Diplomovka/aya_300k_lr_experiments/logs/lr_0p00010.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

12454 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|2026-10-20 21:07:09] 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] 2026-10-20 21:07:09,551 >> 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] 2026-10-20 21:07:09,559 >> 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] 2026-10-20 21:07:11,261 >> 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] 2026-10-20 21:07:11,265 >> 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] 2026-10-20 21:07:11,405 >> 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] 2026-10-20 21:07:11,407 >> 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|2026-10-20 21:07:12] 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] 2026-10-20 21:07:13,035 >> 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] 2026-10-20 21:07:13,038 >> 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|2026-10-20 21:07:13] 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, 52.81it/s] Loading weights: 8%|▊ | 23/291 [00:00<00:02, 112.13it/s] Loading weights: 12%|█▏ | 36/291 [00:00<00:02, 118.28it/s] Loading weights: 17%|█▋ | 49/291 [00:00<00:02, 118.70it/s] Loading weights: 21%|██ | 61/291 [00:00<00:01, 115.46it/s] Loading weights: 26%|██▌ | 76/291 [00:00<00:01, 121.79it/s] Loading weights: 31%|███ | 89/291 [00:00<00:01, 119.67it/s] Loading weights: 35%|███▌ | 103/291 [00:00<00:01, 121.76it/s] Loading weights: 40%|███▉ | 116/291 [00:01<00:01, 111.86it/s] Loading weights: 45%|████▍ | 130/291 [00:01<00:01, 115.07it/s] Loading weights: 49%|████▉ | 142/291 [00:01<00:01, 114.38it/s] Loading weights: 53%|█████▎ | 154/291 [00:01<00:01, 109.52it/s] Loading weights: 59%|█████▉ | 172/291 [00:01<00:00, 128.31it/s] Loading weights: 64%|██████▍ | 186/291 [00:01<00:00, 126.28it/s] Loading weights: 70%|██████▉ | 203/291 [00:01<00:00, 131.73it/s] Loading weights: 75%|███████▍ | 217/291 [00:01<00:00, 127.22it/s] Loading weights: 79%|███████▉ | 230/291 [00:01<00:00, 126.49it/s] Loading weights: 84%|████████▎ | 243/291 [00:02<00:00, 125.26it/s] Loading weights: 88%|████████▊ | 257/291 [00:02<00:00, 127.86it/s] Loading weights: 93%|█████████▎| 270/291 [00:02<00:00, 120.02it/s] Loading weights: 97%|█████████▋| 283/291 [00:02<00:00, 112.53it/s] Loading weights: 100%|██████████| 291/291 [00:02<00:00, 118.80it/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|2026-10-20 21:07:29] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled.
[INFO|2026-10-20 21:07:29] llamafactory.model.adapter:144 >> Upcasting trainable params to float32.
[INFO|2026-10-20 21:07:29] 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|2026-10-20 21:07:33] 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<33:38:20, 6.48s/it] 0%| | 2/18688 [00:10<25:33:52, 4.93s/it] 0%| | 3/18688 [00:14<23:43:00, 4.57s/it] 0%| | 4/18688 [00:18<22:07:34, 4.26s/it] 0%| | 5/18688 [00:22<21:09:58, 4.08s/it] 0%| | 6/18688 [00:26<21:36:28, 4.16s/it] 0%| | 7/18688 [00:31<24:07:23, 4.65s/it] 0%| | 8/18688 [00:35<22:27:01, 4.33s/it] 0%| | 9/18688 [00:40<23:44:46, 4.58s/it] 0%| | 10/18688 [00:44<22:24:56, 4.32s/it] {'loss': '2.106', 'grad_norm': '5.745', 'learning_rate': '6e-07', 'epoch': '0.0005351', 'num_input_tokens_seen': 35216, 'train_runtime': '44.5', 'train_tokens_per_second': '791.4'}
0%| | 10/18688 [00:44<22:24:56, 4.32s/it] 0%| | 11/18688 [00:48<21:59:44, 4.24s/it] 0%| | 12/18688 [00:53<23:52:12, 4.60s/it] 0%| | 13/18688 [00:57<22:41:23, 4.37s/it] 0%| | 14/18688 [01:01<21:52:16, 4.22s/it] 0%| | 15/18688 [01:05<21:37:13, 4.17s/it] 0%| | 16/18688 [01:09<21:04:21, 4.06s/it] 0%| | 17/18688 [01:13<20:29:29, 3.95s/it] 0%| | 18/18688 [01:17<20:14:46, 3.90s/it] 0%| | 19/18688 [01:20<19:55:24, 3.84s/it] 0%| | 20/18688 [01:24<19:43:51, 3.80s/it] {'loss': '2.042', 'grad_norm': '3.87', 'learning_rate': '1.267e-06', 'epoch': '0.00107', 'num_input_tokens_seen': 66488, 'train_runtime': '84.46', 'train_tokens_per_second': '787.2'}
0%| | 20/18688 [01:24<19:43:51, 3.80s/it] 0%| | 21/18688 [01:28<20:10:36, 3.89s/it] 0%| | 22/18688 [01:32<20:16:12, 3.91s/it] 0%| | 23/18688 [01:36<20:25:54, 3.94s/it] 0%| | 24/18688 [01:40<20:18:54, 3.92s/it] 0%| | 25/18688 [01:44<20:31:02, 3.96s/it] 0%| | 26/18688 [01:48<20:04:23, 3.87s/it] 0%| | 27/18688 [01:52<20:19:32, 3.92s/it] 0%| | 28/18688 [01:55<20:07:18, 3.88s/it] 0%| | 29/18688 [02:00<20:35:08, 3.97s/it] 0%| | 30/18688 [02:03<20:21:48, 3.93s/it] {'loss': '1.983', 'grad_norm': '4.375', 'learning_rate': '1.933e-06', 'epoch': '0.001605', 'num_input_tokens_seen': 97280, 'train_runtime': '123.9', 'train_tokens_per_second': '784.9'}
0%| | 30/18688 [02:03<20:21:48, 3.93s/it] 0%| | 31/18688 [02:08<20:57:12, 4.04s/it] 0%| | 32/18688 [02:12<21:03:54, 4.06s/it] 0%| | 33/18688 [02:16<21:01:14, 4.06s/it] 0%| | 34/18688 [02:20<21:03:38, 4.06s/it] 0%| | 35/18688 [02:24<20:25:09, 3.94s/it] 0%| | 36/18688 [02:27<19:56:00, 3.85s/it] 0%| | 37/18688 [02:32<20:43:22, 4.00s/it] 0%| | 38/18688 [02:36<21:48:35, 4.21s/it] 0%| | 39/18688 [02:41<22:19:27, 4.31s/it] 0%| | 40/18688 [02:45<21:22:52, 4.13s/it] {'loss': '1.978', 'grad_norm': '5.51', 'learning_rate': '2.6e-06', 'epoch': '0.00214', 'num_input_tokens_seen': 129200, 'train_runtime': '165.1', 'train_tokens_per_second': '782.7'}
0%| | 40/18688 [02:45<21:22:52, 4.13s/it] 0%| | 41/18688 [02:48<20:46:55, 4.01s/it] 0%| | 42/18688 [02:52<20:30:46, 3.96s/it] 0%| | 43/18688 [02:56<19:59:50, 3.86s/it] 0%| | 44/18688 [03:00<21:16:04, 4.11s/it] 0%| | 45/18688 [03:05<21:55:38, 4.23s/it] 0%| | 46/18688 [03:10<22:33:45, 4.36s/it] 0%| | 47/18688 [03:13<21:29:18, 4.15s/it] 0%| | 48/18688 [03:17<21:01:16, 4.06s/it] 0%| | 49/18688 [03:21<21:17:48, 4.11s/it] 0%| | 50/18688 [03:27<23:11:29, 4.48s/it] {'loss': '1.706', 'grad_norm': '2.015', 'learning_rate': '3.267e-06', 'epoch': '0.002676', 'num_input_tokens_seen': 163208, 'train_runtime': '207.2', 'train_tokens_per_second': '787.6'}
0%| | 50/18688 [03:27<23:11:29, 4.48s/it] 0%| | 51/18688 [03:30<21:51:17, 4.22s/it] 0%| | 52/18688 [03:35<23:04:26, 4.46s/it] 0%| | 53/18688 [03:40<23:08:31, 4.47s/it] 0%| | 54/18688 [03:44<22:13:41, 4.29s/it] 0%| | 55/18688 [03:48<22:38:44, 4.38s/it] 0%| | 56/18688 [03:53<22:36:00, 4.37s/it] 0%| | 57/18688 [03:57<21:53:11, 4.23s/it] 0%| | 58/18688 [04:00<20:50:24, 4.03s/it] 0%| | 59/18688 [04:05<21:34:04, 4.17s/it] 0%| | 60/18688 [04:09<22:25:12, 4.33s/it] {'loss': '1.614', 'grad_norm': '1.952', 'learning_rate': '3.933e-06', 'epoch': '0.003211', 'num_input_tokens_seen': 197328, 'train_runtime': '249.8', 'train_tokens_per_second': '789.8'}
0%| | 60/18688 [04:09<22:25:12, 4.33s/it] 0%| | 61/18688 [04:14<22:48:11, 4.41s/it] 0%| | 62/18688 [04:18<22:48:12, 4.41s/it] 0%| | 63/18688 [04:24<24:47:46, 4.79s/it] 0%| | 64/18688 [04:28<23:25:32, 4.53s/it] 0%| | 65/18688 [04:33<24:07:17, 4.66s/it] 0%| | 66/18688 [04:37<22:37:30, 4.37s/it] 0%| | 67/18688 [04:40<21:48:50, 4.22s/it] 0%| | 68/18688 [04:45<22:28:00, 4.34s/it] 0%| | 69/18688 [04:49<22:30:50, 4.35s/it] 0%| | 70/18688 [04:54<22:27:52, 4.34s/it] {'loss': '1.514', 'grad_norm': '1.759', 'learning_rate': '4.6e-06', 'epoch': '0.003746', 'num_input_tokens_seen': 234064, 'train_runtime': '294.3', 'train_tokens_per_second': '795.4'}
0%| | 70/18688 [04:54<22:27:52, 4.34s/it] 0%| | 71/18688 [04:58<22:39:25, 4.38s/it] 0%| | 72/18688 [05:02<22:13:16, 4.30s/it] 0%| | 73/18688 [05:06<21:43:43, 4.20s/it] 0%| | 74/18688 [05:10<21:36:48, 4.18s/it] 0%| | 75/18688 [05:15<21:39:39, 4.19s/it] 0%| | 76/18688 [05:19<21:27:31, 4.15s/it] 0%| | 77/18688 [05:24<22:29:20, 4.35s/it] 0%| | 78/18688 [05:27<21:19:23, 4.12s/it] 0%| | 79/18688 [05:32<22:38:54, 4.38s/it] 0%| | 80/18688 [05:36<22:24:10, 4.33s/it] {'loss': '1.475', 'grad_norm': '2.054', 'learning_rate': '5.267e-06', 'epoch': '0.004281', 'num_input_tokens_seen': 266768, 'train_runtime': '336.9', 'train_tokens_per_second': '791.9'}
0%| | 80/18688 [05:36<22:24:10, 4.33s/it] 0%| | 81/18688 [05:41<23:12:22, 4.49s/it] 0%| | 82/18688 [05:45<22:41:39, 4.39s/it] 0%| | 83/18688 [05:49<21:52:41, 4.23s/it] 0%| | 84/18688 [05:53<21:31:56, 4.17s/it] 0%| | 85/18688 [05:58<21:41:13, 4.20s/it] 0%| | 86/18688 [06:02<22:23:57, 4.33s/it] 0%| | 87/18688 [06:06<21:15:44, 4.12s/it] 0%| | 88/18688 [06:11<22:28:23, 4.35s/it] 0%| | 89/18688 [06:14<21:39:32, 4.19s/it] 0%| | 90/18688 [06:18<21:19:15, 4.13s/it] {'loss': '1.35', 'grad_norm': '1.874', 'learning_rate': '5.933e-06', 'epoch': '0.004816', 'num_input_tokens_seen': 299720, 'train_runtime': '379', 'train_tokens_per_second': '790.9'}
0%| | 90/18688 [06:18<21:19:15, 4.13s/it] 0%| | 91/18688 [06:23<22:36:24, 4.38s/it] 0%| | 92/18688 [06:28<22:33:58, 4.37s/it] 0%| | 93/18688 [06:32<22:10:01, 4.29s/it] 1%| | 94/18688 [06:37<22:45:28, 4.41s/it] 1%| | 95/18688 [06:41<23:17:15, 4.51s/it] 1%| | 96/18688 [06:46<23:28:57, 4.55s/it] 1%| | 97/18688 [06:50<22:09:59, 4.29s/it] 1%| | 98/18688 [06:54<21:31:34, 4.17s/it] 1%| | 99/18688 [06:58<21:19:50, 4.13s/it] 1%| | 100/18688 [07:01<20:39:49, 4.00s/it] {'loss': '1.311', 'grad_norm': '2.089', 'learning_rate': '6.6e-06', 'epoch': '0.005351', 'num_input_tokens_seen': 333368, 'train_runtime': '421.8', 'train_tokens_per_second': '790.4'}
1%| | 100/18688 [07:01<20:39:49, 4.00s/it] 1%| | 101/18688 [07:07<23:04:08, 4.47s/it] 1%| | 102/18688 [07:11<23:03:26, 4.47s/it] 1%| | 103/18688 [07:15<21:56:28, 4.25s/it] 1%| | 104/18688 [07:19<22:11:56, 4.30s/it] 1%| | 105/18688 [07:24<22:19:10, 4.32s/it] 1%| | 106/18688 [07:29<23:08:37, 4.48s/it] 1%| | 107/18688 [07:33<22:13:36, 4.31s/it] 1%| | 108/18688 [07:37<22:05:25, 4.28s/it] 1%| | 109/18688 [07:41<22:17:32, 4.32s/it] 1%| | 110/18688 [07:45<21:20:38, 4.14s/it] {'loss': '1.256', 'grad_norm': '2.108', 'learning_rate': '7.267e-06', 'epoch': '0.005886', 'num_input_tokens_seen': 370256, 'train_runtime': '465.4', 'train_tokens_per_second': '795.5'}
1%| | 110/18688 [07:45<21:20:38, 4.14s/it] 1%| | 111/18688 [07:49<21:22:04, 4.14s/it] 1%| | 112/18688 [07:53<21:04:23, 4.08s/it] 1%| | 113/18688 [07:57<20:47:01, 4.03s/it] 1%| | 114/18688 [08:01<21:36:22, 4.19s/it] 1%| | 115/18688 [08:05<20:57:39, 4.06s/it] 1%| | 116/18688 [08:09<20:06:47, 3.90s/it] 1%| | 117/18688 [08:13<20:04:33, 3.89s/it] 1%| | 118/18688 [08:17<20:41:44, 4.01s/it] 1%| | 119/18688 [08:22<22:13:50, 4.31s/it] 1%| | 120/18688 [08:26<21:24:30, 4.15s/it] {'loss': '1.131', 'grad_norm': '2.058', 'learning_rate': '7.933e-06', 'epoch': '0.006421', 'num_input_tokens_seen': 402568, 'train_runtime': '506.2', 'train_tokens_per_second': '795.2'}
1%| | 120/18688 [08:26<21:24:30, 4.15s/it] 1%| | 121/18688 [08:30<21:39:45, 4.20s/it] 1%| | 122/18688 [08:34<20:35:39, 3.99s/it] 1%| | 123/18688 [08:37<19:59:31, 3.88s/it] 1%| | 124/18688 [08:41<19:29:29, 3.78s/it] 1%| | 125/18688 [08:45<20:58:05, 4.07s/it] 1%| | 126/18688 [08:49<20:28:03, 3.97s/it] 1%| | 127/18688 [08:53<20:34:50, 3.99s/it] 1%| | 128/18688 [08:57<20:15:17, 3.93s/it] 1%| | 129/18688 [09:02<21:21:37, 4.14s/it] 1%| | 130/18688 [09:07<23:00:06, 4.46s/it] {'loss': '1.14', 'grad_norm': '2.082', 'learning_rate': '8.6e-06', 'epoch': '0.006957', 'num_input_tokens_seen': 434592, 'train_runtime': '547.4', 'train_tokens_per_second': '794'}
1%| | 130/18688 [09:07<23:00:06, 4.46s/it] 1%| | 131/18688 [09:11<22:07:29, 4.29s/it] 1%| | 132/18688 [09:16<24:09:19, 4.69s/it] 1%| | 133/18688 [09:21<23:30:54, 4.56s/it] 1%| | 134/18688 [09:25<22:37:06, 4.39s/it] 1%| | 135/18688 [09:28<21:46:34, 4.23s/it] 1%| | 136/18688 [09:34<24:17:11, 4.71s/it] 1%| | 137/18688 [09:38<22:55:35, 4.45s/it] 1%| | 138/18688 [09:42<22:29:02, 4.36s/it] 1%| | 139/18688 [09:46<21:54:55, 4.25s/it] 1%| | 140/18688 [09:51<21:51:26, 4.24s/it] {'loss': '1.093', 'grad_norm': '1.621', 'learning_rate': '9.267e-06', 'epoch': '0.007492', 'num_input_tokens_seen': 471752, 'train_runtime': '591', 'train_tokens_per_second': '798.2'}
1%| | 140/18688 [09:51<21:51:26, 4.24s/it] 1%| | 141/18688 [09:54<20:51:56, 4.05s/it] 1%| | 142/18688 [09:58<21:13:53, 4.12s/it] 1%| | 143/18688 [10:02<20:57:00, 4.07s/it] 1%| | 144/18688 [10:06<20:08:18, 3.91s/it] 1%| | 145/18688 [10:09<19:37:19, 3.81s/it] 1%| | 146/18688 [10:14<20:36:42, 4.00s/it] 1%| | 147/18688 [10:18<21:17:38, 4.13s/it] 1%| | 148/18688 [10:22<20:49:48, 4.04s/it] 1%| | 149/18688 [10:26<20:06:58, 3.91s/it] 1%| | 150/18688 [10:30<20:16:41, 3.94s/it] {'loss': '1.097', 'grad_norm': '2.129', 'learning_rate': '9.933e-06', 'epoch': '0.008027', 'num_input_tokens_seen': 501528, 'train_runtime': '630.3', 'train_tokens_per_second': '795.7'}
1%| | 150/18688 [10:30<20:16:41, 3.94s/it] 1%| | 151/18688 [10:34<20:35:37, 4.00s/it] 1%| | 152/18688 [10:38<20:12:14, 3.92s/it] 1%| | 153/18688 [10:41<19:40:47, 3.82s/it] 1%| | 154/18688 [10:45<19:32:04, 3.79s/it] 1%| | 155/18688 [10:49<19:28:00, 3.78s/it] 1%| | 156/18688 [10:53<19:59:28, 3.88s/it] 1%| | 157/18688 [10:57<19:57:28, 3.88s/it] 1%| | 158/18688 [11:01<19:52:02, 3.86s/it] 1%| | 159/18688 [11:05<21:29:35, 4.18s/it] 1%| | 160/18688 [11:09<20:37:41, 4.01s/it] {'loss': '1.053', 'grad_norm': '1.585', 'learning_rate': '1.06e-05', 'epoch': '0.008562', 'num_input_tokens_seen': 532056, 'train_runtime': '669.6', 'train_tokens_per_second': '794.6'}
1%| | 160/18688 [11:09<20:37:41, 4.01s/it] 1%| | 161/18688 [11:14<21:17:01, 4.14s/it] 1%| | 162/18688 [11:17<20:44:39, 4.03s/it] 1%| | 163/18688 [11:22<21:30:04, 4.18s/it] 1%| | 164/18688 [11:26<21:07:30, 4.11s/it] 1%| | 165/18688 [11:31<22:46:36, 4.43s/it] 1%| | 166/18688 [11:35<21:41:15, 4.22s/it] 1%| | 167/18688 [11:38<20:35:21, 4.00s/it] 1%| | 168/18688 [11:42<20:46:30, 4.04s/it] 1%| | 169/18688 [11:46<20:09:25, 3.92s/it] 1%| | 170/18688 [11:50<19:51:48, 3.86s/it] {'loss': '1.1', 'grad_norm': '1.915', 'learning_rate': '1.127e-05', 'epoch': '0.009097', 'num_input_tokens_seen': 564304, 'train_runtime': '710.2', 'train_tokens_per_second': '794.6'}
1%| | 170/18688 [11:50<19:51:48, 3.86s/it] 1%| | 171/18688 [11:54<19:52:29, 3.86s/it] 1%| | 172/18688 [11:58<20:05:21, 3.91s/it] 1%| | 173/18688 [12:02<21:12:35, 4.12s/it] 1%| | 174/18688 [12:06<20:09:35, 3.92s/it] 1%| | 175/18688 [12:10<21:14:03, 4.13s/it] 1%| | 176/18688 [12:15<21:54:01, 4.26s/it] 1%| | 177/18688 [12:18<21:00:32, 4.09s/it] 1%| | 178/18688 [12:23<21:14:20, 4.13s/it] 1%| | 179/18688 [12:26<20:13:23, 3.93s/it] 1%| | 180/18688 [12:30<20:21:17, 3.96s/it] {'loss': '1.143', 'grad_norm': '1.899', 'learning_rate': '1.193e-05', 'epoch': '0.009632', 'num_input_tokens_seen': 595424, 'train_runtime': '750.7', 'train_tokens_per_second': '793.2'}
1%| | 180/18688 [12:30<20:21:17, 3.96s/it] 1%| | 181/18688 [12:34<20:00:15, 3.89s/it] 1%| | 182/18688 [12:38<19:48:39, 3.85s/it] 1%| | 183/18688 [12:43<21:32:44, 4.19s/it] 1%| | 184/18688 [12:46<20:53:57, 4.07s/it] 1%| | 185/18688 [12:50<20:45:19, 4.04s/it] 1%| | 186/18688 [12:54<20:43:33, 4.03s/it] 1%| | 187/18688 [12:59<20:57:49, 4.08s/it] 1%| | 188/18688 [13:03<20:48:09, 4.05s/it] 1%| | 189/18688 [13:07<20:38:40, 4.02s/it] 1%| | 190/18688 [13:11<21:29:05, 4.18s/it] {'loss': '1.057', 'grad_norm': '1.933', 'learning_rate': '1.26e-05', 'epoch': '0.01017', 'num_input_tokens_seen': 628248, 'train_runtime': '791.6', 'train_tokens_per_second': '793.6'}
1%| | 190/18688 [13:11<21:29:05, 4.18s/it] 1%| | 191/18688 [13:16<22:11:58, 4.32s/it] 1%| | 192/18688 [13:20<21:30:28, 4.19s/it] 1%| | 193/18688 [13:23<20:36:34, 4.01s/it] 1%| | 194/18688 [13:27<20:27:51, 3.98s/it] 1%| | 195/18688 [13:31<20:58:03, 4.08s/it] 1%| | 196/18688 [13:36<21:35:43, 4.20s/it] 1%| | 197/18688 [13:40<21:45:04, 4.23s/it] 1%| | 198/18688 [13:44<21:23:08, 4.16s/it] 1%| | 199/18688 [13:49<21:44:24, 4.23s/it] 1%| | 200/18688 [13:53<22:08:45, 4.31s/it] {'loss': '1.07', 'grad_norm': '1.568', 'learning_rate': '1.327e-05', 'epoch': '0.0107', 'num_input_tokens_seen': 661320, 'train_runtime': '833.6', 'train_tokens_per_second': '793.3'}
1%| | 200/18688 [13:53<22:08:45, 4.31s/it] 1%| | 201/18688 [13:57<21:30:26, 4.19s/it] 1%| | 202/18688 [14:01<20:39:49, 4.02s/it] 1%| | 203/18688 [14:05<21:30:08, 4.19s/it] 1%| | 204/18688 [14:10<21:50:10, 4.25s/it] 1%| | 205/18688 [14:14<22:03:36, 4.30s/it] 1%| | 206/18688 [14:19<22:19:11, 4.35s/it] 1%| | 207/18688 [14:23<22:30:13, 4.38s/it] 1%| | 208/18688 [14:27<21:43:27, 4.23s/it] 1%| | 209/18688 [14:32<22:57:23, 4.47s/it] 1%| | 210/18688 [14:36<21:38:46, 4.22s/it] {'loss': '1.178', 'grad_norm': '2.181', 'learning_rate': '1.393e-05', 'epoch': '0.01124', 'num_input_tokens_seen': 696656, 'train_runtime': '876', 'train_tokens_per_second': '795.2'}
1%| | 210/18688 [14:36<21:38:46, 4.22s/it] 1%| | 211/18688 [14:39<20:38:13, 4.02s/it] 1%| | 212/18688 [14:43<21:11:02, 4.13s/it] 1%| | 213/18688 [14:47<20:24:13, 3.98s/it] 1%| | 214/18688 [14:51<20:23:30, 3.97s/it] 1%| | 215/18688 [14:55<20:05:45, 3.92s/it] 1%| | 216/18688 [14:59<20:23:28, 3.97s/it] 1%| | 217/18688 [15:03<20:01:16, 3.90s/it] 1%| | 218/18688 [15:07<20:07:21, 3.92s/it] 1%| | 219/18688 [15:10<19:54:52, 3.88s/it] 1%| | 220/18688 [15:15<20:57:12, 4.08s/it] {'loss': '1.094', 'grad_norm': '1.015', 'learning_rate': '1.46e-05', 'epoch': '0.01177', 'num_input_tokens_seen': 727368, 'train_runtime': '915.5', 'train_tokens_per_second': '794.5'}
1%| | 220/18688 [15:15<20:57:12, 4.08s/it] 1%| | 221/18688 [15:19<21:10:46, 4.13s/it] 1%| | 222/18688 [15:23<21:17:54, 4.15s/it] 1%| | 223/18688 [15:28<21:15:54, 4.15s/it] 1%| | 224/18688 [15:32<21:08:42, 4.12s/it] 1%| | 225/18688 [15:36<22:02:48, 4.30s/it] 1%| | 226/18688 [15:40<21:24:12, 4.17s/it] 1%| | 227/18688 [15:44<21:18:36, 4.16s/it] 1%| | 228/18688 [15:50<22:52:55, 4.46s/it] 1%| | 229/18688 [15:53<22:04:33, 4.31s/it] 1%| | 230/18688 [15:57<21:23:43, 4.17s/it] {'loss': '1.075', 'grad_norm': '2.238', 'learning_rate': '1.527e-05', 'epoch': '0.01231', 'num_input_tokens_seen': 761544, 'train_runtime': '957.8', 'train_tokens_per_second': '795.1'}
1%| | 230/18688 [15:57<21:23:43, 4.17s/it] 1%| | 231/18688 [16:01<21:18:17, 4.16s/it] 1%| | 232/18688 [16:07<22:51:48, 4.46s/it] 1%| | 233/18688 [16:11<22:36:10, 4.41s/it] 1%|▏ | 234/18688 [16:15<22:23:09, 4.37s/it] 1%|▏ | 235/18688 [16:19<21:14:43, 4.14s/it] 1%|▏ | 236/18688 [16:24<23:25:46, 4.57s/it] 1%|▏ | 237/18688 [16:28<21:57:25, 4.28s/it] 1%|▏ | 238/18688 [16:32<21:48:28, 4.26s/it] 1%|▏ | 239/18688 [16:36<21:56:31, 4.28s/it] 1%|▏ | 240/18688 [16:40<20:48:58, 4.06s/it] {'loss': '1.083', 'grad_norm': '1.918', 'learning_rate': '1.593e-05', 'epoch': '0.01284', 'num_input_tokens_seen': 797960, 'train_runtime': '1001', 'train_tokens_per_second': '797.5'}
1%|▏ | 240/18688 [16:40<20:48:58, 4.06s/it] 1%|▏ | 241/18688 [16:45<21:33:55, 4.21s/it] 1%|▏ | 242/18688 [16:49<21:43:30, 4.24s/it] 1%|▏ | 243/18688 [16:53<21:25:27, 4.18s/it] 1%|▏ | 244/18688 [16:57<20:47:08, 4.06s/it] 1%|▏ | 245/18688 [17:01<20:23:15, 3.98s/it] 1%|▏ | 246/18688 [17:05<20:25:02, 3.99s/it] 1%|▏ | 247/18688 [17:08<20:11:03, 3.94s/it] 1%|▏ | 248/18688 [17:12<20:03:23, 3.92s/it] 1%|▏ | 249/18688 [17:17<20:55:08, 4.08s/it] 1%|▏ | 250/18688 [17:21<20:53:15, 4.08s/it] {'loss': '1.09', 'grad_norm': '1.295', 'learning_rate': '1.66e-05', 'epoch': '0.01338', 'num_input_tokens_seen': 831064, 'train_runtime': '1041', 'train_tokens_per_second': '798.1'}
1%|▏ | 250/18688 [17:21<20:53:15, 4.08s/it] 1%|▏ | 251/18688 [17:25<20:56:48, 4.09s/it] 1%|▏ | 252/18688 [17:29<20:28:32, 4.00s/it] 1%|▏ | 253/18688 [17:32<19:55:42, 3.89s/it] 1%|▏ | 254/18688 [17:36<19:48:46, 3.87s/it] 1%|▏ | 255/18688 [17:40<19:33:27, 3.82s/it] 1%|▏ | 256/18688 [17:44<20:21:33, 3.98s/it] 1%|▏ | 257/18688 [17:49<21:13:51, 4.15s/it] 1%|▏ | 258/18688 [17:53<20:52:03, 4.08s/it] 1%|▏ | 259/18688 [17:56<20:17:56, 3.97s/it] 1%|▏ | 260/18688 [18:01<20:59:13, 4.10s/it] {'loss': '1.024', 'grad_norm': '1.986', 'learning_rate': '1.727e-05', 'epoch': '0.01391', 'num_input_tokens_seen': 862936, 'train_runtime': '1081', 'train_tokens_per_second': '798.1'}
1%|▏ | 260/18688 [18:01<20:59:13, 4.10s/it] 1%|▏ | 261/18688 [18:05<21:18:21, 4.16s/it] 1%|▏ | 262/18688 [18:09<21:00:51, 4.11s/it] 1%|▏ | 263/18688 [18:13<21:30:25, 4.20s/it] 1%|▏ | 264/18688 [18:17<21:02:29, 4.11s/it] 1%|▏ | 265/18688 [18:21<20:22:04, 3.98s/it] 1%|▏ | 266/18688 [18:25<19:48:47, 3.87s/it] 1%|▏ | 267/18688 [18:30<21:21:11, 4.17s/it] 1%|▏ | 268/18688 [18:33<20:27:53, 4.00s/it] 1%|▏ | 269/18688 [18:37<20:05:39, 3.93s/it] 1%|▏ | 270/18688 [18:40<19:35:59, 3.83s/it] {'loss': '1.021', 'grad_norm': '1.502', 'learning_rate': '1.793e-05', 'epoch': '0.01445', 'num_input_tokens_seen': 893208, 'train_runtime': '1121', 'train_tokens_per_second': '796.8'}
1%|▏ | 270/18688 [18:40<19:35:59, 3.83s/it] 1%|▏ | 271/18688 [18:44<19:26:50, 3.80s/it] 1%|▏ | 272/18688 [18:48<19:51:29, 3.88s/it] 1%|▏ | 273/18688 [18:52<19:46:14, 3.87s/it] 1%|▏ | 274/18688 [18:56<20:32:22, 4.02s/it] 1%|▏ | 275/18688 [19:00<20:06:11, 3.93s/it] 1%|▏ | 276/18688 [19:04<20:07:04, 3.93s/it] 1%|▏ | 277/18688 [19:08<19:49:11, 3.88s/it] 1%|▏ | 278/18688 [19:12<19:46:42, 3.87s/it] 1%|▏ | 279/18688 [19:17<22:09:47, 4.33s/it] 1%|▏ | 280/18688 [19:22<22:35:20, 4.42s/it] {'loss': '1.096', 'grad_norm': '1.577', 'learning_rate': '1.86e-05', 'epoch': '0.01498', 'num_input_tokens_seen': 925664, 'train_runtime': '1162', 'train_tokens_per_second': '796.4'}
1%|▏ | 280/18688 [19:22<22:35:20, 4.42s/it] 2%|▏ | 281/18688 [19:25<21:29:26, 4.20s/it] 2%|▏ | 282/18688 [19:30<22:21:42, 4.37s/it] 2%|▏ | 283/18688 [19:35<23:06:59, 4.52s/it] 2%|▏ | 284/18688 [19:39<22:44:31, 4.45s/it] 2%|▏ | 285/18688 [19:43<21:42:13, 4.25s/it] 2%|▏ | 286/18688 [19:48<22:34:28, 4.42s/it] 2%|▏ | 287/18688 [19:52<21:15:10, 4.16s/it] 2%|▏ | 288/18688 [19:56<21:10:12, 4.14s/it] 2%|▏ | 289/18688 [20:00<21:12:45, 4.15s/it] 2%|▏ | 290/18688 [20:04<21:08:23, 4.14s/it] {'loss': '1.084', 'grad_norm': '1.536', 'learning_rate': '1.927e-05', 'epoch': '0.01552', 'num_input_tokens_seen': 960824, 'train_runtime': '1204', 'train_tokens_per_second': '797.8'}
2%|▏ | 290/18688 [20:04<21:08:23, 4.14s/it] 2%|▏ | 291/18688 [20:08<21:01:28, 4.11s/it] 2%|▏ | 292/18688 [20:12<20:24:45, 3.99s/it] 2%|▏ | 293/18688 [20:15<19:49:51, 3.88s/it] 2%|▏ | 294/18688 [20:19<19:28:33, 3.81s/it] 2%|▏ | 295/18688 [20:23<19:39:46, 3.85s/it] 2%|▏ | 296/18688 [20:27<20:08:55, 3.94s/it] 2%|▏ | 297/18688 [20:31<20:32:11, 4.02s/it] 2%|▏ | 298/18688 [20:36<21:27:05, 4.20s/it] 2%|▏ | 299/18688 [20:40<21:54:43, 4.29s/it] 2%|▏ | 300/18688 [20:45<21:42:00, 4.25s/it] {'loss': '1.065', 'grad_norm': '1.595', 'learning_rate': '1.993e-05', 'epoch': '0.01605', 'num_input_tokens_seen': 993184, 'train_runtime': '1245', 'train_tokens_per_second': '797.7'}
2%|▏ | 300/18688 [20:45<21:42:00, 4.25s/it] 2%|▏ | 301/18688 [20:48<20:44:00, 4.06s/it] 2%|▏ | 302/18688 [20:53<21:35:43, 4.23s/it] 2%|▏ | 303/18688 [20:57<21:07:15, 4.14s/it] 2%|▏ | 304/18688 [21:00<20:32:22, 4.02s/it] 2%|▏ | 305/18688 [21:04<19:46:51, 3.87s/it] 2%|▏ | 306/18688 [21:08<19:38:19, 3.85s/it] 2%|▏ | 307/18688 [21:12<19:30:03, 3.82s/it] 2%|▏ | 308/18688 [21:16<20:00:52, 3.92s/it] 2%|▏ | 309/18688 [21:20<20:21:30, 3.99s/it] 2%|▏ | 310/18688 [21:24<20:07:21, 3.94s/it] {'loss': '1.065', 'grad_norm': '1.243', 'learning_rate': '2.06e-05', 'epoch': '0.01659', 'num_input_tokens_seen': 1022656, 'train_runtime': '1284', 'train_tokens_per_second': '796.4'}
2%|▏ | 310/18688 [21:24<20:07:21, 3.94s/it] 2%|▏ | 311/18688 [21:27<19:39:51, 3.85s/it] 2%|▏ | 312/18688 [21:31<19:13:30, 3.77s/it] 2%|▏ | 313/18688 [21:35<20:02:11, 3.93s/it] 2%|▏ | 314/18688 [21:39<19:42:26, 3.86s/it] 2%|▏ | 315/18688 [21:43<19:55:04, 3.90s/it] 2%|▏ | 316/18688 [21:47<20:29:47, 4.02s/it] 2%|▏ | 317/18688 [21:51<20:41:39, 4.06s/it] 2%|▏ | 318/18688 [21:55<20:15:17, 3.97s/it] 2%|▏ | 319/18688 [21:59<19:46:56, 3.88s/it] 2%|▏ | 320/18688 [22:03<20:20:06, 3.99s/it] {'loss': '1.032', 'grad_norm': '1.311', 'learning_rate': '2.127e-05', 'epoch': '0.01712', 'num_input_tokens_seen': 1053904, 'train_runtime': '1323', 'train_tokens_per_second': '796.3'}
2%|▏ | 320/18688 [22:03<20:20:06, 3.99s/it] 2%|▏ | 321/18688 [22:07<20:39:44, 4.05s/it] 2%|▏ | 322/18688 [22:11<20:46:54, 4.07s/it] 2%|▏ | 323/18688 [22:17<23:00:40, 4.51s/it] 2%|▏ | 324/18688 [22:21<22:17:25, 4.37s/it] 2%|▏ | 325/18688 [22:24<21:05:49, 4.14s/it] 2%|▏ | 326/18688 [22:28<20:36:44, 4.04s/it] 2%|▏ | 327/18688 [22:33<21:57:14, 4.30s/it] 2%|▏ | 328/18688 [22:37<21:55:46, 4.30s/it] 2%|▏ | 329/18688 [22:42<23:00:49, 4.51s/it] 2%|▏ | 330/18688 [22:46<21:41:29, 4.25s/it] {'loss': '1.086', 'grad_norm': '1.431', 'learning_rate': '2.193e-05', 'epoch': '0.01766', 'num_input_tokens_seen': 1090264, 'train_runtime': '1367', 'train_tokens_per_second': '797.8'}
2%|▏ | 330/18688 [22:46<21:41:29, 4.25s/it] 2%|▏ | 331/18688 [22:50<20:52:53, 4.10s/it] 2%|▏ | 332/18688 [22:54<20:22:41, 4.00s/it] 2%|▏ | 333/18688 [22:57<19:33:52, 3.84s/it] 2%|▏ | 334/18688 [23:01<19:13:55, 3.77s/it] 2%|▏ | 335/18688 [23:05<19:16:24, 3.78s/it] 2%|▏ | 336/18688 [23:08<18:56:44, 3.72s/it] 2%|▏ | 337/18688 [23:12<19:22:40, 3.80s/it] 2%|▏ | 338/18688 [23:17<20:38:35, 4.05s/it] 2%|▏ | 339/18688 [23:21<20:48:08, 4.08s/it] 2%|▏ | 340/18688 [23:26<22:05:36, 4.33s/it] {'loss': '0.9826', 'grad_norm': '1.866', 'learning_rate': '2.26e-05', 'epoch': '0.01819', 'num_input_tokens_seen': 1121696, 'train_runtime': '1406', 'train_tokens_per_second': '797.6'}
2%|▏ | 340/18688 [23:26<22:05:36, 4.33s/it] 2%|▏ | 341/18688 [23:29<20:50:25, 4.09s/it] 2%|▏ | 342/18688 [23:33<20:19:30, 3.99s/it] 2%|▏ | 343/18688 [23:37<20:38:26, 4.05s/it] 2%|▏ | 344/18688 [23:41<19:59:35, 3.92s/it] 2%|▏ | 345/18688 [23:45<19:59:56, 3.92s/it] 2%|▏ | 346/18688 [23:49<20:29:16, 4.02s/it] 2%|▏ | 347/18688 [23:54<21:19:40, 4.19s/it] 2%|▏ | 348/18688 [23:58<21:06:12, 4.14s/it] 2%|▏ | 349/18688 [24:02<21:43:16, 4.26s/it] 2%|▏ | 350/18688 [24:06<20:40:36, 4.06s/it] {'loss': '1.071', 'grad_norm': '1.621', 'learning_rate': '2.327e-05', 'epoch': '0.01873', 'num_input_tokens_seen': 1153216, 'train_runtime': '1446', 'train_tokens_per_second': '797.4'}
2%|▏ | 350/18688 [24:06<20:40:36, 4.06s/it] 2%|▏ | 351/18688 [24:09<20:07:23, 3.95s/it] 2%|▏ | 352/18688 [24:14<20:40:51, 4.06s/it] 2%|▏ | 353/18688 [24:18<21:09:00, 4.15s/it] 2%|▏ | 354/18688 [24:22<20:31:14, 4.03s/it] 2%|▏ | 355/18688 [24:26<19:52:03, 3.90s/it] 2%|▏ | 356/18688 [24:31<22:55:05, 4.50s/it] 2%|▏ | 357/18688 [24:35<22:01:46, 4.33s/it] 2%|▏ | 358/18688 [24:40<21:53:49, 4.30s/it] 2%|▏ | 359/18688 [24:44<21:38:19, 4.25s/it] 2%|▏ | 360/18688 [24:48<21:03:12, 4.14s/it] {'loss': '1.057', 'grad_norm': '1.362', 'learning_rate': '2.393e-05', 'epoch': '0.01926', 'num_input_tokens_seen': 1188456, 'train_runtime': '1488', 'train_tokens_per_second': '798.6'}
2%|▏ | 360/18688 [24:48<21:03:12, 4.14s/it] 2%|▏ | 361/18688 [24:51<20:06:09, 3.95s/it] 2%|▏ | 362/18688 [24:56<20:49:58, 4.09s/it] 2%|▏ | 363/18688 [25:00<21:05:49, 4.14s/it] 2%|▏ | 364/18688 [25:04<21:38:05, 4.25s/it] 2%|▏ | 365/18688 [25:09<22:10:37, 4.36s/it] 2%|▏ | 366/18688 [25:12<20:53:33, 4.11s/it] 2%|▏ | 367/18688 [25:17<21:34:29, 4.24s/it] 2%|▏ | 368/18688 [25:21<20:42:18, 4.07s/it] 2%|▏ | 369/18688 [25:24<20:03:28, 3.94s/it] 2%|▏ | 370/18688 [25:29<21:14:17, 4.17s/it] {'loss': '1.083', 'grad_norm': '1.472', 'learning_rate': '2.46e-05', 'epoch': '0.0198', 'num_input_tokens_seen': 1222640, 'train_runtime': '1529', 'train_tokens_per_second': '799.4'}
2%|▏ | 370/18688 [25:29<21:14:17, 4.17s/it] 2%|▏ | 371/18688 [25:33<20:24:55, 4.01s/it] 2%|▏ | 372/18688 [25:36<19:52:41, 3.91s/it] 2%|▏ | 373/18688 [25:40<19:41:48, 3.87s/it] 2%|▏ | 374/18688 [25:44<20:28:35, 4.03s/it] 2%|▏ | 375/18688 [25:49<20:44:27, 4.08s/it] 2%|▏ | 376/18688 [25:53<20:24:13, 4.01s/it] 2%|▏ | 377/18688 [25:57<20:40:30, 4.06s/it] 2%|▏ | 378/18688 [26:01<20:39:26, 4.06s/it] 2%|▏ | 379/18688 [26:04<19:54:01, 3.91s/it] 2%|▏ | 380/18688 [26:09<20:23:52, 4.01s/it] {'loss': '1.089', 'grad_norm': '1.557', 'learning_rate': '2.527e-05', 'epoch': '0.02033', 'num_input_tokens_seen': 1253888, 'train_runtime': '1569', 'train_tokens_per_second': '799.1'}
2%|▏ | 380/18688 [26:09<20:23:52, 4.01s/it] 2%|▏ | 381/18688 [26:12<19:52:04, 3.91s/it] 2%|▏ | 382/18688 [26:16<19:14:11, 3.78s/it] 2%|▏ | 383/18688 [26:20<19:59:33, 3.93s/it] 2%|▏ | 384/18688 [26:24<20:31:30, 4.04s/it] 2%|▏ | 385/18688 [26:28<20:10:38, 3.97s/it] 2%|▏ | 386/18688 [26:33<20:56:49, 4.12s/it] 2%|▏ | 387/18688 [26:37<21:32:05, 4.24s/it] 2%|▏ | 388/18688 [26:41<20:45:05, 4.08s/it] 2%|▏ | 389/18688 [26:44<19:47:50, 3.89s/it] 2%|▏ | 390/18688 [26:49<21:04:11, 4.15s/it] {'loss': '1.014', 'grad_norm': '1.27', 'learning_rate': '2.593e-05', 'epoch': '0.02087', 'num_input_tokens_seen': 1286896, 'train_runtime': '1609', 'train_tokens_per_second': '799.6'}
2%|▏ | 390/18688 [26:49<21:04:11, 4.15s/it] 2%|▏ | 391/18688 [26:53<20:49:11, 4.10s/it] 2%|▏ | 392/18688 [26:57<19:58:59, 3.93s/it] 2%|▏ | 393/18688 [27:00<19:42:23, 3.88s/it] 2%|▏ | 394/18688 [27:04<19:51:06, 3.91s/it] 2%|▏ | 395/18688 [27:09<21:15:58, 4.19s/it] 2%|▏ | 396/18688 [27:13<21:03:11, 4.14s/it] 2%|▏ | 397/18688 [27:17<20:10:00, 3.97s/it] 2%|▏ | 398/18688 [27:20<19:38:47, 3.87s/it] 2%|▏ | 399/18688 [27:25<20:35:22, 4.05s/it] 2%|▏ | 400/18688 [27:29<20:36:46, 4.06s/it] {'loss': '1.037', 'grad_norm': '1.619', 'learning_rate': '2.66e-05', 'epoch': '0.0214', 'num_input_tokens_seen': 1319712, 'train_runtime': '1649', 'train_tokens_per_second': '800.1'}
2%|▏ | 400/18688 [27:29<20:36:46, 4.06s/it] 2%|▏ | 401/18688 [27:34<21:32:22, 4.24s/it] 2%|▏ | 402/18688 [27:37<20:31:06, 4.04s/it] 2%|▏ | 403/18688 [27:41<20:30:20, 4.04s/it] 2%|▏ | 404/18688 [27:45<20:05:06, 3.95s/it] 2%|▏ | 405/18688 [27:49<19:52:14, 3.91s/it] 2%|▏ | 406/18688 [27:54<22:25:11, 4.41s/it] 2%|▏ | 407/18688 [27:58<21:45:41, 4.29s/it] 2%|▏ | 408/18688 [28:02<20:36:43, 4.06s/it] 2%|▏ | 409/18688 [28:06<20:17:56, 4.00s/it] 2%|▏ | 410/18688 [28:09<20:00:47, 3.94s/it] {'loss': '1.088', 'grad_norm': '1.295', 'learning_rate': '2.727e-05', 'epoch': '0.02194', 'num_input_tokens_seen': 1352936, 'train_runtime': '1690', 'train_tokens_per_second': '800.6'}
2%|▏ | 410/18688 [28:09<20:00:47, 3.94s/it] 2%|▏ | 411/18688 [28:14<20:14:34, 3.99s/it] 2%|▏ | 412/18688 [28:17<19:43:19, 3.88s/it] 2%|▏ | 413/18688 [28:22<20:28:12, 4.03s/it] 2%|▏ | 414/18688 [28:26<20:24:22, 4.02s/it] 2%|▏ | 415/18688 [28:30<20:24:02, 4.02s/it] 2%|▏ | 416/18688 [28:34<21:25:16, 4.22s/it] 2%|▏ | 417/18688 [28:38<20:33:36, 4.05s/it] 2%|▏ | 418/18688 [28:41<19:37:21, 3.87s/it] 2%|▏ | 419/18688 [28:46<20:03:17, 3.95s/it] 2%|▏ | 420/18688 [28:49<19:43:35, 3.89s/it] {'loss': '1.083', 'grad_norm': '1.594', 'learning_rate': '2.793e-05', 'epoch': '0.02247', 'num_input_tokens_seen': 1385008, 'train_runtime': '1730', 'train_tokens_per_second': '800.7'}
2%|▏ | 420/18688 [28:49<19:43:35, 3.89s/it] 2%|▏ | 421/18688 [28:54<20:54:19, 4.12s/it] 2%|▏ | 422/18688 [28:59<22:18:18, 4.40s/it] 2%|▏ | 423/18688 [29:03<21:14:20, 4.19s/it] 2%|▏ | 424/18688 [29:07<20:51:58, 4.11s/it] 2%|▏ | 425/18688 [29:10<20:30:20, 4.04s/it] 2%|▏ | 426/18688 [29:14<20:05:27, 3.96s/it] 2%|▏ | 427/18688 [29:18<20:13:15, 3.99s/it] 2%|▏ | 428/18688 [29:24<22:08:02, 4.36s/it] 2%|▏ | 429/18688 [29:28<21:53:59, 4.32s/it] 2%|▏ | 430/18688 [29:31<20:35:00, 4.06s/it] {'loss': '1.038', 'grad_norm': '1.541', 'learning_rate': '2.86e-05', 'epoch': '0.02301', 'num_input_tokens_seen': 1420952, 'train_runtime': '1772', 'train_tokens_per_second': '802'}
2%|▏ | 430/18688 [29:31<20:35:00, 4.06s/it] 2%|▏ | 431/18688 [29:35<20:10:42, 3.98s/it] 2%|▏ | 432/18688 [29:39<20:19:40, 4.01s/it] 2%|▏ | 433/18688 [29:43<20:17:35, 4.00s/it] 2%|▏ | 434/18688 [29:47<20:45:46, 4.09s/it] 2%|▏ | 435/18688 [29:51<20:28:36, 4.04s/it] 2%|▏ | 436/18688 [29:55<19:48:37, 3.91s/it] 2%|▏ | 437/18688 [29:59<20:26:32, 4.03s/it] 2%|▏ | 438/18688 [30:03<19:50:40, 3.91s/it] 2%|▏ | 439/18688 [30:07<19:26:52, 3.84s/it] 2%|▏ | 440/18688 [30:11<19:46:37, 3.90s/it] {'loss': '1.083', 'grad_norm': '1.349', 'learning_rate': '2.927e-05', 'epoch': '0.02355', 'num_input_tokens_seen': 1452280, 'train_runtime': '1811', 'train_tokens_per_second': '801.9'}
2%|▏ | 440/18688 [30:11<19:46:37, 3.90s/it] 2%|▏ | 441/18688 [30:14<19:43:49, 3.89s/it] 2%|▏ | 442/18688 [30:19<19:59:55, 3.95s/it] 2%|▏ | 443/18688 [30:23<20:13:39, 3.99s/it] 2%|▏ | 444/18688 [30:28<22:21:11, 4.41s/it] 2%|▏ | 445/18688 [30:33<23:24:07, 4.62s/it] 2%|▏ | 446/18688 [30:37<22:03:14, 4.35s/it] 2%|▏ | 447/18688 [30:40<20:39:41, 4.08s/it] 2%|▏ | 448/18688 [30:44<20:10:58, 3.98s/it] 2%|▏ | 449/18688 [30:48<20:37:48, 4.07s/it] 2%|▏ | 450/18688 [30:52<20:41:09, 4.08s/it] {'loss': '1.042', 'grad_norm': '1.582', 'learning_rate': '2.993e-05', 'epoch': '0.02408', 'num_input_tokens_seen': 1487680, 'train_runtime': '1853', 'train_tokens_per_second': '802.9'}
2%|▏ | 450/18688 [30:52<20:41:09, 4.08s/it] 2%|▏ | 451/18688 [30:56<19:44:15, 3.90s/it] 2%|▏ | 452/18688 [31:00<20:14:46, 4.00s/it] 2%|▏ | 453/18688 [31:04<19:49:13, 3.91s/it] 2%|▏ | 454/18688 [31:09<21:09:19, 4.18s/it] 2%|▏ | 455/18688 [31:14<22:50:27, 4.51s/it] 2%|▏ | 456/18688 [31:18<22:24:04, 4.42s/it] 2%|▏ | 457/18688 [31:22<21:08:32, 4.17s/it] 2%|▏ | 458/18688 [31:26<21:15:46, 4.20s/it] 2%|▏ | 459/18688 [31:30<20:20:53, 4.02s/it] 2%|▏ | 460/18688 [31:33<20:05:44, 3.97s/it] {'loss': '0.9903', 'grad_norm': '1.518', 'learning_rate': '3.06e-05', 'epoch': '0.02462', 'num_input_tokens_seen': 1521584, 'train_runtime': '1894', 'train_tokens_per_second': '803.4'}
2%|▏ | 460/18688 [31:33<20:05:44, 3.97s/it] 2%|▏ | 461/18688 [31:37<20:14:08, 4.00s/it] 2%|▏ | 462/18688 [31:41<19:43:15, 3.90s/it] 2%|▏ | 463/18688 [31:45<19:56:25, 3.94s/it] 2%|▏ | 464/18688 [31:49<19:23:08, 3.83s/it] 2%|▏ | 465/18688 [31:52<19:10:30, 3.79s/it] 2%|▏ | 466/18688 [31:57<19:57:37, 3.94s/it] 2%|▏ | 467/18688 [32:01<19:50:39, 3.92s/it] 3%|▎ | 468/18688 [32:05<20:44:11, 4.10s/it] 3%|▎ | 469/18688 [32:09<19:46:45, 3.91s/it] 3%|▎ | 470/18688 [32:14<21:21:59, 4.22s/it] {'loss': '1.049', 'grad_norm': '1.247', 'learning_rate': '3.127e-05', 'epoch': '0.02515', 'num_input_tokens_seen': 1553928, 'train_runtime': '1934', 'train_tokens_per_second': '803.5'}
3%|▎ | 470/18688 [32:14<21:21:59, 4.22s/it] 3%|▎ | 471/18688 [32:18<21:53:32, 4.33s/it] 3%|▎ | 472/18688 [32:22<21:51:13, 4.32s/it] 3%|▎ | 473/18688 [32:26<21:24:56, 4.23s/it] 3%|▎ | 474/18688 [32:30<20:57:05, 4.14s/it] 3%|▎ | 475/18688 [32:34<20:06:13, 3.97s/it] 3%|▎ | 476/18688 [32:38<20:24:33, 4.03s/it] 3%|▎ | 477/18688 [32:42<20:14:57, 4.00s/it] 3%|▎ | 478/18688 [32:46<20:10:46, 3.99s/it] 3%|▎ | 479/18688 [32:51<21:10:53, 4.19s/it] 3%|▎ | 480/18688 [32:55<20:41:31, 4.09s/it] {'loss': '1.122', 'grad_norm': '1.555', 'learning_rate': '3.193e-05', 'epoch': '0.02569', 'num_input_tokens_seen': 1588296, 'train_runtime': '1975', 'train_tokens_per_second': '804.2'}
3%|▎ | 480/18688 [32:55<20:41:31, 4.09s/it] 3%|▎ | 481/18688 [32:59<21:42:55, 4.29s/it] 3%|▎ | 482/18688 [33:03<20:56:46, 4.14s/it] 3%|▎ | 483/18688 [33:07<20:14:17, 4.00s/it] 3%|▎ | 484/18688 [33:10<19:26:33, 3.84s/it] 3%|▎ | 485/18688 [33:15<20:19:24, 4.02s/it] 3%|▎ | 486/18688 [33:18<19:27:04, 3.85s/it] 3%|▎ | 487/18688 [33:22<19:51:53, 3.93s/it] 3%|▎ | 488/18688 [33:26<20:00:04, 3.96s/it] 3%|▎ | 489/18688 [33:31<20:34:13, 4.07s/it] 3%|▎ | 490/18688 [33:34<19:51:52, 3.93s/it] {'loss': '1.02', 'grad_norm': '1.702', 'learning_rate': '3.26e-05', 'epoch': '0.02622', 'num_input_tokens_seen': 1620376, 'train_runtime': '2015', 'train_tokens_per_second': '804.3'}
3%|▎ | 490/18688 [33:34<19:51:52, 3.93s/it] 3%|▎ | 491/18688 [33:38<19:10:33, 3.79s/it] 3%|▎ | 492/18688 [33:42<20:00:33, 3.96s/it] 3%|▎ | 493/18688 [33:46<20:00:27, 3.96s/it] 3%|▎ | 494/18688 [33:50<20:39:07, 4.09s/it] 3%|▎ | 495/18688 [33:55<20:52:41, 4.13s/it] 3%|▎ | 496/18688 [33:59<20:33:13, 4.07s/it] 3%|▎ | 497/18688 [34:02<19:46:58, 3.92s/it] 3%|▎ | 498/18688 [34:06<19:16:56, 3.82s/it] 3%|▎ | 499/18688 [34:10<19:36:13, 3.88s/it] 3%|▎ | 500/18688 [34:15<22:06:16, 4.38s/it] {'loss': '1.025', 'grad_norm': '1.184', 'learning_rate': '3.327e-05', 'epoch': '0.02676', 'num_input_tokens_seen': 1653200, 'train_runtime': '2056', 'train_tokens_per_second': '804.2'}
3%|▎ | 500/18688 [34:15<22:06:16, 4.38s/it] 3%|▎ | 501/18688 [34:19<21:30:00, 4.26s/it] 3%|▎ | 502/18688 [34:23<21:03:26, 4.17s/it] 3%|▎ | 503/18688 [34:27<20:11:35, 4.00s/it] 3%|▎ | 504/18688 [34:30<19:34:51, 3.88s/it] 3%|▎ | 505/18688 [34:34<19:55:52, 3.95s/it] 3%|▎ | 506/18688 [34:39<20:31:28, 4.06s/it] 3%|▎ | 507/18688 [34:42<19:43:47, 3.91s/it] 3%|▎ | 508/18688 [34:46<19:41:37, 3.90s/it] 3%|▎ | 509/18688 [34:51<20:27:20, 4.05s/it] 3%|▎ | 510/18688 [34:55<20:27:01, 4.05s/it] {'loss': '1.076', 'grad_norm': '1.553', 'learning_rate': '3.393e-05', 'epoch': '0.02729', 'num_input_tokens_seen': 1682936, 'train_runtime': '2095', 'train_tokens_per_second': '803.2'}
3%|▎ | 510/18688 [34:55<20:27:01, 4.05s/it] 3%|▎ | 511/18688 [34:58<19:32:58, 3.87s/it] 3%|▎ | 512/18688 [35:04<22:07:28, 4.38s/it] 3%|▎ | 513/18688 [35:08<22:20:11, 4.42s/it] 3%|▎ | 514/18688 [35:12<21:52:26, 4.33s/it] 3%|▎ | 515/18688 [35:16<21:01:36, 4.17s/it] 3%|▎ | 516/18688 [35:20<21:02:06, 4.17s/it] 3%|▎ | 517/18688 [35:24<20:44:36, 4.11s/it] 3%|▎ | 518/18688 [35:29<21:17:45, 4.22s/it] 3%|▎ | 519/18688 [35:33<21:48:46, 4.32s/it] 3%|▎ | 520/18688 [35:38<21:46:56, 4.32s/it] {'loss': '1.024', 'grad_norm': '1.43', 'learning_rate': '3.46e-05', 'epoch': '0.02783', 'num_input_tokens_seen': 1718448, 'train_runtime': '2138', 'train_tokens_per_second': '803.7'}
3%|▎ | 520/18688 [35:38<21:46:56, 4.32s/it] 3%|▎ | 521/18688 [35:42<21:39:00, 4.29s/it] 3%|▎ | 522/18688 [35:47<22:45:46, 4.51s/it] 3%|▎ | 523/18688 [35:51<21:42:48, 4.30s/it] 3%|▎ | 524/18688 [35:55<21:17:50, 4.22s/it] 3%|▎ | 525/18688 [35:59<21:35:28, 4.28s/it] 3%|▎ | 526/18688 [36:03<20:52:56, 4.14s/it] 3%|▎ | 527/18688 [36:06<19:54:05, 3.95s/it] 3%|▎ | 528/18688 [36:10<19:55:11, 3.95s/it] 3%|▎ | 529/18688 [36:14<19:39:24, 3.90s/it] 3%|▎ | 530/18688 [36:19<20:27:31, 4.06s/it] {'loss': '0.9793', 'grad_norm': '1.31', 'learning_rate': '3.527e-05', 'epoch': '0.02836', 'num_input_tokens_seen': 1750696, 'train_runtime': '2179', 'train_tokens_per_second': '803.4'}
3%|▎ | 530/18688 [36:19<20:27:31, 4.06s/it] 3%|▎ | 531/18688 [36:23<20:22:40, 4.04s/it] 3%|▎ | 532/18688 [36:27<21:27:19, 4.25s/it] 3%|▎ | 533/18688 [36:31<20:38:35, 4.09s/it] 3%|▎ | 534/18688 [36:35<20:01:12, 3.97s/it] 3%|▎ | 535/18688 [36:39<20:16:47, 4.02s/it] 3%|▎ | 536/18688 [36:42<19:35:10, 3.88s/it] 3%|▎ | 537/18688 [36:47<20:51:32, 4.14s/it] 3%|▎ | 538/18688 [36:51<20:12:59, 4.01s/it] 3%|▎ | 539/18688 [36:54<19:25:37, 3.85s/it] 3%|▎ | 540/18688 [36:58<18:58:31, 3.76s/it] {'loss': '1.08', 'grad_norm': '1.27', 'learning_rate': '3.593e-05', 'epoch': '0.0289', 'num_input_tokens_seen': 1780496, 'train_runtime': '2218', 'train_tokens_per_second': '802.6'}
3%|▎ | 540/18688 [36:58<18:58:31, 3.76s/it] 3%|▎ | 541/18688 [37:02<19:52:57, 3.94s/it] 3%|▎ | 542/18688 [37:06<19:14:23, 3.82s/it] 3%|▎ | 543/18688 [37:10<19:21:11, 3.84s/it] 3%|▎ | 544/18688 [37:14<19:48:04, 3.93s/it] 3%|▎ | 545/18688 [37:19<21:23:29, 4.24s/it] 3%|▎ | 546/18688 [37:23<21:09:54, 4.20s/it] 3%|▎ | 547/18688 [37:27<21:24:28, 4.25s/it] 3%|▎ | 548/18688 [37:31<20:49:39, 4.13s/it] 3%|▎ | 549/18688 [37:35<19:56:46, 3.96s/it] 3%|▎ | 550/18688 [37:39<21:03:23, 4.18s/it] {'loss': '0.9832', 'grad_norm': '1.354', 'learning_rate': '3.66e-05', 'epoch': '0.02943', 'num_input_tokens_seen': 1814136, 'train_runtime': '2260', 'train_tokens_per_second': '802.7'}
3%|▎ | 550/18688 [37:39<21:03:23, 4.18s/it] 3%|▎ | 551/18688 [37:43<20:15:52, 4.02s/it] 3%|▎ | 552/18688 [37:47<20:36:23, 4.09s/it] 3%|▎ | 553/18688 [37:51<19:40:55, 3.91s/it] 3%|▎ | 554/18688 [37:55<20:15:52, 4.02s/it] 3%|▎ | 555/18688 [37:59<19:42:00, 3.91s/it] 3%|▎ | 556/18688 [38:03<20:02:31, 3.98s/it] 3%|▎ | 557/18688 [38:07<20:04:56, 3.99s/it] 3%|▎ | 558/18688 [38:11<20:08:18, 4.00s/it] 3%|▎ | 559/18688 [38:15<20:27:43, 4.06s/it] 3%|▎ | 560/18688 [38:19<19:33:03, 3.88s/it] {'loss': '0.9212', 'grad_norm': '1.275', 'learning_rate': '3.727e-05', 'epoch': '0.02997', 'num_input_tokens_seen': 1844456, 'train_runtime': '2299', 'train_tokens_per_second': '802.3'}
3%|▎ | 560/18688 [38:19<19:33:03, 3.88s/it] 3%|▎ | 561/18688 [38:23<20:25:32, 4.06s/it] 3%|▎ | 562/18688 [38:28<21:13:45, 4.22s/it] 3%|▎ | 563/18688 [38:31<20:05:50, 3.99s/it] 3%|▎ | 564/18688 [38:35<20:28:39, 4.07s/it] 3%|▎ | 565/18688 [38:40<21:27:33, 4.26s/it] 3%|▎ | 566/18688 [38:45<21:51:12, 4.34s/it] 3%|▎ | 567/18688 [38:48<20:41:40, 4.11s/it] 3%|▎ | 568/18688 [38:52<20:45:47, 4.13s/it] 3%|▎ | 569/18688 [38:56<20:04:18, 3.99s/it] 3%|▎ | 570/18688 [39:00<19:55:45, 3.96s/it] {'loss': '1.047', 'grad_norm': '1.204', 'learning_rate': '3.793e-05', 'epoch': '0.0305', 'num_input_tokens_seen': 1877632, 'train_runtime': '2340', 'train_tokens_per_second': '802.3'}
3%|▎ | 570/18688 [39:00<19:55:45, 3.96s/it] 3%|▎ | 571/18688 [39:04<20:49:22, 4.14s/it] 3%|▎ | 572/18688 [39:08<20:18:43, 4.04s/it] 3%|▎ | 573/18688 [39:13<21:17:38, 4.23s/it] 3%|▎ | 574/18688 [39:17<20:57:17, 4.16s/it] 3%|▎ | 575/18688 [39:21<20:03:05, 3.99s/it] 3%|▎ | 576/18688 [39:25<20:22:17, 4.05s/it] 3%|▎ | 577/18688 [39:29<21:12:11, 4.21s/it] 3%|▎ | 578/18688 [39:33<20:48:57, 4.14s/it] 3%|▎ | 579/18688 [39:37<19:49:14, 3.94s/it] 3%|▎ | 580/18688 [39:42<21:17:06, 4.23s/it] {'loss': '0.9657', 'grad_norm': '1.349', 'learning_rate': '3.86e-05', 'epoch': '0.03104', 'num_input_tokens_seen': 1911192, 'train_runtime': '2382', 'train_tokens_per_second': '802.3'}
3%|▎ | 580/18688 [39:42<21:17:06, 4.23s/it] 3%|▎ | 581/18688 [39:46<21:14:32, 4.22s/it] 3%|▎ | 582/18688 [39:50<20:44:58, 4.13s/it] 3%|▎ | 583/18688 [39:54<20:41:13, 4.11s/it] 3%|▎ | 584/18688 [39:58<20:45:06, 4.13s/it] 3%|▎ | 585/18688 [40:02<20:26:49, 4.07s/it] 3%|▎ | 586/18688 [40:07<22:24:28, 4.46s/it] 3%|▎ | 587/18688 [40:11<21:50:32, 4.34s/it] 3%|▎ | 588/18688 [40:16<21:45:45, 4.33s/it] 3%|▎ | 589/18688 [40:20<22:01:59, 4.38s/it] 3%|▎ | 590/18688 [40:24<21:21:39, 4.25s/it] {'loss': '1.005', 'grad_norm': '1.318', 'learning_rate': '3.927e-05', 'epoch': '0.03157', 'num_input_tokens_seen': 1943152, 'train_runtime': '2425', 'train_tokens_per_second': '801.4'}
3%|▎ | 590/18688 [40:24<21:21:39, 4.25s/it] 3%|▎ | 591/18688 [40:28<20:21:31, 4.05s/it] 3%|▎ | 592/18688 [40:32<20:33:23, 4.09s/it] 3%|▎ | 593/18688 [40:36<20:16:15, 4.03s/it] 3%|▎ | 594/18688 [40:40<20:22:09, 4.05s/it] 3%|▎ | 595/18688 [40:44<20:44:26, 4.13s/it] 3%|▎ | 596/18688 [40:48<20:48:58, 4.14s/it] 3%|▎ | 597/18688 [40:52<19:51:46, 3.95s/it] 3%|▎ | 598/18688 [40:56<20:13:24, 4.02s/it] 3%|▎ | 599/18688 [41:01<21:50:41, 4.35s/it] 3%|▎ | 600/18688 [41:05<21:36:57, 4.30s/it] {'loss': '1.008', 'grad_norm': '1.275', 'learning_rate': '3.993e-05', 'epoch': '0.03211', 'num_input_tokens_seen': 1976328, 'train_runtime': '2466', 'train_tokens_per_second': '801.5'}
3%|▎ | 600/18688 [41:05<21:36:57, 4.30s/it] 3%|▎ | 601/18688 [41:09<21:12:35, 4.22s/it] 3%|▎ | 602/18688 [41:14<21:16:55, 4.24s/it] 3%|▎ | 603/18688 [41:17<20:15:57, 4.03s/it] 3%|▎ | 604/18688 [41:21<20:07:00, 4.00s/it] 3%|▎ | 605/18688 [41:26<21:34:26, 4.30s/it] 3%|▎ | 606/18688 [41:30<20:59:20, 4.18s/it] 3%|▎ | 607/18688 [41:34<20:51:47, 4.15s/it] 3%|▎ | 608/18688 [41:38<20:52:10, 4.16s/it] 3%|▎ | 609/18688 [41:43<21:07:20, 4.21s/it] 3%|▎ | 610/18688 [41:46<20:04:16, 4.00s/it] {'loss': '0.9848', 'grad_norm': '1.365', 'learning_rate': '4.06e-05', 'epoch': '0.03264', 'num_input_tokens_seen': 2008480, 'train_runtime': '2507', 'train_tokens_per_second': '801.3'}
3%|▎ | 610/18688 [41:46<20:04:16, 4.00s/it] 3%|▎ | 611/18688 [41:50<19:42:27, 3.92s/it] 3%|▎ | 612/18688 [41:55<21:04:12, 4.20s/it] 3%|▎ | 613/18688 [41:59<21:23:23, 4.26s/it] 3%|▎ | 614/18688 [42:03<20:42:37, 4.13s/it] 3%|▎ | 615/18688 [42:06<19:45:23, 3.94s/it] 3%|▎ | 616/18688 [42:12<22:48:59, 4.55s/it] 3%|▎ | 617/18688 [42:17<23:23:58, 4.66s/it] 3%|▎ | 618/18688 [42:21<21:50:15, 4.35s/it] 3%|▎ | 619/18688 [42:25<21:30:25, 4.28s/it] 3%|▎ | 620/18688 [42:29<20:18:04, 4.04s/it] {'loss': '1.014', 'grad_norm': '1.592', 'learning_rate': '4.127e-05', 'epoch': '0.03318', 'num_input_tokens_seen': 2043576, 'train_runtime': '2549', 'train_tokens_per_second': '801.7'}
3%|▎ | 620/18688 [42:29<20:18:04, 4.04s/it] 3%|▎ | 621/18688 [42:33<20:44:28, 4.13s/it] 3%|▎ | 622/18688 [42:36<19:51:10, 3.96s/it] 3%|▎ | 623/18688 [42:41<20:55:23, 4.17s/it] 3%|▎ | 624/18688 [42:45<20:40:26, 4.12s/it] 3%|▎ | 625/18688 [42:49<19:48:48, 3.95s/it] 3%|▎ | 626/18688 [42:53<19:53:06, 3.96s/it] 3%|▎ | 627/18688 [42:57<20:22:31, 4.06s/it] 3%|▎ | 628/18688 [43:02<21:54:50, 4.37s/it] 3%|▎ | 629/18688 [43:06<20:42:18, 4.13s/it] 3%|▎ | 630/18688 [43:11<21:59:01, 4.38s/it] {'loss': '1.031', 'grad_norm': '1.199', 'learning_rate': '4.193e-05', 'epoch': '0.03371', 'num_input_tokens_seen': 2077600, 'train_runtime': '2591', 'train_tokens_per_second': '801.8'}
3%|▎ | 630/18688 [43:11<21:59:01, 4.38s/it] 3%|▎ | 631/18688 [43:15<22:02:42, 4.40s/it] 3%|▎ | 632/18688 [43:19<21:22:07, 4.26s/it] 3%|▎ | 633/18688 [43:23<20:20:57, 4.06s/it] 3%|▎ | 634/18688 [43:27<20:29:24, 4.09s/it] 3%|▎ | 635/18688 [43:31<20:24:43, 4.07s/it] 3%|▎ | 636/18688 [43:35<20:17:15, 4.05s/it] 3%|▎ | 637/18688 [43:39<20:56:20, 4.18s/it] 3%|▎ | 638/18688 [43:43<21:06:20, 4.21s/it] 3%|▎ | 639/18688 [43:48<21:10:53, 4.22s/it] 3%|▎ | 640/18688 [43:52<20:29:12, 4.09s/it] {'loss': '1.071', 'grad_norm': '1.296', 'learning_rate': '4.26e-05', 'epoch': '0.03425', 'num_input_tokens_seen': 2110816, 'train_runtime': '2632', 'train_tokens_per_second': '802'}
3%|▎ | 640/18688 [43:52<20:29:12, 4.09s/it] 3%|▎ | 641/18688 [43:56<20:59:12, 4.19s/it] 3%|▎ | 642/18688 [44:01<21:34:25, 4.30s/it] 3%|▎ | 643/18688 [44:04<21:00:22, 4.19s/it] 3%|▎ | 644/18688 [44:08<20:32:12, 4.10s/it] 3%|▎ | 645/18688 [44:13<20:59:02, 4.19s/it] 3%|▎ | 646/18688 [44:18<22:15:53, 4.44s/it] 3%|▎ | 647/18688 [44:22<21:15:37, 4.24s/it] 3%|▎ | 648/18688 [44:26<21:53:53, 4.37s/it] 3%|▎ | 649/18688 [44:30<21:42:12, 4.33s/it] 3%|▎ | 650/18688 [44:35<21:28:12, 4.29s/it] {'loss': '0.9838', 'grad_norm': '1.082', 'learning_rate': '4.327e-05', 'epoch': '0.03478', 'num_input_tokens_seen': 2146864, 'train_runtime': '2675', 'train_tokens_per_second': '802.5'}
3%|▎ | 650/18688 [44:35<21:28:12, 4.29s/it] 3%|▎ | 651/18688 [44:39<21:42:57, 4.33s/it] 3%|▎ | 652/18688 [44:44<22:27:17, 4.48s/it] 3%|▎ | 653/18688 [44:48<21:13:34, 4.24s/it] 3%|▎ | 654/18688 [44:51<20:37:32, 4.12s/it] 4%|▎ | 655/18688 [44:55<20:06:29, 4.01s/it] 4%|▎ | 656/18688 [44:59<20:18:51, 4.06s/it] 4%|▎ | 657/18688 [45:04<21:09:25, 4.22s/it] 4%|▎ | 658/18688 [45:07<20:01:43, 4.00s/it] 4%|▎ | 659/18688 [45:11<19:42:17, 3.93s/it] 4%|▎ | 660/18688 [45:15<20:00:20, 3.99s/it] {'loss': '1.065', 'grad_norm': '1.223', 'learning_rate': '4.393e-05', 'epoch': '0.03532', 'num_input_tokens_seen': 2179224, 'train_runtime': '2716', 'train_tokens_per_second': '802.4'}
4%|▎ | 660/18688 [45:15<20:00:20, 3.99s/it] 4%|▎ | 661/18688 [45:19<19:27:59, 3.89s/it] 4%|▎ | 662/18688 [45:23<19:45:14, 3.95s/it] 4%|▎ | 663/18688 [45:27<19:25:13, 3.88s/it] 4%|▎ | 664/18688 [45:32<21:50:13, 4.36s/it] 4%|▎ | 665/18688 [45:36<21:12:42, 4.24s/it] 4%|▎ | 666/18688 [45:40<20:26:18, 4.08s/it] 4%|▎ | 667/18688 [45:44<20:21:25, 4.07s/it] 4%|▎ | 668/18688 [45:47<19:29:19, 3.89s/it] 4%|▎ | 669/18688 [45:51<19:28:30, 3.89s/it] 4%|▎ | 670/18688 [45:56<19:55:03, 3.98s/it] {'loss': '1.075', 'grad_norm': '1.492', 'learning_rate': '4.46e-05', 'epoch': '0.03585', 'num_input_tokens_seen': 2211824, 'train_runtime': '2756', 'train_tokens_per_second': '802.5'}
4%|▎ | 670/18688 [45:56<19:55:03, 3.98s/it] 4%|▎ | 671/18688 [46:00<20:39:45, 4.13s/it] 4%|▎ | 672/18688 [46:05<21:20:55, 4.27s/it] 4%|▎ | 673/18688 [46:09<22:16:01, 4.45s/it] 4%|▎ | 674/18688 [46:14<22:19:06, 4.46s/it] 4%|▎ | 675/18688 [46:18<21:55:38, 4.38s/it] 4%|▎ | 676/18688 [46:22<21:48:47, 4.36s/it] 4%|▎ | 677/18688 [46:27<21:29:54, 4.30s/it] 4%|▎ | 678/18688 [46:31<21:08:44, 4.23s/it] 4%|▎ | 679/18688 [46:35<21:16:32, 4.25s/it] 4%|▎ | 680/18688 [46:39<20:33:39, 4.11s/it] {'loss': '1.053', 'grad_norm': '1.577', 'learning_rate': '4.527e-05', 'epoch': '0.03639', 'num_input_tokens_seen': 2246768, 'train_runtime': '2799', 'train_tokens_per_second': '802.6'}
4%|▎ | 680/18688 [46:39<20:33:39, 4.11s/it] 4%|▎ | 681/18688 [46:43<20:22:43, 4.07s/it] 4%|▎ | 682/18688 [46:47<20:51:13, 4.17s/it] 4%|▎ | 683/18688 [46:51<20:15:55, 4.05s/it] 4%|▎ | 684/18688 [46:55<20:48:03, 4.16s/it] 4%|▎ | 685/18688 [46:59<20:24:02, 4.08s/it] 4%|▎ | 686/18688 [47:03<19:36:56, 3.92s/it] 4%|▎ | 687/18688 [47:07<20:04:26, 4.01s/it] 4%|▎ | 688/18688 [47:11<20:25:40, 4.09s/it] 4%|▎ | 689/18688 [47:15<20:40:35, 4.14s/it] 4%|▎ | 690/18688 [47:20<20:59:29, 4.20s/it] {'loss': '1.03', 'grad_norm': '1.131', 'learning_rate': '4.593e-05', 'epoch': '0.03692', 'num_input_tokens_seen': 2280616, 'train_runtime': '2840', 'train_tokens_per_second': '802.9'}
4%|▎ | 690/18688 [47:20<20:59:29, 4.20s/it] 4%|▎ | 691/18688 [47:24<20:35:02, 4.12s/it] 4%|▎ | 692/18688 [47:28<21:11:26, 4.24s/it] 4%|▎ | 693/18688 [47:32<20:45:15, 4.15s/it] 4%|▎ | 694/18688 [47:36<20:10:36, 4.04s/it] 4%|▎ | 695/18688 [47:40<20:19:30, 4.07s/it] 4%|▎ | 696/18688 [47:45<21:25:31, 4.29s/it] 4%|▎ | 697/18688 [47:49<20:46:47, 4.16s/it] 4%|▎ | 698/18688 [47:52<19:55:01, 3.99s/it] 4%|▎ | 699/18688 [47:56<19:57:27, 3.99s/it] 4%|▎ | 700/18688 [48:01<20:34:52, 4.12s/it] {'loss': '1.004', 'grad_norm': '0.93', 'learning_rate': '4.66e-05', 'epoch': '0.03746', 'num_input_tokens_seen': 2314848, 'train_runtime': '2881', 'train_tokens_per_second': '803.4'}
4%|▎ | 700/18688 [48:01<20:34:52, 4.12s/it] 4%|▍ | 701/18688 [48:05<20:18:35, 4.06s/it] 4%|▍ | 702/18688 [48:09<19:52:48, 3.98s/it] 4%|▍ | 703/18688 [48:13<20:04:30, 4.02s/it] 4%|▍ | 704/18688 [48:17<20:20:46, 4.07s/it] 4%|▍ | 705/18688 [48:21<19:46:34, 3.96s/it] 4%|▍ | 706/18688 [48:25<19:56:18, 3.99s/it] 4%|▍ | 707/18688 [48:29<20:33:28, 4.12s/it] 4%|▍ | 708/18688 [48:33<20:12:44, 4.05s/it] 4%|▍ | 709/18688 [48:37<20:30:37, 4.11s/it] 4%|▍ | 710/18688 [48:41<20:25:13, 4.09s/it] {'loss': '1.101', 'grad_norm': '1.213', 'learning_rate': '4.727e-05', 'epoch': '0.03799', 'num_input_tokens_seen': 2347584, 'train_runtime': '2922', 'train_tokens_per_second': '803.5'}
4%|▍ | 710/18688 [48:41<20:25:13, 4.09s/it] 4%|▍ | 711/18688 [48:45<19:26:49, 3.89s/it] 4%|▍ | 712/18688 [48:48<18:57:29, 3.80s/it] 4%|▍ | 713/18688 [48:52<19:16:53, 3.86s/it] 4%|▍ | 714/18688 [48:56<19:28:59, 3.90s/it] 4%|▍ | 715/18688 [49:00<19:10:40, 3.84s/it] 4%|▍ | 716/18688 [49:04<19:57:38, 4.00s/it] 4%|▍ | 717/18688 [49:09<20:24:19, 4.09s/it] 4%|▍ | 718/18688 [49:12<19:45:35, 3.96s/it] 4%|▍ | 719/18688 [49:16<19:25:15, 3.89s/it] 4%|▍ | 720/18688 [49:21<20:37:51, 4.13s/it] {'loss': '1.06', 'grad_norm': '1.193', 'learning_rate': '4.793e-05', 'epoch': '0.03853', 'num_input_tokens_seen': 2379992, 'train_runtime': '2961', 'train_tokens_per_second': '803.7'}
4%|▍ | 720/18688 [49:21<20:37:51, 4.13s/it] 4%|▍ | 721/18688 [49:24<20:00:48, 4.01s/it] 4%|▍ | 722/18688 [49:29<20:25:59, 4.09s/it] 4%|▍ | 723/18688 [49:33<20:39:25, 4.14s/it] 4%|▍ | 724/18688 [49:38<21:30:01, 4.31s/it] 4%|▍ | 725/18688 [49:42<21:38:34, 4.34s/it] 4%|▍ | 726/18688 [49:46<20:23:20, 4.09s/it] 4%|▍ | 727/18688 [49:50<20:30:53, 4.11s/it] 4%|▍ | 728/18688 [49:54<20:38:29, 4.14s/it] 4%|▍ | 729/18688 [49:58<20:32:40, 4.12s/it] 4%|▍ | 730/18688 [50:01<19:34:35, 3.92s/it] {'loss': '0.9907', 'grad_norm': '1.355', 'learning_rate': '4.86e-05', 'epoch': '0.03906', 'num_input_tokens_seen': 2413688, 'train_runtime': '3002', 'train_tokens_per_second': '804'}
4%|▍ | 730/18688 [50:01<19:34:35, 3.92s/it] 4%|▍ | 731/18688 [50:06<20:11:33, 4.05s/it] 4%|▍ | 732/18688 [50:11<21:52:52, 4.39s/it] 4%|▍ | 733/18688 [50:15<20:40:58, 4.15s/it] 4%|▍ | 734/18688 [50:19<20:58:58, 4.21s/it] 4%|▍ | 735/18688 [50:23<20:32:36, 4.12s/it] 4%|▍ | 736/18688 [50:27<19:58:25, 4.01s/it] 4%|▍ | 737/18688 [50:31<20:29:34, 4.11s/it] 4%|▍ | 738/18688 [50:34<19:27:16, 3.90s/it] 4%|▍ | 739/18688 [50:38<18:57:41, 3.80s/it] 4%|▍ | 740/18688 [50:42<19:14:06, 3.86s/it] {'loss': '1.013', 'grad_norm': '1.226', 'learning_rate': '4.927e-05', 'epoch': '0.0396', 'num_input_tokens_seen': 2446864, 'train_runtime': '3042', 'train_tokens_per_second': '804.3'}
4%|▍ | 740/18688 [50:42<19:14:06, 3.86s/it] 4%|▍ | 741/18688 [50:45<18:53:11, 3.79s/it] 4%|▍ | 742/18688 [50:49<18:39:26, 3.74s/it] 4%|▍ | 743/18688 [50:53<19:03:56, 3.82s/it] 4%|▍ | 744/18688 [50:57<18:32:18, 3.72s/it] 4%|▍ | 745/18688 [51:00<18:12:51, 3.65s/it] 4%|▍ | 746/18688 [51:04<18:22:42, 3.69s/it] 4%|▍ | 747/18688 [51:07<18:02:39, 3.62s/it] 4%|▍ | 748/18688 [51:11<18:03:36, 3.62s/it] 4%|▍ | 749/18688 [51:16<20:14:52, 4.06s/it] 4%|▍ | 750/18688 [51:20<20:46:38, 4.17s/it] {'loss': '0.9822', 'grad_norm': '1.001', 'learning_rate': '4.993e-05', 'epoch': '0.04013', 'num_input_tokens_seen': 2477248, 'train_runtime': '3081', 'train_tokens_per_second': '804'}
4%|▍ | 750/18688 [51:20<20:46:38, 4.17s/it] 4%|▍ | 751/18688 [51:24<19:49:52, 3.98s/it] 4%|▍ | 752/18688 [51:28<19:25:49, 3.90s/it] 4%|▍ | 753/18688 [51:32<19:55:38, 4.00s/it] 4%|▍ | 754/18688 [51:36<19:39:03, 3.94s/it] 4%|▍ | 755/18688 [51:41<20:51:01, 4.19s/it] 4%|▍ | 756/18688 [51:45<21:42:04, 4.36s/it] 4%|▍ | 757/18688 [51:49<20:30:29, 4.12s/it] 4%|▍ | 758/18688 [51:53<20:22:43, 4.09s/it] 4%|▍ | 759/18688 [51:57<20:47:50, 4.18s/it] 4%|▍ | 760/18688 [52:02<22:10:21, 4.45s/it] {'loss': '1.048', 'grad_norm': '1.327', 'learning_rate': '5.06e-05', 'epoch': '0.04067', 'num_input_tokens_seen': 2512440, 'train_runtime': '3123', 'train_tokens_per_second': '804.5'}
4%|▍ | 760/18688 [52:02<22:10:21, 4.45s/it] 4%|▍ | 761/18688 [52:08<23:13:17, 4.66s/it] 4%|▍ | 762/18688 [52:12<22:46:08, 4.57s/it] 4%|▍ | 763/18688 [52:16<22:31:27, 4.52s/it] 4%|▍ | 764/18688 [52:20<21:36:10, 4.34s/it] 4%|▍ | 765/18688 [52:24<20:15:18, 4.07s/it] 4%|▍ | 766/18688 [52:27<19:46:49, 3.97s/it] 4%|▍ | 767/18688 [52:31<19:22:48, 3.89s/it] 4%|▍ | 768/18688 [52:35<19:39:38, 3.95s/it] 4%|▍ | 769/18688 [52:39<19:51:40, 3.99s/it] 4%|▍ | 770/18688 [52:44<20:38:12, 4.15s/it] {'loss': '1.015', 'grad_norm': '1.229', 'learning_rate': '5.127e-05', 'epoch': '0.0412', 'num_input_tokens_seen': 2546856, 'train_runtime': '3164', 'train_tokens_per_second': '804.9'}
4%|▍ | 770/18688 [52:44<20:38:12, 4.15s/it] 4%|▍ | 771/18688 [52:48<20:23:20, 4.10s/it] 4%|▍ | 772/18688 [52:51<19:47:22, 3.98s/it] 4%|▍ | 773/18688 [52:56<20:01:30, 4.02s/it] 4%|▍ | 774/18688 [52:59<19:16:21, 3.87s/it] 4%|▍ | 775/18688 [53:03<18:38:28, 3.75s/it] 4%|▍ | 776/18688 [53:06<18:13:45, 3.66s/it] 4%|▍ | 777/18688 [53:11<20:18:45, 4.08s/it] 4%|▍ | 778/18688 [53:15<20:02:48, 4.03s/it] 4%|▍ | 779/18688 [53:19<20:13:02, 4.06s/it] 4%|▍ | 780/18688 [53:25<22:21:06, 4.49s/it] {'loss': '1.002', 'grad_norm': '1.464', 'learning_rate': '5.193e-05', 'epoch': '0.04174', 'num_input_tokens_seen': 2579896, 'train_runtime': '3205', 'train_tokens_per_second': '804.9'}
4%|▍ | 780/18688 [53:25<22:21:06, 4.49s/it] 4%|▍ | 781/18688 [53:29<22:15:13, 4.47s/it] 4%|▍ | 782/18688 [53:33<21:57:50, 4.42s/it] 4%|▍ | 783/18688 [53:37<20:58:50, 4.22s/it] 4%|▍ | 784/18688 [53:41<20:19:45, 4.09s/it] 4%|▍ | 785/18688 [53:45<20:38:35, 4.15s/it] 4%|▍ | 786/18688 [53:49<19:48:59, 3.98s/it] 4%|▍ | 787/18688 [53:53<20:04:48, 4.04s/it] 4%|▍ | 788/18688 [53:57<20:17:59, 4.08s/it] 4%|▍ | 789/18688 [54:01<20:44:26, 4.17s/it] 4%|▍ | 790/18688 [54:06<21:20:00, 4.29s/it] {'loss': '1.113', 'grad_norm': '1.126', 'learning_rate': '5.26e-05', 'epoch': '0.04227', 'num_input_tokens_seen': 2612736, 'train_runtime': '3247', 'train_tokens_per_second': '804.8'}
4%|▍ | 790/18688 [54:06<21:20:00, 4.29s/it] 4%|▍ | 791/18688 [54:10<21:16:34, 4.28s/it] 4%|▍ | 792/18688 [54:15<22:27:15, 4.52s/it] 4%|▍ | 793/18688 [54:20<22:37:50, 4.55s/it] 4%|▍ | 794/18688 [54:24<21:10:17, 4.26s/it] 4%|▍ | 795/18688 [54:28<21:12:48, 4.27s/it] 4%|▍ | 796/18688 [54:32<21:10:12, 4.26s/it] 4%|▍ | 797/18688 [54:36<20:44:24, 4.17s/it] 4%|▍ | 798/18688 [54:41<21:52:32, 4.40s/it] 4%|▍ | 799/18688 [54:45<21:26:51, 4.32s/it] 4%|▍ | 800/18688 [54:49<21:10:22, 4.26s/it] {'loss': '1.078', 'grad_norm': '1.192', 'learning_rate': '5.327e-05', 'epoch': '0.04281', 'num_input_tokens_seen': 2648032, 'train_runtime': '3290', 'train_tokens_per_second': '804.9'}
4%|▍ | 800/18688 [54:49<21:10:22, 4.26s/it] 4%|▍ | 801/18688 [54:53<20:04:00, 4.04s/it] 4%|▍ | 802/18688 [54:57<20:20:14, 4.09s/it] 4%|▍ | 803/18688 [55:01<20:00:57, 4.03s/it] 4%|▍ | 804/18688 [55:05<19:48:59, 3.99s/it] 4%|▍ | 805/18688 [55:09<20:27:11, 4.12s/it] 4%|▍ | 806/18688 [55:13<19:59:58, 4.03s/it] 4%|▍ | 807/18688 [55:18<21:17:34, 4.29s/it] 4%|▍ | 808/18688 [55:23<21:44:59, 4.38s/it] 4%|▍ | 809/18688 [55:26<20:57:00, 4.22s/it] 4%|▍ | 810/18688 [55:31<21:53:31, 4.41s/it] {'loss': '1.064', 'grad_norm': '0.8856', 'learning_rate': '5.393e-05', 'epoch': '0.04334', 'num_input_tokens_seen': 2683584, 'train_runtime': '3332', 'train_tokens_per_second': '805.5'}
4%|▍ | 810/18688 [55:31<21:53:31, 4.41s/it] 4%|▍ | 811/18688 [55:35<20:47:36, 4.19s/it] 4%|▍ | 812/18688 [55:40<21:26:40, 4.32s/it] 4%|▍ | 813/18688 [55:44<21:21:39, 4.30s/it] 4%|▍ | 814/18688 [55:47<20:26:30, 4.12s/it] 4%|▍ | 815/18688 [55:52<20:36:51, 4.15s/it] 4%|▍ | 816/18688 [55:55<20:01:11, 4.03s/it] 4%|▍ | 817/18688 [56:00<20:44:48, 4.18s/it] 4%|▍ | 818/18688 [56:05<21:34:31, 4.35s/it] 4%|▍ | 819/18688 [56:09<20:47:28, 4.19s/it] 4%|▍ | 820/18688 [56:13<20:35:43, 4.15s/it] {'loss': '1.012', 'grad_norm': '1.636', 'learning_rate': '5.46e-05', 'epoch': '0.04388', 'num_input_tokens_seen': 2716960, 'train_runtime': '3373', 'train_tokens_per_second': '805.5'}
4%|▍ | 820/18688 [56:13<20:35:43, 4.15s/it] 4%|▍ | 821/18688 [56:16<19:53:32, 4.01s/it] 4%|▍ | 822/18688 [56:21<20:14:39, 4.08s/it] 4%|▍ | 823/18688 [56:25<20:48:35, 4.19s/it] 4%|▍ | 824/18688 [56:29<20:10:39, 4.07s/it] 4%|▍ | 825/18688 [56:32<19:27:16, 3.92s/it] 4%|▍ | 826/18688 [56:37<19:58:52, 4.03s/it] 4%|▍ | 827/18688 [56:41<20:04:53, 4.05s/it] 4%|▍ | 828/18688 [56:44<19:40:31, 3.97s/it] 4%|▍ | 829/18688 [56:48<19:39:39, 3.96s/it] 4%|▍ | 830/18688 [56:52<19:23:35, 3.91s/it] {'loss': '0.9945', 'grad_norm': '1.405', 'learning_rate': '5.527e-05', 'epoch': '0.04441', 'num_input_tokens_seen': 2747024, 'train_runtime': '3413', 'train_tokens_per_second': '804.9'}
4%|▍ | 830/18688 [56:52<19:23:35, 3.91s/it] 4%|▍ | 831/18688 [56:56<19:23:04, 3.91s/it] 4%|▍ | 832/18688 [57:00<19:49:37, 4.00s/it] 4%|▍ | 833/18688 [57:04<20:00:32, 4.03s/it] 4%|▍ | 834/18688 [57:09<20:19:15, 4.10s/it] 4%|▍ | 835/18688 [57:13<20:53:52, 4.21s/it] 4%|▍ | 836/18688 [57:17<20:05:12, 4.05s/it] 4%|▍ | 837/18688 [57:21<20:41:24, 4.17s/it] 4%|▍ | 838/18688 [57:25<20:03:45, 4.05s/it] 4%|▍ | 839/18688 [57:29<20:37:08, 4.16s/it] 4%|▍ | 840/18688 [57:34<20:41:20, 4.17s/it] {'loss': '0.9959', 'grad_norm': '0.9362', 'learning_rate': '5.593e-05', 'epoch': '0.04495', 'num_input_tokens_seen': 2780568, 'train_runtime': '3454', 'train_tokens_per_second': '805'}
4%|▍ | 840/18688 [57:34<20:41:20, 4.17s/it] 5%|▍ | 841/18688 [57:37<19:55:10, 4.02s/it] 5%|▍ | 842/18688 [57:42<20:19:50, 4.10s/it] 5%|▍ | 843/18688 [57:45<19:41:14, 3.97s/it] 5%|▍ | 844/18688 [57:50<20:24:49, 4.12s/it] 5%|▍ | 845/18688 [57:53<19:44:07, 3.98s/it] 5%|▍ | 846/18688 [57:59<21:39:02, 4.37s/it] 5%|▍ | 847/18688 [58:02<20:45:51, 4.19s/it] 5%|▍ | 848/18688 [58:07<21:04:28, 4.25s/it] 5%|▍ | 849/18688 [58:12<22:07:05, 4.46s/it] 5%|▍ | 850/18688 [58:16<21:55:46, 4.43s/it] {'loss': '1.015', 'grad_norm': '1.204', 'learning_rate': '5.66e-05', 'epoch': '0.04548', 'num_input_tokens_seen': 2816704, 'train_runtime': '3497', 'train_tokens_per_second': '805.5'}
5%|▍ | 850/18688 [58:16<21:55:46, 4.43s/it] 5%|▍ | 851/18688 [58:21<22:53:20, 4.62s/it] 5%|▍ | 852/18688 [58:26<23:19:40, 4.71s/it] 5%|▍ | 853/18688 [58:30<21:54:48, 4.42s/it] 5%|▍ | 854/18688 [58:34<22:06:33, 4.46s/it] 5%|▍ | 855/18688 [58:39<22:06:42, 4.46s/it] 5%|▍ | 856/18688 [58:43<21:40:30, 4.38s/it] 5%|▍ | 857/18688 [58:47<20:17:25, 4.10s/it] 5%|▍ | 858/18688 [58:51<20:42:27, 4.18s/it] 5%|▍ | 859/18688 [58:55<20:02:55, 4.05s/it] 5%|▍ | 860/18688 [58:59<20:19:11, 4.10s/it] {'loss': '1.033', 'grad_norm': '1.134', 'learning_rate': '5.727e-05', 'epoch': '0.04602', 'num_input_tokens_seen': 2852384, 'train_runtime': '3539', 'train_tokens_per_second': '805.9'}
5%|▍ | 860/18688 [58:59<20:19:11, 4.10s/it] 5%|▍ | 861/18688 [59:03<20:27:21, 4.13s/it] 5%|▍ | 862/18688 [59:07<19:56:50, 4.03s/it] 5%|▍ | 863/18688 [59:12<21:20:03, 4.31s/it] 5%|▍ | 864/18688 [59:16<21:43:06, 4.39s/it] 5%|▍ | 865/18688 [59:20<20:37:07, 4.16s/it] 5%|▍ | 866/18688 [59:24<20:54:27, 4.22s/it] 5%|▍ | 867/18688 [59:28<20:23:20, 4.12s/it] 5%|▍ | 868/18688 [59:33<21:03:40, 4.25s/it] 5%|▍ | 869/18688 [59:38<21:46:56, 4.40s/it] 5%|▍ | 870/18688 [59:42<22:24:16, 4.53s/it] {'loss': '1.057', 'grad_norm': '1.15', 'learning_rate': '5.793e-05', 'epoch': '0.04656', 'num_input_tokens_seen': 2887288, 'train_runtime': '3583', 'train_tokens_per_second': '805.8'}
5%|▍ | 870/18688 [59:42<22:24:16, 4.53s/it] 5%|▍ | 871/18688 [59:47<22:07:09, 4.47s/it] 5%|▍ | 872/18688 [59:51<21:50:57, 4.41s/it] 5%|▍ | 873/18688 [59:56<22:00:27, 4.45s/it] 5%|▍ | 874/18688 [59:59<20:52:47, 4.22s/it] 5%|▍ | 875/18688 [1:00:03<20:18:40, 4.10s/it] 5%|▍ | 876/18688 [1:00:07<19:43:46, 3.99s/it] 5%|▍ | 877/18688 [1:00:11<20:04:19, 4.06s/it] 5%|▍ | 878/18688 [1:00:15<20:14:14, 4.09s/it] 5%|▍ | 879/18688 [1:00:20<20:42:53, 4.19s/it] 5%|▍ | 880/18688 [1:00:23<20:03:04, 4.05s/it] {'loss': '1.072', 'grad_norm': '0.8804', 'learning_rate': '5.86e-05', 'epoch': '0.04709', 'num_input_tokens_seen': 2919984, 'train_runtime': '3624', 'train_tokens_per_second': '805.8'}
5%|▍ | 880/18688 [1:00:23<20:03:04, 4.05s/it] 5%|▍ | 881/18688 [1:00:28<20:56:50, 4.23s/it] 5%|▍ | 882/18688 [1:00:32<21:02:22, 4.25s/it] 5%|▍ | 883/18688 [1:00:36<20:41:01, 4.18s/it] 5%|▍ | 884/18688 [1:00:40<19:44:10, 3.99s/it] 5%|▍ | 885/18688 [1:00:44<20:39:01, 4.18s/it] 5%|▍ | 886/18688 [1:00:49<21:33:56, 4.36s/it] 5%|▍ | 887/18688 [1:00:53<21:14:07, 4.29s/it] 5%|▍ | 888/18688 [1:00:57<20:22:50, 4.12s/it] 5%|▍ | 889/18688 [1:01:02<21:01:47, 4.25s/it] 5%|▍ | 890/18688 [1:01:05<20:09:37, 4.08s/it] {'loss': '1.007', 'grad_norm': '1.038', 'learning_rate': '5.927e-05', 'epoch': '0.04763', 'num_input_tokens_seen': 2954488, 'train_runtime': '3666', 'train_tokens_per_second': '805.9'}
5%|▍ | 890/18688 [1:01:05<20:09:37, 4.08s/it] 5%|▍ | 891/18688 [1:01:09<19:38:49, 3.97s/it] 5%|▍ | 892/18688 [1:01:13<20:08:13, 4.07s/it] 5%|▍ | 893/18688 [1:01:17<19:53:40, 4.02s/it] 5%|▍ | 894/18688 [1:01:21<19:32:58, 3.96s/it] 5%|▍ | 895/18688 [1:01:25<19:37:51, 3.97s/it] 5%|▍ | 896/18688 [1:01:29<20:13:09, 4.09s/it] 5%|▍ | 897/18688 [1:01:34<20:30:06, 4.15s/it] 5%|▍ | 898/18688 [1:01:38<20:11:19, 4.09s/it] 5%|▍ | 899/18688 [1:01:42<20:54:36, 4.23s/it] 5%|▍ | 900/18688 [1:01:46<20:03:56, 4.06s/it] {'loss': '1.013', 'grad_norm': '1.04', 'learning_rate': '5.993e-05', 'epoch': '0.04816', 'num_input_tokens_seen': 2986632, 'train_runtime': '3706', 'train_tokens_per_second': '805.8'}
5%|▍ | 900/18688 [1:01:46<20:03:56, 4.06s/it] 5%|▍ | 901/18688 [1:01:50<20:11:30, 4.09s/it] 5%|▍ | 902/18688 [1:01:54<20:14:39, 4.10s/it] 5%|▍ | 903/18688 [1:01:59<21:08:05, 4.28s/it] 5%|▍ | 904/18688 [1:02:03<20:14:26, 4.10s/it] 5%|▍ | 905/18688 [1:02:07<21:15:08, 4.30s/it] 5%|▍ | 906/18688 [1:02:12<21:06:17, 4.27s/it] 5%|▍ | 907/18688 [1:02:15<20:18:42, 4.11s/it] 5%|▍ | 908/18688 [1:02:20<20:29:12, 4.15s/it] 5%|▍ | 909/18688 [1:02:24<20:31:22, 4.16s/it] 5%|▍ | 910/18688 [1:02:28<20:14:31, 4.10s/it] {'loss': '0.9286', 'grad_norm': '1.072', 'learning_rate': '6.06e-05', 'epoch': '0.0487', 'num_input_tokens_seen': 3021168, 'train_runtime': '3748', 'train_tokens_per_second': '806'}
5%|▍ | 910/18688 [1:02:28<20:14:31, 4.10s/it] 5%|▍ | 911/18688 [1:02:31<19:49:44, 4.02s/it] 5%|▍ | 912/18688 [1:02:35<19:12:07, 3.89s/it] 5%|▍ | 913/18688 [1:02:39<18:35:48, 3.77s/it] 5%|▍ | 914/18688 [1:02:42<18:39:36, 3.78s/it] 5%|▍ | 915/18688 [1:02:46<19:00:49, 3.85s/it] 5%|▍ | 916/18688 [1:02:51<20:02:45, 4.06s/it] 5%|▍ | 917/18688 [1:02:55<20:14:33, 4.10s/it] 5%|▍ | 918/18688 [1:03:00<21:02:26, 4.26s/it] 5%|▍ | 919/18688 [1:03:04<21:01:09, 4.26s/it] 5%|▍ | 920/18688 [1:03:08<21:19:46, 4.32s/it] {'loss': '1.037', 'grad_norm': '1.059', 'learning_rate': '6.127e-05', 'epoch': '0.04923', 'num_input_tokens_seen': 3053520, 'train_runtime': '3789', 'train_tokens_per_second': '805.9'}
5%|▍ | 920/18688 [1:03:08<21:19:46, 4.32s/it] 5%|▍ | 921/18688 [1:03:12<20:03:13, 4.06s/it] 5%|▍ | 922/18688 [1:03:16<19:50:27, 4.02s/it] 5%|▍ | 923/18688 [1:03:21<20:45:32, 4.21s/it] 5%|▍ | 924/18688 [1:03:24<20:07:20, 4.08s/it] 5%|▍ | 925/18688 [1:03:28<20:02:28, 4.06s/it] 5%|▍ | 926/18688 [1:03:32<19:50:44, 4.02s/it] 5%|▍ | 927/18688 [1:03:37<20:35:33, 4.17s/it] 5%|▍ | 928/18688 [1:03:40<19:39:42, 3.99s/it] 5%|▍ | 929/18688 [1:03:44<19:14:29, 3.90s/it] 5%|▍ | 930/18688 [1:03:48<19:55:58, 4.04s/it] {'loss': '0.9633', 'grad_norm': '1.108', 'learning_rate': '6.193e-05', 'epoch': '0.04977', 'num_input_tokens_seen': 3085744, 'train_runtime': '3829', 'train_tokens_per_second': '805.9'}
5%|▍ | 930/18688 [1:03:48<19:55:58, 4.04s/it] 5%|▍ | 931/18688 [1:03:53<20:06:34, 4.08s/it] 5%|▍ | 932/18688 [1:03:57<19:56:37, 4.04s/it] 5%|▍ | 933/18688 [1:04:01<19:56:41, 4.04s/it] 5%|▍ | 934/18688 [1:04:06<21:25:04, 4.34s/it] 5%|▌ | 935/18688 [1:04:09<20:19:43, 4.12s/it] 5%|▌ | 936/18688 [1:04:14<21:03:11, 4.27s/it] 5%|▌ | 937/18688 [1:04:18<20:41:25, 4.20s/it] 5%|▌ | 938/18688 [1:04:21<19:43:22, 4.00s/it] 5%|▌ | 939/18688 [1:04:26<21:21:22, 4.33s/it] 5%|▌ | 940/18688 [1:04:31<21:19:55, 4.33s/it] {'loss': '1.012', 'grad_norm': '0.9644', 'learning_rate': '6.26e-05', 'epoch': '0.0503', 'num_input_tokens_seen': 3119488, 'train_runtime': '3871', 'train_tokens_per_second': '805.8'}
5%|▌ | 940/18688 [1:04:31<21:19:55, 4.33s/it] 5%|▌ | 941/18688 [1:04:35<21:42:04, 4.40s/it] 5%|▌ | 942/18688 [1:04:39<20:24:20, 4.14s/it] 5%|▌ | 943/18688 [1:04:43<20:24:45, 4.14s/it] 5%|▌ | 944/18688 [1:04:47<20:15:46, 4.11s/it] 5%|▌ | 945/18688 [1:04:51<19:35:39, 3.98s/it] 5%|▌ | 946/18688 [1:04:55<20:31:12, 4.16s/it] 5%|▌ | 947/18688 [1:04:59<19:37:17, 3.98s/it] 5%|▌ | 948/18688 [1:05:03<19:16:34, 3.91s/it] 5%|▌ | 949/18688 [1:05:07<19:54:48, 4.04s/it] 5%|▌ | 950/18688 [1:05:11<19:34:22, 3.97s/it] {'loss': '1.019', 'grad_norm': '0.9477', 'learning_rate': '6.327e-05', 'epoch': '0.05084', 'num_input_tokens_seen': 3153640, 'train_runtime': '3911', 'train_tokens_per_second': '806.3'}
5%|▌ | 950/18688 [1:05:11<19:34:22, 3.97s/it] 5%|▌ | 951/18688 [1:05:15<20:12:17, 4.10s/it] 5%|▌ | 952/18688 [1:05:19<20:16:23, 4.12s/it] 5%|▌ | 953/18688 [1:05:23<20:01:23, 4.06s/it] 5%|▌ | 954/18688 [1:05:27<19:48:15, 4.02s/it] 5%|▌ | 955/18688 [1:05:33<21:47:39, 4.42s/it] 5%|▌ | 956/18688 [1:05:37<21:49:07, 4.43s/it] 5%|▌ | 957/18688 [1:05:43<23:49:18, 4.84s/it] 5%|▌ | 958/18688 [1:05:47<22:19:14, 4.53s/it] 5%|▌ | 959/18688 [1:05:50<21:15:57, 4.32s/it] 5%|▌ | 960/18688 [1:05:54<20:08:06, 4.09s/it] {'loss': '1.062', 'grad_norm': '1.175', 'learning_rate': '6.393e-05', 'epoch': '0.05137', 'num_input_tokens_seen': 3189048, 'train_runtime': '3955', 'train_tokens_per_second': '806.4'}
5%|▌ | 960/18688 [1:05:54<20:08:06, 4.09s/it] 5%|▌ | 961/18688 [1:05:57<19:09:34, 3.89s/it] 5%|▌ | 962/18688 [1:06:01<18:30:20, 3.76s/it] 5%|▌ | 963/18688 [1:06:05<18:36:06, 3.78s/it] 5%|▌ | 964/18688 [1:06:08<18:10:02, 3.69s/it] 5%|▌ | 965/18688 [1:06:12<17:52:22, 3.63s/it] 5%|▌ | 966/18688 [1:06:16<18:21:34, 3.73s/it] 5%|▌ | 967/18688 [1:06:20<19:33:33, 3.97s/it] 5%|▌ | 968/18688 [1:06:25<20:05:46, 4.08s/it] 5%|▌ | 969/18688 [1:06:28<19:20:48, 3.93s/it] 5%|▌ | 970/18688 [1:06:32<19:51:41, 4.04s/it] {'loss': '0.959', 'grad_norm': '1.009', 'learning_rate': '6.46e-05', 'epoch': '0.05191', 'num_input_tokens_seen': 3219536, 'train_runtime': '3993', 'train_tokens_per_second': '806.3'}
5%|▌ | 970/18688 [1:06:32<19:51:41, 4.04s/it] 5%|▌ | 971/18688 [1:06:37<21:08:21, 4.30s/it] 5%|▌ | 972/18688 [1:06:42<21:29:39, 4.37s/it] 5%|▌ | 973/18688 [1:06:45<20:19:07, 4.13s/it] 5%|▌ | 974/18688 [1:06:50<21:18:26, 4.33s/it] 5%|▌ | 975/18688 [1:06:55<21:48:04, 4.43s/it] 5%|▌ | 976/18688 [1:06:59<21:35:52, 4.39s/it] 5%|▌ | 977/18688 [1:07:04<21:46:05, 4.42s/it] 5%|▌ | 978/18688 [1:07:07<20:43:33, 4.21s/it] 5%|▌ | 979/18688 [1:07:12<21:21:58, 4.34s/it] 5%|▌ | 980/18688 [1:07:16<20:46:13, 4.22s/it] {'loss': '1.001', 'grad_norm': '0.9758', 'learning_rate': '6.527e-05', 'epoch': '0.05244', 'num_input_tokens_seen': 3255952, 'train_runtime': '4036', 'train_tokens_per_second': '806.6'}
5%|▌ | 980/18688 [1:07:16<20:46:13, 4.22s/it] 5%|▌ | 981/18688 [1:07:20<19:44:36, 4.01s/it] 5%|▌ | 982/18688 [1:07:25<21:12:20, 4.31s/it] 5%|▌ | 983/18688 [1:07:29<22:00:56, 4.48s/it] 5%|▌ | 984/18688 [1:07:34<21:36:38, 4.39s/it] 5%|▌ | 985/18688 [1:07:38<21:14:47, 4.32s/it] 5%|▌ | 986/18688 [1:07:41<20:11:55, 4.11s/it] 5%|▌ | 987/18688 [1:07:46<21:07:19, 4.30s/it] 5%|▌ | 988/18688 [1:07:50<20:44:25, 4.22s/it] 5%|▌ | 989/18688 [1:07:54<20:15:19, 4.12s/it] 5%|▌ | 990/18688 [1:07:58<20:05:03, 4.09s/it] {'loss': '0.9876', 'grad_norm': '0.9507', 'learning_rate': '6.593e-05', 'epoch': '0.05298', 'num_input_tokens_seen': 3289808, 'train_runtime': '4079', 'train_tokens_per_second': '806.6'}
5%|▌ | 990/18688 [1:07:58<20:05:03, 4.09s/it] 5%|▌ | 991/18688 [1:08:02<19:42:41, 4.01s/it] 5%|▌ | 992/18688 [1:08:05<18:52:36, 3.84s/it] 5%|▌ | 993/18688 [1:08:10<20:11:12, 4.11s/it] 5%|▌ | 994/18688 [1:08:14<19:24:13, 3.95s/it] 5%|▌ | 995/18688 [1:08:19<20:59:53, 4.27s/it] 5%|▌ | 996/18688 [1:08:23<21:27:40, 4.37s/it] 5%|▌ | 997/18688 [1:08:27<21:16:22, 4.33s/it] 5%|▌ | 998/18688 [1:08:32<21:26:41, 4.36s/it] 5%|▌ | 999/18688 [1:08:37<22:15:54, 4.53s/it] 5%|▌ | 1000/18688 [1:08:41<21:41:34, 4.42s/it] {'loss': '0.9688', 'grad_norm': '1.105', 'learning_rate': '6.66e-05', 'epoch': '0.05351', 'num_input_tokens_seen': 3324984, 'train_runtime': '4121', 'train_tokens_per_second': '806.7'}
5%|▌ | 1000/18688 [1:08:41<21:41:34, 4.42s/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<01:00, 16.50it/s]
0%| | 5/1000 [00:00<01:03, 15.67it/s]
1%| | 7/1000 [00:00<01:03, 15.65it/s]
1%| | 9/1000 [00:00<01:04, 15.41it/s]
1%| | 11/1000 [00:00<01:03, 15.65it/s]
1%|▏ | 13/1000 [00:00<01:01, 16.05it/s]
2%|▏ | 15/1000 [00:00<01:04, 15.20it/s]
2%|▏ | 17/1000 [00:01<01:04, 15.27it/s]
2%|▏ | 19/1000 [00:01<01:37, 10.03it/s]
2%|▏ | 21/1000 [00:01<01:59, 8.17it/s]
2%|▏ | 23/1000 [00:01<01:45, 9.28it/s]
2%|▎ | 25/1000 [00:02<01:48, 8.95it/s]
3%|▎ | 27/1000 [00:02<01:38, 9.86it/s]
3%|▎ | 29/1000 [00:02<01:30, 10.73it/s]
3%|▎ | 31/1000 [00:02<01:36, 10.03it/s]
3%|▎ | 33/1000 [00:02<01:26, 11.16it/s]
4%|▎ | 35/1000 [00:02<01:20, 12.00it/s]
4%|▎ | 37/1000 [00:03<01:29, 10.81it/s]
4%|▍ | 39/1000 [00:03<01:23, 11.45it/s]
4%|▍ | 41/1000 [00:03<01:51, 8.63it/s]
4%|▍ | 43/1000 [00:03<01:35, 10.00it/s]
4%|▍ | 45/1000 [00:03<01:24, 11.28it/s]
5%|▍ | 47/1000 [00:04<01:17, 12.31it/s]
5%|▍ | 49/1000 [00:04<01:25, 11.18it/s]
5%|▌ | 51/1000 [00:04<01:20, 11.77it/s]
5%|▌ | 53/1000 [00:04<01:12, 13.04it/s]
6%|▌ | 55/1000 [00:04<01:08, 13.76it/s]
6%|▌ | 57/1000 [00:05<01:48, 8.65it/s]
6%|▌ | 59/1000 [00:05<01:35, 9.81it/s]
6%|▌ | 61/1000 [00:05<01:28, 10.64it/s]
6%|▋ | 63/1000 [00:05<01:21, 11.56it/s]
6%|▋ | 65/1000 [00:05<01:15, 12.44it/s]
7%|▋ | 67/1000 [00:05<01:12, 12.84it/s]
7%|▋ | 69/1000 [00:05<01:09, 13.44it/s]
7%|▋ | 71/1000 [00:06<01:07, 13.82it/s]
7%|▋ | 73/1000 [00:06<01:07, 13.78it/s]
8%|▊ | 75/1000 [00:06<01:06, 13.97it/s]
8%|▊ | 77/1000 [00:06<01:02, 14.72it/s]
8%|▊ | 79/1000 [00:06<01:01, 14.93it/s]
8%|▊ | 81/1000 [00:06<01:08, 13.38it/s]
8%|▊ | 83/1000 [00:06<01:04, 14.18it/s]
8%|▊ | 85/1000 [00:07<01:02, 14.54it/s]
9%|▊ | 87/1000 [00:07<01:03, 14.31it/s]
9%|▉ | 89/1000 [00:07<01:04, 14.18it/s]
9%|▉ | 91/1000 [00:07<01:02, 14.44it/s]
9%|▉ | 93/1000 [00:07<01:03, 14.21it/s]
10%|▉ | 95/1000 [00:07<01:04, 14.13it/s]
10%|▉ | 97/1000 [00:07<01:03, 14.13it/s]
10%|▉ | 99/1000 [00:08<01:02, 14.46it/s]
10%|█ | 101/1000 [00:08<01:31, 9.88it/s]
10%|█ | 103/1000 [00:08<01:22, 10.87it/s]
10%|█ | 105/1000 [00:09<02:10, 6.84it/s]
11%|█ | 106/1000 [00:09<02:06, 7.04it/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, 11.34it/s]
11%|█▏ | 114/1000 [00:09<01:13, 12.01it/s]
12%|█▏ | 116/1000 [00:09<01:09, 12.65it/s]
12%|█▏ | 118/1000 [00:10<01:05, 13.42it/s]
12%|█▏ | 120/1000 [00:10<01:08, 12.83it/s]
12%|█▏ | 122/1000 [00:10<01:22, 10.62it/s]
12%|█▏ | 124/1000 [00:10<01:30, 9.69it/s]
13%|█▎ | 126/1000 [00:10<01:21, 10.67it/s]
13%|█▎ | 128/1000 [00:10<01:15, 11.59it/s]
13%|█▎ | 130/1000 [00:11<01:10, 12.27it/s]
13%|█▎ | 132/1000 [00:11<01:05, 13.23it/s]
13%|█▎ | 134/1000 [00:11<01:09, 12.42it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.37it/s]
14%|█▍ | 138/1000 [00:11<01:04, 13.28it/s]
14%|█▍ | 140/1000 [00:12<01:24, 10.13it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.57it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.59it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.61it/s]
15%|█▍ | 148/1000 [00:12<01:13, 11.52it/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<01:02, 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, 13.68it/s]
16%|█▌ | 162/1000 [00:13<01:01, 13.57it/s]
16%|█▋ | 164/1000 [00:13<01:01, 13.66it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.80it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.32it/s]
17%|█▋ | 170/1000 [00:14<01:18, 10.51it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.49it/s]
17%|█▋ | 174/1000 [00:14<01:29, 9.21it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.21it/s]
18%|█▊ | 178/1000 [00:15<01:34, 8.67it/s]
18%|█▊ | 180/1000 [00:15<01:32, 8.82it/s]
18%|█▊ | 182/1000 [00:15<01:35, 8.58it/s]
18%|█▊ | 184/1000 [00:16<01:23, 9.83it/s]
19%|█▊ | 186/1000 [00:16<01:37, 8.35it/s]
19%|█▉ | 188/1000 [00:16<01:23, 9.68it/s]
19%|█▉ | 190/1000 [00:16<01:15, 10.72it/s]
19%|█▉ | 192/1000 [00:16<01:11, 11.37it/s]
19%|█▉ | 194/1000 [00:17<01:16, 10.58it/s]
20%|█▉ | 196/1000 [00:17<01:29, 8.99it/s]
20%|█▉ | 198/1000 [00:17<01:18, 10.21it/s]
20%|██ | 200/1000 [00:17<01:15, 10.55it/s]
20%|██ | 202/1000 [00:17<01:09, 11.46it/s]
20%|██ | 204/1000 [00:17<01:03, 12.55it/s]
21%|██ | 206/1000 [00:18<01:00, 13.16it/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, 14.72it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.75it/s]
22%|██▏ | 216/1000 [00:18<00:54, 14.49it/s]
22%|██▏ | 218/1000 [00:18<00:52, 14.88it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.92it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.19it/s]
22%|██▏ | 224/1000 [00:19<01:20, 9.67it/s]
23%|██▎ | 226/1000 [00:19<01:12, 10.68it/s]
23%|██▎ | 228/1000 [00:19<01:06, 11.59it/s]
23%|██▎ | 230/1000 [00:20<01:03, 12.08it/s]
23%|██▎ | 232/1000 [00:20<01:01, 12.44it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.20it/s]
24%|██▎ | 236/1000 [00:20<00:55, 13.69it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.33it/s]
24%|██▍ | 240/1000 [00:20<00:53, 14.31it/s]
24%|██▍ | 242/1000 [00:20<00:51, 14.83it/s]
24%|██▍ | 244/1000 [00:21<00:50, 15.03it/s]
25%|██▍ | 246/1000 [00:21<00:50, 15.02it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.72it/s]
25%|██▌ | 250/1000 [00:21<00:55, 13.47it/s]
25%|██▌ | 252/1000 [00:21<01:01, 12.19it/s]
25%|██▌ | 254/1000 [00:21<00:56, 13.19it/s]
26%|██▌ | 256/1000 [00:21<00:54, 13.76it/s]
26%|██▌ | 258/1000 [00:22<00:55, 13.31it/s]
26%|██▌ | 260/1000 [00:22<00:58, 12.62it/s]
26%|██▌ | 262/1000 [00:22<00:57, 12.84it/s]
26%|██▋ | 264/1000 [00:22<00:54, 13.47it/s]
27%|██▋ | 266/1000 [00:22<00:52, 13.88it/s]
27%|██▋ | 268/1000 [00:22<00:50, 14.47it/s]
27%|██▋ | 270/1000 [00:23<00:55, 13.24it/s]
27%|██▋ | 272/1000 [00:23<01:03, 11.41it/s]
27%|██▋ | 274/1000 [00:23<01:04, 11.21it/s]
28%|██▊ | 276/1000 [00:23<00:59, 12.15it/s]
28%|██▊ | 278/1000 [00:23<01:00, 11.93it/s]
28%|██▊ | 280/1000 [00:23<00:56, 12.70it/s]
28%|██▊ | 282/1000 [00:23<00:52, 13.70it/s]
28%|██▊ | 284/1000 [00:24<00:51, 13.87it/s]
29%|██▊ | 286/1000 [00:24<00:52, 13.66it/s]
29%|██▉ | 288/1000 [00:24<00:50, 14.12it/s]
29%|██▉ | 290/1000 [00:24<00:48, 14.71it/s]
29%|██▉ | 292/1000 [00:24<00:47, 14.84it/s]
29%|██▉ | 294/1000 [00:24<00:49, 14.34it/s]
30%|██▉ | 296/1000 [00:24<00:48, 14.48it/s]
30%|██▉ | 298/1000 [00:25<00:47, 14.86it/s]
30%|███ | 300/1000 [00:25<00:47, 14.72it/s]
30%|███ | 302/1000 [00:25<00:46, 14.89it/s]
30%|███ | 304/1000 [00:25<00:51, 13.62it/s]
31%|███ | 306/1000 [00:25<00:48, 14.39it/s]
31%|███ | 308/1000 [00:25<00:48, 14.39it/s]
31%|███ | 310/1000 [00:25<00:47, 14.55it/s]
31%|███ | 312/1000 [00:26<00:47, 14.57it/s]
31%|███▏ | 314/1000 [00:26<00:45, 15.21it/s]
32%|███▏ | 316/1000 [00:26<00:44, 15.50it/s]
32%|███▏ | 318/1000 [00:26<00:42, 15.95it/s]
32%|███▏ | 320/1000 [00:26<01:12, 9.36it/s]
32%|███▏ | 322/1000 [00:26<01:03, 10.73it/s]
32%|███▏ | 324/1000 [00:27<00:58, 11.53it/s]
33%|███▎ | 326/1000 [00:27<00:58, 11.49it/s]
33%|███▎ | 328/1000 [00:27<00:58, 11.51it/s]
33%|███▎ | 330/1000 [00:27<00:53, 12.44it/s]
33%|███▎ | 332/1000 [00:27<00:52, 12.71it/s]
33%|███▎ | 334/1000 [00:27<00:53, 12.56it/s]
34%|███▎ | 336/1000 [00:28<00:52, 12.54it/s]
34%|███▍ | 338/1000 [00:28<01:09, 9.56it/s]
34%|███▍ | 340/1000 [00:28<01:04, 10.30it/s]
34%|███▍ | 342/1000 [00:28<00:59, 11.14it/s]
34%|███▍ | 344/1000 [00:28<00:54, 12.06it/s]
35%|███▍ | 346/1000 [00:28<00:50, 12.89it/s]
35%|███▍ | 348/1000 [00:29<00:48, 13.50it/s]
35%|███▌ | 350/1000 [00:29<00:45, 14.23it/s]
35%|███▌ | 352/1000 [00:29<00:44, 14.51it/s]
35%|███▌ | 354/1000 [00:29<00:44, 14.65it/s]
36%|███▌ | 356/1000 [00:29<00:43, 14.79it/s]
36%|███▌ | 358/1000 [00:29<00:44, 14.55it/s]
36%|███▌ | 360/1000 [00:29<00:42, 14.98it/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<00:52, 11.97it/s]
37%|███▋ | 368/1000 [00:30<00:51, 12.38it/s]
37%|███▋ | 370/1000 [00:30<00:47, 13.27it/s]
37%|███▋ | 372/1000 [00:30<00:46, 13.59it/s]
37%|███▋ | 374/1000 [00:31<00:54, 11.43it/s]
38%|███▊ | 376/1000 [00:31<00:56, 11.02it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.70it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.51it/s]
38%|███▊ | 382/1000 [00:31<00:48, 12.69it/s]
38%|███▊ | 384/1000 [00:31<00:45, 13.45it/s]
39%|███▊ | 386/1000 [00:31<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, 13.91it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.70it/s]
40%|███▉ | 396/1000 [00:32<00:43, 13.82it/s]
40%|███▉ | 398/1000 [00:32<00:47, 12.62it/s]
40%|████ | 400/1000 [00:32<00:45, 13.26it/s]
40%|████ | 402/1000 [00:33<00:42, 13.91it/s]
40%|████ | 404/1000 [00:33<00:40, 14.54it/s]
41%|████ | 406/1000 [00:33<00:39, 15.05it/s]
41%|████ | 408/1000 [00:33<00:47, 12.56it/s]
41%|████ | 410/1000 [00:33<00:56, 10.49it/s]
41%|████ | 412/1000 [00:33<00:50, 11.57it/s]
41%|████▏ | 414/1000 [00:34<01:07, 8.66it/s]
42%|████▏ | 416/1000 [00:34<01:24, 6.94it/s]
42%|████▏ | 418/1000 [00:34<01:11, 8.14it/s]
42%|████▏ | 420/1000 [00:35<01:01, 9.39it/s]
42%|████▏ | 422/1000 [00:35<00:55, 10.51it/s]
42%|████▏ | 424/1000 [00:35<01:16, 7.54it/s]
43%|████▎ | 426/1000 [00:35<01:05, 8.79it/s]
43%|████▎ | 428/1000 [00:35<00:59, 9.60it/s]
43%|████▎ | 430/1000 [00:36<01:04, 8.82it/s]
43%|████▎ | 432/1000 [00:36<01:21, 6.93it/s]
43%|████▎ | 434/1000 [00:36<01:09, 8.16it/s]
44%|████▎ | 436/1000 [00:36<01:01, 9.20it/s]
44%|████▍ | 438/1000 [00:37<01:02, 8.94it/s]
44%|████▍ | 440/1000 [00:37<00:59, 9.45it/s]
44%|████▍ | 442/1000 [00:37<00:53, 10.52it/s]
44%|████▍ | 444/1000 [00:37<00:48, 11.39it/s]
45%|████▍ | 446/1000 [00:37<00:45, 12.16it/s]
45%|████▍ | 448/1000 [00:37<00:50, 10.96it/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, 12.30it/s]
46%|████▌ | 456/1000 [00:38<00:42, 12.76it/s]
46%|████▌ | 458/1000 [00:38<00:39, 13.76it/s]
46%|████▌ | 460/1000 [00:38<00:38, 13.86it/s]
46%|████▌ | 462/1000 [00:38<00:37, 14.40it/s]
46%|████▋ | 464/1000 [00:39<00:35, 14.90it/s]
47%|████▋ | 466/1000 [00:39<00:36, 14.79it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.88it/s]
47%|████▋ | 470/1000 [00:39<00:52, 10.05it/s]
47%|████▋ | 472/1000 [00:39<00:51, 10.20it/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, 11.99it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.94it/s]
48%|████▊ | 482/1000 [00:40<00:38, 13.35it/s]
48%|████▊ | 484/1000 [00:40<00:37, 13.86it/s]
49%|████▊ | 486/1000 [00:40<00:36, 14.04it/s]
49%|████▉ | 488/1000 [00:41<00:35, 14.30it/s]
49%|████▉ | 490/1000 [00:41<00:45, 11.15it/s]
49%|████▉ | 492/1000 [00:41<00:40, 12.44it/s]
49%|████▉ | 494/1000 [00:41<00:37, 13.41it/s]
50%|████▉ | 496/1000 [00:41<00:35, 14.19it/s]
50%|████▉ | 498/1000 [00:41<00:34, 14.56it/s]
50%|█████ | 500/1000 [00:42<00:43, 11.61it/s]
50%|█████ | 502/1000 [00:42<00:41, 12.06it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.65it/s]
51%|█████ | 506/1000 [00:42<00:38, 12.82it/s]
51%|█████ | 508/1000 [00:42<00:35, 13.75it/s]
51%|█████ | 510/1000 [00:42<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, 12.63it/s]
52%|█████▏ | 518/1000 [00:43<00:36, 13.16it/s]
52%|█████▏ | 520/1000 [00:43<00:35, 13.51it/s]
52%|█████▏ | 522/1000 [00:43<00:33, 14.24it/s]
52%|█████▏ | 524/1000 [00:43<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, 12.82it/s]
53%|█████▎ | 532/1000 [00:44<00:34, 13.76it/s]
53%|█████▎ | 534/1000 [00:44<00:32, 14.13it/s]
54%|█████▎ | 536/1000 [00:44<00:32, 14.26it/s]
54%|█████▍ | 538/1000 [00:44<00:32, 14.03it/s]
54%|█████▍ | 540/1000 [00:45<00:31, 14.47it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.74it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.31it/s]
55%|█████▍ | 546/1000 [00:45<00:33, 13.66it/s]
55%|█████▍ | 548/1000 [00:45<00:34, 13.25it/s]
55%|█████▌ | 550/1000 [00:45<00:34, 13.00it/s]
55%|█████▌ | 552/1000 [00:45<00:35, 12.76it/s]
55%|█████▌ | 554/1000 [00:46<00:35, 12.64it/s]
56%|█████▌ | 556/1000 [00:46<00:35, 12.66it/s]
56%|█████▌ | 558/1000 [00:46<00:34, 12.80it/s]
56%|█████▌ | 560/1000 [00:46<00:34, 12.84it/s]
56%|█████▌ | 562/1000 [00:46<00:34, 12.74it/s]
56%|█████▋ | 564/1000 [00:47<00:44, 9.71it/s]
57%|█████▋ | 566/1000 [00:47<00:41, 10.36it/s]
57%|█████▋ | 568/1000 [00:47<00:39, 10.85it/s]
57%|█████▋ | 570/1000 [00:47<00:38, 11.29it/s]
57%|█████▋ | 572/1000 [00:47<00:37, 11.33it/s]
57%|█████▋ | 574/1000 [00:47<00:36, 11.65it/s]
58%|█████▊ | 576/1000 [00:48<00:34, 12.17it/s]
58%|█████▊ | 578/1000 [00:48<00:38, 10.88it/s]
58%|█████▊ | 580/1000 [00:48<00:34, 12.23it/s]
58%|█████▊ | 582/1000 [00:48<00:31, 13.12it/s]
58%|█████▊ | 584/1000 [00:48<00:30, 13.49it/s]
59%|█████▊ | 586/1000 [00:48<00:29, 13.93it/s]
59%|█████▉ | 588/1000 [00:48<00:27, 14.82it/s]
59%|█████▉ | 590/1000 [00:49<00:27, 14.90it/s]
59%|█████▉ | 592/1000 [00:49<00:27, 14.73it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.31it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.78it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 13.90it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.29it/s]
60%|██████ | 602/1000 [00:49<00:27, 14.43it/s]
60%|██████ | 604/1000 [00:50<00:26, 14.97it/s]
61%|██████ | 606/1000 [00:50<00:26, 14.75it/s]
61%|██████ | 608/1000 [00:50<00:25, 15.09it/s]
61%|██████ | 610/1000 [00:50<00:26, 14.95it/s]
61%|██████ | 612/1000 [00:50<00:25, 15.13it/s]
61%|██████▏ | 614/1000 [00:50<00:26, 14.38it/s]
62%|██████▏ | 616/1000 [00:50<00:25, 14.81it/s]
62%|██████▏ | 618/1000 [00:50<00:26, 14.23it/s]
62%|██████▏ | 620/1000 [00:51<00:38, 9.80it/s]
62%|██████▏ | 622/1000 [00:51<00:37, 9.98it/s]
62%|██████▏ | 624/1000 [00:51<00:32, 11.42it/s]
63%|██████▎ | 626/1000 [00:51<00:30, 12.12it/s]
63%|██████▎ | 628/1000 [00:51<00:27, 13.31it/s]
63%|██████▎ | 630/1000 [00:52<00:26, 13.82it/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, 10.96it/s]
64%|██████▍ | 638/1000 [00:52<00:33, 10.71it/s]
64%|██████▍ | 640/1000 [00:52<00:31, 11.49it/s]
64%|██████▍ | 642/1000 [00:53<00:28, 12.39it/s]
64%|██████▍ | 644/1000 [00:53<00:28, 12.63it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.92it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.39it/s]
65%|██████▌ | 650/1000 [00:53<00:27, 12.59it/s]
65%|██████▌ | 652/1000 [00:53<00:29, 11.98it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.45it/s]
66%|██████▌ | 656/1000 [00:54<00:31, 10.79it/s]
66%|██████▌ | 658/1000 [00:54<00:28, 11.89it/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, 11.37it/s]
67%|██████▋ | 670/1000 [00:55<00:29, 11.17it/s]
67%|██████▋ | 672/1000 [00:55<00:27, 11.92it/s]
67%|██████▋ | 674/1000 [00:55<00:26, 12.51it/s]
68%|██████▊ | 676/1000 [00:56<00:27, 11.74it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.17it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 12.97it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.15it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.37it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.44it/s]
69%|██████▉ | 688/1000 [00:56<00:22, 13.75it/s]
69%|██████▉ | 690/1000 [00:57<00:22, 13.82it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.29it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.37it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 14.12it/s]
70%|██████▉ | 698/1000 [00:57<00:20, 14.68it/s]
70%|███████ | 700/1000 [00:57<00:24, 12.22it/s]
70%|███████ | 702/1000 [00:57<00:22, 13.04it/s]
70%|███████ | 704/1000 [00:58<00:29, 9.89it/s]
71%|███████ | 706/1000 [00:58<00:27, 10.71it/s]
71%|███████ | 708/1000 [00:58<00:30, 9.65it/s]
71%|███████ | 710/1000 [00:58<00:26, 10.94it/s]
71%|███████ | 712/1000 [00:58<00:24, 11.89it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.98it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 13.07it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.60it/s]
72%|███████▏ | 720/1000 [00:59<00:20, 13.54it/s]
72%|███████▏ | 722/1000 [00:59<00:19, 13.93it/s]
72%|███████▏ | 724/1000 [00:59<00:20, 13.77it/s]
73%|███████▎ | 726/1000 [00:59<00:19, 14.00it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.88it/s]
73%|███████▎ | 730/1000 [01:00<00:32, 8.20it/s]
73%|███████▎ | 732/1000 [01:00<00:32, 8.33it/s]
73%|███████▎ | 734/1000 [01:00<00:30, 8.62it/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, 11.01it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.71it/s]
74%|███████▍ | 743/1000 [01:01<00:20, 12.35it/s]
74%|███████▍ | 745/1000 [01:01<00:24, 10.21it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.59it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.76it/s]
75%|███████▌ | 751/1000 [01:02<00:21, 11.85it/s]
75%|███████▌ | 753/1000 [01:02<00:19, 12.61it/s]
76%|███████▌ | 755/1000 [01:02<00:18, 13.34it/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, 12.32it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.28it/s]
76%|███████▋ | 765/1000 [01:03<00:27, 8.62it/s]
77%|███████▋ | 767/1000 [01:04<00:24, 9.59it/s]
77%|███████▋ | 769/1000 [01:04<00:21, 10.60it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.86it/s]
77%|███████▋ | 773/1000 [01:04<00:17, 12.65it/s]
78%|███████▊ | 775/1000 [01:04<00:17, 13.20it/s]
78%|███████▊ | 777/1000 [01:04<00:15, 14.17it/s]
78%|███████▊ | 779/1000 [01:04<00:20, 10.54it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.41it/s]
78%|███████▊ | 783/1000 [01:05<00:26, 8.30it/s]
78%|███████▊ | 784/1000 [01:05<00:30, 7.07it/s]
78%|███████▊ | 785/1000 [01:05<00:29, 7.38it/s]
79%|███████▊ | 786/1000 [01:06<00:30, 6.97it/s]
79%|███████▉ | 788/1000 [01:06<00:24, 8.82it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.15it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 11.30it/s]
79%|███████▉ | 794/1000 [01:06<00:17, 11.95it/s]
80%|███████▉ | 796/1000 [01:06<00:16, 12.40it/s]
80%|███████▉ | 798/1000 [01:06<00:15, 13.14it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.31it/s]
80%|████████ | 802/1000 [01:07<00:25, 7.74it/s]
80%|████████ | 804/1000 [01:07<00:25, 7.63it/s]
81%|████████ | 806/1000 [01:08<00:23, 8.41it/s]
81%|████████ | 808/1000 [01:08<00:19, 9.79it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.03it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.25it/s]
81%|████████▏ | 814/1000 [01:08<00:15, 12.22it/s]
82%|████████▏ | 816/1000 [01:08<00:16, 10.84it/s]
82%|████████▏ | 818/1000 [01:08<00:15, 11.83it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.36it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 12.95it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.77it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.21it/s]
83%|████████▎ | 828/1000 [01:09<00:11, 14.49it/s]
83%|████████▎ | 830/1000 [01:09<00:12, 13.94it/s]
83%|████████▎ | 832/1000 [01:09<00:11, 14.32it/s]
83%|████████▎ | 834/1000 [01:10<00:11, 14.05it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.37it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 13.97it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.17it/s]
84%|████████▍ | 842/1000 [01:10<00:11, 14.24it/s]
84%|████████▍ | 844/1000 [01:10<00:11, 13.83it/s]
85%|████████▍ | 846/1000 [01:10<00:11, 13.84it/s]
85%|████████▍ | 848/1000 [01:11<00:10, 13.83it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.06it/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 [01:11<00:09, 15.36it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.51it/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, 13.77it/s]
87%|████████▋ | 870/1000 [01:12<00:09, 14.17it/s]
87%|████████▋ | 872/1000 [01:12<00:08, 14.76it/s]
87%|████████▋ | 874/1000 [01:12<00:10, 12.47it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.08it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.96it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.62it/s]
88%|████████▊ | 882/1000 [01:13<00:10, 11.41it/s]
88%|████████▊ | 884/1000 [01:13<00:09, 12.16it/s]
89%|████████▊ | 886/1000 [01:13<00:08, 12.93it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.92it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.33it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.10it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 14.24it/s]
90%|████████▉ | 896/1000 [01:14<00:07, 13.97it/s]
90%|████████▉ | 898/1000 [01:14<00:07, 13.33it/s]
90%|█████████ | 900/1000 [01:14<00:08, 12.28it/s]
90%|█████████ | 902/1000 [01:15<00:07, 13.02it/s]
90%|█████████ | 904/1000 [01:15<00:09, 9.83it/s]
91%|█████████ | 906/1000 [01:15<00:08, 10.90it/s]
91%|█████████ | 908/1000 [01:15<00:07, 11.87it/s]
91%|█████████ | 910/1000 [01:15<00:07, 12.09it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.29it/s]
91%|█████████▏| 914/1000 [01:16<00:12, 6.81it/s]
92%|█████████▏| 916/1000 [01:16<00:10, 8.14it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.40it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.79it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 10.22it/s]
92%|█████████▏| 924/1000 [01:17<00:06, 10.93it/s]
93%|█████████▎| 926/1000 [01:17<00:06, 11.78it/s]
93%|█████████▎| 928/1000 [01:17<00:06, 10.65it/s]
93%|█████████▎| 930/1000 [01:17<00:05, 11.84it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 12.46it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.93it/s]
94%|█████████▎| 936/1000 [01:18<00:04, 13.44it/s]
94%|█████████▍| 938/1000 [01:18<00:04, 14.31it/s]
94%|█████████▍| 940/1000 [01:18<00:04, 14.63it/s]
94%|█████████▍| 942/1000 [01:18<00:04, 12.32it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.85it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.91it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 12.72it/s]
95%|█████████▌| 950/1000 [01:19<00:03, 13.49it/s]
95%|█████████▌| 952/1000 [01:19<00:03, 14.13it/s]
95%|█████████▌| 954/1000 [01:19<00:03, 14.25it/s]
96%|█████████▌| 956/1000 [01:19<00:03, 14.22it/s]
96%|█████████▌| 958/1000 [01:19<00:02, 14.73it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 13.10it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.69it/s]
96%|█████████▋| 964/1000 [01:20<00:03, 10.21it/s]
97%|█████████▋| 966/1000 [01:20<00:02, 11.41it/s]
97%|█████████▋| 968/1000 [01:20<00:02, 12.10it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 12.73it/s]
97%|█████████▋| 972/1000 [01:21<00:02, 9.70it/s]
97%|█████████▋| 974/1000 [01:21<00:02, 10.87it/s]
98%|█████████▊| 976/1000 [01:21<00:02, 11.66it/s]
98%|█████████▊| 978/1000 [01:21<00:02, 9.77it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.82it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.06it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 10.16it/s]
99%|█████████▊| 986/1000 [01:22<00:01, 10.26it/s]
99%|█████████▉| 988/1000 [01:22<00:01, 10.60it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 11.11it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 11.63it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 11.86it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 12.18it/s]
100%|█████████▉| 998/1000 [01:23<00:00, 12.31it/s]
100%|██████████| 1000/1000 [01:23<00:00, 12.64it/s]
{'eval_loss': '1', 'eval_runtime': '83.88', 'eval_samples_per_second': '11.92', 'eval_steps_per_second': '11.92', 'epoch': '0.05351', 'num_input_tokens_seen': 3324984}
5%|▌ | 1000/18688 [1:10:05<21:41:34, 4.42s/it]
100%|██████████| 1000/1000 [01:23<00:00, 12.64it/s]
 5%|▌ | 1001/18688 [1:10:09<145:23:10, 29.59s/it] 5%|▌ | 1002/18688 [1:10:13<107:50:02, 21.95s/it] 5%|▌ | 1003/18688 [1:10:18<82:27:09, 16.78s/it] 5%|▌ | 1004/18688 [1:10:22<63:52:28, 13.00s/it] 5%|▌ | 1005/18688 [1:10:27<51:05:45, 10.40s/it] 5%|▌ | 1006/18688 [1:10:31<42:04:32, 8.57s/it] 5%|▌ | 1007/18688 [1:10:35<35:36:25, 7.25s/it] 5%|▌ | 1008/18688 [1:10:39<30:31:16, 6.21s/it] 5%|▌ | 1009/18688 [1:10:43<27:42:41, 5.64s/it] 5%|▌ | 1010/18688 [1:10:49<27:53:55, 5.68s/it] {'loss': '1.033', 'grad_norm': '0.6336', 'learning_rate': '6.727e-05', 'epoch': '0.05405', 'num_input_tokens_seen': 3363320, 'train_runtime': '4250', 'train_tokens_per_second': '791.5'}
5%|▌ | 1010/18688 [1:10:49<27:53:55, 5.68s/it] 5%|▌ | 1011/18688 [1:10:53<25:02:20, 5.10s/it] 5%|▌ | 1012/18688 [1:10:57<24:06:19, 4.91s/it] 5%|▌ | 1013/18688 [1:11:02<23:47:14, 4.84s/it] 5%|▌ | 1014/18688 [1:11:07<23:28:03, 4.78s/it] 5%|▌ | 1015/18688 [1:11:11<23:31:29, 4.79s/it] 5%|▌ | 1016/18688 [1:11:16<22:34:25, 4.60s/it] 5%|▌ | 1017/18688 [1:11:20<22:49:27, 4.65s/it] 5%|▌ | 1018/18688 [1:11:25<22:16:22, 4.54s/it] 5%|▌ | 1019/18688 [1:11:29<22:47:05, 4.64s/it] 5%|▌ | 1020/18688 [1:11:33<21:21:55, 4.35s/it] {'loss': '1.026', 'grad_norm': '1.465', 'learning_rate': '6.793e-05', 'epoch': '0.05458', 'num_input_tokens_seen': 3400000, 'train_runtime': '4294', 'train_tokens_per_second': '791.9'}
5%|▌ | 1020/18688 [1:11:33<21:21:55, 4.35s/it] 5%|▌ | 1021/18688 [1:11:38<22:07:52, 4.51s/it] 5%|▌ | 1022/18688 [1:11:42<21:54:27, 4.46s/it] 5%|▌ | 1023/18688 [1:11:47<21:49:40, 4.45s/it] 5%|▌ | 1024/18688 [1:11:51<21:21:18, 4.35s/it] 5%|▌ | 1025/18688 [1:11:57<23:18:11, 4.75s/it] 5%|▌ | 1026/18688 [1:12:01<22:10:52, 4.52s/it] 5%|▌ | 1027/18688 [1:12:04<20:56:33, 4.27s/it] 6%|▌ | 1028/18688 [1:12:08<20:50:10, 4.25s/it] 6%|▌ | 1029/18688 [1:12:12<19:58:36, 4.07s/it] 6%|▌ | 1030/18688 [1:12:16<19:20:00, 3.94s/it] {'loss': '1.018', 'grad_norm': '1.227', 'learning_rate': '6.86e-05', 'epoch': '0.05512', 'num_input_tokens_seen': 3435320, 'train_runtime': '4336', 'train_tokens_per_second': '792.2'}
6%|▌ | 1030/18688 [1:12:16<19:20:00, 3.94s/it] 6%|▌ | 1031/18688 [1:12:21<20:44:04, 4.23s/it] 6%|▌ | 1032/18688 [1:12:25<20:44:19, 4.23s/it] 6%|▌ | 1033/18688 [1:12:29<20:32:38, 4.19s/it] 6%|▌ | 1034/18688 [1:12:34<21:33:09, 4.40s/it] 6%|▌ | 1035/18688 [1:12:38<21:11:37, 4.32s/it] 6%|▌ | 1036/18688 [1:12:42<21:06:11, 4.30s/it] 6%|▌ | 1037/18688 [1:12:47<21:12:22, 4.33s/it] 6%|▌ | 1038/18688 [1:12:50<20:12:02, 4.12s/it] 6%|▌ | 1039/18688 [1:12:54<19:15:44, 3.93s/it] 6%|▌ | 1040/18688 [1:12:58<19:33:59, 3.99s/it] {'loss': '0.9953', 'grad_norm': '1.105', 'learning_rate': '6.927e-05', 'epoch': '0.05565', 'num_input_tokens_seen': 3469792, 'train_runtime': '4378', 'train_tokens_per_second': '792.5'}
6%|▌ | 1040/18688 [1:12:58<19:33:59, 3.99s/it] 6%|▌ | 1041/18688 [1:13:02<19:42:05, 4.02s/it] 6%|▌ | 1042/18688 [1:13:06<19:06:29, 3.90s/it] 6%|▌ | 1043/18688 [1:13:10<19:28:46, 3.97s/it] 6%|▌ | 1044/18688 [1:13:14<19:40:22, 4.01s/it] 6%|▌ | 1045/18688 [1:13:18<19:35:56, 4.00s/it] 6%|▌ | 1046/18688 [1:13:23<22:04:37, 4.51s/it] 6%|▌ | 1047/18688 [1:13:27<20:55:28, 4.27s/it] 6%|▌ | 1048/18688 [1:13:31<20:52:07, 4.26s/it] 6%|▌ | 1049/18688 [1:13:35<20:21:48, 4.16s/it] 6%|▌ | 1050/18688 [1:13:39<19:49:57, 4.05s/it] {'loss': '1', 'grad_norm': '1.006', 'learning_rate': '6.993e-05', 'epoch': '0.05619', 'num_input_tokens_seen': 3502400, 'train_runtime': '4420', 'train_tokens_per_second': '792.5'}
6%|▌ | 1050/18688 [1:13:39<19:49:57, 4.05s/it] 6%|▌ | 1051/18688 [1:13:44<20:40:13, 4.22s/it] 6%|▌ | 1052/18688 [1:13:47<19:43:57, 4.03s/it] 6%|▌ | 1053/18688 [1:13:51<19:44:06, 4.03s/it] 6%|▌ | 1054/18688 [1:13:55<19:01:57, 3.89s/it] 6%|▌ | 1055/18688 [1:14:00<20:03:25, 4.09s/it] 6%|▌ | 1056/18688 [1:14:03<19:17:51, 3.94s/it] 6%|▌ | 1057/18688 [1:14:07<19:48:32, 4.04s/it] 6%|▌ | 1058/18688 [1:14:12<20:41:53, 4.23s/it] 6%|▌ | 1059/18688 [1:14:16<20:28:01, 4.18s/it] 6%|▌ | 1060/18688 [1:14:20<20:33:43, 4.20s/it] {'loss': '1.014', 'grad_norm': '1.054', 'learning_rate': '7.06e-05', 'epoch': '0.05672', 'num_input_tokens_seen': 3536816, 'train_runtime': '4461', 'train_tokens_per_second': '792.9'}
6%|▌ | 1060/18688 [1:14:20<20:33:43, 4.20s/it] 6%|▌ | 1061/18688 [1:14:24<20:11:15, 4.12s/it] 6%|▌ | 1062/18688 [1:14:29<20:57:37, 4.28s/it] 6%|▌ | 1063/18688 [1:14:33<20:27:49, 4.18s/it] 6%|▌ | 1064/18688 [1:14:38<21:20:23, 4.36s/it] 6%|▌ | 1065/18688 [1:14:41<20:11:43, 4.13s/it] 6%|▌ | 1066/18688 [1:14:45<19:59:13, 4.08s/it] 6%|▌ | 1067/18688 [1:14:50<20:41:31, 4.23s/it] 6%|▌ | 1068/18688 [1:14:54<20:00:04, 4.09s/it] 6%|▌ | 1069/18688 [1:14:58<20:46:14, 4.24s/it] 6%|▌ | 1070/18688 [1:15:02<20:43:27, 4.23s/it] {'loss': '1.007', 'grad_norm': '1.085', 'learning_rate': '7.127e-05', 'epoch': '0.05726', 'num_input_tokens_seen': 3571048, 'train_runtime': '4503', 'train_tokens_per_second': '793.1'}
6%|▌ | 1070/18688 [1:15:02<20:43:27, 4.23s/it] 6%|▌ | 1071/18688 [1:15:07<20:42:35, 4.23s/it] 6%|▌ | 1072/18688 [1:15:11<20:26:23, 4.18s/it] 6%|▌ | 1073/18688 [1:15:15<20:15:22, 4.14s/it] 6%|▌ | 1074/18688 [1:15:20<21:14:41, 4.34s/it] 6%|▌ | 1075/18688 [1:15:24<21:44:09, 4.44s/it] 6%|▌ | 1076/18688 [1:15:28<21:24:13, 4.38s/it] 6%|▌ | 1077/18688 [1:15:32<20:05:17, 4.11s/it] 6%|▌ | 1078/18688 [1:15:36<20:35:12, 4.21s/it] 6%|▌ | 1079/18688 [1:15:40<19:44:38, 4.04s/it] 6%|▌ | 1080/18688 [1:15:44<19:12:33, 3.93s/it] {'loss': '1.023', 'grad_norm': '1.284', 'learning_rate': '7.193e-05', 'epoch': '0.05779', 'num_input_tokens_seen': 3604416, 'train_runtime': '4544', 'train_tokens_per_second': '793.2'}
6%|▌ | 1080/18688 [1:15:44<19:12:33, 3.93s/it] 6%|▌ | 1081/18688 [1:15:48<19:18:09, 3.95s/it] 6%|▌ | 1082/18688 [1:15:53<20:44:10, 4.24s/it] 6%|▌ | 1083/18688 [1:15:56<19:39:52, 4.02s/it] 6%|▌ | 1084/18688 [1:16:00<19:53:32, 4.07s/it] 6%|▌ | 1085/18688 [1:16:04<20:00:02, 4.09s/it] 6%|▌ | 1086/18688 [1:16:09<20:42:50, 4.24s/it] 6%|▌ | 1087/18688 [1:16:14<21:35:38, 4.42s/it] 6%|▌ | 1088/18688 [1:16:18<21:13:30, 4.34s/it] 6%|▌ | 1089/18688 [1:16:22<21:03:57, 4.31s/it] 6%|▌ | 1090/18688 [1:16:27<21:06:52, 4.32s/it] {'loss': '1.019', 'grad_norm': '0.9586', 'learning_rate': '7.26e-05', 'epoch': '0.05833', 'num_input_tokens_seen': 3639504, 'train_runtime': '4587', 'train_tokens_per_second': '793.4'}
6%|▌ | 1090/18688 [1:16:27<21:06:52, 4.32s/it] 6%|▌ | 1091/18688 [1:16:31<20:35:42, 4.21s/it] 6%|▌ | 1092/18688 [1:16:35<20:37:35, 4.22s/it] 6%|▌ | 1093/18688 [1:16:39<21:08:26, 4.33s/it] 6%|▌ | 1094/18688 [1:16:44<21:28:05, 4.39s/it] 6%|▌ | 1095/18688 [1:16:48<21:11:52, 4.34s/it] 6%|▌ | 1096/18688 [1:16:52<21:09:02, 4.33s/it] 6%|▌ | 1097/18688 [1:16:56<20:02:54, 4.10s/it] 6%|▌ | 1098/18688 [1:17:00<19:58:28, 4.09s/it] 6%|▌ | 1099/18688 [1:17:04<19:18:53, 3.95s/it] 6%|▌ | 1100/18688 [1:17:08<20:03:08, 4.10s/it] {'loss': '1.057', 'grad_norm': '0.8771', 'learning_rate': '7.327e-05', 'epoch': '0.05886', 'num_input_tokens_seen': 3673560, 'train_runtime': '4629', 'train_tokens_per_second': '793.7'}
6%|▌ | 1100/18688 [1:17:08<20:03:08, 4.10s/it] 6%|▌ | 1101/18688 [1:17:13<21:21:42, 4.37s/it] 6%|▌ | 1102/18688 [1:17:17<20:53:28, 4.28s/it] 6%|▌ | 1103/18688 [1:17:21<20:09:45, 4.13s/it] 6%|▌ | 1104/18688 [1:17:25<19:59:34, 4.09s/it] 6%|▌ | 1105/18688 [1:17:29<19:33:48, 4.01s/it] 6%|▌ | 1106/18688 [1:17:32<19:02:47, 3.90s/it] 6%|▌ | 1107/18688 [1:17:37<20:01:03, 4.10s/it] 6%|▌ | 1108/18688 [1:17:41<19:59:58, 4.10s/it] 6%|▌ | 1109/18688 [1:17:45<19:36:15, 4.01s/it] 6%|▌ | 1110/18688 [1:17:49<20:25:37, 4.18s/it] {'loss': '0.9997', 'grad_norm': '0.9367', 'learning_rate': '7.393e-05', 'epoch': '0.0594', 'num_input_tokens_seen': 3706976, 'train_runtime': '4670', 'train_tokens_per_second': '793.8'}
6%|▌ | 1110/18688 [1:17:49<20:25:37, 4.18s/it] 6%|▌ | 1111/18688 [1:17:54<21:36:50, 4.43s/it] 6%|▌ | 1112/18688 [1:17:59<21:09:00, 4.33s/it] 6%|▌ | 1113/18688 [1:18:02<20:02:28, 4.11s/it] 6%|▌ | 1114/18688 [1:18:06<19:08:25, 3.92s/it] 6%|▌ | 1115/18688 [1:18:10<19:51:45, 4.07s/it] 6%|▌ | 1116/18688 [1:18:15<20:29:48, 4.20s/it] 6%|▌ | 1117/18688 [1:18:19<20:09:22, 4.13s/it] 6%|▌ | 1118/18688 [1:18:23<20:09:50, 4.13s/it] 6%|▌ | 1119/18688 [1:18:27<20:03:20, 4.11s/it] 6%|▌ | 1120/18688 [1:18:32<21:11:32, 4.34s/it] {'loss': '0.9749', 'grad_norm': '0.8986', 'learning_rate': '7.46e-05', 'epoch': '0.05993', 'num_input_tokens_seen': 3740912, 'train_runtime': '4712', 'train_tokens_per_second': '793.9'}
6%|▌ | 1120/18688 [1:18:32<21:11:32, 4.34s/it] 6%|▌ | 1121/18688 [1:18:37<22:10:49, 4.55s/it] 6%|▌ | 1122/18688 [1:18:41<22:38:02, 4.64s/it] 6%|▌ | 1123/18688 [1:18:45<21:13:35, 4.35s/it] 6%|▌ | 1124/18688 [1:18:50<21:27:12, 4.40s/it] 6%|▌ | 1125/18688 [1:18:54<21:06:58, 4.33s/it] 6%|▌ | 1126/18688 [1:18:58<20:50:35, 4.27s/it] 6%|▌ | 1127/18688 [1:19:02<19:55:52, 4.09s/it] 6%|▌ | 1128/18688 [1:19:06<20:13:46, 4.15s/it] 6%|▌ | 1129/18688 [1:19:10<19:31:44, 4.00s/it] 6%|▌ | 1130/18688 [1:19:13<19:12:45, 3.94s/it] {'loss': '0.9301', 'grad_norm': '0.9076', 'learning_rate': '7.527e-05', 'epoch': '0.06047', 'num_input_tokens_seen': 3772344, 'train_runtime': '4754', 'train_tokens_per_second': '793.5'}
6%|▌ | 1130/18688 [1:19:13<19:12:45, 3.94s/it] 6%|▌ | 1131/18688 [1:19:18<20:47:09, 4.26s/it] 6%|▌ | 1132/18688 [1:19:22<19:38:27, 4.03s/it] 6%|▌ | 1133/18688 [1:19:26<19:44:25, 4.05s/it] 6%|▌ | 1134/18688 [1:19:30<19:15:52, 3.95s/it] 6%|▌ | 1135/18688 [1:19:33<18:50:03, 3.86s/it] 6%|▌ | 1136/18688 [1:19:37<18:35:16, 3.81s/it] 6%|▌ | 1137/18688 [1:19:41<18:17:02, 3.75s/it] 6%|▌ | 1138/18688 [1:19:46<20:53:10, 4.28s/it] 6%|▌ | 1139/18688 [1:19:51<21:32:46, 4.42s/it] 6%|▌ | 1140/18688 [1:19:55<20:20:30, 4.17s/it] {'loss': '0.9738', 'grad_norm': '1.041', 'learning_rate': '7.593e-05', 'epoch': '0.061', 'num_input_tokens_seen': 3806288, 'train_runtime': '4795', 'train_tokens_per_second': '793.8'}
6%|▌ | 1140/18688 [1:19:55<20:20:30, 4.17s/it] 6%|▌ | 1141/18688 [1:19:59<21:11:41, 4.35s/it] 6%|▌ | 1142/18688 [1:20:03<20:27:13, 4.20s/it] 6%|▌ | 1143/18688 [1:20:07<19:48:20, 4.06s/it] 6%|▌ | 1144/18688 [1:20:11<19:43:11, 4.05s/it] 6%|▌ | 1145/18688 [1:20:15<20:08:46, 4.13s/it] 6%|▌ | 1146/18688 [1:20:19<20:05:20, 4.12s/it] 6%|▌ | 1147/18688 [1:20:23<19:10:32, 3.94s/it] 6%|▌ | 1148/18688 [1:20:27<19:57:04, 4.09s/it] 6%|▌ | 1149/18688 [1:20:32<20:25:44, 4.19s/it] 6%|▌ | 1150/18688 [1:20:36<20:29:07, 4.21s/it] {'loss': '1.06', 'grad_norm': '1.172', 'learning_rate': '7.66e-05', 'epoch': '0.06154', 'num_input_tokens_seen': 3839712, 'train_runtime': '4836', 'train_tokens_per_second': '793.9'}
6%|▌ | 1150/18688 [1:20:36<20:29:07, 4.21s/it] 6%|▌ | 1151/18688 [1:20:40<20:28:05, 4.20s/it] 6%|▌ | 1152/18688 [1:20:44<20:20:53, 4.18s/it] 6%|▌ | 1153/18688 [1:20:49<20:47:47, 4.27s/it] 6%|▌ | 1154/18688 [1:20:53<20:04:53, 4.12s/it] 6%|▌ | 1155/18688 [1:20:56<19:24:51, 3.99s/it] 6%|▌ | 1156/18688 [1:21:02<21:53:09, 4.49s/it] 6%|▌ | 1157/18688 [1:21:06<21:55:54, 4.50s/it] 6%|▌ | 1158/18688 [1:21:11<21:36:48, 4.44s/it] 6%|▌ | 1159/18688 [1:21:16<22:36:33, 4.64s/it] 6%|▌ | 1160/18688 [1:21:20<21:34:20, 4.43s/it] {'loss': '1.069', 'grad_norm': '1.087', 'learning_rate': '7.727e-05', 'epoch': '0.06207', 'num_input_tokens_seen': 3876000, 'train_runtime': '4880', 'train_tokens_per_second': '794.2'}
6%|▌ | 1160/18688 [1:21:20<21:34:20, 4.43s/it] 6%|▌ | 1161/18688 [1:21:23<20:21:52, 4.18s/it] 6%|▌ | 1162/18688 [1:21:28<20:45:47, 4.26s/it] 6%|▌ | 1163/18688 [1:21:33<22:19:27, 4.59s/it] 6%|▌ | 1164/18688 [1:21:37<21:00:49, 4.32s/it] 6%|▌ | 1165/18688 [1:21:41<20:50:46, 4.28s/it] 6%|▌ | 1166/18688 [1:21:45<19:47:54, 4.07s/it] 6%|▌ | 1167/18688 [1:21:48<18:52:49, 3.88s/it] 6%|▋ | 1168/18688 [1:21:52<18:50:58, 3.87s/it] 6%|▋ | 1169/18688 [1:21:56<19:16:52, 3.96s/it] 6%|▋ | 1170/18688 [1:22:00<19:03:24, 3.92s/it] {'loss': '0.9788', 'grad_norm': '0.9733', 'learning_rate': '7.793e-05', 'epoch': '0.06261', 'num_input_tokens_seen': 3908440, 'train_runtime': '4920', 'train_tokens_per_second': '794.3'}
6%|▋ | 1170/18688 [1:22:00<19:03:24, 3.92s/it] 6%|▋ | 1171/18688 [1:22:04<19:02:13, 3.91s/it] 6%|▋ | 1172/18688 [1:22:08<19:38:31, 4.04s/it] 6%|▋ | 1173/18688 [1:22:13<20:27:06, 4.20s/it] 6%|▋ | 1174/18688 [1:22:16<19:49:01, 4.07s/it] 6%|▋ | 1175/18688 [1:22:20<19:26:55, 4.00s/it] 6%|▋ | 1176/18688 [1:22:25<19:50:18, 4.08s/it] 6%|▋ | 1177/18688 [1:22:29<20:27:59, 4.21s/it] 6%|▋ | 1178/18688 [1:22:34<20:51:35, 4.29s/it] 6%|▋ | 1179/18688 [1:22:38<20:53:11, 4.29s/it] 6%|▋ | 1180/18688 [1:22:42<20:22:25, 4.19s/it] {'loss': '0.9809', 'grad_norm': '0.9617', 'learning_rate': '7.86e-05', 'epoch': '0.06314', 'num_input_tokens_seen': 3941608, 'train_runtime': '4962', 'train_tokens_per_second': '794.3'}
6%|▋ | 1180/18688 [1:22:42<20:22:25, 4.19s/it] 6%|▋ | 1181/18688 [1:22:46<20:22:20, 4.19s/it] 6%|▋ | 1182/18688 [1:22:50<19:36:51, 4.03s/it] 6%|▋ | 1183/18688 [1:22:54<20:41:02, 4.25s/it] 6%|▋ | 1184/18688 [1:22:58<19:39:32, 4.04s/it] 6%|▋ | 1185/18688 [1:23:01<18:44:49, 3.86s/it] 6%|▋ | 1186/18688 [1:23:06<20:03:15, 4.13s/it] 6%|▋ | 1187/18688 [1:23:11<20:36:50, 4.24s/it] 6%|▋ | 1188/18688 [1:23:15<20:12:18, 4.16s/it] 6%|▋ | 1189/18688 [1:23:18<19:14:13, 3.96s/it] 6%|▋ | 1190/18688 [1:23:22<19:40:41, 4.05s/it] {'loss': '1.014', 'grad_norm': '1.173', 'learning_rate': '7.927e-05', 'epoch': '0.06368', 'num_input_tokens_seen': 3974776, 'train_runtime': '5003', 'train_tokens_per_second': '794.5'}
6%|▋ | 1190/18688 [1:23:22<19:40:41, 4.05s/it] 6%|▋ | 1191/18688 [1:23:26<19:39:57, 4.05s/it] 6%|▋ | 1192/18688 [1:23:31<20:50:34, 4.29s/it] 6%|▋ | 1193/18688 [1:23:36<21:04:54, 4.34s/it] 6%|▋ | 1194/18688 [1:23:39<19:58:33, 4.11s/it] 6%|▋ | 1195/18688 [1:23:43<19:27:05, 4.00s/it] 6%|▋ | 1196/18688 [1:23:47<19:28:58, 4.01s/it] 6%|▋ | 1197/18688 [1:23:51<19:43:46, 4.06s/it] 6%|▋ | 1198/18688 [1:23:55<19:20:08, 3.98s/it] 6%|▋ | 1199/18688 [1:23:59<19:04:26, 3.93s/it] 6%|▋ | 1200/18688 [1:24:03<19:22:51, 3.99s/it] {'loss': '0.9726', 'grad_norm': '1.046', 'learning_rate': '7.993e-05', 'epoch': '0.06421', 'num_input_tokens_seen': 4008136, 'train_runtime': '5043', 'train_tokens_per_second': '794.7'}
6%|▋ | 1200/18688 [1:24:03<19:22:51, 3.99s/it] 6%|▋ | 1201/18688 [1:24:07<18:52:43, 3.89s/it] 6%|▋ | 1202/18688 [1:24:11<19:26:30, 4.00s/it] 6%|▋ | 1203/18688 [1:24:15<20:11:45, 4.16s/it] 6%|▋ | 1204/18688 [1:24:20<20:34:41, 4.24s/it] 6%|▋ | 1205/18688 [1:24:23<19:38:52, 4.05s/it] 6%|▋ | 1206/18688 [1:24:27<19:20:20, 3.98s/it] 6%|▋ | 1207/18688 [1:24:32<20:31:04, 4.23s/it] 6%|▋ | 1208/18688 [1:24:36<20:28:40, 4.22s/it] 6%|▋ | 1209/18688 [1:24:41<21:54:52, 4.51s/it] 6%|▋ | 1210/18688 [1:24:46<21:21:03, 4.40s/it] {'loss': '1.053', 'grad_norm': '0.9507', 'learning_rate': '8.06e-05', 'epoch': '0.06475', 'num_input_tokens_seen': 4042776, 'train_runtime': '5086', 'train_tokens_per_second': '794.9'}
6%|▋ | 1210/18688 [1:24:46<21:21:03, 4.40s/it] 6%|▋ | 1211/18688 [1:24:49<19:59:39, 4.12s/it] 6%|▋ | 1212/18688 [1:24:53<19:55:12, 4.10s/it] 6%|▋ | 1213/18688 [1:24:57<19:17:01, 3.97s/it] 6%|▋ | 1214/18688 [1:25:01<19:45:39, 4.07s/it] 7%|▋ | 1215/18688 [1:25:05<19:09:08, 3.95s/it] 7%|▋ | 1216/18688 [1:25:08<18:31:47, 3.82s/it] 7%|▋ | 1217/18688 [1:25:12<18:47:48, 3.87s/it] 7%|▋ | 1218/18688 [1:25:16<18:48:34, 3.88s/it] 7%|▋ | 1219/18688 [1:25:21<20:09:59, 4.16s/it] 7%|▋ | 1220/18688 [1:25:25<19:50:08, 4.09s/it] {'loss': '0.9913', 'grad_norm': '0.9128', 'learning_rate': '8.127e-05', 'epoch': '0.06528', 'num_input_tokens_seen': 4074336, 'train_runtime': '5125', 'train_tokens_per_second': '794.9'}
7%|▋ | 1220/18688 [1:25:25<19:50:08, 4.09s/it] 7%|▋ | 1221/18688 [1:25:29<20:14:33, 4.17s/it] 7%|▋ | 1222/18688 [1:25:33<19:07:54, 3.94s/it] 7%|▋ | 1223/18688 [1:25:37<19:01:45, 3.92s/it] 7%|▋ | 1224/18688 [1:25:40<18:32:39, 3.82s/it] 7%|▋ | 1225/18688 [1:25:45<20:32:47, 4.24s/it] 7%|▋ | 1226/18688 [1:25:50<21:23:02, 4.41s/it] 7%|▋ | 1227/18688 [1:25:54<20:03:35, 4.14s/it] 7%|▋ | 1228/18688 [1:25:58<20:05:31, 4.14s/it] 7%|▋ | 1229/18688 [1:26:02<20:19:22, 4.19s/it] 7%|▋ | 1230/18688 [1:26:06<19:55:41, 4.11s/it] {'loss': '1.035', 'grad_norm': '1.272', 'learning_rate': '8.193e-05', 'epoch': '0.06582', 'num_input_tokens_seen': 4107808, 'train_runtime': '5167', 'train_tokens_per_second': '795.1'}
7%|▋ | 1230/18688 [1:26:06<19:55:41, 4.11s/it] 7%|▋ | 1231/18688 [1:26:11<20:30:31, 4.23s/it] 7%|▋ | 1232/18688 [1:26:15<20:16:06, 4.18s/it] 7%|▋ | 1233/18688 [1:26:19<19:56:59, 4.11s/it] 7%|▋ | 1234/18688 [1:26:22<19:41:48, 4.06s/it] 7%|▋ | 1235/18688 [1:26:27<19:42:06, 4.06s/it] 7%|▋ | 1236/18688 [1:26:31<19:59:08, 4.12s/it] 7%|▋ | 1237/18688 [1:26:34<19:03:47, 3.93s/it] 7%|▋ | 1238/18688 [1:26:38<18:43:47, 3.86s/it] 7%|▋ | 1239/18688 [1:26:42<18:58:12, 3.91s/it] 7%|▋ | 1240/18688 [1:26:46<18:44:58, 3.87s/it] {'loss': '0.9365', 'grad_norm': '0.8579', 'learning_rate': '8.26e-05', 'epoch': '0.06635', 'num_input_tokens_seen': 4141480, 'train_runtime': '5206', 'train_tokens_per_second': '795.5'}
7%|▋ | 1240/18688 [1:26:46<18:44:58, 3.87s/it] 7%|▋ | 1241/18688 [1:26:50<18:34:24, 3.83s/it] 7%|▋ | 1242/18688 [1:26:55<21:17:13, 4.39s/it] 7%|▋ | 1243/18688 [1:27:00<21:31:23, 4.44s/it] 7%|▋ | 1244/18688 [1:27:04<21:00:32, 4.34s/it] 7%|▋ | 1245/18688 [1:27:08<20:33:31, 4.24s/it] 7%|▋ | 1246/18688 [1:27:12<20:03:32, 4.14s/it] 7%|▋ | 1247/18688 [1:27:15<19:21:35, 4.00s/it] 7%|▋ | 1248/18688 [1:27:20<19:35:31, 4.04s/it] 7%|▋ | 1249/18688 [1:27:24<19:37:01, 4.05s/it] 7%|▋ | 1250/18688 [1:27:28<20:26:06, 4.22s/it] {'loss': '1.036', 'grad_norm': '0.8608', 'learning_rate': '8.327e-05', 'epoch': '0.06689', 'num_input_tokens_seen': 4175648, 'train_runtime': '5249', 'train_tokens_per_second': '795.5'}
7%|▋ | 1250/18688 [1:27:28<20:26:06, 4.22s/it] 7%|▋ | 1251/18688 [1:27:32<20:08:15, 4.16s/it] 7%|▋ | 1252/18688 [1:27:36<19:39:26, 4.06s/it] 7%|▋ | 1253/18688 [1:27:40<19:14:03, 3.97s/it] 7%|▋ | 1254/18688 [1:27:44<19:57:30, 4.12s/it] 7%|▋ | 1255/18688 [1:27:49<20:23:53, 4.21s/it] 7%|▋ | 1256/18688 [1:27:53<20:32:34, 4.24s/it] 7%|▋ | 1257/18688 [1:27:58<21:15:34, 4.39s/it] 7%|▋ | 1258/18688 [1:28:01<19:56:49, 4.12s/it] 7%|▋ | 1259/18688 [1:28:05<19:31:05, 4.03s/it] 7%|▋ | 1260/18688 [1:28:09<18:43:45, 3.87s/it] {'loss': '0.9782', 'grad_norm': '1.11', 'learning_rate': '8.393e-05', 'epoch': '0.06742', 'num_input_tokens_seen': 4207024, 'train_runtime': '5289', 'train_tokens_per_second': '795.4'}
7%|▋ | 1260/18688 [1:28:09<18:43:45, 3.87s/it] 7%|▋ | 1261/18688 [1:28:13<18:57:27, 3.92s/it] 7%|▋ | 1262/18688 [1:28:17<19:54:52, 4.11s/it] 7%|▋ | 1263/18688 [1:28:21<19:18:10, 3.99s/it] 7%|▋ | 1264/18688 [1:28:25<18:38:05, 3.85s/it] 7%|▋ | 1265/18688 [1:28:28<18:24:56, 3.81s/it] 7%|▋ | 1266/18688 [1:28:32<18:57:55, 3.92s/it] 7%|▋ | 1267/18688 [1:28:36<18:29:41, 3.82s/it] 7%|▋ | 1268/18688 [1:28:39<18:02:58, 3.73s/it] 7%|▋ | 1269/18688 [1:28:43<17:49:31, 3.68s/it] 7%|▋ | 1270/18688 [1:28:48<18:58:25, 3.92s/it] {'loss': '1.003', 'grad_norm': '0.9903', 'learning_rate': '8.46e-05', 'epoch': '0.06796', 'num_input_tokens_seen': 4236392, 'train_runtime': '5328', 'train_tokens_per_second': '795.1'}
7%|▋ | 1270/18688 [1:28:48<18:58:25, 3.92s/it] 7%|▋ | 1271/18688 [1:28:52<19:11:27, 3.97s/it] 7%|▋ | 1272/18688 [1:28:55<18:59:00, 3.92s/it] 7%|▋ | 1273/18688 [1:28:59<18:53:57, 3.91s/it] 7%|▋ | 1274/18688 [1:29:03<18:47:10, 3.88s/it] 7%|▋ | 1275/18688 [1:29:07<19:26:21, 4.02s/it] 7%|▋ | 1276/18688 [1:29:11<19:16:13, 3.98s/it] 7%|▋ | 1277/18688 [1:29:15<18:57:32, 3.92s/it] 7%|▋ | 1278/18688 [1:29:19<18:27:51, 3.82s/it] 7%|▋ | 1279/18688 [1:29:23<18:47:36, 3.89s/it] 7%|▋ | 1280/18688 [1:29:27<18:52:00, 3.90s/it] {'loss': '0.9778', 'grad_norm': '1.029', 'learning_rate': '8.527e-05', 'epoch': '0.06849', 'num_input_tokens_seen': 4265784, 'train_runtime': '5367', 'train_tokens_per_second': '794.8'}
7%|▋ | 1280/18688 [1:29:27<18:52:00, 3.90s/it] 7%|▋ | 1281/18688 [1:29:31<19:30:08, 4.03s/it] 7%|▋ | 1282/18688 [1:29:35<19:16:28, 3.99s/it] 7%|▋ | 1283/18688 [1:29:40<20:08:28, 4.17s/it] 7%|▋ | 1284/18688 [1:29:43<19:27:52, 4.03s/it] 7%|▋ | 1285/18688 [1:29:48<19:53:29, 4.11s/it] 7%|▋ | 1286/18688 [1:29:52<19:52:34, 4.11s/it] 7%|▋ | 1287/18688 [1:29:55<19:03:00, 3.94s/it] 7%|▋ | 1288/18688 [1:29:59<18:33:07, 3.84s/it] 7%|▋ | 1289/18688 [1:30:03<19:20:03, 4.00s/it] 7%|▋ | 1290/18688 [1:30:08<20:11:54, 4.18s/it] {'loss': '1.035', 'grad_norm': '0.9628', 'learning_rate': '8.593e-05', 'epoch': '0.06903', 'num_input_tokens_seen': 4298040, 'train_runtime': '5408', 'train_tokens_per_second': '794.7'}
