'epoch': '0.3987', 'num_input_tokens_seen': 25009160, 'train_runtime': '3.139e+04', 'train_tokens_per_second': '796.7'}
40%|███▉ | 7450/18688 [8:43:10<12:25:55, 3.98s/it] 40%|███▉ | 7451/18688 [8:43:14<12:16:30, 3.93s/it] 40%|███▉ | 7452/18688 [8:43:18<11:51:04, 3.80s/it] 40%|███▉ | 7453/18688 [8:43:22<12:22:56, 3.97s/it] 40%|███▉ | 7454/18688 [8:43:26<12:26:13, 3.99s/it] 40%|███▉ | 7455/18688 [8:43:30<12:46:45, 4.10s/it] 40%|███▉ | 7456/18688 [8:43:34<12:31:05, 4.01s/it] 40%|███▉ | 7457/18688 [8:43:39<12:59:25, 4.16s/it] 40%|███▉ | 7458/18688 [8:43:43<12:37:44, 4.05s/it] 40%|███▉ | 7459/18688 [8:43:48<13:36:01, 4.36s/it] 40%|███▉ | 7460/18688 [8:43:51<12:57:04, 4.15s/it] {'loss': '0.9015', 'grad_norm': '1.227', 'learning_rate': '5.853e-05', 'epoch': '0.3992', 'num_input_tokens_seen': 25041328, 'train_runtime': '3.143e+04', 'train_tokens_per_second': '796.7'}
40%|███▉ | 7460/18688 [8:43:51<12:57:04, 4.15s/it] 40%|███▉ | 7461/18688 [8:43:55<12:25:17, 3.98s/it] 40%|███▉ | 7462/18688 [8:43:59<12:33:28, 4.03s/it] 40%|███▉ | 7463/18688 [8:44:03<12:28:10, 4.00s/it] 40%|███▉ | 7464/18688 [8:44:07<12:08:04, 3.89s/it] 40%|███▉ | 7465/18688 [8:44:10<12:01:15, 3.86s/it] 40%|███▉ | 7466/18688 [8:44:15<12:17:32, 3.94s/it] 40%|███▉ | 7467/18688 [8:44:19<12:19:01, 3.95s/it] 40%|███▉ | 7468/18688 [8:44:22<11:59:17, 3.85s/it] 40%|███▉ | 7469/18688 [8:44:26<11:44:40, 3.77s/it] 40%|███▉ | 7470/18688 [8:44:30<12:20:05, 3.96s/it] {'loss': '0.8764', 'grad_norm': '1.556', 'learning_rate': '5.846e-05', 'epoch': '0.3997', 'num_input_tokens_seen': 25070608, 'train_runtime': '3.147e+04', 'train_tokens_per_second': '796.6'}
40%|███▉ | 7470/18688 [8:44:30<12:20:05, 3.96s/it] 40%|███▉ | 7471/18688 [8:44:34<12:23:13, 3.98s/it] 40%|███▉ | 7472/18688 [8:44:38<12:39:33, 4.06s/it] 40%|███▉ | 7473/18688 [8:44:42<12:09:29, 3.90s/it] 40%|███▉ | 7474/18688 [8:44:46<11:56:39, 3.83s/it] 40%|███▉ | 7475/18688 [8:44:50<12:05:59, 3.88s/it] 40%|████ | 7476/18688 [8:44:54<12:10:40, 3.91s/it] 40%|████ | 7477/18688 [8:44:59<13:09:26, 4.23s/it] 40%|████ | 7478/18688 [8:45:02<12:51:54, 4.13s/it] 40%|████ | 7479/18688 [8:45:06<12:32:57, 4.03s/it] 40%|████ | 7480/18688 [8:45:10<12:06:42, 3.89s/it] {'loss': '0.9352', 'grad_norm': '1.306', 'learning_rate': '5.84e-05', 'epoch': '0.4003', 'num_input_tokens_seen': 25101264, 'train_runtime': '3.151e+04', 'train_tokens_per_second': '796.6'}
40%|████ | 7480/18688 [8:45:10<12:06:42, 3.89s/it] 40%|████ | 7481/18688 [8:45:14<12:27:34, 4.00s/it] 40%|████ | 7482/18688 [8:45:18<12:13:08, 3.93s/it] 40%|████ | 7483/18688 [8:45:22<12:19:22, 3.96s/it] 40%|████ | 7484/18688 [8:45:27<12:59:16, 4.17s/it] 40%|████ | 7485/18688 [8:45:31<13:04:09, 4.20s/it] 40%|████ | 7486/18688 [8:45:34<12:23:19, 3.98s/it] 40%|████ | 7487/18688 [8:45:39<12:52:10, 4.14s/it] 40%|████ | 7488/18688 [8:45:43<12:35:54, 4.05s/it] 40%|████ | 7489/18688 [8:45:47<13:00:33, 4.18s/it] 40%|████ | 7490/18688 [8:45:51<12:38:17, 4.06s/it] {'loss': '0.9094', 'grad_norm': '1.201', 'learning_rate': '5.833e-05', 'epoch': '0.4008', 'num_input_tokens_seen': 25134184, 'train_runtime': '3.155e+04', 'train_tokens_per_second': '796.6'}
40%|████ | 7490/18688 [8:45:51<12:38:17, 4.06s/it] 40%|████ | 7491/18688 [8:45:55<12:38:28, 4.06s/it] 40%|████ | 7492/18688 [8:45:59<12:55:33, 4.16s/it] 40%|████ | 7493/18688 [8:46:04<13:23:38, 4.31s/it] 40%|████ | 7494/18688 [8:46:08<13:17:06, 4.27s/it] 40%|████ | 7495/18688 [8:46:13<13:53:38, 4.47s/it] 40%|████ | 7496/18688 [8:46:17<13:32:48, 4.36s/it] 40%|████ | 7497/18688 [8:46:22<13:56:19, 4.48s/it] 40%|████ | 7498/18688 [8:46:26<13:16:02, 4.27s/it] 40%|████ | 7499/18688 [8:46:29<12:36:52, 4.06s/it] 40%|████ | 7500/18688 [8:46:33<12:31:58, 4.03s/it] {'loss': '0.9155', 'grad_norm': '1.139', 'learning_rate': '5.827e-05', 'epoch': '0.4013', 'num_input_tokens_seen': 25169216, 'train_runtime': '3.159e+04', 'train_tokens_per_second': '796.7'}
40%|████ | 7500/18688 [8:46:33<12:31:58, 4.03s/it] 40%|████ | 7501/18688 [8:46:37<12:29:32, 4.02s/it] 40%|████ | 7502/18688 [8:46:41<12:00:48, 3.87s/it] 40%|████ | 7503/18688 [8:46:46<13:14:59, 4.26s/it] 40%|████ | 7504/18688 [8:46:51<13:33:42, 4.37s/it] 40%|████ | 7505/18688 [8:46:54<12:51:21, 4.14s/it] 40%|████ | 7506/18688 [8:46:58<12:52:14, 4.14s/it] 40%|████ | 7507/18688 [8:47:03<12:53:32, 4.15s/it] 40%|████ | 7508/18688 [8:47:06<12:31:22, 4.03s/it] 40%|████ | 7509/18688 [8:47:11<13:17:13, 4.28s/it] 40%|████ | 7510/18688 [8:47:16<13:24:08, 4.32s/it] {'loss': '0.9403', 'grad_norm': '1.343', 'learning_rate': '5.82e-05', 'epoch': '0.4019', 'num_input_tokens_seen': 25203480, 'train_runtime': '3.164e+04', 'train_tokens_per_second': '796.7'}
40%|████ | 7510/18688 [8:47:16<13:24:08, 4.32s/it] 40%|████ | 7511/18688 [8:47:20<13:40:32, 4.40s/it] 40%|████ | 7512/18688 [8:47:25<14:18:57, 4.61s/it] 40%|████ | 7513/18688 [8:47:29<13:48:37, 4.45s/it] 40%|████ | 7514/18688 [8:47:35<14:32:42, 4.69s/it] 40%|████ | 7515/18688 [8:47:38<13:45:18, 4.43s/it] 40%|████ | 7516/18688 [8:47:43<13:28:49, 4.34s/it] 40%|████ | 7517/18688 [8:47:47<13:37:10, 4.39s/it] 40%|████ | 7518/18688 [8:47:51<12:52:40, 4.15s/it] 40%|████ | 7519/18688 [8:47:54<12:36:02, 4.06s/it] 40%|████ | 7520/18688 [8:47:58<12:09:19, 3.92s/it] {'loss': '0.9439', 'grad_norm': '1.296', 'learning_rate': '5.814e-05', 'epoch': '0.4024', 'num_input_tokens_seen': 25238696, 'train_runtime': '3.168e+04', 'train_tokens_per_second': '796.7'}
40%|████ | 7520/18688 [8:47:58<12:09:19, 3.92s/it] 40%|████ | 7521/18688 [8:48:02<12:29:21, 4.03s/it] 40%|████ | 7522/18688 [8:48:07<12:57:12, 4.18s/it] 40%|████ | 7523/18688 [8:48:11<13:02:32, 4.21s/it] 40%|████ | 7524/18688 [8:48:15<12:33:56, 4.05s/it] 40%|████ | 7525/18688 [8:48:18<12:13:37, 3.94s/it] 40%|████ | 7526/18688 [8:48:23<12:57:40, 4.18s/it] 40%|████ | 7527/18688 [8:48:28<13:27:02, 4.34s/it] 40%|████ | 7528/18688 [8:48:32<13:11:45, 4.26s/it] 40%|████ | 7529/18688 [8:48:36<13:15:35, 4.28s/it] 40%|████ | 7530/18688 [8:48:41<13:16:56, 4.29s/it] {'loss': '0.9394', 'grad_norm': '1.176', 'learning_rate': '5.807e-05', 'epoch': '0.4029', 'num_input_tokens_seen': 25272456, 'train_runtime': '3.172e+04', 'train_tokens_per_second': '796.7'}
40%|████ | 7530/18688 [8:48:41<13:16:56, 4.29s/it] 40%|████ | 7531/18688 [8:48:45<13:26:34, 4.34s/it] 40%|████ | 7532/18688 [8:48:49<13:16:32, 4.28s/it] 40%|████ | 7533/18688 [8:48:53<13:03:59, 4.22s/it] 40%|████ | 7534/18688 [8:48:58<13:09:25, 4.25s/it] 40%|████ | 7535/18688 [8:49:02<13:16:04, 4.28s/it] 40%|████ | 7536/18688 [8:49:06<12:44:25, 4.11s/it] 40%|████ | 7537/18688 [8:49:10<13:04:49, 4.22s/it] 40%|████ | 7538/18688 [8:49:14<12:48:40, 4.14s/it] 40%|████ | 7539/18688 [8:49:18<12:24:26, 4.01s/it] 40%|████ | 7540/18688 [8:49:22<12:41:54, 4.10s/it] {'loss': '0.9633', 'grad_norm': '1.178', 'learning_rate': '5.801e-05', 'epoch': '0.4035', 'num_input_tokens_seen': 25306512, 'train_runtime': '3.176e+04', 'train_tokens_per_second': '796.7'}
40%|████ | 7540/18688 [8:49:22<12:41:54, 4.10s/it] 40%|████ | 7541/18688 [8:49:26<12:47:35, 4.13s/it] 40%|████ | 7542/18688 [8:49:30<12:13:19, 3.95s/it] 40%|████ | 7543/18688 [8:49:34<12:38:47, 4.08s/it] 40%|████ | 7544/18688 [8:49:38<12:05:23, 3.91s/it] 40%|████ | 7545/18688 [8:49:42<12:02:10, 3.89s/it] 40%|████ | 7546/18688 [8:49:46<12:23:44, 4.01s/it] 40%|████ | 7547/18688 [8:49:50<12:10:57, 3.94s/it] 40%|████ | 7548/18688 [8:49:56<14:16:25, 4.61s/it] 40%|████ | 7549/18688 [8:50:00<13:46:40, 4.45s/it] 40%|████ | 7550/18688 [8:50:04<13:00:00, 4.20s/it] {'loss': '0.9122', 'grad_norm': '1.148', 'learning_rate': '5.794e-05', 'epoch': '0.404', 'num_input_tokens_seen': 25339168, 'train_runtime': '3.18e+04', 'train_tokens_per_second': '796.7'}
40%|████ | 7550/18688 [8:50:04<13:00:00, 4.20s/it] 40%|████ | 7551/18688 [8:50:07<12:20:58, 3.99s/it] 40%|████ | 7552/18688 [8:50:11<12:40:00, 4.09s/it] 40%|████ | 7553/18688 [8:50:16<13:00:08, 4.20s/it] 40%|████ | 7554/18688 [8:50:20<12:50:29, 4.15s/it] 40%|████ | 7555/18688 [8:50:24<12:38:43, 4.09s/it] 40%|████ | 7556/18688 [8:50:28<12:45:54, 4.13s/it] 40%|████ | 7557/18688 [8:50:32<12:40:37, 4.10s/it] 40%|████ | 7558/18688 [8:50:36<12:48:54, 4.15s/it] 40%|████ | 7559/18688 [8:50:40<12:30:00, 4.04s/it] 40%|████ | 7560/18688 [8:50:44<11:56:11, 3.86s/it] {'loss': '0.9043', 'grad_norm': '1.353', 'learning_rate': '5.788e-05', 'epoch': '0.4045', 'num_input_tokens_seen': 25370200, 'train_runtime': '3.184e+04', 'train_tokens_per_second': '796.7'}
40%|████ | 7560/18688 [8:50:44<11:56:11, 3.86s/it] 40%|████ | 7561/18688 [8:50:48<12:20:52, 3.99s/it] 40%|████ | 7562/18688 [8:50:51<11:50:55, 3.83s/it] 40%|████ | 7563/18688 [8:50:55<12:02:25, 3.90s/it] 40%|████ | 7564/18688 [8:51:00<12:40:13, 4.10s/it] 40%|████ | 7565/18688 [8:51:04<12:14:51, 3.96s/it] 40%|████ | 7566/18688 [8:51:09<13:40:04, 4.42s/it] 40%|████ | 7567/18688 [8:51:13<13:05:01, 4.24s/it] 40%|████ | 7568/18688 [8:51:17<12:41:17, 4.11s/it] 41%|████ | 7569/18688 [8:51:21<12:30:40, 4.05s/it] 41%|████ | 7570/18688 [8:51:25<12:35:51, 4.08s/it] {'loss': '0.9175', 'grad_norm': '1.355', 'learning_rate': '5.781e-05', 'epoch': '0.4051', 'num_input_tokens_seen': 25404224, 'train_runtime': '3.189e+04', 'train_tokens_per_second': '796.7'}
41%|████ | 7570/18688 [8:51:25<12:35:51, 4.08s/it] 41%|████ | 7571/18688 [8:51:30<13:16:08, 4.30s/it] 41%|████ | 7572/18688 [8:51:34<13:15:44, 4.30s/it] 41%|████ | 7573/18688 [8:51:37<12:32:16, 4.06s/it] 41%|████ | 7574/18688 [8:51:41<12:01:01, 3.89s/it] 41%|████ | 7575/18688 [8:51:45<12:01:01, 3.89s/it] 41%|████ | 7576/18688 [8:51:49<12:18:30, 3.99s/it] 41%|████ | 7577/18688 [8:51:53<11:54:38, 3.86s/it] 41%|████ | 7578/18688 [8:51:57<12:13:06, 3.96s/it] 41%|████ | 7579/18688 [8:52:01<12:40:24, 4.11s/it] 41%|████ | 7580/18688 [8:52:05<12:46:05, 4.14s/it] {'loss': '0.9384', 'grad_norm': '1.363', 'learning_rate': '5.775e-05', 'epoch': '0.4056', 'num_input_tokens_seen': 25435736, 'train_runtime': '3.193e+04', 'train_tokens_per_second': '796.7'}
41%|████ | 7580/18688 [8:52:05<12:46:05, 4.14s/it] 41%|████ | 7581/18688 [8:52:10<13:33:33, 4.39s/it] 41%|████ | 7582/18688 [8:52:15<13:51:20, 4.49s/it] 41%|████ | 7583/18688 [8:52:19<13:38:54, 4.42s/it] 41%|████ | 7584/18688 [8:52:23<12:50:07, 4.16s/it] 41%|████ | 7585/18688 [8:52:27<13:06:59, 4.25s/it] 41%|████ | 7586/18688 [8:52:31<12:45:29, 4.14s/it] 41%|████ | 7587/18688 [8:52:35<12:47:31, 4.15s/it] 41%|████ | 7588/18688 [8:52:39<12:34:10, 4.08s/it] 41%|████ | 7589/18688 [8:52:44<12:44:04, 4.13s/it] 41%|████ | 7590/18688 [8:52:48<12:51:31, 4.17s/it] {'loss': '0.9169', 'grad_norm': '1.201', 'learning_rate': '5.768e-05', 'epoch': '0.4062', 'num_input_tokens_seen': 25470912, 'train_runtime': '3.197e+04', 'train_tokens_per_second': '796.8'}
41%|████ | 7590/18688 [8:52:48<12:51:31, 4.17s/it] 41%|████ | 7591/18688 [8:52:53<13:23:07, 4.34s/it] 41%|████ | 7592/18688 [8:52:57<13:00:57, 4.22s/it] 41%|████ | 7593/18688 [8:53:01<12:53:10, 4.18s/it] 41%|████ | 7594/18688 [8:53:05<13:23:18, 4.34s/it] 41%|████ | 7595/18688 [8:53:09<13:03:24, 4.24s/it] 41%|████ | 7596/18688 [8:53:14<13:08:23, 4.26s/it] 41%|████ | 7597/18688 [8:53:18<12:56:44, 4.20s/it] 41%|████ | 7598/18688 [8:53:22<12:54:24, 4.19s/it] 41%|████ | 7599/18688 [8:53:26<12:56:40, 4.20s/it] 41%|████ | 7600/18688 [8:53:30<13:06:18, 4.25s/it] {'loss': '0.9463', 'grad_norm': '1.237', 'learning_rate': '5.762e-05', 'epoch': '0.4067', 'num_input_tokens_seen': 25506616, 'train_runtime': '3.201e+04', 'train_tokens_per_second': '796.8'}
41%|████ | 7600/18688 [8:53:30<13:06:18, 4.25s/it] 41%|████ | 7601/18688 [8:53:34<12:34:16, 4.08s/it] 41%|████ | 7602/18688 [8:53:39<13:12:36, 4.29s/it] 41%|████ | 7603/18688 [8:53:43<12:58:09, 4.21s/it] 41%|████ | 7604/18688 [8:53:48<13:25:10, 4.36s/it] 41%|████ | 7605/18688 [8:53:52<13:05:30, 4.25s/it] 41%|████ | 7606/18688 [8:53:56<13:30:17, 4.39s/it] 41%|████ | 7607/18688 [8:54:00<13:05:32, 4.25s/it] 41%|████ | 7608/18688 [8:54:04<12:45:12, 4.14s/it] 41%|████ | 7609/18688 [8:54:09<13:11:28, 4.29s/it] 41%|████ | 7610/18688 [8:54:14<14:08:22, 4.59s/it] {'loss': '0.9434', 'grad_norm': '1.296', 'learning_rate': '5.755e-05', 'epoch': '0.4072', 'num_input_tokens_seen': 25543752, 'train_runtime': '3.205e+04', 'train_tokens_per_second': '796.9'}
41%|████ | 7610/18688 [8:54:14<14:08:22, 4.59s/it] 41%|████ | 7611/18688 [8:54:18<13:39:25, 4.44s/it] 41%|████ | 7612/18688 [8:54:23<13:45:30, 4.47s/it] 41%|████ | 7613/18688 [8:54:27<13:43:25, 4.46s/it] 41%|████ | 7614/18688 [8:54:32<13:52:27, 4.51s/it] 41%|████ | 7615/18688 [8:54:36<13:46:39, 4.48s/it] 41%|████ | 7616/18688 [8:54:40<13:26:37, 4.37s/it] 41%|████ | 7617/18688 [8:54:44<12:49:02, 4.17s/it] 41%|████ | 7618/18688 [8:54:48<12:29:58, 4.06s/it] 41%|████ | 7619/18688 [8:54:52<12:11:08, 3.96s/it] 41%|████ | 7620/18688 [8:54:55<12:00:47, 3.91s/it] {'loss': '0.8964', 'grad_norm': '0.9291', 'learning_rate': '5.748e-05', 'epoch': '0.4078', 'num_input_tokens_seen': 25577240, 'train_runtime': '3.21e+04', 'train_tokens_per_second': '796.9'}
41%|████ | 7620/18688 [8:54:55<12:00:47, 3.91s/it] 41%|████ | 7621/18688 [8:55:00<12:38:28, 4.11s/it] 41%|████ | 7622/18688 [8:55:04<12:47:08, 4.16s/it] 41%|████ | 7623/18688 [8:55:08<12:43:36, 4.14s/it] 41%|████ | 7624/18688 [8:55:13<12:57:11, 4.21s/it] 41%|████ | 7625/18688 [8:55:16<12:25:51, 4.05s/it] 41%|████ | 7626/18688 [8:55:21<12:58:50, 4.22s/it] 41%|████ | 7627/18688 [8:55:26<13:34:11, 4.42s/it] 41%|████ | 7628/18688 [8:55:30<13:17:35, 4.33s/it] 41%|████ | 7629/18688 [8:55:35<13:45:25, 4.48s/it] 41%|████ | 7630/18688 [8:55:40<14:03:16, 4.58s/it] {'loss': '0.9881', 'grad_norm': '1.423', 'learning_rate': '5.742e-05', 'epoch': '0.4083', 'num_input_tokens_seen': 25615320, 'train_runtime': '3.214e+04', 'train_tokens_per_second': '797'}
41%|████ | 7630/18688 [8:55:40<14:03:16, 4.58s/it] 41%|████ | 7631/18688 [8:55:44<13:34:52, 4.42s/it] 41%|████ | 7632/18688 [8:55:48<13:43:17, 4.47s/it] 41%|████ | 7633/18688 [8:55:52<13:14:47, 4.31s/it] 41%|████ | 7634/18688 [8:55:57<13:24:43, 4.37s/it] 41%|████ | 7635/18688 [8:56:01<13:38:41, 4.44s/it] 41%|████ | 7636/18688 [8:56:06<13:29:04, 4.39s/it] 41%|████ | 7637/18688 [8:56:10<13:25:41, 4.37s/it] 41%|████ | 7638/18688 [8:56:13<12:39:39, 4.12s/it] 41%|████ | 7639/18688 [8:56:17<12:12:47, 3.98s/it] 41%|████ | 7640/18688 [8:56:21<11:45:11, 3.83s/it] {'loss': '0.8772', 'grad_norm': '1.077', 'learning_rate': '5.735e-05', 'epoch': '0.4088', 'num_input_tokens_seen': 25647048, 'train_runtime': '3.218e+04', 'train_tokens_per_second': '797'}
41%|████ | 7640/18688 [8:56:21<11:45:11, 3.83s/it] 41%|████ | 7641/18688 [8:56:24<11:43:22, 3.82s/it] 41%|████ | 7642/18688 [8:56:29<12:17:48, 4.01s/it] 41%|████ | 7643/18688 [8:56:34<13:02:51, 4.25s/it] 41%|████ | 7644/18688 [8:56:39<13:40:29, 4.46s/it] 41%|████ | 7645/18688 [8:56:43<13:22:53, 4.36s/it] 41%|████ | 7646/18688 [8:56:47<12:50:20, 4.19s/it] 41%|████ | 7647/18688 [8:56:52<13:45:45, 4.49s/it] 41%|████ | 7648/18688 [8:56:56<13:53:27, 4.53s/it] 41%|████ | 7649/18688 [8:57:00<13:11:16, 4.30s/it] 41%|████ | 7650/18688 [8:57:04<12:51:02, 4.19s/it] {'loss': '0.9385', 'grad_norm': '1.163', 'learning_rate': '5.729e-05', 'epoch': '0.4094', 'num_input_tokens_seen': 25682256, 'train_runtime': '3.222e+04', 'train_tokens_per_second': '797'}
41%|████ | 7650/18688 [8:57:04<12:51:02, 4.19s/it] 41%|████ | 7651/18688 [8:57:08<12:12:22, 3.98s/it] 41%|████ | 7652/18688 [8:57:11<11:48:21, 3.85s/it] 41%|████ | 7653/18688 [8:57:16<13:13:52, 4.32s/it] 41%|████ | 7654/18688 [8:57:21<13:36:34, 4.44s/it] 41%|████ | 7655/18688 [8:57:25<12:43:16, 4.15s/it] 41%|████ | 7656/18688 [8:57:29<13:06:12, 4.28s/it] 41%|████ | 7657/18688 [8:57:33<12:21:59, 4.04s/it] 41%|████ | 7658/18688 [8:57:36<11:49:56, 3.86s/it] 41%|████ | 7659/18688 [8:57:41<12:18:47, 4.02s/it] 41%|████ | 7660/18688 [8:57:45<12:20:26, 4.03s/it] {'loss': '0.9282', 'grad_norm': '1.086', 'learning_rate': '5.722e-05', 'epoch': '0.4099', 'num_input_tokens_seen': 25715632, 'train_runtime': '3.227e+04', 'train_tokens_per_second': '797'}
41%|████ | 7660/18688 [8:57:45<12:20:26, 4.03s/it] 41%|████ | 7661/18688 [8:57:49<12:22:49, 4.04s/it] 41%|████ | 7662/18688 [8:57:53<12:25:32, 4.06s/it] 41%|████ | 7663/18688 [8:57:58<13:02:10, 4.26s/it] 41%|████ | 7664/18688 [8:58:01<12:21:29, 4.04s/it] 41%|████ | 7665/18688 [8:58:05<12:04:48, 3.95s/it] 41%|████ | 7666/18688 [8:58:09<12:35:46, 4.11s/it] 41%|████ | 7667/18688 [8:58:14<13:22:42, 4.37s/it] 41%|████ | 7668/18688 [8:58:18<12:44:40, 4.16s/it] 41%|████ | 7669/18688 [8:58:23<13:19:59, 4.36s/it] 41%|████ | 7670/18688 [8:58:28<14:34:17, 4.76s/it] {'loss': '0.9089', 'grad_norm': '1.28', 'learning_rate': '5.715e-05', 'epoch': '0.4104', 'num_input_tokens_seen': 25753384, 'train_runtime': '3.231e+04', 'train_tokens_per_second': '797.1'}
41%|████ | 7670/18688 [8:58:28<14:34:17, 4.76s/it] 41%|████ | 7671/18688 [8:58:32<13:50:57, 4.53s/it] 41%|████ | 7672/18688 [8:58:37<14:10:27, 4.63s/it] 41%|████ | 7673/18688 [8:58:41<13:34:08, 4.43s/it] 41%|████ | 7674/18688 [8:58:46<14:14:53, 4.66s/it] 41%|████ | 7675/18688 [8:58:50<13:31:09, 4.42s/it] 41%|████ | 7676/18688 [8:58:54<12:44:56, 4.17s/it] 41%|████ | 7677/18688 [8:58:58<13:04:56, 4.28s/it] 41%|████ | 7678/18688 [8:59:03<13:12:33, 4.32s/it] 41%|████ | 7679/18688 [8:59:07<12:57:07, 4.24s/it] 41%|████ | 7680/18688 [8:59:11<12:31:29, 4.10s/it] {'loss': '0.9773', 'grad_norm': '1.226', 'learning_rate': '5.709e-05', 'epoch': '0.411', 'num_input_tokens_seen': 25788376, 'train_runtime': '3.235e+04', 'train_tokens_per_second': '797.1'}
41%|████ | 7680/18688 [8:59:11<12:31:29, 4.10s/it] 41%|████ | 7681/18688 [8:59:16<13:18:05, 4.35s/it] 41%|████ | 7682/18688 [8:59:20<13:12:12, 4.32s/it] 41%|████ | 7683/18688 [8:59:24<13:24:42, 4.39s/it] 41%|████ | 7684/18688 [8:59:29<13:50:56, 4.53s/it] 41%|████ | 7685/18688 [8:59:34<13:58:29, 4.57s/it] 41%|████ | 7686/18688 [8:59:39<14:06:14, 4.62s/it] 41%|████ | 7687/18688 [8:59:43<13:25:52, 4.40s/it] 41%|████ | 7688/18688 [8:59:47<13:25:26, 4.39s/it] 41%|████ | 7689/18688 [8:59:51<13:00:33, 4.26s/it] 41%|████ | 7690/18688 [8:59:55<12:50:38, 4.20s/it] {'loss': '0.931', 'grad_norm': '1.246', 'learning_rate': '5.702e-05', 'epoch': '0.4115', 'num_input_tokens_seen': 25826408, 'train_runtime': '3.24e+04', 'train_tokens_per_second': '797.2'}
41%|████ | 7690/18688 [8:59:55<12:50:38, 4.20s/it] 41%|████ | 7691/18688 [8:59:59<12:29:15, 4.09s/it] 41%|████ | 7692/18688 [9:00:03<12:35:47, 4.12s/it] 41%|████ | 7693/18688 [9:00:07<12:23:26, 4.06s/it] 41%|████ | 7694/18688 [9:00:11<12:27:43, 4.08s/it] 41%|████ | 7695/18688 [9:00:15<12:33:08, 4.11s/it] 41%|████ | 7696/18688 [9:00:19<12:38:06, 4.14s/it] 41%|████ | 7697/18688 [9:00:24<12:45:54, 4.18s/it] 41%|████ | 7698/18688 [9:00:28<12:39:01, 4.14s/it] 41%|████ | 7699/18688 [9:00:32<12:50:44, 4.21s/it] 41%|████ | 7700/18688 [9:00:37<13:03:53, 4.28s/it] {'loss': '0.9048', 'grad_norm': '1.09', 'learning_rate': '5.696e-05', 'epoch': '0.412', 'num_input_tokens_seen': 25859680, 'train_runtime': '3.244e+04', 'train_tokens_per_second': '797.2'}
41%|████ | 7700/18688 [9:00:37<13:03:53, 4.28s/it] 41%|████ | 7701/18688 [9:00:40<12:31:38, 4.10s/it] 41%|████ | 7702/18688 [9:00:44<12:09:35, 3.98s/it] 41%|████ | 7703/18688 [9:00:49<13:22:09, 4.38s/it] 41%|████ | 7704/18688 [9:00:55<14:26:04, 4.73s/it] 41%|████ | 7705/18688 [9:00:59<13:34:19, 4.45s/it] 41%|████ | 7706/18688 [9:01:02<12:53:10, 4.22s/it] 41%|████ | 7707/18688 [9:01:07<13:19:36, 4.37s/it] 41%|████ | 7708/18688 [9:01:11<12:37:54, 4.14s/it] 41%|████▏ | 7709/18688 [9:01:15<13:10:49, 4.32s/it] 41%|████▏ | 7710/18688 [9:01:19<12:29:45, 4.10s/it] {'loss': '0.9309', 'grad_norm': '1.195', 'learning_rate': '5.689e-05', 'epoch': '0.4126', 'num_input_tokens_seen': 25895544, 'train_runtime': '3.248e+04', 'train_tokens_per_second': '797.3'}
41%|████▏ | 7710/18688 [9:01:19<12:29:45, 4.10s/it] 41%|████▏ | 7711/18688 [9:01:22<12:01:56, 3.95s/it] 41%|████▏ | 7712/18688 [9:01:27<12:25:59, 4.08s/it] 41%|████▏ | 7713/18688 [9:01:30<11:57:33, 3.92s/it] 41%|████▏ | 7714/18688 [9:01:35<12:28:40, 4.09s/it] 41%|████▏ | 7715/18688 [9:01:39<12:17:26, 4.03s/it] 41%|████▏ | 7716/18688 [9:01:43<12:28:47, 4.09s/it] 41%|████▏ | 7717/18688 [9:01:47<12:44:31, 4.18s/it] 41%|████▏ | 7718/18688 [9:01:52<12:52:38, 4.23s/it] 41%|████▏ | 7719/18688 [9:01:56<13:13:51, 4.34s/it] 41%|████▏ | 7720/18688 [9:02:01<13:49:30, 4.54s/it] {'loss': '0.8969', 'grad_norm': '1.071', 'learning_rate': '5.682e-05', 'epoch': '0.4131', 'num_input_tokens_seen': 25930528, 'train_runtime': '3.252e+04', 'train_tokens_per_second': '797.3'}
41%|████▏ | 7720/18688 [9:02:01<13:49:30, 4.54s/it] 41%|████▏ | 7721/18688 [9:02:06<13:44:36, 4.51s/it] 41%|████▏ | 7722/18688 [9:02:10<13:45:02, 4.51s/it] 41%|████▏ | 7723/18688 [9:02:15<13:35:22, 4.46s/it] 41%|████▏ | 7724/18688 [9:02:18<12:54:54, 4.24s/it] 41%|████▏ | 7725/18688 [9:02:22<12:27:31, 4.09s/it] 41%|████▏ | 7726/18688 [9:02:26<12:20:23, 4.05s/it] 41%|████▏ | 7727/18688 [9:02:30<12:34:11, 4.13s/it] 41%|████▏ | 7728/18688 [9:02:35<12:45:14, 4.19s/it] 41%|████▏ | 7729/18688 [9:02:39<12:55:30, 4.25s/it] 41%|████▏ | 7730/18688 [9:02:43<12:51:57, 4.23s/it] {'loss': '0.9347', 'grad_norm': '1.173', 'learning_rate': '5.676e-05', 'epoch': '0.4136', 'num_input_tokens_seen': 25963880, 'train_runtime': '3.256e+04', 'train_tokens_per_second': '797.3'}
41%|████▏ | 7730/18688 [9:02:43<12:51:57, 4.23s/it] 41%|████▏ | 7731/18688 [9:02:48<13:44:18, 4.51s/it] 41%|████▏ | 7732/18688 [9:02:54<14:25:31, 4.74s/it] 41%|████▏ | 7733/18688 [9:02:57<13:20:25, 4.38s/it] 41%|████▏ | 7734/18688 [9:03:01<13:08:48, 4.32s/it] 41%|████▏ | 7735/18688 [9:03:06<12:54:25, 4.24s/it] 41%|████▏ | 7736/18688 [9:03:10<12:43:14, 4.18s/it] 41%|████▏ | 7737/18688 [9:03:13<12:06:07, 3.98s/it] 41%|████▏ | 7738/18688 [9:03:17<11:53:45, 3.91s/it] 41%|████▏ | 7739/18688 [9:03:21<11:59:43, 3.94s/it] 41%|████▏ | 7740/18688 [9:03:25<12:13:43, 4.02s/it] {'loss': '0.9272', 'grad_norm': '1.237', 'learning_rate': '5.669e-05', 'epoch': '0.4142', 'num_input_tokens_seen': 25999376, 'train_runtime': '3.261e+04', 'train_tokens_per_second': '797.4'}
41%|████▏ | 7740/18688 [9:03:25<12:13:43, 4.02s/it] 41%|████▏ | 7741/18688 [9:03:30<12:35:59, 4.14s/it] 41%|████▏ | 7742/18688 [9:03:33<12:09:17, 4.00s/it] 41%|████▏ | 7743/18688 [9:03:37<12:02:36, 3.96s/it] 41%|████▏ | 7744/18688 [9:03:42<13:18:25, 4.38s/it] 41%|████▏ | 7745/18688 [9:03:46<12:47:30, 4.21s/it] 41%|████▏ | 7746/18688 [9:03:50<12:16:14, 4.04s/it] 41%|████▏ | 7747/18688 [9:03:54<12:23:29, 4.08s/it] 41%|████▏ | 7748/18688 [9:03:59<13:27:15, 4.43s/it] 41%|████▏ | 7749/18688 [9:04:03<12:58:53, 4.27s/it] 41%|████▏ | 7750/18688 [9:04:07<12:19:05, 4.05s/it] {'loss': '0.8849', 'grad_norm': '1.186', 'learning_rate': '5.662e-05', 'epoch': '0.4147', 'num_input_tokens_seen': 26031920, 'train_runtime': '3.265e+04', 'train_tokens_per_second': '797.4'}
41%|████▏ | 7750/18688 [9:04:07<12:19:05, 4.05s/it] 41%|████▏ | 7751/18688 [9:04:11<12:26:30, 4.10s/it] 41%|████▏ | 7752/18688 [9:04:15<12:37:00, 4.15s/it] 41%|████▏ | 7753/18688 [9:04:19<12:34:35, 4.14s/it] 41%|████▏ | 7754/18688 [9:04:24<12:51:25, 4.23s/it] 41%|████▏ | 7755/18688 [9:04:27<12:24:53, 4.09s/it] 42%|████▏ | 7756/18688 [9:04:31<12:08:08, 4.00s/it] 42%|████▏ | 7757/18688 [9:04:36<12:29:12, 4.11s/it] 42%|████▏ | 7758/18688 [9:04:40<12:16:50, 4.04s/it] 42%|████▏ | 7759/18688 [9:04:44<12:43:14, 4.19s/it] 42%|████▏ | 7760/18688 [9:04:48<12:10:42, 4.01s/it] {'loss': '0.9271', 'grad_norm': '1.053', 'learning_rate': '5.656e-05', 'epoch': '0.4153', 'num_input_tokens_seen': 26065816, 'train_runtime': '3.269e+04', 'train_tokens_per_second': '797.4'}
42%|████▏ | 7760/18688 [9:04:48<12:10:42, 4.01s/it] 42%|████▏ | 7761/18688 [9:04:52<12:24:20, 4.09s/it] 42%|████▏ | 7762/18688 [9:04:55<11:52:57, 3.92s/it] 42%|████▏ | 7763/18688 [9:05:00<12:11:28, 4.02s/it] 42%|████▏ | 7764/18688 [9:05:04<12:22:37, 4.08s/it] 42%|████▏ | 7765/18688 [9:05:08<11:58:25, 3.95s/it] 42%|████▏ | 7766/18688 [9:05:12<12:03:48, 3.98s/it] 42%|████▏ | 7767/18688 [9:05:15<11:50:00, 3.90s/it] 42%|████▏ | 7768/18688 [9:05:19<11:45:20, 3.88s/it] 42%|████▏ | 7769/18688 [9:05:23<11:56:04, 3.93s/it] 42%|████▏ | 7770/18688 [9:05:27<11:36:33, 3.83s/it] {'loss': '0.8801', 'grad_norm': '1.204', 'learning_rate': '5.649e-05', 'epoch': '0.4158', 'num_input_tokens_seen': 26096056, 'train_runtime': '3.273e+04', 'train_tokens_per_second': '797.4'}
42%|████▏ | 7770/18688 [9:05:27<11:36:33, 3.83s/it] 42%|████▏ | 7771/18688 [9:05:30<11:28:29, 3.78s/it] 42%|████▏ | 7772/18688 [9:05:35<11:42:55, 3.86s/it] 42%|████▏ | 7773/18688 [9:05:39<12:16:01, 4.05s/it] 42%|████▏ | 7774/18688 [9:05:42<11:45:53, 3.88s/it] 42%|████▏ | 7775/18688 [9:05:47<12:29:25, 4.12s/it] 42%|████▏ | 7776/18688 [9:05:51<12:10:28, 4.02s/it] 42%|████▏ | 7777/18688 [9:05:56<13:12:30, 4.36s/it] 42%|████▏ | 7778/18688 [9:06:00<12:47:24, 4.22s/it] 42%|████▏ | 7779/18688 [9:06:04<12:54:44, 4.26s/it] 42%|████▏ | 7780/18688 [9:06:09<13:04:31, 4.32s/it] {'loss': '0.9149', 'grad_norm': '1.105', 'learning_rate': '5.642e-05', 'epoch': '0.4163', 'num_input_tokens_seen': 26130576, 'train_runtime': '3.277e+04', 'train_tokens_per_second': '797.4'}
42%|████▏ | 7780/18688 [9:06:09<13:04:31, 4.32s/it] 42%|████▏ | 7781/18688 [9:06:13<13:00:35, 4.29s/it] 42%|████▏ | 7782/18688 [9:06:17<12:52:35, 4.25s/it] 42%|████▏ | 7783/18688 [9:06:22<12:59:51, 4.29s/it] 42%|████▏ | 7784/18688 [9:06:27<13:38:13, 4.50s/it] 42%|████▏ | 7785/18688 [9:06:31<13:54:44, 4.59s/it] 42%|████▏ | 7786/18688 [9:06:36<13:29:18, 4.45s/it] 42%|████▏ | 7787/18688 [9:06:40<13:40:45, 4.52s/it] 42%|████▏ | 7788/18688 [9:06:44<12:43:04, 4.20s/it] 42%|████▏ | 7789/18688 [9:06:47<12:02:09, 3.98s/it] 42%|████▏ | 7790/18688 [9:06:51<12:23:20, 4.09s/it] {'loss': '0.9468', 'grad_norm': '1.213', 'learning_rate': '5.636e-05', 'epoch': '0.4169', 'num_input_tokens_seen': 26164784, 'train_runtime': '3.281e+04', 'train_tokens_per_second': '797.4'}
42%|████▏ | 7790/18688 [9:06:51<12:23:20, 4.09s/it] 42%|████▏ | 7791/18688 [9:06:55<11:52:47, 3.92s/it] 42%|████▏ | 7792/18688 [9:06:59<11:59:18, 3.96s/it] 42%|████▏ | 7793/18688 [9:07:03<11:39:18, 3.85s/it] 42%|████▏ | 7794/18688 [9:07:06<11:33:57, 3.82s/it] 42%|████▏ | 7795/18688 [9:07:10<11:46:28, 3.89s/it] 42%|████▏ | 7796/18688 [9:07:15<12:04:23, 3.99s/it] 42%|████▏ | 7797/18688 [9:07:19<12:09:30, 4.02s/it] 42%|████▏ | 7798/18688 [9:07:23<12:12:50, 4.04s/it] 42%|████▏ | 7799/18688 [9:07:28<13:04:12, 4.32s/it] 42%|████▏ | 7800/18688 [9:07:33<13:33:26, 4.48s/it] {'loss': '0.9223', 'grad_norm': '1.197', 'learning_rate': '5.629e-05', 'epoch': '0.4174', 'num_input_tokens_seen': 26199768, 'train_runtime': '3.285e+04', 'train_tokens_per_second': '797.5'}
42%|████▏ | 7800/18688 [9:07:33<13:33:26, 4.48s/it] 42%|████▏ | 7801/18688 [9:07:37<13:20:16, 4.41s/it] 42%|████▏ | 7802/18688 [9:07:41<12:38:45, 4.18s/it] 42%|████▏ | 7803/18688 [9:07:44<12:12:32, 4.04s/it] 42%|████▏ | 7804/18688 [9:07:48<12:01:25, 3.98s/it] 42%|████▏ | 7805/18688 [9:07:52<11:33:00, 3.82s/it] 42%|████▏ | 7806/18688 [9:07:56<12:22:40, 4.09s/it] 42%|████▏ | 7807/18688 [9:08:00<12:23:41, 4.10s/it] 42%|████▏ | 7808/18688 [9:08:04<12:22:36, 4.10s/it] 42%|████▏ | 7809/18688 [9:08:08<12:13:51, 4.05s/it] 42%|████▏ | 7810/18688 [9:08:12<11:42:05, 3.87s/it] {'loss': '0.9765', 'grad_norm': '1.43', 'learning_rate': '5.622e-05', 'epoch': '0.4179', 'num_input_tokens_seen': 26229592, 'train_runtime': '3.289e+04', 'train_tokens_per_second': '797.4'}
42%|████▏ | 7810/18688 [9:08:12<11:42:05, 3.87s/it] 42%|████▏ | 7811/18688 [9:08:16<11:30:35, 3.81s/it] 42%|████▏ | 7812/18688 [9:08:20<12:11:53, 4.04s/it] 42%|████▏ | 7813/18688 [9:08:24<12:12:30, 4.04s/it] 42%|████▏ | 7814/18688 [9:08:28<11:38:23, 3.85s/it] 42%|████▏ | 7815/18688 [9:08:32<11:54:59, 3.95s/it] 42%|████▏ | 7816/18688 [9:08:35<11:30:57, 3.81s/it] 42%|████▏ | 7817/18688 [9:08:40<11:57:22, 3.96s/it] 42%|████▏ | 7818/18688 [9:08:44<12:21:17, 4.09s/it] 42%|████▏ | 7819/18688 [9:08:50<14:01:53, 4.65s/it] 42%|████▏ | 7820/18688 [9:08:54<13:20:20, 4.42s/it] {'loss': '0.9692', 'grad_norm': '1.086', 'learning_rate': '5.616e-05', 'epoch': '0.4185', 'num_input_tokens_seen': 26265024, 'train_runtime': '3.293e+04', 'train_tokens_per_second': '797.5'}
42%|████▏ | 7820/18688 [9:08:54<13:20:20, 4.42s/it] 42%|████▏ | 7821/18688 [9:08:58<13:01:17, 4.31s/it] 42%|████▏ | 7822/18688 [9:09:01<12:21:29, 4.09s/it] 42%|████▏ | 7823/18688 [9:09:06<12:28:26, 4.13s/it] 42%|████▏ | 7824/18688 [9:09:10<12:54:43, 4.28s/it] 42%|████▏ | 7825/18688 [9:09:14<12:38:58, 4.19s/it] 42%|████▏ | 7826/18688 [9:09:18<12:01:28, 3.99s/it] 42%|████▏ | 7827/18688 [9:09:21<11:38:18, 3.86s/it] 42%|████▏ | 7828/18688 [9:09:25<11:16:06, 3.74s/it] 42%|████▏ | 7829/18688 [9:09:30<12:45:01, 4.23s/it] 42%|████▏ | 7830/18688 [9:09:34<12:16:20, 4.07s/it] {'loss': '0.9442', 'grad_norm': '1.248', 'learning_rate': '5.609e-05', 'epoch': '0.419', 'num_input_tokens_seen': 26298200, 'train_runtime': '3.297e+04', 'train_tokens_per_second': '797.5'}
42%|████▏ | 7830/18688 [9:09:34<12:16:20, 4.07s/it] 42%|████▏ | 7831/18688 [9:09:39<12:57:24, 4.30s/it] 42%|████▏ | 7832/18688 [9:09:43<13:14:08, 4.39s/it] 42%|████▏ | 7833/18688 [9:09:48<13:36:08, 4.51s/it] 42%|████▏ | 7834/18688 [9:09:52<12:54:34, 4.28s/it] 42%|████▏ | 7835/18688 [9:09:56<12:30:06, 4.15s/it] 42%|████▏ | 7836/18688 [9:10:00<12:35:19, 4.18s/it] 42%|████▏ | 7837/18688 [9:10:05<12:59:34, 4.31s/it] 42%|████▏ | 7838/18688 [9:10:09<12:49:08, 4.25s/it] 42%|████▏ | 7839/18688 [9:10:12<12:12:57, 4.05s/it] 42%|████▏ | 7840/18688 [9:10:16<12:11:33, 4.05s/it] {'loss': '0.9068', 'grad_norm': '1.393', 'learning_rate': '5.602e-05', 'epoch': '0.4195', 'num_input_tokens_seen': 26332512, 'train_runtime': '3.302e+04', 'train_tokens_per_second': '797.5'}
42%|████▏ | 7840/18688 [9:10:16<12:11:33, 4.05s/it] 42%|████▏ | 7841/18688 [9:10:20<11:54:36, 3.95s/it] 42%|████▏ | 7842/18688 [9:10:24<11:51:26, 3.94s/it] 42%|████▏ | 7843/18688 [9:10:28<11:43:09, 3.89s/it] 42%|████▏ | 7844/18688 [9:10:32<12:23:24, 4.11s/it] 42%|████▏ | 7845/18688 [9:10:37<13:16:49, 4.41s/it] 42%|████▏ | 7846/18688 [9:10:42<13:03:00, 4.33s/it] 42%|████▏ | 7847/18688 [9:10:46<12:45:23, 4.24s/it] 42%|████▏ | 7848/18688 [9:10:49<12:18:31, 4.09s/it] 42%|████▏ | 7849/18688 [9:10:54<12:59:02, 4.31s/it] 42%|████▏ | 7850/18688 [9:10:59<13:12:01, 4.38s/it] {'loss': '0.9911', 'grad_norm': '1.266', 'learning_rate': '5.596e-05', 'epoch': '0.4201', 'num_input_tokens_seen': 26366784, 'train_runtime': '3.306e+04', 'train_tokens_per_second': '797.6'}
42%|████▏ | 7850/18688 [9:10:59<13:12:01, 4.38s/it] 42%|████▏ | 7851/18688 [9:11:03<13:12:06, 4.39s/it] 42%|████▏ | 7852/18688 [9:11:07<12:52:11, 4.28s/it] 42%|████▏ | 7853/18688 [9:11:13<14:15:45, 4.74s/it] 42%|████▏ | 7854/18688 [9:11:17<13:19:24, 4.43s/it] 42%|████▏ | 7855/18688 [9:11:20<12:40:20, 4.21s/it] 42%|████▏ | 7856/18688 [9:11:25<12:39:55, 4.21s/it] 42%|████▏ | 7857/18688 [9:11:28<12:15:58, 4.08s/it] 42%|████▏ | 7858/18688 [9:11:33<12:25:41, 4.13s/it] 42%|████▏ | 7859/18688 [9:11:36<12:09:53, 4.04s/it] 42%|████▏ | 7860/18688 [9:11:40<12:08:21, 4.04s/it] {'loss': '0.919', 'grad_norm': '1.513', 'learning_rate': '5.589e-05', 'epoch': '0.4206', 'num_input_tokens_seen': 26402672, 'train_runtime': '3.31e+04', 'train_tokens_per_second': '797.6'}
42%|████▏ | 7860/18688 [9:11:40<12:08:21, 4.04s/it] 42%|████▏ | 7861/18688 [9:11:46<13:17:55, 4.42s/it] 42%|████▏ | 7862/18688 [9:11:50<13:02:01, 4.33s/it] 42%|████▏ | 7863/18688 [9:11:54<12:47:05, 4.25s/it] 42%|████▏ | 7864/18688 [9:11:58<12:31:18, 4.16s/it] 42%|████▏ | 7865/18688 [9:12:02<12:22:02, 4.11s/it] 42%|████▏ | 7866/18688 [9:12:06<12:00:08, 3.99s/it] 42%|████▏ | 7867/18688 [9:12:09<11:41:46, 3.89s/it] 42%|████▏ | 7868/18688 [9:12:13<11:47:08, 3.92s/it] 42%|████▏ | 7869/18688 [9:12:17<11:45:30, 3.91s/it] 42%|████▏ | 7870/18688 [9:12:21<11:51:19, 3.95s/it] {'loss': '0.9339', 'grad_norm': '1.133', 'learning_rate': '5.582e-05', 'epoch': '0.4211', 'num_input_tokens_seen': 26435008, 'train_runtime': '3.314e+04', 'train_tokens_per_second': '797.6'}
42%|████▏ | 7870/18688 [9:12:21<11:51:19, 3.95s/it] 42%|████▏ | 7871/18688 [9:12:25<11:31:25, 3.84s/it] 42%|████▏ | 7872/18688 [9:12:29<11:56:16, 3.97s/it] 42%|████▏ | 7873/18688 [9:12:33<11:59:33, 3.99s/it] 42%|████▏ | 7874/18688 [9:12:37<11:35:48, 3.86s/it] 42%|████▏ | 7875/18688 [9:12:41<11:52:13, 3.95s/it] 42%|████▏ | 7876/18688 [9:12:45<12:07:12, 4.04s/it] 42%|████▏ | 7877/18688 [9:12:49<12:11:55, 4.06s/it] 42%|████▏ | 7878/18688 [9:12:53<12:17:29, 4.09s/it] 42%|████▏ | 7879/18688 [9:12:57<11:56:35, 3.98s/it] 42%|████▏ | 7880/18688 [9:13:00<11:28:55, 3.82s/it] {'loss': '0.88', 'grad_norm': '1.234', 'learning_rate': '5.576e-05', 'epoch': '0.4217', 'num_input_tokens_seen': 26465800, 'train_runtime': '3.318e+04', 'train_tokens_per_second': '797.6'}
42%|████▏ | 7880/18688 [9:13:00<11:28:55, 3.82s/it] 42%|████▏ | 7881/18688 [9:13:05<12:13:49, 4.07s/it] 42%|████▏ | 7882/18688 [9:13:09<11:52:28, 3.96s/it] 42%|████▏ | 7883/18688 [9:13:13<12:19:15, 4.11s/it] 42%|████▏ | 7884/18688 [9:13:18<12:43:57, 4.24s/it] 42%|████▏ | 7885/18688 [9:13:22<12:46:40, 4.26s/it] 42%|████▏ | 7886/18688 [9:13:26<12:38:14, 4.21s/it] 42%|████▏ | 7887/18688 [9:13:30<11:59:20, 4.00s/it] 42%|████▏ | 7888/18688 [9:13:34<11:55:44, 3.98s/it] 42%|████▏ | 7889/18688 [9:13:38<11:54:03, 3.97s/it] 42%|████▏ | 7890/18688 [9:13:41<11:30:15, 3.84s/it] {'loss': '0.9497', 'grad_norm': '1.225', 'learning_rate': '5.569e-05', 'epoch': '0.4222', 'num_input_tokens_seen': 26498952, 'train_runtime': '3.322e+04', 'train_tokens_per_second': '797.6'}
42%|████▏ | 7890/18688 [9:13:41<11:30:15, 3.84s/it] 42%|████▏ | 7891/18688 [9:13:45<11:50:41, 3.95s/it] 42%|████▏ | 7892/18688 [9:13:51<13:02:12, 4.35s/it] 42%|████▏ | 7893/18688 [9:13:54<12:35:49, 4.20s/it] 42%|████▏ | 7894/18688 [9:13:59<13:13:20, 4.41s/it] 42%|████▏ | 7895/18688 [9:14:03<12:36:53, 4.21s/it] 42%|████▏ | 7896/18688 [9:14:07<12:08:34, 4.05s/it] 42%|████▏ | 7897/18688 [9:14:11<12:19:06, 4.11s/it] 42%|████▏ | 7898/18688 [9:14:15<12:00:10, 4.00s/it] 42%|████▏ | 7899/18688 [9:14:18<11:40:05, 3.89s/it] 42%|████▏ | 7900/18688 [9:14:22<11:47:26, 3.93s/it] {'loss': '0.9192', 'grad_norm': '1.324', 'learning_rate': '5.562e-05', 'epoch': '0.4227', 'num_input_tokens_seen': 26533392, 'train_runtime': '3.326e+04', 'train_tokens_per_second': '797.7'}
42%|████▏ | 7900/18688 [9:14:22<11:47:26, 3.93s/it] 42%|████▏ | 7901/18688 [9:14:26<11:30:34, 3.84s/it] 42%|████▏ | 7902/18688 [9:14:31<12:20:00, 4.12s/it] 42%|████▏ | 7903/18688 [9:14:34<11:45:12, 3.92s/it] 42%|████▏ | 7904/18688 [9:14:38<11:25:51, 3.82s/it] 42%|████▏ | 7905/18688 [9:14:41<11:12:58, 3.74s/it] 42%|████▏ | 7906/18688 [9:14:46<11:41:12, 3.90s/it] 42%|████▏ | 7907/18688 [9:14:51<12:47:58, 4.27s/it] 42%|████▏ | 7908/18688 [9:14:55<12:41:52, 4.24s/it] 42%|████▏ | 7909/18688 [9:14:59<12:33:00, 4.19s/it] 42%|████▏ | 7910/18688 [9:15:03<12:04:22, 4.03s/it] {'loss': '0.9597', 'grad_norm': '1.351', 'learning_rate': '5.555e-05', 'epoch': '0.4233', 'num_input_tokens_seen': 26565960, 'train_runtime': '3.33e+04', 'train_tokens_per_second': '797.7'}
42%|████▏ | 7910/18688 [9:15:03<12:04:22, 4.03s/it] 42%|████▏ | 7911/18688 [9:15:08<12:48:57, 4.28s/it] 42%|████▏ | 7912/18688 [9:15:12<12:58:09, 4.33s/it] 42%|████▏ | 7913/18688 [9:15:16<12:43:18, 4.25s/it] 42%|████▏ | 7914/18688 [9:15:20<12:38:37, 4.22s/it] 42%|████▏ | 7915/18688 [9:15:25<12:37:51, 4.22s/it] 42%|████▏ | 7916/18688 [9:15:28<12:11:16, 4.07s/it] 42%|████▏ | 7917/18688 [9:15:32<11:36:52, 3.88s/it] 42%|████▏ | 7918/18688 [9:15:36<11:32:31, 3.86s/it] 42%|████▏ | 7919/18688 [9:15:39<11:22:39, 3.80s/it] 42%|████▏ | 7920/18688 [9:15:45<12:49:14, 4.29s/it] {'loss': '0.8593', 'grad_norm': '1.039', 'learning_rate': '5.549e-05', 'epoch': '0.4238', 'num_input_tokens_seen': 26599632, 'train_runtime': '3.335e+04', 'train_tokens_per_second': '797.7'}
42%|████▏ | 7920/18688 [9:15:45<12:49:14, 4.29s/it] 42%|████▏ | 7921/18688 [9:15:49<12:35:05, 4.21s/it] 42%|████▏ | 7922/18688 [9:15:53<12:43:47, 4.26s/it] 42%|████▏ | 7923/18688 [9:15:57<12:15:01, 4.10s/it] 42%|████▏ | 7924/18688 [9:16:01<12:16:43, 4.11s/it] 42%|████▏ | 7925/18688 [9:16:05<12:07:15, 4.05s/it] 42%|████▏ | 7926/18688 [9:16:08<11:38:48, 3.90s/it] 42%|████▏ | 7927/18688 [9:16:12<11:32:42, 3.86s/it] 42%|████▏ | 7928/18688 [9:16:16<11:40:37, 3.91s/it] 42%|████▏ | 7929/18688 [9:16:20<11:59:14, 4.01s/it] 42%|████▏ | 7930/18688 [9:16:25<12:20:25, 4.13s/it] {'loss': '0.9481', 'grad_norm': '1.045', 'learning_rate': '5.542e-05', 'epoch': '0.4243', 'num_input_tokens_seen': 26630608, 'train_runtime': '3.339e+04', 'train_tokens_per_second': '797.7'}
42%|████▏ | 7930/18688 [9:16:25<12:20:25, 4.13s/it] 42%|████▏ | 7931/18688 [9:16:28<11:45:09, 3.93s/it] 42%|████▏ | 7932/18688 [9:16:32<11:27:56, 3.84s/it] 42%|████▏ | 7933/18688 [9:16:36<11:59:09, 4.01s/it] 42%|████▏ | 7934/18688 [9:16:42<13:06:11, 4.39s/it] 42%|████▏ | 7935/18688 [9:16:46<12:50:20, 4.30s/it] 42%|████▏ | 7936/18688 [9:16:49<12:24:01, 4.15s/it] 42%|████▏ | 7937/18688 [9:16:54<12:37:56, 4.23s/it] 42%|████▏ | 7938/18688 [9:16:57<12:02:50, 4.03s/it] 42%|████▏ | 7939/18688 [9:17:01<12:02:27, 4.03s/it] 42%|████▏ | 7940/18688 [9:17:05<11:41:38, 3.92s/it] {'loss': '0.8815', 'grad_norm': '1.493', 'learning_rate': '5.535e-05', 'epoch': '0.4249', 'num_input_tokens_seen': 26662440, 'train_runtime': '3.343e+04', 'train_tokens_per_second': '797.7'}
42%|████▏ | 7940/18688 [9:17:05<11:41:38, 3.92s/it] 42%|████▏ | 7941/18688 [9:17:09<12:07:31, 4.06s/it] 42%|████▏ | 7942/18688 [9:17:14<12:15:19, 4.11s/it] 43%|████▎ | 7943/18688 [9:17:17<11:58:11, 4.01s/it] 43%|████▎ | 7944/18688 [9:17:23<13:02:20, 4.37s/it] 43%|████▎ | 7945/18688 [9:17:26<12:24:45, 4.16s/it] 43%|████▎ | 7946/18688 [9:17:31<12:40:29, 4.25s/it] 43%|████▎ | 7947/18688 [9:17:35<12:30:19, 4.19s/it] 43%|████▎ | 7948/18688 [9:17:39<12:15:50, 4.11s/it] 43%|████▎ | 7949/18688 [9:17:42<11:52:32, 3.98s/it] 43%|████▎ | 7950/18688 [9:17:48<13:13:42, 4.43s/it] {'loss': '0.896', 'grad_norm': '1.101', 'learning_rate': '5.528e-05', 'epoch': '0.4254', 'num_input_tokens_seen': 26698152, 'train_runtime': '3.347e+04', 'train_tokens_per_second': '797.7'}
43%|████▎ | 7950/18688 [9:17:48<13:13:42, 4.43s/it] 43%|████▎ | 7951/18688 [9:17:52<13:09:03, 4.41s/it] 43%|████▎ | 7952/18688 [9:17:57<12:59:51, 4.36s/it] 43%|████▎ | 7953/18688 [9:18:00<12:25:13, 4.17s/it] 43%|████▎ | 7954/18688 [9:18:04<11:56:26, 4.00s/it] 43%|████▎ | 7955/18688 [9:18:08<11:48:32, 3.96s/it] 43%|████▎ | 7956/18688 [9:18:12<11:47:03, 3.95s/it] 43%|████▎ | 7957/18688 [9:18:16<11:56:35, 4.01s/it] 43%|████▎ | 7958/18688 [9:18:21<12:53:02, 4.32s/it] 43%|████▎ | 7959/18688 [9:18:24<12:13:32, 4.10s/it] 43%|████▎ | 7960/18688 [9:18:30<13:55:53, 4.68s/it] {'loss': '0.8856', 'grad_norm': '1.262', 'learning_rate': '5.522e-05', 'epoch': '0.426', 'num_input_tokens_seen': 26732688, 'train_runtime': '3.351e+04', 'train_tokens_per_second': '797.7'}
43%|████▎ | 7960/18688 [9:18:30<13:55:53, 4.68s/it] 43%|████▎ | 7961/18688 [9:18:34<13:03:33, 4.38s/it] 43%|████▎ | 7962/18688 [9:18:38<12:16:32, 4.12s/it] 43%|████▎ | 7963/18688 [9:18:42<12:26:00, 4.17s/it] 43%|████▎ | 7964/18688 [9:18:46<12:24:40, 4.17s/it] 43%|████▎ | 7965/18688 [9:18:50<12:17:06, 4.12s/it] 43%|████▎ | 7966/18688 [9:18:54<12:15:48, 4.12s/it] 43%|████▎ | 7967/18688 [9:18:58<11:48:58, 3.97s/it] 43%|████▎ | 7968/18688 [9:19:01<11:23:42, 3.83s/it] 43%|████▎ | 7969/18688 [9:19:06<11:58:30, 4.02s/it] 43%|████▎ | 7970/18688 [9:19:10<11:58:29, 4.02s/it] {'loss': '0.9456', 'grad_norm': '1.312', 'learning_rate': '5.515e-05', 'epoch': '0.4265', 'num_input_tokens_seen': 26762544, 'train_runtime': '3.355e+04', 'train_tokens_per_second': '797.7'}
43%|████▎ | 7970/18688 [9:19:10<11:58:29, 4.02s/it] 43%|████▎ | 7971/18688 [9:19:14<11:52:48, 3.99s/it] 43%|████▎ | 7972/18688 [9:19:17<11:35:18, 3.89s/it] 43%|████▎ | 7973/18688 [9:19:21<11:32:19, 3.88s/it] 43%|████▎ | 7974/18688 [9:19:25<11:41:23, 3.93s/it] 43%|████▎ | 7975/18688 [9:19:29<11:32:44, 3.88s/it] 43%|████▎ | 7976/18688 [9:19:34<12:03:48, 4.05s/it] 43%|████▎ | 7977/18688 [9:19:38<12:14:19, 4.11s/it] 43%|████▎ | 7978/18688 [9:19:43<13:13:51, 4.45s/it] 43%|████▎ | 7979/18688 [9:19:48<13:24:27, 4.51s/it] 43%|████▎ | 7980/18688 [9:19:52<12:55:00, 4.34s/it] {'loss': '0.951', 'grad_norm': '1.31', 'learning_rate': '5.508e-05', 'epoch': '0.427', 'num_input_tokens_seen': 26796120, 'train_runtime': '3.359e+04', 'train_tokens_per_second': '797.7'}
43%|████▎ | 7980/18688 [9:19:52<12:55:00, 4.34s/it] 43%|████▎ | 7981/18688 [9:19:57<13:32:12, 4.55s/it] 43%|████▎ | 7982/18688 [9:20:00<12:44:08, 4.28s/it] 43%|████▎ | 7983/18688 [9:20:05<13:05:44, 4.40s/it] 43%|████▎ | 7984/18688 [9:20:09<12:34:24, 4.23s/it] 43%|████▎ | 7985/18688 [9:20:14<13:04:16, 4.40s/it] 43%|████▎ | 7986/18688 [9:20:18<12:51:07, 4.32s/it] 43%|████▎ | 7987/18688 [9:20:21<12:07:27, 4.08s/it] 43%|████▎ | 7988/18688 [9:20:25<11:57:09, 4.02s/it] 43%|████▎ | 7989/18688 [9:20:29<12:03:35, 4.06s/it] 43%|████▎ | 7990/18688 [9:20:33<11:43:57, 3.95s/it] {'loss': '0.9192', 'grad_norm': '1.419', 'learning_rate': '5.501e-05', 'epoch': '0.4276', 'num_input_tokens_seen': 26828872, 'train_runtime': '3.363e+04', 'train_tokens_per_second': '797.7'}
43%|████▎ | 7990/18688 [9:20:33<11:43:57, 3.95s/it] 43%|████▎ | 7991/18688 [9:20:37<11:55:33, 4.01s/it] 43%|████▎ | 7992/18688 [9:20:42<12:56:21, 4.36s/it] 43%|████▎ | 7993/18688 [9:20:46<12:26:31, 4.19s/it] 43%|████▎ | 7994/18688 [9:20:50<12:25:18, 4.18s/it] 43%|████▎ | 7995/18688 [9:20:54<11:51:53, 3.99s/it] 43%|████▎ | 7996/18688 [9:20:58<11:53:40, 4.00s/it] 43%|████▎ | 7997/18688 [9:21:03<12:30:34, 4.21s/it] 43%|████▎ | 7998/18688 [9:21:07<12:23:56, 4.18s/it] 43%|████▎ | 7999/18688 [9:21:11<12:05:21, 4.07s/it] 43%|████▎ | 8000/18688 [9:21:14<11:33:46, 3.89s/it] {'loss': '0.9426', 'grad_norm': '1.15', 'learning_rate': '5.495e-05', 'epoch': '0.4281', 'num_input_tokens_seen': 26861912, 'train_runtime': '3.367e+04', 'train_tokens_per_second': '797.7'}
43%|████▎ | 8000/18688 [9:21:14<11:33:46, 3.89s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
0%| | 3/1000 [00:00<00:38, 25.95it/s]
1%| | 6/1000 [00:00<00:54, 18.35it/s]
1%| | 8/1000 [00:00<00:56, 17.61it/s]
1%| | 10/1000 [00:00<01:01, 16.12it/s]
1%| | 12/1000 [00:00<01:01, 15.97it/s]
1%|▏ | 14/1000 [00:00<01:03, 15.61it/s]
2%|▏ | 16/1000 [00:00<01:03, 15.49it/s]
2%|▏ | 18/1000 [00:01<01:38, 9.94it/s]
2%|▏ | 20/1000 [00:01<01:33, 10.52it/s]
2%|▏ | 22/1000 [00:01<01:52, 8.68it/s]
2%|▏ | 24/1000 [00:01<01:41, 9.65it/s]
3%|▎ | 26/1000 [00:02<01:46, 9.12it/s]
3%|▎ | 28/1000 [00:02<01:38, 9.89it/s]
3%|▎ | 30/1000 [00:02<01:32, 10.52it/s]
3%|▎ | 32/1000 [00:02<01:38, 9.78it/s]
3%|▎ | 34/1000 [00:02<01:32, 10.47it/s]
4%|▎ | 36/1000 [00:03<01:39, 9.73it/s]
4%|▍ | 38/1000 [00:03<01:32, 10.38it/s]
4%|▍ | 40/1000 [00:03<01:33, 10.26it/s]
4%|▍ | 42/1000 [00:03<01:52, 8.51it/s]
4%|▍ | 44/1000 [00:04<01:41, 9.41it/s]
5%|▍ | 46/1000 [00:04<01:33, 10.21it/s]
5%|▍ | 48/1000 [00:04<01:39, 9.59it/s]
5%|▌ | 50/1000 [00:04<01:31, 10.40it/s]
5%|▌ | 52/1000 [00:04<01:25, 11.03it/s]
5%|▌ | 54/1000 [00:04<01:20, 11.73it/s]
6%|▌ | 56/1000 [00:05<01:14, 12.71it/s]
6%|▌ | 58/1000 [00:05<01:52, 8.38it/s]
6%|▌ | 60/1000 [00:05<01:39, 9.44it/s]
6%|▌ | 62/1000 [00:05<01:29, 10.44it/s]
6%|▋ | 64/1000 [00:05<01:21, 11.55it/s]
7%|▋ | 66/1000 [00:06<01:17, 12.12it/s]
7%|▋ | 68/1000 [00:06<01:13, 12.75it/s]
7%|▋ | 70/1000 [00:06<01:09, 13.37it/s]
7%|▋ | 72/1000 [00:06<01:07, 13.82it/s]
7%|▋ | 74/1000 [00:06<01:07, 13.62it/s]
8%|▊ | 76/1000 [00:06<01:04, 14.23it/s]
8%|▊ | 78/1000 [00:06<01:03, 14.45it/s]
8%|▊ | 80/1000 [00:07<01:08, 13.40it/s]
8%|▊ | 82/1000 [00:07<01:05, 13.95it/s]
8%|▊ | 84/1000 [00:07<01:03, 14.43it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.59it/s]
9%|▉ | 88/1000 [00:07<01:03, 14.44it/s]
9%|▉ | 90/1000 [00:07<01:03, 14.44it/s]
9%|▉ | 92/1000 [00:07<01:04, 14.10it/s]
9%|▉ | 94/1000 [00:07<01:04, 13.95it/s]
10%|▉ | 96/1000 [00:08<01:03, 14.27it/s]
10%|▉ | 98/1000 [00:08<01:03, 14.20it/s]
10%|█ | 100/1000 [00:08<01:31, 9.80it/s]
10%|█ | 102/1000 [00:08<01:23, 10.76it/s]
10%|█ | 104/1000 [00:09<01:54, 7.80it/s]
11%|█ | 106/1000 [00:09<02:02, 7.32it/s]
11%|█ | 108/1000 [00:09<01:43, 8.66it/s]
11%|█ | 110/1000 [00:09<01:28, 10.10it/s]
11%|█ | 112/1000 [00:09<01:18, 11.24it/s]
11%|█▏ | 114/1000 [00:10<01:14, 11.89it/s]
12%|█▏ | 116/1000 [00:10<01:10, 12.53it/s]
12%|█▏ | 118/1000 [00:10<01:06, 13.32it/s]
12%|█▏ | 120/1000 [00:10<01:09, 12.75it/s]
12%|█▏ | 122/1000 [00:10<01:22, 10.59it/s]
12%|█▏ | 124/1000 [00:10<01:30, 9.67it/s]
13%|█▎ | 126/1000 [00:11<01:22, 10.62it/s]
13%|█▎ | 128/1000 [00:11<01:15, 11.54it/s]
13%|█▎ | 130/1000 [00:11<01:11, 12.21it/s]
13%|█▎ | 132/1000 [00:11<01:05, 13.20it/s]
13%|█▎ | 134/1000 [00:11<01:09, 12.40it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.32it/s]
14%|█▍ | 138/1000 [00:11<01:05, 13.23it/s]
14%|█▍ | 140/1000 [00:12<01:25, 10.11it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.52it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.54it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.58it/s]
15%|█▍ | 148/1000 [00:13<01:14, 11.49it/s]
15%|█▌ | 150/1000 [00:13<01:07, 12.58it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.14it/s]
15%|█▌ | 154/1000 [00:13<01:03, 13.41it/s]
16%|█▌ | 156/1000 [00:13<01:03, 13.23it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.73it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.61it/s]
16%|█▌ | 162/1000 [00:14<01:02, 13.51it/s]
16%|█▋ | 164/1000 [00:14<01:01, 13.61it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.77it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.27it/s]
17%|█▋ | 170/1000 [00:14<01:19, 10.50it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.47it/s]
17%|█▋ | 174/1000 [00:15<01:30, 9.17it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.18it/s]
18%|█▊ | 178/1000 [00:15<01:34, 8.65it/s]
18%|█▊ | 180/1000 [00:15<01:33, 8.77it/s]
18%|█▊ | 182/1000 [00:16<01:35, 8.53it/s]
18%|█▊ | 184/1000 [00:16<01:23, 9.77it/s]
19%|█▊ | 186/1000 [00:16<01:38, 8.26it/s]
19%|█▉ | 188/1000 [00:16<01:24, 9.59it/s]
19%|█▉ | 190/1000 [00:16<01:16, 10.64it/s]
19%|█▉ | 192/1000 [00:17<01:11, 11.29it/s]
19%|█▉ | 194/1000 [00:17<01:16, 10.52it/s]
20%|█▉ | 196/1000 [00:17<01:29, 8.94it/s]
20%|█▉ | 198/1000 [00:17<01:19, 10.11it/s]
20%|██ | 200/1000 [00:17<01:16, 10.48it/s]
20%|██ | 202/1000 [00:18<01:09, 11.45it/s]
20%|██ | 204/1000 [00:18<01:03, 12.59it/s]
21%|██ | 206/1000 [00:18<01:00, 13.20it/s]
21%|██ | 208/1000 [00:18<00:58, 13.57it/s]
21%|██ | 210/1000 [00:18<00:54, 14.46it/s]
21%|██ | 212/1000 [00:18<00:53, 14.72it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.73it/s]
22%|██▏ | 216/1000 [00:18<00:54, 14.49it/s]
22%|██▏ | 218/1000 [00:19<00:52, 14.86it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.90it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.17it/s]
22%|██▏ | 224/1000 [00:19<01:20, 9.64it/s]
23%|██▎ | 226/1000 [00:20<01:12, 10.65it/s]
23%|██▎ | 228/1000 [00:20<01:06, 11.56it/s]
23%|██▎ | 230/1000 [00:20<01:03, 12.06it/s]
23%|██▎ | 232/1000 [00:20<01:01, 12.42it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.18it/s]
24%|██▎ | 236/1000 [00:20<00:55, 13.65it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.27it/s]
24%|██▍ | 240/1000 [00:21<00:53, 14.24it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.78it/s]
24%|██▍ | 244/1000 [00:21<00:50, 15.00it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.98it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.66it/s]
25%|██▌ | 250/1000 [00:21<00:55, 13.42it/s]
25%|██▌ | 252/1000 [00:21<01:01, 12.14it/s]
25%|██▌ | 254/1000 [00:22<00:56, 13.16it/s]
26%|██▌ | 256/1000 [00:22<00:54, 13.72it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.76it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.91it/s]
26%|██▌ | 262/1000 [00:22<00:56, 13.06it/s]
26%|██▋ | 264/1000 [00:22<00:53, 13.64it/s]
27%|██▋ | 266/1000 [00:22<00:52, 13.94it/s]
27%|██▋ | 268/1000 [00:23<00:50, 14.44it/s]
27%|██▋ | 270/1000 [00:23<00:55, 13.19it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.58it/s]
27%|██▋ | 274/1000 [00:23<01:02, 11.67it/s]
28%|██▊ | 276/1000 [00:23<00:56, 12.70it/s]
28%|██▊ | 278/1000 [00:23<00:55, 12.94it/s]
28%|██▊ | 280/1000 [00:24<00:53, 13.54it/s]
28%|██▊ | 282/1000 [00:24<00:49, 14.51it/s]
28%|██▊ | 284/1000 [00:24<00:49, 14.46it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.72it/s]
29%|██▉ | 288/1000 [00:24<00:47, 15.08it/s]
29%|██▉ | 290/1000 [00:24<00:45, 15.53it/s]
29%|██▉ | 292/1000 [00:24<00:45, 15.48it/s]
29%|██▉ | 294/1000 [00:24<00:45, 15.36it/s]
30%|██▉ | 296/1000 [00:25<00:45, 15.39it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.43it/s]
30%|███ | 300/1000 [00:25<00:46, 15.10it/s]
30%|███ | 302/1000 [00:25<00:46, 15.13it/s]
30%|███ | 304/1000 [00:25<00:50, 13.72it/s]
31%|███ | 306/1000 [00:25<00:47, 14.48it/s]
31%|███ | 308/1000 [00:25<00:47, 14.45it/s]
31%|███ | 310/1000 [00:26<00:47, 14.65it/s]
31%|███ | 312/1000 [00:26<00:47, 14.62it/s]
31%|███▏ | 314/1000 [00:26<00:45, 15.18it/s]
32%|███▏ | 316/1000 [00:26<00:44, 15.47it/s]
32%|███▏ | 318/1000 [00:26<00:42, 15.96it/s]
32%|███▏ | 320/1000 [00:26<01:12, 9.36it/s]
32%|███▏ | 322/1000 [00:27<01:03, 10.71it/s]
32%|███▏ | 324/1000 [00:27<00:58, 11.48it/s]
33%|███▎ | 326/1000 [00:27<01:01, 10.98it/s]
33%|███▎ | 328/1000 [00:27<01:00, 11.02it/s]
33%|███▎ | 330/1000 [00:27<00:57, 11.57it/s]
33%|███▎ | 332/1000 [00:27<00:56, 11.86it/s]
33%|███▎ | 334/1000 [00:28<00:53, 12.51it/s]
34%|███▎ | 336/1000 [00:28<00:50, 13.05it/s]
34%|███▍ | 338/1000 [00:28<01:08, 9.72it/s]
34%|███▍ | 340/1000 [00:28<01:01, 10.81it/s]
34%|███▍ | 342/1000 [00:28<00:57, 11.44it/s]
34%|███▍ | 344/1000 [00:28<00:56, 11.62it/s]
35%|███▍ | 346/1000 [00:29<00:52, 12.47it/s]
35%|███▍ | 348/1000 [00:29<00:49, 13.16it/s]
35%|███▌ | 350/1000 [00:29<00:46, 13.97it/s]
35%|███▌ | 352/1000 [00:29<00:45, 14.33it/s]
35%|███▌ | 354/1000 [00:29<00:44, 14.49it/s]
36%|███▌ | 356/1000 [00:29<00:43, 14.67it/s]
36%|███▌ | 358/1000 [00:29<00:44, 14.48it/s]
36%|███▌ | 360/1000 [00:30<00:42, 14.93it/s]
36%|███▌ | 362/1000 [00:30<00:47, 13.32it/s]
36%|███▋ | 364/1000 [00:30<00:45, 13.85it/s]
37%|███▋ | 366/1000 [00:30<00:53, 11.94it/s]
37%|███▋ | 368/1000 [00:30<00:51, 12.35it/s]
37%|███▋ | 370/1000 [00:30<00:47, 13.21it/s]
37%|███▋ | 372/1000 [00:30<00:46, 13.54it/s]
37%|███▋ | 374/1000 [00:31<00:54, 11.40it/s]
38%|███▊ | 376/1000 [00:31<00:56, 10.98it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.66it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.48it/s]
38%|███▊ | 382/1000 [00:31<00:48, 12.66it/s]
38%|███▊ | 384/1000 [00:31<00:45, 13.41it/s]
39%|███▊ | 386/1000 [00:32<00:44, 13.72it/s]
39%|███▉ | 388/1000 [00:32<00:44, 13.66it/s]
39%|███▉ | 390/1000 [00:32<00:45, 13.55it/s]
39%|███▉ | 392/1000 [00:32<00:43, 13.88it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.67it/s]
40%|███▉ | 396/1000 [00:32<00:43, 13.79it/s]
40%|███▉ | 398/1000 [00:33<00:47, 12.56it/s]
40%|████ | 400/1000 [00:33<00:45, 13.20it/s]
40%|████ | 402/1000 [00:33<00:43, 13.83it/s]
40%|████ | 404/1000 [00:33<00:41, 14.43it/s]
41%|████ | 406/1000 [00:33<00:39, 14.87it/s]
41%|████ | 408/1000 [00:33<00:47, 12.44it/s]
41%|████ | 410/1000 [00:34<00:56, 10.42it/s]
41%|████ | 412/1000 [00:34<00:51, 11.53it/s]
41%|████▏ | 414/1000 [00:34<01:07, 8.63it/s]
42%|████▏ | 416/1000 [00:34<01:23, 6.96it/s]
42%|████▏ | 418/1000 [00:35<01:10, 8.29it/s]
42%|████▏ | 420/1000 [00:35<01:00, 9.54it/s]
42%|████▏ | 422/1000 [00:35<00:53, 10.88it/s]
42%|████▏ | 424/1000 [00:35<01:14, 7.68it/s]
43%|████▎ | 426/1000 [00:35<01:04, 8.94it/s]
43%|████▎ | 428/1000 [00:36<00:56, 10.14it/s]
43%|████▎ | 430/1000 [00:36<01:03, 9.03it/s]
43%|████▎ | 432/1000 [00:36<01:22, 6.91it/s]
43%|████▎ | 434/1000 [00:36<01:11, 7.94it/s]
44%|████▎ | 436/1000 [00:37<01:03, 8.88it/s]
44%|████▍ | 438/1000 [00:37<01:04, 8.65it/s]
44%|████▍ | 440/1000 [00:37<01:01, 9.15it/s]
44%|████▍ | 442/1000 [00:37<00:56, 9.95it/s]
44%|████▍ | 444/1000 [00:37<00:52, 10.53it/s]
45%|████▍ | 446/1000 [00:38<00:50, 11.02it/s]
45%|████▍ | 448/1000 [00:38<00:54, 10.14it/s]
45%|████▌ | 450/1000 [00:38<00:51, 10.76it/s]
45%|████▌ | 452/1000 [00:38<00:47, 11.61it/s]
45%|████▌ | 454/1000 [00:38<00:47, 11.46it/s]
46%|████▌ | 456/1000 [00:38<00:45, 12.06it/s]
46%|████▌ | 458/1000 [00:39<00:41, 13.16it/s]
46%|████▌ | 460/1000 [00:39<00:40, 13.45it/s]
46%|████▌ | 462/1000 [00:39<00:38, 14.11it/s]
46%|████▋ | 464/1000 [00:39<00:36, 14.69it/s]
47%|████▋ | 466/1000 [00:39<00:36, 14.62it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.79it/s]
47%|████▋ | 470/1000 [00:40<00:52, 10.02it/s]
47%|████▋ | 472/1000 [00:40<00:51, 10.19it/s]
47%|████▋ | 474/1000 [00:40<00:45, 11.47it/s]
48%|████▊ | 476/1000 [00:40<00:43, 12.14it/s]
48%|████▊ | 478/1000 [00:40<00:43, 12.01it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.94it/s]
48%|████▊ | 482/1000 [00:40<00:38, 13.34it/s]
48%|████▊ | 484/1000 [00:41<00:37, 13.87it/s]
49%|████▊ | 486/1000 [00:41<00:36, 14.04it/s]
49%|████▉ | 488/1000 [00:41<00:35, 14.30it/s]
49%|████▉ | 490/1000 [00:41<00:46, 11.09it/s]
49%|████▉ | 492/1000 [00:41<00:41, 12.38it/s]
49%|████▉ | 494/1000 [00:41<00:37, 13.36it/s]
50%|████▉ | 496/1000 [00:41<00:35, 14.12it/s]
50%|████▉ | 498/1000 [00:42<00:34, 14.55it/s]
50%|█████ | 500/1000 [00:42<00:43, 11.58it/s]
50%|█████ | 502/1000 [00:42<00:41, 12.02it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.62it/s]
51%|█████ | 506/1000 [00:42<00:38, 12.81it/s]
51%|█████ | 508/1000 [00:42<00:35, 13.74it/s]
51%|█████ | 510/1000 [00:43<00:47, 10.41it/s]
51%|█████ | 512/1000 [00:43<00:43, 11.18it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 12.04it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.58it/s]
52%|█████▏ | 518/1000 [00:43<00:36, 13.09it/s]
52%|█████▏ | 520/1000 [00:43<00:35, 13.47it/s]
52%|█████▏ | 522/1000 [00:44<00:33, 14.15it/s]
52%|█████▏ | 524/1000 [00:44<00:33, 14.15it/s]
53%|█████▎ | 526/1000 [00:44<00:33, 14.31it/s]
53%|█████▎ | 528/1000 [00:44<00:39, 11.96it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.82it/s]
53%|█████▎ | 532/1000 [00:44<00:33, 13.78it/s]
53%|█████▎ | 534/1000 [00:44<00:32, 14.12it/s]
54%|█████▎ | 536/1000 [00:45<00:32, 14.25it/s]
54%|█████▍ | 538/1000 [00:45<00:32, 14.02it/s]
54%|█████▍ | 540/1000 [00:45<00:31, 14.43it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.71it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.58it/s]
55%|█████▍ | 546/1000 [00:45<00:31, 14.21it/s]
55%|█████▍ | 548/1000 [00:45<00:32, 13.91it/s]
55%|█████▌ | 550/1000 [00:46<00:31, 14.14it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.26it/s]
55%|█████▌ | 554/1000 [00:46<00:32, 13.90it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.00it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.37it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 14.94it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 14.88it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.77it/s]
57%|█████▋ | 566/1000 [00:47<00:37, 11.68it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.22it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.34it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.82it/s]
57%|█████▋ | 574/1000 [00:47<00:31, 13.69it/s]
58%|█████▊ | 576/1000 [00:48<00:31, 13.62it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.65it/s]
58%|█████▊ | 580/1000 [00:48<00:32, 12.89it/s]
58%|█████▊ | 582/1000 [00:48<00:30, 13.61it/s]
58%|█████▊ | 584/1000 [00:48<00:30, 13.84it/s]
59%|█████▊ | 586/1000 [00:48<00:29, 14.22it/s]
59%|█████▉ | 588/1000 [00:48<00:27, 15.05it/s]
59%|█████▉ | 590/1000 [00:49<00:27, 15.05it/s]
59%|█████▉ | 592/1000 [00:49<00:27, 14.78it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.30it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.74it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 13.96it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.36it/s]
60%|██████ | 602/1000 [00:49<00:27, 14.52it/s]
60%|██████ | 604/1000 [00:50<00:26, 15.09it/s]
61%|██████ | 606/1000 [00:50<00:26, 14.83it/s]
61%|██████ | 608/1000 [00:50<00:25, 15.16it/s]
61%|██████ | 610/1000 [00:50<00:26, 14.99it/s]
61%|██████ | 612/1000 [00:50<00:25, 15.16it/s]
61%|██████▏ | 614/1000 [00:50<00:26, 14.41it/s]
62%|██████▏ | 616/1000 [00:50<00:25, 14.80it/s]
62%|██████▏ | 618/1000 [00:50<00:26, 14.21it/s]
62%|██████▏ | 620/1000 [00:51<00:38, 9.75it/s]
62%|██████▏ | 622/1000 [00:51<00:37, 9.96it/s]
62%|██████▏ | 624/1000 [00:51<00:32, 11.40it/s]
63%|██████▎ | 626/1000 [00:51<00:30, 12.07it/s]
63%|██████▎ | 628/1000 [00:51<00:28, 13.26it/s]
63%|██████▎ | 630/1000 [00:52<00:26, 13.78it/s]
63%|██████▎ | 632/1000 [00:52<00:29, 12.45it/s]
63%|██████▎ | 634/1000 [00:52<00:27, 13.18it/s]
64%|██████▎ | 636/1000 [00:52<00:34, 10.44it/s]
64%|██████▍ | 638/1000 [00:52<00:35, 10.13it/s]
64%|██████▍ | 640/1000 [00:53<00:34, 10.42it/s]
64%|██████▍ | 642/1000 [00:53<00:31, 11.49it/s]
64%|██████▍ | 644/1000 [00:53<00:29, 11.92it/s]
65%|██████▍ | 646/1000 [00:53<00:28, 12.33it/s]
65%|██████▍ | 648/1000 [00:53<00:27, 12.85it/s]
65%|██████▌ | 650/1000 [00:53<00:28, 12.23it/s]
65%|██████▌ | 652/1000 [00:53<00:30, 11.46it/s]
65%|██████▌ | 654/1000 [00:54<00:37, 9.18it/s]
66%|██████▌ | 656/1000 [00:54<00:32, 10.56it/s]
66%|██████▌ | 658/1000 [00:54<00:29, 11.64it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.01it/s]
66%|██████▌ | 662/1000 [00:54<00:30, 11.15it/s]
66%|██████▋ | 664/1000 [00:55<00:28, 11.66it/s]
67%|██████▋ | 666/1000 [00:55<00:26, 12.69it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.30it/s]
67%|██████▋ | 670/1000 [00:55<00:29, 11.09it/s]
67%|██████▋ | 672/1000 [00:55<00:27, 11.86it/s]
67%|██████▋ | 674/1000 [00:55<00:26, 12.44it/s]
68%|██████▊ | 676/1000 [00:56<00:27, 11.71it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.16it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 12.95it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.10it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.30it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.32it/s]
69%|██████▉ | 688/1000 [00:57<00:22, 13.63it/s]
69%|██████▉ | 690/1000 [00:57<00:22, 13.71it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.19it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.30it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 14.06it/s]
70%|██████▉ | 698/1000 [00:57<00:20, 14.56it/s]
70%|███████ | 700/1000 [00:57<00:24, 12.16it/s]
70%|███████ | 702/1000 [00:58<00:22, 12.98it/s]
70%|███████ | 704/1000 [00:58<00:30, 9.85it/s]
71%|███████ | 706/1000 [00:58<00:27, 10.66it/s]
71%|███████ | 708/1000 [00:58<00:30, 9.62it/s]
71%|███████ | 710/1000 [00:58<00:26, 10.92it/s]
71%|███████ | 712/1000 [00:59<00:24, 11.88it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.97it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 13.06it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.55it/s]
72%|███████▏ | 720/1000 [00:59<00:20, 13.50it/s]
72%|███████▏ | 722/1000 [00:59<00:19, 13.91it/s]
72%|███████▏ | 724/1000 [00:59<00:20, 13.72it/s]
73%|███████▎ | 726/1000 [01:00<00:19, 13.93it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.87it/s]
73%|███████▎ | 730/1000 [01:00<00:32, 8.18it/s]
73%|███████▎ | 732/1000 [01:00<00:32, 8.31it/s]
73%|███████▎ | 734/1000 [01:01<00:30, 8.59it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.43it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.89it/s]
74%|███████▍ | 739/1000 [01:01<00:23, 11.00it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.68it/s]
74%|███████▍ | 743/1000 [01:01<00:20, 12.30it/s]
74%|███████▍ | 745/1000 [01:02<00:25, 10.17it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.55it/s]
75%|███████▍ | 749/1000 [01:02<00:24, 10.24it/s]
75%|███████▌ | 751/1000 [01:02<00:22, 10.90it/s]
75%|███████▌ | 753/1000 [01:02<00:21, 11.38it/s]
76%|███████▌ | 755/1000 [01:02<00:20, 11.68it/s]
76%|███████▌ | 757/1000 [01:03<00:26, 9.29it/s]
76%|███████▌ | 759/1000 [01:03<00:23, 10.04it/s]
76%|███████▌ | 761/1000 [01:03<00:22, 10.69it/s]
76%|███████▋ | 763/1000 [01:03<00:25, 9.25it/s]
76%|███████▋ | 765/1000 [01:04<00:29, 8.07it/s]
77%|███████▋ | 767/1000 [01:04<00:25, 9.00it/s]
77%|███████▋ | 769/1000 [01:04<00:23, 9.80it/s]
77%|███████▋ | 771/1000 [01:04<00:20, 11.04it/s]
77%|███████▋ | 773/1000 [01:04<00:18, 11.98it/s]
78%|███████▊ | 775/1000 [01:04<00:17, 12.67it/s]
78%|███████▊ | 777/1000 [01:05<00:16, 13.74it/s]
78%|███████▊ | 779/1000 [01:05<00:21, 10.33it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.29it/s]
78%|███████▊ | 783/1000 [01:05<00:26, 8.24it/s]
78%|███████▊ | 784/1000 [01:06<00:30, 7.02it/s]
78%|███████▊ | 785/1000 [01:06<00:29, 7.34it/s]
79%|███████▊ | 786/1000 [01:06<00:30, 6.96it/s]
79%|███████▉ | 788/1000 [01:06<00:24, 8.80it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.12it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 11.25it/s]
79%|███████▉ | 794/1000 [01:07<00:17, 11.87it/s]
80%|███████▉ | 796/1000 [01:07<00:16, 12.33it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 13.08it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.29it/s]
80%|████████ | 802/1000 [01:07<00:24, 8.24it/s]
80%|████████ | 804/1000 [01:08<00:23, 8.29it/s]
81%|████████ | 806/1000 [01:08<00:21, 8.97it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.35it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.48it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.71it/s]
81%|████████▏ | 814/1000 [01:08<00:14, 12.61it/s]
82%|████████▏ | 816/1000 [01:09<00:16, 11.02it/s]
82%|████████▏ | 818/1000 [01:09<00:15, 11.98it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.47it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 13.02it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.78it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.21it/s]
83%|████████▎ | 828/1000 [01:09<00:11, 14.48it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.92it/s]
83%|████████▎ | 832/1000 [01:10<00:11, 14.31it/s]
83%|████████▎ | 834/1000 [01:10<00:11, 14.04it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.42it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.29it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.33it/s]
84%|████████▍ | 842/1000 [01:10<00:11, 14.35it/s]
84%|████████▍ | 844/1000 [01:11<00:11, 13.92it/s]
85%|████████▍ | 846/1000 [01:11<00:11, 13.89it/s]
85%|████████▍ | 848/1000 [01:11<00:10, 13.87it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.09it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.69it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.20it/s]
86%|████████▌ | 856/1000 [01:11<00:10, 14.07it/s]
86%|████████▌ | 858/1000 [01:12<00:09, 14.56it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 15.31it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.48it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 14.01it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.84it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.73it/s]
87%|████████▋ | 870/1000 [01:12<00:09, 14.13it/s]
87%|████████▋ | 872/1000 [01:13<00:08, 14.76it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 12.47it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.07it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.93it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.56it/s]
88%|████████▊ | 882/1000 [01:13<00:10, 11.37it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 12.12it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.91it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.88it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.30it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.07it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 14.21it/s]
90%|████████▉ | 896/1000 [01:14<00:07, 13.96it/s]
90%|████████▉ | 898/1000 [01:15<00:07, 13.34it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.26it/s]
90%|█████████ | 902/1000 [01:15<00:07, 13.00it/s]
90%|█████████ | 904/1000 [01:15<00:09, 9.82it/s]
91%|█████████ | 906/1000 [01:15<00:08, 10.86it/s]
91%|█████████ | 908/1000 [01:16<00:07, 11.81it/s]
91%|█████████ | 910/1000 [01:16<00:07, 12.04it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.24it/s]
91%|█████████▏| 914/1000 [01:16<00:12, 6.79it/s]
92%|█████████▏| 916/1000 [01:17<00:10, 8.11it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.38it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.79it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 10.24it/s]
92%|█████████▏| 924/1000 [01:17<00:07, 10.74it/s]
93%|█████████▎| 926/1000 [01:17<00:06, 11.62it/s]
93%|█████████▎| 928/1000 [01:18<00:06, 10.54it/s]
93%|█████████▎| 930/1000 [01:18<00:05, 11.77it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 12.39it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.88it/s]
94%|█████████▎| 936/1000 [01:18<00:04, 13.46it/s]
94%|█████████▍| 938/1000 [01:18<00:04, 14.32it/s]
94%|█████████▍| 940/1000 [01:18<00:04, 14.63it/s]
94%|█████████▍| 942/1000 [01:19<00:04, 12.30it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.82it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.87it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 12.71it/s]
95%|█████████▌| 950/1000 [01:19<00:03, 13.48it/s]
95%|█████████▌| 952/1000 [01:19<00:03, 14.15it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 14.26it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 14.23it/s]
96%|█████████▌| 958/1000 [01:20<00:02, 14.47it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 12.57it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 12.99it/s]
96%|█████████▋| 964/1000 [01:21<00:03, 9.62it/s]
97%|█████████▋| 966/1000 [01:21<00:03, 10.86it/s]
97%|█████████▋| 968/1000 [01:21<00:02, 11.44it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 11.81it/s]
97%|█████████▋| 972/1000 [01:21<00:03, 9.31it/s]
97%|█████████▋| 974/1000 [01:21<00:02, 10.27it/s]
98%|█████████▊| 976/1000 [01:22<00:02, 11.16it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.63it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.79it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.32it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 10.35it/s]
99%|█████████▊| 986/1000 [01:23<00:01, 10.61it/s]
99%|█████████▉| 988/1000 [01:23<00:01, 10.86it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 11.35it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 12.26it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 13.01it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 13.93it/s]
100%|█████████▉| 998/1000 [01:23<00:00, 14.09it/s]
100%|██████████| 1000/1000 [01:24<00:00, 14.75it/s]
{'eval_loss': '0.9121', 'eval_runtime': '84.11', 'eval_samples_per_second': '11.89', 'eval_steps_per_second': '11.89', 'epoch': '0.4281', 'num_input_tokens_seen': 26861912}
43%|████▎ | 8000/18688 [9:22:38<11:33:46, 3.89s/it]
100%|██████████| 1000/1000 [01:24<00:00, 14.75it/s]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00008/checkpoint-8000
loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
43%|████▎ | 8001/18688 [9:22:44<88:13:55, 29.72s/it] 43%|████▎ | 8002/18688 [9:22:48<65:30:14, 22.07s/it] 43%|████▎ | 8003/18688 [9:22:52<49:32:46, 16.69s/it] 43%|████▎ | 8004/18688 [9:22:56<38:17:04, 12.90s/it] 43%|████▎ | 8005/18688 [9:23:01<30:35:34, 10.31s/it] 43%|████▎ | 8006/18688 [9:23:05<25:17:17, 8.52s/it] 43%|████▎ | 8007/18688 [9:23:09<20:49:11, 7.02s/it] 43%|████▎ | 8008/18688 [9:23:13<18:12:59, 6.14s/it] 43%|████▎ | 8009/18688 [9:23:16<16:07:21, 5.44s/it] 43%|████▎ | 8010/18688 [9:23:21<15:48:07, 5.33s/it] {'loss': '0.8983', 'grad_norm': '1.035', 'learning_rate': '5.488e-05', 'epoch': '0.4286', 'num_input_tokens_seen': 26896032, 'train_runtime': '3.38e+04', 'train_tokens_per_second': '795.7'}
43%|████▎ | 8010/18688 [9:23:21<15:48:07, 5.33s/it] 43%|████▎ | 8011/18688 [9:23:26<15:06:15, 5.09s/it] 43%|████▎ | 8012/18688 [9:23:30<14:28:19, 4.88s/it] 43%|████▎ | 8013/18688 [9:23:35<14:23:50, 4.86s/it] 43%|████▎ | 8014/18688 [9:23:40<14:45:25, 4.98s/it] 43%|████▎ | 8015/18688 [9:23:44<13:46:34, 4.65s/it] 43%|████▎ | 8016/18688 [9:23:49<13:24:52, 4.53s/it] 43%|████▎ | 8017/18688 [9:23:53<13:05:08, 4.41s/it] 43%|████▎ | 8018/18688 [9:23:56<12:22:17, 4.17s/it] 43%|████▎ | 8019/18688 [9:24:01<13:05:57, 4.42s/it] 43%|████▎ | 8020/18688 [9:24:05<12:30:58, 4.22s/it] {'loss': '0.9465', 'grad_norm': '1.364', 'learning_rate': '5.481e-05', 'epoch': '0.4292', 'num_input_tokens_seen': 26933320, 'train_runtime': '3.385e+04', 'train_tokens_per_second': '795.8'}
43%|████▎ | 8020/18688 [9:24:05<12:30:58, 4.22s/it] 43%|████▎ | 8021/18688 [9:24:09<12:03:37, 4.07s/it] 43%|████▎ | 8022/18688 [9:24:13<12:19:29, 4.16s/it] 43%|████▎ | 8023/18688 [9:24:18<12:31:26, 4.23s/it] 43%|████▎ | 8024/18688 [9:24:22<12:48:41, 4.32s/it] 43%|████▎ | 8025/18688 [9:24:27<13:21:01, 4.51s/it] 43%|████▎ | 8026/18688 [9:24:32<14:00:02, 4.73s/it] 43%|████▎ | 8027/18688 [9:24:36<13:23:48, 4.52s/it] 43%|████▎ | 8028/18688 [9:24:40<12:41:42, 4.29s/it] 43%|████▎ | 8029/18688 [9:24:44<12:23:19, 4.18s/it] 43%|████▎ | 8030/18688 [9:24:48<12:36:16, 4.26s/it] {'loss': '1.041', 'grad_norm': '1.122', 'learning_rate': '5.474e-05', 'epoch': '0.4297', 'num_input_tokens_seen': 26969464, 'train_runtime': '3.389e+04', 'train_tokens_per_second': '795.8'}
43%|████▎ | 8030/18688 [9:24:48<12:36:16, 4.26s/it] 43%|████▎ | 8031/18688 [9:24:53<13:16:18, 4.48s/it] 43%|████▎ | 8032/18688 [9:24:57<12:32:57, 4.24s/it] 43%|████▎ | 8033/18688 [9:25:01<12:28:36, 4.22s/it] 43%|████▎ | 8034/18688 [9:25:06<12:43:41, 4.30s/it] 43%|████▎ | 8035/18688 [9:25:10<12:36:28, 4.26s/it] 43%|████▎ | 8036/18688 [9:25:14<12:31:40, 4.23s/it] 43%|████▎ | 8037/18688 [9:25:18<11:54:18, 4.02s/it] 43%|████▎ | 8038/18688 [9:25:23<12:57:21, 4.38s/it] 43%|████▎ | 8039/18688 [9:25:27<12:57:39, 4.38s/it] 43%|████▎ | 8040/18688 [9:25:31<12:37:36, 4.27s/it] {'loss': '0.9162', 'grad_norm': '1.44', 'learning_rate': '5.467e-05', 'epoch': '0.4302', 'num_input_tokens_seen': 27005056, 'train_runtime': '3.393e+04', 'train_tokens_per_second': '795.9'}
43%|████▎ | 8040/18688 [9:25:31<12:37:36, 4.27s/it] 43%|████▎ | 8041/18688 [9:25:35<12:14:21, 4.14s/it] 43%|████▎ | 8042/18688 [9:25:39<11:42:25, 3.96s/it] 43%|████▎ | 8043/18688 [9:25:43<11:40:47, 3.95s/it] 43%|████▎ | 8044/18688 [9:25:47<11:55:13, 4.03s/it] 43%|████▎ | 8045/18688 [9:25:50<11:30:58, 3.90s/it] 43%|████▎ | 8046/18688 [9:25:54<11:20:33, 3.84s/it] 43%|████▎ | 8047/18688 [9:25:59<12:11:57, 4.13s/it] 43%|████▎ | 8048/18688 [9:26:03<11:51:11, 4.01s/it] 43%|████▎ | 8049/18688 [9:26:06<11:25:21, 3.87s/it] 43%|████▎ | 8050/18688 [9:26:11<12:16:23, 4.15s/it] {'loss': '0.9127', 'grad_norm': '1.069', 'learning_rate': '5.461e-05', 'epoch': '0.4308', 'num_input_tokens_seen': 27036192, 'train_runtime': '3.397e+04', 'train_tokens_per_second': '795.8'}
43%|████▎ | 8050/18688 [9:26:11<12:16:23, 4.15s/it] 43%|████▎ | 8051/18688 [9:26:15<11:59:26, 4.06s/it] 43%|████▎ | 8052/18688 [9:26:19<11:57:01, 4.04s/it] 43%|████▎ | 8053/18688 [9:26:23<11:55:08, 4.03s/it] 43%|████▎ | 8054/18688 [9:26:27<11:59:48, 4.06s/it] 43%|████▎ | 8055/18688 [9:26:31<11:44:20, 3.97s/it] 43%|████▎ | 8056/18688 [9:26:36<12:42:33, 4.30s/it] 43%|████▎ | 8057/18688 [9:26:40<12:32:13, 4.25s/it] 43%|████▎ | 8058/18688 [9:26:44<12:30:33, 4.24s/it] 43%|████▎ | 8059/18688 [9:26:48<12:02:07, 4.08s/it] 43%|████▎ | 8060/18688 [9:26:52<12:03:11, 4.08s/it] {'loss': '0.8813', 'grad_norm': '1.127', 'learning_rate': '5.454e-05', 'epoch': '0.4313', 'num_input_tokens_seen': 27069792, 'train_runtime': '3.401e+04', 'train_tokens_per_second': '795.9'}
43%|████▎ | 8060/18688 [9:26:52<12:03:11, 4.08s/it] 43%|████▎ | 8061/18688 [9:26:56<12:23:57, 4.20s/it] 43%|████▎ | 8062/18688 [9:27:01<12:24:24, 4.20s/it] 43%|████▎ | 8063/18688 [9:27:04<11:53:32, 4.03s/it] 43%|████▎ | 8064/18688 [9:27:09<12:07:00, 4.11s/it] 43%|████▎ | 8065/18688 [9:27:12<11:57:33, 4.05s/it] 43%|████▎ | 8066/18688 [9:27:17<11:57:02, 4.05s/it] 43%|████▎ | 8067/18688 [9:27:21<11:59:41, 4.07s/it] 43%|████▎ | 8068/18688 [9:27:26<12:54:06, 4.37s/it] 43%|████▎ | 8069/18688 [9:27:29<12:13:44, 4.15s/it] 43%|████▎ | 8070/18688 [9:27:33<12:13:45, 4.15s/it] {'loss': '0.8889', 'grad_norm': '1.101', 'learning_rate': '5.447e-05', 'epoch': '0.4318', 'num_input_tokens_seen': 27102208, 'train_runtime': '3.405e+04', 'train_tokens_per_second': '795.9'}
43%|████▎ | 8070/18688 [9:27:33<12:13:45, 4.15s/it] 43%|████▎ | 8071/18688 [9:27:38<12:25:02, 4.21s/it] 43%|████▎ | 8072/18688 [9:27:42<12:09:54, 4.13s/it] 43%|████▎ | 8073/18688 [9:27:46<12:34:11, 4.26s/it] 43%|████▎ | 8074/18688 [9:27:50<12:13:35, 4.15s/it] 43%|████▎ | 8075/18688 [9:27:54<12:06:29, 4.11s/it] 43%|████▎ | 8076/18688 [9:27:58<11:50:05, 4.01s/it] 43%|████▎ | 8077/18688 [9:28:02<11:39:01, 3.95s/it] 43%|████▎ | 8078/18688 [9:28:06<11:40:12, 3.96s/it] 43%|████▎ | 8079/18688 [9:28:10<12:04:21, 4.10s/it] 43%|████▎ | 8080/18688 [9:28:15<12:27:23, 4.23s/it] {'loss': '0.947', 'grad_norm': '1.21', 'learning_rate': '5.44e-05', 'epoch': '0.4324', 'num_input_tokens_seen': 27135992, 'train_runtime': '3.41e+04', 'train_tokens_per_second': '795.9'}
43%|████▎ | 8080/18688 [9:28:15<12:27:23, 4.23s/it] 43%|████▎ | 8081/18688 [9:28:18<11:47:42, 4.00s/it] 43%|████▎ | 8082/18688 [9:28:22<11:28:44, 3.90s/it] 43%|████▎ | 8083/18688 [9:28:26<11:43:24, 3.98s/it] 43%|████▎ | 8084/18688 [9:28:30<12:03:31, 4.09s/it] 43%|████▎ | 8085/18688 [9:28:36<13:08:21, 4.46s/it] 43%|████▎ | 8086/18688 [9:28:40<12:52:27, 4.37s/it] 43%|████▎ | 8087/18688 [9:28:44<12:41:48, 4.31s/it] 43%|████▎ | 8088/18688 [9:28:48<12:22:41, 4.20s/it] 43%|████▎ | 8089/18688 [9:28:52<12:05:08, 4.10s/it] 43%|████▎ | 8090/18688 [9:28:56<12:00:58, 4.08s/it] {'loss': '0.9444', 'grad_norm': '1.187', 'learning_rate': '5.433e-05', 'epoch': '0.4329', 'num_input_tokens_seen': 27168608, 'train_runtime': '3.414e+04', 'train_tokens_per_second': '795.9'}
43%|████▎ | 8090/18688 [9:28:56<12:00:58, 4.08s/it] 43%|████▎ | 8091/18688 [9:29:00<12:04:36, 4.10s/it] 43%|████▎ | 8092/18688 [9:29:04<12:03:23, 4.10s/it] 43%|████▎ | 8093/18688 [9:29:09<12:19:27, 4.19s/it] 43%|████▎ | 8094/18688 [9:29:12<11:49:23, 4.02s/it] 43%|████▎ | 8095/18688 [9:29:16<11:43:28, 3.98s/it] 43%|████▎ | 8096/18688 [9:29:20<12:01:10, 4.09s/it] 43%|████▎ | 8097/18688 [9:29:24<11:28:49, 3.90s/it] 43%|████▎ | 8098/18688 [9:29:27<11:09:02, 3.79s/it] 43%|████▎ | 8099/18688 [9:29:31<11:08:08, 3.79s/it] 43%|████▎ | 8100/18688 [9:29:36<11:37:39, 3.95s/it] {'loss': '0.9257', 'grad_norm': '1.087', 'learning_rate': '5.426e-05', 'epoch': '0.4334', 'num_input_tokens_seen': 27198336, 'train_runtime': '3.418e+04', 'train_tokens_per_second': '795.8'}
43%|████▎ | 8100/18688 [9:29:36<11:37:39, 3.95s/it] 43%|████▎ | 8101/18688 [9:29:39<11:34:08, 3.93s/it] 43%|████▎ | 8102/18688 [9:29:44<12:03:22, 4.10s/it] 43%|████▎ | 8103/18688 [9:29:48<11:59:08, 4.08s/it] 43%|████▎ | 8104/18688 [9:29:52<11:52:27, 4.04s/it] 43%|████▎ | 8105/18688 [9:29:56<11:39:09, 3.96s/it] 43%|████▎ | 8106/18688 [9:30:00<12:17:29, 4.18s/it] 43%|████▎ | 8107/18688 [9:30:05<12:39:35, 4.31s/it] 43%|████▎ | 8108/18688 [9:30:09<12:13:22, 4.16s/it] 43%|████▎ | 8109/18688 [9:30:13<12:06:55, 4.12s/it] 43%|████▎ | 8110/18688 [9:30:18<12:51:06, 4.37s/it] {'loss': '0.9603', 'grad_norm': '1.135', 'learning_rate': '5.42e-05', 'epoch': '0.434', 'num_input_tokens_seen': 27231152, 'train_runtime': '3.422e+04', 'train_tokens_per_second': '795.8'}
43%|████▎ | 8110/18688 [9:30:18<12:51:06, 4.37s/it] 43%|████▎ | 8111/18688 [9:30:23<13:35:23, 4.63s/it] 43%|████▎ | 8112/18688 [9:30:27<13:06:25, 4.46s/it] 43%|████▎ | 8113/18688 [9:30:31<12:59:33, 4.42s/it] 43%|████▎ | 8114/18688 [9:30:36<12:53:50, 4.39s/it] 43%|████▎ | 8115/18688 [9:30:40<12:56:11, 4.40s/it] 43%|████▎ | 8116/18688 [9:30:45<13:03:35, 4.45s/it] 43%|████▎ | 8117/18688 [9:30:49<12:47:27, 4.36s/it] 43%|████▎ | 8118/18688 [9:30:52<12:03:26, 4.11s/it] 43%|████▎ | 8119/18688 [9:30:56<11:34:38, 3.94s/it] 43%|████▎ | 8120/18688 [9:31:00<12:00:10, 4.09s/it] {'loss': '0.9675', 'grad_norm': '1.056', 'learning_rate': '5.413e-05', 'epoch': '0.4345', 'num_input_tokens_seen': 27265400, 'train_runtime': '3.426e+04', 'train_tokens_per_second': '795.8'}
43%|████▎ | 8120/18688 [9:31:00<12:00:10, 4.09s/it] 43%|████▎ | 8121/18688 [9:31:05<12:28:12, 4.25s/it] 43%|████▎ | 8122/18688 [9:31:09<12:21:14, 4.21s/it] 43%|████▎ | 8123/18688 [9:31:14<12:48:21, 4.36s/it] 43%|████▎ | 8124/18688 [9:31:19<13:07:04, 4.47s/it] 43%|████▎ | 8125/18688 [9:31:22<12:19:46, 4.20s/it] 43%|████▎ | 8126/18688 [9:31:27<13:19:28, 4.54s/it] 43%|████▎ | 8127/18688 [9:31:31<12:26:29, 4.24s/it] 43%|████▎ | 8128/18688 [9:31:35<11:48:15, 4.02s/it] 43%|████▎ | 8129/18688 [9:31:39<11:57:28, 4.08s/it] 44%|████▎ | 8130/18688 [9:31:43<11:54:24, 4.06s/it] {'loss': '0.9781', 'grad_norm': '1.286', 'learning_rate': '5.406e-05', 'epoch': '0.4351', 'num_input_tokens_seen': 27299592, 'train_runtime': '3.43e+04', 'train_tokens_per_second': '795.8'}
44%|████▎ | 8130/18688 [9:31:43<11:54:24, 4.06s/it] 44%|████▎ | 8131/18688 [9:31:48<12:55:34, 4.41s/it] 44%|████▎ | 8132/18688 [9:31:51<12:10:31, 4.15s/it] 44%|████▎ | 8133/18688 [9:31:56<12:25:44, 4.24s/it] 44%|████▎ | 8134/18688 [9:32:00<12:27:28, 4.25s/it] 44%|████▎ | 8135/18688 [9:32:05<12:47:54, 4.37s/it] 44%|████▎ | 8136/18688 [9:32:10<13:05:22, 4.47s/it] 44%|████▎ | 8137/18688 [9:32:14<13:08:43, 4.49s/it] 44%|████▎ | 8138/18688 [9:32:18<12:20:03, 4.21s/it] 44%|████▎ | 8139/18688 [9:32:21<11:41:17, 3.99s/it] 44%|████▎ | 8140/18688 [9:32:25<11:51:31, 4.05s/it] {'loss': '0.8612', 'grad_norm': '1.279', 'learning_rate': '5.399e-05', 'epoch': '0.4356', 'num_input_tokens_seen': 27336512, 'train_runtime': '3.435e+04', 'train_tokens_per_second': '795.9'}
44%|████▎ | 8140/18688 [9:32:25<11:51:31, 4.05s/it] 44%|████▎ | 8141/18688 [9:32:29<11:42:58, 4.00s/it] 44%|████▎ | 8142/18688 [9:32:33<11:15:37, 3.84s/it] 44%|████▎ | 8143/18688 [9:32:37<11:28:27, 3.92s/it] 44%|████▎ | 8144/18688 [9:32:40<11:08:47, 3.81s/it] 44%|████▎ | 8145/18688 [9:32:44<10:53:18, 3.72s/it] 44%|████▎ | 8146/18688 [9:32:48<11:15:47, 3.85s/it] 44%|████▎ | 8147/18688 [9:32:53<12:02:32, 4.11s/it] 44%|████▎ | 8148/18688 [9:32:58<12:50:35, 4.39s/it] 44%|████▎ | 8149/18688 [9:33:02<12:18:24, 4.20s/it] 44%|████▎ | 8150/18688 [9:33:06<12:20:42, 4.22s/it] {'loss': '0.9743', 'grad_norm': '1.793', 'learning_rate': '5.392e-05', 'epoch': '0.4361', 'num_input_tokens_seen': 27368344, 'train_runtime': '3.439e+04', 'train_tokens_per_second': '795.9'}
44%|████▎ | 8150/18688 [9:33:06<12:20:42, 4.22s/it] 44%|████▎ | 8151/18688 [9:33:10<12:08:17, 4.15s/it] 44%|████▎ | 8152/18688 [9:33:14<12:20:57, 4.22s/it] 44%|████▎ | 8153/18688 [9:33:19<13:14:06, 4.52s/it] 44%|████▎ | 8154/18688 [9:33:24<13:07:35, 4.49s/it] 44%|████▎ | 8155/18688 [9:33:27<12:27:57, 4.26s/it] 44%|████▎ | 8156/18688 [9:33:32<12:16:01, 4.19s/it] 44%|████▎ | 8157/18688 [9:33:37<13:23:47, 4.58s/it] 44%|████▎ | 8158/18688 [9:33:41<12:29:00, 4.27s/it] 44%|████▎ | 8159/18688 [9:33:45<12:27:13, 4.26s/it] 44%|████▎ | 8160/18688 [9:33:49<12:09:24, 4.16s/it] {'loss': '0.9378', 'grad_norm': '1.405', 'learning_rate': '5.385e-05', 'epoch': '0.4367', 'num_input_tokens_seen': 27403936, 'train_runtime': '3.443e+04', 'train_tokens_per_second': '796'}
44%|████▎ | 8160/18688 [9:33:49<12:09:24, 4.16s/it] 44%|████▎ | 8161/18688 [9:33:53<12:23:46, 4.24s/it] 44%|████▎ | 8162/18688 [9:33:57<12:14:49, 4.19s/it] 44%|████▎ | 8163/18688 [9:34:01<12:19:05, 4.21s/it] 44%|████▎ | 8164/18688 [9:34:06<12:15:11, 4.19s/it] 44%|████▎ | 8165/18688 [9:34:11<13:06:22, 4.48s/it] 44%|████▎ | 8166/18688 [9:34:14<12:14:40, 4.19s/it] 44%|████▎ | 8167/18688 [9:34:18<12:09:15, 4.16s/it] 44%|████▎ | 8168/18688 [9:34:22<11:38:39, 3.98s/it] 44%|████▎ | 8169/18688 [9:34:26<11:29:52, 3.94s/it] 44%|████▎ | 8170/18688 [9:34:30<11:37:07, 3.98s/it] {'loss': '0.885', 'grad_norm': '1.101', 'learning_rate': '5.379e-05', 'epoch': '0.4372', 'num_input_tokens_seen': 27436720, 'train_runtime': '3.447e+04', 'train_tokens_per_second': '796'}
44%|████▎ | 8170/18688 [9:34:30<11:37:07, 3.98s/it] 44%|████▎ | 8171/18688 [9:34:34<11:40:57, 4.00s/it] 44%|████▎ | 8172/18688 [9:34:38<11:55:54, 4.08s/it] 44%|████▎ | 8173/18688 [9:34:43<12:22:07, 4.23s/it] 44%|████▎ | 8174/18688 [9:34:47<12:34:25, 4.31s/it] 44%|████▎ | 8175/18688 [9:34:52<13:03:37, 4.47s/it] 44%|████▍ | 8176/18688 [9:34:56<12:48:06, 4.38s/it] 44%|████▍ | 8177/18688 [9:35:00<12:38:22, 4.33s/it] 44%|████▍ | 8178/18688 [9:35:04<12:14:41, 4.19s/it] 44%|████▍ | 8179/18688 [9:35:09<12:20:47, 4.23s/it] 44%|████▍ | 8180/18688 [9:35:12<11:58:53, 4.10s/it] {'loss': '0.9981', 'grad_norm': '1.434', 'learning_rate': '5.372e-05', 'epoch': '0.4377', 'num_input_tokens_seen': 27471312, 'train_runtime': '3.451e+04', 'train_tokens_per_second': '796'}
44%|████▍ | 8180/18688 [9:35:12<11:58:53, 4.10s/it] 44%|████▍ | 8181/18688 [9:35:16<11:37:35, 3.98s/it] 44%|████▍ | 8182/18688 [9:35:21<12:23:11, 4.24s/it] 44%|████▍ | 8183/18688 [9:35:25<11:49:27, 4.05s/it] 44%|████▍ | 8184/18688 [9:35:28<11:30:16, 3.94s/it] 44%|████▍ | 8185/18688 [9:35:32<11:35:15, 3.97s/it] 44%|████▍ | 8186/18688 [9:35:36<11:36:30, 3.98s/it] 44%|████▍ | 8187/18688 [9:35:41<12:16:55, 4.21s/it] 44%|████▍ | 8188/18688 [9:35:45<11:58:52, 4.11s/it] 44%|████▍ | 8189/18688 [9:35:50<12:33:13, 4.30s/it] 44%|████▍ | 8190/18688 [9:35:54<12:11:00, 4.18s/it] {'loss': '0.9548', 'grad_norm': '1.382', 'learning_rate': '5.365e-05', 'epoch': '0.4383', 'num_input_tokens_seen': 27503976, 'train_runtime': '3.455e+04', 'train_tokens_per_second': '796'}
44%|████▍ | 8190/18688 [9:35:54<12:11:00, 4.18s/it] 44%|████▍ | 8191/18688 [9:35:58<12:28:50, 4.28s/it] 44%|████▍ | 8192/18688 [9:36:02<12:08:34, 4.16s/it] 44%|████▍ | 8193/18688 [9:36:07<12:40:32, 4.35s/it] 44%|████▍ | 8194/18688 [9:36:11<12:16:48, 4.21s/it] 44%|████▍ | 8195/18688 [9:36:15<12:30:11, 4.29s/it] 44%|████▍ | 8196/18688 [9:36:19<11:50:43, 4.06s/it] 44%|████▍ | 8197/18688 [9:36:23<11:55:38, 4.09s/it] 44%|████▍ | 8198/18688 [9:36:26<11:28:29, 3.94s/it] 44%|████▍ | 8199/18688 [9:36:31<11:56:02, 4.10s/it] 44%|████▍ | 8200/18688 [9:36:35<12:04:03, 4.14s/it] {'loss': '0.8979', 'grad_norm': '1.299', 'learning_rate': '5.358e-05', 'epoch': '0.4388', 'num_input_tokens_seen': 27537296, 'train_runtime': '3.46e+04', 'train_tokens_per_second': '796'}
44%|████▍ | 8200/18688 [9:36:35<12:04:03, 4.14s/it] 44%|████▍ | 8201/18688 [9:36:39<11:28:28, 3.94s/it] 44%|████▍ | 8202/18688 [9:36:42<11:15:43, 3.87s/it] 44%|████▍ | 8203/18688 [9:36:46<11:17:46, 3.88s/it] 44%|████▍ | 8204/18688 [9:36:50<11:03:43, 3.80s/it] 44%|████▍ | 8205/18688 [9:36:54<11:11:01, 3.84s/it] 44%|████▍ | 8206/18688 [9:36:59<12:24:15, 4.26s/it] 44%|████▍ | 8207/18688 [9:37:04<12:47:29, 4.39s/it] 44%|████▍ | 8208/18688 [9:37:08<12:15:37, 4.21s/it] 44%|████▍ | 8209/18688 [9:37:11<11:36:54, 3.99s/it] 44%|████▍ | 8210/18688 [9:37:16<12:16:50, 4.22s/it] {'loss': '0.9162', 'grad_norm': '1.054', 'learning_rate': '5.351e-05', 'epoch': '0.4393', 'num_input_tokens_seen': 27568400, 'train_runtime': '3.464e+04', 'train_tokens_per_second': '795.9'}
44%|████▍ | 8210/18688 [9:37:16<12:16:50, 4.22s/it] 44%|████▍ | 8211/18688 [9:37:21<13:14:31, 4.55s/it] 44%|████▍ | 8212/18688 [9:37:25<12:19:10, 4.23s/it] 44%|████▍ | 8213/18688 [9:37:28<11:59:11, 4.12s/it] 44%|████▍ | 8214/18688 [9:37:32<11:23:50, 3.92s/it] 44%|████▍ | 8215/18688 [9:37:37<12:12:42, 4.20s/it] 44%|████▍ | 8216/18688 [9:37:42<13:19:28, 4.58s/it] 44%|████▍ | 8217/18688 [9:37:47<13:51:31, 4.76s/it] 44%|████▍ | 8218/18688 [9:37:51<12:53:35, 4.43s/it] 44%|████▍ | 8219/18688 [9:37:55<12:13:47, 4.21s/it] 44%|████▍ | 8220/18688 [9:37:59<12:17:00, 4.22s/it] {'loss': '0.9481', 'grad_norm': '1.395', 'learning_rate': '5.344e-05', 'epoch': '0.4399', 'num_input_tokens_seen': 27604760, 'train_runtime': '3.468e+04', 'train_tokens_per_second': '796'}
44%|████▍ | 8220/18688 [9:37:59<12:17:00, 4.22s/it] 44%|████▍ | 8221/18688 [9:38:03<12:15:07, 4.21s/it] 44%|████▍ | 8222/18688 [9:38:07<12:01:18, 4.14s/it] 44%|████▍ | 8223/18688 [9:38:11<11:48:21, 4.06s/it] 44%|████▍ | 8224/18688 [9:38:16<12:43:30, 4.38s/it] 44%|████▍ | 8225/18688 [9:38:21<13:25:40, 4.62s/it] 44%|████▍ | 8226/18688 [9:38:27<14:01:37, 4.83s/it] 44%|████▍ | 8227/18688 [9:38:30<13:01:38, 4.48s/it] 44%|████▍ | 8228/18688 [9:38:34<12:39:23, 4.36s/it] 44%|████▍ | 8229/18688 [9:38:38<12:23:52, 4.27s/it] 44%|████▍ | 8230/18688 [9:38:43<12:47:36, 4.40s/it] {'loss': '0.8643', 'grad_norm': '1.06', 'learning_rate': '5.337e-05', 'epoch': '0.4404', 'num_input_tokens_seen': 27640848, 'train_runtime': '3.472e+04', 'train_tokens_per_second': '796'}
44%|████▍ | 8230/18688 [9:38:43<12:47:36, 4.40s/it] 44%|████▍ | 8231/18688 [9:38:47<12:24:02, 4.27s/it] 44%|████▍ | 8232/18688 [9:38:51<11:54:42, 4.10s/it] 44%|████▍ | 8233/18688 [9:38:55<11:47:29, 4.06s/it] 44%|████▍ | 8234/18688 [9:38:59<11:53:19, 4.09s/it] 44%|████▍ | 8235/18688 [9:39:04<12:41:12, 4.37s/it] 44%|████▍ | 8236/18688 [9:39:08<12:45:12, 4.39s/it] 44%|████▍ | 8237/18688 [9:39:13<13:05:10, 4.51s/it] 44%|████▍ | 8238/18688 [9:39:17<12:49:50, 4.42s/it] 44%|████▍ | 8239/18688 [9:39:22<12:39:03, 4.36s/it] 44%|████▍ | 8240/18688 [9:39:25<11:58:27, 4.13s/it] {'loss': '0.903', 'grad_norm': '1.258', 'learning_rate': '5.33e-05', 'epoch': '0.4409', 'num_input_tokens_seen': 27674376, 'train_runtime': '3.477e+04', 'train_tokens_per_second': '796'}
44%|████▍ | 8240/18688 [9:39:25<11:58:27, 4.13s/it] 44%|████▍ | 8241/18688 [9:39:29<11:26:23, 3.94s/it] 44%|████▍ | 8242/18688 [9:39:33<11:28:53, 3.96s/it] 44%|████▍ | 8243/18688 [9:39:36<11:05:56, 3.83s/it] 44%|████▍ | 8244/18688 [9:39:40<11:19:56, 3.91s/it] 44%|████▍ | 8245/18688 [9:39:44<10:56:44, 3.77s/it] 44%|████▍ | 8246/18688 [9:39:49<12:28:35, 4.30s/it] 44%|████▍ | 8247/18688 [9:39:53<12:02:45, 4.15s/it] 44%|████▍ | 8248/18688 [9:39:57<12:01:15, 4.15s/it] 44%|████▍ | 8249/18688 [9:40:01<12:01:49, 4.15s/it] 44%|████▍ | 8250/18688 [9:40:06<12:01:41, 4.15s/it] {'loss': '0.9072', 'grad_norm': '1.121', 'learning_rate': '5.323e-05', 'epoch': '0.4415', 'num_input_tokens_seen': 27706056, 'train_runtime': '3.481e+04', 'train_tokens_per_second': '796'}
44%|████▍ | 8250/18688 [9:40:06<12:01:41, 4.15s/it] 44%|████▍ | 8251/18688 [9:40:09<11:38:52, 4.02s/it] 44%|████▍ | 8252/18688 [9:40:13<11:12:02, 3.86s/it] 44%|████▍ | 8253/18688 [9:40:18<12:17:25, 4.24s/it] 44%|████▍ | 8254/18688 [9:40:23<12:46:29, 4.41s/it] 44%|████▍ | 8255/18688 [9:40:27<12:31:41, 4.32s/it] 44%|████▍ | 8256/18688 [9:40:31<12:03:52, 4.16s/it] 44%|████▍ | 8257/18688 [9:40:35<12:06:37, 4.18s/it] 44%|████▍ | 8258/18688 [9:40:39<12:06:12, 4.18s/it] 44%|████▍ | 8259/18688 [9:40:43<11:51:44, 4.09s/it] 44%|████▍ | 8260/18688 [9:40:47<11:38:27, 4.02s/it] {'loss': '0.8511', 'grad_norm': '1.299', 'learning_rate': '5.317e-05', 'epoch': '0.442', 'num_input_tokens_seen': 27738160, 'train_runtime': '3.485e+04', 'train_tokens_per_second': '796'}
44%|████▍ | 8260/18688 [9:40:47<11:38:27, 4.02s/it] 44%|████▍ | 8261/18688 [9:40:51<12:15:49, 4.23s/it] 44%|████▍ | 8262/18688 [9:40:55<11:52:26, 4.10s/it] 44%|████▍ | 8263/18688 [9:40:59<11:22:10, 3.93s/it] 44%|████▍ | 8264/18688 [9:41:03<11:58:30, 4.14s/it] 44%|████▍ | 8265/18688 [9:41:07<11:28:35, 3.96s/it] 44%|████▍ | 8266/18688 [9:41:10<11:02:38, 3.81s/it] 44%|████▍ | 8267/18688 [9:41:14<11:10:31, 3.86s/it] 44%|████▍ | 8268/18688 [9:41:19<11:42:56, 4.05s/it] 44%|████▍ | 8269/18688 [9:41:23<11:37:41, 4.02s/it] 44%|████▍ | 8270/18688 [9:41:27<11:24:43, 3.94s/it] {'loss': '0.9458', 'grad_norm': '1.499', 'learning_rate': '5.31e-05', 'epoch': '0.4425', 'num_input_tokens_seen': 27769024, 'train_runtime': '3.489e+04', 'train_tokens_per_second': '796'}
44%|████▍ | 8270/18688 [9:41:27<11:24:43, 3.94s/it] 44%|████▍ | 8271/18688 [9:41:30<11:00:10, 3.80s/it] 44%|████▍ | 8272/18688 [9:41:34<10:46:11, 3.72s/it] 44%|████▍ | 8273/18688 [9:41:37<10:47:53, 3.73s/it] 44%|████▍ | 8274/18688 [9:41:42<11:20:31, 3.92s/it] 44%|████▍ | 8275/18688 [9:41:46<11:58:52, 4.14s/it] 44%|████▍ | 8276/18688 [9:41:50<11:45:03, 4.06s/it] 44%|████▍ | 8277/18688 [9:41:55<12:36:17, 4.36s/it] 44%|████▍ | 8278/18688 [9:41:59<12:17:33, 4.25s/it] 44%|████▍ | 8279/18688 [9:42:04<12:40:05, 4.38s/it] 44%|████▍ | 8280/18688 [9:42:08<11:58:23, 4.14s/it] {'loss': '0.8965', 'grad_norm': '1.205', 'learning_rate': '5.303e-05', 'epoch': '0.4431', 'num_input_tokens_seen': 27801960, 'train_runtime': '3.493e+04', 'train_tokens_per_second': '796'}
44%|████▍ | 8280/18688 [9:42:08<11:58:23, 4.14s/it] 44%|████▍ | 8281/18688 [9:42:13<12:42:29, 4.40s/it] 44%|████▍ | 8282/18688 [9:42:17<12:22:06, 4.28s/it] 44%|████▍ | 8283/18688 [9:42:20<12:01:30, 4.16s/it] 44%|████▍ | 8284/18688 [9:42:24<11:52:46, 4.11s/it] 44%|████▍ | 8285/18688 [9:42:28<11:46:00, 4.07s/it] 44%|████▍ | 8286/18688 [9:42:32<11:33:56, 4.00s/it] 44%|████▍ | 8287/18688 [9:42:36<11:14:53, 3.89s/it] 44%|████▍ | 8288/18688 [9:42:40<11:22:17, 3.94s/it] 44%|████▍ | 8289/18688 [9:42:44<11:45:56, 4.07s/it] 44%|████▍ | 8290/18688 [9:42:48<11:20:14, 3.93s/it] {'loss': '0.9832', 'grad_norm': '1.406', 'learning_rate': '5.296e-05', 'epoch': '0.4436', 'num_input_tokens_seen': 27833440, 'train_runtime': '3.497e+04', 'train_tokens_per_second': '796'}
44%|████▍ | 8290/18688 [9:42:48<11:20:14, 3.93s/it] 44%|████▍ | 8291/18688 [9:42:51<11:01:42, 3.82s/it] 44%|████▍ | 8292/18688 [9:42:56<11:29:32, 3.98s/it] 44%|████▍ | 8293/18688 [9:43:00<11:50:49, 4.10s/it] 44%|████▍ | 8294/18688 [9:43:05<12:43:48, 4.41s/it] 44%|████▍ | 8295/18688 [9:43:09<12:12:38, 4.23s/it] 44%|████▍ | 8296/18688 [9:43:13<12:06:43, 4.20s/it] 44%|████▍ | 8297/18688 [9:43:18<12:23:42, 4.29s/it] 44%|████▍ | 8298/18688 [9:43:21<11:47:17, 4.08s/it] 44%|████▍ | 8299/18688 [9:43:26<12:05:56, 4.19s/it] 44%|████▍ | 8300/18688 [9:43:30<12:04:34, 4.19s/it] {'loss': '0.9212', 'grad_norm': '1.269', 'learning_rate': '5.289e-05', 'epoch': '0.4441', 'num_input_tokens_seen': 27868144, 'train_runtime': '3.501e+04', 'train_tokens_per_second': '796'}
44%|████▍ | 8300/18688 [9:43:30<12:04:34, 4.19s/it] 44%|████▍ | 8301/18688 [9:43:34<11:44:59, 4.07s/it] 44%|████▍ | 8302/18688 [9:43:38<11:41:43, 4.05s/it] 44%|████▍ | 8303/18688 [9:43:42<12:08:55, 4.21s/it] 44%|████▍ | 8304/18688 [9:43:47<12:20:34, 4.28s/it] 44%|████▍ | 8305/18688 [9:43:51<12:01:36, 4.17s/it] 44%|████▍ | 8306/18688 [9:43:54<11:31:08, 3.99s/it] 44%|████▍ | 8307/18688 [9:43:58<11:15:07, 3.90s/it] 44%|████▍ | 8308/18688 [9:44:02<11:25:48, 3.96s/it] 44%|████▍ | 8309/18688 [9:44:06<11:08:09, 3.86s/it] 44%|████▍ | 8310/18688 [9:44:09<10:57:26, 3.80s/it] {'loss': '0.8904', 'grad_norm': '1.16', 'learning_rate': '5.282e-05', 'epoch': '0.4447', 'num_input_tokens_seen': 27898600, 'train_runtime': '3.505e+04', 'train_tokens_per_second': '796'}
44%|████▍ | 8310/18688 [9:44:09<10:57:26, 3.80s/it] 44%|████▍ | 8311/18688 [9:44:13<10:47:11, 3.74s/it] 44%|████▍ | 8312/18688 [9:44:18<11:39:39, 4.05s/it] 44%|████▍ | 8313/18688 [9:44:22<11:23:30, 3.95s/it] 44%|████▍ | 8314/18688 [9:44:25<11:14:10, 3.90s/it] 44%|████▍ | 8315/18688 [9:44:29<11:20:43, 3.94s/it] 44%|████▍ | 8316/18688 [9:44:34<11:32:55, 4.01s/it] 45%|████▍ | 8317/18688 [9:44:38<12:04:52, 4.19s/it] 45%|████▍ | 8318/18688 [9:44:43<13:02:06, 4.53s/it] 45%|████▍ | 8319/18688 [9:44:48<12:55:31, 4.49s/it] 45%|████▍ | 8320/18688 [9:44:52<12:39:44, 4.40s/it] {'loss': '0.9708', 'grad_norm': '1.177', 'learning_rate': '5.275e-05', 'epoch': '0.4452', 'num_input_tokens_seen': 27933416, 'train_runtime': '3.509e+04', 'train_tokens_per_second': '796'}
45%|████▍ | 8320/18688 [9:44:52<12:39:44, 4.40s/it] 45%|████▍ | 8321/18688 [9:44:56<12:38:59, 4.39s/it] 45%|████▍ | 8322/18688 [9:45:01<12:35:37, 4.37s/it] 45%|████▍ | 8323/18688 [9:45:06<13:12:43, 4.59s/it] 45%|████▍ | 8324/18688 [9:45:10<13:09:07, 4.57s/it] 45%|████▍ | 8325/18688 [9:45:14<12:25:45, 4.32s/it] 45%|████▍ | 8326/18688 [9:45:19<13:02:21, 4.53s/it] 45%|████▍ | 8327/18688 [9:45:23<12:44:33, 4.43s/it] 45%|████▍ | 8328/18688 [9:45:28<12:38:44, 4.39s/it] 45%|████▍ | 8329/18688 [9:45:31<12:00:00, 4.17s/it] 45%|████▍ | 8330/18688 [9:45:35<12:03:39, 4.19s/it] {'loss': '0.9937', 'grad_norm': '1.242', 'learning_rate': '5.268e-05', 'epoch': '0.4458', 'num_input_tokens_seen': 27969768, 'train_runtime': '3.514e+04', 'train_tokens_per_second': '796'}
45%|████▍ | 8330/18688 [9:45:35<12:03:39, 4.19s/it] 45%|████▍ | 8331/18688 [9:45:40<11:55:22, 4.14s/it] 45%|████▍ | 8332/18688 [9:45:44<12:32:26, 4.36s/it] 45%|████▍ | 8333/18688 [9:45:50<13:16:23, 4.61s/it] 45%|████▍ | 8334/18688 [9:45:55<13:58:22, 4.86s/it] 45%|████▍ | 8335/18688 [9:45:59<13:05:14, 4.55s/it] 45%|████▍ | 8336/18688 [9:46:02<12:09:48, 4.23s/it] 45%|████▍ | 8337/18688 [9:46:06<11:32:05, 4.01s/it] 45%|████▍ | 8338/18688 [9:46:10<11:38:30, 4.05s/it] 45%|████▍ | 8339/18688 [9:46:16<13:01:59, 4.53s/it] 45%|████▍ | 8340/18688 [9:46:19<12:18:33, 4.28s/it] {'loss': '0.9417', 'grad_norm': '1.277', 'learning_rate': '5.261e-05', 'epoch': '0.4463', 'num_input_tokens_seen': 28007472, 'train_runtime': '3.518e+04', 'train_tokens_per_second': '796.1'}
45%|████▍ | 8340/18688 [9:46:19<12:18:33, 4.28s/it] 45%|████▍ | 8341/18688 [9:46:23<11:52:32, 4.13s/it] 45%|████▍ | 8342/18688 [9:46:27<11:56:01, 4.15s/it] 45%|████▍ | 8343/18688 [9:46:31<11:48:21, 4.11s/it] 45%|████▍ | 8344/18688 [9:46:35<11:21:54, 3.96s/it] 45%|████▍ | 8345/18688 [9:46:39<11:15:23, 3.92s/it] 45%|████▍ | 8346/18688 [9:46:42<10:55:36, 3.80s/it] 45%|████▍ | 8347/18688 [9:46:47<11:20:42, 3.95s/it] 45%|████▍ | 8348/18688 [9:46:52<12:31:45, 4.36s/it] 45%|████▍ | 8349/18688 [9:46:57<13:02:20, 4.54s/it] 45%|████▍ | 8350/18688 [9:47:01<12:36:08, 4.39s/it] {'loss': '0.92', 'grad_norm': '1.147', 'learning_rate': '5.254e-05', 'epoch': '0.4468', 'num_input_tokens_seen': 28041240, 'train_runtime': '3.522e+04', 'train_tokens_per_second': '796.1'}
45%|████▍ | 8350/18688 [9:47:01<12:36:08, 4.39s/it] 45%|████▍ | 8351/18688 [9:47:06<13:09:43, 4.58s/it] 45%|████▍ | 8352/18688 [9:47:10<12:22:02, 4.31s/it] 45%|████▍ | 8353/18688 [9:47:14<12:03:06, 4.20s/it] 45%|████▍ | 8354/18688 [9:47:18<12:00:25, 4.18s/it] 45%|████▍ | 8355/18688 [9:47:22<11:48:10, 4.11s/it] 45%|████▍ | 8356/18688 [9:47:27<12:28:09, 4.34s/it] 45%|████▍ | 8357/18688 [9:47:30<11:51:26, 4.13s/it] 45%|████▍ | 8358/18688 [9:47:35<12:04:38, 4.21s/it] 45%|████▍ | 8359/18688 [9:47:38<11:34:55, 4.04s/it] 45%|████▍ | 8360/18688 [9:47:43<12:18:55, 4.29s/it] {'loss': '0.9346', 'grad_norm': '1.006', 'learning_rate': '5.247e-05', 'epoch': '0.4474', 'num_input_tokens_seen': 28076200, 'train_runtime': '3.526e+04', 'train_tokens_per_second': '796.2'}
45%|████▍ | 8360/18688 [9:47:43<12:18:55, 4.29s/it] 45%|████▍ | 8361/18688 [9:47:48<12:32:29, 4.37s/it] 45%|████▍ | 8362/18688 [9:47:52<12:25:36, 4.33s/it] 45%|████▍ | 8363/18688 [9:47:56<12:16:02, 4.28s/it] 45%|████▍ | 8364/18688 [9:47:59<11:33:56, 4.03s/it] 45%|████▍ | 8365/18688 [9:48:03<11:07:14, 3.88s/it] 45%|████▍ | 8366/18688 [9:48:08<11:48:47, 4.12s/it] 45%|████▍ | 8367/18688 [9:48:11<11:24:43, 3.98s/it] 45%|████▍ | 8368/18688 [9:48:15<10:59:26, 3.83s/it] 45%|████▍ | 8369/18688 [9:48:20<12:02:21, 4.20s/it] 45%|████▍ | 8370/18688 [9:48:25<12:28:29, 4.35s/it] {'loss': '0.8969', 'grad_norm': '1.21', 'learning_rate': '5.24e-05', 'epoch': '0.4479', 'num_input_tokens_seen': 28110888, 'train_runtime': '3.531e+04', 'train_tokens_per_second': '796.2'}
45%|████▍ | 8370/18688 [9:48:25<12:28:29, 4.35s/it] 45%|████▍ | 8371/18688 [9:48:28<11:50:44, 4.13s/it] 45%|████▍ | 8372/18688 [9:48:32<11:52:04, 4.14s/it] 45%|████▍ | 8373/18688 [9:48:37<12:35:44, 4.40s/it] 45%|████▍ | 8374/18688 [9:48:42<13:01:30, 4.55s/it] 45%|████▍ | 8375/18688 [9:48:47<13:03:04, 4.56s/it] 45%|████▍ | 8376/18688 [9:48:51<12:44:39, 4.45s/it] 45%|████▍ | 8377/18688 [9:48:55<12:20:38, 4.31s/it] 45%|████▍ | 8378/18688 [9:48:59<12:06:47, 4.23s/it] 45%|████▍ | 8379/18688 [9:49:03<12:00:26, 4.19s/it] 45%|████▍ | 8380/18688 [9:49:07<11:45:27, 4.11s/it] {'loss': '0.8815', 'grad_norm': '0.9898', 'learning_rate': '5.233e-05', 'epoch': '0.4484', 'num_input_tokens_seen': 28146016, 'train_runtime': '3.535e+04', 'train_tokens_per_second': '796.3'}
45%|████▍ | 8380/18688 [9:49:07<11:45:27, 4.11s/it] 45%|████▍ | 8381/18688 [9:49:11<11:23:11, 3.98s/it] 45%|████▍ | 8382/18688 [9:49:15<11:37:07, 4.06s/it] 45%|████▍ | 8383/18688 [9:49:20<12:02:21, 4.21s/it] 45%|████▍ | 8384/18688 [9:49:25<13:09:57, 4.60s/it] 45%|████▍ | 8385/18688 [9:49:29<12:48:44, 4.48s/it] 45%|████▍ | 8386/18688 [9:49:34<13:10:56, 4.61s/it] 45%|████▍ | 8387/18688 [9:49:39<13:42:45, 4.79s/it] 45%|████▍ | 8388/18688 [9:49:44<13:20:23, 4.66s/it] 45%|████▍ | 8389/18688 [9:49:47<12:24:49, 4.34s/it] 45%|████▍ | 8390/18688 [9:49:51<11:49:16, 4.13s/it] {'loss': '0.9684', 'grad_norm': '1.327', 'learning_rate': '5.226e-05', 'epoch': '0.449', 'num_input_tokens_seen': 28183992, 'train_runtime': '3.539e+04', 'train_tokens_per_second': '796.3'}
45%|████▍ | 8390/18688 [9:49:51<11:49:16, 4.13s/it] 45%|████▍ | 8391/18688 [9:49:55<11:19:40, 3.96s/it] 45%|████▍ | 8392/18688 [9:49:59<11:31:46, 4.03s/it] 45%|████▍ | 8393/18688 [9:50:02<11:03:07, 3.86s/it] 45%|████▍ | 8394/18688 [9:50:06<10:56:56, 3.83s/it] 45%|████▍ | 8395/18688 [9:50:10<10:41:20, 3.74s/it] 45%|████▍ | 8396/18688 [9:50:13<10:31:36, 3.68s/it] 45%|████▍ | 8397/18688 [9:50:17<11:00:08, 3.85s/it] 45%|████▍ | 8398/18688 [9:50:22<11:22:16, 3.98s/it] 45%|████▍ | 8399/18688 [9:50:25<11:03:30, 3.87s/it] 45%|████▍ | 8400/18688 [9:50:29<11:16:26, 3.95s/it] {'loss': '0.9073', 'grad_norm': '1.531', 'learning_rate': '5.22e-05', 'epoch': '0.4495', 'num_input_tokens_seen': 28213064, 'train_runtime': '3.543e+04', 'train_tokens_per_second': '796.3'}
45%|████▍ | 8400/18688 [9:50:29<11:16:26, 3.95s/it] 45%|████▍ | 8401/18688 [9:50:34<11:54:21, 4.17s/it] 45%|████▍ | 8402/18688 [9:50:39<12:29:19, 4.37s/it] 45%|████▍ | 8403/18688 [9:50:43<12:07:56, 4.25s/it] 45%|████▍ | 8404/18688 [9:50:46<11:35:03, 4.06s/it] 45%|████▍ | 8405/18688 [9:50:51<12:00:22, 4.20s/it] 45%|████▍ | 8406/18688 [9:50:56<12:53:34, 4.51s/it] 45%|████▍ | 8407/18688 [9:51:00<12:08:30, 4.25s/it] 45%|████▍ | 8408/18688 [9:51:04<11:45:06, 4.12s/it] 45%|████▍ | 8409/18688 [9:51:07<11:27:23, 4.01s/it] 45%|████▌ | 8410/18688 [9:51:12<12:15:09, 4.29s/it] {'loss': '0.9988', 'grad_norm': '1.236', 'learning_rate': '5.213e-05', 'epoch': '0.45', 'num_input_tokens_seen': 28248192, 'train_runtime': '3.547e+04', 'train_tokens_per_second': '796.3'}
45%|████▌ | 8410/18688 [9:51:12<12:15:09, 4.29s/it] 45%|████▌ | 8411/18688 [9:51:16<11:45:13, 4.12s/it] 45%|████▌ | 8412/18688 [9:51:20<11:52:59, 4.16s/it] 45%|████▌ | 8413/18688 [9:51:24<11:39:36, 4.09s/it] 45%|████▌ | 8414/18688 [9:51:28<11:13:58, 3.94s/it] 45%|████▌ | 8415/18688 [9:51:32<11:31:23, 4.04s/it] 45%|████▌ | 8416/18688 [9:51:36<11:06:27, 3.89s/it] 45%|████▌ | 8417/18688 [9:51:39<10:52:20, 3.81s/it] 45%|████▌ | 8418/18688 [9:51:44<11:36:53, 4.07s/it] 45%|████▌ | 8419/18688 [9:51:48<11:21:37, 3.98s/it] 45%|████▌ | 8420/18688 [9:51:53<12:31:23, 4.39s/it] {'loss': '0.8952', 'grad_norm': '1.252', 'learning_rate': '5.206e-05', 'epoch': '0.4506', 'num_input_tokens_seen': 28280736, 'train_runtime': '3.551e+04', 'train_tokens_per_second': '796.3'}
45%|████▌ | 8420/18688 [9:51:53<12:31:23, 4.39s/it] 45%|████▌ | 8421/18688 [9:51:57<12:29:34, 4.38s/it] 45%|████▌ | 8422/18688 [9:52:01<12:06:13, 4.24s/it] 45%|████▌ | 8423/18688 [9:52:05<11:36:52, 4.07s/it] 45%|████▌ | 8424/18688 [9:52:09<11:27:24, 4.02s/it] 45%|████▌ | 8425/18688 [9:52:13<11:50:21, 4.15s/it] 45%|████▌ | 8426/18688 [9:52:17<11:41:09, 4.10s/it] 45%|████▌ | 8427/18688 [9:52:22<11:59:04, 4.20s/it] 45%|████▌ | 8428/18688 [9:52:26<12:19:39, 4.33s/it] 45%|████▌ | 8429/18688 [9:52:31<12:20:03, 4.33s/it] 45%|████▌ | 8430/18688 [9:52:35<11:58:34, 4.20s/it] {'loss': '0.8681', 'grad_norm': '1.275', 'learning_rate': '5.199e-05', 'epoch': '0.4511', 'num_input_tokens_seen': 28316296, 'train_runtime': '3.556e+04', 'train_tokens_per_second': '796.4'}
45%|████▌ | 8430/18688 [9:52:35<11:58:34, 4.20s/it] 45%|████▌ | 8431/18688 [9:52:39<11:45:52, 4.13s/it] 45%|████▌ | 8432/18688 [9:52:42<11:24:30, 4.00s/it] 45%|████▌ | 8433/18688 [9:52:46<11:09:31, 3.92s/it] 45%|████▌ | 8434/18688 [9:52:50<11:19:03, 3.97s/it] 45%|████▌ | 8435/18688 [9:52:54<11:21:25, 3.99s/it] 45%|████▌ | 8436/18688 [9:52:59<11:41:10, 4.10s/it] 45%|████▌ | 8437/18688 [9:53:03<11:44:47, 4.13s/it] 45%|████▌ | 8438/18688 [9:53:08<12:56:41, 4.55s/it] 45%|████▌ | 8439/18688 [9:53:13<12:43:35, 4.47s/it] 45%|████▌ | 8440/18688 [9:53:17<12:43:06, 4.47s/it] {'loss': '0.9943', 'grad_norm': '1.139', 'learning_rate': '5.192e-05', 'epoch': '0.4516', 'num_input_tokens_seen': 28350936, 'train_runtime': '3.56e+04', 'train_tokens_per_second': '796.4'}
45%|████▌ | 8440/18688 [9:53:17<12:43:06, 4.47s/it] 45%|████▌ | 8441/18688 [9:53:21<12:21:01, 4.34s/it] 45%|████▌ | 8442/18688 [9:53:26<12:29:37, 4.39s/it] 45%|████▌ | 8443/18688 [9:53:30<12:21:23, 4.34s/it] 45%|████▌ | 8444/18688 [9:53:34<12:20:34, 4.34s/it] 45%|████▌ | 8445/18688 [9:53:39<13:11:22, 4.64s/it] 45%|████▌ | 8446/18688 [9:53:44<13:24:19, 4.71s/it] 45%|████▌ | 8447/18688 [9:53:48<12:50:14, 4.51s/it] 45%|████▌ | 8448/18688 [9:53:52<12:09:08, 4.27s/it] 45%|████▌ | 8449/18688 [9:53:56<11:36:58, 4.08s/it] 45%|████▌ | 8450/18688 [9:54:00<11:49:33, 4.16s/it] {'loss': '0.924', 'grad_norm': '1.304', 'learning_rate': '5.185e-05', 'epoch': '0.4522', 'num_input_tokens_seen': 28387336, 'train_runtime': '3.564e+04', 'train_tokens_per_second': '796.5'}
45%|████▌ | 8450/18688 [9:54:00<11:49:33, 4.16s/it] 45%|████▌ | 8451/18688 [9:54:05<12:51:10, 4.52s/it] 45%|████▌ | 8452/18688 [9:54:10<12:40:23, 4.46s/it] 45%|████▌ | 8453/18688 [9:54:13<12:01:59, 4.23s/it] 45%|████▌ | 8454/18688 [9:54:18<12:08:48, 4.27s/it] 45%|████▌ | 8455/18688 [9:54:22<12:20:17, 4.34s/it] 45%|████▌ | 8456/18688 [9:54:27<12:17:49, 4.33s/it] 45%|████▌ | 8457/18688 [9:54:30<11:54:47, 4.19s/it] 45%|████▌ | 8458/18688 [9:54:35<11:53:25, 4.18s/it] 45%|████▌ | 8459/18688 [9:54:39<11:47:26, 4.15s/it] 45%|████▌ | 8460/18688 [9:54:42<11:11:50, 3.94s/it] {'loss': '0.971', 'grad_norm': '1.258', 'learning_rate': '5.178e-05', 'epoch': '0.4527', 'num_input_tokens_seen': 28422240, 'train_runtime': '3.568e+04', 'train_tokens_per_second': '796.5'}
45%|████▌ | 8460/18688 [9:54:42<11:11:50, 3.94s/it] 45%|████▌ | 8461/18688 [9:54:47<11:44:01, 4.13s/it] 45%|████▌ | 8462/18688 [9:54:51<11:36:35, 4.09s/it] 45%|████▌ | 8463/18688 [9:54:54<11:14:40, 3.96s/it] 45%|████▌ | 8464/18688 [9:54:59<11:25:48, 4.02s/it] 45%|████▌ | 8465/18688 [9:55:02<11:14:13, 3.96s/it] 45%|████▌ | 8466/18688 [9:55:07<11:35:19, 4.08s/it] 45%|████▌ | 8467/18688 [9:55:11<11:34:43, 4.08s/it] 45%|████▌ | 8468/18688 [9:55:14<11:12:45, 3.95s/it] 45%|████▌ | 8469/18688 [9:55:19<11:18:24, 3.98s/it] 45%|████▌ | 8470/18688 [9:55:23<11:20:46, 4.00s/it] {'loss': '0.9634', 'grad_norm': '1.097', 'learning_rate': '5.171e-05', 'epoch': '0.4532', 'num_input_tokens_seen': 28454416, 'train_runtime': '3.572e+04', 'train_tokens_per_second': '796.5'}
45%|████▌ | 8470/18688 [9:55:23<11:20:46, 4.00s/it] 45%|████▌ | 8471/18688 [9:55:27<11:26:05, 4.03s/it] 45%|████▌ | 8472/18688 [9:55:32<12:34:39, 4.43s/it] 45%|████▌ | 8473/18688 [9:55:36<11:51:48, 4.18s/it] 45%|████▌ | 8474/18688 [9:55:39<11:20:34, 4.00s/it] 45%|████▌ | 8475/18688 [9:55:43<11:14:57, 3.97s/it] 45%|████▌ | 8476/18688 [9:55:47<11:33:00, 4.07s/it] 45%|████▌ | 8477/18688 [9:55:52<11:44:17, 4.14s/it] 45%|████▌ | 8478/18688 [9:55:56<11:43:36, 4.13s/it] 45%|████▌ | 8479/18688 [9:56:00<11:43:16, 4.13s/it] 45%|████▌ | 8480/18688 [9:56:04<11:49:04, 4.17s/it] {'loss': '0.9284', 'grad_norm': '1.074', 'learning_rate': '5.164e-05', 'epoch': '0.4538', 'num_input_tokens_seen': 28487064, 'train_runtime': '3.576e+04', 'train_tokens_per_second': '796.5'}
45%|████▌ | 8480/18688 [9:56:04<11:49:04, 4.17s/it] 45%|████▌ | 8481/18688 [9:56:08<11:54:51, 4.20s/it] 45%|████▌ | 8482/18688 [9:56:12<11:32:08, 4.07s/it] 45%|████▌ | 8483/18688 [9:56:17<11:56:36, 4.21s/it] 45%|████▌ | 8484/18688 [9:56:21<11:55:13, 4.21s/it] 45%|████▌ | 8485/18688 [9:56:25<11:52:54, 4.19s/it] 45%|████▌ | 8486/18688 [9:56:29<11:25:27, 4.03s/it] 45%|████▌ | 8487/18688 [9:56:33<11:31:11, 4.07s/it] 45%|████▌ | 8488/18688 [9:56:37<11:37:09, 4.10s/it] 45%|████▌ | 8489/18688 [9:56:42<12:14:00, 4.32s/it] 45%|████▌ | 8490/18688 [9:56:47<13:04:47, 4.62s/it] {'loss': '0.9789', 'grad_norm': '1.279', 'learning_rate': '5.157e-05', 'epoch': '0.4543', 'num_input_tokens_seen': 28524072, 'train_runtime': '3.581e+04', 'train_tokens_per_second': '796.6'}
45%|████▌ | 8490/18688 [9:56:47<13:04:47, 4.62s/it] 45%|████▌ | 8491/18688 [9:56:51<12:12:50, 4.31s/it] 45%|████▌ | 8492/18688 [9:56:56<12:36:37, 4.45s/it] 45%|████▌ | 8493/18688 [9:56:59<11:59:47, 4.24s/it] 45%|████▌ | 8494/18688 [9:57:03<11:27:31, 4.05s/it] 45%|████▌ | 8495/18688 [9:57:07<11:42:25, 4.13s/it] 45%|████▌ | 8496/18688 [9:57:12<12:03:42, 4.26s/it] 45%|████▌ | 8497/18688 [9:57:16<11:44:48, 4.15s/it] 45%|████▌ | 8498/18688 [9:57:20<11:37:43, 4.11s/it] 45%|████▌ | 8499/18688 [9:57:24<11:32:34, 4.08s/it] 45%|████▌ | 8500/18688 [9:57:29<12:08:54, 4.29s/it] {'loss': '0.9554', 'grad_norm': '1.103', 'learning_rate': '5.15e-05', 'epoch': '0.4548', 'num_input_tokens_seen': 28557096, 'train_runtime': '3.585e+04', 'train_tokens_per_second': '796.6'}
45%|████▌ | 8500/18688 [9:57:29<12:08:54, 4.29s/it] 45%|████▌ | 8501/18688 [9:57:32<11:37:33, 4.11s/it] 45%|████▌ | 8502/18688 [9:57:37<11:49:53, 4.18s/it] 45%|████▌ | 8503/18688 [9:57:42<13:14:59, 4.68s/it] 46%|████▌ | 8504/18688 [9:57:46<12:38:35, 4.47s/it] 46%|████▌ | 8505/18688 [9:57:50<11:51:05, 4.19s/it] 46%|████▌ | 8506/18688 [9:57:54<11:52:13, 4.20s/it] 46%|████▌ | 8507/18688 [9:57:58<11:32:02, 4.08s/it] 46%|████▌ | 8508/18688 [9:58:02<11:03:37, 3.91s/it] 46%|████▌ | 8509/18688 [9:58:05<10:41:49, 3.78s/it] 46%|████▌ | 8510/18688 [9:58:09<11:08:06, 3.94s/it] {'loss': '0.9284', 'grad_norm': '1.171', 'learning_rate': '5.143e-05', 'epoch': '0.4554', 'num_input_tokens_seen': 28590184, 'train_runtime': '3.589e+04', 'train_tokens_per_second': '796.6'}
46%|████▌ | 8510/18688 [9:58:09<11:08:06, 3.94s/it] 46%|████▌ | 8511/18688 [9:58:13<10:45:33, 3.81s/it] 46%|████▌ | 8512/18688 [9:58:17<11:02:29, 3.91s/it] 46%|████▌ | 8513/18688 [9:58:21<11:35:23, 4.10s/it] 46%|████▌ | 8514/18688 [9:58:25<11:16:00, 3.99s/it] 46%|████▌ | 8515/18688 [9:58:30<11:34:19, 4.10s/it] 46%|████▌ | 8516/18688 [9:58:34<11:44:15, 4.15s/it] 46%|████▌ | 8517/18688 [9:58:38<11:58:08, 4.24s/it] 46%|████▌ | 8518/18688 [9:58:42<11:33:22, 4.09s/it] 46%|████▌ | 8519/18688 [9:58:47<12:27:50, 4.41s/it] 46%|████▌ | 8520/18688 [9:58:52<12:45:21, 4.52s/it] {'loss': '0.9289', 'grad_norm': '1.047', 'learning_rate': '5.136e-05', 'epoch': '0.4559', 'num_input_tokens_seen': 28625592, 'train_runtime': '3.593e+04', 'train_tokens_per_second': '796.6'}
46%|████▌ | 8520/18688 [9:58:52<12:45:21, 4.52s/it] 46%|████▌ | 8521/18688 [9:58:56<11:58:02, 4.24s/it] 46%|████▌ | 8522/18688 [9:58:59<11:19:50, 4.01s/it] 46%|████▌ | 8523/18688 [9:59:03<11:25:05, 4.04s/it] 46%|████▌ | 8524/18688 [9:59:08<12:02:46, 4.27s/it] 46%|████▌ | 8525/18688 [9:59:12<11:49:41, 4.19s/it] 46%|████▌ | 8526/18688 [9:59:16<11:58:45, 4.24s/it] 46%|████▌ | 8527/18688 [9:59:20<11:27:37, 4.06s/it] 46%|████▌ | 8528/18688 [9:59:24<11:45:38, 4.17s/it] 46%|████▌ | 8529/18688 [9:59:28<11:44:28, 4.16s/it] 46%|████▌ | 8530/18688 [9:59:34<12:30:48, 4.43s/it] {'loss': '1', 'grad_norm': '1.226', 'learning_rate': '5.129e-05', 'epoch': '0.4565', 'num_input_tokens_seen': 28659464, 'train_runtime': '3.597e+04', 'train_tokens_per_second': '796.7'}
46%|████▌ | 8530/18688 [9:59:34<12:30:48, 4.43s/it] 46%|████▌ | 8531/18688 [9:59:37<11:58:02, 4.24s/it] 46%|████▌ | 8532/18688 [9:59:41<11:37:20, 4.12s/it] 46%|████▌ | 8533/18688 [9:59:45<11:37:08, 4.12s/it] 46%|████▌ | 8534/18688 [9:59:50<11:59:45, 4.25s/it] 46%|████▌ | 8535/18688 [9:59:54<11:48:04, 4.18s/it] 46%|████▌ | 8536/18688 [9:59:58<11:48:08, 4.19s/it] 46%|████▌ | 8537/18688 [10:00:02<11:48:20, 4.19s/it] 46%|████▌ | 8538/18688 [10:00:06<11:40:34, 4.14s/it] 46%|████▌ | 8539/18688 [10:00:11<11:53:39, 4.22s/it] 46%|████▌ | 8540/18688 [10:00:15<11:41:47, 4.15s/it] {'loss': '0.9211', 'grad_norm': '0.9869', 'learning_rate': '5.122e-05', 'epoch': '0.457', 'num_input_tokens_seen': 28692144, 'train_runtime': '3.602e+04', 'train_tokens_per_second': '796.7'}
46%|████▌ | 8540/18688 [10:00:15<11:41:47, 4.15s/it] 46%|████▌ | 8541/18688 [10:00:18<11:11:45, 3.97s/it] 46%|████▌ | 8542/18688 [10:00:22<11:05:49, 3.94s/it] 46%|████▌ | 8543/18688 [10:00:26<11:15:59, 4.00s/it] 46%|████▌ | 8544/18688 [10:00:30<11:25:47, 4.06s/it] 46%|████▌ | 8545/18688 [10:00:34<11:05:03, 3.93s/it] 46%|████▌ | 8546/18688 [10:00:38<11:20:13, 4.02s/it] 46%|████▌ | 8547/18688 [10:00:43<12:06:06, 4.30s/it] 46%|████▌ | 8548/18688 [10:00:47<11:38:11, 4.13s/it] 46%|████▌ | 8549/18688 [10:00:51<11:12:01, 3.98s/it] 46%|████▌ | 8550/18688 [10:00:55<11:26:01, 4.06s/it] {'loss': '0.9522', 'grad_norm': '1.482', 'learning_rate': '5.115e-05', 'epoch': '0.4575', 'num_input_tokens_seen': 28724472, 'train_runtime': '3.606e+04', 'train_tokens_per_second': '796.7'}
46%|████▌ | 8550/18688 [10:00:55<11:26:01, 4.06s/it] 46%|████▌ | 8551/18688 [10:00:59<11:17:33, 4.01s/it] 46%|████▌ | 8552/18688 [10:01:02<10:54:17, 3.87s/it] 46%|████▌ | 8553/18688 [10:01:07<11:30:22, 4.09s/it] 46%|████▌ | 8554/18688 [10:01:11<11:34:54, 4.11s/it] 46%|████▌ | 8555/18688 [10:01:15<11:21:19, 4.03s/it] 46%|████▌ | 8556/18688 [10:01:20<11:57:53, 4.25s/it] 46%|████▌ | 8557/18688 [10:01:24<11:53:45, 4.23s/it] 46%|████▌ | 8558/18688 [10:01:28<11:37:04, 4.13s/it] 46%|████▌ | 8559/18688 [10:01:33<12:08:07, 4.31s/it] 46%|████▌ | 8560/18688 [10:01:37<11:57:25, 4.25s/it] {'loss': '0.922', 'grad_norm': '1.165', 'learning_rate': '5.108e-05', 'epoch': '0.4581', 'num_input_tokens_seen': 28757272, 'train_runtime': '3.61e+04', 'train_tokens_per_second': '796.7'}
46%|████▌ | 8560/18688 [10:01:37<11:57:25, 4.25s/it] 46%|████▌ | 8561/18688 [10:01:40<11:28:37, 4.08s/it] 46%|████▌ | 8562/18688 [10:01:45<11:42:20, 4.16s/it] 46%|████▌ | 8563/18688 [10:01:48<11:20:38, 4.03s/it] 46%|████▌ | 8564/18688 [10:01:53<11:27:32, 4.07s/it] 46%|████▌ | 8565/18688 [10:01:57<11:53:20, 4.23s/it] 46%|████▌ | 8566/18688 [10:02:02<12:04:12, 4.29s/it] 46%|████▌ | 8567/18688 [10:02:05<11:27:34, 4.08s/it] 46%|████▌ | 8568/18688 [10:02:10<11:41:35, 4.16s/it] 46%|████▌ | 8569/18688 [10:02:14<11:47:32, 4.20s/it] 46%|████▌ | 8570/18688 [10:02:18<11:29:55, 4.09s/it] {'loss': '0.8534', 'grad_norm': '1.207', 'learning_rate': '5.101e-05', 'epoch': '0.4586', 'num_input_tokens_seen': 28790136, 'train_runtime': '3.614e+04', 'train_tokens_per_second': '796.7'}
46%|████▌ | 8570/18688 [10:02:18<11:29:55, 4.09s/it] 46%|████▌ | 8571/18688 [10:02:22<11:22:36, 4.05s/it] 46%|████▌ | 8572/18688 [10:02:26<11:34:10, 4.12s/it] 46%|████▌ | 8573/18688 [10:02:30<11:29:28, 4.09s/it] 46%|████▌ | 8574/18688 [10:02:34<11:32:50, 4.11s/it] 46%|████▌ | 8575/18688 [10:02:38<11:28:54, 4.09s/it] 46%|████▌ | 8576/18688 [10:02:42<11:06:34, 3.96s/it] 46%|████▌ | 8577/18688 [10:02:46<11:08:26, 3.97s/it] 46%|████▌ | 8578/18688 [10:02:50<11:08:58, 3.97s/it] 46%|████▌ | 8579/18688 [10:02:54<11:05:39, 3.95s/it] 46%|████▌ | 8580/18688 [10:02:58<11:06:48, 3.96s/it] {'loss': '0.9232', 'grad_norm': '1.208', 'learning_rate': '5.094e-05', 'epoch': '0.4591', 'num_input_tokens_seen': 28821128, 'train_runtime': '3.618e+04', 'train_tokens_per_second': '796.6'}
46%|████▌ | 8580/18688 [10:02:58<11:06:48, 3.96s/it] 46%|████▌ | 8581/18688 [10:03:01<11:00:43, 3.92s/it] 46%|████▌ | 8582/18688 [10:03:05<11:02:44, 3.93s/it] 46%|████▌ | 8583/18688 [10:03:10<11:17:53, 4.03s/it] 46%|████▌ | 8584/18688 [10:03:14<11:25:22, 4.07s/it] 46%|████▌ | 8585/18688 [10:03:18<11:23:49, 4.06s/it] 46%|████▌ | 8586/18688 [10:03:21<10:56:29, 3.90s/it] 46%|████▌ | 8587/18688 [10:03:26<11:39:42, 4.16s/it] 46%|████▌ | 8588/18688 [10:03:30<11:08:40, 3.97s/it] 46%|████▌ | 8589/18688 [10:03:35<12:20:12, 4.40s/it] 46%|████▌ | 8590/18688 [10:03:39<11:56:06, 4.25s/it] {'loss': '1.021', 'grad_norm': '1.332', 'learning_rate': '5.087e-05', 'epoch': '0.4597', 'num_input_tokens_seen': 28855456, 'train_runtime': '3.622e+04', 'train_tokens_per_second': '796.7'}
46%|████▌ | 8590/18688 [10:03:39<11:56:06, 4.25s/it] 46%|████▌ | 8591/18688 [10:03:44<12:12:21, 4.35s/it] 46%|████▌ | 8592/18688 [10:03:47<11:45:52, 4.19s/it] 46%|████▌ | 8593/18688 [10:03:51<11:20:51, 4.05s/it] 46%|████▌ | 8594/18688 [10:03:55<11:33:22, 4.12s/it] 46%|████▌ | 8595/18688 [10:04:00<11:34:38, 4.13s/it] 46%|████▌ | 8596/18688 [10:04:04<12:06:07, 4.32s/it] 46%|████▌ | 8597/18688 [10:04:09<12:30:38, 4.46s/it] 46%|████▌ | 8598/18688 [10:04:13<12:23:08, 4.42s/it] 46%|████▌ | 8599/18688 [10:04:17<11:46:57, 4.20s/it] 46%|████▌ | 8600/18688 [10:04:21<11:30:00, 4.10s/it] {'loss': '0.9014', 'grad_norm': '1.117', 'learning_rate': '5.079e-05', 'epoch': '0.4602', 'num_input_tokens_seen': 28888784, 'train_runtime': '3.626e+04', 'train_tokens_per_second': '796.7'}
46%|████▌ | 8600/18688 [10:04:21<11:30:00, 4.10s/it] 46%|████▌ | 8601/18688 [10:04:25<11:24:27, 4.07s/it] 46%|████▌ | 8602/18688 [10:04:31<12:43:25, 4.54s/it] 46%|████▌ | 8603/18688 [10:04:34<11:50:46, 4.23s/it] 46%|████▌ | 8604/18688 [10:04:38<11:37:48, 4.15s/it] 46%|████▌ | 8605/18688 [10:04:42<11:11:45, 4.00s/it] 46%|████▌ | 8606/18688 [10:04:46<11:49:49, 4.22s/it] 46%|████▌ | 8607/18688 [10:04:51<11:55:56, 4.26s/it] 46%|████▌ | 8608/18688 [10:04:55<11:47:52, 4.21s/it] 46%|████▌ | 8609/18688 [10:05:00<12:12:58, 4.36s/it] 46%|████▌ | 8610/18688 [10:05:03<11:29:36, 4.11s/it] {'loss': '0.9107', 'grad_norm': '1.659', 'learning_rate': '5.072e-05', 'epoch': '0.4607', 'num_input_tokens_seen': 28923552, 'train_runtime': '3.63e+04', 'train_tokens_per_second': '796.7'}
46%|████▌ | 8610/18688 [10:05:03<11:29:36, 4.11s/it] 46%|████▌ | 8611/18688 [10:05:07<11:29:17, 4.10s/it] 46%|████▌ | 8612/18688 [10:05:11<11:02:00, 3.94s/it] 46%|████▌ | 8613/18688 [10:05:15<11:09:40, 3.99s/it] 46%|████▌ | 8614/18688 [10:05:19<11:30:59, 4.12s/it] 46%|████▌ | 8615/18688 [10:05:23<11:34:11, 4.13s/it] 46%|████▌ | 8616/18688 [10:05:28<11:58:14, 4.28s/it] 46%|████▌ | 8617/18688 [10:05:32<11:50:30, 4.23s/it] 46%|████▌ | 8618/18688 [10:05:36<11:50:46, 4.23s/it] 46%|████▌ | 8619/18688 [10:05:40<11:33:34, 4.13s/it] 46%|████▌ | 8620/18688 [10:05:45<12:17:49, 4.40s/it] {'loss': '0.8911', 'grad_norm': '1.356', 'learning_rate': '5.065e-05', 'epoch': '0.4613', 'num_input_tokens_seen': 28956720, 'train_runtime': '3.635e+04', 'train_tokens_per_second': '796.7'}
46%|████▌ | 8620/18688 [10:05:45<12:17:49, 4.40s/it] 46%|████▌ | 8621/18688 [10:05:49<11:56:46, 4.27s/it] 46%|████▌ | 8622/18688 [10:05:54<12:23:17, 4.43s/it] 46%|████▌ | 8623/18688 [10:05:58<12:03:31, 4.31s/it] 46%|████▌ | 8624/18688 [10:06:02<11:18:49, 4.05s/it] 46%|████▌ | 8625/18688 [10:06:05<10:49:29, 3.87s/it] 46%|████▌ | 8626/18688 [10:06:10<11:23:06, 4.07s/it] 46%|████▌ | 8627/18688 [10:06:14<12:00:28, 4.30s/it] 46%|████▌ | 8628/18688 [10:06:19<12:22:14, 4.43s/it] 46%|████▌ | 8629/18688 [10:06:23<11:51:07, 4.24s/it] 46%|████▌ | 8630/18688 [10:06:27<11:37:40, 4.16s/it] {'loss': '0.9975', 'grad_norm': '1.478', 'learning_rate': '5.058e-05', 'epoch': '0.4618', 'num_input_tokens_seen': 28990816, 'train_runtime': '3.639e+04', 'train_tokens_per_second': '796.7'}
46%|████▌ | 8630/18688 [10:06:27<11:37:40, 4.16s/it] 46%|████▌ | 8631/18688 [10:06:30<11:06:00, 3.97s/it] 46%|████▌ | 8632/18688 [10:06:34<10:54:42, 3.91s/it] 46%|████▌ | 8633/18688 [10:06:38<11:06:13, 3.98s/it] 46%|████▌ | 8634/18688 [10:06:43<11:34:08, 4.14s/it] 46%|████▌ | 8635/18688 [10:06:47<11:42:57, 4.20s/it] 46%|████▌ | 8636/18688 [10:06:52<11:48:06, 4.23s/it] 46%|████▌ | 8637/18688 [10:06:56<11:44:34, 4.21s/it] 46%|████▌ | 8638/18688 [10:07:00<11:47:18, 4.22s/it] 46%|████▌ | 8639/18688 [10:07:04<11:17:44, 4.05s/it] 46%|████▌ | 8640/18688 [10:07:07<11:03:46, 3.96s/it] {'loss': '0.9408', 'grad_norm': '1.252', 'learning_rate': '5.051e-05', 'epoch': '0.4623', 'num_input_tokens_seen': 29024712, 'train_runtime': '3.643e+04', 'train_tokens_per_second': '796.8'}
46%|████▌ | 8640/18688 [10:07:07<11:03:46, 3.96s/it] 46%|████▌ | 8641/18688 [10:07:12<11:29:14, 4.12s/it] 46%|████▌ | 8642/18688 [10:07:16<11:50:22, 4.24s/it] 46%|████▌ | 8643/18688 [10:07:20<11:18:24, 4.05s/it] 46%|████▋ | 8644/18688 [10:07:24<11:11:22, 4.01s/it] 46%|████▋ | 8645/18688 [10:07:28<11:35:45, 4.16s/it] 46%|████▋ | 8646/18688 [10:07:32<11:05:20, 3.98s/it] 46%|████▋ | 8647/18688 [10:07:35<10:39:31, 3.82s/it] 46%|████▋ | 8648/18688 [10:07:40<10:54:14, 3.91s/it] 46%|████▋ | 8649/18688 [10:07:43<10:37:24, 3.81s/it] 46%|████▋ | 8650/18688 [10:07:48<11:16:01, 4.04s/it] {'loss': '0.9669', 'grad_norm': '1.27', 'learning_rate': '5.044e-05', 'epoch': '0.4629', 'num_input_tokens_seen': 29055992, 'train_runtime': '3.647e+04', 'train_tokens_per_second': '796.7'}
46%|████▋ | 8650/18688 [10:07:48<11:16:01, 4.04s/it] 46%|████▋ | 8651/18688 [10:07:52<11:24:48, 4.09s/it] 46%|████▋ | 8652/18688 [10:07:56<11:26:30, 4.10s/it] 46%|████▋ | 8653/18688 [10:08:00<11:13:36, 4.03s/it] 46%|████▋ | 8654/18688 [10:08:04<11:14:46, 4.03s/it] 46%|████▋ | 8655/18688 [10:08:08<11:21:34, 4.08s/it] 46%|████▋ | 8656/18688 [10:08:13<11:39:36, 4.18s/it] 46%|████▋ | 8657/18688 [10:08:17<11:58:42, 4.30s/it] 46%|████▋ | 8658/18688 [10:08:21<11:40:08, 4.19s/it] 46%|████▋ | 8659/18688 [10:08:26<12:27:29, 4.47s/it] 46%|████▋ | 8660/18688 [10:08:31<12:32:36, 4.50s/it] {'loss': '0.854', 'grad_norm': '1.105', 'learning_rate': '5.037e-05', 'epoch': '0.4634', 'num_input_tokens_seen': 29090240, 'train_runtime': '3.651e+04', 'train_tokens_per_second': '796.7'}
46%|████▋ | 8660/18688 [10:08:31<12:32:36, 4.50s/it] 46%|████▋ | 8661/18688 [10:08:35<12:21:12, 4.44s/it] 46%|████▋ | 8662/18688 [10:08:39<12:07:40, 4.35s/it] 46%|████▋ | 8663/18688 [10:08:43<11:59:06, 4.30s/it] 46%|████▋ | 8664/18688 [10:08:48<12:23:42, 4.45s/it] 46%|████▋ | 8665/18688 [10:08:52<12:06:31, 4.35s/it] 46%|████▋ | 8666/18688 [10:08:56<11:44:31, 4.22s/it] 46%|████▋ | 8667/18688 [10:09:00<11:25:51, 4.11s/it] 46%|████▋ | 8668/18688 [10:09:05<11:53:51, 4.27s/it] 46%|████▋ | 8669/18688 [10:09:09<12:10:31, 4.37s/it] 46%|████▋ | 8670/18688 [10:09:14<12:42:59, 4.57s/it] {'loss': '0.9', 'grad_norm': '1.173', 'learning_rate': '5.03e-05', 'epoch': '0.4639', 'num_input_tokens_seen': 29126040, 'train_runtime': '3.655e+04', 'train_tokens_per_second': '796.8'}
46%|████▋ | 8670/18688 [10:09:14<12:42:59, 4.57s/it] 46%|████▋ | 8671/18688 [10:09:18<11:54:53, 4.28s/it] 46%|████▋ | 8672/18688 [10:09:22<11:27:25, 4.12s/it] 46%|████▋ | 8673/18688 [10:09:26<11:26:57, 4.12s/it] 46%|████▋ | 8674/18688 [10:09:30<11:32:13, 4.15s/it] 46%|████▋ | 8675/18688 [10:09:34<11:43:45, 4.22s/it] 46%|████▋ | 8676/18688 [10:09:39<12:26:32, 4.47s/it] 46%|████▋ | 8677/18688 [10:09:44<12:21:58, 4.45s/it] 46%|████▋ | 8678/18688 [10:09:48<12:30:28, 4.50s/it] 46%|████▋ | 8679/18688 [10:09:53<12:45:55, 4.59s/it] 46%|████▋ | 8680/18688 [10:09:57<12:06:55, 4.36s/it] {'loss': '0.9125', 'grad_norm': '1.145', 'learning_rate': '5.023e-05', 'epoch': '0.4645', 'num_input_tokens_seen': 29161320, 'train_runtime': '3.66e+04', 'train_tokens_per_second': '796.8'}
46%|████▋ | 8680/18688 [10:09:57<12:06:55, 4.36s/it] 46%|████▋ | 8681/18688 [10:10:01<11:42:59, 4.22s/it] 46%|████▋ | 8682/18688 [10:10:06<12:20:11, 4.44s/it] 46%|████▋ | 8683/18688 [10:10:11<12:43:33, 4.58s/it] 46%|████▋ | 8684/18688 [10:10:15<12:25:29, 4.47s/it] 46%|████▋ | 8685/18688 [10:10:19<12:06:34, 4.36s/it] 46%|████▋ | 8686/18688 [10:10:23<11:47:27, 4.24s/it] 46%|████▋ | 8687/18688 [10:10:28<12:26:30, 4.48s/it] 46%|████▋ | 8688/18688 [10:10:32<12:04:38, 4.35s/it] 46%|████▋ | 8689/18688 [10:10:36<11:42:56, 4.22s/it] 47%|████▋ | 8690/18688 [10:10:40<11:14:14, 4.05s/it] {'loss': '0.9242', 'grad_norm': '1.206', 'learning_rate': '5.016e-05', 'epoch': '0.465', 'num_input_tokens_seen': 29196232, 'train_runtime': '3.664e+04', 'train_tokens_per_second': '796.8'}
47%|████▋ | 8690/18688 [10:10:40<11:14:14, 4.05s/it] 47%|████▋ | 8691/18688 [10:10:44<11:12:13, 4.03s/it] 47%|████▋ | 8692/18688 [10:10:47<10:52:59, 3.92s/it] 47%|████▋ | 8693/18688 [10:10:52<11:07:13, 4.01s/it] 47%|████▋ | 8694/18688 [10:10:56<11:30:29, 4.15s/it] 47%|████▋ | 8695/18688 [10:11:00<11:18:57, 4.08s/it] 47%|████▋ | 8696/18688 [10:11:06<12:32:56, 4.52s/it] 47%|████▋ | 8697/18688 [10:11:10<12:53:03, 4.64s/it] 47%|████▋ | 8698/18688 [10:11:16<13:12:26, 4.76s/it] 47%|████▋ | 8699/18688 [10:11:19<12:18:33, 4.44s/it] 47%|████▋ | 8700/18688 [10:11:23<11:58:05, 4.31s/it] {'loss': '0.9361', 'grad_norm': '1.133', 'learning_rate': '5.009e-05', 'epoch': '0.4656', 'num_input_tokens_seen': 29231456, 'train_runtime': '3.668e+04', 'train_tokens_per_second': '796.9'}
47%|████▋ | 8700/18688 [10:11:23<11:58:05, 4.31s/it] 47%|████▋ | 8701/18688 [10:11:27<11:32:58, 4.16s/it] 47%|████▋ | 8702/18688 [10:11:31<11:09:47, 4.02s/it] 47%|████▋ | 8703/18688 [10:11:35<11:31:22, 4.15s/it] 47%|████▋ | 8704/18688 [10:11:39<11:00:12, 3.97s/it] 47%|████▋ | 8705/18688 [10:11:43<11:15:27, 4.06s/it] 47%|████▋ | 8706/18688 [10:11:47<10:51:23, 3.92s/it] 47%|████▋ | 8707/18688 [10:11:51<11:15:22, 4.06s/it] 47%|████▋ | 8708/18688 [10:11:56<11:54:17, 4.29s/it] 47%|████▋ | 8709/18688 [10:12:00<11:51:33, 4.28s/it] 47%|████▋ | 8710/18688 [10:12:04<11:55:43, 4.30s/it] {'loss': '0.905', 'grad_norm': '1.041', 'learning_rate': '5.002e-05', 'epoch': '0.4661', 'num_input_tokens_seen': 29264984, 'train_runtime': '3.672e+04', 'train_tokens_per_second': '796.9'}
47%|████▋ | 8710/18688 [10:12:04<11:55:43, 4.30s/it] 47%|████▋ | 8711/18688 [10:12:09<12:15:36, 4.42s/it] 47%|████▋ | 8712/18688 [10:12:13<11:59:39, 4.33s/it] 47%|████▋ | 8713/18688 [10:12:17<11:43:03, 4.23s/it] 47%|████▋ | 8714/18688 [10:12:21<11:10:46, 4.04s/it] 47%|████▋ | 8715/18688 [10:12:25<11:04:03, 4.00s/it] 47%|████▋ | 8716/18688 [10:12:29<11:17:43, 4.08s/it] 47%|████▋ | 8717/18688 [10:12:34<12:08:50, 4.39s/it] 47%|████▋ | 8718/18688 [10:12:38<12:02:22, 4.35s/it] 47%|████▋ | 8719/18688 [10:12:43<12:08:03, 4.38s/it] 47%|████▋ | 8720/18688 [10:12:47<11:35:07, 4.18s/it] {'loss': '0.9215', 'grad_norm': '1.084', 'learning_rate': '4.995e-05', 'epoch': '0.4666', 'num_input_tokens_seen': 29300640, 'train_runtime': '3.677e+04', 'train_tokens_per_second': '796.9'}
47%|████▋ | 8720/18688 [10:12:47<11:35:07, 4.18s/it] 47%|████▋ | 8721/18688 [10:12:50<11:19:52, 4.09s/it] 47%|████▋ | 8722/18688 [10:12:54<11:15:59, 4.07s/it] 47%|████▋ | 8723/18688 [10:12:58<11:09:33, 4.03s/it] 47%|████▋ | 8724/18688 [10:13:02<10:59:57, 3.97s/it] 47%|████▋ | 8725/18688 [10:13:06<11:15:21, 4.07s/it] 47%|████▋ | 8726/18688 [10:13:11<11:38:54, 4.21s/it] 47%|████▋ | 8727/18688 [10:13:15<11:04:33, 4.00s/it] 47%|████▋ | 8728/18688 [10:13:18<10:51:49, 3.93s/it] 47%|████▋ | 8729/18688 [10:13:23<11:11:08, 4.04s/it] 47%|████▋ | 8730/18688 [10:13:27<11:51:45, 4.29s/it] {'loss': '0.9372', 'grad_norm': '1.237', 'learning_rate': '4.988e-05', 'epoch': '0.4672', 'num_input_tokens_seen': 29333168, 'train_runtime': '3.681e+04', 'train_tokens_per_second': '796.9'}
47%|████▋ | 8730/18688 [10:13:27<11:51:45, 4.29s/it] 47%|████▋ | 8731/18688 [10:13:32<11:41:24, 4.23s/it] 47%|████▋ | 8732/18688 [10:13:35<11:20:09, 4.10s/it] 47%|████▋ | 8733/18688 [10:13:39<10:52:18, 3.93s/it] 47%|████▋ | 8734/18688 [10:13:43<11:17:41, 4.08s/it] 47%|████▋ | 8735/18688 [10:13:48<11:54:47, 4.31s/it] 47%|████▋ | 8736/18688 [10:13:52<11:18:17, 4.09s/it] 47%|████▋ | 8737/18688 [10:13:55<10:46:40, 3.90s/it] 47%|████▋ | 8738/18688 [10:13:59<10:40:58, 3.87s/it] 47%|████▋ | 8739/18688 [10:14:03<11:00:52, 3.99s/it] 47%|████▋ | 8740/18688 [10:14:07<10:42:09, 3.87s/it] {'loss': '0.9098', 'grad_norm': '1.283', 'learning_rate': '4.981e-05', 'epoch': '0.4677', 'num_input_tokens_seen': 29365160, 'train_runtime': '3.685e+04', 'train_tokens_per_second': '796.9'}
47%|████▋ | 8740/18688 [10:14:07<10:42:09, 3.87s/it] 47%|████▋ | 8741/18688 [10:14:11<11:00:22, 3.98s/it] 47%|████▋ | 8742/18688 [10:14:15<10:34:44, 3.83s/it] 47%|████▋ | 8743/18688 [10:14:19<11:03:10, 4.00s/it] 47%|████▋ | 8744/18688 [10:14:23<10:42:12, 3.87s/it] 47%|████▋ | 8745/18688 [10:14:27<10:47:09, 3.91s/it] 47%|████▋ | 8746/18688 [10:14:31<11:22:18, 4.12s/it] 47%|████▋ | 8747/18688 [10:14:36<11:49:27, 4.28s/it] 47%|████▋ | 8748/18688 [10:14:40<11:37:55, 4.21s/it] 47%|████▋ | 8749/18688 [10:14:43<11:05:18, 4.02s/it] 47%|████▋ | 8750/18688 [10:14:47<10:42:17, 3.88s/it] {'loss': '0.9034', 'grad_norm': '1.173', 'learning_rate': '4.973e-05', 'epoch': '0.4682', 'num_input_tokens_seen': 29395544, 'train_runtime': '3.689e+04', 'train_tokens_per_second': '796.9'}
47%|████▋ | 8750/18688 [10:14:47<10:42:17, 3.88s/it] 47%|████▋ | 8751/18688 [10:14:51<10:57:02, 3.97s/it] 47%|████▋ | 8752/18688 [10:14:55<10:43:28, 3.89s/it] 47%|████▋ | 8753/18688 [10:14:59<10:59:06, 3.98s/it] 47%|████▋ | 8754/18688 [10:15:04<11:33:27, 4.19s/it] 47%|████▋ | 8755/18688 [10:15:08<11:25:53, 4.14s/it] 47%|████▋ | 8756/18688 [10:15:12<11:27:27, 4.15s/it] 47%|████▋ | 8757/18688 [10:15:16<11:44:32, 4.26s/it] 47%|████▋ | 8758/18688 [10:15:20<11:19:00, 4.10s/it] 47%|████▋ | 8759/18688 [10:15:24<11:27:28, 4.15s/it] 47%|████▋ | 8760/18688 [10:15:30<12:12:04, 4.42s/it] {'loss': '0.934', 'grad_norm': '1.363', 'learning_rate': '4.966e-05', 'epoch': '0.4688', 'num_input_tokens_seen': 29432168, 'train_runtime': '3.693e+04', 'train_tokens_per_second': '797'}
47%|████▋ | 8760/18688 [10:15:30<12:12:04, 4.42s/it] 47%|████▋ | 8761/18688 [10:15:34<12:27:33, 4.52s/it] 47%|████▋ | 8762/18688 [10:15:39<12:22:51, 4.49s/it] 47%|████▋ | 8763/18688 [10:15:43<12:06:03, 4.39s/it] 47%|████▋ | 8764/18688 [10:15:47<12:05:33, 4.39s/it] 47%|████▋ | 8765/18688 [10:15:51<11:29:44, 4.17s/it] 47%|████▋ | 8766/18688 [10:15:55<11:20:30, 4.12s/it] 47%|████▋ | 8767/18688 [10:15:59<10:59:51, 3.99s/it] 47%|████▋ | 8768/18688 [10:16:04<11:55:02, 4.32s/it] 47%|████▋ | 8769/18688 [10:16:09<12:49:16, 4.65s/it] 47%|████▋ | 8770/18688 [10:16:13<12:31:48, 4.55s/it] {'loss': '0.9732', 'grad_norm': '1.02', 'learning_rate': '4.959e-05', 'epoch': '0.4693', 'num_input_tokens_seen': 29469152, 'train_runtime': '3.697e+04', 'train_tokens_per_second': '797'}
47%|████▋ | 8770/18688 [10:16:13<12:31:48, 4.55s/it] 47%|████▋ | 8771/18688 [10:16:17<12:06:31, 4.40s/it] 47%|████▋ | 8772/18688 [10:16:21<11:45:29, 4.27s/it] 47%|████▋ | 8773/18688 [10:16:26<12:12:23, 4.43s/it] 47%|████▋ | 8774/18688 [10:16:31<12:52:24, 4.67s/it] 47%|████▋ | 8775/18688 [10:16:35<12:12:22, 4.43s/it] 47%|████▋ | 8776/18688 [10:16:41<12:53:59, 4.69s/it] 47%|████▋ | 8777/18688 [10:16:46<13:06:26, 4.76s/it] 47%|████▋ | 8778/18688 [10:16:49<12:02:51, 4.38s/it] 47%|████▋ | 8779/18688 [10:16:53<11:21:59, 4.13s/it] 47%|████▋ | 8780/18688 [10:16:57<11:47:31, 4.28s/it] {'loss': '0.8979', 'grad_norm': '0.9003', 'learning_rate': '4.952e-05', 'epoch': '0.4698', 'num_input_tokens_seen': 29506280, 'train_runtime': '3.702e+04', 'train_tokens_per_second': '797.1'}
47%|████▋ | 8780/18688 [10:16:57<11:47:31, 4.28s/it] 47%|████▋ | 8781/18688 [10:17:03<12:37:50, 4.59s/it] 47%|████▋ | 8782/18688 [10:17:07<12:29:49, 4.54s/it] 47%|████▋ | 8783/18688 [10:17:11<12:21:55, 4.49s/it] 47%|████▋ | 8784/18688 [10:17:15<11:44:56, 4.27s/it] 47%|████▋ | 8785/18688 [10:17:19<11:10:51, 4.06s/it] 47%|████▋ | 8786/18688 [10:17:23<11:09:37, 4.06s/it] 47%|████▋ | 8787/18688 [10:17:26<10:49:44, 3.94s/it] 47%|████▋ | 8788/18688 [10:17:30<10:39:14, 3.87s/it] 47%|████▋ | 8789/18688 [10:17:34<11:00:29, 4.00s/it] 47%|████▋ | 8790/18688 [10:17:39<11:10:05, 4.06s/it] {'loss': '0.8378', 'grad_norm': '1.151', 'learning_rate': '4.945e-05', 'epoch': '0.4704', 'num_input_tokens_seen': 29539216, 'train_runtime': '3.706e+04', 'train_tokens_per_second': '797.1'}
47%|████▋ | 8790/18688 [10:17:39<11:10:05, 4.06s/it] 47%|████▋ | 8791/18688 [10:17:43<11:25:04, 4.15s/it] 47%|████▋ | 8792/18688 [10:17:47<11:29:20, 4.18s/it] 47%|████▋ | 8793/18688 [10:17:51<11:15:05, 4.09s/it] 47%|████▋ | 8794/18688 [10:17:56<11:33:20, 4.20s/it] 47%|████▋ | 8795/18688 [10:18:00<11:26:17, 4.16s/it] 47%|████▋ | 8796/18688 [10:18:04<11:39:33, 4.24s/it] 47%|████▋ | 8797/18688 [10:18:08<11:12:31, 4.08s/it] 47%|████▋ | 8798/18688 [10:18:12<11:12:34, 4.08s/it] 47%|████▋ | 8799/18688 [10:18:16<11:13:48, 4.09s/it] 47%|████▋ | 8800/18688 [10:18:20<10:47:59, 3.93s/it] {'loss': '0.8814', 'grad_norm': '1.339', 'learning_rate': '4.938e-05', 'epoch': '0.4709', 'num_input_tokens_seen': 29572272, 'train_runtime': '3.71e+04', 'train_tokens_per_second': '797.1'}
47%|████▋ | 8800/18688 [10:18:20<10:47:59, 3.93s/it] 47%|████▋ | 8801/18688 [10:18:24<11:22:14, 4.14s/it] 47%|████▋ | 8802/18688 [10:18:28<10:57:52, 3.99s/it] 47%|████▋ | 8803/18688 [10:18:32<11:12:56, 4.08s/it] 47%|████▋ | 8804/18688 [10:18:36<11:15:50, 4.10s/it] 47%|████▋ | 8805/18688 [10:18:40<10:54:54, 3.98s/it] 47%|████▋ | 8806/18688 [10:18:44<10:46:02, 3.92s/it] 47%|████▋ | 8807/18688 [10:18:48<11:01:57, 4.02s/it] 47%|████▋ | 8808/18688 [10:18:52<10:51:36, 3.96s/it] 47%|████▋ | 8809/18688 [10:18:55<10:28:23, 3.82s/it] 47%|████▋ | 8810/18688 [10:19:00<11:01:09, 4.02s/it] {'loss': '0.8989', 'grad_norm': '1.373', 'learning_rate': '4.931e-05', 'epoch': '0.4714', 'num_input_tokens_seen': 29604616, 'train_runtime': '3.714e+04', 'train_tokens_per_second': '797.1'}
47%|████▋ | 8810/18688 [10:19:00<11:01:09, 4.02s/it] 47%|████▋ | 8811/18688 [10:19:04<11:29:18, 4.19s/it] 47%|████▋ | 8812/18688 [10:19:08<11:20:12, 4.13s/it] 47%|████▋ | 8813/18688 [10:19:13<11:31:12, 4.20s/it] 47%|████▋ | 8814/18688 [10:19:17<11:22:44, 4.15s/it] 47%|████▋ | 8815/18688 [10:19:20<10:48:09, 3.94s/it] 47%|████▋ | 8816/18688 [10:19:24<10:33:45, 3.85s/it] 47%|████▋ | 8817/18688 [10:19:28<10:31:10, 3.84s/it] 47%|████▋ | 8818/18688 [10:19:31<10:12:42, 3.72s/it] 47%|████▋ | 8819/18688 [10:19:35<10:07:13, 3.69s/it] 47%|████▋ | 8820/18688 [10:19:39<10:21:27, 3.78s/it] {'loss': '0.8652', 'grad_norm': '1.044', 'learning_rate': '4.924e-05', 'epoch': '0.472', 'num_input_tokens_seen': 29635184, 'train_runtime': '3.718e+04', 'train_tokens_per_second': '797.1'}
47%|████▋ | 8820/18688 [10:19:39<10:21:27, 3.78s/it] 47%|████▋ | 8821/18688 [10:19:43<10:26:25, 3.81s/it] 47%|████▋ | 8822/18688 [10:19:47<11:04:25, 4.04s/it] 47%|████▋ | 8823/18688 [10:19:51<11:18:13, 4.12s/it] 47%|████▋ | 8824/18688 [10:19:56<11:33:03, 4.22s/it] 47%|████▋ | 8825/18688 [10:19:59<10:57:48, 4.00s/it] 47%|████▋ | 8826/18688 [10:20:03<10:47:25, 3.94s/it] 47%|████▋ | 8827/18688 [10:20:07<10:28:27, 3.82s/it] 47%|████▋ | 8828/18688 [10:20:11<11:02:17, 4.03s/it] 47%|████▋ | 8829/18688 [10:20:16<11:14:43, 4.11s/it] 47%|████▋ | 8830/18688 [10:20:20<11:37:07, 4.24s/it] {'loss': '0.9362', 'grad_norm': '0.9197', 'learning_rate': '4.917e-05', 'epoch': '0.4725', 'num_input_tokens_seen': 29669640, 'train_runtime': '3.722e+04', 'train_tokens_per_second': '797.1'}
47%|████▋ | 8830/18688 [10:20:20<11:37:07, 4.24s/it] 47%|████▋ | 8831/18688 [10:20:24<11:33:20, 4.22s/it] 47%|████▋ | 8832/18688 [10:20:28<11:16:51, 4.12s/it] 47%|████▋ | 8833/18688 [10:20:32<11:23:47, 4.16s/it] 47%|████▋ | 8834/18688 [10:20:36<11:04:33, 4.05s/it] 47%|████▋ | 8835/18688 [10:20:41<11:47:17, 4.31s/it] 47%|████▋ | 8836/18688 [10:20:45<11:22:20, 4.16s/it] 47%|████▋ | 8837/18688 [10:20:49<11:33:43, 4.23s/it] 47%|████▋ | 8838/18688 [10:20:54<12:04:05, 4.41s/it] 47%|████▋ | 8839/18688 [10:20:58<11:43:55, 4.29s/it] 47%|████▋ | 8840/18688 [10:21:02<11:03:59, 4.05s/it] {'loss': '0.9452', 'grad_norm': '1.248', 'learning_rate': '4.91e-05', 'epoch': '0.473', 'num_input_tokens_seen': 29703464, 'train_runtime': '3.726e+04', 'train_tokens_per_second': '797.1'}
47%|████▋ | 8840/18688 [10:21:02<11:03:59, 4.05s/it] 47%|████▋ | 8841/18688 [10:21:06<11:05:02, 4.05s/it] 47%|████▋ | 8842/18688 [10:21:10<11:05:23, 4.05s/it] 47%|████▋ | 8843/18688 [10:21:14<11:05:50, 4.06s/it] 47%|████▋ | 8844/18688 [10:21:19<12:12:32, 4.46s/it] 47%|████▋ | 8845/18688 [10:21:23<11:46:09, 4.30s/it] 47%|████▋ | 8846/18688 [10:21:28<11:51:59, 4.34s/it] 47%|████▋ | 8847/18688 [10:21:33<12:23:55, 4.54s/it] 47%|████▋ | 8848/18688 [10:21:36<11:37:55, 4.26s/it] 47%|████▋ | 8849/18688 [10:21:41<12:15:24, 4.48s/it] 47%|████▋ | 8850/18688 [10:21:45<11:37:26, 4.25s/it] {'loss': '1.018', 'grad_norm': '1.146', 'learning_rate': '4.902e-05', 'epoch': '0.4736', 'num_input_tokens_seen': 29739656, 'train_runtime': '3.731e+04', 'train_tokens_per_second': '797.2'}
47%|████▋ | 8850/18688 [10:21:45<11:37:26, 4.25s/it] 47%|████▋ | 8851/18688 [10:21:49<11:21:58, 4.16s/it] 47%|████▋ | 8852/18688 [10:21:52<10:54:39, 3.99s/it] 47%|████▋ | 8853/18688 [10:21:57<10:58:24, 4.02s/it] 47%|████▋ | 8854/18688 [10:22:02<12:08:18, 4.44s/it] 47%|████▋ | 8855/18688 [10:22:06<11:48:36, 4.32s/it] 47%|████▋ | 8856/18688 [10:22:10<11:09:45, 4.09s/it] 47%|████▋ | 8857/18688 [10:22:13<10:52:24, 3.98s/it] 47%|████▋ | 8858/18688 [10:22:17<10:43:00, 3.92s/it] 47%|████▋ | 8859/18688 [10:22:22<11:14:43, 4.12s/it] 47%|████▋ | 8860/18688 [10:22:26<11:11:19, 4.10s/it] {'loss': '0.8837', 'grad_norm': '1.395', 'learning_rate': '4.895e-05', 'epoch': '0.4741', 'num_input_tokens_seen': 29771928, 'train_runtime': '3.735e+04', 'train_tokens_per_second': '797.2'}
47%|████▋ | 8860/18688 [10:22:26<11:11:19, 4.10s/it] 47%|████▋ | 8861/18688 [10:22:30<11:04:56, 4.06s/it] 47%|████▋ | 8862/18688 [10:22:34<11:23:18, 4.17s/it] 47%|████▋ | 8863/18688 [10:22:38<11:16:43, 4.13s/it] 47%|████▋ | 8864/18688 [10:22:42<11:05:09, 4.06s/it] 47%|████▋ | 8865/18688 [10:22:46<11:15:47, 4.13s/it] 47%|████▋ | 8866/18688 [10:22:50<11:09:51, 4.09s/it] 47%|████▋ | 8867/18688 [10:22:54<10:57:17, 4.02s/it] 47%|████▋ | 8868/18688 [10:22:58<11:09:44, 4.09s/it] 47%|████▋ | 8869/18688 [10:23:02<10:53:52, 4.00s/it] 47%|████▋ | 8870/18688 [10:23:06<10:29:27, 3.85s/it] {'loss': '0.9429', 'grad_norm': '1.323', 'learning_rate': '4.888e-05', 'epoch': '0.4746', 'num_input_tokens_seen': 29802992, 'train_runtime': '3.739e+04', 'train_tokens_per_second': '797.2'}
47%|████▋ | 8870/18688 [10:23:06<10:29:27, 3.85s/it] 47%|████▋ | 8871/18688 [10:23:11<11:23:32, 4.18s/it] 47%|████▋ | 8872/18688 [10:23:16<12:01:02, 4.41s/it] 47%|████▋ | 8873/18688 [10:23:20<11:45:15, 4.31s/it] 47%|████▋ | 8874/18688 [10:23:24<11:54:17, 4.37s/it] 47%|████▋ | 8875/18688 [10:23:29<12:16:25, 4.50s/it] 47%|████▋ | 8876/18688 [10:23:33<11:57:24, 4.39s/it] 48%|████▊ | 8877/18688 [10:23:37<11:36:51, 4.26s/it] 48%|████▊ | 8878/18688 [10:23:41<11:08:31, 4.09s/it] 48%|████▊ | 8879/18688 [10:23:45<10:52:13, 3.99s/it] 48%|████▊ | 8880/18688 [10:23:48<10:34:07, 3.88s/it] {'loss': '0.931', 'grad_norm': '1.247', 'learning_rate': '4.881e-05', 'epoch': '0.4752', 'num_input_tokens_seen': 29837816, 'train_runtime': '3.743e+04', 'train_tokens_per_second': '797.2'}
48%|████▊ | 8880/18688 [10:23:48<10:34:07, 3.88s/it] 48%|████▊ | 8881/18688 [10:23:52<10:25:20, 3.83s/it] 48%|████▊ | 8882/18688 [10:23:56<10:34:21, 3.88s/it] 48%|████▊ | 8883/18688 [10:24:01<11:25:59, 4.20s/it] 48%|████▊ | 8884/18688 [10:24:06<11:58:02, 4.39s/it] 48%|████▊ | 8885/18688 [10:24:09<11:13:00, 4.12s/it] 48%|████▊ | 8886/18688 [10:24:13<10:40:06, 3.92s/it] 48%|████▊ | 8887/18688 [10:24:16<10:38:04, 3.91s/it] 48%|████▊ | 8888/18688 [10:24:21<10:52:23, 3.99s/it] 48%|████▊ | 8889/18688 [10:24:25<11:06:57, 4.08s/it] 48%|████▊ | 8890/18688 [10:24:29<11:11:02, 4.11s/it] {'loss': '0.9443', 'grad_norm': '1.278', 'learning_rate': '4.874e-05', 'epoch': '0.4757', 'num_input_tokens_seen': 29872080, 'train_runtime': '3.747e+04', 'train_tokens_per_second': '797.2'}
48%|████▊ | 8890/18688 [10:24:29<11:11:02, 4.11s/it] 48%|████▊ | 8891/18688 [10:24:34<12:06:32, 4.45s/it] 48%|████▊ | 8892/18688 [10:24:38<11:31:40, 4.24s/it] 48%|████▊ | 8893/18688 [10:24:42<11:30:39, 4.23s/it] 48%|████▊ | 8894/18688 [10:24:46<11:22:23, 4.18s/it] 48%|████▊ | 8895/18688 [10:24:52<12:25:37, 4.57s/it] 48%|████▊ | 8896/18688 [10:24:56<12:04:51, 4.44s/it] 48%|████▊ | 8897/18688 [10:25:00<11:57:21, 4.40s/it] 48%|████▊ | 8898/18688 [10:25:04<11:40:17, 4.29s/it] 48%|████▊ | 8899/18688 [10:25:09<11:57:32, 4.40s/it] 48%|████▊ | 8900/18688 [10:25:13<11:39:26, 4.29s/it] {'loss': '0.9314', 'grad_norm': '1.286', 'learning_rate': '4.867e-05', 'epoch': '0.4763', 'num_input_tokens_seen': 29909232, 'train_runtime': '3.751e+04', 'train_tokens_per_second': '797.3'}
48%|████▊ | 8900/18688 [10:25:13<11:39:26, 4.29s/it] 48%|████▊ | 8901/18688 [10:25:17<11:42:03, 4.30s/it] 48%|████▊ | 8902/18688 [10:25:21<11:06:40, 4.09s/it] 48%|████▊ | 8903/18688 [10:25:25<11:00:08, 4.05s/it] 48%|████▊ | 8904/18688 [10:25:31<12:31:11, 4.61s/it] 48%|████▊ | 8905/18688 [10:25:35<12:10:15, 4.48s/it] 48%|████▊ | 8906/18688 [10:25:39<11:36:55, 4.27s/it] 48%|████▊ | 8907/18688 [10:25:43<11:12:46, 4.13s/it] 48%|████▊ | 8908/18688 [10:25:46<11:00:32, 4.05s/it] 48%|████▊ | 8909/18688 [10:25:50<10:48:09, 3.98s/it] 48%|████▊ | 8910/18688 [10:25:54<10:36:03, 3.90s/it] {'loss': '0.9157', 'grad_norm': '1.527', 'learning_rate': '4.86e-05', 'epoch': '0.4768', 'num_input_tokens_seen': 29940944, 'train_runtime': '3.755e+04', 'train_tokens_per_second': '797.3'}
48%|████▊ | 8910/18688 [10:25:54<10:36:03, 3.90s/it] 48%|████▊ | 8911/18688 [10:25:58<10:46:38, 3.97s/it] 48%|████▊ | 8912/18688 [10:26:03<11:33:37, 4.26s/it] 48%|████▊ | 8913/18688 [10:26:08<11:51:52, 4.37s/it] 48%|████▊ | 8914/18688 [10:26:12<11:27:20, 4.22s/it] 48%|████▊ | 8915/18688 [10:26:16<11:34:22, 4.26s/it] 48%|████▊ | 8916/18688 [10:26:20<11:26:11, 4.21s/it] 48%|████▊ | 8917/18688 [10:26:24<11:18:22, 4.17s/it] 48%|████▊ | 8918/18688 [10:26:28<11:26:45, 4.22s/it] 48%|████▊ | 8919/18688 [10:26:32<11:10:45, 4.12s/it] 48%|████▊ | 8920/18688 [10:26:36<10:40:31, 3.93s/it] {'loss': '0.9194', 'grad_norm': '1.146', 'learning_rate': '4.852e-05', 'epoch': '0.4773', 'num_input_tokens_seen': 29974920, 'train_runtime': '3.76e+04', 'train_tokens_per_second': '797.3'}
48%|████▊ | 8920/18688 [10:26:36<10:40:31, 3.93s/it] 48%|████▊ | 8921/18688 [10:26:39<10:27:38, 3.86s/it] 48%|████▊ | 8922/18688 [10:26:43<10:32:35, 3.89s/it] 48%|████▊ | 8923/18688 [10:26:48<11:05:17, 4.09s/it] 48%|████▊ | 8924/18688 [10:26:52<10:56:44, 4.04s/it] 48%|████▊ | 8925/18688 [10:26:55<10:28:55, 3.87s/it] 48%|████▊ | 8926/18688 [10:27:00<10:49:52, 3.99s/it] 48%|████▊ | 8927/18688 [10:27:04<10:45:35, 3.97s/it] 48%|████▊ | 8928/18688 [10:27:08<11:22:41, 4.20s/it] 48%|████▊ | 8929/18688 [10:27:13<11:27:14, 4.23s/it] 48%|████▊ | 8930/18688 [10:27:17<11:12:50, 4.14s/it] {'loss': '0.9229', 'grad_norm': '1.09', 'learning_rate': '4.845e-05', 'epoch': '0.4779', 'num_input_tokens_seen': 30007600, 'train_runtime': '3.764e+04', 'train_tokens_per_second': '797.3'}
48%|████▊ | 8930/18688 [10:27:17<11:12:50, 4.14s/it] 48%|████▊ | 8931/18688 [10:27:20<11:05:21, 4.09s/it] 48%|████▊ | 8932/18688 [10:27:26<11:56:01, 4.40s/it] 48%|████▊ | 8933/18688 [10:27:30<11:57:49, 4.42s/it] 48%|████▊ | 8934/18688 [10:27:35<12:08:04, 4.48s/it] 48%|████▊ | 8935/18688 [10:27:39<11:37:48, 4.29s/it] 48%|████▊ | 8936/18688 [10:27:43<11:52:57, 4.39s/it] 48%|████▊ | 8937/18688 [10:27:48<12:27:37, 4.60s/it] 48%|████▊ | 8938/18688 [10:27:52<11:49:09, 4.36s/it] 48%|████▊ | 8939/18688 [10:27:56<11:43:23, 4.33s/it] 48%|████▊ | 8940/18688 [10:28:00<11:18:18, 4.18s/it] {'loss': '0.9453', 'grad_norm': '1.015', 'learning_rate': '4.838e-05', 'epoch': '0.4784', 'num_input_tokens_seen': 30046312, 'train_runtime': '3.768e+04', 'train_tokens_per_second': '797.4'}
48%|████▊ | 8940/18688 [10:28:00<11:18:18, 4.18s/it] 48%|████▊ | 8941/18688 [10:28:04<11:16:05, 4.16s/it] 48%|████▊ | 8942/18688 [10:28:09<11:26:59, 4.23s/it] 48%|████▊ | 8943/18688 [10:28:13<11:36:37, 4.29s/it] 48%|████▊ | 8944/18688 [10:28:17<11:11:19, 4.13s/it] 48%|████▊ | 8945/18688 [10:28:22<11:42:35, 4.33s/it] 48%|████▊ | 8946/18688 [10:28:26<11:43:32, 4.33s/it] 48%|████▊ | 8947/18688 [10:28:31<11:57:29, 4.42s/it] 48%|████▊ | 8948/18688 [10:28:34<11:22:37, 4.21s/it] 48%|████▊ | 8949/18688 [10:28:38<10:56:17, 4.04s/it] 48%|████▊ | 8950/18688 [10:28:42<10:47:57, 3.99s/it] {'loss': '0.9539', 'grad_norm': '1.255', 'learning_rate': '4.831e-05', 'epoch': '0.4789', 'num_input_tokens_seen': 30080552, 'train_runtime': '3.772e+04', 'train_tokens_per_second': '797.4'}
48%|████▊ | 8950/18688 [10:28:42<10:47:57, 3.99s/it] 48%|████▊ | 8951/18688 [10:28:46<10:52:12, 4.02s/it] 48%|████▊ | 8952/18688 [10:28:50<10:39:03, 3.94s/it] 48%|████▊ | 8953/18688 [10:28:54<11:11:36, 4.14s/it] 48%|████▊ | 8954/18688 [10:28:59<11:41:49, 4.33s/it] 48%|████▊ | 8955/18688 [10:29:03<11:27:36, 4.24s/it] 48%|████▊ | 8956/18688 [10:29:07<11:00:57, 4.07s/it] 48%|████▊ | 8957/18688 [10:29:11<10:53:51, 4.03s/it] 48%|████▊ | 8958/18688 [10:29:14<10:41:49, 3.96s/it] 48%|████▊ | 8959/18688 [10:29:19<11:00:31, 4.07s/it] 48%|████▊ | 8960/18688 [10:29:23<10:58:10, 4.06s/it] {'loss': '0.9589', 'grad_norm': '1.148', 'learning_rate': '4.824e-05', 'epoch': '0.4795', 'num_input_tokens_seen': 30113616, 'train_runtime': '3.776e+04', 'train_tokens_per_second': '797.4'}
48%|████▊ | 8960/18688 [10:29:23<10:58:10, 4.06s/it] 48%|████▊ | 8961/18688 [10:29:27<10:58:24, 4.06s/it] 48%|████▊ | 8962/18688 [10:29:31<11:01:00, 4.08s/it] 48%|████▊ | 8963/18688 [10:29:35<11:04:26, 4.10s/it] 48%|████▊ | 8964/18688 [10:29:39<10:38:19, 3.94s/it] 48%|████▊ | 8965/18688 [10:29:42<10:23:58, 3.85s/it] 48%|████▊ | 8966/18688 [10:29:46<10:17:38, 3.81s/it] 48%|████▊ | 8967/18688 [10:29:51<11:23:22, 4.22s/it] 48%|████▊ | 8968/18688 [10:29:56<11:30:13, 4.26s/it] 48%|████▊ | 8969/18688 [10:30:00<11:20:11, 4.20s/it] 48%|████▊ | 8970/18688 [10:30:04<11:05:58, 4.11s/it] {'loss': '0.9039', 'grad_norm': '0.9881', 'learning_rate': '4.817e-05', 'epoch': '0.48', 'num_input_tokens_seen': 30146696, 'train_runtime': '3.78e+04', 'train_tokens_per_second': '797.4'}
48%|████▊ | 8970/18688 [10:30:04<11:05:58, 4.11s/it] 48%|████▊ | 8971/18688 [10:30:07<10:36:59, 3.93s/it] 48%|████▊ | 8972/18688 [10:30:12<11:14:25, 4.16s/it] 48%|████▊ | 8973/18688 [10:30:16<11:26:10, 4.24s/it] 48%|████▊ | 8974/18688 [10:30:20<11:27:14, 4.24s/it] 48%|████▊ | 8975/18688 [10:30:25<11:26:06, 4.24s/it] 48%|████▊ | 8976/18688 [10:30:29<11:15:54, 4.18s/it] 48%|████▊ | 8977/18688 [10:30:33<11:38:52, 4.32s/it] 48%|████▊ | 8978/18688 [10:30:37<10:58:19, 4.07s/it] 48%|████▊ | 8979/18688 [10:30:41<10:59:14, 4.07s/it] 48%|████▊ | 8980/18688 [10:30:44<10:32:18, 3.91s/it] {'loss': '0.8965', 'grad_norm': '1.267', 'learning_rate': '4.81e-05', 'epoch': '0.4805', 'num_input_tokens_seen': 30180896, 'train_runtime': '3.784e+04', 'train_tokens_per_second': '797.5'}
48%|████▊ | 8980/18688 [10:30:44<10:32:18, 3.91s/it] 48%|████▊ | 8981/18688 [10:30:48<10:32:17, 3.91s/it] 48%|████▊ | 8982/18688 [10:30:53<11:26:20, 4.24s/it] 48%|████▊ | 8983/18688 [10:30:58<11:22:06, 4.22s/it] 48%|████▊ | 8984/18688 [10:31:03<12:02:51, 4.47s/it] 48%|████▊ | 8985/18688 [10:31:06<11:24:29, 4.23s/it] 48%|████▊ | 8986/18688 [10:31:10<10:58:15, 4.07s/it] 48%|████▊ | 8987/18688 [10:31:14<10:53:13, 4.04s/it] 48%|████▊ | 8988/18688 [10:31:18<10:40:34, 3.96s/it] 48%|████▊ | 8989/18688 [10:31:23<11:24:58, 4.24s/it] 48%|████▊ | 8990/18688 [10:31:26<11:03:34, 4.11s/it] {'loss': '0.9151', 'grad_norm': '1.115', 'learning_rate': '4.802e-05', 'epoch': '0.4811', 'num_input_tokens_seen': 30215784, 'train_runtime': '3.789e+04', 'train_tokens_per_second': '797.5'}
48%|████▊ | 8990/18688 [10:31:26<11:03:34, 4.11s/it] 48%|████▊ | 8991/18688 [10:31:31<11:02:35, 4.10s/it] 48%|████▊ | 8992/18688 [10:31:34<10:40:07, 3.96s/it] 48%|████▊ | 8993/18688 [10:31:38<10:32:17, 3.91s/it] 48%|████▊ | 8994/18688 [10:31:42<10:54:08, 4.05s/it] 48%|████▊ | 8995/18688 [10:31:46<10:42:14, 3.98s/it] 48%|████▊ | 8996/18688 [10:31:50<10:16:31, 3.82s/it] 48%|████▊ | 8997/18688 [10:31:54<10:59:50, 4.09s/it] 48%|████▊ | 8998/18688 [10:31:59<11:50:49, 4.40s/it] 48%|████▊ | 8999/18688 [10:32:04<11:52:31, 4.41s/it] 48%|████▊ | 9000/18688 [10:32:08<11:34:23, 4.30s/it] {'loss': '0.8543', 'grad_norm': '1.169', 'learning_rate': '4.795e-05', 'epoch': '0.4816', 'num_input_tokens_seen': 30248528, 'train_runtime': '3.793e+04', 'train_tokens_per_second': '797.5'}
48%|████▊ | 9000/18688 [10:32:08<11:34:23, 4.30s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:71: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:281: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 0/1000 [00:00<?, ?it/s]/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:172: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:202: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:254: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 3/1000 [00:00<00:38, 25.85it/s]
1%| | 6/1000 [00:00<00:54, 18.29it/s]
1%| | 8/1000 [00:00<00:56, 17.56it/s]
1%| | 10/1000 [00:00<00:59, 16.62it/s]
1%| | 12/1000 [00:00<01:00, 16.39it/s]
1%|▏ | 14/1000 [00:00<01:01, 15.94it/s]
2%|▏ | 16/1000 [00:00<01:02, 15.73it/s]
2%|▏ | 18/1000 [00:01<01:38, 10.01it/s]
2%|▏ | 20/1000 [00:01<01:32, 10.56it/s]
2%|▏ | 22/1000 [00:01<01:52, 8.73it/s]
2%|▏ | 24/1000 [00:01<01:39, 9.78it/s]
3%|▎ | 26/1000 [00:02<01:44, 9.29it/s]
3%|▎ | 28/1000 [00:02<01:36, 10.11it/s]
3%|▎ | 30/1000 [00:02<01:28, 11.00it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.14it/s]
3%|▎ | 34/1000 [00:02<01:25, 11.25it/s]
4%|▎ | 36/1000 [00:03<01:32, 10.44it/s]
4%|▍ | 38/1000 [00:03<01:24, 11.34it/s]
4%|▍ | 40/1000 [00:03<01:27, 10.94it/s]
4%|▍ | 42/1000 [00:03<01:46, 9.02it/s]
4%|▍ | 44/1000 [00:03<01:31, 10.46it/s]
5%|▍ | 46/1000 [00:03<01:22, 11.63it/s]
5%|▍ | 48/1000 [00:04<01:29, 10.60it/s]
5%|▌ | 50/1000 [00:04<01:21, 11.59it/s]
5%|▌ | 52/1000 [00:04<01:16, 12.42it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.17it/s]
6%|▌ | 56/1000 [00:04<01:08, 13.86it/s]
6%|▌ | 58/1000 [00:05<01:48, 8.71it/s]
6%|▌ | 60/1000 [00:05<01:36, 9.75it/s]
6%|▌ | 62/1000 [00:05<01:27, 10.72it/s]
6%|▋ | 64/1000 [00:05<01:19, 11.78it/s]
7%|▋ | 66/1000 [00:05<01:16, 12.29it/s]
7%|▋ | 68/1000 [00:05<01:12, 12.87it/s]
7%|▋ | 70/1000 [00:06<01:09, 13.47it/s]
7%|▋ | 72/1000 [00:06<01:06, 13.92it/s]
7%|▋ | 74/1000 [00:06<01:07, 13.71it/s]
8%|▊ | 76/1000 [00:06<01:04, 14.31it/s]
8%|▊ | 78/1000 [00:06<01:03, 14.52it/s]
8%|▊ | 80/1000 [00:06<01:08, 13.49it/s]
8%|▊ | 82/1000 [00:06<01:05, 14.12it/s]
8%|▊ | 84/1000 [00:06<01:02, 14.58it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.70it/s]
9%|▉ | 88/1000 [00:07<01:02, 14.51it/s]
9%|▉ | 90/1000 [00:07<01:02, 14.53it/s]
9%|▉ | 92/1000 [00:07<01:03, 14.20it/s]
9%|▉ | 94/1000 [00:07<01:04, 14.00it/s]
10%|▉ | 96/1000 [00:07<01:03, 14.30it/s]
10%|▉ | 98/1000 [00:07<01:03, 14.21it/s]
10%|█ | 100/1000 [00:08<01:31, 9.79it/s]
10%|█ | 102/1000 [00:08<01:23, 10.76it/s]
10%|█ | 104/1000 [00:08<01:54, 7.81it/s]
11%|█ | 106/1000 [00:09<02:02, 7.32it/s]
11%|█ | 108/1000 [00:09<01:42, 8.66it/s]
11%|█ | 110/1000 [00:09<01:27, 10.12it/s]
11%|█ | 112/1000 [00:09<01:19, 11.21it/s]
11%|█▏ | 114/1000 [00:09<01:14, 11.86it/s]
12%|█▏ | 116/1000 [00:09<01:10, 12.50it/s]
12%|█▏ | 118/1000 [00:09<01:06, 13.29it/s]
12%|█▏ | 120/1000 [00:10<01:09, 12.72it/s]
12%|█▏ | 122/1000 [00:10<01:23, 10.57it/s]
12%|█▏ | 124/1000 [00:10<01:30, 9.65it/s]
13%|█▎ | 126/1000 [00:10<01:22, 10.62it/s]
13%|█▎ | 128/1000 [00:10<01:15, 11.56it/s]
13%|█▎ | 130/1000 [00:11<01:11, 12.24it/s]
13%|█▎ | 132/1000 [00:11<01:05, 13.21it/s]
13%|█▎ | 134/1000 [00:11<01:09, 12.40it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.37it/s]
14%|█▍ | 138/1000 [00:11<01:04, 13.31it/s]
14%|█▍ | 140/1000 [00:11<01:25, 10.11it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.54it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.55it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.59it/s]
15%|█▍ | 148/1000 [00:12<01:14, 11.50it/s]
15%|█▌ | 150/1000 [00:12<01:07, 12.59it/s]
15%|█▌ | 152/1000 [00:12<01:04, 13.14it/s]
15%|█▌ | 154/1000 [00:13<01:03, 13.38it/s]
16%|█▌ | 156/1000 [00:13<01:03, 13.22it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.75it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.66it/s]
16%|█▌ | 162/1000 [00:13<01:01, 13.54it/s]
16%|█▋ | 164/1000 [00:13<01:01, 13.60it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.75it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.22it/s]
17%|█▋ | 170/1000 [00:14<01:20, 10.28it/s]
17%|█▋ | 172/1000 [00:14<01:15, 10.92it/s]
17%|█▋ | 174/1000 [00:14<01:38, 8.41it/s]
18%|█▊ | 176/1000 [00:15<02:00, 6.83it/s]
18%|█▊ | 178/1000 [00:15<01:39, 8.25it/s]
18%|█▊ | 180/1000 [00:15<01:36, 8.46it/s]
18%|█▊ | 182/1000 [00:16<01:40, 8.18it/s]
18%|█▊ | 184/1000 [00:16<01:27, 9.28it/s]
19%|█▊ | 186/1000 [00:16<01:40, 8.10it/s]
19%|█▉ | 188/1000 [00:16<01:26, 9.44it/s]
19%|█▉ | 190/1000 [00:16<01:17, 10.51it/s]
19%|█▉ | 192/1000 [00:16<01:12, 11.16it/s]
19%|█▉ | 194/1000 [00:17<01:17, 10.43it/s]
20%|█▉ | 196/1000 [00:17<01:30, 8.92it/s]
20%|█▉ | 198/1000 [00:17<01:18, 10.16it/s]
20%|██ | 200/1000 [00:17<01:16, 10.52it/s]
20%|██ | 202/1000 [00:17<01:09, 11.44it/s]
20%|██ | 204/1000 [00:17<01:03, 12.55it/s]
21%|██ | 206/1000 [00:18<01:00, 13.17it/s]
21%|██ | 208/1000 [00:18<00:58, 13.57it/s]
21%|██ | 210/1000 [00:18<00:54, 14.44it/s]
21%|██ | 212/1000 [00:18<00:53, 14.68it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.71it/s]
22%|██▏ | 216/1000 [00:18<00:54, 14.47it/s]
22%|██▏ | 218/1000 [00:18<00:52, 14.87it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.90it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.17it/s]
22%|██▏ | 224/1000 [00:19<01:21, 9.49it/s]
23%|██▎ | 226/1000 [00:19<01:13, 10.53it/s]
23%|██▎ | 228/1000 [00:20<01:07, 11.46it/s]
23%|██▎ | 230/1000 [00:20<01:04, 11.96it/s]
23%|██▎ | 232/1000 [00:20<01:02, 12.33it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.09it/s]
24%|██▎ | 236/1000 [00:20<00:56, 13.58it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.21it/s]
24%|██▍ | 240/1000 [00:20<00:53, 14.21it/s]
24%|██▍ | 242/1000 [00:20<00:51, 14.77it/s]
24%|██▍ | 244/1000 [00:21<00:50, 14.99it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.94it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.65it/s]
25%|██▌ | 250/1000 [00:21<00:59, 12.62it/s]
25%|██▌ | 252/1000 [00:21<01:04, 11.51it/s]
25%|██▌ | 254/1000 [00:22<01:03, 11.73it/s]
26%|██▌ | 256/1000 [00:22<01:02, 11.94it/s]
26%|██▌ | 258/1000 [00:22<01:01, 12.13it/s]
26%|██▌ | 260/1000 [00:22<01:04, 11.39it/s]
26%|██▌ | 262/1000 [00:22<01:02, 11.78it/s]
26%|██▋ | 264/1000 [00:22<01:01, 12.01it/s]
27%|██▋ | 266/1000 [00:22<00:59, 12.36it/s]
27%|██▋ | 268/1000 [00:23<00:55, 13.27it/s]
27%|██▋ | 270/1000 [00:23<00:58, 12.47it/s]
27%|██▋ | 272/1000 [00:23<01:05, 11.19it/s]
27%|██▋ | 274/1000 [00:23<01:03, 11.39it/s]
28%|██▊ | 276/1000 [00:23<00:58, 12.42it/s]
28%|██▊ | 278/1000 [00:23<00:56, 12.74it/s]
28%|██▊ | 280/1000 [00:24<00:53, 13.36it/s]
28%|██▊ | 282/1000 [00:24<00:50, 14.30it/s]
28%|██▊ | 284/1000 [00:24<00:50, 14.28it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.59it/s]
29%|██▉ | 288/1000 [00:24<00:47, 14.96it/s]
29%|██▉ | 290/1000 [00:24<00:46, 15.40it/s]
29%|██▉ | 292/1000 [00:24<00:45, 15.40it/s]
29%|██▉ | 294/1000 [00:24<00:46, 15.30it/s]
30%|██▉ | 296/1000 [00:25<00:45, 15.36it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.50it/s]
30%|███ | 300/1000 [00:25<00:46, 15.14it/s]
30%|███ | 302/1000 [00:25<00:46, 15.15it/s]
30%|███ | 304/1000 [00:25<00:50, 13.70it/s]
31%|███ | 306/1000 [00:25<00:48, 14.46it/s]
31%|███ | 308/1000 [00:25<00:47, 14.43it/s]
31%|███ | 310/1000 [00:26<00:47, 14.62it/s]
31%|███ | 312/1000 [00:26<00:47, 14.62it/s]
31%|███▏ | 314/1000 [00:26<00:44, 15.33it/s]
32%|███▏ | 316/1000 [00:26<00:43, 15.62it/s]
32%|███▏ | 318/1000 [00:26<00:42, 16.07it/s]
32%|███▏ | 320/1000 [00:26<01:12, 9.39it/s]
32%|███▏ | 322/1000 [00:27<01:03, 10.76it/s]
32%|███▏ | 324/1000 [00:27<00:58, 11.51it/s]
33%|███▎ | 326/1000 [00:27<00:58, 11.45it/s]
33%|███▎ | 328/1000 [00:27<00:58, 11.50it/s]
33%|███▎ | 330/1000 [00:27<00:53, 12.46it/s]
33%|███▎ | 332/1000 [00:27<00:52, 12.72it/s]
33%|███▎ | 334/1000 [00:28<00:50, 13.18it/s]
34%|███▎ | 336/1000 [00:28<00:48, 13.59it/s]
34%|███▍ | 338/1000 [00:28<01:05, 10.05it/s]
34%|███▍ | 340/1000 [00:28<00:59, 11.12it/s]
34%|███▍ | 342/1000 [00:28<00:53, 12.40it/s]
34%|███▍ | 344/1000 [00:28<00:50, 13.05it/s]
35%|███▍ | 346/1000 [00:29<00:47, 13.65it/s]
35%|███▍ | 348/1000 [00:29<00:46, 14.07it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.71it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.89it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.89it/s]
36%|███▌ | 356/1000 [00:29<00:42, 14.98it/s]
36%|███▌ | 358/1000 [00:29<00:43, 14.69it/s]
36%|███▌ | 360/1000 [00:29<00:42, 15.06it/s]