7%|▋ | 1290/18688 [1:30:08<20:11:54, 4.18s/it] 7%|▋ | 1291/18688 [1:30:11<19:18:09, 3.99s/it] 7%|▋ | 1292/18688 [1:30:16<20:05:15, 4.16s/it] 7%|▋ | 1293/18688 [1:30:20<20:29:34, 4.24s/it] 7%|▋ | 1294/18688 [1:30:25<21:33:51, 4.46s/it] 7%|▋ | 1295/18688 [1:30:29<20:17:10, 4.20s/it] 7%|▋ | 1296/18688 [1:30:33<19:34:27, 4.05s/it] 7%|▋ | 1297/18688 [1:30:36<18:52:55, 3.91s/it] 7%|▋ | 1298/18688 [1:30:40<19:22:35, 4.01s/it] 7%|▋ | 1299/18688 [1:30:45<20:49:19, 4.31s/it] 7%|▋ | 1300/18688 [1:30:50<21:35:40, 4.47s/it] {'loss': '1.055', 'grad_norm': '0.8736', 'learning_rate': '8.66e-05', 'epoch': '0.06957', 'num_input_tokens_seen': 4332752, 'train_runtime': '5451', 'train_tokens_per_second': '794.9'}
7%|▋ | 1300/18688 [1:30:50<21:35:40, 4.47s/it] 7%|▋ | 1301/18688 [1:30:55<22:04:57, 4.57s/it] 7%|▋ | 1302/18688 [1:31:00<22:02:15, 4.56s/it] 7%|▋ | 1303/18688 [1:31:04<21:17:57, 4.41s/it] 7%|▋ | 1304/18688 [1:31:07<20:06:01, 4.16s/it] 7%|▋ | 1305/18688 [1:31:12<20:22:34, 4.22s/it] 7%|▋ | 1306/18688 [1:31:16<20:44:32, 4.30s/it] 7%|▋ | 1307/18688 [1:31:21<21:00:46, 4.35s/it] 7%|▋ | 1308/18688 [1:31:24<20:03:57, 4.16s/it] 7%|▋ | 1309/18688 [1:31:28<19:22:36, 4.01s/it] 7%|▋ | 1310/18688 [1:31:32<19:36:53, 4.06s/it] {'loss': '1.062', 'grad_norm': '0.9519', 'learning_rate': '8.727e-05', 'epoch': '0.0701', 'num_input_tokens_seen': 4365560, 'train_runtime': '5493', 'train_tokens_per_second': '794.8'}
7%|▋ | 1310/18688 [1:31:32<19:36:53, 4.06s/it] 7%|▋ | 1311/18688 [1:31:36<18:56:13, 3.92s/it] 7%|▋ | 1312/18688 [1:31:39<18:36:58, 3.86s/it] 7%|▋ | 1313/18688 [1:31:44<19:03:43, 3.95s/it] 7%|▋ | 1314/18688 [1:31:48<19:26:30, 4.03s/it] 7%|▋ | 1315/18688 [1:31:52<19:39:17, 4.07s/it] 7%|▋ | 1316/18688 [1:31:56<19:12:06, 3.98s/it] 7%|▋ | 1317/18688 [1:32:00<19:32:44, 4.05s/it] 7%|▋ | 1318/18688 [1:32:04<19:15:54, 3.99s/it] 7%|▋ | 1319/18688 [1:32:08<19:22:29, 4.02s/it] 7%|▋ | 1320/18688 [1:32:12<19:40:48, 4.08s/it] {'loss': '0.9857', 'grad_norm': '0.9506', 'learning_rate': '8.793e-05', 'epoch': '0.07064', 'num_input_tokens_seen': 4398144, 'train_runtime': '5533', 'train_tokens_per_second': '795'}
7%|▋ | 1320/18688 [1:32:12<19:40:48, 4.08s/it] 7%|▋ | 1321/18688 [1:32:16<19:46:39, 4.10s/it] 7%|▋ | 1322/18688 [1:32:21<20:03:43, 4.16s/it] 7%|▋ | 1323/18688 [1:32:24<19:07:43, 3.97s/it] 7%|▋ | 1324/18688 [1:32:29<19:56:55, 4.14s/it] 7%|▋ | 1325/18688 [1:32:32<19:31:53, 4.05s/it] 7%|▋ | 1326/18688 [1:32:36<19:30:29, 4.05s/it] 7%|▋ | 1327/18688 [1:32:40<19:05:10, 3.96s/it] 7%|▋ | 1328/18688 [1:32:44<18:44:29, 3.89s/it] 7%|▋ | 1329/18688 [1:32:49<19:47:23, 4.10s/it] 7%|▋ | 1330/18688 [1:32:52<19:01:13, 3.94s/it] {'loss': '1.04', 'grad_norm': '1.139', 'learning_rate': '8.86e-05', 'epoch': '0.07117', 'num_input_tokens_seen': 4428848, 'train_runtime': '5573', 'train_tokens_per_second': '794.8'}
7%|▋ | 1330/18688 [1:32:52<19:01:13, 3.94s/it] 7%|▋ | 1331/18688 [1:32:57<19:50:49, 4.12s/it] 7%|▋ | 1332/18688 [1:33:01<19:46:22, 4.10s/it] 7%|▋ | 1333/18688 [1:33:04<19:05:06, 3.96s/it] 7%|▋ | 1334/18688 [1:33:08<18:23:20, 3.81s/it] 7%|▋ | 1335/18688 [1:33:13<20:25:55, 4.24s/it] 7%|▋ | 1336/18688 [1:33:19<22:51:03, 4.74s/it] 7%|▋ | 1337/18688 [1:33:23<21:10:57, 4.39s/it] 7%|▋ | 1338/18688 [1:33:27<21:00:53, 4.36s/it] 7%|▋ | 1339/18688 [1:33:30<19:50:43, 4.12s/it] 7%|▋ | 1340/18688 [1:33:34<19:22:32, 4.02s/it] {'loss': '1.022', 'grad_norm': '1.049', 'learning_rate': '8.927e-05', 'epoch': '0.07171', 'num_input_tokens_seen': 4462248, 'train_runtime': '5615', 'train_tokens_per_second': '794.7'}
7%|▋ | 1340/18688 [1:33:34<19:22:32, 4.02s/it] 7%|▋ | 1341/18688 [1:33:39<20:08:32, 4.18s/it] 7%|▋ | 1342/18688 [1:33:43<20:17:56, 4.21s/it] 7%|▋ | 1343/18688 [1:33:47<19:21:34, 4.02s/it] 7%|▋ | 1344/18688 [1:33:51<19:27:56, 4.04s/it] 7%|▋ | 1345/18688 [1:33:57<22:05:21, 4.59s/it] 7%|▋ | 1346/18688 [1:34:01<21:13:45, 4.41s/it] 7%|▋ | 1347/18688 [1:34:05<20:51:51, 4.33s/it] 7%|▋ | 1348/18688 [1:34:09<21:10:49, 4.40s/it] 7%|▋ | 1349/18688 [1:34:14<21:01:07, 4.36s/it] 7%|▋ | 1350/18688 [1:34:18<20:36:15, 4.28s/it] {'loss': '1.069', 'grad_norm': '1.142', 'learning_rate': '8.993e-05', 'epoch': '0.07224', 'num_input_tokens_seen': 4497528, 'train_runtime': '5658', 'train_tokens_per_second': '794.9'}
7%|▋ | 1350/18688 [1:34:18<20:36:15, 4.28s/it] 7%|▋ | 1351/18688 [1:34:22<20:57:04, 4.35s/it] 7%|▋ | 1352/18688 [1:34:27<21:56:11, 4.56s/it] 7%|▋ | 1353/18688 [1:34:32<22:14:13, 4.62s/it] 7%|▋ | 1354/18688 [1:34:36<21:14:38, 4.41s/it] 7%|▋ | 1355/18688 [1:34:41<22:08:15, 4.60s/it] 7%|▋ | 1356/18688 [1:34:45<21:37:45, 4.49s/it] 7%|▋ | 1357/18688 [1:34:49<20:19:29, 4.22s/it] 7%|▋ | 1358/18688 [1:34:53<20:18:23, 4.22s/it] 7%|▋ | 1359/18688 [1:34:57<20:25:33, 4.24s/it] 7%|▋ | 1360/18688 [1:35:01<19:50:30, 4.12s/it] {'loss': '1.031', 'grad_norm': '1.145', 'learning_rate': '9.06e-05', 'epoch': '0.07278', 'num_input_tokens_seen': 4534280, 'train_runtime': '5702', 'train_tokens_per_second': '795.3'}
7%|▋ | 1360/18688 [1:35:01<19:50:30, 4.12s/it] 7%|▋ | 1361/18688 [1:35:06<20:20:51, 4.23s/it] 7%|▋ | 1362/18688 [1:35:09<19:49:56, 4.12s/it] 7%|▋ | 1363/18688 [1:35:13<19:27:27, 4.04s/it] 7%|▋ | 1364/18688 [1:35:17<19:16:21, 4.00s/it] 7%|▋ | 1365/18688 [1:35:21<18:29:02, 3.84s/it] 7%|▋ | 1366/18688 [1:35:24<18:25:11, 3.83s/it] 7%|▋ | 1367/18688 [1:35:28<18:24:42, 3.83s/it] 7%|▋ | 1368/18688 [1:35:33<20:05:41, 4.18s/it] 7%|▋ | 1369/18688 [1:35:37<19:07:59, 3.98s/it] 7%|▋ | 1370/18688 [1:35:40<18:41:41, 3.89s/it] {'loss': '1.03', 'grad_norm': '0.9172', 'learning_rate': '9.127e-05', 'epoch': '0.07331', 'num_input_tokens_seen': 4565296, 'train_runtime': '5741', 'train_tokens_per_second': '795.2'}
7%|▋ | 1370/18688 [1:35:40<18:41:41, 3.89s/it] 7%|▋ | 1371/18688 [1:35:45<19:30:01, 4.05s/it] 7%|▋ | 1372/18688 [1:35:49<19:20:27, 4.02s/it] 7%|▋ | 1373/18688 [1:35:53<18:55:39, 3.94s/it] 7%|▋ | 1374/18688 [1:35:57<19:28:16, 4.05s/it] 7%|▋ | 1375/18688 [1:36:01<18:55:36, 3.94s/it] 7%|▋ | 1376/18688 [1:36:04<18:36:51, 3.87s/it] 7%|▋ | 1377/18688 [1:36:08<18:24:15, 3.83s/it] 7%|▋ | 1378/18688 [1:36:13<19:37:58, 4.08s/it] 7%|▋ | 1379/18688 [1:36:17<20:04:49, 4.18s/it] 7%|▋ | 1380/18688 [1:36:21<19:19:50, 4.02s/it] {'loss': '0.9598', 'grad_norm': '1.114', 'learning_rate': '9.193e-05', 'epoch': '0.07385', 'num_input_tokens_seen': 4596896, 'train_runtime': '5781', 'train_tokens_per_second': '795.1'}
7%|▋ | 1380/18688 [1:36:21<19:19:50, 4.02s/it] 7%|▋ | 1381/18688 [1:36:25<19:22:58, 4.03s/it] 7%|▋ | 1382/18688 [1:36:29<19:37:00, 4.08s/it] 7%|▋ | 1383/18688 [1:36:33<19:53:06, 4.14s/it] 7%|▋ | 1384/18688 [1:36:37<19:54:09, 4.14s/it] 7%|▋ | 1385/18688 [1:36:41<19:38:36, 4.09s/it] 7%|▋ | 1386/18688 [1:36:46<20:50:39, 4.34s/it] 7%|▋ | 1387/18688 [1:36:51<20:41:56, 4.31s/it] 7%|▋ | 1388/18688 [1:36:54<20:04:23, 4.18s/it] 7%|▋ | 1389/18688 [1:36:59<20:01:19, 4.17s/it] 7%|▋ | 1390/18688 [1:37:03<19:51:14, 4.13s/it] {'loss': '0.9843', 'grad_norm': '0.9085', 'learning_rate': '9.26e-05', 'epoch': '0.07438', 'num_input_tokens_seen': 4631296, 'train_runtime': '5823', 'train_tokens_per_second': '795.3'}
7%|▋ | 1390/18688 [1:37:03<19:51:14, 4.13s/it] 7%|▋ | 1391/18688 [1:37:06<19:10:46, 3.99s/it] 7%|▋ | 1392/18688 [1:37:12<21:06:42, 4.39s/it] 7%|▋ | 1393/18688 [1:37:16<20:45:58, 4.32s/it] 7%|▋ | 1394/18688 [1:37:20<20:31:55, 4.27s/it] 7%|▋ | 1395/18688 [1:37:25<21:39:24, 4.51s/it] 7%|▋ | 1396/18688 [1:37:29<21:35:12, 4.49s/it] 7%|▋ | 1397/18688 [1:37:34<21:08:04, 4.40s/it] 7%|▋ | 1398/18688 [1:37:38<21:18:30, 4.44s/it] 7%|▋ | 1399/18688 [1:37:42<20:53:45, 4.35s/it] 7%|▋ | 1400/18688 [1:37:47<21:14:17, 4.42s/it] {'loss': '1.052', 'grad_norm': '0.9179', 'learning_rate': '9.327e-05', 'epoch': '0.07492', 'num_input_tokens_seen': 4668640, 'train_runtime': '5867', 'train_tokens_per_second': '795.7'}
7%|▋ | 1400/18688 [1:37:47<21:14:17, 4.42s/it] 7%|▋ | 1401/18688 [1:37:51<21:08:25, 4.40s/it] 8%|▊ | 1402/18688 [1:37:55<20:09:50, 4.20s/it] 8%|▊ | 1403/18688 [1:37:59<19:44:00, 4.11s/it] 8%|▊ | 1404/18688 [1:38:03<19:47:33, 4.12s/it] 8%|▊ | 1405/18688 [1:38:07<18:59:21, 3.96s/it] 8%|▊ | 1406/18688 [1:38:10<18:34:02, 3.87s/it] 8%|▊ | 1407/18688 [1:38:14<18:35:37, 3.87s/it] 8%|▊ | 1408/18688 [1:38:18<19:16:59, 4.02s/it] 8%|▊ | 1409/18688 [1:38:22<18:34:46, 3.87s/it] 8%|▊ | 1410/18688 [1:38:27<19:47:01, 4.12s/it] {'loss': '1.005', 'grad_norm': '1.031', 'learning_rate': '9.393e-05', 'epoch': '0.07545', 'num_input_tokens_seen': 4701160, 'train_runtime': '5907', 'train_tokens_per_second': '795.8'}
8%|▊ | 1410/18688 [1:38:27<19:47:01, 4.12s/it] 8%|▊ | 1411/18688 [1:38:31<19:31:15, 4.07s/it] 8%|▊ | 1412/18688 [1:38:35<20:02:20, 4.18s/it] 8%|▊ | 1413/18688 [1:38:39<19:50:35, 4.14s/it] 8%|▊ | 1414/18688 [1:38:44<20:51:19, 4.35s/it] 8%|▊ | 1415/18688 [1:38:48<20:48:45, 4.34s/it] 8%|▊ | 1416/18688 [1:38:52<19:47:16, 4.12s/it] 8%|▊ | 1417/18688 [1:38:57<21:21:48, 4.45s/it] 8%|▊ | 1418/18688 [1:39:01<20:41:36, 4.31s/it] 8%|▊ | 1419/18688 [1:39:05<20:01:07, 4.17s/it] 8%|▊ | 1420/18688 [1:39:09<19:47:35, 4.13s/it] {'loss': '1.014', 'grad_norm': '1.167', 'learning_rate': '9.46e-05', 'epoch': '0.07599', 'num_input_tokens_seen': 4735568, 'train_runtime': '5949', 'train_tokens_per_second': '796'}
8%|▊ | 1420/18688 [1:39:09<19:47:35, 4.13s/it] 8%|▊ | 1421/18688 [1:39:13<19:53:44, 4.15s/it] 8%|▊ | 1422/18688 [1:39:18<20:52:20, 4.35s/it] 8%|▊ | 1423/18688 [1:39:23<22:24:20, 4.67s/it] 8%|▊ | 1424/18688 [1:39:27<21:19:42, 4.45s/it] 8%|▊ | 1425/18688 [1:39:31<20:09:31, 4.20s/it] 8%|▊ | 1426/18688 [1:39:35<19:24:36, 4.05s/it] 8%|▊ | 1427/18688 [1:39:39<19:48:03, 4.13s/it] 8%|▊ | 1428/18688 [1:39:44<20:33:56, 4.29s/it] 8%|▊ | 1429/18688 [1:39:48<20:53:30, 4.36s/it] 8%|▊ | 1430/18688 [1:39:52<20:07:35, 4.20s/it] {'loss': '1.027', 'grad_norm': '1.127', 'learning_rate': '9.527e-05', 'epoch': '0.07652', 'num_input_tokens_seen': 4771024, 'train_runtime': '5992', 'train_tokens_per_second': '796.2'}
8%|▊ | 1430/18688 [1:39:52<20:07:35, 4.20s/it] 8%|▊ | 1431/18688 [1:39:55<19:04:25, 3.98s/it] 8%|▊ | 1432/18688 [1:39:59<18:36:08, 3.88s/it] 8%|▊ | 1433/18688 [1:40:03<18:35:08, 3.88s/it] 8%|▊ | 1434/18688 [1:40:07<18:20:01, 3.83s/it] 8%|▊ | 1435/18688 [1:40:11<18:55:35, 3.95s/it] 8%|▊ | 1436/18688 [1:40:15<18:36:12, 3.88s/it] 8%|▊ | 1437/18688 [1:40:19<19:27:28, 4.06s/it] 8%|▊ | 1438/18688 [1:40:23<18:31:44, 3.87s/it] 8%|▊ | 1439/18688 [1:40:26<18:20:43, 3.83s/it] 8%|▊ | 1440/18688 [1:40:30<18:28:43, 3.86s/it] {'loss': '0.9426', 'grad_norm': '0.9998', 'learning_rate': '9.593e-05', 'epoch': '0.07706', 'num_input_tokens_seen': 4800712, 'train_runtime': '6031', 'train_tokens_per_second': '796'}
8%|▊ | 1440/18688 [1:40:30<18:28:43, 3.86s/it] 8%|▊ | 1441/18688 [1:40:34<18:33:42, 3.87s/it] 8%|▊ | 1442/18688 [1:40:38<19:15:41, 4.02s/it] 8%|▊ | 1443/18688 [1:40:44<21:19:24, 4.45s/it] 8%|▊ | 1444/18688 [1:40:48<20:27:35, 4.27s/it] 8%|▊ | 1445/18688 [1:40:52<20:20:00, 4.25s/it] 8%|▊ | 1446/18688 [1:40:56<19:25:05, 4.05s/it] 8%|▊ | 1447/18688 [1:41:00<19:31:52, 4.08s/it] 8%|▊ | 1448/18688 [1:41:03<19:06:25, 3.99s/it] 8%|▊ | 1449/18688 [1:41:08<19:28:17, 4.07s/it] 8%|▊ | 1450/18688 [1:41:12<19:22:26, 4.05s/it] {'loss': '1.036', 'grad_norm': '1.012', 'learning_rate': '9.66e-05', 'epoch': '0.07759', 'num_input_tokens_seen': 4835448, 'train_runtime': '6072', 'train_tokens_per_second': '796.3'}
8%|▊ | 1450/18688 [1:41:12<19:22:26, 4.05s/it] 8%|▊ | 1451/18688 [1:41:15<18:52:51, 3.94s/it] 8%|▊ | 1452/18688 [1:41:19<19:03:18, 3.98s/it] 8%|▊ | 1453/18688 [1:41:24<19:11:53, 4.01s/it] 8%|▊ | 1454/18688 [1:41:28<19:37:32, 4.10s/it] 8%|▊ | 1455/18688 [1:41:33<20:33:12, 4.29s/it] 8%|▊ | 1456/18688 [1:41:36<19:40:44, 4.11s/it] 8%|▊ | 1457/18688 [1:41:41<20:31:48, 4.29s/it] 8%|▊ | 1458/18688 [1:41:45<19:38:29, 4.10s/it] 8%|▊ | 1459/18688 [1:41:48<19:04:43, 3.99s/it] 8%|▊ | 1460/18688 [1:41:54<20:50:48, 4.36s/it] {'loss': '0.985', 'grad_norm': '0.8903', 'learning_rate': '9.727e-05', 'epoch': '0.07813', 'num_input_tokens_seen': 4868640, 'train_runtime': '6114', 'train_tokens_per_second': '796.3'}
8%|▊ | 1460/18688 [1:41:54<20:50:48, 4.36s/it] 8%|▊ | 1461/18688 [1:41:58<20:49:33, 4.35s/it] 8%|▊ | 1462/18688 [1:42:03<21:15:13, 4.44s/it] 8%|▊ | 1463/18688 [1:42:07<21:41:37, 4.53s/it] 8%|▊ | 1464/18688 [1:42:13<23:06:07, 4.83s/it] 8%|▊ | 1465/18688 [1:42:17<22:33:43, 4.72s/it] 8%|▊ | 1466/18688 [1:42:22<21:53:30, 4.58s/it] 8%|▊ | 1467/18688 [1:42:26<22:15:29, 4.65s/it] 8%|▊ | 1468/18688 [1:42:31<21:32:51, 4.50s/it] 8%|▊ | 1469/18688 [1:42:35<21:04:54, 4.41s/it] 8%|▊ | 1470/18688 [1:42:39<20:45:01, 4.34s/it] {'loss': '1.045', 'grad_norm': '0.9251', 'learning_rate': '9.793e-05', 'epoch': '0.07866', 'num_input_tokens_seen': 4907240, 'train_runtime': '6159', 'train_tokens_per_second': '796.7'}
8%|▊ | 1470/18688 [1:42:39<20:45:01, 4.34s/it] 8%|▊ | 1471/18688 [1:42:43<20:59:51, 4.39s/it] 8%|▊ | 1472/18688 [1:42:47<19:39:49, 4.11s/it] 8%|▊ | 1473/18688 [1:42:51<20:16:40, 4.24s/it] 8%|▊ | 1474/18688 [1:42:56<20:06:49, 4.21s/it] 8%|▊ | 1475/18688 [1:43:00<20:26:01, 4.27s/it] 8%|▊ | 1476/18688 [1:43:04<19:45:11, 4.13s/it] 8%|▊ | 1477/18688 [1:43:09<20:45:37, 4.34s/it] 8%|▊ | 1478/18688 [1:43:12<19:30:27, 4.08s/it] 8%|▊ | 1479/18688 [1:43:16<18:47:59, 3.93s/it] 8%|▊ | 1480/18688 [1:43:20<19:03:42, 3.99s/it] {'loss': '0.966', 'grad_norm': '0.9409', 'learning_rate': '9.86e-05', 'epoch': '0.0792', 'num_input_tokens_seen': 4940128, 'train_runtime': '6200', 'train_tokens_per_second': '796.8'}
8%|▊ | 1480/18688 [1:43:20<19:03:42, 3.99s/it] 8%|▊ | 1481/18688 [1:43:23<18:36:21, 3.89s/it] 8%|▊ | 1482/18688 [1:43:27<18:03:04, 3.78s/it] 8%|▊ | 1483/18688 [1:43:31<19:00:17, 3.98s/it] 8%|▊ | 1484/18688 [1:43:35<18:52:25, 3.95s/it] 8%|▊ | 1485/18688 [1:43:40<19:19:15, 4.04s/it] 8%|▊ | 1486/18688 [1:43:45<20:54:51, 4.38s/it] 8%|▊ | 1487/18688 [1:43:48<19:39:54, 4.12s/it] 8%|▊ | 1488/18688 [1:43:52<18:44:20, 3.92s/it] 8%|▊ | 1489/18688 [1:43:55<18:03:11, 3.78s/it] 8%|▊ | 1490/18688 [1:43:59<18:43:42, 3.92s/it] {'loss': '1.005', 'grad_norm': '1.159', 'learning_rate': '9.927e-05', 'epoch': '0.07973', 'num_input_tokens_seen': 4971208, 'train_runtime': '6240', 'train_tokens_per_second': '796.7'}
8%|▊ | 1490/18688 [1:43:59<18:43:42, 3.92s/it] 8%|▊ | 1491/18688 [1:44:03<18:34:07, 3.89s/it] 8%|▊ | 1492/18688 [1:44:08<19:50:50, 4.16s/it] 8%|▊ | 1493/18688 [1:44:12<19:43:35, 4.13s/it] 8%|▊ | 1494/18688 [1:44:16<19:45:41, 4.14s/it] 8%|▊ | 1495/18688 [1:44:21<20:08:21, 4.22s/it] 8%|▊ | 1496/18688 [1:44:25<20:26:30, 4.28s/it] 8%|▊ | 1497/18688 [1:44:30<21:03:08, 4.41s/it] 8%|▊ | 1498/18688 [1:44:33<19:49:27, 4.15s/it] 8%|▊ | 1499/18688 [1:44:37<19:52:09, 4.16s/it] 8%|▊ | 1500/18688 [1:44:41<19:14:18, 4.03s/it] {'loss': '1.029', 'grad_norm': '1.121', 'learning_rate': '9.993e-05', 'epoch': '0.08027', 'num_input_tokens_seen': 5004640, 'train_runtime': '6282', 'train_tokens_per_second': '796.7'}
8%|▊ | 1500/18688 [1:44:41<19:14:18, 4.03s/it] 8%|▊ | 1501/18688 [1:44:45<19:26:54, 4.07s/it] 8%|▊ | 1502/18688 [1:44:50<20:03:30, 4.20s/it] 8%|▊ | 1503/18688 [1:44:54<19:50:48, 4.16s/it] 8%|▊ | 1504/18688 [1:44:58<19:36:35, 4.11s/it] 8%|▊ | 1505/18688 [1:45:02<19:50:09, 4.16s/it] 8%|▊ | 1506/18688 [1:45:06<18:59:07, 3.98s/it] 8%|▊ | 1507/18688 [1:45:10<19:10:55, 4.02s/it] 8%|▊ | 1508/18688 [1:45:15<20:15:07, 4.24s/it] 8%|▊ | 1509/18688 [1:45:19<20:23:06, 4.27s/it] 8%|▊ | 1510/18688 [1:45:23<19:54:45, 4.17s/it] {'loss': '1.015', 'grad_norm': '4.587', 'learning_rate': '0.0001', 'epoch': '0.0808', 'num_input_tokens_seen': 5038496, 'train_runtime': '6323', 'train_tokens_per_second': '796.8'}
8%|▊ | 1510/18688 [1:45:23<19:54:45, 4.17s/it] 8%|▊ | 1511/18688 [1:45:27<19:09:59, 4.02s/it] 8%|▊ | 1512/18688 [1:45:31<19:39:51, 4.12s/it] 8%|▊ | 1513/18688 [1:45:35<19:48:28, 4.15s/it] 8%|▊ | 1514/18688 [1:45:39<19:41:17, 4.13s/it] 8%|▊ | 1515/18688 [1:45:44<20:28:57, 4.29s/it] 8%|▊ | 1516/18688 [1:45:48<19:37:08, 4.11s/it] 8%|▊ | 1517/18688 [1:45:52<19:43:48, 4.14s/it] 8%|▊ | 1518/18688 [1:45:56<19:34:56, 4.11s/it] 8%|▊ | 1519/18688 [1:46:00<20:01:32, 4.20s/it] 8%|▊ | 1520/18688 [1:46:04<20:01:06, 4.20s/it] {'loss': '0.9764', 'grad_norm': '1.042', 'learning_rate': '0.0001', 'epoch': '0.08134', 'num_input_tokens_seen': 5072456, 'train_runtime': '6365', 'train_tokens_per_second': '796.9'}
8%|▊ | 1520/18688 [1:46:04<20:01:06, 4.20s/it] 8%|▊ | 1521/18688 [1:46:09<20:07:59, 4.22s/it] 8%|▊ | 1522/18688 [1:46:13<20:34:24, 4.31s/it] 8%|▊ | 1523/18688 [1:46:19<21:58:01, 4.61s/it] 8%|▊ | 1524/18688 [1:46:22<20:17:40, 4.26s/it] 8%|▊ | 1525/18688 [1:46:26<19:59:44, 4.19s/it] 8%|▊ | 1526/18688 [1:46:30<20:13:07, 4.24s/it] 8%|▊ | 1527/18688 [1:46:35<20:31:54, 4.31s/it] 8%|▊ | 1528/18688 [1:46:38<19:23:11, 4.07s/it] 8%|▊ | 1529/18688 [1:46:44<21:03:02, 4.42s/it] 8%|▊ | 1530/18688 [1:46:48<20:58:27, 4.40s/it] {'loss': '0.9734', 'grad_norm': '1.067', 'learning_rate': '0.0001', 'epoch': '0.08187', 'num_input_tokens_seen': 5109856, 'train_runtime': '6408', 'train_tokens_per_second': '797.4'}
8%|▊ | 1530/18688 [1:46:48<20:58:27, 4.40s/it] 8%|▊ | 1531/18688 [1:46:53<21:23:12, 4.49s/it] 8%|▊ | 1532/18688 [1:46:58<22:34:36, 4.74s/it] 8%|▊ | 1533/18688 [1:47:02<20:52:58, 4.38s/it] 8%|▊ | 1534/18688 [1:47:06<21:38:25, 4.54s/it] 8%|▊ | 1535/18688 [1:47:10<20:34:42, 4.32s/it] 8%|▊ | 1536/18688 [1:47:15<20:37:22, 4.33s/it] 8%|▊ | 1537/18688 [1:47:20<21:53:49, 4.60s/it] 8%|▊ | 1538/18688 [1:47:24<20:47:40, 4.37s/it] 8%|▊ | 1539/18688 [1:47:28<20:09:15, 4.23s/it] 8%|▊ | 1540/18688 [1:47:32<20:50:31, 4.38s/it] {'loss': '0.9974', 'grad_norm': '1.075', 'learning_rate': '0.0001', 'epoch': '0.08241', 'num_input_tokens_seen': 5146272, 'train_runtime': '6453', 'train_tokens_per_second': '797.5'}
8%|▊ | 1540/18688 [1:47:32<20:50:31, 4.38s/it] 8%|▊ | 1541/18688 [1:47:36<20:34:11, 4.32s/it] 8%|▊ | 1542/18688 [1:47:41<20:32:22, 4.31s/it] 8%|▊ | 1543/18688 [1:47:45<20:26:18, 4.29s/it] 8%|▊ | 1544/18688 [1:47:49<19:42:34, 4.14s/it] 8%|▊ | 1545/18688 [1:47:53<19:16:02, 4.05s/it] 8%|▊ | 1546/18688 [1:47:57<19:56:23, 4.19s/it] 8%|▊ | 1547/18688 [1:48:01<19:45:46, 4.15s/it] 8%|▊ | 1548/18688 [1:48:06<20:56:12, 4.40s/it] 8%|▊ | 1549/18688 [1:48:10<20:45:13, 4.36s/it] 8%|▊ | 1550/18688 [1:48:14<19:55:58, 4.19s/it] {'loss': '0.9106', 'grad_norm': '0.9676', 'learning_rate': '0.0001', 'epoch': '0.08294', 'num_input_tokens_seen': 5178800, 'train_runtime': '6495', 'train_tokens_per_second': '797.4'}
8%|▊ | 1550/18688 [1:48:14<19:55:58, 4.19s/it] 8%|▊ | 1551/18688 [1:48:18<19:42:43, 4.14s/it] 8%|▊ | 1552/18688 [1:48:23<20:37:20, 4.33s/it] 8%|▊ | 1553/18688 [1:48:28<21:06:12, 4.43s/it] 8%|▊ | 1554/18688 [1:48:32<20:54:04, 4.39s/it] 8%|▊ | 1555/18688 [1:48:36<20:15:10, 4.26s/it] 8%|▊ | 1556/18688 [1:48:40<20:13:27, 4.25s/it] 8%|▊ | 1557/18688 [1:48:46<21:56:45, 4.61s/it] 8%|▊ | 1558/18688 [1:48:49<20:40:55, 4.35s/it] 8%|▊ | 1559/18688 [1:48:53<19:44:25, 4.15s/it] 8%|▊ | 1560/18688 [1:48:58<20:12:26, 4.25s/it] {'loss': '0.9419', 'grad_norm': '1.077', 'learning_rate': '0.0001', 'epoch': '0.08348', 'num_input_tokens_seen': 5214832, 'train_runtime': '6538', 'train_tokens_per_second': '797.6'}
8%|▊ | 1560/18688 [1:48:58<20:12:26, 4.25s/it] 8%|▊ | 1561/18688 [1:49:02<20:14:07, 4.25s/it] 8%|▊ | 1562/18688 [1:49:06<19:58:20, 4.20s/it] 8%|▊ | 1563/18688 [1:49:10<19:26:35, 4.09s/it] 8%|▊ | 1564/18688 [1:49:14<19:22:49, 4.07s/it] 8%|▊ | 1565/18688 [1:49:18<20:11:14, 4.24s/it] 8%|▊ | 1566/18688 [1:49:22<19:19:21, 4.06s/it] 8%|▊ | 1567/18688 [1:49:25<18:29:09, 3.89s/it] 8%|▊ | 1568/18688 [1:49:29<18:00:44, 3.79s/it] 8%|▊ | 1569/18688 [1:49:34<19:59:46, 4.21s/it] 8%|▊ | 1570/18688 [1:49:39<20:32:19, 4.32s/it] {'loss': '0.9905', 'grad_norm': '1.139', 'learning_rate': '0.0001', 'epoch': '0.08401', 'num_input_tokens_seen': 5248400, 'train_runtime': '6579', 'train_tokens_per_second': '797.7'}
8%|▊ | 1570/18688 [1:49:39<20:32:19, 4.32s/it] 8%|▊ | 1571/18688 [1:49:43<20:20:42, 4.28s/it] 8%|▊ | 1572/18688 [1:49:47<20:00:39, 4.21s/it] 8%|▊ | 1573/18688 [1:49:51<19:31:03, 4.11s/it] 8%|▊ | 1574/18688 [1:49:55<19:24:32, 4.08s/it] 8%|▊ | 1575/18688 [1:49:59<19:39:28, 4.14s/it] 8%|▊ | 1576/18688 [1:50:04<19:55:16, 4.19s/it] 8%|▊ | 1577/18688 [1:50:07<19:34:26, 4.12s/it] 8%|▊ | 1578/18688 [1:50:11<19:23:35, 4.08s/it] 8%|▊ | 1579/18688 [1:50:15<18:31:45, 3.90s/it] 8%|▊ | 1580/18688 [1:50:19<18:47:09, 3.95s/it] {'loss': '1.005', 'grad_norm': '0.8142', 'learning_rate': '9.999e-05', 'epoch': '0.08455', 'num_input_tokens_seen': 5280136, 'train_runtime': '6619', 'train_tokens_per_second': '797.7'}
8%|▊ | 1580/18688 [1:50:19<18:47:09, 3.95s/it] 8%|▊ | 1581/18688 [1:50:23<18:57:16, 3.99s/it] 8%|▊ | 1582/18688 [1:50:28<19:47:22, 4.16s/it] 8%|▊ | 1583/18688 [1:50:32<20:40:54, 4.35s/it] 8%|▊ | 1584/18688 [1:50:36<20:04:05, 4.22s/it] 8%|▊ | 1585/18688 [1:50:40<19:44:29, 4.16s/it] 8%|▊ | 1586/18688 [1:50:45<20:24:11, 4.29s/it] 8%|▊ | 1587/18688 [1:50:51<22:10:59, 4.67s/it] 8%|▊ | 1588/18688 [1:50:55<21:59:16, 4.63s/it] 9%|▊ | 1589/18688 [1:50:59<20:30:13, 4.32s/it] 9%|▊ | 1590/18688 [1:51:03<19:56:25, 4.20s/it] {'loss': '1.011', 'grad_norm': '1.107', 'learning_rate': '9.999e-05', 'epoch': '0.08508', 'num_input_tokens_seen': 5316072, 'train_runtime': '6663', 'train_tokens_per_second': '797.8'}
9%|▊ | 1590/18688 [1:51:03<19:56:25, 4.20s/it] 9%|▊ | 1591/18688 [1:51:07<19:41:08, 4.15s/it] 9%|▊ | 1592/18688 [1:51:10<19:01:30, 4.01s/it] 9%|▊ | 1593/18688 [1:51:14<18:41:29, 3.94s/it] 9%|▊ | 1594/18688 [1:51:18<18:15:56, 3.85s/it] 9%|▊ | 1595/18688 [1:51:21<17:53:46, 3.77s/it] 9%|▊ | 1596/18688 [1:51:25<17:31:50, 3.69s/it] 9%|▊ | 1597/18688 [1:51:30<19:06:35, 4.03s/it] 9%|▊ | 1598/18688 [1:51:33<18:16:53, 3.85s/it] 9%|▊ | 1599/18688 [1:51:37<18:49:50, 3.97s/it] 9%|▊ | 1600/18688 [1:51:41<18:16:41, 3.85s/it] {'loss': '0.9718', 'grad_norm': '1.03', 'learning_rate': '9.999e-05', 'epoch': '0.08562', 'num_input_tokens_seen': 5345640, 'train_runtime': '6701', 'train_tokens_per_second': '797.7'}
9%|▊ | 1600/18688 [1:51:41<18:16:41, 3.85s/it] 9%|▊ | 1601/18688 [1:51:44<17:53:53, 3.77s/it] 9%|▊ | 1602/18688 [1:51:49<18:53:49, 3.98s/it] 9%|▊ | 1603/18688 [1:51:55<21:25:14, 4.51s/it] 9%|▊ | 1604/18688 [1:51:59<21:19:11, 4.49s/it] 9%|▊ | 1605/18688 [1:52:03<20:19:09, 4.28s/it] 9%|▊ | 1606/18688 [1:52:07<20:12:00, 4.26s/it] 9%|▊ | 1607/18688 [1:52:11<19:19:49, 4.07s/it] 9%|▊ | 1608/18688 [1:52:15<19:05:08, 4.02s/it] 9%|▊ | 1609/18688 [1:52:19<19:27:39, 4.10s/it] 9%|▊ | 1610/18688 [1:52:23<18:45:58, 3.96s/it] {'loss': '1.032', 'grad_norm': '1.013', 'learning_rate': '9.999e-05', 'epoch': '0.08615', 'num_input_tokens_seen': 5380488, 'train_runtime': '6743', 'train_tokens_per_second': '797.9'}
9%|▊ | 1610/18688 [1:52:23<18:45:58, 3.96s/it] 9%|▊ | 1611/18688 [1:52:26<18:21:24, 3.87s/it] 9%|▊ | 1612/18688 [1:52:30<18:53:24, 3.98s/it] 9%|▊ | 1613/18688 [1:52:34<18:40:22, 3.94s/it] 9%|▊ | 1614/18688 [1:52:39<19:55:26, 4.20s/it] 9%|▊ | 1615/18688 [1:52:43<19:10:48, 4.04s/it] 9%|▊ | 1616/18688 [1:52:46<18:36:33, 3.92s/it] 9%|▊ | 1617/18688 [1:52:51<19:38:15, 4.14s/it] 9%|▊ | 1618/18688 [1:52:56<20:16:02, 4.27s/it] 9%|▊ | 1619/18688 [1:53:00<19:53:28, 4.20s/it] 9%|▊ | 1620/18688 [1:53:03<19:11:22, 4.05s/it] {'loss': '1.003', 'grad_norm': '1', 'learning_rate': '9.999e-05', 'epoch': '0.08669', 'num_input_tokens_seen': 5414080, 'train_runtime': '6784', 'train_tokens_per_second': '798.1'}
9%|▊ | 1620/18688 [1:53:03<19:11:22, 4.05s/it] 9%|▊ | 1621/18688 [1:53:08<19:21:15, 4.08s/it] 9%|▊ | 1622/18688 [1:53:11<18:50:53, 3.98s/it] 9%|▊ | 1623/18688 [1:53:16<20:11:20, 4.26s/it] 9%|▊ | 1624/18688 [1:53:20<20:14:29, 4.27s/it] 9%|▊ | 1625/18688 [1:53:25<20:58:38, 4.43s/it] 9%|▊ | 1626/18688 [1:53:29<20:24:31, 4.31s/it] 9%|▊ | 1627/18688 [1:53:33<19:58:44, 4.22s/it] 9%|▊ | 1628/18688 [1:53:37<19:04:07, 4.02s/it] 9%|▊ | 1629/18688 [1:53:42<19:55:44, 4.21s/it] 9%|▊ | 1630/18688 [1:53:45<18:58:03, 4.00s/it] {'loss': '1.045', 'grad_norm': '1.168', 'learning_rate': '9.999e-05', 'epoch': '0.08722', 'num_input_tokens_seen': 5448424, 'train_runtime': '6826', 'train_tokens_per_second': '798.2'}
9%|▊ | 1630/18688 [1:53:45<18:58:03, 4.00s/it] 9%|▊ | 1631/18688 [1:53:49<19:26:39, 4.10s/it] 9%|▊ | 1632/18688 [1:53:54<20:24:00, 4.31s/it] 9%|▊ | 1633/18688 [1:53:58<20:17:29, 4.28s/it] 9%|▊ | 1634/18688 [1:54:02<20:00:32, 4.22s/it] 9%|▊ | 1635/18688 [1:54:07<20:52:28, 4.41s/it] 9%|▉ | 1636/18688 [1:54:11<19:57:17, 4.21s/it] 9%|▉ | 1637/18688 [1:54:15<19:35:56, 4.14s/it] 9%|▉ | 1638/18688 [1:54:19<19:51:59, 4.19s/it] 9%|▉ | 1639/18688 [1:54:23<19:16:52, 4.07s/it] 9%|▉ | 1640/18688 [1:54:27<19:00:56, 4.02s/it] {'loss': '1.05', 'grad_norm': '0.9863', 'learning_rate': '9.998e-05', 'epoch': '0.08776', 'num_input_tokens_seen': 5483208, 'train_runtime': '6868', 'train_tokens_per_second': '798.4'}
9%|▉ | 1640/18688 [1:54:27<19:00:56, 4.02s/it] 9%|▉ | 1641/18688 [1:54:31<18:23:17, 3.88s/it] 9%|▉ | 1642/18688 [1:54:34<18:21:19, 3.88s/it] 9%|▉ | 1643/18688 [1:54:39<18:54:18, 3.99s/it] 9%|▉ | 1644/18688 [1:54:42<18:13:04, 3.85s/it] 9%|▉ | 1645/18688 [1:54:47<18:58:12, 4.01s/it] 9%|▉ | 1646/18688 [1:54:50<18:29:09, 3.91s/it] 9%|▉ | 1647/18688 [1:54:54<18:17:11, 3.86s/it] 9%|▉ | 1648/18688 [1:54:58<18:42:56, 3.95s/it] 9%|▉ | 1649/18688 [1:55:02<18:06:32, 3.83s/it] 9%|▉ | 1650/18688 [1:55:06<18:22:55, 3.88s/it] {'loss': '0.9483', 'grad_norm': '1.076', 'learning_rate': '9.998e-05', 'epoch': '0.08829', 'num_input_tokens_seen': 5514240, 'train_runtime': '6906', 'train_tokens_per_second': '798.4'}
9%|▉ | 1650/18688 [1:55:06<18:22:55, 3.88s/it] 9%|▉ | 1651/18688 [1:55:10<19:28:36, 4.12s/it] 9%|▉ | 1652/18688 [1:55:14<19:16:43, 4.07s/it] 9%|▉ | 1653/18688 [1:55:19<20:34:02, 4.35s/it] 9%|▉ | 1654/18688 [1:55:24<20:24:19, 4.31s/it] 9%|▉ | 1655/18688 [1:55:28<20:17:48, 4.29s/it] 9%|▉ | 1656/18688 [1:55:32<20:43:40, 4.38s/it] 9%|▉ | 1657/18688 [1:55:37<20:43:40, 4.38s/it] 9%|▉ | 1658/18688 [1:55:41<21:08:06, 4.47s/it] 9%|▉ | 1659/18688 [1:55:46<21:32:47, 4.56s/it] 9%|▉ | 1660/18688 [1:55:50<20:37:14, 4.36s/it] {'loss': '1', 'grad_norm': '0.8861', 'learning_rate': '9.998e-05', 'epoch': '0.08883', 'num_input_tokens_seen': 5552472, 'train_runtime': '6951', 'train_tokens_per_second': '798.8'}
9%|▉ | 1660/18688 [1:55:50<20:37:14, 4.36s/it] 9%|▉ | 1661/18688 [1:55:54<20:32:35, 4.34s/it] 9%|▉ | 1662/18688 [1:55:59<20:21:02, 4.30s/it] 9%|▉ | 1663/18688 [1:56:03<19:48:57, 4.19s/it] 9%|▉ | 1664/18688 [1:56:06<19:03:05, 4.03s/it] 9%|▉ | 1665/18688 [1:56:10<18:35:11, 3.93s/it] 9%|▉ | 1666/18688 [1:56:15<20:05:17, 4.25s/it] 9%|▉ | 1667/18688 [1:56:19<19:17:48, 4.08s/it] 9%|▉ | 1668/18688 [1:56:24<20:27:51, 4.33s/it] 9%|▉ | 1669/18688 [1:56:28<20:26:06, 4.32s/it] 9%|▉ | 1670/18688 [1:56:32<20:01:49, 4.24s/it] {'loss': '1.015', 'grad_norm': '0.9404', 'learning_rate': '9.998e-05', 'epoch': '0.08936', 'num_input_tokens_seen': 5586232, 'train_runtime': '6992', 'train_tokens_per_second': '798.9'}
9%|▉ | 1670/18688 [1:56:32<20:01:49, 4.24s/it] 9%|▉ | 1671/18688 [1:56:37<20:41:25, 4.38s/it] 9%|▉ | 1672/18688 [1:56:40<19:31:51, 4.13s/it] 9%|▉ | 1673/18688 [1:56:44<18:57:51, 4.01s/it] 9%|▉ | 1674/18688 [1:56:48<19:06:45, 4.04s/it] 9%|▉ | 1675/18688 [1:56:52<18:39:06, 3.95s/it] 9%|▉ | 1676/18688 [1:56:55<17:58:52, 3.81s/it] 9%|▉ | 1677/18688 [1:56:59<18:04:12, 3.82s/it] 9%|▉ | 1678/18688 [1:57:03<18:26:12, 3.90s/it] 9%|▉ | 1679/18688 [1:57:07<18:02:12, 3.82s/it] 9%|▉ | 1680/18688 [1:57:10<17:32:11, 3.71s/it] {'loss': '0.9589', 'grad_norm': '0.9883', 'learning_rate': '9.997e-05', 'epoch': '0.0899', 'num_input_tokens_seen': 5615848, 'train_runtime': '7031', 'train_tokens_per_second': '798.8'}
9%|▉ | 1680/18688 [1:57:10<17:32:11, 3.71s/it] 9%|▉ | 1681/18688 [1:57:14<18:00:25, 3.81s/it] 9%|▉ | 1682/18688 [1:57:19<18:41:41, 3.96s/it] 9%|▉ | 1683/18688 [1:57:24<20:27:20, 4.33s/it] 9%|▉ | 1684/18688 [1:57:28<20:08:41, 4.26s/it] 9%|▉ | 1685/18688 [1:57:32<19:21:47, 4.10s/it] 9%|▉ | 1686/18688 [1:57:36<19:36:37, 4.15s/it] 9%|▉ | 1687/18688 [1:57:40<19:16:29, 4.08s/it] 9%|▉ | 1688/18688 [1:57:43<18:36:05, 3.94s/it] 9%|▉ | 1689/18688 [1:57:47<18:35:59, 3.94s/it] 9%|▉ | 1690/18688 [1:57:52<19:07:00, 4.05s/it] {'loss': '1.03', 'grad_norm': '1.114', 'learning_rate': '9.997e-05', 'epoch': '0.09043', 'num_input_tokens_seen': 5649632, 'train_runtime': '7072', 'train_tokens_per_second': '798.9'}
9%|▉ | 1690/18688 [1:57:52<19:07:00, 4.05s/it] 9%|▉ | 1691/18688 [1:57:57<20:40:29, 4.38s/it] 9%|▉ | 1692/18688 [1:58:01<20:07:44, 4.26s/it] 9%|▉ | 1693/18688 [1:58:04<19:13:02, 4.07s/it] 9%|▉ | 1694/18688 [1:58:08<19:05:13, 4.04s/it] 9%|▉ | 1695/18688 [1:58:13<19:40:48, 4.17s/it] 9%|▉ | 1696/18688 [1:58:17<19:38:58, 4.16s/it] 9%|▉ | 1697/18688 [1:58:21<19:28:26, 4.13s/it] 9%|▉ | 1698/18688 [1:58:25<19:30:22, 4.13s/it] 9%|▉ | 1699/18688 [1:58:29<19:17:40, 4.09s/it] 9%|▉ | 1700/18688 [1:58:33<19:24:42, 4.11s/it] {'loss': '0.9821', 'grad_norm': '0.9233', 'learning_rate': '9.997e-05', 'epoch': '0.09097', 'num_input_tokens_seen': 5680272, 'train_runtime': '7114', 'train_tokens_per_second': '798.5'}
9%|▉ | 1700/18688 [1:58:33<19:24:42, 4.11s/it] 9%|▉ | 1701/18688 [1:58:37<18:52:44, 4.00s/it] 9%|▉ | 1702/18688 [1:58:41<19:26:54, 4.12s/it] 9%|▉ | 1703/18688 [1:58:46<19:30:02, 4.13s/it] 9%|▉ | 1704/18688 [1:58:49<19:02:20, 4.04s/it] 9%|▉ | 1705/18688 [1:58:53<18:18:30, 3.88s/it] 9%|▉ | 1706/18688 [1:58:58<20:27:03, 4.34s/it] 9%|▉ | 1707/18688 [1:59:03<20:29:38, 4.34s/it] 9%|▉ | 1708/18688 [1:59:07<19:41:54, 4.18s/it] 9%|▉ | 1709/18688 [1:59:10<18:48:07, 3.99s/it] 9%|▉ | 1710/18688 [1:59:14<19:09:00, 4.06s/it] {'loss': '0.9244', 'grad_norm': '1.161', 'learning_rate': '9.996e-05', 'epoch': '0.09151', 'num_input_tokens_seen': 5711896, 'train_runtime': '7155', 'train_tokens_per_second': '798.3'}
9%|▉ | 1710/18688 [1:59:14<19:09:00, 4.06s/it] 9%|▉ | 1711/18688 [1:59:18<19:04:57, 4.05s/it] 9%|▉ | 1712/18688 [1:59:23<19:48:49, 4.20s/it] 9%|▉ | 1713/18688 [1:59:27<19:37:48, 4.16s/it] 9%|▉ | 1714/18688 [1:59:31<20:11:35, 4.28s/it] 9%|▉ | 1715/18688 [1:59:36<19:52:45, 4.22s/it] 9%|▉ | 1716/18688 [1:59:39<19:17:20, 4.09s/it] 9%|▉ | 1717/18688 [1:59:43<18:45:18, 3.98s/it] 9%|▉ | 1718/18688 [1:59:48<19:54:58, 4.23s/it] 9%|▉ | 1719/18688 [1:59:52<19:20:58, 4.11s/it] 9%|▉ | 1720/18688 [1:59:55<18:24:09, 3.90s/it] {'loss': '1.042', 'grad_norm': '1.112', 'learning_rate': '9.996e-05', 'epoch': '0.09204', 'num_input_tokens_seen': 5744968, 'train_runtime': '7196', 'train_tokens_per_second': '798.4'}
9%|▉ | 1720/18688 [1:59:55<18:24:09, 3.90s/it] 9%|▉ | 1721/18688 [1:59:59<18:10:16, 3.86s/it] 9%|▉ | 1722/18688 [2:00:03<18:10:53, 3.86s/it] 9%|▉ | 1723/18688 [2:00:07<18:42:59, 3.97s/it] 9%|▉ | 1724/18688 [2:00:11<18:05:49, 3.84s/it] 9%|▉ | 1725/18688 [2:00:14<17:44:29, 3.77s/it] 9%|▉ | 1726/18688 [2:00:19<19:28:02, 4.13s/it] 9%|▉ | 1727/18688 [2:00:24<20:20:46, 4.32s/it] 9%|▉ | 1728/18688 [2:00:28<20:25:14, 4.33s/it] 9%|▉ | 1729/18688 [2:00:32<19:57:38, 4.24s/it] 9%|▉ | 1730/18688 [2:00:37<21:07:14, 4.48s/it] {'loss': '0.9877', 'grad_norm': '0.9836', 'learning_rate': '9.996e-05', 'epoch': '0.09258', 'num_input_tokens_seen': 5778576, 'train_runtime': '7238', 'train_tokens_per_second': '798.4'}
9%|▉ | 1730/18688 [2:00:37<21:07:14, 4.48s/it] 9%|▉ | 1731/18688 [2:00:41<20:19:11, 4.31s/it] 9%|▉ | 1732/18688 [2:00:45<19:14:09, 4.08s/it] 9%|▉ | 1733/18688 [2:00:48<18:24:27, 3.91s/it] 9%|▉ | 1734/18688 [2:00:53<19:29:59, 4.14s/it] 9%|▉ | 1735/18688 [2:00:57<19:59:25, 4.25s/it] 9%|▉ | 1736/18688 [2:01:01<19:05:19, 4.05s/it] 9%|▉ | 1737/18688 [2:01:05<19:12:51, 4.08s/it] 9%|▉ | 1738/18688 [2:01:10<19:54:31, 4.23s/it] 9%|▉ | 1739/18688 [2:01:14<19:50:03, 4.21s/it] 9%|▉ | 1740/18688 [2:01:18<19:21:49, 4.11s/it] {'loss': '1.003', 'grad_norm': '1.214', 'learning_rate': '9.995e-05', 'epoch': '0.09311', 'num_input_tokens_seen': 5811808, 'train_runtime': '7278', 'train_tokens_per_second': '798.5'}
9%|▉ | 1740/18688 [2:01:18<19:21:49, 4.11s/it] 9%|▉ | 1741/18688 [2:01:22<19:26:42, 4.13s/it] 9%|▉ | 1742/18688 [2:01:25<18:30:35, 3.93s/it] 9%|▉ | 1743/18688 [2:01:29<18:00:21, 3.83s/it] 9%|▉ | 1744/18688 [2:01:33<17:33:14, 3.73s/it] 9%|▉ | 1745/18688 [2:01:37<18:04:48, 3.84s/it] 9%|▉ | 1746/18688 [2:01:41<18:55:07, 4.02s/it] 9%|▉ | 1747/18688 [2:01:45<18:31:01, 3.93s/it] 9%|▉ | 1748/18688 [2:01:49<18:37:56, 3.96s/it] 9%|▉ | 1749/18688 [2:01:53<18:34:05, 3.95s/it] 9%|▉ | 1750/18688 [2:01:57<19:18:03, 4.10s/it] {'loss': '0.9701', 'grad_norm': '0.9824', 'learning_rate': '9.995e-05', 'epoch': '0.09365', 'num_input_tokens_seen': 5842184, 'train_runtime': '7318', 'train_tokens_per_second': '798.4'}
9%|▉ | 1750/18688 [2:01:57<19:18:03, 4.10s/it] 9%|▉ | 1751/18688 [2:02:01<19:05:23, 4.06s/it] 9%|▉ | 1752/18688 [2:02:05<18:28:51, 3.93s/it] 9%|▉ | 1753/18688 [2:02:09<18:14:49, 3.88s/it] 9%|▉ | 1754/18688 [2:02:13<19:21:06, 4.11s/it] 9%|▉ | 1755/18688 [2:02:17<19:30:27, 4.15s/it] 9%|▉ | 1756/18688 [2:02:22<19:38:36, 4.18s/it] 9%|▉ | 1757/18688 [2:02:27<20:41:07, 4.40s/it] 9%|▉ | 1758/18688 [2:02:31<20:33:45, 4.37s/it] 9%|▉ | 1759/18688 [2:02:35<19:42:41, 4.19s/it] 9%|▉ | 1760/18688 [2:02:38<18:48:29, 4.00s/it] {'loss': '0.974', 'grad_norm': '1.03', 'learning_rate': '9.994e-05', 'epoch': '0.09418', 'num_input_tokens_seen': 5875024, 'train_runtime': '7359', 'train_tokens_per_second': '798.4'}
9%|▉ | 1760/18688 [2:02:38<18:48:29, 4.00s/it] 9%|▉ | 1761/18688 [2:02:43<19:26:14, 4.13s/it] 9%|▉ | 1762/18688 [2:02:48<21:10:14, 4.50s/it] 9%|▉ | 1763/18688 [2:02:52<20:07:35, 4.28s/it] 9%|▉ | 1764/18688 [2:02:56<19:22:26, 4.12s/it] 9%|▉ | 1765/18688 [2:03:00<19:28:51, 4.14s/it] 9%|▉ | 1766/18688 [2:03:03<18:51:10, 4.01s/it] 9%|▉ | 1767/18688 [2:03:07<18:32:21, 3.94s/it] 9%|▉ | 1768/18688 [2:03:12<20:00:58, 4.26s/it] 9%|▉ | 1769/18688 [2:03:16<19:01:34, 4.05s/it] 9%|▉ | 1770/18688 [2:03:20<19:00:56, 4.05s/it] {'loss': '1.003', 'grad_norm': '0.9501', 'learning_rate': '9.994e-05', 'epoch': '0.09472', 'num_input_tokens_seen': 5907784, 'train_runtime': '7400', 'train_tokens_per_second': '798.3'}
9%|▉ | 1770/18688 [2:03:20<19:00:56, 4.05s/it] 9%|▉ | 1771/18688 [2:03:23<18:27:14, 3.93s/it] 9%|▉ | 1772/18688 [2:03:27<18:17:23, 3.89s/it] 9%|▉ | 1773/18688 [2:03:31<18:42:34, 3.98s/it] 9%|▉ | 1774/18688 [2:03:36<19:34:19, 4.17s/it] 9%|▉ | 1775/18688 [2:03:40<19:21:03, 4.12s/it] 10%|▉ | 1776/18688 [2:03:44<18:34:04, 3.95s/it] 10%|▉ | 1777/18688 [2:03:48<18:35:26, 3.96s/it] 10%|▉ | 1778/18688 [2:03:51<18:18:38, 3.90s/it] 10%|▉ | 1779/18688 [2:03:56<19:04:35, 4.06s/it] 10%|▉ | 1780/18688 [2:04:00<18:54:36, 4.03s/it] {'loss': '1.016', 'grad_norm': '1.024', 'learning_rate': '9.994e-05', 'epoch': '0.09525', 'num_input_tokens_seen': 5938800, 'train_runtime': '7440', 'train_tokens_per_second': '798.2'}
10%|▉ | 1780/18688 [2:04:00<18:54:36, 4.03s/it] 10%|▉ | 1781/18688 [2:04:04<19:19:15, 4.11s/it] 10%|▉ | 1782/18688 [2:04:09<20:16:16, 4.32s/it] 10%|▉ | 1783/18688 [2:04:13<20:28:31, 4.36s/it] 10%|▉ | 1784/18688 [2:04:19<21:40:16, 4.62s/it] 10%|▉ | 1785/18688 [2:04:23<21:13:21, 4.52s/it] 10%|▉ | 1786/18688 [2:04:26<19:45:04, 4.21s/it] 10%|▉ | 1787/18688 [2:04:31<19:45:16, 4.21s/it] 10%|▉ | 1788/18688 [2:04:35<19:46:19, 4.21s/it] 10%|▉ | 1789/18688 [2:04:39<19:25:35, 4.14s/it] 10%|▉ | 1790/18688 [2:04:43<19:18:29, 4.11s/it] {'loss': '0.946', 'grad_norm': '1.094', 'learning_rate': '9.993e-05', 'epoch': '0.09579', 'num_input_tokens_seen': 5975136, 'train_runtime': '7483', 'train_tokens_per_second': '798.5'}
10%|▉ | 1790/18688 [2:04:43<19:18:29, 4.11s/it] 10%|▉ | 1791/18688 [2:04:48<20:21:52, 4.34s/it] 10%|▉ | 1792/18688 [2:04:52<20:16:57, 4.32s/it] 10%|▉ | 1793/18688 [2:04:56<19:30:39, 4.16s/it] 10%|▉ | 1794/18688 [2:05:00<19:05:53, 4.07s/it] 10%|▉ | 1795/18688 [2:05:03<18:49:56, 4.01s/it] 10%|▉ | 1796/18688 [2:05:07<18:17:18, 3.90s/it] 10%|▉ | 1797/18688 [2:05:11<18:34:16, 3.96s/it] 10%|▉ | 1798/18688 [2:05:15<18:26:53, 3.93s/it] 10%|▉ | 1799/18688 [2:05:19<18:46:57, 4.00s/it] 10%|▉ | 1800/18688 [2:05:23<18:47:16, 4.00s/it] {'loss': '0.9454', 'grad_norm': '1.151', 'learning_rate': '9.993e-05', 'epoch': '0.09632', 'num_input_tokens_seen': 6004904, 'train_runtime': '7524', 'train_tokens_per_second': '798.1'}
10%|▉ | 1800/18688 [2:05:23<18:47:16, 4.00s/it] 10%|▉ | 1801/18688 [2:05:28<19:34:33, 4.17s/it] 10%|▉ | 1802/18688 [2:05:32<20:07:41, 4.29s/it] 10%|▉ | 1803/18688 [2:05:37<19:59:06, 4.26s/it] 10%|▉ | 1804/18688 [2:05:41<19:56:54, 4.25s/it] 10%|▉ | 1805/18688 [2:05:45<20:09:01, 4.30s/it] 10%|▉ | 1806/18688 [2:05:49<19:50:20, 4.23s/it] 10%|▉ | 1807/18688 [2:05:53<19:34:41, 4.18s/it] 10%|▉ | 1808/18688 [2:05:57<18:53:15, 4.03s/it] 10%|▉ | 1809/18688 [2:06:02<19:47:01, 4.22s/it] 10%|▉ | 1810/18688 [2:06:06<19:46:46, 4.22s/it] {'loss': '0.9675', 'grad_norm': '1.197', 'learning_rate': '9.992e-05', 'epoch': '0.09686', 'num_input_tokens_seen': 6039224, 'train_runtime': '7566', 'train_tokens_per_second': '798.2'}
10%|▉ | 1810/18688 [2:06:06<19:46:46, 4.22s/it] 10%|▉ | 1811/18688 [2:06:10<19:41:20, 4.20s/it] 10%|▉ | 1812/18688 [2:06:14<19:23:58, 4.14s/it] 10%|▉ | 1813/18688 [2:06:18<19:15:12, 4.11s/it] 10%|▉ | 1814/18688 [2:06:22<18:23:07, 3.92s/it] 10%|▉ | 1815/18688 [2:06:26<19:10:07, 4.09s/it] 10%|▉ | 1816/18688 [2:06:32<21:39:08, 4.62s/it] 10%|▉ | 1817/18688 [2:06:36<21:21:21, 4.56s/it] 10%|▉ | 1818/18688 [2:06:40<20:21:49, 4.35s/it] 10%|▉ | 1819/18688 [2:06:44<19:27:38, 4.15s/it] 10%|▉ | 1820/18688 [2:06:47<18:33:24, 3.96s/it] {'loss': '1.015', 'grad_norm': '0.9234', 'learning_rate': '9.992e-05', 'epoch': '0.09739', 'num_input_tokens_seen': 6071920, 'train_runtime': '7608', 'train_tokens_per_second': '798.1'}
10%|▉ | 1820/18688 [2:06:47<18:33:24, 3.96s/it] 10%|▉ | 1821/18688 [2:06:51<18:26:21, 3.94s/it] 10%|▉ | 1822/18688 [2:06:55<17:56:20, 3.83s/it] 10%|▉ | 1823/18688 [2:06:59<18:42:34, 3.99s/it] 10%|▉ | 1824/18688 [2:07:03<18:43:24, 4.00s/it] 10%|▉ | 1825/18688 [2:07:07<18:51:20, 4.03s/it] 10%|▉ | 1826/18688 [2:07:11<18:28:25, 3.94s/it] 10%|▉ | 1827/18688 [2:07:15<18:42:42, 4.00s/it] 10%|▉ | 1828/18688 [2:07:20<19:55:07, 4.25s/it] 10%|▉ | 1829/18688 [2:07:24<20:08:38, 4.30s/it] 10%|▉ | 1830/18688 [2:07:29<20:37:41, 4.41s/it] {'loss': '1.049', 'grad_norm': '0.8678', 'learning_rate': '9.991e-05', 'epoch': '0.09793', 'num_input_tokens_seen': 6107064, 'train_runtime': '7650', 'train_tokens_per_second': '798.4'}
10%|▉ | 1830/18688 [2:07:29<20:37:41, 4.41s/it] 10%|▉ | 1831/18688 [2:07:34<21:25:45, 4.58s/it] 10%|▉ | 1832/18688 [2:07:38<20:09:09, 4.30s/it] 10%|▉ | 1833/18688 [2:07:42<19:48:37, 4.23s/it] 10%|▉ | 1834/18688 [2:07:46<19:38:12, 4.19s/it] 10%|▉ | 1835/18688 [2:07:50<20:06:21, 4.29s/it] 10%|▉ | 1836/18688 [2:07:55<20:31:37, 4.39s/it] 10%|▉ | 1837/18688 [2:07:59<19:44:14, 4.22s/it] 10%|▉ | 1838/18688 [2:08:03<19:01:54, 4.07s/it] 10%|▉ | 1839/18688 [2:08:07<19:30:28, 4.17s/it] 10%|▉ | 1840/18688 [2:08:12<21:21:49, 4.56s/it] {'loss': '0.9821', 'grad_norm': '1.12', 'learning_rate': '9.99e-05', 'epoch': '0.09846', 'num_input_tokens_seen': 6144296, 'train_runtime': '7693', 'train_tokens_per_second': '798.7'}
10%|▉ | 1840/18688 [2:08:12<21:21:49, 4.56s/it] 10%|▉ | 1841/18688 [2:08:16<20:28:40, 4.38s/it] 10%|▉ | 1842/18688 [2:08:20<19:19:14, 4.13s/it] 10%|▉ | 1843/18688 [2:08:24<19:08:49, 4.09s/it] 10%|▉ | 1844/18688 [2:08:29<19:48:42, 4.23s/it] 10%|▉ | 1845/18688 [2:08:33<20:18:27, 4.34s/it] 10%|▉ | 1846/18688 [2:08:38<20:43:54, 4.43s/it] 10%|▉ | 1847/18688 [2:08:42<20:58:23, 4.48s/it] 10%|▉ | 1848/18688 [2:08:48<22:04:27, 4.72s/it] 10%|▉ | 1849/18688 [2:08:52<21:11:16, 4.53s/it] 10%|▉ | 1850/18688 [2:08:56<21:04:26, 4.51s/it] {'loss': '1.017', 'grad_norm': '1.07', 'learning_rate': '9.99e-05', 'epoch': '0.099', 'num_input_tokens_seen': 6179064, 'train_runtime': '7737', 'train_tokens_per_second': '798.7'}
10%|▉ | 1850/18688 [2:08:56<21:04:26, 4.51s/it] 10%|▉ | 1851/18688 [2:09:01<21:24:10, 4.58s/it] 10%|▉ | 1852/18688 [2:09:05<21:02:20, 4.50s/it] 10%|▉ | 1853/18688 [2:09:09<20:21:44, 4.35s/it] 10%|▉ | 1854/18688 [2:09:14<20:21:47, 4.35s/it] 10%|▉ | 1855/18688 [2:09:18<20:05:57, 4.30s/it] 10%|▉ | 1856/18688 [2:09:22<20:02:01, 4.28s/it] 10%|▉ | 1857/18688 [2:09:26<19:22:39, 4.14s/it] 10%|▉ | 1858/18688 [2:09:30<18:57:32, 4.06s/it] 10%|▉ | 1859/18688 [2:09:34<19:06:22, 4.09s/it] 10%|▉ | 1860/18688 [2:09:38<19:26:00, 4.16s/it] {'loss': '1.001', 'grad_norm': '1.111', 'learning_rate': '9.989e-05', 'epoch': '0.09953', 'num_input_tokens_seen': 6213504, 'train_runtime': '7779', 'train_tokens_per_second': '798.8'}
10%|▉ | 1860/18688 [2:09:38<19:26:00, 4.16s/it] 10%|▉ | 1861/18688 [2:09:42<18:52:39, 4.04s/it] 10%|▉ | 1862/18688 [2:09:46<19:19:48, 4.14s/it] 10%|▉ | 1863/18688 [2:09:50<19:25:37, 4.16s/it] 10%|▉ | 1864/18688 [2:09:55<19:18:37, 4.13s/it] 10%|▉ | 1865/18688 [2:09:59<19:47:47, 4.24s/it] 10%|▉ | 1866/18688 [2:10:03<19:40:06, 4.21s/it] 10%|▉ | 1867/18688 [2:10:07<19:09:17, 4.10s/it] 10%|▉ | 1868/18688 [2:10:11<19:37:41, 4.20s/it] 10%|█ | 1869/18688 [2:10:16<20:12:59, 4.33s/it] 10%|█ | 1870/18688 [2:10:20<19:32:30, 4.18s/it] {'loss': '0.9857', 'grad_norm': '1.187', 'learning_rate': '9.989e-05', 'epoch': '0.1001', 'num_input_tokens_seen': 6248336, 'train_runtime': '7820', 'train_tokens_per_second': '799'}
10%|█ | 1870/18688 [2:10:20<19:32:30, 4.18s/it] 10%|█ | 1871/18688 [2:10:25<20:19:53, 4.35s/it] 10%|█ | 1872/18688 [2:10:29<20:41:11, 4.43s/it] 10%|█ | 1873/18688 [2:10:33<19:24:02, 4.15s/it] 10%|█ | 1874/18688 [2:10:37<19:39:09, 4.21s/it] 10%|█ | 1875/18688 [2:10:42<19:57:03, 4.27s/it] 10%|█ | 1876/18688 [2:10:47<21:19:26, 4.57s/it] 10%|█ | 1877/18688 [2:10:51<20:32:44, 4.40s/it] 10%|█ | 1878/18688 [2:10:55<20:36:38, 4.41s/it] 10%|█ | 1879/18688 [2:11:00<20:38:10, 4.42s/it] 10%|█ | 1880/18688 [2:11:03<19:13:33, 4.12s/it] {'loss': '0.9664', 'grad_norm': '1.145', 'learning_rate': '9.988e-05', 'epoch': '0.1006', 'num_input_tokens_seen': 6282464, 'train_runtime': '7864', 'train_tokens_per_second': '798.9'}
10%|█ | 1880/18688 [2:11:03<19:13:33, 4.12s/it] 10%|█ | 1881/18688 [2:11:07<19:17:47, 4.13s/it] 10%|█ | 1882/18688 [2:11:11<18:39:12, 4.00s/it] 10%|█ | 1883/18688 [2:11:17<20:53:01, 4.47s/it] 10%|█ | 1884/18688 [2:11:20<19:51:20, 4.25s/it] 10%|█ | 1885/18688 [2:11:25<20:49:03, 4.46s/it] 10%|█ | 1886/18688 [2:11:30<20:47:49, 4.46s/it] 10%|█ | 1887/18688 [2:11:34<20:35:03, 4.41s/it] 10%|█ | 1888/18688 [2:11:38<20:09:14, 4.32s/it] 10%|█ | 1889/18688 [2:11:42<19:21:58, 4.15s/it] 10%|█ | 1890/18688 [2:11:46<19:28:49, 4.17s/it] {'loss': '0.9614', 'grad_norm': '0.9297', 'learning_rate': '9.987e-05', 'epoch': '0.1011', 'num_input_tokens_seen': 6317720, 'train_runtime': '7907', 'train_tokens_per_second': '799'}
10%|█ | 1890/18688 [2:11:46<19:28:49, 4.17s/it] 10%|█ | 1891/18688 [2:11:50<18:39:26, 4.00s/it] 10%|█ | 1892/18688 [2:11:54<18:49:49, 4.04s/it] 10%|█ | 1893/18688 [2:11:58<19:31:51, 4.19s/it] 10%|█ | 1894/18688 [2:12:02<19:08:49, 4.10s/it] 10%|█ | 1895/18688 [2:12:07<19:26:32, 4.17s/it] 10%|█ | 1896/18688 [2:12:11<19:21:14, 4.15s/it] 10%|█ | 1897/18688 [2:12:15<19:19:09, 4.14s/it] 10%|█ | 1898/18688 [2:12:19<19:22:01, 4.15s/it] 10%|█ | 1899/18688 [2:12:24<20:38:15, 4.43s/it] 10%|█ | 1900/18688 [2:12:28<19:26:54, 4.17s/it] {'loss': '0.9315', 'grad_norm': '1.196', 'learning_rate': '9.987e-05', 'epoch': '0.1017', 'num_input_tokens_seen': 6351128, 'train_runtime': '7948', 'train_tokens_per_second': '799.1'}
10%|█ | 1900/18688 [2:12:28<19:26:54, 4.17s/it] 10%|█ | 1901/18688 [2:12:32<19:14:05, 4.12s/it] 10%|█ | 1902/18688 [2:12:36<19:27:13, 4.17s/it] 10%|█ | 1903/18688 [2:12:40<18:58:00, 4.07s/it] 10%|█ | 1904/18688 [2:12:44<19:01:12, 4.08s/it] 10%|█ | 1905/18688 [2:12:49<20:17:08, 4.35s/it] 10%|█ | 1906/18688 [2:12:53<19:55:45, 4.28s/it] 10%|█ | 1907/18688 [2:12:57<19:43:49, 4.23s/it] 10%|█ | 1908/18688 [2:13:01<18:45:03, 4.02s/it] 10%|█ | 1909/18688 [2:13:05<19:08:45, 4.11s/it] 10%|█ | 1910/18688 [2:13:10<21:09:40, 4.54s/it] {'loss': '1.009', 'grad_norm': '0.9424', 'learning_rate': '9.986e-05', 'epoch': '0.1022', 'num_input_tokens_seen': 6385648, 'train_runtime': '7991', 'train_tokens_per_second': '799.1'}
10%|█ | 1910/18688 [2:13:10<21:09:40, 4.54s/it] 10%|█ | 1911/18688 [2:13:14<19:51:16, 4.26s/it] 10%|█ | 1912/18688 [2:13:18<18:47:15, 4.03s/it] 10%|█ | 1913/18688 [2:13:21<18:35:53, 3.99s/it] 10%|█ | 1914/18688 [2:13:25<18:03:18, 3.87s/it] 10%|█ | 1915/18688 [2:13:29<18:05:33, 3.88s/it] 10%|█ | 1916/18688 [2:13:33<17:50:43, 3.83s/it] 10%|█ | 1917/18688 [2:13:36<17:40:22, 3.79s/it] 10%|█ | 1918/18688 [2:13:40<17:56:50, 3.85s/it] 10%|█ | 1919/18688 [2:13:45<19:43:38, 4.24s/it] 10%|█ | 1920/18688 [2:13:50<19:54:08, 4.27s/it] {'loss': '0.9827', 'grad_norm': '1.154', 'learning_rate': '9.985e-05', 'epoch': '0.1027', 'num_input_tokens_seen': 6414704, 'train_runtime': '8030', 'train_tokens_per_second': '798.8'}
10%|█ | 1920/18688 [2:13:50<19:54:08, 4.27s/it] 10%|█ | 1921/18688 [2:13:54<20:08:59, 4.33s/it] 10%|█ | 1922/18688 [2:13:58<19:55:46, 4.28s/it] 10%|█ | 1923/18688 [2:14:03<20:31:44, 4.41s/it] 10%|█ | 1924/18688 [2:14:07<19:24:45, 4.17s/it] 10%|█ | 1925/18688 [2:14:11<19:12:48, 4.13s/it] 10%|█ | 1926/18688 [2:14:15<19:46:42, 4.25s/it] 10%|█ | 1927/18688 [2:14:19<19:31:55, 4.20s/it] 10%|█ | 1928/18688 [2:14:23<19:13:58, 4.13s/it] 10%|█ | 1929/18688 [2:14:28<20:15:48, 4.35s/it] 10%|█ | 1930/18688 [2:14:32<19:48:29, 4.26s/it] {'loss': '1.006', 'grad_norm': '1.019', 'learning_rate': '9.985e-05', 'epoch': '0.1033', 'num_input_tokens_seen': 6449208, 'train_runtime': '8073', 'train_tokens_per_second': '798.9'}
10%|█ | 1930/18688 [2:14:32<19:48:29, 4.26s/it] 10%|█ | 1931/18688 [2:14:36<19:41:14, 4.23s/it] 10%|█ | 1932/18688 [2:14:40<18:50:57, 4.05s/it] 10%|█ | 1933/18688 [2:14:44<18:08:35, 3.90s/it] 10%|█ | 1934/18688 [2:14:48<19:13:00, 4.13s/it] 10%|█ | 1935/18688 [2:14:53<20:37:55, 4.43s/it] 10%|█ | 1936/18688 [2:14:57<20:06:54, 4.32s/it] 10%|█ | 1937/18688 [2:15:01<19:31:55, 4.20s/it] 10%|█ | 1938/18688 [2:15:07<21:07:58, 4.54s/it] 10%|█ | 1939/18688 [2:15:11<21:01:19, 4.52s/it] 10%|█ | 1940/18688 [2:15:15<20:35:14, 4.43s/it] {'loss': '0.9953', 'grad_norm': '1.105', 'learning_rate': '9.984e-05', 'epoch': '0.1038', 'num_input_tokens_seen': 6482200, 'train_runtime': '8116', 'train_tokens_per_second': '798.7'}
10%|█ | 1940/18688 [2:15:15<20:35:14, 4.43s/it] 10%|█ | 1941/18688 [2:15:19<19:34:11, 4.21s/it] 10%|█ | 1942/18688 [2:15:23<18:49:35, 4.05s/it] 10%|█ | 1943/18688 [2:15:27<18:57:24, 4.08s/it] 10%|█ | 1944/18688 [2:15:31<18:16:41, 3.93s/it] 10%|█ | 1945/18688 [2:15:34<17:55:39, 3.85s/it] 10%|█ | 1946/18688 [2:15:38<18:17:06, 3.93s/it] 10%|█ | 1947/18688 [2:15:43<19:22:02, 4.16s/it] 10%|█ | 1948/18688 [2:15:48<20:17:09, 4.36s/it] 10%|█ | 1949/18688 [2:15:52<19:24:06, 4.17s/it] 10%|█ | 1950/18688 [2:15:56<19:13:23, 4.13s/it] {'loss': '1.047', 'grad_norm': '1.075', 'learning_rate': '9.983e-05', 'epoch': '0.1043', 'num_input_tokens_seen': 6514736, 'train_runtime': '8156', 'train_tokens_per_second': '798.8'}
10%|█ | 1950/18688 [2:15:56<19:13:23, 4.13s/it] 10%|█ | 1951/18688 [2:15:59<18:43:51, 4.03s/it] 10%|█ | 1952/18688 [2:16:04<18:53:19, 4.06s/it] 10%|█ | 1953/18688 [2:16:08<19:17:34, 4.15s/it] 10%|█ | 1954/18688 [2:16:13<20:03:25, 4.31s/it] 10%|█ | 1955/18688 [2:16:18<21:42:23, 4.67s/it] 10%|█ | 1956/18688 [2:16:23<21:32:29, 4.63s/it] 10%|█ | 1957/18688 [2:16:27<21:14:11, 4.57s/it] 10%|█ | 1958/18688 [2:16:32<21:31:28, 4.63s/it] 10%|█ | 1959/18688 [2:16:36<20:34:34, 4.43s/it] 10%|█ | 1960/18688 [2:16:40<19:44:26, 4.25s/it] {'loss': '1.016', 'grad_norm': '1.137', 'learning_rate': '9.982e-05', 'epoch': '0.1049', 'num_input_tokens_seen': 6548928, 'train_runtime': '8200', 'train_tokens_per_second': '798.6'}
10%|█ | 1960/18688 [2:16:40<19:44:26, 4.25s/it] 10%|█ | 1961/18688 [2:16:43<18:52:29, 4.06s/it] 10%|█ | 1962/18688 [2:16:48<20:07:59, 4.33s/it] 11%|█ | 1963/18688 [2:16:53<20:14:35, 4.36s/it] 11%|█ | 1964/18688 [2:16:58<21:23:29, 4.60s/it] 11%|█ | 1965/18688 [2:17:02<20:31:49, 4.42s/it] 11%|█ | 1966/18688 [2:17:07<21:10:05, 4.56s/it] 11%|█ | 1967/18688 [2:17:10<19:59:29, 4.30s/it] 11%|█ | 1968/18688 [2:17:15<19:46:50, 4.26s/it] 11%|█ | 1969/18688 [2:17:19<19:32:05, 4.21s/it] 11%|█ | 1970/18688 [2:17:22<18:35:43, 4.00s/it] {'loss': '0.9624', 'grad_norm': '0.9394', 'learning_rate': '9.982e-05', 'epoch': '0.1054', 'num_input_tokens_seen': 6583256, 'train_runtime': '8243', 'train_tokens_per_second': '798.7'}
11%|█ | 1970/18688 [2:17:22<18:35:43, 4.00s/it] 11%|█ | 1971/18688 [2:17:26<17:59:41, 3.88s/it] 11%|█ | 1972/18688 [2:17:30<18:37:14, 4.01s/it] 11%|█ | 1973/18688 [2:17:34<18:54:46, 4.07s/it] 11%|█ | 1974/18688 [2:17:39<19:21:53, 4.17s/it] 11%|█ | 1975/18688 [2:17:44<21:27:19, 4.62s/it] 11%|█ | 1976/18688 [2:17:48<20:12:48, 4.35s/it] 11%|█ | 1977/18688 [2:17:53<20:25:13, 4.40s/it] 11%|█ | 1978/18688 [2:17:56<19:13:47, 4.14s/it] 11%|█ | 1979/18688 [2:18:01<19:50:42, 4.28s/it] 11%|█ | 1980/18688 [2:18:06<20:43:41, 4.47s/it] {'loss': '1.024', 'grad_norm': '1.232', 'learning_rate': '9.981e-05', 'epoch': '0.106', 'num_input_tokens_seen': 6619632, 'train_runtime': '8286', 'train_tokens_per_second': '798.9'}
11%|█ | 1980/18688 [2:18:06<20:43:41, 4.47s/it] 11%|█ | 1981/18688 [2:18:10<20:13:41, 4.36s/it] 11%|█ | 1982/18688 [2:18:14<20:25:04, 4.40s/it] 11%|█ | 1983/18688 [2:18:18<19:41:42, 4.24s/it] 11%|█ | 1984/18688 [2:18:23<20:02:12, 4.32s/it] 11%|█ | 1985/18688 [2:18:27<19:47:23, 4.27s/it] 11%|█ | 1986/18688 [2:18:31<19:36:32, 4.23s/it] 11%|█ | 1987/18688 [2:18:34<18:38:00, 4.02s/it] 11%|█ | 1988/18688 [2:18:39<19:01:08, 4.10s/it] 11%|█ | 1989/18688 [2:18:43<19:56:24, 4.30s/it] 11%|█ | 1990/18688 [2:18:47<19:14:50, 4.15s/it] {'loss': '0.9771', 'grad_norm': '0.9538', 'learning_rate': '9.98e-05', 'epoch': '0.1065', 'num_input_tokens_seen': 6654592, 'train_runtime': '8328', 'train_tokens_per_second': '799.1'}
11%|█ | 1990/18688 [2:18:47<19:14:50, 4.15s/it] 11%|█ | 1991/18688 [2:18:53<21:18:30, 4.59s/it] 11%|█ | 1992/18688 [2:18:57<21:16:38, 4.59s/it] 11%|█ | 1993/18688 [2:19:01<20:10:54, 4.35s/it] 11%|█ | 1994/18688 [2:19:06<20:06:31, 4.34s/it] 11%|█ | 1995/18688 [2:19:11<21:09:54, 4.56s/it] 11%|█ | 1996/18688 [2:19:15<20:41:52, 4.46s/it] 11%|█ | 1997/18688 [2:19:18<19:28:17, 4.20s/it] 11%|█ | 1998/18688 [2:19:22<18:35:04, 4.01s/it] 11%|█ | 1999/18688 [2:19:26<18:35:13, 4.01s/it] 11%|█ | 2000/18688 [2:19:31<19:29:03, 4.20s/it] {'loss': '0.9931', 'grad_norm': '1.042', 'learning_rate': '9.979e-05', 'epoch': '0.107', 'num_input_tokens_seen': 6689560, 'train_runtime': '8371', 'train_tokens_per_second': '799.1'}
11%|█ | 2000/18688 [2:19:31<19:29:03, 4.20s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
0%| | 3/1000 [00:00<00:38, 26.02it/s]
1%| | 6/1000 [00:00<00:54, 18.39it/s]
1%| | 8/1000 [00:00<00:56, 17.45it/s]
1%| | 10/1000 [00:00<00:59, 16.55it/s]
1%| | 12/1000 [00:00<01:00, 16.24it/s]
1%|▏ | 14/1000 [00:00<01:02, 15.81it/s]
2%|▏ | 16/1000 [00:00<01:02, 15.65it/s]
2%|▏ | 18/1000 [00:01<01:38, 10.00it/s]
2%|▏ | 20/1000 [00:01<01:32, 10.56it/s]
2%|▏ | 22/1000 [00:01<01:51, 8.74it/s]
2%|▏ | 24/1000 [00:01<01:39, 9.82it/s]
3%|▎ | 26/1000 [00:02<01:44, 9.31it/s]
3%|▎ | 28/1000 [00:02<01:35, 10.13it/s]
3%|▎ | 30/1000 [00:02<01:27, 11.03it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.18it/s]
3%|▎ | 34/1000 [00:02<01:25, 11.30it/s]
4%|▎ | 36/1000 [00:03<01:32, 10.47it/s]
4%|▍ | 38/1000 [00:03<01:24, 11.38it/s]
4%|▍ | 40/1000 [00:03<01:27, 10.98it/s]
4%|▍ | 42/1000 [00:03<01:45, 9.05it/s]
4%|▍ | 44/1000 [00:03<01:30, 10.58it/s]
5%|▍ | 46/1000 [00:03<01:21, 11.77it/s]
5%|▍ | 48/1000 [00:04<01:29, 10.69it/s]
5%|▌ | 50/1000 [00:04<01:20, 11.74it/s]
5%|▌ | 52/1000 [00:04<01:15, 12.58it/s]
5%|▌ | 54/1000 [00:04<01:10, 13.34it/s]
6%|▌ | 56/1000 [00:04<01:07, 14.02it/s]
6%|▌ | 58/1000 [00:05<01:47, 8.76it/s]
6%|▌ | 60/1000 [00:05<01:35, 9.80it/s]
6%|▌ | 62/1000 [00:05<01:26, 10.78it/s]
6%|▋ | 64/1000 [00:05<01:18, 11.86it/s]
7%|▋ | 66/1000 [00:05<01:15, 12.38it/s]
7%|▋ | 68/1000 [00:05<01:12, 12.94it/s]
7%|▋ | 70/1000 [00:05<01:08, 13.51it/s]
7%|▋ | 72/1000 [00:06<01:06, 13.94it/s]
7%|▋ | 74/1000 [00:06<01:07, 13.70it/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, 13.43it/s]
8%|▊ | 82/1000 [00:06<01:05, 14.08it/s]
8%|▊ | 84/1000 [00:06<01:02, 14.55it/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, 14.56it/s]
9%|▉ | 92/1000 [00:07<01:03, 14.21it/s]
9%|▉ | 94/1000 [00:07<01:04, 14.04it/s]
10%|▉ | 96/1000 [00:07<01:03, 14.35it/s]
10%|▉ | 98/1000 [00:07<01:03, 14.27it/s]
10%|█ | 100/1000 [00:08<01:31, 9.83it/s]
10%|█ | 102/1000 [00:08<01:23, 10.80it/s]
10%|█ | 104/1000 [00:08<01:54, 7.83it/s]
11%|█ | 106/1000 [00:09<02:01, 7.35it/s]
11%|█ | 108/1000 [00:09<01:42, 8.70it/s]
11%|█ | 110/1000 [00:09<01:27, 10.16it/s]
11%|█ | 112/1000 [00:09<01:18, 11.30it/s]
11%|█▏ | 114/1000 [00:09<01:14, 11.95it/s]
12%|█▏ | 116/1000 [00:09<01:10, 12.56it/s]
12%|█▏ | 118/1000 [00:09<01:06, 13.34it/s]
12%|█▏ | 120/1000 [00:10<01:09, 12.74it/s]
12%|█▏ | 122/1000 [00:10<01:22, 10.59it/s]
12%|█▏ | 124/1000 [00:10<01:30, 9.69it/s]
13%|█▎ | 126/1000 [00:10<01:22, 10.66it/s]
13%|█▎ | 128/1000 [00:10<01:15, 11.57it/s]
13%|█▎ | 130/1000 [00:11<01:11, 12.24it/s]
13%|█▎ | 132/1000 [00:11<01:05, 13.18it/s]
13%|█▎ | 134/1000 [00:11<01:10, 12.36it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.33it/s]
14%|█▍ | 138/1000 [00:11<01:04, 13.28it/s]
14%|█▍ | 140/1000 [00:11<01:24, 10.13it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.56it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.60it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.63it/s]
15%|█▍ | 148/1000 [00:12<01:13, 11.55it/s]
15%|█▌ | 150/1000 [00:12<01:07, 12.64it/s]
15%|█▌ | 152/1000 [00:12<01:04, 13.18it/s]
15%|█▌ | 154/1000 [00:13<01:02, 13.44it/s]
16%|█▌ | 156/1000 [00:13<01:03, 13.27it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.80it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.68it/s]
16%|█▌ | 162/1000 [00:13<01:01, 13.56it/s]
16%|█▋ | 164/1000 [00:13<01:01, 13.68it/s]
17%|█▋ | 166/1000 [00:13<01:00, 13.83it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.33it/s]
17%|█▋ | 170/1000 [00:14<01:18, 10.52it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.49it/s]
17%|█▋ | 174/1000 [00:14<01:29, 9.21it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.22it/s]
18%|█▊ | 178/1000 [00:15<01:34, 8.70it/s]
18%|█▊ | 180/1000 [00:15<01:32, 8.82it/s]
18%|█▊ | 182/1000 [00:15<01:35, 8.61it/s]
18%|█▊ | 184/1000 [00:15<01:22, 9.85it/s]
19%|█▊ | 186/1000 [00:16<01:37, 8.37it/s]
19%|█▉ | 188/1000 [00:16<01:24, 9.66it/s]
19%|█▉ | 190/1000 [00:16<01:15, 10.70it/s]
19%|█▉ | 192/1000 [00:16<01:11, 11.35it/s]
19%|█▉ | 194/1000 [00:16<01:16, 10.57it/s]
20%|█▉ | 196/1000 [00:17<01:29, 8.98it/s]
20%|█▉ | 198/1000 [00:17<01:18, 10.21it/s]
20%|██ | 200/1000 [00:17<01:15, 10.56it/s]
20%|██ | 202/1000 [00:17<01:09, 11.52it/s]
20%|██ | 204/1000 [00:17<01:02, 12.66it/s]
21%|██ | 206/1000 [00:17<00:59, 13.27it/s]
21%|██ | 208/1000 [00:18<00:58, 13.65it/s]
21%|██ | 210/1000 [00:18<00:54, 14.49it/s]
21%|██ | 212/1000 [00:18<00:56, 14.03it/s]
21%|██▏ | 214/1000 [00:18<00:55, 14.09it/s]
22%|██▏ | 216/1000 [00:18<00:57, 13.66it/s]
22%|██▏ | 218/1000 [00:18<00:57, 13.55it/s]
22%|██▏ | 220/1000 [00:19<01:31, 8.51it/s]
22%|██▏ | 222/1000 [00:19<01:27, 8.86it/s]
22%|██▏ | 224/1000 [00:19<01:22, 9.37it/s]
23%|██▎ | 226/1000 [00:19<01:14, 10.40it/s]
23%|██▎ | 228/1000 [00:19<01:07, 11.36it/s]
23%|██▎ | 230/1000 [00:20<01:04, 11.91it/s]
23%|██▎ | 232/1000 [00:20<01:02, 12.32it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.10it/s]
24%|██▎ | 236/1000 [00:20<00:56, 13.61it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.28it/s]
24%|██▍ | 240/1000 [00:20<00:53, 14.29it/s]
24%|██▍ | 242/1000 [00:20<00:51, 14.81it/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, 12.70it/s]
25%|██▌ | 250/1000 [00:21<00:55, 13.44it/s]
25%|██▌ | 252/1000 [00:21<01:01, 12.18it/s]
25%|██▌ | 254/1000 [00:21<00:56, 13.16it/s]
26%|██▌ | 256/1000 [00:21<00:54, 13.70it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.75it/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, 13.61it/s]
27%|██▋ | 266/1000 [00:22<00:52, 13.98it/s]
27%|██▋ | 268/1000 [00:22<00:50, 14.56it/s]
27%|██▋ | 270/1000 [00:22<00:54, 13.31it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.66it/s]
27%|██▋ | 274/1000 [00:23<01:02, 11.57it/s]
28%|██▊ | 276/1000 [00:23<01:01, 11.83it/s]
28%|██▊ | 278/1000 [00:23<01:00, 11.98it/s]
28%|██▊ | 280/1000 [00:23<00:59, 12.10it/s]
28%|██▊ | 282/1000 [00:24<00:58, 12.29it/s]
28%|██▊ | 284/1000 [00:24<00:57, 12.38it/s]
29%|██▊ | 286/1000 [00:24<00:54, 13.12it/s]
29%|██▉ | 288/1000 [00:24<00:51, 13.88it/s]
29%|██▉ | 290/1000 [00:24<00:48, 14.63it/s]
29%|██▉ | 292/1000 [00:24<00:47, 14.87it/s]
29%|██▉ | 294/1000 [00:24<00:47, 14.90it/s]
30%|██▉ | 296/1000 [00:24<00:46, 15.07it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.29it/s]
30%|███ | 300/1000 [00:25<00:46, 15.04it/s]
30%|███ | 302/1000 [00:25<00:46, 15.10it/s]
30%|███ | 304/1000 [00:25<00:50, 13.75it/s]
31%|███ | 306/1000 [00:25<00:47, 14.53it/s]
31%|███ | 308/1000 [00:25<00:47, 14.50it/s]
31%|███ | 310/1000 [00:25<00:47, 14.68it/s]
31%|███ | 312/1000 [00:26<00:46, 14.66it/s]
31%|███▏ | 314/1000 [00:26<00:44, 15.30it/s]
32%|███▏ | 316/1000 [00:26<00:43, 15.59it/s]
32%|███▏ | 318/1000 [00:26<00:42, 15.97it/s]
32%|███▏ | 320/1000 [00:26<01:12, 9.35it/s]
32%|███▏ | 322/1000 [00:26<01:03, 10.71it/s]
32%|███▏ | 324/1000 [00:27<00:58, 11.50it/s]
33%|███▎ | 326/1000 [00:27<00:58, 11.47it/s]
33%|███▎ | 328/1000 [00:27<00:58, 11.49it/s]
33%|███▎ | 330/1000 [00:27<00:54, 12.39it/s]
33%|███▎ | 332/1000 [00:27<00:52, 12.68it/s]
33%|███▎ | 334/1000 [00:27<00:50, 13.20it/s]
34%|███▎ | 336/1000 [00:27<00:48, 13.62it/s]
34%|███▍ | 338/1000 [00:28<01:05, 10.09it/s]
34%|███▍ | 340/1000 [00:28<00:59, 11.16it/s]
34%|███▍ | 342/1000 [00:28<00:54, 12.09it/s]
34%|███▍ | 344/1000 [00:28<00:53, 12.33it/s]
35%|███▍ | 346/1000 [00:28<00:50, 13.00it/s]
35%|███▍ | 348/1000 [00:29<00:52, 12.39it/s]
35%|███▌ | 350/1000 [00:29<00:49, 13.11it/s]
35%|███▌ | 352/1000 [00:29<00:47, 13.68it/s]
35%|███▌ | 354/1000 [00:29<00:46, 13.79it/s]
36%|███▌ | 356/1000 [00:29<00:45, 14.19it/s]
36%|███▌ | 358/1000 [00:29<00:45, 14.14it/s]
36%|███▌ | 360/1000 [00:29<00:47, 13.48it/s]
36%|███▌ | 362/1000 [00:30<00:52, 12.14it/s]
36%|███▋ | 364/1000 [00:30<00:52, 12.21it/s]
37%|███▋ | 366/1000 [00:30<00:57, 10.97it/s]
37%|███▋ | 368/1000 [00:30<00:54, 11.62it/s]
37%|███▋ | 370/1000 [00:30<00:49, 12.63it/s]
37%|███▋ | 372/1000 [00:30<00:47, 13.11it/s]
37%|███▋ | 374/1000 [00:31<00:55, 11.19it/s]
38%|███▊ | 376/1000 [00:31<00:57, 10.85it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.57it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.42it/s]
38%|███▊ | 382/1000 [00:31<00:48, 12.65it/s]
38%|███▊ | 384/1000 [00:31<00:45, 13.42it/s]
39%|███▊ | 386/1000 [00:32<00:44, 13.74it/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, 13.87it/s]
39%|███▉ | 394/1000 [00:32<00:45, 13.25it/s]
40%|███▉ | 396/1000 [00:32<00:44, 13.52it/s]
40%|███▉ | 398/1000 [00:32<00:48, 12.46it/s]
40%|████ | 400/1000 [00:33<00:45, 13.18it/s]
40%|████ | 402/1000 [00:33<00:43, 13.86it/s]
40%|████ | 404/1000 [00:33<00:41, 14.51it/s]
41%|████ | 406/1000 [00:33<00:39, 15.03it/s]
41%|████ | 408/1000 [00:33<00:47, 12.59it/s]
41%|████ | 410/1000 [00:33<00:56, 10.51it/s]
41%|████ | 412/1000 [00:34<00:50, 11.61it/s]
41%|████▏ | 414/1000 [00:34<01:07, 8.66it/s]
42%|████▏ | 416/1000 [00:34<01:23, 6.98it/s]
42%|████▏ | 418/1000 [00:34<01:10, 8.30it/s]
42%|████▏ | 420/1000 [00:35<01:00, 9.57it/s]
42%|████▏ | 422/1000 [00:35<00:52, 10.92it/s]
42%|████▏ | 424/1000 [00:35<01:14, 7.71it/s]
43%|████▎ | 426/1000 [00:35<01:04, 8.97it/s]
43%|████▎ | 428/1000 [00:35<00:56, 10.17it/s]
43%|████▎ | 430/1000 [00:36<01:02, 9.14it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.07it/s]
43%|████▎ | 434/1000 [00:36<01:08, 8.29it/s]
44%|████▎ | 436/1000 [00:36<01:01, 9.16it/s]
44%|████▍ | 438/1000 [00:37<01:02, 8.93it/s]
44%|████▍ | 440/1000 [00:37<00:59, 9.45it/s]
44%|████▍ | 442/1000 [00:37<00:53, 10.51it/s]
44%|████▍ | 444/1000 [00:37<00:48, 11.37it/s]
45%|████▍ | 446/1000 [00:37<00:45, 12.12it/s]
45%|████▍ | 448/1000 [00:38<00:50, 10.95it/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, 12.32it/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, 13.92it/s]
46%|████▌ | 462/1000 [00:39<00:37, 14.51it/s]
46%|████▋ | 464/1000 [00:39<00:35, 15.02it/s]
47%|████▋ | 466/1000 [00:39<00:35, 14.86it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.92it/s]
47%|████▋ | 470/1000 [00:39<00:52, 10.09it/s]
47%|████▋ | 472/1000 [00:39<00:51, 10.24it/s]
47%|████▋ | 474/1000 [00:40<00:45, 11.51it/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<00:38, 13.35it/s]
48%|████▊ | 484/1000 [00:40<00:37, 13.86it/s]
49%|████▊ | 486/1000 [00:40<00:36, 14.06it/s]
49%|████▉ | 488/1000 [00:41<00:35, 14.34it/s]
49%|████▉ | 490/1000 [00:41<00:45, 11.13it/s]
49%|████▉ | 492/1000 [00:41<00:40, 12.44it/s]
49%|████▉ | 494/1000 [00:41<00:37, 13.42it/s]
50%|████▉ | 496/1000 [00:41<00:35, 14.20it/s]
50%|████▉ | 498/1000 [00:41<00:34, 14.40it/s]
50%|█████ | 500/1000 [00:42<00:43, 11.52it/s]
50%|█████ | 502/1000 [00:42<00:41, 12.00it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.62it/s]
51%|█████ | 506/1000 [00:42<00:38, 12.79it/s]
51%|█████ | 508/1000 [00:42<00:35, 13.71it/s]
51%|█████ | 510/1000 [00:42<00:47, 10.36it/s]
51%|█████ | 512/1000 [00:43<00:43, 11.13it/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, 13.10it/s]
52%|█████▏ | 520/1000 [00:43<00:35, 13.48it/s]
52%|█████▏ | 522/1000 [00:43<00:33, 14.22it/s]
52%|█████▏ | 524/1000 [00:43<00:33, 14.25it/s]
53%|█████▎ | 526/1000 [00:44<00:32, 14.41it/s]
53%|█████▎ | 528/1000 [00:44<00:39, 12.02it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.89it/s]
53%|█████▎ | 532/1000 [00:44<00:33, 13.85it/s]
53%|█████▎ | 534/1000 [00:44<00:32, 14.20it/s]
54%|█████▎ | 536/1000 [00:44<00:32, 14.32it/s]
54%|█████▍ | 538/1000 [00:44<00:32, 14.06it/s]
54%|█████▍ | 540/1000 [00:45<00:31, 14.50it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.77it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.66it/s]
55%|█████▍ | 546/1000 [00:45<00:31, 14.27it/s]
55%|█████▍ | 548/1000 [00:45<00:32, 13.95it/s]
55%|█████▌ | 550/1000 [00:45<00:31, 14.19it/s]
55%|█████▌ | 552/1000 [00:45<00:31, 14.34it/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, 14.51it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 15.00it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 14.92it/s]
56%|█████▋ | 564/1000 [00:46<00:40, 10.80it/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, 13.29it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.83it/s]
57%|█████▋ | 574/1000 [00:47<00:31, 13.70it/s]
58%|█████▊ | 576/1000 [00:47<00:31, 13.63it/s]
58%|█████▊ | 578/1000 [00:47<00:36, 11.66it/s]
58%|█████▊ | 580/1000 [00:48<00:34, 12.28it/s]
58%|█████▊ | 582/1000 [00:48<00:33, 12.43it/s]
58%|█████▊ | 584/1000 [00:48<00:33, 12.38it/s]
59%|█████▊ | 586/1000 [00:48<00:33, 12.39it/s]
59%|█████▉ | 588/1000 [00:48<00:32, 12.49it/s]
59%|█████▉ | 590/1000 [00:48<00:32, 12.58it/s]
59%|█████▉ | 592/1000 [00:49<00:31, 12.78it/s]
59%|█████▉ | 594/1000 [00:49<00:33, 11.98it/s]
60%|█████▉ | 596/1000 [00:49<00:33, 12.09it/s]
60%|█████▉ | 598/1000 [00:49<00:32, 12.25it/s]
60%|██████ | 600/1000 [00:49<00:32, 12.35it/s]
60%|██████ | 602/1000 [00:49<00:32, 12.34it/s]
60%|██████ | 604/1000 [00:50<00:31, 12.45it/s]
61%|██████ | 606/1000 [00:50<00:31, 12.41it/s]
61%|██████ | 608/1000 [00:50<00:31, 12.42it/s]
61%|██████ | 610/1000 [00:50<00:30, 12.60it/s]
61%|██████ | 612/1000 [00:50<00:30, 12.55it/s]
61%|██████▏ | 614/1000 [00:50<00:30, 12.48it/s]
62%|██████▏ | 616/1000 [00:50<00:30, 12.61it/s]
62%|██████▏ | 618/1000 [00:51<00:30, 12.52it/s]
62%|██████▏ | 620/1000 [00:51<00:41, 9.16it/s]
62%|██████▏ | 622/1000 [00:51<00:40, 9.43it/s]
62%|██████▏ | 624/1000 [00:51<00:36, 10.27it/s]
63%|██████▎ | 626/1000 [00:52<00:34, 10.85it/s]
63%|██████▎ | 628/1000 [00:52<00:32, 11.46it/s]
63%|██████▎ | 630/1000 [00:52<00:31, 11.75it/s]
63%|██████▎ | 632/1000 [00:52<00:32, 11.18it/s]
63%|██████▎ | 634/1000 [00:52<00:31, 11.52it/s]
64%|██████▎ | 636/1000 [00:52<00:36, 9.95it/s]
64%|██████▍ | 638/1000 [00:53<00:36, 10.01it/s]
64%|██████▍ | 640/1000 [00:53<00:33, 10.62it/s]
64%|██████▍ | 642/1000 [00:53<00:32, 11.06it/s]
64%|██████▍ | 644/1000 [00:53<00:31, 11.35it/s]
65%|██████▍ | 646/1000 [00:53<00:30, 11.63it/s]
65%|██████▍ | 648/1000 [00:53<00:29, 11.84it/s]
65%|██████▌ | 650/1000 [00:54<00:30, 11.49it/s]
65%|██████▌ | 652/1000 [00:54<00:31, 10.89it/s]
65%|██████▌ | 654/1000 [00:54<00:38, 8.94it/s]
66%|██████▌ | 656/1000 [00:54<00:33, 10.33it/s]
66%|██████▌ | 658/1000 [00:54<00:29, 11.49it/s]
66%|██████▌ | 660/1000 [00:55<00:34, 9.96it/s]
66%|██████▌ | 662/1000 [00:55<00:30, 11.12it/s]
66%|██████▋ | 664/1000 [00:55<00:28, 11.66it/s]
67%|██████▋ | 666/1000 [00:55<00:26, 12.72it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.33it/s]
67%|██████▋ | 670/1000 [00:56<00:29, 11.12it/s]
67%|██████▋ | 672/1000 [00:56<00:27, 11.88it/s]
67%|██████▋ | 674/1000 [00:56<00:26, 12.48it/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, 13.00it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.16it/s]
68%|██████▊ | 684/1000 [00:57<00:25, 12.36it/s]
69%|██████▊ | 686/1000 [00:57<00:23, 13.30it/s]
69%|██████▉ | 688/1000 [00:57<00:22, 13.64it/s]
69%|██████▉ | 690/1000 [00:57<00:22, 13.73it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.20it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 13.99it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 13.87it/s]
70%|██████▉ | 698/1000 [00:58<00:20, 14.49it/s]
70%|███████ | 700/1000 [00:58<00:24, 12.16it/s]
70%|███████ | 702/1000 [00:58<00:22, 12.97it/s]
70%|███████ | 704/1000 [00:58<00:30, 9.86it/s]
71%|███████ | 706/1000 [00:58<00:27, 10.67it/s]
71%|███████ | 708/1000 [00:59<00:30, 9.63it/s]
71%|███████ | 710/1000 [00:59<00:26, 10.93it/s]
71%|███████ | 712/1000 [00:59<00:24, 11.90it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.99it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 13.10it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.58it/s]
72%|███████▏ | 720/1000 [00:59<00:20, 13.54it/s]
72%|███████▏ | 722/1000 [01:00<00:19, 14.00it/s]
72%|███████▏ | 724/1000 [01:00<00:19, 13.82it/s]
73%|███████▎ | 726/1000 [01:00<00:19, 14.03it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.97it/s]
73%|███████▎ | 730/1000 [01:01<00:32, 8.22it/s]
73%|███████▎ | 732/1000 [01:01<00:32, 8.35it/s]
73%|███████▎ | 734/1000 [01:01<00:30, 8.64it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.49it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.95it/s]
74%|███████▍ | 739/1000 [01:01<00:23, 11.06it/s]
74%|███████▍ | 741/1000 [01:02<00:22, 11.73it/s]
74%|███████▍ | 743/1000 [01:02<00:20, 12.37it/s]
74%|███████▍ | 745/1000 [01:02<00:24, 10.22it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.61it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.77it/s]
75%|███████▌ | 751/1000 [01:02<00:20, 11.87it/s]
75%|███████▌ | 753/1000 [01:03<00:20, 11.94it/s]
76%|███████▌ | 755/1000 [01:03<00:19, 12.84it/s]
76%|███████▌ | 757/1000 [01:03<00:24, 10.10it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.18it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.20it/s]
76%|███████▋ | 763/1000 [01:04<00:23, 10.18it/s]
76%|███████▋ | 765/1000 [01:04<00:27, 8.57it/s]
77%|███████▋ | 767/1000 [01:04<00:24, 9.55it/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, 12.65it/s]
78%|███████▊ | 775/1000 [01:05<00:17, 13.19it/s]
78%|███████▊ | 777/1000 [01:05<00:15, 14.19it/s]
78%|███████▊ | 779/1000 [01:05<00:20, 10.55it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.44it/s]
78%|███████▊ | 783/1000 [01:06<00:26, 8.32it/s]
78%|███████▊ | 784/1000 [01:06<00:30, 7.07it/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, 8.85it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.17it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 11.31it/s]
79%|███████▉ | 794/1000 [01:07<00:17, 11.94it/s]
80%|███████▉ | 796/1000 [01:07<00:16, 12.39it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 13.13it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.33it/s]
80%|████████ | 802/1000 [01:08<00:23, 8.27it/s]
80%|████████ | 804/1000 [01:08<00:23, 8.32it/s]
81%|████████ | 806/1000 [01:08<00:21, 9.00it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.40it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.53it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.72it/s]
81%|████████▏ | 814/1000 [01:09<00:14, 12.61it/s]
82%|████████▏ | 816/1000 [01:09<00:16, 11.06it/s]
82%|████████▏ | 818/1000 [01:09<00:15, 12.00it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.49it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 13.05it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.80it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.21it/s]
83%|████████▎ | 828/1000 [01:10<00:11, 14.50it/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, 14.05it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.43it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.35it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.45it/s]
84%|████████▍ | 842/1000 [01:11<00:10, 14.47it/s]
84%|████████▍ | 844/1000 [01:11<00:11, 14.00it/s]
85%|████████▍ | 846/1000 [01:11<00:11, 13.95it/s]
85%|████████▍ | 848/1000 [01:11<00:10, 13.94it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.15it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.75it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.35it/s]
86%|████████▌ | 856/1000 [01:12<00:10, 14.19it/s]
86%|████████▌ | 858/1000 [01:12<00:09, 14.69it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 15.44it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.58it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 14.10it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.59it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.56it/s]
87%|████████▋ | 870/1000 [01:13<00:09, 14.00it/s]
87%|████████▋ | 872/1000 [01:13<00:08, 14.66it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 12.45it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.06it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.94it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.59it/s]
88%|████████▊ | 882/1000 [01:14<00:10, 11.43it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 12.18it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.97it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.96it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.35it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.09it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 14.22it/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, 12.29it/s]
90%|█████████ | 902/1000 [01:15<00:07, 13.03it/s]
90%|█████████ | 904/1000 [01:15<00:09, 9.83it/s]
91%|█████████ | 906/1000 [01:15<00:08, 10.90it/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, 12.30it/s]
91%|█████████▏| 914/1000 [01:17<00:12, 6.82it/s]
92%|█████████▏| 916/1000 [01:17<00:10, 8.14it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.40it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.77it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 10.22it/s]
92%|█████████▏| 924/1000 [01:17<00:06, 10.94it/s]
93%|█████████▎| 926/1000 [01:18<00:06, 11.78it/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, 12.44it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.91it/s]
94%|█████████▎| 936/1000 [01:18<00:04, 13.49it/s]
94%|█████████▍| 938/1000 [01:18<00:04, 14.37it/s]
94%|█████████▍| 940/1000 [01:19<00:04, 14.65it/s]
94%|█████████▍| 942/1000 [01:19<00:04, 12.34it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.88it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.84it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 12.19it/s]
95%|█████████▌| 950/1000 [01:19<00:04, 12.26it/s]
95%|█████████▌| 952/1000 [01:20<00:03, 12.36it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 12.93it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 13.25it/s]
96%|█████████▌| 958/1000 [01:20<00:03, 13.34it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 12.21it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 12.98it/s]
96%|█████████▋| 964/1000 [01:21<00:03, 9.92it/s]
97%|█████████▋| 966/1000 [01:21<00:03, 11.09it/s]
97%|█████████▋| 968/1000 [01:21<00:02, 11.84it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 12.53it/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, 9.88it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.96it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.46it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 10.48it/s]
99%|█████████▊| 986/1000 [01:23<00:01, 10.72it/s]
99%|█████████▉| 988/1000 [01:23<00:01, 10.95it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 11.44it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 12.35it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 13.07it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 13.96it/s]
100%|█████████▉| 998/1000 [01:24<00:00, 14.12it/s]
100%|██████████| 1000/1000 [01:24<00:00, 14.80it/s]
{'eval_loss': '0.9659', '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 [2:20:55<19:29:03, 4.20s/it]
100%|██████████| 1000/1000 [01:24<00:00, 14.80it/s]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00010/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 [2:21:01<139:30:42, 30.10s/it] 11%|█ | 2002/18688 [2:21:05<102:51:08, 22.19s/it] 11%|█ | 2003/18688 [2:21:09<77:53:19, 16.81s/it] 11%|█ | 2004/18688 [2:21:14<60:43:05, 13.10s/it] 11%|█ | 2005/18688 [2:21:18<48:17:05, 10.42s/it] 11%|█ | 2006/18688 [2:21:22<40:05:30, 8.65s/it] 11%|█ | 2007/18688 [2:21:27<34:29:13, 7.44s/it] 11%|█ | 2008/18688 [2:21:32<30:36:38, 6.61s/it] 11%|█ | 2009/18688 [2:21:35<26:23:09, 5.70s/it] 11%|█ | 2010/18688 [2:21:39<24:22:44, 5.26s/it] {'loss': '0.9259', 'grad_norm': '0.9119', 'learning_rate': '9.978e-05', 'epoch': '0.1076', 'num_input_tokens_seen': 6727088, 'train_runtime': '8500', 'train_tokens_per_second': '791.4'}
11%|█ | 2010/18688 [2:21:39<24:22:44, 5.26s/it] 11%|█ | 2011/18688 [2:21:45<24:31:55, 5.30s/it] 11%|█ | 2012/18688 [2:21:49<22:32:19, 4.87s/it] 11%|█ | 2013/18688 [2:21:53<21:06:48, 4.56s/it] 11%|█ | 2014/18688 [2:21:56<19:37:56, 4.24s/it] 11%|█ | 2015/18688 [2:22:01<20:17:51, 4.38s/it] 11%|█ | 2016/18688 [2:22:04<19:01:03, 4.11s/it] 11%|█ | 2017/18688 [2:22:08<18:26:21, 3.98s/it] 11%|█ | 2018/18688 [2:22:12<18:07:03, 3.91s/it] 11%|█ | 2019/18688 [2:22:16<18:12:25, 3.93s/it] 11%|█ | 2020/18688 [2:22:20<18:13:24, 3.94s/it] {'loss': '0.9988', 'grad_norm': '1.033', 'learning_rate': '9.978e-05', 'epoch': '0.1081', 'num_input_tokens_seen': 6760040, 'train_runtime': '8540', 'train_tokens_per_second': '791.6'}
11%|█ | 2020/18688 [2:22:20<18:13:24, 3.94s/it] 11%|█ | 2021/18688 [2:22:23<18:11:33, 3.93s/it] 11%|█ | 2022/18688 [2:22:28<19:27:51, 4.20s/it] 11%|█ | 2023/18688 [2:22:33<20:21:23, 4.40s/it] 11%|█ | 2024/18688 [2:22:37<20:07:34, 4.35s/it] 11%|█ | 2025/18688 [2:22:41<19:23:46, 4.19s/it] 11%|█ | 2026/18688 [2:22:45<19:17:00, 4.17s/it] 11%|█ | 2027/18688 [2:22:49<18:26:10, 3.98s/it] 11%|█ | 2028/18688 [2:22:53<17:59:16, 3.89s/it] 11%|█ | 2029/18688 [2:22:56<17:44:12, 3.83s/it] 11%|█ | 2030/18688 [2:23:00<17:50:40, 3.86s/it] {'loss': '1.012', 'grad_norm': '1.385', 'learning_rate': '9.977e-05', 'epoch': '0.1086', 'num_input_tokens_seen': 6791920, 'train_runtime': '8581', 'train_tokens_per_second': '791.5'}
11%|█ | 2030/18688 [2:23:00<17:50:40, 3.86s/it] 11%|█ | 2031/18688 [2:23:05<18:29:55, 4.00s/it] 11%|█ | 2032/18688 [2:23:10<19:55:09, 4.31s/it] 11%|█ | 2033/18688 [2:23:14<20:39:13, 4.46s/it] 11%|█ | 2034/18688 [2:23:18<19:40:25, 4.25s/it] 11%|█ | 2035/18688 [2:23:22<19:42:52, 4.26s/it] 11%|█ | 2036/18688 [2:23:27<19:43:42, 4.27s/it] 11%|█ | 2037/18688 [2:23:30<18:45:55, 4.06s/it] 11%|█ | 2038/18688 [2:23:34<18:52:23, 4.08s/it] 11%|█ | 2039/18688 [2:23:39<19:05:01, 4.13s/it] 11%|█ | 2040/18688 [2:23:43<18:57:23, 4.10s/it] {'loss': '1.007', 'grad_norm': '1.633', 'learning_rate': '9.976e-05', 'epoch': '0.1092', 'num_input_tokens_seen': 6827176, 'train_runtime': '8623', 'train_tokens_per_second': '791.7'}
11%|█ | 2040/18688 [2:23:43<18:57:23, 4.10s/it] 11%|█ | 2041/18688 [2:23:47<19:10:13, 4.15s/it] 11%|█ | 2042/18688 [2:23:50<18:13:09, 3.94s/it] 11%|█ | 2043/18688 [2:23:54<18:24:02, 3.98s/it] 11%|█ | 2044/18688 [2:23:59<19:12:28, 4.15s/it] 11%|█ | 2045/18688 [2:24:04<20:06:23, 4.35s/it] 11%|█ | 2046/18688 [2:24:08<19:40:35, 4.26s/it] 11%|█ | 2047/18688 [2:24:12<18:50:38, 4.08s/it] 11%|█ | 2048/18688 [2:24:16<18:44:43, 4.06s/it] 11%|█ | 2049/18688 [2:24:19<18:21:59, 3.97s/it] 11%|█ | 2050/18688 [2:24:23<18:17:25, 3.96s/it] {'loss': '0.9666', 'grad_norm': '0.9343', 'learning_rate': '9.975e-05', 'epoch': '0.1097', 'num_input_tokens_seen': 6859736, 'train_runtime': '8664', 'train_tokens_per_second': '791.8'}
11%|█ | 2050/18688 [2:24:23<18:17:25, 3.96s/it] 11%|█ | 2051/18688 [2:24:28<19:06:02, 4.13s/it] 11%|█ | 2052/18688 [2:24:33<20:22:47, 4.41s/it] 11%|█ | 2053/18688 [2:24:37<20:20:34, 4.40s/it] 11%|█ | 2054/18688 [2:24:41<20:09:19, 4.36s/it] 11%|█ | 2055/18688 [2:24:45<18:54:46, 4.09s/it] 11%|█ | 2056/18688 [2:24:48<18:03:58, 3.91s/it] 11%|█ | 2057/18688 [2:24:52<17:38:41, 3.82s/it] 11%|█ | 2058/18688 [2:24:56<17:27:55, 3.78s/it] 11%|█ | 2059/18688 [2:25:00<18:11:59, 3.94s/it] 11%|█ | 2060/18688 [2:25:05<19:23:34, 4.20s/it] {'loss': '0.9358', 'grad_norm': '0.7337', 'learning_rate': '9.974e-05', 'epoch': '0.1102', 'num_input_tokens_seen': 6893248, 'train_runtime': '8705', 'train_tokens_per_second': '791.8'}
11%|█ | 2060/18688 [2:25:05<19:23:34, 4.20s/it] 11%|█ | 2061/18688 [2:25:10<20:23:58, 4.42s/it] 11%|█ | 2062/18688 [2:25:14<20:05:39, 4.35s/it] 11%|█ | 2063/18688 [2:25:18<19:12:40, 4.16s/it] 11%|█ | 2064/18688 [2:25:22<19:44:54, 4.28s/it] 11%|█ | 2065/18688 [2:25:26<19:00:40, 4.12s/it] 11%|█ | 2066/18688 [2:25:30<18:32:11, 4.01s/it] 11%|█ | 2067/18688 [2:25:34<19:19:37, 4.19s/it] 11%|█ | 2068/18688 [2:25:38<18:37:54, 4.04s/it] 11%|█ | 2069/18688 [2:25:42<19:14:36, 4.17s/it] 11%|█ | 2070/18688 [2:25:46<18:35:58, 4.03s/it] {'loss': '1.015', 'grad_norm': '0.9413', 'learning_rate': '9.973e-05', 'epoch': '0.1108', 'num_input_tokens_seen': 6926840, 'train_runtime': '8747', 'train_tokens_per_second': '791.9'}
11%|█ | 2070/18688 [2:25:46<18:35:58, 4.03s/it] 11%|█ | 2071/18688 [2:25:51<20:17:40, 4.40s/it] 11%|█ | 2072/18688 [2:25:56<20:57:04, 4.54s/it] 11%|█ | 2073/18688 [2:26:02<21:53:51, 4.74s/it] 11%|█ | 2074/18688 [2:26:05<20:08:58, 4.37s/it] 11%|█ | 2075/18688 [2:26:09<19:27:06, 4.22s/it] 11%|█ | 2076/18688 [2:26:12<18:26:46, 4.00s/it] 11%|█ | 2077/18688 [2:26:16<17:59:31, 3.90s/it] 11%|█ | 2078/18688 [2:26:20<17:25:47, 3.78s/it] 11%|█ | 2079/18688 [2:26:23<17:12:52, 3.73s/it] 11%|█ | 2080/18688 [2:26:28<18:03:58, 3.92s/it] {'loss': '0.9531', 'grad_norm': '1.089', 'learning_rate': '9.972e-05', 'epoch': '0.1113', 'num_input_tokens_seen': 6960856, 'train_runtime': '8788', 'train_tokens_per_second': '792.1'}
11%|█ | 2080/18688 [2:26:28<18:03:58, 3.92s/it] 11%|█ | 2081/18688 [2:26:31<17:26:56, 3.78s/it] 11%|█ | 2082/18688 [2:26:35<17:56:02, 3.89s/it] 11%|█ | 2083/18688 [2:26:40<18:46:58, 4.07s/it] 11%|█ | 2084/18688 [2:26:44<18:54:26, 4.10s/it] 11%|█ | 2085/18688 [2:26:47<18:08:39, 3.93s/it] 11%|█ | 2086/18688 [2:26:52<19:31:39, 4.23s/it] 11%|█ | 2087/18688 [2:26:56<19:09:36, 4.15s/it] 11%|█ | 2088/18688 [2:27:01<19:21:03, 4.20s/it] 11%|█ | 2089/18688 [2:27:04<18:38:02, 4.04s/it] 11%|█ | 2090/18688 [2:27:09<19:01:55, 4.13s/it] {'loss': '0.9233', 'grad_norm': '1.037', 'learning_rate': '9.971e-05', 'epoch': '0.1118', 'num_input_tokens_seen': 6993128, 'train_runtime': '8829', 'train_tokens_per_second': '792.1'}
11%|█ | 2090/18688 [2:27:09<19:01:55, 4.13s/it] 11%|█ | 2091/18688 [2:27:13<19:30:27, 4.23s/it] 11%|█ | 2092/18688 [2:27:17<19:41:57, 4.27s/it] 11%|█ | 2093/18688 [2:27:21<19:06:00, 4.14s/it] 11%|█ | 2094/18688 [2:27:25<19:04:28, 4.14s/it] 11%|█ | 2095/18688 [2:27:30<19:13:34, 4.17s/it] 11%|█ | 2096/18688 [2:27:34<19:43:03, 4.28s/it] 11%|█ | 2097/18688 [2:27:38<19:34:31, 4.25s/it] 11%|█ | 2098/18688 [2:27:44<21:00:43, 4.56s/it] 11%|█ | 2099/18688 [2:27:48<21:20:04, 4.63s/it] 11%|█ | 2100/18688 [2:27:52<20:26:13, 4.44s/it] {'loss': '0.9903', 'grad_norm': '1.16', 'learning_rate': '9.97e-05', 'epoch': '0.1124', 'num_input_tokens_seen': 7027808, 'train_runtime': '8873', 'train_tokens_per_second': '792.1'}
11%|█ | 2100/18688 [2:27:52<20:26:13, 4.44s/it] 11%|█ | 2101/18688 [2:27:57<20:37:06, 4.47s/it] 11%|█ | 2102/18688 [2:28:01<19:44:45, 4.29s/it] 11%|█▏ | 2103/18688 [2:28:05<19:48:19, 4.30s/it] 11%|█▏ | 2104/18688 [2:28:09<18:42:43, 4.06s/it] 11%|█▏ | 2105/18688 [2:28:13<18:47:33, 4.08s/it] 11%|█▏ | 2106/18688 [2:28:16<17:54:16, 3.89s/it] 11%|█▏ | 2107/18688 [2:28:20<17:41:24, 3.84s/it] 11%|█▏ | 2108/18688 [2:28:24<17:21:38, 3.77s/it] 11%|█▏ | 2109/18688 [2:28:28<17:42:58, 3.85s/it] 11%|█▏ | 2110/18688 [2:28:32<18:59:52, 4.13s/it] {'loss': '0.9156', 'grad_norm': '1.195', 'learning_rate': '9.969e-05', 'epoch': '0.1129', 'num_input_tokens_seen': 7060536, 'train_runtime': '8913', 'train_tokens_per_second': '792.2'}
11%|█▏ | 2110/18688 [2:28:32<18:59:52, 4.13s/it] 11%|█▏ | 2111/18688 [2:28:36<18:20:30, 3.98s/it] 11%|█▏ | 2112/18688 [2:28:40<18:14:05, 3.96s/it] 11%|█▏ | 2113/18688 [2:28:44<18:28:45, 4.01s/it] 11%|█▏ | 2114/18688 [2:28:49<19:40:07, 4.27s/it] 11%|█▏ | 2115/18688 [2:28:53<20:08:05, 4.37s/it] 11%|█▏ | 2116/18688 [2:28:57<19:31:17, 4.24s/it] 11%|█▏ | 2117/18688 [2:29:02<20:14:27, 4.40s/it] 11%|█▏ | 2118/18688 [2:29:07<20:19:52, 4.42s/it] 11%|█▏ | 2119/18688 [2:29:11<19:43:03, 4.28s/it] 11%|█▏ | 2120/18688 [2:29:15<19:52:44, 4.32s/it] {'loss': '1.045', 'grad_norm': '0.9974', 'learning_rate': '9.968e-05', 'epoch': '0.1134', 'num_input_tokens_seen': 7095224, 'train_runtime': '8956', 'train_tokens_per_second': '792.3'}
11%|█▏ | 2120/18688 [2:29:15<19:52:44, 4.32s/it] 11%|█▏ | 2121/18688 [2:29:19<19:52:21, 4.32s/it] 11%|█▏ | 2122/18688 [2:29:24<20:09:28, 4.38s/it] 11%|█▏ | 2123/18688 [2:29:28<20:19:18, 4.42s/it] 11%|█▏ | 2124/18688 [2:29:32<19:09:33, 4.16s/it] 11%|█▏ | 2125/18688 [2:29:36<18:45:33, 4.08s/it] 11%|█▏ | 2126/18688 [2:29:39<18:11:08, 3.95s/it] 11%|█▏ | 2127/18688 [2:29:43<17:49:50, 3.88s/it] 11%|█▏ | 2128/18688 [2:29:47<17:45:58, 3.86s/it] 11%|█▏ | 2129/18688 [2:29:52<19:27:14, 4.23s/it] 11%|█▏ | 2130/18688 [2:29:56<18:44:07, 4.07s/it] {'loss': '0.9967', 'grad_norm': '1.187', 'learning_rate': '9.967e-05', 'epoch': '0.114', 'num_input_tokens_seen': 7128176, 'train_runtime': '8996', 'train_tokens_per_second': '792.3'}
11%|█▏ | 2130/18688 [2:29:56<18:44:07, 4.07s/it] 11%|█▏ | 2131/18688 [2:30:00<18:29:19, 4.02s/it] 11%|█▏ | 2132/18688 [2:30:03<17:51:39, 3.88s/it] 11%|█▏ | 2133/18688 [2:30:08<19:07:52, 4.16s/it] 11%|█▏ | 2134/18688 [2:30:13<19:51:55, 4.32s/it] 11%|█▏ | 2135/18688 [2:30:16<18:56:02, 4.12s/it] 11%|█▏ | 2136/18688 [2:30:21<19:10:14, 4.17s/it] 11%|█▏ | 2137/18688 [2:30:25<18:55:07, 4.11s/it] 11%|█▏ | 2138/18688 [2:30:28<18:09:07, 3.95s/it] 11%|█▏ | 2139/18688 [2:30:32<18:24:27, 4.00s/it] 11%|█▏ | 2140/18688 [2:30:36<17:45:00, 3.86s/it] {'loss': '0.984', 'grad_norm': '1.06', 'learning_rate': '9.966e-05', 'epoch': '0.1145', 'num_input_tokens_seen': 7160536, 'train_runtime': '9036', 'train_tokens_per_second': '792.4'}
11%|█▏ | 2140/18688 [2:30:36<17:45:00, 3.86s/it] 11%|█▏ | 2141/18688 [2:30:39<17:20:22, 3.77s/it] 11%|█▏ | 2142/18688 [2:30:43<16:57:52, 3.69s/it] 11%|█▏ | 2143/18688 [2:30:48<18:15:33, 3.97s/it] 11%|█▏ | 2144/18688 [2:30:51<17:53:31, 3.89s/it] 11%|█▏ | 2145/18688 [2:30:56<18:30:15, 4.03s/it] 11%|█▏ | 2146/18688 [2:30:59<18:04:13, 3.93s/it] 11%|█▏ | 2147/18688 [2:31:04<18:58:53, 4.13s/it] 11%|█▏ | 2148/18688 [2:31:08<18:58:50, 4.13s/it] 11%|█▏ | 2149/18688 [2:31:12<19:10:36, 4.17s/it] 12%|█▏ | 2150/18688 [2:31:16<18:28:03, 4.02s/it] {'loss': '0.9701', 'grad_norm': '1.192', 'learning_rate': '9.965e-05', 'epoch': '0.1151', 'num_input_tokens_seen': 7194240, 'train_runtime': '9077', 'train_tokens_per_second': '792.6'}
12%|█▏ | 2150/18688 [2:31:16<18:28:03, 4.02s/it] 12%|█▏ | 2151/18688 [2:31:20<18:08:10, 3.95s/it] 12%|█▏ | 2152/18688 [2:31:24<18:28:58, 4.02s/it] 12%|█▏ | 2153/18688 [2:31:28<18:44:32, 4.08s/it] 12%|█▏ | 2154/18688 [2:31:32<18:03:28, 3.93s/it] 12%|█▏ | 2155/18688 [2:31:36<18:00:59, 3.92s/it] 12%|█▏ | 2156/18688 [2:31:39<17:46:16, 3.87s/it] 12%|█▏ | 2157/18688 [2:31:43<17:58:10, 3.91s/it] 12%|█▏ | 2158/18688 [2:31:47<17:46:50, 3.87s/it] 12%|█▏ | 2159/18688 [2:31:51<17:35:30, 3.83s/it] 12%|█▏ | 2160/18688 [2:31:55<18:17:08, 3.98s/it] {'loss': '0.9871', 'grad_norm': '1.119', 'learning_rate': '9.964e-05', 'epoch': '0.1156', 'num_input_tokens_seen': 7224368, 'train_runtime': '9116', 'train_tokens_per_second': '792.5'}
12%|█▏ | 2160/18688 [2:31:55<18:17:08, 3.98s/it] 12%|█▏ | 2161/18688 [2:32:00<19:44:23, 4.30s/it] 12%|█▏ | 2162/18688 [2:32:04<18:31:12, 4.03s/it] 12%|█▏ | 2163/18688 [2:32:08<18:07:04, 3.95s/it] 12%|█▏ | 2164/18688 [2:32:12<18:23:35, 4.01s/it] 12%|█▏ | 2165/18688 [2:32:15<17:39:16, 3.85s/it] 12%|█▏ | 2166/18688 [2:32:19<18:08:13, 3.95s/it] 12%|█▏ | 2167/18688 [2:32:24<18:31:02, 4.04s/it] 12%|█▏ | 2168/18688 [2:32:28<18:54:28, 4.12s/it] 12%|█▏ | 2169/18688 [2:32:32<19:32:03, 4.26s/it] 12%|█▏ | 2170/18688 [2:32:36<18:51:39, 4.11s/it] {'loss': '0.9779', 'grad_norm': '0.9017', 'learning_rate': '9.963e-05', 'epoch': '0.1161', 'num_input_tokens_seen': 7257872, 'train_runtime': '9157', 'train_tokens_per_second': '792.6'}
12%|█▏ | 2170/18688 [2:32:36<18:51:39, 4.11s/it] 12%|█▏ | 2171/18688 [2:32:40<18:14:00, 3.97s/it] 12%|█▏ | 2172/18688 [2:32:44<19:06:13, 4.16s/it] 12%|█▏ | 2173/18688 [2:32:49<19:15:56, 4.20s/it] 12%|█▏ | 2174/18688 [2:32:52<18:25:19, 4.02s/it] 12%|█▏ | 2175/18688 [2:32:56<18:24:40, 4.01s/it] 12%|█▏ | 2176/18688 [2:33:01<18:35:25, 4.05s/it] 12%|█▏ | 2177/18688 [2:33:04<17:49:38, 3.89s/it] 12%|█▏ | 2178/18688 [2:33:08<17:33:12, 3.83s/it] 12%|█▏ | 2179/18688 [2:33:11<17:22:00, 3.79s/it] 12%|█▏ | 2180/18688 [2:33:16<17:49:42, 3.89s/it] {'loss': '0.9301', 'grad_norm': '1.051', 'learning_rate': '9.962e-05', 'epoch': '0.1167', 'num_input_tokens_seen': 7288792, 'train_runtime': '9196', 'train_tokens_per_second': '792.6'}
12%|█▏ | 2180/18688 [2:33:16<17:49:42, 3.89s/it] 12%|█▏ | 2181/18688 [2:33:20<18:08:23, 3.96s/it] 12%|█▏ | 2182/18688 [2:33:23<17:26:53, 3.81s/it] 12%|█▏ | 2183/18688 [2:33:27<17:47:21, 3.88s/it] 12%|█▏ | 2184/18688 [2:33:31<17:56:48, 3.91s/it] 12%|█▏ | 2185/18688 [2:33:35<17:28:23, 3.81s/it] 12%|█▏ | 2186/18688 [2:33:38<17:11:07, 3.75s/it] 12%|█▏ | 2187/18688 [2:33:43<18:30:07, 4.04s/it] 12%|█▏ | 2188/18688 [2:33:47<17:51:20, 3.90s/it] 12%|█▏ | 2189/18688 [2:33:51<18:27:39, 4.03s/it] 12%|█▏ | 2190/18688 [2:33:55<18:54:34, 4.13s/it] {'loss': '1.02', 'grad_norm': '1.339', 'learning_rate': '9.96e-05', 'epoch': '0.1172', 'num_input_tokens_seen': 7320992, 'train_runtime': '9236', 'train_tokens_per_second': '792.7'}
12%|█▏ | 2190/18688 [2:33:55<18:54:34, 4.13s/it] 12%|█▏ | 2191/18688 [2:33:59<18:18:04, 3.99s/it] 12%|█▏ | 2192/18688 [2:34:02<17:32:28, 3.83s/it] 12%|█▏ | 2193/18688 [2:34:08<19:19:32, 4.22s/it] 12%|█▏ | 2194/18688 [2:34:12<19:24:01, 4.23s/it] 12%|█▏ | 2195/18688 [2:34:16<18:42:13, 4.08s/it] 12%|█▏ | 2196/18688 [2:34:20<19:08:07, 4.18s/it] 12%|█▏ | 2197/18688 [2:34:23<18:09:41, 3.96s/it] 12%|█▏ | 2198/18688 [2:34:28<18:42:56, 4.09s/it] 12%|█▏ | 2199/18688 [2:34:32<19:07:50, 4.18s/it] 12%|█▏ | 2200/18688 [2:34:38<20:55:15, 4.57s/it] {'loss': '0.9924', 'grad_norm': '1.284', 'learning_rate': '9.959e-05', 'epoch': '0.1177', 'num_input_tokens_seen': 7357008, 'train_runtime': '9278', 'train_tokens_per_second': '792.9'}
12%|█▏ | 2200/18688 [2:34:38<20:55:15, 4.57s/it] 12%|█▏ | 2201/18688 [2:34:42<21:16:36, 4.65s/it] 12%|█▏ | 2202/18688 [2:34:47<20:53:05, 4.56s/it] 12%|█▏ | 2203/18688 [2:34:51<19:56:53, 4.36s/it] 12%|█▏ | 2204/18688 [2:34:55<19:45:26, 4.31s/it] 12%|█▏ | 2205/18688 [2:34:59<18:52:43, 4.12s/it] 12%|█▏ | 2206/18688 [2:35:03<19:20:16, 4.22s/it] 12%|█▏ | 2207/18688 [2:35:07<18:31:54, 4.05s/it] 12%|█▏ | 2208/18688 [2:35:11<18:18:31, 4.00s/it] 12%|█▏ | 2209/18688 [2:35:14<17:55:47, 3.92s/it] 12%|█▏ | 2210/18688 [2:35:18<17:51:11, 3.90s/it] {'loss': '1.052', 'grad_norm': '1.127', 'learning_rate': '9.958e-05', 'epoch': '0.1183', 'num_input_tokens_seen': 7390192, 'train_runtime': '9319', 'train_tokens_per_second': '793.1'}
12%|█▏ | 2210/18688 [2:35:18<17:51:11, 3.90s/it] 12%|█▏ | 2211/18688 [2:35:22<17:40:53, 3.86s/it] 12%|█▏ | 2212/18688 [2:35:26<18:15:20, 3.99s/it] 12%|█▏ | 2213/18688 [2:35:30<17:55:22, 3.92s/it] 12%|█▏ | 2214/18688 [2:35:34<17:36:16, 3.85s/it] 12%|█▏ | 2215/18688 [2:35:38<18:30:35, 4.05s/it] 12%|█▏ | 2216/18688 [2:35:42<18:08:06, 3.96s/it] 12%|█▏ | 2217/18688 [2:35:46<18:48:10, 4.11s/it] 12%|█▏ | 2218/18688 [2:35:50<18:45:56, 4.10s/it] 12%|█▏ | 2219/18688 [2:35:54<18:35:34, 4.06s/it] 12%|█▏ | 2220/18688 [2:35:58<18:10:46, 3.97s/it] {'loss': '0.9424', 'grad_norm': '1.04', 'learning_rate': '9.957e-05', 'epoch': '0.1188', 'num_input_tokens_seen': 7422720, 'train_runtime': '9359', 'train_tokens_per_second': '793.1'}
12%|█▏ | 2220/18688 [2:35:58<18:10:46, 3.97s/it] 12%|█▏ | 2221/18688 [2:36:02<18:30:48, 4.05s/it] 12%|█▏ | 2222/18688 [2:36:06<17:54:28, 3.92s/it] 12%|█▏ | 2223/18688 [2:36:11<18:59:35, 4.15s/it] 12%|█▏ | 2224/18688 [2:36:15<19:40:24, 4.30s/it] 12%|█▏ | 2225/18688 [2:36:19<19:01:54, 4.16s/it] 12%|█▏ | 2226/18688 [2:36:23<18:50:32, 4.12s/it] 12%|█▏ | 2227/18688 [2:36:27<18:48:00, 4.11s/it] 12%|█▏ | 2228/18688 [2:36:32<19:23:02, 4.24s/it] 12%|█▏ | 2229/18688 [2:36:36<19:47:02, 4.33s/it] 12%|█▏ | 2230/18688 [2:36:41<19:54:34, 4.35s/it] {'loss': '0.9815', 'grad_norm': '0.9485', 'learning_rate': '9.956e-05', 'epoch': '0.1193', 'num_input_tokens_seen': 7459088, 'train_runtime': '9401', 'train_tokens_per_second': '793.4'}
12%|█▏ | 2230/18688 [2:36:41<19:54:34, 4.35s/it] 12%|█▏ | 2231/18688 [2:36:45<19:58:08, 4.37s/it] 12%|█▏ | 2232/18688 [2:36:49<19:04:45, 4.17s/it] 12%|█▏ | 2233/18688 [2:36:53<18:33:21, 4.06s/it] 12%|█▏ | 2234/18688 [2:36:57<18:37:02, 4.07s/it] 12%|█▏ | 2235/18688 [2:37:01<18:24:22, 4.03s/it] 12%|█▏ | 2236/18688 [2:37:05<18:09:33, 3.97s/it] 12%|█▏ | 2237/18688 [2:37:08<17:55:40, 3.92s/it] 12%|█▏ | 2238/18688 [2:37:12<18:04:54, 3.96s/it] 12%|█▏ | 2239/18688 [2:37:16<17:48:23, 3.90s/it] 12%|█▏ | 2240/18688 [2:37:20<17:38:45, 3.86s/it] {'loss': '0.9815', 'grad_norm': '1.254', 'learning_rate': '9.954e-05', 'epoch': '0.1199', 'num_input_tokens_seen': 7489936, 'train_runtime': '9441', 'train_tokens_per_second': '793.4'}
12%|█▏ | 2240/18688 [2:37:20<17:38:45, 3.86s/it] 12%|█▏ | 2241/18688 [2:37:24<17:13:57, 3.77s/it] 12%|█▏ | 2242/18688 [2:37:27<16:59:16, 3.72s/it] 12%|█▏ | 2243/18688 [2:37:33<19:35:35, 4.29s/it] 12%|█▏ | 2244/18688 [2:37:37<19:46:28, 4.33s/it] 12%|█▏ | 2245/18688 [2:37:41<18:36:34, 4.07s/it] 12%|█▏ | 2246/18688 [2:37:44<17:48:57, 3.90s/it] 12%|█▏ | 2247/18688 [2:37:48<18:01:05, 3.95s/it] 12%|█▏ | 2248/18688 [2:37:53<18:31:30, 4.06s/it] 12%|█▏ | 2249/18688 [2:37:57<18:31:00, 4.06s/it] 12%|█▏ | 2250/18688 [2:38:01<18:36:13, 4.07s/it] {'loss': '0.9427', 'grad_norm': '1.191', 'learning_rate': '9.953e-05', 'epoch': '0.1204', 'num_input_tokens_seen': 7523312, 'train_runtime': '9481', 'train_tokens_per_second': '793.5'}
12%|█▏ | 2250/18688 [2:38:01<18:36:13, 4.07s/it] 12%|█▏ | 2251/18688 [2:38:05<18:31:17, 4.06s/it] 12%|█▏ | 2252/18688 [2:38:09<18:08:38, 3.97s/it] 12%|█▏ | 2253/18688 [2:38:12<17:53:06, 3.92s/it] 12%|█▏ | 2254/18688 [2:38:18<20:30:32, 4.49s/it] 12%|█▏ | 2255/18688 [2:38:23<20:28:17, 4.48s/it] 12%|█▏ | 2256/18688 [2:38:26<19:02:22, 4.17s/it] 12%|█▏ | 2257/18688 [2:38:30<18:44:32, 4.11s/it] 12%|█▏ | 2258/18688 [2:38:35<19:25:12, 4.26s/it] 12%|█▏ | 2259/18688 [2:38:38<18:34:38, 4.07s/it] 12%|█▏ | 2260/18688 [2:38:43<19:28:44, 4.27s/it] {'loss': '1.093', 'grad_norm': '1.014', 'learning_rate': '9.952e-05', 'epoch': '0.1209', 'num_input_tokens_seen': 7558424, 'train_runtime': '9523', 'train_tokens_per_second': '793.7'}
12%|█▏ | 2260/18688 [2:38:43<19:28:44, 4.27s/it] 12%|█▏ | 2261/18688 [2:38:47<18:51:00, 4.13s/it] 12%|█▏ | 2262/18688 [2:38:52<20:31:13, 4.50s/it] 12%|█▏ | 2263/18688 [2:38:56<19:06:53, 4.19s/it] 12%|█▏ | 2264/18688 [2:39:00<19:28:23, 4.27s/it] 12%|█▏ | 2265/18688 [2:39:04<18:46:47, 4.12s/it] 12%|█▏ | 2266/18688 [2:39:08<18:27:13, 4.05s/it] 12%|█▏ | 2267/18688 [2:39:11<17:55:01, 3.93s/it] 12%|█▏ | 2268/18688 [2:39:15<18:05:35, 3.97s/it] 12%|█▏ | 2269/18688 [2:39:20<19:08:57, 4.20s/it] 12%|█▏ | 2270/18688 [2:39:24<18:58:42, 4.16s/it] {'loss': '0.9616', 'grad_norm': '1.185', 'learning_rate': '9.951e-05', 'epoch': '0.1215', 'num_input_tokens_seen': 7591752, 'train_runtime': '9565', 'train_tokens_per_second': '793.7'}
12%|█▏ | 2270/18688 [2:39:24<18:58:42, 4.16s/it] 12%|█▏ | 2271/18688 [2:39:28<18:40:41, 4.10s/it] 12%|█▏ | 2272/18688 [2:39:32<18:12:58, 3.99s/it] 12%|█▏ | 2273/18688 [2:39:36<18:42:26, 4.10s/it] 12%|█▏ | 2274/18688 [2:39:41<19:09:01, 4.20s/it] 12%|█▏ | 2275/18688 [2:39:44<18:11:39, 3.99s/it] 12%|█▏ | 2276/18688 [2:39:48<17:26:56, 3.83s/it] 12%|█▏ | 2277/18688 [2:39:52<18:49:31, 4.13s/it] 12%|█▏ | 2278/18688 [2:39:57<18:58:33, 4.16s/it] 12%|█▏ | 2279/18688 [2:40:01<19:06:13, 4.19s/it] 12%|█▏ | 2280/18688 [2:40:05<18:40:52, 4.10s/it] {'loss': '0.9697', 'grad_norm': '0.8891', 'learning_rate': '9.949e-05', 'epoch': '0.122', 'num_input_tokens_seen': 7626000, 'train_runtime': '9605', 'train_tokens_per_second': '793.9'}
12%|█▏ | 2280/18688 [2:40:05<18:40:52, 4.10s/it] 12%|█▏ | 2281/18688 [2:40:08<18:01:17, 3.95s/it] 12%|█▏ | 2282/18688 [2:40:13<18:25:39, 4.04s/it] 12%|█▏ | 2283/18688 [2:40:17<18:25:54, 4.04s/it] 12%|█▏ | 2284/18688 [2:40:21<19:17:21, 4.23s/it] 12%|█▏ | 2285/18688 [2:40:26<19:15:08, 4.23s/it] 12%|█▏ | 2286/18688 [2:40:30<18:48:00, 4.13s/it] 12%|█▏ | 2287/18688 [2:40:33<18:16:57, 4.01s/it] 12%|█▏ | 2288/18688 [2:40:38<19:03:43, 4.18s/it] 12%|█▏ | 2289/18688 [2:40:42<18:54:52, 4.15s/it] 12%|█▏ | 2290/18688 [2:40:46<18:42:13, 4.11s/it] {'loss': '0.9997', 'grad_norm': '1.072', 'learning_rate': '9.948e-05', 'epoch': '0.1225', 'num_input_tokens_seen': 7659472, 'train_runtime': '9646', 'train_tokens_per_second': '794'}
12%|█▏ | 2290/18688 [2:40:46<18:42:13, 4.11s/it] 12%|█▏ | 2291/18688 [2:40:51<19:58:30, 4.39s/it] 12%|█▏ | 2292/18688 [2:40:56<20:20:00, 4.46s/it] 12%|█▏ | 2293/18688 [2:41:00<20:00:16, 4.39s/it] 12%|█▏ | 2294/18688 [2:41:05<20:50:29, 4.58s/it] 12%|█▏ | 2295/18688 [2:41:08<19:27:28, 4.27s/it] 12%|█▏ | 2296/18688 [2:41:13<19:34:04, 4.30s/it] 12%|█▏ | 2297/18688 [2:41:17<18:51:23, 4.14s/it] 12%|█▏ | 2298/18688 [2:41:21<18:34:53, 4.08s/it] 12%|█▏ | 2299/18688 [2:41:25<19:08:08, 4.20s/it] 12%|█▏ | 2300/18688 [2:41:30<19:57:24, 4.38s/it] {'loss': '1.065', 'grad_norm': '0.9493', 'learning_rate': '9.947e-05', 'epoch': '0.1231', 'num_input_tokens_seen': 7696560, 'train_runtime': '9690', 'train_tokens_per_second': '794.3'}
12%|█▏ | 2300/18688 [2:41:30<19:57:24, 4.38s/it] 12%|█▏ | 2301/18688 [2:41:33<18:57:14, 4.16s/it] 12%|█▏ | 2302/18688 [2:41:37<18:42:53, 4.11s/it] 12%|█▏ | 2303/18688 [2:41:42<18:53:49, 4.15s/it] 12%|█▏ | 2304/18688 [2:41:47<20:31:08, 4.51s/it] 12%|█▏ | 2305/18688 [2:41:51<19:32:40, 4.29s/it] 12%|█▏ | 2306/18688 [2:41:54<18:35:43, 4.09s/it] 12%|█▏ | 2307/18688 [2:41:59<18:44:58, 4.12s/it] 12%|█▏ | 2308/18688 [2:42:02<18:18:35, 4.02s/it] 12%|█▏ | 2309/18688 [2:42:07<19:30:53, 4.29s/it] 12%|█▏ | 2310/18688 [2:42:13<20:52:08, 4.59s/it] {'loss': '0.9928', 'grad_norm': '0.962', 'learning_rate': '9.945e-05', 'epoch': '0.1236', 'num_input_tokens_seen': 7731888, 'train_runtime': '9733', 'train_tokens_per_second': '794.4'}
12%|█▏ | 2310/18688 [2:42:13<20:52:08, 4.59s/it] 12%|█▏ | 2311/18688 [2:42:17<20:16:34, 4.46s/it] 12%|█▏ | 2312/18688 [2:42:21<19:49:46, 4.36s/it] 12%|█▏ | 2313/18688 [2:42:25<19:02:45, 4.19s/it] 12%|█▏ | 2314/18688 [2:42:29<18:47:55, 4.13s/it] 12%|█▏ | 2315/18688 [2:42:33<19:26:01, 4.27s/it] 12%|█▏ | 2316/18688 [2:42:37<18:39:03, 4.10s/it] 12%|█▏ | 2317/18688 [2:42:41<18:04:50, 3.98s/it] 12%|█▏ | 2318/18688 [2:42:45<18:53:09, 4.15s/it] 12%|█▏ | 2319/18688 [2:42:49<18:31:27, 4.07s/it] 12%|█▏ | 2320/18688 [2:42:53<18:05:43, 3.98s/it] {'loss': '0.9082', 'grad_norm': '0.9425', 'learning_rate': '9.944e-05', 'epoch': '0.1241', 'num_input_tokens_seen': 7763960, 'train_runtime': '9773', 'train_tokens_per_second': '794.4'}
12%|█▏ | 2320/18688 [2:42:53<18:05:43, 3.98s/it] 12%|█▏ | 2321/18688 [2:42:57<18:34:11, 4.08s/it] 12%|█▏ | 2322/18688 [2:43:02<19:15:27, 4.24s/it] 12%|█▏ | 2323/18688 [2:43:05<18:11:56, 4.00s/it] 12%|█▏ | 2324/18688 [2:43:10<18:51:38, 4.15s/it] 12%|█▏ | 2325/18688 [2:43:14<18:42:00, 4.11s/it] 12%|█▏ | 2326/18688 [2:43:19<19:34:16, 4.31s/it] 12%|█▏ | 2327/18688 [2:43:23<20:11:56, 4.44s/it] 12%|█▏ | 2328/18688 [2:43:28<20:52:28, 4.59s/it] 12%|█▏ | 2329/18688 [2:43:32<19:48:46, 4.36s/it] 12%|█▏ | 2330/18688 [2:43:36<18:38:41, 4.10s/it] {'loss': '0.9491', 'grad_norm': '1.115', 'learning_rate': '9.943e-05', 'epoch': '0.1247', 'num_input_tokens_seen': 7800728, 'train_runtime': '9816', 'train_tokens_per_second': '794.7'}
12%|█▏ | 2330/18688 [2:43:36<18:38:41, 4.10s/it] 12%|█▏ | 2331/18688 [2:43:39<18:08:04, 3.99s/it] 12%|█▏ | 2332/18688 [2:43:44<18:39:13, 4.11s/it] 12%|█▏ | 2333/18688 [2:43:48<18:54:50, 4.16s/it] 12%|█▏ | 2334/18688 [2:43:53<20:05:49, 4.42s/it] 12%|█▏ | 2335/18688 [2:43:57<19:14:18, 4.24s/it] 12%|█▎ | 2336/18688 [2:44:01<18:31:56, 4.08s/it] 13%|█▎ | 2337/18688 [2:44:05<18:50:13, 4.15s/it] 13%|█▎ | 2338/18688 [2:44:10<20:00:32, 4.41s/it] 13%|█▎ | 2339/18688 [2:44:14<19:07:02, 4.21s/it] 13%|█▎ | 2340/18688 [2:44:18<18:46:09, 4.13s/it] {'loss': '0.9879', 'grad_norm': '0.984', 'learning_rate': '9.941e-05', 'epoch': '0.1252', 'num_input_tokens_seen': 7834536, 'train_runtime': '9858', 'train_tokens_per_second': '794.7'}
13%|█▎ | 2340/18688 [2:44:18<18:46:09, 4.13s/it] 13%|█▎ | 2341/18688 [2:44:21<18:26:20, 4.06s/it] 13%|█▎ | 2342/18688 [2:44:26<18:36:15, 4.10s/it] 13%|█▎ | 2343/18688 [2:44:30<18:54:15, 4.16s/it] 13%|█▎ | 2344/18688 [2:44:34<18:44:27, 4.13s/it] 13%|█▎ | 2345/18688 [2:44:38<18:22:55, 4.05s/it] 13%|█▎ | 2346/18688 [2:44:42<18:36:22, 4.10s/it] 13%|█▎ | 2347/18688 [2:44:46<18:08:39, 4.00s/it] 13%|█▎ | 2348/18688 [2:44:50<18:01:52, 3.97s/it] 13%|█▎ | 2349/18688 [2:44:54<17:53:40, 3.94s/it] 13%|█▎ | 2350/18688 [2:44:57<17:23:19, 3.83s/it] {'loss': '0.9461', 'grad_norm': '1.05', 'learning_rate': '9.94e-05', 'epoch': '0.1258', 'num_input_tokens_seen': 7863608, 'train_runtime': '9898', 'train_tokens_per_second': '794.5'}
13%|█▎ | 2350/18688 [2:44:57<17:23:19, 3.83s/it] 13%|█▎ | 2351/18688 [2:45:02<18:22:57, 4.05s/it] 13%|█▎ | 2352/18688 [2:45:06<18:26:03, 4.06s/it] 13%|█▎ | 2353/18688 [2:45:10<18:59:31, 4.19s/it] 13%|█▎ | 2354/18688 [2:45:14<18:46:36, 4.14s/it] 13%|█▎ | 2355/18688 [2:45:19<19:33:37, 4.31s/it] 13%|█▎ | 2356/18688 [2:45:23<18:36:50, 4.10s/it] 13%|█▎ | 2357/18688 [2:45:27<18:34:21, 4.09s/it] 13%|█▎ | 2358/18688 [2:45:31<18:46:37, 4.14s/it] 13%|█▎ | 2359/18688 [2:45:36<20:33:52, 4.53s/it] 13%|█▎ | 2360/18688 [2:45:40<19:30:00, 4.30s/it] {'loss': '1.036', 'grad_norm': '1.217', 'learning_rate': '9.938e-05', 'epoch': '0.1263', 'num_input_tokens_seen': 7898208, 'train_runtime': '9941', 'train_tokens_per_second': '794.5'}
13%|█▎ | 2360/18688 [2:45:40<19:30:00, 4.30s/it] 13%|█▎ | 2361/18688 [2:45:44<19:19:54, 4.26s/it] 13%|█▎ | 2362/18688 [2:45:49<19:08:40, 4.22s/it] 13%|█▎ | 2363/18688 [2:45:53<19:11:46, 4.23s/it] 13%|█▎ | 2364/18688 [2:45:57<18:56:10, 4.18s/it] 13%|█▎ | 2365/18688 [2:46:01<19:34:03, 4.32s/it] 13%|█▎ | 2366/18688 [2:46:05<18:54:36, 4.17s/it] 13%|█▎ | 2367/18688 [2:46:10<19:21:01, 4.27s/it] 13%|█▎ | 2368/18688 [2:46:14<19:51:08, 4.38s/it] 13%|█▎ | 2369/18688 [2:46:19<19:32:10, 4.31s/it] 13%|█▎ | 2370/18688 [2:46:22<18:52:28, 4.16s/it] {'loss': '0.9658', 'grad_norm': '1.151', 'learning_rate': '9.937e-05', 'epoch': '0.1268', 'num_input_tokens_seen': 7933504, 'train_runtime': '9983', 'train_tokens_per_second': '794.7'}
13%|█▎ | 2370/18688 [2:46:22<18:52:28, 4.16s/it] 13%|█▎ | 2371/18688 [2:46:27<19:39:11, 4.34s/it] 13%|█▎ | 2372/18688 [2:46:31<18:41:37, 4.12s/it] 13%|█▎ | 2373/18688 [2:46:35<18:38:06, 4.11s/it] 13%|█▎ | 2374/18688 [2:46:39<18:21:58, 4.05s/it] 13%|█▎ | 2375/18688 [2:46:43<18:13:44, 4.02s/it] 13%|█▎ | 2376/18688 [2:46:47<18:03:49, 3.99s/it] 13%|█▎ | 2377/18688 [2:46:50<17:42:09, 3.91s/it] 13%|█▎ | 2378/18688 [2:46:54<17:57:30, 3.96s/it] 13%|█▎ | 2379/18688 [2:46:59<18:19:24, 4.04s/it] 13%|█▎ | 2380/18688 [2:47:03<19:09:02, 4.23s/it] {'loss': '0.9895', 'grad_norm': '1.016', 'learning_rate': '9.936e-05', 'epoch': '0.1274', 'num_input_tokens_seen': 7965544, 'train_runtime': '1.002e+04', 'train_tokens_per_second': '794.7'}
13%|█▎ | 2380/18688 [2:47:03<19:09:02, 4.23s/it] 13%|█▎ | 2381/18688 [2:47:07<18:13:53, 4.02s/it] 13%|█▎ | 2382/18688 [2:47:11<17:43:43, 3.91s/it] 13%|█▎ | 2383/18688 [2:47:14<17:14:37, 3.81s/it] 13%|█▎ | 2384/18688 [2:47:18<17:46:37, 3.93s/it] 13%|█▎ | 2385/18688 [2:47:23<18:22:06, 4.06s/it] 13%|█▎ | 2386/18688 [2:47:27<18:48:38, 4.15s/it] 13%|█▎ | 2387/18688 [2:47:31<18:10:14, 4.01s/it] 13%|█▎ | 2388/18688 [2:47:35<18:01:54, 3.98s/it] 13%|█▎ | 2389/18688 [2:47:39<18:39:04, 4.12s/it] 13%|█▎ | 2390/18688 [2:47:43<18:57:13, 4.19s/it] {'loss': '0.9418', 'grad_norm': '1.206', 'learning_rate': '9.934e-05', 'epoch': '0.1279', 'num_input_tokens_seen': 7996976, 'train_runtime': '1.006e+04', 'train_tokens_per_second': '794.6'}
13%|█▎ | 2390/18688 [2:47:43<18:57:13, 4.19s/it] 13%|█▎ | 2391/18688 [2:47:47<18:00:27, 3.98s/it] 13%|█▎ | 2392/18688 [2:47:50<17:29:11, 3.86s/it] 13%|█▎ | 2393/18688 [2:47:54<16:55:43, 3.74s/it] 13%|█▎ | 2394/18688 [2:47:58<17:06:10, 3.78s/it] 13%|█▎ | 2395/18688 [2:48:02<18:10:10, 4.01s/it] 13%|█▎ | 2396/18688 [2:48:06<17:29:19, 3.86s/it] 13%|█▎ | 2397/18688 [2:48:09<16:58:22, 3.75s/it] 13%|█▎ | 2398/18688 [2:48:13<16:40:04, 3.68s/it] 13%|█▎ | 2399/18688 [2:48:18<18:41:22, 4.13s/it] 13%|█▎ | 2400/18688 [2:48:23<19:55:34, 4.40s/it] {'loss': '0.913', 'grad_norm': '1.034', 'learning_rate': '9.933e-05', 'epoch': '0.1284', 'num_input_tokens_seen': 8028608, 'train_runtime': '1.01e+04', 'train_tokens_per_second': '794.6'}
13%|█▎ | 2400/18688 [2:48:23<19:55:34, 4.40s/it] 13%|█▎ | 2401/18688 [2:48:27<18:48:59, 4.16s/it] 13%|█▎ | 2402/18688 [2:48:31<19:02:29, 4.21s/it] 13%|█▎ | 2403/18688 [2:48:34<18:02:03, 3.99s/it] 13%|█▎ | 2404/18688 [2:48:39<19:22:16, 4.28s/it] 13%|█▎ | 2405/18688 [2:48:43<18:26:46, 4.08s/it] 13%|█▎ | 2406/18688 [2:48:47<18:42:11, 4.14s/it] 13%|█▎ | 2407/18688 [2:48:51<18:01:52, 3.99s/it] 13%|█▎ | 2408/18688 [2:48:56<18:46:00, 4.15s/it] 13%|█▎ | 2409/18688 [2:48:59<17:57:09, 3.97s/it] 13%|█▎ | 2410/18688 [2:49:03<18:21:30, 4.06s/it] {'loss': '0.9324', 'grad_norm': '1.159', 'learning_rate': '9.931e-05', 'epoch': '0.129', 'num_input_tokens_seen': 8061768, 'train_runtime': '1.014e+04', 'train_tokens_per_second': '794.7'}
13%|█▎ | 2410/18688 [2:49:03<18:21:30, 4.06s/it] 13%|█▎ | 2411/18688 [2:49:08<19:15:52, 4.26s/it] 13%|█▎ | 2412/18688 [2:49:12<19:27:13, 4.30s/it] 13%|█▎ | 2413/18688 [2:49:17<19:11:16, 4.24s/it] 13%|█▎ | 2414/18688 [2:49:20<18:08:59, 4.01s/it] 13%|█▎ | 2415/18688 [2:49:25<19:15:47, 4.26s/it] 13%|█▎ | 2416/18688 [2:49:29<18:58:54, 4.20s/it] 13%|█▎ | 2417/18688 [2:49:34<19:32:17, 4.32s/it] 13%|█▎ | 2418/18688 [2:49:37<18:43:20, 4.14s/it] 13%|█▎ | 2419/18688 [2:49:41<18:05:43, 4.00s/it] 13%|█▎ | 2420/18688 [2:49:46<19:51:36, 4.39s/it] {'loss': '1.011', 'grad_norm': '1.04', 'learning_rate': '9.93e-05', 'epoch': '0.1295', 'num_input_tokens_seen': 8096248, 'train_runtime': '1.019e+04', 'train_tokens_per_second': '794.8'}
13%|█▎ | 2420/18688 [2:49:46<19:51:36, 4.39s/it] 13%|█▎ | 2421/18688 [2:49:50<18:51:03, 4.17s/it] 13%|█▎ | 2422/18688 [2:49:54<18:11:24, 4.03s/it] 13%|█▎ | 2423/18688 [2:49:57<17:48:25, 3.94s/it] 13%|█▎ | 2424/18688 [2:50:01<17:44:59, 3.93s/it] 13%|█▎ | 2425/18688 [2:50:05<18:09:31, 4.02s/it] 13%|█▎ | 2426/18688 [2:50:10<19:21:18, 4.28s/it] 13%|█▎ | 2427/18688 [2:50:14<18:13:38, 4.04s/it] 13%|█▎ | 2428/18688 [2:50:18<18:00:40, 3.99s/it] 13%|█▎ | 2429/18688 [2:50:21<17:14:36, 3.82s/it] 13%|█▎ | 2430/18688 [2:50:27<19:21:10, 4.29s/it] {'loss': '0.9556', 'grad_norm': '1.15', 'learning_rate': '9.928e-05', 'epoch': '0.13', 'num_input_tokens_seen': 8128752, 'train_runtime': '1.023e+04', 'train_tokens_per_second': '794.8'}
13%|█▎ | 2430/18688 [2:50:27<19:21:10, 4.29s/it] 13%|█▎ | 2431/18688 [2:50:30<18:37:58, 4.13s/it] 13%|█▎ | 2432/18688 [2:50:34<18:44:46, 4.15s/it] 13%|█▎ | 2433/18688 [2:50:39<18:55:01, 4.19s/it] 13%|█▎ | 2434/18688 [2:50:42<17:57:28, 3.98s/it] 13%|█▎ | 2435/18688 [2:50:47<18:47:52, 4.16s/it] 13%|█▎ | 2436/18688 [2:50:50<18:01:10, 3.99s/it] 13%|█▎ | 2437/18688 [2:50:54<17:33:19, 3.89s/it] 13%|█▎ | 2438/18688 [2:50:59<18:43:58, 4.15s/it] 13%|█▎ | 2439/18688 [2:51:03<18:50:39, 4.17s/it] 13%|█▎ | 2440/18688 [2:51:07<19:02:56, 4.22s/it] {'loss': '1.027', 'grad_norm': '1.238', 'learning_rate': '9.927e-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<19:02:56, 4.22s/it] 13%|█▎ | 2441/18688 [2:51:11<17:56:43, 3.98s/it] 13%|█▎ | 2442/18688 [2:51:15<18:01:05, 3.99s/it] 13%|█▎ | 2443/18688 [2:51:19<17:37:36, 3.91s/it] 13%|█▎ | 2444/18688 [2:51:23<17:45:52, 3.94s/it] 13%|█▎ | 2445/18688 [2:51:27<19:03:50, 4.23s/it] 13%|█▎ | 2446/18688 [2:51:31<18:11:53, 4.03s/it] 13%|█▎ | 2447/18688 [2:51:36<19:39:22, 4.36s/it] 13%|█▎ | 2448/18688 [2:51:40<19:07:57, 4.24s/it] 13%|█▎ | 2449/18688 [2:51:44<19:01:26, 4.22s/it] 13%|█▎ | 2450/18688 [2:51:48<18:49:06, 4.17s/it] {'loss': '0.9739', 'grad_norm': '1.165', 'learning_rate': '9.925e-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<18:49:06, 4.17s/it] 13%|█▎ | 2451/18688 [2:51:53<19:31:42, 4.33s/it] 13%|█▎ | 2452/18688 [2:51:57<19:09:12, 4.25s/it] 13%|█▎ | 2453/18688 [2:52:01<18:20:29, 4.07s/it] 13%|█▎ | 2454/18688 [2:52:05<18:42:03, 4.15s/it] 13%|█▎ | 2455/18688 [2:52:10<19:05:22, 4.23s/it] 13%|█▎ | 2456/18688 [2:52:15<21:13:32, 4.71s/it] 13%|█▎ | 2457/18688 [2:52:20<20:42:46, 4.59s/it] 13%|█▎ | 2458/18688 [2:52:25<21:22:26, 4.74s/it] 13%|█▎ | 2459/18688 [2:52:29<20:58:26, 4.65s/it] 13%|█▎ | 2460/18688 [2:52:33<19:45:51, 4.38s/it] {'loss': '0.9608', 'grad_norm': '1.244', 'learning_rate': '9.923e-05', 'epoch': '0.1316', 'num_input_tokens_seen': 8231304, 'train_runtime': '1.035e+04', 'train_tokens_per_second': '795'}
13%|█▎ | 2460/18688 [2:52:33<19:45:51, 4.38s/it] 13%|█▎ | 2461/18688 [2:52:37<19:09:47, 4.25s/it] 13%|█▎ | 2462/18688 [2:52:41<18:33:18, 4.12s/it] 13%|█▎ | 2463/18688 [2:52:45<18:37:25, 4.13s/it] 13%|█▎ | 2464/18688 [2:52:49<18:07:15, 4.02s/it] 13%|█▎ | 2465/18688 [2:52:53<18:22:15, 4.08s/it] 13%|█▎ | 2466/18688 [2:52:57<17:57:21, 3.98s/it] 13%|█▎ | 2467/18688 [2:53:01<17:54:06, 3.97s/it] 13%|█▎ | 2468/18688 [2:53:04<17:38:09, 3.91s/it] 13%|█▎ | 2469/18688 [2:53:09<18:08:39, 4.03s/it] 13%|█▎ | 2470/18688 [2:53:12<17:37:14, 3.91s/it] {'loss': '0.9688', 'grad_norm': '1.077', 'learning_rate': '9.922e-05', 'epoch': '0.1322', 'num_input_tokens_seen': 8264552, 'train_runtime': '1.039e+04', 'train_tokens_per_second': '795.2'}
13%|█▎ | 2470/18688 [2:53:12<17:37:14, 3.91s/it] 13%|█▎ | 2471/18688 [2:53:16<18:02:38, 4.01s/it] 13%|█▎ | 2472/18688 [2:53:21<19:14:16, 4.27s/it] 13%|█▎ | 2473/18688 [2:53:26<19:42:47, 4.38s/it] 13%|█▎ | 2474/18688 [2:53:30<18:39:23, 4.14s/it] 13%|█▎ | 2475/18688 [2:53:33<18:05:26, 4.02s/it] 13%|█▎ | 2476/18688 [2:53:38<18:32:13, 4.12s/it] 13%|█▎ | 2477/18688 [2:53:42<19:04:36, 4.24s/it] 13%|█▎ | 2478/18688 [2:53:47<19:33:42, 4.34s/it] 13%|█▎ | 2479/18688 [2:53:51<19:57:22, 4.43s/it] 13%|█▎ | 2480/18688 [2:53:55<18:40:29, 4.15s/it] {'loss': '0.9879', 'grad_norm': '1.135', 'learning_rate': '9.92e-05', 'epoch': '0.1327', 'num_input_tokens_seen': 8300808, 'train_runtime': '1.044e+04', 'train_tokens_per_second': '795.4'}
13%|█▎ | 2480/18688 [2:53:55<18:40:29, 4.15s/it] 13%|█▎ | 2481/18688 [2:53:59<18:27:44, 4.10s/it] 13%|█▎ | 2482/18688 [2:54:03<18:55:26, 4.20s/it] 13%|█▎ | 2483/18688 [2:54:07<18:01:48, 4.01s/it] 13%|█▎ | 2484/18688 [2:54:11<17:34:28, 3.90s/it] 13%|█▎ | 2485/18688 [2:54:15<18:40:15, 4.15s/it] 13%|█▎ | 2486/18688 [2:54:19<18:02:43, 4.01s/it] 13%|█▎ | 2487/18688 [2:54:23<17:51:38, 3.97s/it] 13%|█▎ | 2488/18688 [2:54:27<18:33:41, 4.12s/it] 13%|█▎ | 2489/18688 [2:54:31<17:58:57, 4.00s/it] 13%|█▎ | 2490/18688 [2:54:35<17:43:00, 3.94s/it] {'loss': '0.9497', 'grad_norm': '1.159', 'learning_rate': '9.919e-05', 'epoch': '0.1332', 'num_input_tokens_seen': 8332048, 'train_runtime': '1.048e+04', 'train_tokens_per_second': '795.4'}
13%|█▎ | 2490/18688 [2:54:35<17:43:00, 3.94s/it] 13%|█▎ | 2491/18688 [2:54:39<18:13:53, 4.05s/it] 13%|█▎ | 2492/18688 [2:54:43<18:30:32, 4.11s/it] 13%|█▎ | 2493/18688 [2:54:48<18:43:32, 4.16s/it] 13%|█▎ | 2494/18688 [2:54:53<20:09:14, 4.48s/it] 13%|█▎ | 2495/18688 [2:54:57<19:55:45, 4.43s/it] 13%|█▎ | 2496/18688 [2:55:01<18:49:54, 4.19s/it] 13%|█▎ | 2497/18688 [2:55:04<18:07:51, 4.03s/it] 13%|█▎ | 2498/18688 [2:55:08<17:36:34, 3.92s/it] 13%|█▎ | 2499/18688 [2:55:12<17:10:39, 3.82s/it] 13%|█▎ | 2500/18688 [2:55:15<16:40:58, 3.71s/it] {'loss': '0.9848', 'grad_norm': '1.111', 'learning_rate': '9.917e-05', 'epoch': '0.1338', 'num_input_tokens_seen': 8362896, 'train_runtime': '1.052e+04', 'train_tokens_per_second': '795.3'}
13%|█▎ | 2500/18688 [2:55:15<16:40:58, 3.71s/it] 13%|█▎ | 2501/18688 [2:55:19<17:11:40, 3.82s/it] 13%|█▎ | 2502/18688 [2:55:23<16:39:38, 3.71s/it] 13%|█▎ | 2503/18688 [2:55:27<17:14:55, 3.84s/it] 13%|█▎ | 2504/18688 [2:55:31<17:46:45, 3.95s/it] 13%|█▎ | 2505/18688 [2:55:34<17:04:29, 3.80s/it] 13%|█▎ | 2506/18688 [2:55:38<17:21:01, 3.86s/it] 13%|█▎ | 2507/18688 [2:55:42<17:00:25, 3.78s/it] 13%|█▎ | 2508/18688 [2:55:46<17:42:12, 3.94s/it] 13%|█▎ | 2509/18688 [2:55:51<18:25:17, 4.10s/it] 13%|█▎ | 2510/18688 [2:55:55<19:02:18, 4.24s/it] {'loss': '0.9027', 'grad_norm': '1.18', 'learning_rate': '9.915e-05', 'epoch': '0.1343', 'num_input_tokens_seen': 8395088, 'train_runtime': '1.056e+04', 'train_tokens_per_second': '795.3'}
13%|█▎ | 2510/18688 [2:55:55<19:02:18, 4.24s/it] 13%|█▎ | 2511/18688 [2:56:00<19:41:13, 4.38s/it] 13%|█▎ | 2512/18688 [2:56:04<18:51:43, 4.20s/it] 13%|█▎ | 2513/18688 [2:56:08<18:55:15, 4.21s/it] 13%|█▎ | 2514/18688 [2:56:12<17:54:20, 3.99s/it] 13%|█▎ | 2515/18688 [2:56:17<19:16:38, 4.29s/it] 13%|█▎ | 2516/18688 [2:56:20<18:11:08, 4.05s/it] 13%|█▎ | 2517/18688 [2:56:24<18:18:25, 4.08s/it] 13%|█▎ | 2518/18688 [2:56:29<19:22:24, 4.31s/it] 13%|█▎ | 2519/18688 [2:56:33<19:16:54, 4.29s/it] 13%|█▎ | 2520/18688 [2:56:37<18:30:09, 4.12s/it] {'loss': '0.9323', 'grad_norm': '1.147', 'learning_rate': '9.914e-05', 'epoch': '0.1348', 'num_input_tokens_seen': 8428832, 'train_runtime': '1.06e+04', 'train_tokens_per_second': '795.4'}
13%|█▎ | 2520/18688 [2:56:37<18:30:09, 4.12s/it] 13%|█▎ | 2521/18688 [2:56:41<17:51:48, 3.98s/it] 13%|█▎ | 2522/18688 [2:56:45<17:47:08, 3.96s/it] 14%|█▎ | 2523/18688 [2:56:48<17:20:31, 3.86s/it] 14%|█▎ | 2524/18688 [2:56:53<18:30:57, 4.12s/it] 14%|█▎ | 2525/18688 [2:56:57<18:44:27, 4.17s/it] 14%|█▎ | 2526/18688 [2:57:03<20:17:08, 4.52s/it] 14%|█▎ | 2527/18688 [2:57:06<18:54:16, 4.21s/it] 14%|█▎ | 2528/18688 [2:57:10<18:55:15, 4.22s/it] 14%|█▎ | 2529/18688 [2:57:15<19:58:02, 4.45s/it] 14%|█▎ | 2530/18688 [2:57:19<18:56:28, 4.22s/it] {'loss': '0.9646', 'grad_norm': '1.262', 'learning_rate': '9.912e-05', 'epoch': '0.1354', 'num_input_tokens_seen': 8462960, 'train_runtime': '1.064e+04', 'train_tokens_per_second': '795.4'}
14%|█▎ | 2530/18688 [2:57:19<18:56:28, 4.22s/it] 14%|█▎ | 2531/18688 [2:57:23<18:19:41, 4.08s/it] 14%|█▎ | 2532/18688 [2:57:27<18:37:16, 4.15s/it] 14%|█▎ | 2533/18688 [2:57:31<18:56:28, 4.22s/it] 14%|█▎ | 2534/18688 [2:57:36<19:10:11, 4.27s/it] 14%|█▎ | 2535/18688 [2:57:40<19:16:07, 4.29s/it] 14%|█▎ | 2536/18688 [2:57:45<20:30:11, 4.57s/it] 14%|█▎ | 2537/18688 [2:57:51<21:16:52, 4.74s/it] 14%|█▎ | 2538/18688 [2:57:55<21:31:21, 4.80s/it] 14%|█▎ | 2539/18688 [2:58:01<22:06:11, 4.93s/it] 14%|█▎ | 2540/18688 [2:58:04<20:11:27, 4.50s/it] {'loss': '0.9684', 'grad_norm': '1.025', 'learning_rate': '9.91e-05', 'epoch': '0.1359', 'num_input_tokens_seen': 8499904, 'train_runtime': '1.068e+04', 'train_tokens_per_second': '795.5'}
14%|█▎ | 2540/18688 [2:58:04<20:11:27, 4.50s/it] 14%|█▎ | 2541/18688 [2:58:10<21:45:39, 4.85s/it] 14%|█▎ | 2542/18688 [2:58:14<21:13:35, 4.73s/it] 14%|█▎ | 2543/18688 [2:58:19<20:52:08, 4.65s/it] 14%|█▎ | 2544/18688 [2:58:23<19:37:06, 4.37s/it] 14%|█▎ | 2545/18688 [2:58:26<18:24:55, 4.11s/it] 14%|█▎ | 2546/18688 [2:58:30<18:21:24, 4.09s/it] 14%|█▎ | 2547/18688 [2:58:34<18:13:48, 4.07s/it] 14%|█▎ | 2548/18688 [2:58:38<17:46:11, 3.96s/it] 14%|█▎ | 2549/18688 [2:58:43<18:48:51, 4.20s/it] 14%|█▎ | 2550/18688 [2:58:47<18:35:41, 4.15s/it] {'loss': '0.9984', 'grad_norm': '1.23', 'learning_rate': '9.908e-05', 'epoch': '0.1365', 'num_input_tokens_seen': 8533320, 'train_runtime': '1.073e+04', 'train_tokens_per_second': '795.5'}
14%|█▎ | 2550/18688 [2:58:47<18:35:41, 4.15s/it] 14%|█▎ | 2551/18688 [2:58:50<17:54:56, 4.00s/it] 14%|█▎ | 2552/18688 [2:58:55<18:45:52, 4.19s/it] 14%|█▎ | 2553/18688 [2:58:59<19:11:02, 4.28s/it] 14%|█▎ | 2554/18688 [2:59:04<19:53:25, 4.44s/it] 14%|█▎ | 2555/18688 [2:59:09<19:48:24, 4.42s/it] 14%|█▎ | 2556/18688 [2:59:13<19:35:41, 4.37s/it] 14%|█▎ | 2557/18688 [2:59:17<18:43:03, 4.18s/it] 14%|█▎ | 2558/18688 [2:59:21<19:23:49, 4.33s/it] 14%|█▎ | 2559/18688 [2:59:26<19:58:28, 4.46s/it] 14%|█▎ | 2560/18688 [2:59:30<19:27:37, 4.34s/it] {'loss': '0.9688', 'grad_norm': '1.066', 'learning_rate': '9.907e-05', 'epoch': '0.137', 'num_input_tokens_seen': 8569816, 'train_runtime': '1.077e+04', 'train_tokens_per_second': '795.7'}
14%|█▎ | 2560/18688 [2:59:30<19:27:37, 4.34s/it] 14%|█▎ | 2561/18688 [2:59:34<19:10:55, 4.28s/it] 14%|█▎ | 2562/18688 [2:59:38<19:00:20, 4.24s/it] 14%|█▎ | 2563/18688 [2:59:43<19:01:14, 4.25s/it] 14%|█▎ | 2564/18688 [2:59:47<18:33:55, 4.15s/it] 14%|█▎ | 2565/18688 [2:59:51<18:33:00, 4.14s/it] 14%|█▎ | 2566/18688 [2:59:54<17:46:02, 3.97s/it] 14%|█▎ | 2567/18688 [2:59:58<18:09:29, 4.05s/it] 14%|█▎ | 2568/18688 [3:00:02<17:42:04, 3.95s/it] 14%|█▎ | 2569/18688 [3:00:06<17:29:53, 3.91s/it] 14%|█▍ | 2570/18688 [3:00:09<16:52:05, 3.77s/it] {'loss': '0.98', 'grad_norm': '1.123', 'learning_rate': '9.905e-05', 'epoch': '0.1375', 'num_input_tokens_seen': 8600752, 'train_runtime': '1.081e+04', 'train_tokens_per_second': '795.6'}
14%|█▍ | 2570/18688 [3:00:09<16:52:05, 3.77s/it] 14%|█▍ | 2571/18688 [3:00:14<18:37:00, 4.16s/it] 14%|█▍ | 2572/18688 [3:00:19<18:34:52, 4.15s/it] 14%|█▍ | 2573/18688 [3:00:23<19:00:21, 4.25s/it] 14%|█▍ | 2574/18688 [3:00:28<19:46:57, 4.42s/it] 14%|█▍ | 2575/18688 [3:00:32<19:57:45, 4.46s/it] 14%|█▍ | 2576/18688 [3:00:37<19:58:38, 4.46s/it] 14%|█▍ | 2577/18688 [3:00:42<20:15:27, 4.53s/it] 14%|█▍ | 2578/18688 [3:00:46<20:14:50, 4.52s/it] 14%|█▍ | 2579/18688 [3:00:50<19:18:53, 4.32s/it] 14%|█▍ | 2580/18688 [3:00:54<18:37:52, 4.16s/it] {'loss': '0.9799', 'grad_norm': '1.305', 'learning_rate': '9.903e-05', 'epoch': '0.1381', 'num_input_tokens_seen': 8638960, 'train_runtime': '1.085e+04', 'train_tokens_per_second': '795.9'}
14%|█▍ | 2580/18688 [3:00:54<18:37:52, 4.16s/it] 14%|█▍ | 2581/18688 [3:00:58<18:24:44, 4.12s/it] 14%|█▍ | 2582/18688 [3:01:03<19:36:51, 4.38s/it] 14%|█▍ | 2583/18688 [3:01:07<19:45:41, 4.42s/it] 14%|█▍ | 2584/18688 [3:01:11<18:36:09, 4.16s/it] 14%|█▍ | 2585/18688 [3:01:15<18:22:30, 4.11s/it] 14%|█▍ | 2586/18688 [3:01:18<17:43:04, 3.96s/it] 14%|█▍ | 2587/18688 [3:01:23<17:58:43, 4.02s/it] 14%|█▍ | 2588/18688 [3:01:27<18:57:21, 4.24s/it] 14%|█▍ | 2589/18688 [3:01:31<18:34:23, 4.15s/it] 14%|█▍ | 2590/18688 [3:01:35<17:58:16, 4.02s/it] {'loss': '1.038', 'grad_norm': '0.9968', 'learning_rate': '9.901e-05', 'epoch': '0.1386', 'num_input_tokens_seen': 8671328, 'train_runtime': '1.09e+04', 'train_tokens_per_second': '795.9'}
14%|█▍ | 2590/18688 [3:01:35<17:58:16, 4.02s/it] 14%|█▍ | 2591/18688 [3:01:39<17:38:33, 3.95s/it] 14%|█▍ | 2592/18688 [3:01:42<17:08:25, 3.83s/it] 14%|█▍ | 2593/18688 [3:01:47<17:52:42, 4.00s/it] 14%|█▍ | 2594/18688 [3:01:51<17:47:12, 3.98s/it] 14%|█▍ | 2595/18688 [3:01:54<17:12:08, 3.85s/it] 14%|█▍ | 2596/18688 [3:01:59<18:39:00, 4.17s/it] 14%|█▍ | 2597/18688 [3:02:03<17:49:52, 3.99s/it] 14%|█▍ | 2598/18688 [3:02:07<18:27:44, 4.13s/it] 14%|█▍ | 2599/18688 [3:02:12<19:25:44, 4.35s/it] 14%|█▍ | 2600/18688 [3:02:16<19:06:19, 4.28s/it] {'loss': '0.9842', 'grad_norm': '1.033', 'learning_rate': '9.899e-05', 'epoch': '0.1391', 'num_input_tokens_seen': 8704528, 'train_runtime': '1.094e+04', 'train_tokens_per_second': '795.9'}
14%|█▍ | 2600/18688 [3:02:16<19:06:19, 4.28s/it] 14%|█▍ | 2601/18688 [3:02:20<19:06:08, 4.27s/it] 14%|█▍ | 2602/18688 [3:02:25<18:54:02, 4.23s/it] 14%|█▍ | 2603/18688 [3:02:29<18:52:16, 4.22s/it] 14%|█▍ | 2604/18688 [3:02:34<19:36:31, 4.39s/it] 14%|█▍ | 2605/18688 [3:02:37<18:40:58, 4.18s/it] 14%|█▍ | 2606/18688 [3:02:41<18:33:04, 4.15s/it] 14%|█▍ | 2607/18688 [3:02:46<18:38:47, 4.17s/it] 14%|█▍ | 2608/18688 [3:02:49<18:20:36, 4.11s/it] 14%|█▍ | 2609/18688 [3:02:53<18:05:39, 4.05s/it] 14%|█▍ | 2610/18688 [3:02:57<17:26:18, 3.90s/it] {'loss': '0.9681', 'grad_norm': '1.312', 'learning_rate': '9.898e-05', 'epoch': '0.1397', 'num_input_tokens_seen': 8737680, 'train_runtime': '1.098e+04', 'train_tokens_per_second': '796'}
14%|█▍ | 2610/18688 [3:02:57<17:26:18, 3.90s/it] 14%|█▍ | 2611/18688 [3:03:01<18:09:40, 4.07s/it] 14%|█▍ | 2612/18688 [3:03:05<18:02:37, 4.04s/it] 14%|█▍ | 2613/18688 [3:03:12<20:50:39, 4.67s/it] 14%|█▍ | 2614/18688 [3:03:16<20:25:51, 4.58s/it] 14%|█▍ | 2615/18688 [3:03:20<19:45:16, 4.42s/it] 14%|█▍ | 2616/18688 [3:03:24<18:37:00, 4.17s/it] 14%|█▍ | 2617/18688 [3:03:27<18:04:55, 4.05s/it] 14%|█▍ | 2618/18688 [3:03:31<18:04:48, 4.05s/it] 14%|█▍ | 2619/18688 [3:03:35<18:02:55, 4.04s/it] 14%|█▍ | 2620/18688 [3:03:39<17:29:36, 3.92s/it] {'loss': '0.9944', 'grad_norm': '1.25', 'learning_rate': '9.896e-05', 'epoch': '0.1402', 'num_input_tokens_seen': 8772632, 'train_runtime': '1.102e+04', 'train_tokens_per_second': '796.1'}
14%|█▍ | 2620/18688 [3:03:39<17:29:36, 3.92s/it] 14%|█▍ | 2621/18688 [3:03:43<18:01:11, 4.04s/it] 14%|█▍ | 2622/18688 [3:03:48<18:51:27, 4.23s/it] 14%|█▍ | 2623/18688 [3:03:52<18:05:40, 4.05s/it] 14%|█▍ | 2624/18688 [3:03:55<17:16:53, 3.87s/it] 14%|█▍ | 2625/18688 [3:03:59<17:37:27, 3.95s/it] 14%|█▍ | 2626/18688 [3:04:03<16:58:43, 3.81s/it] 14%|█▍ | 2627/18688 [3:04:08<18:21:24, 4.11s/it] 14%|█▍ | 2628/18688 [3:04:13<19:59:12, 4.48s/it] 14%|█▍ | 2629/18688 [3:04:17<19:03:08, 4.27s/it] 14%|█▍ | 2630/18688 [3:04:20<18:07:38, 4.06s/it] {'loss': '0.9716', 'grad_norm': '1.041', 'learning_rate': '9.894e-05', 'epoch': '0.1407', 'num_input_tokens_seen': 8806376, 'train_runtime': '1.106e+04', 'train_tokens_per_second': '796.2'}
14%|█▍ | 2630/18688 [3:04:20<18:07:38, 4.06s/it] 14%|█▍ | 2631/18688 [3:04:24<17:31:00, 3.93s/it] 14%|█▍ | 2632/18688 [3:04:28<18:05:54, 4.06s/it] 14%|█▍ | 2633/18688 [3:04:33<18:34:03, 4.16s/it] 14%|█▍ | 2634/18688 [3:04:36<18:01:07, 4.04s/it] 14%|█▍ | 2635/18688 [3:04:41<18:37:25, 4.18s/it] 14%|█▍ | 2636/18688 [3:04:45<17:57:51, 4.03s/it] 14%|█▍ | 2637/18688 [3:04:49<18:01:54, 4.04s/it] 14%|█▍ | 2638/18688 [3:04:53<17:53:49, 4.01s/it] 14%|█▍ | 2639/18688 [3:04:57<18:15:19, 4.09s/it] 14%|█▍ | 2640/18688 [3:05:01<18:16:08, 4.10s/it] {'loss': '0.9807', 'grad_norm': '1.056', 'learning_rate': '9.892e-05', 'epoch': '0.1413', 'num_input_tokens_seen': 8839120, 'train_runtime': '1.11e+04', 'train_tokens_per_second': '796.2'}
14%|█▍ | 2640/18688 [3:05:01<18:16:08, 4.10s/it] 14%|█▍ | 2641/18688 [3:05:05<18:45:54, 4.21s/it] 14%|█▍ | 2642/18688 [3:05:10<19:04:19, 4.28s/it] 14%|█▍ | 2643/18688 [3:05:15<20:04:55, 4.51s/it] 14%|█▍ | 2644/18688 [3:05:19<19:00:28, 4.27s/it] 14%|█▍ | 2645/18688 [3:05:23<18:52:05, 4.23s/it] 14%|█▍ | 2646/18688 [3:05:27<19:25:25, 4.36s/it] 14%|█▍ | 2647/18688 [3:05:31<18:34:15, 4.17s/it] 14%|█▍ | 2648/18688 [3:05:35<18:29:30, 4.15s/it] 14%|█▍ | 2649/18688 [3:05:40<19:21:12, 4.34s/it] 14%|█▍ | 2650/18688 [3:05:46<21:20:08, 4.79s/it] {'loss': '0.937', 'grad_norm': '2.457', 'learning_rate': '9.89e-05', 'epoch': '0.1418', 'num_input_tokens_seen': 8876544, 'train_runtime': '1.115e+04', 'train_tokens_per_second': '796.4'}
14%|█▍ | 2650/18688 [3:05:46<21:20:08, 4.79s/it] 14%|█▍ | 2651/18688 [3:05:49<19:42:26, 4.42s/it] 14%|█▍ | 2652/18688 [3:05:53<19:00:36, 4.27s/it] 14%|█▍ | 2653/18688 [3:05:58<19:36:01, 4.40s/it] 14%|█▍ | 2654/18688 [3:06:03<20:12:34, 4.54s/it] 14%|█▍ | 2655/18688 [3:06:06<18:55:20, 4.25s/it] 14%|█▍ | 2656/18688 [3:06:11<18:48:42, 4.22s/it] 14%|█▍ | 2657/18688 [3:06:14<17:57:01, 4.03s/it] 14%|█▍ | 2658/18688 [3:06:18<18:03:31, 4.06s/it] 14%|█▍ | 2659/18688 [3:06:23<18:23:33, 4.13s/it] 14%|█▍ | 2660/18688 [3:06:26<17:47:47, 4.00s/it] {'loss': '0.9914', 'grad_norm': '1.05', 'learning_rate': '9.888e-05', 'epoch': '0.1423', 'num_input_tokens_seen': 8910464, 'train_runtime': '1.119e+04', 'train_tokens_per_second': '796.5'}
14%|█▍ | 2660/18688 [3:06:26<17:47:47, 4.00s/it] 14%|█▍ | 2661/18688 [3:06:31<18:16:55, 4.11s/it] 14%|█▍ | 2662/18688 [3:06:36<19:15:38, 4.33s/it] 14%|█▍ | 2663/18688 [3:06:39<18:45:33, 4.21s/it] 14%|█▍ | 2664/18688 [3:06:43<17:59:50, 4.04s/it] 14%|█▍ | 2665/18688 [3:06:47<17:27:59, 3.92s/it] 14%|█▍ | 2666/18688 [3:06:50<16:58:55, 3.82s/it] 14%|█▍ | 2667/18688 [3:06:55<18:08:13, 4.08s/it] 14%|█▍ | 2668/18688 [3:06:59<17:21:48, 3.90s/it] 14%|█▍ | 2669/18688 [3:07:02<17:21:38, 3.90s/it] 14%|█▍ | 2670/18688 [3:07:06<17:13:38, 3.87s/it] {'loss': '1.005', 'grad_norm': '1.061', 'learning_rate': '9.886e-05', 'epoch': '0.1429', 'num_input_tokens_seen': 8940648, 'train_runtime': '1.123e+04', 'train_tokens_per_second': '796.4'}
14%|█▍ | 2670/18688 [3:07:06<17:13:38, 3.87s/it] 14%|█▍ | 2671/18688 [3:07:10<16:49:34, 3.78s/it] 14%|█▍ | 2672/18688 [3:07:14<17:00:33, 3.82s/it] 14%|█▍ | 2673/18688 [3:07:18<18:16:27, 4.11s/it] 14%|█▍ | 2674/18688 [3:07:23<18:35:31, 4.18s/it] 14%|█▍ | 2675/18688 [3:07:27<18:42:03, 4.20s/it] 14%|█▍ | 2676/18688 [3:07:32<19:17:27, 4.34s/it] 14%|█▍ | 2677/18688 [3:07:35<18:08:26, 4.08s/it] 14%|█▍ | 2678/18688 [3:07:39<18:24:17, 4.14s/it] 14%|█▍ | 2679/18688 [3:07:44<19:04:09, 4.29s/it] 14%|█▍ | 2680/18688 [3:07:48<19:02:12, 4.28s/it] {'loss': '0.969', 'grad_norm': '1.045', 'learning_rate': '9.884e-05', 'epoch': '0.1434', 'num_input_tokens_seen': 8974040, 'train_runtime': '1.127e+04', 'train_tokens_per_second': '796.4'}
14%|█▍ | 2680/18688 [3:07:48<19:02:12, 4.28s/it] 14%|█▍ | 2681/18688 [3:07:52<17:57:42, 4.04s/it] 14%|█▍ | 2682/18688 [3:07:56<17:35:52, 3.96s/it] 14%|█▍ | 2683/18688 [3:08:00<18:30:45, 4.16s/it] 14%|█▍ | 2684/18688 [3:08:05<19:47:48, 4.45s/it] 14%|█▍ | 2685/18688 [3:08:09<18:34:18, 4.18s/it] 14%|█▍ | 2686/18688 [3:08:14<20:21:12, 4.58s/it] 14%|█▍ | 2687/18688 [3:08:18<19:36:52, 4.41s/it] 14%|█▍ | 2688/18688 [3:08:23<19:57:02, 4.49s/it] 14%|█▍ | 2689/18688 [3:08:27<18:34:39, 4.18s/it] 14%|█▍ | 2690/18688 [3:08:31<18:30:02, 4.16s/it] {'loss': '0.9833', 'grad_norm': '1.2', 'learning_rate': '9.882e-05', 'epoch': '0.1439', 'num_input_tokens_seen': 9008864, 'train_runtime': '1.131e+04', 'train_tokens_per_second': '796.5'}
14%|█▍ | 2690/18688 [3:08:31<18:30:02, 4.16s/it] 14%|█▍ | 2691/18688 [3:08:35<18:05:44, 4.07s/it] 14%|█▍ | 2692/18688 [3:08:39<18:07:03, 4.08s/it] 14%|█▍ | 2693/18688 [3:08:43<17:55:54, 4.04s/it] 14%|█▍ | 2694/18688 [3:08:46<17:09:29, 3.86s/it] 14%|█▍ | 2695/18688 [3:08:51<18:37:10, 4.19s/it] 14%|█▍ | 2696/18688 [3:08:56<20:11:00, 4.54s/it] 14%|█▍ | 2697/18688 [3:09:00<19:25:26, 4.37s/it] 14%|█▍ | 2698/18688 [3:09:05<19:13:28, 4.33s/it] 14%|█▍ | 2699/18688 [3:09:09<19:13:40, 4.33s/it] 14%|█▍ | 2700/18688 [3:09:13<18:23:14, 4.14s/it] {'loss': '0.9483', 'grad_norm': '1.266', 'learning_rate': '9.88e-05', 'epoch': '0.1445', 'num_input_tokens_seen': 9043752, 'train_runtime': '1.135e+04', 'train_tokens_per_second': '796.6'}
14%|█▍ | 2700/18688 [3:09:13<18:23:14, 4.14s/it] 14%|█▍ | 2701/18688 [3:09:16<17:46:12, 4.00s/it] 14%|█▍ | 2702/18688 [3:09:20<17:49:47, 4.02s/it] 14%|█▍ | 2703/18688 [3:09:25<18:18:42, 4.12s/it] 14%|█▍ | 2704/18688 [3:09:30<19:28:50, 4.39s/it] 14%|█▍ | 2705/18688 [3:09:35<20:13:53, 4.56s/it] 14%|█▍ | 2706/18688 [3:09:38<18:49:54, 4.24s/it] 14%|█▍ | 2707/18688 [3:09:42<18:12:54, 4.10s/it] 14%|█▍ | 2708/18688 [3:09:46<18:23:16, 4.14s/it] 14%|█▍ | 2709/18688 [3:09:50<17:59:23, 4.05s/it] 15%|█▍ | 2710/18688 [3:09:54<18:07:51, 4.09s/it] {'loss': '0.9348', 'grad_norm': '1.235', 'learning_rate': '9.878e-05', 'epoch': '0.145', 'num_input_tokens_seen': 9077560, 'train_runtime': '1.139e+04', 'train_tokens_per_second': '796.6'}
15%|█▍ | 2710/18688 [3:09:54<18:07:51, 4.09s/it] 15%|█▍ | 2711/18688 [3:09:59<18:27:26, 4.16s/it] 15%|█▍ | 2712/18688 [3:10:02<18:05:12, 4.08s/it] 15%|█▍ | 2713/18688 [3:10:06<17:41:20, 3.99s/it] 15%|█▍ | 2714/18688 [3:10:11<18:17:28, 4.12s/it] 15%|█▍ | 2715/18688 [3:10:15<18:38:10, 4.20s/it] 15%|█▍ | 2716/18688 [3:10:19<17:51:52, 4.03s/it] 15%|█▍ | 2717/18688 [3:10:23<18:22:03, 4.14s/it] 15%|█▍ | 2718/18688 [3:10:28<19:25:40, 4.38s/it] 15%|█▍ | 2719/18688 [3:10:32<19:17:36, 4.35s/it] 15%|█▍ | 2720/18688 [3:10:38<20:52:17, 4.71s/it] {'loss': '0.9807', 'grad_norm': '1.06', 'learning_rate': '9.876e-05', 'epoch': '0.1456', 'num_input_tokens_seen': 9113984, 'train_runtime': '1.144e+04', 'train_tokens_per_second': '796.8'}
15%|█▍ | 2720/18688 [3:10:38<20:52:17, 4.71s/it] 15%|█▍ | 2721/18688 [3:10:41<19:30:11, 4.40s/it] 15%|█▍ | 2722/18688 [3:10:45<18:28:06, 4.16s/it] 15%|█▍ | 2723/18688 [3:10:49<17:30:53, 3.95s/it] 15%|█▍ | 2724/18688 [3:10:52<17:18:23, 3.90s/it] 15%|█▍ | 2725/18688 [3:10:57<18:29:26, 4.17s/it] 15%|█▍ | 2726/18688 [3:11:02<19:03:59, 4.30s/it] 15%|█▍ | 2727/18688 [3:11:06<19:14:44, 4.34s/it] 15%|█▍ | 2728/18688 [3:11:11<20:23:04, 4.60s/it] 15%|█▍ | 2729/18688 [3:11:16<20:06:27, 4.54s/it] 15%|█▍ | 2730/18688 [3:11:20<19:19:46, 4.36s/it] {'loss': '0.9727', 'grad_norm': '1.079', 'learning_rate': '9.874e-05', 'epoch': '0.1461', 'num_input_tokens_seen': 9148144, 'train_runtime': '1.148e+04', 'train_tokens_per_second': '796.9'}
15%|█▍ | 2730/18688 [3:11:20<19:19:46, 4.36s/it] 15%|█▍ | 2731/18688 [3:11:24<19:00:21, 4.29s/it] 15%|█▍ | 2732/18688 [3:11:28<18:16:17, 4.12s/it] 15%|█▍ | 2733/18688 [3:11:31<17:59:09, 4.06s/it] 15%|█▍ | 2734/18688 [3:11:35<17:15:22, 3.89s/it] 15%|█▍ | 2735/18688 [3:11:40<18:07:12, 4.09s/it] 15%|█▍ | 2736/18688 [3:11:43<17:43:59, 4.00s/it] 15%|█▍ | 2737/18688 [3:11:48<18:43:33, 4.23s/it] 15%|█▍ | 2738/18688 [3:11:52<18:53:25, 4.26s/it] 15%|█▍ | 2739/18688 [3:11:58<20:07:40, 4.54s/it] 15%|█▍ | 2740/18688 [3:12:02<19:30:11, 4.40s/it] {'loss': '1.01', 'grad_norm': '1.05', 'learning_rate': '9.872e-05', 'epoch': '0.1466', 'num_input_tokens_seen': 9182056, 'train_runtime': '1.152e+04', 'train_tokens_per_second': '796.9'}
15%|█▍ | 2740/18688 [3:12:02<19:30:11, 4.40s/it] 15%|█▍ | 2741/18688 [3:12:06<18:53:36, 4.27s/it] 15%|█▍ | 2742/18688 [3:12:09<18:14:15, 4.12s/it] 15%|█▍ | 2743/18688 [3:12:13<17:55:59, 4.05s/it] 15%|█▍ | 2744/18688 [3:12:17<17:44:43, 4.01s/it] 15%|█▍ | 2745/18688 [3:12:21<18:01:48, 4.07s/it] 15%|█▍ | 2746/18688 [3:12:25<17:43:47, 4.00s/it] 15%|█▍ | 2747/18688 [3:12:30<18:25:10, 4.16s/it] 15%|█▍ | 2748/18688 [3:12:34<18:20:40, 4.14s/it] 15%|█▍ | 2749/18688 [3:12:38<18:22:29, 4.15s/it] 15%|█▍ | 2750/18688 [3:12:42<17:35:18, 3.97s/it] {'loss': '1.006', 'grad_norm': '1.109', 'learning_rate': '9.87e-05', 'epoch': '0.1472', 'num_input_tokens_seen': 9213128, 'train_runtime': '1.156e+04', 'train_tokens_per_second': '796.8'}
15%|█▍ | 2750/18688 [3:12:42<17:35:18, 3.97s/it] 15%|█▍ | 2751/18688 [3:12:45<17:18:50, 3.91s/it] 15%|█▍ | 2752/18688 [3:12:49<16:52:44, 3.81s/it] 15%|█▍ | 2753/18688 [3:12:53<17:19:31, 3.91s/it] 15%|█▍ | 2754/18688 [3:12:58<18:29:43, 4.18s/it] 15%|█▍ | 2755/18688 [3:13:03<19:29:14, 4.40s/it] 15%|█▍ | 2756/18688 [3:13:08<20:19:12, 4.59s/it] 15%|█▍ | 2757/18688 [3:13:12<19:23:37, 4.38s/it] 15%|█▍ | 2758/18688 [3:13:15<18:09:39, 4.10s/it] 15%|█▍ | 2759/18688 [3:13:19<17:39:50, 3.99s/it] 15%|█▍ | 2760/18688 [3:13:23<17:27:39, 3.95s/it] {'loss': '0.9885', 'grad_norm': '1.041', 'learning_rate': '9.868e-05', 'epoch': '0.1477', 'num_input_tokens_seen': 9245840, 'train_runtime': '1.16e+04', 'train_tokens_per_second': '796.8'}
15%|█▍ | 2760/18688 [3:13:23<17:27:39, 3.95s/it] 15%|█▍ | 2761/18688 [3:13:27<17:41:13, 4.00s/it] 15%|█▍ | 2762/18688 [3:13:32<18:27:39, 4.17s/it] 15%|█▍ | 2763/18688 [3:13:36<19:03:26, 4.31s/it] 15%|█▍ | 2764/18688 [3:13:40<18:31:06, 4.19s/it] 15%|█▍ | 2765/18688 [3:13:44<17:50:34, 4.03s/it] 15%|█▍ | 2766/18688 [3:13:49<19:06:51, 4.32s/it] 15%|█▍ | 2767/18688 [3:13:53<18:53:58, 4.27s/it] 15%|█▍ | 2768/18688 [3:13:58<19:23:50, 4.39s/it] 15%|█▍ | 2769/18688 [3:14:02<18:58:32, 4.29s/it] 15%|█▍ | 2770/18688 [3:14:05<17:57:17, 4.06s/it] {'loss': '0.9893', 'grad_norm': '1.176', 'learning_rate': '9.866e-05', 'epoch': '0.1482', 'num_input_tokens_seen': 9281336, 'train_runtime': '1.165e+04', 'train_tokens_per_second': '797'}
15%|█▍ | 2770/18688 [3:14:05<17:57:17, 4.06s/it] 15%|█▍ | 2771/18688 [3:14:09<17:54:11, 4.05s/it] 15%|█▍ | 2772/18688 [3:14:13<17:55:41, 4.06s/it] 15%|█▍ | 2773/18688 [3:14:17<17:20:27, 3.92s/it] 15%|█▍ | 2774/18688 [3:14:21<18:20:25, 4.15s/it] 15%|█▍ | 2775/18688 [3:14:26<18:38:52, 4.22s/it] 15%|█▍ | 2776/18688 [3:14:29<17:47:16, 4.02s/it] 15%|█▍ | 2777/18688 [3:14:35<19:25:43, 4.40s/it] 15%|█▍ | 2778/18688 [3:14:39<19:33:18, 4.42s/it] 15%|█▍ | 2779/18688 [3:14:43<18:18:26, 4.14s/it] 15%|█▍ | 2780/18688 [3:14:47<17:56:29, 4.06s/it] {'loss': '0.9731', 'grad_norm': '1.225', 'learning_rate': '9.864e-05', 'epoch': '0.1488', 'num_input_tokens_seen': 9315912, 'train_runtime': '1.169e+04', 'train_tokens_per_second': '797.1'}
15%|█▍ | 2780/18688 [3:14:47<17:56:29, 4.06s/it] 15%|█▍ | 2781/18688 [3:14:51<18:25:39, 4.17s/it] 15%|█▍ | 2782/18688 [3:14:55<17:52:38, 4.05s/it] 15%|█▍ | 2783/18688 [3:14:59<17:58:57, 4.07s/it] 15%|█▍ | 2784/18688 [3:15:02<17:18:57, 3.92s/it] 15%|█▍ | 2785/18688 [3:15:06<17:06:12, 3.87s/it] 15%|█▍ | 2786/18688 [3:15:10<17:20:57, 3.93s/it] 15%|█▍ | 2787/18688 [3:15:15<18:15:38, 4.13s/it] 15%|█▍ | 2788/18688 [3:15:19<18:06:37, 4.10s/it] 15%|█▍ | 2789/18688 [3:15:22<17:16:00, 3.91s/it] 15%|█▍ | 2790/18688 [3:15:26<17:27:14, 3.95s/it] {'loss': '1.008', 'grad_norm': '1.153', 'learning_rate': '9.862e-05', 'epoch': '0.1493', 'num_input_tokens_seen': 9347640, 'train_runtime': '1.173e+04', 'train_tokens_per_second': '797.1'}
15%|█▍ | 2790/18688 [3:15:26<17:27:14, 3.95s/it] 15%|█▍ | 2791/18688 [3:15:31<18:12:48, 4.12s/it] 15%|█▍ | 2792/18688 [3:15:35<18:11:57, 4.12s/it] 15%|█▍ | 2793/18688 [3:15:39<18:08:56, 4.11s/it] 15%|█▍ | 2794/18688 [3:15:43<17:39:34, 4.00s/it] 15%|█▍ | 2795/18688 [3:15:47<18:16:26, 4.14s/it] 15%|█▍ | 2796/18688 [3:15:51<17:34:27, 3.98s/it] 15%|█▍ | 2797/18688 [3:15:55<17:26:02, 3.95s/it] 15%|█▍ | 2798/18688 [3:15:59<18:17:30, 4.14s/it] 15%|█▍ | 2799/18688 [3:16:04<19:18:01, 4.37s/it] 15%|█▍ | 2800/18688 [3:16:09<19:05:35, 4.33s/it] {'loss': '0.9703', 'grad_norm': '1.187', 'learning_rate': '9.86e-05', 'epoch': '0.1498', 'num_input_tokens_seen': 9383216, 'train_runtime': '1.177e+04', 'train_tokens_per_second': '797.3'}
15%|█▍ | 2800/18688 [3:16:09<19:05:35, 4.33s/it] 15%|█▍ | 2801/18688 [3:16:14<20:28:30, 4.64s/it] 15%|█▍ | 2802/18688 [3:16:18<19:06:29, 4.33s/it] 15%|█▍ | 2803/18688 [3:16:21<18:09:56, 4.12s/it] 15%|█▌ | 2804/18688 [3:16:25<17:44:42, 4.02s/it] 15%|█▌ | 2805/18688 [3:16:29<18:09:13, 4.11s/it] 15%|█▌ | 2806/18688 [3:16:34<19:27:53, 4.41s/it] 15%|█▌ | 2807/18688 [3:16:38<18:33:01, 4.21s/it] 15%|█▌ | 2808/18688 [3:16:42<17:57:35, 4.07s/it] 15%|█▌ | 2809/18688 [3:16:46<17:54:28, 4.06s/it] 15%|█▌ | 2810/18688 [3:16:49<17:12:35, 3.90s/it] {'loss': '0.915', 'grad_norm': '1.18', 'learning_rate': '9.858e-05', 'epoch': '0.1504', 'num_input_tokens_seen': 9415184, 'train_runtime': '1.181e+04', 'train_tokens_per_second': '797.2'}
15%|█▌ | 2810/18688 [3:16:49<17:12:35, 3.90s/it] 15%|█▌ | 2811/18688 [3:16:54<18:20:19, 4.16s/it] 15%|█▌ | 2812/18688 [3:16:59<19:11:47, 4.35s/it] 15%|█▌ | 2813/18688 [3:17:02<17:59:13, 4.08s/it] 15%|█▌ | 2814/18688 [3:17:06<17:14:44, 3.91s/it] 15%|█▌ | 2815/18688 [3:17:09<16:38:36, 3.77s/it] 15%|█▌ | 2816/18688 [3:17:14<17:34:01, 3.98s/it] 15%|█▌ | 2817/18688 [3:17:17<16:57:21, 3.85s/it] 15%|█▌ | 2818/18688 [3:17:21<16:55:21, 3.84s/it] 15%|█▌ | 2819/18688 [3:17:25<16:39:56, 3.78s/it] 15%|█▌ | 2820/18688 [3:17:30<17:50:25, 4.05s/it] {'loss': '0.9402', 'grad_norm': '1.039', 'learning_rate': '9.855e-05', 'epoch': '0.1509', 'num_input_tokens_seen': 9447952, 'train_runtime': '1.185e+04', 'train_tokens_per_second': '797.3'}
15%|█▌ | 2820/18688 [3:17:30<17:50:25, 4.05s/it] 15%|█▌ | 2821/18688 [3:17:34<18:33:15, 4.21s/it] 15%|█▌ | 2822/18688 [3:17:39<19:51:30, 4.51s/it] 15%|█▌ | 2823/18688 [3:17:43<18:28:32, 4.19s/it] 15%|█▌ | 2824/18688 [3:17:47<18:13:43, 4.14s/it] 15%|█▌ | 2825/18688 [3:17:51<18:53:19, 4.29s/it] 15%|█▌ | 2826/18688 [3:17:56<18:41:50, 4.24s/it] 15%|█▌ | 2827/18688 [3:18:00<19:07:32, 4.34s/it] 15%|█▌ | 2828/18688 [3:18:04<19:06:18, 4.34s/it] 15%|█▌ | 2829/18688 [3:18:08<18:07:49, 4.12s/it] 15%|█▌ | 2830/18688 [3:18:13<18:42:10, 4.25s/it] {'loss': '0.9812', 'grad_norm': '1.052', 'learning_rate': '9.853e-05', 'epoch': '0.1514', 'num_input_tokens_seen': 9485040, 'train_runtime': '1.189e+04', 'train_tokens_per_second': '797.5'}
15%|█▌ | 2830/18688 [3:18:13<18:42:10, 4.25s/it] 15%|█▌ | 2831/18688 [3:18:18<20:09:40, 4.58s/it] 15%|█▌ | 2832/18688 [3:18:22<19:52:43, 4.51s/it] 15%|█▌ | 2833/18688 [3:18:26<19:06:14, 4.34s/it] 15%|█▌ | 2834/18688 [3:18:30<18:54:42, 4.29s/it] 15%|█▌ | 2835/18688 [3:18:35<18:42:10, 4.25s/it] 15%|█▌ | 2836/18688 [3:18:39<18:29:42, 4.20s/it] 15%|█▌ | 2837/18688 [3:18:43<19:14:59, 4.37s/it] 15%|█▌ | 2838/18688 [3:18:49<20:17:13, 4.61s/it] 15%|█▌ | 2839/18688 [3:18:53<20:18:15, 4.61s/it] 15%|█▌ | 2840/18688 [3:18:58<20:16:54, 4.61s/it] {'loss': '1.019', 'grad_norm': '1.048', 'learning_rate': '9.851e-05', 'epoch': '0.152', 'num_input_tokens_seen': 9523104, 'train_runtime': '1.194e+04', 'train_tokens_per_second': '797.7'}
15%|█▌ | 2840/18688 [3:18:58<20:16:54, 4.61s/it] 15%|█▌ | 2841/18688 [3:19:01<18:49:06, 4.28s/it] 15%|█▌ | 2842/18688 [3:19:05<18:01:57, 4.10s/it] 15%|█▌ | 2843/18688 [3:19:09<17:56:49, 4.08s/it] 15%|█▌ | 2844/18688 [3:19:13<17:52:26, 4.06s/it] 15%|█▌ | 2845/18688 [3:19:17<17:26:38, 3.96s/it] 15%|█▌ | 2846/18688 [3:19:21<18:16:25, 4.15s/it] 15%|█▌ | 2847/18688 [3:19:25<18:07:30, 4.12s/it] 15%|█▌ | 2848/18688 [3:19:30<18:39:18, 4.24s/it] 15%|█▌ | 2849/18688 [3:19:35<19:12:40, 4.37s/it] 15%|█▌ | 2850/18688 [3:19:39<18:45:55, 4.27s/it] {'loss': '0.9274', 'grad_norm': '1.07', 'learning_rate': '9.849e-05', 'epoch': '0.1525', 'num_input_tokens_seen': 9555376, 'train_runtime': '1.198e+04', 'train_tokens_per_second': '797.7'}
15%|█▌ | 2850/18688 [3:19:39<18:45:55, 4.27s/it] 15%|█▌ | 2851/18688 [3:19:43<18:31:15, 4.21s/it] 15%|█▌ | 2852/18688 [3:19:47<18:12:07, 4.14s/it] 15%|█▌ | 2853/18688 [3:19:50<17:24:04, 3.96s/it] 15%|█▌ | 2854/18688 [3:19:54<17:29:20, 3.98s/it] 15%|█▌ | 2855/18688 [3:19:58<17:01:18, 3.87s/it] 15%|█▌ | 2856/18688 [3:20:02<17:34:42, 4.00s/it] 15%|█▌ | 2857/18688 [3:20:07<18:19:22, 4.17s/it] 15%|█▌ | 2858/18688 [3:20:11<18:04:27, 4.11s/it] 15%|█▌ | 2859/18688 [3:20:15<18:17:02, 4.16s/it] 15%|█▌ | 2860/18688 [3:20:19<18:40:43, 4.25s/it] {'loss': '0.9613', 'grad_norm': '0.9634', 'learning_rate': '9.847e-05', 'epoch': '0.153', 'num_input_tokens_seen': 9589488, 'train_runtime': '1.202e+04', 'train_tokens_per_second': '797.8'}
15%|█▌ | 2860/18688 [3:20:19<18:40:43, 4.25s/it] 15%|█▌ | 2861/18688 [3:20:25<20:19:43, 4.62s/it] 15%|█▌ | 2862/18688 [3:20:30<20:49:49, 4.74s/it] 15%|█▌ | 2863/18688 [3:20:34<20:13:10, 4.60s/it] 15%|█▌ | 2864/18688 [3:20:38<19:11:53, 4.37s/it] 15%|█▌ | 2865/18688 [3:20:43<19:32:26, 4.45s/it] 15%|█▌ | 2866/18688 [3:20:47<19:15:03, 4.38s/it] 15%|█▌ | 2867/18688 [3:20:51<18:21:13, 4.18s/it] 15%|█▌ | 2868/18688 [3:20:55<18:22:05, 4.18s/it] 15%|█▌ | 2869/18688 [3:20:59<18:15:26, 4.15s/it] 15%|█▌ | 2870/18688 [3:21:03<17:55:36, 4.08s/it] {'loss': '1.003', 'grad_norm': '1.207', 'learning_rate': '9.844e-05', 'epoch': '0.1536', 'num_input_tokens_seen': 9626728, 'train_runtime': '1.206e+04', 'train_tokens_per_second': '798'}
15%|█▌ | 2870/18688 [3:21:03<17:55:36, 4.08s/it] 15%|█▌ | 2871/18688 [3:21:07<17:41:20, 4.03s/it] 15%|█▌ | 2872/18688 [3:21:10<17:07:09, 3.90s/it] 15%|█▌ | 2873/18688 [3:21:15<17:54:44, 4.08s/it] 15%|█▌ | 2874/18688 [3:21:20<18:44:40, 4.27s/it] 15%|█▌ | 2875/18688 [3:21:24<18:37:08, 4.24s/it] 15%|█▌ | 2876/18688 [3:21:27<18:03:30, 4.11s/it] 15%|█▌ | 2877/18688 [3:21:33<19:46:21, 4.50s/it] 15%|█▌ | 2878/18688 [3:21:38<20:07:11, 4.58s/it] 15%|█▌ | 2879/18688 [3:21:42<19:39:02, 4.47s/it] 15%|█▌ | 2880/18688 [3:21:45<18:16:03, 4.16s/it] {'loss': '0.9487', 'grad_norm': '1.447', 'learning_rate': '9.842e-05', 'epoch': '0.1541', 'num_input_tokens_seen': 9662024, 'train_runtime': '1.211e+04', 'train_tokens_per_second': '798.1'}
15%|█▌ | 2880/18688 [3:21:45<18:16:03, 4.16s/it] 15%|█▌ | 2881/18688 [3:21:49<17:32:29, 4.00s/it] 15%|█▌ | 2882/18688 [3:21:53<17:23:13, 3.96s/it] 15%|█▌ | 2883/18688 [3:21:57<17:09:05, 3.91s/it] 15%|█▌ | 2884/18688 [3:22:01<17:18:19, 3.94s/it] 15%|█▌ | 2885/18688 [3:22:05<17:27:22, 3.98s/it] 15%|█▌ | 2886/18688 [3:22:09<17:30:39, 3.99s/it] 15%|█▌ | 2887/18688 [3:22:12<17:01:19, 3.88s/it] 15%|█▌ | 2888/18688 [3:22:16<17:22:19, 3.96s/it] 15%|█▌ | 2889/18688 [3:22:22<19:07:44, 4.36s/it] 15%|█▌ | 2890/18688 [3:22:27<19:51:44, 4.53s/it] {'loss': '0.9708', 'grad_norm': '1.001', 'learning_rate': '9.84e-05', 'epoch': '0.1546', 'num_input_tokens_seen': 9695896, 'train_runtime': '1.215e+04', 'train_tokens_per_second': '798.2'}
15%|█▌ | 2890/18688 [3:22:27<19:51:44, 4.53s/it] 15%|█▌ | 2891/18688 [3:22:30<18:50:26, 4.29s/it] 15%|█▌ | 2892/18688 [3:22:35<19:11:59, 4.38s/it] 15%|█▌ | 2893/18688 [3:22:39<18:18:48, 4.17s/it] 15%|█▌ | 2894/18688 [3:22:43<17:52:51, 4.08s/it] 15%|█▌ | 2895/18688 [3:22:47<17:46:57, 4.05s/it] 15%|█▌ | 2896/18688 [3:22:50<17:02:15, 3.88s/it] 16%|█▌ | 2897/18688 [3:22:54<17:43:11, 4.04s/it] 16%|█▌ | 2898/18688 [3:23:00<19:29:28, 4.44s/it] 16%|█▌ | 2899/18688 [3:23:04<18:46:40, 4.28s/it] 16%|█▌ | 2900/18688 [3:23:07<17:39:27, 4.03s/it] {'loss': '0.985', 'grad_norm': '1.395', 'learning_rate': '9.837e-05', 'epoch': '0.1552', 'num_input_tokens_seen': 9727832, 'train_runtime': '1.219e+04', 'train_tokens_per_second': '798.2'}
16%|█▌ | 2900/18688 [3:23:07<17:39:27, 4.03s/it] 16%|█▌ | 2901/18688 [3:23:11<17:48:08, 4.06s/it] 16%|█▌ | 2902/18688 [3:23:15<17:23:15, 3.97s/it] 16%|█▌ | 2903/18688 [3:23:18<16:42:14, 3.81s/it] 16%|█▌ | 2904/18688 [3:23:22<16:32:57, 3.77s/it] 16%|█▌ | 2905/18688 [3:23:26<17:02:51, 3.89s/it] 16%|█▌ | 2906/18688 [3:23:30<16:46:57, 3.83s/it] 16%|█▌ | 2907/18688 [3:23:34<17:18:18, 3.95s/it] 16%|█▌ | 2908/18688 [3:23:38<16:54:55, 3.86s/it] 16%|█▌ | 2909/18688 [3:23:42<17:04:09, 3.89s/it] 16%|█▌ | 2910/18688 [3:23:46<17:21:10, 3.96s/it] {'loss': '0.9911', 'grad_norm': '1.509', 'learning_rate': '9.835e-05', 'epoch': '0.1557', 'num_input_tokens_seen': 9759024, 'train_runtime': '1.223e+04', 'train_tokens_per_second': '798.2'}
16%|█▌ | 2910/18688 [3:23:46<17:21:10, 3.96s/it] 16%|█▌ | 2911/18688 [3:23:51<18:33:35, 4.23s/it] 16%|█▌ | 2912/18688 [3:23:55<18:13:29, 4.16s/it] 16%|█▌ | 2913/18688 [3:23:59<17:37:06, 4.02s/it] 16%|█▌ | 2914/18688 [3:24:03<17:37:21, 4.02s/it] 16%|█▌ | 2915/18688 [3:24:07<18:05:11, 4.13s/it] 16%|█▌ | 2916/18688 [3:24:11<18:24:49, 4.20s/it] 16%|█▌ | 2917/18688 [3:24:16<19:36:35, 4.48s/it] 16%|█▌ | 2918/18688 [3:24:20<18:26:31, 4.21s/it] 16%|█▌ | 2919/18688 [3:24:24<17:54:38, 4.09s/it] 16%|█▌ | 2920/18688 [3:24:28<18:16:13, 4.17s/it] {'loss': '0.9855', 'grad_norm': '1.14', 'learning_rate': '9.833e-05', 'epoch': '0.1563', 'num_input_tokens_seen': 9794000, 'train_runtime': '1.227e+04', 'train_tokens_per_second': '798.3'}
16%|█▌ | 2920/18688 [3:24:28<18:16:13, 4.17s/it] 16%|█▌ | 2921/18688 [3:24:33<18:51:26, 4.31s/it] 16%|█▌ | 2922/18688 [3:24:37<18:32:56, 4.24s/it] 16%|█▌ | 2923/18688 [3:24:42<19:19:15, 4.41s/it] 16%|█▌ | 2924/18688 [3:24:46<19:15:56, 4.40s/it] 16%|█▌ | 2925/18688 [3:24:50<18:01:07, 4.12s/it] 16%|█▌ | 2926/18688 [3:24:53<17:14:07, 3.94s/it] 16%|█▌ | 2927/18688 [3:24:57<17:39:21, 4.03s/it] 16%|█▌ | 2928/18688 [3:25:01<17:32:26, 4.01s/it] 16%|█▌ | 2929/18688 [3:25:05<17:24:54, 3.98s/it] 16%|█▌ | 2930/18688 [3:25:09<17:49:25, 4.07s/it] {'loss': '1.025', 'grad_norm': '1.224', 'learning_rate': '9.83e-05', 'epoch': '0.1568', 'num_input_tokens_seen': 9827592, 'train_runtime': '1.231e+04', 'train_tokens_per_second': '798.3'}
16%|█▌ | 2930/18688 [3:25:09<17:49:25, 4.07s/it] 16%|█▌ | 2931/18688 [3:25:14<18:34:54, 4.25s/it] 16%|█▌ | 2932/18688 [3:25:18<17:52:29, 4.08s/it] 16%|█▌ | 2933/18688 [3:25:23<18:52:58, 4.31s/it] 16%|█▌ | 2934/18688 [3:25:27<19:27:05, 4.44s/it] 16%|█▌ | 2935/18688 [3:25:31<18:26:55, 4.22s/it] 16%|█▌ | 2936/18688 [3:25:35<17:38:00, 4.03s/it] 16%|█▌ | 2937/18688 [3:25:39<17:21:30, 3.97s/it] 16%|█▌ | 2938/18688 [3:25:42<17:16:51, 3.95s/it] 16%|█▌ | 2939/18688 [3:25:48<18:54:59, 4.32s/it] 16%|█▌ | 2940/18688 [3:25:52<18:45:21, 4.29s/it] {'loss': '0.9639', 'grad_norm': '1.322', 'learning_rate': '9.828e-05', 'epoch': '0.1573', 'num_input_tokens_seen': 9859472, 'train_runtime': '1.235e+04', 'train_tokens_per_second': '798.2'}
16%|█▌ | 2940/18688 [3:25:52<18:45:21, 4.29s/it] 16%|█▌ | 2941/18688 [3:25:55<17:43:27, 4.05s/it] 16%|█▌ | 2942/18688 [3:26:00<18:34:36, 4.25s/it] 16%|█▌ | 2943/18688 [3:26:04<17:48:52, 4.07s/it] 16%|█▌ | 2944/18688 [3:26:08<18:09:58, 4.15s/it] 16%|█▌ | 2945/18688 [3:26:12<17:26:43, 3.99s/it] 16%|█▌ | 2946/18688 [3:26:16<18:05:34, 4.14s/it] 16%|█▌ | 2947/18688 [3:26:21<18:40:06, 4.27s/it] 16%|█▌ | 2948/18688 [3:26:25<18:52:30, 4.32s/it] 16%|█▌ | 2949/18688 [3:26:29<17:53:33, 4.09s/it] 16%|█▌ | 2950/18688 [3:26:34<18:49:55, 4.31s/it] {'loss': '1.026', 'grad_norm': '1.095', 'learning_rate': '9.826e-05', 'epoch': '0.1579', 'num_input_tokens_seen': 9893912, 'train_runtime': '1.239e+04', 'train_tokens_per_second': '798.3'}
16%|█▌ | 2950/18688 [3:26:34<18:49:55, 4.31s/it] 16%|█▌ | 2951/18688 [3:26:38<18:28:51, 4.23s/it] 16%|█▌ | 2952/18688 [3:26:42<19:11:19, 4.39s/it] 16%|█▌ | 2953/18688 [3:26:46<18:18:45, 4.19s/it] 16%|█▌ | 2954/18688 [3:26:50<18:15:27, 4.18s/it] 16%|█▌ | 2955/18688 [3:26:54<18:08:39, 4.15s/it] 16%|█▌ | 2956/18688 [3:26:58<17:23:49, 3.98s/it] 16%|█▌ | 2957/18688 [3:27:02<18:06:53, 4.15s/it] 16%|█▌ | 2958/18688 [3:27:07<18:53:39, 4.32s/it] 16%|█▌ | 2959/18688 [3:27:11<18:43:36, 4.29s/it] 16%|█▌ | 2960/18688 [3:27:15<17:57:44, 4.11s/it] {'loss': '0.9881', 'grad_norm': '1.07', 'learning_rate': '9.823e-05', 'epoch': '0.1584', 'num_input_tokens_seen': 9928720, 'train_runtime': '1.244e+04', 'train_tokens_per_second': '798.4'}
16%|█▌ | 2960/18688 [3:27:15<17:57:44, 4.11s/it] 16%|█▌ | 2961/18688 [3:27:19<18:05:58, 4.14s/it] 16%|█▌ | 2962/18688 [3:27:24<18:21:44, 4.20s/it] 16%|█▌ | 2963/18688 [3:27:28<18:17:06, 4.19s/it] 16%|█▌ | 2964/18688 [3:27:32<18:11:45, 4.17s/it] 16%|█▌ | 2965/18688 [3:27:36<17:45:10, 4.06s/it] 16%|█▌ | 2966/18688 [3:27:40<18:11:39, 4.17s/it] 16%|█▌ | 2967/18688 [3:27:45<18:45:37, 4.30s/it] 16%|█▌ | 2968/18688 [3:27:48<17:40:41, 4.05s/it] 16%|█▌ | 2969/18688 [3:27:52<17:01:37, 3.90s/it] 16%|█▌ | 2970/18688 [3:27:55<16:25:21, 3.76s/it] {'loss': '1.033', 'grad_norm': '1.291', 'learning_rate': '9.821e-05', 'epoch': '0.1589', 'num_input_tokens_seen': 9960152, 'train_runtime': '1.248e+04', 'train_tokens_per_second': '798.4'}
16%|█▌ | 2970/18688 [3:27:55<16:25:21, 3.76s/it] 16%|█▌ | 2971/18688 [3:27:59<16:15:53, 3.73s/it] 16%|█▌ | 2972/18688 [3:28:04<17:49:25, 4.08s/it] 16%|█▌ | 2973/18688 [3:28:09<19:21:30, 4.43s/it] 16%|█▌ | 2974/18688 [3:28:13<18:39:36, 4.27s/it] 16%|█▌ | 2975/18688 [3:28:17<18:56:52, 4.34s/it] 16%|█▌ | 2976/18688 [3:28:21<18:29:15, 4.24s/it] 16%|█▌ | 2977/18688 [3:28:26<19:15:41, 4.41s/it] 16%|█▌ | 2978/18688 [3:28:30<18:16:57, 4.19s/it] 16%|█▌ | 2979/18688 [3:28:35<19:04:23, 4.37s/it] 16%|█▌ | 2980/18688 [3:28:39<19:13:59, 4.41s/it] {'loss': '0.981', 'grad_norm': '1.147', 'learning_rate': '9.818e-05', 'epoch': '0.1595', 'num_input_tokens_seen': 9997408, 'train_runtime': '1.252e+04', 'train_tokens_per_second': '798.5'}
16%|█▌ | 2980/18688 [3:28:39<19:13:59, 4.41s/it] 16%|█▌ | 2981/18688 [3:28:44<20:23:48, 4.67s/it] 16%|█▌ | 2982/18688 [3:28:48<18:57:33, 4.35s/it] 16%|█▌ | 2983/18688 [3:28:52<18:29:04, 4.24s/it] 16%|█▌ | 2984/18688 [3:28:56<17:54:50, 4.11s/it] 16%|█▌ | 2985/18688 [3:29:00<17:43:58, 4.07s/it] 16%|█▌ | 2986/18688 [3:29:04<17:20:05, 3.97s/it] 16%|█▌ | 2987/18688 [3:29:07<17:16:22, 3.96s/it] 16%|█▌ | 2988/18688 [3:29:12<17:57:28, 4.12s/it] 16%|█▌ | 2989/18688 [3:29:16<17:19:23, 3.97s/it] 16%|█▌ | 2990/18688 [3:29:19<16:57:58, 3.89s/it] {'loss': '0.971', 'grad_norm': '1.158', 'learning_rate': '9.816e-05', 'epoch': '0.16', 'num_input_tokens_seen': 10029264, 'train_runtime': '1.256e+04', 'train_tokens_per_second': '798.5'}
16%|█▌ | 2990/18688 [3:29:19<16:57:58, 3.89s/it] 16%|█▌ | 2991/18688 [3:29:23<17:13:32, 3.95s/it] 16%|█▌ | 2992/18688 [3:29:27<16:53:55, 3.88s/it] 16%|█▌ | 2993/18688 [3:29:31<17:18:16, 3.97s/it] 16%|█▌ | 2994/18688 [3:29:36<17:52:43, 4.10s/it] 16%|█▌ | 2995/18688 [3:29:40<17:57:12, 4.12s/it] 16%|█▌ | 2996/18688 [3:29:44<17:57:40, 4.12s/it] 16%|█▌ | 2997/18688 [3:29:48<17:40:00, 4.05s/it] 16%|█▌ | 2998/18688 [3:29:52<17:54:03, 4.11s/it] 16%|█▌ | 2999/18688 [3:29:56<18:17:04, 4.20s/it] 16%|█▌ | 3000/18688 [3:30:01<18:35:11, 4.27s/it] {'loss': '0.9068', 'grad_norm': '1.122', 'learning_rate': '9.814e-05', 'epoch': '0.1605', 'num_input_tokens_seen': 10063432, 'train_runtime': '1.26e+04', 'train_tokens_per_second': '798.6'}
16%|█▌ | 3000/18688 [3:30:01<18:35:11, 4.27s/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, 25.81it/s]
1%| | 6/1000 [00:00<00:54, 18.29it/s]
1%| | 8/1000 [00:00<00:56, 17.54it/s]
1%| | 10/1000 [00:00<01:03, 15.49it/s]
1%| | 12/1000 [00:00<01:09, 14.19it/s]
1%|▏ | 14/1000 [00:00<01:13, 13.46it/s]
2%|▏ | 16/1000 [00:01<01:15, 12.98it/s]
2%|▏ | 18/1000 [00:01<01:48, 9.02it/s]
2%|▏ | 20/1000 [00:01<01:43, 9.46it/s]
2%|▏ | 22/1000 [00:01<02:00, 8.10it/s]
2%|▏ | 24/1000 [00:02<01:48, 8.97it/s]
3%|▎ | 26/1000 [00:02<01:52, 8.69it/s]
3%|▎ | 28/1000 [00:02<01:42, 9.48it/s]
3%|▎ | 30/1000 [00:02<01:35, 10.10it/s]
3%|▎ | 32/1000 [00:02<01:40, 9.59it/s]
3%|▎ | 34/1000 [00:03<01:29, 10.78it/s]
4%|▎ | 36/1000 [00:03<01:35, 10.15it/s]
4%|▍ | 38/1000 [00:03<01:26, 11.11it/s]
4%|▍ | 40/1000 [00:03<01:28, 10.82it/s]
4%|▍ | 42/1000 [00:03<01:46, 8.97it/s]
4%|▍ | 44/1000 [00:04<01:31, 10.48it/s]
5%|▍ | 46/1000 [00:04<01:21, 11.64it/s]
5%|▍ | 48/1000 [00:04<01:29, 10.63it/s]
5%|▌ | 50/1000 [00:04<01:21, 11.63it/s]
5%|▌ | 52/1000 [00:04<01:16, 12.46it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.21it/s]
6%|▌ | 56/1000 [00:04<01:07, 13.90it/s]
6%|▌ | 58/1000 [00:05<01:47, 8.73it/s]
6%|▌ | 60/1000 [00:05<01:36, 9.77it/s]
6%|▌ | 62/1000 [00:05<01:27, 10.76it/s]
6%|▋ | 64/1000 [00:05<01:19, 11.84it/s]
7%|▋ | 66/1000 [00:05<01:15, 12.33it/s]
7%|▋ | 68/1000 [00:06<01:12, 12.91it/s]
7%|▋ | 70/1000 [00:06<01:08, 13.50it/s]
7%|▋ | 72/1000 [00:06<01:06, 13.93it/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, 14.53it/s]
8%|▊ | 80/1000 [00:06<01:08, 13.49it/s]
8%|▊ | 82/1000 [00:07<01:05, 14.10it/s]
8%|▊ | 84/1000 [00:07<01:02, 14.55it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.69it/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, 14.20it/s]
9%|▉ | 94/1000 [00:07<01:04, 14.03it/s]
10%|▉ | 96/1000 [00:08<01:02, 14.36it/s]
10%|▉ | 98/1000 [00:08<01:03, 14.28it/s]
10%|█ | 100/1000 [00:08<01:31, 9.83it/s]
10%|█ | 102/1000 [00:08<01:23, 10.79it/s]
10%|█ | 104/1000 [00:09<01:54, 7.83it/s]
11%|█ | 106/1000 [00:09<02:01, 7.34it/s]
11%|█ | 108/1000 [00:09<01:42, 8.69it/s]
11%|█ | 110/1000 [00:09<01:27, 10.12it/s]
11%|█ | 112/1000 [00:09<01:19, 11.22it/s]
11%|█▏ | 114/1000 [00:09<01:14, 11.87it/s]
12%|█▏ | 116/1000 [00:10<01:10, 12.52it/s]
12%|█▏ | 118/1000 [00:10<01:06, 13.32it/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, 9.69it/s]
13%|█▎ | 126/1000 [00:11<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, 13.22it/s]
13%|█▎ | 134/1000 [00:11<01:09, 12.42it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.39it/s]
14%|█▍ | 138/1000 [00:11<01:04, 13.34it/s]
14%|█▍ | 140/1000 [00:12<01:24, 10.14it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.56it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.59it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.62it/s]
15%|█▍ | 148/1000 [00:12<01:13, 11.53it/s]
15%|█▌ | 150/1000 [00:13<01:07, 12.63it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.16it/s]
15%|█▌ | 154/1000 [00:13<01:03, 13.43it/s]
16%|█▌ | 156/1000 [00:13<01:03, 13.28it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.80it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.70it/s]
16%|█▌ | 162/1000 [00:13<01:01, 13.57it/s]
16%|█▋ | 164/1000 [00:14<01:01, 13.67it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.82it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.31it/s]
17%|█▋ | 170/1000 [00:14<01:19, 10.48it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.47it/s]
17%|█▋ | 174/1000 [00:15<01:29, 9.20it/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<01:33, 8.80it/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, 8.36it/s]
19%|█▉ | 188/1000 [00:16<01:23, 9.68it/s]
19%|█▉ | 190/1000 [00:16<01:15, 10.72it/s]
19%|█▉ | 192/1000 [00:16<01:11, 11.37it/s]
19%|█▉ | 194/1000 [00:17<01:16, 10.58it/s]
20%|█▉ | 196/1000 [00:17<01:29, 8.99it/s]
20%|█▉ | 198/1000 [00:17<01:18, 10.22it/s]
20%|██ | 200/1000 [00:17<01:15, 10.56it/s]
20%|██ | 202/1000 [00:17<01:09, 11.52it/s]
20%|██ | 204/1000 [00:18<01:02, 12.65it/s]
21%|██ | 206/1000 [00:18<00:59, 13.26it/s]
21%|██ | 208/1000 [00:18<00:57, 13.66it/s]
21%|██ | 210/1000 [00:18<00:54, 14.52it/s]
21%|██ | 212/1000 [00:18<00:53, 14.79it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.81it/s]
22%|██▏ | 216/1000 [00:18<00:53, 14.55it/s]
22%|██▏ | 218/1000 [00:18<00:52, 14.94it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.93it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.19it/s]
22%|██▏ | 224/1000 [00:19<01:20, 9.69it/s]
23%|██▎ | 226/1000 [00:19<01:12, 10.69it/s]
23%|██▎ | 228/1000 [00:20<01:06, 11.60it/s]
23%|██▎ | 230/1000 [00:20<01:03, 12.09it/s]
23%|██▎ | 232/1000 [00:20<01:01, 12.44it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.14it/s]
24%|██▎ | 236/1000 [00:20<00:56, 13.63it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.24it/s]
24%|██▍ | 240/1000 [00:20<00:53, 14.24it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.80it/s]
24%|██▍ | 244/1000 [00:21<00:50, 15.03it/s]
25%|██▍ | 246/1000 [00:21<00:50, 15.01it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.71it/s]
25%|██▌ | 250/1000 [00:21<00:55, 13.46it/s]
25%|██▌ | 252/1000 [00:21<01:01, 12.20it/s]
25%|██▌ | 254/1000 [00:21<00:56, 13.23it/s]
26%|██▌ | 256/1000 [00:22<00:53, 13.80it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.85it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.96it/s]
26%|██▌ | 262/1000 [00:22<00:56, 13.08it/s]
26%|██▋ | 264/1000 [00:22<00:53, 13.66it/s]
27%|██▋ | 266/1000 [00:22<00:52, 14.00it/s]
27%|██▋ | 268/1000 [00:22<00:50, 14.56it/s]
27%|██▋ | 270/1000 [00:23<00:54, 13.31it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.69it/s]
27%|██▋ | 274/1000 [00:23<01:01, 11.76it/s]
28%|██▊ | 276/1000 [00:23<00:56, 12.79it/s]
28%|██▊ | 278/1000 [00:23<00:55, 12.99it/s]
28%|██▊ | 280/1000 [00:23<00:53, 13.58it/s]
28%|██▊ | 282/1000 [00:24<00:49, 14.53it/s]
28%|██▊ | 284/1000 [00:24<00:49, 14.46it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.75it/s]
29%|██▉ | 288/1000 [00:24<00:47, 15.07it/s]
29%|██▉ | 290/1000 [00:24<00:45, 15.47it/s]
29%|██▉ | 292/1000 [00:24<00:45, 15.46it/s]
29%|██▉ | 294/1000 [00:24<00:45, 15.36it/s]
30%|██▉ | 296/1000 [00:24<00:45, 15.42it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.54it/s]
30%|███ | 300/1000 [00:25<00:46, 15.21it/s]
30%|███ | 302/1000 [00:25<00:45, 15.22it/s]
30%|███ | 304/1000 [00:25<00:50, 13.77it/s]
31%|███ | 306/1000 [00:25<00:47, 14.46it/s]
31%|███ | 308/1000 [00:25<00:47, 14.43it/s]
31%|███ | 310/1000 [00:25<00:47, 14.57it/s]
31%|███ | 312/1000 [00:26<00:47, 14.58it/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<00:43, 15.75it/s]
32%|███▏ | 320/1000 [00:26<01:12, 9.32it/s]
32%|███▏ | 322/1000 [00:26<01:03, 10.70it/s]
32%|███▏ | 324/1000 [00:27<00:58, 11.50it/s]
33%|███▎ | 326/1000 [00:27<00:58, 11.46it/s]
33%|███▎ | 328/1000 [00:27<00:58, 11.50it/s]
33%|███▎ | 330/1000 [00:27<00:53, 12.45it/s]
33%|███▎ | 332/1000 [00:27<00:52, 12.71it/s]
33%|███▎ | 334/1000 [00:27<00:50, 13.21it/s]
34%|███▎ | 336/1000 [00:28<00:48, 13.63it/s]
34%|███▍ | 338/1000 [00:28<01:05, 10.09it/s]
34%|███▍ | 340/1000 [00:28<00:59, 11.17it/s]
34%|███▍ | 342/1000 [00:28<00:53, 12.41it/s]
34%|███▍ | 344/1000 [00:28<00:50, 13.08it/s]
35%|███▍ | 346/1000 [00:28<00:47, 13.68it/s]
35%|███▍ | 348/1000 [00:28<00:46, 14.10it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.74it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.92it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.93it/s]
36%|███▌ | 356/1000 [00:29<00:42, 15.02it/s]
36%|███▌ | 358/1000 [00:29<00:43, 14.69it/s]
36%|███▌ | 360/1000 [00:29<00:42, 15.09it/s]
36%|███▌ | 362/1000 [00:29<00:47, 13.43it/s]
36%|███▋ | 364/1000 [00:30<00:45, 13.92it/s]
37%|███▋ | 366/1000 [00:30<00:52, 11.98it/s]
37%|███▋ | 368/1000 [00:30<00:51, 12.38it/s]
37%|███▋ | 370/1000 [00:30<00:47, 13.24it/s]
37%|███▋ | 372/1000 [00:30<00:46, 13.60it/s]
37%|███▋ | 374/1000 [00:30<00:54, 11.43it/s]
38%|███▊ | 376/1000 [00:31<00:56, 10.99it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.67it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.49it/s]
38%|███▊ | 382/1000 [00:31<00:48, 12.67it/s]
38%|███▊ | 384/1000 [00:31<00:45, 13.43it/s]
39%|███▊ | 386/1000 [00:31<00:44, 13.75it/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, 13.71it/s]
40%|███▉ | 396/1000 [00:32<00:43, 13.83it/s]
40%|███▉ | 398/1000 [00:32<00:47, 12.62it/s]
40%|████ | 400/1000 [00:32<00:45, 13.25it/s]
40%|████ | 402/1000 [00:33<00:43, 13.88it/s]
40%|████ | 404/1000 [00:33<00:41, 14.50it/s]
41%|████ | 406/1000 [00:33<00:39, 15.01it/s]
41%|████ | 408/1000 [00:33<00:47, 12.56it/s]
41%|████ | 410/1000 [00:33<00:56, 10.50it/s]
41%|████ | 412/1000 [00:33<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 [00:34<01:10, 8.29it/s]
42%|████▏ | 420/1000 [00:34<01:00, 9.55it/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, 8.96it/s]
43%|████▎ | 428/1000 [00:35<00:56, 10.16it/s]
43%|████▎ | 430/1000 [00:36<01:02, 9.13it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.06it/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, 9.01it/s]
44%|████▍ | 440/1000 [00:37<00:58, 9.52it/s]
44%|████▍ | 442/1000 [00:37<00:52, 10.59it/s]
44%|████▍ | 444/1000 [00:37<00:48, 11.45it/s]
45%|████▍ | 446/1000 [00:37<00:45, 12.21it/s]
45%|████▍ | 448/1000 [00:37<00:50, 11.00it/s]
45%|████▌ | 450/1000 [00:37<00:46, 11.82it/s]
45%|████▌ | 452/1000 [00:38<00:42, 12.85it/s]
45%|████▌ | 454/1000 [00:38<00:44, 12.34it/s]
46%|████▌ | 456/1000 [00:38<00:42, 12.78it/s]
46%|████▌ | 458/1000 [00:38<00:39, 13.79it/s]
46%|████▌ | 460/1000 [00:38<00:38, 13.91it/s]
46%|████▌ | 462/1000 [00:38<00:37, 14.49it/s]
46%|████▋ | 464/1000 [00:38<00:35, 14.99it/s]
47%|████▋ | 466/1000 [00:39<00:35, 14.85it/s]
47%|████▋ | 468/1000 [00:39<00:42, 12.40it/s]
47%|████▋ | 470/1000 [00:39<00:56, 9.44it/s]
47%|████▋ | 472/1000 [00:39<00:55, 9.56it/s]
47%|████▋ | 474/1000 [00:39<00:50, 10.46it/s]
48%|████▊ | 476/1000 [00:40<00:48, 10.90it/s]
48%|████▊ | 478/1000 [00:40<00:47, 10.91it/s]
48%|████▊ | 480/1000 [00:40<00:43, 12.05it/s]
48%|████▊ | 482/1000 [00:40<00:41, 12.62it/s]
48%|████▊ | 484/1000 [00:40<00:38, 13.27it/s]
49%|████▊ | 486/1000 [00:40<00:37, 13.59it/s]
49%|████▉ | 488/1000 [00:41<00:37, 13.82it/s]
49%|████▉ | 490/1000 [00:41<00:47, 10.82it/s]
49%|████▉ | 492/1000 [00:41<00:41, 12.11it/s]
49%|████▉ | 494/1000 [00:41<00:38, 13.02it/s]
50%|████▉ | 496/1000 [00:41<00:36, 13.76it/s]
50%|████▉ | 498/1000 [00:41<00:35, 14.23it/s]
50%|█████ | 500/1000 [00:42<00:43, 11.45it/s]
50%|█████ | 502/1000 [00:42<00:41, 11.95it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.56it/s]
51%|█████ | 506/1000 [00:42<00:38, 12.75it/s]
51%|█████ | 508/1000 [00:42<00:35, 13.68it/s]
51%|█████ | 510/1000 [00:42<00:47, 10.37it/s]
51%|█████ | 512/1000 [00:43<00:43, 11.16it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 12.04it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.60it/s]
52%|█████▏ | 518/1000 [00:43<00:36, 13.12it/s]
52%|█████▏ | 520/1000 [00:43<00:35, 13.49it/s]
52%|█████▏ | 522/1000 [00:43<00:33, 14.23it/s]
52%|█████▏ | 524/1000 [00:43<00:33, 14.25it/s]
53%|█████▎ | 526/1000 [00:44<00:32, 14.40it/s]
53%|█████▎ | 528/1000 [00:44<00:40, 11.79it/s]
53%|█████▎ | 530/1000 [00:44<00:39, 11.94it/s]
53%|█████▎ | 532/1000 [00:44<00:38, 12.09it/s]
53%|█████▎ | 534/1000 [00:44<00:36, 12.72it/s]
54%|█████▎ | 536/1000 [00:44<00:35, 13.22it/s]
54%|█████▍ | 538/1000 [00:44<00:34, 13.31it/s]
54%|█████▍ | 540/1000 [00:45<00:33, 13.91it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.33it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.27it/s]
55%|█████▍ | 546/1000 [00:45<00:32, 14.02it/s]
55%|█████▍ | 548/1000 [00:45<00:32, 13.82it/s]
55%|█████▌ | 550/1000 [00:45<00:31, 14.09it/s]
55%|█████▌ | 552/1000 [00:45<00:31, 14.19it/s]
55%|█████▌ | 554/1000 [00:46<00:32, 13.91it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.07it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.40it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 14.97it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 14.94it/s]
56%|█████▋ | 564/1000 [00:46<00:40, 10.78it/s]
57%|█████▋ | 566/1000 [00:47<00:37, 11.58it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.16it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.18it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.77it/s]
57%|█████▋ | 574/1000 [00:47<00:31, 13.68it/s]
58%|█████▊ | 576/1000 [00:47<00:31, 13.62it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.65it/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<00:29, 13.87it/s]
59%|█████▊ | 586/1000 [00:48<00:29, 14.26it/s]
59%|█████▉ | 588/1000 [00:48<00:27, 15.08it/s]
59%|█████▉ | 590/1000 [00:48<00:27, 15.05it/s]
59%|█████▉ | 592/1000 [00:48<00:27, 14.82it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.37it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.89it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 14.11it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.46it/s]
60%|██████ | 602/1000 [00:49<00:27, 14.54it/s]
60%|██████ | 604/1000 [00:49<00:26, 15.11it/s]
61%|██████ | 606/1000 [00:49<00:26, 14.89it/s]
61%|██████ | 608/1000 [00:50<00:25, 15.20it/s]
61%|██████ | 610/1000 [00:50<00:25, 15.04it/s]
61%|██████ | 612/1000 [00:50<00:25, 15.20it/s]
61%|██████▏ | 614/1000 [00:50<00:26, 14.42it/s]
62%|██████▏ | 616/1000 [00:50<00:26, 14.44it/s]
62%|██████▏ | 618/1000 [00:50<00:28, 13.42it/s]
62%|██████▏ | 620/1000 [00:51<00:43, 8.77it/s]
62%|██████▏ | 622/1000 [00:51<00:40, 9.27it/s]
62%|██████▏ | 624/1000 [00:51<00:35, 10.69it/s]
63%|██████▎ | 626/1000 [00:51<00:32, 11.53it/s]
63%|██████▎ | 628/1000 [00:51<00:29, 12.73it/s]
63%|██████▎ | 630/1000 [00:51<00:27, 13.30it/s]
63%|██████▎ | 632/1000 [00:52<00:30, 12.18it/s]
63%|██████▎ | 634/1000 [00:52<00:28, 12.94it/s]
64%|██████▎ | 636/1000 [00:52<00:33, 10.83it/s]
64%|██████▍ | 638/1000 [00:52<00:34, 10.60it/s]
64%|██████▍ | 640/1000 [00:52<00:31, 11.41it/s]
64%|██████▍ | 642/1000 [00:52<00:29, 12.30it/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, 13.36it/s]
65%|██████▌ | 650/1000 [00:53<00:27, 12.56it/s]
65%|██████▌ | 652/1000 [00:53<00:29, 11.96it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.45it/s]
66%|██████▌ | 656/1000 [00:54<00:32, 10.44it/s]
66%|██████▌ | 658/1000 [00:54<00:29, 11.56it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.03it/s]
66%|██████▌ | 662/1000 [00:54<00:30, 11.17it/s]
66%|██████▋ | 664/1000 [00:54<00:28, 11.69it/s]
67%|██████▋ | 666/1000 [00:54<00:26, 12.71it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.27it/s]
67%|██████▋ | 670/1000 [00:55<00:29, 11.07it/s]
67%|██████▋ | 672/1000 [00:55<00:27, 11.84it/s]
67%|██████▋ | 674/1000 [00:55<00:26, 12.45it/s]
68%|██████▊ | 676/1000 [00:55<00:27, 11.73it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.22it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 12.98it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 11.87it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.16it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.28it/s]
69%|██████▉ | 688/1000 [00:56<00:22, 13.61it/s]
69%|██████▉ | 690/1000 [00:56<00:22, 13.73it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.21it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.31it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 14.05it/s]
70%|██████▉ | 698/1000 [00:57<00:20, 14.56it/s]
70%|███████ | 700/1000 [00:57<00:24, 12.15it/s]
70%|███████ | 702/1000 [00:57<00:22, 12.98it/s]
70%|███████ | 704/1000 [00:58<00:29, 9.89it/s]
71%|███████ | 706/1000 [00:58<00:27, 10.70it/s]
71%|███████ | 708/1000 [00:58<00:31, 9.40it/s]
71%|███████ | 710/1000 [00:58<00:27, 10.48it/s]
71%|███████ | 712/1000 [00:58<00:25, 11.37it/s]
71%|███████▏ | 714/1000 [00:58<00:23, 11.94it/s]
72%|███████▏ | 716/1000 [00:59<00:23, 12.30it/s]
72%|███████▏ | 718/1000 [00:59<00:23, 12.02it/s]
72%|███████▏ | 720/1000 [00:59<00:21, 12.98it/s]
72%|███████▏ | 722/1000 [00:59<00:20, 13.40it/s]
72%|███████▏ | 724/1000 [00:59<00:20, 13.40it/s]
73%|███████▎ | 726/1000 [00:59<00:19, 13.72it/s]
73%|███████▎ | 728/1000 [01:00<00:23, 11.81it/s]
73%|███████▎ | 730/1000 [01:00<00:33, 8.16it/s]
73%|███████▎ | 732/1000 [01:00<00:32, 8.30it/s]
73%|███████▎ | 734/1000 [01:00<00:30, 8.59it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.46it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.92it/s]
74%|███████▍ | 739/1000 [01:01<00:23, 11.03it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.71it/s]
74%|███████▍ | 743/1000 [01:01<00:20, 12.33it/s]
74%|███████▍ | 745/1000 [01:01<00:25, 10.19it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.60it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.78it/s]
75%|███████▌ | 751/1000 [01:02<00:20, 11.88it/s]
75%|███████▌ | 753/1000 [01:02<00:19, 12.58it/s]
76%|███████▌ | 755/1000 [01:02<00:18, 13.25it/s]
76%|███████▌ | 757/1000 [01:02<00:23, 10.24it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.27it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.28it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.25it/s]
76%|███████▋ | 765/1000 [01:03<00:27, 8.55it/s]
77%|███████▋ | 767/1000 [01:04<00:24, 9.36it/s]
77%|███████▋ | 769/1000 [01:04<00:22, 10.16it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.49it/s]
77%|███████▋ | 773/1000 [01:04<00:18, 12.35it/s]
78%|███████▊ | 775/1000 [01:04<00:17, 12.97it/s]
78%|███████▊ | 777/1000 [01:04<00:15, 14.00it/s]
78%|███████▊ | 779/1000 [01:04<00:21, 10.45it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.38it/s]
78%|███████▊ | 783/1000 [01:05<00:26, 8.31it/s]
78%|███████▊ | 784/1000 [01:05<00:30, 7.07it/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<00:24, 8.83it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.14it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 11.29it/s]
79%|███████▉ | 794/1000 [01:06<00:17, 11.93it/s]
80%|███████▉ | 796/1000 [01:06<00:16, 12.39it/s]
80%|███████▉ | 798/1000 [01:06<00:15, 13.15it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.35it/s]
80%|████████ | 802/1000 [01:07<00:23, 8.26it/s]
80%|████████ | 804/1000 [01:07<00:23, 8.32it/s]
81%|████████ | 806/1000 [01:07<00:21, 9.00it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.35it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.49it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.71it/s]
81%|████████▏ | 814/1000 [01:08<00:14, 12.61it/s]
82%|████████▏ | 816/1000 [01:08<00:16, 11.06it/s]
82%|████████▏ | 818/1000 [01:08<00:15, 12.02it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.51it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 13.06it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.85it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.28it/s]
83%|████████▎ | 828/1000 [01:09<00:11, 14.56it/s]
83%|████████▎ | 830/1000 [01:09<00:12, 13.98it/s]
83%|████████▎ | 832/1000 [01:09<00:11, 14.36it/s]
83%|████████▎ | 834/1000 [01:09<00:11, 14.10it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.47it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.37it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.50it/s]
84%|████████▍ | 842/1000 [01:10<00:10, 14.51it/s]
84%|████████▍ | 844/1000 [01:10<00:11, 14.02it/s]
85%|████████▍ | 846/1000 [01:10<00:11, 13.99it/s]
85%|████████▍ | 848/1000 [01:10<00:10, 13.94it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.16it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.74it/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, 14.63it/s]
86%|████████▌ | 860/1000 [01:11<00:09, 15.37it/s]
86%|████████▌ | 862/1000 [01:11<00:10, 13.54it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 14.07it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.88it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.79it/s]
87%|████████▋ | 870/1000 [01:12<00:09, 14.18it/s]
87%|████████▋ | 872/1000 [01:12<00:08, 14.77it/s]
87%|████████▋ | 874/1000 [01:12<00:10, 12.49it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.10it/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, 11.23it/s]
88%|████████▊ | 884/1000 [01:13<00:09, 11.68it/s]
89%|████████▊ | 886/1000 [01:13<00:09, 12.44it/s]
89%|████████▉ | 888/1000 [01:13<00:08, 12.94it/s]
89%|████████▉ | 890/1000 [01:14<00:08, 13.55it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 13.55it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 13.82it/s]
90%|████████▉ | 896/1000 [01:14<00:07, 13.56it/s]
90%|████████▉ | 898/1000 [01:14<00:07, 12.98it/s]
90%|█████████ | 900/1000 [01:14<00:08, 12.03it/s]
90%|█████████ | 902/1000 [01:15<00:07, 12.82it/s]
90%|█████████ | 904/1000 [01:15<00:09, 9.75it/s]
91%|█████████ | 906/1000 [01:15<00:08, 10.82it/s]
91%|█████████ | 908/1000 [01:15<00:07, 11.81it/s]
91%|█████████ | 910/1000 [01:15<00:07, 12.07it/s]
91%|█████████ | 912/1000 [01:15<00:07, 12.27it/s]
91%|█████████▏| 914/1000 [01:16<00:12, 6.81it/s]
92%|█████████▏| 916/1000 [01:16<00:10, 8.14it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.40it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.80it/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, 11.80it/s]
93%|█████████▎| 928/1000 [01:17<00:06, 10.64it/s]
93%|█████████▎| 930/1000 [01:17<00:05, 11.83it/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 [01:18<00:04, 14.24it/s]
94%|█████████▍| 940/1000 [01:18<00:04, 14.52it/s]
94%|█████████▍| 942/1000 [01:18<00:04, 12.23it/s]
94%|█████████▍| 944/1000 [01:18<00:04, 12.78it/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, 13.48it/s]
95%|█████████▌| 952/1000 [01:19<00:03, 14.17it/s]
95%|█████████▌| 954/1000 [01:19<00:03, 14.26it/s]
96%|█████████▌| 956/1000 [01:19<00:03, 13.50it/s]
96%|█████████▌| 958/1000 [01:20<00:03, 13.16it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 12.05it/s]
96%|█████████▌| 962/1000 [01:20<00:03, 12.18it/s]
96%|█████████▋| 964/1000 [01:20<00:03, 9.45it/s]
97%|█████████▋| 966/1000 [01:20<00:03, 10.74it/s]
97%|█████████▋| 968/1000 [01:20<00:02, 11.54it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 12.29it/s]
97%|█████████▋| 972/1000 [01:21<00:02, 9.52it/s]
97%|█████████▋| 974/1000 [01:21<00:02, 10.73it/s]
98%|█████████▊| 976/1000 [01:21<00:02, 11.56it/s]
98%|█████████▊| 978/1000 [01:21<00:02, 9.83it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.93it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.44it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 10.45it/s]
99%|█████████▊| 986/1000 [01:22<00:01, 10.72it/s]
99%|█████████▉| 988/1000 [01:22<00:01, 10.93it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 11.42it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 12.31it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 13.02it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 13.92it/s]
100%|█████████▉| 998/1000 [01:23<00:00, 14.11it/s]
100%|██████████| 1000/1000 [01:23<00:00, 14.78it/s]
{'eval_loss': '0.9527', 'eval_runtime': '83.74', 'eval_samples_per_second': '11.94', 'eval_steps_per_second': '11.94', 'epoch': '0.1605', 'num_input_tokens_seen': 10063432}
16%|█▌ | 3000/18688 [3:31:25<18:35:11, 4.27s/it]
100%|██████████| 1000/1000 [01:23<00:00, 14.78it/s]
 16%|█▌ | 3001/18688 [3:31:29<127:55:01, 29.36s/it] 16%|█▌ | 3002/18688 [3:31:33<95:19:58, 21.88s/it] 16%|█▌ | 3003/18688 [3:31:37<71:30:42, 16.41s/it] 16%|█▌ | 3004/18688 [3:31:41<55:43:36, 12.79s/it] 16%|█▌ | 3005/18688 [3:31:45<44:29:58, 10.21s/it] 16%|█▌ | 3006/18688 [3:31:49<36:25:43, 8.36s/it] 16%|█▌ | 3007/18688 [3:31:53<30:40:16, 7.04s/it] 16%|█▌ | 3008/18688 [3:31:57<26:16:20, 6.03s/it] 16%|█▌ | 3009/18688 [3:32:01<22:55:09, 5.26s/it] 16%|█▌ | 3010/18688 [3:32:04<20:54:27, 4.80s/it] {'loss': '0.9775', 'grad_norm': '1.064', 'learning_rate': '9.811e-05', 'epoch': '0.1611', 'num_input_tokens_seen': 10094960, 'train_runtime': '1.272e+04', 'train_tokens_per_second': '793.3'}
16%|█▌ | 3010/18688 [3:32:04<20:54:27, 4.80s/it] 16%|█▌ | 3011/18688 [3:32:08<19:18:04, 4.43s/it] 16%|█▌ | 3012/18688 [3:32:14<20:50:44, 4.79s/it] 16%|█▌ | 3013/18688 [3:32:17<19:37:12, 4.51s/it] 16%|█▌ | 3014/18688 [3:32:21<18:18:54, 4.21s/it] 16%|█▌ | 3015/18688 [3:32:25<18:51:59, 4.33s/it] 16%|█▌ | 3016/18688 [3:32:29<18:21:47, 4.22s/it] 16%|█▌ | 3017/18688 [3:32:33<18:04:24, 4.15s/it] 16%|█▌ | 3018/18688 [3:32:38<18:05:06, 4.15s/it] 16%|█▌ | 3019/18688 [3:32:41<17:20:31, 3.98s/it] 16%|█▌ | 3020/18688 [3:32:45<16:49:35, 3.87s/it] {'loss': '0.9709', 'grad_norm': '1.003', 'learning_rate': '9.809e-05', 'epoch': '0.1616', 'num_input_tokens_seen': 10128312, 'train_runtime': '1.277e+04', 'train_tokens_per_second': '793.4'}
16%|█▌ | 3020/18688 [3:32:45<16:49:35, 3.87s/it] 16%|█▌ | 3021/18688 [3:32:49<17:01:10, 3.91s/it] 16%|█▌ | 3022/18688 [3:32:52<16:25:58, 3.78s/it] 16%|█▌ | 3023/18688 [3:32:56<16:03:03, 3.69s/it] 16%|█▌ | 3024/18688 [3:33:00<16:20:09, 3.75s/it] 16%|█▌ | 3025/18688 [3:33:04<17:32:51, 4.03s/it] 16%|█▌ | 3026/18688 [3:33:08<16:48:00, 3.86s/it] 16%|█▌ | 3027/18688 [3:33:12<17:16:58, 3.97s/it] 16%|█▌ | 3028/18688 [3:33:16<16:58:43, 3.90s/it] 16%|█▌ | 3029/18688 [3:33:20<16:53:13, 3.88s/it] 16%|█▌ | 3030/18688 [3:33:23<16:42:39, 3.84s/it] {'loss': '0.927', 'grad_norm': '1.028', 'learning_rate': '9.806e-05', 'epoch': '0.1621', 'num_input_tokens_seen': 10158072, 'train_runtime': '1.28e+04', 'train_tokens_per_second': '793.4'}
16%|█▌ | 3030/18688 [3:33:23<16:42:39, 3.84s/it] 16%|█▌ | 3031/18688 [3:33:27<16:51:20, 3.88s/it] 16%|█▌ | 3032/18688 [3:33:31<16:23:50, 3.77s/it] 16%|█▌ | 3033/18688 [3:33:36<18:28:37, 4.25s/it] 16%|█▌ | 3034/18688 [3:33:40<17:48:21, 4.09s/it] 16%|█▌ | 3035/18688 [3:33:44<17:52:31, 4.11s/it] 16%|█▌ | 3036/18688 [3:33:48<17:50:07, 4.10s/it] 16%|█▋ | 3037/18688 [3:33:52<17:55:17, 4.12s/it] 16%|█▋ | 3038/18688 [3:33:56<17:31:09, 4.03s/it] 16%|█▋ | 3039/18688 [3:34:01<18:40:48, 4.30s/it] 16%|█▋ | 3040/18688 [3:34:05<18:40:57, 4.30s/it] {'loss': '0.9566', 'grad_norm': '1.179', 'learning_rate': '9.803e-05', 'epoch': '0.1627', 'num_input_tokens_seen': 10193360, 'train_runtime': '1.285e+04', 'train_tokens_per_second': '793.5'}
16%|█▋ | 3040/18688 [3:34:05<18:40:57, 4.30s/it] 16%|█▋ | 3041/18688 [3:34:09<17:39:56, 4.06s/it] 16%|█▋ | 3042/18688 [3:34:13<18:13:28, 4.19s/it] 16%|█▋ | 3043/18688 [3:34:19<19:37:14, 4.51s/it] 16%|█▋ | 3044/18688 [3:34:23<19:14:44, 4.43s/it] 16%|█▋ | 3045/18688 [3:34:27<18:41:24, 4.30s/it] 16%|█▋ | 3046/18688 [3:34:31<18:24:19, 4.24s/it] 16%|█▋ | 3047/18688 [3:34:34<17:26:28, 4.01s/it] 16%|█▋ | 3048/18688 [3:34:39<17:29:53, 4.03s/it] 16%|█▋ | 3049/18688 [3:34:42<17:00:43, 3.92s/it] 16%|█▋ | 3050/18688 [3:34:46<16:49:30, 3.87s/it] {'loss': '0.9548', 'grad_norm': '1.031', 'learning_rate': '9.801e-05', 'epoch': '0.1632', 'num_input_tokens_seen': 10226736, 'train_runtime': '1.289e+04', 'train_tokens_per_second': '793.6'}
16%|█▋ | 3050/18688 [3:34:46<16:49:30, 3.87s/it] 16%|█▋ | 3051/18688 [3:34:50<17:30:06, 4.03s/it] 16%|█▋ | 3052/18688 [3:34:54<16:59:42, 3.91s/it] 16%|█▋ | 3053/18688 [3:34:57<16:25:13, 3.78s/it] 16%|█▋ | 3054/18688 [3:35:01<16:25:46, 3.78s/it] 16%|█▋ | 3055/18688 [3:35:06<17:10:29, 3.96s/it] 16%|█▋ | 3056/18688 [3:35:11<18:25:33, 4.24s/it] 16%|█▋ | 3057/18688 [3:35:15<18:41:56, 4.31s/it] 16%|█▋ | 3058/18688 [3:35:19<18:04:16, 4.16s/it] 16%|█▋ | 3059/18688 [3:35:23<17:51:45, 4.11s/it] 16%|█▋ | 3060/18688 [3:35:27<17:52:17, 4.12s/it] {'loss': '0.9741', 'grad_norm': '1.147', 'learning_rate': '9.798e-05', 'epoch': '0.1637', 'num_input_tokens_seen': 10259368, 'train_runtime': '1.293e+04', 'train_tokens_per_second': '793.6'}
16%|█▋ | 3060/18688 [3:35:27<17:52:17, 4.12s/it] 16%|█▋ | 3061/18688 [3:35:31<17:54:33, 4.13s/it] 16%|█▋ | 3062/18688 [3:35:35<17:28:54, 4.03s/it] 16%|█▋ | 3063/18688 [3:35:39<17:38:42, 4.07s/it] 16%|█▋ | 3064/18688 [3:35:43<17:19:26, 3.99s/it] 16%|█▋ | 3065/18688 [3:35:46<16:44:00, 3.86s/it] 16%|█▋ | 3066/18688 [3:35:51<17:39:07, 4.07s/it] 16%|█▋ | 3067/18688 [3:35:56<18:48:34, 4.33s/it] 16%|█▋ | 3068/18688 [3:36:00<17:58:06, 4.14s/it] 16%|█▋ | 3069/18688 [3:36:04<18:13:25, 4.20s/it] 16%|█▋ | 3070/18688 [3:36:08<17:41:02, 4.08s/it] {'loss': '0.9484', 'grad_norm': '1.268', 'learning_rate': '9.796e-05', 'epoch': '0.1643', 'num_input_tokens_seen': 10292208, 'train_runtime': '1.297e+04', 'train_tokens_per_second': '793.6'}
16%|█▋ | 3070/18688 [3:36:08<17:41:02, 4.08s/it] 16%|█▋ | 3071/18688 [3:36:11<16:57:22, 3.91s/it] 16%|█▋ | 3072/18688 [3:36:16<17:45:28, 4.09s/it] 16%|█▋ | 3073/18688 [3:36:19<16:56:28, 3.91s/it] 16%|█▋ | 3074/18688 [3:36:24<18:06:39, 4.18s/it] 16%|█▋ | 3075/18688 [3:36:28<18:16:26, 4.21s/it] 16%|█▋ | 3076/18688 [3:36:32<17:41:53, 4.08s/it] 16%|█▋ | 3077/18688 [3:36:36<17:35:31, 4.06s/it] 16%|█▋ | 3078/18688 [3:36:41<18:59:36, 4.38s/it] 16%|█▋ | 3079/18688 [3:36:46<19:55:43, 4.60s/it] 16%|█▋ | 3080/18688 [3:36:50<18:58:48, 4.38s/it] {'loss': '0.9505', 'grad_norm': '1.402', 'learning_rate': '9.793e-05', 'epoch': '0.1648', 'num_input_tokens_seen': 10327872, 'train_runtime': '1.301e+04', 'train_tokens_per_second': '793.8'}
16%|█▋ | 3080/18688 [3:36:50<18:58:48, 4.38s/it] 16%|█▋ | 3081/18688 [3:36:55<19:29:41, 4.50s/it] 16%|█▋ | 3082/18688 [3:36:58<18:09:20, 4.19s/it] 16%|█▋ | 3083/18688 [3:37:03<18:49:55, 4.34s/it] 17%|█▋ | 3084/18688 [3:37:08<18:55:46, 4.37s/it] 17%|█▋ | 3085/18688 [3:37:13<20:02:43, 4.62s/it] 17%|█▋ | 3086/18688 [3:37:17<19:57:12, 4.60s/it] 17%|█▋ | 3087/18688 [3:37:22<20:31:18, 4.74s/it] 17%|█▋ | 3088/18688 [3:37:26<19:38:05, 4.53s/it] 17%|█▋ | 3089/18688 [3:37:31<19:08:01, 4.42s/it] 17%|█▋ | 3090/18688 [3:37:34<18:12:13, 4.20s/it] {'loss': '0.9919', 'grad_norm': '1.208', 'learning_rate': '9.791e-05', 'epoch': '0.1654', 'num_input_tokens_seen': 10365440, 'train_runtime': '1.305e+04', 'train_tokens_per_second': '794'}
17%|█▋ | 3090/18688 [3:37:34<18:12:13, 4.20s/it] 17%|█▋ | 3091/18688 [3:37:38<17:33:27, 4.05s/it] 17%|█▋ | 3092/18688 [3:37:42<17:18:19, 3.99s/it] 17%|█▋ | 3093/18688 [3:37:47<18:41:55, 4.32s/it] 17%|█▋ | 3094/18688 [3:37:51<18:35:39, 4.29s/it] 17%|█▋ | 3095/18688 [3:37:56<19:18:08, 4.46s/it] 17%|█▋ | 3096/18688 [3:38:00<18:56:53, 4.37s/it] 17%|█▋ | 3097/18688 [3:38:05<19:35:18, 4.52s/it] 17%|█▋ | 3098/18688 [3:38:09<19:17:35, 4.46s/it] 17%|█▋ | 3099/18688 [3:38:13<18:14:30, 4.21s/it] 17%|█▋ | 3100/18688 [3:38:17<18:09:31, 4.19s/it] {'loss': '1.007', 'grad_norm': '0.8794', 'learning_rate': '9.788e-05', 'epoch': '0.1659', 'num_input_tokens_seen': 10401096, 'train_runtime': '1.31e+04', 'train_tokens_per_second': '794.1'}
17%|█▋ | 3100/18688 [3:38:17<18:09:31, 4.19s/it] 17%|█▋ | 3101/18688 [3:38:21<17:55:13, 4.14s/it] 17%|█▋ | 3102/18688 [3:38:25<17:19:04, 4.00s/it] 17%|█▋ | 3103/18688 [3:38:30<18:18:09, 4.23s/it] 17%|█▋ | 3104/18688 [3:38:33<17:34:00, 4.06s/it] 17%|█▋ | 3105/18688 [3:38:37<17:33:46, 4.06s/it] 17%|█▋ | 3106/18688 [3:38:42<18:18:56, 4.23s/it] 17%|█▋ | 3107/18688 [3:38:46<17:35:22, 4.06s/it] 17%|█▋ | 3108/18688 [3:38:50<17:58:03, 4.15s/it] 17%|█▋ | 3109/18688 [3:38:54<17:15:01, 3.99s/it] 17%|█▋ | 3110/18688 [3:38:57<17:04:46, 3.95s/it] {'loss': '0.9924', 'grad_norm': '1.387', 'learning_rate': '9.785e-05', 'epoch': '0.1664', 'num_input_tokens_seen': 10432040, 'train_runtime': '1.314e+04', 'train_tokens_per_second': '794'}
17%|█▋ | 3110/18688 [3:38:57<17:04:46, 3.95s/it] 17%|█▋ | 3111/18688 [3:39:01<16:44:23, 3.87s/it] 17%|█▋ | 3112/18688 [3:39:05<16:28:46, 3.81s/it] 17%|█▋ | 3113/18688 [3:39:09<16:40:56, 3.86s/it] 17%|█▋ | 3114/18688 [3:39:12<16:19:31, 3.77s/it] 17%|█▋ | 3115/18688 [3:39:16<16:46:52, 3.88s/it] 17%|█▋ | 3116/18688 [3:39:21<17:54:11, 4.14s/it] 17%|█▋ | 3117/18688 [3:39:25<17:18:49, 4.00s/it] 17%|█▋ | 3118/18688 [3:39:28<16:44:52, 3.87s/it] 17%|█▋ | 3119/18688 [3:39:33<17:41:12, 4.09s/it] 17%|█▋ | 3120/18688 [3:39:38<18:23:38, 4.25s/it] {'loss': '0.9925', 'grad_norm': '1.369', 'learning_rate': '9.783e-05', 'epoch': '0.167', 'num_input_tokens_seen': 10464328, 'train_runtime': '1.318e+04', 'train_tokens_per_second': '794.1'}
17%|█▋ | 3120/18688 [3:39:38<18:23:38, 4.25s/it] 17%|█▋ | 3121/18688 [3:39:42<17:50:20, 4.13s/it] 17%|█▋ | 3122/18688 [3:39:46<17:50:15, 4.13s/it] 17%|█▋ | 3123/18688 [3:39:50<17:34:12, 4.06s/it] 17%|█▋ | 3124/18688 [3:39:53<16:47:52, 3.89s/it] 17%|█▋ | 3125/18688 [3:39:57<16:36:18, 3.84s/it] 17%|█▋ | 3126/18688 [3:40:01<17:00:45, 3.94s/it] 17%|█▋ | 3127/18688 [3:40:05<17:09:46, 3.97s/it] 17%|█▋ | 3128/18688 [3:40:09<16:46:48, 3.88s/it] 17%|█▋ | 3129/18688 [3:40:13<17:53:57, 4.14s/it] 17%|█▋ | 3130/18688 [3:40:18<19:06:35, 4.42s/it] {'loss': '0.975', 'grad_norm': '1.083', 'learning_rate': '9.78e-05', 'epoch': '0.1675', 'num_input_tokens_seen': 10498560, 'train_runtime': '1.322e+04', 'train_tokens_per_second': '794.2'}
17%|█▋ | 3130/18688 [3:40:18<19:06:35, 4.42s/it] 17%|█▋ | 3131/18688 [3:40:22<17:54:48, 4.15s/it] 17%|█▋ | 3132/18688 [3:40:26<17:43:41, 4.10s/it] 17%|█▋ | 3133/18688 [3:40:30<17:25:59, 4.03s/it] 17%|█▋ | 3134/18688 [3:40:35<18:21:44, 4.25s/it] 17%|█▋ | 3135/18688 [3:40:38<17:33:28, 4.06s/it] 17%|█▋ | 3136/18688 [3:40:42<17:12:26, 3.98s/it] 17%|█▋ | 3137/18688 [3:40:47<18:33:31, 4.30s/it] 17%|█▋ | 3138/18688 [3:40:52<19:26:51, 4.50s/it] 17%|█▋ | 3139/18688 [3:40:56<19:12:56, 4.45s/it] 17%|█▋ | 3140/18688 [3:41:00<18:22:29, 4.25s/it] {'loss': '0.9168', 'grad_norm': '1.197', 'learning_rate': '9.777e-05', 'epoch': '0.168', 'num_input_tokens_seen': 10532632, 'train_runtime': '1.326e+04', 'train_tokens_per_second': '794.3'}
17%|█▋ | 3140/18688 [3:41:00<18:22:29, 4.25s/it] 17%|█▋ | 3141/18688 [3:41:04<17:19:36, 4.01s/it] 17%|█▋ | 3142/18688 [3:41:07<16:41:36, 3.87s/it] 17%|█▋ | 3143/18688 [3:41:11<16:45:53, 3.88s/it] 17%|█▋ | 3144/18688 [3:41:15<17:15:04, 4.00s/it] 17%|█▋ | 3145/18688 [3:41:20<18:14:09, 4.22s/it] 17%|█▋ | 3146/18688 [3:41:24<17:25:12, 4.04s/it] 17%|█▋ | 3147/18688 [3:41:28<18:06:34, 4.19s/it] 17%|█▋ | 3148/18688 [3:41:33<18:12:25, 4.22s/it] 17%|█▋ | 3149/18688 [3:41:38<19:18:50, 4.47s/it] 17%|█▋ | 3150/18688 [3:41:42<18:52:47, 4.37s/it] {'loss': '1.008', 'grad_norm': '1.05', 'learning_rate': '9.775e-05', 'epoch': '0.1686', 'num_input_tokens_seen': 10566248, 'train_runtime': '1.33e+04', 'train_tokens_per_second': '794.3'}
17%|█▋ | 3150/18688 [3:41:42<18:52:47, 4.37s/it] 17%|█▋ | 3151/18688 [3:41:47<20:03:21, 4.65s/it] 17%|█▋ | 3152/18688 [3:41:51<19:37:49, 4.55s/it] 17%|█▋ | 3153/18688 [3:41:56<19:57:43, 4.63s/it] 17%|█▋ | 3154/18688 [3:42:00<18:36:54, 4.31s/it] 17%|█▋ | 3155/18688 [3:42:03<17:37:15, 4.08s/it] 17%|█▋ | 3156/18688 [3:42:07<17:38:32, 4.09s/it] 17%|█▋ | 3157/18688 [3:42:12<18:15:16, 4.23s/it] 17%|█▋ | 3158/18688 [3:42:16<18:10:09, 4.21s/it] 17%|█▋ | 3159/18688 [3:42:21<18:50:00, 4.37s/it] 17%|█▋ | 3160/18688 [3:42:25<18:39:56, 4.33s/it] {'loss': '0.9916', 'grad_norm': '1.268', 'learning_rate': '9.772e-05', 'epoch': '0.1691', 'num_input_tokens_seen': 10603952, 'train_runtime': '1.335e+04', 'train_tokens_per_second': '794.6'}
17%|█▋ | 3160/18688 [3:42:25<18:39:56, 4.33s/it] 17%|█▋ | 3161/18688 [3:42:29<18:36:28, 4.31s/it] 17%|█▋ | 3162/18688 [3:42:33<17:29:38, 4.06s/it] 17%|█▋ | 3163/18688 [3:42:37<17:13:30, 3.99s/it] 17%|█▋ | 3164/18688 [3:42:41<17:18:14, 4.01s/it] 17%|█▋ | 3165/18688 [3:42:44<16:44:08, 3.88s/it] 17%|█▋ | 3166/18688 [3:42:49<17:12:35, 3.99s/it] 17%|█▋ | 3167/18688 [3:42:52<16:34:52, 3.85s/it] 17%|█▋ | 3168/18688 [3:42:56<17:14:22, 4.00s/it] 17%|█▋ | 3169/18688 [3:43:01<18:12:03, 4.22s/it] 17%|█▋ | 3170/18688 [3:43:05<18:04:47, 4.19s/it] {'loss': '0.9482', 'grad_norm': '1.371', 'learning_rate': '9.769e-05', 'epoch': '0.1696', 'num_input_tokens_seen': 10636456, 'train_runtime': '1.339e+04', 'train_tokens_per_second': '794.6'}
17%|█▋ | 3170/18688 [3:43:05<18:04:47, 4.19s/it] 17%|█▋ | 3171/18688 [3:43:09<17:38:42, 4.09s/it] 17%|█▋ | 3172/18688 [3:43:13<17:56:12, 4.16s/it] 17%|█▋ | 3173/18688 [3:43:19<19:30:20, 4.53s/it] 17%|█▋ | 3174/18688 [3:43:23<18:45:38, 4.35s/it] 17%|█▋ | 3175/18688 [3:43:27<18:18:47, 4.25s/it] 17%|█▋ | 3176/18688 [3:43:32<18:59:46, 4.41s/it] 17%|█▋ | 3177/18688 [3:43:36<18:48:32, 4.37s/it] 17%|█▋ | 3178/18688 [3:43:39<17:46:14, 4.12s/it] 17%|█▋ | 3179/18688 [3:43:43<17:39:18, 4.10s/it] 17%|█▋ | 3180/18688 [3:43:48<17:56:12, 4.16s/it] {'loss': '0.9748', 'grad_norm': '1.153', 'learning_rate': '9.766e-05', 'epoch': '0.1702', 'num_input_tokens_seen': 10671104, 'train_runtime': '1.343e+04', 'train_tokens_per_second': '794.7'}
17%|█▋ | 3180/18688 [3:43:48<17:56:12, 4.16s/it] 17%|█▋ | 3181/18688 [3:43:52<17:58:33, 4.17s/it] 17%|█▋ | 3182/18688 [3:43:56<17:13:10, 4.00s/it] 17%|█▋ | 3183/18688 [3:43:59<16:52:08, 3.92s/it] 17%|█▋ | 3184/18688 [3:44:03<16:40:48, 3.87s/it] 17%|█▋ | 3185/18688 [3:44:08<18:25:21, 4.28s/it] 17%|█▋ | 3186/18688 [3:44:13<18:37:14, 4.32s/it] 17%|█▋ | 3187/18688 [3:44:16<17:52:41, 4.15s/it] 17%|█▋ | 3188/18688 [3:44:21<18:04:20, 4.20s/it] 17%|█▋ | 3189/18688 [3:44:25<18:29:13, 4.29s/it] 17%|█▋ | 3190/18688 [3:44:30<18:48:52, 4.37s/it] {'loss': '0.9885', 'grad_norm': '0.9974', 'learning_rate': '9.764e-05', 'epoch': '0.1707', 'num_input_tokens_seen': 10707056, 'train_runtime': '1.347e+04', 'train_tokens_per_second': '794.9'}
17%|█▋ | 3190/18688 [3:44:30<18:48:52, 4.37s/it] 17%|█▋ | 3191/18688 [3:44:36<20:55:03, 4.86s/it] 17%|█▋ | 3192/18688 [3:44:40<19:26:49, 4.52s/it] 17%|█▋ | 3193/18688 [3:44:43<18:43:33, 4.35s/it] 17%|█▋ | 3194/18688 [3:44:47<18:00:57, 4.19s/it] 17%|█▋ | 3195/18688 [3:44:51<17:22:03, 4.04s/it] 17%|█▋ | 3196/18688 [3:44:55<17:05:34, 3.97s/it] 17%|█▋ | 3197/18688 [3:44:59<17:02:09, 3.96s/it] 17%|█▋ | 3198/18688 [3:45:03<17:09:50, 3.99s/it] 17%|█▋ | 3199/18688 [3:45:07<17:23:41, 4.04s/it] 17%|█▋ | 3200/18688 [3:45:11<17:11:57, 4.00s/it] {'loss': '1.056', 'grad_norm': '1.207', 'learning_rate': '9.761e-05', 'epoch': '0.1712', 'num_input_tokens_seen': 10740128, 'train_runtime': '1.351e+04', 'train_tokens_per_second': '794.9'}
17%|█▋ | 3200/18688 [3:45:11<17:11:57, 4.00s/it] 17%|█▋ | 3201/18688 [3:45:15<17:13:10, 4.00s/it] 17%|█▋ | 3202/18688 [3:45:19<16:50:47, 3.92s/it] 17%|█▋ | 3203/18688 [3:45:23<17:29:07, 4.07s/it] 17%|█▋ | 3204/18688 [3:45:28<18:57:24, 4.41s/it] 17%|█▋ | 3205/18688 [3:45:32<17:57:26, 4.18s/it] 17%|█▋ | 3206/18688 [3:45:36<17:25:55, 4.05s/it] 17%|█▋ | 3207/18688 [3:45:40<18:19:45, 4.26s/it] 17%|█▋ | 3208/18688 [3:45:44<18:01:56, 4.19s/it] 17%|█▋ | 3209/18688 [3:45:48<17:14:10, 4.01s/it] 17%|█▋ | 3210/18688 [3:45:53<17:57:16, 4.18s/it] {'loss': '0.9646', 'grad_norm': '1.065', 'learning_rate': '9.758e-05', 'epoch': '0.1718', 'num_input_tokens_seen': 10775008, 'train_runtime': '1.355e+04', 'train_tokens_per_second': '795'}
17%|█▋ | 3210/18688 [3:45:53<17:57:16, 4.18s/it] 17%|█▋ | 3211/18688 [3:45:57<17:47:17, 4.14s/it] 17%|█▋ | 3212/18688 [3:46:00<17:30:10, 4.07s/it] 17%|█▋ | 3213/18688 [3:46:04<17:09:20, 3.99s/it] 17%|█▋ | 3214/18688 [3:46:08<16:41:59, 3.89s/it] 17%|█▋ | 3215/18688 [3:46:11<16:07:50, 3.75s/it] 17%|█▋ | 3216/18688 [3:46:16<16:48:05, 3.91s/it] 17%|█▋ | 3217/18688 [3:46:19<16:38:30, 3.87s/it] 17%|█▋ | 3218/18688 [3:46:24<17:05:25, 3.98s/it] 17%|█▋ | 3219/18688 [3:46:29<18:46:03, 4.37s/it] 17%|█▋ | 3220/18688 [3:46:34<19:16:33, 4.49s/it] {'loss': '0.9523', 'grad_norm': '1.002', 'learning_rate': '9.755e-05', 'epoch': '0.1723', 'num_input_tokens_seen': 10809208, 'train_runtime': '1.359e+04', 'train_tokens_per_second': '795.1'}
17%|█▋ | 3220/18688 [3:46:34<19:16:33, 4.49s/it] 17%|█▋ | 3221/18688 [3:46:38<19:15:50, 4.48s/it] 17%|█▋ | 3222/18688 [3:46:42<18:29:40, 4.30s/it] 17%|█▋ | 3223/18688 [3:46:48<20:05:01, 4.68s/it] 17%|█▋ | 3224/18688 [3:46:52<19:46:22, 4.60s/it] 17%|█▋ | 3225/18688 [3:46:56<18:28:53, 4.30s/it] 17%|█▋ | 3226/18688 [3:46:59<17:42:05, 4.12s/it] 17%|█▋ | 3227/18688 [3:47:05<19:07:52, 4.45s/it] 17%|█▋ | 3228/18688 [3:47:08<18:27:53, 4.30s/it] 17%|█▋ | 3229/18688 [3:47:12<17:22:11, 4.05s/it] 17%|█▋ | 3230/18688 [3:47:17<18:25:16, 4.29s/it] {'loss': '0.9962', 'grad_norm': '0.934', 'learning_rate': '9.752e-05', 'epoch': '0.1728', 'num_input_tokens_seen': 10845352, 'train_runtime': '1.364e+04', 'train_tokens_per_second': '795.3'}
17%|█▋ | 3230/18688 [3:47:17<18:25:16, 4.29s/it] 17%|█▋ | 3231/18688 [3:47:22<19:10:55, 4.47s/it] 17%|█▋ | 3232/18688 [3:47:25<18:07:00, 4.22s/it] 17%|█▋ | 3233/18688 [3:47:29<17:47:51, 4.15s/it] 17%|█▋ | 3234/18688 [3:47:34<18:02:13, 4.20s/it] 17%|█▋ | 3235/18688 [3:47:37<17:19:05, 4.03s/it] 17%|█▋ | 3236/18688 [3:47:43<19:42:38, 4.59s/it] 17%|█▋ | 3237/18688 [3:47:47<18:44:42, 4.37s/it] 17%|█▋ | 3238/18688 [3:47:52<19:34:44, 4.56s/it] 17%|█▋ | 3239/18688 [3:47:56<18:52:51, 4.40s/it] 17%|█▋ | 3240/18688 [3:48:00<18:42:58, 4.36s/it] {'loss': '0.9267', 'grad_norm': '1.263', 'learning_rate': '9.75e-05', 'epoch': '0.1734', 'num_input_tokens_seen': 10882248, 'train_runtime': '1.368e+04', 'train_tokens_per_second': '795.4'}
17%|█▋ | 3240/18688 [3:48:00<18:42:58, 4.36s/it] 17%|█▋ | 3241/18688 [3:48:04<17:46:50, 4.14s/it] 17%|█▋ | 3242/18688 [3:48:08<17:31:15, 4.08s/it] 17%|█▋ | 3243/18688 [3:48:12<17:15:44, 4.02s/it] 17%|█▋ | 3244/18688 [3:48:16<17:17:14, 4.03s/it] 17%|█▋ | 3245/18688 [3:48:20<17:15:03, 4.02s/it] 17%|█▋ | 3246/18688 [3:48:24<17:27:11, 4.07s/it] 17%|█▋ | 3247/18688 [3:48:28<16:55:20, 3.95s/it] 17%|█▋ | 3248/18688 [3:48:32<17:52:34, 4.17s/it] 17%|█▋ | 3249/18688 [3:48:36<17:15:47, 4.03s/it] 17%|█▋ | 3250/18688 [3:48:40<17:43:14, 4.13s/it] {'loss': '0.9871', 'grad_norm': '1.229', 'learning_rate': '9.747e-05', 'epoch': '0.1739', 'num_input_tokens_seen': 10913536, 'train_runtime': '1.372e+04', 'train_tokens_per_second': '795.4'}
17%|█▋ | 3250/18688 [3:48:40<17:43:14, 4.13s/it] 17%|█▋ | 3251/18688 [3:48:45<17:46:02, 4.14s/it] 17%|█▋ | 3252/18688 [3:48:49<18:00:49, 4.20s/it] 17%|█▋ | 3253/18688 [3:48:53<17:46:34, 4.15s/it] 17%|█▋ | 3254/18688 [3:48:56<16:56:31, 3.95s/it] 17%|█▋ | 3255/18688 [3:49:00<16:50:18, 3.93s/it] 17%|█▋ | 3256/18688 [3:49:04<16:20:24, 3.81s/it] 17%|█▋ | 3257/18688 [3:49:08<16:45:20, 3.91s/it] 17%|█▋ | 3258/18688 [3:49:13<17:36:51, 4.11s/it] 17%|█▋ | 3259/18688 [3:49:17<18:15:24, 4.26s/it] 17%|█▋ | 3260/18688 [3:49:21<18:03:57, 4.22s/it] {'loss': '0.9338', 'grad_norm': '1.075', 'learning_rate': '9.744e-05', 'epoch': '0.1744', 'num_input_tokens_seen': 10946160, 'train_runtime': '1.376e+04', 'train_tokens_per_second': '795.4'}
17%|█▋ | 3260/18688 [3:49:21<18:03:57, 4.22s/it] 17%|█▋ | 3261/18688 [3:49:26<18:16:02, 4.26s/it] 17%|█▋ | 3262/18688 [3:49:30<18:12:41, 4.25s/it] 17%|█▋ | 3263/18688 [3:49:34<17:58:57, 4.20s/it] 17%|█▋ | 3264/18688 [3:49:37<17:01:17, 3.97s/it] 17%|█▋ | 3265/18688 [3:49:42<18:10:03, 4.24s/it] 17%|█▋ | 3266/18688 [3:49:46<17:44:53, 4.14s/it] 17%|█▋ | 3267/18688 [3:49:50<17:52:33, 4.17s/it] 17%|█▋ | 3268/18688 [3:49:55<18:58:14, 4.43s/it] 17%|█▋ | 3269/18688 [3:49:59<17:50:09, 4.16s/it] 17%|█▋ | 3270/18688 [3:50:03<17:52:40, 4.17s/it] {'loss': '0.9418', 'grad_norm': '1.078', 'learning_rate': '9.741e-05', 'epoch': '0.175', 'num_input_tokens_seen': 10980520, 'train_runtime': '1.38e+04', 'train_tokens_per_second': '795.5'}
17%|█▋ | 3270/18688 [3:50:03<17:52:40, 4.17s/it] 18%|█▊ | 3271/18688 [3:50:07<17:44:28, 4.14s/it] 18%|█▊ | 3272/18688 [3:50:11<17:40:57, 4.13s/it] 18%|█▊ | 3273/18688 [3:50:15<17:39:31, 4.12s/it] 18%|█▊ | 3274/18688 [3:50:20<17:36:53, 4.11s/it] 18%|█▊ | 3275/18688 [3:50:24<18:18:57, 4.28s/it] 18%|█▊ | 3276/18688 [3:50:28<18:03:13, 4.22s/it] 18%|█▊ | 3277/18688 [3:50:32<17:59:08, 4.20s/it] 18%|█▊ | 3278/18688 [3:50:37<18:06:56, 4.23s/it] 18%|█▊ | 3279/18688 [3:50:41<18:33:58, 4.34s/it] 18%|█▊ | 3280/18688 [3:50:46<19:09:52, 4.48s/it] {'loss': '0.9435', 'grad_norm': '1.158', 'learning_rate': '9.738e-05', 'epoch': '0.1755', 'num_input_tokens_seen': 11016736, 'train_runtime': '1.385e+04', 'train_tokens_per_second': '795.6'}
18%|█▊ | 3280/18688 [3:50:46<19:09:52, 4.48s/it] 18%|█▊ | 3281/18688 [3:50:50<18:51:47, 4.41s/it] 18%|█▊ | 3282/18688 [3:50:55<18:36:14, 4.35s/it] 18%|█▊ | 3283/18688 [3:50:58<17:53:55, 4.18s/it] 18%|█▊ | 3284/18688 [3:51:02<17:26:45, 4.08s/it] 18%|█▊ | 3285/18688 [3:51:07<17:59:16, 4.20s/it] 18%|█▊ | 3286/18688 [3:51:11<18:03:59, 4.22s/it] 18%|█▊ | 3287/18688 [3:51:15<18:00:46, 4.21s/it] 18%|█▊ | 3288/18688 [3:51:20<18:40:30, 4.37s/it] 18%|█▊ | 3289/18688 [3:51:24<18:24:06, 4.30s/it] 18%|█▊ | 3290/18688 [3:51:28<17:54:18, 4.19s/it] {'loss': '1.014', 'grad_norm': '1.29', 'learning_rate': '9.735e-05', 'epoch': '0.1761', 'num_input_tokens_seen': 11051480, 'train_runtime': '1.389e+04', 'train_tokens_per_second': '795.7'}
18%|█▊ | 3290/18688 [3:51:28<17:54:18, 4.19s/it] 18%|█▊ | 3291/18688 [3:51:32<17:50:55, 4.17s/it] 18%|█▊ | 3292/18688 [3:51:36<17:22:06, 4.06s/it] 18%|█▊ | 3293/18688 [3:51:40<16:59:18, 3.97s/it] 18%|█▊ | 3294/18688 [3:51:44<17:22:00, 4.06s/it] 18%|█▊ | 3295/18688 [3:51:48<17:54:12, 4.19s/it] 18%|█▊ | 3296/18688 [3:51:52<17:08:24, 4.01s/it] 18%|█▊ | 3297/18688 [3:51:57<17:49:45, 4.17s/it] 18%|█▊ | 3298/18688 [3:52:01<17:47:32, 4.16s/it] 18%|█▊ | 3299/18688 [3:52:04<16:55:17, 3.96s/it] 18%|█▊ | 3300/18688 [3:52:08<16:17:46, 3.81s/it] {'loss': '0.9385', 'grad_norm': '1.066', 'learning_rate': '9.732e-05', 'epoch': '0.1766', 'num_input_tokens_seen': 11083184, 'train_runtime': '1.393e+04', 'train_tokens_per_second': '795.7'}
18%|█▊ | 3300/18688 [3:52:08<16:17:46, 3.81s/it] 18%|█▊ | 3301/18688 [3:52:12<16:42:51, 3.91s/it] 18%|█▊ | 3302/18688 [3:52:15<16:06:50, 3.77s/it] 18%|█▊ | 3303/18688 [3:52:20<17:22:28, 4.07s/it] 18%|█▊ | 3304/18688 [3:52:25<18:12:41, 4.26s/it] 18%|█▊ | 3305/18688 [3:52:28<17:17:48, 4.05s/it] 18%|█▊ | 3306/18688 [3:52:33<17:46:26, 4.16s/it] 18%|█▊ | 3307/18688 [3:52:37<17:23:17, 4.07s/it] 18%|█▊ | 3308/18688 [3:52:40<17:07:39, 4.01s/it] 18%|█▊ | 3309/18688 [3:52:44<16:30:52, 3.87s/it] 18%|█▊ | 3310/18688 [3:52:49<17:37:02, 4.12s/it] {'loss': '1.01', 'grad_norm': '1.439', 'learning_rate': '9.729e-05', 'epoch': '0.1771', 'num_input_tokens_seen': 11115968, 'train_runtime': '1.397e+04', 'train_tokens_per_second': '795.7'}
18%|█▊ | 3310/18688 [3:52:49<17:37:02, 4.12s/it] 18%|█▊ | 3311/18688 [3:52:53<17:26:41, 4.08s/it] 18%|█▊ | 3312/18688 [3:52:57<17:11:21, 4.02s/it] 18%|█▊ | 3313/18688 [3:53:02<18:29:27, 4.33s/it] 18%|█▊ | 3314/18688 [3:53:06<18:30:04, 4.33s/it] 18%|█▊ | 3315/18688 [3:53:10<18:36:12, 4.36s/it] 18%|█▊ | 3316/18688 [3:53:15<18:32:53, 4.34s/it] 18%|█▊ | 3317/18688 [3:53:19<18:38:39, 4.37s/it] 18%|█▊ | 3318/18688 [3:53:24<19:16:47, 4.52s/it] 18%|█▊ | 3319/18688 [3:53:28<18:54:52, 4.43s/it] 18%|█▊ | 3320/18688 [3:53:33<19:15:23, 4.51s/it] {'loss': '1.079', 'grad_norm': '8.578', 'learning_rate': '9.726e-05', 'epoch': '0.1777', 'num_input_tokens_seen': 11152640, 'train_runtime': '1.401e+04', 'train_tokens_per_second': '795.9'}
18%|█▊ | 3320/18688 [3:53:33<19:15:23, 4.51s/it] 18%|█▊ | 3321/18688 [3:53:37<18:41:30, 4.38s/it] 18%|█▊ | 3322/18688 [3:53:41<18:39:11, 4.37s/it] 18%|█▊ | 3323/18688 [3:53:46<19:20:17, 4.53s/it] 18%|█▊ | 3324/18688 [3:53:50<18:59:24, 4.45s/it] 18%|█▊ | 3325/18688 [3:53:54<18:17:04, 4.28s/it] 18%|█▊ | 3326/18688 [3:53:58<17:49:28, 4.18s/it] 18%|█▊ | 3327/18688 [3:54:03<18:50:22, 4.42s/it] 18%|█▊ | 3328/18688 [3:54:08<18:46:34, 4.40s/it] 18%|█▊ | 3329/18688 [3:54:12<18:13:42, 4.27s/it] 18%|█▊ | 3330/18688 [3:54:15<17:25:34, 4.08s/it] {'loss': '1.008', 'grad_norm': '1.615', 'learning_rate': '9.723e-05', 'epoch': '0.1782', 'num_input_tokens_seen': 11186944, 'train_runtime': '1.406e+04', 'train_tokens_per_second': '795.9'}
18%|█▊ | 3330/18688 [3:54:15<17:25:34, 4.08s/it] 18%|█▊ | 3331/18688 [3:54:20<17:39:40, 4.14s/it] 18%|█▊ | 3332/18688 [3:54:23<16:58:33, 3.98s/it] 18%|█▊ | 3333/18688 [3:54:27<16:29:04, 3.86s/it] 18%|█▊ | 3334/18688 [3:54:31<17:19:02, 4.06s/it] 18%|█▊ | 3335/18688 [3:54:35<17:28:59, 4.10s/it] 18%|█▊ | 3336/18688 [3:54:39<17:06:35, 4.01s/it] 18%|█▊ | 3337/18688 [3:54:43<16:39:19, 3.91s/it] 18%|█▊ | 3338/18688 [3:54:48<17:43:38, 4.16s/it] 18%|█▊ | 3339/18688 [3:54:52<17:25:03, 4.09s/it] 18%|█▊ | 3340/18688 [3:54:57<18:36:04, 4.36s/it] {'loss': '0.9586', 'grad_norm': '1.288', 'learning_rate': '9.72e-05', 'epoch': '0.1787', 'num_input_tokens_seen': 11220456, 'train_runtime': '1.41e+04', 'train_tokens_per_second': '795.9'}
18%|█▊ | 3340/18688 [3:54:57<18:36:04, 4.36s/it] 18%|█▊ | 3341/18688 [3:55:00<17:31:18, 4.11s/it] 18%|█▊ | 3342/18688 [3:55:04<17:37:09, 4.13s/it] 18%|█▊ | 3343/18688 [3:55:08<16:50:25, 3.95s/it] 18%|█▊ | 3344/18688 [3:55:11<16:19:02, 3.83s/it] 18%|█▊ | 3345/18688 [3:55:16<17:20:15, 4.07s/it] 18%|█▊ | 3346/18688 [3:55:20<17:36:02, 4.13s/it] 18%|█▊ | 3347/18688 [3:55:24<17:13:39, 4.04s/it] 18%|█▊ | 3348/18688 [3:55:29<17:57:47, 4.22s/it] 18%|█▊ | 3349/18688 [3:55:33<17:40:54, 4.15s/it] 18%|█▊ | 3350/18688 [3:55:37<17:49:10, 4.18s/it] {'loss': '1.034', 'grad_norm': '1.123', 'learning_rate': '9.717e-05', 'epoch': '0.1793', 'num_input_tokens_seen': 11252456, 'train_runtime': '1.414e+04', 'train_tokens_per_second': '795.9'}
18%|█▊ | 3350/18688 [3:55:37<17:49:10, 4.18s/it] 18%|█▊ | 3351/18688 [3:55:41<17:13:07, 4.04s/it] 18%|█▊ | 3352/18688 [3:55:46<18:32:35, 4.35s/it] 18%|█▊ | 3353/18688 [3:55:50<18:55:18, 4.44s/it] 18%|█▊ | 3354/18688 [3:55:54<18:12:43, 4.28s/it] 18%|█▊ | 3355/18688 [3:55:58<17:54:50, 4.21s/it] 18%|█▊ | 3356/18688 [3:56:02<16:59:29, 3.99s/it] 18%|█▊ | 3357/18688 [3:56:06<16:58:14, 3.99s/it] 18%|█▊ | 3358/18688 [3:56:10<17:12:57, 4.04s/it] 18%|█▊ | 3359/18688 [3:56:14<16:52:57, 3.96s/it] 18%|█▊ | 3360/18688 [3:56:18<17:02:59, 4.00s/it] {'loss': '0.9682', 'grad_norm': '1.266', 'learning_rate': '9.714e-05', 'epoch': '0.1798', 'num_input_tokens_seen': 11285240, 'train_runtime': '1.418e+04', 'train_tokens_per_second': '795.9'}
18%|█▊ | 3360/18688 [3:56:18<17:02:59, 4.00s/it] 18%|█▊ | 3361/18688 [3:56:22<17:47:53, 4.18s/it] 18%|█▊ | 3362/18688 [3:56:27<18:13:37, 4.28s/it] 18%|█▊ | 3363/18688 [3:56:32<18:33:58, 4.36s/it] 18%|█▊ | 3364/18688 [3:56:35<17:39:53, 4.15s/it] 18%|█▊ | 3365/18688 [3:56:39<17:12:27, 4.04s/it] 18%|█▊ | 3366/18688 [3:56:43<16:45:43, 3.94s/it] 18%|█▊ | 3367/18688 [3:56:46<16:08:50, 3.79s/it] 18%|█▊ | 3368/18688 [3:56:50<16:06:13, 3.78s/it] 18%|█▊ | 3369/18688 [3:56:54<16:19:19, 3.84s/it] 18%|█▊ | 3370/18688 [3:56:59<17:27:46, 4.10s/it] {'loss': '0.9384', 'grad_norm': '1.04', 'learning_rate': '9.711e-05', 'epoch': '0.1803', 'num_input_tokens_seen': 11316984, 'train_runtime': '1.422e+04', 'train_tokens_per_second': '795.9'}
18%|█▊ | 3370/18688 [3:56:59<17:27:46, 4.10s/it] 18%|█▊ | 3371/18688 [3:57:03<17:39:23, 4.15s/it] 18%|█▊ | 3372/18688 [3:57:08<18:45:51, 4.41s/it] 18%|█▊ | 3373/18688 [3:57:12<18:24:27, 4.33s/it] 18%|█▊ | 3374/18688 [3:57:17<18:45:29, 4.41s/it] 18%|█▊ | 3375/18688 [3:57:21<18:34:37, 4.37s/it] 18%|█▊ | 3376/18688 [3:57:25<17:56:19, 4.22s/it] 18%|█▊ | 3377/18688 [3:57:29<17:44:49, 4.17s/it] 18%|█▊ | 3378/18688 [3:57:33<17:55:09, 4.21s/it] 18%|█▊ | 3379/18688 [3:57:37<17:32:07, 4.12s/it] 18%|█▊ | 3380/18688 [3:57:42<18:08:51, 4.27s/it] {'loss': '0.9409', 'grad_norm': '1.349', 'learning_rate': '9.708e-05', 'epoch': '0.1809', 'num_input_tokens_seen': 11352040, 'train_runtime': '1.426e+04', 'train_tokens_per_second': '796'}
18%|█▊ | 3380/18688 [3:57:42<18:08:51, 4.27s/it] 18%|█▊ | 3381/18688 [3:57:46<18:11:28, 4.28s/it] 18%|█▊ | 3382/18688 [3:57:50<18:26:25, 4.34s/it] 18%|█▊ | 3383/18688 [3:57:54<17:29:32, 4.11s/it] 18%|█▊ | 3384/18688 [3:57:59<18:53:27, 4.44s/it] 18%|█▊ | 3385/18688 [3:58:04<19:29:34, 4.59s/it] 18%|█▊ | 3386/18688 [3:58:08<19:05:24, 4.49s/it] 18%|█▊ | 3387/18688 [3:58:13<18:44:52, 4.41s/it] 18%|█▊ | 3388/18688 [3:58:16<17:55:51, 4.22s/it] 18%|█▊ | 3389/18688 [3:58:20<17:08:49, 4.03s/it] 18%|█▊ | 3390/18688 [3:58:24<17:09:33, 4.04s/it] {'loss': '0.9986', 'grad_norm': '1.295', 'learning_rate': '9.705e-05', 'epoch': '0.1814', 'num_input_tokens_seen': 11386080, 'train_runtime': '1.43e+04', 'train_tokens_per_second': '796'}
18%|█▊ | 3390/18688 [3:58:24<17:09:33, 4.04s/it] 18%|█▊ | 3391/18688 [3:58:29<17:54:05, 4.21s/it] 18%|█▊ | 3392/18688 [3:58:33<18:11:49, 4.28s/it] 18%|█▊ | 3393/18688 [3:58:37<17:19:31, 4.08s/it] 18%|█▊ | 3394/18688 [3:58:41<16:57:43, 3.99s/it] 18%|█▊ | 3395/18688 [3:58:45<17:09:40, 4.04s/it] 18%|█▊ | 3396/18688 [3:58:49<17:38:43, 4.15s/it] 18%|█▊ | 3397/18688 [3:58:53<17:33:36, 4.13s/it] 18%|█▊ | 3398/18688 [3:58:57<16:40:46, 3.93s/it] 18%|█▊ | 3399/18688 [3:59:01<17:03:53, 4.02s/it] 18%|█▊ | 3400/18688 [3:59:05<17:04:34, 4.02s/it] {'loss': '0.9059', 'grad_norm': '1.214', 'learning_rate': '9.702e-05', 'epoch': '0.1819', 'num_input_tokens_seen': 11418256, 'train_runtime': '1.435e+04', 'train_tokens_per_second': '796'}
18%|█▊ | 3400/18688 [3:59:05<17:04:34, 4.02s/it] 18%|█▊ | 3401/18688 [3:59:09<17:00:00, 4.00s/it] 18%|█▊ | 3402/18688 [3:59:13<16:36:20, 3.91s/it] 18%|█▊ | 3403/18688 [3:59:16<16:01:09, 3.77s/it] 18%|█▊ | 3404/18688 [3:59:20<16:47:19, 3.95s/it] 18%|█▊ | 3405/18688 [3:59:25<17:18:44, 4.08s/it] 18%|█▊ | 3406/18688 [3:59:29<18:08:17, 4.27s/it] 18%|█▊ | 3407/18688 [3:59:34<18:28:20, 4.35s/it] 18%|█▊ | 3408/18688 [3:59:38<17:36:33, 4.15s/it] 18%|█▊ | 3409/18688 [3:59:42<17:54:16, 4.22s/it] 18%|█▊ | 3410/18688 [3:59:46<17:26:41, 4.11s/it] {'loss': '1.017', 'grad_norm': '1.045', 'learning_rate': '9.699e-05', 'epoch': '0.1825', 'num_input_tokens_seen': 11451800, 'train_runtime': '1.439e+04', 'train_tokens_per_second': '796'}
18%|█▊ | 3410/18688 [3:59:46<17:26:41, 4.11s/it] 18%|█▊ | 3411/18688 [3:59:49<16:46:59, 3.95s/it] 18%|█▊ | 3412/18688 [3:59:54<17:18:24, 4.08s/it] 18%|█▊ | 3413/18688 [3:59:58<17:41:38, 4.17s/it] 18%|█▊ | 3414/18688 [4:00:03<17:51:00, 4.21s/it] 18%|█▊ | 3415/18688 [4:00:07<17:37:02, 4.15s/it] 18%|█▊ | 3416/18688 [4:00:10<17:01:20, 4.01s/it] 18%|█▊ | 3417/18688 [4:00:14<17:14:05, 4.06s/it] 18%|█▊ | 3418/18688 [4:00:18<16:53:42, 3.98s/it] 18%|█▊ | 3419/18688 [4:00:22<16:21:08, 3.86s/it] 18%|█▊ | 3420/18688 [4:00:26<16:41:23, 3.94s/it] {'loss': '0.9746', 'grad_norm': '1.518', 'learning_rate': '9.696e-05', 'epoch': '0.183', 'num_input_tokens_seen': 11482472, 'train_runtime': '1.443e+04', 'train_tokens_per_second': '795.9'}
18%|█▊ | 3420/18688 [4:00:26<16:41:23, 3.94s/it] 18%|█▊ | 3421/18688 [4:00:30<16:20:47, 3.85s/it] 18%|█▊ | 3422/18688 [4:00:34<17:07:48, 4.04s/it] 18%|█▊ | 3423/18688 [4:00:38<17:32:43, 4.14s/it] 18%|█▊ | 3424/18688 [4:00:43<17:29:30, 4.13s/it] 18%|█▊ | 3425/18688 [4:00:47<18:13:13, 4.30s/it] 18%|█▊ | 3426/18688 [4:00:51<17:26:48, 4.12s/it] 18%|█▊ | 3427/18688 [4:00:54<16:37:32, 3.92s/it] 18%|█▊ | 3428/18688 [4:00:58<16:06:17, 3.80s/it] 18%|█▊ | 3429/18688 [4:01:02<16:24:17, 3.87s/it] 18%|█▊ | 3430/18688 [4:01:07<17:34:39, 4.15s/it] {'loss': '0.9766', 'grad_norm': '1.378', 'learning_rate': '9.692e-05', 'epoch': '0.1835', 'num_input_tokens_seen': 11515112, 'train_runtime': '1.447e+04', 'train_tokens_per_second': '795.9'}
18%|█▊ | 3430/18688 [4:01:07<17:34:39, 4.15s/it] 18%|█▊ | 3431/18688 [4:01:11<17:28:38, 4.12s/it] 18%|█▊ | 3432/18688 [4:01:16<18:31:09, 4.37s/it] 18%|█▊ | 3433/18688 [4:01:21<19:05:01, 4.50s/it] 18%|█▊ | 3434/18688 [4:01:25<18:31:02, 4.37s/it] 18%|█▊ | 3435/18688 [4:01:28<17:47:56, 4.20s/it] 18%|█▊ | 3436/18688 [4:01:32<17:11:59, 4.06s/it] 18%|█▊ | 3437/18688 [4:01:36<16:41:50, 3.94s/it] 18%|█▊ | 3438/18688 [4:01:40<17:36:10, 4.16s/it] 18%|█▊ | 3439/18688 [4:01:44<16:46:44, 3.96s/it] 18%|█▊ | 3440/18688 [4:01:49<18:18:17, 4.32s/it] {'loss': '0.9725', 'grad_norm': '0.8549', 'learning_rate': '9.689e-05', 'epoch': '0.1841', 'num_input_tokens_seen': 11549936, 'train_runtime': '1.451e+04', 'train_tokens_per_second': '796'}
18%|█▊ | 3440/18688 [4:01:49<18:18:17, 4.32s/it] 18%|█▊ | 3441/18688 [4:01:53<17:18:48, 4.09s/it] 18%|█▊ | 3442/18688 [4:01:57<17:35:44, 4.15s/it] 18%|█▊ | 3443/18688 [4:02:03<19:24:45, 4.58s/it] 18%|█▊ | 3444/18688 [4:02:06<18:04:26, 4.27s/it] 18%|█▊ | 3445/18688 [4:02:10<17:24:13, 4.11s/it] 18%|█▊ | 3446/18688 [4:02:15<18:07:00, 4.28s/it] 18%|█▊ | 3447/18688 [4:02:19<18:00:32, 4.25s/it] 18%|█▊ | 3448/18688 [4:02:22<17:16:29, 4.08s/it] 18%|█▊ | 3449/18688 [4:02:27<17:26:03, 4.12s/it] 18%|█▊ | 3450/18688 [4:02:30<16:58:17, 4.01s/it] {'loss': '0.9552', 'grad_norm': '1.286', 'learning_rate': '9.686e-05', 'epoch': '0.1846', 'num_input_tokens_seen': 11583232, 'train_runtime': '1.455e+04', 'train_tokens_per_second': '796.1'}
18%|█▊ | 3450/18688 [4:02:30<16:58:17, 4.01s/it] 18%|█▊ | 3451/18688 [4:02:35<18:11:53, 4.30s/it] 18%|█▊ | 3452/18688 [4:02:40<18:49:34, 4.45s/it] 18%|█▊ | 3453/18688 [4:02:45<19:06:39, 4.52s/it] 18%|█▊ | 3454/18688 [4:02:49<18:53:55, 4.47s/it] 18%|█▊ | 3455/18688 [4:02:53<17:51:39, 4.22s/it] 18%|█▊ | 3456/18688 [4:02:56<17:09:02, 4.05s/it] 18%|█▊ | 3457/18688 [4:03:01<17:47:50, 4.21s/it] 19%|█▊ | 3458/18688 [4:03:05<17:34:30, 4.15s/it] 19%|█▊ | 3459/18688 [4:03:09<17:54:08, 4.23s/it] 19%|█▊ | 3460/18688 [4:03:13<17:32:19, 4.15s/it] {'loss': '0.9296', 'grad_norm': '1.149', 'learning_rate': '9.683e-05', 'epoch': '0.1852', 'num_input_tokens_seen': 11618872, 'train_runtime': '1.459e+04', 'train_tokens_per_second': '796.1'}
19%|█▊ | 3460/18688 [4:03:13<17:32:19, 4.15s/it] 19%|█▊ | 3461/18688 [4:03:17<17:02:57, 4.03s/it] 19%|█▊ | 3462/18688 [4:03:21<16:29:13, 3.90s/it] 19%|█▊ | 3463/18688 [4:03:25<17:13:48, 4.07s/it] 19%|█▊ | 3464/18688 [4:03:29<16:25:30, 3.88s/it] 19%|█▊ | 3465/18688 [4:03:32<15:59:25, 3.78s/it] 19%|█▊ | 3466/18688 [4:03:37<17:09:38, 4.06s/it] 19%|█▊ | 3467/18688 [4:03:41<17:09:01, 4.06s/it] 19%|█▊ | 3468/18688 [4:03:45<16:38:47, 3.94s/it] 19%|█▊ | 3469/18688 [4:03:49<16:54:09, 4.00s/it] 19%|█▊ | 3470/18688 [4:03:53<16:54:41, 4.00s/it] {'loss': '0.902', 'grad_norm': '1.04', 'learning_rate': '9.68e-05', 'epoch': '0.1857', 'num_input_tokens_seen': 11650752, 'train_runtime': '1.463e+04', 'train_tokens_per_second': '796.2'}
19%|█▊ | 3470/18688 [4:03:53<16:54:41, 4.00s/it] 19%|█▊ | 3471/18688 [4:03:57<16:58:00, 4.01s/it] 19%|█▊ | 3472/18688 [4:04:01<16:36:19, 3.93s/it] 19%|█▊ | 3473/18688 [4:04:06<18:47:22, 4.45s/it] 19%|█▊ | 3474/18688 [4:04:10<18:13:47, 4.31s/it] 19%|█▊ | 3475/18688 [4:04:15<18:47:48, 4.45s/it] 19%|█▊ | 3476/18688 [4:04:19<17:40:02, 4.18s/it] 19%|█▊ | 3477/18688 [4:04:23<17:35:33, 4.16s/it] 19%|█▊ | 3478/18688 [4:04:26<17:02:14, 4.03s/it] 19%|█▊ | 3479/18688 [4:04:31<17:11:16, 4.07s/it] 19%|█▊ | 3480/18688 [4:04:34<17:01:39, 4.03s/it] {'loss': '0.9429', 'grad_norm': '1.114', 'learning_rate': '9.676e-05', 'epoch': '0.1862', 'num_input_tokens_seen': 11683496, 'train_runtime': '1.467e+04', 'train_tokens_per_second': '796.2'}
19%|█▊ | 3480/18688 [4:04:34<17:01:39, 4.03s/it] 19%|█▊ | 3481/18688 [4:04:38<16:44:59, 3.97s/it] 19%|█▊ | 3482/18688 [4:04:42<16:56:45, 4.01s/it] 19%|█▊ | 3483/18688 [4:04:46<16:13:32, 3.84s/it] 19%|█▊ | 3484/18688 [4:04:49<15:56:19, 3.77s/it] 19%|█▊ | 3485/18688 [4:04:54<16:30:10, 3.91s/it] 19%|█▊ | 3486/18688 [4:04:58<17:05:19, 4.05s/it] 19%|█▊ | 3487/18688 [4:05:03<18:10:32, 4.30s/it] 19%|█▊ | 3488/18688 [4:05:08<18:55:05, 4.48s/it] 19%|█▊ | 3489/18688 [4:05:12<18:04:55, 4.28s/it] 19%|█▊ | 3490/18688 [4:05:15<17:13:58, 4.08s/it] {'loss': '0.9559', 'grad_norm': '0.9966', 'learning_rate': '9.673e-05', 'epoch': '0.1868', 'num_input_tokens_seen': 11717152, 'train_runtime': '1.472e+04', 'train_tokens_per_second': '796.2'}
19%|█▊ | 3490/18688 [4:05:15<17:13:58, 4.08s/it] 19%|█▊ | 3491/18688 [4:05:20<17:26:24, 4.13s/it] 19%|█▊ | 3492/18688 [4:05:24<17:22:13, 4.12s/it] 19%|█▊ | 3493/18688 [4:05:28<17:39:18, 4.18s/it] 19%|█▊ | 3494/18688 [4:05:31<16:50:13, 3.99s/it] 19%|█▊ | 3495/18688 [4:05:35<16:11:58, 3.84s/it] 19%|█▊ | 3496/18688 [4:05:39<16:34:55, 3.93s/it] 19%|█▊ | 3497/18688 [4:05:43<17:05:51, 4.05s/it] 19%|█▊ | 3498/18688 [4:05:48<17:45:37, 4.21s/it] 19%|█▊ | 3499/18688 [4:05:51<16:46:23, 3.98s/it] 19%|█▊ | 3500/18688 [4:05:55<16:46:41, 3.98s/it] {'loss': '0.8751', 'grad_norm': '1.383', 'learning_rate': '9.67e-05', 'epoch': '0.1873', 'num_input_tokens_seen': 11748664, 'train_runtime': '1.476e+04', 'train_tokens_per_second': '796.2'}
19%|█▊ | 3500/18688 [4:05:55<16:46:41, 3.98s/it] 19%|█▊ | 3501/18688 [4:06:00<17:12:43, 4.08s/it] 19%|█▊ | 3502/18688 [4:06:04<17:42:23, 4.20s/it] 19%|█▊ | 3503/18688 [4:06:09<17:58:36, 4.26s/it] 19%|█▉ | 3504/18688 [4:06:13<18:15:23, 4.33s/it] 19%|█▉ | 3505/18688 [4:06:17<18:02:20, 4.28s/it] 19%|█▉ | 3506/18688 [4:06:22<18:29:49, 4.39s/it] 19%|█▉ | 3507/18688 [4:06:26<18:12:28, 4.32s/it] 19%|█▉ | 3508/18688 [4:06:30<18:18:59, 4.34s/it] 19%|█▉ | 3509/18688 [4:06:36<19:28:57, 4.62s/it] 19%|█▉ | 3510/18688 [4:06:41<20:25:35, 4.84s/it] {'loss': '0.9977', 'grad_norm': '1.02', 'learning_rate': '9.667e-05', 'epoch': '0.1878', 'num_input_tokens_seen': 11788320, 'train_runtime': '1.48e+04', 'train_tokens_per_second': '796.4'}
19%|█▉ | 3510/18688 [4:06:41<20:25:35, 4.84s/it] 19%|█▉ | 3511/18688 [4:06:45<19:00:03, 4.51s/it] 19%|█▉ | 3512/18688 [4:06:49<18:36:05, 4.41s/it] 19%|█▉ | 3513/18688 [4:06:53<17:33:18, 4.16s/it] 19%|█▉ | 3514/18688 [4:06:56<16:45:03, 3.97s/it] 19%|█▉ | 3515/18688 [4:07:00<16:39:06, 3.95s/it] 19%|█▉ | 3516/18688 [4:07:04<16:52:58, 4.01s/it] 19%|█▉ | 3517/18688 [4:07:08<16:35:38, 3.94s/it] 19%|█▉ | 3518/18688 [4:07:12<16:06:14, 3.82s/it] 19%|█▉ | 3519/18688 [4:07:16<17:26:22, 4.14s/it] 19%|█▉ | 3520/18688 [4:07:20<16:44:01, 3.97s/it] {'loss': '0.9416', 'grad_norm': '0.9382', 'learning_rate': '9.663e-05', 'epoch': '0.1884', 'num_input_tokens_seen': 11819968, 'train_runtime': '1.484e+04', 'train_tokens_per_second': '796.5'}
19%|█▉ | 3520/18688 [4:07:20<16:44:01, 3.97s/it] 19%|█▉ | 3521/18688 [4:07:24<16:25:34, 3.90s/it] 19%|█▉ | 3522/18688 [4:07:28<16:59:33, 4.03s/it] 19%|█▉ | 3523/18688 [4:07:32<17:18:56, 4.11s/it] 19%|█▉ | 3524/18688 [4:07:37<17:51:42, 4.24s/it] 19%|█▉ | 3525/18688 [4:07:43<20:05:32, 4.77s/it] 19%|█▉ | 3526/18688 [4:07:46<18:34:52, 4.41s/it] 19%|█▉ | 3527/18688 [4:07:50<17:43:12, 4.21s/it] 19%|█▉ | 3528/18688 [4:07:55<18:48:59, 4.47s/it] 19%|█▉ | 3529/18688 [4:07:59<18:00:23, 4.28s/it] 19%|█▉ | 3530/18688 [4:08:03<16:55:55, 4.02s/it] {'loss': '0.9962', 'grad_norm': '1.296', 'learning_rate': '9.66e-05', 'epoch': '0.1889', 'num_input_tokens_seen': 11856992, 'train_runtime': '1.488e+04', 'train_tokens_per_second': '796.7'}
19%|█▉ | 3530/18688 [4:08:03<16:55:55, 4.02s/it] 19%|█▉ | 3531/18688 [4:08:07<17:04:58, 4.06s/it] 19%|█▉ | 3532/18688 [4:08:12<18:22:01, 4.36s/it] 19%|█▉ | 3533/18688 [4:08:16<17:41:07, 4.20s/it] 19%|█▉ | 3534/18688 [4:08:19<16:54:30, 4.02s/it] 19%|█▉ | 3535/18688 [4:08:24<17:32:28, 4.17s/it] 19%|█▉ | 3536/18688 [4:08:29<18:34:46, 4.41s/it] 19%|█▉ | 3537/18688 [4:08:33<18:13:46, 4.33s/it] 19%|█▉ | 3538/18688 [4:08:37<17:39:41, 4.20s/it] 19%|█▉ | 3539/18688 [4:08:41<17:42:38, 4.21s/it] 19%|█▉ | 3540/18688 [4:08:45<17:53:56, 4.25s/it] {'loss': '0.9556', 'grad_norm': '1.324', 'learning_rate': '9.657e-05', 'epoch': '0.1894', 'num_input_tokens_seen': 11892928, 'train_runtime': '1.493e+04', 'train_tokens_per_second': '796.8'}
19%|█▉ | 3540/18688 [4:08:45<17:53:56, 4.25s/it] 19%|█▉ | 3541/18688 [4:08:49<17:35:36, 4.18s/it] 19%|█▉ | 3542/18688 [4:08:53<16:54:43, 4.02s/it] 19%|█▉ | 3543/18688 [4:08:57<16:56:25, 4.03s/it] 19%|█▉ | 3544/18688 [4:09:01<16:25:31, 3.90s/it] 19%|█▉ | 3545/18688 [4:09:04<16:16:30, 3.87s/it] 19%|█▉ | 3546/18688 [4:09:09<17:13:25, 4.09s/it] 19%|█▉ | 3547/18688 [4:09:13<16:40:30, 3.96s/it] 19%|█▉ | 3548/18688 [4:09:17<16:42:55, 3.97s/it] 19%|█▉ | 3549/18688 [4:09:21<17:06:05, 4.07s/it] 19%|█▉ | 3550/18688 [4:09:25<17:11:08, 4.09s/it] {'loss': '0.9815', 'grad_norm': '1.341', 'learning_rate': '9.653e-05', 'epoch': '0.19', 'num_input_tokens_seen': 11925280, 'train_runtime': '1.497e+04', 'train_tokens_per_second': '796.8'}
19%|█▉ | 3550/18688 [4:09:25<17:11:08, 4.09s/it] 19%|█▉ | 3551/18688 [4:09:29<16:42:39, 3.97s/it] 19%|█▉ | 3552/18688 [4:09:32<16:19:28, 3.88s/it] 19%|█▉ | 3553/18688 [4:09:38<18:42:46, 4.45s/it] 19%|█▉ | 3554/18688 [4:09:43<18:39:03, 4.44s/it] 19%|█▉ | 3555/18688 [4:09:47<17:57:28, 4.27s/it] 19%|█▉ | 3556/18688 [4:09:50<17:33:17, 4.18s/it] 19%|█▉ | 3557/18688 [4:09:55<17:26:50, 4.15s/it] 19%|█▉ | 3558/18688 [4:09:58<16:32:20, 3.94s/it] 19%|█▉ | 3559/18688 [4:10:02<16:45:43, 3.99s/it] 19%|█▉ | 3560/18688 [4:10:07<17:49:21, 4.24s/it] {'loss': '0.9643', 'grad_norm': '1.227', 'learning_rate': '9.65e-05', 'epoch': '0.1905', 'num_input_tokens_seen': 11959592, 'train_runtime': '1.501e+04', 'train_tokens_per_second': '796.9'}
19%|█▉ | 3560/18688 [4:10:07<17:49:21, 4.24s/it] 19%|█▉ | 3561/18688 [4:10:12<19:03:55, 4.54s/it] 19%|█▉ | 3562/18688 [4:10:17<18:55:25, 4.50s/it] 19%|█▉ | 3563/18688 [4:10:21<18:21:11, 4.37s/it] 19%|█▉ | 3564/18688 [4:10:24<17:20:33, 4.13s/it] 19%|█▉ | 3565/18688 [4:10:28<16:39:25, 3.97s/it] 19%|█▉ | 3566/18688 [4:10:32<17:07:12, 4.08s/it] 19%|█▉ | 3567/18688 [4:10:36<16:30:09, 3.93s/it] 19%|█▉ | 3568/18688 [4:10:40<17:12:49, 4.10s/it] 19%|█▉ | 3569/18688 [4:10:44<17:15:06, 4.11s/it] 19%|█▉ | 3570/18688 [4:10:49<17:52:22, 4.26s/it] {'loss': '0.9925', 'grad_norm': '1.139', 'learning_rate': '9.647e-05', 'epoch': '0.191', 'num_input_tokens_seen': 11994864, 'train_runtime': '1.505e+04', 'train_tokens_per_second': '797'}
19%|█▉ | 3570/18688 [4:10:49<17:52:22, 4.26s/it] 19%|█▉ | 3571/18688 [4:10:53<16:59:01, 4.04s/it] 19%|█▉ | 3572/18688 [4:10:57<17:55:56, 4.27s/it] 19%|█▉ | 3573/18688 [4:11:04<20:26:01, 4.87s/it] 19%|█▉ | 3574/18688 [4:11:09<20:40:58, 4.93s/it] 19%|█▉ | 3575/18688 [4:11:12<19:05:18, 4.55s/it] 19%|█▉ | 3576/18688 [4:11:16<17:54:39, 4.27s/it] 19%|█▉ | 3577/18688 [4:11:19<16:55:16, 4.03s/it] 19%|█▉ | 3578/18688 [4:11:23<16:16:54, 3.88s/it] 19%|█▉ | 3579/18688 [4:11:27<16:13:14, 3.86s/it] 19%|█▉ | 3580/18688 [4:11:31<16:57:52, 4.04s/it] {'loss': '0.958', 'grad_norm': '1.143', 'learning_rate': '9.643e-05', 'epoch': '0.1916', 'num_input_tokens_seen': 12028152, 'train_runtime': '1.509e+04', 'train_tokens_per_second': '797'}
19%|█▉ | 3580/18688 [4:11:31<16:57:52, 4.04s/it] 19%|█▉ | 3581/18688 [4:11:35<16:42:16, 3.98s/it] 19%|█▉ | 3582/18688 [4:11:39<17:05:16, 4.07s/it] 19%|█▉ | 3583/18688 [4:11:44<17:18:42, 4.13s/it] 19%|█▉ | 3584/18688 [4:11:47<16:28:00, 3.92s/it] 19%|█▉ | 3585/18688 [4:11:52<17:30:04, 4.17s/it] 19%|█▉ | 3586/18688 [4:11:56<17:34:36, 4.19s/it] 19%|█▉ | 3587/18688 [4:12:00<17:21:24, 4.14s/it] 19%|█▉ | 3588/18688 [4:12:03<16:26:56, 3.92s/it] 19%|█▉ | 3589/18688 [4:12:08<16:40:53, 3.98s/it] 19%|█▉ | 3590/18688 [4:12:12<17:17:23, 4.12s/it] {'loss': '0.931', 'grad_norm': '1.241', 'learning_rate': '9.64e-05', 'epoch': '0.1921', 'num_input_tokens_seen': 12063056, 'train_runtime': '1.513e+04', 'train_tokens_per_second': '797.2'}
19%|█▉ | 3590/18688 [4:12:12<17:17:23, 4.12s/it] 19%|█▉ | 3591/18688 [4:12:16<16:52:38, 4.02s/it] 19%|█▉ | 3592/18688 [4:12:21<18:10:27, 4.33s/it] 19%|█▉ | 3593/18688 [4:12:25<17:29:22, 4.17s/it] 19%|█▉ | 3594/18688 [4:12:29<17:54:58, 4.27s/it] 19%|█▉ | 3595/18688 [4:12:33<17:06:27, 4.08s/it] 19%|█▉ | 3596/18688 [4:12:37<17:40:38, 4.22s/it] 19%|█▉ | 3597/18688 [4:12:41<17:13:56, 4.11s/it] 19%|█▉ | 3598/18688 [4:12:46<17:30:44, 4.18s/it] 19%|█▉ | 3599/18688 [4:12:50<18:25:26, 4.40s/it] 19%|█▉ | 3600/18688 [4:12:54<17:43:14, 4.23s/it] {'loss': '0.9259', 'grad_norm': '1.205', 'learning_rate': '9.637e-05', 'epoch': '0.1926', 'num_input_tokens_seen': 12098216, 'train_runtime': '1.517e+04', 'train_tokens_per_second': '797.3'}
19%|█▉ | 3600/18688 [4:12:54<17:43:14, 4.23s/it] 19%|█▉ | 3601/18688 [4:12:58<16:48:58, 4.01s/it] 19%|█▉ | 3602/18688 [4:13:02<17:41:14, 4.22s/it] 19%|█▉ | 3603/18688 [4:13:08<19:01:47, 4.54s/it] 19%|█▉ | 3604/18688 [4:13:12<18:02:06, 4.30s/it] 19%|█▉ | 3605/18688 [4:13:16<17:38:42, 4.21s/it] 19%|█▉ | 3606/18688 [4:13:20<17:55:20, 4.28s/it] 19%|█▉ | 3607/18688 [4:13:24<17:46:48, 4.24s/it] 19%|█▉ | 3608/18688 [4:13:28<17:18:08, 4.13s/it] 19%|█▉ | 3609/18688 [4:13:31<16:30:00, 3.94s/it] 19%|█▉ | 3610/18688 [4:13:36<16:49:18, 4.02s/it] {'loss': '0.9085', 'grad_norm': '1.409', 'learning_rate': '9.633e-05', 'epoch': '0.1932', 'num_input_tokens_seen': 12131904, 'train_runtime': '1.522e+04', 'train_tokens_per_second': '797.3'}
19%|█▉ | 3610/18688 [4:13:36<16:49:18, 4.02s/it] 19%|█▉ | 3611/18688 [4:13:40<17:28:45, 4.17s/it] 19%|█▉ | 3612/18688 [4:13:44<17:05:19, 4.08s/it] 19%|█▉ | 3613/18688 [4:13:48<17:08:32, 4.09s/it] 19%|█▉ | 3614/18688 [4:13:52<16:39:14, 3.98s/it] 19%|█▉ | 3615/18688 [4:13:57<18:05:10, 4.32s/it] 19%|█▉ | 3616/18688 [4:14:01<17:33:42, 4.19s/it] 19%|█▉ | 3617/18688 [4:14:05<16:49:32, 4.02s/it] 19%|█▉ | 3618/18688 [4:14:09<16:46:41, 4.01s/it] 19%|█▉ | 3619/18688 [4:14:12<16:20:08, 3.90s/it] 19%|█▉ | 3620/18688 [4:14:16<16:31:34, 3.95s/it] {'loss': '0.9856', 'grad_norm': '1.064', 'learning_rate': '9.63e-05', 'epoch': '0.1937', 'num_input_tokens_seen': 12164544, 'train_runtime': '1.526e+04', 'train_tokens_per_second': '797.3'}
19%|█▉ | 3620/18688 [4:14:16<16:31:34, 3.95s/it] 19%|█▉ | 3621/18688 [4:14:20<16:12:55, 3.87s/it] 19%|█▉ | 3622/18688 [4:14:25<17:37:49, 4.21s/it] 19%|█▉ | 3623/18688 [4:14:29<17:12:49, 4.11s/it] 19%|█▉ | 3624/18688 [4:14:32<16:31:19, 3.95s/it] 19%|█▉ | 3625/18688 [4:14:37<17:21:12, 4.15s/it] 19%|█▉ | 3626/18688 [4:14:42<17:51:18, 4.27s/it] 19%|█▉ | 3627/18688 [4:14:45<17:08:47, 4.10s/it] 19%|█▉ | 3628/18688 [4:14:49<16:22:18, 3.91s/it] 19%|█▉ | 3629/18688 [4:14:54<17:26:20, 4.17s/it] 19%|█▉ | 3630/18688 [4:14:57<17:05:07, 4.08s/it] {'loss': '0.9231', 'grad_norm': '1.349', 'learning_rate': '9.626e-05', 'epoch': '0.1942', 'num_input_tokens_seen': 12198296, 'train_runtime': '1.53e+04', 'train_tokens_per_second': '797.4'}
19%|█▉ | 3630/18688 [4:14:57<17:05:07, 4.08s/it] 19%|█▉ | 3631/18688 [4:15:02<17:21:26, 4.15s/it] 19%|█▉ | 3632/18688 [4:15:06<17:02:11, 4.07s/it] 19%|█▉ | 3633/18688 [4:15:09<16:23:21, 3.92s/it] 19%|█▉ | 3634/18688 [4:15:13<16:55:31, 4.05s/it] 19%|█▉ | 3635/18688 [4:15:17<16:18:31, 3.90s/it] 19%|█▉ | 3636/18688 [4:15:21<16:25:50, 3.93s/it] 19%|█▉ | 3637/18688 [4:15:26<17:08:48, 4.10s/it] 19%|█▉ | 3638/18688 [4:15:31<19:16:31, 4.61s/it] 19%|█▉ | 3639/18688 [4:15:35<17:49:33, 4.26s/it] 19%|█▉ | 3640/18688 [4:15:39<17:52:53, 4.28s/it] {'loss': '0.9926', 'grad_norm': '1.239', 'learning_rate': '9.623e-05', 'epoch': '0.1948', 'num_input_tokens_seen': 12232216, 'train_runtime': '1.534e+04', 'train_tokens_per_second': '797.4'}
19%|█▉ | 3640/18688 [4:15:39<17:52:53, 4.28s/it] 19%|█▉ | 3641/18688 [4:15:43<16:58:13, 4.06s/it] 19%|█▉ | 3642/18688 [4:15:46<16:37:47, 3.98s/it] 19%|█▉ | 3643/18688 [4:15:51<16:48:22, 4.02s/it] 19%|█▉ | 3644/18688 [4:15:54<16:15:38, 3.89s/it] 20%|█▉ | 3645/18688 [4:15:58<16:20:05, 3.91s/it] 20%|█▉ | 3646/18688 [4:16:02<16:45:20, 4.01s/it] 20%|█▉ | 3647/18688 [4:16:06<16:52:12, 4.04s/it] 20%|█▉ | 3648/18688 [4:16:11<16:53:05, 4.04s/it] 20%|█▉ | 3649/18688 [4:16:14<16:25:57, 3.93s/it] 20%|█▉ | 3650/18688 [4:16:18<16:27:50, 3.94s/it] {'loss': '0.9635', 'grad_norm': '1.163', 'learning_rate': '9.619e-05', 'epoch': '0.1953', 'num_input_tokens_seen': 12262912, 'train_runtime': '1.538e+04', 'train_tokens_per_second': '797.4'}
20%|█▉ | 3650/18688 [4:16:18<16:27:50, 3.94s/it] 20%|█▉ | 3651/18688 [4:16:23<17:35:54, 4.21s/it] 20%|█▉ | 3652/18688 [4:16:27<16:56:21, 4.06s/it] 20%|█▉ | 3653/18688 [4:16:31<17:46:00, 4.25s/it] 20%|█▉ | 3654/18688 [4:16:35<17:08:20, 4.10s/it] 20%|█▉ | 3655/18688 [4:16:39<17:02:19, 4.08s/it] 20%|█▉ | 3656/18688 [4:16:43<16:40:39, 3.99s/it] 20%|█▉ | 3657/18688 [4:16:47<16:09:20, 3.87s/it] 20%|█▉ | 3658/18688 [4:16:50<15:49:16, 3.79s/it] 20%|█▉ | 3659/18688 [4:16:55<16:47:57, 4.02s/it] 20%|█▉ | 3660/18688 [4:16:59<17:20:46, 4.16s/it] {'loss': '0.9542', 'grad_norm': '1.407', 'learning_rate': '9.616e-05', 'epoch': '0.1959', 'num_input_tokens_seen': 12295296, 'train_runtime': '1.542e+04', 'train_tokens_per_second': '797.4'}
20%|█▉ | 3660/18688 [4:16:59<17:20:46, 4.16s/it] 20%|█▉ | 3661/18688 [4:17:03<17:20:10, 4.15s/it] 20%|█▉ | 3662/18688 [4:17:08<17:28:27, 4.19s/it] 20%|█▉ | 3663/18688 [4:17:13<18:27:19, 4.42s/it] 20%|█▉ | 3664/18688 [4:17:17<18:23:33, 4.41s/it] 20%|█▉ | 3665/18688 [4:17:21<17:31:29, 4.20s/it] 20%|█▉ | 3666/18688 [4:17:24<16:41:11, 4.00s/it] 20%|█▉ | 3667/18688 [4:17:29<17:08:16, 4.11s/it] 20%|█▉ | 3668/18688 [4:17:33<17:56:49, 4.30s/it] 20%|█▉ | 3669/18688 [4:17:37<17:11:10, 4.12s/it] 20%|█▉ | 3670/18688 [4:17:41<17:17:51, 4.15s/it] {'loss': '0.9066', 'grad_norm': '1.082', 'learning_rate': '9.612e-05', 'epoch': '0.1964', 'num_input_tokens_seen': 12330624, 'train_runtime': '1.546e+04', 'train_tokens_per_second': '797.5'}
20%|█▉ | 3670/18688 [4:17:41<17:17:51, 4.15s/it] 20%|█▉ | 3671/18688 [4:17:46<18:01:24, 4.32s/it] 20%|█▉ | 3672/18688 [4:17:50<18:01:43, 4.32s/it] 20%|█▉ | 3673/18688 [4:17:54<17:16:32, 4.14s/it] 20%|█▉ | 3674/18688 [4:17:58<17:13:04, 4.13s/it] 20%|█▉ | 3675/18688 [4:18:02<16:30:49, 3.96s/it] 20%|█▉ | 3676/18688 [4:18:05<15:53:55, 3.81s/it] 20%|█▉ | 3677/18688 [4:18:09<16:35:58, 3.98s/it] 20%|█▉ | 3678/18688 [4:18:14<17:07:25, 4.11s/it] 20%|█▉ | 3679/18688 [4:18:18<17:00:44, 4.08s/it] 20%|█▉ | 3680/18688 [4:18:21<16:14:55, 3.90s/it] {'loss': '0.9908', 'grad_norm': '1.106', 'learning_rate': '9.609e-05', 'epoch': '0.1969', 'num_input_tokens_seen': 12362352, 'train_runtime': '1.55e+04', 'train_tokens_per_second': '797.5'}
20%|█▉ | 3680/18688 [4:18:21<16:14:55, 3.90s/it] 20%|█▉ | 3681/18688 [4:18:25<15:50:54, 3.80s/it] 20%|█▉ | 3682/18688 [4:18:29<16:37:47, 3.99s/it] 20%|█▉ | 3683/18688 [4:18:34<16:59:07, 4.08s/it] 20%|█▉ | 3684/18688 [4:18:38<16:45:46, 4.02s/it] 20%|█▉ | 3685/18688 [4:18:42<16:58:05, 4.07s/it] 20%|█▉ | 3686/18688 [4:18:47<17:49:35, 4.28s/it] 20%|█▉ | 3687/18688 [4:18:51<18:03:26, 4.33s/it] 20%|█▉ | 3688/18688 [4:18:56<18:21:12, 4.40s/it] 20%|█▉ | 3689/18688 [4:19:00<18:49:04, 4.52s/it] 20%|█▉ | 3690/18688 [4:19:05<18:29:17, 4.44s/it] {'loss': '0.9605', 'grad_norm': '1.177', 'learning_rate': '9.605e-05', 'epoch': '0.1975', 'num_input_tokens_seen': 12399136, 'train_runtime': '1.555e+04', 'train_tokens_per_second': '797.6'}
20%|█▉ | 3690/18688 [4:19:05<18:29:17, 4.44s/it] 20%|█▉ | 3691/18688 [4:19:09<18:19:49, 4.40s/it] 20%|█▉ | 3692/18688 [4:19:13<18:11:15, 4.37s/it] 20%|█▉ | 3693/18688 [4:19:17<17:20:04, 4.16s/it] 20%|█▉ | 3694/18688 [4:19:22<18:03:31, 4.34s/it] 20%|█▉ | 3695/18688 [4:19:26<18:26:14, 4.43s/it] 20%|█▉ | 3696/18688 [4:19:31<19:12:59, 4.61s/it] 20%|█▉ | 3697/18688 [4:19:35<17:54:38, 4.30s/it] 20%|█▉ | 3698/18688 [4:19:40<18:55:23, 4.54s/it] 20%|█▉ | 3699/18688 [4:19:44<18:08:47, 4.36s/it] 20%|█▉ | 3700/18688 [4:19:49<18:31:35, 4.45s/it] {'loss': '1.005', 'grad_norm': '1.117', 'learning_rate': '9.602e-05', 'epoch': '0.198', 'num_input_tokens_seen': 12436408, 'train_runtime': '1.559e+04', 'train_tokens_per_second': '797.8'}
20%|█▉ | 3700/18688 [4:19:49<18:31:35, 4.45s/it] 20%|█▉ | 3701/18688 [4:19:52<17:17:30, 4.15s/it] 20%|█▉ | 3702/18688 [4:19:56<16:32:21, 3.97s/it] 20%|█▉ | 3703/18688 [4:19:59<15:57:19, 3.83s/it] 20%|█▉ | 3704/18688 [4:20:04<17:41:49, 4.25s/it] 20%|█▉ | 3705/18688 [4:20:09<17:37:29, 4.23s/it] 20%|█▉ | 3706/18688 [4:20:13<18:16:17, 4.39s/it] 20%|█▉ | 3707/18688 [4:20:17<17:29:54, 4.20s/it] 20%|█▉ | 3708/18688 [4:20:21<16:56:35, 4.07s/it] 20%|█▉ | 3709/18688 [4:20:25<16:36:46, 3.99s/it] 20%|█▉ | 3710/18688 [4:20:28<16:12:33, 3.90s/it] {'loss': '0.9783', 'grad_norm': '1.343', 'learning_rate': '9.598e-05', 'epoch': '0.1985', 'num_input_tokens_seen': 12468512, 'train_runtime': '1.563e+04', 'train_tokens_per_second': '797.8'}
20%|█▉ | 3710/18688 [4:20:28<16:12:33, 3.90s/it] 20%|█▉ | 3711/18688 [4:20:32<15:46:18, 3.79s/it] 20%|█▉ | 3712/18688 [4:20:35<15:31:26, 3.73s/it] 20%|█▉ | 3713/18688 [4:20:39<15:42:15, 3.78s/it] 20%|█▉ | 3714/18688 [4:20:43<15:55:52, 3.83s/it] 20%|█▉ | 3715/18688 [4:20:48<16:33:56, 3.98s/it] 20%|█▉ | 3716/18688 [4:20:52<16:45:18, 4.03s/it] 20%|█▉ | 3717/18688 [4:20:56<17:24:12, 4.18s/it] 20%|█▉ | 3718/18688 [4:21:01<18:10:21, 4.37s/it] 20%|█▉ | 3719/18688 [4:21:06<18:37:42, 4.48s/it] 20%|█▉ | 3720/18688 [4:21:10<18:14:41, 4.39s/it] {'loss': '0.9759', 'grad_norm': '1.244', 'learning_rate': '9.594e-05', 'epoch': '0.1991', 'num_input_tokens_seen': 12501288, 'train_runtime': '1.567e+04', 'train_tokens_per_second': '797.8'}
20%|█▉ | 3720/18688 [4:21:10<18:14:41, 4.39s/it] 20%|█▉ | 3721/18688 [4:21:14<17:23:39, 4.18s/it] 20%|█▉ | 3722/18688 [4:21:17<16:44:17, 4.03s/it] 20%|█▉ | 3723/18688 [4:21:22<17:08:11, 4.12s/it] 20%|█▉ | 3724/18688 [4:21:26<17:53:28, 4.30s/it] 20%|█▉ | 3725/18688 [4:21:31<17:50:16, 4.29s/it] 20%|█▉ | 3726/18688 [4:21:35<17:58:50, 4.33s/it] 20%|█▉ | 3727/18688 [4:21:39<17:19:15, 4.17s/it] 20%|█▉ | 3728/18688 [4:21:42<16:31:11, 3.98s/it] 20%|█▉ | 3729/18688 [4:21:46<15:56:55, 3.84s/it] 20%|█▉ | 3730/18688 [4:21:49<15:29:55, 3.73s/it] {'loss': '0.9732', 'grad_norm': '1.208', 'learning_rate': '9.591e-05', 'epoch': '0.1996', 'num_input_tokens_seen': 12533360, 'train_runtime': '1.571e+04', 'train_tokens_per_second': '797.8'}
20%|█▉ | 3730/18688 [4:21:49<15:29:55, 3.73s/it] 20%|█▉ | 3731/18688 [4:21:54<16:07:15, 3.88s/it] 20%|█▉ | 3732/18688 [4:21:57<15:59:58, 3.85s/it] 20%|█▉ | 3733/18688 [4:22:01<15:56:50, 3.84s/it] 20%|█▉ | 3734/18688 [4:22:05<15:35:10, 3.75s/it] 20%|█▉ | 3735/18688 [4:22:09<15:34:38, 3.75s/it] 20%|█▉ | 3736/18688 [4:22:12<15:30:31, 3.73s/it] 20%|█▉ | 3737/18688 [4:22:16<15:35:55, 3.76s/it] 20%|██ | 3738/18688 [4:22:20<16:25:25, 3.95s/it] 20%|██ | 3739/18688 [4:22:25<17:04:44, 4.11s/it] 20%|██ | 3740/18688 [4:22:28<16:23:07, 3.95s/it] {'loss': '0.9768', 'grad_norm': '1.127', 'learning_rate': '9.587e-05', 'epoch': '0.2001', 'num_input_tokens_seen': 12563192, 'train_runtime': '1.575e+04', 'train_tokens_per_second': '797.7'}
20%|██ | 3740/18688 [4:22:28<16:23:07, 3.95s/it] 20%|██ | 3741/18688 [4:22:32<15:43:26, 3.79s/it] 20%|██ | 3742/18688 [4:22:36<15:33:52, 3.75s/it] 20%|██ | 3743/18688 [4:22:40<16:15:47, 3.92s/it] 20%|██ | 3744/18688 [4:22:44<16:05:52, 3.88s/it] 20%|██ | 3745/18688 [4:22:48<16:38:09, 4.01s/it] 20%|██ | 3746/18688 [4:22:52<16:27:22, 3.96s/it] 20%|██ | 3747/18688 [4:22:56<17:03:37, 4.11s/it] 20%|██ | 3748/18688 [4:23:00<17:08:55, 4.13s/it] 20%|██ | 3749/18688 [4:23:04<16:24:49, 3.96s/it] 20%|██ | 3750/18688 [4:23:09<17:04:54, 4.12s/it] {'loss': '0.8472', 'grad_norm': '0.8537', 'learning_rate': '9.583e-05', 'epoch': '0.2007', 'num_input_tokens_seen': 12595568, 'train_runtime': '1.579e+04', 'train_tokens_per_second': '797.7'}
20%|██ | 3750/18688 [4:23:09<17:04:54, 4.12s/it] 20%|██ | 3751/18688 [4:23:13<17:11:33, 4.14s/it] 20%|██ | 3752/18688 [4:23:17<17:05:26, 4.12s/it] 20%|██ | 3753/18688 [4:23:21<16:41:01, 4.02s/it] 20%|██ | 3754/18688 [4:23:25<16:42:29, 4.03s/it] 20%|██ | 3755/18688 [4:23:31<19:39:08, 4.74s/it] 20%|██ | 3756/18688 [4:23:34<18:02:00, 4.35s/it] 20%|██ | 3757/18688 [4:23:38<17:26:59, 4.21s/it] 20%|██ | 3758/18688 [4:23:43<17:27:43, 4.21s/it] 20%|██ | 3759/18688 [4:23:46<17:06:09, 4.12s/it] 20%|██ | 3760/18688 [4:23:50<16:32:02, 3.99s/it] {'loss': '0.9274', 'grad_norm': '1.085', 'learning_rate': '9.58e-05', 'epoch': '0.2012', 'num_input_tokens_seen': 12630368, 'train_runtime': '1.583e+04', 'train_tokens_per_second': '797.8'}
20%|██ | 3760/18688 [4:23:50<16:32:02, 3.99s/it] 20%|██ | 3761/18688 [4:23:54<16:45:55, 4.04s/it] 20%|██ | 3762/18688 [4:23:59<17:06:10, 4.13s/it] 20%|██ | 3763/18688 [4:24:02<16:27:34, 3.97s/it] 20%|██ | 3764/18688 [4:24:07<17:12:09, 4.15s/it] 20%|██ | 3765/18688 [4:24:11<17:39:25, 4.26s/it] 20%|██ | 3766/18688 [4:24:15<16:55:01, 4.08s/it] 20%|██ | 3767/18688 [4:24:19<17:03:14, 4.11s/it] 20%|██ | 3768/18688 [4:24:24<17:34:03, 4.24s/it] 20%|██ | 3769/18688 [4:24:29<18:21:26, 4.43s/it] 20%|██ | 3770/18688 [4:24:33<17:51:22, 4.31s/it] {'loss': '1.04', 'grad_norm': '1.428', 'learning_rate': '9.576e-05', 'epoch': '0.2017', 'num_input_tokens_seen': 12665824, 'train_runtime': '1.587e+04', 'train_tokens_per_second': '797.9'}