36%|███▌ | 362/1000 [00:30<00:47, 13.38it/s]
36%|███▋ | 364/1000 [00:30<00:45, 13.89it/s]
37%|███▋ | 366/1000 [00:30<00:53, 11.94it/s]
37%|███▋ | 368/1000 [00:30<00:51, 12.34it/s]
37%|███▋ | 370/1000 [00:30<00:47, 13.17it/s]
37%|███▋ | 372/1000 [00:30<00:46, 13.48it/s]
37%|███▋ | 374/1000 [00:31<00:55, 11.37it/s]
38%|███▊ | 376/1000 [00:31<00:56, 10.98it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.66it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.47it/s]
38%|███▊ | 382/1000 [00:31<00:48, 12.68it/s]
38%|███▊ | 384/1000 [00:31<00:45, 13.46it/s]
39%|███▊ | 386/1000 [00:32<00:44, 13.75it/s]
39%|███▉ | 388/1000 [00:32<00:44, 13.69it/s]
39%|███▉ | 390/1000 [00:32<00:44, 13.57it/s]
39%|███▉ | 392/1000 [00:32<00:43, 13.87it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.65it/s]
40%|███▉ | 396/1000 [00:32<00:43, 13.78it/s]
40%|███▉ | 398/1000 [00:32<00:47, 12.57it/s]
40%|████ | 400/1000 [00:33<00:45, 13.20it/s]
40%|████ | 402/1000 [00:33<00:43, 13.83it/s]
40%|████ | 404/1000 [00:33<00:41, 14.43it/s]
41%|████ | 406/1000 [00:33<00:39, 14.97it/s]
41%|████ | 408/1000 [00:33<00:47, 12.53it/s]
41%|████ | 410/1000 [00:33<00:56, 10.45it/s]
41%|████ | 412/1000 [00:34<00:50, 11.54it/s]
41%|████▏ | 414/1000 [00:34<01:07, 8.65it/s]
42%|████▏ | 416/1000 [00:34<01:23, 6.98it/s]
42%|████▏ | 418/1000 [00:34<01:10, 8.30it/s]
42%|████▏ | 420/1000 [00:35<01:00, 9.56it/s]
42%|████▏ | 422/1000 [00:35<00:52, 10.91it/s]
42%|████▏ | 424/1000 [00:35<01:14, 7.68it/s]
43%|████▎ | 426/1000 [00:35<01:04, 8.95it/s]
43%|████▎ | 428/1000 [00:35<00:56, 10.14it/s]
43%|████▎ | 430/1000 [00:36<01:04, 8.81it/s]
43%|████▎ | 432/1000 [00:36<01:27, 6.47it/s]
43%|████▎ | 434/1000 [00:36<01:13, 7.73it/s]
44%|████▎ | 436/1000 [00:37<01:03, 8.82it/s]
44%|████▍ | 438/1000 [00:37<01:05, 8.60it/s]
44%|████▍ | 440/1000 [00:37<01:01, 9.15it/s]
44%|████▍ | 442/1000 [00:37<00:54, 10.17it/s]
44%|████▍ | 444/1000 [00:37<00:50, 10.94it/s]
45%|████▍ | 446/1000 [00:37<00:47, 11.73it/s]
45%|████▍ | 448/1000 [00:38<00:51, 10.63it/s]
45%|████▌ | 450/1000 [00:38<00:47, 11.52it/s]
45%|████▌ | 452/1000 [00:38<00:45, 11.99it/s]
45%|████▌ | 454/1000 [00:38<00:46, 11.77it/s]
46%|████▌ | 456/1000 [00:38<00:44, 12.33it/s]
46%|████▌ | 458/1000 [00:38<00:40, 13.41it/s]
46%|████▌ | 460/1000 [00:39<00:39, 13.63it/s]
46%|████▌ | 462/1000 [00:39<00:37, 14.27it/s]
46%|████▋ | 464/1000 [00:39<00:36, 14.82it/s]
47%|████▋ | 466/1000 [00:39<00:36, 14.71it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.84it/s]
47%|████▋ | 470/1000 [00:39<00:52, 10.04it/s]
47%|████▋ | 472/1000 [00:40<00:51, 10.20it/s]
47%|████▋ | 474/1000 [00:40<00:47, 11.13it/s]
48%|████▊ | 476/1000 [00:40<00:44, 11.86it/s]
48%|████▊ | 478/1000 [00:40<00:44, 11.78it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.74it/s]
48%|████▊ | 482/1000 [00:40<00:39, 13.16it/s]
48%|████▊ | 484/1000 [00:40<00:38, 13.25it/s]
49%|████▊ | 486/1000 [00:41<00:39, 12.96it/s]
49%|████▉ | 488/1000 [00:41<00:40, 12.73it/s]
49%|████▉ | 490/1000 [00:41<00:49, 10.29it/s]
49%|████▉ | 492/1000 [00:41<00:44, 11.47it/s]
49%|████▉ | 494/1000 [00:41<00:40, 12.62it/s]
50%|████▉ | 496/1000 [00:41<00:37, 13.56it/s]
50%|████▉ | 498/1000 [00:42<00:35, 14.12it/s]
50%|█████ | 500/1000 [00:42<00:44, 11.36it/s]
50%|█████ | 502/1000 [00:42<00:42, 11.82it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.46it/s]
51%|█████ | 506/1000 [00:42<00:38, 12.69it/s]
51%|█████ | 508/1000 [00:42<00:36, 13.63it/s]
51%|█████ | 510/1000 [00:43<00:47, 10.34it/s]
51%|█████ | 512/1000 [00:43<00:43, 11.13it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 12.01it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.56it/s]
52%|█████▏ | 518/1000 [00:43<00:36, 13.07it/s]
52%|█████▏ | 520/1000 [00:43<00:35, 13.42it/s]
52%|█████▏ | 522/1000 [00:44<00:33, 14.14it/s]
52%|█████▏ | 524/1000 [00:44<00:33, 14.17it/s]
53%|█████▎ | 526/1000 [00:44<00:33, 14.34it/s]
53%|█████▎ | 528/1000 [00:44<00:39, 11.99it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.85it/s]
53%|█████▎ | 532/1000 [00:44<00:33, 13.79it/s]
53%|█████▎ | 534/1000 [00:44<00:32, 14.14it/s]
54%|█████▎ | 536/1000 [00:45<00:32, 14.26it/s]
54%|█████▍ | 538/1000 [00:45<00:32, 14.03it/s]
54%|█████▍ | 540/1000 [00:45<00:31, 14.46it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.72it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.62it/s]
55%|█████▍ | 546/1000 [00:45<00:31, 14.25it/s]
55%|█████▍ | 548/1000 [00:45<00:32, 13.93it/s]
55%|█████▌ | 550/1000 [00:46<00:31, 14.16it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.29it/s]
55%|█████▌ | 554/1000 [00:46<00:32, 13.93it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.10it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.39it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 14.88it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 14.86it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.76it/s]
57%|█████▋ | 566/1000 [00:47<00:37, 11.66it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.21it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.33it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.85it/s]
57%|█████▋ | 574/1000 [00:47<00:30, 13.74it/s]
58%|█████▊ | 576/1000 [00:47<00:31, 13.62it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.64it/s]
58%|█████▊ | 580/1000 [00:48<00:32, 12.86it/s]
58%|█████▊ | 582/1000 [00:48<00:30, 13.59it/s]
58%|█████▊ | 584/1000 [00:48<00:30, 13.83it/s]
59%|█████▊ | 586/1000 [00:48<00:29, 14.21it/s]
59%|█████▉ | 588/1000 [00:48<00:27, 15.02it/s]
59%|█████▉ | 590/1000 [00:48<00:27, 15.03it/s]
59%|█████▉ | 592/1000 [00:49<00:27, 14.79it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.32it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.84it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 14.09it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.45it/s]
60%|██████ | 602/1000 [00:49<00:27, 14.57it/s]
60%|██████ | 604/1000 [00:49<00:26, 15.14it/s]
61%|██████ | 606/1000 [00:50<00:26, 14.88it/s]
61%|██████ | 608/1000 [00:50<00:25, 15.16it/s]
61%|██████ | 610/1000 [00:50<00:26, 14.64it/s]
61%|██████ | 612/1000 [00:50<00:26, 14.58it/s]
61%|██████▏ | 614/1000 [00:50<00:28, 13.67it/s]
62%|██████▏ | 616/1000 [00:50<00:28, 13.51it/s]
62%|██████▏ | 618/1000 [00:50<00:28, 13.21it/s]
62%|██████▏ | 620/1000 [00:51<00:40, 9.39it/s]
62%|██████▏ | 622/1000 [00:51<00:39, 9.68it/s]
62%|██████▏ | 624/1000 [00:51<00:35, 10.67it/s]
63%|██████▎ | 626/1000 [00:51<00:32, 11.40it/s]
63%|██████▎ | 628/1000 [00:51<00:29, 12.64it/s]
63%|██████▎ | 630/1000 [00:52<00:27, 13.30it/s]
63%|██████▎ | 632/1000 [00:52<00:30, 12.16it/s]
63%|██████▎ | 634/1000 [00:52<00:28, 12.91it/s]
64%|██████▎ | 636/1000 [00:52<00:33, 10.79it/s]
64%|██████▍ | 638/1000 [00:52<00:34, 10.59it/s]
64%|██████▍ | 640/1000 [00:53<00:31, 11.39it/s]
64%|██████▍ | 642/1000 [00:53<00:29, 12.28it/s]
64%|██████▍ | 644/1000 [00:53<00:28, 12.55it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.85it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.33it/s]
65%|██████▌ | 650/1000 [00:53<00:27, 12.52it/s]
65%|██████▌ | 652/1000 [00:53<00:29, 11.88it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.41it/s]
66%|██████▌ | 656/1000 [00:54<00:32, 10.73it/s]
66%|██████▌ | 658/1000 [00:54<00:28, 11.80it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.12it/s]
66%|██████▌ | 662/1000 [00:54<00:30, 11.25it/s]
66%|██████▋ | 664/1000 [00:55<00:28, 11.75it/s]
67%|██████▋ | 666/1000 [00:55<00:28, 11.92it/s]
67%|██████▋ | 668/1000 [00:55<00:31, 10.63it/s]
67%|██████▋ | 670/1000 [00:55<00:31, 10.62it/s]
67%|██████▋ | 672/1000 [00:55<00:29, 11.10it/s]
67%|██████▋ | 674/1000 [00:55<00:28, 11.45it/s]
68%|██████▊ | 676/1000 [00:56<00:29, 11.07it/s]
68%|██████▊ | 678/1000 [00:56<00:27, 11.71it/s]
68%|██████▊ | 680/1000 [00:56<00:25, 12.60it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 11.87it/s]
68%|██████▊ | 684/1000 [00:56<00:26, 12.14it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.24it/s]
69%|██████▉ | 688/1000 [00:57<00:22, 13.57it/s]
69%|██████▉ | 690/1000 [00:57<00:22, 13.67it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.12it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.24it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 14.03it/s]
70%|██████▉ | 698/1000 [00:57<00:20, 14.61it/s]
70%|███████ | 700/1000 [00:57<00:24, 12.19it/s]
70%|███████ | 702/1000 [00:58<00:22, 13.00it/s]
70%|███████ | 704/1000 [00:58<00:30, 9.85it/s]
71%|███████ | 706/1000 [00:58<00:27, 10.65it/s]
71%|███████ | 708/1000 [00:58<00:30, 9.62it/s]
71%|███████ | 710/1000 [00:58<00:26, 10.91it/s]
71%|███████ | 712/1000 [00:59<00:24, 11.89it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.99it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 13.08it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.58it/s]
72%|███████▏ | 720/1000 [00:59<00:20, 13.52it/s]
72%|███████▏ | 722/1000 [00:59<00:19, 13.96it/s]
72%|███████▏ | 724/1000 [00:59<00:20, 13.78it/s]
73%|███████▎ | 726/1000 [01:00<00:19, 13.96it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.92it/s]
73%|███████▎ | 730/1000 [01:00<00:32, 8.20it/s]
73%|███████▎ | 732/1000 [01:00<00:32, 8.31it/s]
73%|███████▎ | 734/1000 [01:01<00:30, 8.59it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.44it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.89it/s]
74%|███████▍ | 739/1000 [01:01<00:23, 11.00it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.50it/s]
74%|███████▍ | 743/1000 [01:01<00:21, 11.70it/s]
74%|███████▍ | 745/1000 [01:02<00:26, 9.59it/s]
75%|███████▍ | 747/1000 [01:02<00:28, 8.88it/s]
75%|███████▍ | 749/1000 [01:02<00:24, 10.08it/s]
75%|███████▌ | 751/1000 [01:02<00:22, 11.24it/s]
75%|███████▌ | 753/1000 [01:02<00:20, 12.02it/s]
76%|███████▌ | 755/1000 [01:02<00:19, 12.68it/s]
76%|███████▌ | 757/1000 [01:03<00:24, 9.99it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.06it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.10it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.14it/s]
76%|███████▋ | 765/1000 [01:04<00:27, 8.53it/s]
77%|███████▋ | 767/1000 [01:04<00:24, 9.50it/s]
77%|███████▋ | 769/1000 [01:04<00:21, 10.52it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.80it/s]
77%|███████▋ | 773/1000 [01:04<00:18, 12.12it/s]
78%|███████▊ | 775/1000 [01:04<00:18, 12.17it/s]
78%|███████▊ | 777/1000 [01:05<00:18, 12.33it/s]
78%|███████▊ | 779/1000 [01:05<00:22, 9.82it/s]
78%|███████▊ | 781/1000 [01:05<00:24, 9.00it/s]
78%|███████▊ | 782/1000 [01:05<00:29, 7.39it/s]
78%|███████▊ | 784/1000 [01:06<00:30, 7.13it/s]
78%|███████▊ | 785/1000 [01:06<00:28, 7.44it/s]
79%|███████▊ | 786/1000 [01:06<00:30, 7.05it/s]
79%|███████▉ | 788/1000 [01:06<00:23, 8.84it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.14it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 11.27it/s]
79%|███████▉ | 794/1000 [01:06<00:17, 11.86it/s]
80%|███████▉ | 796/1000 [01:07<00:16, 12.32it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 13.09it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.32it/s]
80%|████████ | 802/1000 [01:07<00:24, 8.24it/s]
80%|████████ | 804/1000 [01:08<00:23, 8.30it/s]
81%|████████ | 806/1000 [01:08<00:21, 8.97it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.36it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.46it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.43it/s]
81%|████████▏ | 814/1000 [01:08<00:15, 12.15it/s]
82%|████████▏ | 816/1000 [01:09<00:18, 10.13it/s]
82%|████████▏ | 818/1000 [01:09<00:17, 10.53it/s]
82%|████████▏ | 820/1000 [01:09<00:15, 11.35it/s]
82%|████████▏ | 822/1000 [01:09<00:14, 12.07it/s]
82%|████████▏ | 824/1000 [01:09<00:13, 13.03it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 13.54it/s]
83%|████████▎ | 828/1000 [01:10<00:12, 13.62it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.31it/s]
83%|████████▎ | 832/1000 [01:10<00:12, 13.80it/s]
83%|████████▎ | 834/1000 [01:10<00:12, 13.68it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.08it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 13.88it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.10it/s]
84%|████████▍ | 842/1000 [01:11<00:11, 14.19it/s]
84%|████████▍ | 844/1000 [01:11<00:11, 13.80it/s]
85%|████████▍ | 846/1000 [01:11<00:11, 13.83it/s]
85%|████████▍ | 848/1000 [01:11<00:11, 13.80it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.02it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.64it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.25it/s]
86%|████████▌ | 856/1000 [01:12<00:10, 14.10it/s]
86%|████████▌ | 858/1000 [01:12<00:09, 14.61it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 15.37it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.50it/s]
86%|████████▋ | 864/1000 [01:12<00:10, 13.14it/s]
87%|████████▋ | 866/1000 [01:12<00:10, 13.22it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.30it/s]
87%|████████▋ | 870/1000 [01:13<00:09, 13.72it/s]
87%|████████▋ | 872/1000 [01:13<00:08, 14.39it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 12.30it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 10.94it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.85it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.50it/s]
88%|████████▊ | 882/1000 [01:14<00:10, 11.33it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 12.08it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.87it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.82it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.23it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.03it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 14.20it/s]
90%|████████▉ | 896/1000 [01:15<00:07, 13.92it/s]
90%|████████▉ | 898/1000 [01:15<00:07, 13.34it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.27it/s]
90%|█████████ | 902/1000 [01:15<00:07, 13.00it/s]
90%|█████████ | 904/1000 [01:15<00:09, 9.81it/s]
91%|█████████ | 906/1000 [01:16<00:08, 10.88it/s]
91%|█████████ | 908/1000 [01:16<00:07, 11.86it/s]
91%|█████████ | 910/1000 [01:16<00:07, 12.10it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.28it/s]
91%|█████████▏| 914/1000 [01:17<00:12, 6.80it/s]
92%|█████████▏| 916/1000 [01:17<00:10, 7.98it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.25it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.27it/s]
92%|█████████▏| 922/1000 [01:17<00:08, 9.21it/s]
92%|█████████▏| 924/1000 [01:18<00:07, 9.92it/s]
93%|█████████▎| 926/1000 [01:18<00:06, 10.57it/s]
93%|█████████▎| 928/1000 [01:18<00:07, 9.78it/s]
93%|█████████▎| 930/1000 [01:18<00:06, 10.42it/s]
93%|█████████▎| 932/1000 [01:18<00:06, 10.86it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 11.30it/s]
94%|█████████▎| 936/1000 [01:19<00:05, 12.11it/s]
94%|█████████▍| 938/1000 [01:19<00:04, 13.23it/s]
94%|█████████▍| 940/1000 [01:19<00:04, 13.81it/s]
94%|█████████▍| 942/1000 [01:19<00:04, 11.86it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.49it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.67it/s]
95%|█████████▍| 948/1000 [01:20<00:04, 12.53it/s]
95%|█████████▌| 950/1000 [01:20<00:03, 13.35it/s]
95%|█████████▌| 952/1000 [01:20<00:03, 14.08it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 14.21it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 14.23it/s]
96%|█████████▌| 958/1000 [01:20<00:02, 14.73it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 13.08it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.69it/s]
96%|█████████▋| 964/1000 [01:21<00:03, 10.20it/s]
97%|█████████▋| 966/1000 [01:21<00:02, 11.41it/s]
97%|█████████▋| 968/1000 [01:21<00:02, 12.07it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 12.70it/s]
97%|█████████▋| 972/1000 [01:22<00:02, 9.69it/s]
97%|█████████▋| 974/1000 [01:22<00:02, 10.88it/s]
98%|█████████▊| 976/1000 [01:22<00:02, 11.67it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.86it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.95it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.45it/s]
98%|█████████▊| 984/1000 [01:23<00:01, 10.44it/s]
99%|█████████▊| 986/1000 [01:23<00:01, 10.70it/s]
99%|█████████▉| 988/1000 [01:23<00:01, 10.90it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 11.36it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 12.26it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 12.96it/s]
100%|█████████▉| 996/1000 [01:24<00:00, 13.87it/s]
100%|█████████▉| 998/1000 [01:24<00:00, 14.06it/s]
100%|██████████| 1000/1000 [01:24<00:00, 14.74it/s]
{'eval_loss': '0.9066', 'eval_runtime': '84.37', 'eval_samples_per_second': '11.85', 'eval_steps_per_second': '11.85', 'epoch': '0.4816', 'num_input_tokens_seen': 30248528}
48%|████▊ | 9000/18688 [10:33:32<11:34:23, 4.30s/it]
100%|██████████| 1000/1000 [01:24<00:00, 14.74it/s]
 48%|████▊ | 9001/18688 [10:33:36<79:25:22, 29.52s/it] 48%|████▊ | 9002/18688 [10:33:40<58:35:14, 21.78s/it] 48%|████▊ | 9003/18688 [10:33:44<44:02:27, 16.37s/it] 48%|████▊ | 9004/18688 [10:33:48<33:57:24, 12.62s/it] 48%|████▊ | 9005/18688 [10:33:52<27:14:50, 10.13s/it] 48%|████▊ | 9006/18688 [10:33:56<22:12:32, 8.26s/it] 48%|████▊ | 9007/18688 [10:33:59<18:28:19, 6.87s/it] 48%|████▊ | 9008/18688 [10:34:04<16:35:35, 6.17s/it] 48%|████▊ | 9009/18688 [10:34:07<14:25:09, 5.36s/it] 48%|████▊ | 9010/18688 [10:34:11<13:08:56, 4.89s/it] {'loss': '0.9185', 'grad_norm': '1.254', 'learning_rate': '4.788e-05', 'epoch': '0.4821', 'num_input_tokens_seen': 30278768, 'train_runtime': '3.805e+04', 'train_tokens_per_second': '795.7'}
48%|████▊ | 9010/18688 [10:34:11<13:08:56, 4.89s/it] 48%|████▊ | 9011/18688 [10:34:15<12:16:43, 4.57s/it] 48%|████▊ | 9012/18688 [10:34:19<11:39:15, 4.34s/it] 48%|████▊ | 9013/18688 [10:34:24<12:05:51, 4.50s/it] 48%|████▊ | 9014/18688 [10:34:28<12:06:32, 4.51s/it] 48%|████▊ | 9015/18688 [10:34:32<11:50:20, 4.41s/it] 48%|████▊ | 9016/18688 [10:34:37<12:13:50, 4.55s/it] 48%|████▊ | 9017/18688 [10:34:41<11:38:57, 4.34s/it] 48%|████▊ | 9018/18688 [10:34:45<11:34:55, 4.31s/it] 48%|████▊ | 9019/18688 [10:34:49<11:05:49, 4.13s/it] 48%|████▊ | 9020/18688 [10:34:53<11:01:27, 4.10s/it] {'loss': '0.9733', 'grad_norm': '1.221', 'learning_rate': '4.781e-05', 'epoch': '0.4827', 'num_input_tokens_seen': 30311880, 'train_runtime': '3.809e+04', 'train_tokens_per_second': '795.7'}
48%|████▊ | 9020/18688 [10:34:53<11:01:27, 4.10s/it] 48%|████▊ | 9021/18688 [10:34:58<11:44:55, 4.38s/it] 48%|████▊ | 9022/18688 [10:35:02<11:39:55, 4.34s/it] 48%|████▊ | 9023/18688 [10:35:07<11:29:59, 4.28s/it] 48%|████▊ | 9024/18688 [10:35:10<10:49:16, 4.03s/it] 48%|████▊ | 9025/18688 [10:35:13<10:19:36, 3.85s/it] 48%|████▊ | 9026/18688 [10:35:17<10:12:34, 3.80s/it] 48%|████▊ | 9027/18688 [10:35:21<10:24:10, 3.88s/it] 48%|████▊ | 9028/18688 [10:35:25<10:29:53, 3.91s/it] 48%|████▊ | 9029/18688 [10:35:30<10:55:27, 4.07s/it] 48%|████▊ | 9030/18688 [10:35:35<11:34:12, 4.31s/it] {'loss': '0.9349', 'grad_norm': '1.239', 'learning_rate': '4.774e-05', 'epoch': '0.4832', 'num_input_tokens_seen': 30345672, 'train_runtime': '3.814e+04', 'train_tokens_per_second': '795.7'}
48%|████▊ | 9030/18688 [10:35:35<11:34:12, 4.31s/it] 48%|████▊ | 9031/18688 [10:35:39<11:25:18, 4.26s/it] 48%|████▊ | 9032/18688 [10:35:42<10:54:07, 4.06s/it] 48%|████▊ | 9033/18688 [10:35:46<10:41:21, 3.99s/it] 48%|████▊ | 9034/18688 [10:35:50<10:31:04, 3.92s/it] 48%|████▊ | 9035/18688 [10:35:53<10:15:05, 3.82s/it] 48%|████▊ | 9036/18688 [10:35:58<11:07:14, 4.15s/it] 48%|████▊ | 9037/18688 [10:36:03<11:35:58, 4.33s/it] 48%|████▊ | 9038/18688 [10:36:07<11:20:40, 4.23s/it] 48%|████▊ | 9039/18688 [10:36:11<11:27:24, 4.27s/it] 48%|████▊ | 9040/18688 [10:36:16<11:39:57, 4.35s/it] {'loss': '0.9094', 'grad_norm': '1.107', 'learning_rate': '4.767e-05', 'epoch': '0.4837', 'num_input_tokens_seen': 30380232, 'train_runtime': '3.818e+04', 'train_tokens_per_second': '795.8'}
48%|████▊ | 9040/18688 [10:36:16<11:39:57, 4.35s/it] 48%|████▊ | 9041/18688 [10:36:20<11:30:05, 4.29s/it] 48%|████▊ | 9042/18688 [10:36:24<11:30:44, 4.30s/it] 48%|████▊ | 9043/18688 [10:36:29<11:28:00, 4.28s/it] 48%|████▊ | 9044/18688 [10:36:33<11:31:44, 4.30s/it] 48%|████▊ | 9045/18688 [10:36:37<11:33:44, 4.32s/it] 48%|████▊ | 9046/18688 [10:36:42<11:59:21, 4.48s/it] 48%|████▊ | 9047/18688 [10:36:47<12:12:16, 4.56s/it] 48%|████▊ | 9048/18688 [10:36:52<12:26:54, 4.65s/it] 48%|████▊ | 9049/18688 [10:36:55<11:27:50, 4.28s/it] 48%|████▊ | 9050/18688 [10:37:00<11:28:23, 4.29s/it] {'loss': '0.9692', 'grad_norm': '1.257', 'learning_rate': '4.759e-05', 'epoch': '0.4843', 'num_input_tokens_seen': 30415880, 'train_runtime': '3.822e+04', 'train_tokens_per_second': '795.8'}
48%|████▊ | 9050/18688 [10:37:00<11:28:23, 4.29s/it] 48%|████▊ | 9051/18688 [10:37:04<11:15:07, 4.20s/it] 48%|████▊ | 9052/18688 [10:37:07<10:44:59, 4.02s/it] 48%|████▊ | 9053/18688 [10:37:12<11:00:36, 4.11s/it] 48%|████▊ | 9054/18688 [10:37:16<11:01:54, 4.12s/it] 48%|████▊ | 9055/18688 [10:37:20<11:07:34, 4.16s/it] 48%|████▊ | 9056/18688 [10:37:24<10:52:26, 4.06s/it] 48%|████▊ | 9057/18688 [10:37:28<10:42:44, 4.00s/it] 48%|████▊ | 9058/18688 [10:37:32<11:07:31, 4.16s/it] 48%|████▊ | 9059/18688 [10:37:37<11:20:34, 4.24s/it] 48%|████▊ | 9060/18688 [10:37:41<11:30:52, 4.31s/it] {'loss': '0.9185', 'grad_norm': '1.156', 'learning_rate': '4.752e-05', 'epoch': '0.4848', 'num_input_tokens_seen': 30447968, 'train_runtime': '3.826e+04', 'train_tokens_per_second': '795.8'}
48%|████▊ | 9060/18688 [10:37:41<11:30:52, 4.31s/it] 48%|████▊ | 9061/18688 [10:37:45<11:20:20, 4.24s/it] 48%|████▊ | 9062/18688 [10:37:49<11:27:12, 4.28s/it] 48%|████▊ | 9063/18688 [10:37:54<11:23:26, 4.26s/it] 49%|████▊ | 9064/18688 [10:37:57<10:45:10, 4.02s/it] 49%|████▊ | 9065/18688 [10:38:02<11:06:36, 4.16s/it] 49%|████▊ | 9066/18688 [10:38:05<10:48:02, 4.04s/it] 49%|████▊ | 9067/18688 [10:38:10<11:21:51, 4.25s/it] 49%|████▊ | 9068/18688 [10:38:15<11:40:17, 4.37s/it] 49%|████▊ | 9069/18688 [10:38:19<11:36:47, 4.35s/it] 49%|████▊ | 9070/18688 [10:38:23<11:10:32, 4.18s/it] {'loss': '0.9374', 'grad_norm': '1.199', 'learning_rate': '4.745e-05', 'epoch': '0.4854', 'num_input_tokens_seen': 30481944, 'train_runtime': '3.83e+04', 'train_tokens_per_second': '795.8'}
49%|████▊ | 9070/18688 [10:38:23<11:10:32, 4.18s/it] 49%|████▊ | 9071/18688 [10:38:27<11:04:55, 4.15s/it] 49%|████▊ | 9072/18688 [10:38:31<10:54:23, 4.08s/it] 49%|████▊ | 9073/18688 [10:38:35<10:37:01, 3.98s/it] 49%|████▊ | 9074/18688 [10:38:39<10:56:09, 4.10s/it] 49%|████▊ | 9075/18688 [10:38:44<11:47:10, 4.41s/it] 49%|████▊ | 9076/18688 [10:38:48<11:31:56, 4.32s/it] 49%|████▊ | 9077/18688 [10:38:52<11:03:23, 4.14s/it] 49%|████▊ | 9078/18688 [10:38:57<11:26:55, 4.29s/it] 49%|████▊ | 9079/18688 [10:39:02<11:57:10, 4.48s/it] 49%|████▊ | 9080/18688 [10:39:06<11:50:25, 4.44s/it] {'loss': '0.999', 'grad_norm': '1.045', 'learning_rate': '4.738e-05', 'epoch': '0.4859', 'num_input_tokens_seen': 30516040, 'train_runtime': '3.835e+04', 'train_tokens_per_second': '795.8'}
49%|████▊ | 9080/18688 [10:39:06<11:50:25, 4.44s/it] 49%|████▊ | 9081/18688 [10:39:10<11:25:50, 4.28s/it] 49%|████▊ | 9082/18688 [10:39:15<12:02:05, 4.51s/it] 49%|████▊ | 9083/18688 [10:39:19<11:52:30, 4.45s/it] 49%|████▊ | 9084/18688 [10:39:24<11:50:27, 4.44s/it] 49%|████▊ | 9085/18688 [10:39:27<11:10:30, 4.19s/it] 49%|████▊ | 9086/18688 [10:39:31<10:56:41, 4.10s/it] 49%|████▊ | 9087/18688 [10:39:35<10:58:21, 4.11s/it] 49%|████▊ | 9088/18688 [10:39:39<10:32:16, 3.95s/it] 49%|████▊ | 9089/18688 [10:39:43<11:09:25, 4.18s/it] 49%|████▊ | 9090/18688 [10:39:48<11:14:20, 4.22s/it] {'loss': '0.8917', 'grad_norm': '1.202', 'learning_rate': '4.731e-05', 'epoch': '0.4864', 'num_input_tokens_seen': 30550568, 'train_runtime': '3.839e+04', 'train_tokens_per_second': '795.8'}
49%|████▊ | 9090/18688 [10:39:48<11:14:20, 4.22s/it] 49%|████▊ | 9091/18688 [10:39:52<11:06:02, 4.16s/it] 49%|████▊ | 9092/18688 [10:39:56<11:12:46, 4.21s/it] 49%|████▊ | 9093/18688 [10:40:00<11:11:21, 4.20s/it] 49%|████▊ | 9094/18688 [10:40:05<11:19:56, 4.25s/it] 49%|████▊ | 9095/18688 [10:40:09<11:11:01, 4.20s/it] 49%|████▊ | 9096/18688 [10:40:14<11:50:18, 4.44s/it] 49%|████▊ | 9097/18688 [10:40:18<11:35:13, 4.35s/it] 49%|████▊ | 9098/18688 [10:40:22<11:39:14, 4.37s/it] 49%|████▊ | 9099/18688 [10:40:26<11:06:07, 4.17s/it] 49%|████▊ | 9100/18688 [10:40:30<10:36:13, 3.98s/it] {'loss': '0.9016', 'grad_norm': '1.282', 'learning_rate': '4.723e-05', 'epoch': '0.487', 'num_input_tokens_seen': 30583960, 'train_runtime': '3.843e+04', 'train_tokens_per_second': '795.8'}
49%|████▊ | 9100/18688 [10:40:30<10:36:13, 3.98s/it] 49%|████▊ | 9101/18688 [10:40:34<11:19:39, 4.25s/it] 49%|████▊ | 9102/18688 [10:40:38<11:07:09, 4.18s/it] 49%|████▊ | 9103/18688 [10:40:44<11:49:17, 4.44s/it] 49%|████▊ | 9104/18688 [10:40:48<12:00:06, 4.51s/it] 49%|████▊ | 9105/18688 [10:40:53<11:51:26, 4.45s/it] 49%|████▊ | 9106/18688 [10:40:56<11:19:44, 4.26s/it] 49%|████▊ | 9107/18688 [10:41:00<11:09:02, 4.19s/it] 49%|████▊ | 9108/18688 [10:41:04<10:45:21, 4.04s/it] 49%|████▊ | 9109/18688 [10:41:08<10:44:08, 4.03s/it] 49%|████▊ | 9110/18688 [10:41:12<10:59:57, 4.13s/it] {'loss': '0.9669', 'grad_norm': '1.09', 'learning_rate': '4.716e-05', 'epoch': '0.4875', 'num_input_tokens_seen': 30618712, 'train_runtime': '3.847e+04', 'train_tokens_per_second': '795.9'}
49%|████▊ | 9110/18688 [10:41:12<10:59:57, 4.13s/it] 49%|████▉ | 9111/18688 [10:41:17<11:23:49, 4.28s/it] 49%|████▉ | 9112/18688 [10:41:21<11:09:24, 4.19s/it] 49%|████▉ | 9113/18688 [10:41:25<10:58:34, 4.13s/it] 49%|████▉ | 9114/18688 [10:41:29<10:50:57, 4.08s/it] 49%|████▉ | 9115/18688 [10:41:33<10:50:57, 4.08s/it] 49%|████▉ | 9116/18688 [10:41:37<10:37:01, 3.99s/it] 49%|████▉ | 9117/18688 [10:41:40<10:17:26, 3.87s/it] 49%|████▉ | 9118/18688 [10:41:44<10:06:16, 3.80s/it] 49%|████▉ | 9119/18688 [10:41:48<10:33:10, 3.97s/it] 49%|████▉ | 9120/18688 [10:41:52<10:29:53, 3.95s/it] {'loss': '0.9637', 'grad_norm': '0.9584', 'learning_rate': '4.709e-05', 'epoch': '0.488', 'num_input_tokens_seen': 30650416, 'train_runtime': '3.851e+04', 'train_tokens_per_second': '795.8'}
49%|████▉ | 9120/18688 [10:41:52<10:29:53, 3.95s/it] 49%|████▉ | 9121/18688 [10:41:57<10:41:53, 4.03s/it] 49%|████▉ | 9122/18688 [10:42:01<11:02:58, 4.16s/it] 49%|████▉ | 9123/18688 [10:42:05<11:06:30, 4.18s/it] 49%|████▉ | 9124/18688 [10:42:09<10:57:51, 4.13s/it] 49%|████▉ | 9125/18688 [10:42:13<10:32:09, 3.97s/it] 49%|████▉ | 9126/18688 [10:42:17<11:00:04, 4.14s/it] 49%|████▉ | 9127/18688 [10:42:21<10:55:33, 4.11s/it] 49%|████▉ | 9128/18688 [10:42:26<11:23:52, 4.29s/it] 49%|████▉ | 9129/18688 [10:42:30<10:57:47, 4.13s/it] 49%|████▉ | 9130/18688 [10:42:34<10:40:40, 4.02s/it] {'loss': '0.8858', 'grad_norm': '1.051', 'learning_rate': '4.702e-05', 'epoch': '0.4886', 'num_input_tokens_seen': 30683872, 'train_runtime': '3.855e+04', 'train_tokens_per_second': '795.9'}
49%|████▉ | 9130/18688 [10:42:34<10:40:40, 4.02s/it] 49%|████▉ | 9131/18688 [10:42:37<10:16:37, 3.87s/it] 49%|████▉ | 9132/18688 [10:42:42<10:44:58, 4.05s/it] 49%|████▉ | 9133/18688 [10:42:45<10:27:54, 3.94s/it] 49%|████▉ | 9134/18688 [10:42:50<10:44:18, 4.05s/it] 49%|████▉ | 9135/18688 [10:42:54<10:55:38, 4.12s/it] 49%|████▉ | 9136/18688 [10:42:59<11:28:15, 4.32s/it] 49%|████▉ | 9137/18688 [10:43:03<11:09:18, 4.20s/it] 49%|████▉ | 9138/18688 [10:43:08<12:10:45, 4.59s/it] 49%|████▉ | 9139/18688 [10:43:12<11:46:23, 4.44s/it] 49%|████▉ | 9140/18688 [10:43:16<11:28:37, 4.33s/it] {'loss': '0.9283', 'grad_norm': '1.705', 'learning_rate': '4.695e-05', 'epoch': '0.4891', 'num_input_tokens_seen': 30717896, 'train_runtime': '3.86e+04', 'train_tokens_per_second': '795.9'}
49%|████▉ | 9140/18688 [10:43:16<11:28:37, 4.33s/it] 49%|████▉ | 9141/18688 [10:43:21<11:25:28, 4.31s/it] 49%|████▉ | 9142/18688 [10:43:25<11:17:41, 4.26s/it] 49%|████▉ | 9143/18688 [10:43:28<10:41:08, 4.03s/it] 49%|████▉ | 9144/18688 [10:43:32<10:27:27, 3.94s/it] 49%|████▉ | 9145/18688 [10:43:36<10:11:32, 3.84s/it] 49%|████▉ | 9146/18688 [10:43:40<10:42:52, 4.04s/it] 49%|████▉ | 9147/18688 [10:43:44<10:36:39, 4.00s/it] 49%|████▉ | 9148/18688 [10:43:48<10:48:52, 4.08s/it] 49%|████▉ | 9149/18688 [10:43:52<10:26:25, 3.94s/it] 49%|████▉ | 9150/18688 [10:43:55<10:09:52, 3.84s/it] {'loss': '0.8828', 'grad_norm': '1.25', 'learning_rate': '4.687e-05', 'epoch': '0.4896', 'num_input_tokens_seen': 30748976, 'train_runtime': '3.864e+04', 'train_tokens_per_second': '795.9'}
49%|████▉ | 9150/18688 [10:43:55<10:09:52, 3.84s/it] 49%|████▉ | 9151/18688 [10:44:00<10:57:48, 4.14s/it] 49%|████▉ | 9152/18688 [10:44:04<10:40:14, 4.03s/it] 49%|████▉ | 9153/18688 [10:44:09<11:19:19, 4.27s/it] 49%|████▉ | 9154/18688 [10:44:13<10:56:14, 4.13s/it] 49%|████▉ | 9155/18688 [10:44:17<11:12:22, 4.23s/it] 49%|████▉ | 9156/18688 [10:44:22<11:44:27, 4.43s/it] 49%|████▉ | 9157/18688 [10:44:26<11:26:01, 4.32s/it] 49%|████▉ | 9158/18688 [10:44:30<11:26:05, 4.32s/it] 49%|████▉ | 9159/18688 [10:44:35<11:41:21, 4.42s/it] 49%|████▉ | 9160/18688 [10:44:40<12:02:45, 4.55s/it] {'loss': '1.003', 'grad_norm': '1.209', 'learning_rate': '4.68e-05', 'epoch': '0.4902', 'num_input_tokens_seen': 30787328, 'train_runtime': '3.868e+04', 'train_tokens_per_second': '795.9'}
49%|████▉ | 9160/18688 [10:44:40<12:02:45, 4.55s/it] 49%|████▉ | 9161/18688 [10:44:44<11:25:38, 4.32s/it] 49%|████▉ | 9162/18688 [10:44:48<11:24:30, 4.31s/it] 49%|████▉ | 9163/18688 [10:44:52<11:00:55, 4.16s/it] 49%|████▉ | 9164/18688 [10:44:55<10:32:54, 3.99s/it] 49%|████▉ | 9165/18688 [10:44:59<10:14:16, 3.87s/it] 49%|████▉ | 9166/18688 [10:45:03<10:11:52, 3.86s/it] 49%|████▉ | 9167/18688 [10:45:07<10:49:19, 4.09s/it] 49%|████▉ | 9168/18688 [10:45:12<11:01:14, 4.17s/it] 49%|████▉ | 9169/18688 [10:45:16<11:04:31, 4.19s/it] 49%|████▉ | 9170/18688 [10:45:21<11:34:08, 4.38s/it] {'loss': '0.9122', 'grad_norm': '1.164', 'learning_rate': '4.673e-05', 'epoch': '0.4907', 'num_input_tokens_seen': 30820232, 'train_runtime': '3.872e+04', 'train_tokens_per_second': '795.9'}
49%|████▉ | 9170/18688 [10:45:21<11:34:08, 4.38s/it] 49%|████▉ | 9171/18688 [10:45:25<11:21:10, 4.29s/it] 49%|████▉ | 9172/18688 [10:45:29<11:03:11, 4.18s/it] 49%|████▉ | 9173/18688 [10:45:33<10:58:35, 4.15s/it] 49%|████▉ | 9174/18688 [10:45:37<10:40:51, 4.04s/it] 49%|████▉ | 9175/18688 [10:45:41<10:42:21, 4.05s/it] 49%|████▉ | 9176/18688 [10:45:44<10:18:22, 3.90s/it] 49%|████▉ | 9177/18688 [10:45:48<10:00:58, 3.79s/it] 49%|████▉ | 9178/18688 [10:45:52<10:13:30, 3.87s/it] 49%|████▉ | 9179/18688 [10:45:56<10:32:12, 3.99s/it] 49%|████▉ | 9180/18688 [10:46:00<10:42:31, 4.05s/it] {'loss': '0.9098', 'grad_norm': '1.289', 'learning_rate': '4.666e-05', 'epoch': '0.4912', 'num_input_tokens_seen': 30851064, 'train_runtime': '3.876e+04', 'train_tokens_per_second': '795.9'}
49%|████▉ | 9180/18688 [10:46:00<10:42:31, 4.05s/it] 49%|████▉ | 9181/18688 [10:46:05<10:50:49, 4.11s/it] 49%|████▉ | 9182/18688 [10:46:09<11:02:22, 4.18s/it] 49%|████▉ | 9183/18688 [10:46:13<10:41:08, 4.05s/it] 49%|████▉ | 9184/18688 [10:46:18<11:29:17, 4.35s/it] 49%|████▉ | 9185/18688 [10:46:22<11:07:02, 4.21s/it] 49%|████▉ | 9186/18688 [10:46:27<11:36:30, 4.40s/it] 49%|████▉ | 9187/18688 [10:46:30<11:14:48, 4.26s/it] 49%|████▉ | 9188/18688 [10:46:35<11:22:29, 4.31s/it] 49%|████▉ | 9189/18688 [10:46:39<11:00:22, 4.17s/it] 49%|████▉ | 9190/18688 [10:46:43<11:18:55, 4.29s/it] {'loss': '0.9582', 'grad_norm': '1.331', 'learning_rate': '4.659e-05', 'epoch': '0.4918', 'num_input_tokens_seen': 30886736, 'train_runtime': '3.88e+04', 'train_tokens_per_second': '796'}
49%|████▉ | 9190/18688 [10:46:43<11:18:55, 4.29s/it] 49%|████▉ | 9191/18688 [10:46:48<11:22:31, 4.31s/it] 49%|████▉ | 9192/18688 [10:46:53<11:52:29, 4.50s/it] 49%|████▉ | 9193/18688 [10:46:57<11:59:28, 4.55s/it] 49%|████▉ | 9194/18688 [10:47:01<11:23:11, 4.32s/it] 49%|████▉ | 9195/18688 [10:47:05<10:56:30, 4.15s/it] 49%|████▉ | 9196/18688 [10:47:09<10:35:05, 4.01s/it] 49%|████▉ | 9197/18688 [10:47:12<10:27:38, 3.97s/it] 49%|████▉ | 9198/18688 [10:47:16<10:11:06, 3.86s/it] 49%|████▉ | 9199/18688 [10:47:21<10:41:47, 4.06s/it] 49%|████▉ | 9200/18688 [10:47:25<10:50:28, 4.11s/it] {'loss': '0.9444', 'grad_norm': '1.02', 'learning_rate': '4.651e-05', 'epoch': '0.4923', 'num_input_tokens_seen': 30919528, 'train_runtime': '3.885e+04', 'train_tokens_per_second': '796'}
49%|████▉ | 9200/18688 [10:47:25<10:50:28, 4.11s/it] 49%|████▉ | 9201/18688 [10:47:29<10:44:16, 4.07s/it] 49%|████▉ | 9202/18688 [10:47:33<10:35:01, 4.02s/it] 49%|████▉ | 9203/18688 [10:47:38<11:19:16, 4.30s/it] 49%|████▉ | 9204/18688 [10:47:42<11:14:03, 4.26s/it] 49%|████▉ | 9205/18688 [10:47:46<11:11:48, 4.25s/it] 49%|████▉ | 9206/18688 [10:47:49<10:34:12, 4.01s/it] 49%|████▉ | 9207/18688 [10:47:53<10:30:47, 3.99s/it] 49%|████▉ | 9208/18688 [10:47:59<11:46:24, 4.47s/it] 49%|████▉ | 9209/18688 [10:48:04<12:06:50, 4.60s/it] 49%|████▉ | 9210/18688 [10:48:07<11:12:57, 4.26s/it] {'loss': '0.9288', 'grad_norm': '1.177', 'learning_rate': '4.644e-05', 'epoch': '0.4928', 'num_input_tokens_seen': 30954184, 'train_runtime': '3.889e+04', 'train_tokens_per_second': '796'}
49%|████▉ | 9210/18688 [10:48:07<11:12:57, 4.26s/it] 49%|████▉ | 9211/18688 [10:48:11<10:48:34, 4.11s/it] 49%|████▉ | 9212/18688 [10:48:16<11:12:28, 4.26s/it] 49%|████▉ | 9213/18688 [10:48:20<10:53:19, 4.14s/it] 49%|████▉ | 9214/18688 [10:48:23<10:21:54, 3.94s/it] 49%|████▉ | 9215/18688 [10:48:27<10:30:40, 3.99s/it] 49%|████▉ | 9216/18688 [10:48:31<10:24:21, 3.95s/it] 49%|████▉ | 9217/18688 [10:48:35<10:16:39, 3.91s/it] 49%|████▉ | 9218/18688 [10:48:39<10:48:35, 4.11s/it] 49%|████▉ | 9219/18688 [10:48:43<10:28:19, 3.98s/it] 49%|████▉ | 9220/18688 [10:48:48<10:54:17, 4.15s/it] {'loss': '0.9737', 'grad_norm': '1.486', 'learning_rate': '4.637e-05', 'epoch': '0.4934', 'num_input_tokens_seen': 30985872, 'train_runtime': '3.893e+04', 'train_tokens_per_second': '796'}
49%|████▉ | 9220/18688 [10:48:48<10:54:17, 4.15s/it] 49%|████▉ | 9221/18688 [10:48:51<10:29:54, 3.99s/it] 49%|████▉ | 9222/18688 [10:48:55<10:15:51, 3.90s/it] 49%|████▉ | 9223/18688 [10:48:59<10:38:37, 4.05s/it] 49%|████▉ | 9224/18688 [10:49:04<10:59:34, 4.18s/it] 49%|████▉ | 9225/18688 [10:49:07<10:28:13, 3.98s/it] 49%|████▉ | 9226/18688 [10:49:11<10:14:37, 3.90s/it] 49%|████▉ | 9227/18688 [10:49:17<11:29:30, 4.37s/it] 49%|████▉ | 9228/18688 [10:49:20<11:06:27, 4.23s/it] 49%|████▉ | 9229/18688 [10:49:25<11:14:29, 4.28s/it] 49%|████▉ | 9230/18688 [10:49:28<10:41:20, 4.07s/it] {'loss': '0.8509', 'grad_norm': '1.548', 'learning_rate': '4.63e-05', 'epoch': '0.4939', 'num_input_tokens_seen': 31017992, 'train_runtime': '3.897e+04', 'train_tokens_per_second': '796'}
49%|████▉ | 9230/18688 [10:49:28<10:41:20, 4.07s/it] 49%|████▉ | 9231/18688 [10:49:32<10:33:43, 4.02s/it] 49%|████▉ | 9232/18688 [10:49:36<10:28:22, 3.99s/it] 49%|████▉ | 9233/18688 [10:49:40<10:13:06, 3.89s/it] 49%|████▉ | 9234/18688 [10:49:44<10:17:44, 3.92s/it] 49%|████▉ | 9235/18688 [10:49:49<11:11:26, 4.26s/it] 49%|████▉ | 9236/18688 [10:49:54<11:54:34, 4.54s/it] 49%|████▉ | 9237/18688 [10:49:58<11:19:26, 4.31s/it] 49%|████▉ | 9238/18688 [10:50:02<11:07:40, 4.24s/it] 49%|████▉ | 9239/18688 [10:50:06<10:50:22, 4.13s/it] 49%|████▉ | 9240/18688 [10:50:10<10:56:55, 4.17s/it] {'loss': '0.9732', 'grad_norm': '1.329', 'learning_rate': '4.623e-05', 'epoch': '0.4944', 'num_input_tokens_seen': 31050928, 'train_runtime': '3.901e+04', 'train_tokens_per_second': '796'}
49%|████▉ | 9240/18688 [10:50:10<10:56:55, 4.17s/it] 49%|████▉ | 9241/18688 [10:50:14<10:52:26, 4.14s/it] 49%|████▉ | 9242/18688 [10:50:18<10:29:50, 4.00s/it] 49%|████▉ | 9243/18688 [10:50:22<10:30:40, 4.01s/it] 49%|████▉ | 9244/18688 [10:50:26<10:40:34, 4.07s/it] 49%|████▉ | 9245/18688 [10:50:30<10:56:33, 4.17s/it] 49%|████▉ | 9246/18688 [10:50:34<10:46:21, 4.11s/it] 49%|████▉ | 9247/18688 [10:50:39<11:07:32, 4.24s/it] 49%|████▉ | 9248/18688 [10:50:43<10:36:52, 4.05s/it] 49%|████▉ | 9249/18688 [10:50:46<10:26:17, 3.98s/it] 49%|████▉ | 9250/18688 [10:50:50<10:22:25, 3.96s/it] {'loss': '0.9199', 'grad_norm': '1.132', 'learning_rate': '4.615e-05', 'epoch': '0.495', 'num_input_tokens_seen': 31083696, 'train_runtime': '3.905e+04', 'train_tokens_per_second': '796'}
49%|████▉ | 9250/18688 [10:50:50<10:22:25, 3.96s/it] 50%|████▉ | 9251/18688 [10:50:54<10:08:13, 3.87s/it] 50%|████▉ | 9252/18688 [10:50:59<10:46:52, 4.11s/it] 50%|████▉ | 9253/18688 [10:51:03<11:11:53, 4.27s/it] 50%|████▉ | 9254/18688 [10:51:07<10:56:55, 4.18s/it] 50%|████▉ | 9255/18688 [10:51:11<10:39:09, 4.07s/it] 50%|████▉ | 9256/18688 [10:51:15<10:33:00, 4.03s/it] 50%|████▉ | 9257/18688 [10:51:19<10:28:23, 4.00s/it] 50%|████▉ | 9258/18688 [10:51:24<11:07:45, 4.25s/it] 50%|████▉ | 9259/18688 [10:51:28<10:47:37, 4.12s/it] 50%|████▉ | 9260/18688 [10:51:32<11:00:40, 4.20s/it] {'loss': '0.9333', 'grad_norm': '1.138', 'learning_rate': '4.608e-05', 'epoch': '0.4955', 'num_input_tokens_seen': 31116720, 'train_runtime': '3.909e+04', 'train_tokens_per_second': '796'}
50%|████▉ | 9260/18688 [10:51:32<11:00:40, 4.20s/it] 50%|████▉ | 9261/18688 [10:51:36<10:40:54, 4.08s/it] 50%|████▉ | 9262/18688 [10:51:40<10:44:26, 4.10s/it] 50%|████▉ | 9263/18688 [10:51:44<10:33:33, 4.03s/it] 50%|████▉ | 9264/18688 [10:51:48<10:59:33, 4.20s/it] 50%|████▉ | 9265/18688 [10:51:52<10:25:32, 3.98s/it] 50%|████▉ | 9266/18688 [10:51:56<10:30:25, 4.01s/it] 50%|████▉ | 9267/18688 [10:52:00<10:27:14, 3.99s/it] 50%|████▉ | 9268/18688 [10:52:05<11:10:25, 4.27s/it] 50%|████▉ | 9269/18688 [10:52:09<10:56:19, 4.18s/it] 50%|████▉ | 9270/18688 [10:52:12<10:32:26, 4.03s/it] {'loss': '0.9265', 'grad_norm': '1.411', 'learning_rate': '4.601e-05', 'epoch': '0.4961', 'num_input_tokens_seen': 31147920, 'train_runtime': '3.913e+04', 'train_tokens_per_second': '796'}
50%|████▉ | 9270/18688 [10:52:12<10:32:26, 4.03s/it] 50%|████▉ | 9271/18688 [10:52:16<10:30:40, 4.02s/it] 50%|████▉ | 9272/18688 [10:52:20<10:08:45, 3.88s/it] 50%|████▉ | 9273/18688 [10:52:24<10:27:59, 4.00s/it] 50%|████▉ | 9274/18688 [10:52:29<10:42:51, 4.10s/it] 50%|████▉ | 9275/18688 [10:52:32<10:19:38, 3.95s/it] 50%|████▉ | 9276/18688 [10:52:37<10:57:18, 4.19s/it] 50%|████▉ | 9277/18688 [10:52:41<11:09:59, 4.27s/it] 50%|████▉ | 9278/18688 [10:52:46<11:45:33, 4.50s/it] 50%|████▉ | 9279/18688 [10:52:50<11:16:14, 4.31s/it] 50%|████▉ | 9280/18688 [10:52:55<11:19:49, 4.34s/it] {'loss': '0.9309', 'grad_norm': '1.347', 'learning_rate': '4.594e-05', 'epoch': '0.4966', 'num_input_tokens_seen': 31181888, 'train_runtime': '3.918e+04', 'train_tokens_per_second': '796'}
50%|████▉ | 9280/18688 [10:52:55<11:19:49, 4.34s/it] 50%|████▉ | 9281/18688 [10:52:59<10:56:02, 4.18s/it] 50%|████▉ | 9282/18688 [10:53:03<10:55:25, 4.18s/it] 50%|████▉ | 9283/18688 [10:53:08<11:44:43, 4.50s/it] 50%|████▉ | 9284/18688 [10:53:12<11:24:38, 4.37s/it] 50%|████▉ | 9285/18688 [10:53:16<11:11:33, 4.29s/it] 50%|████▉ | 9286/18688 [10:53:20<10:32:40, 4.04s/it] 50%|████▉ | 9287/18688 [10:53:24<11:13:14, 4.30s/it] 50%|████▉ | 9288/18688 [10:53:29<11:16:17, 4.32s/it] 50%|████▉ | 9289/18688 [10:53:33<11:16:09, 4.32s/it] 50%|████▉ | 9290/18688 [10:53:37<11:15:08, 4.31s/it] {'loss': '0.9526', 'grad_norm': '1.134', 'learning_rate': '4.586e-05', 'epoch': '0.4971', 'num_input_tokens_seen': 31217408, 'train_runtime': '3.922e+04', 'train_tokens_per_second': '796'}
50%|████▉ | 9290/18688 [10:53:37<11:15:08, 4.31s/it] 50%|████▉ | 9291/18688 [10:53:42<11:12:30, 4.29s/it] 50%|████▉ | 9292/18688 [10:53:47<11:49:56, 4.53s/it] 50%|████▉ | 9293/18688 [10:53:51<11:39:19, 4.47s/it] 50%|████▉ | 9294/18688 [10:53:56<11:48:58, 4.53s/it] 50%|████▉ | 9295/18688 [10:53:59<11:02:05, 4.23s/it] 50%|████▉ | 9296/18688 [10:54:03<10:24:09, 3.99s/it] 50%|████▉ | 9297/18688 [10:54:07<10:47:38, 4.14s/it] 50%|████▉ | 9298/18688 [10:54:12<10:57:20, 4.20s/it] 50%|████▉ | 9299/18688 [10:54:16<11:13:51, 4.31s/it] 50%|████▉ | 9300/18688 [10:54:21<11:25:21, 4.38s/it] {'loss': '1.029', 'grad_norm': '1.392', 'learning_rate': '4.579e-05', 'epoch': '0.4977', 'num_input_tokens_seen': 31251968, 'train_runtime': '3.926e+04', 'train_tokens_per_second': '796'}
50%|████▉ | 9300/18688 [10:54:21<11:25:21, 4.38s/it] 50%|████▉ | 9301/18688 [10:54:25<11:06:07, 4.26s/it] 50%|████▉ | 9302/18688 [10:54:28<10:30:12, 4.03s/it] 50%|████▉ | 9303/18688 [10:54:33<11:03:02, 4.24s/it] 50%|████▉ | 9304/18688 [10:54:37<11:04:48, 4.25s/it] 50%|████▉ | 9305/18688 [10:54:42<11:22:57, 4.37s/it] 50%|████▉ | 9306/18688 [10:54:46<11:08:34, 4.28s/it] 50%|████▉ | 9307/18688 [10:54:51<11:25:54, 4.39s/it] 50%|████▉ | 9308/18688 [10:54:54<10:51:26, 4.17s/it] 50%|████▉ | 9309/18688 [10:54:58<10:46:09, 4.13s/it] 50%|████▉ | 9310/18688 [10:55:03<11:09:05, 4.28s/it] {'loss': '0.9604', 'grad_norm': '1.51', 'learning_rate': '4.572e-05', 'epoch': '0.4982', 'num_input_tokens_seen': 31286016, 'train_runtime': '3.93e+04', 'train_tokens_per_second': '796'}
50%|████▉ | 9310/18688 [10:55:03<11:09:05, 4.28s/it] 50%|████▉ | 9311/18688 [10:55:07<10:52:18, 4.17s/it] 50%|████▉ | 9312/18688 [10:55:11<11:17:48, 4.34s/it] 50%|████▉ | 9313/18688 [10:55:17<11:50:22, 4.55s/it] 50%|████▉ | 9314/18688 [10:55:20<11:10:42, 4.29s/it] 50%|████▉ | 9315/18688 [10:55:25<11:43:15, 4.50s/it] 50%|████▉ | 9316/18688 [10:55:30<11:40:19, 4.48s/it] 50%|████▉ | 9317/18688 [10:55:33<10:52:01, 4.17s/it] 50%|████▉ | 9318/18688 [10:55:37<10:22:11, 3.98s/it] 50%|████▉ | 9319/18688 [10:55:40<10:02:55, 3.86s/it] 50%|████▉ | 9320/18688 [10:55:45<10:30:07, 4.04s/it] {'loss': '0.9252', 'grad_norm': '1.203', 'learning_rate': '4.565e-05', 'epoch': '0.4987', 'num_input_tokens_seen': 31321136, 'train_runtime': '3.935e+04', 'train_tokens_per_second': '796.1'}
50%|████▉ | 9320/18688 [10:55:45<10:30:07, 4.04s/it] 50%|████▉ | 9321/18688 [10:55:48<10:07:39, 3.89s/it] 50%|████▉ | 9322/18688 [10:55:53<10:55:36, 4.20s/it] 50%|████▉ | 9323/18688 [10:55:58<11:25:42, 4.39s/it] 50%|████▉ | 9324/18688 [10:56:02<10:45:01, 4.13s/it] 50%|████▉ | 9325/18688 [10:56:06<11:00:50, 4.23s/it] 50%|████▉ | 9326/18688 [10:56:10<11:12:02, 4.31s/it] 50%|████▉ | 9327/18688 [10:56:15<11:14:30, 4.32s/it] 50%|████▉ | 9328/18688 [10:56:18<10:43:43, 4.13s/it] 50%|████▉ | 9329/18688 [10:56:22<10:20:52, 3.98s/it] 50%|████▉ | 9330/18688 [10:56:26<10:30:53, 4.05s/it] {'loss': '0.917', 'grad_norm': '1.392', 'learning_rate': '4.557e-05', 'epoch': '0.4993', 'num_input_tokens_seen': 31355072, 'train_runtime': '3.939e+04', 'train_tokens_per_second': '796.1'}
50%|████▉ | 9330/18688 [10:56:26<10:30:53, 4.05s/it] 50%|████▉ | 9331/18688 [10:56:30<10:33:55, 4.06s/it] 50%|████▉ | 9332/18688 [10:56:34<10:11:26, 3.92s/it] 50%|████▉ | 9333/18688 [10:56:38<9:52:20, 3.80s/it] 50%|████▉ | 9334/18688 [10:56:41<9:57:12, 3.83s/it] 50%|████▉ | 9335/18688 [10:56:45<9:53:02, 3.80s/it] 50%|████▉ | 9336/18688 [10:56:50<10:41:42, 4.12s/it] 50%|████▉ | 9337/18688 [10:56:55<11:05:12, 4.27s/it] 50%|████▉ | 9338/18688 [10:56:58<10:39:55, 4.11s/it] 50%|████▉ | 9339/18688 [10:57:03<11:08:18, 4.29s/it] 50%|████▉ | 9340/18688 [10:57:07<10:59:26, 4.23s/it] {'loss': '0.8873', 'grad_norm': '1.275', 'learning_rate': '4.55e-05', 'epoch': '0.4998', 'num_input_tokens_seen': 31387352, 'train_runtime': '3.943e+04', 'train_tokens_per_second': '796.1'}
50%|████▉ | 9340/18688 [10:57:07<10:59:26, 4.23s/it] 50%|████▉ | 9341/18688 [10:57:12<11:17:49, 4.35s/it] 50%|████▉ | 9342/18688 [10:57:16<11:12:03, 4.31s/it] 50%|████▉ | 9343/18688 [10:57:20<10:42:42, 4.13s/it] 50%|█████ | 9344/18688 [10:57:23<10:23:59, 4.01s/it] 50%|█████ | 9345/18688 [10:57:27<10:22:14, 4.00s/it] 50%|█████ | 9346/18688 [10:57:31<10:24:47, 4.01s/it] 50%|█████ | 9347/18688 [10:57:36<10:57:09, 4.22s/it] 50%|█████ | 9348/18688 [10:57:40<10:41:56, 4.12s/it] 50%|█████ | 9349/18688 [10:57:44<10:43:02, 4.13s/it] 50%|█████ | 9350/18688 [10:57:48<10:44:46, 4.14s/it] {'loss': '0.9085', 'grad_norm': '1.381', 'learning_rate': '4.543e-05', 'epoch': '0.5003', 'num_input_tokens_seen': 31421072, 'train_runtime': '3.947e+04', 'train_tokens_per_second': '796.1'}
50%|█████ | 9350/18688 [10:57:48<10:44:46, 4.14s/it] 50%|█████ | 9351/18688 [10:57:52<10:19:49, 3.98s/it] 50%|█████ | 9352/18688 [10:57:56<10:23:54, 4.01s/it] 50%|█████ | 9353/18688 [10:58:01<11:02:54, 4.26s/it] 50%|█████ | 9354/18688 [10:58:06<11:24:12, 4.40s/it] 50%|█████ | 9355/18688 [10:58:10<11:34:46, 4.47s/it] 50%|█████ | 9356/18688 [10:58:15<11:28:17, 4.43s/it] 50%|█████ | 9357/18688 [10:58:18<11:02:18, 4.26s/it] 50%|█████ | 9358/18688 [10:58:23<11:03:51, 4.27s/it] 50%|█████ | 9359/18688 [10:58:27<11:12:12, 4.32s/it] 50%|█████ | 9360/18688 [10:58:31<10:54:37, 4.21s/it] {'loss': '0.8821', 'grad_norm': '1.361', 'learning_rate': '4.536e-05', 'epoch': '0.5009', 'num_input_tokens_seen': 31456456, 'train_runtime': '3.951e+04', 'train_tokens_per_second': '796.1'}
50%|█████ | 9360/18688 [10:58:31<10:54:37, 4.21s/it] 50%|█████ | 9361/18688 [10:58:37<11:52:21, 4.58s/it] 50%|█████ | 9362/18688 [10:58:42<12:35:20, 4.86s/it] 50%|█████ | 9363/18688 [10:58:46<12:01:14, 4.64s/it] 50%|█████ | 9364/18688 [10:58:50<11:07:22, 4.29s/it] 50%|█████ | 9365/18688 [10:58:53<10:35:09, 4.09s/it] 50%|█████ | 9366/18688 [10:58:58<10:42:33, 4.14s/it] 50%|█████ | 9367/18688 [10:59:03<11:19:53, 4.38s/it] 50%|█████ | 9368/18688 [10:59:07<11:00:42, 4.25s/it] 50%|█████ | 9369/18688 [10:59:11<10:49:28, 4.18s/it] 50%|█████ | 9370/18688 [10:59:15<10:55:06, 4.22s/it] {'loss': '0.9798', 'grad_norm': '1.215', 'learning_rate': '4.529e-05', 'epoch': '0.5014', 'num_input_tokens_seen': 31492240, 'train_runtime': '3.956e+04', 'train_tokens_per_second': '796.2'}
50%|█████ | 9370/18688 [10:59:15<10:55:06, 4.22s/it] 50%|█████ | 9371/18688 [10:59:20<11:38:39, 4.50s/it] 50%|█████ | 9372/18688 [10:59:24<11:06:01, 4.29s/it] 50%|█████ | 9373/18688 [10:59:28<11:25:00, 4.41s/it] 50%|█████ | 9374/18688 [10:59:34<12:02:54, 4.66s/it] 50%|█████ | 9375/18688 [10:59:38<12:02:14, 4.65s/it] 50%|█████ | 9376/18688 [10:59:42<11:20:36, 4.39s/it] 50%|█████ | 9377/18688 [10:59:46<11:12:47, 4.34s/it] 50%|█████ | 9378/18688 [10:59:51<11:05:42, 4.29s/it] 50%|█████ | 9379/18688 [10:59:55<11:02:14, 4.27s/it] 50%|█████ | 9380/18688 [10:59:59<10:56:31, 4.23s/it] {'loss': '0.9586', 'grad_norm': '1.118', 'learning_rate': '4.521e-05', 'epoch': '0.5019', 'num_input_tokens_seen': 31527928, 'train_runtime': '3.96e+04', 'train_tokens_per_second': '796.2'}
50%|█████ | 9380/18688 [10:59:59<10:56:31, 4.23s/it] 50%|█████ | 9381/18688 [11:00:04<11:52:44, 4.59s/it] 50%|█████ | 9382/18688 [11:00:09<12:02:57, 4.66s/it] 50%|█████ | 9383/18688 [11:00:13<11:27:15, 4.43s/it] 50%|█████ | 9384/18688 [11:00:18<11:40:29, 4.52s/it] 50%|█████ | 9385/18688 [11:00:22<11:05:26, 4.29s/it] 50%|█████ | 9386/18688 [11:00:25<10:33:54, 4.09s/it] 50%|█████ | 9387/18688 [11:00:29<10:07:56, 3.92s/it] 50%|█████ | 9388/18688 [11:00:33<10:20:17, 4.00s/it] 50%|█████ | 9389/18688 [11:00:37<10:19:26, 4.00s/it] 50%|█████ | 9390/18688 [11:00:42<11:05:57, 4.30s/it] {'loss': '0.8667', 'grad_norm': '1.401', 'learning_rate': '4.514e-05', 'epoch': '0.5025', 'num_input_tokens_seen': 31563432, 'train_runtime': '3.964e+04', 'train_tokens_per_second': '796.2'}
50%|█████ | 9390/18688 [11:00:42<11:05:57, 4.30s/it] 50%|█████ | 9391/18688 [11:00:46<11:10:24, 4.33s/it] 50%|█████ | 9392/18688 [11:00:50<11:03:18, 4.28s/it] 50%|█████ | 9393/18688 [11:00:55<11:03:53, 4.29s/it] 50%|█████ | 9394/18688 [11:01:00<11:39:40, 4.52s/it] 50%|█████ | 9395/18688 [11:01:03<11:00:36, 4.27s/it] 50%|█████ | 9396/18688 [11:01:08<11:08:54, 4.32s/it] 50%|█████ | 9397/18688 [11:01:12<11:03:54, 4.29s/it] 50%|█████ | 9398/18688 [11:01:17<11:38:46, 4.51s/it] 50%|█████ | 9399/18688 [11:01:21<11:11:34, 4.34s/it] 50%|█████ | 9400/18688 [11:01:26<11:32:54, 4.48s/it] {'loss': '0.9408', 'grad_norm': '1.196', 'learning_rate': '4.507e-05', 'epoch': '0.503', 'num_input_tokens_seen': 31599312, 'train_runtime': '3.969e+04', 'train_tokens_per_second': '796.2'}
50%|█████ | 9400/18688 [11:01:26<11:32:54, 4.48s/it] 50%|█████ | 9401/18688 [11:01:30<11:23:55, 4.42s/it] 50%|█████ | 9402/18688 [11:01:34<11:05:47, 4.30s/it] 50%|█████ | 9403/18688 [11:01:38<10:33:27, 4.09s/it] 50%|█████ | 9404/18688 [11:01:43<11:15:29, 4.37s/it] 50%|█████ | 9405/18688 [11:01:48<11:48:18, 4.58s/it] 50%|█████ | 9406/18688 [11:01:52<11:24:10, 4.42s/it] 50%|█████ | 9407/18688 [11:01:55<10:39:12, 4.13s/it] 50%|█████ | 9408/18688 [11:02:00<10:39:51, 4.14s/it] 50%|█████ | 9409/18688 [11:02:03<10:23:38, 4.03s/it] 50%|█████ | 9410/18688 [11:02:07<10:10:41, 3.95s/it] {'loss': '0.9547', 'grad_norm': '1.318', 'learning_rate': '4.5e-05', 'epoch': '0.5035', 'num_input_tokens_seen': 31631528, 'train_runtime': '3.973e+04', 'train_tokens_per_second': '796.2'}
50%|█████ | 9410/18688 [11:02:07<10:10:41, 3.95s/it] 50%|█████ | 9411/18688 [11:02:12<11:10:59, 4.34s/it] 50%|█████ | 9412/18688 [11:02:17<11:26:48, 4.44s/it] 50%|█████ | 9413/18688 [11:02:21<11:06:03, 4.31s/it] 50%|█████ | 9414/18688 [11:02:26<11:31:30, 4.47s/it] 50%|█████ | 9415/18688 [11:02:31<11:52:26, 4.61s/it] 50%|█████ | 9416/18688 [11:02:35<11:21:57, 4.41s/it] 50%|█████ | 9417/18688 [11:02:38<10:43:58, 4.17s/it] 50%|█████ | 9418/18688 [11:02:43<11:14:10, 4.36s/it] 50%|█████ | 9419/18688 [11:02:47<10:59:12, 4.27s/it] 50%|█████ | 9420/18688 [11:02:51<10:29:10, 4.07s/it] {'loss': '0.8755', 'grad_norm': '1.301', 'learning_rate': '4.492e-05', 'epoch': '0.5041', 'num_input_tokens_seen': 31667272, 'train_runtime': '3.977e+04', 'train_tokens_per_second': '796.2'}
50%|█████ | 9420/18688 [11:02:51<10:29:10, 4.07s/it] 50%|█████ | 9421/18688 [11:02:55<10:16:29, 3.99s/it] 50%|█████ | 9422/18688 [11:02:58<10:04:02, 3.91s/it] 50%|█████ | 9423/18688 [11:03:03<10:37:50, 4.13s/it] 50%|█████ | 9424/18688 [11:03:07<10:17:17, 4.00s/it] 50%|█████ | 9425/18688 [11:03:11<10:11:46, 3.96s/it] 50%|█████ | 9426/18688 [11:03:15<10:52:56, 4.23s/it] 50%|█████ | 9427/18688 [11:03:20<11:25:23, 4.44s/it] 50%|█████ | 9428/18688 [11:03:25<11:40:15, 4.54s/it] 50%|█████ | 9429/18688 [11:03:29<11:02:52, 4.30s/it] 50%|█████ | 9430/18688 [11:03:34<11:38:58, 4.53s/it] {'loss': '0.927', 'grad_norm': '1.157', 'learning_rate': '4.485e-05', 'epoch': '0.5046', 'num_input_tokens_seen': 31702000, 'train_runtime': '3.981e+04', 'train_tokens_per_second': '796.2'}
50%|█████ | 9430/18688 [11:03:34<11:38:58, 4.53s/it] 50%|█████ | 9431/18688 [11:03:38<11:15:57, 4.38s/it] 50%|█████ | 9432/18688 [11:03:43<11:28:01, 4.46s/it] 50%|█████ | 9433/18688 [11:03:47<11:08:44, 4.34s/it] 50%|█████ | 9434/18688 [11:03:51<11:04:26, 4.31s/it] 50%|█████ | 9435/18688 [11:03:54<10:27:56, 4.07s/it] 50%|█████ | 9436/18688 [11:03:58<10:09:00, 3.95s/it] 50%|█████ | 9437/18688 [11:04:03<10:44:32, 4.18s/it] 51%|█████ | 9438/18688 [11:04:07<10:51:01, 4.22s/it] 51%|█████ | 9439/18688 [11:04:11<10:49:42, 4.21s/it] 51%|█████ | 9440/18688 [11:04:15<10:29:25, 4.08s/it] {'loss': '0.8601', 'grad_norm': '1.297', 'learning_rate': '4.478e-05', 'epoch': '0.5052', 'num_input_tokens_seen': 31734824, 'train_runtime': '3.986e+04', 'train_tokens_per_second': '796.2'}
51%|█████ | 9440/18688 [11:04:15<10:29:25, 4.08s/it] 51%|█████ | 9441/18688 [11:04:19<10:04:32, 3.92s/it] 51%|█████ | 9442/18688 [11:04:22<9:49:48, 3.83s/it] 51%|█████ | 9443/18688 [11:04:26<9:38:07, 3.75s/it] 51%|█████ | 9444/18688 [11:04:30<9:54:19, 3.86s/it] 51%|█████ | 9445/18688 [11:04:33<9:34:36, 3.73s/it] 51%|█████ | 9446/18688 [11:04:38<9:57:41, 3.88s/it] 51%|█████ | 9447/18688 [11:04:41<9:43:33, 3.79s/it] 51%|█████ | 9448/18688 [11:04:45<9:34:11, 3.73s/it] 51%|█████ | 9449/18688 [11:04:49<10:10:03, 3.96s/it] 51%|█████ | 9450/18688 [11:04:53<9:56:09, 3.87s/it] {'loss': '0.9167', 'grad_norm': '1.47', 'learning_rate': '4.47e-05', 'epoch': '0.5057', 'num_input_tokens_seen': 31765000, 'train_runtime': '3.989e+04', 'train_tokens_per_second': '796.2'}
51%|█████ | 9450/18688 [11:04:53<9:56:09, 3.87s/it] 51%|█████ | 9451/18688 [11:04:56<9:39:46, 3.77s/it] 51%|█████ | 9452/18688 [11:05:01<10:23:41, 4.05s/it] 51%|█████ | 9453/18688 [11:05:06<11:09:11, 4.35s/it] 51%|█████ | 9454/18688 [11:05:10<10:52:08, 4.24s/it] 51%|█████ | 9455/18688 [11:05:15<11:06:41, 4.33s/it] 51%|█████ | 9456/18688 [11:05:19<10:55:15, 4.26s/it] 51%|█████ | 9457/18688 [11:05:23<10:40:14, 4.16s/it] 51%|█████ | 9458/18688 [11:05:27<10:32:39, 4.11s/it] 51%|█████ | 9459/18688 [11:05:31<10:46:59, 4.21s/it] 51%|█████ | 9460/18688 [11:05:36<11:11:38, 4.37s/it] {'loss': '0.9512', 'grad_norm': '1.228', 'learning_rate': '4.463e-05', 'epoch': '0.5062', 'num_input_tokens_seen': 31800568, 'train_runtime': '3.994e+04', 'train_tokens_per_second': '796.3'}
51%|█████ | 9460/18688 [11:05:36<11:11:38, 4.37s/it] 51%|█████ | 9461/18688 [11:05:40<11:11:32, 4.37s/it] 51%|█████ | 9462/18688 [11:05:45<11:25:35, 4.46s/it] 51%|█████ | 9463/18688 [11:05:49<11:10:44, 4.36s/it] 51%|█████ | 9464/18688 [11:05:53<11:04:21, 4.32s/it] 51%|█████ | 9465/18688 [11:05:57<10:34:28, 4.13s/it] 51%|█████ | 9466/18688 [11:06:01<10:14:53, 4.00s/it] 51%|█████ | 9467/18688 [11:06:04<10:02:08, 3.92s/it] 51%|█████ | 9468/18688 [11:06:08<9:52:47, 3.86s/it] 51%|█████ | 9469/18688 [11:06:13<10:33:46, 4.12s/it] 51%|█████ | 9470/18688 [11:06:18<11:24:11, 4.45s/it] {'loss': '0.9081', 'grad_norm': '1.284', 'learning_rate': '4.456e-05', 'epoch': '0.5068', 'num_input_tokens_seen': 31834432, 'train_runtime': '3.998e+04', 'train_tokens_per_second': '796.3'}
51%|█████ | 9470/18688 [11:06:18<11:24:11, 4.45s/it] 51%|█████ | 9471/18688 [11:06:22<10:50:56, 4.24s/it] 51%|█████ | 9472/18688 [11:06:26<11:01:50, 4.31s/it] 51%|█████ | 9473/18688 [11:06:30<10:23:37, 4.06s/it] 51%|█████ | 9474/18688 [11:06:33<10:01:44, 3.92s/it] 51%|█████ | 9475/18688 [11:06:38<10:20:48, 4.04s/it] 51%|█████ | 9476/18688 [11:06:42<10:13:13, 3.99s/it] 51%|█████ | 9477/18688 [11:06:46<10:24:56, 4.07s/it] 51%|█████ | 9478/18688 [11:06:50<10:33:00, 4.12s/it] 51%|█████ | 9479/18688 [11:06:55<10:50:32, 4.24s/it] 51%|█████ | 9480/18688 [11:06:59<10:57:54, 4.29s/it] {'loss': '0.9724', 'grad_norm': '1.391', 'learning_rate': '4.449e-05', 'epoch': '0.5073', 'num_input_tokens_seen': 31867696, 'train_runtime': '4.002e+04', 'train_tokens_per_second': '796.3'}
51%|█████ | 9480/18688 [11:06:59<10:57:54, 4.29s/it] 51%|█████ | 9481/18688 [11:07:04<11:45:47, 4.60s/it] 51%|█████ | 9482/18688 [11:07:08<11:20:19, 4.43s/it] 51%|█████ | 9483/18688 [11:07:13<11:32:15, 4.51s/it] 51%|█████ | 9484/18688 [11:07:17<11:14:37, 4.40s/it] 51%|█████ | 9485/18688 [11:07:21<10:49:47, 4.24s/it] 51%|█████ | 9486/18688 [11:07:25<10:17:11, 4.02s/it] 51%|█████ | 9487/18688 [11:07:29<10:22:35, 4.06s/it] 51%|█████ | 9488/18688 [11:07:33<10:53:18, 4.26s/it] 51%|█████ | 9489/18688 [11:07:38<10:50:55, 4.25s/it] 51%|█████ | 9490/18688 [11:07:42<10:40:03, 4.18s/it] {'loss': '0.9251', 'grad_norm': '1.52', 'learning_rate': '4.441e-05', 'epoch': '0.5078', 'num_input_tokens_seen': 31903608, 'train_runtime': '4.006e+04', 'train_tokens_per_second': '796.4'}
51%|█████ | 9490/18688 [11:07:42<10:40:03, 4.18s/it] 51%|█████ | 9491/18688 [11:07:46<10:59:08, 4.30s/it] 51%|█████ | 9492/18688 [11:07:50<10:31:32, 4.12s/it] 51%|█████ | 9493/18688 [11:07:55<11:09:22, 4.37s/it] 51%|█████ | 9494/18688 [11:07:59<11:03:01, 4.33s/it] 51%|█████ | 9495/18688 [11:08:04<11:17:26, 4.42s/it] 51%|█████ | 9496/18688 [11:08:07<10:34:01, 4.14s/it] 51%|█████ | 9497/18688 [11:08:12<11:06:45, 4.35s/it] 51%|█████ | 9498/18688 [11:08:16<10:46:05, 4.22s/it] 51%|█████ | 9499/18688 [11:08:20<10:43:40, 4.20s/it] 51%|█████ | 9500/18688 [11:08:25<10:56:57, 4.29s/it] {'loss': '0.8722', 'grad_norm': '1.145', 'learning_rate': '4.434e-05', 'epoch': '0.5084', 'num_input_tokens_seen': 31939816, 'train_runtime': '4.011e+04', 'train_tokens_per_second': '796.4'}
51%|█████ | 9500/18688 [11:08:25<10:56:57, 4.29s/it] 51%|█████ | 9501/18688 [11:08:28<10:18:19, 4.04s/it] 51%|█████ | 9502/18688 [11:08:32<10:11:44, 4.00s/it] 51%|█████ | 9503/18688 [11:08:36<10:28:55, 4.11s/it] 51%|█████ | 9504/18688 [11:08:40<9:58:09, 3.91s/it] 51%|█████ | 9505/18688 [11:08:44<9:57:23, 3.90s/it] 51%|█████ | 9506/18688 [11:08:48<9:54:55, 3.89s/it] 51%|█████ | 9507/18688 [11:08:52<9:58:14, 3.91s/it] 51%|█████ | 9508/18688 [11:08:55<9:58:06, 3.91s/it] 51%|█████ | 9509/18688 [11:09:00<10:47:28, 4.23s/it] 51%|█████ | 9510/18688 [11:09:04<10:24:16, 4.08s/it] {'loss': '0.8787', 'grad_norm': '1.179', 'learning_rate': '4.427e-05', 'epoch': '0.5089', 'num_input_tokens_seen': 31969848, 'train_runtime': '4.014e+04', 'train_tokens_per_second': '796.4'}
51%|█████ | 9510/18688 [11:09:04<10:24:16, 4.08s/it] 51%|█████ | 9511/18688 [11:09:08<10:33:11, 4.14s/it] 51%|█████ | 9512/18688 [11:09:13<10:32:56, 4.14s/it] 51%|█████ | 9513/18688 [11:09:17<10:38:55, 4.18s/it] 51%|█████ | 9514/18688 [11:09:21<10:34:32, 4.15s/it] 51%|█████ | 9515/18688 [11:09:25<10:12:30, 4.01s/it] 51%|█████ | 9516/18688 [11:09:29<10:18:13, 4.04s/it] 51%|█████ | 9517/18688 [11:09:34<11:02:21, 4.33s/it] 51%|█████ | 9518/18688 [11:09:37<10:22:23, 4.07s/it] 51%|█████ | 9519/18688 [11:09:41<10:29:58, 4.12s/it] 51%|█████ | 9520/18688 [11:09:46<10:35:45, 4.16s/it] {'loss': '0.8931', 'grad_norm': '1.301', 'learning_rate': '4.42e-05', 'epoch': '0.5094', 'num_input_tokens_seen': 32004080, 'train_runtime': '4.019e+04', 'train_tokens_per_second': '796.4'}
51%|█████ | 9520/18688 [11:09:46<10:35:45, 4.16s/it] 51%|█████ | 9521/18688 [11:09:50<11:04:25, 4.35s/it] 51%|█████ | 9522/18688 [11:09:54<10:42:47, 4.21s/it] 51%|█████ | 9523/18688 [11:09:59<11:10:08, 4.39s/it] 51%|█████ | 9524/18688 [11:10:04<11:12:30, 4.40s/it] 51%|█████ | 9525/18688 [11:10:07<10:36:58, 4.17s/it] 51%|█████ | 9526/18688 [11:10:11<10:27:02, 4.11s/it] 51%|█████ | 9527/18688 [11:10:15<10:18:25, 4.05s/it] 51%|█████ | 9528/18688 [11:10:20<10:44:29, 4.22s/it] 51%|█████ | 9529/18688 [11:10:24<10:31:16, 4.14s/it] 51%|█████ | 9530/18688 [11:10:28<10:48:57, 4.25s/it] {'loss': '0.987', 'grad_norm': '1.215', 'learning_rate': '4.412e-05', 'epoch': '0.51', 'num_input_tokens_seen': 32038736, 'train_runtime': '4.023e+04', 'train_tokens_per_second': '796.4'}
51%|█████ | 9530/18688 [11:10:28<10:48:57, 4.25s/it] 51%|█████ | 9531/18688 [11:10:32<10:50:30, 4.26s/it] 51%|█████ | 9532/18688 [11:10:36<10:13:39, 4.02s/it] 51%|█████ | 9533/18688 [11:10:40<10:09:09, 3.99s/it] 51%|█████ | 9534/18688 [11:10:44<10:32:50, 4.15s/it] 51%|█████ | 9535/18688 [11:10:48<10:23:42, 4.09s/it] 51%|█████ | 9536/18688 [11:10:53<10:48:27, 4.25s/it] 51%|█████ | 9537/18688 [11:10:57<10:39:18, 4.19s/it] 51%|█████ | 9538/18688 [11:11:01<10:15:03, 4.03s/it] 51%|█████ | 9539/18688 [11:11:05<10:28:48, 4.12s/it] 51%|█████ | 9540/18688 [11:11:09<10:16:16, 4.04s/it] {'loss': '0.9091', 'grad_norm': '1.02', 'learning_rate': '4.405e-05', 'epoch': '0.5105', 'num_input_tokens_seen': 32072448, 'train_runtime': '4.027e+04', 'train_tokens_per_second': '796.4'}
51%|█████ | 9540/18688 [11:11:09<10:16:16, 4.04s/it] 51%|█████ | 9541/18688 [11:11:13<10:30:30, 4.14s/it] 51%|█████ | 9542/18688 [11:11:17<10:09:35, 4.00s/it] 51%|█████ | 9543/18688 [11:11:21<10:08:18, 3.99s/it] 51%|█████ | 9544/18688 [11:11:25<10:22:15, 4.08s/it] 51%|█████ | 9545/18688 [11:11:30<11:11:49, 4.41s/it] 51%|█████ | 9546/18688 [11:11:34<10:49:56, 4.27s/it] 51%|█████ | 9547/18688 [11:11:39<10:56:44, 4.31s/it] 51%|█████ | 9548/18688 [11:11:43<11:03:03, 4.35s/it] 51%|█████ | 9549/18688 [11:11:47<10:26:01, 4.11s/it] 51%|█████ | 9550/18688 [11:11:51<10:13:55, 4.03s/it] {'loss': '0.9808', 'grad_norm': '1.087', 'learning_rate': '4.398e-05', 'epoch': '0.511', 'num_input_tokens_seen': 32105416, 'train_runtime': '4.031e+04', 'train_tokens_per_second': '796.4'}
51%|█████ | 9550/18688 [11:11:51<10:13:55, 4.03s/it] 51%|█████ | 9551/18688 [11:11:54<10:04:53, 3.97s/it] 51%|█████ | 9552/18688 [11:11:59<10:29:02, 4.13s/it] 51%|█████ | 9553/18688 [11:12:03<10:09:02, 4.00s/it] 51%|█████ | 9554/18688 [11:12:07<10:24:57, 4.11s/it] 51%|█████ | 9555/18688 [11:12:12<10:51:00, 4.28s/it] 51%|█████ | 9556/18688 [11:12:15<10:34:41, 4.17s/it] 51%|█████ | 9557/18688 [11:12:20<10:38:41, 4.20s/it] 51%|█████ | 9558/18688 [11:12:24<10:55:57, 4.31s/it] 51%|█████ | 9559/18688 [11:12:29<10:49:35, 4.27s/it] 51%|█████ | 9560/18688 [11:12:33<11:20:50, 4.48s/it] {'loss': '0.899', 'grad_norm': '1.039', 'learning_rate': '4.391e-05', 'epoch': '0.5116', 'num_input_tokens_seen': 32140936, 'train_runtime': '4.035e+04', 'train_tokens_per_second': '796.5'}
51%|█████ | 9560/18688 [11:12:33<11:20:50, 4.48s/it] 51%|█████ | 9561/18688 [11:12:37<10:48:32, 4.26s/it] 51%|█████ | 9562/18688 [11:12:41<10:20:26, 4.08s/it] 51%|█████ | 9563/18688 [11:12:44<9:52:37, 3.90s/it] 51%|█████ | 9564/18688 [11:12:49<10:10:04, 4.01s/it] 51%|█████ | 9565/18688 [11:12:52<9:51:34, 3.89s/it] 51%|█████ | 9566/18688 [11:12:56<9:57:01, 3.93s/it] 51%|█████ | 9567/18688 [11:13:00<10:05:16, 3.98s/it] 51%|█████ | 9568/18688 [11:13:05<10:30:09, 4.15s/it] 51%|█████ | 9569/18688 [11:13:09<10:16:32, 4.06s/it] 51%|█████ | 9570/18688 [11:13:13<10:20:44, 4.08s/it] {'loss': '0.8985', 'grad_norm': '1.247', 'learning_rate': '4.383e-05', 'epoch': '0.5121', 'num_input_tokens_seen': 32172424, 'train_runtime': '4.039e+04', 'train_tokens_per_second': '796.5'}
51%|█████ | 9570/18688 [11:13:13<10:20:44, 4.08s/it] 51%|█████ | 9571/18688 [11:13:17<10:01:22, 3.96s/it] 51%|█████ | 9572/18688 [11:13:21<10:03:25, 3.97s/it] 51%|█████ | 9573/18688 [11:13:24<9:55:53, 3.92s/it] 51%|█████ | 9574/18688 [11:13:28<9:39:32, 3.82s/it] 51%|█████ | 9575/18688 [11:13:32<9:59:11, 3.95s/it] 51%|█████ | 9576/18688 [11:13:36<9:49:56, 3.88s/it] 51%|█████ | 9577/18688 [11:13:39<9:34:46, 3.79s/it] 51%|█████▏ | 9578/18688 [11:13:44<10:10:01, 4.02s/it] 51%|█████▏ | 9579/18688 [11:13:49<10:52:38, 4.30s/it] 51%|█████▏ | 9580/18688 [11:13:53<10:34:18, 4.18s/it] {'loss': '0.9073', 'grad_norm': '1.268', 'learning_rate': '4.376e-05', 'epoch': '0.5126', 'num_input_tokens_seen': 32203856, 'train_runtime': '4.043e+04', 'train_tokens_per_second': '796.5'}
51%|█████▏ | 9580/18688 [11:13:53<10:34:18, 4.18s/it] 51%|█████▏ | 9581/18688 [11:13:58<10:55:26, 4.32s/it] 51%|█████▏ | 9582/18688 [11:14:02<11:09:32, 4.41s/it] 51%|█████▏ | 9583/18688 [11:14:06<10:37:07, 4.20s/it] 51%|█████▏ | 9584/18688 [11:14:10<10:13:38, 4.04s/it] 51%|█████▏ | 9585/18688 [11:14:13<9:58:49, 3.95s/it] 51%|█████▏ | 9586/18688 [11:14:18<10:12:20, 4.04s/it] 51%|█████▏ | 9587/18688 [11:14:22<10:33:25, 4.18s/it] 51%|█████▏ | 9588/18688 [11:14:26<10:23:01, 4.11s/it] 51%|█████▏ | 9589/18688 [11:14:30<10:08:03, 4.01s/it] 51%|█████▏ | 9590/18688 [11:14:34<10:20:34, 4.09s/it] {'loss': '0.9524', 'grad_norm': '1.905', 'learning_rate': '4.369e-05', 'epoch': '0.5132', 'num_input_tokens_seen': 32236288, 'train_runtime': '4.047e+04', 'train_tokens_per_second': '796.5'}
51%|█████▏ | 9590/18688 [11:14:34<10:20:34, 4.09s/it] 51%|█████▏ | 9591/18688 [11:14:39<10:55:13, 4.32s/it] 51%|█████▏ | 9592/18688 [11:14:43<10:47:29, 4.27s/it] 51%|█████▏ | 9593/18688 [11:14:47<10:17:27, 4.07s/it] 51%|█████▏ | 9594/18688 [11:14:50<10:05:38, 4.00s/it] 51%|█████▏ | 9595/18688 [11:14:55<10:38:36, 4.21s/it] 51%|█████▏ | 9596/18688 [11:15:00<10:48:39, 4.28s/it] 51%|█████▏ | 9597/18688 [11:15:03<10:29:39, 4.16s/it] 51%|█████▏ | 9598/18688 [11:15:08<10:25:48, 4.13s/it] 51%|█████▏ | 9599/18688 [11:15:12<10:32:08, 4.17s/it] 51%|█████▏ | 9600/18688 [11:15:16<10:33:41, 4.18s/it] {'loss': '0.9535', 'grad_norm': '1.029', 'learning_rate': '4.361e-05', 'epoch': '0.5137', 'num_input_tokens_seen': 32271544, 'train_runtime': '4.052e+04', 'train_tokens_per_second': '796.5'}
51%|█████▏ | 9600/18688 [11:15:16<10:33:41, 4.18s/it] 51%|█████▏ | 9601/18688 [11:15:21<11:04:13, 4.39s/it] 51%|█████▏ | 9602/18688 [11:15:25<10:54:33, 4.32s/it] 51%|█████▏ | 9603/18688 [11:15:29<10:21:40, 4.11s/it] 51%|█████▏ | 9604/18688 [11:15:33<10:31:27, 4.17s/it] 51%|█████▏ | 9605/18688 [11:15:37<10:27:54, 4.15s/it] 51%|█████▏ | 9606/18688 [11:15:41<10:02:53, 3.98s/it] 51%|█████▏ | 9607/18688 [11:15:45<10:30:29, 4.17s/it] 51%|█████▏ | 9608/18688 [11:15:50<11:00:01, 4.36s/it] 51%|█████▏ | 9609/18688 [11:15:54<10:59:25, 4.36s/it] 51%|█████▏ | 9610/18688 [11:15:59<11:30:45, 4.57s/it] {'loss': '0.9498', 'grad_norm': '1.248', 'learning_rate': '4.354e-05', 'epoch': '0.5142', 'num_input_tokens_seen': 32307752, 'train_runtime': '4.056e+04', 'train_tokens_per_second': '796.5'}
51%|█████▏ | 9610/18688 [11:15:59<11:30:45, 4.57s/it] 51%|█████▏ | 9611/18688 [11:16:04<11:09:30, 4.43s/it] 51%|█████▏ | 9612/18688 [11:16:09<12:03:43, 4.78s/it] 51%|█████▏ | 9613/18688 [11:16:14<12:12:41, 4.84s/it] 51%|█████▏ | 9614/18688 [11:16:19<11:49:30, 4.69s/it] 51%|█████▏ | 9615/18688 [11:16:23<11:33:26, 4.59s/it] 51%|█████▏ | 9616/18688 [11:16:27<11:24:18, 4.53s/it] 51%|█████▏ | 9617/18688 [11:16:32<11:23:59, 4.52s/it] 51%|█████▏ | 9618/18688 [11:16:36<11:10:23, 4.43s/it] 51%|█████▏ | 9619/18688 [11:16:41<11:15:58, 4.47s/it] 51%|█████▏ | 9620/18688 [11:16:44<10:46:00, 4.27s/it] {'loss': '0.9479', 'grad_norm': '1.183', 'learning_rate': '4.347e-05', 'epoch': '0.5148', 'num_input_tokens_seen': 32345512, 'train_runtime': '4.06e+04', 'train_tokens_per_second': '796.6'}
51%|█████▏ | 9620/18688 [11:16:44<10:46:00, 4.27s/it] 51%|█████▏ | 9621/18688 [11:16:48<10:23:25, 4.13s/it] 51%|█████▏ | 9622/18688 [11:16:52<9:59:28, 3.97s/it] 51%|█████▏ | 9623/18688 [11:16:56<9:58:06, 3.96s/it] 51%|█████▏ | 9624/18688 [11:17:00<9:57:22, 3.95s/it] 52%|█████▏ | 9625/18688 [11:17:04<9:58:12, 3.96s/it] 52%|█████▏ | 9626/18688 [11:17:07<9:36:05, 3.81s/it] 52%|█████▏ | 9627/18688 [11:17:12<10:19:15, 4.10s/it] 52%|█████▏ | 9628/18688 [11:17:16<10:27:25, 4.16s/it] 52%|█████▏ | 9629/18688 [11:17:21<11:02:09, 4.39s/it] 52%|█████▏ | 9630/18688 [11:17:25<10:40:31, 4.24s/it] {'loss': '0.8688', 'grad_norm': '1.143', 'learning_rate': '4.34e-05', 'epoch': '0.5153', 'num_input_tokens_seen': 32377936, 'train_runtime': '4.065e+04', 'train_tokens_per_second': '796.6'}
52%|█████▏ | 9630/18688 [11:17:25<10:40:31, 4.24s/it] 52%|█████▏ | 9631/18688 [11:17:29<10:22:45, 4.13s/it] 52%|█████▏ | 9632/18688 [11:17:35<11:39:41, 4.64s/it] 52%|█████▏ | 9633/18688 [11:17:39<11:33:39, 4.60s/it] 52%|█████▏ | 9634/18688 [11:17:43<11:11:37, 4.45s/it] 52%|█████▏ | 9635/18688 [11:17:48<11:20:30, 4.51s/it] 52%|█████▏ | 9636/18688 [11:17:52<10:50:41, 4.31s/it] 52%|█████▏ | 9637/18688 [11:17:56<10:32:14, 4.19s/it] 52%|█████▏ | 9638/18688 [11:18:00<10:28:43, 4.17s/it] 52%|█████▏ | 9639/18688 [11:18:05<10:57:19, 4.36s/it] 52%|█████▏ | 9640/18688 [11:18:09<10:48:05, 4.30s/it] {'loss': '0.9107', 'grad_norm': '0.925', 'learning_rate': '4.332e-05', 'epoch': '0.5159', 'num_input_tokens_seen': 32415512, 'train_runtime': '4.069e+04', 'train_tokens_per_second': '796.7'}
52%|█████▏ | 9640/18688 [11:18:09<10:48:05, 4.30s/it] 52%|█████▏ | 9641/18688 [11:18:13<10:51:25, 4.32s/it] 52%|█████▏ | 9642/18688 [11:18:17<10:37:50, 4.23s/it] 52%|█████▏ | 9643/18688 [11:18:22<11:27:43, 4.56s/it] 52%|█████▏ | 9644/18688 [11:18:28<11:55:48, 4.75s/it] 52%|█████▏ | 9645/18688 [11:18:32<11:24:38, 4.54s/it] 52%|█████▏ | 9646/18688 [11:18:35<10:40:18, 4.25s/it] 52%|█████▏ | 9647/18688 [11:18:39<10:06:19, 4.02s/it] 52%|█████▏ | 9648/18688 [11:18:43<10:18:16, 4.10s/it] 52%|█████▏ | 9649/18688 [11:18:47<9:49:38, 3.91s/it] 52%|█████▏ | 9650/18688 [11:18:50<9:45:49, 3.89s/it] {'loss': '0.9232', 'grad_norm': '1.179', 'learning_rate': '4.325e-05', 'epoch': '0.5164', 'num_input_tokens_seen': 32449768, 'train_runtime': '4.073e+04', 'train_tokens_per_second': '796.7'}
52%|█████▏ | 9650/18688 [11:18:50<9:45:49, 3.89s/it] 52%|█████▏ | 9651/18688 [11:18:54<9:46:56, 3.90s/it] 52%|█████▏ | 9652/18688 [11:18:59<10:06:37, 4.03s/it] 52%|█████▏ | 9653/18688 [11:19:02<9:43:12, 3.87s/it] 52%|█████▏ | 9654/18688 [11:19:07<10:42:45, 4.27s/it] 52%|█████▏ | 9655/18688 [11:19:12<10:46:02, 4.29s/it] 52%|█████▏ | 9656/18688 [11:19:15<10:19:09, 4.11s/it] 52%|█████▏ | 9657/18688 [11:19:20<10:33:21, 4.21s/it] 52%|█████▏ | 9658/18688 [11:19:23<10:08:58, 4.05s/it] 52%|█████▏ | 9659/18688 [11:19:27<9:52:18, 3.94s/it] 52%|█████▏ | 9660/18688 [11:19:32<10:23:04, 4.14s/it] {'loss': '0.9288', 'grad_norm': '1.258', 'learning_rate': '4.318e-05', 'epoch': '0.5169', 'num_input_tokens_seen': 32483488, 'train_runtime': '4.077e+04', 'train_tokens_per_second': '796.7'}
52%|█████▏ | 9660/18688 [11:19:32<10:23:04, 4.14s/it] 52%|█████▏ | 9661/18688 [11:19:35<10:03:38, 4.01s/it] 52%|█████▏ | 9662/18688 [11:19:40<10:14:09, 4.08s/it] 52%|█████▏ | 9663/18688 [11:19:44<10:24:42, 4.15s/it] 52%|█████▏ | 9664/18688 [11:19:48<10:18:18, 4.11s/it] 52%|█████▏ | 9665/18688 [11:19:52<10:17:18, 4.10s/it] 52%|█████▏ | 9666/18688 [11:19:57<10:32:51, 4.21s/it] 52%|█████▏ | 9667/18688 [11:20:01<10:24:07, 4.15s/it] 52%|█████▏ | 9668/18688 [11:20:05<10:41:20, 4.27s/it] 52%|█████▏ | 9669/18688 [11:20:09<10:27:29, 4.17s/it] 52%|█████▏ | 9670/18688 [11:20:14<10:42:06, 4.27s/it] {'loss': '0.9576', 'grad_norm': '1.126', 'learning_rate': '4.31e-05', 'epoch': '0.5175', 'num_input_tokens_seen': 32517792, 'train_runtime': '4.081e+04', 'train_tokens_per_second': '796.7'}
52%|█████▏ | 9670/18688 [11:20:14<10:42:06, 4.27s/it] 52%|█████▏ | 9671/18688 [11:20:18<10:53:12, 4.35s/it] 52%|█████▏ | 9672/18688 [11:20:22<10:51:46, 4.34s/it] 52%|█████▏ | 9673/18688 [11:20:27<11:21:24, 4.54s/it] 52%|█████▏ | 9674/18688 [11:20:31<10:42:50, 4.28s/it] 52%|█████▏ | 9675/18688 [11:20:35<10:37:11, 4.24s/it] 52%|█████▏ | 9676/18688 [11:20:39<10:07:03, 4.04s/it] 52%|█████▏ | 9677/18688 [11:20:43<10:03:33, 4.02s/it] 52%|█████▏ | 9678/18688 [11:20:48<10:35:42, 4.23s/it] 52%|█████▏ | 9679/18688 [11:20:52<10:40:34, 4.27s/it] 52%|█████▏ | 9680/18688 [11:20:56<10:38:50, 4.26s/it] {'loss': '0.9531', 'grad_norm': '0.9474', 'learning_rate': '4.303e-05', 'epoch': '0.518', 'num_input_tokens_seen': 32552808, 'train_runtime': '4.086e+04', 'train_tokens_per_second': '796.8'}
52%|█████▏ | 9680/18688 [11:20:56<10:38:50, 4.26s/it] 52%|█████▏ | 9681/18688 [11:21:00<10:35:43, 4.23s/it] 52%|█████▏ | 9682/18688 [11:21:05<10:50:44, 4.34s/it] 52%|█████▏ | 9683/18688 [11:21:08<10:16:19, 4.11s/it] 52%|█████▏ | 9684/18688 [11:21:13<10:20:53, 4.14s/it] 52%|█████▏ | 9685/18688 [11:21:16<9:49:53, 3.93s/it] 52%|█████▏ | 9686/18688 [11:21:20<9:59:44, 4.00s/it] 52%|█████▏ | 9687/18688 [11:21:24<9:50:02, 3.93s/it] 52%|█████▏ | 9688/18688 [11:21:29<10:25:30, 4.17s/it] 52%|█████▏ | 9689/18688 [11:21:33<10:34:22, 4.23s/it] 52%|█████▏ | 9690/18688 [11:21:38<10:44:57, 4.30s/it] {'loss': '0.9515', 'grad_norm': '1.379', 'learning_rate': '4.296e-05', 'epoch': '0.5185', 'num_input_tokens_seen': 32587184, 'train_runtime': '4.09e+04', 'train_tokens_per_second': '796.8'}
52%|█████▏ | 9690/18688 [11:21:38<10:44:57, 4.30s/it] 52%|█████▏ | 9691/18688 [11:21:43<11:16:13, 4.51s/it] 52%|█████▏ | 9692/18688 [11:21:47<10:52:16, 4.35s/it] 52%|█████▏ | 9693/18688 [11:21:50<10:19:17, 4.13s/it] 52%|█████▏ | 9694/18688 [11:21:55<10:33:41, 4.23s/it] 52%|█████▏ | 9695/18688 [11:21:59<10:25:53, 4.18s/it] 52%|█████▏ | 9696/18688 [11:22:04<10:55:05, 4.37s/it] 52%|█████▏ | 9697/18688 [11:22:08<11:03:51, 4.43s/it] 52%|█████▏ | 9698/18688 [11:22:12<10:27:52, 4.19s/it] 52%|█████▏ | 9699/18688 [11:22:15<10:04:40, 4.04s/it] 52%|█████▏ | 9700/18688 [11:22:19<9:39:52, 3.87s/it] {'loss': '0.8928', 'grad_norm': '1.238', 'learning_rate': '4.289e-05', 'epoch': '0.5191', 'num_input_tokens_seen': 32621480, 'train_runtime': '4.094e+04', 'train_tokens_per_second': '796.8'}
52%|█████▏ | 9700/18688 [11:22:19<9:39:52, 3.87s/it] 52%|█████▏ | 9701/18688 [11:22:24<10:19:49, 4.14s/it] 52%|█████▏ | 9702/18688 [11:22:28<10:47:21, 4.32s/it] 52%|█████▏ | 9703/18688 [11:22:33<11:16:16, 4.52s/it] 52%|█████▏ | 9704/18688 [11:22:37<10:55:07, 4.38s/it] 52%|█████▏ | 9705/18688 [11:22:42<10:56:44, 4.39s/it] 52%|█████▏ | 9706/18688 [11:22:46<10:51:33, 4.35s/it] 52%|█████▏ | 9707/18688 [11:22:50<10:22:40, 4.16s/it] 52%|█████▏ | 9708/18688 [11:22:53<9:51:03, 3.95s/it] 52%|█████▏ | 9709/18688 [11:22:57<9:37:56, 3.86s/it] 52%|█████▏ | 9710/18688 [11:23:01<9:40:50, 3.88s/it] {'loss': '0.9519', 'grad_norm': '1.103', 'learning_rate': '4.281e-05', 'epoch': '0.5196', 'num_input_tokens_seen': 32654592, 'train_runtime': '4.098e+04', 'train_tokens_per_second': '796.8'}
52%|█████▏ | 9710/18688 [11:23:01<9:40:50, 3.88s/it] 52%|█████▏ | 9711/18688 [11:23:05<9:41:22, 3.89s/it] 52%|█████▏ | 9712/18688 [11:23:08<9:21:20, 3.75s/it] 52%|█████▏ | 9713/18688 [11:23:12<9:29:49, 3.81s/it] 52%|█████▏ | 9714/18688 [11:23:16<9:45:57, 3.92s/it] 52%|█████▏ | 9715/18688 [11:23:21<10:23:31, 4.17s/it] 52%|█████▏ | 9716/18688 [11:23:25<10:02:06, 4.03s/it] 52%|█████▏ | 9717/18688 [11:23:28<9:41:57, 3.89s/it] 52%|█████▏ | 9718/18688 [11:23:33<10:16:56, 4.13s/it] 52%|█████▏ | 9719/18688 [11:23:38<10:46:08, 4.32s/it] 52%|█████▏ | 9720/18688 [11:23:43<11:09:16, 4.48s/it] {'loss': '0.8946', 'grad_norm': '1.069', 'learning_rate': '4.274e-05', 'epoch': '0.5201', 'num_input_tokens_seen': 32690224, 'train_runtime': '4.102e+04', 'train_tokens_per_second': '796.9'}
52%|█████▏ | 9720/18688 [11:23:43<11:09:16, 4.48s/it] 52%|█████▏ | 9721/18688 [11:23:48<11:30:32, 4.62s/it] 52%|█████▏ | 9722/18688 [11:23:52<11:04:25, 4.45s/it] 52%|█████▏ | 9723/18688 [11:23:56<10:51:06, 4.36s/it] 52%|█████▏ | 9724/18688 [11:23:59<10:21:14, 4.16s/it] 52%|█████▏ | 9725/18688 [11:24:03<10:00:40, 4.02s/it] 52%|█████▏ | 9726/18688 [11:24:07<9:45:08, 3.92s/it] 52%|█████▏ | 9727/18688 [11:24:11<9:57:30, 4.00s/it] 52%|█████▏ | 9728/18688 [11:24:15<9:41:33, 3.89s/it] 52%|█████▏ | 9729/18688 [11:24:18<9:31:23, 3.83s/it] 52%|█████▏ | 9730/18688 [11:24:22<9:14:23, 3.71s/it] {'loss': '0.8491', 'grad_norm': '1.293', 'learning_rate': '4.267e-05', 'epoch': '0.5207', 'num_input_tokens_seen': 32719632, 'train_runtime': '4.106e+04', 'train_tokens_per_second': '796.8'}
52%|█████▏ | 9730/18688 [11:24:22<9:14:23, 3.71s/it] 52%|█████▏ | 9731/18688 [11:24:27<10:05:54, 4.06s/it] 52%|█████▏ | 9732/18688 [11:24:30<9:49:43, 3.95s/it] 52%|█████▏ | 9733/18688 [11:24:34<9:52:51, 3.97s/it] 52%|█████▏ | 9734/18688 [11:24:39<10:26:45, 4.20s/it] 52%|█████▏ | 9735/18688 [11:24:43<9:52:59, 3.97s/it] 52%|█████▏ | 9736/18688 [11:24:46<9:29:54, 3.82s/it] 52%|█████▏ | 9737/18688 [11:24:50<9:44:10, 3.92s/it] 52%|█████▏ | 9738/18688 [11:24:55<10:02:51, 4.04s/it] 52%|█████▏ | 9739/18688 [11:24:59<10:23:58, 4.18s/it] 52%|█████▏ | 9740/18688 [11:25:03<10:26:06, 4.20s/it] {'loss': '0.9362', 'grad_norm': '1.172', 'learning_rate': '4.259e-05', 'epoch': '0.5212', 'num_input_tokens_seen': 32754296, 'train_runtime': '4.11e+04', 'train_tokens_per_second': '796.9'}
52%|█████▏ | 9740/18688 [11:25:03<10:26:06, 4.20s/it] 52%|█████▏ | 9741/18688 [11:25:08<10:55:12, 4.39s/it] 52%|█████▏ | 9742/18688 [11:25:12<10:12:36, 4.11s/it] 52%|█████▏ | 9743/18688 [11:25:15<9:57:01, 4.00s/it] 52%|█████▏ | 9744/18688 [11:25:19<9:40:30, 3.89s/it] 52%|█████▏ | 9745/18688 [11:25:23<9:34:44, 3.86s/it] 52%|█████▏ | 9746/18688 [11:25:26<9:31:13, 3.83s/it] 52%|█████▏ | 9747/18688 [11:25:30<9:18:38, 3.75s/it] 52%|█████▏ | 9748/18688 [11:25:34<9:36:22, 3.87s/it] 52%|█████▏ | 9749/18688 [11:25:39<10:01:10, 4.04s/it] 52%|█████▏ | 9750/18688 [11:25:44<10:53:55, 4.39s/it] {'loss': '0.9476', 'grad_norm': '1.337', 'learning_rate': '4.252e-05', 'epoch': '0.5217', 'num_input_tokens_seen': 32786872, 'train_runtime': '4.114e+04', 'train_tokens_per_second': '796.9'}
52%|█████▏ | 9750/18688 [11:25:44<10:53:55, 4.39s/it] 52%|█████▏ | 9751/18688 [11:25:49<11:40:37, 4.70s/it] 52%|█████▏ | 9752/18688 [11:25:55<12:07:14, 4.88s/it] 52%|█████▏ | 9753/18688 [11:25:59<11:49:34, 4.76s/it] 52%|█████▏ | 9754/18688 [11:26:03<11:15:47, 4.54s/it] 52%|█████▏ | 9755/18688 [11:26:07<11:09:59, 4.50s/it] 52%|█████▏ | 9756/18688 [11:26:12<10:58:39, 4.42s/it] 52%|█████▏ | 9757/18688 [11:26:16<10:37:28, 4.28s/it] 52%|█████▏ | 9758/18688 [11:26:20<10:56:40, 4.41s/it] 52%|█████▏ | 9759/18688 [11:26:24<10:34:52, 4.27s/it] 52%|█████▏ | 9760/18688 [11:26:29<10:52:14, 4.38s/it] {'loss': '0.9053', 'grad_norm': '1.772', 'learning_rate': '4.245e-05', 'epoch': '0.5223', 'num_input_tokens_seen': 32824744, 'train_runtime': '4.119e+04', 'train_tokens_per_second': '796.9'}
52%|█████▏ | 9760/18688 [11:26:29<10:52:14, 4.38s/it] 52%|█████▏ | 9761/18688 [11:26:33<10:42:49, 4.32s/it] 52%|█████▏ | 9762/18688 [11:26:37<10:14:06, 4.13s/it] 52%|█████▏ | 9763/18688 [11:26:41<10:10:56, 4.11s/it] 52%|█████▏ | 9764/18688 [11:26:44<9:42:07, 3.91s/it] 52%|█████▏ | 9765/18688 [11:26:48<9:51:58, 3.98s/it] 52%|█████▏ | 9766/18688 [11:26:53<10:06:05, 4.08s/it] 52%|█████▏ | 9767/18688 [11:26:57<10:16:21, 4.15s/it] 52%|█████▏ | 9768/18688 [11:27:02<10:55:49, 4.41s/it] 52%|█████▏ | 9769/18688 [11:27:06<10:18:36, 4.16s/it] 52%|█████▏ | 9770/18688 [11:27:09<9:54:14, 4.00s/it] {'loss': '0.9295', 'grad_norm': '1.241', 'learning_rate': '4.237e-05', 'epoch': '0.5228', 'num_input_tokens_seen': 32858176, 'train_runtime': '4.123e+04', 'train_tokens_per_second': '797'}
52%|█████▏ | 9770/18688 [11:27:09<9:54:14, 4.00s/it] 52%|█████▏ | 9771/18688 [11:27:14<10:05:26, 4.07s/it] 52%|█████▏ | 9772/18688 [11:27:18<10:01:32, 4.05s/it] 52%|█████▏ | 9773/18688 [11:27:21<9:54:28, 4.00s/it] 52%|█████▏ | 9774/18688 [11:27:27<10:45:47, 4.35s/it] 52%|█████▏ | 9775/18688 [11:27:33<12:02:05, 4.86s/it] 52%|█████▏ | 9776/18688 [11:27:37<11:37:09, 4.69s/it] 52%|█████▏ | 9777/18688 [11:27:42<11:36:14, 4.69s/it] 52%|█████▏ | 9778/18688 [11:27:45<10:54:40, 4.41s/it] 52%|█████▏ | 9779/18688 [11:27:49<10:32:40, 4.26s/it] 52%|█████▏ | 9780/18688 [11:27:54<10:32:36, 4.26s/it] {'loss': '0.9056', 'grad_norm': '1.356', 'learning_rate': '4.23e-05', 'epoch': '0.5233', 'num_input_tokens_seen': 32894288, 'train_runtime': '4.127e+04', 'train_tokens_per_second': '797'}
52%|█████▏ | 9780/18688 [11:27:54<10:32:36, 4.26s/it] 52%|█████▏ | 9781/18688 [11:27:57<10:13:45, 4.13s/it] 52%|█████▏ | 9782/18688 [11:28:01<9:43:02, 3.93s/it] 52%|█████▏ | 9783/18688 [11:28:05<10:05:29, 4.08s/it] 52%|█████▏ | 9784/18688 [11:28:10<10:32:45, 4.26s/it] 52%|█████▏ | 9785/18688 [11:28:14<10:29:37, 4.24s/it] 52%|█████▏ | 9786/18688 [11:28:18<10:29:44, 4.24s/it] 52%|█████▏ | 9787/18688 [11:28:22<10:20:47, 4.18s/it] 52%|█████▏ | 9788/18688 [11:28:26<9:58:15, 4.03s/it] 52%|█████▏ | 9789/18688 [11:28:30<10:02:09, 4.06s/it] 52%|█████▏ | 9790/18688 [11:28:34<9:59:00, 4.04s/it] {'loss': '0.8892', 'grad_norm': '1.066', 'learning_rate': '4.223e-05', 'epoch': '0.5239', 'num_input_tokens_seen': 32928536, 'train_runtime': '4.131e+04', 'train_tokens_per_second': '797'}
52%|█████▏ | 9790/18688 [11:28:34<9:59:00, 4.04s/it] 52%|█████▏ | 9791/18688 [11:28:38<10:03:18, 4.07s/it] 52%|█████▏ | 9792/18688 [11:28:42<10:02:41, 4.06s/it] 52%|█████▏ | 9793/18688 [11:28:47<10:23:07, 4.20s/it] 52%|█████▏ | 9794/18688 [11:28:51<10:17:42, 4.17s/it] 52%|█████▏ | 9795/18688 [11:28:56<10:51:36, 4.40s/it] 52%|█████▏ | 9796/18688 [11:29:00<10:27:45, 4.24s/it] 52%|█████▏ | 9797/18688 [11:29:03<9:53:26, 4.00s/it] 52%|█████▏ | 9798/18688 [11:29:08<10:05:52, 4.09s/it] 52%|█████▏ | 9799/18688 [11:29:11<9:47:58, 3.97s/it] 52%|█████▏ | 9800/18688 [11:29:16<10:37:56, 4.31s/it] {'loss': '0.9108', 'grad_norm': '1.582', 'learning_rate': '4.216e-05', 'epoch': '0.5244', 'num_input_tokens_seen': 32960776, 'train_runtime': '4.136e+04', 'train_tokens_per_second': '797'}
52%|█████▏ | 9800/18688 [11:29:16<10:37:56, 4.31s/it] 52%|█████▏ | 9801/18688 [11:29:21<10:32:46, 4.27s/it] 52%|█████▏ | 9802/18688 [11:29:24<10:02:34, 4.07s/it] 52%|█████▏ | 9803/18688 [11:29:29<10:33:07, 4.28s/it] 52%|█████▏ | 9804/18688 [11:29:33<10:05:09, 4.09s/it] 52%|█████▏ | 9805/18688 [11:29:37<10:22:40, 4.21s/it] 52%|█████▏ | 9806/18688 [11:29:42<10:36:00, 4.30s/it] 52%|█████▏ | 9807/18688 [11:29:46<10:37:57, 4.31s/it] 52%|█████▏ | 9808/18688 [11:29:51<10:54:18, 4.42s/it] 52%|█████▏ | 9809/18688 [11:29:56<11:22:29, 4.61s/it] 52%|█████▏ | 9810/18688 [11:30:00<10:58:56, 4.45s/it] {'loss': '0.9417', 'grad_norm': '1.456', 'learning_rate': '4.208e-05', 'epoch': '0.5249', 'num_input_tokens_seen': 32996760, 'train_runtime': '4.14e+04', 'train_tokens_per_second': '797'}
52%|█████▏ | 9810/18688 [11:30:00<10:58:56, 4.45s/it] 52%|█████▏ | 9811/18688 [11:30:03<10:25:21, 4.23s/it] 53%|█████▎ | 9812/18688 [11:30:07<10:17:19, 4.17s/it] 53%|█████▎ | 9813/18688 [11:30:11<10:04:58, 4.09s/it] 53%|█████▎ | 9814/18688 [11:30:15<9:44:20, 3.95s/it] 53%|█████▎ | 9815/18688 [11:30:19<9:49:14, 3.98s/it] 53%|█████▎ | 9816/18688 [11:30:23<9:54:13, 4.02s/it] 53%|█████▎ | 9817/18688 [11:30:28<10:36:22, 4.30s/it] 53%|█████▎ | 9818/18688 [11:30:33<10:44:26, 4.36s/it] 53%|█████▎ | 9819/18688 [11:30:39<11:55:36, 4.84s/it] 53%|█████▎ | 9820/18688 [11:30:42<11:10:47, 4.54s/it] {'loss': '0.8606', 'grad_norm': '1.236', 'learning_rate': '4.201e-05', 'epoch': '0.5255', 'num_input_tokens_seen': 33030624, 'train_runtime': '4.144e+04', 'train_tokens_per_second': '797'}
53%|█████▎ | 9820/18688 [11:30:42<11:10:47, 4.54s/it] 53%|█████▎ | 9821/18688 [11:30:46<10:46:14, 4.37s/it] 53%|█████▎ | 9822/18688 [11:30:50<10:20:32, 4.20s/it] 53%|█████▎ | 9823/18688 [11:30:54<9:53:03, 4.01s/it] 53%|█████▎ | 9824/18688 [11:30:58<9:59:01, 4.05s/it] 53%|█████▎ | 9825/18688 [11:31:02<9:51:56, 4.01s/it] 53%|█████▎ | 9826/18688 [11:31:05<9:34:22, 3.89s/it] 53%|█████▎ | 9827/18688 [11:31:10<10:13:55, 4.16s/it] 53%|█████▎ | 9828/18688 [11:31:14<9:51:05, 4.00s/it] 53%|█████▎ | 9829/18688 [11:31:18<9:47:15, 3.98s/it] 53%|█████▎ | 9830/18688 [11:31:21<9:33:39, 3.89s/it] {'loss': '0.9463', 'grad_norm': '1.136', 'learning_rate': '4.194e-05', 'epoch': '0.526', 'num_input_tokens_seen': 33062328, 'train_runtime': '4.148e+04', 'train_tokens_per_second': '797'}
53%|█████▎ | 9830/18688 [11:31:21<9:33:39, 3.89s/it] 53%|█████▎ | 9831/18688 [11:31:26<9:47:49, 3.98s/it] 53%|█████▎ | 9832/18688 [11:31:30<10:20:16, 4.20s/it] 53%|█████▎ | 9833/18688 [11:31:34<9:54:00, 4.02s/it] 53%|█████▎ | 9834/18688 [11:31:38<10:05:44, 4.10s/it] 53%|█████▎ | 9835/18688 [11:31:42<9:44:29, 3.96s/it] 53%|█████▎ | 9836/18688 [11:31:46<9:59:34, 4.06s/it] 53%|█████▎ | 9837/18688 [11:31:50<9:59:37, 4.06s/it] 53%|█████▎ | 9838/18688 [11:31:55<10:11:38, 4.15s/it] 53%|█████▎ | 9839/18688 [11:31:59<10:23:19, 4.23s/it] 53%|█████▎ | 9840/18688 [11:32:03<9:50:44, 4.01s/it] {'loss': '0.9269', 'grad_norm': '1.191', 'learning_rate': '4.186e-05', 'epoch': '0.5266', 'num_input_tokens_seen': 33094656, 'train_runtime': '4.152e+04', 'train_tokens_per_second': '797'}
53%|█████▎ | 9840/18688 [11:32:03<9:50:44, 4.01s/it] 53%|█████▎ | 9841/18688 [11:32:06<9:39:04, 3.93s/it] 53%|█████▎ | 9842/18688 [11:32:10<9:37:58, 3.92s/it] 53%|█████▎ | 9843/18688 [11:32:17<11:28:15, 4.67s/it] 53%|█████▎ | 9844/18688 [11:32:20<10:35:07, 4.31s/it] 53%|█████▎ | 9845/18688 [11:32:25<10:59:40, 4.48s/it] 53%|█████▎ | 9846/18688 [11:32:30<11:04:12, 4.51s/it] 53%|█████▎ | 9847/18688 [11:32:34<10:46:16, 4.39s/it] 53%|█████▎ | 9848/18688 [11:32:37<10:05:03, 4.11s/it] 53%|█████▎ | 9849/18688 [11:32:41<10:18:11, 4.20s/it] 53%|█████▎ | 9850/18688 [11:32:46<10:47:45, 4.40s/it] {'loss': '0.9408', 'grad_norm': '1.245', 'learning_rate': '4.179e-05', 'epoch': '0.5271', 'num_input_tokens_seen': 33131424, 'train_runtime': '4.157e+04', 'train_tokens_per_second': '797.1'}
53%|█████▎ | 9850/18688 [11:32:46<10:47:45, 4.40s/it] 53%|█████▎ | 9851/18688 [11:32:51<10:37:29, 4.33s/it] 53%|█████▎ | 9852/18688 [11:32:54<10:04:12, 4.10s/it] 53%|█████▎ | 9853/18688 [11:32:58<10:16:02, 4.18s/it] 53%|█████▎ | 9854/18688 [11:33:02<9:55:07, 4.04s/it] 53%|█████▎ | 9855/18688 [11:33:06<9:36:54, 3.92s/it] 53%|█████▎ | 9856/18688 [11:33:10<9:57:37, 4.06s/it] 53%|█████▎ | 9857/18688 [11:33:15<10:09:49, 4.14s/it] 53%|█████▎ | 9858/18688 [11:33:18<9:56:22, 4.05s/it] 53%|█████▎ | 9859/18688 [11:33:22<9:52:16, 4.03s/it] 53%|█████▎ | 9860/18688 [11:33:28<10:51:54, 4.43s/it] {'loss': '0.9557', 'grad_norm': '1.492', 'learning_rate': '4.172e-05', 'epoch': '0.5276', 'num_input_tokens_seen': 33164080, 'train_runtime': '4.161e+04', 'train_tokens_per_second': '797.1'}
53%|█████▎ | 9860/18688 [11:33:28<10:51:54, 4.43s/it] 53%|█████▎ | 9861/18688 [11:33:32<10:54:10, 4.45s/it] 53%|█████▎ | 9862/18688 [11:33:36<10:45:49, 4.39s/it] 53%|█████▎ | 9863/18688 [11:33:41<10:49:27, 4.42s/it] 53%|█████▎ | 9864/18688 [11:33:45<10:31:39, 4.30s/it] 53%|█████▎ | 9865/18688 [11:33:49<10:12:50, 4.17s/it] 53%|█████▎ | 9866/18688 [11:33:53<10:07:15, 4.13s/it] 53%|█████▎ | 9867/18688 [11:33:58<10:58:48, 4.48s/it] 53%|█████▎ | 9868/18688 [11:34:02<10:22:30, 4.23s/it] 53%|█████▎ | 9869/18688 [11:34:07<11:05:58, 4.53s/it] 53%|█████▎ | 9870/18688 [11:34:11<10:34:35, 4.32s/it] {'loss': '0.8782', 'grad_norm': '1.242', 'learning_rate': '4.164e-05', 'epoch': '0.5282', 'num_input_tokens_seen': 33200528, 'train_runtime': '4.165e+04', 'train_tokens_per_second': '797.1'}
53%|█████▎ | 9870/18688 [11:34:11<10:34:35, 4.32s/it] 53%|█████▎ | 9871/18688 [11:34:15<10:28:04, 4.27s/it] 53%|█████▎ | 9872/18688 [11:34:18<9:52:50, 4.03s/it] 53%|█████▎ | 9873/18688 [11:34:23<9:59:16, 4.08s/it] 53%|█████▎ | 9874/18688 [11:34:27<10:25:13, 4.26s/it] 53%|█████▎ | 9875/18688 [11:34:31<10:05:30, 4.12s/it] 53%|█████▎ | 9876/18688 [11:34:35<9:46:47, 4.00s/it] 53%|█████▎ | 9877/18688 [11:34:39<9:31:45, 3.89s/it] 53%|█████▎ | 9878/18688 [11:34:43<9:42:15, 3.97s/it] 53%|█████▎ | 9879/18688 [11:34:46<9:16:35, 3.79s/it] 53%|█████▎ | 9880/18688 [11:34:51<9:56:37, 4.06s/it] {'loss': '0.8968', 'grad_norm': '1.232', 'learning_rate': '4.157e-05', 'epoch': '0.5287', 'num_input_tokens_seen': 33231392, 'train_runtime': '4.169e+04', 'train_tokens_per_second': '797.1'}
53%|█████▎ | 9880/18688 [11:34:51<9:56:37, 4.06s/it] 53%|█████▎ | 9881/18688 [11:34:54<9:36:50, 3.93s/it] 53%|█████▎ | 9882/18688 [11:34:59<10:26:56, 4.27s/it] 53%|█████▎ | 9883/18688 [11:35:03<9:51:49, 4.03s/it] 53%|█████▎ | 9884/18688 [11:35:07<9:40:35, 3.96s/it] 53%|█████▎ | 9885/18688 [11:35:11<9:50:33, 4.03s/it] 53%|█████▎ | 9886/18688 [11:35:16<10:37:59, 4.35s/it] 53%|█████▎ | 9887/18688 [11:35:20<10:23:30, 4.25s/it] 53%|█████▎ | 9888/18688 [11:35:26<11:20:46, 4.64s/it] 53%|█████▎ | 9889/18688 [11:35:29<10:33:50, 4.32s/it] 53%|█████▎ | 9890/18688 [11:35:33<10:29:50, 4.30s/it] {'loss': '0.9054', 'grad_norm': '1.107', 'learning_rate': '4.15e-05', 'epoch': '0.5292', 'num_input_tokens_seen': 33267288, 'train_runtime': '4.173e+04', 'train_tokens_per_second': '797.1'}
53%|█████▎ | 9890/18688 [11:35:33<10:29:50, 4.30s/it] 53%|█████▎ | 9891/18688 [11:35:38<10:31:55, 4.31s/it] 53%|█████▎ | 9892/18688 [11:35:43<11:14:14, 4.60s/it] 53%|█████▎ | 9893/18688 [11:35:47<11:00:18, 4.50s/it] 53%|█████▎ | 9894/18688 [11:35:51<10:35:18, 4.33s/it] 53%|█████▎ | 9895/18688 [11:35:56<10:38:34, 4.36s/it] 53%|█████▎ | 9896/18688 [11:36:00<10:36:06, 4.34s/it] 53%|█████▎ | 9897/18688 [11:36:05<10:48:36, 4.43s/it] 53%|█████▎ | 9898/18688 [11:36:09<10:33:57, 4.33s/it] 53%|█████▎ | 9899/18688 [11:36:12<10:03:32, 4.12s/it] 53%|█████▎ | 9900/18688 [11:36:16<9:32:27, 3.91s/it] {'loss': '0.9589', 'grad_norm': '1.356', 'learning_rate': '4.143e-05', 'epoch': '0.5298', 'num_input_tokens_seen': 33301776, 'train_runtime': '4.178e+04', 'train_tokens_per_second': '797.1'}
53%|█████▎ | 9900/18688 [11:36:16<9:32:27, 3.91s/it] 53%|█████▎ | 9901/18688 [11:36:20<9:44:33, 3.99s/it] 53%|█████▎ | 9902/18688 [11:36:24<10:04:25, 4.13s/it] 53%|█████▎ | 9903/18688 [11:36:28<9:48:20, 4.02s/it] 53%|█████▎ | 9904/18688 [11:36:32<9:35:21, 3.93s/it] 53%|█████▎ | 9905/18688 [11:36:37<10:34:38, 4.34s/it] 53%|█████▎ | 9906/18688 [11:36:41<10:36:33, 4.35s/it] 53%|█████▎ | 9907/18688 [11:36:46<10:26:50, 4.28s/it] 53%|█████▎ | 9908/18688 [11:36:50<10:27:35, 4.29s/it] 53%|█████▎ | 9909/18688 [11:36:54<10:08:03, 4.16s/it] 53%|█████▎ | 9910/18688 [11:36:59<10:39:34, 4.37s/it] {'loss': '0.9127', 'grad_norm': '1.14', 'learning_rate': '4.135e-05', 'epoch': '0.5303', 'num_input_tokens_seen': 33337120, 'train_runtime': '4.182e+04', 'train_tokens_per_second': '797.2'}
53%|█████▎ | 9910/18688 [11:36:59<10:39:34, 4.37s/it] 53%|█████▎ | 9911/18688 [11:37:02<10:01:17, 4.11s/it] 53%|█████▎ | 9912/18688 [11:37:06<10:11:20, 4.18s/it] 53%|█████▎ | 9913/18688 [11:37:10<9:40:03, 3.97s/it] 53%|█████▎ | 9914/18688 [11:37:15<10:16:02, 4.21s/it] 53%|█████▎ | 9915/18688 [11:37:19<10:32:40, 4.33s/it] 53%|█████▎ | 9916/18688 [11:37:24<10:50:49, 4.45s/it] 53%|█████▎ | 9917/18688 [11:37:29<11:12:11, 4.60s/it] 53%|█████▎ | 9918/18688 [11:37:33<10:58:03, 4.50s/it] 53%|█████▎ | 9919/18688 [11:37:37<10:36:12, 4.35s/it] 53%|█████▎ | 9920/18688 [11:37:41<10:05:19, 4.14s/it] {'loss': '0.8835', 'grad_norm': '1.35', 'learning_rate': '4.128e-05', 'epoch': '0.5308', 'num_input_tokens_seen': 33371000, 'train_runtime': '4.186e+04', 'train_tokens_per_second': '797.2'}
53%|█████▎ | 9920/18688 [11:37:41<10:05:19, 4.14s/it] 53%|█████▎ | 9921/18688 [11:37:46<10:34:09, 4.34s/it] 53%|█████▎ | 9922/18688 [11:37:50<10:35:16, 4.35s/it] 53%|█████▎ | 9923/18688 [11:37:54<10:03:47, 4.13s/it] 53%|█████▎ | 9924/18688 [11:37:58<10:12:33, 4.19s/it] 53%|█████▎ | 9925/18688 [11:38:03<10:30:01, 4.31s/it] 53%|█████▎ | 9926/18688 [11:38:07<10:23:05, 4.27s/it] 53%|█████▎ | 9927/18688 [11:38:11<10:07:29, 4.16s/it] 53%|█████▎ | 9928/18688 [11:38:15<9:56:30, 4.09s/it] 53%|█████▎ | 9929/18688 [11:38:19<10:05:26, 4.15s/it] 53%|█████▎ | 9930/18688 [11:38:23<10:02:57, 4.13s/it] {'loss': '0.9266', 'grad_norm': '1.255', 'learning_rate': '4.121e-05', 'epoch': '0.5314', 'num_input_tokens_seen': 33404656, 'train_runtime': '4.19e+04', 'train_tokens_per_second': '797.2'}
53%|█████▎ | 9930/18688 [11:38:23<10:02:57, 4.13s/it] 53%|█████▎ | 9931/18688 [11:38:27<10:08:44, 4.17s/it] 53%|█████▎ | 9932/18688 [11:38:31<10:00:13, 4.11s/it] 53%|█████▎ | 9933/18688 [11:38:35<9:40:27, 3.98s/it] 53%|█████▎ | 9934/18688 [11:38:39<9:52:46, 4.06s/it] 53%|█████▎ | 9935/18688 [11:38:44<10:41:27, 4.40s/it] 53%|█████▎ | 9936/18688 [11:38:48<10:15:56, 4.22s/it] 53%|█████▎ | 9937/18688 [11:38:52<10:18:18, 4.24s/it] 53%|█████▎ | 9938/18688 [11:38:56<9:59:14, 4.11s/it] 53%|█████▎ | 9939/18688 [11:39:00<9:59:09, 4.11s/it] 53%|█████▎ | 9940/18688 [11:39:05<10:23:24, 4.28s/it] {'loss': '0.8933', 'grad_norm': '1.256', 'learning_rate': '4.113e-05', 'epoch': '0.5319', 'num_input_tokens_seen': 33439232, 'train_runtime': '4.195e+04', 'train_tokens_per_second': '797.2'}
53%|█████▎ | 9940/18688 [11:39:05<10:23:24, 4.28s/it] 53%|█████▎ | 9941/18688 [11:39:10<10:38:29, 4.38s/it] 53%|█████▎ | 9942/18688 [11:39:15<11:02:02, 4.54s/it] 53%|█████▎ | 9943/18688 [11:39:18<10:19:35, 4.25s/it] 53%|█████▎ | 9944/18688 [11:39:22<9:51:56, 4.06s/it] 53%|█████▎ | 9945/18688 [11:39:25<9:26:34, 3.89s/it] 53%|█████▎ | 9946/18688 [11:39:29<9:32:23, 3.93s/it] 53%|█████▎ | 9947/18688 [11:39:33<9:17:23, 3.83s/it] 53%|█████▎ | 9948/18688 [11:39:37<9:32:29, 3.93s/it] 53%|█████▎ | 9949/18688 [11:39:42<10:03:17, 4.14s/it] 53%|█████▎ | 9950/18688 [11:39:45<9:40:22, 3.99s/it] {'loss': '0.8806', 'grad_norm': '1.421', 'learning_rate': '4.106e-05', 'epoch': '0.5324', 'num_input_tokens_seen': 33470712, 'train_runtime': '4.199e+04', 'train_tokens_per_second': '797.2'}
53%|█████▎ | 9950/18688 [11:39:45<9:40:22, 3.99s/it] 53%|█████▎ | 9951/18688 [11:39:49<9:31:21, 3.92s/it] 53%|█████▎ | 9952/18688 [11:39:53<9:46:58, 4.03s/it] 53%|█████▎ | 9953/18688 [11:39:57<9:37:34, 3.97s/it] 53%|█████▎ | 9954/18688 [11:40:01<9:38:20, 3.97s/it] 53%|█████▎ | 9955/18688 [11:40:06<10:28:33, 4.32s/it] 53%|█████▎ | 9956/18688 [11:40:10<10:23:02, 4.28s/it] 53%|█████▎ | 9957/18688 [11:40:14<10:01:53, 4.14s/it] 53%|█████▎ | 9958/18688 [11:40:19<10:10:23, 4.20s/it] 53%|█████▎ | 9959/18688 [11:40:22<9:47:47, 4.04s/it] 53%|█████▎ | 9960/18688 [11:40:27<9:56:31, 4.10s/it] {'loss': '0.9522', 'grad_norm': '1.404', 'learning_rate': '4.099e-05', 'epoch': '0.533', 'num_input_tokens_seen': 33504040, 'train_runtime': '4.203e+04', 'train_tokens_per_second': '797.2'}
53%|█████▎ | 9960/18688 [11:40:27<9:56:31, 4.10s/it] 53%|█████▎ | 9961/18688 [11:40:30<9:37:38, 3.97s/it] 53%|█████▎ | 9962/18688 [11:40:34<9:38:05, 3.97s/it] 53%|█████▎ | 9963/18688 [11:40:38<9:15:57, 3.82s/it] 53%|█████▎ | 9964/18688 [11:40:42<9:22:20, 3.87s/it] 53%|█████▎ | 9965/18688 [11:40:45<9:18:19, 3.84s/it] 53%|█████▎ | 9966/18688 [11:40:50<10:12:11, 4.21s/it] 53%|█████▎ | 9967/18688 [11:40:55<10:16:02, 4.24s/it] 53%|█████▎ | 9968/18688 [11:40:59<10:04:41, 4.16s/it] 53%|█████▎ | 9969/18688 [11:41:03<10:13:01, 4.22s/it] 53%|█████▎ | 9970/18688 [11:41:08<10:24:08, 4.30s/it] {'loss': '0.9938', 'grad_norm': '1.09', 'learning_rate': '4.091e-05', 'epoch': '0.5335', 'num_input_tokens_seen': 33536184, 'train_runtime': '4.207e+04', 'train_tokens_per_second': '797.2'}
53%|█████▎ | 9970/18688 [11:41:08<10:24:08, 4.30s/it] 53%|█████▎ | 9971/18688 [11:41:11<10:04:12, 4.16s/it] 53%|█████▎ | 9972/18688 [11:41:17<10:52:20, 4.49s/it] 53%|█████▎ | 9973/18688 [11:41:21<11:03:25, 4.57s/it] 53%|█████▎ | 9974/18688 [11:41:26<11:05:53, 4.58s/it] 53%|█████▎ | 9975/18688 [11:41:31<11:06:27, 4.59s/it] 53%|█████▎ | 9976/18688 [11:41:35<11:09:38, 4.61s/it] 53%|█████▎ | 9977/18688 [11:41:39<10:29:02, 4.33s/it] 53%|█████▎ | 9978/18688 [11:41:43<9:57:37, 4.12s/it] 53%|█████▎ | 9979/18688 [11:41:47<9:52:06, 4.08s/it] 53%|█████▎ | 9980/18688 [11:41:52<10:32:48, 4.36s/it] {'loss': '0.8829', 'grad_norm': '1.226', 'learning_rate': '4.084e-05', 'epoch': '0.534', 'num_input_tokens_seen': 33573168, 'train_runtime': '4.211e+04', 'train_tokens_per_second': '797.2'}
53%|█████▎ | 9980/18688 [11:41:52<10:32:48, 4.36s/it] 53%|█████▎ | 9981/18688 [11:41:56<10:20:16, 4.27s/it] 53%|█████▎ | 9982/18688 [11:41:59<9:46:49, 4.04s/it] 53%|█████▎ | 9983/18688 [11:42:03<9:33:17, 3.95s/it] 53%|█████▎ | 9984/18688 [11:42:08<10:10:15, 4.21s/it] 53%|█████▎ | 9985/18688 [11:42:13<11:04:40, 4.58s/it] 53%|█████▎ | 9986/18688 [11:42:17<10:21:22, 4.28s/it] 53%|█████▎ | 9987/18688 [11:42:20<9:53:44, 4.09s/it] 53%|█████▎ | 9988/18688 [11:42:25<10:30:55, 4.35s/it] 53%|█████▎ | 9989/18688 [11:42:29<10:14:08, 4.24s/it] 53%|█████▎ | 9990/18688 [11:42:34<10:13:17, 4.23s/it] {'loss': '0.8935', 'grad_norm': '1.283', 'learning_rate': '4.077e-05', 'epoch': '0.5346', 'num_input_tokens_seen': 33608320, 'train_runtime': '4.215e+04', 'train_tokens_per_second': '797.3'}
53%|█████▎ | 9990/18688 [11:42:34<10:13:17, 4.23s/it] 53%|█████▎ | 9991/18688 [11:42:38<10:24:25, 4.31s/it] 53%|█████▎ | 9992/18688 [11:42:42<9:54:41, 4.10s/it] 53%|█████▎ | 9993/18688 [11:42:46<10:24:25, 4.31s/it] 53%|█████▎ | 9994/18688 [11:42:50<10:10:59, 4.22s/it] 53%|█████▎ | 9995/18688 [11:42:55<10:23:54, 4.31s/it] 53%|█████▎ | 9996/18688 [11:43:00<10:56:14, 4.53s/it] 53%|█████▎ | 9997/18688 [11:43:04<10:17:24, 4.26s/it] 53%|█████▎ | 9998/18688 [11:43:08<10:05:43, 4.18s/it] 54%|█████▎ | 9999/18688 [11:43:14<11:52:55, 4.92s/it] 54%|█████▎ | 10000/18688 [11:43:18<11:15:37, 4.67s/it] {'loss': '1.005', 'grad_norm': '1.511', 'learning_rate': '4.069e-05', 'epoch': '0.5351', 'num_input_tokens_seen': 33646792, 'train_runtime': '4.22e+04', 'train_tokens_per_second': '797.3'}
54%|█████▎ | 10000/18688 [11:43:18<11:15:37, 4.67s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
0%| | 3/1000 [00:00<00:39, 25.52it/s]
1%| | 6/1000 [00:00<00:54, 18.20it/s]
1%| | 8/1000 [00:00<00:56, 17.49it/s]
1%| | 10/1000 [00:00<00:59, 16.58it/s]
1%| | 12/1000 [00:00<01:00, 16.35it/s]
1%|▏ | 14/1000 [00:00<01:02, 15.90it/s]
2%|▏ | 16/1000 [00:00<01:07, 14.57it/s]
2%|▏ | 18/1000 [00:01<01:43, 9.51it/s]
2%|▏ | 20/1000 [00:01<01:39, 9.82it/s]
2%|▏ | 22/1000 [00:01<01:57, 8.30it/s]
2%|▏ | 24/1000 [00:02<01:46, 9.18it/s]
3%|▎ | 26/1000 [00:02<01:50, 8.83it/s]
3%|▎ | 28/1000 [00:02<01:40, 9.67it/s]
3%|▎ | 30/1000 [00:02<01:34, 10.31it/s]
3%|▎ | 32/1000 [00:02<01:40, 9.63it/s]
3%|▎ | 34/1000 [00:03<01:32, 10.48it/s]
4%|▎ | 36/1000 [00:03<01:38, 9.78it/s]
4%|▍ | 38/1000 [00:03<01:31, 10.50it/s]
4%|▍ | 40/1000 [00:03<01:32, 10.42it/s]
4%|▍ | 42/1000 [00:03<01:49, 8.73it/s]
4%|▍ | 44/1000 [00:04<01:33, 10.24it/s]
5%|▍ | 46/1000 [00:04<01:23, 11.44it/s]
5%|▍ | 48/1000 [00:04<01:30, 10.49it/s]
5%|▌ | 50/1000 [00:04<01:22, 11.55it/s]
5%|▌ | 52/1000 [00:04<01:16, 12.41it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.17it/s]
6%|▌ | 56/1000 [00:04<01:08, 13.87it/s]
6%|▌ | 58/1000 [00:05<01:48, 8.71it/s]
6%|▌ | 60/1000 [00:05<01:36, 9.74it/s]
6%|▌ | 62/1000 [00:05<01:27, 10.71it/s]
6%|▋ | 64/1000 [00:05<01:19, 11.78it/s]
7%|▋ | 66/1000 [00:05<01:15, 12.29it/s]
7%|▋ | 68/1000 [00:06<01:12, 12.88it/s]
7%|▋ | 70/1000 [00:06<01:09, 13.46it/s]
7%|▋ | 72/1000 [00:06<01:06, 13.91it/s]
7%|▋ | 74/1000 [00:06<01:07, 13.68it/s]
8%|▊ | 76/1000 [00:06<01:04, 14.28it/s]
8%|▊ | 78/1000 [00:06<01:03, 14.48it/s]
8%|▊ | 80/1000 [00:06<01:08, 13.45it/s]
8%|▊ | 82/1000 [00:07<01:05, 14.05it/s]
8%|▊ | 84/1000 [00:07<01:03, 14.53it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.66it/s]
9%|▉ | 88/1000 [00:07<01:03, 14.46it/s]
9%|▉ | 90/1000 [00:07<01:02, 14.47it/s]
9%|▉ | 92/1000 [00:07<01:04, 14.13it/s]
9%|▉ | 94/1000 [00:07<01:04, 13.96it/s]
10%|▉ | 96/1000 [00:07<01:03, 14.28it/s]
10%|▉ | 98/1000 [00:08<01:03, 14.21it/s]
10%|█ | 100/1000 [00:08<01:32, 9.78it/s]
10%|█ | 102/1000 [00:08<01:23, 10.75it/s]
10%|█ | 104/1000 [00:09<01:54, 7.80it/s]
11%|█ | 106/1000 [00:09<02:02, 7.32it/s]
11%|█ | 108/1000 [00:09<01:42, 8.66it/s]
11%|█ | 110/1000 [00:09<01:28, 10.11it/s]
11%|█ | 112/1000 [00:09<01:19, 11.24it/s]
11%|█▏ | 114/1000 [00:09<01:14, 11.89it/s]
12%|█▏ | 116/1000 [00:10<01:10, 12.53it/s]
12%|█▏ | 118/1000 [00:10<01:06, 13.31it/s]
12%|█▏ | 120/1000 [00:10<01:09, 12.73it/s]
12%|█▏ | 122/1000 [00:10<01:22, 10.58it/s]
12%|█▏ | 124/1000 [00:10<01:30, 9.66it/s]
13%|█▎ | 126/1000 [00:10<01:22, 10.61it/s]
13%|█▎ | 128/1000 [00:11<01:15, 11.54it/s]
13%|█▎ | 130/1000 [00:11<01:11, 12.20it/s]
13%|█▎ | 132/1000 [00:11<01:05, 13.17it/s]
13%|█▎ | 134/1000 [00:11<01:10, 12.37it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.32it/s]
14%|█▍ | 138/1000 [00:11<01:04, 13.27it/s]
14%|█▍ | 140/1000 [00:12<01:25, 10.09it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.53it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.56it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.58it/s]
15%|█▍ | 148/1000 [00:12<01:14, 11.50it/s]
15%|█▌ | 150/1000 [00:13<01:12, 11.70it/s]
15%|█▌ | 152/1000 [00:13<01:08, 12.45it/s]
15%|█▌ | 154/1000 [00:13<01:05, 12.88it/s]
16%|█▌ | 156/1000 [00:13<01:05, 12.86it/s]
16%|█▌ | 158/1000 [00:13<01:02, 13.47it/s]
16%|█▌ | 160/1000 [00:13<01:02, 13.47it/s]
16%|█▌ | 162/1000 [00:13<01:02, 13.37it/s]
16%|█▋ | 164/1000 [00:14<01:01, 13.52it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.73it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.23it/s]
17%|█▋ | 170/1000 [00:14<01:19, 10.47it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.45it/s]
17%|█▋ | 174/1000 [00:15<01:30, 9.15it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.18it/s]
18%|█▊ | 178/1000 [00:15<01:35, 8.65it/s]
18%|█▊ | 180/1000 [00:15<01:33, 8.77it/s]
18%|█▊ | 182/1000 [00:16<01:35, 8.55it/s]
18%|█▊ | 184/1000 [00:16<01:23, 9.79it/s]
19%|█▊ | 186/1000 [00:16<01:37, 8.33it/s]
19%|█▉ | 188/1000 [00:16<01:25, 9.53it/s]
19%|█▉ | 190/1000 [00:16<01:18, 10.27it/s]
19%|█▉ | 192/1000 [00:17<01:14, 10.82it/s]
19%|█▉ | 194/1000 [00:17<01:23, 9.65it/s]
20%|█▉ | 196/1000 [00:17<01:34, 8.48it/s]
20%|█▉ | 198/1000 [00:17<01:22, 9.69it/s]
20%|██ | 200/1000 [00:17<01:18, 10.14it/s]
20%|██ | 202/1000 [00:18<01:11, 11.13it/s]
20%|██ | 204/1000 [00:18<01:04, 12.28it/s]
21%|██ | 206/1000 [00:18<01:01, 12.94it/s]
21%|██ | 208/1000 [00:18<00:59, 13.38it/s]
21%|██ | 210/1000 [00:18<00:55, 14.28it/s]
21%|██ | 212/1000 [00:18<00:54, 14.57it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.63it/s]
22%|██▏ | 216/1000 [00:18<00:54, 14.42it/s]
22%|██▏ | 218/1000 [00:19<00:52, 14.79it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.87it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.16it/s]
22%|██▏ | 224/1000 [00:19<01:20, 9.64it/s]
23%|██▎ | 226/1000 [00:20<01:12, 10.64it/s]
23%|██▎ | 228/1000 [00:20<01:06, 11.56it/s]
23%|██▎ | 230/1000 [00:20<01:03, 12.05it/s]
23%|██▎ | 232/1000 [00:20<01:01, 12.41it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.14it/s]
24%|██▎ | 236/1000 [00:20<00:56, 13.64it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.27it/s]
24%|██▍ | 240/1000 [00:21<00:53, 14.24it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.71it/s]
24%|██▍ | 244/1000 [00:21<00:50, 14.92it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.90it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.63it/s]
25%|██▌ | 250/1000 [00:21<00:56, 13.37it/s]
25%|██▌ | 252/1000 [00:21<01:01, 12.12it/s]
25%|██▌ | 254/1000 [00:22<00:56, 13.12it/s]
26%|██▌ | 256/1000 [00:22<00:54, 13.69it/s]
26%|██▌ | 258/1000 [00:22<00:54, 13.73it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.84it/s]
26%|██▌ | 262/1000 [00:22<00:58, 12.68it/s]
26%|██▋ | 264/1000 [00:22<00:55, 13.33it/s]
27%|██▋ | 266/1000 [00:22<00:53, 13.75it/s]
27%|██▋ | 268/1000 [00:23<00:50, 14.37it/s]
27%|██▋ | 270/1000 [00:23<00:55, 13.18it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.56it/s]
27%|██▋ | 274/1000 [00:23<01:02, 11.66it/s]
28%|██▊ | 276/1000 [00:23<00:57, 12.66it/s]
28%|██▊ | 278/1000 [00:23<00:55, 12.92it/s]
28%|██▊ | 280/1000 [00:24<00:53, 13.48it/s]
28%|██▊ | 282/1000 [00:24<00:49, 14.45it/s]
28%|██▊ | 284/1000 [00:24<00:49, 14.41it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.70it/s]
29%|██▉ | 288/1000 [00:24<00:47, 15.05it/s]
29%|██▉ | 290/1000 [00:24<00:45, 15.47it/s]
29%|██▉ | 292/1000 [00:24<00:45, 15.43it/s]
29%|██▉ | 294/1000 [00:24<00:46, 15.32it/s]
30%|██▉ | 296/1000 [00:25<00:45, 15.37it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.48it/s]
30%|███ | 300/1000 [00:25<00:46, 15.15it/s]
30%|███ | 302/1000 [00:25<00:46, 15.16it/s]
30%|███ | 304/1000 [00:25<00:50, 13.74it/s]
31%|███ | 306/1000 [00:25<00:47, 14.49it/s]
31%|███ | 308/1000 [00:25<00:47, 14.46it/s]
31%|███ | 310/1000 [00:26<00:47, 14.63it/s]
31%|███ | 312/1000 [00:26<00:47, 14.62it/s]
31%|███▏ | 314/1000 [00:26<00:44, 15.28it/s]
32%|███▏ | 316/1000 [00:26<00:46, 14.62it/s]
32%|███▏ | 318/1000 [00:26<00:47, 14.45it/s]
32%|███▏ | 320/1000 [00:27<01:17, 8.79it/s]
32%|███▏ | 322/1000 [00:27<01:06, 10.19it/s]
32%|███▏ | 324/1000 [00:27<01:01, 11.08it/s]
33%|███▎ | 326/1000 [00:27<01:03, 10.69it/s]
33%|███▎ | 328/1000 [00:27<01:01, 10.93it/s]
33%|███▎ | 330/1000 [00:27<00:55, 11.97it/s]
33%|███▎ | 332/1000 [00:27<00:54, 12.36it/s]
33%|███▎ | 334/1000 [00:28<00:51, 12.92it/s]
34%|███▎ | 336/1000 [00:28<00:49, 13.40it/s]
34%|███▍ | 338/1000 [00:28<01:06, 9.97it/s]
34%|███▍ | 340/1000 [00:28<00:59, 11.05it/s]
34%|███▍ | 342/1000 [00:28<00:53, 12.30it/s]
34%|███▍ | 344/1000 [00:28<00:50, 12.98it/s]
35%|███▍ | 346/1000 [00:29<00:48, 13.60it/s]
35%|███▍ | 348/1000 [00:29<00:46, 14.04it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.67it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.85it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.86it/s]
36%|███▌ | 356/1000 [00:29<00:43, 14.94it/s]
36%|███▌ | 358/1000 [00:29<00:43, 14.64it/s]
36%|███▌ | 360/1000 [00:30<00:42, 15.04it/s]
36%|███▌ | 362/1000 [00:30<00:47, 13.36it/s]
36%|███▋ | 364/1000 [00:30<00:45, 13.88it/s]
37%|███▋ | 366/1000 [00:30<00:53, 11.95it/s]
37%|███▋ | 368/1000 [00:30<00:52, 12.13it/s]
37%|███▋ | 370/1000 [00:30<00:51, 12.20it/s]
37%|███▋ | 372/1000 [00:31<00:51, 12.28it/s]
37%|███▋ | 374/1000 [00:31<00:58, 10.66it/s]
38%|███▊ | 376/1000 [00:31<01:00, 10.29it/s]
38%|███▊ | 378/1000 [00:31<00:57, 10.83it/s]
38%|███▊ | 380/1000 [00:31<00:55, 11.25it/s]
38%|███▊ | 382/1000 [00:31<00:53, 11.48it/s]
38%|███▊ | 384/1000 [00:32<00:52, 11.75it/s]
39%|███▊ | 386/1000 [00:32<00:51, 11.94it/s]
39%|███▉ | 388/1000 [00:32<00:50, 12.09it/s]
39%|███▉ | 390/1000 [00:32<00:50, 12.09it/s]
39%|███▉ | 392/1000 [00:32<00:49, 12.22it/s]
39%|███▉ | 394/1000 [00:32<00:49, 12.25it/s]
40%|███▉ | 396/1000 [00:33<00:48, 12.38it/s]
40%|███▉ | 398/1000 [00:33<00:51, 11.68it/s]
40%|████ | 400/1000 [00:33<00:50, 11.81it/s]
40%|████ | 402/1000 [00:33<00:49, 11.98it/s]
40%|████ | 404/1000 [00:33<00:49, 12.13it/s]
41%|████ | 406/1000 [00:33<00:48, 12.37it/s]
41%|████ | 408/1000 [00:34<00:54, 10.87it/s]
41%|████ | 410/1000 [00:34<01:01, 9.57it/s]
41%|████ | 412/1000 [00:34<00:56, 10.34it/s]
41%|████▏ | 414/1000 [00:34<01:11, 8.15it/s]
42%|████▏ | 416/1000 [00:35<01:26, 6.74it/s]
42%|████▏ | 418/1000 [00:35<01:12, 8.06it/s]
42%|████▏ | 420/1000 [00:35<01:02, 9.33it/s]
42%|████▏ | 422/1000 [00:35<00:54, 10.70it/s]
42%|████▏ | 424/1000 [00:36<01:15, 7.63it/s]
43%|████▎ | 426/1000 [00:36<01:04, 8.88it/s]
43%|████▎ | 428/1000 [00:36<00:56, 10.10it/s]
43%|████▎ | 430/1000 [00:36<01:02, 9.09it/s]
43%|████▎ | 432/1000 [00:37<01:20, 7.04it/s]
43%|████▎ | 434/1000 [00:37<01:08, 8.26it/s]
44%|████▎ | 436/1000 [00:37<01:00, 9.29it/s]
44%|████▍ | 438/1000 [00:37<01:02, 8.97it/s]
44%|████▍ | 440/1000 [00:37<00:59, 9.49it/s]
44%|████▍ | 442/1000 [00:38<00:52, 10.56it/s]
44%|████▍ | 444/1000 [00:38<00:48, 11.42it/s]
45%|████▍ | 446/1000 [00:38<00:45, 12.19it/s]
45%|████▍ | 448/1000 [00:38<00:50, 10.97it/s]
45%|████▌ | 450/1000 [00:38<00:46, 11.75it/s]
45%|████▌ | 452/1000 [00:38<00:42, 12.76it/s]
45%|████▌ | 454/1000 [00:39<00:44, 12.26it/s]
46%|████▌ | 456/1000 [00:39<00:42, 12.70it/s]
46%|████▌ | 458/1000 [00:39<00:39, 13.73it/s]
46%|████▌ | 460/1000 [00:39<00:38, 13.89it/s]
46%|████▌ | 462/1000 [00:39<00:37, 14.49it/s]
46%|████▋ | 464/1000 [00:39<00:35, 15.01it/s]
47%|████▋ | 466/1000 [00:39<00:36, 14.83it/s]
47%|████▋ | 468/1000 [00:40<00:41, 12.93it/s]
47%|████▋ | 470/1000 [00:40<00:52, 10.09it/s]
47%|████▋ | 472/1000 [00:40<00:51, 10.25it/s]
47%|████▋ | 474/1000 [00:40<00:45, 11.53it/s]
48%|████▊ | 476/1000 [00:40<00:43, 12.17it/s]
48%|████▊ | 478/1000 [00:40<00:43, 12.03it/s]
48%|████▊ | 480/1000 [00:41<00:40, 12.97it/s]
48%|████▊ | 482/1000 [00:41<00:38, 13.35it/s]
48%|████▊ | 484/1000 [00:41<00:37, 13.85it/s]
49%|████▊ | 486/1000 [00:41<00:36, 14.02it/s]
49%|████▉ | 488/1000 [00:41<00:35, 14.30it/s]
49%|████▉ | 490/1000 [00:41<00:45, 11.09it/s]
49%|████▉ | 492/1000 [00:42<00:42, 11.94it/s]
49%|████▉ | 494/1000 [00:42<00:38, 13.01it/s]
50%|████▉ | 496/1000 [00:42<00:36, 13.81it/s]
50%|████▉ | 498/1000 [00:42<00:35, 14.25it/s]
50%|█████ | 500/1000 [00:42<00:43, 11.42it/s]
50%|█████ | 502/1000 [00:42<00:41, 11.90it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.52it/s]
51%|█████ | 506/1000 [00:43<00:38, 12.73it/s]
51%|█████ | 508/1000 [00:43<00:35, 13.69it/s]
51%|█████ | 510/1000 [00:43<00:47, 10.36it/s]
51%|█████ | 512/1000 [00:43<00:43, 11.15it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 12.03it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.56it/s]
52%|█████▏ | 518/1000 [00:44<00:36, 13.08it/s]
52%|█████▏ | 520/1000 [00:44<00:35, 13.45it/s]
52%|█████▏ | 522/1000 [00:44<00:33, 14.17it/s]
52%|█████▏ | 524/1000 [00:44<00:33, 14.22it/s]
53%|█████▎ | 526/1000 [00:44<00:32, 14.37it/s]
53%|█████▎ | 528/1000 [00:44<00:39, 12.00it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.83it/s]
53%|█████▎ | 532/1000 [00:45<00:33, 13.78it/s]
53%|█████▎ | 534/1000 [00:45<00:32, 14.13it/s]
54%|█████▎ | 536/1000 [00:45<00:32, 14.26it/s]
54%|█████▍ | 538/1000 [00:45<00:33, 13.64it/s]
54%|█████▍ | 540/1000 [00:45<00:32, 14.14it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.49it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.47it/s]
55%|█████▍ | 546/1000 [00:46<00:32, 14.15it/s]
55%|█████▍ | 548/1000 [00:46<00:32, 13.87it/s]
55%|█████▌ | 550/1000 [00:46<00:31, 14.11it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.21it/s]
55%|█████▌ | 554/1000 [00:46<00:32, 13.63it/s]
56%|█████▌ | 556/1000 [00:46<00:32, 13.59it/s]
56%|█████▌ | 558/1000 [00:46<00:31, 14.06it/s]
56%|█████▌ | 560/1000 [00:47<00:29, 14.71it/s]
56%|█████▌ | 562/1000 [00:47<00:29, 14.77it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.72it/s]
57%|█████▋ | 566/1000 [00:47<00:37, 11.61it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.16it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.24it/s]
57%|█████▋ | 572/1000 [00:48<00:33, 12.79it/s]
57%|█████▋ | 574/1000 [00:48<00:31, 13.68it/s]
58%|█████▊ | 576/1000 [00:48<00:31, 13.59it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.63it/s]
58%|█████▊ | 580/1000 [00:48<00:32, 12.86it/s]
58%|█████▊ | 582/1000 [00:48<00:30, 13.63it/s]
58%|█████▊ | 584/1000 [00:48<00:30, 13.84it/s]
59%|█████▊ | 586/1000 [00:49<00:29, 14.22it/s]
59%|█████▉ | 588/1000 [00:49<00:27, 15.04it/s]
59%|█████▉ | 590/1000 [00:49<00:27, 15.01it/s]
59%|█████▉ | 592/1000 [00:49<00:27, 14.78it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.35it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.86it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 14.10it/s]
60%|██████ | 600/1000 [00:50<00:27, 14.47it/s]
60%|██████ | 602/1000 [00:50<00:27, 14.57it/s]
60%|██████ | 604/1000 [00:50<00:26, 15.10it/s]
61%|██████ | 606/1000 [00:50<00:26, 14.85it/s]
61%|██████ | 608/1000 [00:50<00:25, 15.16it/s]
61%|██████ | 610/1000 [00:50<00:25, 15.01it/s]
61%|██████ | 612/1000 [00:50<00:25, 15.13it/s]
61%|██████▏ | 614/1000 [00:50<00:26, 14.37it/s]
62%|██████▏ | 616/1000 [00:51<00:25, 14.78it/s]
62%|██████▏ | 618/1000 [00:51<00:26, 14.19it/s]
62%|██████▏ | 620/1000 [00:51<00:38, 9.77it/s]
62%|██████▏ | 622/1000 [00:51<00:37, 9.98it/s]
62%|██████▏ | 624/1000 [00:51<00:33, 11.34it/s]
63%|██████▎ | 626/1000 [00:52<00:31, 12.04it/s]
63%|██████▎ | 628/1000 [00:52<00:28, 13.27it/s]
63%|██████▎ | 630/1000 [00:52<00:26, 13.77it/s]
63%|██████▎ | 632/1000 [00:52<00:29, 12.45it/s]
63%|██████▎ | 634/1000 [00:52<00:27, 13.17it/s]
64%|██████▎ | 636/1000 [00:52<00:33, 10.93it/s]
64%|██████▍ | 638/1000 [00:53<00:33, 10.65it/s]
64%|██████▍ | 640/1000 [00:53<00:31, 11.44it/s]
64%|██████▍ | 642/1000 [00:53<00:29, 12.31it/s]
64%|██████▍ | 644/1000 [00:53<00:28, 12.56it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.85it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.33it/s]
65%|██████▌ | 650/1000 [00:54<00:27, 12.55it/s]
65%|██████▌ | 652/1000 [00:54<00:29, 11.94it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.44it/s]
66%|██████▌ | 656/1000 [00:54<00:31, 10.79it/s]
66%|██████▌ | 658/1000 [00:54<00:28, 11.85it/s]
66%|██████▌ | 660/1000 [00:55<00:33, 10.15it/s]
66%|██████▌ | 662/1000 [00:55<00:29, 11.28it/s]
66%|██████▋ | 664/1000 [00:55<00:28, 11.78it/s]
67%|██████▋ | 666/1000 [00:55<00:26, 12.80it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.32it/s]
67%|██████▋ | 670/1000 [00:55<00:29, 11.10it/s]
67%|██████▋ | 672/1000 [00:55<00:27, 11.86it/s]
67%|██████▋ | 674/1000 [00:56<00:26, 12.45it/s]
68%|██████▊ | 676/1000 [00:56<00:28, 11.47it/s]
68%|██████▊ | 678/1000 [00:56<00:27, 11.75it/s]
68%|██████▊ | 680/1000 [00:56<00:26, 12.29it/s]
68%|██████▊ | 682/1000 [00:56<00:28, 11.10it/s]
68%|██████▊ | 684/1000 [00:57<00:27, 11.54it/s]
69%|██████▊ | 686/1000 [00:57<00:24, 12.62it/s]
69%|██████▉ | 688/1000 [00:57<00:23, 13.00it/s]
69%|██████▉ | 690/1000 [00:57<00:23, 13.21it/s]
69%|██████▉ | 692/1000 [00:57<00:22, 13.72it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 13.95it/s]
70%|██████▉ | 696/1000 [00:57<00:22, 13.81it/s]
70%|██████▉ | 698/1000 [00:57<00:20, 14.41it/s]
70%|███████ | 700/1000 [00:58<00:24, 12.06it/s]
70%|███████ | 702/1000 [00:58<00:23, 12.88it/s]
70%|███████ | 704/1000 [00:58<00:30, 9.81it/s]
71%|███████ | 706/1000 [00:58<00:27, 10.62it/s]
71%|███████ | 708/1000 [00:59<00:30, 9.61it/s]
71%|███████ | 710/1000 [00:59<00:26, 10.88it/s]
71%|███████ | 712/1000 [00:59<00:24, 11.82it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.92it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 13.02it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.46it/s]
72%|███████▏ | 720/1000 [00:59<00:20, 13.43it/s]
72%|███████▏ | 722/1000 [01:00<00:20, 13.87it/s]
72%|███████▏ | 724/1000 [01:00<00:20, 13.73it/s]
73%|███████▎ | 726/1000 [01:00<00:19, 13.96it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.94it/s]
73%|███████▎ | 730/1000 [01:00<00:32, 8.20it/s]
73%|███████▎ | 732/1000 [01:01<00:32, 8.33it/s]
73%|███████▎ | 734/1000 [01:01<00:30, 8.60it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.45it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.90it/s]
74%|███████▍ | 739/1000 [01:01<00:23, 11.00it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.66it/s]
74%|███████▍ | 743/1000 [01:02<00:20, 12.29it/s]
74%|███████▍ | 745/1000 [01:02<00:25, 10.18it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.55it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.70it/s]
75%|███████▌ | 751/1000 [01:02<00:21, 11.79it/s]
75%|███████▌ | 753/1000 [01:03<00:19, 12.53it/s]
76%|███████▌ | 755/1000 [01:03<00:18, 13.26it/s]
76%|███████▌ | 757/1000 [01:03<00:23, 10.22it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.24it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.22it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.23it/s]
76%|███████▋ | 765/1000 [01:04<00:27, 8.58it/s]
77%|███████▋ | 767/1000 [01:04<00:24, 9.54it/s]
77%|███████▋ | 769/1000 [01:04<00:21, 10.56it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.83it/s]
77%|███████▋ | 773/1000 [01:04<00:18, 12.61it/s]
78%|███████▊ | 775/1000 [01:04<00:17, 13.16it/s]
78%|███████▊ | 777/1000 [01:05<00:15, 14.11it/s]
78%|███████▊ | 779/1000 [01:05<00:21, 10.49it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.40it/s]
78%|███████▊ | 783/1000 [01:05<00:26, 8.30it/s]
78%|███████▊ | 784/1000 [01:06<00:30, 7.06it/s]
78%|███████▊ | 785/1000 [01:06<00:29, 7.38it/s]
79%|███████▊ | 786/1000 [01:06<00:30, 6.99it/s]
79%|███████▉ | 788/1000 [01:06<00:24, 8.83it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.15it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 11.29it/s]
79%|███████▉ | 794/1000 [01:07<00:17, 11.90it/s]
80%|███████▉ | 796/1000 [01:07<00:16, 12.35it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 13.11it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.29it/s]
80%|████████ | 802/1000 [01:07<00:24, 8.24it/s]
80%|████████ | 804/1000 [01:08<00:23, 8.29it/s]
81%|████████ | 806/1000 [01:08<00:21, 8.97it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.35it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.46it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.63it/s]
81%|████████▏ | 814/1000 [01:08<00:14, 12.53it/s]
82%|████████▏ | 816/1000 [01:09<00:16, 10.99it/s]
82%|████████▏ | 818/1000 [01:09<00:15, 11.94it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.44it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 13.01it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.80it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.19it/s]
83%|████████▎ | 828/1000 [01:09<00:11, 14.47it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.91it/s]
83%|████████▎ | 832/1000 [01:10<00:11, 14.31it/s]
83%|████████▎ | 834/1000 [01:10<00:11, 14.06it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.44it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.35it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.44it/s]
84%|████████▍ | 842/1000 [01:10<00:10, 14.44it/s]
84%|████████▍ | 844/1000 [01:11<00:11, 13.97it/s]
85%|████████▍ | 846/1000 [01:11<00:11, 13.91it/s]
85%|████████▍ | 848/1000 [01:11<00:10, 13.88it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.07it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.68it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.23it/s]
86%|████████▌ | 856/1000 [01:11<00:10, 14.09it/s]
86%|████████▌ | 858/1000 [01:12<00:09, 14.56it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 15.31it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.46it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 13.99it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.80it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.67it/s]
87%|████████▋ | 870/1000 [01:12<00:09, 14.07it/s]
87%|████████▋ | 872/1000 [01:13<00:08, 14.66it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 12.43it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.03it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.91it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.54it/s]
88%|████████▊ | 882/1000 [01:14<00:10, 11.31it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 12.05it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.83it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.80it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.23it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.04it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 14.19it/s]
90%|████████▉ | 896/1000 [01:14<00:07, 13.93it/s]
90%|████████▉ | 898/1000 [01:15<00:07, 13.32it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.24it/s]
90%|█████████ | 902/1000 [01:15<00:07, 12.98it/s]
90%|█████████ | 904/1000 [01:15<00:09, 9.80it/s]
91%|█████████ | 906/1000 [01:15<00:08, 10.86it/s]
91%|█████████ | 908/1000 [01:16<00:07, 11.85it/s]
91%|█████████ | 910/1000 [01:16<00:07, 12.10it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.30it/s]
91%|█████████▏| 914/1000 [01:16<00:12, 6.81it/s]
92%|█████████▏| 916/1000 [01:17<00:10, 8.01it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.25it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.27it/s]
92%|█████████▏| 922/1000 [01:17<00:08, 9.23it/s]
92%|█████████▏| 924/1000 [01:17<00:07, 9.98it/s]
93%|█████████▎| 926/1000 [01:18<00:06, 10.63it/s]
93%|█████████▎| 928/1000 [01:18<00:07, 9.84it/s]
93%|█████████▎| 930/1000 [01:18<00:06, 10.61it/s]
93%|█████████▎| 932/1000 [01:18<00:06, 11.12it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 11.47it/s]
94%|█████████▎| 936/1000 [01:18<00:05, 11.73it/s]
94%|█████████▍| 938/1000 [01:19<00:05, 11.96it/s]
94%|█████████▍| 940/1000 [01:19<00:04, 12.14it/s]
94%|█████████▍| 942/1000 [01:19<00:05, 10.74it/s]
94%|█████████▍| 944/1000 [01:19<00:05, 11.16it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 10.81it/s]
95%|█████████▍| 948/1000 [01:20<00:04, 11.30it/s]
95%|█████████▌| 950/1000 [01:20<00:04, 11.72it/s]
95%|█████████▌| 952/1000 [01:20<00:04, 11.92it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 12.10it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 12.20it/s]
96%|█████████▌| 958/1000 [01:20<00:03, 12.39it/s]
96%|█████████▌| 960/1000 [01:21<00:03, 11.57it/s]
96%|█████████▌| 962/1000 [01:21<00:03, 11.85it/s]
96%|█████████▋| 964/1000 [01:21<00:03, 9.29it/s]
97%|█████████▋| 966/1000 [01:21<00:03, 10.11it/s]
97%|█████████▋| 968/1000 [01:21<00:02, 10.69it/s]
97%|█████████▋| 970/1000 [01:22<00:02, 11.17it/s]
97%|█████████▋| 972/1000 [01:22<00:03, 9.02it/s]
97%|█████████▋| 974/1000 [01:22<00:02, 9.89it/s]
98%|█████████▊| 976/1000 [01:22<00:02, 10.52it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.24it/s]
98%|█████████▊| 980/1000 [01:23<00:02, 9.41it/s]
98%|█████████▊| 982/1000 [01:23<00:01, 10.00it/s]
98%|█████████▊| 984/1000 [01:23<00:01, 10.01it/s]
99%|█████████▊| 986/1000 [01:23<00:01, 10.38it/s]
99%|█████████▉| 988/1000 [01:23<00:01, 10.70it/s]
99%|█████████▉| 990/1000 [01:24<00:00, 11.25it/s]
99%|█████████▉| 992/1000 [01:24<00:00, 12.17it/s]
99%|█████████▉| 994/1000 [01:24<00:00, 12.94it/s]
100%|█████████▉| 996/1000 [01:24<00:00, 13.84it/s]
100%|█████████▉| 998/1000 [01:24<00:00, 14.02it/s]
100%|██████████| 1000/1000 [01:24<00:00, 14.68it/s]
{'eval_loss': '0.9005', 'eval_runtime': '84.74', 'eval_samples_per_second': '11.8', 'eval_steps_per_second': '11.8', 'epoch': '0.5351', 'num_input_tokens_seen': 33646792}
54%|█████▎ | 10000/18688 [11:44:43<11:15:37, 4.67s/it]
100%|██████████| 1000/1000 [01:24<00:00, 14.68it/s]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00008/checkpoint-10000
loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
54%|█████▎ | 10001/18688 [11:44:48<72:35:30, 30.08s/it] 54%|█████▎ | 10002/18688 [11:44:51<53:26:55, 22.15s/it] 54%|█████▎ | 10003/18688 [11:44:55<40:13:35, 16.67s/it] 54%|█████▎ | 10004/18688 [11:44:59<31:03:12, 12.87s/it] 54%|█████▎ | 10005/18688 [11:45:03<24:41:11, 10.24s/it] 54%|█████▎ | 10006/18688 [11:45:08<20:18:27, 8.42s/it] 54%|█████▎ | 10007/18688 [11:45:12<17:20:04, 7.19s/it] 54%|█████▎ | 10008/18688 [11:45:15<14:43:52, 6.11s/it] 54%|█████▎ | 10009/18688 [11:45:20<13:42:06, 5.68s/it] 54%|█████▎ | 10010/18688 [11:45:24<12:24:51, 5.15s/it] {'loss': '0.946', 'grad_norm': '1.411', 'learning_rate': '4.062e-05', 'epoch': '0.5357', 'num_input_tokens_seen': 33679768, 'train_runtime': '4.232e+04', 'train_tokens_per_second': '795.7'}
54%|█████▎ | 10010/18688 [11:45:24<12:24:51, 5.15s/it] 54%|█████▎ | 10011/18688 [11:45:28<11:30:28, 4.77s/it] 54%|█████▎ | 10012/18688 [11:45:32<10:44:37, 4.46s/it] 54%|█████▎ | 10013/18688 [11:45:37<11:03:51, 4.59s/it] 54%|█████▎ | 10014/18688 [11:45:42<11:42:25, 4.86s/it] 54%|█████▎ | 10015/18688 [11:45:46<11:06:25, 4.61s/it] 54%|█████▎ | 10016/18688 [11:45:50<10:53:39, 4.52s/it] 54%|█████▎ | 10017/18688 [11:45:55<10:54:35, 4.53s/it] 54%|█████▎ | 10018/18688 [11:45:59<10:23:02, 4.31s/it] 54%|█████▎ | 10019/18688 [11:46:03<10:20:30, 4.29s/it] 54%|█████▎ | 10020/18688 [11:46:07<9:52:17, 4.10s/it] {'loss': '0.9708', 'grad_norm': '1.101', 'learning_rate': '4.055e-05', 'epoch': '0.5362', 'num_input_tokens_seen': 33715552, 'train_runtime': '4.237e+04', 'train_tokens_per_second': '795.8'}
54%|█████▎ | 10020/18688 [11:46:07<9:52:17, 4.10s/it] 54%|█████▎ | 10021/18688 [11:46:11<10:15:11, 4.26s/it] 54%|█████▎ | 10022/18688 [11:46:15<10:11:34, 4.23s/it] 54%|█████▎ | 10023/18688 [11:46:20<10:25:31, 4.33s/it] 54%|█████▎ | 10024/18688 [11:46:26<11:26:07, 4.75s/it] 54%|█████▎ | 10025/18688 [11:46:31<11:26:17, 4.75s/it] 54%|█████▎ | 10026/18688 [11:46:34<10:49:50, 4.50s/it] 54%|█████▎ | 10027/18688 [11:46:39<10:31:26, 4.37s/it] 54%|█████▎ | 10028/18688 [11:46:43<10:22:59, 4.32s/it] 54%|█████▎ | 10029/18688 [11:46:47<10:19:21, 4.29s/it] 54%|█████▎ | 10030/18688 [11:46:51<9:51:25, 4.10s/it] {'loss': '1.009', 'grad_norm': '1.398', 'learning_rate': '4.048e-05', 'epoch': '0.5367', 'num_input_tokens_seen': 33752960, 'train_runtime': '4.241e+04', 'train_tokens_per_second': '795.9'}
54%|█████▎ | 10030/18688 [11:46:51<9:51:25, 4.10s/it] 54%|█████▎ | 10031/18688 [11:46:54<9:26:39, 3.93s/it] 54%|█████▎ | 10032/18688 [11:46:59<10:16:10, 4.27s/it] 54%|█████▎ | 10033/18688 [11:47:03<10:02:51, 4.18s/it] 54%|█████▎ | 10034/18688 [11:47:08<10:27:55, 4.35s/it] 54%|█████▎ | 10035/18688 [11:47:12<10:15:45, 4.27s/it] 54%|█████▎ | 10036/18688 [11:47:16<10:06:12, 4.20s/it] 54%|█████▎ | 10037/18688 [11:47:21<10:29:32, 4.37s/it] 54%|█████▎ | 10038/18688 [11:47:25<10:09:14, 4.23s/it] 54%|█████▎ | 10039/18688 [11:47:28<9:45:27, 4.06s/it] 54%|█████▎ | 10040/18688 [11:47:33<10:05:34, 4.20s/it] {'loss': '0.9851', 'grad_norm': '1.217', 'learning_rate': '4.04e-05', 'epoch': '0.5373', 'num_input_tokens_seen': 33787960, 'train_runtime': '4.245e+04', 'train_tokens_per_second': '795.9'}
54%|█████▎ | 10040/18688 [11:47:33<10:05:34, 4.20s/it] 54%|█████▎ | 10041/18688 [11:47:38<10:55:51, 4.55s/it] 54%|█████▎ | 10042/18688 [11:47:42<10:14:29, 4.26s/it] 54%|█████▎ | 10043/18688 [11:47:46<10:28:26, 4.36s/it] 54%|█████▎ | 10044/18688 [11:47:50<9:55:00, 4.13s/it] 54%|█████▍ | 10045/18688 [11:47:54<9:34:22, 3.99s/it] 54%|█████▍ | 10046/18688 [11:47:58<9:54:51, 4.13s/it] 54%|█████▍ | 10047/18688 [11:48:02<9:38:41, 4.02s/it] 54%|█████▍ | 10048/18688 [11:48:07<10:40:30, 4.45s/it] 54%|█████▍ | 10049/18688 [11:48:12<10:45:03, 4.48s/it] 54%|█████▍ | 10050/18688 [11:48:17<11:02:41, 4.60s/it] {'loss': '0.8674', 'grad_norm': '1.095', 'learning_rate': '4.033e-05', 'epoch': '0.5378', 'num_input_tokens_seen': 33824672, 'train_runtime': '4.25e+04', 'train_tokens_per_second': '795.9'}
54%|█████▍ | 10050/18688 [11:48:17<11:02:41, 4.60s/it] 54%|█████▍ | 10051/18688 [11:48:21<10:25:56, 4.35s/it] 54%|█████▍ | 10052/18688 [11:48:26<10:55:02, 4.55s/it] 54%|█████▍ | 10053/18688 [11:48:29<10:15:51, 4.28s/it] 54%|█████▍ | 10054/18688 [11:48:33<9:50:01, 4.10s/it] 54%|█████▍ | 10055/18688 [11:48:37<10:10:28, 4.24s/it] 54%|█████▍ | 10056/18688 [11:48:41<9:50:03, 4.10s/it] 54%|█████▍ | 10057/18688 [11:48:46<10:16:53, 4.29s/it] 54%|█████▍ | 10058/18688 [11:48:50<10:14:12, 4.27s/it] 54%|█████▍ | 10059/18688 [11:48:54<10:00:53, 4.18s/it] 54%|█████▍ | 10060/18688 [11:48:58<10:06:45, 4.22s/it] {'loss': '0.9698', 'grad_norm': '1.598', 'learning_rate': '4.026e-05', 'epoch': '0.5383', 'num_input_tokens_seen': 33856824, 'train_runtime': '4.254e+04', 'train_tokens_per_second': '795.9'}
54%|█████▍ | 10060/18688 [11:48:58<10:06:45, 4.22s/it] 54%|█████▍ | 10061/18688 [11:49:03<10:09:45, 4.24s/it] 54%|█████▍ | 10062/18688 [11:49:07<10:13:36, 4.27s/it] 54%|█████▍ | 10063/18688 [11:49:11<9:56:09, 4.15s/it] 54%|█████▍ | 10064/18688 [11:49:15<10:09:11, 4.24s/it] 54%|█████▍ | 10065/18688 [11:49:19<9:53:57, 4.13s/it] 54%|█████▍ | 10066/18688 [11:49:24<9:59:50, 4.17s/it] 54%|█████▍ | 10067/18688 [11:49:27<9:32:53, 3.99s/it] 54%|█████▍ | 10068/18688 [11:49:31<9:37:29, 4.02s/it] 54%|█████▍ | 10069/18688 [11:49:35<9:27:46, 3.95s/it] 54%|█████▍ | 10070/18688 [11:49:39<9:49:35, 4.10s/it] {'loss': '0.9578', 'grad_norm': '1.06', 'learning_rate': '4.018e-05', 'epoch': '0.5389', 'num_input_tokens_seen': 33890200, 'train_runtime': '4.258e+04', 'train_tokens_per_second': '795.9'}
54%|█████▍ | 10070/18688 [11:49:39<9:49:35, 4.10s/it] 54%|█████▍ | 10071/18688 [11:49:45<11:00:04, 4.60s/it] 54%|█████▍ | 10072/18688 [11:49:49<10:20:52, 4.32s/it] 54%|█████▍ | 10073/18688 [11:49:52<9:44:47, 4.07s/it] 54%|█████▍ | 10074/18688 [11:49:56<9:18:11, 3.89s/it] 54%|█████▍ | 10075/18688 [11:50:00<9:21:24, 3.91s/it] 54%|█████▍ | 10076/18688 [11:50:04<9:23:52, 3.93s/it] 54%|█████▍ | 10077/18688 [11:50:08<9:16:02, 3.87s/it] 54%|█████▍ | 10078/18688 [11:50:12<9:19:24, 3.90s/it] 54%|█████▍ | 10079/18688 [11:50:15<9:16:56, 3.88s/it] 54%|█████▍ | 10080/18688 [11:50:19<9:16:12, 3.88s/it] {'loss': '0.9598', 'grad_norm': '1.301', 'learning_rate': '4.011e-05', 'epoch': '0.5394', 'num_input_tokens_seen': 33920600, 'train_runtime': '4.262e+04', 'train_tokens_per_second': '795.9'}
54%|█████▍ | 10080/18688 [11:50:19<9:16:12, 3.88s/it] 54%|█████▍ | 10081/18688 [11:50:24<10:08:35, 4.24s/it] 54%|█████▍ | 10082/18688 [11:50:30<10:51:27, 4.54s/it] 54%|█████▍ | 10083/18688 [11:50:33<10:19:24, 4.32s/it] 54%|█████▍ | 10084/18688 [11:50:37<9:53:46, 4.14s/it] 54%|█████▍ | 10085/18688 [11:50:42<10:21:55, 4.34s/it] 54%|█████▍ | 10086/18688 [11:50:46<10:01:14, 4.19s/it] 54%|█████▍ | 10087/18688 [11:50:49<9:32:59, 4.00s/it] 54%|█████▍ | 10088/18688 [11:50:53<9:09:34, 3.83s/it] 54%|█████▍ | 10089/18688 [11:50:56<8:54:41, 3.73s/it] 54%|█████▍ | 10090/18688 [11:51:01<9:20:57, 3.91s/it] {'loss': '0.8405', 'grad_norm': '1.183', 'learning_rate': '4.004e-05', 'epoch': '0.5399', 'num_input_tokens_seen': 33953920, 'train_runtime': '4.266e+04', 'train_tokens_per_second': '795.9'}
54%|█████▍ | 10090/18688 [11:51:01<9:20:57, 3.91s/it] 54%|█████▍ | 10091/18688 [11:51:05<9:42:32, 4.07s/it] 54%|█████▍ | 10092/18688 [11:51:10<10:39:13, 4.46s/it] 54%|█████▍ | 10093/18688 [11:51:14<10:24:47, 4.36s/it] 54%|█████▍ | 10094/18688 [11:51:18<10:07:02, 4.24s/it] 54%|█████▍ | 10095/18688 [11:51:23<10:26:50, 4.38s/it] 54%|█████▍ | 10096/18688 [11:51:28<10:35:00, 4.43s/it] 54%|█████▍ | 10097/18688 [11:51:32<10:22:13, 4.35s/it] 54%|█████▍ | 10098/18688 [11:51:38<11:20:51, 4.76s/it] 54%|█████▍ | 10099/18688 [11:51:41<10:43:02, 4.49s/it] 54%|█████▍ | 10100/18688 [11:51:46<10:35:56, 4.44s/it] {'loss': '0.9386', 'grad_norm': '1.343', 'learning_rate': '3.996e-05', 'epoch': '0.5405', 'num_input_tokens_seen': 33993312, 'train_runtime': '4.271e+04', 'train_tokens_per_second': '796'}
54%|█████▍ | 10100/18688 [11:51:46<10:35:56, 4.44s/it] 54%|█████▍ | 10101/18688 [11:51:49<9:58:35, 4.18s/it] 54%|█████▍ | 10102/18688 [11:51:53<9:40:24, 4.06s/it] 54%|█████▍ | 10103/18688 [11:51:57<9:31:17, 3.99s/it] 54%|█████▍ | 10104/18688 [11:52:01<9:54:55, 4.16s/it] 54%|█████▍ | 10105/18688 [11:52:06<10:02:59, 4.22s/it] 54%|█████▍ | 10106/18688 [11:52:10<10:05:50, 4.24s/it] 54%|█████▍ | 10107/18688 [11:52:14<9:41:23, 4.07s/it] 54%|█████▍ | 10108/18688 [11:52:18<9:53:08, 4.15s/it] 54%|█████▍ | 10109/18688 [11:52:22<9:32:37, 4.00s/it] 54%|█████▍ | 10110/18688 [11:52:26<9:20:07, 3.92s/it] {'loss': '0.8985', 'grad_norm': '1.249', 'learning_rate': '3.989e-05', 'epoch': '0.541', 'num_input_tokens_seen': 34024272, 'train_runtime': '4.275e+04', 'train_tokens_per_second': '796'}
54%|█████▍ | 10110/18688 [11:52:26<9:20:07, 3.92s/it] 54%|█████▍ | 10111/18688 [11:52:30<9:31:19, 4.00s/it] 54%|█████▍ | 10112/18688 [11:52:34<9:36:56, 4.04s/it] 54%|█████▍ | 10113/18688 [11:52:37<9:21:16, 3.93s/it] 54%|█████▍ | 10114/18688 [11:52:42<9:29:41, 3.99s/it] 54%|█████▍ | 10115/18688 [11:52:45<9:13:19, 3.87s/it] 54%|█████▍ | 10116/18688 [11:52:49<8:56:30, 3.76s/it] 54%|█████▍ | 10117/18688 [11:52:53<9:13:15, 3.87s/it] 54%|█████▍ | 10118/18688 [11:52:57<9:38:45, 4.05s/it] 54%|█████▍ | 10119/18688 [11:53:01<9:38:34, 4.05s/it] 54%|█████▍ | 10120/18688 [11:53:06<10:03:56, 4.23s/it] {'loss': '0.9473', 'grad_norm': '1.173', 'learning_rate': '3.982e-05', 'epoch': '0.5415', 'num_input_tokens_seen': 34057008, 'train_runtime': '4.279e+04', 'train_tokens_per_second': '796'}
54%|█████▍ | 10120/18688 [11:53:06<10:03:56, 4.23s/it] 54%|█████▍ | 10121/18688 [11:53:10<9:57:23, 4.18s/it] 54%|█████▍ | 10122/18688 [11:53:15<10:13:04, 4.29s/it] 54%|█████▍ | 10123/18688 [11:53:19<10:18:17, 4.33s/it] 54%|█████▍ | 10124/18688 [11:53:23<10:08:09, 4.26s/it] 54%|█████▍ | 10125/18688 [11:53:28<10:13:27, 4.30s/it] 54%|█████▍ | 10126/18688 [11:53:31<9:40:34, 4.07s/it] 54%|█████▍ | 10127/18688 [11:53:35<9:29:44, 3.99s/it] 54%|█████▍ | 10128/18688 [11:53:38<9:09:28, 3.85s/it] 54%|█████▍ | 10129/18688 [11:53:42<9:10:16, 3.86s/it] 54%|█████▍ | 10130/18688 [11:53:47<9:34:47, 4.03s/it] {'loss': '0.8385', 'grad_norm': '1.065', 'learning_rate': '3.974e-05', 'epoch': '0.5421', 'num_input_tokens_seen': 34089432, 'train_runtime': '4.283e+04', 'train_tokens_per_second': '796'}
54%|█████▍ | 10130/18688 [11:53:47<9:34:47, 4.03s/it] 54%|█████▍ | 10131/18688 [11:53:51<9:54:20, 4.17s/it] 54%|█████▍ | 10132/18688 [11:53:56<10:05:44, 4.25s/it] 54%|█████▍ | 10133/18688 [11:54:00<9:59:56, 4.21s/it] 54%|█████▍ | 10134/18688 [11:54:04<10:12:54, 4.30s/it] 54%|█████▍ | 10135/18688 [11:54:09<10:20:17, 4.35s/it] 54%|█████▍ | 10136/18688 [11:54:13<10:12:02, 4.29s/it] 54%|█████▍ | 10137/18688 [11:54:17<10:12:27, 4.30s/it] 54%|█████▍ | 10138/18688 [11:54:21<9:51:09, 4.15s/it] 54%|█████▍ | 10139/18688 [11:54:25<10:01:47, 4.22s/it] 54%|█████▍ | 10140/18688 [11:54:30<10:31:00, 4.43s/it] {'loss': '0.9684', 'grad_norm': '1.212', 'learning_rate': '3.967e-05', 'epoch': '0.5426', 'num_input_tokens_seen': 34125208, 'train_runtime': '4.287e+04', 'train_tokens_per_second': '796'}
54%|█████▍ | 10140/18688 [11:54:30<10:31:00, 4.43s/it] 54%|█████▍ | 10141/18688 [11:54:35<10:48:40, 4.55s/it] 54%|█████▍ | 10142/18688 [11:54:40<11:06:50, 4.68s/it] 54%|█████▍ | 10143/18688 [11:54:46<11:37:25, 4.90s/it] 54%|█████▍ | 10144/18688 [11:54:51<11:44:51, 4.95s/it] 54%|█████▍ | 10145/18688 [11:54:54<10:49:47, 4.56s/it] 54%|█████▍ | 10146/18688 [11:55:00<11:26:15, 4.82s/it] 54%|█████▍ | 10147/18688 [11:55:03<10:34:54, 4.46s/it] 54%|█████▍ | 10148/18688 [11:55:08<10:47:45, 4.55s/it] 54%|█████▍ | 10149/18688 [11:55:12<10:02:20, 4.23s/it] 54%|█████▍ | 10150/18688 [11:55:15<9:38:26, 4.06s/it] {'loss': '0.92', 'grad_norm': '1.313', 'learning_rate': '3.96e-05', 'epoch': '0.5431', 'num_input_tokens_seen': 34164360, 'train_runtime': '4.292e+04', 'train_tokens_per_second': '796.1'}
54%|█████▍ | 10150/18688 [11:55:15<9:38:26, 4.06s/it] 54%|█████▍ | 10151/18688 [11:55:21<10:29:08, 4.42s/it] 54%|█████▍ | 10152/18688 [11:55:24<9:59:53, 4.22s/it] 54%|█████▍ | 10153/18688 [11:55:28<9:51:45, 4.16s/it] 54%|█████▍ | 10154/18688 [11:55:33<10:11:57, 4.30s/it] 54%|█████▍ | 10155/18688 [11:55:37<9:48:15, 4.14s/it] 54%|█████▍ | 10156/18688 [11:55:40<9:28:32, 4.00s/it] 54%|█████▍ | 10157/18688 [11:55:44<9:27:27, 3.99s/it] 54%|█████▍ | 10158/18688 [11:55:48<9:30:13, 4.01s/it] 54%|█████▍ | 10159/18688 [11:55:52<9:28:53, 4.00s/it] 54%|█████▍ | 10160/18688 [11:55:58<10:34:37, 4.46s/it] {'loss': '0.8971', 'grad_norm': '1.398', 'learning_rate': '3.952e-05', 'epoch': '0.5437', 'num_input_tokens_seen': 34198360, 'train_runtime': '4.296e+04', 'train_tokens_per_second': '796.1'}
54%|█████▍ | 10160/18688 [11:55:58<10:34:37, 4.46s/it] 54%|█████▍ | 10161/18688 [11:56:02<10:28:32, 4.42s/it] 54%|█████▍ | 10162/18688 [11:56:06<9:54:31, 4.18s/it] 54%|█████▍ | 10163/18688 [11:56:10<9:44:49, 4.12s/it] 54%|█████▍ | 10164/18688 [11:56:14<9:43:23, 4.11s/it] 54%|█████▍ | 10165/18688 [11:56:18<9:39:17, 4.08s/it] 54%|█████▍ | 10166/18688 [11:56:22<9:35:50, 4.05s/it] 54%|█████▍ | 10167/18688 [11:56:25<9:15:28, 3.91s/it] 54%|█████▍ | 10168/18688 [11:56:30<9:53:45, 4.18s/it] 54%|█████▍ | 10169/18688 [11:56:36<11:00:49, 4.65s/it] 54%|█████▍ | 10170/18688 [11:56:40<10:35:04, 4.47s/it] {'loss': '0.9201', 'grad_norm': '1.291', 'learning_rate': '3.945e-05', 'epoch': '0.5442', 'num_input_tokens_seen': 34233192, 'train_runtime': '4.3e+04', 'train_tokens_per_second': '796.1'}
54%|█████▍ | 10170/18688 [11:56:40<10:35:04, 4.47s/it] 54%|█████▍ | 10171/18688 [11:56:44<10:07:07, 4.28s/it] 54%|█████▍ | 10172/18688 [11:56:48<9:47:24, 4.14s/it] 54%|█████▍ | 10173/18688 [11:56:53<10:18:33, 4.36s/it] 54%|█████▍ | 10174/18688 [11:56:57<10:21:38, 4.38s/it] 54%|█████▍ | 10175/18688 [11:57:01<9:53:05, 4.18s/it] 54%|█████▍ | 10176/18688 [11:57:05<9:59:24, 4.23s/it] 54%|█████▍ | 10177/18688 [11:57:09<10:05:19, 4.27s/it] 54%|█████▍ | 10178/18688 [11:57:14<10:19:56, 4.37s/it] 54%|█████▍ | 10179/18688 [11:57:19<10:38:05, 4.50s/it] 54%|█████▍ | 10180/18688 [11:57:23<10:30:56, 4.45s/it] {'loss': '0.9651', 'grad_norm': '1.311', 'learning_rate': '3.938e-05', 'epoch': '0.5447', 'num_input_tokens_seen': 34269328, 'train_runtime': '4.304e+04', 'train_tokens_per_second': '796.2'}
54%|█████▍ | 10180/18688 [11:57:23<10:30:56, 4.45s/it] 54%|█████▍ | 10181/18688 [11:57:27<9:49:05, 4.15s/it] 54%|█████▍ | 10182/18688 [11:57:31<9:48:32, 4.15s/it] 54%|█████▍ | 10183/18688 [11:57:35<10:10:06, 4.30s/it] 54%|█████▍ | 10184/18688 [11:57:39<9:39:20, 4.09s/it] 55%|█████▍ | 10185/18688 [11:57:43<9:44:18, 4.12s/it] 55%|█████▍ | 10186/18688 [11:57:49<10:39:29, 4.51s/it] 55%|█████▍ | 10187/18688 [11:57:52<10:07:18, 4.29s/it] 55%|█████▍ | 10188/18688 [11:57:56<9:33:34, 4.05s/it] 55%|█████▍ | 10189/18688 [11:58:00<9:54:18, 4.20s/it] 55%|█████▍ | 10190/18688 [11:58:04<9:32:47, 4.04s/it] {'loss': '0.8924', 'grad_norm': '1.396', 'learning_rate': '3.931e-05', 'epoch': '0.5453', 'num_input_tokens_seen': 34302080, 'train_runtime': '4.308e+04', 'train_tokens_per_second': '796.2'}
55%|█████▍ | 10190/18688 [11:58:04<9:32:47, 4.04s/it] 55%|█████▍ | 10191/18688 [11:58:09<9:49:54, 4.17s/it] 55%|█████▍ | 10192/18688 [11:58:13<9:39:03, 4.09s/it] 55%|█████▍ | 10193/18688 [11:58:17<9:52:59, 4.19s/it] 55%|█████▍ | 10194/18688 [11:58:21<9:57:41, 4.22s/it] 55%|█████▍ | 10195/18688 [11:58:26<10:04:25, 4.27s/it] 55%|█████▍ | 10196/18688 [11:58:29<9:35:02, 4.06s/it] 55%|█████▍ | 10197/18688 [11:58:34<10:04:30, 4.27s/it] 55%|█████▍ | 10198/18688 [11:58:38<10:00:53, 4.25s/it] 55%|█████▍ | 10199/18688 [11:58:42<9:36:22, 4.07s/it] 55%|█████▍ | 10200/18688 [11:58:46<9:26:49, 4.01s/it] {'loss': '0.8937', 'grad_norm': '1.241', 'learning_rate': '3.923e-05', 'epoch': '0.5458', 'num_input_tokens_seen': 34335616, 'train_runtime': '4.313e+04', 'train_tokens_per_second': '796.2'}
55%|█████▍ | 10200/18688 [11:58:46<9:26:49, 4.01s/it] 55%|█████▍ | 10201/18688 [11:58:49<9:02:45, 3.84s/it] 55%|█████▍ | 10202/18688 [11:58:53<9:07:32, 3.87s/it] 55%|█████▍ | 10203/18688 [11:58:58<9:35:17, 4.07s/it] 55%|█████▍ | 10204/18688 [11:59:01<9:13:22, 3.91s/it] 55%|█████▍ | 10205/18688 [11:59:05<9:07:50, 3.87s/it] 55%|█████▍ | 10206/18688 [11:59:09<9:33:59, 4.06s/it] 55%|█████▍ | 10207/18688 [11:59:14<9:49:33, 4.17s/it] 55%|█████▍ | 10208/18688 [11:59:18<10:00:52, 4.25s/it] 55%|█████▍ | 10209/18688 [11:59:22<9:33:52, 4.06s/it] 55%|█████▍ | 10210/18688 [11:59:27<10:06:47, 4.29s/it] {'loss': '0.8632', 'grad_norm': '1.078', 'learning_rate': '3.916e-05', 'epoch': '0.5464', 'num_input_tokens_seen': 34367872, 'train_runtime': '4.317e+04', 'train_tokens_per_second': '796.2'}
55%|█████▍ | 10210/18688 [11:59:27<10:06:47, 4.29s/it] 55%|█████▍ | 10211/18688 [11:59:32<10:28:55, 4.45s/it] 55%|█████▍ | 10212/18688 [11:59:35<10:06:18, 4.29s/it] 55%|█████▍ | 10213/18688 [11:59:41<10:37:52, 4.52s/it] 55%|█████▍ | 10214/18688 [11:59:46<11:02:07, 4.69s/it] 55%|█████▍ | 10215/18688 [11:59:51<11:23:36, 4.84s/it] 55%|█████▍ | 10216/18688 [11:59:54<10:25:29, 4.43s/it] 55%|█████▍ | 10217/18688 [11:59:59<10:29:07, 4.46s/it] 55%|█████▍ | 10218/18688 [12:00:03<10:05:16, 4.29s/it] 55%|█████▍ | 10219/18688 [12:00:07<9:58:46, 4.24s/it] 55%|█████▍ | 10220/18688 [12:00:11<9:47:17, 4.16s/it] {'loss': '0.9592', 'grad_norm': '1.45', 'learning_rate': '3.909e-05', 'epoch': '0.5469', 'num_input_tokens_seen': 34405496, 'train_runtime': '4.321e+04', 'train_tokens_per_second': '796.2'}
55%|█████▍ | 10220/18688 [12:00:11<9:47:17, 4.16s/it] 55%|█████▍ | 10221/18688 [12:00:14<9:17:53, 3.95s/it] 55%|█████▍ | 10222/18688 [12:00:18<9:25:02, 4.00s/it] 55%|█████▍ | 10223/18688 [12:00:23<9:48:43, 4.17s/it] 55%|█████▍ | 10224/18688 [12:00:27<9:39:22, 4.11s/it] 55%|█████▍ | 10225/18688 [12:00:30<9:11:43, 3.91s/it] 55%|█████▍ | 10226/18688 [12:00:35<9:43:47, 4.14s/it] 55%|█████▍ | 10227/18688 [12:00:39<9:28:34, 4.03s/it] 55%|█████▍ | 10228/18688 [12:00:43<9:33:57, 4.07s/it] 55%|█████▍ | 10229/18688 [12:00:47<9:21:45, 3.98s/it] 55%|█████▍ | 10230/18688 [12:00:51<9:26:13, 4.02s/it] {'loss': '0.8348', 'grad_norm': '1.114', 'learning_rate': '3.901e-05', 'epoch': '0.5474', 'num_input_tokens_seen': 34436792, 'train_runtime': '4.325e+04', 'train_tokens_per_second': '796.2'}
55%|█████▍ | 10230/18688 [12:00:51<9:26:13, 4.02s/it] 55%|█████▍ | 10231/18688 [12:00:55<9:51:30, 4.20s/it] 55%|█████▍ | 10232/18688 [12:00:59<9:23:52, 4.00s/it] 55%|█████▍ | 10233/18688 [12:01:03<9:25:16, 4.01s/it] 55%|█████▍ | 10234/18688 [12:01:07<9:23:28, 4.00s/it] 55%|█████▍ | 10235/18688 [12:01:12<9:47:22, 4.17s/it] 55%|█████▍ | 10236/18688 [12:01:17<10:38:50, 4.54s/it] 55%|█████▍ | 10237/18688 [12:01:21<9:58:29, 4.25s/it] 55%|█████▍ | 10238/18688 [12:01:25<9:49:00, 4.18s/it] 55%|█████▍ | 10239/18688 [12:01:29<10:11:24, 4.34s/it] 55%|█████▍ | 10240/18688 [12:01:33<9:48:43, 4.18s/it] {'loss': '0.8877', 'grad_norm': '1.14', 'learning_rate': '3.894e-05', 'epoch': '0.548', 'num_input_tokens_seen': 34471432, 'train_runtime': '4.329e+04', 'train_tokens_per_second': '796.2'}
55%|█████▍ | 10240/18688 [12:01:33<9:48:43, 4.18s/it] 55%|█████▍ | 10241/18688 [12:01:38<10:30:42, 4.48s/it] 55%|█████▍ | 10242/18688 [12:01:43<10:23:23, 4.43s/it] 55%|█████▍ | 10243/18688 [12:01:47<10:16:53, 4.38s/it] 55%|█████▍ | 10244/18688 [12:01:50<9:43:11, 4.14s/it] 55%|█████▍ | 10245/18688 [12:01:54<9:23:17, 4.00s/it] 55%|█████▍ | 10246/18688 [12:01:58<9:31:34, 4.06s/it] 55%|█████▍ | 10247/18688 [12:02:02<9:36:29, 4.10s/it] 55%|█████▍ | 10248/18688 [12:02:07<9:46:09, 4.17s/it] 55%|█████▍ | 10249/18688 [12:02:11<9:45:06, 4.16s/it] 55%|█████▍ | 10250/18688 [12:02:16<10:25:17, 4.45s/it] {'loss': '0.8987', 'grad_norm': '1.121', 'learning_rate': '3.887e-05', 'epoch': '0.5485', 'num_input_tokens_seen': 34506080, 'train_runtime': '4.334e+04', 'train_tokens_per_second': '796.2'}
55%|█████▍ | 10250/18688 [12:02:16<10:25:17, 4.45s/it] 55%|█████▍ | 10251/18688 [12:02:20<9:53:13, 4.22s/it] 55%|█████▍ | 10252/18688 [12:02:23<9:23:37, 4.01s/it] 55%|█████▍ | 10253/18688 [12:02:27<9:00:13, 3.84s/it] 55%|█████▍ | 10254/18688 [12:02:32<9:41:30, 4.14s/it] 55%|█████▍ | 10255/18688 [12:02:36<9:51:10, 4.21s/it] 55%|█████▍ | 10256/18688 [12:02:40<9:33:06, 4.08s/it] 55%|█████▍ | 10257/18688 [12:02:44<10:02:44, 4.29s/it] 55%|█████▍ | 10258/18688 [12:02:48<9:40:22, 4.13s/it] 55%|█████▍ | 10259/18688 [12:02:53<10:05:43, 4.31s/it] 55%|█████▍ | 10260/18688 [12:02:57<10:01:38, 4.28s/it] {'loss': '0.9214', 'grad_norm': '1.377', 'learning_rate': '3.879e-05', 'epoch': '0.549', 'num_input_tokens_seen': 34539184, 'train_runtime': '4.338e+04', 'train_tokens_per_second': '796.2'}
55%|█████▍ | 10260/18688 [12:02:57<10:01:38, 4.28s/it] 55%|█████▍ | 10261/18688 [12:03:02<10:14:25, 4.37s/it] 55%|█████▍ | 10262/18688 [12:03:06<10:08:39, 4.33s/it] 55%|█████▍ | 10263/18688 [12:03:10<9:42:34, 4.15s/it] 55%|█████▍ | 10264/18688 [12:03:14<10:05:58, 4.32s/it] 55%|█████▍ | 10265/18688 [12:03:19<10:26:39, 4.46s/it] 55%|█████▍ | 10266/18688 [12:03:23<9:53:16, 4.23s/it] 55%|█████▍ | 10267/18688 [12:03:27<9:37:16, 4.11s/it] 55%|█████▍ | 10268/18688 [12:03:32<10:17:04, 4.40s/it] 55%|█████▍ | 10269/18688 [12:03:36<10:08:08, 4.33s/it] 55%|█████▍ | 10270/18688 [12:03:41<10:22:07, 4.43s/it] {'loss': '0.8677', 'grad_norm': '1.135', 'learning_rate': '3.872e-05', 'epoch': '0.5496', 'num_input_tokens_seen': 34575560, 'train_runtime': '4.342e+04', 'train_tokens_per_second': '796.3'}
55%|█████▍ | 10270/18688 [12:03:41<10:22:07, 4.43s/it] 55%|█████▍ | 10271/18688 [12:03:45<10:26:21, 4.46s/it] 55%|█████▍ | 10272/18688 [12:03:49<9:45:50, 4.18s/it] 55%|█████▍ | 10273/18688 [12:03:54<10:28:38, 4.48s/it] 55%|█████▍ | 10274/18688 [12:03:57<9:47:06, 4.19s/it] 55%|█████▍ | 10275/18688 [12:04:01<9:37:24, 4.12s/it] 55%|█████▍ | 10276/18688 [12:04:05<9:22:34, 4.01s/it] 55%|█████▍ | 10277/18688 [12:04:09<9:11:58, 3.94s/it] 55%|█████▍ | 10278/18688 [12:04:13<9:32:25, 4.08s/it] 55%|█████▌ | 10279/18688 [12:04:17<9:20:31, 4.00s/it] 55%|█████▌ | 10280/18688 [12:04:21<9:04:32, 3.89s/it] {'loss': '0.8944', 'grad_norm': '1.169', 'learning_rate': '3.865e-05', 'epoch': '0.5501', 'num_input_tokens_seen': 34607320, 'train_runtime': '4.346e+04', 'train_tokens_per_second': '796.3'}
55%|█████▌ | 10280/18688 [12:04:21<9:04:32, 3.89s/it] 55%|█████▌ | 10281/18688 [12:04:26<9:58:10, 4.27s/it] 55%|█████▌ | 10282/18688 [12:04:31<10:15:26, 4.39s/it] 55%|█████▌ | 10283/18688 [12:04:34<9:42:52, 4.16s/it] 55%|█████▌ | 10284/18688 [12:04:39<9:51:07, 4.22s/it] 55%|█████▌ | 10285/18688 [12:04:42<9:22:39, 4.02s/it] 55%|█████▌ | 10286/18688 [12:04:46<8:59:23, 3.85s/it] 55%|█████▌ | 10287/18688 [12:04:49<8:59:00, 3.85s/it] 55%|█████▌ | 10288/18688 [12:04:53<9:00:04, 3.86s/it] 55%|█████▌ | 10289/18688 [12:04:57<8:59:04, 3.85s/it] 55%|█████▌ | 10290/18688 [12:05:02<9:32:37, 4.09s/it] {'loss': '0.913', 'grad_norm': '1.095', 'learning_rate': '3.857e-05', 'epoch': '0.5506', 'num_input_tokens_seen': 34639288, 'train_runtime': '4.35e+04', 'train_tokens_per_second': '796.3'}
55%|█████▌ | 10290/18688 [12:05:02<9:32:37, 4.09s/it] 55%|█████▌ | 10291/18688 [12:05:06<9:17:13, 3.98s/it] 55%|█████▌ | 10292/18688 [12:05:10<9:27:05, 4.05s/it] 55%|█████▌ | 10293/18688 [12:05:15<9:59:39, 4.29s/it] 55%|█████▌ | 10294/18688 [12:05:19<10:09:50, 4.36s/it] 55%|█████▌ | 10295/18688 [12:05:24<10:29:03, 4.50s/it] 55%|█████▌ | 10296/18688 [12:05:29<10:33:24, 4.53s/it] 55%|█████▌ | 10297/18688 [12:05:33<10:36:40, 4.55s/it] 55%|█████▌ | 10298/18688 [12:05:37<9:55:21, 4.26s/it] 55%|█████▌ | 10299/18688 [12:05:41<9:43:19, 4.17s/it] 55%|█████▌ | 10300/18688 [12:05:44<9:26:56, 4.06s/it] {'loss': '0.8618', 'grad_norm': '1.194', 'learning_rate': '3.85e-05', 'epoch': '0.5512', 'num_input_tokens_seen': 34673832, 'train_runtime': '4.354e+04', 'train_tokens_per_second': '796.3'}
55%|█████▌ | 10300/18688 [12:05:44<9:26:56, 4.06s/it] 55%|█████▌ | 10301/18688 [12:05:49<9:26:35, 4.05s/it] 55%|█████▌ | 10302/18688 [12:05:53<9:29:16, 4.07s/it] 55%|█████▌ | 10303/18688 [12:05:57<9:22:05, 4.02s/it] 55%|█████▌ | 10304/18688 [12:06:01<9:22:32, 4.03s/it] 55%|█████▌ | 10305/18688 [12:06:05<9:31:45, 4.09s/it] 55%|█████▌ | 10306/18688 [12:06:09<9:24:38, 4.04s/it] 55%|█████▌ | 10307/18688 [12:06:13<9:47:29, 4.21s/it] 55%|█████▌ | 10308/18688 [12:06:17<9:25:14, 4.05s/it] 55%|█████▌ | 10309/18688 [12:06:21<9:10:51, 3.94s/it] 55%|█████▌ | 10310/18688 [12:06:25<9:15:24, 3.98s/it] {'loss': '0.8788', 'grad_norm': '1.328', 'learning_rate': '3.843e-05', 'epoch': '0.5517', 'num_input_tokens_seen': 34705512, 'train_runtime': '4.359e+04', 'train_tokens_per_second': '796.3'}
55%|█████▌ | 10310/18688 [12:06:25<9:15:24, 3.98s/it] 55%|█████▌ | 10311/18688 [12:06:29<9:19:01, 4.00s/it] 55%|█████▌ | 10312/18688 [12:06:33<9:12:32, 3.96s/it] 55%|█████▌ | 10313/18688 [12:06:36<9:01:12, 3.88s/it] 55%|█████▌ | 10314/18688 [12:06:41<9:48:46, 4.22s/it] 55%|█████▌ | 10315/18688 [12:06:46<10:23:57, 4.47s/it] 55%|█████▌ | 10316/18688 [12:06:51<10:06:47, 4.35s/it] 55%|█████▌ | 10317/18688 [12:06:54<9:41:47, 4.17s/it] 55%|█████▌ | 10318/18688 [12:06:58<9:28:18, 4.07s/it] 55%|█████▌ | 10319/18688 [12:07:02<9:09:12, 3.94s/it] 55%|█████▌ | 10320/18688 [12:07:07<9:58:24, 4.29s/it] {'loss': '0.9549', 'grad_norm': '1.016', 'learning_rate': '3.836e-05', 'epoch': '0.5522', 'num_input_tokens_seen': 34739040, 'train_runtime': '4.363e+04', 'train_tokens_per_second': '796.3'}
55%|█████▌ | 10320/18688 [12:07:07<9:58:24, 4.29s/it] 55%|█████▌ | 10321/18688 [12:07:11<9:46:34, 4.21s/it] 55%|█████▌ | 10322/18688 [12:07:16<10:12:21, 4.39s/it] 55%|█████▌ | 10323/18688 [12:07:19<9:42:31, 4.18s/it] 55%|█████▌ | 10324/18688 [12:07:23<9:14:55, 3.98s/it] 55%|█████▌ | 10325/18688 [12:07:28<9:42:14, 4.18s/it] 55%|█████▌ | 10326/18688 [12:07:31<9:16:49, 4.00s/it] 55%|█████▌ | 10327/18688 [12:07:35<9:02:07, 3.89s/it] 55%|█████▌ | 10328/18688 [12:07:39<9:18:27, 4.01s/it] 55%|█████▌ | 10329/18688 [12:07:43<9:31:33, 4.10s/it] 55%|█████▌ | 10330/18688 [12:07:48<10:04:05, 4.34s/it] {'loss': '0.9118', 'grad_norm': '1.159', 'learning_rate': '3.828e-05', 'epoch': '0.5528', 'num_input_tokens_seen': 34772400, 'train_runtime': '4.367e+04', 'train_tokens_per_second': '796.3'}
55%|█████▌ | 10330/18688 [12:07:48<10:04:05, 4.34s/it] 55%|█████▌ | 10331/18688 [12:07:53<10:23:44, 4.48s/it] 55%|█████▌ | 10332/18688 [12:07:57<9:50:01, 4.24s/it] 55%|█████▌ | 10333/18688 [12:08:01<9:32:21, 4.11s/it] 55%|█████▌ | 10334/18688 [12:08:05<9:36:22, 4.14s/it] 55%|█████▌ | 10335/18688 [12:08:08<9:16:27, 4.00s/it] 55%|█████▌ | 10336/18688 [12:08:12<9:02:50, 3.90s/it] 55%|█████▌ | 10337/18688 [12:08:16<8:46:43, 3.78s/it] 55%|█████▌ | 10338/18688 [12:08:19<8:38:25, 3.73s/it] 55%|█████▌ | 10339/18688 [12:08:23<8:36:40, 3.71s/it] 55%|█████▌ | 10340/18688 [12:08:26<8:25:36, 3.63s/it] {'loss': '0.8692', 'grad_norm': '1.392', 'learning_rate': '3.821e-05', 'epoch': '0.5533', 'num_input_tokens_seen': 34800776, 'train_runtime': '4.371e+04', 'train_tokens_per_second': '796.2'}
55%|█████▌ | 10340/18688 [12:08:26<8:25:36, 3.63s/it] 55%|█████▌ | 10341/18688 [12:08:30<8:22:34, 3.61s/it] 55%|█████▌ | 10342/18688 [12:08:34<8:49:14, 3.80s/it] 55%|█████▌ | 10343/18688 [12:08:39<9:21:28, 4.04s/it] 55%|█████▌ | 10344/18688 [12:08:44<9:55:32, 4.28s/it] 55%|█████▌ | 10345/18688 [12:08:48<10:20:17, 4.46s/it] 55%|█████▌ | 10346/18688 [12:08:53<10:10:09, 4.39s/it] 55%|█████▌ | 10347/18688 [12:08:57<9:58:48, 4.31s/it] 55%|█████▌ | 10348/18688 [12:09:01<9:40:26, 4.18s/it] 55%|█████▌ | 10349/18688 [12:09:05<9:38:42, 4.16s/it] 55%|█████▌ | 10350/18688 [12:09:09<9:59:53, 4.32s/it] {'loss': '0.9082', 'grad_norm': '1.018', 'learning_rate': '3.814e-05', 'epoch': '0.5538', 'num_input_tokens_seen': 34835592, 'train_runtime': '4.375e+04', 'train_tokens_per_second': '796.2'}
55%|█████▌ | 10350/18688 [12:09:09<9:59:53, 4.32s/it] 55%|█████▌ | 10351/18688 [12:09:13<9:32:59, 4.12s/it] 55%|█████▌ | 10352/18688 [12:09:18<9:44:06, 4.20s/it] 55%|█████▌ | 10353/18688 [12:09:21<9:22:50, 4.05s/it] 55%|█████▌ | 10354/18688 [12:09:25<9:27:10, 4.08s/it] 55%|█████▌ | 10355/18688 [12:09:30<9:54:33, 4.28s/it] 55%|█████▌ | 10356/18688 [12:09:35<10:10:04, 4.39s/it] 55%|█████▌ | 10357/18688 [12:09:39<10:11:28, 4.40s/it] 55%|█████▌ | 10358/18688 [12:09:45<10:53:12, 4.70s/it] 55%|█████▌ | 10359/18688 [12:09:49<10:57:06, 4.73s/it] 55%|█████▌ | 10360/18688 [12:09:53<10:12:30, 4.41s/it] {'loss': '0.9347', 'grad_norm': '1.632', 'learning_rate': '3.806e-05', 'epoch': '0.5544', 'num_input_tokens_seen': 34872128, 'train_runtime': '4.379e+04', 'train_tokens_per_second': '796.3'}
55%|█████▌ | 10360/18688 [12:09:53<10:12:30, 4.41s/it] 55%|█████▌ | 10361/18688 [12:09:57<9:56:55, 4.30s/it] 55%|█████▌ | 10362/18688 [12:10:01<9:35:50, 4.15s/it] 55%|█████▌ | 10363/18688 [12:10:05<9:46:15, 4.23s/it] 55%|█████▌ | 10364/18688 [12:10:09<9:23:18, 4.06s/it] 55%|█████▌ | 10365/18688 [12:10:12<8:58:34, 3.88s/it] 55%|█████▌ | 10366/18688 [12:10:17<9:23:46, 4.06s/it] 55%|█████▌ | 10367/18688 [12:10:21<9:36:17, 4.16s/it] 55%|█████▌ | 10368/18688 [12:10:26<9:57:05, 4.31s/it] 55%|█████▌ | 10369/18688 [12:10:30<9:35:08, 4.15s/it] 55%|█████▌ | 10370/18688 [12:10:33<9:06:07, 3.94s/it] {'loss': '0.8482', 'grad_norm': '1.163', 'learning_rate': '3.799e-05', 'epoch': '0.5549', 'num_input_tokens_seen': 34904264, 'train_runtime': '4.383e+04', 'train_tokens_per_second': '796.3'}
55%|█████▌ | 10370/18688 [12:10:33<9:06:07, 3.94s/it] 55%|█████▌ | 10371/18688 [12:10:37<9:05:09, 3.93s/it] 56%|█████▌ | 10372/18688 [12:10:41<9:17:31, 4.02s/it] 56%|█████▌ | 10373/18688 [12:10:46<9:47:11, 4.24s/it] 56%|█████▌ | 10374/18688 [12:10:50<9:19:50, 4.04s/it] 56%|█████▌ | 10375/18688 [12:10:54<9:51:08, 4.27s/it] 56%|█████▌ | 10376/18688 [12:10:59<9:44:24, 4.22s/it] 56%|█████▌ | 10377/18688 [12:11:03<10:05:54, 4.37s/it] 56%|█████▌ | 10378/18688 [12:11:08<10:01:50, 4.35s/it] 56%|█████▌ | 10379/18688 [12:11:12<10:02:58, 4.35s/it] 56%|█████▌ | 10380/18688 [12:11:16<9:50:59, 4.27s/it] {'loss': '0.9878', 'grad_norm': '1.199', 'learning_rate': '3.792e-05', 'epoch': '0.5555', 'num_input_tokens_seen': 34938328, 'train_runtime': '4.388e+04', 'train_tokens_per_second': '796.3'}
56%|█████▌ | 10380/18688 [12:11:16<9:50:59, 4.27s/it] 56%|█████▌ | 10381/18688 [12:11:21<10:36:43, 4.60s/it] 56%|█████▌ | 10382/18688 [12:11:26<10:38:38, 4.61s/it] 56%|█████▌ | 10383/18688 [12:11:30<10:00:26, 4.34s/it] 56%|█████▌ | 10384/18688 [12:11:34<9:45:33, 4.23s/it] 56%|█████▌ | 10385/18688 [12:11:37<9:19:16, 4.04s/it] 56%|█████▌ | 10386/18688 [12:11:42<9:46:18, 4.24s/it] 56%|█████▌ | 10387/18688 [12:11:45<9:14:41, 4.01s/it] 56%|█████▌ | 10388/18688 [12:11:50<9:30:26, 4.12s/it] 56%|█████▌ | 10389/18688 [12:11:54<9:18:46, 4.04s/it] 56%|█████▌ | 10390/18688 [12:11:59<9:53:33, 4.29s/it] {'loss': '0.8908', 'grad_norm': '1.336', 'learning_rate': '3.784e-05', 'epoch': '0.556', 'num_input_tokens_seen': 34974160, 'train_runtime': '4.392e+04', 'train_tokens_per_second': '796.3'}
56%|█████▌ | 10390/18688 [12:11:59<9:53:33, 4.29s/it] 56%|█████▌ | 10391/18688 [12:12:03<9:48:49, 4.26s/it] 56%|█████▌ | 10392/18688 [12:12:07<9:52:21, 4.28s/it] 56%|█████▌ | 10393/18688 [12:12:11<9:23:14, 4.07s/it] 56%|█████▌ | 10394/18688 [12:12:14<9:06:38, 3.95s/it] 56%|█████▌ | 10395/18688 [12:12:19<9:24:47, 4.09s/it] 56%|█████▌ | 10396/18688 [12:12:23<9:11:13, 3.99s/it] 56%|█████▌ | 10397/18688 [12:12:27<9:27:36, 4.11s/it] 56%|█████▌ | 10398/18688 [12:12:31<9:30:14, 4.13s/it] 56%|█████▌ | 10399/18688 [12:12:35<9:28:20, 4.11s/it] 56%|█████▌ | 10400/18688 [12:12:39<9:18:03, 4.04s/it] {'loss': '0.9177', 'grad_norm': '1.223', 'learning_rate': '3.777e-05', 'epoch': '0.5565', 'num_input_tokens_seen': 35006040, 'train_runtime': '4.396e+04', 'train_tokens_per_second': '796.3'}
56%|█████▌ | 10400/18688 [12:12:39<9:18:03, 4.04s/it] 56%|█████▌ | 10401/18688 [12:12:43<9:06:13, 3.95s/it] 56%|█████▌ | 10402/18688 [12:12:47<9:00:13, 3.91s/it] 56%|█████▌ | 10403/18688 [12:12:50<8:47:34, 3.82s/it] 56%|█████▌ | 10404/18688 [12:12:54<8:34:56, 3.73s/it] 56%|█████▌ | 10405/18688 [12:12:59<9:35:06, 4.17s/it] 56%|█████▌ | 10406/18688 [12:13:03<9:11:02, 3.99s/it] 56%|█████▌ | 10407/18688 [12:13:07<9:16:22, 4.03s/it] 56%|█████▌ | 10408/18688 [12:13:11<9:30:23, 4.13s/it] 56%|█████▌ | 10409/18688 [12:13:15<9:23:46, 4.09s/it] 56%|█████▌ | 10410/18688 [12:13:19<9:14:08, 4.02s/it] {'loss': '0.9193', 'grad_norm': '1.142', 'learning_rate': '3.77e-05', 'epoch': '0.5571', 'num_input_tokens_seen': 35037200, 'train_runtime': '4.4e+04', 'train_tokens_per_second': '796.3'}
56%|█████▌ | 10410/18688 [12:13:19<9:14:08, 4.02s/it] 56%|█████▌ | 10411/18688 [12:13:23<9:04:07, 3.94s/it] 56%|█████▌ | 10412/18688 [12:13:27<9:43:02, 4.23s/it] 56%|█████▌ | 10413/18688 [12:13:32<9:39:36, 4.20s/it] 56%|█████▌ | 10414/18688 [12:13:35<9:24:59, 4.10s/it] 56%|█████▌ | 10415/18688 [12:13:39<9:08:14, 3.98s/it] 56%|█████▌ | 10416/18688 [12:13:43<9:01:28, 3.93s/it] 56%|█████▌ | 10417/18688 [12:13:47<8:53:10, 3.87s/it] 56%|█████▌ | 10418/18688 [12:13:51<9:14:52, 4.03s/it] 56%|█████▌ | 10419/18688 [12:13:56<9:35:38, 4.18s/it] 56%|█████▌ | 10420/18688 [12:14:00<9:38:54, 4.20s/it] {'loss': '0.9846', 'grad_norm': '1.508', 'learning_rate': '3.763e-05', 'epoch': '0.5576', 'num_input_tokens_seen': 35068880, 'train_runtime': '4.404e+04', 'train_tokens_per_second': '796.3'}
56%|█████▌ | 10420/18688 [12:14:00<9:38:54, 4.20s/it] 56%|█████▌ | 10421/18688 [12:14:04<9:25:51, 4.11s/it] 56%|█████▌ | 10422/18688 [12:14:08<9:21:32, 4.08s/it] 56%|█████▌ | 10423/18688 [12:14:12<9:10:21, 4.00s/it] 56%|█████▌ | 10424/18688 [12:14:16<9:25:51, 4.11s/it] 56%|█████▌ | 10425/18688 [12:14:20<9:29:18, 4.13s/it] 56%|█████▌ | 10426/18688 [12:14:25<9:40:48, 4.22s/it] 56%|█████▌ | 10427/18688 [12:14:28<9:25:11, 4.10s/it] 56%|█████▌ | 10428/18688 [12:14:32<9:17:43, 4.05s/it] 56%|█████▌ | 10429/18688 [12:14:36<8:58:36, 3.91s/it] 56%|█████▌ | 10430/18688 [12:14:40<8:44:22, 3.81s/it] {'loss': '0.929', 'grad_norm': '1.122', 'learning_rate': '3.755e-05', 'epoch': '0.5581', 'num_input_tokens_seen': 35098904, 'train_runtime': '4.408e+04', 'train_tokens_per_second': '796.3'}
56%|█████▌ | 10430/18688 [12:14:40<8:44:22, 3.81s/it] 56%|█████▌ | 10431/18688 [12:14:43<8:49:02, 3.84s/it] 56%|█████▌ | 10432/18688 [12:14:47<8:44:50, 3.81s/it] 56%|█████▌ | 10433/18688 [12:14:52<9:13:07, 4.02s/it] 56%|█████▌ | 10434/18688 [12:14:55<8:54:19, 3.88s/it] 56%|█████▌ | 10435/18688 [12:15:00<9:15:59, 4.04s/it] 56%|█████▌ | 10436/18688 [12:15:04<9:14:02, 4.03s/it] 56%|█████▌ | 10437/18688 [12:15:08<9:31:50, 4.16s/it] 56%|█████▌ | 10438/18688 [12:15:12<9:05:29, 3.97s/it] 56%|█████▌ | 10439/18688 [12:15:16<9:18:42, 4.06s/it] 56%|█████▌ | 10440/18688 [12:15:20<9:35:32, 4.19s/it] {'loss': '0.9516', 'grad_norm': '1.542', 'learning_rate': '3.748e-05', 'epoch': '0.5587', 'num_input_tokens_seen': 35131432, 'train_runtime': '4.412e+04', 'train_tokens_per_second': '796.3'}
56%|█████▌ | 10440/18688 [12:15:20<9:35:32, 4.19s/it] 56%|█████▌ | 10441/18688 [12:15:24<9:18:54, 4.07s/it] 56%|█████▌ | 10442/18688 [12:15:28<8:59:44, 3.93s/it] 56%|█████▌ | 10443/18688 [12:15:33<9:35:36, 4.19s/it] 56%|█████▌ | 10444/18688 [12:15:37<9:27:06, 4.13s/it] 56%|█████▌ | 10445/18688 [12:15:41<9:20:23, 4.08s/it] 56%|█████▌ | 10446/18688 [12:15:44<9:06:56, 3.98s/it] 56%|█████▌ | 10447/18688 [12:15:48<9:08:07, 3.99s/it] 56%|█████▌ | 10448/18688 [12:15:52<8:47:16, 3.84s/it] 56%|█████▌ | 10449/18688 [12:15:55<8:35:21, 3.75s/it] 56%|█████▌ | 10450/18688 [12:15:59<8:27:20, 3.70s/it] {'loss': '0.887', 'grad_norm': '1.302', 'learning_rate': '3.741e-05', 'epoch': '0.5592', 'num_input_tokens_seen': 35160576, 'train_runtime': '4.416e+04', 'train_tokens_per_second': '796.2'}
56%|█████▌ | 10450/18688 [12:15:59<8:27:20, 3.70s/it] 56%|█████▌ | 10451/18688 [12:16:02<8:19:39, 3.64s/it] 56%|█████▌ | 10452/18688 [12:16:07<8:45:46, 3.83s/it] 56%|█████▌ | 10453/18688 [12:16:11<9:08:32, 4.00s/it] 56%|█████▌ | 10454/18688 [12:16:15<8:52:00, 3.88s/it] 56%|█████▌ | 10455/18688 [12:16:19<8:50:26, 3.87s/it] 56%|█████▌ | 10456/18688 [12:16:22<8:38:22, 3.78s/it] 56%|█████▌ | 10457/18688 [12:16:26<9:02:09, 3.95s/it] 56%|█████▌ | 10458/18688 [12:16:31<9:23:10, 4.11s/it] 56%|█████▌ | 10459/18688 [12:16:35<9:25:18, 4.12s/it] 56%|█████▌ | 10460/18688 [12:16:39<9:04:32, 3.97s/it] {'loss': '0.9071', 'grad_norm': '1.205', 'learning_rate': '3.733e-05', 'epoch': '0.5597', 'num_input_tokens_seen': 35190992, 'train_runtime': '4.42e+04', 'train_tokens_per_second': '796.2'}
56%|█████▌ | 10460/18688 [12:16:39<9:04:32, 3.97s/it] 56%|█████▌ | 10461/18688 [12:16:43<9:18:54, 4.08s/it] 56%|█████▌ | 10462/18688 [12:16:47<9:10:23, 4.01s/it] 56%|█████▌ | 10463/18688 [12:16:51<8:55:15, 3.90s/it] 56%|█████▌ | 10464/18688 [12:16:54<8:48:36, 3.86s/it] 56%|█████▌ | 10465/18688 [12:16:59<9:31:44, 4.17s/it] 56%|█████▌ | 10466/18688 [12:17:03<9:16:39, 4.06s/it] 56%|█████▌ | 10467/18688 [12:17:08<9:42:10, 4.25s/it] 56%|█████▌ | 10468/18688 [12:17:12<9:52:57, 4.33s/it] 56%|█████▌ | 10469/18688 [12:17:16<9:50:26, 4.31s/it] 56%|█████▌ | 10470/18688 [12:17:20<9:39:14, 4.23s/it] {'loss': '0.9793', 'grad_norm': '1.351', 'learning_rate': '3.726e-05', 'epoch': '0.5603', 'num_input_tokens_seen': 35226248, 'train_runtime': '4.424e+04', 'train_tokens_per_second': '796.2'}
56%|█████▌ | 10470/18688 [12:17:20<9:39:14, 4.23s/it] 56%|█████▌ | 10471/18688 [12:17:25<9:55:06, 4.35s/it] 56%|█████▌ | 10472/18688 [12:17:30<9:59:45, 4.38s/it] 56%|█████▌ | 10473/18688 [12:17:34<9:48:47, 4.30s/it] 56%|█████▌ | 10474/18688 [12:17:38<9:39:21, 4.23s/it] 56%|█████▌ | 10475/18688 [12:17:41<9:08:51, 4.01s/it] 56%|█████▌ | 10476/18688 [12:17:46<9:28:55, 4.16s/it] 56%|█████▌ | 10477/18688 [12:17:50<9:46:55, 4.29s/it] 56%|█████▌ | 10478/18688 [12:17:54<9:19:22, 4.09s/it] 56%|█████▌ | 10479/18688 [12:17:59<9:39:12, 4.23s/it] 56%|█████▌ | 10480/18688 [12:18:03<9:47:17, 4.29s/it] {'loss': '0.8856', 'grad_norm': '1.424', 'learning_rate': '3.719e-05', 'epoch': '0.5608', 'num_input_tokens_seen': 35261360, 'train_runtime': '4.428e+04', 'train_tokens_per_second': '796.3'}
56%|█████▌ | 10480/18688 [12:18:03<9:47:17, 4.29s/it] 56%|█████▌ | 10481/18688 [12:18:07<9:40:21, 4.24s/it] 56%|█████▌ | 10482/18688 [12:18:12<10:03:18, 4.41s/it] 56%|█████▌ | 10483/18688 [12:18:16<9:44:05, 4.27s/it] 56%|█████▌ | 10484/18688 [12:18:20<9:41:50, 4.26s/it] 56%|█████▌ | 10485/18688 [12:18:24<9:17:59, 4.08s/it] 56%|█████▌ | 10486/18688 [12:18:28<9:10:43, 4.03s/it] 56%|█████▌ | 10487/18688 [12:18:32<9:36:09, 4.22s/it] 56%|█████▌ | 10488/18688 [12:18:38<10:24:52, 4.57s/it] 56%|█████▌ | 10489/18688 [12:18:42<10:03:50, 4.42s/it] 56%|█████▌ | 10490/18688 [12:18:46<10:06:22, 4.44s/it] {'loss': '0.9124', 'grad_norm': '1.98', 'learning_rate': '3.711e-05', 'epoch': '0.5613', 'num_input_tokens_seen': 35295720, 'train_runtime': '4.433e+04', 'train_tokens_per_second': '796.3'}
56%|█████▌ | 10490/18688 [12:18:46<10:06:22, 4.44s/it] 56%|█████▌ | 10491/18688 [12:18:51<10:29:08, 4.61s/it] 56%|█████▌ | 10492/18688 [12:18:55<10:00:36, 4.40s/it] 56%|█████▌ | 10493/18688 [12:18:59<9:36:24, 4.22s/it] 56%|█████▌ | 10494/18688 [12:19:03<9:12:17, 4.04s/it] 56%|█████▌ | 10495/18688 [12:19:07<9:27:10, 4.15s/it] 56%|█████▌ | 10496/18688 [12:19:10<9:00:19, 3.96s/it] 56%|█████▌ | 10497/18688 [12:19:15<9:13:38, 4.06s/it] 56%|█████▌ | 10498/18688 [12:19:19<9:00:19, 3.96s/it] 56%|█████▌ | 10499/18688 [12:19:23<9:42:15, 4.27s/it] 56%|█████▌ | 10500/18688 [12:19:28<9:46:01, 4.29s/it] {'loss': '0.8932', 'grad_norm': '1.112', 'learning_rate': '3.704e-05', 'epoch': '0.5619', 'num_input_tokens_seen': 35329448, 'train_runtime': '4.437e+04', 'train_tokens_per_second': '796.3'}
56%|█████▌ | 10500/18688 [12:19:28<9:46:01, 4.29s/it] 56%|█████▌ | 10501/18688 [12:19:32<9:45:57, 4.29s/it] 56%|█████▌ | 10502/18688 [12:19:36<9:24:37, 4.14s/it] 56%|█████▌ | 10503/18688 [12:19:41<9:52:12, 4.34s/it] 56%|█████▌ | 10504/18688 [12:19:45<9:40:47, 4.26s/it] 56%|█████▌ | 10505/18688 [12:19:49<9:21:09, 4.11s/it] 56%|█████▌ | 10506/18688 [12:19:53<9:24:12, 4.14s/it] 56%|█████▌ | 10507/18688 [12:19:56<9:02:00, 3.98s/it] 56%|█████▌ | 10508/18688 [12:20:00<9:01:02, 3.97s/it] 56%|█████▌ | 10509/18688 [12:20:04<8:49:35, 3.89s/it] 56%|█████▌ | 10510/18688 [12:20:09<9:35:32, 4.22s/it] {'loss': '0.9458', 'grad_norm': '1.014', 'learning_rate': '3.697e-05', 'epoch': '0.5624', 'num_input_tokens_seen': 35363256, 'train_runtime': '4.441e+04', 'train_tokens_per_second': '796.3'}
56%|█████▌ | 10510/18688 [12:20:09<9:35:32, 4.22s/it] 56%|█████▌ | 10511/18688 [12:20:13<9:31:18, 4.19s/it] 56%|█████▋ | 10512/18688 [12:20:17<9:25:02, 4.15s/it] 56%|█████▋ | 10513/18688 [12:20:21<9:11:41, 4.05s/it] 56%|█████▋ | 10514/18688 [12:20:26<9:29:54, 4.18s/it] 56%|█████▋ | 10515/18688 [12:20:30<10:02:46, 4.43s/it] 56%|█████▋ | 10516/18688 [12:20:35<9:46:14, 4.30s/it] 56%|█████▋ | 10517/18688 [12:20:38<9:28:52, 4.18s/it] 56%|█████▋ | 10518/18688 [12:20:42<9:10:32, 4.04s/it] 56%|█████▋ | 10519/18688 [12:20:46<9:01:07, 3.97s/it] 56%|█████▋ | 10520/18688 [12:20:51<9:30:32, 4.19s/it] {'loss': '0.9155', 'grad_norm': '1.25', 'learning_rate': '3.69e-05', 'epoch': '0.5629', 'num_input_tokens_seen': 35396824, 'train_runtime': '4.445e+04', 'train_tokens_per_second': '796.3'}
56%|█████▋ | 10520/18688 [12:20:51<9:30:32, 4.19s/it] 56%|█████▋ | 10521/18688 [12:20:54<9:11:15, 4.05s/it] 56%|█████▋ | 10522/18688 [12:20:58<8:59:47, 3.97s/it] 56%|█████▋ | 10523/18688 [12:21:04<10:29:50, 4.63s/it] 56%|█████▋ | 10524/18688 [12:21:08<9:46:06, 4.31s/it] 56%|█████▋ | 10525/18688 [12:21:12<9:35:53, 4.23s/it] 56%|█████▋ | 10526/18688 [12:21:15<9:09:01, 4.04s/it] 56%|█████▋ | 10527/18688 [12:21:19<8:59:14, 3.96s/it] 56%|█████▋ | 10528/18688 [12:21:24<9:47:49, 4.32s/it] 56%|█████▋ | 10529/18688 [12:21:28<9:22:24, 4.14s/it] 56%|█████▋ | 10530/18688 [12:21:32<9:10:55, 4.05s/it] {'loss': '0.9052', 'grad_norm': '1.225', 'learning_rate': '3.682e-05', 'epoch': '0.5635', 'num_input_tokens_seen': 35431456, 'train_runtime': '4.449e+04', 'train_tokens_per_second': '796.3'}
56%|█████▋ | 10530/18688 [12:21:32<9:10:55, 4.05s/it] 56%|█████▋ | 10531/18688 [12:21:36<8:53:33, 3.92s/it] 56%|█████▋ | 10532/18688 [12:21:39<8:46:26, 3.87s/it] 56%|█████▋ | 10533/18688 [12:21:43<8:49:37, 3.90s/it] 56%|█████▋ | 10534/18688 [12:21:47<8:40:03, 3.83s/it] 56%|█████▋ | 10535/18688 [12:21:51<8:37:44, 3.81s/it] 56%|█████▋ | 10536/18688 [12:21:55<9:03:47, 4.00s/it] 56%|█████▋ | 10537/18688 [12:21:59<9:02:28, 3.99s/it] 56%|█████▋ | 10538/18688 [12:22:04<9:41:58, 4.28s/it] 56%|█████▋ | 10539/18688 [12:22:08<9:16:47, 4.10s/it] 56%|█████▋ | 10540/18688 [12:22:12<9:26:40, 4.17s/it] {'loss': '0.9212', 'grad_norm': '1.24', 'learning_rate': '3.675e-05', 'epoch': '0.564', 'num_input_tokens_seen': 35463280, 'train_runtime': '4.453e+04', 'train_tokens_per_second': '796.3'}
56%|█████▋ | 10540/18688 [12:22:12<9:26:40, 4.17s/it] 56%|█████▋ | 10541/18688 [12:22:16<9:02:18, 3.99s/it] 56%|█████▋ | 10542/18688 [12:22:20<9:21:23, 4.13s/it] 56%|█████▋ | 10543/18688 [12:22:25<9:30:41, 4.20s/it] 56%|█████▋ | 10544/18688 [12:22:29<9:39:24, 4.27s/it] 56%|█████▋ | 10545/18688 [12:22:33<9:26:03, 4.17s/it] 56%|█████▋ | 10546/18688 [12:22:37<9:28:21, 4.19s/it] 56%|█████▋ | 10547/18688 [12:22:41<9:33:05, 4.22s/it] 56%|█████▋ | 10548/18688 [12:22:45<9:21:11, 4.14s/it] 56%|█████▋ | 10549/18688 [12:22:50<9:54:47, 4.38s/it] 56%|█████▋ | 10550/18688 [12:22:54<9:23:17, 4.15s/it] {'loss': '0.9453', 'grad_norm': '1.123', 'learning_rate': '3.668e-05', 'epoch': '0.5645', 'num_input_tokens_seen': 35497984, 'train_runtime': '4.457e+04', 'train_tokens_per_second': '796.4'}
56%|█████▋ | 10550/18688 [12:22:54<9:23:17, 4.15s/it] 56%|█████▋ | 10551/18688 [12:22:58<9:23:28, 4.15s/it] 56%|█████▋ | 10552/18688 [12:23:02<9:19:28, 4.13s/it] 56%|█████▋ | 10553/18688 [12:23:07<9:44:52, 4.31s/it] 56%|█████▋ | 10554/18688 [12:23:11<9:43:37, 4.31s/it] 56%|█████▋ | 10555/18688 [12:23:15<9:08:02, 4.04s/it] 56%|█████▋ | 10556/18688 [12:23:19<9:31:33, 4.22s/it] 56%|█████▋ | 10557/18688 [12:23:24<9:54:55, 4.39s/it] 56%|█████▋ | 10558/18688 [12:23:28<9:33:55, 4.24s/it] 57%|█████▋ | 10559/18688 [12:23:33<9:53:31, 4.38s/it] 57%|█████▋ | 10560/18688 [12:23:37<9:49:13, 4.35s/it] {'loss': '0.9068', 'grad_norm': '1.161', 'learning_rate': '3.66e-05', 'epoch': '0.5651', 'num_input_tokens_seen': 35533728, 'train_runtime': '4.462e+04', 'train_tokens_per_second': '796.4'}
57%|█████▋ | 10560/18688 [12:23:37<9:49:13, 4.35s/it] 57%|█████▋ | 10561/18688 [12:23:41<9:22:37, 4.15s/it] 57%|█████▋ | 10562/18688 [12:23:45<9:19:45, 4.13s/it] 57%|█████▋ | 10563/18688 [12:23:48<8:51:44, 3.93s/it] 57%|█████▋ | 10564/18688 [12:23:53<9:08:35, 4.05s/it] 57%|█████▋ | 10565/18688 [12:23:56<9:02:51, 4.01s/it] 57%|█████▋ | 10566/18688 [12:24:00<9:02:06, 4.00s/it] 57%|█████▋ | 10567/18688 [12:24:04<8:50:31, 3.92s/it] 57%|█████▋ | 10568/18688 [12:24:09<9:16:01, 4.11s/it] 57%|█████▋ | 10569/18688 [12:24:13<9:18:29, 4.13s/it] 57%|█████▋ | 10570/18688 [12:24:17<8:59:44, 3.99s/it] {'loss': '0.9312', 'grad_norm': '1.19', 'learning_rate': '3.653e-05', 'epoch': '0.5656', 'num_input_tokens_seen': 35565456, 'train_runtime': '4.466e+04', 'train_tokens_per_second': '796.4'}
57%|█████▋ | 10570/18688 [12:24:17<8:59:44, 3.99s/it] 57%|█████▋ | 10571/18688 [12:24:21<9:08:19, 4.05s/it] 57%|█████▋ | 10572/18688 [12:24:24<8:51:11, 3.93s/it] 57%|█████▋ | 10573/18688 [12:24:28<8:34:31, 3.80s/it] 57%|█████▋ | 10574/18688 [12:24:32<8:44:25, 3.88s/it] 57%|█████▋ | 10575/18688 [12:24:37<9:18:23, 4.13s/it] 57%|█████▋ | 10576/18688 [12:24:41<9:23:36, 4.17s/it] 57%|█████▋ | 10577/18688 [12:24:45<9:23:54, 4.17s/it] 57%|█████▋ | 10578/18688 [12:24:49<8:54:35, 3.96s/it] 57%|█████▋ | 10579/18688 [12:24:53<9:00:28, 4.00s/it] 57%|█████▋ | 10580/18688 [12:24:56<8:43:27, 3.87s/it] {'loss': '0.8651', 'grad_norm': '1.591', 'learning_rate': '3.646e-05', 'epoch': '0.5662', 'num_input_tokens_seen': 35596224, 'train_runtime': '4.47e+04', 'train_tokens_per_second': '796.4'}
57%|█████▋ | 10580/18688 [12:24:56<8:43:27, 3.87s/it] 57%|█████▋ | 10581/18688 [12:25:00<8:31:11, 3.78s/it] 57%|█████▋ | 10582/18688 [12:25:03<8:17:21, 3.68s/it] 57%|█████▋ | 10583/18688 [12:25:07<8:17:33, 3.68s/it] 57%|█████▋ | 10584/18688 [12:25:11<8:42:14, 3.87s/it] 57%|█████▋ | 10585/18688 [12:25:16<9:18:49, 4.14s/it] 57%|█████▋ | 10586/18688 [12:25:20<8:57:40, 3.98s/it] 57%|█████▋ | 10587/18688 [12:25:24<9:19:34, 4.14s/it] 57%|█████▋ | 10588/18688 [12:25:28<9:27:44, 4.21s/it] 57%|█████▋ | 10589/18688 [12:25:33<9:22:46, 4.17s/it] 57%|█████▋ | 10590/18688 [12:25:37<9:30:21, 4.23s/it] {'loss': '0.8594', 'grad_norm': '1.065', 'learning_rate': '3.639e-05', 'epoch': '0.5667', 'num_input_tokens_seen': 35629064, 'train_runtime': '4.474e+04', 'train_tokens_per_second': '796.4'}
57%|█████▋ | 10590/18688 [12:25:37<9:30:21, 4.23s/it] 57%|█████▋ | 10591/18688 [12:25:41<9:37:48, 4.28s/it] 57%|█████▋ | 10592/18688 [12:25:46<10:00:14, 4.45s/it] 57%|█████▋ | 10593/18688 [12:25:50<9:40:31, 4.30s/it] 57%|█████▋ | 10594/18688 [12:25:54<9:28:39, 4.22s/it] 57%|█████▋ | 10595/18688 [12:25:59<9:41:56, 4.31s/it] 57%|█████▋ | 10596/18688 [12:26:03<9:40:06, 4.30s/it] 57%|█████▋ | 10597/18688 [12:26:07<9:33:55, 4.26s/it] 57%|█████▋ | 10598/18688 [12:26:12<9:42:07, 4.32s/it] 57%|█████▋ | 10599/18688 [12:26:15<9:16:34, 4.13s/it] 57%|█████▋ | 10600/18688 [12:26:20<9:41:23, 4.31s/it] {'loss': '0.9607', 'grad_norm': '1.266', 'learning_rate': '3.631e-05', 'epoch': '0.5672', 'num_input_tokens_seen': 35664616, 'train_runtime': '4.478e+04', 'train_tokens_per_second': '796.4'}
57%|█████▋ | 10600/18688 [12:26:20<9:41:23, 4.31s/it] 57%|█████▋ | 10601/18688 [12:26:25<9:50:36, 4.38s/it] 57%|█████▋ | 10602/18688 [12:26:28<9:22:45, 4.18s/it] 57%|█████▋ | 10603/18688 [12:26:32<9:07:09, 4.06s/it] 57%|█████▋ | 10604/18688 [12:26:38<10:08:13, 4.51s/it] 57%|█████▋ | 10605/18688 [12:26:42<10:00:47, 4.46s/it] 57%|█████▋ | 10606/18688 [12:26:46<9:50:09, 4.38s/it] 57%|█████▋ | 10607/18688 [12:26:50<9:26:07, 4.20s/it] 57%|█████▋ | 10608/18688 [12:26:54<9:14:17, 4.12s/it] 57%|█████▋ | 10609/18688 [12:26:58<9:04:22, 4.04s/it] 57%|█████▋ | 10610/18688 [12:27:01<8:42:38, 3.88s/it] {'loss': '0.8881', 'grad_norm': '1.024', 'learning_rate': '3.624e-05', 'epoch': '0.5678', 'num_input_tokens_seen': 35698272, 'train_runtime': '4.482e+04', 'train_tokens_per_second': '796.5'}
57%|█████▋ | 10610/18688 [12:27:01<8:42:38, 3.88s/it] 57%|█████▋ | 10611/18688 [12:27:06<9:13:58, 4.12s/it] 57%|█████▋ | 10612/18688 [12:27:11<9:44:45, 4.34s/it] 57%|█████▋ | 10613/18688 [12:27:15<9:20:15, 4.16s/it] 57%|█████▋ | 10614/18688 [12:27:18<9:05:31, 4.05s/it] 57%|█████▋ | 10615/18688 [12:27:22<8:43:48, 3.89s/it] 57%|█████▋ | 10616/18688 [12:27:27<9:27:14, 4.22s/it] 57%|█████▋ | 10617/18688 [12:27:31<9:21:41, 4.18s/it] 57%|█████▋ | 10618/18688 [12:27:35<9:21:50, 4.18s/it] 57%|█████▋ | 10619/18688 [12:27:39<9:01:21, 4.03s/it] 57%|█████▋ | 10620/18688 [12:27:44<9:33:23, 4.26s/it] {'loss': '0.8816', 'grad_norm': '1.07', 'learning_rate': '3.617e-05', 'epoch': '0.5683', 'num_input_tokens_seen': 35734088, 'train_runtime': '4.486e+04', 'train_tokens_per_second': '796.5'}
57%|█████▋ | 10620/18688 [12:27:44<9:33:23, 4.26s/it] 57%|█████▋ | 10621/18688 [12:27:49<10:06:03, 4.51s/it] 57%|█████▋ | 10622/18688 [12:27:52<9:28:50, 4.23s/it] 57%|█████▋ | 10623/18688 [12:27:57<9:59:37, 4.46s/it] 57%|█████▋ | 10624/18688 [12:28:02<9:56:31, 4.44s/it] 57%|█████▋ | 10625/18688 [12:28:06<9:51:44, 4.40s/it] 57%|█████▋ | 10626/18688 [12:28:10<9:40:21, 4.32s/it] 57%|█████▋ | 10627/18688 [12:28:14<9:13:31, 4.12s/it] 57%|█████▋ | 10628/18688 [12:28:18<9:13:09, 4.12s/it] 57%|█████▋ | 10629/18688 [12:28:22<9:15:00, 4.13s/it] 57%|█████▋ | 10630/18688 [12:28:26<9:21:36, 4.18s/it] {'loss': '0.9231', 'grad_norm': '1.317', 'learning_rate': '3.609e-05', 'epoch': '0.5688', 'num_input_tokens_seen': 35770040, 'train_runtime': '4.491e+04', 'train_tokens_per_second': '796.5'}
57%|█████▋ | 10630/18688 [12:28:26<9:21:36, 4.18s/it] 57%|█████▋ | 10631/18688 [12:28:31<9:53:39, 4.42s/it] 57%|█████▋ | 10632/18688 [12:28:36<10:09:07, 4.54s/it] 57%|█████▋ | 10633/18688 [12:28:41<10:23:40, 4.65s/it] 57%|█████▋ | 10634/18688 [12:28:45<9:52:01, 4.41s/it] 57%|█████▋ | 10635/18688 [12:28:49<9:39:57, 4.32s/it] 57%|█████▋ | 10636/18688 [12:28:52<9:04:44, 4.06s/it] 57%|█████▋ | 10637/18688 [12:28:57<9:11:51, 4.11s/it] 57%|█████▋ | 10638/18688 [12:29:01<9:15:03, 4.14s/it] 57%|█████▋ | 10639/18688 [12:29:04<8:56:21, 4.00s/it] 57%|█████▋ | 10640/18688 [12:29:08<8:49:03, 3.94s/it] {'loss': '0.9239', 'grad_norm': '1.15', 'learning_rate': '3.602e-05', 'epoch': '0.5694', 'num_input_tokens_seen': 35805136, 'train_runtime': '4.495e+04', 'train_tokens_per_second': '796.6'}
57%|█████▋ | 10640/18688 [12:29:08<8:49:03, 3.94s/it] 57%|█████▋ | 10641/18688 [12:29:13<9:24:27, 4.21s/it] 57%|█████▋ | 10642/18688 [12:29:17<9:04:19, 4.06s/it] 57%|█████▋ | 10643/18688 [12:29:21<9:21:20, 4.19s/it] 57%|█████▋ | 10644/18688 [12:29:25<9:13:49, 4.13s/it] 57%|█████▋ | 10645/18688 [12:29:30<9:18:18, 4.16s/it] 57%|█████▋ | 10646/18688 [12:29:33<8:59:06, 4.02s/it] 57%|█████▋ | 10647/18688 [12:29:38<9:15:43, 4.15s/it] 57%|█████▋ | 10648/18688 [12:29:41<8:46:50, 3.93s/it] 57%|█████▋ | 10649/18688 [12:29:45<9:02:39, 4.05s/it] 57%|█████▋ | 10650/18688 [12:29:49<8:43:37, 3.91s/it] {'loss': '0.8645', 'grad_norm': '1.357', 'learning_rate': '3.595e-05', 'epoch': '0.5699', 'num_input_tokens_seen': 35838216, 'train_runtime': '4.499e+04', 'train_tokens_per_second': '796.6'}
57%|█████▋ | 10650/18688 [12:29:49<8:43:37, 3.91s/it] 57%|█████▋ | 10651/18688 [12:29:53<8:41:10, 3.89s/it] 57%|█████▋ | 10652/18688 [12:29:57<9:08:28, 4.10s/it] 57%|█████▋ | 10653/18688 [12:30:01<8:44:25, 3.92s/it] 57%|█████▋ | 10654/18688 [12:30:06<9:21:37, 4.19s/it] 57%|█████▋ | 10655/18688 [12:30:10<9:37:15, 4.31s/it] 57%|█████▋ | 10656/18688 [12:30:15<9:32:25, 4.28s/it] 57%|█████▋ | 10657/18688 [12:30:18<9:08:40, 4.10s/it] 57%|█████▋ | 10658/18688 [12:30:24<9:57:49, 4.47s/it] 57%|█████▋ | 10659/18688 [12:30:29<10:28:00, 4.69s/it] 57%|█████▋ | 10660/18688 [12:30:33<9:56:46, 4.46s/it] {'loss': '0.9821', 'grad_norm': '1.238', 'learning_rate': '3.588e-05', 'epoch': '0.5704', 'num_input_tokens_seen': 35875008, 'train_runtime': '4.503e+04', 'train_tokens_per_second': '796.6'}
57%|█████▋ | 10660/18688 [12:30:33<9:56:46, 4.46s/it] 57%|█████▋ | 10661/18688 [12:30:37<9:47:21, 4.39s/it] 57%|█████▋ | 10662/18688 [12:30:40<9:10:08, 4.11s/it] 57%|█████▋ | 10663/18688 [12:30:45<9:09:07, 4.11s/it] 57%|█████▋ | 10664/18688 [12:30:49<9:17:38, 4.17s/it] 57%|█████▋ | 10665/18688 [12:30:53<9:37:45, 4.32s/it] 57%|█████▋ | 10666/18688 [12:30:57<9:11:07, 4.12s/it] 57%|█████▋ | 10667/18688 [12:31:01<9:18:48, 4.18s/it] 57%|█████▋ | 10668/18688 [12:31:05<8:50:19, 3.97s/it] 57%|█████▋ | 10669/18688 [12:31:09<8:36:42, 3.87s/it] 57%|█████▋ | 10670/18688 [12:31:13<8:40:48, 3.90s/it] {'loss': '0.8771', 'grad_norm': '1.225', 'learning_rate': '3.58e-05', 'epoch': '0.571', 'num_input_tokens_seen': 35907104, 'train_runtime': '4.507e+04', 'train_tokens_per_second': '796.6'}
57%|█████▋ | 10670/18688 [12:31:13<8:40:48, 3.90s/it] 57%|█████▋ | 10671/18688 [12:31:16<8:37:55, 3.88s/it] 57%|█████▋ | 10672/18688 [12:31:21<8:48:52, 3.96s/it] 57%|█████▋ | 10673/18688 [12:31:24<8:32:12, 3.83s/it] 57%|█████▋ | 10674/18688 [12:31:29<9:14:21, 4.15s/it] 57%|█████▋ | 10675/18688 [12:31:33<9:12:26, 4.14s/it] 57%|█████▋ | 10676/18688 [12:31:38<9:46:53, 4.40s/it] 57%|█████▋ | 10677/18688 [12:31:42<9:16:24, 4.17s/it] 57%|█████▋ | 10678/18688 [12:31:46<9:22:30, 4.21s/it] 57%|█████▋ | 10679/18688 [12:31:50<9:33:15, 4.29s/it] 57%|█████▋ | 10680/18688 [12:31:55<9:46:37, 4.40s/it] {'loss': '0.9362', 'grad_norm': '1.409', 'learning_rate': '3.573e-05', 'epoch': '0.5715', 'num_input_tokens_seen': 35941312, 'train_runtime': '4.512e+04', 'train_tokens_per_second': '796.6'}
57%|█████▋ | 10680/18688 [12:31:55<9:46:37, 4.40s/it] 57%|█████▋ | 10681/18688 [12:31:59<9:13:05, 4.14s/it] 57%|█████▋ | 10682/18688 [12:32:03<9:07:21, 4.10s/it] 57%|█████▋ | 10683/18688 [12:32:07<8:55:58, 4.02s/it] 57%|█████▋ | 10684/18688 [12:32:11<9:03:33, 4.07s/it] 57%|█████▋ | 10685/18688 [12:32:15<8:55:15, 4.01s/it] 57%|█████▋ | 10686/18688 [12:32:18<8:48:31, 3.96s/it] 57%|█████▋ | 10687/18688 [12:32:23<9:23:50, 4.23s/it] 57%|█████▋ | 10688/18688 [12:32:27<8:53:14, 4.00s/it] 57%|█████▋ | 10689/18688 [12:32:31<9:03:18, 4.08s/it] 57%|█████▋ | 10690/18688 [12:32:35<8:58:30, 4.04s/it] {'loss': '0.9448', 'grad_norm': '1.466', 'learning_rate': '3.566e-05', 'epoch': '0.572', 'num_input_tokens_seen': 35971848, 'train_runtime': '4.516e+04', 'train_tokens_per_second': '796.6'}
57%|█████▋ | 10690/18688 [12:32:35<8:58:30, 4.04s/it] 57%|█████▋ | 10691/18688 [12:32:39<8:57:05, 4.03s/it] 57%|█████▋ | 10692/18688 [12:32:43<9:12:34, 4.15s/it] 57%|█████▋ | 10693/18688 [12:32:49<9:59:19, 4.50s/it] 57%|█████▋ | 10694/18688 [12:32:53<9:38:06, 4.34s/it] 57%|█████▋ | 10695/18688 [12:32:57<9:25:16, 4.24s/it] 57%|█████▋ | 10696/18688 [12:33:00<9:02:21, 4.07s/it] 57%|█████▋ | 10697/18688 [12:33:05<9:16:52, 4.18s/it] 57%|█████▋ | 10698/18688 [12:33:09<9:31:13, 4.29s/it] 57%|█████▋ | 10699/18688 [12:33:13<9:05:44, 4.10s/it] 57%|█████▋ | 10700/18688 [12:33:16<8:42:01, 3.92s/it] {'loss': '0.9183', 'grad_norm': '1.101', 'learning_rate': '3.559e-05', 'epoch': '0.5726', 'num_input_tokens_seen': 36006368, 'train_runtime': '4.52e+04', 'train_tokens_per_second': '796.7'}
57%|█████▋ | 10700/18688 [12:33:16<8:42:01, 3.92s/it] 57%|█████▋ | 10701/18688 [12:33:20<8:31:32, 3.84s/it] 57%|█████▋ | 10702/18688 [12:33:25<8:58:46, 4.05s/it] 57%|█████▋ | 10703/18688 [12:33:28<8:48:58, 3.97s/it] 57%|█████▋ | 10704/18688 [12:33:33<9:13:52, 4.16s/it] 57%|█████▋ | 10705/18688 [12:33:37<9:08:37, 4.12s/it] 57%|█████▋ | 10706/18688 [12:33:41<8:41:05, 3.92s/it] 57%|█████▋ | 10707/18688 [12:33:45<9:00:17, 4.06s/it] 57%|█████▋ | 10708/18688 [12:33:49<9:04:15, 4.09s/it] 57%|█████▋ | 10709/18688 [12:33:53<9:03:08, 4.08s/it] 57%|█████▋ | 10710/18688 [12:33:58<9:33:27, 4.31s/it] {'loss': '0.8485', 'grad_norm': '1.013', 'learning_rate': '3.551e-05', 'epoch': '0.5731', 'num_input_tokens_seen': 36041184, 'train_runtime': '4.524e+04', 'train_tokens_per_second': '796.7'}
57%|█████▋ | 10710/18688 [12:33:58<9:33:27, 4.31s/it] 57%|█████▋ | 10711/18688 [12:34:02<9:04:57, 4.10s/it] 57%|█████▋ | 10712/18688 [12:34:05<8:47:01, 3.96s/it] 57%|█████▋ | 10713/18688 [12:34:09<8:31:28, 3.85s/it] 57%|█████▋ | 10714/18688 [12:34:13<8:26:39, 3.81s/it] 57%|█████▋ | 10715/18688 [12:34:16<8:21:14, 3.77s/it] 57%|█████▋ | 10716/18688 [12:34:20<8:23:46, 3.79s/it] 57%|█████▋ | 10717/18688 [12:34:24<8:27:24, 3.82s/it] 57%|█████▋ | 10718/18688 [12:34:29<9:18:58, 4.21s/it] 57%|█████▋ | 10719/18688 [12:34:33<9:09:12, 4.14s/it] 57%|█████▋ | 10720/18688 [12:34:37<8:43:41, 3.94s/it] {'loss': '0.9166', 'grad_norm': '1.367', 'learning_rate': '3.544e-05', 'epoch': '0.5736', 'num_input_tokens_seen': 36070336, 'train_runtime': '4.528e+04', 'train_tokens_per_second': '796.7'}
57%|█████▋ | 10720/18688 [12:34:37<8:43:41, 3.94s/it] 57%|█████▋ | 10721/18688 [12:34:41<8:56:44, 4.04s/it] 57%|█████▋ | 10722/18688 [12:34:44<8:39:34, 3.91s/it] 57%|█████▋ | 10723/18688 [12:34:49<9:17:39, 4.20s/it] 57%|█████▋ | 10724/18688 [12:34:53<9:06:50, 4.12s/it] 57%|█████▋ | 10725/18688 [12:34:57<8:45:06, 3.96s/it] 57%|█████▋ | 10726/18688 [12:35:01<8:45:15, 3.96s/it] 57%|█████▋ | 10727/18688 [12:35:05<9:05:18, 4.11s/it] 57%|█████▋ | 10728/18688 [12:35:09<8:38:37, 3.91s/it] 57%|█████▋ | 10729/18688 [12:35:13<8:40:17, 3.92s/it] 57%|█████▋ | 10730/18688 [12:35:17<8:38:43, 3.91s/it] {'loss': '0.88', 'grad_norm': '0.9875', 'learning_rate': '3.537e-05', 'epoch': '0.5742', 'num_input_tokens_seen': 36102064, 'train_runtime': '4.532e+04', 'train_tokens_per_second': '796.7'}
57%|█████▋ | 10730/18688 [12:35:17<8:38:43, 3.91s/it] 57%|█████▋ | 10731/18688 [12:35:21<9:01:42, 4.08s/it] 57%|█████▋ | 10732/18688 [12:35:25<9:15:35, 4.19s/it] 57%|█████▋ | 10733/18688 [12:35:29<8:48:01, 3.98s/it] 57%|█████▋ | 10734/18688 [12:35:33<8:32:24, 3.87s/it] 57%|█████▋ | 10735/18688 [12:35:37<8:37:36, 3.91s/it] 57%|█████▋ | 10736/18688 [12:35:40<8:21:17, 3.78s/it] 57%|█████▋ | 10737/18688 [12:35:45<9:25:04, 4.26s/it] 57%|█████▋ | 10738/18688 [12:35:50<9:18:33, 4.22s/it] 57%|█████▋ | 10739/18688 [12:35:53<8:58:30, 4.06s/it] 57%|█████▋ | 10740/18688 [12:35:58<9:08:05, 4.14s/it] {'loss': '0.9547', 'grad_norm': '1.118', 'learning_rate': '3.53e-05', 'epoch': '0.5747', 'num_input_tokens_seen': 36135392, 'train_runtime': '4.536e+04', 'train_tokens_per_second': '796.7'}
57%|█████▋ | 10740/18688 [12:35:58<9:08:05, 4.14s/it] 57%|█████▋ | 10741/18688 [12:36:01<8:45:40, 3.97s/it] 57%|█████▋ | 10742/18688 [12:36:05<8:39:16, 3.92s/it] 57%|█████▋ | 10743/18688 [12:36:10<9:12:59, 4.18s/it] 57%|█████▋ | 10744/18688 [12:36:14<9:11:42, 4.17s/it] 57%|█████▋ | 10745/18688 [12:36:18<9:15:53, 4.20s/it] 58%|█████▊ | 10746/18688 [12:36:22<8:58:39, 4.07s/it] 58%|█████▊ | 10747/18688 [12:36:26<8:59:43, 4.08s/it] 58%|█████▊ | 10748/18688 [12:36:30<9:06:46, 4.13s/it] 58%|█████▊ | 10749/18688 [12:36:34<8:54:19, 4.04s/it] 58%|█████▊ | 10750/18688 [12:36:38<8:34:03, 3.89s/it] {'loss': '0.9741', 'grad_norm': '1.569', 'learning_rate': '3.522e-05', 'epoch': '0.5753', 'num_input_tokens_seen': 36167592, 'train_runtime': '4.54e+04', 'train_tokens_per_second': '796.7'}
58%|█████▊ | 10750/18688 [12:36:38<8:34:03, 3.89s/it] 58%|█████▊ | 10751/18688 [12:36:42<9:06:22, 4.13s/it] 58%|█████▊ | 10752/18688 [12:36:46<9:04:57, 4.12s/it] 58%|█████▊ | 10753/18688 [12:36:50<8:53:39, 4.04s/it] 58%|█████▊ | 10754/18688 [12:36:54<8:58:15, 4.07s/it] 58%|█████▊ | 10755/18688 [12:36:59<9:07:09, 4.14s/it] 58%|█████▊ | 10756/18688 [12:37:04<9:49:15, 4.46s/it] 58%|█████▊ | 10757/18688 [12:37:09<10:33:06, 4.79s/it] 58%|█████▊ | 10758/18688 [12:37:13<9:46:06, 4.43s/it] 58%|█████▊ | 10759/18688 [12:37:18<9:59:05, 4.53s/it] 58%|█████▊ | 10760/18688 [12:37:22<9:54:06, 4.50s/it] {'loss': '0.9128', 'grad_norm': '1.077', 'learning_rate': '3.515e-05', 'epoch': '0.5758', 'num_input_tokens_seen': 36207584, 'train_runtime': '4.544e+04', 'train_tokens_per_second': '796.8'}
58%|█████▊ | 10760/18688 [12:37:22<9:54:06, 4.50s/it] 58%|█████▊ | 10761/18688 [12:37:26<9:35:53, 4.36s/it] 58%|█████▊ | 10762/18688 [12:37:30<9:11:45, 4.18s/it] 58%|█████▊ | 10763/18688 [12:37:34<9:21:35, 4.25s/it] 58%|█████▊ | 10764/18688 [12:37:39<9:30:35, 4.32s/it] 58%|█████▊ | 10765/18688 [12:37:42<8:56:13, 4.06s/it] 58%|█████▊ | 10766/18688 [12:37:46<8:34:49, 3.90s/it] 58%|█████▊ | 10767/18688 [12:37:50<8:32:29, 3.88s/it] 58%|█████▊ | 10768/18688 [12:37:53<8:14:44, 3.75s/it] 58%|█████▊ | 10769/18688 [12:37:57<8:19:39, 3.79s/it] 58%|█████▊ | 10770/18688 [12:38:01<8:38:00, 3.93s/it] {'loss': '0.8332', 'grad_norm': '1.3', 'learning_rate': '3.508e-05', 'epoch': '0.5763', 'num_input_tokens_seen': 36238112, 'train_runtime': '4.548e+04', 'train_tokens_per_second': '796.8'}
58%|█████▊ | 10770/18688 [12:38:01<8:38:00, 3.93s/it] 58%|█████▊ | 10771/18688 [12:38:05<8:18:22, 3.78s/it] 58%|█████▊ | 10772/18688 [12:38:09<8:21:31, 3.80s/it] 58%|█████▊ | 10773/18688 [12:38:13<8:31:58, 3.88s/it] 58%|█████▊ | 10774/18688 [12:38:17<8:39:26, 3.94s/it] 58%|█████▊ | 10775/18688 [12:38:20<8:29:05, 3.86s/it] 58%|█████▊ | 10776/18688 [12:38:24<8:18:50, 3.78s/it] 58%|█████▊ | 10777/18688 [12:38:28<8:20:25, 3.80s/it] 58%|█████▊ | 10778/18688 [12:38:32<8:17:22, 3.77s/it] 58%|█████▊ | 10779/18688 [12:38:36<8:58:19, 4.08s/it] 58%|█████▊ | 10780/18688 [12:38:40<8:59:43, 4.10s/it] {'loss': '0.9362', 'grad_norm': '1.6', 'learning_rate': '3.5e-05', 'epoch': '0.5769', 'num_input_tokens_seen': 36269040, 'train_runtime': '4.552e+04', 'train_tokens_per_second': '796.8'}
58%|█████▊ | 10780/18688 [12:38:40<8:59:43, 4.10s/it] 58%|█████▊ | 10781/18688 [12:38:44<8:34:59, 3.91s/it] 58%|█████▊ | 10782/18688 [12:38:48<8:22:13, 3.81s/it] 58%|█████▊ | 10783/18688 [12:38:51<8:15:16, 3.76s/it] 58%|█████▊ | 10784/18688 [12:38:55<8:11:55, 3.73s/it] 58%|█████▊ | 10785/18688 [12:38:59<8:26:13, 3.84s/it] 58%|█████▊ | 10786/18688 [12:39:03<8:15:33, 3.76s/it] 58%|█████▊ | 10787/18688 [12:39:07<8:39:51, 3.95s/it] 58%|█████▊ | 10788/18688 [12:39:11<8:33:58, 3.90s/it] 58%|█████▊ | 10789/18688 [12:39:16<9:46:54, 4.46s/it] 58%|█████▊ | 10790/18688 [12:39:21<10:08:40, 4.62s/it] {'loss': '0.9028', 'grad_norm': '0.8947', 'learning_rate': '3.493e-05', 'epoch': '0.5774', 'num_input_tokens_seen': 36302864, 'train_runtime': '4.556e+04', 'train_tokens_per_second': '796.8'}
58%|█████▊ | 10790/18688 [12:39:21<10:08:40, 4.62s/it] 58%|█████▊ | 10791/18688 [12:39:25<9:27:35, 4.31s/it] 58%|█████▊ | 10792/18688 [12:39:29<9:25:32, 4.30s/it] 58%|█████▊ | 10793/18688 [12:39:33<9:17:19, 4.24s/it] 58%|█████▊ | 10794/18688 [12:39:37<8:59:37, 4.10s/it] 58%|█████▊ | 10795/18688 [12:39:41<9:05:01, 4.14s/it] 58%|█████▊ | 10796/18688 [12:39:45<8:42:56, 3.98s/it] 58%|█████▊ | 10797/18688 [12:39:49<8:47:48, 4.01s/it] 58%|█████▊ | 10798/18688 [12:39:54<9:21:41, 4.27s/it] 58%|█████▊ | 10799/18688 [12:39:57<8:49:14, 4.03s/it] 58%|█████▊ | 10800/18688 [12:40:02<9:04:56, 4.15s/it] {'loss': '0.8992', 'grad_norm': '1.342', 'learning_rate': '3.486e-05', 'epoch': '0.5779', 'num_input_tokens_seen': 36335984, 'train_runtime': '4.56e+04', 'train_tokens_per_second': '796.8'}
58%|█████▊ | 10800/18688 [12:40:02<9:04:56, 4.15s/it] 58%|█████▊ | 10801/18688 [12:40:05<8:39:32, 3.95s/it] 58%|█████▊ | 10802/18688 [12:40:10<9:04:38, 4.14s/it] 58%|█████▊ | 10803/18688 [12:40:15<9:46:44, 4.46s/it] 58%|█████▊ | 10804/18688 [12:40:19<9:34:05, 4.37s/it] 58%|█████▊ | 10805/18688 [12:40:23<9:21:46, 4.28s/it] 58%|█████▊ | 10806/18688 [12:40:29<10:21:21, 4.73s/it] 58%|█████▊ | 10807/18688 [12:40:34<10:24:19, 4.75s/it] 58%|█████▊ | 10808/18688 [12:40:37<9:31:47, 4.35s/it] 58%|█████▊ | 10809/18688 [12:40:41<9:03:55, 4.14s/it] 58%|█████▊ | 10810/18688 [12:40:45<8:53:03, 4.06s/it] {'loss': '0.9075', 'grad_norm': '1.217', 'learning_rate': '3.479e-05', 'epoch': '0.5785', 'num_input_tokens_seen': 36372664, 'train_runtime': '4.565e+04', 'train_tokens_per_second': '796.9'}
58%|█████▊ | 10810/18688 [12:40:45<8:53:03, 4.06s/it] 58%|█████▊ | 10811/18688 [12:40:49<8:50:43, 4.04s/it] 58%|█████▊ | 10812/18688 [12:40:52<8:32:04, 3.90s/it] 58%|█████▊ | 10813/18688 [12:40:57<9:12:38, 4.21s/it] 58%|█████▊ | 10814/18688 [12:41:02<9:14:18, 4.22s/it] 58%|█████▊ | 10815/18688 [12:41:05<8:55:55, 4.08s/it] 58%|█████▊ | 10816/18688 [12:41:10<9:00:04, 4.12s/it] 58%|█████▊ | 10817/18688 [12:41:14<9:24:06, 4.30s/it] 58%|█████▊ | 10818/18688 [12:41:18<9:04:45, 4.15s/it] 58%|█████▊ | 10819/18688 [12:41:23<9:17:56, 4.25s/it] 58%|█████▊ | 10820/18688 [12:41:26<8:45:08, 4.00s/it] {'loss': '0.8826', 'grad_norm': '1.313', 'learning_rate': '3.471e-05', 'epoch': '0.579', 'num_input_tokens_seen': 36405504, 'train_runtime': '4.569e+04', 'train_tokens_per_second': '796.9'}
58%|█████▊ | 10820/18688 [12:41:26<8:45:08, 4.00s/it] 58%|█████▊ | 10821/18688 [12:41:30<8:26:21, 3.86s/it] 58%|█████▊ | 10822/18688 [12:41:34<8:55:18, 4.08s/it] 58%|█████▊ | 10823/18688 [12:41:38<8:44:17, 4.00s/it] 58%|█████▊ | 10824/18688 [12:41:42<8:26:57, 3.87s/it] 58%|█████▊ | 10825/18688 [12:41:46<8:59:18, 4.12s/it] 58%|█████▊ | 10826/18688 [12:41:50<8:33:10, 3.92s/it] 58%|█████▊ | 10827/18688 [12:41:54<8:47:30, 4.03s/it] 58%|█████▊ | 10828/18688 [12:41:59<9:28:06, 4.34s/it] 58%|█████▊ | 10829/18688 [12:42:03<9:19:50, 4.27s/it] 58%|█████▊ | 10830/18688 [12:42:07<8:54:22, 4.08s/it] {'loss': '0.9377', 'grad_norm': '1.182', 'learning_rate': '3.464e-05', 'epoch': '0.5795', 'num_input_tokens_seen': 36439368, 'train_runtime': '4.573e+04', 'train_tokens_per_second': '796.9'}
58%|█████▊ | 10830/18688 [12:42:07<8:54:22, 4.08s/it] 58%|█████▊ | 10831/18688 [12:42:11<8:41:27, 3.98s/it] 58%|█████▊ | 10832/18688 [12:42:15<8:44:29, 4.01s/it] 58%|█████▊ | 10833/18688 [12:42:19<8:58:30, 4.11s/it] 58%|█████▊ | 10834/18688 [12:42:22<8:31:34, 3.91s/it] 58%|█████▊ | 10835/18688 [12:42:26<8:19:02, 3.81s/it] 58%|█████▊ | 10836/18688 [12:42:30<8:19:59, 3.82s/it] 58%|█████▊ | 10837/18688 [12:42:34<8:31:26, 3.91s/it] 58%|█████▊ | 10838/18688 [12:42:38<8:53:10, 4.08s/it] 58%|█████▊ | 10839/18688 [12:42:43<8:57:17, 4.11s/it] 58%|█████▊ | 10840/18688 [12:42:47<9:01:01, 4.14s/it] {'loss': '0.8685', 'grad_norm': '1.238', 'learning_rate': '3.457e-05', 'epoch': '0.5801', 'num_input_tokens_seen': 36471744, 'train_runtime': '4.577e+04', 'train_tokens_per_second': '796.9'}
58%|█████▊ | 10840/18688 [12:42:47<9:01:01, 4.14s/it] 58%|█████▊ | 10841/18688 [12:42:51<9:10:54, 4.21s/it] 58%|█████▊ | 10842/18688 [12:42:57<9:53:50, 4.54s/it] 58%|█████▊ | 10843/18688 [12:43:01<10:08:40, 4.66s/it] 58%|█████▊ | 10844/18688 [12:43:06<9:47:07, 4.49s/it] 58%|█████▊ | 10845/18688 [12:43:09<9:13:26, 4.23s/it] 58%|█████▊ | 10846/18688 [12:43:13<8:56:42, 4.11s/it] 58%|█████▊ | 10847/18688 [12:43:18<9:19:00, 4.28s/it] 58%|█████▊ | 10848/18688 [12:43:23<9:54:58, 4.55s/it] 58%|█████▊ | 10849/18688 [12:43:26<9:14:28, 4.24s/it] 58%|█████▊ | 10850/18688 [12:43:30<9:01:12, 4.14s/it] {'loss': '0.9135', 'grad_norm': '1.288', 'learning_rate': '3.45e-05', 'epoch': '0.5806', 'num_input_tokens_seen': 36507216, 'train_runtime': '4.581e+04', 'train_tokens_per_second': '796.9'}
58%|█████▊ | 10850/18688 [12:43:30<9:01:12, 4.14s/it] 58%|█████▊ | 10851/18688 [12:43:35<9:11:53, 4.23s/it] 58%|█████▊ | 10852/18688 [12:43:39<9:00:40, 4.14s/it] 58%|█████▊ | 10853/18688 [12:43:43<8:59:39, 4.13s/it] 58%|█████▊ | 10854/18688 [12:43:47<8:54:12, 4.09s/it] 58%|█████▊ | 10855/18688 [12:43:50<8:29:21, 3.90s/it] 58%|█████▊ | 10856/18688 [12:43:55<8:57:06, 4.11s/it] 58%|█████▊ | 10857/18688 [12:43:59<9:05:57, 4.18s/it] 58%|█████▊ | 10858/18688 [12:44:03<8:48:12, 4.05s/it] 58%|█████▊ | 10859/18688 [12:44:07<8:33:39, 3.94s/it] 58%|█████▊ | 10860/18688 [12:44:11<8:41:51, 4.00s/it] {'loss': '0.9345', 'grad_norm': '1.223', 'learning_rate': '3.443e-05', 'epoch': '0.5811', 'num_input_tokens_seen': 36540224, 'train_runtime': '4.585e+04', 'train_tokens_per_second': '796.9'}
58%|█████▊ | 10860/18688 [12:44:11<8:41:51, 4.00s/it] 58%|█████▊ | 10861/18688 [12:44:15<8:39:19, 3.98s/it] 58%|█████▊ | 10862/18688 [12:44:19<8:45:14, 4.03s/it] 58%|█████▊ | 10863/18688 [12:44:23<8:39:20, 3.98s/it] 58%|█████▊ | 10864/18688 [12:44:26<8:20:26, 3.84s/it] 58%|█████▊ | 10865/18688 [12:44:30<8:24:09, 3.87s/it] 58%|█████▊ | 10866/18688 [12:44:35<8:54:04, 4.10s/it] 58%|█████▊ | 10867/18688 [12:44:39<9:13:31, 4.25s/it] 58%|█████▊ | 10868/18688 [12:44:43<8:47:38, 4.05s/it] 58%|█████▊ | 10869/18688 [12:44:47<8:45:02, 4.03s/it] 58%|█████▊ | 10870/18688 [12:44:51<8:35:35, 3.96s/it] {'loss': '0.912', 'grad_norm': '1.204', 'learning_rate': '3.435e-05', 'epoch': '0.5817', 'num_input_tokens_seen': 36572264, 'train_runtime': '4.589e+04', 'train_tokens_per_second': '796.9'}
58%|█████▊ | 10870/18688 [12:44:51<8:35:35, 3.96s/it] 58%|█████▊ | 10871/18688 [12:44:55<8:43:19, 4.02s/it] 58%|█████▊ | 10872/18688 [12:44:59<8:59:58, 4.15s/it] 58%|█████▊ | 10873/18688 [12:45:03<8:49:34, 4.07s/it] 58%|█████▊ | 10874/18688 [12:45:08<8:59:30, 4.14s/it] 58%|█████▊ | 10875/18688 [12:45:12<8:56:48, 4.12s/it] 58%|█████▊ | 10876/18688 [12:45:16<9:06:08, 4.19s/it] 58%|█████▊ | 10877/18688 [12:45:20<9:03:23, 4.17s/it] 58%|█████▊ | 10878/18688 [12:45:24<8:37:02, 3.97s/it] 58%|█████▊ | 10879/18688 [12:45:27<8:23:24, 3.87s/it] 58%|█████▊ | 10880/18688 [12:45:31<8:33:24, 3.95s/it] {'loss': '0.9678', 'grad_norm': '1.246', 'learning_rate': '3.428e-05', 'epoch': '0.5822', 'num_input_tokens_seen': 36605224, 'train_runtime': '4.593e+04', 'train_tokens_per_second': '796.9'}
58%|█████▊ | 10880/18688 [12:45:31<8:33:24, 3.95s/it] 58%|█████▊ | 10881/18688 [12:45:36<8:47:22, 4.05s/it] 58%|█████▊ | 10882/18688 [12:45:39<8:29:37, 3.92s/it] 58%|█████▊ | 10883/18688 [12:45:43<8:24:13, 3.88s/it] 58%|█████▊ | 10884/18688 [12:45:48<8:49:49, 4.07s/it] 58%|█████▊ | 10885/18688 [12:45:52<9:06:45, 4.20s/it] 58%|█████▊ | 10886/18688 [12:45:57<9:32:49, 4.41s/it] 58%|█████▊ | 10887/18688 [12:46:01<9:22:25, 4.33s/it] 58%|█████▊ | 10888/18688 [12:46:05<9:22:05, 4.32s/it] 58%|█████▊ | 10889/18688 [12:46:10<9:14:23, 4.27s/it] 58%|█████▊ | 10890/18688 [12:46:13<8:52:52, 4.10s/it] {'loss': '0.8986', 'grad_norm': '1.312', 'learning_rate': '3.421e-05', 'epoch': '0.5827', 'num_input_tokens_seen': 36638776, 'train_runtime': '4.597e+04', 'train_tokens_per_second': '797'}
58%|█████▊ | 10890/18688 [12:46:13<8:52:52, 4.10s/it] 58%|█████▊ | 10891/18688 [12:46:17<8:40:29, 4.01s/it] 58%|█████▊ | 10892/18688 [12:46:21<8:49:42, 4.08s/it] 58%|█████▊ | 10893/18688 [12:46:26<9:19:19, 4.31s/it] 58%|█████▊ | 10894/18688 [12:46:30<9:03:12, 4.18s/it] 58%|█████▊ | 10895/18688 [12:46:34<9:03:48, 4.19s/it] 58%|█████▊ | 10896/18688 [12:46:38<8:37:01, 3.98s/it] 58%|█████▊ | 10897/18688 [12:46:42<8:50:59, 4.09s/it] 58%|█████▊ | 10898/18688 [12:46:45<8:25:19, 3.89s/it] 58%|█████▊ | 10899/18688 [12:46:49<8:18:13, 3.84s/it] 58%|█████▊ | 10900/18688 [12:46:53<8:01:42, 3.71s/it] {'loss': '0.9219', 'grad_norm': '1.189', 'learning_rate': '3.414e-05', 'epoch': '0.5833', 'num_input_tokens_seen': 36669608, 'train_runtime': '4.601e+04', 'train_tokens_per_second': '796.9'}
58%|█████▊ | 10900/18688 [12:46:53<8:01:42, 3.71s/it] 58%|█████▊ | 10901/18688 [12:46:56<7:57:21, 3.68s/it] 58%|█████▊ | 10902/18688 [12:47:00<8:13:31, 3.80s/it] 58%|█████▊ | 10903/18688 [12:47:05<8:31:38, 3.94s/it] 58%|█████▊ | 10904/18688 [12:47:08<8:29:29, 3.93s/it] 58%|█████▊ | 10905/18688 [12:47:12<8:25:39, 3.90s/it] 58%|█████▊ | 10906/18688 [12:47:16<8:27:10, 3.91s/it] 58%|█████▊ | 10907/18688 [12:47:20<8:13:03, 3.80s/it] 58%|█████▊ | 10908/18688 [12:47:23<8:04:38, 3.74s/it] 58%|█████▊ | 10909/18688 [12:47:28<8:28:02, 3.92s/it] 58%|█████▊ | 10910/18688 [12:47:31<8:14:11, 3.81s/it] {'loss': '0.8706', 'grad_norm': '1.133', 'learning_rate': '3.406e-05', 'epoch': '0.5838', 'num_input_tokens_seen': 36699808, 'train_runtime': '4.605e+04', 'train_tokens_per_second': '796.9'}
58%|█████▊ | 10910/18688 [12:47:31<8:14:11, 3.81s/it] 58%|█████▊ | 10911/18688 [12:47:36<8:34:10, 3.97s/it] 58%|█████▊ | 10912/18688 [12:47:41<9:11:19, 4.25s/it] 58%|█████▊ | 10913/18688 [12:47:45<9:35:36, 4.44s/it] 58%|█████▊ | 10914/18688 [12:47:49<9:04:05, 4.20s/it] 58%|█████▊ | 10915/18688 [12:47:53<8:45:05, 4.05s/it] 58%|█████▊ | 10916/18688 [12:47:57<8:55:40, 4.14s/it] 58%|█████▊ | 10917/18688 [12:48:01<8:54:25, 4.13s/it] 58%|█████▊ | 10918/18688 [12:48:05<8:29:10, 3.93s/it] 58%|█████▊ | 10919/18688 [12:48:09<9:04:13, 4.20s/it] 58%|█████▊ | 10920/18688 [12:48:14<9:05:56, 4.22s/it] {'loss': '0.9094', 'grad_norm': '1.184', 'learning_rate': '3.399e-05', 'epoch': '0.5843', 'num_input_tokens_seen': 36734448, 'train_runtime': '4.609e+04', 'train_tokens_per_second': '796.9'}
58%|█████▊ | 10920/18688 [12:48:14<9:05:56, 4.22s/it] 58%|█████▊ | 10921/18688 [12:48:17<8:43:46, 4.05s/it] 58%|█████▊ | 10922/18688 [12:48:21<8:32:07, 3.96s/it] 58%|█████▊ | 10923/18688 [12:48:26<9:04:34, 4.21s/it] 58%|█████▊ | 10924/18688 [12:48:30<9:09:49, 4.25s/it] 58%|█████▊ | 10925/18688 [12:48:34<8:41:20, 4.03s/it] 58%|█████▊ | 10926/18688 [12:48:37<8:27:13, 3.92s/it] 58%|█████▊ | 10927/18688 [12:48:41<8:17:53, 3.85s/it] 58%|█████▊ | 10928/18688 [12:48:45<8:28:24, 3.93s/it] 58%|█████▊ | 10929/18688 [12:48:50<8:41:44, 4.03s/it] 58%|█████▊ | 10930/18688 [12:48:54<8:45:10, 4.06s/it] {'loss': '0.9394', 'grad_norm': '1.227', 'learning_rate': '3.392e-05', 'epoch': '0.5849', 'num_input_tokens_seen': 36767736, 'train_runtime': '4.613e+04', 'train_tokens_per_second': '797'}
58%|█████▊ | 10930/18688 [12:48:54<8:45:10, 4.06s/it] 58%|█████▊ | 10931/18688 [12:48:59<9:16:34, 4.31s/it] 58%|█████▊ | 10932/18688 [12:49:02<8:51:47, 4.11s/it] 59%|█████▊ | 10933/18688 [12:49:07<9:15:30, 4.30s/it] 59%|█████▊ | 10934/18688 [12:49:11<9:06:53, 4.23s/it] 59%|█████▊ | 10935/18688 [12:49:17<9:58:13, 4.63s/it] 59%|█████▊ | 10936/18688 [12:49:20<9:27:07, 4.39s/it] 59%|█████▊ | 10937/18688 [12:49:26<10:23:35, 4.83s/it] 59%|█████▊ | 10938/18688 [12:49:31<10:02:08, 4.66s/it] 59%|█████▊ | 10939/18688 [12:49:34<9:21:03, 4.34s/it] 59%|█████▊ | 10940/18688 [12:49:38<8:59:54, 4.18s/it] {'loss': '0.9201', 'grad_norm': '1.295', 'learning_rate': '3.385e-05', 'epoch': '0.5854', 'num_input_tokens_seen': 36806376, 'train_runtime': '4.618e+04', 'train_tokens_per_second': '797'}
59%|█████▊ | 10940/18688 [12:49:38<8:59:54, 4.18s/it] 59%|█████▊ | 10941/18688 [12:49:42<9:13:22, 4.29s/it] 59%|█████▊ | 10942/18688 [12:49:47<9:23:15, 4.36s/it] 59%|█████▊ | 10943/18688 [12:49:51<8:57:00, 4.16s/it] 59%|█████▊ | 10944/18688 [12:49:55<9:01:24, 4.19s/it] 59%|█████▊ | 10945/18688 [12:49:59<8:50:17, 4.11s/it] 59%|█████▊ | 10946/18688 [12:50:03<8:56:23, 4.16s/it] 59%|█████▊ | 10947/18688 [12:50:07<8:45:41, 4.07s/it] 59%|█████▊ | 10948/18688 [12:50:11<9:01:16, 4.20s/it] 59%|█████▊ | 10949/18688 [12:50:17<9:42:48, 4.52s/it] 59%|█████▊ | 10950/18688 [12:50:21<9:38:58, 4.49s/it] {'loss': '0.9236', 'grad_norm': '0.9966', 'learning_rate': '3.377e-05', 'epoch': '0.586', 'num_input_tokens_seen': 36843568, 'train_runtime': '4.622e+04', 'train_tokens_per_second': '797.1'}
59%|█████▊ | 10950/18688 [12:50:21<9:38:58, 4.49s/it] 59%|█████▊ | 10951/18688 [12:50:25<9:29:49, 4.42s/it] 59%|█████▊ | 10952/18688 [12:50:29<8:56:50, 4.16s/it] 59%|█████▊ | 10953/18688 [12:50:33<8:33:30, 3.98s/it] 59%|█████▊ | 10954/18688 [12:50:37<8:54:32, 4.15s/it] 59%|█████▊ | 10955/18688 [12:50:41<8:37:30, 4.02s/it] 59%|█████▊ | 10956/18688 [12:50:44<8:22:51, 3.90s/it] 59%|█████▊ | 10957/18688 [12:50:48<8:15:23, 3.84s/it] 59%|█████▊ | 10958/18688 [12:50:52<8:20:38, 3.89s/it] 59%|█████▊ | 10959/18688 [12:50:56<8:26:59, 3.94s/it] 59%|█████▊ | 10960/18688 [12:51:00<8:09:20, 3.80s/it] {'loss': '0.9573', 'grad_norm': '1.132', 'learning_rate': '3.37e-05', 'epoch': '0.5865', 'num_input_tokens_seen': 36873216, 'train_runtime': '4.626e+04', 'train_tokens_per_second': '797.1'}
59%|█████▊ | 10960/18688 [12:51:00<8:09:20, 3.80s/it] 59%|█████▊ | 10961/18688 [12:51:03<8:02:49, 3.75s/it] 59%|█████▊ | 10962/18688 [12:51:07<8:16:00, 3.85s/it] 59%|█████▊ | 10963/18688 [12:51:12<8:27:19, 3.94s/it] 59%|█████▊ | 10964/18688 [12:51:16<8:45:31, 4.08s/it] 59%|█████▊ | 10965/18688 [12:51:20<8:52:33, 4.14s/it] 59%|█████▊ | 10966/18688 [12:51:24<8:30:43, 3.97s/it] 59%|█████▊ | 10967/18688 [12:51:27<8:18:15, 3.87s/it] 59%|█████▊ | 10968/18688 [12:51:31<8:06:15, 3.78s/it] 59%|█████▊ | 10969/18688 [12:51:36<8:56:00, 4.17s/it] 59%|█████▊ | 10970/18688 [12:51:40<8:45:16, 4.08s/it] {'loss': '0.9501', 'grad_norm': '1.258', 'learning_rate': '3.363e-05', 'epoch': '0.587', 'num_input_tokens_seen': 36905696, 'train_runtime': '4.63e+04', 'train_tokens_per_second': '797.1'}
59%|█████▊ | 10970/18688 [12:51:40<8:45:16, 4.08s/it] 59%|█████▊ | 10971/18688 [12:51:44<9:00:29, 4.20s/it] 59%|█████▊ | 10972/18688 [12:51:48<8:43:09, 4.07s/it] 59%|█████▊ | 10973/18688 [12:51:52<8:43:24, 4.07s/it] 59%|█████▊ | 10974/18688 [12:51:57<8:57:07, 4.18s/it] 59%|█████▊ | 10975/18688 [12:52:00<8:33:10, 3.99s/it] 59%|█████▊ | 10976/18688 [12:52:04<8:39:08, 4.04s/it] 59%|█████▊ | 10977/18688 [12:52:09<8:58:17, 4.19s/it] 59%|█████▊ | 10978/18688 [12:52:12<8:29:56, 3.97s/it] 59%|█████▊ | 10979/18688 [12:52:16<8:21:51, 3.91s/it] 59%|█████▉ | 10980/18688 [12:52:20<8:06:49, 3.79s/it] {'loss': '0.9257', 'grad_norm': '1.194', 'learning_rate': '3.356e-05', 'epoch': '0.5876', 'num_input_tokens_seen': 36937392, 'train_runtime': '4.634e+04', 'train_tokens_per_second': '797.1'}
59%|█████▉ | 10980/18688 [12:52:20<8:06:49, 3.79s/it] 59%|█████▉ | 10981/18688 [12:52:23<7:52:24, 3.68s/it] 59%|█████▉ | 10982/18688 [12:52:27<7:57:26, 3.72s/it] 59%|█████▉ | 10983/18688 [12:52:31<8:25:29, 3.94s/it] 59%|█████▉ | 10984/18688 [12:52:36<8:38:07, 4.04s/it] 59%|█████▉ | 10985/18688 [12:52:40<8:35:42, 4.02s/it] 59%|█████▉ | 10986/18688 [12:52:44<8:49:18, 4.12s/it] 59%|█████▉ | 10987/18688 [12:52:48<8:27:19, 3.95s/it] 59%|█████▉ | 10988/18688 [12:52:51<8:17:45, 3.88s/it] 59%|█████▉ | 10989/18688 [12:52:55<8:11:49, 3.83s/it] 59%|█████▉ | 10990/18688 [12:53:00<9:00:22, 4.21s/it] {'loss': '0.8597', 'grad_norm': '1.169', 'learning_rate': '3.349e-05', 'epoch': '0.5881', 'num_input_tokens_seen': 36969240, 'train_runtime': '4.638e+04', 'train_tokens_per_second': '797.1'}
59%|█████▉ | 10990/18688 [12:53:00<9:00:22, 4.21s/it] 59%|█████▉ | 10991/18688 [12:53:04<9:08:50, 4.28s/it] 59%|█████▉ | 10992/18688 [12:53:09<9:07:24, 4.27s/it] 59%|█████▉ | 10993/18688 [12:53:13<9:21:13, 4.38s/it] 59%|█████▉ | 10994/18688 [12:53:17<9:07:09, 4.27s/it] 59%|█████▉ | 10995/18688 [12:53:22<9:22:54, 4.39s/it] 59%|█████▉ | 10996/18688 [12:53:26<9:10:08, 4.29s/it] 59%|█████▉ | 10997/18688 [12:53:31<9:14:04, 4.32s/it] 59%|█████▉ | 10998/18688 [12:53:34<8:44:36, 4.09s/it] 59%|█████▉ | 10999/18688 [12:53:39<9:19:52, 4.37s/it] 59%|█████▉ | 11000/18688 [12:53:43<9:19:31, 4.37s/it] {'loss': '0.8838', 'grad_norm': '1.255', 'learning_rate': '3.341e-05', 'epoch': '0.5886', 'num_input_tokens_seen': 37004632, 'train_runtime': '4.642e+04', 'train_tokens_per_second': '797.1'}
59%|█████▉ | 11000/18688 [12:53:43<9:19:31, 4.37s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:71: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:281: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 0/1000 [00:00<?, ?it/s]/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:172: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:202: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:254: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 3/1000 [00:00<00:38, 25.93it/s]
1%| | 6/1000 [00:00<00:54, 18.30it/s]
1%| | 8/1000 [00:00<00:56, 17.56it/s]
1%| | 10/1000 [00:00<00:59, 16.65it/s]
1%| | 12/1000 [00:00<01:03, 15.59it/s]
1%|▏ | 14/1000 [00:00<01:07, 14.53it/s]
2%|▏ | 16/1000 [00:01<01:06, 14.71it/s]
2%|▏ | 18/1000 [00:01<01:41, 9.71it/s]
2%|▏ | 20/1000 [00:01<01:35, 10.31it/s]
2%|▏ | 22/1000 [00:01<01:53, 8.59it/s]
2%|▏ | 24/1000 [00:02<01:41, 9.66it/s]
3%|▎ | 26/1000 [00:02<01:45, 9.22it/s]
3%|▎ | 28/1000 [00:02<01:36, 10.05it/s]
3%|▎ | 30/1000 [00:02<01:28, 10.96it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.08it/s]
3%|▎ | 34/1000 [00:02<01:28, 10.91it/s]
4%|▎ | 36/1000 [00:03<01:37, 9.92it/s]
4%|▍ | 38/1000 [00:03<01:33, 10.34it/s]
4%|▍ | 40/1000 [00:03<01:33, 10.28it/s]
4%|▍ | 42/1000 [00:03<01:50, 8.65it/s]
4%|▍ | 44/1000 [00:04<01:39, 9.66it/s]
5%|▍ | 46/1000 [00:04<01:26, 10.99it/s]
5%|▍ | 48/1000 [00:04<01:33, 10.23it/s]
5%|▌ | 50/1000 [00:04<01:23, 11.32it/s]
5%|▌ | 52/1000 [00:04<01:17, 12.22it/s]
5%|▌ | 54/1000 [00:04<01:12, 13.02it/s]
6%|▌ | 56/1000 [00:04<01:08, 13.75it/s]
6%|▌ | 58/1000 [00:05<01:48, 8.68it/s]
6%|▌ | 60/1000 [00:05<01:36, 9.72it/s]
6%|▌ | 62/1000 [00:05<01:31, 10.30it/s]
6%|▋ | 64/1000 [00:05<01:22, 11.31it/s]
7%|▋ | 66/1000 [00:05<01:18, 11.95it/s]
7%|▋ | 68/1000 [00:06<01:13, 12.61it/s]
7%|▋ | 70/1000 [00:06<01:10, 13.26it/s]
7%|▋ | 72/1000 [00:06<01:07, 13.77it/s]
7%|▋ | 74/1000 [00:06<01:08, 13.57it/s]
8%|▊ | 76/1000 [00:06<01:05, 14.18it/s]
8%|▊ | 78/1000 [00:06<01:03, 14.44it/s]
8%|▊ | 80/1000 [00:06<01:08, 13.38it/s]
8%|▊ | 82/1000 [00:07<01:05, 14.01it/s]
8%|▊ | 84/1000 [00:07<01:03, 14.49it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.64it/s]
9%|▉ | 88/1000 [00:07<01:03, 14.47it/s]
9%|▉ | 90/1000 [00:07<01:02, 14.47it/s]
9%|▉ | 92/1000 [00:07<01:04, 14.14it/s]
9%|▉ | 94/1000 [00:07<01:04, 13.97it/s]
10%|▉ | 96/1000 [00:07<01:03, 14.28it/s]
10%|▉ | 98/1000 [00:08<01:03, 14.20it/s]
10%|█ | 100/1000 [00:08<01:31, 9.79it/s]
10%|█ | 102/1000 [00:08<01:23, 10.74it/s]
10%|█ | 104/1000 [00:09<01:54, 7.80it/s]
11%|█ | 106/1000 [00:09<02:02, 7.31it/s]
11%|█ | 108/1000 [00:09<01:42, 8.66it/s]
11%|█ | 110/1000 [00:09<01:28, 10.11it/s]
11%|█ | 112/1000 [00:09<01:19, 11.23it/s]
11%|█▏ | 114/1000 [00:09<01:15, 11.76it/s]
12%|█▏ | 116/1000 [00:10<01:13, 11.95it/s]
12%|█▏ | 118/1000 [00:10<01:08, 12.84it/s]
12%|█▏ | 120/1000 [00:10<01:13, 11.97it/s]
12%|█▏ | 122/1000 [00:10<01:29, 9.85it/s]
12%|█▏ | 124/1000 [00:10<01:34, 9.22it/s]
13%|█▎ | 126/1000 [00:11<01:26, 10.16it/s]
13%|█▎ | 128/1000 [00:11<01:20, 10.80it/s]
13%|█▎ | 130/1000 [00:11<01:14, 11.62it/s]
13%|█▎ | 132/1000 [00:11<01:08, 12.68it/s]
13%|█▎ | 134/1000 [00:11<01:11, 12.07it/s]
14%|█▎ | 136/1000 [00:11<01:06, 13.06it/s]
14%|█▍ | 138/1000 [00:11<01:06, 13.05it/s]
14%|█▍ | 140/1000 [00:12<01:25, 10.02it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.50it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.53it/s]
15%|█▍ | 146/1000 [00:12<01:24, 10.16it/s]
15%|█▍ | 148/1000 [00:13<01:17, 10.99it/s]
15%|█▌ | 150/1000 [00:13<01:09, 12.17it/s]
15%|█▌ | 152/1000 [00:13<01:06, 12.82it/s]
15%|█▌ | 154/1000 [00:13<01:04, 13.18it/s]
16%|█▌ | 156/1000 [00:13<01:04, 13.09it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.60it/s]
16%|█▌ | 160/1000 [00:13<01:02, 13.49it/s]
16%|█▌ | 162/1000 [00:14<01:02, 13.40it/s]
16%|█▋ | 164/1000 [00:14<01:01, 13.56it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.73it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.21it/s]
17%|█▋ | 170/1000 [00:14<01:19, 10.46it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.42it/s]
17%|█▋ | 174/1000 [00:15<01:30, 9.16it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.18it/s]
18%|█▊ | 178/1000 [00:15<01:35, 8.65it/s]
18%|█▊ | 180/1000 [00:15<01:33, 8.78it/s]
18%|█▊ | 182/1000 [00:16<01:35, 8.57it/s]
18%|█▊ | 184/1000 [00:16<01:23, 9.82it/s]
19%|█▊ | 186/1000 [00:16<01:37, 8.34it/s]
19%|█▉ | 188/1000 [00:16<01:24, 9.64it/s]
19%|█▉ | 190/1000 [00:16<01:15, 10.68it/s]
19%|█▉ | 192/1000 [00:17<01:11, 11.32it/s]
19%|█▉ | 194/1000 [00:17<01:18, 10.27it/s]
20%|█▉ | 196/1000 [00:17<01:34, 8.50it/s]
20%|█▉ | 198/1000 [00:17<01:23, 9.58it/s]
20%|██ | 200/1000 [00:17<01:19, 10.09it/s]
20%|██ | 202/1000 [00:18<01:12, 11.07it/s]
20%|██ | 204/1000 [00:18<01:06, 12.04it/s]
21%|██ | 206/1000 [00:18<01:02, 12.78it/s]
21%|██ | 208/1000 [00:18<00:59, 13.27it/s]
21%|██ | 210/1000 [00:18<00:55, 14.17it/s]
21%|██ | 212/1000 [00:18<00:54, 14.49it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.56it/s]
22%|██▏ | 216/1000 [00:19<00:54, 14.33it/s]
22%|██▏ | 218/1000 [00:19<00:53, 14.67it/s]
22%|██▏ | 220/1000 [00:19<01:28, 8.85it/s]
22%|██▏ | 222/1000 [00:19<01:25, 9.13it/s]
22%|██▏ | 224/1000 [00:20<01:20, 9.62it/s]
23%|██▎ | 226/1000 [00:20<01:12, 10.63it/s]
23%|██▎ | 228/1000 [00:20<01:06, 11.55it/s]
23%|██▎ | 230/1000 [00:20<01:03, 12.04it/s]
23%|██▎ | 232/1000 [00:20<01:01, 12.42it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.16it/s]
24%|██▎ | 236/1000 [00:20<00:55, 13.65it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.28it/s]
24%|██▍ | 240/1000 [00:21<00:53, 14.27it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.81it/s]
24%|██▍ | 244/1000 [00:21<00:50, 14.99it/s]
25%|██▍ | 246/1000 [00:21<00:51, 14.76it/s]
25%|██▍ | 248/1000 [00:21<01:03, 11.92it/s]
25%|██▌ | 250/1000 [00:21<00:59, 12.69it/s]
25%|██▌ | 252/1000 [00:22<01:08, 10.88it/s]
25%|██▌ | 254/1000 [00:22<01:01, 12.06it/s]
26%|██▌ | 256/1000 [00:22<00:58, 12.83it/s]
26%|██▌ | 258/1000 [00:22<00:56, 13.11it/s]
26%|██▌ | 260/1000 [00:22<01:00, 12.22it/s]
26%|██▌ | 262/1000 [00:22<00:59, 12.46it/s]
26%|██▋ | 264/1000 [00:23<00:55, 13.16it/s]
27%|██▋ | 266/1000 [00:23<00:53, 13.64it/s]
27%|██▋ | 268/1000 [00:23<00:51, 14.30it/s]
27%|██▋ | 270/1000 [00:23<00:55, 13.13it/s]
27%|██▋ | 272/1000 [00:23<01:03, 11.55it/s]
27%|██▋ | 274/1000 [00:23<01:02, 11.65it/s]
28%|██▊ | 276/1000 [00:23<00:57, 12.66it/s]
28%|██▊ | 278/1000 [00:24<00:57, 12.58it/s]
28%|██▊ | 280/1000 [00:24<00:56, 12.73it/s]
28%|██▊ | 282/1000 [00:24<00:52, 13.74it/s]
28%|██▊ | 284/1000 [00:24<00:51, 13.90it/s]
29%|██▊ | 286/1000 [00:24<00:49, 14.32it/s]
29%|██▉ | 288/1000 [00:24<00:48, 14.78it/s]
29%|██▉ | 290/1000 [00:24<00:46, 15.29it/s]
29%|██▉ | 292/1000 [00:25<00:46, 15.31it/s]
29%|██▉ | 294/1000 [00:25<00:46, 15.24it/s]
30%|██▉ | 296/1000 [00:25<00:45, 15.32it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.46it/s]
30%|███ | 300/1000 [00:25<00:46, 15.13it/s]
30%|███ | 302/1000 [00:25<00:46, 15.17it/s]
30%|███ | 304/1000 [00:25<00:50, 13.77it/s]
31%|███ | 306/1000 [00:26<00:47, 14.50it/s]
31%|███ | 308/1000 [00:26<00:47, 14.46it/s]
31%|███ | 310/1000 [00:26<00:47, 14.65it/s]
31%|███ | 312/1000 [00:26<00:47, 14.63it/s]
31%|███▏ | 314/1000 [00:26<00:44, 15.35it/s]
32%|███▏ | 316/1000 [00:26<00:43, 15.64it/s]
32%|███▏ | 318/1000 [00:26<00:42, 16.06it/s]
32%|███▏ | 320/1000 [00:27<01:12, 9.37it/s]
32%|███▏ | 322/1000 [00:27<01:03, 10.72it/s]
32%|███▏ | 324/1000 [00:27<00:58, 11.50it/s]
33%|███▎ | 326/1000 [00:27<00:58, 11.44it/s]
33%|███▎ | 328/1000 [00:27<01:01, 10.92it/s]
33%|███▎ | 330/1000 [00:27<00:56, 11.92it/s]
33%|███▎ | 332/1000 [00:28<00:56, 11.86it/s]
33%|███▎ | 334/1000 [00:28<00:57, 11.59it/s]
34%|███▎ | 336/1000 [00:28<00:53, 12.38it/s]
34%|███▍ | 338/1000 [00:28<01:09, 9.47it/s]
34%|███▍ | 340/1000 [00:28<01:03, 10.34it/s]
34%|███▍ | 342/1000 [00:29<00:56, 11.69it/s]
34%|███▍ | 344/1000 [00:29<00:52, 12.49it/s]
35%|███▍ | 346/1000 [00:29<00:49, 13.22it/s]
35%|███▍ | 348/1000 [00:29<00:47, 13.75it/s]
35%|███▌ | 350/1000 [00:29<00:45, 14.40it/s]
35%|███▌ | 352/1000 [00:29<00:44, 14.60it/s]
35%|███▌ | 354/1000 [00:29<00:44, 14.68it/s]
36%|███▌ | 356/1000 [00:29<00:43, 14.84it/s]
36%|███▌ | 358/1000 [00:30<00:44, 14.41it/s]
36%|███▌ | 360/1000 [00:30<00:46, 13.80it/s]
36%|███▌ | 362/1000 [00:30<00:50, 12.71it/s]
36%|███▋ | 364/1000 [00:30<00:47, 13.39it/s]
37%|███▋ | 366/1000 [00:30<00:54, 11.69it/s]
37%|███▋ | 368/1000 [00:30<00:51, 12.16it/s]
37%|███▋ | 370/1000 [00:31<00:48, 13.08it/s]
37%|███▋ | 372/1000 [00:31<00:46, 13.45it/s]
37%|███▋ | 374/1000 [00:31<00:55, 11.36it/s]
38%|███▊ | 376/1000 [00:31<00:56, 10.95it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.64it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.46it/s]
38%|███▊ | 382/1000 [00:32<00:48, 12.65it/s]
38%|███▊ | 384/1000 [00:32<00:45, 13.40it/s]
39%|███▊ | 386/1000 [00:32<00:44, 13.69it/s]
39%|███▉ | 388/1000 [00:32<00:44, 13.62it/s]
39%|███▉ | 390/1000 [00:32<00:45, 13.51it/s]
39%|███▉ | 392/1000 [00:32<00:43, 13.84it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.64it/s]
40%|███▉ | 396/1000 [00:33<00:43, 13.78it/s]
40%|███▉ | 398/1000 [00:33<00:47, 12.61it/s]
40%|████ | 400/1000 [00:33<00:45, 13.26it/s]
40%|████ | 402/1000 [00:33<00:43, 13.90it/s]
40%|████ | 404/1000 [00:33<00:41, 14.50it/s]
41%|████ | 406/1000 [00:33<00:39, 15.01it/s]
41%|████ | 408/1000 [00:34<00:50, 11.79it/s]
41%|████ | 410/1000 [00:34<01:01, 9.62it/s]
41%|████ | 412/1000 [00:34<00:55, 10.59it/s]
41%|████▏ | 414/1000 [00:34<01:10, 8.25it/s]
42%|████▏ | 416/1000 [00:35<01:27, 6.68it/s]
42%|████▏ | 418/1000 [00:35<01:12, 7.99it/s]
42%|████▏ | 420/1000 [00:35<01:02, 9.27it/s]
42%|████▏ | 422/1000 [00:35<00:54, 10.64it/s]
42%|████▏ | 424/1000 [00:36<01:15, 7.59it/s]
43%|████▎ | 426/1000 [00:36<01:05, 8.71it/s]
43%|████▎ | 428/1000 [00:36<00:59, 9.55it/s]
43%|████▎ | 430/1000 [00:36<01:04, 8.79it/s]
43%|████▎ | 432/1000 [00:37<01:22, 6.91it/s]
43%|████▎ | 434/1000 [00:37<01:09, 8.13it/s]
44%|████▎ | 436/1000 [00:37<01:01, 9.18it/s]
44%|████▍ | 438/1000 [00:37<01:02, 8.92it/s]
44%|████▍ | 440/1000 [00:37<00:59, 9.45it/s]
44%|████▍ | 442/1000 [00:38<00:52, 10.53it/s]
44%|████▍ | 444/1000 [00:38<00:48, 11.39it/s]
45%|████▍ | 446/1000 [00:38<00:45, 12.12it/s]
45%|████▍ | 448/1000 [00:38<00:50, 10.93it/s]
45%|████▌ | 450/1000 [00:38<00:46, 11.77it/s]
45%|████▌ | 452/1000 [00:38<00:42, 12.80it/s]
45%|████▌ | 454/1000 [00:38<00:44, 12.30it/s]
46%|████▌ | 456/1000 [00:39<00:42, 12.74it/s]
46%|████▌ | 458/1000 [00:39<00:39, 13.75it/s]
46%|████▌ | 460/1000 [00:39<00:38, 13.88it/s]
46%|████▌ | 462/1000 [00:39<00:37, 14.48it/s]
46%|████▋ | 464/1000 [00:39<00:35, 15.01it/s]
47%|████▋ | 466/1000 [00:39<00:36, 14.81it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.83it/s]
47%|████▋ | 470/1000 [00:40<00:54, 9.64it/s]
47%|████▋ | 472/1000 [00:40<00:55, 9.50it/s]
47%|████▋ | 474/1000 [00:40<00:48, 10.82it/s]
48%|████▊ | 476/1000 [00:40<00:45, 11.60it/s]
48%|████▊ | 478/1000 [00:40<00:45, 11.53it/s]
48%|████▊ | 480/1000 [00:41<00:41, 12.48it/s]
48%|████▊ | 482/1000 [00:41<00:39, 12.99it/s]
48%|████▊ | 484/1000 [00:41<00:38, 13.56it/s]
49%|████▊ | 486/1000 [00:41<00:37, 13.80it/s]
49%|████▉ | 488/1000 [00:41<00:36, 14.08it/s]
49%|████▉ | 490/1000 [00:41<00:46, 11.02it/s]
49%|████▉ | 492/1000 [00:42<00:41, 12.29it/s]
49%|████▉ | 494/1000 [00:42<00:38, 13.26it/s]
50%|████▉ | 496/1000 [00:42<00:35, 14.02it/s]
50%|████▉ | 498/1000 [00:42<00:35, 14.04it/s]
50%|█████ | 500/1000 [00:42<00:45, 11.05it/s]
50%|█████ | 502/1000 [00:42<00:42, 11.60it/s]
50%|█████ | 504/1000 [00:42<00:40, 12.30it/s]
51%|█████ | 506/1000 [00:43<00:39, 12.56it/s]
51%|█████ | 508/1000 [00:43<00:36, 13.53it/s]
51%|█████ | 510/1000 [00:43<00:47, 10.30it/s]
51%|█████ | 512/1000 [00:43<00:44, 11.09it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 11.97it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.56it/s]
52%|█████▏ | 518/1000 [00:44<00:36, 13.09it/s]
52%|█████▏ | 520/1000 [00:44<00:35, 13.46it/s]
52%|█████▏ | 522/1000 [00:44<00:33, 14.17it/s]
52%|█████▏ | 524/1000 [00:44<00:33, 14.20it/s]
53%|█████▎ | 526/1000 [00:44<00:32, 14.37it/s]
53%|█████▎ | 528/1000 [00:44<00:39, 11.95it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.79it/s]
53%|█████▎ | 532/1000 [00:45<00:34, 13.72it/s]
53%|█████▎ | 534/1000 [00:45<00:33, 13.75it/s]
54%|█████▎ | 536/1000 [00:45<00:34, 13.58it/s]
54%|█████▍ | 538/1000 [00:45<00:34, 13.21it/s]
54%|█████▍ | 540/1000 [00:45<00:34, 13.28it/s]
54%|█████▍ | 542/1000 [00:45<00:33, 13.84it/s]
54%|█████▍ | 544/1000 [00:45<00:32, 13.98it/s]
55%|█████▍ | 546/1000 [00:46<00:33, 13.72it/s]
55%|█████▍ | 548/1000 [00:46<00:33, 13.53it/s]
55%|█████▌ | 550/1000 [00:46<00:32, 13.87it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.04it/s]
55%|█████▌ | 554/1000 [00:46<00:32, 13.78it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 13.98it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.34it/s]
56%|█████▌ | 560/1000 [00:47<00:29, 14.90it/s]
56%|█████▌ | 562/1000 [00:47<00:29, 14.89it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.74it/s]
57%|█████▋ | 566/1000 [00:47<00:37, 11.64it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.21it/s]
57%|█████▋ | 570/1000 [00:48<00:34, 12.29it/s]
57%|█████▋ | 572/1000 [00:48<00:35, 12.09it/s]
57%|█████▋ | 574/1000 [00:48<00:32, 12.97it/s]
58%|█████▊ | 576/1000 [00:48<00:32, 13.09it/s]
58%|█████▊ | 578/1000 [00:48<00:37, 11.37it/s]
58%|█████▊ | 580/1000 [00:48<00:33, 12.63it/s]
58%|█████▊ | 582/1000 [00:48<00:31, 13.40it/s]
58%|█████▊ | 584/1000 [00:49<00:30, 13.69it/s]
59%|█████▊ | 586/1000 [00:49<00:29, 14.12it/s]
59%|█████▉ | 588/1000 [00:49<00:27, 14.96it/s]
59%|█████▉ | 590/1000 [00:49<00:27, 14.98it/s]
59%|█████▉ | 592/1000 [00:49<00:27, 14.75it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.32it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.84it/s]
60%|█████▉ | 598/1000 [00:50<00:28, 14.08it/s]
60%|██████ | 600/1000 [00:50<00:27, 14.44it/s]
60%|██████ | 602/1000 [00:50<00:27, 14.54it/s]
60%|██████ | 604/1000 [00:50<00:26, 15.09it/s]
61%|██████ | 606/1000 [00:50<00:26, 14.84it/s]
61%|██████ | 608/1000 [00:50<00:25, 15.08it/s]
61%|██████ | 610/1000 [00:50<00:26, 14.92it/s]
61%|██████ | 612/1000 [00:50<00:25, 15.02it/s]
61%|██████▏ | 614/1000 [00:51<00:26, 14.30it/s]
62%|██████▏ | 616/1000 [00:51<00:26, 14.73it/s]
62%|██████▏ | 618/1000 [00:51<00:26, 14.16it/s]
62%|██████▏ | 620/1000 [00:51<00:38, 9.77it/s]
62%|██████▏ | 622/1000 [00:51<00:38, 9.94it/s]
62%|██████▏ | 624/1000 [00:52<00:33, 11.34it/s]
63%|██████▎ | 626/1000 [00:52<00:31, 12.04it/s]
63%|██████▎ | 628/1000 [00:52<00:28, 13.24it/s]
63%|██████▎ | 630/1000 [00:52<00:26, 13.77it/s]
63%|██████▎ | 632/1000 [00:52<00:30, 12.08it/s]
63%|██████▎ | 634/1000 [00:52<00:29, 12.60it/s]
64%|██████▎ | 636/1000 [00:53<00:36, 10.05it/s]
64%|██████▍ | 638/1000 [00:53<00:36, 9.97it/s]
64%|██████▍ | 640/1000 [00:53<00:33, 10.88it/s]
64%|██████▍ | 642/1000 [00:53<00:30, 11.69it/s]
64%|██████▍ | 644/1000 [00:53<00:29, 12.11it/s]
65%|██████▍ | 646/1000 [00:53<00:28, 12.51it/s]
65%|██████▍ | 648/1000 [00:54<00:26, 13.05it/s]
65%|██████▌ | 650/1000 [00:54<00:28, 12.38it/s]
65%|██████▌ | 652/1000 [00:54<00:29, 11.81it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.38it/s]
66%|██████▌ | 656/1000 [00:54<00:32, 10.48it/s]
66%|██████▌ | 658/1000 [00:54<00:29, 11.59it/s]
66%|██████▌ | 660/1000 [00:55<00:33, 10.04it/s]
66%|██████▌ | 662/1000 [00:55<00:30, 11.18it/s]
66%|██████▋ | 664/1000 [00:55<00:28, 11.69it/s]
67%|██████▋ | 666/1000 [00:55<00:26, 12.73it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.28it/s]
67%|██████▋ | 670/1000 [00:56<00:29, 11.07it/s]
67%|██████▋ | 672/1000 [00:56<00:27, 11.84it/s]
67%|██████▋ | 674/1000 [00:56<00:26, 12.43it/s]
68%|██████▊ | 676/1000 [00:56<00:27, 11.70it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.20it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 12.95it/s]
68%|██████▊ | 682/1000 [00:57<00:26, 11.85it/s]
68%|██████▊ | 684/1000 [00:57<00:26, 11.77it/s]
69%|██████▊ | 686/1000 [00:57<00:24, 12.83it/s]
69%|██████▉ | 688/1000 [00:57<00:24, 12.56it/s]
69%|██████▉ | 690/1000 [00:57<00:23, 12.94it/s]
69%|██████▉ | 692/1000 [00:57<00:22, 13.52it/s]
69%|██████▉ | 694/1000 [00:57<00:22, 13.68it/s]
70%|██████▉ | 696/1000 [00:58<00:22, 13.63it/s]
70%|██████▉ | 698/1000 [00:58<00:21, 14.27it/s]
70%|███████ | 700/1000 [00:58<00:24, 12.04it/s]
70%|███████ | 702/1000 [00:58<00:23, 12.87it/s]
70%|███████ | 704/1000 [00:58<00:30, 9.80it/s]
71%|███████ | 706/1000 [00:59<00:28, 10.33it/s]
71%|███████ | 708/1000 [00:59<00:30, 9.45it/s]
71%|███████ | 710/1000 [00:59<00:26, 10.77it/s]
71%|███████ | 712/1000 [00:59<00:24, 11.77it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.88it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 13.01it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.52it/s]
72%|███████▏ | 720/1000 [01:00<00:20, 13.49it/s]
72%|███████▏ | 722/1000 [01:00<00:19, 13.94it/s]
72%|███████▏ | 724/1000 [01:00<00:20, 13.77it/s]
73%|███████▎ | 726/1000 [01:00<00:19, 13.98it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.91it/s]
73%|███████▎ | 730/1000 [01:01<00:33, 8.00it/s]
73%|███████▎ | 732/1000 [01:01<00:33, 7.98it/s]
73%|███████▎ | 734/1000 [01:01<00:32, 8.13it/s]
74%|███████▎ | 735/1000 [01:01<00:32, 8.08it/s]
74%|███████▎ | 737/1000 [01:01<00:27, 9.55it/s]
74%|███████▍ | 739/1000 [01:02<00:24, 10.63it/s]
74%|███████▍ | 741/1000 [01:02<00:22, 11.38it/s]
74%|███████▍ | 743/1000 [01:02<00:21, 12.06it/s]
74%|███████▍ | 745/1000 [01:02<00:25, 10.05it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.46it/s]
75%|███████▍ | 749/1000 [01:03<00:23, 10.64it/s]
75%|███████▌ | 751/1000 [01:03<00:21, 11.76it/s]
75%|███████▌ | 753/1000 [01:03<00:19, 12.53it/s]
76%|███████▌ | 755/1000 [01:03<00:18, 13.11it/s]
76%|███████▌ | 757/1000 [01:03<00:24, 9.78it/s]
76%|███████▌ | 759/1000 [01:03<00:23, 10.48it/s]
76%|███████▌ | 761/1000 [01:04<00:20, 11.55it/s]
76%|███████▋ | 763/1000 [01:04<00:24, 9.83it/s]
76%|███████▋ | 765/1000 [01:04<00:28, 8.38it/s]
77%|███████▋ | 767/1000 [01:04<00:24, 9.36it/s]
77%|███████▋ | 769/1000 [01:04<00:22, 10.16it/s]
77%|███████▋ | 771/1000 [01:05<00:20, 11.38it/s]
77%|███████▋ | 773/1000 [01:05<00:18, 12.27it/s]
78%|███████▊ | 775/1000 [01:05<00:17, 12.90it/s]
78%|███████▊ | 777/1000 [01:05<00:16, 13.93it/s]
78%|███████▊ | 779/1000 [01:05<00:21, 10.39it/s]
78%|███████▊ | 781/1000 [01:06<00:24, 8.93it/s]
78%|███████▊ | 783/1000 [01:06<00:28, 7.69it/s]
78%|███████▊ | 784/1000 [01:06<00:32, 6.69it/s]
78%|███████▊ | 785/1000 [01:06<00:30, 6.97it/s]
79%|███████▊ | 786/1000 [01:06<00:32, 6.66it/s]
79%|███████▉ | 788/1000 [01:07<00:25, 8.33it/s]
79%|███████▉ | 790/1000 [01:07<00:21, 9.71it/s]
79%|███████▉ | 792/1000 [01:07<00:19, 10.94it/s]
79%|███████▉ | 794/1000 [01:07<00:17, 11.66it/s]
80%|███████▉ | 796/1000 [01:07<00:16, 12.17it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 12.98it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.19it/s]
80%|████████ | 802/1000 [01:08<00:24, 8.21it/s]
80%|████████ | 804/1000 [01:08<00:24, 8.16it/s]
81%|████████ | 806/1000 [01:08<00:22, 8.76it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.16it/s]
81%|████████ | 810/1000 [01:09<00:18, 10.33it/s]
81%|████████ | 812/1000 [01:09<00:16, 11.58it/s]
81%|████████▏ | 814/1000 [01:09<00:14, 12.51it/s]
82%|████████▏ | 816/1000 [01:09<00:16, 10.97it/s]
82%|████████▏ | 818/1000 [01:09<00:15, 11.95it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.45it/s]
82%|████████▏ | 822/1000 [01:10<00:13, 13.02it/s]
82%|████████▏ | 824/1000 [01:10<00:12, 13.85it/s]
83%|████████▎ | 826/1000 [01:10<00:12, 14.25it/s]
83%|████████▎ | 828/1000 [01:10<00:11, 14.53it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.96it/s]
83%|████████▎ | 832/1000 [01:10<00:11, 14.37it/s]
83%|████████▎ | 834/1000 [01:10<00:11, 14.09it/s]
84%|████████▎ | 836/1000 [01:11<00:11, 14.47it/s]
84%|████████▍ | 838/1000 [01:11<00:11, 14.37it/s]
84%|████████▍ | 840/1000 [01:11<00:11, 14.49it/s]
84%|████████▍ | 842/1000 [01:11<00:10, 14.49it/s]
84%|████████▍ | 844/1000 [01:11<00:11, 14.01it/s]
85%|████████▍ | 846/1000 [01:11<00:11, 13.95it/s]
85%|████████▍ | 848/1000 [01:11<00:10, 13.90it/s]
85%|████████▌ | 850/1000 [01:12<00:10, 13.76it/s]
85%|████████▌ | 852/1000 [01:12<00:10, 13.47it/s]
85%|████████▌ | 854/1000 [01:12<00:10, 13.81it/s]
86%|████████▌ | 856/1000 [01:12<00:10, 13.22it/s]
86%|████████▌ | 858/1000 [01:12<00:10, 13.69it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 14.26it/s]
86%|████████▌ | 862/1000 [01:12<00:11, 12.16it/s]
86%|████████▋ | 864/1000 [01:13<00:10, 12.97it/s]
87%|████████▋ | 866/1000 [01:13<00:10, 13.09it/s]
87%|████████▋ | 868/1000 [01:13<00:10, 13.04it/s]
87%|████████▋ | 870/1000 [01:13<00:09, 13.12it/s]
87%|████████▋ | 872/1000 [01:13<00:09, 13.88it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 12.02it/s]
88%|████████▊ | 876/1000 [01:14<00:11, 10.82it/s]
88%|████████▊ | 878/1000 [01:14<00:10, 11.76it/s]
88%|████████▊ | 880/1000 [01:14<00:09, 12.09it/s]
88%|████████▊ | 882/1000 [01:14<00:10, 11.14it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 11.93it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.73it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.74it/s]
89%|████████▉ | 890/1000 [01:15<00:07, 14.24it/s]
89%|████████▉ | 892/1000 [01:15<00:07, 14.05it/s]
89%|████████▉ | 894/1000 [01:15<00:07, 14.20it/s]
90%|████████▉ | 896/1000 [01:15<00:07, 13.92it/s]
90%|████████▉ | 898/1000 [01:15<00:07, 13.34it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.22it/s]
90%|█████████ | 902/1000 [01:16<00:07, 12.98it/s]
90%|█████████ | 904/1000 [01:16<00:10, 9.53it/s]
91%|█████████ | 906/1000 [01:16<00:08, 10.48it/s]
91%|█████████ | 908/1000 [01:16<00:08, 11.36it/s]
91%|█████████ | 910/1000 [01:16<00:08, 11.24it/s]
91%|█████████ | 912/1000 [01:17<00:07, 11.64it/s]
91%|█████████▏| 914/1000 [01:17<00:12, 6.63it/s]
92%|█████████▏| 916/1000 [01:17<00:10, 7.94it/s]
92%|█████████▏| 918/1000 [01:18<00:11, 7.29it/s]
92%|█████████▏| 920/1000 [01:18<00:09, 8.50it/s]
92%|█████████▏| 922/1000 [01:18<00:07, 9.94it/s]
92%|█████████▏| 924/1000 [01:18<00:07, 10.60it/s]
93%|█████████▎| 926/1000 [01:18<00:06, 11.16it/s]
93%|█████████▎| 928/1000 [01:18<00:07, 10.27it/s]
93%|█████████▎| 930/1000 [01:19<00:06, 11.53it/s]
93%|█████████▎| 932/1000 [01:19<00:05, 12.16it/s]
93%|█████████▎| 934/1000 [01:19<00:05, 12.71it/s]
94%|█████████▎| 936/1000 [01:19<00:04, 13.31it/s]
94%|█████████▍| 938/1000 [01:19<00:04, 14.22it/s]
94%|█████████▍| 940/1000 [01:19<00:04, 14.55it/s]
94%|█████████▍| 942/1000 [01:19<00:04, 12.27it/s]
94%|█████████▍| 944/1000 [01:20<00:04, 12.80it/s]
95%|█████████▍| 946/1000 [01:20<00:04, 11.86it/s]
95%|█████████▍| 948/1000 [01:20<00:04, 12.68it/s]
95%|█████████▌| 950/1000 [01:20<00:03, 13.46it/s]
95%|█████████▌| 952/1000 [01:20<00:03, 14.16it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 14.26it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 14.24it/s]
96%|█████████▌| 958/1000 [01:21<00:02, 14.79it/s]
96%|█████████▌| 960/1000 [01:21<00:03, 12.40it/s]
96%|█████████▌| 962/1000 [01:21<00:02, 13.12it/s]
96%|█████████▋| 964/1000 [01:21<00:03, 9.34it/s]
97%|█████████▋| 966/1000 [01:21<00:03, 10.61it/s]
97%|█████████▋| 968/1000 [01:22<00:02, 11.41it/s]
97%|█████████▋| 970/1000 [01:22<00:02, 12.04it/s]
97%|█████████▋| 972/1000 [01:22<00:02, 9.43it/s]
97%|█████████▋| 974/1000 [01:22<00:02, 10.63it/s]
98%|█████████▊| 976/1000 [01:22<00:02, 11.45it/s]
98%|█████████▊| 978/1000 [01:23<00:02, 9.75it/s]
98%|█████████▊| 980/1000 [01:23<00:02, 9.85it/s]
98%|█████████▊| 982/1000 [01:23<00:01, 10.04it/s]
98%|█████████▊| 984/1000 [01:23<00:01, 10.21it/s]
99%|█████████▊| 986/1000 [01:23<00:01, 10.52it/s]
99%|█████████▉| 988/1000 [01:23<00:01, 10.78it/s]
99%|█████████▉| 990/1000 [01:24<00:00, 11.31it/s]
99%|█████████▉| 992/1000 [01:24<00:00, 12.22it/s]
99%|█████████▉| 994/1000 [01:24<00:00, 12.96it/s]
100%|█████████▉| 996/1000 [01:24<00:00, 13.90it/s]
100%|█████████▉| 998/1000 [01:24<00:00, 14.09it/s]
100%|██████████| 1000/1000 [01:24<00:00, 14.75it/s]
{'eval_loss': '0.893', 'eval_runtime': '84.84', 'eval_samples_per_second': '11.79', 'eval_steps_per_second': '11.79', 'epoch': '0.5886', 'num_input_tokens_seen': 37004632}
59%|█████▉ | 11000/18688 [12:55:08<9:19:31, 4.37s/it]
100%|██████████| 1000/1000 [01:24<00:00, 14.75it/s]
 59%|█████▉ | 11001/18688 [12:55:13<63:52:49, 29.92s/it] 59%|█████▉ | 11002/18688 [12:55:18<47:47:46, 22.39s/it] 59%|█████▉ | 11003/18688 [12:55:22<36:14:10, 16.97s/it] 59%|█████▉ | 11004/18688 [12:55:26<28:04:44, 13.16s/it] 59%|█████▉ | 11005/18688 [12:55:30<21:58:43, 10.30s/it] 59%|█████▉ | 11006/18688 [12:55:35<18:32:33, 8.69s/it] 59%|█████▉ | 11007/18688 [12:55:39<15:34:16, 7.30s/it] 59%|█████▉ | 11008/18688 [12:55:44<13:53:42, 6.51s/it] 59%|█████▉ | 11009/18688 [12:55:48<12:30:27, 5.86s/it] 59%|█████▉ | 11010/18688 [12:55:52<11:16:03, 5.28s/it] {'loss': '0.9075', 'grad_norm': '1.145', 'learning_rate': '3.334e-05', 'epoch': '0.5892', 'num_input_tokens_seen': 37041936, 'train_runtime': '4.655e+04', 'train_tokens_per_second': '795.7'}
59%|█████▉ | 11010/18688 [12:55:52<11:16:03, 5.28s/it] 59%|█████▉ | 11011/18688 [12:55:57<10:50:03, 5.08s/it] 59%|█████▉ | 11012/18688 [12:56:01<10:38:39, 4.99s/it] 59%|█████▉ | 11013/18688 [12:56:05<10:06:17, 4.74s/it] 59%|█████▉ | 11014/18688 [12:56:09<9:28:52, 4.45s/it] 59%|█████▉ | 11015/18688 [12:56:14<9:55:11, 4.65s/it] 59%|█████▉ | 11016/18688 [12:56:18<9:20:47, 4.39s/it] 59%|█████▉ | 11017/18688 [12:56:22<8:58:03, 4.21s/it] 59%|█████▉ | 11018/18688 [12:56:26<8:53:29, 4.17s/it] 59%|█████▉ | 11019/18688 [12:56:31<9:10:20, 4.31s/it] 59%|█████▉ | 11020/18688 [12:56:35<9:25:42, 4.43s/it] {'loss': '0.9432', 'grad_norm': '1.125', 'learning_rate': '3.327e-05', 'epoch': '0.5897', 'num_input_tokens_seen': 37078200, 'train_runtime': '4.66e+04', 'train_tokens_per_second': '795.7'}
59%|█████▉ | 11020/18688 [12:56:35<9:25:42, 4.43s/it] 59%|█████▉ | 11021/18688 [12:56:39<9:13:13, 4.33s/it] 59%|█████▉ | 11022/18688 [12:56:43<8:52:21, 4.17s/it] 59%|█████▉ | 11023/18688 [12:56:47<8:31:26, 4.00s/it] 59%|█████▉ | 11024/18688 [12:56:51<8:38:55, 4.06s/it] 59%|█████▉ | 11025/18688 [12:56:56<9:20:57, 4.39s/it] 59%|█████▉ | 11026/18688 [12:57:01<9:21:31, 4.40s/it] 59%|█████▉ | 11027/18688 [12:57:05<9:26:45, 4.44s/it] 59%|█████▉ | 11028/18688 [12:57:09<8:53:32, 4.18s/it] 59%|█████▉ | 11029/18688 [12:57:12<8:35:47, 4.04s/it] 59%|█████▉ | 11030/18688 [12:57:16<8:29:12, 3.99s/it] {'loss': '0.861', 'grad_norm': '1.254', 'learning_rate': '3.32e-05', 'epoch': '0.5902', 'num_input_tokens_seen': 37111288, 'train_runtime': '4.664e+04', 'train_tokens_per_second': '795.8'}
59%|█████▉ | 11030/18688 [12:57:16<8:29:12, 3.99s/it] 59%|█████▉ | 11031/18688 [12:57:21<9:03:05, 4.26s/it] 59%|█████▉ | 11032/18688 [12:57:25<8:36:48, 4.05s/it] 59%|█████▉ | 11033/18688 [12:57:28<8:20:54, 3.93s/it] 59%|█████▉ | 11034/18688 [12:57:33<8:39:37, 4.07s/it] 59%|█████▉ | 11035/18688 [12:57:37<8:29:19, 3.99s/it] 59%|█████▉ | 11036/18688 [12:57:42<9:10:42, 4.32s/it] 59%|█████▉ | 11037/18688 [12:57:45<8:38:03, 4.06s/it] 59%|█████▉ | 11038/18688 [12:57:49<8:31:50, 4.01s/it] 59%|█████▉ | 11039/18688 [12:57:53<8:14:18, 3.88s/it] 59%|█████▉ | 11040/18688 [12:57:57<8:24:29, 3.96s/it] {'loss': '0.9391', 'grad_norm': '0.9957', 'learning_rate': '3.313e-05', 'epoch': '0.5908', 'num_input_tokens_seen': 37144120, 'train_runtime': '4.668e+04', 'train_tokens_per_second': '795.8'}
59%|█████▉ | 11040/18688 [12:57:57<8:24:29, 3.96s/it] 59%|█████▉ | 11041/18688 [12:58:01<8:20:00, 3.92s/it] 59%|█████▉ | 11042/18688 [12:58:04<8:15:21, 3.89s/it] 59%|█████▉ | 11043/18688 [12:58:09<8:48:39, 4.15s/it] 59%|█████▉ | 11044/18688 [12:58:14<9:04:16, 4.27s/it] 59%|█████▉ | 11045/18688 [12:58:17<8:32:16, 4.02s/it] 59%|█████▉ | 11046/18688 [12:58:21<8:37:19, 4.06s/it] 59%|█████▉ | 11047/18688 [12:58:25<8:27:15, 3.98s/it] 59%|█████▉ | 11048/18688 [12:58:29<8:15:28, 3.89s/it] 59%|█████▉ | 11049/18688 [12:58:33<8:42:58, 4.11s/it] 59%|█████▉ | 11050/18688 [12:58:37<8:37:40, 4.07s/it] {'loss': '0.9168', 'grad_norm': '1.264', 'learning_rate': '3.305e-05', 'epoch': '0.5913', 'num_input_tokens_seen': 37176384, 'train_runtime': '4.672e+04', 'train_tokens_per_second': '795.8'}
59%|█████▉ | 11050/18688 [12:58:37<8:37:40, 4.07s/it] 59%|█████▉ | 11051/18688 [12:58:42<8:41:50, 4.10s/it] 59%|█████▉ | 11052/18688 [12:58:45<8:34:16, 4.04s/it] 59%|█████▉ | 11053/18688 [12:58:50<8:43:28, 4.11s/it] 59%|█████▉ | 11054/18688 [12:58:54<8:41:28, 4.10s/it] 59%|█████▉ | 11055/18688 [12:58:58<8:30:32, 4.01s/it] 59%|█████▉ | 11056/18688 [12:59:03<9:14:47, 4.36s/it] 59%|█████▉ | 11057/18688 [12:59:07<8:51:48, 4.18s/it] 59%|█████▉ | 11058/18688 [12:59:11<9:06:22, 4.30s/it] 59%|█████▉ | 11059/18688 [12:59:15<8:40:02, 4.09s/it] 59%|█████▉ | 11060/18688 [12:59:19<8:48:09, 4.15s/it] {'loss': '0.9546', 'grad_norm': '1.018', 'learning_rate': '3.298e-05', 'epoch': '0.5918', 'num_input_tokens_seen': 37209768, 'train_runtime': '4.676e+04', 'train_tokens_per_second': '795.8'}
59%|█████▉ | 11060/18688 [12:59:19<8:48:09, 4.15s/it] 59%|█████▉ | 11061/18688 [12:59:23<8:53:51, 4.20s/it] 59%|█████▉ | 11062/18688 [12:59:27<8:32:39, 4.03s/it] 59%|█████▉ | 11063/18688 [12:59:32<8:54:36, 4.21s/it] 59%|█████▉ | 11064/18688 [12:59:35<8:26:38, 3.99s/it] 59%|█████▉ | 11065/18688 [12:59:40<9:04:46, 4.29s/it] 59%|█████▉ | 11066/18688 [12:59:44<8:52:03, 4.19s/it] 59%|█████▉ | 11067/18688 [12:59:48<8:46:20, 4.14s/it] 59%|█████▉ | 11068/18688 [12:59:52<8:46:46, 4.15s/it] 59%|█████▉ | 11069/18688 [12:59:56<8:33:14, 4.04s/it] 59%|█████▉ | 11070/18688 [13:00:00<8:36:51, 4.07s/it] {'loss': '0.8757', 'grad_norm': '1.334', 'learning_rate': '3.291e-05', 'epoch': '0.5924', 'num_input_tokens_seen': 37243352, 'train_runtime': '4.68e+04', 'train_tokens_per_second': '795.8'}
59%|█████▉ | 11070/18688 [13:00:00<8:36:51, 4.07s/it] 59%|█████▉ | 11071/18688 [13:00:04<8:45:13, 4.14s/it] 59%|█████▉ | 11072/18688 [13:00:09<8:55:58, 4.22s/it] 59%|█████▉ | 11073/18688 [13:00:13<9:00:34, 4.26s/it] 59%|█████▉ | 11074/18688 [13:00:18<9:11:51, 4.35s/it] 59%|█████▉ | 11075/18688 [13:00:22<8:53:38, 4.21s/it] 59%|█████▉ | 11076/18688 [13:00:25<8:32:20, 4.04s/it] 59%|█████▉ | 11077/18688 [13:00:29<8:23:38, 3.97s/it] 59%|█████▉ | 11078/18688 [13:00:34<9:08:19, 4.32s/it] 59%|█████▉ | 11079/18688 [13:00:38<8:38:26, 4.09s/it] 59%|█████▉ | 11080/18688 [13:00:42<8:23:40, 3.97s/it] {'loss': '0.8712', 'grad_norm': '1.185', 'learning_rate': '3.284e-05', 'epoch': '0.5929', 'num_input_tokens_seen': 37276464, 'train_runtime': '4.684e+04', 'train_tokens_per_second': '795.8'}
59%|█████▉ | 11080/18688 [13:00:42<8:23:40, 3.97s/it] 59%|█████▉ | 11081/18688 [13:00:45<8:15:40, 3.91s/it] 59%|█████▉ | 11082/18688 [13:00:50<8:55:29, 4.22s/it] 59%|█████▉ | 11083/18688 [13:00:55<9:17:03, 4.39s/it] 59%|█████▉ | 11084/18688 [13:00:59<9:05:48, 4.31s/it] 59%|█████▉ | 11085/18688 [13:01:04<9:18:10, 4.40s/it] 59%|█████▉ | 11086/18688 [13:01:08<9:02:17, 4.28s/it] 59%|█████▉ | 11087/18688 [13:01:12<9:12:28, 4.36s/it] 59%|█████▉ | 11088/18688 [13:01:16<8:57:45, 4.25s/it] 59%|█████▉ | 11089/18688 [13:01:21<9:02:44, 4.29s/it] 59%|█████▉ | 11090/18688 [13:01:25<8:49:03, 4.18s/it] {'loss': '0.9094', 'grad_norm': '1.198', 'learning_rate': '3.277e-05', 'epoch': '0.5934', 'num_input_tokens_seen': 37311592, 'train_runtime': '4.689e+04', 'train_tokens_per_second': '795.8'}
59%|█████▉ | 11090/18688 [13:01:25<8:49:03, 4.18s/it] 59%|█████▉ | 11091/18688 [13:01:29<8:47:22, 4.17s/it] 59%|█████▉ | 11092/18688 [13:01:34<9:17:44, 4.41s/it] 59%|█████▉ | 11093/18688 [13:01:38<9:10:55, 4.35s/it] 59%|█████▉ | 11094/18688 [13:01:43<9:21:21, 4.44s/it] 59%|█████▉ | 11095/18688 [13:01:46<8:57:15, 4.25s/it] 59%|█████▉ | 11096/18688 [13:01:51<9:00:48, 4.27s/it] 59%|█████▉ | 11097/18688 [13:01:55<8:53:22, 4.22s/it] 59%|█████▉ | 11098/18688 [13:01:58<8:27:35, 4.01s/it] 59%|█████▉ | 11099/18688 [13:02:03<8:45:57, 4.16s/it] 59%|█████▉ | 11100/18688 [13:02:07<8:58:25, 4.26s/it] {'loss': '0.896', 'grad_norm': '1.266', 'learning_rate': '3.269e-05', 'epoch': '0.594', 'num_input_tokens_seen': 37346912, 'train_runtime': '4.693e+04', 'train_tokens_per_second': '795.8'}
59%|█████▉ | 11100/18688 [13:02:07<8:58:25, 4.26s/it] 59%|█████▉ | 11101/18688 [13:02:11<8:52:45, 4.21s/it] 59%|█████▉ | 11102/18688 [13:02:16<9:15:18, 4.39s/it] 59%|█████▉ | 11103/18688 [13:02:20<8:43:54, 4.14s/it] 59%|█████▉ | 11104/18688 [13:02:24<8:47:53, 4.18s/it] 59%|█████▉ | 11105/18688 [13:02:28<8:28:33, 4.02s/it] 59%|█████▉ | 11106/18688 [13:02:32<8:33:49, 4.07s/it] 59%|█████▉ | 11107/18688 [13:02:36<8:43:26, 4.14s/it] 59%|█████▉ | 11108/18688 [13:02:40<8:33:13, 4.06s/it] 59%|█████▉ | 11109/18688 [13:02:44<8:37:11, 4.09s/it] 59%|█████▉ | 11110/18688 [13:02:48<8:21:13, 3.97s/it] {'loss': '0.9349', 'grad_norm': '1.402', 'learning_rate': '3.262e-05', 'epoch': '0.5945', 'num_input_tokens_seen': 37378160, 'train_runtime': '4.697e+04', 'train_tokens_per_second': '795.8'}
59%|█████▉ | 11110/18688 [13:02:48<8:21:13, 3.97s/it] 59%|█████▉ | 11111/18688 [13:02:52<8:19:05, 3.95s/it] 59%|█████▉ | 11112/18688 [13:02:57<9:07:28, 4.34s/it] 59%|█████▉ | 11113/18688 [13:03:01<9:11:13, 4.37s/it] 59%|█████▉ | 11114/18688 [13:03:05<8:46:21, 4.17s/it] 59%|█████▉ | 11115/18688 [13:03:10<9:05:42, 4.32s/it] 59%|█████▉ | 11116/18688 [13:03:14<8:44:28, 4.16s/it] 59%|█████▉ | 11117/18688 [13:03:17<8:31:26, 4.05s/it] 59%|█████▉ | 11118/18688 [13:03:22<8:40:00, 4.12s/it] 59%|█████▉ | 11119/18688 [13:03:26<8:34:14, 4.08s/it] 60%|█████▉ | 11120/18688 [13:03:30<8:31:45, 4.06s/it] {'loss': '0.8821', 'grad_norm': '1.296', 'learning_rate': '3.255e-05', 'epoch': '0.5951', 'num_input_tokens_seen': 37411824, 'train_runtime': '4.701e+04', 'train_tokens_per_second': '795.8'}
60%|█████▉ | 11120/18688 [13:03:30<8:31:45, 4.06s/it] 60%|█████▉ | 11121/18688 [13:03:34<8:36:43, 4.10s/it] 60%|█████▉ | 11122/18688 [13:03:38<8:21:47, 3.98s/it] 60%|█████▉ | 11123/18688 [13:03:42<8:18:45, 3.96s/it] 60%|█████▉ | 11124/18688 [13:03:47<9:03:09, 4.31s/it] 60%|█████▉ | 11125/18688 [13:03:51<9:11:22, 4.37s/it] 60%|█████▉ | 11126/18688 [13:03:55<8:51:37, 4.22s/it] 60%|█████▉ | 11127/18688 [13:04:00<9:06:11, 4.33s/it] 60%|█████▉ | 11128/18688 [13:04:04<8:55:16, 4.25s/it] 60%|█████▉ | 11129/18688 [13:04:08<8:49:15, 4.20s/it] 60%|█████▉ | 11130/18688 [13:04:12<8:31:50, 4.06s/it] {'loss': '0.908', 'grad_norm': '1.33', 'learning_rate': '3.248e-05', 'epoch': '0.5956', 'num_input_tokens_seen': 37445888, 'train_runtime': '4.705e+04', 'train_tokens_per_second': '795.8'}
60%|█████▉ | 11130/18688 [13:04:12<8:31:50, 4.06s/it] 60%|█████▉ | 11131/18688 [13:04:16<8:37:06, 4.11s/it] 60%|█████▉ | 11132/18688 [13:04:20<9:01:00, 4.30s/it] 60%|█████▉ | 11133/18688 [13:04:25<8:54:02, 4.24s/it] 60%|█████▉ | 11134/18688 [13:04:30<9:36:16, 4.58s/it] 60%|█████▉ | 11135/18688 [13:04:34<9:12:28, 4.39s/it] 60%|█████▉ | 11136/18688 [13:04:38<8:44:06, 4.16s/it] 60%|█████▉ | 11137/18688 [13:04:42<8:54:04, 4.24s/it] 60%|█████▉ | 11138/18688 [13:04:46<8:30:16, 4.06s/it] 60%|█████▉ | 11139/18688 [13:04:50<8:25:51, 4.02s/it] 60%|█████▉ | 11140/18688 [13:04:53<8:10:19, 3.90s/it] {'loss': '0.8467', 'grad_norm': '1.192', 'learning_rate': '3.241e-05', 'epoch': '0.5961', 'num_input_tokens_seen': 37479048, 'train_runtime': '4.709e+04', 'train_tokens_per_second': '795.8'}
60%|█████▉ | 11140/18688 [13:04:53<8:10:19, 3.90s/it] 60%|█████▉ | 11141/18688 [13:04:57<8:09:48, 3.89s/it] 60%|█████▉ | 11142/18688 [13:05:01<7:59:53, 3.82s/it] 60%|█████▉ | 11143/18688 [13:05:06<8:46:59, 4.19s/it] 60%|█████▉ | 11144/18688 [13:05:10<8:39:19, 4.13s/it] 60%|█████▉ | 11145/18688 [13:05:14<8:40:18, 4.14s/it] 60%|█████▉ | 11146/18688 [13:05:18<8:22:20, 4.00s/it] 60%|█████▉ | 11147/18688 [13:05:21<8:19:37, 3.98s/it] 60%|█████▉ | 11148/18688 [13:05:25<8:10:00, 3.90s/it] 60%|█████▉ | 11149/18688 [13:05:29<8:24:26, 4.01s/it] 60%|█████▉ | 11150/18688 [13:05:34<8:38:24, 4.13s/it] {'loss': '0.8991', 'grad_norm': '1.225', 'learning_rate': '3.233e-05', 'epoch': '0.5967', 'num_input_tokens_seen': 37512280, 'train_runtime': '4.713e+04', 'train_tokens_per_second': '795.9'}
60%|█████▉ | 11150/18688 [13:05:34<8:38:24, 4.13s/it] 60%|█████▉ | 11151/18688 [13:05:38<8:53:30, 4.25s/it] 60%|█████▉ | 11152/18688 [13:05:42<8:29:39, 4.06s/it] 60%|█████▉ | 11153/18688 [13:05:46<8:21:37, 3.99s/it] 60%|█████▉ | 11154/18688 [13:05:50<8:43:24, 4.17s/it] 60%|█████▉ | 11155/18688 [13:05:55<8:51:16, 4.23s/it] 60%|█████▉ | 11156/18688 [13:05:59<8:43:22, 4.17s/it] 60%|█████▉ | 11157/18688 [13:06:03<8:36:12, 4.11s/it] 60%|█████▉ | 11158/18688 [13:06:07<8:45:16, 4.19s/it] 60%|█████▉ | 11159/18688 [13:06:11<8:40:27, 4.15s/it] 60%|█████▉ | 11160/18688 [13:06:15<8:36:03, 4.11s/it] {'loss': '0.9222', 'grad_norm': '1.074', 'learning_rate': '3.226e-05', 'epoch': '0.5972', 'num_input_tokens_seen': 37544480, 'train_runtime': '4.718e+04', 'train_tokens_per_second': '795.8'}
60%|█████▉ | 11160/18688 [13:06:15<8:36:03, 4.11s/it] 60%|█████▉ | 11161/18688 [13:06:19<8:28:43, 4.06s/it] 60%|█████▉ | 11162/18688 [13:06:23<8:23:23, 4.01s/it] 60%|█████▉ | 11163/18688 [13:06:27<8:29:19, 4.06s/it] 60%|█████▉ | 11164/18688 [13:06:32<8:46:15, 4.20s/it] 60%|█████▉ | 11165/18688 [13:06:36<8:41:50, 4.16s/it] 60%|█████▉ | 11166/18688 [13:06:40<8:35:55, 4.12s/it] 60%|█████▉ | 11167/18688 [13:06:43<8:12:51, 3.93s/it] 60%|█████▉ | 11168/18688 [13:06:48<8:34:56, 4.11s/it] 60%|█████▉ | 11169/18688 [13:06:53<9:08:24, 4.38s/it] 60%|█████▉ | 11170/18688 [13:06:56<8:36:26, 4.12s/it] {'loss': '0.9622', 'grad_norm': '1.159', 'learning_rate': '3.219e-05', 'epoch': '0.5977', 'num_input_tokens_seen': 37577344, 'train_runtime': '4.722e+04', 'train_tokens_per_second': '795.8'}
60%|█████▉ | 11170/18688 [13:06:56<8:36:26, 4.12s/it] 60%|█████▉ | 11171/18688 [13:07:00<8:19:08, 3.98s/it] 60%|█████▉ | 11172/18688 [13:07:04<7:59:30, 3.83s/it] 60%|█████▉ | 11173/18688 [13:07:07<7:59:29, 3.83s/it] 60%|█████▉ | 11174/18688 [13:07:11<8:09:45, 3.91s/it] 60%|█████▉ | 11175/18688 [13:07:16<8:25:27, 4.04s/it] 60%|█████▉ | 11176/18688 [13:07:21<8:55:16, 4.28s/it] 60%|█████▉ | 11177/18688 [13:07:24<8:33:23, 4.10s/it] 60%|█████▉ | 11178/18688 [13:07:29<8:43:09, 4.18s/it] 60%|█████▉ | 11179/18688 [13:07:32<8:21:30, 4.01s/it] 60%|█████▉ | 11180/18688 [13:07:36<8:15:03, 3.96s/it] {'loss': '0.9129', 'grad_norm': '1.489', 'learning_rate': '3.212e-05', 'epoch': '0.5983', 'num_input_tokens_seen': 37609192, 'train_runtime': '4.726e+04', 'train_tokens_per_second': '795.9'}
60%|█████▉ | 11180/18688 [13:07:36<8:15:03, 3.96s/it] 60%|█████▉ | 11181/18688 [13:07:40<7:59:47, 3.83s/it] 60%|█████▉ | 11182/18688 [13:07:43<7:52:55, 3.78s/it] 60%|█████▉ | 11183/18688 [13:07:47<8:04:52, 3.88s/it] 60%|█████▉ | 11184/18688 [13:07:51<8:04:14, 3.87s/it] 60%|█████▉ | 11185/18688 [13:07:56<8:51:52, 4.25s/it] 60%|█████▉ | 11186/18688 [13:08:01<8:48:05, 4.22s/it] 60%|█████▉ | 11187/18688 [13:08:05<8:49:02, 4.23s/it] 60%|█████▉ | 11188/18688 [13:08:09<8:43:59, 4.19s/it] 60%|█████▉ | 11189/18688 [13:08:14<9:07:32, 4.38s/it] 60%|█████▉ | 11190/18688 [13:08:18<9:08:51, 4.39s/it] {'loss': '0.8851', 'grad_norm': '1.351', 'learning_rate': '3.205e-05', 'epoch': '0.5988', 'num_input_tokens_seen': 37643704, 'train_runtime': '4.73e+04', 'train_tokens_per_second': '795.9'}
60%|█████▉ | 11190/18688 [13:08:18<9:08:51, 4.39s/it] 60%|█████▉ | 11191/18688 [13:08:22<8:37:51, 4.14s/it] 60%|█████▉ | 11192/18688 [13:08:25<8:23:04, 4.03s/it] 60%|█████▉ | 11193/18688 [13:08:30<8:56:13, 4.29s/it] 60%|█████▉ | 11194/18688 [13:08:36<9:32:40, 4.59s/it] 60%|█████▉ | 11195/18688 [13:08:41<10:04:35, 4.84s/it] 60%|█████▉ | 11196/18688 [13:08:45<9:20:14, 4.49s/it] 60%|█████▉ | 11197/18688 [13:08:49<9:14:26, 4.44s/it] 60%|█████▉ | 11198/18688 [13:08:53<8:48:16, 4.23s/it] 60%|█████▉ | 11199/18688 [13:08:57<8:31:54, 4.10s/it] 60%|█████▉ | 11200/18688 [13:09:01<8:31:43, 4.10s/it] {'loss': '0.859', 'grad_norm': '1.128', 'learning_rate': '3.198e-05', 'epoch': '0.5993', 'num_input_tokens_seen': 37678432, 'train_runtime': '4.734e+04', 'train_tokens_per_second': '795.9'}
60%|█████▉ | 11200/18688 [13:09:01<8:31:43, 4.10s/it] 60%|█████▉ | 11201/18688 [13:09:04<8:17:15, 3.98s/it] 60%|█████▉ | 11202/18688 [13:09:08<8:08:06, 3.91s/it] 60%|█████▉ | 11203/18688 [13:09:13<8:22:58, 4.03s/it] 60%|█████▉ | 11204/18688 [13:09:17<8:24:38, 4.05s/it] 60%|█████▉ | 11205/18688 [13:09:21<8:21:38, 4.02s/it] 60%|█████▉ | 11206/18688 [13:09:25<8:31:48, 4.10s/it] 60%|█████▉ | 11207/18688 [13:09:29<8:51:19, 4.26s/it] 60%|█████▉ | 11208/18688 [13:09:33<8:38:36, 4.16s/it] 60%|█████▉ | 11209/18688 [13:09:38<8:50:44, 4.26s/it] 60%|█████▉ | 11210/18688 [13:09:42<8:32:45, 4.11s/it] {'loss': '0.8767', 'grad_norm': '1.559', 'learning_rate': '3.19e-05', 'epoch': '0.5999', 'num_input_tokens_seen': 37711928, 'train_runtime': '4.738e+04', 'train_tokens_per_second': '795.9'}
60%|█████▉ | 11210/18688 [13:09:42<8:32:45, 4.11s/it] 60%|█████▉ | 11211/18688 [13:09:46<8:37:21, 4.15s/it] 60%|█████▉ | 11212/18688 [13:09:50<8:21:43, 4.03s/it] 60%|██████ | 11213/18688 [13:09:54<8:31:57, 4.11s/it] 60%|██████ | 11214/18688 [13:09:59<9:24:20, 4.53s/it] 60%|██████ | 11215/18688 [13:10:03<8:54:29, 4.29s/it] 60%|██████ | 11216/18688 [13:10:08<8:56:23, 4.31s/it] 60%|██████ | 11217/18688 [13:10:11<8:25:12, 4.06s/it] 60%|██████ | 11218/18688 [13:10:15<8:40:39, 4.18s/it] 60%|██████ | 11219/18688 [13:10:19<8:26:21, 4.07s/it] 60%|██████ | 11220/18688 [13:10:23<8:17:16, 4.00s/it] {'loss': '0.9133', 'grad_norm': '1.395', 'learning_rate': '3.183e-05', 'epoch': '0.6004', 'num_input_tokens_seen': 37746016, 'train_runtime': '4.742e+04', 'train_tokens_per_second': '795.9'}
60%|██████ | 11220/18688 [13:10:23<8:17:16, 4.00s/it] 60%|██████ | 11221/18688 [13:10:28<8:37:47, 4.16s/it] 60%|██████ | 11222/18688 [13:10:32<8:29:26, 4.09s/it] 60%|██████ | 11223/18688 [13:10:36<8:27:08, 4.08s/it] 60%|██████ | 11224/18688 [13:10:41<9:00:27, 4.34s/it] 60%|██████ | 11225/18688 [13:10:45<8:53:12, 4.29s/it] 60%|██████ | 11226/18688 [13:10:49<8:49:09, 4.25s/it] 60%|██████ | 11227/18688 [13:10:53<8:33:18, 4.13s/it] 60%|██████ | 11228/18688 [13:10:57<8:48:36, 4.25s/it] 60%|██████ | 11229/18688 [13:11:02<8:51:21, 4.27s/it] 60%|██████ | 11230/18688 [13:11:06<8:46:37, 4.24s/it] {'loss': '0.93', 'grad_norm': '1.608', 'learning_rate': '3.176e-05', 'epoch': '0.6009', 'num_input_tokens_seen': 37781880, 'train_runtime': '4.747e+04', 'train_tokens_per_second': '796'}
60%|██████ | 11230/18688 [13:11:06<8:46:37, 4.24s/it] 60%|██████ | 11231/18688 [13:11:09<8:24:16, 4.06s/it] 60%|██████ | 11232/18688 [13:11:14<8:36:07, 4.15s/it] 60%|██████ | 11233/18688 [13:11:18<8:32:55, 4.13s/it] 60%|██████ | 11234/18688 [13:11:22<8:29:05, 4.10s/it] 60%|██████ | 11235/18688 [13:11:26<8:34:50, 4.14s/it] 60%|██████ | 11236/18688 [13:11:31<8:54:04, 4.30s/it] 60%|██████ | 11237/18688 [13:11:35<9:04:13, 4.38s/it] 60%|██████ | 11238/18688 [13:11:41<9:48:48, 4.74s/it] 60%|██████ | 11239/18688 [13:11:45<9:14:43, 4.47s/it] 60%|██████ | 11240/18688 [13:11:49<9:04:44, 4.39s/it] {'loss': '0.9182', 'grad_norm': '1.114', 'learning_rate': '3.169e-05', 'epoch': '0.6015', 'num_input_tokens_seen': 37819928, 'train_runtime': '4.751e+04', 'train_tokens_per_second': '796'}
60%|██████ | 11240/18688 [13:11:49<9:04:44, 4.39s/it] 60%|██████ | 11241/18688 [13:11:53<9:00:06, 4.35s/it] 60%|██████ | 11242/18688 [13:11:58<9:11:50, 4.45s/it] 60%|██████ | 11243/18688 [13:12:02<9:00:54, 4.36s/it] 60%|██████ | 11244/18688 [13:12:06<9:01:03, 4.36s/it] 60%|██████ | 11245/18688 [13:12:11<9:10:23, 4.44s/it] 60%|██████ | 11246/18688 [13:12:15<8:41:25, 4.20s/it] 60%|██████ | 11247/18688 [13:12:18<8:24:08, 4.07s/it] 60%|██████ | 11248/18688 [13:12:22<8:22:07, 4.05s/it] 60%|██████ | 11249/18688 [13:12:27<8:53:06, 4.30s/it] 60%|██████ | 11250/18688 [13:12:32<9:09:46, 4.43s/it] {'loss': '0.9662', 'grad_norm': '1.295', 'learning_rate': '3.162e-05', 'epoch': '0.602', 'num_input_tokens_seen': 37855248, 'train_runtime': '4.755e+04', 'train_tokens_per_second': '796.1'}
60%|██████ | 11250/18688 [13:12:32<9:09:46, 4.43s/it] 60%|██████ | 11251/18688 [13:12:36<8:39:02, 4.19s/it] 60%|██████ | 11252/18688 [13:12:39<8:21:24, 4.05s/it] 60%|██████ | 11253/18688 [13:12:43<8:12:27, 3.97s/it] 60%|██████ | 11254/18688 [13:12:47<8:03:42, 3.90s/it] 60%|██████ | 11255/18688 [13:12:51<7:57:07, 3.85s/it] 60%|██████ | 11256/18688 [13:12:55<8:15:48, 4.00s/it] 60%|██████ | 11257/18688 [13:12:59<8:28:30, 4.11s/it] 60%|██████ | 11258/18688 [13:13:04<8:38:18, 4.19s/it] 60%|██████ | 11259/18688 [13:13:08<8:39:06, 4.19s/it] 60%|██████ | 11260/18688 [13:13:12<8:49:37, 4.28s/it] {'loss': '0.8963', 'grad_norm': '1.34', 'learning_rate': '3.155e-05', 'epoch': '0.6025', 'num_input_tokens_seen': 37886672, 'train_runtime': '4.759e+04', 'train_tokens_per_second': '796.1'}
60%|██████ | 11260/18688 [13:13:12<8:49:37, 4.28s/it] 60%|██████ | 11261/18688 [13:13:16<8:30:32, 4.12s/it] 60%|██████ | 11262/18688 [13:13:20<8:33:27, 4.15s/it] 60%|██████ | 11263/18688 [13:13:24<8:16:08, 4.01s/it] 60%|██████ | 11264/18688 [13:13:28<8:04:42, 3.92s/it] 60%|██████ | 11265/18688 [13:13:32<8:20:48, 4.05s/it] 60%|██████ | 11266/18688 [13:13:37<8:35:57, 4.17s/it] 60%|██████ | 11267/18688 [13:13:41<8:24:56, 4.08s/it] 60%|██████ | 11268/18688 [13:13:44<8:09:24, 3.96s/it] 60%|██████ | 11269/18688 [13:13:48<8:21:36, 4.06s/it] 60%|██████ | 11270/18688 [13:13:52<8:10:13, 3.97s/it] {'loss': '0.9362', 'grad_norm': '1.196', 'learning_rate': '3.148e-05', 'epoch': '0.6031', 'num_input_tokens_seen': 37917320, 'train_runtime': '4.763e+04', 'train_tokens_per_second': '796'}
60%|██████ | 11270/18688 [13:13:52<8:10:13, 3.97s/it] 60%|██████ | 11271/18688 [13:13:56<8:18:53, 4.04s/it] 60%|██████ | 11272/18688 [13:14:01<8:53:30, 4.32s/it] 60%|██████ | 11273/18688 [13:14:05<8:38:38, 4.20s/it] 60%|██████ | 11274/18688 [13:14:09<8:30:20, 4.13s/it] 60%|██████ | 11275/18688 [13:14:14<8:40:57, 4.22s/it] 60%|██████ | 11276/18688 [13:14:19<9:25:08, 4.57s/it] 60%|██████ | 11277/18688 [13:14:23<8:59:09, 4.37s/it] 60%|██████ | 11278/18688 [13:14:27<9:02:48, 4.40s/it] 60%|██████ | 11279/18688 [13:14:32<8:55:25, 4.34s/it] 60%|██████ | 11280/18688 [13:14:36<8:50:28, 4.30s/it] {'loss': '0.9212', 'grad_norm': '1.133', 'learning_rate': '3.14e-05', 'epoch': '0.6036', 'num_input_tokens_seen': 37953256, 'train_runtime': '4.768e+04', 'train_tokens_per_second': '796.1'}
60%|██████ | 11280/18688 [13:14:36<8:50:28, 4.30s/it] 60%|██████ | 11281/18688 [13:14:40<8:45:07, 4.25s/it] 60%|██████ | 11282/18688 [13:14:44<8:26:08, 4.10s/it] 60%|██████ | 11283/18688 [13:14:49<9:17:17, 4.52s/it] 60%|██████ | 11284/18688 [13:14:53<9:05:35, 4.42s/it] 60%|██████ | 11285/18688 [13:14:58<9:12:03, 4.47s/it] 60%|██████ | 11286/18688 [13:15:02<8:37:54, 4.20s/it] 60%|██████ | 11287/18688 [13:15:05<8:13:30, 4.00s/it] 60%|██████ | 11288/18688 [13:15:09<8:01:29, 3.90s/it] 60%|██████ | 11289/18688 [13:15:13<8:30:02, 4.14s/it] 60%|██████ | 11290/18688 [13:15:17<8:13:57, 4.01s/it] {'loss': '0.8558', 'grad_norm': '1.284', 'learning_rate': '3.133e-05', 'epoch': '0.6041', 'num_input_tokens_seen': 37986632, 'train_runtime': '4.772e+04', 'train_tokens_per_second': '796.1'}
60%|██████ | 11290/18688 [13:15:17<8:13:57, 4.01s/it] 60%|██████ | 11291/18688 [13:15:22<8:27:17, 4.11s/it] 60%|██████ | 11292/18688 [13:15:25<8:11:19, 3.99s/it] 60%|██████ | 11293/18688 [13:15:30<8:27:29, 4.12s/it] 60%|██████ | 11294/18688 [13:15:35<9:12:24, 4.48s/it] 60%|██████ | 11295/18688 [13:15:39<8:46:26, 4.27s/it] 60%|██████ | 11296/18688 [13:15:42<8:23:36, 4.09s/it] 60%|██████ | 11297/18688 [13:15:46<8:01:36, 3.91s/it] 60%|██████ | 11298/18688 [13:15:50<8:01:07, 3.91s/it] 60%|██████ | 11299/18688 [13:15:54<8:24:23, 4.10s/it] 60%|██████ | 11300/18688 [13:15:58<8:22:46, 4.08s/it] {'loss': '0.9377', 'grad_norm': '1.205', 'learning_rate': '3.126e-05', 'epoch': '0.6047', 'num_input_tokens_seen': 38021816, 'train_runtime': '4.776e+04', 'train_tokens_per_second': '796.1'}
60%|██████ | 11300/18688 [13:15:58<8:22:46, 4.08s/it] 60%|██████ | 11301/18688 [13:16:03<8:22:33, 4.08s/it] 60%|██████ | 11302/18688 [13:16:07<8:33:33, 4.17s/it] 60%|██████ | 11303/18688 [13:16:11<8:18:08, 4.05s/it] 60%|██████ | 11304/18688 [13:16:15<8:46:22, 4.28s/it] 60%|██████ | 11305/18688 [13:16:19<8:19:10, 4.06s/it] 60%|██████ | 11306/18688 [13:16:23<8:15:04, 4.02s/it] 61%|██████ | 11307/18688 [13:16:27<8:29:03, 4.14s/it] 61%|██████ | 11308/18688 [13:16:31<8:04:19, 3.94s/it] 61%|██████ | 11309/18688 [13:16:35<8:02:49, 3.93s/it] 61%|██████ | 11310/18688 [13:16:39<8:09:14, 3.98s/it] {'loss': '0.8797', 'grad_norm': '1.315', 'learning_rate': '3.119e-05', 'epoch': '0.6052', 'num_input_tokens_seen': 38053976, 'train_runtime': '4.78e+04', 'train_tokens_per_second': '796.1'}
61%|██████ | 11310/18688 [13:16:39<8:09:14, 3.98s/it] 61%|██████ | 11311/18688 [13:16:44<8:49:25, 4.31s/it] 61%|██████ | 11312/18688 [13:16:47<8:21:26, 4.08s/it] 61%|██████ | 11313/18688 [13:16:51<8:11:43, 4.00s/it] 61%|██████ | 11314/18688 [13:16:55<8:13:29, 4.02s/it] 61%|██████ | 11315/18688 [13:17:00<8:27:34, 4.13s/it] 61%|██████ | 11316/18688 [13:17:03<8:11:16, 4.00s/it] 61%|██████ | 11317/18688 [13:17:07<8:00:26, 3.91s/it] 61%|██████ | 11318/18688 [13:17:11<7:53:17, 3.85s/it] 61%|██████ | 11319/18688 [13:17:15<8:22:01, 4.09s/it] 61%|██████ | 11320/18688 [13:17:20<8:34:02, 4.19s/it] {'loss': '0.898', 'grad_norm': '1.194', 'learning_rate': '3.112e-05', 'epoch': '0.6058', 'num_input_tokens_seen': 38086360, 'train_runtime': '4.784e+04', 'train_tokens_per_second': '796.1'}
61%|██████ | 11320/18688 [13:17:20<8:34:02, 4.19s/it] 61%|██████ | 11321/18688 [13:17:25<8:50:27, 4.32s/it] 61%|██████ | 11322/18688 [13:17:29<8:45:17, 4.28s/it] 61%|██████ | 11323/18688 [13:17:33<8:30:32, 4.16s/it] 61%|██████ | 11324/18688 [13:17:36<8:04:04, 3.94s/it] 61%|██████ | 11325/18688 [13:17:40<8:04:28, 3.95s/it] 61%|██████ | 11326/18688 [13:17:44<8:18:43, 4.06s/it] 61%|██████ | 11327/18688 [13:17:49<8:26:43, 4.13s/it] 61%|██████ | 11328/18688 [13:17:52<8:03:01, 3.94s/it] 61%|██████ | 11329/18688 [13:17:56<7:49:18, 3.83s/it] 61%|██████ | 11330/18688 [13:17:59<7:38:59, 3.74s/it] {'loss': '0.8665', 'grad_norm': '1.365', 'learning_rate': '3.105e-05', 'epoch': '0.6063', 'num_input_tokens_seen': 38117032, 'train_runtime': '4.788e+04', 'train_tokens_per_second': '796.1'}
61%|██████ | 11330/18688 [13:17:59<7:38:59, 3.74s/it] 61%|██████ | 11331/18688 [13:18:05<8:56:56, 4.38s/it] 61%|██████ | 11332/18688 [13:18:10<9:10:09, 4.49s/it] 61%|██████ | 11333/18688 [13:18:13<8:40:12, 4.24s/it] 61%|██████ | 11334/18688 [13:18:19<9:10:50, 4.49s/it] 61%|██████ | 11335/18688 [13:18:24<9:34:13, 4.69s/it] 61%|██████ | 11336/18688 [13:18:28<9:10:10, 4.49s/it] 61%|██████ | 11337/18688 [13:18:32<9:13:45, 4.52s/it] 61%|██████ | 11338/18688 [13:18:36<8:51:04, 4.34s/it] 61%|██████ | 11339/18688 [13:18:40<8:34:10, 4.20s/it] 61%|██████ | 11340/18688 [13:18:44<8:15:09, 4.04s/it] {'loss': '0.986', 'grad_norm': '1.238', 'learning_rate': '3.098e-05', 'epoch': '0.6068', 'num_input_tokens_seen': 38154872, 'train_runtime': '4.792e+04', 'train_tokens_per_second': '796.1'}
61%|██████ | 11340/18688 [13:18:44<8:15:09, 4.04s/it] 61%|██████ | 11341/18688 [13:18:48<8:33:50, 4.20s/it] 61%|██████ | 11342/18688 [13:18:52<8:19:53, 4.08s/it] 61%|██████ | 11343/18688 [13:18:56<8:12:20, 4.02s/it] 61%|██████ | 11344/18688 [13:19:00<8:12:07, 4.02s/it] 61%|██████ | 11345/18688 [13:19:04<8:26:21, 4.14s/it] 61%|██████ | 11346/18688 [13:19:09<8:25:59, 4.14s/it] 61%|██████ | 11347/18688 [13:19:12<8:03:46, 3.95s/it] 61%|██████ | 11348/18688 [13:19:17<8:51:02, 4.34s/it] 61%|██████ | 11349/18688 [13:19:23<9:26:22, 4.63s/it] 61%|██████ | 11350/18688 [13:19:27<9:15:01, 4.54s/it] {'loss': '0.9104', 'grad_norm': '1.182', 'learning_rate': '3.09e-05', 'epoch': '0.6074', 'num_input_tokens_seen': 38191624, 'train_runtime': '4.797e+04', 'train_tokens_per_second': '796.2'}
61%|██████ | 11350/18688 [13:19:27<9:15:01, 4.54s/it] 61%|██████ | 11351/18688 [13:19:31<8:47:03, 4.31s/it] 61%|██████ | 11352/18688 [13:19:35<8:45:10, 4.30s/it] 61%|██████ | 11353/18688 [13:19:39<8:34:39, 4.21s/it] 61%|██████ | 11354/18688 [13:19:43<8:32:18, 4.19s/it] 61%|██████ | 11355/18688 [13:19:47<8:29:06, 4.17s/it] 61%|██████ | 11356/18688 [13:19:52<8:42:21, 4.27s/it] 61%|██████ | 11357/18688 [13:19:55<8:19:41, 4.09s/it] 61%|██████ | 11358/18688 [13:19:59<8:15:07, 4.05s/it] 61%|██████ | 11359/18688 [13:20:05<9:15:29, 4.55s/it] 61%|██████ | 11360/18688 [13:20:09<8:44:57, 4.30s/it] {'loss': '0.9929', 'grad_norm': '1.543', 'learning_rate': '3.083e-05', 'epoch': '0.6079', 'num_input_tokens_seen': 38224632, 'train_runtime': '4.801e+04', 'train_tokens_per_second': '796.2'}
61%|██████ | 11360/18688 [13:20:09<8:44:57, 4.30s/it] 61%|██████ | 11361/18688 [13:20:13<8:39:08, 4.25s/it] 61%|██████ | 11362/18688 [13:20:17<8:28:25, 4.16s/it] 61%|██████ | 11363/18688 [13:20:21<8:11:36, 4.03s/it] 61%|██████ | 11364/18688 [13:20:25<8:37:01, 4.24s/it] 61%|██████ | 11365/18688 [13:20:30<8:45:18, 4.30s/it] 61%|██████ | 11366/18688 [13:20:35<9:17:43, 4.57s/it] 61%|██████ | 11367/18688 [13:20:39<8:50:30, 4.35s/it] 61%|██████ | 11368/18688 [13:20:42<8:21:18, 4.11s/it] 61%|██████ | 11369/18688 [13:20:46<8:16:38, 4.07s/it] 61%|██████ | 11370/18688 [13:20:50<8:06:29, 3.99s/it] {'loss': '0.9833', 'grad_norm': '1.291', 'learning_rate': '3.076e-05', 'epoch': '0.6084', 'num_input_tokens_seen': 38257520, 'train_runtime': '4.805e+04', 'train_tokens_per_second': '796.2'}
61%|██████ | 11370/18688 [13:20:50<8:06:29, 3.99s/it] 61%|██████ | 11371/18688 [13:20:54<8:13:47, 4.05s/it] 61%|██████ | 11372/18688 [13:20:59<8:30:59, 4.19s/it] 61%|██████ | 11373/18688 [13:21:03<8:09:56, 4.02s/it] 61%|██████ | 11374/18688 [13:21:07<8:13:08, 4.05s/it] 61%|██████ | 11375/18688 [13:21:11<8:14:06, 4.05s/it] 61%|██████ | 11376/18688 [13:21:15<8:37:34, 4.25s/it] 61%|██████ | 11377/18688 [13:21:19<8:23:12, 4.13s/it] 61%|██████ | 11378/18688 [13:21:24<8:30:34, 4.19s/it] 61%|██████ | 11379/18688 [13:21:27<8:15:51, 4.07s/it] 61%|██████ | 11380/18688 [13:21:32<8:31:55, 4.20s/it] {'loss': '0.9', 'grad_norm': '1.236', 'learning_rate': '3.069e-05', 'epoch': '0.609', 'num_input_tokens_seen': 38292432, 'train_runtime': '4.809e+04', 'train_tokens_per_second': '796.2'}
61%|██████ | 11380/18688 [13:21:32<8:31:55, 4.20s/it] 61%|██████ | 11381/18688 [13:21:36<8:09:23, 4.02s/it] 61%|██████ | 11382/18688 [13:21:40<8:16:08, 4.07s/it] 61%|██████ | 11383/18688 [13:21:45<8:43:37, 4.30s/it] 61%|██████ | 11384/18688 [13:21:49<9:01:08, 4.45s/it] 61%|██████ | 11385/18688 [13:21:53<8:40:35, 4.28s/it] 61%|██████ | 11386/18688 [13:21:57<8:33:52, 4.22s/it] 61%|██████ | 11387/18688 [13:22:02<8:35:41, 4.24s/it] 61%|██████ | 11388/18688 [13:22:06<8:26:27, 4.16s/it] 61%|██████ | 11389/18688 [13:22:09<8:07:28, 4.01s/it] 61%|██████ | 11390/18688 [13:22:13<8:08:13, 4.01s/it] {'loss': '0.9086', 'grad_norm': '1.273', 'learning_rate': '3.062e-05', 'epoch': '0.6095', 'num_input_tokens_seen': 38326040, 'train_runtime': '4.813e+04', 'train_tokens_per_second': '796.2'}
61%|██████ | 11390/18688 [13:22:13<8:08:13, 4.01s/it] 61%|██████ | 11391/18688 [13:22:17<7:50:24, 3.87s/it] 61%|██████ | 11392/18688 [13:22:20<7:45:31, 3.83s/it] 61%|██████ | 11393/18688 [13:22:24<7:46:45, 3.84s/it] 61%|██████ | 11394/18688 [13:22:28<7:49:53, 3.87s/it] 61%|██████ | 11395/18688 [13:22:32<7:49:03, 3.86s/it] 61%|██████ | 11396/18688 [13:22:36<8:00:25, 3.95s/it] 61%|██████ | 11397/18688 [13:22:41<8:09:55, 4.03s/it] 61%|██████ | 11398/18688 [13:22:44<8:03:37, 3.98s/it] 61%|██████ | 11399/18688 [13:22:48<7:54:17, 3.90s/it] 61%|██████ | 11400/18688 [13:22:52<7:57:45, 3.93s/it] {'loss': '0.9435', 'grad_norm': '1.288', 'learning_rate': '3.055e-05', 'epoch': '0.61', 'num_input_tokens_seen': 38355688, 'train_runtime': '4.817e+04', 'train_tokens_per_second': '796.2'}
61%|██████ | 11400/18688 [13:22:52<7:57:45, 3.93s/it] 61%|██████ | 11401/18688 [13:22:57<8:34:44, 4.24s/it] 61%|██████ | 11402/18688 [13:23:02<8:43:59, 4.32s/it] 61%|██████ | 11403/18688 [13:23:05<8:18:07, 4.10s/it] 61%|██████ | 11404/18688 [13:23:09<8:12:32, 4.06s/it] 61%|██████ | 11405/18688 [13:23:14<8:24:46, 4.16s/it] 61%|██████ | 11406/18688 [13:23:17<8:02:43, 3.98s/it] 61%|██████ | 11407/18688 [13:23:21<7:54:37, 3.91s/it] 61%|██████ | 11408/18688 [13:23:26<8:49:21, 4.36s/it] 61%|██████ | 11409/18688 [13:23:31<8:52:56, 4.39s/it] 61%|██████ | 11410/18688 [13:23:35<8:47:24, 4.35s/it] {'loss': '0.8939', 'grad_norm': '1.42', 'learning_rate': '3.048e-05', 'epoch': '0.6106', 'num_input_tokens_seen': 38391136, 'train_runtime': '4.822e+04', 'train_tokens_per_second': '796.2'}
61%|██████ | 11410/18688 [13:23:35<8:47:24, 4.35s/it] 61%|██████ | 11411/18688 [13:23:40<8:59:51, 4.45s/it] 61%|██████ | 11412/18688 [13:23:43<8:34:28, 4.24s/it] 61%|██████ | 11413/18688 [13:23:47<8:13:47, 4.07s/it] 61%|██████ | 11414/18688 [13:23:52<8:55:16, 4.42s/it] 61%|██████ | 11415/18688 [13:23:56<8:39:44, 4.29s/it] 61%|██████ | 11416/18688 [13:24:01<8:49:35, 4.37s/it] 61%|██████ | 11417/18688 [13:24:05<8:30:44, 4.21s/it] 61%|██████ | 11418/18688 [13:24:09<8:16:28, 4.10s/it] 61%|██████ | 11419/18688 [13:24:12<8:08:17, 4.03s/it] 61%|██████ | 11420/18688 [13:24:17<8:28:06, 4.19s/it] {'loss': '0.9387', 'grad_norm': '1.317', 'learning_rate': '3.041e-05', 'epoch': '0.6111', 'num_input_tokens_seen': 38424824, 'train_runtime': '4.826e+04', 'train_tokens_per_second': '796.2'}
61%|██████ | 11420/18688 [13:24:17<8:28:06, 4.19s/it] 61%|██████ | 11421/18688 [13:24:21<8:22:07, 4.15s/it] 61%|██████ | 11422/18688 [13:24:25<8:11:10, 4.06s/it] 61%|██████ | 11423/18688 [13:24:29<8:32:31, 4.23s/it] 61%|██████ | 11424/18688 [13:24:34<8:30:50, 4.22s/it] 61%|██████ | 11425/18688 [13:24:38<8:21:06, 4.14s/it] 61%|██████ | 11426/18688 [13:24:42<8:37:56, 4.28s/it] 61%|██████ | 11427/18688 [13:24:46<8:16:10, 4.10s/it] 61%|██████ | 11428/18688 [13:24:50<8:21:37, 4.15s/it] 61%|██████ | 11429/18688 [13:24:55<8:57:02, 4.44s/it] 61%|██████ | 11430/18688 [13:24:59<8:26:59, 4.19s/it] {'loss': '0.9059', 'grad_norm': '1.265', 'learning_rate': '3.034e-05', 'epoch': '0.6116', 'num_input_tokens_seen': 38460160, 'train_runtime': '4.83e+04', 'train_tokens_per_second': '796.3'}
61%|██████ | 11430/18688 [13:24:59<8:26:59, 4.19s/it] 61%|██████ | 11431/18688 [13:25:03<8:31:48, 4.23s/it] 61%|██████ | 11432/18688 [13:25:07<8:24:54, 4.18s/it] 61%|██████ | 11433/18688 [13:25:12<8:31:47, 4.23s/it] 61%|██████ | 11434/18688 [13:25:16<8:30:34, 4.22s/it] 61%|██████ | 11435/18688 [13:25:19<8:08:57, 4.04s/it] 61%|██████ | 11436/18688 [13:25:23<7:56:17, 3.94s/it] 61%|██████ | 11437/18688 [13:25:27<7:43:47, 3.84s/it] 61%|██████ | 11438/18688 [13:25:31<8:11:46, 4.07s/it] 61%|██████ | 11439/18688 [13:25:36<8:26:47, 4.19s/it] 61%|██████ | 11440/18688 [13:25:40<8:30:54, 4.23s/it] {'loss': '0.909', 'grad_norm': '1.244', 'learning_rate': '3.027e-05', 'epoch': '0.6122', 'num_input_tokens_seen': 38493712, 'train_runtime': '4.834e+04', 'train_tokens_per_second': '796.3'}
61%|██████ | 11440/18688 [13:25:40<8:30:54, 4.23s/it] 61%|██████ | 11441/18688 [13:25:45<8:37:32, 4.28s/it] 61%|██████ | 11442/18688 [13:25:48<8:13:34, 4.09s/it] 61%|██████ | 11443/18688 [13:25:52<8:16:57, 4.12s/it] 61%|██████ | 11444/18688 [13:25:57<8:20:19, 4.14s/it] 61%|██████ | 11445/18688 [13:26:01<8:40:18, 4.31s/it] 61%|██████ | 11446/18688 [13:26:05<8:30:24, 4.23s/it] 61%|██████▏ | 11447/18688 [13:26:10<8:36:19, 4.28s/it] 61%|██████▏ | 11448/18688 [13:26:14<8:19:55, 4.14s/it] 61%|██████▏ | 11449/18688 [13:26:18<8:46:39, 4.37s/it] 61%|██████▏ | 11450/18688 [13:26:23<8:36:02, 4.28s/it] {'loss': '0.8585', 'grad_norm': '0.9442', 'learning_rate': '3.019e-05', 'epoch': '0.6127', 'num_input_tokens_seen': 38529016, 'train_runtime': '4.838e+04', 'train_tokens_per_second': '796.3'}
61%|██████▏ | 11450/18688 [13:26:23<8:36:02, 4.28s/it] 61%|██████▏ | 11451/18688 [13:26:26<8:20:23, 4.15s/it] 61%|██████▏ | 11452/18688 [13:26:30<8:13:16, 4.09s/it] 61%|██████▏ | 11453/18688 [13:26:36<9:00:58, 4.49s/it] 61%|██████▏ | 11454/18688 [13:26:40<8:36:23, 4.28s/it] 61%|██████▏ | 11455/18688 [13:26:43<8:12:02, 4.08s/it] 61%|██████▏ | 11456/18688 [13:26:48<8:30:20, 4.23s/it] 61%|██████▏ | 11457/18688 [13:26:51<8:12:52, 4.09s/it] 61%|██████▏ | 11458/18688 [13:26:55<8:00:40, 3.99s/it] 61%|██████▏ | 11459/18688 [13:26:59<7:55:00, 3.94s/it] 61%|██████▏ | 11460/18688 [13:27:03<7:51:17, 3.91s/it] {'loss': '0.8829', 'grad_norm': '1.298', 'learning_rate': '3.012e-05', 'epoch': '0.6132', 'num_input_tokens_seen': 38560368, 'train_runtime': '4.842e+04', 'train_tokens_per_second': '796.3'}
61%|██████▏ | 11460/18688 [13:27:03<7:51:17, 3.91s/it] 61%|██████▏ | 11461/18688 [13:27:07<7:58:30, 3.97s/it] 61%|██████▏ | 11462/18688 [13:27:11<7:55:07, 3.95s/it] 61%|██████▏ | 11463/18688 [13:27:15<8:02:49, 4.01s/it] 61%|██████▏ | 11464/18688 [13:27:19<8:10:53, 4.08s/it] 61%|██████▏ | 11465/18688 [13:27:23<8:08:35, 4.06s/it] 61%|██████▏ | 11466/18688 [13:27:28<8:19:13, 4.15s/it] 61%|██████▏ | 11467/18688 [13:27:33<8:53:53, 4.44s/it] 61%|██████▏ | 11468/18688 [13:27:37<8:46:20, 4.37s/it] 61%|██████▏ | 11469/18688 [13:27:42<9:09:29, 4.57s/it] 61%|██████▏ | 11470/18688 [13:27:47<9:07:45, 4.55s/it] {'loss': '0.9598', 'grad_norm': '0.9658', 'learning_rate': '3.005e-05', 'epoch': '0.6138', 'num_input_tokens_seen': 38596616, 'train_runtime': '4.847e+04', 'train_tokens_per_second': '796.3'}
61%|██████▏ | 11470/18688 [13:27:47<9:07:45, 4.55s/it] 61%|██████▏ | 11471/18688 [13:27:50<8:38:25, 4.31s/it] 61%|██████▏ | 11472/18688 [13:27:54<8:30:46, 4.25s/it] 61%|██████▏ | 11473/18688 [13:27:58<8:08:23, 4.06s/it] 61%|██████▏ | 11474/18688 [13:28:02<8:19:09, 4.15s/it] 61%|██████▏ | 11475/18688 [13:28:06<8:01:24, 4.00s/it] 61%|██████▏ | 11476/18688 [13:28:10<7:59:47, 3.99s/it] 61%|██████▏ | 11477/18688 [13:28:14<7:47:55, 3.89s/it] 61%|██████▏ | 11478/18688 [13:28:17<7:35:02, 3.79s/it] 61%|██████▏ | 11479/18688 [13:28:21<7:37:32, 3.81s/it] 61%|██████▏ | 11480/18688 [13:28:25<7:48:48, 3.90s/it] {'loss': '0.8998', 'grad_norm': '1.183', 'learning_rate': '2.998e-05', 'epoch': '0.6143', 'num_input_tokens_seen': 38625808, 'train_runtime': '4.851e+04', 'train_tokens_per_second': '796.3'}
61%|██████▏ | 11480/18688 [13:28:25<7:48:48, 3.90s/it] 61%|██████▏ | 11481/18688 [13:28:29<8:01:40, 4.01s/it] 61%|██████▏ | 11482/18688 [13:28:35<8:39:31, 4.33s/it] 61%|██████▏ | 11483/18688 [13:28:38<8:10:44, 4.09s/it] 61%|██████▏ | 11484/18688 [13:28:42<7:51:42, 3.93s/it] 61%|██████▏ | 11485/18688 [13:28:46<8:17:53, 4.15s/it] 61%|██████▏ | 11486/18688 [13:28:50<8:04:21, 4.04s/it] 61%|██████▏ | 11487/18688 [13:28:54<7:54:51, 3.96s/it] 61%|██████▏ | 11488/18688 [13:28:58<7:49:11, 3.91s/it] 61%|██████▏ | 11489/18688 [13:29:02<7:54:22, 3.95s/it] 61%|██████▏ | 11490/18688 [13:29:07<8:26:30, 4.22s/it] {'loss': '0.9268', 'grad_norm': '1.248', 'learning_rate': '2.991e-05', 'epoch': '0.6148', 'num_input_tokens_seen': 38659568, 'train_runtime': '4.855e+04', 'train_tokens_per_second': '796.3'}
61%|██████▏ | 11490/18688 [13:29:07<8:26:30, 4.22s/it] 61%|██████▏ | 11491/18688 [13:29:10<8:07:07, 4.06s/it] 61%|██████▏ | 11492/18688 [13:29:15<8:18:40, 4.16s/it] 61%|██████▏ | 11493/18688 [13:29:18<8:03:48, 4.03s/it] 62%|██████▏ | 11494/18688 [13:29:23<8:08:47, 4.08s/it] 62%|██████▏ | 11495/18688 [13:29:26<7:57:28, 3.98s/it] 62%|██████▏ | 11496/18688 [13:29:30<7:52:08, 3.94s/it] 62%|██████▏ | 11497/18688 [13:29:34<7:41:37, 3.85s/it] 62%|██████▏ | 11498/18688 [13:29:38<7:47:24, 3.90s/it] 62%|██████▏ | 11499/18688 [13:29:43<8:21:21, 4.18s/it] 62%|██████▏ | 11500/18688 [13:29:47<8:19:27, 4.17s/it] {'loss': '0.9661', 'grad_norm': '1.376', 'learning_rate': '2.984e-05', 'epoch': '0.6154', 'num_input_tokens_seen': 38691136, 'train_runtime': '4.859e+04', 'train_tokens_per_second': '796.3'}
62%|██████▏ | 11500/18688 [13:29:47<8:19:27, 4.17s/it] 62%|██████▏ | 11501/18688 [13:29:52<8:51:50, 4.44s/it] 62%|██████▏ | 11502/18688 [13:29:56<8:39:34, 4.34s/it] 62%|██████▏ | 11503/18688 [13:30:00<8:38:08, 4.33s/it] 62%|██████▏ | 11504/18688 [13:30:04<8:31:44, 4.27s/it] 62%|██████▏ | 11505/18688 [13:30:09<8:28:16, 4.25s/it] 62%|██████▏ | 11506/18688 [13:30:12<8:04:47, 4.05s/it] 62%|██████▏ | 11507/18688 [13:30:16<8:08:09, 4.08s/it] 62%|██████▏ | 11508/18688 [13:30:20<7:55:14, 3.97s/it] 62%|██████▏ | 11509/18688 [13:30:25<8:22:01, 4.20s/it] 62%|██████▏ | 11510/18688 [13:30:28<8:06:31, 4.07s/it] {'loss': '0.9389', 'grad_norm': '1.345', 'learning_rate': '2.977e-05', 'epoch': '0.6159', 'num_input_tokens_seen': 38724736, 'train_runtime': '4.863e+04', 'train_tokens_per_second': '796.3'}
62%|██████▏ | 11510/18688 [13:30:28<8:06:31, 4.07s/it] 62%|██████▏ | 11511/18688 [13:30:33<8:08:51, 4.09s/it] 62%|██████▏ | 11512/18688 [13:30:36<7:49:27, 3.93s/it] 62%|██████▏ | 11513/18688 [13:30:40<7:39:22, 3.84s/it] 62%|██████▏ | 11514/18688 [13:30:44<7:54:46, 3.97s/it] 62%|██████▏ | 11515/18688 [13:30:48<7:55:47, 3.98s/it] 62%|██████▏ | 11516/18688 [13:30:53<8:13:29, 4.13s/it] 62%|██████▏ | 11517/18688 [13:30:56<7:55:04, 3.97s/it] 62%|██████▏ | 11518/18688 [13:31:00<7:46:59, 3.91s/it] 62%|██████▏ | 11519/18688 [13:31:05<8:26:27, 4.24s/it] 62%|██████▏ | 11520/18688 [13:31:10<8:52:35, 4.46s/it] {'loss': '0.9057', 'grad_norm': '1.194', 'learning_rate': '2.97e-05', 'epoch': '0.6165', 'num_input_tokens_seen': 38758400, 'train_runtime': '4.867e+04', 'train_tokens_per_second': '796.3'}
62%|██████▏ | 11520/18688 [13:31:10<8:52:35, 4.46s/it] 62%|██████▏ | 11521/18688 [13:31:15<9:01:20, 4.53s/it] 62%|██████▏ | 11522/18688 [13:31:20<9:20:31, 4.69s/it] 62%|██████▏ | 11523/18688 [13:31:25<9:43:18, 4.88s/it] 62%|██████▏ | 11524/18688 [13:31:29<9:01:01, 4.53s/it] 62%|██████▏ | 11525/18688 [13:31:32<8:33:24, 4.30s/it] 62%|██████▏ | 11526/18688 [13:31:36<8:06:55, 4.08s/it] 62%|██████▏ | 11527/18688 [13:31:40<8:02:05, 4.04s/it] 62%|██████▏ | 11528/18688 [13:31:44<7:53:09, 3.96s/it] 62%|██████▏ | 11529/18688 [13:31:48<8:05:36, 4.07s/it] 62%|██████▏ | 11530/18688 [13:31:52<7:44:52, 3.90s/it] {'loss': '0.893', 'grad_norm': '1.216', 'learning_rate': '2.963e-05', 'epoch': '0.617', 'num_input_tokens_seen': 38793344, 'train_runtime': '4.871e+04', 'train_tokens_per_second': '796.4'}
62%|██████▏ | 11530/18688 [13:31:52<7:44:52, 3.90s/it] 62%|██████▏ | 11531/18688 [13:31:56<8:08:59, 4.10s/it] 62%|██████▏ | 11532/18688 [13:32:00<8:03:09, 4.05s/it] 62%|██████▏ | 11533/18688 [13:32:04<7:52:59, 3.97s/it] 62%|██████▏ | 11534/18688 [13:32:09<8:42:41, 4.38s/it] 62%|██████▏ | 11535/18688 [13:32:13<8:25:57, 4.24s/it] 62%|██████▏ | 11536/18688 [13:32:17<7:58:47, 4.02s/it] 62%|██████▏ | 11537/18688 [13:32:20<7:44:37, 3.90s/it] 62%|██████▏ | 11538/18688 [13:32:24<7:32:52, 3.80s/it] 62%|██████▏ | 11539/18688 [13:32:28<7:43:21, 3.89s/it] 62%|██████▏ | 11540/18688 [13:32:32<7:49:33, 3.94s/it] {'loss': '0.9154', 'grad_norm': '1.286', 'learning_rate': '2.956e-05', 'epoch': '0.6175', 'num_input_tokens_seen': 38825920, 'train_runtime': '4.875e+04', 'train_tokens_per_second': '796.4'}
62%|██████▏ | 11540/18688 [13:32:32<7:49:33, 3.94s/it] 62%|██████▏ | 11541/18688 [13:32:36<7:41:40, 3.88s/it] 62%|██████▏ | 11542/18688 [13:32:40<7:53:48, 3.98s/it] 62%|██████▏ | 11543/18688 [13:32:44<7:54:08, 3.98s/it] 62%|██████▏ | 11544/18688 [13:32:48<7:40:20, 3.87s/it] 62%|██████▏ | 11545/18688 [13:32:52<8:06:53, 4.09s/it] 62%|██████▏ | 11546/18688 [13:32:56<7:44:23, 3.90s/it] 62%|██████▏ | 11547/18688 [13:33:00<7:44:29, 3.90s/it] 62%|██████▏ | 11548/18688 [13:33:03<7:34:32, 3.82s/it] 62%|██████▏ | 11549/18688 [13:33:07<7:32:22, 3.80s/it] 62%|██████▏ | 11550/18688 [13:33:11<7:44:30, 3.90s/it] {'loss': '0.8846', 'grad_norm': '1.376', 'learning_rate': '2.949e-05', 'epoch': '0.6181', 'num_input_tokens_seen': 38856632, 'train_runtime': '4.879e+04', 'train_tokens_per_second': '796.4'}
62%|██████▏ | 11550/18688 [13:33:11<7:44:30, 3.90s/it] 62%|██████▏ | 11551/18688 [13:33:15<7:54:40, 3.99s/it] 62%|██████▏ | 11552/18688 [13:33:19<8:01:30, 4.05s/it] 62%|██████▏ | 11553/18688 [13:33:23<7:45:14, 3.91s/it] 62%|██████▏ | 11554/18688 [13:33:27<7:37:19, 3.85s/it] 62%|██████▏ | 11555/18688 [13:33:31<7:55:30, 4.00s/it] 62%|██████▏ | 11556/18688 [13:33:35<7:49:46, 3.95s/it] 62%|██████▏ | 11557/18688 [13:33:39<7:43:04, 3.90s/it] 62%|██████▏ | 11558/18688 [13:33:43<8:01:45, 4.05s/it] 62%|██████▏ | 11559/18688 [13:33:47<7:52:59, 3.98s/it] 62%|██████▏ | 11560/18688 [13:33:52<8:15:54, 4.17s/it] {'loss': '0.9225', 'grad_norm': '1.399', 'learning_rate': '2.942e-05', 'epoch': '0.6186', 'num_input_tokens_seen': 38889464, 'train_runtime': '4.883e+04', 'train_tokens_per_second': '796.4'}
62%|██████▏ | 11560/18688 [13:33:52<8:15:54, 4.17s/it] 62%|██████▏ | 11561/18688 [13:33:55<7:59:14, 4.03s/it] 62%|██████▏ | 11562/18688 [13:33:59<8:05:11, 4.09s/it] 62%|██████▏ | 11563/18688 [13:34:04<8:12:06, 4.14s/it] 62%|██████▏ | 11564/18688 [13:34:08<8:34:14, 4.33s/it] 62%|██████▏ | 11565/18688 [13:34:14<9:05:47, 4.60s/it] 62%|██████▏ | 11566/18688 [13:34:17<8:28:00, 4.28s/it] 62%|██████▏ | 11567/18688 [13:34:21<8:23:54, 4.25s/it] 62%|██████▏ | 11568/18688 [13:34:25<8:15:36, 4.18s/it] 62%|██████▏ | 11569/18688 [13:34:29<8:08:41, 4.12s/it] 62%|██████▏ | 11570/18688 [13:34:33<7:58:30, 4.03s/it] {'loss': '0.8986', 'grad_norm': '1.384', 'learning_rate': '2.935e-05', 'epoch': '0.6191', 'num_input_tokens_seen': 38923384, 'train_runtime': '4.887e+04', 'train_tokens_per_second': '796.4'}
62%|██████▏ | 11570/18688 [13:34:33<7:58:30, 4.03s/it] 62%|██████▏ | 11571/18688 [13:34:38<8:07:02, 4.11s/it] 62%|██████▏ | 11572/18688 [13:34:42<8:07:33, 4.11s/it] 62%|██████▏ | 11573/18688 [13:34:46<8:16:18, 4.19s/it] 62%|██████▏ | 11574/18688 [13:34:50<8:14:19, 4.17s/it] 62%|██████▏ | 11575/18688 [13:34:56<8:59:21, 4.55s/it] 62%|██████▏ | 11576/18688 [13:34:59<8:35:17, 4.35s/it] 62%|██████▏ | 11577/18688 [13:35:04<8:45:17, 4.43s/it] 62%|██████▏ | 11578/18688 [13:35:08<8:30:42, 4.31s/it] 62%|██████▏ | 11579/18688 [13:35:12<8:30:46, 4.31s/it] 62%|██████▏ | 11580/18688 [13:35:17<8:30:57, 4.31s/it] {'loss': '0.9603', 'grad_norm': '1.134', 'learning_rate': '2.928e-05', 'epoch': '0.6197', 'num_input_tokens_seen': 38959992, 'train_runtime': '4.892e+04', 'train_tokens_per_second': '796.4'}
62%|██████▏ | 11580/18688 [13:35:17<8:30:57, 4.31s/it] 62%|██████▏ | 11581/18688 [13:35:20<8:11:33, 4.15s/it] 62%|██████▏ | 11582/18688 [13:35:24<8:04:08, 4.09s/it] 62%|██████▏ | 11583/18688 [13:35:29<8:09:23, 4.13s/it] 62%|██████▏ | 11584/18688 [13:35:33<8:14:27, 4.18s/it] 62%|██████▏ | 11585/18688 [13:35:37<8:02:52, 4.08s/it] 62%|██████▏ | 11586/18688 [13:35:41<8:10:59, 4.15s/it] 62%|██████▏ | 11587/18688 [13:35:45<8:13:44, 4.17s/it] 62%|██████▏ | 11588/18688 [13:35:49<7:50:28, 3.98s/it] 62%|██████▏ | 11589/18688 [13:35:53<7:42:40, 3.91s/it] 62%|██████▏ | 11590/18688 [13:35:57<8:06:58, 4.12s/it] {'loss': '0.9047', 'grad_norm': '1.157', 'learning_rate': '2.921e-05', 'epoch': '0.6202', 'num_input_tokens_seen': 38992168, 'train_runtime': '4.896e+04', 'train_tokens_per_second': '796.4'}
62%|██████▏ | 11590/18688 [13:35:57<8:06:58, 4.12s/it] 62%|██████▏ | 11591/18688 [13:36:01<8:09:17, 4.14s/it] 62%|██████▏ | 11592/18688 [13:36:06<8:09:01, 4.13s/it] 62%|██████▏ | 11593/18688 [13:36:10<8:04:19, 4.10s/it] 62%|██████▏ | 11594/18688 [13:36:14<8:02:21, 4.08s/it] 62%|██████▏ | 11595/18688 [13:36:18<8:11:14, 4.16s/it] 62%|██████▏ | 11596/18688 [13:36:22<8:15:52, 4.20s/it] 62%|██████▏ | 11597/18688 [13:36:26<7:51:03, 3.99s/it] 62%|██████▏ | 11598/18688 [13:36:29<7:43:27, 3.92s/it] 62%|██████▏ | 11599/18688 [13:36:33<7:41:22, 3.90s/it] 62%|██████▏ | 11600/18688 [13:36:37<7:47:49, 3.96s/it] {'loss': '0.9076', 'grad_norm': '1.465', 'learning_rate': '2.913e-05', 'epoch': '0.6207', 'num_input_tokens_seen': 39022800, 'train_runtime': '4.9e+04', 'train_tokens_per_second': '796.4'}
62%|██████▏ | 11600/18688 [13:36:37<7:47:49, 3.96s/it] 62%|██████▏ | 11601/18688 [13:36:42<8:03:10, 4.09s/it] 62%|██████▏ | 11602/18688 [13:36:46<8:11:21, 4.16s/it] 62%|██████▏ | 11603/18688 [13:36:51<8:18:44, 4.22s/it] 62%|██████▏ | 11604/18688 [13:36:54<8:01:15, 4.08s/it] 62%|██████▏ | 11605/18688 [13:36:58<7:56:47, 4.04s/it] 62%|██████▏ | 11606/18688 [13:37:02<7:41:41, 3.91s/it] 62%|██████▏ | 11607/18688 [13:37:06<7:48:15, 3.97s/it] 62%|██████▏ | 11608/18688 [13:37:10<8:00:13, 4.07s/it] 62%|██████▏ | 11609/18688 [13:37:14<7:42:44, 3.92s/it] 62%|██████▏ | 11610/18688 [13:37:18<7:45:02, 3.94s/it] {'loss': '0.834', 'grad_norm': '1.272', 'learning_rate': '2.906e-05', 'epoch': '0.6213', 'num_input_tokens_seen': 39053456, 'train_runtime': '4.904e+04', 'train_tokens_per_second': '796.4'}
62%|██████▏ | 11610/18688 [13:37:18<7:45:02, 3.94s/it] 62%|██████▏ | 11611/18688 [13:37:21<7:35:31, 3.86s/it] 62%|██████▏ | 11612/18688 [13:37:26<8:07:01, 4.13s/it] 62%|██████▏ | 11613/18688 [13:37:31<8:23:05, 4.27s/it] 62%|██████▏ | 11614/18688 [13:37:35<8:06:29, 4.13s/it] 62%|██████▏ | 11615/18688 [13:37:39<8:12:23, 4.18s/it] 62%|██████▏ | 11616/18688 [13:37:43<8:24:46, 4.28s/it] 62%|██████▏ | 11617/18688 [13:37:48<8:22:18, 4.26s/it] 62%|██████▏ | 11618/18688 [13:37:51<7:56:05, 4.04s/it] 62%|██████▏ | 11619/18688 [13:37:55<7:46:03, 3.96s/it] 62%|██████▏ | 11620/18688 [13:37:59<7:53:36, 4.02s/it] {'loss': '0.8859', 'grad_norm': '1.104', 'learning_rate': '2.899e-05', 'epoch': '0.6218', 'num_input_tokens_seen': 39087064, 'train_runtime': '4.908e+04', 'train_tokens_per_second': '796.4'}
62%|██████▏ | 11620/18688 [13:37:59<7:53:36, 4.02s/it] 62%|██████▏ | 11621/18688 [13:38:03<7:42:48, 3.93s/it] 62%|██████▏ | 11622/18688 [13:38:07<7:43:49, 3.94s/it] 62%|██████▏ | 11623/18688 [13:38:11<7:51:26, 4.00s/it] 62%|██████▏ | 11624/18688 [13:38:15<7:42:03, 3.92s/it] 62%|██████▏ | 11625/18688 [13:38:19<8:01:17, 4.09s/it] 62%|██████▏ | 11626/18688 [13:38:24<8:22:23, 4.27s/it] 62%|██████▏ | 11627/18688 [13:38:29<9:05:14, 4.63s/it] 62%|██████▏ | 11628/18688 [13:38:34<8:52:39, 4.53s/it] 62%|██████▏ | 11629/18688 [13:38:37<8:22:49, 4.27s/it] 62%|██████▏ | 11630/18688 [13:38:41<7:59:10, 4.07s/it] {'loss': '0.9205', 'grad_norm': '1.245', 'learning_rate': '2.892e-05', 'epoch': '0.6223', 'num_input_tokens_seen': 39121248, 'train_runtime': '4.912e+04', 'train_tokens_per_second': '796.4'}
62%|██████▏ | 11630/18688 [13:38:41<7:59:10, 4.07s/it] 62%|██████▏ | 11631/18688 [13:38:45<7:57:48, 4.06s/it] 62%|██████▏ | 11632/18688 [13:38:49<7:56:14, 4.05s/it] 62%|██████▏ | 11633/18688 [13:38:53<7:39:38, 3.91s/it] 62%|██████▏ | 11634/18688 [13:38:56<7:38:54, 3.90s/it] 62%|██████▏ | 11635/18688 [13:39:00<7:31:50, 3.84s/it] 62%|██████▏ | 11636/18688 [13:39:04<7:32:40, 3.85s/it] 62%|██████▏ | 11637/18688 [13:39:09<8:09:53, 4.17s/it] 62%|██████▏ | 11638/18688 [13:39:13<8:20:54, 4.26s/it] 62%|██████▏ | 11639/18688 [13:39:18<8:18:11, 4.24s/it] 62%|██████▏ | 11640/18688 [13:39:21<8:01:49, 4.10s/it] {'loss': '0.8568', 'grad_norm': '1.204', 'learning_rate': '2.885e-05', 'epoch': '0.6229', 'num_input_tokens_seen': 39152528, 'train_runtime': '4.916e+04', 'train_tokens_per_second': '796.4'}
62%|██████▏ | 11640/18688 [13:39:21<8:01:49, 4.10s/it] 62%|██████▏ | 11641/18688 [13:39:25<7:54:43, 4.04s/it] 62%|██████▏ | 11642/18688 [13:39:29<8:02:08, 4.11s/it] 62%|██████▏ | 11643/18688 [13:39:33<7:57:07, 4.06s/it] 62%|██████▏ | 11644/18688 [13:39:37<7:38:13, 3.90s/it] 62%|██████▏ | 11645/18688 [13:39:41<7:46:05, 3.97s/it] 62%|██████▏ | 11646/18688 [13:39:45<7:55:05, 4.05s/it] 62%|██████▏ | 11647/18688 [13:39:50<8:03:07, 4.12s/it] 62%|██████▏ | 11648/18688 [13:39:55<8:34:44, 4.39s/it] 62%|██████▏ | 11649/18688 [13:40:00<9:10:50, 4.70s/it] 62%|██████▏ | 11650/18688 [13:40:05<9:17:36, 4.75s/it] {'loss': '0.9599', 'grad_norm': '1.202', 'learning_rate': '2.878e-05', 'epoch': '0.6234', 'num_input_tokens_seen': 39188112, 'train_runtime': '4.921e+04', 'train_tokens_per_second': '796.4'}
62%|██████▏ | 11650/18688 [13:40:05<9:17:36, 4.75s/it] 62%|██████▏ | 11651/18688 [13:40:09<8:41:58, 4.45s/it] 62%|██████▏ | 11652/18688 [13:40:13<8:25:50, 4.31s/it] 62%|██████▏ | 11653/18688 [13:40:17<8:33:44, 4.38s/it] 62%|██████▏ | 11654/18688 [13:40:21<8:04:54, 4.14s/it] 62%|██████▏ | 11655/18688 [13:40:25<7:55:57, 4.06s/it] 62%|██████▏ | 11656/18688 [13:40:29<7:52:27, 4.03s/it] 62%|██████▏ | 11657/18688 [13:40:32<7:41:35, 3.94s/it] 62%|██████▏ | 11658/18688 [13:40:37<8:09:18, 4.18s/it] 62%|██████▏ | 11659/18688 [13:40:41<7:48:42, 4.00s/it] 62%|██████▏ | 11660/18688 [13:40:45<7:55:08, 4.06s/it] {'loss': '0.8916', 'grad_norm': '1.393', 'learning_rate': '2.871e-05', 'epoch': '0.6239', 'num_input_tokens_seen': 39219224, 'train_runtime': '4.925e+04', 'train_tokens_per_second': '796.4'}
62%|██████▏ | 11660/18688 [13:40:45<7:55:08, 4.06s/it] 62%|██████▏ | 11661/18688 [13:40:49<7:42:22, 3.95s/it] 62%|██████▏ | 11662/18688 [13:40:53<7:51:10, 4.02s/it] 62%|██████▏ | 11663/18688 [13:40:57<7:51:45, 4.03s/it] 62%|██████▏ | 11664/18688 [13:41:01<7:44:48, 3.97s/it] 62%|██████▏ | 11665/18688 [13:41:05<7:50:12, 4.02s/it] 62%|██████▏ | 11666/18688 [13:41:09<7:52:35, 4.04s/it] 62%|██████▏ | 11667/18688 [13:41:12<7:34:05, 3.88s/it] 62%|██████▏ | 11668/18688 [13:41:17<7:44:08, 3.97s/it] 62%|██████▏ | 11669/18688 [13:41:21<7:46:44, 3.99s/it] 62%|██████▏ | 11670/18688 [13:41:24<7:37:37, 3.91s/it] {'loss': '0.8937', 'grad_norm': '1.409', 'learning_rate': '2.864e-05', 'epoch': '0.6245', 'num_input_tokens_seen': 39250448, 'train_runtime': '4.928e+04', 'train_tokens_per_second': '796.4'}
62%|██████▏ | 11670/18688 [13:41:24<7:37:37, 3.91s/it] 62%|██████▏ | 11671/18688 [13:41:28<7:44:15, 3.97s/it] 62%|██████▏ | 11672/18688 [13:41:34<8:33:09, 4.39s/it] 62%|██████▏ | 11673/18688 [13:41:38<8:22:06, 4.29s/it] 62%|██████▏ | 11674/18688 [13:41:42<8:23:32, 4.31s/it] 62%|██████▏ | 11675/18688 [13:41:46<8:04:18, 4.14s/it] 62%|██████▏ | 11676/18688 [13:41:50<7:55:49, 4.07s/it] 62%|██████▏ | 11677/18688 [13:41:54<8:08:42, 4.18s/it] 62%|██████▏ | 11678/18688 [13:41:58<7:53:25, 4.05s/it] 62%|██████▏ | 11679/18688 [13:42:02<8:01:51, 4.12s/it] 62%|██████▎ | 11680/18688 [13:42:07<8:05:59, 4.16s/it] {'loss': '0.8904', 'grad_norm': '1.262', 'learning_rate': '2.857e-05', 'epoch': '0.625', 'num_input_tokens_seen': 39284104, 'train_runtime': '4.933e+04', 'train_tokens_per_second': '796.4'}
62%|██████▎ | 11680/18688 [13:42:07<8:05:59, 4.16s/it] 63%|██████▎ | 11681/18688 [13:42:11<8:10:50, 4.20s/it] 63%|██████▎ | 11682/18688 [13:42:15<7:51:00, 4.03s/it] 63%|██████▎ | 11683/18688 [13:42:19<7:58:52, 4.10s/it] 63%|██████▎ | 11684/18688 [13:42:23<8:09:41, 4.19s/it] 63%|██████▎ | 11685/18688 [13:42:28<8:15:35, 4.25s/it] 63%|██████▎ | 11686/18688 [13:42:32<8:24:17, 4.32s/it] 63%|██████▎ | 11687/18688 [13:42:36<8:04:36, 4.15s/it] 63%|██████▎ | 11688/18688 [13:42:40<8:05:01, 4.16s/it] 63%|██████▎ | 11689/18688 [13:42:44<8:09:55, 4.20s/it] 63%|██████▎ | 11690/18688 [13:42:48<7:55:20, 4.08s/it] {'loss': '0.9016', 'grad_norm': '1.129', 'learning_rate': '2.85e-05', 'epoch': '0.6256', 'num_input_tokens_seen': 39317120, 'train_runtime': '4.937e+04', 'train_tokens_per_second': '796.4'}
63%|██████▎ | 11690/18688 [13:42:48<7:55:20, 4.08s/it] 63%|██████▎ | 11691/18688 [13:42:52<7:39:27, 3.94s/it] 63%|██████▎ | 11692/18688 [13:42:56<8:08:53, 4.19s/it] 63%|██████▎ | 11693/18688 [13:43:00<8:01:01, 4.13s/it] 63%|██████▎ | 11694/18688 [13:43:05<8:08:42, 4.19s/it] 63%|██████▎ | 11695/18688 [13:43:09<8:23:56, 4.32s/it] 63%|██████▎ | 11696/18688 [13:43:13<8:07:07, 4.18s/it] 63%|██████▎ | 11697/18688 [13:43:17<7:52:24, 4.05s/it] 63%|██████▎ | 11698/18688 [13:43:21<7:42:05, 3.97s/it] 63%|██████▎ | 11699/18688 [13:43:25<7:52:42, 4.06s/it] 63%|██████▎ | 11700/18688 [13:43:30<8:14:08, 4.24s/it] {'loss': '0.8739', 'grad_norm': '1.202', 'learning_rate': '2.843e-05', 'epoch': '0.6261', 'num_input_tokens_seen': 39351192, 'train_runtime': '4.941e+04', 'train_tokens_per_second': '796.4'}
63%|██████▎ | 11700/18688 [13:43:30<8:14:08, 4.24s/it] 63%|██████▎ | 11701/18688 [13:43:34<8:18:43, 4.28s/it] 63%|██████▎ | 11702/18688 [13:43:38<7:55:06, 4.08s/it] 63%|██████▎ | 11703/18688 [13:43:42<8:07:16, 4.19s/it] 63%|██████▎ | 11704/18688 [13:43:46<8:01:52, 4.14s/it] 63%|██████▎ | 11705/18688 [13:43:50<8:06:45, 4.18s/it] 63%|██████▎ | 11706/18688 [13:43:54<7:48:35, 4.03s/it] 63%|██████▎ | 11707/18688 [13:43:58<7:35:29, 3.91s/it] 63%|██████▎ | 11708/18688 [13:44:02<7:40:21, 3.96s/it] 63%|██████▎ | 11709/18688 [13:44:06<7:40:56, 3.96s/it] 63%|██████▎ | 11710/18688 [13:44:09<7:26:55, 3.84s/it] {'loss': '0.9136', 'grad_norm': '1.091', 'learning_rate': '2.836e-05', 'epoch': '0.6266', 'num_input_tokens_seen': 39382248, 'train_runtime': '4.945e+04', 'train_tokens_per_second': '796.4'}
63%|██████▎ | 11710/18688 [13:44:09<7:26:55, 3.84s/it] 63%|██████▎ | 11711/18688 [13:44:14<7:39:48, 3.95s/it] 63%|██████▎ | 11712/18688 [13:44:19<8:26:20, 4.35s/it] 63%|██████▎ | 11713/18688 [13:44:23<8:20:36, 4.31s/it] 63%|██████▎ | 11714/18688 [13:44:27<7:59:05, 4.12s/it] 63%|██████▎ | 11715/18688 [13:44:31<8:06:50, 4.19s/it] 63%|██████▎ | 11716/18688 [13:44:35<7:59:27, 4.13s/it] 63%|██████▎ | 11717/18688 [13:44:40<8:15:42, 4.27s/it] 63%|██████▎ | 11718/18688 [13:44:43<7:50:33, 4.05s/it] 63%|██████▎ | 11719/18688 [13:44:48<8:03:31, 4.16s/it] 63%|██████▎ | 11720/18688 [13:44:52<8:07:03, 4.19s/it] {'loss': '0.9486', 'grad_norm': '1.427', 'learning_rate': '2.829e-05', 'epoch': '0.6272', 'num_input_tokens_seen': 39416704, 'train_runtime': '4.949e+04', 'train_tokens_per_second': '796.4'}
63%|██████▎ | 11720/18688 [13:44:52<8:07:03, 4.19s/it] 63%|██████▎ | 11721/18688 [13:44:56<7:47:34, 4.03s/it] 63%|██████▎ | 11722/18688 [13:45:00<7:46:35, 4.02s/it] 63%|██████▎ | 11723/18688 [13:45:05<8:27:59, 4.38s/it] 63%|██████▎ | 11724/18688 [13:45:08<7:59:23, 4.13s/it] 63%|██████▎ | 11725/18688 [13:45:13<8:06:56, 4.20s/it] 63%|██████▎ | 11726/18688 [13:45:17<8:08:00, 4.21s/it] 63%|██████▎ | 11727/18688 [13:45:21<8:13:34, 4.25s/it] 63%|██████▎ | 11728/18688 [13:45:25<8:07:05, 4.20s/it] 63%|██████▎ | 11729/18688 [13:45:30<8:19:02, 4.30s/it] 63%|██████▎ | 11730/18688 [13:45:34<8:09:10, 4.22s/it] {'loss': '0.8671', 'grad_norm': '1.265', 'learning_rate': '2.822e-05', 'epoch': '0.6277', 'num_input_tokens_seen': 39450600, 'train_runtime': '4.953e+04', 'train_tokens_per_second': '796.4'}
63%|██████▎ | 11730/18688 [13:45:34<8:09:10, 4.22s/it] 63%|██████▎ | 11731/18688 [13:45:38<8:06:01, 4.19s/it] 63%|██████▎ | 11732/18688 [13:45:42<8:11:13, 4.24s/it] 63%|██████▎ | 11733/18688 [13:45:47<8:39:11, 4.48s/it] 63%|██████▎ | 11734/18688 [13:45:52<8:35:56, 4.45s/it] 63%|██████▎ | 11735/18688 [13:45:56<8:18:12, 4.30s/it] 63%|██████▎ | 11736/18688 [13:46:00<8:02:46, 4.17s/it] 63%|██████▎ | 11737/18688 [13:46:04<7:57:08, 4.12s/it] 63%|██████▎ | 11738/18688 [13:46:08<8:16:45, 4.29s/it] 63%|██████▎ | 11739/18688 [13:46:12<7:52:09, 4.08s/it] 63%|██████▎ | 11740/18688 [13:46:16<7:55:51, 4.11s/it] {'loss': '0.9324', 'grad_norm': '1.175', 'learning_rate': '2.815e-05', 'epoch': '0.6282', 'num_input_tokens_seen': 39485416, 'train_runtime': '4.958e+04', 'train_tokens_per_second': '796.5'}
63%|██████▎ | 11740/18688 [13:46:16<7:55:51, 4.11s/it] 63%|██████▎ | 11741/18688 [13:46:20<7:49:31, 4.06s/it] 63%|██████▎ | 11742/18688 [13:46:24<7:38:49, 3.96s/it] 63%|██████▎ | 11743/18688 [13:46:28<7:32:44, 3.91s/it] 63%|██████▎ | 11744/18688 [13:46:31<7:23:48, 3.83s/it] 63%|██████▎ | 11745/18688 [13:46:35<7:17:55, 3.78s/it] 63%|██████▎ | 11746/18688 [13:46:39<7:31:44, 3.90s/it] 63%|██████▎ | 11747/18688 [13:46:44<8:00:17, 4.15s/it] 63%|██████▎ | 11748/18688 [13:46:49<8:23:30, 4.35s/it] 63%|██████▎ | 11749/18688 [13:46:53<8:35:31, 4.46s/it] 63%|██████▎ | 11750/18688 [13:46:58<8:42:03, 4.51s/it] {'loss': '0.9131', 'grad_norm': '1.141', 'learning_rate': '2.808e-05', 'epoch': '0.6288', 'num_input_tokens_seen': 39518704, 'train_runtime': '4.962e+04', 'train_tokens_per_second': '796.5'}
63%|██████▎ | 11750/18688 [13:46:58<8:42:03, 4.51s/it] 63%|██████▎ | 11751/18688 [13:47:02<8:15:35, 4.29s/it] 63%|██████▎ | 11752/18688 [13:47:05<7:55:27, 4.11s/it] 63%|██████▎ | 11753/18688 [13:47:09<7:52:50, 4.09s/it] 63%|██████▎ | 11754/18688 [13:47:13<7:46:55, 4.04s/it] 63%|██████▎ | 11755/18688 [13:47:17<7:36:56, 3.95s/it] 63%|██████▎ | 11756/18688 [13:47:22<7:58:22, 4.14s/it] 63%|██████▎ | 11757/18688 [13:47:26<7:48:59, 4.06s/it] 63%|██████▎ | 11758/18688 [13:47:31<8:22:38, 4.35s/it] 63%|██████▎ | 11759/18688 [13:47:35<8:37:45, 4.48s/it] 63%|██████▎ | 11760/18688 [13:47:40<8:25:43, 4.38s/it] {'loss': '0.9298', 'grad_norm': '1.106', 'learning_rate': '2.801e-05', 'epoch': '0.6293', 'num_input_tokens_seen': 39552712, 'train_runtime': '4.966e+04', 'train_tokens_per_second': '796.5'}
63%|██████▎ | 11760/18688 [13:47:40<8:25:43, 4.38s/it] 63%|██████▎ | 11761/18688 [13:47:43<7:56:08, 4.12s/it] 63%|██████▎ | 11762/18688 [13:47:47<7:56:14, 4.13s/it] 63%|██████▎ | 11763/18688 [13:47:51<7:38:31, 3.97s/it] 63%|██████▎ | 11764/18688 [13:47:55<7:39:33, 3.98s/it] 63%|██████▎ | 11765/18688 [13:47:59<7:47:52, 4.05s/it] 63%|██████▎ | 11766/18688 [13:48:03<7:35:26, 3.95s/it] 63%|██████▎ | 11767/18688 [13:48:07<7:33:33, 3.93s/it] 63%|██████▎ | 11768/18688 [13:48:11<7:42:20, 4.01s/it] 63%|██████▎ | 11769/18688 [13:48:15<7:39:35, 3.99s/it] 63%|██████▎ | 11770/18688 [13:48:19<7:32:59, 3.93s/it] {'loss': '0.8823', 'grad_norm': '1.038', 'learning_rate': '2.794e-05', 'epoch': '0.6298', 'num_input_tokens_seen': 39582008, 'train_runtime': '4.97e+04', 'train_tokens_per_second': '796.4'}
63%|██████▎ | 11770/18688 [13:48:19<7:32:59, 3.93s/it] 63%|██████▎ | 11771/18688 [13:48:23<7:44:35, 4.03s/it] 63%|██████▎ | 11772/18688 [13:48:28<8:10:13, 4.25s/it] 63%|██████▎ | 11773/18688 [13:48:31<7:57:58, 4.15s/it] 63%|██████▎ | 11774/18688 [13:48:36<8:13:41, 4.28s/it] 63%|██████▎ | 11775/18688 [13:48:40<7:57:56, 4.15s/it] 63%|██████▎ | 11776/18688 [13:48:44<7:47:06, 4.05s/it] 63%|██████▎ | 11777/18688 [13:48:48<7:57:11, 4.14s/it] 63%|██████▎ | 11778/18688 [13:48:52<8:00:50, 4.18s/it] 63%|██████▎ | 11779/18688 [13:48:56<7:52:40, 4.10s/it] 63%|██████▎ | 11780/18688 [13:49:00<7:47:37, 4.06s/it] {'loss': '0.9416', 'grad_norm': '1.276', 'learning_rate': '2.787e-05', 'epoch': '0.6304', 'num_input_tokens_seen': 39615360, 'train_runtime': '4.974e+04', 'train_tokens_per_second': '796.4'}
63%|██████▎ | 11780/18688 [13:49:00<7:47:37, 4.06s/it] 63%|██████▎ | 11781/18688 [13:49:04<7:42:17, 4.02s/it] 63%|██████▎ | 11782/18688 [13:49:08<7:47:27, 4.06s/it] 63%|██████▎ | 11783/18688 [13:49:12<7:38:25, 3.98s/it] 63%|██████▎ | 11784/18688 [13:49:16<7:32:49, 3.94s/it] 63%|██████▎ | 11785/18688 [13:49:21<8:23:57, 4.38s/it] 63%|██████▎ | 11786/18688 [13:49:26<8:29:33, 4.43s/it] 63%|██████▎ | 11787/18688 [13:49:31<8:50:09, 4.61s/it] 63%|██████▎ | 11788/18688 [13:49:35<8:24:36, 4.39s/it] 63%|██████▎ | 11789/18688 [13:49:39<8:30:00, 4.44s/it] 63%|██████▎ | 11790/18688 [13:49:44<8:25:14, 4.39s/it] {'loss': '0.8735', 'grad_norm': '1.211', 'learning_rate': '2.78e-05', 'epoch': '0.6309', 'num_input_tokens_seen': 39650600, 'train_runtime': '4.978e+04', 'train_tokens_per_second': '796.5'}
63%|██████▎ | 11790/18688 [13:49:44<8:25:14, 4.39s/it] 63%|██████▎ | 11791/18688 [13:49:47<7:58:52, 4.17s/it] 63%|██████▎ | 11792/18688 [13:49:51<7:38:23, 3.99s/it] 63%|██████▎ | 11793/18688 [13:49:56<8:23:24, 4.38s/it] 63%|██████▎ | 11794/18688 [13:50:01<8:28:23, 4.42s/it] 63%|██████▎ | 11795/18688 [13:50:04<7:59:03, 4.17s/it] 63%|██████▎ | 11796/18688 [13:50:09<8:04:56, 4.22s/it] 63%|██████▎ | 11797/18688 [13:50:12<7:42:23, 4.03s/it] 63%|██████▎ | 11798/18688 [13:50:16<7:33:07, 3.95s/it] 63%|██████▎ | 11799/18688 [13:50:20<7:28:56, 3.91s/it] 63%|██████▎ | 11800/18688 [13:50:24<7:30:51, 3.93s/it] {'loss': '0.8879', 'grad_norm': '1.459', 'learning_rate': '2.774e-05', 'epoch': '0.6314', 'num_input_tokens_seen': 39682520, 'train_runtime': '4.982e+04', 'train_tokens_per_second': '796.5'}
63%|██████▎ | 11800/18688 [13:50:24<7:30:51, 3.93s/it] 63%|██████▎ | 11801/18688 [13:50:27<7:13:02, 3.77s/it] 63%|██████▎ | 11802/18688 [13:50:31<7:25:20, 3.88s/it] 63%|██████▎ | 11803/18688 [13:50:35<7:17:47, 3.82s/it] 63%|██████▎ | 11804/18688 [13:50:39<7:13:28, 3.78s/it] 63%|██████▎ | 11805/18688 [13:50:42<7:06:38, 3.72s/it] 63%|██████▎ | 11806/18688 [13:50:46<6:58:15, 3.65s/it] 63%|██████▎ | 11807/18688 [13:50:50<7:20:38, 3.84s/it] 63%|██████▎ | 11808/18688 [13:50:55<7:44:31, 4.05s/it] 63%|██████▎ | 11809/18688 [13:50:58<7:31:25, 3.94s/it] 63%|██████▎ | 11810/18688 [13:51:02<7:18:15, 3.82s/it] {'loss': '0.891', 'grad_norm': '1.232', 'learning_rate': '2.767e-05', 'epoch': '0.632', 'num_input_tokens_seen': 39710928, 'train_runtime': '4.986e+04', 'train_tokens_per_second': '796.4'}
63%|██████▎ | 11810/18688 [13:51:02<7:18:15, 3.82s/it] 63%|██████▎ | 11811/18688 [13:51:07<7:51:16, 4.11s/it] 63%|██████▎ | 11812/18688 [13:51:11<7:55:56, 4.15s/it] 63%|██████▎ | 11813/18688 [13:51:15<7:53:10, 4.13s/it] 63%|██████▎ | 11814/18688 [13:51:19<8:04:42, 4.23s/it] 63%|██████▎ | 11815/18688 [13:51:23<7:41:06, 4.03s/it] 63%|██████▎ | 11816/18688 [13:51:26<7:25:05, 3.89s/it] 63%|██████▎ | 11817/18688 [13:51:31<7:37:51, 4.00s/it] 63%|██████▎ | 11818/18688 [13:51:35<7:47:50, 4.09s/it] 63%|██████▎ | 11819/18688 [13:51:39<7:42:38, 4.04s/it] 63%|██████▎ | 11820/18688 [13:51:43<7:39:26, 4.01s/it] {'loss': '0.9233', 'grad_norm': '1.714', 'learning_rate': '2.76e-05', 'epoch': '0.6325', 'num_input_tokens_seen': 39743904, 'train_runtime': '4.99e+04', 'train_tokens_per_second': '796.4'}
63%|██████▎ | 11820/18688 [13:51:43<7:39:26, 4.01s/it] 63%|██████▎ | 11821/18688 [13:51:47<8:00:14, 4.20s/it] 63%|██████▎ | 11822/18688 [13:51:52<7:59:13, 4.19s/it] 63%|██████▎ | 11823/18688 [13:51:56<7:54:22, 4.15s/it] 63%|██████▎ | 11824/18688 [13:52:00<7:52:13, 4.13s/it] 63%|██████▎ | 11825/18688 [13:52:04<7:53:16, 4.14s/it] 63%|██████▎ | 11826/18688 [13:52:08<7:49:37, 4.11s/it] 63%|██████▎ | 11827/18688 [13:52:12<7:36:51, 4.00s/it] 63%|██████▎ | 11828/18688 [13:52:16<7:39:19, 4.02s/it] 63%|██████▎ | 11829/18688 [13:52:21<8:10:29, 4.29s/it] 63%|██████▎ | 11830/18688 [13:52:24<7:50:54, 4.12s/it] {'loss': '0.9008', 'grad_norm': '1.208', 'learning_rate': '2.753e-05', 'epoch': '0.633', 'num_input_tokens_seen': 39778240, 'train_runtime': '4.994e+04', 'train_tokens_per_second': '796.4'}
63%|██████▎ | 11830/18688 [13:52:24<7:50:54, 4.12s/it] 63%|██████▎ | 11831/18688 [13:52:29<8:03:22, 4.23s/it] 63%|██████▎ | 11832/18688 [13:52:34<8:25:02, 4.42s/it] 63%|██████▎ | 11833/18688 [13:52:38<8:29:24, 4.46s/it] 63%|██████▎ | 11834/18688 [13:52:42<8:04:07, 4.24s/it] 63%|██████▎ | 11835/18688 [13:52:47<8:37:31, 4.53s/it] 63%|██████▎ | 11836/18688 [13:52:51<8:24:37, 4.42s/it] 63%|██████▎ | 11837/18688 [13:52:56<8:30:44, 4.47s/it] 63%|██████▎ | 11838/18688 [13:53:00<8:17:03, 4.35s/it] 63%|██████▎ | 11839/18688 [13:53:04<8:06:06, 4.26s/it] 63%|██████▎ | 11840/18688 [13:53:08<8:00:49, 4.21s/it] {'loss': '0.9686', 'grad_norm': '1.124', 'learning_rate': '2.746e-05', 'epoch': '0.6336', 'num_input_tokens_seen': 39816056, 'train_runtime': '4.999e+04', 'train_tokens_per_second': '796.5'}
63%|██████▎ | 11840/18688 [13:53:08<8:00:49, 4.21s/it] 63%|██████▎ | 11841/18688 [13:53:12<7:45:38, 4.08s/it] 63%|██████▎ | 11842/18688 [13:53:16<7:55:51, 4.17s/it] 63%|██████▎ | 11843/18688 [13:53:20<7:30:47, 3.95s/it] 63%|██████▎ | 11844/18688 [13:53:24<7:26:21, 3.91s/it] 63%|██████▎ | 11845/18688 [13:53:28<7:32:02, 3.96s/it] 63%|██████▎ | 11846/18688 [13:53:33<8:29:13, 4.47s/it] 63%|██████▎ | 11847/18688 [13:53:38<8:50:59, 4.66s/it] 63%|██████▎ | 11848/18688 [13:53:42<8:15:12, 4.34s/it] 63%|██████▎ | 11849/18688 [13:53:47<8:19:26, 4.38s/it] 63%|██████▎ | 11850/18688 [13:53:51<8:14:41, 4.34s/it] {'loss': '0.8467', 'grad_norm': '1.424', 'learning_rate': '2.739e-05', 'epoch': '0.6341', 'num_input_tokens_seen': 39851384, 'train_runtime': '5.003e+04', 'train_tokens_per_second': '796.5'}
63%|██████▎ | 11850/18688 [13:53:51<8:14:41, 4.34s/it] 63%|██████▎ | 11851/18688 [13:53:55<8:09:23, 4.29s/it] 63%|██████▎ | 11852/18688 [13:53:59<8:00:22, 4.22s/it] 63%|██████▎ | 11853/18688 [13:54:03<8:04:31, 4.25s/it] 63%|██████▎ | 11854/18688 [13:54:07<7:39:31, 4.03s/it] 63%|██████▎ | 11855/18688 [13:54:11<7:48:14, 4.11s/it] 63%|██████▎ | 11856/18688 [13:54:15<7:36:09, 4.01s/it] 63%|██████▎ | 11857/18688 [13:54:19<7:53:31, 4.16s/it] 63%|██████▎ | 11858/18688 [13:54:24<7:50:07, 4.13s/it] 63%|██████▎ | 11859/18688 [13:54:27<7:35:54, 4.01s/it] 63%|██████▎ | 11860/18688 [13:54:32<7:51:13, 4.14s/it] {'loss': '0.8733', 'grad_norm': '1.039', 'learning_rate': '2.732e-05', 'epoch': '0.6346', 'num_input_tokens_seen': 39884640, 'train_runtime': '5.007e+04', 'train_tokens_per_second': '796.5'}
63%|██████▎ | 11860/18688 [13:54:32<7:51:13, 4.14s/it] 63%|██████▎ | 11861/18688 [13:54:36<7:52:21, 4.15s/it] 63%|██████▎ | 11862/18688 [13:54:40<7:33:54, 3.99s/it] 63%|██████▎ | 11863/18688 [13:54:43<7:24:44, 3.91s/it] 63%|██████▎ | 11864/18688 [13:54:48<7:39:35, 4.04s/it] 63%|██████▎ | 11865/18688 [13:54:52<7:50:18, 4.14s/it] 63%|██████▎ | 11866/18688 [13:54:56<7:31:36, 3.97s/it] 64%|██████▎ | 11867/18688 [13:55:00<7:46:52, 4.11s/it] 64%|██████▎ | 11868/18688 [13:55:03<7:26:47, 3.93s/it] 64%|██████▎ | 11869/18688 [13:55:07<7:24:06, 3.91s/it] 64%|██████▎ | 11870/18688 [13:55:12<7:48:18, 4.12s/it] {'loss': '0.9205', 'grad_norm': '1.451', 'learning_rate': '2.725e-05', 'epoch': '0.6352', 'num_input_tokens_seen': 39916648, 'train_runtime': '5.011e+04', 'train_tokens_per_second': '796.5'}
64%|██████▎ | 11870/18688 [13:55:12<7:48:18, 4.12s/it] 64%|██████▎ | 11871/18688 [13:55:16<7:49:01, 4.13s/it] 64%|██████▎ | 11872/18688 [13:55:20<7:54:39, 4.18s/it] 64%|██████▎ | 11873/18688 [13:55:24<7:28:29, 3.95s/it] 64%|██████▎ | 11874/18688 [13:55:28<7:30:35, 3.97s/it] 64%|██████▎ | 11875/18688 [13:55:32<7:44:56, 4.09s/it] 64%|██████▎ | 11876/18688 [13:55:36<7:35:22, 4.01s/it] 64%|██████▎ | 11877/18688 [13:55:40<7:27:23, 3.94s/it] 64%|██████▎ | 11878/18688 [13:55:44<7:32:41, 3.99s/it] 64%|██████▎ | 11879/18688 [13:55:50<8:31:28, 4.51s/it] 64%|██████▎ | 11880/18688 [13:55:53<7:59:13, 4.22s/it] {'loss': '0.9208', 'grad_norm': '1.397', 'learning_rate': '2.718e-05', 'epoch': '0.6357', 'num_input_tokens_seen': 39950848, 'train_runtime': '5.015e+04', 'train_tokens_per_second': '796.6'}
64%|██████▎ | 11880/18688 [13:55:53<7:59:13, 4.22s/it] 64%|██████▎ | 11881/18688 [13:55:58<8:09:08, 4.31s/it] 64%|██████▎ | 11882/18688 [13:56:02<8:00:08, 4.23s/it] 64%|██████▎ | 11883/18688 [13:56:06<7:53:33, 4.18s/it] 64%|██████▎ | 11884/18688 [13:56:10<7:49:22, 4.14s/it] 64%|██████▎ | 11885/18688 [13:56:14<7:49:33, 4.14s/it] 64%|██████▎ | 11886/18688 [13:56:18<7:46:22, 4.11s/it] 64%|██████▎ | 11887/18688 [13:56:22<7:28:59, 3.96s/it] 64%|██████▎ | 11888/18688 [13:56:26<7:56:22, 4.20s/it] 64%|██████▎ | 11889/18688 [13:56:31<7:57:36, 4.21s/it] 64%|██████▎ | 11890/18688 [13:56:35<7:51:12, 4.16s/it] {'loss': '0.9643', 'grad_norm': '1.364', 'learning_rate': '2.711e-05', 'epoch': '0.6363', 'num_input_tokens_seen': 39985264, 'train_runtime': '5.02e+04', 'train_tokens_per_second': '796.6'}
64%|██████▎ | 11890/18688 [13:56:35<7:51:12, 4.16s/it] 64%|██████▎ | 11891/18688 [13:56:38<7:28:06, 3.96s/it] 64%|██████▎ | 11892/18688 [13:56:42<7:13:41, 3.83s/it] 64%|██████▎ | 11893/18688 [13:56:46<7:44:46, 4.10s/it] 64%|██████▎ | 11894/18688 [13:56:50<7:38:47, 4.05s/it] 64%|██████▎ | 11895/18688 [13:56:54<7:23:34, 3.92s/it] 64%|██████▎ | 11896/18688 [13:56:58<7:26:12, 3.94s/it] 64%|██████▎ | 11897/18688 [13:57:02<7:23:55, 3.92s/it] 64%|██████▎ | 11898/18688 [13:57:06<7:23:07, 3.92s/it] 64%|██████▎ | 11899/18688 [13:57:10<7:18:40, 3.88s/it] 64%|██████▎ | 11900/18688 [13:57:13<7:17:19, 3.87s/it] {'loss': '0.916', 'grad_norm': '1.263', 'learning_rate': '2.704e-05', 'epoch': '0.6368', 'num_input_tokens_seen': 40015912, 'train_runtime': '5.023e+04', 'train_tokens_per_second': '796.6'}
64%|██████▎ | 11900/18688 [13:57:13<7:17:19, 3.87s/it] 64%|██████▎ | 11901/18688 [13:57:17<7:11:24, 3.81s/it] 64%|██████▎ | 11902/18688 [13:57:21<7:27:39, 3.96s/it] 64%|██████▎ | 11903/18688 [13:57:26<7:49:15, 4.15s/it] 64%|██████▎ | 11904/18688 [13:57:31<8:34:45, 4.55s/it] 64%|██████▎ | 11905/18688 [13:57:35<8:08:43, 4.32s/it] 64%|██████▎ | 11906/18688 [13:57:39<7:47:36, 4.14s/it] 64%|██████▎ | 11907/18688 [13:57:43<7:37:30, 4.05s/it] 64%|██████▎ | 11908/18688 [13:57:47<7:54:46, 4.20s/it] 64%|██████▎ | 11909/18688 [13:57:51<7:28:42, 3.97s/it] 64%|██████▎ | 11910/18688 [13:57:54<7:13:47, 3.84s/it] {'loss': '0.9207', 'grad_norm': '1.314', 'learning_rate': '2.697e-05', 'epoch': '0.6373', 'num_input_tokens_seen': 40049264, 'train_runtime': '5.027e+04', 'train_tokens_per_second': '796.6'}
64%|██████▎ | 11910/18688 [13:57:54<7:13:47, 3.84s/it] 64%|██████▎ | 11911/18688 [13:57:59<7:36:40, 4.04s/it] 64%|██████▎ | 11912/18688 [13:58:03<7:44:20, 4.11s/it] 64%|██████▎ | 11913/18688 [13:58:07<7:30:04, 3.99s/it] 64%|██████▍ | 11914/18688 [13:58:10<7:17:15, 3.87s/it] 64%|██████▍ | 11915/18688 [13:58:16<8:01:26, 4.26s/it] 64%|██████▍ | 11916/18688 [13:58:19<7:47:37, 4.14s/it] 64%|██████▍ | 11917/18688 [13:58:24<7:48:06, 4.15s/it] 64%|██████▍ | 11918/18688 [13:58:28<8:08:46, 4.33s/it] 64%|██████▍ | 11919/18688 [13:58:33<8:04:28, 4.29s/it] 64%|██████▍ | 11920/18688 [13:58:37<8:10:48, 4.35s/it] {'loss': '0.9561', 'grad_norm': '1.032', 'learning_rate': '2.69e-05', 'epoch': '0.6379', 'num_input_tokens_seen': 40083792, 'train_runtime': '5.032e+04', 'train_tokens_per_second': '796.6'}
64%|██████▍ | 11920/18688 [13:58:37<8:10:48, 4.35s/it] 64%|██████▍ | 11921/18688 [13:58:41<7:58:21, 4.24s/it] 64%|██████▍ | 11922/18688 [13:58:45<7:42:55, 4.11s/it] 64%|██████▍ | 11923/18688 [13:58:49<7:42:06, 4.10s/it] 64%|██████▍ | 11924/18688 [13:58:53<7:40:22, 4.08s/it] 64%|██████▍ | 11925/18688 [13:58:57<7:23:39, 3.94s/it] 64%|██████▍ | 11926/18688 [13:59:00<7:20:26, 3.91s/it] 64%|██████▍ | 11927/18688 [13:59:06<8:05:48, 4.31s/it] 64%|██████▍ | 11928/18688 [13:59:11<8:27:52, 4.51s/it] 64%|██████▍ | 11929/18688 [13:59:15<8:12:33, 4.37s/it] 64%|██████▍ | 11930/18688 [13:59:18<7:45:30, 4.13s/it] {'loss': '0.8734', 'grad_norm': '1.225', 'learning_rate': '2.683e-05', 'epoch': '0.6384', 'num_input_tokens_seen': 40116656, 'train_runtime': '5.036e+04', 'train_tokens_per_second': '796.6'}
64%|██████▍ | 11930/18688 [13:59:18<7:45:30, 4.13s/it] 64%|██████▍ | 11931/18688 [13:59:22<7:43:52, 4.12s/it] 64%|██████▍ | 11932/18688 [13:59:26<7:30:25, 4.00s/it] 64%|██████▍ | 11933/18688 [13:59:30<7:23:06, 3.94s/it] 64%|██████▍ | 11934/18688 [13:59:35<8:10:34, 4.36s/it] 64%|██████▍ | 11935/18688 [13:59:39<8:01:13, 4.28s/it] 64%|██████▍ | 11936/18688 [13:59:44<8:25:03, 4.49s/it] 64%|██████▍ | 11937/18688 [13:59:49<8:32:03, 4.55s/it] 64%|██████▍ | 11938/18688 [13:59:54<8:40:04, 4.62s/it] 64%|██████▍ | 11939/18688 [13:59:57<8:06:38, 4.33s/it] 64%|██████▍ | 11940/18688 [14:00:03<8:35:14, 4.58s/it] {'loss': '0.8909', 'grad_norm': '1.215', 'learning_rate': '2.677e-05', 'epoch': '0.6389', 'num_input_tokens_seen': 40154472, 'train_runtime': '5.04e+04', 'train_tokens_per_second': '796.7'}
64%|██████▍ | 11940/18688 [14:00:03<8:35:14, 4.58s/it] 64%|██████▍ | 11941/18688 [14:00:07<8:28:23, 4.52s/it] 64%|██████▍ | 11942/18688 [14:00:12<8:38:10, 4.61s/it] 64%|██████▍ | 11943/18688 [14:00:16<8:29:08, 4.53s/it] 64%|██████▍ | 11944/18688 [14:00:20<8:02:42, 4.29s/it] 64%|██████▍ | 11945/18688 [14:00:24<7:51:52, 4.20s/it] 64%|██████▍ | 11946/18688 [14:00:27<7:31:58, 4.02s/it] 64%|██████▍ | 11947/18688 [14:00:31<7:31:32, 4.02s/it] 64%|██████▍ | 11948/18688 [14:00:36<7:55:01, 4.23s/it] 64%|██████▍ | 11949/18688 [14:00:41<8:03:13, 4.30s/it] 64%|██████▍ | 11950/18688 [14:00:44<7:38:36, 4.08s/it] {'loss': '0.9242', 'grad_norm': '1.677', 'learning_rate': '2.67e-05', 'epoch': '0.6395', 'num_input_tokens_seen': 40187520, 'train_runtime': '5.044e+04', 'train_tokens_per_second': '796.7'}
64%|██████▍ | 11950/18688 [14:00:44<7:38:36, 4.08s/it] 64%|██████▍ | 11951/18688 [14:00:48<7:18:19, 3.90s/it] 64%|██████▍ | 11952/18688 [14:00:51<7:13:48, 3.86s/it] 64%|██████▍ | 11953/18688 [14:00:55<7:02:25, 3.76s/it] 64%|██████▍ | 11954/18688 [14:00:59<7:14:23, 3.87s/it] 64%|██████▍ | 11955/18688 [14:01:04<8:06:21, 4.33s/it] 64%|██████▍ | 11956/18688 [14:01:08<7:36:37, 4.07s/it] 64%|██████▍ | 11957/18688 [14:01:13<8:11:25, 4.38s/it] 64%|██████▍ | 11958/18688 [14:01:18<8:18:14, 4.44s/it] 64%|██████▍ | 11959/18688 [14:01:21<7:56:22, 4.25s/it] 64%|██████▍ | 11960/18688 [14:01:26<8:16:36, 4.43s/it] {'loss': '0.8801', 'grad_norm': '1.028', 'learning_rate': '2.663e-05', 'epoch': '0.64', 'num_input_tokens_seen': 40222440, 'train_runtime': '5.049e+04', 'train_tokens_per_second': '796.7'}
64%|██████▍ | 11960/18688 [14:01:26<8:16:36, 4.43s/it] 64%|██████▍ | 11961/18688 [14:01:32<8:46:13, 4.69s/it] 64%|██████▍ | 11962/18688 [14:01:36<8:30:56, 4.56s/it] 64%|██████▍ | 11963/18688 [14:01:40<8:32:26, 4.57s/it] 64%|██████▍ | 11964/18688 [14:01:44<8:05:48, 4.34s/it] 64%|██████▍ | 11965/18688 [14:01:48<7:36:43, 4.08s/it] 64%|██████▍ | 11966/18688 [14:01:53<8:02:00, 4.30s/it] 64%|██████▍ | 11967/18688 [14:01:57<8:00:23, 4.29s/it] 64%|██████▍ | 11968/18688 [14:02:01<7:57:40, 4.26s/it] 64%|██████▍ | 11969/18688 [14:02:06<8:11:52, 4.39s/it] 64%|██████▍ | 11970/18688 [14:02:10<7:57:12, 4.26s/it] {'loss': '0.9276', 'grad_norm': '1.432', 'learning_rate': '2.656e-05', 'epoch': '0.6405', 'num_input_tokens_seen': 40259536, 'train_runtime': '5.053e+04', 'train_tokens_per_second': '796.7'}
64%|██████▍ | 11970/18688 [14:02:10<7:57:12, 4.26s/it] 64%|██████▍ | 11971/18688 [14:02:15<8:28:56, 4.55s/it] 64%|██████▍ | 11972/18688 [14:02:19<8:25:38, 4.52s/it] 64%|██████▍ | 11973/18688 [14:02:24<8:17:24, 4.44s/it] 64%|██████▍ | 11974/18688 [14:02:27<7:49:23, 4.19s/it] 64%|██████▍ | 11975/18688 [14:02:31<7:32:44, 4.05s/it] 64%|██████▍ | 11976/18688 [14:02:36<7:59:13, 4.28s/it] 64%|██████▍ | 11977/18688 [14:02:39<7:41:01, 4.12s/it] 64%|██████▍ | 11978/18688 [14:02:43<7:37:04, 4.09s/it] 64%|██████▍ | 11979/18688 [14:02:48<7:40:08, 4.12s/it] 64%|██████▍ | 11980/18688 [14:02:52<7:40:46, 4.12s/it] {'loss': '0.9988', 'grad_norm': '1.32', 'learning_rate': '2.649e-05', 'epoch': '0.6411', 'num_input_tokens_seen': 40293704, 'train_runtime': '5.057e+04', 'train_tokens_per_second': '796.8'}
64%|██████▍ | 11980/18688 [14:02:52<7:40:46, 4.12s/it] 64%|██████▍ | 11981/18688 [14:02:55<7:25:02, 3.98s/it] 64%|██████▍ | 11982/18688 [14:02:59<7:26:23, 3.99s/it] 64%|██████▍ | 11983/18688 [14:03:04<7:39:49, 4.11s/it] 64%|██████▍ | 11984/18688 [14:03:08<7:47:55, 4.19s/it] 64%|██████▍ | 11985/18688 [14:03:12<7:50:37, 4.21s/it] 64%|██████▍ | 11986/18688 [14:03:17<7:55:48, 4.26s/it] 64%|██████▍ | 11987/18688 [14:03:21<7:39:53, 4.12s/it] 64%|██████▍ | 11988/18688 [14:03:25<7:51:30, 4.22s/it] 64%|██████▍ | 11989/18688 [14:03:29<7:34:07, 4.07s/it] 64%|██████▍ | 11990/18688 [14:03:33<7:43:01, 4.15s/it] {'loss': '0.9105', 'grad_norm': '1.114', 'learning_rate': '2.642e-05', 'epoch': '0.6416', 'num_input_tokens_seen': 40325872, 'train_runtime': '5.061e+04', 'train_tokens_per_second': '796.7'}
64%|██████▍ | 11990/18688 [14:03:33<7:43:01, 4.15s/it] 64%|██████▍ | 11991/18688 [14:03:38<8:00:56, 4.31s/it] 64%|██████▍ | 11992/18688 [14:03:42<7:59:08, 4.29s/it] 64%|██████▍ | 11993/18688 [14:03:47<8:29:12, 4.56s/it] 64%|██████▍ | 11994/18688 [14:03:51<7:55:19, 4.26s/it] 64%|██████▍ | 11995/18688 [14:03:54<7:32:51, 4.06s/it] 64%|██████▍ | 11996/18688 [14:03:59<7:37:22, 4.10s/it] 64%|██████▍ | 11997/18688 [14:04:02<7:27:09, 4.01s/it] 64%|██████▍ | 11998/18688 [14:04:07<7:54:12, 4.25s/it] 64%|██████▍ | 11999/18688 [14:04:11<7:48:21, 4.20s/it] 64%|██████▍ | 12000/18688 [14:04:15<7:33:17, 4.07s/it] {'loss': '0.9069', 'grad_norm': '1.33', 'learning_rate': '2.635e-05', 'epoch': '0.6421', 'num_input_tokens_seen': 40360224, 'train_runtime': '5.066e+04', 'train_tokens_per_second': '796.8'}
64%|██████▍ | 12000/18688 [14:04:15<7:33:17, 4.07s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
0%| | 3/1000 [00:00<00:38, 25.93it/s]
1%| | 6/1000 [00:00<00:54, 18.26it/s]
1%| | 8/1000 [00:00<00:56, 17.52it/s]
1%| | 10/1000 [00:00<00:59, 16.60it/s]
1%| | 12/1000 [00:00<01:00, 16.30it/s]
1%|▏ | 14/1000 [00:00<01:02, 15.81it/s]
2%|▏ | 16/1000 [00:00<01:03, 15.61it/s]
2%|▏ | 18/1000 [00:01<01:38, 9.96it/s]
2%|▏ | 20/1000 [00:01<01:36, 10.17it/s]
2%|▏ | 22/1000 [00:01<01:55, 8.50it/s]
2%|▏ | 24/1000 [00:02<01:44, 9.37it/s]
3%|▎ | 26/1000 [00:02<01:47, 9.03it/s]
3%|▎ | 28/1000 [00:02<01:38, 9.89it/s]
3%|▎ | 30/1000 [00:02<01:29, 10.81it/s]
3%|▎ | 32/1000 [00:02<01:36, 10.02it/s]
3%|▎ | 34/1000 [00:02<01:26, 11.13it/s]
4%|▎ | 36/1000 [00:03<01:33, 10.36it/s]
4%|▍ | 38/1000 [00:03<01:25, 11.29it/s]
4%|▍ | 40/1000 [00:03<01:27, 10.92it/s]
4%|▍ | 42/1000 [00:03<01:46, 9.00it/s]
4%|▍ | 44/1000 [00:03<01:31, 10.50it/s]
5%|▍ | 46/1000 [00:04<01:21, 11.67it/s]
5%|▍ | 48/1000 [00:04<01:29, 10.62it/s]
5%|▌ | 50/1000 [00:04<01:21, 11.65it/s]
5%|▌ | 52/1000 [00:04<01:15, 12.50it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.26it/s]
6%|▌ | 56/1000 [00:04<01:07, 13.92it/s]
6%|▌ | 58/1000 [00:05<01:48, 8.71it/s]
6%|▌ | 60/1000 [00:05<01:36, 9.75it/s]
6%|▌ | 62/1000 [00:05<01:27, 10.73it/s]
6%|▋ | 64/1000 [00:05<01:19, 11.79it/s]
7%|▋ | 66/1000 [00:05<01:15, 12.30it/s]
7%|▋ | 68/1000 [00:05<01:12, 12.88it/s]
7%|▋ | 70/1000 [00:06<01:09, 13.46it/s]
7%|▋ | 72/1000 [00:06<01:06, 13.90it/s]
7%|▋ | 74/1000 [00:06<01:08, 13.55it/s]
8%|▊ | 76/1000 [00:06<01:06, 13.84it/s]
8%|▊ | 78/1000 [00:06<01:05, 14.00it/s]
8%|▊ | 80/1000 [00:06<01:14, 12.40it/s]
8%|▊ | 82/1000 [00:06<01:09, 13.16it/s]
8%|▊ | 84/1000 [00:07<01:06, 13.81it/s]
9%|▊ | 86/1000 [00:07<01:05, 14.02it/s]
9%|▉ | 88/1000 [00:07<01:04, 14.04it/s]
9%|▉ | 90/1000 [00:07<01:04, 14.20it/s]
9%|▉ | 92/1000 [00:07<01:05, 13.96it/s]
9%|▉ | 94/1000 [00:07<01:05, 13.83it/s]
10%|▉ | 96/1000 [00:07<01:03, 14.18it/s]
10%|▉ | 98/1000 [00:08<01:03, 14.12it/s]
10%|█ | 100/1000 [00:08<01:32, 9.76it/s]
10%|█ | 102/1000 [00:08<01:23, 10.72it/s]
10%|█ | 104/1000 [00:08<01:55, 7.79it/s]
11%|█ | 106/1000 [00:09<02:02, 7.31it/s]
11%|█ | 108/1000 [00:09<01:43, 8.66it/s]
11%|█ | 110/1000 [00:09<01:28, 10.09it/s]
11%|█ | 112/1000 [00:09<01:19, 11.21it/s]
11%|█▏ | 114/1000 [00:09<01:14, 11.88it/s]
12%|█▏ | 116/1000 [00:09<01:10, 12.52it/s]
12%|█▏ | 118/1000 [00:10<01:06, 13.28it/s]
12%|█▏ | 120/1000 [00:10<01:09, 12.71it/s]
12%|█▏ | 122/1000 [00:10<01:23, 10.57it/s]
12%|█▏ | 124/1000 [00:10<01:30, 9.64it/s]
13%|█▎ | 126/1000 [00:10<01:25, 10.27it/s]
13%|█▎ | 128/1000 [00:11<01:20, 10.84it/s]
13%|█▎ | 130/1000 [00:11<01:17, 11.26it/s]
13%|█▎ | 132/1000 [00:11<01:15, 11.57it/s]