20%|██ | 3770/18688 [4:24:33<17:51:22, 4.31s/it] 20%|██ | 3771/18688 [4:24:37<17:28:43, 4.22s/it] 20%|██ | 3772/18688 [4:24:41<17:15:09, 4.16s/it] 20%|██ | 3773/18688 [4:24:45<17:16:54, 4.17s/it] 20%|██ | 3774/18688 [4:24:49<16:52:44, 4.07s/it] 20%|██ | 3775/18688 [4:24:54<18:00:02, 4.35s/it] 20%|██ | 3776/18688 [4:24:57<16:53:39, 4.08s/it] 20%|██ | 3777/18688 [4:25:01<16:43:00, 4.04s/it] 20%|██ | 3778/18688 [4:25:05<16:45:07, 4.04s/it] 20%|██ | 3779/18688 [4:25:09<16:08:00, 3.90s/it] 20%|██ | 3780/18688 [4:25:13<16:14:53, 3.92s/it] {'loss': '0.8903', 'grad_norm': '1.122', 'learning_rate': '9.572e-05', 'epoch': '0.2023', 'num_input_tokens_seen': 12697808, 'train_runtime': '1.591e+04', 'train_tokens_per_second': '797.9'}
20%|██ | 3780/18688 [4:25:13<16:14:53, 3.92s/it] 20%|██ | 3781/18688 [4:25:17<16:49:55, 4.06s/it] 20%|██ | 3782/18688 [4:25:21<16:53:02, 4.08s/it] 20%|██ | 3783/18688 [4:25:26<17:24:38, 4.21s/it] 20%|██ | 3784/18688 [4:25:30<17:55:01, 4.33s/it] 20%|██ | 3785/18688 [4:25:34<17:31:38, 4.23s/it] 20%|██ | 3786/18688 [4:25:39<17:35:07, 4.25s/it] 20%|██ | 3787/18688 [4:25:43<17:16:51, 4.17s/it] 20%|██ | 3788/18688 [4:25:47<17:09:16, 4.14s/it] 20%|██ | 3789/18688 [4:25:51<16:53:12, 4.08s/it] 20%|██ | 3790/18688 [4:25:55<17:17:57, 4.18s/it] {'loss': '1.031', 'grad_norm': '1.276', 'learning_rate': '9.569e-05', 'epoch': '0.2028', 'num_input_tokens_seen': 12733016, 'train_runtime': '1.596e+04', 'train_tokens_per_second': '798'}
20%|██ | 3790/18688 [4:25:55<17:17:57, 4.18s/it] 20%|██ | 3791/18688 [4:25:59<16:46:55, 4.06s/it] 20%|██ | 3792/18688 [4:26:03<16:25:09, 3.97s/it] 20%|██ | 3793/18688 [4:26:06<16:08:08, 3.90s/it] 20%|██ | 3794/18688 [4:26:10<15:46:15, 3.81s/it] 20%|██ | 3795/18688 [4:26:14<16:25:13, 3.97s/it] 20%|██ | 3796/18688 [4:26:18<15:48:14, 3.82s/it] 20%|██ | 3797/18688 [4:26:22<16:25:59, 3.97s/it] 20%|██ | 3798/18688 [4:26:26<16:37:39, 4.02s/it] 20%|██ | 3799/18688 [4:26:30<16:11:57, 3.92s/it] 20%|██ | 3800/18688 [4:26:34<16:01:07, 3.87s/it] {'loss': '0.9099', 'grad_norm': '1.319', 'learning_rate': '9.565e-05', 'epoch': '0.2033', 'num_input_tokens_seen': 12763008, 'train_runtime': '1.599e+04', 'train_tokens_per_second': '798'}
20%|██ | 3800/18688 [4:26:34<16:01:07, 3.87s/it] 20%|██ | 3801/18688 [4:26:38<17:17:18, 4.18s/it] 20%|██ | 3802/18688 [4:26:43<17:06:08, 4.14s/it] 20%|██ | 3803/18688 [4:26:47<18:03:27, 4.37s/it] 20%|██ | 3804/18688 [4:26:52<18:18:50, 4.43s/it] 20%|██ | 3805/18688 [4:26:56<17:53:10, 4.33s/it] 20%|██ | 3806/18688 [4:27:00<17:40:51, 4.28s/it] 20%|██ | 3807/18688 [4:27:04<17:35:42, 4.26s/it] 20%|██ | 3808/18688 [4:27:09<17:33:49, 4.25s/it] 20%|██ | 3809/18688 [4:27:13<17:10:10, 4.15s/it] 20%|██ | 3810/18688 [4:27:17<17:10:12, 4.15s/it] {'loss': '0.99', 'grad_norm': '1.064', 'learning_rate': '9.561e-05', 'epoch': '0.2039', 'num_input_tokens_seen': 12799280, 'train_runtime': '1.604e+04', 'train_tokens_per_second': '798.1'}
20%|██ | 3810/18688 [4:27:17<17:10:12, 4.15s/it] 20%|██ | 3811/18688 [4:27:20<16:30:27, 3.99s/it] 20%|██ | 3812/18688 [4:27:24<16:29:19, 3.99s/it] 20%|██ | 3813/18688 [4:27:29<17:38:32, 4.27s/it] 20%|██ | 3814/18688 [4:27:33<17:02:19, 4.12s/it] 20%|██ | 3815/18688 [4:27:37<16:51:49, 4.08s/it] 20%|██ | 3816/18688 [4:27:41<16:37:35, 4.02s/it] 20%|██ | 3817/18688 [4:27:44<15:55:58, 3.86s/it] 20%|██ | 3818/18688 [4:27:49<16:52:08, 4.08s/it] 20%|██ | 3819/18688 [4:27:53<16:42:58, 4.05s/it] 20%|██ | 3820/18688 [4:27:57<16:27:35, 3.99s/it] {'loss': '0.9755', 'grad_norm': '1.098', 'learning_rate': '9.558e-05', 'epoch': '0.2044', 'num_input_tokens_seen': 12830240, 'train_runtime': '1.608e+04', 'train_tokens_per_second': '798'}
20%|██ | 3820/18688 [4:27:57<16:27:35, 3.99s/it] 20%|██ | 3821/18688 [4:28:01<16:29:27, 3.99s/it] 20%|██ | 3822/18688 [4:28:05<16:20:55, 3.96s/it] 20%|██ | 3823/18688 [4:28:08<15:47:32, 3.82s/it] 20%|██ | 3824/18688 [4:28:12<15:22:33, 3.72s/it] 20%|██ | 3825/18688 [4:28:15<15:06:23, 3.66s/it] 20%|██ | 3826/18688 [4:28:19<14:54:42, 3.61s/it] 20%|██ | 3827/18688 [4:28:23<15:43:16, 3.81s/it] 20%|██ | 3828/18688 [4:28:28<17:24:59, 4.22s/it] 20%|██ | 3829/18688 [4:28:32<16:27:40, 3.99s/it] 20%|██ | 3830/18688 [4:28:35<16:07:58, 3.91s/it] {'loss': '0.9339', 'grad_norm': '1.093', 'learning_rate': '9.554e-05', 'epoch': '0.2049', 'num_input_tokens_seen': 12862056, 'train_runtime': '1.612e+04', 'train_tokens_per_second': '798.1'}
20%|██ | 3830/18688 [4:28:35<16:07:58, 3.91s/it] 20%|██ | 3831/18688 [4:28:39<15:56:34, 3.86s/it] 21%|██ | 3832/18688 [4:28:44<17:13:32, 4.17s/it] 21%|██ | 3833/18688 [4:28:48<16:52:42, 4.09s/it] 21%|██ | 3834/18688 [4:28:53<18:08:05, 4.40s/it] 21%|██ | 3835/18688 [4:28:57<17:53:05, 4.33s/it] 21%|██ | 3836/18688 [4:29:01<17:36:48, 4.27s/it] 21%|██ | 3837/18688 [4:29:05<16:41:52, 4.05s/it] 21%|██ | 3838/18688 [4:29:09<16:56:58, 4.11s/it] 21%|██ | 3839/18688 [4:29:14<17:19:16, 4.20s/it] 21%|██ | 3840/18688 [4:29:17<16:31:34, 4.01s/it] {'loss': '0.9418', 'grad_norm': '1.431', 'learning_rate': '9.55e-05', 'epoch': '0.2055', 'num_input_tokens_seen': 12895912, 'train_runtime': '1.616e+04', 'train_tokens_per_second': '798.1'}
21%|██ | 3840/18688 [4:29:17<16:31:34, 4.01s/it] 21%|██ | 3841/18688 [4:29:21<15:52:22, 3.85s/it] 21%|██ | 3842/18688 [4:29:25<16:01:45, 3.89s/it] 21%|██ | 3843/18688 [4:29:28<15:32:11, 3.77s/it] 21%|██ | 3844/18688 [4:29:34<18:34:46, 4.51s/it] 21%|██ | 3845/18688 [4:29:39<18:26:56, 4.47s/it] 21%|██ | 3846/18688 [4:29:43<18:08:50, 4.40s/it] 21%|██ | 3847/18688 [4:29:47<17:29:22, 4.24s/it] 21%|██ | 3848/18688 [4:29:51<17:28:06, 4.24s/it] 21%|██ | 3849/18688 [4:29:55<17:19:26, 4.20s/it] 21%|██ | 3850/18688 [4:30:00<18:22:22, 4.46s/it] {'loss': '0.9655', 'grad_norm': '0.9975', 'learning_rate': '9.546e-05', 'epoch': '0.206', 'num_input_tokens_seen': 12932544, 'train_runtime': '1.62e+04', 'train_tokens_per_second': '798.3'}
21%|██ | 3850/18688 [4:30:00<18:22:22, 4.46s/it] 21%|██ | 3851/18688 [4:30:04<17:17:16, 4.19s/it] 21%|██ | 3852/18688 [4:30:08<16:47:08, 4.07s/it] 21%|██ | 3853/18688 [4:30:11<16:37:50, 4.04s/it] 21%|██ | 3854/18688 [4:30:16<17:42:14, 4.30s/it] 21%|██ | 3855/18688 [4:30:20<17:15:08, 4.19s/it] 21%|██ | 3856/18688 [4:30:25<17:38:33, 4.28s/it] 21%|██ | 3857/18688 [4:30:29<17:09:40, 4.17s/it] 21%|██ | 3858/18688 [4:30:33<17:12:27, 4.18s/it] 21%|██ | 3859/18688 [4:30:37<16:41:15, 4.05s/it] 21%|██ | 3860/18688 [4:30:41<17:12:41, 4.18s/it] {'loss': '0.9661', 'grad_norm': '1.176', 'learning_rate': '9.542e-05', 'epoch': '0.2066', 'num_input_tokens_seen': 12965064, 'train_runtime': '1.624e+04', 'train_tokens_per_second': '798.3'}
21%|██ | 3860/18688 [4:30:41<17:12:41, 4.18s/it] 21%|██ | 3861/18688 [4:30:47<18:48:23, 4.57s/it] 21%|██ | 3862/18688 [4:30:50<17:27:31, 4.24s/it] 21%|██ | 3863/18688 [4:30:54<17:28:39, 4.24s/it] 21%|██ | 3864/18688 [4:30:58<16:48:53, 4.08s/it] 21%|██ | 3865/18688 [4:31:03<17:32:44, 4.26s/it] 21%|██ | 3866/18688 [4:31:07<17:11:29, 4.18s/it] 21%|██ | 3867/18688 [4:31:11<17:16:41, 4.20s/it] 21%|██ | 3868/18688 [4:31:16<18:02:20, 4.38s/it] 21%|██ | 3869/18688 [4:31:20<18:02:13, 4.38s/it] 21%|██ | 3870/18688 [4:31:24<17:11:54, 4.18s/it] {'loss': '0.9903', 'grad_norm': '1.314', 'learning_rate': '9.539e-05', 'epoch': '0.2071', 'num_input_tokens_seen': 12999672, 'train_runtime': '1.628e+04', 'train_tokens_per_second': '798.3'}
21%|██ | 3870/18688 [4:31:24<17:11:54, 4.18s/it] 21%|██ | 3871/18688 [4:31:28<16:51:05, 4.09s/it] 21%|██ | 3872/18688 [4:31:32<16:31:20, 4.01s/it] 21%|██ | 3873/18688 [4:31:36<16:57:02, 4.12s/it] 21%|██ | 3874/18688 [4:31:40<17:06:02, 4.16s/it] 21%|██ | 3875/18688 [4:31:44<16:31:16, 4.02s/it] 21%|██ | 3876/18688 [4:31:48<16:42:26, 4.06s/it] 21%|██ | 3877/18688 [4:31:52<16:54:53, 4.11s/it] 21%|██ | 3878/18688 [4:31:57<17:12:17, 4.18s/it] 21%|██ | 3879/18688 [4:32:01<17:05:09, 4.15s/it] 21%|██ | 3880/18688 [4:32:04<16:35:04, 4.03s/it] {'loss': '0.9622', 'grad_norm': '1.534', 'learning_rate': '9.535e-05', 'epoch': '0.2076', 'num_input_tokens_seen': 13032520, 'train_runtime': '1.632e+04', 'train_tokens_per_second': '798.3'}
21%|██ | 3880/18688 [4:32:04<16:35:04, 4.03s/it] 21%|██ | 3881/18688 [4:32:08<16:21:18, 3.98s/it] 21%|██ | 3882/18688 [4:32:12<16:11:57, 3.94s/it] 21%|██ | 3883/18688 [4:32:16<15:59:02, 3.89s/it] 21%|██ | 3884/18688 [4:32:20<16:17:37, 3.96s/it] 21%|██ | 3885/18688 [4:32:24<16:14:04, 3.95s/it] 21%|██ | 3886/18688 [4:32:27<15:39:14, 3.81s/it] 21%|██ | 3887/18688 [4:32:32<16:04:03, 3.91s/it] 21%|██ | 3888/18688 [4:32:36<16:42:43, 4.07s/it] 21%|██ | 3889/18688 [4:32:40<16:37:54, 4.05s/it] 21%|██ | 3890/18688 [4:32:44<16:08:44, 3.93s/it] {'loss': '0.9169', 'grad_norm': '1.289', 'learning_rate': '9.531e-05', 'epoch': '0.2082', 'num_input_tokens_seen': 13063912, 'train_runtime': '1.636e+04', 'train_tokens_per_second': '798.3'}
21%|██ | 3890/18688 [4:32:44<16:08:44, 3.93s/it] 21%|██ | 3891/18688 [4:32:48<16:26:31, 4.00s/it] 21%|██ | 3892/18688 [4:32:53<18:04:00, 4.40s/it] 21%|██ | 3893/18688 [4:32:58<18:07:36, 4.41s/it] 21%|██ | 3894/18688 [4:33:02<17:54:39, 4.36s/it] 21%|██ | 3895/18688 [4:33:05<16:59:02, 4.13s/it] 21%|██ | 3896/18688 [4:33:09<16:17:26, 3.96s/it] 21%|██ | 3897/18688 [4:33:13<16:47:01, 4.08s/it] 21%|██ | 3898/18688 [4:33:17<16:27:51, 4.01s/it] 21%|██ | 3899/18688 [4:33:22<17:15:22, 4.20s/it] 21%|██ | 3900/18688 [4:33:26<16:52:21, 4.11s/it] {'loss': '0.9836', 'grad_norm': '1.102', 'learning_rate': '9.527e-05', 'epoch': '0.2087', 'num_input_tokens_seen': 13097456, 'train_runtime': '1.641e+04', 'train_tokens_per_second': '798.3'}
21%|██ | 3900/18688 [4:33:26<16:52:21, 4.11s/it] 21%|██ | 3901/18688 [4:33:31<18:19:25, 4.46s/it] 21%|██ | 3902/18688 [4:33:35<18:15:47, 4.45s/it] 21%|██ | 3903/18688 [4:33:40<17:56:49, 4.37s/it] 21%|██ | 3904/18688 [4:33:44<17:35:33, 4.28s/it] 21%|██ | 3905/18688 [4:33:48<17:09:36, 4.18s/it] 21%|██ | 3906/18688 [4:33:53<18:24:01, 4.48s/it] 21%|██ | 3907/18688 [4:33:58<19:42:30, 4.80s/it] 21%|██ | 3908/18688 [4:34:04<20:45:43, 5.06s/it] 21%|██ | 3909/18688 [4:34:08<19:33:09, 4.76s/it] 21%|██ | 3910/18688 [4:34:12<18:11:05, 4.43s/it] {'loss': '1.005', 'grad_norm': '1.051', 'learning_rate': '9.523e-05', 'epoch': '0.2092', 'num_input_tokens_seen': 13135768, 'train_runtime': '1.645e+04', 'train_tokens_per_second': '798.4'}
21%|██ | 3910/18688 [4:34:12<18:11:05, 4.43s/it] 21%|██ | 3911/18688 [4:34:15<17:08:19, 4.18s/it] 21%|██ | 3912/18688 [4:34:19<16:26:17, 4.00s/it] 21%|██ | 3913/18688 [4:34:23<15:56:38, 3.88s/it] 21%|██ | 3914/18688 [4:34:27<17:09:07, 4.18s/it] 21%|██ | 3915/18688 [4:34:31<16:57:35, 4.13s/it] 21%|██ | 3916/18688 [4:34:35<16:14:34, 3.96s/it] 21%|██ | 3917/18688 [4:34:39<16:00:57, 3.90s/it] 21%|██ | 3918/18688 [4:34:43<16:03:34, 3.91s/it] 21%|██ | 3919/18688 [4:34:47<16:04:01, 3.92s/it] 21%|██ | 3920/18688 [4:34:51<16:14:00, 3.96s/it] {'loss': '0.9207', 'grad_norm': '1.336', 'learning_rate': '9.519e-05', 'epoch': '0.2098', 'num_input_tokens_seen': 13165712, 'train_runtime': '1.649e+04', 'train_tokens_per_second': '798.3'}
21%|██ | 3920/18688 [4:34:51<16:14:00, 3.96s/it] 21%|██ | 3921/18688 [4:34:54<15:53:50, 3.88s/it] 21%|██ | 3922/18688 [4:34:59<16:32:36, 4.03s/it] 21%|██ | 3923/18688 [4:35:03<16:25:52, 4.01s/it] 21%|██ | 3924/18688 [4:35:08<17:45:21, 4.33s/it] 21%|██ | 3925/18688 [4:35:12<17:16:19, 4.21s/it] 21%|██ | 3926/18688 [4:35:16<16:44:00, 4.08s/it] 21%|██ | 3927/18688 [4:35:20<17:10:57, 4.19s/it] 21%|██ | 3928/18688 [4:35:25<18:02:13, 4.40s/it] 21%|██ | 3929/18688 [4:35:30<18:20:44, 4.47s/it] 21%|██ | 3930/18688 [4:35:34<18:05:11, 4.41s/it] {'loss': '0.999', 'grad_norm': '1.119', 'learning_rate': '9.515e-05', 'epoch': '0.2103', 'num_input_tokens_seen': 13200840, 'train_runtime': '1.653e+04', 'train_tokens_per_second': '798.4'}
21%|██ | 3930/18688 [4:35:34<18:05:11, 4.41s/it] 21%|██ | 3931/18688 [4:35:38<17:54:23, 4.37s/it] 21%|██ | 3932/18688 [4:35:42<17:22:17, 4.24s/it] 21%|██ | 3933/18688 [4:35:46<16:50:13, 4.11s/it] 21%|██ | 3934/18688 [4:35:51<18:00:58, 4.40s/it] 21%|██ | 3935/18688 [4:35:55<17:22:40, 4.24s/it] 21%|██ | 3936/18688 [4:35:59<17:02:21, 4.16s/it] 21%|██ | 3937/18688 [4:36:02<16:24:12, 4.00s/it] 21%|██ | 3938/18688 [4:36:06<16:25:45, 4.01s/it] 21%|██ | 3939/18688 [4:36:10<15:46:46, 3.85s/it] 21%|██ | 3940/18688 [4:36:14<16:10:39, 3.95s/it] {'loss': '0.9277', 'grad_norm': '1.167', 'learning_rate': '9.511e-05', 'epoch': '0.2108', 'num_input_tokens_seen': 13232512, 'train_runtime': '1.657e+04', 'train_tokens_per_second': '798.4'}
21%|██ | 3940/18688 [4:36:14<16:10:39, 3.95s/it] 21%|██ | 3941/18688 [4:36:18<16:26:10, 4.01s/it] 21%|██ | 3942/18688 [4:36:23<17:20:29, 4.23s/it] 21%|██ | 3943/18688 [4:36:26<16:30:32, 4.03s/it] 21%|██ | 3944/18688 [4:36:30<16:21:37, 3.99s/it] 21%|██ | 3945/18688 [4:36:35<16:33:46, 4.04s/it] 21%|██ | 3946/18688 [4:36:39<17:17:10, 4.22s/it] 21%|██ | 3947/18688 [4:36:43<16:35:26, 4.05s/it] 21%|██ | 3948/18688 [4:36:47<16:37:57, 4.06s/it] 21%|██ | 3949/18688 [4:36:51<16:03:59, 3.92s/it] 21%|██ | 3950/18688 [4:36:54<15:39:54, 3.83s/it] {'loss': '0.9445', 'grad_norm': '1.03', 'learning_rate': '9.507e-05', 'epoch': '0.2114', 'num_input_tokens_seen': 13265784, 'train_runtime': '1.661e+04', 'train_tokens_per_second': '798.4'}
21%|██ | 3950/18688 [4:36:54<15:39:54, 3.83s/it] 21%|██ | 3951/18688 [4:36:58<15:56:55, 3.90s/it] 21%|██ | 3952/18688 [4:37:04<18:26:32, 4.51s/it] 21%|██ | 3953/18688 [4:37:08<17:50:07, 4.36s/it] 21%|██ | 3954/18688 [4:37:12<17:25:55, 4.26s/it] 21%|██ | 3955/18688 [4:37:16<16:58:59, 4.15s/it] 21%|██ | 3956/18688 [4:37:20<16:09:55, 3.95s/it] 21%|██ | 3957/18688 [4:37:23<15:44:50, 3.85s/it] 21%|██ | 3958/18688 [4:37:28<16:34:40, 4.05s/it] 21%|██ | 3959/18688 [4:37:32<16:41:07, 4.08s/it] 21%|██ | 3960/18688 [4:37:36<16:18:21, 3.99s/it] {'loss': '0.9679', 'grad_norm': '1.354', 'learning_rate': '9.503e-05', 'epoch': '0.2119', 'num_input_tokens_seen': 13299808, 'train_runtime': '1.666e+04', 'train_tokens_per_second': '798.5'}
21%|██ | 3960/18688 [4:37:36<16:18:21, 3.99s/it] 21%|██ | 3961/18688 [4:37:40<17:05:47, 4.18s/it] 21%|██ | 3962/18688 [4:37:44<16:43:42, 4.09s/it] 21%|██ | 3963/18688 [4:37:48<16:41:55, 4.08s/it] 21%|██ | 3964/18688 [4:37:52<16:51:41, 4.12s/it] 21%|██ | 3965/18688 [4:37:57<17:07:51, 4.19s/it] 21%|██ | 3966/18688 [4:38:02<18:00:01, 4.40s/it] 21%|██ | 3967/18688 [4:38:05<17:06:50, 4.19s/it] 21%|██ | 3968/18688 [4:38:09<16:17:29, 3.98s/it] 21%|██ | 3969/18688 [4:38:13<16:22:35, 4.01s/it] 21%|██ | 3970/18688 [4:38:17<16:25:37, 4.02s/it] {'loss': '0.9113', 'grad_norm': '1.242', 'learning_rate': '9.499e-05', 'epoch': '0.2124', 'num_input_tokens_seen': 13334904, 'train_runtime': '1.67e+04', 'train_tokens_per_second': '798.6'}
21%|██ | 3970/18688 [4:38:17<16:25:37, 4.02s/it] 21%|██ | 3971/18688 [4:38:21<16:17:36, 3.99s/it] 21%|██▏ | 3972/18688 [4:38:27<18:27:54, 4.52s/it] 21%|██▏ | 3973/18688 [4:38:31<17:43:10, 4.34s/it] 21%|██▏ | 3974/18688 [4:38:34<16:56:03, 4.14s/it] 21%|██▏ | 3975/18688 [4:38:38<16:59:33, 4.16s/it] 21%|██▏ | 3976/18688 [4:38:42<16:08:19, 3.95s/it] 21%|██▏ | 3977/18688 [4:38:46<16:41:22, 4.08s/it] 21%|██▏ | 3978/18688 [4:38:50<16:12:30, 3.97s/it] 21%|██▏ | 3979/18688 [4:38:56<18:12:05, 4.45s/it] 21%|██▏ | 3980/18688 [4:39:00<17:40:25, 4.33s/it] {'loss': '0.9527', 'grad_norm': '1.414', 'learning_rate': '9.495e-05', 'epoch': '0.213', 'num_input_tokens_seen': 13370056, 'train_runtime': '1.674e+04', 'train_tokens_per_second': '798.7'}
21%|██▏ | 3980/18688 [4:39:00<17:40:25, 4.33s/it] 21%|██▏ | 3981/18688 [4:39:03<16:37:35, 4.07s/it] 21%|██▏ | 3982/18688 [4:39:07<16:20:12, 4.00s/it] 21%|██▏ | 3983/18688 [4:39:11<16:13:35, 3.97s/it] 21%|██▏ | 3984/18688 [4:39:14<15:51:34, 3.88s/it] 21%|██▏ | 3985/18688 [4:39:19<16:42:26, 4.09s/it] 21%|██▏ | 3986/18688 [4:39:23<16:45:53, 4.11s/it] 21%|██▏ | 3987/18688 [4:39:28<17:31:57, 4.29s/it] 21%|██▏ | 3988/18688 [4:39:32<17:32:58, 4.30s/it] 21%|██▏ | 3989/18688 [4:39:36<17:29:29, 4.28s/it] 21%|██▏ | 3990/18688 [4:39:40<16:46:11, 4.11s/it] {'loss': '1.037', 'grad_norm': '1.213', 'learning_rate': '9.491e-05', 'epoch': '0.2135', 'num_input_tokens_seen': 13401352, 'train_runtime': '1.678e+04', 'train_tokens_per_second': '798.6'}
21%|██▏ | 3990/18688 [4:39:40<16:46:11, 4.11s/it] 21%|██▏ | 3991/18688 [4:39:44<16:31:46, 4.05s/it] 21%|██▏ | 3992/18688 [4:39:48<16:04:41, 3.94s/it] 21%|██▏ | 3993/18688 [4:39:51<15:47:52, 3.87s/it] 21%|██▏ | 3994/18688 [4:39:55<15:27:23, 3.79s/it] 21%|██▏ | 3995/18688 [4:39:59<15:11:35, 3.72s/it] 21%|██▏ | 3996/18688 [4:40:03<16:06:08, 3.95s/it] 21%|██▏ | 3997/18688 [4:40:07<15:32:24, 3.81s/it] 21%|██▏ | 3998/18688 [4:40:11<16:37:35, 4.07s/it] 21%|██▏ | 3999/18688 [4:40:15<16:01:04, 3.93s/it] 21%|██▏ | 4000/18688 [4:40:19<16:50:47, 4.13s/it] {'loss': '0.9506', 'grad_norm': '1.013', 'learning_rate': '9.487e-05', 'epoch': '0.214', 'num_input_tokens_seen': 13433360, 'train_runtime': '1.682e+04', 'train_tokens_per_second': '798.7'}
21%|██▏ | 4000/18688 [4:40:19<16:50:47, 4.13s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
0%| | 3/1000 [00:00<00:51, 19.30it/s]
0%| | 5/1000 [00:00<01:05, 15.26it/s]
1%| | 7/1000 [00:00<01:10, 14.13it/s]
1%| | 9/1000 [00:00<01:13, 13.46it/s]
1%| | 11/1000 [00:00<01:14, 13.31it/s]
1%|▏ | 13/1000 [00:00<01:15, 13.05it/s]
2%|▏ | 15/1000 [00:01<01:17, 12.75it/s]
2%|▏ | 17/1000 [00:01<01:17, 12.65it/s]
2%|▏ | 19/1000 [00:01<01:50, 8.89it/s]
2%|▏ | 21/1000 [00:01<02:08, 7.60it/s]
2%|▏ | 23/1000 [00:02<01:53, 8.60it/s]
2%|▎ | 25/1000 [00:02<01:55, 8.46it/s]
3%|▎ | 27/1000 [00:02<01:44, 9.35it/s]
3%|▎ | 29/1000 [00:02<01:36, 10.06it/s]
3%|▎ | 31/1000 [00:02<01:42, 9.48it/s]
3%|▎ | 33/1000 [00:03<01:34, 10.27it/s]
4%|▎ | 35/1000 [00:03<01:29, 10.83it/s]
4%|▎ | 37/1000 [00:03<01:36, 9.94it/s]
4%|▍ | 39/1000 [00:03<01:31, 10.56it/s]
4%|▍ | 41/1000 [00:04<01:56, 8.23it/s]
4%|▍ | 43/1000 [00:04<01:44, 9.16it/s]
4%|▍ | 45/1000 [00:04<01:35, 10.02it/s]
5%|▍ | 47/1000 [00:04<01:29, 10.62it/s]
5%|▍ | 49/1000 [00:04<01:35, 9.91it/s]
5%|▌ | 51/1000 [00:04<01:28, 10.74it/s]
5%|▌ | 53/1000 [00:05<01:18, 12.07it/s]
6%|▌ | 55/1000 [00:05<01:13, 12.95it/s]
6%|▌ | 57/1000 [00:05<01:51, 8.42it/s]
6%|▌ | 59/1000 [00:05<01:38, 9.59it/s]
6%|▌ | 61/1000 [00:05<01:29, 10.47it/s]
6%|▋ | 63/1000 [00:06<01:22, 11.42it/s]
6%|▋ | 65/1000 [00:06<01:15, 12.31it/s]
7%|▋ | 67/1000 [00:06<01:13, 12.76it/s]
7%|▋ | 69/1000 [00:06<01:09, 13.39it/s]
7%|▋ | 71/1000 [00:06<01:06, 13.88it/s]
7%|▋ | 73/1000 [00:06<01:07, 13.83it/s]
8%|▊ | 75/1000 [00:06<01:05, 14.02it/s]
8%|▊ | 77/1000 [00:06<01:02, 14.78it/s]
8%|▊ | 79/1000 [00:07<01:01, 14.96it/s]
8%|▊ | 81/1000 [00:07<01:08, 13.44it/s]
8%|▊ | 83/1000 [00:07<01:04, 14.15it/s]
8%|▊ | 85/1000 [00:07<01:03, 14.51it/s]
9%|▊ | 87/1000 [00:07<01:03, 14.30it/s]
9%|▉ | 89/1000 [00:07<01:04, 14.17it/s]
9%|▉ | 91/1000 [00:07<01:02, 14.43it/s]
9%|▉ | 93/1000 [00:08<01:03, 14.20it/s]
10%|▉ | 95/1000 [00:08<01:03, 14.14it/s]
10%|▉ | 97/1000 [00:08<01:03, 14.13it/s]
10%|▉ | 99/1000 [00:08<01:02, 14.46it/s]
10%|█ | 101/1000 [00:08<01:31, 9.86it/s]
10%|█ | 103/1000 [00:09<01:22, 10.85it/s]
10%|█ | 105/1000 [00:09<02:10, 6.83it/s]
11%|█ | 106/1000 [00:09<02:06, 7.04it/s]
11%|█ | 108/1000 [00:09<01:44, 8.58it/s]
11%|█ | 110/1000 [00:09<01:27, 10.17it/s]
11%|█ | 112/1000 [00:10<01:18, 11.36it/s]
11%|█▏ | 114/1000 [00:10<01:13, 12.01it/s]
12%|█▏ | 116/1000 [00:10<01:09, 12.63it/s]
12%|█▏ | 118/1000 [00:10<01:06, 13.36it/s]
12%|█▏ | 120/1000 [00:10<01:08, 12.76it/s]
12%|█▏ | 122/1000 [00:10<01:22, 10.59it/s]
12%|█▏ | 124/1000 [00:11<01:30, 9.67it/s]
13%|█▎ | 126/1000 [00:11<01:22, 10.64it/s]
13%|█▎ | 128/1000 [00:11<01:15, 11.55it/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, 12.39it/s]
14%|█▎ | 136/1000 [00:12<01:04, 13.33it/s]
14%|█▍ | 138/1000 [00:12<01:05, 13.26it/s]
14%|█▍ | 140/1000 [00:12<01:24, 10.12it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.57it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.59it/s]
15%|█▍ | 146/1000 [00:13<01:20, 10.62it/s]
15%|█▍ | 148/1000 [00:13<01:13, 11.53it/s]
15%|█▌ | 150/1000 [00:13<01:07, 12.64it/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, 13.80it/s]
16%|█▌ | 160/1000 [00:14<01:01, 13.72it/s]
16%|█▌ | 162/1000 [00:14<01:01, 13.57it/s]
16%|█▋ | 164/1000 [00:14<01:01, 13.63it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.77it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.29it/s]
17%|█▋ | 170/1000 [00:14<01:18, 10.52it/s]
17%|█▋ | 172/1000 [00:15<01:12, 11.48it/s]
17%|█▋ | 174/1000 [00:15<01:29, 9.19it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.18it/s]
18%|█▊ | 178/1000 [00:15<01:35, 8.64it/s]
18%|█▊ | 180/1000 [00:16<01:33, 8.78it/s]
18%|█▊ | 182/1000 [00:16<01:35, 8.57it/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 [00:17<01:15, 10.71it/s]
19%|█▉ | 192/1000 [00:17<01:11, 11.36it/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, 10.20it/s]
20%|██ | 200/1000 [00:18<01:15, 10.57it/s]
20%|██ | 202/1000 [00:18<01:09, 11.51it/s]
20%|██ | 204/1000 [00:18<01:03, 12.62it/s]
21%|██ | 206/1000 [00:18<01:00, 13.23it/s]
21%|██ | 208/1000 [00:18<00:58, 13.63it/s]
21%|██ | 210/1000 [00:18<00:54, 14.44it/s]
21%|██ | 212/1000 [00:18<00:53, 14.66it/s]
21%|██▏ | 214/1000 [00:19<00:53, 14.69it/s]
22%|██▏ | 216/1000 [00:19<00:54, 14.46it/s]
22%|██▏ | 218/1000 [00:19<00:52, 14.87it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.92it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.19it/s]
22%|██▏ | 224/1000 [00:20<01:20, 9.68it/s]
23%|██▎ | 226/1000 [00:20<01:12, 10.69it/s]
23%|██▎ | 228/1000 [00:20<01:06, 11.59it/s]
23%|██▎ | 230/1000 [00:20<01:03, 12.09it/s]
23%|██▎ | 232/1000 [00:20<01:01, 12.45it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.20it/s]
24%|██▎ | 236/1000 [00:20<00:56, 13.59it/s]
24%|██▍ | 238/1000 [00:21<00:53, 14.20it/s]
24%|██▍ | 240/1000 [00:21<00:53, 14.20it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.74it/s]
24%|██▍ | 244/1000 [00:21<00:50, 14.98it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.96it/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, 12.17it/s]
25%|██▌ | 254/1000 [00:22<00:56, 13.20it/s]
26%|██▌ | 256/1000 [00:22<00:54, 13.73it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.77it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.89it/s]
26%|██▌ | 262/1000 [00:22<00:56, 13.04it/s]
26%|██▋ | 264/1000 [00:23<00:54, 13.60it/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, 13.30it/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, 12.99it/s]
28%|██▊ | 280/1000 [00:24<00:53, 13.54it/s]
28%|██▊ | 282/1000 [00:24<00:49, 14.40it/s]
28%|██▊ | 284/1000 [00:24<00:52, 13.69it/s]
29%|██▊ | 286/1000 [00:24<00:50, 14.15it/s]
29%|██▉ | 288/1000 [00:24<00:48, 14.65it/s]
29%|██▉ | 290/1000 [00:24<00:46, 15.17it/s]
29%|██▉ | 292/1000 [00:25<00:46, 15.25it/s]
29%|██▉ | 294/1000 [00:25<00:46, 15.21it/s]
30%|██▉ | 296/1000 [00:25<00:46, 15.28it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.44it/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, 13.77it/s]
31%|███ | 306/1000 [00:25<00:47, 14.52it/s]
31%|███ | 308/1000 [00:26<00:47, 14.49it/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, 15.20it/s]
32%|███▏ | 316/1000 [00:26<00:44, 15.53it/s]
32%|███▏ | 318/1000 [00:26<00:42, 15.97it/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, 11.50it/s]
33%|███▎ | 326/1000 [00:27<00:58, 11.46it/s]
33%|███▎ | 328/1000 [00:27<00:58, 11.45it/s]
33%|███▎ | 330/1000 [00:27<00:54, 12.40it/s]
33%|███▎ | 332/1000 [00:28<00:52, 12.69it/s]
33%|███▎ | 334/1000 [00:28<00:50, 13.19it/s]
34%|███▎ | 336/1000 [00:28<00:48, 13.60it/s]
34%|███▍ | 338/1000 [00:28<01:05, 10.07it/s]
34%|███▍ | 340/1000 [00:28<00:59, 11.13it/s]
34%|███▍ | 342/1000 [00:28<00:53, 12.39it/s]
34%|███▍ | 344/1000 [00:29<00:50, 13.05it/s]
35%|███▍ | 346/1000 [00:29<00:47, 13.66it/s]
35%|███▍ | 348/1000 [00:29<00:46, 14.09it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.71it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.88it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.88it/s]
36%|███▌ | 356/1000 [00:29<00:42, 15.00it/s]
36%|███▌ | 358/1000 [00:29<00:43, 14.71it/s]
36%|███▌ | 360/1000 [00:30<00:42, 15.10it/s]
36%|███▌ | 362/1000 [00:30<00:47, 13.43it/s]
36%|███▋ | 364/1000 [00:30<00:45, 13.88it/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 [00:31<00:46, 13.54it/s]
37%|███▋ | 374/1000 [00:31<00:54, 11.41it/s]
38%|███▊ | 376/1000 [00:31<00:56, 10.99it/s]
38%|███▊ | 378/1000 [00:31<00:55, 11.31it/s]
38%|███▊ | 380/1000 [00:31<00:52, 11.91it/s]
38%|███▊ | 382/1000 [00:31<00:50, 12.27it/s]
38%|███▊ | 384/1000 [00:32<00:46, 13.12it/s]
39%|███▊ | 386/1000 [00:32<00:45, 13.52it/s]
39%|███▉ | 388/1000 [00:32<00:45, 13.55it/s]
39%|███▉ | 390/1000 [00:32<00:45, 13.47it/s]
39%|███▉ | 392/1000 [00:32<00:43, 13.82it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.63it/s]
40%|███▉ | 396/1000 [00:32<00:43, 13.77it/s]
40%|███▉ | 398/1000 [00:33<00:47, 12.59it/s]
40%|████ | 400/1000 [00:33<00:45, 13.27it/s]
40%|████ | 402/1000 [00:33<00:42, 13.92it/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, 12.58it/s]
41%|████ | 410/1000 [00:34<00:56, 10.51it/s]
41%|████ | 412/1000 [00:34<00:51, 11.36it/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, 9.54it/s]
42%|████▏ | 422/1000 [00:35<00:53, 10.88it/s]
42%|████▏ | 424/1000 [00:35<01:14, 7.70it/s]
43%|████▎ | 426/1000 [00:36<01:04, 8.95it/s]
43%|████▎ | 428/1000 [00:36<00:56, 10.15it/s]
43%|████▎ | 430/1000 [00:36<01:02, 9.13it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.06it/s]
43%|████▎ | 434/1000 [00:36<01:08, 8.29it/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, 10.56it/s]
44%|████▍ | 444/1000 [00:37<00:48, 11.42it/s]
45%|████▍ | 446/1000 [00:37<00:45, 12.18it/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, 12.81it/s]
45%|████▌ | 454/1000 [00:38<00:44, 12.28it/s]
46%|████▌ | 456/1000 [00:38<00:43, 12.58it/s]
46%|████▌ | 458/1000 [00:38<00:39, 13.61it/s]
46%|████▌ | 460/1000 [00:39<00:39, 13.78it/s]
46%|████▌ | 462/1000 [00:39<00:40, 13.42it/s]
46%|████▋ | 464/1000 [00:39<00:38, 13.75it/s]
47%|████▋ | 466/1000 [00:39<00:38, 13.74it/s]
47%|████▋ | 468/1000 [00:39<00:46, 11.55it/s]
47%|████▋ | 470/1000 [00:40<00:56, 9.42it/s]
47%|████▋ | 472/1000 [00:40<00:54, 9.74it/s]
47%|████▋ | 474/1000 [00:40<00:47, 10.99it/s]
48%|████▊ | 476/1000 [00:40<00:44, 11.65it/s]
48%|████▊ | 478/1000 [00:40<00:44, 11.63it/s]
48%|████▊ | 480/1000 [00:40<00:41, 12.59it/s]
48%|████▊ | 482/1000 [00:40<00:39, 13.09it/s]
48%|████▊ | 484/1000 [00:41<00:37, 13.64it/s]
49%|████▊ | 486/1000 [00:41<00:37, 13.88it/s]
49%|████▉ | 488/1000 [00:41<00:36, 14.18it/s]
49%|████▉ | 490/1000 [00:41<00:46, 11.08it/s]
49%|████▉ | 492/1000 [00:41<00:41, 12.38it/s]
49%|████▉ | 494/1000 [00:41<00:37, 13.34it/s]
50%|████▉ | 496/1000 [00:41<00:35, 14.12it/s]
50%|████▉ | 498/1000 [00:42<00:34, 14.54it/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, 12.61it/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, 12.05it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.60it/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, 14.20it/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, 12.01it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.81it/s]
53%|█████▎ | 532/1000 [00:44<00:34, 13.74it/s]
53%|█████▎ | 534/1000 [00:44<00:33, 14.12it/s]
54%|█████▎ | 536/1000 [00:45<00:32, 14.25it/s]
54%|█████▍ | 538/1000 [00:45<00:32, 14.01it/s]
54%|█████▍ | 540/1000 [00:45<00:31, 14.44it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.72it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.61it/s]
55%|█████▍ | 546/1000 [00:45<00:31, 14.24it/s]
55%|█████▍ | 548/1000 [00:45<00:32, 13.94it/s]
55%|█████▌ | 550/1000 [00:46<00:31, 14.15it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.28it/s]
55%|█████▌ | 554/1000 [00:46<00:31, 13.95it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.13it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.49it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 14.99it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 14.96it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.80it/s]
57%|█████▋ | 566/1000 [00:47<00:37, 11.65it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.21it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.18it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.74it/s]
57%|█████▋ | 574/1000 [00:47<00:31, 13.62it/s]
58%|█████▊ | 576/1000 [00:48<00:31, 13.56it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.61it/s]
58%|█████▊ | 580/1000 [00:48<00:32, 12.85it/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, 14.20it/s]
59%|█████▉ | 588/1000 [00:48<00:27, 14.96it/s]
59%|█████▉ | 590/1000 [00:49<00:27, 14.97it/s]
59%|█████▉ | 592/1000 [00:49<00:27, 14.76it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.32it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.82it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 14.04it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.42it/s]
60%|██████ | 602/1000 [00:49<00:27, 14.55it/s]
60%|██████ | 604/1000 [00:50<00:26, 15.13it/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<00:25, 15.01it/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, 14.78it/s]
62%|██████▏ | 618/1000 [00:50<00:26, 14.17it/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<00:32, 11.41it/s]
63%|██████▎ | 626/1000 [00:51<00:30, 12.11it/s]
63%|██████▎ | 628/1000 [00:51<00:27, 13.31it/s]
63%|██████▎ | 630/1000 [00:52<00:26, 13.83it/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, 10.97it/s]
64%|██████▍ | 638/1000 [00:52<00:33, 10.69it/s]
64%|██████▍ | 640/1000 [00:52<00:31, 11.46it/s]
64%|██████▍ | 642/1000 [00:53<00:29, 12.33it/s]
64%|██████▍ | 644/1000 [00:53<00:28, 12.60it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.87it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.35it/s]
65%|██████▌ | 650/1000 [00:53<00:27, 12.59it/s]
65%|██████▌ | 652/1000 [00:53<00:29, 11.97it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.46it/s]
66%|██████▌ | 656/1000 [00:54<00:31, 10.81it/s]
66%|██████▌ | 658/1000 [00:54<00:28, 11.87it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.17it/s]
66%|██████▌ | 662/1000 [00:54<00:29, 11.30it/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, 11.34it/s]
67%|██████▋ | 670/1000 [00:55<00:29, 11.11it/s]
67%|██████▋ | 672/1000 [00:55<00:27, 11.90it/s]
67%|██████▋ | 674/1000 [00:55<00:26, 12.49it/s]
68%|██████▊ | 676/1000 [00:56<00:27, 11.74it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.23it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 13.01it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.14it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.33it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.25it/s]
69%|██████▉ | 688/1000 [00:56<00:22, 13.60it/s]
69%|██████▉ | 690/1000 [00:57<00:22, 13.69it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.17it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.28it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 14.03it/s]
70%|██████▉ | 698/1000 [00:57<00:20, 14.55it/s]
70%|███████ | 700/1000 [00:57<00:24, 12.17it/s]
70%|███████ | 702/1000 [00:57<00:22, 12.98it/s]
70%|███████ | 704/1000 [00:58<00:30, 9.86it/s]
71%|███████ | 706/1000 [00:58<00:27, 10.52it/s]
71%|███████ | 708/1000 [00:58<00:31, 9.40it/s]
71%|███████ | 710/1000 [00:58<00:28, 10.09it/s]
71%|███████ | 712/1000 [00:59<00:27, 10.65it/s]
71%|███████▏ | 714/1000 [00:59<00:25, 11.25it/s]
72%|███████▏ | 716/1000 [00:59<00:24, 11.69it/s]
72%|███████▏ | 718/1000 [00:59<00:25, 11.25it/s]
72%|███████▏ | 720/1000 [00:59<00:23, 11.84it/s]
72%|███████▏ | 722/1000 [00:59<00:22, 12.16it/s]
72%|███████▏ | 724/1000 [00:59<00:22, 12.21it/s]
73%|███████▎ | 726/1000 [01:00<00:22, 12.29it/s]
73%|███████▎ | 728/1000 [01:00<00:25, 10.66it/s]
73%|███████▎ | 730/1000 [01:00<00:35, 7.67it/s]
73%|███████▎ | 732/1000 [01:01<00:34, 7.86it/s]
73%|███████▎ | 734/1000 [01:01<00:33, 8.04it/s]
74%|███████▎ | 735/1000 [01:01<00:33, 7.99it/s]
74%|███████▎ | 737/1000 [01:01<00:28, 9.18it/s]
74%|███████▍ | 739/1000 [01:01<00:25, 10.18it/s]
74%|███████▍ | 741/1000 [01:01<00:23, 10.89it/s]
74%|███████▍ | 743/1000 [01:02<00:22, 11.26it/s]
74%|███████▍ | 745/1000 [01:02<00:26, 9.58it/s]
75%|███████▍ | 747/1000 [01:02<00:27, 9.13it/s]
75%|███████▍ | 749/1000 [01:02<00:25, 9.98it/s]
75%|███████▌ | 751/1000 [01:02<00:23, 10.57it/s]
75%|███████▌ | 753/1000 [01:03<00:22, 11.05it/s]
76%|███████▌ | 755/1000 [01:03<00:21, 11.41it/s]
76%|███████▌ | 757/1000 [01:03<00:26, 9.17it/s]
76%|███████▌ | 759/1000 [01:03<00:24, 9.93it/s]
76%|███████▌ | 761/1000 [01:03<00:22, 10.57it/s]
76%|███████▋ | 763/1000 [01:04<00:25, 9.37it/s]
76%|███████▋ | 765/1000 [01:04<00:28, 8.16it/s]
77%|███████▋ | 767/1000 [01:04<00:25, 9.18it/s]
77%|███████▋ | 769/1000 [01:04<00:22, 10.23it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.54it/s]
77%|███████▋ | 773/1000 [01:05<00:18, 12.36it/s]
78%|███████▊ | 775/1000 [01:05<00:17, 12.97it/s]
78%|███████▊ | 777/1000 [01:05<00:15, 13.99it/s]
78%|███████▊ | 779/1000 [01:05<00:21, 10.47it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.39it/s]
78%|███████▊ | 783/1000 [01:06<00:26, 8.31it/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<00:23, 8.84it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.17it/s]
79%|███████▉ | 792/1000 [01:07<00:18, 11.31it/s]
79%|███████▉ | 794/1000 [01:07<00:17, 11.95it/s]
80%|███████▉ | 796/1000 [01:07<00:16, 12.39it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 13.14it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.32it/s]
80%|████████ | 802/1000 [01:08<00:23, 8.25it/s]
80%|████████ | 804/1000 [01:08<00:23, 8.31it/s]
81%|████████ | 806/1000 [01:08<00:21, 8.99it/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, 11.65it/s]
81%|████████▏ | 814/1000 [01:09<00:14, 12.56it/s]
82%|████████▏ | 816/1000 [01:09<00:16, 11.03it/s]
82%|████████▏ | 818/1000 [01:09<00:15, 12.00it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.49it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 13.06it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.85it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.25it/s]
83%|████████▎ | 828/1000 [01:10<00:11, 14.53it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.95it/s]
83%|████████▎ | 832/1000 [01:10<00:11, 14.34it/s]
83%|████████▎ | 834/1000 [01:10<00:11, 14.10it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.45it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.36it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.39it/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, 13.96it/s]
85%|████████▍ | 848/1000 [01:11<00:10, 13.94it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.14it/s]
85%|████████▌ | 852/1000 [01:11<00:11, 12.92it/s]
85%|████████▌ | 854/1000 [01:12<00:10, 13.34it/s]
86%|████████▌ | 856/1000 [01:12<00:10, 13.48it/s]
86%|████████▌ | 858/1000 [01:12<00:10, 14.15it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 15.01it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.36it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 13.93it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.77it/s]
87%|████████▋ | 868/1000 [01:13<00:09, 13.69it/s]
87%|████████▋ | 870/1000 [01:13<00:09, 14.12it/s]
87%|████████▋ | 872/1000 [01:13<00:08, 14.75it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 12.50it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.10it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.97it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.59it/s]
88%|████████▊ | 882/1000 [01:14<00:10, 11.36it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 12.11it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.89it/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, 14.11it/s]
89%|████████▉ | 894/1000 [01:15<00:07, 14.25it/s]
90%|████████▉ | 896/1000 [01:15<00:07, 13.28it/s]
90%|████████▉ | 898/1000 [01:15<00:07, 12.92it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.09it/s]
90%|█████████ | 902/1000 [01:15<00:07, 12.87it/s]
90%|█████████ | 904/1000 [01:15<00:09, 9.74it/s]
91%|█████████ | 906/1000 [01:16<00:08, 10.81it/s]
91%|█████████ | 908/1000 [01:16<00:07, 11.82it/s]
91%|█████████ | 910/1000 [01:16<00:07, 12.11it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.33it/s]
91%|█████████▏| 914/1000 [01:17<00:12, 6.82it/s]
92%|█████████▏| 916/1000 [01:17<00:10, 8.15it/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, 10.27it/s]
92%|█████████▏| 924/1000 [01:18<00:06, 10.95it/s]
93%|█████████▎| 926/1000 [01:18<00:06, 11.80it/s]
93%|█████████▎| 928/1000 [01:18<00:06, 10.64it/s]
93%|█████████▎| 930/1000 [01:18<00:05, 11.86it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 12.46it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.94it/s]
94%|█████████▎| 936/1000 [01:18<00:04, 13.49it/s]
94%|█████████▍| 938/1000 [01:19<00:04, 14.38it/s]
94%|█████████▍| 940/1000 [01:19<00:04, 14.67it/s]
94%|█████████▍| 942/1000 [01:19<00:04, 12.32it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.82it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.89it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 12.72it/s]
95%|█████████▌| 950/1000 [01:20<00:03, 13.51it/s]
95%|█████████▌| 952/1000 [01:20<00:03, 14.24it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 14.34it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 14.31it/s]
96%|█████████▌| 958/1000 [01:20<00:02, 14.86it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 13.21it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.85it/s]
96%|█████████▋| 964/1000 [01:21<00:03, 10.27it/s]
97%|█████████▋| 966/1000 [01:21<00:02, 11.48it/s]
97%|█████████▋| 968/1000 [01:21<00:02, 12.15it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 12.76it/s]
97%|█████████▋| 972/1000 [01:21<00:02, 9.74it/s]
97%|█████████▋| 974/1000 [01:22<00:02, 10.94it/s]
98%|█████████▊| 976/1000 [01:22<00:02, 11.73it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.92it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.98it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.49it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 10.50it/s]
99%|█████████▊| 986/1000 [01:23<00:01, 10.74it/s]
99%|█████████▉| 988/1000 [01:23<00:01, 10.95it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 11.44it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 12.34it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 13.09it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 14.00it/s]
100%|█████████▉| 998/1000 [01:24<00:00, 14.17it/s]
100%|██████████| 1000/1000 [01:24<00:00, 14.84it/s]
{'eval_loss': '0.9393', 'eval_runtime': '84.23', 'eval_samples_per_second': '11.87', 'eval_steps_per_second': '11.87', 'epoch': '0.214', 'num_input_tokens_seen': 13433360}
21%|██▏ | 4000/18688 [4:41:44<16:50:47, 4.13s/it]
100%|██████████| 1000/1000 [01:24<00:00, 14.84it/s]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00010/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 [4:41:49<121:08:40, 29.69s/it] 21%|██▏ | 4002/18688 [4:41:53<89:43:54, 22.00s/it] 21%|██▏ | 4003/18688 [4:41:57<68:27:41, 16.78s/it] 21%|██▏ | 4004/18688 [4:42:02<53:04:31, 13.01s/it] 21%|██▏ | 4005/18688 [4:42:06<42:34:35, 10.44s/it] 21%|██▏ | 4006/18688 [4:42:10<34:58:44, 8.58s/it] 21%|██▏ | 4007/18688 [4:42:15<29:47:40, 7.31s/it] 21%|██▏ | 4008/18688 [4:42:18<25:11:07, 6.18s/it] 21%|██▏ | 4009/18688 [4:42:23<23:08:29, 5.68s/it] 21%|██▏ | 4010/18688 [4:42:27<20:59:36, 5.15s/it] {'loss': '0.9656', 'grad_norm': '1.239', 'learning_rate': '9.483e-05', 'epoch': '0.2146', 'num_input_tokens_seen': 13467800, 'train_runtime': '1.695e+04', 'train_tokens_per_second': '794.7'}
21%|██▏ | 4010/18688 [4:42:27<20:59:36, 5.15s/it] 21%|██▏ | 4011/18688 [4:42:31<20:18:48, 4.98s/it] 21%|██▏ | 4012/18688 [4:42:36<19:42:41, 4.84s/it] 21%|██▏ | 4013/18688 [4:42:40<19:06:52, 4.69s/it] 21%|██▏ | 4014/18688 [4:42:44<17:55:56, 4.40s/it] 21%|██▏ | 4015/18688 [4:42:48<18:05:48, 4.44s/it] 21%|██▏ | 4016/18688 [4:42:53<17:46:53, 4.36s/it] 21%|██▏ | 4017/18688 [4:42:57<17:57:16, 4.41s/it] 22%|██▏ | 4018/18688 [4:43:01<17:33:26, 4.31s/it] 22%|██▏ | 4019/18688 [4:43:05<17:20:24, 4.26s/it] 22%|██▏ | 4020/18688 [4:43:09<16:49:07, 4.13s/it] {'loss': '0.9684', 'grad_norm': '1.214', 'learning_rate': '9.479e-05', 'epoch': '0.2151', 'num_input_tokens_seen': 13502624, 'train_runtime': '1.699e+04', 'train_tokens_per_second': '794.8'}
22%|██▏ | 4020/18688 [4:43:09<16:49:07, 4.13s/it] 22%|██▏ | 4021/18688 [4:43:14<17:31:28, 4.30s/it] 22%|██▏ | 4022/18688 [4:43:18<17:05:06, 4.19s/it] 22%|██▏ | 4023/18688 [4:43:22<16:50:04, 4.13s/it] 22%|██▏ | 4024/18688 [4:43:26<17:34:18, 4.31s/it] 22%|██▏ | 4025/18688 [4:43:30<17:01:16, 4.18s/it] 22%|██▏ | 4026/18688 [4:43:35<17:26:33, 4.28s/it] 22%|██▏ | 4027/18688 [4:43:39<17:29:53, 4.30s/it] 22%|██▏ | 4028/18688 [4:43:43<16:58:23, 4.17s/it] 22%|██▏ | 4029/18688 [4:43:47<16:38:22, 4.09s/it] 22%|██▏ | 4030/18688 [4:43:51<16:17:28, 4.00s/it] {'loss': '1.036', 'grad_norm': '1.217', 'learning_rate': '9.475e-05', 'epoch': '0.2157', 'num_input_tokens_seen': 13535688, 'train_runtime': '1.703e+04', 'train_tokens_per_second': '794.8'}
22%|██▏ | 4030/18688 [4:43:51<16:17:28, 4.00s/it] 22%|██▏ | 4031/18688 [4:43:55<16:19:05, 4.01s/it] 22%|██▏ | 4032/18688 [4:43:59<16:04:14, 3.95s/it] 22%|██▏ | 4033/18688 [4:44:03<16:16:36, 4.00s/it] 22%|██▏ | 4034/18688 [4:44:06<15:55:51, 3.91s/it] 22%|██▏ | 4035/18688 [4:44:10<15:40:42, 3.85s/it] 22%|██▏ | 4036/18688 [4:44:14<16:02:10, 3.94s/it] 22%|██▏ | 4037/18688 [4:44:18<16:14:36, 3.99s/it] 22%|██▏ | 4038/18688 [4:44:22<16:04:45, 3.95s/it] 22%|██▏ | 4039/18688 [4:44:26<16:19:07, 4.01s/it] 22%|██▏ | 4040/18688 [4:44:30<15:41:39, 3.86s/it] {'loss': '0.9776', 'grad_norm': '1.42', 'learning_rate': '9.471e-05', 'epoch': '0.2162', 'num_input_tokens_seen': 13566128, 'train_runtime': '1.707e+04', 'train_tokens_per_second': '794.7'}
22%|██▏ | 4040/18688 [4:44:30<15:41:39, 3.86s/it] 22%|██▏ | 4041/18688 [4:44:33<15:24:23, 3.79s/it] 22%|██▏ | 4042/18688 [4:44:38<15:44:47, 3.87s/it] 22%|██▏ | 4043/18688 [4:44:42<15:58:59, 3.93s/it] 22%|██▏ | 4044/18688 [4:44:45<15:49:28, 3.89s/it] 22%|██▏ | 4045/18688 [4:44:50<16:21:57, 4.02s/it] 22%|██▏ | 4046/18688 [4:44:54<16:26:10, 4.04s/it] 22%|██▏ | 4047/18688 [4:44:58<16:01:44, 3.94s/it] 22%|██▏ | 4048/18688 [4:45:02<17:13:21, 4.24s/it] 22%|██▏ | 4049/18688 [4:45:07<17:47:18, 4.37s/it] 22%|██▏ | 4050/18688 [4:45:11<16:45:01, 4.12s/it] {'loss': '0.9271', 'grad_norm': '1.163', 'learning_rate': '9.467e-05', 'epoch': '0.2167', 'num_input_tokens_seen': 13599768, 'train_runtime': '1.711e+04', 'train_tokens_per_second': '794.8'}
22%|██▏ | 4050/18688 [4:45:11<16:45:01, 4.12s/it] 22%|██▏ | 4051/18688 [4:45:14<16:16:43, 4.00s/it] 22%|██▏ | 4052/18688 [4:45:19<17:16:32, 4.25s/it] 22%|██▏ | 4053/18688 [4:45:23<16:53:45, 4.16s/it] 22%|██▏ | 4054/18688 [4:45:27<16:30:56, 4.06s/it] 22%|██▏ | 4055/18688 [4:45:32<17:42:54, 4.36s/it] 22%|██▏ | 4056/18688 [4:45:37<17:48:09, 4.38s/it] 22%|██▏ | 4057/18688 [4:45:41<17:32:39, 4.32s/it] 22%|██▏ | 4058/18688 [4:45:44<16:34:22, 4.08s/it] 22%|██▏ | 4059/18688 [4:45:48<16:14:34, 4.00s/it] 22%|██▏ | 4060/18688 [4:45:51<15:33:38, 3.83s/it] {'loss': '0.8776', 'grad_norm': '1.348', 'learning_rate': '9.463e-05', 'epoch': '0.2173', 'num_input_tokens_seen': 13631920, 'train_runtime': '1.715e+04', 'train_tokens_per_second': '794.8'}
22%|██▏ | 4060/18688 [4:45:51<15:33:38, 3.83s/it] 22%|██▏ | 4061/18688 [4:45:55<15:47:20, 3.89s/it] 22%|██▏ | 4062/18688 [4:46:00<16:54:14, 4.16s/it] 22%|██▏ | 4063/18688 [4:46:05<17:23:36, 4.28s/it] 22%|██▏ | 4064/18688 [4:46:09<16:49:06, 4.14s/it] 22%|██▏ | 4065/18688 [4:46:13<16:53:57, 4.16s/it] 22%|██▏ | 4066/18688 [4:46:17<16:29:43, 4.06s/it] 22%|██▏ | 4067/18688 [4:46:22<17:30:18, 4.31s/it] 22%|██▏ | 4068/18688 [4:46:26<17:34:20, 4.33s/it] 22%|██▏ | 4069/18688 [4:46:31<18:03:01, 4.45s/it] 22%|██▏ | 4070/18688 [4:46:35<17:53:41, 4.41s/it] {'loss': '1.029', 'grad_norm': '1.083', 'learning_rate': '9.459e-05', 'epoch': '0.2178', 'num_input_tokens_seen': 13668632, 'train_runtime': '1.72e+04', 'train_tokens_per_second': '794.9'}
22%|██▏ | 4070/18688 [4:46:35<17:53:41, 4.41s/it] 22%|██▏ | 4071/18688 [4:46:39<16:54:25, 4.16s/it] 22%|██▏ | 4072/18688 [4:46:43<16:49:50, 4.15s/it] 22%|██▏ | 4073/18688 [4:46:46<16:14:20, 4.00s/it] 22%|██▏ | 4074/18688 [4:46:50<15:35:13, 3.84s/it] 22%|██▏ | 4075/18688 [4:46:54<15:58:00, 3.93s/it] 22%|██▏ | 4076/18688 [4:46:59<16:47:27, 4.14s/it] 22%|██▏ | 4077/18688 [4:47:03<16:50:59, 4.15s/it] 22%|██▏ | 4078/18688 [4:47:07<16:29:33, 4.06s/it] 22%|██▏ | 4079/18688 [4:47:10<16:07:33, 3.97s/it] 22%|██▏ | 4080/18688 [4:47:14<15:57:00, 3.93s/it] {'loss': '0.9747', 'grad_norm': '1.126', 'learning_rate': '9.455e-05', 'epoch': '0.2183', 'num_input_tokens_seen': 13699016, 'train_runtime': '1.723e+04', 'train_tokens_per_second': '794.9'}
22%|██▏ | 4080/18688 [4:47:14<15:57:00, 3.93s/it] 22%|██▏ | 4081/18688 [4:47:18<15:25:56, 3.80s/it] 22%|██▏ | 4082/18688 [4:47:22<16:28:54, 4.06s/it] 22%|██▏ | 4083/18688 [4:47:28<18:29:47, 4.56s/it] 22%|██▏ | 4084/18688 [4:47:32<17:39:16, 4.35s/it] 22%|██▏ | 4085/18688 [4:47:35<16:36:58, 4.10s/it] 22%|██▏ | 4086/18688 [4:47:39<15:49:39, 3.90s/it] 22%|██▏ | 4087/18688 [4:47:43<15:47:13, 3.89s/it] 22%|██▏ | 4088/18688 [4:47:46<15:20:08, 3.78s/it] 22%|██▏ | 4089/18688 [4:47:50<15:17:55, 3.77s/it] 22%|██▏ | 4090/18688 [4:47:55<16:14:03, 4.00s/it] {'loss': '0.9342', 'grad_norm': '1.135', 'learning_rate': '9.451e-05', 'epoch': '0.2189', 'num_input_tokens_seen': 13733208, 'train_runtime': '1.728e+04', 'train_tokens_per_second': '795'}
22%|██▏ | 4090/18688 [4:47:55<16:14:03, 4.00s/it] 22%|██▏ | 4091/18688 [4:47:59<17:04:29, 4.21s/it] 22%|██▏ | 4092/18688 [4:48:04<17:10:15, 4.24s/it] 22%|██▏ | 4093/18688 [4:48:09<18:23:02, 4.53s/it] 22%|██▏ | 4094/18688 [4:48:13<17:22:35, 4.29s/it] 22%|██▏ | 4095/18688 [4:48:17<17:11:55, 4.24s/it] 22%|██▏ | 4096/18688 [4:48:21<17:00:53, 4.20s/it] 22%|██▏ | 4097/18688 [4:48:24<16:12:57, 4.00s/it] 22%|██▏ | 4098/18688 [4:48:28<15:43:45, 3.88s/it] 22%|██▏ | 4099/18688 [4:48:32<16:05:50, 3.97s/it] 22%|██▏ | 4100/18688 [4:48:36<16:10:04, 3.99s/it] {'loss': '0.9167', 'grad_norm': '1.07', 'learning_rate': '9.446e-05', 'epoch': '0.2194', 'num_input_tokens_seen': 13767232, 'train_runtime': '1.732e+04', 'train_tokens_per_second': '795'}
22%|██▏ | 4100/18688 [4:48:36<16:10:04, 3.99s/it] 22%|██▏ | 4101/18688 [4:48:40<16:21:43, 4.04s/it] 22%|██▏ | 4102/18688 [4:48:46<18:03:52, 4.46s/it] 22%|██▏ | 4103/18688 [4:48:49<17:10:51, 4.24s/it] 22%|██▏ | 4104/18688 [4:48:54<17:09:24, 4.24s/it] 22%|██▏ | 4105/18688 [4:48:58<17:06:02, 4.22s/it] 22%|██▏ | 4106/18688 [4:49:03<18:31:48, 4.57s/it] 22%|██▏ | 4107/18688 [4:49:08<18:17:07, 4.51s/it] 22%|██▏ | 4108/18688 [4:49:12<17:43:32, 4.38s/it] 22%|██▏ | 4109/18688 [4:49:16<17:03:27, 4.21s/it] 22%|██▏ | 4110/18688 [4:49:20<17:32:03, 4.33s/it] {'loss': '0.9444', 'grad_norm': '1.067', 'learning_rate': '9.442e-05', 'epoch': '0.2199', 'num_input_tokens_seen': 13803928, 'train_runtime': '1.736e+04', 'train_tokens_per_second': '795.1'}
22%|██▏ | 4110/18688 [4:49:20<17:32:03, 4.33s/it] 22%|██▏ | 4111/18688 [4:49:24<17:31:13, 4.33s/it] 22%|██▏ | 4112/18688 [4:49:28<17:04:16, 4.22s/it] 22%|██▏ | 4113/18688 [4:49:32<16:40:25, 4.12s/it] 22%|██▏ | 4114/18688 [4:49:36<16:23:39, 4.05s/it] 22%|██▏ | 4115/18688 [4:49:41<17:12:21, 4.25s/it] 22%|██▏ | 4116/18688 [4:49:45<16:37:06, 4.11s/it] 22%|██▏ | 4117/18688 [4:49:49<17:15:04, 4.26s/it] 22%|██▏ | 4118/18688 [4:49:53<17:00:09, 4.20s/it] 22%|██▏ | 4119/18688 [4:49:58<17:16:48, 4.27s/it] 22%|██▏ | 4120/18688 [4:50:04<19:11:33, 4.74s/it] {'loss': '0.9823', 'grad_norm': '1.11', 'learning_rate': '9.438e-05', 'epoch': '0.2205', 'num_input_tokens_seen': 13839704, 'train_runtime': '1.74e+04', 'train_tokens_per_second': '795.2'}
22%|██▏ | 4120/18688 [4:50:04<19:11:33, 4.74s/it] 22%|██▏ | 4121/18688 [4:50:08<18:43:36, 4.63s/it] 22%|██▏ | 4122/18688 [4:50:12<17:51:46, 4.41s/it] 22%|██▏ | 4123/18688 [4:50:16<18:02:42, 4.46s/it] 22%|██▏ | 4124/18688 [4:50:21<18:05:32, 4.47s/it] 22%|██▏ | 4125/18688 [4:50:25<17:11:04, 4.25s/it] 22%|██▏ | 4126/18688 [4:50:28<16:28:53, 4.07s/it] 22%|██▏ | 4127/18688 [4:50:33<17:04:11, 4.22s/it] 22%|██▏ | 4128/18688 [4:50:37<17:12:38, 4.26s/it] 22%|██▏ | 4129/18688 [4:50:42<18:14:37, 4.51s/it] 22%|██▏ | 4130/18688 [4:50:47<18:43:00, 4.63s/it] {'loss': '0.9943', 'grad_norm': '1.337', 'learning_rate': '9.434e-05', 'epoch': '0.221', 'num_input_tokens_seen': 13873808, 'train_runtime': '1.745e+04', 'train_tokens_per_second': '795.2'}
22%|██▏ | 4130/18688 [4:50:47<18:43:00, 4.63s/it] 22%|██▏ | 4131/18688 [4:50:51<17:30:10, 4.33s/it] 22%|██▏ | 4132/18688 [4:50:56<18:05:36, 4.47s/it] 22%|██▏ | 4133/18688 [4:50:59<17:05:15, 4.23s/it] 22%|██▏ | 4134/18688 [4:51:03<16:27:22, 4.07s/it] 22%|██▏ | 4135/18688 [4:51:07<16:31:50, 4.09s/it] 22%|██▏ | 4136/18688 [4:51:12<17:07:59, 4.24s/it] 22%|██▏ | 4137/18688 [4:51:16<17:23:03, 4.30s/it] 22%|██▏ | 4138/18688 [4:51:21<17:40:44, 4.37s/it] 22%|██▏ | 4139/18688 [4:51:25<17:55:43, 4.44s/it] 22%|██▏ | 4140/18688 [4:51:29<17:04:01, 4.22s/it] {'loss': '1.037', 'grad_norm': '1.133', 'learning_rate': '9.43e-05', 'epoch': '0.2215', 'num_input_tokens_seen': 13908144, 'train_runtime': '1.749e+04', 'train_tokens_per_second': '795.2'}
22%|██▏ | 4140/18688 [4:51:29<17:04:01, 4.22s/it] 22%|██▏ | 4141/18688 [4:51:33<16:52:47, 4.18s/it] 22%|██▏ | 4142/18688 [4:51:38<17:14:14, 4.27s/it] 22%|██▏ | 4143/18688 [4:51:43<18:29:00, 4.57s/it] 22%|██▏ | 4144/18688 [4:51:47<17:41:31, 4.38s/it] 22%|██▏ | 4145/18688 [4:51:51<17:19:02, 4.29s/it] 22%|██▏ | 4146/18688 [4:51:55<16:45:18, 4.15s/it] 22%|██▏ | 4147/18688 [4:51:59<16:17:19, 4.03s/it] 22%|██▏ | 4148/18688 [4:52:03<17:06:32, 4.24s/it] 22%|██▏ | 4149/18688 [4:52:07<16:16:42, 4.03s/it] 22%|██▏ | 4150/18688 [4:52:10<15:43:55, 3.90s/it] {'loss': '0.9222', 'grad_norm': '0.9551', 'learning_rate': '9.425e-05', 'epoch': '0.2221', 'num_input_tokens_seen': 13941400, 'train_runtime': '1.753e+04', 'train_tokens_per_second': '795.2'}
22%|██▏ | 4150/18688 [4:52:10<15:43:55, 3.90s/it] 22%|██▏ | 4151/18688 [4:52:14<15:14:32, 3.77s/it] 22%|██▏ | 4152/18688 [4:52:18<15:46:43, 3.91s/it] 22%|██▏ | 4153/18688 [4:52:22<15:43:42, 3.90s/it] 22%|██▏ | 4154/18688 [4:52:26<15:52:26, 3.93s/it] 22%|██▏ | 4155/18688 [4:52:30<15:40:58, 3.88s/it] 22%|██▏ | 4156/18688 [4:52:34<16:19:55, 4.05s/it] 22%|██▏ | 4157/18688 [4:52:38<16:39:59, 4.13s/it] 22%|██▏ | 4158/18688 [4:52:42<16:11:23, 4.01s/it] 22%|██▏ | 4159/18688 [4:52:48<18:15:25, 4.52s/it] 22%|██▏ | 4160/18688 [4:52:52<18:05:50, 4.48s/it] {'loss': '1.012', 'grad_norm': '1.199', 'learning_rate': '9.421e-05', 'epoch': '0.2226', 'num_input_tokens_seen': 13975592, 'train_runtime': '1.757e+04', 'train_tokens_per_second': '795.3'}
22%|██▏ | 4160/18688 [4:52:52<18:05:50, 4.48s/it] 22%|██▏ | 4161/18688 [4:52:57<18:32:01, 4.59s/it] 22%|██▏ | 4162/18688 [4:53:01<18:01:23, 4.47s/it] 22%|██▏ | 4163/18688 [4:53:05<17:07:09, 4.24s/it] 22%|██▏ | 4164/18688 [4:53:10<17:44:00, 4.40s/it] 22%|██▏ | 4165/18688 [4:53:14<17:35:54, 4.36s/it] 22%|██▏ | 4166/18688 [4:53:18<17:28:33, 4.33s/it] 22%|██▏ | 4167/18688 [4:53:23<17:17:41, 4.29s/it] 22%|██▏ | 4168/18688 [4:53:26<16:48:25, 4.17s/it] 22%|██▏ | 4169/18688 [4:53:31<16:54:54, 4.19s/it] 22%|██▏ | 4170/18688 [4:53:35<17:00:09, 4.22s/it] {'loss': '0.9434', 'grad_norm': '1.06', 'learning_rate': '9.417e-05', 'epoch': '0.2231', 'num_input_tokens_seen': 14010624, 'train_runtime': '1.762e+04', 'train_tokens_per_second': '795.4'}
22%|██▏ | 4170/18688 [4:53:35<17:00:09, 4.22s/it] 22%|██▏ | 4171/18688 [4:53:39<16:59:24, 4.21s/it] 22%|██▏ | 4172/18688 [4:53:44<17:10:53, 4.26s/it] 22%|██▏ | 4173/18688 [4:53:48<17:02:49, 4.23s/it] 22%|██▏ | 4174/18688 [4:53:53<17:52:15, 4.43s/it] 22%|██▏ | 4175/18688 [4:53:56<17:07:51, 4.25s/it] 22%|██▏ | 4176/18688 [4:54:01<16:58:17, 4.21s/it] 22%|██▏ | 4177/18688 [4:54:05<16:43:29, 4.15s/it] 22%|██▏ | 4178/18688 [4:54:09<16:32:53, 4.11s/it] 22%|██▏ | 4179/18688 [4:54:13<16:57:28, 4.21s/it] 22%|██▏ | 4180/18688 [4:54:17<16:27:25, 4.08s/it] {'loss': '0.9403', 'grad_norm': '1.24', 'learning_rate': '9.412e-05', 'epoch': '0.2237', 'num_input_tokens_seen': 14042928, 'train_runtime': '1.766e+04', 'train_tokens_per_second': '795.3'}
22%|██▏ | 4180/18688 [4:54:17<16:27:25, 4.08s/it] 22%|██▏ | 4181/18688 [4:54:21<16:40:17, 4.14s/it] 22%|██▏ | 4182/18688 [4:54:26<17:22:45, 4.31s/it] 22%|██▏ | 4183/18688 [4:54:30<16:44:55, 4.16s/it] 22%|██▏ | 4184/18688 [4:54:34<17:03:34, 4.23s/it] 22%|██▏ | 4185/18688 [4:54:38<16:57:55, 4.21s/it] 22%|██▏ | 4186/18688 [4:54:43<17:39:45, 4.38s/it] 22%|██▏ | 4187/18688 [4:54:46<16:33:14, 4.11s/it] 22%|██▏ | 4188/18688 [4:54:50<16:15:44, 4.04s/it] 22%|██▏ | 4189/18688 [4:54:55<16:32:03, 4.11s/it] 22%|██▏ | 4190/18688 [4:54:58<15:50:16, 3.93s/it] {'loss': '0.9836', 'grad_norm': '1.702', 'learning_rate': '9.408e-05', 'epoch': '0.2242', 'num_input_tokens_seen': 14075792, 'train_runtime': '1.77e+04', 'train_tokens_per_second': '795.3'}
22%|██▏ | 4190/18688 [4:54:58<15:50:16, 3.93s/it] 22%|██▏ | 4191/18688 [4:55:02<16:25:31, 4.08s/it] 22%|██▏ | 4192/18688 [4:55:08<17:48:24, 4.42s/it] 22%|██▏ | 4193/18688 [4:55:11<16:44:12, 4.16s/it] 22%|██▏ | 4194/18688 [4:55:15<16:23:07, 4.07s/it] 22%|██▏ | 4195/18688 [4:55:19<16:20:14, 4.06s/it] 22%|██▏ | 4196/18688 [4:55:23<16:29:10, 4.10s/it] 22%|██▏ | 4197/18688 [4:55:27<15:57:21, 3.96s/it] 22%|██▏ | 4198/18688 [4:55:31<15:32:03, 3.86s/it] 22%|██▏ | 4199/18688 [4:55:35<15:53:03, 3.95s/it] 22%|██▏ | 4200/18688 [4:55:39<15:48:06, 3.93s/it] {'loss': '0.9678', 'grad_norm': '1.299', 'learning_rate': '9.404e-05', 'epoch': '0.2247', 'num_input_tokens_seen': 14106968, 'train_runtime': '1.774e+04', 'train_tokens_per_second': '795.2'}
22%|██▏ | 4200/18688 [4:55:39<15:48:06, 3.93s/it] 22%|██▏ | 4201/18688 [4:55:42<15:23:54, 3.83s/it] 22%|██▏ | 4202/18688 [4:55:46<15:33:44, 3.87s/it] 22%|██▏ | 4203/18688 [4:55:51<16:48:42, 4.18s/it] 22%|██▏ | 4204/18688 [4:55:55<16:50:48, 4.19s/it] 23%|██▎ | 4205/18688 [4:55:59<16:48:00, 4.18s/it] 23%|██▎ | 4206/18688 [4:56:04<17:07:42, 4.26s/it] 23%|██▎ | 4207/18688 [4:56:08<16:27:43, 4.09s/it] 23%|██▎ | 4208/18688 [4:56:12<16:27:55, 4.09s/it] 23%|██▎ | 4209/18688 [4:56:15<15:45:03, 3.92s/it] 23%|██▎ | 4210/18688 [4:56:20<16:58:44, 4.22s/it] {'loss': '0.9422', 'grad_norm': '1.071', 'learning_rate': '9.399e-05', 'epoch': '0.2253', 'num_input_tokens_seen': 14138728, 'train_runtime': '1.778e+04', 'train_tokens_per_second': '795.2'}
23%|██▎ | 4210/18688 [4:56:20<16:58:44, 4.22s/it] 23%|██▎ | 4211/18688 [4:56:24<16:15:01, 4.04s/it] 23%|██▎ | 4212/18688 [4:56:28<16:51:06, 4.19s/it] 23%|██▎ | 4213/18688 [4:56:32<16:20:13, 4.06s/it] 23%|██▎ | 4214/18688 [4:56:36<16:25:57, 4.09s/it] 23%|██▎ | 4215/18688 [4:56:41<17:31:37, 4.36s/it] 23%|██▎ | 4216/18688 [4:56:45<16:52:16, 4.20s/it] 23%|██▎ | 4217/18688 [4:56:49<16:46:44, 4.17s/it] 23%|██▎ | 4218/18688 [4:56:54<17:46:41, 4.42s/it] 23%|██▎ | 4219/18688 [4:56:59<18:17:08, 4.55s/it] 23%|██▎ | 4220/18688 [4:57:03<18:09:56, 4.52s/it] {'loss': '0.965', 'grad_norm': '1.104', 'learning_rate': '9.395e-05', 'epoch': '0.2258', 'num_input_tokens_seen': 14173688, 'train_runtime': '1.782e+04', 'train_tokens_per_second': '795.2'}
23%|██▎ | 4220/18688 [4:57:03<18:09:56, 4.52s/it] 23%|██▎ | 4221/18688 [4:57:07<17:00:12, 4.23s/it] 23%|██▎ | 4222/18688 [4:57:12<17:23:10, 4.33s/it] 23%|██▎ | 4223/18688 [4:57:16<17:16:15, 4.30s/it] 23%|██▎ | 4224/18688 [4:57:20<17:40:45, 4.40s/it] 23%|██▎ | 4225/18688 [4:57:24<16:58:30, 4.23s/it] 23%|██▎ | 4226/18688 [4:57:28<16:25:52, 4.09s/it] 23%|██▎ | 4227/18688 [4:57:32<16:55:18, 4.21s/it] 23%|██▎ | 4228/18688 [4:57:37<17:40:15, 4.40s/it] 23%|██▎ | 4229/18688 [4:57:42<17:26:24, 4.34s/it] 23%|██▎ | 4230/18688 [4:57:45<16:39:52, 4.15s/it] {'loss': '0.9569', 'grad_norm': '1.195', 'learning_rate': '9.391e-05', 'epoch': '0.2264', 'num_input_tokens_seen': 14206008, 'train_runtime': '1.787e+04', 'train_tokens_per_second': '795.2'}
23%|██▎ | 4230/18688 [4:57:45<16:39:52, 4.15s/it] 23%|██▎ | 4231/18688 [4:57:50<17:31:41, 4.36s/it] 23%|██▎ | 4232/18688 [4:57:54<16:27:41, 4.10s/it] 23%|██▎ | 4233/18688 [4:57:58<16:17:07, 4.06s/it] 23%|██▎ | 4234/18688 [4:58:02<16:34:43, 4.13s/it] 23%|██▎ | 4235/18688 [4:58:06<16:40:42, 4.15s/it] 23%|██▎ | 4236/18688 [4:58:10<16:47:22, 4.18s/it] 23%|██▎ | 4237/18688 [4:58:16<18:10:49, 4.53s/it] 23%|██▎ | 4238/18688 [4:58:20<18:00:15, 4.49s/it] 23%|██▎ | 4239/18688 [4:58:24<16:59:42, 4.23s/it] 23%|██▎ | 4240/18688 [4:58:28<17:01:21, 4.24s/it] {'loss': '0.9211', 'grad_norm': '1.197', 'learning_rate': '9.386e-05', 'epoch': '0.2269', 'num_input_tokens_seen': 14241608, 'train_runtime': '1.791e+04', 'train_tokens_per_second': '795.2'}
23%|██▎ | 4240/18688 [4:58:28<17:01:21, 4.24s/it] 23%|██▎ | 4241/18688 [4:58:32<16:55:06, 4.22s/it] 23%|██▎ | 4242/18688 [4:58:36<16:39:29, 4.15s/it] 23%|██▎ | 4243/18688 [4:58:40<16:56:57, 4.22s/it] 23%|██▎ | 4244/18688 [4:58:46<18:21:30, 4.58s/it] 23%|██▎ | 4245/18688 [4:58:50<17:42:51, 4.42s/it] 23%|██▎ | 4246/18688 [4:58:54<17:22:26, 4.33s/it] 23%|██▎ | 4247/18688 [4:58:58<16:29:42, 4.11s/it] 23%|██▎ | 4248/18688 [4:59:02<16:59:16, 4.24s/it] 23%|██▎ | 4249/18688 [4:59:06<16:01:52, 4.00s/it] 23%|██▎ | 4250/18688 [4:59:10<15:58:09, 3.98s/it] {'loss': '0.9509', 'grad_norm': '1.216', 'learning_rate': '9.382e-05', 'epoch': '0.2274', 'num_input_tokens_seen': 14275344, 'train_runtime': '1.795e+04', 'train_tokens_per_second': '795.3'}
23%|██▎ | 4250/18688 [4:59:10<15:58:09, 3.98s/it] 23%|██▎ | 4251/18688 [4:59:14<16:45:20, 4.18s/it] 23%|██▎ | 4252/18688 [4:59:18<16:37:00, 4.14s/it] 23%|██▎ | 4253/18688 [4:59:23<16:57:58, 4.23s/it] 23%|██▎ | 4254/18688 [4:59:27<17:13:06, 4.29s/it] 23%|██▎ | 4255/18688 [4:59:31<17:07:22, 4.27s/it] 23%|██▎ | 4256/18688 [4:59:35<16:57:27, 4.23s/it] 23%|██▎ | 4257/18688 [4:59:41<18:15:00, 4.55s/it] 23%|██▎ | 4258/18688 [4:59:45<17:53:13, 4.46s/it] 23%|██▎ | 4259/18688 [4:59:49<16:45:59, 4.18s/it] 23%|██▎ | 4260/18688 [4:59:53<16:48:28, 4.19s/it] {'loss': '0.9887', 'grad_norm': '1.224', 'learning_rate': '9.378e-05', 'epoch': '0.228', 'num_input_tokens_seen': 14310808, 'train_runtime': '1.799e+04', 'train_tokens_per_second': '795.3'}
23%|██▎ | 4260/18688 [4:59:53<16:48:28, 4.19s/it] 23%|██▎ | 4261/18688 [4:59:57<17:06:34, 4.27s/it] 23%|██▎ | 4262/18688 [5:00:01<16:42:24, 4.17s/it] 23%|██▎ | 4263/18688 [5:00:05<16:34:48, 4.14s/it] 23%|██▎ | 4264/18688 [5:00:09<15:55:58, 3.98s/it] 23%|██▎ | 4265/18688 [5:00:13<16:05:34, 4.02s/it] 23%|██▎ | 4266/18688 [5:00:18<16:55:15, 4.22s/it] 23%|██▎ | 4267/18688 [5:00:22<17:24:11, 4.34s/it] 23%|██▎ | 4268/18688 [5:00:27<17:21:45, 4.33s/it] 23%|██▎ | 4269/18688 [5:00:31<16:52:26, 4.21s/it] 23%|██▎ | 4270/18688 [5:00:35<17:37:15, 4.40s/it] {'loss': '0.9686', 'grad_norm': '1.334', 'learning_rate': '9.373e-05', 'epoch': '0.2285', 'num_input_tokens_seen': 14345096, 'train_runtime': '1.804e+04', 'train_tokens_per_second': '795.4'}
23%|██▎ | 4270/18688 [5:00:35<17:37:15, 4.40s/it] 23%|██▎ | 4271/18688 [5:00:40<18:06:32, 4.52s/it] 23%|██▎ | 4272/18688 [5:00:44<17:24:49, 4.35s/it] 23%|██▎ | 4273/18688 [5:00:48<17:10:09, 4.29s/it] 23%|██▎ | 4274/18688 [5:00:52<16:21:44, 4.09s/it] 23%|██▎ | 4275/18688 [5:00:56<16:11:21, 4.04s/it] 23%|██▎ | 4276/18688 [5:00:59<15:37:56, 3.90s/it] 23%|██▎ | 4277/18688 [5:01:03<15:29:18, 3.87s/it] 23%|██▎ | 4278/18688 [5:01:07<15:47:48, 3.95s/it] 23%|██▎ | 4279/18688 [5:01:11<15:45:44, 3.94s/it] 23%|██▎ | 4280/18688 [5:01:15<15:23:49, 3.85s/it] {'loss': '0.9787', 'grad_norm': '1.226', 'learning_rate': '9.369e-05', 'epoch': '0.229', 'num_input_tokens_seen': 14376600, 'train_runtime': '1.808e+04', 'train_tokens_per_second': '795.4'}
23%|██▎ | 4280/18688 [5:01:15<15:23:49, 3.85s/it] 23%|██▎ | 4281/18688 [5:01:19<15:16:35, 3.82s/it] 23%|██▎ | 4282/18688 [5:01:23<15:29:41, 3.87s/it] 23%|██▎ | 4283/18688 [5:01:26<15:14:51, 3.81s/it] 23%|██▎ | 4284/18688 [5:01:30<15:16:59, 3.82s/it] 23%|██▎ | 4285/18688 [5:01:34<15:50:19, 3.96s/it] 23%|██▎ | 4286/18688 [5:01:39<16:12:40, 4.05s/it] 23%|██▎ | 4287/18688 [5:01:42<15:49:16, 3.96s/it] 23%|██▎ | 4288/18688 [5:01:46<15:13:43, 3.81s/it] 23%|██▎ | 4289/18688 [5:01:50<15:14:14, 3.81s/it] 23%|██▎ | 4290/18688 [5:01:53<14:48:04, 3.70s/it] {'loss': '0.9404', 'grad_norm': '1.284', 'learning_rate': '9.364e-05', 'epoch': '0.2296', 'num_input_tokens_seen': 14405560, 'train_runtime': '1.811e+04', 'train_tokens_per_second': '795.3'}
23%|██▎ | 4290/18688 [5:01:53<14:48:04, 3.70s/it] 23%|██▎ | 4291/18688 [5:01:57<15:31:22, 3.88s/it] 23%|██▎ | 4292/18688 [5:02:02<16:08:52, 4.04s/it] 23%|██▎ | 4293/18688 [5:02:05<15:42:02, 3.93s/it] 23%|██▎ | 4294/18688 [5:02:09<15:27:05, 3.86s/it] 23%|██▎ | 4295/18688 [5:02:13<15:38:19, 3.91s/it] 23%|██▎ | 4296/18688 [5:02:17<15:27:49, 3.87s/it] 23%|██▎ | 4297/18688 [5:02:21<16:12:31, 4.05s/it] 23%|██▎ | 4298/18688 [5:02:25<16:09:07, 4.04s/it] 23%|██▎ | 4299/18688 [5:02:29<15:44:02, 3.94s/it] 23%|██▎ | 4300/18688 [5:02:33<15:41:58, 3.93s/it] {'loss': '0.8987', 'grad_norm': '1.092', 'learning_rate': '9.36e-05', 'epoch': '0.2301', 'num_input_tokens_seen': 14436256, 'train_runtime': '1.815e+04', 'train_tokens_per_second': '795.2'}
23%|██▎ | 4300/18688 [5:02:33<15:41:58, 3.93s/it] 23%|██▎ | 4301/18688 [5:02:38<16:36:03, 4.15s/it] 23%|██▎ | 4302/18688 [5:02:42<16:46:57, 4.20s/it] 23%|██▎ | 4303/18688 [5:02:46<16:07:53, 4.04s/it] 23%|██▎ | 4304/18688 [5:02:51<17:19:33, 4.34s/it] 23%|██▎ | 4305/18688 [5:02:55<17:10:17, 4.30s/it] 23%|██▎ | 4306/18688 [5:03:00<18:30:25, 4.63s/it] 23%|██▎ | 4307/18688 [5:03:04<17:49:42, 4.46s/it] 23%|██▎ | 4308/18688 [5:03:09<17:53:25, 4.48s/it] 23%|██▎ | 4309/18688 [5:03:13<17:04:45, 4.28s/it] 23%|██▎ | 4310/18688 [5:03:16<16:14:57, 4.07s/it] {'loss': '0.9696', 'grad_norm': '1.008', 'learning_rate': '9.355e-05', 'epoch': '0.2306', 'num_input_tokens_seen': 14470816, 'train_runtime': '1.82e+04', 'train_tokens_per_second': '795.2'}
23%|██▎ | 4310/18688 [5:03:16<16:14:57, 4.07s/it] 23%|██▎ | 4311/18688 [5:03:20<16:07:03, 4.04s/it] 23%|██▎ | 4312/18688 [5:03:24<15:24:51, 3.86s/it] 23%|██▎ | 4313/18688 [5:03:28<15:33:50, 3.90s/it] 23%|██▎ | 4314/18688 [5:03:31<15:15:44, 3.82s/it] 23%|██▎ | 4315/18688 [5:03:36<15:49:53, 3.97s/it] 23%|██▎ | 4316/18688 [5:03:40<16:22:42, 4.10s/it] 23%|██▎ | 4317/18688 [5:03:44<16:28:09, 4.13s/it] 23%|██▎ | 4318/18688 [5:03:48<16:05:28, 4.03s/it] 23%|██▎ | 4319/18688 [5:03:52<16:07:15, 4.04s/it] 23%|██▎ | 4320/18688 [5:03:57<17:09:27, 4.30s/it] {'loss': '0.9594', 'grad_norm': '1.249', 'learning_rate': '9.351e-05', 'epoch': '0.2312', 'num_input_tokens_seen': 14504184, 'train_runtime': '1.824e+04', 'train_tokens_per_second': '795.3'}
23%|██▎ | 4320/18688 [5:03:57<17:09:27, 4.30s/it] 23%|██▎ | 4321/18688 [5:04:02<17:29:05, 4.38s/it] 23%|██▎ | 4322/18688 [5:04:05<16:44:24, 4.19s/it] 23%|██▎ | 4323/18688 [5:04:10<17:05:30, 4.28s/it] 23%|██▎ | 4324/18688 [5:04:15<18:27:04, 4.62s/it] 23%|██▎ | 4325/18688 [5:04:19<17:52:38, 4.48s/it] 23%|██▎ | 4326/18688 [5:04:23<17:16:51, 4.33s/it] 23%|██▎ | 4327/18688 [5:04:28<17:16:33, 4.33s/it] 23%|██▎ | 4328/18688 [5:04:32<17:22:03, 4.35s/it] 23%|██▎ | 4329/18688 [5:04:36<16:27:09, 4.12s/it] 23%|██▎ | 4330/18688 [5:04:39<15:40:43, 3.93s/it] {'loss': '0.9395', 'grad_norm': '1.062', 'learning_rate': '9.346e-05', 'epoch': '0.2317', 'num_input_tokens_seen': 14538640, 'train_runtime': '1.828e+04', 'train_tokens_per_second': '795.3'}
23%|██▎ | 4330/18688 [5:04:39<15:40:43, 3.93s/it] 23%|██▎ | 4331/18688 [5:04:43<15:18:51, 3.84s/it] 23%|██▎ | 4332/18688 [5:04:47<15:31:31, 3.89s/it] 23%|██▎ | 4333/18688 [5:04:51<15:16:05, 3.83s/it] 23%|██▎ | 4334/18688 [5:04:55<16:14:04, 4.07s/it] 23%|██▎ | 4335/18688 [5:04:59<16:27:42, 4.13s/it] 23%|██▎ | 4336/18688 [5:05:03<16:18:06, 4.09s/it] 23%|██▎ | 4337/18688 [5:05:08<16:20:01, 4.10s/it] 23%|██▎ | 4338/18688 [5:05:13<17:19:48, 4.35s/it] 23%|██▎ | 4339/18688 [5:05:16<16:51:14, 4.23s/it] 23%|██▎ | 4340/18688 [5:05:20<16:26:28, 4.13s/it] {'loss': '0.9784', 'grad_norm': '1.998', 'learning_rate': '9.342e-05', 'epoch': '0.2322', 'num_input_tokens_seen': 14570032, 'train_runtime': '1.832e+04', 'train_tokens_per_second': '795.3'}
23%|██▎ | 4340/18688 [5:05:20<16:26:28, 4.13s/it] 23%|██▎ | 4341/18688 [5:05:25<16:29:29, 4.14s/it] 23%|██▎ | 4342/18688 [5:05:29<16:22:38, 4.11s/it] 23%|██▎ | 4343/18688 [5:05:32<15:50:35, 3.98s/it] 23%|██▎ | 4344/18688 [5:05:36<15:48:44, 3.97s/it] 23%|██▎ | 4345/18688 [5:05:40<15:57:06, 4.00s/it] 23%|██▎ | 4346/18688 [5:05:45<17:00:20, 4.27s/it] 23%|██▎ | 4347/18688 [5:05:49<16:46:44, 4.21s/it] 23%|██▎ | 4348/18688 [5:05:54<16:49:40, 4.22s/it] 23%|██▎ | 4349/18688 [5:05:58<16:36:26, 4.17s/it] 23%|██▎ | 4350/18688 [5:06:03<17:55:04, 4.50s/it] {'loss': '0.9467', 'grad_norm': '1.543', 'learning_rate': '9.337e-05', 'epoch': '0.2328', 'num_input_tokens_seen': 14605536, 'train_runtime': '1.836e+04', 'train_tokens_per_second': '795.4'}
23%|██▎ | 4350/18688 [5:06:03<17:55:04, 4.50s/it] 23%|██▎ | 4351/18688 [5:06:07<17:47:48, 4.47s/it] 23%|██▎ | 4352/18688 [5:06:12<18:10:09, 4.56s/it] 23%|██▎ | 4353/18688 [5:06:17<18:56:50, 4.76s/it] 23%|██▎ | 4354/18688 [5:06:22<18:39:35, 4.69s/it] 23%|██▎ | 4355/18688 [5:06:26<17:35:42, 4.42s/it] 23%|██▎ | 4356/18688 [5:06:30<17:45:11, 4.46s/it] 23%|██▎ | 4357/18688 [5:06:35<18:11:52, 4.57s/it] 23%|██▎ | 4358/18688 [5:06:39<17:06:18, 4.30s/it] 23%|██▎ | 4359/18688 [5:06:43<17:38:08, 4.43s/it] 23%|██▎ | 4360/18688 [5:06:47<16:24:19, 4.12s/it] {'loss': '0.9778', 'grad_norm': '1.444', 'learning_rate': '9.333e-05', 'epoch': '0.2333', 'num_input_tokens_seen': 14641800, 'train_runtime': '1.841e+04', 'train_tokens_per_second': '795.4'}
23%|██▎ | 4360/18688 [5:06:47<16:24:19, 4.12s/it] 23%|██▎ | 4361/18688 [5:06:51<16:22:45, 4.12s/it] 23%|██▎ | 4362/18688 [5:06:55<16:17:18, 4.09s/it] 23%|██▎ | 4363/18688 [5:06:59<15:50:18, 3.98s/it] 23%|██▎ | 4364/18688 [5:07:02<15:27:28, 3.88s/it] 23%|██▎ | 4365/18688 [5:07:06<15:26:22, 3.88s/it] 23%|██▎ | 4366/18688 [5:07:10<15:00:15, 3.77s/it] 23%|██▎ | 4367/18688 [5:07:14<15:27:04, 3.88s/it] 23%|██▎ | 4368/18688 [5:07:19<16:42:19, 4.20s/it] 23%|██▎ | 4369/18688 [5:07:23<16:30:59, 4.15s/it] 23%|██▎ | 4370/18688 [5:07:26<15:49:02, 3.98s/it] {'loss': '0.9302', 'grad_norm': '1.185', 'learning_rate': '9.328e-05', 'epoch': '0.2338', 'num_input_tokens_seen': 14674688, 'train_runtime': '1.845e+04', 'train_tokens_per_second': '795.5'}
23%|██▎ | 4370/18688 [5:07:26<15:49:02, 3.98s/it] 23%|██▎ | 4371/18688 [5:07:31<17:09:51, 4.32s/it] 23%|██▎ | 4372/18688 [5:07:36<16:56:24, 4.26s/it] 23%|██▎ | 4373/18688 [5:07:40<16:40:32, 4.19s/it] 23%|██▎ | 4374/18688 [5:07:43<16:04:21, 4.04s/it] 23%|██▎ | 4375/18688 [5:07:47<15:40:33, 3.94s/it] 23%|██▎ | 4376/18688 [5:07:51<16:10:07, 4.07s/it] 23%|██▎ | 4377/18688 [5:07:55<15:25:43, 3.88s/it] 23%|██▎ | 4378/18688 [5:07:59<15:20:32, 3.86s/it] 23%|██▎ | 4379/18688 [5:08:02<15:18:28, 3.85s/it] 23%|██▎ | 4380/18688 [5:08:06<15:11:27, 3.82s/it] {'loss': '0.9553', 'grad_norm': '1.447', 'learning_rate': '9.324e-05', 'epoch': '0.2344', 'num_input_tokens_seen': 14705816, 'train_runtime': '1.849e+04', 'train_tokens_per_second': '795.5'}
23%|██▎ | 4380/18688 [5:08:06<15:11:27, 3.82s/it] 23%|██▎ | 4381/18688 [5:08:11<16:18:07, 4.10s/it] 23%|██▎ | 4382/18688 [5:08:15<16:12:32, 4.08s/it] 23%|██▎ | 4383/18688 [5:08:19<15:45:28, 3.97s/it] 23%|██▎ | 4384/18688 [5:08:24<16:59:34, 4.28s/it] 23%|██▎ | 4385/18688 [5:08:28<16:34:18, 4.17s/it] 23%|██▎ | 4386/18688 [5:08:33<17:31:55, 4.41s/it] 23%|██▎ | 4387/18688 [5:08:37<17:12:47, 4.33s/it] 23%|██▎ | 4388/18688 [5:08:41<17:22:58, 4.38s/it] 23%|██▎ | 4389/18688 [5:08:47<19:02:44, 4.80s/it] 23%|██▎ | 4390/18688 [5:08:52<18:47:11, 4.73s/it] {'loss': '0.9924', 'grad_norm': '1.285', 'learning_rate': '9.319e-05', 'epoch': '0.2349', 'num_input_tokens_seen': 14744160, 'train_runtime': '1.853e+04', 'train_tokens_per_second': '795.6'}
23%|██▎ | 4390/18688 [5:08:52<18:47:11, 4.73s/it] 23%|██▎ | 4391/18688 [5:08:56<18:18:30, 4.61s/it] 24%|██▎ | 4392/18688 [5:09:00<18:04:43, 4.55s/it] 24%|██▎ | 4393/18688 [5:09:04<17:11:52, 4.33s/it] 24%|██▎ | 4394/18688 [5:09:08<16:35:35, 4.18s/it] 24%|██▎ | 4395/18688 [5:09:13<17:30:33, 4.41s/it] 24%|██▎ | 4396/18688 [5:09:17<16:52:00, 4.25s/it] 24%|██▎ | 4397/18688 [5:09:21<16:41:52, 4.21s/it] 24%|██▎ | 4398/18688 [5:09:26<17:59:03, 4.53s/it] 24%|██▎ | 4399/18688 [5:09:31<18:14:21, 4.60s/it] 24%|██▎ | 4400/18688 [5:09:35<17:46:23, 4.48s/it] {'loss': '1.031', 'grad_norm': '1.079', 'learning_rate': '9.314e-05', 'epoch': '0.2355', 'num_input_tokens_seen': 14780304, 'train_runtime': '1.858e+04', 'train_tokens_per_second': '795.7'}
24%|██▎ | 4400/18688 [5:09:35<17:46:23, 4.48s/it] 24%|██▎ | 4401/18688 [5:09:39<17:20:18, 4.37s/it] 24%|██▎ | 4402/18688 [5:09:44<17:48:12, 4.49s/it] 24%|██▎ | 4403/18688 [5:09:47<16:36:50, 4.19s/it] 24%|██▎ | 4404/18688 [5:09:52<16:39:07, 4.20s/it] 24%|██▎ | 4405/18688 [5:09:56<16:36:13, 4.18s/it] 24%|██▎ | 4406/18688 [5:10:01<17:46:22, 4.48s/it] 24%|██▎ | 4407/18688 [5:10:06<18:38:55, 4.70s/it] 24%|██▎ | 4408/18688 [5:10:10<18:02:51, 4.55s/it] 24%|██▎ | 4409/18688 [5:10:15<17:32:21, 4.42s/it] 24%|██▎ | 4410/18688 [5:10:19<17:45:44, 4.48s/it] {'loss': '1.005', 'grad_norm': '1.357', 'learning_rate': '9.31e-05', 'epoch': '0.236', 'num_input_tokens_seen': 14818472, 'train_runtime': '1.862e+04', 'train_tokens_per_second': '795.9'}
24%|██▎ | 4410/18688 [5:10:19<17:45:44, 4.48s/it] 24%|██▎ | 4411/18688 [5:10:25<18:55:06, 4.77s/it] 24%|██▎ | 4412/18688 [5:10:28<17:31:07, 4.42s/it] 24%|██▎ | 4413/18688 [5:10:32<16:32:42, 4.17s/it] 24%|██▎ | 4414/18688 [5:10:36<16:30:23, 4.16s/it] 24%|██▎ | 4415/18688 [5:10:40<15:48:05, 3.99s/it] 24%|██▎ | 4416/18688 [5:10:44<15:55:27, 4.02s/it] 24%|██▎ | 4417/18688 [5:10:47<15:38:09, 3.94s/it] 24%|██▎ | 4418/18688 [5:10:52<15:54:50, 4.01s/it] 24%|██▎ | 4419/18688 [5:10:56<16:01:13, 4.04s/it] 24%|██▎ | 4420/18688 [5:11:00<16:23:52, 4.14s/it] {'loss': '0.9363', 'grad_norm': '1.208', 'learning_rate': '9.305e-05', 'epoch': '0.2365', 'num_input_tokens_seen': 14850488, 'train_runtime': '1.866e+04', 'train_tokens_per_second': '795.8'}
24%|██▎ | 4420/18688 [5:11:00<16:23:52, 4.14s/it] 24%|██▎ | 4421/18688 [5:11:04<16:43:44, 4.22s/it] 24%|██▎ | 4422/18688 [5:11:09<17:17:25, 4.36s/it] 24%|██▎ | 4423/18688 [5:11:13<16:34:18, 4.18s/it] 24%|██▎ | 4424/18688 [5:11:17<16:11:01, 4.08s/it] 24%|██▎ | 4425/18688 [5:11:22<17:06:21, 4.32s/it] 24%|██▎ | 4426/18688 [5:11:27<18:29:49, 4.67s/it] 24%|██▎ | 4427/18688 [5:11:32<18:10:44, 4.59s/it] 24%|██▎ | 4428/18688 [5:11:35<16:51:03, 4.25s/it] 24%|██▎ | 4429/18688 [5:11:39<16:38:26, 4.20s/it] 24%|██▎ | 4430/18688 [5:11:43<16:22:25, 4.13s/it] {'loss': '1.035', 'grad_norm': '1.018', 'learning_rate': '9.3e-05', 'epoch': '0.2371', 'num_input_tokens_seen': 14886120, 'train_runtime': '1.87e+04', 'train_tokens_per_second': '795.9'}
24%|██▎ | 4430/18688 [5:11:43<16:22:25, 4.13s/it] 24%|██▎ | 4431/18688 [5:11:47<15:35:35, 3.94s/it] 24%|██▎ | 4432/18688 [5:11:51<15:59:38, 4.04s/it] 24%|██▎ | 4433/18688 [5:11:55<15:45:54, 3.98s/it] 24%|██▎ | 4434/18688 [5:11:59<16:33:57, 4.18s/it] 24%|██▎ | 4435/18688 [5:12:03<16:07:44, 4.07s/it] 24%|██▎ | 4436/18688 [5:12:07<15:31:46, 3.92s/it] 24%|██▎ | 4437/18688 [5:12:11<16:05:25, 4.06s/it] 24%|██▎ | 4438/18688 [5:12:15<16:08:48, 4.08s/it] 24%|██▍ | 4439/18688 [5:12:19<15:30:24, 3.92s/it] 24%|██▍ | 4440/18688 [5:12:23<16:04:42, 4.06s/it] {'loss': '0.9696', 'grad_norm': '1.366', 'learning_rate': '9.296e-05', 'epoch': '0.2376', 'num_input_tokens_seen': 14916592, 'train_runtime': '1.874e+04', 'train_tokens_per_second': '795.8'}
24%|██▍ | 4440/18688 [5:12:23<16:04:42, 4.06s/it] 24%|██▍ | 4441/18688 [5:12:28<17:19:28, 4.38s/it] 24%|██▍ | 4442/18688 [5:12:32<16:21:12, 4.13s/it] 24%|██▍ | 4443/18688 [5:12:37<17:09:35, 4.34s/it] 24%|██▍ | 4444/18688 [5:12:41<16:46:15, 4.24s/it] 24%|██▍ | 4445/18688 [5:12:45<16:50:31, 4.26s/it] 24%|██▍ | 4446/18688 [5:12:49<16:10:01, 4.09s/it] 24%|██▍ | 4447/18688 [5:12:52<15:26:57, 3.91s/it] 24%|██▍ | 4448/18688 [5:12:56<15:02:04, 3.80s/it] 24%|██▍ | 4449/18688 [5:13:00<15:21:57, 3.88s/it] 24%|██▍ | 4450/18688 [5:13:04<15:18:01, 3.87s/it] {'loss': '0.9522', 'grad_norm': '1.286', 'learning_rate': '9.291e-05', 'epoch': '0.2381', 'num_input_tokens_seen': 14949064, 'train_runtime': '1.878e+04', 'train_tokens_per_second': '795.8'}
24%|██▍ | 4450/18688 [5:13:04<15:18:01, 3.87s/it] 24%|██▍ | 4451/18688 [5:13:08<16:14:30, 4.11s/it] 24%|██▍ | 4452/18688 [5:13:12<16:03:40, 4.06s/it] 24%|██▍ | 4453/18688 [5:13:17<16:30:26, 4.17s/it] 24%|██▍ | 4454/18688 [5:13:21<16:41:51, 4.22s/it] 24%|██▍ | 4455/18688 [5:13:26<17:55:28, 4.53s/it] 24%|██▍ | 4456/18688 [5:13:31<18:41:37, 4.73s/it] 24%|██▍ | 4457/18688 [5:13:35<17:47:50, 4.50s/it] 24%|██▍ | 4458/18688 [5:13:39<16:39:29, 4.21s/it] 24%|██▍ | 4459/18688 [5:13:42<15:49:53, 4.01s/it] 24%|██▍ | 4460/18688 [5:13:46<15:13:44, 3.85s/it] {'loss': '0.906', 'grad_norm': '1.25', 'learning_rate': '9.286e-05', 'epoch': '0.2387', 'num_input_tokens_seen': 14984544, 'train_runtime': '1.883e+04', 'train_tokens_per_second': '795.9'}
24%|██▍ | 4460/18688 [5:13:46<15:13:44, 3.85s/it] 24%|██▍ | 4461/18688 [5:13:50<15:45:48, 3.99s/it] 24%|██▍ | 4462/18688 [5:13:54<16:04:08, 4.07s/it] 24%|██▍ | 4463/18688 [5:13:58<15:31:18, 3.93s/it] 24%|██▍ | 4464/18688 [5:14:02<15:13:26, 3.85s/it] 24%|██▍ | 4465/18688 [5:14:05<14:54:56, 3.78s/it] 24%|██▍ | 4466/18688 [5:14:09<15:16:12, 3.87s/it] 24%|██▍ | 4467/18688 [5:14:13<14:48:45, 3.75s/it] 24%|██▍ | 4468/18688 [5:14:18<16:05:26, 4.07s/it] 24%|██▍ | 4469/18688 [5:14:21<15:29:39, 3.92s/it] 24%|██▍ | 4470/18688 [5:14:26<16:29:55, 4.18s/it] {'loss': '0.929', 'grad_norm': '1.315', 'learning_rate': '9.282e-05', 'epoch': '0.2392', 'num_input_tokens_seen': 15015072, 'train_runtime': '1.887e+04', 'train_tokens_per_second': '795.9'}
24%|██▍ | 4470/18688 [5:14:26<16:29:55, 4.18s/it] 24%|██▍ | 4471/18688 [5:14:30<16:40:37, 4.22s/it] 24%|██▍ | 4472/18688 [5:14:35<16:43:47, 4.24s/it] 24%|██▍ | 4473/18688 [5:14:40<18:00:31, 4.56s/it] 24%|██▍ | 4474/18688 [5:14:44<17:28:45, 4.43s/it] 24%|██▍ | 4475/18688 [5:14:48<17:01:52, 4.31s/it] 24%|██▍ | 4476/18688 [5:14:52<16:41:50, 4.23s/it] 24%|██▍ | 4477/18688 [5:14:57<17:49:55, 4.52s/it] 24%|██▍ | 4478/18688 [5:15:02<17:42:08, 4.48s/it] 24%|██▍ | 4479/18688 [5:15:06<16:56:39, 4.29s/it] 24%|██▍ | 4480/18688 [5:15:10<17:00:28, 4.31s/it] {'loss': '0.9694', 'grad_norm': '1.489', 'learning_rate': '9.277e-05', 'epoch': '0.2397', 'num_input_tokens_seen': 15052064, 'train_runtime': '1.891e+04', 'train_tokens_per_second': '796'}
24%|██▍ | 4480/18688 [5:15:10<17:00:28, 4.31s/it] 24%|██▍ | 4481/18688 [5:15:15<17:28:16, 4.43s/it] 24%|██▍ | 4482/18688 [5:15:19<17:05:13, 4.33s/it] 24%|██▍ | 4483/18688 [5:15:22<16:09:57, 4.10s/it] 24%|██▍ | 4484/18688 [5:15:26<15:27:31, 3.92s/it] 24%|██▍ | 4485/18688 [5:15:30<16:03:10, 4.07s/it] 24%|██▍ | 4486/18688 [5:15:36<17:27:14, 4.42s/it] 24%|██▍ | 4487/18688 [5:15:39<16:43:32, 4.24s/it] 24%|██▍ | 4488/18688 [5:15:44<16:53:56, 4.28s/it] 24%|██▍ | 4489/18688 [5:15:49<17:42:47, 4.49s/it] 24%|██▍ | 4490/18688 [5:15:52<16:47:04, 4.26s/it] {'loss': '0.982', 'grad_norm': '1.062', 'learning_rate': '9.272e-05', 'epoch': '0.2403', 'num_input_tokens_seen': 15086400, 'train_runtime': '1.895e+04', 'train_tokens_per_second': '796'}
24%|██▍ | 4490/18688 [5:15:52<16:47:04, 4.26s/it] 24%|██▍ | 4491/18688 [5:15:56<16:30:10, 4.18s/it] 24%|██▍ | 4492/18688 [5:16:00<15:37:43, 3.96s/it] 24%|██▍ | 4493/18688 [5:16:03<15:13:43, 3.86s/it] 24%|██▍ | 4494/18688 [5:16:09<16:53:42, 4.29s/it] 24%|██▍ | 4495/18688 [5:16:13<16:24:19, 4.16s/it] 24%|██▍ | 4496/18688 [5:16:16<15:34:00, 3.95s/it] 24%|██▍ | 4497/18688 [5:16:20<15:17:01, 3.88s/it] 24%|██▍ | 4498/18688 [5:16:24<15:30:29, 3.93s/it] 24%|██▍ | 4499/18688 [5:16:28<16:18:55, 4.14s/it] 24%|██▍ | 4500/18688 [5:16:33<16:31:13, 4.19s/it] {'loss': '0.9599', 'grad_norm': '1.606', 'learning_rate': '9.267e-05', 'epoch': '0.2408', 'num_input_tokens_seen': 15120512, 'train_runtime': '1.899e+04', 'train_tokens_per_second': '796.1'}
24%|██▍ | 4500/18688 [5:16:33<16:31:13, 4.19s/it] 24%|██▍ | 4501/18688 [5:16:37<17:00:27, 4.32s/it] 24%|██▍ | 4502/18688 [5:16:42<17:24:51, 4.42s/it] 24%|██▍ | 4503/18688 [5:16:46<16:32:08, 4.20s/it] 24%|██▍ | 4504/18688 [5:16:50<16:20:57, 4.15s/it] 24%|██▍ | 4505/18688 [5:16:54<16:03:03, 4.07s/it] 24%|██▍ | 4506/18688 [5:16:58<15:49:55, 4.02s/it] 24%|██▍ | 4507/18688 [5:17:02<16:44:57, 4.25s/it] 24%|██▍ | 4508/18688 [5:17:06<16:32:01, 4.20s/it] 24%|██▍ | 4509/18688 [5:17:10<16:10:11, 4.11s/it] 24%|██▍ | 4510/18688 [5:17:15<16:50:43, 4.28s/it] {'loss': '0.9931', 'grad_norm': '1.257', 'learning_rate': '9.263e-05', 'epoch': '0.2413', 'num_input_tokens_seen': 15155280, 'train_runtime': '1.904e+04', 'train_tokens_per_second': '796.2'}
24%|██▍ | 4510/18688 [5:17:15<16:50:43, 4.28s/it] 24%|██▍ | 4511/18688 [5:17:19<16:41:21, 4.24s/it] 24%|██▍ | 4512/18688 [5:17:24<17:13:20, 4.37s/it] 24%|██▍ | 4513/18688 [5:17:28<17:13:13, 4.37s/it] 24%|██▍ | 4514/18688 [5:17:33<17:54:45, 4.55s/it] 24%|██▍ | 4515/18688 [5:17:38<18:15:09, 4.64s/it] 24%|██▍ | 4516/18688 [5:17:42<17:02:21, 4.33s/it] 24%|██▍ | 4517/18688 [5:17:46<16:44:02, 4.25s/it] 24%|██▍ | 4518/18688 [5:17:49<16:01:45, 4.07s/it] 24%|██▍ | 4519/18688 [5:17:53<15:26:58, 3.93s/it] 24%|██▍ | 4520/18688 [5:17:57<15:37:27, 3.97s/it] {'loss': '0.9504', 'grad_norm': '1.446', 'learning_rate': '9.258e-05', 'epoch': '0.2419', 'num_input_tokens_seen': 15189120, 'train_runtime': '1.908e+04', 'train_tokens_per_second': '796.2'}
24%|██▍ | 4520/18688 [5:17:57<15:37:27, 3.97s/it] 24%|██▍ | 4521/18688 [5:18:02<16:31:29, 4.20s/it] 24%|██▍ | 4522/18688 [5:18:06<16:33:53, 4.21s/it] 24%|██▍ | 4523/18688 [5:18:10<15:51:20, 4.03s/it] 24%|██▍ | 4524/18688 [5:18:14<16:17:55, 4.14s/it] 24%|██▍ | 4525/18688 [5:18:19<16:52:05, 4.29s/it] 24%|██▍ | 4526/18688 [5:18:23<16:41:53, 4.24s/it] 24%|██▍ | 4527/18688 [5:18:27<16:44:35, 4.26s/it] 24%|██▍ | 4528/18688 [5:18:31<16:23:42, 4.17s/it] 24%|██▍ | 4529/18688 [5:18:36<17:02:39, 4.33s/it] 24%|██▍ | 4530/18688 [5:18:40<16:42:50, 4.25s/it] {'loss': '0.9346', 'grad_norm': '1.656', 'learning_rate': '9.253e-05', 'epoch': '0.2424', 'num_input_tokens_seen': 15223632, 'train_runtime': '1.912e+04', 'train_tokens_per_second': '796.2'}
24%|██▍ | 4530/18688 [5:18:40<16:42:50, 4.25s/it] 24%|██▍ | 4531/18688 [5:18:43<15:48:49, 4.02s/it] 24%|██▍ | 4532/18688 [5:18:47<15:40:28, 3.99s/it] 24%|██▍ | 4533/18688 [5:18:52<16:07:54, 4.10s/it] 24%|██▍ | 4534/18688 [5:18:55<15:50:27, 4.03s/it] 24%|██▍ | 4535/18688 [5:19:00<16:04:25, 4.09s/it] 24%|██▍ | 4536/18688 [5:19:04<16:13:38, 4.13s/it] 24%|██▍ | 4537/18688 [5:19:08<16:16:11, 4.14s/it] 24%|██▍ | 4538/18688 [5:19:12<16:00:57, 4.07s/it] 24%|██▍ | 4539/18688 [5:19:16<15:25:47, 3.93s/it] 24%|██▍ | 4540/18688 [5:19:20<15:34:03, 3.96s/it] {'loss': '0.9844', 'grad_norm': '1.164', 'learning_rate': '9.248e-05', 'epoch': '0.2429', 'num_input_tokens_seen': 15255144, 'train_runtime': '1.916e+04', 'train_tokens_per_second': '796.2'}
24%|██▍ | 4540/18688 [5:19:20<15:34:03, 3.96s/it] 24%|██▍ | 4541/18688 [5:19:24<15:40:24, 3.99s/it] 24%|██▍ | 4542/18688 [5:19:29<16:56:48, 4.31s/it] 24%|██▍ | 4543/18688 [5:19:33<16:40:27, 4.24s/it] 24%|██▍ | 4544/18688 [5:19:37<16:34:39, 4.22s/it] 24%|██▍ | 4545/18688 [5:19:41<15:56:29, 4.06s/it] 24%|██▍ | 4546/18688 [5:19:45<16:14:52, 4.14s/it] 24%|██▍ | 4547/18688 [5:19:49<15:38:00, 3.98s/it] 24%|██▍ | 4548/18688 [5:19:52<15:18:09, 3.90s/it] 24%|██▍ | 4549/18688 [5:19:56<15:29:24, 3.94s/it] 24%|██▍ | 4550/18688 [5:20:02<17:10:03, 4.37s/it] {'loss': '0.9821', 'grad_norm': '1.177', 'learning_rate': '9.243e-05', 'epoch': '0.2435', 'num_input_tokens_seen': 15289496, 'train_runtime': '1.92e+04', 'train_tokens_per_second': '796.2'}
24%|██▍ | 4550/18688 [5:20:02<17:10:03, 4.37s/it] 24%|██▍ | 4551/18688 [5:20:06<16:50:15, 4.29s/it] 24%|██▍ | 4552/18688 [5:20:10<16:39:30, 4.24s/it] 24%|██▍ | 4553/18688 [5:20:14<16:27:55, 4.19s/it] 24%|██▍ | 4554/18688 [5:20:18<16:33:59, 4.22s/it] 24%|██▍ | 4555/18688 [5:20:22<16:00:55, 4.08s/it] 24%|██▍ | 4556/18688 [5:20:26<16:05:24, 4.10s/it] 24%|██▍ | 4557/18688 [5:20:30<15:45:00, 4.01s/it] 24%|██▍ | 4558/18688 [5:20:34<15:45:57, 4.02s/it] 24%|██▍ | 4559/18688 [5:20:38<15:18:36, 3.90s/it] 24%|██▍ | 4560/18688 [5:20:41<15:12:36, 3.88s/it] {'loss': '0.9172', 'grad_norm': '1.217', 'learning_rate': '9.239e-05', 'epoch': '0.244', 'num_input_tokens_seen': 15321784, 'train_runtime': '1.924e+04', 'train_tokens_per_second': '796.3'}
24%|██▍ | 4560/18688 [5:20:41<15:12:36, 3.88s/it] 24%|██▍ | 4561/18688 [5:20:45<14:46:24, 3.76s/it] 24%|██▍ | 4562/18688 [5:20:49<15:17:09, 3.90s/it] 24%|██▍ | 4563/18688 [5:20:53<14:59:51, 3.82s/it] 24%|██▍ | 4564/18688 [5:20:57<15:01:58, 3.83s/it] 24%|██▍ | 4565/18688 [5:21:00<14:44:32, 3.76s/it] 24%|██▍ | 4566/18688 [5:21:04<14:35:21, 3.72s/it] 24%|██▍ | 4567/18688 [5:21:09<16:08:47, 4.12s/it] 24%|██▍ | 4568/18688 [5:21:13<16:09:10, 4.12s/it] 24%|██▍ | 4569/18688 [5:21:17<15:32:37, 3.96s/it] 24%|██▍ | 4570/18688 [5:21:21<15:49:55, 4.04s/it] {'loss': '0.9353', 'grad_norm': '1.298', 'learning_rate': '9.234e-05', 'epoch': '0.2445', 'num_input_tokens_seen': 15351872, 'train_runtime': '1.928e+04', 'train_tokens_per_second': '796.2'}
24%|██▍ | 4570/18688 [5:21:21<15:49:55, 4.04s/it] 24%|██▍ | 4571/18688 [5:21:25<15:55:17, 4.06s/it] 24%|██▍ | 4572/18688 [5:21:29<16:17:10, 4.15s/it] 24%|██▍ | 4573/18688 [5:21:33<15:55:22, 4.06s/it] 24%|██▍ | 4574/18688 [5:21:37<16:01:54, 4.09s/it] 24%|██▍ | 4575/18688 [5:21:41<15:25:16, 3.93s/it] 24%|██▍ | 4576/18688 [5:21:45<15:37:45, 3.99s/it] 24%|██▍ | 4577/18688 [5:21:49<15:50:17, 4.04s/it] 24%|██▍ | 4578/18688 [5:21:54<16:12:15, 4.13s/it] 25%|██▍ | 4579/18688 [5:21:57<15:36:26, 3.98s/it] 25%|██▍ | 4580/18688 [5:22:02<17:01:53, 4.35s/it] {'loss': '0.9728', 'grad_norm': '1.185', 'learning_rate': '9.229e-05', 'epoch': '0.2451', 'num_input_tokens_seen': 15386312, 'train_runtime': '1.932e+04', 'train_tokens_per_second': '796.3'}
25%|██▍ | 4580/18688 [5:22:02<17:01:53, 4.35s/it] 25%|██▍ | 4581/18688 [5:22:07<17:56:41, 4.58s/it] 25%|██▍ | 4582/18688 [5:22:13<18:35:26, 4.74s/it] 25%|██▍ | 4583/18688 [5:22:17<17:44:54, 4.53s/it] 25%|██▍ | 4584/18688 [5:22:21<17:57:31, 4.58s/it] 25%|██▍ | 4585/18688 [5:22:26<17:29:05, 4.46s/it] 25%|██▍ | 4586/18688 [5:22:30<17:19:22, 4.42s/it] 25%|██▍ | 4587/18688 [5:22:34<16:33:39, 4.23s/it] 25%|██▍ | 4588/18688 [5:22:37<15:44:09, 4.02s/it] 25%|██▍ | 4589/18688 [5:22:41<15:05:51, 3.86s/it] 25%|██▍ | 4590/18688 [5:22:44<14:44:29, 3.76s/it] {'loss': '0.9433', 'grad_norm': '1.111', 'learning_rate': '9.224e-05', 'epoch': '0.2456', 'num_input_tokens_seen': 15420224, 'train_runtime': '1.936e+04', 'train_tokens_per_second': '796.3'}
25%|██▍ | 4590/18688 [5:22:44<14:44:29, 3.76s/it] 25%|██▍ | 4591/18688 [5:22:49<15:24:19, 3.93s/it] 25%|██▍ | 4592/18688 [5:22:52<14:53:20, 3.80s/it] 25%|██▍ | 4593/18688 [5:22:56<15:39:08, 4.00s/it] 25%|██▍ | 4594/18688 [5:23:00<15:02:05, 3.84s/it] 25%|██▍ | 4595/18688 [5:23:04<14:57:13, 3.82s/it] 25%|██▍ | 4596/18688 [5:23:08<15:36:23, 3.99s/it] 25%|██▍ | 4597/18688 [5:23:12<15:19:12, 3.91s/it] 25%|██▍ | 4598/18688 [5:23:16<15:12:33, 3.89s/it] 25%|██▍ | 4599/18688 [5:23:19<14:46:30, 3.78s/it] 25%|██▍ | 4600/18688 [5:23:24<15:38:07, 4.00s/it] {'loss': '0.9512', 'grad_norm': '1.275', 'learning_rate': '9.219e-05', 'epoch': '0.2462', 'num_input_tokens_seen': 15452360, 'train_runtime': '1.94e+04', 'train_tokens_per_second': '796.3'}
25%|██▍ | 4600/18688 [5:23:24<15:38:07, 4.00s/it] 25%|██▍ | 4601/18688 [5:23:29<16:36:56, 4.25s/it] 25%|██▍ | 4602/18688 [5:23:33<16:45:01, 4.28s/it] 25%|██▍ | 4603/18688 [5:23:37<16:31:35, 4.22s/it] 25%|██▍ | 4604/18688 [5:23:41<16:29:49, 4.22s/it] 25%|██▍ | 4605/18688 [5:23:46<16:41:14, 4.27s/it] 25%|██▍ | 4606/18688 [5:23:49<16:15:45, 4.16s/it] 25%|██▍ | 4607/18688 [5:23:54<16:30:21, 4.22s/it] 25%|██▍ | 4608/18688 [5:23:58<16:28:48, 4.21s/it] 25%|██▍ | 4609/18688 [5:24:02<16:34:07, 4.24s/it] 25%|██▍ | 4610/18688 [5:24:07<16:51:14, 4.31s/it] {'loss': '1.007', 'grad_norm': '1.263', 'learning_rate': '9.214e-05', 'epoch': '0.2467', 'num_input_tokens_seen': 15488664, 'train_runtime': '1.945e+04', 'train_tokens_per_second': '796.4'}
25%|██▍ | 4610/18688 [5:24:07<16:51:14, 4.31s/it] 25%|██▍ | 4611/18688 [5:24:11<16:53:23, 4.32s/it] 25%|██▍ | 4612/18688 [5:24:16<16:57:04, 4.34s/it] 25%|██▍ | 4613/18688 [5:24:19<16:03:32, 4.11s/it] 25%|██▍ | 4614/18688 [5:24:23<15:19:18, 3.92s/it] 25%|██▍ | 4615/18688 [5:24:27<15:30:57, 3.97s/it] 25%|██▍ | 4616/18688 [5:24:31<16:03:04, 4.11s/it] 25%|██▍ | 4617/18688 [5:24:35<15:36:50, 3.99s/it] 25%|██▍ | 4618/18688 [5:24:39<15:31:23, 3.97s/it] 25%|██▍ | 4619/18688 [5:24:43<15:59:50, 4.09s/it] 25%|██▍ | 4620/18688 [5:24:48<17:20:18, 4.44s/it] {'loss': '0.9259', 'grad_norm': '1.271', 'learning_rate': '9.209e-05', 'epoch': '0.2472', 'num_input_tokens_seen': 15522336, 'train_runtime': '1.949e+04', 'train_tokens_per_second': '796.5'}
25%|██▍ | 4620/18688 [5:24:48<17:20:18, 4.44s/it] 25%|██▍ | 4621/18688 [5:24:53<18:00:23, 4.61s/it] 25%|██▍ | 4622/18688 [5:24:57<16:48:39, 4.30s/it] 25%|██▍ | 4623/18688 [5:25:01<16:14:56, 4.16s/it] 25%|██▍ | 4624/18688 [5:25:04<15:34:13, 3.99s/it] 25%|██▍ | 4625/18688 [5:25:09<16:17:57, 4.17s/it] 25%|██▍ | 4626/18688 [5:25:13<16:08:17, 4.13s/it] 25%|██▍ | 4627/18688 [5:25:16<15:21:23, 3.93s/it] 25%|██▍ | 4628/18688 [5:25:20<14:48:59, 3.79s/it] 25%|██▍ | 4629/18688 [5:25:25<15:49:25, 4.05s/it] 25%|██▍ | 4630/18688 [5:25:30<17:33:01, 4.49s/it] {'loss': '0.9586', 'grad_norm': '1.163', 'learning_rate': '9.204e-05', 'epoch': '0.2478', 'num_input_tokens_seen': 15557696, 'train_runtime': '1.953e+04', 'train_tokens_per_second': '796.6'}
25%|██▍ | 4630/18688 [5:25:30<17:33:01, 4.49s/it] 25%|██▍ | 4631/18688 [5:25:34<17:08:39, 4.39s/it] 25%|██▍ | 4632/18688 [5:25:38<16:35:37, 4.25s/it] 25%|██▍ | 4633/18688 [5:25:42<16:27:50, 4.22s/it] 25%|██▍ | 4634/18688 [5:25:46<16:14:35, 4.16s/it] 25%|██▍ | 4635/18688 [5:25:50<15:51:43, 4.06s/it] 25%|██▍ | 4636/18688 [5:25:54<15:47:07, 4.04s/it] 25%|██▍ | 4637/18688 [5:25:58<15:34:57, 3.99s/it] 25%|██▍ | 4638/18688 [5:26:02<15:06:41, 3.87s/it] 25%|██▍ | 4639/18688 [5:26:06<15:43:41, 4.03s/it] 25%|██▍ | 4640/18688 [5:26:10<15:37:18, 4.00s/it] {'loss': '0.957', 'grad_norm': '1.007', 'learning_rate': '9.199e-05', 'epoch': '0.2483', 'num_input_tokens_seen': 15590776, 'train_runtime': '1.957e+04', 'train_tokens_per_second': '796.6'}
25%|██▍ | 4640/18688 [5:26:10<15:37:18, 4.00s/it] 25%|██▍ | 4641/18688 [5:26:14<15:53:51, 4.07s/it] 25%|██▍ | 4642/18688 [5:26:19<17:16:57, 4.43s/it] 25%|██▍ | 4643/18688 [5:26:23<16:41:11, 4.28s/it] 25%|██▍ | 4644/18688 [5:26:27<16:07:22, 4.13s/it] 25%|██▍ | 4645/18688 [5:26:31<15:20:54, 3.93s/it] 25%|██▍ | 4646/18688 [5:26:34<15:11:55, 3.90s/it] 25%|██▍ | 4647/18688 [5:26:38<15:01:26, 3.85s/it] 25%|██▍ | 4648/18688 [5:26:42<15:28:23, 3.97s/it] 25%|██▍ | 4649/18688 [5:26:46<14:52:15, 3.81s/it] 25%|██▍ | 4650/18688 [5:26:50<15:07:12, 3.88s/it] {'loss': '0.9572', 'grad_norm': '1.046', 'learning_rate': '9.194e-05', 'epoch': '0.2488', 'num_input_tokens_seen': 15622168, 'train_runtime': '1.961e+04', 'train_tokens_per_second': '796.6'}
25%|██▍ | 4650/18688 [5:26:50<15:07:12, 3.88s/it] 25%|██▍ | 4651/18688 [5:26:53<14:44:35, 3.78s/it] 25%|██▍ | 4652/18688 [5:26:57<14:35:31, 3.74s/it] 25%|██▍ | 4653/18688 [5:27:01<14:27:41, 3.71s/it] 25%|██▍ | 4654/18688 [5:27:05<15:10:04, 3.89s/it] 25%|██▍ | 4655/18688 [5:27:09<14:50:28, 3.81s/it] 25%|██▍ | 4656/18688 [5:27:13<15:07:50, 3.88s/it] 25%|██▍ | 4657/18688 [5:27:17<15:52:11, 4.07s/it] 25%|██▍ | 4658/18688 [5:27:21<15:32:54, 3.99s/it] 25%|██▍ | 4659/18688 [5:27:27<17:31:28, 4.50s/it] 25%|██▍ | 4660/18688 [5:27:31<17:00:16, 4.36s/it] {'loss': '0.9173', 'grad_norm': '1.318', 'learning_rate': '9.189e-05', 'epoch': '0.2494', 'num_input_tokens_seen': 15654144, 'train_runtime': '1.965e+04', 'train_tokens_per_second': '796.6'}
25%|██▍ | 4660/18688 [5:27:31<17:00:16, 4.36s/it] 25%|██▍ | 4661/18688 [5:27:35<16:28:32, 4.23s/it] 25%|██▍ | 4662/18688 [5:27:38<15:53:55, 4.08s/it] 25%|██▍ | 4663/18688 [5:27:42<15:48:50, 4.06s/it] 25%|██▍ | 4664/18688 [5:27:46<15:26:31, 3.96s/it] 25%|██▍ | 4665/18688 [5:27:50<14:59:44, 3.85s/it] 25%|██▍ | 4666/18688 [5:27:54<15:20:35, 3.94s/it] 25%|██▍ | 4667/18688 [5:27:58<15:00:15, 3.85s/it] 25%|██▍ | 4668/18688 [5:28:01<14:42:14, 3.78s/it] 25%|██▍ | 4669/18688 [5:28:05<14:24:53, 3.70s/it] 25%|██▍ | 4670/18688 [5:28:09<14:47:30, 3.80s/it] {'loss': '0.9459', 'grad_norm': '1.221', 'learning_rate': '9.184e-05', 'epoch': '0.2499', 'num_input_tokens_seen': 15684704, 'train_runtime': '1.969e+04', 'train_tokens_per_second': '796.6'}
25%|██▍ | 4670/18688 [5:28:09<14:47:30, 3.80s/it] 25%|██▍ | 4671/18688 [5:28:13<15:36:27, 4.01s/it] 25%|██▌ | 4672/18688 [5:28:17<15:45:03, 4.05s/it] 25%|██▌ | 4673/18688 [5:28:21<15:09:51, 3.90s/it] 25%|██▌ | 4674/18688 [5:28:25<15:21:14, 3.94s/it] 25%|██▌ | 4675/18688 [5:28:29<15:28:05, 3.97s/it] 25%|██▌ | 4676/18688 [5:28:33<14:58:52, 3.85s/it] 25%|██▌ | 4677/18688 [5:28:37<16:13:31, 4.17s/it] 25%|██▌ | 4678/18688 [5:28:42<16:38:41, 4.28s/it] 25%|██▌ | 4679/18688 [5:28:46<16:39:21, 4.28s/it] 25%|██▌ | 4680/18688 [5:28:50<16:24:56, 4.22s/it] {'loss': '0.9646', 'grad_norm': '1.525', 'learning_rate': '9.179e-05', 'epoch': '0.2504', 'num_input_tokens_seen': 15717224, 'train_runtime': '1.973e+04', 'train_tokens_per_second': '796.6'}
25%|██▌ | 4680/18688 [5:28:50<16:24:56, 4.22s/it] 25%|██▌ | 4681/18688 [5:28:54<15:39:12, 4.02s/it] 25%|██▌ | 4682/18688 [5:28:57<15:01:53, 3.86s/it] 25%|██▌ | 4683/18688 [5:29:01<14:38:54, 3.77s/it] 25%|██▌ | 4684/18688 [5:29:05<14:35:12, 3.75s/it] 25%|██▌ | 4685/18688 [5:29:10<15:51:59, 4.08s/it] 25%|██▌ | 4686/18688 [5:29:14<16:02:20, 4.12s/it] 25%|██▌ | 4687/18688 [5:29:17<15:24:47, 3.96s/it] 25%|██▌ | 4688/18688 [5:29:22<16:11:58, 4.17s/it] 25%|██▌ | 4689/18688 [5:29:26<16:12:10, 4.17s/it] 25%|██▌ | 4690/18688 [5:29:30<15:27:12, 3.97s/it] {'loss': '0.9392', 'grad_norm': '1.181', 'learning_rate': '9.174e-05', 'epoch': '0.251', 'num_input_tokens_seen': 15749296, 'train_runtime': '1.977e+04', 'train_tokens_per_second': '796.6'}
25%|██▌ | 4690/18688 [5:29:30<15:27:12, 3.97s/it] 25%|██▌ | 4691/18688 [5:29:34<16:11:34, 4.16s/it] 25%|██▌ | 4692/18688 [5:29:38<15:31:48, 3.99s/it] 25%|██▌ | 4693/18688 [5:29:42<15:32:58, 4.00s/it] 25%|██▌ | 4694/18688 [5:29:46<15:36:00, 4.01s/it] 25%|██▌ | 4695/18688 [5:29:49<14:57:37, 3.85s/it] 25%|██▌ | 4696/18688 [5:29:53<14:37:26, 3.76s/it] 25%|██▌ | 4697/18688 [5:29:57<15:23:24, 3.96s/it] 25%|██▌ | 4698/18688 [5:30:02<16:41:54, 4.30s/it] 25%|██▌ | 4699/18688 [5:30:06<15:57:59, 4.11s/it] 25%|██▌ | 4700/18688 [5:30:10<16:08:11, 4.15s/it] {'loss': '0.8748', 'grad_norm': '1.405', 'learning_rate': '9.169e-05', 'epoch': '0.2515', 'num_input_tokens_seen': 15783216, 'train_runtime': '1.981e+04', 'train_tokens_per_second': '796.7'}
25%|██▌ | 4700/18688 [5:30:10<16:08:11, 4.15s/it] 25%|██▌ | 4701/18688 [5:30:14<15:50:26, 4.08s/it] 25%|██▌ | 4702/18688 [5:30:19<16:23:50, 4.22s/it] 25%|██▌ | 4703/18688 [5:30:22<15:39:52, 4.03s/it] 25%|██▌ | 4704/18688 [5:30:27<15:51:46, 4.08s/it] 25%|██▌ | 4705/18688 [5:30:31<16:10:26, 4.16s/it] 25%|██▌ | 4706/18688 [5:30:36<16:53:36, 4.35s/it] 25%|██▌ | 4707/18688 [5:30:40<17:01:07, 4.38s/it] 25%|██▌ | 4708/18688 [5:30:44<15:56:52, 4.11s/it] 25%|██▌ | 4709/18688 [5:30:47<15:31:12, 4.00s/it] 25%|██▌ | 4710/18688 [5:30:52<16:27:11, 4.24s/it] {'loss': '0.9118', 'grad_norm': '1.169', 'learning_rate': '9.164e-05', 'epoch': '0.252', 'num_input_tokens_seen': 15817536, 'train_runtime': '1.985e+04', 'train_tokens_per_second': '796.7'}
25%|██▌ | 4710/18688 [5:30:52<16:27:11, 4.24s/it] 25%|██▌ | 4711/18688 [5:30:57<17:11:44, 4.43s/it] 25%|██▌ | 4712/18688 [5:31:02<17:40:51, 4.55s/it] 25%|██▌ | 4713/18688 [5:31:06<17:24:49, 4.49s/it] 25%|██▌ | 4714/18688 [5:31:10<16:55:17, 4.36s/it] 25%|██▌ | 4715/18688 [5:31:14<16:00:03, 4.12s/it] 25%|██▌ | 4716/18688 [5:31:18<15:21:52, 3.96s/it] 25%|██▌ | 4717/18688 [5:31:22<15:25:33, 3.97s/it] 25%|██▌ | 4718/18688 [5:31:26<15:33:04, 4.01s/it] 25%|██▌ | 4719/18688 [5:31:30<15:51:08, 4.09s/it] 25%|██▌ | 4720/18688 [5:31:34<15:49:28, 4.08s/it] {'loss': '0.8885', 'grad_norm': '1.251', 'learning_rate': '9.159e-05', 'epoch': '0.2526', 'num_input_tokens_seen': 15852056, 'train_runtime': '1.989e+04', 'train_tokens_per_second': '796.8'}
25%|██▌ | 4720/18688 [5:31:34<15:49:28, 4.08s/it] 25%|██▌ | 4721/18688 [5:31:38<15:41:40, 4.05s/it] 25%|██▌ | 4722/18688 [5:31:42<15:25:39, 3.98s/it] 25%|██▌ | 4723/18688 [5:31:45<14:47:33, 3.81s/it] 25%|██▌ | 4724/18688 [5:31:49<14:51:54, 3.83s/it] 25%|██▌ | 4725/18688 [5:31:53<14:38:05, 3.77s/it] 25%|██▌ | 4726/18688 [5:31:57<15:33:13, 4.01s/it] 25%|██▌ | 4727/18688 [5:32:01<15:20:48, 3.96s/it] 25%|██▌ | 4728/18688 [5:32:05<15:15:11, 3.93s/it] 25%|██▌ | 4729/18688 [5:32:09<14:52:39, 3.84s/it] 25%|██▌ | 4730/18688 [5:32:12<14:33:42, 3.76s/it] {'loss': '0.9704', 'grad_norm': '1.286', 'learning_rate': '9.154e-05', 'epoch': '0.2531', 'num_input_tokens_seen': 15882192, 'train_runtime': '1.993e+04', 'train_tokens_per_second': '796.8'}
25%|██▌ | 4730/18688 [5:32:12<14:33:42, 3.76s/it] 25%|██▌ | 4731/18688 [5:32:16<14:29:52, 3.74s/it] 25%|██▌ | 4732/18688 [5:32:21<15:42:26, 4.05s/it] 25%|██▌ | 4733/18688 [5:32:25<16:15:02, 4.19s/it] 25%|██▌ | 4734/18688 [5:32:29<16:14:30, 4.19s/it] 25%|██▌ | 4735/18688 [5:32:34<16:33:25, 4.27s/it] 25%|██▌ | 4736/18688 [5:32:38<16:30:32, 4.26s/it] 25%|██▌ | 4737/18688 [5:32:42<15:58:35, 4.12s/it] 25%|██▌ | 4738/18688 [5:32:45<15:26:39, 3.99s/it] 25%|██▌ | 4739/18688 [5:32:49<15:04:00, 3.89s/it] 25%|██▌ | 4740/18688 [5:32:53<15:18:14, 3.95s/it] {'loss': '0.989', 'grad_norm': '0.9123', 'learning_rate': '9.149e-05', 'epoch': '0.2536', 'num_input_tokens_seen': 15916552, 'train_runtime': '1.997e+04', 'train_tokens_per_second': '796.9'}
25%|██▌ | 4740/18688 [5:32:53<15:18:14, 3.95s/it] 25%|██▌ | 4741/18688 [5:32:57<15:02:33, 3.88s/it] 25%|██▌ | 4742/18688 [5:33:01<14:43:54, 3.80s/it] 25%|██▌ | 4743/18688 [5:33:04<14:21:52, 3.71s/it] 25%|██▌ | 4744/18688 [5:33:08<14:22:31, 3.71s/it] 25%|██▌ | 4745/18688 [5:33:12<14:57:33, 3.86s/it] 25%|██▌ | 4746/18688 [5:33:16<14:52:27, 3.84s/it] 25%|██▌ | 4747/18688 [5:33:20<14:53:56, 3.85s/it] 25%|██▌ | 4748/18688 [5:33:25<16:26:26, 4.25s/it] 25%|██▌ | 4749/18688 [5:33:29<15:48:20, 4.08s/it] 25%|██▌ | 4750/18688 [5:33:33<16:11:05, 4.18s/it] {'loss': '0.9514', 'grad_norm': '1.251', 'learning_rate': '9.144e-05', 'epoch': '0.2542', 'num_input_tokens_seen': 15948256, 'train_runtime': '2.001e+04', 'train_tokens_per_second': '796.9'}
25%|██▌ | 4750/18688 [5:33:33<16:11:05, 4.18s/it] 25%|██▌ | 4751/18688 [5:33:37<16:12:30, 4.19s/it] 25%|██▌ | 4752/18688 [5:33:42<17:22:13, 4.49s/it] 25%|██▌ | 4753/18688 [5:33:47<17:23:58, 4.50s/it] 25%|██▌ | 4754/18688 [5:33:50<16:10:42, 4.18s/it] 25%|██▌ | 4755/18688 [5:33:54<16:00:43, 4.14s/it] 25%|██▌ | 4756/18688 [5:33:58<15:59:04, 4.13s/it] 25%|██▌ | 4757/18688 [5:34:03<16:15:13, 4.20s/it] 25%|██▌ | 4758/18688 [5:34:06<15:38:28, 4.04s/it] 25%|██▌ | 4759/18688 [5:34:10<15:27:00, 3.99s/it] 25%|██▌ | 4760/18688 [5:34:15<15:54:56, 4.11s/it] {'loss': '0.9395', 'grad_norm': '1.338', 'learning_rate': '9.139e-05', 'epoch': '0.2547', 'num_input_tokens_seen': 15982480, 'train_runtime': '2.006e+04', 'train_tokens_per_second': '796.9'}
25%|██▌ | 4760/18688 [5:34:15<15:54:56, 4.11s/it] 25%|██▌ | 4761/18688 [5:34:19<15:58:53, 4.13s/it] 25%|██▌ | 4762/18688 [5:34:23<16:22:19, 4.23s/it] 25%|██▌ | 4763/18688 [5:34:28<16:53:58, 4.37s/it] 25%|██▌ | 4764/18688 [5:34:32<16:05:56, 4.16s/it] 25%|██▌ | 4765/18688 [5:34:36<15:59:22, 4.13s/it] 26%|██▌ | 4766/18688 [5:34:40<16:36:08, 4.29s/it] 26%|██▌ | 4767/18688 [5:34:45<16:48:50, 4.35s/it] 26%|██▌ | 4768/18688 [5:34:49<16:33:42, 4.28s/it] 26%|██▌ | 4769/18688 [5:34:53<15:57:10, 4.13s/it] 26%|██▌ | 4770/18688 [5:34:56<15:16:29, 3.95s/it] {'loss': '0.9663', 'grad_norm': '1.373', 'learning_rate': '9.134e-05', 'epoch': '0.2553', 'num_input_tokens_seen': 16017888, 'train_runtime': '2.01e+04', 'train_tokens_per_second': '797'}
26%|██▌ | 4770/18688 [5:34:56<15:16:29, 3.95s/it] 26%|██▌ | 4771/18688 [5:35:00<14:51:50, 3.84s/it] 26%|██▌ | 4772/18688 [5:35:05<16:10:46, 4.19s/it] 26%|██▌ | 4773/18688 [5:35:11<18:10:07, 4.70s/it] 26%|██▌ | 4774/18688 [5:35:15<17:13:44, 4.46s/it] 26%|██▌ | 4775/18688 [5:35:19<16:39:53, 4.31s/it] 26%|██▌ | 4776/18688 [5:35:23<16:31:57, 4.28s/it] 26%|██▌ | 4777/18688 [5:35:28<17:17:50, 4.48s/it] 26%|██▌ | 4778/18688 [5:35:32<16:53:20, 4.37s/it] 26%|██▌ | 4779/18688 [5:35:36<16:45:05, 4.34s/it] 26%|██▌ | 4780/18688 [5:35:40<16:24:46, 4.25s/it] {'loss': '0.9366', 'grad_norm': '1.161', 'learning_rate': '9.129e-05', 'epoch': '0.2558', 'num_input_tokens_seen': 16053640, 'train_runtime': '2.014e+04', 'train_tokens_per_second': '797.1'}
26%|██▌ | 4780/18688 [5:35:40<16:24:46, 4.25s/it] 26%|██▌ | 4781/18688 [5:35:44<16:18:59, 4.22s/it] 26%|██▌ | 4782/18688 [5:35:49<16:25:40, 4.25s/it] 26%|██▌ | 4783/18688 [5:35:53<16:43:39, 4.33s/it] 26%|██▌ | 4784/18688 [5:35:58<17:30:12, 4.53s/it] 26%|██▌ | 4785/18688 [5:36:02<16:33:12, 4.29s/it] 26%|██▌ | 4786/18688 [5:36:06<16:20:13, 4.23s/it] 26%|██▌ | 4787/18688 [5:36:10<16:07:55, 4.18s/it] 26%|██▌ | 4788/18688 [5:36:14<15:49:40, 4.10s/it] 26%|██▌ | 4789/18688 [5:36:18<15:27:32, 4.00s/it] 26%|██▌ | 4790/18688 [5:36:21<15:00:48, 3.89s/it] {'loss': '0.9031', 'grad_norm': '1.026', 'learning_rate': '9.123e-05', 'epoch': '0.2563', 'num_input_tokens_seen': 16089048, 'train_runtime': '2.018e+04', 'train_tokens_per_second': '797.2'}
26%|██▌ | 4790/18688 [5:36:21<15:00:48, 3.89s/it] 26%|██▌ | 4791/18688 [5:36:26<15:34:34, 4.03s/it] 26%|██▌ | 4792/18688 [5:36:30<16:12:22, 4.20s/it] 26%|██▌ | 4793/18688 [5:36:35<16:16:43, 4.22s/it] 26%|██▌ | 4794/18688 [5:36:39<16:50:56, 4.37s/it] 26%|██▌ | 4795/18688 [5:36:43<16:25:37, 4.26s/it] 26%|██▌ | 4796/18688 [5:36:48<16:56:06, 4.39s/it] 26%|██▌ | 4797/18688 [5:36:52<15:53:36, 4.12s/it] 26%|██▌ | 4798/18688 [5:36:55<15:23:06, 3.99s/it] 26%|██▌ | 4799/18688 [5:36:59<14:54:00, 3.86s/it] 26%|██▌ | 4800/18688 [5:37:03<14:41:11, 3.81s/it] {'loss': '0.9796', 'grad_norm': '1.305', 'learning_rate': '9.118e-05', 'epoch': '0.2569', 'num_input_tokens_seen': 16120568, 'train_runtime': '2.022e+04', 'train_tokens_per_second': '797.1'}
26%|██▌ | 4800/18688 [5:37:03<14:41:11, 3.81s/it] 26%|██▌ | 4801/18688 [5:37:06<14:25:21, 3.74s/it] 26%|██▌ | 4802/18688 [5:37:11<16:12:50, 4.20s/it] 26%|██▌ | 4803/18688 [5:37:15<15:28:45, 4.01s/it] 26%|██▌ | 4804/18688 [5:37:21<17:41:52, 4.59s/it] 26%|██▌ | 4805/18688 [5:37:26<18:05:15, 4.69s/it] 26%|██▌ | 4806/18688 [5:37:31<18:10:20, 4.71s/it] 26%|██▌ | 4807/18688 [5:37:35<17:27:27, 4.53s/it] 26%|██▌ | 4808/18688 [5:37:38<16:29:43, 4.28s/it] 26%|██▌ | 4809/18688 [5:37:42<15:46:11, 4.09s/it] 26%|██▌ | 4810/18688 [5:37:46<15:24:45, 4.00s/it] {'loss': '0.949', 'grad_norm': '1.361', 'learning_rate': '9.113e-05', 'epoch': '0.2574', 'num_input_tokens_seen': 16156104, 'train_runtime': '2.027e+04', 'train_tokens_per_second': '797.2'}
26%|██▌ | 4810/18688 [5:37:46<15:24:45, 4.00s/it] 26%|██▌ | 4811/18688 [5:37:50<15:16:35, 3.96s/it] 26%|██▌ | 4812/18688 [5:37:53<15:03:20, 3.91s/it] 26%|██▌ | 4813/18688 [5:37:58<15:35:26, 4.05s/it] 26%|██▌ | 4814/18688 [5:38:02<15:38:30, 4.06s/it] 26%|██▌ | 4815/18688 [5:38:06<15:14:46, 3.96s/it] 26%|██▌ | 4816/18688 [5:38:10<15:28:35, 4.02s/it] 26%|██▌ | 4817/18688 [5:38:13<14:49:10, 3.85s/it] 26%|██▌ | 4818/18688 [5:38:18<15:58:11, 4.15s/it] 26%|██▌ | 4819/18688 [5:38:22<15:56:53, 4.14s/it] 26%|██▌ | 4820/18688 [5:38:26<15:15:24, 3.96s/it] {'loss': '0.9532', 'grad_norm': '1.185', 'learning_rate': '9.108e-05', 'epoch': '0.2579', 'num_input_tokens_seen': 16188728, 'train_runtime': '2.031e+04', 'train_tokens_per_second': '797.2'}
26%|██▌ | 4820/18688 [5:38:26<15:15:24, 3.96s/it] 26%|██▌ | 4821/18688 [5:38:30<15:53:29, 4.13s/it] 26%|██▌ | 4822/18688 [5:38:35<16:40:07, 4.33s/it] 26%|██▌ | 4823/18688 [5:38:40<17:09:38, 4.46s/it] 26%|██▌ | 4824/18688 [5:38:43<16:02:06, 4.16s/it] 26%|██▌ | 4825/18688 [5:38:48<16:34:55, 4.31s/it] 26%|██▌ | 4826/18688 [5:38:53<16:56:53, 4.40s/it] 26%|██▌ | 4827/18688 [5:38:57<17:08:07, 4.45s/it] 26%|██▌ | 4828/18688 [5:39:01<16:14:56, 4.22s/it] 26%|██▌ | 4829/18688 [5:39:05<16:04:21, 4.18s/it] 26%|██▌ | 4830/18688 [5:39:10<17:04:28, 4.44s/it] {'loss': '0.9333', 'grad_norm': '1.15', 'learning_rate': '9.103e-05', 'epoch': '0.2585', 'num_input_tokens_seen': 16227520, 'train_runtime': '2.035e+04', 'train_tokens_per_second': '797.4'}
26%|██▌ | 4830/18688 [5:39:10<17:04:28, 4.44s/it] 26%|██▌ | 4831/18688 [5:39:14<16:40:16, 4.33s/it] 26%|██▌ | 4832/18688 [5:39:18<15:53:05, 4.13s/it] 26%|██▌ | 4833/18688 [5:39:23<16:50:58, 4.38s/it] 26%|██▌ | 4834/18688 [5:39:27<16:39:12, 4.33s/it] 26%|██▌ | 4835/18688 [5:39:31<16:20:02, 4.24s/it] 26%|██▌ | 4836/18688 [5:39:35<15:47:35, 4.10s/it] 26%|██▌ | 4837/18688 [5:39:39<15:42:36, 4.08s/it] 26%|██▌ | 4838/18688 [5:39:43<15:23:50, 4.00s/it] 26%|██▌ | 4839/18688 [5:39:46<15:14:20, 3.96s/it] 26%|██▌ | 4840/18688 [5:39:50<15:20:26, 3.99s/it] {'loss': '0.878', 'grad_norm': '1.134', 'learning_rate': '9.097e-05', 'epoch': '0.259', 'num_input_tokens_seen': 16261304, 'train_runtime': '2.039e+04', 'train_tokens_per_second': '797.5'}
26%|██▌ | 4840/18688 [5:39:50<15:20:26, 3.99s/it] 26%|██▌ | 4841/18688 [5:39:55<15:29:54, 4.03s/it] 26%|██▌ | 4842/18688 [5:39:59<16:24:52, 4.27s/it] 26%|██▌ | 4843/18688 [5:40:04<16:50:38, 4.38s/it] 26%|██▌ | 4844/18688 [5:40:08<16:11:44, 4.21s/it] 26%|██▌ | 4845/18688 [5:40:12<15:58:30, 4.15s/it] 26%|██▌ | 4846/18688 [5:40:16<16:23:05, 4.26s/it] 26%|██▌ | 4847/18688 [5:40:21<16:43:11, 4.35s/it] 26%|██▌ | 4848/18688 [5:40:25<16:43:50, 4.35s/it] 26%|██▌ | 4849/18688 [5:40:29<15:51:59, 4.13s/it] 26%|██▌ | 4850/18688 [5:40:33<15:49:39, 4.12s/it] {'loss': '0.9882', 'grad_norm': '1.336', 'learning_rate': '9.092e-05', 'epoch': '0.2595', 'num_input_tokens_seen': 16297392, 'train_runtime': '2.043e+04', 'train_tokens_per_second': '797.6'}
26%|██▌ | 4850/18688 [5:40:33<15:49:39, 4.12s/it] 26%|██▌ | 4851/18688 [5:40:37<15:54:32, 4.14s/it] 26%|██▌ | 4852/18688 [5:40:41<15:19:38, 3.99s/it] 26%|██▌ | 4853/18688 [5:40:45<15:26:04, 4.02s/it] 26%|██▌ | 4854/18688 [5:40:49<15:07:30, 3.94s/it] 26%|██▌ | 4855/18688 [5:40:53<15:24:01, 4.01s/it] 26%|██▌ | 4856/18688 [5:40:57<15:06:22, 3.93s/it] 26%|██▌ | 4857/18688 [5:41:01<15:45:18, 4.10s/it] 26%|██▌ | 4858/18688 [5:41:05<15:30:04, 4.04s/it] 26%|██▌ | 4859/18688 [5:41:08<14:52:26, 3.87s/it] 26%|██▌ | 4860/18688 [5:41:13<15:10:48, 3.95s/it] {'loss': '0.9926', 'grad_norm': '1.752', 'learning_rate': '9.087e-05', 'epoch': '0.2601', 'num_input_tokens_seen': 16328984, 'train_runtime': '2.047e+04', 'train_tokens_per_second': '797.6'}
26%|██▌ | 4860/18688 [5:41:13<15:10:48, 3.95s/it] 26%|██▌ | 4861/18688 [5:41:16<14:58:17, 3.90s/it] 26%|██▌ | 4862/18688 [5:41:20<14:56:55, 3.89s/it] 26%|██▌ | 4863/18688 [5:41:24<14:32:22, 3.79s/it] 26%|██▌ | 4864/18688 [5:41:28<14:29:50, 3.78s/it] 26%|██▌ | 4865/18688 [5:41:32<14:44:35, 3.84s/it] 26%|██▌ | 4866/18688 [5:41:36<15:11:06, 3.96s/it] 26%|██▌ | 4867/18688 [5:41:40<15:23:23, 4.01s/it] 26%|██▌ | 4868/18688 [5:41:45<16:10:58, 4.22s/it] 26%|██▌ | 4869/18688 [5:41:49<16:10:14, 4.21s/it] 26%|██▌ | 4870/18688 [5:41:52<15:22:28, 4.01s/it] {'loss': '0.9527', 'grad_norm': '1.27', 'learning_rate': '9.082e-05', 'epoch': '0.2606', 'num_input_tokens_seen': 16360632, 'train_runtime': '2.051e+04', 'train_tokens_per_second': '797.6'}
26%|██▌ | 4870/18688 [5:41:52<15:22:28, 4.01s/it] 26%|██▌ | 4871/18688 [5:41:56<14:43:12, 3.84s/it] 26%|██▌ | 4872/18688 [5:42:00<15:31:59, 4.05s/it] 26%|██▌ | 4873/18688 [5:42:05<15:56:32, 4.15s/it] 26%|██▌ | 4874/18688 [5:42:09<15:51:25, 4.13s/it] 26%|██▌ | 4875/18688 [5:42:13<16:01:58, 4.18s/it] 26%|██▌ | 4876/18688 [5:42:17<15:51:27, 4.13s/it] 26%|██▌ | 4877/18688 [5:42:21<16:01:00, 4.17s/it] 26%|██▌ | 4878/18688 [5:42:25<15:39:10, 4.08s/it] 26%|██▌ | 4879/18688 [5:42:29<15:48:10, 4.12s/it] 26%|██▌ | 4880/18688 [5:42:34<15:56:11, 4.15s/it] {'loss': '0.9822', 'grad_norm': '1.317', 'learning_rate': '9.076e-05', 'epoch': '0.2611', 'num_input_tokens_seen': 16394784, 'train_runtime': '2.055e+04', 'train_tokens_per_second': '797.6'}
26%|██▌ | 4880/18688 [5:42:34<15:56:11, 4.15s/it] 26%|██▌ | 4881/18688 [5:42:38<15:53:29, 4.14s/it] 26%|██▌ | 4882/18688 [5:42:42<15:33:57, 4.06s/it] 26%|██▌ | 4883/18688 [5:42:46<15:35:23, 4.07s/it] 26%|██▌ | 4884/18688 [5:42:50<15:51:24, 4.14s/it] 26%|██▌ | 4885/18688 [5:42:54<16:03:01, 4.19s/it] 26%|██▌ | 4886/18688 [5:42:58<15:55:36, 4.15s/it] 26%|██▌ | 4887/18688 [5:43:02<15:11:45, 3.96s/it] 26%|██▌ | 4888/18688 [5:43:06<15:06:28, 3.94s/it] 26%|██▌ | 4889/18688 [5:43:10<15:23:05, 4.01s/it] 26%|██▌ | 4890/18688 [5:43:15<15:58:19, 4.17s/it] {'loss': '0.9374', 'grad_norm': '1.648', 'learning_rate': '9.071e-05', 'epoch': '0.2617', 'num_input_tokens_seen': 16427848, 'train_runtime': '2.06e+04', 'train_tokens_per_second': '797.7'}
26%|██▌ | 4890/18688 [5:43:15<15:58:19, 4.17s/it] 26%|██▌ | 4891/18688 [5:43:18<15:29:35, 4.04s/it] 26%|██▌ | 4892/18688 [5:43:23<16:01:11, 4.18s/it] 26%|██▌ | 4893/18688 [5:43:26<15:14:21, 3.98s/it] 26%|██▌ | 4894/18688 [5:43:31<16:25:50, 4.29s/it] 26%|██▌ | 4895/18688 [5:43:35<16:08:30, 4.21s/it] 26%|██▌ | 4896/18688 [5:43:40<16:15:57, 4.25s/it] 26%|██▌ | 4897/18688 [5:43:46<18:35:40, 4.85s/it] 26%|██▌ | 4898/18688 [5:43:50<18:10:25, 4.74s/it] 26%|██▌ | 4899/18688 [5:43:54<17:05:10, 4.46s/it] 26%|██▌ | 4900/18688 [5:43:58<16:46:22, 4.38s/it] {'loss': '1.054', 'grad_norm': '1.222', 'learning_rate': '9.066e-05', 'epoch': '0.2622', 'num_input_tokens_seen': 16465344, 'train_runtime': '2.064e+04', 'train_tokens_per_second': '797.8'}
26%|██▌ | 4900/18688 [5:43:58<16:46:22, 4.38s/it] 26%|██▌ | 4901/18688 [5:44:02<16:04:16, 4.20s/it] 26%|██▌ | 4902/18688 [5:44:06<15:31:08, 4.05s/it] 26%|██▌ | 4903/18688 [5:44:10<15:44:50, 4.11s/it] 26%|██▌ | 4904/18688 [5:44:14<15:30:10, 4.05s/it] 26%|██▌ | 4905/18688 [5:44:18<15:23:53, 4.02s/it] 26%|██▋ | 4906/18688 [5:44:23<16:01:53, 4.19s/it] 26%|██▋ | 4907/18688 [5:44:26<15:37:19, 4.08s/it] 26%|██▋ | 4908/18688 [5:44:30<15:03:27, 3.93s/it] 26%|██▋ | 4909/18688 [5:44:34<14:39:58, 3.83s/it] 26%|██▋ | 4910/18688 [5:44:37<14:22:09, 3.75s/it] {'loss': '0.937', 'grad_norm': '1.097', 'learning_rate': '9.06e-05', 'epoch': '0.2627', 'num_input_tokens_seen': 16495096, 'train_runtime': '2.068e+04', 'train_tokens_per_second': '797.7'}
26%|██▋ | 4910/18688 [5:44:37<14:22:09, 3.75s/it] 26%|██▋ | 4911/18688 [5:44:41<14:32:13, 3.80s/it] 26%|██▋ | 4912/18688 [5:44:45<14:57:09, 3.91s/it] 26%|██▋ | 4913/18688 [5:44:49<15:04:59, 3.94s/it] 26%|██▋ | 4914/18688 [5:44:53<14:53:44, 3.89s/it] 26%|██▋ | 4915/18688 [5:44:56<14:23:18, 3.76s/it] 26%|██▋ | 4916/18688 [5:45:00<14:09:22, 3.70s/it] 26%|██▋ | 4917/18688 [5:45:04<14:44:29, 3.85s/it] 26%|██▋ | 4918/18688 [5:45:10<16:31:49, 4.32s/it] 26%|██▋ | 4919/18688 [5:45:13<15:46:33, 4.12s/it] 26%|██▋ | 4920/18688 [5:45:17<15:35:02, 4.07s/it] {'loss': '1.041', 'grad_norm': '1.381', 'learning_rate': '9.055e-05', 'epoch': '0.2633', 'num_input_tokens_seen': 16528224, 'train_runtime': '2.072e+04', 'train_tokens_per_second': '797.8'}
26%|██▋ | 4920/18688 [5:45:17<15:35:02, 4.07s/it] 26%|██▋ | 4921/18688 [5:45:22<16:32:09, 4.32s/it] 26%|██▋ | 4922/18688 [5:45:28<18:16:58, 4.78s/it] 26%|██▋ | 4923/18688 [5:45:32<16:55:28, 4.43s/it] 26%|██▋ | 4924/18688 [5:45:36<17:09:10, 4.49s/it] 26%|██▋ | 4925/18688 [5:45:40<16:28:08, 4.31s/it] 26%|██▋ | 4926/18688 [5:45:44<15:28:32, 4.05s/it] 26%|██▋ | 4927/18688 [5:45:47<15:01:05, 3.93s/it] 26%|██▋ | 4928/18688 [5:45:51<14:42:56, 3.85s/it] 26%|██▋ | 4929/18688 [5:45:55<14:25:56, 3.78s/it] 26%|██▋ | 4930/18688 [5:45:59<14:49:38, 3.88s/it] {'loss': '0.9425', 'grad_norm': '1.084', 'learning_rate': '9.05e-05', 'epoch': '0.2638', 'num_input_tokens_seen': 16562256, 'train_runtime': '2.076e+04', 'train_tokens_per_second': '797.8'}
26%|██▋ | 4930/18688 [5:45:59<14:49:38, 3.88s/it] 26%|██▋ | 4931/18688 [5:46:03<15:52:37, 4.15s/it] 26%|██▋ | 4932/18688 [5:46:08<16:20:03, 4.27s/it] 26%|██▋ | 4933/18688 [5:46:11<15:23:24, 4.03s/it] 26%|██▋ | 4934/18688 [5:46:15<14:45:52, 3.86s/it] 26%|██▋ | 4935/18688 [5:46:19<15:03:11, 3.94s/it] 26%|██▋ | 4936/18688 [5:46:23<14:44:06, 3.86s/it] 26%|██▋ | 4937/18688 [5:46:26<14:34:59, 3.82s/it] 26%|██▋ | 4938/18688 [5:46:30<14:43:42, 3.86s/it] 26%|██▋ | 4939/18688 [5:46:35<15:08:52, 3.97s/it] 26%|██▋ | 4940/18688 [5:46:39<15:26:19, 4.04s/it] {'loss': '0.8835', 'grad_norm': '1.279', 'learning_rate': '9.044e-05', 'epoch': '0.2643', 'num_input_tokens_seen': 16593744, 'train_runtime': '2.08e+04', 'train_tokens_per_second': '797.8'}
26%|██▋ | 4940/18688 [5:46:39<15:26:19, 4.04s/it] 26%|██▋ | 4941/18688 [5:46:43<15:45:42, 4.13s/it] 26%|██▋ | 4942/18688 [5:46:47<15:13:47, 3.99s/it] 26%|██▋ | 4943/18688 [5:46:51<15:39:04, 4.10s/it] 26%|██▋ | 4944/18688 [5:46:55<15:42:42, 4.12s/it] 26%|██▋ | 4945/18688 [5:47:00<16:24:55, 4.30s/it] 26%|██▋ | 4946/18688 [5:47:04<15:57:15, 4.18s/it] 26%|██▋ | 4947/18688 [5:47:08<15:51:47, 4.16s/it] 26%|██▋ | 4948/18688 [5:47:13<16:40:56, 4.37s/it] 26%|██▋ | 4949/18688 [5:47:18<17:12:45, 4.51s/it] 26%|██▋ | 4950/18688 [5:47:22<17:05:17, 4.48s/it] {'loss': '0.9066', 'grad_norm': '1.261', 'learning_rate': '9.039e-05', 'epoch': '0.2649', 'num_input_tokens_seen': 16628392, 'train_runtime': '2.084e+04', 'train_tokens_per_second': '797.8'}
26%|██▋ | 4950/18688 [5:47:22<17:05:17, 4.48s/it] 26%|██▋ | 4951/18688 [5:47:26<15:55:25, 4.17s/it] 26%|██▋ | 4952/18688 [5:47:30<15:40:20, 4.11s/it] 27%|██▋ | 4953/18688 [5:47:34<15:53:32, 4.17s/it] 27%|██▋ | 4954/18688 [5:47:38<15:22:33, 4.03s/it] 27%|██▋ | 4955/18688 [5:47:41<14:58:27, 3.93s/it] 27%|██▋ | 4956/18688 [5:47:45<14:35:50, 3.83s/it] 27%|██▋ | 4957/18688 [5:47:49<14:23:35, 3.77s/it] 27%|██▋ | 4958/18688 [5:47:53<14:59:27, 3.93s/it] 27%|██▋ | 4959/18688 [5:47:57<14:47:01, 3.88s/it] 27%|██▋ | 4960/18688 [5:48:01<15:23:49, 4.04s/it] {'loss': '0.9299', 'grad_norm': '1.406', 'learning_rate': '9.034e-05', 'epoch': '0.2654', 'num_input_tokens_seen': 16659344, 'train_runtime': '2.088e+04', 'train_tokens_per_second': '797.8'}
27%|██▋ | 4960/18688 [5:48:01<15:23:49, 4.04s/it] 27%|██▋ | 4961/18688 [5:48:05<15:00:08, 3.93s/it] 27%|██▋ | 4962/18688 [5:48:08<14:36:42, 3.83s/it] 27%|██▋ | 4963/18688 [5:48:12<14:36:02, 3.83s/it] 27%|██▋ | 4964/18688 [5:48:17<15:33:49, 4.08s/it] 27%|██▋ | 4965/18688 [5:48:20<15:06:26, 3.96s/it] 27%|██▋ | 4966/18688 [5:48:24<14:37:58, 3.84s/it] 27%|██▋ | 4967/18688 [5:48:28<14:34:12, 3.82s/it] 27%|██▋ | 4968/18688 [5:48:32<15:02:03, 3.94s/it] 27%|██▋ | 4969/18688 [5:48:37<16:31:33, 4.34s/it] 27%|██▋ | 4970/18688 [5:48:42<16:47:53, 4.41s/it] {'loss': '0.9711', 'grad_norm': '1.042', 'learning_rate': '9.028e-05', 'epoch': '0.266', 'num_input_tokens_seen': 16692968, 'train_runtime': '2.092e+04', 'train_tokens_per_second': '797.9'}
27%|██▋ | 4970/18688 [5:48:42<16:47:53, 4.41s/it] 27%|██▋ | 4971/18688 [5:48:46<16:35:14, 4.35s/it] 27%|██▋ | 4972/18688 [5:48:50<16:38:53, 4.37s/it] 27%|██▋ | 4973/18688 [5:48:55<16:59:24, 4.46s/it] 27%|██▋ | 4974/18688 [5:49:00<17:38:29, 4.63s/it] 27%|██▋ | 4975/18688 [5:49:04<16:42:25, 4.39s/it] 27%|██▋ | 4976/18688 [5:49:08<16:19:23, 4.29s/it] 27%|██▋ | 4977/18688 [5:49:12<15:25:15, 4.05s/it] 27%|██▋ | 4978/18688 [5:49:16<15:44:28, 4.13s/it] 27%|██▋ | 4979/18688 [5:49:20<15:47:27, 4.15s/it] 27%|██▋ | 4980/18688 [5:49:24<15:39:39, 4.11s/it] {'loss': '0.9571', 'grad_norm': '1.275', 'learning_rate': '9.023e-05', 'epoch': '0.2665', 'num_input_tokens_seen': 16727232, 'train_runtime': '2.096e+04', 'train_tokens_per_second': '797.9'}
27%|██▋ | 4980/18688 [5:49:24<15:39:39, 4.11s/it] 27%|██▋ | 4981/18688 [5:49:28<15:09:52, 3.98s/it] 27%|██▋ | 4982/18688 [5:49:31<14:52:38, 3.91s/it] 27%|██▋ | 4983/18688 [5:49:36<15:25:22, 4.05s/it] 27%|██▋ | 4984/18688 [5:49:40<15:23:47, 4.04s/it] 27%|██▋ | 4985/18688 [5:49:44<15:01:39, 3.95s/it] 27%|██▋ | 4986/18688 [5:49:48<15:45:51, 4.14s/it] 27%|██▋ | 4987/18688 [5:49:52<15:07:58, 3.98s/it] 27%|██▋ | 4988/18688 [5:49:56<15:03:35, 3.96s/it] 27%|██▋ | 4989/18688 [5:50:01<16:06:33, 4.23s/it] 27%|██▋ | 4990/18688 [5:50:05<15:53:37, 4.18s/it] {'loss': '0.9699', 'grad_norm': '1.166', 'learning_rate': '9.017e-05', 'epoch': '0.267', 'num_input_tokens_seen': 16760360, 'train_runtime': '2.101e+04', 'train_tokens_per_second': '797.9'}
27%|██▋ | 4990/18688 [5:50:05<15:53:37, 4.18s/it] 27%|██▋ | 4991/18688 [5:50:09<15:45:16, 4.14s/it] 27%|██▋ | 4992/18688 [5:50:13<15:59:44, 4.20s/it] 27%|██▋ | 4993/18688 [5:50:18<16:48:48, 4.42s/it] 27%|██▋ | 4994/18688 [5:50:22<15:48:43, 4.16s/it] 27%|██▋ | 4995/18688 [5:50:25<15:27:07, 4.06s/it] 27%|██▋ | 4996/18688 [5:50:30<15:31:56, 4.08s/it] 27%|██▋ | 4997/18688 [5:50:34<16:04:44, 4.23s/it] 27%|██▋ | 4998/18688 [5:50:38<16:07:25, 4.24s/it] 27%|██▋ | 4999/18688 [5:50:42<15:44:49, 4.14s/it] 27%|██▋ | 5000/18688 [5:50:47<15:55:44, 4.19s/it] {'loss': '0.9629', 'grad_norm': '1.235', 'learning_rate': '9.012e-05', 'epoch': '0.2676', 'num_input_tokens_seen': 16794000, 'train_runtime': '2.105e+04', 'train_tokens_per_second': '797.9'}