13%|█▎ | 134/1000 [00:11<01:16, 11.33it/s]
14%|█▎ | 136/1000 [00:11<01:13, 11.82it/s]
14%|█▍ | 138/1000 [00:11<01:10, 12.24it/s]
14%|█▍ | 140/1000 [00:12<01:28, 9.66it/s]
14%|█▍ | 142/1000 [00:12<01:43, 8.31it/s]
14%|█▍ | 144/1000 [00:12<01:31, 9.35it/s]
15%|█▍ | 146/1000 [00:12<01:21, 10.42it/s]
15%|█▍ | 148/1000 [00:12<01:14, 11.36it/s]
15%|█▌ | 150/1000 [00:13<01:08, 12.40it/s]
15%|█▌ | 152/1000 [00:13<01:05, 12.97it/s]
15%|█▌ | 154/1000 [00:13<01:03, 13.27it/s]
16%|█▌ | 156/1000 [00:13<01:04, 13.15it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.67it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.58it/s]
16%|█▌ | 162/1000 [00:13<01:02, 13.46it/s]
16%|█▋ | 164/1000 [00:14<01:01, 13.59it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.77it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.27it/s]
17%|█▋ | 170/1000 [00:14<01:19, 10.48it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.46it/s]
17%|█▋ | 174/1000 [00:15<01:30, 9.17it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.18it/s]
18%|█▊ | 178/1000 [00:15<01:35, 8.65it/s]
18%|█▊ | 180/1000 [00:15<01:33, 8.78it/s]
18%|█▊ | 182/1000 [00:16<01:35, 8.56it/s]
18%|█▊ | 184/1000 [00:16<01:23, 9.81it/s]
19%|█▊ | 186/1000 [00:16<01:37, 8.32it/s]
19%|█▉ | 188/1000 [00:16<01:24, 9.63it/s]
19%|█▉ | 190/1000 [00:16<01:15, 10.66it/s]
19%|█▉ | 192/1000 [00:17<01:11, 11.31it/s]
19%|█▉ | 194/1000 [00:17<01:16, 10.55it/s]
20%|█▉ | 196/1000 [00:17<01:31, 8.82it/s]
20%|█▉ | 198/1000 [00:17<01:20, 10.01it/s]
20%|██ | 200/1000 [00:17<01:19, 10.13it/s]
20%|██ | 202/1000 [00:18<01:13, 10.83it/s]
20%|██ | 204/1000 [00:18<01:06, 11.99it/s]
21%|██ | 206/1000 [00:18<01:02, 12.70it/s]
21%|██ | 208/1000 [00:18<01:00, 13.19it/s]
21%|██ | 210/1000 [00:18<00:56, 13.88it/s]
21%|██ | 212/1000 [00:18<00:55, 14.23it/s]
21%|██▏ | 214/1000 [00:18<00:54, 14.35it/s]
22%|██▏ | 216/1000 [00:19<00:56, 13.91it/s]
22%|██▏ | 218/1000 [00:19<00:56, 13.82it/s]
22%|██▏ | 220/1000 [00:19<01:30, 8.65it/s]
22%|██▏ | 222/1000 [00:19<01:26, 9.01it/s]
22%|██▏ | 224/1000 [00:19<01:21, 9.49it/s]
23%|██▎ | 226/1000 [00:20<01:13, 10.50it/s]
23%|██▎ | 228/1000 [00:20<01:07, 11.43it/s]
23%|██▎ | 230/1000 [00:20<01:04, 11.96it/s]
23%|██▎ | 232/1000 [00:20<01:02, 12.36it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.07it/s]
24%|██▎ | 236/1000 [00:20<00:56, 13.55it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.19it/s]
24%|██▍ | 240/1000 [00:21<00:53, 14.19it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.75it/s]
24%|██▍ | 244/1000 [00:21<00:50, 14.97it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.95it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.67it/s]
25%|██▌ | 250/1000 [00:21<00:55, 13.42it/s]
25%|██▌ | 252/1000 [00:22<01:01, 12.17it/s]
25%|██▌ | 254/1000 [00:22<00:56, 13.18it/s]
26%|██▌ | 256/1000 [00:22<00:54, 13.75it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.81it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.93it/s]
26%|██▌ | 262/1000 [00:22<00:56, 13.06it/s]
26%|██▋ | 264/1000 [00:22<00:54, 13.62it/s]
27%|██▋ | 266/1000 [00:23<00:52, 13.99it/s]
27%|██▋ | 268/1000 [00:23<00:50, 14.48it/s]
27%|██▋ | 270/1000 [00:23<00:55, 13.21it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.62it/s]
27%|██▋ | 274/1000 [00:23<01:02, 11.68it/s]
28%|██▊ | 276/1000 [00:23<00:57, 12.65it/s]
28%|██▊ | 278/1000 [00:23<00:55, 12.91it/s]
28%|██▊ | 280/1000 [00:24<00:53, 13.48it/s]
28%|██▊ | 282/1000 [00:24<00:49, 14.41it/s]
28%|██▊ | 284/1000 [00:24<00:49, 14.37it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.66it/s]
29%|██▉ | 288/1000 [00:24<00:47, 15.02it/s]
29%|██▉ | 290/1000 [00:24<00:46, 15.42it/s]
29%|██▉ | 292/1000 [00:24<00:45, 15.40it/s]
29%|██▉ | 294/1000 [00:25<00:46, 15.31it/s]
30%|██▉ | 296/1000 [00:25<00:45, 15.34it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.37it/s]
30%|███ | 300/1000 [00:25<00:46, 15.05it/s]
30%|███ | 302/1000 [00:25<00:46, 15.09it/s]
30%|███ | 304/1000 [00:25<00:50, 13.71it/s]
31%|███ | 306/1000 [00:25<00:48, 14.39it/s]
31%|███ | 308/1000 [00:25<00:48, 14.38it/s]
31%|███ | 310/1000 [00:26<00:47, 14.57it/s]
31%|███ | 312/1000 [00:26<00:47, 14.59it/s]
31%|███▏ | 314/1000 [00:26<00:44, 15.29it/s]
32%|███▏ | 316/1000 [00:26<00:43, 15.58it/s]
32%|███▏ | 318/1000 [00:26<00:42, 15.96it/s]
32%|███▏ | 320/1000 [00:27<01:12, 9.35it/s]
32%|███▏ | 322/1000 [00:27<01:03, 10.71it/s]
32%|███▏ | 324/1000 [00:27<00:58, 11.50it/s]
33%|███▎ | 326/1000 [00:27<00:58, 11.44it/s]
33%|███▎ | 328/1000 [00:27<00:58, 11.48it/s]
33%|███▎ | 330/1000 [00:27<00:53, 12.41it/s]
33%|███▎ | 332/1000 [00:27<00:52, 12.67it/s]
33%|███▎ | 334/1000 [00:28<00:50, 13.15it/s]
34%|███▎ | 336/1000 [00:28<00:48, 13.56it/s]
34%|███▍ | 338/1000 [00:28<01:05, 10.08it/s]
34%|███▍ | 340/1000 [00:28<00:59, 11.14it/s]
34%|███▍ | 342/1000 [00:28<00:53, 12.36it/s]
34%|███▍ | 344/1000 [00:28<00:50, 13.02it/s]
35%|███▍ | 346/1000 [00:29<00:48, 13.56it/s]
35%|███▍ | 348/1000 [00:29<00:46, 13.90it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.54it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.75it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.83it/s]
36%|███▌ | 356/1000 [00:29<00:43, 14.96it/s]
36%|███▌ | 358/1000 [00:29<00:43, 14.66it/s]
36%|███▌ | 360/1000 [00:29<00:42, 15.05it/s]
36%|███▌ | 362/1000 [00:30<00:47, 13.36it/s]
36%|███▋ | 364/1000 [00:30<00:45, 13.85it/s]
37%|███▋ | 366/1000 [00:30<00:53, 11.94it/s]
37%|███▋ | 368/1000 [00:30<00:51, 12.34it/s]
37%|███▋ | 370/1000 [00:30<00:47, 13.16it/s]
37%|███▋ | 372/1000 [00:30<00:46, 13.50it/s]
37%|███▋ | 374/1000 [00:31<00:55, 11.38it/s]
38%|███▊ | 376/1000 [00:31<00:56, 10.96it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.63it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.46it/s]
38%|███▊ | 382/1000 [00:31<00:48, 12.65it/s]
38%|███▊ | 384/1000 [00:31<00:45, 13.41it/s]
39%|███▊ | 386/1000 [00:32<00:44, 13.72it/s]
39%|███▉ | 388/1000 [00:32<00:44, 13.66it/s]
39%|███▉ | 390/1000 [00:32<00:45, 13.54it/s]
39%|███▉ | 392/1000 [00:32<00:43, 13.86it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.66it/s]
40%|███▉ | 396/1000 [00:32<00:43, 13.78it/s]
40%|███▉ | 398/1000 [00:32<00:47, 12.58it/s]
40%|████ | 400/1000 [00:33<00:45, 13.23it/s]
40%|████ | 402/1000 [00:33<00:43, 13.88it/s]
40%|████ | 404/1000 [00:33<00:41, 14.48it/s]
41%|████ | 406/1000 [00:33<00:39, 14.99it/s]
41%|████ | 408/1000 [00:33<00:47, 12.52it/s]
41%|████ | 410/1000 [00:33<00:56, 10.40it/s]
41%|████ | 412/1000 [00:34<00:53, 11.02it/s]
41%|████▏ | 414/1000 [00:34<01:09, 8.47it/s]
42%|████▏ | 416/1000 [00:34<01:26, 6.74it/s]
42%|████▏ | 418/1000 [00:35<01:14, 7.78it/s]
42%|████▏ | 420/1000 [00:35<01:06, 8.72it/s]
42%|████▏ | 422/1000 [00:35<01:00, 9.53it/s]
42%|████▏ | 424/1000 [00:35<01:20, 7.16it/s]
43%|████▎ | 426/1000 [00:36<01:09, 8.21it/s]
43%|████▎ | 428/1000 [00:36<01:02, 9.11it/s]
43%|████▎ | 430/1000 [00:36<01:07, 8.49it/s]
43%|████▎ | 432/1000 [00:36<01:24, 6.73it/s]
43%|████▎ | 434/1000 [00:37<01:12, 7.76it/s]
44%|████▎ | 436/1000 [00:37<01:04, 8.73it/s]
44%|████▍ | 438/1000 [00:37<01:05, 8.54it/s]
44%|████▍ | 440/1000 [00:37<01:02, 9.02it/s]
44%|████▍ | 442/1000 [00:37<00:56, 9.80it/s]
44%|████▍ | 444/1000 [00:37<00:53, 10.44it/s]
45%|████▍ | 446/1000 [00:38<00:50, 10.92it/s]
45%|████▍ | 448/1000 [00:38<00:54, 10.05it/s]
45%|████▌ | 450/1000 [00:38<00:51, 10.64it/s]
45%|████▌ | 452/1000 [00:38<00:49, 11.13it/s]
45%|████▌ | 454/1000 [00:38<00:49, 10.95it/s]
46%|████▌ | 456/1000 [00:39<00:47, 11.37it/s]
46%|████▌ | 458/1000 [00:39<00:45, 11.79it/s]
46%|████▌ | 460/1000 [00:39<00:44, 12.09it/s]
46%|████▌ | 462/1000 [00:39<00:41, 12.99it/s]
46%|████▋ | 464/1000 [00:39<00:38, 13.81it/s]
47%|████▋ | 466/1000 [00:39<00:38, 14.02it/s]
47%|████▋ | 468/1000 [00:39<00:42, 12.48it/s]
47%|████▋ | 470/1000 [00:40<00:53, 9.86it/s]
47%|████▋ | 472/1000 [00:40<00:52, 10.09it/s]
47%|████▋ | 474/1000 [00:40<00:46, 11.39it/s]
48%|████▊ | 476/1000 [00:40<00:43, 12.07it/s]
48%|████▊ | 478/1000 [00:40<00:43, 11.92it/s]
48%|████▊ | 480/1000 [00:41<00:40, 12.85it/s]
48%|████▊ | 482/1000 [00:41<00:38, 13.29it/s]
48%|████▊ | 484/1000 [00:41<00:37, 13.82it/s]
49%|████▊ | 486/1000 [00:41<00:36, 14.00it/s]
49%|████▉ | 488/1000 [00:41<00:35, 14.28it/s]
49%|████▉ | 490/1000 [00:41<00:45, 11.11it/s]
49%|████▉ | 492/1000 [00:41<00:40, 12.41it/s]
49%|████▉ | 494/1000 [00:42<00:37, 13.38it/s]
50%|████▉ | 496/1000 [00:42<00:35, 14.15it/s]
50%|████▉ | 498/1000 [00:42<00:34, 14.56it/s]
50%|█████ | 500/1000 [00:42<00:43, 11.60it/s]
50%|█████ | 502/1000 [00:42<00:41, 12.05it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.64it/s]
51%|█████ | 506/1000 [00:43<00:38, 12.79it/s]
51%|█████ | 508/1000 [00:43<00:35, 13.71it/s]
51%|█████ | 510/1000 [00:43<00:47, 10.38it/s]
51%|█████ | 512/1000 [00:43<00:43, 11.15it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 12.03it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.59it/s]
52%|█████▏ | 518/1000 [00:44<00:36, 13.10it/s]
52%|█████▏ | 520/1000 [00:44<00:35, 13.46it/s]
52%|█████▏ | 522/1000 [00:44<00:33, 14.19it/s]
52%|█████▏ | 524/1000 [00:44<00:33, 14.22it/s]
53%|█████▎ | 526/1000 [00:44<00:32, 14.38it/s]
53%|█████▎ | 528/1000 [00:44<00:39, 11.96it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.77it/s]
53%|█████▎ | 532/1000 [00:45<00:34, 13.70it/s]
53%|█████▎ | 534/1000 [00:45<00:33, 14.08it/s]
54%|█████▎ | 536/1000 [00:45<00:32, 14.21it/s]
54%|█████▍ | 538/1000 [00:45<00:33, 13.98it/s]
54%|█████▍ | 540/1000 [00:45<00:31, 14.40it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.68it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.56it/s]
55%|█████▍ | 546/1000 [00:46<00:31, 14.19it/s]
55%|█████▍ | 548/1000 [00:46<00:32, 13.89it/s]
55%|█████▌ | 550/1000 [00:46<00:31, 14.13it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.26it/s]
55%|█████▌ | 554/1000 [00:46<00:32, 13.92it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.08it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.41it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 14.95it/s]
56%|█████▌ | 562/1000 [00:47<00:29, 14.91it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.76it/s]
57%|█████▋ | 566/1000 [00:47<00:37, 11.52it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.09it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.22it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.76it/s]
57%|█████▋ | 574/1000 [00:48<00:31, 13.67it/s]
58%|█████▊ | 576/1000 [00:48<00:31, 13.58it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.62it/s]
58%|█████▊ | 580/1000 [00:48<00:32, 12.84it/s]
58%|█████▊ | 582/1000 [00:48<00:30, 13.60it/s]
58%|█████▊ | 584/1000 [00:48<00:30, 13.84it/s]
59%|█████▊ | 586/1000 [00:49<00:29, 14.22it/s]
59%|█████▉ | 588/1000 [00:49<00:27, 14.98it/s]
59%|█████▉ | 590/1000 [00:49<00:27, 14.96it/s]
59%|█████▉ | 592/1000 [00:49<00:27, 14.73it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.32it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.86it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 14.09it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.38it/s]
60%|██████ | 602/1000 [00:50<00:27, 14.43it/s]
60%|██████ | 604/1000 [00:50<00:26, 14.99it/s]
61%|██████ | 606/1000 [00:50<00:26, 14.79it/s]
61%|██████ | 608/1000 [00:50<00:25, 15.11it/s]
61%|██████ | 610/1000 [00:50<00:26, 14.96it/s]
61%|██████ | 612/1000 [00:50<00:25, 15.05it/s]
61%|██████▏ | 614/1000 [00:50<00:26, 14.32it/s]
62%|██████▏ | 616/1000 [00:51<00:26, 14.73it/s]
62%|██████▏ | 618/1000 [00:51<00:26, 14.17it/s]
62%|██████▏ | 620/1000 [00:51<00:38, 9.75it/s]
62%|██████▏ | 622/1000 [00:51<00:37, 9.95it/s]
62%|██████▏ | 624/1000 [00:51<00:32, 11.39it/s]
63%|██████▎ | 626/1000 [00:52<00:30, 12.09it/s]
63%|██████▎ | 628/1000 [00:52<00:27, 13.31it/s]
63%|██████▎ | 630/1000 [00:52<00:26, 13.81it/s]
63%|██████▎ | 632/1000 [00:52<00:29, 12.48it/s]
63%|██████▎ | 634/1000 [00:52<00:27, 13.19it/s]
64%|██████▎ | 636/1000 [00:52<00:33, 10.96it/s]
64%|██████▍ | 638/1000 [00:53<00:33, 10.68it/s]
64%|██████▍ | 640/1000 [00:53<00:31, 11.46it/s]
64%|██████▍ | 642/1000 [00:53<00:29, 12.33it/s]
64%|██████▍ | 644/1000 [00:53<00:28, 12.58it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.86it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.34it/s]
65%|██████▌ | 650/1000 [00:53<00:27, 12.56it/s]
65%|██████▌ | 652/1000 [00:54<00:29, 11.95it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.42it/s]
66%|██████▌ | 656/1000 [00:54<00:31, 10.78it/s]
66%|██████▌ | 658/1000 [00:54<00:28, 11.86it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.16it/s]
66%|██████▌ | 662/1000 [00:55<00:29, 11.29it/s]
66%|██████▋ | 664/1000 [00:55<00:28, 11.76it/s]
67%|██████▋ | 666/1000 [00:55<00:26, 12.73it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.29it/s]
67%|██████▋ | 670/1000 [00:55<00:29, 11.06it/s]
67%|██████▋ | 672/1000 [00:55<00:27, 11.83it/s]
67%|██████▋ | 674/1000 [00:56<00:26, 12.42it/s]
68%|██████▊ | 676/1000 [00:56<00:27, 11.69it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.19it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 12.98it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.10it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.30it/s]
69%|██████▊ | 686/1000 [00:57<00:23, 13.37it/s]
69%|██████▉ | 688/1000 [00:57<00:22, 13.70it/s]
69%|██████▉ | 690/1000 [00:57<00:22, 13.77it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.24it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.35it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 14.08it/s]
70%|██████▉ | 698/1000 [00:57<00:20, 14.59it/s]
70%|███████ | 700/1000 [00:58<00:24, 12.15it/s]
70%|███████ | 702/1000 [00:58<00:22, 12.97it/s]
70%|███████ | 704/1000 [00:58<00:29, 9.87it/s]
71%|███████ | 706/1000 [00:58<00:27, 10.68it/s]
71%|███████ | 708/1000 [00:58<00:30, 9.61it/s]
71%|███████ | 710/1000 [00:59<00:26, 10.91it/s]
71%|███████ | 712/1000 [00:59<00:24, 11.86it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.95it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 13.03it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.53it/s]
72%|███████▏ | 720/1000 [00:59<00:20, 13.49it/s]
72%|███████▏ | 722/1000 [00:59<00:19, 13.94it/s]
72%|███████▏ | 724/1000 [01:00<00:20, 13.78it/s]
73%|███████▎ | 726/1000 [01:00<00:19, 14.00it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.93it/s]
73%|███████▎ | 730/1000 [01:00<00:32, 8.19it/s]
73%|███████▎ | 732/1000 [01:01<00:32, 8.31it/s]
73%|███████▎ | 734/1000 [01:01<00:30, 8.59it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.42it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.88it/s]
74%|███████▍ | 739/1000 [01:01<00:23, 10.99it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.67it/s]
74%|███████▍ | 743/1000 [01:01<00:20, 12.32it/s]
74%|███████▍ | 745/1000 [01:02<00:25, 10.20it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.56it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.72it/s]
75%|███████▌ | 751/1000 [01:02<00:21, 11.81it/s]
75%|███████▌ | 753/1000 [01:02<00:19, 12.57it/s]
76%|███████▌ | 755/1000 [01:02<00:18, 13.29it/s]
76%|███████▌ | 757/1000 [01:03<00:23, 10.25it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.29it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.28it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 9.99it/s]
76%|███████▋ | 765/1000 [01:04<00:28, 8.13it/s]
77%|███████▋ | 766/1000 [01:04<00:28, 8.31it/s]
77%|███████▋ | 768/1000 [01:04<00:24, 9.49it/s]
77%|███████▋ | 770/1000 [01:04<00:21, 10.85it/s]
77%|███████▋ | 772/1000 [01:04<00:19, 11.89it/s]
77%|███████▋ | 774/1000 [01:04<00:17, 12.86it/s]
78%|███████▊ | 776/1000 [01:04<00:16, 13.36it/s]
78%|███████▊ | 778/1000 [01:05<00:21, 10.19it/s]
78%|███████▊ | 780/1000 [01:05<00:23, 9.29it/s]
78%|███████▊ | 782/1000 [01:05<00:26, 8.14it/s]
78%|███████▊ | 784/1000 [01:06<00:28, 7.61it/s]
78%|███████▊ | 785/1000 [01:06<00:27, 7.83it/s]
79%|███████▊ | 786/1000 [01:06<00:29, 7.35it/s]
79%|███████▉ | 788/1000 [01:06<00:23, 9.04it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.25it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 11.33it/s]
79%|███████▉ | 794/1000 [01:06<00:17, 11.90it/s]
80%|███████▉ | 796/1000 [01:07<00:16, 12.34it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 13.09it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.32it/s]
80%|████████ | 802/1000 [01:07<00:24, 8.25it/s]
80%|████████ | 804/1000 [01:08<00:23, 8.29it/s]
81%|████████ | 806/1000 [01:08<00:21, 8.97it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.35it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.47it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.69it/s]
81%|████████▏ | 814/1000 [01:08<00:14, 12.58it/s]
82%|████████▏ | 816/1000 [01:09<00:16, 11.01it/s]
82%|████████▏ | 818/1000 [01:09<00:15, 11.97it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.45it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 13.00it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.81it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.22it/s]
83%|████████▎ | 828/1000 [01:09<00:11, 14.48it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.93it/s]
83%|████████▎ | 832/1000 [01:10<00:11, 14.33it/s]
83%|████████▎ | 834/1000 [01:10<00:11, 14.07it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.42it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.32it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.38it/s]
84%|████████▍ | 842/1000 [01:10<00:10, 14.42it/s]
84%|████████▍ | 844/1000 [01:11<00:11, 13.97it/s]
85%|████████▍ | 846/1000 [01:11<00:11, 13.89it/s]
85%|████████▍ | 848/1000 [01:11<00:10, 13.84it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.05it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.66it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.25it/s]
86%|████████▌ | 856/1000 [01:11<00:10, 14.11it/s]
86%|████████▌ | 858/1000 [01:12<00:09, 14.60it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 15.35it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.50it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 14.03it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.83it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.70it/s]
87%|████████▋ | 870/1000 [01:12<00:09, 14.10it/s]
87%|████████▋ | 872/1000 [01:13<00:08, 14.73it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 12.46it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.07it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.92it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.55it/s]
88%|████████▊ | 882/1000 [01:13<00:10, 11.36it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 12.10it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.87it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.82it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.25it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.05it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 14.19it/s]
90%|████████▉ | 896/1000 [01:14<00:07, 13.91it/s]
90%|████████▉ | 898/1000 [01:15<00:07, 13.32it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.26it/s]
90%|█████████ | 902/1000 [01:15<00:07, 12.98it/s]
90%|█████████ | 904/1000 [01:15<00:09, 9.80it/s]
91%|█████████ | 906/1000 [01:15<00:08, 10.84it/s]
91%|█████████ | 908/1000 [01:15<00:07, 11.80it/s]
91%|█████████ | 910/1000 [01:16<00:07, 12.06it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.26it/s]
91%|█████████▏| 914/1000 [01:16<00:12, 6.80it/s]
92%|█████████▏| 916/1000 [01:17<00:10, 7.84it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.17it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.20it/s]
92%|█████████▏| 922/1000 [01:17<00:08, 9.17it/s]
92%|█████████▏| 924/1000 [01:17<00:07, 9.94it/s]
93%|█████████▎| 926/1000 [01:18<00:06, 10.65it/s]
93%|█████████▎| 928/1000 [01:18<00:07, 9.85it/s]
93%|█████████▎| 930/1000 [01:18<00:06, 10.51it/s]
93%|█████████▎| 932/1000 [01:18<00:06, 11.00it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 11.58it/s]
94%|█████████▎| 936/1000 [01:18<00:05, 11.81it/s]
94%|█████████▍| 938/1000 [01:19<00:05, 12.17it/s]
94%|█████████▍| 940/1000 [01:19<00:04, 12.20it/s]
94%|█████████▍| 942/1000 [01:19<00:05, 10.74it/s]
94%|█████████▍| 944/1000 [01:19<00:05, 11.16it/s]
95%|█████████▍| 946/1000 [01:19<00:05, 10.78it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 11.28it/s]
95%|█████████▌| 950/1000 [01:20<00:04, 12.00it/s]
95%|█████████▌| 952/1000 [01:20<00:03, 12.99it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 13.40it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 13.63it/s]
96%|█████████▌| 958/1000 [01:20<00:02, 14.26it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 12.81it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.51it/s]
96%|█████████▋| 964/1000 [01:21<00:03, 10.14it/s]
97%|█████████▋| 966/1000 [01:21<00:02, 11.36it/s]
97%|█████████▋| 968/1000 [01:21<00:02, 12.04it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 12.66it/s]
97%|█████████▋| 972/1000 [01:22<00:02, 9.66it/s]
97%|█████████▋| 974/1000 [01:22<00:02, 10.83it/s]
98%|█████████▊| 976/1000 [01:22<00:02, 11.64it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.86it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.93it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.42it/s]
98%|█████████▊| 984/1000 [01:23<00:01, 10.44it/s]
99%|█████████▊| 986/1000 [01:23<00:01, 10.69it/s]
99%|█████████▉| 988/1000 [01:23<00:01, 10.90it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 11.40it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 12.29it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 13.03it/s]
100%|█████████▉| 996/1000 [01:24<00:00, 13.93it/s]
100%|█████████▉| 998/1000 [01:24<00:00, 14.08it/s]
100%|██████████| 1000/1000 [01:24<00:00, 14.76it/s]
{'eval_loss': '0.8898', 'eval_runtime': '84.36', 'eval_samples_per_second': '11.85', 'eval_steps_per_second': '11.85', 'epoch': '0.6421', 'num_input_tokens_seen': 40360224}
64%|██████▍ | 12000/18688 [14:05:39<7:33:17, 4.07s/it]
100%|██████████| 1000/1000 [01:24<00:00, 14.76it/s]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00008/checkpoint-12000
loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
64%|██████▍ | 12001/18688 [14:05:44<54:52:54, 29.55s/it] 64%|██████▍ | 12002/18688 [14:05:48<40:39:15, 21.89s/it] 64%|██████▍ | 12003/18688 [14:05:52<30:32:15, 16.45s/it] 64%|██████▍ | 12004/18688 [14:05:56<23:39:19, 12.74s/it] 64%|██████▍ | 12005/18688 [14:06:00<18:58:44, 10.22s/it] 64%|██████▍ | 12006/18688 [14:06:04<15:20:38, 8.27s/it] 64%|██████▍ | 12007/18688 [14:06:09<13:30:40, 7.28s/it] 64%|██████▍ | 12008/18688 [14:06:13<11:51:24, 6.39s/it] 64%|██████▍ | 12009/18688 [14:06:17<10:16:48, 5.54s/it] 64%|██████▍ | 12010/18688 [14:06:22<9:49:52, 5.30s/it] {'loss': '0.8769', 'grad_norm': '1.262', 'learning_rate': '2.628e-05', 'epoch': '0.6427', 'num_input_tokens_seen': 40393544, 'train_runtime': '5.078e+04', 'train_tokens_per_second': '795.4'}
64%|██████▍ | 12010/18688 [14:06:22<9:49:52, 5.30s/it] 64%|██████▍ | 12011/18688 [14:06:26<9:15:38, 4.99s/it] 64%|██████▍ | 12012/18688 [14:06:30<8:43:31, 4.71s/it] 64%|██████▍ | 12013/18688 [14:06:34<8:15:37, 4.46s/it] 64%|██████▍ | 12014/18688 [14:06:38<8:17:26, 4.47s/it] 64%|██████▍ | 12015/18688 [14:06:42<7:44:06, 4.17s/it] 64%|██████▍ | 12016/18688 [14:06:46<7:38:24, 4.12s/it] 64%|██████▍ | 12017/18688 [14:06:49<7:18:25, 3.94s/it] 64%|██████▍ | 12018/18688 [14:06:53<7:09:11, 3.86s/it] 64%|██████▍ | 12019/18688 [14:06:57<7:02:23, 3.80s/it] 64%|██████▍ | 12020/18688 [14:07:01<7:30:54, 4.06s/it] {'loss': '0.8661', 'grad_norm': '1.159', 'learning_rate': '2.621e-05', 'epoch': '0.6432', 'num_input_tokens_seen': 40423536, 'train_runtime': '5.082e+04', 'train_tokens_per_second': '795.4'}
64%|██████▍ | 12020/18688 [14:07:01<7:30:54, 4.06s/it] 64%|██████▍ | 12021/18688 [14:07:06<7:53:02, 4.26s/it] 64%|██████▍ | 12022/18688 [14:07:10<7:30:05, 4.05s/it] 64%|██████▍ | 12023/18688 [14:07:13<7:10:23, 3.87s/it] 64%|██████▍ | 12024/18688 [14:07:17<7:14:53, 3.92s/it] 64%|██████▍ | 12025/18688 [14:07:22<7:41:11, 4.15s/it] 64%|██████▍ | 12026/18688 [14:07:26<7:50:25, 4.24s/it] 64%|██████▍ | 12027/18688 [14:07:30<7:40:12, 4.15s/it] 64%|██████▍ | 12028/18688 [14:07:34<7:31:47, 4.07s/it] 64%|██████▍ | 12029/18688 [14:07:38<7:45:37, 4.20s/it] 64%|██████▍ | 12030/18688 [14:07:42<7:23:37, 4.00s/it] {'loss': '0.8818', 'grad_norm': '1.091', 'learning_rate': '2.615e-05', 'epoch': '0.6437', 'num_input_tokens_seen': 40455808, 'train_runtime': '5.086e+04', 'train_tokens_per_second': '795.4'}
64%|██████▍ | 12030/18688 [14:07:42<7:23:37, 4.00s/it] 64%|██████▍ | 12031/18688 [14:07:46<7:32:37, 4.08s/it] 64%|██████▍ | 12032/18688 [14:07:50<7:13:33, 3.91s/it] 64%|██████▍ | 12033/18688 [14:07:53<6:59:40, 3.78s/it] 64%|██████▍ | 12034/18688 [14:07:57<7:11:28, 3.89s/it] 64%|██████▍ | 12035/18688 [14:08:01<7:04:20, 3.83s/it] 64%|██████▍ | 12036/18688 [14:08:05<7:05:42, 3.84s/it] 64%|██████▍ | 12037/18688 [14:08:09<6:57:19, 3.76s/it] 64%|██████▍ | 12038/18688 [14:08:13<7:16:28, 3.94s/it] 64%|██████▍ | 12039/18688 [14:08:18<8:02:30, 4.35s/it] 64%|██████▍ | 12040/18688 [14:08:22<7:47:22, 4.22s/it] {'loss': '0.8647', 'grad_norm': '1.173', 'learning_rate': '2.608e-05', 'epoch': '0.6443', 'num_input_tokens_seen': 40487512, 'train_runtime': '5.09e+04', 'train_tokens_per_second': '795.4'}
64%|██████▍ | 12040/18688 [14:08:22<7:47:22, 4.22s/it] 64%|██████▍ | 12041/18688 [14:08:26<7:30:56, 4.07s/it] 64%|██████▍ | 12042/18688 [14:08:30<7:35:40, 4.11s/it] 64%|██████▍ | 12043/18688 [14:08:34<7:17:48, 3.95s/it] 64%|██████▍ | 12044/18688 [14:08:39<7:57:31, 4.31s/it] 64%|██████▍ | 12045/18688 [14:08:43<7:43:14, 4.18s/it] 64%|██████▍ | 12046/18688 [14:08:47<7:49:56, 4.25s/it] 64%|██████▍ | 12047/18688 [14:08:51<7:35:20, 4.11s/it] 64%|██████▍ | 12048/18688 [14:08:56<8:11:02, 4.44s/it] 64%|██████▍ | 12049/18688 [14:09:00<7:46:17, 4.21s/it] 64%|██████▍ | 12050/18688 [14:09:03<7:28:34, 4.05s/it] {'loss': '0.9248', 'grad_norm': '1.32', 'learning_rate': '2.601e-05', 'epoch': '0.6448', 'num_input_tokens_seen': 40520616, 'train_runtime': '5.094e+04', 'train_tokens_per_second': '795.4'}
64%|██████▍ | 12050/18688 [14:09:03<7:28:34, 4.05s/it] 64%|██████▍ | 12051/18688 [14:09:08<7:48:46, 4.24s/it] 64%|██████▍ | 12052/18688 [14:09:12<7:48:17, 4.23s/it] 64%|██████▍ | 12053/18688 [14:09:16<7:40:52, 4.17s/it] 65%|██████▍ | 12054/18688 [14:09:21<8:07:24, 4.41s/it] 65%|██████▍ | 12055/18688 [14:09:27<8:42:11, 4.72s/it] 65%|██████▍ | 12056/18688 [14:09:31<8:24:14, 4.56s/it] 65%|██████▍ | 12057/18688 [14:09:35<8:02:38, 4.37s/it] 65%|██████▍ | 12058/18688 [14:09:40<8:13:50, 4.47s/it] 65%|██████▍ | 12059/18688 [14:09:44<8:10:53, 4.44s/it] 65%|██████▍ | 12060/18688 [14:09:48<8:01:12, 4.36s/it] {'loss': '0.9781', 'grad_norm': '1.088', 'learning_rate': '2.594e-05', 'epoch': '0.6454', 'num_input_tokens_seen': 40558168, 'train_runtime': '5.099e+04', 'train_tokens_per_second': '795.4'}
65%|██████▍ | 12060/18688 [14:09:48<8:01:12, 4.36s/it] 65%|██████▍ | 12061/18688 [14:09:52<7:57:08, 4.32s/it] 65%|██████▍ | 12062/18688 [14:09:56<7:36:13, 4.13s/it] 65%|██████▍ | 12063/18688 [14:10:00<7:28:16, 4.06s/it] 65%|██████▍ | 12064/18688 [14:10:05<7:58:04, 4.33s/it] 65%|██████▍ | 12065/18688 [14:10:10<8:17:34, 4.51s/it] 65%|██████▍ | 12066/18688 [14:10:16<9:11:34, 5.00s/it] 65%|██████▍ | 12067/18688 [14:10:20<8:32:39, 4.65s/it] 65%|██████▍ | 12068/18688 [14:10:24<8:01:14, 4.36s/it] 65%|██████▍ | 12069/18688 [14:10:27<7:45:21, 4.22s/it] 65%|██████▍ | 12070/18688 [14:10:32<7:53:25, 4.29s/it] {'loss': '0.9227', 'grad_norm': '1.177', 'learning_rate': '2.587e-05', 'epoch': '0.6459', 'num_input_tokens_seen': 40595384, 'train_runtime': '5.103e+04', 'train_tokens_per_second': '795.5'}
65%|██████▍ | 12070/18688 [14:10:32<7:53:25, 4.29s/it] 65%|██████▍ | 12071/18688 [14:10:36<7:35:35, 4.13s/it] 65%|██████▍ | 12072/18688 [14:10:39<7:14:11, 3.94s/it] 65%|██████▍ | 12073/18688 [14:10:44<7:34:36, 4.12s/it] 65%|██████▍ | 12074/18688 [14:10:48<7:49:11, 4.26s/it] 65%|██████▍ | 12075/18688 [14:10:53<7:52:22, 4.29s/it] 65%|██████▍ | 12076/18688 [14:10:56<7:25:11, 4.04s/it] 65%|██████▍ | 12077/18688 [14:11:00<7:30:30, 4.09s/it] 65%|██████▍ | 12078/18688 [14:11:04<7:30:16, 4.09s/it] 65%|██████▍ | 12079/18688 [14:11:08<7:13:55, 3.94s/it] 65%|██████▍ | 12080/18688 [14:11:12<7:19:55, 3.99s/it] {'loss': '0.9391', 'grad_norm': '1.16', 'learning_rate': '2.58e-05', 'epoch': '0.6464', 'num_input_tokens_seen': 40628992, 'train_runtime': '5.107e+04', 'train_tokens_per_second': '795.5'}
65%|██████▍ | 12080/18688 [14:11:12<7:19:55, 3.99s/it] 65%|██████▍ | 12081/18688 [14:11:16<7:16:05, 3.96s/it] 65%|██████▍ | 12082/18688 [14:11:20<7:16:55, 3.97s/it] 65%|██████▍ | 12083/18688 [14:11:24<7:32:41, 4.11s/it] 65%|██████▍ | 12084/18688 [14:11:29<7:53:56, 4.31s/it] 65%|██████▍ | 12085/18688 [14:11:33<7:44:03, 4.22s/it] 65%|██████▍ | 12086/18688 [14:11:38<7:59:41, 4.36s/it] 65%|██████▍ | 12087/18688 [14:11:42<7:52:01, 4.29s/it] 65%|██████▍ | 12088/18688 [14:11:46<7:54:37, 4.31s/it] 65%|██████▍ | 12089/18688 [14:11:50<7:40:01, 4.18s/it] 65%|██████▍ | 12090/18688 [14:11:54<7:30:51, 4.10s/it] {'loss': '0.9043', 'grad_norm': '1.079', 'learning_rate': '2.574e-05', 'epoch': '0.647', 'num_input_tokens_seen': 40663528, 'train_runtime': '5.111e+04', 'train_tokens_per_second': '795.5'}
65%|██████▍ | 12090/18688 [14:11:54<7:30:51, 4.10s/it] 65%|██████▍ | 12091/18688 [14:11:58<7:35:20, 4.14s/it] 65%|██████▍ | 12092/18688 [14:12:02<7:16:35, 3.97s/it] 65%|██████▍ | 12093/18688 [14:12:06<7:16:28, 3.97s/it] 65%|██████▍ | 12094/18688 [14:12:10<7:20:01, 4.00s/it] 65%|██████▍ | 12095/18688 [14:12:14<7:28:06, 4.08s/it] 65%|██████▍ | 12096/18688 [14:12:18<7:19:08, 4.00s/it] 65%|██████▍ | 12097/18688 [14:12:23<7:57:38, 4.35s/it] 65%|██████▍ | 12098/18688 [14:12:27<7:47:04, 4.25s/it] 65%|██████▍ | 12099/18688 [14:12:31<7:33:15, 4.13s/it] 65%|██████▍ | 12100/18688 [14:12:34<7:10:33, 3.92s/it] {'loss': '0.9237', 'grad_norm': '1.149', 'learning_rate': '2.567e-05', 'epoch': '0.6475', 'num_input_tokens_seen': 40696336, 'train_runtime': '5.115e+04', 'train_tokens_per_second': '795.5'}
65%|██████▍ | 12100/18688 [14:12:34<7:10:33, 3.92s/it] 65%|██████▍ | 12101/18688 [14:12:39<7:15:21, 3.97s/it] 65%|██████▍ | 12102/18688 [14:12:42<7:02:47, 3.85s/it] 65%|██████▍ | 12103/18688 [14:12:46<6:53:57, 3.77s/it] 65%|██████▍ | 12104/18688 [14:12:49<6:49:23, 3.73s/it] 65%|██████▍ | 12105/18688 [14:12:54<7:09:37, 3.92s/it] 65%|██████▍ | 12106/18688 [14:12:59<7:48:40, 4.27s/it] 65%|██████▍ | 12107/18688 [14:13:04<8:06:37, 4.44s/it] 65%|██████▍ | 12108/18688 [14:13:08<8:02:19, 4.40s/it] 65%|██████▍ | 12109/18688 [14:13:12<7:49:39, 4.28s/it] 65%|██████▍ | 12110/18688 [14:13:15<7:21:03, 4.02s/it] {'loss': '0.9173', 'grad_norm': '1.125', 'learning_rate': '2.56e-05', 'epoch': '0.648', 'num_input_tokens_seen': 40729808, 'train_runtime': '5.12e+04', 'train_tokens_per_second': '795.6'}
65%|██████▍ | 12110/18688 [14:13:15<7:21:03, 4.02s/it] 65%|██████▍ | 12111/18688 [14:13:20<7:43:09, 4.23s/it] 65%|██████▍ | 12112/18688 [14:13:24<7:29:24, 4.10s/it] 65%|██████▍ | 12113/18688 [14:13:28<7:29:13, 4.10s/it] 65%|██████▍ | 12114/18688 [14:13:32<7:30:34, 4.11s/it] 65%|██████▍ | 12115/18688 [14:13:36<7:19:41, 4.01s/it] 65%|██████▍ | 12116/18688 [14:13:40<7:23:25, 4.05s/it] 65%|██████▍ | 12117/18688 [14:13:44<7:26:52, 4.08s/it] 65%|██████▍ | 12118/18688 [14:13:49<7:48:38, 4.28s/it] 65%|██████▍ | 12119/18688 [14:13:54<8:00:39, 4.39s/it] 65%|██████▍ | 12120/18688 [14:13:58<7:45:01, 4.25s/it] {'loss': '0.8643', 'grad_norm': '1.163', 'learning_rate': '2.553e-05', 'epoch': '0.6486', 'num_input_tokens_seen': 40763240, 'train_runtime': '5.124e+04', 'train_tokens_per_second': '795.6'}
65%|██████▍ | 12120/18688 [14:13:58<7:45:01, 4.25s/it] 65%|██████▍ | 12121/18688 [14:14:01<7:28:30, 4.10s/it] 65%|██████▍ | 12122/18688 [14:14:06<7:43:20, 4.23s/it] 65%|██████▍ | 12123/18688 [14:14:09<7:22:21, 4.04s/it] 65%|██████▍ | 12124/18688 [14:14:13<7:12:44, 3.96s/it] 65%|██████▍ | 12125/18688 [14:14:18<7:27:09, 4.09s/it] 65%|██████▍ | 12126/18688 [14:14:21<7:19:55, 4.02s/it] 65%|██████▍ | 12127/18688 [14:14:25<7:18:19, 4.01s/it] 65%|██████▍ | 12128/18688 [14:14:29<7:14:54, 3.98s/it] 65%|██████▍ | 12129/18688 [14:14:34<7:49:06, 4.29s/it] 65%|██████▍ | 12130/18688 [14:14:38<7:21:14, 4.04s/it] {'loss': '0.8637', 'grad_norm': '1.085', 'learning_rate': '2.546e-05', 'epoch': '0.6491', 'num_input_tokens_seen': 40795904, 'train_runtime': '5.128e+04', 'train_tokens_per_second': '795.6'}
65%|██████▍ | 12130/18688 [14:14:38<7:21:14, 4.04s/it] 65%|██████▍ | 12131/18688 [14:14:42<7:27:58, 4.10s/it] 65%|██████▍ | 12132/18688 [14:14:46<7:20:42, 4.03s/it] 65%|██████▍ | 12133/18688 [14:14:50<7:27:50, 4.10s/it] 65%|██████▍ | 12134/18688 [14:14:54<7:12:06, 3.96s/it] 65%|██████▍ | 12135/18688 [14:14:57<7:03:46, 3.88s/it] 65%|██████▍ | 12136/18688 [14:15:02<7:20:13, 4.03s/it] 65%|██████▍ | 12137/18688 [14:15:05<7:00:23, 3.85s/it] 65%|██████▍ | 12138/18688 [14:15:09<7:00:57, 3.86s/it] 65%|██████▍ | 12139/18688 [14:15:14<7:25:39, 4.08s/it] 65%|██████▍ | 12140/18688 [14:15:18<7:15:45, 3.99s/it] {'loss': '0.8988', 'grad_norm': '1.541', 'learning_rate': '2.539e-05', 'epoch': '0.6496', 'num_input_tokens_seen': 40827344, 'train_runtime': '5.132e+04', 'train_tokens_per_second': '795.6'}
65%|██████▍ | 12140/18688 [14:15:18<7:15:45, 3.99s/it] 65%|██████▍ | 12141/18688 [14:15:21<7:02:12, 3.87s/it] 65%|██████▍ | 12142/18688 [14:15:25<7:02:11, 3.87s/it] 65%|██████▍ | 12143/18688 [14:15:29<7:07:43, 3.92s/it] 65%|██████▍ | 12144/18688 [14:15:33<6:59:55, 3.85s/it] 65%|██████▍ | 12145/18688 [14:15:36<6:51:11, 3.77s/it] 65%|██████▍ | 12146/18688 [14:15:40<7:03:43, 3.89s/it] 65%|██████▍ | 12147/18688 [14:15:46<7:52:23, 4.33s/it] 65%|██████▌ | 12148/18688 [14:15:51<8:11:44, 4.51s/it] 65%|██████▌ | 12149/18688 [14:15:55<8:05:33, 4.46s/it] 65%|██████▌ | 12150/18688 [14:15:59<7:38:42, 4.21s/it] {'loss': '0.908', 'grad_norm': '1.28', 'learning_rate': '2.533e-05', 'epoch': '0.6502', 'num_input_tokens_seen': 40858048, 'train_runtime': '5.136e+04', 'train_tokens_per_second': '795.5'}
65%|██████▌ | 12150/18688 [14:15:59<7:38:42, 4.21s/it] 65%|██████▌ | 12151/18688 [14:16:03<7:24:52, 4.08s/it] 65%|██████▌ | 12152/18688 [14:16:06<7:11:07, 3.96s/it] 65%|██████▌ | 12153/18688 [14:16:10<6:58:30, 3.84s/it] 65%|██████▌ | 12154/18688 [14:16:14<7:22:27, 4.06s/it] 65%|██████▌ | 12155/18688 [14:16:19<7:28:46, 4.12s/it] 65%|██████▌ | 12156/18688 [14:16:22<7:10:09, 3.95s/it] 65%|██████▌ | 12157/18688 [14:16:26<7:00:06, 3.86s/it] 65%|██████▌ | 12158/18688 [14:16:30<7:04:58, 3.90s/it] 65%|██████▌ | 12159/18688 [14:16:33<6:54:07, 3.81s/it] 65%|██████▌ | 12160/18688 [14:16:37<7:02:42, 3.89s/it] {'loss': '0.9484', 'grad_norm': '1.23', 'learning_rate': '2.526e-05', 'epoch': '0.6507', 'num_input_tokens_seen': 40889384, 'train_runtime': '5.14e+04', 'train_tokens_per_second': '795.5'}
65%|██████▌ | 12160/18688 [14:16:37<7:02:42, 3.89s/it] 65%|██████▌ | 12161/18688 [14:16:41<6:56:02, 3.82s/it] 65%|██████▌ | 12162/18688 [14:16:45<7:04:02, 3.90s/it] 65%|██████▌ | 12163/18688 [14:16:49<6:51:54, 3.79s/it] 65%|██████▌ | 12164/18688 [14:16:53<6:57:57, 3.84s/it] 65%|██████▌ | 12165/18688 [14:16:57<7:23:36, 4.08s/it] 65%|██████▌ | 12166/18688 [14:17:01<7:13:59, 3.99s/it] 65%|██████▌ | 12167/18688 [14:17:05<7:15:20, 4.01s/it] 65%|██████▌ | 12168/18688 [14:17:10<7:42:30, 4.26s/it] 65%|██████▌ | 12169/18688 [14:17:14<7:35:08, 4.19s/it] 65%|██████▌ | 12170/18688 [14:17:18<7:34:07, 4.18s/it] {'loss': '0.8389', 'grad_norm': '1.401', 'learning_rate': '2.519e-05', 'epoch': '0.6512', 'num_input_tokens_seen': 40921552, 'train_runtime': '5.144e+04', 'train_tokens_per_second': '795.5'}
65%|██████▌ | 12170/18688 [14:17:18<7:34:07, 4.18s/it] 65%|██████▌ | 12171/18688 [14:17:22<7:34:47, 4.19s/it] 65%|██████▌ | 12172/18688 [14:17:26<7:18:04, 4.03s/it] 65%|██████▌ | 12173/18688 [14:17:30<7:29:10, 4.14s/it] 65%|██████▌ | 12174/18688 [14:17:34<7:10:51, 3.97s/it] 65%|██████▌ | 12175/18688 [14:17:38<6:57:03, 3.84s/it] 65%|██████▌ | 12176/18688 [14:17:42<7:08:02, 3.94s/it] 65%|██████▌ | 12177/18688 [14:17:45<6:54:17, 3.82s/it] 65%|██████▌ | 12178/18688 [14:17:50<7:11:03, 3.97s/it] 65%|██████▌ | 12179/18688 [14:17:55<7:44:17, 4.28s/it] 65%|██████▌ | 12180/18688 [14:17:59<7:53:33, 4.37s/it] {'loss': '0.9132', 'grad_norm': '1.202', 'learning_rate': '2.512e-05', 'epoch': '0.6518', 'num_input_tokens_seen': 40953680, 'train_runtime': '5.148e+04', 'train_tokens_per_second': '795.5'}
65%|██████▌ | 12180/18688 [14:17:59<7:53:33, 4.37s/it] 65%|██████▌ | 12181/18688 [14:18:04<8:11:48, 4.53s/it] 65%|██████▌ | 12182/18688 [14:18:08<7:43:08, 4.27s/it] 65%|██████▌ | 12183/18688 [14:18:12<7:54:34, 4.38s/it] 65%|██████▌ | 12184/18688 [14:18:17<8:02:15, 4.45s/it] 65%|██████▌ | 12185/18688 [14:18:21<7:33:43, 4.19s/it] 65%|██████▌ | 12186/18688 [14:18:24<7:17:13, 4.03s/it] 65%|██████▌ | 12187/18688 [14:18:29<7:25:51, 4.11s/it] 65%|██████▌ | 12188/18688 [14:18:32<7:10:06, 3.97s/it] 65%|██████▌ | 12189/18688 [14:18:36<7:15:15, 4.02s/it] 65%|██████▌ | 12190/18688 [14:18:40<7:02:06, 3.90s/it] {'loss': '0.9247', 'grad_norm': '1.338', 'learning_rate': '2.505e-05', 'epoch': '0.6523', 'num_input_tokens_seen': 40986968, 'train_runtime': '5.152e+04', 'train_tokens_per_second': '795.5'}
65%|██████▌ | 12190/18688 [14:18:40<7:02:06, 3.90s/it] 65%|██████▌ | 12191/18688 [14:18:44<6:52:59, 3.81s/it] 65%|██████▌ | 12192/18688 [14:18:47<6:44:59, 3.74s/it] 65%|██████▌ | 12193/18688 [14:18:51<7:01:37, 3.89s/it] 65%|██████▌ | 12194/18688 [14:18:57<7:55:26, 4.39s/it] 65%|██████▌ | 12195/18688 [14:19:02<8:12:42, 4.55s/it] 65%|██████▌ | 12196/18688 [14:19:06<8:03:40, 4.47s/it] 65%|██████▌ | 12197/18688 [14:19:10<7:48:48, 4.33s/it] 65%|██████▌ | 12198/18688 [14:19:14<7:31:05, 4.17s/it] 65%|██████▌ | 12199/18688 [14:19:18<7:30:37, 4.17s/it] 65%|██████▌ | 12200/18688 [14:19:22<7:08:02, 3.96s/it] {'loss': '0.9181', 'grad_norm': '1.199', 'learning_rate': '2.499e-05', 'epoch': '0.6528', 'num_input_tokens_seen': 41021120, 'train_runtime': '5.156e+04', 'train_tokens_per_second': '795.6'}
65%|██████▌ | 12200/18688 [14:19:22<7:08:02, 3.96s/it] 65%|██████▌ | 12201/18688 [14:19:25<6:54:40, 3.84s/it] 65%|██████▌ | 12202/18688 [14:19:29<7:05:02, 3.93s/it] 65%|██████▌ | 12203/18688 [14:19:33<6:50:19, 3.80s/it] 65%|██████▌ | 12204/18688 [14:19:37<6:59:24, 3.88s/it] 65%|██████▌ | 12205/18688 [14:19:41<7:06:04, 3.94s/it] 65%|██████▌ | 12206/18688 [14:19:45<6:54:23, 3.84s/it] 65%|██████▌ | 12207/18688 [14:19:48<6:49:26, 3.79s/it] 65%|██████▌ | 12208/18688 [14:19:53<7:16:56, 4.05s/it] 65%|██████▌ | 12209/18688 [14:19:58<7:45:51, 4.31s/it] 65%|██████▌ | 12210/18688 [14:20:02<7:27:21, 4.14s/it] {'loss': '0.9525', 'grad_norm': '1.149', 'learning_rate': '2.492e-05', 'epoch': '0.6534', 'num_input_tokens_seen': 41051400, 'train_runtime': '5.16e+04', 'train_tokens_per_second': '795.5'}
65%|██████▌ | 12210/18688 [14:20:02<7:27:21, 4.14s/it] 65%|██████▌ | 12211/18688 [14:20:05<7:06:42, 3.95s/it] 65%|██████▌ | 12212/18688 [14:20:09<7:00:11, 3.89s/it] 65%|██████▌ | 12213/18688 [14:20:13<7:04:21, 3.93s/it] 65%|██████▌ | 12214/18688 [14:20:17<7:28:44, 4.16s/it] 65%|██████▌ | 12215/18688 [14:20:23<7:57:32, 4.43s/it] 65%|██████▌ | 12216/18688 [14:20:27<8:00:43, 4.46s/it] 65%|██████▌ | 12217/18688 [14:20:31<7:42:39, 4.29s/it] 65%|██████▌ | 12218/18688 [14:20:36<7:55:30, 4.41s/it] 65%|██████▌ | 12219/18688 [14:20:40<7:49:00, 4.35s/it] 65%|██████▌ | 12220/18688 [14:20:44<7:46:17, 4.33s/it] {'loss': '0.9565', 'grad_norm': '1.282', 'learning_rate': '2.485e-05', 'epoch': '0.6539', 'num_input_tokens_seen': 41087720, 'train_runtime': '5.164e+04', 'train_tokens_per_second': '795.6'}
65%|██████▌ | 12220/18688 [14:20:44<7:46:17, 4.33s/it] 65%|██████▌ | 12221/18688 [14:20:48<7:37:05, 4.24s/it] 65%|██████▌ | 12222/18688 [14:20:52<7:20:09, 4.08s/it] 65%|██████▌ | 12223/18688 [14:20:56<7:10:08, 3.99s/it] 65%|██████▌ | 12224/18688 [14:21:00<7:05:23, 3.95s/it] 65%|██████▌ | 12225/18688 [14:21:04<7:20:23, 4.09s/it] 65%|██████▌ | 12226/18688 [14:21:09<8:03:11, 4.49s/it] 65%|██████▌ | 12227/18688 [14:21:13<7:45:39, 4.32s/it] 65%|██████▌ | 12228/18688 [14:21:18<7:46:45, 4.34s/it] 65%|██████▌ | 12229/18688 [14:21:22<7:46:50, 4.34s/it] 65%|██████▌ | 12230/18688 [14:21:26<7:22:59, 4.12s/it] {'loss': '0.9385', 'grad_norm': '1.174', 'learning_rate': '2.478e-05', 'epoch': '0.6544', 'num_input_tokens_seen': 41119864, 'train_runtime': '5.169e+04', 'train_tokens_per_second': '795.6'}
65%|██████▌ | 12230/18688 [14:21:26<7:22:59, 4.12s/it] 65%|██████▌ | 12231/18688 [14:21:29<7:13:08, 4.02s/it] 65%|██████▌ | 12232/18688 [14:21:33<7:06:52, 3.97s/it] 65%|██████▌ | 12233/18688 [14:21:38<7:33:55, 4.22s/it] 65%|██████▌ | 12234/18688 [14:21:42<7:17:31, 4.07s/it] 65%|██████▌ | 12235/18688 [14:21:45<7:04:56, 3.95s/it] 65%|██████▌ | 12236/18688 [14:21:49<6:55:47, 3.87s/it] 65%|██████▌ | 12237/18688 [14:21:53<7:10:49, 4.01s/it] 65%|██████▌ | 12238/18688 [14:21:58<7:15:40, 4.05s/it] 65%|██████▌ | 12239/18688 [14:22:01<7:05:05, 3.95s/it] 65%|██████▌ | 12240/18688 [14:22:06<7:15:13, 4.05s/it] {'loss': '0.9281', 'grad_norm': '1.237', 'learning_rate': '2.472e-05', 'epoch': '0.655', 'num_input_tokens_seen': 41151696, 'train_runtime': '5.173e+04', 'train_tokens_per_second': '795.6'}
65%|██████▌ | 12240/18688 [14:22:06<7:15:13, 4.05s/it] 66%|██████▌ | 12241/18688 [14:22:10<7:11:44, 4.02s/it] 66%|██████▌ | 12242/18688 [14:22:13<7:00:15, 3.91s/it] 66%|██████▌ | 12243/18688 [14:22:17<7:04:44, 3.95s/it] 66%|██████▌ | 12244/18688 [14:22:21<6:52:44, 3.84s/it] 66%|██████▌ | 12245/18688 [14:22:24<6:43:54, 3.76s/it] 66%|██████▌ | 12246/18688 [14:22:28<6:33:23, 3.66s/it] 66%|██████▌ | 12247/18688 [14:22:32<6:36:43, 3.70s/it] 66%|██████▌ | 12248/18688 [14:22:37<7:23:11, 4.13s/it] 66%|██████▌ | 12249/18688 [14:22:41<7:35:34, 4.25s/it] 66%|██████▌ | 12250/18688 [14:22:46<7:34:32, 4.24s/it] {'loss': '0.9237', 'grad_norm': '1.164', 'learning_rate': '2.465e-05', 'epoch': '0.6555', 'num_input_tokens_seen': 41181688, 'train_runtime': '5.177e+04', 'train_tokens_per_second': '795.5'}
66%|██████▌ | 12250/18688 [14:22:46<7:34:32, 4.24s/it] 66%|██████▌ | 12251/18688 [14:22:49<7:17:41, 4.08s/it] 66%|██████▌ | 12252/18688 [14:22:54<7:28:12, 4.18s/it] 66%|██████▌ | 12253/18688 [14:22:57<7:13:16, 4.04s/it] 66%|██████▌ | 12254/18688 [14:23:02<7:22:47, 4.13s/it] 66%|██████▌ | 12255/18688 [14:23:06<7:14:46, 4.06s/it] 66%|██████▌ | 12256/18688 [14:23:10<7:41:10, 4.30s/it] 66%|██████▌ | 12257/18688 [14:23:14<7:32:16, 4.22s/it] 66%|██████▌ | 12258/18688 [14:23:19<7:28:39, 4.19s/it] 66%|██████▌ | 12259/18688 [14:23:23<7:24:12, 4.15s/it] 66%|██████▌ | 12260/18688 [14:23:27<7:20:41, 4.11s/it] {'loss': '0.9127', 'grad_norm': '1.228', 'learning_rate': '2.458e-05', 'epoch': '0.6561', 'num_input_tokens_seen': 41215008, 'train_runtime': '5.181e+04', 'train_tokens_per_second': '795.5'}
66%|██████▌ | 12260/18688 [14:23:27<7:20:41, 4.11s/it] 66%|██████▌ | 12261/18688 [14:23:32<7:55:26, 4.44s/it] 66%|██████▌ | 12262/18688 [14:23:36<7:41:31, 4.31s/it] 66%|██████▌ | 12263/18688 [14:23:39<7:16:37, 4.08s/it] 66%|██████▌ | 12264/18688 [14:23:44<7:28:49, 4.19s/it] 66%|██████▌ | 12265/18688 [14:23:48<7:17:25, 4.09s/it] 66%|██████▌ | 12266/18688 [14:23:52<7:17:30, 4.09s/it] 66%|██████▌ | 12267/18688 [14:23:56<7:06:15, 3.98s/it] 66%|██████▌ | 12268/18688 [14:24:00<7:33:08, 4.23s/it] 66%|██████▌ | 12269/18688 [14:24:05<7:41:09, 4.31s/it] 66%|██████▌ | 12270/18688 [14:24:09<7:47:27, 4.37s/it] {'loss': '0.909', 'grad_norm': '1.285', 'learning_rate': '2.451e-05', 'epoch': '0.6566', 'num_input_tokens_seen': 41250544, 'train_runtime': '5.185e+04', 'train_tokens_per_second': '795.6'}
66%|██████▌ | 12270/18688 [14:24:09<7:47:27, 4.37s/it] 66%|██████▌ | 12271/18688 [14:24:13<7:23:16, 4.14s/it] 66%|██████▌ | 12272/18688 [14:24:18<7:46:51, 4.37s/it] 66%|██████▌ | 12273/18688 [14:24:23<8:02:17, 4.51s/it] 66%|██████▌ | 12274/18688 [14:24:27<7:47:11, 4.37s/it] 66%|██████▌ | 12275/18688 [14:24:31<7:36:52, 4.27s/it] 66%|██████▌ | 12276/18688 [14:24:35<7:32:06, 4.23s/it] 66%|██████▌ | 12277/18688 [14:24:39<7:16:55, 4.09s/it] 66%|██████▌ | 12278/18688 [14:24:43<7:19:34, 4.11s/it] 66%|██████▌ | 12279/18688 [14:24:47<7:20:28, 4.12s/it] 66%|██████▌ | 12280/18688 [14:24:51<7:06:07, 3.99s/it] {'loss': '0.9187', 'grad_norm': '1.418', 'learning_rate': '2.445e-05', 'epoch': '0.6571', 'num_input_tokens_seen': 41284480, 'train_runtime': '5.189e+04', 'train_tokens_per_second': '795.6'}
66%|██████▌ | 12280/18688 [14:24:51<7:06:07, 3.99s/it] 66%|██████▌ | 12281/18688 [14:24:55<7:15:33, 4.08s/it] 66%|██████▌ | 12282/18688 [14:24:58<6:56:29, 3.90s/it] 66%|██████▌ | 12283/18688 [14:25:03<7:05:01, 3.98s/it] 66%|██████▌ | 12284/18688 [14:25:06<6:48:44, 3.83s/it] 66%|██████▌ | 12285/18688 [14:25:10<6:36:29, 3.72s/it] 66%|██████▌ | 12286/18688 [14:25:13<6:28:56, 3.65s/it] 66%|██████▌ | 12287/18688 [14:25:17<6:45:44, 3.80s/it] 66%|██████▌ | 12288/18688 [14:25:21<7:01:24, 3.95s/it] 66%|██████▌ | 12289/18688 [14:25:26<7:06:08, 4.00s/it] 66%|██████▌ | 12290/18688 [14:25:30<7:16:16, 4.09s/it] {'loss': '0.9241', 'grad_norm': '1.396', 'learning_rate': '2.438e-05', 'epoch': '0.6577', 'num_input_tokens_seen': 41315560, 'train_runtime': '5.193e+04', 'train_tokens_per_second': '795.6'}
66%|██████▌ | 12290/18688 [14:25:30<7:16:16, 4.09s/it] 66%|██████▌ | 12291/18688 [14:25:35<7:33:34, 4.25s/it] 66%|██████▌ | 12292/18688 [14:25:38<7:22:48, 4.15s/it] 66%|██████▌ | 12293/18688 [14:25:44<7:52:12, 4.43s/it] 66%|██████▌ | 12294/18688 [14:25:47<7:27:55, 4.20s/it] 66%|██████▌ | 12295/18688 [14:25:51<7:06:27, 4.00s/it] 66%|██████▌ | 12296/18688 [14:25:56<7:45:24, 4.37s/it] 66%|██████▌ | 12297/18688 [14:26:00<7:33:57, 4.26s/it] 66%|██████▌ | 12298/18688 [14:26:04<7:14:30, 4.08s/it] 66%|██████▌ | 12299/18688 [14:26:07<7:01:19, 3.96s/it] 66%|██████▌ | 12300/18688 [14:26:11<7:02:58, 3.97s/it] {'loss': '0.9215', 'grad_norm': '1.08', 'learning_rate': '2.431e-05', 'epoch': '0.6582', 'num_input_tokens_seen': 41349464, 'train_runtime': '5.197e+04', 'train_tokens_per_second': '795.6'}
66%|██████▌ | 12300/18688 [14:26:11<7:02:58, 3.97s/it] 66%|██████▌ | 12301/18688 [14:26:15<6:54:51, 3.90s/it] 66%|██████▌ | 12302/18688 [14:26:19<7:02:17, 3.97s/it] 66%|██████▌ | 12303/18688 [14:26:23<6:51:03, 3.86s/it] 66%|██████▌ | 12304/18688 [14:26:26<6:45:45, 3.81s/it] 66%|██████▌ | 12305/18688 [14:26:31<7:22:48, 4.16s/it] 66%|██████▌ | 12306/18688 [14:26:36<7:28:47, 4.22s/it] 66%|██████▌ | 12307/18688 [14:26:40<7:29:11, 4.22s/it] 66%|██████▌ | 12308/18688 [14:26:44<7:22:56, 4.17s/it] 66%|██████▌ | 12309/18688 [14:26:49<7:33:35, 4.27s/it] 66%|██████▌ | 12310/18688 [14:26:53<7:24:44, 4.18s/it] {'loss': '0.9415', 'grad_norm': '1.39', 'learning_rate': '2.424e-05', 'epoch': '0.6587', 'num_input_tokens_seen': 41381760, 'train_runtime': '5.201e+04', 'train_tokens_per_second': '795.6'}
66%|██████▌ | 12310/18688 [14:26:53<7:24:44, 4.18s/it] 66%|██████▌ | 12311/18688 [14:26:56<7:00:35, 3.96s/it] 66%|██████▌ | 12312/18688 [14:27:01<7:22:01, 4.16s/it] 66%|██████▌ | 12313/18688 [14:27:04<7:07:42, 4.03s/it] 66%|██████▌ | 12314/18688 [14:27:08<7:07:04, 4.02s/it] 66%|██████▌ | 12315/18688 [14:27:12<7:09:12, 4.04s/it] 66%|██████▌ | 12316/18688 [14:27:16<6:53:56, 3.90s/it] 66%|██████▌ | 12317/18688 [14:27:20<6:57:28, 3.93s/it] 66%|██████▌ | 12318/18688 [14:27:24<6:47:35, 3.84s/it] 66%|██████▌ | 12319/18688 [14:27:28<6:55:09, 3.91s/it] 66%|██████▌ | 12320/18688 [14:27:32<7:10:00, 4.05s/it] {'loss': '0.8761', 'grad_norm': '1.077', 'learning_rate': '2.418e-05', 'epoch': '0.6593', 'num_input_tokens_seen': 41413256, 'train_runtime': '5.205e+04', 'train_tokens_per_second': '795.6'}
66%|██████▌ | 12320/18688 [14:27:32<7:10:00, 4.05s/it] 66%|██████▌ | 12321/18688 [14:27:36<6:54:28, 3.91s/it] 66%|██████▌ | 12322/18688 [14:27:40<6:59:31, 3.95s/it] 66%|██████▌ | 12323/18688 [14:27:43<6:43:21, 3.80s/it] 66%|██████▌ | 12324/18688 [14:27:48<7:04:37, 4.00s/it] 66%|██████▌ | 12325/18688 [14:27:52<7:24:03, 4.19s/it] 66%|██████▌ | 12326/18688 [14:27:56<7:23:59, 4.19s/it] 66%|██████▌ | 12327/18688 [14:28:00<7:04:38, 4.01s/it] 66%|██████▌ | 12328/18688 [14:28:04<7:11:35, 4.07s/it] 66%|██████▌ | 12329/18688 [14:28:08<7:01:50, 3.98s/it] 66%|██████▌ | 12330/18688 [14:28:13<7:20:44, 4.16s/it] {'loss': '0.9697', 'grad_norm': '1.242', 'learning_rate': '2.411e-05', 'epoch': '0.6598', 'num_input_tokens_seen': 41445536, 'train_runtime': '5.209e+04', 'train_tokens_per_second': '795.6'}
66%|██████▌ | 12330/18688 [14:28:13<7:20:44, 4.16s/it] 66%|██████▌ | 12331/18688 [14:28:17<7:24:25, 4.19s/it] 66%|██████▌ | 12332/18688 [14:28:21<7:16:28, 4.12s/it] 66%|██████▌ | 12333/18688 [14:28:25<7:03:29, 4.00s/it] 66%|██████▌ | 12334/18688 [14:28:28<6:51:50, 3.89s/it] 66%|██████▌ | 12335/18688 [14:28:32<6:49:58, 3.87s/it] 66%|██████▌ | 12336/18688 [14:28:36<6:38:10, 3.76s/it] 66%|██████▌ | 12337/18688 [14:28:39<6:34:12, 3.72s/it] 66%|██████▌ | 12338/18688 [14:28:43<6:40:14, 3.78s/it] 66%|██████▌ | 12339/18688 [14:28:47<6:46:00, 3.84s/it] 66%|██████▌ | 12340/18688 [14:28:52<7:09:23, 4.06s/it] {'loss': '0.936', 'grad_norm': '1.225', 'learning_rate': '2.404e-05', 'epoch': '0.6603', 'num_input_tokens_seen': 41476336, 'train_runtime': '5.213e+04', 'train_tokens_per_second': '795.6'}
66%|██████▌ | 12340/18688 [14:28:52<7:09:23, 4.06s/it] 66%|██████▌ | 12341/18688 [14:28:57<7:45:34, 4.40s/it] 66%|██████▌ | 12342/18688 [14:29:02<8:06:33, 4.60s/it] 66%|██████▌ | 12343/18688 [14:29:05<7:30:54, 4.26s/it] 66%|██████▌ | 12344/18688 [14:29:10<7:28:47, 4.24s/it] 66%|██████▌ | 12345/18688 [14:29:13<7:03:43, 4.01s/it] 66%|██████▌ | 12346/18688 [14:29:17<7:09:12, 4.06s/it] 66%|██████▌ | 12347/18688 [14:29:22<7:35:06, 4.31s/it] 66%|██████▌ | 12348/18688 [14:29:26<7:37:53, 4.33s/it] 66%|██████▌ | 12349/18688 [14:29:31<7:38:56, 4.34s/it] 66%|██████▌ | 12350/18688 [14:29:36<7:55:05, 4.50s/it] {'loss': '0.9693', 'grad_norm': '1.31', 'learning_rate': '2.398e-05', 'epoch': '0.6609', 'num_input_tokens_seen': 41513968, 'train_runtime': '5.218e+04', 'train_tokens_per_second': '795.6'}
66%|██████▌ | 12350/18688 [14:29:36<7:55:05, 4.50s/it] 66%|██████▌ | 12351/18688 [14:29:40<7:43:53, 4.39s/it] 66%|██████▌ | 12352/18688 [14:29:44<7:41:01, 4.37s/it] 66%|██████▌ | 12353/18688 [14:29:48<7:31:03, 4.27s/it] 66%|██████▌ | 12354/18688 [14:29:52<7:31:08, 4.27s/it] 66%|██████▌ | 12355/18688 [14:29:56<7:19:12, 4.16s/it] 66%|██████▌ | 12356/18688 [14:30:00<7:09:39, 4.07s/it] 66%|██████▌ | 12357/18688 [14:30:04<7:02:49, 4.01s/it] 66%|██████▌ | 12358/18688 [14:30:08<7:01:54, 4.00s/it] 66%|██████▌ | 12359/18688 [14:30:12<7:08:36, 4.06s/it] 66%|██████▌ | 12360/18688 [14:30:16<7:03:07, 4.01s/it] {'loss': '0.9503', 'grad_norm': '1.237', 'learning_rate': '2.391e-05', 'epoch': '0.6614', 'num_input_tokens_seen': 41545912, 'train_runtime': '5.222e+04', 'train_tokens_per_second': '795.6'}
66%|██████▌ | 12360/18688 [14:30:16<7:03:07, 4.01s/it] 66%|██████▌ | 12361/18688 [14:30:20<6:43:39, 3.83s/it] 66%|██████▌ | 12362/18688 [14:30:23<6:33:18, 3.73s/it] 66%|██████▌ | 12363/18688 [14:30:27<6:50:47, 3.90s/it] 66%|██████▌ | 12364/18688 [14:30:31<6:53:27, 3.92s/it] 66%|██████▌ | 12365/18688 [14:30:36<7:05:13, 4.03s/it] 66%|██████▌ | 12366/18688 [14:30:40<7:18:41, 4.16s/it] 66%|██████▌ | 12367/18688 [14:30:44<7:19:40, 4.17s/it] 66%|██████▌ | 12368/18688 [14:30:49<7:24:28, 4.22s/it] 66%|██████▌ | 12369/18688 [14:30:52<7:01:00, 4.00s/it] 66%|██████▌ | 12370/18688 [14:30:56<7:07:28, 4.06s/it] {'loss': '0.9051', 'grad_norm': '1.217', 'learning_rate': '2.384e-05', 'epoch': '0.6619', 'num_input_tokens_seen': 41577536, 'train_runtime': '5.226e+04', 'train_tokens_per_second': '795.6'}
66%|██████▌ | 12370/18688 [14:30:56<7:07:28, 4.06s/it] 66%|██████▌ | 12371/18688 [14:31:00<7:06:41, 4.05s/it] 66%|██████▌ | 12372/18688 [14:31:04<6:51:48, 3.91s/it] 66%|██████▌ | 12373/18688 [14:31:08<7:11:19, 4.10s/it] 66%|██████▌ | 12374/18688 [14:31:13<7:19:20, 4.18s/it] 66%|██████▌ | 12375/18688 [14:31:17<7:27:25, 4.25s/it] 66%|██████▌ | 12376/18688 [14:31:21<7:23:22, 4.21s/it] 66%|██████▌ | 12377/18688 [14:31:25<7:18:28, 4.17s/it] 66%|██████▌ | 12378/18688 [14:31:30<7:30:04, 4.28s/it] 66%|██████▌ | 12379/18688 [14:31:34<7:09:03, 4.08s/it] 66%|██████▌ | 12380/18688 [14:31:37<6:59:12, 3.99s/it] {'loss': '0.8725', 'grad_norm': '1.275', 'learning_rate': '2.378e-05', 'epoch': '0.6625', 'num_input_tokens_seen': 41611592, 'train_runtime': '5.23e+04', 'train_tokens_per_second': '795.7'}
66%|██████▌ | 12380/18688 [14:31:37<6:59:12, 3.99s/it] 66%|██████▋ | 12381/18688 [14:31:41<6:53:32, 3.93s/it] 66%|██████▋ | 12382/18688 [14:31:45<6:47:26, 3.88s/it] 66%|██████▋ | 12383/18688 [14:31:49<7:07:51, 4.07s/it] 66%|██████▋ | 12384/18688 [14:31:54<7:27:44, 4.26s/it] 66%|██████▋ | 12385/18688 [14:31:58<7:23:45, 4.22s/it] 66%|██████▋ | 12386/18688 [14:32:02<7:16:01, 4.15s/it] 66%|██████▋ | 12387/18688 [14:32:06<6:53:29, 3.94s/it] 66%|██████▋ | 12388/18688 [14:32:10<6:49:25, 3.90s/it] 66%|██████▋ | 12389/18688 [14:32:13<6:41:15, 3.82s/it] 66%|██████▋ | 12390/18688 [14:32:17<6:36:51, 3.78s/it] {'loss': '0.8825', 'grad_norm': '1.258', 'learning_rate': '2.371e-05', 'epoch': '0.663', 'num_input_tokens_seen': 41642264, 'train_runtime': '5.234e+04', 'train_tokens_per_second': '795.7'}
66%|██████▋ | 12390/18688 [14:32:17<6:36:51, 3.78s/it] 66%|██████▋ | 12391/18688 [14:32:21<6:38:13, 3.79s/it] 66%|██████▋ | 12392/18688 [14:32:24<6:30:24, 3.72s/it] 66%|██████▋ | 12393/18688 [14:32:29<7:09:38, 4.10s/it] 66%|██████▋ | 12394/18688 [14:32:33<7:12:33, 4.12s/it] 66%|██████▋ | 12395/18688 [14:32:38<7:17:58, 4.18s/it] 66%|██████▋ | 12396/18688 [14:32:41<7:01:32, 4.02s/it] 66%|██████▋ | 12397/18688 [14:32:46<7:22:59, 4.22s/it] 66%|██████▋ | 12398/18688 [14:32:50<7:17:45, 4.18s/it] 66%|██████▋ | 12399/18688 [14:32:54<7:13:16, 4.13s/it] 66%|██████▋ | 12400/18688 [14:32:58<7:18:38, 4.19s/it] {'loss': '0.9214', 'grad_norm': '1.132', 'learning_rate': '2.364e-05', 'epoch': '0.6635', 'num_input_tokens_seen': 41676704, 'train_runtime': '5.238e+04', 'train_tokens_per_second': '795.7'}
66%|██████▋ | 12400/18688 [14:32:58<7:18:38, 4.19s/it] 66%|██████▋ | 12401/18688 [14:33:03<7:21:09, 4.21s/it] 66%|██████▋ | 12402/18688 [14:33:07<7:22:27, 4.22s/it] 66%|██████▋ | 12403/18688 [14:33:11<7:03:57, 4.05s/it] 66%|██████▋ | 12404/18688 [14:33:15<7:00:32, 4.02s/it] 66%|██████▋ | 12405/18688 [14:33:18<6:43:04, 3.85s/it] 66%|██████▋ | 12406/18688 [14:33:22<6:58:32, 4.00s/it] 66%|██████▋ | 12407/18688 [14:33:26<6:43:57, 3.86s/it] 66%|██████▋ | 12408/18688 [14:33:30<6:53:37, 3.95s/it] 66%|██████▋ | 12409/18688 [14:33:35<7:16:44, 4.17s/it] 66%|██████▋ | 12410/18688 [14:33:40<8:02:52, 4.61s/it] {'loss': '0.9698', 'grad_norm': '1.234', 'learning_rate': '2.358e-05', 'epoch': '0.6641', 'num_input_tokens_seen': 41709352, 'train_runtime': '5.242e+04', 'train_tokens_per_second': '795.7'}
66%|██████▋ | 12410/18688 [14:33:40<8:02:52, 4.61s/it] 66%|██████▋ | 12411/18688 [14:33:45<8:00:16, 4.59s/it] 66%|██████▋ | 12412/18688 [14:33:49<7:31:21, 4.32s/it] 66%|██████▋ | 12413/18688 [14:33:53<7:31:56, 4.32s/it] 66%|██████▋ | 12414/18688 [14:33:56<7:04:54, 4.06s/it] 66%|██████▋ | 12415/18688 [14:34:00<6:45:33, 3.88s/it] 66%|██████▋ | 12416/18688 [14:34:03<6:37:35, 3.80s/it] 66%|██████▋ | 12417/18688 [14:34:08<7:10:11, 4.12s/it] 66%|██████▋ | 12418/18688 [14:34:13<7:15:09, 4.16s/it] 66%|██████▋ | 12419/18688 [14:34:16<7:00:13, 4.02s/it] 66%|██████▋ | 12420/18688 [14:34:21<7:10:17, 4.12s/it] {'loss': '0.9072', 'grad_norm': '1.19', 'learning_rate': '2.351e-05', 'epoch': '0.6646', 'num_input_tokens_seen': 41742408, 'train_runtime': '5.246e+04', 'train_tokens_per_second': '795.7'}
66%|██████▋ | 12420/18688 [14:34:21<7:10:17, 4.12s/it] 66%|██████▋ | 12421/18688 [14:34:24<6:59:44, 4.02s/it] 66%|██████▋ | 12422/18688 [14:34:29<7:02:46, 4.05s/it] 66%|██████▋ | 12423/18688 [14:34:34<7:47:06, 4.47s/it] 66%|██████▋ | 12424/18688 [14:34:39<8:04:56, 4.65s/it] 66%|██████▋ | 12425/18688 [14:34:43<7:42:52, 4.43s/it] 66%|██████▋ | 12426/18688 [14:34:47<7:30:26, 4.32s/it] 66%|██████▋ | 12427/18688 [14:34:51<7:14:37, 4.17s/it] 67%|██████▋ | 12428/18688 [14:34:55<7:01:22, 4.04s/it] 67%|██████▋ | 12429/18688 [14:34:58<6:49:45, 3.93s/it] 67%|██████▋ | 12430/18688 [14:35:02<6:41:10, 3.85s/it] {'loss': '0.9076', 'grad_norm': '1.456', 'learning_rate': '2.344e-05', 'epoch': '0.6652', 'num_input_tokens_seen': 41776160, 'train_runtime': '5.25e+04', 'train_tokens_per_second': '795.7'}
67%|██████▋ | 12430/18688 [14:35:02<6:41:10, 3.85s/it] 67%|██████▋ | 12431/18688 [14:35:06<6:58:13, 4.01s/it] 67%|██████▋ | 12432/18688 [14:35:11<7:08:15, 4.11s/it] 67%|██████▋ | 12433/18688 [14:35:14<6:47:44, 3.91s/it] 67%|██████▋ | 12434/18688 [14:35:18<6:46:01, 3.90s/it] 67%|██████▋ | 12435/18688 [14:35:22<6:47:53, 3.91s/it] 67%|██████▋ | 12436/18688 [14:35:26<6:51:55, 3.95s/it] 67%|██████▋ | 12437/18688 [14:35:30<6:50:37, 3.94s/it] 67%|██████▋ | 12438/18688 [14:35:34<7:07:41, 4.11s/it] 67%|██████▋ | 12439/18688 [14:35:38<6:55:11, 3.99s/it] 67%|██████▋ | 12440/18688 [14:35:42<7:03:43, 4.07s/it] {'loss': '0.9748', 'grad_norm': '1.565', 'learning_rate': '2.338e-05', 'epoch': '0.6657', 'num_input_tokens_seen': 41808800, 'train_runtime': '5.254e+04', 'train_tokens_per_second': '795.7'}
67%|██████▋ | 12440/18688 [14:35:42<7:03:43, 4.07s/it] 67%|██████▋ | 12441/18688 [14:35:47<7:14:05, 4.17s/it] 67%|██████▋ | 12442/18688 [14:35:50<6:52:37, 3.96s/it] 67%|██████▋ | 12443/18688 [14:35:55<7:16:15, 4.19s/it] 67%|██████▋ | 12444/18688 [14:35:59<7:15:52, 4.19s/it] 67%|██████▋ | 12445/18688 [14:36:03<7:14:09, 4.17s/it] 67%|██████▋ | 12446/18688 [14:36:07<7:01:00, 4.05s/it] 67%|██████▋ | 12447/18688 [14:36:11<6:55:03, 3.99s/it] 67%|██████▋ | 12448/18688 [14:36:16<7:19:09, 4.22s/it] 67%|██████▋ | 12449/18688 [14:36:21<7:44:38, 4.47s/it] 67%|██████▋ | 12450/18688 [14:36:25<7:29:44, 4.33s/it] {'loss': '0.8893', 'grad_norm': '1.422', 'learning_rate': '2.331e-05', 'epoch': '0.6662', 'num_input_tokens_seen': 41843544, 'train_runtime': '5.259e+04', 'train_tokens_per_second': '795.7'}
67%|██████▋ | 12450/18688 [14:36:25<7:29:44, 4.33s/it] 67%|██████▋ | 12451/18688 [14:36:29<7:25:59, 4.29s/it] 67%|██████▋ | 12452/18688 [14:36:33<7:19:03, 4.22s/it] 67%|██████▋ | 12453/18688 [14:36:37<7:05:05, 4.09s/it] 67%|██████▋ | 12454/18688 [14:36:41<7:03:00, 4.07s/it] 67%|██████▋ | 12455/18688 [14:36:44<6:51:31, 3.96s/it] 67%|██████▋ | 12456/18688 [14:36:48<6:35:59, 3.81s/it] 67%|██████▋ | 12457/18688 [14:36:52<6:34:51, 3.80s/it] 67%|██████▋ | 12458/18688 [14:36:56<6:45:49, 3.91s/it] 67%|██████▋ | 12459/18688 [14:37:00<6:49:32, 3.94s/it] 67%|██████▋ | 12460/18688 [14:37:04<6:47:36, 3.93s/it] {'loss': '0.8944', 'grad_norm': '1.271', 'learning_rate': '2.324e-05', 'epoch': '0.6668', 'num_input_tokens_seen': 41874896, 'train_runtime': '5.262e+04', 'train_tokens_per_second': '795.7'}
67%|██████▋ | 12460/18688 [14:37:04<6:47:36, 3.93s/it] 67%|██████▋ | 12461/18688 [14:37:09<7:26:14, 4.30s/it] 67%|██████▋ | 12462/18688 [14:37:14<7:36:12, 4.40s/it] 67%|██████▋ | 12463/18688 [14:37:17<7:10:15, 4.15s/it] 67%|██████▋ | 12464/18688 [14:37:23<7:49:53, 4.53s/it] 67%|██████▋ | 12465/18688 [14:37:27<7:33:09, 4.37s/it] 67%|██████▋ | 12466/18688 [14:37:30<7:18:47, 4.23s/it] 67%|██████▋ | 12467/18688 [14:37:35<7:16:20, 4.21s/it] 67%|██████▋ | 12468/18688 [14:37:40<7:52:48, 4.56s/it] 67%|██████▋ | 12469/18688 [14:37:44<7:37:11, 4.41s/it] 67%|██████▋ | 12470/18688 [14:37:48<7:14:39, 4.19s/it] {'loss': '0.9518', 'grad_norm': '1.296', 'learning_rate': '2.318e-05', 'epoch': '0.6673', 'num_input_tokens_seen': 41912360, 'train_runtime': '5.267e+04', 'train_tokens_per_second': '795.8'}
67%|██████▋ | 12470/18688 [14:37:48<7:14:39, 4.19s/it] 67%|██████▋ | 12471/18688 [14:37:52<7:15:28, 4.20s/it] 67%|██████▋ | 12472/18688 [14:37:56<7:01:20, 4.07s/it] 67%|██████▋ | 12473/18688 [14:38:01<7:30:45, 4.35s/it] 67%|██████▋ | 12474/18688 [14:38:05<7:17:09, 4.22s/it] 67%|██████▋ | 12475/18688 [14:38:09<7:06:19, 4.12s/it] 67%|██████▋ | 12476/18688 [14:38:12<6:56:43, 4.02s/it] 67%|██████▋ | 12477/18688 [14:38:17<7:23:46, 4.29s/it] 67%|██████▋ | 12478/18688 [14:38:22<7:39:55, 4.44s/it] 67%|██████▋ | 12479/18688 [14:38:26<7:10:51, 4.16s/it] 67%|██████▋ | 12480/18688 [14:38:30<7:25:49, 4.31s/it] {'loss': '0.8548', 'grad_norm': '1.27', 'learning_rate': '2.311e-05', 'epoch': '0.6678', 'num_input_tokens_seen': 41946928, 'train_runtime': '5.271e+04', 'train_tokens_per_second': '795.8'}
67%|██████▋ | 12480/18688 [14:38:30<7:25:49, 4.31s/it] 67%|██████▋ | 12481/18688 [14:38:35<7:34:32, 4.39s/it] 67%|██████▋ | 12482/18688 [14:38:38<7:06:10, 4.12s/it] 67%|██████▋ | 12483/18688 [14:38:42<7:03:39, 4.10s/it] 67%|██████▋ | 12484/18688 [14:38:47<7:13:12, 4.19s/it] 67%|██████▋ | 12485/18688 [14:38:51<7:10:03, 4.16s/it] 67%|██████▋ | 12486/18688 [14:38:56<7:47:29, 4.52s/it] 67%|██████▋ | 12487/18688 [14:39:00<7:34:03, 4.39s/it] 67%|██████▋ | 12488/18688 [14:39:04<7:22:52, 4.29s/it] 67%|██████▋ | 12489/18688 [14:39:08<7:07:06, 4.13s/it] 67%|██████▋ | 12490/18688 [14:39:13<7:20:09, 4.26s/it] {'loss': '0.9411', 'grad_norm': '1.132', 'learning_rate': '2.304e-05', 'epoch': '0.6684', 'num_input_tokens_seen': 41981944, 'train_runtime': '5.275e+04', 'train_tokens_per_second': '795.8'}
67%|██████▋ | 12490/18688 [14:39:13<7:20:09, 4.26s/it] 67%|██████▋ | 12491/18688 [14:39:17<7:34:03, 4.40s/it] 67%|██████▋ | 12492/18688 [14:39:21<7:19:50, 4.26s/it] 67%|██████▋ | 12493/18688 [14:39:25<6:58:41, 4.06s/it] 67%|██████▋ | 12494/18688 [14:39:29<7:04:12, 4.11s/it] 67%|██████▋ | 12495/18688 [14:39:34<7:23:00, 4.29s/it] 67%|██████▋ | 12496/18688 [14:39:38<7:22:48, 4.29s/it] 67%|██████▋ | 12497/18688 [14:39:42<7:21:36, 4.28s/it] 67%|██████▋ | 12498/18688 [14:39:47<7:18:50, 4.25s/it] 67%|██████▋ | 12499/18688 [14:39:51<7:15:47, 4.22s/it] 67%|██████▋ | 12500/18688 [14:39:55<7:07:48, 4.15s/it] {'loss': '0.8905', 'grad_norm': '1.174', 'learning_rate': '2.298e-05', 'epoch': '0.6689', 'num_input_tokens_seen': 42016024, 'train_runtime': '5.28e+04', 'train_tokens_per_second': '795.8'}
67%|██████▋ | 12500/18688 [14:39:55<7:07:48, 4.15s/it] 67%|██████▋ | 12501/18688 [14:39:59<7:15:34, 4.22s/it] 67%|██████▋ | 12502/18688 [14:40:03<7:10:05, 4.17s/it] 67%|██████▋ | 12503/18688 [14:40:08<7:37:39, 4.44s/it] 67%|██████▋ | 12504/18688 [14:40:12<7:32:12, 4.39s/it] 67%|██████▋ | 12505/18688 [14:40:18<7:58:59, 4.65s/it] 67%|██████▋ | 12506/18688 [14:40:22<7:35:15, 4.42s/it] 67%|██████▋ | 12507/18688 [14:40:26<7:26:00, 4.33s/it] 67%|██████▋ | 12508/18688 [14:40:31<8:06:18, 4.72s/it] 67%|██████▋ | 12509/18688 [14:40:36<8:14:06, 4.80s/it] 67%|██████▋ | 12510/18688 [14:40:40<7:45:57, 4.53s/it] {'loss': '0.9259', 'grad_norm': '1.017', 'learning_rate': '2.291e-05', 'epoch': '0.6694', 'num_input_tokens_seen': 42055312, 'train_runtime': '5.284e+04', 'train_tokens_per_second': '795.9'}
67%|██████▋ | 12510/18688 [14:40:40<7:45:57, 4.53s/it] 67%|██████▋ | 12511/18688 [14:40:45<7:39:02, 4.46s/it] 67%|██████▋ | 12512/18688 [14:40:50<8:16:57, 4.83s/it] 67%|██████▋ | 12513/18688 [14:40:54<7:42:47, 4.50s/it] 67%|██████▋ | 12514/18688 [14:40:58<7:24:45, 4.32s/it] 67%|██████▋ | 12515/18688 [14:41:02<7:08:17, 4.16s/it] 67%|██████▋ | 12516/18688 [14:41:06<7:08:42, 4.17s/it] 67%|██████▋ | 12517/18688 [14:41:10<7:18:55, 4.27s/it] 67%|██████▋ | 12518/18688 [14:41:16<7:50:57, 4.58s/it] 67%|██████▋ | 12519/18688 [14:41:20<7:42:22, 4.50s/it] 67%|██████▋ | 12520/18688 [14:41:24<7:23:45, 4.32s/it] {'loss': '0.8958', 'grad_norm': '1.312', 'learning_rate': '2.285e-05', 'epoch': '0.67', 'num_input_tokens_seen': 42091352, 'train_runtime': '5.288e+04', 'train_tokens_per_second': '795.9'}
67%|██████▋ | 12520/18688 [14:41:24<7:23:45, 4.32s/it] 67%|██████▋ | 12521/18688 [14:41:29<7:48:54, 4.56s/it] 67%|██████▋ | 12522/18688 [14:41:33<7:42:37, 4.50s/it] 67%|██████▋ | 12523/18688 [14:41:38<7:49:12, 4.57s/it] 67%|██████▋ | 12524/18688 [14:41:42<7:36:32, 4.44s/it] 67%|██████▋ | 12525/18688 [14:41:47<7:34:26, 4.42s/it] 67%|██████▋ | 12526/18688 [14:41:51<7:30:37, 4.39s/it] 67%|██████▋ | 12527/18688 [14:41:55<7:32:40, 4.41s/it] 67%|██████▋ | 12528/18688 [14:41:59<7:16:54, 4.26s/it] 67%|██████▋ | 12529/18688 [14:42:03<7:01:40, 4.11s/it] 67%|██████▋ | 12530/18688 [14:42:07<6:48:07, 3.98s/it] {'loss': '0.9275', 'grad_norm': '1.332', 'learning_rate': '2.278e-05', 'epoch': '0.6705', 'num_input_tokens_seen': 42125880, 'train_runtime': '5.293e+04', 'train_tokens_per_second': '795.9'}
67%|██████▋ | 12530/18688 [14:42:07<6:48:07, 3.98s/it] 67%|██████▋ | 12531/18688 [14:42:10<6:35:55, 3.86s/it] 67%|██████▋ | 12532/18688 [14:42:14<6:44:35, 3.94s/it] 67%|██████▋ | 12533/18688 [14:42:18<6:29:22, 3.80s/it] 67%|██████▋ | 12534/18688 [14:42:22<6:39:23, 3.89s/it] 67%|██████▋ | 12535/18688 [14:42:26<6:33:28, 3.84s/it] 67%|██████▋ | 12536/18688 [14:42:29<6:32:39, 3.83s/it] 67%|██████▋ | 12537/18688 [14:42:33<6:33:30, 3.84s/it] 67%|██████▋ | 12538/18688 [14:42:37<6:36:10, 3.87s/it] 67%|██████▋ | 12539/18688 [14:42:42<6:58:18, 4.08s/it] 67%|██████▋ | 12540/18688 [14:42:46<7:03:56, 4.14s/it] {'loss': '0.907', 'grad_norm': '1.052', 'learning_rate': '2.271e-05', 'epoch': '0.671', 'num_input_tokens_seen': 42154864, 'train_runtime': '5.297e+04', 'train_tokens_per_second': '795.9'}
67%|██████▋ | 12540/18688 [14:42:46<7:03:56, 4.14s/it] 67%|██████▋ | 12541/18688 [14:42:50<6:43:42, 3.94s/it] 67%|██████▋ | 12542/18688 [14:42:53<6:38:52, 3.89s/it] 67%|██████▋ | 12543/18688 [14:42:57<6:44:24, 3.95s/it] 67%|██████▋ | 12544/18688 [14:43:02<6:55:48, 4.06s/it] 67%|██████▋ | 12545/18688 [14:43:06<7:06:10, 4.16s/it] 67%|██████▋ | 12546/18688 [14:43:10<6:57:15, 4.08s/it] 67%|██████▋ | 12547/18688 [14:43:14<6:58:34, 4.09s/it] 67%|██████▋ | 12548/18688 [14:43:19<7:14:05, 4.24s/it] 67%|██████▋ | 12549/18688 [14:43:23<7:23:41, 4.34s/it] 67%|██████▋ | 12550/18688 [14:43:28<7:22:12, 4.32s/it] {'loss': '0.9402', 'grad_norm': '1.086', 'learning_rate': '2.265e-05', 'epoch': '0.6716', 'num_input_tokens_seen': 42189640, 'train_runtime': '5.301e+04', 'train_tokens_per_second': '795.9'}
67%|██████▋ | 12550/18688 [14:43:28<7:22:12, 4.32s/it] 67%|██████▋ | 12551/18688 [14:43:33<7:40:24, 4.50s/it] 67%|██████▋ | 12552/18688 [14:43:37<7:24:05, 4.34s/it] 67%|██████▋ | 12553/18688 [14:43:41<7:31:44, 4.42s/it] 67%|██████▋ | 12554/18688 [14:43:45<7:07:57, 4.19s/it] 67%|██████▋ | 12555/18688 [14:43:48<6:53:40, 4.05s/it] 67%|██████▋ | 12556/18688 [14:43:52<6:47:37, 3.99s/it] 67%|██████▋ | 12557/18688 [14:43:56<6:44:10, 3.96s/it] 67%|██████▋ | 12558/18688 [14:44:01<7:08:34, 4.19s/it] 67%|██████▋ | 12559/18688 [14:44:05<7:02:44, 4.14s/it] 67%|██████▋ | 12560/18688 [14:44:09<7:05:33, 4.17s/it] {'loss': '0.9464', 'grad_norm': '1.301', 'learning_rate': '2.258e-05', 'epoch': '0.6721', 'num_input_tokens_seen': 42222776, 'train_runtime': '5.305e+04', 'train_tokens_per_second': '795.9'}
67%|██████▋ | 12560/18688 [14:44:09<7:05:33, 4.17s/it] 67%|██████▋ | 12561/18688 [14:44:13<6:49:37, 4.01s/it] 67%|██████▋ | 12562/18688 [14:44:17<6:43:52, 3.96s/it] 67%|██████▋ | 12563/18688 [14:44:20<6:35:28, 3.87s/it] 67%|██████▋ | 12564/18688 [14:44:24<6:36:46, 3.89s/it] 67%|██████▋ | 12565/18688 [14:44:28<6:27:44, 3.80s/it] 67%|██████▋ | 12566/18688 [14:44:32<6:44:07, 3.96s/it] 67%|██████▋ | 12567/18688 [14:44:37<7:07:37, 4.19s/it] 67%|██████▋ | 12568/18688 [14:44:41<7:03:26, 4.15s/it] 67%|██████▋ | 12569/18688 [14:44:45<6:47:46, 4.00s/it] 67%|██████▋ | 12570/18688 [14:44:48<6:35:58, 3.88s/it] {'loss': '0.8625', 'grad_norm': '1.149', 'learning_rate': '2.252e-05', 'epoch': '0.6726', 'num_input_tokens_seen': 42252760, 'train_runtime': '5.309e+04', 'train_tokens_per_second': '795.9'}
67%|██████▋ | 12570/18688 [14:44:48<6:35:58, 3.88s/it] 67%|██████▋ | 12571/18688 [14:44:53<7:00:12, 4.12s/it] 67%|██████▋ | 12572/18688 [14:44:58<7:29:00, 4.40s/it] 67%|██████▋ | 12573/18688 [14:45:02<7:31:19, 4.43s/it] 67%|██████▋ | 12574/18688 [14:45:06<7:08:07, 4.20s/it] 67%|██████▋ | 12575/18688 [14:45:10<7:05:46, 4.18s/it] 67%|██████▋ | 12576/18688 [14:45:15<7:14:12, 4.26s/it] 67%|██████▋ | 12577/18688 [14:45:18<6:49:39, 4.02s/it] 67%|██████▋ | 12578/18688 [14:45:22<6:45:47, 3.98s/it] 67%|██████▋ | 12579/18688 [14:45:26<6:58:02, 4.11s/it] 67%|██████▋ | 12580/18688 [14:45:30<6:38:39, 3.92s/it] {'loss': '0.9213', 'grad_norm': '1.401', 'learning_rate': '2.245e-05', 'epoch': '0.6732', 'num_input_tokens_seen': 42287328, 'train_runtime': '5.313e+04', 'train_tokens_per_second': '795.9'}
67%|██████▋ | 12580/18688 [14:45:30<6:38:39, 3.92s/it] 67%|██████▋ | 12581/18688 [14:45:34<6:38:28, 3.91s/it] 67%|██████▋ | 12582/18688 [14:45:39<7:17:56, 4.30s/it] 67%|██████▋ | 12583/18688 [14:45:43<7:04:49, 4.18s/it] 67%|██████▋ | 12584/18688 [14:45:46<6:43:49, 3.97s/it] 67%|██████▋ | 12585/18688 [14:45:50<6:35:24, 3.89s/it] 67%|██████▋ | 12586/18688 [14:45:54<6:46:25, 4.00s/it] 67%|██████▋ | 12587/18688 [14:45:58<6:48:12, 4.01s/it] 67%|██████▋ | 12588/18688 [14:46:02<6:35:41, 3.89s/it] 67%|██████▋ | 12589/18688 [14:46:06<6:22:30, 3.76s/it] 67%|██████▋ | 12590/18688 [14:46:10<6:36:21, 3.90s/it] {'loss': '0.8945', 'grad_norm': '1.254', 'learning_rate': '2.238e-05', 'epoch': '0.6737', 'num_input_tokens_seen': 42319456, 'train_runtime': '5.317e+04', 'train_tokens_per_second': '795.9'}
67%|██████▋ | 12590/18688 [14:46:10<6:36:21, 3.90s/it] 67%|██████▋ | 12591/18688 [14:46:14<6:49:06, 4.03s/it] 67%|██████▋ | 12592/18688 [14:46:18<6:57:51, 4.11s/it] 67%|██████▋ | 12593/18688 [14:46:22<6:37:02, 3.91s/it] 67%|██████▋ | 12594/18688 [14:46:26<6:49:15, 4.03s/it] 67%|██████▋ | 12595/18688 [14:46:31<7:11:10, 4.25s/it] 67%|██████▋ | 12596/18688 [14:46:35<7:05:24, 4.19s/it] 67%|██████▋ | 12597/18688 [14:46:40<7:35:48, 4.49s/it] 67%|██████▋ | 12598/18688 [14:46:45<7:40:40, 4.54s/it] 67%|██████▋ | 12599/18688 [14:46:49<7:36:11, 4.50s/it] 67%|██████▋ | 12600/18688 [14:46:54<7:45:54, 4.59s/it] {'loss': '0.878', 'grad_norm': '1.354', 'learning_rate': '2.232e-05', 'epoch': '0.6742', 'num_input_tokens_seen': 42357016, 'train_runtime': '5.321e+04', 'train_tokens_per_second': '796'}
67%|██████▋ | 12600/18688 [14:46:54<7:45:54, 4.59s/it] 67%|██████▋ | 12601/18688 [14:46:58<7:42:44, 4.56s/it] 67%|██████▋ | 12602/18688 [14:47:02<7:26:28, 4.40s/it] 67%|██████▋ | 12603/18688 [14:47:06<7:07:01, 4.21s/it] 67%|██████▋ | 12604/18688 [14:47:10<6:50:29, 4.05s/it] 67%|██████▋ | 12605/18688 [14:47:15<7:10:12, 4.24s/it] 67%|██████▋ | 12606/18688 [14:47:19<7:25:48, 4.40s/it] 67%|██████▋ | 12607/18688 [14:47:23<7:08:28, 4.23s/it] 67%|██████▋ | 12608/18688 [14:47:28<7:22:57, 4.37s/it] 67%|██████▋ | 12609/18688 [14:47:32<7:17:47, 4.32s/it] 67%|██████▋ | 12610/18688 [14:47:36<7:04:26, 4.19s/it] {'loss': '0.9133', 'grad_norm': '1.258', 'learning_rate': '2.225e-05', 'epoch': '0.6748', 'num_input_tokens_seen': 42389648, 'train_runtime': '5.326e+04', 'train_tokens_per_second': '796'}
67%|██████▋ | 12610/18688 [14:47:36<7:04:26, 4.19s/it] 67%|██████▋ | 12611/18688 [14:47:40<7:06:12, 4.21s/it] 67%|██████▋ | 12612/18688 [14:47:44<6:56:33, 4.11s/it] 67%|██████▋ | 12613/18688 [14:47:48<6:49:52, 4.05s/it] 67%|██████▋ | 12614/18688 [14:47:52<6:58:42, 4.14s/it] 68%|██████▊ | 12615/18688 [14:47:56<6:39:57, 3.95s/it] 68%|██████▊ | 12616/18688 [14:48:00<6:38:16, 3.94s/it] 68%|██████▊ | 12617/18688 [14:48:04<6:38:09, 3.94s/it] 68%|██████▊ | 12618/18688 [14:48:08<6:48:51, 4.04s/it] 68%|██████▊ | 12619/18688 [14:48:12<6:56:24, 4.12s/it] 68%|██████▊ | 12620/18688 [14:48:16<6:56:03, 4.11s/it] {'loss': '0.9504', 'grad_norm': '1.102', 'learning_rate': '2.219e-05', 'epoch': '0.6753', 'num_input_tokens_seen': 42420432, 'train_runtime': '5.33e+04', 'train_tokens_per_second': '795.9'}
68%|██████▊ | 12620/18688 [14:48:16<6:56:03, 4.11s/it] 68%|██████▊ | 12621/18688 [14:48:20<6:46:26, 4.02s/it] 68%|██████▊ | 12622/18688 [14:48:25<6:59:10, 4.15s/it] 68%|██████▊ | 12623/18688 [14:48:30<7:39:24, 4.54s/it] 68%|██████▊ | 12624/18688 [14:48:34<7:27:22, 4.43s/it] 68%|██████▊ | 12625/18688 [14:48:38<6:58:00, 4.14s/it] 68%|██████▊ | 12626/18688 [14:48:41<6:38:49, 3.95s/it] 68%|██████▊ | 12627/18688 [14:48:45<6:29:00, 3.85s/it] 68%|██████▊ | 12628/18688 [14:48:49<6:24:24, 3.81s/it] 68%|██████▊ | 12629/18688 [14:48:53<6:35:52, 3.92s/it] 68%|██████▊ | 12630/18688 [14:48:57<6:32:02, 3.88s/it] {'loss': '0.8739', 'grad_norm': '1.006', 'learning_rate': '2.212e-05', 'epoch': '0.6759', 'num_input_tokens_seen': 42452464, 'train_runtime': '5.334e+04', 'train_tokens_per_second': '795.9'}
68%|██████▊ | 12630/18688 [14:48:57<6:32:02, 3.88s/it] 68%|██████▊ | 12631/18688 [14:49:00<6:31:41, 3.88s/it] 68%|██████▊ | 12632/18688 [14:49:04<6:24:44, 3.81s/it] 68%|██████▊ | 12633/18688 [14:49:08<6:17:10, 3.74s/it] 68%|██████▊ | 12634/18688 [14:49:11<6:17:51, 3.74s/it] 68%|██████▊ | 12635/18688 [14:49:15<6:25:51, 3.82s/it] 68%|██████▊ | 12636/18688 [14:49:20<6:33:07, 3.90s/it] 68%|██████▊ | 12637/18688 [14:49:24<6:37:06, 3.94s/it] 68%|██████▊ | 12638/18688 [14:49:28<6:43:26, 4.00s/it] 68%|██████▊ | 12639/18688 [14:49:32<6:58:28, 4.15s/it] 68%|██████▊ | 12640/18688 [14:49:37<7:05:39, 4.22s/it] {'loss': '0.8561', 'grad_norm': '1.146', 'learning_rate': '2.206e-05', 'epoch': '0.6764', 'num_input_tokens_seen': 42483392, 'train_runtime': '5.338e+04', 'train_tokens_per_second': '795.9'}
68%|██████▊ | 12640/18688 [14:49:37<7:05:39, 4.22s/it] 68%|██████▊ | 12641/18688 [14:49:41<7:10:45, 4.27s/it] 68%|██████▊ | 12642/18688 [14:49:46<7:27:54, 4.45s/it] 68%|██████▊ | 12643/18688 [14:49:50<7:24:52, 4.42s/it] 68%|██████▊ | 12644/18688 [14:49:54<7:02:49, 4.20s/it] 68%|██████▊ | 12645/18688 [14:49:58<7:14:22, 4.31s/it] 68%|██████▊ | 12646/18688 [14:50:02<6:52:57, 4.10s/it] 68%|██████▊ | 12647/18688 [14:50:06<6:47:44, 4.05s/it] 68%|██████▊ | 12648/18688 [14:50:10<6:51:49, 4.09s/it] 68%|██████▊ | 12649/18688 [14:50:14<6:36:02, 3.93s/it] 68%|██████▊ | 12650/18688 [14:50:18<6:47:08, 4.05s/it] {'loss': '0.8915', 'grad_norm': '1.414', 'learning_rate': '2.199e-05', 'epoch': '0.6769', 'num_input_tokens_seen': 42515232, 'train_runtime': '5.342e+04', 'train_tokens_per_second': '795.9'}
68%|██████▊ | 12650/18688 [14:50:18<6:47:08, 4.05s/it] 68%|██████▊ | 12651/18688 [14:50:22<6:35:23, 3.93s/it] 68%|██████▊ | 12652/18688 [14:50:25<6:30:18, 3.88s/it] 68%|██████▊ | 12653/18688 [14:50:30<7:00:19, 4.18s/it] 68%|██████▊ | 12654/18688 [14:50:34<6:51:44, 4.09s/it] 68%|██████▊ | 12655/18688 [14:50:38<6:41:55, 4.00s/it] 68%|██████▊ | 12656/18688 [14:50:42<6:28:34, 3.87s/it] 68%|██████▊ | 12657/18688 [14:50:45<6:27:11, 3.85s/it] 68%|██████▊ | 12658/18688 [14:50:50<6:49:03, 4.07s/it] 68%|██████▊ | 12659/18688 [14:50:54<6:57:58, 4.16s/it] 68%|██████▊ | 12660/18688 [14:50:58<6:40:38, 3.99s/it] {'loss': '0.8691', 'grad_norm': '1.265', 'learning_rate': '2.193e-05', 'epoch': '0.6775', 'num_input_tokens_seen': 42545840, 'train_runtime': '5.346e+04', 'train_tokens_per_second': '795.9'}
68%|██████▊ | 12660/18688 [14:50:58<6:40:38, 3.99s/it] 68%|██████▊ | 12661/18688 [14:51:02<6:42:51, 4.01s/it] 68%|██████▊ | 12662/18688 [14:51:06<6:53:17, 4.12s/it] 68%|██████▊ | 12663/18688 [14:51:11<6:58:21, 4.17s/it] 68%|██████▊ | 12664/18688 [14:51:14<6:46:35, 4.05s/it] 68%|██████▊ | 12665/18688 [14:51:18<6:44:49, 4.03s/it] 68%|██████▊ | 12666/18688 [14:51:22<6:33:10, 3.92s/it] 68%|██████▊ | 12667/18688 [14:51:26<6:30:35, 3.89s/it] 68%|██████▊ | 12668/18688 [14:51:30<6:34:06, 3.93s/it] 68%|██████▊ | 12669/18688 [14:51:34<6:53:05, 4.12s/it] 68%|██████▊ | 12670/18688 [14:51:39<6:53:59, 4.13s/it] {'loss': '0.9244', 'grad_norm': '1.103', 'learning_rate': '2.186e-05', 'epoch': '0.678', 'num_input_tokens_seen': 42579744, 'train_runtime': '5.35e+04', 'train_tokens_per_second': '795.9'}
68%|██████▊ | 12670/18688 [14:51:39<6:53:59, 4.13s/it] 68%|██████▊ | 12671/18688 [14:51:44<7:22:18, 4.41s/it] 68%|██████▊ | 12672/18688 [14:51:48<7:07:46, 4.27s/it] 68%|██████▊ | 12673/18688 [14:51:52<7:12:03, 4.31s/it] 68%|██████▊ | 12674/18688 [14:51:57<7:29:27, 4.48s/it] 68%|██████▊ | 12675/18688 [14:52:01<7:12:23, 4.31s/it] 68%|██████▊ | 12676/18688 [14:52:05<6:59:09, 4.18s/it] 68%|██████▊ | 12677/18688 [14:52:09<7:15:53, 4.35s/it] 68%|██████▊ | 12678/18688 [14:52:14<7:24:33, 4.44s/it] 68%|██████▊ | 12679/18688 [14:52:19<7:24:13, 4.44s/it] 68%|██████▊ | 12680/18688 [14:52:22<7:03:21, 4.23s/it] {'loss': '0.9772', 'grad_norm': '1.203', 'learning_rate': '2.18e-05', 'epoch': '0.6785', 'num_input_tokens_seen': 42615848, 'train_runtime': '5.354e+04', 'train_tokens_per_second': '795.9'}
68%|██████▊ | 12680/18688 [14:52:22<7:03:21, 4.23s/it] 68%|██████▊ | 12681/18688 [14:52:26<6:41:00, 4.01s/it] 68%|██████▊ | 12682/18688 [14:52:30<6:45:57, 4.06s/it] 68%|██████▊ | 12683/18688 [14:52:34<6:51:19, 4.11s/it] 68%|██████▊ | 12684/18688 [14:52:38<6:58:12, 4.18s/it] 68%|██████▊ | 12685/18688 [14:52:43<7:11:56, 4.32s/it] 68%|██████▊ | 12686/18688 [14:52:48<7:34:02, 4.54s/it] 68%|██████▊ | 12687/18688 [14:52:52<7:10:41, 4.31s/it] 68%|██████▊ | 12688/18688 [14:52:56<6:59:36, 4.20s/it] 68%|██████▊ | 12689/18688 [14:53:00<7:04:49, 4.25s/it] 68%|██████▊ | 12690/18688 [14:53:05<7:10:10, 4.30s/it] {'loss': '0.9255', 'grad_norm': '1.259', 'learning_rate': '2.173e-05', 'epoch': '0.6791', 'num_input_tokens_seen': 42649984, 'train_runtime': '5.359e+04', 'train_tokens_per_second': '795.9'}
68%|██████▊ | 12690/18688 [14:53:05<7:10:10, 4.30s/it] 68%|██████▊ | 12691/18688 [14:53:08<6:54:41, 4.15s/it] 68%|██████▊ | 12692/18688 [14:53:13<6:51:30, 4.12s/it] 68%|██████▊ | 12693/18688 [14:53:16<6:33:41, 3.94s/it] 68%|██████▊ | 12694/18688 [14:53:20<6:32:51, 3.93s/it] 68%|██████▊ | 12695/18688 [14:53:24<6:50:50, 4.11s/it] 68%|██████▊ | 12696/18688 [14:53:28<6:37:43, 3.98s/it] 68%|██████▊ | 12697/18688 [14:53:33<7:07:10, 4.28s/it] 68%|██████▊ | 12698/18688 [14:53:37<6:51:07, 4.12s/it] 68%|██████▊ | 12699/18688 [14:53:41<6:42:21, 4.03s/it] 68%|██████▊ | 12700/18688 [14:53:45<6:39:37, 4.00s/it] {'loss': '0.9329', 'grad_norm': '1.198', 'learning_rate': '2.167e-05', 'epoch': '0.6796', 'num_input_tokens_seen': 42681544, 'train_runtime': '5.363e+04', 'train_tokens_per_second': '795.9'}
68%|██████▊ | 12700/18688 [14:53:45<6:39:37, 4.00s/it] 68%|██████▊ | 12701/18688 [14:53:49<6:34:59, 3.96s/it] 68%|██████▊ | 12702/18688 [14:53:53<6:55:14, 4.16s/it] 68%|██████▊ | 12703/18688 [14:53:57<6:44:09, 4.05s/it] 68%|██████▊ | 12704/18688 [14:54:01<6:30:26, 3.91s/it] 68%|██████▊ | 12705/18688 [14:54:05<6:56:38, 4.18s/it] 68%|██████▊ | 12706/18688 [14:54:09<6:46:52, 4.08s/it] 68%|██████▊ | 12707/18688 [14:54:13<6:45:28, 4.07s/it] 68%|██████▊ | 12708/18688 [14:54:17<6:28:19, 3.90s/it] 68%|██████▊ | 12709/18688 [14:54:21<6:29:50, 3.91s/it] 68%|██████▊ | 12710/18688 [14:54:26<6:59:36, 4.21s/it] {'loss': '0.8551', 'grad_norm': '1.108', 'learning_rate': '2.16e-05', 'epoch': '0.6801', 'num_input_tokens_seen': 42713200, 'train_runtime': '5.367e+04', 'train_tokens_per_second': '795.9'}
68%|██████▊ | 12710/18688 [14:54:26<6:59:36, 4.21s/it] 68%|██████▊ | 12711/18688 [14:54:29<6:39:47, 4.01s/it] 68%|██████▊ | 12712/18688 [14:54:33<6:47:54, 4.10s/it] 68%|██████▊ | 12713/18688 [14:54:37<6:46:55, 4.09s/it] 68%|██████▊ | 12714/18688 [14:54:41<6:28:49, 3.91s/it] 68%|██████▊ | 12715/18688 [14:54:45<6:25:22, 3.87s/it] 68%|██████▊ | 12716/18688 [14:54:49<6:43:44, 4.06s/it] 68%|██████▊ | 12717/18688 [14:54:54<7:14:14, 4.36s/it] 68%|██████▊ | 12718/18688 [14:54:58<6:47:35, 4.10s/it] 68%|██████▊ | 12719/18688 [14:55:02<6:59:08, 4.21s/it] 68%|██████▊ | 12720/18688 [14:55:07<7:01:57, 4.24s/it] {'loss': '0.9037', 'grad_norm': '1.384', 'learning_rate': '2.154e-05', 'epoch': '0.6807', 'num_input_tokens_seen': 42746688, 'train_runtime': '5.371e+04', 'train_tokens_per_second': '795.9'}
68%|██████▊ | 12720/18688 [14:55:07<7:01:57, 4.24s/it] 68%|██████▊ | 12721/18688 [14:55:11<7:11:14, 4.34s/it] 68%|██████▊ | 12722/18688 [14:55:16<7:24:09, 4.47s/it] 68%|██████▊ | 12723/18688 [14:55:20<7:00:53, 4.23s/it] 68%|██████▊ | 12724/18688 [14:55:23<6:47:18, 4.10s/it] 68%|██████▊ | 12725/18688 [14:55:27<6:44:51, 4.07s/it] 68%|██████▊ | 12726/18688 [14:55:32<7:00:36, 4.23s/it] 68%|██████▊ | 12727/18688 [14:55:36<6:42:09, 4.05s/it] 68%|██████▊ | 12728/18688 [14:55:39<6:34:06, 3.97s/it] 68%|██████▊ | 12729/18688 [14:55:43<6:28:35, 3.91s/it] 68%|██████▊ | 12730/18688 [14:55:48<6:43:04, 4.06s/it] {'loss': '0.8909', 'grad_norm': '1.39', 'learning_rate': '2.147e-05', 'epoch': '0.6812', 'num_input_tokens_seen': 42779592, 'train_runtime': '5.375e+04', 'train_tokens_per_second': '795.9'}
68%|██████▊ | 12730/18688 [14:55:48<6:43:04, 4.06s/it] 68%|██████▊ | 12731/18688 [14:55:52<6:41:08, 4.04s/it] 68%|██████▊ | 12732/18688 [14:55:55<6:31:47, 3.95s/it] 68%|██████▊ | 12733/18688 [14:56:00<6:42:43, 4.06s/it] 68%|██████▊ | 12734/18688 [14:56:04<6:37:44, 4.01s/it] 68%|██████▊ | 12735/18688 [14:56:07<6:22:05, 3.85s/it] 68%|██████▊ | 12736/18688 [14:56:11<6:15:22, 3.78s/it] 68%|██████▊ | 12737/18688 [14:56:15<6:29:37, 3.93s/it] 68%|██████▊ | 12738/18688 [14:56:19<6:41:00, 4.04s/it] 68%|██████▊ | 12739/18688 [14:56:24<7:14:29, 4.38s/it] 68%|██████▊ | 12740/18688 [14:56:28<7:06:01, 4.30s/it] {'loss': '0.9073', 'grad_norm': '1.017', 'learning_rate': '2.141e-05', 'epoch': '0.6817', 'num_input_tokens_seen': 42812264, 'train_runtime': '5.379e+04', 'train_tokens_per_second': '795.9'}
68%|██████▊ | 12740/18688 [14:56:28<7:06:01, 4.30s/it] 68%|██████▊ | 12741/18688 [14:56:33<6:58:26, 4.22s/it] 68%|██████▊ | 12742/18688 [14:56:36<6:40:03, 4.04s/it] 68%|██████▊ | 12743/18688 [14:56:40<6:35:03, 3.99s/it] 68%|██████▊ | 12744/18688 [14:56:44<6:26:11, 3.90s/it] 68%|██████▊ | 12745/18688 [14:56:48<6:44:47, 4.09s/it] 68%|██████▊ | 12746/18688 [14:56:53<6:52:19, 4.16s/it] 68%|██████▊ | 12747/18688 [14:56:56<6:33:02, 3.97s/it] 68%|██████▊ | 12748/18688 [14:57:00<6:29:46, 3.94s/it] 68%|██████▊ | 12749/18688 [14:57:04<6:35:29, 4.00s/it] 68%|██████▊ | 12750/18688 [14:57:08<6:29:43, 3.94s/it] {'loss': '0.8869', 'grad_norm': '1.173', 'learning_rate': '2.134e-05', 'epoch': '0.6823', 'num_input_tokens_seen': 42843488, 'train_runtime': '5.383e+04', 'train_tokens_per_second': '795.9'}
68%|██████▊ | 12750/18688 [14:57:08<6:29:43, 3.94s/it] 68%|██████▊ | 12751/18688 [14:57:12<6:35:55, 4.00s/it] 68%|██████▊ | 12752/18688 [14:57:16<6:41:51, 4.06s/it] 68%|██████▊ | 12753/18688 [14:57:21<6:48:35, 4.13s/it] 68%|██████▊ | 12754/18688 [14:57:24<6:32:37, 3.97s/it] 68%|██████▊ | 12755/18688 [14:57:29<6:45:43, 4.10s/it] 68%|██████▊ | 12756/18688 [14:57:33<7:07:53, 4.33s/it] 68%|██████▊ | 12757/18688 [14:57:37<6:49:37, 4.14s/it] 68%|██████▊ | 12758/18688 [14:57:41<6:46:34, 4.11s/it] 68%|██████▊ | 12759/18688 [14:57:45<6:34:14, 3.99s/it] 68%|██████▊ | 12760/18688 [14:57:49<6:25:10, 3.90s/it] {'loss': '0.9203', 'grad_norm': '1.215', 'learning_rate': '2.128e-05', 'epoch': '0.6828', 'num_input_tokens_seen': 42876112, 'train_runtime': '5.387e+04', 'train_tokens_per_second': '795.9'}
68%|██████▊ | 12760/18688 [14:57:49<6:25:10, 3.90s/it] 68%|██████▊ | 12761/18688 [14:57:52<6:20:52, 3.86s/it] 68%|██████▊ | 12762/18688 [14:57:58<7:01:05, 4.26s/it] 68%|██████▊ | 12763/18688 [14:58:02<6:54:28, 4.20s/it] 68%|██████▊ | 12764/18688 [14:58:06<7:02:40, 4.28s/it] 68%|██████▊ | 12765/18688 [14:58:10<6:51:33, 4.17s/it] 68%|██████▊ | 12766/18688 [14:58:15<7:07:22, 4.33s/it] 68%|██████▊ | 12767/18688 [14:58:19<7:10:44, 4.36s/it] 68%|██████▊ | 12768/18688 [14:58:23<6:45:02, 4.11s/it] 68%|██████▊ | 12769/18688 [14:58:27<6:45:35, 4.11s/it] 68%|██████▊ | 12770/18688 [14:58:32<7:07:57, 4.34s/it] {'loss': '0.9608', 'grad_norm': '1.141', 'learning_rate': '2.121e-05', 'epoch': '0.6833', 'num_input_tokens_seen': 42912456, 'train_runtime': '5.391e+04', 'train_tokens_per_second': '796'}
68%|██████▊ | 12770/18688 [14:58:32<7:07:57, 4.34s/it] 68%|██████▊ | 12771/18688 [14:58:35<6:44:37, 4.10s/it] 68%|██████▊ | 12772/18688 [14:58:39<6:39:00, 4.05s/it] 68%|██████▊ | 12773/18688 [14:58:43<6:41:48, 4.08s/it] 68%|██████▊ | 12774/18688 [14:58:48<7:01:02, 4.27s/it] 68%|██████▊ | 12775/18688 [14:58:52<6:58:13, 4.24s/it] 68%|██████▊ | 12776/18688 [14:58:57<7:19:11, 4.46s/it] 68%|██████▊ | 12777/18688 [14:59:01<7:16:03, 4.43s/it] 68%|██████▊ | 12778/18688 [14:59:05<6:49:50, 4.16s/it] 68%|██████▊ | 12779/18688 [14:59:10<7:06:32, 4.33s/it] 68%|██████▊ | 12780/18688 [14:59:13<6:45:53, 4.12s/it] {'loss': '0.9317', 'grad_norm': '1.137', 'learning_rate': '2.115e-05', 'epoch': '0.6839', 'num_input_tokens_seen': 42946800, 'train_runtime': '5.395e+04', 'train_tokens_per_second': '796'}
68%|██████▊ | 12780/18688 [14:59:13<6:45:53, 4.12s/it] 68%|██████▊ | 12781/18688 [14:59:17<6:40:08, 4.06s/it] 68%|██████▊ | 12782/18688 [14:59:21<6:42:15, 4.09s/it] 68%|██████▊ | 12783/18688 [14:59:25<6:31:53, 3.98s/it] 68%|██████▊ | 12784/18688 [14:59:30<6:52:00, 4.19s/it] 68%|██████▊ | 12785/18688 [14:59:34<6:45:56, 4.13s/it] 68%|██████▊ | 12786/18688 [14:59:38<6:45:15, 4.12s/it] 68%|██████▊ | 12787/18688 [14:59:42<6:37:57, 4.05s/it] 68%|██████▊ | 12788/18688 [14:59:46<6:32:53, 4.00s/it] 68%|██████▊ | 12789/18688 [14:59:49<6:19:08, 3.86s/it] 68%|██████▊ | 12790/18688 [14:59:53<6:18:18, 3.85s/it] {'loss': '0.9529', 'grad_norm': '1.471', 'learning_rate': '2.108e-05', 'epoch': '0.6844', 'num_input_tokens_seen': 42977776, 'train_runtime': '5.399e+04', 'train_tokens_per_second': '796'}
68%|██████▊ | 12790/18688 [14:59:53<6:18:18, 3.85s/it] 68%|██████▊ | 12791/18688 [14:59:57<6:18:09, 3.85s/it] 68%|██████▊ | 12792/18688 [15:00:00<6:09:46, 3.76s/it] 68%|██████▊ | 12793/18688 [15:00:04<6:08:53, 3.75s/it] 68%|██████▊ | 12794/18688 [15:00:09<6:28:39, 3.96s/it] 68%|██████▊ | 12795/18688 [15:00:13<6:38:27, 4.06s/it] 68%|██████▊ | 12796/18688 [15:00:17<6:46:38, 4.14s/it] 68%|██████▊ | 12797/18688 [15:00:21<6:48:03, 4.16s/it] 68%|██████▊ | 12798/18688 [15:00:27<7:18:10, 4.46s/it] 68%|██████▊ | 12799/18688 [15:00:31<7:13:35, 4.42s/it] 68%|██████▊ | 12800/18688 [15:00:34<6:50:01, 4.18s/it] {'loss': '0.9028', 'grad_norm': '1.083', 'learning_rate': '2.102e-05', 'epoch': '0.6849', 'num_input_tokens_seen': 43011528, 'train_runtime': '5.403e+04', 'train_tokens_per_second': '796'}
68%|██████▊ | 12800/18688 [15:00:34<6:50:01, 4.18s/it] 68%|██████▊ | 12801/18688 [15:00:38<6:33:01, 4.01s/it] 69%|██████▊ | 12802/18688 [15:00:42<6:23:04, 3.90s/it] 69%|██████▊ | 12803/18688 [15:00:46<6:18:52, 3.86s/it] 69%|██████▊ | 12804/18688 [15:00:49<6:05:49, 3.73s/it] 69%|██████▊ | 12805/18688 [15:00:53<6:17:44, 3.85s/it] 69%|██████▊ | 12806/18688 [15:00:57<6:09:44, 3.77s/it] 69%|██████▊ | 12807/18688 [15:01:02<6:49:47, 4.18s/it] 69%|██████▊ | 12808/18688 [15:01:06<6:45:44, 4.14s/it] 69%|██████▊ | 12809/18688 [15:01:10<6:37:10, 4.05s/it] 69%|██████▊ | 12810/18688 [15:01:14<6:37:03, 4.05s/it] {'loss': '0.9167', 'grad_norm': '1.346', 'learning_rate': '2.095e-05', 'epoch': '0.6855', 'num_input_tokens_seen': 43043032, 'train_runtime': '5.407e+04', 'train_tokens_per_second': '796'}
69%|██████▊ | 12810/18688 [15:01:14<6:37:03, 4.05s/it] 69%|██████▊ | 12811/18688 [15:01:19<7:00:08, 4.29s/it] 69%|██████▊ | 12812/18688 [15:01:22<6:43:03, 4.12s/it] 69%|██████▊ | 12813/18688 [15:01:27<6:53:30, 4.22s/it] 69%|██████▊ | 12814/18688 [15:01:31<6:43:55, 4.13s/it] 69%|██████▊ | 12815/18688 [15:01:35<6:43:47, 4.13s/it] 69%|██████▊ | 12816/18688 [15:01:38<6:29:22, 3.98s/it] 69%|██████▊ | 12817/18688 [15:01:43<6:41:23, 4.10s/it] 69%|██████▊ | 12818/18688 [15:01:47<6:50:25, 4.20s/it] 69%|██████▊ | 12819/18688 [15:01:51<6:39:35, 4.09s/it] 69%|██████▊ | 12820/18688 [15:01:56<7:05:01, 4.35s/it] {'loss': '0.919', 'grad_norm': '1.299', 'learning_rate': '2.089e-05', 'epoch': '0.686', 'num_input_tokens_seen': 43077904, 'train_runtime': '5.412e+04', 'train_tokens_per_second': '796'}
69%|██████▊ | 12820/18688 [15:01:56<7:05:01, 4.35s/it] 69%|██████▊ | 12821/18688 [15:02:01<7:16:08, 4.46s/it] 69%|██████▊ | 12822/18688 [15:02:05<7:05:44, 4.35s/it] 69%|██████▊ | 12823/18688 [15:02:09<7:02:50, 4.33s/it] 69%|██████▊ | 12824/18688 [15:02:13<6:53:30, 4.23s/it] 69%|██████▊ | 12825/18688 [15:02:17<6:38:27, 4.08s/it] 69%|██████▊ | 12826/18688 [15:02:21<6:45:52, 4.15s/it] 69%|██████▊ | 12827/18688 [15:02:25<6:37:13, 4.07s/it] 69%|██████▊ | 12828/18688 [15:02:29<6:35:32, 4.05s/it] 69%|██████▊ | 12829/18688 [15:02:33<6:25:52, 3.95s/it] 69%|██████▊ | 12830/18688 [15:02:37<6:28:35, 3.98s/it] {'loss': '0.8613', 'grad_norm': '1.192', 'learning_rate': '2.083e-05', 'epoch': '0.6866', 'num_input_tokens_seen': 43110640, 'train_runtime': '5.416e+04', 'train_tokens_per_second': '796'}
69%|██████▊ | 12830/18688 [15:02:37<6:28:35, 3.98s/it] 69%|██████▊ | 12831/18688 [15:02:41<6:46:46, 4.17s/it] 69%|██████▊ | 12832/18688 [15:02:45<6:42:17, 4.12s/it] 69%|██████▊ | 12833/18688 [15:02:50<6:43:01, 4.13s/it] 69%|██████▊ | 12834/18688 [15:02:53<6:24:10, 3.94s/it] 69%|██████▊ | 12835/18688 [15:02:57<6:22:45, 3.92s/it] 69%|██████▊ | 12836/18688 [15:03:01<6:30:30, 4.00s/it] 69%|██████▊ | 12837/18688 [15:03:05<6:30:37, 4.01s/it] 69%|██████▊ | 12838/18688 [15:03:09<6:31:34, 4.02s/it] 69%|██████▊ | 12839/18688 [15:03:13<6:15:48, 3.86s/it] 69%|██████▊ | 12840/18688 [15:03:17<6:14:36, 3.84s/it] {'loss': '0.8894', 'grad_norm': '1.185', 'learning_rate': '2.076e-05', 'epoch': '0.6871', 'num_input_tokens_seen': 43141536, 'train_runtime': '5.42e+04', 'train_tokens_per_second': '796'}
69%|██████▊ | 12840/18688 [15:03:17<6:14:36, 3.84s/it] 69%|██████▊ | 12841/18688 [15:03:20<6:10:20, 3.80s/it] 69%|██████▊ | 12842/18688 [15:03:25<6:41:25, 4.12s/it] 69%|██████▊ | 12843/18688 [15:03:29<6:25:12, 3.95s/it] 69%|██████▊ | 12844/18688 [15:03:33<6:39:45, 4.10s/it] 69%|██████▊ | 12845/18688 [15:03:38<6:50:18, 4.21s/it] 69%|██████▊ | 12846/18688 [15:03:41<6:33:43, 4.04s/it] 69%|██████▊ | 12847/18688 [15:03:46<6:51:58, 4.23s/it] 69%|██████▉ | 12848/18688 [15:03:50<6:44:03, 4.15s/it] 69%|██████▉ | 12849/18688 [15:03:54<6:42:20, 4.13s/it] 69%|██████▉ | 12850/18688 [15:03:59<7:00:40, 4.32s/it] {'loss': '0.8942', 'grad_norm': '1.119', 'learning_rate': '2.07e-05', 'epoch': '0.6876', 'num_input_tokens_seen': 43176720, 'train_runtime': '5.424e+04', 'train_tokens_per_second': '796'}
69%|██████▉ | 12850/18688 [15:03:59<7:00:40, 4.32s/it] 69%|██████▉ | 12851/18688 [15:04:02<6:41:24, 4.13s/it] 69%|██████▉ | 12852/18688 [15:04:07<6:59:54, 4.32s/it] 69%|██████▉ | 12853/18688 [15:04:11<6:53:35, 4.25s/it] 69%|██████▉ | 12854/18688 [15:04:15<6:41:01, 4.12s/it] 69%|██████▉ | 12855/18688 [15:04:19<6:29:32, 4.01s/it] 69%|██████▉ | 12856/18688 [15:04:23<6:22:22, 3.93s/it] 69%|██████▉ | 12857/18688 [15:04:27<6:29:44, 4.01s/it] 69%|██████▉ | 12858/18688 [15:04:31<6:28:51, 4.00s/it] 69%|██████▉ | 12859/18688 [15:04:35<6:31:49, 4.03s/it] 69%|██████▉ | 12860/18688 [15:04:38<6:17:52, 3.89s/it] {'loss': '0.9463', 'grad_norm': '1.263', 'learning_rate': '2.063e-05', 'epoch': '0.6882', 'num_input_tokens_seen': 43208576, 'train_runtime': '5.428e+04', 'train_tokens_per_second': '796'}
69%|██████▉ | 12860/18688 [15:04:38<6:17:52, 3.89s/it] 69%|██████▉ | 12861/18688 [15:04:42<6:14:38, 3.86s/it] 69%|██████▉ | 12862/18688 [15:04:46<6:23:20, 3.95s/it] 69%|██████▉ | 12863/18688 [15:04:50<6:14:15, 3.85s/it] 69%|██████▉ | 12864/18688 [15:04:54<6:11:06, 3.82s/it] 69%|██████▉ | 12865/18688 [15:04:57<6:04:01, 3.75s/it] 69%|██████▉ | 12866/18688 [15:05:01<6:15:59, 3.87s/it] 69%|██████▉ | 12867/18688 [15:05:06<6:23:34, 3.95s/it] 69%|██████▉ | 12868/18688 [15:05:09<6:20:07, 3.92s/it] 69%|██████▉ | 12869/18688 [15:05:14<6:26:21, 3.98s/it] 69%|██████▉ | 12870/18688 [15:05:18<6:33:43, 4.06s/it] {'loss': '0.8913', 'grad_norm': '1.171', 'learning_rate': '2.057e-05', 'epoch': '0.6887', 'num_input_tokens_seen': 43239616, 'train_runtime': '5.432e+04', 'train_tokens_per_second': '796'}
69%|██████▉ | 12870/18688 [15:05:18<6:33:43, 4.06s/it] 69%|██████▉ | 12871/18688 [15:05:22<6:23:44, 3.96s/it] 69%|██████▉ | 12872/18688 [15:05:26<6:32:56, 4.05s/it] 69%|██████▉ | 12873/18688 [15:05:30<6:43:53, 4.17s/it] 69%|██████▉ | 12874/18688 [15:05:35<6:52:36, 4.26s/it] 69%|██████▉ | 12875/18688 [15:05:39<6:42:55, 4.16s/it] 69%|██████▉ | 12876/18688 [15:05:42<6:29:58, 4.03s/it] 69%|██████▉ | 12877/18688 [15:05:47<6:45:36, 4.19s/it] 69%|██████▉ | 12878/18688 [15:05:51<6:48:21, 4.22s/it] 69%|██████▉ | 12879/18688 [15:05:55<6:43:35, 4.17s/it] 69%|██████▉ | 12880/18688 [15:06:00<6:55:32, 4.29s/it] {'loss': '0.929', 'grad_norm': '1.143', 'learning_rate': '2.051e-05', 'epoch': '0.6892', 'num_input_tokens_seen': 43273640, 'train_runtime': '5.436e+04', 'train_tokens_per_second': '796.1'}
69%|██████▉ | 12880/18688 [15:06:00<6:55:32, 4.29s/it] 69%|██████▉ | 12881/18688 [15:06:03<6:35:12, 4.08s/it] 69%|██████▉ | 12882/18688 [15:06:07<6:24:36, 3.97s/it] 69%|██████▉ | 12883/18688 [15:06:11<6:33:59, 4.07s/it] 69%|██████▉ | 12884/18688 [15:06:16<6:34:27, 4.08s/it] 69%|██████▉ | 12885/18688 [15:06:19<6:19:09, 3.92s/it] 69%|██████▉ | 12886/18688 [15:06:23<6:21:10, 3.94s/it] 69%|██████▉ | 12887/18688 [15:06:28<6:37:42, 4.11s/it] 69%|██████▉ | 12888/18688 [15:06:32<6:51:53, 4.26s/it] 69%|██████▉ | 12889/18688 [15:06:37<7:03:00, 4.38s/it] 69%|██████▉ | 12890/18688 [15:06:42<7:20:00, 4.55s/it] {'loss': '0.9658', 'grad_norm': '1.246', 'learning_rate': '2.044e-05', 'epoch': '0.6898', 'num_input_tokens_seen': 43307600, 'train_runtime': '5.44e+04', 'train_tokens_per_second': '796.1'}
69%|██████▉ | 12890/18688 [15:06:42<7:20:00, 4.55s/it] 69%|██████▉ | 12891/18688 [15:06:46<6:55:56, 4.31s/it] 69%|██████▉ | 12892/18688 [15:06:49<6:43:54, 4.18s/it] 69%|██████▉ | 12893/18688 [15:06:53<6:22:41, 3.96s/it] 69%|██████▉ | 12894/18688 [15:06:57<6:29:38, 4.03s/it] 69%|██████▉ | 12895/18688 [15:07:01<6:16:45, 3.90s/it] 69%|██████▉ | 12896/18688 [15:07:05<6:18:19, 3.92s/it] 69%|██████▉ | 12897/18688 [15:07:09<6:21:49, 3.96s/it] 69%|██████▉ | 12898/18688 [15:07:14<6:57:46, 4.33s/it] 69%|██████▉ | 12899/18688 [15:07:20<7:54:56, 4.92s/it] 69%|██████▉ | 12900/18688 [15:07:25<7:43:41, 4.81s/it] {'loss': '0.9079', 'grad_norm': '1.399', 'learning_rate': '2.038e-05', 'epoch': '0.6903', 'num_input_tokens_seen': 43342592, 'train_runtime': '5.445e+04', 'train_tokens_per_second': '796.1'}
69%|██████▉ | 12900/18688 [15:07:25<7:43:41, 4.81s/it] 69%|██████▉ | 12901/18688 [15:07:29<7:22:13, 4.59s/it] 69%|██████▉ | 12902/18688 [15:07:33<7:03:15, 4.39s/it] 69%|██████▉ | 12903/18688 [15:07:36<6:36:50, 4.12s/it] 69%|██████▉ | 12904/18688 [15:07:41<6:51:29, 4.27s/it] 69%|██████▉ | 12905/18688 [15:07:44<6:33:41, 4.08s/it] 69%|██████▉ | 12906/18688 [15:07:48<6:18:49, 3.93s/it] 69%|██████▉ | 12907/18688 [15:07:52<6:19:37, 3.94s/it] 69%|██████▉ | 12908/18688 [15:07:56<6:13:20, 3.88s/it] 69%|██████▉ | 12909/18688 [15:07:59<6:03:19, 3.77s/it] 69%|██████▉ | 12910/18688 [15:08:03<6:14:22, 3.89s/it] {'loss': '0.8451', 'grad_norm': '1.187', 'learning_rate': '2.032e-05', 'epoch': '0.6908', 'num_input_tokens_seen': 43373352, 'train_runtime': '5.448e+04', 'train_tokens_per_second': '796.1'}
69%|██████▉ | 12910/18688 [15:08:03<6:14:22, 3.89s/it] 69%|██████▉ | 12911/18688 [15:08:07<6:15:00, 3.89s/it] 69%|██████▉ | 12912/18688 [15:08:12<6:27:48, 4.03s/it] 69%|██████▉ | 12913/18688 [15:08:15<6:20:27, 3.95s/it] 69%|██████▉ | 12914/18688 [15:08:19<6:06:08, 3.80s/it] 69%|██████▉ | 12915/18688 [15:08:23<6:17:17, 3.92s/it] 69%|██████▉ | 12916/18688 [15:08:27<6:07:24, 3.82s/it] 69%|██████▉ | 12917/18688 [15:08:31<6:19:53, 3.95s/it] 69%|██████▉ | 12918/18688 [15:08:35<6:26:21, 4.02s/it] 69%|██████▉ | 12919/18688 [15:08:39<6:10:05, 3.85s/it] 69%|██████▉ | 12920/18688 [15:08:42<6:05:00, 3.80s/it] {'loss': '0.8926', 'grad_norm': '1.255', 'learning_rate': '2.025e-05', 'epoch': '0.6914', 'num_input_tokens_seen': 43402664, 'train_runtime': '5.452e+04', 'train_tokens_per_second': '796'}
69%|██████▉ | 12920/18688 [15:08:42<6:05:00, 3.80s/it] 69%|██████▉ | 12921/18688 [15:08:46<6:02:39, 3.77s/it] 69%|██████▉ | 12922/18688 [15:08:50<5:56:44, 3.71s/it] 69%|██████▉ | 12923/18688 [15:08:54<6:04:00, 3.79s/it] 69%|██████▉ | 12924/18688 [15:08:58<6:13:19, 3.89s/it] 69%|██████▉ | 12925/18688 [15:09:02<6:27:45, 4.04s/it] 69%|██████▉ | 12926/18688 [15:09:06<6:18:29, 3.94s/it] 69%|██████▉ | 12927/18688 [15:09:10<6:32:40, 4.09s/it] 69%|██████▉ | 12928/18688 [15:09:15<6:53:27, 4.31s/it] 69%|██████▉ | 12929/18688 [15:09:19<6:37:11, 4.14s/it] 69%|██████▉ | 12930/18688 [15:09:24<6:58:00, 4.36s/it] {'loss': '0.9045', 'grad_norm': '1.072', 'learning_rate': '2.019e-05', 'epoch': '0.6919', 'num_input_tokens_seen': 43435184, 'train_runtime': '5.456e+04', 'train_tokens_per_second': '796'}
69%|██████▉ | 12930/18688 [15:09:24<6:58:00, 4.36s/it] 69%|██████▉ | 12931/18688 [15:09:28<6:55:41, 4.33s/it] 69%|██████▉ | 12932/18688 [15:09:32<6:41:52, 4.19s/it] 69%|██████▉ | 12933/18688 [15:09:36<6:52:26, 4.30s/it] 69%|██████▉ | 12934/18688 [15:09:41<6:58:44, 4.37s/it] 69%|██████▉ | 12935/18688 [15:09:46<7:09:34, 4.48s/it] 69%|██████▉ | 12936/18688 [15:09:49<6:46:47, 4.24s/it] 69%|██████▉ | 12937/18688 [15:09:53<6:33:26, 4.10s/it] 69%|██████▉ | 12938/18688 [15:09:57<6:39:41, 4.17s/it] 69%|██████▉ | 12939/18688 [15:10:01<6:31:36, 4.09s/it] 69%|██████▉ | 12940/18688 [15:10:05<6:34:07, 4.11s/it] {'loss': '0.9227', 'grad_norm': '1.044', 'learning_rate': '2.012e-05', 'epoch': '0.6924', 'num_input_tokens_seen': 43470608, 'train_runtime': '5.461e+04', 'train_tokens_per_second': '796.1'}
69%|██████▉ | 12940/18688 [15:10:05<6:34:07, 4.11s/it] 69%|██████▉ | 12941/18688 [15:10:09<6:31:09, 4.08s/it] 69%|██████▉ | 12942/18688 [15:10:13<6:28:51, 4.06s/it] 69%|██████▉ | 12943/18688 [15:10:18<6:34:34, 4.12s/it] 69%|██████▉ | 12944/18688 [15:10:22<6:39:19, 4.17s/it] 69%|██████▉ | 12945/18688 [15:10:26<6:42:29, 4.21s/it] 69%|██████▉ | 12946/18688 [15:10:30<6:24:09, 4.01s/it] 69%|██████▉ | 12947/18688 [15:10:34<6:19:44, 3.97s/it] 69%|██████▉ | 12948/18688 [15:10:38<6:26:55, 4.04s/it] 69%|██████▉ | 12949/18688 [15:10:42<6:20:22, 3.98s/it] 69%|██████▉ | 12950/18688 [15:10:46<6:20:27, 3.98s/it] {'loss': '0.9067', 'grad_norm': '1.142', 'learning_rate': '2.006e-05', 'epoch': '0.693', 'num_input_tokens_seen': 43503288, 'train_runtime': '5.465e+04', 'train_tokens_per_second': '796.1'}
69%|██████▉ | 12950/18688 [15:10:46<6:20:27, 3.98s/it] 69%|██████▉ | 12951/18688 [15:10:51<7:07:58, 4.48s/it] 69%|██████▉ | 12952/18688 [15:10:55<6:47:29, 4.26s/it] 69%|██████▉ | 12953/18688 [15:10:59<6:39:42, 4.18s/it] 69%|██████▉ | 12954/18688 [15:11:04<7:01:06, 4.41s/it] 69%|██████▉ | 12955/18688 [15:11:08<6:48:50, 4.28s/it] 69%|██████▉ | 12956/18688 [15:11:13<7:05:39, 4.46s/it] 69%|██████▉ | 12957/18688 [15:11:17<6:57:31, 4.37s/it] 69%|██████▉ | 12958/18688 [15:11:21<6:53:01, 4.32s/it] 69%|██████▉ | 12959/18688 [15:11:25<6:35:01, 4.14s/it] 69%|██████▉ | 12960/18688 [15:11:30<6:47:45, 4.27s/it] {'loss': '0.9348', 'grad_norm': '1.032', 'learning_rate': '2e-05', 'epoch': '0.6935', 'num_input_tokens_seen': 43539128, 'train_runtime': '5.469e+04', 'train_tokens_per_second': '796.1'}
69%|██████▉ | 12960/18688 [15:11:30<6:47:45, 4.27s/it] 69%|██████▉ | 12961/18688 [15:11:33<6:31:27, 4.10s/it] 69%|██████▉ | 12962/18688 [15:11:38<6:48:47, 4.28s/it] 69%|██████▉ | 12963/18688 [15:11:42<6:43:16, 4.23s/it] 69%|██████▉ | 12964/18688 [15:11:46<6:30:56, 4.10s/it] 69%|██████▉ | 12965/18688 [15:11:50<6:41:09, 4.21s/it] 69%|██████▉ | 12966/18688 [15:11:54<6:38:36, 4.18s/it] 69%|██████▉ | 12967/18688 [15:12:00<7:14:43, 4.56s/it] 69%|██████▉ | 12968/18688 [15:12:04<6:54:11, 4.34s/it] 69%|██████▉ | 12969/18688 [15:12:08<6:59:18, 4.40s/it] 69%|██████▉ | 12970/18688 [15:12:12<6:37:27, 4.17s/it] {'loss': '0.8501', 'grad_norm': '1.115', 'learning_rate': '1.993e-05', 'epoch': '0.694', 'num_input_tokens_seen': 43573992, 'train_runtime': '5.473e+04', 'train_tokens_per_second': '796.1'}
69%|██████▉ | 12970/18688 [15:12:12<6:37:27, 4.17s/it] 69%|██████▉ | 12971/18688 [15:12:16<6:21:46, 4.01s/it] 69%|██████▉ | 12972/18688 [15:12:20<6:31:40, 4.11s/it] 69%|██████▉ | 12973/18688 [15:12:23<6:14:27, 3.93s/it] 69%|██████▉ | 12974/18688 [15:12:28<6:45:57, 4.26s/it] 69%|██████▉ | 12975/18688 [15:12:32<6:38:06, 4.18s/it] 69%|██████▉ | 12976/18688 [15:12:37<6:39:26, 4.20s/it] 69%|██████▉ | 12977/18688 [15:12:41<6:46:09, 4.27s/it] 69%|██████▉ | 12978/18688 [15:12:45<6:33:42, 4.14s/it] 69%|██████▉ | 12979/18688 [15:12:49<6:23:29, 4.03s/it] 69%|██████▉ | 12980/18688 [15:12:53<6:37:22, 4.18s/it] {'loss': '0.8404', 'grad_norm': '1.359', 'learning_rate': '1.987e-05', 'epoch': '0.6946', 'num_input_tokens_seen': 43608880, 'train_runtime': '5.477e+04', 'train_tokens_per_second': '796.2'}
69%|██████▉ | 12980/18688 [15:12:53<6:37:22, 4.18s/it] 69%|██████▉ | 12981/18688 [15:12:57<6:28:29, 4.08s/it] 69%|██████▉ | 12982/18688 [15:13:01<6:31:00, 4.11s/it] 69%|██████▉ | 12983/18688 [15:13:05<6:16:16, 3.96s/it] 69%|██████▉ | 12984/18688 [15:13:09<6:13:03, 3.92s/it] 69%|██████▉ | 12985/18688 [15:13:14<6:40:58, 4.22s/it] 69%|██████▉ | 12986/18688 [15:13:17<6:23:41, 4.04s/it] 69%|██████▉ | 12987/18688 [15:13:21<6:09:37, 3.89s/it] 69%|██████▉ | 12988/18688 [15:13:25<6:05:17, 3.85s/it] 70%|██████▉ | 12989/18688 [15:13:28<5:56:35, 3.75s/it] 70%|██████▉ | 12990/18688 [15:13:32<6:11:52, 3.92s/it] {'loss': '0.8816', 'grad_norm': '1.022', 'learning_rate': '1.981e-05', 'epoch': '0.6951', 'num_input_tokens_seen': 43640872, 'train_runtime': '5.481e+04', 'train_tokens_per_second': '796.2'}
70%|██████▉ | 12990/18688 [15:13:32<6:11:52, 3.92s/it] 70%|██████▉ | 12991/18688 [15:13:36<6:17:30, 3.98s/it] 70%|██████▉ | 12992/18688 [15:13:40<6:15:03, 3.95s/it] 70%|██████▉ | 12993/18688 [15:13:45<6:34:13, 4.15s/it] 70%|██████▉ | 12994/18688 [15:13:49<6:27:06, 4.08s/it] 70%|██████▉ | 12995/18688 [15:13:53<6:18:02, 3.98s/it] 70%|██████▉ | 12996/18688 [15:13:57<6:26:19, 4.07s/it] 70%|██████▉ | 12997/18688 [15:14:01<6:21:19, 4.02s/it] 70%|██████▉ | 12998/18688 [15:14:05<6:25:10, 4.06s/it] 70%|██████▉ | 12999/18688 [15:14:09<6:15:08, 3.96s/it] 70%|██████▉ | 13000/18688 [15:14:12<6:05:39, 3.86s/it] {'loss': '0.9334', 'grad_norm': '1.255', 'learning_rate': '1.975e-05', 'epoch': '0.6957', 'num_input_tokens_seen': 43672984, 'train_runtime': '5.485e+04', 'train_tokens_per_second': '796.2'}
70%|██████▉ | 13000/18688 [15:14:12<6:05:39, 3.86s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:71: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:281: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 0/1000 [00:00<?, ?it/s]/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:172: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:202: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:254: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 3/1000 [00:00<00:38, 25.90it/s]
1%| | 6/1000 [00:00<00:54, 18.34it/s]
1%| | 8/1000 [00:00<00:56, 17.61it/s]
1%| | 10/1000 [00:00<00:59, 16.63it/s]
1%| | 12/1000 [00:00<01:00, 16.35it/s]
1%|▏ | 14/1000 [00:00<01:02, 15.89it/s]
2%|▏ | 16/1000 [00:00<01:02, 15.68it/s]
2%|▏ | 18/1000 [00:01<01:38, 9.98it/s]
2%|▏ | 20/1000 [00:01<01:33, 10.53it/s]
2%|▏ | 22/1000 [00:01<01:52, 8.70it/s]
2%|▏ | 24/1000 [00:01<01:39, 9.77it/s]
3%|▎ | 26/1000 [00:02<01:45, 9.28it/s]
3%|▎ | 28/1000 [00:02<01:36, 10.10it/s]
3%|▎ | 30/1000 [00:02<01:28, 11.00it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.13it/s]
3%|▎ | 34/1000 [00:02<01:26, 11.22it/s]
4%|▎ | 36/1000 [00:03<01:32, 10.41it/s]
4%|▍ | 38/1000 [00:03<01:24, 11.32it/s]
4%|▍ | 40/1000 [00:03<01:27, 10.94it/s]
4%|▍ | 42/1000 [00:03<01:46, 8.99it/s]
4%|▍ | 44/1000 [00:03<01:31, 10.47it/s]
5%|▍ | 46/1000 [00:03<01:21, 11.66it/s]
5%|▍ | 48/1000 [00:04<01:29, 10.62it/s]
5%|▌ | 50/1000 [00:04<01:21, 11.66it/s]
5%|▌ | 52/1000 [00:04<01:15, 12.50it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.25it/s]
6%|▌ | 56/1000 [00:04<01:07, 13.91it/s]
6%|▌ | 58/1000 [00:05<01:47, 8.72it/s]
6%|▌ | 60/1000 [00:05<01:36, 9.75it/s]
6%|▌ | 62/1000 [00:05<01:27, 10.72it/s]
6%|▋ | 64/1000 [00:05<01:19, 11.79it/s]
7%|▋ | 66/1000 [00:05<01:15, 12.30it/s]
7%|▋ | 68/1000 [00:05<01:12, 12.88it/s]
7%|▋ | 70/1000 [00:06<01:09, 13.46it/s]
7%|▋ | 72/1000 [00:06<01:06, 13.92it/s]
7%|▋ | 74/1000 [00:06<01:07, 13.68it/s]
8%|▊ | 76/1000 [00:06<01:04, 14.28it/s]
8%|▊ | 78/1000 [00:06<01:03, 14.51it/s]
8%|▊ | 80/1000 [00:06<01:08, 13.47it/s]
8%|▊ | 82/1000 [00:06<01:05, 14.06it/s]
8%|▊ | 84/1000 [00:06<01:03, 14.52it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.66it/s]
9%|▉ | 88/1000 [00:07<01:03, 14.47it/s]
9%|▉ | 90/1000 [00:07<01:02, 14.50it/s]
9%|▉ | 92/1000 [00:07<01:04, 14.16it/s]
9%|▉ | 94/1000 [00:07<01:04, 13.99it/s]
10%|▉ | 96/1000 [00:07<01:03, 14.28it/s]
10%|▉ | 98/1000 [00:07<01:03, 14.18it/s]
10%|█ | 100/1000 [00:08<01:32, 9.78it/s]
10%|█ | 102/1000 [00:08<01:23, 10.74it/s]
10%|█ | 104/1000 [00:08<01:55, 7.79it/s]
11%|█ | 106/1000 [00:09<02:02, 7.30it/s]
11%|█ | 108/1000 [00:09<01:47, 8.31it/s]
11%|█ | 110/1000 [00:09<01:36, 9.23it/s]
11%|█ | 112/1000 [00:09<01:29, 9.95it/s]
11%|█▏ | 114/1000 [00:09<01:24, 10.52it/s]
12%|█▏ | 116/1000 [00:10<01:20, 10.96it/s]
12%|█▏ | 118/1000 [00:10<01:18, 11.28it/s]
12%|█▏ | 120/1000 [00:10<01:19, 11.12it/s]
12%|█▏ | 122/1000 [00:10<01:31, 9.63it/s]
12%|█▏ | 124/1000 [00:10<01:38, 8.89it/s]
13%|█▎ | 126/1000 [00:11<01:30, 9.63it/s]
13%|█▎ | 128/1000 [00:11<01:24, 10.29it/s]
13%|█▎ | 130/1000 [00:11<01:18, 11.07it/s]
13%|█▎ | 132/1000 [00:11<01:11, 12.22it/s]
13%|█▎ | 134/1000 [00:11<01:13, 11.76it/s]
14%|█▎ | 136/1000 [00:11<01:07, 12.84it/s]
14%|█▍ | 138/1000 [00:11<01:06, 12.91it/s]
14%|█▍ | 140/1000 [00:12<01:26, 9.96it/s]
14%|█▍ | 142/1000 [00:12<01:41, 8.47it/s]
14%|█▍ | 144/1000 [00:12<01:30, 9.51it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.55it/s]
15%|█▍ | 148/1000 [00:13<01:14, 11.46it/s]
15%|█▌ | 150/1000 [00:13<01:07, 12.56it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.10it/s]
15%|█▌ | 154/1000 [00:13<01:03, 13.35it/s]
16%|█▌ | 156/1000 [00:13<01:04, 13.17it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.69it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.62it/s]
16%|█▌ | 162/1000 [00:14<01:02, 13.50it/s]
16%|█▋ | 164/1000 [00:14<01:01, 13.60it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.75it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.28it/s]
17%|█▋ | 170/1000 [00:14<01:19, 10.50it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.46it/s]
17%|█▋ | 174/1000 [00:15<01:30, 9.17it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.18it/s]
18%|█▊ | 178/1000 [00:15<01:34, 8.66it/s]
18%|█▊ | 180/1000 [00:15<01:33, 8.79it/s]
18%|█▊ | 182/1000 [00:16<01:35, 8.58it/s]
18%|█▊ | 184/1000 [00:16<01:23, 9.82it/s]
19%|█▊ | 186/1000 [00:16<01:37, 8.34it/s]
19%|█▉ | 188/1000 [00:16<01:24, 9.66it/s]
19%|█▉ | 190/1000 [00:16<01:15, 10.69it/s]
19%|█▉ | 192/1000 [00:17<01:11, 11.33it/s]
19%|█▉ | 194/1000 [00:17<01:16, 10.55it/s]
20%|█▉ | 196/1000 [00:17<01:29, 8.96it/s]
20%|█▉ | 198/1000 [00:17<01:18, 10.19it/s]
20%|██ | 200/1000 [00:17<01:15, 10.56it/s]
20%|██ | 202/1000 [00:18<01:09, 11.48it/s]
20%|██ | 204/1000 [00:18<01:03, 12.59it/s]
21%|██ | 206/1000 [00:18<01:00, 13.20it/s]
21%|██ | 208/1000 [00:18<00:58, 13.60it/s]
21%|██ | 210/1000 [00:18<00:54, 14.44it/s]
21%|██ | 212/1000 [00:18<00:53, 14.62it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.61it/s]
22%|██▏ | 216/1000 [00:18<00:54, 14.38it/s]
22%|██▏ | 218/1000 [00:19<00:52, 14.80it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.87it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.15it/s]
22%|██▏ | 224/1000 [00:19<01:20, 9.64it/s]
23%|██▎ | 226/1000 [00:20<01:12, 10.63it/s]
23%|██▎ | 228/1000 [00:20<01:06, 11.55it/s]
23%|██▎ | 230/1000 [00:20<01:03, 12.05it/s]
23%|██▎ | 232/1000 [00:20<01:01, 12.42it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.17it/s]
24%|██▎ | 236/1000 [00:20<00:55, 13.66it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.27it/s]
24%|██▍ | 240/1000 [00:21<00:53, 14.26it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.81it/s]
24%|██▍ | 244/1000 [00:21<00:50, 15.03it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.99it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.67it/s]
25%|██▌ | 250/1000 [00:21<00:55, 13.42it/s]
25%|██▌ | 252/1000 [00:21<01:01, 12.15it/s]
25%|██▌ | 254/1000 [00:22<00:56, 13.16it/s]
26%|██▌ | 256/1000 [00:22<00:54, 13.72it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.76it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.90it/s]
26%|██▌ | 262/1000 [00:22<00:56, 13.06it/s]
26%|██▋ | 264/1000 [00:22<00:53, 13.63it/s]
27%|██▋ | 266/1000 [00:22<00:52, 13.98it/s]
27%|██▋ | 268/1000 [00:23<00:50, 14.55it/s]
27%|██▋ | 270/1000 [00:23<00:54, 13.29it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.61it/s]
27%|██▋ | 274/1000 [00:23<01:02, 11.67it/s]
28%|██▊ | 276/1000 [00:23<00:57, 12.68it/s]
28%|██▊ | 278/1000 [00:23<00:55, 12.91it/s]
28%|██▊ | 280/1000 [00:24<00:53, 13.44it/s]
28%|██▊ | 282/1000 [00:24<00:50, 14.32it/s]
28%|██▊ | 284/1000 [00:24<00:50, 14.30it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.61it/s]
29%|██▉ | 288/1000 [00:24<00:47, 14.99it/s]
29%|██▉ | 290/1000 [00:24<00:45, 15.44it/s]
29%|██▉ | 292/1000 [00:24<00:45, 15.41it/s]
29%|██▉ | 294/1000 [00:24<00:46, 15.33it/s]
30%|██▉ | 296/1000 [00:25<00:45, 15.39it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.50it/s]
30%|███ | 300/1000 [00:25<00:46, 15.16it/s]
30%|███ | 302/1000 [00:25<00:46, 15.17it/s]
30%|███ | 304/1000 [00:25<00:50, 13.74it/s]
31%|███ | 306/1000 [00:25<00:47, 14.48it/s]
31%|███ | 308/1000 [00:25<00:47, 14.44it/s]
31%|███ | 310/1000 [00:26<00:47, 14.64it/s]
31%|███ | 312/1000 [00:26<00:46, 14.64it/s]
31%|███▏ | 314/1000 [00:26<00:44, 15.35it/s]
32%|███▏ | 316/1000 [00:26<00:43, 15.63it/s]
32%|███▏ | 318/1000 [00:26<00:42, 16.08it/s]
32%|███▏ | 320/1000 [00:26<01:12, 9.38it/s]
32%|███▏ | 322/1000 [00:27<01:03, 10.73it/s]
32%|███▏ | 324/1000 [00:27<00:58, 11.50it/s]
33%|███▎ | 326/1000 [00:27<00:58, 11.44it/s]
33%|███▎ | 328/1000 [00:27<00:58, 11.47it/s]
33%|███▎ | 330/1000 [00:27<00:53, 12.41it/s]
33%|███▎ | 332/1000 [00:27<00:52, 12.68it/s]
33%|███▎ | 334/1000 [00:27<00:50, 13.14it/s]
34%|███▎ | 336/1000 [00:28<00:49, 13.54it/s]
34%|███▍ | 338/1000 [00:28<01:05, 10.05it/s]
34%|███▍ | 340/1000 [00:28<00:59, 11.12it/s]
34%|███▍ | 342/1000 [00:28<00:53, 12.34it/s]
34%|███▍ | 344/1000 [00:28<00:50, 13.02it/s]
35%|███▍ | 346/1000 [00:28<00:47, 13.64it/s]
35%|███▍ | 348/1000 [00:29<00:46, 14.06it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.63it/s]
35%|███▌ | 352/1000 [00:29<00:44, 14.69it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.76it/s]
36%|███▌ | 356/1000 [00:29<00:43, 14.86it/s]
36%|███▌ | 358/1000 [00:29<00:43, 14.60it/s]
36%|███▌ | 360/1000 [00:29<00:42, 15.00it/s]
36%|███▌ | 362/1000 [00:30<00:47, 13.31it/s]
36%|███▋ | 364/1000 [00:30<00:45, 13.84it/s]
37%|███▋ | 366/1000 [00:30<00:53, 11.93it/s]
37%|███▋ | 368/1000 [00:30<00:51, 12.32it/s]
37%|███▋ | 370/1000 [00:30<00:47, 13.20it/s]
37%|███▋ | 372/1000 [00:30<00:46, 13.51it/s]
37%|███▋ | 374/1000 [00:31<00:54, 11.39it/s]
38%|███▊ | 376/1000 [00:31<00:56, 10.97it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.65it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.46it/s]
38%|███▊ | 382/1000 [00:31<00:48, 12.66it/s]
38%|███▊ | 384/1000 [00:31<00:45, 13.43it/s]
39%|███▊ | 386/1000 [00:31<00:44, 13.73it/s]
39%|███▉ | 388/1000 [00:32<00:44, 13.67it/s]
39%|███▉ | 390/1000 [00:32<00:45, 13.55it/s]
39%|███▉ | 392/1000 [00:32<00:43, 13.87it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.66it/s]
40%|███▉ | 396/1000 [00:32<00:43, 13.80it/s]
40%|███▉ | 398/1000 [00:32<00:47, 12.58it/s]
40%|████ | 400/1000 [00:33<00:45, 13.26it/s]
40%|████ | 402/1000 [00:33<00:43, 13.91it/s]
40%|████ | 404/1000 [00:33<00:41, 14.53it/s]
41%|████ | 406/1000 [00:33<00:39, 15.04it/s]
41%|████ | 408/1000 [00:33<00:47, 12.54it/s]
41%|████ | 410/1000 [00:33<00:56, 10.47it/s]
41%|████ | 412/1000 [00:34<00:50, 11.56it/s]
41%|████▏ | 414/1000 [00:34<01:07, 8.64it/s]
42%|████▏ | 416/1000 [00:34<01:23, 6.97it/s]
42%|████▏ | 418/1000 [00:34<01:10, 8.27it/s]
42%|████▏ | 420/1000 [00:35<01:00, 9.53it/s]
42%|████▏ | 422/1000 [00:35<00:53, 10.88it/s]
42%|████▏ | 424/1000 [00:35<01:15, 7.67it/s]
43%|████▎ | 426/1000 [00:35<01:04, 8.93it/s]
43%|████▎ | 428/1000 [00:35<00:56, 10.12it/s]
43%|████▎ | 430/1000 [00:36<01:02, 9.11it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.04it/s]
43%|████▎ | 434/1000 [00:36<01:08, 8.27it/s]
44%|████▎ | 436/1000 [00:36<01:00, 9.31it/s]
44%|████▍ | 438/1000 [00:37<01:03, 8.85it/s]
44%|████▍ | 440/1000 [00:37<01:01, 9.16it/s]
44%|████▍ | 442/1000 [00:37<00:55, 10.03it/s]
44%|████▍ | 444/1000 [00:37<00:53, 10.44it/s]
45%|████▍ | 446/1000 [00:37<00:48, 11.35it/s]
45%|████▍ | 448/1000 [00:38<00:52, 10.46it/s]
45%|████▌ | 450/1000 [00:38<00:48, 11.25it/s]
45%|████▌ | 452/1000 [00:38<00:44, 12.29it/s]
45%|████▌ | 454/1000 [00:38<00:45, 11.91it/s]
46%|████▌ | 456/1000 [00:38<00:45, 12.04it/s]
46%|████▌ | 458/1000 [00:38<00:41, 13.15it/s]
46%|████▌ | 460/1000 [00:38<00:40, 13.43it/s]
46%|████▌ | 462/1000 [00:39<00:38, 14.12it/s]
46%|████▋ | 464/1000 [00:39<00:36, 14.72it/s]
47%|████▋ | 466/1000 [00:39<00:36, 14.68it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.80it/s]
47%|████▋ | 470/1000 [00:39<00:52, 10.01it/s]
47%|████▋ | 472/1000 [00:40<00:51, 10.16it/s]
47%|████▋ | 474/1000 [00:40<00:46, 11.40it/s]
48%|████▊ | 476/1000 [00:40<00:43, 12.09it/s]
48%|████▊ | 478/1000 [00:40<00:43, 11.97it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.93it/s]
48%|████▊ | 482/1000 [00:40<00:38, 13.33it/s]
48%|████▊ | 484/1000 [00:40<00:37, 13.85it/s]
49%|████▊ | 486/1000 [00:41<00:36, 14.00it/s]
49%|████▉ | 488/1000 [00:41<00:35, 14.28it/s]
49%|████▉ | 490/1000 [00:41<00:45, 11.10it/s]
49%|████▉ | 492/1000 [00:41<00:40, 12.40it/s]
49%|████▉ | 494/1000 [00:41<00:37, 13.38it/s]
50%|████▉ | 496/1000 [00:41<00:35, 14.13it/s]
50%|████▉ | 498/1000 [00:41<00:34, 14.53it/s]
50%|█████ | 500/1000 [00:42<00:43, 11.55it/s]
50%|█████ | 502/1000 [00:42<00:41, 12.00it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.60it/s]
51%|█████ | 506/1000 [00:42<00:38, 12.79it/s]
51%|█████ | 508/1000 [00:42<00:35, 13.72it/s]
51%|█████ | 510/1000 [00:43<00:47, 10.41it/s]
51%|█████ | 512/1000 [00:43<00:43, 11.18it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 11.92it/s]
52%|█████▏ | 516/1000 [00:43<00:40, 12.01it/s]
52%|█████▏ | 518/1000 [00:43<00:39, 12.11it/s]
52%|█████▏ | 520/1000 [00:43<00:39, 12.16it/s]
52%|█████▏ | 522/1000 [00:43<00:39, 12.25it/s]
52%|█████▏ | 524/1000 [00:44<00:38, 12.28it/s]
53%|█████▎ | 526/1000 [00:44<00:38, 12.35it/s]
53%|█████▎ | 528/1000 [00:44<00:43, 10.77it/s]
53%|█████▎ | 530/1000 [00:44<00:39, 11.82it/s]
53%|█████▎ | 532/1000 [00:44<00:36, 12.93it/s]
53%|█████▎ | 534/1000 [00:44<00:34, 13.49it/s]
54%|█████▎ | 536/1000 [00:45<00:33, 13.79it/s]
54%|█████▍ | 538/1000 [00:45<00:33, 13.68it/s]
54%|█████▍ | 540/1000 [00:45<00:32, 14.15it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.48it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.45it/s]
55%|█████▍ | 546/1000 [00:45<00:32, 14.13it/s]
55%|█████▍ | 548/1000 [00:45<00:32, 13.82it/s]
55%|█████▌ | 550/1000 [00:46<00:31, 14.08it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.23it/s]
55%|█████▌ | 554/1000 [00:46<00:32, 13.88it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.08it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.46it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 15.01it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 14.97it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.79it/s]
57%|█████▋ | 566/1000 [00:47<00:37, 11.68it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.22it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.32it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.80it/s]
57%|█████▋ | 574/1000 [00:47<00:33, 12.77it/s]
58%|█████▊ | 576/1000 [00:48<00:32, 12.94it/s]
58%|█████▊ | 578/1000 [00:48<00:37, 11.29it/s]
58%|█████▊ | 580/1000 [00:48<00:33, 12.59it/s]
58%|█████▊ | 582/1000 [00:48<00:31, 13.41it/s]
58%|█████▊ | 584/1000 [00:48<00:30, 13.70it/s]
59%|█████▊ | 586/1000 [00:48<00:29, 14.06it/s]
59%|█████▉ | 588/1000 [00:48<00:27, 14.92it/s]
59%|█████▉ | 590/1000 [00:49<00:27, 14.95it/s]
59%|█████▉ | 592/1000 [00:49<00:27, 14.74it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.31it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.73it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 13.99it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.37it/s]
60%|██████ | 602/1000 [00:49<00:27, 14.49it/s]
60%|██████ | 604/1000 [00:50<00:26, 15.01it/s]
61%|██████ | 606/1000 [00:50<00:26, 14.70it/s]
61%|██████ | 608/1000 [00:50<00:26, 15.05it/s]
61%|██████ | 610/1000 [00:50<00:26, 14.74it/s]
61%|██████ | 612/1000 [00:50<00:26, 14.92it/s]
61%|██████▏ | 614/1000 [00:50<00:27, 14.22it/s]
62%|██████▏ | 616/1000 [00:50<00:26, 14.66it/s]
62%|██████▏ | 618/1000 [00:50<00:27, 14.11it/s]
62%|██████▏ | 620/1000 [00:51<00:39, 9.73it/s]
62%|██████▏ | 622/1000 [00:51<00:38, 9.93it/s]
62%|██████▏ | 624/1000 [00:51<00:33, 11.24it/s]
63%|██████▎ | 626/1000 [00:51<00:31, 11.98it/s]
63%|██████▎ | 628/1000 [00:51<00:28, 13.18it/s]
63%|██████▎ | 630/1000 [00:52<00:27, 13.69it/s]
63%|██████▎ | 632/1000 [00:52<00:29, 12.40it/s]
63%|██████▎ | 634/1000 [00:52<00:27, 13.13it/s]
64%|██████▎ | 636/1000 [00:52<00:33, 10.81it/s]
64%|██████▍ | 638/1000 [00:52<00:35, 10.17it/s]
64%|██████▍ | 640/1000 [00:53<00:32, 10.99it/s]
64%|██████▍ | 642/1000 [00:53<00:30, 11.58it/s]
64%|██████▍ | 644/1000 [00:53<00:30, 11.53it/s]
65%|██████▍ | 646/1000 [00:53<00:29, 12.05it/s]
65%|██████▍ | 648/1000 [00:53<00:27, 12.70it/s]
65%|██████▌ | 650/1000 [00:53<00:28, 12.08it/s]
65%|██████▌ | 652/1000 [00:53<00:29, 11.62it/s]
65%|██████▌ | 654/1000 [00:54<00:37, 9.25it/s]
66%|██████▌ | 656/1000 [00:54<00:32, 10.50it/s]
66%|██████▌ | 658/1000 [00:54<00:29, 11.61it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.03it/s]
66%|██████▌ | 662/1000 [00:54<00:30, 11.17it/s]
66%|██████▋ | 664/1000 [00:55<00:28, 11.69it/s]
67%|██████▋ | 666/1000 [00:55<00:26, 12.73it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.27it/s]
67%|██████▋ | 670/1000 [00:55<00:29, 11.05it/s]
67%|██████▋ | 672/1000 [00:55<00:27, 11.80it/s]
67%|██████▋ | 674/1000 [00:55<00:26, 12.39it/s]
68%|██████▊ | 676/1000 [00:56<00:27, 11.67it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.17it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 12.96it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.11it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.31it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.38it/s]
69%|██████▉ | 688/1000 [00:57<00:22, 13.71it/s]
69%|██████▉ | 690/1000 [00:57<00:22, 13.78it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.23it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.31it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 14.00it/s]
70%|██████▉ | 698/1000 [00:57<00:20, 14.56it/s]
70%|███████ | 700/1000 [00:57<00:24, 12.13it/s]
70%|███████ | 702/1000 [00:58<00:23, 12.93it/s]
70%|███████ | 704/1000 [00:58<00:30, 9.84it/s]
71%|███████ | 706/1000 [00:58<00:27, 10.65it/s]
71%|███████ | 708/1000 [00:58<00:30, 9.60it/s]
71%|███████ | 710/1000 [00:58<00:26, 10.88it/s]
71%|███████ | 712/1000 [00:59<00:24, 11.85it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.94it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 13.03it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.49it/s]
72%|███████▏ | 720/1000 [00:59<00:20, 13.45it/s]
72%|███████▏ | 722/1000 [00:59<00:20, 13.89it/s]
72%|███████▏ | 724/1000 [00:59<00:20, 13.73it/s]
73%|███████▎ | 726/1000 [01:00<00:19, 13.95it/s]
73%|███████▎ | 728/1000 [01:00<00:23, 11.46it/s]
73%|███████▎ | 730/1000 [01:00<00:34, 7.90it/s]
73%|███████▎ | 732/1000 [01:00<00:33, 8.01it/s]
73%|███████▎ | 734/1000 [01:01<00:32, 8.14it/s]
74%|███████▎ | 735/1000 [01:01<00:32, 8.06it/s]
74%|███████▎ | 737/1000 [01:01<00:28, 9.13it/s]
74%|███████▍ | 739/1000 [01:01<00:26, 9.93it/s]
74%|███████▍ | 741/1000 [01:01<00:24, 10.73it/s]
74%|███████▍ | 743/1000 [01:01<00:22, 11.25it/s]
74%|███████▍ | 745/1000 [01:02<00:26, 9.66it/s]
75%|███████▍ | 747/1000 [01:02<00:27, 9.19it/s]
75%|███████▍ | 749/1000 [01:02<00:24, 10.41it/s]
75%|███████▌ | 751/1000 [01:02<00:21, 11.57it/s]
75%|███████▌ | 753/1000 [01:02<00:19, 12.39it/s]
76%|███████▌ | 755/1000 [01:03<00:18, 13.17it/s]
76%|███████▌ | 757/1000 [01:03<00:23, 10.23it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.27it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.23it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.20it/s]
76%|███████▋ | 765/1000 [01:04<00:27, 8.57it/s]
77%|███████▋ | 767/1000 [01:04<00:24, 9.53it/s]
77%|███████▋ | 769/1000 [01:04<00:21, 10.56it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.84it/s]
77%|███████▋ | 773/1000 [01:04<00:17, 12.64it/s]
78%|███████▊ | 775/1000 [01:04<00:17, 13.19it/s]
78%|███████▊ | 777/1000 [01:04<00:15, 14.17it/s]
78%|███████▊ | 779/1000 [01:05<00:21, 10.51it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.40it/s]
78%|███████▊ | 783/1000 [01:05<00:26, 8.30it/s]
78%|███████▊ | 784/1000 [01:06<00:30, 7.06it/s]
78%|███████▊ | 785/1000 [01:06<00:29, 7.37it/s]
79%|███████▊ | 786/1000 [01:06<00:30, 6.98it/s]
79%|███████▉ | 788/1000 [01:06<00:24, 8.80it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.12it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 11.26it/s]
79%|███████▉ | 794/1000 [01:06<00:17, 11.88it/s]
80%|███████▉ | 796/1000 [01:07<00:16, 12.33it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 13.10it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.31it/s]
80%|████████ | 802/1000 [01:07<00:24, 8.24it/s]
80%|████████ | 804/1000 [01:08<00:23, 8.28it/s]
81%|████████ | 806/1000 [01:08<00:21, 8.96it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.34it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.46it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.65it/s]
81%|████████▏ | 814/1000 [01:08<00:14, 12.54it/s]
82%|████████▏ | 816/1000 [01:09<00:16, 11.01it/s]
82%|████████▏ | 818/1000 [01:09<00:15, 11.97it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.48it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 13.01it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.80it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.21it/s]
83%|████████▎ | 828/1000 [01:09<00:11, 14.50it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.93it/s]
83%|████████▎ | 832/1000 [01:10<00:11, 14.32it/s]
83%|████████▎ | 834/1000 [01:10<00:11, 14.06it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.42it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.29it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.38it/s]
84%|████████▍ | 842/1000 [01:10<00:10, 14.42it/s]
84%|████████▍ | 844/1000 [01:10<00:11, 13.96it/s]
85%|████████▍ | 846/1000 [01:11<00:11, 13.93it/s]
85%|████████▍ | 848/1000 [01:11<00:10, 13.91it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.12it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.68it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.19it/s]
86%|████████▌ | 856/1000 [01:11<00:10, 14.02it/s]
86%|████████▌ | 858/1000 [01:11<00:09, 14.52it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 15.30it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.47it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 14.01it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.82it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.70it/s]
87%|████████▋ | 870/1000 [01:12<00:09, 14.09it/s]
87%|████████▋ | 872/1000 [01:12<00:08, 14.71it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 12.44it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.06it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.92it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.56it/s]
88%|████████▊ | 882/1000 [01:13<00:10, 11.37it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 12.11it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.86it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.82it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.26it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.02it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 13.67it/s]
90%|████████▉ | 896/1000 [01:14<00:07, 13.30it/s]
90%|████████▉ | 898/1000 [01:15<00:08, 12.24it/s]
90%|█████████ | 900/1000 [01:15<00:08, 11.49it/s]
90%|█████████ | 902/1000 [01:15<00:07, 12.36it/s]
90%|█████████ | 904/1000 [01:15<00:10, 9.48it/s]
91%|█████████ | 906/1000 [01:15<00:09, 10.35it/s]
91%|█████████ | 908/1000 [01:16<00:08, 11.12it/s]
91%|█████████ | 910/1000 [01:16<00:07, 11.35it/s]
91%|█████████ | 912/1000 [01:16<00:07, 11.76it/s]
91%|█████████▏| 914/1000 [01:16<00:12, 6.68it/s]
92%|█████████▏| 916/1000 [01:17<00:10, 7.99it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.31it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.72it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 10.18it/s]
92%|█████████▏| 924/1000 [01:17<00:06, 10.88it/s]
93%|█████████▎| 926/1000 [01:17<00:06, 11.72it/s]
93%|█████████▎| 928/1000 [01:18<00:06, 10.58it/s]
93%|█████████▎| 930/1000 [01:18<00:05, 11.77it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 12.38it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.85it/s]
94%|█████████▎| 936/1000 [01:18<00:04, 13.37it/s]
94%|█████████▍| 938/1000 [01:18<00:04, 14.23it/s]
94%|█████████▍| 940/1000 [01:18<00:04, 14.54it/s]
94%|█████████▍| 942/1000 [01:19<00:04, 12.26it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.80it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.85it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 12.66it/s]
95%|█████████▌| 950/1000 [01:19<00:03, 13.46it/s]
95%|█████████▌| 952/1000 [01:19<00:03, 14.15it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 14.26it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 14.25it/s]
96%|█████████▌| 958/1000 [01:20<00:02, 14.72it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 13.08it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.69it/s]
96%|█████████▋| 964/1000 [01:20<00:03, 10.21it/s]
97%|█████████▋| 966/1000 [01:21<00:02, 11.41it/s]
97%|█████████▋| 968/1000 [01:21<00:02, 12.08it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 12.71it/s]
97%|█████████▋| 972/1000 [01:21<00:02, 9.67it/s]
97%|█████████▋| 974/1000 [01:21<00:02, 10.85it/s]
98%|█████████▊| 976/1000 [01:21<00:02, 11.65it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.84it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.84it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.06it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 10.17it/s]
99%|█████████▊| 986/1000 [01:23<00:01, 10.26it/s]
99%|█████████▉| 988/1000 [01:23<00:01, 10.51it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 10.98it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 11.40it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 11.64it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 11.93it/s]
100%|█████████▉| 998/1000 [01:24<00:00, 12.05it/s]
100%|██████████| 1000/1000 [01:24<00:00, 12.34it/s]
{'eval_loss': '0.8845', 'eval_runtime': '84.24', 'eval_samples_per_second': '11.87', 'eval_steps_per_second': '11.87', 'epoch': '0.6957', 'num_input_tokens_seen': 43672984}
70%|██████▉ | 13000/18688 [15:15:37<6:05:39, 3.86s/it]
100%|██████████| 1000/1000 [01:24<00:00, 12.34it/s]
 70%|██████▉ | 13001/18688 [15:15:41<46:21:10, 29.34s/it] 70%|██████▉ | 13002/18688 [15:15:45<34:10:40, 21.64s/it] 70%|██████▉ | 13003/18688 [15:15:49<26:01:16, 16.48s/it] 70%|██████▉ | 13004/18688 [15:15:53<20:04:10, 12.71s/it] 70%|██████▉ | 13005/18688 [15:15:58<16:12:11, 10.26s/it] 70%|██████▉ | 13006/18688 [15:16:02<13:15:30, 8.40s/it] 70%|██████▉ | 13007/18688 [15:16:05<10:56:14, 6.93s/it] 70%|██████▉ | 13008/18688 [15:16:09<9:20:34, 5.92s/it] 70%|██████▉ | 13009/18688 [15:16:13<8:40:29, 5.50s/it] 70%|██████▉ | 13010/18688 [15:16:18<8:07:12, 5.15s/it] {'loss': '0.8841', 'grad_norm': '1.17', 'learning_rate': '1.968e-05', 'epoch': '0.6962', 'num_input_tokens_seen': 43704864, 'train_runtime': '5.498e+04', 'train_tokens_per_second': '794.9'}
70%|██████▉ | 13010/18688 [15:16:18<8:07:12, 5.15s/it] 70%|██████▉ | 13011/18688 [15:16:22<7:38:59, 4.85s/it] 70%|██████▉ | 13012/18688 [15:16:26<7:12:11, 4.57s/it] 70%|██████▉ | 13013/18688 [15:16:30<7:00:43, 4.45s/it] 70%|██████▉ | 13014/18688 [15:16:34<6:40:35, 4.24s/it] 70%|██████▉ | 13015/18688 [15:16:38<6:33:31, 4.16s/it] 70%|██████▉ | 13016/18688 [15:16:42<6:40:26, 4.24s/it] 70%|██████▉ | 13017/18688 [15:16:47<7:08:34, 4.53s/it] 70%|██████▉ | 13018/18688 [15:16:52<7:26:35, 4.73s/it] 70%|██████▉ | 13019/18688 [15:16:56<6:55:32, 4.40s/it] 70%|██████▉ | 13020/18688 [15:17:01<7:04:40, 4.50s/it] {'loss': '0.9033', 'grad_norm': '1.41', 'learning_rate': '1.962e-05', 'epoch': '0.6967', 'num_input_tokens_seen': 43739792, 'train_runtime': '5.502e+04', 'train_tokens_per_second': '795'}
70%|██████▉ | 13020/18688 [15:17:01<7:04:40, 4.50s/it] 70%|██████▉ | 13021/18688 [15:17:05<6:46:28, 4.30s/it] 70%|██████▉ | 13022/18688 [15:17:09<6:48:06, 4.32s/it] 70%|██████▉ | 13023/18688 [15:17:13<6:43:36, 4.27s/it] 70%|██████▉ | 13024/18688 [15:17:17<6:28:33, 4.12s/it] 70%|██████▉ | 13025/18688 [15:17:21<6:22:46, 4.06s/it] 70%|██████▉ | 13026/18688 [15:17:25<6:36:15, 4.20s/it] 70%|██████▉ | 13027/18688 [15:17:29<6:27:05, 4.10s/it] 70%|██████▉ | 13028/18688 [15:17:33<6:15:43, 3.98s/it] 70%|██████▉ | 13029/18688 [15:17:39<6:59:59, 4.45s/it] 70%|██████▉ | 13030/18688 [15:17:43<6:52:16, 4.37s/it] {'loss': '0.9479', 'grad_norm': '1.016', 'learning_rate': '1.956e-05', 'epoch': '0.6973', 'num_input_tokens_seen': 43774432, 'train_runtime': '5.506e+04', 'train_tokens_per_second': '795'}
70%|██████▉ | 13030/18688 [15:17:43<6:52:16, 4.37s/it] 70%|██████▉ | 13031/18688 [15:17:47<6:37:43, 4.22s/it] 70%|██████▉ | 13032/18688 [15:17:51<6:48:53, 4.34s/it] 70%|██████▉ | 13033/18688 [15:17:55<6:32:12, 4.16s/it] 70%|██████▉ | 13034/18688 [15:17:58<6:13:02, 3.96s/it] 70%|██████▉ | 13035/18688 [15:18:02<6:06:29, 3.89s/it] 70%|██████▉ | 13036/18688 [15:18:06<6:05:01, 3.88s/it] 70%|██████▉ | 13037/18688 [15:18:10<5:59:58, 3.82s/it] 70%|██████▉ | 13038/18688 [15:18:15<6:29:05, 4.13s/it] 70%|██████▉ | 13039/18688 [15:18:19<6:50:38, 4.36s/it] 70%|██████▉ | 13040/18688 [15:18:24<6:47:17, 4.33s/it] {'loss': '0.9542', 'grad_norm': '1.507', 'learning_rate': '1.949e-05', 'epoch': '0.6978', 'num_input_tokens_seen': 43807728, 'train_runtime': '5.51e+04', 'train_tokens_per_second': '795'}
70%|██████▉ | 13040/18688 [15:18:24<6:47:17, 4.33s/it] 70%|██████▉ | 13041/18688 [15:18:28<6:46:37, 4.32s/it] 70%|██████▉ | 13042/18688 [15:18:32<6:37:22, 4.22s/it] 70%|██████▉ | 13043/18688 [15:18:36<6:25:02, 4.09s/it] 70%|██████▉ | 13044/18688 [15:18:41<6:55:33, 4.42s/it] 70%|██████▉ | 13045/18688 [15:18:45<6:34:52, 4.20s/it] 70%|██████▉ | 13046/18688 [15:18:48<6:16:17, 4.00s/it] 70%|██████▉ | 13047/18688 [15:18:52<6:21:42, 4.06s/it] 70%|██████▉ | 13048/18688 [15:18:57<6:25:39, 4.10s/it] 70%|██████▉ | 13049/18688 [15:19:01<6:37:01, 4.22s/it] 70%|██████▉ | 13050/18688 [15:19:06<6:43:44, 4.30s/it] {'loss': '0.9088', 'grad_norm': '1.237', 'learning_rate': '1.943e-05', 'epoch': '0.6983', 'num_input_tokens_seen': 43842104, 'train_runtime': '5.515e+04', 'train_tokens_per_second': '795'}
70%|██████▉ | 13050/18688 [15:19:06<6:43:44, 4.30s/it] 70%|██████▉ | 13051/18688 [15:19:10<6:58:04, 4.45s/it] 70%|██████▉ | 13052/18688 [15:19:14<6:41:17, 4.27s/it] 70%|██████▉ | 13053/18688 [15:19:18<6:23:05, 4.08s/it] 70%|██████▉ | 13054/18688 [15:19:24<7:09:02, 4.57s/it] 70%|██████▉ | 13055/18688 [15:19:27<6:47:09, 4.34s/it] 70%|██████▉ | 13056/18688 [15:19:32<6:55:26, 4.43s/it] 70%|██████▉ | 13057/18688 [15:19:37<6:59:18, 4.47s/it] 70%|██████▉ | 13058/18688 [15:19:41<6:50:30, 4.37s/it] 70%|██████▉ | 13059/18688 [15:19:46<7:08:40, 4.57s/it] 70%|██████▉ | 13060/18688 [15:19:49<6:42:44, 4.29s/it] {'loss': '0.9239', 'grad_norm': '1.33', 'learning_rate': '1.937e-05', 'epoch': '0.6989', 'num_input_tokens_seen': 43879256, 'train_runtime': '5.519e+04', 'train_tokens_per_second': '795.1'}
70%|██████▉ | 13060/18688 [15:19:49<6:42:44, 4.29s/it] 70%|██████▉ | 13061/18688 [15:19:53<6:35:18, 4.22s/it] 70%|██████▉ | 13062/18688 [15:19:58<6:35:46, 4.22s/it] 70%|██████▉ | 13063/18688 [15:20:02<6:31:17, 4.17s/it] 70%|██████▉ | 13064/18688 [15:20:05<6:12:37, 3.98s/it] 70%|██████▉ | 13065/18688 [15:20:09<6:11:20, 3.96s/it] 70%|██████▉ | 13066/18688 [15:20:14<6:41:48, 4.29s/it] 70%|██████▉ | 13067/18688 [15:20:18<6:38:55, 4.26s/it] 70%|██████▉ | 13068/18688 [15:20:23<6:43:25, 4.31s/it] 70%|██████▉ | 13069/18688 [15:20:28<6:55:54, 4.44s/it] 70%|██████▉ | 13070/18688 [15:20:31<6:38:14, 4.25s/it] {'loss': '0.8743', 'grad_norm': '1.322', 'learning_rate': '1.931e-05', 'epoch': '0.6994', 'num_input_tokens_seen': 43914584, 'train_runtime': '5.523e+04', 'train_tokens_per_second': '795.1'}
70%|██████▉ | 13070/18688 [15:20:31<6:38:14, 4.25s/it] 70%|██████▉ | 13071/18688 [15:20:35<6:20:49, 4.07s/it] 70%|██████▉ | 13072/18688 [15:20:39<6:17:05, 4.03s/it] 70%|██████▉ | 13073/18688 [15:20:42<6:01:29, 3.86s/it] 70%|██████▉ | 13074/18688 [15:20:47<6:33:06, 4.20s/it] 70%|██████▉ | 13075/18688 [15:20:52<6:36:13, 4.24s/it] 70%|██████▉ | 13076/18688 [15:20:56<6:27:38, 4.14s/it] 70%|██████▉ | 13077/18688 [15:20:59<6:08:11, 3.94s/it] 70%|██████▉ | 13078/18688 [15:21:03<5:54:19, 3.79s/it] 70%|██████▉ | 13079/18688 [15:21:07<6:00:12, 3.85s/it] 70%|██████▉ | 13080/18688 [15:21:10<5:52:18, 3.77s/it] {'loss': '0.8904', 'grad_norm': '1.213', 'learning_rate': '1.924e-05', 'epoch': '0.6999', 'num_input_tokens_seen': 43945088, 'train_runtime': '5.527e+04', 'train_tokens_per_second': '795.1'}
70%|██████▉ | 13080/18688 [15:21:10<5:52:18, 3.77s/it] 70%|██████▉ | 13081/18688 [15:21:14<6:00:37, 3.86s/it] 70%|███████ | 13082/18688 [15:21:18<5:51:26, 3.76s/it] 70%|███████ | 13083/18688 [15:21:22<6:10:40, 3.97s/it] 70%|███████ | 13084/18688 [15:21:28<6:48:37, 4.37s/it] 70%|███████ | 13085/18688 [15:21:31<6:35:59, 4.24s/it] 70%|███████ | 13086/18688 [15:21:36<6:48:40, 4.38s/it] 70%|███████ | 13087/18688 [15:21:40<6:36:14, 4.24s/it] 70%|███████ | 13088/18688 [15:21:44<6:32:20, 4.20s/it] 70%|███████ | 13089/18688 [15:21:48<6:16:13, 4.03s/it] 70%|███████ | 13090/18688 [15:21:53<6:34:42, 4.23s/it] {'loss': '0.8643', 'grad_norm': '1.025', 'learning_rate': '1.918e-05', 'epoch': '0.7005', 'num_input_tokens_seen': 43980344, 'train_runtime': '5.531e+04', 'train_tokens_per_second': '795.1'}
70%|███████ | 13090/18688 [15:21:53<6:34:42, 4.23s/it] 70%|███████ | 13091/18688 [15:21:57<6:46:08, 4.35s/it] 70%|███████ | 13092/18688 [15:22:01<6:31:52, 4.20s/it] 70%|███████ | 13093/18688 [15:22:05<6:30:51, 4.19s/it] 70%|███████ | 13094/18688 [15:22:09<6:26:37, 4.15s/it] 70%|███████ | 13095/18688 [15:22:13<6:24:53, 4.13s/it] 70%|███████ | 13096/18688 [15:22:17<6:12:57, 4.00s/it] 70%|███████ | 13097/18688 [15:22:22<6:43:50, 4.33s/it] 70%|███████ | 13098/18688 [15:22:26<6:22:25, 4.10s/it] 70%|███████ | 13099/18688 [15:22:30<6:24:23, 4.13s/it] 70%|███████ | 13100/18688 [15:22:35<6:44:45, 4.35s/it] {'loss': '0.8488', 'grad_norm': '1.05', 'learning_rate': '1.912e-05', 'epoch': '0.701', 'num_input_tokens_seen': 44015280, 'train_runtime': '5.536e+04', 'train_tokens_per_second': '795.1'}
70%|███████ | 13100/18688 [15:22:35<6:44:45, 4.35s/it] 70%|███████ | 13101/18688 [15:22:39<6:45:39, 4.36s/it] 70%|███████ | 13102/18688 [15:22:44<6:58:52, 4.50s/it] 70%|███████ | 13103/18688 [15:22:48<6:55:16, 4.46s/it] 70%|███████ | 13104/18688 [15:22:52<6:44:36, 4.35s/it] 70%|███████ | 13105/18688 [15:22:56<6:35:53, 4.25s/it] 70%|███████ | 13106/18688 [15:23:00<6:16:43, 4.05s/it] 70%|███████ | 13107/18688 [15:23:05<6:31:51, 4.21s/it] 70%|███████ | 13108/18688 [15:23:09<6:26:33, 4.16s/it] 70%|███████ | 13109/18688 [15:23:12<6:11:51, 4.00s/it] 70%|███████ | 13110/18688 [15:23:17<6:20:52, 4.10s/it] {'loss': '0.8695', 'grad_norm': '1.191', 'learning_rate': '1.906e-05', 'epoch': '0.7015', 'num_input_tokens_seen': 44048352, 'train_runtime': '5.54e+04', 'train_tokens_per_second': '795.1'}
70%|███████ | 13110/18688 [15:23:17<6:20:52, 4.10s/it] 70%|███████ | 13111/18688 [15:23:20<6:14:13, 4.03s/it] 70%|███████ | 13112/18688 [15:23:24<6:03:11, 3.91s/it] 70%|███████ | 13113/18688 [15:23:29<6:27:19, 4.17s/it] 70%|███████ | 13114/18688 [15:23:33<6:14:13, 4.03s/it] 70%|███████ | 13115/18688 [15:23:36<6:07:27, 3.96s/it] 70%|███████ | 13116/18688 [15:23:40<6:00:19, 3.88s/it] 70%|███████ | 13117/18688 [15:23:44<5:50:21, 3.77s/it] 70%|███████ | 13118/18688 [15:23:47<5:49:46, 3.77s/it] 70%|███████ | 13119/18688 [15:23:52<6:13:54, 4.03s/it] 70%|███████ | 13120/18688 [15:23:56<6:23:15, 4.13s/it] {'loss': '0.8579', 'grad_norm': '1.211', 'learning_rate': '1.899e-05', 'epoch': '0.7021', 'num_input_tokens_seen': 44080432, 'train_runtime': '5.544e+04', 'train_tokens_per_second': '795.1'}
70%|███████ | 13120/18688 [15:23:56<6:23:15, 4.13s/it] 70%|███████ | 13121/18688 [15:24:00<6:13:11, 4.02s/it] 70%|███████ | 13122/18688 [15:24:05<6:44:24, 4.36s/it] 70%|███████ | 13123/18688 [15:24:09<6:38:17, 4.29s/it] 70%|███████ | 13124/18688 [15:24:14<6:40:17, 4.32s/it] 70%|███████ | 13125/18688 [15:24:18<6:41:25, 4.33s/it] 70%|███████ | 13126/18688 [15:24:22<6:28:31, 4.19s/it] 70%|███████ | 13127/18688 [15:24:26<6:11:13, 4.01s/it] 70%|███████ | 13128/18688 [15:24:30<6:21:10, 4.11s/it] 70%|███████ | 13129/18688 [15:24:34<6:16:44, 4.07s/it] 70%|███████ | 13130/18688 [15:24:38<6:18:50, 4.09s/it] {'loss': '0.9542', 'grad_norm': '1.176', 'learning_rate': '1.893e-05', 'epoch': '0.7026', 'num_input_tokens_seen': 44113840, 'train_runtime': '5.548e+04', 'train_tokens_per_second': '795.2'}
70%|███████ | 13130/18688 [15:24:38<6:18:50, 4.09s/it] 70%|███████ | 13131/18688 [15:24:42<6:13:18, 4.03s/it] 70%|███████ | 13132/18688 [15:24:46<6:17:48, 4.08s/it] 70%|███████ | 13133/18688 [15:24:50<6:09:06, 3.99s/it] 70%|███████ | 13134/18688 [15:24:54<6:05:30, 3.95s/it] 70%|███████ | 13135/18688 [15:24:58<6:23:09, 4.14s/it] 70%|███████ | 13136/18688 [15:25:03<6:36:11, 4.28s/it] 70%|███████ | 13137/18688 [15:25:06<6:13:30, 4.04s/it] 70%|███████ | 13138/18688 [15:25:11<6:23:01, 4.14s/it] 70%|███████ | 13139/18688 [15:25:15<6:20:08, 4.11s/it] 70%|███████ | 13140/18688 [15:25:19<6:19:04, 4.10s/it] {'loss': '0.8643', 'grad_norm': '1.179', 'learning_rate': '1.887e-05', 'epoch': '0.7031', 'num_input_tokens_seen': 44147016, 'train_runtime': '5.552e+04', 'train_tokens_per_second': '795.2'}
70%|███████ | 13140/18688 [15:25:19<6:19:04, 4.10s/it] 70%|███████ | 13141/18688 [15:25:23<6:24:57, 4.16s/it] 70%|███████ | 13142/18688 [15:25:28<6:35:35, 4.28s/it] 70%|███████ | 13143/18688 [15:25:31<6:18:12, 4.09s/it] 70%|███████ | 13144/18688 [15:25:35<6:13:34, 4.04s/it] 70%|███████ | 13145/18688 [15:25:40<6:17:00, 4.08s/it] 70%|███████ | 13146/18688 [15:25:44<6:19:57, 4.11s/it] 70%|███████ | 13147/18688 [15:25:48<6:15:41, 4.07s/it] 70%|███████ | 13148/18688 [15:25:52<6:11:34, 4.02s/it] 70%|███████ | 13149/18688 [15:25:55<6:06:24, 3.97s/it] 70%|███████ | 13150/18688 [15:26:00<6:21:08, 4.13s/it] {'loss': '0.9988', 'grad_norm': '1.287', 'learning_rate': '1.881e-05', 'epoch': '0.7037', 'num_input_tokens_seen': 44178912, 'train_runtime': '5.556e+04', 'train_tokens_per_second': '795.2'}
70%|███████ | 13150/18688 [15:26:00<6:21:08, 4.13s/it] 70%|███████ | 13151/18688 [15:26:04<6:30:22, 4.23s/it] 70%|███████ | 13152/18688 [15:26:08<6:08:41, 4.00s/it] 70%|███████ | 13153/18688 [15:26:12<6:04:52, 3.96s/it] 70%|███████ | 13154/18688 [15:26:16<6:06:35, 3.97s/it] 70%|███████ | 13155/18688 [15:26:20<6:03:54, 3.95s/it] 70%|███████ | 13156/18688 [15:26:24<6:05:12, 3.96s/it] 70%|███████ | 13157/18688 [15:26:28<6:12:51, 4.04s/it] 70%|███████ | 13158/18688 [15:26:32<6:21:47, 4.14s/it] 70%|███████ | 13159/18688 [15:26:38<6:56:00, 4.51s/it] 70%|███████ | 13160/18688 [15:26:41<6:37:26, 4.31s/it] {'loss': '0.9028', 'grad_norm': '1.142', 'learning_rate': '1.875e-05', 'epoch': '0.7042', 'num_input_tokens_seen': 44212928, 'train_runtime': '5.56e+04', 'train_tokens_per_second': '795.2'}
70%|███████ | 13160/18688 [15:26:41<6:37:26, 4.31s/it] 70%|███████ | 13161/18688 [15:26:46<6:51:20, 4.47s/it] 70%|███████ | 13162/18688 [15:26:52<7:26:23, 4.85s/it] 70%|███████ | 13163/18688 [15:26:56<7:04:01, 4.60s/it] 70%|███████ | 13164/18688 [15:27:00<6:56:06, 4.52s/it] 70%|███████ | 13165/18688 [15:27:05<6:50:20, 4.46s/it] 70%|███████ | 13166/18688 [15:27:09<6:44:04, 4.39s/it] 70%|███████ | 13167/18688 [15:27:13<6:39:15, 4.34s/it] 70%|███████ | 13168/18688 [15:27:17<6:32:54, 4.27s/it] 70%|███████ | 13169/18688 [15:27:22<6:42:09, 4.37s/it] 70%|███████ | 13170/18688 [15:27:27<6:54:45, 4.51s/it] {'loss': '0.9358', 'grad_norm': '1.303', 'learning_rate': '1.868e-05', 'epoch': '0.7047', 'num_input_tokens_seen': 44252128, 'train_runtime': '5.565e+04', 'train_tokens_per_second': '795.2'}
70%|███████ | 13170/18688 [15:27:27<6:54:45, 4.51s/it] 70%|███████ | 13171/18688 [15:27:31<6:41:32, 4.37s/it] 70%|███████ | 13172/18688 [15:27:35<6:34:56, 4.30s/it] 70%|███████ | 13173/18688 [15:27:39<6:18:22, 4.12s/it] 70%|███████ | 13174/18688 [15:27:42<6:00:20, 3.92s/it] 70%|███████ | 13175/18688 [15:27:46<5:54:52, 3.86s/it] 71%|███████ | 13176/18688 [15:27:50<6:00:09, 3.92s/it] 71%|███████ | 13177/18688 [15:27:54<6:07:21, 4.00s/it] 71%|███████ | 13178/18688 [15:27:59<6:27:56, 4.22s/it] 71%|███████ | 13179/18688 [15:28:02<6:12:40, 4.06s/it] 71%|███████ | 13180/18688 [15:28:06<6:02:05, 3.94s/it] {'loss': '0.8337', 'grad_norm': '1.216', 'learning_rate': '1.862e-05', 'epoch': '0.7053', 'num_input_tokens_seen': 44284016, 'train_runtime': '5.569e+04', 'train_tokens_per_second': '795.2'}
71%|███████ | 13180/18688 [15:28:06<6:02:05, 3.94s/it] 71%|███████ | 13181/18688 [15:28:10<6:04:10, 3.97s/it] 71%|███████ | 13182/18688 [15:28:15<6:18:33, 4.13s/it] 71%|███████ | 13183/18688 [15:28:19<6:22:48, 4.17s/it] 71%|███████ | 13184/18688 [15:28:23<6:23:02, 4.18s/it] 71%|███████ | 13185/18688 [15:28:27<6:29:34, 4.25s/it] 71%|███████ | 13186/18688 [15:28:31<6:14:41, 4.09s/it] 71%|███████ | 13187/18688 [15:28:36<6:26:52, 4.22s/it] 71%|███████ | 13188/18688 [15:28:39<6:07:36, 4.01s/it] 71%|███████ | 13189/18688 [15:28:44<6:16:36, 4.11s/it] 71%|███████ | 13190/18688 [15:28:49<6:42:49, 4.40s/it] {'loss': '1.059', 'grad_norm': '1.314', 'learning_rate': '1.856e-05', 'epoch': '0.7058', 'num_input_tokens_seen': 44319968, 'train_runtime': '5.573e+04', 'train_tokens_per_second': '795.3'}
71%|███████ | 13190/18688 [15:28:49<6:42:49, 4.40s/it] 71%|███████ | 13191/18688 [15:28:53<6:55:51, 4.54s/it] 71%|███████ | 13192/18688 [15:28:57<6:35:03, 4.31s/it] 71%|███████ | 13193/18688 [15:29:01<6:10:41, 4.05s/it] 71%|███████ | 13194/18688 [15:29:04<5:59:14, 3.92s/it] 71%|███████ | 13195/18688 [15:29:08<5:53:57, 3.87s/it] 71%|███████ | 13196/18688 [15:29:12<5:48:23, 3.81s/it] 71%|███████ | 13197/18688 [15:29:15<5:40:35, 3.72s/it] 71%|███████ | 13198/18688 [15:29:19<5:33:42, 3.65s/it] 71%|███████ | 13199/18688 [15:29:23<5:53:12, 3.86s/it] 71%|███████ | 13200/18688 [15:29:28<6:18:05, 4.13s/it] {'loss': '0.9396', 'grad_norm': '1.453', 'learning_rate': '1.85e-05', 'epoch': '0.7064', 'num_input_tokens_seen': 44349832, 'train_runtime': '5.577e+04', 'train_tokens_per_second': '795.3'}
71%|███████ | 13200/18688 [15:29:28<6:18:05, 4.13s/it] 71%|███████ | 13201/18688 [15:29:32<6:28:56, 4.25s/it] 71%|███████ | 13202/18688 [15:29:37<6:27:00, 4.23s/it] 71%|███████ | 13203/18688 [15:29:41<6:27:36, 4.24s/it] 71%|███████ | 13204/18688 [15:29:45<6:21:54, 4.18s/it] 71%|███████ | 13205/18688 [15:29:49<6:17:52, 4.14s/it] 71%|███████ | 13206/18688 [15:29:53<6:12:49, 4.08s/it] 71%|███████ | 13207/18688 [15:29:57<6:10:33, 4.06s/it] 71%|███████ | 13208/18688 [15:30:01<6:05:24, 4.00s/it] 71%|███████ | 13209/18688 [15:30:04<5:51:41, 3.85s/it] 71%|███████ | 13210/18688 [15:30:08<5:42:31, 3.75s/it] {'loss': '0.9073', 'grad_norm': '1.724', 'learning_rate': '1.844e-05', 'epoch': '0.7069', 'num_input_tokens_seen': 44380488, 'train_runtime': '5.581e+04', 'train_tokens_per_second': '795.2'}
71%|███████ | 13210/18688 [15:30:08<5:42:31, 3.75s/it] 71%|███████ | 13211/18688 [15:30:12<5:56:26, 3.90s/it] 71%|███████ | 13212/18688 [15:30:16<5:58:36, 3.93s/it] 71%|███████ | 13213/18688 [15:30:20<5:55:05, 3.89s/it] 71%|███████ | 13214/18688 [15:30:24<5:50:36, 3.84s/it] 71%|███████ | 13215/18688 [15:30:28<5:57:01, 3.91s/it] 71%|███████ | 13216/18688 [15:30:32<6:20:27, 4.17s/it] 71%|███████ | 13217/18688 [15:30:36<6:01:39, 3.97s/it] 71%|███████ | 13218/18688 [15:30:40<6:08:40, 4.04s/it] 71%|███████ | 13219/18688 [15:30:44<5:54:17, 3.89s/it] 71%|███████ | 13220/18688 [15:30:47<5:50:37, 3.85s/it] {'loss': '0.9258', 'grad_norm': '1.427', 'learning_rate': '1.837e-05', 'epoch': '0.7074', 'num_input_tokens_seen': 44411288, 'train_runtime': '5.585e+04', 'train_tokens_per_second': '795.2'}
71%|███████ | 13220/18688 [15:30:47<5:50:37, 3.85s/it] 71%|███████ | 13221/18688 [15:30:52<6:13:54, 4.10s/it] 71%|███████ | 13222/18688 [15:30:57<6:25:49, 4.24s/it] 71%|███████ | 13223/18688 [15:31:00<6:08:49, 4.05s/it] 71%|███████ | 13224/18688 [15:31:04<6:09:29, 4.06s/it] 71%|███████ | 13225/18688 [15:31:09<6:31:54, 4.30s/it] 71%|███████ | 13226/18688 [15:31:13<6:28:22, 4.27s/it] 71%|███████ | 13227/18688 [15:31:17<6:23:50, 4.22s/it] 71%|███████ | 13228/18688 [15:31:21<6:12:29, 4.09s/it] 71%|███████ | 13229/18688 [15:31:26<6:39:27, 4.39s/it] 71%|███████ | 13230/18688 [15:31:32<7:06:19, 4.69s/it] {'loss': '1.038', 'grad_norm': '1.237', 'learning_rate': '1.831e-05', 'epoch': '0.708', 'num_input_tokens_seen': 44448992, 'train_runtime': '5.589e+04', 'train_tokens_per_second': '795.3'}
71%|███████ | 13230/18688 [15:31:32<7:06:19, 4.69s/it] 71%|███████ | 13231/18688 [15:31:35<6:38:58, 4.39s/it] 71%|███████ | 13232/18688 [15:31:39<6:17:42, 4.15s/it] 71%|███████ | 13233/18688 [15:31:43<6:03:04, 3.99s/it] 71%|███████ | 13234/18688 [15:31:46<5:57:39, 3.93s/it] 71%|███████ | 13235/18688 [15:31:51<6:12:02, 4.09s/it] 71%|███████ | 13236/18688 [15:31:55<6:10:18, 4.08s/it] 71%|███████ | 13237/18688 [15:31:59<6:12:18, 4.10s/it] 71%|███████ | 13238/18688 [15:32:04<6:23:04, 4.22s/it] 71%|███████ | 13239/18688 [15:32:08<6:37:06, 4.37s/it] 71%|███████ | 13240/18688 [15:32:13<6:35:18, 4.35s/it] {'loss': '0.8704', 'grad_norm': '1.262', 'learning_rate': '1.825e-05', 'epoch': '0.7085', 'num_input_tokens_seen': 44482184, 'train_runtime': '5.593e+04', 'train_tokens_per_second': '795.3'}
71%|███████ | 13240/18688 [15:32:13<6:35:18, 4.35s/it] 71%|███████ | 13241/18688 [15:32:17<6:23:33, 4.23s/it] 71%|███████ | 13242/18688 [15:32:21<6:23:45, 4.23s/it] 71%|███████ | 13243/18688 [15:32:25<6:32:22, 4.32s/it] 71%|███████ | 13244/18688 [15:32:29<6:12:25, 4.10s/it] 71%|███████ | 13245/18688 [15:32:34<6:32:22, 4.33s/it] 71%|███████ | 13246/18688 [15:32:38<6:19:58, 4.19s/it] 71%|███████ | 13247/18688 [15:32:41<6:00:33, 3.98s/it] 71%|███████ | 13248/18688 [15:32:46<6:28:43, 4.29s/it] 71%|███████ | 13249/18688 [15:32:51<6:34:05, 4.35s/it] 71%|███████ | 13250/18688 [15:32:55<6:28:43, 4.29s/it] {'loss': '0.913', 'grad_norm': '1.491', 'learning_rate': '1.819e-05', 'epoch': '0.709', 'num_input_tokens_seen': 44517360, 'train_runtime': '5.598e+04', 'train_tokens_per_second': '795.3'}
71%|███████ | 13250/18688 [15:32:55<6:28:43, 4.29s/it] 71%|███████ | 13251/18688 [15:32:59<6:13:31, 4.12s/it] 71%|███████ | 13252/18688 [15:33:02<5:59:50, 3.97s/it] 71%|███████ | 13253/18688 [15:33:07<6:21:02, 4.21s/it] 71%|███████ | 13254/18688 [15:33:10<6:01:34, 3.99s/it] 71%|███████ | 13255/18688 [15:33:15<6:16:06, 4.15s/it] 71%|███████ | 13256/18688 [15:33:18<5:55:09, 3.92s/it] 71%|███████ | 13257/18688 [15:33:22<6:01:42, 4.00s/it] 71%|███████ | 13258/18688 [15:33:26<5:56:21, 3.94s/it] 71%|███████ | 13259/18688 [15:33:30<6:02:33, 4.01s/it] 71%|███████ | 13260/18688 [15:33:34<5:51:47, 3.89s/it] {'loss': '0.8655', 'grad_norm': '1.254', 'learning_rate': '1.813e-05', 'epoch': '0.7096', 'num_input_tokens_seen': 44547576, 'train_runtime': '5.601e+04', 'train_tokens_per_second': '795.3'}
71%|███████ | 13260/18688 [15:33:34<5:51:47, 3.89s/it] 71%|███████ | 13261/18688 [15:33:38<5:51:00, 3.88s/it] 71%|███████ | 13262/18688 [15:33:42<6:05:00, 4.04s/it] 71%|███████ | 13263/18688 [15:33:46<6:05:25, 4.04s/it] 71%|███████ | 13264/18688 [15:33:51<6:22:05, 4.23s/it] 71%|███████ | 13265/18688 [15:33:55<6:13:40, 4.13s/it] 71%|███████ | 13266/18688 [15:33:59<6:02:33, 4.01s/it] 71%|███████ | 13267/18688 [15:34:03<5:59:58, 3.98s/it] 71%|███████ | 13268/18688 [15:34:07<6:18:01, 4.18s/it] 71%|███████ | 13269/18688 [15:34:12<6:37:48, 4.40s/it] 71%|███████ | 13270/18688 [15:34:16<6:22:30, 4.24s/it] {'loss': '0.9001', 'grad_norm': '1.159', 'learning_rate': '1.807e-05', 'epoch': '0.7101', 'num_input_tokens_seen': 44581544, 'train_runtime': '5.606e+04', 'train_tokens_per_second': '795.3'}
71%|███████ | 13270/18688 [15:34:16<6:22:30, 4.24s/it] 71%|███████ | 13271/18688 [15:34:20<6:28:07, 4.30s/it] 71%|███████ | 13272/18688 [15:34:24<6:21:17, 4.22s/it] 71%|███████ | 13273/18688 [15:34:28<6:08:49, 4.09s/it] 71%|███████ | 13274/18688 [15:34:32<5:54:48, 3.93s/it] 71%|███████ | 13275/18688 [15:34:36<5:51:48, 3.90s/it] 71%|███████ | 13276/18688 [15:34:39<5:48:48, 3.87s/it] 71%|███████ | 13277/18688 [15:34:43<5:39:23, 3.76s/it] 71%|███████ | 13278/18688 [15:34:47<5:39:14, 3.76s/it] 71%|███████ | 13279/18688 [15:34:51<6:04:23, 4.04s/it] 71%|███████ | 13280/18688 [15:34:56<6:11:57, 4.13s/it] {'loss': '0.9039', 'grad_norm': '1.097', 'learning_rate': '1.801e-05', 'epoch': '0.7106', 'num_input_tokens_seen': 44612840, 'train_runtime': '5.61e+04', 'train_tokens_per_second': '795.3'}
71%|███████ | 13280/18688 [15:34:56<6:11:57, 4.13s/it] 71%|███████ | 13281/18688 [15:34:59<5:59:45, 3.99s/it] 71%|███████ | 13282/18688 [15:35:03<5:47:51, 3.86s/it] 71%|███████ | 13283/18688 [15:35:07<5:40:29, 3.78s/it] 71%|███████ | 13284/18688 [15:35:11<5:53:20, 3.92s/it] 71%|███████ | 13285/18688 [15:35:15<6:08:14, 4.09s/it] 71%|███████ | 13286/18688 [15:35:20<6:17:06, 4.19s/it] 71%|███████ | 13287/18688 [15:35:25<6:35:18, 4.39s/it] 71%|███████ | 13288/18688 [15:35:29<6:24:01, 4.27s/it] 71%|███████ | 13289/18688 [15:35:33<6:25:38, 4.29s/it] 71%|███████ | 13290/18688 [15:35:37<6:13:09, 4.15s/it] {'loss': '0.9304', 'grad_norm': '1.272', 'learning_rate': '1.795e-05', 'epoch': '0.7112', 'num_input_tokens_seen': 44645984, 'train_runtime': '5.614e+04', 'train_tokens_per_second': '795.3'}
71%|███████ | 13290/18688 [15:35:37<6:13:09, 4.15s/it] 71%|███████ | 13291/18688 [15:35:41<6:04:52, 4.06s/it] 71%|███████ | 13292/18688 [15:35:45<6:22:31, 4.25s/it] 71%|███████ | 13293/18688 [15:35:50<6:28:28, 4.32s/it] 71%|███████ | 13294/18688 [15:35:54<6:21:34, 4.24s/it] 71%|███████ | 13295/18688 [15:35:58<6:09:37, 4.11s/it] 71%|███████ | 13296/18688 [15:36:02<6:14:06, 4.16s/it] 71%|███████ | 13297/18688 [15:36:06<6:12:09, 4.14s/it] 71%|███████ | 13298/18688 [15:36:10<6:03:42, 4.05s/it] 71%|███████ | 13299/18688 [15:36:14<6:01:51, 4.03s/it] 71%|███████ | 13300/18688 [15:36:17<5:51:56, 3.92s/it] {'loss': '0.9288', 'grad_norm': '1.584', 'learning_rate': '1.789e-05', 'epoch': '0.7117', 'num_input_tokens_seen': 44678200, 'train_runtime': '5.618e+04', 'train_tokens_per_second': '795.3'}
71%|███████ | 13300/18688 [15:36:17<5:51:56, 3.92s/it] 71%|███████ | 13301/18688 [15:36:22<6:07:25, 4.09s/it] 71%|███████ | 13302/18688 [15:36:26<6:03:56, 4.05s/it] 71%|███████ | 13303/18688 [15:36:30<6:07:44, 4.10s/it] 71%|███████ | 13304/18688 [15:36:34<6:13:25, 4.16s/it] 71%|███████ | 13305/18688 [15:36:38<6:08:27, 4.11s/it] 71%|███████ | 13306/18688 [15:36:43<6:13:19, 4.16s/it] 71%|███████ | 13307/18688 [15:36:46<6:01:04, 4.03s/it] 71%|███████ | 13308/18688 [15:36:51<6:06:51, 4.09s/it] 71%|███████ | 13309/18688 [15:36:55<6:11:07, 4.14s/it] 71%|███████ | 13310/18688 [15:37:00<6:25:32, 4.30s/it] {'loss': '0.9538', 'grad_norm': '1.345', 'learning_rate': '1.782e-05', 'epoch': '0.7122', 'num_input_tokens_seen': 44712528, 'train_runtime': '5.622e+04', 'train_tokens_per_second': '795.3'}
71%|███████ | 13310/18688 [15:37:00<6:25:32, 4.30s/it] 71%|███████ | 13311/18688 [15:37:03<6:06:06, 4.09s/it] 71%|███████ | 13312/18688 [15:37:07<6:02:54, 4.05s/it] 71%|███████ | 13313/18688 [15:37:12<6:14:44, 4.18s/it] 71%|███████ | 13314/18688 [15:37:16<6:11:55, 4.15s/it] 71%|███████ | 13315/18688 [15:37:20<6:12:41, 4.16s/it] 71%|███████▏ | 13316/18688 [15:37:24<6:14:50, 4.19s/it] 71%|███████▏ | 13317/18688 [15:37:28<6:01:12, 4.04s/it] 71%|███████▏ | 13318/18688 [15:37:32<6:09:57, 4.13s/it] 71%|███████▏ | 13319/18688 [15:37:37<6:17:38, 4.22s/it] 71%|███████▏ | 13320/18688 [15:37:40<6:06:45, 4.10s/it] {'loss': '0.8824', 'grad_norm': '1.003', 'learning_rate': '1.776e-05', 'epoch': '0.7128', 'num_input_tokens_seen': 44745936, 'train_runtime': '5.626e+04', 'train_tokens_per_second': '795.3'}
71%|███████▏ | 13320/18688 [15:37:40<6:06:45, 4.10s/it] 71%|███████▏ | 13321/18688 [15:37:45<6:20:59, 4.26s/it] 71%|███████▏ | 13322/18688 [15:37:49<6:18:38, 4.23s/it] 71%|███████▏ | 13323/18688 [15:37:53<6:09:32, 4.13s/it] 71%|███████▏ | 13324/18688 [15:37:58<6:32:41, 4.39s/it] 71%|███████▏ | 13325/18688 [15:38:02<6:07:15, 4.11s/it] 71%|███████▏ | 13326/18688 [15:38:06<6:14:22, 4.19s/it] 71%|███████▏ | 13327/18688 [15:38:10<6:01:34, 4.05s/it] 71%|███████▏ | 13328/18688 [15:38:15<6:28:02, 4.34s/it] 71%|███████▏ | 13329/18688 [15:38:19<6:26:48, 4.33s/it] 71%|███████▏ | 13330/18688 [15:38:24<6:40:44, 4.49s/it] {'loss': '0.9387', 'grad_norm': '9.15', 'learning_rate': '1.77e-05', 'epoch': '0.7133', 'num_input_tokens_seen': 44781648, 'train_runtime': '5.63e+04', 'train_tokens_per_second': '795.3'}
71%|███████▏ | 13330/18688 [15:38:24<6:40:44, 4.49s/it] 71%|███████▏ | 13331/18688 [15:38:29<6:52:16, 4.62s/it] 71%|███████▏ | 13332/18688 [15:38:34<7:00:32, 4.71s/it] 71%|███████▏ | 13333/18688 [15:38:38<6:37:00, 4.45s/it] 71%|███████▏ | 13334/18688 [15:38:42<6:27:48, 4.35s/it] 71%|███████▏ | 13335/18688 [15:38:45<6:12:19, 4.17s/it] 71%|███████▏ | 13336/18688 [15:38:51<6:42:37, 4.51s/it] 71%|███████▏ | 13337/18688 [15:38:56<6:51:05, 4.61s/it] 71%|███████▏ | 13338/18688 [15:38:59<6:26:32, 4.34s/it] 71%|███████▏ | 13339/18688 [15:39:03<6:15:14, 4.21s/it] 71%|███████▏ | 13340/18688 [15:39:07<6:01:15, 4.05s/it] {'loss': '0.9804', 'grad_norm': '1.199', 'learning_rate': '1.764e-05', 'epoch': '0.7138', 'num_input_tokens_seen': 44817080, 'train_runtime': '5.635e+04', 'train_tokens_per_second': '795.4'}
71%|███████▏ | 13340/18688 [15:39:07<6:01:15, 4.05s/it] 71%|███████▏ | 13341/18688 [15:39:11<5:53:36, 3.97s/it] 71%|███████▏ | 13342/18688 [15:39:14<5:49:51, 3.93s/it] 71%|███████▏ | 13343/18688 [15:39:18<5:46:35, 3.89s/it] 71%|███████▏ | 13344/18688 [15:39:22<5:44:45, 3.87s/it] 71%|███████▏ | 13345/18688 [15:39:26<5:58:10, 4.02s/it] 71%|███████▏ | 13346/18688 [15:39:31<6:15:43, 4.22s/it] 71%|███████▏ | 13347/18688 [15:39:35<5:54:52, 3.99s/it] 71%|███████▏ | 13348/18688 [15:39:38<5:43:00, 3.85s/it] 71%|███████▏ | 13349/18688 [15:39:42<5:31:58, 3.73s/it] 71%|███████▏ | 13350/18688 [15:39:46<5:49:17, 3.93s/it] {'loss': '0.9205', 'grad_norm': '1.036', 'learning_rate': '1.758e-05', 'epoch': '0.7144', 'num_input_tokens_seen': 44846864, 'train_runtime': '5.639e+04', 'train_tokens_per_second': '795.3'}
71%|███████▏ | 13350/18688 [15:39:46<5:49:17, 3.93s/it] 71%|███████▏ | 13351/18688 [15:39:49<5:36:55, 3.79s/it] 71%|███████▏ | 13352/18688 [15:39:54<5:47:49, 3.91s/it] 71%|███████▏ | 13353/18688 [15:39:59<6:31:48, 4.41s/it] 71%|███████▏ | 13354/18688 [15:40:03<6:19:57, 4.27s/it] 71%|███████▏ | 13355/18688 [15:40:07<6:19:39, 4.27s/it] 71%|███████▏ | 13356/18688 [15:40:11<6:04:01, 4.10s/it] 71%|███████▏ | 13357/18688 [15:40:16<6:32:56, 4.42s/it] 71%|███████▏ | 13358/18688 [15:40:21<6:47:38, 4.59s/it] 71%|███████▏ | 13359/18688 [15:40:25<6:28:03, 4.37s/it] 71%|███████▏ | 13360/18688 [15:40:29<6:08:57, 4.15s/it] {'loss': '0.9579', 'grad_norm': '1.16', 'learning_rate': '1.752e-05', 'epoch': '0.7149', 'num_input_tokens_seen': 44883600, 'train_runtime': '5.643e+04', 'train_tokens_per_second': '795.4'}
71%|███████▏ | 13360/18688 [15:40:29<6:08:57, 4.15s/it] 71%|███████▏ | 13361/18688 [15:40:33<5:58:59, 4.04s/it] 72%|███████▏ | 13362/18688 [15:40:37<6:05:24, 4.12s/it] 72%|███████▏ | 13363/18688 [15:40:41<6:05:53, 4.12s/it] 72%|███████▏ | 13364/18688 [15:40:45<5:55:55, 4.01s/it] 72%|███████▏ | 13365/18688 [15:40:49<5:50:12, 3.95s/it] 72%|███████▏ | 13366/18688 [15:40:53<5:50:39, 3.95s/it] 72%|███████▏ | 13367/18688 [15:40:56<5:49:11, 3.94s/it] 72%|███████▏ | 13368/18688 [15:41:00<5:48:58, 3.94s/it] 72%|███████▏ | 13369/18688 [15:41:05<6:10:36, 4.18s/it] 72%|███████▏ | 13370/18688 [15:41:10<6:35:16, 4.46s/it] {'loss': '0.8826', 'grad_norm': '1.159', 'learning_rate': '1.746e-05', 'epoch': '0.7155', 'num_input_tokens_seen': 44916888, 'train_runtime': '5.647e+04', 'train_tokens_per_second': '795.4'}
72%|███████▏ | 13370/18688 [15:41:10<6:35:16, 4.46s/it] 72%|███████▏ | 13371/18688 [15:41:14<6:14:50, 4.23s/it] 72%|███████▏ | 13372/18688 [15:41:18<6:02:42, 4.09s/it] 72%|███████▏ | 13373/18688 [15:41:21<5:52:33, 3.98s/it] 72%|███████▏ | 13374/18688 [15:41:25<5:47:57, 3.93s/it] 72%|███████▏ | 13375/18688 [15:41:29<5:53:55, 4.00s/it] 72%|███████▏ | 13376/18688 [15:41:33<5:42:14, 3.87s/it] 72%|███████▏ | 13377/18688 [15:41:37<5:47:18, 3.92s/it] 72%|███████▏ | 13378/18688 [15:41:41<5:54:39, 4.01s/it] 72%|███████▏ | 13379/18688 [15:41:45<6:01:28, 4.09s/it] 72%|███████▏ | 13380/18688 [15:41:49<5:54:05, 4.00s/it] {'loss': '0.8837', 'grad_norm': '1.243', 'learning_rate': '1.74e-05', 'epoch': '0.716', 'num_input_tokens_seen': 44946248, 'train_runtime': '5.651e+04', 'train_tokens_per_second': '795.4'}
72%|███████▏ | 13380/18688 [15:41:49<5:54:05, 4.00s/it] 72%|███████▏ | 13381/18688 [15:41:54<6:24:03, 4.34s/it] 72%|███████▏ | 13382/18688 [15:41:59<6:37:17, 4.49s/it] 72%|███████▏ | 13383/18688 [15:42:04<6:34:22, 4.46s/it] 72%|███████▏ | 13384/18688 [15:42:07<6:11:29, 4.20s/it] 72%|███████▏ | 13385/18688 [15:42:12<6:28:18, 4.39s/it] 72%|███████▏ | 13386/18688 [15:42:16<6:12:03, 4.21s/it] 72%|███████▏ | 13387/18688 [15:42:20<6:04:53, 4.13s/it] 72%|███████▏ | 13388/18688 [15:42:24<5:55:21, 4.02s/it] 72%|███████▏ | 13389/18688 [15:42:27<5:46:04, 3.92s/it] 72%|███████▏ | 13390/18688 [15:42:31<5:43:22, 3.89s/it] {'loss': '0.8393', 'grad_norm': '1.207', 'learning_rate': '1.734e-05', 'epoch': '0.7165', 'num_input_tokens_seen': 44979392, 'train_runtime': '5.655e+04', 'train_tokens_per_second': '795.4'}
72%|███████▏ | 13390/18688 [15:42:31<5:43:22, 3.89s/it] 72%|███████▏ | 13391/18688 [15:42:35<5:44:42, 3.90s/it] 72%|███████▏ | 13392/18688 [15:42:39<5:44:39, 3.90s/it] 72%|███████▏ | 13393/18688 [15:42:43<5:41:51, 3.87s/it] 72%|███████▏ | 13394/18688 [15:42:47<5:44:24, 3.90s/it] 72%|███████▏ | 13395/18688 [15:42:51<5:48:57, 3.96s/it] 72%|███████▏ | 13396/18688 [15:42:55<6:09:31, 4.19s/it] 72%|███████▏ | 13397/18688 [15:43:00<6:10:51, 4.21s/it] 72%|███████▏ | 13398/18688 [15:43:03<5:54:12, 4.02s/it] 72%|███████▏ | 13399/18688 [15:43:08<6:04:30, 4.14s/it] 72%|███████▏ | 13400/18688 [15:43:12<6:11:55, 4.22s/it] {'loss': '0.8968', 'grad_norm': '1.28', 'learning_rate': '1.728e-05', 'epoch': '0.7171', 'num_input_tokens_seen': 45012672, 'train_runtime': '5.659e+04', 'train_tokens_per_second': '795.4'}
72%|███████▏ | 13400/18688 [15:43:12<6:11:55, 4.22s/it] 72%|███████▏ | 13401/18688 [15:43:16<5:55:50, 4.04s/it] 72%|███████▏ | 13402/18688 [15:43:21<6:14:48, 4.25s/it] 72%|███████▏ | 13403/18688 [15:43:24<5:59:26, 4.08s/it] 72%|███████▏ | 13404/18688 [15:43:28<6:00:47, 4.10s/it] 72%|███████▏ | 13405/18688 [15:43:32<6:01:41, 4.11s/it] 72%|███████▏ | 13406/18688 [15:43:37<6:10:54, 4.21s/it] 72%|███████▏ | 13407/18688 [15:43:41<6:12:43, 4.23s/it] 72%|███████▏ | 13408/18688 [15:43:46<6:23:22, 4.36s/it] 72%|███████▏ | 13409/18688 [15:43:50<6:25:22, 4.38s/it] 72%|███████▏ | 13410/18688 [15:43:54<6:20:12, 4.32s/it] {'loss': '0.9044', 'grad_norm': '1.342', 'learning_rate': '1.722e-05', 'epoch': '0.7176', 'num_input_tokens_seen': 45047216, 'train_runtime': '5.663e+04', 'train_tokens_per_second': '795.4'}
72%|███████▏ | 13410/18688 [15:43:54<6:20:12, 4.32s/it] 72%|███████▏ | 13411/18688 [15:43:58<5:58:40, 4.08s/it] 72%|███████▏ | 13412/18688 [15:44:02<5:45:26, 3.93s/it] 72%|███████▏ | 13413/18688 [15:44:07<6:29:34, 4.43s/it] 72%|███████▏ | 13414/18688 [15:44:12<6:33:56, 4.48s/it] 72%|███████▏ | 13415/18688 [15:44:15<6:10:41, 4.22s/it] 72%|███████▏ | 13416/18688 [15:44:19<6:01:32, 4.11s/it] 72%|███████▏ | 13417/18688 [15:44:23<5:51:19, 4.00s/it] 72%|███████▏ | 13418/18688 [15:44:27<5:44:12, 3.92s/it] 72%|███████▏ | 13419/18688 [15:44:31<5:47:46, 3.96s/it] 72%|███████▏ | 13420/18688 [15:44:35<5:48:21, 3.97s/it] {'loss': '0.9146', 'grad_norm': '1.271', 'learning_rate': '1.716e-05', 'epoch': '0.7181', 'num_input_tokens_seen': 45079448, 'train_runtime': '5.668e+04', 'train_tokens_per_second': '795.4'}
72%|███████▏ | 13420/18688 [15:44:35<5:48:21, 3.97s/it] 72%|███████▏ | 13421/18688 [15:44:39<5:55:01, 4.04s/it] 72%|███████▏ | 13422/18688 [15:44:43<5:43:44, 3.92s/it] 72%|███████▏ | 13423/18688 [15:44:47<6:02:20, 4.13s/it] 72%|███████▏ | 13424/18688 [15:44:51<6:06:05, 4.17s/it] 72%|███████▏ | 13425/18688 [15:44:55<5:58:51, 4.09s/it] 72%|███████▏ | 13426/18688 [15:45:01<6:26:47, 4.41s/it] 72%|███████▏ | 13427/18688 [15:45:04<6:13:32, 4.26s/it] 72%|███████▏ | 13428/18688 [15:45:08<6:03:50, 4.15s/it] 72%|███████▏ | 13429/18688 [15:45:13<6:18:33, 4.32s/it] 72%|███████▏ | 13430/18688 [15:45:18<6:26:59, 4.42s/it] {'loss': '0.915', 'grad_norm': '1.542', 'learning_rate': '1.71e-05', 'epoch': '0.7187', 'num_input_tokens_seen': 45113168, 'train_runtime': '5.672e+04', 'train_tokens_per_second': '795.4'}
72%|███████▏ | 13430/18688 [15:45:18<6:26:59, 4.42s/it] 72%|███████▏ | 13431/18688 [15:45:22<6:27:22, 4.42s/it] 72%|███████▏ | 13432/18688 [15:45:27<6:42:21, 4.59s/it] 72%|███████▏ | 13433/18688 [15:45:32<6:49:00, 4.67s/it] 72%|███████▏ | 13434/18688 [15:45:36<6:29:05, 4.44s/it] 72%|███████▏ | 13435/18688 [15:45:41<6:46:30, 4.64s/it] 72%|███████▏ | 13436/18688 [15:45:45<6:31:27, 4.47s/it] 72%|███████▏ | 13437/18688 [15:45:50<6:35:50, 4.52s/it] 72%|███████▏ | 13438/18688 [15:45:56<7:24:16, 5.08s/it] 72%|███████▏ | 13439/18688 [15:46:01<7:21:24, 5.05s/it] 72%|███████▏ | 13440/18688 [15:46:05<6:59:01, 4.79s/it] {'loss': '0.8914', 'grad_norm': '0.964', 'learning_rate': '1.704e-05', 'epoch': '0.7192', 'num_input_tokens_seen': 45155632, 'train_runtime': '5.677e+04', 'train_tokens_per_second': '795.5'}
72%|███████▏ | 13440/18688 [15:46:05<6:59:01, 4.79s/it] 72%|███████▏ | 13441/18688 [15:46:10<6:51:02, 4.70s/it] 72%|███████▏ | 13442/18688 [15:46:14<6:48:48, 4.68s/it] 72%|███████▏ | 13443/18688 [15:46:19<6:37:52, 4.55s/it] 72%|███████▏ | 13444/18688 [15:46:23<6:23:54, 4.39s/it] 72%|███████▏ | 13445/18688 [15:46:27<6:18:53, 4.34s/it] 72%|███████▏ | 13446/18688 [15:46:30<6:01:01, 4.13s/it] 72%|███████▏ | 13447/18688 [15:46:35<6:06:18, 4.19s/it] 72%|███████▏ | 13448/18688 [15:46:39<6:02:17, 4.15s/it] 72%|███████▏ | 13449/18688 [15:46:44<6:17:40, 4.33s/it] 72%|███████▏ | 13450/18688 [15:46:48<6:07:25, 4.21s/it] {'loss': '0.957', 'grad_norm': '1.713', 'learning_rate': '1.698e-05', 'epoch': '0.7197', 'num_input_tokens_seen': 45188776, 'train_runtime': '5.681e+04', 'train_tokens_per_second': '795.5'}
72%|███████▏ | 13450/18688 [15:46:48<6:07:25, 4.21s/it] 72%|███████▏ | 13451/18688 [15:46:52<6:13:51, 4.28s/it] 72%|███████▏ | 13452/18688 [15:46:56<6:18:44, 4.34s/it] 72%|███████▏ | 13453/18688 [15:47:01<6:27:09, 4.44s/it] 72%|███████▏ | 13454/18688 [15:47:05<6:04:43, 4.18s/it] 72%|███████▏ | 13455/18688 [15:47:09<5:59:45, 4.12s/it] 72%|███████▏ | 13456/18688 [15:47:13<5:54:17, 4.06s/it] 72%|███████▏ | 13457/18688 [15:47:18<6:25:35, 4.42s/it] 72%|███████▏ | 13458/18688 [15:47:23<6:40:59, 4.60s/it] 72%|███████▏ | 13459/18688 [15:47:27<6:21:15, 4.37s/it] 72%|███████▏ | 13460/18688 [15:47:31<6:16:59, 4.33s/it] {'loss': '0.9321', 'grad_norm': '1.421', 'learning_rate': '1.692e-05', 'epoch': '0.7203', 'num_input_tokens_seen': 45225200, 'train_runtime': '5.685e+04', 'train_tokens_per_second': '795.5'}
72%|███████▏ | 13460/18688 [15:47:31<6:16:59, 4.33s/it] 72%|███████▏ | 13461/18688 [15:47:35<6:03:25, 4.17s/it] 72%|███████▏ | 13462/18688 [15:47:39<6:12:27, 4.28s/it] 72%|███████▏ | 13463/18688 [15:47:45<6:38:01, 4.57s/it] 72%|███████▏ | 13464/18688 [15:47:48<6:13:24, 4.29s/it] 72%|███████▏ | 13465/18688 [15:47:52<6:10:07, 4.25s/it] 72%|███████▏ | 13466/18688 [15:47:57<6:08:12, 4.23s/it] 72%|███████▏ | 13467/18688 [15:48:01<6:26:47, 4.44s/it] 72%|███████▏ | 13468/18688 [15:48:06<6:20:48, 4.38s/it] 72%|███████▏ | 13469/18688 [15:48:10<6:10:04, 4.25s/it] 72%|███████▏ | 13470/18688 [15:48:13<5:51:42, 4.04s/it] {'loss': '0.8741', 'grad_norm': '1.513', 'learning_rate': '1.686e-05', 'epoch': '0.7208', 'num_input_tokens_seen': 45260024, 'train_runtime': '5.689e+04', 'train_tokens_per_second': '795.5'}
72%|███████▏ | 13470/18688 [15:48:13<5:51:42, 4.04s/it] 72%|███████▏ | 13471/18688 [15:48:17<5:45:37, 3.97s/it] 72%|███████▏ | 13472/18688 [15:48:21<5:40:21, 3.92s/it] 72%|███████▏ | 13473/18688 [15:48:25<5:49:09, 4.02s/it] 72%|███████▏ | 13474/18688 [15:48:30<6:16:59, 4.34s/it] 72%|███████▏ | 13475/18688 [15:48:34<6:11:59, 4.28s/it] 72%|███████▏ | 13476/18688 [15:48:38<6:05:43, 4.21s/it] 72%|███████▏ | 13477/18688 [15:48:42<6:01:21, 4.16s/it] 72%|███████▏ | 13478/18688 [15:48:47<6:20:56, 4.39s/it] 72%|███████▏ | 13479/18688 [15:48:51<6:10:06, 4.26s/it] 72%|███████▏ | 13480/18688 [15:48:55<6:02:36, 4.18s/it] {'loss': '0.9361', 'grad_norm': '1.1', 'learning_rate': '1.68e-05', 'epoch': '0.7213', 'num_input_tokens_seen': 45293200, 'train_runtime': '5.694e+04', 'train_tokens_per_second': '795.5'}
72%|███████▏ | 13480/18688 [15:48:55<6:02:36, 4.18s/it] 72%|███████▏ | 13481/18688 [15:48:59<5:55:01, 4.09s/it] 72%|███████▏ | 13482/18688 [15:49:03<5:55:06, 4.09s/it] 72%|███████▏ | 13483/18688 [15:49:07<5:56:00, 4.10s/it] 72%|███████▏ | 13484/18688 [15:49:11<5:44:50, 3.98s/it] 72%|███████▏ | 13485/18688 [15:49:15<5:46:24, 3.99s/it] 72%|███████▏ | 13486/18688 [15:49:19<5:51:33, 4.05s/it] 72%|███████▏ | 13487/18688 [15:49:23<5:39:15, 3.91s/it] 72%|███████▏ | 13488/18688 [15:49:27<5:44:15, 3.97s/it] 72%|███████▏ | 13489/18688 [15:49:31<5:38:10, 3.90s/it] 72%|███████▏ | 13490/18688 [15:49:35<5:39:57, 3.92s/it] {'loss': '0.8524', 'grad_norm': '1.274', 'learning_rate': '1.674e-05', 'epoch': '0.7219', 'num_input_tokens_seen': 45323704, 'train_runtime': '5.698e+04', 'train_tokens_per_second': '795.5'}
72%|███████▏ | 13490/18688 [15:49:35<5:39:57, 3.92s/it] 72%|███████▏ | 13491/18688 [15:49:39<5:48:20, 4.02s/it] 72%|███████▏ | 13492/18688 [15:49:44<6:15:46, 4.34s/it] 72%|███████▏ | 13493/18688 [15:49:48<6:06:17, 4.23s/it] 72%|███████▏ | 13494/18688 [15:49:52<5:56:35, 4.12s/it] 72%|███████▏ | 13495/18688 [15:49:55<5:42:23, 3.96s/it] 72%|███████▏ | 13496/18688 [15:49:59<5:35:42, 3.88s/it] 72%|███████▏ | 13497/18688 [15:50:04<5:54:45, 4.10s/it] 72%|███████▏ | 13498/18688 [15:50:08<5:49:44, 4.04s/it] 72%|███████▏ | 13499/18688 [15:50:12<5:52:11, 4.07s/it] 72%|███████▏ | 13500/18688 [15:50:16<6:04:13, 4.21s/it] {'loss': '0.9251', 'grad_norm': '1.212', 'learning_rate': '1.668e-05', 'epoch': '0.7224', 'num_input_tokens_seen': 45357712, 'train_runtime': '5.702e+04', 'train_tokens_per_second': '795.5'}
72%|███████▏ | 13500/18688 [15:50:16<6:04:13, 4.21s/it] 72%|███████▏ | 13501/18688 [15:50:20<5:50:27, 4.05s/it] 72%|███████▏ | 13502/18688 [15:50:24<5:38:06, 3.91s/it] 72%|███████▏ | 13503/18688 [15:50:28<5:50:27, 4.06s/it] 72%|███████▏ | 13504/18688 [15:50:32<5:42:57, 3.97s/it] 72%|███████▏ | 13505/18688 [15:50:36<5:54:25, 4.10s/it] 72%|███████▏ | 13506/18688 [15:50:42<6:27:31, 4.49s/it] 72%|███████▏ | 13507/18688 [15:50:45<6:00:50, 4.18s/it] 72%|███████▏ | 13508/18688 [15:50:49<5:57:40, 4.14s/it] 72%|███████▏ | 13509/18688 [15:50:53<6:01:17, 4.19s/it] 72%|███████▏ | 13510/18688 [15:50:58<6:01:04, 4.18s/it] {'loss': '0.9297', 'grad_norm': '1.137', 'learning_rate': '1.662e-05', 'epoch': '0.7229', 'num_input_tokens_seen': 45390832, 'train_runtime': '5.706e+04', 'train_tokens_per_second': '795.5'}
72%|███████▏ | 13510/18688 [15:50:58<6:01:04, 4.18s/it] 72%|███████▏ | 13511/18688 [15:51:02<6:03:36, 4.21s/it] 72%|███████▏ | 13512/18688 [15:51:06<6:09:23, 4.28s/it] 72%|███████▏ | 13513/18688 [15:51:11<6:13:35, 4.33s/it] 72%|███████▏ | 13514/18688 [15:51:15<5:59:24, 4.17s/it] 72%|███████▏ | 13515/18688 [15:51:18<5:53:48, 4.10s/it] 72%|███████▏ | 13516/18688 [15:51:22<5:38:04, 3.92s/it] 72%|███████▏ | 13517/18688 [15:51:27<6:13:18, 4.33s/it] 72%|███████▏ | 13518/18688 [15:51:31<6:02:42, 4.21s/it] 72%|███████▏ | 13519/18688 [15:51:35<5:47:05, 4.03s/it] 72%|███████▏ | 13520/18688 [15:51:40<6:07:00, 4.26s/it] {'loss': '0.9416', 'grad_norm': '0.9414', 'learning_rate': '1.656e-05', 'epoch': '0.7235', 'num_input_tokens_seen': 45426936, 'train_runtime': '5.71e+04', 'train_tokens_per_second': '795.6'}
72%|███████▏ | 13520/18688 [15:51:40<6:07:00, 4.26s/it] 72%|███████▏ | 13521/18688 [15:51:44<6:00:24, 4.19s/it] 72%|███████▏ | 13522/18688 [15:51:47<5:41:59, 3.97s/it] 72%|███████▏ | 13523/18688 [15:51:51<5:50:35, 4.07s/it] 72%|███████▏ | 13524/18688 [15:51:55<5:51:10, 4.08s/it] 72%|███████▏ | 13525/18688 [15:51:59<5:37:03, 3.92s/it] 72%|███████▏ | 13526/18688 [15:52:03<5:47:14, 4.04s/it] 72%|███████▏ | 13527/18688 [15:52:07<5:33:34, 3.88s/it] 72%|███████▏ | 13528/18688 [15:52:12<6:04:59, 4.24s/it] 72%|███████▏ | 13529/18688 [15:52:16<5:57:57, 4.16s/it] 72%|███████▏ | 13530/18688 [15:52:20<5:49:22, 4.06s/it] {'loss': '0.9039', 'grad_norm': '1.472', 'learning_rate': '1.65e-05', 'epoch': '0.724', 'num_input_tokens_seen': 45459144, 'train_runtime': '5.714e+04', 'train_tokens_per_second': '795.6'}
72%|███████▏ | 13530/18688 [15:52:20<5:49:22, 4.06s/it] 72%|███████▏ | 13531/18688 [15:52:24<5:58:07, 4.17s/it] 72%|███████▏ | 13532/18688 [15:52:28<5:52:41, 4.10s/it] 72%|███████▏ | 13533/18688 [15:52:32<5:41:13, 3.97s/it] 72%|███████▏ | 13534/18688 [15:52:36<6:00:23, 4.20s/it] 72%|███████▏ | 13535/18688 [15:52:41<6:00:18, 4.20s/it] 72%|███████▏ | 13536/18688 [15:52:45<5:55:41, 4.14s/it] 72%|███████▏ | 13537/18688 [15:52:49<6:10:07, 4.31s/it] 72%|███████▏ | 13538/18688 [15:52:54<6:25:38, 4.49s/it] 72%|███████▏ | 13539/18688 [15:52:59<6:17:59, 4.40s/it] 72%|███████▏ | 13540/18688 [15:53:03<6:27:11, 4.51s/it] {'loss': '1.001', 'grad_norm': '1.226', 'learning_rate': '1.644e-05', 'epoch': '0.7245', 'num_input_tokens_seen': 45495864, 'train_runtime': '5.718e+04', 'train_tokens_per_second': '795.6'}
72%|███████▏ | 13540/18688 [15:53:03<6:27:11, 4.51s/it] 72%|███████▏ | 13541/18688 [15:53:07<6:04:16, 4.25s/it] 72%|███████▏ | 13542/18688 [15:53:10<5:46:19, 4.04s/it] 72%|███████▏ | 13543/18688 [15:53:14<5:34:45, 3.90s/it] 72%|███████▏ | 13544/18688 [15:53:18<5:43:10, 4.00s/it] 72%|███████▏ | 13545/18688 [15:53:22<5:47:34, 4.05s/it] 72%|███████▏ | 13546/18688 [15:53:27<5:54:41, 4.14s/it] 72%|███████▏ | 13547/18688 [15:53:32<6:09:25, 4.31s/it] 72%|███████▏ | 13548/18688 [15:53:36<6:21:02, 4.45s/it] 73%|███████▎ | 13549/18688 [15:53:40<6:12:52, 4.35s/it] 73%|███████▎ | 13550/18688 [15:53:44<5:50:30, 4.09s/it] {'loss': '0.8058', 'grad_norm': '1.162', 'learning_rate': '1.639e-05', 'epoch': '0.7251', 'num_input_tokens_seen': 45528392, 'train_runtime': '5.722e+04', 'train_tokens_per_second': '795.6'}
73%|███████▎ | 13550/18688 [15:53:44<5:50:30, 4.09s/it] 73%|███████▎ | 13551/18688 [15:53:48<5:47:36, 4.06s/it] 73%|███████▎ | 13552/18688 [15:53:52<5:42:06, 4.00s/it] 73%|███████▎ | 13553/18688 [15:53:56<5:38:00, 3.95s/it] 73%|███████▎ | 13554/18688 [15:53:59<5:31:11, 3.87s/it] 73%|███████▎ | 13555/18688 [15:54:04<6:05:13, 4.27s/it] 73%|███████▎ | 13556/18688 [15:54:08<5:49:27, 4.09s/it] 73%|███████▎ | 13557/18688 [15:54:12<5:42:57, 4.01s/it] 73%|███████▎ | 13558/18688 [15:54:17<6:04:02, 4.26s/it] 73%|███████▎ | 13559/18688 [15:54:21<6:09:41, 4.32s/it] 73%|███████▎ | 13560/18688 [15:54:27<6:43:42, 4.72s/it] {'loss': '0.8972', 'grad_norm': '1.33', 'learning_rate': '1.633e-05', 'epoch': '0.7256', 'num_input_tokens_seen': 45563968, 'train_runtime': '5.727e+04', 'train_tokens_per_second': '795.6'}
73%|███████▎ | 13560/18688 [15:54:27<6:43:42, 4.72s/it] 73%|███████▎ | 13561/18688 [15:54:30<6:14:20, 4.38s/it] 73%|███████▎ | 13562/18688 [15:54:35<6:09:17, 4.32s/it] 73%|███████▎ | 13563/18688 [15:54:38<5:55:50, 4.17s/it] 73%|███████▎ | 13564/18688 [15:54:42<5:46:09, 4.05s/it] 73%|███████▎ | 13565/18688 [15:54:46<5:32:35, 3.90s/it] 73%|███████▎ | 13566/18688 [15:54:51<6:03:22, 4.26s/it] 73%|███████▎ | 13567/18688 [15:54:55<6:01:59, 4.24s/it] 73%|███████▎ | 13568/18688 [15:54:59<5:57:48, 4.19s/it] 73%|███████▎ | 13569/18688 [15:55:03<5:40:18, 3.99s/it] 73%|███████▎ | 13570/18688 [15:55:07<5:38:18, 3.97s/it] {'loss': '0.8242', 'grad_norm': '1.378', 'learning_rate': '1.627e-05', 'epoch': '0.7262', 'num_input_tokens_seen': 45596000, 'train_runtime': '5.731e+04', 'train_tokens_per_second': '795.6'}
73%|███████▎ | 13570/18688 [15:55:07<5:38:18, 3.97s/it] 73%|███████▎ | 13571/18688 [15:55:11<5:44:59, 4.05s/it] 73%|███████▎ | 13572/18688 [15:55:16<6:11:30, 4.36s/it] 73%|███████▎ | 13573/18688 [15:55:21<6:22:24, 4.49s/it] 73%|███████▎ | 13574/18688 [15:55:24<5:56:03, 4.18s/it] 73%|███████▎ | 13575/18688 [15:55:28<5:58:51, 4.21s/it] 73%|███████▎ | 13576/18688 [15:55:32<5:41:20, 4.01s/it] 73%|███████▎ | 13577/18688 [15:55:35<5:27:26, 3.84s/it] 73%|███████▎ | 13578/18688 [15:55:39<5:28:08, 3.85s/it] 73%|███████▎ | 13579/18688 [15:55:44<5:54:53, 4.17s/it] 73%|███████▎ | 13580/18688 [15:55:49<6:12:25, 4.37s/it] {'loss': '0.8964', 'grad_norm': '1.397', 'learning_rate': '1.621e-05', 'epoch': '0.7267', 'num_input_tokens_seen': 45630320, 'train_runtime': '5.735e+04', 'train_tokens_per_second': '795.7'}
73%|███████▎ | 13580/18688 [15:55:49<6:12:25, 4.37s/it] 73%|███████▎ | 13581/18688 [15:55:53<6:05:56, 4.30s/it] 73%|███████▎ | 13582/18688 [15:55:57<5:56:51, 4.19s/it] 73%|███████▎ | 13583/18688 [15:56:01<5:49:39, 4.11s/it] 73%|███████▎ | 13584/18688 [15:56:05<5:43:22, 4.04s/it] 73%|███████▎ | 13585/18688 [15:56:09<5:42:58, 4.03s/it] 73%|███████▎ | 13586/18688 [15:56:12<5:29:55, 3.88s/it] 73%|███████▎ | 13587/18688 [15:56:17<5:53:13, 4.15s/it] 73%|███████▎ | 13588/18688 [15:56:22<5:56:24, 4.19s/it] 73%|███████▎ | 13589/18688 [15:56:26<5:52:06, 4.14s/it] 73%|███████▎ | 13590/18688 [15:56:29<5:36:03, 3.96s/it] {'loss': '0.9195', 'grad_norm': '1.1', 'learning_rate': '1.615e-05', 'epoch': '0.7272', 'num_input_tokens_seen': 45661896, 'train_runtime': '5.739e+04', 'train_tokens_per_second': '795.6'}
73%|███████▎ | 13590/18688 [15:56:29<5:36:03, 3.96s/it] 73%|███████▎ | 13591/18688 [15:56:33<5:29:47, 3.88s/it] 73%|███████▎ | 13592/18688 [15:56:37<5:44:02, 4.05s/it] 73%|███████▎ | 13593/18688 [15:56:41<5:38:48, 3.99s/it] 73%|███████▎ | 13594/18688 [15:56:45<5:39:52, 4.00s/it] 73%|███████▎ | 13595/18688 [15:56:49<5:39:29, 4.00s/it] 73%|███████▎ | 13596/18688 [15:56:53<5:45:19, 4.07s/it] 73%|███████▎ | 13597/18688 [15:56:58<5:50:53, 4.14s/it] 73%|███████▎ | 13598/18688 [15:57:02<6:04:44, 4.30s/it] 73%|███████▎ | 13599/18688 [15:57:06<5:48:21, 4.11s/it] 73%|███████▎ | 13600/18688 [15:57:10<5:41:41, 4.03s/it] {'loss': '0.9541', 'grad_norm': '1.251', 'learning_rate': '1.609e-05', 'epoch': '0.7278', 'num_input_tokens_seen': 45693664, 'train_runtime': '5.743e+04', 'train_tokens_per_second': '795.6'}
73%|███████▎ | 13600/18688 [15:57:10<5:41:41, 4.03s/it] 73%|███████▎ | 13601/18688 [15:57:13<5:30:27, 3.90s/it] 73%|███████▎ | 13602/18688 [15:57:18<5:41:12, 4.03s/it] 73%|███████▎ | 13603/18688 [15:57:22<5:45:51, 4.08s/it] 73%|███████▎ | 13604/18688 [15:57:27<6:07:59, 4.34s/it] 73%|███████▎ | 13605/18688 [15:57:31<6:00:51, 4.26s/it] 73%|███████▎ | 13606/18688 [15:57:36<6:13:48, 4.41s/it] 73%|███████▎ | 13607/18688 [15:57:41<6:38:20, 4.70s/it] 73%|███████▎ | 13608/18688 [15:57:45<6:28:01, 4.58s/it] 73%|███████▎ | 13609/18688 [15:57:49<6:01:22, 4.27s/it] 73%|███████▎ | 13610/18688 [15:57:53<6:07:31, 4.34s/it] {'loss': '0.9994', 'grad_norm': '1.199', 'learning_rate': '1.603e-05', 'epoch': '0.7283', 'num_input_tokens_seen': 45730440, 'train_runtime': '5.747e+04', 'train_tokens_per_second': '795.7'}
73%|███████▎ | 13610/18688 [15:57:53<6:07:31, 4.34s/it] 73%|███████▎ | 13611/18688 [15:57:58<6:19:48, 4.49s/it] 73%|███████▎ | 13612/18688 [15:58:03<6:22:40, 4.52s/it] 73%|███████▎ | 13613/18688 [15:58:08<6:31:16, 4.63s/it] 73%|███████▎ | 13614/18688 [15:58:11<6:05:55, 4.33s/it] 73%|███████▎ | 13615/18688 [15:58:15<5:46:04, 4.09s/it] 73%|███████▎ | 13616/18688 [15:58:19<5:39:06, 4.01s/it] 73%|███████▎ | 13617/18688 [15:58:23<5:52:40, 4.17s/it] 73%|███████▎ | 13618/18688 [15:58:28<6:11:37, 4.40s/it] 73%|███████▎ | 13619/18688 [15:58:34<6:46:33, 4.81s/it] 73%|███████▎ | 13620/18688 [15:58:38<6:32:49, 4.65s/it] {'loss': '0.8813', 'grad_norm': '1.31', 'learning_rate': '1.597e-05', 'epoch': '0.7288', 'num_input_tokens_seen': 45768744, 'train_runtime': '5.752e+04', 'train_tokens_per_second': '795.7'}
73%|███████▎ | 13620/18688 [15:58:38<6:32:49, 4.65s/it] 73%|███████▎ | 13621/18688 [15:58:42<6:12:21, 4.41s/it] 73%|███████▎ | 13622/18688 [15:58:47<6:18:23, 4.48s/it] 73%|███████▎ | 13623/18688 [15:58:51<6:06:21, 4.34s/it] 73%|███████▎ | 13624/18688 [15:58:56<6:18:23, 4.48s/it] 73%|███████▎ | 13625/18688 [15:59:00<6:18:59, 4.49s/it] 73%|███████▎ | 13626/18688 [15:59:04<6:13:27, 4.43s/it] 73%|███████▎ | 13627/18688 [15:59:08<5:56:29, 4.23s/it] 73%|███████▎ | 13628/18688 [15:59:12<5:47:30, 4.12s/it] 73%|███████▎ | 13629/18688 [15:59:16<5:43:05, 4.07s/it] 73%|███████▎ | 13630/18688 [15:59:20<5:37:32, 4.00s/it] {'loss': '0.9578', 'grad_norm': '1.412', 'learning_rate': '1.592e-05', 'epoch': '0.7294', 'num_input_tokens_seen': 45803008, 'train_runtime': '5.756e+04', 'train_tokens_per_second': '795.7'}
73%|███████▎ | 13630/18688 [15:59:20<5:37:32, 4.00s/it] 73%|███████▎ | 13631/18688 [15:59:23<5:27:05, 3.88s/it] 73%|███████▎ | 13632/18688 [15:59:28<5:33:49, 3.96s/it] 73%|███████▎ | 13633/18688 [15:59:32<5:56:10, 4.23s/it] 73%|███████▎ | 13634/18688 [15:59:36<5:46:19, 4.11s/it] 73%|███████▎ | 13635/18688 [15:59:40<5:44:11, 4.09s/it] 73%|███████▎ | 13636/18688 [15:59:45<6:09:06, 4.38s/it] 73%|███████▎ | 13637/18688 [15:59:49<6:00:19, 4.28s/it] 73%|███████▎ | 13638/18688 [15:59:53<5:51:58, 4.18s/it] 73%|███████▎ | 13639/18688 [15:59:58<5:50:25, 4.16s/it] 73%|███████▎ | 13640/18688 [16:00:02<6:00:16, 4.28s/it] {'loss': '0.9311', 'grad_norm': '1.386', 'learning_rate': '1.586e-05', 'epoch': '0.7299', 'num_input_tokens_seen': 45837280, 'train_runtime': '5.76e+04', 'train_tokens_per_second': '795.8'}
73%|███████▎ | 13640/18688 [16:00:02<6:00:16, 4.28s/it] 73%|███████▎ | 13641/18688 [16:00:06<5:46:52, 4.12s/it] 73%|███████▎ | 13642/18688 [16:00:10<5:37:54, 4.02s/it] 73%|███████▎ | 13643/18688 [16:00:13<5:34:47, 3.98s/it] 73%|███████▎ | 13644/18688 [16:00:17<5:28:05, 3.90s/it] 73%|███████▎ | 13645/18688 [16:00:21<5:35:38, 3.99s/it] 73%|███████▎ | 13646/18688 [16:00:25<5:29:37, 3.92s/it] 73%|███████▎ | 13647/18688 [16:00:29<5:28:09, 3.91s/it] 73%|███████▎ | 13648/18688 [16:00:33<5:20:16, 3.81s/it] 73%|███████▎ | 13649/18688 [16:00:36<5:19:24, 3.80s/it] 73%|███████▎ | 13650/18688 [16:00:42<5:52:32, 4.20s/it] {'loss': '0.9152', 'grad_norm': '0.9772', 'learning_rate': '1.58e-05', 'epoch': '0.7304', 'num_input_tokens_seen': 45867312, 'train_runtime': '5.764e+04', 'train_tokens_per_second': '795.7'}
73%|███████▎ | 13650/18688 [16:00:42<5:52:32, 4.20s/it] 73%|███████▎ | 13651/18688 [16:00:45<5:40:05, 4.05s/it] 73%|███████▎ | 13652/18688 [16:00:49<5:39:02, 4.04s/it] 73%|███████▎ | 13653/18688 [16:00:54<5:47:55, 4.15s/it] 73%|███████▎ | 13654/18688 [16:00:57<5:33:44, 3.98s/it] 73%|███████▎ | 13655/18688 [16:01:01<5:26:36, 3.89s/it] 73%|███████▎ | 13656/18688 [16:01:05<5:23:32, 3.86s/it] 73%|███████▎ | 13657/18688 [16:01:09<5:26:03, 3.89s/it] 73%|███████▎ | 13658/18688 [16:01:13<5:33:45, 3.98s/it] 73%|███████▎ | 13659/18688 [16:01:17<5:31:14, 3.95s/it] 73%|███████▎ | 13660/18688 [16:01:21<5:41:19, 4.07s/it] {'loss': '0.9683', 'grad_norm': '1.183', 'learning_rate': '1.574e-05', 'epoch': '0.731', 'num_input_tokens_seen': 45898472, 'train_runtime': '5.768e+04', 'train_tokens_per_second': '795.7'}
73%|███████▎ | 13660/18688 [16:01:21<5:41:19, 4.07s/it] 73%|███████▎ | 13661/18688 [16:01:25<5:43:15, 4.10s/it] 73%|███████▎ | 13662/18688 [16:01:29<5:43:08, 4.10s/it] 73%|███████▎ | 13663/18688 [16:01:35<6:14:44, 4.47s/it] 73%|███████▎ | 13664/18688 [16:01:38<5:53:17, 4.22s/it] 73%|███████▎ | 13665/18688 [16:01:42<5:43:39, 4.11s/it] 73%|███████▎ | 13666/18688 [16:01:46<5:34:17, 3.99s/it] 73%|███████▎ | 13667/18688 [16:01:51<5:52:27, 4.21s/it] 73%|███████▎ | 13668/18688 [16:01:55<5:50:10, 4.19s/it] 73%|███████▎ | 13669/18688 [16:02:00<6:10:59, 4.43s/it] 73%|███████▎ | 13670/18688 [16:02:04<5:56:33, 4.26s/it] {'loss': '0.9309', 'grad_norm': '1.206', 'learning_rate': '1.568e-05', 'epoch': '0.7315', 'num_input_tokens_seen': 45934840, 'train_runtime': '5.772e+04', 'train_tokens_per_second': '795.8'}
73%|███████▎ | 13670/18688 [16:02:04<5:56:33, 4.26s/it] 73%|███████▎ | 13671/18688 [16:02:07<5:44:16, 4.12s/it] 73%|███████▎ | 13672/18688 [16:02:11<5:28:40, 3.93s/it] 73%|███████▎ | 13673/18688 [16:02:15<5:38:33, 4.05s/it] 73%|███████▎ | 13674/18688 [16:02:19<5:32:47, 3.98s/it] 73%|███████▎ | 13675/18688 [16:02:23<5:28:02, 3.93s/it] 73%|███████▎ | 13676/18688 [16:02:26<5:17:04, 3.80s/it] 73%|███████▎ | 13677/18688 [16:02:30<5:16:00, 3.78s/it] 73%|███████▎ | 13678/18688 [16:02:34<5:30:53, 3.96s/it] 73%|███████▎ | 13679/18688 [16:02:38<5:28:57, 3.94s/it] 73%|███████▎ | 13680/18688 [16:02:45<6:24:03, 4.60s/it] {'loss': '0.8977', 'grad_norm': '1.03', 'learning_rate': '1.563e-05', 'epoch': '0.732', 'num_input_tokens_seen': 45967560, 'train_runtime': '5.777e+04', 'train_tokens_per_second': '795.8'}
73%|███████▎ | 13680/18688 [16:02:45<6:24:03, 4.60s/it] 73%|███████▎ | 13681/18688 [16:02:49<6:17:15, 4.52s/it] 73%|███████▎ | 13682/18688 [16:02:53<6:01:07, 4.33s/it] 73%|███████▎ | 13683/18688 [16:02:57<6:10:25, 4.44s/it] 73%|███████▎ | 13684/18688 [16:03:01<5:49:10, 4.19s/it] 73%|███████▎ | 13685/18688 [16:03:05<5:37:30, 4.05s/it] 73%|███████▎ | 13686/18688 [16:03:09<5:33:48, 4.00s/it] 73%|███████▎ | 13687/18688 [16:03:12<5:26:02, 3.91s/it] 73%|███████▎ | 13688/18688 [16:03:17<5:43:52, 4.13s/it] 73%|███████▎ | 13689/18688 [16:03:22<5:56:53, 4.28s/it] 73%|███████▎ | 13690/18688 [16:03:25<5:38:02, 4.06s/it] {'loss': '0.9316', 'grad_norm': '1.132', 'learning_rate': '1.557e-05', 'epoch': '0.7326', 'num_input_tokens_seen': 46000784, 'train_runtime': '5.781e+04', 'train_tokens_per_second': '795.8'}
73%|███████▎ | 13690/18688 [16:03:25<5:38:02, 4.06s/it] 73%|███████▎ | 13691/18688 [16:03:29<5:43:38, 4.13s/it] 73%|███████▎ | 13692/18688 [16:03:33<5:30:26, 3.97s/it] 73%|███████▎ | 13693/18688 [16:03:37<5:27:06, 3.93s/it] 73%|███████▎ | 13694/18688 [16:03:41<5:43:41, 4.13s/it] 73%|███████▎ | 13695/18688 [16:03:46<6:02:51, 4.36s/it] 73%|███████▎ | 13696/18688 [16:03:50<5:44:27, 4.14s/it] 73%|███████▎ | 13697/18688 [16:03:54<5:33:55, 4.01s/it] 73%|███████▎ | 13698/18688 [16:03:57<5:25:30, 3.91s/it] 73%|███████▎ | 13699/18688 [16:04:01<5:22:01, 3.87s/it] 73%|███████▎ | 13700/18688 [16:04:05<5:22:53, 3.88s/it] {'loss': '0.9209', 'grad_norm': '1.151', 'learning_rate': '1.551e-05', 'epoch': '0.7331', 'num_input_tokens_seen': 46031872, 'train_runtime': '5.785e+04', 'train_tokens_per_second': '795.8'}
73%|███████▎ | 13700/18688 [16:04:05<5:22:53, 3.88s/it] 73%|███████▎ | 13701/18688 [16:04:09<5:27:49, 3.94s/it] 73%|███████▎ | 13702/18688 [16:04:13<5:26:55, 3.93s/it] 73%|███████▎ | 13703/18688 [16:04:17<5:27:47, 3.95s/it] 73%|███████▎ | 13704/18688 [16:04:21<5:23:09, 3.89s/it] 73%|███████▎ | 13705/18688 [16:04:25<5:38:29, 4.08s/it] 73%|███████▎ | 13706/18688 [16:04:30<5:46:37, 4.17s/it] 73%|███████▎ | 13707/18688 [16:04:33<5:36:42, 4.06s/it] 73%|███████▎ | 13708/18688 [16:04:38<5:47:25, 4.19s/it] 73%|███████▎ | 13709/18688 [16:04:42<5:35:14, 4.04s/it] 73%|███████▎ | 13710/18688 [16:04:45<5:21:01, 3.87s/it] {'loss': '0.9091', 'grad_norm': '1.355', 'learning_rate': '1.545e-05', 'epoch': '0.7336', 'num_input_tokens_seen': 46062608, 'train_runtime': '5.789e+04', 'train_tokens_per_second': '795.8'}
73%|███████▎ | 13710/18688 [16:04:45<5:21:01, 3.87s/it] 73%|███████▎ | 13711/18688 [16:04:49<5:10:58, 3.75s/it] 73%|███████▎ | 13712/18688 [16:04:52<5:05:55, 3.69s/it] 73%|███████▎ | 13713/18688 [16:04:57<5:29:49, 3.98s/it] 73%|███████▎ | 13714/18688 [16:05:01<5:41:49, 4.12s/it] 73%|███████▎ | 13715/18688 [16:05:05<5:32:48, 4.02s/it] 73%|███████▎ | 13716/18688 [16:05:10<5:58:43, 4.33s/it] 73%|███████▎ | 13717/18688 [16:05:15<6:11:15, 4.48s/it] 73%|███████▎ | 13718/18688 [16:05:19<5:55:10, 4.29s/it] 73%|███████▎ | 13719/18688 [16:05:24<6:20:26, 4.59s/it] 73%|███████▎ | 13720/18688 [16:05:29<6:18:47, 4.57s/it] {'loss': '0.9273', 'grad_norm': '1.267', 'learning_rate': '1.539e-05', 'epoch': '0.7342', 'num_input_tokens_seen': 46098736, 'train_runtime': '5.793e+04', 'train_tokens_per_second': '795.8'}
73%|███████▎ | 13720/18688 [16:05:29<6:18:47, 4.57s/it] 73%|███████▎ | 13721/18688 [16:05:33<6:20:42, 4.60s/it] 73%|███████▎ | 13722/18688 [16:05:37<5:56:30, 4.31s/it] 73%|███████▎ | 13723/18688 [16:05:42<6:14:58, 4.53s/it] 73%|███████▎ | 13724/18688 [16:05:46<6:11:50, 4.49s/it] 73%|███████▎ | 13725/18688 [16:05:51<6:13:48, 4.52s/it] 73%|███████▎ | 13726/18688 [16:05:55<6:04:10, 4.40s/it] 73%|███████▎ | 13727/18688 [16:06:00<6:05:16, 4.42s/it] 73%|███████▎ | 13728/18688 [16:06:04<6:06:08, 4.43s/it] 73%|███████▎ | 13729/18688 [16:06:09<6:10:33, 4.48s/it] 73%|███████▎ | 13730/18688 [16:06:13<6:01:29, 4.37s/it] {'loss': '0.9159', 'grad_norm': '1.463', 'learning_rate': '1.534e-05', 'epoch': '0.7347', 'num_input_tokens_seen': 46134672, 'train_runtime': '5.797e+04', 'train_tokens_per_second': '795.8'}
73%|███████▎ | 13730/18688 [16:06:13<6:01:29, 4.37s/it] 73%|███████▎ | 13731/18688 [16:06:16<5:42:53, 4.15s/it] 73%|███████▎ | 13732/18688 [16:06:20<5:35:01, 4.06s/it] 73%|███████▎ | 13733/18688 [16:06:24<5:27:49, 3.97s/it] 73%|███████▎ | 13734/18688 [16:06:28<5:19:41, 3.87s/it] 73%|███████▎ | 13735/18688 [16:06:32<5:28:25, 3.98s/it] 74%|███████▎ | 13736/18688 [16:06:36<5:32:13, 4.03s/it] 74%|███████▎ | 13737/18688 [16:06:40<5:24:42, 3.94s/it] 74%|███████▎ | 13738/18688 [16:06:44<5:39:18, 4.11s/it] 74%|███████▎ | 13739/18688 [16:06:49<5:56:08, 4.32s/it] 74%|███████▎ | 13740/18688 [16:06:53<5:58:49, 4.35s/it] {'loss': '0.9151', 'grad_norm': '1.266', 'learning_rate': '1.528e-05', 'epoch': '0.7353', 'num_input_tokens_seen': 46165248, 'train_runtime': '5.801e+04', 'train_tokens_per_second': '795.8'}
74%|███████▎ | 13740/18688 [16:06:53<5:58:49, 4.35s/it] 74%|███████▎ | 13741/18688 [16:06:57<5:45:36, 4.19s/it] 74%|███████▎ | 13742/18688 [16:07:01<5:34:22, 4.06s/it] 74%|███████▎ | 13743/18688 [16:07:05<5:29:33, 4.00s/it] 74%|███████▎ | 13744/18688 [16:07:09<5:22:39, 3.92s/it] 74%|███████▎ | 13745/18688 [16:07:13<5:37:01, 4.09s/it] 74%|███████▎ | 13746/18688 [16:07:18<5:57:30, 4.34s/it] 74%|███████▎ | 13747/18688 [16:07:23<6:04:32, 4.43s/it] 74%|███████▎ | 13748/18688 [16:07:27<6:15:02, 4.56s/it] 74%|███████▎ | 13749/18688 [16:07:32<6:08:46, 4.48s/it] 74%|███████▎ | 13750/18688 [16:07:36<5:53:36, 4.30s/it] {'loss': '0.8798', 'grad_norm': '1.11', 'learning_rate': '1.522e-05', 'epoch': '0.7358', 'num_input_tokens_seen': 46200256, 'train_runtime': '5.806e+04', 'train_tokens_per_second': '795.8'}
74%|███████▎ | 13750/18688 [16:07:36<5:53:36, 4.30s/it] 74%|███████▎ | 13751/18688 [16:07:40<5:45:11, 4.20s/it] 74%|███████▎ | 13752/18688 [16:07:43<5:30:59, 4.02s/it] 74%|███████▎ | 13753/18688 [16:07:47<5:22:07, 3.92s/it] 74%|███████▎ | 13754/18688 [16:07:52<5:41:32, 4.15s/it] 74%|███████▎ | 13755/18688 [16:07:55<5:30:59, 4.03s/it] 74%|███████▎ | 13756/18688 [16:08:00<5:55:51, 4.33s/it] 74%|███████▎ | 13757/18688 [16:08:04<5:49:58, 4.26s/it] 74%|███████▎ | 13758/18688 [16:08:08<5:35:17, 4.08s/it] 74%|███████▎ | 13759/18688 [16:08:12<5:37:42, 4.11s/it] 74%|███████▎ | 13760/18688 [16:08:16<5:25:28, 3.96s/it] {'loss': '0.875', 'grad_norm': '1.128', 'learning_rate': '1.516e-05', 'epoch': '0.7363', 'num_input_tokens_seen': 46232192, 'train_runtime': '5.81e+04', 'train_tokens_per_second': '795.8'}
74%|███████▎ | 13760/18688 [16:08:16<5:25:28, 3.96s/it] 74%|███████▎ | 13761/18688 [16:08:20<5:29:27, 4.01s/it] 74%|███████▎ | 13762/18688 [16:08:25<5:56:46, 4.35s/it] 74%|███████▎ | 13763/18688 [16:08:29<5:47:47, 4.24s/it] 74%|███████▎ | 13764/18688 [16:08:34<5:53:36, 4.31s/it] 74%|███████▎ | 13765/18688 [16:08:37<5:34:30, 4.08s/it] 74%|███████▎ | 13766/18688 [16:08:41<5:19:21, 3.89s/it] 74%|███████▎ | 13767/18688 [16:08:46<5:58:54, 4.38s/it] 74%|███████▎ | 13768/18688 [16:08:51<6:01:05, 4.40s/it] 74%|███████▎ | 13769/18688 [16:08:55<5:54:22, 4.32s/it] 74%|███████▎ | 13770/18688 [16:08:58<5:36:19, 4.10s/it] {'loss': '0.8614', 'grad_norm': '1.425', 'learning_rate': '1.511e-05', 'epoch': '0.7369', 'num_input_tokens_seen': 46266584, 'train_runtime': '5.814e+04', 'train_tokens_per_second': '795.8'}
74%|███████▎ | 13770/18688 [16:08:58<5:36:19, 4.10s/it] 74%|███████▎ | 13771/18688 [16:09:04<6:11:28, 4.53s/it] 74%|███████▎ | 13772/18688 [16:09:08<6:11:42, 4.54s/it] 74%|███████▎ | 13773/18688 [16:09:13<6:01:56, 4.42s/it] 74%|███████▎ | 13774/18688 [16:09:16<5:42:51, 4.19s/it] 74%|███████▎ | 13775/18688 [16:09:21<5:50:54, 4.29s/it] 74%|███████▎ | 13776/18688 [16:09:24<5:35:14, 4.10s/it] 74%|███████▎ | 13777/18688 [16:09:29<5:43:25, 4.20s/it] 74%|███████▎ | 13778/18688 [16:09:33<5:35:24, 4.10s/it] 74%|███████▎ | 13779/18688 [16:09:37<5:46:47, 4.24s/it] 74%|███████▎ | 13780/18688 [16:09:42<5:55:13, 4.34s/it] {'loss': '0.9478', 'grad_norm': '1.251', 'learning_rate': '1.505e-05', 'epoch': '0.7374', 'num_input_tokens_seen': 46302480, 'train_runtime': '5.818e+04', 'train_tokens_per_second': '795.8'}
74%|███████▎ | 13780/18688 [16:09:42<5:55:13, 4.34s/it] 74%|███████▎ | 13781/18688 [16:09:46<5:45:13, 4.22s/it] 74%|███████▎ | 13782/18688 [16:09:49<5:30:23, 4.04s/it] 74%|███████▍ | 13783/18688 [16:09:54<5:33:07, 4.07s/it] 74%|███████▍ | 13784/18688 [16:09:57<5:28:41, 4.02s/it] 74%|███████▍ | 13785/18688 [16:10:01<5:25:08, 3.98s/it] 74%|███████▍ | 13786/18688 [16:10:06<5:34:59, 4.10s/it] 74%|███████▍ | 13787/18688 [16:10:10<5:35:06, 4.10s/it] 74%|███████▍ | 13788/18688 [16:10:14<5:37:50, 4.14s/it] 74%|███████▍ | 13789/18688 [16:10:18<5:23:01, 3.96s/it] 74%|███████▍ | 13790/18688 [16:10:22<5:27:48, 4.02s/it] {'loss': '0.9588', 'grad_norm': '1.298', 'learning_rate': '1.499e-05', 'epoch': '0.7379', 'num_input_tokens_seen': 46333888, 'train_runtime': '5.822e+04', 'train_tokens_per_second': '795.8'}
74%|███████▍ | 13790/18688 [16:10:22<5:27:48, 4.02s/it] 74%|███████▍ | 13791/18688 [16:10:25<5:18:02, 3.90s/it] 74%|███████▍ | 13792/18688 [16:10:29<5:11:04, 3.81s/it] 74%|███████▍ | 13793/18688 [16:10:33<5:23:09, 3.96s/it] 74%|███████▍ | 13794/18688 [16:10:38<5:41:19, 4.18s/it] 74%|███████▍ | 13795/18688 [16:10:42<5:31:43, 4.07s/it] 74%|███████▍ | 13796/18688 [16:10:45<5:22:47, 3.96s/it] 74%|███████▍ | 13797/18688 [16:10:50<5:25:51, 4.00s/it] 74%|███████▍ | 13798/18688 [16:10:53<5:18:52, 3.91s/it] 74%|███████▍ | 13799/18688 [16:10:58<5:40:56, 4.18s/it] 74%|███████▍ | 13800/18688 [16:11:02<5:40:57, 4.19s/it] {'loss': '0.8842', 'grad_norm': '1.059', 'learning_rate': '1.494e-05', 'epoch': '0.7385', 'num_input_tokens_seen': 46365576, 'train_runtime': '5.826e+04', 'train_tokens_per_second': '795.8'}
74%|███████▍ | 13800/18688 [16:11:02<5:40:57, 4.19s/it] 74%|███████▍ | 13801/18688 [16:11:07<5:44:25, 4.23s/it] 74%|███████▍ | 13802/18688 [16:11:12<6:03:56, 4.47s/it] 74%|███████▍ | 13803/18688 [16:11:16<6:07:55, 4.52s/it] 74%|███████▍ | 13804/18688 [16:11:21<6:08:25, 4.53s/it] 74%|███████▍ | 13805/18688 [16:11:25<6:06:52, 4.51s/it] 74%|███████▍ | 13806/18688 [16:11:30<6:17:31, 4.64s/it] 74%|███████▍ | 13807/18688 [16:11:34<5:58:44, 4.41s/it] 74%|███████▍ | 13808/18688 [16:11:38<5:49:32, 4.30s/it] 74%|███████▍ | 13809/18688 [16:11:42<5:49:18, 4.30s/it] 74%|███████▍ | 13810/18688 [16:11:46<5:37:30, 4.15s/it] {'loss': '0.9', 'grad_norm': '1.412', 'learning_rate': '1.488e-05', 'epoch': '0.739', 'num_input_tokens_seen': 46402096, 'train_runtime': '5.831e+04', 'train_tokens_per_second': '795.8'}
74%|███████▍ | 13810/18688 [16:11:46<5:37:30, 4.15s/it] 74%|███████▍ | 13811/18688 [16:11:52<6:10:33, 4.56s/it] 74%|███████▍ | 13812/18688 [16:11:56<6:01:44, 4.45s/it] 74%|███████▍ | 13813/18688 [16:12:01<6:10:01, 4.55s/it] 74%|███████▍ | 13814/18688 [16:12:05<5:50:47, 4.32s/it] 74%|███████▍ | 13815/18688 [16:12:09<5:50:38, 4.32s/it] 74%|███████▍ | 13816/18688 [16:12:12<5:33:27, 4.11s/it] 74%|███████▍ | 13817/18688 [16:12:18<5:57:04, 4.40s/it] 74%|███████▍ | 13818/18688 [16:12:22<6:04:54, 4.50s/it] 74%|███████▍ | 13819/18688 [16:12:26<5:56:54, 4.40s/it] 74%|███████▍ | 13820/18688 [16:12:30<5:37:21, 4.16s/it] {'loss': '0.9476', 'grad_norm': '1.814', 'learning_rate': '1.482e-05', 'epoch': '0.7395', 'num_input_tokens_seen': 46438560, 'train_runtime': '5.835e+04', 'train_tokens_per_second': '795.9'}
74%|███████▍ | 13820/18688 [16:12:30<5:37:21, 4.16s/it] 74%|███████▍ | 13821/18688 [16:12:34<5:40:14, 4.19s/it] 74%|███████▍ | 13822/18688 [16:12:38<5:40:00, 4.19s/it] 74%|███████▍ | 13823/18688 [16:12:43<5:56:18, 4.39s/it] 74%|███████▍ | 13824/18688 [16:12:47<5:41:33, 4.21s/it] 74%|███████▍ | 13825/18688 [16:12:52<6:03:17, 4.48s/it] 74%|███████▍ | 13826/18688 [16:12:57<6:09:51, 4.56s/it] 74%|███████▍ | 13827/18688 [16:13:01<5:57:54, 4.42s/it] 74%|███████▍ | 13828/18688 [16:13:05<5:54:59, 4.38s/it] 74%|███████▍ | 13829/18688 [16:13:09<5:32:51, 4.11s/it] 74%|███████▍ | 13830/18688 [16:13:14<5:55:01, 4.38s/it] {'loss': '0.9678', 'grad_norm': '1.162', 'learning_rate': '1.477e-05', 'epoch': '0.7401', 'num_input_tokens_seen': 46476488, 'train_runtime': '5.839e+04', 'train_tokens_per_second': '795.9'}
74%|███████▍ | 13830/18688 [16:13:14<5:55:01, 4.38s/it] 74%|███████▍ | 13831/18688 [16:13:19<6:02:07, 4.47s/it] 74%|███████▍ | 13832/18688 [16:13:23<5:58:45, 4.43s/it] 74%|███████▍ | 13833/18688 [16:13:28<6:20:18, 4.70s/it] 74%|███████▍ | 13834/18688 [16:13:33<6:21:58, 4.72s/it] 74%|███████▍ | 13835/18688 [16:13:37<6:01:57, 4.48s/it] 74%|███████▍ | 13836/18688 [16:13:41<6:03:46, 4.50s/it] 74%|███████▍ | 13837/18688 [16:13:46<5:53:18, 4.37s/it] 74%|███████▍ | 13838/18688 [16:13:50<5:51:54, 4.35s/it] 74%|███████▍ | 13839/18688 [16:13:54<5:42:58, 4.24s/it] 74%|███████▍ | 13840/18688 [16:13:58<5:30:42, 4.09s/it] {'loss': '0.9435', 'grad_norm': '0.976', 'learning_rate': '1.471e-05', 'epoch': '0.7406', 'num_input_tokens_seen': 46512696, 'train_runtime': '5.844e+04', 'train_tokens_per_second': '795.9'}
74%|███████▍ | 13840/18688 [16:13:58<5:30:42, 4.09s/it] 74%|███████▍ | 13841/18688 [16:14:03<6:02:07, 4.48s/it] 74%|███████▍ | 13842/18688 [16:14:07<5:52:10, 4.36s/it] 74%|███████▍ | 13843/18688 [16:14:11<5:45:33, 4.28s/it] 74%|███████▍ | 13844/18688 [16:14:15<5:34:09, 4.14s/it] 74%|███████▍ | 13845/18688 [16:14:19<5:44:31, 4.27s/it] 74%|███████▍ | 13846/18688 [16:14:24<5:57:51, 4.43s/it] 74%|███████▍ | 13847/18688 [16:14:28<5:39:01, 4.20s/it] 74%|███████▍ | 13848/18688 [16:14:32<5:37:46, 4.19s/it] 74%|███████▍ | 13849/18688 [16:14:36<5:40:16, 4.22s/it] 74%|███████▍ | 13850/18688 [16:14:40<5:30:38, 4.10s/it] {'loss': '0.9231', 'grad_norm': '1.156', 'learning_rate': '1.465e-05', 'epoch': '0.7411', 'num_input_tokens_seen': 46546440, 'train_runtime': '5.848e+04', 'train_tokens_per_second': '795.9'}
74%|███████▍ | 13850/18688 [16:14:40<5:30:38, 4.10s/it] 74%|███████▍ | 13851/18688 [16:14:44<5:31:53, 4.12s/it] 74%|███████▍ | 13852/18688 [16:14:48<5:16:31, 3.93s/it] 74%|███████▍ | 13853/18688 [16:14:51<5:06:16, 3.80s/it] 74%|███████▍ | 13854/18688 [16:14:56<5:16:08, 3.92s/it] 74%|███████▍ | 13855/18688 [16:15:00<5:33:35, 4.14s/it] 74%|███████▍ | 13856/18688 [16:15:05<5:38:19, 4.20s/it] 74%|███████▍ | 13857/18688 [16:15:08<5:27:45, 4.07s/it] 74%|███████▍ | 13858/18688 [16:15:12<5:15:26, 3.92s/it] 74%|███████▍ | 13859/18688 [16:15:17<5:33:51, 4.15s/it] 74%|███████▍ | 13860/18688 [16:15:20<5:19:09, 3.97s/it] {'loss': '0.9031', 'grad_norm': '1.328', 'learning_rate': '1.46e-05', 'epoch': '0.7417', 'num_input_tokens_seen': 46577360, 'train_runtime': '5.852e+04', 'train_tokens_per_second': '795.9'}
74%|███████▍ | 13860/18688 [16:15:20<5:19:09, 3.97s/it] 74%|███████▍ | 13861/18688 [16:15:24<5:13:17, 3.89s/it] 74%|███████▍ | 13862/18688 [16:15:28<5:08:08, 3.83s/it] 74%|███████▍ | 13863/18688 [16:15:33<5:36:18, 4.18s/it] 74%|███████▍ | 13864/18688 [16:15:36<5:27:58, 4.08s/it] 74%|███████▍ | 13865/18688 [16:15:41<5:32:43, 4.14s/it] 74%|███████▍ | 13866/18688 [16:15:45<5:34:17, 4.16s/it] 74%|███████▍ | 13867/18688 [16:15:49<5:26:24, 4.06s/it] 74%|███████▍ | 13868/18688 [16:15:53<5:24:27, 4.04s/it] 74%|███████▍ | 13869/18688 [16:15:56<5:15:38, 3.93s/it] 74%|███████▍ | 13870/18688 [16:16:00<5:12:54, 3.90s/it] {'loss': '0.9214', 'grad_norm': '1.272', 'learning_rate': '1.454e-05', 'epoch': '0.7422', 'num_input_tokens_seen': 46609400, 'train_runtime': '5.856e+04', 'train_tokens_per_second': '795.9'}
74%|███████▍ | 13870/18688 [16:16:00<5:12:54, 3.90s/it] 74%|███████▍ | 13871/18688 [16:16:05<5:27:13, 4.08s/it] 74%|███████▍ | 13872/18688 [16:16:09<5:39:19, 4.23s/it] 74%|███████▍ | 13873/18688 [16:16:13<5:33:58, 4.16s/it] 74%|███████▍ | 13874/18688 [16:16:19<6:03:50, 4.53s/it] 74%|███████▍ | 13875/18688 [16:16:23<5:46:48, 4.32s/it] 74%|███████▍ | 13876/18688 [16:16:26<5:37:33, 4.21s/it] 74%|███████▍ | 13877/18688 [16:16:31<5:34:53, 4.18s/it] 74%|███████▍ | 13878/18688 [16:16:35<5:37:05, 4.20s/it] 74%|███████▍ | 13879/18688 [16:16:40<6:07:16, 4.58s/it] 74%|███████▍ | 13880/18688 [16:16:45<6:10:44, 4.63s/it] {'loss': '1.015', 'grad_norm': '1.068', 'learning_rate': '1.448e-05', 'epoch': '0.7427', 'num_input_tokens_seen': 46648576, 'train_runtime': '5.861e+04', 'train_tokens_per_second': '796'}
74%|███████▍ | 13880/18688 [16:16:45<6:10:44, 4.63s/it] 74%|███████▍ | 13881/18688 [16:16:49<5:59:19, 4.49s/it] 74%|███████▍ | 13882/18688 [16:16:53<5:50:00, 4.37s/it] 74%|███████▍ | 13883/18688 [16:16:57<5:34:09, 4.17s/it] 74%|███████▍ | 13884/18688 [16:17:01<5:20:02, 4.00s/it] 74%|███████▍ | 13885/18688 [16:17:05<5:33:29, 4.17s/it] 74%|███████▍ | 13886/18688 [16:17:09<5:27:31, 4.09s/it] 74%|███████▍ | 13887/18688 [16:17:13<5:19:48, 4.00s/it] 74%|███████▍ | 13888/18688 [16:17:17<5:20:34, 4.01s/it] 74%|███████▍ | 13889/18688 [16:17:21<5:16:47, 3.96s/it] 74%|███████▍ | 13890/18688 [16:17:25<5:14:14, 3.93s/it] {'loss': '0.8478', 'grad_norm': '1.312', 'learning_rate': '1.443e-05', 'epoch': '0.7433', 'num_input_tokens_seen': 46679336, 'train_runtime': '5.865e+04', 'train_tokens_per_second': '796'}
74%|███████▍ | 13890/18688 [16:17:25<5:14:14, 3.93s/it] 74%|███████▍ | 13891/18688 [16:17:28<5:04:37, 3.81s/it] 74%|███████▍ | 13892/18688 [16:17:32<5:01:50, 3.78s/it] 74%|███████▍ | 13893/18688 [16:17:36<5:17:19, 3.97s/it] 74%|███████▍ | 13894/18688 [16:17:40<5:20:35, 4.01s/it] 74%|███████▍ | 13895/18688 [16:17:45<5:24:33, 4.06s/it] 74%|███████▍ | 13896/18688 [16:17:49<5:41:32, 4.28s/it] 74%|███████▍ | 13897/18688 [16:17:53<5:33:06, 4.17s/it] 74%|███████▍ | 13898/18688 [16:17:57<5:19:13, 4.00s/it] 74%|███████▍ | 13899/18688 [16:18:00<5:05:14, 3.82s/it] 74%|███████▍ | 13900/18688 [16:18:04<5:15:04, 3.95s/it] {'loss': '0.8384', 'grad_norm': '1.303', 'learning_rate': '1.437e-05', 'epoch': '0.7438', 'num_input_tokens_seen': 46710680, 'train_runtime': '5.868e+04', 'train_tokens_per_second': '796'}
74%|███████▍ | 13900/18688 [16:18:04<5:15:04, 3.95s/it] 74%|███████▍ | 13901/18688 [16:18:08<5:12:25, 3.92s/it] 74%|███████▍ | 13902/18688 [16:18:12<5:08:42, 3.87s/it] 74%|███████▍ | 13903/18688 [16:18:16<5:12:07, 3.91s/it] 74%|███████▍ | 13904/18688 [16:18:21<5:29:34, 4.13s/it] 74%|███████▍ | 13905/18688 [16:18:26<5:46:37, 4.35s/it] 74%|███████▍ | 13906/18688 [16:18:30<5:42:47, 4.30s/it] 74%|███████▍ | 13907/18688 [16:18:35<6:07:40, 4.61s/it] 74%|███████▍ | 13908/18688 [16:18:39<5:58:05, 4.49s/it] 74%|███████▍ | 13909/18688 [16:18:43<5:47:43, 4.37s/it] 74%|███████▍ | 13910/18688 [16:18:48<6:00:20, 4.52s/it] {'loss': '0.911', 'grad_norm': '1.381', 'learning_rate': '1.431e-05', 'epoch': '0.7443', 'num_input_tokens_seen': 46747080, 'train_runtime': '5.873e+04', 'train_tokens_per_second': '796'}
74%|███████▍ | 13910/18688 [16:18:48<6:00:20, 4.52s/it] 74%|███████▍ | 13911/18688 [16:18:53<5:57:52, 4.49s/it] 74%|███████▍ | 13912/18688 [16:18:57<5:45:13, 4.34s/it] 74%|███████▍ | 13913/18688 [16:19:02<6:13:24, 4.69s/it] 74%|███████▍ | 13914/18688 [16:19:06<5:48:47, 4.38s/it] 74%|███████▍ | 13915/18688 [16:19:10<5:42:51, 4.31s/it] 74%|███████▍ | 13916/18688 [16:19:14<5:45:27, 4.34s/it] 74%|███████▍ | 13917/18688 [16:19:19<5:44:33, 4.33s/it] 74%|███████▍ | 13918/18688 [16:19:23<5:33:18, 4.19s/it] 74%|███████▍ | 13919/18688 [16:19:27<5:26:36, 4.11s/it] 74%|███████▍ | 13920/18688 [16:19:31<5:34:25, 4.21s/it] {'loss': '0.8854', 'grad_norm': '1.151', 'learning_rate': '1.426e-05', 'epoch': '0.7449', 'num_input_tokens_seen': 46782264, 'train_runtime': '5.877e+04', 'train_tokens_per_second': '796'}
74%|███████▍ | 13920/18688 [16:19:31<5:34:25, 4.21s/it] 74%|███████▍ | 13921/18688 [16:19:36<5:47:31, 4.37s/it] 74%|███████▍ | 13922/18688 [16:19:40<5:54:29, 4.46s/it] 75%|███████▍ | 13923/18688 [16:19:44<5:36:33, 4.24s/it] 75%|███████▍ | 13924/18688 [16:19:48<5:23:10, 4.07s/it] 75%|███████▍ | 13925/18688 [16:19:52<5:20:50, 4.04s/it] 75%|███████▍ | 13926/18688 [16:19:57<5:59:05, 4.52s/it] 75%|███████▍ | 13927/18688 [16:20:02<5:56:15, 4.49s/it] 75%|███████▍ | 13928/18688 [16:20:05<5:31:53, 4.18s/it] 75%|███████▍ | 13929/18688 [16:20:10<5:44:45, 4.35s/it] 75%|███████▍ | 13930/18688 [16:20:14<5:43:50, 4.34s/it] {'loss': '0.8662', 'grad_norm': '1.088', 'learning_rate': '1.42e-05', 'epoch': '0.7454', 'num_input_tokens_seen': 46817480, 'train_runtime': '5.881e+04', 'train_tokens_per_second': '796'}
75%|███████▍ | 13930/18688 [16:20:14<5:43:50, 4.34s/it] 75%|███████▍ | 13931/18688 [16:20:19<5:41:46, 4.31s/it] 75%|███████▍ | 13932/18688 [16:20:22<5:27:18, 4.13s/it] 75%|███████▍ | 13933/18688 [16:20:26<5:19:58, 4.04s/it] 75%|███████▍ | 13934/18688 [16:20:31<5:35:48, 4.24s/it] 75%|███████▍ | 13935/18688 [16:20:34<5:19:47, 4.04s/it] 75%|███████▍ | 13936/18688 [16:20:38<5:08:58, 3.90s/it] 75%|███████▍ | 13937/18688 [16:20:42<5:16:52, 4.00s/it] 75%|███████▍ | 13938/18688 [16:20:47<5:28:07, 4.14s/it] 75%|███████▍ | 13939/18688 [16:20:50<5:12:01, 3.94s/it] 75%|███████▍ | 13940/18688 [16:20:54<5:17:54, 4.02s/it] {'loss': '0.9342', 'grad_norm': '1.094', 'learning_rate': '1.415e-05', 'epoch': '0.746', 'num_input_tokens_seen': 46847224, 'train_runtime': '5.885e+04', 'train_tokens_per_second': '796'}
75%|███████▍ | 13940/18688 [16:20:54<5:17:54, 4.02s/it] 75%|███████▍ | 13941/18688 [16:20:58<5:12:36, 3.95s/it] 75%|███████▍ | 13942/18688 [16:21:03<5:29:52, 4.17s/it] 75%|███████▍ | 13943/18688 [16:21:06<5:12:55, 3.96s/it] 75%|███████▍ | 13944/18688 [16:21:10<5:04:24, 3.85s/it] 75%|███████▍ | 13945/18688 [16:21:14<5:16:42, 4.01s/it] 75%|███████▍ | 13946/18688 [16:21:18<5:16:55, 4.01s/it] 75%|███████▍ | 13947/18688 [16:21:23<5:31:52, 4.20s/it] 75%|███████▍ | 13948/18688 [16:21:28<5:51:56, 4.46s/it] 75%|███████▍ | 13949/18688 [16:21:33<5:58:32, 4.54s/it] 75%|███████▍ | 13950/18688 [16:21:37<5:55:45, 4.51s/it] {'loss': '0.8553', 'grad_norm': '1.379', 'learning_rate': '1.409e-05', 'epoch': '0.7465', 'num_input_tokens_seen': 46881720, 'train_runtime': '5.89e+04', 'train_tokens_per_second': '796'}
75%|███████▍ | 13950/18688 [16:21:37<5:55:45, 4.51s/it] 75%|███████▍ | 13951/18688 [16:21:41<5:41:03, 4.32s/it] 75%|███████▍ | 13952/18688 [16:21:46<5:54:47, 4.49s/it] 75%|███████▍ | 13953/18688 [16:21:50<5:50:36, 4.44s/it] 75%|███████▍ | 13954/18688 [16:21:54<5:37:22, 4.28s/it] 75%|███████▍ | 13955/18688 [16:21:58<5:25:34, 4.13s/it] 75%|███████▍ | 13956/18688 [16:22:02<5:25:42, 4.13s/it] 75%|███████▍ | 13957/18688 [16:22:06<5:16:55, 4.02s/it] 75%|███████▍ | 13958/18688 [16:22:10<5:22:13, 4.09s/it] 75%|███████▍ | 13959/18688 [16:22:14<5:29:13, 4.18s/it] 75%|███████▍ | 13960/18688 [16:22:19<5:29:10, 4.18s/it] {'loss': '0.8947', 'grad_norm': '1.147', 'learning_rate': '1.403e-05', 'epoch': '0.747', 'num_input_tokens_seen': 46914040, 'train_runtime': '5.894e+04', 'train_tokens_per_second': '796'}
75%|███████▍ | 13960/18688 [16:22:19<5:29:10, 4.18s/it] 75%|███████▍ | 13961/18688 [16:22:22<5:20:38, 4.07s/it] 75%|███████▍ | 13962/18688 [16:22:26<5:20:09, 4.06s/it] 75%|███████▍ | 13963/18688 [16:22:31<5:37:15, 4.28s/it] 75%|███████▍ | 13964/18688 [16:22:35<5:35:08, 4.26s/it] 75%|███████▍ | 13965/18688 [16:22:40<5:34:48, 4.25s/it] 75%|███████▍ | 13966/18688 [16:22:44<5:37:16, 4.29s/it] 75%|███████▍ | 13967/18688 [16:22:48<5:21:18, 4.08s/it] 75%|███████▍ | 13968/18688 [16:22:51<5:11:47, 3.96s/it] 75%|███████▍ | 13969/18688 [16:22:55<5:13:31, 3.99s/it] 75%|███████▍ | 13970/18688 [16:22:59<5:09:22, 3.93s/it] {'loss': '0.8988', 'grad_norm': '1.226', 'learning_rate': '1.398e-05', 'epoch': '0.7476', 'num_input_tokens_seen': 46945168, 'train_runtime': '5.898e+04', 'train_tokens_per_second': '796'}
75%|███████▍ | 13970/18688 [16:22:59<5:09:22, 3.93s/it] 75%|███████▍ | 13971/18688 [16:23:03<5:03:39, 3.86s/it] 75%|███████▍ | 13972/18688 [16:23:07<5:10:22, 3.95s/it] 75%|███████▍ | 13973/18688 [16:23:11<5:13:22, 3.99s/it] 75%|███████▍ | 13974/18688 [16:23:15<5:16:06, 4.02s/it] 75%|███████▍ | 13975/18688 [16:23:19<5:11:24, 3.96s/it] 75%|███████▍ | 13976/18688 [16:23:23<5:07:34, 3.92s/it] 75%|███████▍ | 13977/18688 [16:23:27<5:07:48, 3.92s/it] 75%|███████▍ | 13978/18688 [16:23:31<5:12:56, 3.99s/it] 75%|███████▍ | 13979/18688 [16:23:36<5:26:10, 4.16s/it] 75%|███████▍ | 13980/18688 [16:23:39<5:13:46, 4.00s/it] {'loss': '0.9255', 'grad_norm': '1.401', 'learning_rate': '1.392e-05', 'epoch': '0.7481', 'num_input_tokens_seen': 46975808, 'train_runtime': '5.902e+04', 'train_tokens_per_second': '795.9'}
75%|███████▍ | 13980/18688 [16:23:39<5:13:46, 4.00s/it] 75%|███████▍ | 13981/18688 [16:23:44<5:24:25, 4.14s/it] 75%|███████▍ | 13982/18688 [16:23:48<5:21:21, 4.10s/it] 75%|███████▍ | 13983/18688 [16:23:52<5:27:43, 4.18s/it] 75%|███████▍ | 13984/18688 [16:23:56<5:23:19, 4.12s/it] 75%|███████▍ | 13985/18688 [16:24:00<5:17:36, 4.05s/it] 75%|███████▍ | 13986/18688 [16:24:04<5:16:29, 4.04s/it] 75%|███████▍ | 13987/18688 [16:24:07<5:02:59, 3.87s/it] 75%|███████▍ | 13988/18688 [16:24:13<5:36:42, 4.30s/it] 75%|███████▍ | 13989/18688 [16:24:16<5:18:29, 4.07s/it] 75%|███████▍ | 13990/18688 [16:24:21<5:25:17, 4.15s/it] {'loss': '0.8793', 'grad_norm': '1.898', 'learning_rate': '1.387e-05', 'epoch': '0.7486', 'num_input_tokens_seen': 47007592, 'train_runtime': '5.906e+04', 'train_tokens_per_second': '795.9'}
75%|███████▍ | 13990/18688 [16:24:21<5:25:17, 4.15s/it] 75%|███████▍ | 13991/18688 [16:24:24<5:15:31, 4.03s/it] 75%|███████▍ | 13992/18688 [16:24:29<5:30:23, 4.22s/it] 75%|███████▍ | 13993/18688 [16:24:33<5:18:50, 4.07s/it] 75%|███████▍ | 13994/18688 [16:24:36<5:12:30, 3.99s/it] 75%|███████▍ | 13995/18688 [16:24:41<5:15:31, 4.03s/it] 75%|███████▍ | 13996/18688 [16:24:44<5:07:59, 3.94s/it] 75%|███████▍ | 13997/18688 [16:24:49<5:17:28, 4.06s/it] 75%|███████▍ | 13998/18688 [16:24:53<5:15:33, 4.04s/it] 75%|███████▍ | 13999/18688 [16:24:57<5:16:17, 4.05s/it] 75%|███████▍ | 14000/18688 [16:25:01<5:33:35, 4.27s/it] {'loss': '0.9312', 'grad_norm': '1.339', 'learning_rate': '1.381e-05', 'epoch': '0.7492', 'num_input_tokens_seen': 47040648, 'train_runtime': '5.91e+04', 'train_tokens_per_second': '795.9'}
75%|███████▍ | 14000/18688 [16:25:01<5:33:35, 4.27s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
0%| | 3/1000 [00:00<00:38, 25.62it/s]
1%| | 6/1000 [00:00<00:56, 17.59it/s]
1%| | 8/1000 [00:00<01:00, 16.32it/s]
1%| | 10/1000 [00:00<01:04, 15.27it/s]
1%| | 12/1000 [00:00<01:03, 15.44it/s]
1%|▏ | 14/1000 [00:00<01:04, 15.31it/s]
2%|▏ | 16/1000 [00:01<01:04, 15.29it/s]
2%|▏ | 18/1000 [00:01<01:39, 9.87it/s]
2%|▏ | 20/1000 [00:01<01:33, 10.46it/s]
2%|▏ | 22/1000 [00:01<01:52, 8.69it/s]
2%|▏ | 24/1000 [00:01<01:40, 9.75it/s]
3%|▎ | 26/1000 [00:02<01:45, 9.27it/s]
3%|▎ | 28/1000 [00:02<01:36, 10.09it/s]
3%|▎ | 30/1000 [00:02<01:28, 10.99it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.14it/s]
3%|▎ | 34/1000 [00:02<01:25, 11.25it/s]
4%|▎ | 36/1000 [00:03<01:32, 10.43it/s]
4%|▍ | 38/1000 [00:03<01:24, 11.34it/s]
4%|▍ | 40/1000 [00:03<01:27, 10.93it/s]
4%|▍ | 42/1000 [00:03<01:46, 9.02it/s]
4%|▍ | 44/1000 [00:03<01:30, 10.52it/s]
5%|▍ | 46/1000 [00:04<01:21, 11.66it/s]
5%|▍ | 48/1000 [00:04<01:29, 10.59it/s]
5%|▌ | 50/1000 [00:04<01:21, 11.61it/s]
5%|▌ | 52/1000 [00:04<01:16, 12.47it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.24it/s]
6%|▌ | 56/1000 [00:04<01:07, 13.89it/s]
6%|▌ | 58/1000 [00:05<01:48, 8.71it/s]
6%|▌ | 60/1000 [00:05<01:36, 9.76it/s]
6%|▌ | 62/1000 [00:05<01:27, 10.73it/s]
6%|▋ | 64/1000 [00:05<01:19, 11.79it/s]
7%|▋ | 66/1000 [00:05<01:16, 12.28it/s]
7%|▋ | 68/1000 [00:05<01:12, 12.88it/s]
7%|▋ | 70/1000 [00:06<01:09, 13.47it/s]
7%|▋ | 72/1000 [00:06<01:06, 13.92it/s]
7%|▋ | 74/1000 [00:06<01:07, 13.69it/s]
8%|▊ | 76/1000 [00:06<01:04, 14.29it/s]
8%|▊ | 78/1000 [00:06<01:03, 14.52it/s]
8%|▊ | 80/1000 [00:06<01:10, 13.00it/s]
8%|▊ | 82/1000 [00:06<01:11, 12.89it/s]
8%|▊ | 84/1000 [00:07<01:11, 12.83it/s]
9%|▊ | 86/1000 [00:07<01:11, 12.80it/s]
9%|▉ | 88/1000 [00:07<01:11, 12.70it/s]
9%|▉ | 90/1000 [00:07<01:12, 12.60it/s]
9%|▉ | 92/1000 [00:07<01:12, 12.52it/s]
9%|▉ | 94/1000 [00:07<01:11, 12.58it/s]
10%|▉ | 96/1000 [00:08<01:11, 12.63it/s]
10%|▉ | 98/1000 [00:08<01:10, 12.80it/s]
10%|█ | 100/1000 [00:08<01:38, 9.14it/s]
10%|█ | 102/1000 [00:08<01:30, 9.91it/s]
10%|█ | 104/1000 [00:09<02:02, 7.33it/s]
10%|█ | 105/1000 [00:09<02:10, 6.85it/s]
11%|█ | 106/1000 [00:09<02:06, 7.08it/s]
11%|█ | 108/1000 [00:09<01:41, 8.78it/s]
11%|█ | 110/1000 [00:09<01:25, 10.47it/s]
11%|█ | 112/1000 [00:09<01:15, 11.69it/s]
11%|█▏ | 114/1000 [00:10<01:12, 12.27it/s]
12%|█▏ | 116/1000 [00:10<01:08, 12.84it/s]
12%|█▏ | 118/1000 [00:10<01:05, 13.53it/s]
12%|█▏ | 120/1000 [00:10<01:08, 12.86it/s]
12%|█▏ | 122/1000 [00:10<01:22, 10.63it/s]
12%|█▏ | 124/1000 [00:10<01:30, 9.68it/s]
13%|█▎ | 126/1000 [00:11<01:22, 10.63it/s]
13%|█▎ | 128/1000 [00:11<01:15, 11.55it/s]
13%|█▎ | 130/1000 [00:11<01:11, 12.22it/s]
13%|█▎ | 132/1000 [00:11<01:05, 13.22it/s]
13%|█▎ | 134/1000 [00:11<01:09, 12.41it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.38it/s]
14%|█▍ | 138/1000 [00:11<01:04, 13.28it/s]
14%|█▍ | 140/1000 [00:12<01:25, 10.11it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.54it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.58it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.60it/s]
15%|█▍ | 148/1000 [00:13<01:14, 11.51it/s]
15%|█▌ | 150/1000 [00:13<01:07, 12.60it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.15it/s]
15%|█▌ | 154/1000 [00:13<01:03, 13.40it/s]
16%|█▌ | 156/1000 [00:13<01:03, 13.25it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.76it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.66it/s]
16%|█▌ | 162/1000 [00:14<01:01, 13.53it/s]
16%|█▋ | 164/1000 [00:14<01:01, 13.64it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.78it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.24it/s]
17%|█▋ | 170/1000 [00:14<01:19, 10.45it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.43it/s]
17%|█▋ | 174/1000 [00:15<01:30, 9.16it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.18it/s]
18%|█▊ | 178/1000 [00:15<01:34, 8.66it/s]
18%|█▊ | 180/1000 [00:15<01:33, 8.77it/s]
18%|█▊ | 182/1000 [00:16<01:35, 8.55it/s]
18%|█▊ | 184/1000 [00:16<01:23, 9.79it/s]
19%|█▊ | 186/1000 [00:16<01:37, 8.33it/s]
19%|█▉ | 188/1000 [00:16<01:23, 9.67it/s]
19%|█▉ | 190/1000 [00:16<01:15, 10.70it/s]
19%|█▉ | 192/1000 [00:17<01:11, 11.34it/s]
19%|█▉ | 194/1000 [00:17<01:17, 10.36it/s]
20%|█▉ | 196/1000 [00:17<01:34, 8.52it/s]
20%|█▉ | 198/1000 [00:17<01:24, 9.48it/s]
20%|██ | 200/1000 [00:17<01:19, 10.01it/s]
20%|██ | 202/1000 [00:18<01:12, 11.01it/s]
20%|██ | 204/1000 [00:18<01:05, 12.15it/s]
21%|██ | 206/1000 [00:18<01:02, 12.79it/s]
21%|██ | 208/1000 [00:18<00:59, 13.23it/s]
21%|██ | 210/1000 [00:18<00:55, 14.16it/s]
21%|██ | 212/1000 [00:18<00:55, 14.32it/s]
21%|██▏ | 214/1000 [00:18<00:54, 14.42it/s]
22%|██▏ | 216/1000 [00:19<00:54, 14.26it/s]
22%|██▏ | 218/1000 [00:19<00:53, 14.68it/s]
22%|██▏ | 220/1000 [00:19<01:28, 8.85it/s]
22%|██▏ | 222/1000 [00:19<01:25, 9.13it/s]
22%|██▏ | 224/1000 [00:20<01:20, 9.59it/s]
23%|██▎ | 226/1000 [00:20<01:12, 10.60it/s]
23%|██▎ | 228/1000 [00:20<01:07, 11.49it/s]
23%|██▎ | 230/1000 [00:20<01:04, 11.99it/s]
23%|██▎ | 232/1000 [00:20<01:02, 12.38it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.13it/s]
24%|██▎ | 236/1000 [00:20<00:56, 13.62it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.27it/s]
24%|██▍ | 240/1000 [00:21<00:53, 14.26it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.79it/s]
24%|██▍ | 244/1000 [00:21<00:50, 14.99it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.97it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.67it/s]
25%|██▌ | 250/1000 [00:21<00:55, 13.41it/s]
25%|██▌ | 252/1000 [00:22<01:01, 12.15it/s]
25%|██▌ | 254/1000 [00:22<00:56, 13.18it/s]
26%|██▌ | 256/1000 [00:22<00:53, 13.78it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.81it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.95it/s]
26%|██▌ | 262/1000 [00:22<00:56, 13.09it/s]
26%|██▋ | 264/1000 [00:22<00:56, 12.92it/s]
27%|██▋ | 266/1000 [00:23<00:57, 12.70it/s]
27%|██▋ | 268/1000 [00:23<00:57, 12.77it/s]
27%|██▋ | 270/1000 [00:23<01:00, 11.98it/s]
27%|██▋ | 272/1000 [00:23<01:07, 10.72it/s]
27%|██▋ | 274/1000 [00:23<01:06, 10.89it/s]
28%|██▊ | 276/1000 [00:24<01:03, 11.35it/s]
28%|██▊ | 278/1000 [00:24<01:00, 11.89it/s]
28%|██▊ | 280/1000 [00:24<00:56, 12.69it/s]
28%|██▊ | 282/1000 [00:24<00:52, 13.80it/s]
28%|██▊ | 284/1000 [00:24<00:51, 13.94it/s]
29%|██▊ | 286/1000 [00:24<00:49, 14.35it/s]
29%|██▉ | 288/1000 [00:24<00:48, 14.81it/s]
29%|██▉ | 290/1000 [00:24<00:46, 15.35it/s]
29%|██▉ | 292/1000 [00:25<00:46, 15.36it/s]
29%|██▉ | 294/1000 [00:25<00:46, 15.25it/s]
30%|██▉ | 296/1000 [00:25<00:46, 15.28it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.35it/s]
30%|███ | 300/1000 [00:25<00:46, 15.04it/s]
30%|███ | 302/1000 [00:25<00:46, 15.09it/s]
30%|███ | 304/1000 [00:25<00:50, 13.71it/s]
31%|███ | 306/1000 [00:26<00:48, 14.44it/s]
31%|███ | 308/1000 [00:26<00:47, 14.42it/s]
31%|███ | 310/1000 [00:26<00:47, 14.65it/s]
31%|███ | 312/1000 [00:26<00:47, 14.63it/s]
31%|███▏ | 314/1000 [00:26<00:44, 15.39it/s]
32%|███▏ | 316/1000 [00:26<00:43, 15.67it/s]
32%|███▏ | 318/1000 [00:26<00:42, 16.14it/s]
32%|███▏ | 320/1000 [00:27<01:12, 9.39it/s]
32%|███▏ | 322/1000 [00:27<01:03, 10.75it/s]
32%|███▏ | 324/1000 [00:27<00:58, 11.53it/s]
33%|███▎ | 326/1000 [00:27<00:58, 11.47it/s]
33%|███▎ | 328/1000 [00:27<00:58, 11.52it/s]
33%|███▎ | 330/1000 [00:27<00:53, 12.47it/s]
33%|███▎ | 332/1000 [00:28<00:52, 12.70it/s]
33%|███▎ | 334/1000 [00:28<00:50, 13.18it/s]
34%|███▎ | 336/1000 [00:28<00:48, 13.59it/s]
34%|███▍ | 338/1000 [00:28<01:05, 10.07it/s]
34%|███▍ | 340/1000 [00:28<00:59, 11.14it/s]
34%|███▍ | 342/1000 [00:28<00:53, 12.38it/s]
34%|███▍ | 344/1000 [00:29<00:50, 13.04it/s]
35%|███▍ | 346/1000 [00:29<00:50, 12.99it/s]
35%|███▍ | 348/1000 [00:29<00:47, 13.59it/s]
35%|███▌ | 350/1000 [00:29<00:45, 14.30it/s]
35%|███▌ | 352/1000 [00:29<00:44, 14.61it/s]
35%|███▌ | 354/1000 [00:29<00:44, 14.67it/s]
36%|███▌ | 356/1000 [00:29<00:43, 14.83it/s]
36%|███▌ | 358/1000 [00:30<00:44, 14.57it/s]
36%|███▌ | 360/1000 [00:30<00:42, 14.91it/s]
36%|███▌ | 362/1000 [00:30<00:48, 13.29it/s]
36%|███▋ | 364/1000 [00:30<00:46, 13.78it/s]
37%|███▋ | 366/1000 [00:30<00:53, 11.91it/s]
37%|███▋ | 368/1000 [00:30<00:51, 12.31it/s]
37%|███▋ | 370/1000 [00:30<00:47, 13.21it/s]
37%|███▋ | 372/1000 [00:31<00:46, 13.55it/s]
37%|███▋ | 374/1000 [00:31<00:55, 11.35it/s]
38%|███▊ | 376/1000 [00:31<00:56, 10.95it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.64it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.48it/s]
38%|███▊ | 382/1000 [00:31<00:48, 12.66it/s]
38%|███▊ | 384/1000 [00:32<00:45, 13.44it/s]
39%|███▊ | 386/1000 [00:32<00:44, 13.75it/s]
39%|███▉ | 388/1000 [00:32<00:45, 13.31it/s]
39%|███▉ | 390/1000 [00:32<00:46, 13.00it/s]
39%|███▉ | 392/1000 [00:32<00:46, 13.11it/s]
39%|███▉ | 394/1000 [00:32<00:49, 12.32it/s]
40%|███▉ | 396/1000 [00:33<00:47, 12.84it/s]
40%|███▉ | 398/1000 [00:33<00:50, 11.98it/s]
40%|████ | 400/1000 [00:33<00:47, 12.70it/s]
40%|████ | 402/1000 [00:33<00:44, 13.37it/s]
40%|████ | 404/1000 [00:33<00:42, 14.07it/s]
41%|████ | 406/1000 [00:33<00:40, 14.66it/s]
41%|████ | 408/1000 [00:33<00:47, 12.38it/s]
41%|████ | 410/1000 [00:34<00:57, 10.35it/s]
41%|████ | 412/1000 [00:34<00:51, 11.45it/s]
41%|████▏ | 414/1000 [00:34<01:08, 8.60it/s]
42%|████▏ | 416/1000 [00:35<01:24, 6.95it/s]
42%|████▏ | 418/1000 [00:35<01:10, 8.27it/s]
42%|████▏ | 420/1000 [00:35<01:00, 9.53it/s]
42%|████▏ | 422/1000 [00:35<00:53, 10.88it/s]
42%|████▏ | 424/1000 [00:35<01:15, 7.68it/s]
43%|████▎ | 426/1000 [00:36<01:04, 8.93it/s]
43%|████▎ | 428/1000 [00:36<00:56, 10.14it/s]
43%|████▎ | 430/1000 [00:36<01:02, 9.10it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.04it/s]
43%|████▎ | 434/1000 [00:37<01:08, 8.27it/s]
44%|████▎ | 436/1000 [00:37<01:00, 9.31it/s]
44%|████▍ | 438/1000 [00:37<01:02, 8.98it/s]
44%|████▍ | 440/1000 [00:37<00:59, 9.48it/s]
44%|████▍ | 442/1000 [00:37<00:52, 10.55it/s]
44%|████▍ | 444/1000 [00:37<00:48, 11.41it/s]
45%|████▍ | 446/1000 [00:38<00:45, 12.18it/s]
45%|████▍ | 448/1000 [00:38<00:50, 10.96it/s]
45%|████▌ | 450/1000 [00:38<00:46, 11.77it/s]
45%|████▌ | 452/1000 [00:38<00:42, 12.81it/s]
45%|████▌ | 454/1000 [00:38<00:44, 12.28it/s]
46%|████▌ | 456/1000 [00:38<00:42, 12.69it/s]
46%|████▌ | 458/1000 [00:39<00:40, 13.42it/s]
46%|████▌ | 460/1000 [00:39<00:41, 13.15it/s]
46%|████▌ | 462/1000 [00:39<00:41, 12.92it/s]
46%|████▋ | 464/1000 [00:39<00:41, 12.82it/s]
47%|████▋ | 466/1000 [00:39<00:42, 12.70it/s]
47%|████▋ | 468/1000 [00:39<00:45, 11.72it/s]
47%|████▋ | 470/1000 [00:40<00:56, 9.34it/s]
47%|████▋ | 472/1000 [00:40<00:55, 9.57it/s]
47%|████▋ | 474/1000 [00:40<00:51, 10.27it/s]
48%|████▊ | 476/1000 [00:40<00:48, 10.82it/s]
48%|████▊ | 478/1000 [00:40<00:48, 10.70it/s]
48%|████▊ | 480/1000 [00:41<00:44, 11.78it/s]
48%|████▊ | 482/1000 [00:41<00:41, 12.45it/s]
48%|████▊ | 484/1000 [00:41<00:39, 13.14it/s]
49%|████▊ | 486/1000 [00:41<00:38, 13.48it/s]
49%|████▉ | 488/1000 [00:41<00:36, 13.90it/s]
49%|████▉ | 490/1000 [00:41<00:46, 10.93it/s]
49%|████▉ | 492/1000 [00:41<00:41, 12.27it/s]
49%|████▉ | 494/1000 [00:42<00:38, 13.30it/s]
50%|████▉ | 496/1000 [00:42<00:35, 14.12it/s]
50%|████▉ | 498/1000 [00:42<00:34, 14.56it/s]
50%|█████ | 500/1000 [00:42<00:43, 11.57it/s]
50%|█████ | 502/1000 [00:42<00:41, 12.02it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.62it/s]
51%|█████ | 506/1000 [00:43<00:38, 12.78it/s]
51%|█████ | 508/1000 [00:43<00:35, 13.73it/s]
51%|█████ | 510/1000 [00:43<00:47, 10.41it/s]
51%|█████ | 512/1000 [00:43<00:43, 11.18it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 12.06it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.60it/s]
52%|█████▏ | 518/1000 [00:44<00:36, 13.10it/s]
52%|█████▏ | 520/1000 [00:44<00:35, 13.47it/s]
52%|█████▏ | 522/1000 [00:44<00:33, 14.20it/s]
52%|█████▏ | 524/1000 [00:44<00:33, 14.21it/s]
53%|█████▎ | 526/1000 [00:44<00:33, 14.36it/s]
53%|█████▎ | 528/1000 [00:44<00:39, 11.99it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.83it/s]
53%|█████▎ | 532/1000 [00:45<00:33, 13.79it/s]
53%|█████▎ | 534/1000 [00:45<00:32, 14.14it/s]
54%|█████▎ | 536/1000 [00:45<00:32, 14.26it/s]
54%|█████▍ | 538/1000 [00:45<00:32, 14.02it/s]
54%|█████▍ | 540/1000 [00:45<00:31, 14.45it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.74it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.60it/s]
55%|█████▍ | 546/1000 [00:46<00:31, 14.22it/s]
55%|█████▍ | 548/1000 [00:46<00:32, 13.90it/s]
55%|█████▌ | 550/1000 [00:46<00:31, 14.07it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.17it/s]
55%|█████▌ | 554/1000 [00:46<00:32, 13.87it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.04it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.41it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 14.98it/s]
56%|█████▌ | 562/1000 [00:47<00:29, 14.96it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.79it/s]
57%|█████▋ | 566/1000 [00:47<00:37, 11.69it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.25it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.39it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.88it/s]
57%|█████▋ | 574/1000 [00:48<00:30, 13.77it/s]
58%|█████▊ | 576/1000 [00:48<00:30, 13.68it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.68it/s]
58%|█████▊ | 580/1000 [00:48<00:32, 12.93it/s]
58%|█████▊ | 582/1000 [00:48<00:30, 13.67it/s]
58%|█████▊ | 584/1000 [00:48<00:31, 13.34it/s]
59%|█████▊ | 586/1000 [00:49<00:30, 13.52it/s]
59%|█████▉ | 588/1000 [00:49<00:29, 14.19it/s]
59%|█████▉ | 590/1000 [00:49<00:29, 13.71it/s]
59%|█████▉ | 592/1000 [00:49<00:29, 13.83it/s]
59%|█████▉ | 594/1000 [00:49<00:31, 12.76it/s]
60%|█████▉ | 596/1000 [00:49<00:31, 12.63it/s]
60%|█████▉ | 598/1000 [00:49<00:30, 13.17it/s]
60%|██████ | 600/1000 [00:50<00:29, 13.76it/s]
60%|██████ | 602/1000 [00:50<00:28, 13.95it/s]
60%|██████ | 604/1000 [00:50<00:27, 14.64it/s]
61%|██████ | 606/1000 [00:50<00:27, 14.53it/s]
61%|██████ | 608/1000 [00:50<00:26, 14.91it/s]
61%|██████ | 610/1000 [00:50<00:26, 14.85it/s]
61%|██████ | 612/1000 [00:50<00:25, 15.00it/s]
61%|██████▏ | 614/1000 [00:51<00:27, 14.28it/s]
62%|██████▏ | 616/1000 [00:51<00:26, 14.71it/s]
62%|██████▏ | 618/1000 [00:51<00:26, 14.16it/s]
62%|██████▏ | 620/1000 [00:51<00:39, 9.72it/s]
62%|██████▏ | 622/1000 [00:51<00:38, 9.93it/s]
62%|██████▏ | 624/1000 [00:51<00:33, 11.33it/s]
63%|██████▎ | 626/1000 [00:52<00:31, 12.03it/s]
63%|██████▎ | 628/1000 [00:52<00:28, 13.28it/s]
63%|██████▎ | 630/1000 [00:52<00:26, 13.78it/s]
63%|██████▎ | 632/1000 [00:52<00:29, 12.46it/s]
63%|██████▎ | 634/1000 [00:52<00:27, 13.18it/s]
64%|██████▎ | 636/1000 [00:52<00:33, 10.92it/s]
64%|██████▍ | 638/1000 [00:53<00:33, 10.67it/s]
64%|██████▍ | 640/1000 [00:53<00:31, 11.47it/s]
64%|██████▍ | 642/1000 [00:53<00:28, 12.35it/s]
64%|██████▍ | 644/1000 [00:53<00:28, 12.58it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.87it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.34it/s]
65%|██████▌ | 650/1000 [00:54<00:27, 12.52it/s]
65%|██████▌ | 652/1000 [00:54<00:29, 11.94it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.43it/s]
66%|██████▌ | 656/1000 [00:54<00:31, 10.78it/s]
66%|██████▌ | 658/1000 [00:54<00:28, 11.85it/s]
66%|██████▌ | 660/1000 [00:55<00:33, 10.15it/s]
66%|██████▌ | 662/1000 [00:55<00:29, 11.27it/s]
66%|██████▋ | 664/1000 [00:55<00:28, 11.76it/s]
67%|██████▋ | 666/1000 [00:55<00:26, 12.79it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.33it/s]
67%|██████▋ | 670/1000 [00:55<00:29, 11.11it/s]
67%|██████▋ | 672/1000 [00:56<00:27, 11.87it/s]
67%|██████▋ | 674/1000 [00:56<00:26, 12.45it/s]
68%|██████▊ | 676/1000 [00:56<00:27, 11.71it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.20it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 12.97it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.12it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.31it/s]
69%|██████▊ | 686/1000 [00:57<00:23, 13.32it/s]
69%|██████▉ | 688/1000 [00:57<00:22, 13.64it/s]
69%|██████▉ | 690/1000 [00:57<00:22, 13.72it/s]
69%|██████▉ | 692/1000 [00:57<00:23, 13.25it/s]
69%|██████▉ | 694/1000 [00:57<00:23, 12.96it/s]
70%|██████▉ | 696/1000 [00:57<00:23, 12.76it/s]
70%|██████▉ | 698/1000 [00:58<00:23, 12.65it/s]
70%|███████ | 700/1000 [00:58<00:27, 11.05it/s]
70%|███████ | 702/1000 [00:58<00:26, 11.44it/s]
70%|███████ | 704/1000 [00:58<00:32, 9.10it/s]
71%|███████ | 706/1000 [00:58<00:29, 9.81it/s]
71%|███████ | 708/1000 [00:59<00:32, 8.98it/s]
71%|███████ | 710/1000 [00:59<00:29, 9.80it/s]
71%|███████ | 712/1000 [00:59<00:27, 10.44it/s]
71%|███████▏ | 714/1000 [00:59<00:25, 11.02it/s]
72%|███████▏ | 716/1000 [00:59<00:24, 11.61it/s]
72%|███████▏ | 718/1000 [00:59<00:24, 11.61it/s]
72%|███████▏ | 720/1000 [01:00<00:22, 12.71it/s]
72%|███████▏ | 722/1000 [01:00<00:20, 13.32it/s]
72%|███████▏ | 724/1000 [01:00<00:20, 13.29it/s]
73%|███████▎ | 726/1000 [01:00<00:20, 13.62it/s]
73%|███████▎ | 728/1000 [01:00<00:23, 11.76it/s]
73%|███████▎ | 730/1000 [01:01<00:33, 8.15it/s]
73%|███████▎ | 732/1000 [01:01<00:32, 8.27it/s]
73%|███████▎ | 734/1000 [01:01<00:31, 8.58it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.42it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.87it/s]
74%|███████▍ | 739/1000 [01:02<00:23, 10.98it/s]
74%|███████▍ | 741/1000 [01:02<00:22, 11.67it/s]
74%|███████▍ | 743/1000 [01:02<00:20, 12.28it/s]
74%|███████▍ | 745/1000 [01:02<00:25, 10.16it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.54it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.73it/s]
75%|███████▌ | 751/1000 [01:03<00:21, 11.85it/s]
75%|███████▌ | 753/1000 [01:03<00:19, 12.63it/s]
76%|███████▌ | 755/1000 [01:03<00:18, 13.36it/s]
76%|███████▌ | 757/1000 [01:03<00:23, 10.31it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.33it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.32it/s]
76%|███████▋ | 763/1000 [01:04<00:23, 10.26it/s]
76%|███████▋ | 765/1000 [01:04<00:27, 8.60it/s]
77%|███████▋ | 767/1000 [01:04<00:24, 9.56it/s]
77%|███████▋ | 769/1000 [01:04<00:21, 10.58it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.84it/s]
77%|███████▋ | 773/1000 [01:05<00:17, 12.65it/s]
78%|███████▊ | 775/1000 [01:05<00:17, 13.18it/s]
78%|███████▊ | 777/1000 [01:05<00:15, 14.20it/s]
78%|███████▊ | 779/1000 [01:05<00:20, 10.53it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.42it/s]
78%|███████▊ | 783/1000 [01:06<00:26, 8.33it/s]
78%|███████▊ | 784/1000 [01:06<00:30, 7.07it/s]
78%|███████▊ | 785/1000 [01:06<00:29, 7.38it/s]
79%|███████▊ | 786/1000 [01:06<00:30, 6.98it/s]
79%|███████▉ | 788/1000 [01:06<00:24, 8.81it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.14it/s]
79%|███████▉ | 792/1000 [01:07<00:18, 11.29it/s]
79%|███████▉ | 794/1000 [01:07<00:17, 11.89it/s]
80%|███████▉ | 796/1000 [01:07<00:16, 12.35it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 13.10it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.30it/s]
80%|████████ | 802/1000 [01:08<00:24, 8.23it/s]
80%|████████ | 804/1000 [01:08<00:23, 8.28it/s]
81%|████████ | 806/1000 [01:08<00:21, 8.96it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.35it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.47it/s]
81%|████████ | 812/1000 [01:09<00:16, 11.69it/s]
81%|████████▏ | 814/1000 [01:09<00:14, 12.59it/s]
82%|████████▏ | 816/1000 [01:09<00:16, 11.02it/s]
82%|████████▏ | 818/1000 [01:09<00:15, 11.95it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.21it/s]
82%|████████▏ | 822/1000 [01:09<00:14, 12.39it/s]
82%|████████▏ | 824/1000 [01:09<00:13, 13.25it/s]
83%|████████▎ | 826/1000 [01:10<00:13, 13.15it/s]
83%|████████▎ | 828/1000 [01:10<00:12, 13.28it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.09it/s]
83%|████████▎ | 832/1000 [01:10<00:12, 13.64it/s]
83%|████████▎ | 834/1000 [01:10<00:12, 13.29it/s]
84%|████████▎ | 836/1000 [01:10<00:12, 13.66it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 13.78it/s]
84%|████████▍ | 840/1000 [01:11<00:11, 14.07it/s]
84%|████████▍ | 842/1000 [01:11<00:11, 14.19it/s]
84%|████████▍ | 844/1000 [01:11<00:11, 13.77it/s]
85%|████████▍ | 846/1000 [01:11<00:11, 13.81it/s]
85%|████████▍ | 848/1000 [01:11<00:11, 13.79it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.03it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.66it/s]
85%|████████▌ | 854/1000 [01:12<00:10, 14.26it/s]
86%|████████▌ | 856/1000 [01:12<00:10, 14.07it/s]
86%|████████▌ | 858/1000 [01:12<00:09, 14.59it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 15.34it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.49it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 14.02it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.81it/s]
87%|████████▋ | 868/1000 [01:13<00:09, 13.71it/s]
87%|████████▋ | 870/1000 [01:13<00:09, 14.10it/s]
87%|████████▋ | 872/1000 [01:13<00:08, 14.71it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 12.43it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.03it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.91it/s]
88%|████████▊ | 880/1000 [01:14<00:09, 12.55it/s]
88%|████████▊ | 882/1000 [01:14<00:10, 11.34it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 12.07it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.87it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.84it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.27it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.03it/s]
89%|████████▉ | 894/1000 [01:15<00:07, 14.18it/s]
90%|████████▉ | 896/1000 [01:15<00:07, 13.92it/s]
90%|████████▉ | 898/1000 [01:15<00:07, 13.31it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.24it/s]
90%|█████████ | 902/1000 [01:15<00:07, 12.98it/s]
90%|█████████ | 904/1000 [01:16<00:09, 9.80it/s]
91%|█████████ | 906/1000 [01:16<00:08, 10.86it/s]
91%|█████████ | 908/1000 [01:16<00:07, 11.82it/s]
91%|█████████ | 910/1000 [01:16<00:07, 12.06it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.11it/s]
91%|█████████▏| 914/1000 [01:17<00:12, 6.76it/s]
92%|█████████▏| 916/1000 [01:17<00:10, 7.82it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.18it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.25it/s]
92%|█████████▏| 922/1000 [01:18<00:08, 9.26it/s]
92%|█████████▏| 924/1000 [01:18<00:07, 10.07it/s]
93%|█████████▎| 926/1000 [01:18<00:06, 10.75it/s]
93%|█████████▎| 928/1000 [01:18<00:07, 10.00it/s]
93%|█████████▎| 930/1000 [01:18<00:06, 10.79it/s]
93%|█████████▎| 932/1000 [01:18<00:06, 11.19it/s]
93%|█████████▎| 934/1000 [01:19<00:05, 11.78it/s]
94%|█████████▎| 936/1000 [01:19<00:05, 12.59it/s]
94%|█████████▍| 938/1000 [01:19<00:04, 13.68it/s]
94%|█████████▍| 940/1000 [01:19<00:04, 14.12it/s]
94%|█████████▍| 942/1000 [01:19<00:04, 11.98it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.58it/s]
95%|█████████▍| 946/1000 [01:20<00:04, 11.72it/s]
95%|█████████▍| 948/1000 [01:20<00:04, 12.55it/s]
95%|█████████▌| 950/1000 [01:20<00:03, 13.37it/s]
95%|█████████▌| 952/1000 [01:20<00:03, 14.10it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 14.22it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 14.21it/s]
96%|█████████▌| 958/1000 [01:20<00:02, 14.77it/s]
96%|█████████▌| 960/1000 [01:21<00:03, 13.11it/s]
96%|█████████▌| 962/1000 [01:21<00:02, 13.74it/s]
96%|█████████▋| 964/1000 [01:21<00:03, 10.23it/s]
97%|█████████▋| 966/1000 [01:21<00:02, 11.46it/s]
97%|█████████▋| 968/1000 [01:21<00:02, 12.11it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 12.72it/s]
97%|█████████▋| 972/1000 [01:22<00:02, 9.70it/s]
97%|█████████▋| 974/1000 [01:22<00:02, 10.89it/s]
98%|█████████▊| 976/1000 [01:22<00:02, 11.69it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.86it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.95it/s]
98%|█████████▊| 982/1000 [01:23<00:01, 10.45it/s]
98%|█████████▊| 984/1000 [01:23<00:01, 10.46it/s]
99%|█████████▊| 986/1000 [01:23<00:01, 10.68it/s]
99%|█████████▉| 988/1000 [01:23<00:01, 10.87it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 11.36it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 12.27it/s]
99%|█████████▉| 994/1000 [01:24<00:00, 13.02it/s]
100%|█████████▉| 996/1000 [01:24<00:00, 13.93it/s]
100%|█████████▉| 998/1000 [01:24<00:00, 14.09it/s]
100%|██████████| 1000/1000 [01:24<00:00, 14.74it/s]
{'eval_loss': '0.8801', 'eval_runtime': '84.53', 'eval_samples_per_second': '11.83', 'eval_steps_per_second': '11.83', 'epoch': '0.7492', 'num_input_tokens_seen': 47040648}
75%|███████▍ | 14000/18688 [16:26:26<5:33:35, 4.27s/it]
100%|██████████| 1000/1000 [01:24<00:00, 14.74it/s]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00008/checkpoint-14000
loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
75%|███████▍ | 14001/18688 [16:26:31<39:01:03, 29.97s/it] 75%|███████▍ | 14002/18688 [16:26:36<29:09:58, 22.41s/it] 75%|███████▍ | 14003/18688 [16:26:41<22:10:02, 17.03s/it] 75%|███████▍ | 14004/18688 [16:26:45<17:12:07, 13.22s/it] 75%|███████▍ | 14005/18688 [16:26:50<13:47:56, 10.61s/it] 75%|███████▍ | 14006/18688 [16:26:54<11:33:51, 8.89s/it] 75%|███████▍ | 14007/18688 [16:26:58<9:39:31, 7.43s/it] 75%|███████▍ | 14008/18688 [16:27:02<8:08:34, 6.26s/it] 75%|███████▍ | 14009/18688 [16:27:08<7:56:57, 6.12s/it] 75%|███████▍ | 14010/18688 [16:27:12<7:10:46, 5.53s/it] {'loss': '0.9996', 'grad_norm': '1.645', 'learning_rate': '1.376e-05', 'epoch': '0.7497', 'num_input_tokens_seen': 47078616, 'train_runtime': '5.923e+04', 'train_tokens_per_second': '794.8'}
75%|███████▍ | 14010/18688 [16:27:12<7:10:46, 5.53s/it] 75%|███████▍ | 14011/18688 [16:27:16<6:28:12, 4.98s/it] 75%|███████▍ | 14012/18688 [16:27:19<6:01:35, 4.64s/it] 75%|███████▍ | 14013/18688 [16:27:25<6:14:00, 4.80s/it] 75%|███████▍ | 14014/18688 [16:27:29<6:04:16, 4.68s/it] 75%|███████▍ | 14015/18688 [16:27:33<5:59:12, 4.61s/it] 75%|███████▌ | 14016/18688 [16:27:37<5:38:11, 4.34s/it] 75%|███████▌ | 14017/18688 [16:27:41<5:32:07, 4.27s/it] 75%|███████▌ | 14018/18688 [16:27:45<5:21:12, 4.13s/it] 75%|███████▌ | 14019/18688 [16:27:49<5:10:05, 3.98s/it] 75%|███████▌ | 14020/18688 [16:27:53<5:11:20, 4.00s/it] {'loss': '0.9518', 'grad_norm': '1.251', 'learning_rate': '1.37e-05', 'epoch': '0.7502', 'num_input_tokens_seen': 47112024, 'train_runtime': '5.927e+04', 'train_tokens_per_second': '794.8'}
75%|███████▌ | 14020/18688 [16:27:53<5:11:20, 4.00s/it] 75%|███████▌ | 14021/18688 [16:27:57<5:05:30, 3.93s/it] 75%|███████▌ | 14022/18688 [16:28:01<5:20:32, 4.12s/it] 75%|███████▌ | 14023/18688 [16:28:05<5:16:33, 4.07s/it] 75%|███████▌ | 14024/18688 [16:28:10<5:38:03, 4.35s/it] 75%|███████▌ | 14025/18688 [16:28:14<5:27:51, 4.22s/it] 75%|███████▌ | 14026/18688 [16:28:18<5:13:31, 4.04s/it] 75%|███████▌ | 14027/18688 [16:28:23<5:54:41, 4.57s/it] 75%|███████▌ | 14028/18688 [16:28:28<5:47:03, 4.47s/it] 75%|███████▌ | 14029/18688 [16:28:32<5:34:38, 4.31s/it] 75%|███████▌ | 14030/18688 [16:28:36<5:32:23, 4.28s/it] {'loss': '0.897', 'grad_norm': '1.135', 'learning_rate': '1.365e-05', 'epoch': '0.7508', 'num_input_tokens_seen': 47146904, 'train_runtime': '5.932e+04', 'train_tokens_per_second': '794.8'}
75%|███████▌ | 14030/18688 [16:28:36<5:32:23, 4.28s/it] 75%|███████▌ | 14031/18688 [16:28:40<5:20:56, 4.14s/it] 75%|███████▌ | 14032/18688 [16:28:45<5:44:57, 4.45s/it] 75%|███████▌ | 14033/18688 [16:28:49<5:42:23, 4.41s/it] 75%|███████▌ | 14034/18688 [16:28:53<5:33:42, 4.30s/it] 75%|███████▌ | 14035/18688 [16:28:58<5:39:53, 4.38s/it] 75%|███████▌ | 14036/18688 [16:29:02<5:40:57, 4.40s/it] 75%|███████▌ | 14037/18688 [16:29:07<6:00:26, 4.65s/it] 75%|███████▌ | 14038/18688 [16:29:11<5:47:14, 4.48s/it] 75%|███████▌ | 14039/18688 [16:29:15<5:28:37, 4.24s/it] 75%|███████▌ | 14040/18688 [16:29:20<5:37:48, 4.36s/it] {'loss': '0.8856', 'grad_norm': '1.253', 'learning_rate': '1.359e-05', 'epoch': '0.7513', 'num_input_tokens_seen': 47184824, 'train_runtime': '5.936e+04', 'train_tokens_per_second': '794.9'}
75%|███████▌ | 14040/18688 [16:29:20<5:37:48, 4.36s/it] 75%|███████▌ | 14041/18688 [16:29:24<5:40:18, 4.39s/it] 75%|███████▌ | 14042/18688 [16:29:28<5:29:11, 4.25s/it] 75%|███████▌ | 14043/18688 [16:29:32<5:16:27, 4.09s/it] 75%|███████▌ | 14044/18688 [16:29:36<5:27:37, 4.23s/it] 75%|███████▌ | 14045/18688 [16:29:41<5:29:34, 4.26s/it] 75%|███████▌ | 14046/18688 [16:29:45<5:28:54, 4.25s/it] 75%|███████▌ | 14047/18688 [16:29:50<5:41:43, 4.42s/it] 75%|███████▌ | 14048/18688 [16:29:54<5:25:45, 4.21s/it] 75%|███████▌ | 14049/18688 [16:29:58<5:32:32, 4.30s/it] 75%|███████▌ | 14050/18688 [16:30:02<5:20:08, 4.14s/it] {'loss': '0.9233', 'grad_norm': '1.214', 'learning_rate': '1.354e-05', 'epoch': '0.7518', 'num_input_tokens_seen': 47218264, 'train_runtime': '5.94e+04', 'train_tokens_per_second': '794.9'}
75%|███████▌ | 14050/18688 [16:30:02<5:20:08, 4.14s/it] 75%|███████▌ | 14051/18688 [16:30:06<5:31:00, 4.28s/it] 75%|███████▌ | 14052/18688 [16:30:10<5:24:25, 4.20s/it] 75%|███████▌ | 14053/18688 [16:30:14<5:09:24, 4.01s/it] 75%|███████▌ | 14054/18688 [16:30:18<5:03:05, 3.92s/it] 75%|███████▌ | 14055/18688 [16:30:22<5:21:12, 4.16s/it] 75%|███████▌ | 14056/18688 [16:30:26<5:14:36, 4.08s/it] 75%|███████▌ | 14057/18688 [16:30:30<5:14:11, 4.07s/it] 75%|███████▌ | 14058/18688 [16:30:35<5:21:19, 4.16s/it] 75%|███████▌ | 14059/18688 [16:30:39<5:28:11, 4.25s/it] 75%|███████▌ | 14060/18688 [16:30:43<5:22:26, 4.18s/it] {'loss': '0.9035', 'grad_norm': '1.33', 'learning_rate': '1.348e-05', 'epoch': '0.7524', 'num_input_tokens_seen': 47251056, 'train_runtime': '5.944e+04', 'train_tokens_per_second': '794.9'}
75%|███████▌ | 14060/18688 [16:30:43<5:22:26, 4.18s/it] 75%|███████▌ | 14061/18688 [16:30:47<5:19:53, 4.15s/it] 75%|███████▌ | 14062/18688 [16:30:53<6:05:10, 4.74s/it] 75%|███████▌ | 14063/18688 [16:30:57<5:43:00, 4.45s/it] 75%|███████▌ | 14064/18688 [16:31:01<5:28:43, 4.27s/it] 75%|███████▌ | 14065/18688 [16:31:05<5:29:46, 4.28s/it] 75%|███████▌ | 14066/18688 [16:31:10<5:45:01, 4.48s/it] 75%|███████▌ | 14067/18688 [16:31:15<5:51:41, 4.57s/it] 75%|███████▌ | 14068/18688 [16:31:20<6:08:18, 4.78s/it] 75%|███████▌ | 14069/18688 [16:31:25<5:57:03, 4.64s/it] 75%|███████▌ | 14070/18688 [16:31:29<5:41:06, 4.43s/it] {'loss': '0.9387', 'grad_norm': '1.327', 'learning_rate': '1.343e-05', 'epoch': '0.7529', 'num_input_tokens_seen': 47289704, 'train_runtime': '5.949e+04', 'train_tokens_per_second': '794.9'}
75%|███████▌ | 14070/18688 [16:31:29<5:41:06, 4.43s/it] 75%|███████▌ | 14071/18688 [16:31:33<5:42:50, 4.46s/it] 75%|███████▌ | 14072/18688 [16:31:37<5:31:46, 4.31s/it] 75%|███████▌ | 14073/18688 [16:31:41<5:23:55, 4.21s/it] 75%|███████▌ | 14074/18688 [16:31:45<5:19:00, 4.15s/it] 75%|███████▌ | 14075/18688 [16:31:49<5:19:46, 4.16s/it] 75%|███████▌ | 14076/18688 [16:31:53<5:03:50, 3.95s/it] 75%|███████▌ | 14077/18688 [16:31:57<5:10:54, 4.05s/it] 75%|███████▌ | 14078/18688 [16:32:02<5:27:52, 4.27s/it] 75%|███████▌ | 14079/18688 [16:32:06<5:25:08, 4.23s/it] 75%|███████▌ | 14080/18688 [16:32:10<5:30:26, 4.30s/it] {'loss': '0.9788', 'grad_norm': '1.446', 'learning_rate': '1.337e-05', 'epoch': '0.7534', 'num_input_tokens_seen': 47323048, 'train_runtime': '5.953e+04', 'train_tokens_per_second': '794.9'}
75%|███████▌ | 14080/18688 [16:32:10<5:30:26, 4.30s/it] 75%|███████▌ | 14081/18688 [16:32:15<5:27:51, 4.27s/it] 75%|███████▌ | 14082/18688 [16:32:19<5:33:50, 4.35s/it] 75%|███████▌ | 14083/18688 [16:32:23<5:34:02, 4.35s/it] 75%|███████▌ | 14084/18688 [16:32:27<5:16:43, 4.13s/it] 75%|███████▌ | 14085/18688 [16:32:31<5:07:11, 4.00s/it] 75%|███████▌ | 14086/18688 [16:32:35<5:07:47, 4.01s/it] 75%|███████▌ | 14087/18688 [16:32:38<4:57:11, 3.88s/it] 75%|███████▌ | 14088/18688 [16:32:43<5:19:16, 4.16s/it] 75%|███████▌ | 14089/18688 [16:32:49<5:54:17, 4.62s/it] 75%|███████▌ | 14090/18688 [16:32:53<5:42:01, 4.46s/it] {'loss': '0.9462', 'grad_norm': '1.351', 'learning_rate': '1.332e-05', 'epoch': '0.754', 'num_input_tokens_seen': 47358160, 'train_runtime': '5.957e+04', 'train_tokens_per_second': '795'}
75%|███████▌ | 14090/18688 [16:32:53<5:42:01, 4.46s/it] 75%|███████▌ | 14091/18688 [16:32:58<5:59:50, 4.70s/it] 75%|███████▌ | 14092/18688 [16:33:02<5:49:29, 4.56s/it] 75%|███████▌ | 14093/18688 [16:33:07<5:48:47, 4.55s/it] 75%|███████▌ | 14094/18688 [16:33:11<5:31:28, 4.33s/it] 75%|███████▌ | 14095/18688 [16:33:15<5:26:14, 4.26s/it] 75%|███████▌ | 14096/18688 [16:33:20<5:36:48, 4.40s/it] 75%|███████▌ | 14097/18688 [16:33:24<5:25:30, 4.25s/it] 75%|███████▌ | 14098/18688 [16:33:29<5:49:32, 4.57s/it] 75%|███████▌ | 14099/18688 [16:33:33<5:38:13, 4.42s/it] 75%|███████▌ | 14100/18688 [16:33:37<5:19:12, 4.17s/it] {'loss': '0.8551', 'grad_norm': '1.251', 'learning_rate': '1.326e-05', 'epoch': '0.7545', 'num_input_tokens_seen': 47394824, 'train_runtime': '5.962e+04', 'train_tokens_per_second': '795'}
75%|███████▌ | 14100/18688 [16:33:37<5:19:12, 4.17s/it] 75%|███████▌ | 14101/18688 [16:33:40<5:09:00, 4.04s/it] 75%|███████▌ | 14102/18688 [16:33:45<5:19:56, 4.19s/it] 75%|███████▌ | 14103/18688 [16:33:49<5:21:28, 4.21s/it] 75%|███████▌ | 14104/18688 [16:33:53<5:21:41, 4.21s/it] 75%|███████▌ | 14105/18688 [16:33:57<5:16:21, 4.14s/it] 75%|███████▌ | 14106/18688 [16:34:01<5:14:38, 4.12s/it] 75%|███████▌ | 14107/18688 [16:34:05<5:10:53, 4.07s/it] 75%|███████▌ | 14108/18688 [16:34:10<5:21:22, 4.21s/it] 75%|███████▌ | 14109/18688 [16:34:14<5:20:10, 4.20s/it] 76%|███████▌ | 14110/18688 [16:34:18<5:16:37, 4.15s/it] {'loss': '0.9044', 'grad_norm': '1.302', 'learning_rate': '1.321e-05', 'epoch': '0.7551', 'num_input_tokens_seen': 47428384, 'train_runtime': '5.966e+04', 'train_tokens_per_second': '795'}
76%|███████▌ | 14110/18688 [16:34:18<5:16:37, 4.15s/it] 76%|███████▌ | 14111/18688 [16:34:22<5:18:13, 4.17s/it] 76%|███████▌ | 14112/18688 [16:34:26<5:14:04, 4.12s/it] 76%|███████▌ | 14113/18688 [16:34:30<5:08:12, 4.04s/it] 76%|███████▌ | 14114/18688 [16:34:34<5:12:45, 4.10s/it] 76%|███████▌ | 14115/18688 [16:34:39<5:22:10, 4.23s/it] 76%|███████▌ | 14116/18688 [16:34:43<5:13:38, 4.12s/it] 76%|███████▌ | 14117/18688 [16:34:47<5:06:32, 4.02s/it] 76%|███████▌ | 14118/18688 [16:34:51<5:06:46, 4.03s/it] 76%|███████▌ | 14119/18688 [16:34:55<5:24:46, 4.26s/it] 76%|███████▌ | 14120/18688 [16:35:00<5:34:13, 4.39s/it] {'loss': '0.8263', 'grad_norm': '1.229', 'learning_rate': '1.316e-05', 'epoch': '0.7556', 'num_input_tokens_seen': 47462432, 'train_runtime': '5.97e+04', 'train_tokens_per_second': '795'}
76%|███████▌ | 14120/18688 [16:35:00<5:34:13, 4.39s/it] 76%|███████▌ | 14121/18688 [16:35:04<5:28:06, 4.31s/it] 76%|███████▌ | 14122/18688 [16:35:08<5:27:16, 4.30s/it] 76%|███████▌ | 14123/18688 [16:35:13<5:31:40, 4.36s/it] 76%|███████▌ | 14124/18688 [16:35:17<5:18:38, 4.19s/it] 76%|███████▌ | 14125/18688 [16:35:21<5:13:16, 4.12s/it] 76%|███████▌ | 14126/18688 [16:35:25<5:09:42, 4.07s/it] 76%|███████▌ | 14127/18688 [16:35:29<5:12:16, 4.11s/it] 76%|███████▌ | 14128/18688 [16:35:33<5:02:12, 3.98s/it] 76%|███████▌ | 14129/18688 [16:35:37<5:08:52, 4.07s/it] 76%|███████▌ | 14130/18688 [16:35:41<5:01:53, 3.97s/it] {'loss': '0.9197', 'grad_norm': '1.467', 'learning_rate': '1.31e-05', 'epoch': '0.7561', 'num_input_tokens_seen': 47493992, 'train_runtime': '5.974e+04', 'train_tokens_per_second': '795'}
76%|███████▌ | 14130/18688 [16:35:41<5:01:53, 3.97s/it] 76%|███████▌ | 14131/18688 [16:35:45<5:05:13, 4.02s/it] 76%|███████▌ | 14132/18688 [16:35:48<5:00:15, 3.95s/it] 76%|███████▌ | 14133/18688 [16:35:52<4:52:09, 3.85s/it] 76%|███████▌ | 14134/18688 [16:35:56<4:58:11, 3.93s/it] 76%|███████▌ | 14135/18688 [16:36:00<4:51:04, 3.84s/it] 76%|███████▌ | 14136/18688 [16:36:05<5:26:31, 4.30s/it] 76%|███████▌ | 14137/18688 [16:36:09<5:22:42, 4.25s/it] 76%|███████▌ | 14138/18688 [16:36:14<5:27:37, 4.32s/it] 76%|███████▌ | 14139/18688 [16:36:18<5:16:40, 4.18s/it] 76%|███████▌ | 14140/18688 [16:36:21<5:02:44, 3.99s/it] {'loss': '0.8964', 'grad_norm': '1.505', 'learning_rate': '1.305e-05', 'epoch': '0.7567', 'num_input_tokens_seen': 47526208, 'train_runtime': '5.978e+04', 'train_tokens_per_second': '795'}
76%|███████▌ | 14140/18688 [16:36:21<5:02:44, 3.99s/it] 76%|███████▌ | 14141/18688 [16:36:26<5:09:20, 4.08s/it] 76%|███████▌ | 14142/18688 [16:36:30<5:17:22, 4.19s/it] 76%|███████▌ | 14143/18688 [16:36:35<5:34:37, 4.42s/it] 76%|███████▌ | 14144/18688 [16:36:39<5:29:00, 4.34s/it] 76%|███████▌ | 14145/18688 [16:36:43<5:27:28, 4.32s/it] 76%|███████▌ | 14146/18688 [16:36:47<5:13:03, 4.14s/it] 76%|███████▌ | 14147/18688 [16:36:51<5:04:11, 4.02s/it] 76%|███████▌ | 14148/18688 [16:36:55<5:02:20, 4.00s/it] 76%|███████▌ | 14149/18688 [16:37:00<5:28:09, 4.34s/it] 76%|███████▌ | 14150/18688 [16:37:04<5:14:36, 4.16s/it] {'loss': '0.895', 'grad_norm': '1.155', 'learning_rate': '1.299e-05', 'epoch': '0.7572', 'num_input_tokens_seen': 47562168, 'train_runtime': '5.982e+04', 'train_tokens_per_second': '795'}
76%|███████▌ | 14150/18688 [16:37:04<5:14:36, 4.16s/it] 76%|███████▌ | 14151/18688 [16:37:08<5:13:34, 4.15s/it] 76%|███████▌ | 14152/18688 [16:37:12<5:22:21, 4.26s/it] 76%|███████▌ | 14153/18688 [16:37:16<5:18:59, 4.22s/it] 76%|███████▌ | 14154/18688 [16:37:20<5:02:05, 4.00s/it] 76%|███████▌ | 14155/18688 [16:37:24<4:54:59, 3.90s/it] 76%|███████▌ | 14156/18688 [16:37:28<5:11:24, 4.12s/it] 76%|███████▌ | 14157/18688 [16:37:32<5:03:25, 4.02s/it] 76%|███████▌ | 14158/18688 [16:37:36<4:56:35, 3.93s/it] 76%|███████▌ | 14159/18688 [16:37:40<4:55:07, 3.91s/it] 76%|███████▌ | 14160/18688 [16:37:44<4:57:10, 3.94s/it] {'loss': '0.935', 'grad_norm': '1.414', 'learning_rate': '1.294e-05', 'epoch': '0.7577', 'num_input_tokens_seen': 47594240, 'train_runtime': '5.986e+04', 'train_tokens_per_second': '795'}
76%|███████▌ | 14160/18688 [16:37:44<4:57:10, 3.94s/it] 76%|███████▌ | 14161/18688 [16:37:48<5:14:38, 4.17s/it] 76%|███████▌ | 14162/18688 [16:37:52<5:04:44, 4.04s/it] 76%|███████▌ | 14163/18688 [16:37:56<5:00:28, 3.98s/it] 76%|███████▌ | 14164/18688 [16:38:01<5:23:37, 4.29s/it] 76%|███████▌ | 14165/18688 [16:38:05<5:15:42, 4.19s/it] 76%|███████▌ | 14166/18688 [16:38:09<5:21:11, 4.26s/it] 76%|███████▌ | 14167/18688 [16:38:13<5:11:12, 4.13s/it] 76%|███████▌ | 14168/18688 [16:38:17<4:59:08, 3.97s/it] 76%|███████▌ | 14169/18688 [16:38:21<5:11:01, 4.13s/it] 76%|███████▌ | 14170/18688 [16:38:25<5:01:35, 4.01s/it] {'loss': '0.8785', 'grad_norm': '1.412', 'learning_rate': '1.289e-05', 'epoch': '0.7583', 'num_input_tokens_seen': 47625832, 'train_runtime': '5.991e+04', 'train_tokens_per_second': '795'}
76%|███████▌ | 14170/18688 [16:38:25<5:01:35, 4.01s/it] 76%|███████▌ | 14171/18688 [16:38:29<5:09:55, 4.12s/it] 76%|███████▌ | 14172/18688 [16:38:34<5:17:54, 4.22s/it] 76%|███████▌ | 14173/18688 [16:38:38<5:29:09, 4.37s/it] 76%|███████▌ | 14174/18688 [16:38:42<5:18:51, 4.24s/it] 76%|███████▌ | 14175/18688 [16:38:46<5:04:06, 4.04s/it] 76%|███████▌ | 14176/18688 [16:38:50<5:12:29, 4.16s/it] 76%|███████▌ | 14177/18688 [16:38:55<5:15:30, 4.20s/it] 76%|███████▌ | 14178/18688 [16:38:59<5:14:30, 4.18s/it] 76%|███████▌ | 14179/18688 [16:39:03<5:08:51, 4.11s/it] 76%|███████▌ | 14180/18688 [16:39:07<5:12:38, 4.16s/it] {'loss': '0.9733', 'grad_norm': '1.036', 'learning_rate': '1.283e-05', 'epoch': '0.7588', 'num_input_tokens_seen': 47660328, 'train_runtime': '5.995e+04', 'train_tokens_per_second': '795'}
76%|███████▌ | 14180/18688 [16:39:07<5:12:38, 4.16s/it] 76%|███████▌ | 14181/18688 [16:39:11<5:16:48, 4.22s/it] 76%|███████▌ | 14182/18688 [16:39:15<5:12:07, 4.16s/it] 76%|███████▌ | 14183/18688 [16:39:20<5:30:40, 4.40s/it] 76%|███████▌ | 14184/18688 [16:39:25<5:38:19, 4.51s/it] 76%|███████▌ | 14185/18688 [16:39:29<5:15:29, 4.20s/it] 76%|███████▌ | 14186/18688 [16:39:32<5:06:07, 4.08s/it] 76%|███████▌ | 14187/18688 [16:39:38<5:39:56, 4.53s/it] 76%|███████▌ | 14188/18688 [16:39:42<5:21:45, 4.29s/it] 76%|███████▌ | 14189/18688 [16:39:46<5:14:47, 4.20s/it] 76%|███████▌ | 14190/18688 [16:39:49<5:02:49, 4.04s/it] {'loss': '0.9278', 'grad_norm': '1.356', 'learning_rate': '1.278e-05', 'epoch': '0.7593', 'num_input_tokens_seen': 47694656, 'train_runtime': '5.999e+04', 'train_tokens_per_second': '795'}
76%|███████▌ | 14190/18688 [16:39:49<5:02:49, 4.04s/it] 76%|███████▌ | 14191/18688 [16:39:54<5:14:26, 4.20s/it] 76%|███████▌ | 14192/18688 [16:39:58<5:03:21, 4.05s/it] 76%|███████▌ | 14193/18688 [16:40:01<4:50:41, 3.88s/it] 76%|███████▌ | 14194/18688 [16:40:05<4:58:35, 3.99s/it] 76%|███████▌ | 14195/18688 [16:40:10<5:18:45, 4.26s/it] 76%|███████▌ | 14196/18688 [16:40:14<5:16:38, 4.23s/it] 76%|███████▌ | 14197/18688 [16:40:18<5:12:17, 4.17s/it] 76%|███████▌ | 14198/18688 [16:40:22<5:01:20, 4.03s/it] 76%|███████▌ | 14199/18688 [16:40:26<4:50:52, 3.89s/it] 76%|███████▌ | 14200/18688 [16:40:30<5:01:07, 4.03s/it] {'loss': '0.8727', 'grad_norm': '1.187', 'learning_rate': '1.273e-05', 'epoch': '0.7599', 'num_input_tokens_seen': 47727040, 'train_runtime': '6.003e+04', 'train_tokens_per_second': '795'}
76%|███████▌ | 14200/18688 [16:40:30<5:01:07, 4.03s/it] 76%|███████▌ | 14201/18688 [16:40:34<4:54:14, 3.93s/it] 76%|███████▌ | 14202/18688 [16:40:38<4:57:18, 3.98s/it] 76%|███████▌ | 14203/18688 [16:40:41<4:47:29, 3.85s/it] 76%|███████▌ | 14204/18688 [16:40:45<4:45:53, 3.83s/it] 76%|███████▌ | 14205/18688 [16:40:49<4:48:56, 3.87s/it] 76%|███████▌ | 14206/18688 [16:40:53<4:52:09, 3.91s/it] 76%|███████▌ | 14207/18688 [16:40:57<4:57:28, 3.98s/it] 76%|███████▌ | 14208/18688 [16:41:01<4:51:08, 3.90s/it] 76%|███████▌ | 14209/18688 [16:41:06<5:24:50, 4.35s/it] 76%|███████▌ | 14210/18688 [16:41:11<5:21:18, 4.31s/it] {'loss': '0.8463', 'grad_norm': '1.16', 'learning_rate': '1.267e-05', 'epoch': '0.7604', 'num_input_tokens_seen': 47759160, 'train_runtime': '6.007e+04', 'train_tokens_per_second': '795'}
76%|███████▌ | 14210/18688 [16:41:11<5:21:18, 4.31s/it] 76%|███████▌ | 14211/18688 [16:41:15<5:31:28, 4.44s/it] 76%|███████▌ | 14212/18688 [16:41:20<5:35:43, 4.50s/it] 76%|███████▌ | 14213/18688 [16:41:25<5:43:50, 4.61s/it] 76%|███████▌ | 14214/18688 [16:41:29<5:30:25, 4.43s/it] 76%|███████▌ | 14215/18688 [16:41:33<5:14:43, 4.22s/it] 76%|███████▌ | 14216/18688 [16:41:38<5:42:57, 4.60s/it] 76%|███████▌ | 14217/18688 [16:41:43<5:55:43, 4.77s/it] 76%|███████▌ | 14218/18688 [16:41:47<5:33:18, 4.47s/it] 76%|███████▌ | 14219/18688 [16:41:51<5:26:44, 4.39s/it] 76%|███████▌ | 14220/18688 [16:41:56<5:31:25, 4.45s/it] {'loss': '0.9327', 'grad_norm': '1.026', 'learning_rate': '1.262e-05', 'epoch': '0.7609', 'num_input_tokens_seen': 47797224, 'train_runtime': '6.012e+04', 'train_tokens_per_second': '795.1'}
76%|███████▌ | 14220/18688 [16:41:56<5:31:25, 4.45s/it] 76%|███████▌ | 14221/18688 [16:42:02<6:00:38, 4.84s/it] 76%|███████▌ | 14222/18688 [16:42:06<5:53:44, 4.75s/it] 76%|███████▌ | 14223/18688 [16:42:11<5:51:29, 4.72s/it] 76%|███████▌ | 14224/18688 [16:42:15<5:31:04, 4.45s/it] 76%|███████▌ | 14225/18688 [16:42:18<5:17:31, 4.27s/it] 76%|███████▌ | 14226/18688 [16:42:23<5:31:55, 4.46s/it] 76%|███████▌ | 14227/18688 [16:42:28<5:27:02, 4.40s/it] 76%|███████▌ | 14228/18688 [16:42:31<5:09:28, 4.16s/it] 76%|███████▌ | 14229/18688 [16:42:35<4:53:40, 3.95s/it] 76%|███████▌ | 14230/18688 [16:42:39<5:05:18, 4.11s/it] {'loss': '0.8597', 'grad_norm': '1.339', 'learning_rate': '1.257e-05', 'epoch': '0.7615', 'num_input_tokens_seen': 47833792, 'train_runtime': '6.016e+04', 'train_tokens_per_second': '795.1'}
76%|███████▌ | 14230/18688 [16:42:39<5:05:18, 4.11s/it] 76%|███████▌ | 14231/18688 [16:42:44<5:20:58, 4.32s/it] 76%|███████▌ | 14232/18688 [16:42:48<5:11:43, 4.20s/it] 76%|███████▌ | 14233/18688 [16:42:52<5:05:37, 4.12s/it] 76%|███████▌ | 14234/18688 [16:42:56<5:00:33, 4.05s/it] 76%|███████▌ | 14235/18688 [16:43:00<4:58:05, 4.02s/it] 76%|███████▌ | 14236/18688 [16:43:04<5:11:30, 4.20s/it] 76%|███████▌ | 14237/18688 [16:43:09<5:28:33, 4.43s/it] 76%|███████▌ | 14238/18688 [16:43:15<5:51:27, 4.74s/it] 76%|███████▌ | 14239/18688 [16:43:19<5:32:42, 4.49s/it] 76%|███████▌ | 14240/18688 [16:43:22<5:13:11, 4.22s/it] {'loss': '0.9638', 'grad_norm': '1.545', 'learning_rate': '1.251e-05', 'epoch': '0.762', 'num_input_tokens_seen': 47870392, 'train_runtime': '6.02e+04', 'train_tokens_per_second': '795.2'}
76%|███████▌ | 14240/18688 [16:43:22<5:13:11, 4.22s/it] 76%|███████▌ | 14241/18688 [16:43:26<5:12:12, 4.21s/it] 76%|███████▌ | 14242/18688 [16:43:31<5:12:01, 4.21s/it] 76%|███████▌ | 14243/18688 [16:43:36<5:30:04, 4.46s/it] 76%|███████▌ | 14244/18688 [16:43:40<5:17:44, 4.29s/it] 76%|███████▌ | 14245/18688 [16:43:44<5:31:19, 4.47s/it] 76%|███████▌ | 14246/18688 [16:43:48<5:13:35, 4.24s/it] 76%|███████▌ | 14247/18688 [16:43:52<4:59:14, 4.04s/it] 76%|███████▌ | 14248/18688 [16:43:56<4:59:22, 4.05s/it] 76%|███████▌ | 14249/18688 [16:44:00<4:59:37, 4.05s/it] 76%|███████▋ | 14250/18688 [16:44:03<4:48:39, 3.90s/it] {'loss': '0.9063', 'grad_norm': '1.679', 'learning_rate': '1.246e-05', 'epoch': '0.7625', 'num_input_tokens_seen': 47903680, 'train_runtime': '6.024e+04', 'train_tokens_per_second': '795.2'}
76%|███████▋ | 14250/18688 [16:44:03<4:48:39, 3.90s/it] 76%|███████▋ | 14251/18688 [16:44:07<4:44:49, 3.85s/it] 76%|███████▋ | 14252/18688 [16:44:11<4:50:58, 3.94s/it] 76%|███████▋ | 14253/18688 [16:44:16<5:02:14, 4.09s/it] 76%|███████▋ | 14254/18688 [16:44:20<4:58:01, 4.03s/it] 76%|███████▋ | 14255/18688 [16:44:24<4:55:37, 4.00s/it] 76%|███████▋ | 14256/18688 [16:44:28<5:03:36, 4.11s/it] 76%|███████▋ | 14257/18688 [16:44:32<4:54:23, 3.99s/it] 76%|███████▋ | 14258/18688 [16:44:36<5:07:37, 4.17s/it] 76%|███████▋ | 14259/18688 [16:44:40<5:10:28, 4.21s/it] 76%|███████▋ | 14260/18688 [16:44:44<4:54:13, 3.99s/it] {'loss': '0.8643', 'grad_norm': '1.191', 'learning_rate': '1.241e-05', 'epoch': '0.7631', 'num_input_tokens_seen': 47935776, 'train_runtime': '6.028e+04', 'train_tokens_per_second': '795.2'}
76%|███████▋ | 14260/18688 [16:44:44<4:54:13, 3.99s/it] 76%|███████▋ | 14261/18688 [16:44:49<5:16:44, 4.29s/it] 76%|███████▋ | 14262/18688 [16:44:53<5:06:57, 4.16s/it] 76%|███████▋ | 14263/18688 [16:44:57<5:08:24, 4.18s/it] 76%|███████▋ | 14264/18688 [16:45:02<5:27:08, 4.44s/it] 76%|███████▋ | 14265/18688 [16:45:06<5:20:28, 4.35s/it] 76%|███████▋ | 14266/18688 [16:45:11<5:29:37, 4.47s/it] 76%|███████▋ | 14267/18688 [16:45:16<5:34:55, 4.55s/it] 76%|███████▋ | 14268/18688 [16:45:19<5:12:50, 4.25s/it] 76%|███████▋ | 14269/18688 [16:45:23<5:04:35, 4.14s/it] 76%|███████▋ | 14270/18688 [16:45:27<4:50:18, 3.94s/it] {'loss': '0.8398', 'grad_norm': '1.414', 'learning_rate': '1.235e-05', 'epoch': '0.7636', 'num_input_tokens_seen': 47971408, 'train_runtime': '6.033e+04', 'train_tokens_per_second': '795.2'}
76%|███████▋ | 14270/18688 [16:45:27<4:50:18, 3.94s/it] 76%|███████▋ | 14271/18688 [16:45:32<5:14:07, 4.27s/it] 76%|███████▋ | 14272/18688 [16:45:35<5:04:19, 4.13s/it] 76%|███████▋ | 14273/18688 [16:45:39<4:59:23, 4.07s/it] 76%|███████▋ | 14274/18688 [16:45:44<5:17:06, 4.31s/it] 76%|███████▋ | 14275/18688 [16:45:48<5:05:32, 4.15s/it] 76%|███████▋ | 14276/18688 [16:45:52<5:04:28, 4.14s/it] 76%|███████▋ | 14277/18688 [16:45:56<4:53:54, 4.00s/it] 76%|███████▋ | 14278/18688 [16:46:01<5:09:20, 4.21s/it] 76%|███████▋ | 14279/18688 [16:46:04<4:57:57, 4.05s/it] 76%|███████▋ | 14280/18688 [16:46:09<5:06:30, 4.17s/it] {'loss': '0.9209', 'grad_norm': '1.289', 'learning_rate': '1.23e-05', 'epoch': '0.7641', 'num_input_tokens_seen': 48006968, 'train_runtime': '6.037e+04', 'train_tokens_per_second': '795.2'}
76%|███████▋ | 14280/18688 [16:46:09<5:06:30, 4.17s/it] 76%|███████▋ | 14281/18688 [16:46:14<5:27:08, 4.45s/it] 76%|███████▋ | 14282/18688 [16:46:18<5:29:57, 4.49s/it] 76%|███████▋ | 14283/18688 [16:46:22<5:12:14, 4.25s/it] 76%|███████▋ | 14284/18688 [16:46:27<5:21:44, 4.38s/it] 76%|███████▋ | 14285/18688 [16:46:30<5:03:44, 4.14s/it] 76%|███████▋ | 14286/18688 [16:46:35<5:08:11, 4.20s/it] 76%|███████▋ | 14287/18688 [16:46:39<5:05:02, 4.16s/it] 76%|███████▋ | 14288/18688 [16:46:42<4:52:32, 3.99s/it] 76%|███████▋ | 14289/18688 [16:46:46<4:50:19, 3.96s/it] 76%|███████▋ | 14290/18688 [16:46:51<5:03:15, 4.14s/it] {'loss': '0.8973', 'grad_norm': '1.393', 'learning_rate': '1.225e-05', 'epoch': '0.7647', 'num_input_tokens_seen': 48040176, 'train_runtime': '6.041e+04', 'train_tokens_per_second': '795.2'}
76%|███████▋ | 14290/18688 [16:46:51<5:03:15, 4.14s/it] 76%|███████▋ | 14291/18688 [16:46:54<4:52:24, 3.99s/it] 76%|███████▋ | 14292/18688 [16:46:58<4:53:30, 4.01s/it] 76%|███████▋ | 14293/18688 [16:47:02<4:54:13, 4.02s/it] 76%|███████▋ | 14294/18688 [16:47:08<5:33:52, 4.56s/it] 76%|███████▋ | 14295/18688 [16:47:12<5:17:14, 4.33s/it] 76%|███████▋ | 14296/18688 [16:47:16<5:17:44, 4.34s/it] 77%|███████▋ | 14297/18688 [16:47:21<5:21:07, 4.39s/it] 77%|███████▋ | 14298/18688 [16:47:25<5:09:27, 4.23s/it] 77%|███████▋ | 14299/18688 [16:47:29<5:01:13, 4.12s/it] 77%|███████▋ | 14300/18688 [16:47:32<4:52:35, 4.00s/it] {'loss': '0.9466', 'grad_norm': '1.237', 'learning_rate': '1.22e-05', 'epoch': '0.7652', 'num_input_tokens_seen': 48074240, 'train_runtime': '6.045e+04', 'train_tokens_per_second': '795.2'}
77%|███████▋ | 14300/18688 [16:47:32<4:52:35, 4.00s/it] 77%|███████▋ | 14301/18688 [16:47:36<4:45:32, 3.91s/it] 77%|███████▋ | 14302/18688 [16:47:41<5:06:47, 4.20s/it] 77%|███████▋ | 14303/18688 [16:47:45<4:56:33, 4.06s/it] 77%|███████▋ | 14304/18688 [16:47:49<5:06:11, 4.19s/it] 77%|███████▋ | 14305/18688 [16:47:53<4:58:59, 4.09s/it] 77%|███████▋ | 14306/18688 [16:47:57<5:01:35, 4.13s/it] 77%|███████▋ | 14307/18688 [16:48:01<4:55:59, 4.05s/it] 77%|███████▋ | 14308/18688 [16:48:05<4:54:12, 4.03s/it] 77%|███████▋ | 14309/18688 [16:48:09<4:56:30, 4.06s/it] 77%|███████▋ | 14310/18688 [16:48:13<4:44:56, 3.91s/it] {'loss': '0.853', 'grad_norm': '1.136', 'learning_rate': '1.214e-05', 'epoch': '0.7658', 'num_input_tokens_seen': 48106104, 'train_runtime': '6.049e+04', 'train_tokens_per_second': '795.2'}
77%|███████▋ | 14310/18688 [16:48:13<4:44:56, 3.91s/it] 77%|███████▋ | 14311/18688 [16:48:17<4:41:28, 3.86s/it] 77%|███████▋ | 14312/18688 [16:48:21<5:01:02, 4.13s/it] 77%|███████▋ | 14313/18688 [16:48:25<4:58:13, 4.09s/it] 77%|███████▋ | 14314/18688 [16:48:29<4:47:03, 3.94s/it] 77%|███████▋ | 14315/18688 [16:48:33<4:54:15, 4.04s/it] 77%|███████▋ | 14316/18688 [16:48:37<4:47:28, 3.95s/it] 77%|███████▋ | 14317/18688 [16:48:40<4:37:53, 3.81s/it] 77%|███████▋ | 14318/18688 [16:48:44<4:37:55, 3.82s/it] 77%|███████▋ | 14319/18688 [16:48:48<4:35:27, 3.78s/it] 77%|███████▋ | 14320/18688 [16:48:53<5:01:36, 4.14s/it] {'loss': '0.8572', 'grad_norm': '1.218', 'learning_rate': '1.209e-05', 'epoch': '0.7663', 'num_input_tokens_seen': 48137712, 'train_runtime': '6.053e+04', 'train_tokens_per_second': '795.2'}
77%|███████▋ | 14320/18688 [16:48:53<5:01:36, 4.14s/it] 77%|███████▋ | 14321/18688 [16:48:58<5:14:09, 4.32s/it] 77%|███████▋ | 14322/18688 [16:49:02<5:17:09, 4.36s/it] 77%|███████▋ | 14323/18688 [16:49:07<5:24:17, 4.46s/it] 77%|███████▋ | 14324/18688 [16:49:10<5:06:45, 4.22s/it] 77%|███████▋ | 14325/18688 [16:49:15<5:14:26, 4.32s/it] 77%|███████▋ | 14326/18688 [16:49:19<5:12:11, 4.29s/it] 77%|███████▋ | 14327/18688 [16:49:24<5:32:42, 4.58s/it] 77%|███████▋ | 14328/18688 [16:49:29<5:22:59, 4.44s/it] 77%|███████▋ | 14329/18688 [16:49:33<5:16:34, 4.36s/it] 77%|███████▋ | 14330/18688 [16:49:37<5:07:01, 4.23s/it] {'loss': '0.9078', 'grad_norm': '1.092', 'learning_rate': '1.204e-05', 'epoch': '0.7668', 'num_input_tokens_seen': 48174248, 'train_runtime': '6.058e+04', 'train_tokens_per_second': '795.3'}
77%|███████▋ | 14330/18688 [16:49:37<5:07:01, 4.23s/it] 77%|███████▋ | 14331/18688 [16:49:41<5:13:09, 4.31s/it] 77%|███████▋ | 14332/18688 [16:49:45<5:08:32, 4.25s/it] 77%|███████▋ | 14333/18688 [16:49:50<5:21:27, 4.43s/it] 77%|███████▋ | 14334/18688 [16:49:54<5:17:04, 4.37s/it] 77%|███████▋ | 14335/18688 [16:49:59<5:20:22, 4.42s/it] 77%|███████▋ | 14336/18688 [16:50:04<5:27:04, 4.51s/it] 77%|███████▋ | 14337/18688 [16:50:08<5:15:51, 4.36s/it] 77%|███████▋ | 14338/18688 [16:50:12<5:16:17, 4.36s/it] 77%|███████▋ | 14339/18688 [16:50:17<5:38:13, 4.67s/it] 77%|███████▋ | 14340/18688 [16:50:22<5:26:24, 4.50s/it] {'loss': '0.9219', 'grad_norm': '1.257', 'learning_rate': '1.199e-05', 'epoch': '0.7674', 'num_input_tokens_seen': 48211768, 'train_runtime': '6.062e+04', 'train_tokens_per_second': '795.3'}
77%|███████▋ | 14340/18688 [16:50:22<5:26:24, 4.50s/it] 77%|███████▋ | 14341/18688 [16:50:25<5:05:40, 4.22s/it] 77%|███████▋ | 14342/18688 [16:50:29<4:51:46, 4.03s/it] 77%|███████▋ | 14343/18688 [16:50:33<4:48:28, 3.98s/it] 77%|███████▋ | 14344/18688 [16:50:36<4:37:23, 3.83s/it] 77%|███████▋ | 14345/18688 [16:50:40<4:31:36, 3.75s/it] 77%|███████▋ | 14346/18688 [16:50:43<4:33:21, 3.78s/it] 77%|███████▋ | 14347/18688 [16:50:49<5:01:56, 4.17s/it] 77%|███████▋ | 14348/18688 [16:50:53<5:13:08, 4.33s/it] 77%|███████▋ | 14349/18688 [16:50:57<5:10:25, 4.29s/it] 77%|███████▋ | 14350/18688 [16:51:01<5:04:11, 4.21s/it] {'loss': '0.8899', 'grad_norm': '1.414', 'learning_rate': '1.193e-05', 'epoch': '0.7679', 'num_input_tokens_seen': 48242496, 'train_runtime': '6.066e+04', 'train_tokens_per_second': '795.3'}
77%|███████▋ | 14350/18688 [16:51:01<5:04:11, 4.21s/it] 77%|███████▋ | 14351/18688 [16:51:06<5:12:15, 4.32s/it] 77%|███████▋ | 14352/18688 [16:51:10<5:09:11, 4.28s/it] 77%|███████▋ | 14353/18688 [16:51:14<5:08:47, 4.27s/it] 77%|███████▋ | 14354/18688 [16:51:18<4:56:43, 4.11s/it] 77%|███████▋ | 14355/18688 [16:51:23<5:04:21, 4.21s/it] 77%|███████▋ | 14356/18688 [16:51:28<5:19:00, 4.42s/it] 77%|███████▋ | 14357/18688 [16:51:32<5:25:00, 4.50s/it] 77%|███████▋ | 14358/18688 [16:51:36<5:16:19, 4.38s/it] 77%|███████▋ | 14359/18688 [16:51:40<5:06:34, 4.25s/it] 77%|███████▋ | 14360/18688 [16:51:44<4:56:05, 4.10s/it] {'loss': '0.9031', 'grad_norm': '1.64', 'learning_rate': '1.188e-05', 'epoch': '0.7684', 'num_input_tokens_seen': 48278152, 'train_runtime': '6.07e+04', 'train_tokens_per_second': '795.3'}
77%|███████▋ | 14360/18688 [16:51:44<4:56:05, 4.10s/it] 77%|███████▋ | 14361/18688 [16:51:48<4:48:06, 4.00s/it] 77%|███████▋ | 14362/18688 [16:51:51<4:37:41, 3.85s/it] 77%|███████▋ | 14363/18688 [16:51:55<4:41:23, 3.90s/it] 77%|███████▋ | 14364/18688 [16:52:00<4:49:16, 4.01s/it] 77%|███████▋ | 14365/18688 [16:52:04<4:52:11, 4.06s/it] 77%|███████▋ | 14366/18688 [16:52:08<4:46:25, 3.98s/it] 77%|███████▋ | 14367/18688 [16:52:12<4:57:20, 4.13s/it] 77%|███████▋ | 14368/18688 [16:52:16<4:55:07, 4.10s/it] 77%|███████▋ | 14369/18688 [16:52:20<4:54:27, 4.09s/it] 77%|███████▋ | 14370/18688 [16:52:25<5:06:29, 4.26s/it] {'loss': '0.8619', 'grad_norm': '1.215', 'learning_rate': '1.183e-05', 'epoch': '0.769', 'num_input_tokens_seen': 48311496, 'train_runtime': '6.075e+04', 'train_tokens_per_second': '795.3'}
77%|███████▋ | 14370/18688 [16:52:25<5:06:29, 4.26s/it] 77%|███████▋ | 14371/18688 [16:52:30<5:28:15, 4.56s/it] 77%|███████▋ | 14372/18688 [16:52:34<5:11:37, 4.33s/it] 77%|███████▋ | 14373/18688 [16:52:38<5:03:49, 4.22s/it] 77%|███████▋ | 14374/18688 [16:52:41<4:50:53, 4.05s/it] 77%|███████▋ | 14375/18688 [16:52:45<4:49:22, 4.03s/it] 77%|███████▋ | 14376/18688 [16:52:49<4:36:55, 3.85s/it] 77%|███████▋ | 14377/18688 [16:52:53<4:52:47, 4.07s/it] 77%|███████▋ | 14378/18688 [16:52:57<4:49:28, 4.03s/it] 77%|███████▋ | 14379/18688 [16:53:01<4:43:05, 3.94s/it] 77%|███████▋ | 14380/18688 [16:53:05<4:51:16, 4.06s/it] {'loss': '0.8773', 'grad_norm': '1.275', 'learning_rate': '1.178e-05', 'epoch': '0.7695', 'num_input_tokens_seen': 48343040, 'train_runtime': '6.079e+04', 'train_tokens_per_second': '795.3'}
77%|███████▋ | 14380/18688 [16:53:05<4:51:16, 4.06s/it] 77%|███████▋ | 14381/18688 [16:53:09<4:41:29, 3.92s/it] 77%|███████▋ | 14382/18688 [16:53:13<4:39:58, 3.90s/it] 77%|███████▋ | 14383/18688 [16:53:18<5:00:48, 4.19s/it] 77%|███████▋ | 14384/18688 [16:53:22<4:56:57, 4.14s/it] 77%|███████▋ | 14385/18688 [16:53:26<4:53:46, 4.10s/it] 77%|███████▋ | 14386/18688 [16:53:31<5:09:38, 4.32s/it] 77%|███████▋ | 14387/18688 [16:53:34<4:54:05, 4.10s/it] 77%|███████▋ | 14388/18688 [16:53:38<4:55:56, 4.13s/it] 77%|███████▋ | 14389/18688 [16:53:43<5:01:22, 4.21s/it] 77%|███████▋ | 14390/18688 [16:53:46<4:46:25, 4.00s/it] {'loss': '0.8501', 'grad_norm': '1.149', 'learning_rate': '1.173e-05', 'epoch': '0.77', 'num_input_tokens_seen': 48375824, 'train_runtime': '6.083e+04', 'train_tokens_per_second': '795.3'}
77%|███████▋ | 14390/18688 [16:53:46<4:46:25, 4.00s/it] 77%|███████▋ | 14391/18688 [16:53:51<4:52:23, 4.08s/it] 77%|███████▋ | 14392/18688 [16:53:55<5:00:21, 4.19s/it] 77%|███████▋ | 14393/18688 [16:53:59<4:51:59, 4.08s/it] 77%|███████▋ | 14394/18688 [16:54:02<4:40:03, 3.91s/it] 77%|███████▋ | 14395/18688 [16:54:07<4:48:11, 4.03s/it] 77%|███████▋ | 14396/18688 [16:54:10<4:36:49, 3.87s/it] 77%|███████▋ | 14397/18688 [16:54:15<4:52:57, 4.10s/it] 77%|███████▋ | 14398/18688 [16:54:19<5:02:16, 4.23s/it] 77%|███████▋ | 14399/18688 [16:54:23<4:52:15, 4.09s/it] 77%|███████▋ | 14400/18688 [16:54:27<4:49:20, 4.05s/it] {'loss': '0.8873', 'grad_norm': '1.144', 'learning_rate': '1.167e-05', 'epoch': '0.7706', 'num_input_tokens_seen': 48407544, 'train_runtime': '6.087e+04', 'train_tokens_per_second': '795.3'}
77%|███████▋ | 14400/18688 [16:54:27<4:49:20, 4.05s/it] 77%|███████▋ | 14401/18688 [16:54:32<5:06:24, 4.29s/it] 77%|███████▋ | 14402/18688 [16:54:36<5:09:41, 4.34s/it] 77%|███████▋ | 14403/18688 [16:54:40<5:01:32, 4.22s/it] 77%|███████▋ | 14404/18688 [16:54:44<4:53:35, 4.11s/it] 77%|███████▋ | 14405/18688 [16:54:48<4:54:36, 4.13s/it] 77%|███████▋ | 14406/18688 [16:54:53<5:04:35, 4.27s/it] 77%|███████▋ | 14407/18688 [16:54:57<4:51:23, 4.08s/it] 77%|███████▋ | 14408/18688 [16:55:00<4:43:31, 3.97s/it] 77%|███████▋ | 14409/18688 [16:55:04<4:36:17, 3.87s/it] 77%|███████▋ | 14410/18688 [16:55:09<4:52:03, 4.10s/it] {'loss': '0.9552', 'grad_norm': '1.278', 'learning_rate': '1.162e-05', 'epoch': '0.7711', 'num_input_tokens_seen': 48441672, 'train_runtime': '6.091e+04', 'train_tokens_per_second': '795.3'}
77%|███████▋ | 14410/18688 [16:55:09<4:52:03, 4.10s/it] 77%|███████▋ | 14411/18688 [16:55:13<4:48:53, 4.05s/it] 77%|███████▋ | 14412/18688 [16:55:16<4:44:28, 3.99s/it] 77%|███████▋ | 14413/18688 [16:55:21<4:48:57, 4.06s/it] 77%|███████▋ | 14414/18688 [16:55:25<4:53:06, 4.11s/it] 77%|███████▋ | 14415/18688 [16:55:29<5:01:15, 4.23s/it] 77%|███████▋ | 14416/18688 [16:55:33<4:47:37, 4.04s/it] 77%|███████▋ | 14417/18688 [16:55:38<4:59:30, 4.21s/it] 77%|███████▋ | 14418/18688 [16:55:42<4:59:45, 4.21s/it] 77%|███████▋ | 14419/18688 [16:55:46<4:58:14, 4.19s/it] 77%|███████▋ | 14420/18688 [16:55:50<4:57:12, 4.18s/it] {'loss': '0.9089', 'grad_norm': '1.185', 'learning_rate': '1.157e-05', 'epoch': '0.7716', 'num_input_tokens_seen': 48474872, 'train_runtime': '6.095e+04', 'train_tokens_per_second': '795.3'}
77%|███████▋ | 14420/18688 [16:55:50<4:57:12, 4.18s/it] 77%|███████▋ | 14421/18688 [16:55:54<4:55:14, 4.15s/it] 77%|███████▋ | 14422/18688 [16:55:58<4:44:26, 4.00s/it] 77%|███████▋ | 14423/18688 [16:56:01<4:36:11, 3.89s/it] 77%|███████▋ | 14424/18688 [16:56:06<4:49:51, 4.08s/it] 77%|███████▋ | 14425/18688 [16:56:12<5:22:29, 4.54s/it] 77%|███████▋ | 14426/18688 [16:56:15<5:07:45, 4.33s/it] 77%|███████▋ | 14427/18688 [16:56:19<5:00:29, 4.23s/it] 77%|███████▋ | 14428/18688 [16:56:23<4:55:45, 4.17s/it] 77%|███████▋ | 14429/18688 [16:56:27<4:53:33, 4.14s/it] 77%|███████▋ | 14430/18688 [16:56:32<4:55:15, 4.16s/it] {'loss': '0.895', 'grad_norm': '1.3', 'learning_rate': '1.152e-05', 'epoch': '0.7722', 'num_input_tokens_seen': 48508912, 'train_runtime': '6.099e+04', 'train_tokens_per_second': '795.3'}
77%|███████▋ | 14430/18688 [16:56:32<4:55:15, 4.16s/it] 77%|███████▋ | 14431/18688 [16:56:35<4:46:36, 4.04s/it] 77%|███████▋ | 14432/18688 [16:56:39<4:41:11, 3.96s/it] 77%|███████▋ | 14433/18688 [16:56:45<5:19:44, 4.51s/it] 77%|███████▋ | 14434/18688 [16:56:49<5:16:30, 4.46s/it] 77%|███████▋ | 14435/18688 [16:56:54<5:20:22, 4.52s/it] 77%|███████▋ | 14436/18688 [16:56:58<5:07:38, 4.34s/it] 77%|███████▋ | 14437/18688 [16:57:03<5:13:09, 4.42s/it] 77%|███████▋ | 14438/18688 [16:57:06<4:54:20, 4.16s/it] 77%|███████▋ | 14439/18688 [16:57:11<5:12:36, 4.41s/it] 77%|███████▋ | 14440/18688 [16:57:15<5:11:33, 4.40s/it] {'loss': '0.8589', 'grad_norm': '1.132', 'learning_rate': '1.147e-05', 'epoch': '0.7727', 'num_input_tokens_seen': 48545432, 'train_runtime': '6.104e+04', 'train_tokens_per_second': '795.4'}
77%|███████▋ | 14440/18688 [16:57:15<5:11:33, 4.40s/it] 77%|███████▋ | 14441/18688 [16:57:20<5:18:25, 4.50s/it] 77%|███████▋ | 14442/18688 [16:57:24<5:09:47, 4.38s/it] 77%|███████▋ | 14443/18688 [16:57:28<4:52:24, 4.13s/it] 77%|███████▋ | 14444/18688 [16:57:32<4:46:42, 4.05s/it] 77%|███████▋ | 14445/18688 [16:57:36<4:57:37, 4.21s/it] 77%|███████▋ | 14446/18688 [16:57:41<5:00:38, 4.25s/it] 77%|███████▋ | 14447/18688 [16:57:45<4:53:37, 4.15s/it] 77%|███████▋ | 14448/18688 [16:57:49<5:06:23, 4.34s/it] 77%|███████▋ | 14449/18688 [16:57:53<4:51:24, 4.12s/it] 77%|███████▋ | 14450/18688 [16:57:58<5:13:23, 4.44s/it] {'loss': '0.9247', 'grad_norm': '1.195', 'learning_rate': '1.142e-05', 'epoch': '0.7732', 'num_input_tokens_seen': 48579560, 'train_runtime': '6.108e+04', 'train_tokens_per_second': '795.4'}
77%|███████▋ | 14450/18688 [16:57:58<5:13:23, 4.44s/it] 77%|███████▋ | 14451/18688 [16:58:02<5:07:44, 4.36s/it] 77%|███████▋ | 14452/18688 [16:58:08<5:26:57, 4.63s/it] 77%|███████▋ | 14453/18688 [16:58:12<5:24:58, 4.60s/it] 77%|███████▋ | 14454/18688 [16:58:16<5:13:26, 4.44s/it] 77%|███████▋ | 14455/18688 [16:58:21<5:13:48, 4.45s/it] 77%|███████▋ | 14456/18688 [16:58:24<4:55:48, 4.19s/it] 77%|███████▋ | 14457/18688 [16:58:28<4:55:08, 4.19s/it] 77%|███████▋ | 14458/18688 [16:58:33<4:58:20, 4.23s/it] 77%|███████▋ | 14459/18688 [16:58:37<4:59:58, 4.26s/it] 77%|███████▋ | 14460/18688 [16:58:42<5:10:22, 4.40s/it] {'loss': '0.8898', 'grad_norm': '1.191', 'learning_rate': '1.137e-05', 'epoch': '0.7738', 'num_input_tokens_seen': 48615864, 'train_runtime': '6.112e+04', 'train_tokens_per_second': '795.4'}
77%|███████▋ | 14460/18688 [16:58:42<5:10:22, 4.40s/it] 77%|███████▋ | 14461/18688 [16:58:46<4:58:21, 4.24s/it] 77%|███████▋ | 14462/18688 [16:58:49<4:48:49, 4.10s/it] 77%|███████▋ | 14463/18688 [16:58:53<4:37:07, 3.94s/it] 77%|███████▋ | 14464/18688 [16:58:58<4:52:53, 4.16s/it] 77%|███████▋ | 14465/18688 [16:59:03<5:10:51, 4.42s/it] 77%|███████▋ | 14466/18688 [16:59:07<5:18:26, 4.53s/it] 77%|███████▋ | 14467/18688 [16:59:11<5:00:22, 4.27s/it] 77%|███████▋ | 14468/18688 [16:59:16<5:09:24, 4.40s/it] 77%|███████▋ | 14469/18688 [16:59:20<4:58:55, 4.25s/it] 77%|███████▋ | 14470/18688 [16:59:23<4:45:26, 4.06s/it] {'loss': '0.9314', 'grad_norm': '1.307', 'learning_rate': '1.132e-05', 'epoch': '0.7743', 'num_input_tokens_seen': 48648616, 'train_runtime': '6.116e+04', 'train_tokens_per_second': '795.4'}
77%|███████▋ | 14470/18688 [16:59:23<4:45:26, 4.06s/it] 77%|███████▋ | 14471/18688 [16:59:27<4:41:29, 4.01s/it] 77%|███████▋ | 14472/18688 [16:59:31<4:34:01, 3.90s/it] 77%|███████▋ | 14473/18688 [16:59:34<4:27:33, 3.81s/it] 77%|███████▋ | 14474/18688 [16:59:39<4:35:01, 3.92s/it] 77%|███████▋ | 14475/18688 [16:59:43<4:42:27, 4.02s/it] 77%|███████▋ | 14476/18688 [16:59:47<4:46:21, 4.08s/it] 77%|███████▋ | 14477/18688 [16:59:51<4:46:54, 4.09s/it] 77%|███████▋ | 14478/18688 [16:59:55<4:38:31, 3.97s/it] 77%|███████▋ | 14479/18688 [16:59:59<4:38:46, 3.97s/it] 77%|███████▋ | 14480/18688 [17:00:03<4:37:03, 3.95s/it] {'loss': '0.892', 'grad_norm': '1.226', 'learning_rate': '1.126e-05', 'epoch': '0.7748', 'num_input_tokens_seen': 48678584, 'train_runtime': '6.12e+04', 'train_tokens_per_second': '795.4'}
77%|███████▋ | 14480/18688 [17:00:03<4:37:03, 3.95s/it] 77%|███████▋ | 14481/18688 [17:00:06<4:31:01, 3.87s/it] 77%|███████▋ | 14482/18688 [17:00:10<4:23:38, 3.76s/it] 77%|███████▋ | 14483/18688 [17:00:15<4:45:13, 4.07s/it] 78%|███████▊ | 14484/18688 [17:00:19<4:49:31, 4.13s/it] 78%|███████▊ | 14485/18688 [17:00:23<4:44:18, 4.06s/it] 78%|███████▊ | 14486/18688 [17:00:27<4:54:48, 4.21s/it] 78%|███████▊ | 14487/18688 [17:00:32<4:59:52, 4.28s/it] 78%|███████▊ | 14488/18688 [17:00:37<5:07:28, 4.39s/it] 78%|███████▊ | 14489/18688 [17:00:41<5:12:56, 4.47s/it] 78%|███████▊ | 14490/18688 [17:00:46<5:18:03, 4.55s/it] {'loss': '0.859', 'grad_norm': '1.171', 'learning_rate': '1.121e-05', 'epoch': '0.7754', 'num_input_tokens_seen': 48715240, 'train_runtime': '6.125e+04', 'train_tokens_per_second': '795.4'}
78%|███████▊ | 14490/18688 [17:00:46<5:18:03, 4.55s/it] 78%|███████▊ | 14491/18688 [17:00:51<5:26:58, 4.67s/it] 78%|███████▊ | 14492/18688 [17:00:54<5:02:56, 4.33s/it] 78%|███████▊ | 14493/18688 [17:00:59<5:04:05, 4.35s/it] 78%|███████▊ | 14494/18688 [17:01:03<5:00:34, 4.30s/it] 78%|███████▊ | 14495/18688 [17:01:07<4:45:05, 4.08s/it] 78%|███████▊ | 14496/18688 [17:01:11<4:41:57, 4.04s/it] 78%|███████▊ | 14497/18688 [17:01:15<4:59:33, 4.29s/it] 78%|███████▊ | 14498/18688 [17:01:20<5:00:03, 4.30s/it] 78%|███████▊ | 14499/18688 [17:01:23<4:43:35, 4.06s/it] 78%|███████▊ | 14500/18688 [17:01:28<4:57:22, 4.26s/it] {'loss': '0.9034', 'grad_norm': '1.2', 'learning_rate': '1.116e-05', 'epoch': '0.7759', 'num_input_tokens_seen': 48748360, 'train_runtime': '6.129e+04', 'train_tokens_per_second': '795.4'}
78%|███████▊ | 14500/18688 [17:01:28<4:57:22, 4.26s/it] 78%|███████▊ | 14501/18688 [17:01:32<4:57:57, 4.27s/it] 78%|███████▊ | 14502/18688 [17:01:36<4:47:03, 4.11s/it] 78%|███████▊ | 14503/18688 [17:01:40<4:44:42, 4.08s/it] 78%|███████▊ | 14504/18688 [17:01:44<4:41:07, 4.03s/it] 78%|███████▊ | 14505/18688 [17:01:49<5:01:55, 4.33s/it] 78%|███████▊ | 14506/18688 [17:01:54<5:18:19, 4.57s/it] 78%|███████▊ | 14507/18688 [17:01:59<5:25:45, 4.67s/it] 78%|███████▊ | 14508/18688 [17:02:03<5:14:16, 4.51s/it] 78%|███████▊ | 14509/18688 [17:02:07<5:09:48, 4.45s/it] 78%|███████▊ | 14510/18688 [17:02:12<5:09:24, 4.44s/it] {'loss': '0.9456', 'grad_norm': '1.247', 'learning_rate': '1.111e-05', 'epoch': '0.7765', 'num_input_tokens_seen': 48784912, 'train_runtime': '6.133e+04', 'train_tokens_per_second': '795.4'}
78%|███████▊ | 14510/18688 [17:02:12<5:09:24, 4.44s/it] 78%|███████▊ | 14511/18688 [17:02:16<5:02:45, 4.35s/it] 78%|███████▊ | 14512/18688 [17:02:20<4:50:34, 4.17s/it] 78%|███████▊ | 14513/18688 [17:02:24<4:51:00, 4.18s/it] 78%|███████▊ | 14514/18688 [17:02:27<4:36:10, 3.97s/it] 78%|███████▊ | 14515/18688 [17:02:32<4:53:25, 4.22s/it] 78%|███████▊ | 14516/18688 [17:02:36<4:49:20, 4.16s/it] 78%|███████▊ | 14517/18688 [17:02:40<4:37:41, 3.99s/it] 78%|███████▊ | 14518/18688 [17:02:45<4:50:35, 4.18s/it] 78%|███████▊ | 14519/18688 [17:02:49<4:53:07, 4.22s/it] 78%|███████▊ | 14520/18688 [17:02:53<5:02:33, 4.36s/it] {'loss': '0.9019', 'grad_norm': '1.259', 'learning_rate': '1.106e-05', 'epoch': '0.777', 'num_input_tokens_seen': 48818280, 'train_runtime': '6.137e+04', 'train_tokens_per_second': '795.4'}
78%|███████▊ | 14520/18688 [17:02:53<5:02:33, 4.36s/it] 78%|███████▊ | 14521/18688 [17:02:57<4:49:11, 4.16s/it] 78%|███████▊ | 14522/18688 [17:03:01<4:50:38, 4.19s/it] 78%|███████▊ | 14523/18688 [17:03:06<4:52:45, 4.22s/it] 78%|███████▊ | 14524/18688 [17:03:11<5:13:49, 4.52s/it] 78%|███████▊ | 14525/18688 [17:03:15<5:05:02, 4.40s/it] 78%|███████▊ | 14526/18688 [17:03:19<4:53:44, 4.23s/it] 78%|███████▊ | 14527/18688 [17:03:23<4:46:23, 4.13s/it] 78%|███████▊ | 14528/18688 [17:03:27<4:53:05, 4.23s/it] 78%|███████▊ | 14529/18688 [17:03:32<4:56:32, 4.28s/it] 78%|███████▊ | 14530/18688 [17:03:35<4:46:26, 4.13s/it] {'loss': '0.9244', 'grad_norm': '1.263', 'learning_rate': '1.101e-05', 'epoch': '0.7775', 'num_input_tokens_seen': 48852544, 'train_runtime': '6.142e+04', 'train_tokens_per_second': '795.4'}
78%|███████▊ | 14530/18688 [17:03:35<4:46:26, 4.13s/it] 78%|███████▊ | 14531/18688 [17:03:39<4:38:11, 4.02s/it] 78%|███████▊ | 14532/18688 [17:03:43<4:40:15, 4.05s/it] 78%|███████▊ | 14533/18688 [17:03:48<4:50:25, 4.19s/it] 78%|███████▊ | 14534/18688 [17:03:53<5:07:14, 4.44s/it] 78%|███████▊ | 14535/18688 [17:03:56<4:49:35, 4.18s/it] 78%|███████▊ | 14536/18688 [17:04:01<4:56:56, 4.29s/it] 78%|███████▊ | 14537/18688 [17:04:06<5:09:18, 4.47s/it] 78%|███████▊ | 14538/18688 [17:04:10<4:55:12, 4.27s/it] 78%|███████▊ | 14539/18688 [17:04:14<5:00:32, 4.35s/it] 78%|███████▊ | 14540/18688 [17:04:18<4:56:25, 4.29s/it] {'loss': '0.9837', 'grad_norm': '1.292', 'learning_rate': '1.096e-05', 'epoch': '0.7781', 'num_input_tokens_seen': 48889080, 'train_runtime': '6.146e+04', 'train_tokens_per_second': '795.5'}
78%|███████▊ | 14540/18688 [17:04:18<4:56:25, 4.29s/it] 78%|███████▊ | 14541/18688 [17:04:22<4:52:12, 4.23s/it] 78%|███████▊ | 14542/18688 [17:04:27<4:49:02, 4.18s/it] 78%|███████▊ | 14543/18688 [17:04:31<4:52:17, 4.23s/it] 78%|███████▊ | 14544/18688 [17:04:35<4:52:30, 4.24s/it] 78%|███████▊ | 14545/18688 [17:04:39<4:37:02, 4.01s/it] 78%|███████▊ | 14546/18688 [17:04:42<4:29:14, 3.90s/it] 78%|███████▊ | 14547/18688 [17:04:46<4:35:46, 4.00s/it] 78%|███████▊ | 14548/18688 [17:04:50<4:35:40, 4.00s/it] 78%|███████▊ | 14549/18688 [17:04:55<4:38:48, 4.04s/it] 78%|███████▊ | 14550/18688 [17:05:00<4:59:21, 4.34s/it] {'loss': '0.9322', 'grad_norm': '1.212', 'learning_rate': '1.091e-05', 'epoch': '0.7786', 'num_input_tokens_seen': 48921000, 'train_runtime': '6.15e+04', 'train_tokens_per_second': '795.5'}
78%|███████▊ | 14550/18688 [17:05:00<4:59:21, 4.34s/it] 78%|███████▊ | 14551/18688 [17:05:03<4:44:05, 4.12s/it] 78%|███████▊ | 14552/18688 [17:05:08<4:50:42, 4.22s/it] 78%|███████▊ | 14553/18688 [17:05:12<4:49:20, 4.20s/it] 78%|███████▊ | 14554/18688 [17:05:16<4:40:53, 4.08s/it] 78%|███████▊ | 14555/18688 [17:05:19<4:33:22, 3.97s/it] 78%|███████▊ | 14556/18688 [17:05:23<4:29:20, 3.91s/it] 78%|███████▊ | 14557/18688 [17:05:28<4:45:08, 4.14s/it] 78%|███████▊ | 14558/18688 [17:05:32<4:35:41, 4.01s/it] 78%|███████▊ | 14559/18688 [17:05:36<4:40:16, 4.07s/it] 78%|███████▊ | 14560/18688 [17:05:39<4:31:53, 3.95s/it] {'loss': '0.8991', 'grad_norm': '1.529', 'learning_rate': '1.086e-05', 'epoch': '0.7791', 'num_input_tokens_seen': 48950784, 'train_runtime': '6.154e+04', 'train_tokens_per_second': '795.4'}
78%|███████▊ | 14560/18688 [17:05:39<4:31:53, 3.95s/it] 78%|███████▊ | 14561/18688 [17:05:44<4:55:10, 4.29s/it] 78%|███████▊ | 14562/18688 [17:05:48<4:37:52, 4.04s/it] 78%|███████▊ | 14563/18688 [17:05:52<4:28:19, 3.90s/it] 78%|███████▊ | 14564/18688 [17:05:55<4:26:06, 3.87s/it] 78%|███████▊ | 14565/18688 [17:05:59<4:29:25, 3.92s/it] 78%|███████▊ | 14566/18688 [17:06:03<4:24:29, 3.85s/it] 78%|███████▊ | 14567/18688 [17:06:07<4:30:08, 3.93s/it] 78%|███████▊ | 14568/18688 [17:06:12<4:45:00, 4.15s/it] 78%|███████▊ | 14569/18688 [17:06:16<4:42:56, 4.12s/it] 78%|███████▊ | 14570/18688 [17:06:20<4:34:54, 4.01s/it] {'loss': '0.8928', 'grad_norm': '1.268', 'learning_rate': '1.081e-05', 'epoch': '0.7797', 'num_input_tokens_seen': 48981576, 'train_runtime': '6.158e+04', 'train_tokens_per_second': '795.4'}
78%|███████▊ | 14570/18688 [17:06:20<4:34:54, 4.01s/it] 78%|███████▊ | 14571/18688 [17:06:24<4:43:01, 4.12s/it] 78%|███████▊ | 14572/18688 [17:06:28<4:37:05, 4.04s/it] 78%|███████▊ | 14573/18688 [17:06:32<4:39:34, 4.08s/it] 78%|███████▊ | 14574/18688 [17:06:37<4:56:11, 4.32s/it] 78%|███████▊ | 14575/18688 [17:06:41<4:46:05, 4.17s/it] 78%|███████▊ | 14576/18688 [17:06:45<4:53:43, 4.29s/it] 78%|███████▊ | 14577/18688 [17:06:49<4:43:13, 4.13s/it] 78%|███████▊ | 14578/18688 [17:06:53<4:43:25, 4.14s/it] 78%|███████▊ | 14579/18688 [17:06:58<4:57:25, 4.34s/it] 78%|███████▊ | 14580/18688 [17:07:02<4:55:33, 4.32s/it] {'loss': '0.9027', 'grad_norm': '1.441', 'learning_rate': '1.076e-05', 'epoch': '0.7802', 'num_input_tokens_seen': 49016560, 'train_runtime': '6.162e+04', 'train_tokens_per_second': '795.4'}
78%|███████▊ | 14580/18688 [17:07:02<4:55:33, 4.32s/it] 78%|███████▊ | 14581/18688 [17:07:07<4:58:26, 4.36s/it] 78%|███████▊ | 14582/18688 [17:07:11<4:54:43, 4.31s/it] 78%|███████▊ | 14583/18688 [17:07:16<5:01:38, 4.41s/it] 78%|███████▊ | 14584/18688 [17:07:20<5:02:58, 4.43s/it] 78%|███████▊ | 14585/18688 [17:07:24<4:45:08, 4.17s/it] 78%|███████▊ | 14586/18688 [17:07:27<4:34:07, 4.01s/it] 78%|███████▊ | 14587/18688 [17:07:31<4:31:55, 3.98s/it] 78%|███████▊ | 14588/18688 [17:07:35<4:29:29, 3.94s/it] 78%|███████▊ | 14589/18688 [17:07:40<4:48:48, 4.23s/it] 78%|███████▊ | 14590/18688 [17:07:44<4:46:15, 4.19s/it] {'loss': '0.8272', 'grad_norm': '1.272', 'learning_rate': '1.071e-05', 'epoch': '0.7807', 'num_input_tokens_seen': 49048816, 'train_runtime': '6.166e+04', 'train_tokens_per_second': '795.4'}
78%|███████▊ | 14590/18688 [17:07:44<4:46:15, 4.19s/it] 78%|███████▊ | 14591/18688 [17:07:48<4:35:59, 4.04s/it] 78%|███████▊ | 14592/18688 [17:07:52<4:43:23, 4.15s/it] 78%|███████▊ | 14593/18688 [17:07:57<4:50:53, 4.26s/it] 78%|███████▊ | 14594/18688 [17:08:01<4:42:26, 4.14s/it] 78%|███████▊ | 14595/18688 [17:08:05<4:42:26, 4.14s/it] 78%|███████▊ | 14596/18688 [17:08:09<4:54:36, 4.32s/it] 78%|███████▊ | 14597/18688 [17:08:14<5:02:28, 4.44s/it] 78%|███████▊ | 14598/18688 [17:08:18<4:47:59, 4.22s/it] 78%|███████▊ | 14599/18688 [17:08:22<4:39:30, 4.10s/it] 78%|███████▊ | 14600/18688 [17:08:26<4:40:00, 4.11s/it] {'loss': '0.8846', 'grad_norm': '1.129', 'learning_rate': '1.066e-05', 'epoch': '0.7813', 'num_input_tokens_seen': 49082728, 'train_runtime': '6.171e+04', 'train_tokens_per_second': '795.4'}
78%|███████▊ | 14600/18688 [17:08:26<4:40:00, 4.11s/it] 78%|███████▊ | 14601/18688 [17:08:30<4:42:50, 4.15s/it] 78%|███████▊ | 14602/18688 [17:08:35<5:04:06, 4.47s/it] 78%|███████▊ | 14603/18688 [17:08:39<4:47:00, 4.22s/it] 78%|███████▊ | 14604/18688 [17:08:43<4:36:31, 4.06s/it] 78%|███████▊ | 14605/18688 [17:08:47<4:36:06, 4.06s/it] 78%|███████▊ | 14606/18688 [17:08:51<4:45:31, 4.20s/it] 78%|███████▊ | 14607/18688 [17:08:55<4:34:58, 4.04s/it] 78%|███████▊ | 14608/18688 [17:08:59<4:46:14, 4.21s/it] 78%|███████▊ | 14609/18688 [17:09:04<4:44:03, 4.18s/it] 78%|███████▊ | 14610/18688 [17:09:07<4:35:57, 4.06s/it] {'loss': '0.9039', 'grad_norm': '1.168', 'learning_rate': '1.061e-05', 'epoch': '0.7818', 'num_input_tokens_seen': 49115320, 'train_runtime': '6.175e+04', 'train_tokens_per_second': '795.4'}
78%|███████▊ | 14610/18688 [17:09:07<4:35:57, 4.06s/it] 78%|███████▊ | 14611/18688 [17:09:11<4:38:24, 4.10s/it] 78%|███████▊ | 14612/18688 [17:09:15<4:26:33, 3.92s/it] 78%|███████▊ | 14613/18688 [17:09:19<4:18:12, 3.80s/it] 78%|███████▊ | 14614/18688 [17:09:23<4:37:40, 4.09s/it] 78%|███████▊ | 14615/18688 [17:09:27<4:34:47, 4.05s/it] 78%|███████▊ | 14616/18688 [17:09:31<4:37:08, 4.08s/it] 78%|███████▊ | 14617/18688 [17:09:35<4:33:45, 4.03s/it] 78%|███████▊ | 14618/18688 [17:09:39<4:31:35, 4.00s/it] 78%|███████▊ | 14619/18688 [17:09:43<4:32:23, 4.02s/it] 78%|███████▊ | 14620/18688 [17:09:48<4:44:57, 4.20s/it] {'loss': '0.8522', 'grad_norm': '1.303', 'learning_rate': '1.056e-05', 'epoch': '0.7823', 'num_input_tokens_seen': 49146664, 'train_runtime': '6.179e+04', 'train_tokens_per_second': '795.4'}
78%|███████▊ | 14620/18688 [17:09:48<4:44:57, 4.20s/it] 78%|███████▊ | 14621/18688 [17:09:52<4:45:38, 4.21s/it] 78%|███████▊ | 14622/18688 [17:09:57<4:49:09, 4.27s/it] 78%|███████▊ | 14623/18688 [17:10:01<4:54:51, 4.35s/it] 78%|███████▊ | 14624/18688 [17:10:05<4:52:20, 4.32s/it] 78%|███████▊ | 14625/18688 [17:10:09<4:41:13, 4.15s/it] 78%|███████▊ | 14626/18688 [17:10:13<4:44:03, 4.20s/it] 78%|███████▊ | 14627/18688 [17:10:17<4:31:26, 4.01s/it] 78%|███████▊ | 14628/18688 [17:10:21<4:36:15, 4.08s/it] 78%|███████▊ | 14629/18688 [17:10:26<4:53:16, 4.34s/it] 78%|███████▊ | 14630/18688 [17:10:31<4:58:17, 4.41s/it] {'loss': '0.9496', 'grad_norm': '1.17', 'learning_rate': '1.051e-05', 'epoch': '0.7829', 'num_input_tokens_seen': 49181440, 'train_runtime': '6.183e+04', 'train_tokens_per_second': '795.4'}
78%|███████▊ | 14630/18688 [17:10:31<4:58:17, 4.41s/it] 78%|███████▊ | 14631/18688 [17:10:35<4:48:36, 4.27s/it] 78%|███████▊ | 14632/18688 [17:10:39<4:45:30, 4.22s/it] 78%|███████▊ | 14633/18688 [17:10:43<4:48:26, 4.27s/it] 78%|███████▊ | 14634/18688 [17:10:47<4:39:27, 4.14s/it] 78%|███████▊ | 14635/18688 [17:10:52<4:53:15, 4.34s/it] 78%|███████▊ | 14636/18688 [17:10:55<4:36:10, 4.09s/it] 78%|███████▊ | 14637/18688 [17:11:00<4:40:14, 4.15s/it] 78%|███████▊ | 14638/18688 [17:11:03<4:34:11, 4.06s/it] 78%|███████▊ | 14639/18688 [17:11:08<4:35:09, 4.08s/it] 78%|███████▊ | 14640/18688 [17:11:12<4:35:07, 4.08s/it] {'loss': '0.8983', 'grad_norm': '1.167', 'learning_rate': '1.046e-05', 'epoch': '0.7834', 'num_input_tokens_seen': 49213704, 'train_runtime': '6.187e+04', 'train_tokens_per_second': '795.4'}
78%|███████▊ | 14640/18688 [17:11:12<4:35:07, 4.08s/it] 78%|███████▊ | 14641/18688 [17:11:17<5:03:54, 4.51s/it] 78%|███████▊ | 14642/18688 [17:11:21<4:55:08, 4.38s/it] 78%|███████▊ | 14643/18688 [17:11:25<4:51:31, 4.32s/it] 78%|███████▊ | 14644/18688 [17:11:30<4:53:17, 4.35s/it] 78%|███████▊ | 14645/18688 [17:11:33<4:37:32, 4.12s/it] 78%|███████▊ | 14646/18688 [17:11:39<5:07:30, 4.56s/it] 78%|███████▊ | 14647/18688 [17:11:43<4:59:46, 4.45s/it] 78%|███████▊ | 14648/18688 [17:11:48<5:14:10, 4.67s/it] 78%|███████▊ | 14649/18688 [17:11:52<4:57:05, 4.41s/it] 78%|███████▊ | 14650/18688 [17:11:57<4:58:44, 4.44s/it] {'loss': '0.9486', 'grad_norm': '1.225', 'learning_rate': '1.041e-05', 'epoch': '0.7839', 'num_input_tokens_seen': 49251928, 'train_runtime': '6.192e+04', 'train_tokens_per_second': '795.4'}
78%|███████▊ | 14650/18688 [17:11:57<4:58:44, 4.44s/it] 78%|███████▊ | 14651/18688 [17:12:02<5:10:24, 4.61s/it] 78%|███████▊ | 14652/18688 [17:12:06<5:10:26, 4.62s/it] 78%|███████▊ | 14653/18688 [17:12:10<4:50:11, 4.32s/it] 78%|███████▊ | 14654/18688 [17:12:14<4:37:14, 4.12s/it] 78%|███████▊ | 14655/18688 [17:12:19<4:55:22, 4.39s/it] 78%|███████▊ | 14656/18688 [17:12:23<4:44:20, 4.23s/it] 78%|███████▊ | 14657/18688 [17:12:27<4:43:29, 4.22s/it] 78%|███████▊ | 14658/18688 [17:12:30<4:33:15, 4.07s/it] 78%|███████▊ | 14659/18688 [17:12:34<4:25:25, 3.95s/it] 78%|███████▊ | 14660/18688 [17:12:39<4:46:33, 4.27s/it] {'loss': '0.8731', 'grad_norm': '1.246', 'learning_rate': '1.036e-05', 'epoch': '0.7845', 'num_input_tokens_seen': 49285664, 'train_runtime': '6.196e+04', 'train_tokens_per_second': '795.4'}
78%|███████▊ | 14660/18688 [17:12:39<4:46:33, 4.27s/it] 78%|███████▊ | 14661/18688 [17:12:43<4:42:24, 4.21s/it] 78%|███████▊ | 14662/18688 [17:12:47<4:37:50, 4.14s/it] 78%|███████▊ | 14663/18688 [17:12:52<4:49:48, 4.32s/it] 78%|███████▊ | 14664/18688 [17:12:56<4:48:53, 4.31s/it] 78%|███████▊ | 14665/18688 [17:13:00<4:44:08, 4.24s/it] 78%|███████▊ | 14666/18688 [17:13:04<4:41:48, 4.20s/it] 78%|███████▊ | 14667/18688 [17:13:08<4:37:52, 4.15s/it] 78%|███████▊ | 14668/18688 [17:13:12<4:33:59, 4.09s/it] 78%|███████▊ | 14669/18688 [17:13:17<4:49:49, 4.33s/it] 78%|███████▊ | 14670/18688 [17:13:21<4:42:55, 4.22s/it] {'loss': '0.9307', 'grad_norm': '1.105', 'learning_rate': '1.032e-05', 'epoch': '0.785', 'num_input_tokens_seen': 49319416, 'train_runtime': '6.2e+04', 'train_tokens_per_second': '795.5'}
78%|███████▊ | 14670/18688 [17:13:21<4:42:55, 4.22s/it] 79%|███████▊ | 14671/18688 [17:13:26<4:47:00, 4.29s/it] 79%|███████▊ | 14672/18688 [17:13:30<4:49:41, 4.33s/it] 79%|███████▊ | 14673/18688 [17:13:34<4:41:44, 4.21s/it] 79%|███████▊ | 14674/18688 [17:13:38<4:27:33, 4.00s/it] 79%|███████▊ | 14675/18688 [17:13:43<4:54:02, 4.40s/it] 79%|███████▊ | 14676/18688 [17:13:47<4:47:42, 4.30s/it] 79%|███████▊ | 14677/18688 [17:13:51<4:34:45, 4.11s/it] 79%|███████▊ | 14678/18688 [17:13:55<4:32:10, 4.07s/it] 79%|███████▊ | 14679/18688 [17:13:59<4:46:00, 4.28s/it] 79%|███████▊ | 14680/18688 [17:14:03<4:30:25, 4.05s/it] {'loss': '0.8955', 'grad_norm': '1.077', 'learning_rate': '1.027e-05', 'epoch': '0.7856', 'num_input_tokens_seen': 49353336, 'train_runtime': '6.204e+04', 'train_tokens_per_second': '795.5'}
79%|███████▊ | 14680/18688 [17:14:03<4:30:25, 4.05s/it] 79%|███████▊ | 14681/18688 [17:14:07<4:35:48, 4.13s/it] 79%|███████▊ | 14682/18688 [17:14:12<4:55:18, 4.42s/it] 79%|███████▊ | 14683/18688 [17:14:17<4:51:26, 4.37s/it] 79%|███████▊ | 14684/18688 [17:14:20<4:35:50, 4.13s/it] 79%|███████▊ | 14685/18688 [17:14:25<4:46:43, 4.30s/it] 79%|███████▊ | 14686/18688 [17:14:29<4:35:32, 4.13s/it] 79%|███████▊ | 14687/18688 [17:14:32<4:30:36, 4.06s/it] 79%|███████▊ | 14688/18688 [17:14:37<4:32:14, 4.08s/it] 79%|███████▊ | 14689/18688 [17:14:40<4:22:32, 3.94s/it] 79%|███████▊ | 14690/18688 [17:14:45<4:42:46, 4.24s/it] {'loss': '0.9', 'grad_norm': '0.9636', 'learning_rate': '1.022e-05', 'epoch': '0.7861', 'num_input_tokens_seen': 49387728, 'train_runtime': '6.209e+04', 'train_tokens_per_second': '795.5'}
79%|███████▊ | 14690/18688 [17:14:45<4:42:46, 4.24s/it] 79%|███████▊ | 14691/18688 [17:14:51<5:05:38, 4.59s/it] 79%|███████▊ | 14692/18688 [17:14:55<4:59:36, 4.50s/it] 79%|███████▊ | 14693/18688 [17:14:59<4:46:18, 4.30s/it] 79%|███████▊ | 14694/18688 [17:15:02<4:36:11, 4.15s/it] 79%|███████▊ | 14695/18688 [17:15:06<4:31:58, 4.09s/it] 79%|███████▊ | 14696/18688 [17:15:11<4:52:16, 4.39s/it] 79%|███████▊ | 14697/18688 [17:15:16<4:48:15, 4.33s/it] 79%|███████▊ | 14698/18688 [17:15:19<4:37:23, 4.17s/it] 79%|███████▊ | 14699/18688 [17:15:24<4:38:10, 4.18s/it] 79%|███████▊ | 14700/18688 [17:15:28<4:42:24, 4.25s/it] {'loss': '0.9062', 'grad_norm': '1.218', 'learning_rate': '1.017e-05', 'epoch': '0.7866', 'num_input_tokens_seen': 49423416, 'train_runtime': '6.213e+04', 'train_tokens_per_second': '795.5'}
79%|███████▊ | 14700/18688 [17:15:28<4:42:24, 4.25s/it] 79%|███████▊ | 14701/18688 [17:15:32<4:43:14, 4.26s/it] 79%|███████▊ | 14702/18688 [17:15:37<4:50:04, 4.37s/it] 79%|███████▊ | 14703/18688 [17:15:41<4:35:23, 4.15s/it] 79%|███████▊ | 14704/18688 [17:15:46<4:50:36, 4.38s/it] 79%|███████▊ | 14705/18688 [17:15:49<4:38:50, 4.20s/it] 79%|███████▊ | 14706/18688 [17:15:54<4:44:34, 4.29s/it] 79%|███████▊ | 14707/18688 [17:15:59<4:55:02, 4.45s/it] 79%|███████▊ | 14708/18688 [17:16:03<4:43:55, 4.28s/it] 79%|███████▊ | 14709/18688 [17:16:07<4:44:17, 4.29s/it] 79%|███████▊ | 14710/18688 [17:16:11<4:33:33, 4.13s/it] {'loss': '0.8951', 'grad_norm': '1.282', 'learning_rate': '1.012e-05', 'epoch': '0.7872', 'num_input_tokens_seen': 49458488, 'train_runtime': '6.217e+04', 'train_tokens_per_second': '795.5'}
79%|███████▊ | 14710/18688 [17:16:11<4:33:33, 4.13s/it] 79%|███████▊ | 14711/18688 [17:16:14<4:25:16, 4.00s/it] 79%|███████▊ | 14712/18688 [17:16:19<4:32:29, 4.11s/it] 79%|███████▊ | 14713/18688 [17:16:23<4:28:39, 4.06s/it] 79%|███████▊ | 14714/18688 [17:16:27<4:28:44, 4.06s/it] 79%|███████▊ | 14715/18688 [17:16:31<4:26:06, 4.02s/it] 79%|███████▊ | 14716/18688 [17:16:35<4:35:11, 4.16s/it] 79%|███████▉ | 14717/18688 [17:16:39<4:27:53, 4.05s/it] 79%|███████▉ | 14718/18688 [17:16:43<4:30:28, 4.09s/it] 79%|███████▉ | 14719/18688 [17:16:48<4:42:45, 4.27s/it] 79%|███████▉ | 14720/18688 [17:16:52<4:37:16, 4.19s/it] {'loss': '0.9233', 'grad_norm': '1.343', 'learning_rate': '1.007e-05', 'epoch': '0.7877', 'num_input_tokens_seen': 49490616, 'train_runtime': '6.221e+04', 'train_tokens_per_second': '795.5'}
79%|███████▉ | 14720/18688 [17:16:52<4:37:16, 4.19s/it] 79%|███████▉ | 14721/18688 [17:16:56<4:39:35, 4.23s/it] 79%|███████▉ | 14722/18688 [17:17:01<4:52:26, 4.42s/it] 79%|███████▉ | 14723/18688 [17:17:04<4:33:21, 4.14s/it] 79%|███████▉ | 14724/18688 [17:17:08<4:29:44, 4.08s/it] 79%|███████▉ | 14725/18688 [17:17:13<4:41:43, 4.27s/it] 79%|███████▉ | 14726/18688 [17:17:17<4:32:09, 4.12s/it] 79%|███████▉ | 14727/18688 [17:17:20<4:21:07, 3.96s/it] 79%|███████▉ | 14728/18688 [17:17:25<4:40:52, 4.26s/it] 79%|███████▉ | 14729/18688 [17:17:29<4:34:13, 4.16s/it] 79%|███████▉ | 14730/18688 [17:17:33<4:29:24, 4.08s/it] {'loss': '0.9438', 'grad_norm': '1.662', 'learning_rate': '1.002e-05', 'epoch': '0.7882', 'num_input_tokens_seen': 49523712, 'train_runtime': '6.225e+04', 'train_tokens_per_second': '795.5'}
79%|███████▉ | 14730/18688 [17:17:33<4:29:24, 4.08s/it] 79%|███████▉ | 14731/18688 [17:17:37<4:32:36, 4.13s/it] 79%|███████▉ | 14732/18688 [17:17:42<4:44:35, 4.32s/it] 79%|███████▉ | 14733/18688 [17:17:47<4:47:33, 4.36s/it] 79%|███████▉ | 14734/18688 [17:17:52<5:07:06, 4.66s/it] 79%|███████▉ | 14735/18688 [17:17:57<5:15:08, 4.78s/it] 79%|███████▉ | 14736/18688 [17:18:01<5:03:12, 4.60s/it] 79%|███████▉ | 14737/18688 [17:18:05<4:46:45, 4.35s/it] 79%|███████▉ | 14738/18688 [17:18:09<4:42:22, 4.29s/it] 79%|███████▉ | 14739/18688 [17:18:14<4:53:15, 4.46s/it] 79%|███████▉ | 14740/18688 [17:18:18<4:37:47, 4.22s/it] {'loss': '0.8524', 'grad_norm': '1.263', 'learning_rate': '9.975e-06', 'epoch': '0.7888', 'num_input_tokens_seen': 49562040, 'train_runtime': '6.23e+04', 'train_tokens_per_second': '795.6'}
79%|███████▉ | 14740/18688 [17:18:18<4:37:47, 4.22s/it] 79%|███████▉ | 14741/18688 [17:18:22<4:35:50, 4.19s/it] 79%|███████▉ | 14742/18688 [17:18:26<4:38:05, 4.23s/it] 79%|███████▉ | 14743/18688 [17:18:30<4:31:49, 4.13s/it] 79%|███████▉ | 14744/18688 [17:18:34<4:34:08, 4.17s/it] 79%|███████▉ | 14745/18688 [17:18:38<4:31:33, 4.13s/it] 79%|███████▉ | 14746/18688 [17:18:42<4:29:14, 4.10s/it] 79%|███████▉ | 14747/18688 [17:18:46<4:24:52, 4.03s/it] 79%|███████▉ | 14748/18688 [17:18:50<4:23:09, 4.01s/it] 79%|███████▉ | 14749/18688 [17:18:54<4:24:54, 4.04s/it] 79%|███████▉ | 14750/18688 [17:18:58<4:25:53, 4.05s/it] {'loss': '0.9228', 'grad_norm': '1.227', 'learning_rate': '9.927e-06', 'epoch': '0.7893', 'num_input_tokens_seen': 49594568, 'train_runtime': '6.234e+04', 'train_tokens_per_second': '795.6'}
79%|███████▉ | 14750/18688 [17:18:58<4:25:53, 4.05s/it] 79%|███████▉ | 14751/18688 [17:19:03<4:33:41, 4.17s/it] 79%|███████▉ | 14752/18688 [17:19:09<5:03:18, 4.62s/it] 79%|███████▉ | 14753/18688 [17:19:12<4:46:49, 4.37s/it] 79%|███████▉ | 14754/18688 [17:19:16<4:43:18, 4.32s/it] 79%|███████▉ | 14755/18688 [17:19:20<4:29:32, 4.11s/it] 79%|███████▉ | 14756/18688 [17:19:24<4:26:20, 4.06s/it] 79%|███████▉ | 14757/18688 [17:19:28<4:20:41, 3.98s/it] 79%|███████▉ | 14758/18688 [17:19:33<4:51:19, 4.45s/it] 79%|███████▉ | 14759/18688 [17:19:37<4:43:41, 4.33s/it] 79%|███████▉ | 14760/18688 [17:19:42<4:42:51, 4.32s/it] {'loss': '0.9316', 'grad_norm': '1.174', 'learning_rate': '9.879e-06', 'epoch': '0.7898', 'num_input_tokens_seen': 49628960, 'train_runtime': '6.238e+04', 'train_tokens_per_second': '795.6'}
79%|███████▉ | 14760/18688 [17:19:42<4:42:51, 4.32s/it] 79%|███████▉ | 14761/18688 [17:19:46<4:33:26, 4.18s/it] 79%|███████▉ | 14762/18688 [17:19:49<4:26:35, 4.07s/it] 79%|███████▉ | 14763/18688 [17:19:53<4:19:08, 3.96s/it] 79%|███████▉ | 14764/18688 [17:19:57<4:20:11, 3.98s/it] 79%|███████▉ | 14765/18688 [17:20:01<4:11:30, 3.85s/it] 79%|███████▉ | 14766/18688 [17:20:05<4:19:20, 3.97s/it] 79%|███████▉ | 14767/18688 [17:20:09<4:15:29, 3.91s/it] 79%|███████▉ | 14768/18688 [17:20:13<4:18:07, 3.95s/it] 79%|███████▉ | 14769/18688 [17:20:16<4:09:13, 3.82s/it] 79%|███████▉ | 14770/18688 [17:20:20<4:14:18, 3.89s/it] {'loss': '0.933', 'grad_norm': '1.056', 'learning_rate': '9.831e-06', 'epoch': '0.7904', 'num_input_tokens_seen': 49658712, 'train_runtime': '6.242e+04', 'train_tokens_per_second': '795.5'}
79%|███████▉ | 14770/18688 [17:20:20<4:14:18, 3.89s/it] 79%|███████▉ | 14771/18688 [17:20:25<4:30:35, 4.14s/it] 79%|███████▉ | 14772/18688 [17:20:29<4:32:45, 4.18s/it] 79%|███████▉ | 14773/18688 [17:20:34<4:37:28, 4.25s/it] 79%|███████▉ | 14774/18688 [17:20:38<4:29:58, 4.14s/it] 79%|███████▉ | 14775/18688 [17:20:42<4:29:44, 4.14s/it] 79%|███████▉ | 14776/18688 [17:20:46<4:23:50, 4.05s/it] 79%|███████▉ | 14777/18688 [17:20:51<4:43:42, 4.35s/it] 79%|███████▉ | 14778/18688 [17:20:55<4:42:18, 4.33s/it] 79%|███████▉ | 14779/18688 [17:20:59<4:40:01, 4.30s/it] 79%|███████▉ | 14780/18688 [17:21:04<4:52:51, 4.50s/it] {'loss': '0.9066', 'grad_norm': '1.292', 'learning_rate': '9.783e-06', 'epoch': '0.7909', 'num_input_tokens_seen': 49694280, 'train_runtime': '6.246e+04', 'train_tokens_per_second': '795.6'}
79%|███████▉ | 14780/18688 [17:21:04<4:52:51, 4.50s/it] 79%|███████▉ | 14781/18688 [17:21:08<4:44:33, 4.37s/it] 79%|███████▉ | 14782/18688 [17:21:12<4:33:23, 4.20s/it] 79%|███████▉ | 14783/18688 [17:21:16<4:21:33, 4.02s/it] 79%|███████▉ | 14784/18688 [17:21:20<4:21:03, 4.01s/it] 79%|███████▉ | 14785/18688 [17:21:24<4:23:49, 4.06s/it] 79%|███████▉ | 14786/18688 [17:21:28<4:18:42, 3.98s/it] 79%|███████▉ | 14787/18688 [17:21:31<4:11:26, 3.87s/it] 79%|███████▉ | 14788/18688 [17:21:35<4:06:09, 3.79s/it] 79%|███████▉ | 14789/18688 [17:21:39<4:11:27, 3.87s/it] 79%|███████▉ | 14790/18688 [17:21:43<4:20:54, 4.02s/it] {'loss': '0.9064', 'grad_norm': '1.394', 'learning_rate': '9.735e-06', 'epoch': '0.7914', 'num_input_tokens_seen': 49723480, 'train_runtime': '6.25e+04', 'train_tokens_per_second': '795.5'}
79%|███████▉ | 14790/18688 [17:21:43<4:20:54, 4.02s/it] 79%|███████▉ | 14791/18688 [17:21:47<4:17:23, 3.96s/it] 79%|███████▉ | 14792/18688 [17:21:51<4:15:06, 3.93s/it] 79%|███████▉ | 14793/18688 [17:21:56<4:30:05, 4.16s/it] 79%|███████▉ | 14794/18688 [17:21:59<4:24:28, 4.07s/it] 79%|███████▉ | 14795/18688 [17:22:05<4:55:35, 4.56s/it] 79%|███████▉ | 14796/18688 [17:22:09<4:50:26, 4.48s/it] 79%|███████▉ | 14797/18688 [17:22:13<4:41:36, 4.34s/it] 79%|███████▉ | 14798/18688 [17:22:17<4:29:51, 4.16s/it] 79%|███████▉ | 14799/18688 [17:22:21<4:28:37, 4.14s/it] 79%|███████▉ | 14800/18688 [17:22:26<4:34:46, 4.24s/it] {'loss': '0.8966', 'grad_norm': '1.054', 'learning_rate': '9.687e-06', 'epoch': '0.792', 'num_input_tokens_seen': 49759216, 'train_runtime': '6.255e+04', 'train_tokens_per_second': '795.6'}
79%|███████▉ | 14800/18688 [17:22:26<4:34:46, 4.24s/it] 79%|███████▉ | 14801/18688 [17:22:31<4:52:21, 4.51s/it] 79%|███████▉ | 14802/18688 [17:22:35<4:48:56, 4.46s/it] 79%|███████▉ | 14803/18688 [17:22:40<4:45:44, 4.41s/it] 79%|███████▉ | 14804/18688 [17:22:44<4:42:36, 4.37s/it] 79%|███████▉ | 14805/18688 [17:22:48<4:31:03, 4.19s/it] 79%|███████▉ | 14806/18688 [17:22:51<4:18:37, 4.00s/it] 79%|███████▉ | 14807/18688 [17:22:55<4:14:22, 3.93s/it] 79%|███████▉ | 14808/18688 [17:23:00<4:28:26, 4.15s/it] 79%|███████▉ | 14809/18688 [17:23:03<4:18:57, 4.01s/it] 79%|███████▉ | 14810/18688 [17:23:07<4:15:02, 3.95s/it] {'loss': '0.8851', 'grad_norm': '1.124', 'learning_rate': '9.639e-06', 'epoch': '0.7925', 'num_input_tokens_seen': 49792744, 'train_runtime': '6.259e+04', 'train_tokens_per_second': '795.6'}
79%|███████▉ | 14810/18688 [17:23:07<4:15:02, 3.95s/it] 79%|███████▉ | 14811/18688 [17:23:11<4:16:37, 3.97s/it] 79%|███████▉ | 14812/18688 [17:23:16<4:31:44, 4.21s/it] 79%|███████▉ | 14813/18688 [17:23:20<4:27:59, 4.15s/it] 79%|███████▉ | 14814/18688 [17:23:24<4:22:56, 4.07s/it] 79%|███████▉ | 14815/18688 [17:23:28<4:19:10, 4.02s/it] 79%|███████▉ | 14816/18688 [17:23:32<4:26:48, 4.13s/it] 79%|███████▉ | 14817/18688 [17:23:36<4:28:32, 4.16s/it] 79%|███████▉ | 14818/18688 [17:23:41<4:34:50, 4.26s/it] 79%|███████▉ | 14819/18688 [17:23:45<4:31:15, 4.21s/it] 79%|███████▉ | 14820/18688 [17:23:49<4:34:51, 4.26s/it] {'loss': '0.92', 'grad_norm': '1.015', 'learning_rate': '9.592e-06', 'epoch': '0.793', 'num_input_tokens_seen': 49826648, 'train_runtime': '6.263e+04', 'train_tokens_per_second': '795.6'}
79%|███████▉ | 14820/18688 [17:23:49<4:34:51, 4.26s/it] 79%|███████▉ | 14821/18688 [17:23:53<4:20:42, 4.05s/it] 79%|███████▉ | 14822/18688 [17:23:56<4:11:20, 3.90s/it] 79%|███████▉ | 14823/18688 [17:24:01<4:17:05, 3.99s/it] 79%|███████▉ | 14824/18688 [17:24:05<4:19:51, 4.03s/it] 79%|███████▉ | 14825/18688 [17:24:09<4:16:21, 3.98s/it] 79%|███████▉ | 14826/18688 [17:24:12<4:14:45, 3.96s/it] 79%|███████▉ | 14827/18688 [17:24:16<4:07:26, 3.85s/it] 79%|███████▉ | 14828/18688 [17:24:21<4:21:12, 4.06s/it] 79%|███████▉ | 14829/18688 [17:24:24<4:16:46, 3.99s/it] 79%|███████▉ | 14830/18688 [17:24:29<4:21:27, 4.07s/it] {'loss': '0.9376', 'grad_norm': '1.214', 'learning_rate': '9.544e-06', 'epoch': '0.7936', 'num_input_tokens_seen': 49857272, 'train_runtime': '6.267e+04', 'train_tokens_per_second': '795.6'}
79%|███████▉ | 14830/18688 [17:24:29<4:21:27, 4.07s/it] 79%|███████▉ | 14831/18688 [17:24:33<4:35:45, 4.29s/it] 79%|███████▉ | 14832/18688 [17:24:38<4:39:12, 4.34s/it] 79%|███████▉ | 14833/18688 [17:24:42<4:29:44, 4.20s/it] 79%|███████▉ | 14834/18688 [17:24:46<4:36:26, 4.30s/it] 79%|███████▉ | 14835/18688 [17:24:51<4:35:28, 4.29s/it] 79%|███████▉ | 14836/18688 [17:24:54<4:22:36, 4.09s/it] 79%|███████▉ | 14837/18688 [17:24:59<4:40:52, 4.38s/it] 79%|███████▉ | 14838/18688 [17:25:04<4:47:23, 4.48s/it] 79%|███████▉ | 14839/18688 [17:25:08<4:45:35, 4.45s/it] 79%|███████▉ | 14840/18688 [17:25:12<4:36:21, 4.31s/it] {'loss': '0.9432', 'grad_norm': '1.913', 'learning_rate': '9.497e-06', 'epoch': '0.7941', 'num_input_tokens_seen': 49892864, 'train_runtime': '6.271e+04', 'train_tokens_per_second': '795.6'}
79%|███████▉ | 14840/18688 [17:25:12<4:36:21, 4.31s/it] 79%|███████▉ | 14841/18688 [17:25:16<4:29:01, 4.20s/it] 79%|███████▉ | 14842/18688 [17:25:20<4:22:03, 4.09s/it] 79%|███████▉ | 14843/18688 [17:25:24<4:24:41, 4.13s/it] 79%|███████▉ | 14844/18688 [17:25:29<4:41:20, 4.39s/it] 79%|███████▉ | 14845/18688 [17:25:34<4:39:20, 4.36s/it] 79%|███████▉ | 14846/18688 [17:25:37<4:23:04, 4.11s/it] 79%|███████▉ | 14847/18688 [17:25:41<4:24:46, 4.14s/it] 79%|███████▉ | 14848/18688 [17:25:47<4:45:06, 4.45s/it] 79%|███████▉ | 14849/18688 [17:25:51<4:43:31, 4.43s/it] 79%|███████▉ | 14850/18688 [17:25:55<4:40:11, 4.38s/it] {'loss': '0.9132', 'grad_norm': '1.249', 'learning_rate': '9.45e-06', 'epoch': '0.7946', 'num_input_tokens_seen': 49928696, 'train_runtime': '6.276e+04', 'train_tokens_per_second': '795.6'}
79%|███████▉ | 14850/18688 [17:25:55<4:40:11, 4.38s/it] 79%|███████▉ | 14851/18688 [17:26:00<4:47:07, 4.49s/it] 79%|███████▉ | 14852/18688 [17:26:05<5:00:32, 4.70s/it] 79%|███████▉ | 14853/18688 [17:26:09<4:37:01, 4.33s/it] 79%|███████▉ | 14854/18688 [17:26:13<4:45:43, 4.47s/it] 79%|███████▉ | 14855/18688 [17:26:17<4:30:23, 4.23s/it] 79%|███████▉ | 14856/18688 [17:26:21<4:16:30, 4.02s/it] 80%|███████▉ | 14857/18688 [17:26:24<4:06:36, 3.86s/it] 80%|███████▉ | 14858/18688 [17:26:29<4:20:36, 4.08s/it] 80%|███████▉ | 14859/18688 [17:26:33<4:20:12, 4.08s/it] 80%|███████▉ | 14860/18688 [17:26:37<4:16:44, 4.02s/it] {'loss': '0.8901', 'grad_norm': '1.125', 'learning_rate': '9.403e-06', 'epoch': '0.7952', 'num_input_tokens_seen': 49962016, 'train_runtime': '6.28e+04', 'train_tokens_per_second': '795.6'}
80%|███████▉ | 14860/18688 [17:26:37<4:16:44, 4.02s/it] 80%|███████▉ | 14861/18688 [17:26:41<4:21:47, 4.10s/it] 80%|███████▉ | 14862/18688 [17:26:45<4:16:00, 4.01s/it] 80%|███████▉ | 14863/18688 [17:26:48<4:09:22, 3.91s/it] 80%|███████▉ | 14864/18688 [17:26:52<4:06:22, 3.87s/it] 80%|███████▉ | 14865/18688 [17:26:56<4:10:14, 3.93s/it] 80%|███████▉ | 14866/18688 [17:27:00<4:12:47, 3.97s/it] 80%|███████▉ | 14867/18688 [17:27:05<4:23:17, 4.13s/it] 80%|███████▉ | 14868/18688 [17:27:08<4:10:17, 3.93s/it] 80%|███████▉ | 14869/18688 [17:27:12<4:12:49, 3.97s/it] 80%|███████▉ | 14870/18688 [17:27:16<4:05:07, 3.85s/it] {'loss': '0.8684', 'grad_norm': '1.275', 'learning_rate': '9.356e-06', 'epoch': '0.7957', 'num_input_tokens_seen': 49992136, 'train_runtime': '6.284e+04', 'train_tokens_per_second': '795.6'}
80%|███████▉ | 14870/18688 [17:27:16<4:05:07, 3.85s/it] 80%|███████▉ | 14871/18688 [17:27:21<4:19:44, 4.08s/it] 80%|███████▉ | 14872/18688 [17:27:24<4:12:29, 3.97s/it] 80%|███████▉ | 14873/18688 [17:27:29<4:23:02, 4.14s/it] 80%|███████▉ | 14874/18688 [17:27:33<4:23:30, 4.15s/it] 80%|███████▉ | 14875/18688 [17:27:37<4:24:58, 4.17s/it] 80%|███████▉ | 14876/18688 [17:27:41<4:17:28, 4.05s/it] 80%|███████▉ | 14877/18688 [17:27:45<4:21:44, 4.12s/it] 80%|███████▉ | 14878/18688 [17:27:49<4:13:12, 3.99s/it] 80%|███████▉ | 14879/18688 [17:27:53<4:08:40, 3.92s/it] 80%|███████▉ | 14880/18688 [17:27:57<4:16:47, 4.05s/it] {'loss': '0.9021', 'grad_norm': '1.153', 'learning_rate': '9.309e-06', 'epoch': '0.7963', 'num_input_tokens_seen': 50025056, 'train_runtime': '6.288e+04', 'train_tokens_per_second': '795.6'}
80%|███████▉ | 14880/18688 [17:27:57<4:16:47, 4.05s/it] 80%|███████▉ | 14881/18688 [17:28:02<4:28:12, 4.23s/it] 80%|███████▉ | 14882/18688 [17:28:06<4:22:33, 4.14s/it] 80%|███████▉ | 14883/18688 [17:28:09<4:13:36, 4.00s/it] 80%|███████▉ | 14884/18688 [17:28:13<4:07:21, 3.90s/it] 80%|███████▉ | 14885/18688 [17:28:17<4:07:09, 3.90s/it] 80%|███████▉ | 14886/18688 [17:28:22<4:22:24, 4.14s/it] 80%|███████▉ | 14887/18688 [17:28:27<4:42:44, 4.46s/it] 80%|███████▉ | 14888/18688 [17:28:31<4:40:15, 4.43s/it] 80%|███████▉ | 14889/18688 [17:28:36<4:43:48, 4.48s/it] 80%|███████▉ | 14890/18688 [17:28:40<4:44:43, 4.50s/it] {'loss': '0.9023', 'grad_norm': '1.054', 'learning_rate': '9.262e-06', 'epoch': '0.7968', 'num_input_tokens_seen': 50060816, 'train_runtime': '6.292e+04', 'train_tokens_per_second': '795.6'}