27%|██▋ | 5000/18688 [5:50:47<15:55:44, 4.19s/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, 25.94it/s]
1%| | 6/1000 [00:00<00:54, 18.35it/s]
1%| | 8/1000 [00:00<00:56, 17.58it/s]
1%| | 10/1000 [00:00<00:59, 16.64it/s]
1%| | 12/1000 [00:00<01:00, 16.33it/s]
1%|▏ | 14/1000 [00:00<01:02, 15.86it/s]
2%|▏ | 16/1000 [00:00<01:02, 15.67it/s]
2%|▏ | 18/1000 [00:01<01:38, 9.99it/s]
2%|▏ | 20/1000 [00:01<01:32, 10.54it/s]
2%|▏ | 22/1000 [00:01<01:52, 8.73it/s]
2%|▏ | 24/1000 [00:01<01:39, 9.79it/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, 11.02it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.17it/s]
3%|▎ | 34/1000 [00:02<01:25, 11.26it/s]
4%|▎ | 36/1000 [00:03<01:32, 10.44it/s]
4%|▍ | 38/1000 [00:03<01:24, 11.36it/s]
4%|▍ | 40/1000 [00:03<01:27, 10.96it/s]
4%|▍ | 42/1000 [00:03<01:46, 9.04it/s]
4%|▍ | 44/1000 [00:03<01:30, 10.51it/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, 11.67it/s]
5%|▌ | 52/1000 [00:04<01:15, 12.53it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.28it/s]
6%|▌ | 56/1000 [00:04<01:07, 13.96it/s]
6%|▌ | 58/1000 [00:05<01:47, 8.74it/s]
6%|▌ | 60/1000 [00:05<01:36, 9.78it/s]
6%|▌ | 62/1000 [00:05<01:27, 10.77it/s]
6%|▋ | 64/1000 [00:05<01:19, 11.83it/s]
7%|▋ | 66/1000 [00:05<01:15, 12.34it/s]
7%|▋ | 68/1000 [00:05<01:12, 12.93it/s]
7%|▋ | 70/1000 [00:05<01:08, 13.52it/s]
7%|▋ | 72/1000 [00:06<01:06, 13.97it/s]
7%|▋ | 74/1000 [00:06<01:07, 13.72it/s]
8%|▊ | 76/1000 [00:06<01:04, 14.33it/s]
8%|▊ | 78/1000 [00:06<01:03, 14.56it/s]
8%|▊ | 80/1000 [00:06<01:08, 13.53it/s]
8%|▊ | 82/1000 [00:06<01:04, 14.14it/s]
8%|▊ | 84/1000 [00:06<01:02, 14.61it/s]
9%|▊ | 86/1000 [00:07<01:01, 14.75it/s]
9%|▉ | 88/1000 [00:07<01:02, 14.55it/s]
9%|▉ | 90/1000 [00:07<01:02, 14.57it/s]
9%|▉ | 92/1000 [00:07<01:03, 14.24it/s]
9%|▉ | 94/1000 [00:07<01:04, 14.03it/s]
10%|▉ | 96/1000 [00:07<01:03, 14.34it/s]
10%|▉ | 98/1000 [00:07<01:03, 14.25it/s]
10%|█ | 100/1000 [00:08<01:31, 9.81it/s]
10%|█ | 102/1000 [00:08<01:23, 10.77it/s]
10%|█ | 104/1000 [00:08<01:54, 7.83it/s]
11%|█ | 106/1000 [00:09<02:01, 7.34it/s]
11%|█ | 108/1000 [00:09<01:42, 8.69it/s]
11%|█ | 110/1000 [00:09<01:27, 10.15it/s]
11%|█ | 112/1000 [00:09<01:18, 11.26it/s]
11%|█▏ | 114/1000 [00:09<01:14, 11.93it/s]
12%|█▏ | 116/1000 [00:09<01:10, 12.56it/s]
12%|█▏ | 118/1000 [00:09<01:06, 13.36it/s]
12%|█▏ | 120/1000 [00:10<01:08, 12.78it/s]
12%|█▏ | 122/1000 [00:10<01:22, 10.61it/s]
12%|█▏ | 124/1000 [00:10<01:30, 9.69it/s]
13%|█▎ | 126/1000 [00:10<01:22, 10.66it/s]
13%|█▎ | 128/1000 [00:10<01:15, 11.59it/s]
13%|█▎ | 130/1000 [00:11<01:10, 12.27it/s]
13%|█▎ | 132/1000 [00:11<01:05, 13.25it/s]
13%|█▎ | 134/1000 [00:11<01:09, 12.42it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.39it/s]
14%|█▍ | 138/1000 [00:11<01:04, 13.30it/s]
14%|█▍ | 140/1000 [00:11<01:24, 10.14it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.57it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.60it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.63it/s]
15%|█▍ | 148/1000 [00:12<01:13, 11.55it/s]
15%|█▌ | 150/1000 [00:12<01:07, 12.64it/s]
15%|█▌ | 152/1000 [00:12<01:04, 13.18it/s]
15%|█▌ | 154/1000 [00:13<01:02, 13.44it/s]
16%|█▌ | 156/1000 [00:13<01:03, 13.27it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.69it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.59it/s]
16%|█▌ | 162/1000 [00:13<01:02, 13.49it/s]
16%|█▋ | 164/1000 [00:13<01:01, 13.60it/s]
17%|█▋ | 166/1000 [00:13<01:00, 13.73it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.23it/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<01:29, 9.19it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.20it/s]
18%|█▊ | 178/1000 [00:15<01:34, 8.68it/s]
18%|█▊ | 180/1000 [00:15<01:33, 8.81it/s]
18%|█▊ | 182/1000 [00:15<01:35, 8.59it/s]
18%|█▊ | 184/1000 [00:16<01:23, 9.83it/s]
19%|█▊ | 186/1000 [00:16<01:37, 8.37it/s]
19%|█▉ | 188/1000 [00:16<01:23, 9.69it/s]
19%|█▉ | 190/1000 [00:16<01:15, 10.73it/s]
19%|█▉ | 192/1000 [00:16<01:10, 11.38it/s]
19%|█▉ | 194/1000 [00:16<01:16, 10.60it/s]
20%|█▉ | 196/1000 [00:17<01:29, 8.99it/s]
20%|█▉ | 198/1000 [00:17<01:18, 10.23it/s]
20%|██ | 200/1000 [00:17<01:15, 10.57it/s]
20%|██ | 202/1000 [00:17<01:09, 11.51it/s]
20%|██ | 204/1000 [00:17<01:03, 12.61it/s]
21%|██ | 206/1000 [00:17<00:59, 13.24it/s]
21%|██ | 208/1000 [00:18<00:58, 13.62it/s]
21%|██ | 210/1000 [00:18<00:54, 14.49it/s]
21%|██ | 212/1000 [00:18<00:53, 14.76it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.72it/s]
22%|██▏ | 216/1000 [00:18<00:54, 14.43it/s]
22%|██▏ | 218/1000 [00:18<00:52, 14.76it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.90it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.21it/s]
22%|██▏ | 224/1000 [00:19<01:20, 9.69it/s]
23%|██▎ | 226/1000 [00:19<01:12, 10.68it/s]
23%|██▎ | 228/1000 [00:19<01:06, 11.58it/s]
23%|██▎ | 230/1000 [00:20<01:03, 12.08it/s]
23%|██▎ | 232/1000 [00:20<01:01, 12.45it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.17it/s]
24%|██▎ | 236/1000 [00:20<00:56, 13.62it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.25it/s]
24%|██▍ | 240/1000 [00:20<00:55, 13.66it/s]
24%|██▍ | 242/1000 [00:20<00:52, 14.35it/s]
24%|██▍ | 244/1000 [00:20<00:51, 14.66it/s]
25%|██▍ | 246/1000 [00:21<00:51, 14.77it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.58it/s]
25%|██▌ | 250/1000 [00:21<00:56, 13.36it/s]
25%|██▌ | 252/1000 [00:21<01:01, 12.11it/s]
25%|██▌ | 254/1000 [00:21<00:56, 13.10it/s]
26%|██▌ | 256/1000 [00:21<00:54, 13.70it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.76it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.92it/s]
26%|██▌ | 262/1000 [00:22<00:56, 13.08it/s]
26%|██▋ | 264/1000 [00:22<00:53, 13.68it/s]
27%|██▋ | 266/1000 [00:22<00:52, 14.02it/s]
27%|██▋ | 268/1000 [00:22<00:50, 14.57it/s]
27%|██▋ | 270/1000 [00:22<00:54, 13.29it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.66it/s]
27%|██▋ | 274/1000 [00:23<01:01, 11.75it/s]
28%|██▊ | 276/1000 [00:23<00:56, 12.78it/s]
28%|██▊ | 278/1000 [00:23<00:55, 13.00it/s]
28%|██▊ | 280/1000 [00:23<00:53, 13.53it/s]
28%|██▊ | 282/1000 [00:23<00:49, 14.40it/s]
28%|██▊ | 284/1000 [00:23<00:49, 14.36it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.65it/s]
29%|██▉ | 288/1000 [00:24<00:47, 15.04it/s]
29%|██▉ | 290/1000 [00:24<00:45, 15.48it/s]
29%|██▉ | 292/1000 [00:24<00:45, 15.45it/s]
29%|██▉ | 294/1000 [00:24<00:45, 15.35it/s]
30%|██▉ | 296/1000 [00:24<00:45, 15.41it/s]
30%|██▉ | 298/1000 [00:24<00:45, 15.47it/s]
30%|███ | 300/1000 [00:25<00:46, 15.14it/s]
30%|███ | 302/1000 [00:25<00:46, 15.17it/s]
30%|███ | 304/1000 [00:25<00:50, 13.78it/s]
31%|███ | 306/1000 [00:25<00:47, 14.53it/s]
31%|███ | 308/1000 [00:25<00:47, 14.49it/s]
31%|███ | 310/1000 [00:25<00:46, 14.69it/s]
31%|███ | 312/1000 [00:25<00:46, 14.66it/s]
31%|███▏ | 314/1000 [00:25<00:44, 15.37it/s]
32%|███▏ | 316/1000 [00:26<00:43, 15.65it/s]
32%|███▏ | 318/1000 [00:26<00:42, 16.09it/s]
32%|███▏ | 320/1000 [00:26<01:12, 9.39it/s]
32%|███▏ | 322/1000 [00:26<01:02, 10.77it/s]
32%|███▏ | 324/1000 [00:26<00:58, 11.57it/s]
33%|███▎ | 326/1000 [00:27<00:58, 11.51it/s]
33%|███▎ | 328/1000 [00:27<00:58, 11.56it/s]
33%|███▎ | 330/1000 [00:27<00:53, 12.51it/s]
33%|███▎ | 332/1000 [00:27<00:52, 12.76it/s]
33%|███▎ | 334/1000 [00:27<00:50, 13.24it/s]
34%|███▎ | 336/1000 [00:27<00:48, 13.63it/s]
34%|███▍ | 338/1000 [00:28<01:05, 10.09it/s]
34%|███▍ | 340/1000 [00:28<00:59, 11.15it/s]
34%|███▍ | 342/1000 [00:28<00:52, 12.42it/s]
34%|███▍ | 344/1000 [00:28<00:50, 13.10it/s]
35%|███▍ | 346/1000 [00:28<00:47, 13.70it/s]
35%|███▍ | 348/1000 [00:28<00:46, 14.12it/s]
35%|███▌ | 350/1000 [00:28<00:44, 14.75it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.88it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.88it/s]
36%|███▌ | 356/1000 [00:29<00:43, 14.95it/s]
36%|███▌ | 358/1000 [00:29<00:43, 14.68it/s]
36%|███▌ | 360/1000 [00:29<00:42, 15.07it/s]
36%|███▌ | 362/1000 [00:29<00:47, 13.40it/s]
36%|███▋ | 364/1000 [00:29<00:45, 13.89it/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<00:47, 13.21it/s]
37%|███▋ | 372/1000 [00:30<00:46, 13.55it/s]
37%|███▋ | 374/1000 [00:30<00:54, 11.40it/s]
38%|███▊ | 376/1000 [00:30<00:57, 10.80it/s]
38%|███▊ | 378/1000 [00:31<00:55, 11.16it/s]
38%|███▊ | 380/1000 [00:31<00:51, 11.98it/s]
38%|███▊ | 382/1000 [00:31<00:52, 11.86it/s]
38%|███▊ | 384/1000 [00:31<00:49, 12.54it/s]
39%|███▊ | 386/1000 [00:31<00:47, 13.06it/s]
39%|███▉ | 388/1000 [00:31<00:46, 13.16it/s]
39%|███▉ | 390/1000 [00:32<00:46, 13.21it/s]
39%|███▉ | 392/1000 [00:32<00:44, 13.52it/s]
39%|███▉ | 394/1000 [00:32<00:45, 13.34it/s]
40%|███▉ | 396/1000 [00:32<00:44, 13.46it/s]
40%|███▉ | 398/1000 [00:32<00:48, 12.40it/s]
40%|████ | 400/1000 [00:32<00:45, 13.06it/s]
40%|████ | 402/1000 [00:32<00:43, 13.71it/s]
40%|████ | 404/1000 [00:33<00:41, 14.37it/s]
41%|████ | 406/1000 [00:33<00:39, 14.93it/s]
41%|████ | 408/1000 [00:33<00:47, 12.52it/s]
41%|████ | 410/1000 [00:33<00:56, 10.48it/s]
41%|████ | 412/1000 [00:33<00:50, 11.58it/s]
41%|████▏ | 414/1000 [00:34<01:07, 8.65it/s]
42%|████▏ | 416/1000 [00:34<01:23, 6.97it/s]
42%|████▏ | 418/1000 [00:34<01:10, 8.26it/s]
42%|████▏ | 420/1000 [00:34<01:00, 9.52it/s]
42%|████▏ | 422/1000 [00:34<00:53, 10.86it/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 [00:35<01:02, 9.12it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.05it/s]
43%|████▎ | 434/1000 [00:36<01:08, 8.28it/s]
44%|████▎ | 436/1000 [00:36<01:00, 9.34it/s]
44%|████▍ | 438/1000 [00:36<01:02, 9.02it/s]
44%|████▍ | 440/1000 [00:37<00:58, 9.52it/s]
44%|████▍ | 442/1000 [00:37<00:52, 10.59it/s]
44%|████▍ | 444/1000 [00:37<00:48, 11.45it/s]
45%|████▍ | 446/1000 [00:37<00:45, 12.21it/s]
45%|████▍ | 448/1000 [00:37<00:50, 11.00it/s]
45%|████▌ | 450/1000 [00:37<00:46, 11.83it/s]
45%|████▌ | 452/1000 [00:37<00:42, 12.83it/s]
45%|████▌ | 454/1000 [00:38<00:44, 12.32it/s]
46%|████▌ | 456/1000 [00:38<00:42, 12.77it/s]
46%|████▌ | 458/1000 [00:38<00:39, 13.77it/s]
46%|████▌ | 460/1000 [00:38<00:38, 13.92it/s]
46%|████▌ | 462/1000 [00:38<00:37, 14.51it/s]
46%|████▋ | 464/1000 [00:38<00:35, 15.01it/s]
47%|████▋ | 466/1000 [00:38<00:35, 14.85it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.95it/s]
47%|████▋ | 470/1000 [00:39<00:52, 10.10it/s]
47%|████▋ | 472/1000 [00:39<00:51, 10.26it/s]
47%|████▋ | 474/1000 [00:39<00:45, 11.49it/s]
48%|████▊ | 476/1000 [00:39<00:43, 12.14it/s]
48%|████▊ | 478/1000 [00:40<00:43, 11.97it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.91it/s]
48%|████▊ | 482/1000 [00:40<00:38, 13.31it/s]
48%|████▊ | 484/1000 [00:40<00:37, 13.82it/s]
49%|████▊ | 486/1000 [00:40<00:36, 14.01it/s]
49%|████▉ | 488/1000 [00:40<00:35, 14.24it/s]
49%|████▉ | 490/1000 [00:41<00:45, 11.12it/s]
49%|████▉ | 492/1000 [00:41<00:40, 12.41it/s]
49%|████▉ | 494/1000 [00:41<00:37, 13.41it/s]
50%|████▉ | 496/1000 [00:41<00:35, 14.17it/s]
50%|████▉ | 498/1000 [00:41<00:34, 14.57it/s]
50%|█████ | 500/1000 [00:41<00:43, 11.60it/s]
50%|█████ | 502/1000 [00:41<00:41, 12.05it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.64it/s]
51%|█████ | 506/1000 [00:42<00:38, 12.83it/s]
51%|█████ | 508/1000 [00:42<00:35, 13.76it/s]
51%|█████ | 510/1000 [00:42<00:47, 10.41it/s]
51%|█████ | 512/1000 [00:42<00:43, 11.19it/s]
51%|█████▏ | 514/1000 [00:42<00:40, 12.06it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.62it/s]
52%|█████▏ | 518/1000 [00:43<00:36, 13.14it/s]
52%|█████▏ | 520/1000 [00:43<00:35, 13.51it/s]
52%|█████▏ | 522/1000 [00:43<00:33, 14.23it/s]
52%|█████▏ | 524/1000 [00:43<00:33, 14.24it/s]
53%|█████▎ | 526/1000 [00:43<00:32, 14.38it/s]
53%|█████▎ | 528/1000 [00:43<00:39, 12.00it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.83it/s]
53%|█████▎ | 532/1000 [00:44<00:33, 13.77it/s]
53%|█████▎ | 534/1000 [00:44<00:32, 14.13it/s]
54%|█████▎ | 536/1000 [00:44<00:32, 14.26it/s]
54%|█████▍ | 538/1000 [00:44<00:32, 14.01it/s]
54%|█████▍ | 540/1000 [00:44<00:31, 14.44it/s]
54%|█████▍ | 542/1000 [00:44<00:31, 14.72it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.50it/s]
55%|█████▍ | 546/1000 [00:45<00:32, 14.15it/s]
55%|█████▍ | 548/1000 [00:45<00:32, 13.87it/s]
55%|█████▌ | 550/1000 [00:45<00:31, 14.12it/s]
55%|█████▌ | 552/1000 [00:45<00:31, 14.24it/s]
55%|█████▌ | 554/1000 [00:45<00:32, 13.93it/s]
56%|█████▌ | 556/1000 [00:45<00:31, 14.12it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.47it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 15.01it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 14.98it/s]
56%|█████▋ | 564/1000 [00:46<00:40, 10.81it/s]
57%|█████▋ | 566/1000 [00:46<00:37, 11.72it/s]
57%|█████▋ | 568/1000 [00:46<00:35, 12.27it/s]
57%|█████▋ | 570/1000 [00:46<00:32, 13.36it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.87it/s]
57%|█████▋ | 574/1000 [00:47<00:32, 13.06it/s]
58%|█████▊ | 576/1000 [00:47<00:33, 12.76it/s]
58%|█████▊ | 578/1000 [00:47<00:38, 11.04it/s]
58%|█████▊ | 580/1000 [00:47<00:36, 11.44it/s]
58%|█████▊ | 582/1000 [00:48<00:35, 11.72it/s]
58%|█████▊ | 584/1000 [00:48<00:35, 11.82it/s]
59%|█████▊ | 586/1000 [00:48<00:34, 12.08it/s]
59%|█████▉ | 588/1000 [00:48<00:33, 12.31it/s]
59%|█████▉ | 590/1000 [00:48<00:33, 12.34it/s]
59%|█████▉ | 592/1000 [00:48<00:33, 12.34it/s]
59%|█████▉ | 594/1000 [00:49<00:34, 11.69it/s]
60%|█████▉ | 596/1000 [00:49<00:34, 11.86it/s]
60%|█████▉ | 598/1000 [00:49<00:33, 12.15it/s]
60%|██████ | 600/1000 [00:49<00:32, 12.33it/s]
60%|██████ | 602/1000 [00:49<00:31, 12.45it/s]
60%|██████ | 604/1000 [00:49<00:31, 12.44it/s]
61%|██████ | 606/1000 [00:49<00:31, 12.38it/s]
61%|██████ | 608/1000 [00:50<00:31, 12.31it/s]
61%|██████ | 610/1000 [00:50<00:32, 12.14it/s]
61%|██████ | 612/1000 [00:50<00:32, 11.99it/s]
61%|██████▏ | 614/1000 [00:50<00:32, 11.98it/s]
62%|██████▏ | 616/1000 [00:50<00:29, 12.86it/s]
62%|██████▏ | 618/1000 [00:50<00:29, 12.90it/s]
62%|██████▏ | 620/1000 [00:51<00:40, 9.29it/s]
62%|██████▏ | 622/1000 [00:51<00:39, 9.63it/s]
62%|██████▏ | 624/1000 [00:51<00:33, 11.09it/s]
63%|██████▎ | 626/1000 [00:51<00:31, 11.84it/s]
63%|██████▎ | 628/1000 [00:51<00:28, 13.08it/s]
63%|██████▎ | 630/1000 [00:51<00:27, 13.66it/s]
63%|██████▎ | 632/1000 [00:52<00:29, 12.39it/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, 10.66it/s]
64%|██████▍ | 640/1000 [00:52<00:31, 11.45it/s]
64%|██████▍ | 642/1000 [00:53<00:28, 12.35it/s]
64%|██████▍ | 644/1000 [00:53<00:28, 12.62it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.91it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.40it/s]
65%|██████▌ | 650/1000 [00:53<00:27, 12.60it/s]
65%|██████▌ | 652/1000 [00:53<00:29, 11.99it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.46it/s]
66%|██████▌ | 656/1000 [00:54<00:31, 10.79it/s]
66%|██████▌ | 658/1000 [00:54<00:29, 11.75it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.12it/s]
66%|██████▌ | 662/1000 [00:54<00:30, 11.26it/s]
66%|██████▋ | 664/1000 [00:54<00:28, 11.75it/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 [00:55<00:26, 12.45it/s]
68%|██████▊ | 676/1000 [00:55<00:27, 11.72it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.21it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 12.99it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.15it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.37it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.44it/s]
69%|██████▉ | 688/1000 [00:56<00:22, 13.75it/s]
69%|██████▉ | 690/1000 [00:56<00:22, 13.84it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.29it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.36it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 14.11it/s]
70%|██████▉ | 698/1000 [00:57<00:20, 14.66it/s]
70%|███████ | 700/1000 [00:57<00:24, 12.23it/s]
70%|███████ | 702/1000 [00:57<00:22, 13.03it/s]
70%|███████ | 704/1000 [00:58<00:29, 9.90it/s]
71%|███████ | 706/1000 [00:58<00:27, 10.70it/s]
71%|███████ | 708/1000 [00:58<00:30, 9.65it/s]
71%|███████ | 710/1000 [00:58<00:26, 10.95it/s]
71%|███████ | 712/1000 [00:58<00:24, 11.92it/s]
71%|███████▏ | 714/1000 [00:58<00:21, 13.01it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 13.12it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.61it/s]
72%|███████▏ | 720/1000 [00:59<00:20, 13.57it/s]
72%|███████▏ | 722/1000 [00:59<00:19, 14.01it/s]
72%|███████▏ | 724/1000 [00:59<00:19, 13.83it/s]
73%|███████▎ | 726/1000 [00:59<00:19, 14.03it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.95it/s]
73%|███████▎ | 730/1000 [01:00<00:32, 8.21it/s]
73%|███████▎ | 732/1000 [01:00<00:32, 8.33it/s]
73%|███████▎ | 734/1000 [01:00<00:30, 8.59it/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 [01:01<00:22, 11.70it/s]
74%|███████▍ | 743/1000 [01:01<00:20, 12.33it/s]
74%|███████▍ | 745/1000 [01:01<00:24, 10.20it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.58it/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, 12.62it/s]
76%|███████▌ | 755/1000 [01:02<00:18, 13.34it/s]
76%|███████▌ | 757/1000 [01:02<00:23, 10.31it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.35it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.34it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.28it/s]
76%|███████▋ | 765/1000 [01:03<00:27, 8.62it/s]
77%|███████▋ | 767/1000 [01:03<00:24, 9.58it/s]
77%|███████▋ | 769/1000 [01:04<00:21, 10.60it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.86it/s]
77%|███████▋ | 773/1000 [01:04<00:17, 12.63it/s]
78%|███████▊ | 775/1000 [01:04<00:17, 13.18it/s]
78%|███████▊ | 777/1000 [01:04<00:15, 14.11it/s]
78%|███████▊ | 779/1000 [01:04<00:21, 10.50it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.41it/s]
78%|███████▊ | 783/1000 [01:05<00:26, 8.31it/s]
78%|███████▊ | 784/1000 [01:05<00:30, 7.07it/s]
78%|███████▊ | 785/1000 [01:05<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, 10.15it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 11.30it/s]
79%|███████▉ | 794/1000 [01:06<00:17, 11.94it/s]
80%|███████▉ | 796/1000 [01:06<00:16, 12.00it/s]
80%|███████▉ | 798/1000 [01:06<00:15, 12.86it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.19it/s]
80%|████████ | 802/1000 [01:07<00:24, 8.22it/s]
80%|████████ | 804/1000 [01:07<00:23, 8.26it/s]
81%|████████ | 806/1000 [01:07<00:21, 8.95it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.35it/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, 12.61it/s]
82%|████████▏ | 816/1000 [01:08<00:16, 11.04it/s]
82%|████████▏ | 818/1000 [01:08<00:15, 12.00it/s]
82%|████████▏ | 820/1000 [01:08<00:14, 12.49it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 13.05it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.85it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.27it/s]
83%|████████▎ | 828/1000 [01:09<00:11, 14.55it/s]
83%|████████▎ | 830/1000 [01:09<00:12, 13.98it/s]
83%|████████▎ | 832/1000 [01:09<00:11, 14.37it/s]
83%|████████▎ | 834/1000 [01:09<00:11, 14.11it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.48it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.36it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.37it/s]
84%|████████▍ | 842/1000 [01:10<00:10, 14.40it/s]
84%|████████▍ | 844/1000 [01:10<00:11, 13.92it/s]
85%|████████▍ | 846/1000 [01:10<00:11, 13.91it/s]
85%|████████▍ | 848/1000 [01:10<00:10, 13.91it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.13it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.74it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.26it/s]
86%|████████▌ | 856/1000 [01:11<00:10, 14.11it/s]
86%|████████▌ | 858/1000 [01:11<00:09, 14.50it/s]
86%|████████▌ | 860/1000 [01:11<00:09, 15.23it/s]
86%|████████▌ | 862/1000 [01:11<00:10, 13.47it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 14.02it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.85it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.72it/s]
87%|████████▋ | 870/1000 [01:12<00:09, 14.11it/s]
87%|████████▋ | 872/1000 [01:12<00:08, 14.73it/s]
87%|████████▋ | 874/1000 [01:12<00:10, 12.49it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.09it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.97it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.61it/s]
88%|████████▊ | 882/1000 [01:13<00:10, 11.40it/s]
88%|████████▊ | 884/1000 [01:13<00:09, 12.14it/s]
89%|████████▊ | 886/1000 [01:13<00:08, 12.92it/s]
89%|████████▉ | 888/1000 [01:13<00:08, 13.87it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.29it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.09it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 14.24it/s]
90%|████████▉ | 896/1000 [01:14<00:07, 13.97it/s]
90%|████████▉ | 898/1000 [01:14<00:07, 13.36it/s]
90%|█████████ | 900/1000 [01:14<00:08, 12.25it/s]
90%|█████████ | 902/1000 [01:14<00:07, 12.99it/s]
90%|█████████ | 904/1000 [01:15<00:09, 9.82it/s]
91%|█████████ | 906/1000 [01:15<00:08, 10.90it/s]
91%|█████████ | 908/1000 [01:15<00:07, 11.87it/s]
91%|█████████ | 910/1000 [01:15<00:07, 12.12it/s]
91%|█████████ | 912/1000 [01:15<00:07, 12.33it/s]
91%|█████████▏| 914/1000 [01:16<00:12, 6.83it/s]
92%|█████████▏| 916/1000 [01:16<00:10, 8.15it/s]
92%|█████████▏| 918/1000 [01:16<00:11, 7.40it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.81it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 10.27it/s]
92%|█████████▏| 924/1000 [01:17<00:06, 10.98it/s]
93%|█████████▎| 926/1000 [01:17<00:06, 11.83it/s]
93%|█████████▎| 928/1000 [01:17<00:06, 10.65it/s]
93%|█████████▎| 930/1000 [01:17<00:05, 11.85it/s]
93%|█████████▎| 932/1000 [01:17<00:05, 12.45it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.92it/s]
94%|█████████▎| 936/1000 [01:18<00:04, 13.48it/s]
94%|█████████▍| 938/1000 [01:18<00:04, 14.35it/s]
94%|█████████▍| 940/1000 [01:18<00:04, 14.63it/s]
94%|█████████▍| 942/1000 [01:18<00:04, 12.32it/s]
94%|█████████▍| 944/1000 [01:18<00:04, 12.85it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.91it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 12.71it/s]
95%|█████████▌| 950/1000 [01:19<00:03, 13.49it/s]
95%|█████████▌| 952/1000 [01:19<00:03, 14.21it/s]
95%|█████████▌| 954/1000 [01:19<00:03, 14.31it/s]
96%|█████████▌| 956/1000 [01:19<00:03, 14.29it/s]
96%|█████████▌| 958/1000 [01:19<00:02, 14.81it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 13.17it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.79it/s]
96%|█████████▋| 964/1000 [01:20<00:03, 10.25it/s]
97%|█████████▋| 966/1000 [01:20<00:02, 11.42it/s]
97%|█████████▋| 968/1000 [01:20<00:02, 12.09it/s]
97%|█████████▋| 970/1000 [01:20<00:02, 12.71it/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, 11.70it/s]
98%|█████████▊| 978/1000 [01:21<00:02, 9.89it/s]
98%|█████████▊| 980/1000 [01:21<00:02, 9.97it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.44it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 10.46it/s]
99%|█████████▊| 986/1000 [01:22<00:01, 10.71it/s]
99%|█████████▉| 988/1000 [01:22<00:01, 10.92it/s]
99%|█████████▉| 990/1000 [01:22<00:00, 11.42it/s]
99%|█████████▉| 992/1000 [01:22<00:00, 12.32it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 13.05it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 13.97it/s]
100%|█████████▉| 998/1000 [01:23<00:00, 14.14it/s]
100%|██████████| 1000/1000 [01:23<00:00, 14.82it/s]
{'eval_loss': '0.9337', 'eval_runtime': '83.54', 'eval_samples_per_second': '11.97', 'eval_steps_per_second': '11.97', 'epoch': '0.2676', 'num_input_tokens_seen': 16794000}
27%|██▋ | 5000/18688 [5:52:10<15:55:44, 4.19s/it]
100%|██████████| 1000/1000 [01:23<00:00, 14.82it/s]
 27%|██▋ | 5001/18688 [5:52:14<111:01:05, 29.20s/it] 27%|██▋ | 5002/18688 [5:52:18<82:29:07, 21.70s/it] 27%|██▋ | 5003/18688 [5:52:22<62:04:23, 16.33s/it] 27%|██▋ | 5004/18688 [5:52:26<47:46:34, 12.57s/it] 27%|██▋ | 5005/18688 [5:52:30<37:47:45, 9.94s/it] 27%|██▋ | 5006/18688 [5:52:34<30:58:55, 8.15s/it] 27%|██▋ | 5007/18688 [5:52:38<26:48:26, 7.05s/it] 27%|██▋ | 5008/18688 [5:52:42<22:48:30, 6.00s/it] 27%|██▋ | 5009/18688 [5:52:45<20:02:45, 5.28s/it] 27%|██▋ | 5010/18688 [5:52:49<18:13:09, 4.80s/it] {'loss': '0.8942', 'grad_norm': '1.294', 'learning_rate': '9.006e-05', 'epoch': '0.2681', 'num_input_tokens_seen': 16824824, 'train_runtime': '2.117e+04', 'train_tokens_per_second': '794.8'}
27%|██▋ | 5010/18688 [5:52:49<18:13:09, 4.80s/it] 27%|██▋ | 5011/18688 [5:52:52<16:43:46, 4.40s/it] 27%|██▋ | 5012/18688 [5:52:56<15:49:22, 4.17s/it] 27%|██▋ | 5013/18688 [5:53:01<16:23:43, 4.32s/it] 27%|██▋ | 5014/18688 [5:53:05<16:36:22, 4.37s/it] 27%|██▋ | 5015/18688 [5:53:09<16:19:46, 4.30s/it] 27%|██▋ | 5016/18688 [5:53:13<15:56:17, 4.20s/it] 27%|██▋ | 5017/18688 [5:53:17<15:37:31, 4.11s/it] 27%|██▋ | 5018/18688 [5:53:21<14:55:06, 3.93s/it] 27%|██▋ | 5019/18688 [5:53:24<14:38:05, 3.85s/it] 27%|██▋ | 5020/18688 [5:53:28<14:14:29, 3.75s/it] {'loss': '0.9244', 'grad_norm': '1.356', 'learning_rate': '9.001e-05', 'epoch': '0.2686', 'num_input_tokens_seen': 16854992, 'train_runtime': '2.121e+04', 'train_tokens_per_second': '794.7'}
27%|██▋ | 5020/18688 [5:53:28<14:14:29, 3.75s/it] 27%|██▋ | 5021/18688 [5:53:32<14:37:15, 3.85s/it] 27%|██▋ | 5022/18688 [5:53:36<14:12:01, 3.74s/it] 27%|██▋ | 5023/18688 [5:53:39<14:14:38, 3.75s/it] 27%|██▋ | 5024/18688 [5:53:43<14:40:57, 3.87s/it] 27%|██▋ | 5025/18688 [5:53:49<16:19:08, 4.30s/it] 27%|██▋ | 5026/18688 [5:53:53<16:10:23, 4.26s/it] 27%|██▋ | 5027/18688 [5:53:57<16:17:27, 4.29s/it] 27%|██▋ | 5028/18688 [5:54:01<16:03:50, 4.23s/it] 27%|██▋ | 5029/18688 [5:54:06<16:44:11, 4.41s/it] 27%|██▋ | 5030/18688 [5:54:10<15:59:27, 4.21s/it] {'loss': '0.9396', 'grad_norm': '1.337', 'learning_rate': '8.995e-05', 'epoch': '0.2692', 'num_input_tokens_seen': 16890760, 'train_runtime': '2.125e+04', 'train_tokens_per_second': '794.8'}
27%|██▋ | 5030/18688 [5:54:10<15:59:27, 4.21s/it] 27%|██▋ | 5031/18688 [5:54:14<15:31:56, 4.09s/it] 27%|██▋ | 5032/18688 [5:54:17<14:51:44, 3.92s/it] 27%|██▋ | 5033/18688 [5:54:22<15:35:24, 4.11s/it] 27%|██▋ | 5034/18688 [5:54:27<16:25:20, 4.33s/it] 27%|██▋ | 5035/18688 [5:54:31<16:03:23, 4.23s/it] 27%|██▋ | 5036/18688 [5:54:34<15:21:55, 4.05s/it] 27%|██▋ | 5037/18688 [5:54:39<15:56:27, 4.20s/it] 27%|██▋ | 5038/18688 [5:54:44<16:56:46, 4.47s/it] 27%|██▋ | 5039/18688 [5:54:48<16:43:09, 4.41s/it] 27%|██▋ | 5040/18688 [5:54:52<16:08:31, 4.26s/it] {'loss': '1.054', 'grad_norm': '1.31', 'learning_rate': '8.99e-05', 'epoch': '0.2697', 'num_input_tokens_seen': 16926128, 'train_runtime': '2.129e+04', 'train_tokens_per_second': '794.9'}
27%|██▋ | 5040/18688 [5:54:52<16:08:31, 4.26s/it] 27%|██▋ | 5041/18688 [5:54:56<15:14:27, 4.02s/it] 27%|██▋ | 5042/18688 [5:55:00<15:15:02, 4.02s/it] 27%|██▋ | 5043/18688 [5:55:03<14:44:07, 3.89s/it] 27%|██▋ | 5044/18688 [5:55:07<14:16:54, 3.77s/it] 27%|██▋ | 5045/18688 [5:55:10<14:18:46, 3.78s/it] 27%|██▋ | 5046/18688 [5:55:15<14:44:15, 3.89s/it] 27%|██▋ | 5047/18688 [5:55:20<15:58:19, 4.22s/it] 27%|██▋ | 5048/18688 [5:55:24<15:41:49, 4.14s/it] 27%|██▋ | 5049/18688 [5:55:28<15:50:52, 4.18s/it] 27%|██▋ | 5050/18688 [5:55:32<16:07:12, 4.26s/it] {'loss': '0.9808', 'grad_norm': '1.143', 'learning_rate': '8.984e-05', 'epoch': '0.2702', 'num_input_tokens_seen': 16959600, 'train_runtime': '2.133e+04', 'train_tokens_per_second': '795'}
27%|██▋ | 5050/18688 [5:55:32<16:07:12, 4.26s/it] 27%|██▋ | 5051/18688 [5:55:36<15:49:14, 4.18s/it] 27%|██▋ | 5052/18688 [5:55:40<15:15:26, 4.03s/it] 27%|██▋ | 5053/18688 [5:55:44<15:14:32, 4.02s/it] 27%|██▋ | 5054/18688 [5:55:48<15:40:54, 4.14s/it] 27%|██▋ | 5055/18688 [5:55:53<15:42:28, 4.15s/it] 27%|██▋ | 5056/18688 [5:55:57<15:49:49, 4.18s/it] 27%|██▋ | 5057/18688 [5:56:01<15:55:30, 4.21s/it] 27%|██▋ | 5058/18688 [5:56:05<15:11:03, 4.01s/it] 27%|██▋ | 5059/18688 [5:56:09<15:21:42, 4.06s/it] 27%|██▋ | 5060/18688 [5:56:12<14:56:40, 3.95s/it] {'loss': '1.009', 'grad_norm': '1.381', 'learning_rate': '8.979e-05', 'epoch': '0.2708', 'num_input_tokens_seen': 16991944, 'train_runtime': '2.137e+04', 'train_tokens_per_second': '795'}
27%|██▋ | 5060/18688 [5:56:12<14:56:40, 3.95s/it] 27%|██▋ | 5061/18688 [5:56:16<14:38:41, 3.87s/it] 27%|██▋ | 5062/18688 [5:56:21<15:51:54, 4.19s/it] 27%|██▋ | 5063/18688 [5:56:25<15:18:11, 4.04s/it] 27%|██▋ | 5064/18688 [5:56:29<15:48:03, 4.18s/it] 27%|██▋ | 5065/18688 [5:56:34<16:19:53, 4.32s/it] 27%|██▋ | 5066/18688 [5:56:38<16:12:34, 4.28s/it] 27%|██▋ | 5067/18688 [5:56:42<15:25:04, 4.07s/it] 27%|██▋ | 5068/18688 [5:56:46<15:38:12, 4.13s/it] 27%|██▋ | 5069/18688 [5:56:50<15:54:57, 4.21s/it] 27%|██▋ | 5070/18688 [5:56:54<15:06:05, 3.99s/it] {'loss': '0.9901', 'grad_norm': '1.229', 'learning_rate': '8.973e-05', 'epoch': '0.2713', 'num_input_tokens_seen': 17026992, 'train_runtime': '2.141e+04', 'train_tokens_per_second': '795.1'}
27%|██▋ | 5070/18688 [5:56:54<15:06:05, 3.99s/it] 27%|██▋ | 5071/18688 [5:56:58<15:31:01, 4.10s/it] 27%|██▋ | 5072/18688 [5:57:02<15:07:54, 4.00s/it] 27%|██▋ | 5073/18688 [5:57:06<15:20:59, 4.06s/it] 27%|██▋ | 5074/18688 [5:57:11<16:02:27, 4.24s/it] 27%|██▋ | 5075/18688 [5:57:14<15:19:15, 4.05s/it] 27%|██▋ | 5076/18688 [5:57:18<15:05:56, 3.99s/it] 27%|██▋ | 5077/18688 [5:57:22<14:59:56, 3.97s/it] 27%|██▋ | 5078/18688 [5:57:26<15:07:51, 4.00s/it] 27%|██▋ | 5079/18688 [5:57:30<14:36:29, 3.86s/it] 27%|██▋ | 5080/18688 [5:57:35<15:29:23, 4.10s/it] {'loss': '0.9538', 'grad_norm': '1.694', 'learning_rate': '8.968e-05', 'epoch': '0.2718', 'num_input_tokens_seen': 17059280, 'train_runtime': '2.146e+04', 'train_tokens_per_second': '795.1'}
27%|██▋ | 5080/18688 [5:57:35<15:29:23, 4.10s/it] 27%|██▋ | 5081/18688 [5:57:40<16:41:26, 4.42s/it] 27%|██▋ | 5082/18688 [5:57:45<18:06:03, 4.79s/it] 27%|██▋ | 5083/18688 [5:57:50<17:39:12, 4.67s/it] 27%|██▋ | 5084/18688 [5:57:55<18:25:56, 4.88s/it] 27%|██▋ | 5085/18688 [5:57:59<17:05:45, 4.52s/it] 27%|██▋ | 5086/18688 [5:58:04<18:04:21, 4.78s/it] 27%|██▋ | 5087/18688 [5:58:08<16:55:06, 4.48s/it] 27%|██▋ | 5088/18688 [5:58:12<16:05:11, 4.26s/it] 27%|██▋ | 5089/18688 [5:58:15<15:28:40, 4.10s/it] 27%|██▋ | 5090/18688 [5:58:19<15:10:22, 4.02s/it] {'loss': '0.9705', 'grad_norm': '1.43', 'learning_rate': '8.962e-05', 'epoch': '0.2724', 'num_input_tokens_seen': 17099072, 'train_runtime': '2.15e+04', 'train_tokens_per_second': '795.3'}
27%|██▋ | 5090/18688 [5:58:19<15:10:22, 4.02s/it] 27%|██▋ | 5091/18688 [5:58:23<15:04:44, 3.99s/it] 27%|██▋ | 5092/18688 [5:58:27<15:09:46, 4.01s/it] 27%|██▋ | 5093/18688 [5:58:32<15:31:20, 4.11s/it] 27%|██▋ | 5094/18688 [5:58:35<15:16:33, 4.05s/it] 27%|██▋ | 5095/18688 [5:58:40<15:26:32, 4.09s/it] 27%|██▋ | 5096/18688 [5:58:44<15:22:32, 4.07s/it] 27%|██▋ | 5097/18688 [5:58:47<14:47:33, 3.92s/it] 27%|██▋ | 5098/18688 [5:58:51<14:35:39, 3.87s/it] 27%|██▋ | 5099/18688 [5:58:54<14:11:13, 3.76s/it] 27%|██▋ | 5100/18688 [5:58:58<14:23:08, 3.81s/it] {'loss': '0.9115', 'grad_norm': '1.421', 'learning_rate': '8.957e-05', 'epoch': '0.2729', 'num_input_tokens_seen': 17129952, 'train_runtime': '2.154e+04', 'train_tokens_per_second': '795.3'}
27%|██▋ | 5100/18688 [5:58:58<14:23:08, 3.81s/it] 27%|██▋ | 5101/18688 [5:59:03<14:50:30, 3.93s/it] 27%|██▋ | 5102/18688 [5:59:06<14:38:53, 3.88s/it] 27%|██▋ | 5103/18688 [5:59:11<15:41:31, 4.16s/it] 27%|██▋ | 5104/18688 [5:59:15<15:43:12, 4.17s/it] 27%|██▋ | 5105/18688 [5:59:20<15:42:42, 4.16s/it] 27%|██▋ | 5106/18688 [5:59:23<15:07:54, 4.01s/it] 27%|██▋ | 5107/18688 [5:59:27<15:13:58, 4.04s/it] 27%|██▋ | 5108/18688 [5:59:31<14:45:49, 3.91s/it] 27%|██▋ | 5109/18688 [5:59:35<14:37:52, 3.88s/it] 27%|██▋ | 5110/18688 [5:59:39<15:11:48, 4.03s/it] {'loss': '0.9201', 'grad_norm': '1.244', 'learning_rate': '8.951e-05', 'epoch': '0.2734', 'num_input_tokens_seen': 17163160, 'train_runtime': '2.158e+04', 'train_tokens_per_second': '795.3'}
27%|██▋ | 5110/18688 [5:59:39<15:11:48, 4.03s/it] 27%|██▋ | 5111/18688 [5:59:44<15:39:45, 4.15s/it] 27%|██▋ | 5112/18688 [5:59:48<15:50:28, 4.20s/it] 27%|██▋ | 5113/18688 [5:59:52<15:18:47, 4.06s/it] 27%|██▋ | 5114/18688 [5:59:56<15:30:02, 4.11s/it] 27%|██▋ | 5115/18688 [6:00:00<15:27:17, 4.10s/it] 27%|██▋ | 5116/18688 [6:00:04<14:56:21, 3.96s/it] 27%|██▋ | 5117/18688 [6:00:07<14:29:11, 3.84s/it] 27%|██▋ | 5118/18688 [6:00:11<14:24:28, 3.82s/it] 27%|██▋ | 5119/18688 [6:00:15<14:44:55, 3.91s/it] 27%|██▋ | 5120/18688 [6:00:19<14:51:55, 3.94s/it] {'loss': '0.9723', 'grad_norm': '1.307', 'learning_rate': '8.945e-05', 'epoch': '0.274', 'num_input_tokens_seen': 17196272, 'train_runtime': '2.162e+04', 'train_tokens_per_second': '795.4'}
27%|██▋ | 5120/18688 [6:00:19<14:51:55, 3.94s/it] 27%|██▋ | 5121/18688 [6:00:23<15:23:23, 4.08s/it] 27%|██▋ | 5122/18688 [6:00:28<15:48:52, 4.20s/it] 27%|██▋ | 5123/18688 [6:00:31<14:57:40, 3.97s/it] 27%|██▋ | 5124/18688 [6:00:36<15:11:47, 4.03s/it] 27%|██▋ | 5125/18688 [6:00:39<14:49:22, 3.93s/it] 27%|██▋ | 5126/18688 [6:00:43<14:32:03, 3.86s/it] 27%|██▋ | 5127/18688 [6:00:47<14:24:31, 3.83s/it] 27%|██▋ | 5128/18688 [6:00:51<14:43:10, 3.91s/it] 27%|██▋ | 5129/18688 [6:00:54<14:31:45, 3.86s/it] 27%|██▋ | 5130/18688 [6:00:58<14:05:10, 3.74s/it] {'loss': '0.9612', 'grad_norm': '1.062', 'learning_rate': '8.94e-05', 'epoch': '0.2745', 'num_input_tokens_seen': 17226720, 'train_runtime': '2.166e+04', 'train_tokens_per_second': '795.4'}
27%|██▋ | 5130/18688 [6:00:58<14:05:10, 3.74s/it] 27%|██▋ | 5131/18688 [6:01:02<14:26:14, 3.83s/it] 27%|██▋ | 5132/18688 [6:01:06<14:53:48, 3.96s/it] 27%|██▋ | 5133/18688 [6:01:10<14:58:03, 3.98s/it] 27%|██▋ | 5134/18688 [6:01:14<15:05:17, 4.01s/it] 27%|██▋ | 5135/18688 [6:01:18<15:13:36, 4.04s/it] 27%|██▋ | 5136/18688 [6:01:22<15:10:02, 4.03s/it] 27%|██▋ | 5137/18688 [6:01:26<14:50:46, 3.94s/it] 27%|██▋ | 5138/18688 [6:01:31<15:14:56, 4.05s/it] 27%|██▋ | 5139/18688 [6:01:35<15:29:02, 4.11s/it] 28%|██▊ | 5140/18688 [6:01:39<15:31:40, 4.13s/it] {'loss': '0.975', 'grad_norm': '1.248', 'learning_rate': '8.934e-05', 'epoch': '0.2751', 'num_input_tokens_seen': 17260824, 'train_runtime': '2.17e+04', 'train_tokens_per_second': '795.5'}
28%|██▊ | 5140/18688 [6:01:39<15:31:40, 4.13s/it] 28%|██▊ | 5141/18688 [6:01:43<15:39:07, 4.16s/it] 28%|██▊ | 5142/18688 [6:01:48<15:56:20, 4.24s/it] 28%|██▊ | 5143/18688 [6:01:52<15:40:16, 4.17s/it] 28%|██▊ | 5144/18688 [6:01:56<15:33:44, 4.14s/it] 28%|██▊ | 5145/18688 [6:02:00<15:35:11, 4.14s/it] 28%|██▊ | 5146/18688 [6:02:04<15:25:58, 4.10s/it] 28%|██▊ | 5147/18688 [6:02:08<16:02:29, 4.26s/it] 28%|██▊ | 5148/18688 [6:02:12<15:10:43, 4.04s/it] 28%|██▊ | 5149/18688 [6:02:17<15:45:48, 4.19s/it] 28%|██▊ | 5150/18688 [6:02:20<15:24:52, 4.10s/it] {'loss': '1.026', 'grad_norm': '1.468', 'learning_rate': '8.929e-05', 'epoch': '0.2756', 'num_input_tokens_seen': 17295792, 'train_runtime': '2.174e+04', 'train_tokens_per_second': '795.5'}
28%|██▊ | 5150/18688 [6:02:20<15:24:52, 4.10s/it] 28%|██▊ | 5151/18688 [6:02:24<15:14:23, 4.05s/it] 28%|██▊ | 5152/18688 [6:02:28<15:02:21, 4.00s/it] 28%|██▊ | 5153/18688 [6:02:33<15:33:29, 4.14s/it] 28%|██▊ | 5154/18688 [6:02:37<15:46:30, 4.20s/it] 28%|██▊ | 5155/18688 [6:02:41<15:17:50, 4.07s/it] 28%|██▊ | 5156/18688 [6:02:44<14:46:00, 3.93s/it] 28%|██▊ | 5157/18688 [6:02:48<14:54:37, 3.97s/it] 28%|██▊ | 5158/18688 [6:02:54<16:36:48, 4.42s/it] 28%|██▊ | 5159/18688 [6:02:58<15:51:08, 4.22s/it] 28%|██▊ | 5160/18688 [6:03:02<16:00:59, 4.26s/it] {'loss': '0.9326', 'grad_norm': '1.241', 'learning_rate': '8.923e-05', 'epoch': '0.2761', 'num_input_tokens_seen': 17328232, 'train_runtime': '2.178e+04', 'train_tokens_per_second': '795.5'}
28%|██▊ | 5160/18688 [6:03:02<16:00:59, 4.26s/it] 28%|██▊ | 5161/18688 [6:03:06<15:38:30, 4.16s/it] 28%|██▊ | 5162/18688 [6:03:10<15:57:01, 4.25s/it] 28%|██▊ | 5163/18688 [6:03:16<17:01:44, 4.53s/it] 28%|██▊ | 5164/18688 [6:03:20<16:30:47, 4.40s/it] 28%|██▊ | 5165/18688 [6:03:24<15:51:27, 4.22s/it] 28%|██▊ | 5166/18688 [6:03:28<15:40:59, 4.18s/it] 28%|██▊ | 5167/18688 [6:03:33<16:49:21, 4.48s/it] 28%|██▊ | 5168/18688 [6:03:38<17:08:33, 4.56s/it] 28%|██▊ | 5169/18688 [6:03:43<17:50:07, 4.75s/it] 28%|██▊ | 5170/18688 [6:03:49<19:25:38, 5.17s/it] {'loss': '0.925', 'grad_norm': '1.098', 'learning_rate': '8.917e-05', 'epoch': '0.2767', 'num_input_tokens_seen': 17368832, 'train_runtime': '2.183e+04', 'train_tokens_per_second': '795.7'}
28%|██▊ | 5170/18688 [6:03:49<19:25:38, 5.17s/it] 28%|██▊ | 5171/18688 [6:03:53<18:09:09, 4.83s/it] 28%|██▊ | 5172/18688 [6:03:57<16:46:44, 4.47s/it] 28%|██▊ | 5173/18688 [6:04:02<17:32:45, 4.67s/it] 28%|██▊ | 5174/18688 [6:04:06<17:30:36, 4.66s/it] 28%|██▊ | 5175/18688 [6:04:11<17:20:40, 4.62s/it] 28%|██▊ | 5176/18688 [6:04:15<17:07:49, 4.56s/it] 28%|██▊ | 5177/18688 [6:04:19<16:00:17, 4.26s/it] 28%|██▊ | 5178/18688 [6:04:23<15:19:47, 4.08s/it] 28%|██▊ | 5179/18688 [6:04:27<15:13:53, 4.06s/it] 28%|██▊ | 5180/18688 [6:04:31<15:45:03, 4.20s/it] {'loss': '0.9404', 'grad_norm': '1.069', 'learning_rate': '8.912e-05', 'epoch': '0.2772', 'num_input_tokens_seen': 17402472, 'train_runtime': '2.187e+04', 'train_tokens_per_second': '795.7'}
28%|██▊ | 5180/18688 [6:04:31<15:45:03, 4.20s/it] 28%|██▊ | 5181/18688 [6:04:35<15:23:38, 4.10s/it] 28%|██▊ | 5182/18688 [6:04:39<15:20:58, 4.09s/it] 28%|██▊ | 5183/18688 [6:04:43<15:01:17, 4.00s/it] 28%|██▊ | 5184/18688 [6:04:46<14:31:17, 3.87s/it] 28%|██▊ | 5185/18688 [6:04:50<14:22:52, 3.83s/it] 28%|██▊ | 5186/18688 [6:04:54<14:20:10, 3.82s/it] 28%|██▊ | 5187/18688 [6:04:58<14:36:53, 3.90s/it] 28%|██▊ | 5188/18688 [6:05:03<15:23:56, 4.11s/it] 28%|██▊ | 5189/18688 [6:05:08<16:41:46, 4.45s/it] 28%|██▊ | 5190/18688 [6:05:12<16:21:58, 4.37s/it] {'loss': '0.9284', 'grad_norm': '1.226', 'learning_rate': '8.906e-05', 'epoch': '0.2777', 'num_input_tokens_seen': 17436336, 'train_runtime': '2.191e+04', 'train_tokens_per_second': '795.7'}
28%|██▊ | 5190/18688 [6:05:12<16:21:58, 4.37s/it] 28%|██▊ | 5191/18688 [6:05:16<15:38:00, 4.17s/it] 28%|██▊ | 5192/18688 [6:05:20<15:45:09, 4.20s/it] 28%|██▊ | 5193/18688 [6:05:24<15:49:42, 4.22s/it] 28%|██▊ | 5194/18688 [6:05:29<16:43:18, 4.46s/it] 28%|██▊ | 5195/18688 [6:05:33<15:47:37, 4.21s/it] 28%|██▊ | 5196/18688 [6:05:37<15:08:17, 4.04s/it] 28%|██▊ | 5197/18688 [6:05:41<15:12:19, 4.06s/it] 28%|██▊ | 5198/18688 [6:05:44<14:32:47, 3.88s/it] 28%|██▊ | 5199/18688 [6:05:48<14:58:16, 4.00s/it] 28%|██▊ | 5200/18688 [6:05:52<14:50:36, 3.96s/it] {'loss': '0.9761', 'grad_norm': '1.162', 'learning_rate': '8.9e-05', 'epoch': '0.2783', 'num_input_tokens_seen': 17468840, 'train_runtime': '2.195e+04', 'train_tokens_per_second': '795.7'}
28%|██▊ | 5200/18688 [6:05:52<14:50:36, 3.96s/it] 28%|██▊ | 5201/18688 [6:05:56<14:18:44, 3.82s/it] 28%|██▊ | 5202/18688 [6:06:00<14:49:18, 3.96s/it] 28%|██▊ | 5203/18688 [6:06:05<15:33:19, 4.15s/it] 28%|██▊ | 5204/18688 [6:06:09<16:10:46, 4.32s/it] 28%|██▊ | 5205/18688 [6:06:15<17:12:07, 4.59s/it] 28%|██▊ | 5206/18688 [6:06:18<15:54:52, 4.25s/it] 28%|██▊ | 5207/18688 [6:06:22<15:07:51, 4.04s/it] 28%|██▊ | 5208/18688 [6:06:25<14:31:33, 3.88s/it] 28%|██▊ | 5209/18688 [6:06:29<14:49:52, 3.96s/it] 28%|██▊ | 5210/18688 [6:06:33<14:48:02, 3.95s/it] {'loss': '1.007', 'grad_norm': '1.582', 'learning_rate': '8.894e-05', 'epoch': '0.2788', 'num_input_tokens_seen': 17501400, 'train_runtime': '2.199e+04', 'train_tokens_per_second': '795.7'}
28%|██▊ | 5210/18688 [6:06:33<14:48:02, 3.95s/it] 28%|██▊ | 5211/18688 [6:06:37<14:15:55, 3.81s/it] 28%|██▊ | 5212/18688 [6:06:42<16:08:25, 4.31s/it] 28%|██▊ | 5213/18688 [6:06:46<16:11:39, 4.33s/it] 28%|██▊ | 5214/18688 [6:06:51<16:35:34, 4.43s/it] 28%|██▊ | 5215/18688 [6:06:55<15:53:49, 4.25s/it] 28%|██▊ | 5216/18688 [6:06:59<15:47:40, 4.22s/it] 28%|██▊ | 5217/18688 [6:07:03<15:34:41, 4.16s/it] 28%|██▊ | 5218/18688 [6:07:07<15:08:47, 4.05s/it] 28%|██▊ | 5219/18688 [6:07:11<15:06:26, 4.04s/it] 28%|██▊ | 5220/18688 [6:07:14<14:26:51, 3.86s/it] {'loss': '0.9205', 'grad_norm': '1.369', 'learning_rate': '8.889e-05', 'epoch': '0.2793', 'num_input_tokens_seen': 17534272, 'train_runtime': '2.203e+04', 'train_tokens_per_second': '795.8'}
28%|██▊ | 5220/18688 [6:07:14<14:26:51, 3.86s/it] 28%|██▊ | 5221/18688 [6:07:19<15:49:33, 4.23s/it] 28%|██▊ | 5222/18688 [6:07:24<16:26:47, 4.40s/it] 28%|██▊ | 5223/18688 [6:07:30<17:36:38, 4.71s/it] 28%|██▊ | 5224/18688 [6:07:34<16:56:58, 4.53s/it] 28%|██▊ | 5225/18688 [6:07:38<16:22:32, 4.38s/it] 28%|██▊ | 5226/18688 [6:07:42<16:18:10, 4.36s/it] 28%|██▊ | 5227/18688 [6:07:48<17:32:38, 4.69s/it] 28%|██▊ | 5228/18688 [6:07:51<16:29:38, 4.41s/it] 28%|██▊ | 5229/18688 [6:07:56<16:42:18, 4.47s/it] 28%|██▊ | 5230/18688 [6:08:00<15:57:11, 4.27s/it] {'loss': '0.9932', 'grad_norm': '1.189', 'learning_rate': '8.883e-05', 'epoch': '0.2799', 'num_input_tokens_seen': 17572440, 'train_runtime': '2.208e+04', 'train_tokens_per_second': '795.8'}
28%|██▊ | 5230/18688 [6:08:00<15:57:11, 4.27s/it] 28%|██▊ | 5231/18688 [6:08:04<16:03:03, 4.29s/it] 28%|██▊ | 5232/18688 [6:08:08<16:00:58, 4.28s/it] 28%|██▊ | 5233/18688 [6:08:13<16:05:56, 4.31s/it] 28%|██▊ | 5234/18688 [6:08:17<16:27:20, 4.40s/it] 28%|██▊ | 5235/18688 [6:08:22<17:13:45, 4.61s/it] 28%|██▊ | 5236/18688 [6:08:27<17:27:49, 4.67s/it] 28%|██▊ | 5237/18688 [6:08:32<17:05:52, 4.58s/it] 28%|██▊ | 5238/18688 [6:08:37<17:30:35, 4.69s/it] 28%|██▊ | 5239/18688 [6:08:40<16:24:03, 4.39s/it] 28%|██▊ | 5240/18688 [6:08:44<15:39:16, 4.19s/it] {'loss': '0.9561', 'grad_norm': '1.436', 'learning_rate': '8.877e-05', 'epoch': '0.2804', 'num_input_tokens_seen': 17609416, 'train_runtime': '2.212e+04', 'train_tokens_per_second': '795.9'}
28%|██▊ | 5240/18688 [6:08:44<15:39:16, 4.19s/it] 28%|██▊ | 5241/18688 [6:08:48<15:09:22, 4.06s/it] 28%|██▊ | 5242/18688 [6:08:51<14:29:10, 3.88s/it] 28%|██▊ | 5243/18688 [6:08:55<14:45:24, 3.95s/it] 28%|██▊ | 5244/18688 [6:09:00<15:17:14, 4.09s/it] 28%|██▊ | 5245/18688 [6:09:04<15:27:47, 4.14s/it] 28%|██▊ | 5246/18688 [6:09:08<15:35:17, 4.17s/it] 28%|██▊ | 5247/18688 [6:09:12<15:02:56, 4.03s/it] 28%|██▊ | 5248/18688 [6:09:16<15:03:46, 4.03s/it] 28%|██▊ | 5249/18688 [6:09:20<14:33:12, 3.90s/it] 28%|██▊ | 5250/18688 [6:09:24<14:40:34, 3.93s/it] {'loss': '1.055', 'grad_norm': '1.449', 'learning_rate': '8.871e-05', 'epoch': '0.2809', 'num_input_tokens_seen': 17639536, 'train_runtime': '2.216e+04', 'train_tokens_per_second': '795.9'}
28%|██▊ | 5250/18688 [6:09:24<14:40:34, 3.93s/it] 28%|██▊ | 5251/18688 [6:09:28<15:22:06, 4.12s/it] 28%|██▊ | 5252/18688 [6:09:32<14:57:50, 4.01s/it] 28%|██▊ | 5253/18688 [6:09:36<14:48:31, 3.97s/it] 28%|██▊ | 5254/18688 [6:09:39<14:22:54, 3.85s/it] 28%|██▊ | 5255/18688 [6:09:44<14:43:45, 3.95s/it] 28%|██▊ | 5256/18688 [6:09:48<15:33:53, 4.17s/it] 28%|██▊ | 5257/18688 [6:09:52<14:46:13, 3.96s/it] 28%|██▊ | 5258/18688 [6:09:55<14:13:39, 3.81s/it] 28%|██▊ | 5259/18688 [6:09:59<14:45:01, 3.95s/it] 28%|██▊ | 5260/18688 [6:10:03<14:24:05, 3.86s/it] {'loss': '0.9193', 'grad_norm': '1.195', 'learning_rate': '8.866e-05', 'epoch': '0.2815', 'num_input_tokens_seen': 17669064, 'train_runtime': '2.22e+04', 'train_tokens_per_second': '795.8'}
28%|██▊ | 5260/18688 [6:10:03<14:24:05, 3.86s/it] 28%|██▊ | 5261/18688 [6:10:08<15:58:21, 4.28s/it] 28%|██▊ | 5262/18688 [6:10:12<15:16:15, 4.09s/it] 28%|██▊ | 5263/18688 [6:10:16<15:33:04, 4.17s/it] 28%|██▊ | 5264/18688 [6:10:21<15:52:11, 4.26s/it] 28%|██▊ | 5265/18688 [6:10:25<15:16:06, 4.09s/it] 28%|██▊ | 5266/18688 [6:10:28<15:02:03, 4.03s/it] 28%|██▊ | 5267/18688 [6:10:33<15:53:16, 4.26s/it] 28%|██▊ | 5268/18688 [6:10:38<16:39:02, 4.47s/it] 28%|██▊ | 5269/18688 [6:10:42<16:16:05, 4.36s/it] 28%|██▊ | 5270/18688 [6:10:46<16:01:03, 4.30s/it] {'loss': '0.9238', 'grad_norm': '1.827', 'learning_rate': '8.86e-05', 'epoch': '0.282', 'num_input_tokens_seen': 17705704, 'train_runtime': '2.225e+04', 'train_tokens_per_second': '795.9'}
28%|██▊ | 5270/18688 [6:10:46<16:01:03, 4.30s/it] 28%|██▊ | 5271/18688 [6:10:50<15:33:28, 4.17s/it] 28%|██▊ | 5272/18688 [6:10:54<15:01:03, 4.03s/it] 28%|██▊ | 5273/18688 [6:10:58<15:12:20, 4.08s/it] 28%|██▊ | 5274/18688 [6:11:03<15:41:17, 4.21s/it] 28%|██▊ | 5275/18688 [6:11:07<15:55:22, 4.27s/it] 28%|██▊ | 5276/18688 [6:11:11<15:19:28, 4.11s/it] 28%|██▊ | 5277/18688 [6:11:15<15:15:11, 4.09s/it] 28%|██▊ | 5278/18688 [6:11:20<16:31:42, 4.44s/it] 28%|██▊ | 5279/18688 [6:11:24<15:35:34, 4.19s/it] 28%|██▊ | 5280/18688 [6:11:28<15:32:20, 4.17s/it] {'loss': '1.018', 'grad_norm': '1.146', 'learning_rate': '8.854e-05', 'epoch': '0.2825', 'num_input_tokens_seen': 17737472, 'train_runtime': '2.229e+04', 'train_tokens_per_second': '795.8'}
28%|██▊ | 5280/18688 [6:11:28<15:32:20, 4.17s/it] 28%|██▊ | 5281/18688 [6:11:32<15:06:37, 4.06s/it] 28%|██▊ | 5282/18688 [6:11:36<15:17:49, 4.11s/it] 28%|██▊ | 5283/18688 [6:11:41<16:43:42, 4.49s/it] 28%|██▊ | 5284/18688 [6:11:45<15:59:57, 4.30s/it] 28%|██▊ | 5285/18688 [6:11:49<15:28:26, 4.16s/it] 28%|██▊ | 5286/18688 [6:11:53<15:32:24, 4.17s/it] 28%|██▊ | 5287/18688 [6:11:58<15:54:11, 4.27s/it] 28%|██▊ | 5288/18688 [6:12:02<15:28:30, 4.16s/it] 28%|██▊ | 5289/18688 [6:12:06<16:08:23, 4.34s/it] 28%|██▊ | 5290/18688 [6:12:10<15:49:59, 4.25s/it] {'loss': '0.9102', 'grad_norm': '1.131', 'learning_rate': '8.848e-05', 'epoch': '0.2831', 'num_input_tokens_seen': 17773016, 'train_runtime': '2.233e+04', 'train_tokens_per_second': '795.9'}
28%|██▊ | 5290/18688 [6:12:10<15:49:59, 4.25s/it] 28%|██▊ | 5291/18688 [6:12:16<17:09:17, 4.61s/it] 28%|██▊ | 5292/18688 [6:12:20<16:10:42, 4.35s/it] 28%|██▊ | 5293/18688 [6:12:24<15:52:46, 4.27s/it] 28%|██▊ | 5294/18688 [6:12:28<15:45:38, 4.24s/it] 28%|██▊ | 5295/18688 [6:12:33<16:29:48, 4.43s/it] 28%|██▊ | 5296/18688 [6:12:37<16:32:39, 4.45s/it] 28%|██▊ | 5297/18688 [6:12:41<15:34:21, 4.19s/it] 28%|██▊ | 5298/18688 [6:12:44<14:55:10, 4.01s/it] 28%|██▊ | 5299/18688 [6:12:48<14:55:39, 4.01s/it] 28%|██▊ | 5300/18688 [6:12:52<14:44:12, 3.96s/it] {'loss': '0.9404', 'grad_norm': '1.305', 'learning_rate': '8.842e-05', 'epoch': '0.2836', 'num_input_tokens_seen': 17806056, 'train_runtime': '2.237e+04', 'train_tokens_per_second': '795.9'}
28%|██▊ | 5300/18688 [6:12:52<14:44:12, 3.96s/it] 28%|██▊ | 5301/18688 [6:12:56<14:25:39, 3.88s/it] 28%|██▊ | 5302/18688 [6:13:00<14:54:32, 4.01s/it] 28%|██▊ | 5303/18688 [6:13:05<15:33:33, 4.18s/it] 28%|██▊ | 5304/18688 [6:13:09<15:06:02, 4.06s/it] 28%|██▊ | 5305/18688 [6:13:12<14:49:46, 3.99s/it] 28%|██▊ | 5306/18688 [6:13:17<15:09:37, 4.08s/it] 28%|██▊ | 5307/18688 [6:13:21<15:38:02, 4.21s/it] 28%|██▊ | 5308/18688 [6:13:26<16:33:26, 4.45s/it] 28%|██▊ | 5309/18688 [6:13:30<15:44:15, 4.23s/it] 28%|██▊ | 5310/18688 [6:13:34<15:04:24, 4.06s/it] {'loss': '0.9354', 'grad_norm': '1.242', 'learning_rate': '8.836e-05', 'epoch': '0.2841', 'num_input_tokens_seen': 17837904, 'train_runtime': '2.241e+04', 'train_tokens_per_second': '795.8'}
28%|██▊ | 5310/18688 [6:13:34<15:04:24, 4.06s/it] 28%|██▊ | 5311/18688 [6:13:38<15:17:14, 4.11s/it] 28%|██▊ | 5312/18688 [6:13:42<15:28:48, 4.17s/it] 28%|██▊ | 5313/18688 [6:13:47<16:08:28, 4.34s/it] 28%|██▊ | 5314/18688 [6:13:51<15:40:01, 4.22s/it] 28%|██▊ | 5315/18688 [6:13:55<15:03:58, 4.06s/it] 28%|██▊ | 5316/18688 [6:13:59<15:51:56, 4.27s/it] 28%|██▊ | 5317/18688 [6:14:03<15:31:39, 4.18s/it] 28%|██▊ | 5318/18688 [6:14:08<15:47:09, 4.25s/it] 28%|██▊ | 5319/18688 [6:14:12<16:24:38, 4.42s/it] 28%|██▊ | 5320/18688 [6:14:16<15:50:35, 4.27s/it] {'loss': '1.024', 'grad_norm': '1.22', 'learning_rate': '8.831e-05', 'epoch': '0.2847', 'num_input_tokens_seen': 17873048, 'train_runtime': '2.246e+04', 'train_tokens_per_second': '795.9'}
28%|██▊ | 5320/18688 [6:14:16<15:50:35, 4.27s/it] 28%|██▊ | 5321/18688 [6:14:20<15:03:48, 4.06s/it] 28%|██▊ | 5322/18688 [6:14:24<15:21:50, 4.14s/it] 28%|██▊ | 5323/18688 [6:14:28<15:20:18, 4.13s/it] 28%|██▊ | 5324/18688 [6:14:32<15:04:59, 4.06s/it] 28%|██▊ | 5325/18688 [6:14:38<16:25:30, 4.42s/it] 28%|██▊ | 5326/18688 [6:14:41<15:35:02, 4.20s/it] 29%|██▊ | 5327/18688 [6:14:45<15:19:51, 4.13s/it] 29%|██▊ | 5328/18688 [6:14:49<14:56:05, 4.02s/it] 29%|██▊ | 5329/18688 [6:14:54<16:05:01, 4.33s/it] 29%|██▊ | 5330/18688 [6:14:59<16:58:19, 4.57s/it] {'loss': '0.9714', 'grad_norm': '1.303', 'learning_rate': '8.825e-05', 'epoch': '0.2852', 'num_input_tokens_seen': 17907056, 'train_runtime': '2.25e+04', 'train_tokens_per_second': '795.9'}
29%|██▊ | 5330/18688 [6:14:59<16:58:19, 4.57s/it] 29%|██▊ | 5331/18688 [6:15:03<16:10:08, 4.36s/it] 29%|██▊ | 5332/18688 [6:15:07<16:06:58, 4.34s/it] 29%|██▊ | 5333/18688 [6:15:11<15:30:34, 4.18s/it] 29%|██▊ | 5334/18688 [6:15:15<15:28:24, 4.17s/it] 29%|██▊ | 5335/18688 [6:15:19<15:06:34, 4.07s/it] 29%|██▊ | 5336/18688 [6:15:24<15:33:35, 4.20s/it] 29%|██▊ | 5337/18688 [6:15:27<14:50:37, 4.00s/it] 29%|██▊ | 5338/18688 [6:15:31<14:50:25, 4.00s/it] 29%|██▊ | 5339/18688 [6:15:36<15:48:25, 4.26s/it] 29%|██▊ | 5340/18688 [6:15:40<15:53:27, 4.29s/it] {'loss': '0.9874', 'grad_norm': '1.113', 'learning_rate': '8.819e-05', 'epoch': '0.2858', 'num_input_tokens_seen': 17939856, 'train_runtime': '2.254e+04', 'train_tokens_per_second': '795.9'}
29%|██▊ | 5340/18688 [6:15:40<15:53:27, 4.29s/it] 29%|██▊ | 5341/18688 [6:15:44<15:26:02, 4.16s/it] 29%|██▊ | 5342/18688 [6:15:48<15:22:55, 4.15s/it] 29%|██▊ | 5343/18688 [6:15:53<15:33:25, 4.20s/it] 29%|██▊ | 5344/18688 [6:15:56<15:04:46, 4.07s/it] 29%|██▊ | 5345/18688 [6:16:00<14:53:34, 4.02s/it] 29%|██▊ | 5346/18688 [6:16:05<15:41:54, 4.24s/it] 29%|██▊ | 5347/18688 [6:16:09<15:02:55, 4.06s/it] 29%|██▊ | 5348/18688 [6:16:14<16:16:50, 4.39s/it] 29%|██▊ | 5349/18688 [6:16:19<17:12:39, 4.64s/it] 29%|██▊ | 5350/18688 [6:16:23<16:37:09, 4.49s/it] {'loss': '0.9237', 'grad_norm': '1.179', 'learning_rate': '8.813e-05', 'epoch': '0.2863', 'num_input_tokens_seen': 17974912, 'train_runtime': '2.258e+04', 'train_tokens_per_second': '795.9'}
29%|██▊ | 5350/18688 [6:16:23<16:37:09, 4.49s/it] 29%|██▊ | 5351/18688 [6:16:27<16:13:29, 4.38s/it] 29%|██▊ | 5352/18688 [6:16:32<16:28:23, 4.45s/it] 29%|██▊ | 5353/18688 [6:16:37<17:22:29, 4.69s/it] 29%|██▊ | 5354/18688 [6:16:41<16:15:12, 4.39s/it] 29%|██▊ | 5355/18688 [6:16:45<15:57:20, 4.31s/it] 29%|██▊ | 5356/18688 [6:16:50<16:36:38, 4.49s/it] 29%|██▊ | 5357/18688 [6:16:54<16:26:33, 4.44s/it] 29%|██▊ | 5358/18688 [6:16:58<15:23:52, 4.16s/it] 29%|██▊ | 5359/18688 [6:17:02<15:09:04, 4.09s/it] 29%|██▊ | 5360/18688 [6:17:06<15:18:07, 4.13s/it] {'loss': '0.9912', 'grad_norm': '1.111', 'learning_rate': '8.807e-05', 'epoch': '0.2868', 'num_input_tokens_seen': 18011176, 'train_runtime': '2.263e+04', 'train_tokens_per_second': '796'}
29%|██▊ | 5360/18688 [6:17:06<15:18:07, 4.13s/it] 29%|██▊ | 5361/18688 [6:17:10<15:05:10, 4.08s/it] 29%|██▊ | 5362/18688 [6:17:14<15:10:37, 4.10s/it] 29%|██▊ | 5363/18688 [6:17:20<16:55:03, 4.57s/it] 29%|██▊ | 5364/18688 [6:17:24<16:20:02, 4.41s/it] 29%|██▊ | 5365/18688 [6:17:28<15:54:18, 4.30s/it] 29%|██▊ | 5366/18688 [6:17:31<15:01:00, 4.06s/it] 29%|██▊ | 5367/18688 [6:17:35<14:35:29, 3.94s/it] 29%|██▊ | 5368/18688 [6:17:39<14:20:34, 3.88s/it] 29%|██▊ | 5369/18688 [6:17:43<14:37:45, 3.95s/it] 29%|██▊ | 5370/18688 [6:17:47<15:00:52, 4.06s/it] {'loss': '0.8999', 'grad_norm': '1.146', 'learning_rate': '8.801e-05', 'epoch': '0.2874', 'num_input_tokens_seen': 18042736, 'train_runtime': '2.267e+04', 'train_tokens_per_second': '796'}
29%|██▊ | 5370/18688 [6:17:47<15:00:52, 4.06s/it] 29%|██▊ | 5371/18688 [6:17:53<16:32:27, 4.47s/it] 29%|██▊ | 5372/18688 [6:17:57<16:31:21, 4.47s/it] 29%|██▉ | 5373/18688 [6:18:01<15:51:03, 4.29s/it] 29%|██▉ | 5374/18688 [6:18:06<16:18:21, 4.41s/it] 29%|██▉ | 5375/18688 [6:18:09<15:14:17, 4.12s/it] 29%|██▉ | 5376/18688 [6:18:13<15:33:08, 4.21s/it] 29%|██▉ | 5377/18688 [6:18:18<16:16:13, 4.40s/it] 29%|██▉ | 5378/18688 [6:18:24<17:19:39, 4.69s/it] 29%|██▉ | 5379/18688 [6:18:28<17:03:53, 4.62s/it] 29%|██▉ | 5380/18688 [6:18:32<16:32:30, 4.47s/it] {'loss': '0.9942', 'grad_norm': '1.414', 'learning_rate': '8.795e-05', 'epoch': '0.2879', 'num_input_tokens_seen': 18081032, 'train_runtime': '2.271e+04', 'train_tokens_per_second': '796.1'}
29%|██▉ | 5380/18688 [6:18:32<16:32:30, 4.47s/it] 29%|██▉ | 5381/18688 [6:18:37<16:41:24, 4.52s/it] 29%|██▉ | 5382/18688 [6:18:41<16:17:00, 4.41s/it] 29%|██▉ | 5383/18688 [6:18:45<15:42:49, 4.25s/it] 29%|██▉ | 5384/18688 [6:18:50<16:13:31, 4.39s/it] 29%|██▉ | 5385/18688 [6:18:54<16:00:40, 4.33s/it] 29%|██▉ | 5386/18688 [6:18:58<16:22:00, 4.43s/it] 29%|██▉ | 5387/18688 [6:19:03<16:05:12, 4.35s/it] 29%|██▉ | 5388/18688 [6:19:07<16:08:15, 4.37s/it] 29%|██▉ | 5389/18688 [6:19:11<15:58:39, 4.33s/it] 29%|██▉ | 5390/18688 [6:19:16<16:31:58, 4.48s/it] {'loss': '0.9043', 'grad_norm': '0.9772', 'learning_rate': '8.789e-05', 'epoch': '0.2884', 'num_input_tokens_seen': 18118448, 'train_runtime': '2.276e+04', 'train_tokens_per_second': '796.2'}
29%|██▉ | 5390/18688 [6:19:16<16:31:58, 4.48s/it] 29%|██▉ | 5391/18688 [6:19:20<16:18:36, 4.42s/it] 29%|██▉ | 5392/18688 [6:19:26<17:16:53, 4.68s/it] 29%|██▉ | 5393/18688 [6:19:30<16:41:31, 4.52s/it] 29%|██▉ | 5394/18688 [6:19:33<15:41:01, 4.25s/it] 29%|██▉ | 5395/18688 [6:19:37<14:50:57, 4.02s/it] 29%|██▉ | 5396/18688 [6:19:41<15:09:17, 4.10s/it] 29%|██▉ | 5397/18688 [6:19:46<15:27:20, 4.19s/it] 29%|██▉ | 5398/18688 [6:19:49<14:42:07, 3.98s/it] 29%|██▉ | 5399/18688 [6:19:54<15:45:28, 4.27s/it] 29%|██▉ | 5400/18688 [6:19:59<16:09:12, 4.38s/it] {'loss': '0.93', 'grad_norm': '1.283', 'learning_rate': '8.783e-05', 'epoch': '0.289', 'num_input_tokens_seen': 18152576, 'train_runtime': '2.28e+04', 'train_tokens_per_second': '796.2'}
29%|██▉ | 5400/18688 [6:19:59<16:09:12, 4.38s/it] 29%|██▉ | 5401/18688 [6:20:02<15:09:39, 4.11s/it] 29%|██▉ | 5402/18688 [6:20:07<15:38:55, 4.24s/it] 29%|██▉ | 5403/18688 [6:20:12<16:32:40, 4.48s/it] 29%|██▉ | 5404/18688 [6:20:17<16:54:10, 4.58s/it] 29%|██▉ | 5405/18688 [6:20:21<16:52:15, 4.57s/it] 29%|██▉ | 5406/18688 [6:20:26<17:37:42, 4.78s/it] 29%|██▉ | 5407/18688 [6:20:30<16:10:06, 4.38s/it] 29%|██▉ | 5408/18688 [6:20:34<15:31:16, 4.21s/it] 29%|██▉ | 5409/18688 [6:20:38<15:42:18, 4.26s/it] 29%|██▉ | 5410/18688 [6:20:42<14:57:25, 4.06s/it] {'loss': '0.944', 'grad_norm': '1.361', 'learning_rate': '8.777e-05', 'epoch': '0.2895', 'num_input_tokens_seen': 18188768, 'train_runtime': '2.284e+04', 'train_tokens_per_second': '796.3'}
29%|██▉ | 5410/18688 [6:20:42<14:57:25, 4.06s/it] 29%|██▉ | 5411/18688 [6:20:45<14:29:02, 3.93s/it] 29%|██▉ | 5412/18688 [6:20:50<15:04:47, 4.09s/it] 29%|██▉ | 5413/18688 [6:20:54<15:30:23, 4.21s/it] 29%|██▉ | 5414/18688 [6:20:58<15:33:15, 4.22s/it] 29%|██▉ | 5415/18688 [6:21:02<15:13:53, 4.13s/it] 29%|██▉ | 5416/18688 [6:21:06<14:31:32, 3.94s/it] 29%|██▉ | 5417/18688 [6:21:10<14:34:29, 3.95s/it] 29%|██▉ | 5418/18688 [6:21:15<15:25:13, 4.18s/it] 29%|██▉ | 5419/18688 [6:21:18<14:56:43, 4.05s/it] 29%|██▉ | 5420/18688 [6:21:23<15:37:01, 4.24s/it] {'loss': '0.9468', 'grad_norm': '1.107', 'learning_rate': '8.771e-05', 'epoch': '0.29', 'num_input_tokens_seen': 18221832, 'train_runtime': '2.288e+04', 'train_tokens_per_second': '796.3'}
29%|██▉ | 5420/18688 [6:21:23<15:37:01, 4.24s/it] 29%|██▉ | 5421/18688 [6:21:27<15:47:17, 4.28s/it] 29%|██▉ | 5422/18688 [6:21:31<15:20:49, 4.16s/it] 29%|██▉ | 5423/18688 [6:21:35<14:48:40, 4.02s/it] 29%|██▉ | 5424/18688 [6:21:39<14:48:40, 4.02s/it] 29%|██▉ | 5425/18688 [6:21:43<14:57:34, 4.06s/it] 29%|██▉ | 5426/18688 [6:21:48<15:21:51, 4.17s/it] 29%|██▉ | 5427/18688 [6:21:51<14:50:52, 4.03s/it] 29%|██▉ | 5428/18688 [6:21:55<14:57:02, 4.06s/it] 29%|██▉ | 5429/18688 [6:22:00<15:51:08, 4.30s/it] 29%|██▉ | 5430/18688 [6:22:04<15:36:40, 4.24s/it] {'loss': '0.934', 'grad_norm': '1.339', 'learning_rate': '8.765e-05', 'epoch': '0.2906', 'num_input_tokens_seen': 18254664, 'train_runtime': '2.292e+04', 'train_tokens_per_second': '796.3'}
29%|██▉ | 5430/18688 [6:22:04<15:36:40, 4.24s/it] 29%|██▉ | 5431/18688 [6:22:08<14:50:12, 4.03s/it] 29%|██▉ | 5432/18688 [6:22:12<14:32:08, 3.95s/it] 29%|██▉ | 5433/18688 [6:22:15<14:10:42, 3.85s/it] 29%|██▉ | 5434/18688 [6:22:20<14:40:24, 3.99s/it] 29%|██▉ | 5435/18688 [6:22:23<14:11:22, 3.85s/it] 29%|██▉ | 5436/18688 [6:22:27<14:08:46, 3.84s/it] 29%|██▉ | 5437/18688 [6:22:31<14:36:07, 3.97s/it] 29%|██▉ | 5438/18688 [6:22:35<14:30:47, 3.94s/it] 29%|██▉ | 5439/18688 [6:22:39<14:00:47, 3.81s/it] 29%|██▉ | 5440/18688 [6:22:43<14:41:55, 3.99s/it] {'loss': '0.928', 'grad_norm': '1.364', 'learning_rate': '8.759e-05', 'epoch': '0.2911', 'num_input_tokens_seen': 18284960, 'train_runtime': '2.296e+04', 'train_tokens_per_second': '796.3'}
29%|██▉ | 5440/18688 [6:22:43<14:41:55, 3.99s/it] 29%|██▉ | 5441/18688 [6:22:47<14:23:08, 3.91s/it] 29%|██▉ | 5442/18688 [6:22:50<13:53:40, 3.78s/it] 29%|██▉ | 5443/18688 [6:22:54<13:53:48, 3.78s/it] 29%|██▉ | 5444/18688 [6:22:58<14:39:33, 3.98s/it] 29%|██▉ | 5445/18688 [6:23:02<14:20:50, 3.90s/it] 29%|██▉ | 5446/18688 [6:23:06<14:53:17, 4.05s/it] 29%|██▉ | 5447/18688 [6:23:10<14:30:14, 3.94s/it] 29%|██▉ | 5448/18688 [6:23:14<14:04:07, 3.83s/it] 29%|██▉ | 5449/18688 [6:23:18<14:13:11, 3.87s/it] 29%|██▉ | 5450/18688 [6:23:22<14:20:11, 3.90s/it] {'loss': '0.8823', 'grad_norm': '1.291', 'learning_rate': '8.753e-05', 'epoch': '0.2916', 'num_input_tokens_seen': 18315640, 'train_runtime': '2.3e+04', 'train_tokens_per_second': '796.3'}
29%|██▉ | 5450/18688 [6:23:22<14:20:11, 3.90s/it] 29%|██▉ | 5451/18688 [6:23:25<13:58:22, 3.80s/it] 29%|██▉ | 5452/18688 [6:23:29<13:41:45, 3.73s/it] 29%|██▉ | 5453/18688 [6:23:33<14:13:56, 3.87s/it] 29%|██▉ | 5454/18688 [6:23:37<14:41:41, 4.00s/it] 29%|██▉ | 5455/18688 [6:23:41<14:34:59, 3.97s/it] 29%|██▉ | 5456/18688 [6:23:45<14:31:15, 3.95s/it] 29%|██▉ | 5457/18688 [6:23:50<15:38:08, 4.25s/it] 29%|██▉ | 5458/18688 [6:23:54<15:01:48, 4.09s/it] 29%|██▉ | 5459/18688 [6:23:58<14:51:44, 4.04s/it] 29%|██▉ | 5460/18688 [6:24:02<14:45:29, 4.02s/it] {'loss': '0.9031', 'grad_norm': '1.609', 'learning_rate': '8.747e-05', 'epoch': '0.2922', 'num_input_tokens_seen': 18346304, 'train_runtime': '2.304e+04', 'train_tokens_per_second': '796.2'}
29%|██▉ | 5460/18688 [6:24:02<14:45:29, 4.02s/it] 29%|██▉ | 5461/18688 [6:24:06<14:43:16, 4.01s/it] 29%|██▉ | 5462/18688 [6:24:10<15:29:34, 4.22s/it] 29%|██▉ | 5463/18688 [6:24:14<15:08:22, 4.12s/it] 29%|██▉ | 5464/18688 [6:24:19<15:31:56, 4.23s/it] 29%|██▉ | 5465/18688 [6:24:23<15:29:57, 4.22s/it] 29%|██▉ | 5466/18688 [6:24:27<15:38:22, 4.26s/it] 29%|██▉ | 5467/18688 [6:24:31<14:55:42, 4.06s/it] 29%|██▉ | 5468/18688 [6:24:35<14:33:50, 3.97s/it] 29%|██▉ | 5469/18688 [6:24:40<15:47:11, 4.30s/it] 29%|██▉ | 5470/18688 [6:24:44<15:56:14, 4.34s/it] {'loss': '0.9231', 'grad_norm': '1.228', 'learning_rate': '8.741e-05', 'epoch': '0.2927', 'num_input_tokens_seen': 18380496, 'train_runtime': '2.308e+04', 'train_tokens_per_second': '796.2'}
29%|██▉ | 5470/18688 [6:24:44<15:56:14, 4.34s/it] 29%|██▉ | 5471/18688 [6:24:49<16:03:43, 4.37s/it] 29%|██▉ | 5472/18688 [6:24:53<15:53:46, 4.33s/it] 29%|██▉ | 5473/18688 [6:24:56<15:07:57, 4.12s/it] 29%|██▉ | 5474/18688 [6:25:01<15:11:12, 4.14s/it] 29%|██▉ | 5475/18688 [6:25:04<14:40:45, 4.00s/it] 29%|██▉ | 5476/18688 [6:25:09<15:17:35, 4.17s/it] 29%|██▉ | 5477/18688 [6:25:13<15:41:06, 4.27s/it] 29%|██▉ | 5478/18688 [6:25:18<15:36:39, 4.25s/it] 29%|██▉ | 5479/18688 [6:25:22<16:04:03, 4.38s/it] 29%|██▉ | 5480/18688 [6:25:26<15:03:33, 4.10s/it] {'loss': '0.9301', 'grad_norm': '1.236', 'learning_rate': '8.735e-05', 'epoch': '0.2932', 'num_input_tokens_seen': 18415344, 'train_runtime': '2.313e+04', 'train_tokens_per_second': '796.3'}
29%|██▉ | 5480/18688 [6:25:26<15:03:33, 4.10s/it] 29%|██▉ | 5481/18688 [6:25:29<14:37:06, 3.98s/it] 29%|██▉ | 5482/18688 [6:25:33<14:19:26, 3.90s/it] 29%|██▉ | 5483/18688 [6:25:37<13:48:58, 3.77s/it] 29%|██▉ | 5484/18688 [6:25:42<15:16:55, 4.17s/it] 29%|██▉ | 5485/18688 [6:25:46<15:19:24, 4.18s/it] 29%|██▉ | 5486/18688 [6:25:50<15:18:48, 4.18s/it] 29%|██▉ | 5487/18688 [6:25:54<14:46:26, 4.03s/it] 29%|██▉ | 5488/18688 [6:25:58<14:30:57, 3.96s/it] 29%|██▉ | 5489/18688 [6:26:01<14:10:50, 3.87s/it] 29%|██▉ | 5490/18688 [6:26:05<14:02:03, 3.83s/it] {'loss': '0.9996', 'grad_norm': '1.296', 'learning_rate': '8.729e-05', 'epoch': '0.2938', 'num_input_tokens_seen': 18445968, 'train_runtime': '2.317e+04', 'train_tokens_per_second': '796.3'}
29%|██▉ | 5490/18688 [6:26:05<14:02:03, 3.83s/it] 29%|██▉ | 5491/18688 [6:26:10<15:08:56, 4.13s/it] 29%|██▉ | 5492/18688 [6:26:14<15:18:16, 4.18s/it] 29%|██▉ | 5493/18688 [6:26:19<15:48:24, 4.31s/it] 29%|██▉ | 5494/18688 [6:26:23<15:59:08, 4.36s/it] 29%|██▉ | 5495/18688 [6:26:27<15:24:41, 4.21s/it] 29%|██▉ | 5496/18688 [6:26:31<14:52:58, 4.06s/it] 29%|██▉ | 5497/18688 [6:26:35<14:56:28, 4.08s/it] 29%|██▉ | 5498/18688 [6:26:40<15:44:32, 4.30s/it] 29%|██▉ | 5499/18688 [6:26:44<15:38:51, 4.27s/it] 29%|██▉ | 5500/18688 [6:26:47<14:48:22, 4.04s/it] {'loss': '0.9484', 'grad_norm': '1.438', 'learning_rate': '8.723e-05', 'epoch': '0.2943', 'num_input_tokens_seen': 18479888, 'train_runtime': '2.321e+04', 'train_tokens_per_second': '796.3'}
29%|██▉ | 5500/18688 [6:26:47<14:48:22, 4.04s/it] 29%|██▉ | 5501/18688 [6:26:52<15:20:04, 4.19s/it] 29%|██▉ | 5502/18688 [6:26:56<14:43:18, 4.02s/it] 29%|██▉ | 5503/18688 [6:27:00<15:21:29, 4.19s/it] 29%|██▉ | 5504/18688 [6:27:05<16:25:23, 4.48s/it] 29%|██▉ | 5505/18688 [6:27:09<15:33:56, 4.25s/it] 29%|██▉ | 5506/18688 [6:27:13<15:48:29, 4.32s/it] 29%|██▉ | 5507/18688 [6:27:18<16:12:08, 4.43s/it] 29%|██▉ | 5508/18688 [6:27:22<15:18:56, 4.18s/it] 29%|██▉ | 5509/18688 [6:27:26<15:45:57, 4.31s/it] 29%|██▉ | 5510/18688 [6:27:31<16:11:46, 4.42s/it] {'loss': '0.9604', 'grad_norm': '1.422', 'learning_rate': '8.717e-05', 'epoch': '0.2948', 'num_input_tokens_seen': 18515552, 'train_runtime': '2.325e+04', 'train_tokens_per_second': '796.3'}
29%|██▉ | 5510/18688 [6:27:31<16:11:46, 4.42s/it] 29%|██▉ | 5511/18688 [6:27:35<15:39:43, 4.28s/it] 29%|██▉ | 5512/18688 [6:27:39<15:33:54, 4.25s/it] 30%|██▉ | 5513/18688 [6:27:44<15:36:39, 4.27s/it] 30%|██▉ | 5514/18688 [6:27:47<15:03:24, 4.11s/it] 30%|██▉ | 5515/18688 [6:27:51<14:41:01, 4.01s/it] 30%|██▉ | 5516/18688 [6:27:55<14:43:03, 4.02s/it] 30%|██▉ | 5517/18688 [6:28:00<15:12:30, 4.16s/it] 30%|██▉ | 5518/18688 [6:28:03<14:47:22, 4.04s/it] 30%|██▉ | 5519/18688 [6:28:07<14:19:29, 3.92s/it] 30%|██▉ | 5520/18688 [6:28:11<14:28:28, 3.96s/it] {'loss': '0.8749', 'grad_norm': '1.288', 'learning_rate': '8.711e-05', 'epoch': '0.2954', 'num_input_tokens_seen': 18546288, 'train_runtime': '2.329e+04', 'train_tokens_per_second': '796.3'}
30%|██▉ | 5520/18688 [6:28:11<14:28:28, 3.96s/it] 30%|██▉ | 5521/18688 [6:28:15<14:00:01, 3.83s/it] 30%|██▉ | 5522/18688 [6:28:18<13:54:41, 3.80s/it] 30%|██▉ | 5523/18688 [6:28:22<13:38:31, 3.73s/it] 30%|██▉ | 5524/18688 [6:28:27<15:08:58, 4.14s/it] 30%|██▉ | 5525/18688 [6:28:31<15:26:11, 4.22s/it] 30%|██▉ | 5526/18688 [6:28:36<15:37:58, 4.28s/it] 30%|██▉ | 5527/18688 [6:28:40<15:48:39, 4.32s/it] 30%|██▉ | 5528/18688 [6:28:44<15:11:30, 4.16s/it] 30%|██▉ | 5529/18688 [6:28:48<15:09:26, 4.15s/it] 30%|██▉ | 5530/18688 [6:28:52<14:25:31, 3.95s/it] {'loss': '0.908', 'grad_norm': '1.223', 'learning_rate': '8.704e-05', 'epoch': '0.2959', 'num_input_tokens_seen': 18577384, 'train_runtime': '2.333e+04', 'train_tokens_per_second': '796.2'}
30%|██▉ | 5530/18688 [6:28:52<14:25:31, 3.95s/it] 30%|██▉ | 5531/18688 [6:28:55<14:14:33, 3.90s/it] 30%|██▉ | 5532/18688 [6:28:59<13:59:51, 3.83s/it] 30%|██▉ | 5533/18688 [6:29:03<14:33:49, 3.99s/it] 30%|██▉ | 5534/18688 [6:29:07<14:19:12, 3.92s/it] 30%|██▉ | 5535/18688 [6:29:11<14:01:17, 3.84s/it] 30%|██▉ | 5536/18688 [6:29:16<15:01:38, 4.11s/it] 30%|██▉ | 5537/18688 [6:29:20<15:18:03, 4.19s/it] 30%|██▉ | 5538/18688 [6:29:24<15:22:24, 4.21s/it] 30%|██▉ | 5539/18688 [6:29:29<15:52:48, 4.35s/it] 30%|██▉ | 5540/18688 [6:29:34<16:36:14, 4.55s/it] {'loss': '0.9226', 'grad_norm': '1.171', 'learning_rate': '8.698e-05', 'epoch': '0.2965', 'num_input_tokens_seen': 18611152, 'train_runtime': '2.337e+04', 'train_tokens_per_second': '796.2'}
30%|██▉ | 5540/18688 [6:29:34<16:36:14, 4.55s/it] 30%|██▉ | 5541/18688 [6:29:39<16:48:58, 4.60s/it] 30%|██▉ | 5542/18688 [6:29:43<16:04:54, 4.40s/it] 30%|██▉ | 5543/18688 [6:29:46<15:35:18, 4.27s/it] 30%|██▉ | 5544/18688 [6:29:51<15:32:41, 4.26s/it] 30%|██▉ | 5545/18688 [6:29:56<16:24:39, 4.50s/it] 30%|██▉ | 5546/18688 [6:30:00<16:30:22, 4.52s/it] 30%|██▉ | 5547/18688 [6:30:04<15:47:35, 4.33s/it] 30%|██▉ | 5548/18688 [6:30:09<16:04:52, 4.41s/it] 30%|██▉ | 5549/18688 [6:30:14<16:35:52, 4.55s/it] 30%|██▉ | 5550/18688 [6:30:17<15:47:45, 4.33s/it] {'loss': '0.9805', 'grad_norm': '1.142', 'learning_rate': '8.692e-05', 'epoch': '0.297', 'num_input_tokens_seen': 18648848, 'train_runtime': '2.342e+04', 'train_tokens_per_second': '796.3'}
30%|██▉ | 5550/18688 [6:30:17<15:47:45, 4.33s/it] 30%|██▉ | 5551/18688 [6:30:22<15:55:23, 4.36s/it] 30%|██▉ | 5552/18688 [6:30:26<15:24:16, 4.22s/it] 30%|██▉ | 5553/18688 [6:30:30<15:43:41, 4.31s/it] 30%|██▉ | 5554/18688 [6:30:35<16:05:30, 4.41s/it] 30%|██▉ | 5555/18688 [6:30:39<15:19:27, 4.20s/it] 30%|██▉ | 5556/18688 [6:30:43<15:11:49, 4.17s/it] 30%|██▉ | 5557/18688 [6:30:46<14:34:43, 4.00s/it] 30%|██▉ | 5558/18688 [6:30:50<14:24:32, 3.95s/it] 30%|██▉ | 5559/18688 [6:30:54<14:09:59, 3.88s/it] 30%|██▉ | 5560/18688 [6:30:58<14:38:12, 4.01s/it] {'loss': '0.8733', 'grad_norm': '1.114', 'learning_rate': '8.686e-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<14:38:12, 4.01s/it] 30%|██▉ | 5561/18688 [6:31:02<14:11:41, 3.89s/it] 30%|██▉ | 5562/18688 [6:31:07<15:32:52, 4.26s/it] 30%|██▉ | 5563/18688 [6:31:12<16:32:58, 4.54s/it] 30%|██▉ | 5564/18688 [6:31:17<17:00:20, 4.66s/it] 30%|██▉ | 5565/18688 [6:31:21<15:52:52, 4.36s/it] 30%|██▉ | 5566/18688 [6:31:25<15:52:01, 4.35s/it] 30%|██▉ | 5567/18688 [6:31:29<15:51:05, 4.35s/it] 30%|██▉ | 5568/18688 [6:31:34<15:57:57, 4.38s/it] 30%|██▉ | 5569/18688 [6:31:38<15:50:45, 4.35s/it] 30%|██▉ | 5570/18688 [6:31:42<15:29:44, 4.25s/it] {'loss': '0.9495', 'grad_norm': '1.362', 'learning_rate': '8.68e-05', 'epoch': '0.2981', 'num_input_tokens_seen': 18714848, 'train_runtime': '2.35e+04', 'train_tokens_per_second': '796.3'}
30%|██▉ | 5570/18688 [6:31:42<15:29:44, 4.25s/it] 30%|██▉ | 5571/18688 [6:31:46<15:06:47, 4.15s/it] 30%|██▉ | 5572/18688 [6:31:50<15:00:07, 4.12s/it] 30%|██▉ | 5573/18688 [6:31:54<14:21:49, 3.94s/it] 30%|██▉ | 5574/18688 [6:31:59<15:25:13, 4.23s/it] 30%|██▉ | 5575/18688 [6:32:03<15:05:08, 4.14s/it] 30%|██▉ | 5576/18688 [6:32:06<14:22:37, 3.95s/it] 30%|██▉ | 5577/18688 [6:32:11<15:14:05, 4.18s/it] 30%|██▉ | 5578/18688 [6:32:16<15:57:47, 4.38s/it] 30%|██▉ | 5579/18688 [6:32:19<15:15:09, 4.19s/it] 30%|██▉ | 5580/18688 [6:32:23<14:45:35, 4.05s/it] {'loss': '0.9411', 'grad_norm': '1.263', 'learning_rate': '8.674e-05', 'epoch': '0.2986', 'num_input_tokens_seen': 18747136, 'train_runtime': '2.354e+04', 'train_tokens_per_second': '796.3'}
30%|██▉ | 5580/18688 [6:32:23<14:45:35, 4.05s/it] 30%|██▉ | 5581/18688 [6:32:27<14:28:52, 3.98s/it] 30%|██▉ | 5582/18688 [6:32:32<15:17:44, 4.20s/it] 30%|██▉ | 5583/18688 [6:32:35<14:55:00, 4.10s/it] 30%|██▉ | 5584/18688 [6:32:40<14:59:55, 4.12s/it] 30%|██▉ | 5585/18688 [6:32:43<14:32:02, 3.99s/it] 30%|██▉ | 5586/18688 [6:32:47<14:41:53, 4.04s/it] 30%|██▉ | 5587/18688 [6:32:51<14:08:41, 3.89s/it] 30%|██▉ | 5588/18688 [6:32:55<14:04:29, 3.87s/it] 30%|██▉ | 5589/18688 [6:33:00<14:58:14, 4.11s/it] 30%|██▉ | 5590/18688 [6:33:04<14:55:59, 4.10s/it] {'loss': '0.8989', 'grad_norm': '0.9977', 'learning_rate': '8.667e-05', 'epoch': '0.2991', 'num_input_tokens_seen': 18779008, 'train_runtime': '2.358e+04', 'train_tokens_per_second': '796.3'}
30%|██▉ | 5590/18688 [6:33:04<14:55:59, 4.10s/it] 30%|██▉ | 5591/18688 [6:33:08<15:18:36, 4.21s/it] 30%|██▉ | 5592/18688 [6:33:12<15:15:52, 4.20s/it] 30%|██▉ | 5593/18688 [6:33:16<14:46:05, 4.06s/it] 30%|██▉ | 5594/18688 [6:33:20<14:31:16, 3.99s/it] 30%|██▉ | 5595/18688 [6:33:23<14:00:09, 3.85s/it] 30%|██▉ | 5596/18688 [6:33:28<14:33:24, 4.00s/it] 30%|██▉ | 5597/18688 [6:33:31<14:07:40, 3.89s/it] 30%|██▉ | 5598/18688 [6:33:35<14:25:11, 3.97s/it] 30%|██▉ | 5599/18688 [6:33:40<14:37:35, 4.02s/it] 30%|██▉ | 5600/18688 [6:33:44<14:35:35, 4.01s/it] {'loss': '0.8879', 'grad_norm': '1.534', 'learning_rate': '8.661e-05', 'epoch': '0.2997', 'num_input_tokens_seen': 18810528, 'train_runtime': '2.362e+04', 'train_tokens_per_second': '796.2'}
30%|██▉ | 5600/18688 [6:33:44<14:35:35, 4.01s/it] 30%|██▉ | 5601/18688 [6:33:48<14:43:52, 4.05s/it] 30%|██▉ | 5602/18688 [6:33:52<15:15:04, 4.20s/it] 30%|██▉ | 5603/18688 [6:33:57<15:55:06, 4.38s/it] 30%|██▉ | 5604/18688 [6:34:01<15:37:39, 4.30s/it] 30%|██▉ | 5605/18688 [6:34:05<14:50:40, 4.08s/it] 30%|██▉ | 5606/18688 [6:34:08<14:10:51, 3.90s/it] 30%|███ | 5607/18688 [6:34:12<13:56:44, 3.84s/it] 30%|███ | 5608/18688 [6:34:16<14:02:10, 3.86s/it] 30%|███ | 5609/18688 [6:34:19<13:38:59, 3.76s/it] 30%|███ | 5610/18688 [6:34:24<14:35:13, 4.02s/it] {'loss': '0.8874', 'grad_norm': '1.32', 'learning_rate': '8.655e-05', 'epoch': '0.3002', 'num_input_tokens_seen': 18841392, 'train_runtime': '2.366e+04', 'train_tokens_per_second': '796.2'}
30%|███ | 5610/18688 [6:34:24<14:35:13, 4.02s/it] 30%|███ | 5611/18688 [6:34:29<15:47:48, 4.35s/it] 30%|███ | 5612/18688 [6:34:33<15:41:15, 4.32s/it] 30%|███ | 5613/18688 [6:34:37<14:47:02, 4.07s/it] 30%|███ | 5614/18688 [6:34:41<14:29:05, 3.99s/it] 30%|███ | 5615/18688 [6:34:44<14:18:59, 3.94s/it] 30%|███ | 5616/18688 [6:34:48<14:08:19, 3.89s/it] 30%|███ | 5617/18688 [6:34:52<13:43:08, 3.78s/it] 30%|███ | 5618/18688 [6:34:56<14:03:00, 3.87s/it] 30%|███ | 5619/18688 [6:35:00<14:34:04, 4.01s/it] 30%|███ | 5620/18688 [6:35:05<15:32:43, 4.28s/it] {'loss': '0.9183', 'grad_norm': '1.119', 'learning_rate': '8.649e-05', 'epoch': '0.3007', 'num_input_tokens_seen': 18874376, 'train_runtime': '2.371e+04', 'train_tokens_per_second': '796.2'}
30%|███ | 5620/18688 [6:35:05<15:32:43, 4.28s/it] 30%|███ | 5621/18688 [6:35:09<15:07:38, 4.17s/it] 30%|███ | 5622/18688 [6:35:13<15:00:31, 4.14s/it] 30%|███ | 5623/18688 [6:35:18<16:03:23, 4.42s/it] 30%|███ | 5624/18688 [6:35:23<16:29:32, 4.54s/it] 30%|███ | 5625/18688 [6:35:28<16:35:38, 4.57s/it] 30%|███ | 5626/18688 [6:35:32<16:08:15, 4.45s/it] 30%|███ | 5627/18688 [6:35:35<15:17:48, 4.22s/it] 30%|███ | 5628/18688 [6:35:39<14:49:11, 4.09s/it] 30%|███ | 5629/18688 [6:35:44<15:27:59, 4.26s/it] 30%|███ | 5630/18688 [6:35:49<16:07:34, 4.45s/it] {'loss': '0.9848', 'grad_norm': '0.992', 'learning_rate': '8.642e-05', 'epoch': '0.3013', 'num_input_tokens_seen': 18908680, 'train_runtime': '2.375e+04', 'train_tokens_per_second': '796.2'}
30%|███ | 5630/18688 [6:35:49<16:07:34, 4.45s/it] 30%|███ | 5631/18688 [6:35:53<16:10:47, 4.46s/it] 30%|███ | 5632/18688 [6:35:57<15:23:40, 4.24s/it] 30%|███ | 5633/18688 [6:36:02<15:45:50, 4.35s/it] 30%|███ | 5634/18688 [6:36:06<15:50:38, 4.37s/it] 30%|███ | 5635/18688 [6:36:10<15:54:30, 4.39s/it] 30%|███ | 5636/18688 [6:36:15<15:52:42, 4.38s/it] 30%|███ | 5637/18688 [6:36:19<15:47:29, 4.36s/it] 30%|███ | 5638/18688 [6:36:23<15:41:14, 4.33s/it] 30%|███ | 5639/18688 [6:36:27<14:51:14, 4.10s/it] 30%|███ | 5640/18688 [6:36:30<14:09:29, 3.91s/it] {'loss': '0.9668', 'grad_norm': '1.455', 'learning_rate': '8.636e-05', 'epoch': '0.3018', 'num_input_tokens_seen': 18941968, 'train_runtime': '2.379e+04', 'train_tokens_per_second': '796.2'}
30%|███ | 5640/18688 [6:36:30<14:09:29, 3.91s/it] 30%|███ | 5641/18688 [6:36:35<15:05:26, 4.16s/it] 30%|███ | 5642/18688 [6:36:39<14:22:18, 3.97s/it] 30%|███ | 5643/18688 [6:36:43<14:12:38, 3.92s/it] 30%|███ | 5644/18688 [6:36:46<14:09:26, 3.91s/it] 30%|███ | 5645/18688 [6:36:50<13:45:47, 3.80s/it] 30%|███ | 5646/18688 [6:36:54<14:12:23, 3.92s/it] 30%|███ | 5647/18688 [6:36:58<14:11:07, 3.92s/it] 30%|███ | 5648/18688 [6:37:03<15:11:48, 4.20s/it] 30%|███ | 5649/18688 [6:37:07<15:25:41, 4.26s/it] 30%|███ | 5650/18688 [6:37:11<14:34:16, 4.02s/it] {'loss': '0.9699', 'grad_norm': '1.292', 'learning_rate': '8.63e-05', 'epoch': '0.3023', 'num_input_tokens_seen': 18974096, 'train_runtime': '2.383e+04', 'train_tokens_per_second': '796.2'}
30%|███ | 5650/18688 [6:37:11<14:34:16, 4.02s/it] 30%|███ | 5651/18688 [6:37:14<14:10:41, 3.92s/it] 30%|███ | 5652/18688 [6:37:19<14:29:58, 4.00s/it] 30%|███ | 5653/18688 [6:37:23<14:40:03, 4.05s/it] 30%|███ | 5654/18688 [6:37:28<15:36:55, 4.31s/it] 30%|███ | 5655/18688 [6:37:32<15:24:11, 4.25s/it] 30%|███ | 5656/18688 [6:37:36<14:48:02, 4.09s/it] 30%|███ | 5657/18688 [6:37:40<14:38:40, 4.05s/it] 30%|███ | 5658/18688 [6:37:44<14:47:24, 4.09s/it] 30%|███ | 5659/18688 [6:37:47<14:14:02, 3.93s/it] 30%|███ | 5660/18688 [6:37:51<14:20:13, 3.96s/it] {'loss': '0.8858', 'grad_norm': '1.171', 'learning_rate': '8.624e-05', 'epoch': '0.3029', 'num_input_tokens_seen': 19005576, 'train_runtime': '2.387e+04', 'train_tokens_per_second': '796.2'}
30%|███ | 5660/18688 [6:37:51<14:20:13, 3.96s/it] 30%|███ | 5661/18688 [6:37:55<14:34:16, 4.03s/it] 30%|███ | 5662/18688 [6:37:59<14:15:40, 3.94s/it] 30%|███ | 5663/18688 [6:38:04<15:35:35, 4.31s/it] 30%|███ | 5664/18688 [6:38:09<16:16:21, 4.50s/it] 30%|███ | 5665/18688 [6:38:13<15:24:30, 4.26s/it] 30%|███ | 5666/18688 [6:38:16<14:31:14, 4.01s/it] 30%|███ | 5667/18688 [6:38:20<14:30:20, 4.01s/it] 30%|███ | 5668/18688 [6:38:25<14:48:26, 4.09s/it] 30%|███ | 5669/18688 [6:38:29<15:13:13, 4.21s/it] 30%|███ | 5670/18688 [6:38:33<14:39:48, 4.06s/it] {'loss': '0.9263', 'grad_norm': '1.384', 'learning_rate': '8.617e-05', 'epoch': '0.3034', 'num_input_tokens_seen': 19039616, 'train_runtime': '2.391e+04', 'train_tokens_per_second': '796.2'}
30%|███ | 5670/18688 [6:38:33<14:39:48, 4.06s/it] 30%|███ | 5671/18688 [6:38:37<14:44:08, 4.08s/it] 30%|███ | 5672/18688 [6:38:41<14:28:38, 4.00s/it] 30%|███ | 5673/18688 [6:38:46<15:14:45, 4.22s/it] 30%|███ | 5674/18688 [6:38:51<16:20:19, 4.52s/it] 30%|███ | 5675/18688 [6:38:55<15:50:43, 4.38s/it] 30%|███ | 5676/18688 [6:38:59<15:15:37, 4.22s/it] 30%|███ | 5677/18688 [6:39:02<14:27:41, 4.00s/it] 30%|███ | 5678/18688 [6:39:06<14:38:13, 4.05s/it] 30%|███ | 5679/18688 [6:39:10<14:08:51, 3.92s/it] 30%|███ | 5680/18688 [6:39:14<13:46:53, 3.81s/it] {'loss': '0.8941', 'grad_norm': '1.472', 'learning_rate': '8.611e-05', 'epoch': '0.3039', 'num_input_tokens_seen': 19069448, 'train_runtime': '2.395e+04', 'train_tokens_per_second': '796.1'}
30%|███ | 5680/18688 [6:39:14<13:46:53, 3.81s/it] 30%|███ | 5681/18688 [6:39:17<13:44:24, 3.80s/it] 30%|███ | 5682/18688 [6:39:21<13:49:33, 3.83s/it] 30%|███ | 5683/18688 [6:39:25<14:01:57, 3.88s/it] 30%|███ | 5684/18688 [6:39:29<14:19:48, 3.97s/it] 30%|███ | 5685/18688 [6:39:34<15:24:37, 4.27s/it] 30%|███ | 5686/18688 [6:39:39<15:26:52, 4.28s/it] 30%|███ | 5687/18688 [6:39:42<14:39:25, 4.06s/it] 30%|███ | 5688/18688 [6:39:46<14:46:08, 4.09s/it] 30%|███ | 5689/18688 [6:39:51<15:10:46, 4.20s/it] 30%|███ | 5690/18688 [6:39:55<15:23:28, 4.26s/it] {'loss': '0.9811', 'grad_norm': '1.341', 'learning_rate': '8.605e-05', 'epoch': '0.3045', 'num_input_tokens_seen': 19103552, 'train_runtime': '2.4e+04', 'train_tokens_per_second': '796.1'}
30%|███ | 5690/18688 [6:39:55<15:23:28, 4.26s/it] 30%|███ | 5691/18688 [6:39:59<14:33:19, 4.03s/it] 30%|███ | 5692/18688 [6:40:03<15:01:31, 4.16s/it] 30%|███ | 5693/18688 [6:40:08<15:16:17, 4.23s/it] 30%|███ | 5694/18688 [6:40:14<17:14:14, 4.78s/it] 30%|███ | 5695/18688 [6:40:17<16:03:06, 4.45s/it] 30%|███ | 5696/18688 [6:40:21<15:23:30, 4.26s/it] 30%|███ | 5697/18688 [6:40:26<16:31:34, 4.58s/it] 30%|███ | 5698/18688 [6:40:31<16:20:40, 4.53s/it] 30%|███ | 5699/18688 [6:40:35<16:19:11, 4.52s/it] 31%|███ | 5700/18688 [6:40:39<15:36:44, 4.33s/it] {'loss': '0.9412', 'grad_norm': '1.181', 'learning_rate': '8.598e-05', 'epoch': '0.305', 'num_input_tokens_seen': 19140408, 'train_runtime': '2.404e+04', 'train_tokens_per_second': '796.2'}
31%|███ | 5700/18688 [6:40:39<15:36:44, 4.33s/it] 31%|███ | 5701/18688 [6:40:43<15:07:40, 4.19s/it] 31%|███ | 5702/18688 [6:40:47<14:40:43, 4.07s/it] 31%|███ | 5703/18688 [6:40:51<14:24:53, 4.00s/it] 31%|███ | 5704/18688 [6:40:55<14:46:21, 4.10s/it] 31%|███ | 5705/18688 [6:40:59<14:28:55, 4.02s/it] 31%|███ | 5706/18688 [6:41:03<14:16:53, 3.96s/it] 31%|███ | 5707/18688 [6:41:07<14:03:55, 3.90s/it] 31%|███ | 5708/18688 [6:41:10<13:42:37, 3.80s/it] 31%|███ | 5709/18688 [6:41:15<14:36:35, 4.05s/it] 31%|███ | 5710/18688 [6:41:19<14:25:53, 4.00s/it] {'loss': '0.956', 'grad_norm': '1.193', 'learning_rate': '8.592e-05', 'epoch': '0.3056', 'num_input_tokens_seen': 19171408, 'train_runtime': '2.408e+04', 'train_tokens_per_second': '796.2'}
31%|███ | 5710/18688 [6:41:19<14:25:53, 4.00s/it] 31%|███ | 5711/18688 [6:41:23<15:17:55, 4.24s/it] 31%|███ | 5712/18688 [6:41:28<15:22:27, 4.27s/it] 31%|███ | 5713/18688 [6:41:32<15:11:34, 4.22s/it] 31%|███ | 5714/18688 [6:41:36<14:36:35, 4.05s/it] 31%|███ | 5715/18688 [6:41:39<14:11:25, 3.94s/it] 31%|███ | 5716/18688 [6:41:43<14:32:11, 4.03s/it] 31%|███ | 5717/18688 [6:41:48<14:41:09, 4.08s/it] 31%|███ | 5718/18688 [6:41:52<15:15:03, 4.23s/it] 31%|███ | 5719/18688 [6:41:57<15:46:24, 4.38s/it] 31%|███ | 5720/18688 [6:42:02<16:12:35, 4.50s/it] {'loss': '0.9676', 'grad_norm': '1.341', 'learning_rate': '8.586e-05', 'epoch': '0.3061', 'num_input_tokens_seen': 19207288, 'train_runtime': '2.412e+04', 'train_tokens_per_second': '796.2'}
31%|███ | 5720/18688 [6:42:02<16:12:35, 4.50s/it] 31%|███ | 5721/18688 [6:42:06<16:24:47, 4.56s/it] 31%|███ | 5722/18688 [6:42:12<17:08:20, 4.76s/it] 31%|███ | 5723/18688 [6:42:15<15:48:52, 4.39s/it] 31%|███ | 5724/18688 [6:42:20<16:01:11, 4.45s/it] 31%|███ | 5725/18688 [6:42:24<16:02:55, 4.46s/it] 31%|███ | 5726/18688 [6:42:28<15:36:43, 4.34s/it] 31%|███ | 5727/18688 [6:42:33<16:30:45, 4.59s/it] 31%|███ | 5728/18688 [6:42:37<15:19:30, 4.26s/it] 31%|███ | 5729/18688 [6:42:41<14:53:06, 4.14s/it] 31%|███ | 5730/18688 [6:42:44<14:18:08, 3.97s/it] {'loss': '0.9388', 'grad_norm': '1.356', 'learning_rate': '8.579e-05', 'epoch': '0.3066', 'num_input_tokens_seen': 19242856, 'train_runtime': '2.416e+04', 'train_tokens_per_second': '796.3'}
31%|███ | 5730/18688 [6:42:44<14:18:08, 3.97s/it] 31%|███ | 5731/18688 [6:42:49<14:46:21, 4.10s/it] 31%|███ | 5732/18688 [6:42:52<14:13:45, 3.95s/it] 31%|███ | 5733/18688 [6:42:56<13:54:39, 3.87s/it] 31%|███ | 5734/18688 [6:43:00<13:38:50, 3.79s/it] 31%|███ | 5735/18688 [6:43:05<14:56:47, 4.15s/it] 31%|███ | 5736/18688 [6:43:08<14:28:14, 4.02s/it] 31%|███ | 5737/18688 [6:43:13<14:37:40, 4.07s/it] 31%|███ | 5738/18688 [6:43:16<14:14:38, 3.96s/it] 31%|███ | 5739/18688 [6:43:21<14:48:33, 4.12s/it] 31%|███ | 5740/18688 [6:43:24<14:05:49, 3.92s/it] {'loss': '0.8947', 'grad_norm': '1.356', 'learning_rate': '8.573e-05', 'epoch': '0.3072', 'num_input_tokens_seen': 19275640, 'train_runtime': '2.42e+04', 'train_tokens_per_second': '796.4'}
31%|███ | 5740/18688 [6:43:24<14:05:49, 3.92s/it] 31%|███ | 5741/18688 [6:43:28<14:02:41, 3.91s/it] 31%|███ | 5742/18688 [6:43:32<13:55:27, 3.87s/it] 31%|███ | 5743/18688 [6:43:36<14:14:20, 3.96s/it] 31%|███ | 5744/18688 [6:43:40<14:10:43, 3.94s/it] 31%|███ | 5745/18688 [6:43:44<14:17:37, 3.98s/it] 31%|███ | 5746/18688 [6:43:48<14:31:06, 4.04s/it] 31%|███ | 5747/18688 [6:43:52<14:09:51, 3.94s/it] 31%|███ | 5748/18688 [6:43:56<14:43:11, 4.10s/it] 31%|███ | 5749/18688 [6:44:01<14:50:17, 4.13s/it] 31%|███ | 5750/18688 [6:44:05<14:43:06, 4.10s/it] {'loss': '0.9165', 'grad_norm': '1.199', 'learning_rate': '8.566e-05', 'epoch': '0.3077', 'num_input_tokens_seen': 19307320, 'train_runtime': '2.425e+04', 'train_tokens_per_second': '796.3'}
31%|███ | 5750/18688 [6:44:05<14:43:06, 4.10s/it] 31%|███ | 5751/18688 [6:44:09<14:39:06, 4.08s/it] 31%|███ | 5752/18688 [6:44:14<15:42:46, 4.37s/it] 31%|███ | 5753/18688 [6:44:18<15:33:39, 4.33s/it] 31%|███ | 5754/18688 [6:44:21<14:39:26, 4.08s/it] 31%|███ | 5755/18688 [6:44:25<14:35:54, 4.06s/it] 31%|███ | 5756/18688 [6:44:29<14:16:29, 3.97s/it] 31%|███ | 5757/18688 [6:44:33<13:48:42, 3.85s/it] 31%|███ | 5758/18688 [6:44:37<13:47:24, 3.84s/it] 31%|███ | 5759/18688 [6:44:40<13:32:37, 3.77s/it] 31%|███ | 5760/18688 [6:44:45<14:15:28, 3.97s/it] {'loss': '0.9126', 'grad_norm': '1.12', 'learning_rate': '8.56e-05', 'epoch': '0.3082', 'num_input_tokens_seen': 19338200, 'train_runtime': '2.429e+04', 'train_tokens_per_second': '796.3'}
31%|███ | 5760/18688 [6:44:45<14:15:28, 3.97s/it] 31%|███ | 5761/18688 [6:44:49<14:34:21, 4.06s/it] 31%|███ | 5762/18688 [6:44:53<15:10:32, 4.23s/it] 31%|███ | 5763/18688 [6:44:58<14:57:23, 4.17s/it] 31%|███ | 5764/18688 [6:45:02<15:16:53, 4.26s/it] 31%|███ | 5765/18688 [6:45:06<15:24:58, 4.29s/it] 31%|███ | 5766/18688 [6:45:10<14:33:28, 4.06s/it] 31%|███ | 5767/18688 [6:45:14<14:41:41, 4.09s/it] 31%|███ | 5768/18688 [6:45:19<15:40:24, 4.37s/it] 31%|███ | 5769/18688 [6:45:23<15:27:56, 4.31s/it] 31%|███ | 5770/18688 [6:45:28<15:41:45, 4.37s/it] {'loss': '0.9974', 'grad_norm': '1.23', 'learning_rate': '8.554e-05', 'epoch': '0.3088', 'num_input_tokens_seen': 19373864, 'train_runtime': '2.433e+04', 'train_tokens_per_second': '796.4'}
31%|███ | 5770/18688 [6:45:28<15:41:45, 4.37s/it] 31%|███ | 5771/18688 [6:45:33<16:17:46, 4.54s/it] 31%|███ | 5772/18688 [6:45:38<17:16:23, 4.81s/it] 31%|███ | 5773/18688 [6:45:42<16:35:23, 4.62s/it] 31%|███ | 5774/18688 [6:45:46<15:57:17, 4.45s/it] 31%|███ | 5775/18688 [6:45:51<15:40:22, 4.37s/it] 31%|███ | 5776/18688 [6:45:54<14:48:15, 4.13s/it] 31%|███ | 5777/18688 [6:45:58<15:05:28, 4.21s/it] 31%|███ | 5778/18688 [6:46:02<14:48:14, 4.13s/it] 31%|███ | 5779/18688 [6:46:06<14:29:21, 4.04s/it] 31%|███ | 5780/18688 [6:46:10<14:35:10, 4.07s/it] {'loss': '0.9124', 'grad_norm': '1.625', 'learning_rate': '8.547e-05', 'epoch': '0.3093', 'num_input_tokens_seen': 19409144, 'train_runtime': '2.437e+04', 'train_tokens_per_second': '796.4'}
31%|███ | 5780/18688 [6:46:10<14:35:10, 4.07s/it] 31%|███ | 5781/18688 [6:46:14<14:25:43, 4.02s/it] 31%|███ | 5782/18688 [6:46:19<14:35:45, 4.07s/it] 31%|███ | 5783/18688 [6:46:23<14:42:55, 4.11s/it] 31%|███ | 5784/18688 [6:46:27<15:27:12, 4.31s/it] 31%|███ | 5785/18688 [6:46:31<14:50:07, 4.14s/it] 31%|███ | 5786/18688 [6:46:36<15:57:04, 4.45s/it] 31%|███ | 5787/18688 [6:46:41<15:59:01, 4.46s/it] 31%|███ | 5788/18688 [6:46:46<16:17:55, 4.55s/it] 31%|███ | 5789/18688 [6:46:50<15:47:10, 4.41s/it] 31%|███ | 5790/18688 [6:46:54<15:29:40, 4.32s/it] {'loss': '0.9846', 'grad_norm': '1.401', 'learning_rate': '8.541e-05', 'epoch': '0.3098', 'num_input_tokens_seen': 19443104, 'train_runtime': '2.441e+04', 'train_tokens_per_second': '796.4'}
31%|███ | 5790/18688 [6:46:54<15:29:40, 4.32s/it] 31%|███ | 5791/18688 [6:46:58<15:28:40, 4.32s/it] 31%|███ | 5792/18688 [6:47:02<14:56:23, 4.17s/it] 31%|███ | 5793/18688 [6:47:06<15:04:00, 4.21s/it] 31%|███ | 5794/18688 [6:47:12<16:28:04, 4.60s/it] 31%|███ | 5795/18688 [6:47:16<15:31:34, 4.34s/it] 31%|███ | 5796/18688 [6:47:19<14:35:22, 4.07s/it] 31%|███ | 5797/18688 [6:47:23<14:30:39, 4.05s/it] 31%|███ | 5798/18688 [6:47:27<14:47:06, 4.13s/it] 31%|███ | 5799/18688 [6:47:31<14:17:52, 3.99s/it] 31%|███ | 5800/18688 [6:47:35<14:08:57, 3.95s/it] {'loss': '0.9561', 'grad_norm': '1.303', 'learning_rate': '8.534e-05', 'epoch': '0.3104', 'num_input_tokens_seen': 19475768, 'train_runtime': '2.446e+04', 'train_tokens_per_second': '796.4'}
31%|███ | 5800/18688 [6:47:35<14:08:57, 3.95s/it] 31%|███ | 5801/18688 [6:47:40<15:32:32, 4.34s/it] 31%|███ | 5802/18688 [6:47:45<15:39:20, 4.37s/it] 31%|███ | 5803/18688 [6:47:48<15:09:10, 4.23s/it] 31%|███ | 5804/18688 [6:47:52<14:46:15, 4.13s/it] 31%|███ | 5805/18688 [6:47:56<14:30:23, 4.05s/it] 31%|███ | 5806/18688 [6:48:01<15:14:41, 4.26s/it] 31%|███ | 5807/18688 [6:48:05<14:52:14, 4.16s/it] 31%|███ | 5808/18688 [6:48:08<14:18:37, 4.00s/it] 31%|███ | 5809/18688 [6:48:13<14:53:50, 4.16s/it] 31%|███ | 5810/18688 [6:48:17<14:17:11, 3.99s/it] {'loss': '0.914', 'grad_norm': '1.179', 'learning_rate': '8.528e-05', 'epoch': '0.3109', 'num_input_tokens_seen': 19510000, 'train_runtime': '2.45e+04', 'train_tokens_per_second': '796.4'}
31%|███ | 5810/18688 [6:48:17<14:17:11, 3.99s/it] 31%|███ | 5811/18688 [6:48:20<14:02:42, 3.93s/it] 31%|███ | 5812/18688 [6:48:25<14:53:17, 4.16s/it] 31%|███ | 5813/18688 [6:48:29<14:32:34, 4.07s/it] 31%|███ | 5814/18688 [6:48:33<14:03:07, 3.93s/it] 31%|███ | 5815/18688 [6:48:38<15:14:09, 4.26s/it] 31%|███ | 5816/18688 [6:48:41<14:44:18, 4.12s/it] 31%|███ | 5817/18688 [6:48:46<14:46:30, 4.13s/it] 31%|███ | 5818/18688 [6:48:50<14:49:13, 4.15s/it] 31%|███ | 5819/18688 [6:48:54<15:08:29, 4.24s/it] 31%|███ | 5820/18688 [6:48:58<15:14:19, 4.26s/it] {'loss': '0.9081', 'grad_norm': '1.035', 'learning_rate': '8.521e-05', 'epoch': '0.3114', 'num_input_tokens_seen': 19544144, 'train_runtime': '2.454e+04', 'train_tokens_per_second': '796.5'}
31%|███ | 5820/18688 [6:48:58<15:14:19, 4.26s/it] 31%|███ | 5821/18688 [6:49:03<15:23:48, 4.31s/it] 31%|███ | 5822/18688 [6:49:06<14:29:40, 4.06s/it] 31%|███ | 5823/18688 [6:49:11<14:57:58, 4.19s/it] 31%|███ | 5824/18688 [6:49:15<15:02:09, 4.21s/it] 31%|███ | 5825/18688 [6:49:19<14:31:43, 4.07s/it] 31%|███ | 5826/18688 [6:49:23<14:50:04, 4.15s/it] 31%|███ | 5827/18688 [6:49:27<14:22:48, 4.03s/it] 31%|███ | 5828/18688 [6:49:31<14:03:29, 3.94s/it] 31%|███ | 5829/18688 [6:49:35<14:01:44, 3.93s/it] 31%|███ | 5830/18688 [6:49:38<13:49:10, 3.87s/it] {'loss': '0.934', 'grad_norm': '1.053', 'learning_rate': '8.515e-05', 'epoch': '0.312', 'num_input_tokens_seen': 19575072, 'train_runtime': '2.458e+04', 'train_tokens_per_second': '796.4'}
31%|███ | 5830/18688 [6:49:38<13:49:10, 3.87s/it] 31%|███ | 5831/18688 [6:49:43<14:34:12, 4.08s/it] 31%|███ | 5832/18688 [6:49:47<14:32:35, 4.07s/it] 31%|███ | 5833/18688 [6:49:51<14:32:08, 4.07s/it] 31%|███ | 5834/18688 [6:49:55<14:35:36, 4.09s/it] 31%|███ | 5835/18688 [6:49:59<14:08:26, 3.96s/it] 31%|███ | 5836/18688 [6:50:03<14:03:04, 3.94s/it] 31%|███ | 5837/18688 [6:50:07<14:23:22, 4.03s/it] 31%|███ | 5838/18688 [6:50:11<14:18:44, 4.01s/it] 31%|███ | 5839/18688 [6:50:15<14:36:44, 4.09s/it] 31%|███▏ | 5840/18688 [6:50:19<14:25:28, 4.04s/it] {'loss': '1.002', 'grad_norm': '1.468', 'learning_rate': '8.508e-05', 'epoch': '0.3125', 'num_input_tokens_seen': 19607816, 'train_runtime': '2.462e+04', 'train_tokens_per_second': '796.4'}
31%|███▏ | 5840/18688 [6:50:19<14:25:28, 4.04s/it] 31%|███▏ | 5841/18688 [6:50:23<13:57:16, 3.91s/it] 31%|███▏ | 5842/18688 [6:50:27<14:22:57, 4.03s/it] 31%|███▏ | 5843/18688 [6:50:31<13:59:42, 3.92s/it] 31%|███▏ | 5844/18688 [6:50:34<13:40:15, 3.83s/it] 31%|███▏ | 5845/18688 [6:50:39<14:06:08, 3.95s/it] 31%|███▏ | 5846/18688 [6:50:42<13:46:51, 3.86s/it] 31%|███▏ | 5847/18688 [6:50:46<13:42:57, 3.85s/it] 31%|███▏ | 5848/18688 [6:50:50<14:22:06, 4.03s/it] 31%|███▏ | 5849/18688 [6:50:55<14:39:12, 4.11s/it] 31%|███▏ | 5850/18688 [6:50:59<14:17:52, 4.01s/it] {'loss': '0.9919', 'grad_norm': '1.395', 'learning_rate': '8.502e-05', 'epoch': '0.313', 'num_input_tokens_seen': 19639928, 'train_runtime': '2.466e+04', 'train_tokens_per_second': '796.5'}
31%|███▏ | 5850/18688 [6:50:59<14:17:52, 4.01s/it] 31%|███▏ | 5851/18688 [6:51:03<14:19:07, 4.02s/it] 31%|███▏ | 5852/18688 [6:51:07<14:18:47, 4.01s/it] 31%|███▏ | 5853/18688 [6:51:11<14:22:46, 4.03s/it] 31%|███▏ | 5854/18688 [6:51:16<15:47:16, 4.43s/it] 31%|███▏ | 5855/18688 [6:51:20<15:06:17, 4.24s/it] 31%|███▏ | 5856/18688 [6:51:24<14:52:56, 4.18s/it] 31%|███▏ | 5857/18688 [6:51:27<14:06:52, 3.96s/it] 31%|███▏ | 5858/18688 [6:51:32<14:56:24, 4.19s/it] 31%|███▏ | 5859/18688 [6:51:36<14:30:28, 4.07s/it] 31%|███▏ | 5860/18688 [6:51:40<14:36:28, 4.10s/it] {'loss': '0.9472', 'grad_norm': '1.425', 'learning_rate': '8.495e-05', 'epoch': '0.3136', 'num_input_tokens_seen': 19674232, 'train_runtime': '2.47e+04', 'train_tokens_per_second': '796.5'}
31%|███▏ | 5860/18688 [6:51:40<14:36:28, 4.10s/it] 31%|███▏ | 5861/18688 [6:51:44<14:18:39, 4.02s/it] 31%|███▏ | 5862/18688 [6:51:48<14:00:44, 3.93s/it] 31%|███▏ | 5863/18688 [6:51:52<14:18:10, 4.01s/it] 31%|███▏ | 5864/18688 [6:51:56<15:04:41, 4.23s/it] 31%|███▏ | 5865/18688 [6:52:01<15:06:15, 4.24s/it] 31%|███▏ | 5866/18688 [6:52:04<14:24:26, 4.05s/it] 31%|███▏ | 5867/18688 [6:52:08<14:12:30, 3.99s/it] 31%|███▏ | 5868/18688 [6:52:12<13:57:37, 3.92s/it] 31%|███▏ | 5869/18688 [6:52:16<14:28:12, 4.06s/it] 31%|███▏ | 5870/18688 [6:52:22<15:39:44, 4.40s/it] {'loss': '0.9033', 'grad_norm': '1.126', 'learning_rate': '8.489e-05', 'epoch': '0.3141', 'num_input_tokens_seen': 19708232, 'train_runtime': '2.474e+04', 'train_tokens_per_second': '796.5'}
31%|███▏ | 5870/18688 [6:52:22<15:39:44, 4.40s/it] 31%|███▏ | 5871/18688 [6:52:25<14:53:57, 4.18s/it] 31%|███▏ | 5872/18688 [6:52:29<14:29:57, 4.07s/it] 31%|███▏ | 5873/18688 [6:52:33<14:21:16, 4.03s/it] 31%|███▏ | 5874/18688 [6:52:38<15:29:48, 4.35s/it] 31%|███▏ | 5875/18688 [6:52:42<14:52:25, 4.18s/it] 31%|███▏ | 5876/18688 [6:52:46<14:48:59, 4.16s/it] 31%|███▏ | 5877/18688 [6:52:51<15:33:43, 4.37s/it] 31%|███▏ | 5878/18688 [6:52:55<14:56:35, 4.20s/it] 31%|███▏ | 5879/18688 [6:53:00<16:04:59, 4.52s/it] 31%|███▏ | 5880/18688 [6:53:05<16:26:11, 4.62s/it] {'loss': '0.9743', 'grad_norm': '1.016', 'learning_rate': '8.482e-05', 'epoch': '0.3146', 'num_input_tokens_seen': 19744040, 'train_runtime': '2.479e+04', 'train_tokens_per_second': '796.6'}
31%|███▏ | 5880/18688 [6:53:05<16:26:11, 4.62s/it] 31%|███▏ | 5881/18688 [6:53:09<16:16:49, 4.58s/it] 31%|███▏ | 5882/18688 [6:53:13<15:20:07, 4.31s/it] 31%|███▏ | 5883/18688 [6:53:17<14:47:46, 4.16s/it] 31%|███▏ | 5884/18688 [6:53:21<14:51:56, 4.18s/it] 31%|███▏ | 5885/18688 [6:53:26<15:22:33, 4.32s/it] 31%|███▏ | 5886/18688 [6:53:29<14:39:29, 4.12s/it] 32%|███▏ | 5887/18688 [6:53:34<15:00:36, 4.22s/it] 32%|███▏ | 5888/18688 [6:53:37<14:33:51, 4.10s/it] 32%|███▏ | 5889/18688 [6:53:41<14:07:13, 3.97s/it] 32%|███▏ | 5890/18688 [6:53:45<13:50:10, 3.89s/it] {'loss': '0.9689', 'grad_norm': '1.511', 'learning_rate': '8.476e-05', 'epoch': '0.3152', 'num_input_tokens_seen': 19775696, 'train_runtime': '2.483e+04', 'train_tokens_per_second': '796.6'}
32%|███▏ | 5890/18688 [6:53:45<13:50:10, 3.89s/it] 32%|███▏ | 5891/18688 [6:53:49<13:55:13, 3.92s/it] 32%|███▏ | 5892/18688 [6:53:53<14:26:16, 4.06s/it] 32%|███▏ | 5893/18688 [6:53:57<14:23:28, 4.05s/it] 32%|███▏ | 5894/18688 [6:54:02<14:47:07, 4.16s/it] 32%|███▏ | 5895/18688 [6:54:06<15:06:00, 4.25s/it] 32%|███▏ | 5896/18688 [6:54:10<14:45:10, 4.15s/it] 32%|███▏ | 5897/18688 [6:54:14<14:27:10, 4.07s/it] 32%|███▏ | 5898/18688 [6:54:18<13:58:45, 3.93s/it] 32%|███▏ | 5899/18688 [6:54:22<14:45:31, 4.15s/it] 32%|███▏ | 5900/18688 [6:54:26<14:16:07, 4.02s/it] {'loss': '0.8964', 'grad_norm': '1.242', 'learning_rate': '8.469e-05', 'epoch': '0.3157', 'num_input_tokens_seen': 19808688, 'train_runtime': '2.487e+04', 'train_tokens_per_second': '796.6'}
32%|███▏ | 5900/18688 [6:54:26<14:16:07, 4.02s/it] 32%|███▏ | 5901/18688 [6:54:30<13:47:52, 3.88s/it] 32%|███▏ | 5902/18688 [6:54:33<13:35:30, 3.83s/it] 32%|███▏ | 5903/18688 [6:54:39<15:10:06, 4.27s/it] 32%|███▏ | 5904/18688 [6:54:42<14:41:49, 4.14s/it] 32%|███▏ | 5905/18688 [6:54:47<15:24:23, 4.34s/it] 32%|███▏ | 5906/18688 [6:54:51<15:05:01, 4.25s/it] 32%|███▏ | 5907/18688 [6:54:55<14:33:50, 4.10s/it] 32%|███▏ | 5908/18688 [6:54:59<14:12:46, 4.00s/it] 32%|███▏ | 5909/18688 [6:55:03<14:37:46, 4.12s/it] 32%|███▏ | 5910/18688 [6:55:07<14:13:38, 4.01s/it] {'loss': '0.9739', 'grad_norm': '1.225', 'learning_rate': '8.462e-05', 'epoch': '0.3163', 'num_input_tokens_seen': 19842304, 'train_runtime': '2.491e+04', 'train_tokens_per_second': '796.6'}
32%|███▏ | 5910/18688 [6:55:07<14:13:38, 4.01s/it] 32%|███▏ | 5911/18688 [6:55:11<14:25:59, 4.07s/it] 32%|███▏ | 5912/18688 [6:55:15<14:04:30, 3.97s/it] 32%|███▏ | 5913/18688 [6:55:19<14:02:01, 3.95s/it] 32%|███▏ | 5914/18688 [6:55:22<13:29:04, 3.80s/it] 32%|███▏ | 5915/18688 [6:55:26<13:50:24, 3.90s/it] 32%|███▏ | 5916/18688 [6:55:30<13:34:52, 3.83s/it] 32%|███▏ | 5917/18688 [6:55:35<14:45:06, 4.16s/it] 32%|███▏ | 5918/18688 [6:55:39<14:31:45, 4.10s/it] 32%|███▏ | 5919/18688 [6:55:43<14:23:31, 4.06s/it] 32%|███▏ | 5920/18688 [6:55:47<14:42:51, 4.15s/it] {'loss': '0.9834', 'grad_norm': '1.334', 'learning_rate': '8.456e-05', 'epoch': '0.3168', 'num_input_tokens_seen': 19875192, 'train_runtime': '2.495e+04', 'train_tokens_per_second': '796.7'}
32%|███▏ | 5920/18688 [6:55:47<14:42:51, 4.15s/it] 32%|███▏ | 5921/18688 [6:55:52<15:20:25, 4.33s/it] 32%|███▏ | 5922/18688 [6:55:55<14:32:32, 4.10s/it] 32%|███▏ | 5923/18688 [6:56:00<14:32:00, 4.10s/it] 32%|███▏ | 5924/18688 [6:56:03<14:00:18, 3.95s/it] 32%|███▏ | 5925/18688 [6:56:07<13:55:28, 3.93s/it] 32%|███▏ | 5926/18688 [6:56:11<14:04:12, 3.97s/it] 32%|███▏ | 5927/18688 [6:56:15<13:29:39, 3.81s/it] 32%|███▏ | 5928/18688 [6:56:20<15:07:11, 4.27s/it] 32%|███▏ | 5929/18688 [6:56:24<14:31:59, 4.10s/it] 32%|███▏ | 5930/18688 [6:56:29<16:16:44, 4.59s/it] {'loss': '0.9707', 'grad_norm': '1.526', 'learning_rate': '8.449e-05', 'epoch': '0.3173', 'num_input_tokens_seen': 19910424, 'train_runtime': '2.499e+04', 'train_tokens_per_second': '796.7'}
32%|███▏ | 5930/18688 [6:56:29<16:16:44, 4.59s/it] 32%|███▏ | 5931/18688 [6:56:33<15:33:39, 4.39s/it] 32%|███▏ | 5932/18688 [6:56:37<14:52:55, 4.20s/it] 32%|███▏ | 5933/18688 [6:56:41<14:10:06, 4.00s/it] 32%|███▏ | 5934/18688 [6:56:44<13:52:26, 3.92s/it] 32%|███▏ | 5935/18688 [6:56:48<13:59:09, 3.95s/it] 32%|███▏ | 5936/18688 [6:56:53<15:00:31, 4.24s/it] 32%|███▏ | 5937/18688 [6:56:58<15:06:37, 4.27s/it] 32%|███▏ | 5938/18688 [6:57:02<15:02:24, 4.25s/it] 32%|███▏ | 5939/18688 [6:57:06<14:40:26, 4.14s/it] 32%|███▏ | 5940/18688 [6:57:09<13:59:26, 3.95s/it] {'loss': '0.9121', 'grad_norm': '1.366', 'learning_rate': '8.443e-05', 'epoch': '0.3179', 'num_input_tokens_seen': 19942736, 'train_runtime': '2.503e+04', 'train_tokens_per_second': '796.8'}
32%|███▏ | 5940/18688 [6:57:09<13:59:26, 3.95s/it] 32%|███▏ | 5941/18688 [6:57:14<14:30:16, 4.10s/it] 32%|███▏ | 5942/18688 [6:57:17<13:57:53, 3.94s/it] 32%|███▏ | 5943/18688 [6:57:22<14:32:25, 4.11s/it] 32%|███▏ | 5944/18688 [6:57:27<15:42:19, 4.44s/it] 32%|███▏ | 5945/18688 [6:57:31<15:06:27, 4.27s/it] 32%|███▏ | 5946/18688 [6:57:35<14:57:28, 4.23s/it] 32%|███▏ | 5947/18688 [6:57:38<14:06:56, 3.99s/it] 32%|███▏ | 5948/18688 [6:57:42<13:59:01, 3.95s/it] 32%|███▏ | 5949/18688 [6:57:46<13:41:29, 3.87s/it] 32%|███▏ | 5950/18688 [6:57:50<13:32:02, 3.82s/it] {'loss': '0.9291', 'grad_norm': '1.101', 'learning_rate': '8.436e-05', 'epoch': '0.3184', 'num_input_tokens_seen': 19975128, 'train_runtime': '2.507e+04', 'train_tokens_per_second': '796.8'}
32%|███▏ | 5950/18688 [6:57:50<13:32:02, 3.82s/it] 32%|███▏ | 5951/18688 [6:57:53<13:27:45, 3.81s/it] 32%|███▏ | 5952/18688 [6:57:58<14:13:12, 4.02s/it] 32%|███▏ | 5953/18688 [6:58:01<13:36:51, 3.85s/it] 32%|███▏ | 5954/18688 [6:58:05<13:38:10, 3.86s/it] 32%|███▏ | 5955/18688 [6:58:09<14:01:50, 3.97s/it] 32%|███▏ | 5956/18688 [6:58:13<13:40:13, 3.87s/it] 32%|███▏ | 5957/18688 [6:58:17<13:31:14, 3.82s/it] 32%|███▏ | 5958/18688 [6:58:20<13:17:36, 3.76s/it] 32%|███▏ | 5959/18688 [6:58:24<13:08:24, 3.72s/it] 32%|███▏ | 5960/18688 [6:58:28<13:21:00, 3.78s/it] {'loss': '0.9568', 'grad_norm': '1.255', 'learning_rate': '8.429e-05', 'epoch': '0.3189', 'num_input_tokens_seen': 20004584, 'train_runtime': '2.511e+04', 'train_tokens_per_second': '796.7'}
32%|███▏ | 5960/18688 [6:58:28<13:21:00, 3.78s/it] 32%|███▏ | 5961/18688 [6:58:32<14:11:49, 4.02s/it] 32%|███▏ | 5962/18688 [6:58:37<14:19:27, 4.05s/it] 32%|███▏ | 5963/18688 [6:58:40<14:03:53, 3.98s/it] 32%|███▏ | 5964/18688 [6:58:44<13:43:02, 3.88s/it] 32%|███▏ | 5965/18688 [6:58:48<13:26:29, 3.80s/it] 32%|███▏ | 5966/18688 [6:58:51<13:22:52, 3.79s/it] 32%|███▏ | 5967/18688 [6:58:56<14:17:52, 4.05s/it] 32%|███▏ | 5968/18688 [6:59:01<15:07:18, 4.28s/it] 32%|███▏ | 5969/18688 [6:59:05<14:50:33, 4.20s/it] 32%|███▏ | 5970/18688 [6:59:09<15:06:18, 4.28s/it] {'loss': '0.9798', 'grad_norm': '1.483', 'learning_rate': '8.423e-05', 'epoch': '0.3195', 'num_input_tokens_seen': 20038552, 'train_runtime': '2.515e+04', 'train_tokens_per_second': '796.8'}
32%|███▏ | 5970/18688 [6:59:09<15:06:18, 4.28s/it] 32%|███▏ | 5971/18688 [6:59:14<15:22:36, 4.35s/it] 32%|███▏ | 5972/18688 [6:59:19<15:38:58, 4.43s/it] 32%|███▏ | 5973/18688 [6:59:23<15:27:48, 4.38s/it] 32%|███▏ | 5974/18688 [6:59:26<14:46:36, 4.18s/it] 32%|███▏ | 5975/18688 [6:59:31<14:45:01, 4.18s/it] 32%|███▏ | 5976/18688 [6:59:34<13:59:21, 3.96s/it] 32%|███▏ | 5977/18688 [6:59:38<14:00:16, 3.97s/it] 32%|███▏ | 5978/18688 [6:59:42<13:33:36, 3.84s/it] 32%|███▏ | 5979/18688 [6:59:46<14:25:29, 4.09s/it] 32%|███▏ | 5980/18688 [6:59:51<14:34:16, 4.13s/it] {'loss': '0.9632', 'grad_norm': '1.395', 'learning_rate': '8.416e-05', 'epoch': '0.32', 'num_input_tokens_seen': 20073584, 'train_runtime': '2.519e+04', 'train_tokens_per_second': '796.9'}
32%|███▏ | 5980/18688 [6:59:51<14:34:16, 4.13s/it] 32%|███▏ | 5981/18688 [6:59:55<15:08:09, 4.29s/it] 32%|███▏ | 5982/18688 [6:59:59<14:29:52, 4.11s/it] 32%|███▏ | 5983/18688 [7:00:03<14:22:13, 4.07s/it] 32%|███▏ | 5984/18688 [7:00:07<14:23:47, 4.08s/it] 32%|███▏ | 5985/18688 [7:00:12<15:31:31, 4.40s/it] 32%|███▏ | 5986/18688 [7:00:16<14:44:03, 4.18s/it] 32%|███▏ | 5987/18688 [7:00:20<14:34:18, 4.13s/it] 32%|███▏ | 5988/18688 [7:00:24<14:39:27, 4.15s/it] 32%|███▏ | 5989/18688 [7:00:28<14:47:39, 4.19s/it] 32%|███▏ | 5990/18688 [7:00:32<14:12:50, 4.03s/it] {'loss': '0.9167', 'grad_norm': '1.205', 'learning_rate': '8.409e-05', 'epoch': '0.3205', 'num_input_tokens_seen': 20106224, 'train_runtime': '2.523e+04', 'train_tokens_per_second': '796.8'}
32%|███▏ | 5990/18688 [7:00:32<14:12:50, 4.03s/it] 32%|███▏ | 5991/18688 [7:00:36<13:52:11, 3.93s/it] 32%|███▏ | 5992/18688 [7:00:40<13:58:26, 3.96s/it] 32%|███▏ | 5993/18688 [7:00:44<14:24:14, 4.08s/it] 32%|███▏ | 5994/18688 [7:00:48<14:48:05, 4.20s/it] 32%|███▏ | 5995/18688 [7:00:52<13:59:19, 3.97s/it] 32%|███▏ | 5996/18688 [7:00:56<14:31:17, 4.12s/it] 32%|███▏ | 5997/18688 [7:01:00<14:23:52, 4.08s/it] 32%|███▏ | 5998/18688 [7:01:05<14:28:45, 4.11s/it] 32%|███▏ | 5999/18688 [7:01:08<13:59:08, 3.97s/it] 32%|███▏ | 6000/18688 [7:01:13<14:28:20, 4.11s/it] {'loss': '0.9718', 'grad_norm': '1.012', 'learning_rate': '8.403e-05', 'epoch': '0.3211', 'num_input_tokens_seen': 20140120, 'train_runtime': '2.527e+04', 'train_tokens_per_second': '796.9'}
32%|███▏ | 6000/18688 [7:01:13<14:28:20, 4.11s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
0%| | 3/1000 [00:00<00:39, 25.51it/s]
1%| | 6/1000 [00:00<00:54, 18.10it/s]
1%| | 8/1000 [00:00<00:56, 17.45it/s]
1%| | 10/1000 [00:00<00:59, 16.61it/s]
1%| | 12/1000 [00:00<01:00, 16.39it/s]
1%|▏ | 14/1000 [00:00<01:01, 15.95it/s]
2%|▏ | 16/1000 [00:00<01:02, 15.75it/s]
2%|▏ | 18/1000 [00:01<01:38, 10.00it/s]
2%|▏ | 20/1000 [00:01<01:32, 10.58it/s]
2%|▏ | 22/1000 [00:01<01:51, 8.75it/s]
2%|▏ | 24/1000 [00:01<01:39, 9.80it/s]
3%|▎ | 26/1000 [00:02<01:44, 9.30it/s]
3%|▎ | 28/1000 [00:02<01:35, 10.14it/s]
3%|▎ | 30/1000 [00:02<01:27, 11.04it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.19it/s]
3%|▎ | 34/1000 [00:02<01:25, 11.30it/s]
4%|▎ | 36/1000 [00:03<01:32, 10.48it/s]
4%|▍ | 38/1000 [00:03<01:24, 11.39it/s]
4%|▍ | 40/1000 [00:03<01:27, 10.99it/s]
4%|▍ | 42/1000 [00:03<01:45, 9.05it/s]
4%|▍ | 44/1000 [00:03<01:30, 10.55it/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, 11.64it/s]
5%|▌ | 52/1000 [00:04<01:16, 12.47it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.19it/s]
6%|▌ | 56/1000 [00:04<01:08, 13.88it/s]
6%|▌ | 58/1000 [00:05<01:47, 8.72it/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<01:15, 12.31it/s]
7%|▋ | 68/1000 [00:05<01:12, 12.90it/s]
7%|▋ | 70/1000 [00:05<01:09, 13.36it/s]
7%|▋ | 72/1000 [00:06<01:09, 13.36it/s]
7%|▋ | 74/1000 [00:06<01:10, 13.05it/s]
8%|▊ | 76/1000 [00:06<01:07, 13.60it/s]
8%|▊ | 78/1000 [00:06<01:07, 13.72it/s]
8%|▊ | 80/1000 [00:06<01:10, 12.97it/s]
8%|▊ | 82/1000 [00:06<01:07, 13.65it/s]
8%|▊ | 84/1000 [00:07<01:04, 14.11it/s]
9%|▊ | 86/1000 [00:07<01:03, 14.37it/s]
9%|▉ | 88/1000 [00:07<01:03, 14.28it/s]
9%|▉ | 90/1000 [00:07<01:03, 14.34it/s]
9%|▉ | 92/1000 [00:07<01:04, 14.07it/s]
9%|▉ | 94/1000 [00:07<01:04, 13.95it/s]
10%|▉ | 96/1000 [00:07<01:03, 14.27it/s]
10%|▉ | 98/1000 [00:07<01:03, 14.21it/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, 7.82it/s]
11%|█ | 106/1000 [00:09<02:01, 7.33it/s]
11%|█ | 108/1000 [00:09<01:42, 8.68it/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 [00:10<01:06, 13.32it/s]
12%|█▏ | 120/1000 [00:10<01:09, 12.75it/s]
12%|█▏ | 122/1000 [00:10<01:22, 10.61it/s]
12%|█▏ | 124/1000 [00:10<01:30, 9.68it/s]
13%|█▎ | 126/1000 [00:10<01:22, 10.63it/s]
13%|█▎ | 128/1000 [00:10<01:15, 11.55it/s]
13%|█▎ | 130/1000 [00:11<01:11, 12.22it/s]
13%|█▎ | 132/1000 [00:11<01:05, 13.19it/s]
13%|█▎ | 134/1000 [00:11<01:09, 12.40it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.36it/s]
14%|█▍ | 138/1000 [00:11<01:04, 13.30it/s]
14%|█▍ | 140/1000 [00:12<01:24, 10.13it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.55it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.58it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.61it/s]
15%|█▍ | 148/1000 [00:12<01:13, 11.52it/s]
15%|█▌ | 150/1000 [00:12<01:07, 12.61it/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, 13.78it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.69it/s]
16%|█▌ | 162/1000 [00:13<01:01, 13.55it/s]
16%|█▋ | 164/1000 [00:13<01:01, 13.66it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.79it/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, 9.19it/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, 8.81it/s]
18%|█▊ | 182/1000 [00:15<01:35, 8.57it/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<01:24, 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, 10.58it/s]
20%|█▉ | 196/1000 [00:17<01:29, 8.99it/s]
20%|█▉ | 198/1000 [00:17<01:20, 10.00it/s]
20%|██ | 200/1000 [00:17<01:17, 10.38it/s]
20%|██ | 202/1000 [00:17<01:13, 10.85it/s]
20%|██ | 204/1000 [00:17<01:11, 11.16it/s]
21%|██ | 206/1000 [00:18<01:09, 11.40it/s]
21%|██ | 208/1000 [00:18<01:08, 11.63it/s]
21%|██ | 210/1000 [00:18<01:04, 12.18it/s]
21%|██ | 212/1000 [00:18<01:04, 12.24it/s]
21%|██▏ | 214/1000 [00:18<01:04, 12.19it/s]
22%|██▏ | 216/1000 [00:18<01:04, 12.15it/s]
22%|██▏ | 218/1000 [00:19<01:04, 12.18it/s]
22%|██▏ | 220/1000 [00:19<01:35, 8.18it/s]
22%|██▏ | 221/1000 [00:19<01:34, 8.21it/s]
22%|██▏ | 223/1000 [00:19<01:27, 8.83it/s]
22%|██▎ | 225/1000 [00:20<01:18, 9.91it/s]
23%|██▎ | 227/1000 [00:20<01:09, 11.16it/s]
23%|██▎ | 229/1000 [00:20<01:05, 11.76it/s]
23%|██▎ | 231/1000 [00:20<01:02, 12.32it/s]
23%|██▎ | 233/1000 [00:20<01:00, 12.68it/s]
24%|██▎ | 235/1000 [00:20<00:57, 13.31it/s]
24%|██▎ | 237/1000 [00:20<00:53, 14.35it/s]
24%|██▍ | 239/1000 [00:20<00:53, 14.22it/s]
24%|██▍ | 241/1000 [00:21<00:52, 14.59it/s]
24%|██▍ | 243/1000 [00:21<00:51, 14.79it/s]
24%|██▍ | 245/1000 [00:21<00:51, 14.76it/s]
25%|██▍ | 247/1000 [00:21<00:49, 15.14it/s]
25%|██▍ | 249/1000 [00:21<00:57, 12.95it/s]
25%|██▌ | 251/1000 [00:21<01:02, 11.97it/s]
25%|██▌ | 253/1000 [00:22<00:58, 12.80it/s]
26%|██▌ | 255/1000 [00:22<00:54, 13.74it/s]
26%|██▌ | 257/1000 [00:22<00:53, 13.92it/s]
26%|██▌ | 259/1000 [00:22<00:52, 14.17it/s]
26%|██▌ | 261/1000 [00:22<00:58, 12.69it/s]
26%|██▋ | 263/1000 [00:22<00:54, 13.43it/s]
26%|██▋ | 265/1000 [00:22<00:52, 13.90it/s]
27%|██▋ | 267/1000 [00:23<00:52, 14.03it/s]
27%|██▋ | 269/1000 [00:23<00:49, 14.75it/s]
27%|██▋ | 271/1000 [00:23<00:54, 13.26it/s]
27%|██▋ | 273/1000 [00:23<01:03, 11.53it/s]
28%|██▊ | 275/1000 [00:23<01:01, 11.78it/s]
28%|██▊ | 277/1000 [00:23<00:56, 12.69it/s]
28%|██▊ | 279/1000 [00:24<00:56, 12.86it/s]
28%|██▊ | 281/1000 [00:24<00:51, 13.93it/s]
28%|██▊ | 283/1000 [00:24<00:50, 14.14it/s]
28%|██▊ | 285/1000 [00:24<00:49, 14.50it/s]
29%|██▊ | 287/1000 [00:24<00:48, 14.73it/s]
29%|██▉ | 289/1000 [00:24<00:46, 15.29it/s]
29%|██▉ | 291/1000 [00:24<00:45, 15.46it/s]
29%|██▉ | 293/1000 [00:24<00:45, 15.41it/s]
30%|██▉ | 295/1000 [00:25<00:45, 15.62it/s]
30%|██▉ | 297/1000 [00:25<00:44, 15.69it/s]
30%|██▉ | 299/1000 [00:25<00:45, 15.36it/s]
30%|███ | 301/1000 [00:25<00:46, 15.04it/s]
30%|███ | 303/1000 [00:25<00:50, 13.72it/s]
30%|███ | 305/1000 [00:25<00:49, 14.16it/s]
31%|███ | 307/1000 [00:25<00:47, 14.54it/s]
31%|███ | 309/1000 [00:25<00:46, 14.98it/s]
31%|███ | 311/1000 [00:26<00:47, 14.54it/s]
31%|███▏ | 313/1000 [00:26<00:45, 15.01it/s]
32%|███▏ | 315/1000 [00:26<00:44, 15.49it/s]
32%|███▏ | 317/1000 [00:26<00:42, 15.90it/s]
32%|███▏ | 319/1000 [00:26<00:42, 15.93it/s]
32%|███▏ | 321/1000 [00:27<01:12, 9.41it/s]
32%|███▏ | 323/1000 [00:27<01:03, 10.66it/s]
32%|███▎ | 325/1000 [00:27<00:58, 11.52it/s]
33%|███▎ | 327/1000 [00:27<00:57, 11.66it/s]
33%|███▎ | 329/1000 [00:27<00:57, 11.60it/s]
33%|███▎ | 331/1000 [00:27<00:55, 12.14it/s]
33%|███▎ | 333/1000 [00:27<00:53, 12.58it/s]
34%|███▎ | 335/1000 [00:28<00:50, 13.08it/s]
34%|███▎ | 337/1000 [00:28<01:06, 10.03it/s]
34%|███▍ | 339/1000 [00:28<00:59, 11.16it/s]
34%|███▍ | 341/1000 [00:28<00:54, 12.05it/s]
34%|███▍ | 343/1000 [00:28<00:50, 12.96it/s]
34%|███▍ | 345/1000 [00:28<00:48, 13.43it/s]
35%|███▍ | 347/1000 [00:29<00:45, 14.32it/s]
35%|███▍ | 349/1000 [00:29<00:44, 14.55it/s]
35%|███▌ | 351/1000 [00:29<00:44, 14.67it/s]
35%|███▌ | 353/1000 [00:29<00:42, 15.20it/s]
36%|███▌ | 355/1000 [00:29<00:42, 15.24it/s]
36%|███▌ | 357/1000 [00:29<00:42, 15.04it/s]
36%|███▌ | 359/1000 [00:29<00:42, 14.93it/s]
36%|███▌ | 361/1000 [00:30<00:47, 13.35it/s]
36%|███▋ | 363/1000 [00:30<00:46, 13.64it/s]
36%|███▋ | 365/1000 [00:30<00:44, 14.38it/s]
37%|███▋ | 367/1000 [00:30<00:53, 11.91it/s]
37%|███▋ | 369/1000 [00:30<00:50, 12.56it/s]
37%|███▋ | 371/1000 [00:30<00:47, 13.25it/s]
37%|███▋ | 373/1000 [00:31<00:54, 11.45it/s]
38%|███▊ | 375/1000 [00:31<00:57, 10.86it/s]
38%|███▊ | 377/1000 [00:31<00:53, 11.69it/s]
38%|███▊ | 379/1000 [00:31<00:50, 12.20it/s]
38%|███▊ | 381/1000 [00:31<00:47, 12.93it/s]
38%|███▊ | 383/1000 [00:31<00:46, 13.36it/s]
38%|███▊ | 385/1000 [00:31<00:45, 13.57it/s]
39%|███▊ | 387/1000 [00:32<00:44, 13.73it/s]
39%|███▉ | 389/1000 [00:32<00:44, 13.77it/s]
39%|███▉ | 391/1000 [00:32<00:43, 13.90it/s]
39%|███▉ | 393/1000 [00:32<00:44, 13.75it/s]
40%|███▉ | 395/1000 [00:32<00:44, 13.61it/s]
40%|███▉ | 397/1000 [00:32<00:43, 13.79it/s]
40%|███▉ | 399/1000 [00:32<00:47, 12.65it/s]
40%|████ | 401/1000 [00:33<00:43, 13.72it/s]
40%|████ | 403/1000 [00:33<00:41, 14.28it/s]
40%|████ | 405/1000 [00:33<00:40, 14.82it/s]
41%|████ | 407/1000 [00:33<00:38, 15.29it/s]
41%|████ | 409/1000 [00:33<00:57, 10.23it/s]
41%|████ | 411/1000 [00:33<00:52, 11.13it/s]
41%|████▏ | 413/1000 [00:34<01:04, 9.14it/s]
42%|████▏ | 415/1000 [00:34<01:00, 9.62it/s]
42%|████▏ | 417/1000 [00:34<01:18, 7.40it/s]
42%|████▏ | 419/1000 [00:34<01:08, 8.54it/s]
42%|████▏ | 421/1000 [00:35<00:57, 10.04it/s]
42%|████▏ | 423/1000 [00:35<01:17, 7.45it/s]
42%|████▎ | 425/1000 [00:35<01:05, 8.71it/s]
43%|████▎ | 427/1000 [00:35<00:58, 9.76it/s]
43%|████▎ | 429/1000 [00:36<01:02, 9.13it/s]
43%|████▎ | 431/1000 [00:36<00:55, 10.18it/s]
43%|████▎ | 433/1000 [00:36<01:15, 7.49it/s]
44%|████▎ | 435/1000 [00:36<01:05, 8.62it/s]
44%|████▎ | 437/1000 [00:37<01:06, 8.52it/s]
44%|████▍ | 439/1000 [00:37<00:58, 9.56it/s]
44%|████▍ | 441/1000 [00:37<00:56, 9.88it/s]
44%|████▍ | 443/1000 [00:37<00:51, 10.87it/s]
44%|████▍ | 445/1000 [00:37<00:46, 11.91it/s]
45%|████▍ | 447/1000 [00:37<00:51, 10.69it/s]
45%|████▍ | 449/1000 [00:38<00:47, 11.57it/s]
45%|████▌ | 451/1000 [00:38<00:43, 12.50it/s]
45%|████▌ | 453/1000 [00:38<00:41, 13.27it/s]
46%|████▌ | 455/1000 [00:38<00:43, 12.40it/s]
46%|████▌ | 457/1000 [00:38<00:41, 13.23it/s]
46%|████▌ | 459/1000 [00:38<00:39, 13.78it/s]
46%|████▌ | 461/1000 [00:38<00:37, 14.20it/s]
46%|████▋ | 463/1000 [00:38<00:35, 14.99it/s]
46%|████▋ | 465/1000 [00:39<00:35, 14.88it/s]
47%|████▋ | 467/1000 [00:39<00:40, 13.19it/s]
47%|████▋ | 469/1000 [00:39<00:38, 13.66it/s]
47%|████▋ | 471/1000 [00:39<00:51, 10.20it/s]
47%|████▋ | 473/1000 [00:39<00:49, 10.55it/s]
48%|████▊ | 475/1000 [00:40<00:44, 11.68it/s]
48%|████▊ | 477/1000 [00:40<00:41, 12.55it/s]
48%|████▊ | 479/1000 [00:40<00:43, 12.05it/s]
48%|████▊ | 481/1000 [00:40<00:40, 12.80it/s]
48%|████▊ | 483/1000 [00:40<00:37, 13.68it/s]
48%|████▊ | 485/1000 [00:40<00:37, 13.87it/s]
49%|████▊ | 487/1000 [00:40<00:35, 14.30it/s]
49%|████▉ | 489/1000 [00:41<00:45, 11.17it/s]
49%|████▉ | 491/1000 [00:41<00:42, 12.08it/s]
49%|████▉ | 493/1000 [00:41<00:38, 13.10it/s]
50%|████▉ | 495/1000 [00:41<00:35, 14.10it/s]
50%|████▉ | 497/1000 [00:41<00:34, 14.72it/s]
50%|████▉ | 499/1000 [00:41<00:33, 14.83it/s]
50%|█████ | 501/1000 [00:42<00:43, 11.44it/s]
50%|█████ | 503/1000 [00:42<00:41, 12.00it/s]
50%|█████ | 505/1000 [00:42<00:39, 12.65it/s]
51%|█████ | 507/1000 [00:42<00:37, 13.10it/s]
51%|█████ | 509/1000 [00:42<00:48, 10.07it/s]
51%|█████ | 511/1000 [00:42<00:43, 11.19it/s]
51%|█████▏ | 513/1000 [00:43<00:41, 11.78it/s]
52%|█████▏ | 515/1000 [00:43<00:37, 12.83it/s]
52%|█████▏ | 517/1000 [00:43<00:36, 13.23it/s]
52%|█████▏ | 519/1000 [00:43<00:36, 13.35it/s]
52%|█████▏ | 521/1000 [00:43<00:33, 14.10it/s]
52%|█████▏ | 523/1000 [00:43<00:33, 14.23it/s]
52%|█████▎ | 525/1000 [00:43<00:33, 14.30it/s]
53%|█████▎ | 527/1000 [00:44<00:39, 12.03it/s]
53%|█████▎ | 529/1000 [00:44<00:37, 12.60it/s]
53%|█████▎ | 531/1000 [00:44<00:34, 13.77it/s]
53%|█████▎ | 533/1000 [00:44<00:32, 14.19it/s]
54%|█████▎ | 535/1000 [00:44<00:32, 14.48it/s]
54%|█████▎ | 537/1000 [00:44<00:32, 14.15it/s]
54%|█████▍ | 539/1000 [00:44<00:31, 14.52it/s]
54%|█████▍ | 541/1000 [00:45<00:31, 14.62it/s]
54%|█████▍ | 543/1000 [00:45<00:30, 14.87it/s]
55%|█████▍ | 545/1000 [00:45<00:31, 14.31it/s]
55%|█████▍ | 547/1000 [00:45<00:32, 14.14it/s]
55%|█████▍ | 549/1000 [00:45<00:31, 14.12it/s]
55%|█████▌ | 551/1000 [00:45<00:32, 13.93it/s]
55%|█████▌ | 553/1000 [00:45<00:31, 14.24it/s]
56%|█████▌ | 555/1000 [00:46<00:31, 14.20it/s]
56%|█████▌ | 557/1000 [00:46<00:30, 14.61it/s]
56%|█████▌ | 559/1000 [00:46<00:30, 14.55it/s]
56%|█████▌ | 561/1000 [00:46<00:29, 14.76it/s]
56%|█████▋ | 563/1000 [00:46<00:40, 10.84it/s]
56%|█████▋ | 565/1000 [00:46<00:37, 11.62it/s]
57%|█████▋ | 567/1000 [00:47<00:35, 12.11it/s]
57%|█████▋ | 569/1000 [00:47<00:34, 12.64it/s]
57%|█████▋ | 571/1000 [00:47<00:34, 12.52it/s]
57%|█████▋ | 573/1000 [00:47<00:33, 12.85it/s]
57%|█████▊ | 575/1000 [00:47<00:33, 12.78it/s]
58%|█████▊ | 577/1000 [00:47<00:32, 13.11it/s]
58%|█████▊ | 579/1000 [00:48<00:37, 11.15it/s]
58%|█████▊ | 581/1000 [00:48<00:33, 12.41it/s]
58%|█████▊ | 583/1000 [00:48<00:32, 13.01it/s]
58%|█████▊ | 585/1000 [00:48<00:31, 13.38it/s]
59%|█████▊ | 587/1000 [00:48<00:30, 13.57it/s]
59%|█████▉ | 589/1000 [00:48<00:29, 13.98it/s]
59%|█████▉ | 591/1000 [00:48<00:29, 13.74it/s]
59%|█████▉ | 593/1000 [00:49<00:31, 12.94it/s]
60%|█████▉ | 595/1000 [00:49<00:29, 13.53it/s]
60%|█████▉ | 597/1000 [00:49<00:29, 13.51it/s]
60%|█████▉ | 599/1000 [00:49<00:27, 14.51it/s]
60%|██████ | 601/1000 [00:49<00:28, 14.10it/s]
60%|██████ | 603/1000 [00:49<00:26, 14.79it/s]
60%|██████ | 605/1000 [00:49<00:26, 14.86it/s]
61%|██████ | 607/1000 [00:49<00:26, 14.95it/s]
61%|██████ | 609/1000 [00:50<00:25, 15.12it/s]
61%|██████ | 611/1000 [00:50<00:25, 15.37it/s]
61%|██████▏ | 613/1000 [00:50<00:26, 14.83it/s]
62%|██████▏ | 615/1000 [00:50<00:27, 14.09it/s]
62%|██████▏ | 617/1000 [00:50<00:28, 13.52it/s]
62%|██████▏ | 619/1000 [00:50<00:33, 11.30it/s]
62%|██████▏ | 621/1000 [00:51<00:40, 9.30it/s]
62%|██████▏ | 623/1000 [00:51<00:35, 10.51it/s]
62%|██████▎ | 625/1000 [00:51<00:31, 11.77it/s]
63%|██████▎ | 627/1000 [00:51<00:29, 12.58it/s]
63%|██████▎ | 629/1000 [00:51<00:27, 13.52it/s]
63%|██████▎ | 631/1000 [00:51<00:29, 12.55it/s]
63%|██████▎ | 633/1000 [00:52<00:28, 12.99it/s]
64%|██████▎ | 635/1000 [00:52<00:33, 10.82it/s]
64%|██████▎ | 637/1000 [00:52<00:31, 11.66it/s]
64%|██████▍ | 639/1000 [00:52<00:32, 11.10it/s]
64%|██████▍ | 641/1000 [00:52<00:29, 12.10it/s]
64%|██████▍ | 643/1000 [00:52<00:28, 12.64it/s]
64%|██████▍ | 645/1000 [00:53<00:27, 12.89it/s]
65%|██████▍ | 647/1000 [00:53<00:26, 13.08it/s]
65%|██████▍ | 649/1000 [00:53<00:27, 12.58it/s]
65%|██████▌ | 651/1000 [00:53<00:26, 13.30it/s]
65%|██████▌ | 653/1000 [00:53<00:28, 12.13it/s]
66%|██████▌ | 655/1000 [00:54<00:35, 9.72it/s]
66%|██████▌ | 657/1000 [00:54<00:31, 10.82it/s]
66%|██████▌ | 659/1000 [00:54<00:34, 9.80it/s]
66%|██████▌ | 661/1000 [00:54<00:31, 10.84it/s]
66%|██████▋ | 663/1000 [00:54<00:28, 11.72it/s]
66%|██████▋ | 665/1000 [00:54<00:27, 12.29it/s]
67%|██████▋ | 667/1000 [00:55<00:29, 11.24it/s]
67%|██████▋ | 669/1000 [00:55<00:29, 11.22it/s]
67%|██████▋ | 671/1000 [00:55<00:27, 11.94it/s]
67%|██████▋ | 673/1000 [00:55<00:26, 12.28it/s]
68%|██████▊ | 675/1000 [00:55<00:27, 11.79it/s]
68%|██████▊ | 677/1000 [00:55<00:26, 12.24it/s]
68%|██████▊ | 679/1000 [00:56<00:26, 12.27it/s]
68%|██████▊ | 681/1000 [00:56<00:25, 12.64it/s]
68%|██████▊ | 683/1000 [00:56<00:27, 11.51it/s]
68%|██████▊ | 685/1000 [00:56<00:26, 11.79it/s]
69%|██████▊ | 687/1000 [00:56<00:24, 12.60it/s]
69%|██████▉ | 689/1000 [00:56<00:23, 13.25it/s]
69%|██████▉ | 691/1000 [00:56<00:22, 13.58it/s]
69%|██████▉ | 693/1000 [00:57<00:22, 13.91it/s]
70%|██████▉ | 695/1000 [00:57<00:22, 13.80it/s]
70%|██████▉ | 697/1000 [00:57<00:21, 14.14it/s]
70%|██████▉ | 699/1000 [00:57<00:21, 14.20it/s]
70%|███████ | 701/1000 [00:57<00:24, 12.16it/s]
70%|███████ | 703/1000 [00:57<00:23, 12.78it/s]
70%|███████ | 705/1000 [00:58<00:30, 9.79it/s]
71%|███████ | 707/1000 [00:58<00:32, 8.95it/s]
71%|███████ | 709/1000 [00:58<00:28, 10.21it/s]
71%|███████ | 711/1000 [00:58<00:25, 11.28it/s]
71%|███████▏ | 713/1000 [00:58<00:23, 12.43it/s]
72%|███████▏ | 715/1000 [00:58<00:21, 13.15it/s]
72%|███████▏ | 717/1000 [00:59<00:23, 12.22it/s]
72%|███████▏ | 719/1000 [00:59<00:21, 13.21it/s]
72%|███████▏ | 721/1000 [00:59<00:19, 14.16it/s]
72%|███████▏ | 723/1000 [00:59<00:19, 13.92it/s]
72%|███████▎ | 725/1000 [00:59<00:19, 14.11it/s]
73%|███████▎ | 727/1000 [00:59<00:23, 11.72it/s]
73%|███████▎ | 729/1000 [01:00<00:33, 8.20it/s]
73%|███████▎ | 731/1000 [01:00<00:28, 9.41it/s]
73%|███████▎ | 733/1000 [01:00<00:28, 9.32it/s]
74%|███████▎ | 735/1000 [01:00<00:31, 8.53it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.79it/s]
74%|███████▍ | 739/1000 [01:01<00:24, 10.82it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.51it/s]
74%|███████▍ | 743/1000 [01:01<00:21, 12.16it/s]
74%|███████▍ | 745/1000 [01:01<00:25, 10.17it/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, 12.62it/s]
76%|███████▌ | 755/1000 [01:02<00:18, 13.34it/s]
76%|███████▌ | 757/1000 [01:02<00:23, 10.29it/s]
76%|███████▌ | 759/1000 [01:02<00:21, 11.33it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.33it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.27it/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, 10.60it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.86it/s]
77%|███████▋ | 773/1000 [01:04<00:18, 12.60it/s]
78%|███████▊ | 775/1000 [01:04<00:17, 13.14it/s]
78%|███████▊ | 777/1000 [01:04<00:15, 14.03it/s]
78%|███████▊ | 779/1000 [01:04<00:21, 10.47it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.39it/s]
78%|███████▊ | 783/1000 [01:05<00:26, 8.30it/s]
78%|███████▊ | 784/1000 [01:05<00:30, 7.06it/s]
78%|███████▊ | 785/1000 [01:05<00:29, 7.38it/s]
79%|███████▊ | 786/1000 [01:05<00:30, 6.99it/s]
79%|███████▉ | 788/1000 [01:06<00:24, 8.82it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.15it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 11.31it/s]
79%|███████▉ | 794/1000 [01:06<00:17, 11.93it/s]
80%|███████▉ | 796/1000 [01:06<00:16, 12.38it/s]
80%|███████▉ | 798/1000 [01:06<00:15, 13.14it/s]
80%|████████ | 800/1000 [01:06<00:16, 12.33it/s]
80%|████████ | 802/1000 [01:07<00:23, 8.26it/s]
80%|████████ | 804/1000 [01:07<00:23, 8.31it/s]
81%|████████ | 806/1000 [01:07<00:21, 9.00it/s]
81%|████████ | 808/1000 [01:07<00:18, 10.33it/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, 12.59it/s]
82%|████████▏ | 816/1000 [01:08<00:16, 11.05it/s]
82%|████████▏ | 818/1000 [01:08<00:15, 12.00it/s]
82%|████████▏ | 820/1000 [01:08<00:14, 12.25it/s]
82%|████████▏ | 822/1000 [01:09<00:14, 12.18it/s]
82%|████████▏ | 824/1000 [01:09<00:14, 12.33it/s]
83%|████████▎ | 826/1000 [01:09<00:14, 12.40it/s]
83%|████████▎ | 828/1000 [01:09<00:13, 12.39it/s]
83%|████████▎ | 830/1000 [01:09<00:13, 12.26it/s]
83%|████████▎ | 832/1000 [01:09<00:13, 12.23it/s]
83%|████████▎ | 834/1000 [01:10<00:13, 12.20it/s]
84%|████████▎ | 836/1000 [01:10<00:13, 12.35it/s]
84%|████████▍ | 838/1000 [01:10<00:13, 12.43it/s]
84%|████████▍ | 840/1000 [01:10<00:12, 12.32it/s]
84%|████████▍ | 842/1000 [01:10<00:12, 12.26it/s]
84%|████████▍ | 844/1000 [01:10<00:12, 12.23it/s]
85%|████████▍ | 846/1000 [01:10<00:12, 12.65it/s]
85%|████████▍ | 848/1000 [01:11<00:11, 13.01it/s]
85%|████████▌ | 850/1000 [01:11<00:11, 13.47it/s]
85%|████████▌ | 852/1000 [01:11<00:11, 13.26it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 13.97it/s]
86%|████████▌ | 856/1000 [01:11<00:10, 13.92it/s]
86%|████████▌ | 858/1000 [01:11<00:09, 14.48it/s]
86%|████████▌ | 860/1000 [01:11<00:09, 15.28it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.49it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 14.03it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.86it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.74it/s]
87%|████████▋ | 870/1000 [01:12<00:09, 14.15it/s]
87%|████████▋ | 872/1000 [01:12<00:08, 14.74it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 12.45it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.07it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.96it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.61it/s]
88%|████████▊ | 882/1000 [01:13<00:10, 11.43it/s]
88%|████████▊ | 884/1000 [01:13<00:09, 12.17it/s]
89%|████████▊ | 886/1000 [01:13<00:08, 12.95it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.88it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.25it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.02it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 14.16it/s]
90%|████████▉ | 896/1000 [01:14<00:07, 13.90it/s]
90%|████████▉ | 898/1000 [01:14<00:07, 13.31it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.29it/s]
90%|█████████ | 902/1000 [01:15<00:07, 13.03it/s]
90%|█████████ | 904/1000 [01:15<00:09, 9.82it/s]
91%|█████████ | 906/1000 [01:15<00:08, 10.86it/s]
91%|█████████ | 908/1000 [01:15<00:07, 11.85it/s]
91%|█████████ | 910/1000 [01:15<00:07, 12.12it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.31it/s]
91%|█████████▏| 914/1000 [01:16<00:12, 6.82it/s]
92%|█████████▏| 916/1000 [01:16<00:10, 8.15it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.41it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.83it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 10.29it/s]
92%|█████████▏| 924/1000 [01:17<00:06, 10.97it/s]
93%|█████████▎| 926/1000 [01:17<00:06, 11.82it/s]
93%|█████████▎| 928/1000 [01:17<00:06, 10.65it/s]
93%|█████████▎| 930/1000 [01:18<00:05, 11.86it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 12.46it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.96it/s]
94%|█████████▎| 936/1000 [01:18<00:04, 13.51it/s]
94%|█████████▍| 938/1000 [01:18<00:04, 14.42it/s]
94%|█████████▍| 940/1000 [01:18<00:04, 14.73it/s]
94%|█████████▍| 942/1000 [01:18<00:04, 12.36it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.87it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.92it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 12.73it/s]
95%|█████████▌| 950/1000 [01:19<00:03, 13.51it/s]
95%|█████████▌| 952/1000 [01:19<00:03, 14.16it/s]
95%|█████████▌| 954/1000 [01:19<00:03, 14.27it/s]
96%|█████████▌| 956/1000 [01:19<00:03, 14.27it/s]
96%|█████████▌| 958/1000 [01:20<00:02, 14.81it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 13.17it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.78it/s]
96%|█████████▋| 964/1000 [01:20<00:03, 10.25it/s]
97%|█████████▋| 966/1000 [01:20<00:02, 11.49it/s]
97%|█████████▋| 968/1000 [01:20<00:02, 12.15it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 12.77it/s]
97%|█████████▋| 972/1000 [01:21<00:02, 9.73it/s]
97%|█████████▋| 974/1000 [01:21<00:02, 10.91it/s]
98%|█████████▊| 976/1000 [01:21<00:02, 11.71it/s]
98%|█████████▊| 978/1000 [01:21<00:02, 9.90it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.99it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.50it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 10.50it/s]
99%|█████████▊| 986/1000 [01:22<00:01, 10.73it/s]
99%|█████████▉| 988/1000 [01:22<00:01, 10.94it/s]
99%|█████████▉| 990/1000 [01:22<00:00, 11.44it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 12.34it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 13.08it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 13.98it/s]
100%|█████████▉| 998/1000 [01:23<00:00, 14.14it/s]
100%|██████████| 1000/1000 [01:23<00:00, 14.80it/s]
{'eval_loss': '0.9318', 'eval_runtime': '83.71', 'eval_samples_per_second': '11.95', 'eval_steps_per_second': '11.95', 'epoch': '0.3211', 'num_input_tokens_seen': 20140120}
32%|███▏ | 6000/18688 [7:02:36<14:28:20, 4.11s/it]
100%|██████████| 1000/1000 [01:23<00:00, 14.80it/s]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00010/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 [7:02:42<104:07:12, 29.54s/it] 32%|███▏ | 6002/18688 [7:02:45<76:45:13, 21.78s/it] 32%|███▏ | 6003/18688 [7:02:50<58:32:59, 16.62s/it] 32%|███▏ | 6004/18688 [7:02:54<45:23:56, 12.89s/it] 32%|███▏ | 6005/18688 [7:02:57<35:26:27, 10.06s/it] 32%|███▏ | 6006/18688 [7:03:02<29:36:27, 8.40s/it] 32%|███▏ | 6007/18688 [7:03:07<25:36:44, 7.27s/it] 32%|███▏ | 6008/18688 [7:03:11<22:55:03, 6.51s/it] 32%|███▏ | 6009/18688 [7:03:15<20:05:43, 5.71s/it] 32%|███▏ | 6010/18688 [7:03:19<18:34:41, 5.28s/it] {'loss': '0.9126', 'grad_norm': '1.113', 'learning_rate': '8.396e-05', 'epoch': '0.3216', 'num_input_tokens_seen': 20175880, 'train_runtime': '2.54e+04', 'train_tokens_per_second': '794.3'}
32%|███▏ | 6010/18688 [7:03:19<18:34:41, 5.28s/it] 32%|███▏ | 6011/18688 [7:03:24<17:27:08, 4.96s/it] 32%|███▏ | 6012/18688 [7:03:28<16:48:04, 4.77s/it] 32%|███▏ | 6013/18688 [7:03:32<15:53:20, 4.51s/it] 32%|███▏ | 6014/18688 [7:03:35<14:46:58, 4.20s/it] 32%|███▏ | 6015/18688 [7:03:39<14:03:27, 3.99s/it] 32%|███▏ | 6016/18688 [7:03:45<15:49:26, 4.50s/it] 32%|███▏ | 6017/18688 [7:03:49<15:21:38, 4.36s/it] 32%|███▏ | 6018/18688 [7:03:52<14:48:57, 4.21s/it] 32%|███▏ | 6019/18688 [7:03:56<14:11:51, 4.03s/it] 32%|███▏ | 6020/18688 [7:04:00<13:45:33, 3.91s/it] {'loss': '0.9304', 'grad_norm': '1.077', 'learning_rate': '8.389e-05', 'epoch': '0.3221', 'num_input_tokens_seen': 20208592, 'train_runtime': '2.544e+04', 'train_tokens_per_second': '794.4'}
32%|███▏ | 6020/18688 [7:04:00<13:45:33, 3.91s/it] 32%|███▏ | 6021/18688 [7:04:04<13:51:55, 3.94s/it] 32%|███▏ | 6022/18688 [7:04:08<14:32:32, 4.13s/it] 32%|███▏ | 6023/18688 [7:04:13<14:44:20, 4.19s/it] 32%|███▏ | 6024/18688 [7:04:17<15:22:41, 4.37s/it] 32%|███▏ | 6025/18688 [7:04:21<14:40:20, 4.17s/it] 32%|███▏ | 6026/18688 [7:04:25<14:07:54, 4.02s/it] 32%|███▏ | 6027/18688 [7:04:28<13:50:32, 3.94s/it] 32%|███▏ | 6028/18688 [7:04:33<14:48:24, 4.21s/it] 32%|███▏ | 6029/18688 [7:04:38<15:10:35, 4.32s/it] 32%|███▏ | 6030/18688 [7:04:42<14:40:45, 4.17s/it] {'loss': '0.9041', 'grad_norm': '1.179', 'learning_rate': '8.382e-05', 'epoch': '0.3227', 'num_input_tokens_seen': 20243008, 'train_runtime': '2.548e+04', 'train_tokens_per_second': '794.4'}
32%|███▏ | 6030/18688 [7:04:42<14:40:45, 4.17s/it] 32%|███▏ | 6031/18688 [7:04:45<13:57:01, 3.97s/it] 32%|███▏ | 6032/18688 [7:04:49<13:25:21, 3.82s/it] 32%|███▏ | 6033/18688 [7:04:53<14:16:12, 4.06s/it] 32%|███▏ | 6034/18688 [7:04:57<13:49:13, 3.93s/it] 32%|███▏ | 6035/18688 [7:05:01<14:16:40, 4.06s/it] 32%|███▏ | 6036/18688 [7:05:05<14:22:29, 4.09s/it] 32%|███▏ | 6037/18688 [7:05:09<13:47:08, 3.92s/it] 32%|███▏ | 6038/18688 [7:05:13<13:58:32, 3.98s/it] 32%|███▏ | 6039/18688 [7:05:17<13:44:34, 3.91s/it] 32%|███▏ | 6040/18688 [7:05:21<13:54:07, 3.96s/it] {'loss': '0.9529', 'grad_norm': '1.138', 'learning_rate': '8.376e-05', 'epoch': '0.3232', 'num_input_tokens_seen': 20273568, 'train_runtime': '2.552e+04', 'train_tokens_per_second': '794.4'}
32%|███▏ | 6040/18688 [7:05:21<13:54:07, 3.96s/it] 32%|███▏ | 6041/18688 [7:05:25<14:13:44, 4.05s/it] 32%|███▏ | 6042/18688 [7:05:30<14:49:20, 4.22s/it] 32%|███▏ | 6043/18688 [7:05:34<14:21:19, 4.09s/it] 32%|███▏ | 6044/18688 [7:05:37<14:05:32, 4.01s/it] 32%|███▏ | 6045/18688 [7:05:42<14:39:00, 4.17s/it] 32%|███▏ | 6046/18688 [7:05:46<14:05:30, 4.01s/it] 32%|███▏ | 6047/18688 [7:05:49<13:32:44, 3.86s/it] 32%|███▏ | 6048/18688 [7:05:53<13:52:48, 3.95s/it] 32%|███▏ | 6049/18688 [7:05:58<14:24:07, 4.10s/it] 32%|███▏ | 6050/18688 [7:06:02<14:48:08, 4.22s/it] {'loss': '0.9122', 'grad_norm': '1.351', 'learning_rate': '8.369e-05', 'epoch': '0.3237', 'num_input_tokens_seen': 20306824, 'train_runtime': '2.556e+04', 'train_tokens_per_second': '794.4'}
32%|███▏ | 6050/18688 [7:06:02<14:48:08, 4.22s/it] 32%|███▏ | 6051/18688 [7:06:06<14:25:06, 4.11s/it] 32%|███▏ | 6052/18688 [7:06:10<14:40:18, 4.18s/it] 32%|███▏ | 6053/18688 [7:06:15<14:34:52, 4.15s/it] 32%|███▏ | 6054/18688 [7:06:18<14:08:25, 4.03s/it] 32%|███▏ | 6055/18688 [7:06:22<14:00:29, 3.99s/it] 32%|███▏ | 6056/18688 [7:06:26<14:13:58, 4.06s/it] 32%|███▏ | 6057/18688 [7:06:31<14:26:14, 4.11s/it] 32%|███▏ | 6058/18688 [7:06:35<14:43:38, 4.20s/it] 32%|███▏ | 6059/18688 [7:06:39<14:16:10, 4.07s/it] 32%|███▏ | 6060/18688 [7:06:42<13:48:34, 3.94s/it] {'loss': '0.9551', 'grad_norm': '1.229', 'learning_rate': '8.362e-05', 'epoch': '0.3243', 'num_input_tokens_seen': 20338936, 'train_runtime': '2.56e+04', 'train_tokens_per_second': '794.4'}
32%|███▏ | 6060/18688 [7:06:42<13:48:34, 3.94s/it] 32%|███▏ | 6061/18688 [7:06:46<13:16:36, 3.79s/it] 32%|███▏ | 6062/18688 [7:06:50<13:52:42, 3.96s/it] 32%|███▏ | 6063/18688 [7:06:54<13:54:13, 3.96s/it] 32%|███▏ | 6064/18688 [7:06:58<14:10:45, 4.04s/it] 32%|███▏ | 6065/18688 [7:07:02<13:52:18, 3.96s/it] 32%|███▏ | 6066/18688 [7:07:06<13:41:06, 3.90s/it] 32%|███▏ | 6067/18688 [7:07:10<13:44:48, 3.92s/it] 32%|███▏ | 6068/18688 [7:07:15<14:46:34, 4.22s/it] 32%|███▏ | 6069/18688 [7:07:20<15:22:20, 4.39s/it] 32%|███▏ | 6070/18688 [7:07:23<14:48:41, 4.23s/it] {'loss': '0.9794', 'grad_norm': '1.332', 'learning_rate': '8.355e-05', 'epoch': '0.3248', 'num_input_tokens_seen': 20371608, 'train_runtime': '2.564e+04', 'train_tokens_per_second': '794.4'}
32%|███▏ | 6070/18688 [7:07:23<14:48:41, 4.23s/it] 32%|███▏ | 6071/18688 [7:07:27<14:36:19, 4.17s/it] 32%|███▏ | 6072/18688 [7:07:31<14:01:12, 4.00s/it] 32%|███▏ | 6073/18688 [7:07:35<13:25:09, 3.83s/it] 33%|███▎ | 6074/18688 [7:07:38<13:31:58, 3.86s/it] 33%|███▎ | 6075/18688 [7:07:42<13:05:06, 3.73s/it] 33%|███▎ | 6076/18688 [7:07:45<12:55:43, 3.69s/it] 33%|███▎ | 6077/18688 [7:07:49<13:10:06, 3.76s/it] 33%|███▎ | 6078/18688 [7:07:53<13:21:10, 3.81s/it] 33%|███▎ | 6079/18688 [7:07:58<14:18:11, 4.08s/it] 33%|███▎ | 6080/18688 [7:08:02<14:00:10, 4.00s/it] {'loss': '0.8594', 'grad_norm': '1.16', 'learning_rate': '8.349e-05', 'epoch': '0.3254', 'num_input_tokens_seen': 20402328, 'train_runtime': '2.568e+04', 'train_tokens_per_second': '794.4'}
33%|███▎ | 6080/18688 [7:08:02<14:00:10, 4.00s/it] 33%|███▎ | 6081/18688 [7:08:07<14:55:48, 4.26s/it] 33%|███▎ | 6082/18688 [7:08:11<14:25:20, 4.12s/it] 33%|███▎ | 6083/18688 [7:08:15<15:05:27, 4.31s/it] 33%|███▎ | 6084/18688 [7:08:19<14:55:45, 4.26s/it] 33%|███▎ | 6085/18688 [7:08:23<14:41:24, 4.20s/it] 33%|███▎ | 6086/18688 [7:08:27<14:11:31, 4.05s/it] 33%|███▎ | 6087/18688 [7:08:31<13:40:56, 3.91s/it] 33%|███▎ | 6088/18688 [7:08:34<13:17:59, 3.80s/it] 33%|███▎ | 6089/18688 [7:08:38<13:21:29, 3.82s/it] 33%|███▎ | 6090/18688 [7:08:42<12:59:25, 3.71s/it] {'loss': '0.9857', 'grad_norm': '1.37', 'learning_rate': '8.342e-05', 'epoch': '0.3259', 'num_input_tokens_seen': 20433016, 'train_runtime': '2.572e+04', 'train_tokens_per_second': '794.4'}
33%|███▎ | 6090/18688 [7:08:42<12:59:25, 3.71s/it] 33%|███▎ | 6091/18688 [7:08:46<13:46:09, 3.94s/it] 33%|███▎ | 6092/18688 [7:08:50<13:27:38, 3.85s/it] 33%|███▎ | 6093/18688 [7:08:53<13:00:21, 3.72s/it] 33%|███▎ | 6094/18688 [7:08:57<13:04:33, 3.74s/it] 33%|███▎ | 6095/18688 [7:09:01<13:45:30, 3.93s/it] 33%|███▎ | 6096/18688 [7:09:06<14:27:47, 4.13s/it] 33%|███▎ | 6097/18688 [7:09:10<14:52:49, 4.25s/it] 33%|███▎ | 6098/18688 [7:09:15<15:19:29, 4.38s/it] 33%|███▎ | 6099/18688 [7:09:19<14:40:09, 4.19s/it] 33%|███▎ | 6100/18688 [7:09:23<14:15:44, 4.08s/it] {'loss': '0.8996', 'grad_norm': '1.175', 'learning_rate': '8.335e-05', 'epoch': '0.3264', 'num_input_tokens_seen': 20466352, 'train_runtime': '2.576e+04', 'train_tokens_per_second': '794.4'}
33%|███▎ | 6100/18688 [7:09:23<14:15:44, 4.08s/it] 33%|███▎ | 6101/18688 [7:09:28<15:02:01, 4.30s/it] 33%|███▎ | 6102/18688 [7:09:32<15:06:59, 4.32s/it] 33%|███▎ | 6103/18688 [7:09:35<14:19:27, 4.10s/it] 33%|███▎ | 6104/18688 [7:09:39<13:44:25, 3.93s/it] 33%|███▎ | 6105/18688 [7:09:43<13:40:28, 3.91s/it] 33%|███▎ | 6106/18688 [7:09:48<14:37:53, 4.19s/it] 33%|███▎ | 6107/18688 [7:09:53<15:30:41, 4.44s/it] 33%|███▎ | 6108/18688 [7:09:56<14:36:07, 4.18s/it] 33%|███▎ | 6109/18688 [7:10:00<14:26:12, 4.13s/it] 33%|███▎ | 6110/18688 [7:10:04<13:44:50, 3.93s/it] {'loss': '0.9788', 'grad_norm': '1.204', 'learning_rate': '8.328e-05', 'epoch': '0.327', 'num_input_tokens_seen': 20499760, 'train_runtime': '2.58e+04', 'train_tokens_per_second': '794.4'}
33%|███▎ | 6110/18688 [7:10:04<13:44:50, 3.93s/it] 33%|███▎ | 6111/18688 [7:10:08<13:49:40, 3.96s/it] 33%|███▎ | 6112/18688 [7:10:11<13:24:34, 3.84s/it] 33%|███▎ | 6113/18688 [7:10:17<15:29:11, 4.43s/it] 33%|███▎ | 6114/18688 [7:10:21<14:39:59, 4.20s/it] 33%|███▎ | 6115/18688 [7:10:25<14:35:38, 4.18s/it] 33%|███▎ | 6116/18688 [7:10:29<14:09:25, 4.05s/it] 33%|███▎ | 6117/18688 [7:10:33<14:42:47, 4.21s/it] 33%|███▎ | 6118/18688 [7:10:37<14:34:00, 4.17s/it] 33%|███▎ | 6119/18688 [7:10:41<14:08:15, 4.05s/it] 33%|███▎ | 6120/18688 [7:10:45<14:13:00, 4.07s/it] {'loss': '0.8844', 'grad_norm': '1.234', 'learning_rate': '8.321e-05', 'epoch': '0.3275', 'num_input_tokens_seen': 20533952, 'train_runtime': '2.585e+04', 'train_tokens_per_second': '794.5'}
33%|███▎ | 6120/18688 [7:10:45<14:13:00, 4.07s/it] 33%|███▎ | 6121/18688 [7:10:50<15:01:28, 4.30s/it] 33%|███▎ | 6122/18688 [7:10:54<14:34:21, 4.17s/it] 33%|███▎ | 6123/18688 [7:10:58<13:59:06, 4.01s/it] 33%|███▎ | 6124/18688 [7:11:02<14:34:41, 4.18s/it] 33%|███▎ | 6125/18688 [7:11:06<14:08:41, 4.05s/it] 33%|███▎ | 6126/18688 [7:11:10<13:45:11, 3.94s/it] 33%|███▎ | 6127/18688 [7:11:14<14:25:49, 4.14s/it] 33%|███▎ | 6128/18688 [7:11:18<14:08:55, 4.06s/it] 33%|███▎ | 6129/18688 [7:11:22<13:37:21, 3.90s/it] 33%|███▎ | 6130/18688 [7:11:25<13:04:56, 3.75s/it] {'loss': '0.9788', 'grad_norm': '1.64', 'learning_rate': '8.315e-05', 'epoch': '0.328', 'num_input_tokens_seen': 20565120, 'train_runtime': '2.589e+04', 'train_tokens_per_second': '794.5'}
33%|███▎ | 6130/18688 [7:11:25<13:04:56, 3.75s/it] 33%|███▎ | 6131/18688 [7:11:30<14:30:49, 4.16s/it] 33%|███▎ | 6132/18688 [7:11:36<16:00:40, 4.59s/it] 33%|███▎ | 6133/18688 [7:11:40<15:17:32, 4.38s/it] 33%|███▎ | 6134/18688 [7:11:43<14:18:18, 4.10s/it] 33%|███▎ | 6135/18688 [7:11:47<13:37:11, 3.91s/it] 33%|███▎ | 6136/18688 [7:11:50<13:33:17, 3.89s/it] 33%|███▎ | 6137/18688 [7:11:54<13:11:58, 3.79s/it] 33%|███▎ | 6138/18688 [7:11:58<13:58:02, 4.01s/it] 33%|███▎ | 6139/18688 [7:12:02<13:42:44, 3.93s/it] 33%|███▎ | 6140/18688 [7:12:06<13:58:08, 4.01s/it] {'loss': '0.9683', 'grad_norm': '1.289', 'learning_rate': '8.308e-05', 'epoch': '0.3286', 'num_input_tokens_seen': 20598680, 'train_runtime': '2.593e+04', 'train_tokens_per_second': '794.5'}
33%|███▎ | 6140/18688 [7:12:06<13:58:08, 4.01s/it] 33%|███▎ | 6141/18688 [7:12:10<13:47:32, 3.96s/it] 33%|███▎ | 6142/18688 [7:12:14<13:55:52, 4.00s/it] 33%|███▎ | 6143/18688 [7:12:18<13:25:06, 3.85s/it] 33%|███▎ | 6144/18688 [7:12:22<14:04:30, 4.04s/it] 33%|███▎ | 6145/18688 [7:12:26<13:55:01, 3.99s/it] 33%|███▎ | 6146/18688 [7:12:30<13:47:30, 3.96s/it] 33%|███▎ | 6147/18688 [7:12:34<13:58:52, 4.01s/it] 33%|███▎ | 6148/18688 [7:12:38<13:39:40, 3.92s/it] 33%|███▎ | 6149/18688 [7:12:42<13:20:29, 3.83s/it] 33%|███▎ | 6150/18688 [7:12:46<13:34:20, 3.90s/it] {'loss': '0.976', 'grad_norm': '1.223', 'learning_rate': '8.301e-05', 'epoch': '0.3291', 'num_input_tokens_seen': 20629968, 'train_runtime': '2.597e+04', 'train_tokens_per_second': '794.5'}
33%|███▎ | 6150/18688 [7:12:46<13:34:20, 3.90s/it] 33%|███▎ | 6151/18688 [7:12:51<14:47:40, 4.25s/it] 33%|███▎ | 6152/18688 [7:12:54<14:02:10, 4.03s/it] 33%|███▎ | 6153/18688 [7:12:58<13:34:16, 3.90s/it] 33%|███▎ | 6154/18688 [7:13:02<13:27:59, 3.87s/it] 33%|███▎ | 6155/18688 [7:13:05<13:23:57, 3.85s/it] 33%|███▎ | 6156/18688 [7:13:09<13:19:04, 3.83s/it] 33%|███▎ | 6157/18688 [7:13:13<13:12:26, 3.79s/it] 33%|███▎ | 6158/18688 [7:13:17<13:58:05, 4.01s/it] 33%|███▎ | 6159/18688 [7:13:22<14:11:43, 4.08s/it] 33%|███▎ | 6160/18688 [7:13:25<13:53:29, 3.99s/it] {'loss': '1.004', 'grad_norm': '1.054', 'learning_rate': '8.294e-05', 'epoch': '0.3296', 'num_input_tokens_seen': 20661592, 'train_runtime': '2.601e+04', 'train_tokens_per_second': '794.5'}
33%|███▎ | 6160/18688 [7:13:25<13:53:29, 3.99s/it] 33%|███▎ | 6161/18688 [7:13:29<13:27:11, 3.87s/it] 33%|███▎ | 6162/18688 [7:13:32<13:02:41, 3.75s/it] 33%|███▎ | 6163/18688 [7:13:37<14:00:17, 4.03s/it] 33%|███▎ | 6164/18688 [7:13:42<14:44:46, 4.24s/it] 33%|███▎ | 6165/18688 [7:13:45<13:55:10, 4.00s/it] 33%|███▎ | 6166/18688 [7:13:49<13:53:44, 3.99s/it] 33%|███▎ | 6167/18688 [7:13:53<13:47:08, 3.96s/it] 33%|███▎ | 6168/18688 [7:13:57<13:54:41, 4.00s/it] 33%|███▎ | 6169/18688 [7:14:02<14:16:36, 4.11s/it] 33%|███▎ | 6170/18688 [7:14:06<14:04:09, 4.05s/it] {'loss': '0.9023', 'grad_norm': '1.159', 'learning_rate': '8.287e-05', 'epoch': '0.3302', 'num_input_tokens_seen': 20692840, 'train_runtime': '2.605e+04', 'train_tokens_per_second': '794.5'}
33%|███▎ | 6170/18688 [7:14:06<14:04:09, 4.05s/it] 33%|███▎ | 6171/18688 [7:14:10<14:10:55, 4.08s/it] 33%|███▎ | 6172/18688 [7:14:14<14:12:33, 4.09s/it] 33%|███▎ | 6173/18688 [7:14:18<14:17:58, 4.11s/it] 33%|███▎ | 6174/18688 [7:14:22<13:58:08, 4.02s/it] 33%|███▎ | 6175/18688 [7:14:25<13:32:39, 3.90s/it] 33%|███▎ | 6176/18688 [7:14:29<13:22:11, 3.85s/it] 33%|███▎ | 6177/18688 [7:14:33<13:25:58, 3.87s/it] 33%|███▎ | 6178/18688 [7:14:38<14:28:10, 4.16s/it] 33%|███▎ | 6179/18688 [7:14:42<14:41:19, 4.23s/it] 33%|███▎ | 6180/18688 [7:14:46<14:25:44, 4.15s/it] {'loss': '0.9003', 'grad_norm': '1.379', 'learning_rate': '8.28e-05', 'epoch': '0.3307', 'num_input_tokens_seen': 20726280, 'train_runtime': '2.609e+04', 'train_tokens_per_second': '794.5'}
33%|███▎ | 6180/18688 [7:14:46<14:25:44, 4.15s/it] 33%|███▎ | 6181/18688 [7:14:51<14:43:30, 4.24s/it] 33%|███▎ | 6182/18688 [7:14:54<13:59:59, 4.03s/it] 33%|███▎ | 6183/18688 [7:14:59<14:27:11, 4.16s/it] 33%|███▎ | 6184/18688 [7:15:03<14:40:44, 4.23s/it] 33%|███▎ | 6185/18688 [7:15:07<14:16:06, 4.11s/it] 33%|███▎ | 6186/18688 [7:15:11<14:12:12, 4.09s/it] 33%|███▎ | 6187/18688 [7:15:15<13:49:29, 3.98s/it] 33%|███▎ | 6188/18688 [7:15:19<13:40:46, 3.94s/it] 33%|███▎ | 6189/18688 [7:15:23<14:45:16, 4.25s/it] 33%|███▎ | 6190/18688 [7:15:27<14:20:50, 4.13s/it] {'loss': '0.9473', 'grad_norm': '1.28', 'learning_rate': '8.273e-05', 'epoch': '0.3312', 'num_input_tokens_seen': 20759248, 'train_runtime': '2.613e+04', 'train_tokens_per_second': '794.5'}
33%|███▎ | 6190/18688 [7:15:27<14:20:50, 4.13s/it] 33%|███▎ | 6191/18688 [7:15:32<15:05:38, 4.35s/it] 33%|███▎ | 6192/18688 [7:15:37<15:22:16, 4.43s/it] 33%|███▎ | 6193/18688 [7:15:42<16:05:27, 4.64s/it] 33%|███▎ | 6194/18688 [7:15:46<15:29:30, 4.46s/it] 33%|███▎ | 6195/18688 [7:15:51<15:47:52, 4.55s/it] 33%|███▎ | 6196/18688 [7:15:55<14:58:16, 4.31s/it] 33%|███▎ | 6197/18688 [7:15:59<15:15:18, 4.40s/it] 33%|███▎ | 6198/18688 [7:16:03<14:58:16, 4.32s/it] 33%|███▎ | 6199/18688 [7:16:08<15:10:59, 4.38s/it] 33%|███▎ | 6200/18688 [7:16:12<14:35:04, 4.20s/it] {'loss': '1.034', 'grad_norm': '1.327', 'learning_rate': '8.266e-05', 'epoch': '0.3318', 'num_input_tokens_seen': 20796600, 'train_runtime': '2.617e+04', 'train_tokens_per_second': '794.6'}
33%|███▎ | 6200/18688 [7:16:12<14:35:04, 4.20s/it] 33%|███▎ | 6201/18688 [7:16:16<14:21:45, 4.14s/it] 33%|███▎ | 6202/18688 [7:16:20<14:10:35, 4.09s/it] 33%|███▎ | 6203/18688 [7:16:23<13:31:24, 3.90s/it] 33%|███▎ | 6204/18688 [7:16:27<13:09:43, 3.80s/it] 33%|███▎ | 6205/18688 [7:16:30<13:20:32, 3.85s/it] 33%|███▎ | 6206/18688 [7:16:35<13:35:17, 3.92s/it] 33%|███▎ | 6207/18688 [7:16:40<14:51:42, 4.29s/it] 33%|███▎ | 6208/18688 [7:16:44<14:27:36, 4.17s/it] 33%|███▎ | 6209/18688 [7:16:47<13:45:54, 3.97s/it] 33%|███▎ | 6210/18688 [7:16:51<13:22:21, 3.86s/it] {'loss': '0.9268', 'grad_norm': '1.116', 'learning_rate': '8.26e-05', 'epoch': '0.3323', 'num_input_tokens_seen': 20825928, 'train_runtime': '2.621e+04', 'train_tokens_per_second': '794.5'}
33%|███▎ | 6210/18688 [7:16:51<13:22:21, 3.86s/it] 33%|███▎ | 6211/18688 [7:16:55<13:27:34, 3.88s/it] 33%|███▎ | 6212/18688 [7:16:58<12:59:03, 3.75s/it] 33%|███▎ | 6213/18688 [7:17:02<13:01:48, 3.76s/it] 33%|███▎ | 6214/18688 [7:17:05<12:42:35, 3.67s/it] 33%|███▎ | 6215/18688 [7:17:10<13:39:59, 3.94s/it] 33%|███▎ | 6216/18688 [7:17:13<13:07:27, 3.79s/it] 33%|███▎ | 6217/18688 [7:17:18<13:59:58, 4.04s/it] 33%|███▎ | 6218/18688 [7:17:22<14:01:18, 4.05s/it] 33%|███▎ | 6219/18688 [7:17:26<13:45:27, 3.97s/it] 33%|███▎ | 6220/18688 [7:17:30<14:19:06, 4.13s/it] {'loss': '0.919', 'grad_norm': '1.276', 'learning_rate': '8.253e-05', 'epoch': '0.3328', 'num_input_tokens_seen': 20856744, 'train_runtime': '2.625e+04', 'train_tokens_per_second': '794.5'}
33%|███▎ | 6220/18688 [7:17:30<14:19:06, 4.13s/it] 33%|███▎ | 6221/18688 [7:17:34<13:42:33, 3.96s/it] 33%|███▎ | 6222/18688 [7:17:37<13:13:35, 3.82s/it] 33%|███▎ | 6223/18688 [7:17:41<12:57:33, 3.74s/it] 33%|███▎ | 6224/18688 [7:17:46<14:15:48, 4.12s/it] 33%|███▎ | 6225/18688 [7:17:50<14:18:07, 4.13s/it] 33%|███▎ | 6226/18688 [7:17:54<13:40:47, 3.95s/it] 33%|███▎ | 6227/18688 [7:17:58<13:55:15, 4.02s/it] 33%|███▎ | 6228/18688 [7:18:03<14:42:40, 4.25s/it] 33%|███▎ | 6229/18688 [7:18:07<14:35:33, 4.22s/it] 33%|███▎ | 6230/18688 [7:18:11<14:10:56, 4.10s/it] {'loss': '0.9322', 'grad_norm': '1.077', 'learning_rate': '8.246e-05', 'epoch': '0.3334', 'num_input_tokens_seen': 20888192, 'train_runtime': '2.629e+04', 'train_tokens_per_second': '794.5'}
33%|███▎ | 6230/18688 [7:18:11<14:10:56, 4.10s/it] 33%|███▎ | 6231/18688 [7:18:16<15:04:05, 4.35s/it] 33%|███▎ | 6232/18688 [7:18:19<14:10:30, 4.10s/it] 33%|███▎ | 6233/18688 [7:18:23<13:52:40, 4.01s/it] 33%|███▎ | 6234/18688 [7:18:27<13:32:27, 3.91s/it] 33%|███▎ | 6235/18688 [7:18:31<14:13:20, 4.11s/it] 33%|███▎ | 6236/18688 [7:18:36<14:41:22, 4.25s/it] 33%|███▎ | 6237/18688 [7:18:39<14:13:49, 4.11s/it] 33%|███▎ | 6238/18688 [7:18:44<14:23:19, 4.16s/it] 33%|███▎ | 6239/18688 [7:18:48<14:08:10, 4.09s/it] 33%|███▎ | 6240/18688 [7:18:52<13:54:49, 4.02s/it] {'loss': '0.9291', 'grad_norm': '1.408', 'learning_rate': '8.239e-05', 'epoch': '0.3339', 'num_input_tokens_seen': 20921856, 'train_runtime': '2.633e+04', 'train_tokens_per_second': '794.5'}
33%|███▎ | 6240/18688 [7:18:52<13:54:49, 4.02s/it] 33%|███▎ | 6241/18688 [7:18:56<14:24:51, 4.17s/it] 33%|███▎ | 6242/18688 [7:19:01<14:52:19, 4.30s/it] 33%|███▎ | 6243/18688 [7:19:05<14:52:48, 4.30s/it] 33%|███▎ | 6244/18688 [7:19:09<14:22:06, 4.16s/it] 33%|███▎ | 6245/18688 [7:19:13<14:55:01, 4.32s/it] 33%|███▎ | 6246/18688 [7:19:18<14:38:02, 4.23s/it] 33%|███▎ | 6247/18688 [7:19:22<14:29:08, 4.19s/it] 33%|███▎ | 6248/18688 [7:19:26<14:11:14, 4.11s/it] 33%|███▎ | 6249/18688 [7:19:29<13:44:31, 3.98s/it] 33%|███▎ | 6250/18688 [7:19:34<14:09:06, 4.10s/it] {'loss': '0.9725', 'grad_norm': '1.259', 'learning_rate': '8.232e-05', 'epoch': '0.3344', 'num_input_tokens_seen': 20955112, 'train_runtime': '2.637e+04', 'train_tokens_per_second': '794.5'}
33%|███▎ | 6250/18688 [7:19:34<14:09:06, 4.10s/it] 33%|███▎ | 6251/18688 [7:19:37<13:29:43, 3.91s/it] 33%|███▎ | 6252/18688 [7:19:41<13:45:08, 3.98s/it] 33%|███▎ | 6253/18688 [7:19:45<13:28:53, 3.90s/it] 33%|███▎ | 6254/18688 [7:19:48<13:03:27, 3.78s/it] 33%|███▎ | 6255/18688 [7:19:52<12:49:26, 3.71s/it] 33%|███▎ | 6256/18688 [7:19:56<13:33:31, 3.93s/it] 33%|███▎ | 6257/18688 [7:20:01<14:01:48, 4.06s/it] 33%|███▎ | 6258/18688 [7:20:04<13:33:22, 3.93s/it] 33%|███▎ | 6259/18688 [7:20:09<14:20:27, 4.15s/it] 33%|███▎ | 6260/18688 [7:20:13<13:48:05, 4.00s/it] {'loss': '0.9037', 'grad_norm': '1.203', 'learning_rate': '8.225e-05', 'epoch': '0.335', 'num_input_tokens_seen': 20986040, 'train_runtime': '2.641e+04', 'train_tokens_per_second': '794.5'}
33%|███▎ | 6260/18688 [7:20:13<13:48:05, 4.00s/it] 34%|███▎ | 6261/18688 [7:20:17<13:48:38, 4.00s/it] 34%|███▎ | 6262/18688 [7:20:21<13:58:21, 4.05s/it] 34%|███▎ | 6263/18688 [7:20:26<14:41:34, 4.26s/it] 34%|███▎ | 6264/18688 [7:20:29<14:18:44, 4.15s/it] 34%|███▎ | 6265/18688 [7:20:33<14:09:37, 4.10s/it] 34%|███▎ | 6266/18688 [7:20:37<13:35:53, 3.94s/it] 34%|███▎ | 6267/18688 [7:20:42<14:11:00, 4.11s/it] 34%|███▎ | 6268/18688 [7:20:45<13:52:15, 4.02s/it] 34%|███▎ | 6269/18688 [7:20:50<14:29:12, 4.20s/it] 34%|███▎ | 6270/18688 [7:20:54<14:17:52, 4.14s/it] {'loss': '0.9901', 'grad_norm': '1.419', 'learning_rate': '8.218e-05', 'epoch': '0.3355', 'num_input_tokens_seen': 21020464, 'train_runtime': '2.645e+04', 'train_tokens_per_second': '794.6'}
34%|███▎ | 6270/18688 [7:20:54<14:17:52, 4.14s/it] 34%|███▎ | 6271/18688 [7:20:59<14:51:03, 4.31s/it] 34%|███▎ | 6272/18688 [7:21:03<15:03:36, 4.37s/it] 34%|███▎ | 6273/18688 [7:21:07<14:44:03, 4.27s/it] 34%|███▎ | 6274/18688 [7:21:11<14:06:50, 4.09s/it] 34%|███▎ | 6275/18688 [7:21:15<14:14:37, 4.13s/it] 34%|███▎ | 6276/18688 [7:21:19<14:25:10, 4.18s/it] 34%|███▎ | 6277/18688 [7:21:23<13:43:11, 3.98s/it] 34%|███▎ | 6278/18688 [7:21:27<13:18:51, 3.86s/it] 34%|███▎ | 6279/18688 [7:21:32<14:34:33, 4.23s/it] 34%|███▎ | 6280/18688 [7:21:37<15:23:56, 4.47s/it] {'loss': '0.9473', 'grad_norm': '1.126', 'learning_rate': '8.211e-05', 'epoch': '0.3361', 'num_input_tokens_seen': 21055680, 'train_runtime': '2.65e+04', 'train_tokens_per_second': '794.6'}
34%|███▎ | 6280/18688 [7:21:37<15:23:56, 4.47s/it] 34%|███▎ | 6281/18688 [7:21:40<14:33:35, 4.22s/it] 34%|███▎ | 6282/18688 [7:21:45<15:01:14, 4.36s/it] 34%|███▎ | 6283/18688 [7:21:49<14:24:29, 4.18s/it] 34%|███▎ | 6284/18688 [7:21:53<13:59:38, 4.06s/it] 34%|███▎ | 6285/18688 [7:21:57<14:16:50, 4.14s/it] 34%|███▎ | 6286/18688 [7:22:00<13:41:55, 3.98s/it] 34%|███▎ | 6287/18688 [7:22:04<13:31:11, 3.92s/it] 34%|███▎ | 6288/18688 [7:22:09<14:48:27, 4.30s/it] 34%|███▎ | 6289/18688 [7:22:13<14:20:05, 4.16s/it] 34%|███▎ | 6290/18688 [7:22:17<14:06:25, 4.10s/it] {'loss': '0.9393', 'grad_norm': '1.065', 'learning_rate': '8.204e-05', 'epoch': '0.3366', 'num_input_tokens_seen': 21088384, 'train_runtime': '2.654e+04', 'train_tokens_per_second': '794.7'}
34%|███▎ | 6290/18688 [7:22:17<14:06:25, 4.10s/it] 34%|███▎ | 6291/18688 [7:22:21<13:51:08, 4.02s/it] 34%|███▎ | 6292/18688 [7:22:25<13:53:50, 4.04s/it] 34%|███▎ | 6293/18688 [7:22:29<13:58:28, 4.06s/it] 34%|███▎ | 6294/18688 [7:22:33<13:41:42, 3.98s/it] 34%|███▎ | 6295/18688 [7:22:37<14:08:27, 4.11s/it] 34%|███▎ | 6296/18688 [7:22:42<14:39:31, 4.26s/it] 34%|███▎ | 6297/18688 [7:22:46<13:50:22, 4.02s/it] 34%|███▎ | 6298/18688 [7:22:50<13:55:10, 4.04s/it] 34%|███▎ | 6299/18688 [7:22:54<14:13:10, 4.13s/it] 34%|███▎ | 6300/18688 [7:22:58<14:24:24, 4.19s/it] {'loss': '0.9408', 'grad_norm': '1.034', 'learning_rate': '8.197e-05', 'epoch': '0.3371', 'num_input_tokens_seen': 21120936, 'train_runtime': '2.658e+04', 'train_tokens_per_second': '794.7'}
34%|███▎ | 6300/18688 [7:22:58<14:24:24, 4.19s/it] 34%|███▎ | 6301/18688 [7:23:02<14:05:44, 4.10s/it] 34%|███▎ | 6302/18688 [7:23:06<14:07:17, 4.10s/it] 34%|███▎ | 6303/18688 [7:23:10<13:38:58, 3.97s/it] 34%|███▎ | 6304/18688 [7:23:14<13:22:48, 3.89s/it] 34%|███▎ | 6305/18688 [7:23:18<13:24:02, 3.90s/it] 34%|███▎ | 6306/18688 [7:23:21<13:10:24, 3.83s/it] 34%|███▎ | 6307/18688 [7:23:26<13:44:47, 4.00s/it] 34%|███▍ | 6308/18688 [7:23:30<14:38:29, 4.26s/it] 34%|███▍ | 6309/18688 [7:23:34<13:50:48, 4.03s/it] 34%|███▍ | 6310/18688 [7:23:38<13:58:21, 4.06s/it] {'loss': '0.9641', 'grad_norm': '1.214', 'learning_rate': '8.19e-05', 'epoch': '0.3377', 'num_input_tokens_seen': 21152248, 'train_runtime': '2.662e+04', 'train_tokens_per_second': '794.6'}
34%|███▍ | 6310/18688 [7:23:38<13:58:21, 4.06s/it] 34%|███▍ | 6311/18688 [7:23:42<13:57:29, 4.06s/it] 34%|███▍ | 6312/18688 [7:23:46<14:05:22, 4.10s/it] 34%|███▍ | 6313/18688 [7:23:51<14:19:18, 4.17s/it] 34%|███▍ | 6314/18688 [7:23:55<14:24:32, 4.19s/it] 34%|███▍ | 6315/18688 [7:23:59<14:37:04, 4.25s/it] 34%|███▍ | 6316/18688 [7:24:03<14:13:40, 4.14s/it] 34%|███▍ | 6317/18688 [7:24:07<14:22:59, 4.19s/it] 34%|███▍ | 6318/18688 [7:24:12<14:53:02, 4.33s/it] 34%|███▍ | 6319/18688 [7:24:17<14:56:51, 4.35s/it] 34%|███▍ | 6320/18688 [7:24:22<15:43:56, 4.58s/it] {'loss': '0.9344', 'grad_norm': '1.231', 'learning_rate': '8.183e-05', 'epoch': '0.3382', 'num_input_tokens_seen': 21188680, 'train_runtime': '2.666e+04', 'train_tokens_per_second': '794.7'}
34%|███▍ | 6320/18688 [7:24:22<15:43:56, 4.58s/it] 34%|███▍ | 6321/18688 [7:24:27<16:45:25, 4.88s/it] 34%|███▍ | 6322/18688 [7:24:31<15:33:16, 4.53s/it] 34%|███▍ | 6323/18688 [7:24:34<14:32:07, 4.23s/it] 34%|███▍ | 6324/18688 [7:24:39<14:19:26, 4.17s/it] 34%|███▍ | 6325/18688 [7:24:43<14:44:25, 4.29s/it] 34%|███▍ | 6326/18688 [7:24:47<14:49:34, 4.32s/it] 34%|███▍ | 6327/18688 [7:24:51<14:09:34, 4.12s/it] 34%|███▍ | 6328/18688 [7:24:56<15:05:37, 4.40s/it] 34%|███▍ | 6329/18688 [7:25:00<14:39:51, 4.27s/it] 34%|███▍ | 6330/18688 [7:25:05<14:44:43, 4.30s/it] {'loss': '0.9541', 'grad_norm': '1.505', 'learning_rate': '8.176e-05', 'epoch': '0.3387', 'num_input_tokens_seen': 21223512, 'train_runtime': '2.671e+04', 'train_tokens_per_second': '794.7'}
34%|███▍ | 6330/18688 [7:25:05<14:44:43, 4.30s/it] 34%|███▍ | 6331/18688 [7:25:09<15:23:36, 4.48s/it] 34%|███▍ | 6332/18688 [7:25:14<15:10:46, 4.42s/it] 34%|███▍ | 6333/18688 [7:25:17<14:19:18, 4.17s/it] 34%|███▍ | 6334/18688 [7:25:21<13:39:23, 3.98s/it] 34%|███▍ | 6335/18688 [7:25:25<14:15:10, 4.15s/it] 34%|███▍ | 6336/18688 [7:25:29<13:37:07, 3.97s/it] 34%|███▍ | 6337/18688 [7:25:34<14:45:38, 4.30s/it] 34%|███▍ | 6338/18688 [7:25:38<14:02:16, 4.09s/it] 34%|███▍ | 6339/18688 [7:25:42<14:19:07, 4.17s/it] 34%|███▍ | 6340/18688 [7:25:46<14:11:49, 4.14s/it] {'loss': '0.9054', 'grad_norm': '1.29', 'learning_rate': '8.169e-05', 'epoch': '0.3393', 'num_input_tokens_seen': 21257224, 'train_runtime': '2.675e+04', 'train_tokens_per_second': '794.8'}
34%|███▍ | 6340/18688 [7:25:46<14:11:49, 4.14s/it] 34%|███▍ | 6341/18688 [7:25:51<14:36:01, 4.26s/it] 34%|███▍ | 6342/18688 [7:25:54<14:05:14, 4.11s/it] 34%|███▍ | 6343/18688 [7:25:58<13:33:58, 3.96s/it] 34%|███▍ | 6344/18688 [7:26:03<14:27:01, 4.21s/it] 34%|███▍ | 6345/18688 [7:26:07<14:05:15, 4.11s/it] 34%|███▍ | 6346/18688 [7:26:11<14:28:51, 4.22s/it] 34%|███▍ | 6347/18688 [7:26:16<15:02:08, 4.39s/it] 34%|███▍ | 6348/18688 [7:26:19<14:06:08, 4.11s/it] 34%|███▍ | 6349/18688 [7:26:23<13:57:07, 4.07s/it] 34%|███▍ | 6350/18688 [7:26:28<14:18:41, 4.18s/it] {'loss': '0.8909', 'grad_norm': '1.139', 'learning_rate': '8.161e-05', 'epoch': '0.3398', 'num_input_tokens_seen': 21291256, 'train_runtime': '2.679e+04', 'train_tokens_per_second': '794.8'}
34%|███▍ | 6350/18688 [7:26:28<14:18:41, 4.18s/it] 34%|███▍ | 6351/18688 [7:26:31<13:50:26, 4.04s/it] 34%|███▍ | 6352/18688 [7:26:35<13:36:42, 3.97s/it] 34%|███▍ | 6353/18688 [7:26:39<13:20:21, 3.89s/it] 34%|███▍ | 6354/18688 [7:26:43<13:46:12, 4.02s/it] 34%|███▍ | 6355/18688 [7:26:49<15:05:13, 4.40s/it] 34%|███▍ | 6356/18688 [7:26:53<15:31:10, 4.53s/it] 34%|███▍ | 6357/18688 [7:26:57<14:27:01, 4.22s/it] 34%|███▍ | 6358/18688 [7:27:01<13:48:56, 4.03s/it] 34%|███▍ | 6359/18688 [7:27:05<13:58:03, 4.08s/it] 34%|███▍ | 6360/18688 [7:27:08<13:21:08, 3.90s/it] {'loss': '0.8904', 'grad_norm': '1.128', 'learning_rate': '8.154e-05', 'epoch': '0.3403', 'num_input_tokens_seen': 21322936, 'train_runtime': '2.683e+04', 'train_tokens_per_second': '794.8'}
34%|███▍ | 6360/18688 [7:27:08<13:21:08, 3.90s/it] 34%|███▍ | 6361/18688 [7:27:12<13:38:27, 3.98s/it] 34%|███▍ | 6362/18688 [7:27:16<13:19:57, 3.89s/it] 34%|███▍ | 6363/18688 [7:27:20<13:12:13, 3.86s/it] 34%|███▍ | 6364/18688 [7:27:24<13:24:37, 3.92s/it] 34%|███▍ | 6365/18688 [7:27:28<13:51:34, 4.05s/it] 34%|███▍ | 6366/18688 [7:27:33<14:20:50, 4.19s/it] 34%|███▍ | 6367/18688 [7:27:37<14:51:32, 4.34s/it] 34%|███▍ | 6368/18688 [7:27:42<15:17:08, 4.47s/it] 34%|███▍ | 6369/18688 [7:27:46<15:02:55, 4.40s/it] 34%|███▍ | 6370/18688 [7:27:51<15:09:31, 4.43s/it] {'loss': '0.9797', 'grad_norm': '1.572', 'learning_rate': '8.147e-05', 'epoch': '0.3409', 'num_input_tokens_seen': 21359280, 'train_runtime': '2.687e+04', 'train_tokens_per_second': '794.9'}
34%|███▍ | 6370/18688 [7:27:51<15:09:31, 4.43s/it] 34%|███▍ | 6371/18688 [7:27:55<15:00:23, 4.39s/it] 34%|███▍ | 6372/18688 [7:28:00<15:13:47, 4.45s/it] 34%|███▍ | 6373/18688 [7:28:04<14:26:19, 4.22s/it] 34%|███▍ | 6374/18688 [7:28:07<14:10:24, 4.14s/it] 34%|███▍ | 6375/18688 [7:28:11<13:49:02, 4.04s/it] 34%|███▍ | 6376/18688 [7:28:16<14:39:36, 4.29s/it] 34%|███▍ | 6377/18688 [7:28:21<14:45:00, 4.31s/it] 34%|███▍ | 6378/18688 [7:28:25<14:28:15, 4.23s/it] 34%|███▍ | 6379/18688 [7:28:29<14:54:27, 4.36s/it] 34%|███▍ | 6380/18688 [7:28:33<14:21:56, 4.20s/it] {'loss': '0.9268', 'grad_norm': '1.14', 'learning_rate': '8.14e-05', 'epoch': '0.3414', 'num_input_tokens_seen': 21392696, 'train_runtime': '2.691e+04', 'train_tokens_per_second': '794.9'}
34%|███▍ | 6380/18688 [7:28:33<14:21:56, 4.20s/it] 34%|███▍ | 6381/18688 [7:28:37<13:49:02, 4.04s/it] 34%|███▍ | 6382/18688 [7:28:40<13:29:38, 3.95s/it] 34%|███▍ | 6383/18688 [7:28:45<14:16:34, 4.18s/it] 34%|███▍ | 6384/18688 [7:28:50<15:28:28, 4.53s/it] 34%|███▍ | 6385/18688 [7:28:55<15:43:44, 4.60s/it] 34%|███▍ | 6386/18688 [7:29:00<15:24:37, 4.51s/it] 34%|███▍ | 6387/18688 [7:29:03<14:29:14, 4.24s/it] 34%|███▍ | 6388/18688 [7:29:07<13:50:45, 4.05s/it] 34%|███▍ | 6389/18688 [7:29:11<13:31:22, 3.96s/it] 34%|███▍ | 6390/18688 [7:29:14<13:20:19, 3.90s/it] {'loss': '0.9537', 'grad_norm': '1.283', 'learning_rate': '8.133e-05', 'epoch': '0.3419', 'num_input_tokens_seen': 21424928, 'train_runtime': '2.695e+04', 'train_tokens_per_second': '794.8'}
34%|███▍ | 6390/18688 [7:29:14<13:20:19, 3.90s/it] 34%|███▍ | 6391/18688 [7:29:18<13:12:41, 3.87s/it] 34%|███▍ | 6392/18688 [7:29:22<13:23:47, 3.92s/it] 34%|███▍ | 6393/18688 [7:29:26<13:08:59, 3.85s/it] 34%|███▍ | 6394/18688 [7:29:30<13:43:32, 4.02s/it] 34%|███▍ | 6395/18688 [7:29:34<13:25:44, 3.93s/it] 34%|███▍ | 6396/18688 [7:29:38<13:27:30, 3.94s/it] 34%|███▍ | 6397/18688 [7:29:42<13:14:44, 3.88s/it] 34%|███▍ | 6398/18688 [7:29:46<14:05:33, 4.13s/it] 34%|███▍ | 6399/18688 [7:29:51<14:44:50, 4.32s/it] 34%|███▍ | 6400/18688 [7:29:56<14:52:47, 4.36s/it] {'loss': '0.9382', 'grad_norm': '1.131', 'learning_rate': '8.126e-05', 'epoch': '0.3425', 'num_input_tokens_seen': 21458152, 'train_runtime': '2.7e+04', 'train_tokens_per_second': '794.9'}
34%|███▍ | 6400/18688 [7:29:56<14:52:47, 4.36s/it] 34%|███▍ | 6401/18688 [7:30:00<15:10:26, 4.45s/it] 34%|███▍ | 6402/18688 [7:30:05<15:14:29, 4.47s/it] 34%|███▍ | 6403/18688 [7:30:09<14:58:38, 4.39s/it] 34%|███▍ | 6404/18688 [7:30:13<14:15:15, 4.18s/it] 34%|███▍ | 6405/18688 [7:30:17<14:23:47, 4.22s/it] 34%|███▍ | 6406/18688 [7:30:20<13:40:12, 4.01s/it] 34%|███▍ | 6407/18688 [7:30:24<13:32:01, 3.97s/it] 34%|███▍ | 6408/18688 [7:30:28<13:10:20, 3.86s/it] 34%|███▍ | 6409/18688 [7:30:31<12:48:19, 3.75s/it] 34%|███▍ | 6410/18688 [7:30:36<13:48:17, 4.05s/it] {'loss': '0.9179', 'grad_norm': '1.183', 'learning_rate': '8.119e-05', 'epoch': '0.343', 'num_input_tokens_seen': 21490280, 'train_runtime': '2.704e+04', 'train_tokens_per_second': '794.9'}
34%|███▍ | 6410/18688 [7:30:36<13:48:17, 4.05s/it] 34%|███▍ | 6411/18688 [7:30:41<14:53:11, 4.37s/it] 34%|███▍ | 6412/18688 [7:30:45<14:21:55, 4.21s/it] 34%|███▍ | 6413/18688 [7:30:49<13:43:43, 4.03s/it] 34%|███▍ | 6414/18688 [7:30:52<13:17:54, 3.90s/it] 34%|███▍ | 6415/18688 [7:30:57<13:41:10, 4.01s/it] 34%|███▍ | 6416/18688 [7:31:01<13:39:29, 4.01s/it] 34%|███▍ | 6417/18688 [7:31:04<13:21:03, 3.92s/it] 34%|███▍ | 6418/18688 [7:31:09<13:55:18, 4.08s/it] 34%|███▍ | 6419/18688 [7:31:14<14:49:21, 4.35s/it] 34%|███▍ | 6420/18688 [7:31:19<15:26:10, 4.53s/it] {'loss': '0.9808', 'grad_norm': '1.298', 'learning_rate': '8.112e-05', 'epoch': '0.3435', 'num_input_tokens_seen': 21525752, 'train_runtime': '2.708e+04', 'train_tokens_per_second': '794.9'}
34%|███▍ | 6420/18688 [7:31:19<15:26:10, 4.53s/it] 34%|███▍ | 6421/18688 [7:31:23<14:41:52, 4.31s/it] 34%|███▍ | 6422/18688 [7:31:26<13:50:17, 4.06s/it] 34%|███▍ | 6423/18688 [7:31:30<13:19:13, 3.91s/it] 34%|███▍ | 6424/18688 [7:31:33<12:55:40, 3.79s/it] 34%|███▍ | 6425/18688 [7:31:37<13:11:39, 3.87s/it] 34%|███▍ | 6426/18688 [7:31:42<13:46:59, 4.05s/it] 34%|███▍ | 6427/18688 [7:31:45<13:34:08, 3.98s/it] 34%|███▍ | 6428/18688 [7:31:50<13:45:38, 4.04s/it] 34%|███▍ | 6429/18688 [7:31:53<13:19:26, 3.91s/it] 34%|███▍ | 6430/18688 [7:31:57<13:06:44, 3.85s/it] {'loss': '0.8902', 'grad_norm': '1.425', 'learning_rate': '8.104e-05', 'epoch': '0.3441', 'num_input_tokens_seen': 21556080, 'train_runtime': '2.712e+04', 'train_tokens_per_second': '794.9'}
34%|███▍ | 6430/18688 [7:31:57<13:06:44, 3.85s/it] 34%|███▍ | 6431/18688 [7:32:01<13:10:11, 3.87s/it] 34%|███▍ | 6432/18688 [7:32:05<13:26:05, 3.95s/it] 34%|███▍ | 6433/18688 [7:32:09<13:48:45, 4.06s/it] 34%|███▍ | 6434/18688 [7:32:14<14:17:22, 4.20s/it] 34%|███▍ | 6435/18688 [7:32:18<14:31:19, 4.27s/it] 34%|███▍ | 6436/18688 [7:32:22<14:08:45, 4.16s/it] 34%|███▍ | 6437/18688 [7:32:27<14:37:52, 4.30s/it] 34%|███▍ | 6438/18688 [7:32:31<14:48:57, 4.35s/it] 34%|███▍ | 6439/18688 [7:32:35<14:37:48, 4.30s/it] 34%|███▍ | 6440/18688 [7:32:39<13:54:41, 4.09s/it] {'loss': '0.9357', 'grad_norm': '1.338', 'learning_rate': '8.097e-05', 'epoch': '0.3446', 'num_input_tokens_seen': 21591096, 'train_runtime': '2.716e+04', 'train_tokens_per_second': '795'}
34%|███▍ | 6440/18688 [7:32:39<13:54:41, 4.09s/it] 34%|███▍ | 6441/18688 [7:32:44<14:38:20, 4.30s/it] 34%|███▍ | 6442/18688 [7:32:48<14:45:09, 4.34s/it] 34%|███▍ | 6443/18688 [7:32:52<14:00:00, 4.12s/it] 34%|███▍ | 6444/18688 [7:32:56<13:48:16, 4.06s/it] 34%|███▍ | 6445/18688 [7:33:01<14:46:05, 4.34s/it] 34%|███▍ | 6446/18688 [7:33:05<14:19:42, 4.21s/it] 34%|███▍ | 6447/18688 [7:33:08<13:53:28, 4.09s/it] 35%|███▍ | 6448/18688 [7:33:12<13:30:17, 3.97s/it] 35%|███▍ | 6449/18688 [7:33:16<13:20:50, 3.93s/it] 35%|███▍ | 6450/18688 [7:33:21<13:59:40, 4.12s/it] {'loss': '0.9036', 'grad_norm': '1.293', 'learning_rate': '8.09e-05', 'epoch': '0.3452', 'num_input_tokens_seen': 21624784, 'train_runtime': '2.72e+04', 'train_tokens_per_second': '795'}
35%|███▍ | 6450/18688 [7:33:21<13:59:40, 4.12s/it] 35%|███▍ | 6451/18688 [7:33:26<15:08:19, 4.45s/it] 35%|███▍ | 6452/18688 [7:33:31<15:34:48, 4.58s/it] 35%|███▍ | 6453/18688 [7:33:34<14:36:07, 4.30s/it] 35%|███▍ | 6454/18688 [7:33:38<14:07:22, 4.16s/it] 35%|███▍ | 6455/18688 [7:33:42<13:33:21, 3.99s/it] 35%|███▍ | 6456/18688 [7:33:46<13:38:39, 4.02s/it] 35%|███▍ | 6457/18688 [7:33:49<13:17:46, 3.91s/it] 35%|███▍ | 6458/18688 [7:33:54<13:42:31, 4.04s/it] 35%|███▍ | 6459/18688 [7:33:58<13:46:26, 4.05s/it] 35%|███▍ | 6460/18688 [7:34:02<14:13:56, 4.19s/it] {'loss': '0.9618', 'grad_norm': '1.331', 'learning_rate': '8.083e-05', 'epoch': '0.3457', 'num_input_tokens_seen': 21657664, 'train_runtime': '2.724e+04', 'train_tokens_per_second': '795'}
35%|███▍ | 6460/18688 [7:34:02<14:13:56, 4.19s/it] 35%|███▍ | 6461/18688 [7:34:07<14:30:40, 4.27s/it] 35%|███▍ | 6462/18688 [7:34:11<14:43:41, 4.34s/it] 35%|███▍ | 6463/18688 [7:34:15<14:02:13, 4.13s/it] 35%|███▍ | 6464/18688 [7:34:19<13:38:52, 4.02s/it] 35%|███▍ | 6465/18688 [7:34:24<14:35:03, 4.30s/it] 35%|███▍ | 6466/18688 [7:34:27<13:50:21, 4.08s/it] 35%|███▍ | 6467/18688 [7:34:31<13:24:17, 3.95s/it] 35%|███▍ | 6468/18688 [7:34:36<14:04:33, 4.15s/it] 35%|███▍ | 6469/18688 [7:34:41<15:03:53, 4.44s/it] 35%|███▍ | 6470/18688 [7:34:45<14:26:47, 4.26s/it] {'loss': '0.9642', 'grad_norm': '1.477', 'learning_rate': '8.076e-05', 'epoch': '0.3462', 'num_input_tokens_seen': 21691824, 'train_runtime': '2.729e+04', 'train_tokens_per_second': '795'}
35%|███▍ | 6470/18688 [7:34:45<14:26:47, 4.26s/it] 35%|███▍ | 6471/18688 [7:34:49<14:19:30, 4.22s/it] 35%|███▍ | 6472/18688 [7:34:52<13:38:25, 4.02s/it] 35%|███▍ | 6473/18688 [7:34:56<13:37:25, 4.02s/it] 35%|███▍ | 6474/18688 [7:35:00<13:11:57, 3.89s/it] 35%|███▍ | 6475/18688 [7:35:04<13:15:52, 3.91s/it] 35%|███▍ | 6476/18688 [7:35:08<13:54:17, 4.10s/it] 35%|███▍ | 6477/18688 [7:35:12<13:42:18, 4.04s/it] 35%|███▍ | 6478/18688 [7:35:16<13:57:55, 4.12s/it] 35%|███▍ | 6479/18688 [7:35:21<14:19:02, 4.22s/it] 35%|███▍ | 6480/18688 [7:35:26<14:58:08, 4.41s/it] {'loss': '0.9121', 'grad_norm': '1.058', 'learning_rate': '8.069e-05', 'epoch': '0.3468', 'num_input_tokens_seen': 21723792, 'train_runtime': '2.733e+04', 'train_tokens_per_second': '795'}
35%|███▍ | 6480/18688 [7:35:26<14:58:08, 4.41s/it] 35%|███▍ | 6481/18688 [7:35:30<14:18:17, 4.22s/it] 35%|███▍ | 6482/18688 [7:35:34<14:42:51, 4.34s/it] 35%|███▍ | 6483/18688 [7:35:38<14:34:51, 4.30s/it] 35%|███▍ | 6484/18688 [7:35:43<14:58:45, 4.42s/it] 35%|███▍ | 6485/18688 [7:35:47<14:21:48, 4.24s/it] 35%|███▍ | 6486/18688 [7:35:52<14:42:32, 4.34s/it] 35%|███▍ | 6487/18688 [7:35:56<14:39:15, 4.32s/it] 35%|███▍ | 6488/18688 [7:36:00<14:28:58, 4.27s/it] 35%|███▍ | 6489/18688 [7:36:05<15:02:04, 4.44s/it] 35%|███▍ | 6490/18688 [7:36:09<14:44:28, 4.35s/it] {'loss': '0.943', 'grad_norm': '1.29', 'learning_rate': '8.061e-05', 'epoch': '0.3473', 'num_input_tokens_seen': 21759184, 'train_runtime': '2.737e+04', 'train_tokens_per_second': '795'}
35%|███▍ | 6490/18688 [7:36:09<14:44:28, 4.35s/it] 35%|███▍ | 6491/18688 [7:36:13<14:37:20, 4.32s/it] 35%|███▍ | 6492/18688 [7:36:17<13:51:15, 4.09s/it] 35%|███▍ | 6493/18688 [7:36:21<13:42:41, 4.05s/it] 35%|███▍ | 6494/18688 [7:36:24<13:25:19, 3.96s/it] 35%|███▍ | 6495/18688 [7:36:29<14:12:11, 4.19s/it] 35%|███▍ | 6496/18688 [7:36:33<13:56:27, 4.12s/it] 35%|███▍ | 6497/18688 [7:36:37<13:29:18, 3.98s/it] 35%|███▍ | 6498/18688 [7:36:41<13:20:08, 3.94s/it] 35%|███▍ | 6499/18688 [7:36:44<12:57:31, 3.83s/it] 35%|███▍ | 6500/18688 [7:36:48<12:54:07, 3.81s/it] {'loss': '0.9715', 'grad_norm': '1.534', 'learning_rate': '8.054e-05', 'epoch': '0.3478', 'num_input_tokens_seen': 21790976, 'train_runtime': '2.741e+04', 'train_tokens_per_second': '795'}
35%|███▍ | 6500/18688 [7:36:48<12:54:07, 3.81s/it] 35%|███▍ | 6501/18688 [7:36:53<14:24:53, 4.26s/it] 35%|███▍ | 6502/18688 [7:36:58<14:32:03, 4.29s/it] 35%|███▍ | 6503/18688 [7:37:02<14:35:20, 4.31s/it] 35%|███▍ | 6504/18688 [7:37:06<14:24:47, 4.26s/it] 35%|███▍ | 6505/18688 [7:37:10<14:22:49, 4.25s/it] 35%|███▍ | 6506/18688 [7:37:14<13:33:15, 4.01s/it] 35%|███▍ | 6507/18688 [7:37:18<13:44:46, 4.06s/it] 35%|███▍ | 6508/18688 [7:37:23<14:35:19, 4.31s/it] 35%|███▍ | 6509/18688 [7:37:26<13:51:48, 4.10s/it] 35%|███▍ | 6510/18688 [7:37:30<13:36:04, 4.02s/it] {'loss': '0.8769', 'grad_norm': '1.101', 'learning_rate': '8.047e-05', 'epoch': '0.3484', 'num_input_tokens_seen': 21825512, 'train_runtime': '2.745e+04', 'train_tokens_per_second': '795.1'}
35%|███▍ | 6510/18688 [7:37:30<13:36:04, 4.02s/it] 35%|███▍ | 6511/18688 [7:37:34<13:20:52, 3.95s/it] 35%|███▍ | 6512/18688 [7:37:38<13:14:29, 3.92s/it] 35%|███▍ | 6513/18688 [7:37:43<14:22:12, 4.25s/it] 35%|███▍ | 6514/18688 [7:37:47<14:30:30, 4.29s/it] 35%|███▍ | 6515/18688 [7:37:51<14:14:57, 4.21s/it] 35%|███▍ | 6516/18688 [7:37:56<14:35:57, 4.32s/it] 35%|███▍ | 6517/18688 [7:38:00<14:19:08, 4.24s/it] 35%|███▍ | 6518/18688 [7:38:04<13:46:18, 4.07s/it] 35%|███▍ | 6519/18688 [7:38:08<13:41:54, 4.05s/it] 35%|███▍ | 6520/18688 [7:38:12<14:12:05, 4.20s/it] {'loss': '0.9026', 'grad_norm': '1.148', 'learning_rate': '8.04e-05', 'epoch': '0.3489', 'num_input_tokens_seen': 21858352, 'train_runtime': '2.749e+04', 'train_tokens_per_second': '795.1'}
35%|███▍ | 6520/18688 [7:38:12<14:12:05, 4.20s/it] 35%|███▍ | 6521/18688 [7:38:16<13:34:46, 4.02s/it] 35%|███▍ | 6522/18688 [7:38:19<13:01:01, 3.85s/it] 35%|███▍ | 6523/18688 [7:38:23<13:22:53, 3.96s/it] 35%|███▍ | 6524/18688 [7:38:27<13:16:05, 3.93s/it] 35%|███▍ | 6525/18688 [7:38:32<13:43:18, 4.06s/it] 35%|███▍ | 6526/18688 [7:38:35<13:16:16, 3.93s/it] 35%|███▍ | 6527/18688 [7:38:40<14:20:54, 4.25s/it] 35%|███▍ | 6528/18688 [7:38:45<14:57:19, 4.43s/it] 35%|███▍ | 6529/18688 [7:38:49<14:38:08, 4.33s/it] 35%|███▍ | 6530/18688 [7:38:54<14:32:21, 4.31s/it] {'loss': '0.9857', 'grad_norm': '1.48', 'learning_rate': '8.032e-05', 'epoch': '0.3494', 'num_input_tokens_seen': 21891648, 'train_runtime': '2.753e+04', 'train_tokens_per_second': '795.1'}
35%|███▍ | 6530/18688 [7:38:54<14:32:21, 4.31s/it] 35%|███▍ | 6531/18688 [7:38:58<14:42:24, 4.36s/it] 35%|███▍ | 6532/18688 [7:39:02<14:03:52, 4.17s/it] 35%|███▍ | 6533/18688 [7:39:06<13:50:36, 4.10s/it] 35%|███▍ | 6534/18688 [7:39:10<14:00:03, 4.15s/it] 35%|███▍ | 6535/18688 [7:39:14<13:31:45, 4.01s/it] 35%|███▍ | 6536/18688 [7:39:17<12:57:17, 3.84s/it] 35%|███▍ | 6537/18688 [7:39:21<12:37:22, 3.74s/it] 35%|███▍ | 6538/18688 [7:39:25<13:01:29, 3.86s/it] 35%|███▍ | 6539/18688 [7:39:29<13:15:37, 3.93s/it] 35%|███▍ | 6540/18688 [7:39:33<13:23:27, 3.97s/it] {'loss': '0.9033', 'grad_norm': '1.452', 'learning_rate': '8.025e-05', 'epoch': '0.35', 'num_input_tokens_seen': 21922472, 'train_runtime': '2.757e+04', 'train_tokens_per_second': '795.1'}
35%|███▍ | 6540/18688 [7:39:33<13:23:27, 3.97s/it] 35%|███▌ | 6541/18688 [7:39:37<13:09:59, 3.90s/it] 35%|███▌ | 6542/18688 [7:39:40<12:57:51, 3.84s/it] 35%|███▌ | 6543/18688 [7:39:44<12:51:55, 3.81s/it] 35%|███▌ | 6544/18688 [7:39:48<13:29:00, 4.00s/it] 35%|███▌ | 6545/18688 [7:39:53<13:38:01, 4.04s/it] 35%|███▌ | 6546/18688 [7:39:57<14:02:23, 4.16s/it] 35%|███▌ | 6547/18688 [7:40:01<13:38:19, 4.04s/it] 35%|███▌ | 6548/18688 [7:40:05<13:22:17, 3.97s/it] 35%|███▌ | 6549/18688 [7:40:09<14:18:05, 4.24s/it] 35%|███▌ | 6550/18688 [7:40:13<13:33:56, 4.02s/it] {'loss': '0.9585', 'grad_norm': '1.275', 'learning_rate': '8.018e-05', 'epoch': '0.3505', 'num_input_tokens_seen': 21955056, 'train_runtime': '2.761e+04', 'train_tokens_per_second': '795.1'}
35%|███▌ | 6550/18688 [7:40:13<13:33:56, 4.02s/it] 35%|███▌ | 6551/18688 [7:40:18<14:20:06, 4.25s/it] 35%|███▌ | 6552/18688 [7:40:22<14:09:36, 4.20s/it] 35%|███▌ | 6553/18688 [7:40:27<14:47:28, 4.39s/it] 35%|███▌ | 6554/18688 [7:40:32<15:24:32, 4.57s/it] 35%|███▌ | 6555/18688 [7:40:36<14:57:07, 4.44s/it] 35%|███▌ | 6556/18688 [7:40:39<14:04:10, 4.17s/it] 35%|███▌ | 6557/18688 [7:40:43<13:27:35, 3.99s/it] 35%|███▌ | 6558/18688 [7:40:47<13:37:02, 4.04s/it] 35%|███▌ | 6559/18688 [7:40:53<15:00:23, 4.45s/it] 35%|███▌ | 6560/18688 [7:40:57<15:00:39, 4.46s/it] {'loss': '0.9089', 'grad_norm': '1.286', 'learning_rate': '8.01e-05', 'epoch': '0.351', 'num_input_tokens_seen': 21991416, 'train_runtime': '2.766e+04', 'train_tokens_per_second': '795.1'}
35%|███▌ | 6560/18688 [7:40:57<15:00:39, 4.46s/it] 35%|███▌ | 6561/18688 [7:41:01<14:37:56, 4.34s/it] 35%|███▌ | 6562/18688 [7:41:05<14:20:39, 4.26s/it] 35%|███▌ | 6563/18688 [7:41:09<13:59:38, 4.15s/it] 35%|███▌ | 6564/18688 [7:41:14<14:32:24, 4.32s/it] 35%|███▌ | 6565/18688 [7:41:18<14:49:47, 4.40s/it] 35%|███▌ | 6566/18688 [7:41:23<14:59:08, 4.45s/it] 35%|███▌ | 6567/18688 [7:41:26<13:58:57, 4.15s/it] 35%|███▌ | 6568/18688 [7:41:30<13:19:35, 3.96s/it] 35%|███▌ | 6569/18688 [7:41:34<13:02:17, 3.87s/it] 35%|███▌ | 6570/18688 [7:41:37<12:59:04, 3.86s/it] {'loss': '0.8742', 'grad_norm': '1.28', 'learning_rate': '8.003e-05', 'epoch': '0.3516', 'num_input_tokens_seen': 22024552, 'train_runtime': '2.77e+04', 'train_tokens_per_second': '795.2'}
35%|███▌ | 6570/18688 [7:41:37<12:59:04, 3.86s/it] 35%|███▌ | 6571/18688 [7:41:42<13:36:18, 4.04s/it] 35%|███▌ | 6572/18688 [7:41:46<13:16:10, 3.94s/it] 35%|███▌ | 6573/18688 [7:41:51<14:46:27, 4.39s/it] 35%|███▌ | 6574/18688 [7:41:56<15:00:37, 4.46s/it] 35%|███▌ | 6575/18688 [7:41:59<14:13:31, 4.23s/it] 35%|███▌ | 6576/18688 [7:42:03<14:03:35, 4.18s/it] 35%|███▌ | 6577/18688 [7:42:07<13:34:36, 4.04s/it] 35%|███▌ | 6578/18688 [7:42:12<14:57:13, 4.45s/it] 35%|███▌ | 6579/18688 [7:42:16<14:31:43, 4.32s/it] 35%|███▌ | 6580/18688 [7:42:20<13:53:44, 4.13s/it] {'loss': '0.9435', 'grad_norm': '1.321', 'learning_rate': '7.996e-05', 'epoch': '0.3521', 'num_input_tokens_seen': 22060344, 'train_runtime': '2.774e+04', 'train_tokens_per_second': '795.2'}
35%|███▌ | 6580/18688 [7:42:20<13:53:44, 4.13s/it] 35%|███▌ | 6581/18688 [7:42:25<14:14:03, 4.23s/it] 35%|███▌ | 6582/18688 [7:42:29<14:42:42, 4.37s/it] 35%|███▌ | 6583/18688 [7:42:34<14:37:54, 4.35s/it] 35%|███▌ | 6584/18688 [7:42:37<13:59:03, 4.16s/it] 35%|███▌ | 6585/18688 [7:42:42<14:13:25, 4.23s/it] 35%|███▌ | 6586/18688 [7:42:46<14:00:00, 4.16s/it] 35%|███▌ | 6587/18688 [7:42:50<14:15:19, 4.24s/it] 35%|███▌ | 6588/18688 [7:42:55<15:00:08, 4.46s/it] 35%|███▌ | 6589/18688 [7:43:00<14:58:20, 4.45s/it] 35%|███▌ | 6590/18688 [7:43:03<14:01:33, 4.17s/it] {'loss': '1.006', 'grad_norm': '1.195', 'learning_rate': '7.989e-05', 'epoch': '0.3526', 'num_input_tokens_seen': 22095392, 'train_runtime': '2.778e+04', 'train_tokens_per_second': '795.3'}
35%|███▌ | 6590/18688 [7:43:03<14:01:33, 4.17s/it] 35%|███▌ | 6591/18688 [7:43:07<13:34:52, 4.04s/it] 35%|███▌ | 6592/18688 [7:43:11<13:39:42, 4.07s/it] 35%|███▌ | 6593/18688 [7:43:15<13:11:36, 3.93s/it] 35%|███▌ | 6594/18688 [7:43:19<13:26:09, 4.00s/it] 35%|███▌ | 6595/18688 [7:43:22<13:06:49, 3.90s/it] 35%|███▌ | 6596/18688 [7:43:26<13:13:04, 3.94s/it] 35%|███▌ | 6597/18688 [7:43:32<14:31:33, 4.33s/it] 35%|███▌ | 6598/18688 [7:43:36<14:03:29, 4.19s/it] 35%|███▌ | 6599/18688 [7:43:40<13:55:25, 4.15s/it] 35%|███▌ | 6600/18688 [7:43:43<13:19:45, 3.97s/it] {'loss': '0.9643', 'grad_norm': '1.384', 'learning_rate': '7.981e-05', 'epoch': '0.3532', 'num_input_tokens_seen': 22126984, 'train_runtime': '2.782e+04', 'train_tokens_per_second': '795.3'}
35%|███▌ | 6600/18688 [7:43:43<13:19:45, 3.97s/it] 35%|███▌ | 6601/18688 [7:43:47<13:36:04, 4.05s/it] 35%|███▌ | 6602/18688 [7:43:52<14:05:05, 4.20s/it] 35%|███▌ | 6603/18688 [7:43:56<14:01:29, 4.18s/it] 35%|███▌ | 6604/18688 [7:44:01<14:20:23, 4.27s/it] 35%|███▌ | 6605/18688 [7:44:05<14:53:53, 4.44s/it] 35%|███▌ | 6606/18688 [7:44:09<13:57:45, 4.16s/it] 35%|███▌ | 6607/18688 [7:44:13<14:04:20, 4.19s/it] 35%|███▌ | 6608/18688 [7:44:18<14:47:51, 4.41s/it] 35%|███▌ | 6609/18688 [7:44:22<14:14:09, 4.24s/it] 35%|███▌ | 6610/18688 [7:44:27<14:48:51, 4.42s/it] {'loss': '0.9656', 'grad_norm': '1.573', 'learning_rate': '7.974e-05', 'epoch': '0.3537', 'num_input_tokens_seen': 22163488, 'train_runtime': '2.787e+04', 'train_tokens_per_second': '795.3'}
35%|███▌ | 6610/18688 [7:44:27<14:48:51, 4.42s/it] 35%|███▌ | 6611/18688 [7:44:31<14:47:28, 4.41s/it] 35%|███▌ | 6612/18688 [7:44:35<14:32:19, 4.33s/it] 35%|███▌ | 6613/18688 [7:44:39<14:18:11, 4.26s/it] 35%|███▌ | 6614/18688 [7:44:43<13:37:24, 4.06s/it] 35%|███▌ | 6615/18688 [7:44:47<13:47:24, 4.11s/it] 35%|███▌ | 6616/18688 [7:44:52<14:12:36, 4.24s/it] 35%|███▌ | 6617/18688 [7:44:56<13:50:18, 4.13s/it] 35%|███▌ | 6618/18688 [7:45:00<14:10:37, 4.23s/it] 35%|███▌ | 6619/18688 [7:45:05<14:30:49, 4.33s/it] 35%|███▌ | 6620/18688 [7:45:10<15:06:24, 4.51s/it] {'loss': '0.9794', 'grad_norm': '1.194', 'learning_rate': '7.967e-05', 'epoch': '0.3542', 'num_input_tokens_seen': 22198272, 'train_runtime': '2.791e+04', 'train_tokens_per_second': '795.4'}
35%|███▌ | 6620/18688 [7:45:10<15:06:24, 4.51s/it] 35%|███▌ | 6621/18688 [7:45:14<15:05:34, 4.50s/it] 35%|███▌ | 6622/18688 [7:45:18<14:44:08, 4.40s/it] 35%|███▌ | 6623/18688 [7:45:22<14:08:49, 4.22s/it] 35%|███▌ | 6624/18688 [7:45:27<14:34:57, 4.35s/it] 35%|███▌ | 6625/18688 [7:45:31<15:01:38, 4.48s/it] 35%|███▌ | 6626/18688 [7:45:36<14:51:40, 4.44s/it] 35%|███▌ | 6627/18688 [7:45:40<14:49:35, 4.43s/it] 35%|███▌ | 6628/18688 [7:45:45<15:13:44, 4.55s/it] 35%|███▌ | 6629/18688 [7:45:50<15:53:08, 4.74s/it] 35%|███▌ | 6630/18688 [7:45:54<15:18:33, 4.57s/it] {'loss': '0.9627', 'grad_norm': '1.309', 'learning_rate': '7.959e-05', 'epoch': '0.3548', 'num_input_tokens_seen': 22236368, 'train_runtime': '2.795e+04', 'train_tokens_per_second': '795.4'}
35%|███▌ | 6630/18688 [7:45:54<15:18:33, 4.57s/it] 35%|███▌ | 6631/18688 [7:45:59<14:54:46, 4.45s/it] 35%|███▌ | 6632/18688 [7:46:03<14:43:35, 4.40s/it] 35%|███▌ | 6633/18688 [7:46:07<14:34:47, 4.35s/it] 35%|███▌ | 6634/18688 [7:46:11<14:31:41, 4.34s/it] 36%|███▌ | 6635/18688 [7:46:17<15:18:25, 4.57s/it] 36%|███▌ | 6636/18688 [7:46:21<15:30:01, 4.63s/it] 36%|███▌ | 6637/18688 [7:46:26<15:13:15, 4.55s/it] 36%|███▌ | 6638/18688 [7:46:30<14:33:26, 4.35s/it] 36%|███▌ | 6639/18688 [7:46:33<14:06:53, 4.22s/it] 36%|███▌ | 6640/18688 [7:46:38<14:22:04, 4.29s/it] {'loss': '0.9279', 'grad_norm': '1.151', 'learning_rate': '7.952e-05', 'epoch': '0.3553', 'num_input_tokens_seen': 22272800, 'train_runtime': '2.8e+04', 'train_tokens_per_second': '795.5'}
36%|███▌ | 6640/18688 [7:46:38<14:22:04, 4.29s/it] 36%|███▌ | 6641/18688 [7:46:41<13:39:21, 4.08s/it] 36%|███▌ | 6642/18688 [7:46:45<13:13:39, 3.95s/it] 36%|███▌ | 6643/18688 [7:46:50<14:00:28, 4.19s/it] 36%|███▌ | 6644/18688 [7:46:54<14:03:43, 4.20s/it] 36%|███▌ | 6645/18688 [7:46:58<13:37:34, 4.07s/it] 36%|███▌ | 6646/18688 [7:47:03<14:12:02, 4.25s/it] 36%|███▌ | 6647/18688 [7:47:07<14:02:09, 4.20s/it] 36%|███▌ | 6648/18688 [7:47:10<13:20:50, 3.99s/it] 36%|███▌ | 6649/18688 [7:47:15<14:09:25, 4.23s/it] 36%|███▌ | 6650/18688 [7:47:19<13:43:07, 4.10s/it] {'loss': '0.9865', 'grad_norm': '1.389', 'learning_rate': '7.944e-05', 'epoch': '0.3559', 'num_input_tokens_seen': 22305800, 'train_runtime': '2.804e+04', 'train_tokens_per_second': '795.5'}
36%|███▌ | 6650/18688 [7:47:19<13:43:07, 4.10s/it] 36%|███▌ | 6651/18688 [7:47:23<13:26:10, 4.02s/it] 36%|███▌ | 6652/18688 [7:47:27<13:42:47, 4.10s/it] 36%|███▌ | 6653/18688 [7:47:31<13:29:53, 4.04s/it] 36%|███▌ | 6654/18688 [7:47:34<13:05:41, 3.92s/it] 36%|███▌ | 6655/18688 [7:47:39<13:58:02, 4.18s/it] 36%|███▌ | 6656/18688 [7:47:43<13:56:28, 4.17s/it] 36%|███▌ | 6657/18688 [7:47:48<14:06:07, 4.22s/it] 36%|███▌ | 6658/18688 [7:47:51<13:28:47, 4.03s/it] 36%|███▌ | 6659/18688 [7:47:55<13:37:30, 4.08s/it] 36%|███▌ | 6660/18688 [7:47:59<13:30:25, 4.04s/it] {'loss': '1.019', 'grad_norm': '1.684', 'learning_rate': '7.937e-05', 'epoch': '0.3564', 'num_input_tokens_seen': 22338624, 'train_runtime': '2.808e+04', 'train_tokens_per_second': '795.5'}
36%|███▌ | 6660/18688 [7:47:59<13:30:25, 4.04s/it] 36%|███▌ | 6661/18688 [7:48:04<14:05:12, 4.22s/it] 36%|███▌ | 6662/18688 [7:48:08<13:48:47, 4.13s/it] 36%|███▌ | 6663/18688 [7:48:13<14:33:03, 4.36s/it] 36%|███▌ | 6664/18688 [7:48:17<14:06:08, 4.22s/it] 36%|███▌ | 6665/18688 [7:48:21<14:08:31, 4.23s/it] 36%|███▌ | 6666/18688 [7:48:25<14:19:56, 4.29s/it] 36%|███▌ | 6667/18688 [7:48:30<14:10:05, 4.24s/it] 36%|███▌ | 6668/18688 [7:48:34<14:11:30, 4.25s/it] 36%|███▌ | 6669/18688 [7:48:38<14:33:20, 4.36s/it] 36%|███▌ | 6670/18688 [7:48:42<13:51:51, 4.15s/it] {'loss': '0.9886', 'grad_norm': '1.487', 'learning_rate': '7.93e-05', 'epoch': '0.3569', 'num_input_tokens_seen': 22374912, 'train_runtime': '2.812e+04', 'train_tokens_per_second': '795.6'}
36%|███▌ | 6670/18688 [7:48:42<13:51:51, 4.15s/it] 36%|███▌ | 6671/18688 [7:48:46<14:05:27, 4.22s/it] 36%|███▌ | 6672/18688 [7:48:51<14:18:16, 4.29s/it] 36%|███▌ | 6673/18688 [7:48:55<14:10:59, 4.25s/it] 36%|███▌ | 6674/18688 [7:48:59<13:52:13, 4.16s/it] 36%|███▌ | 6675/18688 [7:49:03<13:18:33, 3.99s/it] 36%|███▌ | 6676/18688 [7:49:06<12:45:31, 3.82s/it] 36%|███▌ | 6677/18688 [7:49:11<13:39:21, 4.09s/it] 36%|███▌ | 6678/18688 [7:49:15<13:24:31, 4.02s/it] 36%|███▌ | 6679/18688 [7:49:20<14:55:03, 4.47s/it] 36%|███▌ | 6680/18688 [7:49:25<15:20:42, 4.60s/it] {'loss': '0.9785', 'grad_norm': '1.055', 'learning_rate': '7.922e-05', 'epoch': '0.3575', 'num_input_tokens_seen': 22411704, 'train_runtime': '2.817e+04', 'train_tokens_per_second': '795.7'}
36%|███▌ | 6680/18688 [7:49:25<15:20:42, 4.60s/it] 36%|███▌ | 6681/18688 [7:49:30<15:36:22, 4.68s/it] 36%|███▌ | 6682/18688 [7:49:34<14:42:32, 4.41s/it] 36%|███▌ | 6683/18688 [7:49:38<14:25:02, 4.32s/it] 36%|███▌ | 6684/18688 [7:49:42<14:06:08, 4.23s/it] 36%|███▌ | 6685/18688 [7:49:46<13:36:43, 4.08s/it] 36%|███▌ | 6686/18688 [7:49:50<13:35:22, 4.08s/it] 36%|███▌ | 6687/18688 [7:49:53<13:08:18, 3.94s/it] 36%|███▌ | 6688/18688 [7:49:58<14:08:19, 4.24s/it] 36%|███▌ | 6689/18688 [7:50:03<14:22:48, 4.31s/it] 36%|███▌ | 6690/18688 [7:50:07<14:38:06, 4.39s/it] {'loss': '0.9464', 'grad_norm': '1.251', 'learning_rate': '7.915e-05', 'epoch': '0.358', 'num_input_tokens_seen': 22447064, 'train_runtime': '2.821e+04', 'train_tokens_per_second': '795.8'}
36%|███▌ | 6690/18688 [7:50:07<14:38:06, 4.39s/it] 36%|███▌ | 6691/18688 [7:50:11<14:03:28, 4.22s/it] 36%|███▌ | 6692/18688 [7:50:15<13:35:39, 4.08s/it] 36%|███▌ | 6693/18688 [7:50:20<14:30:37, 4.35s/it] 36%|███▌ | 6694/18688 [7:50:24<14:27:06, 4.34s/it] 36%|███▌ | 6695/18688 [7:50:28<14:01:44, 4.21s/it] 36%|███▌ | 6696/18688 [7:50:32<13:50:37, 4.16s/it] 36%|███▌ | 6697/18688 [7:50:36<13:16:32, 3.99s/it] 36%|███▌ | 6698/18688 [7:50:40<13:43:03, 4.12s/it] 36%|███▌ | 6699/18688 [7:50:45<14:53:26, 4.47s/it] 36%|███▌ | 6700/18688 [7:50:49<14:16:59, 4.29s/it] {'loss': '0.9332', 'grad_norm': '1.223', 'learning_rate': '7.907e-05', 'epoch': '0.3585', 'num_input_tokens_seen': 22482968, 'train_runtime': '2.825e+04', 'train_tokens_per_second': '795.9'}
36%|███▌ | 6700/18688 [7:50:49<14:16:59, 4.29s/it] 36%|███▌ | 6701/18688 [7:50:54<14:18:32, 4.30s/it] 36%|███▌ | 6702/18688 [7:50:58<14:31:22, 4.36s/it] 36%|███▌ | 6703/18688 [7:51:03<14:39:06, 4.40s/it] 36%|███▌ | 6704/18688 [7:51:06<13:47:45, 4.14s/it] 36%|███▌ | 6705/18688 [7:51:10<13:42:59, 4.12s/it] 36%|███▌ | 6706/18688 [7:51:14<13:37:04, 4.09s/it] 36%|███▌ | 6707/18688 [7:51:18<13:17:25, 3.99s/it] 36%|███▌ | 6708/18688 [7:51:22<13:10:27, 3.96s/it] 36%|███▌ | 6709/18688 [7:51:25<12:49:28, 3.85s/it] 36%|███▌ | 6710/18688 [7:51:29<12:34:09, 3.78s/it] {'loss': '0.9294', 'grad_norm': '1.55', 'learning_rate': '7.9e-05', 'epoch': '0.3591', 'num_input_tokens_seen': 22513312, 'train_runtime': '2.829e+04', 'train_tokens_per_second': '795.8'}
36%|███▌ | 6710/18688 [7:51:29<12:34:09, 3.78s/it] 36%|███▌ | 6711/18688 [7:51:33<12:21:01, 3.71s/it] 36%|███▌ | 6712/18688 [7:51:37<12:59:28, 3.91s/it] 36%|███▌ | 6713/18688 [7:51:40<12:33:55, 3.78s/it] 36%|███▌ | 6714/18688 [7:51:44<12:25:50, 3.74s/it] 36%|███▌ | 6715/18688 [7:51:48<12:38:52, 3.80s/it] 36%|███▌ | 6716/18688 [7:51:52<13:05:41, 3.94s/it] 36%|███▌ | 6717/18688 [7:51:56<12:43:19, 3.83s/it] 36%|███▌ | 6718/18688 [7:52:00<13:22:12, 4.02s/it] 36%|███▌ | 6719/18688 [7:52:04<13:22:11, 4.02s/it] 36%|███▌ | 6720/18688 [7:52:08<13:16:16, 3.99s/it] {'loss': '0.9042', 'grad_norm': '1.077', 'learning_rate': '7.892e-05', 'epoch': '0.3596', 'num_input_tokens_seen': 22544488, 'train_runtime': '2.833e+04', 'train_tokens_per_second': '795.8'}
36%|███▌ | 6720/18688 [7:52:08<13:16:16, 3.99s/it] 36%|███▌ | 6721/18688 [7:52:12<12:54:04, 3.88s/it] 36%|███▌ | 6722/18688 [7:52:16<13:04:01, 3.93s/it] 36%|███▌ | 6723/18688 [7:52:20<12:49:19, 3.86s/it] 36%|███▌ | 6724/18688 [7:52:23<12:30:49, 3.77s/it] 36%|███▌ | 6725/18688 [7:52:28<13:05:23, 3.94s/it] 36%|███▌ | 6726/18688 [7:52:33<14:17:55, 4.30s/it] 36%|███▌ | 6727/18688 [7:52:36<13:42:31, 4.13s/it] 36%|███▌ | 6728/18688 [7:52:41<14:29:40, 4.36s/it] 36%|███▌ | 6729/18688 [7:52:46<14:50:11, 4.47s/it] 36%|███▌ | 6730/18688 [7:52:50<14:13:16, 4.28s/it] {'loss': '0.9179', 'grad_norm': '1.104', 'learning_rate': '7.885e-05', 'epoch': '0.3601', 'num_input_tokens_seen': 22580160, 'train_runtime': '2.837e+04', 'train_tokens_per_second': '795.9'}
36%|███▌ | 6730/18688 [7:52:50<14:13:16, 4.28s/it] 36%|███▌ | 6731/18688 [7:52:53<13:31:36, 4.07s/it] 36%|███▌ | 6732/18688 [7:52:57<13:06:08, 3.95s/it] 36%|███▌ | 6733/18688 [7:53:01<13:06:46, 3.95s/it] 36%|███▌ | 6734/18688 [7:53:05<13:27:26, 4.05s/it] 36%|███▌ | 6735/18688 [7:53:09<12:58:11, 3.91s/it] 36%|███▌ | 6736/18688 [7:53:13<12:44:13, 3.84s/it] 36%|███▌ | 6737/18688 [7:53:17<12:49:33, 3.86s/it] 36%|███▌ | 6738/18688 [7:53:20<12:34:47, 3.79s/it] 36%|███▌ | 6739/18688 [7:53:24<12:47:48, 3.86s/it] 36%|███▌ | 6740/18688 [7:53:28<12:54:16, 3.89s/it] {'loss': '0.8875', 'grad_norm': '1.185', 'learning_rate': '7.878e-05', 'epoch': '0.3607', 'num_input_tokens_seen': 22609760, 'train_runtime': '2.841e+04', 'train_tokens_per_second': '795.9'}
36%|███▌ | 6740/18688 [7:53:28<12:54:16, 3.89s/it] 36%|███▌ | 6741/18688 [7:53:32<12:48:06, 3.86s/it] 36%|███▌ | 6742/18688 [7:53:36<13:24:29, 4.04s/it] 36%|███▌ | 6743/18688 [7:53:40<13:18:07, 4.01s/it] 36%|███▌ | 6744/18688 [7:53:44<13:11:18, 3.98s/it] 36%|███▌ | 6745/18688 [7:53:48<12:53:42, 3.89s/it] 36%|███▌ | 6746/18688 [7:53:52<12:48:28, 3.86s/it] 36%|███▌ | 6747/18688 [7:53:55<12:45:48, 3.85s/it] 36%|███▌ | 6748/18688 [7:54:00<13:18:29, 4.01s/it] 36%|███▌ | 6749/18688 [7:54:03<12:46:20, 3.85s/it] 36%|███▌ | 6750/18688 [7:54:07<12:47:10, 3.86s/it] {'loss': '0.99', 'grad_norm': '2.12', 'learning_rate': '7.87e-05', 'epoch': '0.3612', 'num_input_tokens_seen': 22640632, 'train_runtime': '2.845e+04', 'train_tokens_per_second': '795.9'}
36%|███▌ | 6750/18688 [7:54:07<12:47:10, 3.86s/it] 36%|███▌ | 6751/18688 [7:54:11<13:08:08, 3.96s/it] 36%|███▌ | 6752/18688 [7:54:15<12:53:10, 3.89s/it] 36%|███▌ | 6753/18688 [7:54:19<13:09:09, 3.97s/it] 36%|███▌ | 6754/18688 [7:54:23<12:57:08, 3.91s/it] 36%|███▌ | 6755/18688 [7:54:28<13:31:18, 4.08s/it] 36%|███▌ | 6756/18688 [7:54:32<13:35:04, 4.10s/it] 36%|███▌ | 6757/18688 [7:54:36<14:02:54, 4.24s/it] 36%|███▌ | 6758/18688 [7:54:40<13:29:40, 4.07s/it] 36%|███▌ | 6759/18688 [7:54:44<13:48:36, 4.17s/it] 36%|███▌ | 6760/18688 [7:54:49<13:50:02, 4.18s/it] {'loss': '1.047', 'grad_norm': '1.23', 'learning_rate': '7.863e-05', 'epoch': '0.3617', 'num_input_tokens_seen': 22674000, 'train_runtime': '2.849e+04', 'train_tokens_per_second': '795.9'}
36%|███▌ | 6760/18688 [7:54:49<13:50:02, 4.18s/it] 36%|███▌ | 6761/18688 [7:54:53<13:43:33, 4.14s/it] 36%|███▌ | 6762/18688 [7:54:58<14:33:34, 4.39s/it] 36%|███▌ | 6763/18688 [7:55:01<13:46:42, 4.16s/it] 36%|███▌ | 6764/18688 [7:55:06<14:22:52, 4.34s/it] 36%|███▌ | 6765/18688 [7:55:10<13:44:38, 4.15s/it] 36%|███▌ | 6766/18688 [7:55:13<13:10:30, 3.98s/it] 36%|███▌ | 6767/18688 [7:55:18<13:28:13, 4.07s/it] 36%|███▌ | 6768/18688 [7:55:22<13:25:16, 4.05s/it] 36%|███▌ | 6769/18688 [7:55:25<13:03:51, 3.95s/it] 36%|███▌ | 6770/18688 [7:55:31<14:30:01, 4.38s/it] {'loss': '0.9527', 'grad_norm': '1.327', 'learning_rate': '7.855e-05', 'epoch': '0.3623', 'num_input_tokens_seen': 22710184, 'train_runtime': '2.853e+04', 'train_tokens_per_second': '796'}
36%|███▌ | 6770/18688 [7:55:31<14:30:01, 4.38s/it] 36%|███▌ | 6771/18688 [7:55:34<13:57:51, 4.22s/it] 36%|███▌ | 6772/18688 [7:55:39<14:14:06, 4.30s/it] 36%|███▌ | 6773/18688 [7:55:42<13:24:06, 4.05s/it] 36%|███▌ | 6774/18688 [7:55:46<13:01:29, 3.94s/it] 36%|███▋ | 6775/18688 [7:55:50<12:59:03, 3.92s/it] 36%|███▋ | 6776/18688 [7:55:54<13:09:50, 3.98s/it] 36%|███▋ | 6777/18688 [7:55:58<13:26:23, 4.06s/it] 36%|███▋ | 6778/18688 [7:56:03<13:59:22, 4.23s/it] 36%|███▋ | 6779/18688 [7:56:09<15:43:00, 4.75s/it] 36%|███▋ | 6780/18688 [7:56:13<14:38:02, 4.42s/it] {'loss': '0.9686', 'grad_norm': '1.442', 'learning_rate': '7.848e-05', 'epoch': '0.3628', 'num_input_tokens_seen': 22745592, 'train_runtime': '2.857e+04', 'train_tokens_per_second': '796'}
36%|███▋ | 6780/18688 [7:56:13<14:38:02, 4.42s/it] 36%|███▋ | 6781/18688 [7:56:17<14:41:43, 4.44s/it] 36%|███▋ | 6782/18688 [7:56:22<14:46:14, 4.47s/it] 36%|███▋ | 6783/18688 [7:56:26<14:19:30, 4.33s/it] 36%|███▋ | 6784/18688 [7:56:29<13:37:50, 4.12s/it] 36%|███▋ | 6785/18688 [7:56:33<13:23:09, 4.05s/it] 36%|███▋ | 6786/18688 [7:56:37<13:07:40, 3.97s/it] 36%|███▋ | 6787/18688 [7:56:41<13:18:34, 4.03s/it] 36%|███▋ | 6788/18688 [7:56:45<12:55:58, 3.91s/it] 36%|███▋ | 6789/18688 [7:56:49<13:15:14, 4.01s/it] 36%|███▋ | 6790/18688 [7:56:53<13:17:03, 4.02s/it] {'loss': '0.9539', 'grad_norm': '1.298', 'learning_rate': '7.84e-05', 'epoch': '0.3633', 'num_input_tokens_seen': 22778088, 'train_runtime': '2.861e+04', 'train_tokens_per_second': '796.1'}
36%|███▋ | 6790/18688 [7:56:53<13:17:03, 4.02s/it] 36%|███▋ | 6791/18688 [7:56:57<12:48:43, 3.88s/it] 36%|███▋ | 6792/18688 [7:57:01<13:43:31, 4.15s/it] 36%|███▋ | 6793/18688 [7:57:05<13:42:17, 4.15s/it] 36%|███▋ | 6794/18688 [7:57:10<13:48:38, 4.18s/it] 36%|███▋ | 6795/18688 [7:57:14<13:31:24, 4.09s/it] 36%|███▋ | 6796/18688 [7:57:17<13:02:26, 3.95s/it] 36%|███▋ | 6797/18688 [7:57:22<13:57:06, 4.22s/it] 36%|███▋ | 6798/18688 [7:57:26<13:21:37, 4.05s/it] 36%|███▋ | 6799/18688 [7:57:30<13:52:14, 4.20s/it] 36%|███▋ | 6800/18688 [7:57:35<14:00:16, 4.24s/it] {'loss': '0.97', 'grad_norm': '1.159', 'learning_rate': '7.833e-05', 'epoch': '0.3639', 'num_input_tokens_seen': 22812664, 'train_runtime': '2.866e+04', 'train_tokens_per_second': '796.1'}
36%|███▋ | 6800/18688 [7:57:35<14:00:16, 4.24s/it] 36%|███▋ | 6801/18688 [7:57:39<14:10:56, 4.30s/it] 36%|███▋ | 6802/18688 [7:57:43<13:31:14, 4.10s/it] 36%|███▋ | 6803/18688 [7:57:47<13:42:34, 4.15s/it] 36%|███▋ | 6804/18688 [7:57:51<13:09:24, 3.99s/it] 36%|███▋ | 6805/18688 [7:57:54<13:00:54, 3.94s/it] 36%|███▋ | 6806/18688 [7:57:58<12:46:31, 3.87s/it] 36%|███▋ | 6807/18688 [7:58:02<13:07:31, 3.98s/it] 36%|███▋ | 6808/18688 [7:58:06<12:42:55, 3.85s/it] 36%|███▋ | 6809/18688 [7:58:09<12:26:05, 3.77s/it] 36%|███▋ | 6810/18688 [7:58:13<12:08:44, 3.68s/it] {'loss': '0.8932', 'grad_norm': '1.339', 'learning_rate': '7.825e-05', 'epoch': '0.3644', 'num_input_tokens_seen': 22841872, 'train_runtime': '2.869e+04', 'train_tokens_per_second': '796.1'}
36%|███▋ | 6810/18688 [7:58:13<12:08:44, 3.68s/it] 36%|███▋ | 6811/18688 [7:58:17<12:51:01, 3.90s/it] 36%|███▋ | 6812/18688 [7:58:21<12:37:42, 3.83s/it] 36%|███▋ | 6813/18688 [7:58:26<14:07:28, 4.28s/it] 36%|███▋ | 6814/18688 [7:58:31<14:52:44, 4.51s/it] 36%|███▋ | 6815/18688 [7:58:35<14:01:27, 4.25s/it] 36%|███▋ | 6816/18688 [7:58:39<14:00:01, 4.25s/it] 36%|███▋ | 6817/18688 [7:58:44<14:06:56, 4.28s/it] 36%|███▋ | 6818/18688 [7:58:48<14:02:24, 4.26s/it] 36%|███▋ | 6819/18688 [7:58:52<14:18:49, 4.34s/it] 36%|███▋ | 6820/18688 [7:58:56<13:45:53, 4.18s/it] {'loss': '0.9287', 'grad_norm': '1.221', 'learning_rate': '7.817e-05', 'epoch': '0.3649', 'num_input_tokens_seen': 22878008, 'train_runtime': '2.874e+04', 'train_tokens_per_second': '796.1'}
36%|███▋ | 6820/18688 [7:58:56<13:45:53, 4.18s/it] 36%|███▋ | 6821/18688 [7:59:00<13:23:46, 4.06s/it] 37%|███▋ | 6822/18688 [7:59:04<13:34:35, 4.12s/it] 37%|███▋ | 6823/18688 [7:59:08<13:27:05, 4.08s/it] 37%|███▋ | 6824/18688 [7:59:12<13:21:36, 4.05s/it] 37%|███▋ | 6825/18688 [7:59:17<13:41:52, 4.16s/it] 37%|███▋ | 6826/18688 [7:59:20<13:06:58, 3.98s/it] 37%|███▋ | 6827/18688 [7:59:24<13:22:38, 4.06s/it] 37%|███▋ | 6828/18688 [7:59:29<13:28:39, 4.09s/it] 37%|███▋ | 6829/18688 [7:59:32<13:00:04, 3.95s/it] 37%|███▋ | 6830/18688 [7:59:37<13:32:43, 4.11s/it] {'loss': '0.9216', 'grad_norm': '1.039', 'learning_rate': '7.81e-05', 'epoch': '0.3655', 'num_input_tokens_seen': 22910608, 'train_runtime': '2.878e+04', 'train_tokens_per_second': '796.1'}
37%|███▋ | 6830/18688 [7:59:37<13:32:43, 4.11s/it] 37%|███▋ | 6831/18688 [7:59:41<13:40:51, 4.15s/it] 37%|███▋ | 6832/18688 [7:59:45<13:12:58, 4.01s/it] 37%|███▋ | 6833/18688 [7:59:49<13:39:45, 4.15s/it] 37%|███▋ | 6834/18688 [7:59:54<13:56:27, 4.23s/it] 37%|███▋ | 6835/18688 [7:59:58<13:46:46, 4.19s/it] 37%|███▋ | 6836/18688 [8:00:02<13:56:09, 4.23s/it] 37%|███▋ | 6837/18688 [8:00:06<13:29:05, 4.10s/it] 37%|███▋ | 6838/18688 [8:00:11<14:35:38, 4.43s/it] 37%|███▋ | 6839/18688 [8:00:16<14:48:16, 4.50s/it] 37%|███▋ | 6840/18688 [8:00:20<14:38:12, 4.45s/it] {'loss': '0.9319', 'grad_norm': '1.366', 'learning_rate': '7.802e-05', 'epoch': '0.366', 'num_input_tokens_seen': 22947848, 'train_runtime': '2.882e+04', 'train_tokens_per_second': '796.2'}
37%|███▋ | 6840/18688 [8:00:20<14:38:12, 4.45s/it] 37%|███▋ | 6841/18688 [8:00:24<13:58:05, 4.24s/it] 37%|███▋ | 6842/18688 [8:00:28<14:10:13, 4.31s/it] 37%|███▋ | 6843/18688 [8:00:33<14:53:31, 4.53s/it] 37%|███▋ | 6844/18688 [8:00:37<14:21:39, 4.37s/it] 37%|███▋ | 6845/18688 [8:00:41<13:51:51, 4.21s/it] 37%|███▋ | 6846/18688 [8:00:45<13:26:53, 4.09s/it] 37%|███▋ | 6847/18688 [8:00:48<13:01:28, 3.96s/it] 37%|███▋ | 6848/18688 [8:00:52<12:49:00, 3.90s/it] 37%|███▋ | 6849/18688 [8:00:56<12:56:09, 3.93s/it] 37%|███▋ | 6850/18688 [8:01:00<13:13:14, 4.02s/it] {'loss': '0.8914', 'grad_norm': '1.376', 'learning_rate': '7.795e-05', 'epoch': '0.3666', 'num_input_tokens_seen': 22979960, 'train_runtime': '2.886e+04', 'train_tokens_per_second': '796.2'}
37%|███▋ | 6850/18688 [8:01:00<13:13:14, 4.02s/it] 37%|███▋ | 6851/18688 [8:01:05<13:19:51, 4.05s/it] 37%|███▋ | 6852/18688 [8:01:08<12:58:53, 3.95s/it] 37%|███▋ | 6853/18688 [8:01:12<13:07:29, 3.99s/it] 37%|███▋ | 6854/18688 [8:01:16<13:01:51, 3.96s/it] 37%|███▋ | 6855/18688 [8:01:20<12:50:03, 3.90s/it] 37%|███▋ | 6856/18688 [8:01:24<13:14:17, 4.03s/it] 37%|███▋ | 6857/18688 [8:01:28<12:55:14, 3.93s/it] 37%|███▋ | 6858/18688 [8:01:32<12:25:34, 3.78s/it] 37%|███▋ | 6859/18688 [8:01:35<12:16:13, 3.73s/it] 37%|███▋ | 6860/18688 [8:01:39<12:38:08, 3.85s/it] {'loss': '0.9858', 'grad_norm': '1.428', 'learning_rate': '7.787e-05', 'epoch': '0.3671', 'num_input_tokens_seen': 23010608, 'train_runtime': '2.89e+04', 'train_tokens_per_second': '796.2'}
37%|███▋ | 6860/18688 [8:01:39<12:38:08, 3.85s/it] 37%|███▋ | 6861/18688 [8:01:44<13:06:51, 3.99s/it] 37%|███▋ | 6862/18688 [8:01:47<12:43:13, 3.87s/it] 37%|███▋ | 6863/18688 [8:01:52<13:18:35, 4.05s/it] 37%|███▋ | 6864/18688 [8:01:56<13:18:30, 4.05s/it] 37%|███▋ | 6865/18688 [8:02:00<13:54:37, 4.24s/it] 37%|███▋ | 6866/18688 [8:02:04<13:22:56, 4.08s/it] 37%|███▋ | 6867/18688 [8:02:09<13:55:25, 4.24s/it] 37%|███▋ | 6868/18688 [8:02:12<13:27:31, 4.10s/it] 37%|███▋ | 6869/18688 [8:02:16<12:53:41, 3.93s/it] 37%|███▋ | 6870/18688 [8:02:20<13:15:49, 4.04s/it] {'loss': '0.9541', 'grad_norm': '1.262', 'learning_rate': '7.78e-05', 'epoch': '0.3676', 'num_input_tokens_seen': 23044024, 'train_runtime': '2.894e+04', 'train_tokens_per_second': '796.2'}
37%|███▋ | 6870/18688 [8:02:20<13:15:49, 4.04s/it] 37%|███▋ | 6871/18688 [8:02:25<13:48:35, 4.21s/it] 37%|███▋ | 6872/18688 [8:02:29<13:15:44, 4.04s/it] 37%|███▋ | 6873/18688 [8:02:32<12:57:01, 3.95s/it] 37%|███▋ | 6874/18688 [8:02:37<13:46:51, 4.20s/it] 37%|███▋ | 6875/18688 [8:02:41<13:10:18, 4.01s/it] 37%|███▋ | 6876/18688 [8:02:45<13:31:30, 4.12s/it] 37%|███▋ | 6877/18688 [8:02:49<13:25:21, 4.09s/it] 37%|███▋ | 6878/18688 [8:02:53<13:38:32, 4.16s/it] 37%|███▋ | 6879/18688 [8:02:57<13:00:33, 3.97s/it] 37%|███▋ | 6880/18688 [8:03:01<13:15:52, 4.04s/it] {'loss': '0.9758', 'grad_norm': '1.554', 'learning_rate': '7.772e-05', 'epoch': '0.3682', 'num_input_tokens_seen': 23076920, 'train_runtime': '2.898e+04', 'train_tokens_per_second': '796.3'}
37%|███▋ | 6880/18688 [8:03:01<13:15:52, 4.04s/it] 37%|███▋ | 6881/18688 [8:03:05<12:59:37, 3.96s/it] 37%|███▋ | 6882/18688 [8:03:09<12:45:47, 3.89s/it] 37%|███▋ | 6883/18688 [8:03:12<12:32:58, 3.83s/it] 37%|███▋ | 6884/18688 [8:03:16<12:38:27, 3.86s/it] 37%|███▋ | 6885/18688 [8:03:20<12:32:26, 3.83s/it] 37%|███▋ | 6886/18688 [8:03:23<12:16:57, 3.75s/it] 37%|███▋ | 6887/18688 [8:03:27<12:22:38, 3.78s/it] 37%|███▋ | 6888/18688 [8:03:32<12:47:37, 3.90s/it] 37%|███▋ | 6889/18688 [8:03:36<12:52:08, 3.93s/it] 37%|███▋ | 6890/18688 [8:03:39<12:42:13, 3.88s/it] {'loss': '0.9348', 'grad_norm': '1.106', 'learning_rate': '7.764e-05', 'epoch': '0.3687', 'num_input_tokens_seen': 23106328, 'train_runtime': '2.902e+04', 'train_tokens_per_second': '796.2'}
37%|███▋ | 6890/18688 [8:03:39<12:42:13, 3.88s/it] 37%|███▋ | 6891/18688 [8:03:45<14:09:55, 4.32s/it] 37%|███▋ | 6892/18688 [8:03:49<13:58:35, 4.27s/it] 37%|███▋ | 6893/18688 [8:03:53<13:50:44, 4.23s/it] 37%|███▋ | 6894/18688 [8:03:56<13:04:54, 3.99s/it] 37%|███▋ | 6895/18688 [8:04:00<12:40:23, 3.87s/it] 37%|███▋ | 6896/18688 [8:04:04<13:16:21, 4.05s/it] 37%|███▋ | 6897/18688 [8:04:08<13:12:19, 4.03s/it] 37%|███▋ | 6898/18688 [8:04:12<12:57:08, 3.95s/it] 37%|███▋ | 6899/18688 [8:04:16<13:14:51, 4.05s/it] 37%|███▋ | 6900/18688 [8:04:20<12:51:29, 3.93s/it] {'loss': '0.8966', 'grad_norm': '1.247', 'learning_rate': '7.757e-05', 'epoch': '0.3692', 'num_input_tokens_seen': 23139560, 'train_runtime': '2.906e+04', 'train_tokens_per_second': '796.3'}
37%|███▋ | 6900/18688 [8:04:20<12:51:29, 3.93s/it] 37%|███▋ | 6901/18688 [8:04:24<13:04:10, 3.99s/it] 37%|███▋ | 6902/18688 [8:04:28<12:43:24, 3.89s/it] 37%|███▋ | 6903/18688 [8:04:32<12:55:47, 3.95s/it] 37%|███▋ | 6904/18688 [8:04:36<13:01:43, 3.98s/it] 37%|███▋ | 6905/18688 [8:04:41<13:35:09, 4.15s/it] 37%|███▋ | 6906/18688 [8:04:45<13:32:29, 4.14s/it] 37%|███▋ | 6907/18688 [8:04:49<14:09:16, 4.33s/it] 37%|███▋ | 6908/18688 [8:04:54<14:17:15, 4.37s/it] 37%|███▋ | 6909/18688 [8:04:58<13:52:58, 4.24s/it] 37%|███▋ | 6910/18688 [8:05:02<14:02:58, 4.29s/it] {'loss': '0.9697', 'grad_norm': '1.217', 'learning_rate': '7.749e-05', 'epoch': '0.3698', 'num_input_tokens_seen': 23175688, 'train_runtime': '2.91e+04', 'train_tokens_per_second': '796.3'}
37%|███▋ | 6910/18688 [8:05:02<14:02:58, 4.29s/it] 37%|███▋ | 6911/18688 [8:05:07<14:25:14, 4.41s/it] 37%|███▋ | 6912/18688 [8:05:11<14:07:59, 4.32s/it] 37%|███▋ | 6913/18688 [8:05:15<13:31:30, 4.14s/it] 37%|███▋ | 6914/18688 [8:05:19<13:48:13, 4.22s/it] 37%|███▋ | 6915/18688 [8:05:23<13:43:03, 4.19s/it] 37%|███▋ | 6916/18688 [8:05:27<13:08:14, 4.02s/it] 37%|███▋ | 6917/18688 [8:05:31<13:11:13, 4.03s/it] 37%|███▋ | 6918/18688 [8:05:36<13:39:48, 4.18s/it] 37%|███▋ | 6919/18688 [8:05:39<13:20:15, 4.08s/it] 37%|███▋ | 6920/18688 [8:05:43<13:15:23, 4.06s/it] {'loss': '0.9542', 'grad_norm': '1.359', 'learning_rate': '7.741e-05', 'epoch': '0.3703', 'num_input_tokens_seen': 23209168, 'train_runtime': '2.914e+04', 'train_tokens_per_second': '796.4'}
37%|███▋ | 6920/18688 [8:05:43<13:15:23, 4.06s/it] 37%|███▋ | 6921/18688 [8:05:48<13:21:50, 4.09s/it] 37%|███▋ | 6922/18688 [8:05:51<13:02:05, 3.99s/it] 37%|███▋ | 6923/18688 [8:05:55<13:14:43, 4.05s/it] 37%|███▋ | 6924/18688 [8:06:00<13:41:53, 4.19s/it] 37%|███▋ | 6925/18688 [8:06:04<13:41:00, 4.19s/it] 37%|███▋ | 6926/18688 [8:06:09<14:07:11, 4.32s/it] 37%|███▋ | 6927/18688 [8:06:13<14:02:16, 4.30s/it] 37%|███▋ | 6928/18688 [8:06:17<13:45:11, 4.21s/it] 37%|███▋ | 6929/18688 [8:06:21<13:38:18, 4.18s/it] 37%|███▋ | 6930/18688 [8:06:26<13:52:26, 4.25s/it] {'loss': '0.9051', 'grad_norm': '1.13', 'learning_rate': '7.734e-05', 'epoch': '0.3708', 'num_input_tokens_seen': 23243400, 'train_runtime': '2.919e+04', 'train_tokens_per_second': '796.4'}
37%|███▋ | 6930/18688 [8:06:26<13:52:26, 4.25s/it] 37%|███▋ | 6931/18688 [8:06:29<13:20:36, 4.09s/it] 37%|███▋ | 6932/18688 [8:06:33<13:25:58, 4.11s/it] 37%|███▋ | 6933/18688 [8:06:37<13:20:18, 4.08s/it] 37%|███▋ | 6934/18688 [8:06:41<13:12:03, 4.04s/it] 37%|███▋ | 6935/18688 [8:06:45<12:54:11, 3.95s/it] 37%|███▋ | 6936/18688 [8:06:49<12:45:09, 3.91s/it] 37%|███▋ | 6937/18688 [8:06:53<12:43:51, 3.90s/it] 37%|███▋ | 6938/18688 [8:06:57<13:11:20, 4.04s/it] 37%|███▋ | 6939/18688 [8:07:01<12:50:54, 3.94s/it] 37%|███▋ | 6940/18688 [8:07:05<12:44:53, 3.91s/it] {'loss': '1.007', 'grad_norm': '1.202', 'learning_rate': '7.726e-05', 'epoch': '0.3714', 'num_input_tokens_seen': 23273432, 'train_runtime': '2.923e+04', 'train_tokens_per_second': '796.3'}
37%|███▋ | 6940/18688 [8:07:05<12:44:53, 3.91s/it] 37%|███▋ | 6941/18688 [8:07:09<13:30:58, 4.14s/it] 37%|███▋ | 6942/18688 [8:07:13<13:26:26, 4.12s/it] 37%|███▋ | 6943/18688 [8:07:18<13:31:03, 4.14s/it] 37%|███▋ | 6944/18688 [8:07:23<14:29:12, 4.44s/it] 37%|███▋ | 6945/18688 [8:07:27<14:10:08, 4.34s/it] 37%|███▋ | 6946/18688 [8:07:32<14:54:54, 4.57s/it] 37%|███▋ | 6947/18688 [8:07:36<14:34:29, 4.47s/it] 37%|███▋ | 6948/18688 [8:07:40<14:04:06, 4.31s/it] 37%|███▋ | 6949/18688 [8:07:45<14:19:05, 4.39s/it] 37%|███▋ | 6950/18688 [8:07:50<15:01:38, 4.61s/it] {'loss': '0.9545', 'grad_norm': '1.212', 'learning_rate': '7.718e-05', 'epoch': '0.3719', 'num_input_tokens_seen': 23313368, 'train_runtime': '2.927e+04', 'train_tokens_per_second': '796.5'}
37%|███▋ | 6950/18688 [8:07:50<15:01:38, 4.61s/it] 37%|███▋ | 6951/18688 [8:07:53<13:59:13, 4.29s/it] 37%|███▋ | 6952/18688 [8:07:57<13:29:56, 4.14s/it] 37%|███▋ | 6953/18688 [8:08:01<13:34:00, 4.16s/it] 37%|███▋ | 6954/18688 [8:08:07<14:46:02, 4.53s/it] 37%|███▋ | 6955/18688 [8:08:11<14:15:17, 4.37s/it] 37%|███▋ | 6956/18688 [8:08:14<13:23:00, 4.11s/it] 37%|███▋ | 6957/18688 [8:08:19<13:25:36, 4.12s/it] 37%|███▋ | 6958/18688 [8:08:23<14:01:44, 4.31s/it] 37%|███▋ | 6959/18688 [8:08:27<13:37:47, 4.18s/it] 37%|███▋ | 6960/18688 [8:08:31<12:53:33, 3.96s/it] {'loss': '0.9287', 'grad_norm': '1.44', 'learning_rate': '7.711e-05', 'epoch': '0.3724', 'num_input_tokens_seen': 23347136, 'train_runtime': '2.931e+04', 'train_tokens_per_second': '796.5'}
37%|███▋ | 6960/18688 [8:08:31<12:53:33, 3.96s/it] 37%|███▋ | 6961/18688 [8:08:34<12:34:42, 3.86s/it] 37%|███▋ | 6962/18688 [8:08:39<13:24:02, 4.11s/it] 37%|███▋ | 6963/18688 [8:08:43<13:46:53, 4.23s/it] 37%|███▋ | 6964/18688 [8:08:47<13:32:25, 4.16s/it] 37%|███▋ | 6965/18688 [8:08:51<13:09:15, 4.04s/it] 37%|███▋ | 6966/18688 [8:08:55<13:22:43, 4.11s/it] 37%|███▋ | 6967/18688 [8:09:00<14:07:44, 4.34s/it] 37%|███▋ | 6968/18688 [8:09:04<13:22:23, 4.11s/it] 37%|███▋ | 6969/18688 [8:09:08<13:30:35, 4.15s/it] 37%|███▋ | 6970/18688 [8:09:12<13:03:26, 4.01s/it] {'loss': '0.9237', 'grad_norm': '1.63', 'learning_rate': '7.703e-05', 'epoch': '0.373', 'num_input_tokens_seen': 23381240, 'train_runtime': '2.935e+04', 'train_tokens_per_second': '796.6'}
37%|███▋ | 6970/18688 [8:09:12<13:03:26, 4.01s/it] 37%|███▋ | 6971/18688 [8:09:16<13:09:05, 4.04s/it] 37%|███▋ | 6972/18688 [8:09:20<12:41:25, 3.90s/it] 37%|███▋ | 6973/18688 [8:09:24<12:59:09, 3.99s/it] 37%|███▋ | 6974/18688 [8:09:29<13:57:54, 4.29s/it] 37%|███▋ | 6975/18688 [8:09:32<13:12:59, 4.06s/it] 37%|███▋ | 6976/18688 [8:09:37<13:59:17, 4.30s/it] 37%|███▋ | 6977/18688 [8:09:42<14:11:22, 4.36s/it] 37%|███▋ | 6978/18688 [8:09:46<14:19:15, 4.40s/it] 37%|███▋ | 6979/18688 [8:09:50<14:09:22, 4.35s/it] 37%|███▋ | 6980/18688 [8:09:55<14:03:02, 4.32s/it] {'loss': '0.9101', 'grad_norm': '1.593', 'learning_rate': '7.695e-05', 'epoch': '0.3735', 'num_input_tokens_seen': 23417216, 'train_runtime': '2.94e+04', 'train_tokens_per_second': '796.6'}
37%|███▋ | 6980/18688 [8:09:55<14:03:02, 4.32s/it] 37%|███▋ | 6981/18688 [8:09:59<14:16:41, 4.39s/it] 37%|███▋ | 6982/18688 [8:10:03<13:56:46, 4.29s/it] 37%|███▋ | 6983/18688 [8:10:07<13:13:53, 4.07s/it] 37%|███▋ | 6984/18688 [8:10:11<13:23:02, 4.12s/it] 37%|███▋ | 6985/18688 [8:10:15<13:14:41, 4.07s/it] 37%|███▋ | 6986/18688 [8:10:19<13:15:33, 4.08s/it] 37%|███▋ | 6987/18688 [8:10:23<13:04:53, 4.02s/it] 37%|███▋ | 6988/18688 [8:10:27<13:17:58, 4.09s/it] 37%|███▋ | 6989/18688 [8:10:31<12:53:13, 3.97s/it] 37%|███▋ | 6990/18688 [8:10:35<12:51:02, 3.95s/it] {'loss': '0.9407', 'grad_norm': '1.704', 'learning_rate': '7.688e-05', 'epoch': '0.374', 'num_input_tokens_seen': 23448592, 'train_runtime': '2.944e+04', 'train_tokens_per_second': '796.6'}
37%|███▋ | 6990/18688 [8:10:35<12:51:02, 3.95s/it] 37%|███▋ | 6991/18688 [8:10:38<12:32:29, 3.86s/it] 37%|███▋ | 6992/18688 [8:10:43<13:03:38, 4.02s/it] 37%|███▋ | 6993/18688 [8:10:47<13:33:24, 4.17s/it] 37%|███▋ | 6994/18688 [8:10:51<12:55:57, 3.98s/it] 37%|███▋ | 6995/18688 [8:10:55<12:39:54, 3.90s/it] 37%|███▋ | 6996/18688 [8:10:58<12:16:41, 3.78s/it] 37%|███▋ | 6997/18688 [8:11:02<12:46:14, 3.93s/it] 37%|███▋ | 6998/18688 [8:11:06<12:29:35, 3.85s/it] 37%|███▋ | 6999/18688 [8:11:10<12:47:51, 3.94s/it] 37%|███▋ | 7000/18688 [8:11:15<13:20:03, 4.11s/it] {'loss': '0.8742', 'grad_norm': '1.72', 'learning_rate': '7.68e-05', 'epoch': '0.3746', 'num_input_tokens_seen': 23480632, 'train_runtime': '2.948e+04', 'train_tokens_per_second': '796.6'}
37%|███▋ | 7000/18688 [8:11:15<13:20:03, 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<00:38, 25.74it/s]
1%| | 6/1000 [00:00<01:01, 16.29it/s]
1%| | 8/1000 [00:00<01:03, 15.68it/s]
1%| | 10/1000 [00:00<01:08, 14.48it/s]
1%| | 12/1000 [00:00<01:06, 14.94it/s]
1%|▏ | 14/1000 [00:00<01:05, 14.96it/s]
2%|▏ | 16/1000 [00:01<01:05, 15.06it/s]
2%|▏ | 18/1000 [00:01<01:39, 9.83it/s]
2%|▏ | 20/1000 [00:01<01:33, 10.44it/s]
2%|▏ | 22/1000 [00:01<01:52, 8.68it/s]
2%|▏ | 24/1000 [00:02<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, 10.16it/s]
3%|▎ | 34/1000 [00:02<01:25, 11.26it/s]
4%|▎ | 36/1000 [00:03<01:32, 10.44it/s]
4%|▍ | 38/1000 [00:03<01:24, 11.36it/s]
4%|▍ | 40/1000 [00:03<01:27, 10.98it/s]
4%|▍ | 42/1000 [00:03<01:45, 9.05it/s]
4%|▍ | 44/1000 [00:03<01:30, 10.53it/s]
5%|▍ | 46/1000 [00:04<01:22, 11.60it/s]
5%|▍ | 48/1000 [00:04<01:34, 10.08it/s]
5%|▌ | 50/1000 [00:04<01:26, 11.03it/s]
5%|▌ | 52/1000 [00:04<01:20, 11.76it/s]
5%|▌ | 54/1000 [00:04<01:17, 12.25it/s]
6%|▌ | 56/1000 [00:04<01:12, 13.07it/s]
6%|▌ | 58/1000 [00:05<01:52, 8.40it/s]
6%|▌ | 60/1000 [00:05<01:39, 9.47it/s]
6%|▌ | 62/1000 [00:05<01:29, 10.48it/s]
6%|▋ | 64/1000 [00:05<01:20, 11.58it/s]
7%|▋ | 66/1000 [00:05<01:16, 12.13it/s]
7%|▋ | 68/1000 [00:06<01:13, 12.75it/s]
7%|▋ | 70/1000 [00:06<01:09, 13.38it/s]
7%|▋ | 72/1000 [00:06<01:06, 13.86it/s]
7%|▋ | 74/1000 [00:06<01:07, 13.66it/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, 13.53it/s]
8%|▊ | 82/1000 [00:06<01:04, 14.17it/s]
8%|▊ | 84/1000 [00:07<01:02, 14.61it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.74it/s]
9%|▉ | 88/1000 [00:07<01:02, 14.54it/s]
9%|▉ | 90/1000 [00:07<01:02, 14.56it/s]
9%|▉ | 92/1000 [00:07<01:03, 14.23it/s]
9%|▉ | 94/1000 [00:07<01:04, 14.04it/s]
10%|▉ | 96/1000 [00:07<01:02, 14.39it/s]
10%|▉ | 98/1000 [00:08<01:03, 14.31it/s]
10%|█ | 100/1000 [00:08<01:31, 9.82it/s]
10%|█ | 102/1000 [00:08<01:23, 10.79it/s]
10%|█ | 104/1000 [00:09<01:54, 7.83it/s]
11%|█ | 106/1000 [00:09<02:02, 7.33it/s]
11%|█ | 108/1000 [00:09<01:47, 8.33it/s]
11%|█ | 110/1000 [00:09<01:33, 9.53it/s]
11%|█ | 112/1000 [00:09<01:22, 10.73it/s]
11%|█▏ | 114/1000 [00:09<01:16, 11.51it/s]
12%|█▏ | 116/1000 [00:10<01:12, 12.23it/s]
12%|█▏ | 118/1000 [00:10<01:07, 13.08it/s]
12%|█▏ | 120/1000 [00:10<01:09, 12.61it/s]
12%|█▏ | 122/1000 [00:10<01:23, 10.51it/s]
12%|█▏ | 124/1000 [00:10<01:30, 9.63it/s]
13%|█▎ | 126/1000 [00:10<01:22, 10.60it/s]
13%|█▎ | 128/1000 [00:11<01:15, 11.52it/s]
13%|█▎ | 130/1000 [00:11<01:11, 12.21it/s]
13%|█▎ | 132/1000 [00:11<01:05, 13.19it/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, 13.27it/s]
14%|█▍ | 140/1000 [00:12<01:24, 10.13it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.55it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.59it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.61it/s]
15%|█▍ | 148/1000 [00:12<01:13, 11.53it/s]
15%|█▌ | 150/1000 [00:13<01:07, 12.63it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.19it/s]
15%|█▌ | 154/1000 [00:13<01:03, 13.43it/s]
16%|█▌ | 156/1000 [00:13<01:03, 13.27it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.76it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.65it/s]
16%|█▌ | 162/1000 [00:13<01:01, 13.54it/s]
16%|█▋ | 164/1000 [00:14<01:01, 13.65it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.83it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.33it/s]
17%|█▋ | 170/1000 [00:14<01:18, 10.52it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.49it/s]
17%|█▋ | 174/1000 [00:15<01:29, 9.19it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.21it/s]
18%|█▊ | 178/1000 [00:15<01:34, 8.67it/s]
18%|█▊ | 180/1000 [00:15<01:33, 8.79it/s]
18%|█▊ | 182/1000 [00:16<01:35, 8.57it/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<01:24, 9.65it/s]
19%|█▉ | 190/1000 [00:16<01:15, 10.68it/s]
19%|█▉ | 192/1000 [00:16<01:11, 11.34it/s]
19%|█▉ | 194/1000 [00:17<01:16, 10.57it/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 [00:17<01:10, 11.31it/s]
20%|██ | 204/1000 [00:18<01:04, 12.27it/s]
21%|██ | 206/1000 [00:18<01:03, 12.52it/s]
21%|██ | 208/1000 [00:18<01:04, 12.26it/s]
21%|██ | 210/1000 [00:18<00:59, 13.32it/s]
21%|██ | 212/1000 [00:18<00:57, 13.81it/s]
21%|██▏ | 214/1000 [00:18<00:56, 13.97it/s]
22%|██▏ | 216/1000 [00:18<00:56, 13.78it/s]
22%|██▏ | 218/1000 [00:19<00:56, 13.73it/s]
22%|██▏ | 220/1000 [00:19<01:30, 8.63it/s]
22%|██▏ | 222/1000 [00:19<01:26, 9.00it/s]
22%|██▏ | 224/1000 [00:19<01:21, 9.51it/s]
23%|██▎ | 226/1000 [00:20<01:13, 10.52it/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, 12.34it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.09it/s]
24%|██▎ | 236/1000 [00:20<00:56, 13.60it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.24it/s]
24%|██▍ | 240/1000 [00:21<00:56, 13.53it/s]
24%|██▍ | 242/1000 [00:21<00:57, 13.13it/s]
24%|██▍ | 244/1000 [00:21<00:55, 13.57it/s]
25%|██▍ | 246/1000 [00:21<00:54, 13.91it/s]
25%|██▍ | 248/1000 [00:21<01:01, 12.14it/s]
25%|██▌ | 250/1000 [00:21<00:57, 12.95it/s]
25%|██▌ | 252/1000 [00:22<01:02, 11.89it/s]
25%|██▌ | 254/1000 [00:22<00:57, 12.93it/s]
26%|██▌ | 256/1000 [00:22<00:55, 13.47it/s]
26%|██▌ | 258/1000 [00:22<00:54, 13.58it/s]
26%|██▌ | 260/1000 [00:22<00:58, 12.70it/s]
26%|██▌ | 262/1000 [00:22<00:57, 12.90it/s]
26%|██▋ | 264/1000 [00:22<00:54, 13.52it/s]
27%|██▋ | 266/1000 [00:23<00:52, 13.89it/s]
27%|██▋ | 268/1000 [00:23<00:50, 14.47it/s]
27%|██▋ | 270/1000 [00:23<00:55, 13.25it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.64it/s]
27%|██▋ | 274/1000 [00:23<01:02, 11.70it/s]
28%|██▊ | 276/1000 [00:23<00:56, 12.72it/s]
28%|██▊ | 278/1000 [00:23<00:55, 12.98it/s]
28%|██▊ | 280/1000 [00:24<00:53, 13.51it/s]
28%|██▊ | 282/1000 [00:24<00:49, 14.40it/s]
28%|██▊ | 284/1000 [00:24<00:49, 14.39it/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<00:45, 15.46it/s]
29%|██▉ | 292/1000 [00:24<00:45, 15.44it/s]
29%|██▉ | 294/1000 [00:25<00:45, 15.35it/s]
30%|██▉ | 296/1000 [00:25<00:45, 15.38it/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<00:45, 15.18it/s]
30%|███ | 304/1000 [00:25<00:50, 13.79it/s]
31%|███ | 306/1000 [00:25<00:47, 14.48it/s]
31%|███ | 308/1000 [00:25<00:47, 14.46it/s]
31%|███ | 310/1000 [00:26<00:47, 14.62it/s]
31%|███ | 312/1000 [00:26<00:49, 14.01it/s]
31%|███▏ | 314/1000 [00:26<00:46, 14.73it/s]
32%|███▏ | 316/1000 [00:26<00:45, 14.98it/s]
32%|███▏ | 318/1000 [00:26<00:45, 15.14it/s]
32%|███▏ | 320/1000 [00:27<01:14, 9.12it/s]
32%|███▏ | 322/1000 [00:27<01:05, 10.37it/s]
32%|███▏ | 324/1000 [00:27<01:03, 10.70it/s]
33%|███▎ | 326/1000 [00:27<01:03, 10.61it/s]
33%|███▎ | 328/1000 [00:27<01:01, 10.88it/s]
33%|███▎ | 330/1000 [00:27<00:56, 11.93it/s]
33%|███▎ | 332/1000 [00:28<00:54, 12.32it/s]
33%|███▎ | 334/1000 [00:28<00:51, 12.92it/s]
34%|███▎ | 336/1000 [00:28<00:49, 13.37it/s]
34%|███▍ | 338/1000 [00:28<01:06, 9.99it/s]
34%|███▍ | 340/1000 [00:28<00:59, 11.07it/s]
34%|███▍ | 342/1000 [00:28<00:53, 12.34it/s]
34%|███▍ | 344/1000 [00:28<00:50, 13.01it/s]
35%|███▍ | 346/1000 [00:29<00:48, 13.62it/s]
35%|███▍ | 348/1000 [00:29<00:46, 14.05it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.66it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.82it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.89it/s]
36%|███▌ | 356/1000 [00:29<00:42, 15.02it/s]
36%|███▌ | 358/1000 [00:29<00:43, 14.73it/s]
36%|███▌ | 360/1000 [00:30<00:42, 15.10it/s]
36%|███▌ | 362/1000 [00:30<00:47, 13.39it/s]
36%|███▋ | 364/1000 [00:30<00:45, 13.87it/s]
37%|███▋ | 366/1000 [00:30<00:53, 11.95it/s]
37%|███▋ | 368/1000 [00:30<00:51, 12.33it/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<00:55, 11.24it/s]
38%|███▊ | 376/1000 [00:31<00:57, 10.77it/s]
38%|███▊ | 378/1000 [00:31<00:55, 11.21it/s]
38%|███▊ | 380/1000 [00:31<00:53, 11.54it/s]
38%|███▊ | 382/1000 [00:31<00:53, 11.66it/s]
38%|███▊ | 384/1000 [00:32<00:50, 12.25it/s]
39%|███▊ | 386/1000 [00:32<00:47, 12.82it/s]
39%|███▉ | 388/1000 [00:32<00:47, 13.02it/s]
39%|███▉ | 390/1000 [00:32<00:46, 13.06it/s]
39%|███▉ | 392/1000 [00:32<00:45, 13.48it/s]
39%|███▉ | 394/1000 [00:32<00:45, 13.40it/s]
40%|███▉ | 396/1000 [00:32<00:44, 13.61it/s]
40%|███▉ | 398/1000 [00:33<00:48, 12.52it/s]
40%|████ | 400/1000 [00:33<00:45, 13.17it/s]
40%|████ | 402/1000 [00:33<00:43, 13.83it/s]
40%|████ | 404/1000 [00:33<00:41, 14.46it/s]
41%|████ | 406/1000 [00:33<00:39, 14.91it/s]
41%|████ | 408/1000 [00:33<00:47, 12.52it/s]
41%|████ | 410/1000 [00:34<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 [00:35<01:23, 6.97it/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, 7.69it/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, 9.13it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.06it/s]
43%|████▎ | 434/1000 [00:36<01:08, 8.29it/s]
44%|████▎ | 436/1000 [00:37<01:00, 9.34it/s]
44%|████▍ | 438/1000 [00:37<01:02, 9.01it/s]
44%|████▍ | 440/1000 [00:37<00:58, 9.51it/s]
44%|████▍ | 442/1000 [00:37<00:52, 10.57it/s]
44%|████▍ | 444/1000 [00:37<00:48, 11.42it/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, 11.81it/s]
45%|████▌ | 452/1000 [00:38<00:42, 12.82it/s]
45%|████▌ | 454/1000 [00:38<00:44, 12.32it/s]
46%|████▌ | 456/1000 [00:38<00:42, 12.76it/s]
46%|████▌ | 458/1000 [00:38<00:39, 13.78it/s]
46%|████▌ | 460/1000 [00:39<00:38, 13.94it/s]
46%|████▌ | 462/1000 [00:39<00:36, 14.54it/s]
46%|████▋ | 464/1000 [00:39<00:35, 15.05it/s]
47%|████▋ | 466/1000 [00:39<00:35, 14.85it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.94it/s]
47%|████▋ | 470/1000 [00:39<00:52, 10.09it/s]
47%|████▋ | 472/1000 [00:40<00:51, 10.27it/s]
47%|████▋ | 474/1000 [00:40<00:45, 11.55it/s]
48%|████▊ | 476/1000 [00:40<00:42, 12.22it/s]
48%|████▊ | 478/1000 [00:40<00:43, 12.05it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.97it/s]
48%|████▊ | 482/1000 [00:40<00:38, 13.37it/s]
48%|████▊ | 484/1000 [00:40<00:37, 13.89it/s]
49%|████▊ | 486/1000 [00:41<00:37, 13.85it/s]
49%|████▉ | 488/1000 [00:41<00:36, 13.96it/s]
49%|████▉ | 490/1000 [00:41<00:49, 10.33it/s]
49%|████▉ | 492/1000 [00:41<00:44, 11.43it/s]
49%|████▉ | 494/1000 [00:41<00:40, 12.57it/s]
50%|████▉ | 496/1000 [00:41<00:37, 13.46it/s]
50%|████▉ | 498/1000 [00:42<00:35, 13.96it/s]
50%|█████ | 500/1000 [00:42<00:44, 11.21it/s]
50%|█████ | 502/1000 [00:42<00:42, 11.75it/s]
50%|█████ | 504/1000 [00:42<00:40, 12.37it/s]
51%|█████ | 506/1000 [00:42<00:39, 12.61it/s]
51%|█████ | 508/1000 [00:42<00:36, 13.55it/s]
51%|█████ | 510/1000 [00:43<00:47, 10.30it/s]
51%|█████ | 512/1000 [00:43<00:43, 11.10it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 11.95it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.53it/s]
52%|█████▏ | 518/1000 [00:43<00:36, 13.07it/s]
52%|█████▏ | 520/1000 [00:43<00:35, 13.45it/s]
52%|█████▏ | 522/1000 [00:44<00:33, 14.19it/s]
52%|█████▏ | 524/1000 [00:44<00:33, 14.22it/s]
53%|█████▎ | 526/1000 [00:44<00:32, 14.39it/s]
53%|█████▎ | 528/1000 [00:44<00:39, 12.00it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.84it/s]
53%|█████▎ | 532/1000 [00:44<00:33, 13.78it/s]
53%|█████▎ | 534/1000 [00:44<00:32, 14.15it/s]
54%|█████▎ | 536/1000 [00:45<00:32, 14.28it/s]
54%|█████▍ | 538/1000 [00:45<00:32, 14.03it/s]
54%|█████▍ | 540/1000 [00:45<00:31, 14.43it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.70it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.62it/s]
55%|█████▍ | 546/1000 [00:45<00:31, 14.26it/s]
55%|█████▍ | 548/1000 [00:45<00:32, 13.92it/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<00:31, 13.95it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.12it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.44it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 14.95it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 14.91it/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, 12.24it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.33it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.87it/s]
57%|█████▋ | 574/1000 [00:47<00:31, 13.70it/s]
58%|█████▊ | 576/1000 [00:47<00:31, 13.63it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.64it/s]
58%|█████▊ | 580/1000 [00:48<00:32, 12.85it/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<00:30, 13.74it/s]
59%|█████▉ | 588/1000 [00:48<00:30, 13.61it/s]
59%|█████▉ | 590/1000 [00:49<00:30, 13.51it/s]
59%|█████▉ | 592/1000 [00:49<00:30, 13.28it/s]
59%|█████▉ | 594/1000 [00:49<00:33, 12.27it/s]
60%|█████▉ | 596/1000 [00:49<00:33, 12.23it/s]
60%|█████▉ | 598/1000 [00:49<00:32, 12.26it/s]
60%|██████ | 600/1000 [00:49<00:32, 12.30it/s]
60%|██████ | 602/1000 [00:50<00:32, 12.21it/s]
60%|██████ | 604/1000 [00:50<00:32, 12.33it/s]
61%|██████ | 606/1000 [00:50<00:30, 12.82it/s]
61%|██████ | 608/1000 [00:50<00:28, 13.62it/s]
61%|██████ | 610/1000 [00:50<00:28, 13.92it/s]
61%|██████ | 612/1000 [00:50<00:27, 14.35it/s]
61%|██████▏ | 614/1000 [00:50<00:27, 13.88it/s]
62%|██████▏ | 616/1000 [00:51<00:26, 14.40it/s]
62%|██████▏ | 618/1000 [00:51<00:27, 13.90it/s]
62%|██████▏ | 620/1000 [00:51<00:39, 9.69it/s]
62%|██████▏ | 622/1000 [00:51<00:38, 9.94it/s]
62%|██████▏ | 624/1000 [00:51<00:32, 11.39it/s]
63%|██████▎ | 626/1000 [00:51<00:30, 12.09it/s]
63%|██████▎ | 628/1000 [00:52<00:28, 13.26it/s]
63%|██████▎ | 630/1000 [00:52<00:26, 13.77it/s]
63%|██████▎ | 632/1000 [00:52<00:29, 12.46it/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, 10.69it/s]
64%|██████▍ | 640/1000 [00:53<00:31, 11.48it/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, 13.37it/s]
65%|██████▌ | 650/1000 [00:53<00:27, 12.55it/s]
65%|██████▌ | 652/1000 [00:54<00:29, 11.97it/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, 11.88it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.17it/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, 12.82it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.37it/s]
67%|██████▋ | 670/1000 [00:55<00:29, 11.14it/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, 11.75it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.22it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 13.01it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.16it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.35it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.41it/s]
69%|██████▉ | 688/1000 [00:57<00:22, 13.72it/s]
69%|██████▉ | 690/1000 [00:57<00:22, 13.81it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.25it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.36it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 14.14it/s]
70%|██████▉ | 698/1000 [00:57<00:20, 14.69it/s]
70%|███████ | 700/1000 [00:58<00:24, 12.24it/s]
70%|███████ | 702/1000 [00:58<00:22, 13.05it/s]
70%|███████ | 704/1000 [00:58<00:29, 9.88it/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 [00:59<00:24, 11.91it/s]
71%|███████▏ | 714/1000 [00:59<00:21, 13.02it/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, 13.55it/s]
72%|███████▏ | 722/1000 [00:59<00:19, 13.99it/s]
72%|███████▏ | 724/1000 [00:59<00:19, 13.80it/s]
73%|███████▎ | 726/1000 [01:00<00:19, 14.03it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.96it/s]
73%|███████▎ | 730/1000 [01:00<00:32, 8.22it/s]
73%|███████▎ | 732/1000 [01:00<00:32, 8.34it/s]
73%|███████▎ | 734/1000 [01:01<00:30, 8.61it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.46it/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, 11.71it/s]
74%|███████▍ | 743/1000 [01:01<00:20, 12.34it/s]
74%|███████▍ | 745/1000 [01:02<00:24, 10.22it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.59it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.74it/s]
75%|███████▌ | 751/1000 [01:02<00:21, 11.83it/s]
75%|███████▌ | 753/1000 [01:02<00:19, 12.56it/s]
76%|███████▌ | 755/1000 [01:02<00:18, 13.28it/s]
76%|███████▌ | 757/1000 [01:03<00:23, 10.29it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.33it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.32it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.25it/s]
76%|███████▋ | 765/1000 [01:04<00:27, 8.59it/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, 11.87it/s]
77%|███████▋ | 773/1000 [01:04<00:17, 12.66it/s]
78%|███████▊ | 775/1000 [01:04<00:17, 13.22it/s]
78%|███████▊ | 777/1000 [01:04<00:15, 14.16it/s]
78%|███████▊ | 779/1000 [01:05<00:21, 10.32it/s]
78%|███████▊ | 781/1000 [01:05<00:24, 8.90it/s]
78%|███████▊ | 783/1000 [01:05<00:28, 7.70it/s]
78%|███████▊ | 784/1000 [01:06<00:32, 6.69it/s]
78%|███████▊ | 785/1000 [01:06<00:30, 7.06it/s]
79%|███████▊ | 786/1000 [01:06<00:31, 6.74it/s]
79%|███████▉ | 788/1000 [01:06<00:24, 8.55it/s]
79%|███████▉ | 790/1000 [01:06<00:21, 9.92it/s]
79%|███████▉ | 792/1000 [01:06<00:19, 10.73it/s]
79%|███████▉ | 794/1000 [01:06<00:18, 11.08it/s]
80%|███████▉ | 796/1000 [01:07<00:17, 11.35it/s]
80%|███████▉ | 798/1000 [01:07<00:17, 11.73it/s]
80%|████████ | 800/1000 [01:07<00:17, 11.50it/s]
80%|████████ | 802/1000 [01:07<00:24, 7.99it/s]
80%|████████ | 803/1000 [01:08<00:26, 7.56it/s]
80%|████████ | 805/1000 [01:08<00:21, 8.99it/s]
81%|████████ | 807/1000 [01:08<00:19, 9.69it/s]
81%|████████ | 809/1000 [01:08<00:17, 10.77it/s]
81%|████████ | 811/1000 [01:08<00:17, 10.98it/s]
81%|████████▏ | 813/1000 [01:08<00:15, 12.19it/s]
82%|████████▏ | 815/1000 [01:08<00:14, 12.63it/s]
82%|████████▏ | 817/1000 [01:09<00:16, 11.03it/s]
82%|████████▏ | 819/1000 [01:09<00:15, 11.77it/s]
82%|████████▏ | 821/1000 [01:09<00:14, 12.49it/s]
82%|████████▏ | 823/1000 [01:09<00:13, 13.22it/s]
82%|████████▎ | 825/1000 [01:09<00:12, 14.06it/s]
83%|████████▎ | 827/1000 [01:09<00:12, 14.24it/s]
83%|████████▎ | 829/1000 [01:10<00:12, 13.54it/s]
83%|████████▎ | 831/1000 [01:10<00:12, 13.87it/s]
83%|████████▎ | 833/1000 [01:10<00:12, 13.79it/s]
84%|████████▎ | 835/1000 [01:10<00:11, 14.19it/s]
84%|████████▎ | 837/1000 [01:10<00:11, 14.30it/s]
84%|████████▍ | 839/1000 [01:10<00:11, 13.97it/s]
84%|████████▍ | 841/1000 [01:10<00:11, 14.31it/s]
84%|████████▍ | 843/1000 [01:11<00:11, 14.24it/s]
84%|████████▍ | 845/1000 [01:11<00:11, 13.93it/s]
85%|████████▍ | 847/1000 [01:11<00:10, 13.97it/s]
85%|████████▍ | 849/1000 [01:11<00:10, 14.09it/s]
85%|████████▌ | 851/1000 [01:11<00:10, 13.91it/s]
85%|████████▌ | 853/1000 [01:11<00:10, 13.87it/s]
86%|████████▌ | 855/1000 [01:11<00:10, 14.20it/s]
86%|████████▌ | 857/1000 [01:12<00:10, 14.30it/s]
86%|████████▌ | 859/1000 [01:12<00:09, 15.01it/s]
86%|████████▌ | 861/1000 [01:12<00:09, 13.90it/s]
86%|████████▋ | 863/1000 [01:12<00:09, 13.87it/s]
86%|████████▋ | 865/1000 [01:12<00:09, 13.97it/s]
87%|████████▋ | 867/1000 [01:12<00:09, 13.79it/s]
87%|████████▋ | 869/1000 [01:12<00:09, 13.74it/s]
87%|████████▋ | 871/1000 [01:12<00:08, 14.50it/s]
87%|████████▋ | 873/1000 [01:13<00:09, 13.60it/s]
88%|████████▊ | 875/1000 [01:13<00:09, 12.68it/s]
88%|████████▊ | 877/1000 [01:13<00:10, 11.38it/s]
88%|████████▊ | 879/1000 [01:13<00:10, 12.01it/s]
88%|████████▊ | 881/1000 [01:13<00:10, 10.88it/s]
88%|████████▊ | 883/1000 [01:14<00:09, 12.16it/s]
88%|████████▊ | 885/1000 [01:14<00:09, 12.50it/s]
89%|████████▊ | 887/1000 [01:14<00:08, 13.57it/s]
89%|████████▉ | 889/1000 [01:14<00:07, 14.21it/s]
89%|████████▉ | 891/1000 [01:14<00:07, 14.01it/s]
89%|████████▉ | 893/1000 [01:14<00:07, 14.22it/s]
90%|████████▉ | 895/1000 [01:14<00:07, 13.96it/s]
90%|████████▉ | 897/1000 [01:15<00:07, 14.24it/s]
90%|████████▉ | 899/1000 [01:15<00:08, 12.13it/s]
90%|█████████ | 901/1000 [01:15<00:07, 12.64it/s]
90%|█████████ | 903/1000 [01:15<00:07, 12.98it/s]
90%|█████████ | 905/1000 [01:15<00:09, 9.80it/s]
91%|█████████ | 907/1000 [01:15<00:08, 10.94it/s]
91%|█████████ | 909/1000 [01:16<00:07, 12.26it/s]
91%|█████████ | 911/1000 [01:16<00:07, 11.89it/s]
91%|█████████▏| 913/1000 [01:16<00:09, 9.38it/s]
92%|█████████▏| 915/1000 [01:17<00:11, 7.11it/s]
92%|█████████▏| 917/1000 [01:17<00:12, 6.72it/s]
92%|█████████▏| 919/1000 [01:17<00:10, 7.75it/s]
92%|█████████▏| 921/1000 [01:17<00:09, 8.72it/s]
92%|█████████▏| 923/1000 [01:17<00:08, 9.58it/s]
92%|█████████▎| 925/1000 [01:18<00:07, 10.29it/s]
93%|█████████▎| 927/1000 [01:18<00:06, 10.76it/s]
93%|█████████▎| 929/1000 [01:18<00:07, 9.92it/s]
93%|█████████▎| 931/1000 [01:18<00:06, 10.53it/s]
93%|█████████▎| 933/1000 [01:18<00:06, 11.12it/s]
94%|█████████▎| 935/1000 [01:18<00:05, 12.25it/s]
94%|█████████▎| 937/1000 [01:18<00:04, 12.93it/s]
94%|█████████▍| 939/1000 [01:19<00:04, 13.87it/s]
94%|█████████▍| 941/1000 [01:19<00:04, 14.12it/s]
94%|█████████▍| 943/1000 [01:19<00:04, 12.00it/s]
94%|█████████▍| 945/1000 [01:19<00:04, 12.36it/s]
95%|█████████▍| 947/1000 [01:19<00:04, 11.81it/s]
95%|█████████▍| 949/1000 [01:19<00:04, 12.66it/s]
95%|█████████▌| 951/1000 [01:20<00:03, 13.65it/s]
95%|█████████▌| 953/1000 [01:20<00:03, 14.18it/s]
96%|█████████▌| 955/1000 [01:20<00:03, 13.96it/s]
96%|█████████▌| 957/1000 [01:20<00:02, 14.39it/s]
96%|█████████▌| 959/1000 [01:20<00:03, 13.32it/s]
96%|█████████▌| 961/1000 [01:20<00:02, 13.41it/s]
96%|█████████▋| 963/1000 [01:20<00:02, 14.09it/s]
96%|█████████▋| 965/1000 [01:21<00:03, 10.39it/s]
97%|█████████▋| 967/1000 [01:21<00:02, 11.61it/s]
97%|█████████▋| 969/1000 [01:21<00:02, 12.06it/s]
97%|█████████▋| 971/1000 [01:21<00:02, 12.61it/s]
97%|█████████▋| 973/1000 [01:21<00:02, 9.89it/s]
98%|█████████▊| 975/1000 [01:22<00:02, 10.76it/s]
98%|█████████▊| 977/1000 [01:22<00:02, 9.55it/s]
98%|█████████▊| 979/1000 [01:22<00:02, 9.70it/s]
98%|█████████▊| 981/1000 [01:22<00:01, 10.01it/s]
98%|█████████▊| 983/1000 [01:22<00:01, 11.00it/s]
98%|█████████▊| 985/1000 [01:23<00:01, 10.36it/s]
99%|█████████▊| 987/1000 [01:23<00:01, 11.26it/s]
99%|█████████▉| 989/1000 [01:23<00:00, 11.21it/s]
99%|█████████▉| 991/1000 [01:23<00:00, 11.92it/s]
99%|█████████▉| 993/1000 [01:23<00:00, 12.55it/s]
100%|█████████▉| 995/1000 [01:23<00:00, 13.46it/s]
100%|█████████▉| 997/1000 [01:23<00:00, 14.05it/s]
100%|█████████▉| 999/1000 [01:24<00:00, 14.65it/s]
{'eval_loss': '0.9227', 'eval_runtime': '84.22', 'eval_samples_per_second': '11.87', 'eval_steps_per_second': '11.87', 'epoch': '0.3746', 'num_input_tokens_seen': 23480632}
37%|███▋ | 7000/18688 [8:12:39<13:20:03, 4.11s/it]
100%|██████████| 1000/1000 [01:24<00:00, 14.65it/s]
 37%|███▋ | 7001/18688 [8:12:42<94:52:16, 29.22s/it] 37%|███▋ | 7002/18688 [8:12:46<69:52:25, 21.53s/it] 37%|███▋ | 7003/18688 [8:12:50<52:19:40, 16.12s/it] 37%|███▋ | 7004/18688 [8:12:53<40:15:02, 12.40s/it] 37%|███▋ | 7005/18688 [8:12:57<31:59:29, 9.86s/it] 37%|███▋ | 7006/18688 [8:13:01<25:55:29, 7.99s/it] 37%|███▋ | 7007/18688 [8:13:05<22:15:04, 6.86s/it] 38%|███▊ | 7008/18688 [8:13:09<19:07:30, 5.89s/it] 38%|███▊ | 7009/18688 [8:13:13<17:34:14, 5.42s/it] 38%|███▊ | 7010/18688 [8:13:17<15:55:54, 4.91s/it] {'loss': '0.9399', 'grad_norm': '1.315', 'learning_rate': '7.672e-05', 'epoch': '0.3751', 'num_input_tokens_seen': 23509808, 'train_runtime': '2.96e+04', 'train_tokens_per_second': '794.3'}
38%|███▊ | 7010/18688 [8:13:17<15:55:54, 4.91s/it] 38%|███▊ | 7011/18688 [8:13:21<15:16:30, 4.71s/it] 38%|███▊ | 7012/18688 [8:13:26<15:07:09, 4.66s/it] 38%|███▊ | 7013/18688 [8:13:29<14:12:00, 4.38s/it] 38%|███▊ | 7014/18688 [8:13:33<13:54:56, 4.29s/it] 38%|███▊ | 7015/18688 [8:13:38<13:52:51, 4.28s/it] 38%|███▊ | 7016/18688 [8:13:42<14:08:13, 4.36s/it] 38%|███▊ | 7017/18688 [8:13:46<13:30:24, 4.17s/it] 38%|███▊ | 7018/18688 [8:13:50<13:29:12, 4.16s/it] 38%|███▊ | 7019/18688 [8:13:54<13:00:21, 4.01s/it] 38%|███▊ | 7020/18688 [8:13:58<13:15:48, 4.09s/it] {'loss': '0.9205', 'grad_norm': '1.224', 'learning_rate': '7.665e-05', 'epoch': '0.3757', 'num_input_tokens_seen': 23542376, 'train_runtime': '2.964e+04', 'train_tokens_per_second': '794.3'}
38%|███▊ | 7020/18688 [8:13:58<13:15:48, 4.09s/it] 38%|███▊ | 7021/18688 [8:14:03<13:53:27, 4.29s/it] 38%|███▊ | 7022/18688 [8:14:07<13:59:49, 4.32s/it] 38%|███▊ | 7023/18688 [8:14:11<13:46:50, 4.25s/it] 38%|███▊ | 7024/18688 [8:14:16<14:03:44, 4.34s/it] 38%|███▊ | 7025/18688 [8:14:19<13:22:29, 4.13s/it] 38%|███▊ | 7026/18688 [8:14:23<12:55:33, 3.99s/it] 38%|███▊ | 7027/18688 [8:14:27<12:31:33, 3.87s/it] 38%|███▊ | 7028/18688 [8:14:31<12:41:48, 3.92s/it] 38%|███▊ | 7029/18688 [8:14:34<12:36:59, 3.90s/it] 38%|███▊ | 7030/18688 [8:14:38<12:33:17, 3.88s/it] {'loss': '0.9781', 'grad_norm': '1.233', 'learning_rate': '7.657e-05', 'epoch': '0.3762', 'num_input_tokens_seen': 23574728, 'train_runtime': '2.968e+04', 'train_tokens_per_second': '794.3'}
38%|███▊ | 7030/18688 [8:14:38<12:33:17, 3.88s/it] 38%|███▊ | 7031/18688 [8:14:42<12:30:18, 3.86s/it] 38%|███▊ | 7032/18688 [8:14:46<12:44:03, 3.93s/it] 38%|███▊ | 7033/18688 [8:14:51<13:53:12, 4.29s/it] 38%|███▊ | 7034/18688 [8:14:55<13:30:29, 4.17s/it] 38%|███▊ | 7035/18688 [8:14:59<13:17:36, 4.11s/it] 38%|███▊ | 7036/18688 [8:15:04<13:34:22, 4.19s/it] 38%|███▊ | 7037/18688 [8:15:08<13:37:09, 4.21s/it] 38%|███▊ | 7038/18688 [8:15:12<13:46:19, 4.26s/it] 38%|███▊ | 7039/18688 [8:15:17<14:24:59, 4.46s/it] 38%|███▊ | 7040/18688 [8:15:21<14:00:47, 4.33s/it] {'loss': '0.9258', 'grad_norm': '1.351', 'learning_rate': '7.649e-05', 'epoch': '0.3767', 'num_input_tokens_seen': 23608888, 'train_runtime': '2.972e+04', 'train_tokens_per_second': '794.3'}
38%|███▊ | 7040/18688 [8:15:21<14:00:47, 4.33s/it] 38%|███▊ | 7041/18688 [8:15:26<14:19:19, 4.43s/it] 38%|███▊ | 7042/18688 [8:15:30<14:16:05, 4.41s/it] 38%|███▊ | 7043/18688 [8:15:35<14:18:06, 4.42s/it] 38%|███▊ | 7044/18688 [8:15:38<13:37:45, 4.21s/it] 38%|███▊ | 7045/18688 [8:15:42<13:22:30, 4.14s/it] 38%|███▊ | 7046/18688 [8:15:46<12:58:29, 4.01s/it] 38%|███▊ | 7047/18688 [8:15:51<13:28:24, 4.17s/it] 38%|███▊ | 7048/18688 [8:15:55<14:09:32, 4.38s/it] 38%|███▊ | 7049/18688 [8:15:59<13:26:54, 4.16s/it] 38%|███▊ | 7050/18688 [8:16:03<13:34:07, 4.20s/it] {'loss': '0.9475', 'grad_norm': '1.315', 'learning_rate': '7.641e-05', 'epoch': '0.3773', 'num_input_tokens_seen': 23643440, 'train_runtime': '2.976e+04', 'train_tokens_per_second': '794.4'}
38%|███▊ | 7050/18688 [8:16:03<13:34:07, 4.20s/it] 38%|███▊ | 7051/18688 [8:16:07<13:26:48, 4.16s/it] 38%|███▊ | 7052/18688 [8:16:11<13:14:49, 4.10s/it] 38%|███▊ | 7053/18688 [8:16:16<13:27:58, 4.17s/it] 38%|███▊ | 7054/18688 [8:16:20<13:21:31, 4.13s/it] 38%|███▊ | 7055/18688 [8:16:24<13:10:01, 4.07s/it] 38%|███▊ | 7056/18688 [8:16:27<12:49:05, 3.97s/it] 38%|███▊ | 7057/18688 [8:16:31<12:41:00, 3.93s/it] 38%|███▊ | 7058/18688 [8:16:36<13:35:43, 4.21s/it] 38%|███▊ | 7059/18688 [8:16:40<13:04:02, 4.05s/it] 38%|███▊ | 7060/18688 [8:16:44<12:49:34, 3.97s/it] {'loss': '0.9229', 'grad_norm': '1.232', 'learning_rate': '7.634e-05', 'epoch': '0.3778', 'num_input_tokens_seen': 23676168, 'train_runtime': '2.98e+04', 'train_tokens_per_second': '794.4'}
38%|███▊ | 7060/18688 [8:16:44<12:49:34, 3.97s/it] 38%|███▊ | 7061/18688 [8:16:47<12:39:50, 3.92s/it] 38%|███▊ | 7062/18688 [8:16:52<13:24:24, 4.15s/it] 38%|███▊ | 7063/18688 [8:16:56<13:26:04, 4.16s/it] 38%|███▊ | 7064/18688 [8:17:01<13:45:55, 4.26s/it] 38%|███▊ | 7065/18688 [8:17:05<13:54:07, 4.31s/it] 38%|███▊ | 7066/18688 [8:17:09<13:17:26, 4.12s/it] 38%|███▊ | 7067/18688 [8:17:13<13:14:15, 4.10s/it] 38%|███▊ | 7068/18688 [8:17:17<13:35:33, 4.21s/it] 38%|███▊ | 7069/18688 [8:17:21<12:59:52, 4.03s/it] 38%|███▊ | 7070/18688 [8:17:25<12:54:33, 4.00s/it] {'loss': '0.9295', 'grad_norm': '1.352', 'learning_rate': '7.626e-05', 'epoch': '0.3783', 'num_input_tokens_seen': 23709624, 'train_runtime': '2.985e+04', 'train_tokens_per_second': '794.4'}
38%|███▊ | 7070/18688 [8:17:25<12:54:33, 4.00s/it] 38%|███▊ | 7071/18688 [8:17:29<13:23:28, 4.15s/it] 38%|███▊ | 7072/18688 [8:17:34<13:40:37, 4.24s/it] 38%|███▊ | 7073/18688 [8:17:38<13:50:43, 4.29s/it] 38%|███▊ | 7074/18688 [8:17:43<13:57:19, 4.33s/it] 38%|███▊ | 7075/18688 [8:17:46<13:07:20, 4.07s/it] 38%|███▊ | 7076/18688 [8:17:50<13:11:33, 4.09s/it] 38%|███▊ | 7077/18688 [8:17:55<13:25:48, 4.16s/it] 38%|███▊ | 7078/18688 [8:17:58<12:55:56, 4.01s/it] 38%|███▊ | 7079/18688 [8:18:03<13:09:06, 4.08s/it] 38%|███▊ | 7080/18688 [8:18:07<13:35:16, 4.21s/it] {'loss': '0.9938', 'grad_norm': '1.169', 'learning_rate': '7.618e-05', 'epoch': '0.3789', 'num_input_tokens_seen': 23743736, 'train_runtime': '2.989e+04', 'train_tokens_per_second': '794.4'}
38%|███▊ | 7080/18688 [8:18:07<13:35:16, 4.21s/it] 38%|███▊ | 7081/18688 [8:18:11<13:21:08, 4.14s/it] 38%|███▊ | 7082/18688 [8:18:16<13:49:10, 4.29s/it] 38%|███▊ | 7083/18688 [8:18:20<13:54:02, 4.31s/it] 38%|███▊ | 7084/18688 [8:18:25<14:55:38, 4.63s/it] 38%|███▊ | 7085/18688 [8:18:29<14:07:30, 4.38s/it] 38%|███▊ | 7086/18688 [8:18:33<13:40:11, 4.24s/it] 38%|███▊ | 7087/18688 [8:18:37<13:11:44, 4.09s/it] 38%|███▊ | 7088/18688 [8:18:41<13:26:58, 4.17s/it] 38%|███▊ | 7089/18688 [8:18:45<13:17:42, 4.13s/it] 38%|███▊ | 7090/18688 [8:18:50<13:28:29, 4.18s/it] {'loss': '0.8926', 'grad_norm': '1.113', 'learning_rate': '7.61e-05', 'epoch': '0.3794', 'num_input_tokens_seen': 23778216, 'train_runtime': '2.993e+04', 'train_tokens_per_second': '794.5'}
38%|███▊ | 7090/18688 [8:18:50<13:28:29, 4.18s/it] 38%|███▊ | 7091/18688 [8:18:54<14:10:26, 4.40s/it] 38%|███▊ | 7092/18688 [8:18:58<13:17:13, 4.12s/it] 38%|███▊ | 7093/18688 [8:19:02<12:52:42, 4.00s/it] 38%|███▊ | 7094/18688 [8:19:05<12:22:08, 3.84s/it] 38%|███▊ | 7095/18688 [8:19:09<12:18:17, 3.82s/it] 38%|███▊ | 7096/18688 [8:19:13<12:36:53, 3.92s/it] 38%|███▊ | 7097/18688 [8:19:17<12:47:00, 3.97s/it] 38%|███▊ | 7098/18688 [8:19:21<12:39:34, 3.93s/it] 38%|███▊ | 7099/18688 [8:19:25<12:17:52, 3.82s/it] 38%|███▊ | 7100/18688 [8:19:28<12:15:25, 3.81s/it] {'loss': '0.9101', 'grad_norm': '1.319', 'learning_rate': '7.602e-05', 'epoch': '0.3799', 'num_input_tokens_seen': 23808680, 'train_runtime': '2.997e+04', 'train_tokens_per_second': '794.4'}