80%|███████▉ | 14890/18688 [17:28:40<4:44:43, 4.50s/it] 80%|███████▉ | 14891/18688 [17:28:45<4:49:53, 4.58s/it] 80%|███████▉ | 14892/18688 [17:28:49<4:31:18, 4.29s/it] 80%|███████▉ | 14893/18688 [17:28:53<4:30:23, 4.28s/it] 80%|███████▉ | 14894/18688 [17:28:57<4:26:25, 4.21s/it] 80%|███████▉ | 14895/18688 [17:29:01<4:22:42, 4.16s/it] 80%|███████▉ | 14896/18688 [17:29:05<4:19:42, 4.11s/it] 80%|███████▉ | 14897/18688 [17:29:10<4:30:18, 4.28s/it] 80%|███████▉ | 14898/18688 [17:29:13<4:14:54, 4.04s/it] 80%|███████▉ | 14899/18688 [17:29:17<4:21:26, 4.14s/it] 80%|███████▉ | 14900/18688 [17:29:21<4:15:57, 4.05s/it] {'loss': '0.8974', 'grad_norm': '1.267', 'learning_rate': '9.215e-06', 'epoch': '0.7973', 'num_input_tokens_seen': 50093656, 'train_runtime': '6.296e+04', 'train_tokens_per_second': '795.6'}
80%|███████▉ | 14900/18688 [17:29:21<4:15:57, 4.05s/it] 80%|███████▉ | 14901/18688 [17:29:25<4:08:04, 3.93s/it] 80%|███████▉ | 14902/18688 [17:29:29<4:09:15, 3.95s/it] 80%|███████▉ | 14903/18688 [17:29:33<4:14:51, 4.04s/it] 80%|███████▉ | 14904/18688 [17:29:37<4:13:02, 4.01s/it] 80%|███████▉ | 14905/18688 [17:29:41<4:11:28, 3.99s/it] 80%|███████▉ | 14906/18688 [17:29:46<4:31:10, 4.30s/it] 80%|███████▉ | 14907/18688 [17:29:50<4:20:13, 4.13s/it] 80%|███████▉ | 14908/18688 [17:29:54<4:20:32, 4.14s/it] 80%|███████▉ | 14909/18688 [17:29:58<4:22:08, 4.16s/it] 80%|███████▉ | 14910/18688 [17:30:02<4:14:03, 4.03s/it] {'loss': '0.9391', 'grad_norm': '1.32', 'learning_rate': '9.168e-06', 'epoch': '0.7979', 'num_input_tokens_seen': 50125760, 'train_runtime': '6.3e+04', 'train_tokens_per_second': '795.6'}
80%|███████▉ | 14910/18688 [17:30:02<4:14:03, 4.03s/it] 80%|███████▉ | 14911/18688 [17:30:07<4:31:01, 4.31s/it] 80%|███████▉ | 14912/18688 [17:30:11<4:30:07, 4.29s/it] 80%|███████▉ | 14913/18688 [17:30:15<4:18:51, 4.11s/it] 80%|███████▉ | 14914/18688 [17:30:20<4:32:02, 4.32s/it] 80%|███████▉ | 14915/18688 [17:30:23<4:20:21, 4.14s/it] 80%|███████▉ | 14916/18688 [17:30:28<4:23:10, 4.19s/it] 80%|███████▉ | 14917/18688 [17:30:32<4:20:35, 4.15s/it] 80%|███████▉ | 14918/18688 [17:30:36<4:23:31, 4.19s/it] 80%|███████▉ | 14919/18688 [17:30:40<4:26:59, 4.25s/it] 80%|███████▉ | 14920/18688 [17:30:46<4:45:23, 4.54s/it] {'loss': '0.9857', 'grad_norm': '1.16', 'learning_rate': '9.122e-06', 'epoch': '0.7984', 'num_input_tokens_seen': 50161592, 'train_runtime': '6.305e+04', 'train_tokens_per_second': '795.6'}
80%|███████▉ | 14920/18688 [17:30:46<4:45:23, 4.54s/it] 80%|███████▉ | 14921/18688 [17:30:49<4:27:14, 4.26s/it] 80%|███████▉ | 14922/18688 [17:30:54<4:29:27, 4.29s/it] 80%|███████▉ | 14923/18688 [17:30:57<4:17:51, 4.11s/it] 80%|███████▉ | 14924/18688 [17:31:03<4:41:37, 4.49s/it] 80%|███████▉ | 14925/18688 [17:31:07<4:33:13, 4.36s/it] 80%|███████▉ | 14926/18688 [17:31:10<4:15:15, 4.07s/it] 80%|███████▉ | 14927/18688 [17:31:15<4:21:11, 4.17s/it] 80%|███████▉ | 14928/18688 [17:31:19<4:20:46, 4.16s/it] 80%|███████▉ | 14929/18688 [17:31:23<4:29:24, 4.30s/it] 80%|███████▉ | 14930/18688 [17:31:28<4:27:56, 4.28s/it] {'loss': '0.8798', 'grad_norm': '1.454', 'learning_rate': '9.076e-06', 'epoch': '0.7989', 'num_input_tokens_seen': 50195760, 'train_runtime': '6.309e+04', 'train_tokens_per_second': '795.6'}
80%|███████▉ | 14930/18688 [17:31:28<4:27:56, 4.28s/it] 80%|███████▉ | 14931/18688 [17:31:32<4:32:06, 4.35s/it] 80%|███████▉ | 14932/18688 [17:31:37<4:39:19, 4.46s/it] 80%|███████▉ | 14933/18688 [17:31:40<4:23:31, 4.21s/it] 80%|███████▉ | 14934/18688 [17:31:45<4:29:27, 4.31s/it] 80%|███████▉ | 14935/18688 [17:31:49<4:28:47, 4.30s/it] 80%|███████▉ | 14936/18688 [17:31:54<4:32:33, 4.36s/it] 80%|███████▉ | 14937/18688 [17:31:58<4:25:20, 4.24s/it] 80%|███████▉ | 14938/18688 [17:32:01<4:15:56, 4.09s/it] 80%|███████▉ | 14939/18688 [17:32:05<4:14:45, 4.08s/it] 80%|███████▉ | 14940/18688 [17:32:09<4:04:16, 3.91s/it] {'loss': '0.8789', 'grad_norm': '1.364', 'learning_rate': '9.029e-06', 'epoch': '0.7995', 'num_input_tokens_seen': 50228848, 'train_runtime': '6.313e+04', 'train_tokens_per_second': '795.6'}
80%|███████▉ | 14940/18688 [17:32:09<4:04:16, 3.91s/it] 80%|███████▉ | 14941/18688 [17:32:13<3:58:15, 3.82s/it] 80%|███████▉ | 14942/18688 [17:32:16<3:52:14, 3.72s/it] 80%|███████▉ | 14943/18688 [17:32:20<4:00:48, 3.86s/it] 80%|███████▉ | 14944/18688 [17:32:24<4:00:48, 3.86s/it] 80%|███████▉ | 14945/18688 [17:32:28<4:02:02, 3.88s/it] 80%|███████▉ | 14946/18688 [17:32:32<4:07:18, 3.97s/it] 80%|███████▉ | 14947/18688 [17:32:36<4:08:30, 3.99s/it] 80%|███████▉ | 14948/18688 [17:32:40<4:08:40, 3.99s/it] 80%|███████▉ | 14949/18688 [17:32:44<4:00:06, 3.85s/it] 80%|███████▉ | 14950/18688 [17:32:48<4:13:02, 4.06s/it] {'loss': '0.8459', 'grad_norm': '1.097', 'learning_rate': '8.983e-06', 'epoch': '0.8', 'num_input_tokens_seen': 50260192, 'train_runtime': '6.317e+04', 'train_tokens_per_second': '795.6'}
80%|███████▉ | 14950/18688 [17:32:48<4:13:02, 4.06s/it] 80%|████████ | 14951/18688 [17:32:52<4:10:56, 4.03s/it] 80%|████████ | 14952/18688 [17:32:56<4:00:40, 3.87s/it] 80%|████████ | 14953/18688 [17:33:00<4:14:59, 4.10s/it] 80%|████████ | 14954/18688 [17:33:04<4:13:57, 4.08s/it] 80%|████████ | 14955/18688 [17:33:09<4:16:04, 4.12s/it] 80%|████████ | 14956/18688 [17:33:12<4:11:14, 4.04s/it] 80%|████████ | 14957/18688 [17:33:17<4:23:26, 4.24s/it] 80%|████████ | 14958/18688 [17:33:21<4:17:57, 4.15s/it] 80%|████████ | 14959/18688 [17:33:25<4:14:36, 4.10s/it] 80%|████████ | 14960/18688 [17:33:30<4:33:23, 4.40s/it] {'loss': '0.9196', 'grad_norm': '1.527', 'learning_rate': '8.937e-06', 'epoch': '0.8005', 'num_input_tokens_seen': 50293264, 'train_runtime': '6.321e+04', 'train_tokens_per_second': '795.6'}
80%|████████ | 14960/18688 [17:33:30<4:33:23, 4.40s/it] 80%|████████ | 14961/18688 [17:33:34<4:26:19, 4.29s/it] 80%|████████ | 14962/18688 [17:33:38<4:17:25, 4.15s/it] 80%|████████ | 14963/18688 [17:33:42<4:16:41, 4.13s/it] 80%|████████ | 14964/18688 [17:33:46<4:09:57, 4.03s/it] 80%|████████ | 14965/18688 [17:33:51<4:21:35, 4.22s/it] 80%|████████ | 14966/18688 [17:33:55<4:20:10, 4.19s/it] 80%|████████ | 14967/18688 [17:33:58<4:06:49, 3.98s/it] 80%|████████ | 14968/18688 [17:34:02<4:12:14, 4.07s/it] 80%|████████ | 14969/18688 [17:34:07<4:12:08, 4.07s/it] 80%|████████ | 14970/18688 [17:34:12<4:30:10, 4.36s/it] {'loss': '0.9064', 'grad_norm': '1.248', 'learning_rate': '8.891e-06', 'epoch': '0.8011', 'num_input_tokens_seen': 50325944, 'train_runtime': '6.325e+04', 'train_tokens_per_second': '795.6'}
80%|████████ | 14970/18688 [17:34:12<4:30:10, 4.36s/it] 80%|████████ | 14971/18688 [17:34:16<4:37:17, 4.48s/it] 80%|████████ | 14972/18688 [17:34:20<4:28:31, 4.34s/it] 80%|████████ | 14973/18688 [17:34:25<4:28:27, 4.34s/it] 80%|████████ | 14974/18688 [17:34:28<4:15:58, 4.14s/it] 80%|████████ | 14975/18688 [17:34:33<4:16:47, 4.15s/it] 80%|████████ | 14976/18688 [17:34:36<4:10:23, 4.05s/it] 80%|████████ | 14977/18688 [17:34:40<4:04:06, 3.95s/it] 80%|████████ | 14978/18688 [17:34:45<4:21:34, 4.23s/it] 80%|████████ | 14979/18688 [17:34:49<4:21:38, 4.23s/it] 80%|████████ | 14980/18688 [17:34:55<4:45:37, 4.62s/it] {'loss': '0.9507', 'grad_norm': '1.211', 'learning_rate': '8.845e-06', 'epoch': '0.8016', 'num_input_tokens_seen': 50361112, 'train_runtime': '6.33e+04', 'train_tokens_per_second': '795.7'}
80%|████████ | 14980/18688 [17:34:55<4:45:37, 4.62s/it] 80%|████████ | 14981/18688 [17:34:59<4:38:33, 4.51s/it] 80%|████████ | 14982/18688 [17:35:03<4:31:56, 4.40s/it] 80%|████████ | 14983/18688 [17:35:08<4:35:50, 4.47s/it] 80%|████████ | 14984/18688 [17:35:12<4:24:29, 4.28s/it] 80%|████████ | 14985/18688 [17:35:17<4:43:39, 4.60s/it] 80%|████████ | 14986/18688 [17:35:22<4:46:18, 4.64s/it] 80%|████████ | 14987/18688 [17:35:25<4:28:18, 4.35s/it] 80%|████████ | 14988/18688 [17:35:29<4:16:31, 4.16s/it] 80%|████████ | 14989/18688 [17:35:33<4:18:39, 4.20s/it] 80%|████████ | 14990/18688 [17:35:37<4:08:09, 4.03s/it] {'loss': '0.9186', 'grad_norm': '1.307', 'learning_rate': '8.799e-06', 'epoch': '0.8021', 'num_input_tokens_seen': 50396304, 'train_runtime': '6.334e+04', 'train_tokens_per_second': '795.7'}
80%|████████ | 14990/18688 [17:35:37<4:08:09, 4.03s/it] 80%|████████ | 14991/18688 [17:35:40<3:57:50, 3.86s/it] 80%|████████ | 14992/18688 [17:35:45<4:04:10, 3.96s/it] 80%|████████ | 14993/18688 [17:35:49<4:20:28, 4.23s/it] 80%|████████ | 14994/18688 [17:35:55<4:36:53, 4.50s/it] 80%|████████ | 14995/18688 [17:35:58<4:25:25, 4.31s/it] 80%|████████ | 14996/18688 [17:36:03<4:25:55, 4.32s/it] 80%|████████ | 14997/18688 [17:36:07<4:27:26, 4.35s/it] 80%|████████ | 14998/18688 [17:36:11<4:16:10, 4.17s/it] 80%|████████ | 14999/18688 [17:36:15<4:08:05, 4.04s/it] 80%|████████ | 15000/18688 [17:36:19<4:12:34, 4.11s/it] {'loss': '0.8773', 'grad_norm': '1.387', 'learning_rate': '8.753e-06', 'epoch': '0.8027', 'num_input_tokens_seen': 50429344, 'train_runtime': '6.338e+04', 'train_tokens_per_second': '795.7'}
80%|████████ | 15000/18688 [17:36:19<4:12:34, 4.11s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:71: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:281: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 0/1000 [00:00<?, ?it/s]/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:172: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:202: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:254: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 3/1000 [00:00<00:43, 22.93it/s]
1%| | 6/1000 [00:00<00:56, 17.56it/s]
1%| | 8/1000 [00:00<00:58, 17.06it/s]
1%| | 10/1000 [00:00<01:00, 16.26it/s]
1%| | 12/1000 [00:00<01:01, 16.12it/s]
1%|▏ | 14/1000 [00:00<01:02, 15.78it/s]
2%|▏ | 16/1000 [00:00<01:02, 15.64it/s]
2%|▏ | 18/1000 [00:01<01:38, 9.98it/s]
2%|▏ | 20/1000 [00:01<01:33, 10.51it/s]
2%|▏ | 22/1000 [00:01<01:52, 8.72it/s]
2%|▏ | 24/1000 [00:01<01:39, 9.77it/s]
3%|▎ | 26/1000 [00:02<01:45, 9.26it/s]
3%|▎ | 28/1000 [00:02<01:36, 10.09it/s]
3%|▎ | 30/1000 [00:02<01:28, 10.99it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.15it/s]
3%|▎ | 34/1000 [00:02<01:25, 11.26it/s]
4%|▎ | 36/1000 [00:03<01:32, 10.43it/s]
4%|▍ | 38/1000 [00:03<01:24, 11.33it/s]
4%|▍ | 40/1000 [00:03<01:27, 10.93it/s]
4%|▍ | 42/1000 [00:03<01:46, 9.02it/s]
4%|▍ | 44/1000 [00:03<01:30, 10.54it/s]
5%|▍ | 46/1000 [00:03<01:21, 11.73it/s]
5%|▍ | 48/1000 [00:04<01:29, 10.65it/s]
5%|▌ | 50/1000 [00:04<01:21, 11.68it/s]
5%|▌ | 52/1000 [00:04<01:15, 12.54it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.30it/s]
6%|▌ | 56/1000 [00:04<01:07, 13.98it/s]
6%|▌ | 58/1000 [00:05<01:47, 8.75it/s]
6%|▌ | 60/1000 [00:05<01:36, 9.77it/s]
6%|▌ | 62/1000 [00:05<01:27, 10.75it/s]
6%|▋ | 64/1000 [00:05<01:19, 11.81it/s]
7%|▋ | 66/1000 [00:05<01:15, 12.30it/s]
7%|▋ | 68/1000 [00:05<01:12, 12.87it/s]
7%|▋ | 70/1000 [00:06<01:09, 13.45it/s]
7%|▋ | 72/1000 [00:06<01:06, 13.89it/s]
7%|▋ | 74/1000 [00:06<01:07, 13.68it/s]
8%|▊ | 76/1000 [00:06<01:04, 14.29it/s]
8%|▊ | 78/1000 [00:06<01:03, 14.54it/s]
8%|▊ | 80/1000 [00:06<01:08, 13.51it/s]
8%|▊ | 82/1000 [00:06<01:05, 14.10it/s]
8%|▊ | 84/1000 [00:06<01:02, 14.55it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.69it/s]
9%|▉ | 88/1000 [00:07<01:02, 14.49it/s]
9%|▉ | 90/1000 [00:07<01:02, 14.52it/s]
9%|▉ | 92/1000 [00:07<01:04, 14.17it/s]
9%|▉ | 94/1000 [00:07<01:04, 13.99it/s]
10%|▉ | 96/1000 [00:07<01:03, 14.31it/s]
10%|▉ | 98/1000 [00:07<01:03, 14.23it/s]
10%|█ | 100/1000 [00:08<01:31, 9.81it/s]
10%|█ | 102/1000 [00:08<01:23, 10.78it/s]
10%|█ | 104/1000 [00:08<01:54, 7.81it/s]
11%|█ | 106/1000 [00:09<02:02, 7.32it/s]
11%|█ | 108/1000 [00:09<01:42, 8.67it/s]
11%|█ | 110/1000 [00:09<01:27, 10.12it/s]
11%|█ | 112/1000 [00:09<01:18, 11.26it/s]
11%|█▏ | 114/1000 [00:09<01:14, 11.91it/s]
12%|█▏ | 116/1000 [00:09<01:10, 12.55it/s]
12%|█▏ | 118/1000 [00:09<01:06, 13.34it/s]
12%|█▏ | 120/1000 [00:10<01:08, 12.76it/s]
12%|█▏ | 122/1000 [00:10<01:22, 10.60it/s]
12%|█▏ | 124/1000 [00:10<01:30, 9.66it/s]
13%|█▎ | 126/1000 [00:10<01:22, 10.63it/s]
13%|█▎ | 128/1000 [00:10<01:15, 11.56it/s]
13%|█▎ | 130/1000 [00:11<01:11, 12.21it/s]
13%|█▎ | 132/1000 [00:11<01:05, 13.16it/s]
13%|█▎ | 134/1000 [00:11<01:10, 12.35it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.31it/s]
14%|█▍ | 138/1000 [00:11<01:05, 13.26it/s]
14%|█▍ | 140/1000 [00:11<01:25, 10.10it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.53it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.57it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.60it/s]
15%|█▍ | 148/1000 [00:12<01:14, 11.51it/s]
15%|█▌ | 150/1000 [00:12<01:07, 12.60it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.15it/s]
15%|█▌ | 154/1000 [00:13<01:03, 13.40it/s]
16%|█▌ | 156/1000 [00:13<01:03, 13.24it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.73it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.62it/s]
16%|█▌ | 162/1000 [00:13<01:01, 13.52it/s]
16%|█▋ | 164/1000 [00:13<01:01, 13.62it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.78it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.31it/s]
17%|█▋ | 170/1000 [00:14<01:19, 10.49it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.46it/s]
17%|█▋ | 174/1000 [00:14<01:29, 9.18it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.20it/s]
18%|█▊ | 178/1000 [00:15<01:34, 8.67it/s]
18%|█▊ | 180/1000 [00:15<01:35, 8.57it/s]
18%|█▊ | 182/1000 [00:15<01:39, 8.22it/s]
18%|█▊ | 183/1000 [00:16<01:36, 8.48it/s]
18%|█▊ | 185/1000 [00:16<01:45, 7.71it/s]
19%|█▊ | 187/1000 [00:16<01:31, 8.93it/s]
19%|█▉ | 189/1000 [00:16<01:23, 9.76it/s]
19%|█▉ | 191/1000 [00:16<01:14, 10.86it/s]
19%|█▉ | 193/1000 [00:17<01:20, 10.01it/s]
20%|█▉ | 195/1000 [00:17<01:10, 11.34it/s]
20%|█▉ | 197/1000 [00:17<01:26, 9.30it/s]
20%|█▉ | 199/1000 [00:17<01:20, 9.96it/s]
20%|██ | 201/1000 [00:17<01:15, 10.65it/s]
20%|██ | 203/1000 [00:17<01:07, 11.89it/s]
20%|██ | 205/1000 [00:18<01:02, 12.81it/s]
21%|██ | 207/1000 [00:18<00:59, 13.27it/s]
21%|██ | 209/1000 [00:18<00:56, 14.05it/s]
21%|██ | 211/1000 [00:18<00:53, 14.79it/s]
21%|██▏ | 213/1000 [00:18<00:52, 14.95it/s]
22%|██▏ | 215/1000 [00:18<00:54, 14.30it/s]
22%|██▏ | 217/1000 [00:18<00:52, 14.89it/s]
22%|██▏ | 219/1000 [00:19<01:27, 8.89it/s]
22%|██▏ | 221/1000 [00:19<01:23, 9.32it/s]
22%|██▏ | 223/1000 [00:19<01:20, 9.60it/s]
22%|██▎ | 225/1000 [00:19<01:13, 10.53it/s]
23%|██▎ | 227/1000 [00:19<01:06, 11.62it/s]
23%|██▎ | 229/1000 [00:20<01:03, 12.08it/s]
23%|██▎ | 231/1000 [00:20<01:01, 12.54it/s]
23%|██▎ | 233/1000 [00:20<00:59, 12.83it/s]
24%|██▎ | 235/1000 [00:20<00:57, 13.38it/s]
24%|██▎ | 237/1000 [00:20<00:52, 14.40it/s]
24%|██▍ | 239/1000 [00:20<00:53, 14.25it/s]
24%|██▍ | 241/1000 [00:20<00:51, 14.60it/s]
24%|██▍ | 243/1000 [00:21<00:50, 14.87it/s]
24%|██▍ | 245/1000 [00:21<00:50, 14.84it/s]
25%|██▍ | 247/1000 [00:21<00:49, 15.20it/s]
25%|██▍ | 249/1000 [00:21<00:58, 12.95it/s]
25%|██▌ | 251/1000 [00:21<01:02, 11.93it/s]
25%|██▌ | 253/1000 [00:21<00:58, 12.72it/s]
26%|██▌ | 255/1000 [00:21<00:54, 13.61it/s]
26%|██▌ | 257/1000 [00:22<00:53, 13.79it/s]
26%|██▌ | 259/1000 [00:22<00:52, 14.05it/s]
26%|██▌ | 261/1000 [00:22<00:58, 12.60it/s]
26%|██▋ | 263/1000 [00:22<00:55, 13.34it/s]
26%|██▋ | 265/1000 [00:22<00:53, 13.76it/s]
27%|██▋ | 267/1000 [00:22<00:52, 13.90it/s]
27%|██▋ | 269/1000 [00:22<00:49, 14.62it/s]
27%|██▋ | 271/1000 [00:23<00:55, 13.17it/s]
27%|██▋ | 273/1000 [00:23<01:03, 11.44it/s]
28%|██▊ | 275/1000 [00:23<01:01, 11.72it/s]
28%|██▊ | 277/1000 [00:23<00:57, 12.64it/s]
28%|██▊ | 279/1000 [00:23<00:56, 12.84it/s]
28%|██▊ | 281/1000 [00:23<00:51, 13.96it/s]
28%|██▊ | 283/1000 [00:24<00:50, 14.25it/s]
28%|██▊ | 285/1000 [00:24<00:48, 14.60it/s]
29%|██▊ | 287/1000 [00:24<00:48, 14.80it/s]
29%|██▉ | 289/1000 [00:24<00:46, 15.38it/s]
29%|██▉ | 291/1000 [00:24<00:46, 15.11it/s]
29%|██▉ | 293/1000 [00:24<00:46, 15.15it/s]
30%|██▉ | 295/1000 [00:24<00:45, 15.41it/s]
30%|██▉ | 297/1000 [00:24<00:48, 14.53it/s]
30%|██▉ | 299/1000 [00:25<00:50, 13.86it/s]
30%|███ | 301/1000 [00:25<00:52, 13.38it/s]
30%|███ | 303/1000 [00:25<00:55, 12.45it/s]
30%|███ | 305/1000 [00:25<00:55, 12.63it/s]
31%|███ | 307/1000 [00:25<00:54, 12.66it/s]
31%|███ | 309/1000 [00:25<00:54, 12.61it/s]
31%|███ | 311/1000 [00:26<00:54, 12.54it/s]
31%|███▏ | 313/1000 [00:26<00:54, 12.60it/s]
32%|███▏ | 315/1000 [00:26<00:53, 12.71it/s]
32%|███▏ | 317/1000 [00:26<00:53, 12.84it/s]
32%|███▏ | 319/1000 [00:26<00:52, 12.88it/s]
32%|███▏ | 321/1000 [00:27<01:21, 8.31it/s]
32%|███▏ | 323/1000 [00:27<01:13, 9.20it/s]
32%|███▎ | 325/1000 [00:27<01:08, 9.90it/s]
33%|███▎ | 327/1000 [00:27<01:04, 10.39it/s]
33%|███▎ | 329/1000 [00:27<01:02, 10.70it/s]
33%|███▎ | 331/1000 [00:27<00:58, 11.44it/s]
33%|███▎ | 333/1000 [00:28<00:55, 12.04it/s]
34%|███▎ | 335/1000 [00:28<00:52, 12.65it/s]
34%|███▎ | 337/1000 [00:28<01:07, 9.83it/s]
34%|███▍ | 339/1000 [00:28<01:00, 10.97it/s]
34%|███▍ | 341/1000 [00:28<00:55, 11.91it/s]
34%|███▍ | 343/1000 [00:28<00:51, 12.85it/s]
34%|███▍ | 345/1000 [00:29<00:49, 13.33it/s]
35%|███▍ | 347/1000 [00:29<00:45, 14.26it/s]
35%|███▍ | 349/1000 [00:29<00:44, 14.55it/s]
35%|███▌ | 351/1000 [00:29<00:44, 14.66it/s]
35%|███▌ | 353/1000 [00:29<00:42, 15.19it/s]
36%|███▌ | 355/1000 [00:29<00:42, 15.23it/s]
36%|███▌ | 357/1000 [00:29<00:42, 15.02it/s]
36%|███▌ | 359/1000 [00:30<00:42, 14.94it/s]
36%|███▌ | 361/1000 [00:30<00:47, 13.32it/s]
36%|███▋ | 363/1000 [00:30<00:46, 13.60it/s]
36%|███▋ | 365/1000 [00:30<00:44, 14.40it/s]
37%|███▋ | 367/1000 [00:30<00:53, 11.90it/s]
37%|███▋ | 369/1000 [00:30<00:50, 12.54it/s]
37%|███▋ | 371/1000 [00:30<00:47, 13.24it/s]
37%|███▋ | 373/1000 [00:31<00:55, 11.39it/s]
38%|███▊ | 375/1000 [00:31<00:57, 10.80it/s]
38%|███▊ | 377/1000 [00:31<00:53, 11.62it/s]
38%|███▊ | 379/1000 [00:31<00:51, 12.12it/s]
38%|███▊ | 381/1000 [00:31<00:48, 12.88it/s]
38%|███▊ | 383/1000 [00:31<00:46, 13.36it/s]
38%|███▊ | 385/1000 [00:32<00:45, 13.56it/s]
39%|███▊ | 387/1000 [00:32<00:44, 13.71it/s]
39%|███▉ | 389/1000 [00:32<00:44, 13.74it/s]
39%|███▉ | 391/1000 [00:32<00:43, 13.87it/s]
39%|███▉ | 393/1000 [00:32<00:44, 13.71it/s]
40%|███▉ | 395/1000 [00:32<00:44, 13.53it/s]
40%|███▉ | 397/1000 [00:32<00:43, 13.73it/s]
40%|███▉ | 399/1000 [00:33<00:47, 12.58it/s]
40%|████ | 401/1000 [00:33<00:43, 13.65it/s]
40%|████ | 403/1000 [00:33<00:42, 14.20it/s]
40%|████ | 405/1000 [00:33<00:40, 14.77it/s]
41%|████ | 407/1000 [00:33<00:38, 15.26it/s]
41%|████ | 409/1000 [00:34<00:57, 10.20it/s]
41%|████ | 411/1000 [00:34<00:53, 11.10it/s]
41%|████▏ | 413/1000 [00:34<01:04, 9.11it/s]
42%|████▏ | 415/1000 [00:34<01:00, 9.61it/s]
42%|████▏ | 417/1000 [00:35<01:18, 7.40it/s]
42%|████▏ | 419/1000 [00:35<01:08, 8.53it/s]
42%|████▏ | 421/1000 [00:35<00:57, 10.03it/s]
42%|████▏ | 423/1000 [00:35<01:17, 7.44it/s]
42%|████▎ | 425/1000 [00:35<01:06, 8.71it/s]
43%|████▎ | 427/1000 [00:36<00:58, 9.75it/s]
43%|████▎ | 429/1000 [00:36<01:02, 9.10it/s]
43%|████▎ | 431/1000 [00:36<00:56, 10.16it/s]
43%|████▎ | 433/1000 [00:36<01:15, 7.47it/s]
44%|████▎ | 435/1000 [00:37<01:05, 8.59it/s]
44%|████▎ | 437/1000 [00:37<01:06, 8.48it/s]
44%|████▍ | 439/1000 [00:37<00:58, 9.51it/s]
44%|████▍ | 441/1000 [00:37<00:56, 9.84it/s]
44%|████▍ | 443/1000 [00:37<00:51, 10.82it/s]
44%|████▍ | 445/1000 [00:37<00:46, 11.85it/s]
45%|████▍ | 447/1000 [00:38<00:51, 10.65it/s]
45%|████▍ | 449/1000 [00:38<00:47, 11.55it/s]
45%|████▌ | 451/1000 [00:38<00:44, 12.47it/s]
45%|████▌ | 453/1000 [00:38<00:41, 13.26it/s]
46%|████▌ | 455/1000 [00:38<00:44, 12.37it/s]
46%|████▌ | 457/1000 [00:38<00:41, 13.20it/s]
46%|████▌ | 459/1000 [00:38<00:39, 13.76it/s]
46%|████▌ | 461/1000 [00:39<00:37, 14.20it/s]
46%|████▋ | 463/1000 [00:39<00:35, 15.02it/s]
46%|████▋ | 465/1000 [00:39<00:35, 14.89it/s]
47%|████▋ | 467/1000 [00:39<00:40, 13.16it/s]
47%|████▋ | 469/1000 [00:39<00:38, 13.66it/s]
47%|████▋ | 471/1000 [00:39<00:51, 10.19it/s]
47%|████▋ | 473/1000 [00:40<00:50, 10.54it/s]
48%|████▊ | 475/1000 [00:40<00:45, 11.66it/s]
48%|████▊ | 477/1000 [00:40<00:41, 12.51it/s]
48%|████▊ | 479/1000 [00:40<00:43, 12.04it/s]
48%|████▊ | 481/1000 [00:40<00:40, 12.80it/s]
48%|████▊ | 483/1000 [00:40<00:37, 13.67it/s]
48%|████▊ | 485/1000 [00:41<00:37, 13.85it/s]
49%|████▊ | 487/1000 [00:41<00:35, 14.27it/s]
49%|████▉ | 489/1000 [00:41<00:45, 11.14it/s]
49%|████▉ | 491/1000 [00:41<00:42, 12.04it/s]
49%|████▉ | 493/1000 [00:41<00:38, 13.06it/s]
50%|████▉ | 495/1000 [00:41<00:35, 14.08it/s]
50%|████▉ | 497/1000 [00:41<00:35, 14.36it/s]
50%|████▉ | 499/1000 [00:42<00:34, 14.60it/s]
50%|█████ | 501/1000 [00:42<00:44, 11.33it/s]
50%|█████ | 503/1000 [00:42<00:41, 11.90it/s]
50%|█████ | 505/1000 [00:42<00:39, 12.57it/s]
51%|█████ | 507/1000 [00:42<00:38, 12.89it/s]
51%|█████ | 509/1000 [00:43<00:51, 9.48it/s]
51%|█████ | 511/1000 [00:43<00:47, 10.39it/s]
51%|█████▏ | 513/1000 [00:43<00:43, 11.14it/s]
52%|█████▏ | 515/1000 [00:43<00:39, 12.24it/s]
52%|█████▏ | 517/1000 [00:43<00:38, 12.40it/s]
52%|█████▏ | 519/1000 [00:43<00:38, 12.53it/s]
52%|█████▏ | 521/1000 [00:43<00:35, 13.41it/s]
52%|█████▏ | 523/1000 [00:44<00:34, 13.70it/s]
52%|█████▎ | 525/1000 [00:44<00:34, 13.93it/s]
53%|█████▎ | 527/1000 [00:44<00:39, 11.83it/s]
53%|█████▎ | 529/1000 [00:44<00:37, 12.44it/s]
53%|█████▎ | 531/1000 [00:44<00:34, 13.65it/s]
53%|█████▎ | 533/1000 [00:44<00:33, 14.10it/s]
54%|█████▎ | 535/1000 [00:44<00:32, 14.42it/s]
54%|█████▎ | 537/1000 [00:45<00:32, 14.09it/s]
54%|█████▍ | 539/1000 [00:45<00:31, 14.47it/s]
54%|█████▍ | 541/1000 [00:45<00:31, 14.56it/s]
54%|█████▍ | 543/1000 [00:45<00:30, 14.80it/s]
55%|█████▍ | 545/1000 [00:45<00:31, 14.26it/s]
55%|█████▍ | 547/1000 [00:45<00:32, 14.10it/s]
55%|█████▍ | 549/1000 [00:45<00:32, 14.09it/s]
55%|█████▌ | 551/1000 [00:46<00:32, 13.91it/s]
55%|█████▌ | 553/1000 [00:46<00:31, 14.20it/s]
56%|█████▌ | 555/1000 [00:46<00:31, 14.15it/s]
56%|█████▌ | 557/1000 [00:46<00:30, 14.58it/s]
56%|█████▌ | 559/1000 [00:46<00:30, 14.55it/s]
56%|█████▌ | 561/1000 [00:46<00:29, 14.77it/s]
56%|█████▋ | 563/1000 [00:47<00:40, 10.79it/s]
56%|█████▋ | 565/1000 [00:47<00:37, 11.56it/s]
57%|█████▋ | 567/1000 [00:47<00:34, 12.41it/s]
57%|█████▋ | 569/1000 [00:47<00:33, 13.05it/s]
57%|█████▋ | 571/1000 [00:47<00:32, 13.04it/s]
57%|█████▋ | 573/1000 [00:47<00:31, 13.56it/s]
57%|█████▊ | 575/1000 [00:47<00:30, 13.72it/s]
58%|█████▊ | 577/1000 [00:48<00:30, 13.78it/s]
58%|█████▊ | 579/1000 [00:48<00:35, 11.94it/s]
58%|█████▊ | 581/1000 [00:48<00:31, 13.14it/s]
58%|█████▊ | 583/1000 [00:48<00:30, 13.65it/s]
58%|█████▊ | 585/1000 [00:48<00:29, 13.88it/s]
59%|█████▊ | 587/1000 [00:48<00:28, 14.57it/s]
59%|█████▉ | 589/1000 [00:48<00:26, 15.26it/s]
59%|█████▉ | 591/1000 [00:49<00:28, 14.57it/s]
59%|█████▉ | 593/1000 [00:49<00:30, 13.42it/s]
60%|█████▉ | 595/1000 [00:49<00:29, 13.90it/s]
60%|█████▉ | 597/1000 [00:49<00:29, 13.76it/s]
60%|█████▉ | 599/1000 [00:49<00:27, 14.62it/s]
60%|██████ | 601/1000 [00:49<00:28, 14.20it/s]
60%|██████ | 603/1000 [00:49<00:26, 14.87it/s]
60%|██████ | 605/1000 [00:50<00:26, 14.91it/s]
61%|██████ | 607/1000 [00:50<00:26, 14.98it/s]
61%|██████ | 609/1000 [00:50<00:25, 15.14it/s]
61%|██████ | 611/1000 [00:50<00:25, 15.38it/s]
61%|██████▏ | 613/1000 [00:50<00:26, 14.83it/s]
62%|██████▏ | 615/1000 [00:50<00:26, 14.46it/s]
62%|██████▏ | 617/1000 [00:50<00:26, 14.51it/s]
62%|██████▏ | 619/1000 [00:51<00:32, 11.87it/s]
62%|██████▏ | 621/1000 [00:51<00:39, 9.50it/s]
62%|██████▏ | 623/1000 [00:51<00:37, 10.19it/s]
62%|██████▎ | 625/1000 [00:51<00:34, 10.83it/s]
63%|██████▎ | 627/1000 [00:51<00:33, 11.27it/s]
63%|██████▎ | 629/1000 [00:52<00:31, 11.61it/s]
63%|██████▎ | 631/1000 [00:52<00:33, 11.07it/s]
63%|██████▎ | 633/1000 [00:52<00:32, 11.40it/s]
64%|██████▎ | 635/1000 [00:52<00:37, 9.81it/s]
64%|██████▎ | 637/1000 [00:52<00:34, 10.43it/s]
64%|██████▍ | 639/1000 [00:53<00:35, 10.25it/s]
64%|██████▍ | 641/1000 [00:53<00:32, 11.20it/s]
64%|██████▍ | 643/1000 [00:53<00:29, 11.92it/s]
64%|██████▍ | 645/1000 [00:53<00:28, 12.34it/s]
65%|██████▍ | 647/1000 [00:53<00:27, 12.67it/s]
65%|██████▍ | 649/1000 [00:53<00:28, 12.29it/s]
65%|██████▌ | 651/1000 [00:53<00:26, 13.06it/s]
65%|██████▌ | 653/1000 [00:54<00:28, 11.97it/s]
66%|██████▌ | 655/1000 [00:54<00:35, 9.63it/s]
66%|██████▌ | 657/1000 [00:54<00:31, 10.73it/s]
66%|██████▌ | 659/1000 [00:54<00:34, 9.78it/s]
66%|██████▌ | 661/1000 [00:54<00:31, 10.84it/s]
66%|██████▋ | 663/1000 [00:55<00:28, 11.71it/s]
66%|██████▋ | 665/1000 [00:55<00:27, 12.28it/s]
67%|██████▋ | 667/1000 [00:55<00:29, 11.21it/s]
67%|██████▋ | 669/1000 [00:55<00:29, 11.20it/s]
67%|██████▋ | 671/1000 [00:55<00:27, 11.93it/s]
67%|██████▋ | 673/1000 [00:55<00:26, 12.28it/s]
68%|██████▊ | 675/1000 [00:56<00:27, 11.78it/s]
68%|██████▊ | 677/1000 [00:56<00:26, 12.24it/s]
68%|██████▊ | 679/1000 [00:56<00:25, 12.63it/s]
68%|██████▊ | 681/1000 [00:56<00:24, 13.10it/s]
68%|██████▊ | 683/1000 [00:56<00:25, 12.23it/s]
68%|██████▊ | 685/1000 [00:56<00:24, 12.84it/s]
69%|██████▊ | 687/1000 [00:57<00:23, 13.46it/s]
69%|██████▉ | 689/1000 [00:57<00:22, 13.93it/s]
69%|██████▉ | 691/1000 [00:57<00:21, 14.19it/s]
69%|██████▉ | 693/1000 [00:57<00:21, 14.37it/s]
70%|██████▉ | 695/1000 [00:57<00:21, 14.10it/s]
70%|██████▉ | 697/1000 [00:57<00:21, 14.40it/s]
70%|██████▉ | 699/1000 [00:57<00:20, 14.44it/s]
70%|███████ | 701/1000 [00:58<00:24, 12.27it/s]
70%|███████ | 703/1000 [00:58<00:23, 12.86it/s]
70%|███████ | 705/1000 [00:58<00:30, 9.80it/s]
71%|███████ | 707/1000 [00:58<00:32, 8.97it/s]
71%|███████ | 709/1000 [00:58<00:28, 10.20it/s]
71%|███████ | 711/1000 [00:59<00:25, 11.28it/s]
71%|███████▏ | 713/1000 [00:59<00:23, 12.45it/s]
72%|███████▏ | 715/1000 [00:59<00:21, 13.16it/s]
72%|███████▏ | 717/1000 [00:59<00:23, 12.20it/s]
72%|███████▏ | 719/1000 [00:59<00:21, 13.19it/s]
72%|███████▏ | 721/1000 [00:59<00:19, 14.16it/s]
72%|███████▏ | 723/1000 [00:59<00:19, 13.91it/s]
72%|███████▎ | 725/1000 [01:00<00:19, 14.08it/s]
73%|███████▎ | 727/1000 [01:00<00:23, 11.71it/s]
73%|███████▎ | 729/1000 [01:00<00:33, 8.19it/s]
73%|███████▎ | 731/1000 [01:00<00:28, 9.40it/s]
73%|███████▎ | 733/1000 [01:01<00:28, 9.33it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.52it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.78it/s]
74%|███████▍ | 739/1000 [01:01<00:24, 10.80it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.48it/s]
74%|███████▍ | 743/1000 [01:01<00:21, 12.13it/s]
74%|███████▍ | 745/1000 [01:02<00:25, 10.15it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.55it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.73it/s]
75%|███████▌ | 751/1000 [01:02<00:21, 11.84it/s]
75%|███████▌ | 753/1000 [01:02<00:19, 12.56it/s]
76%|███████▌ | 755/1000 [01:02<00:18, 13.30it/s]
76%|███████▌ | 757/1000 [01:03<00:23, 10.28it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.31it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.29it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.26it/s]
76%|███████▋ | 765/1000 [01:04<00:27, 8.60it/s]
77%|███████▋ | 767/1000 [01:04<00:24, 9.55it/s]
77%|███████▋ | 769/1000 [01:04<00:21, 10.57it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.83it/s]
77%|███████▋ | 773/1000 [01:04<00:17, 12.63it/s]
78%|███████▊ | 775/1000 [01:04<00:17, 13.17it/s]
78%|███████▊ | 777/1000 [01:04<00:15, 14.19it/s]
78%|███████▊ | 779/1000 [01:05<00:21, 10.51it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.41it/s]
78%|███████▊ | 783/1000 [01:05<00:26, 8.33it/s]
78%|███████▊ | 784/1000 [01:05<00:30, 7.08it/s]
78%|███████▊ | 785/1000 [01:06<00:29, 7.37it/s]
79%|███████▊ | 786/1000 [01:06<00:30, 6.99it/s]
79%|███████▉ | 788/1000 [01:06<00:24, 8.73it/s]
79%|███████▉ | 790/1000 [01:06<00:21, 9.74it/s]
79%|███████▉ | 792/1000 [01:06<00:19, 10.82it/s]
79%|███████▉ | 794/1000 [01:06<00:18, 11.17it/s]
80%|███████▉ | 796/1000 [01:07<00:18, 11.19it/s]
80%|███████▉ | 798/1000 [01:07<00:16, 12.18it/s]
80%|████████ | 800/1000 [01:07<00:17, 11.69it/s]
80%|████████ | 802/1000 [01:07<00:24, 7.99it/s]
80%|████████ | 803/1000 [01:07<00:26, 7.55it/s]
80%|████████ | 805/1000 [01:08<00:21, 8.98it/s]
81%|████████ | 807/1000 [01:08<00:19, 9.66it/s]
81%|████████ | 809/1000 [01:08<00:17, 10.75it/s]
81%|████████ | 811/1000 [01:08<00:17, 10.97it/s]
81%|████████▏ | 813/1000 [01:08<00:15, 12.16it/s]
82%|████████▏ | 815/1000 [01:08<00:14, 12.92it/s]
82%|████████▏ | 817/1000 [01:09<00:16, 11.15it/s]
82%|████████▏ | 819/1000 [01:09<00:15, 11.83it/s]
82%|████████▏ | 821/1000 [01:09<00:14, 12.51it/s]
82%|████████▏ | 823/1000 [01:09<00:13, 13.22it/s]
82%|████████▎ | 825/1000 [01:09<00:12, 14.08it/s]
83%|████████▎ | 827/1000 [01:09<00:12, 14.28it/s]
83%|████████▎ | 829/1000 [01:09<00:12, 14.14it/s]
83%|████████▎ | 831/1000 [01:10<00:11, 14.33it/s]
83%|████████▎ | 833/1000 [01:10<00:11, 14.08it/s]
84%|████████▎ | 835/1000 [01:10<00:11, 14.42it/s]
84%|████████▎ | 837/1000 [01:10<00:11, 14.45it/s]
84%|████████▍ | 839/1000 [01:10<00:11, 14.02it/s]
84%|████████▍ | 841/1000 [01:10<00:11, 14.38it/s]
84%|████████▍ | 843/1000 [01:10<00:10, 14.30it/s]
84%|████████▍ | 845/1000 [01:11<00:11, 13.97it/s]
85%|████████▍ | 847/1000 [01:11<00:10, 13.98it/s]
85%|████████▍ | 849/1000 [01:11<00:10, 14.09it/s]
85%|████████▌ | 851/1000 [01:11<00:10, 13.87it/s]
85%|████████▌ | 853/1000 [01:11<00:10, 13.83it/s]
86%|████████▌ | 855/1000 [01:11<00:10, 14.15it/s]
86%|████████▌ | 857/1000 [01:11<00:10, 14.27it/s]
86%|████████▌ | 859/1000 [01:11<00:09, 15.00it/s]
86%|████████▌ | 861/1000 [01:12<00:10, 13.89it/s]
86%|████████▋ | 863/1000 [01:12<00:09, 13.90it/s]
86%|████████▋ | 865/1000 [01:12<00:09, 13.97it/s]
87%|████████▋ | 867/1000 [01:12<00:09, 13.77it/s]
87%|████████▋ | 869/1000 [01:12<00:09, 13.71it/s]
87%|████████▋ | 871/1000 [01:12<00:08, 14.49it/s]
87%|████████▋ | 873/1000 [01:13<00:09, 13.59it/s]
88%|████████▊ | 875/1000 [01:13<00:09, 12.66it/s]
88%|████████▊ | 877/1000 [01:13<00:10, 11.37it/s]
88%|████████▊ | 879/1000 [01:13<00:10, 11.97it/s]
88%|████████▊ | 881/1000 [01:13<00:10, 10.84it/s]
88%|████████▊ | 883/1000 [01:13<00:09, 12.12it/s]
88%|████████▊ | 885/1000 [01:14<00:09, 12.48it/s]
89%|████████▊ | 887/1000 [01:14<00:08, 13.65it/s]
89%|████████▉ | 889/1000 [01:14<00:07, 14.37it/s]
89%|████████▉ | 891/1000 [01:14<00:07, 14.12it/s]
89%|████████▉ | 893/1000 [01:14<00:07, 14.27it/s]
90%|████████▉ | 895/1000 [01:14<00:07, 13.95it/s]
90%|████████▉ | 897/1000 [01:14<00:07, 14.29it/s]
90%|████████▉ | 899/1000 [01:15<00:08, 12.17it/s]
90%|█████████ | 901/1000 [01:15<00:07, 12.66it/s]
90%|█████████ | 903/1000 [01:15<00:07, 12.99it/s]
90%|█████████ | 905/1000 [01:15<00:09, 9.73it/s]
91%|█████████ | 907/1000 [01:15<00:08, 10.44it/s]
91%|█████████ | 909/1000 [01:16<00:08, 11.03it/s]
91%|█████████ | 911/1000 [01:16<00:08, 11.05it/s]
91%|█████████▏| 913/1000 [01:16<00:09, 8.92it/s]
92%|█████████▏| 915/1000 [01:16<00:12, 6.91it/s]
92%|█████████▏| 917/1000 [01:17<00:12, 6.62it/s]
92%|█████████▏| 919/1000 [01:17<00:10, 7.84it/s]
92%|█████████▏| 921/1000 [01:17<00:08, 9.38it/s]
92%|█████████▏| 923/1000 [01:17<00:07, 10.44it/s]
92%|█████████▎| 925/1000 [01:17<00:06, 11.26it/s]
93%|█████████▎| 927/1000 [01:18<00:06, 11.86it/s]
93%|█████████▎| 929/1000 [01:18<00:06, 10.83it/s]
93%|█████████▎| 931/1000 [01:18<00:05, 12.09it/s]
93%|█████████▎| 933/1000 [01:18<00:05, 12.34it/s]
94%|█████████▎| 935/1000 [01:18<00:04, 13.27it/s]
94%|█████████▎| 937/1000 [01:18<00:04, 13.77it/s]
94%|█████████▍| 939/1000 [01:18<00:04, 14.62it/s]
94%|█████████▍| 941/1000 [01:19<00:04, 14.64it/s]
94%|█████████▍| 943/1000 [01:19<00:04, 12.25it/s]
94%|█████████▍| 945/1000 [01:19<00:04, 12.53it/s]
95%|█████████▍| 947/1000 [01:19<00:04, 11.90it/s]
95%|█████████▍| 949/1000 [01:19<00:04, 12.72it/s]
95%|█████████▌| 951/1000 [01:19<00:03, 13.66it/s]
95%|█████████▌| 953/1000 [01:19<00:03, 14.12it/s]
96%|█████████▌| 955/1000 [01:20<00:03, 13.89it/s]
96%|█████████▌| 957/1000 [01:20<00:03, 14.33it/s]
96%|█████████▌| 959/1000 [01:20<00:03, 13.28it/s]
96%|█████████▌| 961/1000 [01:20<00:02, 13.37it/s]
96%|█████████▋| 963/1000 [01:20<00:02, 14.06it/s]
96%|█████████▋| 965/1000 [01:20<00:03, 10.38it/s]
97%|█████████▋| 967/1000 [01:21<00:02, 11.64it/s]
97%|█████████▋| 969/1000 [01:21<00:02, 12.06it/s]
97%|█████████▋| 971/1000 [01:21<00:02, 12.58it/s]
97%|█████████▋| 973/1000 [01:21<00:02, 9.88it/s]
98%|█████████▊| 975/1000 [01:21<00:02, 10.75it/s]
98%|█████████▊| 977/1000 [01:22<00:02, 9.54it/s]
98%|█████████▊| 979/1000 [01:22<00:02, 9.69it/s]
98%|█████████▊| 981/1000 [01:22<00:01, 9.99it/s]
98%|█████████▊| 983/1000 [01:22<00:01, 10.98it/s]
98%|█████████▊| 985/1000 [01:22<00:01, 10.32it/s]
99%|█████████▊| 987/1000 [01:23<00:01, 11.21it/s]
99%|█████████▉| 989/1000 [01:23<00:00, 11.20it/s]
99%|█████████▉| 991/1000 [01:23<00:00, 11.90it/s]
99%|█████████▉| 993/1000 [01:23<00:00, 12.54it/s]
100%|█████████▉| 995/1000 [01:23<00:00, 13.46it/s]
100%|█████████▉| 997/1000 [01:23<00:00, 14.03it/s]
100%|█████████▉| 999/1000 [01:23<00:00, 14.63it/s]
{'eval_loss': '0.8778', 'eval_runtime': '84.02', 'eval_samples_per_second': '11.9', 'eval_steps_per_second': '11.9', 'epoch': '0.8027', 'num_input_tokens_seen': 50429344}
80%|████████ | 15000/18688 [17:37:43<4:12:34, 4.11s/it]
100%|██████████| 1000/1000 [01:23<00:00, 14.63it/s]
 80%|████████ | 15001/18688 [17:37:47<29:59:20, 29.28s/it] 80%|████████ | 15002/18688 [17:37:51<22:20:47, 21.83s/it] 80%|████████ | 15003/18688 [17:37:55<16:52:55, 16.49s/it] 80%|████████ | 15004/18688 [17:38:00<13:06:03, 12.80s/it] 80%|████████ | 15005/18688 [17:38:04<10:24:52, 10.18s/it] 80%|████████ | 15006/18688 [17:38:08<8:33:50, 8.37s/it] 80%|████████ | 15007/18688 [17:38:12<7:16:39, 7.12s/it] 80%|████████ | 15008/18688 [17:38:16<6:18:03, 6.16s/it] 80%|████████ | 15009/18688 [17:38:20<5:32:08, 5.42s/it] 80%|████████ | 15010/18688 [17:38:23<4:58:07, 4.86s/it] {'loss': '0.8339', 'grad_norm': '1.321', 'learning_rate': '8.708e-06', 'epoch': '0.8032', 'num_input_tokens_seen': 50461320, 'train_runtime': '6.35e+04', 'train_tokens_per_second': '794.6'}
80%|████████ | 15010/18688 [17:38:23<4:58:07, 4.86s/it] 80%|████████ | 15011/18688 [17:38:27<4:43:15, 4.62s/it] 80%|████████ | 15012/18688 [17:38:31<4:26:36, 4.35s/it] 80%|████████ | 15013/18688 [17:38:36<4:34:23, 4.48s/it] 80%|████████ | 15014/18688 [17:38:40<4:31:43, 4.44s/it] 80%|████████ | 15015/18688 [17:38:45<4:43:01, 4.62s/it] 80%|████████ | 15016/18688 [17:38:49<4:27:34, 4.37s/it] 80%|████████ | 15017/18688 [17:38:54<4:31:11, 4.43s/it] 80%|████████ | 15018/18688 [17:38:57<4:21:21, 4.27s/it] 80%|████████ | 15019/18688 [17:39:01<4:10:04, 4.09s/it] 80%|████████ | 15020/18688 [17:39:06<4:30:08, 4.42s/it] {'loss': '0.9493', 'grad_norm': '1.099', 'learning_rate': '8.662e-06', 'epoch': '0.8037', 'num_input_tokens_seen': 50497304, 'train_runtime': '6.355e+04', 'train_tokens_per_second': '794.6'}
80%|████████ | 15020/18688 [17:39:06<4:30:08, 4.42s/it] 80%|████████ | 15021/18688 [17:39:11<4:38:17, 4.55s/it] 80%|████████ | 15022/18688 [17:39:15<4:32:16, 4.46s/it] 80%|████████ | 15023/18688 [17:39:19<4:16:28, 4.20s/it] 80%|████████ | 15024/18688 [17:39:23<4:13:41, 4.15s/it] 80%|████████ | 15025/18688 [17:39:27<4:10:36, 4.11s/it] 80%|████████ | 15026/18688 [17:39:31<4:09:25, 4.09s/it] 80%|████████ | 15027/18688 [17:39:35<4:13:01, 4.15s/it] 80%|████████ | 15028/18688 [17:39:40<4:21:23, 4.29s/it] 80%|████████ | 15029/18688 [17:39:44<4:16:20, 4.20s/it] 80%|████████ | 15030/18688 [17:39:49<4:28:46, 4.41s/it] {'loss': '0.8844', 'grad_norm': '1.228', 'learning_rate': '8.617e-06', 'epoch': '0.8043', 'num_input_tokens_seen': 50533168, 'train_runtime': '6.359e+04', 'train_tokens_per_second': '794.7'}
80%|████████ | 15030/18688 [17:39:49<4:28:46, 4.41s/it] 80%|████████ | 15031/18688 [17:39:53<4:19:22, 4.26s/it] 80%|████████ | 15032/18688 [17:39:57<4:17:41, 4.23s/it] 80%|████████ | 15033/18688 [17:40:01<4:06:26, 4.05s/it] 80%|████████ | 15034/18688 [17:40:04<4:00:18, 3.95s/it] 80%|████████ | 15035/18688 [17:40:09<4:10:00, 4.11s/it] 80%|████████ | 15036/18688 [17:40:13<4:05:59, 4.04s/it] 80%|████████ | 15037/18688 [17:40:17<4:08:31, 4.08s/it] 80%|████████ | 15038/18688 [17:40:21<4:16:56, 4.22s/it] 80%|████████ | 15039/18688 [17:40:25<4:14:27, 4.18s/it] 80%|████████ | 15040/18688 [17:40:30<4:14:27, 4.19s/it] {'loss': '0.9207', 'grad_norm': '0.9882', 'learning_rate': '8.572e-06', 'epoch': '0.8048', 'num_input_tokens_seen': 50565640, 'train_runtime': '6.363e+04', 'train_tokens_per_second': '794.7'}
80%|████████ | 15040/18688 [17:40:30<4:14:27, 4.19s/it] 80%|████████ | 15041/18688 [17:40:34<4:09:36, 4.11s/it] 80%|████████ | 15042/18688 [17:40:37<4:02:19, 3.99s/it] 80%|████████ | 15043/18688 [17:40:42<4:08:51, 4.10s/it] 81%|████████ | 15044/18688 [17:40:45<3:59:56, 3.95s/it] 81%|████████ | 15045/18688 [17:40:50<4:17:12, 4.24s/it] 81%|████████ | 15046/18688 [17:40:54<4:17:48, 4.25s/it] 81%|████████ | 15047/18688 [17:40:58<4:03:54, 4.02s/it] 81%|████████ | 15048/18688 [17:41:02<3:58:40, 3.93s/it] 81%|████████ | 15049/18688 [17:41:06<4:00:30, 3.97s/it] 81%|████████ | 15050/18688 [17:41:10<4:08:30, 4.10s/it] {'loss': '0.8507', 'grad_norm': '1.164', 'learning_rate': '8.527e-06', 'epoch': '0.8054', 'num_input_tokens_seen': 50599192, 'train_runtime': '6.367e+04', 'train_tokens_per_second': '794.7'}
81%|████████ | 15050/18688 [17:41:10<4:08:30, 4.10s/it] 81%|████████ | 15051/18688 [17:41:15<4:26:24, 4.39s/it] 81%|████████ | 15052/18688 [17:41:19<4:21:08, 4.31s/it] 81%|████████ | 15053/18688 [17:41:23<4:14:39, 4.20s/it] 81%|████████ | 15054/18688 [17:41:27<4:12:54, 4.18s/it] 81%|████████ | 15055/18688 [17:41:31<4:07:29, 4.09s/it] 81%|████████ | 15056/18688 [17:41:35<3:59:03, 3.95s/it] 81%|████████ | 15057/18688 [17:41:40<4:13:42, 4.19s/it] 81%|████████ | 15058/18688 [17:41:43<4:07:27, 4.09s/it] 81%|████████ | 15059/18688 [17:41:48<4:11:38, 4.16s/it] 81%|████████ | 15060/18688 [17:41:52<4:06:47, 4.08s/it] {'loss': '0.9072', 'grad_norm': '1.132', 'learning_rate': '8.481e-06', 'epoch': '0.8059', 'num_input_tokens_seen': 50632520, 'train_runtime': '6.371e+04', 'train_tokens_per_second': '794.7'}
81%|████████ | 15060/18688 [17:41:52<4:06:47, 4.08s/it] 81%|████████ | 15061/18688 [17:41:55<3:58:10, 3.94s/it] 81%|████████ | 15062/18688 [17:41:59<3:53:33, 3.86s/it] 81%|████████ | 15063/18688 [17:42:03<3:57:06, 3.92s/it] 81%|████████ | 15064/18688 [17:42:07<4:00:58, 3.99s/it] 81%|████████ | 15065/18688 [17:42:11<4:02:28, 4.02s/it] 81%|████████ | 15066/18688 [17:42:16<4:08:15, 4.11s/it] 81%|████████ | 15067/18688 [17:42:20<4:06:31, 4.08s/it] 81%|████████ | 15068/18688 [17:42:24<4:10:35, 4.15s/it] 81%|████████ | 15069/18688 [17:42:28<4:03:57, 4.04s/it] 81%|████████ | 15070/18688 [17:42:31<3:57:38, 3.94s/it] {'loss': '0.8846', 'grad_norm': '1.222', 'learning_rate': '8.437e-06', 'epoch': '0.8064', 'num_input_tokens_seen': 50663544, 'train_runtime': '6.375e+04', 'train_tokens_per_second': '794.7'}
81%|████████ | 15070/18688 [17:42:31<3:57:38, 3.94s/it] 81%|████████ | 15071/18688 [17:42:36<4:15:52, 4.24s/it] 81%|████████ | 15072/18688 [17:42:41<4:19:51, 4.31s/it] 81%|████████ | 15073/18688 [17:42:45<4:19:52, 4.31s/it] 81%|████████ | 15074/18688 [17:42:49<4:05:47, 4.08s/it] 81%|████████ | 15075/18688 [17:42:52<3:57:30, 3.94s/it] 81%|████████ | 15076/18688 [17:42:57<4:10:19, 4.16s/it] 81%|████████ | 15077/18688 [17:43:01<4:09:52, 4.15s/it] 81%|████████ | 15078/18688 [17:43:05<4:06:38, 4.10s/it] 81%|████████ | 15079/18688 [17:43:09<4:06:00, 4.09s/it] 81%|████████ | 15080/18688 [17:43:14<4:14:21, 4.23s/it] {'loss': '0.9022', 'grad_norm': '1.197', 'learning_rate': '8.392e-06', 'epoch': '0.807', 'num_input_tokens_seen': 50698608, 'train_runtime': '6.379e+04', 'train_tokens_per_second': '794.7'}
81%|████████ | 15080/18688 [17:43:14<4:14:21, 4.23s/it] 81%|████████ | 15081/18688 [17:43:18<4:11:59, 4.19s/it] 81%|████████ | 15082/18688 [17:43:23<4:27:32, 4.45s/it] 81%|████████ | 15083/18688 [17:43:27<4:19:27, 4.32s/it] 81%|████████ | 15084/18688 [17:43:31<4:17:21, 4.28s/it] 81%|████████ | 15085/18688 [17:43:36<4:19:05, 4.31s/it] 81%|████████ | 15086/18688 [17:43:39<4:07:45, 4.13s/it] 81%|████████ | 15087/18688 [17:43:44<4:11:03, 4.18s/it] 81%|████████ | 15088/18688 [17:43:49<4:39:00, 4.65s/it] 81%|████████ | 15089/18688 [17:43:53<4:24:46, 4.41s/it] 81%|████████ | 15090/18688 [17:43:57<4:15:44, 4.26s/it] {'loss': '0.9004', 'grad_norm': '1.81', 'learning_rate': '8.347e-06', 'epoch': '0.8075', 'num_input_tokens_seen': 50734696, 'train_runtime': '6.384e+04', 'train_tokens_per_second': '794.7'}
81%|████████ | 15090/18688 [17:43:57<4:15:44, 4.26s/it] 81%|████████ | 15091/18688 [17:44:01<4:06:29, 4.11s/it] 81%|████████ | 15092/18688 [17:44:04<3:54:44, 3.92s/it] 81%|████████ | 15093/18688 [17:44:08<3:50:03, 3.84s/it] 81%|████████ | 15094/18688 [17:44:12<3:46:01, 3.77s/it] 81%|████████ | 15095/18688 [17:44:16<4:00:08, 4.01s/it] 81%|████████ | 15096/18688 [17:44:20<3:58:30, 3.98s/it] 81%|████████ | 15097/18688 [17:44:24<3:54:32, 3.92s/it] 81%|████████ | 15098/18688 [17:44:28<4:00:38, 4.02s/it] 81%|████████ | 15099/18688 [17:44:32<4:04:52, 4.09s/it] 81%|████████ | 15100/18688 [17:44:36<3:56:18, 3.95s/it] {'loss': '0.8924', 'grad_norm': '1.14', 'learning_rate': '8.302e-06', 'epoch': '0.808', 'num_input_tokens_seen': 50764760, 'train_runtime': '6.388e+04', 'train_tokens_per_second': '794.7'}
81%|████████ | 15100/18688 [17:44:36<3:56:18, 3.95s/it] 81%|████████ | 15101/18688 [17:44:40<3:55:53, 3.95s/it] 81%|████████ | 15102/18688 [17:44:44<3:57:48, 3.98s/it] 81%|████████ | 15103/18688 [17:44:49<4:10:16, 4.19s/it] 81%|████████ | 15104/18688 [17:44:53<4:16:28, 4.29s/it] 81%|████████ | 15105/18688 [17:44:57<4:14:39, 4.26s/it] 81%|████████ | 15106/18688 [17:45:02<4:16:29, 4.30s/it] 81%|████████ | 15107/18688 [17:45:05<4:02:36, 4.06s/it] 81%|████████ | 15108/18688 [17:45:09<4:00:10, 4.03s/it] 81%|████████ | 15109/18688 [17:45:14<4:17:13, 4.31s/it] 81%|████████ | 15110/18688 [17:45:18<4:10:33, 4.20s/it] {'loss': '0.8785', 'grad_norm': '1.134', 'learning_rate': '8.258e-06', 'epoch': '0.8086', 'num_input_tokens_seen': 50798992, 'train_runtime': '6.392e+04', 'train_tokens_per_second': '794.7'}
81%|████████ | 15110/18688 [17:45:18<4:10:33, 4.20s/it] 81%|████████ | 15111/18688 [17:45:22<4:12:02, 4.23s/it] 81%|████████ | 15112/18688 [17:45:26<4:02:44, 4.07s/it] 81%|████████ | 15113/18688 [17:45:30<4:01:12, 4.05s/it] 81%|████████ | 15114/18688 [17:45:35<4:24:58, 4.45s/it] 81%|████████ | 15115/18688 [17:45:40<4:25:44, 4.46s/it] 81%|████████ | 15116/18688 [17:45:45<4:32:27, 4.58s/it] 81%|████████ | 15117/18688 [17:45:49<4:25:58, 4.47s/it] 81%|████████ | 15118/18688 [17:45:53<4:09:11, 4.19s/it] 81%|████████ | 15119/18688 [17:45:56<3:59:12, 4.02s/it] 81%|████████ | 15120/18688 [17:46:01<4:16:08, 4.31s/it] {'loss': '0.936', 'grad_norm': '1.092', 'learning_rate': '8.213e-06', 'epoch': '0.8091', 'num_input_tokens_seen': 50834728, 'train_runtime': '6.396e+04', 'train_tokens_per_second': '794.8'}
81%|████████ | 15120/18688 [17:46:01<4:16:08, 4.31s/it] 81%|████████ | 15121/18688 [17:46:06<4:18:51, 4.35s/it] 81%|████████ | 15122/18688 [17:46:09<4:08:50, 4.19s/it] 81%|████████ | 15123/18688 [17:46:13<3:58:27, 4.01s/it] 81%|████████ | 15124/18688 [17:46:17<4:03:27, 4.10s/it] 81%|████████ | 15125/18688 [17:46:22<4:10:56, 4.23s/it] 81%|████████ | 15126/18688 [17:46:26<4:06:22, 4.15s/it] 81%|████████ | 15127/18688 [17:46:30<3:59:12, 4.03s/it] 81%|████████ | 15128/18688 [17:46:34<4:05:18, 4.13s/it] 81%|████████ | 15129/18688 [17:46:38<3:57:06, 4.00s/it] 81%|████████ | 15130/18688 [17:46:42<3:57:31, 4.01s/it] {'loss': '0.9139', 'grad_norm': '1.303', 'learning_rate': '8.169e-06', 'epoch': '0.8096', 'num_input_tokens_seen': 50865640, 'train_runtime': '6.4e+04', 'train_tokens_per_second': '794.7'}
81%|████████ | 15130/18688 [17:46:42<3:57:31, 4.01s/it] 81%|████████ | 15131/18688 [17:46:45<3:48:05, 3.85s/it] 81%|████████ | 15132/18688 [17:46:50<4:05:10, 4.14s/it] 81%|████████ | 15133/18688 [17:46:54<4:03:41, 4.11s/it] 81%|████████ | 15134/18688 [17:46:59<4:10:46, 4.23s/it] 81%|████████ | 15135/18688 [17:47:02<4:02:01, 4.09s/it] 81%|████████ | 15136/18688 [17:47:06<4:03:17, 4.11s/it] 81%|████████ | 15137/18688 [17:47:11<4:10:05, 4.23s/it] 81%|████████ | 15138/18688 [17:47:15<4:09:11, 4.21s/it] 81%|████████ | 15139/18688 [17:47:20<4:12:38, 4.27s/it] 81%|████████ | 15140/18688 [17:47:24<4:20:14, 4.40s/it] {'loss': '0.8895', 'grad_norm': '1.315', 'learning_rate': '8.125e-06', 'epoch': '0.8102', 'num_input_tokens_seen': 50901552, 'train_runtime': '6.404e+04', 'train_tokens_per_second': '794.8'}
81%|████████ | 15140/18688 [17:47:24<4:20:14, 4.40s/it] 81%|████████ | 15141/18688 [17:47:28<4:10:03, 4.23s/it] 81%|████████ | 15142/18688 [17:47:32<4:00:01, 4.06s/it] 81%|████████ | 15143/18688 [17:47:36<3:58:43, 4.04s/it] 81%|████████ | 15144/18688 [17:47:41<4:13:17, 4.29s/it] 81%|████████ | 15145/18688 [17:47:45<4:08:15, 4.20s/it] 81%|████████ | 15146/18688 [17:47:49<4:18:24, 4.38s/it] 81%|████████ | 15147/18688 [17:47:54<4:29:04, 4.56s/it] 81%|████████ | 15148/18688 [17:47:59<4:30:05, 4.58s/it] 81%|████████ | 15149/18688 [17:48:05<4:47:40, 4.88s/it] 81%|████████ | 15150/18688 [17:48:10<4:53:40, 4.98s/it] {'loss': '0.9549', 'grad_norm': '1.668', 'learning_rate': '8.081e-06', 'epoch': '0.8107', 'num_input_tokens_seen': 50940912, 'train_runtime': '6.409e+04', 'train_tokens_per_second': '794.8'}
81%|████████ | 15150/18688 [17:48:10<4:53:40, 4.98s/it] 81%|████████ | 15151/18688 [17:48:14<4:42:23, 4.79s/it] 81%|████████ | 15152/18688 [17:48:18<4:34:42, 4.66s/it] 81%|████████ | 15153/18688 [17:48:23<4:32:18, 4.62s/it] 81%|████████ | 15154/18688 [17:48:28<4:39:03, 4.74s/it] 81%|████████ | 15155/18688 [17:48:32<4:29:49, 4.58s/it] 81%|████████ | 15156/18688 [17:48:36<4:16:50, 4.36s/it] 81%|████████ | 15157/18688 [17:48:41<4:33:28, 4.65s/it] 81%|████████ | 15158/18688 [17:48:46<4:39:19, 4.75s/it] 81%|████████ | 15159/18688 [17:48:51<4:38:41, 4.74s/it] 81%|████████ | 15160/18688 [17:48:55<4:27:43, 4.55s/it] {'loss': '0.9255', 'grad_norm': '1.721', 'learning_rate': '8.037e-06', 'epoch': '0.8112', 'num_input_tokens_seen': 50980088, 'train_runtime': '6.414e+04', 'train_tokens_per_second': '794.9'}
81%|████████ | 15160/18688 [17:48:55<4:27:43, 4.55s/it] 81%|████████ | 15161/18688 [17:49:00<4:24:04, 4.49s/it] 81%|████████ | 15162/18688 [17:49:03<4:10:39, 4.27s/it] 81%|████████ | 15163/18688 [17:49:07<4:08:26, 4.23s/it] 81%|████████ | 15164/18688 [17:49:12<4:14:10, 4.33s/it] 81%|████████ | 15165/18688 [17:49:16<4:03:17, 4.14s/it] 81%|████████ | 15166/18688 [17:49:19<3:51:33, 3.94s/it] 81%|████████ | 15167/18688 [17:49:23<3:54:03, 3.99s/it] 81%|████████ | 15168/18688 [17:49:30<4:48:29, 4.92s/it] 81%|████████ | 15169/18688 [17:49:34<4:30:36, 4.61s/it] 81%|████████ | 15170/18688 [17:49:38<4:22:47, 4.48s/it] {'loss': '0.8742', 'grad_norm': '1.273', 'learning_rate': '7.993e-06', 'epoch': '0.8118', 'num_input_tokens_seen': 51016928, 'train_runtime': '6.418e+04', 'train_tokens_per_second': '794.9'}
81%|████████ | 15170/18688 [17:49:38<4:22:47, 4.48s/it] 81%|████████ | 15171/18688 [17:49:43<4:19:15, 4.42s/it] 81%|████████ | 15172/18688 [17:49:47<4:24:59, 4.52s/it] 81%|████████ | 15173/18688 [17:49:52<4:21:59, 4.47s/it] 81%|████████ | 15174/18688 [17:49:56<4:17:15, 4.39s/it] 81%|████████ | 15175/18688 [17:50:00<4:03:22, 4.16s/it] 81%|████████ | 15176/18688 [17:50:04<4:03:59, 4.17s/it] 81%|████████ | 15177/18688 [17:50:08<3:56:04, 4.03s/it] 81%|████████ | 15178/18688 [17:50:12<3:57:53, 4.07s/it] 81%|████████ | 15179/18688 [17:50:15<3:49:14, 3.92s/it] 81%|████████ | 15180/18688 [17:50:21<4:15:58, 4.38s/it] {'loss': '0.8808', 'grad_norm': '1.714', 'learning_rate': '7.949e-06', 'epoch': '0.8123', 'num_input_tokens_seen': 51052032, 'train_runtime': '6.422e+04', 'train_tokens_per_second': '794.9'}
81%|████████ | 15180/18688 [17:50:21<4:15:58, 4.38s/it] 81%|████████ | 15181/18688 [17:50:25<4:13:09, 4.33s/it] 81%|████████ | 15182/18688 [17:50:29<4:16:08, 4.38s/it] 81%|████████ | 15183/18688 [17:50:34<4:17:33, 4.41s/it] 81%|████████▏ | 15184/18688 [17:50:38<4:18:26, 4.43s/it] 81%|████████▏ | 15185/18688 [17:50:42<4:05:47, 4.21s/it] 81%|████████▏ | 15186/18688 [17:50:47<4:11:36, 4.31s/it] 81%|████████▏ | 15187/18688 [17:50:51<4:15:43, 4.38s/it] 81%|████████▏ | 15188/18688 [17:50:56<4:20:31, 4.47s/it] 81%|████████▏ | 15189/18688 [17:51:00<4:14:52, 4.37s/it] 81%|████████▏ | 15190/18688 [17:51:04<4:05:10, 4.21s/it] {'loss': '0.9107', 'grad_norm': '1.432', 'learning_rate': '7.905e-06', 'epoch': '0.8128', 'num_input_tokens_seen': 51087368, 'train_runtime': '6.426e+04', 'train_tokens_per_second': '795'}
81%|████████▏ | 15190/18688 [17:51:04<4:05:10, 4.21s/it] 81%|████████▏ | 15191/18688 [17:51:08<4:08:17, 4.26s/it] 81%|████████▏ | 15192/18688 [17:51:12<4:05:22, 4.21s/it] 81%|████████▏ | 15193/18688 [17:51:16<3:54:56, 4.03s/it] 81%|████████▏ | 15194/18688 [17:51:20<3:49:13, 3.94s/it] 81%|████████▏ | 15195/18688 [17:51:24<3:59:08, 4.11s/it] 81%|████████▏ | 15196/18688 [17:51:28<4:01:38, 4.15s/it] 81%|████████▏ | 15197/18688 [17:51:32<3:54:59, 4.04s/it] 81%|████████▏ | 15198/18688 [17:51:36<3:59:27, 4.12s/it] 81%|████████▏ | 15199/18688 [17:51:41<3:59:42, 4.12s/it] 81%|████████▏ | 15200/18688 [17:51:44<3:51:18, 3.98s/it] {'loss': '0.8973', 'grad_norm': '1.14', 'learning_rate': '7.862e-06', 'epoch': '0.8134', 'num_input_tokens_seen': 51118880, 'train_runtime': '6.43e+04', 'train_tokens_per_second': '794.9'}
81%|████████▏ | 15200/18688 [17:51:44<3:51:18, 3.98s/it] 81%|████████▏ | 15201/18688 [17:51:49<4:05:09, 4.22s/it] 81%|████████▏ | 15202/18688 [17:51:53<4:04:09, 4.20s/it] 81%|████████▏ | 15203/18688 [17:51:57<4:05:35, 4.23s/it] 81%|████████▏ | 15204/18688 [17:52:02<4:05:26, 4.23s/it] 81%|████████▏ | 15205/18688 [17:52:06<4:01:31, 4.16s/it] 81%|████████▏ | 15206/18688 [17:52:11<4:18:15, 4.45s/it] 81%|████████▏ | 15207/18688 [17:52:15<4:15:13, 4.40s/it] 81%|████████▏ | 15208/18688 [17:52:19<4:11:34, 4.34s/it] 81%|████████▏ | 15209/18688 [17:52:23<4:06:46, 4.26s/it] 81%|████████▏ | 15210/18688 [17:52:27<3:55:07, 4.06s/it] {'loss': '0.8225', 'grad_norm': '0.9945', 'learning_rate': '7.818e-06', 'epoch': '0.8139', 'num_input_tokens_seen': 51153248, 'train_runtime': '6.435e+04', 'train_tokens_per_second': '795'}
81%|████████▏ | 15210/18688 [17:52:27<3:55:07, 4.06s/it] 81%|████████▏ | 15211/18688 [17:52:31<3:57:53, 4.11s/it] 81%|████████▏ | 15212/18688 [17:52:35<3:56:49, 4.09s/it] 81%|████████▏ | 15213/18688 [17:52:39<3:46:32, 3.91s/it] 81%|████████▏ | 15214/18688 [17:52:42<3:41:43, 3.83s/it] 81%|████████▏ | 15215/18688 [17:52:47<3:50:11, 3.98s/it] 81%|████████▏ | 15216/18688 [17:52:52<4:04:34, 4.23s/it] 81%|████████▏ | 15217/18688 [17:52:55<3:58:53, 4.13s/it] 81%|████████▏ | 15218/18688 [17:52:59<3:55:26, 4.07s/it] 81%|████████▏ | 15219/18688 [17:53:04<4:04:24, 4.23s/it] 81%|████████▏ | 15220/18688 [17:53:08<3:53:12, 4.03s/it] {'loss': '0.9184', 'grad_norm': '1.225', 'learning_rate': '7.775e-06', 'epoch': '0.8144', 'num_input_tokens_seen': 51184888, 'train_runtime': '6.439e+04', 'train_tokens_per_second': '794.9'}
81%|████████▏ | 15220/18688 [17:53:08<3:53:12, 4.03s/it] 81%|████████▏ | 15221/18688 [17:53:12<3:55:28, 4.08s/it] 81%|████████▏ | 15222/18688 [17:53:15<3:48:26, 3.95s/it] 81%|████████▏ | 15223/18688 [17:53:20<3:57:45, 4.12s/it] 81%|████████▏ | 15224/18688 [17:53:24<3:53:27, 4.04s/it] 81%|████████▏ | 15225/18688 [17:53:28<3:56:08, 4.09s/it] 81%|████████▏ | 15226/18688 [17:53:32<4:01:20, 4.18s/it] 81%|████████▏ | 15227/18688 [17:53:36<3:59:29, 4.15s/it] 81%|████████▏ | 15228/18688 [17:53:41<4:09:19, 4.32s/it] 81%|████████▏ | 15229/18688 [17:53:46<4:17:14, 4.46s/it] 81%|████████▏ | 15230/18688 [17:53:51<4:19:22, 4.50s/it] {'loss': '0.8655', 'grad_norm': '1.239', 'learning_rate': '7.732e-06', 'epoch': '0.815', 'num_input_tokens_seen': 51219576, 'train_runtime': '6.443e+04', 'train_tokens_per_second': '795'}
81%|████████▏ | 15230/18688 [17:53:51<4:19:22, 4.50s/it] 82%|████████▏ | 15231/18688 [17:53:55<4:16:06, 4.44s/it] 82%|████████▏ | 15232/18688 [17:53:59<4:08:12, 4.31s/it] 82%|████████▏ | 15233/18688 [17:54:03<4:04:07, 4.24s/it] 82%|████████▏ | 15234/18688 [17:54:07<4:07:24, 4.30s/it] 82%|████████▏ | 15235/18688 [17:54:12<4:08:11, 4.31s/it] 82%|████████▏ | 15236/18688 [17:54:15<3:53:05, 4.05s/it] 82%|████████▏ | 15237/18688 [17:54:19<3:43:03, 3.88s/it] 82%|████████▏ | 15238/18688 [17:54:22<3:37:19, 3.78s/it] 82%|████████▏ | 15239/18688 [17:54:26<3:34:10, 3.73s/it] 82%|████████▏ | 15240/18688 [17:54:30<3:48:13, 3.97s/it] {'loss': '0.9029', 'grad_norm': '1.168', 'learning_rate': '7.688e-06', 'epoch': '0.8155', 'num_input_tokens_seen': 51250712, 'train_runtime': '6.447e+04', 'train_tokens_per_second': '794.9'}
82%|████████▏ | 15240/18688 [17:54:30<3:48:13, 3.97s/it] 82%|████████▏ | 15241/18688 [17:54:34<3:40:47, 3.84s/it] 82%|████████▏ | 15242/18688 [17:54:38<3:42:39, 3.88s/it] 82%|████████▏ | 15243/18688 [17:54:43<4:03:24, 4.24s/it] 82%|████████▏ | 15244/18688 [17:54:47<4:09:42, 4.35s/it] 82%|████████▏ | 15245/18688 [17:54:52<4:05:40, 4.28s/it] 82%|████████▏ | 15246/18688 [17:54:55<3:51:29, 4.04s/it] 82%|████████▏ | 15247/18688 [17:55:00<3:59:49, 4.18s/it] 82%|████████▏ | 15248/18688 [17:55:04<3:59:47, 4.18s/it] 82%|████████▏ | 15249/18688 [17:55:07<3:50:01, 4.01s/it] 82%|████████▏ | 15250/18688 [17:55:11<3:46:38, 3.96s/it] {'loss': '0.9461', 'grad_norm': '1.327', 'learning_rate': '7.645e-06', 'epoch': '0.8161', 'num_input_tokens_seen': 51283456, 'train_runtime': '6.451e+04', 'train_tokens_per_second': '794.9'}
82%|████████▏ | 15250/18688 [17:55:11<3:46:38, 3.96s/it] 82%|████████▏ | 15251/18688 [17:55:16<3:53:31, 4.08s/it] 82%|████████▏ | 15252/18688 [17:55:20<3:59:23, 4.18s/it] 82%|████████▏ | 15253/18688 [17:55:23<3:47:32, 3.97s/it] 82%|████████▏ | 15254/18688 [17:55:27<3:45:16, 3.94s/it] 82%|████████▏ | 15255/18688 [17:55:31<3:37:30, 3.80s/it] 82%|████████▏ | 15256/18688 [17:55:35<3:45:17, 3.94s/it] 82%|████████▏ | 15257/18688 [17:55:39<3:46:24, 3.96s/it] 82%|████████▏ | 15258/18688 [17:55:43<3:52:48, 4.07s/it] 82%|████████▏ | 15259/18688 [17:55:48<4:06:37, 4.32s/it] 82%|████████▏ | 15260/18688 [17:55:52<3:58:49, 4.18s/it] {'loss': '0.9152', 'grad_norm': '1.264', 'learning_rate': '7.602e-06', 'epoch': '0.8166', 'num_input_tokens_seen': 51316544, 'train_runtime': '6.455e+04', 'train_tokens_per_second': '795'}
82%|████████▏ | 15260/18688 [17:55:52<3:58:49, 4.18s/it] 82%|████████▏ | 15261/18688 [17:55:58<4:21:40, 4.58s/it] 82%|████████▏ | 15262/18688 [17:56:02<4:21:27, 4.58s/it] 82%|████████▏ | 15263/18688 [17:56:06<4:02:21, 4.25s/it] 82%|████████▏ | 15264/18688 [17:56:10<4:03:10, 4.26s/it] 82%|████████▏ | 15265/18688 [17:56:14<4:01:13, 4.23s/it] 82%|████████▏ | 15266/18688 [17:56:18<3:55:51, 4.14s/it] 82%|████████▏ | 15267/18688 [17:56:22<3:57:18, 4.16s/it] 82%|████████▏ | 15268/18688 [17:56:27<4:06:57, 4.33s/it] 82%|████████▏ | 15269/18688 [17:56:31<4:00:55, 4.23s/it] 82%|████████▏ | 15270/18688 [17:56:36<4:07:10, 4.34s/it] {'loss': '0.8928', 'grad_norm': '1.168', 'learning_rate': '7.56e-06', 'epoch': '0.8171', 'num_input_tokens_seen': 51352344, 'train_runtime': '6.46e+04', 'train_tokens_per_second': '795'}
82%|████████▏ | 15270/18688 [17:56:36<4:07:10, 4.34s/it] 82%|████████▏ | 15271/18688 [17:56:39<3:52:59, 4.09s/it] 82%|████████▏ | 15272/18688 [17:56:43<3:49:04, 4.02s/it] 82%|████████▏ | 15273/18688 [17:56:47<3:41:15, 3.89s/it] 82%|████████▏ | 15274/18688 [17:56:51<3:42:44, 3.91s/it] 82%|████████▏ | 15275/18688 [17:56:55<3:52:19, 4.08s/it] 82%|████████▏ | 15276/18688 [17:56:59<3:47:10, 3.99s/it] 82%|████████▏ | 15277/18688 [17:57:03<3:52:33, 4.09s/it] 82%|████████▏ | 15278/18688 [17:57:08<4:02:12, 4.26s/it] 82%|████████▏ | 15279/18688 [17:57:12<4:03:06, 4.28s/it] 82%|████████▏ | 15280/18688 [17:57:16<3:52:33, 4.09s/it] {'loss': '0.9039', 'grad_norm': '1.391', 'learning_rate': '7.517e-06', 'epoch': '0.8177', 'num_input_tokens_seen': 51383304, 'train_runtime': '6.464e+04', 'train_tokens_per_second': '795'}
82%|████████▏ | 15280/18688 [17:57:16<3:52:33, 4.09s/it] 82%|████████▏ | 15281/18688 [17:57:20<3:47:10, 4.00s/it] 82%|████████▏ | 15282/18688 [17:57:24<3:59:45, 4.22s/it] 82%|████████▏ | 15283/18688 [17:57:28<3:52:25, 4.10s/it] 82%|████████▏ | 15284/18688 [17:57:32<3:46:45, 4.00s/it] 82%|████████▏ | 15285/18688 [17:57:36<3:49:59, 4.06s/it] 82%|████████▏ | 15286/18688 [17:57:41<3:57:33, 4.19s/it] 82%|████████▏ | 15287/18688 [17:57:45<3:58:05, 4.20s/it] 82%|████████▏ | 15288/18688 [17:57:50<4:07:11, 4.36s/it] 82%|████████▏ | 15289/18688 [17:57:54<4:09:08, 4.40s/it] 82%|████████▏ | 15290/18688 [17:57:57<3:52:51, 4.11s/it] {'loss': '0.9258', 'grad_norm': '1.325', 'learning_rate': '7.474e-06', 'epoch': '0.8182', 'num_input_tokens_seen': 51417800, 'train_runtime': '6.468e+04', 'train_tokens_per_second': '795'}
82%|████████▏ | 15290/18688 [17:57:57<3:52:51, 4.11s/it] 82%|████████▏ | 15291/18688 [17:58:03<4:15:31, 4.51s/it] 82%|████████▏ | 15292/18688 [17:58:07<4:11:18, 4.44s/it] 82%|████████▏ | 15293/18688 [17:58:11<4:05:31, 4.34s/it] 82%|████████▏ | 15294/18688 [17:58:16<4:04:20, 4.32s/it] 82%|████████▏ | 15295/18688 [17:58:20<4:09:17, 4.41s/it] 82%|████████▏ | 15296/18688 [17:58:25<4:16:07, 4.53s/it] 82%|████████▏ | 15297/18688 [17:58:29<4:07:24, 4.38s/it] 82%|████████▏ | 15298/18688 [17:58:33<4:07:44, 4.38s/it] 82%|████████▏ | 15299/18688 [17:58:37<3:56:38, 4.19s/it] 82%|████████▏ | 15300/18688 [17:58:41<3:53:36, 4.14s/it] {'loss': '0.9109', 'grad_norm': '1.13', 'learning_rate': '7.432e-06', 'epoch': '0.8187', 'num_input_tokens_seen': 51452920, 'train_runtime': '6.472e+04', 'train_tokens_per_second': '795'}
82%|████████▏ | 15300/18688 [17:58:41<3:53:36, 4.14s/it] 82%|████████▏ | 15301/18688 [17:58:45<3:48:08, 4.04s/it] 82%|████████▏ | 15302/18688 [17:58:49<3:48:53, 4.06s/it] 82%|████████▏ | 15303/18688 [17:58:53<3:55:16, 4.17s/it] 82%|████████▏ | 15304/18688 [17:58:58<4:03:47, 4.32s/it] 82%|████████▏ | 15305/18688 [17:59:02<3:57:30, 4.21s/it] 82%|████████▏ | 15306/18688 [17:59:07<4:14:53, 4.52s/it] 82%|████████▏ | 15307/18688 [17:59:11<4:03:40, 4.32s/it] 82%|████████▏ | 15308/18688 [17:59:15<3:54:13, 4.16s/it] 82%|████████▏ | 15309/18688 [17:59:19<3:43:45, 3.97s/it] 82%|████████▏ | 15310/18688 [17:59:23<3:53:49, 4.15s/it] {'loss': '0.9587', 'grad_norm': '1.128', 'learning_rate': '7.389e-06', 'epoch': '0.8193', 'num_input_tokens_seen': 51486696, 'train_runtime': '6.476e+04', 'train_tokens_per_second': '795'}
82%|████████▏ | 15310/18688 [17:59:23<3:53:49, 4.15s/it] 82%|████████▏ | 15311/18688 [17:59:28<4:07:23, 4.40s/it] 82%|████████▏ | 15312/18688 [17:59:33<4:10:59, 4.46s/it] 82%|████████▏ | 15313/18688 [17:59:37<4:05:34, 4.37s/it] 82%|████████▏ | 15314/18688 [17:59:41<3:56:39, 4.21s/it] 82%|████████▏ | 15315/18688 [17:59:45<4:00:16, 4.27s/it] 82%|████████▏ | 15316/18688 [17:59:49<4:02:04, 4.31s/it] 82%|████████▏ | 15317/18688 [17:59:54<3:58:24, 4.24s/it] 82%|████████▏ | 15318/18688 [17:59:57<3:50:49, 4.11s/it] 82%|████████▏ | 15319/18688 [18:00:01<3:43:54, 3.99s/it] 82%|████████▏ | 15320/18688 [18:00:05<3:43:58, 3.99s/it] {'loss': '0.8898', 'grad_norm': '1.234', 'learning_rate': '7.347e-06', 'epoch': '0.8198', 'num_input_tokens_seen': 51520216, 'train_runtime': '6.481e+04', 'train_tokens_per_second': '795'}
82%|████████▏ | 15320/18688 [18:00:05<3:43:58, 3.99s/it] 82%|████████▏ | 15321/18688 [18:00:09<3:40:49, 3.94s/it] 82%|████████▏ | 15322/18688 [18:00:13<3:46:44, 4.04s/it] 82%|████████▏ | 15323/18688 [18:00:17<3:46:40, 4.04s/it] 82%|████████▏ | 15324/18688 [18:00:21<3:47:47, 4.06s/it] 82%|████████▏ | 15325/18688 [18:00:26<4:01:13, 4.30s/it] 82%|████████▏ | 15326/18688 [18:00:30<3:52:50, 4.16s/it] 82%|████████▏ | 15327/18688 [18:00:33<3:41:04, 3.95s/it] 82%|████████▏ | 15328/18688 [18:00:37<3:34:22, 3.83s/it] 82%|████████▏ | 15329/18688 [18:00:41<3:34:01, 3.82s/it] 82%|████████▏ | 15330/18688 [18:00:45<3:45:05, 4.02s/it] {'loss': '0.8739', 'grad_norm': '1.035', 'learning_rate': '7.305e-06', 'epoch': '0.8203', 'num_input_tokens_seen': 51551488, 'train_runtime': '6.485e+04', 'train_tokens_per_second': '795'}
82%|████████▏ | 15330/18688 [18:00:45<3:45:05, 4.02s/it] 82%|████████▏ | 15331/18688 [18:00:49<3:41:19, 3.96s/it] 82%|████████▏ | 15332/18688 [18:00:54<3:56:36, 4.23s/it] 82%|████████▏ | 15333/18688 [18:00:58<3:53:07, 4.17s/it] 82%|████████▏ | 15334/18688 [18:01:02<3:47:47, 4.07s/it] 82%|████████▏ | 15335/18688 [18:01:06<3:43:45, 4.00s/it] 82%|████████▏ | 15336/18688 [18:01:10<3:48:06, 4.08s/it] 82%|████████▏ | 15337/18688 [18:01:14<3:55:10, 4.21s/it] 82%|████████▏ | 15338/18688 [18:01:20<4:10:02, 4.48s/it] 82%|████████▏ | 15339/18688 [18:01:23<3:57:44, 4.26s/it] 82%|████████▏ | 15340/18688 [18:01:28<4:05:07, 4.39s/it] {'loss': '0.9441', 'grad_norm': '1.043', 'learning_rate': '7.263e-06', 'epoch': '0.8209', 'num_input_tokens_seen': 51585536, 'train_runtime': '6.489e+04', 'train_tokens_per_second': '795'}
82%|████████▏ | 15340/18688 [18:01:28<4:05:07, 4.39s/it] 82%|████████▏ | 15341/18688 [18:01:32<3:53:52, 4.19s/it] 82%|████████▏ | 15342/18688 [18:01:36<3:56:24, 4.24s/it] 82%|████████▏ | 15343/18688 [18:01:40<3:46:48, 4.07s/it] 82%|████████▏ | 15344/18688 [18:01:44<3:43:26, 4.01s/it] 82%|████████▏ | 15345/18688 [18:01:47<3:34:48, 3.86s/it] 82%|████████▏ | 15346/18688 [18:01:51<3:42:02, 3.99s/it] 82%|████████▏ | 15347/18688 [18:01:56<3:44:56, 4.04s/it] 82%|████████▏ | 15348/18688 [18:02:00<3:45:06, 4.04s/it] 82%|████████▏ | 15349/18688 [18:02:04<3:49:53, 4.13s/it] 82%|████████▏ | 15350/18688 [18:02:08<3:42:27, 4.00s/it] {'loss': '0.9077', 'grad_norm': '1.318', 'learning_rate': '7.221e-06', 'epoch': '0.8214', 'num_input_tokens_seen': 51615568, 'train_runtime': '6.493e+04', 'train_tokens_per_second': '795'}
82%|████████▏ | 15350/18688 [18:02:08<3:42:27, 4.00s/it] 82%|████████▏ | 15351/18688 [18:02:12<3:44:14, 4.03s/it] 82%|████████▏ | 15352/18688 [18:02:15<3:35:21, 3.87s/it] 82%|████████▏ | 15353/18688 [18:02:20<3:42:44, 4.01s/it] 82%|████████▏ | 15354/18688 [18:02:24<3:45:39, 4.06s/it] 82%|████████▏ | 15355/18688 [18:02:27<3:38:32, 3.93s/it] 82%|████████▏ | 15356/18688 [18:02:32<3:49:38, 4.14s/it] 82%|████████▏ | 15357/18688 [18:02:36<3:48:38, 4.12s/it] 82%|████████▏ | 15358/18688 [18:02:40<3:50:55, 4.16s/it] 82%|████████▏ | 15359/18688 [18:02:44<3:44:19, 4.04s/it] 82%|████████▏ | 15360/18688 [18:02:49<3:53:12, 4.20s/it] {'loss': '0.914', 'grad_norm': '1.15', 'learning_rate': '7.179e-06', 'epoch': '0.8219', 'num_input_tokens_seen': 51647704, 'train_runtime': '6.497e+04', 'train_tokens_per_second': '795'}
82%|████████▏ | 15360/18688 [18:02:49<3:53:12, 4.20s/it] 82%|████████▏ | 15361/18688 [18:02:53<3:45:56, 4.07s/it] 82%|████████▏ | 15362/18688 [18:02:56<3:36:20, 3.90s/it] 82%|████████▏ | 15363/18688 [18:03:00<3:41:35, 4.00s/it] 82%|████████▏ | 15364/18688 [18:03:05<3:56:18, 4.27s/it] 82%|████████▏ | 15365/18688 [18:03:09<3:48:00, 4.12s/it] 82%|████████▏ | 15366/18688 [18:03:13<3:54:23, 4.23s/it] 82%|████████▏ | 15367/18688 [18:03:17<3:51:54, 4.19s/it] 82%|████████▏ | 15368/18688 [18:03:23<4:16:00, 4.63s/it] 82%|████████▏ | 15369/18688 [18:03:27<4:07:24, 4.47s/it] 82%|████████▏ | 15370/18688 [18:03:31<4:03:23, 4.40s/it] {'loss': '0.893', 'grad_norm': '1.175', 'learning_rate': '7.137e-06', 'epoch': '0.8225', 'num_input_tokens_seen': 51683864, 'train_runtime': '6.501e+04', 'train_tokens_per_second': '795'}
82%|████████▏ | 15370/18688 [18:03:31<4:03:23, 4.40s/it] 82%|████████▏ | 15371/18688 [18:03:36<3:58:11, 4.31s/it] 82%|████████▏ | 15372/18688 [18:03:40<3:56:25, 4.28s/it] 82%|████████▏ | 15373/18688 [18:03:44<3:49:05, 4.15s/it] 82%|████████▏ | 15374/18688 [18:03:47<3:39:19, 3.97s/it] 82%|████████▏ | 15375/18688 [18:03:51<3:41:14, 4.01s/it] 82%|████████▏ | 15376/18688 [18:03:55<3:44:19, 4.06s/it] 82%|████████▏ | 15377/18688 [18:03:59<3:36:09, 3.92s/it] 82%|████████▏ | 15378/18688 [18:04:03<3:29:24, 3.80s/it] 82%|████████▏ | 15379/18688 [18:04:07<3:45:22, 4.09s/it] 82%|████████▏ | 15380/18688 [18:04:12<3:48:14, 4.14s/it] {'loss': '0.9197', 'grad_norm': '1.598', 'learning_rate': '7.096e-06', 'epoch': '0.823', 'num_input_tokens_seen': 51713808, 'train_runtime': '6.505e+04', 'train_tokens_per_second': '795'}
82%|████████▏ | 15380/18688 [18:04:12<3:48:14, 4.14s/it] 82%|████████▏ | 15381/18688 [18:04:16<3:48:38, 4.15s/it] 82%|████████▏ | 15382/18688 [18:04:20<3:45:53, 4.10s/it] 82%|████████▏ | 15383/18688 [18:04:24<3:52:43, 4.22s/it] 82%|████████▏ | 15384/18688 [18:04:28<3:51:54, 4.21s/it] 82%|████████▏ | 15385/18688 [18:04:33<3:56:36, 4.30s/it] 82%|████████▏ | 15386/18688 [18:04:37<3:49:10, 4.16s/it] 82%|████████▏ | 15387/18688 [18:04:41<3:47:31, 4.14s/it] 82%|████████▏ | 15388/18688 [18:04:46<3:56:17, 4.30s/it] 82%|████████▏ | 15389/18688 [18:04:50<3:52:16, 4.22s/it] 82%|████████▏ | 15390/18688 [18:04:53<3:40:33, 4.01s/it] {'loss': '0.8904', 'grad_norm': '1.495', 'learning_rate': '7.054e-06', 'epoch': '0.8235', 'num_input_tokens_seen': 51747464, 'train_runtime': '6.509e+04', 'train_tokens_per_second': '795'}
82%|████████▏ | 15390/18688 [18:04:53<3:40:33, 4.01s/it] 82%|████████▏ | 15391/18688 [18:04:57<3:42:55, 4.06s/it] 82%|████████▏ | 15392/18688 [18:05:01<3:33:01, 3.88s/it] 82%|████████▏ | 15393/18688 [18:05:04<3:30:56, 3.84s/it] 82%|████████▏ | 15394/18688 [18:05:08<3:27:50, 3.79s/it] 82%|████████▏ | 15395/18688 [18:05:12<3:24:43, 3.73s/it] 82%|████████▏ | 15396/18688 [18:05:16<3:29:17, 3.81s/it] 82%|████████▏ | 15397/18688 [18:05:20<3:38:22, 3.98s/it] 82%|████████▏ | 15398/18688 [18:05:24<3:37:51, 3.97s/it] 82%|████████▏ | 15399/18688 [18:05:28<3:43:10, 4.07s/it] 82%|████████▏ | 15400/18688 [18:05:33<3:49:56, 4.20s/it] {'loss': '0.9012', 'grad_norm': '1.275', 'learning_rate': '7.013e-06', 'epoch': '0.8241', 'num_input_tokens_seen': 51777744, 'train_runtime': '6.513e+04', 'train_tokens_per_second': '795'}
82%|████████▏ | 15400/18688 [18:05:33<3:49:56, 4.20s/it] 82%|████████▏ | 15401/18688 [18:05:37<3:50:44, 4.21s/it] 82%|████████▏ | 15402/18688 [18:05:41<3:43:42, 4.08s/it] 82%|████████▏ | 15403/18688 [18:05:45<3:48:01, 4.16s/it] 82%|████████▏ | 15404/18688 [18:05:49<3:44:31, 4.10s/it] 82%|████████▏ | 15405/18688 [18:05:54<3:53:46, 4.27s/it] 82%|████████▏ | 15406/18688 [18:05:58<3:58:51, 4.37s/it] 82%|████████▏ | 15407/18688 [18:06:03<4:03:46, 4.46s/it] 82%|████████▏ | 15408/18688 [18:06:07<3:58:21, 4.36s/it] 82%|████████▏ | 15409/18688 [18:06:12<3:59:07, 4.38s/it] 82%|████████▏ | 15410/18688 [18:06:16<3:59:47, 4.39s/it] {'loss': '0.9115', 'grad_norm': '1.271', 'learning_rate': '6.971e-06', 'epoch': '0.8246', 'num_input_tokens_seen': 51813432, 'train_runtime': '6.518e+04', 'train_tokens_per_second': '795'}
82%|████████▏ | 15410/18688 [18:06:16<3:59:47, 4.39s/it] 82%|████████▏ | 15411/18688 [18:06:22<4:16:25, 4.70s/it] 82%|████████▏ | 15412/18688 [18:06:25<3:59:08, 4.38s/it] 82%|████████▏ | 15413/18688 [18:06:29<3:44:07, 4.11s/it] 82%|████████▏ | 15414/18688 [18:06:33<3:54:19, 4.29s/it] 82%|████████▏ | 15415/18688 [18:06:38<4:07:02, 4.53s/it] 82%|████████▏ | 15416/18688 [18:06:44<4:16:42, 4.71s/it] 82%|████████▏ | 15417/18688 [18:06:48<4:06:50, 4.53s/it] 83%|████████▎ | 15418/18688 [18:06:51<3:52:04, 4.26s/it] 83%|████████▎ | 15419/18688 [18:06:55<3:50:41, 4.23s/it] 83%|████████▎ | 15420/18688 [18:07:00<3:51:23, 4.25s/it] {'loss': '0.9167', 'grad_norm': '1.186', 'learning_rate': '6.93e-06', 'epoch': '0.8252', 'num_input_tokens_seen': 51850784, 'train_runtime': '6.522e+04', 'train_tokens_per_second': '795'}
83%|████████▎ | 15420/18688 [18:07:00<3:51:23, 4.25s/it] 83%|████████▎ | 15421/18688 [18:07:05<4:01:48, 4.44s/it] 83%|████████▎ | 15422/18688 [18:07:09<4:00:11, 4.41s/it] 83%|████████▎ | 15423/18688 [18:07:13<3:49:38, 4.22s/it] 83%|████████▎ | 15424/18688 [18:07:17<3:41:50, 4.08s/it] 83%|████████▎ | 15425/18688 [18:07:21<3:54:21, 4.31s/it] 83%|████████▎ | 15426/18688 [18:07:25<3:40:44, 4.06s/it] 83%|████████▎ | 15427/18688 [18:07:29<3:35:37, 3.97s/it] 83%|████████▎ | 15428/18688 [18:07:33<3:37:21, 4.00s/it] 83%|████████▎ | 15429/18688 [18:07:37<3:41:21, 4.08s/it] 83%|████████▎ | 15430/18688 [18:07:41<3:42:37, 4.10s/it] {'loss': '0.9254', 'grad_norm': '1.379', 'learning_rate': '6.889e-06', 'epoch': '0.8257', 'num_input_tokens_seen': 51883152, 'train_runtime': '6.526e+04', 'train_tokens_per_second': '795'}
83%|████████▎ | 15430/18688 [18:07:41<3:42:37, 4.10s/it] 83%|████████▎ | 15431/18688 [18:07:45<3:32:46, 3.92s/it] 83%|████████▎ | 15432/18688 [18:07:48<3:29:56, 3.87s/it] 83%|████████▎ | 15433/18688 [18:07:53<3:43:26, 4.12s/it] 83%|████████▎ | 15434/18688 [18:07:57<3:49:02, 4.22s/it] 83%|████████▎ | 15435/18688 [18:08:01<3:42:35, 4.11s/it] 83%|████████▎ | 15436/18688 [18:08:05<3:32:46, 3.93s/it] 83%|████████▎ | 15437/18688 [18:08:09<3:37:59, 4.02s/it] 83%|████████▎ | 15438/18688 [18:08:13<3:40:49, 4.08s/it] 83%|████████▎ | 15439/18688 [18:08:17<3:38:37, 4.04s/it] 83%|████████▎ | 15440/18688 [18:08:21<3:31:42, 3.91s/it] {'loss': '0.8346', 'grad_norm': '1.152', 'learning_rate': '6.848e-06', 'epoch': '0.8262', 'num_input_tokens_seen': 51914592, 'train_runtime': '6.53e+04', 'train_tokens_per_second': '795'}
83%|████████▎ | 15440/18688 [18:08:21<3:31:42, 3.91s/it] 83%|████████▎ | 15441/18688 [18:08:24<3:24:40, 3.78s/it] 83%|████████▎ | 15442/18688 [18:08:30<3:51:40, 4.28s/it] 83%|████████▎ | 15443/18688 [18:08:34<3:54:00, 4.33s/it] 83%|████████▎ | 15444/18688 [18:08:39<4:03:19, 4.50s/it] 83%|████████▎ | 15445/18688 [18:08:43<3:50:58, 4.27s/it] 83%|████████▎ | 15446/18688 [18:08:48<3:57:48, 4.40s/it] 83%|████████▎ | 15447/18688 [18:08:52<3:56:36, 4.38s/it] 83%|████████▎ | 15448/18688 [18:08:56<3:50:00, 4.26s/it] 83%|████████▎ | 15449/18688 [18:09:00<3:47:47, 4.22s/it] 83%|████████▎ | 15450/18688 [18:09:04<3:48:55, 4.24s/it] {'loss': '0.9408', 'grad_norm': '1.246', 'learning_rate': '6.807e-06', 'epoch': '0.8268', 'num_input_tokens_seen': 51950680, 'train_runtime': '6.534e+04', 'train_tokens_per_second': '795'}
83%|████████▎ | 15450/18688 [18:09:04<3:48:55, 4.24s/it] 83%|████████▎ | 15451/18688 [18:09:09<3:52:23, 4.31s/it] 83%|████████▎ | 15452/18688 [18:09:14<4:01:53, 4.48s/it] 83%|████████▎ | 15453/18688 [18:09:18<3:55:13, 4.36s/it] 83%|████████▎ | 15454/18688 [18:09:22<3:56:57, 4.40s/it] 83%|████████▎ | 15455/18688 [18:09:26<3:46:59, 4.21s/it] 83%|████████▎ | 15456/18688 [18:09:30<3:42:15, 4.13s/it] 83%|████████▎ | 15457/18688 [18:09:34<3:43:36, 4.15s/it] 83%|████████▎ | 15458/18688 [18:09:38<3:40:16, 4.09s/it] 83%|████████▎ | 15459/18688 [18:09:42<3:34:23, 3.98s/it] 83%|████████▎ | 15460/18688 [18:09:47<3:46:32, 4.21s/it] {'loss': '0.9685', 'grad_norm': '1.104', 'learning_rate': '6.767e-06', 'epoch': '0.8273', 'num_input_tokens_seen': 51985304, 'train_runtime': '6.539e+04', 'train_tokens_per_second': '795'}
83%|████████▎ | 15460/18688 [18:09:47<3:46:32, 4.21s/it] 83%|████████▎ | 15461/18688 [18:09:51<3:47:46, 4.24s/it] 83%|████████▎ | 15462/18688 [18:09:55<3:49:52, 4.28s/it] 83%|████████▎ | 15463/18688 [18:09:59<3:36:08, 4.02s/it] 83%|████████▎ | 15464/18688 [18:10:03<3:40:30, 4.10s/it] 83%|████████▎ | 15465/18688 [18:10:07<3:34:20, 3.99s/it] 83%|████████▎ | 15466/18688 [18:10:11<3:47:27, 4.24s/it] 83%|████████▎ | 15467/18688 [18:10:16<3:48:17, 4.25s/it] 83%|████████▎ | 15468/18688 [18:10:20<3:47:27, 4.24s/it] 83%|████████▎ | 15469/18688 [18:10:23<3:35:43, 4.02s/it] 83%|████████▎ | 15470/18688 [18:10:28<3:36:29, 4.04s/it] {'loss': '0.9007', 'grad_norm': '1.217', 'learning_rate': '6.726e-06', 'epoch': '0.8278', 'num_input_tokens_seen': 52018432, 'train_runtime': '6.543e+04', 'train_tokens_per_second': '795'}
83%|████████▎ | 15470/18688 [18:10:28<3:36:29, 4.04s/it] 83%|████████▎ | 15471/18688 [18:10:32<3:47:30, 4.24s/it] 83%|████████▎ | 15472/18688 [18:10:37<3:51:56, 4.33s/it] 83%|████████▎ | 15473/18688 [18:10:41<3:45:53, 4.22s/it] 83%|████████▎ | 15474/18688 [18:10:45<3:39:39, 4.10s/it] 83%|████████▎ | 15475/18688 [18:10:49<3:43:42, 4.18s/it] 83%|████████▎ | 15476/18688 [18:10:53<3:41:00, 4.13s/it] 83%|████████▎ | 15477/18688 [18:10:56<3:30:02, 3.92s/it] 83%|████████▎ | 15478/18688 [18:11:00<3:29:43, 3.92s/it] 83%|████████▎ | 15479/18688 [18:11:05<3:34:09, 4.00s/it] 83%|████████▎ | 15480/18688 [18:11:09<3:46:17, 4.23s/it] {'loss': '0.9372', 'grad_norm': '1.281', 'learning_rate': '6.685e-06', 'epoch': '0.8284', 'num_input_tokens_seen': 52050600, 'train_runtime': '6.547e+04', 'train_tokens_per_second': '795'}
83%|████████▎ | 15480/18688 [18:11:09<3:46:17, 4.23s/it] 83%|████████▎ | 15481/18688 [18:11:14<3:59:56, 4.49s/it] 83%|████████▎ | 15482/18688 [18:11:19<3:56:48, 4.43s/it] 83%|████████▎ | 15483/18688 [18:11:22<3:45:39, 4.22s/it] 83%|████████▎ | 15484/18688 [18:11:27<3:55:08, 4.40s/it] 83%|████████▎ | 15485/18688 [18:11:32<4:06:23, 4.62s/it] 83%|████████▎ | 15486/18688 [18:11:37<4:00:32, 4.51s/it] 83%|████████▎ | 15487/18688 [18:11:42<4:11:05, 4.71s/it] 83%|████████▎ | 15488/18688 [18:11:46<4:07:10, 4.63s/it] 83%|████████▎ | 15489/18688 [18:11:51<4:01:41, 4.53s/it] 83%|████████▎ | 15490/18688 [18:11:54<3:47:49, 4.27s/it] {'loss': '0.9235', 'grad_norm': '1.191', 'learning_rate': '6.645e-06', 'epoch': '0.8289', 'num_input_tokens_seen': 52087808, 'train_runtime': '6.551e+04', 'train_tokens_per_second': '795.1'}
83%|████████▎ | 15490/18688 [18:11:54<3:47:49, 4.27s/it] 83%|████████▎ | 15491/18688 [18:11:58<3:47:51, 4.28s/it] 83%|████████▎ | 15492/18688 [18:12:02<3:42:29, 4.18s/it] 83%|████████▎ | 15493/18688 [18:12:06<3:34:30, 4.03s/it] 83%|████████▎ | 15494/18688 [18:12:10<3:26:45, 3.88s/it] 83%|████████▎ | 15495/18688 [18:12:13<3:23:15, 3.82s/it] 83%|████████▎ | 15496/18688 [18:12:18<3:32:32, 4.00s/it] 83%|████████▎ | 15497/18688 [18:12:22<3:37:51, 4.10s/it] 83%|████████▎ | 15498/18688 [18:12:27<3:48:35, 4.30s/it] 83%|████████▎ | 15499/18688 [18:12:32<3:59:01, 4.50s/it] 83%|████████▎ | 15500/18688 [18:12:36<3:49:12, 4.31s/it] {'loss': '0.8899', 'grad_norm': '1.162', 'learning_rate': '6.605e-06', 'epoch': '0.8294', 'num_input_tokens_seen': 52121240, 'train_runtime': '6.556e+04', 'train_tokens_per_second': '795.1'}
83%|████████▎ | 15500/18688 [18:12:36<3:49:12, 4.31s/it] 83%|████████▎ | 15501/18688 [18:12:41<4:09:34, 4.70s/it] 83%|████████▎ | 15502/18688 [18:12:45<4:00:58, 4.54s/it] 83%|████████▎ | 15503/18688 [18:12:50<4:04:22, 4.60s/it] 83%|████████▎ | 15504/18688 [18:12:54<3:48:35, 4.31s/it] 83%|████████▎ | 15505/18688 [18:12:59<3:56:56, 4.47s/it] 83%|████████▎ | 15506/18688 [18:13:03<3:52:18, 4.38s/it] 83%|████████▎ | 15507/18688 [18:13:07<3:44:16, 4.23s/it] 83%|████████▎ | 15508/18688 [18:13:11<3:48:41, 4.31s/it] 83%|████████▎ | 15509/18688 [18:13:16<3:52:13, 4.38s/it] 83%|████████▎ | 15510/18688 [18:13:20<3:43:27, 4.22s/it] {'loss': '0.9006', 'grad_norm': '1.15', 'learning_rate': '6.565e-06', 'epoch': '0.83', 'num_input_tokens_seen': 52158440, 'train_runtime': '6.56e+04', 'train_tokens_per_second': '795.1'}
83%|████████▎ | 15510/18688 [18:13:20<3:43:27, 4.22s/it] 83%|████████▎ | 15511/18688 [18:13:24<3:38:37, 4.13s/it] 83%|████████▎ | 15512/18688 [18:13:28<3:40:57, 4.17s/it] 83%|████████▎ | 15513/18688 [18:13:32<3:35:03, 4.06s/it] 83%|████████▎ | 15514/18688 [18:13:36<3:40:35, 4.17s/it] 83%|████████▎ | 15515/18688 [18:13:39<3:28:34, 3.94s/it] 83%|████████▎ | 15516/18688 [18:13:45<3:48:35, 4.32s/it] 83%|████████▎ | 15517/18688 [18:13:49<3:44:47, 4.25s/it] 83%|████████▎ | 15518/18688 [18:13:53<3:46:50, 4.29s/it] 83%|████████▎ | 15519/18688 [18:13:57<3:41:29, 4.19s/it] 83%|████████▎ | 15520/18688 [18:14:02<3:45:42, 4.27s/it] {'loss': '0.8692', 'grad_norm': '1.345', 'learning_rate': '6.524e-06', 'epoch': '0.8305', 'num_input_tokens_seen': 52193000, 'train_runtime': '6.564e+04', 'train_tokens_per_second': '795.1'}
83%|████████▎ | 15520/18688 [18:14:02<3:45:42, 4.27s/it] 83%|████████▎ | 15521/18688 [18:14:06<3:42:09, 4.21s/it] 83%|████████▎ | 15522/18688 [18:14:10<3:46:40, 4.30s/it] 83%|████████▎ | 15523/18688 [18:14:14<3:42:27, 4.22s/it] 83%|████████▎ | 15524/18688 [18:14:19<3:45:29, 4.28s/it] 83%|████████▎ | 15525/18688 [18:14:23<3:42:30, 4.22s/it] 83%|████████▎ | 15526/18688 [18:14:27<3:41:20, 4.20s/it] 83%|████████▎ | 15527/18688 [18:14:31<3:46:06, 4.29s/it] 83%|████████▎ | 15528/18688 [18:14:35<3:34:09, 4.07s/it] 83%|████████▎ | 15529/18688 [18:14:39<3:28:55, 3.97s/it] 83%|████████▎ | 15530/18688 [18:14:43<3:36:27, 4.11s/it] {'loss': '0.8653', 'grad_norm': '1.143', 'learning_rate': '6.485e-06', 'epoch': '0.831', 'num_input_tokens_seen': 52226032, 'train_runtime': '6.568e+04', 'train_tokens_per_second': '795.1'}
83%|████████▎ | 15530/18688 [18:14:43<3:36:27, 4.11s/it] 83%|████████▎ | 15531/18688 [18:14:47<3:29:48, 3.99s/it] 83%|████████▎ | 15532/18688 [18:14:51<3:34:22, 4.08s/it] 83%|████████▎ | 15533/18688 [18:14:55<3:27:47, 3.95s/it] 83%|████████▎ | 15534/18688 [18:14:59<3:30:55, 4.01s/it] 83%|████████▎ | 15535/18688 [18:15:03<3:27:31, 3.95s/it] 83%|████████▎ | 15536/18688 [18:15:07<3:35:09, 4.10s/it] 83%|████████▎ | 15537/18688 [18:15:12<3:45:24, 4.29s/it] 83%|████████▎ | 15538/18688 [18:15:16<3:36:40, 4.13s/it] 83%|████████▎ | 15539/18688 [18:15:19<3:33:36, 4.07s/it] 83%|████████▎ | 15540/18688 [18:15:23<3:29:45, 4.00s/it] {'loss': '0.914', 'grad_norm': '1.195', 'learning_rate': '6.445e-06', 'epoch': '0.8316', 'num_input_tokens_seen': 52258680, 'train_runtime': '6.572e+04', 'train_tokens_per_second': '795.1'}
83%|████████▎ | 15540/18688 [18:15:23<3:29:45, 4.00s/it] 83%|████████▎ | 15541/18688 [18:15:28<3:33:49, 4.08s/it] 83%|████████▎ | 15542/18688 [18:15:33<3:52:11, 4.43s/it] 83%|████████▎ | 15543/18688 [18:15:37<3:46:54, 4.33s/it] 83%|████████▎ | 15544/18688 [18:15:40<3:33:15, 4.07s/it] 83%|████████▎ | 15545/18688 [18:15:45<3:41:21, 4.23s/it] 83%|████████▎ | 15546/18688 [18:15:49<3:45:06, 4.30s/it] 83%|████████▎ | 15547/18688 [18:15:54<3:50:16, 4.40s/it] 83%|████████▎ | 15548/18688 [18:15:58<3:45:34, 4.31s/it] 83%|████████▎ | 15549/18688 [18:16:02<3:35:14, 4.11s/it] 83%|████████▎ | 15550/18688 [18:16:07<3:45:40, 4.31s/it] {'loss': '0.8938', 'grad_norm': '1.248', 'learning_rate': '6.405e-06', 'epoch': '0.8321', 'num_input_tokens_seen': 52295024, 'train_runtime': '6.577e+04', 'train_tokens_per_second': '795.2'}
83%|████████▎ | 15550/18688 [18:16:07<3:45:40, 4.31s/it] 83%|████████▎ | 15551/18688 [18:16:11<3:41:34, 4.24s/it] 83%|████████▎ | 15552/18688 [18:16:15<3:46:35, 4.34s/it] 83%|████████▎ | 15553/18688 [18:16:19<3:40:10, 4.21s/it] 83%|████████▎ | 15554/18688 [18:16:23<3:40:15, 4.22s/it] 83%|████████▎ | 15555/18688 [18:16:27<3:34:25, 4.11s/it] 83%|████████▎ | 15556/18688 [18:16:33<3:57:44, 4.55s/it] 83%|████████▎ | 15557/18688 [18:16:36<3:41:36, 4.25s/it] 83%|████████▎ | 15558/18688 [18:16:40<3:34:31, 4.11s/it] 83%|████████▎ | 15559/18688 [18:16:44<3:30:41, 4.04s/it] 83%|████████▎ | 15560/18688 [18:16:48<3:35:55, 4.14s/it] {'loss': '0.8121', 'grad_norm': '1.095', 'learning_rate': '6.365e-06', 'epoch': '0.8326', 'num_input_tokens_seen': 52328728, 'train_runtime': '6.581e+04', 'train_tokens_per_second': '795.2'}
83%|████████▎ | 15560/18688 [18:16:48<3:35:55, 4.14s/it] 83%|████████▎ | 15561/18688 [18:16:53<3:39:06, 4.20s/it] 83%|████████▎ | 15562/18688 [18:16:57<3:32:26, 4.08s/it] 83%|████████▎ | 15563/18688 [18:17:01<3:37:54, 4.18s/it] 83%|████████▎ | 15564/18688 [18:17:05<3:30:31, 4.04s/it] 83%|████████▎ | 15565/18688 [18:17:08<3:24:43, 3.93s/it] 83%|████████▎ | 15566/18688 [18:17:12<3:21:37, 3.87s/it] 83%|████████▎ | 15567/18688 [18:17:16<3:21:33, 3.87s/it] 83%|████████▎ | 15568/18688 [18:17:20<3:27:13, 3.98s/it] 83%|████████▎ | 15569/18688 [18:17:25<3:33:23, 4.10s/it] 83%|████████▎ | 15570/18688 [18:17:28<3:26:31, 3.97s/it] {'loss': '0.9056', 'grad_norm': '1.719', 'learning_rate': '6.326e-06', 'epoch': '0.8332', 'num_input_tokens_seen': 52357432, 'train_runtime': '6.585e+04', 'train_tokens_per_second': '795.1'}
83%|████████▎ | 15570/18688 [18:17:28<3:26:31, 3.97s/it] 83%|████████▎ | 15571/18688 [18:17:32<3:25:00, 3.95s/it] 83%|████████▎ | 15572/18688 [18:17:37<3:39:21, 4.22s/it] 83%|████████▎ | 15573/18688 [18:17:41<3:32:47, 4.10s/it] 83%|████████▎ | 15574/18688 [18:17:45<3:30:35, 4.06s/it] 83%|████████▎ | 15575/18688 [18:17:49<3:36:31, 4.17s/it] 83%|████████▎ | 15576/18688 [18:17:54<3:44:34, 4.33s/it] 83%|████████▎ | 15577/18688 [18:17:58<3:33:36, 4.12s/it] 83%|████████▎ | 15578/18688 [18:18:02<3:35:13, 4.15s/it] 83%|████████▎ | 15579/18688 [18:18:06<3:40:25, 4.25s/it] 83%|████████▎ | 15580/18688 [18:18:10<3:28:40, 4.03s/it] {'loss': '0.9589', 'grad_norm': '1.38', 'learning_rate': '6.286e-06', 'epoch': '0.8337', 'num_input_tokens_seen': 52389688, 'train_runtime': '6.589e+04', 'train_tokens_per_second': '795.1'}
83%|████████▎ | 15580/18688 [18:18:10<3:28:40, 4.03s/it] 83%|████████▎ | 15581/18688 [18:18:14<3:29:23, 4.04s/it] 83%|████████▎ | 15582/18688 [18:18:18<3:23:46, 3.94s/it] 83%|████████▎ | 15583/18688 [18:18:22<3:29:47, 4.05s/it] 83%|████████▎ | 15584/18688 [18:18:26<3:30:17, 4.06s/it] 83%|████████▎ | 15585/18688 [18:18:30<3:26:02, 3.98s/it] 83%|████████▎ | 15586/18688 [18:18:33<3:17:46, 3.83s/it] 83%|████████▎ | 15587/18688 [18:18:38<3:38:20, 4.22s/it] 83%|████████▎ | 15588/18688 [18:18:44<3:53:40, 4.52s/it] 83%|████████▎ | 15589/18688 [18:18:48<3:58:34, 4.62s/it] 83%|████████▎ | 15590/18688 [18:18:53<3:54:57, 4.55s/it] {'loss': '0.9664', 'grad_norm': '1.084', 'learning_rate': '6.247e-06', 'epoch': '0.8342', 'num_input_tokens_seen': 52424536, 'train_runtime': '6.593e+04', 'train_tokens_per_second': '795.1'}
83%|████████▎ | 15590/18688 [18:18:53<3:54:57, 4.55s/it] 83%|████████▎ | 15591/18688 [18:18:57<3:52:15, 4.50s/it] 83%|████████▎ | 15592/18688 [18:19:01<3:41:09, 4.29s/it] 83%|████████▎ | 15593/18688 [18:19:05<3:30:12, 4.08s/it] 83%|████████▎ | 15594/18688 [18:19:09<3:36:34, 4.20s/it] 83%|████████▎ | 15595/18688 [18:19:13<3:34:52, 4.17s/it] 83%|████████▎ | 15596/18688 [18:19:17<3:34:12, 4.16s/it] 83%|████████▎ | 15597/18688 [18:19:21<3:26:30, 4.01s/it] 83%|████████▎ | 15598/18688 [18:19:25<3:33:53, 4.15s/it] 83%|████████▎ | 15599/18688 [18:19:29<3:28:59, 4.06s/it] 83%|████████▎ | 15600/18688 [18:19:34<3:39:40, 4.27s/it] {'loss': '0.9275', 'grad_norm': '1.106', 'learning_rate': '6.208e-06', 'epoch': '0.8348', 'num_input_tokens_seen': 52458416, 'train_runtime': '6.597e+04', 'train_tokens_per_second': '795.1'}
83%|████████▎ | 15600/18688 [18:19:34<3:39:40, 4.27s/it] 83%|████████▎ | 15601/18688 [18:19:38<3:33:31, 4.15s/it] 83%|████████▎ | 15602/18688 [18:19:41<3:24:23, 3.97s/it] 83%|████████▎ | 15603/18688 [18:19:45<3:20:40, 3.90s/it] 83%|████████▎ | 15604/18688 [18:19:49<3:25:32, 4.00s/it] 84%|████████▎ | 15605/18688 [18:19:53<3:16:44, 3.83s/it] 84%|████████▎ | 15606/18688 [18:19:57<3:18:05, 3.86s/it] 84%|████████▎ | 15607/18688 [18:20:00<3:14:40, 3.79s/it] 84%|████████▎ | 15608/18688 [18:20:05<3:21:05, 3.92s/it] 84%|████████▎ | 15609/18688 [18:20:09<3:22:16, 3.94s/it] 84%|████████▎ | 15610/18688 [18:20:13<3:29:24, 4.08s/it] {'loss': '0.9128', 'grad_norm': '1.132', 'learning_rate': '6.169e-06', 'epoch': '0.8353', 'num_input_tokens_seen': 52488808, 'train_runtime': '6.601e+04', 'train_tokens_per_second': '795.1'}
84%|████████▎ | 15610/18688 [18:20:13<3:29:24, 4.08s/it] 84%|████████▎ | 15611/18688 [18:20:17<3:34:00, 4.17s/it] 84%|████████▎ | 15612/18688 [18:20:21<3:30:35, 4.11s/it] 84%|████████▎ | 15613/18688 [18:20:25<3:30:03, 4.10s/it] 84%|████████▎ | 15614/18688 [18:20:30<3:39:49, 4.29s/it] 84%|████████▎ | 15615/18688 [18:20:34<3:36:01, 4.22s/it] 84%|████████▎ | 15616/18688 [18:20:38<3:32:07, 4.14s/it] 84%|████████▎ | 15617/18688 [18:20:42<3:32:04, 4.14s/it] 84%|████████▎ | 15618/18688 [18:20:46<3:30:55, 4.12s/it] 84%|████████▎ | 15619/18688 [18:20:51<3:41:50, 4.34s/it] 84%|████████▎ | 15620/18688 [18:20:55<3:39:42, 4.30s/it] {'loss': '0.881', 'grad_norm': '0.9716', 'learning_rate': '6.13e-06', 'epoch': '0.8359', 'num_input_tokens_seen': 52523424, 'train_runtime': '6.606e+04', 'train_tokens_per_second': '795.1'}
84%|████████▎ | 15620/18688 [18:20:55<3:39:42, 4.30s/it] 84%|████████▎ | 15621/18688 [18:20:59<3:28:21, 4.08s/it] 84%|████████▎ | 15622/18688 [18:21:03<3:31:31, 4.14s/it] 84%|████████▎ | 15623/18688 [18:21:08<3:34:20, 4.20s/it] 84%|████████▎ | 15624/18688 [18:21:12<3:30:18, 4.12s/it] 84%|████████▎ | 15625/18688 [18:21:15<3:22:57, 3.98s/it] 84%|████████▎ | 15626/18688 [18:21:19<3:16:48, 3.86s/it] 84%|████████▎ | 15627/18688 [18:21:23<3:19:10, 3.90s/it] 84%|████████▎ | 15628/18688 [18:21:28<3:31:53, 4.15s/it] 84%|████████▎ | 15629/18688 [18:21:33<3:48:50, 4.49s/it] 84%|████████▎ | 15630/18688 [18:21:37<3:45:49, 4.43s/it] {'loss': '0.8628', 'grad_norm': '1.177', 'learning_rate': '6.091e-06', 'epoch': '0.8364', 'num_input_tokens_seen': 52556456, 'train_runtime': '6.61e+04', 'train_tokens_per_second': '795.1'}
84%|████████▎ | 15630/18688 [18:21:37<3:45:49, 4.43s/it] 84%|████████▎ | 15631/18688 [18:21:42<3:45:55, 4.43s/it] 84%|████████▎ | 15632/18688 [18:21:46<3:38:11, 4.28s/it] 84%|████████▎ | 15633/18688 [18:21:49<3:29:14, 4.11s/it] 84%|████████▎ | 15634/18688 [18:21:53<3:27:39, 4.08s/it] 84%|████████▎ | 15635/18688 [18:21:57<3:19:47, 3.93s/it] 84%|████████▎ | 15636/18688 [18:22:00<3:13:08, 3.80s/it] 84%|████████▎ | 15637/18688 [18:22:05<3:19:47, 3.93s/it] 84%|████████▎ | 15638/18688 [18:22:08<3:18:39, 3.91s/it] 84%|████████▎ | 15639/18688 [18:22:12<3:12:04, 3.78s/it] 84%|████████▎ | 15640/18688 [18:22:16<3:18:17, 3.90s/it] {'loss': '0.898', 'grad_norm': '1.191', 'learning_rate': '6.052e-06', 'epoch': '0.8369', 'num_input_tokens_seen': 52586576, 'train_runtime': '6.614e+04', 'train_tokens_per_second': '795.1'}
84%|████████▎ | 15640/18688 [18:22:16<3:18:17, 3.90s/it] 84%|████████▎ | 15641/18688 [18:22:21<3:39:52, 4.33s/it] 84%|████████▎ | 15642/18688 [18:22:26<3:44:30, 4.42s/it] 84%|████████▎ | 15643/18688 [18:22:30<3:34:24, 4.22s/it] 84%|████████▎ | 15644/18688 [18:22:35<3:42:24, 4.38s/it] 84%|████████▎ | 15645/18688 [18:22:39<3:37:20, 4.29s/it] 84%|████████▎ | 15646/18688 [18:22:43<3:32:00, 4.18s/it] 84%|████████▎ | 15647/18688 [18:22:47<3:30:53, 4.16s/it] 84%|████████▎ | 15648/18688 [18:22:50<3:23:12, 4.01s/it] 84%|████████▎ | 15649/18688 [18:22:55<3:39:05, 4.33s/it] 84%|████████▎ | 15650/18688 [18:23:00<3:43:48, 4.42s/it] {'loss': '0.9422', 'grad_norm': '0.8063', 'learning_rate': '6.014e-06', 'epoch': '0.8375', 'num_input_tokens_seen': 52623808, 'train_runtime': '6.618e+04', 'train_tokens_per_second': '795.2'}
84%|████████▎ | 15650/18688 [18:23:00<3:43:48, 4.42s/it] 84%|████████▎ | 15651/18688 [18:23:04<3:42:11, 4.39s/it] 84%|████████▍ | 15652/18688 [18:23:08<3:36:39, 4.28s/it] 84%|████████▍ | 15653/18688 [18:23:13<3:35:01, 4.25s/it] 84%|████████▍ | 15654/18688 [18:23:16<3:27:04, 4.09s/it] 84%|████████▍ | 15655/18688 [18:23:20<3:23:26, 4.02s/it] 84%|████████▍ | 15656/18688 [18:23:24<3:23:04, 4.02s/it] 84%|████████▍ | 15657/18688 [18:23:29<3:42:09, 4.40s/it] 84%|████████▍ | 15658/18688 [18:23:33<3:35:58, 4.28s/it] 84%|████████▍ | 15659/18688 [18:23:38<3:35:37, 4.27s/it] 84%|████████▍ | 15660/18688 [18:23:42<3:38:09, 4.32s/it] {'loss': '0.9204', 'grad_norm': '1.313', 'learning_rate': '5.975e-06', 'epoch': '0.838', 'num_input_tokens_seen': 52657304, 'train_runtime': '6.622e+04', 'train_tokens_per_second': '795.2'}
84%|████████▍ | 15660/18688 [18:23:42<3:38:09, 4.32s/it] 84%|████████▍ | 15661/18688 [18:23:47<3:41:25, 4.39s/it] 84%|████████▍ | 15662/18688 [18:23:51<3:44:44, 4.46s/it] 84%|████████▍ | 15663/18688 [18:23:56<3:46:54, 4.50s/it] 84%|████████▍ | 15664/18688 [18:24:01<3:54:10, 4.65s/it] 84%|████████▍ | 15665/18688 [18:24:05<3:47:44, 4.52s/it] 84%|████████▍ | 15666/18688 [18:24:11<4:04:38, 4.86s/it] 84%|████████▍ | 15667/18688 [18:24:15<3:57:45, 4.72s/it] 84%|████████▍ | 15668/18688 [18:24:20<3:55:53, 4.69s/it] 84%|████████▍ | 15669/18688 [18:24:24<3:49:02, 4.55s/it] 84%|████████▍ | 15670/18688 [18:24:29<3:48:59, 4.55s/it] {'loss': '0.8968', 'grad_norm': '1.202', 'learning_rate': '5.937e-06', 'epoch': '0.8385', 'num_input_tokens_seen': 52696808, 'train_runtime': '6.627e+04', 'train_tokens_per_second': '795.2'}
84%|████████▍ | 15670/18688 [18:24:29<3:48:59, 4.55s/it] 84%|████████▍ | 15671/18688 [18:24:32<3:35:52, 4.29s/it] 84%|████████▍ | 15672/18688 [18:24:36<3:35:20, 4.28s/it] 84%|████████▍ | 15673/18688 [18:24:41<3:34:11, 4.26s/it] 84%|████████▍ | 15674/18688 [18:24:45<3:32:38, 4.23s/it] 84%|████████▍ | 15675/18688 [18:24:49<3:32:48, 4.24s/it] 84%|████████▍ | 15676/18688 [18:24:54<3:35:09, 4.29s/it] 84%|████████▍ | 15677/18688 [18:24:58<3:42:20, 4.43s/it] 84%|████████▍ | 15678/18688 [18:25:03<3:39:49, 4.38s/it] 84%|████████▍ | 15679/18688 [18:25:07<3:48:05, 4.55s/it] 84%|████████▍ | 15680/18688 [18:25:11<3:33:53, 4.27s/it] {'loss': '0.9953', 'grad_norm': '1.679', 'learning_rate': '5.899e-06', 'epoch': '0.8391', 'num_input_tokens_seen': 52731144, 'train_runtime': '6.631e+04', 'train_tokens_per_second': '795.2'}
84%|████████▍ | 15680/18688 [18:25:11<3:33:53, 4.27s/it] 84%|████████▍ | 15681/18688 [18:25:15<3:30:29, 4.20s/it] 84%|████████▍ | 15682/18688 [18:25:20<3:42:54, 4.45s/it] 84%|████████▍ | 15683/18688 [18:25:25<3:43:14, 4.46s/it] 84%|████████▍ | 15684/18688 [18:25:29<3:39:33, 4.39s/it] 84%|████████▍ | 15685/18688 [18:25:33<3:33:57, 4.27s/it] 84%|████████▍ | 15686/18688 [18:25:37<3:30:36, 4.21s/it] 84%|████████▍ | 15687/18688 [18:25:41<3:31:29, 4.23s/it] 84%|████████▍ | 15688/18688 [18:25:46<3:37:11, 4.34s/it] 84%|████████▍ | 15689/18688 [18:25:50<3:37:32, 4.35s/it] 84%|████████▍ | 15690/18688 [18:25:54<3:27:59, 4.16s/it] {'loss': '0.9784', 'grad_norm': '1.474', 'learning_rate': '5.86e-06', 'epoch': '0.8396', 'num_input_tokens_seen': 52764968, 'train_runtime': '6.635e+04', 'train_tokens_per_second': '795.2'}
84%|████████▍ | 15690/18688 [18:25:54<3:27:59, 4.16s/it] 84%|████████▍ | 15691/18688 [18:25:57<3:17:53, 3.96s/it] 84%|████████▍ | 15692/18688 [18:26:01<3:13:32, 3.88s/it] 84%|████████▍ | 15693/18688 [18:26:05<3:19:26, 4.00s/it] 84%|████████▍ | 15694/18688 [18:26:09<3:16:41, 3.94s/it] 84%|████████▍ | 15695/18688 [18:26:13<3:11:09, 3.83s/it] 84%|████████▍ | 15696/18688 [18:26:17<3:10:52, 3.83s/it] 84%|████████▍ | 15697/18688 [18:26:21<3:14:02, 3.89s/it] 84%|████████▍ | 15698/18688 [18:26:25<3:18:16, 3.98s/it] 84%|████████▍ | 15699/18688 [18:26:29<3:28:02, 4.18s/it] 84%|████████▍ | 15700/18688 [18:26:33<3:22:57, 4.08s/it] {'loss': '0.885', 'grad_norm': '1.361', 'learning_rate': '5.822e-06', 'epoch': '0.8401', 'num_input_tokens_seen': 52795136, 'train_runtime': '6.639e+04', 'train_tokens_per_second': '795.2'}
84%|████████▍ | 15700/18688 [18:26:33<3:22:57, 4.08s/it] 84%|████████▍ | 15701/18688 [18:26:37<3:21:26, 4.05s/it] 84%|████████▍ | 15702/18688 [18:26:42<3:33:55, 4.30s/it] 84%|████████▍ | 15703/18688 [18:26:46<3:24:22, 4.11s/it] 84%|████████▍ | 15704/18688 [18:26:50<3:22:31, 4.07s/it] 84%|████████▍ | 15705/18688 [18:26:54<3:23:15, 4.09s/it] 84%|████████▍ | 15706/18688 [18:26:58<3:16:22, 3.95s/it] 84%|████████▍ | 15707/18688 [18:27:02<3:18:28, 3.99s/it] 84%|████████▍ | 15708/18688 [18:27:06<3:25:04, 4.13s/it] 84%|████████▍ | 15709/18688 [18:27:10<3:26:01, 4.15s/it] 84%|████████▍ | 15710/18688 [18:27:14<3:21:34, 4.06s/it] {'loss': '0.9405', 'grad_norm': '1.233', 'learning_rate': '5.784e-06', 'epoch': '0.8407', 'num_input_tokens_seen': 52827016, 'train_runtime': '6.643e+04', 'train_tokens_per_second': '795.2'}
84%|████████▍ | 15710/18688 [18:27:14<3:21:34, 4.06s/it] 84%|████████▍ | 15711/18688 [18:27:19<3:30:49, 4.25s/it] 84%|████████▍ | 15712/18688 [18:27:23<3:28:37, 4.21s/it] 84%|████████▍ | 15713/18688 [18:27:26<3:19:01, 4.01s/it] 84%|████████▍ | 15714/18688 [18:27:31<3:21:49, 4.07s/it] 84%|████████▍ | 15715/18688 [18:27:36<3:35:16, 4.34s/it] 84%|████████▍ | 15716/18688 [18:27:40<3:33:24, 4.31s/it] 84%|████████▍ | 15717/18688 [18:27:44<3:22:57, 4.10s/it] 84%|████████▍ | 15718/18688 [18:27:48<3:23:44, 4.12s/it] 84%|████████▍ | 15719/18688 [18:27:52<3:23:01, 4.10s/it] 84%|████████▍ | 15720/18688 [18:27:56<3:22:26, 4.09s/it] {'loss': '0.9153', 'grad_norm': '1.354', 'learning_rate': '5.747e-06', 'epoch': '0.8412', 'num_input_tokens_seen': 52860536, 'train_runtime': '6.648e+04', 'train_tokens_per_second': '795.2'}
84%|████████▍ | 15720/18688 [18:27:56<3:22:26, 4.09s/it] 84%|████████▍ | 15721/18688 [18:27:59<3:13:59, 3.92s/it] 84%|████████▍ | 15722/18688 [18:28:04<3:18:47, 4.02s/it] 84%|████████▍ | 15723/18688 [18:28:07<3:10:21, 3.85s/it] 84%|████████▍ | 15724/18688 [18:28:12<3:22:55, 4.11s/it] 84%|████████▍ | 15725/18688 [18:28:16<3:27:24, 4.20s/it] 84%|████████▍ | 15726/18688 [18:28:20<3:27:19, 4.20s/it] 84%|████████▍ | 15727/18688 [18:28:24<3:20:24, 4.06s/it] 84%|████████▍ | 15728/18688 [18:28:28<3:13:14, 3.92s/it] 84%|████████▍ | 15729/18688 [18:28:33<3:27:48, 4.21s/it] 84%|████████▍ | 15730/18688 [18:28:37<3:30:55, 4.28s/it] {'loss': '0.8342', 'grad_norm': '1.37', 'learning_rate': '5.709e-06', 'epoch': '0.8417', 'num_input_tokens_seen': 52893392, 'train_runtime': '6.652e+04', 'train_tokens_per_second': '795.2'}
84%|████████▍ | 15730/18688 [18:28:37<3:30:55, 4.28s/it] 84%|████████▍ | 15731/18688 [18:28:41<3:28:49, 4.24s/it] 84%|████████▍ | 15732/18688 [18:28:45<3:19:26, 4.05s/it] 84%|████████▍ | 15733/18688 [18:28:49<3:20:49, 4.08s/it] 84%|████████▍ | 15734/18688 [18:28:53<3:21:48, 4.10s/it] 84%|████████▍ | 15735/18688 [18:28:57<3:19:37, 4.06s/it] 84%|████████▍ | 15736/18688 [18:29:01<3:14:40, 3.96s/it] 84%|████████▍ | 15737/18688 [18:29:05<3:15:44, 3.98s/it] 84%|████████▍ | 15738/18688 [18:29:10<3:29:54, 4.27s/it] 84%|████████▍ | 15739/18688 [18:29:14<3:33:27, 4.34s/it] 84%|████████▍ | 15740/18688 [18:29:18<3:22:18, 4.12s/it] {'loss': '0.8845', 'grad_norm': '1.156', 'learning_rate': '5.671e-06', 'epoch': '0.8423', 'num_input_tokens_seen': 52924496, 'train_runtime': '6.656e+04', 'train_tokens_per_second': '795.2'}
84%|████████▍ | 15740/18688 [18:29:18<3:22:18, 4.12s/it] 84%|████████▍ | 15741/18688 [18:29:22<3:24:42, 4.17s/it] 84%|████████▍ | 15742/18688 [18:29:27<3:32:41, 4.33s/it] 84%|████████▍ | 15743/18688 [18:29:31<3:28:08, 4.24s/it] 84%|████████▍ | 15744/18688 [18:29:34<3:17:32, 4.03s/it] 84%|████████▍ | 15745/18688 [18:29:38<3:12:54, 3.93s/it] 84%|████████▍ | 15746/18688 [18:29:42<3:15:09, 3.98s/it] 84%|████████▍ | 15747/18688 [18:29:46<3:13:58, 3.96s/it] 84%|████████▍ | 15748/18688 [18:29:51<3:25:42, 4.20s/it] 84%|████████▍ | 15749/18688 [18:29:55<3:28:23, 4.25s/it] 84%|████████▍ | 15750/18688 [18:29:59<3:23:11, 4.15s/it] {'loss': '0.8826', 'grad_norm': '1.369', 'learning_rate': '5.634e-06', 'epoch': '0.8428', 'num_input_tokens_seen': 52957624, 'train_runtime': '6.66e+04', 'train_tokens_per_second': '795.2'}
84%|████████▍ | 15750/18688 [18:29:59<3:23:11, 4.15s/it] 84%|████████▍ | 15751/18688 [18:30:03<3:25:34, 4.20s/it] 84%|████████▍ | 15752/18688 [18:30:09<3:40:03, 4.50s/it] 84%|████████▍ | 15753/18688 [18:30:12<3:26:37, 4.22s/it] 84%|████████▍ | 15754/18688 [18:30:16<3:20:52, 4.11s/it] 84%|████████▍ | 15755/18688 [18:30:21<3:30:02, 4.30s/it] 84%|████████▍ | 15756/18688 [18:30:26<3:37:01, 4.44s/it] 84%|████████▍ | 15757/18688 [18:30:30<3:35:36, 4.41s/it] 84%|████████▍ | 15758/18688 [18:30:34<3:29:54, 4.30s/it] 84%|████████▍ | 15759/18688 [18:30:38<3:21:29, 4.13s/it] 84%|████████▍ | 15760/18688 [18:30:42<3:24:39, 4.19s/it] {'loss': '0.8899', 'grad_norm': '1.188', 'learning_rate': '5.597e-06', 'epoch': '0.8433', 'num_input_tokens_seen': 52993224, 'train_runtime': '6.664e+04', 'train_tokens_per_second': '795.2'}
84%|████████▍ | 15760/18688 [18:30:42<3:24:39, 4.19s/it] 84%|████████▍ | 15761/18688 [18:30:46<3:27:29, 4.25s/it] 84%|████████▍ | 15762/18688 [18:30:51<3:27:45, 4.26s/it] 84%|████████▍ | 15763/18688 [18:30:55<3:27:50, 4.26s/it] 84%|████████▍ | 15764/18688 [18:30:59<3:17:18, 4.05s/it] 84%|████████▍ | 15765/18688 [18:31:04<3:33:16, 4.38s/it] 84%|████████▍ | 15766/18688 [18:31:08<3:25:17, 4.22s/it] 84%|████████▍ | 15767/18688 [18:31:11<3:16:09, 4.03s/it] 84%|████████▍ | 15768/18688 [18:31:16<3:27:55, 4.27s/it] 84%|████████▍ | 15769/18688 [18:31:21<3:40:57, 4.54s/it] 84%|████████▍ | 15770/18688 [18:31:25<3:32:48, 4.38s/it] {'loss': '0.8921', 'grad_norm': '1.272', 'learning_rate': '5.559e-06', 'epoch': '0.8439', 'num_input_tokens_seen': 53029128, 'train_runtime': '6.669e+04', 'train_tokens_per_second': '795.2'}
84%|████████▍ | 15770/18688 [18:31:25<3:32:48, 4.38s/it] 84%|████████▍ | 15771/18688 [18:31:29<3:23:28, 4.19s/it] 84%|████████▍ | 15772/18688 [18:31:33<3:26:36, 4.25s/it] 84%|████████▍ | 15773/18688 [18:31:38<3:31:53, 4.36s/it] 84%|████████▍ | 15774/18688 [18:31:42<3:23:49, 4.20s/it] 84%|████████▍ | 15775/18688 [18:31:46<3:28:51, 4.30s/it] 84%|████████▍ | 15776/18688 [18:31:50<3:19:55, 4.12s/it] 84%|████████▍ | 15777/18688 [18:31:54<3:21:39, 4.16s/it] 84%|████████▍ | 15778/18688 [18:31:58<3:13:39, 3.99s/it] 84%|████████▍ | 15779/18688 [18:32:02<3:11:21, 3.95s/it] 84%|████████▍ | 15780/18688 [18:32:06<3:19:21, 4.11s/it] {'loss': '0.9251', 'grad_norm': '1.436', 'learning_rate': '5.522e-06', 'epoch': '0.8444', 'num_input_tokens_seen': 53062584, 'train_runtime': '6.673e+04', 'train_tokens_per_second': '795.2'}
84%|████████▍ | 15780/18688 [18:32:06<3:19:21, 4.11s/it] 84%|████████▍ | 15781/18688 [18:32:10<3:21:14, 4.15s/it] 84%|████████▍ | 15782/18688 [18:32:14<3:19:29, 4.12s/it] 84%|████████▍ | 15783/18688 [18:32:18<3:17:40, 4.08s/it] 84%|████████▍ | 15784/18688 [18:32:23<3:18:14, 4.10s/it] 84%|████████▍ | 15785/18688 [18:32:27<3:20:32, 4.14s/it] 84%|████████▍ | 15786/18688 [18:32:31<3:21:30, 4.17s/it] 84%|████████▍ | 15787/18688 [18:32:35<3:24:42, 4.23s/it] 84%|████████▍ | 15788/18688 [18:32:39<3:21:44, 4.17s/it] 84%|████████▍ | 15789/18688 [18:32:44<3:34:05, 4.43s/it] 84%|████████▍ | 15790/18688 [18:32:48<3:21:39, 4.18s/it] {'loss': '0.8747', 'grad_norm': '1.013', 'learning_rate': '5.485e-06', 'epoch': '0.8449', 'num_input_tokens_seen': 53095576, 'train_runtime': '6.677e+04', 'train_tokens_per_second': '795.2'}
84%|████████▍ | 15790/18688 [18:32:48<3:21:39, 4.18s/it] 84%|████████▍ | 15791/18688 [18:32:52<3:12:09, 3.98s/it] 85%|████████▍ | 15792/18688 [18:32:56<3:13:04, 4.00s/it] 85%|████████▍ | 15793/18688 [18:32:59<3:07:31, 3.89s/it] 85%|████████▍ | 15794/18688 [18:33:03<3:10:47, 3.96s/it] 85%|████████▍ | 15795/18688 [18:33:09<3:29:51, 4.35s/it] 85%|████████▍ | 15796/18688 [18:33:13<3:24:15, 4.24s/it] 85%|████████▍ | 15797/18688 [18:33:17<3:26:23, 4.28s/it] 85%|████████▍ | 15798/18688 [18:33:21<3:24:42, 4.25s/it] 85%|████████▍ | 15799/18688 [18:33:25<3:18:16, 4.12s/it] 85%|████████▍ | 15800/18688 [18:33:29<3:23:32, 4.23s/it] {'loss': '0.8401', 'grad_norm': '1.188', 'learning_rate': '5.448e-06', 'epoch': '0.8455', 'num_input_tokens_seen': 53128888, 'train_runtime': '6.681e+04', 'train_tokens_per_second': '795.2'}
85%|████████▍ | 15800/18688 [18:33:29<3:23:32, 4.23s/it] 85%|████████▍ | 15801/18688 [18:33:34<3:30:04, 4.37s/it] 85%|████████▍ | 15802/18688 [18:33:38<3:29:13, 4.35s/it] 85%|████████▍ | 15803/18688 [18:33:42<3:23:18, 4.23s/it] 85%|████████▍ | 15804/18688 [18:33:46<3:15:40, 4.07s/it] 85%|████████▍ | 15805/18688 [18:33:50<3:19:42, 4.16s/it] 85%|████████▍ | 15806/18688 [18:33:55<3:23:30, 4.24s/it] 85%|████████▍ | 15807/18688 [18:33:59<3:16:03, 4.08s/it] 85%|████████▍ | 15808/18688 [18:34:04<3:29:46, 4.37s/it] 85%|████████▍ | 15809/18688 [18:34:08<3:28:03, 4.34s/it] 85%|████████▍ | 15810/18688 [18:34:12<3:24:25, 4.26s/it] {'loss': '0.9094', 'grad_norm': '1.433', 'learning_rate': '5.411e-06', 'epoch': '0.846', 'num_input_tokens_seen': 53163248, 'train_runtime': '6.685e+04', 'train_tokens_per_second': '795.2'}
85%|████████▍ | 15810/18688 [18:34:12<3:24:25, 4.26s/it] 85%|████████▍ | 15811/18688 [18:34:16<3:22:35, 4.23s/it] 85%|████████▍ | 15812/18688 [18:34:20<3:22:22, 4.22s/it] 85%|████████▍ | 15813/18688 [18:34:25<3:21:58, 4.22s/it] 85%|████████▍ | 15814/18688 [18:34:29<3:32:25, 4.43s/it] 85%|████████▍ | 15815/18688 [18:34:34<3:36:45, 4.53s/it] 85%|████████▍ | 15816/18688 [18:34:38<3:27:57, 4.34s/it] 85%|████████▍ | 15817/18688 [18:34:42<3:15:07, 4.08s/it] 85%|████████▍ | 15818/18688 [18:34:46<3:12:21, 4.02s/it] 85%|████████▍ | 15819/18688 [18:34:50<3:15:57, 4.10s/it] 85%|████████▍ | 15820/18688 [18:34:54<3:14:55, 4.08s/it] {'loss': '0.8989', 'grad_norm': '1.274', 'learning_rate': '5.375e-06', 'epoch': '0.8466', 'num_input_tokens_seen': 53196960, 'train_runtime': '6.689e+04', 'train_tokens_per_second': '795.2'}
85%|████████▍ | 15820/18688 [18:34:54<3:14:55, 4.08s/it] 85%|████████▍ | 15821/18688 [18:34:58<3:11:37, 4.01s/it] 85%|████████▍ | 15822/18688 [18:35:01<3:07:47, 3.93s/it] 85%|████████▍ | 15823/18688 [18:35:05<3:05:13, 3.88s/it] 85%|████████▍ | 15824/18688 [18:35:09<3:01:46, 3.81s/it] 85%|████████▍ | 15825/18688 [18:35:13<3:04:27, 3.87s/it] 85%|████████▍ | 15826/18688 [18:35:17<3:04:58, 3.88s/it] 85%|████████▍ | 15827/18688 [18:35:20<3:01:36, 3.81s/it] 85%|████████▍ | 15828/18688 [18:35:24<3:03:30, 3.85s/it] 85%|████████▍ | 15829/18688 [18:35:28<3:03:07, 3.84s/it] 85%|████████▍ | 15830/18688 [18:35:32<3:07:09, 3.93s/it] {'loss': '0.9129', 'grad_norm': '0.985', 'learning_rate': '5.338e-06', 'epoch': '0.8471', 'num_input_tokens_seen': 53225760, 'train_runtime': '6.693e+04', 'train_tokens_per_second': '795.2'}
85%|████████▍ | 15830/18688 [18:35:32<3:07:09, 3.93s/it] 85%|████████▍ | 15831/18688 [18:35:36<3:04:54, 3.88s/it] 85%|████████▍ | 15832/18688 [18:35:40<3:11:31, 4.02s/it] 85%|████████▍ | 15833/18688 [18:35:45<3:20:38, 4.22s/it] 85%|████████▍ | 15834/18688 [18:35:49<3:17:57, 4.16s/it] 85%|████████▍ | 15835/18688 [18:35:54<3:28:29, 4.38s/it] 85%|████████▍ | 15836/18688 [18:35:58<3:25:26, 4.32s/it] 85%|████████▍ | 15837/18688 [18:36:03<3:32:07, 4.46s/it] 85%|████████▍ | 15838/18688 [18:36:07<3:26:44, 4.35s/it] 85%|████████▍ | 15839/18688 [18:36:11<3:24:18, 4.30s/it] 85%|████████▍ | 15840/18688 [18:36:16<3:26:20, 4.35s/it] {'loss': '0.9653', 'grad_norm': '1.65', 'learning_rate': '5.302e-06', 'epoch': '0.8476', 'num_input_tokens_seen': 53261736, 'train_runtime': '6.698e+04', 'train_tokens_per_second': '795.2'}
85%|████████▍ | 15840/18688 [18:36:16<3:26:20, 4.35s/it] 85%|████████▍ | 15841/18688 [18:36:20<3:23:47, 4.29s/it] 85%|████████▍ | 15842/18688 [18:36:24<3:21:15, 4.24s/it] 85%|████████▍ | 15843/18688 [18:36:28<3:12:45, 4.07s/it] 85%|████████▍ | 15844/18688 [18:36:32<3:13:54, 4.09s/it] 85%|████████▍ | 15845/18688 [18:36:36<3:13:57, 4.09s/it] 85%|████████▍ | 15846/18688 [18:36:41<3:24:18, 4.31s/it] 85%|████████▍ | 15847/18688 [18:36:45<3:28:46, 4.41s/it] 85%|████████▍ | 15848/18688 [18:36:49<3:24:51, 4.33s/it] 85%|████████▍ | 15849/18688 [18:36:54<3:23:29, 4.30s/it] 85%|████████▍ | 15850/18688 [18:36:58<3:21:11, 4.25s/it] {'loss': '0.8932', 'grad_norm': '1.383', 'learning_rate': '5.266e-06', 'epoch': '0.8482', 'num_input_tokens_seen': 53296088, 'train_runtime': '6.702e+04', 'train_tokens_per_second': '795.2'}
85%|████████▍ | 15850/18688 [18:36:58<3:21:11, 4.25s/it] 85%|████████▍ | 15851/18688 [18:37:02<3:16:49, 4.16s/it] 85%|████████▍ | 15852/18688 [18:37:07<3:31:04, 4.47s/it] 85%|████████▍ | 15853/18688 [18:37:11<3:20:40, 4.25s/it] 85%|████████▍ | 15854/18688 [18:37:14<3:10:58, 4.04s/it] 85%|████████▍ | 15855/18688 [18:37:19<3:17:30, 4.18s/it] 85%|████████▍ | 15856/18688 [18:37:22<3:07:37, 3.97s/it] 85%|████████▍ | 15857/18688 [18:37:27<3:14:08, 4.11s/it] 85%|████████▍ | 15858/18688 [18:37:31<3:09:46, 4.02s/it] 85%|████████▍ | 15859/18688 [18:37:35<3:21:08, 4.27s/it] 85%|████████▍ | 15860/18688 [18:37:39<3:18:33, 4.21s/it] {'loss': '0.8813', 'grad_norm': '1.374', 'learning_rate': '5.229e-06', 'epoch': '0.8487', 'num_input_tokens_seen': 53329832, 'train_runtime': '6.706e+04', 'train_tokens_per_second': '795.3'}
85%|████████▍ | 15860/18688 [18:37:39<3:18:33, 4.21s/it] 85%|████████▍ | 15861/18688 [18:37:43<3:10:31, 4.04s/it] 85%|████████▍ | 15862/18688 [18:37:48<3:17:52, 4.20s/it] 85%|████████▍ | 15863/18688 [18:37:52<3:16:21, 4.17s/it] 85%|████████▍ | 15864/18688 [18:37:56<3:14:54, 4.14s/it] 85%|████████▍ | 15865/18688 [18:38:00<3:09:15, 4.02s/it] 85%|████████▍ | 15866/18688 [18:38:04<3:13:58, 4.12s/it] 85%|████████▍ | 15867/18688 [18:38:08<3:13:42, 4.12s/it] 85%|████████▍ | 15868/18688 [18:38:12<3:15:34, 4.16s/it] 85%|████████▍ | 15869/18688 [18:38:17<3:17:54, 4.21s/it] 85%|████████▍ | 15870/18688 [18:38:20<3:11:18, 4.07s/it] {'loss': '0.8842', 'grad_norm': '1.304', 'learning_rate': '5.193e-06', 'epoch': '0.8492', 'num_input_tokens_seen': 53362512, 'train_runtime': '6.71e+04', 'train_tokens_per_second': '795.3'}
85%|████████▍ | 15870/18688 [18:38:20<3:11:18, 4.07s/it] 85%|████████▍ | 15871/18688 [18:38:24<3:07:10, 3.99s/it] 85%|████████▍ | 15872/18688 [18:38:29<3:24:06, 4.35s/it] 85%|████████▍ | 15873/18688 [18:38:34<3:25:01, 4.37s/it] 85%|████████▍ | 15874/18688 [18:38:38<3:20:44, 4.28s/it] 85%|████████▍ | 15875/18688 [18:38:42<3:16:00, 4.18s/it] 85%|████████▍ | 15876/18688 [18:38:46<3:14:36, 4.15s/it] 85%|████████▍ | 15877/18688 [18:38:50<3:16:13, 4.19s/it] 85%|████████▍ | 15878/18688 [18:38:55<3:18:14, 4.23s/it] 85%|████████▍ | 15879/18688 [18:38:59<3:14:40, 4.16s/it] 85%|████████▍ | 15880/18688 [18:39:02<3:11:52, 4.10s/it] {'loss': '0.9485', 'grad_norm': '1.097', 'learning_rate': '5.157e-06', 'epoch': '0.8498', 'num_input_tokens_seen': 53397128, 'train_runtime': '6.714e+04', 'train_tokens_per_second': '795.3'}
85%|████████▍ | 15880/18688 [18:39:02<3:11:52, 4.10s/it] 85%|████████▍ | 15881/18688 [18:39:06<3:03:01, 3.91s/it] 85%|████████▍ | 15882/18688 [18:39:10<3:09:37, 4.05s/it] 85%|████████▍ | 15883/18688 [18:39:15<3:12:12, 4.11s/it] 85%|████████▍ | 15884/18688 [18:39:19<3:15:31, 4.18s/it] 85%|████████▌ | 15885/18688 [18:39:23<3:08:17, 4.03s/it] 85%|████████▌ | 15886/18688 [18:39:26<3:04:12, 3.94s/it] 85%|████████▌ | 15887/18688 [18:39:30<2:59:17, 3.84s/it] 85%|████████▌ | 15888/18688 [18:39:34<2:59:36, 3.85s/it] 85%|████████▌ | 15889/18688 [18:39:37<2:54:22, 3.74s/it] 85%|████████▌ | 15890/18688 [18:39:41<2:52:15, 3.69s/it] {'loss': '0.8767', 'grad_norm': '1.401', 'learning_rate': '5.121e-06', 'epoch': '0.8503', 'num_input_tokens_seen': 53425792, 'train_runtime': '6.718e+04', 'train_tokens_per_second': '795.2'}
85%|████████▌ | 15890/18688 [18:39:41<2:52:15, 3.69s/it] 85%|████████▌ | 15891/18688 [18:39:45<2:54:08, 3.74s/it] 85%|████████▌ | 15892/18688 [18:39:48<2:51:44, 3.69s/it] 85%|████████▌ | 15893/18688 [18:39:52<2:58:34, 3.83s/it] 85%|████████▌ | 15894/18688 [18:39:57<3:09:12, 4.06s/it] 85%|████████▌ | 15895/18688 [18:40:01<3:07:00, 4.02s/it] 85%|████████▌ | 15896/18688 [18:40:05<3:10:50, 4.10s/it] 85%|████████▌ | 15897/18688 [18:40:09<3:09:41, 4.08s/it] 85%|████████▌ | 15898/18688 [18:40:13<3:03:35, 3.95s/it] 85%|████████▌ | 15899/18688 [18:40:17<3:04:22, 3.97s/it] 85%|████████▌ | 15900/18688 [18:40:21<3:08:50, 4.06s/it] {'loss': '0.878', 'grad_norm': '1.231', 'learning_rate': '5.086e-06', 'epoch': '0.8508', 'num_input_tokens_seen': 53455944, 'train_runtime': '6.722e+04', 'train_tokens_per_second': '795.2'}
85%|████████▌ | 15900/18688 [18:40:21<3:08:50, 4.06s/it] 85%|████████▌ | 15901/18688 [18:40:25<3:02:56, 3.94s/it] 85%|████████▌ | 15902/18688 [18:40:28<2:57:45, 3.83s/it] 85%|████████▌ | 15903/18688 [18:40:33<3:14:24, 4.19s/it] 85%|████████▌ | 15904/18688 [18:40:38<3:16:23, 4.23s/it] 85%|████████▌ | 15905/18688 [18:40:41<3:08:15, 4.06s/it] 85%|████████▌ | 15906/18688 [18:40:47<3:26:58, 4.46s/it] 85%|████████▌ | 15907/18688 [18:40:51<3:17:41, 4.27s/it] 85%|████████▌ | 15908/18688 [18:40:56<3:31:14, 4.56s/it] 85%|████████▌ | 15909/18688 [18:41:01<3:35:17, 4.65s/it] 85%|████████▌ | 15910/18688 [18:41:04<3:22:06, 4.37s/it] {'loss': '0.8955', 'grad_norm': '1.2', 'learning_rate': '5.05e-06', 'epoch': '0.8514', 'num_input_tokens_seen': 53491384, 'train_runtime': '6.726e+04', 'train_tokens_per_second': '795.2'}
85%|████████▌ | 15910/18688 [18:41:04<3:22:06, 4.37s/it] 85%|████████▌ | 15911/18688 [18:41:09<3:19:19, 4.31s/it] 85%|████████▌ | 15912/18688 [18:41:12<3:12:00, 4.15s/it] 85%|████████▌ | 15913/18688 [18:41:17<3:14:55, 4.21s/it] 85%|████████▌ | 15914/18688 [18:41:21<3:11:28, 4.14s/it] 85%|████████▌ | 15915/18688 [18:41:25<3:14:15, 4.20s/it] 85%|████████▌ | 15916/18688 [18:41:29<3:12:20, 4.16s/it] 85%|████████▌ | 15917/18688 [18:41:33<3:12:19, 4.16s/it] 85%|████████▌ | 15918/18688 [18:41:38<3:23:37, 4.41s/it] 85%|████████▌ | 15919/18688 [18:41:42<3:17:19, 4.28s/it] 85%|████████▌ | 15920/18688 [18:41:47<3:21:35, 4.37s/it] {'loss': '0.9485', 'grad_norm': '1.254', 'learning_rate': '5.015e-06', 'epoch': '0.8519', 'num_input_tokens_seen': 53525408, 'train_runtime': '6.731e+04', 'train_tokens_per_second': '795.2'}
85%|████████▌ | 15920/18688 [18:41:47<3:21:35, 4.37s/it] 85%|████████▌ | 15921/18688 [18:41:51<3:19:51, 4.33s/it] 85%|████████▌ | 15922/18688 [18:41:55<3:10:31, 4.13s/it] 85%|████████▌ | 15923/18688 [18:42:00<3:20:11, 4.34s/it] 85%|████████▌ | 15924/18688 [18:42:04<3:18:03, 4.30s/it] 85%|████████▌ | 15925/18688 [18:42:09<3:35:27, 4.68s/it] 85%|████████▌ | 15926/18688 [18:42:13<3:18:51, 4.32s/it] 85%|████████▌ | 15927/18688 [18:42:18<3:30:43, 4.58s/it] 85%|████████▌ | 15928/18688 [18:42:22<3:27:02, 4.50s/it] 85%|████████▌ | 15929/18688 [18:42:27<3:25:36, 4.47s/it] 85%|████████▌ | 15930/18688 [18:42:31<3:18:12, 4.31s/it] {'loss': '0.9362', 'grad_norm': '1.08', 'learning_rate': '4.979e-06', 'epoch': '0.8524', 'num_input_tokens_seen': 53562496, 'train_runtime': '6.735e+04', 'train_tokens_per_second': '795.3'}
85%|████████▌ | 15930/18688 [18:42:31<3:18:12, 4.31s/it] 85%|████████▌ | 15931/18688 [18:42:36<3:30:13, 4.58s/it] 85%|████████▌ | 15932/18688 [18:42:40<3:29:07, 4.55s/it] 85%|████████▌ | 15933/18688 [18:42:46<3:38:43, 4.76s/it] 85%|████████▌ | 15934/18688 [18:42:50<3:31:42, 4.61s/it] 85%|████████▌ | 15935/18688 [18:42:55<3:43:05, 4.86s/it] 85%|████████▌ | 15936/18688 [18:43:00<3:34:29, 4.68s/it] 85%|████████▌ | 15937/18688 [18:43:04<3:29:11, 4.56s/it] 85%|████████▌ | 15938/18688 [18:43:08<3:26:46, 4.51s/it] 85%|████████▌ | 15939/18688 [18:43:12<3:20:10, 4.37s/it] 85%|████████▌ | 15940/18688 [18:43:16<3:11:56, 4.19s/it] {'loss': '0.861', 'grad_norm': '1.1', 'learning_rate': '4.944e-06', 'epoch': '0.853', 'num_input_tokens_seen': 53601520, 'train_runtime': '6.74e+04', 'train_tokens_per_second': '795.3'}
85%|████████▌ | 15940/18688 [18:43:16<3:11:56, 4.19s/it] 85%|████████▌ | 15941/18688 [18:43:20<3:13:08, 4.22s/it] 85%|████████▌ | 15942/18688 [18:43:24<3:11:04, 4.17s/it] 85%|████████▌ | 15943/18688 [18:43:29<3:12:21, 4.20s/it] 85%|████████▌ | 15944/18688 [18:43:32<3:04:48, 4.04s/it] 85%|████████▌ | 15945/18688 [18:43:37<3:10:38, 4.17s/it] 85%|████████▌ | 15946/18688 [18:43:41<3:08:01, 4.11s/it] 85%|████████▌ | 15947/18688 [18:43:45<3:13:11, 4.23s/it] 85%|████████▌ | 15948/18688 [18:43:50<3:19:27, 4.37s/it] 85%|████████▌ | 15949/18688 [18:43:56<3:42:25, 4.87s/it] 85%|████████▌ | 15950/18688 [18:44:00<3:30:31, 4.61s/it] {'loss': '0.932', 'grad_norm': '1.107', 'learning_rate': '4.909e-06', 'epoch': '0.8535', 'num_input_tokens_seen': 53638512, 'train_runtime': '6.744e+04', 'train_tokens_per_second': '795.3'}
85%|████████▌ | 15950/18688 [18:44:00<3:30:31, 4.61s/it] 85%|████████▌ | 15951/18688 [18:44:05<3:35:43, 4.73s/it] 85%|████████▌ | 15952/18688 [18:44:09<3:27:38, 4.55s/it] 85%|████████▌ | 15953/18688 [18:44:13<3:21:43, 4.43s/it] 85%|████████▌ | 15954/18688 [18:44:17<3:09:26, 4.16s/it] 85%|████████▌ | 15955/18688 [18:44:21<3:06:12, 4.09s/it] 85%|████████▌ | 15956/18688 [18:44:26<3:16:32, 4.32s/it] 85%|████████▌ | 15957/18688 [18:44:30<3:13:50, 4.26s/it] 85%|████████▌ | 15958/18688 [18:44:34<3:06:39, 4.10s/it] 85%|████████▌ | 15959/18688 [18:44:37<3:01:36, 3.99s/it] 85%|████████▌ | 15960/18688 [18:44:42<3:11:24, 4.21s/it] {'loss': '0.9268', 'grad_norm': '1.327', 'learning_rate': '4.874e-06', 'epoch': '0.854', 'num_input_tokens_seen': 53671096, 'train_runtime': '6.748e+04', 'train_tokens_per_second': '795.3'}
85%|████████▌ | 15960/18688 [18:44:42<3:11:24, 4.21s/it] 85%|████████▌ | 15961/18688 [18:44:46<3:11:19, 4.21s/it] 85%|████████▌ | 15962/18688 [18:44:50<3:01:26, 3.99s/it] 85%|████████▌ | 15963/18688 [18:44:54<3:05:49, 4.09s/it] 85%|████████▌ | 15964/18688 [18:44:59<3:14:07, 4.28s/it] 85%|████████▌ | 15965/18688 [18:45:03<3:12:48, 4.25s/it] 85%|████████▌ | 15966/18688 [18:45:07<3:09:33, 4.18s/it] 85%|████████▌ | 15967/18688 [18:45:12<3:24:29, 4.51s/it] 85%|████████▌ | 15968/18688 [18:45:17<3:22:29, 4.47s/it] 85%|████████▌ | 15969/18688 [18:45:21<3:24:18, 4.51s/it] 85%|████████▌ | 15970/18688 [18:45:25<3:12:12, 4.24s/it] {'loss': '0.9203', 'grad_norm': '1.086', 'learning_rate': '4.839e-06', 'epoch': '0.8546', 'num_input_tokens_seen': 53706720, 'train_runtime': '6.753e+04', 'train_tokens_per_second': '795.4'}
85%|████████▌ | 15970/18688 [18:45:25<3:12:12, 4.24s/it] 85%|████████▌ | 15971/18688 [18:45:29<3:09:40, 4.19s/it] 85%|████████▌ | 15972/18688 [18:45:33<3:03:01, 4.04s/it] 85%|████████▌ | 15973/18688 [18:45:38<3:16:53, 4.35s/it] 85%|████████▌ | 15974/18688 [18:45:42<3:16:56, 4.35s/it] 85%|████████▌ | 15975/18688 [18:45:46<3:06:30, 4.12s/it] 85%|████████▌ | 15976/18688 [18:45:50<3:03:52, 4.07s/it] 85%|████████▌ | 15977/18688 [18:45:54<3:12:47, 4.27s/it] 85%|████████▌ | 15978/18688 [18:45:58<3:10:01, 4.21s/it] 86%|████████▌ | 15979/18688 [18:46:03<3:13:12, 4.28s/it] 86%|████████▌ | 15980/18688 [18:46:07<3:13:12, 4.28s/it] {'loss': '0.8772', 'grad_norm': '1.011', 'learning_rate': '4.804e-06', 'epoch': '0.8551', 'num_input_tokens_seen': 53740784, 'train_runtime': '6.757e+04', 'train_tokens_per_second': '795.4'}
86%|████████▌ | 15980/18688 [18:46:07<3:13:12, 4.28s/it] 86%|████████▌ | 15981/18688 [18:46:11<3:07:21, 4.15s/it] 86%|████████▌ | 15982/18688 [18:46:15<3:02:19, 4.04s/it] 86%|████████▌ | 15983/18688 [18:46:19<3:08:46, 4.19s/it] 86%|████████▌ | 15984/18688 [18:46:24<3:22:06, 4.48s/it] 86%|████████▌ | 15985/18688 [18:46:28<3:15:04, 4.33s/it] 86%|████████▌ | 15986/18688 [18:46:33<3:13:51, 4.30s/it] 86%|████████▌ | 15987/18688 [18:46:36<3:06:39, 4.15s/it] 86%|████████▌ | 15988/18688 [18:46:41<3:14:32, 4.32s/it] 86%|████████▌ | 15989/18688 [18:46:45<3:04:47, 4.11s/it] 86%|████████▌ | 15990/18688 [18:46:50<3:13:59, 4.31s/it] {'loss': '0.9354', 'grad_norm': '1.025', 'learning_rate': '4.769e-06', 'epoch': '0.8557', 'num_input_tokens_seen': 53775136, 'train_runtime': '6.761e+04', 'train_tokens_per_second': '795.4'}
86%|████████▌ | 15990/18688 [18:46:50<3:13:59, 4.31s/it] 86%|████████▌ | 15991/18688 [18:46:54<3:17:31, 4.39s/it] 86%|████████▌ | 15992/18688 [18:46:58<3:15:24, 4.35s/it] 86%|████████▌ | 15993/18688 [18:47:02<3:09:18, 4.21s/it] 86%|████████▌ | 15994/18688 [18:47:06<3:01:56, 4.05s/it] 86%|████████▌ | 15995/18688 [18:47:10<3:04:50, 4.12s/it] 86%|████████▌ | 15996/18688 [18:47:14<3:04:50, 4.12s/it] 86%|████████▌ | 15997/18688 [18:47:18<3:02:31, 4.07s/it] 86%|████████▌ | 15998/18688 [18:47:23<3:07:25, 4.18s/it] 86%|████████▌ | 15999/18688 [18:47:26<2:59:29, 4.01s/it] 86%|████████▌ | 16000/18688 [18:47:30<3:01:09, 4.04s/it] {'loss': '0.9011', 'grad_norm': '1.127', 'learning_rate': '4.735e-06', 'epoch': '0.8562', 'num_input_tokens_seen': 53808416, 'train_runtime': '6.765e+04', 'train_tokens_per_second': '795.4'}
86%|████████▌ | 16000/18688 [18:47:30<3:01:09, 4.04s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
0%| | 3/1000 [00:00<00:38, 25.81it/s]
1%| | 6/1000 [00:00<00:54, 18.25it/s]
1%| | 8/1000 [00:00<00:56, 17.43it/s]
1%| | 10/1000 [00:00<00:59, 16.51it/s]
1%| | 12/1000 [00:00<01:00, 16.29it/s]
1%|▏ | 14/1000 [00:00<01:02, 15.85it/s]
2%|▏ | 16/1000 [00:00<01:03, 15.41it/s]
2%|▏ | 18/1000 [00:01<01:40, 9.74it/s]
2%|▏ | 20/1000 [00:01<01:38, 9.98it/s]
2%|▏ | 22/1000 [00:01<01:56, 8.40it/s]
2%|▏ | 24/1000 [00:02<01:44, 9.30it/s]
3%|▎ | 26/1000 [00:02<01:48, 8.97it/s]
3%|▎ | 28/1000 [00:02<01:39, 9.81it/s]
3%|▎ | 30/1000 [00:02<01:32, 10.46it/s]
3%|▎ | 32/1000 [00:02<01:39, 9.75it/s]
3%|▎ | 34/1000 [00:02<01:31, 10.59it/s]
4%|▎ | 36/1000 [00:03<01:36, 10.04it/s]
4%|▍ | 38/1000 [00:03<01:27, 11.01it/s]
4%|▍ | 40/1000 [00:03<01:29, 10.71it/s]
4%|▍ | 42/1000 [00:03<01:47, 8.88it/s]
4%|▍ | 44/1000 [00:03<01:32, 10.38it/s]
5%|▍ | 46/1000 [00:04<01:22, 11.57it/s]
5%|▍ | 48/1000 [00:04<01:30, 10.56it/s]
5%|▌ | 50/1000 [00:04<01:22, 11.55it/s]
5%|▌ | 52/1000 [00:04<01:16, 12.36it/s]
5%|▌ | 54/1000 [00:04<01:12, 13.11it/s]
6%|▌ | 56/1000 [00:04<01:08, 13.79it/s]
6%|▌ | 58/1000 [00:05<01:48, 8.69it/s]
6%|▌ | 60/1000 [00:05<01:36, 9.73it/s]
6%|▌ | 62/1000 [00:05<01:27, 10.70it/s]
6%|▋ | 64/1000 [00:05<01:19, 11.76it/s]
7%|▋ | 66/1000 [00:05<01:16, 12.26it/s]
7%|▋ | 68/1000 [00:05<01:12, 12.85it/s]
7%|▋ | 70/1000 [00:06<01:09, 13.45it/s]
7%|▋ | 72/1000 [00:06<01:06, 13.90it/s]
7%|▋ | 74/1000 [00:06<01:07, 13.69it/s]
8%|▊ | 76/1000 [00:06<01:04, 14.26it/s]
8%|▊ | 78/1000 [00:06<01:03, 14.44it/s]
8%|▊ | 80/1000 [00:06<01:08, 13.41it/s]
8%|▊ | 82/1000 [00:06<01:05, 14.03it/s]
8%|▊ | 84/1000 [00:07<01:03, 14.52it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.65it/s]
9%|▉ | 88/1000 [00:07<01:02, 14.49it/s]
9%|▉ | 90/1000 [00:07<01:02, 14.52it/s]
9%|▉ | 92/1000 [00:07<01:03, 14.19it/s]
9%|▉ | 94/1000 [00:07<01:04, 14.01it/s]
10%|▉ | 96/1000 [00:07<01:03, 14.29it/s]
10%|▉ | 98/1000 [00:08<01:03, 14.20it/s]
10%|█ | 100/1000 [00:08<01:31, 9.79it/s]
10%|█ | 102/1000 [00:08<01:23, 10.76it/s]
10%|█ | 104/1000 [00:08<01:54, 7.80it/s]
11%|█ | 106/1000 [00:09<02:02, 7.31it/s]
11%|█ | 108/1000 [00:09<01:43, 8.65it/s]
11%|█ | 110/1000 [00:09<01:28, 10.10it/s]
11%|█ | 112/1000 [00:09<01:19, 11.18it/s]
11%|█▏ | 114/1000 [00:09<01:14, 11.82it/s]
12%|█▏ | 116/1000 [00:09<01:11, 12.28it/s]
12%|█▏ | 118/1000 [00:10<01:08, 12.91it/s]
12%|█▏ | 120/1000 [00:10<01:12, 12.12it/s]
12%|█▏ | 122/1000 [00:10<01:29, 9.82it/s]
12%|█▏ | 124/1000 [00:10<01:35, 9.21it/s]
13%|█▎ | 126/1000 [00:11<01:25, 10.18it/s]
13%|█▎ | 128/1000 [00:11<01:18, 11.17it/s]
13%|█▎ | 130/1000 [00:11<01:13, 11.89it/s]
13%|█▎ | 132/1000 [00:11<01:07, 12.87it/s]
13%|█▎ | 134/1000 [00:11<01:11, 12.18it/s]
14%|█▎ | 136/1000 [00:11<01:05, 13.11it/s]
14%|█▍ | 138/1000 [00:11<01:05, 13.11it/s]
14%|█▍ | 140/1000 [00:12<01:25, 10.04it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.51it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.54it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.57it/s]
15%|█▍ | 148/1000 [00:12<01:14, 11.48it/s]
15%|█▌ | 150/1000 [00:13<01:07, 12.57it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.11it/s]
15%|█▌ | 154/1000 [00:13<01:03, 13.38it/s]
16%|█▌ | 156/1000 [00:13<01:03, 13.23it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.73it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.61it/s]
16%|█▌ | 162/1000 [00:13<01:02, 13.48it/s]
16%|█▋ | 164/1000 [00:14<01:01, 13.59it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.74it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.23it/s]
17%|█▋ | 170/1000 [00:14<01:19, 10.47it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.44it/s]
17%|█▋ | 174/1000 [00:15<01:30, 9.16it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.17it/s]
18%|█▊ | 178/1000 [00:15<01:35, 8.63it/s]
18%|█▊ | 180/1000 [00:15<01:33, 8.76it/s]
18%|█▊ | 182/1000 [00:16<01:38, 8.32it/s]
18%|█▊ | 184/1000 [00:16<01:28, 9.24it/s]
19%|█▊ | 186/1000 [00:16<01:41, 8.02it/s]
19%|█▉ | 188/1000 [00:16<01:31, 8.91it/s]
19%|█▉ | 190/1000 [00:16<01:23, 9.71it/s]
19%|█▉ | 192/1000 [00:17<01:17, 10.37it/s]
19%|█▉ | 194/1000 [00:17<01:22, 9.80it/s]
20%|█▉ | 196/1000 [00:17<01:35, 8.44it/s]
20%|█▉ | 198/1000 [00:17<01:22, 9.72it/s]
20%|██ | 200/1000 [00:17<01:18, 10.17it/s]
20%|██ | 202/1000 [00:18<01:11, 11.18it/s]
20%|██ | 204/1000 [00:18<01:04, 12.33it/s]
21%|██ | 206/1000 [00:18<01:01, 12.98it/s]
21%|██ | 208/1000 [00:18<00:59, 13.42it/s]
21%|██ | 210/1000 [00:18<00:55, 14.32it/s]
21%|██ | 212/1000 [00:18<00:53, 14.61it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.62it/s]
22%|██▏ | 216/1000 [00:19<00:54, 14.42it/s]
22%|██▏ | 218/1000 [00:19<00:52, 14.80it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.89it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.16it/s]
22%|██▏ | 224/1000 [00:19<01:20, 9.64it/s]
23%|██▎ | 226/1000 [00:20<01:12, 10.64it/s]
23%|██▎ | 228/1000 [00:20<01:06, 11.53it/s]
23%|██▎ | 230/1000 [00:20<01:04, 12.02it/s]
23%|██▎ | 232/1000 [00:20<01:02, 12.37it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.10it/s]
24%|██▎ | 236/1000 [00:20<00:56, 13.60it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.26it/s]
24%|██▍ | 240/1000 [00:21<00:53, 14.23it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.71it/s]
24%|██▍ | 244/1000 [00:21<00:50, 14.93it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.92it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.64it/s]
25%|██▌ | 250/1000 [00:21<00:55, 13.40it/s]
25%|██▌ | 252/1000 [00:22<01:01, 12.14it/s]
25%|██▌ | 254/1000 [00:22<00:56, 13.14it/s]
26%|██▌ | 256/1000 [00:22<00:54, 13.71it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.77it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.90it/s]
26%|██▌ | 262/1000 [00:22<00:56, 13.05it/s]
26%|██▋ | 264/1000 [00:22<00:54, 13.62it/s]
27%|██▋ | 266/1000 [00:23<00:52, 14.01it/s]
27%|██▋ | 268/1000 [00:23<00:50, 14.53it/s]
27%|██▋ | 270/1000 [00:23<00:55, 13.26it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.61it/s]
27%|██▋ | 274/1000 [00:23<01:02, 11.70it/s]
28%|██▊ | 276/1000 [00:23<00:57, 12.69it/s]
28%|██▊ | 278/1000 [00:23<00:55, 12.94it/s]
28%|██▊ | 280/1000 [00:24<00:53, 13.51it/s]
28%|██▊ | 282/1000 [00:24<00:49, 14.47it/s]
28%|██▊ | 284/1000 [00:24<00:49, 14.41it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.69it/s]
29%|██▉ | 288/1000 [00:24<00:47, 15.06it/s]
29%|██▉ | 290/1000 [00:24<00:45, 15.49it/s]
29%|██▉ | 292/1000 [00:24<00:45, 15.45it/s]
29%|██▉ | 294/1000 [00:25<00:45, 15.35it/s]
30%|██▉ | 296/1000 [00:25<00:45, 15.37it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.47it/s]
30%|███ | 300/1000 [00:25<00:46, 15.09it/s]
30%|███ | 302/1000 [00:25<00:46, 15.12it/s]
30%|███ | 304/1000 [00:25<00:50, 13.71it/s]
31%|███ | 306/1000 [00:25<00:48, 14.40it/s]
31%|███ | 308/1000 [00:25<00:48, 14.39it/s]
31%|███ | 310/1000 [00:26<00:47, 14.59it/s]
31%|███ | 312/1000 [00:26<00:47, 14.58it/s]
31%|███▏ | 314/1000 [00:26<00:47, 14.55it/s]
32%|███▏ | 316/1000 [00:26<00:47, 14.28it/s]
32%|███▏ | 318/1000 [00:26<00:46, 14.76it/s]
32%|███▏ | 320/1000 [00:27<01:16, 8.84it/s]
32%|███▏ | 322/1000 [00:27<01:06, 10.15it/s]
32%|███▏ | 324/1000 [00:27<01:01, 11.04it/s]
33%|███▎ | 326/1000 [00:27<01:00, 11.11it/s]
33%|███▎ | 328/1000 [00:27<00:59, 11.25it/s]
33%|███▎ | 330/1000 [00:27<00:54, 12.21it/s]
33%|███▎ | 332/1000 [00:27<00:53, 12.53it/s]
33%|███▎ | 334/1000 [00:28<00:51, 13.05it/s]
34%|███▎ | 336/1000 [00:28<00:49, 13.47it/s]
34%|███▍ | 338/1000 [00:28<01:05, 10.04it/s]
34%|███▍ | 340/1000 [00:28<00:59, 11.10it/s]
34%|███▍ | 342/1000 [00:28<00:53, 12.33it/s]
34%|███▍ | 344/1000 [00:28<00:50, 12.99it/s]
35%|███▍ | 346/1000 [00:29<00:48, 13.56it/s]
35%|███▍ | 348/1000 [00:29<00:46, 13.96it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.58it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.76it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.80it/s]
36%|███▌ | 356/1000 [00:29<00:43, 14.92it/s]
36%|███▌ | 358/1000 [00:29<00:43, 14.62it/s]
36%|███▌ | 360/1000 [00:30<00:42, 14.98it/s]
36%|███▌ | 362/1000 [00:30<00:47, 13.33it/s]
36%|███▋ | 364/1000 [00:30<00:45, 13.84it/s]
37%|███▋ | 366/1000 [00:30<00:53, 11.93it/s]
37%|███▋ | 368/1000 [00:30<00:51, 12.33it/s]
37%|███▋ | 370/1000 [00:30<00:47, 13.16it/s]
37%|███▋ | 372/1000 [00:30<00:46, 13.50it/s]
37%|███▋ | 374/1000 [00:31<00:55, 11.35it/s]
38%|███▊ | 376/1000 [00:31<00:57, 10.93it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.62it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.44it/s]
38%|███▊ | 382/1000 [00:31<00:48, 12.61it/s]
38%|███▊ | 384/1000 [00:31<00:46, 13.35it/s]
39%|███▊ | 386/1000 [00:32<00:44, 13.67it/s]
39%|███▉ | 388/1000 [00:32<00:44, 13.63it/s]
39%|███▉ | 390/1000 [00:32<00:45, 13.48it/s]
39%|███▉ | 392/1000 [00:32<00:46, 13.19it/s]
39%|███▉ | 394/1000 [00:32<00:46, 12.91it/s]
40%|███▉ | 396/1000 [00:32<00:47, 12.66it/s]
40%|███▉ | 398/1000 [00:33<00:51, 11.78it/s]
40%|████ | 400/1000 [00:33<00:50, 11.87it/s]
40%|████ | 402/1000 [00:33<00:49, 12.01it/s]
40%|████ | 404/1000 [00:33<00:49, 12.13it/s]
41%|████ | 406/1000 [00:33<00:46, 12.67it/s]
41%|████ | 408/1000 [00:33<00:52, 11.33it/s]
41%|████ | 410/1000 [00:34<00:59, 9.83it/s]
41%|████ | 412/1000 [00:34<00:53, 10.99it/s]
41%|████▏ | 414/1000 [00:34<01:09, 8.41it/s]
42%|████▏ | 416/1000 [00:35<01:25, 6.87it/s]
42%|████▏ | 418/1000 [00:35<01:11, 8.19it/s]
42%|████▏ | 420/1000 [00:35<01:01, 9.44it/s]
42%|████▏ | 422/1000 [00:35<00:53, 10.78it/s]
42%|████▏ | 424/1000 [00:35<01:15, 7.65it/s]
43%|████▎ | 426/1000 [00:36<01:04, 8.90it/s]
43%|████▎ | 428/1000 [00:36<00:56, 10.06it/s]
43%|████▎ | 430/1000 [00:36<01:02, 9.06it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.03it/s]
43%|████▎ | 434/1000 [00:37<01:08, 8.26it/s]
44%|████▎ | 436/1000 [00:37<01:00, 9.29it/s]
44%|████▍ | 438/1000 [00:37<01:02, 8.97it/s]
44%|████▍ | 440/1000 [00:37<00:59, 9.48it/s]
44%|████▍ | 442/1000 [00:37<00:52, 10.55it/s]
44%|████▍ | 444/1000 [00:37<00:48, 11.41it/s]
45%|████▍ | 446/1000 [00:38<00:45, 12.17it/s]
45%|████▍ | 448/1000 [00:38<00:50, 10.96it/s]
45%|████▌ | 450/1000 [00:38<00:46, 11.77it/s]
45%|████▌ | 452/1000 [00:38<00:42, 12.79it/s]
45%|████▌ | 454/1000 [00:38<00:44, 12.26it/s]
46%|████▌ | 456/1000 [00:38<00:42, 12.73it/s]
46%|████▌ | 458/1000 [00:39<00:39, 13.72it/s]
46%|████▌ | 460/1000 [00:39<00:38, 13.86it/s]
46%|████▌ | 462/1000 [00:39<00:37, 14.46it/s]
46%|████▋ | 464/1000 [00:39<00:35, 14.99it/s]
47%|████▋ | 466/1000 [00:39<00:36, 14.82it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.90it/s]
47%|████▋ | 470/1000 [00:40<00:52, 10.04it/s]
47%|████▋ | 472/1000 [00:40<00:51, 10.21it/s]
47%|████▋ | 474/1000 [00:40<00:45, 11.49it/s]
48%|████▊ | 476/1000 [00:40<00:43, 12.14it/s]
48%|████▊ | 478/1000 [00:40<00:44, 11.74it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.74it/s]
48%|████▊ | 482/1000 [00:40<00:39, 13.19it/s]
48%|████▊ | 484/1000 [00:41<00:37, 13.74it/s]
49%|████▊ | 486/1000 [00:41<00:36, 13.92it/s]
49%|████▉ | 488/1000 [00:41<00:36, 14.14it/s]
49%|████▉ | 490/1000 [00:41<00:46, 10.99it/s]
49%|████▉ | 492/1000 [00:41<00:41, 12.26it/s]
49%|████▉ | 494/1000 [00:41<00:38, 13.27it/s]
50%|████▉ | 496/1000 [00:42<00:35, 14.07it/s]
50%|████▉ | 498/1000 [00:42<00:34, 14.46it/s]
50%|█████ | 500/1000 [00:42<00:43, 11.53it/s]
50%|█████ | 502/1000 [00:42<00:41, 11.98it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.59it/s]
51%|█████ | 506/1000 [00:42<00:38, 12.78it/s]
51%|█████ | 508/1000 [00:42<00:35, 13.72it/s]
51%|█████ | 510/1000 [00:43<00:47, 10.40it/s]
51%|█████ | 512/1000 [00:43<00:43, 11.18it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 12.05it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.61it/s]
52%|█████▏ | 518/1000 [00:43<00:36, 13.11it/s]
52%|█████▏ | 520/1000 [00:43<00:35, 13.48it/s]
52%|█████▏ | 522/1000 [00:44<00:33, 14.17it/s]
52%|█████▏ | 524/1000 [00:44<00:33, 14.19it/s]
53%|█████▎ | 526/1000 [00:44<00:33, 14.36it/s]
53%|█████▎ | 528/1000 [00:44<00:40, 11.58it/s]
53%|█████▎ | 530/1000 [00:44<00:38, 12.25it/s]
53%|█████▎ | 532/1000 [00:44<00:35, 13.05it/s]
53%|█████▎ | 534/1000 [00:45<00:35, 12.99it/s]
54%|█████▎ | 536/1000 [00:45<00:35, 13.04it/s]
54%|█████▍ | 538/1000 [00:45<00:35, 13.14it/s]
54%|█████▍ | 540/1000 [00:45<00:33, 13.70it/s]
54%|█████▍ | 542/1000 [00:45<00:32, 14.01it/s]
54%|█████▍ | 544/1000 [00:45<00:32, 14.10it/s]
55%|█████▍ | 546/1000 [00:45<00:32, 13.90it/s]
55%|█████▍ | 548/1000 [00:46<00:33, 13.68it/s]
55%|█████▌ | 550/1000 [00:46<00:32, 13.96it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.10it/s]
55%|█████▌ | 554/1000 [00:46<00:32, 13.82it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.02it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.35it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 14.83it/s]
56%|█████▌ | 562/1000 [00:47<00:29, 14.76it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.72it/s]
57%|█████▋ | 566/1000 [00:47<00:37, 11.63it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.17it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.25it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.80it/s]
57%|█████▋ | 574/1000 [00:48<00:31, 13.67it/s]
58%|█████▊ | 576/1000 [00:48<00:31, 13.61it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.62it/s]
58%|█████▊ | 580/1000 [00:48<00:32, 12.79it/s]
58%|█████▊ | 582/1000 [00:48<00:30, 13.57it/s]
58%|█████▊ | 584/1000 [00:48<00:30, 13.80it/s]
59%|█████▊ | 586/1000 [00:48<00:29, 14.19it/s]
59%|█████▉ | 588/1000 [00:49<00:27, 14.97it/s]
59%|█████▉ | 590/1000 [00:49<00:27, 14.96it/s]
59%|█████▉ | 592/1000 [00:49<00:27, 14.75it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.30it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.81it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 14.00it/s]
60%|██████ | 600/1000 [00:49<00:28, 14.09it/s]
60%|██████ | 602/1000 [00:50<00:27, 14.30it/s]
60%|██████ | 604/1000 [00:50<00:26, 14.91it/s]
61%|██████ | 606/1000 [00:50<00:26, 14.73it/s]
61%|██████ | 608/1000 [00:50<00:27, 14.30it/s]
61%|██████ | 610/1000 [00:50<00:28, 13.62it/s]
61%|██████ | 612/1000 [00:50<00:29, 13.23it/s]
61%|██████▏ | 614/1000 [00:50<00:29, 12.97it/s]
62%|██████▏ | 616/1000 [00:51<00:29, 12.97it/s]
62%|██████▏ | 618/1000 [00:51<00:29, 12.78it/s]
62%|██████▏ | 620/1000 [00:51<00:41, 9.20it/s]
62%|██████▏ | 622/1000 [00:51<00:39, 9.47it/s]
62%|██████▏ | 624/1000 [00:51<00:35, 10.50it/s]
63%|██████▎ | 626/1000 [00:52<00:32, 11.36it/s]
63%|██████▎ | 628/1000 [00:52<00:29, 12.61it/s]
63%|██████▎ | 630/1000 [00:52<00:27, 13.26it/s]
63%|██████▎ | 632/1000 [00:52<00:30, 12.15it/s]
63%|██████▎ | 634/1000 [00:52<00:28, 12.92it/s]
64%|██████▎ | 636/1000 [00:52<00:33, 10.81it/s]
64%|██████▍ | 638/1000 [00:53<00:34, 10.59it/s]
64%|██████▍ | 640/1000 [00:53<00:31, 11.40it/s]
64%|██████▍ | 642/1000 [00:53<00:29, 12.30it/s]
64%|██████▍ | 644/1000 [00:53<00:28, 12.56it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.85it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.33it/s]
65%|██████▌ | 650/1000 [00:54<00:27, 12.52it/s]
65%|██████▌ | 652/1000 [00:54<00:29, 11.93it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.43it/s]
66%|██████▌ | 656/1000 [00:54<00:31, 10.76it/s]
66%|██████▌ | 658/1000 [00:54<00:28, 11.83it/s]
66%|██████▌ | 660/1000 [00:55<00:33, 10.15it/s]
66%|██████▌ | 662/1000 [00:55<00:29, 11.28it/s]
66%|██████▋ | 664/1000 [00:55<00:28, 11.77it/s]
67%|██████▋ | 666/1000 [00:55<00:26, 12.77it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.32it/s]
67%|██████▋ | 670/1000 [00:55<00:29, 11.10it/s]
67%|██████▋ | 672/1000 [00:55<00:27, 11.86it/s]
67%|██████▋ | 674/1000 [00:56<00:26, 12.45it/s]
68%|██████▊ | 676/1000 [00:56<00:27, 11.70it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.17it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 12.96it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.13it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.32it/s]
69%|██████▊ | 686/1000 [00:57<00:23, 13.38it/s]
69%|██████▉ | 688/1000 [00:57<00:22, 13.70it/s]
69%|██████▉ | 690/1000 [00:57<00:22, 13.77it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.17it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.26it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 14.02it/s]
70%|██████▉ | 698/1000 [00:57<00:20, 14.55it/s]
70%|███████ | 700/1000 [00:58<00:25, 11.56it/s]
70%|███████ | 702/1000 [00:58<00:23, 12.42it/s]
70%|███████ | 704/1000 [00:58<00:32, 9.09it/s]
71%|███████ | 706/1000 [00:58<00:29, 10.00it/s]
71%|███████ | 708/1000 [00:59<00:31, 9.17it/s]
71%|███████ | 710/1000 [00:59<00:27, 10.50it/s]
71%|███████ | 712/1000 [00:59<00:25, 11.51it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.66it/s]
72%|███████▏ | 716/1000 [00:59<00:22, 12.83it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.35it/s]
72%|███████▏ | 720/1000 [00:59<00:21, 13.31it/s]
72%|███████▏ | 722/1000 [01:00<00:20, 13.75it/s]
72%|███████▏ | 724/1000 [01:00<00:20, 13.63it/s]
73%|███████▎ | 726/1000 [01:00<00:19, 13.88it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.83it/s]
73%|███████▎ | 730/1000 [01:00<00:33, 8.16it/s]
73%|███████▎ | 732/1000 [01:01<00:32, 8.29it/s]
73%|███████▎ | 734/1000 [01:01<00:31, 8.55it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.40it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.83it/s]
74%|███████▍ | 739/1000 [01:01<00:23, 10.94it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.64it/s]
74%|███████▍ | 743/1000 [01:02<00:20, 12.27it/s]
74%|███████▍ | 745/1000 [01:02<00:25, 10.17it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.54it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.67it/s]
75%|███████▌ | 751/1000 [01:02<00:21, 11.74it/s]
75%|███████▌ | 753/1000 [01:03<00:19, 12.50it/s]
76%|███████▌ | 755/1000 [01:03<00:18, 13.23it/s]
76%|███████▌ | 757/1000 [01:03<00:23, 10.24it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.27it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.25it/s]
76%|███████▋ | 763/1000 [01:04<00:23, 10.02it/s]
76%|███████▋ | 765/1000 [01:04<00:27, 8.45it/s]
77%|███████▋ | 767/1000 [01:04<00:25, 9.32it/s]
77%|███████▋ | 769/1000 [01:04<00:22, 10.14it/s]
77%|███████▋ | 771/1000 [01:04<00:21, 10.87it/s]
77%|███████▋ | 773/1000 [01:04<00:20, 11.28it/s]
78%|███████▊ | 775/1000 [01:05<00:19, 11.60it/s]
78%|███████▊ | 777/1000 [01:05<00:18, 11.91it/s]
78%|███████▊ | 779/1000 [01:05<00:23, 9.51it/s]
78%|███████▊ | 781/1000 [01:05<00:24, 8.81it/s]
78%|███████▊ | 782/1000 [01:06<00:29, 7.29it/s]
78%|███████▊ | 784/1000 [01:06<00:30, 7.06it/s]
78%|███████▊ | 785/1000 [01:06<00:29, 7.37it/s]
79%|███████▊ | 786/1000 [01:06<00:30, 6.99it/s]
79%|███████▉ | 788/1000 [01:06<00:24, 8.79it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.10it/s]
79%|███████▉ | 792/1000 [01:07<00:18, 11.22it/s]
79%|███████▉ | 794/1000 [01:07<00:17, 11.85it/s]
80%|███████▉ | 796/1000 [01:07<00:16, 12.32it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 13.07it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.29it/s]
80%|████████ | 802/1000 [01:08<00:24, 8.23it/s]
80%|████████ | 804/1000 [01:08<00:23, 8.27it/s]
81%|████████ | 806/1000 [01:08<00:21, 8.95it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.34it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.47it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.70it/s]
81%|████████▏ | 814/1000 [01:09<00:14, 12.58it/s]
82%|████████▏ | 816/1000 [01:09<00:16, 11.01it/s]
82%|████████▏ | 818/1000 [01:09<00:15, 11.97it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.47it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 13.01it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.74it/s]
83%|████████▎ | 826/1000 [01:10<00:12, 14.16it/s]
83%|████████▎ | 828/1000 [01:10<00:11, 14.45it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.90it/s]
83%|████████▎ | 832/1000 [01:10<00:11, 14.29it/s]
83%|████████▎ | 834/1000 [01:10<00:11, 14.01it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.38it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.28it/s]
84%|████████▍ | 840/1000 [01:11<00:11, 14.41it/s]
84%|████████▍ | 842/1000 [01:11<00:10, 14.41it/s]
84%|████████▍ | 844/1000 [01:11<00:11, 13.90it/s]
85%|████████▍ | 846/1000 [01:11<00:11, 13.86it/s]
85%|████████▍ | 848/1000 [01:11<00:11, 13.44it/s]
85%|████████▌ | 850/1000 [01:11<00:11, 13.46it/s]
85%|████████▌ | 852/1000 [01:11<00:11, 12.84it/s]
85%|████████▌ | 854/1000 [01:12<00:11, 13.26it/s]
86%|████████▌ | 856/1000 [01:12<00:10, 13.37it/s]
86%|████████▌ | 858/1000 [01:12<00:10, 13.97it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 14.59it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.03it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 13.67it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.58it/s]
87%|████████▋ | 868/1000 [01:13<00:09, 13.52it/s]
87%|████████▋ | 870/1000 [01:13<00:09, 13.97it/s]
87%|████████▋ | 872/1000 [01:13<00:08, 14.56it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 12.36it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.00it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.86it/s]
88%|████████▊ | 880/1000 [01:14<00:09, 12.51it/s]
88%|████████▊ | 882/1000 [01:14<00:10, 11.35it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 12.08it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.82it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.79it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.23it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.02it/s]
89%|████████▉ | 894/1000 [01:15<00:07, 14.17it/s]
90%|████████▉ | 896/1000 [01:15<00:07, 13.90it/s]
90%|████████▉ | 898/1000 [01:15<00:07, 13.27it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.25it/s]
90%|█████████ | 902/1000 [01:15<00:07, 12.99it/s]
90%|█████████ | 904/1000 [01:16<00:09, 9.80it/s]
91%|█████████ | 906/1000 [01:16<00:08, 10.84it/s]
91%|█████████ | 908/1000 [01:16<00:07, 11.82it/s]
91%|█████████ | 910/1000 [01:16<00:07, 12.08it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.08it/s]
91%|█████████▏| 914/1000 [01:17<00:12, 6.75it/s]
92%|█████████▏| 916/1000 [01:17<00:10, 7.80it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.16it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.31it/s]
92%|█████████▏| 922/1000 [01:18<00:08, 9.34it/s]
92%|█████████▏| 924/1000 [01:18<00:07, 10.22it/s]
93%|█████████▎| 926/1000 [01:18<00:06, 11.21it/s]
93%|█████████▎| 928/1000 [01:18<00:07, 10.28it/s]
93%|█████████▎| 930/1000 [01:18<00:06, 11.50it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 12.13it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.66it/s]
94%|█████████▎| 936/1000 [01:19<00:04, 13.29it/s]
94%|█████████▍| 938/1000 [01:19<00:04, 14.22it/s]
94%|█████████▍| 940/1000 [01:19<00:04, 14.58it/s]
94%|█████████▍| 942/1000 [01:19<00:04, 12.29it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.82it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.82it/s]
95%|█████████▍| 948/1000 [01:20<00:04, 12.63it/s]
95%|█████████▌| 950/1000 [01:20<00:03, 13.42it/s]
95%|█████████▌| 952/1000 [01:20<00:03, 14.14it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 14.23it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 14.21it/s]
96%|█████████▌| 958/1000 [01:20<00:02, 14.72it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 13.09it/s]
96%|█████████▌| 962/1000 [01:21<00:02, 13.73it/s]
96%|█████████▋| 964/1000 [01:21<00:03, 10.22it/s]
97%|█████████▋| 966/1000 [01:21<00:02, 11.43it/s]
97%|█████████▋| 968/1000 [01:21<00:02, 12.09it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 12.71it/s]
97%|█████████▋| 972/1000 [01:22<00:02, 9.68it/s]
97%|█████████▋| 974/1000 [01:22<00:02, 10.87it/s]
98%|█████████▊| 976/1000 [01:22<00:02, 11.67it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.87it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.94it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.43it/s]
98%|█████████▊| 984/1000 [01:23<00:01, 10.07it/s]
99%|█████████▊| 986/1000 [01:23<00:01, 10.20it/s]
99%|█████████▉| 988/1000 [01:23<00:01, 9.94it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 10.71it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 11.71it/s]
99%|█████████▉| 994/1000 [01:24<00:00, 12.51it/s]
100%|█████████▉| 996/1000 [01:24<00:00, 13.11it/s]
100%|█████████▉| 998/1000 [01:24<00:00, 13.50it/s]
100%|██████████| 1000/1000 [01:24<00:00, 14.28it/s]
{'eval_loss': '0.8767', 'eval_runtime': '84.51', 'eval_samples_per_second': '11.83', 'eval_steps_per_second': '11.83', 'epoch': '0.8562', 'num_input_tokens_seen': 53808416}
86%|████████▌ | 16000/18688 [18:48:55<3:01:09, 4.04s/it]
100%|██████████| 1000/1000 [01:24<00:00, 14.28it/s]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00008/checkpoint-16000
loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
86%|████████▌ | 16001/18688 [18:49:00<22:11:42, 29.74s/it] 86%|████████▌ | 16002/18688 [18:49:05<16:31:07, 22.14s/it] 86%|████████▌ | 16003/18688 [18:49:08<12:19:57, 16.54s/it] 86%|████████▌ | 16004/18688 [18:49:12<9:33:28, 12.82s/it] 86%|████████▌ | 16005/18688 [18:49:17<7:49:32, 10.50s/it] 86%|████████▌ | 16006/18688 [18:49:22<6:32:51, 8.79s/it] 86%|████████▌ | 16007/18688 [18:49:26<5:29:14, 7.37s/it] 86%|████████▌ | 16008/18688 [18:49:31<4:57:24, 6.66s/it] 86%|████████▌ | 16009/18688 [18:49:35<4:23:13, 5.90s/it] 86%|████████▌ | 16010/18688 [18:49:39<3:57:16, 5.32s/it] {'loss': '0.9649', 'grad_norm': '1.874', 'learning_rate': '4.7e-06', 'epoch': '0.8567', 'num_input_tokens_seen': 53843200, 'train_runtime': '6.778e+04', 'train_tokens_per_second': '794.4'}
86%|████████▌ | 16010/18688 [18:49:39<3:57:16, 5.32s/it] 86%|████████▌ | 16011/18688 [18:49:44<3:49:01, 5.13s/it] 86%|████████▌ | 16012/18688 [18:49:48<3:34:48, 4.82s/it] 86%|████████▌ | 16013/18688 [18:49:52<3:21:36, 4.52s/it] 86%|████████▌ | 16014/18688 [18:49:56<3:22:36, 4.55s/it] 86%|████████▌ | 16015/18688 [18:50:00<3:09:34, 4.26s/it] 86%|████████▌ | 16016/18688 [18:50:04<3:04:42, 4.15s/it] 86%|████████▌ | 16017/18688 [18:50:07<2:55:38, 3.95s/it] 86%|████████▌ | 16018/18688 [18:50:11<2:52:51, 3.88s/it] 86%|████████▌ | 16019/18688 [18:50:15<2:54:05, 3.91s/it] 86%|████████▌ | 16020/18688 [18:50:20<3:09:53, 4.27s/it] {'loss': '0.8984', 'grad_norm': '1.011', 'learning_rate': '4.666e-06', 'epoch': '0.8573', 'num_input_tokens_seen': 53874888, 'train_runtime': '6.782e+04', 'train_tokens_per_second': '794.4'}
86%|████████▌ | 16020/18688 [18:50:20<3:09:53, 4.27s/it] 86%|████████▌ | 16021/18688 [18:50:24<3:02:29, 4.11s/it] 86%|████████▌ | 16022/18688 [18:50:28<3:03:02, 4.12s/it] 86%|████████▌ | 16023/18688 [18:50:33<3:10:40, 4.29s/it] 86%|████████▌ | 16024/18688 [18:50:37<3:11:45, 4.32s/it] 86%|████████▌ | 16025/18688 [18:50:41<3:05:13, 4.17s/it] 86%|████████▌ | 16026/18688 [18:50:45<3:07:41, 4.23s/it] 86%|████████▌ | 16027/18688 [18:50:49<3:06:45, 4.21s/it] 86%|████████▌ | 16028/18688 [18:50:54<3:05:00, 4.17s/it] 86%|████████▌ | 16029/18688 [18:50:58<3:11:52, 4.33s/it] 86%|████████▌ | 16030/18688 [18:51:02<3:10:01, 4.29s/it] {'loss': '0.9042', 'grad_norm': '1.228', 'learning_rate': '4.632e-06', 'epoch': '0.8578', 'num_input_tokens_seen': 53910296, 'train_runtime': '6.786e+04', 'train_tokens_per_second': '794.4'}
86%|████████▌ | 16030/18688 [18:51:02<3:10:01, 4.29s/it] 86%|████████▌ | 16031/18688 [18:51:06<3:01:24, 4.10s/it] 86%|████████▌ | 16032/18688 [18:51:11<3:14:36, 4.40s/it] 86%|████████▌ | 16033/18688 [18:51:15<3:05:34, 4.19s/it] 86%|████████▌ | 16034/18688 [18:51:19<3:04:06, 4.16s/it] 86%|████████▌ | 16035/18688 [18:51:24<3:16:22, 4.44s/it] 86%|████████▌ | 16036/18688 [18:51:28<3:03:29, 4.15s/it] 86%|████████▌ | 16037/18688 [18:51:32<3:07:04, 4.23s/it] 86%|████████▌ | 16038/18688 [18:51:37<3:11:41, 4.34s/it] 86%|████████▌ | 16039/18688 [18:51:42<3:24:09, 4.62s/it] 86%|████████▌ | 16040/18688 [18:51:46<3:19:15, 4.51s/it] {'loss': '0.8546', 'grad_norm': '1.519', 'learning_rate': '4.598e-06', 'epoch': '0.8583', 'num_input_tokens_seen': 53946480, 'train_runtime': '6.791e+04', 'train_tokens_per_second': '794.4'}
86%|████████▌ | 16040/18688 [18:51:46<3:19:15, 4.51s/it] 86%|████████▌ | 16041/18688 [18:51:51<3:19:46, 4.53s/it] 86%|████████▌ | 16042/18688 [18:51:56<3:27:41, 4.71s/it] 86%|████████▌ | 16043/18688 [18:52:00<3:14:04, 4.40s/it] 86%|████████▌ | 16044/18688 [18:52:04<3:10:10, 4.32s/it] 86%|████████▌ | 16045/18688 [18:52:08<3:12:34, 4.37s/it] 86%|████████▌ | 16046/18688 [18:52:12<3:04:28, 4.19s/it] 86%|████████▌ | 16047/18688 [18:52:16<3:06:44, 4.24s/it] 86%|████████▌ | 16048/18688 [18:52:21<3:10:59, 4.34s/it] 86%|████████▌ | 16049/18688 [18:52:25<3:13:05, 4.39s/it] 86%|████████▌ | 16050/18688 [18:52:29<3:03:10, 4.17s/it] {'loss': '0.9058', 'grad_norm': '1.195', 'learning_rate': '4.564e-06', 'epoch': '0.8589', 'num_input_tokens_seen': 53982744, 'train_runtime': '6.795e+04', 'train_tokens_per_second': '794.5'}
86%|████████▌ | 16050/18688 [18:52:29<3:03:10, 4.17s/it] 86%|████████▌ | 16051/18688 [18:52:33<2:59:36, 4.09s/it] 86%|████████▌ | 16052/18688 [18:52:38<3:16:30, 4.47s/it] 86%|████████▌ | 16053/18688 [18:52:43<3:18:34, 4.52s/it] 86%|████████▌ | 16054/18688 [18:52:47<3:15:11, 4.45s/it] 86%|████████▌ | 16055/18688 [18:52:51<3:13:12, 4.40s/it] 86%|████████▌ | 16056/18688 [18:52:56<3:15:34, 4.46s/it] 86%|████████▌ | 16057/18688 [18:53:00<3:05:34, 4.23s/it] 86%|████████▌ | 16058/18688 [18:53:05<3:17:46, 4.51s/it] 86%|████████▌ | 16059/18688 [18:53:09<3:08:45, 4.31s/it] 86%|████████▌ | 16060/18688 [18:53:13<3:04:51, 4.22s/it] {'loss': '0.916', 'grad_norm': '1.249', 'learning_rate': '4.53e-06', 'epoch': '0.8594', 'num_input_tokens_seen': 54018688, 'train_runtime': '6.799e+04', 'train_tokens_per_second': '794.5'}
86%|████████▌ | 16060/18688 [18:53:13<3:04:51, 4.22s/it] 86%|████████▌ | 16061/18688 [18:53:17<3:05:29, 4.24s/it] 86%|████████▌ | 16062/18688 [18:53:21<2:58:40, 4.08s/it] 86%|████████▌ | 16063/18688 [18:53:25<3:02:20, 4.17s/it] 86%|████████▌ | 16064/18688 [18:53:29<3:00:46, 4.13s/it] 86%|████████▌ | 16065/18688 [18:53:33<3:00:49, 4.14s/it] 86%|████████▌ | 16066/18688 [18:53:37<2:59:15, 4.10s/it] 86%|████████▌ | 16067/18688 [18:53:41<2:55:30, 4.02s/it] 86%|████████▌ | 16068/18688 [18:53:45<2:56:35, 4.04s/it] 86%|████████▌ | 16069/18688 [18:53:49<2:50:17, 3.90s/it] 86%|████████▌ | 16070/18688 [18:53:53<2:52:06, 3.94s/it] {'loss': '0.9329', 'grad_norm': '1.538', 'learning_rate': '4.496e-06', 'epoch': '0.8599', 'num_input_tokens_seen': 54050688, 'train_runtime': '6.803e+04', 'train_tokens_per_second': '794.5'}
86%|████████▌ | 16070/18688 [18:53:53<2:52:06, 3.94s/it] 86%|████████▌ | 16071/18688 [18:53:58<3:01:00, 4.15s/it] 86%|████████▌ | 16072/18688 [18:54:03<3:14:57, 4.47s/it] 86%|████████▌ | 16073/18688 [18:54:07<3:14:26, 4.46s/it] 86%|████████▌ | 16074/18688 [18:54:12<3:22:04, 4.64s/it] 86%|████████▌ | 16075/18688 [18:54:18<3:33:02, 4.89s/it] 86%|████████▌ | 16076/18688 [18:54:22<3:18:46, 4.57s/it] 86%|████████▌ | 16077/18688 [18:54:27<3:29:24, 4.81s/it] 86%|████████▌ | 16078/18688 [18:54:32<3:28:24, 4.79s/it] 86%|████████▌ | 16079/18688 [18:54:36<3:20:29, 4.61s/it] 86%|████████▌ | 16080/18688 [18:54:40<3:14:46, 4.48s/it] {'loss': '0.9291', 'grad_norm': '1.106', 'learning_rate': '4.463e-06', 'epoch': '0.8605', 'num_input_tokens_seen': 54091488, 'train_runtime': '6.808e+04', 'train_tokens_per_second': '794.5'}
86%|████████▌ | 16080/18688 [18:54:40<3:14:46, 4.48s/it] 86%|████████▌ | 16081/18688 [18:54:44<3:09:42, 4.37s/it] 86%|████████▌ | 16082/18688 [18:54:48<3:07:45, 4.32s/it] 86%|████████▌ | 16083/18688 [18:54:52<3:00:00, 4.15s/it] 86%|████████▌ | 16084/18688 [18:54:56<2:55:32, 4.04s/it] 86%|████████▌ | 16085/18688 [18:55:00<2:58:12, 4.11s/it] 86%|████████▌ | 16086/18688 [18:55:05<3:07:49, 4.33s/it] 86%|████████▌ | 16087/18688 [18:55:09<3:07:55, 4.34s/it] 86%|████████▌ | 16088/18688 [18:55:14<3:10:21, 4.39s/it] 86%|████████▌ | 16089/18688 [18:55:18<3:01:34, 4.19s/it] 86%|████████▌ | 16090/18688 [18:55:22<3:02:27, 4.21s/it] {'loss': '0.9673', 'grad_norm': '1.305', 'learning_rate': '4.429e-06', 'epoch': '0.861', 'num_input_tokens_seen': 54126304, 'train_runtime': '6.812e+04', 'train_tokens_per_second': '794.5'}
86%|████████▌ | 16090/18688 [18:55:22<3:02:27, 4.21s/it] 86%|████████▌ | 16091/18688 [18:55:27<3:08:25, 4.35s/it] 86%|████████▌ | 16092/18688 [18:55:30<2:56:21, 4.08s/it] 86%|████████▌ | 16093/18688 [18:55:34<2:50:15, 3.94s/it] 86%|████████▌ | 16094/18688 [18:55:37<2:46:41, 3.86s/it] 86%|████████▌ | 16095/18688 [18:55:41<2:47:06, 3.87s/it] 86%|████████▌ | 16096/18688 [18:55:45<2:47:24, 3.88s/it] 86%|████████▌ | 16097/18688 [18:55:50<2:59:03, 4.15s/it] 86%|████████▌ | 16098/18688 [18:55:54<3:04:59, 4.29s/it] 86%|████████▌ | 16099/18688 [18:55:58<2:55:23, 4.06s/it] 86%|████████▌ | 16100/18688 [18:56:03<3:01:25, 4.21s/it] {'loss': '0.8939', 'grad_norm': '1.237', 'learning_rate': '4.396e-06', 'epoch': '0.8615', 'num_input_tokens_seen': 54157960, 'train_runtime': '6.816e+04', 'train_tokens_per_second': '794.5'}
86%|████████▌ | 16100/18688 [18:56:03<3:01:25, 4.21s/it] 86%|████████▌ | 16101/18688 [18:56:06<2:53:42, 4.03s/it] 86%|████████▌ | 16102/18688 [18:56:10<2:47:01, 3.88s/it] 86%|████████▌ | 16103/18688 [18:56:14<2:48:20, 3.91s/it] 86%|████████▌ | 16104/18688 [18:56:18<2:57:29, 4.12s/it] 86%|████████▌ | 16105/18688 [18:56:23<3:00:29, 4.19s/it] 86%|████████▌ | 16106/18688 [18:56:27<3:00:02, 4.18s/it] 86%|████████▌ | 16107/18688 [18:56:31<2:58:53, 4.16s/it] 86%|████████▌ | 16108/18688 [18:56:35<2:57:11, 4.12s/it] 86%|████████▌ | 16109/18688 [18:56:39<2:56:14, 4.10s/it] 86%|████████▌ | 16110/18688 [18:56:43<2:58:27, 4.15s/it] {'loss': '0.972', 'grad_norm': '1.166', 'learning_rate': '4.362e-06', 'epoch': '0.8621', 'num_input_tokens_seen': 54190576, 'train_runtime': '6.82e+04', 'train_tokens_per_second': '794.5'}
86%|████████▌ | 16110/18688 [18:56:43<2:58:27, 4.15s/it] 86%|████████▌ | 16111/18688 [18:56:48<3:10:03, 4.43s/it] 86%|████████▌ | 16112/18688 [18:56:53<3:13:49, 4.51s/it] 86%|████████▌ | 16113/18688 [18:56:57<3:03:12, 4.27s/it] 86%|████████▌ | 16114/18688 [18:57:02<3:10:38, 4.44s/it] 86%|████████▌ | 16115/18688 [18:57:06<3:14:11, 4.53s/it] 86%|████████▌ | 16116/18688 [18:57:10<3:05:46, 4.33s/it] 86%|████████▌ | 16117/18688 [18:57:14<3:00:46, 4.22s/it] 86%|████████▌ | 16118/18688 [18:57:18<3:01:53, 4.25s/it] 86%|████████▋ | 16119/18688 [18:57:22<2:57:23, 4.14s/it] 86%|████████▋ | 16120/18688 [18:57:26<2:49:06, 3.95s/it] {'loss': '0.874', 'grad_norm': '1.381', 'learning_rate': '4.329e-06', 'epoch': '0.8626', 'num_input_tokens_seen': 54224232, 'train_runtime': '6.825e+04', 'train_tokens_per_second': '794.5'}
86%|████████▋ | 16120/18688 [18:57:26<2:49:06, 3.95s/it] 86%|████████▋ | 16121/18688 [18:57:30<2:51:13, 4.00s/it] 86%|████████▋ | 16122/18688 [18:57:34<2:57:23, 4.15s/it] 86%|████████▋ | 16123/18688 [18:57:38<2:54:36, 4.08s/it] 86%|████████▋ | 16124/18688 [18:57:42<2:48:40, 3.95s/it] 86%|████████▋ | 16125/18688 [18:57:46<2:43:11, 3.82s/it] 86%|████████▋ | 16126/18688 [18:57:49<2:42:17, 3.80s/it] 86%|████████▋ | 16127/18688 [18:57:53<2:45:41, 3.88s/it] 86%|████████▋ | 16128/18688 [18:57:58<2:56:26, 4.14s/it] 86%|████████▋ | 16129/18688 [18:58:02<2:55:47, 4.12s/it] 86%|████████▋ | 16130/18688 [18:58:06<2:52:01, 4.03s/it] {'loss': '0.8693', 'grad_norm': '1.276', 'learning_rate': '4.296e-06', 'epoch': '0.8631', 'num_input_tokens_seen': 54255880, 'train_runtime': '6.829e+04', 'train_tokens_per_second': '794.5'}
86%|████████▋ | 16130/18688 [18:58:06<2:52:01, 4.03s/it] 86%|████████▋ | 16131/18688 [18:58:10<2:45:20, 3.88s/it] 86%|████████▋ | 16132/18688 [18:58:14<2:54:19, 4.09s/it] 86%|████████▋ | 16133/18688 [18:58:18<2:50:59, 4.02s/it] 86%|████████▋ | 16134/18688 [18:58:22<2:48:58, 3.97s/it] 86%|████████▋ | 16135/18688 [18:58:26<2:53:58, 4.09s/it] 86%|████████▋ | 16136/18688 [18:58:30<2:52:05, 4.05s/it] 86%|████████▋ | 16137/18688 [18:58:35<2:59:15, 4.22s/it] 86%|████████▋ | 16138/18688 [18:58:40<3:10:22, 4.48s/it] 86%|████████▋ | 16139/18688 [18:58:45<3:16:24, 4.62s/it] 86%|████████▋ | 16140/18688 [18:58:50<3:20:23, 4.72s/it] {'loss': '0.9007', 'grad_norm': '1.257', 'learning_rate': '4.263e-06', 'epoch': '0.8637', 'num_input_tokens_seen': 54291480, 'train_runtime': '6.833e+04', 'train_tokens_per_second': '794.5'}
86%|████████▋ | 16140/18688 [18:58:50<3:20:23, 4.72s/it] 86%|████████▋ | 16141/18688 [18:58:54<3:18:42, 4.68s/it] 86%|████████▋ | 16142/18688 [18:58:58<3:10:06, 4.48s/it] 86%|████████▋ | 16143/18688 [18:59:03<3:12:00, 4.53s/it] 86%|████████▋ | 16144/18688 [18:59:07<3:03:11, 4.32s/it] 86%|████████▋ | 16145/18688 [18:59:11<3:01:01, 4.27s/it] 86%|████████▋ | 16146/18688 [18:59:16<3:06:29, 4.40s/it] 86%|████████▋ | 16147/18688 [18:59:20<3:02:24, 4.31s/it] 86%|████████▋ | 16148/18688 [18:59:24<3:01:13, 4.28s/it] 86%|████████▋ | 16149/18688 [18:59:28<2:51:45, 4.06s/it] 86%|████████▋ | 16150/18688 [18:59:33<3:03:29, 4.34s/it] {'loss': '0.9206', 'grad_norm': '1.242', 'learning_rate': '4.231e-06', 'epoch': '0.8642', 'num_input_tokens_seen': 54325880, 'train_runtime': '6.837e+04', 'train_tokens_per_second': '794.6'}
86%|████████▋ | 16150/18688 [18:59:33<3:03:29, 4.34s/it] 86%|████████▋ | 16151/18688 [18:59:36<2:55:44, 4.16s/it] 86%|████████▋ | 16152/18688 [18:59:40<2:56:42, 4.18s/it] 86%|████████▋ | 16153/18688 [18:59:44<2:54:14, 4.12s/it] 86%|████████▋ | 16154/18688 [18:59:48<2:52:01, 4.07s/it] 86%|████████▋ | 16155/18688 [18:59:52<2:49:59, 4.03s/it] 86%|████████▋ | 16156/18688 [18:59:58<3:05:26, 4.39s/it] 86%|████████▋ | 16157/18688 [19:00:02<3:09:45, 4.50s/it] 86%|████████▋ | 16158/18688 [19:00:07<3:08:52, 4.48s/it] 86%|████████▋ | 16159/18688 [19:00:12<3:20:11, 4.75s/it] 86%|████████▋ | 16160/18688 [19:00:17<3:16:57, 4.67s/it] {'loss': '0.9167', 'grad_norm': '1.209', 'learning_rate': '4.198e-06', 'epoch': '0.8647', 'num_input_tokens_seen': 54363088, 'train_runtime': '6.842e+04', 'train_tokens_per_second': '794.6'}
86%|████████▋ | 16160/18688 [19:00:17<3:16:57, 4.67s/it] 86%|████████▋ | 16161/18688 [19:00:20<3:02:57, 4.34s/it] 86%|████████▋ | 16162/18688 [19:00:24<2:59:43, 4.27s/it] 86%|████████▋ | 16163/18688 [19:00:29<3:11:01, 4.54s/it] 86%|████████▋ | 16164/18688 [19:00:34<3:08:39, 4.48s/it] 86%|████████▋ | 16165/18688 [19:00:38<3:04:43, 4.39s/it] 87%|████████▋ | 16166/18688 [19:00:42<3:01:21, 4.31s/it] 87%|████████▋ | 16167/18688 [19:00:46<2:59:17, 4.27s/it] 87%|████████▋ | 16168/18688 [19:00:50<2:54:01, 4.14s/it] 87%|████████▋ | 16169/18688 [19:00:54<2:48:43, 4.02s/it] 87%|████████▋ | 16170/18688 [19:00:57<2:43:07, 3.89s/it] {'loss': '0.9497', 'grad_norm': '1.474', 'learning_rate': '4.165e-06', 'epoch': '0.8653', 'num_input_tokens_seen': 54395472, 'train_runtime': '6.846e+04', 'train_tokens_per_second': '794.6'}
87%|████████▋ | 16170/18688 [19:00:57<2:43:07, 3.89s/it] 87%|████████▋ | 16171/18688 [19:01:02<2:46:36, 3.97s/it] 87%|████████▋ | 16172/18688 [19:01:06<2:49:07, 4.03s/it] 87%|████████▋ | 16173/18688 [19:01:10<2:45:00, 3.94s/it] 87%|████████▋ | 16174/18688 [19:01:15<3:02:30, 4.36s/it] 87%|████████▋ | 16175/18688 [19:01:19<2:58:53, 4.27s/it] 87%|████████▋ | 16176/18688 [19:01:23<2:51:24, 4.09s/it] 87%|████████▋ | 16177/18688 [19:01:26<2:47:24, 4.00s/it] 87%|████████▋ | 16178/18688 [19:01:31<2:57:01, 4.23s/it] 87%|████████▋ | 16179/18688 [19:01:37<3:19:19, 4.77s/it] 87%|████████▋ | 16180/18688 [19:01:41<3:11:38, 4.58s/it] {'loss': '0.8939', 'grad_norm': '1.25', 'learning_rate': '4.133e-06', 'epoch': '0.8658', 'num_input_tokens_seen': 54432584, 'train_runtime': '6.85e+04', 'train_tokens_per_second': '794.6'}
87%|████████▋ | 16180/18688 [19:01:41<3:11:38, 4.58s/it] 87%|████████▋ | 16181/18688 [19:01:45<2:57:13, 4.24s/it] 87%|████████▋ | 16182/18688 [19:01:50<3:03:35, 4.40s/it] 87%|████████▋ | 16183/18688 [19:01:53<2:55:37, 4.21s/it] 87%|████████▋ | 16184/18688 [19:01:58<3:05:52, 4.45s/it] 87%|████████▋ | 16185/18688 [19:02:02<2:56:42, 4.24s/it] 87%|████████▋ | 16186/18688 [19:02:07<3:11:03, 4.58s/it] 87%|████████▋ | 16187/18688 [19:02:12<3:09:59, 4.56s/it] 87%|████████▋ | 16188/18688 [19:02:16<2:59:02, 4.30s/it] 87%|████████▋ | 16189/18688 [19:02:19<2:52:42, 4.15s/it] 87%|████████▋ | 16190/18688 [19:02:25<3:07:19, 4.50s/it] {'loss': '0.9166', 'grad_norm': '1.461', 'learning_rate': '4.101e-06', 'epoch': '0.8664', 'num_input_tokens_seen': 54467968, 'train_runtime': '6.855e+04', 'train_tokens_per_second': '794.6'}
87%|████████▋ | 16190/18688 [19:02:25<3:07:19, 4.50s/it] 87%|████████▋ | 16191/18688 [19:02:29<2:59:13, 4.31s/it] 87%|████████▋ | 16192/18688 [19:02:33<2:56:30, 4.24s/it] 87%|████████▋ | 16193/18688 [19:02:38<3:08:16, 4.53s/it] 87%|████████▋ | 16194/18688 [19:02:42<3:00:21, 4.34s/it] 87%|████████▋ | 16195/18688 [19:02:45<2:49:50, 4.09s/it] 87%|████████▋ | 16196/18688 [19:02:49<2:45:33, 3.99s/it] 87%|████████▋ | 16197/18688 [19:02:53<2:44:33, 3.96s/it] 87%|████████▋ | 16198/18688 [19:02:57<2:48:46, 4.07s/it] 87%|████████▋ | 16199/18688 [19:03:02<2:54:04, 4.20s/it] 87%|████████▋ | 16200/18688 [19:03:06<2:50:56, 4.12s/it] {'loss': '0.901', 'grad_norm': '1.189', 'learning_rate': '4.068e-06', 'epoch': '0.8669', 'num_input_tokens_seen': 54499824, 'train_runtime': '6.859e+04', 'train_tokens_per_second': '794.6'}
87%|████████▋ | 16200/18688 [19:03:06<2:50:56, 4.12s/it] 87%|████████▋ | 16201/18688 [19:03:09<2:43:37, 3.95s/it] 87%|████████▋ | 16202/18688 [19:03:13<2:41:17, 3.89s/it] 87%|████████▋ | 16203/18688 [19:03:17<2:40:29, 3.87s/it] 87%|████████▋ | 16204/18688 [19:03:22<2:50:12, 4.11s/it] 87%|████████▋ | 16205/18688 [19:03:26<2:51:43, 4.15s/it] 87%|████████▋ | 16206/18688 [19:03:30<2:53:34, 4.20s/it] 87%|████████▋ | 16207/18688 [19:03:34<2:50:18, 4.12s/it] 87%|████████▋ | 16208/18688 [19:03:38<2:52:16, 4.17s/it] 87%|████████▋ | 16209/18688 [19:03:42<2:52:03, 4.16s/it] 87%|████████▋ | 16210/18688 [19:03:46<2:45:31, 4.01s/it] {'loss': '0.933', 'grad_norm': '1.264', 'learning_rate': '4.036e-06', 'epoch': '0.8674', 'num_input_tokens_seen': 54531792, 'train_runtime': '6.863e+04', 'train_tokens_per_second': '794.6'}
87%|████████▋ | 16210/18688 [19:03:46<2:45:31, 4.01s/it] 87%|████████▋ | 16211/18688 [19:03:50<2:47:04, 4.05s/it] 87%|████████▋ | 16212/18688 [19:03:54<2:43:39, 3.97s/it] 87%|████████▋ | 16213/18688 [19:03:58<2:40:44, 3.90s/it] 87%|████████▋ | 16214/18688 [19:04:01<2:37:02, 3.81s/it] 87%|████████▋ | 16215/18688 [19:04:05<2:40:25, 3.89s/it] 87%|████████▋ | 16216/18688 [19:04:09<2:38:27, 3.85s/it] 87%|████████▋ | 16217/18688 [19:04:14<2:48:22, 4.09s/it] 87%|████████▋ | 16218/18688 [19:04:18<2:45:07, 4.01s/it] 87%|████████▋ | 16219/18688 [19:04:22<2:54:26, 4.24s/it] 87%|████████▋ | 16220/18688 [19:04:27<2:55:09, 4.26s/it] {'loss': '0.9031', 'grad_norm': '1.282', 'learning_rate': '4.004e-06', 'epoch': '0.868', 'num_input_tokens_seen': 54562368, 'train_runtime': '6.867e+04', 'train_tokens_per_second': '794.6'}
87%|████████▋ | 16220/18688 [19:04:27<2:55:09, 4.26s/it] 87%|████████▋ | 16221/18688 [19:04:31<2:51:31, 4.17s/it] 87%|████████▋ | 16222/18688 [19:04:34<2:42:04, 3.94s/it] 87%|████████▋ | 16223/18688 [19:04:39<2:49:24, 4.12s/it] 87%|████████▋ | 16224/18688 [19:04:43<2:55:30, 4.27s/it] 87%|████████▋ | 16225/18688 [19:04:47<2:50:39, 4.16s/it] 87%|████████▋ | 16226/18688 [19:04:51<2:47:32, 4.08s/it] 87%|████████▋ | 16227/18688 [19:04:56<2:54:13, 4.25s/it] 87%|████████▋ | 16228/18688 [19:05:00<2:52:58, 4.22s/it] 87%|████████▋ | 16229/18688 [19:05:04<2:48:33, 4.11s/it] 87%|████████▋ | 16230/18688 [19:05:08<2:56:26, 4.31s/it] {'loss': '0.8864', 'grad_norm': '1.342', 'learning_rate': '3.973e-06', 'epoch': '0.8685', 'num_input_tokens_seen': 54595720, 'train_runtime': '6.871e+04', 'train_tokens_per_second': '794.6'}
87%|████████▋ | 16230/18688 [19:05:08<2:56:26, 4.31s/it] 87%|████████▋ | 16231/18688 [19:05:13<2:56:20, 4.31s/it] 87%|████████▋ | 16232/18688 [19:05:16<2:47:31, 4.09s/it] 87%|████████▋ | 16233/18688 [19:05:20<2:41:21, 3.94s/it] 87%|████████▋ | 16234/18688 [19:05:24<2:43:37, 4.00s/it] 87%|████████▋ | 16235/18688 [19:05:28<2:38:58, 3.89s/it] 87%|████████▋ | 16236/18688 [19:05:31<2:34:27, 3.78s/it] 87%|████████▋ | 16237/18688 [19:05:35<2:37:18, 3.85s/it] 87%|████████▋ | 16238/18688 [19:05:40<2:42:32, 3.98s/it] 87%|████████▋ | 16239/18688 [19:05:43<2:41:22, 3.95s/it] 87%|████████▋ | 16240/18688 [19:05:48<2:43:07, 4.00s/it] {'loss': '0.8707', 'grad_norm': '1.08', 'learning_rate': '3.941e-06', 'epoch': '0.869', 'num_input_tokens_seen': 54626424, 'train_runtime': '6.875e+04', 'train_tokens_per_second': '794.6'}
87%|████████▋ | 16240/18688 [19:05:48<2:43:07, 4.00s/it] 87%|████████▋ | 16241/18688 [19:05:52<2:48:04, 4.12s/it] 87%|████████▋ | 16242/18688 [19:05:57<2:56:58, 4.34s/it] 87%|████████▋ | 16243/18688 [19:06:00<2:46:46, 4.09s/it] 87%|████████▋ | 16244/18688 [19:06:04<2:43:02, 4.00s/it] 87%|████████▋ | 16245/18688 [19:06:08<2:45:55, 4.08s/it] 87%|████████▋ | 16246/18688 [19:06:13<2:52:10, 4.23s/it] 87%|████████▋ | 16247/18688 [19:06:17<2:52:31, 4.24s/it] 87%|████████▋ | 16248/18688 [19:06:21<2:47:29, 4.12s/it] 87%|████████▋ | 16249/18688 [19:06:25<2:45:08, 4.06s/it] 87%|████████▋ | 16250/18688 [19:06:29<2:45:28, 4.07s/it] {'loss': '0.8842', 'grad_norm': '1.15', 'learning_rate': '3.909e-06', 'epoch': '0.8696', 'num_input_tokens_seen': 54660032, 'train_runtime': '6.879e+04', 'train_tokens_per_second': '794.6'}
87%|████████▋ | 16250/18688 [19:06:29<2:45:28, 4.07s/it] 87%|████████▋ | 16251/18688 [19:06:33<2:44:35, 4.05s/it] 87%|████████▋ | 16252/18688 [19:06:37<2:45:08, 4.07s/it] 87%|████████▋ | 16253/18688 [19:06:41<2:46:18, 4.10s/it] 87%|████████▋ | 16254/18688 [19:06:46<2:49:17, 4.17s/it] 87%|████████▋ | 16255/18688 [19:06:50<2:52:00, 4.24s/it] 87%|████████▋ | 16256/18688 [19:06:54<2:46:30, 4.11s/it] 87%|████████▋ | 16257/18688 [19:06:58<2:48:50, 4.17s/it] 87%|████████▋ | 16258/18688 [19:07:03<2:50:34, 4.21s/it] 87%|████████▋ | 16259/18688 [19:07:07<2:51:46, 4.24s/it] 87%|████████▋ | 16260/18688 [19:07:11<2:51:55, 4.25s/it] {'loss': '0.8958', 'grad_norm': '1.132', 'learning_rate': '3.878e-06', 'epoch': '0.8701', 'num_input_tokens_seen': 54694552, 'train_runtime': '6.883e+04', 'train_tokens_per_second': '794.6'}
87%|████████▋ | 16260/18688 [19:07:11<2:51:55, 4.25s/it] 87%|████████▋ | 16261/18688 [19:07:15<2:47:56, 4.15s/it] 87%|████████▋ | 16262/18688 [19:07:20<2:55:27, 4.34s/it] 87%|████████▋ | 16263/18688 [19:07:25<3:01:54, 4.50s/it] 87%|████████▋ | 16264/18688 [19:07:29<3:00:21, 4.46s/it] 87%|████████▋ | 16265/18688 [19:07:33<2:53:55, 4.31s/it] 87%|████████▋ | 16266/18688 [19:07:38<2:57:35, 4.40s/it] 87%|████████▋ | 16267/18688 [19:07:42<2:51:28, 4.25s/it] 87%|████████▋ | 16268/18688 [19:07:46<2:51:09, 4.24s/it] 87%|████████▋ | 16269/18688 [19:07:50<2:49:02, 4.19s/it] 87%|████████▋ | 16270/18688 [19:07:54<2:53:17, 4.30s/it] {'loss': '0.893', 'grad_norm': '1.523', 'learning_rate': '3.846e-06', 'epoch': '0.8706', 'num_input_tokens_seen': 54730696, 'train_runtime': '6.887e+04', 'train_tokens_per_second': '794.6'}
87%|████████▋ | 16270/18688 [19:07:54<2:53:17, 4.30s/it] 87%|████████▋ | 16271/18688 [19:07:59<2:58:32, 4.43s/it] 87%|████████▋ | 16272/18688 [19:08:03<2:51:47, 4.27s/it] 87%|████████▋ | 16273/18688 [19:08:08<2:58:52, 4.44s/it] 87%|████████▋ | 16274/18688 [19:08:12<2:53:57, 4.32s/it] 87%|████████▋ | 16275/18688 [19:08:16<2:53:21, 4.31s/it] 87%|████████▋ | 16276/18688 [19:08:20<2:45:44, 4.12s/it] 87%|████████▋ | 16277/18688 [19:08:25<2:52:51, 4.30s/it] 87%|████████▋ | 16278/18688 [19:08:29<2:53:10, 4.31s/it] 87%|████████▋ | 16279/18688 [19:08:33<2:51:19, 4.27s/it] 87%|████████▋ | 16280/18688 [19:08:37<2:45:57, 4.14s/it] {'loss': '0.9094', 'grad_norm': '1.16', 'learning_rate': '3.815e-06', 'epoch': '0.8712', 'num_input_tokens_seen': 54765720, 'train_runtime': '6.892e+04', 'train_tokens_per_second': '794.7'}
87%|████████▋ | 16280/18688 [19:08:37<2:45:57, 4.14s/it] 87%|████████▋ | 16281/18688 [19:08:41<2:42:43, 4.06s/it] 87%|████████▋ | 16282/18688 [19:08:45<2:38:56, 3.96s/it] 87%|████████▋ | 16283/18688 [19:08:49<2:41:27, 4.03s/it] 87%|████████▋ | 16284/18688 [19:08:53<2:42:10, 4.05s/it] 87%|████████▋ | 16285/18688 [19:08:57<2:40:02, 4.00s/it] 87%|████████▋ | 16286/18688 [19:09:01<2:48:09, 4.20s/it] 87%|████████▋ | 16287/18688 [19:09:06<2:50:54, 4.27s/it] 87%|████████▋ | 16288/18688 [19:09:10<2:51:23, 4.28s/it] 87%|████████▋ | 16289/18688 [19:09:15<2:55:38, 4.39s/it] 87%|████████▋ | 16290/18688 [19:09:19<2:50:29, 4.27s/it] {'loss': '0.9391', 'grad_norm': '1.537', 'learning_rate': '3.784e-06', 'epoch': '0.8717', 'num_input_tokens_seen': 54797360, 'train_runtime': '6.896e+04', 'train_tokens_per_second': '794.6'}
87%|████████▋ | 16290/18688 [19:09:19<2:50:29, 4.27s/it] 87%|████████▋ | 16291/18688 [19:09:23<2:54:07, 4.36s/it] 87%|████████▋ | 16292/18688 [19:09:29<3:08:32, 4.72s/it] 87%|████████▋ | 16293/18688 [19:09:34<3:13:57, 4.86s/it] 87%|████████▋ | 16294/18688 [19:09:39<3:10:44, 4.78s/it] 87%|████████▋ | 16295/18688 [19:09:43<3:03:52, 4.61s/it] 87%|████████▋ | 16296/18688 [19:09:47<3:01:17, 4.55s/it] 87%|████████▋ | 16297/18688 [19:09:52<3:01:06, 4.54s/it] 87%|████████▋ | 16298/18688 [19:09:55<2:50:15, 4.27s/it] 87%|████████▋ | 16299/18688 [19:09:59<2:43:04, 4.10s/it] 87%|████████▋ | 16300/18688 [19:10:03<2:45:50, 4.17s/it] {'loss': '0.8897', 'grad_norm': '1.244', 'learning_rate': '3.753e-06', 'epoch': '0.8722', 'num_input_tokens_seen': 54835080, 'train_runtime': '6.9e+04', 'train_tokens_per_second': '794.7'}
87%|████████▋ | 16300/18688 [19:10:03<2:45:50, 4.17s/it] 87%|████████▋ | 16301/18688 [19:10:08<2:49:59, 4.27s/it] 87%|████████▋ | 16302/18688 [19:10:12<2:44:51, 4.15s/it] 87%|████████▋ | 16303/18688 [19:10:17<2:56:10, 4.43s/it] 87%|████████▋ | 16304/18688 [19:10:21<2:56:58, 4.45s/it] 87%|████████▋ | 16305/18688 [19:10:25<2:48:29, 4.24s/it] 87%|████████▋ | 16306/18688 [19:10:29<2:40:59, 4.06s/it] 87%|████████▋ | 16307/18688 [19:10:32<2:36:12, 3.94s/it] 87%|████████▋ | 16308/18688 [19:10:36<2:34:43, 3.90s/it] 87%|████████▋ | 16309/18688 [19:10:41<2:41:24, 4.07s/it] 87%|████████▋ | 16310/18688 [19:10:45<2:43:43, 4.13s/it] {'loss': '0.9294', 'grad_norm': '1.077', 'learning_rate': '3.722e-06', 'epoch': '0.8728', 'num_input_tokens_seen': 54868472, 'train_runtime': '6.905e+04', 'train_tokens_per_second': '794.7'}
87%|████████▋ | 16310/18688 [19:10:45<2:43:43, 4.13s/it] 87%|████████▋ | 16311/18688 [19:10:49<2:45:00, 4.17s/it] 87%|████████▋ | 16312/18688 [19:10:53<2:42:42, 4.11s/it] 87%|████████▋ | 16313/18688 [19:10:58<2:49:50, 4.29s/it] 87%|████████▋ | 16314/18688 [19:11:02<2:47:57, 4.25s/it] 87%|████████▋ | 16315/18688 [19:11:06<2:47:26, 4.23s/it] 87%|████████▋ | 16316/18688 [19:11:11<2:51:43, 4.34s/it] 87%|████████▋ | 16317/18688 [19:11:16<3:00:07, 4.56s/it] 87%|████████▋ | 16318/18688 [19:11:20<2:56:28, 4.47s/it] 87%|████████▋ | 16319/18688 [19:11:24<2:50:52, 4.33s/it] 87%|████████▋ | 16320/18688 [19:11:29<2:52:49, 4.38s/it] {'loss': '0.9593', 'grad_norm': '1.18', 'learning_rate': '3.692e-06', 'epoch': '0.8733', 'num_input_tokens_seen': 54904520, 'train_runtime': '6.909e+04', 'train_tokens_per_second': '794.7'}
87%|████████▋ | 16320/18688 [19:11:29<2:52:49, 4.38s/it] 87%|████████▋ | 16321/18688 [19:11:33<2:52:06, 4.36s/it] 87%|████████▋ | 16322/18688 [19:11:37<2:51:54, 4.36s/it] 87%|████████▋ | 16323/18688 [19:11:41<2:47:08, 4.24s/it] 87%|████████▋ | 16324/18688 [19:11:46<2:50:32, 4.33s/it] 87%|████████▋ | 16325/18688 [19:11:50<2:48:05, 4.27s/it] 87%|████████▋ | 16326/18688 [19:11:54<2:42:51, 4.14s/it] 87%|████████▋ | 16327/18688 [19:11:58<2:42:30, 4.13s/it] 87%|████████▋ | 16328/18688 [19:12:02<2:46:12, 4.23s/it] 87%|████████▋ | 16329/18688 [19:12:07<2:54:10, 4.43s/it] 87%|████████▋ | 16330/18688 [19:12:11<2:49:01, 4.30s/it] {'loss': '0.8772', 'grad_norm': '1.419', 'learning_rate': '3.661e-06', 'epoch': '0.8738', 'num_input_tokens_seen': 54938024, 'train_runtime': '6.913e+04', 'train_tokens_per_second': '794.7'}
87%|████████▋ | 16330/18688 [19:12:11<2:49:01, 4.30s/it] 87%|████████▋ | 16331/18688 [19:12:16<2:54:45, 4.45s/it] 87%|████████▋ | 16332/18688 [19:12:20<2:47:54, 4.28s/it] 87%|████████▋ | 16333/18688 [19:12:24<2:44:03, 4.18s/it] 87%|████████▋ | 16334/18688 [19:12:28<2:45:15, 4.21s/it] 87%|████████▋ | 16335/18688 [19:12:33<2:46:34, 4.25s/it] 87%|████████▋ | 16336/18688 [19:12:36<2:37:24, 4.02s/it] 87%|████████▋ | 16337/18688 [19:12:40<2:34:43, 3.95s/it] 87%|████████▋ | 16338/18688 [19:12:44<2:33:34, 3.92s/it] 87%|████████▋ | 16339/18688 [19:12:49<2:47:10, 4.27s/it] 87%|████████▋ | 16340/18688 [19:12:53<2:44:10, 4.20s/it] {'loss': '0.9057', 'grad_norm': '1.337', 'learning_rate': '3.63e-06', 'epoch': '0.8744', 'num_input_tokens_seen': 54970040, 'train_runtime': '6.917e+04', 'train_tokens_per_second': '794.7'}
87%|████████▋ | 16340/18688 [19:12:53<2:44:10, 4.20s/it] 87%|████████▋ | 16341/18688 [19:12:57<2:46:07, 4.25s/it] 87%|████████▋ | 16342/18688 [19:13:02<2:55:52, 4.50s/it] 87%|████████▋ | 16343/18688 [19:13:06<2:52:57, 4.43s/it] 87%|████████▋ | 16344/18688 [19:13:11<2:58:46, 4.58s/it] 87%|████████▋ | 16345/18688 [19:13:16<2:56:15, 4.51s/it] 87%|████████▋ | 16346/18688 [19:13:20<2:54:08, 4.46s/it] 87%|████████▋ | 16347/18688 [19:13:24<2:47:03, 4.28s/it] 87%|████████▋ | 16348/18688 [19:13:28<2:41:29, 4.14s/it] 87%|████████▋ | 16349/18688 [19:13:33<2:50:59, 4.39s/it] 87%|████████▋ | 16350/18688 [19:13:37<2:49:23, 4.35s/it] {'loss': '0.9229', 'grad_norm': '1.157', 'learning_rate': '3.6e-06', 'epoch': '0.8749', 'num_input_tokens_seen': 55006632, 'train_runtime': '6.922e+04', 'train_tokens_per_second': '794.7'}
87%|████████▋ | 16350/18688 [19:13:37<2:49:23, 4.35s/it] 87%|████████▋ | 16351/18688 [19:13:41<2:40:45, 4.13s/it] 88%|████████▊ | 16352/18688 [19:13:45<2:39:57, 4.11s/it] 88%|████████▊ | 16353/18688 [19:13:49<2:40:01, 4.11s/it] 88%|████████▊ | 16354/18688 [19:13:53<2:35:23, 3.99s/it] 88%|████████▊ | 16355/18688 [19:13:56<2:34:05, 3.96s/it] 88%|████████▊ | 16356/18688 [19:14:00<2:31:00, 3.89s/it] 88%|████████▊ | 16357/18688 [19:14:05<2:37:53, 4.06s/it] 88%|████████▊ | 16358/18688 [19:14:09<2:38:03, 4.07s/it] 88%|████████▊ | 16359/18688 [19:14:13<2:38:19, 4.08s/it] 88%|████████▊ | 16360/18688 [19:14:17<2:41:46, 4.17s/it] {'loss': '0.8893', 'grad_norm': '1.158', 'learning_rate': '3.57e-06', 'epoch': '0.8755', 'num_input_tokens_seen': 55037856, 'train_runtime': '6.926e+04', 'train_tokens_per_second': '794.7'}
88%|████████▊ | 16360/18688 [19:14:17<2:41:46, 4.17s/it] 88%|████████▊ | 16361/18688 [19:14:21<2:42:08, 4.18s/it] 88%|████████▊ | 16362/18688 [19:14:25<2:35:38, 4.01s/it] 88%|████████▊ | 16363/18688 [19:14:30<2:43:55, 4.23s/it] 88%|████████▊ | 16364/18688 [19:14:34<2:40:26, 4.14s/it] 88%|████████▊ | 16365/18688 [19:14:38<2:39:05, 4.11s/it] 88%|████████▊ | 16366/18688 [19:14:42<2:42:51, 4.21s/it] 88%|████████▊ | 16367/18688 [19:14:47<2:45:24, 4.28s/it] 88%|████████▊ | 16368/18688 [19:14:51<2:42:02, 4.19s/it] 88%|████████▊ | 16369/18688 [19:14:55<2:43:35, 4.23s/it] 88%|████████▊ | 16370/18688 [19:15:00<2:48:35, 4.36s/it] {'loss': '0.9083', 'grad_norm': '1.133', 'learning_rate': '3.54e-06', 'epoch': '0.876', 'num_input_tokens_seen': 55071192, 'train_runtime': '6.93e+04', 'train_tokens_per_second': '794.7'}
88%|████████▊ | 16370/18688 [19:15:00<2:48:35, 4.36s/it] 88%|████████▊ | 16371/18688 [19:15:03<2:40:00, 4.14s/it] 88%|████████▊ | 16372/18688 [19:15:07<2:32:29, 3.95s/it] 88%|████████▊ | 16373/18688 [19:15:11<2:38:47, 4.12s/it] 88%|████████▊ | 16374/18688 [19:15:16<2:41:37, 4.19s/it] 88%|████████▊ | 16375/18688 [19:15:20<2:43:32, 4.24s/it] 88%|████████▊ | 16376/18688 [19:15:24<2:40:42, 4.17s/it] 88%|████████▊ | 16377/18688 [19:15:28<2:41:13, 4.19s/it] 88%|████████▊ | 16378/18688 [19:15:32<2:42:01, 4.21s/it] 88%|████████▊ | 16379/18688 [19:15:36<2:36:19, 4.06s/it] 88%|████████▊ | 16380/18688 [19:15:40<2:36:36, 4.07s/it] {'loss': '0.9093', 'grad_norm': '1.055', 'learning_rate': '3.51e-06', 'epoch': '0.8765', 'num_input_tokens_seen': 55103616, 'train_runtime': '6.934e+04', 'train_tokens_per_second': '794.7'}
88%|████████▊ | 16380/18688 [19:15:40<2:36:36, 4.07s/it] 88%|████████▊ | 16381/18688 [19:15:46<2:51:42, 4.47s/it] 88%|████████▊ | 16382/18688 [19:15:50<2:51:10, 4.45s/it] 88%|████████▊ | 16383/18688 [19:15:55<2:53:55, 4.53s/it] 88%|████████▊ | 16384/18688 [19:16:00<2:59:03, 4.66s/it] 88%|████████▊ | 16385/18688 [19:16:04<2:58:41, 4.66s/it] 88%|████████▊ | 16386/18688 [19:16:08<2:44:42, 4.29s/it] 88%|████████▊ | 16387/18688 [19:16:12<2:40:02, 4.17s/it] 88%|████████▊ | 16388/18688 [19:16:15<2:34:59, 4.04s/it] 88%|████████▊ | 16389/18688 [19:16:21<2:54:53, 4.56s/it] 88%|████████▊ | 16390/18688 [19:16:26<2:57:48, 4.64s/it] {'loss': '0.8797', 'grad_norm': '1.363', 'learning_rate': '3.48e-06', 'epoch': '0.8771', 'num_input_tokens_seen': 55142560, 'train_runtime': '6.939e+04', 'train_tokens_per_second': '794.7'}
88%|████████▊ | 16390/18688 [19:16:26<2:57:48, 4.64s/it] 88%|████████▊ | 16391/18688 [19:16:30<2:47:35, 4.38s/it] 88%|████████▊ | 16392/18688 [19:16:34<2:45:01, 4.31s/it] 88%|████████▊ | 16393/18688 [19:16:38<2:39:57, 4.18s/it] 88%|████████▊ | 16394/18688 [19:16:41<2:32:26, 3.99s/it] 88%|████████▊ | 16395/18688 [19:16:46<2:38:38, 4.15s/it] 88%|████████▊ | 16396/18688 [19:16:51<2:51:00, 4.48s/it] 88%|████████▊ | 16397/18688 [19:16:55<2:41:21, 4.23s/it] 88%|████████▊ | 16398/18688 [19:16:59<2:41:12, 4.22s/it] 88%|████████▊ | 16399/18688 [19:17:04<2:46:23, 4.36s/it] 88%|████████▊ | 16400/18688 [19:17:08<2:43:08, 4.28s/it] {'loss': '0.857', 'grad_norm': '1.022', 'learning_rate': '3.45e-06', 'epoch': '0.8776', 'num_input_tokens_seen': 55176144, 'train_runtime': '6.943e+04', 'train_tokens_per_second': '794.7'}
88%|████████▊ | 16400/18688 [19:17:08<2:43:08, 4.28s/it] 88%|████████▊ | 16401/18688 [19:17:12<2:38:36, 4.16s/it] 88%|████████▊ | 16402/18688 [19:17:16<2:39:37, 4.19s/it] 88%|████████▊ | 16403/18688 [19:17:20<2:34:24, 4.05s/it] 88%|████████▊ | 16404/18688 [19:17:24<2:33:51, 4.04s/it] 88%|████████▊ | 16405/18688 [19:17:27<2:30:06, 3.95s/it] 88%|████████▊ | 16406/18688 [19:17:32<2:34:02, 4.05s/it] 88%|████████▊ | 16407/18688 [19:17:36<2:42:26, 4.27s/it] 88%|████████▊ | 16408/18688 [19:17:41<2:42:12, 4.27s/it] 88%|████████▊ | 16409/18688 [19:17:44<2:33:51, 4.05s/it] 88%|████████▊ | 16410/18688 [19:17:49<2:40:33, 4.23s/it] {'loss': '0.881', 'grad_norm': '1.216', 'learning_rate': '3.42e-06', 'epoch': '0.8781', 'num_input_tokens_seen': 55208216, 'train_runtime': '6.947e+04', 'train_tokens_per_second': '794.7'}
88%|████████▊ | 16410/18688 [19:17:49<2:40:33, 4.23s/it] 88%|████████▊ | 16411/18688 [19:17:53<2:44:04, 4.32s/it] 88%|████████▊ | 16412/18688 [19:17:57<2:34:26, 4.07s/it] 88%|████████▊ | 16413/18688 [19:18:01<2:28:36, 3.92s/it] 88%|████████▊ | 16414/18688 [19:18:06<2:41:06, 4.25s/it] 88%|████████▊ | 16415/18688 [19:18:09<2:31:30, 4.00s/it] 88%|████████▊ | 16416/18688 [19:18:13<2:27:28, 3.89s/it] 88%|████████▊ | 16417/18688 [19:18:17<2:30:25, 3.97s/it] 88%|████████▊ | 16418/18688 [19:18:21<2:34:50, 4.09s/it] 88%|████████▊ | 16419/18688 [19:18:25<2:28:45, 3.93s/it] 88%|████████▊ | 16420/18688 [19:18:29<2:30:47, 3.99s/it] {'loss': '0.9142', 'grad_norm': '1.473', 'learning_rate': '3.391e-06', 'epoch': '0.8787', 'num_input_tokens_seen': 55239688, 'train_runtime': '6.951e+04', 'train_tokens_per_second': '794.7'}
88%|████████▊ | 16420/18688 [19:18:29<2:30:47, 3.99s/it] 88%|████████▊ | 16421/18688 [19:18:32<2:26:45, 3.88s/it] 88%|████████▊ | 16422/18688 [19:18:37<2:33:27, 4.06s/it] 88%|████████▊ | 16423/18688 [19:18:42<2:41:44, 4.28s/it] 88%|████████▊ | 16424/18688 [19:18:45<2:35:00, 4.11s/it] 88%|████████▊ | 16425/18688 [19:18:49<2:30:47, 4.00s/it] 88%|████████▊ | 16426/18688 [19:18:53<2:30:06, 3.98s/it] 88%|████████▊ | 16427/18688 [19:18:57<2:25:37, 3.86s/it] 88%|████████▊ | 16428/18688 [19:19:00<2:24:42, 3.84s/it] 88%|████████▊ | 16429/18688 [19:19:04<2:24:55, 3.85s/it] 88%|████████▊ | 16430/18688 [19:19:09<2:31:31, 4.03s/it] {'loss': '0.8755', 'grad_norm': '1.255', 'learning_rate': '3.361e-06', 'epoch': '0.8792', 'num_input_tokens_seen': 55271168, 'train_runtime': '6.955e+04', 'train_tokens_per_second': '794.7'}
88%|████████▊ | 16430/18688 [19:19:09<2:31:31, 4.03s/it] 88%|████████▊ | 16431/18688 [19:19:13<2:32:12, 4.05s/it] 88%|████████▊ | 16432/18688 [19:19:17<2:28:17, 3.94s/it] 88%|████████▊ | 16433/18688 [19:19:21<2:35:31, 4.14s/it] 88%|████████▊ | 16434/18688 [19:19:25<2:31:44, 4.04s/it] 88%|████████▊ | 16435/18688 [19:19:29<2:31:05, 4.02s/it] 88%|████████▊ | 16436/18688 [19:19:33<2:33:04, 4.08s/it] 88%|████████▊ | 16437/18688 [19:19:37<2:30:16, 4.01s/it] 88%|████████▊ | 16438/18688 [19:19:41<2:35:05, 4.14s/it] 88%|████████▊ | 16439/18688 [19:19:46<2:42:58, 4.35s/it] 88%|████████▊ | 16440/18688 [19:19:50<2:34:34, 4.13s/it] {'loss': '0.9292', 'grad_norm': '1.439', 'learning_rate': '3.332e-06', 'epoch': '0.8797', 'num_input_tokens_seen': 55304424, 'train_runtime': '6.959e+04', 'train_tokens_per_second': '794.7'}
88%|████████▊ | 16440/18688 [19:19:50<2:34:34, 4.13s/it] 88%|████████▊ | 16441/18688 [19:19:54<2:36:03, 4.17s/it] 88%|████████▊ | 16442/18688 [19:19:59<2:39:49, 4.27s/it] 88%|████████▊ | 16443/18688 [19:20:03<2:35:10, 4.15s/it] 88%|████████▊ | 16444/18688 [19:20:06<2:32:02, 4.07s/it] 88%|████████▊ | 16445/18688 [19:20:11<2:33:53, 4.12s/it] 88%|████████▊ | 16446/18688 [19:20:15<2:31:36, 4.06s/it] 88%|████████▊ | 16447/18688 [19:20:18<2:27:31, 3.95s/it] 88%|████████▊ | 16448/18688 [19:20:22<2:26:07, 3.91s/it] 88%|████████▊ | 16449/18688 [19:20:27<2:35:07, 4.16s/it] 88%|████████▊ | 16450/18688 [19:20:32<2:41:03, 4.32s/it] {'loss': '0.9463', 'grad_norm': '1.206', 'learning_rate': '3.303e-06', 'epoch': '0.8803', 'num_input_tokens_seen': 55338856, 'train_runtime': '6.963e+04', 'train_tokens_per_second': '794.7'}
88%|████████▊ | 16450/18688 [19:20:32<2:41:03, 4.32s/it] 88%|████████▊ | 16451/18688 [19:20:36<2:47:05, 4.48s/it] 88%|████████▊ | 16452/18688 [19:20:40<2:40:39, 4.31s/it] 88%|████████▊ | 16453/18688 [19:20:44<2:38:45, 4.26s/it] 88%|████████▊ | 16454/18688 [19:20:48<2:32:43, 4.10s/it] 88%|████████▊ | 16455/18688 [19:20:53<2:45:59, 4.46s/it] 88%|████████▊ | 16456/18688 [19:20:57<2:35:23, 4.18s/it] 88%|████████▊ | 16457/18688 [19:21:01<2:39:00, 4.28s/it] 88%|████████▊ | 16458/18688 [19:21:05<2:34:48, 4.17s/it] 88%|████████▊ | 16459/18688 [19:21:11<2:53:58, 4.68s/it] 88%|████████▊ | 16460/18688 [19:21:16<2:49:32, 4.57s/it] {'loss': '0.9067', 'grad_norm': '1.396', 'learning_rate': '3.274e-06', 'epoch': '0.8808', 'num_input_tokens_seen': 55375240, 'train_runtime': '6.968e+04', 'train_tokens_per_second': '794.8'}
88%|████████▊ | 16460/18688 [19:21:16<2:49:32, 4.57s/it] 88%|████████▊ | 16461/18688 [19:21:19<2:37:52, 4.25s/it] 88%|████████▊ | 16462/18688 [19:21:23<2:31:08, 4.07s/it] 88%|████████▊ | 16463/18688 [19:21:28<2:39:21, 4.30s/it] 88%|████████▊ | 16464/18688 [19:21:31<2:31:32, 4.09s/it] 88%|████████▊ | 16465/18688 [19:21:36<2:34:08, 4.16s/it] 88%|████████▊ | 16466/18688 [19:21:40<2:36:24, 4.22s/it] 88%|████████▊ | 16467/18688 [19:21:44<2:32:37, 4.12s/it] 88%|████████▊ | 16468/18688 [19:21:48<2:34:31, 4.18s/it] 88%|████████▊ | 16469/18688 [19:21:54<2:54:43, 4.72s/it] 88%|████████▊ | 16470/18688 [19:21:58<2:50:58, 4.62s/it] {'loss': '0.936', 'grad_norm': '1.468', 'learning_rate': '3.245e-06', 'epoch': '0.8813', 'num_input_tokens_seen': 55411336, 'train_runtime': '6.972e+04', 'train_tokens_per_second': '794.8'}
88%|████████▊ | 16470/18688 [19:21:58<2:50:58, 4.62s/it] 88%|████████▊ | 16471/18688 [19:22:03<2:44:35, 4.45s/it] 88%|████████▊ | 16472/18688 [19:22:07<2:40:46, 4.35s/it] 88%|████████▊ | 16473/18688 [19:22:11<2:36:06, 4.23s/it] 88%|████████▊ | 16474/18688 [19:22:14<2:27:59, 4.01s/it] 88%|████████▊ | 16475/18688 [19:22:18<2:32:09, 4.13s/it] 88%|████████▊ | 16476/18688 [19:22:23<2:37:47, 4.28s/it] 88%|████████▊ | 16477/18688 [19:22:27<2:30:40, 4.09s/it] 88%|████████▊ | 16478/18688 [19:22:30<2:25:28, 3.95s/it] 88%|████████▊ | 16479/18688 [19:22:34<2:24:27, 3.92s/it] 88%|████████▊ | 16480/18688 [19:22:39<2:31:01, 4.10s/it] {'loss': '0.9103', 'grad_norm': '1.348', 'learning_rate': '3.216e-06', 'epoch': '0.8819', 'num_input_tokens_seen': 55443536, 'train_runtime': '6.976e+04', 'train_tokens_per_second': '794.8'}
88%|████████▊ | 16480/18688 [19:22:39<2:31:01, 4.10s/it] 88%|████████▊ | 16481/18688 [19:22:44<2:40:00, 4.35s/it] 88%|████████▊ | 16482/18688 [19:22:47<2:31:11, 4.11s/it] 88%|████████▊ | 16483/18688 [19:22:52<2:41:23, 4.39s/it] 88%|████████▊ | 16484/18688 [19:22:56<2:31:32, 4.13s/it] 88%|████████▊ | 16485/18688 [19:22:59<2:26:50, 4.00s/it] 88%|████████▊ | 16486/18688 [19:23:03<2:24:59, 3.95s/it] 88%|████████▊ | 16487/18688 [19:23:08<2:28:13, 4.04s/it] 88%|████████▊ | 16488/18688 [19:23:11<2:26:40, 4.00s/it] 88%|████████▊ | 16489/18688 [19:23:15<2:25:10, 3.96s/it] 88%|████████▊ | 16490/18688 [19:23:20<2:27:32, 4.03s/it] {'loss': '0.8702', 'grad_norm': '1.284', 'learning_rate': '3.188e-06', 'epoch': '0.8824', 'num_input_tokens_seen': 55474704, 'train_runtime': '6.98e+04', 'train_tokens_per_second': '794.8'}
88%|████████▊ | 16490/18688 [19:23:20<2:27:32, 4.03s/it] 88%|████████▊ | 16491/18688 [19:23:24<2:31:02, 4.12s/it] 88%|████████▊ | 16492/18688 [19:23:29<2:45:09, 4.51s/it] 88%|████████▊ | 16493/18688 [19:23:34<2:48:29, 4.61s/it] 88%|████████▊ | 16494/18688 [19:23:38<2:41:56, 4.43s/it] 88%|████████▊ | 16495/18688 [19:23:43<2:47:06, 4.57s/it] 88%|████████▊ | 16496/18688 [19:23:47<2:38:37, 4.34s/it] 88%|████████▊ | 16497/18688 [19:23:52<2:42:03, 4.44s/it] 88%|████████▊ | 16498/18688 [19:23:55<2:32:45, 4.19s/it] 88%|████████▊ | 16499/18688 [19:24:00<2:38:18, 4.34s/it] 88%|████████▊ | 16500/18688 [19:24:05<2:42:09, 4.45s/it] {'loss': '0.9547', 'grad_norm': '0.9884', 'learning_rate': '3.159e-06', 'epoch': '0.8829', 'num_input_tokens_seen': 55512608, 'train_runtime': '6.985e+04', 'train_tokens_per_second': '794.8'}
88%|████████▊ | 16500/18688 [19:24:05<2:42:09, 4.45s/it] 88%|████████▊ | 16501/18688 [19:24:08<2:36:59, 4.31s/it] 88%|████████▊ | 16502/18688 [19:24:13<2:35:40, 4.27s/it] 88%|████████▊ | 16503/18688 [19:24:16<2:30:06, 4.12s/it] 88%|████████▊ | 16504/18688 [19:24:21<2:30:46, 4.14s/it] 88%|████████▊ | 16505/18688 [19:24:24<2:25:36, 4.00s/it] 88%|████████▊ | 16506/18688 [19:24:28<2:23:46, 3.95s/it] 88%|████████▊ | 16507/18688 [19:24:32<2:22:04, 3.91s/it] 88%|████████▊ | 16508/18688 [19:24:36<2:26:19, 4.03s/it] 88%|████████▊ | 16509/18688 [19:24:41<2:35:20, 4.28s/it] 88%|████████▊ | 16510/18688 [19:24:45<2:35:12, 4.28s/it] {'loss': '0.9264', 'grad_norm': '1.3', 'learning_rate': '3.131e-06', 'epoch': '0.8835', 'num_input_tokens_seen': 55544984, 'train_runtime': '6.989e+04', 'train_tokens_per_second': '794.8'}
88%|████████▊ | 16510/18688 [19:24:45<2:35:12, 4.28s/it] 88%|████████▊ | 16511/18688 [19:24:50<2:33:49, 4.24s/it] 88%|████████▊ | 16512/18688 [19:24:54<2:40:32, 4.43s/it] 88%|████████▊ | 16513/18688 [19:24:58<2:31:13, 4.17s/it] 88%|████████▊ | 16514/18688 [19:25:02<2:26:34, 4.05s/it] 88%|████████▊ | 16515/18688 [19:25:05<2:21:44, 3.91s/it] 88%|████████▊ | 16516/18688 [19:25:10<2:27:24, 4.07s/it] 88%|████████▊ | 16517/18688 [19:25:14<2:27:42, 4.08s/it] 88%|████████▊ | 16518/18688 [19:25:17<2:22:13, 3.93s/it] 88%|████████▊ | 16519/18688 [19:25:22<2:23:52, 3.98s/it] 88%|████████▊ | 16520/18688 [19:25:26<2:23:26, 3.97s/it] {'loss': '0.8766', 'grad_norm': '1.499', 'learning_rate': '3.102e-06', 'epoch': '0.884', 'num_input_tokens_seen': 55575064, 'train_runtime': '6.993e+04', 'train_tokens_per_second': '794.8'}
88%|████████▊ | 16520/18688 [19:25:26<2:23:26, 3.97s/it] 88%|████████▊ | 16521/18688 [19:25:29<2:22:48, 3.95s/it] 88%|████████▊ | 16522/18688 [19:25:33<2:23:45, 3.98s/it] 88%|████████▊ | 16523/18688 [19:25:37<2:23:56, 3.99s/it] 88%|████████▊ | 16524/18688 [19:25:42<2:30:34, 4.18s/it] 88%|████████▊ | 16525/18688 [19:25:46<2:27:38, 4.10s/it] 88%|████████▊ | 16526/18688 [19:25:51<2:38:31, 4.40s/it] 88%|████████▊ | 16527/18688 [19:25:56<2:42:08, 4.50s/it] 88%|████████▊ | 16528/18688 [19:26:00<2:43:19, 4.54s/it] 88%|████████▊ | 16529/18688 [19:26:04<2:36:53, 4.36s/it] 88%|████████▊ | 16530/18688 [19:26:08<2:31:32, 4.21s/it] {'loss': '0.9511', 'grad_norm': '1.381', 'learning_rate': '3.074e-06', 'epoch': '0.8845', 'num_input_tokens_seen': 55609112, 'train_runtime': '6.997e+04', 'train_tokens_per_second': '794.8'}
88%|████████▊ | 16530/18688 [19:26:08<2:31:32, 4.21s/it] 88%|████████▊ | 16531/18688 [19:26:12<2:27:31, 4.10s/it] 88%|████████▊ | 16532/18688 [19:26:16<2:28:33, 4.13s/it] 88%|████████▊ | 16533/18688 [19:26:21<2:30:45, 4.20s/it] 88%|████████▊ | 16534/18688 [19:26:24<2:24:50, 4.03s/it] 88%|████████▊ | 16535/18688 [19:26:29<2:27:49, 4.12s/it] 88%|████████▊ | 16536/18688 [19:26:33<2:26:23, 4.08s/it] 88%|████████▊ | 16537/18688 [19:26:39<2:45:46, 4.62s/it] 88%|████████▊ | 16538/18688 [19:26:42<2:37:47, 4.40s/it] 89%|████████▊ | 16539/18688 [19:26:47<2:39:23, 4.45s/it] 89%|████████▊ | 16540/18688 [19:26:51<2:32:00, 4.25s/it] {'loss': '0.8849', 'grad_norm': '1.146', 'learning_rate': '3.046e-06', 'epoch': '0.8851', 'num_input_tokens_seen': 55642976, 'train_runtime': '7.001e+04', 'train_tokens_per_second': '794.8'}
89%|████████▊ | 16540/18688 [19:26:51<2:32:00, 4.25s/it] 89%|████████▊ | 16541/18688 [19:26:55<2:32:13, 4.25s/it] 89%|████████▊ | 16542/18688 [19:26:59<2:32:51, 4.27s/it] 89%|████████▊ | 16543/18688 [19:27:04<2:37:16, 4.40s/it] 89%|████████▊ | 16544/18688 [19:27:08<2:30:39, 4.22s/it] 89%|████████▊ | 16545/18688 [19:27:12<2:34:13, 4.32s/it] 89%|████████▊ | 16546/18688 [19:27:17<2:35:04, 4.34s/it] 89%|████████▊ | 16547/18688 [19:27:21<2:38:13, 4.43s/it] 89%|████████▊ | 16548/18688 [19:27:25<2:33:24, 4.30s/it] 89%|████████▊ | 16549/18688 [19:27:30<2:34:50, 4.34s/it] 89%|████████▊ | 16550/18688 [19:27:34<2:29:46, 4.20s/it] {'loss': '0.9219', 'grad_norm': '1.922', 'learning_rate': '3.018e-06', 'epoch': '0.8856', 'num_input_tokens_seen': 55678856, 'train_runtime': '7.005e+04', 'train_tokens_per_second': '794.8'}
89%|████████▊ | 16550/18688 [19:27:34<2:29:46, 4.20s/it] 89%|████████▊ | 16551/18688 [19:27:37<2:21:41, 3.98s/it] 89%|████████▊ | 16552/18688 [19:27:41<2:22:22, 4.00s/it] 89%|████████▊ | 16553/18688 [19:27:45<2:20:04, 3.94s/it] 89%|████████▊ | 16554/18688 [19:27:50<2:28:17, 4.17s/it] 89%|████████▊ | 16555/18688 [19:27:53<2:21:13, 3.97s/it] 89%|████████▊ | 16556/18688 [19:27:57<2:19:55, 3.94s/it] 89%|████████▊ | 16557/18688 [19:28:01<2:16:59, 3.86s/it] 89%|████████▊ | 16558/18688 [19:28:05<2:20:24, 3.96s/it] 89%|████████▊ | 16559/18688 [19:28:09<2:25:11, 4.09s/it] 89%|████████▊ | 16560/18688 [19:28:14<2:28:19, 4.18s/it] {'loss': '0.8595', 'grad_norm': '1.443', 'learning_rate': '2.99e-06', 'epoch': '0.8862', 'num_input_tokens_seen': 55709232, 'train_runtime': '7.009e+04', 'train_tokens_per_second': '794.8'}
89%|████████▊ | 16560/18688 [19:28:14<2:28:19, 4.18s/it] 89%|████████▊ | 16561/18688 [19:28:18<2:30:08, 4.24s/it] 89%|████████▊ | 16562/18688 [19:28:23<2:31:27, 4.27s/it] 89%|████████▊ | 16563/18688 [19:28:27<2:31:48, 4.29s/it] 89%|████████▊ | 16564/18688 [19:28:31<2:33:49, 4.35s/it] 89%|████████▊ | 16565/18688 [19:28:36<2:35:18, 4.39s/it] 89%|████████▊ | 16566/18688 [19:28:40<2:38:04, 4.47s/it] 89%|████████▊ | 16567/18688 [19:28:45<2:35:15, 4.39s/it] 89%|████████▊ | 16568/18688 [19:28:49<2:31:19, 4.28s/it] 89%|████████▊ | 16569/18688 [19:28:53<2:29:06, 4.22s/it] 89%|████████▊ | 16570/18688 [19:28:56<2:22:32, 4.04s/it] {'loss': '0.952', 'grad_norm': '1.04', 'learning_rate': '2.963e-06', 'epoch': '0.8867', 'num_input_tokens_seen': 55743240, 'train_runtime': '7.014e+04', 'train_tokens_per_second': '794.8'}
89%|████████▊ | 16570/18688 [19:28:56<2:22:32, 4.04s/it] 89%|████████▊ | 16571/18688 [19:29:01<2:26:53, 4.16s/it] 89%|████████▊ | 16572/18688 [19:29:04<2:21:26, 4.01s/it] 89%|████████▊ | 16573/18688 [19:29:09<2:25:15, 4.12s/it] 89%|████████▊ | 16574/18688 [19:29:13<2:29:38, 4.25s/it] 89%|████████▊ | 16575/18688 [19:29:18<2:35:49, 4.42s/it] 89%|████████▊ | 16576/18688 [19:29:22<2:32:39, 4.34s/it] 89%|████████▊ | 16577/18688 [19:29:26<2:30:07, 4.27s/it] 89%|████████▊ | 16578/18688 [19:29:30<2:27:10, 4.19s/it] 89%|████████▊ | 16579/18688 [19:29:36<2:35:58, 4.44s/it] 89%|████████▊ | 16580/18688 [19:29:40<2:34:25, 4.40s/it] {'loss': '0.8755', 'grad_norm': '1.473', 'learning_rate': '2.935e-06', 'epoch': '0.8872', 'num_input_tokens_seen': 55779448, 'train_runtime': '7.018e+04', 'train_tokens_per_second': '794.8'}
89%|████████▊ | 16580/18688 [19:29:40<2:34:25, 4.40s/it] 89%|████████▊ | 16581/18688 [19:29:44<2:36:42, 4.46s/it] 89%|████████▊ | 16582/18688 [19:29:48<2:27:35, 4.20s/it] 89%|████████▊ | 16583/18688 [19:29:51<2:19:43, 3.98s/it] 89%|████████▊ | 16584/18688 [19:29:56<2:23:47, 4.10s/it] 89%|████████▊ | 16585/18688 [19:30:00<2:24:48, 4.13s/it] 89%|████████▉ | 16586/18688 [19:30:05<2:31:36, 4.33s/it] 89%|████████▉ | 16587/18688 [19:30:09<2:32:49, 4.36s/it] 89%|████████▉ | 16588/18688 [19:30:14<2:33:33, 4.39s/it] 89%|████████▉ | 16589/18688 [19:30:18<2:35:55, 4.46s/it] 89%|████████▉ | 16590/18688 [19:30:22<2:27:36, 4.22s/it] {'loss': '0.9028', 'grad_norm': '1.341', 'learning_rate': '2.908e-06', 'epoch': '0.8878', 'num_input_tokens_seen': 55814120, 'train_runtime': '7.022e+04', 'train_tokens_per_second': '794.8'}
89%|████████▉ | 16590/18688 [19:30:22<2:27:36, 4.22s/it] 89%|████████▉ | 16591/18688 [19:30:27<2:34:46, 4.43s/it] 89%|████████▉ | 16592/18688 [19:30:32<2:36:36, 4.48s/it] 89%|████████▉ | 16593/18688 [19:30:36<2:38:00, 4.53s/it] 89%|████████▉ | 16594/18688 [19:30:41<2:38:38, 4.55s/it] 89%|████████▉ | 16595/18688 [19:30:45<2:34:23, 4.43s/it] 89%|████████▉ | 16596/18688 [19:30:49<2:28:14, 4.25s/it] 89%|████████▉ | 16597/18688 [19:30:53<2:27:31, 4.23s/it] 89%|████████▉ | 16598/18688 [19:30:58<2:34:27, 4.43s/it] 89%|████████▉ | 16599/18688 [19:31:02<2:28:34, 4.27s/it] 89%|████████▉ | 16600/18688 [19:31:06<2:29:45, 4.30s/it] {'loss': '0.9484', 'grad_norm': '1.265', 'learning_rate': '2.881e-06', 'epoch': '0.8883', 'num_input_tokens_seen': 55851920, 'train_runtime': '7.027e+04', 'train_tokens_per_second': '794.9'}
89%|████████▉ | 16600/18688 [19:31:06<2:29:45, 4.30s/it] 89%|████████▉ | 16601/18688 [19:31:10<2:27:52, 4.25s/it] 89%|████████▉ | 16602/18688 [19:31:15<2:33:23, 4.41s/it] 89%|████████▉ | 16603/18688 [19:31:19<2:26:18, 4.21s/it] 89%|████████▉ | 16604/18688 [19:31:22<2:19:32, 4.02s/it] 89%|████████▉ | 16605/18688 [19:31:26<2:20:18, 4.04s/it] 89%|████████▉ | 16606/18688 [19:31:31<2:29:17, 4.30s/it] 89%|████████▉ | 16607/18688 [19:31:35<2:26:21, 4.22s/it] 89%|████████▉ | 16608/18688 [19:31:40<2:26:35, 4.23s/it] 89%|████████▉ | 16609/18688 [19:31:43<2:21:08, 4.07s/it] 89%|████████▉ | 16610/18688 [19:31:48<2:23:29, 4.14s/it] {'loss': '0.9074', 'grad_norm': '1.656', 'learning_rate': '2.853e-06', 'epoch': '0.8888', 'num_input_tokens_seen': 55883696, 'train_runtime': '7.031e+04', 'train_tokens_per_second': '794.8'}
89%|████████▉ | 16610/18688 [19:31:48<2:23:29, 4.14s/it] 89%|████████▉ | 16611/18688 [19:31:53<2:31:08, 4.37s/it] 89%|████████▉ | 16612/18688 [19:31:57<2:37:04, 4.54s/it] 89%|████████▉ | 16613/18688 [19:32:01<2:28:15, 4.29s/it] 89%|████████▉ | 16614/18688 [19:32:05<2:22:15, 4.12s/it] 89%|████████▉ | 16615/18688 [19:32:09<2:21:58, 4.11s/it] 89%|████████▉ | 16616/18688 [19:32:13<2:21:36, 4.10s/it] 89%|████████▉ | 16617/18688 [19:32:17<2:17:26, 3.98s/it] 89%|████████▉ | 16618/18688 [19:32:21<2:17:05, 3.97s/it] 89%|████████▉ | 16619/18688 [19:32:25<2:17:42, 3.99s/it] 89%|████████▉ | 16620/18688 [19:32:29<2:24:01, 4.18s/it] {'loss': '0.8971', 'grad_norm': '1.38', 'learning_rate': '2.826e-06', 'epoch': '0.8894', 'num_input_tokens_seen': 55917848, 'train_runtime': '7.035e+04', 'train_tokens_per_second': '794.9'}
89%|████████▉ | 16620/18688 [19:32:29<2:24:01, 4.18s/it] 89%|████████▉ | 16621/18688 [19:32:34<2:25:18, 4.22s/it] 89%|████████▉ | 16622/18688 [19:32:38<2:30:24, 4.37s/it] 89%|████████▉ | 16623/18688 [19:32:43<2:33:27, 4.46s/it] 89%|████████▉ | 16624/18688 [19:32:47<2:24:54, 4.21s/it] 89%|████████▉ | 16625/18688 [19:32:50<2:18:12, 4.02s/it] 89%|████████▉ | 16626/18688 [19:32:54<2:16:42, 3.98s/it] 89%|████████▉ | 16627/18688 [19:32:58<2:12:23, 3.85s/it] 89%|████████▉ | 16628/18688 [19:33:02<2:14:55, 3.93s/it] 89%|████████▉ | 16629/18688 [19:33:06<2:15:32, 3.95s/it] 89%|████████▉ | 16630/18688 [19:33:10<2:17:00, 3.99s/it] {'loss': '0.9026', 'grad_norm': '1.232', 'learning_rate': '2.799e-06', 'epoch': '0.8899', 'num_input_tokens_seen': 55949896, 'train_runtime': '7.039e+04', 'train_tokens_per_second': '794.9'}
89%|████████▉ | 16630/18688 [19:33:10<2:17:00, 3.99s/it] 89%|████████▉ | 16631/18688 [19:33:14<2:20:12, 4.09s/it] 89%|████████▉ | 16632/18688 [19:33:19<2:23:37, 4.19s/it] 89%|████████▉ | 16633/18688 [19:33:22<2:18:13, 4.04s/it] 89%|████████▉ | 16634/18688 [19:33:27<2:24:17, 4.21s/it] 89%|████████▉ | 16635/18688 [19:33:31<2:21:52, 4.15s/it] 89%|████████▉ | 16636/18688 [19:33:35<2:19:37, 4.08s/it] 89%|████████▉ | 16637/18688 [19:33:39<2:16:02, 3.98s/it] 89%|████████▉ | 16638/18688 [19:33:43<2:17:46, 4.03s/it] 89%|████████▉ | 16639/18688 [19:33:47<2:16:06, 3.99s/it] 89%|████████▉ | 16640/18688 [19:33:50<2:13:29, 3.91s/it] {'loss': '0.8821', 'grad_norm': '1.167', 'learning_rate': '2.773e-06', 'epoch': '0.8904', 'num_input_tokens_seen': 55983352, 'train_runtime': '7.043e+04', 'train_tokens_per_second': '794.9'}
89%|████████▉ | 16640/18688 [19:33:50<2:13:29, 3.91s/it] 89%|████████▉ | 16641/18688 [19:33:55<2:16:14, 3.99s/it] 89%|████████▉ | 16642/18688 [19:33:59<2:23:02, 4.19s/it] 89%|████████▉ | 16643/18688 [19:34:04<2:30:43, 4.42s/it] 89%|████████▉ | 16644/18688 [19:34:08<2:21:30, 4.15s/it] 89%|████████▉ | 16645/18688 [19:34:13<2:33:42, 4.51s/it] 89%|████████▉ | 16646/18688 [19:34:18<2:36:22, 4.59s/it] 89%|████████▉ | 16647/18688 [19:34:22<2:33:20, 4.51s/it] 89%|████████▉ | 16648/18688 [19:34:26<2:23:08, 4.21s/it] 89%|████████▉ | 16649/18688 [19:34:30<2:27:34, 4.34s/it] 89%|████████▉ | 16650/18688 [19:34:35<2:33:43, 4.53s/it] {'loss': '0.8807', 'grad_norm': '1.189', 'learning_rate': '2.746e-06', 'epoch': '0.891', 'num_input_tokens_seen': 56021224, 'train_runtime': '7.048e+04', 'train_tokens_per_second': '794.9'}
89%|████████▉ | 16650/18688 [19:34:35<2:33:43, 4.53s/it] 89%|████████▉ | 16651/18688 [19:34:39<2:27:48, 4.35s/it] 89%|████████▉ | 16652/18688 [19:34:43<2:21:27, 4.17s/it] 89%|████████▉ | 16653/18688 [19:34:48<2:29:42, 4.41s/it] 89%|████████▉ | 16654/18688 [19:34:52<2:29:28, 4.41s/it] 89%|████████▉ | 16655/18688 [19:34:56<2:22:47, 4.21s/it] 89%|████████▉ | 16656/18688 [19:35:00<2:17:03, 4.05s/it] 89%|████████▉ | 16657/18688 [19:35:06<2:34:49, 4.57s/it] 89%|████████▉ | 16658/18688 [19:35:09<2:26:57, 4.34s/it] 89%|████████▉ | 16659/18688 [19:35:13<2:20:57, 4.17s/it] 89%|████████▉ | 16660/18688 [19:35:17<2:21:34, 4.19s/it] {'loss': '0.9538', 'grad_norm': '2.047', 'learning_rate': '2.719e-06', 'epoch': '0.8915', 'num_input_tokens_seen': 56055168, 'train_runtime': '7.052e+04', 'train_tokens_per_second': '794.9'}
89%|████████▉ | 16660/18688 [19:35:17<2:21:34, 4.19s/it] 89%|████████▉ | 16661/18688 [19:35:22<2:29:07, 4.41s/it] 89%|████████▉ | 16662/18688 [19:35:27<2:33:10, 4.54s/it] 89%|████████▉ | 16663/18688 [19:35:31<2:22:27, 4.22s/it] 89%|████████▉ | 16664/18688 [19:35:35<2:25:11, 4.30s/it] 89%|████████▉ | 16665/18688 [19:35:39<2:22:15, 4.22s/it] 89%|████████▉ | 16666/18688 [19:35:43<2:20:03, 4.16s/it] 89%|████████▉ | 16667/18688 [19:35:47<2:18:14, 4.10s/it] 89%|████████▉ | 16668/18688 [19:35:51<2:20:17, 4.17s/it] 89%|████████▉ | 16669/18688 [19:35:56<2:25:47, 4.33s/it] 89%|████████▉ | 16670/18688 [19:36:01<2:28:55, 4.43s/it] {'loss': '0.9668', 'grad_norm': '1.252', 'learning_rate': '2.693e-06', 'epoch': '0.892', 'num_input_tokens_seen': 56091040, 'train_runtime': '7.056e+04', 'train_tokens_per_second': '794.9'}
89%|████████▉ | 16670/18688 [19:36:01<2:28:55, 4.43s/it] 89%|████████▉ | 16671/18688 [19:36:05<2:25:43, 4.33s/it] 89%|████████▉ | 16672/18688 [19:36:09<2:24:36, 4.30s/it] 89%|████████▉ | 16673/18688 [19:36:13<2:22:01, 4.23s/it] 89%|████████▉ | 16674/18688 [19:36:17<2:17:46, 4.10s/it] 89%|████████▉ | 16675/18688 [19:36:21<2:14:32, 4.01s/it] 89%|████████▉ | 16676/18688 [19:36:25<2:12:57, 3.96s/it] 89%|████████▉ | 16677/18688 [19:36:28<2:09:29, 3.86s/it] 89%|████████▉ | 16678/18688 [19:36:32<2:08:09, 3.83s/it] 89%|████████▉ | 16679/18688 [19:36:37<2:20:02, 4.18s/it] 89%|████████▉ | 16680/18688 [19:36:41<2:18:28, 4.14s/it] {'loss': '0.939', 'grad_norm': '1.231', 'learning_rate': '2.667e-06', 'epoch': '0.8926', 'num_input_tokens_seen': 56121448, 'train_runtime': '7.06e+04', 'train_tokens_per_second': '794.9'}
89%|████████▉ | 16680/18688 [19:36:41<2:18:28, 4.14s/it] 89%|████████▉ | 16681/18688 [19:36:45<2:14:38, 4.02s/it] 89%|████████▉ | 16682/18688 [19:36:49<2:10:52, 3.91s/it] 89%|████████▉ | 16683/18688 [19:36:53<2:16:40, 4.09s/it] 89%|████████▉ | 16684/18688 [19:36:57<2:13:23, 3.99s/it] 89%|████████▉ | 16685/18688 [19:37:01<2:16:35, 4.09s/it] 89%|████████▉ | 16686/18688 [19:37:06<2:20:02, 4.20s/it] 89%|████████▉ | 16687/18688 [19:37:10<2:19:47, 4.19s/it] 89%|████████▉ | 16688/18688 [19:37:14<2:17:38, 4.13s/it] 89%|████████▉ | 16689/18688 [19:37:18<2:14:13, 4.03s/it] 89%|████████▉ | 16690/18688 [19:37:21<2:13:17, 4.00s/it] {'loss': '0.9302', 'grad_norm': '1.293', 'learning_rate': '2.64e-06', 'epoch': '0.8931', 'num_input_tokens_seen': 56152576, 'train_runtime': '7.064e+04', 'train_tokens_per_second': '794.9'}
89%|████████▉ | 16690/18688 [19:37:21<2:13:17, 4.00s/it] 89%|████████▉ | 16691/18688 [19:37:26<2:14:17, 4.04s/it] 89%|████████▉ | 16692/18688 [19:37:30<2:17:04, 4.12s/it] 89%|████████▉ | 16693/18688 [19:37:35<2:22:48, 4.30s/it] 89%|████████▉ | 16694/18688 [19:37:39<2:22:58, 4.30s/it] 89%|████████▉ | 16695/18688 [19:37:42<2:14:59, 4.06s/it] 89%|████████▉ | 16696/18688 [19:37:47<2:16:17, 4.11s/it] 89%|████████▉ | 16697/18688 [19:37:51<2:19:05, 4.19s/it] 89%|████████▉ | 16698/18688 [19:37:56<2:27:34, 4.45s/it] 89%|████████▉ | 16699/18688 [19:38:00<2:20:01, 4.22s/it] 89%|████████▉ | 16700/18688 [19:38:06<2:36:36, 4.73s/it] {'loss': '0.9058', 'grad_norm': '1.303', 'learning_rate': '2.614e-06', 'epoch': '0.8936', 'num_input_tokens_seen': 56189952, 'train_runtime': '7.069e+04', 'train_tokens_per_second': '794.9'}
89%|████████▉ | 16700/18688 [19:38:06<2:36:36, 4.73s/it] 89%|████████▉ | 16701/18688 [19:38:09<2:24:01, 4.35s/it] 89%|████████▉ | 16702/18688 [19:38:14<2:24:29, 4.37s/it] 89%|████████▉ | 16703/18688 [19:38:18<2:28:03, 4.48s/it] 89%|████████▉ | 16704/18688 [19:38:23<2:34:11, 4.66s/it] 89%|████████▉ | 16705/18688 [19:38:28<2:28:45, 4.50s/it] 89%|████████▉ | 16706/18688 [19:38:32<2:24:57, 4.39s/it] 89%|████████▉ | 16707/18688 [19:38:36<2:28:21, 4.49s/it] 89%|████████▉ | 16708/18688 [19:38:41<2:26:04, 4.43s/it] 89%|████████▉ | 16709/18688 [19:38:45<2:21:53, 4.30s/it] 89%|████████▉ | 16710/18688 [19:38:49<2:21:50, 4.30s/it] {'loss': '0.8828', 'grad_norm': '1.1', 'learning_rate': '2.588e-06', 'epoch': '0.8942', 'num_input_tokens_seen': 56226176, 'train_runtime': '7.073e+04', 'train_tokens_per_second': '794.9'}
89%|████████▉ | 16710/18688 [19:38:49<2:21:50, 4.30s/it] 89%|████████▉ | 16711/18688 [19:38:53<2:19:22, 4.23s/it] 89%|████████▉ | 16712/18688 [19:38:57<2:12:27, 4.02s/it] 89%|████████▉ | 16713/18688 [19:39:00<2:09:17, 3.93s/it] 89%|████████▉ | 16714/18688 [19:39:04<2:05:43, 3.82s/it] 89%|████████▉ | 16715/18688 [19:39:09<2:15:18, 4.11s/it] 89%|████████▉ | 16716/18688 [19:39:13<2:17:11, 4.17s/it] 89%|████████▉ | 16717/18688 [19:39:17<2:18:32, 4.22s/it] 89%|████████▉ | 16718/18688 [19:39:21<2:12:24, 4.03s/it] 89%|████████▉ | 16719/18688 [19:39:25<2:13:33, 4.07s/it] 89%|████████▉ | 16720/18688 [19:39:29<2:14:15, 4.09s/it] {'loss': '0.8801', 'grad_norm': '1.19', 'learning_rate': '2.563e-06', 'epoch': '0.8947', 'num_input_tokens_seen': 56258272, 'train_runtime': '7.077e+04', 'train_tokens_per_second': '794.9'}
89%|████████▉ | 16720/18688 [19:39:29<2:14:15, 4.09s/it] 89%|████████▉ | 16721/18688 [19:39:34<2:20:02, 4.27s/it] 89%|████████▉ | 16722/18688 [19:39:38<2:15:42, 4.14s/it] 89%|████████▉ | 16723/18688 [19:39:42<2:14:47, 4.12s/it] 89%|████████▉ | 16724/18688 [19:39:46<2:14:55, 4.12s/it] 89%|████████▉ | 16725/18688 [19:39:50<2:13:19, 4.07s/it] 90%|████████▉ | 16726/18688 [19:39:54<2:14:04, 4.10s/it] 90%|████████▉ | 16727/18688 [19:39:58<2:15:59, 4.16s/it] 90%|████████▉ | 16728/18688 [19:40:02<2:10:49, 4.01s/it] 90%|████████▉ | 16729/18688 [19:40:06<2:11:27, 4.03s/it] 90%|████████▉ | 16730/18688 [19:40:10<2:15:07, 4.14s/it] {'loss': '0.8223', 'grad_norm': '1.209', 'learning_rate': '2.537e-06', 'epoch': '0.8953', 'num_input_tokens_seen': 56290536, 'train_runtime': '7.081e+04', 'train_tokens_per_second': '794.9'}
90%|████████▉ | 16730/18688 [19:40:10<2:15:07, 4.14s/it] 90%|████████▉ | 16731/18688 [19:40:14<2:14:07, 4.11s/it] 90%|████████▉ | 16732/18688 [19:40:19<2:15:15, 4.15s/it] 90%|████████▉ | 16733/18688 [19:40:23<2:20:22, 4.31s/it] 90%|████████▉ | 16734/18688 [19:40:28<2:22:29, 4.38s/it] 90%|████████▉ | 16735/18688 [19:40:32<2:17:31, 4.22s/it] 90%|████████▉ | 16736/18688 [19:40:36<2:15:53, 4.18s/it] 90%|████████▉ | 16737/18688 [19:40:41<2:25:56, 4.49s/it] 90%|████████▉ | 16738/18688 [19:40:45<2:23:26, 4.41s/it] 90%|████████▉ | 16739/18688 [19:40:51<2:33:20, 4.72s/it] 90%|████████▉ | 16740/18688 [19:40:55<2:29:43, 4.61s/it] {'loss': '0.8814', 'grad_norm': '1.414', 'learning_rate': '2.511e-06', 'epoch': '0.8958', 'num_input_tokens_seen': 56329184, 'train_runtime': '7.086e+04', 'train_tokens_per_second': '795'}
90%|████████▉ | 16740/18688 [19:40:55<2:29:43, 4.61s/it] 90%|████████▉ | 16741/18688 [19:41:00<2:28:10, 4.57s/it] 90%|████████▉ | 16742/18688 [19:41:05<2:37:29, 4.86s/it] 90%|████████▉ | 16743/18688 [19:41:09<2:27:58, 4.56s/it] 90%|████████▉ | 16744/18688 [19:41:13<2:20:37, 4.34s/it] 90%|████████▉ | 16745/18688 [19:41:16<2:13:07, 4.11s/it] 90%|████████▉ | 16746/18688 [19:41:20<2:12:32, 4.09s/it] 90%|████████▉ | 16747/18688 [19:41:24<2:11:16, 4.06s/it] 90%|████████▉ | 16748/18688 [19:41:29<2:13:31, 4.13s/it] 90%|████████▉ | 16749/18688 [19:41:34<2:20:09, 4.34s/it] 90%|████████▉ | 16750/18688 [19:41:38<2:22:24, 4.41s/it] {'loss': '0.9649', 'grad_norm': '1.127', 'learning_rate': '2.486e-06', 'epoch': '0.8963', 'num_input_tokens_seen': 56364320, 'train_runtime': '7.09e+04', 'train_tokens_per_second': '795'}
90%|████████▉ | 16750/18688 [19:41:38<2:22:24, 4.41s/it] 90%|████████▉ | 16751/18688 [19:41:42<2:20:50, 4.36s/it] 90%|████████▉ | 16752/18688 [19:41:46<2:17:46, 4.27s/it] 90%|████████▉ | 16753/18688 [19:41:51<2:22:13, 4.41s/it] 90%|████████▉ | 16754/18688 [19:41:55<2:13:33, 4.14s/it] 90%|████████▉ | 16755/18688 [19:41:58<2:08:29, 3.99s/it] 90%|████████▉ | 16756/18688 [19:42:02<2:09:55, 4.04s/it] 90%|████████▉ | 16757/18688 [19:42:07<2:11:29, 4.09s/it] 90%|████████▉ | 16758/18688 [19:42:10<2:06:10, 3.92s/it] 90%|████████▉ | 16759/18688 [19:42:15<2:17:06, 4.26s/it] 90%|████████▉ | 16760/18688 [19:42:19<2:14:47, 4.19s/it] {'loss': '0.9637', 'grad_norm': '1.313', 'learning_rate': '2.461e-06', 'epoch': '0.8969', 'num_input_tokens_seen': 56398024, 'train_runtime': '7.094e+04', 'train_tokens_per_second': '795'}
90%|████████▉ | 16760/18688 [19:42:19<2:14:47, 4.19s/it] 90%|████████▉ | 16761/18688 [19:42:24<2:19:02, 4.33s/it] 90%|████████▉ | 16762/18688 [19:42:28<2:16:17, 4.25s/it] 90%|████████▉ | 16763/18688 [19:42:32<2:18:41, 4.32s/it] 90%|████████▉ | 16764/18688 [19:42:38<2:25:47, 4.55s/it] 90%|████████▉ | 16765/18688 [19:42:42<2:20:08, 4.37s/it] 90%|████████▉ | 16766/18688 [19:42:47<2:26:55, 4.59s/it] 90%|████████▉ | 16767/18688 [19:42:51<2:24:21, 4.51s/it] 90%|████████▉ | 16768/18688 [19:42:55<2:15:37, 4.24s/it] 90%|████████▉ | 16769/18688 [19:42:58<2:10:58, 4.09s/it] 90%|████████▉ | 16770/18688 [19:43:03<2:18:41, 4.34s/it] {'loss': '0.8981', 'grad_norm': '1.139', 'learning_rate': '2.435e-06', 'epoch': '0.8974', 'num_input_tokens_seen': 56433360, 'train_runtime': '7.098e+04', 'train_tokens_per_second': '795'}
90%|████████▉ | 16770/18688 [19:43:03<2:18:41, 4.34s/it] 90%|████████▉ | 16771/18688 [19:43:08<2:22:29, 4.46s/it] 90%|████████▉ | 16772/18688 [19:43:12<2:15:13, 4.23s/it] 90%|████████▉ | 16773/18688 [19:43:16<2:19:42, 4.38s/it] 90%|████████▉ | 16774/18688 [19:43:21<2:22:39, 4.47s/it] 90%|████████▉ | 16775/18688 [19:43:25<2:14:25, 4.22s/it] 90%|████████▉ | 16776/18688 [19:43:29<2:13:49, 4.20s/it] 90%|████████▉ | 16777/18688 [19:43:33<2:11:13, 4.12s/it] 90%|████████▉ | 16778/18688 [19:43:37<2:11:09, 4.12s/it] 90%|████████▉ | 16779/18688 [19:43:41<2:12:16, 4.16s/it] 90%|████████▉ | 16780/18688 [19:43:45<2:13:25, 4.20s/it] {'loss': '0.8946', 'grad_norm': '1.353', 'learning_rate': '2.41e-06', 'epoch': '0.8979', 'num_input_tokens_seen': 56466600, 'train_runtime': '7.103e+04', 'train_tokens_per_second': '795'}
90%|████████▉ | 16780/18688 [19:43:45<2:13:25, 4.20s/it] 90%|████████▉ | 16781/18688 [19:43:49<2:10:10, 4.10s/it] 90%|████████▉ | 16782/18688 [19:43:53<2:09:00, 4.06s/it] 90%|████████▉ | 16783/18688 [19:43:58<2:12:10, 4.16s/it] 90%|████████▉ | 16784/18688 [19:44:01<2:08:27, 4.05s/it] 90%|████████▉ | 16785/18688 [19:44:05<2:03:26, 3.89s/it] 90%|████████▉ | 16786/18688 [19:44:10<2:12:42, 4.19s/it] 90%|████████▉ | 16787/18688 [19:44:15<2:18:13, 4.36s/it] 90%|████████▉ | 16788/18688 [19:44:18<2:12:22, 4.18s/it] 90%|████████▉ | 16789/18688 [19:44:23<2:13:00, 4.20s/it] 90%|████████▉ | 16790/18688 [19:44:27<2:11:17, 4.15s/it] {'loss': '0.8476', 'grad_norm': '1.1', 'learning_rate': '2.385e-06', 'epoch': '0.8985', 'num_input_tokens_seen': 56497552, 'train_runtime': '7.107e+04', 'train_tokens_per_second': '795'}
90%|████████▉ | 16790/18688 [19:44:27<2:11:17, 4.15s/it] 90%|████████▉ | 16791/18688 [19:44:31<2:16:20, 4.31s/it] 90%|████████▉ | 16792/18688 [19:44:36<2:17:13, 4.34s/it] 90%|████████▉ | 16793/18688 [19:44:40<2:14:54, 4.27s/it] 90%|████████▉ | 16794/18688 [19:44:44<2:09:55, 4.12s/it] 90%|████████▉ | 16795/18688 [19:44:47<2:07:25, 4.04s/it] 90%|████████▉ | 16796/18688 [19:44:51<2:03:16, 3.91s/it] 90%|████████▉ | 16797/18688 [19:44:56<2:08:59, 4.09s/it] 90%|████████▉ | 16798/18688 [19:44:59<2:03:19, 3.92s/it] 90%|████████▉ | 16799/18688 [19:45:03<2:04:37, 3.96s/it] 90%|████████▉ | 16800/18688 [19:45:08<2:09:55, 4.13s/it] {'loss': '0.8379', 'grad_norm': '1.37', 'learning_rate': '2.361e-06', 'epoch': '0.899', 'num_input_tokens_seen': 56530520, 'train_runtime': '7.111e+04', 'train_tokens_per_second': '795'}
90%|████████▉ | 16800/18688 [19:45:08<2:09:55, 4.13s/it] 90%|████████▉ | 16801/18688 [19:45:12<2:13:28, 4.24s/it] 90%|████████▉ | 16802/18688 [19:45:17<2:14:21, 4.27s/it] 90%|████████▉ | 16803/18688 [19:45:21<2:12:35, 4.22s/it] 90%|████████▉ | 16804/18688 [19:45:25<2:16:30, 4.35s/it] 90%|████████▉ | 16805/18688 [19:45:29<2:09:20, 4.12s/it] 90%|████████▉ | 16806/18688 [19:45:32<2:04:22, 3.97s/it] 90%|████████▉ | 16807/18688 [19:45:36<2:04:13, 3.96s/it] 90%|████████▉ | 16808/18688 [19:45:40<2:01:07, 3.87s/it] 90%|████████▉ | 16809/18688 [19:45:44<1:58:09, 3.77s/it] 90%|████████▉ | 16810/18688 [19:45:49<2:17:32, 4.39s/it] {'loss': '0.8989', 'grad_norm': '1.065', 'learning_rate': '2.336e-06', 'epoch': '0.8995', 'num_input_tokens_seen': 56564096, 'train_runtime': '7.115e+04', 'train_tokens_per_second': '795'}
90%|████████▉ | 16810/18688 [19:45:49<2:17:32, 4.39s/it] 90%|████████▉ | 16811/18688 [19:45:54<2:20:21, 4.49s/it] 90%|████████▉ | 16812/18688 [19:45:58<2:18:30, 4.43s/it] 90%|████████▉ | 16813/18688 [19:46:03<2:19:16, 4.46s/it] 90%|████████▉ | 16814/18688 [19:46:08<2:23:30, 4.59s/it] 90%|████████▉ | 16815/18688 [19:46:12<2:21:20, 4.53s/it] 90%|████████▉ | 16816/18688 [19:46:16<2:14:11, 4.30s/it] 90%|████████▉ | 16817/18688 [19:46:21<2:22:08, 4.56s/it] 90%|████████▉ | 16818/18688 [19:46:26<2:24:05, 4.62s/it] 90%|████████▉ | 16819/18688 [19:46:30<2:19:20, 4.47s/it] 90%|█████████ | 16820/18688 [19:46:35<2:21:27, 4.54s/it] {'loss': '1.002', 'grad_norm': '0.9998', 'learning_rate': '2.311e-06', 'epoch': '0.9001', 'num_input_tokens_seen': 56602408, 'train_runtime': '7.12e+04', 'train_tokens_per_second': '795'}
90%|█████████ | 16820/18688 [19:46:35<2:21:27, 4.54s/it] 90%|█████████ | 16821/18688 [19:46:39<2:20:09, 4.50s/it] 90%|█████████ | 16822/18688 [19:46:43<2:17:26, 4.42s/it] 90%|█████████ | 16823/18688 [19:46:48<2:16:20, 4.39s/it] 90%|█████████ | 16824/18688 [19:46:52<2:16:16, 4.39s/it] 90%|█████████ | 16825/18688 [19:46:57<2:18:58, 4.48s/it] 90%|█████████ | 16826/18688 [19:47:01<2:13:23, 4.30s/it] 90%|█████████ | 16827/18688 [19:47:04<2:07:00, 4.10s/it] 90%|█████████ | 16828/18688 [19:47:08<2:04:41, 4.02s/it] 90%|█████████ | 16829/18688 [19:47:12<2:06:22, 4.08s/it] 90%|█████████ | 16830/18688 [19:47:17<2:07:39, 4.12s/it] {'loss': '0.9358', 'grad_norm': '1.143', 'learning_rate': '2.287e-06', 'epoch': '0.9006', 'num_input_tokens_seen': 56636688, 'train_runtime': '7.124e+04', 'train_tokens_per_second': '795'}
90%|█████████ | 16830/18688 [19:47:17<2:07:39, 4.12s/it] 90%|█████████ | 16831/18688 [19:47:21<2:08:15, 4.14s/it] 90%|█████████ | 16832/18688 [19:47:25<2:07:39, 4.13s/it] 90%|█████████ | 16833/18688 [19:47:29<2:08:10, 4.15s/it] 90%|█████████ | 16834/18688 [19:47:35<2:23:26, 4.64s/it] 90%|█████████ | 16835/18688 [19:47:39<2:13:46, 4.33s/it] 90%|█████████ | 16836/18688 [19:47:43<2:18:18, 4.48s/it] 90%|█████████ | 16837/18688 [19:47:48<2:17:57, 4.47s/it] 90%|█████████ | 16838/18688 [19:47:52<2:11:13, 4.26s/it] 90%|█████████ | 16839/18688 [19:47:56<2:12:57, 4.31s/it] 90%|█████████ | 16840/18688 [19:48:00<2:11:08, 4.26s/it] {'loss': '0.9113', 'grad_norm': '1.098', 'learning_rate': '2.263e-06', 'epoch': '0.9011', 'num_input_tokens_seen': 56673048, 'train_runtime': '7.128e+04', 'train_tokens_per_second': '795.1'}
90%|█████████ | 16840/18688 [19:48:00<2:11:08, 4.26s/it] 90%|█████████ | 16841/18688 [19:48:04<2:05:40, 4.08s/it] 90%|█████████ | 16842/18688 [19:48:08<2:10:04, 4.23s/it] 90%|█████████ | 16843/18688 [19:48:12<2:04:25, 4.05s/it] 90%|█████████ | 16844/18688 [19:48:16<2:06:13, 4.11s/it] 90%|█████████ | 16845/18688 [19:48:20<2:04:04, 4.04s/it] 90%|█████████ | 16846/18688 [19:48:24<2:02:36, 3.99s/it] 90%|█████████ | 16847/18688 [19:48:28<2:03:57, 4.04s/it] 90%|█████████ | 16848/18688 [19:48:33<2:09:25, 4.22s/it] 90%|█████████ | 16849/18688 [19:48:37<2:05:44, 4.10s/it] 90%|█████████ | 16850/18688 [19:48:41<2:09:48, 4.24s/it] {'loss': '0.9224', 'grad_norm': '1.15', 'learning_rate': '2.238e-06', 'epoch': '0.9017', 'num_input_tokens_seen': 56705840, 'train_runtime': '7.132e+04', 'train_tokens_per_second': '795.1'}
90%|█████████ | 16850/18688 [19:48:41<2:09:48, 4.24s/it] 90%|█████████ | 16851/18688 [19:48:46<2:14:34, 4.40s/it] 90%|█████████ | 16852/18688 [19:48:51<2:17:22, 4.49s/it] 90%|█████████ | 16853/18688 [19:48:55<2:18:12, 4.52s/it] 90%|█████████ | 16854/18688 [19:48:59<2:12:13, 4.33s/it] 90%|█████████ | 16855/18688 [19:49:03<2:07:07, 4.16s/it] 90%|█████████ | 16856/18688 [19:49:07<2:05:29, 4.11s/it] 90%|█████████ | 16857/18688 [19:49:11<2:05:37, 4.12s/it] 90%|█████████ | 16858/18688 [19:49:15<2:03:07, 4.04s/it] 90%|█████████ | 16859/18688 [19:49:20<2:11:59, 4.33s/it] 90%|█████████ | 16860/18688 [19:49:24<2:14:30, 4.41s/it] {'loss': '0.9398', 'grad_norm': '1.025', 'learning_rate': '2.214e-06', 'epoch': '0.9022', 'num_input_tokens_seen': 56741496, 'train_runtime': '7.136e+04', 'train_tokens_per_second': '795.1'}
90%|█████████ | 16860/18688 [19:49:24<2:14:30, 4.41s/it] 90%|█████████ | 16861/18688 [19:49:29<2:12:18, 4.34s/it] 90%|█████████ | 16862/18688 [19:49:33<2:11:56, 4.34s/it] 90%|█████████ | 16863/18688 [19:49:37<2:06:07, 4.15s/it] 90%|█████████ | 16864/18688 [19:49:41<2:09:17, 4.25s/it] 90%|█████████ | 16865/18688 [19:49:45<2:07:19, 4.19s/it] 90%|█████████ | 16866/18688 [19:49:50<2:11:18, 4.32s/it] 90%|█████████ | 16867/18688 [19:49:54<2:07:32, 4.20s/it] 90%|█████████ | 16868/18688 [19:49:59<2:14:09, 4.42s/it] 90%|█████████ | 16869/18688 [19:50:02<2:06:23, 4.17s/it] 90%|█████████ | 16870/18688 [19:50:06<2:05:41, 4.15s/it] {'loss': '0.8625', 'grad_norm': '1.138', 'learning_rate': '2.19e-06', 'epoch': '0.9027', 'num_input_tokens_seen': 56775752, 'train_runtime': '7.141e+04', 'train_tokens_per_second': '795.1'}
90%|█████████ | 16870/18688 [19:50:06<2:05:41, 4.15s/it] 90%|█████████ | 16871/18688 [19:50:11<2:09:50, 4.29s/it] 90%|█████████ | 16872/18688 [19:50:15<2:02:41, 4.05s/it] 90%|█████████ | 16873/18688 [19:50:19<2:05:29, 4.15s/it] 90%|█████████ | 16874/18688 [19:50:22<2:00:09, 3.97s/it] 90%|█████████ | 16875/18688 [19:50:27<2:01:42, 4.03s/it] 90%|█████████ | 16876/18688 [19:50:31<2:02:50, 4.07s/it] 90%|█████████ | 16877/18688 [19:50:35<1:59:43, 3.97s/it] 90%|█████████ | 16878/18688 [19:50:39<2:05:12, 4.15s/it] 90%|█████████ | 16879/18688 [19:50:44<2:14:21, 4.46s/it] 90%|█████████ | 16880/18688 [19:50:48<2:10:45, 4.34s/it] {'loss': '0.888', 'grad_norm': '1.137', 'learning_rate': '2.167e-06', 'epoch': '0.9033', 'num_input_tokens_seen': 56810064, 'train_runtime': '7.145e+04', 'train_tokens_per_second': '795.1'}
90%|█████████ | 16880/18688 [19:50:48<2:10:45, 4.34s/it] 90%|█████████ | 16881/18688 [19:50:52<2:04:06, 4.12s/it] 90%|█████████ | 16882/18688 [19:50:56<1:59:06, 3.96s/it] 90%|█████████ | 16883/18688 [19:50:59<1:55:49, 3.85s/it] 90%|█████████ | 16884/18688 [19:51:05<2:11:12, 4.36s/it] 90%|█████████ | 16885/18688 [19:51:09<2:08:19, 4.27s/it] 90%|█████████ | 16886/18688 [19:51:14<2:13:07, 4.43s/it] 90%|█████████ | 16887/18688 [19:51:18<2:10:48, 4.36s/it] 90%|█████████ | 16888/18688 [19:51:22<2:12:40, 4.42s/it] 90%|█████████ | 16889/18688 [19:51:27<2:15:01, 4.50s/it] 90%|█████████ | 16890/18688 [19:51:31<2:14:32, 4.49s/it] {'loss': '0.9013', 'grad_norm': '1.145', 'learning_rate': '2.143e-06', 'epoch': '0.9038', 'num_input_tokens_seen': 56845384, 'train_runtime': '7.149e+04', 'train_tokens_per_second': '795.1'}
90%|█████████ | 16890/18688 [19:51:31<2:14:32, 4.49s/it] 90%|█████████ | 16891/18688 [19:51:35<2:07:34, 4.26s/it] 90%|█████████ | 16892/18688 [19:51:39<2:05:46, 4.20s/it] 90%|█████████ | 16893/18688 [19:51:43<1:58:56, 3.98s/it] 90%|█████████ | 16894/18688 [19:51:46<1:54:12, 3.82s/it] 90%|█████████ | 16895/18688 [19:51:50<1:56:27, 3.90s/it] 90%|█████████ | 16896/18688 [19:51:54<1:56:09, 3.89s/it] 90%|█████████ | 16897/18688 [19:52:00<2:11:39, 4.41s/it] 90%|█████████ | 16898/18688 [19:52:03<2:05:04, 4.19s/it] 90%|█████████ | 16899/18688 [19:52:08<2:06:29, 4.24s/it] 90%|█████████ | 16900/18688 [19:52:12<2:04:10, 4.17s/it] {'loss': '0.8752', 'grad_norm': '1.466', 'learning_rate': '2.119e-06', 'epoch': '0.9043', 'num_input_tokens_seen': 56875712, 'train_runtime': '7.153e+04', 'train_tokens_per_second': '795.1'}
90%|█████████ | 16900/18688 [19:52:12<2:04:10, 4.17s/it] 90%|█████████ | 16901/18688 [19:52:16<2:05:10, 4.20s/it] 90%|█████████ | 16902/18688 [19:52:21<2:10:07, 4.37s/it] 90%|█████████ | 16903/18688 [19:52:25<2:12:58, 4.47s/it] 90%|█████████ | 16904/18688 [19:52:29<2:08:22, 4.32s/it] 90%|█████████ | 16905/18688 [19:52:34<2:10:15, 4.38s/it] 90%|█████████ | 16906/18688 [19:52:39<2:17:37, 4.63s/it] 90%|█████████ | 16907/18688 [19:52:43<2:07:44, 4.30s/it] 90%|█████████ | 16908/18688 [19:52:47<2:03:07, 4.15s/it] 90%|█████████ | 16909/18688 [19:52:50<2:00:07, 4.05s/it] 90%|█████████ | 16910/18688 [19:52:54<1:58:33, 4.00s/it] {'loss': '0.871', 'grad_norm': '1.463', 'learning_rate': '2.096e-06', 'epoch': '0.9049', 'num_input_tokens_seen': 56910152, 'train_runtime': '7.157e+04', 'train_tokens_per_second': '795.1'}
90%|█████████ | 16910/18688 [19:52:54<1:58:33, 4.00s/it] 90%|█████████ | 16911/18688 [19:52:58<1:54:26, 3.86s/it] 90%|█████████ | 16912/18688 [19:53:02<1:56:55, 3.95s/it] 91%|█████████ | 16913/18688 [19:53:07<2:07:44, 4.32s/it] 91%|█████████ | 16914/18688 [19:53:11<2:01:34, 4.11s/it] 91%|█████████ | 16915/18688 [19:53:15<1:59:11, 4.03s/it] 91%|█████████ | 16916/18688 [19:53:19<2:01:08, 4.10s/it] 91%|█████████ | 16917/18688 [19:53:23<1:59:44, 4.06s/it] 91%|█████████ | 16918/18688 [19:53:27<1:59:48, 4.06s/it] 91%|█████████ | 16919/18688 [19:53:31<2:00:25, 4.08s/it] 91%|█████████ | 16920/18688 [19:53:35<2:02:08, 4.14s/it] {'loss': '0.8587', 'grad_norm': '1.362', 'learning_rate': '2.073e-06', 'epoch': '0.9054', 'num_input_tokens_seen': 56940672, 'train_runtime': '7.162e+04', 'train_tokens_per_second': '795.1'}
91%|█████████ | 16920/18688 [19:53:35<2:02:08, 4.14s/it] 91%|█████████ | 16921/18688 [19:53:39<1:59:44, 4.07s/it] 91%|█████████ | 16922/18688 [19:53:44<2:02:28, 4.16s/it] 91%|█████████ | 16923/18688 [19:53:47<1:58:02, 4.01s/it] 91%|█████████ | 16924/18688 [19:53:51<1:57:55, 4.01s/it] 91%|█████████ | 16925/18688 [19:53:55<1:59:28, 4.07s/it] 91%|█████████ | 16926/18688 [19:54:00<2:02:33, 4.17s/it] 91%|█████████ | 16927/18688 [19:54:04<2:04:05, 4.23s/it] 91%|█████████ | 16928/18688 [19:54:08<2:00:53, 4.12s/it] 91%|█████████ | 16929/18688 [19:54:12<1:58:10, 4.03s/it] 91%|█████████ | 16930/18688 [19:54:16<1:57:38, 4.01s/it] {'loss': '0.8837', 'grad_norm': '1.182', 'learning_rate': '2.05e-06', 'epoch': '0.906', 'num_input_tokens_seen': 56972728, 'train_runtime': '7.166e+04', 'train_tokens_per_second': '795.1'}
91%|█████████ | 16930/18688 [19:54:16<1:57:38, 4.01s/it] 91%|█████████ | 16931/18688 [19:54:19<1:53:14, 3.87s/it] 91%|█████████ | 16932/18688 [19:54:25<2:04:12, 4.24s/it] 91%|█████████ | 16933/18688 [19:54:28<1:58:02, 4.04s/it] 91%|█████████ | 16934/18688 [19:54:33<2:05:15, 4.28s/it] 91%|█████████ | 16935/18688 [19:54:37<2:03:54, 4.24s/it] 91%|█████████ | 16936/18688 [19:54:41<1:57:45, 4.03s/it] 91%|█████████ | 16937/18688 [19:54:44<1:53:13, 3.88s/it] 91%|█████████ | 16938/18688 [19:54:48<1:55:39, 3.97s/it] 91%|█████████ | 16939/18688 [19:54:53<2:05:53, 4.32s/it] 91%|█████████ | 16940/18688 [19:54:57<1:59:46, 4.11s/it] {'loss': '0.8407', 'grad_norm': '1.523', 'learning_rate': '2.027e-06', 'epoch': '0.9065', 'num_input_tokens_seen': 57006328, 'train_runtime': '7.17e+04', 'train_tokens_per_second': '795.1'}
91%|█████████ | 16940/18688 [19:54:57<1:59:46, 4.11s/it] 91%|█████████ | 16941/18688 [19:55:02<2:05:51, 4.32s/it] 91%|█████████ | 16942/18688 [19:55:06<2:05:51, 4.33s/it] 91%|█████████ | 16943/18688 [19:55:10<2:04:00, 4.26s/it] 91%|█████████ | 16944/18688 [19:55:14<1:57:21, 4.04s/it] 91%|█████████ | 16945/18688 [19:55:18<1:54:16, 3.93s/it] 91%|█████████ | 16946/18688 [19:55:22<2:00:55, 4.17s/it] 91%|█████████ | 16947/18688 [19:55:27<2:06:06, 4.35s/it] 91%|█████████ | 16948/18688 [19:55:32<2:08:30, 4.43s/it] 91%|█████████ | 16949/18688 [19:55:36<2:10:49, 4.51s/it] 91%|█████████ | 16950/18688 [19:55:40<2:02:19, 4.22s/it] {'loss': '0.9243', 'grad_norm': '1.363', 'learning_rate': '2.004e-06', 'epoch': '0.907', 'num_input_tokens_seen': 57042000, 'train_runtime': '7.174e+04', 'train_tokens_per_second': '795.1'}
91%|█████████ | 16950/18688 [19:55:40<2:02:19, 4.22s/it] 91%|█████████ | 16951/18688 [19:55:44<1:59:23, 4.12s/it] 91%|█████████ | 16952/18688 [19:55:48<2:00:23, 4.16s/it] 91%|█████████ | 16953/18688 [19:55:52<1:56:02, 4.01s/it] 91%|█████████ | 16954/18688 [19:55:55<1:53:02, 3.91s/it] 91%|█████████ | 16955/18688 [19:55:59<1:52:05, 3.88s/it] 91%|█████████ | 16956/18688 [19:56:04<1:57:50, 4.08s/it] 91%|█████████ | 16957/18688 [19:56:09<2:04:21, 4.31s/it] 91%|█████████ | 16958/18688 [19:56:12<1:59:31, 4.15s/it] 91%|█████████ | 16959/18688 [19:56:17<2:01:44, 4.22s/it] 91%|█████████ | 16960/18688 [19:56:21<1:59:16, 4.14s/it] {'loss': '0.8835', 'grad_norm': '1.172', 'learning_rate': '1.981e-06', 'epoch': '0.9076', 'num_input_tokens_seen': 57074336, 'train_runtime': '7.178e+04', 'train_tokens_per_second': '795.1'}
91%|█████████ | 16960/18688 [19:56:21<1:59:16, 4.14s/it] 91%|█████████ | 16961/18688 [19:56:25<2:00:48, 4.20s/it] 91%|█████████ | 16962/18688 [19:56:29<2:01:08, 4.21s/it] 91%|█████████ | 16963/18688 [19:56:33<1:56:13, 4.04s/it] 91%|█████████ | 16964/18688 [19:56:37<1:58:19, 4.12s/it] 91%|█████████ | 16965/18688 [19:56:42<2:00:56, 4.21s/it] 91%|█████████ | 16966/18688 [19:56:47<2:08:32, 4.48s/it] 91%|█████████ | 16967/18688 [19:56:51<2:06:18, 4.40s/it] 91%|█████████ | 16968/18688 [19:56:54<1:58:07, 4.12s/it] 91%|█████████ | 16969/18688 [19:56:59<2:03:50, 4.32s/it] 91%|█████████ | 16970/18688 [19:57:03<2:02:54, 4.29s/it] {'loss': '0.9692', 'grad_norm': '1.342', 'learning_rate': '1.958e-06', 'epoch': '0.9081', 'num_input_tokens_seen': 57108760, 'train_runtime': '7.182e+04', 'train_tokens_per_second': '795.1'}
91%|█████████ | 16970/18688 [19:57:03<2:02:54, 4.29s/it] 91%|█████████ | 16971/18688 [19:57:07<1:56:15, 4.06s/it] 91%|█████████ | 16972/18688 [19:57:11<1:52:28, 3.93s/it] 91%|█████████ | 16973/18688 [19:57:15<1:58:43, 4.15s/it] 91%|█████████ | 16974/18688 [19:57:19<1:53:04, 3.96s/it] 91%|█████████ | 16975/18688 [19:57:24<2:05:21, 4.39s/it] 91%|█████████ | 16976/18688 [19:57:28<1:58:50, 4.16s/it] 91%|█████████ | 16977/18688 [19:57:32<1:55:35, 4.05s/it] 91%|█████████ | 16978/18688 [19:57:35<1:52:54, 3.96s/it] 91%|█████████ | 16979/18688 [19:57:40<1:55:58, 4.07s/it] 91%|█████████ | 16980/18688 [19:57:44<1:55:31, 4.06s/it] {'loss': '0.9299', 'grad_norm': '1.417', 'learning_rate': '1.936e-06', 'epoch': '0.9086', 'num_input_tokens_seen': 57140504, 'train_runtime': '7.186e+04', 'train_tokens_per_second': '795.1'}
91%|█████████ | 16980/18688 [19:57:44<1:55:31, 4.06s/it] 91%|█████████ | 16981/18688 [19:57:47<1:50:44, 3.89s/it] 91%|█████████ | 16982/18688 [19:57:51<1:52:26, 3.95s/it] 91%|█████████ | 16983/18688 [19:57:56<1:58:29, 4.17s/it] 91%|█████████ | 16984/18688 [19:58:00<1:54:27, 4.03s/it] 91%|█████████ | 16985/18688 [19:58:04<1:58:25, 4.17s/it] 91%|█████████ | 16986/18688 [19:58:08<1:57:49, 4.15s/it] 91%|█████████ | 16987/18688 [19:58:14<2:11:37, 4.64s/it] 91%|█████████ | 16988/18688 [19:58:18<2:04:52, 4.41s/it] 91%|█████████ | 16989/18688 [19:58:22<1:58:57, 4.20s/it] 91%|█████████ | 16990/18688 [19:58:26<1:58:27, 4.19s/it] {'loss': '0.9065', 'grad_norm': '1.186', 'learning_rate': '1.913e-06', 'epoch': '0.9092', 'num_input_tokens_seen': 57174824, 'train_runtime': '7.191e+04', 'train_tokens_per_second': '795.1'}
91%|█████████ | 16990/18688 [19:58:26<1:58:27, 4.19s/it] 91%|█████████ | 16991/18688 [19:58:30<1:59:15, 4.22s/it] 91%|█████████ | 16992/18688 [19:58:34<1:54:42, 4.06s/it] 91%|█████████ | 16993/18688 [19:58:37<1:50:25, 3.91s/it] 91%|█████████ | 16994/18688 [19:58:41<1:51:13, 3.94s/it] 91%|█████████ | 16995/18688 [19:58:46<1:55:11, 4.08s/it] 91%|█████████ | 16996/18688 [19:58:50<1:54:33, 4.06s/it] 91%|█████████ | 16997/18688 [19:58:54<1:53:13, 4.02s/it] 91%|█████████ | 16998/18688 [19:58:58<1:58:21, 4.20s/it] 91%|█████████ | 16999/18688 [19:59:03<1:58:58, 4.23s/it] 91%|█████████ | 17000/18688 [19:59:07<1:59:07, 4.23s/it] {'loss': '0.9221', 'grad_norm': '1.267', 'learning_rate': '1.891e-06', 'epoch': '0.9097', 'num_input_tokens_seen': 57206512, 'train_runtime': '7.195e+04', 'train_tokens_per_second': '795.1'}
91%|█████████ | 17000/18688 [19:59:07<1:59:07, 4.23s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:71: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:281: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 0/1000 [00:00<?, ?it/s]/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:172: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:202: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:254: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 3/1000 [00:00<00:38, 25.90it/s]
1%| | 6/1000 [00:00<00:54, 18.34it/s]
1%| | 8/1000 [00:00<00:56, 17.63it/s]
1%| | 10/1000 [00:00<00:59, 16.68it/s]
1%| | 12/1000 [00:00<01:00, 16.45it/s]
1%|▏ | 14/1000 [00:00<01:01, 16.00it/s]
2%|▏ | 16/1000 [00:00<01:02, 15.80it/s]
2%|▏ | 18/1000 [00:01<01:37, 10.03it/s]
2%|▏ | 20/1000 [00:01<01:32, 10.59it/s]
2%|▏ | 22/1000 [00:01<01:51, 8.74it/s]
2%|▏ | 24/1000 [00:01<01:39, 9.77it/s]
3%|▎ | 26/1000 [00:02<01:45, 9.26it/s]
3%|▎ | 28/1000 [00:02<01:36, 10.09it/s]
3%|▎ | 30/1000 [00:02<01:28, 10.99it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.16it/s]
3%|▎ | 34/1000 [00:02<01:25, 11.26it/s]
4%|▎ | 36/1000 [00:03<01:32, 10.42it/s]
4%|▍ | 38/1000 [00:03<01:24, 11.34it/s]
4%|▍ | 40/1000 [00:03<01:27, 10.93it/s]
4%|▍ | 42/1000 [00:03<01:46, 9.01it/s]
4%|▍ | 44/1000 [00:03<01:30, 10.52it/s]
5%|▍ | 46/1000 [00:03<01:21, 11.69it/s]
5%|▍ | 48/1000 [00:04<01:29, 10.62it/s]
5%|▌ | 50/1000 [00:04<01:21, 11.65it/s]
5%|▌ | 52/1000 [00:04<01:15, 12.51it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.26it/s]
6%|▌ | 56/1000 [00:04<01:07, 13.94it/s]
6%|▌ | 58/1000 [00:05<01:48, 8.72it/s]
6%|▌ | 60/1000 [00:05<01:36, 9.76it/s]
6%|▌ | 62/1000 [00:05<01:27, 10.74it/s]
6%|▋ | 64/1000 [00:05<01:19, 11.80it/s]
7%|▋ | 66/1000 [00:05<01:15, 12.31it/s]
7%|▋ | 68/1000 [00:05<01:12, 12.90it/s]
7%|▋ | 70/1000 [00:05<01:08, 13.48it/s]
7%|▋ | 72/1000 [00:06<01:06, 13.93it/s]
7%|▋ | 74/1000 [00:06<01:07, 13.68it/s]
8%|▊ | 76/1000 [00:06<01:04, 14.25it/s]
8%|▊ | 78/1000 [00:06<01:03, 14.48it/s]
8%|▊ | 80/1000 [00:06<01:08, 13.46it/s]
8%|▊ | 82/1000 [00:06<01:05, 14.10it/s]
8%|▊ | 84/1000 [00:06<01:02, 14.57it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.68it/s]
9%|▉ | 88/1000 [00:07<01:03, 14.47it/s]
9%|▉ | 90/1000 [00:07<01:02, 14.46it/s]
9%|▉ | 92/1000 [00:07<01:04, 14.11it/s]
9%|▉ | 94/1000 [00:07<01:04, 13.94it/s]
10%|▉ | 96/1000 [00:07<01:03, 14.28it/s]
10%|▉ | 98/1000 [00:07<01:03, 14.21it/s]
10%|█ | 100/1000 [00:08<01:31, 9.79it/s]
10%|█ | 102/1000 [00:08<01:23, 10.76it/s]
10%|█ | 104/1000 [00:08<01:54, 7.81it/s]
11%|█ | 106/1000 [00:09<02:02, 7.31it/s]
11%|█ | 108/1000 [00:09<01:43, 8.65it/s]
11%|█ | 110/1000 [00:09<01:28, 10.03it/s]
11%|█ | 112/1000 [00:09<01:20, 11.00it/s]
11%|█▏ | 114/1000 [00:09<01:16, 11.53it/s]
12%|█▏ | 116/1000 [00:09<01:14, 11.84it/s]
12%|█▏ | 118/1000 [00:10<01:10, 12.53it/s]
12%|█▏ | 120/1000 [00:10<01:12, 12.20it/s]
12%|█▏ | 122/1000 [00:10<01:28, 9.88it/s]
12%|█▏ | 124/1000 [00:10<01:34, 9.27it/s]
13%|█▎ | 126/1000 [00:10<01:25, 10.18it/s]
13%|█▎ | 128/1000 [00:11<01:17, 11.19it/s]
13%|█▎ | 130/1000 [00:11<01:13, 11.89it/s]
13%|█▎ | 132/1000 [00:11<01:07, 12.92it/s]
13%|█▎ | 134/1000 [00:11<01:10, 12.21it/s]
14%|█▎ | 136/1000 [00:11<01:05, 13.21it/s]
14%|█▍ | 138/1000 [00:11<01:05, 13.19it/s]
14%|█▍ | 140/1000 [00:12<01:25, 10.08it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.52it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.54it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.55it/s]
15%|█▍ | 148/1000 [00:12<01:14, 11.44it/s]
15%|█▌ | 150/1000 [00:12<01:07, 12.54it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.09it/s]
15%|█▌ | 154/1000 [00:13<01:03, 13.37it/s]
16%|█▌ | 156/1000 [00:13<01:03, 13.20it/s]
16%|█▌ | 158/1000 [00:13<01:02, 13.56it/s]
16%|█▌ | 160/1000 [00:13<01:02, 13.52it/s]
16%|█▌ | 162/1000 [00:13<01:02, 13.43it/s]
16%|█▋ | 164/1000 [00:13<01:01, 13.56it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.69it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.19it/s]
17%|█▋ | 170/1000 [00:14<01:19, 10.46it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.43it/s]
17%|█▋ | 174/1000 [00:14<01:30, 9.15it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.18it/s]
18%|█▊ | 178/1000 [00:15<01:35, 8.65it/s]
18%|█▊ | 180/1000 [00:15<01:33, 8.77it/s]
18%|█▊ | 182/1000 [00:16<01:38, 8.34it/s]
18%|█▊ | 184/1000 [00:16<01:27, 9.27it/s]
19%|█▊ | 186/1000 [00:16<01:40, 8.06it/s]
19%|█▉ | 188/1000 [00:16<01:30, 8.99it/s]
19%|█▉ | 190/1000 [00:16<01:23, 9.74it/s]
19%|█▉ | 192/1000 [00:17<01:17, 10.37it/s]
19%|█▉ | 194/1000 [00:17<01:20, 9.95it/s]
20%|█▉ | 196/1000 [00:17<01:32, 8.65it/s]
20%|█▉ | 198/1000 [00:17<01:20, 9.91it/s]
20%|██ | 200/1000 [00:17<01:17, 10.28it/s]
20%|██ | 202/1000 [00:17<01:10, 11.25it/s]
20%|██ | 204/1000 [00:18<01:04, 12.38it/s]
21%|██ | 206/1000 [00:18<01:00, 13.03it/s]
21%|██ | 208/1000 [00:18<00:58, 13.45it/s]
21%|██ | 210/1000 [00:18<00:54, 14.36it/s]
21%|██ | 212/1000 [00:18<00:53, 14.68it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.71it/s]
22%|██▏ | 216/1000 [00:18<00:54, 14.44it/s]
22%|██▏ | 218/1000 [00:19<00:52, 14.81it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.90it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.18it/s]
22%|██▏ | 224/1000 [00:19<01:20, 9.65it/s]
23%|██▎ | 226/1000 [00:19<01:12, 10.65it/s]
23%|██▎ | 228/1000 [00:20<01:06, 11.54it/s]
23%|██▎ | 230/1000 [00:20<01:03, 12.05it/s]
23%|██▎ | 232/1000 [00:20<01:01, 12.42it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.19it/s]
24%|██▎ | 236/1000 [00:20<00:55, 13.68it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.32it/s]
24%|██▍ | 240/1000 [00:20<00:53, 14.28it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.84it/s]
24%|██▍ | 244/1000 [00:21<00:50, 15.04it/s]
25%|██▍ | 246/1000 [00:21<00:50, 15.01it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.69it/s]
25%|██▌ | 250/1000 [00:21<00:55, 13.44it/s]
25%|██▌ | 252/1000 [00:21<01:01, 12.16it/s]
25%|██▌ | 254/1000 [00:22<00:56, 13.18it/s]
26%|██▌ | 256/1000 [00:22<00:54, 13.74it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.78it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.91it/s]
26%|██▌ | 262/1000 [00:22<00:56, 13.05it/s]
26%|██▋ | 264/1000 [00:22<00:54, 13.62it/s]
27%|██▋ | 266/1000 [00:22<00:52, 14.00it/s]
27%|██▋ | 268/1000 [00:22<00:50, 14.60it/s]
27%|██▋ | 270/1000 [00:23<00:54, 13.30it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.64it/s]
27%|██▋ | 274/1000 [00:23<01:02, 11.68it/s]
28%|██▊ | 276/1000 [00:23<00:57, 12.70it/s]
28%|██▊ | 278/1000 [00:23<00:55, 12.92it/s]
28%|██▊ | 280/1000 [00:23<00:53, 13.51it/s]
28%|██▊ | 282/1000 [00:24<00:49, 14.42it/s]
28%|██▊ | 284/1000 [00:24<00:49, 14.39it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.68it/s]
29%|██▉ | 288/1000 [00:24<00:50, 14.22it/s]
29%|██▉ | 290/1000 [00:24<00:47, 14.91it/s]
29%|██▉ | 292/1000 [00:24<00:47, 15.05it/s]
29%|██▉ | 294/1000 [00:24<00:46, 15.02it/s]
30%|██▉ | 296/1000 [00:25<00:46, 15.15it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.34it/s]
30%|███ | 300/1000 [00:25<00:46, 15.05it/s]
30%|███ | 302/1000 [00:25<00:46, 15.09it/s]
30%|███ | 304/1000 [00:25<00:50, 13.71it/s]
31%|███ | 306/1000 [00:25<00:49, 14.13it/s]
31%|███ | 308/1000 [00:25<00:48, 14.23it/s]
31%|███ | 310/1000 [00:25<00:47, 14.50it/s]
31%|███ | 312/1000 [00:26<00:47, 14.51it/s]
31%|███▏ | 314/1000 [00:26<00:44, 15.32it/s]
32%|███▏ | 316/1000 [00:26<00:43, 15.64it/s]
32%|███▏ | 318/1000 [00:26<00:44, 15.47it/s]
32%|███▏ | 320/1000 [00:26<01:14, 9.17it/s]
32%|███▏ | 322/1000 [00:27<01:05, 10.33it/s]
32%|███▏ | 324/1000 [00:27<01:00, 11.14it/s]
33%|███▎ | 326/1000 [00:27<01:02, 10.70it/s]
33%|███▎ | 328/1000 [00:27<01:01, 10.89it/s]
33%|███▎ | 330/1000 [00:27<00:56, 11.88it/s]
33%|███▎ | 332/1000 [00:27<00:54, 12.27it/s]
33%|███▎ | 334/1000 [00:28<00:52, 12.73it/s]
34%|███▎ | 336/1000 [00:28<00:50, 13.22it/s]
34%|███▍ | 338/1000 [00:28<01:06, 9.91it/s]
34%|███▍ | 340/1000 [00:28<01:00, 10.98it/s]
34%|███▍ | 342/1000 [00:28<00:55, 11.88it/s]
34%|███▍ | 344/1000 [00:28<00:51, 12.66it/s]
35%|███▍ | 346/1000 [00:29<00:49, 13.34it/s]
35%|███▍ | 348/1000 [00:29<00:47, 13.82it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.53it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.74it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.79it/s]
36%|███▌ | 356/1000 [00:29<00:43, 14.88it/s]
36%|███▌ | 358/1000 [00:29<00:43, 14.61it/s]
36%|███▌ | 360/1000 [00:29<00:42, 15.01it/s]
36%|███▌ | 362/1000 [00:30<00:48, 13.23it/s]
36%|███▋ | 364/1000 [00:30<00:46, 13.79it/s]
37%|███▋ | 366/1000 [00:30<00:53, 11.91it/s]
37%|███▋ | 368/1000 [00:30<00:51, 12.31it/s]
37%|███▋ | 370/1000 [00:30<00:47, 13.22it/s]
37%|███▋ | 372/1000 [00:30<00:46, 13.55it/s]
37%|███▋ | 374/1000 [00:31<00:55, 11.38it/s]
38%|███▊ | 376/1000 [00:31<00:56, 10.97it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.65it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.47it/s]
38%|███▊ | 382/1000 [00:31<00:48, 12.67it/s]
38%|███▊ | 384/1000 [00:31<00:45, 13.45it/s]
39%|███▊ | 386/1000 [00:32<00:44, 13.77it/s]
39%|███▉ | 388/1000 [00:32<00:44, 13.69it/s]
39%|███▉ | 390/1000 [00:32<00:44, 13.56it/s]
39%|███▉ | 392/1000 [00:32<00:43, 13.88it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.66it/s]
40%|███▉ | 396/1000 [00:32<00:43, 13.77it/s]
40%|███▉ | 398/1000 [00:32<00:47, 12.59it/s]
40%|████ | 400/1000 [00:33<00:45, 13.25it/s]
40%|████ | 402/1000 [00:33<00:42, 13.91it/s]
40%|████ | 404/1000 [00:33<00:41, 14.34it/s]
41%|████ | 406/1000 [00:33<00:43, 13.62it/s]
41%|████ | 408/1000 [00:33<00:51, 11.42it/s]
41%|████ | 410/1000 [00:34<01:00, 9.82it/s]
41%|████ | 412/1000 [00:34<00:56, 10.42it/s]
41%|████▏ | 414/1000 [00:34<01:11, 8.21it/s]
42%|████▏ | 416/1000 [00:34<01:28, 6.63it/s]
42%|████▏ | 418/1000 [00:35<01:15, 7.74it/s]
42%|████▏ | 420/1000 [00:35<01:04, 9.04it/s]
42%|████▏ | 422/1000 [00:35<00:55, 10.44it/s]
42%|████▏ | 424/1000 [00:35<01:16, 7.53it/s]
43%|████▎ | 426/1000 [00:35<01:05, 8.77it/s]
43%|████▎ | 428/1000 [00:36<00:57, 9.99it/s]
43%|████▎ | 430/1000 [00:36<01:03, 9.04it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.02it/s]
43%|████▎ | 434/1000 [00:36<01:08, 8.24it/s]
44%|████▎ | 436/1000 [00:37<01:00, 9.29it/s]
44%|████▍ | 438/1000 [00:37<01:02, 8.98it/s]
44%|████▍ | 440/1000 [00:37<00:59, 9.49it/s]
44%|████▍ | 442/1000 [00:37<00:52, 10.55it/s]
44%|████▍ | 444/1000 [00:37<00:48, 11.42it/s]
45%|████▍ | 446/1000 [00:37<00:45, 12.17it/s]
45%|████▍ | 448/1000 [00:38<00:50, 10.97it/s]
45%|████▌ | 450/1000 [00:38<00:46, 11.79it/s]
45%|████▌ | 452/1000 [00:38<00:42, 12.85it/s]
45%|████▌ | 454/1000 [00:38<00:44, 12.32it/s]
46%|████▌ | 456/1000 [00:38<00:42, 12.75it/s]
46%|████▌ | 458/1000 [00:38<00:39, 13.71it/s]
46%|████▌ | 460/1000 [00:39<00:39, 13.83it/s]
46%|████▌ | 462/1000 [00:39<00:39, 13.53it/s]
46%|████▋ | 464/1000 [00:39<00:37, 14.25it/s]
47%|████▋ | 466/1000 [00:39<00:37, 14.15it/s]
47%|████▋ | 468/1000 [00:39<00:42, 12.57it/s]
47%|████▋ | 470/1000 [00:39<00:53, 9.96it/s]
47%|████▋ | 472/1000 [00:40<00:52, 10.11it/s]
47%|████▋ | 474/1000 [00:40<00:46, 11.42it/s]
48%|████▊ | 476/1000 [00:40<00:43, 12.10it/s]
48%|████▊ | 478/1000 [00:40<00:43, 11.97it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.92it/s]
48%|████▊ | 482/1000 [00:40<00:38, 13.32it/s]
48%|████▊ | 484/1000 [00:40<00:37, 13.84it/s]
49%|████▊ | 486/1000 [00:41<00:36, 14.00it/s]
49%|████▉ | 488/1000 [00:41<00:35, 14.30it/s]
49%|████▉ | 490/1000 [00:41<00:45, 11.11it/s]
49%|████▉ | 492/1000 [00:41<00:40, 12.41it/s]
49%|████▉ | 494/1000 [00:41<00:37, 13.39it/s]
50%|████▉ | 496/1000 [00:41<00:35, 14.18it/s]
50%|████▉ | 498/1000 [00:41<00:34, 14.61it/s]
50%|█████ | 500/1000 [00:42<00:43, 11.59it/s]
50%|█████ | 502/1000 [00:42<00:42, 11.65it/s]
50%|█████ | 504/1000 [00:42<00:40, 12.12it/s]
51%|█████ | 506/1000 [00:42<00:41, 12.01it/s]
51%|█████ | 508/1000 [00:42<00:39, 12.49it/s]
51%|█████ | 510/1000 [00:43<00:49, 9.87it/s]
51%|█████ | 512/1000 [00:43<00:45, 10.66it/s]
51%|█████▏ | 514/1000 [00:43<00:41, 11.60it/s]
52%|█████▏ | 516/1000 [00:43<00:39, 12.25it/s]
52%|█████▏ | 518/1000 [00:43<00:37, 12.80it/s]
52%|█████▏ | 520/1000 [00:43<00:36, 13.24it/s]
52%|█████▏ | 522/1000 [00:44<00:34, 14.00it/s]
52%|█████▏ | 524/1000 [00:44<00:33, 14.06it/s]
53%|█████▎ | 526/1000 [00:44<00:33, 14.24it/s]
53%|█████▎ | 528/1000 [00:44<00:39, 11.94it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.79it/s]
53%|█████▎ | 532/1000 [00:44<00:34, 13.71it/s]
53%|█████▎ | 534/1000 [00:44<00:33, 14.02it/s]
54%|█████▎ | 536/1000 [00:45<00:32, 14.18it/s]
54%|█████▍ | 538/1000 [00:45<00:33, 13.97it/s]
54%|█████▍ | 540/1000 [00:45<00:31, 14.41it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.68it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.59it/s]
55%|█████▍ | 546/1000 [00:45<00:31, 14.22it/s]
55%|█████▍ | 548/1000 [00:45<00:32, 13.91it/s]
55%|█████▌ | 550/1000 [00:46<00:31, 14.14it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.26it/s]
55%|█████▌ | 554/1000 [00:46<00:31, 13.94it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.10it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.47it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 15.02it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 14.93it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.76it/s]
57%|█████▋ | 566/1000 [00:47<00:37, 11.67it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.21it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.34it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.83it/s]
57%|█████▋ | 574/1000 [00:47<00:31, 13.73it/s]
58%|█████▊ | 576/1000 [00:47<00:31, 13.64it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.66it/s]
58%|█████▊ | 580/1000 [00:48<00:32, 12.90it/s]
58%|█████▊ | 582/1000 [00:48<00:30, 13.63it/s]
58%|█████▊ | 584/1000 [00:48<00:30, 13.85it/s]
59%|█████▊ | 586/1000 [00:48<00:30, 13.46it/s]
59%|█████▉ | 588/1000 [00:48<00:31, 13.18it/s]
59%|█████▉ | 590/1000 [00:49<00:31, 13.01it/s]
59%|█████▉ | 592/1000 [00:49<00:31, 12.80it/s]
59%|█████▉ | 594/1000 [00:49<00:33, 12.00it/s]
60%|█████▉ | 596/1000 [00:49<00:33, 12.11it/s]
60%|█████▉ | 598/1000 [00:49<00:33, 12.08it/s]
60%|██████ | 600/1000 [00:49<00:32, 12.18it/s]
60%|██████ | 602/1000 [00:50<00:32, 12.22it/s]
60%|██████ | 604/1000 [00:50<00:30, 12.81it/s]
61%|██████ | 606/1000 [00:50<00:29, 13.23it/s]
61%|██████ | 608/1000 [00:50<00:28, 13.95it/s]
61%|██████ | 610/1000 [00:50<00:27, 14.17it/s]
61%|██████ | 612/1000 [00:50<00:26, 14.56it/s]
61%|██████▏ | 614/1000 [00:50<00:27, 14.00it/s]
62%|██████▏ | 616/1000 [00:51<00:26, 14.49it/s]
62%|██████▏ | 618/1000 [00:51<00:27, 14.01it/s]
62%|██████▏ | 620/1000 [00:51<00:39, 9.70it/s]
62%|██████▏ | 622/1000 [00:51<00:38, 9.95it/s]
62%|██████▏ | 624/1000 [00:51<00:32, 11.41it/s]
63%|██████▎ | 626/1000 [00:51<00:30, 12.09it/s]
63%|██████▎ | 628/1000 [00:52<00:28, 13.24it/s]
63%|██████▎ | 630/1000 [00:52<00:26, 13.75it/s]
63%|██████▎ | 632/1000 [00:52<00:29, 12.43it/s]
63%|██████▎ | 634/1000 [00:52<00:27, 13.15it/s]
64%|██████▎ | 636/1000 [00:52<00:33, 10.93it/s]
64%|██████▍ | 638/1000 [00:53<00:33, 10.66it/s]
64%|██████▍ | 640/1000 [00:53<00:31, 11.45it/s]
64%|██████▍ | 642/1000 [00:53<00:29, 12.34it/s]
64%|██████▍ | 644/1000 [00:53<00:29, 12.10it/s]
65%|██████▍ | 646/1000 [00:53<00:28, 12.49it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.06it/s]
65%|██████▌ | 650/1000 [00:53<00:28, 12.41it/s]
65%|██████▌ | 652/1000 [00:54<00:29, 11.85it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.38it/s]
66%|██████▌ | 656/1000 [00:54<00:32, 10.74it/s]
66%|██████▌ | 658/1000 [00:54<00:29, 11.78it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.11it/s]
66%|██████▌ | 662/1000 [00:55<00:30, 11.24it/s]
66%|██████▋ | 664/1000 [00:55<00:28, 11.74it/s]
67%|██████▋ | 666/1000 [00:55<00:26, 12.77it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.30it/s]
67%|██████▋ | 670/1000 [00:55<00:29, 11.08it/s]
67%|██████▋ | 672/1000 [00:55<00:27, 11.86it/s]
67%|██████▋ | 674/1000 [00:56<00:26, 12.44it/s]
68%|██████▊ | 676/1000 [00:56<00:27, 11.71it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.21it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 12.98it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.11it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.30it/s]
69%|██████▊ | 686/1000 [00:57<00:25, 12.50it/s]
69%|██████▉ | 688/1000 [00:57<00:23, 13.02it/s]
69%|██████▉ | 690/1000 [00:57<00:23, 13.28it/s]
69%|██████▉ | 692/1000 [00:57<00:22, 13.86it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.05it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 13.90it/s]
70%|██████▉ | 698/1000 [00:57<00:20, 14.50it/s]
70%|███████ | 700/1000 [00:58<00:24, 12.12it/s]
70%|███████ | 702/1000 [00:58<00:23, 12.94it/s]
70%|███████ | 704/1000 [00:58<00:30, 9.85it/s]
71%|███████ | 706/1000 [00:58<00:27, 10.67it/s]
71%|███████ | 708/1000 [00:58<00:31, 9.26it/s]
71%|███████ | 710/1000 [00:59<00:27, 10.60it/s]
71%|███████ | 712/1000 [00:59<00:24, 11.60it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.74it/s]
72%|███████▏ | 716/1000 [00:59<00:22, 12.90it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.45it/s]
72%|███████▏ | 720/1000 [00:59<00:21, 13.06it/s]
72%|███████▏ | 722/1000 [00:59<00:20, 13.33it/s]
72%|███████▏ | 724/1000 [01:00<00:21, 12.70it/s]
73%|███████▎ | 726/1000 [01:00<00:21, 12.49it/s]
73%|███████▎ | 728/1000 [01:00<00:24, 11.09it/s]
73%|███████▎ | 730/1000 [01:00<00:34, 7.88it/s]
73%|███████▎ | 732/1000 [01:01<00:33, 8.07it/s]
73%|███████▎ | 734/1000 [01:01<00:31, 8.41it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.30it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.77it/s]
74%|███████▍ | 739/1000 [01:01<00:23, 10.89it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.58it/s]
74%|███████▍ | 743/1000 [01:02<00:21, 12.23it/s]
74%|███████▍ | 745/1000 [01:02<00:25, 10.15it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.56it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.74it/s]
75%|███████▌ | 751/1000 [01:02<00:21, 11.85it/s]
75%|███████▌ | 753/1000 [01:02<00:19, 12.61it/s]
76%|███████▌ | 755/1000 [01:03<00:18, 13.33it/s]
76%|███████▌ | 757/1000 [01:03<00:23, 10.25it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.28it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.26it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.24it/s]
76%|███████▋ | 765/1000 [01:04<00:27, 8.59it/s]
77%|███████▋ | 767/1000 [01:04<00:24, 9.55it/s]
77%|███████▋ | 769/1000 [01:04<00:21, 10.57it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.82it/s]
77%|███████▋ | 773/1000 [01:04<00:18, 12.56it/s]
78%|███████▊ | 775/1000 [01:04<00:17, 13.11it/s]
78%|███████▊ | 777/1000 [01:05<00:15, 14.08it/s]
78%|███████▊ | 779/1000 [01:05<00:21, 10.46it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.37it/s]
78%|███████▊ | 783/1000 [01:05<00:26, 8.29it/s]
78%|███████▊ | 784/1000 [01:06<00:30, 7.06it/s]
78%|███████▊ | 785/1000 [01:06<00:29, 7.37it/s]
79%|███████▊ | 786/1000 [01:06<00:30, 6.98it/s]
79%|███████▉ | 788/1000 [01:06<00:25, 8.46it/s]
79%|███████▉ | 790/1000 [01:06<00:21, 9.55it/s]
79%|███████▉ | 792/1000 [01:06<00:20, 10.36it/s]
79%|███████▉ | 794/1000 [01:07<00:18, 11.00it/s]
80%|███████▉ | 796/1000 [01:07<00:17, 11.38it/s]
80%|███████▉ | 798/1000 [01:07<00:16, 11.96it/s]
80%|████████ | 800/1000 [01:07<00:17, 11.27it/s]
80%|████████ | 802/1000 [01:08<00:25, 7.79it/s]
80%|████████ | 803/1000 [01:08<00:26, 7.42it/s]
80%|████████ | 805/1000 [01:08<00:22, 8.69it/s]
81%|████████ | 806/1000 [01:08<00:22, 8.69it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.42it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.54it/s]
81%|████████ | 812/1000 [01:08<00:15, 11.86it/s]
81%|████████▏ | 814/1000 [01:09<00:14, 12.77it/s]
82%|████████▏ | 816/1000 [01:09<00:16, 11.06it/s]
82%|████████▏ | 818/1000 [01:09<00:15, 12.03it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.52it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 13.08it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.88it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.28it/s]
83%|████████▎ | 828/1000 [01:10<00:11, 14.54it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.97it/s]
83%|████████▎ | 832/1000 [01:10<00:11, 14.39it/s]
83%|████████▎ | 834/1000 [01:10<00:11, 14.09it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.41it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.29it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.41it/s]
84%|████████▍ | 842/1000 [01:11<00:10, 14.43it/s]
84%|████████▍ | 844/1000 [01:11<00:11, 13.96it/s]
85%|████████▍ | 846/1000 [01:11<00:11, 13.92it/s]
85%|████████▍ | 848/1000 [01:11<00:10, 13.87it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.09it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.67it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.29it/s]
86%|████████▌ | 856/1000 [01:12<00:10, 14.12it/s]
86%|████████▌ | 858/1000 [01:12<00:09, 14.63it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 15.40it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.52it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 14.04it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.85it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.73it/s]
87%|████████▋ | 870/1000 [01:13<00:09, 14.12it/s]
87%|████████▋ | 872/1000 [01:13<00:08, 14.74it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 12.44it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.04it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.93it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.57it/s]
88%|████████▊ | 882/1000 [01:14<00:10, 11.36it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 12.10it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.90it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.88it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.33it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.08it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 14.21it/s]
90%|████████▉ | 896/1000 [01:15<00:07, 13.96it/s]
90%|████████▉ | 898/1000 [01:15<00:07, 13.36it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.23it/s]
90%|█████████ | 902/1000 [01:15<00:07, 12.97it/s]
90%|█████████ | 904/1000 [01:15<00:09, 9.80it/s]
91%|█████████ | 906/1000 [01:16<00:08, 10.85it/s]
91%|█████████ | 908/1000 [01:16<00:07, 11.82it/s]
91%|█████████ | 910/1000 [01:16<00:07, 12.06it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.25it/s]
91%|█████████▏| 914/1000 [01:17<00:12, 6.80it/s]
92%|█████████▏| 916/1000 [01:17<00:10, 8.13it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.38it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.80it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 10.27it/s]
92%|█████████▏| 924/1000 [01:17<00:06, 10.96it/s]
93%|█████████▎| 926/1000 [01:18<00:06, 11.81it/s]
93%|█████████▎| 928/1000 [01:18<00:06, 10.65it/s]
93%|█████████▎| 930/1000 [01:18<00:05, 11.87it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 12.26it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.28it/s]
94%|█████████▎| 936/1000 [01:18<00:04, 12.80it/s]
94%|█████████▍| 938/1000 [01:19<00:04, 13.64it/s]
94%|█████████▍| 940/1000 [01:19<00:04, 13.99it/s]
94%|█████████▍| 942/1000 [01:19<00:04, 11.96it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.50it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.63it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 12.49it/s]
95%|█████████▌| 950/1000 [01:19<00:03, 13.33it/s]
95%|█████████▌| 952/1000 [01:20<00:03, 14.05it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 14.18it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 14.19it/s]
96%|█████████▌| 958/1000 [01:20<00:02, 14.72it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 13.10it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.72it/s]
96%|█████████▋| 964/1000 [01:21<00:03, 10.21it/s]
97%|█████████▋| 966/1000 [01:21<00:02, 11.39it/s]
97%|█████████▋| 968/1000 [01:21<00:02, 12.05it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 12.67it/s]
97%|█████████▋| 972/1000 [01:21<00:02, 9.68it/s]
97%|█████████▋| 974/1000 [01:22<00:02, 10.86it/s]
98%|█████████▊| 976/1000 [01:22<00:02, 11.65it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.86it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.94it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.43it/s]
98%|█████████▊| 984/1000 [01:23<00:01, 10.44it/s]
99%|█████████▊| 986/1000 [01:23<00:01, 10.69it/s]
99%|█████████▉| 988/1000 [01:23<00:01, 10.90it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 11.38it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 12.28it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 13.03it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 13.93it/s]
100%|█████████▉| 998/1000 [01:24<00:00, 13.45it/s]
100%|██████████| 1000/1000 [01:24<00:00, 14.25it/s]
{'eval_loss': '0.8758', 'eval_runtime': '84.25', 'eval_samples_per_second': '11.87', 'eval_steps_per_second': '11.87', 'epoch': '0.9097', 'num_input_tokens_seen': 57206512}
91%|█████████ | 17000/18688 [20:00:31<1:59:07, 4.23s/it]
100%|██████████| 1000/1000 [01:24<00:00, 14.25it/s]
 91%|█████████ | 17001/18688 [20:00:36<13:52:09, 29.60s/it] 91%|█████████ | 17002/18688 [20:00:39<10:14:07, 21.86s/it] 91%|█████████ | 17003/18688 [20:00:44<7:47:28, 16.65s/it] 91%|█████████ | 17004/18688 [20:00:48<5:57:12, 12.73s/it] 91%|█████████ | 17005/18688 [20:00:52<4:44:06, 10.13s/it] 91%|█████████ | 17006/18688 [20:00:55<3:48:02, 8.13s/it] 91%|█████████ | 17007/18688 [20:01:00<3:20:01, 7.14s/it] 91%|█████████ | 17008/18688 [20:01:05<3:02:00, 6.50s/it] 91%|█████████ | 17009/18688 [20:01:10<2:46:29, 5.95s/it] 91%|█████████ | 17010/18688 [20:01:13<2:28:02, 5.29s/it] {'loss': '0.9099', 'grad_norm': '1.259', 'learning_rate': '1.869e-06', 'epoch': '0.9102', 'num_input_tokens_seen': 57241360, 'train_runtime': '7.207e+04', 'train_tokens_per_second': '794.2'}
91%|█████████ | 17010/18688 [20:01:13<2:28:02, 5.29s/it] 91%|█████████ | 17011/18688 [20:01:18<2:21:07, 5.05s/it] 91%|█████████ | 17012/18688 [20:01:22<2:11:09, 4.70s/it] 91%|█████████ | 17013/18688 [20:01:26<2:11:08, 4.70s/it] 91%|█████████ | 17014/18688 [20:01:31<2:11:52, 4.73s/it] 91%|█████████ | 17015/18688 [20:01:35<2:02:26, 4.39s/it] 91%|█████████ | 17016/18688 [20:01:40<2:06:09, 4.53s/it] 91%|█████████ | 17017/18688 [20:01:45<2:10:39, 4.69s/it] 91%|█████████ | 17018/18688 [20:01:48<2:02:54, 4.42s/it] 91%|█████████ | 17019/18688 [20:01:53<2:04:53, 4.49s/it] 91%|█████████ | 17020/18688 [20:01:57<1:59:16, 4.29s/it] {'loss': '0.942', 'grad_norm': '1.195', 'learning_rate': '1.847e-06', 'epoch': '0.9108', 'num_input_tokens_seen': 57278416, 'train_runtime': '7.212e+04', 'train_tokens_per_second': '794.2'}
91%|█████████ | 17020/18688 [20:01:57<1:59:16, 4.29s/it] 91%|█████████ | 17021/18688 [20:02:02<2:04:03, 4.47s/it] 91%|█████████ | 17022/18688 [20:02:06<1:59:39, 4.31s/it] 91%|█████████ | 17023/18688 [20:02:10<1:54:46, 4.14s/it] 91%|█████████ | 17024/18688 [20:02:14<1:56:41, 4.21s/it] 91%|█████████ | 17025/18688 [20:02:18<1:56:54, 4.22s/it] 91%|█████████ | 17026/18688 [20:02:22<1:56:17, 4.20s/it] 91%|█████████ | 17027/18688 [20:02:26<1:51:48, 4.04s/it] 91%|█████████ | 17028/18688 [20:02:30<1:50:29, 3.99s/it] 91%|█████████ | 17029/18688 [20:02:35<1:56:39, 4.22s/it] 91%|█████████ | 17030/18688 [20:02:39<1:55:11, 4.17s/it] {'loss': '0.8736', 'grad_norm': '1.686', 'learning_rate': '1.825e-06', 'epoch': '0.9113', 'num_input_tokens_seen': 57311688, 'train_runtime': '7.216e+04', 'train_tokens_per_second': '794.2'}
91%|█████████ | 17030/18688 [20:02:39<1:55:11, 4.17s/it] 91%|█████████ | 17031/18688 [20:02:43<1:57:36, 4.26s/it] 91%|█████████ | 17032/18688 [20:02:47<1:56:52, 4.23s/it] 91%|█████████ | 17033/18688 [20:02:51<1:53:11, 4.10s/it] 91%|█████████ | 17034/18688 [20:02:56<1:58:26, 4.30s/it] 91%|█████████ | 17035/18688 [20:03:00<1:57:30, 4.27s/it] 91%|█████████ | 17036/18688 [20:03:04<1:53:55, 4.14s/it] 91%|█████████ | 17037/18688 [20:03:08<1:49:58, 4.00s/it] 91%|█████████ | 17038/18688 [20:03:11<1:47:47, 3.92s/it] 91%|█████████ | 17039/18688 [20:03:15<1:45:10, 3.83s/it] 91%|█████████ | 17040/18688 [20:03:19<1:48:57, 3.97s/it] {'loss': '0.9546', 'grad_norm': '1.406', 'learning_rate': '1.803e-06', 'epoch': '0.9118', 'num_input_tokens_seen': 57342880, 'train_runtime': '7.22e+04', 'train_tokens_per_second': '794.2'}
91%|█████████ | 17040/18688 [20:03:19<1:48:57, 3.97s/it] 91%|█████████ | 17041/18688 [20:03:23<1:48:01, 3.94s/it] 91%|█████████ | 17042/18688 [20:03:27<1:50:31, 4.03s/it] 91%|█████████ | 17043/18688 [20:03:32<1:54:01, 4.16s/it] 91%|█████████ | 17044/18688 [20:03:36<1:54:35, 4.18s/it] 91%|█████████ | 17045/18688 [20:03:41<1:59:34, 4.37s/it] 91%|█████████ | 17046/18688 [20:03:45<2:00:06, 4.39s/it] 91%|█████████ | 17047/18688 [20:03:49<1:57:19, 4.29s/it] 91%|█████████ | 17048/18688 [20:03:54<1:59:23, 4.37s/it] 91%|█████████ | 17049/18688 [20:03:58<2:01:16, 4.44s/it] 91%|█████████ | 17050/18688 [20:04:03<2:00:10, 4.40s/it] {'loss': '0.9168', 'grad_norm': '1.028', 'learning_rate': '1.782e-06', 'epoch': '0.9124', 'num_input_tokens_seen': 57378656, 'train_runtime': '7.224e+04', 'train_tokens_per_second': '794.2'}
91%|█████████ | 17050/18688 [20:04:03<2:00:10, 4.40s/it] 91%|█████████ | 17051/18688 [20:04:07<1:56:18, 4.26s/it] 91%|█████████ | 17052/18688 [20:04:12<2:00:40, 4.43s/it] 91%|█████████▏| 17053/18688 [20:04:17<2:08:30, 4.72s/it] 91%|█████████▏| 17054/18688 [20:04:21<2:00:48, 4.44s/it] 91%|█████████▏| 17055/18688 [20:04:25<2:01:59, 4.48s/it] 91%|█████████▏| 17056/18688 [20:04:30<2:05:15, 4.61s/it] 91%|█████████▏| 17057/18688 [20:04:34<1:58:54, 4.37s/it] 91%|█████████▏| 17058/18688 [20:04:38<1:56:55, 4.30s/it] 91%|█████████▏| 17059/18688 [20:04:42<1:55:45, 4.26s/it] 91%|█████████▏| 17060/18688 [20:04:46<1:51:15, 4.10s/it] {'loss': '0.974', 'grad_norm': '1.378', 'learning_rate': '1.76e-06', 'epoch': '0.9129', 'num_input_tokens_seen': 57414632, 'train_runtime': '7.229e+04', 'train_tokens_per_second': '794.3'}
91%|█████████▏| 17060/18688 [20:04:46<1:51:15, 4.10s/it] 91%|█████████▏| 17061/18688 [20:04:50<1:46:10, 3.92s/it] 91%|█████████▏| 17062/18688 [20:04:54<1:47:58, 3.98s/it] 91%|█████████▏| 17063/18688 [20:04:58<1:48:25, 4.00s/it] 91%|█████████▏| 17064/18688 [20:05:02<1:54:41, 4.24s/it] 91%|█████████▏| 17065/18688 [20:05:07<1:53:22, 4.19s/it] 91%|█████████▏| 17066/18688 [20:05:12<1:59:45, 4.43s/it] 91%|█████████▏| 17067/18688 [20:05:15<1:52:43, 4.17s/it] 91%|█████████▏| 17068/18688 [20:05:19<1:50:21, 4.09s/it] 91%|█████████▏| 17069/18688 [20:05:24<1:55:00, 4.26s/it] 91%|█████████▏| 17070/18688 [20:05:28<1:53:53, 4.22s/it] {'loss': '0.9135', 'grad_norm': '1.2', 'learning_rate': '1.739e-06', 'epoch': '0.9134', 'num_input_tokens_seen': 57449088, 'train_runtime': '7.233e+04', 'train_tokens_per_second': '794.3'}
91%|█████████▏| 17070/18688 [20:05:28<1:53:53, 4.22s/it] 91%|█████████▏| 17071/18688 [20:05:32<1:56:03, 4.31s/it] 91%|█████████▏| 17072/18688 [20:05:37<1:56:15, 4.32s/it] 91%|█████████▏| 17073/18688 [20:05:41<1:59:37, 4.44s/it] 91%|█████████▏| 17074/18688 [20:05:47<2:08:35, 4.78s/it] 91%|█████████▏| 17075/18688 [20:05:51<2:01:39, 4.53s/it] 91%|█████████▏| 17076/18688 [20:05:56<2:05:05, 4.66s/it] 91%|█████████▏| 17077/18688 [20:06:00<2:01:06, 4.51s/it] 91%|█████████▏| 17078/18688 [20:06:04<1:56:38, 4.35s/it] 91%|█████████▏| 17079/18688 [20:06:09<1:58:51, 4.43s/it] 91%|█████████▏| 17080/18688 [20:06:12<1:53:17, 4.23s/it] {'loss': '0.9477', 'grad_norm': '1.251', 'learning_rate': '1.717e-06', 'epoch': '0.914', 'num_input_tokens_seen': 57488120, 'train_runtime': '7.237e+04', 'train_tokens_per_second': '794.3'}
91%|█████████▏| 17080/18688 [20:06:12<1:53:17, 4.23s/it] 91%|█████████▏| 17081/18688 [20:06:16<1:50:08, 4.11s/it] 91%|█████████▏| 17082/18688 [20:06:21<1:55:10, 4.30s/it] 91%|█████████▏| 17083/18688 [20:06:25<1:51:49, 4.18s/it] 91%|█████████▏| 17084/18688 [20:06:29<1:48:48, 4.07s/it] 91%|█████████▏| 17085/18688 [20:06:33<1:49:34, 4.10s/it] 91%|█████████▏| 17086/18688 [20:06:38<1:54:29, 4.29s/it] 91%|█████████▏| 17087/18688 [20:06:42<1:55:37, 4.33s/it] 91%|█████████▏| 17088/18688 [20:06:47<2:00:38, 4.52s/it] 91%|█████████▏| 17089/18688 [20:06:51<1:53:57, 4.28s/it] 91%|█████████▏| 17090/18688 [20:06:55<1:50:18, 4.14s/it] {'loss': '0.9343', 'grad_norm': '1.151', 'learning_rate': '1.696e-06', 'epoch': '0.9145', 'num_input_tokens_seen': 57522368, 'train_runtime': '7.242e+04', 'train_tokens_per_second': '794.3'}
91%|█████████▏| 17090/18688 [20:06:55<1:50:18, 4.14s/it] 91%|█████████▏| 17091/18688 [20:06:58<1:46:56, 4.02s/it] 91%|█████████▏| 17092/18688 [20:07:03<1:54:33, 4.31s/it] 91%|█████████▏| 17093/18688 [20:07:07<1:48:44, 4.09s/it] 91%|█████████▏| 17094/18688 [20:07:11<1:47:20, 4.04s/it] 91%|█████████▏| 17095/18688 [20:07:15<1:45:26, 3.97s/it] 91%|█████████▏| 17096/18688 [20:07:18<1:44:03, 3.92s/it] 91%|█████████▏| 17097/18688 [20:07:22<1:42:32, 3.87s/it] 91%|█████████▏| 17098/18688 [20:07:26<1:40:52, 3.81s/it] 91%|█████████▏| 17099/18688 [20:07:30<1:42:33, 3.87s/it] 92%|█████████▏| 17100/18688 [20:07:34<1:43:04, 3.89s/it] {'loss': '0.8777', 'grad_norm': '1.258', 'learning_rate': '1.675e-06', 'epoch': '0.9151', 'num_input_tokens_seen': 57552504, 'train_runtime': '7.245e+04', 'train_tokens_per_second': '794.3'}
92%|█████████▏| 17100/18688 [20:07:34<1:43:04, 3.89s/it] 92%|█████████▏| 17101/18688 [20:07:37<1:40:33, 3.80s/it] 92%|█████████▏| 17102/18688 [20:07:42<1:43:47, 3.93s/it] 92%|█████████▏| 17103/18688 [20:07:46<1:47:02, 4.05s/it] 92%|█████████▏| 17104/18688 [20:07:50<1:50:26, 4.18s/it] 92%|█████████▏| 17105/18688 [20:07:55<1:52:50, 4.28s/it] 92%|█████████▏| 17106/18688 [20:07:59<1:53:06, 4.29s/it] 92%|█████████▏| 17107/18688 [20:08:03<1:52:15, 4.26s/it] 92%|█████████▏| 17108/18688 [20:08:07<1:47:42, 4.09s/it] 92%|█████████▏| 17109/18688 [20:08:11<1:48:26, 4.12s/it] 92%|█████████▏| 17110/18688 [20:08:15<1:44:58, 3.99s/it] {'loss': '0.8437', 'grad_norm': '1.199', 'learning_rate': '1.654e-06', 'epoch': '0.9156', 'num_input_tokens_seen': 57586208, 'train_runtime': '7.25e+04', 'train_tokens_per_second': '794.3'}
92%|█████████▏| 17110/18688 [20:08:15<1:44:58, 3.99s/it] 92%|█████████▏| 17111/18688 [20:08:19<1:42:09, 3.89s/it] 92%|█████████▏| 17112/18688 [20:08:23<1:47:14, 4.08s/it] 92%|█████████▏| 17113/18688 [20:08:27<1:48:02, 4.12s/it] 92%|█████████▏| 17114/18688 [20:08:31<1:47:26, 4.10s/it] 92%|█████████▏| 17115/18688 [20:08:35<1:43:37, 3.95s/it] 92%|█████████▏| 17116/18688 [20:08:40<1:48:31, 4.14s/it] 92%|█████████▏| 17117/18688 [20:08:43<1:45:12, 4.02s/it] 92%|█████████▏| 17118/18688 [20:08:47<1:44:15, 3.98s/it] 92%|█████████▏| 17119/18688 [20:08:53<1:56:45, 4.46s/it] 92%|█████████▏| 17120/18688 [20:08:57<1:57:29, 4.50s/it] {'loss': '0.9716', 'grad_norm': '1.616', 'learning_rate': '1.634e-06', 'epoch': '0.9161', 'num_input_tokens_seen': 57620560, 'train_runtime': '7.254e+04', 'train_tokens_per_second': '794.4'}
92%|█████████▏| 17120/18688 [20:08:57<1:57:29, 4.50s/it] 92%|█████████▏| 17121/18688 [20:09:03<2:03:12, 4.72s/it] 92%|█████████▏| 17122/18688 [20:09:06<1:55:32, 4.43s/it] 92%|█████████▏| 17123/18688 [20:09:10<1:52:07, 4.30s/it] 92%|█████████▏| 17124/18688 [20:09:15<1:52:20, 4.31s/it] 92%|█████████▏| 17125/18688 [20:09:19<1:49:11, 4.19s/it] 92%|█████████▏| 17126/18688 [20:09:22<1:45:13, 4.04s/it] 92%|█████████▏| 17127/18688 [20:09:26<1:44:21, 4.01s/it] 92%|█████████▏| 17128/18688 [20:09:31<1:46:56, 4.11s/it] 92%|█████████▏| 17129/18688 [20:09:35<1:48:35, 4.18s/it] 92%|█████████▏| 17130/18688 [20:09:39<1:46:15, 4.09s/it] {'loss': '0.9415', 'grad_norm': '1.114', 'learning_rate': '1.613e-06', 'epoch': '0.9167', 'num_input_tokens_seen': 57653336, 'train_runtime': '7.258e+04', 'train_tokens_per_second': '794.4'}
92%|█████████▏| 17130/18688 [20:09:39<1:46:15, 4.09s/it] 92%|█████████▏| 17131/18688 [20:09:43<1:43:19, 3.98s/it] 92%|█████████▏| 17132/18688 [20:09:46<1:41:41, 3.92s/it] 92%|█████████▏| 17133/18688 [20:09:50<1:39:54, 3.86s/it] 92%|█████████▏| 17134/18688 [20:09:55<1:49:43, 4.24s/it] 92%|█████████▏| 17135/18688 [20:09:59<1:46:38, 4.12s/it] 92%|█████████▏| 17136/18688 [20:10:04<1:54:43, 4.44s/it] 92%|█████████▏| 17137/18688 [20:10:08<1:49:18, 4.23s/it] 92%|█████████▏| 17138/18688 [20:10:12<1:49:06, 4.22s/it] 92%|█████████▏| 17139/18688 [20:10:16<1:46:34, 4.13s/it] 92%|█████████▏| 17140/18688 [20:10:20<1:43:59, 4.03s/it] {'loss': '0.9415', 'grad_norm': '1.393', 'learning_rate': '1.592e-06', 'epoch': '0.9172', 'num_input_tokens_seen': 57685760, 'train_runtime': '7.262e+04', 'train_tokens_per_second': '794.3'}
92%|█████████▏| 17140/18688 [20:10:20<1:43:59, 4.03s/it] 92%|█████████▏| 17141/18688 [20:10:24<1:43:45, 4.02s/it] 92%|█████████▏| 17142/18688 [20:10:28<1:47:38, 4.18s/it] 92%|█████████▏| 17143/18688 [20:10:33<1:47:56, 4.19s/it] 92%|█████████▏| 17144/18688 [20:10:37<1:50:52, 4.31s/it] 92%|█████████▏| 17145/18688 [20:10:41<1:46:47, 4.15s/it] 92%|█████████▏| 17146/18688 [20:10:45<1:42:47, 4.00s/it] 92%|█████████▏| 17147/18688 [20:10:49<1:42:04, 3.97s/it] 92%|█████████▏| 17148/18688 [20:10:53<1:43:04, 4.02s/it] 92%|█████████▏| 17149/18688 [20:10:58<1:53:09, 4.41s/it] 92%|█████████▏| 17150/18688 [20:11:02<1:50:03, 4.29s/it] {'loss': '0.8792', 'grad_norm': '1.05', 'learning_rate': '1.572e-06', 'epoch': '0.9177', 'num_input_tokens_seen': 57719592, 'train_runtime': '7.266e+04', 'train_tokens_per_second': '794.4'}
92%|█████████▏| 17150/18688 [20:11:02<1:50:03, 4.29s/it] 92%|█████████▏| 17151/18688 [20:11:06<1:50:09, 4.30s/it] 92%|█████████▏| 17152/18688 [20:11:10<1:47:49, 4.21s/it] 92%|█████████▏| 17153/18688 [20:11:15<1:49:23, 4.28s/it] 92%|█████████▏| 17154/18688 [20:11:18<1:45:16, 4.12s/it] 92%|█████████▏| 17155/18688 [20:11:23<1:45:52, 4.14s/it] 92%|█████████▏| 17156/18688 [20:11:28<1:51:03, 4.35s/it] 92%|█████████▏| 17157/18688 [20:11:32<1:51:30, 4.37s/it] 92%|█████████▏| 17158/18688 [20:11:36<1:51:24, 4.37s/it] 92%|█████████▏| 17159/18688 [20:11:40<1:47:30, 4.22s/it] 92%|█████████▏| 17160/18688 [20:11:44<1:43:44, 4.07s/it] {'loss': '0.8255', 'grad_norm': '1.335', 'learning_rate': '1.552e-06', 'epoch': '0.9183', 'num_input_tokens_seen': 57752144, 'train_runtime': '7.27e+04', 'train_tokens_per_second': '794.3'}
92%|█████████▏| 17160/18688 [20:11:44<1:43:44, 4.07s/it] 92%|█████████▏| 17161/18688 [20:11:48<1:40:29, 3.95s/it] 92%|█████████▏| 17162/18688 [20:11:51<1:39:45, 3.92s/it] 92%|█████████▏| 17163/18688 [20:11:56<1:41:18, 3.99s/it] 92%|█████████▏| 17164/18688 [20:12:00<1:41:21, 3.99s/it] 92%|█████████▏| 17165/18688 [20:12:04<1:44:12, 4.11s/it] 92%|█████████▏| 17166/18688 [20:12:08<1:46:12, 4.19s/it] 92%|█████████▏| 17167/18688 [20:12:12<1:45:32, 4.16s/it] 92%|█████████▏| 17168/18688 [20:12:17<1:52:06, 4.43s/it] 92%|█████████▏| 17169/18688 [20:12:22<1:50:59, 4.38s/it] 92%|█████████▏| 17170/18688 [20:12:27<1:54:02, 4.51s/it] {'loss': '0.8361', 'grad_norm': '1.181', 'learning_rate': '1.532e-06', 'epoch': '0.9188', 'num_input_tokens_seen': 57786352, 'train_runtime': '7.275e+04', 'train_tokens_per_second': '794.3'}
92%|█████████▏| 17170/18688 [20:12:27<1:54:02, 4.51s/it] 92%|█████████▏| 17171/18688 [20:12:31<1:53:57, 4.51s/it] 92%|█████████▏| 17172/18688 [20:12:35<1:52:05, 4.44s/it] 92%|█████████▏| 17173/18688 [20:12:40<1:54:31, 4.54s/it] 92%|█████████▏| 17174/18688 [20:12:44<1:50:41, 4.39s/it] 92%|█████████▏| 17175/18688 [20:12:49<1:52:47, 4.47s/it] 92%|█████████▏| 17176/18688 [20:12:52<1:46:16, 4.22s/it] 92%|█████████▏| 17177/18688 [20:12:56<1:42:13, 4.06s/it] 92%|█████████▏| 17178/18688 [20:13:00<1:43:43, 4.12s/it] 92%|█████████▏| 17179/18688 [20:13:05<1:44:14, 4.14s/it] 92%|█████████▏| 17180/18688 [20:13:08<1:42:20, 4.07s/it] {'loss': '0.9032', 'grad_norm': '1.111', 'learning_rate': '1.512e-06', 'epoch': '0.9193', 'num_input_tokens_seen': 57820360, 'train_runtime': '7.279e+04', 'train_tokens_per_second': '794.4'}
92%|█████████▏| 17180/18688 [20:13:08<1:42:20, 4.07s/it] 92%|█████████▏| 17181/18688 [20:13:13<1:43:33, 4.12s/it] 92%|█████████▏| 17182/18688 [20:13:17<1:41:33, 4.05s/it] 92%|█████████▏| 17183/18688 [20:13:21<1:45:15, 4.20s/it] 92%|█████████▏| 17184/18688 [20:13:25<1:45:45, 4.22s/it] 92%|█████████▏| 17185/18688 [20:13:29<1:43:25, 4.13s/it] 92%|█████████▏| 17186/18688 [20:13:34<1:46:36, 4.26s/it] 92%|█████████▏| 17187/18688 [20:13:38<1:43:20, 4.13s/it] 92%|█████████▏| 17188/18688 [20:13:42<1:41:10, 4.05s/it] 92%|█████████▏| 17189/18688 [20:13:46<1:45:11, 4.21s/it] 92%|█████████▏| 17190/18688 [20:13:50<1:40:13, 4.01s/it] {'loss': '0.8874', 'grad_norm': '1.376', 'learning_rate': '1.492e-06', 'epoch': '0.9199', 'num_input_tokens_seen': 57852184, 'train_runtime': '7.283e+04', 'train_tokens_per_second': '794.3'}
92%|█████████▏| 17190/18688 [20:13:50<1:40:13, 4.01s/it] 92%|█████████▏| 17191/18688 [20:13:54<1:38:33, 3.95s/it] 92%|█████████▏| 17192/18688 [20:13:57<1:37:34, 3.91s/it] 92%|█████████▏| 17193/18688 [20:14:01<1:36:54, 3.89s/it] 92%|█████████▏| 17194/18688 [20:14:05<1:38:00, 3.94s/it] 92%|█████████▏| 17195/18688 [20:14:09<1:40:26, 4.04s/it] 92%|█████████▏| 17196/18688 [20:14:13<1:39:48, 4.01s/it] 92%|█████████▏| 17197/18688 [20:14:18<1:41:33, 4.09s/it] 92%|█████████▏| 17198/18688 [20:14:22<1:42:11, 4.11s/it] 92%|█████████▏| 17199/18688 [20:14:26<1:44:36, 4.22s/it] 92%|█████████▏| 17200/18688 [20:14:30<1:39:14, 4.00s/it] {'loss': '0.9175', 'grad_norm': '1.113', 'learning_rate': '1.472e-06', 'epoch': '0.9204', 'num_input_tokens_seen': 57883952, 'train_runtime': '7.287e+04', 'train_tokens_per_second': '794.3'}
92%|█████████▏| 17200/18688 [20:14:30<1:39:14, 4.00s/it] 92%|█████████▏| 17201/18688 [20:14:34<1:42:06, 4.12s/it] 92%|█████████▏| 17202/18688 [20:14:39<1:43:33, 4.18s/it] 92%|█████████▏| 17203/18688 [20:14:44<1:52:34, 4.55s/it] 92%|█████████▏| 17204/18688 [20:14:48<1:51:27, 4.51s/it] 92%|█████████▏| 17205/18688 [20:14:52<1:48:34, 4.39s/it] 92%|█████████▏| 17206/18688 [20:14:56<1:45:08, 4.26s/it] 92%|█████████▏| 17207/18688 [20:15:00<1:39:24, 4.03s/it] 92%|█████████▏| 17208/18688 [20:15:05<1:46:29, 4.32s/it] 92%|█████████▏| 17209/18688 [20:15:09<1:43:56, 4.22s/it] 92%|█████████▏| 17210/18688 [20:15:13<1:44:25, 4.24s/it] {'loss': '0.9201', 'grad_norm': '1.172', 'learning_rate': '1.453e-06', 'epoch': '0.9209', 'num_input_tokens_seen': 57920480, 'train_runtime': '7.291e+04', 'train_tokens_per_second': '794.4'}
92%|█████████▏| 17210/18688 [20:15:13<1:44:25, 4.24s/it] 92%|█████████▏| 17211/18688 [20:15:18<1:46:28, 4.33s/it] 92%|█████████▏| 17212/18688 [20:15:23<1:51:30, 4.53s/it] 92%|█████████▏| 17213/18688 [20:15:27<1:51:04, 4.52s/it] 92%|█████████▏| 17214/18688 [20:15:32<1:56:09, 4.73s/it] 92%|█████████▏| 17215/18688 [20:15:36<1:50:51, 4.52s/it] 92%|█████████▏| 17216/18688 [20:15:41<1:49:20, 4.46s/it] 92%|█████████▏| 17217/18688 [20:15:45<1:46:55, 4.36s/it] 92%|█████████▏| 17218/18688 [20:15:48<1:40:15, 4.09s/it] 92%|█████████▏| 17219/18688 [20:15:53<1:42:10, 4.17s/it] 92%|█████████▏| 17220/18688 [20:15:56<1:38:12, 4.01s/it] {'loss': '0.876', 'grad_norm': '1.155', 'learning_rate': '1.433e-06', 'epoch': '0.9215', 'num_input_tokens_seen': 57956232, 'train_runtime': '7.296e+04', 'train_tokens_per_second': '794.4'}
92%|█████████▏| 17220/18688 [20:15:56<1:38:12, 4.01s/it] 92%|█████████▏| 17221/18688 [20:16:01<1:39:59, 4.09s/it] 92%|█████████▏| 17222/18688 [20:16:05<1:42:51, 4.21s/it] 92%|█████████▏| 17223/18688 [20:16:09<1:38:48, 4.05s/it] 92%|█████████▏| 17224/18688 [20:16:13<1:36:46, 3.97s/it] 92%|█████████▏| 17225/18688 [20:16:17<1:39:42, 4.09s/it] 92%|█████████▏| 17226/18688 [20:16:21<1:38:40, 4.05s/it] 92%|█████████▏| 17227/18688 [20:16:25<1:41:56, 4.19s/it] 92%|█████████▏| 17228/18688 [20:16:29<1:39:36, 4.09s/it] 92%|█████████▏| 17229/18688 [20:16:34<1:43:40, 4.26s/it] 92%|█████████▏| 17230/18688 [20:16:38<1:40:03, 4.12s/it] {'loss': '0.9004', 'grad_norm': '1.782', 'learning_rate': '1.414e-06', 'epoch': '0.922', 'num_input_tokens_seen': 57989112, 'train_runtime': '7.3e+04', 'train_tokens_per_second': '794.4'}
92%|█████████▏| 17230/18688 [20:16:38<1:40:03, 4.12s/it] 92%|█████████▏| 17231/18688 [20:16:42<1:40:42, 4.15s/it] 92%|█████████▏| 17232/18688 [20:16:47<1:49:46, 4.52s/it] 92%|█████████▏| 17233/18688 [20:16:52<1:50:42, 4.57s/it] 92%|█████████▏| 17234/18688 [20:16:56<1:47:35, 4.44s/it] 92%|█████████▏| 17235/18688 [20:17:02<1:56:54, 4.83s/it] 92%|█████████▏| 17236/18688 [20:17:06<1:50:22, 4.56s/it] 92%|█████████▏| 17237/18688 [20:17:10<1:48:00, 4.47s/it] 92%|█████████▏| 17238/18688 [20:17:14<1:44:28, 4.32s/it] 92%|█████████▏| 17239/18688 [20:17:18<1:43:47, 4.30s/it] 92%|█████████▏| 17240/18688 [20:17:23<1:43:23, 4.28s/it] {'loss': '0.8643', 'grad_norm': '1.14', 'learning_rate': '1.395e-06', 'epoch': '0.9225', 'num_input_tokens_seen': 58025944, 'train_runtime': '7.304e+04', 'train_tokens_per_second': '794.4'}
92%|█████████▏| 17240/18688 [20:17:23<1:43:23, 4.28s/it] 92%|█████████▏| 17241/18688 [20:17:28<1:49:36, 4.54s/it] 92%|█████████▏| 17242/18688 [20:17:32<1:45:50, 4.39s/it] 92%|█████████▏| 17243/18688 [20:17:36<1:46:31, 4.42s/it] 92%|█████████▏| 17244/18688 [20:17:40<1:42:23, 4.25s/it] 92%|█████████▏| 17245/18688 [20:17:44<1:37:33, 4.06s/it] 92%|█████████▏| 17246/18688 [20:17:48<1:38:36, 4.10s/it] 92%|█████████▏| 17247/18688 [20:17:52<1:37:04, 4.04s/it] 92%|█████████▏| 17248/18688 [20:17:56<1:38:13, 4.09s/it] 92%|█████████▏| 17249/18688 [20:18:00<1:34:31, 3.94s/it] 92%|█████████▏| 17250/18688 [20:18:04<1:38:56, 4.13s/it] {'loss': '0.9386', 'grad_norm': '1.404', 'learning_rate': '1.376e-06', 'epoch': '0.9231', 'num_input_tokens_seen': 58059080, 'train_runtime': '7.308e+04', 'train_tokens_per_second': '794.4'}
92%|█████████▏| 17250/18688 [20:18:04<1:38:56, 4.13s/it] 92%|█████████▏| 17251/18688 [20:18:09<1:40:53, 4.21s/it] 92%|█████████▏| 17252/18688 [20:18:13<1:42:41, 4.29s/it] 92%|█████████▏| 17253/18688 [20:18:18<1:44:39, 4.38s/it] 92%|█████████▏| 17254/18688 [20:18:22<1:45:52, 4.43s/it] 92%|█████████▏| 17255/18688 [20:18:26<1:39:56, 4.18s/it] 92%|█████████▏| 17256/18688 [20:18:31<1:44:33, 4.38s/it] 92%|█████████▏| 17257/18688 [20:18:34<1:40:32, 4.22s/it] 92%|█████████▏| 17258/18688 [20:18:38<1:35:36, 4.01s/it] 92%|█████████▏| 17259/18688 [20:18:42<1:34:24, 3.96s/it] 92%|█████████▏| 17260/18688 [20:18:47<1:39:48, 4.19s/it] {'loss': '0.8609', 'grad_norm': '1.253', 'learning_rate': '1.357e-06', 'epoch': '0.9236', 'num_input_tokens_seen': 58093128, 'train_runtime': '7.313e+04', 'train_tokens_per_second': '794.4'}
92%|█████████▏| 17260/18688 [20:18:47<1:39:48, 4.19s/it] 92%|█████████▏| 17261/18688 [20:18:51<1:40:38, 4.23s/it] 92%|█████████▏| 17262/18688 [20:18:55<1:39:44, 4.20s/it] 92%|█████████▏| 17263/18688 [20:18:59<1:37:05, 4.09s/it] 92%|█████████▏| 17264/18688 [20:19:03<1:39:10, 4.18s/it] 92%|█████████▏| 17265/18688 [20:19:07<1:38:47, 4.17s/it] 92%|█████████▏| 17266/18688 [20:19:11<1:36:13, 4.06s/it] 92%|█████████▏| 17267/18688 [20:19:15<1:33:53, 3.96s/it] 92%|█████████▏| 17268/18688 [20:19:19<1:36:48, 4.09s/it] 92%|█████████▏| 17269/18688 [20:19:23<1:34:08, 3.98s/it] 92%|█████████▏| 17270/18688 [20:19:27<1:34:25, 4.00s/it] {'loss': '0.8995', 'grad_norm': '1.443', 'learning_rate': '1.338e-06', 'epoch': '0.9241', 'num_input_tokens_seen': 58122328, 'train_runtime': '7.317e+04', 'train_tokens_per_second': '794.4'}
92%|█████████▏| 17270/18688 [20:19:27<1:34:25, 4.00s/it] 92%|█████████▏| 17271/18688 [20:19:31<1:34:54, 4.02s/it] 92%|█████████▏| 17272/18688 [20:19:35<1:34:15, 3.99s/it] 92%|█████████▏| 17273/18688 [20:19:39<1:36:19, 4.08s/it] 92%|█████████▏| 17274/18688 [20:19:44<1:39:19, 4.21s/it] 92%|█████████▏| 17275/18688 [20:19:48<1:35:43, 4.06s/it] 92%|█████████▏| 17276/18688 [20:19:52<1:35:09, 4.04s/it] 92%|█████████▏| 17277/18688 [20:19:56<1:36:34, 4.11s/it] 92%|█████████▏| 17278/18688 [20:19:59<1:32:59, 3.96s/it] 92%|█████████▏| 17279/18688 [20:20:03<1:31:55, 3.91s/it] 92%|█████████▏| 17280/18688 [20:20:08<1:36:15, 4.10s/it] {'loss': '0.8694', 'grad_norm': '1.125', 'learning_rate': '1.319e-06', 'epoch': '0.9247', 'num_input_tokens_seen': 58154592, 'train_runtime': '7.321e+04', 'train_tokens_per_second': '794.4'}
92%|█████████▏| 17280/18688 [20:20:08<1:36:15, 4.10s/it] 92%|█████████▏| 17281/18688 [20:20:12<1:34:00, 4.01s/it] 92%|█████████▏| 17282/18688 [20:20:15<1:32:10, 3.93s/it] 92%|█████████▏| 17283/18688 [20:20:20<1:35:38, 4.08s/it] 92%|█████████▏| 17284/18688 [20:20:24<1:37:52, 4.18s/it] 92%|█████████▏| 17285/18688 [20:20:29<1:42:23, 4.38s/it] 92%|█████████▏| 17286/18688 [20:20:33<1:38:46, 4.23s/it] 93%|█████████▎| 17287/18688 [20:20:37<1:39:50, 4.28s/it] 93%|█████████▎| 17288/18688 [20:20:42<1:43:33, 4.44s/it] 93%|█████████▎| 17289/18688 [20:20:46<1:39:05, 4.25s/it] 93%|█████████▎| 17290/18688 [20:20:50<1:39:39, 4.28s/it] {'loss': '0.9457', 'grad_norm': '1.265', 'learning_rate': '1.301e-06', 'epoch': '0.9252', 'num_input_tokens_seen': 58188728, 'train_runtime': '7.325e+04', 'train_tokens_per_second': '794.4'}
93%|█████████▎| 17290/18688 [20:20:50<1:39:39, 4.28s/it] 93%|█████████▎| 17291/18688 [20:20:54<1:35:25, 4.10s/it] 93%|█████████▎| 17292/18688 [20:20:58<1:35:18, 4.10s/it] 93%|█████████▎| 17293/18688 [20:21:02<1:34:56, 4.08s/it] 93%|█████████▎| 17294/18688 [20:21:07<1:42:30, 4.41s/it] 93%|█████████▎| 17295/18688 [20:21:12<1:41:39, 4.38s/it] 93%|█████████▎| 17296/18688 [20:21:16<1:39:20, 4.28s/it] 93%|█████████▎| 17297/18688 [20:21:20<1:37:14, 4.19s/it] 93%|█████████▎| 17298/18688 [20:21:24<1:36:22, 4.16s/it] 93%|█████████▎| 17299/18688 [20:21:28<1:38:14, 4.24s/it] 93%|█████████▎| 17300/18688 [20:21:32<1:35:11, 4.12s/it] {'loss': '0.9067', 'grad_norm': '1.147', 'learning_rate': '1.282e-06', 'epoch': '0.9258', 'num_input_tokens_seen': 58221144, 'train_runtime': '7.329e+04', 'train_tokens_per_second': '794.4'}
93%|█████████▎| 17300/18688 [20:21:32<1:35:11, 4.12s/it] 93%|█████████▎| 17301/18688 [20:21:36<1:37:47, 4.23s/it] 93%|█████████▎| 17302/18688 [20:21:41<1:40:58, 4.37s/it] 93%|█████████▎| 17303/18688 [20:21:46<1:42:59, 4.46s/it] 93%|█████████▎| 17304/18688 [20:21:50<1:40:27, 4.36s/it] 93%|█████████▎| 17305/18688 [20:21:54<1:38:55, 4.29s/it] 93%|█████████▎| 17306/18688 [20:21:59<1:39:58, 4.34s/it] 93%|█████████▎| 17307/18688 [20:22:03<1:37:18, 4.23s/it] 93%|█████████▎| 17308/18688 [20:22:07<1:39:09, 4.31s/it] 93%|█████████▎| 17309/18688 [20:22:12<1:40:21, 4.37s/it] 93%|█████████▎| 17310/18688 [20:22:15<1:37:11, 4.23s/it] {'loss': '0.9618', 'grad_norm': '1.464', 'learning_rate': '1.264e-06', 'epoch': '0.9263', 'num_input_tokens_seen': 58256976, 'train_runtime': '7.334e+04', 'train_tokens_per_second': '794.4'}
93%|█████████▎| 17310/18688 [20:22:15<1:37:11, 4.23s/it] 93%|█████████▎| 17311/18688 [20:22:20<1:38:20, 4.28s/it] 93%|█████████▎| 17312/18688 [20:22:24<1:39:50, 4.35s/it] 93%|█████████▎| 17313/18688 [20:22:29<1:39:48, 4.36s/it] 93%|█████████▎| 17314/18688 [20:22:34<1:43:03, 4.50s/it] 93%|█████████▎| 17315/18688 [20:22:38<1:42:15, 4.47s/it] 93%|█████████▎| 17316/18688 [20:22:42<1:40:02, 4.38s/it] 93%|█████████▎| 17317/18688 [20:22:46<1:37:14, 4.26s/it] 93%|█████████▎| 17318/18688 [20:22:50<1:33:29, 4.09s/it] 93%|█████████▎| 17319/18688 [20:22:54<1:31:25, 4.01s/it] 93%|█████████▎| 17320/18688 [20:22:59<1:40:12, 4.40s/it] {'loss': '0.9478', 'grad_norm': '1.307', 'learning_rate': '1.246e-06', 'epoch': '0.9268', 'num_input_tokens_seen': 58293144, 'train_runtime': '7.338e+04', 'train_tokens_per_second': '794.4'}
93%|█████████▎| 17320/18688 [20:22:59<1:40:12, 4.40s/it] 93%|█████████▎| 17321/18688 [20:23:03<1:35:39, 4.20s/it] 93%|█████████▎| 17322/18688 [20:23:08<1:44:00, 4.57s/it] 93%|█████████▎| 17323/18688 [20:23:12<1:38:55, 4.35s/it] 93%|█████████▎| 17324/18688 [20:23:16<1:37:40, 4.30s/it] 93%|█████████▎| 17325/18688 [20:23:20<1:33:50, 4.13s/it] 93%|█████████▎| 17326/18688 [20:23:25<1:38:01, 4.32s/it] 93%|█████████▎| 17327/18688 [20:23:28<1:34:29, 4.17s/it] 93%|█████████▎| 17328/18688 [20:23:32<1:33:15, 4.11s/it] 93%|█████████▎| 17329/18688 [20:23:36<1:32:16, 4.07s/it] 93%|█████████▎| 17330/18688 [20:23:40<1:30:54, 4.02s/it] {'loss': '0.8304', 'grad_norm': '1.143', 'learning_rate': '1.228e-06', 'epoch': '0.9274', 'num_input_tokens_seen': 58325712, 'train_runtime': '7.342e+04', 'train_tokens_per_second': '794.4'}
93%|█████████▎| 17330/18688 [20:23:40<1:30:54, 4.02s/it] 93%|█████████▎| 17331/18688 [20:23:45<1:34:15, 4.17s/it] 93%|█████████▎| 17332/18688 [20:23:49<1:31:46, 4.06s/it] 93%|█████████▎| 17333/18688 [20:23:53<1:33:28, 4.14s/it] 93%|█████████▎| 17334/18688 [20:23:58<1:37:59, 4.34s/it] 93%|█████████▎| 17335/18688 [20:24:01<1:32:17, 4.09s/it] 93%|█████████▎| 17336/18688 [20:24:06<1:38:24, 4.37s/it] 93%|█████████▎| 17337/18688 [20:24:10<1:35:53, 4.26s/it] 93%|█████████▎| 17338/18688 [20:24:15<1:36:40, 4.30s/it] 93%|█████████▎| 17339/18688 [20:24:19<1:36:52, 4.31s/it] 93%|█████████▎| 17340/18688 [20:24:23<1:33:29, 4.16s/it] {'loss': '0.916', 'grad_norm': '1.202', 'learning_rate': '1.21e-06', 'epoch': '0.9279', 'num_input_tokens_seen': 58359832, 'train_runtime': '7.346e+04', 'train_tokens_per_second': '794.4'}
93%|█████████▎| 17340/18688 [20:24:23<1:33:29, 4.16s/it] 93%|█████████▎| 17341/18688 [20:24:27<1:31:29, 4.08s/it] 93%|█████████▎| 17342/18688 [20:24:30<1:28:43, 3.96s/it] 93%|█████████▎| 17343/18688 [20:24:34<1:29:20, 3.99s/it] 93%|█████████▎| 17344/18688 [20:24:39<1:31:54, 4.10s/it] 93%|█████████▎| 17345/18688 [20:24:43<1:35:02, 4.25s/it] 93%|█████████▎| 17346/18688 [20:24:47<1:32:28, 4.13s/it] 93%|█████████▎| 17347/18688 [20:24:51<1:30:50, 4.06s/it] 93%|█████████▎| 17348/18688 [20:24:55<1:30:41, 4.06s/it] 93%|█████████▎| 17349/18688 [20:24:59<1:30:33, 4.06s/it] 93%|█████████▎| 17350/18688 [20:25:03<1:30:10, 4.04s/it] {'loss': '0.9595', 'grad_norm': '1.158', 'learning_rate': '1.192e-06', 'epoch': '0.9284', 'num_input_tokens_seen': 58390064, 'train_runtime': '7.35e+04', 'train_tokens_per_second': '794.4'}
93%|█████████▎| 17350/18688 [20:25:03<1:30:10, 4.04s/it] 93%|█████████▎| 17351/18688 [20:25:07<1:30:25, 4.06s/it] 93%|█████████▎| 17352/18688 [20:25:12<1:34:26, 4.24s/it] 93%|█████████▎| 17353/18688 [20:25:16<1:31:48, 4.13s/it] 93%|█████████▎| 17354/18688 [20:25:20<1:29:10, 4.01s/it] 93%|█████████▎| 17355/18688 [20:25:24<1:28:41, 3.99s/it] 93%|█████████▎| 17356/18688 [20:25:29<1:35:16, 4.29s/it] 93%|█████████▎| 17357/18688 [20:25:32<1:32:03, 4.15s/it] 93%|█████████▎| 17358/18688 [20:25:36<1:29:48, 4.05s/it] 93%|█████████▎| 17359/18688 [20:25:40<1:28:38, 4.00s/it] 93%|█████████▎| 17360/18688 [20:25:44<1:26:26, 3.91s/it] {'loss': '0.8715', 'grad_norm': '1.146', 'learning_rate': '1.174e-06', 'epoch': '0.929', 'num_input_tokens_seen': 58420528, 'train_runtime': '7.354e+04', 'train_tokens_per_second': '794.4'}
93%|█████████▎| 17360/18688 [20:25:44<1:26:26, 3.91s/it] 93%|█████████▎| 17361/18688 [20:25:48<1:30:17, 4.08s/it] 93%|█████████▎| 17362/18688 [20:25:52<1:28:20, 4.00s/it] 93%|█████████▎| 17363/18688 [20:25:57<1:34:46, 4.29s/it] 93%|█████████▎| 17364/18688 [20:26:01<1:31:18, 4.14s/it] 93%|█████████▎| 17365/18688 [20:26:05<1:30:41, 4.11s/it] 93%|█████████▎| 17366/18688 [20:26:10<1:35:15, 4.32s/it] 93%|█████████▎| 17367/18688 [20:26:15<1:39:28, 4.52s/it] 93%|█████████▎| 17368/18688 [20:26:18<1:34:39, 4.30s/it] 93%|█████████▎| 17369/18688 [20:26:23<1:35:25, 4.34s/it] 93%|█████████▎| 17370/18688 [20:26:27<1:33:23, 4.25s/it] {'loss': '0.9323', 'grad_norm': '1.104', 'learning_rate': '1.157e-06', 'epoch': '0.9295', 'num_input_tokens_seen': 58455184, 'train_runtime': '7.359e+04', 'train_tokens_per_second': '794.4'}
93%|█████████▎| 17370/18688 [20:26:27<1:33:23, 4.25s/it] 93%|█████████▎| 17371/18688 [20:26:32<1:36:40, 4.40s/it] 93%|█████████▎| 17372/18688 [20:26:36<1:32:53, 4.24s/it] 93%|█████████▎| 17373/18688 [20:26:40<1:31:41, 4.18s/it] 93%|█████████▎| 17374/18688 [20:26:44<1:30:14, 4.12s/it] 93%|█████████▎| 17375/18688 [20:26:48<1:30:47, 4.15s/it] 93%|█████████▎| 17376/18688 [20:26:52<1:30:35, 4.14s/it] 93%|█████████▎| 17377/18688 [20:26:56<1:30:18, 4.13s/it] 93%|█████████▎| 17378/18688 [20:27:01<1:35:06, 4.36s/it] 93%|█████████▎| 17379/18688 [20:27:05<1:34:58, 4.35s/it] 93%|█████████▎| 17380/18688 [20:27:10<1:40:41, 4.62s/it] {'loss': '0.9773', 'grad_norm': '1.384', 'learning_rate': '1.139e-06', 'epoch': '0.93', 'num_input_tokens_seen': 58491344, 'train_runtime': '7.363e+04', 'train_tokens_per_second': '794.4'}
93%|█████████▎| 17380/18688 [20:27:10<1:40:41, 4.62s/it] 93%|█████████▎| 17381/18688 [20:27:15<1:38:29, 4.52s/it] 93%|█████████▎| 17382/18688 [20:27:19<1:38:52, 4.54s/it] 93%|█████████▎| 17383/18688 [20:27:24<1:40:44, 4.63s/it] 93%|█████████▎| 17384/18688 [20:27:29<1:42:04, 4.70s/it] 93%|█████████▎| 17385/18688 [20:27:34<1:41:24, 4.67s/it] 93%|█████████▎| 17386/18688 [20:27:37<1:34:30, 4.36s/it] 93%|█████████▎| 17387/18688 [20:27:41<1:29:34, 4.13s/it] 93%|█████████▎| 17388/18688 [20:27:45<1:29:01, 4.11s/it] 93%|█████████▎| 17389/18688 [20:27:49<1:26:35, 4.00s/it] 93%|█████████▎| 17390/18688 [20:27:53<1:25:58, 3.97s/it] {'loss': '0.9076', 'grad_norm': '1.185', 'learning_rate': '1.122e-06', 'epoch': '0.9306', 'num_input_tokens_seen': 58524768, 'train_runtime': '7.367e+04', 'train_tokens_per_second': '794.4'}
93%|█████████▎| 17390/18688 [20:27:53<1:25:58, 3.97s/it] 93%|█████████▎| 17391/18688 [20:27:57<1:28:01, 4.07s/it] 93%|█████████▎| 17392/18688 [20:28:01<1:28:44, 4.11s/it] 93%|█████████▎| 17393/18688 [20:28:05<1:26:12, 3.99s/it] 93%|█████████▎| 17394/18688 [20:28:09<1:28:15, 4.09s/it] 93%|█████████▎| 17395/18688 [20:28:13<1:26:58, 4.04s/it] 93%|█████████▎| 17396/18688 [20:28:18<1:33:19, 4.33s/it] 93%|█████████▎| 17397/18688 [20:28:22<1:29:26, 4.16s/it] 93%|█████████▎| 17398/18688 [20:28:26<1:31:01, 4.23s/it] 93%|█████████▎| 17399/18688 [20:28:30<1:27:38, 4.08s/it] 93%|█████████▎| 17400/18688 [20:28:34<1:24:56, 3.96s/it] {'loss': '0.8838', 'grad_norm': '1.26', 'learning_rate': '1.105e-06', 'epoch': '0.9311', 'num_input_tokens_seen': 58556856, 'train_runtime': '7.371e+04', 'train_tokens_per_second': '794.4'}
93%|█████████▎| 17400/18688 [20:28:34<1:24:56, 3.96s/it] 93%|█████████▎| 17401/18688 [20:28:37<1:22:12, 3.83s/it] 93%|█████████▎| 17402/18688 [20:28:41<1:22:08, 3.83s/it] 93%|█████████▎| 17403/18688 [20:28:46<1:27:37, 4.09s/it] 93%|█████████▎| 17404/18688 [20:28:50<1:28:01, 4.11s/it] 93%|█████████▎| 17405/18688 [20:28:54<1:28:46, 4.15s/it] 93%|█████████▎| 17406/18688 [20:28:58<1:27:16, 4.08s/it] 93%|█████████▎| 17407/18688 [20:29:02<1:25:58, 4.03s/it] 93%|█████████▎| 17408/18688 [20:29:07<1:31:48, 4.30s/it] 93%|█████████▎| 17409/18688 [20:29:12<1:36:28, 4.53s/it] 93%|█████████▎| 17410/18688 [20:29:16<1:33:01, 4.37s/it] {'loss': '0.9519', 'grad_norm': '1.152', 'learning_rate': '1.088e-06', 'epoch': '0.9316', 'num_input_tokens_seen': 58591696, 'train_runtime': '7.376e+04', 'train_tokens_per_second': '794.4'}
93%|█████████▎| 17410/18688 [20:29:16<1:33:01, 4.37s/it] 93%|█████████▎| 17411/18688 [20:29:21<1:34:39, 4.45s/it] 93%|█████████▎| 17412/18688 [20:29:25<1:32:42, 4.36s/it] 93%|█████████▎| 17413/18688 [20:29:28<1:27:16, 4.11s/it] 93%|█████████▎| 17414/18688 [20:29:32<1:24:23, 3.97s/it] 93%|█████████▎| 17415/18688 [20:29:37<1:30:39, 4.27s/it] 93%|█████████▎| 17416/18688 [20:29:41<1:27:07, 4.11s/it] 93%|█████████▎| 17417/18688 [20:29:45<1:27:21, 4.12s/it] 93%|█████████▎| 17418/18688 [20:29:48<1:24:17, 3.98s/it] 93%|█████████▎| 17419/18688 [20:29:52<1:23:13, 3.93s/it] 93%|█████████▎| 17420/18688 [20:29:56<1:23:34, 3.95s/it] {'loss': '0.9275', 'grad_norm': '1.221', 'learning_rate': '1.071e-06', 'epoch': '0.9322', 'num_input_tokens_seen': 58623808, 'train_runtime': '7.38e+04', 'train_tokens_per_second': '794.4'}
93%|█████████▎| 17420/18688 [20:29:56<1:23:34, 3.95s/it] 93%|█████████▎| 17421/18688 [20:30:00<1:23:29, 3.95s/it] 93%|█████████▎| 17422/18688 [20:30:05<1:26:00, 4.08s/it] 93%|█████████▎| 17423/18688 [20:30:09<1:30:39, 4.30s/it] 93%|█████████▎| 17424/18688 [20:30:13<1:25:48, 4.07s/it] 93%|█████████▎| 17425/18688 [20:30:17<1:27:23, 4.15s/it] 93%|█████████▎| 17426/18688 [20:30:21<1:27:17, 4.15s/it] 93%|█████████▎| 17427/18688 [20:30:25<1:24:29, 4.02s/it] 93%|█████████▎| 17428/18688 [20:30:30<1:28:22, 4.21s/it] 93%|█████████▎| 17429/18688 [20:30:34<1:26:58, 4.14s/it] 93%|█████████▎| 17430/18688 [20:30:38<1:27:06, 4.15s/it] {'loss': '0.8819', 'grad_norm': '1.054', 'learning_rate': '1.054e-06', 'epoch': '0.9327', 'num_input_tokens_seen': 58656896, 'train_runtime': '7.384e+04', 'train_tokens_per_second': '794.4'}
93%|█████████▎| 17430/18688 [20:30:38<1:27:06, 4.15s/it] 93%|█████████▎| 17431/18688 [20:30:43<1:30:18, 4.31s/it] 93%|█████████▎| 17432/18688 [20:30:47<1:29:21, 4.27s/it] 93%|█████████▎| 17433/18688 [20:30:51<1:29:57, 4.30s/it] 93%|█████████▎| 17434/18688 [20:30:55<1:25:34, 4.09s/it] 93%|█████████▎| 17435/18688 [20:30:59<1:27:31, 4.19s/it] 93%|█████████▎| 17436/18688 [20:31:04<1:34:08, 4.51s/it] 93%|█████████▎| 17437/18688 [20:31:08<1:29:02, 4.27s/it] 93%|█████████▎| 17438/18688 [20:31:12<1:26:04, 4.13s/it] 93%|█████████▎| 17439/18688 [20:31:16<1:26:37, 4.16s/it] 93%|█████████▎| 17440/18688 [20:31:21<1:32:28, 4.45s/it] {'loss': '0.9765', 'grad_norm': '1.098', 'learning_rate': '1.038e-06', 'epoch': '0.9332', 'num_input_tokens_seen': 58693384, 'train_runtime': '7.388e+04', 'train_tokens_per_second': '794.4'}
93%|█████████▎| 17440/18688 [20:31:21<1:32:28, 4.45s/it] 93%|█████████▎| 17441/18688 [20:31:25<1:28:43, 4.27s/it] 93%|█████████▎| 17442/18688 [20:31:29<1:28:36, 4.27s/it] 93%|█████████▎| 17443/18688 [20:31:33<1:26:35, 4.17s/it] 93%|█████████▎| 17444/18688 [20:31:37<1:26:06, 4.15s/it] 93%|█████████▎| 17445/18688 [20:31:42<1:30:09, 4.35s/it] 93%|█████████▎| 17446/18688 [20:31:46<1:25:49, 4.15s/it] 93%|█████████▎| 17447/18688 [20:31:50<1:22:48, 4.00s/it] 93%|█████████▎| 17448/18688 [20:31:53<1:21:17, 3.93s/it] 93%|█████████▎| 17449/18688 [20:31:58<1:22:27, 3.99s/it] 93%|█████████▎| 17450/18688 [20:32:02<1:24:19, 4.09s/it] {'loss': '0.9037', 'grad_norm': '1.557', 'learning_rate': '1.021e-06', 'epoch': '0.9338', 'num_input_tokens_seen': 58724152, 'train_runtime': '7.392e+04', 'train_tokens_per_second': '794.4'}
93%|█████████▎| 17450/18688 [20:32:02<1:24:19, 4.09s/it] 93%|█████████▎| 17451/18688 [20:32:06<1:23:36, 4.06s/it] 93%|█████████▎| 17452/18688 [20:32:09<1:20:38, 3.91s/it] 93%|█████████▎| 17453/18688 [20:32:14<1:23:38, 4.06s/it] 93%|█████████▎| 17454/18688 [20:32:18<1:24:23, 4.10s/it] 93%|█████████▎| 17455/18688 [20:32:23<1:30:03, 4.38s/it] 93%|█████████▎| 17456/18688 [20:32:27<1:25:55, 4.18s/it] 93%|█████████▎| 17457/18688 [20:32:31<1:23:56, 4.09s/it] 93%|█████████▎| 17458/18688 [20:32:35<1:25:51, 4.19s/it] 93%|█████████▎| 17459/18688 [20:32:39<1:26:19, 4.21s/it] 93%|█████████▎| 17460/18688 [20:32:44<1:30:32, 4.42s/it] {'loss': '0.9008', 'grad_norm': '1.241', 'learning_rate': '1.005e-06', 'epoch': '0.9343', 'num_input_tokens_seen': 58760104, 'train_runtime': '7.396e+04', 'train_tokens_per_second': '794.4'}
93%|█████████▎| 17460/18688 [20:32:44<1:30:32, 4.42s/it] 93%|█████████▎| 17461/18688 [20:32:48<1:26:59, 4.25s/it] 93%|█████████▎| 17462/18688 [20:32:52<1:23:02, 4.06s/it] 93%|█████████▎| 17463/18688 [20:32:55<1:20:54, 3.96s/it] 93%|█████████▎| 17464/18688 [20:32:59<1:19:45, 3.91s/it] 93%|█████████▎| 17465/18688 [20:33:05<1:29:00, 4.37s/it] 93%|█████████▎| 17466/18688 [20:33:08<1:25:16, 4.19s/it] 93%|█████████▎| 17467/18688 [20:33:13<1:27:21, 4.29s/it] 93%|█████████▎| 17468/18688 [20:33:17<1:24:34, 4.16s/it] 93%|█████████▎| 17469/18688 [20:33:21<1:24:13, 4.15s/it] 93%|█████████▎| 17470/18688 [20:33:25<1:24:02, 4.14s/it] {'loss': '0.8785', 'grad_norm': '1.201', 'learning_rate': '9.888e-07', 'epoch': '0.9348', 'num_input_tokens_seen': 58792120, 'train_runtime': '7.401e+04', 'train_tokens_per_second': '794.4'}
93%|█████████▎| 17470/18688 [20:33:25<1:24:02, 4.14s/it] 93%|█████████▎| 17471/18688 [20:33:29<1:21:07, 4.00s/it] 93%|█████████▎| 17472/18688 [20:33:33<1:24:29, 4.17s/it] 93%|█████████▎| 17473/18688 [20:33:37<1:21:20, 4.02s/it] 94%|█████████▎| 17474/18688 [20:33:41<1:19:52, 3.95s/it] 94%|█████████▎| 17475/18688 [20:33:46<1:26:01, 4.25s/it] 94%|█████████▎| 17476/18688 [20:33:49<1:22:23, 4.08s/it] 94%|█████████▎| 17477/18688 [20:33:53<1:20:25, 3.98s/it] 94%|█████████▎| 17478/18688 [20:33:58<1:26:20, 4.28s/it] 94%|█████████▎| 17479/18688 [20:34:02<1:26:54, 4.31s/it] 94%|█████████▎| 17480/18688 [20:34:07<1:25:23, 4.24s/it] {'loss': '0.8597', 'grad_norm': '1.218', 'learning_rate': '9.727e-07', 'epoch': '0.9354', 'num_input_tokens_seen': 58825248, 'train_runtime': '7.405e+04', 'train_tokens_per_second': '794.4'}
94%|█████████▎| 17480/18688 [20:34:07<1:25:23, 4.24s/it] 94%|█████████▎| 17481/18688 [20:34:11<1:24:39, 4.21s/it] 94%|█████████▎| 17482/18688 [20:34:15<1:25:28, 4.25s/it] 94%|█████████▎| 17483/18688 [20:34:20<1:27:25, 4.35s/it] 94%|█████████▎| 17484/18688 [20:34:24<1:26:42, 4.32s/it] 94%|█████████▎| 17485/18688 [20:34:28<1:23:42, 4.17s/it] 94%|█████████▎| 17486/18688 [20:34:31<1:21:03, 4.05s/it] 94%|█████████▎| 17487/18688 [20:34:35<1:18:53, 3.94s/it] 94%|█████████▎| 17488/18688 [20:34:39<1:19:41, 3.98s/it] 94%|█████████▎| 17489/18688 [20:34:43<1:18:26, 3.92s/it] 94%|█████████▎| 17490/18688 [20:34:48<1:24:22, 4.23s/it] {'loss': '0.9052', 'grad_norm': '1.182', 'learning_rate': '9.567e-07', 'epoch': '0.9359', 'num_input_tokens_seen': 58858952, 'train_runtime': '7.409e+04', 'train_tokens_per_second': '794.4'}
94%|█████████▎| 17490/18688 [20:34:48<1:24:22, 4.23s/it] 94%|█████████▎| 17491/18688 [20:34:52<1:22:16, 4.12s/it] 94%|█████████▎| 17492/18688 [20:34:56<1:24:40, 4.25s/it] 94%|█████████▎| 17493/18688 [20:35:01<1:24:09, 4.23s/it] 94%|█████████▎| 17494/18688 [20:35:05<1:26:16, 4.34s/it] 94%|█████████▎| 17495/18688 [20:35:09<1:22:33, 4.15s/it] 94%|█████████▎| 17496/18688 [20:35:14<1:27:37, 4.41s/it] 94%|█████████▎| 17497/18688 [20:35:18<1:23:57, 4.23s/it] 94%|█████████▎| 17498/18688 [20:35:22<1:24:00, 4.24s/it] 94%|█████████▎| 17499/18688 [20:35:26<1:21:19, 4.10s/it] 94%|█████████▎| 17500/18688 [20:35:30<1:21:55, 4.14s/it] {'loss': '0.9753', 'grad_norm': '1.431', 'learning_rate': '9.409e-07', 'epoch': '0.9365', 'num_input_tokens_seen': 58892912, 'train_runtime': '7.413e+04', 'train_tokens_per_second': '794.4'}
94%|█████████▎| 17500/18688 [20:35:30<1:21:55, 4.14s/it] 94%|█████████▎| 17501/18688 [20:35:34<1:18:37, 3.97s/it] 94%|█████████▎| 17502/18688 [20:35:38<1:22:43, 4.19s/it] 94%|█████████▎| 17503/18688 [20:35:43<1:27:57, 4.45s/it] 94%|█████████▎| 17504/18688 [20:35:47<1:25:44, 4.35s/it] 94%|█████████▎| 17505/18688 [20:35:51<1:23:19, 4.23s/it] 94%|█████████▎| 17506/18688 [20:35:55<1:19:25, 4.03s/it] 94%|█████████▎| 17507/18688 [20:36:00<1:25:52, 4.36s/it] 94%|█████████▎| 17508/18688 [20:36:04<1:21:21, 4.14s/it] 94%|█████████▎| 17509/18688 [20:36:08<1:20:55, 4.12s/it] 94%|█████████▎| 17510/18688 [20:36:11<1:18:23, 3.99s/it] {'loss': '0.8782', 'grad_norm': '1.037', 'learning_rate': '9.252e-07', 'epoch': '0.937', 'num_input_tokens_seen': 58928048, 'train_runtime': '7.417e+04', 'train_tokens_per_second': '794.5'}
94%|█████████▎| 17510/18688 [20:36:11<1:18:23, 3.99s/it] 94%|█████████▎| 17511/18688 [20:36:15<1:18:33, 4.00s/it] 94%|█████████▎| 17512/18688 [20:36:21<1:28:16, 4.50s/it] 94%|█████████▎| 17513/18688 [20:36:25<1:26:43, 4.43s/it] 94%|█████████▎| 17514/18688 [20:36:29<1:20:58, 4.14s/it] 94%|█████████▎| 17515/18688 [20:36:33<1:21:59, 4.19s/it] 94%|█████████▎| 17516/18688 [20:36:38<1:25:12, 4.36s/it] 94%|█████████▎| 17517/18688 [20:36:43<1:27:17, 4.47s/it] 94%|█████████▎| 17518/18688 [20:36:47<1:26:55, 4.46s/it] 94%|█████████▎| 17519/18688 [20:36:51<1:21:49, 4.20s/it] 94%|█████████▍| 17520/18688 [20:36:54<1:17:33, 3.98s/it] {'loss': '0.9317', 'grad_norm': '1.482', 'learning_rate': '9.096e-07', 'epoch': '0.9375', 'num_input_tokens_seen': 58963264, 'train_runtime': '7.421e+04', 'train_tokens_per_second': '794.5'}
94%|█████████▍| 17520/18688 [20:36:54<1:17:33, 3.98s/it] 94%|█████████▍| 17521/18688 [20:36:58<1:17:29, 3.98s/it] 94%|█████████▍| 17522/18688 [20:37:02<1:16:32, 3.94s/it] 94%|█████████▍| 17523/18688 [20:37:05<1:13:54, 3.81s/it] 94%|█████████▍| 17524/18688 [20:37:10<1:17:40, 4.00s/it] 94%|█████████▍| 17525/18688 [20:37:14<1:17:38, 4.01s/it] 94%|█████████▍| 17526/18688 [20:37:19<1:21:21, 4.20s/it] 94%|█████████▍| 17527/18688 [20:37:23<1:21:13, 4.20s/it] 94%|█████████▍| 17528/18688 [20:37:27<1:22:44, 4.28s/it] 94%|█████████▍| 17529/18688 [20:37:32<1:23:15, 4.31s/it] 94%|█████████▍| 17530/18688 [20:37:36<1:20:36, 4.18s/it] {'loss': '0.8975', 'grad_norm': '1.179', 'learning_rate': '8.942e-07', 'epoch': '0.9381', 'num_input_tokens_seen': 58996040, 'train_runtime': '7.426e+04', 'train_tokens_per_second': '794.5'}
94%|█████████▍| 17530/18688 [20:37:36<1:20:36, 4.18s/it] 94%|█████████▍| 17531/18688 [20:37:39<1:18:26, 4.07s/it] 94%|█████████▍| 17532/18688 [20:37:43<1:16:04, 3.95s/it] 94%|█████████▍| 17533/18688 [20:37:47<1:13:29, 3.82s/it] 94%|█████████▍| 17534/18688 [20:37:51<1:19:19, 4.12s/it] 94%|█████████▍| 17535/18688 [20:37:56<1:22:22, 4.29s/it] 94%|█████████▍| 17536/18688 [20:38:00<1:20:09, 4.17s/it] 94%|█████████▍| 17537/18688 [20:38:04<1:20:00, 4.17s/it] 94%|█████████▍| 17538/18688 [20:38:09<1:26:08, 4.49s/it] 94%|█████████▍| 17539/18688 [20:38:13<1:21:38, 4.26s/it] 94%|█████████▍| 17540/18688 [20:38:17<1:17:28, 4.05s/it] {'loss': '0.8358', 'grad_norm': '1.348', 'learning_rate': '8.789e-07', 'epoch': '0.9386', 'num_input_tokens_seen': 59029056, 'train_runtime': '7.43e+04', 'train_tokens_per_second': '794.5'}
94%|█████████▍| 17540/18688 [20:38:17<1:17:28, 4.05s/it] 94%|█████████▍| 17541/18688 [20:38:20<1:14:29, 3.90s/it] 94%|█████████▍| 17542/18688 [20:38:25<1:17:56, 4.08s/it] 94%|█████████▍| 17543/18688 [20:38:28<1:15:39, 3.96s/it] 94%|█████████▍| 17544/18688 [20:38:33<1:19:51, 4.19s/it] 94%|█████████▍| 17545/18688 [20:38:38<1:21:29, 4.28s/it] 94%|█████████▍| 17546/18688 [20:38:42<1:20:07, 4.21s/it] 94%|█████████▍| 17547/18688 [20:38:45<1:17:29, 4.07s/it] 94%|█████████▍| 17548/18688 [20:38:49<1:16:33, 4.03s/it] 94%|█████████▍| 17549/18688 [20:38:53<1:15:19, 3.97s/it] 94%|█████████▍| 17550/18688 [20:38:58<1:19:39, 4.20s/it] {'loss': '0.9401', 'grad_norm': '0.9436', 'learning_rate': '8.637e-07', 'epoch': '0.9391', 'num_input_tokens_seen': 59060984, 'train_runtime': '7.434e+04', 'train_tokens_per_second': '794.5'}
94%|█████████▍| 17550/18688 [20:38:58<1:19:39, 4.20s/it] 94%|█████████▍| 17551/18688 [20:39:02<1:18:59, 4.17s/it] 94%|█████████▍| 17552/18688 [20:39:07<1:23:09, 4.39s/it] 94%|█████████▍| 17553/18688 [20:39:11<1:22:23, 4.36s/it] 94%|█████████▍| 17554/18688 [20:39:15<1:21:30, 4.31s/it] 94%|█████████▍| 17555/18688 [20:39:19<1:19:11, 4.19s/it] 94%|█████████▍| 17556/18688 [20:39:24<1:19:24, 4.21s/it] 94%|█████████▍| 17557/18688 [20:39:28<1:20:15, 4.26s/it] 94%|█████████▍| 17558/18688 [20:39:32<1:22:06, 4.36s/it] 94%|█████████▍| 17559/18688 [20:39:36<1:18:39, 4.18s/it] 94%|█████████▍| 17560/18688 [20:39:40<1:15:17, 4.01s/it] {'loss': '0.9538', 'grad_norm': '1.444', 'learning_rate': '8.486e-07', 'epoch': '0.9397', 'num_input_tokens_seen': 59094352, 'train_runtime': '7.438e+04', 'train_tokens_per_second': '794.5'}
94%|█████████▍| 17560/18688 [20:39:40<1:15:17, 4.01s/it] 94%|█████████▍| 17561/18688 [20:39:43<1:12:50, 3.88s/it] 94%|█████████▍| 17562/18688 [20:39:47<1:11:32, 3.81s/it] 94%|█████████▍| 17563/18688 [20:39:51<1:14:47, 3.99s/it] 94%|█████████▍| 17564/18688 [20:39:57<1:20:45, 4.31s/it] 94%|█████████▍| 17565/18688 [20:40:01<1:20:35, 4.31s/it] 94%|█████████▍| 17566/18688 [20:40:05<1:17:11, 4.13s/it] 94%|█████████▍| 17567/18688 [20:40:09<1:18:01, 4.18s/it] 94%|█████████▍| 17568/18688 [20:40:13<1:17:49, 4.17s/it] 94%|█████████▍| 17569/18688 [20:40:18<1:25:01, 4.56s/it] 94%|█████████▍| 17570/18688 [20:40:23<1:22:57, 4.45s/it] {'loss': '0.9673', 'grad_norm': '1.377', 'learning_rate': '8.337e-07', 'epoch': '0.9402', 'num_input_tokens_seen': 59129360, 'train_runtime': '7.442e+04', 'train_tokens_per_second': '794.5'}
94%|█████████▍| 17570/18688 [20:40:23<1:22:57, 4.45s/it] 94%|█████████▍| 17571/18688 [20:40:27<1:21:47, 4.39s/it] 94%|█████████▍| 17572/18688 [20:40:31<1:19:46, 4.29s/it] 94%|█████████▍| 17573/18688 [20:40:35<1:15:32, 4.06s/it] 94%|█████████▍| 17574/18688 [20:40:38<1:14:44, 4.03s/it] 94%|█████████▍| 17575/18688 [20:40:42<1:14:25, 4.01s/it] 94%|█████████▍| 17576/18688 [20:40:46<1:12:37, 3.92s/it] 94%|█████████▍| 17577/18688 [20:40:51<1:16:57, 4.16s/it] 94%|█████████▍| 17578/18688 [20:40:55<1:18:21, 4.24s/it] 94%|█████████▍| 17579/18688 [20:41:00<1:21:38, 4.42s/it] 94%|█████████▍| 17580/18688 [20:41:04<1:19:50, 4.32s/it] {'loss': '0.8556', 'grad_norm': '0.9945', 'learning_rate': '8.189e-07', 'epoch': '0.9407', 'num_input_tokens_seen': 59163280, 'train_runtime': '7.446e+04', 'train_tokens_per_second': '794.5'}
94%|█████████▍| 17580/18688 [20:41:04<1:19:50, 4.32s/it] 94%|█████████▍| 17581/18688 [20:41:09<1:21:21, 4.41s/it] 94%|█████████▍| 17582/18688 [20:41:13<1:22:03, 4.45s/it] 94%|█████████▍| 17583/18688 [20:41:18<1:24:14, 4.57s/it] 94%|█████████▍| 17584/18688 [20:41:23<1:24:03, 4.57s/it] 94%|█████████▍| 17585/18688 [20:41:27<1:19:54, 4.35s/it] 94%|█████████▍| 17586/18688 [20:41:31<1:17:29, 4.22s/it] 94%|█████████▍| 17587/18688 [20:41:35<1:17:09, 4.20s/it] 94%|█████████▍| 17588/18688 [20:41:39<1:15:46, 4.13s/it] 94%|█████████▍| 17589/18688 [20:41:42<1:13:32, 4.01s/it] 94%|█████████▍| 17590/18688 [20:41:47<1:14:21, 4.06s/it] {'loss': '0.941', 'grad_norm': '1.298', 'learning_rate': '8.043e-07', 'epoch': '0.9413', 'num_input_tokens_seen': 59198392, 'train_runtime': '7.451e+04', 'train_tokens_per_second': '794.5'}
94%|█████████▍| 17590/18688 [20:41:47<1:14:21, 4.06s/it] 94%|█████████▍| 17591/18688 [20:41:51<1:16:41, 4.20s/it] 94%|█████████▍| 17592/18688 [20:41:56<1:18:53, 4.32s/it] 94%|█████████▍| 17593/18688 [20:41:59<1:15:34, 4.14s/it] 94%|█████████▍| 17594/18688 [20:42:04<1:15:23, 4.13s/it] 94%|█████████▍| 17595/18688 [20:42:08<1:18:35, 4.31s/it] 94%|█████████▍| 17596/18688 [20:42:13<1:21:02, 4.45s/it] 94%|█████████▍| 17597/18688 [20:42:17<1:18:18, 4.31s/it] 94%|█████████▍| 17598/18688 [20:42:21<1:15:29, 4.16s/it] 94%|█████████▍| 17599/18688 [20:42:26<1:19:27, 4.38s/it] 94%|█████████▍| 17600/18688 [20:42:30<1:17:17, 4.26s/it] {'loss': '0.9437', 'grad_norm': '1.255', 'learning_rate': '7.898e-07', 'epoch': '0.9418', 'num_input_tokens_seen': 59233504, 'train_runtime': '7.455e+04', 'train_tokens_per_second': '794.5'}
94%|█████████▍| 17600/18688 [20:42:30<1:17:17, 4.26s/it] 94%|█████████▍| 17601/18688 [20:42:34<1:15:32, 4.17s/it] 94%|█████████▍| 17602/18688 [20:42:39<1:19:44, 4.41s/it] 94%|█████████▍| 17603/18688 [20:42:43<1:17:19, 4.28s/it] 94%|█████████▍| 17604/18688 [20:42:47<1:16:43, 4.25s/it] 94%|█████████▍| 17605/18688 [20:42:50<1:13:38, 4.08s/it] 94%|█████████▍| 17606/18688 [20:42:54<1:12:55, 4.04s/it] 94%|█████████▍| 17607/18688 [20:42:58<1:11:36, 3.97s/it] 94%|█████████▍| 17608/18688 [20:43:02<1:09:41, 3.87s/it] 94%|█████████▍| 17609/18688 [20:43:06<1:12:01, 4.01s/it] 94%|█████████▍| 17610/18688 [20:43:11<1:14:31, 4.15s/it] {'loss': '0.9205', 'grad_norm': '1.201', 'learning_rate': '7.754e-07', 'epoch': '0.9423', 'num_input_tokens_seen': 59266768, 'train_runtime': '7.459e+04', 'train_tokens_per_second': '794.6'}
94%|█████████▍| 17610/18688 [20:43:11<1:14:31, 4.15s/it] 94%|█████████▍| 17611/18688 [20:43:15<1:13:59, 4.12s/it] 94%|█████████▍| 17612/18688 [20:43:18<1:11:31, 3.99s/it] 94%|█████████▍| 17613/18688 [20:43:23<1:15:22, 4.21s/it] 94%|█████████▍| 17614/18688 [20:43:28<1:19:02, 4.42s/it] 94%|█████████▍| 17615/18688 [20:43:32<1:17:03, 4.31s/it] 94%|█████████▍| 17616/18688 [20:43:36<1:14:24, 4.16s/it] 94%|█████████▍| 17617/18688 [20:43:40<1:13:27, 4.12s/it] 94%|█████████▍| 17618/18688 [20:43:44<1:11:22, 4.00s/it] 94%|█████████▍| 17619/18688 [20:43:49<1:17:18, 4.34s/it] 94%|█████████▍| 17620/18688 [20:43:53<1:14:30, 4.19s/it] {'loss': '0.9332', 'grad_norm': '1.118', 'learning_rate': '7.611e-07', 'epoch': '0.9429', 'num_input_tokens_seen': 59301600, 'train_runtime': '7.463e+04', 'train_tokens_per_second': '794.6'}
94%|█████████▍| 17620/18688 [20:43:53<1:14:30, 4.19s/it] 94%|█████████▍| 17621/18688 [20:43:57<1:15:06, 4.22s/it] 94%|█████████▍| 17622/18688 [20:44:01<1:13:58, 4.16s/it] 94%|█████████▍| 17623/18688 [20:44:07<1:22:10, 4.63s/it] 94%|█████████▍| 17624/18688 [20:44:11<1:19:13, 4.47s/it] 94%|█████████▍| 17625/18688 [20:44:15<1:19:17, 4.48s/it] 94%|█████████▍| 17626/18688 [20:44:19<1:15:55, 4.29s/it] 94%|█████████▍| 17627/18688 [20:44:23<1:11:59, 4.07s/it] 94%|█████████▍| 17628/18688 [20:44:26<1:09:44, 3.95s/it] 94%|█████████▍| 17629/18688 [20:44:31<1:12:44, 4.12s/it] 94%|█████████▍| 17630/18688 [20:44:35<1:14:21, 4.22s/it] {'loss': '0.8892', 'grad_norm': '1.088', 'learning_rate': '7.47e-07', 'epoch': '0.9434', 'num_input_tokens_seen': 59336528, 'train_runtime': '7.468e+04', 'train_tokens_per_second': '794.6'}
94%|█████████▍| 17630/18688 [20:44:35<1:14:21, 4.22s/it] 94%|█████████▍| 17631/18688 [20:44:39<1:12:01, 4.09s/it] 94%|█████████▍| 17632/18688 [20:44:44<1:14:49, 4.25s/it] 94%|█████████▍| 17633/18688 [20:44:48<1:15:00, 4.27s/it] 94%|█████████▍| 17634/18688 [20:44:52<1:13:37, 4.19s/it] 94%|█████████▍| 17635/18688 [20:44:58<1:20:21, 4.58s/it] 94%|█████████▍| 17636/18688 [20:45:02<1:20:42, 4.60s/it] 94%|█████████▍| 17637/18688 [20:45:07<1:21:39, 4.66s/it] 94%|█████████▍| 17638/18688 [20:45:11<1:17:32, 4.43s/it] 94%|█████████▍| 17639/18688 [20:45:15<1:16:43, 4.39s/it] 94%|█████████▍| 17640/18688 [20:45:20<1:18:17, 4.48s/it] {'loss': '0.9095', 'grad_norm': '1.205', 'learning_rate': '7.33e-07', 'epoch': '0.9439', 'num_input_tokens_seen': 59374240, 'train_runtime': '7.472e+04', 'train_tokens_per_second': '794.6'}
94%|█████████▍| 17640/18688 [20:45:20<1:18:17, 4.48s/it] 94%|█████████▍| 17641/18688 [20:45:25<1:19:27, 4.55s/it] 94%|█████████▍| 17642/18688 [20:45:29<1:19:23, 4.55s/it] 94%|█████████▍| 17643/18688 [20:45:33<1:13:56, 4.25s/it] 94%|█████████▍| 17644/18688 [20:45:36<1:11:10, 4.09s/it] 94%|█████████▍| 17645/18688 [20:45:41<1:13:00, 4.20s/it] 94%|█████████▍| 17646/18688 [20:45:45<1:15:15, 4.33s/it] 94%|█████████▍| 17647/18688 [20:45:50<1:16:28, 4.41s/it] 94%|█████████▍| 17648/18688 [20:45:54<1:14:41, 4.31s/it] 94%|█████████▍| 17649/18688 [20:45:58<1:11:42, 4.14s/it] 94%|█████████▍| 17650/18688 [20:46:02<1:12:49, 4.21s/it] {'loss': '0.9462', 'grad_norm': '1.581', 'learning_rate': '7.191e-07', 'epoch': '0.9445', 'num_input_tokens_seen': 59408248, 'train_runtime': '7.476e+04', 'train_tokens_per_second': '794.6'}
94%|█████████▍| 17650/18688 [20:46:02<1:12:49, 4.21s/it] 94%|█████████▍| 17651/18688 [20:46:07<1:13:07, 4.23s/it] 94%|█████████▍| 17652/18688 [20:46:11<1:15:32, 4.37s/it] 94%|█████████▍| 17653/18688 [20:46:16<1:14:55, 4.34s/it] 94%|█████████▍| 17654/18688 [20:46:19<1:11:26, 4.15s/it] 94%|█████████▍| 17655/18688 [20:46:23<1:09:34, 4.04s/it] 94%|█████████▍| 17656/18688 [20:46:28<1:13:52, 4.30s/it] 94%|█████████▍| 17657/18688 [20:46:32<1:15:06, 4.37s/it] 94%|█████████▍| 17658/18688 [20:46:36<1:12:59, 4.25s/it] 94%|█████████▍| 17659/18688 [20:46:40<1:09:33, 4.06s/it] 94%|█████████▍| 17660/18688 [20:46:44<1:10:04, 4.09s/it] {'loss': '0.9863', 'grad_norm': '1.326', 'learning_rate': '7.054e-07', 'epoch': '0.945', 'num_input_tokens_seen': 59441992, 'train_runtime': '7.48e+04', 'train_tokens_per_second': '794.6'}
94%|█████████▍| 17660/18688 [20:46:44<1:10:04, 4.09s/it] 95%|█████████▍| 17661/18688 [20:46:48<1:07:08, 3.92s/it] 95%|█████████▍| 17662/18688 [20:46:53<1:11:36, 4.19s/it] 95%|█████████▍| 17663/18688 [20:46:57<1:11:45, 4.20s/it] 95%|█████████▍| 17664/18688 [20:47:01<1:12:32, 4.25s/it] 95%|█████████▍| 17665/18688 [20:47:05<1:10:30, 4.14s/it] 95%|█████████▍| 17666/18688 [20:47:09<1:08:27, 4.02s/it] 95%|█████████▍| 17667/18688 [20:47:13<1:08:42, 4.04s/it] 95%|█████████▍| 17668/18688 [20:47:17<1:07:58, 4.00s/it] 95%|█████████▍| 17669/18688 [20:47:21<1:11:17, 4.20s/it] 95%|█████████▍| 17670/18688 [20:47:26<1:11:11, 4.20s/it] {'loss': '0.8568', 'grad_norm': '1.296', 'learning_rate': '6.918e-07', 'epoch': '0.9456', 'num_input_tokens_seen': 59474960, 'train_runtime': '7.485e+04', 'train_tokens_per_second': '794.6'}
95%|█████████▍| 17670/18688 [20:47:26<1:11:11, 4.20s/it] 95%|█████████▍| 17671/18688 [20:47:29<1:09:09, 4.08s/it] 95%|█████████▍| 17672/18688 [20:47:34<1:10:45, 4.18s/it] 95%|█████████▍| 17673/18688 [20:47:38<1:13:07, 4.32s/it] 95%|█████████▍| 17674/18688 [20:47:42<1:10:34, 4.18s/it] 95%|█████████▍| 17675/18688 [20:47:47<1:11:39, 4.24s/it] 95%|█████████▍| 17676/18688 [20:47:50<1:08:37, 4.07s/it] 95%|█████████▍| 17677/18688 [20:47:55<1:11:52, 4.27s/it] 95%|█████████▍| 17678/18688 [20:47:59<1:09:59, 4.16s/it] 95%|█████████▍| 17679/18688 [20:48:03<1:07:58, 4.04s/it] 95%|█████████▍| 17680/18688 [20:48:07<1:06:59, 3.99s/it] {'loss': '0.8668', 'grad_norm': '1.132', 'learning_rate': '6.783e-07', 'epoch': '0.9461', 'num_input_tokens_seen': 59507024, 'train_runtime': '7.489e+04', 'train_tokens_per_second': '794.6'}
95%|█████████▍| 17680/18688 [20:48:07<1:06:59, 3.99s/it] 95%|█████████▍| 17681/18688 [20:48:11<1:07:49, 4.04s/it] 95%|█████████▍| 17682/18688 [20:48:15<1:08:26, 4.08s/it] 95%|█████████▍| 17683/18688 [20:48:19<1:08:37, 4.10s/it] 95%|█████████▍| 17684/18688 [20:48:24<1:10:50, 4.23s/it] 95%|█████████▍| 17685/18688 [20:48:28<1:09:00, 4.13s/it] 95%|█████████▍| 17686/18688 [20:48:32<1:09:06, 4.14s/it] 95%|█████████▍| 17687/18688 [20:48:35<1:07:05, 4.02s/it] 95%|█████████▍| 17688/18688 [20:48:40<1:08:29, 4.11s/it] 95%|█████████▍| 17689/18688 [20:48:44<1:09:57, 4.20s/it] 95%|█████████▍| 17690/18688 [20:48:48<1:10:06, 4.22s/it] {'loss': '0.8836', 'grad_norm': '1.282', 'learning_rate': '6.65e-07', 'epoch': '0.9466', 'num_input_tokens_seen': 59540920, 'train_runtime': '7.493e+04', 'train_tokens_per_second': '794.6'}
95%|█████████▍| 17690/18688 [20:48:48<1:10:06, 4.22s/it] 95%|█████████▍| 17691/18688 [20:48:52<1:08:56, 4.15s/it] 95%|█████████▍| 17692/18688 [20:48:56<1:06:47, 4.02s/it] 95%|█████████▍| 17693/18688 [20:49:01<1:09:21, 4.18s/it] 95%|█████████▍| 17694/18688 [20:49:05<1:09:32, 4.20s/it] 95%|█████████▍| 17695/18688 [20:49:09<1:09:02, 4.17s/it] 95%|█████████▍| 17696/18688 [20:49:13<1:06:50, 4.04s/it] 95%|█████████▍| 17697/18688 [20:49:17<1:06:28, 4.02s/it] 95%|█████████▍| 17698/18688 [20:49:20<1:04:27, 3.91s/it] 95%|█████████▍| 17699/18688 [20:49:25<1:07:00, 4.07s/it] 95%|█████████▍| 17700/18688 [20:49:30<1:12:47, 4.42s/it] {'loss': '0.8867', 'grad_norm': '1.139', 'learning_rate': '6.518e-07', 'epoch': '0.9472', 'num_input_tokens_seen': 59574736, 'train_runtime': '7.497e+04', 'train_tokens_per_second': '794.6'}
95%|█████████▍| 17700/18688 [20:49:30<1:12:47, 4.42s/it] 95%|█████████▍| 17701/18688 [20:49:34<1:09:29, 4.22s/it] 95%|█████████▍| 17702/18688 [20:49:37<1:06:08, 4.02s/it] 95%|█████████▍| 17703/18688 [20:49:42<1:07:57, 4.14s/it] 95%|█████████▍| 17704/18688 [20:49:46<1:06:03, 4.03s/it] 95%|█████████▍| 17705/18688 [20:49:49<1:03:46, 3.89s/it] 95%|█████████▍| 17706/18688 [20:49:53<1:02:03, 3.79s/it] 95%|█████████▍| 17707/18688 [20:49:57<1:02:33, 3.83s/it] 95%|█████████▍| 17708/18688 [20:50:01<1:05:04, 3.98s/it] 95%|█████████▍| 17709/18688 [20:50:05<1:04:05, 3.93s/it] 95%|█████████▍| 17710/18688 [20:50:10<1:08:18, 4.19s/it] {'loss': '0.8274', 'grad_norm': '1.155', 'learning_rate': '6.387e-07', 'epoch': '0.9477', 'num_input_tokens_seen': 59605200, 'train_runtime': '7.501e+04', 'train_tokens_per_second': '794.6'}