38%|███▊ | 7100/18688 [8:19:28<12:15:25, 3.81s/it] 38%|███▊ | 7101/18688 [8:19:32<12:08:12, 3.77s/it] 38%|███▊ | 7102/18688 [8:19:36<12:45:41, 3.97s/it] 38%|███▊ | 7103/18688 [8:19:40<12:30:16, 3.89s/it] 38%|███▊ | 7104/18688 [8:19:45<13:37:14, 4.23s/it] 38%|███▊ | 7105/18688 [8:19:50<13:54:43, 4.32s/it] 38%|███▊ | 7106/18688 [8:19:54<13:25:53, 4.17s/it] 38%|███▊ | 7107/18688 [8:19:57<12:54:35, 4.01s/it] 38%|███▊ | 7108/18688 [8:20:01<13:05:40, 4.07s/it] 38%|███▊ | 7109/18688 [8:20:05<13:07:36, 4.08s/it] 38%|███▊ | 7110/18688 [8:20:12<14:59:36, 4.66s/it] {'loss': '0.9637', 'grad_norm': '1.509', 'learning_rate': '7.595e-05', 'epoch': '0.3805', 'num_input_tokens_seen': 23845016, 'train_runtime': '3.001e+04', 'train_tokens_per_second': '794.5'}
38%|███▊ | 7110/18688 [8:20:12<14:59:36, 4.66s/it] 38%|███▊ | 7111/18688 [8:20:15<13:52:18, 4.31s/it] 38%|███▊ | 7112/18688 [8:20:19<14:00:48, 4.36s/it] 38%|███▊ | 7113/18688 [8:20:24<14:06:03, 4.39s/it] 38%|███▊ | 7114/18688 [8:20:29<14:29:23, 4.51s/it] 38%|███▊ | 7115/18688 [8:20:33<14:42:14, 4.57s/it] 38%|███▊ | 7116/18688 [8:20:37<13:50:06, 4.30s/it] 38%|███▊ | 7117/18688 [8:20:41<13:48:17, 4.30s/it] 38%|███▊ | 7118/18688 [8:20:46<14:31:09, 4.52s/it] 38%|███▊ | 7119/18688 [8:20:50<13:30:38, 4.20s/it] 38%|███▊ | 7120/18688 [8:20:54<13:00:51, 4.05s/it] {'loss': '0.9181', 'grad_norm': '1.431', 'learning_rate': '7.587e-05', 'epoch': '0.381', 'num_input_tokens_seen': 23880984, 'train_runtime': '3.005e+04', 'train_tokens_per_second': '794.6'}
38%|███▊ | 7120/18688 [8:20:54<13:00:51, 4.05s/it] 38%|███▊ | 7121/18688 [8:20:58<13:00:47, 4.05s/it] 38%|███▊ | 7122/18688 [8:21:02<13:39:15, 4.25s/it] 38%|███▊ | 7123/18688 [8:21:07<14:01:32, 4.37s/it] 38%|███▊ | 7124/18688 [8:21:12<15:05:14, 4.70s/it] 38%|███▊ | 7125/18688 [8:21:17<15:06:16, 4.70s/it] 38%|███▊ | 7126/18688 [8:21:21<14:08:24, 4.40s/it] 38%|███▊ | 7127/18688 [8:21:24<13:19:35, 4.15s/it] 38%|███▊ | 7128/18688 [8:21:29<13:20:34, 4.16s/it] 38%|███▊ | 7129/18688 [8:21:32<12:44:35, 3.97s/it] 38%|███▊ | 7130/18688 [8:21:36<12:28:50, 3.89s/it] {'loss': '0.9119', 'grad_norm': '1.363', 'learning_rate': '7.579e-05', 'epoch': '0.3815', 'num_input_tokens_seen': 23915912, 'train_runtime': '3.01e+04', 'train_tokens_per_second': '794.6'}
38%|███▊ | 7130/18688 [8:21:36<12:28:50, 3.89s/it] 38%|███▊ | 7131/18688 [8:21:40<12:38:31, 3.94s/it] 38%|███▊ | 7132/18688 [8:21:44<13:01:40, 4.06s/it] 38%|███▊ | 7133/18688 [8:21:48<13:08:08, 4.09s/it] 38%|███▊ | 7134/18688 [8:21:52<12:40:39, 3.95s/it] 38%|███▊ | 7135/18688 [8:21:56<12:39:40, 3.95s/it] 38%|███▊ | 7136/18688 [8:22:00<12:34:50, 3.92s/it] 38%|███▊ | 7137/18688 [8:22:04<12:33:00, 3.91s/it] 38%|███▊ | 7138/18688 [8:22:07<12:23:50, 3.86s/it] 38%|███▊ | 7139/18688 [8:22:12<13:14:23, 4.13s/it] 38%|███▊ | 7140/18688 [8:22:16<12:39:24, 3.95s/it] {'loss': '1.046', 'grad_norm': '1.634', 'learning_rate': '7.571e-05', 'epoch': '0.3821', 'num_input_tokens_seen': 23948352, 'train_runtime': '3.014e+04', 'train_tokens_per_second': '794.7'}
38%|███▊ | 7140/18688 [8:22:16<12:39:24, 3.95s/it] 38%|███▊ | 7141/18688 [8:22:20<13:12:46, 4.12s/it] 38%|███▊ | 7142/18688 [8:22:25<13:53:41, 4.33s/it] 38%|███▊ | 7143/18688 [8:22:29<13:01:37, 4.06s/it] 38%|███▊ | 7144/18688 [8:22:32<12:28:32, 3.89s/it] 38%|███▊ | 7145/18688 [8:22:36<12:47:28, 3.99s/it] 38%|███▊ | 7146/18688 [8:22:40<12:54:25, 4.03s/it] 38%|███▊ | 7147/18688 [8:22:45<13:23:39, 4.18s/it] 38%|███▊ | 7148/18688 [8:22:49<13:18:20, 4.15s/it] 38%|███▊ | 7149/18688 [8:22:53<13:12:16, 4.12s/it] 38%|███▊ | 7150/18688 [8:22:56<12:34:35, 3.92s/it] {'loss': '0.9218', 'grad_norm': '1.788', 'learning_rate': '7.563e-05', 'epoch': '0.3826', 'num_input_tokens_seen': 23981296, 'train_runtime': '3.018e+04', 'train_tokens_per_second': '794.7'}
38%|███▊ | 7150/18688 [8:22:56<12:34:35, 3.92s/it] 38%|███▊ | 7151/18688 [8:23:01<12:47:18, 3.99s/it] 38%|███▊ | 7152/18688 [8:23:05<12:56:04, 4.04s/it] 38%|███▊ | 7153/18688 [8:23:08<12:29:38, 3.90s/it] 38%|███▊ | 7154/18688 [8:23:12<12:40:59, 3.96s/it] 38%|███▊ | 7155/18688 [8:23:16<12:14:42, 3.82s/it] 38%|███▊ | 7156/18688 [8:23:21<13:29:45, 4.21s/it] 38%|███▊ | 7157/18688 [8:23:27<14:56:35, 4.67s/it] 38%|███▊ | 7158/18688 [8:23:31<14:24:37, 4.50s/it] 38%|███▊ | 7159/18688 [8:23:35<14:29:46, 4.53s/it] 38%|███▊ | 7160/18688 [8:23:39<13:39:06, 4.26s/it] {'loss': '0.9447', 'grad_norm': '1.36', 'learning_rate': '7.555e-05', 'epoch': '0.3831', 'num_input_tokens_seen': 24016600, 'train_runtime': '3.022e+04', 'train_tokens_per_second': '794.7'}
38%|███▊ | 7160/18688 [8:23:39<13:39:06, 4.26s/it] 38%|███▊ | 7161/18688 [8:23:43<13:38:14, 4.26s/it] 38%|███▊ | 7162/18688 [8:23:48<13:40:26, 4.27s/it] 38%|███▊ | 7163/18688 [8:23:51<12:57:05, 4.05s/it] 38%|███▊ | 7164/18688 [8:23:56<14:04:49, 4.40s/it] 38%|███▊ | 7165/18688 [8:24:00<13:42:23, 4.28s/it] 38%|███▊ | 7166/18688 [8:24:04<12:57:35, 4.05s/it] 38%|███▊ | 7167/18688 [8:24:07<12:23:42, 3.87s/it] 38%|███▊ | 7168/18688 [8:24:11<12:27:29, 3.89s/it] 38%|███▊ | 7169/18688 [8:24:17<13:42:59, 4.29s/it] 38%|███▊ | 7170/18688 [8:24:20<13:03:46, 4.08s/it] {'loss': '0.9521', 'grad_norm': '1.262', 'learning_rate': '7.548e-05', 'epoch': '0.3837', 'num_input_tokens_seen': 24050728, 'train_runtime': '3.026e+04', 'train_tokens_per_second': '794.8'}
38%|███▊ | 7170/18688 [8:24:20<13:03:46, 4.08s/it] 38%|███▊ | 7171/18688 [8:24:24<13:12:14, 4.13s/it] 38%|███▊ | 7172/18688 [8:24:29<13:45:12, 4.30s/it] 38%|███▊ | 7173/18688 [8:24:34<14:20:50, 4.49s/it] 38%|███▊ | 7174/18688 [8:24:38<13:48:23, 4.32s/it] 38%|███▊ | 7175/18688 [8:24:42<13:31:25, 4.23s/it] 38%|███▊ | 7176/18688 [8:24:46<13:19:09, 4.17s/it] 38%|███▊ | 7177/18688 [8:24:51<13:41:35, 4.28s/it] 38%|███▊ | 7178/18688 [8:24:55<13:36:56, 4.26s/it] 38%|███▊ | 7179/18688 [8:24:59<13:20:57, 4.18s/it] 38%|███▊ | 7180/18688 [8:25:03<13:43:54, 4.30s/it] {'loss': '0.9182', 'grad_norm': '1.298', 'learning_rate': '7.54e-05', 'epoch': '0.3842', 'num_input_tokens_seen': 24088088, 'train_runtime': '3.03e+04', 'train_tokens_per_second': '794.9'}
38%|███▊ | 7180/18688 [8:25:03<13:43:54, 4.30s/it] 38%|███▊ | 7181/18688 [8:25:07<13:24:26, 4.19s/it] 38%|███▊ | 7182/18688 [8:25:11<12:59:06, 4.06s/it] 38%|███▊ | 7183/18688 [8:25:16<13:37:14, 4.26s/it] 38%|███▊ | 7184/18688 [8:25:20<13:50:30, 4.33s/it] 38%|███▊ | 7185/18688 [8:25:24<13:39:57, 4.28s/it] 38%|███▊ | 7186/18688 [8:25:29<13:34:15, 4.25s/it] 38%|███▊ | 7187/18688 [8:25:33<13:26:13, 4.21s/it] 38%|███▊ | 7188/18688 [8:25:36<12:56:30, 4.05s/it] 38%|███▊ | 7189/18688 [8:25:40<12:56:04, 4.05s/it] 38%|███▊ | 7190/18688 [8:25:44<12:58:33, 4.06s/it] {'loss': '1.003', 'grad_norm': '1.192', 'learning_rate': '7.532e-05', 'epoch': '0.3847', 'num_input_tokens_seen': 24121392, 'train_runtime': '3.034e+04', 'train_tokens_per_second': '794.9'}
38%|███▊ | 7190/18688 [8:25:44<12:58:33, 4.06s/it] 38%|███▊ | 7191/18688 [8:25:49<13:43:19, 4.30s/it] 38%|███▊ | 7192/18688 [8:25:54<13:39:30, 4.28s/it] 38%|███▊ | 7193/18688 [8:25:58<13:36:51, 4.26s/it] 38%|███▊ | 7194/18688 [8:26:02<13:46:55, 4.32s/it] 39%|███▊ | 7195/18688 [8:26:06<13:36:39, 4.26s/it] 39%|███▊ | 7196/18688 [8:26:11<13:30:36, 4.23s/it] 39%|███▊ | 7197/18688 [8:26:15<14:10:26, 4.44s/it] 39%|███▊ | 7198/18688 [8:26:20<14:25:23, 4.52s/it] 39%|███▊ | 7199/18688 [8:26:25<15:00:21, 4.70s/it] 39%|███▊ | 7200/18688 [8:26:30<14:43:10, 4.61s/it] {'loss': '0.9514', 'grad_norm': '1.383', 'learning_rate': '7.524e-05', 'epoch': '0.3853', 'num_input_tokens_seen': 24160816, 'train_runtime': '3.039e+04', 'train_tokens_per_second': '795'}
39%|███▊ | 7200/18688 [8:26:30<14:43:10, 4.61s/it] 39%|███▊ | 7201/18688 [8:26:34<14:20:01, 4.49s/it] 39%|███▊ | 7202/18688 [8:26:38<13:42:49, 4.30s/it] 39%|███▊ | 7203/18688 [8:26:41<12:59:57, 4.07s/it] 39%|███▊ | 7204/18688 [8:26:45<12:42:44, 3.99s/it] 39%|███▊ | 7205/18688 [8:26:49<12:34:38, 3.94s/it] 39%|███▊ | 7206/18688 [8:26:53<12:28:05, 3.91s/it] 39%|███▊ | 7207/18688 [8:26:57<12:42:00, 3.98s/it] 39%|███▊ | 7208/18688 [8:27:01<12:56:04, 4.06s/it] 39%|███▊ | 7209/18688 [8:27:06<13:19:27, 4.18s/it] 39%|███▊ | 7210/18688 [8:27:09<13:01:56, 4.09s/it] {'loss': '0.9616', 'grad_norm': '1.219', 'learning_rate': '7.516e-05', 'epoch': '0.3858', 'num_input_tokens_seen': 24192264, 'train_runtime': '3.043e+04', 'train_tokens_per_second': '795'}
39%|███▊ | 7210/18688 [8:27:09<13:01:56, 4.09s/it] 39%|███▊ | 7211/18688 [8:27:13<12:47:11, 4.01s/it] 39%|███▊ | 7212/18688 [8:27:18<13:20:21, 4.18s/it] 39%|███▊ | 7213/18688 [8:27:22<12:52:38, 4.04s/it] 39%|███▊ | 7214/18688 [8:27:26<12:58:08, 4.07s/it] 39%|███▊ | 7215/18688 [8:27:30<13:12:46, 4.15s/it] 39%|███▊ | 7216/18688 [8:27:34<12:45:37, 4.00s/it] 39%|███▊ | 7217/18688 [8:27:38<12:51:52, 4.04s/it] 39%|███▊ | 7218/18688 [8:27:42<12:36:15, 3.96s/it] 39%|███▊ | 7219/18688 [8:27:45<12:13:56, 3.84s/it] 39%|███▊ | 7220/18688 [8:27:50<12:55:29, 4.06s/it] {'loss': '0.9', 'grad_norm': '1.122', 'learning_rate': '7.508e-05', 'epoch': '0.3864', 'num_input_tokens_seen': 24224664, 'train_runtime': '3.047e+04', 'train_tokens_per_second': '795'}
39%|███▊ | 7220/18688 [8:27:50<12:55:29, 4.06s/it] 39%|███▊ | 7221/18688 [8:27:54<13:10:06, 4.13s/it] 39%|███▊ | 7222/18688 [8:27:58<12:32:47, 3.94s/it] 39%|███▊ | 7223/18688 [8:28:01<12:17:13, 3.86s/it] 39%|███▊ | 7224/18688 [8:28:05<12:15:06, 3.85s/it] 39%|███▊ | 7225/18688 [8:28:09<12:30:00, 3.93s/it] 39%|███▊ | 7226/18688 [8:28:14<13:18:37, 4.18s/it] 39%|███▊ | 7227/18688 [8:28:18<13:25:14, 4.22s/it] 39%|███▊ | 7228/18688 [8:28:23<13:55:45, 4.38s/it] 39%|███▊ | 7229/18688 [8:28:27<13:53:01, 4.36s/it] 39%|███▊ | 7230/18688 [8:28:31<13:10:22, 4.14s/it] {'loss': '0.9246', 'grad_norm': '1.099', 'learning_rate': '7.5e-05', 'epoch': '0.3869', 'num_input_tokens_seen': 24258840, 'train_runtime': '3.051e+04', 'train_tokens_per_second': '795.1'}
39%|███▊ | 7230/18688 [8:28:31<13:10:22, 4.14s/it] 39%|███▊ | 7231/18688 [8:28:35<13:29:32, 4.24s/it] 39%|███▊ | 7232/18688 [8:28:39<13:19:27, 4.19s/it] 39%|███▊ | 7233/18688 [8:28:43<12:56:32, 4.07s/it] 39%|███▊ | 7234/18688 [8:28:48<13:37:06, 4.28s/it] 39%|███▊ | 7235/18688 [8:28:53<14:06:48, 4.44s/it] 39%|███▊ | 7236/18688 [8:28:57<13:29:29, 4.24s/it] 39%|███▊ | 7237/18688 [8:29:02<14:08:42, 4.45s/it] 39%|███▊ | 7238/18688 [8:29:06<13:52:43, 4.36s/it] 39%|███▊ | 7239/18688 [8:29:10<13:42:48, 4.31s/it] 39%|███▊ | 7240/18688 [8:29:14<13:13:31, 4.16s/it] {'loss': '0.9122', 'grad_norm': '1.459', 'learning_rate': '7.492e-05', 'epoch': '0.3874', 'num_input_tokens_seen': 24295616, 'train_runtime': '3.055e+04', 'train_tokens_per_second': '795.2'}
39%|███▊ | 7240/18688 [8:29:14<13:13:31, 4.16s/it] 39%|███▊ | 7241/18688 [8:29:18<13:13:35, 4.16s/it] 39%|███▉ | 7242/18688 [8:29:22<12:51:10, 4.04s/it] 39%|███▉ | 7243/18688 [8:29:26<13:14:42, 4.17s/it] 39%|███▉ | 7244/18688 [8:29:30<12:55:13, 4.06s/it] 39%|███▉ | 7245/18688 [8:29:34<12:35:18, 3.96s/it] 39%|███▉ | 7246/18688 [8:29:37<12:24:10, 3.90s/it] 39%|███▉ | 7247/18688 [8:29:41<12:02:31, 3.79s/it] 39%|███▉ | 7248/18688 [8:29:46<12:50:14, 4.04s/it] 39%|███▉ | 7249/18688 [8:29:50<13:05:15, 4.12s/it] 39%|███▉ | 7250/18688 [8:29:54<13:10:26, 4.15s/it] {'loss': '0.896', 'grad_norm': '2.369', 'learning_rate': '7.484e-05', 'epoch': '0.388', 'num_input_tokens_seen': 24327648, 'train_runtime': '3.059e+04', 'train_tokens_per_second': '795.2'}
39%|███▉ | 7250/18688 [8:29:54<13:10:26, 4.15s/it] 39%|███▉ | 7251/18688 [8:29:58<13:26:21, 4.23s/it] 39%|███▉ | 7252/18688 [8:30:02<13:02:14, 4.10s/it] 39%|███▉ | 7253/18688 [8:30:07<13:13:04, 4.16s/it] 39%|███▉ | 7254/18688 [8:30:10<12:50:28, 4.04s/it] 39%|███▉ | 7255/18688 [8:30:16<14:08:31, 4.45s/it] 39%|███▉ | 7256/18688 [8:30:20<13:58:46, 4.40s/it] 39%|███▉ | 7257/18688 [8:30:24<13:34:56, 4.28s/it] 39%|███▉ | 7258/18688 [8:30:29<14:31:49, 4.58s/it] 39%|███▉ | 7259/18688 [8:30:33<14:05:57, 4.44s/it] 39%|███▉ | 7260/18688 [8:30:38<14:00:19, 4.41s/it] {'loss': '0.9603', 'grad_norm': '1.362', 'learning_rate': '7.476e-05', 'epoch': '0.3885', 'num_input_tokens_seen': 24365472, 'train_runtime': '3.064e+04', 'train_tokens_per_second': '795.3'}
39%|███▉ | 7260/18688 [8:30:38<14:00:19, 4.41s/it] 39%|███▉ | 7261/18688 [8:30:42<13:26:52, 4.24s/it] 39%|███▉ | 7262/18688 [8:30:46<13:33:23, 4.27s/it] 39%|███▉ | 7263/18688 [8:30:49<12:49:16, 4.04s/it] 39%|███▉ | 7264/18688 [8:30:54<13:12:51, 4.16s/it] 39%|███▉ | 7265/18688 [8:30:57<12:32:19, 3.95s/it] 39%|███▉ | 7266/18688 [8:31:02<12:59:23, 4.09s/it] 39%|███▉ | 7267/18688 [8:31:06<13:03:54, 4.12s/it] 39%|███▉ | 7268/18688 [8:31:11<13:31:49, 4.27s/it] 39%|███▉ | 7269/18688 [8:31:15<13:17:47, 4.19s/it] 39%|███▉ | 7270/18688 [8:31:19<13:09:59, 4.15s/it] {'loss': '0.9447', 'grad_norm': '1.141', 'learning_rate': '7.469e-05', 'epoch': '0.389', 'num_input_tokens_seen': 24399032, 'train_runtime': '3.068e+04', 'train_tokens_per_second': '795.3'}
39%|███▉ | 7270/18688 [8:31:19<13:09:59, 4.15s/it] 39%|███▉ | 7271/18688 [8:31:23<13:16:30, 4.19s/it] 39%|███▉ | 7272/18688 [8:31:28<13:42:45, 4.32s/it] 39%|███▉ | 7273/18688 [8:31:32<13:40:48, 4.31s/it] 39%|███▉ | 7274/18688 [8:31:38<15:01:04, 4.74s/it] 39%|███▉ | 7275/18688 [8:31:41<14:13:24, 4.49s/it] 39%|███▉ | 7276/18688 [8:31:45<13:15:47, 4.18s/it] 39%|███▉ | 7277/18688 [8:31:49<13:22:17, 4.22s/it] 39%|███▉ | 7278/18688 [8:31:54<13:33:04, 4.28s/it] 39%|███▉ | 7279/18688 [8:31:59<14:10:33, 4.47s/it] 39%|███▉ | 7280/18688 [8:32:03<14:21:28, 4.53s/it] {'loss': '1.026', 'grad_norm': '1.135', 'learning_rate': '7.461e-05', 'epoch': '0.3896', 'num_input_tokens_seen': 24436864, 'train_runtime': '3.072e+04', 'train_tokens_per_second': '795.4'}
39%|███▉ | 7280/18688 [8:32:03<14:21:28, 4.53s/it] 39%|███▉ | 7281/18688 [8:32:09<15:05:32, 4.76s/it] 39%|███▉ | 7282/18688 [8:32:13<14:46:53, 4.67s/it] 39%|███▉ | 7283/18688 [8:32:17<14:30:00, 4.58s/it] 39%|███▉ | 7284/18688 [8:32:21<13:48:58, 4.36s/it] 39%|███▉ | 7285/18688 [8:32:26<13:54:44, 4.39s/it] 39%|███▉ | 7286/18688 [8:32:30<13:48:39, 4.36s/it] 39%|███▉ | 7287/18688 [8:32:34<13:30:16, 4.26s/it] 39%|███▉ | 7288/18688 [8:32:39<13:44:15, 4.34s/it] 39%|███▉ | 7289/18688 [8:32:43<14:06:12, 4.45s/it] 39%|███▉ | 7290/18688 [8:32:47<13:47:24, 4.36s/it] {'loss': '0.874', 'grad_norm': '1.311', 'learning_rate': '7.453e-05', 'epoch': '0.3901', 'num_input_tokens_seen': 24472512, 'train_runtime': '3.077e+04', 'train_tokens_per_second': '795.4'}
39%|███▉ | 7290/18688 [8:32:47<13:47:24, 4.36s/it] 39%|███▉ | 7291/18688 [8:32:51<13:19:12, 4.21s/it] 39%|███▉ | 7292/18688 [8:32:56<14:14:55, 4.50s/it] 39%|███▉ | 7293/18688 [8:33:01<14:27:13, 4.57s/it] 39%|███▉ | 7294/18688 [8:33:05<13:44:25, 4.34s/it] 39%|███▉ | 7295/18688 [8:33:09<13:28:48, 4.26s/it] 39%|███▉ | 7296/18688 [8:33:13<13:03:51, 4.13s/it] 39%|███▉ | 7297/18688 [8:33:17<13:29:55, 4.27s/it] 39%|███▉ | 7298/18688 [8:33:21<13:00:18, 4.11s/it] 39%|███▉ | 7299/18688 [8:33:25<13:02:41, 4.12s/it] 39%|███▉ | 7300/18688 [8:33:29<12:49:25, 4.05s/it] {'loss': '0.9345', 'grad_norm': '1.125', 'learning_rate': '7.445e-05', 'epoch': '0.3906', 'num_input_tokens_seen': 24506688, 'train_runtime': '3.081e+04', 'train_tokens_per_second': '795.4'}
39%|███▉ | 7300/18688 [8:33:29<12:49:25, 4.05s/it] 39%|███▉ | 7301/18688 [8:33:33<12:34:36, 3.98s/it] 39%|███▉ | 7302/18688 [8:33:37<12:19:34, 3.90s/it] 39%|███▉ | 7303/18688 [8:33:40<11:58:10, 3.78s/it] 39%|███▉ | 7304/18688 [8:33:44<12:03:53, 3.82s/it] 39%|███▉ | 7305/18688 [8:33:48<12:28:00, 3.94s/it] 39%|███▉ | 7306/18688 [8:33:53<13:23:40, 4.24s/it] 39%|███▉ | 7307/18688 [8:33:59<14:26:35, 4.57s/it] 39%|███▉ | 7308/18688 [8:34:03<14:24:53, 4.56s/it] 39%|███▉ | 7309/18688 [8:34:07<14:07:28, 4.47s/it] 39%|███▉ | 7310/18688 [8:34:12<13:55:50, 4.41s/it] {'loss': '0.9314', 'grad_norm': '1.3', 'learning_rate': '7.437e-05', 'epoch': '0.3912', 'num_input_tokens_seen': 24542768, 'train_runtime': '3.085e+04', 'train_tokens_per_second': '795.5'}
39%|███▉ | 7310/18688 [8:34:12<13:55:50, 4.41s/it] 39%|███▉ | 7311/18688 [8:34:15<13:08:41, 4.16s/it] 39%|███▉ | 7312/18688 [8:34:19<12:46:18, 4.04s/it] 39%|███▉ | 7313/18688 [8:34:23<12:45:47, 4.04s/it] 39%|███▉ | 7314/18688 [8:34:27<12:14:12, 3.87s/it] 39%|███▉ | 7315/18688 [8:34:30<11:56:23, 3.78s/it] 39%|███▉ | 7316/18688 [8:34:35<13:05:07, 4.14s/it] 39%|███▉ | 7317/18688 [8:34:39<13:16:46, 4.20s/it] 39%|███▉ | 7318/18688 [8:34:43<13:03:42, 4.14s/it] 39%|███▉ | 7319/18688 [8:34:47<12:52:12, 4.08s/it] 39%|███▉ | 7320/18688 [8:34:52<13:16:24, 4.20s/it] {'loss': '0.9832', 'grad_norm': '1.318', 'learning_rate': '7.429e-05', 'epoch': '0.3917', 'num_input_tokens_seen': 24574384, 'train_runtime': '3.089e+04', 'train_tokens_per_second': '795.5'}
39%|███▉ | 7320/18688 [8:34:52<13:16:24, 4.20s/it] 39%|███▉ | 7321/18688 [8:34:56<12:52:02, 4.08s/it] 39%|███▉ | 7322/18688 [8:35:00<13:07:38, 4.16s/it] 39%|███▉ | 7323/18688 [8:35:04<13:15:49, 4.20s/it] 39%|███▉ | 7324/18688 [8:35:08<12:57:37, 4.11s/it] 39%|███▉ | 7325/18688 [8:35:12<12:58:44, 4.11s/it] 39%|███▉ | 7326/18688 [8:35:16<12:23:27, 3.93s/it] 39%|███▉ | 7327/18688 [8:35:20<12:14:39, 3.88s/it] 39%|███▉ | 7328/18688 [8:35:24<12:29:44, 3.96s/it] 39%|███▉ | 7329/18688 [8:35:28<12:54:43, 4.09s/it] 39%|███▉ | 7330/18688 [8:35:32<12:36:42, 4.00s/it] {'loss': '0.9245', 'grad_norm': '1.286', 'learning_rate': '7.421e-05', 'epoch': '0.3922', 'num_input_tokens_seen': 24606744, 'train_runtime': '3.093e+04', 'train_tokens_per_second': '795.5'}
39%|███▉ | 7330/18688 [8:35:32<12:36:42, 4.00s/it] 39%|███▉ | 7331/18688 [8:35:36<12:48:57, 4.06s/it] 39%|███▉ | 7332/18688 [8:35:40<12:29:17, 3.96s/it] 39%|███▉ | 7333/18688 [8:35:44<12:20:38, 3.91s/it] 39%|███▉ | 7334/18688 [8:35:48<12:15:47, 3.89s/it] 39%|███▉ | 7335/18688 [8:35:52<12:35:31, 3.99s/it] 39%|███▉ | 7336/18688 [8:35:56<12:38:16, 4.01s/it] 39%|███▉ | 7337/18688 [8:36:00<13:00:15, 4.12s/it] 39%|███▉ | 7338/18688 [8:36:04<12:47:48, 4.06s/it] 39%|███▉ | 7339/18688 [8:36:09<13:52:45, 4.40s/it] 39%|███▉ | 7340/18688 [8:36:13<13:00:32, 4.13s/it] {'loss': '0.934', 'grad_norm': '1.208', 'learning_rate': '7.413e-05', 'epoch': '0.3928', 'num_input_tokens_seen': 24638920, 'train_runtime': '3.097e+04', 'train_tokens_per_second': '795.5'}
39%|███▉ | 7340/18688 [8:36:13<13:00:32, 4.13s/it] 39%|███▉ | 7341/18688 [8:36:16<12:25:49, 3.94s/it] 39%|███▉ | 7342/18688 [8:36:20<12:23:44, 3.93s/it] 39%|███▉ | 7343/18688 [8:36:24<12:08:40, 3.85s/it] 39%|███▉ | 7344/18688 [8:36:28<12:34:18, 3.99s/it] 39%|███▉ | 7345/18688 [8:36:32<12:07:59, 3.85s/it] 39%|███▉ | 7346/18688 [8:36:35<11:46:45, 3.74s/it] 39%|███▉ | 7347/18688 [8:36:40<12:24:08, 3.94s/it] 39%|███▉ | 7348/18688 [8:36:44<12:56:43, 4.11s/it] 39%|███▉ | 7349/18688 [8:36:48<12:59:27, 4.12s/it] 39%|███▉ | 7350/18688 [8:36:52<12:59:53, 4.13s/it] {'loss': '0.8763', 'grad_norm': '1.305', 'learning_rate': '7.405e-05', 'epoch': '0.3933', 'num_input_tokens_seen': 24669840, 'train_runtime': '3.101e+04', 'train_tokens_per_second': '795.5'}
39%|███▉ | 7350/18688 [8:36:52<12:59:53, 4.13s/it] 39%|███▉ | 7351/18688 [8:36:56<12:41:42, 4.03s/it] 39%|███▉ | 7352/18688 [8:37:00<12:49:24, 4.07s/it] 39%|███▉ | 7353/18688 [8:37:05<13:08:20, 4.17s/it] 39%|███▉ | 7354/18688 [8:37:10<13:58:24, 4.44s/it] 39%|███▉ | 7355/18688 [8:37:15<14:52:15, 4.72s/it] 39%|███▉ | 7356/18688 [8:37:19<13:50:12, 4.40s/it] 39%|███▉ | 7357/18688 [8:37:23<13:46:30, 4.38s/it] 39%|███▉ | 7358/18688 [8:37:28<14:14:55, 4.53s/it] 39%|███▉ | 7359/18688 [8:37:33<14:13:55, 4.52s/it] 39%|███▉ | 7360/18688 [8:37:38<14:54:58, 4.74s/it] {'loss': '0.9151', 'grad_norm': '1.205', 'learning_rate': '7.397e-05', 'epoch': '0.3938', 'num_input_tokens_seen': 24709056, 'train_runtime': '3.106e+04', 'train_tokens_per_second': '795.6'}
39%|███▉ | 7360/18688 [8:37:38<14:54:58, 4.74s/it] 39%|███▉ | 7361/18688 [8:37:42<14:15:19, 4.53s/it] 39%|███▉ | 7362/18688 [8:37:47<14:27:29, 4.60s/it] 39%|███▉ | 7363/18688 [8:37:52<15:40:00, 4.98s/it] 39%|███▉ | 7364/18688 [8:37:56<14:40:25, 4.66s/it] 39%|███▉ | 7365/18688 [8:38:00<13:45:13, 4.37s/it] 39%|███▉ | 7366/18688 [8:38:04<12:54:45, 4.11s/it] 39%|███▉ | 7367/18688 [8:38:07<12:36:57, 4.01s/it] 39%|███▉ | 7368/18688 [8:38:12<12:48:40, 4.07s/it] 39%|███▉ | 7369/18688 [8:38:16<12:41:35, 4.04s/it] 39%|███▉ | 7370/18688 [8:38:20<13:07:57, 4.18s/it] {'loss': '0.8887', 'grad_norm': '1.019', 'learning_rate': '7.389e-05', 'epoch': '0.3944', 'num_input_tokens_seen': 24743888, 'train_runtime': '3.11e+04', 'train_tokens_per_second': '795.6'}
39%|███▉ | 7370/18688 [8:38:20<13:07:57, 4.18s/it] 39%|███▉ | 7371/18688 [8:38:24<12:46:27, 4.06s/it] 39%|███▉ | 7372/18688 [8:38:28<12:47:49, 4.07s/it] 39%|███▉ | 7373/18688 [8:38:31<12:15:21, 3.90s/it] 39%|███▉ | 7374/18688 [8:38:35<12:19:18, 3.92s/it] 39%|███▉ | 7375/18688 [8:38:40<13:12:25, 4.20s/it] 39%|███▉ | 7376/18688 [8:38:44<13:08:59, 4.18s/it] 39%|███▉ | 7377/18688 [8:38:49<13:18:34, 4.24s/it] 39%|███▉ | 7378/18688 [8:38:53<12:52:58, 4.10s/it] 39%|███▉ | 7379/18688 [8:38:57<13:06:30, 4.17s/it] 39%|███▉ | 7380/18688 [8:39:00<12:25:49, 3.96s/it] {'loss': '0.9086', 'grad_norm': '1.485', 'learning_rate': '7.381e-05', 'epoch': '0.3949', 'num_input_tokens_seen': 24775520, 'train_runtime': '3.114e+04', 'train_tokens_per_second': '795.6'}
39%|███▉ | 7380/18688 [8:39:00<12:25:49, 3.96s/it] 39%|███▉ | 7381/18688 [8:39:05<12:41:00, 4.04s/it] 40%|███▉ | 7382/18688 [8:39:10<13:53:35, 4.42s/it] 40%|███▉ | 7383/18688 [8:39:14<13:52:25, 4.42s/it] 40%|███▉ | 7384/18688 [8:39:18<13:06:31, 4.17s/it] 40%|███▉ | 7385/18688 [8:39:22<13:16:54, 4.23s/it] 40%|███▉ | 7386/18688 [8:39:27<13:21:51, 4.26s/it] 40%|███▉ | 7387/18688 [8:39:30<12:51:42, 4.10s/it] 40%|███▉ | 7388/18688 [8:39:34<12:33:50, 4.00s/it] 40%|███▉ | 7389/18688 [8:39:39<13:09:26, 4.19s/it] 40%|███▉ | 7390/18688 [8:39:42<12:28:28, 3.97s/it] {'loss': '0.969', 'grad_norm': '1.489', 'learning_rate': '7.373e-05', 'epoch': '0.3955', 'num_input_tokens_seen': 24809368, 'train_runtime': '3.118e+04', 'train_tokens_per_second': '795.6'}
40%|███▉ | 7390/18688 [8:39:42<12:28:28, 3.97s/it] 40%|███▉ | 7391/18688 [8:39:47<13:20:09, 4.25s/it] 40%|███▉ | 7392/18688 [8:39:51<12:46:54, 4.07s/it] 40%|███▉ | 7393/18688 [8:39:55<13:06:47, 4.18s/it] 40%|███▉ | 7394/18688 [8:39:59<13:11:58, 4.21s/it] 40%|███▉ | 7395/18688 [8:40:04<13:21:05, 4.26s/it] 40%|███▉ | 7396/18688 [8:40:08<12:58:35, 4.14s/it] 40%|███▉ | 7397/18688 [8:40:12<13:08:09, 4.19s/it] 40%|███▉ | 7398/18688 [8:40:16<12:50:33, 4.10s/it] 40%|███▉ | 7399/18688 [8:40:19<12:23:56, 3.95s/it] 40%|███▉ | 7400/18688 [8:40:24<13:12:35, 4.21s/it] {'loss': '0.9238', 'grad_norm': '1.167', 'learning_rate': '7.365e-05', 'epoch': '0.396', 'num_input_tokens_seen': 24842808, 'train_runtime': '3.122e+04', 'train_tokens_per_second': '795.6'}
40%|███▉ | 7400/18688 [8:40:24<13:12:35, 4.21s/it] 40%|███▉ | 7401/18688 [8:40:28<12:33:48, 4.01s/it] 40%|███▉ | 7402/18688 [8:40:32<12:39:51, 4.04s/it] 40%|███▉ | 7403/18688 [8:40:36<12:49:28, 4.09s/it] 40%|███▉ | 7404/18688 [8:40:40<12:58:55, 4.14s/it] 40%|███▉ | 7405/18688 [8:40:44<12:27:51, 3.98s/it] 40%|███▉ | 7406/18688 [8:40:48<12:08:38, 3.88s/it] 40%|███▉ | 7407/18688 [8:40:52<12:09:31, 3.88s/it] 40%|███▉ | 7408/18688 [8:40:56<12:37:44, 4.03s/it] 40%|███▉ | 7409/18688 [8:41:00<12:40:21, 4.04s/it] 40%|███▉ | 7410/18688 [8:41:05<13:23:58, 4.28s/it] {'loss': '0.8813', 'grad_norm': '1.249', 'learning_rate': '7.356e-05', 'epoch': '0.3965', 'num_input_tokens_seen': 24874784, 'train_runtime': '3.127e+04', 'train_tokens_per_second': '795.6'}
40%|███▉ | 7410/18688 [8:41:05<13:23:58, 4.28s/it] 40%|███▉ | 7411/18688 [8:41:09<13:20:49, 4.26s/it] 40%|███▉ | 7412/18688 [8:41:12<12:35:08, 4.02s/it] 40%|███▉ | 7413/18688 [8:41:16<12:07:08, 3.87s/it] 40%|███▉ | 7414/18688 [8:41:21<13:24:08, 4.28s/it] 40%|███▉ | 7415/18688 [8:41:25<12:44:39, 4.07s/it] 40%|███▉ | 7416/18688 [8:41:28<12:15:57, 3.92s/it] 40%|███▉ | 7417/18688 [8:41:33<12:30:04, 3.99s/it] 40%|███▉ | 7418/18688 [8:41:37<12:27:31, 3.98s/it] 40%|███▉ | 7419/18688 [8:41:41<12:43:57, 4.07s/it] 40%|███▉ | 7420/18688 [8:41:44<12:15:44, 3.92s/it] {'loss': '0.8807', 'grad_norm': '1.384', 'learning_rate': '7.348e-05', 'epoch': '0.3971', 'num_input_tokens_seen': 24906192, 'train_runtime': '3.13e+04', 'train_tokens_per_second': '795.6'}
40%|███▉ | 7420/18688 [8:41:44<12:15:44, 3.92s/it] 40%|███▉ | 7421/18688 [8:41:48<11:49:13, 3.78s/it] 40%|███▉ | 7422/18688 [8:41:51<11:37:01, 3.71s/it] 40%|███▉ | 7423/18688 [8:41:55<11:58:54, 3.83s/it] 40%|███▉ | 7424/18688 [8:42:00<12:46:32, 4.08s/it] 40%|███▉ | 7425/18688 [8:42:05<13:13:17, 4.23s/it] 40%|███▉ | 7426/18688 [8:42:09<13:19:03, 4.26s/it] 40%|███▉ | 7427/18688 [8:42:13<13:29:12, 4.31s/it] 40%|███▉ | 7428/18688 [8:42:18<13:57:16, 4.46s/it] 40%|███▉ | 7429/18688 [8:42:22<13:28:04, 4.31s/it] 40%|███▉ | 7430/18688 [8:42:26<13:18:08, 4.25s/it] {'loss': '0.9886', 'grad_norm': '1.211', 'learning_rate': '7.34e-05', 'epoch': '0.3976', 'num_input_tokens_seen': 24941200, 'train_runtime': '3.135e+04', 'train_tokens_per_second': '795.7'}
40%|███▉ | 7430/18688 [8:42:26<13:18:08, 4.25s/it] 40%|███▉ | 7431/18688 [8:42:31<13:26:27, 4.30s/it] 40%|███▉ | 7432/18688 [8:42:35<12:59:38, 4.16s/it] 40%|███▉ | 7433/18688 [8:42:39<12:48:29, 4.10s/it] 40%|███▉ | 7434/18688 [8:42:42<12:31:32, 4.01s/it] 40%|███▉ | 7435/18688 [8:42:47<13:22:14, 4.28s/it] 40%|███▉ | 7436/18688 [8:42:51<13:04:49, 4.19s/it] 40%|███▉ | 7437/18688 [8:42:55<12:57:49, 4.15s/it] 40%|███▉ | 7438/18688 [8:43:00<13:10:25, 4.22s/it] 40%|███▉ | 7439/18688 [8:43:04<13:05:28, 4.19s/it] 40%|███▉ | 7440/18688 [8:43:08<13:34:39, 4.35s/it] {'loss': '0.9579', 'grad_norm': '1.451', 'learning_rate': '7.332e-05', 'epoch': '0.3981', 'num_input_tokens_seen': 24975024, 'train_runtime': '3.139e+04', 'train_tokens_per_second': '795.7'}
40%|███▉ | 7440/18688 [8:43:08<13:34:39, 4.35s/it] 40%|███▉ | 7441/18688 [8:43:13<13:42:34, 4.39s/it] 40%|███▉ | 7442/18688 [8:43:18<13:53:44, 4.45s/it] 40%|███▉ | 7443/18688 [8:43:21<13:23:43, 4.29s/it] 40%|███▉ | 7444/18688 [8:43:26<13:17:18, 4.25s/it] 40%|███▉ | 7445/18688 [8:43:29<12:49:13, 4.11s/it] 40%|███▉ | 7446/18688 [8:43:33<12:38:39, 4.05s/it] 40%|███▉ | 7447/18688 [8:43:38<13:11:13, 4.22s/it] 40%|███▉ | 7448/18688 [8:43:42<12:44:26, 4.08s/it] 40%|███▉ | 7449/18688 [8:43:46<12:36:07, 4.04s/it] 40%|███▉ | 7450/18688 [8:43:50<12:27:30, 3.99s/it] {'loss': '0.9524', 'grad_norm': '1.377', 'learning_rate': '7.324e-05', 'epoch': '0.3987', 'num_input_tokens_seen': 25009160, 'train_runtime': '3.143e+04', 'train_tokens_per_second': '795.7'}
40%|███▉ | 7450/18688 [8:43:50<12:27:30, 3.99s/it] 40%|███▉ | 7451/18688 [8:43:53<12:16:41, 3.93s/it] 40%|███▉ | 7452/18688 [8:43:57<11:55:56, 3.82s/it] 40%|███▉ | 7453/18688 [8:44:01<12:23:14, 3.97s/it] 40%|███▉ | 7454/18688 [8:44:05<12:13:04, 3.92s/it] 40%|███▉ | 7455/18688 [8:44:09<12:30:23, 4.01s/it] 40%|███▉ | 7456/18688 [8:44:13<12:08:24, 3.89s/it] 40%|███▉ | 7457/18688 [8:44:17<12:42:04, 4.07s/it] 40%|███▉ | 7458/18688 [8:44:21<12:22:01, 3.96s/it] 40%|███▉ | 7459/18688 [8:44:27<13:52:26, 4.45s/it] 40%|███▉ | 7460/18688 [8:44:30<13:10:14, 4.22s/it] {'loss': '0.9086', 'grad_norm': '1.32', 'learning_rate': '7.316e-05', 'epoch': '0.3992', 'num_input_tokens_seen': 25041328, 'train_runtime': '3.147e+04', 'train_tokens_per_second': '795.7'}
40%|███▉ | 7460/18688 [8:44:30<13:10:14, 4.22s/it] 40%|███▉ | 7461/18688 [8:44:34<12:32:56, 4.02s/it] 40%|███▉ | 7462/18688 [8:44:38<12:32:35, 4.02s/it] 40%|███▉ | 7463/18688 [8:44:41<12:06:32, 3.88s/it] 40%|███▉ | 7464/18688 [8:44:45<11:51:43, 3.80s/it] 40%|███▉ | 7465/18688 [8:44:49<11:48:02, 3.79s/it] 40%|███▉ | 7466/18688 [8:44:53<12:06:11, 3.88s/it] 40%|███▉ | 7467/18688 [8:44:57<12:17:23, 3.94s/it] 40%|███▉ | 7468/18688 [8:45:01<11:56:06, 3.83s/it] 40%|███▉ | 7469/18688 [8:45:04<11:41:25, 3.75s/it] 40%|███▉ | 7470/18688 [8:45:08<12:01:05, 3.86s/it] {'loss': '0.8817', 'grad_norm': '1.342', 'learning_rate': '7.308e-05', 'epoch': '0.3997', 'num_input_tokens_seen': 25070608, 'train_runtime': '3.151e+04', 'train_tokens_per_second': '795.7'}
40%|███▉ | 7470/18688 [8:45:08<12:01:05, 3.86s/it] 40%|███▉ | 7471/18688 [8:45:12<12:15:22, 3.93s/it] 40%|███▉ | 7472/18688 [8:45:16<12:03:23, 3.87s/it] 40%|███▉ | 7473/18688 [8:45:20<11:48:33, 3.79s/it] 40%|███▉ | 7474/18688 [8:45:23<11:42:18, 3.76s/it] 40%|███▉ | 7475/18688 [8:45:28<12:10:40, 3.91s/it] 40%|████ | 7476/18688 [8:45:32<12:52:02, 4.13s/it] 40%|████ | 7477/18688 [8:45:37<13:18:45, 4.27s/it] 40%|████ | 7478/18688 [8:45:41<13:00:11, 4.18s/it] 40%|████ | 7479/18688 [8:45:45<12:36:57, 4.05s/it] 40%|████ | 7480/18688 [8:45:48<12:13:28, 3.93s/it] {'loss': '0.9371', 'grad_norm': '1.443', 'learning_rate': '7.3e-05', 'epoch': '0.4003', 'num_input_tokens_seen': 25101264, 'train_runtime': '3.155e+04', 'train_tokens_per_second': '795.6'}
40%|████ | 7480/18688 [8:45:48<12:13:28, 3.93s/it] 40%|████ | 7481/18688 [8:45:53<12:33:50, 4.04s/it] 40%|████ | 7482/18688 [8:45:56<12:20:07, 3.96s/it] 40%|████ | 7483/18688 [8:46:00<12:25:38, 3.99s/it] 40%|████ | 7484/18688 [8:46:05<12:46:10, 4.10s/it] 40%|████ | 7485/18688 [8:46:09<12:32:48, 4.03s/it] 40%|████ | 7486/18688 [8:46:13<12:31:54, 4.03s/it] 40%|████ | 7487/18688 [8:46:17<12:59:55, 4.18s/it] 40%|████ | 7488/18688 [8:46:21<12:35:51, 4.05s/it] 40%|████ | 7489/18688 [8:46:25<13:03:10, 4.20s/it] 40%|████ | 7490/18688 [8:46:29<12:35:42, 4.05s/it] {'loss': '0.9166', 'grad_norm': '1.211', 'learning_rate': '7.292e-05', 'epoch': '0.4008', 'num_input_tokens_seen': 25134184, 'train_runtime': '3.159e+04', 'train_tokens_per_second': '795.6'}
40%|████ | 7490/18688 [8:46:29<12:35:42, 4.05s/it] 40%|████ | 7491/18688 [8:46:34<12:57:42, 4.17s/it] 40%|████ | 7492/18688 [8:46:38<13:07:30, 4.22s/it] 40%|████ | 7493/18688 [8:46:42<13:25:27, 4.32s/it] 40%|████ | 7494/18688 [8:46:47<13:11:35, 4.24s/it] 40%|████ | 7495/18688 [8:46:51<13:11:25, 4.24s/it] 40%|████ | 7496/18688 [8:46:55<13:02:39, 4.20s/it] 40%|████ | 7497/18688 [8:47:00<13:35:48, 4.37s/it] 40%|████ | 7498/18688 [8:47:03<13:01:18, 4.19s/it] 40%|████ | 7499/18688 [8:47:08<12:59:57, 4.18s/it] 40%|████ | 7500/18688 [8:47:12<13:03:24, 4.20s/it] {'loss': '0.9207', 'grad_norm': '1.189', 'learning_rate': '7.284e-05', 'epoch': '0.4013', 'num_input_tokens_seen': 25169216, 'train_runtime': '3.163e+04', 'train_tokens_per_second': '795.7'}
40%|████ | 7500/18688 [8:47:12<13:03:24, 4.20s/it] 40%|████ | 7501/18688 [8:47:16<12:52:44, 4.14s/it] 40%|████ | 7502/18688 [8:47:19<12:15:55, 3.95s/it] 40%|████ | 7503/18688 [8:47:24<12:41:43, 4.09s/it] 40%|████ | 7504/18688 [8:47:28<12:57:59, 4.17s/it] 40%|████ | 7505/18688 [8:47:32<12:25:18, 4.00s/it] 40%|████ | 7506/18688 [8:47:36<12:33:30, 4.04s/it] 40%|████ | 7507/18688 [8:47:40<12:40:12, 4.08s/it] 40%|████ | 7508/18688 [8:47:44<12:32:40, 4.04s/it] 40%|████ | 7509/18688 [8:47:49<13:16:56, 4.28s/it] 40%|████ | 7510/18688 [8:47:53<13:32:16, 4.36s/it] {'loss': '0.9483', 'grad_norm': '1.49', 'learning_rate': '7.275e-05', 'epoch': '0.4019', 'num_input_tokens_seen': 25203480, 'train_runtime': '3.167e+04', 'train_tokens_per_second': '795.7'}
40%|████ | 7510/18688 [8:47:53<13:32:16, 4.36s/it] 40%|████ | 7511/18688 [8:47:58<14:05:23, 4.54s/it] 40%|████ | 7512/18688 [8:48:03<14:02:43, 4.52s/it] 40%|████ | 7513/18688 [8:48:07<13:33:41, 4.37s/it] 40%|████ | 7514/18688 [8:48:12<14:21:22, 4.63s/it] 40%|████ | 7515/18688 [8:48:16<13:42:21, 4.42s/it] 40%|████ | 7516/18688 [8:48:20<13:22:52, 4.31s/it] 40%|████ | 7517/18688 [8:48:24<13:31:50, 4.36s/it] 40%|████ | 7518/18688 [8:48:28<12:49:26, 4.13s/it] 40%|████ | 7519/18688 [8:48:32<12:32:48, 4.04s/it] 40%|████ | 7520/18688 [8:48:36<12:23:33, 3.99s/it] {'loss': '0.9521', 'grad_norm': '1.358', 'learning_rate': '7.267e-05', 'epoch': '0.4024', 'num_input_tokens_seen': 25238696, 'train_runtime': '3.172e+04', 'train_tokens_per_second': '795.8'}
40%|████ | 7520/18688 [8:48:36<12:23:33, 3.99s/it] 40%|████ | 7521/18688 [8:48:40<12:47:36, 4.12s/it] 40%|████ | 7522/18688 [8:48:45<12:58:57, 4.19s/it] 40%|████ | 7523/18688 [8:48:49<13:16:20, 4.28s/it] 40%|████ | 7524/18688 [8:48:53<13:16:32, 4.28s/it] 40%|████ | 7525/18688 [8:48:57<12:36:57, 4.07s/it] 40%|████ | 7526/18688 [8:49:01<12:53:50, 4.16s/it] 40%|████ | 7527/18688 [8:49:06<13:19:26, 4.30s/it] 40%|████ | 7528/18688 [8:49:10<12:42:04, 4.10s/it] 40%|████ | 7529/18688 [8:49:14<12:41:10, 4.09s/it] 40%|████ | 7530/18688 [8:49:18<12:36:01, 4.07s/it] {'loss': '0.9483', 'grad_norm': '1.295', 'learning_rate': '7.259e-05', 'epoch': '0.4029', 'num_input_tokens_seen': 25272456, 'train_runtime': '3.176e+04', 'train_tokens_per_second': '795.8'}
40%|████ | 7530/18688 [8:49:18<12:36:01, 4.07s/it] 40%|████ | 7531/18688 [8:49:22<12:47:24, 4.13s/it] 40%|████ | 7532/18688 [8:49:26<13:00:02, 4.20s/it] 40%|████ | 7533/18688 [8:49:30<12:51:57, 4.15s/it] 40%|████ | 7534/18688 [8:49:35<13:35:03, 4.38s/it] 40%|████ | 7535/18688 [8:49:39<13:24:10, 4.33s/it] 40%|████ | 7536/18688 [8:49:43<12:48:15, 4.13s/it] 40%|████ | 7537/18688 [8:49:47<12:58:52, 4.19s/it] 40%|████ | 7538/18688 [8:49:51<12:48:49, 4.14s/it] 40%|████ | 7539/18688 [8:49:55<12:24:20, 4.01s/it] 40%|████ | 7540/18688 [8:50:00<12:44:53, 4.12s/it] {'loss': '0.969', 'grad_norm': '1.209', 'learning_rate': '7.251e-05', 'epoch': '0.4035', 'num_input_tokens_seen': 25306512, 'train_runtime': '3.18e+04', 'train_tokens_per_second': '795.8'}
40%|████ | 7540/18688 [8:50:00<12:44:53, 4.12s/it] 40%|████ | 7541/18688 [8:50:04<13:00:19, 4.20s/it] 40%|████ | 7542/18688 [8:50:07<12:19:47, 3.98s/it] 40%|████ | 7543/18688 [8:50:11<12:24:02, 4.01s/it] 40%|████ | 7544/18688 [8:50:15<11:52:33, 3.84s/it] 40%|████ | 7545/18688 [8:50:19<11:43:14, 3.79s/it] 40%|████ | 7546/18688 [8:50:23<12:09:00, 3.93s/it] 40%|████ | 7547/18688 [8:50:26<11:51:55, 3.83s/it] 40%|████ | 7548/18688 [8:50:32<13:50:20, 4.47s/it] 40%|████ | 7549/18688 [8:50:37<13:41:20, 4.42s/it] 40%|████ | 7550/18688 [8:50:41<13:09:24, 4.25s/it] {'loss': '0.9188', 'grad_norm': '1.293', 'learning_rate': '7.243e-05', 'epoch': '0.404', 'num_input_tokens_seen': 25339168, 'train_runtime': '3.184e+04', 'train_tokens_per_second': '795.8'}
40%|████ | 7550/18688 [8:50:41<13:09:24, 4.25s/it] 40%|████ | 7551/18688 [8:50:44<12:22:20, 4.00s/it] 40%|████ | 7552/18688 [8:50:48<12:35:48, 4.07s/it] 40%|████ | 7553/18688 [8:50:52<12:42:58, 4.11s/it] 40%|████ | 7554/18688 [8:50:56<12:25:28, 4.02s/it] 40%|████ | 7555/18688 [8:51:00<12:06:30, 3.92s/it] 40%|████ | 7556/18688 [8:51:03<11:48:01, 3.82s/it] 40%|████ | 7557/18688 [8:51:07<11:56:01, 3.86s/it] 40%|████ | 7558/18688 [8:51:12<12:32:49, 4.06s/it] 40%|████ | 7559/18688 [8:51:16<12:27:32, 4.03s/it] 40%|████ | 7560/18688 [8:51:20<12:14:55, 3.96s/it] {'loss': '0.9097', 'grad_norm': '1.505', 'learning_rate': '7.235e-05', 'epoch': '0.4045', 'num_input_tokens_seen': 25370200, 'train_runtime': '3.188e+04', 'train_tokens_per_second': '795.8'}
40%|████ | 7560/18688 [8:51:20<12:14:55, 3.96s/it] 40%|████ | 7561/18688 [8:51:24<12:50:22, 4.15s/it] 40%|████ | 7562/18688 [8:51:28<12:12:23, 3.95s/it] 40%|████ | 7563/18688 [8:51:31<11:51:24, 3.84s/it] 40%|████ | 7564/18688 [8:51:36<12:33:03, 4.06s/it] 40%|████ | 7565/18688 [8:51:40<12:11:16, 3.94s/it] 40%|████ | 7566/18688 [8:51:45<13:44:34, 4.45s/it] 40%|████ | 7567/18688 [8:51:49<13:15:52, 4.29s/it] 40%|████ | 7568/18688 [8:51:54<13:17:11, 4.30s/it] 41%|████ | 7569/18688 [8:51:58<13:16:26, 4.30s/it] 41%|████ | 7570/18688 [8:52:02<13:13:05, 4.28s/it] {'loss': '0.9272', 'grad_norm': '1.409', 'learning_rate': '7.227e-05', 'epoch': '0.4051', 'num_input_tokens_seen': 25404224, 'train_runtime': '3.192e+04', 'train_tokens_per_second': '795.8'}
41%|████ | 7570/18688 [8:52:02<13:13:05, 4.28s/it] 41%|████ | 7571/18688 [8:52:06<13:07:25, 4.25s/it] 41%|████ | 7572/18688 [8:52:10<13:04:19, 4.23s/it] 41%|████ | 7573/18688 [8:52:14<12:26:07, 4.03s/it] 41%|████ | 7574/18688 [8:52:18<12:08:47, 3.93s/it] 41%|████ | 7575/18688 [8:52:22<12:16:30, 3.98s/it] 41%|████ | 7576/18688 [8:52:26<12:36:39, 4.09s/it] 41%|████ | 7577/18688 [8:52:30<12:27:59, 4.04s/it] 41%|████ | 7578/18688 [8:52:34<12:33:27, 4.07s/it] 41%|████ | 7579/18688 [8:52:38<12:41:16, 4.11s/it] 41%|████ | 7580/18688 [8:52:43<12:47:10, 4.14s/it] {'loss': '0.9461', 'grad_norm': '1.514', 'learning_rate': '7.218e-05', 'epoch': '0.4056', 'num_input_tokens_seen': 25435736, 'train_runtime': '3.196e+04', 'train_tokens_per_second': '795.8'}
41%|████ | 7580/18688 [8:52:43<12:47:10, 4.14s/it] 41%|████ | 7581/18688 [8:52:47<13:23:32, 4.34s/it] 41%|████ | 7582/18688 [8:52:52<13:28:15, 4.37s/it] 41%|████ | 7583/18688 [8:52:56<13:24:55, 4.35s/it] 41%|████ | 7584/18688 [8:53:00<12:48:16, 4.15s/it] 41%|████ | 7585/18688 [8:53:05<13:44:24, 4.46s/it] 41%|████ | 7586/18688 [8:53:09<13:14:32, 4.29s/it] 41%|████ | 7587/18688 [8:53:13<13:05:47, 4.25s/it] 41%|████ | 7588/18688 [8:53:17<12:46:04, 4.14s/it] 41%|████ | 7589/18688 [8:53:21<12:41:02, 4.11s/it] 41%|████ | 7590/18688 [8:53:25<12:49:26, 4.16s/it] {'loss': '0.9205', 'grad_norm': '1.322', 'learning_rate': '7.21e-05', 'epoch': '0.4062', 'num_input_tokens_seen': 25470912, 'train_runtime': '3.201e+04', 'train_tokens_per_second': '795.8'}
41%|████ | 7590/18688 [8:53:25<12:49:26, 4.16s/it] 41%|████ | 7591/18688 [8:53:30<13:15:33, 4.30s/it] 41%|████ | 7592/18688 [8:53:34<12:55:17, 4.19s/it] 41%|████ | 7593/18688 [8:53:38<12:57:48, 4.21s/it] 41%|████ | 7594/18688 [8:53:42<13:06:05, 4.25s/it] 41%|████ | 7595/18688 [8:53:47<12:59:49, 4.22s/it] 41%|████ | 7596/18688 [8:53:51<13:06:35, 4.25s/it] 41%|████ | 7597/18688 [8:53:55<12:58:02, 4.21s/it] 41%|████ | 7598/18688 [8:53:59<12:56:09, 4.20s/it] 41%|████ | 7599/18688 [8:54:03<12:55:48, 4.20s/it] 41%|████ | 7600/18688 [8:54:08<13:17:52, 4.32s/it] {'loss': '0.9559', 'grad_norm': '1.24', 'learning_rate': '7.202e-05', 'epoch': '0.4067', 'num_input_tokens_seen': 25506616, 'train_runtime': '3.205e+04', 'train_tokens_per_second': '795.9'}
41%|████ | 7600/18688 [8:54:08<13:17:52, 4.32s/it] 41%|████ | 7601/18688 [8:54:12<13:15:11, 4.30s/it] 41%|████ | 7602/18688 [8:54:17<13:42:20, 4.45s/it] 41%|████ | 7603/18688 [8:54:21<13:11:40, 4.29s/it] 41%|████ | 7604/18688 [8:54:25<13:13:14, 4.29s/it] 41%|████ | 7605/18688 [8:54:29<13:07:07, 4.26s/it] 41%|████ | 7606/18688 [8:54:34<13:45:22, 4.47s/it] 41%|████ | 7607/18688 [8:54:39<13:31:25, 4.39s/it] 41%|████ | 7608/18688 [8:54:43<13:04:25, 4.25s/it] 41%|████ | 7609/18688 [8:54:48<14:01:26, 4.56s/it] 41%|████ | 7610/18688 [8:54:53<14:43:40, 4.79s/it] {'loss': '0.951', 'grad_norm': '1.485', 'learning_rate': '7.194e-05', 'epoch': '0.4072', 'num_input_tokens_seen': 25543752, 'train_runtime': '3.209e+04', 'train_tokens_per_second': '795.9'}
41%|████ | 7610/18688 [8:54:53<14:43:40, 4.79s/it] 41%|████ | 7611/18688 [8:54:57<14:03:05, 4.57s/it] 41%|████ | 7612/18688 [8:55:02<13:55:45, 4.53s/it] 41%|████ | 7613/18688 [8:55:06<13:46:13, 4.48s/it] 41%|████ | 7614/18688 [8:55:11<13:53:58, 4.52s/it] 41%|████ | 7615/18688 [8:55:15<13:34:55, 4.42s/it] 41%|████ | 7616/18688 [8:55:19<13:25:35, 4.37s/it] 41%|████ | 7617/18688 [8:55:23<13:04:59, 4.25s/it] 41%|████ | 7618/18688 [8:55:27<12:41:44, 4.13s/it] 41%|████ | 7619/18688 [8:55:31<12:18:16, 4.00s/it] 41%|████ | 7620/18688 [8:55:34<12:05:51, 3.93s/it] {'loss': '0.903', 'grad_norm': '1.178', 'learning_rate': '7.186e-05', 'epoch': '0.4078', 'num_input_tokens_seen': 25577240, 'train_runtime': '3.213e+04', 'train_tokens_per_second': '795.9'}
41%|████ | 7620/18688 [8:55:34<12:05:51, 3.93s/it] 41%|████ | 7621/18688 [8:55:39<12:39:46, 4.12s/it] 41%|████ | 7622/18688 [8:55:43<12:45:05, 4.15s/it] 41%|████ | 7623/18688 [8:55:47<12:42:50, 4.14s/it] 41%|████ | 7624/18688 [8:55:51<12:19:30, 4.01s/it] 41%|████ | 7625/18688 [8:55:55<12:26:47, 4.05s/it] 41%|████ | 7626/18688 [8:56:01<13:51:06, 4.51s/it] 41%|████ | 7627/18688 [8:56:06<14:22:01, 4.68s/it] 41%|████ | 7628/18688 [8:56:10<13:56:43, 4.54s/it] 41%|████ | 7629/18688 [8:56:15<14:14:01, 4.63s/it] 41%|████ | 7630/18688 [8:56:19<14:12:42, 4.63s/it] {'loss': '0.9941', 'grad_norm': '1.595', 'learning_rate': '7.177e-05', 'epoch': '0.4083', 'num_input_tokens_seen': 25615320, 'train_runtime': '3.218e+04', 'train_tokens_per_second': '796'}
41%|████ | 7630/18688 [8:56:19<14:12:42, 4.63s/it] 41%|████ | 7631/18688 [8:56:23<13:16:33, 4.32s/it] 41%|████ | 7632/18688 [8:56:28<13:28:01, 4.39s/it] 41%|████ | 7633/18688 [8:56:31<13:05:25, 4.26s/it] 41%|████ | 7634/18688 [8:56:36<13:12:46, 4.30s/it] 41%|████ | 7635/18688 [8:56:40<13:16:55, 4.33s/it] 41%|████ | 7636/18688 [8:56:45<13:12:20, 4.30s/it] 41%|████ | 7637/18688 [8:56:49<12:58:36, 4.23s/it] 41%|████ | 7638/18688 [8:56:52<12:29:46, 4.07s/it] 41%|████ | 7639/18688 [8:56:57<12:45:34, 4.16s/it] 41%|████ | 7640/18688 [8:57:00<12:11:22, 3.97s/it] {'loss': '0.8813', 'grad_norm': '1.178', 'learning_rate': '7.169e-05', 'epoch': '0.4088', 'num_input_tokens_seen': 25647048, 'train_runtime': '3.222e+04', 'train_tokens_per_second': '796'}
41%|████ | 7640/18688 [8:57:00<12:11:22, 3.97s/it] 41%|████ | 7641/18688 [8:57:04<12:00:42, 3.91s/it] 41%|████ | 7642/18688 [8:57:08<12:14:04, 3.99s/it] 41%|████ | 7643/18688 [8:57:13<12:59:39, 4.24s/it] 41%|████ | 7644/18688 [8:57:18<13:24:45, 4.37s/it] 41%|████ | 7645/18688 [8:57:21<12:52:12, 4.20s/it] 41%|████ | 7646/18688 [8:57:26<12:55:25, 4.21s/it] 41%|████ | 7647/18688 [8:57:31<13:50:29, 4.51s/it] 41%|████ | 7648/18688 [8:57:35<13:54:07, 4.53s/it] 41%|████ | 7649/18688 [8:57:39<13:23:33, 4.37s/it] 41%|████ | 7650/18688 [8:57:43<13:02:09, 4.25s/it] {'loss': '0.9403', 'grad_norm': '1.364', 'learning_rate': '7.161e-05', 'epoch': '0.4094', 'num_input_tokens_seen': 25682256, 'train_runtime': '3.226e+04', 'train_tokens_per_second': '796'}
41%|████ | 7650/18688 [8:57:43<13:02:09, 4.25s/it] 41%|████ | 7651/18688 [8:57:47<12:35:08, 4.11s/it] 41%|████ | 7652/18688 [8:57:51<12:47:04, 4.17s/it] 41%|████ | 7653/18688 [8:57:57<13:48:58, 4.51s/it] 41%|████ | 7654/18688 [8:58:01<13:39:54, 4.46s/it] 41%|████ | 7655/18688 [8:58:05<12:45:06, 4.16s/it] 41%|████ | 7656/18688 [8:58:09<13:07:54, 4.29s/it] 41%|████ | 7657/18688 [8:58:13<12:28:30, 4.07s/it] 41%|████ | 7658/18688 [8:58:16<11:55:34, 3.89s/it] 41%|████ | 7659/18688 [8:58:21<12:17:20, 4.01s/it] 41%|████ | 7660/18688 [8:58:25<12:16:37, 4.01s/it] {'loss': '0.9323', 'grad_norm': '1.233', 'learning_rate': '7.153e-05', 'epoch': '0.4099', 'num_input_tokens_seen': 25715632, 'train_runtime': '3.231e+04', 'train_tokens_per_second': '796'}
41%|████ | 7660/18688 [8:58:25<12:16:37, 4.01s/it] 41%|████ | 7661/18688 [8:58:29<12:16:36, 4.01s/it] 41%|████ | 7662/18688 [8:58:33<13:04:55, 4.27s/it] 41%|████ | 7663/18688 [8:58:38<13:30:32, 4.41s/it] 41%|████ | 7664/18688 [8:58:42<12:43:00, 4.15s/it] 41%|████ | 7665/18688 [8:58:45<12:14:31, 4.00s/it] 41%|████ | 7666/18688 [8:58:50<12:34:13, 4.11s/it] 41%|████ | 7667/18688 [8:58:55<13:26:29, 4.39s/it] 41%|████ | 7668/18688 [8:58:59<12:52:05, 4.20s/it] 41%|████ | 7669/18688 [8:59:03<13:23:34, 4.38s/it] 41%|████ | 7670/18688 [8:59:09<14:18:15, 4.67s/it] {'loss': '0.9137', 'grad_norm': '1.336', 'learning_rate': '7.144e-05', 'epoch': '0.4104', 'num_input_tokens_seen': 25753384, 'train_runtime': '3.235e+04', 'train_tokens_per_second': '796.1'}
41%|████ | 7670/18688 [8:59:09<14:18:15, 4.67s/it] 41%|████ | 7671/18688 [8:59:13<13:40:58, 4.47s/it] 41%|████ | 7672/18688 [8:59:17<14:00:06, 4.58s/it] 41%|████ | 7673/18688 [8:59:22<13:49:47, 4.52s/it] 41%|████ | 7674/18688 [8:59:27<14:02:40, 4.59s/it] 41%|████ | 7675/18688 [8:59:30<13:15:48, 4.34s/it] 41%|████ | 7676/18688 [8:59:34<12:32:55, 4.10s/it] 41%|████ | 7677/18688 [8:59:39<13:02:57, 4.27s/it] 41%|████ | 7678/18688 [8:59:43<13:11:03, 4.31s/it] 41%|████ | 7679/18688 [8:59:47<12:57:53, 4.24s/it] 41%|████ | 7680/18688 [8:59:51<12:33:51, 4.11s/it] {'loss': '0.9823', 'grad_norm': '1.39', 'learning_rate': '7.136e-05', 'epoch': '0.411', 'num_input_tokens_seen': 25788376, 'train_runtime': '3.239e+04', 'train_tokens_per_second': '796.1'}
41%|████ | 7680/18688 [8:59:51<12:33:51, 4.11s/it] 41%|████ | 7681/18688 [8:59:55<13:03:06, 4.27s/it] 41%|████ | 7682/18688 [9:00:00<13:03:51, 4.27s/it] 41%|████ | 7683/18688 [9:00:04<13:27:56, 4.40s/it] 41%|████ | 7684/18688 [9:00:09<13:56:19, 4.56s/it] 41%|████ | 7685/18688 [9:00:14<14:05:31, 4.61s/it] 41%|████ | 7686/18688 [9:00:19<14:13:20, 4.65s/it] 41%|████ | 7687/18688 [9:00:23<13:20:46, 4.37s/it] 41%|████ | 7688/18688 [9:00:27<12:59:37, 4.25s/it] 41%|████ | 7689/18688 [9:00:31<13:08:00, 4.30s/it] 41%|████ | 7690/18688 [9:00:35<12:53:37, 4.22s/it] {'loss': '0.9366', 'grad_norm': '1.369', 'learning_rate': '7.128e-05', 'epoch': '0.4115', 'num_input_tokens_seen': 25826408, 'train_runtime': '3.244e+04', 'train_tokens_per_second': '796.2'}
41%|████ | 7690/18688 [9:00:35<12:53:37, 4.22s/it] 41%|████ | 7691/18688 [9:00:39<12:30:33, 4.10s/it] 41%|████ | 7692/18688 [9:00:43<12:34:08, 4.12s/it] 41%|████ | 7693/18688 [9:00:47<12:15:19, 4.01s/it] 41%|████ | 7694/18688 [9:00:51<12:08:45, 3.98s/it] 41%|████ | 7695/18688 [9:00:54<11:42:21, 3.83s/it] 41%|████ | 7696/18688 [9:00:58<11:55:10, 3.90s/it] 41%|████ | 7697/18688 [9:01:02<12:13:18, 4.00s/it] 41%|████ | 7698/18688 [9:01:07<12:51:39, 4.21s/it] 41%|████ | 7699/18688 [9:01:11<12:58:28, 4.25s/it] 41%|████ | 7700/18688 [9:01:16<13:04:21, 4.28s/it] {'loss': '0.9111', 'grad_norm': '1.155', 'learning_rate': '7.12e-05', 'epoch': '0.412', 'num_input_tokens_seen': 25859680, 'train_runtime': '3.248e+04', 'train_tokens_per_second': '796.3'}
41%|████ | 7700/18688 [9:01:16<13:04:21, 4.28s/it] 41%|████ | 7701/18688 [9:01:20<12:31:09, 4.10s/it] 41%|████ | 7702/18688 [9:01:23<12:19:33, 4.04s/it] 41%|████ | 7703/18688 [9:01:29<13:32:12, 4.44s/it] 41%|████ | 7704/18688 [9:01:34<13:53:00, 4.55s/it] 41%|████ | 7705/18688 [9:01:38<13:23:39, 4.39s/it] 41%|████ | 7706/18688 [9:01:42<13:02:18, 4.27s/it] 41%|████ | 7707/18688 [9:01:46<13:22:06, 4.38s/it] 41%|████ | 7708/18688 [9:01:50<12:37:11, 4.14s/it] 41%|████▏ | 7709/18688 [9:01:55<13:17:20, 4.36s/it] 41%|████▏ | 7710/18688 [9:01:59<12:48:20, 4.20s/it] {'loss': '0.937', 'grad_norm': '1.263', 'learning_rate': '7.111e-05', 'epoch': '0.4126', 'num_input_tokens_seen': 25895544, 'train_runtime': '3.252e+04', 'train_tokens_per_second': '796.3'}
41%|████▏ | 7710/18688 [9:01:59<12:48:20, 4.20s/it] 41%|████▏ | 7711/18688 [9:02:02<12:13:42, 4.01s/it] 41%|████▏ | 7712/18688 [9:02:07<13:00:16, 4.27s/it] 41%|████▏ | 7713/18688 [9:02:10<12:16:45, 4.03s/it] 41%|████▏ | 7714/18688 [9:02:15<12:30:18, 4.10s/it] 41%|████▏ | 7715/18688 [9:02:19<12:15:20, 4.02s/it] 41%|████▏ | 7716/18688 [9:02:22<12:06:43, 3.97s/it] 41%|████▏ | 7717/18688 [9:02:27<12:38:55, 4.15s/it] 41%|████▏ | 7718/18688 [9:02:31<12:44:07, 4.18s/it] 41%|████▏ | 7719/18688 [9:02:36<13:08:10, 4.31s/it] 41%|████▏ | 7720/18688 [9:02:41<13:48:06, 4.53s/it] {'loss': '0.903', 'grad_norm': '1.17', 'learning_rate': '7.103e-05', 'epoch': '0.4131', 'num_input_tokens_seen': 25930528, 'train_runtime': '3.256e+04', 'train_tokens_per_second': '796.4'}
41%|████▏ | 7720/18688 [9:02:41<13:48:06, 4.53s/it] 41%|████▏ | 7721/18688 [9:02:45<13:01:23, 4.27s/it] 41%|████▏ | 7722/18688 [9:02:49<13:13:40, 4.34s/it] 41%|████▏ | 7723/18688 [9:02:54<13:20:31, 4.38s/it] 41%|████▏ | 7724/18688 [9:02:57<12:48:43, 4.21s/it] 41%|████▏ | 7725/18688 [9:03:01<12:22:04, 4.06s/it] 41%|████▏ | 7726/18688 [9:03:05<12:37:39, 4.15s/it] 41%|████▏ | 7727/18688 [9:03:10<12:55:07, 4.24s/it] 41%|████▏ | 7728/18688 [9:03:14<12:45:48, 4.19s/it] 41%|████▏ | 7729/18688 [9:03:18<12:54:42, 4.24s/it] 41%|████▏ | 7730/18688 [9:03:22<12:23:03, 4.07s/it] {'loss': '0.9399', 'grad_norm': '1.233', 'learning_rate': '7.095e-05', 'epoch': '0.4136', 'num_input_tokens_seen': 25963880, 'train_runtime': '3.26e+04', 'train_tokens_per_second': '796.4'}
41%|████▏ | 7730/18688 [9:03:22<12:23:03, 4.07s/it] 41%|████▏ | 7731/18688 [9:03:27<13:19:22, 4.38s/it] 41%|████▏ | 7732/18688 [9:03:32<14:13:58, 4.68s/it] 41%|████▏ | 7733/18688 [9:03:36<13:08:54, 4.32s/it] 41%|████▏ | 7734/18688 [9:03:41<13:38:59, 4.49s/it] 41%|████▏ | 7735/18688 [9:03:45<13:09:02, 4.32s/it] 41%|████▏ | 7736/18688 [9:03:49<12:46:08, 4.20s/it] 41%|████▏ | 7737/18688 [9:03:52<12:06:46, 3.98s/it] 41%|████▏ | 7738/18688 [9:03:56<11:48:52, 3.88s/it] 41%|████▏ | 7739/18688 [9:04:00<12:03:23, 3.96s/it] 41%|████▏ | 7740/18688 [9:04:04<12:13:29, 4.02s/it] {'loss': '0.9307', 'grad_norm': '1.494', 'learning_rate': '7.086e-05', 'epoch': '0.4142', 'num_input_tokens_seen': 25999376, 'train_runtime': '3.264e+04', 'train_tokens_per_second': '796.4'}
41%|████▏ | 7740/18688 [9:04:04<12:13:29, 4.02s/it] 41%|████▏ | 7741/18688 [9:04:09<12:40:10, 4.17s/it] 41%|████▏ | 7742/18688 [9:04:13<12:31:47, 4.12s/it] 41%|████▏ | 7743/18688 [9:04:16<12:00:42, 3.95s/it] 41%|████▏ | 7744/18688 [9:04:21<12:28:44, 4.10s/it] 41%|████▏ | 7745/18688 [9:04:24<12:08:45, 4.00s/it] 41%|████▏ | 7746/18688 [9:04:28<11:44:59, 3.87s/it] 41%|████▏ | 7747/18688 [9:04:32<11:58:45, 3.94s/it] 41%|████▏ | 7748/18688 [9:04:37<13:15:15, 4.36s/it] 41%|████▏ | 7749/18688 [9:04:41<12:45:30, 4.20s/it] 41%|████▏ | 7750/18688 [9:04:45<12:08:41, 4.00s/it] {'loss': '0.8894', 'grad_norm': '1.287', 'learning_rate': '7.078e-05', 'epoch': '0.4147', 'num_input_tokens_seen': 26031920, 'train_runtime': '3.269e+04', 'train_tokens_per_second': '796.4'}
41%|████▏ | 7750/18688 [9:04:45<12:08:41, 4.00s/it] 41%|████▏ | 7751/18688 [9:04:49<12:33:23, 4.13s/it] 41%|████▏ | 7752/18688 [9:04:53<12:40:34, 4.17s/it] 41%|████▏ | 7753/18688 [9:04:58<12:50:52, 4.23s/it] 41%|████▏ | 7754/18688 [9:05:02<12:52:58, 4.24s/it] 41%|████▏ | 7755/18688 [9:05:06<12:16:41, 4.04s/it] 42%|████▏ | 7756/18688 [9:05:09<11:54:07, 3.92s/it] 42%|████▏ | 7757/18688 [9:05:14<12:24:16, 4.09s/it] 42%|████▏ | 7758/18688 [9:05:19<13:01:44, 4.29s/it] 42%|████▏ | 7759/18688 [9:05:23<13:19:10, 4.39s/it] 42%|████▏ | 7760/18688 [9:05:27<12:35:00, 4.15s/it] {'loss': '0.9313', 'grad_norm': '1.108', 'learning_rate': '7.07e-05', 'epoch': '0.4153', 'num_input_tokens_seen': 26065816, 'train_runtime': '3.273e+04', 'train_tokens_per_second': '796.5'}
42%|████▏ | 7760/18688 [9:05:27<12:35:00, 4.15s/it] 42%|████▏ | 7761/18688 [9:05:30<12:14:40, 4.03s/it] 42%|████▏ | 7762/18688 [9:05:34<11:44:48, 3.87s/it] 42%|████▏ | 7763/18688 [9:05:38<12:05:36, 3.99s/it] 42%|████▏ | 7764/18688 [9:05:42<12:19:01, 4.06s/it] 42%|████▏ | 7765/18688 [9:05:46<11:56:06, 3.93s/it] 42%|████▏ | 7766/18688 [9:05:50<12:01:49, 3.97s/it] 42%|████▏ | 7767/18688 [9:05:54<11:42:33, 3.86s/it] 42%|████▏ | 7768/18688 [9:05:58<11:36:54, 3.83s/it] 42%|████▏ | 7769/18688 [9:06:02<11:51:40, 3.91s/it] 42%|████▏ | 7770/18688 [9:06:05<11:35:59, 3.82s/it] {'loss': '0.8861', 'grad_norm': '1.375', 'learning_rate': '7.061e-05', 'epoch': '0.4158', 'num_input_tokens_seen': 26096056, 'train_runtime': '3.277e+04', 'train_tokens_per_second': '796.4'}
42%|████▏ | 7770/18688 [9:06:05<11:35:59, 3.82s/it] 42%|████▏ | 7771/18688 [9:06:10<12:00:03, 3.96s/it] 42%|████▏ | 7772/18688 [9:06:13<11:38:38, 3.84s/it] 42%|████▏ | 7773/18688 [9:06:18<12:10:50, 4.02s/it] 42%|████▏ | 7774/18688 [9:06:21<11:40:39, 3.85s/it] 42%|████▏ | 7775/18688 [9:06:26<12:20:42, 4.07s/it] 42%|████▏ | 7776/18688 [9:06:29<12:03:09, 3.98s/it] 42%|████▏ | 7777/18688 [9:06:34<12:51:39, 4.24s/it] 42%|████▏ | 7778/18688 [9:06:39<13:12:31, 4.36s/it] 42%|████▏ | 7779/18688 [9:06:43<13:13:20, 4.36s/it] 42%|████▏ | 7780/18688 [9:06:48<13:21:18, 4.41s/it] {'loss': '0.9246', 'grad_norm': '1.27', 'learning_rate': '7.053e-05', 'epoch': '0.4163', 'num_input_tokens_seen': 26130576, 'train_runtime': '3.281e+04', 'train_tokens_per_second': '796.5'}
42%|████▏ | 7780/18688 [9:06:48<13:21:18, 4.41s/it] 42%|████▏ | 7781/18688 [9:06:52<13:07:28, 4.33s/it] 42%|████▏ | 7782/18688 [9:06:56<12:44:48, 4.21s/it] 42%|████▏ | 7783/18688 [9:07:00<12:35:55, 4.16s/it] 42%|████▏ | 7784/18688 [9:07:05<13:14:17, 4.37s/it] 42%|████▏ | 7785/18688 [9:07:09<13:38:50, 4.51s/it] 42%|████▏ | 7786/18688 [9:07:14<13:17:42, 4.39s/it] 42%|████▏ | 7787/18688 [9:07:18<13:18:06, 4.39s/it] 42%|████▏ | 7788/18688 [9:07:21<12:27:05, 4.11s/it] 42%|████▏ | 7789/18688 [9:07:25<11:55:18, 3.94s/it] 42%|████▏ | 7790/18688 [9:07:29<12:19:06, 4.07s/it] {'loss': '0.9526', 'grad_norm': '1.399', 'learning_rate': '7.045e-05', 'epoch': '0.4169', 'num_input_tokens_seen': 26164784, 'train_runtime': '3.285e+04', 'train_tokens_per_second': '796.5'}
42%|████▏ | 7790/18688 [9:07:29<12:19:06, 4.07s/it] 42%|████▏ | 7791/18688 [9:07:34<12:21:48, 4.08s/it] 42%|████▏ | 7792/18688 [9:07:38<12:16:58, 4.06s/it] 42%|████▏ | 7793/18688 [9:07:41<11:48:12, 3.90s/it] 42%|████▏ | 7794/18688 [9:07:45<11:39:13, 3.85s/it] 42%|████▏ | 7795/18688 [9:07:48<11:32:18, 3.81s/it] 42%|████▏ | 7796/18688 [9:07:53<11:51:35, 3.92s/it] 42%|████▏ | 7797/18688 [9:07:57<12:00:08, 3.97s/it] 42%|████▏ | 7798/18688 [9:08:01<12:06:46, 4.00s/it] 42%|████▏ | 7799/18688 [9:08:06<13:19:35, 4.41s/it] 42%|████▏ | 7800/18688 [9:08:11<13:43:19, 4.54s/it] {'loss': '0.9263', 'grad_norm': '1.314', 'learning_rate': '7.036e-05', 'epoch': '0.4174', 'num_input_tokens_seen': 26199768, 'train_runtime': '3.289e+04', 'train_tokens_per_second': '796.6'}
42%|████▏ | 7800/18688 [9:08:11<13:43:19, 4.54s/it] 42%|████▏ | 7801/18688 [9:08:15<13:26:40, 4.45s/it] 42%|████▏ | 7802/18688 [9:08:19<12:42:47, 4.20s/it] 42%|████▏ | 7803/18688 [9:08:23<12:14:12, 4.05s/it] 42%|████▏ | 7804/18688 [9:08:26<12:01:48, 3.98s/it] 42%|████▏ | 7805/18688 [9:08:30<11:40:56, 3.86s/it] 42%|████▏ | 7806/18688 [9:08:34<11:28:00, 3.79s/it] 42%|████▏ | 7807/18688 [9:08:38<11:42:27, 3.87s/it] 42%|████▏ | 7808/18688 [9:08:42<11:47:06, 3.90s/it] 42%|████▏ | 7809/18688 [9:08:46<11:49:05, 3.91s/it] 42%|████▏ | 7810/18688 [9:08:49<11:24:30, 3.78s/it] {'loss': '0.9851', 'grad_norm': '1.376', 'learning_rate': '7.028e-05', 'epoch': '0.4179', 'num_input_tokens_seen': 26229592, 'train_runtime': '3.293e+04', 'train_tokens_per_second': '796.5'}
42%|████▏ | 7810/18688 [9:08:49<11:24:30, 3.78s/it] 42%|████▏ | 7811/18688 [9:08:53<11:23:34, 3.77s/it] 42%|████▏ | 7812/18688 [9:08:57<12:07:12, 4.01s/it] 42%|████▏ | 7813/18688 [9:09:02<12:30:14, 4.14s/it] 42%|████▏ | 7814/18688 [9:09:05<11:50:17, 3.92s/it] 42%|████▏ | 7815/18688 [9:09:09<12:03:05, 3.99s/it] 42%|████▏ | 7816/18688 [9:09:13<11:38:16, 3.85s/it] 42%|████▏ | 7817/18688 [9:09:17<12:02:46, 3.99s/it] 42%|████▏ | 7818/18688 [9:09:21<12:17:35, 4.07s/it] 42%|████▏ | 7819/18688 [9:09:28<14:04:28, 4.66s/it] 42%|████▏ | 7820/18688 [9:09:32<13:33:19, 4.49s/it] {'loss': '0.9772', 'grad_norm': '1.233', 'learning_rate': '7.02e-05', 'epoch': '0.4185', 'num_input_tokens_seen': 26265024, 'train_runtime': '3.297e+04', 'train_tokens_per_second': '796.6'}
42%|████▏ | 7820/18688 [9:09:32<13:33:19, 4.49s/it] 42%|████▏ | 7821/18688 [9:09:36<13:10:54, 4.37s/it] 42%|████▏ | 7822/18688 [9:09:39<12:28:33, 4.13s/it] 42%|████▏ | 7823/18688 [9:09:43<12:27:36, 4.13s/it] 42%|████▏ | 7824/18688 [9:09:48<12:41:51, 4.21s/it] 42%|████▏ | 7825/18688 [9:09:52<12:28:53, 4.14s/it] 42%|████▏ | 7826/18688 [9:09:55<11:53:16, 3.94s/it] 42%|████▏ | 7827/18688 [9:09:59<11:32:54, 3.83s/it] 42%|████▏ | 7828/18688 [9:10:03<11:59:50, 3.98s/it] 42%|████▏ | 7829/18688 [9:10:08<13:14:55, 4.39s/it] 42%|████▏ | 7830/18688 [9:10:12<12:36:54, 4.18s/it] {'loss': '0.9498', 'grad_norm': '1.306', 'learning_rate': '7.011e-05', 'epoch': '0.419', 'num_input_tokens_seen': 26298200, 'train_runtime': '3.301e+04', 'train_tokens_per_second': '796.6'}
42%|████▏ | 7830/18688 [9:10:12<12:36:54, 4.18s/it] 42%|████▏ | 7831/18688 [9:10:17<13:11:32, 4.37s/it] 42%|████▏ | 7832/18688 [9:10:20<12:22:19, 4.10s/it] 42%|████▏ | 7833/18688 [9:10:25<12:29:48, 4.14s/it] 42%|████▏ | 7834/18688 [9:10:28<11:59:38, 3.98s/it] 42%|████▏ | 7835/18688 [9:10:32<11:48:28, 3.92s/it] 42%|████▏ | 7836/18688 [9:10:37<12:34:12, 4.17s/it] 42%|████▏ | 7837/18688 [9:10:41<12:58:15, 4.30s/it] 42%|████▏ | 7838/18688 [9:10:46<12:48:06, 4.25s/it] 42%|████▏ | 7839/18688 [9:10:49<12:12:14, 4.05s/it] 42%|████▏ | 7840/18688 [9:10:53<12:14:31, 4.06s/it] {'loss': '0.9128', 'grad_norm': '1.519', 'learning_rate': '7.003e-05', 'epoch': '0.4195', 'num_input_tokens_seen': 26332512, 'train_runtime': '3.305e+04', 'train_tokens_per_second': '796.7'}
42%|████▏ | 7840/18688 [9:10:53<12:14:31, 4.06s/it] 42%|████▏ | 7841/18688 [9:10:57<12:11:44, 4.05s/it] 42%|████▏ | 7842/18688 [9:11:01<12:16:43, 4.08s/it] 42%|████▏ | 7843/18688 [9:11:05<12:09:14, 4.03s/it] 42%|████▏ | 7844/18688 [9:11:11<13:18:56, 4.42s/it] 42%|████▏ | 7845/18688 [9:11:16<13:42:54, 4.55s/it] 42%|████▏ | 7846/18688 [9:11:19<13:08:31, 4.36s/it] 42%|████▏ | 7847/18688 [9:11:23<12:44:31, 4.23s/it] 42%|████▏ | 7848/18688 [9:11:27<12:15:53, 4.07s/it] 42%|████▏ | 7849/18688 [9:11:32<12:35:35, 4.18s/it] 42%|████▏ | 7850/18688 [9:11:36<12:53:25, 4.28s/it] {'loss': '0.9952', 'grad_norm': '1.32', 'learning_rate': '6.995e-05', 'epoch': '0.4201', 'num_input_tokens_seen': 26366784, 'train_runtime': '3.31e+04', 'train_tokens_per_second': '796.7'}
42%|████▏ | 7850/18688 [9:11:36<12:53:25, 4.28s/it] 42%|████▏ | 7851/18688 [9:11:40<12:59:34, 4.32s/it] 42%|████▏ | 7852/18688 [9:11:44<12:44:02, 4.23s/it] 42%|████▏ | 7853/18688 [9:11:51<14:36:16, 4.85s/it] 42%|████▏ | 7854/18688 [9:11:54<13:33:16, 4.50s/it] 42%|████▏ | 7855/18688 [9:11:58<12:49:50, 4.26s/it] 42%|████▏ | 7856/18688 [9:12:02<12:43:30, 4.23s/it] 42%|████▏ | 7857/18688 [9:12:06<12:21:43, 4.11s/it] 42%|████▏ | 7858/18688 [9:12:10<12:28:23, 4.15s/it] 42%|████▏ | 7859/18688 [9:12:14<12:15:58, 4.08s/it] 42%|████▏ | 7860/18688 [9:12:18<12:13:25, 4.06s/it] {'loss': '0.9248', 'grad_norm': '1.623', 'learning_rate': '6.986e-05', 'epoch': '0.4206', 'num_input_tokens_seen': 26402672, 'train_runtime': '3.314e+04', 'train_tokens_per_second': '796.7'}
42%|████▏ | 7860/18688 [9:12:18<12:13:25, 4.06s/it] 42%|████▏ | 7861/18688 [9:12:23<12:53:47, 4.29s/it] 42%|████▏ | 7862/18688 [9:12:27<12:08:42, 4.04s/it] 42%|████▏ | 7863/18688 [9:12:31<12:56:10, 4.30s/it] 42%|████▏ | 7864/18688 [9:12:36<13:03:10, 4.34s/it] 42%|████▏ | 7865/18688 [9:12:40<12:54:22, 4.29s/it] 42%|████▏ | 7866/18688 [9:12:44<12:27:27, 4.14s/it] 42%|████▏ | 7867/18688 [9:12:48<12:00:38, 4.00s/it] 42%|████▏ | 7868/18688 [9:12:52<11:59:55, 3.99s/it] 42%|████▏ | 7869/18688 [9:12:55<11:54:23, 3.96s/it] 42%|████▏ | 7870/18688 [9:13:00<12:03:17, 4.01s/it] {'loss': '0.9412', 'grad_norm': '1.303', 'learning_rate': '6.978e-05', 'epoch': '0.4211', 'num_input_tokens_seen': 26435008, 'train_runtime': '3.318e+04', 'train_tokens_per_second': '796.7'}
42%|████▏ | 7870/18688 [9:13:00<12:03:17, 4.01s/it] 42%|████▏ | 7871/18688 [9:13:03<11:40:41, 3.89s/it] 42%|████▏ | 7872/18688 [9:13:08<12:17:02, 4.09s/it] 42%|████▏ | 7873/18688 [9:13:11<12:00:41, 4.00s/it] 42%|████▏ | 7874/18688 [9:13:15<11:36:32, 3.86s/it] 42%|████▏ | 7875/18688 [9:13:19<11:50:17, 3.94s/it] 42%|████▏ | 7876/18688 [9:13:23<12:05:30, 4.03s/it] 42%|████▏ | 7877/18688 [9:13:27<11:48:07, 3.93s/it] 42%|████▏ | 7878/18688 [9:13:32<12:31:49, 4.17s/it] 42%|████▏ | 7879/18688 [9:13:36<12:06:09, 4.03s/it] 42%|████▏ | 7880/18688 [9:13:39<11:35:47, 3.86s/it] {'loss': '0.8869', 'grad_norm': '1.347', 'learning_rate': '6.969e-05', 'epoch': '0.4217', 'num_input_tokens_seen': 26465800, 'train_runtime': '3.322e+04', 'train_tokens_per_second': '796.7'}
42%|████▏ | 7880/18688 [9:13:39<11:35:47, 3.86s/it] 42%|████▏ | 7881/18688 [9:13:44<12:13:52, 4.07s/it] 42%|████▏ | 7882/18688 [9:13:47<11:51:27, 3.95s/it] 42%|████▏ | 7883/18688 [9:13:52<12:24:42, 4.14s/it] 42%|████▏ | 7884/18688 [9:13:56<12:41:16, 4.23s/it] 42%|████▏ | 7885/18688 [9:14:01<12:49:08, 4.27s/it] 42%|████▏ | 7886/18688 [9:14:05<12:39:30, 4.22s/it] 42%|████▏ | 7887/18688 [9:14:08<12:06:25, 4.04s/it] 42%|████▏ | 7888/18688 [9:14:12<11:56:08, 3.98s/it] 42%|████▏ | 7889/18688 [9:14:16<11:43:00, 3.91s/it] 42%|████▏ | 7890/18688 [9:14:19<11:23:18, 3.80s/it] {'loss': '0.9554', 'grad_norm': '1.285', 'learning_rate': '6.961e-05', 'epoch': '0.4222', 'num_input_tokens_seen': 26498952, 'train_runtime': '3.326e+04', 'train_tokens_per_second': '796.7'}
42%|████▏ | 7890/18688 [9:14:19<11:23:18, 3.80s/it] 42%|████▏ | 7891/18688 [9:14:24<12:08:21, 4.05s/it] 42%|████▏ | 7892/18688 [9:14:30<13:30:00, 4.50s/it] 42%|████▏ | 7893/18688 [9:14:33<12:53:23, 4.30s/it] 42%|████▏ | 7894/18688 [9:14:38<13:25:07, 4.48s/it] 42%|████▏ | 7895/18688 [9:14:42<12:42:22, 4.24s/it] 42%|████▏ | 7896/18688 [9:14:46<12:12:04, 4.07s/it] 42%|████▏ | 7897/18688 [9:14:50<12:11:20, 4.07s/it] 42%|████▏ | 7898/18688 [9:14:54<11:56:30, 3.98s/it] 42%|████▏ | 7899/18688 [9:14:57<11:31:18, 3.84s/it] 42%|████▏ | 7900/18688 [9:15:01<11:38:25, 3.88s/it] {'loss': '0.9232', 'grad_norm': '1.296', 'learning_rate': '6.953e-05', 'epoch': '0.4227', 'num_input_tokens_seen': 26533392, 'train_runtime': '3.33e+04', 'train_tokens_per_second': '796.8'}
42%|████▏ | 7900/18688 [9:15:01<11:38:25, 3.88s/it] 42%|████▏ | 7901/18688 [9:15:05<11:25:28, 3.81s/it] 42%|████▏ | 7902/18688 [9:15:09<11:45:59, 3.93s/it] 42%|████▏ | 7903/18688 [9:15:12<11:21:42, 3.79s/it] 42%|████▏ | 7904/18688 [9:15:17<11:49:05, 3.95s/it] 42%|████▏ | 7905/18688 [9:15:21<11:51:03, 3.96s/it] 42%|████▏ | 7906/18688 [9:15:25<11:59:42, 4.01s/it] 42%|████▏ | 7907/18688 [9:15:30<12:55:13, 4.31s/it] 42%|████▏ | 7908/18688 [9:15:34<12:46:00, 4.26s/it] 42%|████▏ | 7909/18688 [9:15:38<12:35:17, 4.20s/it] 42%|████▏ | 7910/18688 [9:15:42<12:01:16, 4.02s/it] {'loss': '0.964', 'grad_norm': '1.303', 'learning_rate': '6.944e-05', 'epoch': '0.4233', 'num_input_tokens_seen': 26565960, 'train_runtime': '3.334e+04', 'train_tokens_per_second': '796.8'}
42%|████▏ | 7910/18688 [9:15:42<12:01:16, 4.02s/it] 42%|████▏ | 7911/18688 [9:15:45<11:48:32, 3.94s/it] 42%|████▏ | 7912/18688 [9:15:50<12:15:23, 4.09s/it] 42%|████▏ | 7913/18688 [9:15:54<12:22:13, 4.13s/it] 42%|████▏ | 7914/18688 [9:15:58<12:23:47, 4.14s/it] 42%|████▏ | 7915/18688 [9:16:02<12:30:10, 4.18s/it] 42%|████▏ | 7916/18688 [9:16:06<12:05:43, 4.04s/it] 42%|████▏ | 7917/18688 [9:16:10<11:53:31, 3.97s/it] 42%|████▏ | 7918/18688 [9:16:14<11:38:47, 3.89s/it] 42%|████▏ | 7919/18688 [9:16:17<11:15:37, 3.76s/it] 42%|████▏ | 7920/18688 [9:16:23<12:40:46, 4.24s/it] {'loss': '0.8646', 'grad_norm': '1.149', 'learning_rate': '6.936e-05', 'epoch': '0.4238', 'num_input_tokens_seen': 26599632, 'train_runtime': '3.338e+04', 'train_tokens_per_second': '796.8'}
42%|████▏ | 7920/18688 [9:16:23<12:40:46, 4.24s/it] 42%|████▏ | 7921/18688 [9:16:27<12:28:31, 4.17s/it] 42%|████▏ | 7922/18688 [9:16:31<12:31:30, 4.19s/it] 42%|████▏ | 7923/18688 [9:16:35<12:07:28, 4.05s/it] 42%|████▏ | 7924/18688 [9:16:39<12:09:58, 4.07s/it] 42%|████▏ | 7925/18688 [9:16:43<12:02:41, 4.03s/it] 42%|████▏ | 7926/18688 [9:16:46<11:35:47, 3.88s/it] 42%|████▏ | 7927/18688 [9:16:50<11:28:52, 3.84s/it] 42%|████▏ | 7928/18688 [9:16:54<11:23:40, 3.81s/it] 42%|████▏ | 7929/18688 [9:16:58<11:57:15, 4.00s/it] 42%|████▏ | 7930/18688 [9:17:02<12:09:53, 4.07s/it] {'loss': '0.9554', 'grad_norm': '1.12', 'learning_rate': '6.927e-05', 'epoch': '0.4243', 'num_input_tokens_seen': 26630608, 'train_runtime': '3.342e+04', 'train_tokens_per_second': '796.8'}
42%|████▏ | 7930/18688 [9:17:02<12:09:53, 4.07s/it] 42%|████▏ | 7931/18688 [9:17:06<11:37:19, 3.89s/it] 42%|████▏ | 7932/18688 [9:17:09<11:26:36, 3.83s/it] 42%|████▏ | 7933/18688 [9:17:14<11:57:06, 4.00s/it] 42%|████▏ | 7934/18688 [9:17:19<13:00:35, 4.36s/it] 42%|████▏ | 7935/18688 [9:17:23<12:36:52, 4.22s/it] 42%|████▏ | 7936/18688 [9:17:27<12:12:41, 4.09s/it] 42%|████▏ | 7937/18688 [9:17:31<12:28:56, 4.18s/it] 42%|████▏ | 7938/18688 [9:17:35<11:55:00, 3.99s/it] 42%|████▏ | 7939/18688 [9:17:39<11:57:45, 4.01s/it] 42%|████▏ | 7940/18688 [9:17:42<11:42:09, 3.92s/it] {'loss': '0.8863', 'grad_norm': '1.447', 'learning_rate': '6.919e-05', 'epoch': '0.4249', 'num_input_tokens_seen': 26662440, 'train_runtime': '3.346e+04', 'train_tokens_per_second': '796.8'}
42%|████▏ | 7940/18688 [9:17:42<11:42:09, 3.92s/it] 42%|████▏ | 7941/18688 [9:17:47<12:07:55, 4.06s/it] 42%|████▏ | 7942/18688 [9:17:51<12:12:39, 4.09s/it] 43%|████▎ | 7943/18688 [9:17:55<12:28:21, 4.18s/it] 43%|████▎ | 7944/18688 [9:18:00<13:04:29, 4.38s/it] 43%|████▎ | 7945/18688 [9:18:04<12:24:55, 4.16s/it] 43%|████▎ | 7946/18688 [9:18:08<12:39:45, 4.24s/it] 43%|████▎ | 7947/18688 [9:18:12<12:29:00, 4.18s/it] 43%|████▎ | 7948/18688 [9:18:16<12:09:59, 4.08s/it] 43%|████▎ | 7949/18688 [9:18:20<11:47:03, 3.95s/it] 43%|████▎ | 7950/18688 [9:18:25<13:14:24, 4.44s/it] {'loss': '0.9006', 'grad_norm': '1.317', 'learning_rate': '6.91e-05', 'epoch': '0.4254', 'num_input_tokens_seen': 26698152, 'train_runtime': '3.351e+04', 'train_tokens_per_second': '796.8'}
43%|████▎ | 7950/18688 [9:18:25<13:14:24, 4.44s/it] 43%|████▎ | 7951/18688 [9:18:30<13:03:01, 4.38s/it] 43%|████▎ | 7952/18688 [9:18:34<12:54:58, 4.33s/it] 43%|████▎ | 7953/18688 [9:18:37<12:13:18, 4.10s/it] 43%|████▎ | 7954/18688 [9:18:41<11:39:12, 3.91s/it] 43%|████▎ | 7955/18688 [9:18:45<11:35:33, 3.89s/it] 43%|████▎ | 7956/18688 [9:18:48<11:18:05, 3.79s/it] 43%|████▎ | 7957/18688 [9:18:52<11:23:16, 3.82s/it] 43%|████▎ | 7958/18688 [9:18:57<12:29:00, 4.19s/it] 43%|████▎ | 7959/18688 [9:19:02<12:43:21, 4.27s/it] 43%|████▎ | 7960/18688 [9:19:08<14:13:37, 4.77s/it] {'loss': '0.8877', 'grad_norm': '1.307', 'learning_rate': '6.902e-05', 'epoch': '0.426', 'num_input_tokens_seen': 26732688, 'train_runtime': '3.355e+04', 'train_tokens_per_second': '796.8'}
43%|████▎ | 7960/18688 [9:19:08<14:13:37, 4.77s/it] 43%|████▎ | 7961/18688 [9:19:11<13:02:37, 4.38s/it] 43%|████▎ | 7962/18688 [9:19:14<12:10:43, 4.09s/it] 43%|████▎ | 7963/18688 [9:19:19<12:20:17, 4.14s/it] 43%|████▎ | 7964/18688 [9:19:22<11:55:51, 4.01s/it] 43%|████▎ | 7965/18688 [9:19:26<11:27:19, 3.85s/it] 43%|████▎ | 7966/18688 [9:19:30<11:38:53, 3.91s/it] 43%|████▎ | 7967/18688 [9:19:34<11:22:48, 3.82s/it] 43%|████▎ | 7968/18688 [9:19:37<11:13:17, 3.77s/it] 43%|████▎ | 7969/18688 [9:19:42<12:12:23, 4.10s/it] 43%|████▎ | 7970/18688 [9:19:46<12:00:10, 4.03s/it] {'loss': '0.9528', 'grad_norm': '1.575', 'learning_rate': '6.894e-05', 'epoch': '0.4265', 'num_input_tokens_seen': 26762544, 'train_runtime': '3.359e+04', 'train_tokens_per_second': '796.8'}
43%|████▎ | 7970/18688 [9:19:46<12:00:10, 4.03s/it] 43%|████▎ | 7971/18688 [9:19:50<11:41:44, 3.93s/it] 43%|████▎ | 7972/18688 [9:19:53<11:27:25, 3.85s/it] 43%|████▎ | 7973/18688 [9:19:57<11:17:42, 3.79s/it] 43%|████▎ | 7974/18688 [9:20:01<11:34:16, 3.89s/it] 43%|████▎ | 7975/18688 [9:20:05<11:16:09, 3.79s/it] 43%|████▎ | 7976/18688 [9:20:09<11:53:30, 4.00s/it] 43%|████▎ | 7977/18688 [9:20:13<12:05:30, 4.06s/it] 43%|████▎ | 7978/18688 [9:20:19<13:08:54, 4.42s/it] 43%|████▎ | 7979/18688 [9:20:23<13:04:19, 4.39s/it] 43%|████▎ | 7980/18688 [9:20:27<12:38:00, 4.25s/it] {'loss': '0.9567', 'grad_norm': '1.417', 'learning_rate': '6.885e-05', 'epoch': '0.427', 'num_input_tokens_seen': 26796120, 'train_runtime': '3.363e+04', 'train_tokens_per_second': '796.9'}
43%|████▎ | 7980/18688 [9:20:27<12:38:00, 4.25s/it] 43%|████▎ | 7981/18688 [9:20:32<13:46:27, 4.63s/it] 43%|████▎ | 7982/18688 [9:20:36<12:41:47, 4.27s/it] 43%|████▎ | 7983/18688 [9:20:40<12:28:55, 4.20s/it] 43%|████▎ | 7984/18688 [9:20:43<12:00:23, 4.04s/it] 43%|████▎ | 7985/18688 [9:20:48<12:37:13, 4.24s/it] 43%|████▎ | 7986/18688 [9:20:52<12:34:50, 4.23s/it] 43%|████▎ | 7987/18688 [9:20:56<11:55:10, 4.01s/it] 43%|████▎ | 7988/18688 [9:21:00<11:45:29, 3.96s/it] 43%|████▎ | 7989/18688 [9:21:05<12:31:03, 4.21s/it] 43%|████▎ | 7990/18688 [9:21:08<11:51:41, 3.99s/it] {'loss': '0.926', 'grad_norm': '1.543', 'learning_rate': '6.877e-05', 'epoch': '0.4276', 'num_input_tokens_seen': 26828872, 'train_runtime': '3.367e+04', 'train_tokens_per_second': '796.9'}
43%|████▎ | 7990/18688 [9:21:08<11:51:41, 3.99s/it] 43%|████▎ | 7991/18688 [9:21:12<11:56:20, 4.02s/it] 43%|████▎ | 7992/18688 [9:21:17<12:33:24, 4.23s/it] 43%|████▎ | 7993/18688 [9:21:20<11:59:33, 4.04s/it] 43%|████▎ | 7994/18688 [9:21:25<12:05:47, 4.07s/it] 43%|████▎ | 7995/18688 [9:21:28<11:38:52, 3.92s/it] 43%|████▎ | 7996/18688 [9:21:32<11:43:44, 3.95s/it] 43%|████▎ | 7997/18688 [9:21:37<12:20:24, 4.16s/it] 43%|████▎ | 7998/18688 [9:21:41<12:44:57, 4.29s/it] 43%|████▎ | 7999/18688 [9:21:45<12:19:37, 4.15s/it] 43%|████▎ | 8000/18688 [9:21:49<11:42:22, 3.94s/it] {'loss': '0.945', 'grad_norm': '1.2', 'learning_rate': '6.868e-05', 'epoch': '0.4281', 'num_input_tokens_seen': 26861912, 'train_runtime': '3.371e+04', 'train_tokens_per_second': '796.9'}
43%|████▎ | 8000/18688 [9:21:49<11:42:22, 3.94s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
0%| | 3/1000 [00:00<00:38, 26.19it/s]
1%| | 6/1000 [00:00<00:54, 18.32it/s]
1%| | 8/1000 [00:00<00:56, 17.52it/s]
1%| | 10/1000 [00:00<00:59, 16.61it/s]
1%| | 12/1000 [00:00<01:00, 16.38it/s]
1%|▏ | 14/1000 [00:00<01:01, 15.93it/s]
2%|▏ | 16/1000 [00:00<01:02, 15.74it/s]
2%|▏ | 18/1000 [00:01<01:38, 10.02it/s]
2%|▏ | 20/1000 [00:01<01:32, 10.59it/s]
2%|▏ | 22/1000 [00:01<01:51, 8.76it/s]
2%|▏ | 24/1000 [00:01<01:39, 9.80it/s]
3%|▎ | 26/1000 [00:02<01:44, 9.30it/s]
3%|▎ | 28/1000 [00:02<01:36, 10.12it/s]
3%|▎ | 30/1000 [00:02<01:27, 11.02it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.19it/s]
3%|▎ | 34/1000 [00:02<01:25, 11.30it/s]
4%|▎ | 36/1000 [00:03<01:32, 10.47it/s]
4%|▍ | 38/1000 [00:03<01:24, 11.37it/s]
4%|▍ | 40/1000 [00:03<01:27, 10.98it/s]
4%|▍ | 42/1000 [00:03<01:45, 9.05it/s]
4%|▍ | 44/1000 [00:03<01:30, 10.56it/s]
5%|▍ | 46/1000 [00:03<01:21, 11.75it/s]
5%|▍ | 48/1000 [00:04<01:29, 10.68it/s]
5%|▌ | 50/1000 [00:04<01:20, 11.73it/s]
5%|▌ | 52/1000 [00:04<01:15, 12.56it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.30it/s]
6%|▌ | 56/1000 [00:04<01:07, 13.99it/s]
6%|▌ | 58/1000 [00:05<01:47, 8.75it/s]
6%|▌ | 60/1000 [00:05<01:36, 9.79it/s]
6%|▌ | 62/1000 [00:05<01:27, 10.77it/s]
6%|▋ | 64/1000 [00:05<01:19, 11.82it/s]
7%|▋ | 66/1000 [00:05<01:15, 12.32it/s]
7%|▋ | 68/1000 [00:05<01:12, 12.92it/s]
7%|▋ | 70/1000 [00:05<01:08, 13.50it/s]
7%|▋ | 72/1000 [00:06<01:06, 13.95it/s]
7%|▋ | 74/1000 [00:06<01:07, 13.73it/s]
8%|▊ | 76/1000 [00:06<01:04, 14.31it/s]
8%|▊ | 78/1000 [00:06<01:03, 14.50it/s]
8%|▊ | 80/1000 [00:06<01:08, 13.46it/s]
8%|▊ | 82/1000 [00:06<01:04, 14.12it/s]
8%|▊ | 84/1000 [00:06<01:02, 14.59it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.72it/s]
9%|▉ | 88/1000 [00:07<01:02, 14.53it/s]
9%|▉ | 90/1000 [00:07<01:02, 14.57it/s]
9%|▉ | 92/1000 [00:07<01:03, 14.23it/s]
9%|▉ | 94/1000 [00:07<01:04, 14.06it/s]
10%|▉ | 96/1000 [00:07<01:02, 14.35it/s]
10%|▉ | 98/1000 [00:07<01:03, 14.28it/s]
10%|█ | 100/1000 [00:08<01:31, 9.82it/s]
10%|█ | 102/1000 [00:08<01:23, 10.78it/s]
10%|█ | 104/1000 [00:08<01:54, 7.82it/s]
11%|█ | 106/1000 [00:09<02:01, 7.33it/s]
11%|█ | 108/1000 [00:09<01:42, 8.68it/s]
11%|█ | 110/1000 [00:09<01:27, 10.14it/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 [00:09<01:06, 13.35it/s]
12%|█▏ | 120/1000 [00:10<01:08, 12.78it/s]
12%|█▏ | 122/1000 [00:10<01:22, 10.61it/s]
12%|█▏ | 124/1000 [00:10<01:30, 9.68it/s]
13%|█▎ | 126/1000 [00:10<01:22, 10.64it/s]
13%|█▎ | 128/1000 [00:10<01:15, 11.56it/s]
13%|█▎ | 130/1000 [00:11<01:11, 12.23it/s]
13%|█▎ | 132/1000 [00:11<01:05, 13.19it/s]
13%|█▎ | 134/1000 [00:11<01:09, 12.39it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.36it/s]
14%|█▍ | 138/1000 [00:11<01:04, 13.32it/s]
14%|█▍ | 140/1000 [00:11<01:24, 10.14it/s]
14%|█▍ | 142/1000 [00:12<01:43, 8.31it/s]
14%|█▍ | 144/1000 [00:12<01:32, 9.24it/s]
15%|█▍ | 146/1000 [00:12<01:25, 10.04it/s]
15%|█▍ | 148/1000 [00:12<01:20, 10.62it/s]
15%|█▌ | 150/1000 [00:12<01:12, 11.79it/s]
15%|█▌ | 152/1000 [00:13<01:07, 12.51it/s]
15%|█▌ | 154/1000 [00:13<01:05, 12.90it/s]
16%|█▌ | 156/1000 [00:13<01:05, 12.80it/s]
16%|█▌ | 158/1000 [00:13<01:02, 13.43it/s]
16%|█▌ | 160/1000 [00:13<01:02, 13.40it/s]
16%|█▌ | 162/1000 [00:13<01:02, 13.35it/s]
16%|█▋ | 164/1000 [00:13<01:01, 13.51it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.73it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.28it/s]
17%|█▋ | 170/1000 [00:14<01:18, 10.51it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.48it/s]
17%|█▋ | 174/1000 [00:14<01:30, 9.17it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.20it/s]
18%|█▊ | 178/1000 [00:15<01:34, 8.68it/s]
18%|█▊ | 180/1000 [00:15<01:33, 8.81it/s]
18%|█▊ | 182/1000 [00:15<01:35, 8.59it/s]
18%|█▊ | 184/1000 [00:16<01:22, 9.84it/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 [00:16<01:11, 11.36it/s]
19%|█▉ | 194/1000 [00:17<01:16, 10.58it/s]
20%|█▉ | 196/1000 [00:17<01:29, 8.99it/s]
20%|█▉ | 198/1000 [00:17<01:18, 10.23it/s]
20%|██ | 200/1000 [00:17<01:15, 10.57it/s]
20%|██ | 202/1000 [00:17<01:09, 11.54it/s]
20%|██ | 204/1000 [00:17<01:02, 12.65it/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, 14.48it/s]
21%|██ | 212/1000 [00:18<00:53, 14.72it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.66it/s]
22%|██▏ | 216/1000 [00:18<00:54, 14.44it/s]
22%|██▏ | 218/1000 [00:18<00:52, 14.83it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.91it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.18it/s]
22%|██▏ | 224/1000 [00:19<01:20, 9.66it/s]
23%|██▎ | 226/1000 [00:19<01:12, 10.66it/s]
23%|██▎ | 228/1000 [00:19<01:06, 11.56it/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, 13.20it/s]
24%|██▎ | 236/1000 [00:20<00:55, 13.69it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.33it/s]
24%|██▍ | 240/1000 [00:20<00:53, 14.32it/s]
24%|██▍ | 242/1000 [00:20<00:51, 14.85it/s]
24%|██▍ | 244/1000 [00:21<00:50, 15.03it/s]
25%|██▍ | 246/1000 [00:21<00:50, 15.02it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.71it/s]
25%|██▌ | 250/1000 [00:21<00:55, 13.40it/s]
25%|██▌ | 252/1000 [00:21<01:01, 12.15it/s]
25%|██▌ | 254/1000 [00:21<00:56, 13.15it/s]
26%|██▌ | 256/1000 [00:21<00:54, 13.69it/s]
26%|██▌ | 258/1000 [00:22<00:54, 13.74it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.89it/s]
26%|██▌ | 262/1000 [00:22<00:56, 13.06it/s]
26%|██▋ | 264/1000 [00:22<00:53, 13.65it/s]
27%|██▋ | 266/1000 [00:22<00:52, 13.99it/s]
27%|██▋ | 268/1000 [00:22<00:50, 14.53it/s]
27%|██▋ | 270/1000 [00:22<00:55, 13.26it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.65it/s]
27%|██▋ | 274/1000 [00:23<01:01, 11.74it/s]
28%|██▊ | 276/1000 [00:23<00:56, 12.78it/s]
28%|██▊ | 278/1000 [00:23<00:55, 13.00it/s]
28%|██▊ | 280/1000 [00:23<00:53, 13.54it/s]
28%|██▊ | 282/1000 [00:23<00:49, 14.48it/s]
28%|██▊ | 284/1000 [00:24<00:53, 13.44it/s]
29%|██▊ | 286/1000 [00:24<00:50, 14.06it/s]
29%|██▉ | 288/1000 [00:24<00:48, 14.59it/s]
29%|██▉ | 290/1000 [00:24<00:46, 15.12it/s]
29%|██▉ | 292/1000 [00:24<00:49, 14.28it/s]
29%|██▉ | 294/1000 [00:24<00:51, 13.65it/s]
30%|██▉ | 296/1000 [00:24<00:53, 13.24it/s]
30%|██▉ | 298/1000 [00:25<00:53, 13.14it/s]
30%|███ | 300/1000 [00:25<00:54, 12.89it/s]
30%|███ | 302/1000 [00:25<00:55, 12.65it/s]
30%|███ | 304/1000 [00:25<00:58, 11.94it/s]
31%|███ | 306/1000 [00:25<00:57, 12.12it/s]
31%|███ | 308/1000 [00:25<00:56, 12.16it/s]
31%|███ | 310/1000 [00:26<00:56, 12.23it/s]
31%|███ | 312/1000 [00:26<00:55, 12.38it/s]
31%|███▏ | 314/1000 [00:26<00:53, 12.71it/s]
32%|███▏ | 316/1000 [00:26<00:53, 12.80it/s]
32%|███▏ | 318/1000 [00:26<00:53, 12.74it/s]
32%|███▏ | 320/1000 [00:27<01:21, 8.34it/s]
32%|███▏ | 322/1000 [00:27<01:13, 9.22it/s]
32%|███▏ | 324/1000 [00:27<01:07, 9.97it/s]
33%|███▎ | 326/1000 [00:27<01:06, 10.07it/s]
33%|███▎ | 328/1000 [00:27<01:05, 10.25it/s]
33%|███▎ | 330/1000 [00:27<00:58, 11.40it/s]
33%|███▎ | 332/1000 [00:28<00:55, 11.93it/s]
33%|███▎ | 334/1000 [00:28<00:52, 12.60it/s]
34%|███▎ | 336/1000 [00:28<00:50, 13.15it/s]
34%|███▍ | 338/1000 [00:28<01:06, 9.90it/s]
34%|███▍ | 340/1000 [00:28<01:00, 10.99it/s]
34%|███▍ | 342/1000 [00:28<00:53, 12.28it/s]
34%|███▍ | 344/1000 [00:29<00:50, 12.99it/s]
35%|███▍ | 346/1000 [00:29<00:48, 13.61it/s]
35%|███▍ | 348/1000 [00:29<00:46, 14.05it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.68it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.86it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.88it/s]
36%|███▌ | 356/1000 [00:29<00:42, 14.99it/s]
36%|███▌ | 358/1000 [00:30<00:43, 14.71it/s]
36%|███▌ | 360/1000 [00:30<00:42, 15.10it/s]
36%|███▌ | 362/1000 [00:30<00:47, 13.44it/s]
36%|███▋ | 364/1000 [00:30<00:45, 13.95it/s]
37%|███▋ | 366/1000 [00:30<00:52, 12.01it/s]
37%|███▋ | 368/1000 [00:30<00:50, 12.41it/s]
37%|███▋ | 370/1000 [00:30<00:47, 13.30it/s]
37%|███▋ | 372/1000 [00:31<00:46, 13.62it/s]
37%|███▋ | 374/1000 [00:31<00:54, 11.44it/s]
38%|███▊ | 376/1000 [00:31<00:56, 11.02it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.71it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.52it/s]
38%|███▊ | 382/1000 [00:31<00:48, 12.68it/s]
38%|███▊ | 384/1000 [00:32<00:45, 13.40it/s]
39%|███▊ | 386/1000 [00:32<00:44, 13.72it/s]
39%|███▉ | 388/1000 [00:32<00:44, 13.68it/s]
39%|███▉ | 390/1000 [00:32<00:44, 13.56it/s]
39%|███▉ | 392/1000 [00:32<00:43, 13.90it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.69it/s]
40%|███▉ | 396/1000 [00:32<00:43, 13.81it/s]
40%|███▉ | 398/1000 [00:33<00:47, 12.62it/s]
40%|████ | 400/1000 [00:33<00:45, 13.29it/s]
40%|████ | 402/1000 [00:33<00:42, 13.95it/s]
40%|████ | 404/1000 [00:33<00:40, 14.57it/s]
41%|████ | 406/1000 [00:33<00:39, 15.10it/s]
41%|████ | 408/1000 [00:33<00:46, 12.63it/s]
41%|████ | 410/1000 [00:34<00:56, 10.51it/s]
41%|████ | 412/1000 [00:34<00:50, 11.60it/s]
41%|████▏ | 414/1000 [00:34<01:07, 8.68it/s]
42%|████▏ | 416/1000 [00:35<01:23, 7.00it/s]
42%|████▏ | 418/1000 [00:35<01:09, 8.32it/s]
42%|████▏ | 420/1000 [00:35<01:00, 9.59it/s]
42%|████▏ | 422/1000 [00:35<00:52, 10.94it/s]
42%|████▏ | 424/1000 [00:35<01:14, 7.72it/s]
43%|████▎ | 426/1000 [00:36<01:03, 8.97it/s]
43%|████▎ | 428/1000 [00:36<00:56, 10.17it/s]
43%|████▎ | 430/1000 [00:36<01:02, 9.15it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.07it/s]
43%|████▎ | 434/1000 [00:37<01:08, 8.30it/s]
44%|████▎ | 436/1000 [00:37<01:00, 9.35it/s]
44%|████▍ | 438/1000 [00:37<01:02, 9.02it/s]
44%|████▍ | 440/1000 [00:37<00:58, 9.52it/s]
44%|████▍ | 442/1000 [00:37<00:52, 10.58it/s]
44%|████▍ | 444/1000 [00:37<00:48, 11.44it/s]
45%|████▍ | 446/1000 [00:37<00:45, 12.22it/s]
45%|████▍ | 448/1000 [00:38<00:50, 10.99it/s]
45%|████▌ | 450/1000 [00:38<00:46, 11.82it/s]
45%|████▌ | 452/1000 [00:38<00:42, 12.82it/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, 13.76it/s]
46%|████▌ | 460/1000 [00:39<00:38, 13.91it/s]
46%|████▌ | 462/1000 [00:39<00:37, 14.52it/s]
46%|████▋ | 464/1000 [00:39<00:35, 15.03it/s]
47%|████▋ | 466/1000 [00:39<00:35, 14.87it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.96it/s]
47%|████▋ | 470/1000 [00:39<00:52, 10.10it/s]
47%|████▋ | 472/1000 [00:40<00:51, 10.27it/s]
47%|████▋ | 474/1000 [00:40<00:45, 11.57it/s]
48%|████▊ | 476/1000 [00:40<00:42, 12.22it/s]
48%|████▊ | 478/1000 [00:40<00:43, 12.09it/s]
48%|████▊ | 480/1000 [00:40<00:39, 13.04it/s]
48%|████▊ | 482/1000 [00:40<00:38, 13.44it/s]
48%|████▊ | 484/1000 [00:40<00:37, 13.95it/s]
49%|████▊ | 486/1000 [00:41<00:36, 14.11it/s]
49%|████▉ | 488/1000 [00:41<00:35, 14.39it/s]
49%|████▉ | 490/1000 [00:41<00:45, 11.16it/s]
49%|████▉ | 492/1000 [00:41<00:40, 12.46it/s]
49%|████▉ | 494/1000 [00:41<00:37, 13.45it/s]
50%|████▉ | 496/1000 [00:41<00:35, 14.18it/s]
50%|████▉ | 498/1000 [00:41<00:34, 14.57it/s]
50%|█████ | 500/1000 [00:42<00:43, 11.61it/s]
50%|█████ | 502/1000 [00:42<00:41, 12.06it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.65it/s]
51%|█████ | 506/1000 [00:42<00:38, 12.81it/s]
51%|█████ | 508/1000 [00:42<00:35, 13.71it/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, 12.09it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.66it/s]
52%|█████▏ | 518/1000 [00:43<00:36, 13.17it/s]
52%|█████▏ | 520/1000 [00:43<00:35, 13.53it/s]
52%|█████▏ | 522/1000 [00:43<00:33, 14.26it/s]
52%|█████▏ | 524/1000 [00:44<00:33, 14.26it/s]
53%|█████▎ | 526/1000 [00:44<00:32, 14.42it/s]
53%|█████▎ | 528/1000 [00:44<00:39, 12.02it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.89it/s]
53%|█████▎ | 532/1000 [00:44<00:33, 13.85it/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, 14.06it/s]
54%|█████▍ | 540/1000 [00:45<00:31, 14.49it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.77it/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, 13.96it/s]
55%|█████▌ | 550/1000 [00:45<00:31, 14.20it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.34it/s]
55%|█████▌ | 554/1000 [00:46<00:31, 14.00it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.16it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.53it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 15.09it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 15.06it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.84it/s]
57%|█████▋ | 566/1000 [00:47<00:36, 11.74it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.28it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.41it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.91it/s]
57%|█████▋ | 574/1000 [00:47<00:30, 13.79it/s]
58%|█████▊ | 576/1000 [00:47<00:31, 13.67it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.65it/s]
58%|█████▊ | 580/1000 [00:48<00:32, 12.79it/s]
58%|█████▊ | 582/1000 [00:48<00:30, 13.58it/s]
58%|█████▊ | 584/1000 [00:48<00:30, 13.82it/s]
59%|█████▊ | 586/1000 [00:48<00:29, 14.23it/s]
59%|█████▉ | 588/1000 [00:48<00:27, 15.06it/s]
59%|█████▉ | 590/1000 [00:48<00:27, 15.06it/s]
59%|█████▉ | 592/1000 [00:49<00:27, 14.83it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.34it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.87it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 14.10it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.43it/s]
60%|██████ | 602/1000 [00:49<00:27, 14.30it/s]
60%|██████ | 604/1000 [00:49<00:27, 14.48it/s]
61%|██████ | 606/1000 [00:50<00:28, 14.01it/s]
61%|██████ | 608/1000 [00:50<00:28, 13.86it/s]
61%|██████ | 610/1000 [00:50<00:27, 14.08it/s]
61%|██████ | 612/1000 [00:50<00:26, 14.42it/s]
61%|██████▏ | 614/1000 [00:50<00:27, 13.90it/s]
62%|██████▏ | 616/1000 [00:50<00:26, 14.35it/s]
62%|██████▏ | 618/1000 [00:50<00:27, 13.84it/s]
62%|██████▏ | 620/1000 [00:51<00:39, 9.63it/s]
62%|██████▏ | 622/1000 [00:51<00:38, 9.89it/s]
62%|██████▏ | 624/1000 [00:51<00:33, 11.34it/s]
63%|██████▎ | 626/1000 [00:51<00:31, 12.03it/s]
63%|██████▎ | 628/1000 [00:51<00:28, 13.22it/s]
63%|██████▎ | 630/1000 [00:51<00:26, 13.76it/s]
63%|██████▎ | 632/1000 [00:52<00:29, 12.44it/s]
63%|██████▎ | 634/1000 [00:52<00:27, 13.15it/s]
64%|██████▎ | 636/1000 [00:52<00:33, 10.94it/s]
64%|██████▍ | 638/1000 [00:52<00:33, 10.68it/s]
64%|██████▍ | 640/1000 [00:52<00:31, 11.47it/s]
64%|██████▍ | 642/1000 [00:53<00:28, 12.35it/s]
64%|██████▍ | 644/1000 [00:53<00:28, 12.59it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.86it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.35it/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, 9.45it/s]
66%|██████▌ | 656/1000 [00:54<00:32, 10.71it/s]
66%|██████▌ | 658/1000 [00:54<00:28, 11.80it/s]
66%|██████▌ | 660/1000 [00:54<00:34, 9.95it/s]
66%|██████▌ | 662/1000 [00:54<00:32, 10.48it/s]
66%|██████▋ | 664/1000 [00:54<00:30, 10.88it/s]
67%|██████▋ | 666/1000 [00:55<00:28, 11.68it/s]
67%|██████▋ | 668/1000 [00:55<00:30, 10.72it/s]
67%|██████▋ | 670/1000 [00:55<00:30, 10.70it/s]
67%|██████▋ | 672/1000 [00:55<00:28, 11.55it/s]
67%|██████▋ | 674/1000 [00:55<00:26, 12.21it/s]
68%|██████▊ | 676/1000 [00:56<00:28, 11.55it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.08it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 12.90it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.11it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.33it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.43it/s]
69%|██████▉ | 688/1000 [00:56<00:22, 13.74it/s]
69%|██████▉ | 690/1000 [00:57<00:22, 13.82it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.25it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.35it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 14.10it/s]
70%|██████▉ | 698/1000 [00:57<00:20, 14.68it/s]
70%|███████ | 700/1000 [00:57<00:24, 12.25it/s]
70%|███████ | 702/1000 [00:57<00:22, 13.05it/s]
70%|███████ | 704/1000 [00:58<00:29, 9.90it/s]
71%|███████ | 706/1000 [00:58<00:27, 10.69it/s]
71%|███████ | 708/1000 [00:58<00:30, 9.63it/s]
71%|███████ | 710/1000 [00:58<00:26, 10.93it/s]
71%|███████ | 712/1000 [00:58<00:24, 11.89it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.98it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 13.06it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.58it/s]
72%|███████▏ | 720/1000 [00:59<00:20, 13.53it/s]
72%|███████▏ | 722/1000 [00:59<00:19, 13.92it/s]
72%|███████▏ | 724/1000 [00:59<00:20, 13.77it/s]
73%|███████▎ | 726/1000 [00:59<00:19, 14.01it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.94it/s]
73%|███████▎ | 730/1000 [01:00<00:32, 8.22it/s]
73%|███████▎ | 732/1000 [01:00<00:32, 8.35it/s]
73%|███████▎ | 734/1000 [01:00<00:30, 8.64it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.48it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.93it/s]
74%|███████▍ | 739/1000 [01:01<00:23, 11.04it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.72it/s]
74%|███████▍ | 743/1000 [01:01<00:20, 12.35it/s]
74%|███████▍ | 745/1000 [01:01<00:24, 10.21it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.59it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.77it/s]
75%|███████▌ | 751/1000 [01:02<00:21, 11.85it/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, 10.30it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.35it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.33it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.27it/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, 10.59it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.87it/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, 14.18it/s]
78%|███████▊ | 779/1000 [01:05<00:21, 10.20it/s]
78%|███████▊ | 781/1000 [01:05<00:24, 8.97it/s]
78%|███████▊ | 783/1000 [01:05<00:27, 7.81it/s]
78%|███████▊ | 784/1000 [01:05<00:31, 6.76it/s]
78%|███████▊ | 785/1000 [01:05<00:30, 7.08it/s]
79%|███████▊ | 786/1000 [01:06<00:31, 6.77it/s]
79%|███████▉ | 788/1000 [01:06<00:24, 8.60it/s]
79%|███████▉ | 790/1000 [01:06<00:21, 9.96it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 11.14it/s]
79%|███████▉ | 794/1000 [01:06<00:17, 11.81it/s]
80%|███████▉ | 796/1000 [01:06<00:16, 12.30it/s]
80%|███████▉ | 798/1000 [01:06<00:15, 13.10it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.29it/s]
80%|████████ | 802/1000 [01:07<00:24, 8.24it/s]
80%|████████ | 804/1000 [01:07<00:23, 8.30it/s]
81%|████████ | 806/1000 [01:08<00:21, 8.99it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.37it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.49it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.68it/s]
81%|████████▏ | 814/1000 [01:08<00:14, 12.58it/s]
82%|████████▏ | 816/1000 [01:08<00:16, 11.04it/s]
82%|████████▏ | 818/1000 [01:08<00:15, 12.01it/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, 13.79it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.22it/s]
83%|████████▎ | 828/1000 [01:09<00:11, 14.51it/s]
83%|████████▎ | 830/1000 [01:09<00:12, 13.94it/s]
83%|████████▎ | 832/1000 [01:09<00:11, 14.35it/s]
83%|████████▎ | 834/1000 [01:10<00:11, 14.07it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.41it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.34it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.46it/s]
84%|████████▍ | 842/1000 [01:10<00:10, 14.48it/s]
84%|████████▍ | 844/1000 [01:10<00:11, 14.02it/s]
85%|████████▍ | 846/1000 [01:10<00:11, 13.99it/s]
85%|████████▍ | 848/1000 [01:11<00:10, 13.92it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.13it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.73it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.30it/s]
86%|████████▌ | 856/1000 [01:11<00:10, 14.14it/s]
86%|████████▌ | 858/1000 [01:11<00:09, 14.63it/s]
86%|████████▌ | 860/1000 [01:11<00:09, 15.34it/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 [01:12<00:09, 14.14it/s]
87%|████████▋ | 872/1000 [01:12<00:09, 13.47it/s]
87%|████████▋ | 874/1000 [01:12<00:10, 11.79it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 10.53it/s]
88%|████████▊ | 878/1000 [01:13<00:11, 11.02it/s]
88%|████████▊ | 880/1000 [01:13<00:10, 11.44it/s]
88%|████████▊ | 882/1000 [01:13<00:10, 10.75it/s]
88%|████████▊ | 884/1000 [01:13<00:09, 11.63it/s]
89%|████████▊ | 886/1000 [01:14<00:09, 12.53it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.59it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.11it/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, 13.89it/s]
90%|████████▉ | 898/1000 [01:14<00:07, 13.30it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.25it/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, 10.88it/s]
91%|█████████ | 908/1000 [01:15<00:07, 11.87it/s]
91%|█████████ | 910/1000 [01:15<00:07, 12.13it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.30it/s]
91%|█████████▏| 914/1000 [01:16<00:12, 6.82it/s]
92%|█████████▏| 916/1000 [01:16<00:10, 8.15it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.41it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.83it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 10.29it/s]
92%|█████████▏| 924/1000 [01:17<00:06, 10.97it/s]
93%|█████████▎| 926/1000 [01:17<00:06, 11.82it/s]
93%|█████████▎| 928/1000 [01:17<00:06, 10.65it/s]
93%|█████████▎| 930/1000 [01:18<00:05, 11.86it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 12.46it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.93it/s]
94%|█████████▎| 936/1000 [01:18<00:04, 13.47it/s]
94%|█████████▍| 938/1000 [01:18<00:04, 14.28it/s]
94%|█████████▍| 940/1000 [01:18<00:04, 14.61it/s]
94%|█████████▍| 942/1000 [01:18<00:04, 12.31it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.84it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.91it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 12.73it/s]
95%|█████████▌| 950/1000 [01:19<00:03, 13.50it/s]
95%|█████████▌| 952/1000 [01:19<00:03, 14.14it/s]
95%|█████████▌| 954/1000 [01:19<00:03, 14.24it/s]
96%|█████████▌| 956/1000 [01:19<00:03, 14.23it/s]
96%|█████████▌| 958/1000 [01:20<00:02, 14.77it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 13.12it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.74it/s]
96%|█████████▋| 964/1000 [01:20<00:03, 10.24it/s]
97%|█████████▋| 966/1000 [01:20<00:02, 11.47it/s]
97%|█████████▋| 968/1000 [01:20<00:02, 12.12it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 12.74it/s]
97%|█████████▋| 972/1000 [01:21<00:02, 9.71it/s]
97%|█████████▋| 974/1000 [01:21<00:02, 10.89it/s]
98%|█████████▊| 976/1000 [01:21<00:02, 11.69it/s]
98%|█████████▊| 978/1000 [01:21<00:02, 9.90it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.98it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.47it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 10.48it/s]
99%|█████████▊| 986/1000 [01:22<00:01, 10.73it/s]
99%|█████████▉| 988/1000 [01:22<00:01, 10.95it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 11.43it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 12.32it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 13.06it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 13.97it/s]
100%|█████████▉| 998/1000 [01:23<00:00, 14.14it/s]
100%|██████████| 1000/1000 [01:23<00:00, 14.82it/s]
{'eval_loss': '0.9158', 'eval_runtime': '83.74', 'eval_samples_per_second': '11.94', 'eval_steps_per_second': '11.94', 'epoch': '0.4281', 'num_input_tokens_seen': 26861912}
43%|████▎ | 8000/18688 [9:23:12<11:42:22, 3.94s/it]
100%|██████████| 1000/1000 [01:23<00:00, 14.82it/s]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00010/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 [9:23:18<87:43:26, 29.55s/it] 43%|████▎ | 8002/18688 [9:23:22<65:13:10, 21.97s/it] 43%|████▎ | 8003/18688 [9:23:26<49:14:14, 16.59s/it] 43%|████▎ | 8004/18688 [9:23:30<37:42:50, 12.71s/it] 43%|████▎ | 8005/18688 [9:23:34<30:13:12, 10.18s/it] 43%|████▎ | 8006/18688 [9:23:38<24:26:45, 8.24s/it] 43%|████▎ | 8007/18688 [9:23:41<20:15:11, 6.83s/it] 43%|████▎ | 8008/18688 [9:23:46<17:50:14, 6.01s/it] 43%|████▎ | 8009/18688 [9:23:49<15:54:34, 5.36s/it] 43%|████▎ | 8010/18688 [9:23:55<15:41:28, 5.29s/it] {'loss': '0.9048', 'grad_norm': '1.118', 'learning_rate': '6.86e-05', 'epoch': '0.4286', 'num_input_tokens_seen': 26896032, 'train_runtime': '3.384e+04', 'train_tokens_per_second': '794.9'}
43%|████▎ | 8010/18688 [9:23:55<15:41:28, 5.29s/it] 43%|████▎ | 8011/18688 [9:23:59<14:57:20, 5.04s/it] 43%|████▎ | 8012/18688 [9:24:03<14:25:35, 4.86s/it] 43%|████▎ | 8013/18688 [9:24:08<13:59:44, 4.72s/it] 43%|████▎ | 8014/18688 [9:24:13<14:33:13, 4.91s/it] 43%|████▎ | 8015/18688 [9:24:17<13:34:43, 4.58s/it] 43%|████▎ | 8016/18688 [9:24:21<13:16:35, 4.48s/it] 43%|████▎ | 8017/18688 [9:24:25<12:57:48, 4.37s/it] 43%|████▎ | 8018/18688 [9:24:29<12:19:00, 4.16s/it] 43%|████▎ | 8019/18688 [9:24:34<13:01:54, 4.40s/it] 43%|████▎ | 8020/18688 [9:24:38<12:16:53, 4.14s/it] {'loss': '0.9553', 'grad_norm': '1.565', 'learning_rate': '6.851e-05', 'epoch': '0.4292', 'num_input_tokens_seen': 26933320, 'train_runtime': '3.388e+04', 'train_tokens_per_second': '795'}
43%|████▎ | 8020/18688 [9:24:38<12:16:53, 4.14s/it] 43%|████▎ | 8021/18688 [9:24:41<11:52:10, 4.01s/it] 43%|████▎ | 8022/18688 [9:24:46<12:19:29, 4.16s/it] 43%|████▎ | 8023/18688 [9:24:49<11:57:19, 4.04s/it] 43%|████▎ | 8024/18688 [9:24:54<12:27:31, 4.21s/it] 43%|████▎ | 8025/18688 [9:24:59<13:09:57, 4.45s/it] 43%|████▎ | 8026/18688 [9:25:05<14:02:42, 4.74s/it] 43%|████▎ | 8027/18688 [9:25:08<13:17:38, 4.49s/it] 43%|████▎ | 8028/18688 [9:25:12<12:41:01, 4.28s/it] 43%|████▎ | 8029/18688 [9:25:16<12:16:45, 4.15s/it] 43%|████▎ | 8030/18688 [9:25:20<12:14:24, 4.13s/it] {'loss': '1.047', 'grad_norm': '1.146', 'learning_rate': '6.843e-05', 'epoch': '0.4297', 'num_input_tokens_seen': 26969464, 'train_runtime': '3.392e+04', 'train_tokens_per_second': '795.1'}
43%|████▎ | 8030/18688 [9:25:20<12:14:24, 4.13s/it] 43%|████▎ | 8031/18688 [9:25:25<12:55:12, 4.36s/it] 43%|████▎ | 8032/18688 [9:25:29<12:37:08, 4.26s/it] 43%|████▎ | 8033/18688 [9:25:33<12:35:31, 4.25s/it] 43%|████▎ | 8034/18688 [9:25:38<12:55:37, 4.37s/it] 43%|████▎ | 8035/18688 [9:25:42<12:50:07, 4.34s/it] 43%|████▎ | 8036/18688 [9:25:46<12:38:51, 4.27s/it] 43%|████▎ | 8037/18688 [9:25:50<12:11:40, 4.12s/it] 43%|████▎ | 8038/18688 [9:25:55<12:44:58, 4.31s/it] 43%|████▎ | 8039/18688 [9:25:59<12:33:52, 4.25s/it] 43%|████▎ | 8040/18688 [9:26:03<12:27:08, 4.21s/it] {'loss': '0.921', 'grad_norm': '1.405', 'learning_rate': '6.834e-05', 'epoch': '0.4302', 'num_input_tokens_seen': 27005056, 'train_runtime': '3.396e+04', 'train_tokens_per_second': '795.1'}
43%|████▎ | 8040/18688 [9:26:03<12:27:08, 4.21s/it] 43%|████▎ | 8041/18688 [9:26:07<12:18:56, 4.16s/it] 43%|████▎ | 8042/18688 [9:26:11<11:47:03, 3.98s/it] 43%|████▎ | 8043/18688 [9:26:15<11:42:36, 3.96s/it] 43%|████▎ | 8044/18688 [9:26:19<11:43:27, 3.97s/it] 43%|████▎ | 8045/18688 [9:26:22<11:33:47, 3.91s/it] 43%|████▎ | 8046/18688 [9:26:26<11:22:04, 3.85s/it] 43%|████▎ | 8047/18688 [9:26:30<11:43:40, 3.97s/it] 43%|████▎ | 8048/18688 [9:26:34<11:23:22, 3.85s/it] 43%|████▎ | 8049/18688 [9:26:37<11:06:49, 3.76s/it] 43%|████▎ | 8050/18688 [9:26:42<12:03:03, 4.08s/it] {'loss': '0.9177', 'grad_norm': '1.137', 'learning_rate': '6.826e-05', 'epoch': '0.4308', 'num_input_tokens_seen': 27036192, 'train_runtime': '3.4e+04', 'train_tokens_per_second': '795.1'}
43%|████▎ | 8050/18688 [9:26:42<12:03:03, 4.08s/it] 43%|████▎ | 8051/18688 [9:26:46<11:48:39, 4.00s/it] 43%|████▎ | 8052/18688 [9:26:50<11:52:53, 4.02s/it] 43%|████▎ | 8053/18688 [9:26:54<12:08:26, 4.11s/it] 43%|████▎ | 8054/18688 [9:26:59<12:05:18, 4.09s/it] 43%|████▎ | 8055/18688 [9:27:02<11:48:14, 4.00s/it] 43%|████▎ | 8056/18688 [9:27:08<12:59:25, 4.40s/it] 43%|████▎ | 8057/18688 [9:27:12<12:51:26, 4.35s/it] 43%|████▎ | 8058/18688 [9:27:16<12:45:26, 4.32s/it] 43%|████▎ | 8059/18688 [9:27:20<12:11:39, 4.13s/it] 43%|████▎ | 8060/18688 [9:27:24<12:13:14, 4.14s/it] {'loss': '0.8881', 'grad_norm': '1.248', 'learning_rate': '6.817e-05', 'epoch': '0.4313', 'num_input_tokens_seen': 27069792, 'train_runtime': '3.404e+04', 'train_tokens_per_second': '795.1'}
43%|████▎ | 8060/18688 [9:27:24<12:13:14, 4.14s/it] 43%|████▎ | 8061/18688 [9:27:28<12:14:55, 4.15s/it] 43%|████▎ | 8062/18688 [9:27:32<12:16:30, 4.16s/it] 43%|████▎ | 8063/18688 [9:27:36<11:52:40, 4.02s/it] 43%|████▎ | 8064/18688 [9:27:40<12:13:10, 4.14s/it] 43%|████▎ | 8065/18688 [9:27:44<11:54:41, 4.04s/it] 43%|████▎ | 8066/18688 [9:27:48<11:52:54, 4.03s/it] 43%|████▎ | 8067/18688 [9:27:52<12:00:54, 4.07s/it] 43%|████▎ | 8068/18688 [9:27:57<12:28:31, 4.23s/it] 43%|████▎ | 8069/18688 [9:28:01<11:53:39, 4.03s/it] 43%|████▎ | 8070/18688 [9:28:05<12:07:36, 4.11s/it] {'loss': '0.8967', 'grad_norm': '1.37', 'learning_rate': '6.809e-05', 'epoch': '0.4318', 'num_input_tokens_seen': 27102208, 'train_runtime': '3.409e+04', 'train_tokens_per_second': '795.1'}
43%|████▎ | 8070/18688 [9:28:05<12:07:36, 4.11s/it] 43%|████▎ | 8071/18688 [9:28:09<12:20:15, 4.18s/it] 43%|████▎ | 8072/18688 [9:28:13<12:06:49, 4.11s/it] 43%|████▎ | 8073/18688 [9:28:18<12:56:28, 4.39s/it] 43%|████▎ | 8074/18688 [9:28:22<12:29:51, 4.24s/it] 43%|████▎ | 8075/18688 [9:28:26<12:01:17, 4.08s/it] 43%|████▎ | 8076/18688 [9:28:30<11:46:22, 3.99s/it] 43%|████▎ | 8077/18688 [9:28:33<11:25:15, 3.87s/it] 43%|████▎ | 8078/18688 [9:28:37<11:17:07, 3.83s/it] 43%|████▎ | 8079/18688 [9:28:41<11:48:36, 4.01s/it] 43%|████▎ | 8080/18688 [9:28:46<12:15:07, 4.16s/it] {'loss': '0.9529', 'grad_norm': '1.302', 'learning_rate': '6.8e-05', 'epoch': '0.4324', 'num_input_tokens_seen': 27135992, 'train_runtime': '3.413e+04', 'train_tokens_per_second': '795.2'}
43%|████▎ | 8080/18688 [9:28:46<12:15:07, 4.16s/it] 43%|████▎ | 8081/18688 [9:28:49<11:40:16, 3.96s/it] 43%|████▎ | 8082/18688 [9:28:53<11:33:39, 3.92s/it] 43%|████▎ | 8083/18688 [9:28:57<11:45:36, 3.99s/it] 43%|████▎ | 8084/18688 [9:29:02<12:07:11, 4.11s/it] 43%|████▎ | 8085/18688 [9:29:07<12:49:04, 4.35s/it] 43%|████▎ | 8086/18688 [9:29:11<12:37:20, 4.29s/it] 43%|████▎ | 8087/18688 [9:29:15<12:33:15, 4.26s/it] 43%|████▎ | 8088/18688 [9:29:19<12:09:36, 4.13s/it] 43%|████▎ | 8089/18688 [9:29:23<11:51:47, 4.03s/it] 43%|████▎ | 8090/18688 [9:29:26<11:26:06, 3.88s/it] {'loss': '0.9509', 'grad_norm': '1.263', 'learning_rate': '6.792e-05', 'epoch': '0.4329', 'num_input_tokens_seen': 27168608, 'train_runtime': '3.417e+04', 'train_tokens_per_second': '795.2'}
43%|████▎ | 8090/18688 [9:29:26<11:26:06, 3.88s/it] 43%|████▎ | 8091/18688 [9:29:30<11:34:14, 3.93s/it] 43%|████▎ | 8092/18688 [9:29:34<11:39:07, 3.96s/it] 43%|████▎ | 8093/18688 [9:29:38<11:55:38, 4.05s/it] 43%|████▎ | 8094/18688 [9:29:42<11:31:17, 3.92s/it] 43%|████▎ | 8095/18688 [9:29:46<11:25:19, 3.88s/it] 43%|████▎ | 8096/18688 [9:29:50<11:22:27, 3.87s/it] 43%|████▎ | 8097/18688 [9:29:53<11:10:15, 3.80s/it] 43%|████▎ | 8098/18688 [9:29:57<10:55:51, 3.72s/it] 43%|████▎ | 8099/18688 [9:30:01<10:54:55, 3.71s/it] 43%|████▎ | 8100/18688 [9:30:05<11:27:55, 3.90s/it] {'loss': '0.9312', 'grad_norm': '1.191', 'learning_rate': '6.783e-05', 'epoch': '0.4334', 'num_input_tokens_seen': 27198336, 'train_runtime': '3.421e+04', 'train_tokens_per_second': '795.1'}
43%|████▎ | 8100/18688 [9:30:05<11:27:55, 3.90s/it] 43%|████▎ | 8101/18688 [9:30:09<11:41:08, 3.97s/it] 43%|████▎ | 8102/18688 [9:30:13<12:03:49, 4.10s/it] 43%|████▎ | 8103/18688 [9:30:18<12:04:50, 4.11s/it] 43%|████▎ | 8104/18688 [9:30:21<11:44:50, 4.00s/it] 43%|████▎ | 8105/18688 [9:30:25<11:34:01, 3.93s/it] 43%|████▎ | 8106/18688 [9:30:30<12:02:40, 4.10s/it] 43%|████▎ | 8107/18688 [9:30:34<12:31:27, 4.26s/it] 43%|████▎ | 8108/18688 [9:30:38<12:07:09, 4.12s/it] 43%|████▎ | 8109/18688 [9:30:42<11:51:24, 4.03s/it] 43%|████▎ | 8110/18688 [9:30:47<12:39:43, 4.31s/it] {'loss': '0.9645', 'grad_norm': '1.285', 'learning_rate': '6.775e-05', 'epoch': '0.434', 'num_input_tokens_seen': 27231152, 'train_runtime': '3.425e+04', 'train_tokens_per_second': '795.1'}
43%|████▎ | 8110/18688 [9:30:47<12:39:43, 4.31s/it] 43%|████▎ | 8111/18688 [9:30:52<13:25:00, 4.57s/it] 43%|████▎ | 8112/18688 [9:30:57<13:25:20, 4.57s/it] 43%|████▎ | 8113/18688 [9:31:01<13:04:32, 4.45s/it] 43%|████▎ | 8114/18688 [9:31:04<12:23:02, 4.22s/it] 43%|████▎ | 8115/18688 [9:31:09<12:34:22, 4.28s/it] 43%|████▎ | 8116/18688 [9:31:13<12:45:31, 4.34s/it] 43%|████▎ | 8117/18688 [9:31:17<12:35:26, 4.29s/it] 43%|████▎ | 8118/18688 [9:31:21<11:55:03, 4.06s/it] 43%|████▎ | 8119/18688 [9:31:24<11:24:24, 3.89s/it] 43%|████▎ | 8120/18688 [9:31:29<11:56:09, 4.07s/it] {'loss': '0.974', 'grad_norm': '1.092', 'learning_rate': '6.766e-05', 'epoch': '0.4345', 'num_input_tokens_seen': 27265400, 'train_runtime': '3.429e+04', 'train_tokens_per_second': '795.2'}
43%|████▎ | 8120/18688 [9:31:29<11:56:09, 4.07s/it] 43%|████▎ | 8121/18688 [9:31:34<12:32:57, 4.28s/it] 43%|████▎ | 8122/18688 [9:31:38<12:25:00, 4.23s/it] 43%|████▎ | 8123/18688 [9:31:42<12:45:43, 4.35s/it] 43%|████▎ | 8124/18688 [9:31:47<13:06:49, 4.47s/it] 43%|████▎ | 8125/18688 [9:31:51<12:31:18, 4.27s/it] 43%|████▎ | 8126/18688 [9:31:56<12:54:48, 4.40s/it] 43%|████▎ | 8127/18688 [9:31:59<12:10:13, 4.15s/it] 43%|████▎ | 8128/18688 [9:32:03<11:35:07, 3.95s/it] 43%|████▎ | 8129/18688 [9:32:07<11:47:27, 4.02s/it] 44%|████▎ | 8130/18688 [9:32:11<11:46:54, 4.02s/it] {'loss': '0.9836', 'grad_norm': '1.436', 'learning_rate': '6.757e-05', 'epoch': '0.4351', 'num_input_tokens_seen': 27299592, 'train_runtime': '3.433e+04', 'train_tokens_per_second': '795.2'}
44%|████▎ | 8130/18688 [9:32:11<11:46:54, 4.02s/it] 44%|████▎ | 8131/18688 [9:32:17<13:07:50, 4.48s/it] 44%|████▎ | 8132/18688 [9:32:20<12:36:00, 4.30s/it] 44%|████▎ | 8133/18688 [9:32:25<12:37:55, 4.31s/it] 44%|████▎ | 8134/18688 [9:32:29<12:35:30, 4.30s/it] 44%|████▎ | 8135/18688 [9:32:33<12:39:51, 4.32s/it] 44%|████▎ | 8136/18688 [9:32:38<13:06:03, 4.47s/it] 44%|████▎ | 8137/18688 [9:32:43<13:19:50, 4.55s/it] 44%|████▎ | 8138/18688 [9:32:46<12:25:33, 4.24s/it] 44%|████▎ | 8139/18688 [9:32:50<12:13:02, 4.17s/it] 44%|████▎ | 8140/18688 [9:32:55<12:17:06, 4.19s/it] {'loss': '0.8678', 'grad_norm': '1.294', 'learning_rate': '6.749e-05', 'epoch': '0.4356', 'num_input_tokens_seen': 27336512, 'train_runtime': '3.438e+04', 'train_tokens_per_second': '795.2'}
44%|████▎ | 8140/18688 [9:32:55<12:17:06, 4.19s/it] 44%|████▎ | 8141/18688 [9:32:58<11:52:11, 4.05s/it] 44%|████▎ | 8142/18688 [9:33:02<11:28:35, 3.92s/it] 44%|████▎ | 8143/18688 [9:33:06<11:42:54, 4.00s/it] 44%|████▎ | 8144/18688 [9:33:10<11:22:18, 3.88s/it] 44%|████▎ | 8145/18688 [9:33:13<11:04:16, 3.78s/it] 44%|████▎ | 8146/18688 [9:33:18<11:28:16, 3.92s/it] 44%|████▎ | 8147/18688 [9:33:22<12:13:17, 4.17s/it] 44%|████▎ | 8148/18688 [9:33:27<12:36:16, 4.31s/it] 44%|████▎ | 8149/18688 [9:33:31<12:14:05, 4.18s/it] 44%|████▎ | 8150/18688 [9:33:35<12:19:03, 4.21s/it] {'loss': '0.9767', 'grad_norm': '1.727', 'learning_rate': '6.74e-05', 'epoch': '0.4361', 'num_input_tokens_seen': 27368344, 'train_runtime': '3.442e+04', 'train_tokens_per_second': '795.2'}
44%|████▎ | 8150/18688 [9:33:35<12:19:03, 4.21s/it] 44%|████▎ | 8151/18688 [9:33:39<12:15:25, 4.19s/it] 44%|████▎ | 8152/18688 [9:33:44<12:33:57, 4.29s/it] 44%|████▎ | 8153/18688 [9:33:49<13:32:53, 4.63s/it] 44%|████▎ | 8154/18688 [9:33:54<13:36:29, 4.65s/it] 44%|████▎ | 8155/18688 [9:33:58<12:43:06, 4.35s/it] 44%|████▎ | 8156/18688 [9:34:01<12:07:18, 4.14s/it] 44%|████▎ | 8157/18688 [9:34:07<13:15:48, 4.53s/it] 44%|████▎ | 8158/18688 [9:34:10<12:19:58, 4.22s/it] 44%|████▎ | 8159/18688 [9:34:14<12:22:05, 4.23s/it] 44%|████▎ | 8160/18688 [9:34:18<11:53:40, 4.07s/it] {'loss': '0.9426', 'grad_norm': '1.43', 'learning_rate': '6.732e-05', 'epoch': '0.4367', 'num_input_tokens_seen': 27403936, 'train_runtime': '3.446e+04', 'train_tokens_per_second': '795.3'}
44%|████▎ | 8160/18688 [9:34:18<11:53:40, 4.07s/it] 44%|████▎ | 8161/18688 [9:34:22<11:54:19, 4.07s/it] 44%|████▎ | 8162/18688 [9:34:26<11:41:36, 4.00s/it] 44%|████▎ | 8163/18688 [9:34:31<12:18:10, 4.21s/it] 44%|████▎ | 8164/18688 [9:34:35<12:25:41, 4.25s/it] 44%|████▎ | 8165/18688 [9:34:40<13:14:03, 4.53s/it] 44%|████▎ | 8166/18688 [9:34:44<12:19:44, 4.22s/it] 44%|████▎ | 8167/18688 [9:34:47<11:45:38, 4.02s/it] 44%|████▎ | 8168/18688 [9:34:51<11:21:16, 3.89s/it] 44%|████▎ | 8169/18688 [9:34:55<11:19:07, 3.87s/it] 44%|████▎ | 8170/18688 [9:34:59<11:29:52, 3.94s/it] {'loss': '0.89', 'grad_norm': '1.178', 'learning_rate': '6.723e-05', 'epoch': '0.4372', 'num_input_tokens_seen': 27436720, 'train_runtime': '3.45e+04', 'train_tokens_per_second': '795.3'}
44%|████▎ | 8170/18688 [9:34:59<11:29:52, 3.94s/it] 44%|████▎ | 8171/18688 [9:35:03<11:45:40, 4.03s/it] 44%|████▎ | 8172/18688 [9:35:07<11:55:23, 4.08s/it] 44%|████▎ | 8173/18688 [9:35:12<12:12:38, 4.18s/it] 44%|████▎ | 8174/18688 [9:35:16<12:01:33, 4.12s/it] 44%|████▎ | 8175/18688 [9:35:21<12:41:17, 4.34s/it] 44%|████▍ | 8176/18688 [9:35:25<12:29:03, 4.28s/it] 44%|████▍ | 8177/18688 [9:35:29<12:39:53, 4.34s/it] 44%|████▍ | 8178/18688 [9:35:33<12:04:47, 4.14s/it] 44%|████▍ | 8179/18688 [9:35:37<12:15:11, 4.20s/it] 44%|████▍ | 8180/18688 [9:35:41<11:54:46, 4.08s/it] {'loss': '1.006', 'grad_norm': '1.408', 'learning_rate': '6.715e-05', 'epoch': '0.4377', 'num_input_tokens_seen': 27471312, 'train_runtime': '3.454e+04', 'train_tokens_per_second': '795.3'}
44%|████▍ | 8180/18688 [9:35:41<11:54:46, 4.08s/it] 44%|████▍ | 8181/18688 [9:35:45<11:35:06, 3.97s/it] 44%|████▍ | 8182/18688 [9:35:48<11:21:38, 3.89s/it] 44%|████▍ | 8183/18688 [9:35:52<11:10:50, 3.83s/it] 44%|████▍ | 8184/18688 [9:35:56<11:13:43, 3.85s/it] 44%|████▍ | 8185/18688 [9:36:00<11:43:09, 4.02s/it] 44%|████▍ | 8186/18688 [9:36:04<11:46:11, 4.03s/it] 44%|████▍ | 8187/18688 [9:36:09<12:25:25, 4.26s/it] 44%|████▍ | 8188/18688 [9:36:13<12:03:59, 4.14s/it] 44%|████▍ | 8189/18688 [9:36:18<12:34:38, 4.31s/it] 44%|████▍ | 8190/18688 [9:36:22<12:11:53, 4.18s/it] {'loss': '0.9602', 'grad_norm': '1.379', 'learning_rate': '6.706e-05', 'epoch': '0.4383', 'num_input_tokens_seen': 27503976, 'train_runtime': '3.458e+04', 'train_tokens_per_second': '795.3'}
44%|████▍ | 8190/18688 [9:36:22<12:11:53, 4.18s/it] 44%|████▍ | 8191/18688 [9:36:26<12:32:19, 4.30s/it] 44%|████▍ | 8192/18688 [9:36:30<12:08:09, 4.16s/it] 44%|████▍ | 8193/18688 [9:36:34<11:42:17, 4.01s/it] 44%|████▍ | 8194/18688 [9:36:38<11:34:16, 3.97s/it] 44%|████▍ | 8195/18688 [9:36:42<12:01:26, 4.13s/it] 44%|████▍ | 8196/18688 [9:36:46<11:32:50, 3.96s/it] 44%|████▍ | 8197/18688 [9:36:50<11:44:07, 4.03s/it] 44%|████▍ | 8198/18688 [9:36:54<11:28:11, 3.94s/it] 44%|████▍ | 8199/18688 [9:36:59<12:27:23, 4.28s/it] 44%|████▍ | 8200/18688 [9:37:03<12:23:25, 4.25s/it] {'loss': '0.902', 'grad_norm': '1.348', 'learning_rate': '6.697e-05', 'epoch': '0.4388', 'num_input_tokens_seen': 27537296, 'train_runtime': '3.462e+04', 'train_tokens_per_second': '795.3'}
44%|████▍ | 8200/18688 [9:37:03<12:23:25, 4.25s/it] 44%|████▍ | 8201/18688 [9:37:06<11:44:41, 4.03s/it] 44%|████▍ | 8202/18688 [9:37:10<11:27:55, 3.94s/it] 44%|████▍ | 8203/18688 [9:37:14<11:27:16, 3.93s/it] 44%|████▍ | 8204/18688 [9:37:18<11:12:41, 3.85s/it] 44%|████▍ | 8205/18688 [9:37:21<11:03:34, 3.80s/it] 44%|████▍ | 8206/18688 [9:37:25<11:16:06, 3.87s/it] 44%|████▍ | 8207/18688 [9:37:30<11:44:22, 4.03s/it] 44%|████▍ | 8208/18688 [9:37:34<11:30:19, 3.95s/it] 44%|████▍ | 8209/18688 [9:37:37<11:07:08, 3.82s/it] 44%|████▍ | 8210/18688 [9:37:42<12:11:19, 4.19s/it] {'loss': '0.9197', 'grad_norm': '1.124', 'learning_rate': '6.689e-05', 'epoch': '0.4393', 'num_input_tokens_seen': 27568400, 'train_runtime': '3.466e+04', 'train_tokens_per_second': '795.3'}
44%|████▍ | 8210/18688 [9:37:42<12:11:19, 4.19s/it] 44%|████▍ | 8211/18688 [9:37:48<13:25:03, 4.61s/it] 44%|████▍ | 8212/18688 [9:37:52<12:42:45, 4.37s/it] 44%|████▍ | 8213/18688 [9:37:55<12:15:57, 4.22s/it] 44%|████▍ | 8214/18688 [9:37:59<11:36:05, 3.99s/it] 44%|████▍ | 8215/18688 [9:38:04<12:13:08, 4.20s/it] 44%|████▍ | 8216/18688 [9:38:09<13:15:31, 4.56s/it] 44%|████▍ | 8217/18688 [9:38:14<13:25:18, 4.61s/it] 44%|████▍ | 8218/18688 [9:38:17<12:37:25, 4.34s/it] 44%|████▍ | 8219/18688 [9:38:22<12:26:43, 4.28s/it] 44%|████▍ | 8220/18688 [9:38:26<12:35:08, 4.33s/it] {'loss': '0.9524', 'grad_norm': '1.383', 'learning_rate': '6.68e-05', 'epoch': '0.4399', 'num_input_tokens_seen': 27604760, 'train_runtime': '3.471e+04', 'train_tokens_per_second': '795.4'}
44%|████▍ | 8220/18688 [9:38:26<12:35:08, 4.33s/it] 44%|████▍ | 8221/18688 [9:38:30<12:30:32, 4.30s/it] 44%|████▍ | 8222/18688 [9:38:34<11:59:34, 4.13s/it] 44%|████▍ | 8223/18688 [9:38:38<11:55:17, 4.10s/it] 44%|████▍ | 8224/18688 [9:38:43<12:42:26, 4.37s/it] 44%|████▍ | 8225/18688 [9:38:48<13:27:17, 4.63s/it] 44%|████▍ | 8226/18688 [9:38:52<13:09:45, 4.53s/it] 44%|████▍ | 8227/18688 [9:38:56<12:25:03, 4.27s/it] 44%|████▍ | 8228/18688 [9:39:00<12:13:42, 4.21s/it] 44%|████▍ | 8229/18688 [9:39:04<12:06:15, 4.17s/it] 44%|████▍ | 8230/18688 [9:39:09<12:38:38, 4.35s/it] {'loss': '0.8714', 'grad_norm': '1.14', 'learning_rate': '6.672e-05', 'epoch': '0.4404', 'num_input_tokens_seen': 27640848, 'train_runtime': '3.475e+04', 'train_tokens_per_second': '795.4'}
44%|████▍ | 8230/18688 [9:39:09<12:38:38, 4.35s/it] 44%|████▍ | 8231/18688 [9:39:13<12:20:51, 4.25s/it] 44%|████▍ | 8232/18688 [9:39:17<11:43:36, 4.04s/it] 44%|████▍ | 8233/18688 [9:39:20<11:33:20, 3.98s/it] 44%|████▍ | 8234/18688 [9:39:25<12:14:08, 4.21s/it] 44%|████▍ | 8235/18688 [9:39:30<12:56:25, 4.46s/it] 44%|████▍ | 8236/18688 [9:39:35<12:56:04, 4.46s/it] 44%|████▍ | 8237/18688 [9:39:38<12:19:07, 4.24s/it] 44%|████▍ | 8238/18688 [9:39:43<12:13:35, 4.21s/it] 44%|████▍ | 8239/18688 [9:39:47<12:20:58, 4.25s/it] 44%|████▍ | 8240/18688 [9:39:51<11:44:57, 4.05s/it] {'loss': '0.9051', 'grad_norm': '1.419', 'learning_rate': '6.663e-05', 'epoch': '0.4409', 'num_input_tokens_seen': 27674376, 'train_runtime': '3.479e+04', 'train_tokens_per_second': '795.4'}
44%|████▍ | 8240/18688 [9:39:51<11:44:57, 4.05s/it] 44%|████▍ | 8241/18688 [9:39:54<11:16:18, 3.88s/it] 44%|████▍ | 8242/18688 [9:39:58<11:23:01, 3.92s/it] 44%|████▍ | 8243/18688 [9:40:02<11:05:37, 3.82s/it] 44%|████▍ | 8244/18688 [9:40:06<11:26:59, 3.95s/it] 44%|████▍ | 8245/18688 [9:40:10<11:16:47, 3.89s/it] 44%|████▍ | 8246/18688 [9:40:16<13:05:57, 4.52s/it] 44%|████▍ | 8247/18688 [9:40:19<12:30:27, 4.31s/it] 44%|████▍ | 8248/18688 [9:40:24<12:18:11, 4.24s/it] 44%|████▍ | 8249/18688 [9:40:27<12:03:40, 4.16s/it] 44%|████▍ | 8250/18688 [9:40:31<11:40:43, 4.03s/it] {'loss': '0.9174', 'grad_norm': '1.247', 'learning_rate': '6.654e-05', 'epoch': '0.4415', 'num_input_tokens_seen': 27706056, 'train_runtime': '3.483e+04', 'train_tokens_per_second': '795.4'}
44%|████▍ | 8250/18688 [9:40:31<11:40:43, 4.03s/it] 44%|████▍ | 8251/18688 [9:40:35<11:23:07, 3.93s/it] 44%|████▍ | 8252/18688 [9:40:38<10:58:40, 3.79s/it] 44%|████▍ | 8253/18688 [9:40:43<11:56:47, 4.12s/it] 44%|████▍ | 8254/18688 [9:40:48<12:11:46, 4.21s/it] 44%|████▍ | 8255/18688 [9:40:52<12:03:19, 4.16s/it] 44%|████▍ | 8256/18688 [9:40:56<11:54:16, 4.11s/it] 44%|████▍ | 8257/18688 [9:41:00<12:16:32, 4.24s/it] 44%|████▍ | 8258/18688 [9:41:04<12:12:51, 4.22s/it] 44%|████▍ | 8259/18688 [9:41:08<11:49:46, 4.08s/it] 44%|████▍ | 8260/18688 [9:41:12<11:25:56, 3.95s/it] {'loss': '0.8555', 'grad_norm': '1.639', 'learning_rate': '6.646e-05', 'epoch': '0.442', 'num_input_tokens_seen': 27738160, 'train_runtime': '3.487e+04', 'train_tokens_per_second': '795.4'}
44%|████▍ | 8260/18688 [9:41:12<11:25:56, 3.95s/it] 44%|████▍ | 8261/18688 [9:41:16<11:43:39, 4.05s/it] 44%|████▍ | 8262/18688 [9:41:20<11:26:54, 3.95s/it] 44%|████▍ | 8263/18688 [9:41:23<11:05:40, 3.83s/it] 44%|████▍ | 8264/18688 [9:41:28<12:04:47, 4.17s/it] 44%|████▍ | 8265/18688 [9:41:32<11:49:38, 4.09s/it] 44%|████▍ | 8266/18688 [9:41:36<11:20:24, 3.92s/it] 44%|████▍ | 8267/18688 [9:41:40<11:45:52, 4.06s/it] 44%|████▍ | 8268/18688 [9:41:45<12:21:28, 4.27s/it] 44%|████▍ | 8269/18688 [9:41:49<12:04:29, 4.17s/it] 44%|████▍ | 8270/18688 [9:41:53<11:47:39, 4.08s/it] {'loss': '0.9524', 'grad_norm': '1.444', 'learning_rate': '6.637e-05', 'epoch': '0.4425', 'num_input_tokens_seen': 27769024, 'train_runtime': '3.491e+04', 'train_tokens_per_second': '795.4'}
44%|████▍ | 8270/18688 [9:41:53<11:47:39, 4.08s/it] 44%|████▍ | 8271/18688 [9:41:56<11:22:23, 3.93s/it] 44%|████▍ | 8272/18688 [9:42:00<11:08:21, 3.85s/it] 44%|████▍ | 8273/18688 [9:42:04<11:03:00, 3.82s/it] 44%|████▍ | 8274/18688 [9:42:08<11:29:08, 3.97s/it] 44%|████▍ | 8275/18688 [9:42:12<11:40:46, 4.04s/it] 44%|████▍ | 8276/18688 [9:42:16<11:31:57, 3.99s/it] 44%|████▍ | 8277/18688 [9:42:21<12:23:03, 4.28s/it] 44%|████▍ | 8278/18688 [9:42:25<12:08:23, 4.20s/it] 44%|████▍ | 8279/18688 [9:42:30<12:57:22, 4.48s/it] 44%|████▍ | 8280/18688 [9:42:34<12:14:06, 4.23s/it] {'loss': '0.9021', 'grad_norm': '1.272', 'learning_rate': '6.628e-05', 'epoch': '0.4431', 'num_input_tokens_seen': 27801960, 'train_runtime': '3.495e+04', 'train_tokens_per_second': '795.4'}