95%|█████████▍| 17710/18688 [20:50:10<1:08:18, 4.19s/it] 95%|█████████▍| 17711/18688 [20:50:14<1:11:01, 4.36s/it] 95%|█████████▍| 17712/18688 [20:50:18<1:09:23, 4.27s/it] 95%|█████████▍| 17713/18688 [20:50:22<1:07:08, 4.13s/it] 95%|█████████▍| 17714/18688 [20:50:26<1:07:32, 4.16s/it] 95%|█████████▍| 17715/18688 [20:50:30<1:04:51, 4.00s/it] 95%|█████████▍| 17716/18688 [20:50:34<1:02:58, 3.89s/it] 95%|█████████▍| 17717/18688 [20:50:39<1:08:52, 4.26s/it] 95%|█████████▍| 17718/18688 [20:50:42<1:06:08, 4.09s/it] 95%|█████████▍| 17719/18688 [20:50:46<1:04:29, 3.99s/it] 95%|█████████▍| 17720/18688 [20:50:50<1:04:58, 4.03s/it] {'loss': '0.8765', 'grad_norm': '1.463', 'learning_rate': '6.257e-07', 'epoch': '0.9482', 'num_input_tokens_seen': 59637096, 'train_runtime': '7.505e+04', 'train_tokens_per_second': '794.6'}
95%|█████████▍| 17720/18688 [20:50:50<1:04:58, 4.03s/it] 95%|█████████▍| 17721/18688 [20:50:55<1:05:58, 4.09s/it] 95%|█████████▍| 17722/18688 [20:50:58<1:03:36, 3.95s/it] 95%|█████████▍| 17723/18688 [20:51:02<1:02:45, 3.90s/it] 95%|█████████▍| 17724/18688 [20:51:06<1:03:42, 3.97s/it] 95%|█████████▍| 17725/18688 [20:51:10<1:02:13, 3.88s/it] 95%|█████████▍| 17726/18688 [20:51:13<1:01:15, 3.82s/it] 95%|█████████▍| 17727/18688 [20:51:17<1:00:35, 3.78s/it] 95%|█████████▍| 17728/18688 [20:51:21<59:38, 3.73s/it] 95%|█████████▍| 17729/18688 [20:51:25<1:01:57, 3.88s/it] 95%|█████████▍| 17730/18688 [20:51:30<1:05:19, 4.09s/it] {'loss': '0.9189', 'grad_norm': '1.206', 'learning_rate': '6.129e-07', 'epoch': '0.9488', 'num_input_tokens_seen': 59667800, 'train_runtime': '7.509e+04', 'train_tokens_per_second': '794.6'}
95%|█████████▍| 17730/18688 [20:51:30<1:05:19, 4.09s/it] 95%|█████████▍| 17731/18688 [20:51:33<1:02:55, 3.95s/it] 95%|█████████▍| 17732/18688 [20:51:37<1:04:16, 4.03s/it] 95%|█████████▍| 17733/18688 [20:51:42<1:05:17, 4.10s/it] 95%|█████████▍| 17734/18688 [20:51:46<1:04:12, 4.04s/it] 95%|█████████▍| 17735/18688 [20:51:50<1:05:29, 4.12s/it] 95%|█████████▍| 17736/18688 [20:51:55<1:09:11, 4.36s/it] 95%|█████████▍| 17737/18688 [20:51:59<1:06:06, 4.17s/it] 95%|█████████▍| 17738/18688 [20:52:02<1:03:57, 4.04s/it] 95%|█████████▍| 17739/18688 [20:52:06<1:02:32, 3.95s/it] 95%|█████████▍| 17740/18688 [20:52:12<1:10:07, 4.44s/it] {'loss': '0.8423', 'grad_norm': '1.153', 'learning_rate': '6.002e-07', 'epoch': '0.9493', 'num_input_tokens_seen': 59700792, 'train_runtime': '7.513e+04', 'train_tokens_per_second': '794.6'}
95%|█████████▍| 17740/18688 [20:52:12<1:10:07, 4.44s/it] 95%|█████████▍| 17741/18688 [20:52:16<1:11:48, 4.55s/it] 95%|█████████▍| 17742/18688 [20:52:20<1:08:37, 4.35s/it] 95%|█████████▍| 17743/18688 [20:52:25<1:09:05, 4.39s/it] 95%|█████████▍| 17744/18688 [20:52:29<1:08:51, 4.38s/it] 95%|█████████▍| 17745/18688 [20:52:34<1:11:00, 4.52s/it] 95%|█████████▍| 17746/18688 [20:52:39<1:14:12, 4.73s/it] 95%|█████████▍| 17747/18688 [20:52:43<1:11:25, 4.55s/it] 95%|█████████▍| 17748/18688 [20:52:48<1:10:03, 4.47s/it] 95%|█████████▍| 17749/18688 [20:52:51<1:06:42, 4.26s/it] 95%|█████████▍| 17750/18688 [20:52:55<1:04:25, 4.12s/it] {'loss': '0.9283', 'grad_norm': '1.229', 'learning_rate': '5.877e-07', 'epoch': '0.9498', 'num_input_tokens_seen': 59735976, 'train_runtime': '7.518e+04', 'train_tokens_per_second': '794.6'}
95%|█████████▍| 17750/18688 [20:52:55<1:04:25, 4.12s/it] 95%|█████████▍| 17751/18688 [20:52:59<1:04:46, 4.15s/it] 95%|█████████▍| 17752/18688 [20:53:04<1:06:12, 4.24s/it] 95%|█████████▍| 17753/18688 [20:53:08<1:03:25, 4.07s/it] 95%|█████████▌| 17754/18688 [20:53:11<1:02:14, 4.00s/it] 95%|█████████▌| 17755/18688 [20:53:15<1:02:41, 4.03s/it] 95%|█████████▌| 17756/18688 [20:53:19<1:01:17, 3.95s/it] 95%|█████████▌| 17757/18688 [20:53:23<1:02:33, 4.03s/it] 95%|█████████▌| 17758/18688 [20:53:27<1:00:39, 3.91s/it] 95%|█████████▌| 17759/18688 [20:53:31<1:00:03, 3.88s/it] 95%|█████████▌| 17760/18688 [20:53:35<1:02:25, 4.04s/it] {'loss': '0.8672', 'grad_norm': '1.259', 'learning_rate': '5.753e-07', 'epoch': '0.9504', 'num_input_tokens_seen': 59767968, 'train_runtime': '7.522e+04', 'train_tokens_per_second': '794.6'}
95%|█████████▌| 17760/18688 [20:53:35<1:02:25, 4.04s/it] 95%|█████████▌| 17761/18688 [20:53:39<1:02:11, 4.03s/it] 95%|█████████▌| 17762/18688 [20:53:43<1:01:17, 3.97s/it] 95%|█████████▌| 17763/18688 [20:53:47<59:38, 3.87s/it] 95%|█████████▌| 17764/18688 [20:53:52<1:03:42, 4.14s/it] 95%|█████████▌| 17765/18688 [20:53:55<1:02:01, 4.03s/it] 95%|█████████▌| 17766/18688 [20:54:00<1:02:48, 4.09s/it] 95%|█████████▌| 17767/18688 [20:54:03<1:01:51, 4.03s/it] 95%|█████████▌| 17768/18688 [20:54:07<1:00:21, 3.94s/it] 95%|█████████▌| 17769/18688 [20:54:11<1:00:45, 3.97s/it] 95%|█████████▌| 17770/18688 [20:54:15<1:00:55, 3.98s/it] {'loss': '0.9243', 'grad_norm': '1.561', 'learning_rate': '5.63e-07', 'epoch': '0.9509', 'num_input_tokens_seen': 59799216, 'train_runtime': '7.526e+04', 'train_tokens_per_second': '794.6'}
95%|█████████▌| 17770/18688 [20:54:15<1:00:55, 3.98s/it] 95%|█████████▌| 17771/18688 [20:54:19<1:00:09, 3.94s/it] 95%|█████████▌| 17772/18688 [20:54:24<1:03:39, 4.17s/it] 95%|█████████▌| 17773/18688 [20:54:28<1:02:14, 4.08s/it] 95%|█████████▌| 17774/18688 [20:54:32<1:05:41, 4.31s/it] 95%|█████████▌| 17775/18688 [20:54:37<1:05:45, 4.32s/it] 95%|█████████▌| 17776/18688 [20:54:41<1:03:39, 4.19s/it] 95%|█████████▌| 17777/18688 [20:54:44<1:01:48, 4.07s/it] 95%|█████████▌| 17778/18688 [20:54:48<59:52, 3.95s/it] 95%|█████████▌| 17779/18688 [20:54:53<1:05:24, 4.32s/it] 95%|█████████▌| 17780/18688 [20:54:59<1:09:23, 4.59s/it] {'loss': '0.9281', 'grad_norm': '1.341', 'learning_rate': '5.508e-07', 'epoch': '0.9514', 'num_input_tokens_seen': 59834888, 'train_runtime': '7.53e+04', 'train_tokens_per_second': '794.6'}
95%|█████████▌| 17780/18688 [20:54:59<1:09:23, 4.59s/it] 95%|█████████▌| 17781/18688 [20:55:03<1:07:45, 4.48s/it] 95%|█████████▌| 17782/18688 [20:55:07<1:04:27, 4.27s/it] 95%|█████████▌| 17783/18688 [20:55:11<1:04:46, 4.29s/it] 95%|█████████▌| 17784/18688 [20:55:16<1:07:09, 4.46s/it] 95%|█████████▌| 17785/18688 [20:55:19<1:03:43, 4.23s/it] 95%|█████████▌| 17786/18688 [20:55:23<1:01:22, 4.08s/it] 95%|█████████▌| 17787/18688 [20:55:28<1:04:25, 4.29s/it] 95%|█████████▌| 17788/18688 [20:55:32<1:02:26, 4.16s/it] 95%|█████████▌| 17789/18688 [20:55:36<1:01:49, 4.13s/it] 95%|█████████▌| 17790/18688 [20:55:40<59:40, 3.99s/it] {'loss': '0.8804', 'grad_norm': '1.366', 'learning_rate': '5.388e-07', 'epoch': '0.952', 'num_input_tokens_seen': 59866856, 'train_runtime': '7.534e+04', 'train_tokens_per_second': '794.6'}
95%|█████████▌| 17790/18688 [20:55:40<59:40, 3.99s/it] 95%|█████████▌| 17791/18688 [20:55:43<58:46, 3.93s/it] 95%|█████████▌| 17792/18688 [20:55:47<58:27, 3.91s/it] 95%|█████████▌| 17793/18688 [20:55:51<58:52, 3.95s/it] 95%|█████████▌| 17794/18688 [20:55:55<59:53, 4.02s/it] 95%|█████████▌| 17795/18688 [20:55:59<58:15, 3.91s/it] 95%|█████████▌| 17796/18688 [20:56:03<57:20, 3.86s/it] 95%|█████████▌| 17797/18688 [20:56:07<58:14, 3.92s/it] 95%|█████████▌| 17798/18688 [20:56:11<59:13, 3.99s/it] 95%|█████████▌| 17799/18688 [20:56:16<1:01:17, 4.14s/it] 95%|█████████▌| 17800/18688 [20:56:19<59:54, 4.05s/it] {'loss': '0.8813', 'grad_norm': '1.274', 'learning_rate': '5.269e-07', 'epoch': '0.9525', 'num_input_tokens_seen': 59898904, 'train_runtime': '7.538e+04', 'train_tokens_per_second': '794.6'}
95%|█████████▌| 17800/18688 [20:56:19<59:54, 4.05s/it] 95%|█████████▌| 17801/18688 [20:56:23<58:28, 3.95s/it] 95%|█████████▌| 17802/18688 [20:56:27<58:53, 3.99s/it] 95%|█████████▌| 17803/18688 [20:56:31<59:07, 4.01s/it] 95%|█████████▌| 17804/18688 [20:56:35<58:22, 3.96s/it] 95%|█████████▌| 17805/18688 [20:56:39<57:14, 3.89s/it] 95%|█████████▌| 17806/18688 [20:56:43<57:06, 3.88s/it] 95%|█████████▌| 17807/18688 [20:56:46<55:49, 3.80s/it] 95%|█████████▌| 17808/18688 [20:56:50<57:33, 3.92s/it] 95%|█████████▌| 17809/18688 [20:56:54<57:03, 3.89s/it] 95%|█████████▌| 17810/18688 [20:56:58<56:11, 3.84s/it] {'loss': '0.9013', 'grad_norm': '1.205', 'learning_rate': '5.151e-07', 'epoch': '0.953', 'num_input_tokens_seen': 59928400, 'train_runtime': '7.542e+04', 'train_tokens_per_second': '794.6'}
95%|█████████▌| 17810/18688 [20:56:58<56:11, 3.84s/it] 95%|█████████▌| 17811/18688 [20:57:02<57:36, 3.94s/it] 95%|█████████▌| 17812/18688 [20:57:07<59:07, 4.05s/it] 95%|█████████▌| 17813/18688 [20:57:10<58:10, 3.99s/it] 95%|█████████▌| 17814/18688 [20:57:15<1:01:36, 4.23s/it] 95%|█████████▌| 17815/18688 [20:57:20<1:03:29, 4.36s/it] 95%|█████████▌| 17816/18688 [20:57:25<1:05:32, 4.51s/it] 95%|█████████▌| 17817/18688 [20:57:29<1:04:40, 4.46s/it] 95%|█████████▌| 17818/18688 [20:57:33<1:01:20, 4.23s/it] 95%|█████████▌| 17819/18688 [20:57:36<58:05, 4.01s/it] 95%|█████████▌| 17820/18688 [20:57:40<58:22, 4.03s/it] {'loss': '0.9278', 'grad_norm': '1.229', 'learning_rate': '5.035e-07', 'epoch': '0.9536', 'num_input_tokens_seen': 59963056, 'train_runtime': '7.546e+04', 'train_tokens_per_second': '794.6'}
95%|█████████▌| 17820/18688 [20:57:40<58:22, 4.03s/it] 95%|█████████▌| 17821/18688 [20:57:44<56:45, 3.93s/it] 95%|█████████▌| 17822/18688 [20:57:48<55:12, 3.82s/it] 95%|█████████▌| 17823/18688 [20:57:52<57:08, 3.96s/it] 95%|█████████▌| 17824/18688 [20:57:55<55:20, 3.84s/it] 95%|█████████▌| 17825/18688 [20:58:00<56:28, 3.93s/it] 95%|█████████▌| 17826/18688 [20:58:03<54:58, 3.83s/it] 95%|█████████▌| 17827/18688 [20:58:07<55:16, 3.85s/it] 95%|█████████▌| 17828/18688 [20:58:10<53:33, 3.74s/it] 95%|█████████▌| 17829/18688 [20:58:14<54:07, 3.78s/it] 95%|█████████▌| 17830/18688 [20:58:18<52:41, 3.68s/it] {'loss': '0.8055', 'grad_norm': '1.266', 'learning_rate': '4.92e-07', 'epoch': '0.9541', 'num_input_tokens_seen': 59990576, 'train_runtime': '7.55e+04', 'train_tokens_per_second': '794.6'}
95%|█████████▌| 17830/18688 [20:58:18<52:41, 3.68s/it] 95%|█████████▌| 17831/18688 [20:58:22<52:46, 3.70s/it] 95%|█████████▌| 17832/18688 [20:58:26<54:14, 3.80s/it] 95%|█████████▌| 17833/18688 [20:58:30<55:09, 3.87s/it] 95%|█████████▌| 17834/18688 [20:58:34<56:33, 3.97s/it] 95%|█████████▌| 17835/18688 [20:58:38<56:58, 4.01s/it] 95%|█████████▌| 17836/18688 [20:58:42<56:59, 4.01s/it] 95%|█████████▌| 17837/18688 [20:58:47<59:09, 4.17s/it] 95%|█████████▌| 17838/18688 [20:58:50<57:27, 4.06s/it] 95%|█████████▌| 17839/18688 [20:58:55<58:03, 4.10s/it] 95%|█████████▌| 17840/18688 [20:58:59<1:00:16, 4.26s/it] {'loss': '0.9503', 'grad_norm': '1.221', 'learning_rate': '4.806e-07', 'epoch': '0.9546', 'num_input_tokens_seen': 60023656, 'train_runtime': '7.554e+04', 'train_tokens_per_second': '794.6'}
95%|█████████▌| 17840/18688 [20:58:59<1:00:16, 4.26s/it] 95%|█████████▌| 17841/18688 [20:59:04<1:01:16, 4.34s/it] 95%|█████████▌| 17842/18688 [20:59:08<1:02:59, 4.47s/it] 95%|█████████▌| 17843/18688 [20:59:13<1:01:51, 4.39s/it] 95%|█████████▌| 17844/18688 [20:59:16<59:27, 4.23s/it] 95%|█████████▌| 17845/18688 [20:59:20<56:54, 4.05s/it] 95%|█████████▌| 17846/18688 [20:59:25<58:38, 4.18s/it] 95%|█████████▌| 17847/18688 [20:59:28<55:37, 3.97s/it] 96%|█████████▌| 17848/18688 [20:59:32<53:31, 3.82s/it] 96%|█████████▌| 17849/18688 [20:59:36<56:18, 4.03s/it] 96%|█████████▌| 17850/18688 [20:59:40<55:46, 3.99s/it] {'loss': '0.9072', 'grad_norm': '1.098', 'learning_rate': '4.694e-07', 'epoch': '0.9552', 'num_input_tokens_seen': 60057400, 'train_runtime': '7.558e+04', 'train_tokens_per_second': '794.6'}
96%|█████████▌| 17850/18688 [20:59:40<55:46, 3.99s/it] 96%|█████████▌| 17851/18688 [20:59:44<55:10, 3.96s/it] 96%|█████████▌| 17852/18688 [20:59:47<53:06, 3.81s/it] 96%|█████████▌| 17853/18688 [20:59:52<56:00, 4.02s/it] 96%|█████████▌| 17854/18688 [20:59:56<54:59, 3.96s/it] 96%|█████████▌| 17855/18688 [21:00:00<55:05, 3.97s/it] 96%|█████████▌| 17856/18688 [21:00:03<53:34, 3.86s/it] 96%|█████████▌| 17857/18688 [21:00:07<52:03, 3.76s/it] 96%|█████████▌| 17858/18688 [21:00:11<52:02, 3.76s/it] 96%|█████████▌| 17859/18688 [21:00:14<52:22, 3.79s/it] 96%|█████████▌| 17860/18688 [21:00:19<53:56, 3.91s/it] {'loss': '0.9512', 'grad_norm': '1.277', 'learning_rate': '4.583e-07', 'epoch': '0.9557', 'num_input_tokens_seen': 60086904, 'train_runtime': '7.562e+04', 'train_tokens_per_second': '794.6'}
96%|█████████▌| 17860/18688 [21:00:19<53:56, 3.91s/it] 96%|█████████▌| 17861/18688 [21:00:23<54:28, 3.95s/it] 96%|█████████▌| 17862/18688 [21:00:27<57:11, 4.15s/it] 96%|█████████▌| 17863/18688 [21:00:31<57:03, 4.15s/it] 96%|█████████▌| 17864/18688 [21:00:36<57:37, 4.20s/it] 96%|█████████▌| 17865/18688 [21:00:40<57:12, 4.17s/it] 96%|█████████▌| 17866/18688 [21:00:44<58:36, 4.28s/it] 96%|█████████▌| 17867/18688 [21:00:48<55:32, 4.06s/it] 96%|█████████▌| 17868/18688 [21:00:52<54:44, 4.01s/it] 96%|█████████▌| 17869/18688 [21:00:56<55:35, 4.07s/it] 96%|█████████▌| 17870/18688 [21:01:00<56:40, 4.16s/it] {'loss': '0.8687', 'grad_norm': '1.13', 'learning_rate': '4.473e-07', 'epoch': '0.9563', 'num_input_tokens_seen': 60119624, 'train_runtime': '7.566e+04', 'train_tokens_per_second': '794.6'}
96%|█████████▌| 17870/18688 [21:01:00<56:40, 4.16s/it] 96%|█████████▌| 17871/18688 [21:01:05<59:50, 4.39s/it] 96%|█████████▌| 17872/18688 [21:01:10<1:00:26, 4.44s/it] 96%|█████████▌| 17873/18688 [21:01:14<57:22, 4.22s/it] 96%|█████████▌| 17874/18688 [21:01:19<1:00:52, 4.49s/it] 96%|█████████▌| 17875/18688 [21:01:24<1:04:48, 4.78s/it] 96%|█████████▌| 17876/18688 [21:01:29<1:04:12, 4.74s/it] 96%|█████████▌| 17877/18688 [21:01:33<1:02:52, 4.65s/it] 96%|█████████▌| 17878/18688 [21:01:37<59:50, 4.43s/it] 96%|█████████▌| 17879/18688 [21:01:43<1:04:26, 4.78s/it] 96%|█████████▌| 17880/18688 [21:01:49<1:09:38, 5.17s/it] {'loss': '0.9522', 'grad_norm': '1.079', 'learning_rate': '4.365e-07', 'epoch': '0.9568', 'num_input_tokens_seen': 60161744, 'train_runtime': '7.571e+04', 'train_tokens_per_second': '794.6'}
96%|█████████▌| 17880/18688 [21:01:49<1:09:38, 5.17s/it] 96%|█████████▌| 17881/18688 [21:01:52<1:03:16, 4.70s/it] 96%|█████████▌| 17882/18688 [21:01:57<1:01:08, 4.55s/it] 96%|█████████▌| 17883/18688 [21:02:00<57:32, 4.29s/it] 96%|█████████▌| 17884/18688 [21:02:04<55:39, 4.15s/it] 96%|█████████▌| 17885/18688 [21:02:09<57:55, 4.33s/it] 96%|█████████▌| 17886/18688 [21:02:12<54:44, 4.10s/it] 96%|█████████▌| 17887/18688 [21:02:16<53:51, 4.03s/it] 96%|█████████▌| 17888/18688 [21:02:20<53:33, 4.02s/it] 96%|█████████▌| 17889/18688 [21:02:24<52:44, 3.96s/it] 96%|█████████▌| 17890/18688 [21:02:28<52:04, 3.92s/it] {'loss': '0.9176', 'grad_norm': '1.293', 'learning_rate': '4.258e-07', 'epoch': '0.9573', 'num_input_tokens_seen': 60191768, 'train_runtime': '7.575e+04', 'train_tokens_per_second': '794.6'}
96%|█████████▌| 17890/18688 [21:02:28<52:04, 3.92s/it] 96%|█████████▌| 17891/18688 [21:02:32<52:07, 3.92s/it] 96%|█████████▌| 17892/18688 [21:02:36<52:38, 3.97s/it] 96%|█████████▌| 17893/18688 [21:02:40<51:04, 3.85s/it] 96%|█████████▌| 17894/18688 [21:02:44<51:59, 3.93s/it] 96%|█████████▌| 17895/18688 [21:02:48<54:22, 4.11s/it] 96%|█████████▌| 17896/18688 [21:02:52<52:10, 3.95s/it] 96%|█████████▌| 17897/18688 [21:02:57<55:18, 4.20s/it] 96%|█████████▌| 17898/18688 [21:03:01<56:26, 4.29s/it] 96%|█████████▌| 17899/18688 [21:03:05<56:27, 4.29s/it] 96%|█████████▌| 17900/18688 [21:03:10<57:32, 4.38s/it] {'loss': '0.9258', 'grad_norm': '1.255', 'learning_rate': '4.152e-07', 'epoch': '0.9579', 'num_input_tokens_seen': 60225864, 'train_runtime': '7.579e+04', 'train_tokens_per_second': '794.6'}
96%|█████████▌| 17900/18688 [21:03:10<57:32, 4.38s/it] 96%|█████████▌| 17901/18688 [21:03:14<56:32, 4.31s/it] 96%|█████████▌| 17902/18688 [21:03:19<57:20, 4.38s/it] 96%|█████████▌| 17903/18688 [21:03:23<56:52, 4.35s/it] 96%|█████████▌| 17904/18688 [21:03:28<58:47, 4.50s/it] 96%|█████████▌| 17905/18688 [21:03:32<57:11, 4.38s/it] 96%|█████████▌| 17906/18688 [21:03:36<54:22, 4.17s/it] 96%|█████████▌| 17907/18688 [21:03:40<54:00, 4.15s/it] 96%|█████████▌| 17908/18688 [21:03:44<54:04, 4.16s/it] 96%|█████████▌| 17909/18688 [21:03:48<54:06, 4.17s/it] 96%|█████████▌| 17910/18688 [21:03:52<53:35, 4.13s/it] {'loss': '0.8799', 'grad_norm': '1.803', 'learning_rate': '4.048e-07', 'epoch': '0.9584', 'num_input_tokens_seen': 60261128, 'train_runtime': '7.583e+04', 'train_tokens_per_second': '794.7'}
96%|█████████▌| 17910/18688 [21:03:52<53:35, 4.13s/it] 96%|█████████▌| 17911/18688 [21:03:56<51:19, 3.96s/it] 96%|█████████▌| 17912/18688 [21:03:59<50:22, 3.90s/it] 96%|█████████▌| 17913/18688 [21:04:04<51:46, 4.01s/it] 96%|█████████▌| 17914/18688 [21:04:08<53:29, 4.15s/it] 96%|█████████▌| 17915/18688 [21:04:12<52:58, 4.11s/it] 96%|█████████▌| 17916/18688 [21:04:16<52:27, 4.08s/it] 96%|█████████▌| 17917/18688 [21:04:21<53:35, 4.17s/it] 96%|█████████▌| 17918/18688 [21:04:26<59:59, 4.67s/it] 96%|█████████▌| 17919/18688 [21:04:31<58:28, 4.56s/it] 96%|█████████▌| 17920/18688 [21:04:35<56:34, 4.42s/it] {'loss': '0.9209', 'grad_norm': '1.312', 'learning_rate': '3.945e-07', 'epoch': '0.9589', 'num_input_tokens_seen': 60296848, 'train_runtime': '7.588e+04', 'train_tokens_per_second': '794.7'}
96%|█████████▌| 17920/18688 [21:04:35<56:34, 4.42s/it] 96%|█████████▌| 17921/18688 [21:04:39<55:57, 4.38s/it] 96%|█████████▌| 17922/18688 [21:04:44<57:12, 4.48s/it] 96%|█████████▌| 17923/18688 [21:04:49<58:27, 4.58s/it] 96%|█████████▌| 17924/18688 [21:04:52<54:29, 4.28s/it] 96%|█████████▌| 17925/18688 [21:04:56<53:30, 4.21s/it] 96%|█████████▌| 17926/18688 [21:05:00<50:55, 4.01s/it] 96%|█████████▌| 17927/18688 [21:05:04<52:19, 4.13s/it] 96%|█████████▌| 17928/18688 [21:05:08<50:10, 3.96s/it] 96%|█████████▌| 17929/18688 [21:05:12<51:11, 4.05s/it] 96%|█████████▌| 17930/18688 [21:05:16<51:27, 4.07s/it] {'loss': '0.8895', 'grad_norm': '1.515', 'learning_rate': '3.843e-07', 'epoch': '0.9595', 'num_input_tokens_seen': 60330792, 'train_runtime': '7.592e+04', 'train_tokens_per_second': '794.7'}
96%|█████████▌| 17930/18688 [21:05:16<51:27, 4.07s/it] 96%|█████████▌| 17931/18688 [21:05:21<54:56, 4.36s/it] 96%|█████████▌| 17932/18688 [21:05:25<51:42, 4.10s/it] 96%|█████████▌| 17933/18688 [21:05:29<53:05, 4.22s/it] 96%|█████████▌| 17934/18688 [21:05:33<51:06, 4.07s/it] 96%|█████████▌| 17935/18688 [21:05:38<54:51, 4.37s/it] 96%|█████████▌| 17936/18688 [21:05:42<52:23, 4.18s/it] 96%|█████████▌| 17937/18688 [21:05:46<51:51, 4.14s/it] 96%|█████████▌| 17938/18688 [21:05:50<50:53, 4.07s/it] 96%|█████████▌| 17939/18688 [21:05:54<50:17, 4.03s/it] 96%|█████████▌| 17940/18688 [21:05:59<56:53, 4.56s/it] {'loss': '0.8825', 'grad_norm': '1.14', 'learning_rate': '3.743e-07', 'epoch': '0.96', 'num_input_tokens_seen': 60367736, 'train_runtime': '7.596e+04', 'train_tokens_per_second': '794.7'}
96%|█████████▌| 17940/18688 [21:05:59<56:53, 4.56s/it] 96%|█████████▌| 17941/18688 [21:06:04<56:38, 4.55s/it] 96%|█████████▌| 17942/18688 [21:06:07<52:32, 4.23s/it] 96%|█████████▌| 17943/18688 [21:06:11<51:54, 4.18s/it] 96%|█████████▌| 17944/18688 [21:06:15<49:17, 3.98s/it] 96%|█████████▌| 17945/18688 [21:06:19<48:37, 3.93s/it] 96%|█████████▌| 17946/18688 [21:06:23<48:08, 3.89s/it] 96%|█████████▌| 17947/18688 [21:06:27<49:42, 4.02s/it] 96%|█████████▌| 17948/18688 [21:06:30<47:58, 3.89s/it] 96%|█████████▌| 17949/18688 [21:06:35<52:08, 4.23s/it] 96%|█████████▌| 17950/18688 [21:06:39<50:08, 4.08s/it] {'loss': '0.9154', 'grad_norm': '1.36', 'learning_rate': '3.643e-07', 'epoch': '0.9605', 'num_input_tokens_seen': 60399488, 'train_runtime': '7.6e+04', 'train_tokens_per_second': '794.7'}
96%|█████████▌| 17950/18688 [21:06:39<50:08, 4.08s/it] 96%|█████████▌| 17951/18688 [21:06:44<52:05, 4.24s/it] 96%|█████████▌| 17952/18688 [21:06:49<53:50, 4.39s/it] 96%|█████████▌| 17953/18688 [21:06:53<52:29, 4.29s/it] 96%|█████████▌| 17954/18688 [21:06:56<50:13, 4.11s/it] 96%|█████████▌| 17955/18688 [21:07:01<52:54, 4.33s/it] 96%|█████████▌| 17956/18688 [21:07:06<54:07, 4.44s/it] 96%|█████████▌| 17957/18688 [21:07:10<53:20, 4.38s/it] 96%|█████████▌| 17958/18688 [21:07:14<51:52, 4.26s/it] 96%|█████████▌| 17959/18688 [21:07:18<51:50, 4.27s/it] 96%|█████████▌| 17960/18688 [21:07:22<50:23, 4.15s/it] {'loss': '0.9025', 'grad_norm': '1.202', 'learning_rate': '3.546e-07', 'epoch': '0.9611', 'num_input_tokens_seen': 60435944, 'train_runtime': '7.604e+04', 'train_tokens_per_second': '794.8'}
96%|█████████▌| 17960/18688 [21:07:22<50:23, 4.15s/it] 96%|█████████▌| 17961/18688 [21:07:26<49:28, 4.08s/it] 96%|█████████▌| 17962/18688 [21:07:30<48:19, 3.99s/it] 96%|█████████▌| 17963/18688 [21:07:34<47:55, 3.97s/it] 96%|█████████▌| 17964/18688 [21:07:37<46:30, 3.85s/it] 96%|█████████▌| 17965/18688 [21:07:41<45:34, 3.78s/it] 96%|█████████▌| 17966/18688 [21:07:46<51:16, 4.26s/it] 96%|█████████▌| 17967/18688 [21:07:51<53:01, 4.41s/it] 96%|█████████▌| 17968/18688 [21:07:55<52:28, 4.37s/it] 96%|█████████▌| 17969/18688 [21:08:00<51:28, 4.30s/it] 96%|█████████▌| 17970/18688 [21:08:03<49:06, 4.10s/it] {'loss': '0.8943', 'grad_norm': '1.513', 'learning_rate': '3.449e-07', 'epoch': '0.9616', 'num_input_tokens_seen': 60469000, 'train_runtime': '7.608e+04', 'train_tokens_per_second': '794.8'}
96%|█████████▌| 17970/18688 [21:08:03<49:06, 4.10s/it] 96%|█████████▌| 17971/18688 [21:08:08<51:03, 4.27s/it] 96%|█████████▌| 17972/18688 [21:08:13<52:46, 4.42s/it] 96%|█████████▌| 17973/18688 [21:08:17<51:47, 4.35s/it] 96%|█████████▌| 17974/18688 [21:08:20<49:11, 4.13s/it] 96%|█████████▌| 17975/18688 [21:08:24<46:48, 3.94s/it] 96%|█████████▌| 17976/18688 [21:08:28<46:23, 3.91s/it] 96%|█████████▌| 17977/18688 [21:08:31<45:23, 3.83s/it] 96%|█████████▌| 17978/18688 [21:08:36<47:25, 4.01s/it] 96%|█████████▌| 17979/18688 [21:08:40<47:37, 4.03s/it] 96%|█████████▌| 17980/18688 [21:08:44<48:59, 4.15s/it] {'loss': '0.8857', 'grad_norm': '1.506', 'learning_rate': '3.354e-07', 'epoch': '0.9621', 'num_input_tokens_seen': 60501200, 'train_runtime': '7.612e+04', 'train_tokens_per_second': '794.8'}
96%|█████████▌| 17980/18688 [21:08:44<48:59, 4.15s/it] 96%|█████████▌| 17981/18688 [21:08:48<47:42, 4.05s/it] 96%|█████████▌| 17982/18688 [21:08:52<47:07, 4.01s/it] 96%|█████████▌| 17983/18688 [21:08:56<45:14, 3.85s/it] 96%|█████████▌| 17984/18688 [21:08:59<44:29, 3.79s/it] 96%|█████████▌| 17985/18688 [21:09:03<45:37, 3.89s/it] 96%|█████████▌| 17986/18688 [21:09:07<43:56, 3.76s/it] 96%|█████████▌| 17987/18688 [21:09:11<47:08, 4.03s/it] 96%|█████████▋| 17988/18688 [21:09:16<49:53, 4.28s/it] 96%|█████████▋| 17989/18688 [21:09:20<49:00, 4.21s/it] 96%|█████████▋| 17990/18688 [21:09:25<49:50, 4.28s/it] {'loss': '0.9346', 'grad_norm': '1.589', 'learning_rate': '3.26e-07', 'epoch': '0.9627', 'num_input_tokens_seen': 60533800, 'train_runtime': '7.617e+04', 'train_tokens_per_second': '794.8'}
96%|█████████▋| 17990/18688 [21:09:25<49:50, 4.28s/it] 96%|█████████▋| 17991/18688 [21:09:28<46:58, 4.04s/it] 96%|█████████▋| 17992/18688 [21:09:32<46:54, 4.04s/it] 96%|█████████▋| 17993/18688 [21:09:37<47:47, 4.13s/it] 96%|█████████▋| 17994/18688 [21:09:40<45:45, 3.96s/it] 96%|█████████▋| 17995/18688 [21:09:44<45:32, 3.94s/it] 96%|█████████▋| 17996/18688 [21:09:50<52:24, 4.54s/it] 96%|█████████▋| 17997/18688 [21:09:54<50:00, 4.34s/it] 96%|█████████▋| 17998/18688 [21:10:00<54:07, 4.71s/it] 96%|█████████▋| 17999/18688 [21:10:04<53:33, 4.66s/it] 96%|█████████▋| 18000/18688 [21:10:08<49:45, 4.34s/it] {'loss': '0.8773', 'grad_norm': '1.299', 'learning_rate': '3.168e-07', 'epoch': '0.9632', 'num_input_tokens_seen': 60569592, 'train_runtime': '7.621e+04', 'train_tokens_per_second': '794.8'}
96%|█████████▋| 18000/18688 [21:10:08<49:45, 4.34s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
0%| | 3/1000 [00:00<00:38, 26.15it/s]
1%| | 6/1000 [00:00<00:54, 18.33it/s]
1%| | 8/1000 [00:00<00:56, 17.61it/s]
1%| | 10/1000 [00:00<00:59, 16.61it/s]
1%| | 12/1000 [00:00<01:00, 16.29it/s]
1%|▏ | 14/1000 [00:00<01:02, 15.80it/s]
2%|▏ | 16/1000 [00:00<01:02, 15.63it/s]
2%|▏ | 18/1000 [00:01<01:38, 9.96it/s]
2%|▏ | 20/1000 [00:01<01:33, 10.49it/s]
2%|▏ | 22/1000 [00:01<01:52, 8.70it/s]
2%|▏ | 24/1000 [00:01<01:39, 9.76it/s]
3%|▎ | 26/1000 [00:02<01:45, 9.26it/s]
3%|▎ | 28/1000 [00:02<01:36, 10.09it/s]
3%|▎ | 30/1000 [00:02<01:28, 10.99it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.13it/s]
3%|▎ | 34/1000 [00:02<01:25, 11.24it/s]
4%|▎ | 36/1000 [00:03<01:32, 10.42it/s]
4%|▍ | 38/1000 [00:03<01:25, 11.32it/s]
4%|▍ | 40/1000 [00:03<01:28, 10.90it/s]
4%|▍ | 42/1000 [00:03<01:48, 8.81it/s]
4%|▍ | 44/1000 [00:03<01:39, 9.65it/s]
5%|▍ | 46/1000 [00:04<01:32, 10.34it/s]
5%|▍ | 48/1000 [00:04<01:38, 9.62it/s]
5%|▌ | 50/1000 [00:04<01:28, 10.71it/s]
5%|▌ | 52/1000 [00:04<01:20, 11.72it/s]
5%|▌ | 54/1000 [00:04<01:14, 12.64it/s]
6%|▌ | 56/1000 [00:04<01:10, 13.46it/s]
6%|▌ | 58/1000 [00:05<01:49, 8.59it/s]
6%|▌ | 60/1000 [00:05<01:37, 9.64it/s]
6%|▌ | 62/1000 [00:05<01:28, 10.63it/s]
6%|▋ | 64/1000 [00:05<01:19, 11.71it/s]
7%|▋ | 66/1000 [00:05<01:16, 12.22it/s]
7%|▋ | 68/1000 [00:06<01:14, 12.52it/s]
7%|▋ | 70/1000 [00:06<01:12, 12.84it/s]
7%|▋ | 72/1000 [00:06<01:09, 13.44it/s]
7%|▋ | 74/1000 [00:06<01:09, 13.36it/s]
8%|▊ | 76/1000 [00:06<01:05, 14.06it/s]
8%|▊ | 78/1000 [00:06<01:04, 14.37it/s]
8%|▊ | 80/1000 [00:06<01:08, 13.40it/s]
8%|▊ | 82/1000 [00:06<01:05, 14.06it/s]
8%|▊ | 84/1000 [00:07<01:03, 14.54it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.67it/s]
9%|▉ | 88/1000 [00:07<01:02, 14.49it/s]
9%|▉ | 90/1000 [00:07<01:02, 14.54it/s]
9%|▉ | 92/1000 [00:07<01:03, 14.20it/s]
9%|▉ | 94/1000 [00:07<01:04, 14.01it/s]
10%|▉ | 96/1000 [00:07<01:03, 14.31it/s]
10%|▉ | 98/1000 [00:08<01:03, 14.19it/s]
10%|█ | 100/1000 [00:08<01:32, 9.78it/s]
10%|█ | 102/1000 [00:08<01:23, 10.74it/s]
10%|█ | 104/1000 [00:09<01:54, 7.80it/s]
11%|█ | 106/1000 [00:09<02:02, 7.31it/s]
11%|█ | 108/1000 [00:09<01:43, 8.66it/s]
11%|█ | 110/1000 [00:09<01:28, 10.10it/s]
11%|█ | 112/1000 [00:09<01:19, 11.24it/s]
11%|█▏ | 114/1000 [00:09<01:14, 11.90it/s]
12%|█▏ | 116/1000 [00:09<01:10, 12.54it/s]
12%|█▏ | 118/1000 [00:10<01:06, 13.33it/s]
12%|█▏ | 120/1000 [00:10<01:09, 12.75it/s]
12%|█▏ | 122/1000 [00:10<01:22, 10.58it/s]
12%|█▏ | 124/1000 [00:10<01:30, 9.66it/s]
13%|█▎ | 126/1000 [00:10<01:22, 10.63it/s]
13%|█▎ | 128/1000 [00:11<01:15, 11.54it/s]
13%|█▎ | 130/1000 [00:11<01:11, 12.19it/s]
13%|█▎ | 132/1000 [00:11<01:06, 13.14it/s]
13%|█▎ | 134/1000 [00:11<01:10, 12.33it/s]
14%|█▎ | 136/1000 [00:11<01:05, 13.29it/s]
14%|█▍ | 138/1000 [00:11<01:05, 13.22it/s]
14%|█▍ | 140/1000 [00:12<01:25, 10.10it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.54it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.55it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.56it/s]
15%|█▍ | 148/1000 [00:12<01:14, 11.47it/s]
15%|█▌ | 150/1000 [00:13<01:07, 12.58it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.12it/s]
15%|█▌ | 154/1000 [00:13<01:03, 13.36it/s]
16%|█▌ | 156/1000 [00:13<01:03, 13.21it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.72it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.61it/s]
16%|█▌ | 162/1000 [00:13<01:02, 13.49it/s]
16%|█▋ | 164/1000 [00:14<01:01, 13.60it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.74it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.25it/s]
17%|█▋ | 170/1000 [00:14<01:19, 10.45it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.43it/s]
17%|█▋ | 174/1000 [00:15<01:30, 9.16it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.19it/s]
18%|█▊ | 178/1000 [00:15<01:34, 8.67it/s]
18%|█▊ | 180/1000 [00:15<01:33, 8.80it/s]
18%|█▊ | 182/1000 [00:16<01:35, 8.59it/s]
18%|█▊ | 184/1000 [00:16<01:23, 9.83it/s]
19%|█▊ | 186/1000 [00:16<01:37, 8.33it/s]
19%|█▉ | 188/1000 [00:16<01:24, 9.63it/s]
19%|█▉ | 190/1000 [00:16<01:16, 10.66it/s]
19%|█▉ | 192/1000 [00:16<01:11, 11.31it/s]
19%|█▉ | 194/1000 [00:17<01:16, 10.55it/s]
20%|█▉ | 196/1000 [00:17<01:31, 8.78it/s]
20%|█▉ | 198/1000 [00:17<01:20, 10.00it/s]
20%|██ | 200/1000 [00:17<01:19, 10.10it/s]
20%|██ | 202/1000 [00:17<01:15, 10.55it/s]
20%|██ | 204/1000 [00:18<01:07, 11.77it/s]
21%|██ | 206/1000 [00:18<01:03, 12.52it/s]
21%|██ | 208/1000 [00:18<01:01, 12.85it/s]
21%|██ | 210/1000 [00:18<00:59, 13.21it/s]
21%|██ | 212/1000 [00:18<00:57, 13.62it/s]
21%|██▏ | 214/1000 [00:18<01:00, 13.09it/s]
22%|██▏ | 216/1000 [00:18<00:59, 13.08it/s]
22%|██▏ | 218/1000 [00:19<00:56, 13.83it/s]
22%|██▏ | 220/1000 [00:19<01:30, 8.63it/s]
22%|██▏ | 222/1000 [00:19<01:26, 8.96it/s]
22%|██▏ | 224/1000 [00:19<01:21, 9.48it/s]
23%|██▎ | 226/1000 [00:20<01:13, 10.51it/s]
23%|██▎ | 228/1000 [00:20<01:07, 11.44it/s]
23%|██▎ | 230/1000 [00:20<01:04, 11.96it/s]
23%|██▎ | 232/1000 [00:20<01:02, 12.35it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.12it/s]
24%|██▎ | 236/1000 [00:20<00:56, 13.63it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.29it/s]
24%|██▍ | 240/1000 [00:21<00:53, 14.28it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.84it/s]
24%|██▍ | 244/1000 [00:21<00:50, 15.02it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.99it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.67it/s]
25%|██▌ | 250/1000 [00:21<00:55, 13.39it/s]
25%|██▌ | 252/1000 [00:21<01:01, 12.13it/s]
25%|██▌ | 254/1000 [00:22<00:56, 13.14it/s]
26%|██▌ | 256/1000 [00:22<00:54, 13.74it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.78it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.91it/s]
26%|██▌ | 262/1000 [00:22<00:56, 13.01it/s]
26%|██▋ | 264/1000 [00:22<00:54, 13.57it/s]
27%|██▋ | 266/1000 [00:22<00:52, 13.95it/s]
27%|██▋ | 268/1000 [00:23<00:50, 14.53it/s]
27%|██▋ | 270/1000 [00:23<00:55, 13.25it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.61it/s]
27%|██▋ | 274/1000 [00:23<01:02, 11.68it/s]
28%|██▊ | 276/1000 [00:23<00:56, 12.72it/s]
28%|██▊ | 278/1000 [00:23<00:55, 12.95it/s]
28%|██▊ | 280/1000 [00:24<00:56, 12.84it/s]
28%|██▊ | 282/1000 [00:24<00:56, 12.69it/s]
28%|██▊ | 284/1000 [00:24<00:57, 12.50it/s]
29%|██▊ | 286/1000 [00:24<00:57, 12.37it/s]
29%|██▉ | 288/1000 [00:24<00:58, 12.26it/s]
29%|██▉ | 290/1000 [00:24<00:57, 12.28it/s]
29%|██▉ | 292/1000 [00:25<00:57, 12.32it/s]
29%|██▉ | 294/1000 [00:25<00:56, 12.46it/s]
30%|██▉ | 296/1000 [00:25<00:54, 12.98it/s]
30%|██▉ | 298/1000 [00:25<00:51, 13.71it/s]
30%|███ | 300/1000 [00:25<00:50, 13.91it/s]
30%|███ | 302/1000 [00:25<00:49, 14.23it/s]
30%|███ | 304/1000 [00:25<00:52, 13.20it/s]
31%|███ | 306/1000 [00:26<00:49, 14.07it/s]
31%|███ | 308/1000 [00:26<00:48, 14.15it/s]
31%|███ | 310/1000 [00:26<00:47, 14.42it/s]
31%|███ | 312/1000 [00:26<00:47, 14.47it/s]
31%|███▏ | 314/1000 [00:26<00:45, 15.21it/s]
32%|███▏ | 316/1000 [00:26<00:45, 15.10it/s]
32%|███▏ | 318/1000 [00:26<00:43, 15.70it/s]
32%|███▏ | 320/1000 [00:27<01:13, 9.28it/s]
32%|███▏ | 322/1000 [00:27<01:03, 10.64it/s]
32%|███▏ | 324/1000 [00:27<00:59, 11.44it/s]
33%|███▎ | 326/1000 [00:27<00:59, 11.40it/s]
33%|███▎ | 328/1000 [00:27<00:58, 11.46it/s]
33%|███▎ | 330/1000 [00:28<00:53, 12.42it/s]
33%|███▎ | 332/1000 [00:28<00:52, 12.69it/s]
33%|███▎ | 334/1000 [00:28<00:50, 13.16it/s]
34%|███▎ | 336/1000 [00:28<00:48, 13.57it/s]
34%|███▍ | 338/1000 [00:28<01:05, 10.06it/s]
34%|███▍ | 340/1000 [00:28<00:59, 11.12it/s]
34%|███▍ | 342/1000 [00:29<00:53, 12.41it/s]
34%|███▍ | 344/1000 [00:29<00:50, 13.06it/s]
35%|███▍ | 346/1000 [00:29<00:47, 13.68it/s]
35%|███▍ | 348/1000 [00:29<00:46, 14.10it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.68it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.88it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.90it/s]
36%|███▌ | 356/1000 [00:29<00:42, 14.99it/s]
36%|███▌ | 358/1000 [00:30<00:43, 14.69it/s]
36%|███▌ | 360/1000 [00:30<00:42, 15.06it/s]
36%|███▌ | 362/1000 [00:30<00:47, 13.38it/s]
36%|███▋ | 364/1000 [00:30<00:45, 13.89it/s]
37%|███▋ | 366/1000 [00:30<00:53, 11.96it/s]
37%|███▋ | 368/1000 [00:30<00:51, 12.36it/s]
37%|███▋ | 370/1000 [00:31<00:47, 13.25it/s]
37%|███▋ | 372/1000 [00:31<00:46, 13.57it/s]
37%|███▋ | 374/1000 [00:31<00:55, 11.38it/s]
38%|███▊ | 376/1000 [00:31<00:56, 10.96it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.65it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.47it/s]
38%|███▊ | 382/1000 [00:32<00:48, 12.65it/s]
38%|███▊ | 384/1000 [00:32<00:45, 13.43it/s]
39%|███▊ | 386/1000 [00:32<00:44, 13.73it/s]
39%|███▉ | 388/1000 [00:32<00:44, 13.65it/s]
39%|███▉ | 390/1000 [00:32<00:45, 13.52it/s]
39%|███▉ | 392/1000 [00:32<00:43, 13.82it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.63it/s]
40%|███▉ | 396/1000 [00:33<00:43, 13.74it/s]
40%|███▉ | 398/1000 [00:33<00:47, 12.54it/s]
40%|████ | 400/1000 [00:33<00:45, 13.19it/s]
40%|████ | 402/1000 [00:33<00:43, 13.86it/s]
40%|████ | 404/1000 [00:33<00:41, 14.50it/s]
41%|████ | 406/1000 [00:33<00:39, 14.87it/s]
41%|████ | 408/1000 [00:33<00:47, 12.48it/s]
41%|████ | 410/1000 [00:34<00:56, 10.43it/s]
41%|████ | 412/1000 [00:34<00:51, 11.53it/s]
41%|████▏ | 414/1000 [00:34<01:07, 8.63it/s]
42%|████▏ | 416/1000 [00:35<01:23, 6.96it/s]
42%|████▏ | 418/1000 [00:35<01:10, 8.28it/s]
42%|████▏ | 420/1000 [00:35<01:00, 9.55it/s]
42%|████▏ | 422/1000 [00:35<00:52, 10.91it/s]
42%|████▏ | 424/1000 [00:35<01:14, 7.68it/s]
43%|████▎ | 426/1000 [00:36<01:04, 8.94it/s]
43%|████▎ | 428/1000 [00:36<00:56, 10.15it/s]
43%|████▎ | 430/1000 [00:36<01:02, 9.11it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.05it/s]
43%|████▎ | 434/1000 [00:37<01:08, 8.27it/s]
44%|████▎ | 436/1000 [00:37<01:00, 9.31it/s]
44%|████▍ | 438/1000 [00:37<01:02, 8.99it/s]
44%|████▍ | 440/1000 [00:37<00:58, 9.50it/s]
44%|████▍ | 442/1000 [00:37<00:52, 10.55it/s]
44%|████▍ | 444/1000 [00:37<00:49, 11.20it/s]
45%|████▍ | 446/1000 [00:38<00:47, 11.60it/s]
45%|████▍ | 448/1000 [00:38<00:53, 10.30it/s]
45%|████▌ | 450/1000 [00:38<00:51, 10.76it/s]
45%|████▌ | 452/1000 [00:38<00:46, 11.90it/s]
45%|████▌ | 454/1000 [00:38<00:46, 11.67it/s]
46%|████▌ | 456/1000 [00:38<00:45, 11.96it/s]
46%|████▌ | 458/1000 [00:39<00:41, 12.94it/s]
46%|████▌ | 460/1000 [00:39<00:41, 13.16it/s]
46%|████▌ | 462/1000 [00:39<00:38, 13.93it/s]
46%|████▋ | 464/1000 [00:39<00:36, 14.58it/s]
47%|████▋ | 466/1000 [00:39<00:36, 14.56it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.76it/s]
47%|████▋ | 470/1000 [00:40<00:53, 9.99it/s]
47%|████▋ | 472/1000 [00:40<00:51, 10.18it/s]
47%|████▋ | 474/1000 [00:40<00:45, 11.47it/s]
48%|████▊ | 476/1000 [00:40<00:43, 12.14it/s]
48%|████▊ | 478/1000 [00:40<00:43, 12.00it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.94it/s]
48%|████▊ | 482/1000 [00:41<00:38, 13.33it/s]
48%|████▊ | 484/1000 [00:41<00:37, 13.84it/s]
49%|████▊ | 486/1000 [00:41<00:38, 13.46it/s]
49%|████▉ | 488/1000 [00:41<00:38, 13.27it/s]
49%|████▉ | 490/1000 [00:41<00:47, 10.65it/s]
49%|████▉ | 492/1000 [00:41<00:42, 12.03it/s]
49%|████▉ | 494/1000 [00:41<00:38, 13.09it/s]
50%|████▉ | 496/1000 [00:42<00:36, 13.93it/s]
50%|████▉ | 498/1000 [00:42<00:34, 14.42it/s]
50%|█████ | 500/1000 [00:42<00:43, 11.52it/s]
50%|█████ | 502/1000 [00:42<00:41, 11.96it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.56it/s]
51%|█████ | 506/1000 [00:42<00:38, 12.74it/s]
51%|█████ | 508/1000 [00:43<00:36, 13.63it/s]
51%|█████ | 510/1000 [00:43<00:47, 10.32it/s]
51%|█████ | 512/1000 [00:43<00:44, 11.09it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 11.99it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.54it/s]
52%|█████▏ | 518/1000 [00:43<00:36, 13.05it/s]
52%|█████▏ | 520/1000 [00:44<00:35, 13.42it/s]
52%|█████▏ | 522/1000 [00:44<00:33, 14.12it/s]
52%|█████▏ | 524/1000 [00:44<00:33, 14.16it/s]
53%|█████▎ | 526/1000 [00:44<00:33, 14.34it/s]
53%|█████▎ | 528/1000 [00:44<00:39, 11.94it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.79it/s]
53%|█████▎ | 532/1000 [00:44<00:34, 13.73it/s]
53%|█████▎ | 534/1000 [00:45<00:33, 14.09it/s]
54%|█████▎ | 536/1000 [00:45<00:32, 14.23it/s]
54%|█████▍ | 538/1000 [00:45<00:33, 13.98it/s]
54%|█████▍ | 540/1000 [00:45<00:31, 14.42it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.69it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.54it/s]
55%|█████▍ | 546/1000 [00:45<00:32, 14.18it/s]
55%|█████▍ | 548/1000 [00:46<00:32, 13.89it/s]
55%|█████▌ | 550/1000 [00:46<00:31, 14.13it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.28it/s]
55%|█████▌ | 554/1000 [00:46<00:32, 13.93it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.11it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.47it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 15.00it/s]
56%|█████▌ | 562/1000 [00:47<00:29, 14.97it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.79it/s]
57%|█████▋ | 566/1000 [00:47<00:38, 11.41it/s]
57%|█████▋ | 568/1000 [00:47<00:37, 11.54it/s]
57%|█████▋ | 570/1000 [00:47<00:33, 12.68it/s]
57%|█████▋ | 572/1000 [00:47<00:35, 11.99it/s]
57%|█████▋ | 574/1000 [00:48<00:33, 12.61it/s]
58%|█████▊ | 576/1000 [00:48<00:33, 12.82it/s]
58%|█████▊ | 578/1000 [00:48<00:37, 11.19it/s]
58%|█████▊ | 580/1000 [00:48<00:34, 12.11it/s]
58%|█████▊ | 582/1000 [00:48<00:32, 13.02it/s]
58%|█████▊ | 584/1000 [00:48<00:31, 13.40it/s]
59%|█████▊ | 586/1000 [00:49<00:30, 13.40it/s]
59%|█████▉ | 588/1000 [00:49<00:30, 13.53it/s]
59%|█████▉ | 590/1000 [00:49<00:30, 13.60it/s]
59%|█████▉ | 592/1000 [00:49<00:29, 13.79it/s]
59%|█████▉ | 594/1000 [00:49<00:31, 12.75it/s]
60%|█████▉ | 596/1000 [00:49<00:30, 13.39it/s]
60%|█████▉ | 598/1000 [00:49<00:29, 13.75it/s]
60%|██████ | 600/1000 [00:50<00:28, 14.16it/s]
60%|██████ | 602/1000 [00:50<00:27, 14.34it/s]
60%|██████ | 604/1000 [00:50<00:26, 14.97it/s]
61%|██████ | 606/1000 [00:50<00:26, 14.76it/s]
61%|██████ | 608/1000 [00:50<00:25, 15.10it/s]
61%|██████ | 610/1000 [00:50<00:26, 14.99it/s]
61%|██████ | 612/1000 [00:50<00:25, 15.16it/s]
61%|██████▏ | 614/1000 [00:50<00:26, 14.37it/s]
62%|██████▏ | 616/1000 [00:51<00:26, 14.76it/s]
62%|██████▏ | 618/1000 [00:51<00:26, 14.18it/s]
62%|██████▏ | 620/1000 [00:51<00:39, 9.74it/s]
62%|██████▏ | 622/1000 [00:51<00:37, 9.96it/s]
62%|██████▏ | 624/1000 [00:51<00:32, 11.40it/s]
63%|██████▎ | 626/1000 [00:52<00:30, 12.07it/s]
63%|██████▎ | 628/1000 [00:52<00:27, 13.31it/s]
63%|██████▎ | 630/1000 [00:52<00:26, 13.80it/s]
63%|██████▎ | 632/1000 [00:52<00:29, 12.48it/s]
63%|██████▎ | 634/1000 [00:52<00:27, 13.18it/s]
64%|██████▎ | 636/1000 [00:52<00:33, 10.93it/s]
64%|██████▍ | 638/1000 [00:53<00:33, 10.66it/s]
64%|██████▍ | 640/1000 [00:53<00:31, 11.44it/s]
64%|██████▍ | 642/1000 [00:53<00:29, 12.31it/s]
64%|██████▍ | 644/1000 [00:53<00:28, 12.56it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.82it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.28it/s]
65%|██████▌ | 650/1000 [00:53<00:28, 12.50it/s]
65%|██████▌ | 652/1000 [00:54<00:29, 11.88it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.40it/s]
66%|██████▌ | 656/1000 [00:54<00:31, 10.76it/s]
66%|██████▌ | 658/1000 [00:54<00:28, 11.84it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.13it/s]
66%|██████▌ | 662/1000 [00:55<00:30, 11.25it/s]
66%|██████▋ | 664/1000 [00:55<00:28, 11.75it/s]
67%|██████▋ | 666/1000 [00:55<00:26, 12.78it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.33it/s]
67%|██████▋ | 670/1000 [00:55<00:29, 11.10it/s]
67%|██████▋ | 672/1000 [00:55<00:27, 11.86it/s]
67%|██████▋ | 674/1000 [00:56<00:26, 12.45it/s]
68%|██████▊ | 676/1000 [00:56<00:27, 11.71it/s]
68%|██████▊ | 678/1000 [00:56<00:27, 11.77it/s]
68%|██████▊ | 680/1000 [00:56<00:26, 11.94it/s]
68%|██████▊ | 682/1000 [00:56<00:27, 11.40it/s]
68%|██████▊ | 684/1000 [00:56<00:27, 11.57it/s]
69%|██████▊ | 686/1000 [00:57<00:26, 11.70it/s]
69%|██████▉ | 688/1000 [00:57<00:26, 11.81it/s]
69%|██████▉ | 690/1000 [00:57<00:25, 12.08it/s]
69%|██████▉ | 692/1000 [00:57<00:25, 12.23it/s]
69%|██████▉ | 694/1000 [00:57<00:24, 12.33it/s]
70%|██████▉ | 696/1000 [00:57<00:23, 12.67it/s]
70%|██████▉ | 698/1000 [00:58<00:22, 13.58it/s]
70%|███████ | 700/1000 [00:58<00:25, 11.67it/s]
70%|███████ | 702/1000 [00:58<00:23, 12.57it/s]
70%|███████ | 704/1000 [00:58<00:30, 9.65it/s]
71%|███████ | 706/1000 [00:58<00:27, 10.50it/s]
71%|███████ | 708/1000 [00:59<00:30, 9.52it/s]
71%|███████ | 710/1000 [00:59<00:26, 10.84it/s]
71%|███████ | 712/1000 [00:59<00:24, 11.83it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.92it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 13.03it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.56it/s]
72%|███████▏ | 720/1000 [00:59<00:20, 13.51it/s]
72%|███████▏ | 722/1000 [01:00<00:19, 13.97it/s]
72%|███████▏ | 724/1000 [01:00<00:20, 13.80it/s]
73%|███████▎ | 726/1000 [01:00<00:19, 14.00it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.94it/s]
73%|███████▎ | 730/1000 [01:01<00:32, 8.20it/s]
73%|███████▎ | 732/1000 [01:01<00:32, 8.32it/s]
73%|███████▎ | 734/1000 [01:01<00:30, 8.61it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.44it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.90it/s]
74%|███████▍ | 739/1000 [01:01<00:23, 11.01it/s]
74%|███████▍ | 741/1000 [01:02<00:22, 11.68it/s]
74%|███████▍ | 743/1000 [01:02<00:20, 12.32it/s]
74%|███████▍ | 745/1000 [01:02<00:25, 10.18it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.56it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.73it/s]
75%|███████▌ | 751/1000 [01:02<00:21, 11.85it/s]
75%|███████▌ | 753/1000 [01:03<00:19, 12.58it/s]
76%|███████▌ | 755/1000 [01:03<00:18, 13.27it/s]
76%|███████▌ | 757/1000 [01:03<00:23, 10.26it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.30it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.29it/s]
76%|███████▋ | 763/1000 [01:04<00:23, 10.23it/s]
76%|███████▋ | 765/1000 [01:04<00:27, 8.57it/s]
77%|███████▋ | 767/1000 [01:04<00:24, 9.53it/s]
77%|███████▋ | 769/1000 [01:04<00:21, 10.55it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.82it/s]
77%|███████▋ | 773/1000 [01:04<00:17, 12.63it/s]
78%|███████▊ | 775/1000 [01:05<00:17, 13.17it/s]
78%|███████▊ | 777/1000 [01:05<00:15, 14.14it/s]
78%|███████▊ | 779/1000 [01:05<00:21, 10.52it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.40it/s]
78%|███████▊ | 783/1000 [01:06<00:26, 8.31it/s]
78%|███████▊ | 784/1000 [01:06<00:30, 7.07it/s]
78%|███████▊ | 785/1000 [01:06<00:29, 7.38it/s]
79%|███████▊ | 786/1000 [01:06<00:30, 6.98it/s]
79%|███████▉ | 788/1000 [01:06<00:23, 8.83it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.15it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 11.27it/s]
79%|███████▉ | 794/1000 [01:07<00:17, 11.89it/s]
80%|███████▉ | 796/1000 [01:07<00:16, 12.33it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 13.10it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.33it/s]
80%|████████ | 802/1000 [01:08<00:24, 8.24it/s]
80%|████████ | 804/1000 [01:08<00:23, 8.29it/s]
81%|████████ | 806/1000 [01:08<00:21, 8.97it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.35it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.47it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.72it/s]
81%|████████▏ | 814/1000 [01:09<00:14, 12.60it/s]
82%|████████▏ | 816/1000 [01:09<00:16, 11.02it/s]
82%|████████▏ | 818/1000 [01:09<00:15, 11.96it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.46it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 13.00it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.75it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.19it/s]
83%|████████▎ | 828/1000 [01:10<00:11, 14.47it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.92it/s]
83%|████████▎ | 832/1000 [01:10<00:11, 14.35it/s]
83%|████████▎ | 834/1000 [01:10<00:11, 14.08it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.46it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.32it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.46it/s]
84%|████████▍ | 842/1000 [01:11<00:10, 14.47it/s]
84%|████████▍ | 844/1000 [01:11<00:11, 13.99it/s]
85%|████████▍ | 846/1000 [01:11<00:11, 13.93it/s]
85%|████████▍ | 848/1000 [01:11<00:10, 13.90it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.11it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.69it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.29it/s]
86%|████████▌ | 856/1000 [01:12<00:10, 14.15it/s]
86%|████████▌ | 858/1000 [01:12<00:09, 14.66it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 15.42it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.55it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 14.04it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.83it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.68it/s]
87%|████████▋ | 870/1000 [01:13<00:09, 14.08it/s]
87%|████████▋ | 872/1000 [01:13<00:08, 14.73it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 12.45it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.05it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.92it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.56it/s]
88%|████████▊ | 882/1000 [01:14<00:10, 11.31it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 12.04it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.84it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.82it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.28it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.06it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 14.19it/s]
90%|████████▉ | 896/1000 [01:15<00:07, 13.90it/s]
90%|████████▉ | 898/1000 [01:15<00:07, 13.29it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.20it/s]
90%|█████████ | 902/1000 [01:15<00:07, 12.96it/s]
90%|█████████ | 904/1000 [01:15<00:09, 9.78it/s]
91%|█████████ | 906/1000 [01:16<00:08, 10.84it/s]
91%|█████████ | 908/1000 [01:16<00:07, 11.83it/s]
91%|█████████ | 910/1000 [01:16<00:07, 12.08it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.29it/s]
91%|█████████▏| 914/1000 [01:17<00:12, 6.80it/s]
92%|█████████▏| 916/1000 [01:17<00:10, 8.13it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.39it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.67it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 10.11it/s]
92%|█████████▏| 924/1000 [01:17<00:07, 10.50it/s]
93%|█████████▎| 926/1000 [01:18<00:06, 10.84it/s]
93%|█████████▎| 928/1000 [01:18<00:07, 10.05it/s]
93%|█████████▎| 930/1000 [01:18<00:06, 11.30it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 11.62it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.25it/s]
94%|█████████▎| 936/1000 [01:18<00:05, 12.54it/s]
94%|█████████▍| 938/1000 [01:19<00:04, 13.61it/s]
94%|█████████▍| 940/1000 [01:19<00:04, 14.10it/s]
94%|█████████▍| 942/1000 [01:19<00:04, 12.03it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.60it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.72it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 12.56it/s]
95%|█████████▌| 950/1000 [01:20<00:03, 13.38it/s]
95%|█████████▌| 952/1000 [01:20<00:03, 14.05it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 14.15it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 13.60it/s]
96%|█████████▌| 958/1000 [01:20<00:03, 13.24it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 12.24it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.07it/s]
96%|█████████▋| 964/1000 [01:21<00:03, 9.96it/s]
97%|█████████▋| 966/1000 [01:21<00:03, 11.20it/s]
97%|█████████▋| 968/1000 [01:21<00:02, 11.87it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 12.54it/s]
97%|█████████▋| 972/1000 [01:21<00:02, 9.63it/s]
97%|█████████▋| 974/1000 [01:22<00:02, 10.84it/s]
98%|█████████▊| 976/1000 [01:22<00:02, 11.64it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.84it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.91it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.41it/s]
98%|█████████▊| 984/1000 [01:23<00:01, 10.43it/s]
99%|█████████▊| 986/1000 [01:23<00:01, 10.69it/s]
99%|█████████▉| 988/1000 [01:23<00:01, 10.89it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 11.37it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 12.27it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 13.03it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 13.95it/s]
100%|█████████▉| 998/1000 [01:24<00:00, 14.07it/s]
100%|██████████| 1000/1000 [01:24<00:00, 14.73it/s]
{'eval_loss': '0.8753', 'eval_runtime': '84.28', 'eval_samples_per_second': '11.87', 'eval_steps_per_second': '11.87', 'epoch': '0.9632', 'num_input_tokens_seen': 60569592}
96%|█████████▋| 18000/18688 [21:11:32<49:45, 4.34s/it]
100%|██████████| 1000/1000 [01:24<00:00, 14.73it/s]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00008/checkpoint-18000
loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
96%|█████████▋| 18001/18688 [21:11:37<5:42:31, 29.91s/it] 96%|█████████▋| 18002/18688 [21:11:41<4:13:04, 22.14s/it] 96%|█████████▋| 18003/18688 [21:11:45<3:09:54, 16.63s/it] 96%|█████████▋| 18004/18688 [21:11:49<2:24:51, 12.71s/it] 96%|█████████▋| 18005/18688 [21:11:53<1:54:39, 10.07s/it] 96%|█████████▋| 18006/18688 [21:11:56<1:33:07, 8.19s/it] 96%|█████████▋| 18007/18688 [21:12:01<1:22:09, 7.24s/it] 96%|█████████▋| 18008/18688 [21:12:05<1:10:12, 6.20s/it] 96%|█████████▋| 18009/18688 [21:12:09<1:01:02, 5.39s/it] 96%|█████████▋| 18010/18688 [21:12:13<57:05, 5.05s/it] {'loss': '0.8961', 'grad_norm': '1.408', 'learning_rate': '3.077e-07', 'epoch': '0.9637', 'num_input_tokens_seen': 60601344, 'train_runtime': '7.633e+04', 'train_tokens_per_second': '793.9'}
96%|█████████▋| 18010/18688 [21:12:13<57:05, 5.05s/it] 96%|█████████▋| 18011/18688 [21:12:17<53:37, 4.75s/it] 96%|█████████▋| 18012/18688 [21:12:21<51:48, 4.60s/it] 96%|█████████▋| 18013/18688 [21:12:26<51:17, 4.56s/it] 96%|█████████▋| 18014/18688 [21:12:30<49:17, 4.39s/it] 96%|█████████▋| 18015/18688 [21:12:34<49:15, 4.39s/it] 96%|█████████▋| 18016/18688 [21:12:38<46:19, 4.14s/it] 96%|█████████▋| 18017/18688 [21:12:42<46:26, 4.15s/it] 96%|█████████▋| 18018/18688 [21:12:47<49:18, 4.42s/it] 96%|█████████▋| 18019/18688 [21:12:51<48:06, 4.31s/it] 96%|█████████▋| 18020/18688 [21:12:55<47:07, 4.23s/it] {'loss': '0.8779', 'grad_norm': '1.371', 'learning_rate': '2.987e-07', 'epoch': '0.9643', 'num_input_tokens_seen': 60636728, 'train_runtime': '7.638e+04', 'train_tokens_per_second': '793.9'}
96%|█████████▋| 18020/18688 [21:12:55<47:07, 4.23s/it] 96%|█████████▋| 18021/18688 [21:12:59<47:03, 4.23s/it] 96%|█████████▋| 18022/18688 [21:13:04<47:44, 4.30s/it] 96%|█████████▋| 18023/18688 [21:13:09<51:06, 4.61s/it] 96%|█████████▋| 18024/18688 [21:13:14<50:54, 4.60s/it] 96%|█████████▋| 18025/18688 [21:13:18<51:48, 4.69s/it] 96%|█████████▋| 18026/18688 [21:13:22<48:53, 4.43s/it] 96%|█████████▋| 18027/18688 [21:13:26<46:17, 4.20s/it] 96%|█████████▋| 18028/18688 [21:13:30<45:53, 4.17s/it] 96%|█████████▋| 18029/18688 [21:13:34<44:01, 4.01s/it] 96%|█████████▋| 18030/18688 [21:13:38<44:20, 4.04s/it] {'loss': '0.8995', 'grad_norm': '1.315', 'learning_rate': '2.898e-07', 'epoch': '0.9648', 'num_input_tokens_seen': 60673048, 'train_runtime': '7.642e+04', 'train_tokens_per_second': '794'}
96%|█████████▋| 18030/18688 [21:13:38<44:20, 4.04s/it] 96%|█████████▋| 18031/18688 [21:13:41<42:30, 3.88s/it] 96%|█████████▋| 18032/18688 [21:13:45<43:31, 3.98s/it] 96%|█████████▋| 18033/18688 [21:13:50<44:13, 4.05s/it] 97%|█████████▋| 18034/18688 [21:13:53<42:54, 3.94s/it] 97%|█████████▋| 18035/18688 [21:13:57<41:42, 3.83s/it] 97%|█████████▋| 18036/18688 [21:14:01<41:50, 3.85s/it] 97%|█████████▋| 18037/18688 [21:14:05<41:24, 3.82s/it] 97%|█████████▋| 18038/18688 [21:14:08<41:31, 3.83s/it] 97%|█████████▋| 18039/18688 [21:14:13<45:19, 4.19s/it] 97%|█████████▋| 18040/18688 [21:14:18<45:08, 4.18s/it] {'loss': '0.9001', 'grad_norm': '1.204', 'learning_rate': '2.811e-07', 'epoch': '0.9654', 'num_input_tokens_seen': 60705056, 'train_runtime': '7.646e+04', 'train_tokens_per_second': '794'}
97%|█████████▋| 18040/18688 [21:14:18<45:08, 4.18s/it] 97%|█████████▋| 18041/18688 [21:14:21<42:42, 3.96s/it] 97%|█████████▋| 18042/18688 [21:14:26<44:25, 4.13s/it] 97%|█████████▋| 18043/18688 [21:14:30<44:04, 4.10s/it] 97%|█████████▋| 18044/18688 [21:14:34<43:36, 4.06s/it] 97%|█████████▋| 18045/18688 [21:14:37<42:03, 3.92s/it] 97%|█████████▋| 18046/18688 [21:14:41<42:54, 4.01s/it] 97%|█████████▋| 18047/18688 [21:14:45<42:08, 3.94s/it] 97%|█████████▋| 18048/18688 [21:14:49<41:09, 3.86s/it] 97%|█████████▋| 18049/18688 [21:14:53<41:16, 3.88s/it] 97%|█████████▋| 18050/18688 [21:14:57<42:41, 4.01s/it] {'loss': '0.9523', 'grad_norm': '1.617', 'learning_rate': '2.725e-07', 'epoch': '0.9659', 'num_input_tokens_seen': 60736776, 'train_runtime': '7.65e+04', 'train_tokens_per_second': '794'}
97%|█████████▋| 18050/18688 [21:14:57<42:41, 4.01s/it] 97%|█████████▋| 18051/18688 [21:15:02<44:09, 4.16s/it] 97%|█████████▋| 18052/18688 [21:15:06<43:13, 4.08s/it] 97%|█████████▋| 18053/18688 [21:15:12<49:14, 4.65s/it] 97%|█████████▋| 18054/18688 [21:15:15<45:29, 4.31s/it] 97%|█████████▋| 18055/18688 [21:15:19<43:42, 4.14s/it] 97%|█████████▋| 18056/18688 [21:15:23<44:06, 4.19s/it] 97%|█████████▋| 18057/18688 [21:15:27<43:36, 4.15s/it] 97%|█████████▋| 18058/18688 [21:15:31<42:27, 4.04s/it] 97%|█████████▋| 18059/18688 [21:15:36<46:21, 4.42s/it] 97%|█████████▋| 18060/18688 [21:15:40<45:26, 4.34s/it] {'loss': '0.9148', 'grad_norm': '1.595', 'learning_rate': '2.641e-07', 'epoch': '0.9664', 'num_input_tokens_seen': 60772824, 'train_runtime': '7.654e+04', 'train_tokens_per_second': '794'}
97%|█████████▋| 18060/18688 [21:15:40<45:26, 4.34s/it] 97%|█████████▋| 18061/18688 [21:15:44<43:35, 4.17s/it] 97%|█████████▋| 18062/18688 [21:15:48<41:34, 3.98s/it] 97%|█████████▋| 18063/18688 [21:15:51<40:06, 3.85s/it] 97%|█████████▋| 18064/18688 [21:15:57<44:57, 4.32s/it] 97%|█████████▋| 18065/18688 [21:16:02<46:51, 4.51s/it] 97%|█████████▋| 18066/18688 [21:16:06<47:18, 4.56s/it] 97%|█████████▋| 18067/18688 [21:16:11<46:27, 4.49s/it] 97%|█████████▋| 18068/18688 [21:16:16<47:55, 4.64s/it] 97%|█████████▋| 18069/18688 [21:16:19<44:27, 4.31s/it] 97%|█████████▋| 18070/18688 [21:16:24<46:03, 4.47s/it] {'loss': '0.92', 'grad_norm': '1.382', 'learning_rate': '2.557e-07', 'epoch': '0.967', 'num_input_tokens_seen': 60809920, 'train_runtime': '7.658e+04', 'train_tokens_per_second': '794'}
97%|█████████▋| 18070/18688 [21:16:24<46:03, 4.47s/it] 97%|█████████▋| 18071/18688 [21:16:28<45:44, 4.45s/it] 97%|█████████▋| 18072/18688 [21:16:33<45:07, 4.40s/it] 97%|█████████▋| 18073/18688 [21:16:36<42:36, 4.16s/it] 97%|█████████▋| 18074/18688 [21:16:40<41:03, 4.01s/it] 97%|█████████▋| 18075/18688 [21:16:44<42:01, 4.11s/it] 97%|█████████▋| 18076/18688 [21:16:48<39:58, 3.92s/it] 97%|█████████▋| 18077/18688 [21:16:51<39:08, 3.84s/it] 97%|█████████▋| 18078/18688 [21:16:57<43:59, 4.33s/it] 97%|█████████▋| 18079/18688 [21:17:00<41:19, 4.07s/it] 97%|█████████▋| 18080/18688 [21:17:04<40:11, 3.97s/it] {'loss': '0.8862', 'grad_norm': '1.371', 'learning_rate': '2.476e-07', 'epoch': '0.9675', 'num_input_tokens_seen': 60842784, 'train_runtime': '7.662e+04', 'train_tokens_per_second': '794'}
97%|█████████▋| 18080/18688 [21:17:04<40:11, 3.97s/it] 97%|█████████▋| 18081/18688 [21:17:08<40:38, 4.02s/it] 97%|█████████▋| 18082/18688 [21:17:13<42:15, 4.18s/it] 97%|█████████▋| 18083/18688 [21:17:16<40:14, 3.99s/it] 97%|█████████▋| 18084/18688 [21:17:20<38:44, 3.85s/it] 97%|█████████▋| 18085/18688 [21:17:24<39:49, 3.96s/it] 97%|█████████▋| 18086/18688 [21:17:27<38:09, 3.80s/it] 97%|█████████▋| 18087/18688 [21:17:33<41:49, 4.18s/it] 97%|█████████▋| 18088/18688 [21:17:36<39:58, 4.00s/it] 97%|█████████▋| 18089/18688 [21:17:40<38:39, 3.87s/it] 97%|█████████▋| 18090/18688 [21:17:44<39:30, 3.96s/it] {'loss': '0.8645', 'grad_norm': '1.253', 'learning_rate': '2.395e-07', 'epoch': '0.968', 'num_input_tokens_seen': 60874792, 'train_runtime': '7.666e+04', 'train_tokens_per_second': '794'}
97%|█████████▋| 18090/18688 [21:17:44<39:30, 3.96s/it] 97%|█████████▋| 18091/18688 [21:17:48<39:50, 4.00s/it] 97%|█████████▋| 18092/18688 [21:17:52<38:43, 3.90s/it] 97%|█████████▋| 18093/18688 [21:17:56<39:53, 4.02s/it] 97%|█████████▋| 18094/18688 [21:18:00<41:01, 4.14s/it] 97%|█████████▋| 18095/18688 [21:18:05<41:34, 4.21s/it] 97%|█████████▋| 18096/18688 [21:18:09<41:19, 4.19s/it] 97%|█████████▋| 18097/18688 [21:18:13<41:12, 4.18s/it] 97%|█████████▋| 18098/18688 [21:18:17<39:12, 3.99s/it] 97%|█████████▋| 18099/18688 [21:18:20<37:42, 3.84s/it] 97%|█████████▋| 18100/18688 [21:18:24<38:20, 3.91s/it] {'loss': '0.9263', 'grad_norm': '1.084', 'learning_rate': '2.316e-07', 'epoch': '0.9686', 'num_input_tokens_seen': 60908184, 'train_runtime': '7.67e+04', 'train_tokens_per_second': '794.1'}
97%|█████████▋| 18100/18688 [21:18:24<38:20, 3.91s/it] 97%|█████████▋| 18101/18688 [21:18:28<38:34, 3.94s/it] 97%|█████████▋| 18102/18688 [21:18:32<39:06, 4.00s/it] 97%|█████████▋| 18103/18688 [21:18:37<39:54, 4.09s/it] 97%|█████████▋| 18104/18688 [21:18:41<40:59, 4.21s/it] 97%|█████████▋| 18105/18688 [21:18:46<44:01, 4.53s/it] 97%|█████████▋| 18106/18688 [21:18:51<44:09, 4.55s/it] 97%|█████████▋| 18107/18688 [21:18:55<42:16, 4.37s/it] 97%|█████████▋| 18108/18688 [21:18:59<41:39, 4.31s/it] 97%|█████████▋| 18109/18688 [21:19:04<42:00, 4.35s/it] 97%|█████████▋| 18110/18688 [21:19:08<42:58, 4.46s/it] {'loss': '0.9223', 'grad_norm': '0.9887', 'learning_rate': '2.238e-07', 'epoch': '0.9691', 'num_input_tokens_seen': 60945736, 'train_runtime': '7.675e+04', 'train_tokens_per_second': '794.1'}
97%|█████████▋| 18110/18688 [21:19:08<42:58, 4.46s/it] 97%|█████████▋| 18111/18688 [21:19:12<40:24, 4.20s/it] 97%|█████████▋| 18112/18688 [21:19:16<41:30, 4.32s/it] 97%|█████████▋| 18113/18688 [21:19:20<39:37, 4.13s/it] 97%|█████████▋| 18114/18688 [21:19:24<38:31, 4.03s/it] 97%|█████████▋| 18115/18688 [21:19:28<37:12, 3.90s/it] 97%|█████████▋| 18116/18688 [21:19:31<36:45, 3.86s/it] 97%|█████████▋| 18117/18688 [21:19:35<36:12, 3.81s/it] 97%|█████████▋| 18118/18688 [21:19:39<37:12, 3.92s/it] 97%|█████████▋| 18119/18688 [21:19:43<36:02, 3.80s/it] 97%|█████████▋| 18120/18688 [21:19:48<39:11, 4.14s/it] {'loss': '0.8908', 'grad_norm': '1.084', 'learning_rate': '2.161e-07', 'epoch': '0.9696', 'num_input_tokens_seen': 60976584, 'train_runtime': '7.679e+04', 'train_tokens_per_second': '794.1'}
97%|█████████▋| 18120/18688 [21:19:48<39:11, 4.14s/it] 97%|█████████▋| 18121/18688 [21:19:52<38:38, 4.09s/it] 97%|█████████▋| 18122/18688 [21:19:56<38:22, 4.07s/it] 97%|█████████▋| 18123/18688 [21:19:59<37:35, 3.99s/it] 97%|█████████▋| 18124/18688 [21:20:03<36:36, 3.89s/it] 97%|█████████▋| 18125/18688 [21:20:07<35:57, 3.83s/it] 97%|█████████▋| 18126/18688 [21:20:11<37:54, 4.05s/it] 97%|█████████▋| 18127/18688 [21:20:15<37:33, 4.02s/it] 97%|█████████▋| 18128/18688 [21:20:19<37:29, 4.02s/it] 97%|█████████▋| 18129/18688 [21:20:23<36:28, 3.91s/it] 97%|█████████▋| 18130/18688 [21:20:27<35:59, 3.87s/it] {'loss': '0.8671', 'grad_norm': '1.607', 'learning_rate': '2.086e-07', 'epoch': '0.9702', 'num_input_tokens_seen': 61006880, 'train_runtime': '7.683e+04', 'train_tokens_per_second': '794.1'}
97%|█████████▋| 18130/18688 [21:20:27<35:59, 3.87s/it] 97%|█████████▋| 18131/18688 [21:20:31<36:02, 3.88s/it] 97%|█████████▋| 18132/18688 [21:20:34<35:29, 3.83s/it] 97%|█████████▋| 18133/18688 [21:20:38<35:22, 3.82s/it] 97%|█████████▋| 18134/18688 [21:20:42<36:08, 3.92s/it] 97%|█████████▋| 18135/18688 [21:20:46<35:46, 3.88s/it] 97%|█████████▋| 18136/18688 [21:20:51<37:21, 4.06s/it] 97%|█████████▋| 18137/18688 [21:20:55<37:25, 4.08s/it] 97%|█████████▋| 18138/18688 [21:20:59<37:20, 4.07s/it] 97%|█████████▋| 18139/18688 [21:21:02<35:50, 3.92s/it] 97%|█████████▋| 18140/18688 [21:21:06<34:37, 3.79s/it] {'loss': '0.8611', 'grad_norm': '0.9739', 'learning_rate': '2.012e-07', 'epoch': '0.9707', 'num_input_tokens_seen': 61037216, 'train_runtime': '7.687e+04', 'train_tokens_per_second': '794.1'}
97%|█████████▋| 18140/18688 [21:21:06<34:37, 3.79s/it] 97%|█████████▋| 18141/18688 [21:21:09<34:02, 3.73s/it] 97%|█████████▋| 18142/18688 [21:21:14<36:45, 4.04s/it] 97%|█████████▋| 18143/18688 [21:21:18<36:47, 4.05s/it] 97%|█████████▋| 18144/18688 [21:21:22<36:38, 4.04s/it] 97%|█████████▋| 18145/18688 [21:21:26<35:04, 3.88s/it] 97%|█████████▋| 18146/18688 [21:21:29<34:17, 3.80s/it] 97%|█████████▋| 18147/18688 [21:21:33<34:58, 3.88s/it] 97%|█████████▋| 18148/18688 [21:21:37<34:30, 3.83s/it] 97%|█████████▋| 18149/18688 [21:21:42<36:01, 4.01s/it] 97%|█████████▋| 18150/18688 [21:21:46<36:32, 4.07s/it] {'loss': '0.9425', 'grad_norm': '1.605', 'learning_rate': '1.94e-07', 'epoch': '0.9712', 'num_input_tokens_seen': 61067976, 'train_runtime': '7.691e+04', 'train_tokens_per_second': '794.1'}
97%|█████████▋| 18150/18688 [21:21:46<36:32, 4.07s/it] 97%|█████████▋| 18151/18688 [21:21:50<36:27, 4.07s/it] 97%|█████████▋| 18152/18688 [21:21:53<35:03, 3.93s/it] 97%|█████████▋| 18153/18688 [21:21:58<37:35, 4.22s/it] 97%|█████████▋| 18154/18688 [21:22:02<35:34, 4.00s/it] 97%|█████████▋| 18155/18688 [21:22:06<36:05, 4.06s/it] 97%|█████████▋| 18156/18688 [21:22:11<37:37, 4.24s/it] 97%|█████████▋| 18157/18688 [21:22:15<37:50, 4.28s/it] 97%|█████████▋| 18158/18688 [21:22:19<35:51, 4.06s/it] 97%|█████████▋| 18159/18688 [21:22:22<34:44, 3.94s/it] 97%|█████████▋| 18160/18688 [21:22:27<36:36, 4.16s/it] {'loss': '0.8801', 'grad_norm': '1.131', 'learning_rate': '1.868e-07', 'epoch': '0.9718', 'num_input_tokens_seen': 61101848, 'train_runtime': '7.695e+04', 'train_tokens_per_second': '794.1'}
97%|█████████▋| 18160/18688 [21:22:27<36:36, 4.16s/it] 97%|█████████▋| 18161/18688 [21:22:31<36:26, 4.15s/it] 97%|█████████▋| 18162/18688 [21:22:35<36:07, 4.12s/it] 97%|█████████▋| 18163/18688 [21:22:39<35:05, 4.01s/it] 97%|█████████▋| 18164/18688 [21:22:43<35:01, 4.01s/it] 97%|█████████▋| 18165/18688 [21:22:48<37:25, 4.29s/it] 97%|█████████▋| 18166/18688 [21:22:52<37:41, 4.33s/it] 97%|█████████▋| 18167/18688 [21:22:56<36:53, 4.25s/it] 97%|█████████▋| 18168/18688 [21:23:00<34:40, 4.00s/it] 97%|█████████▋| 18169/18688 [21:23:04<34:30, 3.99s/it] 97%|█████████▋| 18170/18688 [21:23:08<34:49, 4.03s/it] {'loss': '0.9546', 'grad_norm': '1.448', 'learning_rate': '1.798e-07', 'epoch': '0.9723', 'num_input_tokens_seen': 61136040, 'train_runtime': '7.699e+04', 'train_tokens_per_second': '794.1'}
97%|█████████▋| 18170/18688 [21:23:08<34:49, 4.03s/it] 97%|█████████▋| 18171/18688 [21:23:12<34:25, 3.99s/it] 97%|█████████▋| 18172/18688 [21:23:15<33:05, 3.85s/it] 97%|█████████▋| 18173/18688 [21:23:19<32:48, 3.82s/it] 97%|█████████▋| 18174/18688 [21:23:23<33:19, 3.89s/it] 97%|█████████▋| 18175/18688 [21:23:27<33:26, 3.91s/it] 97%|█████████▋| 18176/18688 [21:23:31<32:53, 3.85s/it] 97%|█████████▋| 18177/18688 [21:23:35<33:35, 3.94s/it] 97%|█████████▋| 18178/18688 [21:23:39<32:46, 3.86s/it] 97%|█████████▋| 18179/18688 [21:23:42<32:51, 3.87s/it] 97%|█████████▋| 18180/18688 [21:23:46<32:52, 3.88s/it] {'loss': '0.9023', 'grad_norm': '1.328', 'learning_rate': '1.73e-07', 'epoch': '0.9728', 'num_input_tokens_seen': 61166064, 'train_runtime': '7.703e+04', 'train_tokens_per_second': '794.1'}
97%|█████████▋| 18180/18688 [21:23:46<32:52, 3.88s/it] 97%|█████████▋| 18181/18688 [21:23:50<32:39, 3.86s/it] 97%|█████████▋| 18182/18688 [21:23:54<32:33, 3.86s/it] 97%|█████████▋| 18183/18688 [21:23:58<32:26, 3.85s/it] 97%|█████████▋| 18184/18688 [21:24:02<34:11, 4.07s/it] 97%|█████████▋| 18185/18688 [21:24:06<33:03, 3.94s/it] 97%|█████████▋| 18186/18688 [21:24:10<33:09, 3.96s/it] 97%|█████████▋| 18187/18688 [21:24:14<33:15, 3.98s/it] 97%|█████████▋| 18188/18688 [21:24:19<34:55, 4.19s/it] 97%|█████████▋| 18189/18688 [21:24:23<33:44, 4.06s/it] 97%|█████████▋| 18190/18688 [21:24:26<32:12, 3.88s/it] {'loss': '0.9091', 'grad_norm': '1.449', 'learning_rate': '1.663e-07', 'epoch': '0.9734', 'num_input_tokens_seen': 61198296, 'train_runtime': '7.707e+04', 'train_tokens_per_second': '794.1'}
97%|█████████▋| 18190/18688 [21:24:26<32:12, 3.88s/it] 97%|█████████▋| 18191/18688 [21:24:30<32:00, 3.87s/it] 97%|█████████▋| 18192/18688 [21:24:33<30:56, 3.74s/it] 97%|█████████▋| 18193/18688 [21:24:37<31:19, 3.80s/it] 97%|█████████▋| 18194/18688 [21:24:41<31:33, 3.83s/it] 97%|█████████▋| 18195/18688 [21:24:45<31:44, 3.86s/it] 97%|█████████▋| 18196/18688 [21:24:49<31:29, 3.84s/it] 97%|█████████▋| 18197/18688 [21:24:53<31:34, 3.86s/it] 97%|█████████▋| 18198/18688 [21:24:57<32:22, 3.96s/it] 97%|█████████▋| 18199/18688 [21:25:02<34:20, 4.21s/it] 97%|█████████▋| 18200/18688 [21:25:05<32:24, 3.98s/it] {'loss': '0.9', 'grad_norm': '1.368', 'learning_rate': '1.597e-07', 'epoch': '0.9739', 'num_input_tokens_seen': 61229808, 'train_runtime': '7.711e+04', 'train_tokens_per_second': '794.1'}
97%|█████████▋| 18200/18688 [21:25:05<32:24, 3.98s/it] 97%|█████████▋| 18201/18688 [21:25:10<33:50, 4.17s/it] 97%|█████████▋| 18202/18688 [21:25:14<33:31, 4.14s/it] 97%|█████████▋| 18203/18688 [21:25:18<32:40, 4.04s/it] 97%|█████████▋| 18204/18688 [21:25:22<32:01, 3.97s/it] 97%|█████████▋| 18205/18688 [21:25:26<32:08, 3.99s/it] 97%|█████████▋| 18206/18688 [21:25:30<33:35, 4.18s/it] 97%|█████████▋| 18207/18688 [21:25:34<33:43, 4.21s/it] 97%|█████████▋| 18208/18688 [21:25:39<33:47, 4.22s/it] 97%|█████████▋| 18209/18688 [21:25:43<33:17, 4.17s/it] 97%|█████████▋| 18210/18688 [21:25:48<35:35, 4.47s/it] {'loss': '0.935', 'grad_norm': '1.328', 'learning_rate': '1.532e-07', 'epoch': '0.9744', 'num_input_tokens_seen': 61264992, 'train_runtime': '7.715e+04', 'train_tokens_per_second': '794.1'}
97%|█████████▋| 18210/18688 [21:25:48<35:35, 4.47s/it] 97%|█████████▋| 18211/18688 [21:25:52<34:41, 4.36s/it] 97%|█████████▋| 18212/18688 [21:25:56<32:52, 4.14s/it] 97%|█████████▋| 18213/18688 [21:25:59<31:18, 3.96s/it] 97%|█████████▋| 18214/18688 [21:26:04<33:13, 4.21s/it] 97%|█████████▋| 18215/18688 [21:26:08<32:15, 4.09s/it] 97%|█████████▋| 18216/18688 [21:26:12<31:32, 4.01s/it] 97%|█████████▋| 18217/18688 [21:26:16<31:38, 4.03s/it] 97%|█████████▋| 18218/18688 [21:26:19<30:13, 3.86s/it] 97%|█████████▋| 18219/18688 [21:26:23<29:15, 3.74s/it] 97%|█████████▋| 18220/18688 [21:26:26<28:59, 3.72s/it] {'loss': '0.8838', 'grad_norm': '1.169', 'learning_rate': '1.469e-07', 'epoch': '0.975', 'num_input_tokens_seen': 61295160, 'train_runtime': '7.719e+04', 'train_tokens_per_second': '794.1'}
97%|█████████▋| 18220/18688 [21:26:26<28:59, 3.72s/it] 98%|█████████▊| 18221/18688 [21:26:31<32:05, 4.12s/it] 98%|█████████▊| 18222/18688 [21:26:35<30:29, 3.93s/it] 98%|█████████▊| 18223/18688 [21:26:39<30:12, 3.90s/it] 98%|█████████▊| 18224/18688 [21:26:43<30:30, 3.94s/it] 98%|█████████▊| 18225/18688 [21:26:46<29:55, 3.88s/it] 98%|█████████▊| 18226/18688 [21:26:51<31:13, 4.05s/it] 98%|█████████▊| 18227/18688 [21:26:55<31:58, 4.16s/it] 98%|█████████▊| 18228/18688 [21:26:59<31:13, 4.07s/it] 98%|█████████▊| 18229/18688 [21:27:03<29:56, 3.91s/it] 98%|█████████▊| 18230/18688 [21:27:06<29:16, 3.83s/it] {'loss': '0.8737', 'grad_norm': '1.341', 'learning_rate': '1.407e-07', 'epoch': '0.9755', 'num_input_tokens_seen': 61327592, 'train_runtime': '7.723e+04', 'train_tokens_per_second': '794.1'}
98%|█████████▊| 18230/18688 [21:27:06<29:16, 3.83s/it] 98%|█████████▊| 18231/18688 [21:27:11<30:11, 3.96s/it] 98%|█████████▊| 18232/18688 [21:27:15<31:00, 4.08s/it] 98%|█████████▊| 18233/18688 [21:27:19<31:08, 4.11s/it] 98%|█████████▊| 18234/18688 [21:27:23<31:03, 4.10s/it] 98%|█████████▊| 18235/18688 [21:27:27<29:59, 3.97s/it] 98%|█████████▊| 18236/18688 [21:27:31<29:31, 3.92s/it] 98%|█████████▊| 18237/18688 [21:27:35<29:17, 3.90s/it] 98%|█████████▊| 18238/18688 [21:27:40<32:29, 4.33s/it] 98%|█████████▊| 18239/18688 [21:27:44<32:11, 4.30s/it] 98%|█████████▊| 18240/18688 [21:27:48<30:47, 4.12s/it] {'loss': '0.9055', 'grad_norm': '1.168', 'learning_rate': '1.346e-07', 'epoch': '0.9761', 'num_input_tokens_seen': 61363024, 'train_runtime': '7.727e+04', 'train_tokens_per_second': '794.2'}
98%|█████████▊| 18240/18688 [21:27:48<30:47, 4.12s/it] 98%|█████████▊| 18241/18688 [21:27:51<29:25, 3.95s/it] 98%|█████████▊| 18242/18688 [21:27:55<28:45, 3.87s/it] 98%|█████████▊| 18243/18688 [21:27:59<29:12, 3.94s/it] 98%|█████████▊| 18244/18688 [21:28:03<28:40, 3.87s/it] 98%|█████████▊| 18245/18688 [21:28:07<29:11, 3.95s/it] 98%|█████████▊| 18246/18688 [21:28:12<30:45, 4.18s/it] 98%|█████████▊| 18247/18688 [21:28:16<30:29, 4.15s/it] 98%|█████████▊| 18248/18688 [21:28:19<29:13, 3.98s/it] 98%|█████████▊| 18249/18688 [21:28:24<30:38, 4.19s/it] 98%|█████████▊| 18250/18688 [21:28:28<29:13, 4.00s/it] {'loss': '0.833', 'grad_norm': '1.121', 'learning_rate': '1.287e-07', 'epoch': '0.9766', 'num_input_tokens_seen': 61395528, 'train_runtime': '7.731e+04', 'train_tokens_per_second': '794.2'}
98%|█████████▊| 18250/18688 [21:28:28<29:13, 4.00s/it] 98%|█████████▊| 18251/18688 [21:28:31<27:58, 3.84s/it] 98%|█████████▊| 18252/18688 [21:28:35<28:55, 3.98s/it] 98%|█████████▊| 18253/18688 [21:28:40<30:35, 4.22s/it] 98%|█████████▊| 18254/18688 [21:28:44<29:37, 4.10s/it] 98%|█████████▊| 18255/18688 [21:28:48<29:34, 4.10s/it] 98%|█████████▊| 18256/18688 [21:28:52<29:53, 4.15s/it] 98%|█████████▊| 18257/18688 [21:28:56<28:58, 4.03s/it] 98%|█████████▊| 18258/18688 [21:29:00<28:29, 3.97s/it] 98%|█████████▊| 18259/18688 [21:29:05<30:28, 4.26s/it] 98%|█████████▊| 18260/18688 [21:29:09<30:33, 4.28s/it] {'loss': '0.8923', 'grad_norm': '1.374', 'learning_rate': '1.229e-07', 'epoch': '0.9771', 'num_input_tokens_seen': 61428936, 'train_runtime': '7.735e+04', 'train_tokens_per_second': '794.2'}
98%|█████████▊| 18260/18688 [21:29:09<30:33, 4.28s/it] 98%|█████████▊| 18261/18688 [21:29:13<29:36, 4.16s/it] 98%|█████████▊| 18262/18688 [21:29:18<31:15, 4.40s/it] 98%|█████████▊| 18263/18688 [21:29:22<31:06, 4.39s/it] 98%|█████████▊| 18264/18688 [21:29:26<29:16, 4.14s/it] 98%|█████████▊| 18265/18688 [21:29:30<28:24, 4.03s/it] 98%|█████████▊| 18266/18688 [21:29:34<28:44, 4.09s/it] 98%|█████████▊| 18267/18688 [21:29:37<27:15, 3.89s/it] 98%|█████████▊| 18268/18688 [21:29:41<26:40, 3.81s/it] 98%|█████████▊| 18269/18688 [21:29:45<27:39, 3.96s/it] 98%|█████████▊| 18270/18688 [21:29:49<27:23, 3.93s/it] {'loss': '0.8883', 'grad_norm': '1.304', 'learning_rate': '1.172e-07', 'epoch': '0.9777', 'num_input_tokens_seen': 61458864, 'train_runtime': '7.739e+04', 'train_tokens_per_second': '794.1'}
98%|█████████▊| 18270/18688 [21:29:49<27:23, 3.93s/it] 98%|█████████▊| 18271/18688 [21:29:53<27:56, 4.02s/it] 98%|█████████▊| 18272/18688 [21:29:57<27:19, 3.94s/it] 98%|█████████▊| 18273/18688 [21:30:02<28:58, 4.19s/it] 98%|█████████▊| 18274/18688 [21:30:06<29:28, 4.27s/it] 98%|█████████▊| 18275/18688 [21:30:10<28:45, 4.18s/it] 98%|█████████▊| 18276/18688 [21:30:15<28:37, 4.17s/it] 98%|█████████▊| 18277/18688 [21:30:19<29:09, 4.26s/it] 98%|█████████▊| 18278/18688 [21:30:23<27:42, 4.05s/it] 98%|█████████▊| 18279/18688 [21:30:27<29:23, 4.31s/it] 98%|█████████▊| 18280/18688 [21:30:32<29:24, 4.32s/it] {'loss': '0.9104', 'grad_norm': '1.205', 'learning_rate': '1.117e-07', 'epoch': '0.9782', 'num_input_tokens_seen': 61492032, 'train_runtime': '7.743e+04', 'train_tokens_per_second': '794.1'}
98%|█████████▊| 18280/18688 [21:30:32<29:24, 4.32s/it] 98%|█████████▊| 18281/18688 [21:30:36<29:17, 4.32s/it] 98%|█████████▊| 18282/18688 [21:30:41<29:22, 4.34s/it] 98%|█████████▊| 18283/18688 [21:30:44<28:11, 4.18s/it] 98%|█████████▊| 18284/18688 [21:30:48<26:56, 4.00s/it] 98%|█████████▊| 18285/18688 [21:30:52<26:48, 3.99s/it] 98%|█████████▊| 18286/18688 [21:30:56<27:02, 4.04s/it] 98%|█████████▊| 18287/18688 [21:31:00<26:16, 3.93s/it] 98%|█████████▊| 18288/18688 [21:31:04<26:44, 4.01s/it] 98%|█████████▊| 18289/18688 [21:31:08<25:56, 3.90s/it] 98%|█████████▊| 18290/18688 [21:31:12<27:52, 4.20s/it] {'loss': '0.955', 'grad_norm': '1.267', 'learning_rate': '1.063e-07', 'epoch': '0.9787', 'num_input_tokens_seen': 61525008, 'train_runtime': '7.747e+04', 'train_tokens_per_second': '794.1'}
98%|█████████▊| 18290/18688 [21:31:12<27:52, 4.20s/it] 98%|█████████▊| 18291/18688 [21:31:17<27:58, 4.23s/it] 98%|█████████▊| 18292/18688 [21:31:20<26:29, 4.01s/it] 98%|█████████▊| 18293/18688 [21:31:26<29:38, 4.50s/it] 98%|█████████▊| 18294/18688 [21:31:30<28:17, 4.31s/it] 98%|█████████▊| 18295/18688 [21:31:34<28:49, 4.40s/it] 98%|█████████▊| 18296/18688 [21:31:39<28:42, 4.39s/it] 98%|█████████▊| 18297/18688 [21:31:43<28:07, 4.32s/it] 98%|█████████▊| 18298/18688 [21:31:46<26:19, 4.05s/it] 98%|█████████▊| 18299/18688 [21:31:51<27:06, 4.18s/it] 98%|█████████▊| 18300/18688 [21:31:54<26:02, 4.03s/it] {'loss': '0.8992', 'grad_norm': '1.266', 'learning_rate': '1.011e-07', 'epoch': '0.9793', 'num_input_tokens_seen': 61559520, 'train_runtime': '7.751e+04', 'train_tokens_per_second': '794.2'}
98%|█████████▊| 18300/18688 [21:31:54<26:02, 4.03s/it] 98%|█████████▊| 18301/18688 [21:31:58<25:27, 3.95s/it] 98%|█████████▊| 18302/18688 [21:32:02<25:44, 4.00s/it] 98%|█████████▊| 18303/18688 [21:32:07<27:16, 4.25s/it] 98%|█████████▊| 18304/18688 [21:32:11<26:58, 4.22s/it] 98%|█████████▊| 18305/18688 [21:32:15<26:42, 4.18s/it] 98%|█████████▊| 18306/18688 [21:32:21<28:39, 4.50s/it] 98%|█████████▊| 18307/18688 [21:32:25<28:26, 4.48s/it] 98%|█████████▊| 18308/18688 [21:32:29<27:05, 4.28s/it] 98%|█████████▊| 18309/18688 [21:32:33<25:45, 4.08s/it] 98%|█████████▊| 18310/18688 [21:32:37<25:39, 4.07s/it] {'loss': '0.8465', 'grad_norm': '1.269', 'learning_rate': '9.594e-08', 'epoch': '0.9798', 'num_input_tokens_seen': 61592872, 'train_runtime': '7.756e+04', 'train_tokens_per_second': '794.2'}
98%|█████████▊| 18310/18688 [21:32:37<25:39, 4.07s/it] 98%|█████████▊| 18311/18688 [21:32:40<24:39, 3.92s/it] 98%|█████████▊| 18312/18688 [21:32:44<23:55, 3.82s/it] 98%|█████████▊| 18313/18688 [21:32:48<23:48, 3.81s/it] 98%|█████████▊| 18314/18688 [21:32:51<23:46, 3.81s/it] 98%|█████████▊| 18315/18688 [21:32:55<23:57, 3.85s/it] 98%|█████████▊| 18316/18688 [21:32:59<23:37, 3.81s/it] 98%|█████████▊| 18317/18688 [21:33:03<23:39, 3.83s/it] 98%|█████████▊| 18318/18688 [21:33:08<25:17, 4.10s/it] 98%|█████████▊| 18319/18688 [21:33:12<25:06, 4.08s/it] 98%|█████████▊| 18320/18688 [21:33:17<26:39, 4.35s/it] {'loss': '0.8746', 'grad_norm': '1.167', 'learning_rate': '9.094e-08', 'epoch': '0.9803', 'num_input_tokens_seen': 61625152, 'train_runtime': '7.76e+04', 'train_tokens_per_second': '794.2'}
98%|█████████▊| 18320/18688 [21:33:17<26:39, 4.35s/it] 98%|█████████▊| 18321/18688 [21:33:22<27:48, 4.55s/it] 98%|█████████▊| 18322/18688 [21:33:26<26:59, 4.43s/it] 98%|█████████▊| 18323/18688 [21:33:30<25:46, 4.24s/it] 98%|█████████▊| 18324/18688 [21:33:34<25:13, 4.16s/it] 98%|█████████▊| 18325/18688 [21:33:37<24:35, 4.06s/it] 98%|█████████▊| 18326/18688 [21:33:42<25:09, 4.17s/it] 98%|█████████▊| 18327/18688 [21:33:46<25:11, 4.19s/it] 98%|█████████▊| 18328/18688 [21:33:50<24:34, 4.10s/it] 98%|█████████▊| 18329/18688 [21:33:54<25:03, 4.19s/it] 98%|█████████▊| 18330/18688 [21:33:59<25:44, 4.31s/it] {'loss': '0.883', 'grad_norm': '1.111', 'learning_rate': '8.608e-08', 'epoch': '0.9809', 'num_input_tokens_seen': 61659880, 'train_runtime': '7.764e+04', 'train_tokens_per_second': '794.2'}
98%|█████████▊| 18330/18688 [21:33:59<25:44, 4.31s/it] 98%|█████████▊| 18331/18688 [21:34:04<27:45, 4.66s/it] 98%|█████████▊| 18332/18688 [21:34:08<25:31, 4.30s/it] 98%|█████████▊| 18333/18688 [21:34:11<23:59, 4.05s/it] 98%|█████████▊| 18334/18688 [21:34:16<24:36, 4.17s/it] 98%|█████████▊| 18335/18688 [21:34:20<24:24, 4.15s/it] 98%|█████████▊| 18336/18688 [21:34:24<24:39, 4.20s/it] 98%|█████████▊| 18337/18688 [21:34:28<24:41, 4.22s/it] 98%|█████████▊| 18338/18688 [21:34:32<23:20, 4.00s/it] 98%|█████████▊| 18339/18688 [21:34:36<23:19, 4.01s/it] 98%|█████████▊| 18340/18688 [21:34:40<23:25, 4.04s/it] {'loss': '0.849', 'grad_norm': '1.294', 'learning_rate': '8.135e-08', 'epoch': '0.9814', 'num_input_tokens_seen': 61693224, 'train_runtime': '7.768e+04', 'train_tokens_per_second': '794.2'}
98%|█████████▊| 18340/18688 [21:34:40<23:25, 4.04s/it] 98%|█████████▊| 18341/18688 [21:34:45<24:18, 4.20s/it] 98%|█████████▊| 18342/18688 [21:34:49<24:13, 4.20s/it] 98%|█████████▊| 18343/18688 [21:34:54<25:11, 4.38s/it] 98%|█████████▊| 18344/18688 [21:34:58<24:48, 4.33s/it] 98%|█████████▊| 18345/18688 [21:35:02<24:32, 4.29s/it] 98%|█████████▊| 18346/18688 [21:35:07<25:26, 4.46s/it] 98%|█████████▊| 18347/18688 [21:35:11<24:13, 4.26s/it] 98%|█████████▊| 18348/18688 [21:35:15<23:47, 4.20s/it] 98%|█████████▊| 18349/18688 [21:35:19<24:12, 4.29s/it] 98%|█████████▊| 18350/18688 [21:35:23<23:15, 4.13s/it] {'loss': '0.8881', 'grad_norm': '1.22', 'learning_rate': '7.676e-08', 'epoch': '0.9819', 'num_input_tokens_seen': 61728552, 'train_runtime': '7.772e+04', 'train_tokens_per_second': '794.2'}
98%|█████████▊| 18350/18688 [21:35:23<23:15, 4.13s/it] 98%|█████████▊| 18351/18688 [21:35:28<23:52, 4.25s/it] 98%|█████████▊| 18352/18688 [21:35:32<23:56, 4.28s/it] 98%|█████████▊| 18353/18688 [21:35:37<25:02, 4.49s/it] 98%|█████████▊| 18354/18688 [21:35:41<23:47, 4.27s/it] 98%|█████████▊| 18355/18688 [21:35:45<24:35, 4.43s/it] 98%|█████████▊| 18356/18688 [21:35:50<24:02, 4.35s/it] 98%|█████████▊| 18357/18688 [21:35:54<24:46, 4.49s/it] 98%|█████████▊| 18358/18688 [21:35:59<24:05, 4.38s/it] 98%|█████████▊| 18359/18688 [21:36:03<23:54, 4.36s/it] 98%|█████████▊| 18360/18688 [21:36:07<23:37, 4.32s/it] {'loss': '0.9002', 'grad_norm': '1.578', 'learning_rate': '7.23e-08', 'epoch': '0.9825', 'num_input_tokens_seen': 61763968, 'train_runtime': '7.777e+04', 'train_tokens_per_second': '794.2'}
98%|█████████▊| 18360/18688 [21:36:07<23:37, 4.32s/it] 98%|█████████▊| 18361/18688 [21:36:11<22:28, 4.12s/it] 98%|█████████▊| 18362/18688 [21:36:16<24:33, 4.52s/it] 98%|█████████▊| 18363/18688 [21:36:21<24:46, 4.58s/it] 98%|█████████▊| 18364/18688 [21:36:25<23:18, 4.32s/it] 98%|█████████▊| 18365/18688 [21:36:29<22:55, 4.26s/it] 98%|█████████▊| 18366/18688 [21:36:35<25:34, 4.77s/it] 98%|█████████▊| 18367/18688 [21:36:39<24:00, 4.49s/it] 98%|█████████▊| 18368/18688 [21:36:43<24:17, 4.55s/it] 98%|█████████▊| 18369/18688 [21:36:48<23:49, 4.48s/it] 98%|█████████▊| 18370/18688 [21:36:51<22:20, 4.21s/it] {'loss': '0.8876', 'grad_norm': '1.233', 'learning_rate': '6.797e-08', 'epoch': '0.983', 'num_input_tokens_seen': 61801088, 'train_runtime': '7.781e+04', 'train_tokens_per_second': '794.2'}
98%|█████████▊| 18370/18688 [21:36:51<22:20, 4.21s/it] 98%|█████████▊| 18371/18688 [21:36:55<22:26, 4.25s/it] 98%|█████████▊| 18372/18688 [21:36:59<21:44, 4.13s/it] 98%|█████████▊| 18373/18688 [21:37:03<20:40, 3.94s/it] 98%|█████████▊| 18374/18688 [21:37:07<21:14, 4.06s/it] 98%|█████████▊| 18375/18688 [21:37:11<21:11, 4.06s/it] 98%|█████████▊| 18376/18688 [21:37:15<21:09, 4.07s/it] 98%|█████████▊| 18377/18688 [21:37:20<22:42, 4.38s/it] 98%|█████████▊| 18378/18688 [21:37:26<24:44, 4.79s/it] 98%|█████████▊| 18379/18688 [21:37:31<23:59, 4.66s/it] 98%|█████████▊| 18380/18688 [21:37:36<24:26, 4.76s/it] {'loss': '0.9711', 'grad_norm': '1.486', 'learning_rate': '6.378e-08', 'epoch': '0.9835', 'num_input_tokens_seen': 61837960, 'train_runtime': '7.786e+04', 'train_tokens_per_second': '794.3'}
98%|█████████▊| 18380/18688 [21:37:36<24:26, 4.76s/it] 98%|█████████▊| 18381/18688 [21:37:40<23:24, 4.57s/it] 98%|█████████▊| 18382/18688 [21:37:44<22:51, 4.48s/it] 98%|█████████▊| 18383/18688 [21:37:49<23:33, 4.63s/it] 98%|█████████▊| 18384/18688 [21:37:53<23:01, 4.54s/it] 98%|█████████▊| 18385/18688 [21:37:57<22:21, 4.43s/it] 98%|█████████▊| 18386/18688 [21:38:02<22:56, 4.56s/it] 98%|█████████▊| 18387/18688 [21:38:06<21:52, 4.36s/it] 98%|█████████▊| 18388/18688 [21:38:11<23:04, 4.62s/it] 98%|█████████▊| 18389/18688 [21:38:16<22:55, 4.60s/it] 98%|█████████▊| 18390/18688 [21:38:21<22:49, 4.60s/it] {'loss': '0.9454', 'grad_norm': '1.158', 'learning_rate': '5.972e-08', 'epoch': '0.9841', 'num_input_tokens_seen': 61878304, 'train_runtime': '7.79e+04', 'train_tokens_per_second': '794.3'}
98%|█████████▊| 18390/18688 [21:38:21<22:49, 4.60s/it] 98%|█████████▊| 18391/18688 [21:38:24<21:25, 4.33s/it] 98%|█████████▊| 18392/18688 [21:38:28<20:35, 4.17s/it] 98%|█████████▊| 18393/18688 [21:38:33<21:43, 4.42s/it] 98%|█████████▊| 18394/18688 [21:38:37<20:39, 4.22s/it] 98%|█████████▊| 18395/18688 [21:38:41<20:13, 4.14s/it] 98%|█████████▊| 18396/18688 [21:38:45<20:14, 4.16s/it] 98%|█████████▊| 18397/18688 [21:38:49<20:31, 4.23s/it] 98%|█████████▊| 18398/18688 [21:38:53<19:35, 4.05s/it] 98%|█████████▊| 18399/18688 [21:38:57<18:49, 3.91s/it] 98%|█████████▊| 18400/18688 [21:39:01<19:42, 4.11s/it] {'loss': '0.8861', 'grad_norm': '1.298', 'learning_rate': '5.579e-08', 'epoch': '0.9846', 'num_input_tokens_seen': 61909184, 'train_runtime': '7.794e+04', 'train_tokens_per_second': '794.3'}
98%|█████████▊| 18400/18688 [21:39:01<19:42, 4.11s/it] 98%|█████████▊| 18401/18688 [21:39:05<19:38, 4.11s/it] 98%|█████████▊| 18402/18688 [21:39:10<20:21, 4.27s/it] 98%|█████████▊| 18403/18688 [21:39:14<19:54, 4.19s/it] 98%|█████████▊| 18404/18688 [21:39:18<19:13, 4.06s/it] 98%|█████████▊| 18405/18688 [21:39:22<19:40, 4.17s/it] 98%|█████████▊| 18406/18688 [21:39:26<19:38, 4.18s/it] 98%|█████████▊| 18407/18688 [21:39:30<18:55, 4.04s/it] 99%|█████████▊| 18408/18688 [21:39:35<20:08, 4.32s/it] 99%|█████████▊| 18409/18688 [21:39:39<19:08, 4.12s/it] 99%|█████████▊| 18410/18688 [21:39:43<19:55, 4.30s/it] {'loss': '0.8953', 'grad_norm': '1.329', 'learning_rate': '5.2e-08', 'epoch': '0.9852', 'num_input_tokens_seen': 61945000, 'train_runtime': '7.798e+04', 'train_tokens_per_second': '794.3'}
99%|█████████▊| 18410/18688 [21:39:43<19:55, 4.30s/it] 99%|█████████▊| 18411/18688 [21:39:47<19:10, 4.15s/it] 99%|█████████▊| 18412/18688 [21:39:52<19:54, 4.33s/it] 99%|█████████▊| 18413/18688 [21:39:56<20:10, 4.40s/it] 99%|█████████▊| 18414/18688 [21:40:02<22:11, 4.86s/it] 99%|█████████▊| 18415/18688 [21:40:08<22:45, 5.00s/it] 99%|█████████▊| 18416/18688 [21:40:12<21:32, 4.75s/it] 99%|█████████▊| 18417/18688 [21:40:17<21:55, 4.86s/it] 99%|█████████▊| 18418/18688 [21:40:21<20:57, 4.66s/it] 99%|█████████▊| 18419/18688 [21:40:25<20:06, 4.49s/it] 99%|█████████▊| 18420/18688 [21:40:29<18:51, 4.22s/it] {'loss': '0.8754', 'grad_norm': '1.339', 'learning_rate': '4.834e-08', 'epoch': '0.9857', 'num_input_tokens_seen': 61982512, 'train_runtime': '7.803e+04', 'train_tokens_per_second': '794.3'}
99%|█████████▊| 18420/18688 [21:40:29<18:51, 4.22s/it] 99%|█████████▊| 18421/18688 [21:40:34<19:33, 4.39s/it] 99%|█████████▊| 18422/18688 [21:40:38<19:18, 4.36s/it] 99%|█████████▊| 18423/18688 [21:40:42<18:45, 4.25s/it] 99%|█████████▊| 18424/18688 [21:40:45<17:38, 4.01s/it] 99%|█████████▊| 18425/18688 [21:40:50<18:07, 4.13s/it] 99%|█████████▊| 18426/18688 [21:40:55<18:47, 4.30s/it] 99%|█████████▊| 18427/18688 [21:40:59<18:46, 4.32s/it] 99%|█████████▊| 18428/18688 [21:41:03<18:48, 4.34s/it] 99%|█████████▊| 18429/18688 [21:41:07<17:57, 4.16s/it] 99%|█████████▊| 18430/18688 [21:41:11<17:28, 4.06s/it] {'loss': '0.9381', 'grad_norm': '1.376', 'learning_rate': '4.481e-08', 'epoch': '0.9862', 'num_input_tokens_seen': 62016416, 'train_runtime': '7.807e+04', 'train_tokens_per_second': '794.4'}
99%|█████████▊| 18430/18688 [21:41:11<17:28, 4.06s/it] 99%|█████████▊| 18431/18688 [21:41:14<16:49, 3.93s/it] 99%|█████████▊| 18432/18688 [21:41:19<17:12, 4.03s/it] 99%|█████████▊| 18433/18688 [21:41:23<17:57, 4.22s/it] 99%|█████████▊| 18434/18688 [21:41:27<17:27, 4.12s/it] 99%|█████████▊| 18435/18688 [21:41:31<17:15, 4.09s/it] 99%|█████████▊| 18436/18688 [21:41:35<16:42, 3.98s/it] 99%|█████████▊| 18437/18688 [21:41:38<15:58, 3.82s/it] 99%|█████████▊| 18438/18688 [21:41:42<16:06, 3.86s/it] 99%|█████████▊| 18439/18688 [21:41:46<15:58, 3.85s/it] 99%|█████████▊| 18440/18688 [21:41:50<15:35, 3.77s/it] {'loss': '0.8786', 'grad_norm': '1.187', 'learning_rate': '4.142e-08', 'epoch': '0.9868', 'num_input_tokens_seen': 62046720, 'train_runtime': '7.811e+04', 'train_tokens_per_second': '794.3'}
99%|█████████▊| 18440/18688 [21:41:50<15:35, 3.77s/it] 99%|█████████▊| 18441/18688 [21:41:54<15:52, 3.86s/it] 99%|█████████▊| 18442/18688 [21:41:58<16:27, 4.02s/it] 99%|█████████▊| 18443/18688 [21:42:02<16:01, 3.92s/it] 99%|█████████▊| 18444/18688 [21:42:05<15:23, 3.78s/it] 99%|█████████▊| 18445/18688 [21:42:09<15:16, 3.77s/it] 99%|█████████▊| 18446/18688 [21:42:14<16:34, 4.11s/it] 99%|█████████▊| 18447/18688 [21:42:18<16:32, 4.12s/it] 99%|█████████▊| 18448/18688 [21:42:23<16:58, 4.24s/it] 99%|█████████▊| 18449/18688 [21:42:27<17:16, 4.34s/it] 99%|█████████▊| 18450/18688 [21:42:32<17:05, 4.31s/it] {'loss': '0.9303', 'grad_norm': '1.312', 'learning_rate': '3.816e-08', 'epoch': '0.9873', 'num_input_tokens_seen': 62079696, 'train_runtime': '7.815e+04', 'train_tokens_per_second': '794.3'}
99%|█████████▊| 18450/18688 [21:42:32<17:05, 4.31s/it] 99%|█████████▊| 18451/18688 [21:42:36<17:21, 4.39s/it] 99%|█████████▊| 18452/18688 [21:42:40<16:41, 4.24s/it] 99%|█████████▊| 18453/18688 [21:42:44<16:43, 4.27s/it] 99%|█████████▊| 18454/18688 [21:42:49<16:37, 4.26s/it] 99%|█████████▉| 18455/18688 [21:42:54<18:04, 4.65s/it] 99%|█████████▉| 18456/18688 [21:42:58<17:17, 4.47s/it] 99%|█████████▉| 18457/18688 [21:43:03<17:03, 4.43s/it] 99%|█████████▉| 18458/18688 [21:43:07<16:55, 4.42s/it] 99%|█████████▉| 18459/18688 [21:43:11<16:11, 4.24s/it] 99%|█████████▉| 18460/18688 [21:43:15<16:12, 4.26s/it] {'loss': '0.9837', 'grad_norm': '1.351', 'learning_rate': '3.503e-08', 'epoch': '0.9878', 'num_input_tokens_seen': 62114832, 'train_runtime': '7.82e+04', 'train_tokens_per_second': '794.4'}
99%|█████████▉| 18460/18688 [21:43:15<16:12, 4.26s/it] 99%|█████████▉| 18461/18688 [21:43:20<16:59, 4.49s/it] 99%|█████████▉| 18462/18688 [21:43:24<16:20, 4.34s/it] 99%|█████████▉| 18463/18688 [21:43:28<15:59, 4.27s/it] 99%|█████████▉| 18464/18688 [21:43:33<16:05, 4.31s/it] 99%|█████████▉| 18465/18688 [21:43:37<16:00, 4.31s/it] 99%|█████████▉| 18466/18688 [21:43:42<16:43, 4.52s/it] 99%|█████████▉| 18467/18688 [21:43:46<16:21, 4.44s/it] 99%|█████████▉| 18468/18688 [21:43:51<16:16, 4.44s/it] 99%|█████████▉| 18469/18688 [21:43:54<15:25, 4.23s/it] 99%|█████████▉| 18470/18688 [21:43:58<14:31, 4.00s/it] {'loss': '0.943', 'grad_norm': '1.534', 'learning_rate': '3.204e-08', 'epoch': '0.9884', 'num_input_tokens_seen': 62149288, 'train_runtime': '7.824e+04', 'train_tokens_per_second': '794.4'}
99%|█████████▉| 18470/18688 [21:43:58<14:31, 4.00s/it] 99%|█████████▉| 18471/18688 [21:44:02<14:12, 3.93s/it] 99%|█████████▉| 18472/18688 [21:44:07<15:53, 4.41s/it] 99%|█████████▉| 18473/18688 [21:44:12<16:30, 4.61s/it] 99%|█████████▉| 18474/18688 [21:44:17<17:07, 4.80s/it] 99%|█████████▉| 18475/18688 [21:44:21<15:54, 4.48s/it] 99%|█████████▉| 18476/18688 [21:44:25<14:56, 4.23s/it] 99%|█████████▉| 18477/18688 [21:44:28<14:03, 4.00s/it] 99%|█████████▉| 18478/18688 [21:44:32<13:27, 3.85s/it] 99%|█████████▉| 18479/18688 [21:44:36<14:14, 4.09s/it] 99%|█████████▉| 18480/18688 [21:44:41<14:57, 4.32s/it] {'loss': '0.9473', 'grad_norm': '1.017', 'learning_rate': '2.918e-08', 'epoch': '0.9889', 'num_input_tokens_seen': 62184432, 'train_runtime': '7.828e+04', 'train_tokens_per_second': '794.4'}
99%|█████████▉| 18480/18688 [21:44:41<14:57, 4.32s/it] 99%|█████████▉| 18481/18688 [21:44:45<14:36, 4.24s/it] 99%|█████████▉| 18482/18688 [21:44:49<14:15, 4.15s/it] 99%|█████████▉| 18483/18688 [21:44:53<13:45, 4.03s/it] 99%|█████████▉| 18484/18688 [21:44:57<13:30, 3.97s/it] 99%|█████████▉| 18485/18688 [21:45:01<13:29, 3.99s/it] 99%|█████████▉| 18486/18688 [21:45:06<14:21, 4.26s/it] 99%|█████████▉| 18487/18688 [21:45:10<14:27, 4.31s/it] 99%|█████████▉| 18488/18688 [21:45:14<14:18, 4.29s/it] 99%|█████████▉| 18489/18688 [21:45:19<14:06, 4.25s/it] 99%|█████████▉| 18490/18688 [21:45:24<15:07, 4.58s/it] {'loss': '0.8943', 'grad_norm': '1.075', 'learning_rate': '2.646e-08', 'epoch': '0.9894', 'num_input_tokens_seen': 62220832, 'train_runtime': '7.832e+04', 'train_tokens_per_second': '794.4'}
99%|█████████▉| 18490/18688 [21:45:24<15:07, 4.58s/it] 99%|█████████▉| 18491/18688 [21:45:27<13:58, 4.25s/it] 99%|█████████▉| 18492/18688 [21:45:31<13:27, 4.12s/it] 99%|█████████▉| 18493/18688 [21:45:36<14:26, 4.44s/it] 99%|█████████▉| 18494/18688 [21:45:41<14:15, 4.41s/it] 99%|█████████▉| 18495/18688 [21:45:46<14:32, 4.52s/it] 99%|█████████▉| 18496/18688 [21:45:50<13:55, 4.35s/it] 99%|█████████▉| 18497/18688 [21:45:55<14:56, 4.69s/it] 99%|█████████▉| 18498/18688 [21:45:59<13:53, 4.39s/it] 99%|█████████▉| 18499/18688 [21:46:03<13:43, 4.36s/it] 99%|█████████▉| 18500/18688 [21:46:07<13:36, 4.34s/it] {'loss': '0.8539', 'grad_norm': '1.099', 'learning_rate': '2.386e-08', 'epoch': '0.99', 'num_input_tokens_seen': 62254984, 'train_runtime': '7.837e+04', 'train_tokens_per_second': '794.4'}
99%|█████████▉| 18500/18688 [21:46:07<13:36, 4.34s/it] 99%|█████████▉| 18501/18688 [21:46:11<12:52, 4.13s/it] 99%|█████████▉| 18502/18688 [21:46:15<12:55, 4.17s/it] 99%|█████████▉| 18503/18688 [21:46:19<12:51, 4.17s/it] 99%|█████████▉| 18504/18688 [21:46:23<12:43, 4.15s/it] 99%|█████████▉| 18505/18688 [21:46:27<12:18, 4.04s/it] 99%|█████████▉| 18506/18688 [21:46:31<12:20, 4.07s/it] 99%|█████████▉| 18507/18688 [21:46:36<12:36, 4.18s/it] 99%|█████████▉| 18508/18688 [21:46:40<12:06, 4.04s/it] 99%|█████████▉| 18509/18688 [21:46:43<11:40, 3.91s/it] 99%|█████████▉| 18510/18688 [21:46:48<12:08, 4.09s/it] {'loss': '0.9265', 'grad_norm': '1.415', 'learning_rate': '2.141e-08', 'epoch': '0.9905', 'num_input_tokens_seen': 62283384, 'train_runtime': '7.841e+04', 'train_tokens_per_second': '794.3'}
99%|█████████▉| 18510/18688 [21:46:48<12:08, 4.09s/it] 99%|█████████▉| 18511/18688 [21:46:51<11:34, 3.92s/it] 99%|█████████▉| 18512/18688 [21:46:55<11:24, 3.89s/it] 99%|█████████▉| 18513/18688 [21:46:59<11:26, 3.92s/it] 99%|█████████▉| 18514/18688 [21:47:04<12:39, 4.37s/it] 99%|█████████▉| 18515/18688 [21:47:08<12:00, 4.16s/it] 99%|█████████▉| 18516/18688 [21:47:13<12:39, 4.42s/it] 99%|█████████▉| 18517/18688 [21:47:17<12:14, 4.29s/it] 99%|█████████▉| 18518/18688 [21:47:21<11:37, 4.11s/it] 99%|█████████▉| 18519/18688 [21:47:25<11:41, 4.15s/it] 99%|█████████▉| 18520/18688 [21:47:29<11:14, 4.01s/it] {'loss': '0.9366', 'grad_norm': '1.229', 'learning_rate': '1.908e-08', 'epoch': '0.991', 'num_input_tokens_seen': 62315792, 'train_runtime': '7.845e+04', 'train_tokens_per_second': '794.3'}
99%|█████████▉| 18520/18688 [21:47:29<11:14, 4.01s/it] 99%|█████████▉| 18521/18688 [21:47:34<12:16, 4.41s/it] 99%|█████████▉| 18522/18688 [21:47:38<11:54, 4.31s/it] 99%|█████████▉| 18523/18688 [21:47:44<12:53, 4.69s/it] 99%|█████████▉| 18524/18688 [21:47:48<12:23, 4.53s/it] 99%|█████████▉| 18525/18688 [21:47:52<12:01, 4.43s/it] 99%|█████████▉| 18526/18688 [21:47:56<11:52, 4.40s/it] 99%|█████████▉| 18527/18688 [21:48:00<11:15, 4.19s/it] 99%|█████████▉| 18528/18688 [21:48:05<11:47, 4.42s/it] 99%|█████████▉| 18529/18688 [21:48:10<11:59, 4.53s/it] 99%|█████████▉| 18530/18688 [21:48:14<11:52, 4.51s/it] {'loss': '0.895', 'grad_norm': '1.124', 'learning_rate': '1.689e-08', 'epoch': '0.9916', 'num_input_tokens_seen': 62354968, 'train_runtime': '7.849e+04', 'train_tokens_per_second': '794.4'}
99%|█████████▉| 18530/18688 [21:48:14<11:52, 4.51s/it] 99%|█████████▉| 18531/18688 [21:48:19<12:04, 4.61s/it] 99%|█████████▉| 18532/18688 [21:48:23<11:29, 4.42s/it] 99%|█████████▉| 18533/18688 [21:48:27<11:15, 4.36s/it] 99%|█████████▉| 18534/18688 [21:48:32<11:31, 4.49s/it] 99%|█████████▉| 18535/18688 [21:48:36<10:50, 4.25s/it] 99%|█████████▉| 18536/18688 [21:48:40<11:02, 4.36s/it] 99%|█████████▉| 18537/18688 [21:48:44<10:21, 4.12s/it] 99%|█████████▉| 18538/18688 [21:48:48<10:34, 4.23s/it] 99%|█████████▉| 18539/18688 [21:48:53<10:25, 4.20s/it] 99%|█████████▉| 18540/18688 [21:48:57<10:51, 4.40s/it] {'loss': '0.8903', 'grad_norm': '1.032', 'learning_rate': '1.483e-08', 'epoch': '0.9921', 'num_input_tokens_seen': 62390624, 'train_runtime': '7.854e+04', 'train_tokens_per_second': '794.4'}
99%|█████████▉| 18540/18688 [21:48:57<10:51, 4.40s/it] 99%|█████████▉| 18541/18688 [21:49:02<10:44, 4.38s/it] 99%|█████████▉| 18542/18688 [21:49:07<11:12, 4.60s/it] 99%|█████████▉| 18543/18688 [21:49:12<11:45, 4.86s/it] 99%|█████████▉| 18544/18688 [21:49:16<10:56, 4.56s/it] 99%|█████████▉| 18545/18688 [21:49:21<10:56, 4.59s/it] 99%|█████████▉| 18546/18688 [21:49:24<10:04, 4.25s/it] 99%|█████████▉| 18547/18688 [21:49:29<09:58, 4.25s/it] 99%|█████████▉| 18548/18688 [21:49:32<09:38, 4.13s/it] 99%|█████████▉| 18549/18688 [21:49:36<09:18, 4.02s/it] 99%|█████████▉| 18550/18688 [21:49:40<09:00, 3.91s/it] {'loss': '0.9454', 'grad_norm': '1.05', 'learning_rate': '1.291e-08', 'epoch': '0.9926', 'num_input_tokens_seen': 62425328, 'train_runtime': '7.858e+04', 'train_tokens_per_second': '794.4'}
99%|█████████▉| 18550/18688 [21:49:40<09:00, 3.91s/it] 99%|█████████▉| 18551/18688 [21:49:44<09:08, 4.01s/it] 99%|█████████▉| 18552/18688 [21:49:48<09:15, 4.09s/it] 99%|█████████▉| 18553/18688 [21:49:53<09:31, 4.23s/it] 99%|█████████▉| 18554/18688 [21:49:57<09:30, 4.26s/it] 99%|█████████▉| 18555/18688 [21:50:01<09:10, 4.14s/it] 99%|█████████▉| 18556/18688 [21:50:05<09:02, 4.11s/it] 99%|█████████▉| 18557/18688 [21:50:09<08:52, 4.06s/it] 99%|█████████▉| 18558/18688 [21:50:13<08:31, 3.94s/it] 99%|█████████▉| 18559/18688 [21:50:17<08:32, 3.97s/it] 99%|█████████▉| 18560/18688 [21:50:21<08:21, 3.92s/it] {'loss': '0.8621', 'grad_norm': '1.231', 'learning_rate': '1.112e-08', 'epoch': '0.9932', 'num_input_tokens_seen': 62456656, 'train_runtime': '7.862e+04', 'train_tokens_per_second': '794.4'}
99%|█████████▉| 18560/18688 [21:50:21<08:21, 3.92s/it] 99%|█████████▉| 18561/18688 [21:50:25<08:30, 4.02s/it] 99%|█████████▉| 18562/18688 [21:50:30<09:01, 4.29s/it] 99%|█████████▉| 18563/18688 [21:50:33<08:32, 4.10s/it] 99%|█████████▉| 18564/18688 [21:50:38<08:27, 4.09s/it] 99%|█████████▉| 18565/18688 [21:50:42<08:26, 4.12s/it] 99%|█████████▉| 18566/18688 [21:50:46<08:11, 4.03s/it] 99%|█████████▉| 18567/18688 [21:50:49<07:53, 3.92s/it] 99%|█████████▉| 18568/18688 [21:50:53<07:47, 3.90s/it] 99%|█████████▉| 18569/18688 [21:50:58<08:03, 4.07s/it] 99%|█████████▉| 18570/18688 [21:51:01<07:41, 3.91s/it] {'loss': '0.8973', 'grad_norm': '1.195', 'learning_rate': '9.461e-09', 'epoch': '0.9937', 'num_input_tokens_seen': 62488152, 'train_runtime': '7.866e+04', 'train_tokens_per_second': '794.4'}
99%|█████████▉| 18570/18688 [21:51:01<07:41, 3.91s/it] 99%|█████████▉| 18571/18688 [21:51:05<07:31, 3.86s/it] 99%|█████████▉| 18572/18688 [21:51:10<08:02, 4.16s/it] 99%|█████████▉| 18573/18688 [21:51:14<08:00, 4.18s/it] 99%|█████████▉| 18574/18688 [21:51:19<08:26, 4.44s/it] 99%|█████████▉| 18575/18688 [21:51:24<08:37, 4.58s/it] 99%|█████████▉| 18576/18688 [21:51:28<08:13, 4.41s/it] 99%|█████████▉| 18577/18688 [21:51:33<08:27, 4.57s/it] 99%|█████████▉| 18578/18688 [21:51:37<08:08, 4.44s/it] 99%|█████████▉| 18579/18688 [21:51:41<08:02, 4.42s/it] 99%|█████████▉| 18580/18688 [21:51:46<08:16, 4.59s/it] {'loss': '0.9764', 'grad_norm': '1.459', 'learning_rate': '7.938e-09', 'epoch': '0.9942', 'num_input_tokens_seen': 62524560, 'train_runtime': '7.871e+04', 'train_tokens_per_second': '794.4'}
99%|█████████▉| 18580/18688 [21:51:46<08:16, 4.59s/it] 99%|█████████▉| 18581/18688 [21:51:51<07:58, 4.47s/it] 99%|█████████▉| 18582/18688 [21:51:56<08:10, 4.63s/it] 99%|█████████▉| 18583/18688 [21:52:00<08:03, 4.61s/it] 99%|█████████▉| 18584/18688 [21:52:04<07:34, 4.37s/it] 99%|█████████▉| 18585/18688 [21:52:08<07:11, 4.19s/it] 99%|█████████▉| 18586/18688 [21:52:13<07:40, 4.51s/it] 99%|█████████▉| 18587/18688 [21:52:18<07:48, 4.64s/it] 99%|█████████▉| 18588/18688 [21:52:23<08:00, 4.81s/it] 99%|█████████▉| 18589/18688 [21:52:27<07:37, 4.62s/it] 99%|█████████▉| 18590/18688 [21:52:31<07:01, 4.30s/it] {'loss': '0.8648', 'grad_norm': '1.418', 'learning_rate': '6.548e-09', 'epoch': '0.9948', 'num_input_tokens_seen': 62562056, 'train_runtime': '7.875e+04', 'train_tokens_per_second': '794.4'}
99%|█████████▉| 18590/18688 [21:52:31<07:01, 4.30s/it] 99%|█████████▉| 18591/18688 [21:52:35<07:00, 4.33s/it] 99%|█████████▉| 18592/18688 [21:52:39<06:30, 4.07s/it] 99%|█████████▉| 18593/18688 [21:52:43<06:20, 4.01s/it] 99%|█████████▉| 18594/18688 [21:52:47<06:24, 4.09s/it] 100%|█████████▉| 18595/18688 [21:52:51<06:19, 4.08s/it] 100%|█████████▉| 18596/18688 [21:52:55<06:20, 4.13s/it] 100%|█████████▉| 18597/18688 [21:52:59<05:56, 3.91s/it] 100%|█████████▉| 18598/18688 [21:53:03<05:59, 3.99s/it] 100%|█████████▉| 18599/18688 [21:53:07<06:05, 4.10s/it] 100%|█████████▉| 18600/18688 [21:53:12<06:20, 4.32s/it] {'loss': '0.9362', 'grad_norm': '1.214', 'learning_rate': '5.292e-09', 'epoch': '0.9953', 'num_input_tokens_seen': 62593856, 'train_runtime': '7.879e+04', 'train_tokens_per_second': '794.4'}
100%|█████████▉| 18600/18688 [21:53:12<06:20, 4.32s/it] 100%|█████████▉| 18601/18688 [21:53:16<06:11, 4.27s/it] 100%|█████████▉| 18602/18688 [21:53:20<06:02, 4.21s/it] 100%|█████████▉| 18603/18688 [21:53:25<06:03, 4.28s/it] 100%|█████████▉| 18604/18688 [21:53:29<06:11, 4.43s/it] 100%|█████████▉| 18605/18688 [21:53:35<06:37, 4.79s/it] 100%|█████████▉| 18606/18688 [21:53:39<06:20, 4.64s/it] 100%|█████████▉| 18607/18688 [21:53:44<06:20, 4.70s/it] 100%|█████████▉| 18608/18688 [21:53:48<06:00, 4.51s/it] 100%|█████████▉| 18609/18688 [21:53:52<05:38, 4.29s/it] 100%|█████████▉| 18610/18688 [21:53:56<05:33, 4.27s/it] {'loss': '0.8831', 'grad_norm': '1.084', 'learning_rate': '4.17e-09', 'epoch': '0.9959', 'num_input_tokens_seen': 62632432, 'train_runtime': '7.884e+04', 'train_tokens_per_second': '794.5'}
100%|█████████▉| 18610/18688 [21:53:56<05:33, 4.27s/it] 100%|█████████▉| 18611/18688 [21:54:01<05:36, 4.38s/it] 100%|█████████▉| 18612/18688 [21:54:05<05:33, 4.38s/it] 100%|█████████▉| 18613/18688 [21:54:10<05:39, 4.53s/it] 100%|█████████▉| 18614/18688 [21:54:14<05:24, 4.39s/it] 100%|█████████▉| 18615/18688 [21:54:20<05:49, 4.79s/it] 100%|█████████▉| 18616/18688 [21:54:24<05:21, 4.46s/it] 100%|█████████▉| 18617/18688 [21:54:28<05:08, 4.35s/it] 100%|█████████▉| 18618/18688 [21:54:31<04:49, 4.13s/it] 100%|█████████▉| 18619/18688 [21:54:35<04:40, 4.06s/it] 100%|█████████▉| 18620/18688 [21:54:39<04:31, 4.00s/it] {'loss': '0.9428', 'grad_norm': '1.072', 'learning_rate': '3.181e-09', 'epoch': '0.9964', 'num_input_tokens_seen': 62668504, 'train_runtime': '7.888e+04', 'train_tokens_per_second': '794.5'}
100%|█████████▉| 18620/18688 [21:54:39<04:31, 4.00s/it] 100%|█████████▉| 18621/18688 [21:54:43<04:22, 3.91s/it] 100%|█████████▉| 18622/18688 [21:54:47<04:27, 4.06s/it] 100%|█████████▉| 18623/18688 [21:54:51<04:19, 4.00s/it] 100%|█████████▉| 18624/18688 [21:54:55<04:23, 4.12s/it] 100%|█████████▉| 18625/18688 [21:54:59<04:17, 4.09s/it] 100%|█████████▉| 18626/18688 [21:55:03<04:02, 3.91s/it] 100%|█████████▉| 18627/18688 [21:55:07<03:56, 3.88s/it] 100%|█████████▉| 18628/18688 [21:55:11<03:56, 3.95s/it] 100%|█████████▉| 18629/18688 [21:55:15<03:53, 3.95s/it] 100%|█████████▉| 18630/18688 [21:55:19<03:56, 4.08s/it] {'loss': '0.9368', 'grad_norm': '0.9813', 'learning_rate': '2.326e-09', 'epoch': '0.9969', 'num_input_tokens_seen': 62700024, 'train_runtime': '7.892e+04', 'train_tokens_per_second': '794.5'}
100%|█████████▉| 18630/18688 [21:55:19<03:56, 4.08s/it] 100%|█████████▉| 18631/18688 [21:55:23<03:57, 4.17s/it] 100%|█████████▉| 18632/18688 [21:55:28<04:04, 4.37s/it] 100%|█████████▉| 18633/18688 [21:55:32<03:54, 4.27s/it] 100%|█████████▉| 18634/18688 [21:55:36<03:45, 4.17s/it] 100%|█████████▉| 18635/18688 [21:55:40<03:40, 4.16s/it] 100%|█████████▉| 18636/18688 [21:55:45<03:35, 4.15s/it] 100%|█████████▉| 18637/18688 [21:55:48<03:26, 4.04s/it] 100%|█████████▉| 18638/18688 [21:55:52<03:21, 4.04s/it] 100%|█████████▉| 18639/18688 [21:55:56<03:16, 4.01s/it] 100%|█████████▉| 18640/18688 [21:56:02<03:38, 4.55s/it] {'loss': '0.9139', 'grad_norm': '1.261', 'learning_rate': '1.604e-09', 'epoch': '0.9975', 'num_input_tokens_seen': 62736400, 'train_runtime': '7.896e+04', 'train_tokens_per_second': '794.5'}
100%|█████████▉| 18640/18688 [21:56:02<03:38, 4.55s/it] 100%|█████████▉| 18641/18688 [21:56:07<03:33, 4.55s/it] 100%|█████████▉| 18642/18688 [21:56:11<03:25, 4.47s/it] 100%|█████████▉| 18643/18688 [21:56:15<03:20, 4.46s/it] 100%|█████████▉| 18644/18688 [21:56:20<03:12, 4.37s/it] 100%|█████████▉| 18645/18688 [21:56:24<03:03, 4.28s/it] 100%|█████████▉| 18646/18688 [21:56:28<02:58, 4.25s/it] 100%|█████████▉| 18647/18688 [21:56:32<02:51, 4.18s/it] 100%|█████████▉| 18648/18688 [21:56:36<02:43, 4.09s/it] 100%|█████████▉| 18649/18688 [21:56:40<02:38, 4.06s/it] 100%|█████████▉| 18650/18688 [21:56:43<02:29, 3.93s/it] {'loss': '0.9505', 'grad_norm': '1.239', 'learning_rate': '1.016e-09', 'epoch': '0.998', 'num_input_tokens_seen': 62770736, 'train_runtime': '7.9e+04', 'train_tokens_per_second': '794.5'}
100%|█████████▉| 18650/18688 [21:56:43<02:29, 3.93s/it] 100%|█████████▉| 18651/18688 [21:56:47<02:20, 3.81s/it] 100%|█████████▉| 18652/18688 [21:56:51<02:21, 3.94s/it] 100%|█████████▉| 18653/18688 [21:56:56<02:32, 4.36s/it] 100%|█████████▉| 18654/18688 [21:57:00<02:22, 4.18s/it] 100%|█████████▉| 18655/18688 [21:57:04<02:18, 4.19s/it] 100%|█████████▉| 18656/18688 [21:57:09<02:16, 4.25s/it] 100%|█████████▉| 18657/18688 [21:57:13<02:10, 4.20s/it] 100%|█████████▉| 18658/18688 [21:57:17<02:05, 4.19s/it] 100%|█████████▉| 18659/18688 [21:57:21<01:57, 4.07s/it] 100%|█████████▉| 18660/18688 [21:57:24<01:49, 3.91s/it] {'loss': '0.9458', 'grad_norm': '1.377', 'learning_rate': '5.619e-10', 'epoch': '0.9985', 'num_input_tokens_seen': 62801816, 'train_runtime': '7.904e+04', 'train_tokens_per_second': '794.5'}
100%|█████████▉| 18660/18688 [21:57:24<01:49, 3.91s/it] 100%|█████████▉| 18661/18688 [21:57:28<01:45, 3.91s/it] 100%|█████████▉| 18662/18688 [21:57:32<01:38, 3.79s/it] 100%|█████████▉| 18663/18688 [21:57:37<01:41, 4.07s/it] 100%|█████████▉| 18664/18688 [21:57:42<01:45, 4.40s/it] 100%|█████████▉| 18665/18688 [21:57:45<01:36, 4.19s/it] 100%|█████████▉| 18666/18688 [21:57:49<01:27, 4.00s/it] 100%|█████████▉| 18667/18688 [21:57:53<01:24, 4.01s/it] 100%|█████████▉| 18668/18688 [21:57:56<01:17, 3.85s/it] 100%|█████████▉| 18669/18688 [21:58:02<01:20, 4.21s/it] 100%|█████████▉| 18670/18688 [21:58:05<01:13, 4.08s/it] {'loss': '0.866', 'grad_norm': '1.244', 'learning_rate': '2.412e-10', 'epoch': '0.9991', 'num_input_tokens_seen': 62835552, 'train_runtime': '7.909e+04', 'train_tokens_per_second': '794.5'}
100%|█████████▉| 18670/18688 [21:58:05<01:13, 4.08s/it] 100%|█████████▉| 18671/18688 [21:58:09<01:07, 3.98s/it] 100%|█████████▉| 18672/18688 [21:58:13<01:04, 4.02s/it] 100%|█████████▉| 18673/18688 [21:58:17<00:58, 3.89s/it] 100%|█████████▉| 18674/18688 [21:58:21<00:53, 3.85s/it] 100%|█████████▉| 18675/18688 [21:58:25<00:51, 3.98s/it] 100%|█████████▉| 18676/18688 [21:58:29<00:49, 4.15s/it] 100%|█████████▉| 18677/18688 [21:58:33<00:44, 4.01s/it] 100%|█████████▉| 18678/18688 [21:58:38<00:41, 4.17s/it] 100%|█████████▉| 18679/18688 [21:58:42<00:37, 4.13s/it] 100%|█████████▉| 18680/18688 [21:58:46<00:33, 4.14s/it] {'loss': '0.8504', 'grad_norm': '1.118', 'learning_rate': '5.412e-11', 'epoch': '0.9996', 'num_input_tokens_seen': 62866392, 'train_runtime': '7.913e+04', 'train_tokens_per_second': '794.5'}
100%|█████████▉| 18680/18688 [21:58:46<00:33, 4.14s/it] 100%|█████████▉| 18681/18688 [21:58:50<00:28, 4.05s/it] 100%|█████████▉| 18682/18688 [21:58:53<00:23, 3.99s/it] 100%|█████████▉| 18683/18688 [21:58:57<00:19, 3.96s/it] 100%|█████████▉| 18684/18688 [21:59:02<00:17, 4.26s/it] 100%|█████████▉| 18685/18688 [21:59:06<00:12, 4.23s/it] 100%|█████████▉| 18686/18688 [21:59:10<00:08, 4.12s/it] 100%|█████████▉| 18687/18688 [21:59:15<00:04, 4.33s/it] 100%|██████████| 18688/18688 [21:59:17<00:00, 3.57s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:71: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:281: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 0/1000 [00:00<?, ?it/s]/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:172: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:202: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:254: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 3/1000 [00:00<00:38, 25.96it/s]
1%| | 6/1000 [00:00<00:54, 18.28it/s]
1%| | 8/1000 [00:00<00:56, 17.53it/s]
1%| | 10/1000 [00:00<00:59, 16.63it/s]
1%| | 12/1000 [00:00<01:00, 16.41it/s]
1%|▏ | 14/1000 [00:00<01:01, 15.92it/s]
2%|▏ | 16/1000 [00:00<01:02, 15.69it/s]
2%|▏ | 18/1000 [00:01<01:38, 9.98it/s]
2%|▏ | 20/1000 [00:01<01:33, 10.53it/s]
2%|▏ | 22/1000 [00:01<01:52, 8.72it/s]
2%|▏ | 24/1000 [00:01<01:39, 9.76it/s]
3%|▎ | 26/1000 [00:02<01:44, 9.28it/s]
3%|▎ | 28/1000 [00:02<01:36, 10.09it/s]
3%|▎ | 30/1000 [00:02<01:28, 10.98it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.15it/s]
3%|▎ | 34/1000 [00:02<01:25, 11.26it/s]
4%|▎ | 36/1000 [00:03<01:32, 10.43it/s]
4%|▍ | 38/1000 [00:03<01:24, 11.34it/s]
4%|▍ | 40/1000 [00:03<01:27, 10.92it/s]
4%|▍ | 42/1000 [00:03<01:46, 9.01it/s]
4%|▍ | 44/1000 [00:03<01:31, 10.50it/s]
5%|▍ | 46/1000 [00:03<01:21, 11.65it/s]
5%|▍ | 48/1000 [00:04<01:29, 10.62it/s]
5%|▌ | 50/1000 [00:04<01:21, 11.65it/s]
5%|▌ | 52/1000 [00:04<01:15, 12.49it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.23it/s]
6%|▌ | 56/1000 [00:04<01:08, 13.86it/s]
6%|▌ | 58/1000 [00:05<01:48, 8.69it/s]
6%|▌ | 60/1000 [00:05<01:36, 9.73it/s]
6%|▌ | 62/1000 [00:05<01:27, 10.71it/s]
6%|▋ | 64/1000 [00:05<01:19, 11.77it/s]
7%|▋ | 66/1000 [00:05<01:16, 12.27it/s]
7%|▋ | 68/1000 [00:05<01:12, 12.86it/s]
7%|▋ | 70/1000 [00:06<01:09, 13.45it/s]
7%|▋ | 72/1000 [00:06<01:06, 13.91it/s]
7%|▋ | 74/1000 [00:06<01:07, 13.68it/s]
8%|▊ | 76/1000 [00:06<01:04, 14.30it/s]
8%|▊ | 78/1000 [00:06<01:03, 14.51it/s]
8%|▊ | 80/1000 [00:06<01:08, 13.47it/s]
8%|▊ | 82/1000 [00:06<01:05, 14.08it/s]
8%|▊ | 84/1000 [00:06<01:03, 14.54it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.69it/s]
9%|▉ | 88/1000 [00:07<01:02, 14.49it/s]
9%|▉ | 90/1000 [00:07<01:02, 14.53it/s]
9%|▉ | 92/1000 [00:07<01:03, 14.19it/s]
9%|▉ | 94/1000 [00:07<01:04, 14.00it/s]
10%|▉ | 96/1000 [00:07<01:03, 14.28it/s]
10%|▉ | 98/1000 [00:07<01:03, 14.19it/s]
10%|█ | 100/1000 [00:08<01:31, 9.79it/s]
10%|█ | 102/1000 [00:08<01:23, 10.76it/s]
10%|█ | 104/1000 [00:08<01:54, 7.80it/s]
11%|█ | 106/1000 [00:09<02:02, 7.30it/s]
11%|█ | 108/1000 [00:09<01:43, 8.63it/s]
11%|█ | 110/1000 [00:09<01:28, 10.06it/s]
11%|█ | 112/1000 [00:09<01:19, 11.18it/s]
11%|█▏ | 114/1000 [00:09<01:14, 11.84it/s]
12%|█▏ | 116/1000 [00:09<01:10, 12.48it/s]
12%|█▏ | 118/1000 [00:09<01:06, 13.25it/s]
12%|█▏ | 120/1000 [00:10<01:12, 12.07it/s]
12%|█▏ | 122/1000 [00:10<01:25, 10.30it/s]
12%|█▏ | 124/1000 [00:10<01:32, 9.52it/s]
13%|█▎ | 126/1000 [00:10<01:23, 10.49it/s]
13%|█▎ | 128/1000 [00:10<01:16, 11.44it/s]
13%|█▎ | 130/1000 [00:11<01:11, 12.09it/s]
13%|█▎ | 132/1000 [00:11<01:06, 13.10it/s]
13%|█▎ | 134/1000 [00:11<01:10, 12.32it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.30it/s]
14%|█▍ | 138/1000 [00:11<01:05, 13.24it/s]
14%|█▍ | 140/1000 [00:12<01:25, 10.09it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.53it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.56it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.58it/s]
15%|█▍ | 148/1000 [00:12<01:14, 11.47it/s]
15%|█▌ | 150/1000 [00:12<01:07, 12.58it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.12it/s]
15%|█▌ | 154/1000 [00:13<01:05, 12.86it/s]
16%|█▌ | 156/1000 [00:13<01:06, 12.68it/s]
16%|█▌ | 158/1000 [00:13<01:07, 12.55it/s]
16%|█▌ | 160/1000 [00:13<01:07, 12.48it/s]
16%|█▌ | 162/1000 [00:13<01:07, 12.44it/s]
16%|█▋ | 164/1000 [00:14<01:07, 12.44it/s]
17%|█▋ | 166/1000 [00:14<01:05, 12.68it/s]
17%|█▋ | 168/1000 [00:14<01:04, 12.84it/s]
17%|█▋ | 170/1000 [00:14<01:25, 9.73it/s]
17%|█▋ | 172/1000 [00:14<01:19, 10.41it/s]
17%|█▋ | 174/1000 [00:15<01:35, 8.63it/s]
18%|█▊ | 175/1000 [00:15<02:10, 6.31it/s]
18%|█▊ | 177/1000 [00:15<01:49, 7.54it/s]
18%|█▊ | 179/1000 [00:15<01:43, 7.96it/s]
18%|█▊ | 181/1000 [00:16<01:45, 7.79it/s]
18%|█▊ | 183/1000 [00:16<01:32, 8.88it/s]
18%|█▊ | 185/1000 [00:16<01:43, 7.84it/s]
19%|█▊ | 187/1000 [00:16<01:32, 8.83it/s]
19%|█▉ | 189/1000 [00:16<01:23, 9.74it/s]
19%|█▉ | 191/1000 [00:17<01:16, 10.52it/s]
19%|█▉ | 193/1000 [00:17<01:22, 9.81it/s]
20%|█▉ | 195/1000 [00:17<01:16, 10.54it/s]
20%|█▉ | 197/1000 [00:17<01:31, 8.75it/s]
20%|█▉ | 199/1000 [00:17<01:25, 9.40it/s]
20%|██ | 201/1000 [00:18<01:18, 10.13it/s]
20%|██ | 203/1000 [00:18<01:13, 10.79it/s]
20%|██ | 205/1000 [00:18<01:10, 11.28it/s]
21%|██ | 207/1000 [00:18<01:08, 11.62it/s]
21%|██ | 209/1000 [00:18<01:06, 11.93it/s]
21%|██ | 211/1000 [00:18<01:05, 12.05it/s]
21%|██▏ | 213/1000 [00:19<01:04, 12.14it/s]
22%|██▏ | 215/1000 [00:19<01:04, 12.19it/s]
22%|██▏ | 217/1000 [00:19<01:03, 12.33it/s]
22%|██▏ | 219/1000 [00:19<01:36, 8.08it/s]
22%|██▏ | 221/1000 [00:20<01:29, 8.71it/s]
22%|██▏ | 223/1000 [00:20<01:24, 9.14it/s]
22%|██▎ | 225/1000 [00:20<01:16, 10.09it/s]
23%|██▎ | 227/1000 [00:20<01:08, 11.23it/s]
23%|██▎ | 229/1000 [00:20<01:05, 11.76it/s]
23%|██▎ | 231/1000 [00:20<01:04, 11.90it/s]
23%|██▎ | 233/1000 [00:20<01:01, 12.37it/s]
24%|██▎ | 235/1000 [00:21<00:58, 13.06it/s]
24%|██▎ | 237/1000 [00:21<00:53, 14.14it/s]
24%|██▍ | 239/1000 [00:21<00:54, 14.05it/s]
24%|██▍ | 241/1000 [00:21<00:52, 14.45it/s]
24%|██▍ | 243/1000 [00:21<00:51, 14.76it/s]
24%|██▍ | 245/1000 [00:21<00:51, 14.74it/s]
25%|██▍ | 247/1000 [00:21<00:49, 15.12it/s]
25%|██▍ | 249/1000 [00:22<00:57, 12.95it/s]
25%|██▌ | 251/1000 [00:22<01:02, 11.95it/s]
25%|██▌ | 253/1000 [00:22<00:58, 12.79it/s]
26%|██▌ | 255/1000 [00:22<00:54, 13.74it/s]
26%|██▌ | 257/1000 [00:22<00:53, 13.89it/s]
26%|██▌ | 259/1000 [00:22<00:52, 14.14it/s]
26%|██▌ | 261/1000 [00:23<00:58, 12.66it/s]
26%|██▋ | 263/1000 [00:23<00:55, 13.39it/s]
26%|██▋ | 265/1000 [00:23<00:53, 13.84it/s]
27%|██▋ | 267/1000 [00:23<00:55, 13.14it/s]
27%|██▋ | 269/1000 [00:23<00:52, 14.03it/s]
27%|██▋ | 271/1000 [00:23<00:56, 12.85it/s]
27%|██▋ | 273/1000 [00:23<01:04, 11.29it/s]
28%|██▊ | 275/1000 [00:24<01:02, 11.60it/s]
28%|██▊ | 277/1000 [00:24<00:57, 12.54it/s]
28%|██▊ | 279/1000 [00:24<00:56, 12.72it/s]
28%|██▊ | 281/1000 [00:24<00:51, 13.86it/s]
28%|██▊ | 283/1000 [00:24<00:50, 14.18it/s]
28%|██▊ | 285/1000 [00:24<00:49, 14.53it/s]
29%|██▊ | 287/1000 [00:24<00:48, 14.73it/s]
29%|██▉ | 289/1000 [00:25<00:46, 15.29it/s]
29%|██▉ | 291/1000 [00:25<00:45, 15.43it/s]
29%|██▉ | 293/1000 [00:25<00:46, 15.36it/s]
30%|██▉ | 295/1000 [00:25<00:45, 15.56it/s]
30%|██▉ | 297/1000 [00:25<00:44, 15.66it/s]
30%|██▉ | 299/1000 [00:25<00:45, 15.35it/s]
30%|███ | 301/1000 [00:25<00:46, 15.01it/s]
30%|███ | 303/1000 [00:26<00:50, 13.67it/s]
30%|███ | 305/1000 [00:26<00:49, 14.10it/s]
31%|███ | 307/1000 [00:26<00:47, 14.47it/s]
31%|███ | 309/1000 [00:26<00:46, 14.92it/s]
31%|███ | 311/1000 [00:26<00:47, 14.49it/s]
31%|███▏ | 313/1000 [00:26<00:45, 15.01it/s]
32%|███▏ | 315/1000 [00:26<00:44, 15.52it/s]
32%|███▏ | 317/1000 [00:26<00:42, 16.02it/s]
32%|███▏ | 319/1000 [00:27<00:42, 16.08it/s]
32%|███▏ | 321/1000 [00:27<01:13, 9.26it/s]
32%|███▏ | 323/1000 [00:27<01:04, 10.53it/s]
32%|███▎ | 325/1000 [00:27<00:59, 11.40it/s]
33%|███▎ | 327/1000 [00:27<00:58, 11.56it/s]
33%|███▎ | 329/1000 [00:28<00:58, 11.52it/s]
33%|███▎ | 331/1000 [00:28<00:55, 12.07it/s]
33%|███▎ | 333/1000 [00:28<00:53, 12.54it/s]
34%|███▎ | 335/1000 [00:28<00:50, 13.04it/s]
34%|███▎ | 337/1000 [00:28<01:06, 9.99it/s]
34%|███▍ | 339/1000 [00:28<00:59, 11.09it/s]
34%|███▍ | 341/1000 [00:29<00:54, 11.98it/s]
34%|███▍ | 343/1000 [00:29<00:50, 12.89it/s]
34%|███▍ | 345/1000 [00:29<00:49, 13.34it/s]
35%|███▍ | 347/1000 [00:29<00:46, 14.18it/s]
35%|███▍ | 349/1000 [00:29<00:44, 14.47it/s]
35%|███▌ | 351/1000 [00:29<00:44, 14.59it/s]
35%|███▌ | 353/1000 [00:29<00:42, 15.13it/s]
36%|███▌ | 355/1000 [00:29<00:42, 15.17it/s]
36%|███▌ | 357/1000 [00:30<00:42, 14.98it/s]
36%|███▌ | 359/1000 [00:30<00:43, 14.90it/s]
36%|███▌ | 361/1000 [00:30<00:48, 13.31it/s]
36%|███▋ | 363/1000 [00:30<00:46, 13.60it/s]
36%|███▋ | 365/1000 [00:30<00:44, 14.35it/s]
37%|███▋ | 367/1000 [00:30<00:53, 11.87it/s]
37%|███▋ | 369/1000 [00:31<00:50, 12.52it/s]
37%|███▋ | 371/1000 [00:31<00:47, 13.16it/s]
37%|███▋ | 373/1000 [00:31<00:55, 11.35it/s]
38%|███▊ | 375/1000 [00:31<00:57, 10.81it/s]
38%|███▊ | 377/1000 [00:31<00:53, 11.63it/s]
38%|███▊ | 379/1000 [00:31<00:51, 12.12it/s]
38%|███▊ | 381/1000 [00:32<00:52, 11.88it/s]
38%|███▊ | 383/1000 [00:32<00:48, 12.62it/s]
38%|███▊ | 385/1000 [00:32<00:47, 13.03it/s]
39%|███▊ | 387/1000 [00:32<00:45, 13.33it/s]
39%|███▉ | 389/1000 [00:32<00:45, 13.49it/s]
39%|███▉ | 391/1000 [00:32<00:44, 13.68it/s]
39%|███▉ | 393/1000 [00:32<00:44, 13.56it/s]
40%|███▉ | 395/1000 [00:33<00:44, 13.47it/s]
40%|███▉ | 397/1000 [00:33<00:44, 13.66it/s]
40%|███▉ | 399/1000 [00:33<00:47, 12.56it/s]
40%|████ | 401/1000 [00:33<00:43, 13.62it/s]
40%|████ | 403/1000 [00:33<00:42, 14.20it/s]
40%|████ | 405/1000 [00:33<00:40, 14.74it/s]
41%|████ | 407/1000 [00:33<00:39, 15.20it/s]
41%|████ | 409/1000 [00:34<00:58, 10.19it/s]
41%|████ | 411/1000 [00:34<00:53, 11.08it/s]
41%|████▏ | 413/1000 [00:34<01:04, 9.09it/s]
42%|████▏ | 415/1000 [00:34<01:01, 9.58it/s]
42%|████▏ | 417/1000 [00:35<01:19, 7.38it/s]
42%|████▏ | 419/1000 [00:35<01:08, 8.51it/s]
42%|████▏ | 421/1000 [00:35<01:01, 9.39it/s]
42%|████▏ | 423/1000 [00:36<01:20, 7.19it/s]
42%|████▎ | 425/1000 [00:36<01:07, 8.46it/s]
43%|████▎ | 427/1000 [00:36<01:00, 9.54it/s]
43%|████▎ | 429/1000 [00:36<01:03, 8.95it/s]
43%|████▎ | 431/1000 [00:36<00:56, 10.01it/s]
43%|████▎ | 433/1000 [00:37<01:16, 7.41it/s]
44%|████▎ | 435/1000 [00:37<01:06, 8.55it/s]
44%|████▎ | 437/1000 [00:37<01:06, 8.45it/s]
44%|████▍ | 439/1000 [00:37<00:59, 9.51it/s]
44%|████▍ | 441/1000 [00:37<00:56, 9.84it/s]
44%|████▍ | 443/1000 [00:38<00:51, 10.83it/s]
44%|████▍ | 445/1000 [00:38<00:46, 11.85it/s]
45%|████▍ | 447/1000 [00:38<00:51, 10.65it/s]
45%|████▍ | 449/1000 [00:38<00:47, 11.52it/s]
45%|████▌ | 451/1000 [00:38<00:44, 12.46it/s]
45%|████▌ | 453/1000 [00:38<00:41, 13.23it/s]
46%|████▌ | 455/1000 [00:39<00:44, 12.34it/s]
46%|████▌ | 457/1000 [00:39<00:41, 13.16it/s]
46%|████▌ | 459/1000 [00:39<00:39, 13.72it/s]
46%|████▌ | 461/1000 [00:39<00:38, 14.16it/s]
46%|████▋ | 463/1000 [00:39<00:35, 14.96it/s]
46%|████▋ | 465/1000 [00:39<00:36, 14.83it/s]
47%|████▋ | 467/1000 [00:39<00:40, 13.16it/s]
47%|████▋ | 469/1000 [00:39<00:38, 13.64it/s]
47%|████▋ | 471/1000 [00:40<00:52, 10.15it/s]
47%|████▋ | 473/1000 [00:40<00:50, 10.50it/s]
48%|████▊ | 475/1000 [00:40<00:45, 11.62it/s]
48%|████▊ | 477/1000 [00:40<00:41, 12.46it/s]
48%|████▊ | 479/1000 [00:40<00:43, 11.98it/s]
48%|████▊ | 481/1000 [00:41<00:40, 12.73it/s]
48%|████▊ | 483/1000 [00:41<00:37, 13.61it/s]
48%|████▊ | 485/1000 [00:41<00:37, 13.80it/s]
49%|████▊ | 487/1000 [00:41<00:36, 14.23it/s]
49%|████▉ | 489/1000 [00:41<00:45, 11.15it/s]
49%|████▉ | 491/1000 [00:41<00:42, 12.03it/s]
49%|████▉ | 493/1000 [00:41<00:38, 13.01it/s]
50%|████▉ | 495/1000 [00:42<00:36, 13.98it/s]
50%|████▉ | 497/1000 [00:42<00:34, 14.61it/s]
50%|████▉ | 499/1000 [00:42<00:33, 14.75it/s]
50%|█████ | 501/1000 [00:42<00:43, 11.36it/s]
50%|█████ | 503/1000 [00:42<00:41, 11.91it/s]
50%|█████ | 505/1000 [00:42<00:39, 12.59it/s]
51%|█████ | 507/1000 [00:43<00:37, 13.13it/s]
51%|█████ | 509/1000 [00:43<00:51, 9.52it/s]
51%|█████ | 511/1000 [00:43<00:46, 10.54it/s]
51%|█████▏ | 513/1000 [00:43<00:45, 10.75it/s]
52%|█████▏ | 515/1000 [00:43<00:40, 11.90it/s]
52%|█████▏ | 517/1000 [00:43<00:38, 12.48it/s]
52%|█████▏ | 519/1000 [00:44<00:37, 12.76it/s]
52%|█████▏ | 521/1000 [00:44<00:35, 13.51it/s]
52%|█████▏ | 523/1000 [00:44<00:34, 13.69it/s]
52%|█████▎ | 525/1000 [00:44<00:34, 13.88it/s]
53%|█████▎ | 527/1000 [00:44<00:40, 11.78it/s]
53%|█████▎ | 529/1000 [00:44<00:38, 12.39it/s]
53%|█████▎ | 531/1000 [00:45<00:36, 12.83it/s]
53%|█████▎ | 533/1000 [00:45<00:36, 12.96it/s]
54%|█████▎ | 535/1000 [00:45<00:35, 13.07it/s]
54%|█████▎ | 537/1000 [00:45<00:35, 13.11it/s]
54%|█████▍ | 539/1000 [00:45<00:34, 13.48it/s]
54%|█████▍ | 541/1000 [00:45<00:33, 13.86it/s]
54%|█████▍ | 543/1000 [00:45<00:32, 14.21it/s]
55%|█████▍ | 545/1000 [00:46<00:32, 13.89it/s]
55%|█████▍ | 547/1000 [00:46<00:32, 13.84it/s]
55%|█████▍ | 549/1000 [00:46<00:32, 13.91it/s]
55%|█████▌ | 551/1000 [00:46<00:32, 13.77it/s]
55%|█████▌ | 553/1000 [00:46<00:31, 14.07it/s]
56%|█████▌ | 555/1000 [00:46<00:31, 14.04it/s]
56%|█████▌ | 557/1000 [00:46<00:30, 14.49it/s]
56%|█████▌ | 559/1000 [00:47<00:30, 14.46it/s]
56%|█████▌ | 561/1000 [00:47<00:30, 14.63it/s]
56%|█████▋ | 563/1000 [00:47<00:40, 10.75it/s]
56%|█████▋ | 565/1000 [00:47<00:37, 11.52it/s]
57%|█████▋ | 567/1000 [00:47<00:34, 12.39it/s]
57%|█████▋ | 569/1000 [00:47<00:32, 13.10it/s]
57%|█████▋ | 571/1000 [00:48<00:33, 12.90it/s]
57%|█████▋ | 573/1000 [00:48<00:31, 13.44it/s]
57%|█████▊ | 575/1000 [00:48<00:31, 13.62it/s]
58%|█████▊ | 577/1000 [00:48<00:30, 13.73it/s]
58%|█████▊ | 579/1000 [00:48<00:35, 11.96it/s]
58%|█████▊ | 581/1000 [00:48<00:31, 13.16it/s]
58%|█████▊ | 583/1000 [00:48<00:30, 13.65it/s]
58%|█████▊ | 585/1000 [00:49<00:29, 13.87it/s]
59%|█████▊ | 587/1000 [00:49<00:28, 14.55it/s]
59%|█████▉ | 589/1000 [00:49<00:26, 15.29it/s]
59%|█████▉ | 591/1000 [00:49<00:28, 14.59it/s]
59%|█████▉ | 593/1000 [00:49<00:30, 13.41it/s]
60%|█████▉ | 595/1000 [00:49<00:29, 13.86it/s]
60%|█████▉ | 597/1000 [00:49<00:29, 13.72it/s]
60%|█████▉ | 599/1000 [00:50<00:27, 14.68it/s]
60%|██████ | 601/1000 [00:50<00:28, 14.25it/s]
60%|██████ | 603/1000 [00:50<00:26, 14.85it/s]
60%|██████ | 605/1000 [00:50<00:26, 14.87it/s]
61%|██████ | 607/1000 [00:50<00:26, 14.96it/s]
61%|██████ | 609/1000 [00:50<00:25, 15.10it/s]
61%|██████ | 611/1000 [00:50<00:25, 15.36it/s]
61%|██████▏ | 613/1000 [00:50<00:26, 14.83it/s]
62%|██████▏ | 615/1000 [00:51<00:26, 14.45it/s]
62%|██████▏ | 617/1000 [00:51<00:26, 14.50it/s]
62%|██████▏ | 619/1000 [00:51<00:32, 11.85it/s]
62%|██████▏ | 621/1000 [00:51<00:39, 9.51it/s]
62%|██████▏ | 623/1000 [00:51<00:35, 10.69it/s]
62%|██████▎ | 625/1000 [00:52<00:31, 11.92it/s]
63%|██████▎ | 627/1000 [00:52<00:29, 12.69it/s]
63%|██████▎ | 629/1000 [00:52<00:27, 13.58it/s]
63%|██████▎ | 631/1000 [00:52<00:29, 12.57it/s]
63%|██████▎ | 633/1000 [00:52<00:28, 12.96it/s]
64%|██████▎ | 635/1000 [00:52<00:33, 10.78it/s]
64%|██████▎ | 637/1000 [00:53<00:31, 11.60it/s]
64%|██████▍ | 639/1000 [00:53<00:32, 11.07it/s]
64%|██████▍ | 641/1000 [00:53<00:29, 12.04it/s]
64%|██████▍ | 643/1000 [00:53<00:28, 12.58it/s]
64%|██████▍ | 645/1000 [00:53<00:27, 12.85it/s]
65%|██████▍ | 647/1000 [00:53<00:27, 13.05it/s]
65%|██████▍ | 649/1000 [00:54<00:28, 12.52it/s]
65%|██████▌ | 651/1000 [00:54<00:26, 13.20it/s]
65%|██████▌ | 653/1000 [00:54<00:28, 12.06it/s]
66%|██████▌ | 655/1000 [00:54<00:35, 9.64it/s]
66%|██████▌ | 657/1000 [00:54<00:31, 10.73it/s]
66%|██████▌ | 659/1000 [00:55<00:34, 9.75it/s]
66%|██████▌ | 661/1000 [00:55<00:31, 10.80it/s]
66%|██████▋ | 663/1000 [00:55<00:28, 11.68it/s]
66%|██████▋ | 665/1000 [00:55<00:27, 12.21it/s]
67%|██████▋ | 667/1000 [00:55<00:29, 11.15it/s]
67%|██████▋ | 669/1000 [00:55<00:29, 11.15it/s]
67%|██████▋ | 671/1000 [00:55<00:27, 11.86it/s]
67%|██████▋ | 673/1000 [00:56<00:26, 12.23it/s]
68%|██████▊ | 675/1000 [00:56<00:27, 11.75it/s]
68%|██████▊ | 677/1000 [00:56<00:26, 12.22it/s]
68%|██████▊ | 679/1000 [00:56<00:25, 12.60it/s]
68%|██████▊ | 681/1000 [00:56<00:24, 13.07it/s]
68%|██████▊ | 683/1000 [00:56<00:25, 12.20it/s]
68%|██████▊ | 685/1000 [00:57<00:24, 12.86it/s]
69%|██████▊ | 687/1000 [00:57<00:23, 13.45it/s]
69%|██████▉ | 689/1000 [00:57<00:22, 13.90it/s]
69%|██████▉ | 691/1000 [00:57<00:21, 14.15it/s]
69%|██████▉ | 693/1000 [00:57<00:21, 14.34it/s]
70%|██████▉ | 695/1000 [00:57<00:21, 14.07it/s]
70%|██████▉ | 697/1000 [00:57<00:21, 14.33it/s]
70%|██████▉ | 699/1000 [00:58<00:20, 14.38it/s]
70%|███████ | 701/1000 [00:58<00:24, 12.23it/s]
70%|███████ | 703/1000 [00:58<00:23, 12.82it/s]
70%|███████ | 705/1000 [00:58<00:30, 9.77it/s]
71%|███████ | 707/1000 [00:58<00:32, 8.95it/s]
71%|███████ | 709/1000 [00:59<00:28, 10.21it/s]
71%|███████ | 711/1000 [00:59<00:25, 11.28it/s]
71%|███████▏ | 713/1000 [00:59<00:23, 12.42it/s]
72%|███████▏ | 715/1000 [00:59<00:21, 13.13it/s]
72%|███████▏ | 717/1000 [00:59<00:23, 12.18it/s]
72%|███████▏ | 719/1000 [00:59<00:21, 13.17it/s]
72%|███████▏ | 721/1000 [00:59<00:19, 14.14it/s]
72%|███████▏ | 723/1000 [01:00<00:19, 13.88it/s]
72%|███████▎ | 725/1000 [01:00<00:19, 14.07it/s]
73%|███████▎ | 727/1000 [01:00<00:23, 11.70it/s]
73%|███████▎ | 729/1000 [01:00<00:33, 8.17it/s]
73%|███████▎ | 731/1000 [01:01<00:28, 9.39it/s]
73%|███████▎ | 733/1000 [01:01<00:28, 9.32it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.51it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.78it/s]
74%|███████▍ | 739/1000 [01:01<00:24, 10.81it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.49it/s]
74%|███████▍ | 743/1000 [01:02<00:21, 12.14it/s]
74%|███████▍ | 745/1000 [01:02<00:25, 10.14it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.56it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.72it/s]
75%|███████▌ | 751/1000 [01:02<00:21, 11.81it/s]
75%|███████▌ | 753/1000 [01:02<00:19, 12.55it/s]
76%|███████▌ | 755/1000 [01:03<00:18, 13.24it/s]
76%|███████▌ | 757/1000 [01:03<00:23, 10.23it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.26it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.25it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.23it/s]
76%|███████▋ | 765/1000 [01:04<00:27, 8.58it/s]
77%|███████▋ | 767/1000 [01:04<00:24, 9.54it/s]
77%|███████▋ | 769/1000 [01:04<00:21, 10.55it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.82it/s]
77%|███████▋ | 773/1000 [01:04<00:19, 11.74it/s]
78%|███████▊ | 775/1000 [01:05<00:19, 11.76it/s]
78%|███████▊ | 777/1000 [01:05<00:17, 12.98it/s]
78%|███████▊ | 779/1000 [01:05<00:22, 10.02it/s]
78%|███████▊ | 781/1000 [01:05<00:24, 9.12it/s]
78%|███████▊ | 783/1000 [01:06<00:26, 8.13it/s]
78%|███████▊ | 784/1000 [01:06<00:31, 6.96it/s]
78%|███████▊ | 785/1000 [01:06<00:29, 7.30it/s]
79%|███████▊ | 786/1000 [01:06<00:30, 6.93it/s]
79%|███████▉ | 788/1000 [01:06<00:24, 8.77it/s]
79%|███████▉ | 790/1000 [01:06<00:21, 9.92it/s]
79%|███████▉ | 792/1000 [01:07<00:19, 10.64it/s]
79%|███████▉ | 794/1000 [01:07<00:18, 11.15it/s]
80%|███████▉ | 796/1000 [01:07<00:17, 11.51it/s]
80%|███████▉ | 798/1000 [01:07<00:17, 11.79it/s]
80%|████████ | 800/1000 [01:07<00:17, 11.26it/s]
80%|████████ | 802/1000 [01:08<00:25, 7.78it/s]
80%|████████ | 803/1000 [01:08<00:26, 7.41it/s]
80%|████████ | 805/1000 [01:08<00:22, 8.61it/s]
81%|████████ | 806/1000 [01:08<00:22, 8.64it/s]
81%|████████ | 808/1000 [01:08<00:19, 9.78it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.02it/s]
81%|████████ | 812/1000 [01:09<00:17, 10.77it/s]
81%|████████▏ | 814/1000 [01:09<00:16, 11.33it/s]
82%|████████▏ | 816/1000 [01:09<00:18, 10.09it/s]
82%|████████▏ | 818/1000 [01:09<00:17, 10.69it/s]
82%|████████▏ | 820/1000 [01:09<00:16, 11.16it/s]
82%|████████▏ | 822/1000 [01:09<00:15, 11.73it/s]
82%|████████▏ | 824/1000 [01:10<00:14, 12.00it/s]
83%|████████▎ | 826/1000 [01:10<00:14, 12.33it/s]
83%|████████▎ | 828/1000 [01:10<00:13, 12.42it/s]
83%|████████▎ | 830/1000 [01:10<00:13, 12.43it/s]
83%|████████▎ | 832/1000 [01:10<00:13, 12.46it/s]
83%|████████▎ | 834/1000 [01:10<00:13, 12.40it/s]
84%|████████▎ | 836/1000 [01:11<00:13, 12.38it/s]
84%|████████▍ | 838/1000 [01:11<00:13, 12.36it/s]
84%|████████▍ | 840/1000 [01:11<00:12, 12.40it/s]
84%|████████▍ | 842/1000 [01:11<00:12, 12.59it/s]
84%|████████▍ | 844/1000 [01:11<00:12, 12.47it/s]
85%|████████▍ | 846/1000 [01:11<00:12, 12.40it/s]
85%|████████▍ | 848/1000 [01:12<00:12, 12.39it/s]
85%|████████▌ | 850/1000 [01:12<00:12, 12.43it/s]
85%|████████▌ | 852/1000 [01:12<00:11, 12.45it/s]
85%|████████▌ | 854/1000 [01:12<00:11, 12.50it/s]
86%|████████▌ | 856/1000 [01:12<00:11, 12.46it/s]
86%|████████▌ | 858/1000 [01:12<00:11, 12.47it/s]
86%|████████▌ | 860/1000 [01:12<00:11, 12.67it/s]
86%|████████▌ | 862/1000 [01:13<00:11, 11.98it/s]
86%|████████▋ | 864/1000 [01:13<00:11, 12.07it/s]
87%|████████▋ | 866/1000 [01:13<00:11, 12.08it/s]
87%|████████▋ | 868/1000 [01:13<00:10, 12.14it/s]
87%|████████▋ | 870/1000 [01:13<00:10, 12.24it/s]
87%|████████▋ | 872/1000 [01:13<00:10, 12.27it/s]
87%|████████▋ | 874/1000 [01:14<00:11, 11.11it/s]
88%|████████▊ | 876/1000 [01:14<00:12, 10.18it/s]
88%|████████▊ | 878/1000 [01:14<00:11, 10.89it/s]
88%|████████▊ | 880/1000 [01:14<00:10, 11.24it/s]
88%|████████▊ | 882/1000 [01:14<00:11, 10.25it/s]
88%|████████▊ | 884/1000 [01:15<00:10, 10.81it/s]
89%|████████▊ | 886/1000 [01:15<00:10, 11.25it/s]
89%|████████▉ | 888/1000 [01:15<00:09, 11.67it/s]
89%|████████▉ | 890/1000 [01:15<00:09, 11.94it/s]
89%|████████▉ | 892/1000 [01:15<00:08, 12.13it/s]
89%|████████▉ | 894/1000 [01:15<00:08, 12.23it/s]
90%|████████▉ | 896/1000 [01:16<00:08, 12.54it/s]
90%|████████▉ | 898/1000 [01:16<00:08, 12.39it/s]
90%|█████████ | 900/1000 [01:16<00:08, 11.68it/s]
90%|█████████ | 902/1000 [01:16<00:07, 12.54it/s]
90%|█████████ | 904/1000 [01:16<00:09, 9.62it/s]
91%|█████████ | 906/1000 [01:17<00:08, 10.72it/s]
91%|█████████ | 908/1000 [01:17<00:07, 11.74it/s]
91%|█████████ | 910/1000 [01:17<00:07, 12.00it/s]
91%|█████████ | 912/1000 [01:17<00:07, 12.21it/s]
91%|█████████▏| 914/1000 [01:18<00:12, 6.80it/s]
92%|█████████▏| 916/1000 [01:18<00:10, 8.12it/s]
92%|█████████▏| 918/1000 [01:18<00:11, 7.38it/s]
92%|█████████▏| 920/1000 [01:18<00:09, 8.59it/s]
92%|█████████▏| 922/1000 [01:18<00:07, 10.04it/s]
92%|█████████▏| 924/1000 [01:18<00:07, 10.75it/s]
93%|█████████▎| 926/1000 [01:19<00:06, 11.63it/s]
93%|█████████▎| 928/1000 [01:19<00:06, 10.54it/s]
93%|█████████▎| 930/1000 [01:19<00:05, 11.78it/s]
93%|█████████▎| 932/1000 [01:19<00:05, 12.33it/s]
93%|█████████▎| 934/1000 [01:19<00:05, 12.83it/s]
94%|█████████▎| 936/1000 [01:19<00:04, 13.41it/s]
94%|█████████▍| 938/1000 [01:20<00:04, 14.34it/s]
94%|█████████▍| 940/1000 [01:20<00:04, 14.67it/s]
94%|█████████▍| 942/1000 [01:20<00:04, 12.33it/s]
94%|█████████▍| 944/1000 [01:20<00:04, 12.85it/s]
95%|█████████▍| 946/1000 [01:20<00:04, 11.88it/s]
95%|█████████▍| 948/1000 [01:20<00:04, 12.68it/s]
95%|█████████▌| 950/1000 [01:20<00:03, 13.47it/s]
95%|█████████▌| 952/1000 [01:21<00:03, 14.19it/s]
95%|█████████▌| 954/1000 [01:21<00:03, 14.27it/s]
96%|█████████▌| 956/1000 [01:21<00:03, 14.25it/s]
96%|█████████▌| 958/1000 [01:21<00:02, 14.78it/s]
96%|█████████▌| 960/1000 [01:21<00:03, 13.12it/s]
96%|█████████▌| 962/1000 [01:21<00:02, 13.75it/s]
96%|█████████▋| 964/1000 [01:22<00:03, 10.23it/s]
97%|█████████▋| 966/1000 [01:22<00:02, 11.45it/s]
97%|█████████▋| 968/1000 [01:22<00:02, 12.11it/s]
97%|█████████▋| 970/1000 [01:22<00:02, 12.72it/s]
97%|█████████▋| 972/1000 [01:22<00:02, 9.70it/s]
97%|█████████▋| 974/1000 [01:22<00:02, 10.87it/s]
98%|█████████▊| 976/1000 [01:23<00:02, 11.66it/s]
98%|█████████▊| 978/1000 [01:23<00:02, 9.86it/s]
98%|█████████▊| 980/1000 [01:23<00:02, 9.94it/s]
98%|█████████▊| 982/1000 [01:23<00:01, 10.43it/s]
98%|█████████▊| 984/1000 [01:23<00:01, 10.44it/s]
99%|█████████▊| 986/1000 [01:24<00:01, 10.66it/s]
99%|█████████▉| 988/1000 [01:24<00:01, 10.87it/s]
99%|█████████▉| 990/1000 [01:24<00:00, 11.35it/s]
99%|█████████▉| 992/1000 [01:24<00:00, 12.25it/s]
99%|█████████▉| 994/1000 [01:24<00:00, 12.72it/s]
100%|█████████▉| 996/1000 [01:24<00:00, 13.47it/s]
100%|█████████▉| 998/1000 [01:25<00:00, 13.76it/s]
100%|██████████| 1000/1000 [01:25<00:00, 14.49it/s]
{'eval_loss': '0.8752', 'eval_runtime': '85.19', 'eval_samples_per_second': '11.74', 'eval_steps_per_second': '11.74', 'epoch': '1', 'num_input_tokens_seen': 62891360}
100%|██████████| 18688/18688 [22:00:42<00:00, 3.57s/it]
100%|██████████| 1000/1000 [01:25<00:00, 14.49it/s]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00008/checkpoint-18688
loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
Training completed. Do not forget to share your model on huggingface.co/models =)
{'train_runtime': '7.924e+04', 'train_samples_per_second': '3.773', 'train_steps_per_second': '0.236', 'train_loss': '0.9402', 'epoch': '1', 'num_input_tokens_seen': 62891360}
100%|██████████| 18688/18688 [22:00:43<00:00, 3.57s/it] 100%|██████████| 18688/18688 [22:00:43<00:00, 4.24s/it]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00008
loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
***** train metrics *****
epoch = 1.0
num_input_tokens_seen = 62891360
total_flos = 2513659775GF
train_loss = 0.9402
train_runtime = 22:00:43.64
train_samples_per_second = 3.773
train_steps_per_second = 0.236
Figure saved at: /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00008/training_loss.png
Figure saved at: /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00008/training_eval_loss.png
[WARNING|2026-10-22 17:12:41] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot.
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:71: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:281: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 0/1000 [00:00<?, ?it/s]/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:172: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:202: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:254: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 3/1000 [00:00<00:38, 25.90it/s] 1%| | 6/1000 [00:00<00:54, 18.37it/s] 1%| | 8/1000 [00:00<00:56, 17.61it/s] 1%| | 10/1000 [00:00<00:59, 16.65it/s] 1%| | 12/1000 [00:00<01:00, 16.41it/s] 1%|▏ | 14/1000 [00:00<01:01, 15.94it/s] 2%|▏ | 16/1000 [00:00<01:02, 15.69it/s] 2%|▏ | 18/1000 [00:01<01:38, 10.00it/s] 2%|▏ | 20/1000 [00:01<01:32, 10.55it/s] 2%|▏ | 22/1000 [00:01<01:53, 8.65it/s] 2%|▏ | 24/1000 [00:01<01:41, 9.57it/s] 3%|▎ | 26/1000 [00:02<01:46, 9.17it/s] 3%|▎ | 28/1000 [00:02<01:37, 10.02it/s] 3%|▎ | 30/1000 [00:02<01:31, 10.55it/s] 3%|▎ | 32/1000 [00:02<01:37, 9.89it/s] 3%|▎ | 34/1000 [00:02<01:27, 11.04it/s] 4%|▎ | 36/1000 [00:03<01:33, 10.31it/s] 4%|▍ | 38/1000 [00:03<01:25, 11.25it/s] 4%|▍ | 40/1000 [00:03<01:28, 10.86it/s] 4%|▍ | 42/1000 [00:03<01:46, 8.98it/s] 4%|▍ | 44/1000 [00:03<01:35, 9.98it/s] 5%|▍ | 46/1000 [00:04<01:25, 11.22it/s] 5%|▍ | 48/1000 [00:04<01:31, 10.37it/s] 5%|▌ | 50/1000 [00:04<01:23, 11.43it/s] 5%|▌ | 52/1000 [00:04<01:17, 12.31it/s] 5%|▌ | 54/1000 [00:04<01:12, 13.08it/s] 6%|▌ | 56/1000 [00:04<01:08, 13.79it/s] 6%|▌ | 58/1000 [00:05<01:48, 8.69it/s] 6%|▌ | 60/1000 [00:05<01:36, 9.73it/s] 6%|▌ | 62/1000 [00:05<01:27, 10.71it/s] 6%|▋ | 64/1000 [00:05<01:19, 11.77it/s] 7%|▋ | 66/1000 [00:05<01:15, 12.29it/s] 7%|▋ | 68/1000 [00:05<01:15, 12.40it/s] 7%|▋ | 70/1000 [00:06<01:11, 13.09it/s] 7%|▋ | 72/1000 [00:06<01:08, 13.63it/s] 7%|▋ | 74/1000 [00:06<01:13, 12.62it/s] 8%|▊ | 76/1000 [00:06<01:08, 13.47it/s] 8%|▊ | 78/1000 [00:06<01:06, 13.90it/s] 8%|▊ | 80/1000 [00:06<01:10, 13.09it/s] 8%|▊ | 82/1000 [00:06<01:06, 13.74it/s] 8%|▊ | 84/1000 [00:07<01:04, 14.25it/s] 9%|▊ | 86/1000 [00:07<01:03, 14.43it/s] 9%|▉ | 88/1000 [00:07<01:03, 14.33it/s] 9%|▉ | 90/1000 [00:07<01:03, 14.40it/s] 9%|▉ | 92/1000 [00:07<01:04, 14.12it/s] 9%|▉ | 94/1000 [00:07<01:04, 13.95it/s] 10%|▉ | 96/1000 [00:07<01:03, 14.24it/s] 10%|▉ | 98/1000 [00:08<01:04, 13.92it/s] 10%|█ | 100/1000 [00:08<01:32, 9.70it/s] 10%|█ | 102/1000 [00:08<01:24, 10.67it/s] 10%|█ | 104/1000 [00:09<01:55, 7.77it/s] 10%|█ | 105/1000 [00:09<02:05, 7.15it/s] 11%|█ | 106/1000 [00:09<02:01, 7.34it/s] 11%|█ | 108/1000 [00:09<01:38, 9.03it/s] 11%|█ | 110/1000 [00:09<01:23, 10.69it/s] 11%|█ | 112/1000 [00:09<01:15, 11.84it/s] 11%|█▏ | 114/1000 [00:09<01:11, 12.39it/s] 12%|█▏ | 116/1000 [00:09<01:08, 12.92it/s] 12%|█▏ | 118/1000 [00:10<01:04, 13.61it/s] 12%|█▏ | 120/1000 [00:10<01:08, 12.92it/s] 12%|█▏ | 122/1000 [00:10<01:22, 10.65it/s] 12%|█▏ | 124/1000 [00:10<01:30, 9.70it/s] 13%|█▎ | 126/1000 [00:10<01:22, 10.65it/s] 13%|█▎ | 128/1000 [00:11<01:15, 11.57it/s] 13%|█▎ | 130/1000 [00:11<01:11, 12.24it/s] 13%|█▎ | 132/1000 [00:11<01:05, 13.20it/s] 13%|█▎ | 134/1000 [00:11<01:09, 12.39it/s] 14%|█▎ | 136/1000 [00:11<01:04, 13.34it/s] 14%|█▍ | 138/1000 [00:11<01:04, 13.26it/s] 14%|█▍ | 140/1000 [00:12<01:25, 10.09it/s] 14%|█▍ | 142/1000 [00:12<01:40, 8.54it/s] 14%|█▍ | 144/1000 [00:12<01:29, 9.57it/s] 15%|█▍ | 146/1000 [00:12<01:20, 10.59it/s] 15%|█▍ | 148/1000 [00:12<01:14, 11.50it/s] 15%|█▌ | 150/1000 [00:12<01:07, 12.55it/s] 15%|█▌ | 152/1000 [00:13<01:04, 13.07it/s] 15%|█▌ | 154/1000 [00:13<01:03, 13.33it/s] 16%|█▌ | 156/1000 [00:13<01:03, 13.20it/s] 16%|█▌ | 158/1000 [00:13<01:01, 13.72it/s] 16%|█▌ | 160/1000 [00:13<01:01, 13.63it/s] 16%|█▌ | 162/1000 [00:13<01:02, 13.51it/s] 16%|█▋ | 164/1000 [00:14<01:01, 13.60it/s] 17%|█▋ | 166/1000 [00:14<01:00, 13.74it/s] 17%|█▋ | 168/1000 [00:14<00:58, 14.24it/s] 17%|█▋ | 170/1000 [00:14<01:19, 10.47it/s] 17%|█▋ | 172/1000 [00:14<01:12, 11.45it/s] 17%|█▋ | 174/1000 [00:15<01:30, 9.17it/s] 18%|█▊ | 176/1000 [00:15<01:54, 7.18it/s] 18%|█▊ | 178/1000 [00:15<01:35, 8.65it/s] 18%|█▊ | 180/1000 [00:15<01:33, 8.79it/s] 18%|█▊ | 182/1000 [00:16<01:35, 8.56it/s] 18%|█▊ | 184/1000 [00:16<01:23, 9.80it/s] 19%|█▊ | 186/1000 [00:16<01:37, 8.33it/s] 19%|█▉ | 188/1000 [00:16<01:24, 9.65it/s] 19%|█▉ | 190/1000 [00:16<01:15, 10.69it/s] 19%|█▉ | 192/1000 [00:16<01:11, 11.33it/s] 19%|█▉ | 194/1000 [00:17<01:16, 10.54it/s] 20%|█▉ | 196/1000 [00:17<01:29, 8.95it/s] 20%|█▉ | 198/1000 [00:17<01:18, 10.19it/s] 20%|██ | 200/1000 [00:17<01:16, 10.50it/s] 20%|██ | 202/1000 [00:17<01:09, 11.43it/s] 20%|██ | 204/1000 [00:18<01:03, 12.53it/s] 21%|██ | 206/1000 [00:18<01:00, 13.16it/s] 21%|██ | 208/1000 [00:18<00:58, 13.55it/s] 21%|██ | 210/1000 [00:18<00:55, 14.35it/s] 21%|██ | 212/1000 [00:18<00:54, 14.58it/s] 21%|██▏ | 214/1000 [00:18<00:53, 14.58it/s] 22%|██▏ | 216/1000 [00:18<00:54, 14.38it/s] 22%|██▏ | 218/1000 [00:18<00:52, 14.77it/s] 22%|██▏ | 220/1000 [00:19<01:31, 8.48it/s] 22%|██▏ | 222/1000 [00:19<01:31, 8.54it/s] 22%|██▏ | 224/1000 [00:19<01:26, 8.93it/s] 23%|██▎ | 226/1000 [00:20<01:17, 10.01it/s] 23%|██▎ | 228/1000 [00:20<01:10, 11.00it/s] 23%|██▎ | 230/1000 [00:20<01:07, 11.47it/s] 23%|██▎ | 232/1000 [00:20<01:04, 11.91it/s] 23%|██▎ | 234/1000 [00:20<01:00, 12.69it/s] 24%|██▎ | 236/1000 [00:20<00:57, 13.26it/s] 24%|██▍ | 238/1000 [00:20<00:54, 13.95it/s] 24%|██▍ | 240/1000 [00:20<00:54, 14.03it/s] 24%|██▍ | 242/1000 [00:21<00:51, 14.59it/s] 24%|██▍ | 244/1000 [00:21<00:50, 14.85it/s] 25%|██▍ | 246/1000 [00:21<00:50, 14.86it/s] 25%|██▍ | 248/1000 [00:21<00:59, 12.61it/s] 25%|██▌ | 250/1000 [00:21<00:57, 13.06it/s] 25%|██▌ | 252/1000 [00:21<01:02, 11.92it/s] 25%|██▌ | 254/1000 [00:22<00:57, 12.95it/s] 26%|██▌ | 256/1000 [00:22<00:55, 13.52it/s] 26%|██▌ | 258/1000 [00:22<00:54, 13.62it/s] 26%|██▌ | 260/1000 [00:22<00:57, 12.78it/s] 26%|██▌ | 262/1000 [00:22<00:56, 12.96it/s] 26%|██▋ | 264/1000 [00:22<00:54, 13.54it/s] 27%|██▋ | 266/1000 [00:22<00:52, 13.90it/s] 27%|██▋ | 268/1000 [00:23<00:50, 14.47it/s] 27%|██▋ | 270/1000 [00:23<00:55, 13.21it/s] 27%|██▋ | 272/1000 [00:23<01:06, 11.02it/s] 27%|██▋ | 274/1000 [00:23<01:05, 11.11it/s] 28%|██▊ | 276/1000 [00:23<01:00, 11.98it/s] 28%|██▊ | 278/1000 [00:23<00:59, 12.13it/s] 28%|██▊ | 280/1000 [00:24<00:56, 12.80it/s] 28%|██▊ | 282/1000 [00:24<00:52, 13.77it/s] 28%|██▊ | 284/1000 [00:24<00:51, 13.84it/s] 29%|██▊ | 286/1000 [00:24<00:50, 14.25it/s] 29%|██▉ | 288/1000 [00:24<00:48, 14.69it/s] 29%|██▉ | 290/1000 [00:24<00:46, 15.19it/s] 29%|██▉ | 292/1000 [00:24<00:46, 15.24it/s] 29%|██▉ | 294/1000 [00:25<00:46, 15.18it/s] 30%|██▉ | 296/1000 [00:25<00:46, 15.23it/s] 30%|██▉ | 298/1000 [00:25<00:45, 15.34it/s] 30%|███ | 300/1000 [00:25<00:46, 15.04it/s] 30%|███ | 302/1000 [00:25<00:46, 15.08it/s] 30%|███ | 304/1000 [00:25<00:50, 13.70it/s] 31%|███ | 306/1000 [00:25<00:48, 14.41it/s] 31%|███ | 308/1000 [00:25<00:48, 14.39it/s] 31%|███ | 310/1000 [00:26<00:47, 14.58it/s] 31%|███ | 312/1000 [00:26<00:47, 14.57it/s] 31%|███▏ | 314/1000 [00:26<00:45, 15.08it/s] 32%|███▏ | 316/1000 [00:26<00:44, 15.36it/s] 32%|███▏ | 318/1000 [00:26<00:43, 15.80it/s] 32%|███▏ | 320/1000 [00:27<01:13, 9.31it/s] 32%|███▏ | 322/1000 [00:27<01:03, 10.66it/s] 32%|███▏ | 324/1000 [00:27<00:59, 11.42it/s] 33%|███▎ | 326/1000 [00:27<00:59, 11.38it/s] 33%|███▎ | 328/1000 [00:27<00:58, 11.44it/s] 33%|███▎ | 330/1000 [00:27<00:54, 12.35it/s] 33%|███▎ | 332/1000 [00:27<00:52, 12.62it/s] 33%|███▎ | 334/1000 [00:28<00:52, 12.58it/s] 34%|███▎ | 336/1000 [00:28<00:53, 12.44it/s] 34%|███▍ | 338/1000 [00:28<01:09, 9.48it/s] 34%|███▍ | 340/1000 [00:28<01:04, 10.23it/s] 34%|███▍ | 342/1000 [00:28<00:57, 11.46it/s] 34%|███▍ | 344/1000 [00:28<00:53, 12.31it/s] 35%|███▍ | 346/1000 [00:29<00:50, 13.00it/s] 35%|███▍ | 348/1000 [00:29<00:48, 13.50it/s] 35%|███▌ | 350/1000 [00:29<00:45, 14.19it/s] 35%|███▌ | 352/1000 [00:29<00:44, 14.48it/s] 35%|███▌ | 354/1000 [00:29<00:44, 14.63it/s] 36%|███▌ | 356/1000 [00:29<00:43, 14.81it/s] 36%|███▌ | 358/1000 [00:29<00:44, 14.55it/s] 36%|███▌ | 360/1000 [00:30<00:42, 14.96it/s] 36%|███▌ | 362/1000 [00:30<00:47, 13.33it/s] 36%|███▋ | 364/1000 [00:30<00:46, 13.82it/s] 37%|███▋ | 366/1000 [00:30<00:53, 11.93it/s] 37%|███▋ | 368/1000 [00:30<00:51, 12.33it/s] 37%|███▋ | 370/1000 [00:30<00:47, 13.19it/s] 37%|███▋ | 372/1000 [00:31<00:46, 13.51it/s] 37%|███▋ | 374/1000 [00:31<00:55, 11.37it/s] 38%|███▊ | 376/1000 [00:31<00:56, 10.96it/s] 38%|███▊ | 378/1000 [00:31<00:53, 11.64it/s] 38%|███▊ | 380/1000 [00:31<00:49, 12.45it/s] 38%|███▊ | 382/1000 [00:31<00:48, 12.64it/s] 38%|███▊ | 384/1000 [00:32<00:46, 13.38it/s] 39%|███▊ | 386/1000 [00:32<00:44, 13.71it/s] 39%|███▉ | 388/1000 [00:32<00:44, 13.65it/s] 39%|███▉ | 390/1000 [00:32<00:45, 13.52it/s] 39%|███▉ | 392/1000 [00:32<00:43, 13.83it/s] 39%|███▉ | 394/1000 [00:32<00:44, 13.62it/s] 40%|███▉ | 396/1000 [00:32<00:43, 13.73it/s] 40%|███▉ | 398/1000 [00:33<00:47, 12.55it/s] 40%|████ | 400/1000 [00:33<00:45, 13.21it/s] 40%|████ | 402/1000 [00:33<00:43, 13.86it/s] 40%|████ | 404/1000 [00:33<00:41, 14.45it/s] 41%|████ | 406/1000 [00:33<00:39, 14.95it/s] 41%|████ | 408/1000 [00:33<00:47, 12.50it/s] 41%|████ | 410/1000 [00:34<00:56, 10.45it/s] 41%|████ | 412/1000 [00:34<00:51, 11.53it/s] 41%|████▏ | 414/1000 [00:34<01:07, 8.62it/s] 42%|████▏ | 416/1000 [00:34<01:23, 6.96it/s] 42%|████▏ | 418/1000 [00:35<01:10, 8.27it/s] 42%|████▏ | 420/1000 [00:35<01:00, 9.53it/s] 42%|████▏ | 422/1000 [00:35<00:53, 10.87it/s] 42%|████▏ | 424/1000 [00:35<01:15, 7.67it/s] 43%|████▎ | 426/1000 [00:35<01:04, 8.93it/s] 43%|████▎ | 428/1000 [00:36<00:56, 10.13it/s] 43%|████▎ | 430/1000 [00:36<01:02, 9.09it/s] 43%|████▎ | 432/1000 [00:36<01:20, 7.03it/s] 43%|████▎ | 434/1000 [00:36<01:08, 8.25it/s] 44%|████▎ | 436/1000 [00:37<01:00, 9.28it/s] 44%|████▍ | 438/1000 [00:37<01:02, 8.96it/s] 44%|████▍ | 440/1000 [00:37<00:59, 9.47it/s] 44%|████▍ | 442/1000 [00:37<00:53, 10.37it/s] 44%|████▍ | 444/1000 [00:37<00:51, 10.87it/s] 45%|████▍ | 446/1000 [00:37<00:47, 11.66it/s] 45%|████▍ | 448/1000 [00:38<00:55, 9.99it/s] 45%|████▌ | 450/1000 [00:38<00:51, 10.69it/s] 45%|████▌ | 452/1000 [00:38<00:46, 11.84it/s] 45%|████▌ | 454/1000 [00:38<00:47, 11.56it/s] 46%|████▌ | 456/1000 [00:38<00:44, 12.12it/s] 46%|████▌ | 458/1000 [00:38<00:41, 13.21it/s] 46%|████▌ | 460/1000 [00:39<00:42, 12.84it/s] 46%|████▌ | 462/1000 [00:39<00:42, 12.58it/s] 46%|████▋ | 464/1000 [00:39<00:42, 12.64it/s] 47%|████▋ | 466/1000 [00:39<00:40, 13.18it/s] 47%|████▋ | 468/1000 [00:39<00:44, 11.97it/s] 47%|████▋ | 470/1000 [00:40<00:55, 9.62it/s] 47%|████▋ | 472/1000 [00:40<00:53, 9.85it/s] 47%|████▋ | 474/1000 [00:40<00:47, 11.14it/s] 48%|████▊ | 476/1000 [00:40<00:44, 11.87it/s] 48%|████▊ | 478/1000 [00:40<00:44, 11.81it/s] 48%|████▊ | 480/1000 [00:40<00:40, 12.77it/s] 48%|████▊ | 482/1000 [00:41<00:39, 13.22it/s] 48%|████▊ | 484/1000 [00:41<00:37, 13.76it/s] 49%|████▊ | 486/1000 [00:41<00:36, 13.94it/s] 49%|████▉ | 488/1000 [00:41<00:36, 14.20it/s] 49%|████▉ | 490/1000 [00:41<00:46, 11.06it/s] 49%|████▉ | 492/1000 [00:41<00:41, 12.35it/s] 49%|████▉ | 494/1000 [00:41<00:38, 13.31it/s] 50%|████▉ | 496/1000 [00:42<00:35, 14.08it/s] 50%|████▉ | 498/1000 [00:42<00:34, 14.48it/s] 50%|█████ | 500/1000 [00:42<00:43, 11.53it/s] 50%|█████ | 502/1000 [00:42<00:41, 11.98it/s] 50%|█████ | 504/1000 [00:42<00:39, 12.58it/s] 51%|█████ | 506/1000 [00:42<00:38, 12.75it/s] 51%|█████ | 508/1000 [00:42<00:35, 13.68it/s] 51%|█████ | 510/1000 [00:43<00:47, 10.38it/s] 51%|█████ | 512/1000 [00:43<00:43, 11.15it/s] 51%|█████▏ | 514/1000 [00:43<00:40, 12.00it/s] 52%|█████▏ | 516/1000 [00:43<00:38, 12.57it/s] 52%|█████▏ | 518/1000 [00:43<00:36, 13.07it/s] 52%|█████▏ | 520/1000 [00:44<00:35, 13.45it/s] 52%|█████▏ | 522/1000 [00:44<00:33, 14.17it/s] 52%|█████▏ | 524/1000 [00:44<00:33, 14.20it/s] 53%|█████▎ | 526/1000 [00:44<00:33, 14.35it/s] 53%|█████▎ | 528/1000 [00:44<00:39, 11.96it/s] 53%|█████▎ | 530/1000 [00:44<00:36, 12.74it/s] 53%|█████▎ | 532/1000 [00:44<00:34, 13.63it/s] 53%|█████▎ | 534/1000 [00:45<00:33, 14.01it/s] 54%|█████▎ | 536/1000 [00:45<00:32, 14.15it/s] 54%|█████▍ | 538/1000 [00:45<00:33, 13.93it/s] 54%|█████▍ | 540/1000 [00:45<00:32, 14.37it/s] 54%|█████▍ | 542/1000 [00:45<00:31, 14.67it/s] 54%|█████▍ | 544/1000 [00:45<00:31, 14.51it/s] 55%|█████▍ | 546/1000 [00:45<00:32, 14.15it/s] 55%|█████▍ | 548/1000 [00:46<00:32, 13.86it/s] 55%|█████▌ | 550/1000 [00:46<00:31, 14.10it/s] 55%|█████▌ | 552/1000 [00:46<00:31, 14.19it/s] 55%|█████▌ | 554/1000 [00:46<00:32, 13.83it/s] 56%|█████▌ | 556/1000 [00:46<00:31, 14.02it/s] 56%|█████▌ | 558/1000 [00:46<00:30, 14.38it/s] 56%|█████▌ | 560/1000 [00:46<00:29, 14.92it/s] 56%|█████▌ | 562/1000 [00:46<00:29, 14.85it/s] 56%|█████▋ | 564/1000 [00:47<00:40, 10.74it/s] 57%|█████▋ | 566/1000 [00:47<00:37, 11.64it/s] 57%|█████▋ | 568/1000 [00:47<00:35, 12.19it/s] 57%|█████▋ | 570/1000 [00:47<00:32, 13.27it/s] 57%|█████▋ | 572/1000 [00:47<00:33, 12.78it/s] 57%|█████▋ | 574/1000 [00:47<00:31, 13.63it/s] 58%|█████▊ | 576/1000 [00:48<00:31, 13.56it/s] 58%|█████▊ | 578/1000 [00:48<00:37, 11.40it/s] 58%|█████▊ | 580/1000 [00:48<00:35, 11.72it/s] 58%|█████▊ | 582/1000 [00:48<00:35, 11.94it/s] 58%|█████▊ | 584/1000 [00:48<00:34, 11.99it/s] 59%|█████▊ | 586/1000 [00:49<00:34, 11.98it/s] 59%|█████▉ | 588/1000 [00:49<00:34, 12.11it/s] 59%|█████▉ | 590/1000 [00:49<00:33, 12.22it/s] 59%|█████▉ | 592/1000 [00:49<00:33, 12.17it/s] 59%|█████▉ | 594/1000 [00:49<00:35, 11.53it/s] 60%|█████▉ | 596/1000 [00:49<00:34, 11.66it/s] 60%|█████▉ | 598/1000 [00:50<00:33, 11.84it/s] 60%|██████ | 600/1000 [00:50<00:33, 11.98it/s] 60%|██████ | 602/1000 [00:50<00:32, 12.12it/s] 60%|██████ | 604/1000 [00:50<00:30, 13.16it/s] 61%|██████ | 606/1000 [00:50<00:29, 13.47it/s] 61%|██████ | 608/1000 [00:50<00:27, 14.10it/s] 61%|██████ | 610/1000 [00:50<00:27, 14.26it/s] 61%|██████ | 612/1000 [00:51<00:26, 14.59it/s] 61%|██████▏ | 614/1000 [00:51<00:27, 14.03it/s] 62%|██████▏ | 616/1000 [00:51<00:26, 14.52it/s] 62%|██████▏ | 618/1000 [00:51<00:27, 14.02it/s] 62%|██████▏ | 620/1000 [00:51<00:39, 9.69it/s] 62%|██████▏ | 622/1000 [00:51<00:38, 9.91it/s] 62%|██████▏ | 624/1000 [00:52<00:33, 11.31it/s] 63%|██████▎ | 626/1000 [00:52<00:31, 12.03it/s] 63%|██████▎ | 628/1000 [00:52<00:28, 13.25it/s] 63%|██████▎ | 630/1000 [00:52<00:26, 13.75it/s] 63%|██████▎ | 632/1000 [00:52<00:29, 12.44it/s] 63%|██████▎ | 634/1000 [00:52<00:27, 13.17it/s] 64%|██████▎ | 636/1000 [00:53<00:33, 10.90it/s] 64%|██████▍ | 638/1000 [00:53<00:33, 10.66it/s] 64%|██████▍ | 640/1000 [00:53<00:31, 11.45it/s] 64%|██████▍ | 642/1000 [00:53<00:29, 12.32it/s] 64%|██████▍ | 644/1000 [00:53<00:28, 12.57it/s] 65%|██████▍ | 646/1000 [00:53<00:27, 12.85it/s] 65%|██████▍ | 648/1000 [00:53<00:26, 13.33it/s] 65%|██████▌ | 650/1000 [00:54<00:27, 12.55it/s] 65%|██████▌ | 652/1000 [00:54<00:29, 11.95it/s] 65%|██████▌ | 654/1000 [00:54<00:36, 9.43it/s] 66%|██████▌ | 656/1000 [00:54<00:31, 10.77it/s] 66%|██████▌ | 658/1000 [00:54<00:28, 11.81it/s] 66%|██████▌ | 660/1000 [00:55<00:33, 10.13it/s] 66%|██████▌ | 662/1000 [00:55<00:30, 11.25it/s] 66%|██████▋ | 664/1000 [00:55<00:28, 11.73it/s] 67%|██████▋ | 666/1000 [00:55<00:26, 12.70it/s] 67%|██████▋ | 668/1000 [00:55<00:29, 11.28it/s] 67%|██████▋ | 670/1000 [00:56<00:29, 11.05it/s] 67%|██████▋ | 672/1000 [00:56<00:27, 11.82it/s] 67%|██████▋ | 674/1000 [00:56<00:26, 12.42it/s] 68%|██████▊ | 676/1000 [00:56<00:27, 11.68it/s] 68%|██████▊ | 678/1000 [00:56<00:26, 12.18it/s] 68%|██████▊ | 680/1000 [00:56<00:24, 12.96it/s] 68%|██████▊ | 682/1000 [00:56<00:26, 12.10it/s] 68%|██████▊ | 684/1000 [00:57<00:25, 12.30it/s] 69%|██████▊ | 686/1000 [00:57<00:23, 13.34it/s] 69%|██████▉ | 688/1000 [00:57<00:22, 13.67it/s] 69%|██████▉ | 690/1000 [00:57<00:22, 13.75it/s] 69%|██████▉ | 692/1000 [00:57<00:21, 14.20it/s] 69%|██████▉ | 694/1000 [00:57<00:21, 14.29it/s] 70%|██████▉ | 696/1000 [00:57<00:21, 14.03it/s] 70%|██████▉ | 698/1000 [00:58<00:20, 14.57it/s] 70%|███████ | 700/1000 [00:58<00:24, 12.18it/s] 70%|███████ | 702/1000 [00:58<00:22, 13.00it/s] 70%|███████ | 704/1000 [00:58<00:30, 9.84it/s] 71%|███████ | 706/1000 [00:58<00:27, 10.65it/s] 71%|███████ | 708/1000 [00:59<00:30, 9.60it/s] 71%|███████ | 710/1000 [00:59<00:26, 10.90it/s] 71%|███████ | 712/1000 [00:59<00:24, 11.86it/s] 71%|███████▏ | 714/1000 [00:59<00:22, 12.95it/s] 72%|███████▏ | 716/1000 [00:59<00:21, 13.04it/s] 72%|███████▏ | 718/1000 [00:59<00:22, 12.49it/s] 72%|███████▏ | 720/1000 [00:59<00:20, 13.46it/s] 72%|███████▏ | 722/1000 [01:00<00:20, 13.88it/s] 72%|███████▏ | 724/1000 [01:00<00:20, 13.73it/s] 73%|███████▎ | 726/1000 [01:00<00:19, 13.93it/s] 73%|███████▎ | 728/1000 [01:00<00:23, 11.80it/s] 73%|███████▎ | 730/1000 [01:01<00:33, 8.16it/s] 73%|███████▎ | 732/1000 [01:01<00:32, 8.30it/s] 73%|███████▎ | 734/1000 [01:01<00:31, 8.58it/s] 74%|███████▎ | 735/1000 [01:01<00:31, 8.41it/s] 74%|███████▎ | 737/1000 [01:01<00:26, 9.85it/s] 74%|███████▍ | 739/1000 [01:01<00:23, 10.95it/s] 74%|███████▍ | 741/1000 [01:02<00:22, 11.64it/s] 74%|███████▍ | 743/1000 [01:02<00:20, 12.29it/s] 74%|███████▍ | 745/1000 [01:02<00:25, 10.18it/s] 75%|███████▍ | 747/1000 [01:02<00:26, 9.53it/s] 75%|███████▍ | 749/1000 [01:02<00:23, 10.69it/s] 75%|███████▌ | 751/1000 [01:02<00:21, 11.78it/s] 75%|███████▌ | 753/1000 [01:03<00:19, 12.55it/s] 76%|███████▌ | 755/1000 [01:03<00:18, 13.26it/s] 76%|███████▌ | 757/1000 [01:03<00:23, 10.24it/s] 76%|███████▌ | 759/1000 [01:03<00:21, 11.28it/s] 76%|███████▌ | 761/1000 [01:03<00:19, 12.26it/s] 76%|███████▋ | 763/1000 [01:04<00:23, 10.24it/s] 76%|███████▋ | 765/1000 [01:04<00:27, 8.58it/s] 77%|███████▋ | 767/1000 [01:04<00:24, 9.53it/s] 77%|███████▋ | 769/1000 [01:04<00:21, 10.55it/s] 77%|███████▋ | 771/1000 [01:04<00:19, 11.81it/s] 77%|███████▋ | 773/1000 [01:04<00:18, 12.59it/s] 78%|███████▊ | 775/1000 [01:05<00:17, 13.14it/s] 78%|███████▊ | 777/1000 [01:05<00:15, 14.10it/s] 78%|███████▊ | 779/1000 [01:05<00:21, 10.48it/s] 78%|███████▊ | 781/1000 [01:05<00:23, 9.39it/s] 78%|███████▊ | 783/1000 [01:06<00:26, 8.27it/s] 78%|███████▊ | 784/1000 [01:06<00:30, 7.04it/s] 78%|███████▊ | 785/1000 [01:06<00:29, 7.35it/s] 79%|███████▊ | 786/1000 [01:06<00:30, 6.96it/s] 79%|███████▉ | 788/1000 [01:06<00:24, 8.81it/s] 79%|███████▉ | 790/1000 [01:06<00:20, 10.13it/s] 79%|███████▉ | 792/1000 [01:07<00:18, 11.25it/s] 79%|███████▉ | 794/1000 [01:07<00:17, 11.88it/s] 80%|███████▉ | 796/1000 [01:07<00:16, 12.32it/s] 80%|███████▉ | 798/1000 [01:07<00:15, 13.07it/s] 80%|████████ | 800/1000 [01:07<00:16, 12.28it/s] 80%|████████ | 802/1000 [01:08<00:24, 8.23it/s] 80%|████████ | 804/1000 [01:08<00:23, 8.28it/s] 81%|████████ | 806/1000 [01:08<00:21, 8.96it/s] 81%|████████ | 808/1000 [01:08<00:18, 10.33it/s] 81%|████████ | 810/1000 [01:08<00:18, 10.45it/s] 81%|████████ | 812/1000 [01:08<00:16, 11.66it/s] 81%|████████▏ | 814/1000 [01:09<00:14, 12.57it/s] 82%|████████▏ | 816/1000 [01:09<00:16, 11.02it/s] 82%|████████▏ | 818/1000 [01:09<00:15, 11.97it/s] 82%|████████▏ | 820/1000 [01:09<00:14, 12.46it/s] 82%|████████▏ | 822/1000 [01:09<00:13, 13.02it/s] 82%|████████▏ | 824/1000 [01:09<00:12, 13.80it/s] 83%|████████▎ | 826/1000 [01:09<00:12, 14.20it/s] 83%|████████▎ | 828/1000 [01:10<00:11, 14.48it/s] 83%|████████▎ | 830/1000 [01:10<00:12, 13.89it/s] 83%|████████▎ | 832/1000 [01:10<00:11, 14.28it/s] 83%|████████▎ | 834/1000 [01:10<00:11, 14.03it/s] 84%|████████▎ | 836/1000 [01:10<00:11, 14.37it/s] 84%|████████▍ | 838/1000 [01:10<00:11, 14.28it/s] 84%|████████▍ | 840/1000 [01:10<00:11, 14.38it/s] 84%|████████▍ | 842/1000 [01:11<00:11, 14.07it/s] 84%|████████▍ | 844/1000 [01:11<00:11, 13.10it/s] 85%|████████▍ | 846/1000 [01:11<00:11, 13.14it/s] 85%|████████▍ | 848/1000 [01:11<00:12, 12.65it/s] 85%|████████▌ | 850/1000 [01:11<00:11, 12.83it/s] 85%|████████▌ | 852/1000 [01:11<00:11, 12.82it/s] 85%|████████▌ | 854/1000 [01:12<00:10, 13.55it/s] 86%|████████▌ | 856/1000 [01:12<00:10, 13.35it/s] 86%|████████▌ | 858/1000 [01:12<00:10, 13.97it/s] 86%|████████▌ | 860/1000 [01:12<00:09, 14.52it/s] 86%|████████▌ | 862/1000 [01:12<00:10, 12.98it/s] 86%|████████▋ | 864/1000 [01:12<00:10, 13.57it/s] 87%|████████▋ | 866/1000 [01:12<00:09, 13.52it/s] 87%|████████▋ | 868/1000 [01:13<00:09, 13.51it/s] 87%|████████▋ | 870/1000 [01:13<00:09, 13.95it/s] 87%|████████▋ | 872/1000 [01:13<00:08, 14.58it/s] 87%|████████▋ | 874/1000 [01:13<00:10, 12.35it/s] 88%|████████▊ | 876/1000 [01:13<00:11, 10.99it/s] 88%|████████▊ | 878/1000 [01:13<00:10, 11.87it/s] 88%|████████▊ | 880/1000 [01:14<00:09, 12.51it/s] 88%|████████▊ | 882/1000 [01:14<00:10, 11.33it/s] 88%|████████▊ | 884/1000 [01:14<00:09, 12.06it/s] 89%|████████▊ | 886/1000 [01:14<00:08, 12.83it/s] 89%|████████▉ | 888/1000 [01:14<00:08, 13.79it/s] 89%|████████▉ | 890/1000 [01:14<00:07, 14.23it/s] 89%|████████▉ | 892/1000 [01:14<00:07, 14.00it/s] 89%|████████▉ | 894/1000 [01:15<00:07, 14.15it/s] 90%|████████▉ | 896/1000 [01:15<00:07, 13.92it/s] 90%|████████▉ | 898/1000 [01:15<00:07, 13.27it/s] 90%|█████████ | 900/1000 [01:15<00:08, 12.23it/s] 90%|█████████ | 902/1000 [01:15<00:07, 12.97it/s] 90%|█████████ | 904/1000 [01:16<00:09, 9.78it/s] 91%|█████████ | 906/1000 [01:16<00:09, 10.42it/s] 91%|█████████ | 908/1000 [01:16<00:08, 10.85it/s] 91%|█████████ | 910/1000 [01:16<00:08, 10.96it/s] 91%|█████████ | 912/1000 [01:16<00:07, 11.38it/s] 91%|█████████▏| 914/1000 [01:17<00:13, 6.58it/s] 92%|█████████▏| 916/1000 [01:17<00:10, 7.90it/s] 92%|█████████▏| 918/1000 [01:17<00:11, 7.27it/s] 92%|█████████▏| 920/1000 [01:17<00:09, 8.66it/s] 92%|█████████▏| 922/1000 [01:17<00:07, 10.10it/s] 92%|█████████▏| 924/1000 [01:18<00:07, 10.83it/s] 93%|█████████▎| 926/1000 [01:18<00:06, 11.70it/s] 93%|█████████▎| 928/1000 [01:18<00:06, 10.57it/s] 93%|█████████▎| 930/1000 [01:18<00:05, 11.77it/s] 93%|█████████▎| 932/1000 [01:18<00:05, 12.37it/s] 93%|█████████▎| 934/1000 [01:18<00:05, 12.85it/s] 94%|█████████▎| 936/1000 [01:19<00:04, 13.40it/s] 94%|█████████▍| 938/1000 [01:19<00:04, 14.26it/s] 94%|█████████▍| 940/1000 [01:19<00:04, 14.55it/s] 94%|█████████▍| 942/1000 [01:19<00:04, 12.26it/s] 94%|█████████▍| 944/1000 [01:19<00:04, 12.79it/s] 95%|█████████▍| 946/1000 [01:19<00:04, 11.86it/s] 95%|█████████▍| 948/1000 [01:20<00:04, 12.68it/s] 95%|█████████▌| 950/1000 [01:20<00:03, 13.44it/s] 95%|█████████▌| 952/1000 [01:20<00:03, 14.12it/s] 95%|█████████▌| 954/1000 [01:20<00:03, 14.23it/s] 96%|█████████▌| 956/1000 [01:20<00:03, 14.22it/s] 96%|█████████▌| 958/1000 [01:20<00:02, 14.74it/s] 96%|█████████▌| 960/1000 [01:20<00:03, 13.10it/s] 96%|█████████▌| 962/1000 [01:20<00:02, 13.69it/s] 96%|█████████▋| 964/1000 [01:21<00:03, 10.21it/s] 97%|█████████▋| 966/1000 [01:21<00:02, 11.42it/s] 97%|█████████▋| 968/1000 [01:21<00:02, 12.08it/s] 97%|█████████▋| 970/1000 [01:21<00:02, 12.71it/s] 97%|█████████▋| 972/1000 [01:22<00:02, 9.67it/s] 97%|█████████▋| 974/1000 [01:22<00:02, 10.82it/s] 98%|█████████▊| 976/1000 [01:22<00:02, 11.62it/s] 98%|█████████▊| 978/1000 [01:22<00:02, 9.86it/s] 98%|█████████▊| 980/1000 [01:22<00:02, 9.94it/s] 98%|█████████▊| 982/1000 [01:22<00:01, 10.43it/s] 98%|█████████▊| 984/1000 [01:23<00:01, 10.42it/s] 99%|█████████▊| 986/1000 [01:23<00:01, 10.67it/s] 99%|█████████▉| 988/1000 [01:23<00:01, 10.89it/s] 99%|█████████▉| 990/1000 [01:23<00:00, 11.39it/s] 99%|█████████▉| 992/1000 [01:23<00:00, 12.29it/s] 99%|█████████▉| 994/1000 [01:23<00:00, 13.02it/s] 100%|█████████▉| 996/1000 [01:24<00:00, 13.92it/s] 100%|█████████▉| 998/1000 [01:24<00:00, 14.08it/s] 100%|██████████| 1000/1000 [01:24<00:00, 14.74it/s] 100%|██████████| 1000/1000 [01:24<00:00, 11.86it/s]
***** eval metrics *****
epoch = 1.0
eval_loss = 0.8752
eval_runtime = 0:01:24.35
eval_samples_per_second = 11.854
eval_steps_per_second = 11.854
num_input_tokens_seen = 62891360
Dropping the following result as it does not have all the necessary fields:
{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}}
Reference in New Issue View Git Blame Copy Permalink