44%|████▍ | 8280/18688 [9:42:34<12:14:06, 4.23s/it] 44%|████▍ | 8281/18688 [9:42:38<12:29:31, 4.32s/it] 44%|████▍ | 8282/18688 [9:42:42<12:10:29, 4.21s/it] 44%|████▍ | 8283/18688 [9:42:46<11:50:03, 4.09s/it] 44%|████▍ | 8284/18688 [9:42:50<11:40:39, 4.04s/it] 44%|████▍ | 8285/18688 [9:42:54<11:26:59, 3.96s/it] 44%|████▍ | 8286/18688 [9:42:58<11:31:41, 3.99s/it] 44%|████▍ | 8287/18688 [9:43:01<11:11:30, 3.87s/it] 44%|████▍ | 8288/18688 [9:43:05<10:55:42, 3.78s/it] 44%|████▍ | 8289/18688 [9:43:10<11:43:28, 4.06s/it] 44%|████▍ | 8290/18688 [9:43:13<11:22:47, 3.94s/it] {'loss': '0.9866', 'grad_norm': '1.453', 'learning_rate': '6.62e-05', 'epoch': '0.4436', 'num_input_tokens_seen': 27833440, 'train_runtime': '3.499e+04', 'train_tokens_per_second': '795.4'}
44%|████▍ | 8290/18688 [9:43:13<11:22:47, 3.94s/it] 44%|████▍ | 8291/18688 [9:43:17<11:10:52, 3.87s/it] 44%|████▍ | 8292/18688 [9:43:22<11:37:21, 4.02s/it] 44%|████▍ | 8293/18688 [9:43:26<12:00:24, 4.16s/it] 44%|████▍ | 8294/18688 [9:43:31<12:41:25, 4.40s/it] 44%|████▍ | 8295/18688 [9:43:35<12:05:58, 4.19s/it] 44%|████▍ | 8296/18688 [9:43:39<12:12:22, 4.23s/it] 44%|████▍ | 8297/18688 [9:43:44<12:43:02, 4.41s/it] 44%|████▍ | 8298/18688 [9:43:47<11:56:33, 4.14s/it] 44%|████▍ | 8299/18688 [9:43:52<12:02:56, 4.18s/it] 44%|████▍ | 8300/18688 [9:43:56<12:02:47, 4.17s/it] {'loss': '0.9288', 'grad_norm': '1.333', 'learning_rate': '6.611e-05', 'epoch': '0.4441', 'num_input_tokens_seen': 27868144, 'train_runtime': '3.504e+04', 'train_tokens_per_second': '795.4'}
44%|████▍ | 8300/18688 [9:43:56<12:02:47, 4.17s/it] 44%|████▍ | 8301/18688 [9:44:00<11:43:49, 4.07s/it] 44%|████▍ | 8302/18688 [9:44:04<11:43:58, 4.07s/it] 44%|████▍ | 8303/18688 [9:44:07<11:13:39, 3.89s/it] 44%|████▍ | 8304/18688 [9:44:12<11:41:10, 4.05s/it] 44%|████▍ | 8305/18688 [9:44:16<11:39:29, 4.04s/it] 44%|████▍ | 8306/18688 [9:44:19<11:18:42, 3.92s/it] 44%|████▍ | 8307/18688 [9:44:23<11:09:21, 3.87s/it] 44%|████▍ | 8308/18688 [9:44:27<11:32:55, 4.01s/it] 44%|████▍ | 8309/18688 [9:44:31<11:17:04, 3.91s/it] 44%|████▍ | 8310/18688 [9:44:35<11:08:35, 3.87s/it] {'loss': '0.8971', 'grad_norm': '1.172', 'learning_rate': '6.603e-05', 'epoch': '0.4447', 'num_input_tokens_seen': 27898600, 'train_runtime': '3.508e+04', 'train_tokens_per_second': '795.4'}
44%|████▍ | 8310/18688 [9:44:35<11:08:35, 3.87s/it] 44%|████▍ | 8311/18688 [9:44:38<10:54:52, 3.79s/it] 44%|████▍ | 8312/18688 [9:44:43<11:26:35, 3.97s/it] 44%|████▍ | 8313/18688 [9:44:46<11:10:27, 3.88s/it] 44%|████▍ | 8314/18688 [9:44:50<11:06:15, 3.85s/it] 44%|████▍ | 8315/18688 [9:44:54<11:27:09, 3.97s/it] 44%|████▍ | 8316/18688 [9:44:58<11:24:31, 3.96s/it] 45%|████▍ | 8317/18688 [9:45:03<12:00:44, 4.17s/it] 45%|████▍ | 8318/18688 [9:45:08<13:01:21, 4.52s/it] 45%|████▍ | 8319/18688 [9:45:13<12:46:45, 4.44s/it] 45%|████▍ | 8320/18688 [9:45:17<12:47:40, 4.44s/it] {'loss': '0.9751', 'grad_norm': '1.227', 'learning_rate': '6.594e-05', 'epoch': '0.4452', 'num_input_tokens_seen': 27933416, 'train_runtime': '3.512e+04', 'train_tokens_per_second': '795.4'}
45%|████▍ | 8320/18688 [9:45:17<12:47:40, 4.44s/it] 45%|████▍ | 8321/18688 [9:45:22<13:13:51, 4.59s/it] 45%|████▍ | 8322/18688 [9:45:26<13:00:08, 4.52s/it] 45%|████▍ | 8323/18688 [9:45:32<13:35:20, 4.72s/it] 45%|████▍ | 8324/18688 [9:45:36<13:13:43, 4.60s/it] 45%|████▍ | 8325/18688 [9:45:39<12:15:45, 4.26s/it] 45%|████▍ | 8326/18688 [9:45:44<12:41:53, 4.41s/it] 45%|████▍ | 8327/18688 [9:45:48<12:04:20, 4.19s/it] 45%|████▍ | 8328/18688 [9:45:52<12:12:46, 4.24s/it] 45%|████▍ | 8329/18688 [9:45:56<11:41:39, 4.06s/it] 45%|████▍ | 8330/18688 [9:46:00<12:03:50, 4.19s/it] {'loss': '0.9997', 'grad_norm': '1.337', 'learning_rate': '6.585e-05', 'epoch': '0.4458', 'num_input_tokens_seen': 27969768, 'train_runtime': '3.516e+04', 'train_tokens_per_second': '795.5'}
45%|████▍ | 8330/18688 [9:46:00<12:03:50, 4.19s/it] 45%|████▍ | 8331/18688 [9:46:04<11:55:04, 4.14s/it] 45%|████▍ | 8332/18688 [9:46:09<12:17:14, 4.27s/it] 45%|████▍ | 8333/18688 [9:46:14<13:03:20, 4.54s/it] 45%|████▍ | 8334/18688 [9:46:19<13:30:42, 4.70s/it] 45%|████▍ | 8335/18688 [9:46:23<13:02:20, 4.53s/it] 45%|████▍ | 8336/18688 [9:46:27<12:31:26, 4.36s/it] 45%|████▍ | 8337/18688 [9:46:31<11:44:46, 4.09s/it] 45%|████▍ | 8338/18688 [9:46:35<11:45:36, 4.09s/it] 45%|████▍ | 8339/18688 [9:46:40<12:58:49, 4.52s/it] 45%|████▍ | 8340/18688 [9:46:44<12:11:36, 4.24s/it] {'loss': '0.9431', 'grad_norm': '1.196', 'learning_rate': '6.577e-05', 'epoch': '0.4463', 'num_input_tokens_seen': 28007472, 'train_runtime': '3.52e+04', 'train_tokens_per_second': '795.6'}
45%|████▍ | 8340/18688 [9:46:44<12:11:36, 4.24s/it] 45%|████▍ | 8341/18688 [9:46:48<11:59:10, 4.17s/it] 45%|████▍ | 8342/18688 [9:46:51<11:25:08, 3.97s/it] 45%|████▍ | 8343/18688 [9:46:56<11:34:26, 4.03s/it] 45%|████▍ | 8344/18688 [9:46:59<11:12:21, 3.90s/it] 45%|████▍ | 8345/18688 [9:47:03<11:08:08, 3.88s/it] 45%|████▍ | 8346/18688 [9:47:06<10:49:50, 3.77s/it] 45%|████▍ | 8347/18688 [9:47:11<11:09:48, 3.89s/it] 45%|████▍ | 8348/18688 [9:47:16<12:13:27, 4.26s/it] 45%|████▍ | 8349/18688 [9:47:21<12:48:04, 4.46s/it] 45%|████▍ | 8350/18688 [9:47:25<12:18:03, 4.28s/it] {'loss': '0.9255', 'grad_norm': '1.238', 'learning_rate': '6.568e-05', 'epoch': '0.4468', 'num_input_tokens_seen': 28041240, 'train_runtime': '3.525e+04', 'train_tokens_per_second': '795.6'}
45%|████▍ | 8350/18688 [9:47:25<12:18:03, 4.28s/it] 45%|████▍ | 8351/18688 [9:47:29<12:40:05, 4.41s/it] 45%|████▍ | 8352/18688 [9:47:33<12:12:07, 4.25s/it] 45%|████▍ | 8353/18688 [9:47:37<12:07:03, 4.22s/it] 45%|████▍ | 8354/18688 [9:47:42<12:18:48, 4.29s/it] 45%|████▍ | 8355/18688 [9:47:46<11:58:45, 4.17s/it] 45%|████▍ | 8356/18688 [9:47:50<12:31:19, 4.36s/it] 45%|████▍ | 8357/18688 [9:47:54<11:55:18, 4.15s/it] 45%|████▍ | 8358/18688 [9:47:58<11:53:51, 4.15s/it] 45%|████▍ | 8359/18688 [9:48:02<11:39:29, 4.06s/it] 45%|████▍ | 8360/18688 [9:48:07<12:22:31, 4.31s/it] {'loss': '0.9399', 'grad_norm': '1.107', 'learning_rate': '6.559e-05', 'epoch': '0.4474', 'num_input_tokens_seen': 28076200, 'train_runtime': '3.529e+04', 'train_tokens_per_second': '795.6'}
45%|████▍ | 8360/18688 [9:48:07<12:22:31, 4.31s/it] 45%|████▍ | 8361/18688 [9:48:11<12:18:54, 4.29s/it] 45%|████▍ | 8362/18688 [9:48:16<12:18:44, 4.29s/it] 45%|████▍ | 8363/18688 [9:48:20<12:12:17, 4.26s/it] 45%|████▍ | 8364/18688 [9:48:23<11:32:48, 4.03s/it] 45%|████▍ | 8365/18688 [9:48:27<11:03:20, 3.86s/it] 45%|████▍ | 8366/18688 [9:48:31<11:51:41, 4.14s/it] 45%|████▍ | 8367/18688 [9:48:35<11:27:44, 4.00s/it] 45%|████▍ | 8368/18688 [9:48:39<11:01:00, 3.84s/it] 45%|████▍ | 8369/18688 [9:48:44<12:10:46, 4.25s/it] 45%|████▍ | 8370/18688 [9:48:49<12:34:17, 4.39s/it] {'loss': '0.9007', 'grad_norm': '1.244', 'learning_rate': '6.55e-05', 'epoch': '0.4479', 'num_input_tokens_seen': 28110888, 'train_runtime': '3.533e+04', 'train_tokens_per_second': '795.7'}
45%|████▍ | 8370/18688 [9:48:49<12:34:17, 4.39s/it] 45%|████▍ | 8371/18688 [9:48:52<11:49:21, 4.13s/it] 45%|████▍ | 8372/18688 [9:48:56<11:40:33, 4.07s/it] 45%|████▍ | 8373/18688 [9:49:01<12:19:18, 4.30s/it] 45%|████▍ | 8374/18688 [9:49:06<12:42:22, 4.43s/it] 45%|████▍ | 8375/18688 [9:49:10<12:42:25, 4.44s/it] 45%|████▍ | 8376/18688 [9:49:14<12:20:24, 4.31s/it] 45%|████▍ | 8377/18688 [9:49:18<12:11:35, 4.26s/it] 45%|████▍ | 8378/18688 [9:49:22<11:46:30, 4.11s/it] 45%|████▍ | 8379/18688 [9:49:26<11:44:05, 4.10s/it] 45%|████▍ | 8380/18688 [9:49:30<11:41:15, 4.08s/it] {'loss': '0.8889', 'grad_norm': '1.116', 'learning_rate': '6.542e-05', 'epoch': '0.4484', 'num_input_tokens_seen': 28146016, 'train_runtime': '3.537e+04', 'train_tokens_per_second': '795.7'}
45%|████▍ | 8380/18688 [9:49:30<11:41:15, 4.08s/it] 45%|████▍ | 8381/18688 [9:49:34<11:29:44, 4.02s/it] 45%|████▍ | 8382/18688 [9:49:38<11:56:50, 4.17s/it] 45%|████▍ | 8383/18688 [9:49:43<12:05:20, 4.22s/it] 45%|████▍ | 8384/18688 [9:49:48<13:07:38, 4.59s/it] 45%|████▍ | 8385/18688 [9:49:52<12:46:51, 4.47s/it] 45%|████▍ | 8386/18688 [9:49:57<13:13:49, 4.62s/it] 45%|████▍ | 8387/18688 [9:50:02<13:29:13, 4.71s/it] 45%|████▍ | 8388/18688 [9:50:07<13:09:52, 4.60s/it] 45%|████▍ | 8389/18688 [9:50:10<12:15:37, 4.29s/it] 45%|████▍ | 8390/18688 [9:50:14<11:38:32, 4.07s/it] {'loss': '0.9734', 'grad_norm': '1.32', 'learning_rate': '6.533e-05', 'epoch': '0.449', 'num_input_tokens_seen': 28183992, 'train_runtime': '3.541e+04', 'train_tokens_per_second': '795.8'}
45%|████▍ | 8390/18688 [9:50:14<11:38:32, 4.07s/it] 45%|████▍ | 8391/18688 [9:50:17<11:15:02, 3.93s/it] 45%|████▍ | 8392/18688 [9:50:21<11:11:00, 3.91s/it] 45%|████▍ | 8393/18688 [9:50:25<11:02:01, 3.86s/it] 45%|████▍ | 8394/18688 [9:50:29<10:54:55, 3.82s/it] 45%|████▍ | 8395/18688 [9:50:32<10:39:58, 3.73s/it] 45%|████▍ | 8396/18688 [9:50:36<10:25:51, 3.65s/it] 45%|████▍ | 8397/18688 [9:50:39<10:34:45, 3.70s/it] 45%|████▍ | 8398/18688 [9:50:44<11:05:33, 3.88s/it] 45%|████▍ | 8399/18688 [9:50:47<10:54:44, 3.82s/it] 45%|████▍ | 8400/18688 [9:50:52<11:17:35, 3.95s/it] {'loss': '0.9117', 'grad_norm': '1.759', 'learning_rate': '6.524e-05', 'epoch': '0.4495', 'num_input_tokens_seen': 28213064, 'train_runtime': '3.545e+04', 'train_tokens_per_second': '795.8'}
45%|████▍ | 8400/18688 [9:50:52<11:17:35, 3.95s/it] 45%|████▍ | 8401/18688 [9:50:56<11:38:18, 4.07s/it] 45%|████▍ | 8402/18688 [9:51:01<12:05:06, 4.23s/it] 45%|████▍ | 8403/18688 [9:51:04<11:43:15, 4.10s/it] 45%|████▍ | 8404/18688 [9:51:08<11:22:04, 3.98s/it] 45%|████▍ | 8405/18688 [9:51:13<11:42:53, 4.10s/it] 45%|████▍ | 8406/18688 [9:51:18<12:35:39, 4.41s/it] 45%|████▍ | 8407/18688 [9:51:21<11:55:23, 4.18s/it] 45%|████▍ | 8408/18688 [9:51:25<11:34:27, 4.05s/it] 45%|████▍ | 8409/18688 [9:51:29<11:09:51, 3.91s/it] 45%|████▌ | 8410/18688 [9:51:33<11:35:42, 4.06s/it] {'loss': '1.002', 'grad_norm': '1.526', 'learning_rate': '6.516e-05', 'epoch': '0.45', 'num_input_tokens_seen': 28248192, 'train_runtime': '3.549e+04', 'train_tokens_per_second': '795.9'}
45%|████▌ | 8410/18688 [9:51:33<11:35:42, 4.06s/it] 45%|████▌ | 8411/18688 [9:51:37<11:13:44, 3.93s/it] 45%|████▌ | 8412/18688 [9:51:41<11:29:22, 4.03s/it] 45%|████▌ | 8413/18688 [9:51:45<11:16:22, 3.95s/it] 45%|████▌ | 8414/18688 [9:51:49<11:20:45, 3.98s/it] 45%|████▌ | 8415/18688 [9:51:53<11:23:33, 3.99s/it] 45%|████▌ | 8416/18688 [9:51:56<11:00:34, 3.86s/it] 45%|████▌ | 8417/18688 [9:52:00<10:48:16, 3.79s/it] 45%|████▌ | 8418/18688 [9:52:05<11:30:49, 4.04s/it] 45%|████▌ | 8419/18688 [9:52:08<11:19:09, 3.97s/it] 45%|████▌ | 8420/18688 [9:52:13<12:05:35, 4.24s/it] {'loss': '0.8974', 'grad_norm': '1.389', 'learning_rate': '6.507e-05', 'epoch': '0.4506', 'num_input_tokens_seen': 28280736, 'train_runtime': '3.553e+04', 'train_tokens_per_second': '795.9'}
45%|████▌ | 8420/18688 [9:52:13<12:05:35, 4.24s/it] 45%|████▌ | 8421/18688 [9:52:18<12:11:32, 4.28s/it] 45%|████▌ | 8422/18688 [9:52:22<12:18:04, 4.31s/it] 45%|████▌ | 8423/18688 [9:52:26<11:44:48, 4.12s/it] 45%|████▌ | 8424/18688 [9:52:30<11:28:51, 4.03s/it] 45%|████▌ | 8425/18688 [9:52:34<11:50:47, 4.16s/it] 45%|████▌ | 8426/18688 [9:52:38<11:22:36, 3.99s/it] 45%|████▌ | 8427/18688 [9:52:42<11:45:54, 4.13s/it] 45%|████▌ | 8428/18688 [9:52:47<12:09:40, 4.27s/it] 45%|████▌ | 8429/18688 [9:52:51<12:18:18, 4.32s/it] 45%|████▌ | 8430/18688 [9:52:55<12:10:58, 4.28s/it] {'loss': '0.8718', 'grad_norm': '1.368', 'learning_rate': '6.498e-05', 'epoch': '0.4511', 'num_input_tokens_seen': 28316296, 'train_runtime': '3.558e+04', 'train_tokens_per_second': '795.9'}
45%|████▌ | 8430/18688 [9:52:55<12:10:58, 4.28s/it] 45%|████▌ | 8431/18688 [9:52:59<11:34:19, 4.06s/it] 45%|████▌ | 8432/18688 [9:53:02<11:14:38, 3.95s/it] 45%|████▌ | 8433/18688 [9:53:06<10:56:21, 3.84s/it] 45%|████▌ | 8434/18688 [9:53:10<11:12:28, 3.93s/it] 45%|████▌ | 8435/18688 [9:53:14<11:23:33, 4.00s/it] 45%|████▌ | 8436/18688 [9:53:19<12:03:13, 4.23s/it] 45%|████▌ | 8437/18688 [9:53:23<11:52:33, 4.17s/it] 45%|████▌ | 8438/18688 [9:53:29<12:53:59, 4.53s/it] 45%|████▌ | 8439/18688 [9:53:32<12:21:33, 4.34s/it] 45%|████▌ | 8440/18688 [9:53:37<12:32:46, 4.41s/it] {'loss': '1', 'grad_norm': '1.267', 'learning_rate': '6.49e-05', 'epoch': '0.4516', 'num_input_tokens_seen': 28350936, 'train_runtime': '3.562e+04', 'train_tokens_per_second': '796'}
45%|████▌ | 8440/18688 [9:53:37<12:32:46, 4.41s/it] 45%|████▌ | 8441/18688 [9:53:41<12:11:16, 4.28s/it] 45%|████▌ | 8442/18688 [9:53:46<12:34:28, 4.42s/it] 45%|████▌ | 8443/18688 [9:53:50<12:24:35, 4.36s/it] 45%|████▌ | 8444/18688 [9:53:54<12:21:14, 4.34s/it] 45%|████▌ | 8445/18688 [9:53:59<12:46:08, 4.49s/it] 45%|████▌ | 8446/18688 [9:54:04<13:11:09, 4.63s/it] 45%|████▌ | 8447/18688 [9:54:08<12:41:57, 4.46s/it] 45%|████▌ | 8448/18688 [9:54:12<12:11:30, 4.29s/it] 45%|████▌ | 8449/18688 [9:54:16<11:54:58, 4.19s/it] 45%|████▌ | 8450/18688 [9:54:20<12:13:07, 4.30s/it] {'loss': '0.9262', 'grad_norm': '1.459', 'learning_rate': '6.481e-05', 'epoch': '0.4522', 'num_input_tokens_seen': 28387336, 'train_runtime': '3.566e+04', 'train_tokens_per_second': '796'}
45%|████▌ | 8450/18688 [9:54:20<12:13:07, 4.30s/it] 45%|████▌ | 8451/18688 [9:54:26<13:01:01, 4.58s/it] 45%|████▌ | 8452/18688 [9:54:30<12:51:31, 4.52s/it] 45%|████▌ | 8453/18688 [9:54:34<12:08:22, 4.27s/it] 45%|████▌ | 8454/18688 [9:54:38<12:07:56, 4.27s/it] 45%|████▌ | 8455/18688 [9:54:43<12:18:30, 4.33s/it] 45%|████▌ | 8456/18688 [9:54:46<11:52:33, 4.18s/it] 45%|████▌ | 8457/18688 [9:54:50<11:42:06, 4.12s/it] 45%|████▌ | 8458/18688 [9:54:55<11:44:19, 4.13s/it] 45%|████▌ | 8459/18688 [9:54:59<11:43:08, 4.12s/it] 45%|████▌ | 8460/18688 [9:55:02<11:16:52, 3.97s/it] {'loss': '0.9796', 'grad_norm': '1.411', 'learning_rate': '6.472e-05', 'epoch': '0.4527', 'num_input_tokens_seen': 28422240, 'train_runtime': '3.57e+04', 'train_tokens_per_second': '796.1'}
45%|████▌ | 8460/18688 [9:55:02<11:16:52, 3.97s/it] 45%|████▌ | 8461/18688 [9:55:07<11:51:26, 4.17s/it] 45%|████▌ | 8462/18688 [9:55:11<11:32:46, 4.06s/it] 45%|████▌ | 8463/18688 [9:55:14<11:10:22, 3.93s/it] 45%|████▌ | 8464/18688 [9:55:18<11:22:02, 4.00s/it] 45%|████▌ | 8465/18688 [9:55:22<11:10:20, 3.93s/it] 45%|████▌ | 8466/18688 [9:55:27<11:39:36, 4.11s/it] 45%|████▌ | 8467/18688 [9:55:31<11:36:06, 4.09s/it] 45%|████▌ | 8468/18688 [9:55:34<11:11:44, 3.94s/it] 45%|████▌ | 8469/18688 [9:55:38<11:16:57, 3.97s/it] 45%|████▌ | 8470/18688 [9:55:42<11:19:15, 3.99s/it] {'loss': '0.9688', 'grad_norm': '1.14', 'learning_rate': '6.463e-05', 'epoch': '0.4532', 'num_input_tokens_seen': 28454416, 'train_runtime': '3.574e+04', 'train_tokens_per_second': '796.1'}
45%|████▌ | 8470/18688 [9:55:42<11:19:15, 3.99s/it] 45%|████▌ | 8471/18688 [9:55:46<11:09:25, 3.93s/it] 45%|████▌ | 8472/18688 [9:55:51<11:25:15, 4.02s/it] 45%|████▌ | 8473/18688 [9:55:54<11:09:55, 3.93s/it] 45%|████▌ | 8474/18688 [9:55:58<10:50:12, 3.82s/it] 45%|████▌ | 8475/18688 [9:56:02<10:52:36, 3.83s/it] 45%|████▌ | 8476/18688 [9:56:06<11:18:06, 3.98s/it] 45%|████▌ | 8477/18688 [9:56:10<11:35:42, 4.09s/it] 45%|████▌ | 8478/18688 [9:56:14<11:32:40, 4.07s/it] 45%|████▌ | 8479/18688 [9:56:18<11:31:03, 4.06s/it] 45%|████▌ | 8480/18688 [9:56:23<11:40:13, 4.12s/it] {'loss': '0.9331', 'grad_norm': '1.103', 'learning_rate': '6.455e-05', 'epoch': '0.4538', 'num_input_tokens_seen': 28487064, 'train_runtime': '3.578e+04', 'train_tokens_per_second': '796.1'}
45%|████▌ | 8480/18688 [9:56:23<11:40:13, 4.12s/it] 45%|████▌ | 8481/18688 [9:56:27<11:53:30, 4.19s/it] 45%|████▌ | 8482/18688 [9:56:31<11:44:14, 4.14s/it] 45%|████▌ | 8483/18688 [9:56:36<12:13:46, 4.31s/it] 45%|████▌ | 8484/18688 [9:56:40<12:07:56, 4.28s/it] 45%|████▌ | 8485/18688 [9:56:44<12:02:09, 4.25s/it] 45%|████▌ | 8486/18688 [9:56:48<11:27:57, 4.05s/it] 45%|████▌ | 8487/18688 [9:56:52<11:29:34, 4.06s/it] 45%|████▌ | 8488/18688 [9:56:56<11:35:03, 4.09s/it] 45%|████▌ | 8489/18688 [9:57:01<12:05:22, 4.27s/it] 45%|████▌ | 8490/18688 [9:57:06<12:57:28, 4.57s/it] {'loss': '0.9801', 'grad_norm': '1.353', 'learning_rate': '6.446e-05', 'epoch': '0.4543', 'num_input_tokens_seen': 28524072, 'train_runtime': '3.583e+04', 'train_tokens_per_second': '796.2'}
45%|████▌ | 8490/18688 [9:57:06<12:57:28, 4.57s/it] 45%|████▌ | 8491/18688 [9:57:10<12:07:00, 4.28s/it] 45%|████▌ | 8492/18688 [9:57:14<12:35:03, 4.44s/it] 45%|████▌ | 8493/18688 [9:57:18<11:55:18, 4.21s/it] 45%|████▌ | 8494/18688 [9:57:22<11:37:46, 4.11s/it] 45%|████▌ | 8495/18688 [9:57:26<11:40:32, 4.12s/it] 45%|████▌ | 8496/18688 [9:57:30<11:21:29, 4.01s/it] 45%|████▌ | 8497/18688 [9:57:34<11:10:19, 3.95s/it] 45%|████▌ | 8498/18688 [9:57:38<11:12:51, 3.96s/it] 45%|████▌ | 8499/18688 [9:57:42<11:19:04, 4.00s/it] 45%|████▌ | 8500/18688 [9:57:46<11:59:41, 4.24s/it] {'loss': '0.958', 'grad_norm': '1.135', 'learning_rate': '6.437e-05', 'epoch': '0.4548', 'num_input_tokens_seen': 28557096, 'train_runtime': '3.587e+04', 'train_tokens_per_second': '796.2'}
45%|████▌ | 8500/18688 [9:57:46<11:59:41, 4.24s/it] 45%|████▌ | 8501/18688 [9:57:50<11:27:34, 4.05s/it] 45%|████▌ | 8502/18688 [9:57:54<11:44:45, 4.15s/it] 45%|████▌ | 8503/18688 [9:58:00<13:05:56, 4.63s/it] 46%|████▌ | 8504/18688 [9:58:04<12:49:04, 4.53s/it] 46%|████▌ | 8505/18688 [9:58:08<11:57:36, 4.23s/it] 46%|████▌ | 8506/18688 [9:58:11<11:18:26, 4.00s/it] 46%|████▌ | 8507/18688 [9:58:15<11:07:33, 3.93s/it] 46%|████▌ | 8508/18688 [9:58:19<10:45:01, 3.80s/it] 46%|████▌ | 8509/18688 [9:58:22<10:27:26, 3.70s/it] 46%|████▌ | 8510/18688 [9:58:27<11:06:13, 3.93s/it] {'loss': '0.9278', 'grad_norm': '1.257', 'learning_rate': '6.428e-05', 'epoch': '0.4554', 'num_input_tokens_seen': 28590184, 'train_runtime': '3.591e+04', 'train_tokens_per_second': '796.2'}
46%|████▌ | 8510/18688 [9:58:27<11:06:13, 3.93s/it] 46%|████▌ | 8511/18688 [9:58:30<10:43:38, 3.79s/it] 46%|████▌ | 8512/18688 [9:58:35<11:14:55, 3.98s/it] 46%|████▌ | 8513/18688 [9:58:39<11:43:44, 4.15s/it] 46%|████▌ | 8514/18688 [9:58:43<11:20:12, 4.01s/it] 46%|████▌ | 8515/18688 [9:58:47<11:35:58, 4.10s/it] 46%|████▌ | 8516/18688 [9:58:51<11:15:52, 3.99s/it] 46%|████▌ | 8517/18688 [9:58:55<11:18:58, 4.01s/it] 46%|████▌ | 8518/18688 [9:58:59<11:09:47, 3.95s/it] 46%|████▌ | 8519/18688 [9:59:04<12:10:50, 4.31s/it] 46%|████▌ | 8520/18688 [9:59:09<12:48:08, 4.53s/it] {'loss': '0.9337', 'grad_norm': '1.12', 'learning_rate': '6.42e-05', 'epoch': '0.4559', 'num_input_tokens_seen': 28625592, 'train_runtime': '3.595e+04', 'train_tokens_per_second': '796.3'}
46%|████▌ | 8520/18688 [9:59:09<12:48:08, 4.53s/it] 46%|████▌ | 8521/18688 [9:59:12<11:58:48, 4.24s/it] 46%|████▌ | 8522/18688 [9:59:16<11:18:47, 4.01s/it] 46%|████▌ | 8523/18688 [9:59:20<11:18:52, 4.01s/it] 46%|████▌ | 8524/18688 [9:59:25<11:57:42, 4.24s/it] 46%|████▌ | 8525/18688 [9:59:29<11:41:40, 4.14s/it] 46%|████▌ | 8526/18688 [9:59:33<11:54:48, 4.22s/it] 46%|████▌ | 8527/18688 [9:59:37<11:23:53, 4.04s/it] 46%|████▌ | 8528/18688 [9:59:41<11:16:25, 3.99s/it] 46%|████▌ | 8529/18688 [9:59:45<11:17:04, 4.00s/it] 46%|████▌ | 8530/18688 [9:59:50<12:17:05, 4.35s/it] {'loss': '1.002', 'grad_norm': '1.342', 'learning_rate': '6.411e-05', 'epoch': '0.4565', 'num_input_tokens_seen': 28659464, 'train_runtime': '3.599e+04', 'train_tokens_per_second': '796.3'}
46%|████▌ | 8530/18688 [9:59:50<12:17:05, 4.35s/it] 46%|████▌ | 8531/18688 [9:59:54<11:53:31, 4.21s/it] 46%|████▌ | 8532/18688 [9:59:57<11:31:54, 4.09s/it] 46%|████▌ | 8533/18688 [10:00:01<11:30:17, 4.08s/it] 46%|████▌ | 8534/18688 [10:00:06<12:00:08, 4.26s/it] 46%|████▌ | 8535/18688 [10:00:10<11:38:56, 4.13s/it] 46%|████▌ | 8536/18688 [10:00:14<11:40:41, 4.14s/it] 46%|████▌ | 8537/18688 [10:00:18<11:46:36, 4.18s/it] 46%|████▌ | 8538/18688 [10:00:22<11:42:02, 4.15s/it] 46%|████▌ | 8539/18688 [10:00:26<11:34:23, 4.11s/it] 46%|████▌ | 8540/18688 [10:00:30<11:26:03, 4.06s/it] {'loss': '0.9225', 'grad_norm': '0.9882', 'learning_rate': '6.402e-05', 'epoch': '0.457', 'num_input_tokens_seen': 28692144, 'train_runtime': '3.603e+04', 'train_tokens_per_second': '796.3'}
46%|████▌ | 8540/18688 [10:00:30<11:26:03, 4.06s/it] 46%|████▌ | 8541/18688 [10:00:34<10:58:52, 3.90s/it] 46%|████▌ | 8542/18688 [10:00:38<11:09:49, 3.96s/it] 46%|████▌ | 8543/18688 [10:00:42<11:17:51, 4.01s/it] 46%|████▌ | 8544/18688 [10:00:46<11:20:03, 4.02s/it] 46%|████▌ | 8545/18688 [10:00:50<11:00:03, 3.90s/it] 46%|████▌ | 8546/18688 [10:00:54<11:13:06, 3.98s/it] 46%|████▌ | 8547/18688 [10:00:59<11:48:55, 4.19s/it] 46%|████▌ | 8548/18688 [10:01:03<11:32:08, 4.10s/it] 46%|████▌ | 8549/18688 [10:01:06<11:08:34, 3.96s/it] 46%|████▌ | 8550/18688 [10:01:11<11:48:24, 4.19s/it] {'loss': '0.9562', 'grad_norm': '1.649', 'learning_rate': '6.393e-05', 'epoch': '0.4575', 'num_input_tokens_seen': 28724472, 'train_runtime': '3.607e+04', 'train_tokens_per_second': '796.3'}
46%|████▌ | 8550/18688 [10:01:11<11:48:24, 4.19s/it] 46%|████▌ | 8551/18688 [10:01:15<11:26:50, 4.07s/it] 46%|████▌ | 8552/18688 [10:01:18<10:57:01, 3.89s/it] 46%|████▌ | 8553/18688 [10:01:23<11:19:43, 4.02s/it] 46%|████▌ | 8554/18688 [10:01:26<10:51:53, 3.86s/it] 46%|████▌ | 8555/18688 [10:01:30<10:32:22, 3.74s/it] 46%|████▌ | 8556/18688 [10:01:34<11:20:51, 4.03s/it] 46%|████▌ | 8557/18688 [10:01:38<11:27:12, 4.07s/it] 46%|████▌ | 8558/18688 [10:01:42<11:23:00, 4.05s/it] 46%|████▌ | 8559/18688 [10:01:47<12:01:51, 4.28s/it] 46%|████▌ | 8560/18688 [10:01:51<11:55:10, 4.24s/it] {'loss': '0.9261', 'grad_norm': '1.269', 'learning_rate': '6.384e-05', 'epoch': '0.4581', 'num_input_tokens_seen': 28757272, 'train_runtime': '3.611e+04', 'train_tokens_per_second': '796.3'}
46%|████▌ | 8560/18688 [10:01:51<11:55:10, 4.24s/it] 46%|████▌ | 8561/18688 [10:01:55<11:26:50, 4.07s/it] 46%|████▌ | 8562/18688 [10:01:59<11:31:34, 4.10s/it] 46%|████▌ | 8563/18688 [10:02:03<11:14:43, 4.00s/it] 46%|████▌ | 8564/18688 [10:02:07<11:20:23, 4.03s/it] 46%|████▌ | 8565/18688 [10:02:11<11:28:36, 4.08s/it] 46%|████▌ | 8566/18688 [10:02:16<11:51:24, 4.22s/it] 46%|████▌ | 8567/18688 [10:02:19<11:21:46, 4.04s/it] 46%|████▌ | 8568/18688 [10:02:24<11:27:03, 4.07s/it] 46%|████▌ | 8569/18688 [10:02:28<11:41:04, 4.16s/it] 46%|████▌ | 8570/18688 [10:02:32<11:27:22, 4.08s/it] {'loss': '0.856', 'grad_norm': '1.241', 'learning_rate': '6.376e-05', 'epoch': '0.4586', 'num_input_tokens_seen': 28790136, 'train_runtime': '3.615e+04', 'train_tokens_per_second': '796.4'}
46%|████▌ | 8570/18688 [10:02:32<11:27:22, 4.08s/it] 46%|████▌ | 8571/18688 [10:02:36<11:21:21, 4.04s/it] 46%|████▌ | 8572/18688 [10:02:40<11:26:52, 4.07s/it] 46%|████▌ | 8573/18688 [10:02:44<11:24:40, 4.06s/it] 46%|████▌ | 8574/18688 [10:02:48<11:32:18, 4.11s/it] 46%|████▌ | 8575/18688 [10:02:52<11:35:21, 4.13s/it] 46%|████▌ | 8576/18688 [10:02:56<11:04:54, 3.95s/it] 46%|████▌ | 8577/18688 [10:03:00<10:56:11, 3.89s/it] 46%|████▌ | 8578/18688 [10:03:04<11:03:23, 3.94s/it] 46%|████▌ | 8579/18688 [10:03:07<10:53:34, 3.88s/it] 46%|████▌ | 8580/18688 [10:03:11<10:44:59, 3.83s/it] {'loss': '0.9295', 'grad_norm': '1.168', 'learning_rate': '6.367e-05', 'epoch': '0.4591', 'num_input_tokens_seen': 28821128, 'train_runtime': '3.619e+04', 'train_tokens_per_second': '796.3'}
46%|████▌ | 8580/18688 [10:03:11<10:44:59, 3.83s/it] 46%|████▌ | 8581/18688 [10:03:15<10:54:27, 3.89s/it] 46%|████▌ | 8582/18688 [10:03:19<10:58:01, 3.91s/it] 46%|████▌ | 8583/18688 [10:03:23<11:10:29, 3.98s/it] 46%|████▌ | 8584/18688 [10:03:27<11:20:05, 4.04s/it] 46%|████▌ | 8585/18688 [10:03:31<11:17:25, 4.02s/it] 46%|████▌ | 8586/18688 [10:03:35<10:49:49, 3.86s/it] 46%|████▌ | 8587/18688 [10:03:40<11:38:12, 4.15s/it] 46%|████▌ | 8588/18688 [10:03:43<11:08:04, 3.97s/it] 46%|████▌ | 8589/18688 [10:03:49<12:30:48, 4.46s/it] 46%|████▌ | 8590/18688 [10:03:52<11:46:24, 4.20s/it] {'loss': '1.026', 'grad_norm': '1.393', 'learning_rate': '6.358e-05', 'epoch': '0.4597', 'num_input_tokens_seen': 28855456, 'train_runtime': '3.623e+04', 'train_tokens_per_second': '796.4'}
46%|████▌ | 8590/18688 [10:03:52<11:46:24, 4.20s/it] 46%|████▌ | 8591/18688 [10:03:57<12:04:28, 4.31s/it] 46%|████▌ | 8592/18688 [10:04:01<11:27:03, 4.08s/it] 46%|████▌ | 8593/18688 [10:04:04<10:57:05, 3.91s/it] 46%|████▌ | 8594/18688 [10:04:08<10:53:33, 3.88s/it] 46%|████▌ | 8595/18688 [10:04:12<11:02:40, 3.94s/it] 46%|████▌ | 8596/18688 [10:04:17<11:36:38, 4.14s/it] 46%|████▌ | 8597/18688 [10:04:21<12:05:24, 4.31s/it] 46%|████▌ | 8598/18688 [10:04:25<11:53:54, 4.25s/it] 46%|████▌ | 8599/18688 [10:04:29<11:27:27, 4.09s/it] 46%|████▌ | 8600/18688 [10:04:33<11:13:46, 4.01s/it] {'loss': '0.9074', 'grad_norm': '1.166', 'learning_rate': '6.349e-05', 'epoch': '0.4602', 'num_input_tokens_seen': 28888784, 'train_runtime': '3.627e+04', 'train_tokens_per_second': '796.4'}
46%|████▌ | 8600/18688 [10:04:33<11:13:46, 4.01s/it] 46%|████▌ | 8601/18688 [10:04:37<11:14:18, 4.01s/it] 46%|████▌ | 8602/18688 [10:04:43<12:34:40, 4.49s/it] 46%|████▌ | 8603/18688 [10:04:46<11:47:58, 4.21s/it] 46%|████▌ | 8604/18688 [10:04:50<11:37:53, 4.15s/it] 46%|████▌ | 8605/18688 [10:04:54<11:17:00, 4.03s/it] 46%|████▌ | 8606/18688 [10:04:59<11:49:00, 4.22s/it] 46%|████▌ | 8607/18688 [10:05:03<11:54:29, 4.25s/it] 46%|████▌ | 8608/18688 [10:05:07<11:58:41, 4.28s/it] 46%|████▌ | 8609/18688 [10:05:12<12:26:23, 4.44s/it] 46%|████▌ | 8610/18688 [10:05:15<11:35:19, 4.14s/it] {'loss': '0.9192', 'grad_norm': '1.595', 'learning_rate': '6.341e-05', 'epoch': '0.4607', 'num_input_tokens_seen': 28923552, 'train_runtime': '3.632e+04', 'train_tokens_per_second': '796.4'}
46%|████▌ | 8610/18688 [10:05:15<11:35:19, 4.14s/it] 46%|████▌ | 8611/18688 [10:05:19<11:03:59, 3.95s/it] 46%|████▌ | 8612/18688 [10:05:23<10:46:36, 3.85s/it] 46%|████▌ | 8613/18688 [10:05:27<10:56:44, 3.91s/it] 46%|████▌ | 8614/18688 [10:05:31<11:21:00, 4.06s/it] 46%|████▌ | 8615/18688 [10:05:35<11:14:42, 4.02s/it] 46%|████▌ | 8616/18688 [10:05:40<11:58:10, 4.28s/it] 46%|████▌ | 8617/18688 [10:05:44<11:29:28, 4.11s/it] 46%|████▌ | 8618/18688 [10:05:48<11:32:01, 4.12s/it] 46%|████▌ | 8619/18688 [10:05:52<11:20:15, 4.05s/it] 46%|████▌ | 8620/18688 [10:05:57<12:11:12, 4.36s/it] {'loss': '0.8987', 'grad_norm': '1.461', 'learning_rate': '6.332e-05', 'epoch': '0.4613', 'num_input_tokens_seen': 28956720, 'train_runtime': '3.636e+04', 'train_tokens_per_second': '796.5'}
46%|████▌ | 8620/18688 [10:05:57<12:11:12, 4.36s/it] 46%|████▌ | 8621/18688 [10:06:01<11:45:17, 4.20s/it] 46%|████▌ | 8622/18688 [10:06:05<12:14:02, 4.38s/it] 46%|████▌ | 8623/18688 [10:06:09<11:58:00, 4.28s/it] 46%|████▌ | 8624/18688 [10:06:13<11:15:45, 4.03s/it] 46%|████▌ | 8625/18688 [10:06:16<10:48:41, 3.87s/it] 46%|████▌ | 8626/18688 [10:06:21<11:14:28, 4.02s/it] 46%|████▌ | 8627/18688 [10:06:26<12:00:28, 4.30s/it] 46%|████▌ | 8628/18688 [10:06:30<12:12:42, 4.37s/it] 46%|████▌ | 8629/18688 [10:06:34<11:44:19, 4.20s/it] 46%|████▌ | 8630/18688 [10:06:38<11:33:34, 4.14s/it] {'loss': '1.005', 'grad_norm': '1.467', 'learning_rate': '6.323e-05', 'epoch': '0.4618', 'num_input_tokens_seen': 28990816, 'train_runtime': '3.64e+04', 'train_tokens_per_second': '796.5'}
46%|████▌ | 8630/18688 [10:06:38<11:33:34, 4.14s/it] 46%|████▌ | 8631/18688 [10:06:41<11:00:03, 3.94s/it] 46%|████▌ | 8632/18688 [10:06:45<10:50:55, 3.88s/it] 46%|████▌ | 8633/18688 [10:06:49<10:55:37, 3.91s/it] 46%|████▌ | 8634/18688 [10:06:54<11:35:21, 4.15s/it] 46%|████▌ | 8635/18688 [10:06:58<11:46:23, 4.22s/it] 46%|████▌ | 8636/18688 [10:07:02<11:48:07, 4.23s/it] 46%|████▌ | 8637/18688 [10:07:07<11:48:49, 4.23s/it] 46%|████▌ | 8638/18688 [10:07:11<12:02:33, 4.31s/it] 46%|████▌ | 8639/18688 [10:07:15<11:28:58, 4.11s/it] 46%|████▌ | 8640/18688 [10:07:19<11:14:44, 4.03s/it] {'loss': '0.9452', 'grad_norm': '1.383', 'learning_rate': '6.314e-05', 'epoch': '0.4623', 'num_input_tokens_seen': 29024712, 'train_runtime': '3.644e+04', 'train_tokens_per_second': '796.5'}
46%|████▌ | 8640/18688 [10:07:19<11:14:44, 4.03s/it] 46%|████▌ | 8641/18688 [10:07:23<11:34:00, 4.14s/it] 46%|████▌ | 8642/18688 [10:07:27<11:39:50, 4.18s/it] 46%|████▌ | 8643/18688 [10:07:31<11:13:11, 4.02s/it] 46%|████▋ | 8644/18688 [10:07:35<11:12:08, 4.02s/it] 46%|████▋ | 8645/18688 [10:07:39<11:33:43, 4.14s/it] 46%|████▋ | 8646/18688 [10:07:43<11:12:09, 4.02s/it] 46%|████▋ | 8647/18688 [10:07:47<10:44:17, 3.85s/it] 46%|████▋ | 8648/18688 [10:07:50<10:43:49, 3.85s/it] 46%|████▋ | 8649/18688 [10:07:54<10:29:58, 3.77s/it] 46%|████▋ | 8650/18688 [10:07:59<11:15:59, 4.04s/it] {'loss': '0.973', 'grad_norm': '1.311', 'learning_rate': '6.305e-05', 'epoch': '0.4629', 'num_input_tokens_seen': 29055992, 'train_runtime': '3.648e+04', 'train_tokens_per_second': '796.5'}
46%|████▋ | 8650/18688 [10:07:59<11:15:59, 4.04s/it] 46%|████▋ | 8651/18688 [10:08:03<11:20:21, 4.07s/it] 46%|████▋ | 8652/18688 [10:08:07<11:22:37, 4.08s/it] 46%|████▋ | 8653/18688 [10:08:11<11:10:21, 4.01s/it] 46%|████▋ | 8654/18688 [10:08:15<11:00:52, 3.95s/it] 46%|████▋ | 8655/18688 [10:08:19<11:24:56, 4.10s/it] 46%|████▋ | 8656/18688 [10:08:24<11:43:05, 4.21s/it] 46%|████▋ | 8657/18688 [10:08:28<11:55:31, 4.28s/it] 46%|████▋ | 8658/18688 [10:08:32<11:39:20, 4.18s/it] 46%|████▋ | 8659/18688 [10:08:37<12:21:04, 4.43s/it] 46%|████▋ | 8660/18688 [10:08:41<12:12:24, 4.38s/it] {'loss': '0.8594', 'grad_norm': '1.218', 'learning_rate': '6.296e-05', 'epoch': '0.4634', 'num_input_tokens_seen': 29090240, 'train_runtime': '3.652e+04', 'train_tokens_per_second': '796.5'}
46%|████▋ | 8660/18688 [10:08:41<12:12:24, 4.38s/it] 46%|████▋ | 8661/18688 [10:08:45<11:59:12, 4.30s/it] 46%|████▋ | 8662/18688 [10:08:49<11:33:24, 4.15s/it] 46%|████▋ | 8663/18688 [10:08:53<11:39:09, 4.18s/it] 46%|████▋ | 8664/18688 [10:08:58<12:08:02, 4.36s/it] 46%|████▋ | 8665/18688 [10:09:02<11:58:28, 4.30s/it] 46%|████▋ | 8666/18688 [10:09:06<11:32:13, 4.14s/it] 46%|████▋ | 8667/18688 [10:09:10<11:19:17, 4.07s/it] 46%|████▋ | 8668/18688 [10:09:15<11:47:29, 4.24s/it] 46%|████▋ | 8669/18688 [10:09:19<11:51:24, 4.26s/it] 46%|████▋ | 8670/18688 [10:09:24<12:07:10, 4.36s/it] {'loss': '0.903', 'grad_norm': '1.295', 'learning_rate': '6.288e-05', 'epoch': '0.4639', 'num_input_tokens_seen': 29126040, 'train_runtime': '3.656e+04', 'train_tokens_per_second': '796.6'}
46%|████▋ | 8670/18688 [10:09:24<12:07:10, 4.36s/it] 46%|████▋ | 8671/18688 [10:09:27<11:37:54, 4.18s/it] 46%|████▋ | 8672/18688 [10:09:31<11:15:39, 4.05s/it] 46%|████▋ | 8673/18688 [10:09:35<11:22:51, 4.09s/it] 46%|████▋ | 8674/18688 [10:09:39<11:17:56, 4.06s/it] 46%|████▋ | 8675/18688 [10:09:43<10:54:04, 3.92s/it] 46%|████▋ | 8676/18688 [10:09:48<11:51:05, 4.26s/it] 46%|████▋ | 8677/18688 [10:09:52<11:56:11, 4.29s/it] 46%|████▋ | 8678/18688 [10:09:57<12:17:53, 4.42s/it] 46%|████▋ | 8679/18688 [10:10:02<12:30:05, 4.50s/it] 46%|████▋ | 8680/18688 [10:10:05<11:49:10, 4.25s/it] {'loss': '0.9173', 'grad_norm': '1.256', 'learning_rate': '6.279e-05', 'epoch': '0.4645', 'num_input_tokens_seen': 29161320, 'train_runtime': '3.661e+04', 'train_tokens_per_second': '796.6'}
46%|████▋ | 8680/18688 [10:10:05<11:49:10, 4.25s/it] 46%|████▋ | 8681/18688 [10:10:09<11:22:40, 4.09s/it] 46%|████▋ | 8682/18688 [10:10:14<11:56:14, 4.29s/it] 46%|████▋ | 8683/18688 [10:10:19<12:23:49, 4.46s/it] 46%|████▋ | 8684/18688 [10:10:23<12:18:52, 4.43s/it] 46%|████▋ | 8685/18688 [10:10:27<11:51:30, 4.27s/it] 46%|████▋ | 8686/18688 [10:10:30<11:16:22, 4.06s/it] 46%|████▋ | 8687/18688 [10:10:35<11:57:37, 4.31s/it] 46%|████▋ | 8688/18688 [10:10:39<11:33:05, 4.16s/it] 46%|████▋ | 8689/18688 [10:10:43<11:07:35, 4.01s/it] 47%|████▋ | 8690/18688 [10:10:46<10:43:07, 3.86s/it] {'loss': '0.9285', 'grad_norm': '1.345', 'learning_rate': '6.27e-05', 'epoch': '0.465', 'num_input_tokens_seen': 29196232, 'train_runtime': '3.665e+04', 'train_tokens_per_second': '796.7'}
47%|████▋ | 8690/18688 [10:10:46<10:43:07, 3.86s/it] 47%|████▋ | 8691/18688 [10:10:50<10:35:24, 3.81s/it] 47%|████▋ | 8692/18688 [10:10:54<10:22:06, 3.73s/it] 47%|████▋ | 8693/18688 [10:10:58<10:39:50, 3.84s/it] 47%|████▋ | 8694/18688 [10:11:02<11:19:46, 4.08s/it] 47%|████▋ | 8695/18688 [10:11:06<11:07:12, 4.01s/it] 47%|████▋ | 8696/18688 [10:11:11<12:12:33, 4.40s/it] 47%|████▋ | 8697/18688 [10:11:16<12:19:00, 4.44s/it] 47%|████▋ | 8698/18688 [10:11:21<12:28:59, 4.50s/it] 47%|████▋ | 8699/18688 [10:11:24<11:42:51, 4.22s/it] 47%|████▋ | 8700/18688 [10:11:28<11:29:04, 4.14s/it] {'loss': '0.9376', 'grad_norm': '1.225', 'learning_rate': '6.261e-05', 'epoch': '0.4656', 'num_input_tokens_seen': 29231456, 'train_runtime': '3.669e+04', 'train_tokens_per_second': '796.7'}
47%|████▋ | 8700/18688 [10:11:28<11:29:04, 4.14s/it] 47%|████▋ | 8701/18688 [10:11:32<11:08:53, 4.02s/it] 47%|████▋ | 8702/18688 [10:11:36<10:56:08, 3.94s/it] 47%|████▋ | 8703/18688 [10:11:40<11:11:01, 4.03s/it] 47%|████▋ | 8704/18688 [10:11:44<10:54:57, 3.94s/it] 47%|████▋ | 8705/18688 [10:11:48<11:11:25, 4.04s/it] 47%|████▋ | 8706/18688 [10:11:51<10:48:33, 3.90s/it] 47%|████▋ | 8707/18688 [10:11:56<10:56:09, 3.94s/it] 47%|████▋ | 8708/18688 [10:12:00<11:09:54, 4.03s/it] 47%|████▋ | 8709/18688 [10:12:04<11:20:13, 4.09s/it] 47%|████▋ | 8710/18688 [10:12:08<11:33:29, 4.17s/it] {'loss': '0.9097', 'grad_norm': '1.156', 'learning_rate': '6.252e-05', 'epoch': '0.4661', 'num_input_tokens_seen': 29264984, 'train_runtime': '3.673e+04', 'train_tokens_per_second': '796.8'}
47%|████▋ | 8710/18688 [10:12:08<11:33:29, 4.17s/it] 47%|████▋ | 8711/18688 [10:12:13<12:01:46, 4.34s/it] 47%|████▋ | 8712/18688 [10:12:17<12:04:47, 4.36s/it] 47%|████▋ | 8713/18688 [10:12:21<11:46:22, 4.25s/it] 47%|████▋ | 8714/18688 [10:12:25<11:14:06, 4.06s/it] 47%|████▋ | 8715/18688 [10:12:29<11:06:13, 4.01s/it] 47%|████▋ | 8716/18688 [10:12:33<11:22:36, 4.11s/it] 47%|████▋ | 8717/18688 [10:12:38<12:02:03, 4.34s/it] 47%|████▋ | 8718/18688 [10:12:42<11:34:42, 4.18s/it] 47%|████▋ | 8719/18688 [10:12:47<12:10:20, 4.40s/it] 47%|████▋ | 8720/18688 [10:12:51<11:36:52, 4.19s/it] {'loss': '0.9264', 'grad_norm': '1.131', 'learning_rate': '6.243e-05', 'epoch': '0.4666', 'num_input_tokens_seen': 29300640, 'train_runtime': '3.677e+04', 'train_tokens_per_second': '796.8'}
47%|████▋ | 8720/18688 [10:12:51<11:36:52, 4.19s/it] 47%|████▋ | 8721/18688 [10:12:54<11:15:52, 4.07s/it] 47%|████▋ | 8722/18688 [10:12:58<11:03:08, 3.99s/it] 47%|████▋ | 8723/18688 [10:13:02<11:00:30, 3.98s/it] 47%|████▋ | 8724/18688 [10:13:06<10:37:36, 3.84s/it] 47%|████▋ | 8725/18688 [10:13:10<10:54:29, 3.94s/it] 47%|████▋ | 8726/18688 [10:13:14<11:23:55, 4.12s/it] 47%|████▋ | 8727/18688 [10:13:18<10:57:47, 3.96s/it] 47%|████▋ | 8728/18688 [10:13:22<10:48:01, 3.90s/it] 47%|████▋ | 8729/18688 [10:13:25<10:36:20, 3.83s/it] 47%|████▋ | 8730/18688 [10:13:30<11:21:25, 4.11s/it] {'loss': '0.9437', 'grad_norm': '1.324', 'learning_rate': '6.235e-05', 'epoch': '0.4672', 'num_input_tokens_seen': 29333168, 'train_runtime': '3.681e+04', 'train_tokens_per_second': '796.9'}
47%|████▋ | 8730/18688 [10:13:30<11:21:25, 4.11s/it] 47%|████▋ | 8731/18688 [10:13:34<11:21:37, 4.11s/it] 47%|████▋ | 8732/18688 [10:13:38<11:06:15, 4.02s/it] 47%|████▋ | 8733/18688 [10:13:42<10:43:01, 3.88s/it] 47%|████▋ | 8734/18688 [10:13:46<11:10:27, 4.04s/it] 47%|████▋ | 8735/18688 [10:13:51<11:52:46, 4.30s/it] 47%|████▋ | 8736/18688 [10:13:55<11:17:24, 4.08s/it] 47%|████▋ | 8737/18688 [10:13:58<10:51:37, 3.93s/it] 47%|████▋ | 8738/18688 [10:14:02<11:02:31, 4.00s/it] 47%|████▋ | 8739/18688 [10:14:06<11:14:26, 4.07s/it] 47%|████▋ | 8740/18688 [10:14:10<10:52:17, 3.93s/it] {'loss': '0.9173', 'grad_norm': '1.328', 'learning_rate': '6.226e-05', 'epoch': '0.4677', 'num_input_tokens_seen': 29365160, 'train_runtime': '3.685e+04', 'train_tokens_per_second': '796.9'}
47%|████▋ | 8740/18688 [10:14:10<10:52:17, 3.93s/it] 47%|████▋ | 8741/18688 [10:14:14<10:58:43, 3.97s/it] 47%|████▋ | 8742/18688 [10:14:18<10:35:29, 3.83s/it] 47%|████▋ | 8743/18688 [10:14:22<11:03:49, 4.00s/it] 47%|████▋ | 8744/18688 [10:14:26<10:57:20, 3.97s/it] 47%|████▋ | 8745/18688 [10:14:30<10:52:19, 3.94s/it] 47%|████▋ | 8746/18688 [10:14:34<10:51:52, 3.93s/it] 47%|████▋ | 8747/18688 [10:14:38<11:09:35, 4.04s/it] 47%|████▋ | 8748/18688 [10:14:42<11:18:01, 4.09s/it] 47%|████▋ | 8749/18688 [10:14:46<10:57:08, 3.97s/it] 47%|████▋ | 8750/18688 [10:14:50<10:44:09, 3.89s/it] {'loss': '0.9073', 'grad_norm': '1.327', 'learning_rate': '6.217e-05', 'epoch': '0.4682', 'num_input_tokens_seen': 29395544, 'train_runtime': '3.689e+04', 'train_tokens_per_second': '796.8'}
47%|████▋ | 8750/18688 [10:14:50<10:44:09, 3.89s/it] 47%|████▋ | 8751/18688 [10:14:54<11:06:13, 4.02s/it] 47%|████▋ | 8752/18688 [10:14:58<10:57:47, 3.97s/it] 47%|████▋ | 8753/18688 [10:15:02<11:13:45, 4.07s/it] 47%|████▋ | 8754/18688 [10:15:07<11:43:58, 4.25s/it] 47%|████▋ | 8755/18688 [10:15:11<11:36:56, 4.21s/it] 47%|████▋ | 8756/18688 [10:15:15<11:42:03, 4.24s/it] 47%|████▋ | 8757/18688 [10:15:20<11:54:29, 4.32s/it] 47%|████▋ | 8758/18688 [10:15:23<11:23:18, 4.13s/it] 47%|████▋ | 8759/18688 [10:15:28<11:22:17, 4.12s/it] 47%|████▋ | 8760/18688 [10:15:33<12:10:36, 4.42s/it] {'loss': '0.9401', 'grad_norm': '1.361', 'learning_rate': '6.208e-05', 'epoch': '0.4688', 'num_input_tokens_seen': 29432168, 'train_runtime': '3.693e+04', 'train_tokens_per_second': '796.9'}
47%|████▋ | 8760/18688 [10:15:33<12:10:36, 4.42s/it] 47%|████▋ | 8761/18688 [10:15:37<12:34:16, 4.56s/it] 47%|████▋ | 8762/18688 [10:15:42<12:27:11, 4.52s/it] 47%|████▋ | 8763/18688 [10:15:46<12:13:40, 4.44s/it] 47%|████▋ | 8764/18688 [10:15:50<11:36:12, 4.21s/it] 47%|████▋ | 8765/18688 [10:15:54<11:21:11, 4.12s/it] 47%|████▋ | 8766/18688 [10:15:58<11:14:24, 4.08s/it] 47%|████▋ | 8767/18688 [10:16:01<10:55:43, 3.97s/it] 47%|████▋ | 8768/18688 [10:16:07<11:50:59, 4.30s/it] 47%|████▋ | 8769/18688 [10:16:12<12:50:17, 4.66s/it] 47%|████▋ | 8770/18688 [10:16:16<12:24:36, 4.50s/it] {'loss': '0.979', 'grad_norm': '1.117', 'learning_rate': '6.199e-05', 'epoch': '0.4693', 'num_input_tokens_seen': 29469152, 'train_runtime': '3.698e+04', 'train_tokens_per_second': '797'}
47%|████▋ | 8770/18688 [10:16:16<12:24:36, 4.50s/it] 47%|████▋ | 8771/18688 [10:16:20<11:55:52, 4.33s/it] 47%|████▋ | 8772/18688 [10:16:24<11:32:47, 4.19s/it] 47%|████▋ | 8773/18688 [10:16:29<12:07:45, 4.40s/it] 47%|████▋ | 8774/18688 [10:16:34<12:28:06, 4.53s/it] 47%|████▋ | 8775/18688 [10:16:38<12:10:11, 4.42s/it] 47%|████▋ | 8776/18688 [10:16:43<12:56:48, 4.70s/it] 47%|████▋ | 8777/18688 [10:16:48<13:16:43, 4.82s/it] 47%|████▋ | 8778/18688 [10:16:52<12:24:09, 4.51s/it] 47%|████▋ | 8779/18688 [10:16:56<11:37:52, 4.23s/it] 47%|████▋ | 8780/18688 [10:17:00<12:00:30, 4.36s/it] {'loss': '0.9018', 'grad_norm': '1.013', 'learning_rate': '6.19e-05', 'epoch': '0.4698', 'num_input_tokens_seen': 29506280, 'train_runtime': '3.702e+04', 'train_tokens_per_second': '797'}
47%|████▋ | 8780/18688 [10:17:00<12:00:30, 4.36s/it] 47%|████▋ | 8781/18688 [10:17:06<12:47:09, 4.65s/it] 47%|████▋ | 8782/18688 [10:17:10<12:29:01, 4.54s/it] 47%|████▋ | 8783/18688 [10:17:14<12:28:31, 4.53s/it] 47%|████▋ | 8784/18688 [10:17:18<11:52:43, 4.32s/it] 47%|████▋ | 8785/18688 [10:17:22<11:11:35, 4.07s/it] 47%|████▋ | 8786/18688 [10:17:25<10:45:15, 3.91s/it] 47%|████▋ | 8787/18688 [10:17:29<10:32:00, 3.83s/it] 47%|████▋ | 8788/18688 [10:17:33<10:38:25, 3.87s/it] 47%|████▋ | 8789/18688 [10:17:37<10:59:33, 4.00s/it] 47%|████▋ | 8790/18688 [10:17:41<11:01:26, 4.01s/it] {'loss': '0.8431', 'grad_norm': '1.205', 'learning_rate': '6.181e-05', 'epoch': '0.4704', 'num_input_tokens_seen': 29539216, 'train_runtime': '3.706e+04', 'train_tokens_per_second': '797'}
47%|████▋ | 8790/18688 [10:17:41<11:01:26, 4.01s/it] 47%|████▋ | 8791/18688 [10:17:46<11:17:09, 4.11s/it] 47%|████▋ | 8792/18688 [10:17:49<11:07:10, 4.05s/it] 47%|████▋ | 8793/18688 [10:17:53<11:06:48, 4.04s/it] 47%|████▋ | 8794/18688 [10:17:58<11:28:35, 4.18s/it] 47%|████▋ | 8795/18688 [10:18:02<11:24:41, 4.15s/it] 47%|████▋ | 8796/18688 [10:18:07<11:42:54, 4.26s/it] 47%|████▋ | 8797/18688 [10:18:10<11:06:15, 4.04s/it] 47%|████▋ | 8798/18688 [10:18:14<11:07:14, 4.05s/it] 47%|████▋ | 8799/18688 [10:18:18<11:09:17, 4.06s/it] 47%|████▋ | 8800/18688 [10:18:22<10:43:36, 3.91s/it] {'loss': '0.8845', 'grad_norm': '1.503', 'learning_rate': '6.172e-05', 'epoch': '0.4709', 'num_input_tokens_seen': 29572272, 'train_runtime': '3.71e+04', 'train_tokens_per_second': '797'}
47%|████▋ | 8800/18688 [10:18:22<10:43:36, 3.91s/it] 47%|████▋ | 8801/18688 [10:18:26<10:56:08, 3.98s/it] 47%|████▋ | 8802/18688 [10:18:30<10:55:12, 3.98s/it] 47%|████▋ | 8803/18688 [10:18:34<11:11:44, 4.08s/it] 47%|████▋ | 8804/18688 [10:18:38<11:15:20, 4.10s/it] 47%|████▋ | 8805/18688 [10:18:42<10:48:34, 3.94s/it] 47%|████▋ | 8806/18688 [10:18:46<10:33:17, 3.85s/it] 47%|████▋ | 8807/18688 [10:18:50<11:14:37, 4.10s/it] 47%|████▋ | 8808/18688 [10:18:54<10:56:37, 3.99s/it] 47%|████▋ | 8809/18688 [10:18:58<10:32:09, 3.84s/it] 47%|████▋ | 8810/18688 [10:19:02<11:04:02, 4.03s/it] {'loss': '0.9068', 'grad_norm': '1.397', 'learning_rate': '6.164e-05', 'epoch': '0.4714', 'num_input_tokens_seen': 29604616, 'train_runtime': '3.714e+04', 'train_tokens_per_second': '797.1'}
47%|████▋ | 8810/18688 [10:19:02<11:04:02, 4.03s/it] 47%|████▋ | 8811/18688 [10:19:06<11:05:40, 4.04s/it] 47%|████▋ | 8812/18688 [10:19:10<11:11:52, 4.08s/it] 47%|████▋ | 8813/18688 [10:19:15<11:35:52, 4.23s/it] 47%|████▋ | 8814/18688 [10:19:19<11:27:38, 4.18s/it] 47%|████▋ | 8815/18688 [10:19:22<10:52:46, 3.97s/it] 47%|████▋ | 8816/18688 [10:19:26<10:45:10, 3.92s/it] 47%|████▋ | 8817/18688 [10:19:30<10:40:24, 3.89s/it] 47%|████▋ | 8818/18688 [10:19:33<10:21:51, 3.78s/it] 47%|████▋ | 8819/18688 [10:19:37<10:21:12, 3.78s/it] 47%|████▋ | 8820/18688 [10:19:41<10:23:37, 3.79s/it] {'loss': '0.8653', 'grad_norm': '1.164', 'learning_rate': '6.155e-05', 'epoch': '0.472', 'num_input_tokens_seen': 29635184, 'train_runtime': '3.718e+04', 'train_tokens_per_second': '797'}
47%|████▋ | 8820/18688 [10:19:41<10:23:37, 3.79s/it] 47%|████▋ | 8821/18688 [10:19:45<10:28:37, 3.82s/it] 47%|████▋ | 8822/18688 [10:19:49<10:54:39, 3.98s/it] 47%|████▋ | 8823/18688 [10:19:54<11:21:48, 4.15s/it] 47%|████▋ | 8824/18688 [10:19:58<11:35:48, 4.23s/it] 47%|████▋ | 8825/18688 [10:20:02<11:02:03, 4.03s/it] 47%|████▋ | 8826/18688 [10:20:06<10:49:46, 3.95s/it] 47%|████▋ | 8827/18688 [10:20:09<10:36:42, 3.87s/it] 47%|████▋ | 8828/18688 [10:20:14<11:04:44, 4.05s/it] 47%|████▋ | 8829/18688 [10:20:18<11:16:49, 4.12s/it] 47%|████▋ | 8830/18688 [10:20:23<11:38:18, 4.25s/it] {'loss': '0.9409', 'grad_norm': '1.002', 'learning_rate': '6.146e-05', 'epoch': '0.4725', 'num_input_tokens_seen': 29669640, 'train_runtime': '3.722e+04', 'train_tokens_per_second': '797.1'}
47%|████▋ | 8830/18688 [10:20:23<11:38:18, 4.25s/it] 47%|████▋ | 8831/18688 [10:20:27<11:37:23, 4.25s/it] 47%|████▋ | 8832/18688 [10:20:31<11:18:07, 4.13s/it] 47%|████▋ | 8833/18688 [10:20:35<11:28:06, 4.19s/it] 47%|████▋ | 8834/18688 [10:20:39<11:11:27, 4.09s/it] 47%|████▋ | 8835/18688 [10:20:44<11:53:40, 4.35s/it] 47%|████▋ | 8836/18688 [10:20:48<11:25:18, 4.17s/it] 47%|████▋ | 8837/18688 [10:20:52<11:34:16, 4.23s/it] 47%|████▋ | 8838/18688 [10:20:57<12:08:51, 4.44s/it] 47%|████▋ | 8839/18688 [10:21:01<12:06:48, 4.43s/it] 47%|████▋ | 8840/18688 [10:21:05<11:28:15, 4.19s/it] {'loss': '0.9506', 'grad_norm': '1.331', 'learning_rate': '6.137e-05', 'epoch': '0.473', 'num_input_tokens_seen': 29703464, 'train_runtime': '3.727e+04', 'train_tokens_per_second': '797.1'}
47%|████▋ | 8840/18688 [10:21:05<11:28:15, 4.19s/it] 47%|████▋ | 8841/18688 [10:21:09<11:16:39, 4.12s/it] 47%|████▋ | 8842/18688 [10:21:13<11:16:00, 4.12s/it] 47%|████▋ | 8843/18688 [10:21:17<11:05:47, 4.06s/it] 47%|████▋ | 8844/18688 [10:21:23<12:21:48, 4.52s/it] 47%|████▋ | 8845/18688 [10:21:27<12:08:54, 4.44s/it] 47%|████▋ | 8846/18688 [10:21:31<11:55:36, 4.36s/it] 47%|████▋ | 8847/18688 [10:21:36<12:17:23, 4.50s/it] 47%|████▋ | 8848/18688 [10:21:39<11:28:35, 4.20s/it] 47%|████▋ | 8849/18688 [10:21:44<12:04:55, 4.42s/it] 47%|████▋ | 8850/18688 [10:21:48<11:34:19, 4.23s/it] {'loss': '1.025', 'grad_norm': '1.396', 'learning_rate': '6.128e-05', 'epoch': '0.4736', 'num_input_tokens_seen': 29739656, 'train_runtime': '3.731e+04', 'train_tokens_per_second': '797.1'}
47%|████▋ | 8850/18688 [10:21:48<11:34:19, 4.23s/it] 47%|████▋ | 8851/18688 [10:21:52<11:12:38, 4.10s/it] 47%|████▋ | 8852/18688 [10:21:56<11:11:30, 4.10s/it] 47%|████▋ | 8853/18688 [10:22:00<11:10:40, 4.09s/it] 47%|████▋ | 8854/18688 [10:22:05<11:58:34, 4.38s/it] 47%|████▋ | 8855/18688 [10:22:09<11:47:08, 4.31s/it] 47%|████▋ | 8856/18688 [10:22:13<11:12:27, 4.10s/it] 47%|████▋ | 8857/18688 [10:22:17<11:00:02, 4.03s/it] 47%|████▋ | 8858/18688 [10:22:21<10:55:08, 4.00s/it] 47%|████▋ | 8859/18688 [10:22:25<11:28:36, 4.20s/it] 47%|████▋ | 8860/18688 [10:22:30<11:54:07, 4.36s/it] {'loss': '0.889', 'grad_norm': '1.456', 'learning_rate': '6.119e-05', 'epoch': '0.4741', 'num_input_tokens_seen': 29771928, 'train_runtime': '3.735e+04', 'train_tokens_per_second': '797.1'}
47%|████▋ | 8860/18688 [10:22:30<11:54:07, 4.36s/it] 47%|████▋ | 8861/18688 [10:22:34<11:30:27, 4.22s/it] 47%|████▋ | 8862/18688 [10:22:38<11:40:28, 4.28s/it] 47%|████▋ | 8863/18688 [10:22:42<11:04:50, 4.06s/it] 47%|████▋ | 8864/18688 [10:22:46<10:58:59, 4.02s/it] 47%|████▋ | 8865/18688 [10:22:50<11:09:50, 4.09s/it] 47%|████▋ | 8866/18688 [10:22:54<11:07:15, 4.08s/it] 47%|████▋ | 8867/18688 [10:22:58<10:59:25, 4.03s/it] 47%|████▋ | 8868/18688 [10:23:02<11:20:32, 4.16s/it] 47%|████▋ | 8869/18688 [10:23:06<11:05:55, 4.07s/it] 47%|████▋ | 8870/18688 [10:23:10<10:36:33, 3.89s/it] {'loss': '0.9459', 'grad_norm': '1.39', 'learning_rate': '6.11e-05', 'epoch': '0.4746', 'num_input_tokens_seen': 29802992, 'train_runtime': '3.739e+04', 'train_tokens_per_second': '797.1'}
47%|████▋ | 8870/18688 [10:23:10<10:36:33, 3.89s/it] 47%|████▋ | 8871/18688 [10:23:15<11:24:08, 4.18s/it] 47%|████▋ | 8872/18688 [10:23:20<12:01:46, 4.41s/it] 47%|████▋ | 8873/18688 [10:23:24<11:45:35, 4.31s/it] 47%|████▋ | 8874/18688 [10:23:28<11:44:02, 4.30s/it] 47%|████▋ | 8875/18688 [10:23:33<12:11:54, 4.48s/it] 47%|████▋ | 8876/18688 [10:23:37<11:56:03, 4.38s/it] 48%|████▊ | 8877/18688 [10:23:41<11:35:54, 4.26s/it] 48%|████▊ | 8878/18688 [10:23:45<11:08:02, 4.09s/it] 48%|████▊ | 8879/18688 [10:23:48<10:47:27, 3.96s/it] 48%|████▊ | 8880/18688 [10:23:52<10:28:50, 3.85s/it] {'loss': '0.9397', 'grad_norm': '1.334', 'learning_rate': '6.101e-05', 'epoch': '0.4752', 'num_input_tokens_seen': 29837816, 'train_runtime': '3.743e+04', 'train_tokens_per_second': '797.1'}
48%|████▊ | 8880/18688 [10:23:52<10:28:50, 3.85s/it] 48%|████▊ | 8881/18688 [10:23:56<10:23:12, 3.81s/it] 48%|████▊ | 8882/18688 [10:24:00<10:31:09, 3.86s/it] 48%|████▊ | 8883/18688 [10:24:04<11:03:43, 4.06s/it] 48%|████▊ | 8884/18688 [10:24:09<12:04:23, 4.43s/it] 48%|████▊ | 8885/18688 [10:24:13<11:17:54, 4.15s/it] 48%|████▊ | 8886/18688 [10:24:16<10:43:05, 3.94s/it] 48%|████▊ | 8887/18688 [10:24:20<10:40:16, 3.92s/it] 48%|████▊ | 8888/18688 [10:24:24<10:56:09, 4.02s/it] 48%|████▊ | 8889/18688 [10:24:29<11:02:58, 4.06s/it] 48%|████▊ | 8890/18688 [10:24:33<11:04:04, 4.07s/it] {'loss': '0.9496', 'grad_norm': '1.219', 'learning_rate': '6.092e-05', 'epoch': '0.4757', 'num_input_tokens_seen': 29872080, 'train_runtime': '3.747e+04', 'train_tokens_per_second': '797.2'}
48%|████▊ | 8890/18688 [10:24:33<11:04:04, 4.07s/it] 48%|████▊ | 8891/18688 [10:24:38<11:59:52, 4.41s/it] 48%|████▊ | 8892/18688 [10:24:42<11:28:08, 4.21s/it] 48%|████▊ | 8893/18688 [10:24:46<11:47:12, 4.33s/it] 48%|████▊ | 8894/18688 [10:24:50<11:37:59, 4.28s/it] 48%|████▊ | 8895/18688 [10:24:56<12:28:30, 4.59s/it] 48%|████▊ | 8896/18688 [10:25:00<11:51:41, 4.36s/it] 48%|████▊ | 8897/18688 [10:25:04<11:47:57, 4.34s/it] 48%|████▊ | 8898/18688 [10:25:08<11:35:31, 4.26s/it] 48%|████▊ | 8899/18688 [10:25:13<12:00:55, 4.42s/it] 48%|████▊ | 8900/18688 [10:25:17<11:52:56, 4.37s/it] {'loss': '0.9374', 'grad_norm': '1.419', 'learning_rate': '6.083e-05', 'epoch': '0.4763', 'num_input_tokens_seen': 29909232, 'train_runtime': '3.752e+04', 'train_tokens_per_second': '797.2'}
48%|████▊ | 8900/18688 [10:25:17<11:52:56, 4.37s/it] 48%|████▊ | 8901/18688 [10:25:21<11:40:07, 4.29s/it] 48%|████▊ | 8902/18688 [10:25:25<11:06:22, 4.09s/it] 48%|████▊ | 8903/18688 [10:25:29<11:05:06, 4.08s/it] 48%|████▊ | 8904/18688 [10:25:34<12:17:06, 4.52s/it] 48%|████▊ | 8905/18688 [10:25:38<11:49:26, 4.35s/it] 48%|████▊ | 8906/18688 [10:25:42<11:28:26, 4.22s/it] 48%|████▊ | 8907/18688 [10:25:46<10:49:06, 3.98s/it] 48%|████▊ | 8908/18688 [10:25:49<10:39:42, 3.92s/it] 48%|████▊ | 8909/18688 [10:25:53<10:32:35, 3.88s/it] 48%|████▊ | 8910/18688 [10:25:57<10:19:25, 3.80s/it] {'loss': '0.9184', 'grad_norm': '1.639', 'learning_rate': '6.075e-05', 'epoch': '0.4768', 'num_input_tokens_seen': 29940944, 'train_runtime': '3.756e+04', 'train_tokens_per_second': '797.2'}
48%|████▊ | 8910/18688 [10:25:57<10:19:25, 3.80s/it] 48%|████▊ | 8911/18688 [10:26:01<10:34:27, 3.89s/it] 48%|████▊ | 8912/18688 [10:26:05<11:04:20, 4.08s/it] 48%|████▊ | 8913/18688 [10:26:10<11:23:05, 4.19s/it] 48%|████▊ | 8914/18688 [10:26:13<10:53:21, 4.01s/it] 48%|████▊ | 8915/18688 [10:26:18<11:13:28, 4.13s/it] 48%|████▊ | 8916/18688 [10:26:22<11:11:08, 4.12s/it] 48%|████▊ | 8917/18688 [10:26:26<11:03:43, 4.08s/it] 48%|████▊ | 8918/18688 [10:26:30<11:12:37, 4.13s/it] 48%|████▊ | 8919/18688 [10:26:34<11:00:38, 4.06s/it] 48%|████▊ | 8920/18688 [10:26:38<10:32:44, 3.89s/it] {'loss': '0.9239', 'grad_norm': '1.265', 'learning_rate': '6.066e-05', 'epoch': '0.4773', 'num_input_tokens_seen': 29974920, 'train_runtime': '3.76e+04', 'train_tokens_per_second': '797.2'}
48%|████▊ | 8920/18688 [10:26:38<10:32:44, 3.89s/it] 48%|████▊ | 8921/18688 [10:26:41<10:25:31, 3.84s/it] 48%|████▊ | 8922/18688 [10:26:45<10:33:37, 3.89s/it] 48%|████▊ | 8923/18688 [10:26:50<11:05:06, 4.09s/it] 48%|████▊ | 8924/18688 [10:26:54<10:55:08, 4.03s/it] 48%|████▊ | 8925/18688 [10:26:57<10:28:01, 3.86s/it] 48%|████▊ | 8926/18688 [10:27:02<10:51:16, 4.00s/it] 48%|████▊ | 8927/18688 [10:27:05<10:46:19, 3.97s/it] 48%|████▊ | 8928/18688 [10:27:10<11:21:53, 4.19s/it] 48%|████▊ | 8929/18688 [10:27:14<11:03:44, 4.08s/it] 48%|████▊ | 8930/18688 [10:27:18<10:44:55, 3.97s/it] {'loss': '0.93', 'grad_norm': '1.118', 'learning_rate': '6.057e-05', 'epoch': '0.4779', 'num_input_tokens_seen': 30007600, 'train_runtime': '3.764e+04', 'train_tokens_per_second': '797.3'}
48%|████▊ | 8930/18688 [10:27:18<10:44:55, 3.97s/it] 48%|████▊ | 8931/18688 [10:27:22<10:48:32, 3.99s/it] 48%|████▊ | 8932/18688 [10:27:27<11:44:46, 4.33s/it] 48%|████▊ | 8933/18688 [10:27:31<11:50:34, 4.37s/it] 48%|████▊ | 8934/18688 [10:27:36<12:01:17, 4.44s/it] 48%|████▊ | 8935/18688 [10:27:40<11:32:52, 4.26s/it] 48%|████▊ | 8936/18688 [10:27:44<11:50:02, 4.37s/it] 48%|████▊ | 8937/18688 [10:27:49<12:25:06, 4.58s/it] 48%|████▊ | 8938/18688 [10:27:53<11:47:04, 4.35s/it] 48%|████▊ | 8939/18688 [10:27:58<11:42:50, 4.33s/it] 48%|████▊ | 8940/18688 [10:28:01<11:22:12, 4.20s/it] {'loss': '0.9507', 'grad_norm': '1.173', 'learning_rate': '6.048e-05', 'epoch': '0.4784', 'num_input_tokens_seen': 30046312, 'train_runtime': '3.768e+04', 'train_tokens_per_second': '797.4'}
48%|████▊ | 8940/18688 [10:28:01<11:22:12, 4.20s/it] 48%|████▊ | 8941/18688 [10:28:06<11:24:17, 4.21s/it] 48%|████▊ | 8942/18688 [10:28:10<11:31:02, 4.25s/it] 48%|████▊ | 8943/18688 [10:28:15<11:48:14, 4.36s/it] 48%|████▊ | 8944/18688 [10:28:18<11:20:12, 4.19s/it] 48%|████▊ | 8945/18688 [10:28:23<11:17:35, 4.17s/it] 48%|████▊ | 8946/18688 [10:28:27<11:29:24, 4.25s/it] 48%|████▊ | 8947/18688 [10:28:32<11:53:07, 4.39s/it] 48%|████▊ | 8948/18688 [10:28:36<11:22:25, 4.20s/it] 48%|████▊ | 8949/18688 [10:28:39<11:01:51, 4.08s/it] 48%|████▊ | 8950/18688 [10:28:43<10:51:21, 4.01s/it] {'loss': '0.9598', 'grad_norm': '1.36', 'learning_rate': '6.039e-05', 'epoch': '0.4789', 'num_input_tokens_seen': 30080552, 'train_runtime': '3.772e+04', 'train_tokens_per_second': '797.4'}
48%|████▊ | 8950/18688 [10:28:43<10:51:21, 4.01s/it] 48%|████▊ | 8951/18688 [10:28:47<11:06:59, 4.11s/it] 48%|████▊ | 8952/18688 [10:28:52<11:09:03, 4.12s/it] 48%|████▊ | 8953/18688 [10:28:56<11:21:54, 4.20s/it] 48%|████▊ | 8954/18688 [10:29:00<11:28:39, 4.24s/it] 48%|████▊ | 8955/18688 [10:29:04<11:20:08, 4.19s/it] 48%|████▊ | 8956/18688 [10:29:08<10:59:50, 4.07s/it] 48%|████▊ | 8957/18688 [10:29:12<10:57:15, 4.05s/it] 48%|████▊ | 8958/18688 [10:29:17<11:17:52, 4.18s/it] 48%|████▊ | 8959/18688 [10:29:21<11:30:45, 4.26s/it] 48%|████▊ | 8960/18688 [10:29:25<11:19:12, 4.19s/it] {'loss': '0.9659', 'grad_norm': '1.321', 'learning_rate': '6.03e-05', 'epoch': '0.4795', 'num_input_tokens_seen': 30113616, 'train_runtime': '3.777e+04', 'train_tokens_per_second': '797.4'}
48%|████▊ | 8960/18688 [10:29:25<11:19:12, 4.19s/it] 48%|████▊ | 8961/18688 [10:29:29<11:03:14, 4.09s/it] 48%|████▊ | 8962/18688 [10:29:33<10:49:46, 4.01s/it] 48%|████▊ | 8963/18688 [10:29:37<11:00:48, 4.08s/it] 48%|████▊ | 8964/18688 [10:29:41<10:37:37, 3.93s/it] 48%|████▊ | 8965/18688 [10:29:45<10:38:54, 3.94s/it] 48%|████▊ | 8966/18688 [10:29:48<10:29:59, 3.89s/it] 48%|████▊ | 8967/18688 [10:29:53<11:27:03, 4.24s/it] 48%|████▊ | 8968/18688 [10:29:58<11:33:23, 4.28s/it] 48%|████▊ | 8969/18688 [10:30:02<11:03:39, 4.10s/it] 48%|████▊ | 8970/18688 [10:30:06<11:03:21, 4.10s/it] {'loss': '0.9082', 'grad_norm': '1.144', 'learning_rate': '6.021e-05', 'epoch': '0.48', 'num_input_tokens_seen': 30146696, 'train_runtime': '3.781e+04', 'train_tokens_per_second': '797.4'}
48%|████▊ | 8970/18688 [10:30:06<11:03:21, 4.10s/it] 48%|████▊ | 8971/18688 [10:30:09<10:40:09, 3.95s/it] 48%|████▊ | 8972/18688 [10:30:14<11:23:19, 4.22s/it] 48%|████▊ | 8973/18688 [10:30:19<11:40:03, 4.32s/it] 48%|████▊ | 8974/18688 [10:30:23<11:38:25, 4.31s/it] 48%|████▊ | 8975/18688 [10:30:27<11:29:42, 4.26s/it] 48%|████▊ | 8976/18688 [10:30:31<11:17:37, 4.19s/it] 48%|████▊ | 8977/18688 [10:30:36<11:46:18, 4.36s/it] 48%|████▊ | 8978/18688 [10:30:39<11:09:38, 4.14s/it] 48%|████▊ | 8979/18688 [10:30:44<11:04:33, 4.11s/it] 48%|████▊ | 8980/18688 [10:30:47<10:39:31, 3.95s/it] {'loss': '0.8985', 'grad_norm': '1.357', 'learning_rate': '6.012e-05', 'epoch': '0.4805', 'num_input_tokens_seen': 30180896, 'train_runtime': '3.785e+04', 'train_tokens_per_second': '797.4'}
48%|████▊ | 8980/18688 [10:30:47<10:39:31, 3.95s/it] 48%|████▊ | 8981/18688 [10:30:51<10:49:44, 4.02s/it] 48%|████▊ | 8982/18688 [10:30:56<11:24:29, 4.23s/it] 48%|████▊ | 8983/18688 [10:31:00<11:24:42, 4.23s/it] 48%|████▊ | 8984/18688 [10:31:05<12:13:09, 4.53s/it] 48%|████▊ | 8985/18688 [10:31:09<11:37:04, 4.31s/it] 48%|████▊ | 8986/18688 [10:31:13<11:09:15, 4.14s/it] 48%|████▊ | 8987/18688 [10:31:17<11:05:34, 4.12s/it] 48%|████▊ | 8988/18688 [10:31:21<10:56:17, 4.06s/it] 48%|████▊ | 8989/18688 [10:31:25<11:13:13, 4.16s/it] 48%|████▊ | 8990/18688 [10:31:29<10:50:06, 4.02s/it] {'loss': '0.922', 'grad_norm': '1.229', 'learning_rate': '6.003e-05', 'epoch': '0.4811', 'num_input_tokens_seen': 30215784, 'train_runtime': '3.789e+04', 'train_tokens_per_second': '797.5'}
48%|████▊ | 8990/18688 [10:31:29<10:50:06, 4.02s/it] 48%|████▊ | 8991/18688 [10:31:33<10:59:40, 4.08s/it] 48%|████▊ | 8992/18688 [10:31:37<10:50:18, 4.02s/it] 48%|████▊ | 8993/18688 [10:31:41<10:27:33, 3.88s/it] 48%|████▊ | 8994/18688 [10:31:45<10:49:30, 4.02s/it] 48%|████▊ | 8995/18688 [10:31:49<10:28:11, 3.89s/it] 48%|████▊ | 8996/18688 [10:31:52<10:13:24, 3.80s/it] 48%|████▊ | 8997/18688 [10:31:57<10:55:24, 4.06s/it] 48%|████▊ | 8998/18688 [10:32:02<11:48:03, 4.38s/it] 48%|████▊ | 8999/18688 [10:32:07<11:51:42, 4.41s/it] 48%|████▊ | 9000/18688 [10:32:10<11:07:46, 4.14s/it] {'loss': '0.8589', 'grad_norm': '1.235', 'learning_rate': '5.994e-05', 'epoch': '0.4816', 'num_input_tokens_seen': 30248528, 'train_runtime': '3.793e+04', 'train_tokens_per_second': '797.5'}
48%|████▊ | 9000/18688 [10:32:10<11:07:46, 4.14s/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, 18.32it/s]
1%| | 8/1000 [00:00<00:56, 17.47it/s]
1%| | 10/1000 [00:00<00:59, 16.54it/s]
1%| | 12/1000 [00:00<01:00, 16.33it/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, 10.00it/s]
2%|▏ | 20/1000 [00:01<01:32, 10.54it/s]
2%|▏ | 22/1000 [00:01<01:51, 8.74it/s]
2%|▏ | 24/1000 [00:01<01:39, 9.79it/s]
3%|▎ | 26/1000 [00:02<01:44, 9.28it/s]
3%|▎ | 28/1000 [00:02<01:36, 10.12it/s]
3%|▎ | 30/1000 [00:02<01:28, 11.02it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.18it/s]
3%|▎ | 34/1000 [00:02<01:25, 11.30it/s]
4%|▎ | 36/1000 [00:03<01:32, 10.45it/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, 9.03it/s]
4%|▍ | 44/1000 [00:03<01:30, 10.51it/s]
5%|▍ | 46/1000 [00:03<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, 12.51it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.24it/s]
6%|▌ | 56/1000 [00:04<01:07, 13.92it/s]
6%|▌ | 58/1000 [00:05<01:47, 8.73it/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, 11.82it/s]
7%|▋ | 66/1000 [00:05<01:15, 12.31it/s]
7%|▋ | 68/1000 [00:05<01:12, 12.91it/s]
7%|▋ | 70/1000 [00:05<01:08, 13.50it/s]
7%|▋ | 72/1000 [00:06<01:06, 13.94it/s]
7%|▋ | 74/1000 [00:06<01:07, 13.73it/s]
8%|▊ | 76/1000 [00:06<01:04, 14.30it/s]
8%|▊ | 78/1000 [00:06<01:03, 14.52it/s]
8%|▊ | 80/1000 [00:06<01:08, 13.47it/s]
8%|▊ | 82/1000 [00:06<01:05, 14.02it/s]
8%|▊ | 84/1000 [00:06<01:03, 14.49it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.64it/s]
9%|▉ | 88/1000 [00:07<01:03, 14.46it/s]
9%|▉ | 90/1000 [00:07<01:03, 14.32it/s]
9%|▉ | 92/1000 [00:07<01:06, 13.73it/s]
9%|▉ | 94/1000 [00:07<01:08, 13.25it/s]
10%|▉ | 96/1000 [00:07<01:09, 12.92it/s]
10%|▉ | 98/1000 [00:08<01:07, 13.27it/s]
10%|█ | 100/1000 [00:08<01:35, 9.45it/s]
10%|█ | 102/1000 [00:08<01:26, 10.42it/s]
10%|█ | 104/1000 [00:08<01:56, 7.66it/s]
10%|█ | 105/1000 [00:09<02:06, 7.08it/s]
11%|█ | 106/1000 [00:09<02:02, 7.28it/s]
11%|█ | 108/1000 [00:09<01:39, 8.96it/s]
11%|█ | 110/1000 [00:09<01:23, 10.60it/s]
11%|█ | 112/1000 [00:09<01:15, 11.74it/s]
11%|█▏ | 114/1000 [00:09<01:11, 12.34it/s]
12%|█▏ | 116/1000 [00:09<01:08, 12.91it/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, 9.68it/s]
13%|█▎ | 126/1000 [00:10<01:21, 10.66it/s]
13%|█▎ | 128/1000 [00:11<01:15, 11.59it/s]
13%|█▎ | 130/1000 [00:11<01:10, 12.27it/s]
13%|█▎ | 132/1000 [00:11<01:05, 13.22it/s]
13%|█▎ | 134/1000 [00:11<01:09, 12.40it/s]
14%|█▎ | 136/1000 [00:11<01:05, 13.28it/s]
14%|█▍ | 138/1000 [00:11<01:05, 13.24it/s]
14%|█▍ | 140/1000 [00:12<01:25, 10.11it/s]
14%|█▍ | 142/1000 [00:12<01:41, 8.46it/s]
14%|█▍ | 144/1000 [00:12<01:30, 9.50it/s]
15%|█▍ | 146/1000 [00:12<01:21, 10.54it/s]
15%|█▍ | 148/1000 [00:12<01:14, 11.47it/s]
15%|█▌ | 150/1000 [00:12<01:07, 12.55it/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, 13.66it/s]
16%|█▌ | 162/1000 [00:13<01:02, 13.51it/s]
16%|█▋ | 164/1000 [00:13<01:01, 13.60it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.76it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.28it/s]
17%|█▋ | 170/1000 [00:14<01:19, 10.49it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.47it/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<01:33, 8.80it/s]
18%|█▊ | 182/1000 [00:15<01:35, 8.58it/s]
18%|█▊ | 184/1000 [00:16<01:23, 9.82it/s]
19%|█▊ | 186/1000 [00:16<01:37, 8.35it/s]
19%|█▉ | 188/1000 [00:16<01:23, 9.68it/s]
19%|█▉ | 190/1000 [00:16<01:15, 10.71it/s]
19%|█▉ | 192/1000 [00:16<01:11, 11.35it/s]
19%|█▉ | 194/1000 [00:17<01:16, 10.55it/s]
20%|█▉ | 196/1000 [00:17<01:29, 8.97it/s]
20%|█▉ | 198/1000 [00:17<01:18, 10.19it/s]
20%|██ | 200/1000 [00:17<01:15, 10.53it/s]
20%|██ | 202/1000 [00:17<01:09, 11.48it/s]
20%|██ | 204/1000 [00:17<01:03, 12.60it/s]
21%|██ | 206/1000 [00:18<01:00, 13.20it/s]
21%|██ | 208/1000 [00:18<00:58, 13.58it/s]
21%|██ | 210/1000 [00:18<00:54, 14.43it/s]
21%|██ | 212/1000 [00:18<00:53, 14.69it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.70it/s]
22%|██▏ | 216/1000 [00:18<00:54, 14.45it/s]
22%|██▏ | 218/1000 [00:18<00:52, 14.81it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.89it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.18it/s]
22%|██▏ | 224/1000 [00:19<01:20, 9.67it/s]
23%|██▎ | 226/1000 [00:19<01:12, 10.66it/s]
23%|██▎ | 228/1000 [00:19<01:06, 11.56it/s]
23%|██▎ | 230/1000 [00:20<01:03, 12.06it/s]
23%|██▎ | 232/1000 [00:20<01:01, 12.44it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.13it/s]
24%|██▎ | 236/1000 [00:20<00:56, 13.58it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.23it/s]
24%|██▍ | 240/1000 [00:20<00:53, 14.25it/s]
24%|██▍ | 242/1000 [00:20<00:51, 14.74it/s]
24%|██▍ | 244/1000 [00:21<00:51, 14.63it/s]
25%|██▍ | 246/1000 [00:21<00:53, 14.09it/s]
25%|██▍ | 248/1000 [00:21<01:03, 11.89it/s]
25%|██▌ | 250/1000 [00:21<01:00, 12.33it/s]
25%|██▌ | 252/1000 [00:21<01:05, 11.48it/s]
25%|██▌ | 254/1000 [00:21<00:59, 12.54it/s]
26%|██▌ | 256/1000 [00:22<00:56, 13.11it/s]
26%|██▌ | 258/1000 [00:22<00:55, 13.30it/s]
26%|██▌ | 260/1000 [00:22<00:59, 12.53it/s]
26%|██▌ | 262/1000 [00:22<00:58, 12.70it/s]
26%|██▋ | 264/1000 [00:22<00:55, 13.36it/s]
27%|██▋ | 266/1000 [00:22<00:53, 13.81it/s]
27%|██▋ | 268/1000 [00:22<00:50, 14.39it/s]
27%|██▋ | 270/1000 [00:23<00:55, 13.18it/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<00:56, 12.72it/s]
28%|██▊ | 278/1000 [00:23<00:55, 12.95it/s]
28%|██▊ | 280/1000 [00:23<00:53, 13.53it/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<00:50, 14.03it/s]
29%|██▉ | 290/1000 [00:24<00:48, 14.65it/s]
29%|██▉ | 292/1000 [00:24<00:48, 14.61it/s]
29%|██▉ | 294/1000 [00:24<00:47, 14.78it/s]
30%|██▉ | 296/1000 [00:24<00:47, 14.97it/s]
30%|██▉ | 298/1000 [00:25<00:46, 15.19it/s]
30%|███ | 300/1000 [00:25<00:46, 14.97it/s]
30%|███ | 302/1000 [00:25<00:46, 15.01it/s]
30%|███ | 304/1000 [00:25<00:50, 13.67it/s]
31%|███ | 306/1000 [00:25<00:48, 14.43it/s]
31%|███ | 308/1000 [00:25<00:48, 14.41it/s]
31%|███ | 310/1000 [00:25<00:47, 14.60it/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, 15.60it/s]
32%|███▏ | 318/1000 [00:26<00:42, 16.04it/s]
32%|███▏ | 320/1000 [00:26<01:12, 9.37it/s]
32%|███▏ | 322/1000 [00:27<01:07, 10.04it/s]
32%|███▏ | 324/1000 [00:27<01:04, 10.54it/s]
33%|███▎ | 326/1000 [00:27<01:03, 10.56it/s]
33%|███▎ | 328/1000 [00:27<01:04, 10.49it/s]
33%|███▎ | 330/1000 [00:27<01:00, 11.16it/s]
33%|███▎ | 332/1000 [00:27<00:56, 11.74it/s]
33%|███▎ | 334/1000 [00:28<00:53, 12.45it/s]
34%|███▎ | 336/1000 [00:28<00:50, 13.04it/s]
34%|███▍ | 338/1000 [00:28<01:07, 9.87it/s]
34%|███▍ | 340/1000 [00:28<01:00, 10.96it/s]
34%|███▍ | 342/1000 [00:28<00:53, 12.24it/s]
34%|███▍ | 344/1000 [00:28<00:50, 12.95it/s]
35%|███▍ | 346/1000 [00:28<00:48, 13.56it/s]
35%|███▍ | 348/1000 [00:29<00:46, 14.01it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.66it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.79it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.83it/s]
36%|███▌ | 356/1000 [00:29<00:43, 14.91it/s]
36%|███▌ | 358/1000 [00:29<00:43, 14.60it/s]
36%|███▌ | 360/1000 [00:29<00:42, 14.97it/s]
36%|███▌ | 362/1000 [00:30<00:47, 13.35it/s]
36%|███▋ | 364/1000 [00:30<00:45, 13.86it/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, 13.54it/s]
37%|███▋ | 374/1000 [00:31<00:54, 11.40it/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, 12.67it/s]
38%|███▊ | 384/1000 [00:31<00:45, 13.40it/s]
39%|███▊ | 386/1000 [00:32<00:44, 13.72it/s]
39%|███▉ | 388/1000 [00:32<00:44, 13.68it/s]
39%|███▉ | 390/1000 [00:32<00:44, 13.57it/s]
39%|███▉ | 392/1000 [00:32<00:43, 13.90it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.69it/s]
40%|███▉ | 396/1000 [00:32<00:43, 13.80it/s]
40%|███▉ | 398/1000 [00:32<00:47, 12.61it/s]
40%|████ | 400/1000 [00:33<00:45, 13.25it/s]
40%|████ | 402/1000 [00:33<00:43, 13.88it/s]
40%|████ | 404/1000 [00:33<00:41, 14.51it/s]
41%|████ | 406/1000 [00:33<00:39, 15.01it/s]
41%|████ | 408/1000 [00:33<00:47, 12.56it/s]
41%|████ | 410/1000 [00:33<00:56, 10.48it/s]
41%|████ | 412/1000 [00:34<00:50, 11.55it/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, 8.26it/s]
42%|████▏ | 420/1000 [00:35<01:01, 9.50it/s]
42%|████▏ | 422/1000 [00:35<00:53, 10.84it/s]
42%|████▏ | 424/1000 [00:35<01:15, 7.66it/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, 9.12it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.05it/s]
43%|████▎ | 434/1000 [00:36<01:08, 8.28it/s]
44%|████▎ | 436/1000 [00:36<01:00, 9.31it/s]
44%|████▍ | 438/1000 [00:37<01:03, 8.91it/s]
44%|████▍ | 440/1000 [00:37<00:59, 9.44it/s]
44%|████▍ | 442/1000 [00:37<00:53, 10.50it/s]
44%|████▍ | 444/1000 [00:37<00:48, 11.38it/s]
45%|████▍ | 446/1000 [00:37<00:45, 12.16it/s]
45%|████▍ | 448/1000 [00:38<00:50, 10.95it/s]
45%|████▌ | 450/1000 [00:38<00:46, 11.78it/s]
45%|████▌ | 452/1000 [00:38<00:42, 12.79it/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, 13.89it/s]
46%|████▌ | 462/1000 [00:38<00:37, 14.48it/s]
46%|████▋ | 464/1000 [00:39<00:35, 14.99it/s]
47%|████▋ | 466/1000 [00:39<00:35, 14.84it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.94it/s]
47%|████▋ | 470/1000 [00:39<00:52, 10.08it/s]
47%|████▋ | 472/1000 [00:39<00:51, 10.24it/s]
47%|████▋ | 474/1000 [00:40<00:46, 11.25it/s]
48%|████▊ | 476/1000 [00:40<00:45, 11.54it/s]
48%|████▊ | 478/1000 [00:40<00:46, 11.28it/s]
48%|████▊ | 480/1000 [00:40<00:43, 12.08it/s]
48%|████▊ | 482/1000 [00:40<00:41, 12.36it/s]
48%|████▊ | 484/1000 [00:40<00:39, 13.09it/s]
49%|████▊ | 486/1000 [00:40<00:38, 13.45it/s]
49%|████▉ | 488/1000 [00:41<00:37, 13.72it/s]
49%|████▉ | 490/1000 [00:41<00:46, 10.86it/s]
49%|████▉ | 492/1000 [00:41<00:43, 11.79it/s]
49%|████▉ | 494/1000 [00:41<00:41, 12.28it/s]
50%|████▉ | 496/1000 [00:41<00:39, 12.69it/s]
50%|████▉ | 498/1000 [00:41<00:38, 12.90it/s]
50%|█████ | 500/1000 [00:42<00:46, 10.75it/s]
50%|█████ | 502/1000 [00:42<00:43, 11.40it/s]
50%|█████ | 504/1000 [00:42<00:40, 12.12it/s]
51%|█████ | 506/1000 [00:42<00:39, 12.39it/s]
51%|█████ | 508/1000 [00:42<00:36, 13.37it/s]
51%|█████ | 510/1000 [00:43<00:47, 10.25it/s]
51%|█████ | 512/1000 [00:43<00:44, 11.04it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 11.93it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.51it/s]
52%|█████▏ | 518/1000 [00:43<00:36, 13.05it/s]
52%|█████▏ | 520/1000 [00:43<00:35, 13.44it/s]
52%|█████▏ | 522/1000 [00:43<00:33, 14.16it/s]
52%|█████▏ | 524/1000 [00:44<00:33, 14.18it/s]
53%|█████▎ | 526/1000 [00:44<00:32, 14.37it/s]
53%|█████▎ | 528/1000 [00:44<00:39, 11.97it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.82it/s]
53%|█████▎ | 532/1000 [00:44<00:33, 13.77it/s]
53%|█████▎ | 534/1000 [00:44<00:32, 14.15it/s]
54%|█████▎ | 536/1000 [00:44<00:32, 14.27it/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, 14.73it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.56it/s]
55%|█████▍ | 546/1000 [00:45<00:32, 14.17it/s]
55%|█████▍ | 548/1000 [00:45<00:32, 13.87it/s]
55%|█████▌ | 550/1000 [00:45<00:31, 14.12it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.23it/s]
55%|█████▌ | 554/1000 [00:46<00:32, 13.91it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.11it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.48it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 15.03it/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, 11.64it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.21it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.30it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.84it/s]
57%|█████▋ | 574/1000 [00:47<00:31, 13.70it/s]
58%|█████▊ | 576/1000 [00:47<00:31, 13.60it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.64it/s]
58%|█████▊ | 580/1000 [00:48<00:32, 12.81it/s]
58%|█████▊ | 582/1000 [00:48<00:30, 13.59it/s]
58%|█████▊ | 584/1000 [00:48<00:30, 13.84it/s]
59%|█████▊ | 586/1000 [00:48<00:29, 14.23it/s]
59%|█████▉ | 588/1000 [00:48<00:27, 15.03it/s]
59%|█████▉ | 590/1000 [00:48<00:27, 15.04it/s]
59%|█████▉ | 592/1000 [00:49<00:27, 14.80it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.32it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.83it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 14.06it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.38it/s]
60%|██████ | 602/1000 [00:49<00:27, 14.47it/s]
60%|██████ | 604/1000 [00:49<00:26, 15.03it/s]
61%|██████ | 606/1000 [00:50<00:26, 14.81it/s]
61%|██████ | 608/1000 [00:50<00:25, 15.09it/s]
61%|██████ | 610/1000 [00:50<00:26, 14.98it/s]
61%|██████ | 612/1000 [00:50<00:25, 15.13it/s]
61%|██████▏ | 614/1000 [00:50<00:26, 14.39it/s]
62%|██████▏ | 616/1000 [00:50<00:25, 14.78it/s]
62%|██████▏ | 618/1000 [00:50<00:26, 14.17it/s]
62%|██████▏ | 620/1000 [00:51<00:39, 9.74it/s]
62%|██████▏ | 622/1000 [00:51<00:37, 9.97it/s]
62%|██████▏ | 624/1000 [00:51<00:35, 10.67it/s]
63%|██████▎ | 626/1000 [00:51<00:33, 11.06it/s]
63%|██████▎ | 628/1000 [00:51<00:32, 11.43it/s]
63%|██████▎ | 630/1000 [00:52<00:31, 11.59it/s]
63%|██████▎ | 632/1000 [00:52<00:33, 11.02it/s]
63%|██████▎ | 634/1000 [00:52<00:32, 11.34it/s]
64%|██████▎ | 636/1000 [00:52<00:36, 9.87it/s]
64%|██████▍ | 638/1000 [00:52<00:36, 9.88it/s]
64%|██████▍ | 640/1000 [00:53<00:33, 10.80it/s]
64%|██████▍ | 642/1000 [00:53<00:30, 11.80it/s]
64%|██████▍ | 644/1000 [00:53<00:29, 12.19it/s]
65%|██████▍ | 646/1000 [00:53<00:28, 12.58it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.13it/s]
65%|██████▌ | 650/1000 [00:53<00:28, 12.42it/s]
65%|██████▌ | 652/1000 [00:53<00:29, 11.85it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.41it/s]
66%|██████▌ | 656/1000 [00:54<00:32, 10.74it/s]
66%|██████▌ | 658/1000 [00:54<00:28, 11.81it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.15it/s]
66%|██████▌ | 662/1000 [00:54<00:29, 11.28it/s]
66%|██████▋ | 664/1000 [00:55<00:28, 11.77it/s]
67%|██████▋ | 666/1000 [00:55<00:26, 12.79it/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, 11.88it/s]
67%|██████▋ | 674/1000 [00:55<00:26, 12.46it/s]
68%|██████▊ | 676/1000 [00:56<00:27, 11.72it/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, 12.32it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.27it/s]
69%|██████▉ | 688/1000 [00:56<00:22, 13.63it/s]
69%|██████▉ | 690/1000 [00:57<00:22, 13.71it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.16it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.29it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 14.06it/s]
70%|██████▉ | 698/1000 [00:57<00:20, 14.62it/s]
70%|███████ | 700/1000 [00:57<00:24, 12.18it/s]
70%|███████ | 702/1000 [00:58<00:22, 12.99it/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, 9.63it/s]
71%|███████ | 710/1000 [00:58<00:26, 10.93it/s]
71%|███████ | 712/1000 [00:58<00:24, 11.89it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.98it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 13.08it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.54it/s]
72%|███████▏ | 720/1000 [00:59<00:20, 13.49it/s]
72%|███████▏ | 722/1000 [00:59<00:19, 13.91it/s]
72%|███████▏ | 724/1000 [00:59<00:20, 13.76it/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, 8.19it/s]
73%|███████▎ | 732/1000 [01:00<00:32, 8.32it/s]
73%|███████▎ | 734/1000 [01:01<00:30, 8.59it/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, 11.00it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.68it/s]
74%|███████▍ | 743/1000 [01:01<00:20, 12.31it/s]
74%|███████▍ | 745/1000 [01:02<00:25, 10.18it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.59it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.76it/s]
75%|███████▌ | 751/1000 [01:02<00:21, 11.85it/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, 10.30it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.34it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.32it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.27it/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, 10.60it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.86it/s]
77%|███████▋ | 773/1000 [01:04<00:17, 12.63it/s]
78%|███████▊ | 775/1000 [01:04<00:17, 13.18it/s]
78%|███████▊ | 777/1000 [01:04<00:15, 14.17it/s]
78%|███████▊ | 779/1000 [01:05<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, 7.07it/s]
78%|███████▊ | 785/1000 [01:05<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 [01:06<00:16, 12.34it/s]
80%|███████▉ | 798/1000 [01:06<00:15, 13.09it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.28it/s]
80%|████████ | 802/1000 [01:07<00:24, 8.24it/s]
80%|████████ | 804/1000 [01:07<00:23, 8.29it/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, 10.48it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.68it/s]
81%|████████▏ | 814/1000 [01:08<00:14, 12.59it/s]
82%|████████▏ | 816/1000 [01:08<00:16, 11.02it/s]
82%|████████▏ | 818/1000 [01:08<00:15, 11.97it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.47it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 13.03it/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, 14.51it/s]
83%|████████▎ | 830/1000 [01:09<00:12, 13.95it/s]
83%|████████▎ | 832/1000 [01:09<00:11, 14.30it/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, 14.30it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.37it/s]
84%|████████▍ | 842/1000 [01:10<00:10, 14.39it/s]
84%|████████▍ | 844/1000 [01:10<00:11, 13.95it/s]
85%|████████▍ | 846/1000 [01:10<00:11, 13.93it/s]
85%|████████▍ | 848/1000 [01:11<00:10, 13.90it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.13it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.70it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.29it/s]
86%|████████▌ | 856/1000 [01:11<00:10, 14.10it/s]
86%|████████▌ | 858/1000 [01:11<00:09, 14.56it/s]
86%|████████▌ | 860/1000 [01:11<00:09, 15.32it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.47it/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, 13.72it/s]
87%|████████▋ | 870/1000 [01:12<00:09, 14.11it/s]
87%|████████▋ | 872/1000 [01:12<00:08, 14.72it/s]
87%|████████▋ | 874/1000 [01:12<00:10, 12.43it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.07it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.96it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.59it/s]
88%|████████▊ | 882/1000 [01:13<00:10, 11.41it/s]
88%|████████▊ | 884/1000 [01:13<00:09, 12.15it/s]
89%|████████▊ | 886/1000 [01:13<00:08, 12.92it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.89it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.32it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.10it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 14.22it/s]
90%|████████▉ | 896/1000 [01:14<00:07, 13.96it/s]
90%|████████▉ | 898/1000 [01:14<00:07, 13.36it/s]
90%|█████████ | 900/1000 [01:14<00:08, 12.26it/s]
90%|█████████ | 902/1000 [01:15<00:07, 12.99it/s]
90%|█████████ | 904/1000 [01:15<00:09, 9.80it/s]
91%|█████████ | 906/1000 [01:15<00:08, 10.89it/s]
91%|█████████ | 908/1000 [01:15<00:07, 11.88it/s]
91%|█████████ | 910/1000 [01:15<00:07, 12.11it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.31it/s]
91%|█████████▏| 914/1000 [01:16<00:12, 6.82it/s]
92%|█████████▏| 916/1000 [01:16<00:10, 8.15it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.40it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.78it/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 [01:17<00:06, 10.60it/s]
93%|█████████▎| 930/1000 [01:17<00:05, 11.81it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 12.41it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.89it/s]
94%|█████████▎| 936/1000 [01:18<00:04, 13.44it/s]
94%|█████████▍| 938/1000 [01:18<00:04, 14.23it/s]
94%|█████████▍| 940/1000 [01:18<00:04, 14.56it/s]
94%|█████████▍| 942/1000 [01:18<00:04, 12.26it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.77it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.85it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 12.68it/s]
95%|█████████▌| 950/1000 [01:19<00:03, 13.47it/s]
95%|█████████▌| 952/1000 [01:19<00:03, 14.16it/s]
95%|█████████▌| 954/1000 [01:19<00:03, 14.26it/s]
96%|█████████▌| 956/1000 [01:19<00:03, 14.25it/s]
96%|█████████▌| 958/1000 [01:19<00:02, 14.71it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 13.09it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.71it/s]
96%|█████████▋| 964/1000 [01:20<00:03, 10.23it/s]
97%|█████████▋| 966/1000 [01:20<00:02, 11.42it/s]
97%|█████████▋| 968/1000 [01:20<00:02, 12.10it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 12.74it/s]
97%|█████████▋| 972/1000 [01:21<00:02, 9.70it/s]
97%|█████████▋| 974/1000 [01:21<00:02, 10.87it/s]
98%|█████████▊| 976/1000 [01:21<00:02, 11.66it/s]
98%|█████████▊| 978/1000 [01:21<00:02, 9.86it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.77it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.06it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 9.84it/s]
99%|█████████▊| 986/1000 [01:22<00:01, 9.88it/s]
99%|█████████▉| 988/1000 [01:22<00:01, 10.30it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 10.93it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 11.85it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 12.57it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 13.48it/s]
100%|█████████▉| 998/1000 [01:23<00:00, 13.78it/s]
100%|██████████| 1000/1000 [01:23<00:00, 14.50it/s]
{'eval_loss': '0.91', 'eval_runtime': '83.77', 'eval_samples_per_second': '11.94', 'eval_steps_per_second': '11.94', 'epoch': '0.4816', 'num_input_tokens_seen': 30248528}
48%|████▊ | 9000/18688 [10:33:34<11:07:46, 4.14s/it]
100%|██████████| 1000/1000 [01:23<00:00, 14.50it/s]
 48%|████▊ | 9001/18688 [10:33:38<78:36:54, 29.22s/it] 48%|████▊ | 9002/18688 [10:33:42<58:02:40, 21.57s/it] 48%|████▊ | 9003/18688 [10:33:45<43:31:55, 16.18s/it] 48%|████▊ | 9004/18688 [10:33:49<33:29:59, 12.45s/it] 48%|████▊ | 9005/18688 [10:33:53<26:46:41, 9.96s/it] 48%|████▊ | 9006/18688 [10:33:57<21:45:30, 8.09s/it] 48%|████▊ | 9007/18688 [10:34:00<18:13:48, 6.78s/it] 48%|████▊ | 9008/18688 [10:34:05<16:34:37, 6.17s/it] 48%|████▊ | 9009/18688 [10:34:09<14:24:01, 5.36s/it] 48%|████▊ | 9010/18688 [10:34:12<13:05:38, 4.87s/it] {'loss': '0.9232', 'grad_norm': '1.271', 'learning_rate': '5.985e-05', 'epoch': '0.4821', 'num_input_tokens_seen': 30278768, 'train_runtime': '3.805e+04', 'train_tokens_per_second': '795.7'}
48%|████▊ | 9010/18688 [10:34:12<13:05:38, 4.87s/it] 48%|████▊ | 9011/18688 [10:34:16<12:16:02, 4.56s/it] 48%|████▊ | 9012/18688 [10:34:20<11:49:32, 4.40s/it] 48%|████▊ | 9013/18688 [10:34:25<11:42:14, 4.35s/it] 48%|████▊ | 9014/18688 [10:34:29<11:53:36, 4.43s/it] 48%|████▊ | 9015/18688 [10:34:34<11:54:02, 4.43s/it] 48%|████▊ | 9016/18688 [10:34:39<12:20:19, 4.59s/it] 48%|████▊ | 9017/18688 [10:34:43<11:54:07, 4.43s/it] 48%|████▊ | 9018/18688 [10:34:47<11:44:37, 4.37s/it] 48%|████▊ | 9019/18688 [10:34:51<11:25:11, 4.25s/it] 48%|████▊ | 9020/18688 [10:34:55<11:07:57, 4.15s/it] {'loss': '0.9824', 'grad_norm': '1.259', 'learning_rate': '5.976e-05', 'epoch': '0.4827', 'num_input_tokens_seen': 30311880, 'train_runtime': '3.81e+04', 'train_tokens_per_second': '795.7'}
48%|████▊ | 9020/18688 [10:34:55<11:07:57, 4.15s/it] 48%|████▊ | 9021/18688 [10:34:59<11:22:12, 4.23s/it] 48%|████▊ | 9022/18688 [10:35:03<11:26:30, 4.26s/it] 48%|████▊ | 9023/18688 [10:35:08<11:27:31, 4.27s/it] 48%|████▊ | 9024/18688 [10:35:11<10:49:39, 4.03s/it] 48%|████▊ | 9025/18688 [10:35:15<10:20:59, 3.86s/it] 48%|████▊ | 9026/18688 [10:35:18<10:16:33, 3.83s/it] 48%|████▊ | 9027/18688 [10:35:23<10:31:08, 3.92s/it] 48%|████▊ | 9028/18688 [10:35:26<10:28:55, 3.91s/it] 48%|████▊ | 9029/18688 [10:35:31<10:52:10, 4.05s/it] 48%|████▊ | 9030/18688 [10:35:35<11:11:24, 4.17s/it] {'loss': '0.9392', 'grad_norm': '1.373', 'learning_rate': '5.967e-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<11:11:24, 4.17s/it] 48%|████▊ | 9031/18688 [10:35:39<11:10:53, 4.17s/it] 48%|████▊ | 9032/18688 [10:35:43<10:53:30, 4.06s/it] 48%|████▊ | 9033/18688 [10:35:47<10:41:22, 3.99s/it] 48%|████▊ | 9034/18688 [10:35:51<10:29:53, 3.91s/it] 48%|████▊ | 9035/18688 [10:35:54<10:17:37, 3.84s/it] 48%|████▊ | 9036/18688 [10:35:59<10:59:32, 4.10s/it] 48%|████▊ | 9037/18688 [10:36:04<11:39:23, 4.35s/it] 48%|████▊ | 9038/18688 [10:36:08<11:24:50, 4.26s/it] 48%|████▊ | 9039/18688 [10:36:12<11:25:08, 4.26s/it] 48%|████▊ | 9040/18688 [10:36:17<11:44:53, 4.38s/it] {'loss': '0.9169', 'grad_norm': '1.183', 'learning_rate': '5.958e-05', 'epoch': '0.4837', 'num_input_tokens_seen': 30380232, 'train_runtime': '3.818e+04', 'train_tokens_per_second': '795.8'}
48%|████▊ | 9040/18688 [10:36:17<11:44:53, 4.38s/it] 48%|████▊ | 9041/18688 [10:36:21<11:25:57, 4.27s/it] 48%|████▊ | 9042/18688 [10:36:25<11:14:04, 4.19s/it] 48%|████▊ | 9043/18688 [10:36:29<11:21:05, 4.24s/it] 48%|████▊ | 9044/18688 [10:36:34<11:27:47, 4.28s/it] 48%|████▊ | 9045/18688 [10:36:38<11:22:25, 4.25s/it] 48%|████▊ | 9046/18688 [10:36:43<11:46:36, 4.40s/it] 48%|████▊ | 9047/18688 [10:36:47<11:54:03, 4.44s/it] 48%|████▊ | 9048/18688 [10:36:52<12:09:11, 4.54s/it] 48%|████▊ | 9049/18688 [10:36:56<11:33:18, 4.32s/it] 48%|████▊ | 9050/18688 [10:37:00<11:37:42, 4.34s/it] {'loss': '0.9735', 'grad_norm': '1.25', 'learning_rate': '5.949e-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<11:37:42, 4.34s/it] 48%|████▊ | 9051/18688 [10:37:04<11:04:24, 4.14s/it] 48%|████▊ | 9052/18688 [10:37:08<10:41:35, 3.99s/it] 48%|████▊ | 9053/18688 [10:37:12<10:48:45, 4.04s/it] 48%|████▊ | 9054/18688 [10:37:16<10:44:25, 4.01s/it] 48%|████▊ | 9055/18688 [10:37:20<10:53:18, 4.07s/it] 48%|████▊ | 9056/18688 [10:37:24<10:42:20, 4.00s/it] 48%|████▊ | 9057/18688 [10:37:27<10:26:44, 3.90s/it] 48%|████▊ | 9058/18688 [10:37:32<10:43:26, 4.01s/it] 48%|████▊ | 9059/18688 [10:37:36<10:51:48, 4.06s/it] 48%|████▊ | 9060/18688 [10:37:40<11:02:17, 4.13s/it] {'loss': '0.9205', 'grad_norm': '1.277', 'learning_rate': '5.94e-05', 'epoch': '0.4848', 'num_input_tokens_seen': 30447968, 'train_runtime': '3.826e+04', 'train_tokens_per_second': '795.8'}
48%|████▊ | 9060/18688 [10:37:40<11:02:17, 4.13s/it] 48%|████▊ | 9061/18688 [10:37:44<11:06:13, 4.15s/it] 48%|████▊ | 9062/18688 [10:37:49<11:19:01, 4.23s/it] 48%|████▊ | 9063/18688 [10:37:53<11:28:41, 4.29s/it] 49%|████▊ | 9064/18688 [10:37:57<10:56:17, 4.09s/it] 49%|████▊ | 9065/18688 [10:38:01<11:13:28, 4.20s/it] 49%|████▊ | 9066/18688 [10:38:05<11:11:34, 4.19s/it] 49%|████▊ | 9067/18688 [10:38:10<11:20:09, 4.24s/it] 49%|████▊ | 9068/18688 [10:38:14<11:38:12, 4.35s/it] 49%|████▊ | 9069/18688 [10:38:19<11:38:50, 4.36s/it] 49%|████▊ | 9070/18688 [10:38:23<11:14:57, 4.21s/it] {'loss': '0.9406', 'grad_norm': '1.255', 'learning_rate': '5.931e-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<11:14:57, 4.21s/it] 49%|████▊ | 9071/18688 [10:38:26<10:55:59, 4.09s/it] 49%|████▊ | 9072/18688 [10:38:31<10:55:43, 4.09s/it] 49%|████▊ | 9073/18688 [10:38:35<10:57:19, 4.10s/it] 49%|████▊ | 9074/18688 [10:38:39<11:06:54, 4.16s/it] 49%|████▊ | 9075/18688 [10:38:44<11:39:50, 4.37s/it] 49%|████▊ | 9076/18688 [10:38:48<11:26:18, 4.28s/it] 49%|████▊ | 9077/18688 [10:38:52<10:57:49, 4.11s/it] 49%|████▊ | 9078/18688 [10:38:56<11:26:51, 4.29s/it] 49%|████▊ | 9079/18688 [10:39:01<11:42:22, 4.39s/it] 49%|████▊ | 9080/18688 [10:39:05<11:27:44, 4.29s/it] {'loss': '1', 'grad_norm': '1.088', 'learning_rate': '5.922e-05', 'epoch': '0.4859', 'num_input_tokens_seen': 30516040, 'train_runtime': '3.835e+04', 'train_tokens_per_second': '795.8'}
49%|████▊ | 9080/18688 [10:39:05<11:27:44, 4.29s/it] 49%|████▊ | 9081/18688 [10:39:09<11:10:07, 4.19s/it] 49%|████▊ | 9082/18688 [10:39:13<11:26:07, 4.29s/it] 49%|████▊ | 9083/18688 [10:39:18<11:38:22, 4.36s/it] 49%|████▊ | 9084/18688 [10:39:22<11:41:25, 4.38s/it] 49%|████▊ | 9085/18688 [10:39:26<11:05:13, 4.16s/it] 49%|████▊ | 9086/18688 [10:39:30<10:59:38, 4.12s/it] 49%|████▊ | 9087/18688 [10:39:34<11:11:59, 4.20s/it] 49%|████▊ | 9088/18688 [10:39:38<10:46:45, 4.04s/it] 49%|████▊ | 9089/18688 [10:39:43<11:22:20, 4.27s/it] 49%|████▊ | 9090/18688 [10:39:47<11:21:31, 4.26s/it] {'loss': '0.8943', 'grad_norm': '1.339', 'learning_rate': '5.913e-05', 'epoch': '0.4864', 'num_input_tokens_seen': 30550568, 'train_runtime': '3.839e+04', 'train_tokens_per_second': '795.8'}
49%|████▊ | 9090/18688 [10:39:47<11:21:31, 4.26s/it] 49%|████▊ | 9091/18688 [10:39:51<11:03:21, 4.15s/it] 49%|████▊ | 9092/18688 [10:39:55<10:51:29, 4.07s/it] 49%|████▊ | 9093/18688 [10:39:59<11:11:34, 4.20s/it] 49%|████▊ | 9094/18688 [10:40:04<11:16:04, 4.23s/it] 49%|████▊ | 9095/18688 [10:40:08<11:14:06, 4.22s/it] 49%|████▊ | 9096/18688 [10:40:13<11:51:48, 4.45s/it] 49%|████▊ | 9097/18688 [10:40:17<11:50:01, 4.44s/it] 49%|████▊ | 9098/18688 [10:40:22<11:56:39, 4.48s/it] 49%|████▊ | 9099/18688 [10:40:26<11:30:42, 4.32s/it] 49%|████▊ | 9100/18688 [10:40:29<10:54:06, 4.09s/it] {'loss': '0.9076', 'grad_norm': '1.428', 'learning_rate': '5.904e-05', 'epoch': '0.487', 'num_input_tokens_seen': 30583960, 'train_runtime': '3.843e+04', 'train_tokens_per_second': '795.8'}
49%|████▊ | 9100/18688 [10:40:29<10:54:06, 4.09s/it] 49%|████▊ | 9101/18688 [10:40:34<11:16:54, 4.24s/it] 49%|████▊ | 9102/18688 [10:40:38<10:52:33, 4.08s/it] 49%|████▊ | 9103/18688 [10:40:43<11:38:17, 4.37s/it] 49%|████▊ | 9104/18688 [10:40:47<11:54:25, 4.47s/it] 49%|████▊ | 9105/18688 [10:40:51<11:31:38, 4.33s/it] 49%|████▊ | 9106/18688 [10:40:56<11:17:27, 4.24s/it] 49%|████▊ | 9107/18688 [10:41:00<11:12:28, 4.21s/it] 49%|████▊ | 9108/18688 [10:41:03<10:46:57, 4.05s/it] 49%|████▊ | 9109/18688 [10:41:07<10:40:52, 4.01s/it] 49%|████▊ | 9110/18688 [10:41:12<10:56:05, 4.11s/it] {'loss': '0.9687', 'grad_norm': '1.189', 'learning_rate': '5.895e-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<10:56:05, 4.11s/it] 49%|████▉ | 9111/18688 [10:41:16<11:27:19, 4.31s/it] 49%|████▉ | 9112/18688 [10:41:20<11:01:39, 4.15s/it] 49%|████▉ | 9113/18688 [10:41:24<11:06:35, 4.18s/it] 49%|████▉ | 9114/18688 [10:41:29<11:14:17, 4.23s/it] 49%|████▉ | 9115/18688 [10:41:33<11:06:08, 4.18s/it] 49%|████▉ | 9116/18688 [10:41:37<10:46:35, 4.05s/it] 49%|████▉ | 9117/18688 [10:41:40<10:23:36, 3.91s/it] 49%|████▉ | 9118/18688 [10:41:44<10:04:55, 3.79s/it] 49%|████▉ | 9119/18688 [10:41:48<10:27:17, 3.93s/it] 49%|████▉ | 9120/18688 [10:41:52<10:24:43, 3.92s/it] {'loss': '0.9691', 'grad_norm': '1.052', 'learning_rate': '5.886e-05', 'epoch': '0.488', 'num_input_tokens_seen': 30650416, 'train_runtime': '3.851e+04', 'train_tokens_per_second': '795.9'}
49%|████▉ | 9120/18688 [10:41:52<10:24:43, 3.92s/it] 49%|████▉ | 9121/18688 [10:41:56<10:40:48, 4.02s/it] 49%|████▉ | 9122/18688 [10:42:01<11:05:53, 4.18s/it] 49%|████▉ | 9123/18688 [10:42:04<10:51:17, 4.09s/it] 49%|████▉ | 9124/18688 [10:42:08<10:41:46, 4.03s/it] 49%|████▉ | 9125/18688 [10:42:12<10:19:40, 3.89s/it] 49%|████▉ | 9126/18688 [10:42:17<10:53:55, 4.10s/it] 49%|████▉ | 9127/18688 [10:42:21<10:52:10, 4.09s/it] 49%|████▉ | 9128/18688 [10:42:25<11:05:54, 4.18s/it] 49%|████▉ | 9129/18688 [10:42:29<10:57:17, 4.13s/it] 49%|████▉ | 9130/18688 [10:42:33<11:00:24, 4.15s/it] {'loss': '0.8902', 'grad_norm': '1.19', 'learning_rate': '5.877e-05', 'epoch': '0.4886', 'num_input_tokens_seen': 30683872, 'train_runtime': '3.855e+04', 'train_tokens_per_second': '795.9'}
49%|████▉ | 9130/18688 [10:42:33<11:00:24, 4.15s/it] 49%|████▉ | 9131/18688 [10:42:37<10:29:14, 3.95s/it] 49%|████▉ | 9132/18688 [10:42:41<10:55:28, 4.12s/it] 49%|████▉ | 9133/18688 [10:42:45<10:30:33, 3.96s/it] 49%|████▉ | 9134/18688 [10:42:49<10:53:37, 4.10s/it] 49%|████▉ | 9135/18688 [10:42:54<11:09:57, 4.21s/it] 49%|████▉ | 9136/18688 [10:42:58<11:32:51, 4.35s/it] 49%|████▉ | 9137/18688 [10:43:03<11:29:47, 4.33s/it] 49%|████▉ | 9138/18688 [10:43:08<12:14:26, 4.61s/it] 49%|████▉ | 9139/18688 [10:43:12<11:42:51, 4.42s/it] 49%|████▉ | 9140/18688 [10:43:16<11:10:55, 4.22s/it] {'loss': '0.9322', 'grad_norm': '1.665', 'learning_rate': '5.868e-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<11:10:55, 4.22s/it] 49%|████▉ | 9141/18688 [10:43:20<11:19:38, 4.27s/it] 49%|████▉ | 9142/18688 [10:43:24<11:23:50, 4.30s/it] 49%|████▉ | 9143/18688 [10:43:28<11:07:16, 4.19s/it] 49%|████▉ | 9144/18688 [10:43:32<10:46:43, 4.07s/it] 49%|████▉ | 9145/18688 [10:43:36<10:40:52, 4.03s/it] 49%|████▉ | 9146/18688 [10:43:40<10:52:12, 4.10s/it] 49%|████▉ | 9147/18688 [10:43:44<10:44:04, 4.05s/it] 49%|████▉ | 9148/18688 [10:43:48<10:54:45, 4.12s/it] 49%|████▉ | 9149/18688 [10:43:52<10:25:26, 3.93s/it] 49%|████▉ | 9150/18688 [10:43:56<10:10:17, 3.84s/it] {'loss': '0.8892', 'grad_norm': '1.266', 'learning_rate': '5.859e-05', 'epoch': '0.4896', 'num_input_tokens_seen': 30748976, 'train_runtime': '3.864e+04', 'train_tokens_per_second': '795.9'}
49%|████▉ | 9150/18688 [10:43:56<10:10:17, 3.84s/it] 49%|████▉ | 9151/18688 [10:44:00<10:50:22, 4.09s/it] 49%|████▉ | 9152/18688 [10:44:04<10:44:50, 4.06s/it] 49%|████▉ | 9153/18688 [10:44:09<11:23:22, 4.30s/it] 49%|████▉ | 9154/18688 [10:44:13<11:01:27, 4.16s/it] 49%|████▉ | 9155/18688 [10:44:17<11:11:55, 4.23s/it] 49%|████▉ | 9156/18688 [10:44:22<11:45:34, 4.44s/it] 49%|████▉ | 9157/18688 [10:44:26<11:13:50, 4.24s/it] 49%|████▉ | 9158/18688 [10:44:30<11:18:38, 4.27s/it] 49%|████▉ | 9159/18688 [10:44:35<11:43:48, 4.43s/it] 49%|████▉ | 9160/18688 [10:44:40<12:04:06, 4.56s/it] {'loss': '1.007', 'grad_norm': '1.249', 'learning_rate': '5.85e-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<12:04:06, 4.56s/it] 49%|████▉ | 9161/18688 [10:44:44<11:26:31, 4.32s/it] 49%|████▉ | 9162/18688 [10:44:48<11:22:24, 4.30s/it] 49%|████▉ | 9163/18688 [10:44:52<10:54:34, 4.12s/it] 49%|████▉ | 9164/18688 [10:44:55<10:28:57, 3.96s/it] 49%|████▉ | 9165/18688 [10:44:59<10:18:32, 3.90s/it] 49%|████▉ | 9166/18688 [10:45:03<10:14:19, 3.87s/it] 49%|████▉ | 9167/18688 [10:45:07<10:28:11, 3.96s/it] 49%|████▉ | 9168/18688 [10:45:11<10:44:02, 4.06s/it] 49%|████▉ | 9169/18688 [10:45:16<10:57:53, 4.15s/it] 49%|████▉ | 9170/18688 [10:45:21<11:38:15, 4.40s/it] {'loss': '0.9184', 'grad_norm': '1.256', 'learning_rate': '5.841e-05', 'epoch': '0.4907', 'num_input_tokens_seen': 30820232, 'train_runtime': '3.872e+04', 'train_tokens_per_second': '796'}
49%|████▉ | 9170/18688 [10:45:21<11:38:15, 4.40s/it] 49%|████▉ | 9171/18688 [10:45:25<11:26:40, 4.33s/it] 49%|████▉ | 9172/18688 [10:45:29<11:13:10, 4.24s/it] 49%|████▉ | 9173/18688 [10:45:33<10:44:55, 4.07s/it] 49%|████▉ | 9174/18688 [10:45:36<10:31:18, 3.98s/it] 49%|████▉ | 9175/18688 [10:45:40<10:36:23, 4.01s/it] 49%|████▉ | 9176/18688 [10:45:44<10:10:21, 3.85s/it] 49%|████▉ | 9177/18688 [10:45:47<9:53:39, 3.75s/it] 49%|████▉ | 9178/18688 [10:45:52<10:22:11, 3.93s/it] 49%|████▉ | 9179/18688 [10:45:55<10:07:04, 3.83s/it] 49%|████▉ | 9180/18688 [10:45:59<10:17:00, 3.89s/it] {'loss': '0.9154', 'grad_norm': '1.368', 'learning_rate': '5.832e-05', 'epoch': '0.4912', 'num_input_tokens_seen': 30851064, 'train_runtime': '3.876e+04', 'train_tokens_per_second': '796'}
49%|████▉ | 9180/18688 [10:45:59<10:17:00, 3.89s/it] 49%|████▉ | 9181/18688 [10:46:04<10:31:29, 3.99s/it] 49%|████▉ | 9182/18688 [10:46:08<10:48:40, 4.09s/it] 49%|████▉ | 9183/18688 [10:46:12<10:33:16, 4.00s/it] 49%|████▉ | 9184/18688 [10:46:17<11:18:37, 4.28s/it] 49%|████▉ | 9185/18688 [10:46:21<10:58:31, 4.16s/it] 49%|████▉ | 9186/18688 [10:46:25<11:05:23, 4.20s/it] 49%|████▉ | 9187/18688 [10:46:29<10:52:40, 4.12s/it] 49%|████▉ | 9188/18688 [10:46:34<11:22:27, 4.31s/it] 49%|████▉ | 9189/18688 [10:46:37<11:00:11, 4.17s/it] 49%|████▉ | 9190/18688 [10:46:42<11:13:47, 4.26s/it] {'loss': '0.9622', 'grad_norm': '1.227', 'learning_rate': '5.823e-05', 'epoch': '0.4918', 'num_input_tokens_seen': 30886736, 'train_runtime': '3.88e+04', 'train_tokens_per_second': '796'}
49%|████▉ | 9190/18688 [10:46:42<11:13:47, 4.26s/it] 49%|████▉ | 9191/18688 [10:46:46<11:17:41, 4.28s/it] 49%|████▉ | 9192/18688 [10:46:51<11:47:37, 4.47s/it] 49%|████▉ | 9193/18688 [10:46:55<11:09:59, 4.23s/it] 49%|████▉ | 9194/18688 [10:46:59<10:59:18, 4.17s/it] 49%|████▉ | 9195/18688 [10:47:03<10:38:54, 4.04s/it] 49%|████▉ | 9196/18688 [10:47:06<10:32:26, 4.00s/it] 49%|████▉ | 9197/18688 [10:47:10<10:24:37, 3.95s/it] 49%|████▉ | 9198/18688 [10:47:14<10:00:53, 3.80s/it] 49%|████▉ | 9199/18688 [10:47:19<10:47:58, 4.10s/it] 49%|████▉ | 9200/18688 [10:47:23<10:54:36, 4.14s/it] {'loss': '0.9486', 'grad_norm': '1.264', 'learning_rate': '5.814e-05', 'epoch': '0.4923', 'num_input_tokens_seen': 30919528, 'train_runtime': '3.884e+04', 'train_tokens_per_second': '796'}
49%|████▉ | 9200/18688 [10:47:23<10:54:36, 4.14s/it] 49%|████▉ | 9201/18688 [10:47:27<10:36:12, 4.02s/it] 49%|████▉ | 9202/18688 [10:47:30<10:29:54, 3.98s/it] 49%|████▉ | 9203/18688 [10:47:35<11:16:49, 4.28s/it] 49%|████▉ | 9204/18688 [10:47:40<11:10:28, 4.24s/it] 49%|████▉ | 9205/18688 [10:47:44<11:15:04, 4.27s/it] 49%|████▉ | 9206/18688 [10:47:48<10:52:26, 4.13s/it] 49%|████▉ | 9207/18688 [10:47:52<10:43:53, 4.07s/it] 49%|████▉ | 9208/18688 [10:47:57<11:34:53, 4.40s/it] 49%|████▉ | 9209/18688 [10:48:01<11:35:34, 4.40s/it] 49%|████▉ | 9210/18688 [10:48:05<10:58:42, 4.17s/it] {'loss': '0.9347', 'grad_norm': '1.201', 'learning_rate': '5.805e-05', 'epoch': '0.4928', 'num_input_tokens_seen': 30954184, 'train_runtime': '3.889e+04', 'train_tokens_per_second': '796'}
49%|████▉ | 9210/18688 [10:48:05<10:58:42, 4.17s/it] 49%|████▉ | 9211/18688 [10:48:08<10:33:47, 4.01s/it] 49%|████▉ | 9212/18688 [10:48:13<11:02:48, 4.20s/it] 49%|████▉ | 9213/18688 [10:48:17<10:47:40, 4.10s/it] 49%|████▉ | 9214/18688 [10:48:21<10:21:01, 3.93s/it] 49%|████▉ | 9215/18688 [10:48:25<10:36:03, 4.03s/it] 49%|████▉ | 9216/18688 [10:48:29<10:46:17, 4.09s/it] 49%|████▉ | 9217/18688 [10:48:33<10:31:51, 4.00s/it] 49%|████▉ | 9218/18688 [10:48:37<10:38:58, 4.05s/it] 49%|████▉ | 9219/18688 [10:48:41<10:21:23, 3.94s/it] 49%|████▉ | 9220/18688 [10:48:45<10:48:57, 4.11s/it] {'loss': '0.9788', 'grad_norm': '1.5', 'learning_rate': '5.796e-05', 'epoch': '0.4934', 'num_input_tokens_seen': 30985872, 'train_runtime': '3.893e+04', 'train_tokens_per_second': '796'}
49%|████▉ | 9220/18688 [10:48:45<10:48:57, 4.11s/it] 49%|████▉ | 9221/18688 [10:48:49<10:23:18, 3.95s/it] 49%|████▉ | 9222/18688 [10:48:53<10:14:36, 3.90s/it] 49%|████▉ | 9223/18688 [10:48:57<10:40:41, 4.06s/it] 49%|████▉ | 9224/18688 [10:49:00<10:13:51, 3.89s/it] 49%|████▉ | 9225/18688 [10:49:04<9:55:39, 3.78s/it] 49%|████▉ | 9226/18688 [10:49:08<9:57:19, 3.79s/it] 49%|████▉ | 9227/18688 [10:49:13<11:14:15, 4.28s/it] 49%|████▉ | 9228/18688 [10:49:17<11:08:00, 4.24s/it] 49%|████▉ | 9229/18688 [10:49:21<11:03:55, 4.21s/it] 49%|████▉ | 9230/18688 [10:49:25<10:33:33, 4.02s/it] {'loss': '0.8566', 'grad_norm': '1.477', 'learning_rate': '5.787e-05', 'epoch': '0.4939', 'num_input_tokens_seen': 31017992, 'train_runtime': '3.897e+04', 'train_tokens_per_second': '796'}
49%|████▉ | 9230/18688 [10:49:25<10:33:33, 4.02s/it] 49%|████▉ | 9231/18688 [10:49:29<10:28:59, 3.99s/it] 49%|████▉ | 9232/18688 [10:49:33<10:25:24, 3.97s/it] 49%|████▉ | 9233/18688 [10:49:37<10:28:53, 3.99s/it] 49%|████▉ | 9234/18688 [10:49:41<10:34:29, 4.03s/it] 49%|████▉ | 9235/18688 [10:49:45<10:44:27, 4.09s/it] 49%|████▉ | 9236/18688 [10:49:50<11:00:55, 4.20s/it] 49%|████▉ | 9237/18688 [10:49:54<10:42:33, 4.08s/it] 49%|████▉ | 9238/18688 [10:49:58<10:57:56, 4.18s/it] 49%|████▉ | 9239/18688 [10:50:02<10:43:59, 4.09s/it] 49%|████▉ | 9240/18688 [10:50:06<10:52:06, 4.14s/it] {'loss': '0.9777', 'grad_norm': '1.443', 'learning_rate': '5.778e-05', 'epoch': '0.4944', 'num_input_tokens_seen': 31050928, 'train_runtime': '3.901e+04', 'train_tokens_per_second': '796'}
49%|████▉ | 9240/18688 [10:50:06<10:52:06, 4.14s/it] 49%|████▉ | 9241/18688 [10:50:10<10:49:55, 4.13s/it] 49%|████▉ | 9242/18688 [10:50:14<10:28:48, 3.99s/it] 49%|████▉ | 9243/18688 [10:50:18<10:28:57, 4.00s/it] 49%|████▉ | 9244/18688 [10:50:22<10:38:48, 4.06s/it] 49%|████▉ | 9245/18688 [10:50:27<11:04:14, 4.22s/it] 49%|████▉ | 9246/18688 [10:50:31<11:08:12, 4.25s/it] 49%|████▉ | 9247/18688 [10:50:35<11:02:27, 4.21s/it] 49%|████▉ | 9248/18688 [10:50:39<10:37:27, 4.05s/it] 49%|████▉ | 9249/18688 [10:50:43<10:30:30, 4.01s/it] 49%|████▉ | 9250/18688 [10:50:47<10:29:19, 4.00s/it] {'loss': '0.9219', 'grad_norm': '1.146', 'learning_rate': '5.769e-05', 'epoch': '0.495', 'num_input_tokens_seen': 31083696, 'train_runtime': '3.905e+04', 'train_tokens_per_second': '796.1'}
49%|████▉ | 9250/18688 [10:50:47<10:29:19, 4.00s/it] 50%|████▉ | 9251/18688 [10:50:50<10:17:13, 3.92s/it] 50%|████▉ | 9252/18688 [10:50:55<10:30:48, 4.01s/it] 50%|████▉ | 9253/18688 [10:50:59<11:00:07, 4.20s/it] 50%|████▉ | 9254/18688 [10:51:03<10:59:31, 4.19s/it] 50%|████▉ | 9255/18688 [10:51:08<10:54:29, 4.16s/it] 50%|████▉ | 9256/18688 [10:51:11<10:41:59, 4.08s/it] 50%|████▉ | 9257/18688 [10:51:15<10:32:29, 4.02s/it] 50%|████▉ | 9258/18688 [10:51:19<10:37:48, 4.06s/it] 50%|████▉ | 9259/18688 [10:51:23<10:33:59, 4.03s/it] 50%|████▉ | 9260/18688 [10:51:28<10:50:16, 4.14s/it] {'loss': '0.9345', 'grad_norm': '1.247', 'learning_rate': '5.76e-05', 'epoch': '0.4955', 'num_input_tokens_seen': 31116720, 'train_runtime': '3.909e+04', 'train_tokens_per_second': '796.1'}
50%|████▉ | 9260/18688 [10:51:28<10:50:16, 4.14s/it] 50%|████▉ | 9261/18688 [10:51:32<10:30:41, 4.01s/it] 50%|████▉ | 9262/18688 [10:51:36<10:35:43, 4.05s/it] 50%|████▉ | 9263/18688 [10:51:39<10:18:06, 3.93s/it] 50%|████▉ | 9264/18688 [10:51:44<10:39:14, 4.07s/it] 50%|████▉ | 9265/18688 [10:51:47<10:10:17, 3.89s/it] 50%|████▉ | 9266/18688 [10:51:51<10:19:41, 3.95s/it] 50%|████▉ | 9267/18688 [10:51:55<10:15:39, 3.92s/it] 50%|████▉ | 9268/18688 [10:52:00<11:01:40, 4.21s/it] 50%|████▉ | 9269/18688 [10:52:04<10:39:51, 4.08s/it] 50%|████▉ | 9270/18688 [10:52:07<10:16:15, 3.93s/it] {'loss': '0.9342', 'grad_norm': '1.722', 'learning_rate': '5.751e-05', 'epoch': '0.4961', 'num_input_tokens_seen': 31147920, 'train_runtime': '3.913e+04', 'train_tokens_per_second': '796.1'}
50%|████▉ | 9270/18688 [10:52:07<10:16:15, 3.93s/it] 50%|████▉ | 9271/18688 [10:52:11<10:12:49, 3.90s/it] 50%|████▉ | 9272/18688 [10:52:15<10:03:40, 3.85s/it] 50%|████▉ | 9273/18688 [10:52:19<10:06:20, 3.86s/it] 50%|████▉ | 9274/18688 [10:52:23<10:18:36, 3.94s/it] 50%|████▉ | 9275/18688 [10:52:27<10:01:38, 3.83s/it] 50%|████▉ | 9276/18688 [10:52:31<10:45:01, 4.11s/it] 50%|████▉ | 9277/18688 [10:52:36<11:01:21, 4.22s/it] 50%|████▉ | 9278/18688 [10:52:41<11:41:32, 4.47s/it] 50%|████▉ | 9279/18688 [10:52:45<11:13:47, 4.30s/it] 50%|████▉ | 9280/18688 [10:52:49<11:04:54, 4.24s/it] {'loss': '0.9361', 'grad_norm': '1.382', 'learning_rate': '5.742e-05', 'epoch': '0.4966', 'num_input_tokens_seen': 31181888, 'train_runtime': '3.917e+04', 'train_tokens_per_second': '796.1'}
50%|████▉ | 9280/18688 [10:52:49<11:04:54, 4.24s/it] 50%|████▉ | 9281/18688 [10:52:53<10:44:36, 4.11s/it] 50%|████▉ | 9282/18688 [10:52:57<10:47:24, 4.13s/it] 50%|████▉ | 9283/18688 [10:53:02<11:38:50, 4.46s/it] 50%|████▉ | 9284/18688 [10:53:06<11:20:57, 4.34s/it] 50%|████▉ | 9285/18688 [10:53:10<10:52:32, 4.16s/it] 50%|████▉ | 9286/18688 [10:53:13<10:22:17, 3.97s/it] 50%|████▉ | 9287/18688 [10:53:18<11:06:47, 4.26s/it] 50%|████▉ | 9288/18688 [10:53:23<11:23:39, 4.36s/it] 50%|████▉ | 9289/18688 [10:53:27<11:14:00, 4.30s/it] 50%|████▉ | 9290/18688 [10:53:31<11:14:25, 4.31s/it] {'loss': '0.9565', 'grad_norm': '1.212', 'learning_rate': '5.733e-05', 'epoch': '0.4971', 'num_input_tokens_seen': 31217408, 'train_runtime': '3.921e+04', 'train_tokens_per_second': '796.1'}
50%|████▉ | 9290/18688 [10:53:31<11:14:25, 4.31s/it] 50%|████▉ | 9291/18688 [10:53:35<11:03:18, 4.24s/it] 50%|████▉ | 9292/18688 [10:53:40<11:39:49, 4.47s/it] 50%|████▉ | 9293/18688 [10:53:45<11:32:02, 4.42s/it] 50%|████▉ | 9294/18688 [10:53:50<11:53:43, 4.56s/it] 50%|████▉ | 9295/18688 [10:53:53<11:04:33, 4.25s/it] 50%|████▉ | 9296/18688 [10:53:57<10:26:41, 4.00s/it] 50%|████▉ | 9297/18688 [10:54:01<10:37:37, 4.07s/it] 50%|████▉ | 9298/18688 [10:54:05<10:48:23, 4.14s/it] 50%|████▉ | 9299/18688 [10:54:09<10:55:29, 4.19s/it] 50%|████▉ | 9300/18688 [10:54:14<10:54:11, 4.18s/it] {'loss': '1.035', 'grad_norm': '1.417', 'learning_rate': '5.724e-05', 'epoch': '0.4977', 'num_input_tokens_seen': 31251968, 'train_runtime': '3.925e+04', 'train_tokens_per_second': '796.1'}
50%|████▉ | 9300/18688 [10:54:14<10:54:11, 4.18s/it] 50%|████▉ | 9301/18688 [10:54:18<10:45:37, 4.13s/it] 50%|████▉ | 9302/18688 [10:54:22<10:34:47, 4.06s/it] 50%|████▉ | 9303/18688 [10:54:26<11:08:19, 4.27s/it] 50%|████▉ | 9304/18688 [10:54:30<11:03:49, 4.24s/it] 50%|████▉ | 9305/18688 [10:54:35<11:19:16, 4.34s/it] 50%|████▉ | 9306/18688 [10:54:39<11:10:00, 4.28s/it] 50%|████▉ | 9307/18688 [10:54:43<10:46:07, 4.13s/it] 50%|████▉ | 9308/18688 [10:54:47<10:33:02, 4.05s/it] 50%|████▉ | 9309/18688 [10:54:51<10:39:33, 4.09s/it] 50%|████▉ | 9310/18688 [10:54:56<11:04:56, 4.25s/it] {'loss': '0.9668', 'grad_norm': '1.607', 'learning_rate': '5.715e-05', 'epoch': '0.4982', 'num_input_tokens_seen': 31286016, 'train_runtime': '3.93e+04', 'train_tokens_per_second': '796.2'}
50%|████▉ | 9310/18688 [10:54:56<11:04:56, 4.25s/it] 50%|████▉ | 9311/18688 [10:55:00<10:49:56, 4.16s/it] 50%|████▉ | 9312/18688 [10:55:04<11:14:01, 4.31s/it] 50%|████▉ | 9313/18688 [10:55:09<11:53:37, 4.57s/it] 50%|████▉ | 9314/18688 [10:55:13<11:07:13, 4.27s/it] 50%|████▉ | 9315/18688 [10:55:18<11:26:47, 4.40s/it] 50%|████▉ | 9316/18688 [10:55:22<11:33:26, 4.44s/it] 50%|████▉ | 9317/18688 [10:55:26<10:48:09, 4.15s/it] 50%|████▉ | 9318/18688 [10:55:29<10:20:57, 3.98s/it] 50%|████▉ | 9319/18688 [10:55:33<10:03:23, 3.86s/it] 50%|████▉ | 9320/18688 [10:55:37<10:22:29, 3.99s/it] {'loss': '0.9304', 'grad_norm': '1.388', 'learning_rate': '5.706e-05', 'epoch': '0.4987', 'num_input_tokens_seen': 31321136, 'train_runtime': '3.934e+04', 'train_tokens_per_second': '796.2'}
50%|████▉ | 9320/18688 [10:55:37<10:22:29, 3.99s/it] 50%|████▉ | 9321/18688 [10:55:41<10:03:02, 3.86s/it] 50%|████▉ | 9322/18688 [10:55:45<10:33:26, 4.06s/it] 50%|████▉ | 9323/18688 [10:55:50<11:06:08, 4.27s/it] 50%|████▉ | 9324/18688 [10:55:53<10:30:22, 4.04s/it] 50%|████▉ | 9325/18688 [10:55:58<10:52:43, 4.18s/it] 50%|████▉ | 9326/18688 [10:56:03<11:08:49, 4.29s/it] 50%|████▉ | 9327/18688 [10:56:07<10:59:31, 4.23s/it] 50%|████▉ | 9328/18688 [10:56:10<10:32:52, 4.06s/it] 50%|████▉ | 9329/18688 [10:56:14<10:15:02, 3.94s/it] 50%|████▉ | 9330/18688 [10:56:18<10:22:13, 3.99s/it] {'loss': '0.9225', 'grad_norm': '1.342', 'learning_rate': '5.697e-05', 'epoch': '0.4993', 'num_input_tokens_seen': 31355072, 'train_runtime': '3.938e+04', 'train_tokens_per_second': '796.2'}
50%|████▉ | 9330/18688 [10:56:18<10:22:13, 3.99s/it] 50%|████▉ | 9331/18688 [10:56:22<10:17:36, 3.96s/it] 50%|████▉ | 9332/18688 [10:56:26<10:00:29, 3.85s/it] 50%|████▉ | 9333/18688 [10:56:29<9:45:58, 3.76s/it] 50%|████▉ | 9334/18688 [10:56:33<9:53:26, 3.81s/it] 50%|████▉ | 9335/18688 [10:56:37<9:51:46, 3.80s/it] 50%|████▉ | 9336/18688 [10:56:42<10:39:22, 4.10s/it] 50%|████▉ | 9337/18688 [10:56:46<11:10:23, 4.30s/it] 50%|████▉ | 9338/18688 [10:56:50<10:51:14, 4.18s/it] 50%|████▉ | 9339/18688 [10:56:54<10:31:58, 4.06s/it] 50%|████▉ | 9340/18688 [10:56:58<10:38:34, 4.10s/it] {'loss': '0.8905', 'grad_norm': '1.572', 'learning_rate': '5.688e-05', 'epoch': '0.4998', 'num_input_tokens_seen': 31387352, 'train_runtime': '3.942e+04', 'train_tokens_per_second': '796.3'}
50%|████▉ | 9340/18688 [10:56:58<10:38:34, 4.10s/it] 50%|████▉ | 9341/18688 [10:57:03<11:03:30, 4.26s/it] 50%|████▉ | 9342/18688 [10:57:07<11:09:18, 4.30s/it] 50%|████▉ | 9343/18688 [10:57:11<10:55:25, 4.21s/it] 50%|█████ | 9344/18688 [10:57:15<10:30:00, 4.05s/it] 50%|█████ | 9345/18688 [10:57:19<10:25:59, 4.02s/it] 50%|█████ | 9346/18688 [10:57:23<10:28:32, 4.04s/it] 50%|█████ | 9347/18688 [10:57:27<10:34:44, 4.08s/it] 50%|█████ | 9348/18688 [10:57:31<10:27:37, 4.03s/it] 50%|█████ | 9349/18688 [10:57:35<10:39:20, 4.11s/it] 50%|█████ | 9350/18688 [10:57:40<10:50:41, 4.18s/it] {'loss': '0.9155', 'grad_norm': '1.413', 'learning_rate': '5.679e-05', 'epoch': '0.5003', 'num_input_tokens_seen': 31421072, 'train_runtime': '3.946e+04', 'train_tokens_per_second': '796.3'}
50%|█████ | 9350/18688 [10:57:40<10:50:41, 4.18s/it] 50%|█████ | 9351/18688 [10:57:43<10:24:08, 4.01s/it] 50%|█████ | 9352/18688 [10:57:47<10:26:22, 4.03s/it] 50%|█████ | 9353/18688 [10:57:52<11:10:14, 4.31s/it] 50%|█████ | 9354/18688 [10:57:57<11:06:29, 4.28s/it] 50%|█████ | 9355/18688 [10:58:01<11:18:24, 4.36s/it] 50%|█████ | 9356/18688 [10:58:05<11:15:44, 4.34s/it] 50%|█████ | 9357/18688 [10:58:09<10:54:49, 4.21s/it] 50%|█████ | 9358/18688 [10:58:14<11:11:15, 4.32s/it] 50%|█████ | 9359/18688 [10:58:18<11:18:34, 4.36s/it] 50%|█████ | 9360/18688 [10:58:22<10:50:19, 4.18s/it] {'loss': '0.8885', 'grad_norm': '1.474', 'learning_rate': '5.67e-05', 'epoch': '0.5009', 'num_input_tokens_seen': 31456456, 'train_runtime': '3.95e+04', 'train_tokens_per_second': '796.3'}
50%|█████ | 9360/18688 [10:58:22<10:50:19, 4.18s/it] 50%|█████ | 9361/18688 [10:58:27<11:42:25, 4.52s/it] 50%|█████ | 9362/18688 [10:58:32<12:00:50, 4.64s/it] 50%|█████ | 9363/18688 [10:58:37<11:40:53, 4.51s/it] 50%|█████ | 9364/18688 [10:58:40<10:54:10, 4.21s/it] 50%|█████ | 9365/18688 [10:58:44<10:24:54, 4.02s/it] 50%|█████ | 9366/18688 [10:58:48<10:38:14, 4.11s/it] 50%|█████ | 9367/18688 [10:58:53<11:14:41, 4.34s/it] 50%|█████ | 9368/18688 [10:58:56<10:40:08, 4.12s/it] 50%|█████ | 9369/18688 [10:59:00<10:35:02, 4.09s/it] 50%|█████ | 9370/18688 [10:59:05<10:47:03, 4.17s/it] {'loss': '0.9827', 'grad_norm': '1.243', 'learning_rate': '5.661e-05', 'epoch': '0.5014', 'num_input_tokens_seen': 31492240, 'train_runtime': '3.955e+04', 'train_tokens_per_second': '796.4'}
50%|█████ | 9370/18688 [10:59:05<10:47:03, 4.17s/it] 50%|█████ | 9371/18688 [10:59:10<11:28:35, 4.43s/it] 50%|█████ | 9372/18688 [10:59:14<10:58:15, 4.24s/it] 50%|█████ | 9373/18688 [10:59:18<11:19:04, 4.37s/it] 50%|█████ | 9374/18688 [10:59:23<11:30:46, 4.45s/it] 50%|█████ | 9375/18688 [10:59:28<11:44:26, 4.54s/it] 50%|█████ | 9376/18688 [10:59:31<10:55:22, 4.22s/it] 50%|█████ | 9377/18688 [10:59:35<10:50:50, 4.19s/it] 50%|█████ | 9378/18688 [10:59:39<10:36:37, 4.10s/it] 50%|█████ | 9379/18688 [10:59:43<10:37:57, 4.11s/it] 50%|█████ | 9380/18688 [10:59:48<10:54:11, 4.22s/it] {'loss': '0.9629', 'grad_norm': '1.188', 'learning_rate': '5.652e-05', 'epoch': '0.5019', 'num_input_tokens_seen': 31527928, 'train_runtime': '3.959e+04', 'train_tokens_per_second': '796.4'}
50%|█████ | 9380/18688 [10:59:48<10:54:11, 4.22s/it] 50%|█████ | 9381/18688 [10:59:53<11:46:58, 4.56s/it] 50%|█████ | 9382/18688 [10:59:57<11:29:44, 4.45s/it] 50%|█████ | 9383/18688 [11:00:01<10:44:23, 4.16s/it] 50%|█████ | 9384/18688 [11:00:06<11:10:30, 4.32s/it] 50%|█████ | 9385/18688 [11:00:09<10:40:35, 4.13s/it] 50%|█████ | 9386/18688 [11:00:13<10:25:11, 4.03s/it] 50%|█████ | 9387/18688 [11:00:17<10:00:22, 3.87s/it] 50%|█████ | 9388/18688 [11:00:21<10:12:32, 3.95s/it] 50%|█████ | 9389/18688 [11:00:24<9:55:20, 3.84s/it] 50%|█████ | 9390/18688 [11:00:29<10:41:32, 4.14s/it] {'loss': '0.8729', 'grad_norm': '1.506', 'learning_rate': '5.643e-05', 'epoch': '0.5025', 'num_input_tokens_seen': 31563432, 'train_runtime': '3.963e+04', 'train_tokens_per_second': '796.5'}
50%|█████ | 9390/18688 [11:00:29<10:41:32, 4.14s/it] 50%|█████ | 9391/18688 [11:00:33<10:54:59, 4.23s/it] 50%|█████ | 9392/18688 [11:00:38<11:01:28, 4.27s/it] 50%|█████ | 9393/18688 [11:00:42<10:34:49, 4.10s/it] 50%|█████ | 9394/18688 [11:00:46<10:56:02, 4.24s/it] 50%|█████ | 9395/18688 [11:00:50<10:34:56, 4.10s/it] 50%|█████ | 9396/18688 [11:00:54<10:57:47, 4.25s/it] 50%|█████ | 9397/18688 [11:00:59<10:46:44, 4.18s/it] 50%|█████ | 9398/18688 [11:01:04<11:27:51, 4.44s/it] 50%|█████ | 9399/18688 [11:01:07<11:03:47, 4.29s/it] 50%|█████ | 9400/18688 [11:01:12<11:26:44, 4.44s/it] {'loss': '0.9455', 'grad_norm': '1.235', 'learning_rate': '5.633e-05', 'epoch': '0.503', 'num_input_tokens_seen': 31599312, 'train_runtime': '3.967e+04', 'train_tokens_per_second': '796.5'}
50%|█████ | 9400/18688 [11:01:12<11:26:44, 4.44s/it] 50%|█████ | 9401/18688 [11:01:16<11:16:12, 4.37s/it] 50%|█████ | 9402/18688 [11:01:20<10:47:51, 4.19s/it] 50%|█████ | 9403/18688 [11:01:24<10:22:10, 4.02s/it] 50%|█████ | 9404/18688 [11:01:29<11:17:26, 4.38s/it] 50%|█████ | 9405/18688 [11:01:33<11:04:34, 4.30s/it] 50%|█████ | 9406/18688 [11:01:37<10:48:13, 4.19s/it] 50%|█████ | 9407/18688 [11:01:41<10:13:40, 3.97s/it] 50%|█████ | 9408/18688 [11:01:45<10:21:11, 4.02s/it] 50%|█████ | 9409/18688 [11:01:48<10:07:29, 3.93s/it] 50%|█████ | 9410/18688 [11:01:52<10:01:54, 3.89s/it] {'loss': '0.9591', 'grad_norm': '1.432', 'learning_rate': '5.624e-05', 'epoch': '0.5035', 'num_input_tokens_seen': 31631528, 'train_runtime': '3.971e+04', 'train_tokens_per_second': '796.5'}
50%|█████ | 9410/18688 [11:01:52<10:01:54, 3.89s/it] 50%|█████ | 9411/18688 [11:01:58<11:07:04, 4.31s/it] 50%|█████ | 9412/18688 [11:02:02<11:12:27, 4.35s/it] 50%|█████ | 9413/18688 [11:02:06<10:40:41, 4.14s/it] 50%|█████ | 9414/18688 [11:02:11<11:18:35, 4.39s/it] 50%|█████ | 9415/18688 [11:02:16<11:57:51, 4.64s/it] 50%|█████ | 9416/18688 [11:02:20<11:18:29, 4.39s/it] 50%|█████ | 9417/18688 [11:02:23<10:41:42, 4.15s/it] 50%|█████ | 9418/18688 [11:02:28<11:02:47, 4.29s/it] 50%|█████ | 9419/18688 [11:02:32<10:47:47, 4.19s/it] 50%|█████ | 9420/18688 [11:02:35<10:20:40, 4.02s/it] {'loss': '0.8813', 'grad_norm': '1.482', 'learning_rate': '5.615e-05', 'epoch': '0.5041', 'num_input_tokens_seen': 31667272, 'train_runtime': '3.976e+04', 'train_tokens_per_second': '796.5'}
50%|█████ | 9420/18688 [11:02:35<10:20:40, 4.02s/it] 50%|█████ | 9421/18688 [11:02:39<10:05:18, 3.92s/it] 50%|█████ | 9422/18688 [11:02:43<9:55:19, 3.85s/it] 50%|█████ | 9423/18688 [11:02:47<10:32:14, 4.09s/it] 50%|█████ | 9424/18688 [11:02:51<10:10:15, 3.95s/it] 50%|█████ | 9425/18688 [11:02:55<10:06:45, 3.93s/it] 50%|█████ | 9426/18688 [11:03:00<10:54:48, 4.24s/it] 50%|█████ | 9427/18688 [11:03:05<11:49:33, 4.60s/it] 50%|█████ | 9428/18688 [11:03:09<11:01:55, 4.29s/it] 50%|█████ | 9429/18688 [11:03:13<10:36:41, 4.13s/it] 50%|█████ | 9430/18688 [11:03:18<11:20:39, 4.41s/it] {'loss': '0.9298', 'grad_norm': '1.249', 'learning_rate': '5.606e-05', 'epoch': '0.5046', 'num_input_tokens_seen': 31702000, 'train_runtime': '3.98e+04', 'train_tokens_per_second': '796.6'}
50%|█████ | 9430/18688 [11:03:18<11:20:39, 4.41s/it] 50%|█████ | 9431/18688 [11:03:22<11:03:03, 4.30s/it] 50%|█████ | 9432/18688 [11:03:26<11:18:53, 4.40s/it] 50%|█████ | 9433/18688 [11:03:31<11:04:01, 4.30s/it] 50%|█████ | 9434/18688 [11:03:35<11:13:44, 4.37s/it] 50%|█████ | 9435/18688 [11:03:39<10:46:57, 4.20s/it] 50%|█████ | 9436/18688 [11:03:43<10:36:26, 4.13s/it] 50%|█████ | 9437/18688 [11:03:48<11:16:31, 4.39s/it] 51%|█████ | 9438/18688 [11:03:52<11:02:40, 4.30s/it] 51%|█████ | 9439/18688 [11:03:56<10:33:34, 4.11s/it] 51%|█████ | 9440/18688 [11:03:59<10:22:50, 4.04s/it] {'loss': '0.8675', 'grad_norm': '1.477', 'learning_rate': '5.597e-05', 'epoch': '0.5052', 'num_input_tokens_seen': 31734824, 'train_runtime': '3.984e+04', 'train_tokens_per_second': '796.6'}
51%|█████ | 9440/18688 [11:03:59<10:22:50, 4.04s/it] 51%|█████ | 9441/18688 [11:04:03<10:23:56, 4.05s/it] 51%|█████ | 9442/18688 [11:04:07<10:12:08, 3.97s/it] 51%|█████ | 9443/18688 [11:04:11<10:04:42, 3.92s/it] 51%|█████ | 9444/18688 [11:04:15<10:19:29, 4.02s/it] 51%|█████ | 9445/18688 [11:04:20<10:46:29, 4.20s/it] 51%|█████ | 9446/18688 [11:04:24<10:50:10, 4.22s/it] 51%|█████ | 9447/18688 [11:04:28<10:25:12, 4.06s/it] 51%|█████ | 9448/18688 [11:04:32<10:03:52, 3.92s/it] 51%|█████ | 9449/18688 [11:04:36<10:31:01, 4.10s/it] 51%|█████ | 9450/18688 [11:04:40<10:13:55, 3.99s/it] {'loss': '0.9206', 'grad_norm': '1.567', 'learning_rate': '5.588e-05', 'epoch': '0.5057', 'num_input_tokens_seen': 31765000, 'train_runtime': '3.988e+04', 'train_tokens_per_second': '796.5'}
51%|█████ | 9450/18688 [11:04:40<10:13:55, 3.99s/it] 51%|█████ | 9451/18688 [11:04:43<9:52:17, 3.85s/it] 51%|█████ | 9452/18688 [11:04:47<9:44:44, 3.80s/it] 51%|█████ | 9453/18688 [11:04:52<10:23:06, 4.05s/it] 51%|█████ | 9454/18688 [11:04:56<10:35:17, 4.13s/it] 51%|█████ | 9455/18688 [11:05:01<11:14:15, 4.38s/it] 51%|█████ | 9456/18688 [11:05:05<11:02:45, 4.31s/it] 51%|█████ | 9457/18688 [11:05:09<10:45:19, 4.19s/it] 51%|█████ | 9458/18688 [11:05:13<10:41:32, 4.17s/it] 51%|█████ | 9459/18688 [11:05:18<11:04:55, 4.32s/it] 51%|█████ | 9460/18688 [11:05:22<11:16:39, 4.40s/it] {'loss': '0.9603', 'grad_norm': '1.262', 'learning_rate': '5.579e-05', 'epoch': '0.5062', 'num_input_tokens_seen': 31800568, 'train_runtime': '3.992e+04', 'train_tokens_per_second': '796.6'}
51%|█████ | 9460/18688 [11:05:22<11:16:39, 4.40s/it] 51%|█████ | 9461/18688 [11:05:27<11:17:04, 4.40s/it] 51%|█████ | 9462/18688 [11:05:31<11:18:12, 4.41s/it] 51%|█████ | 9463/18688 [11:05:35<11:04:39, 4.32s/it] 51%|█████ | 9464/18688 [11:05:40<11:15:12, 4.39s/it] 51%|█████ | 9465/18688 [11:05:44<10:47:53, 4.21s/it] 51%|█████ | 9466/18688 [11:05:48<10:50:48, 4.23s/it] 51%|█████ | 9467/18688 [11:05:52<10:29:19, 4.09s/it] 51%|█████ | 9468/18688 [11:05:55<10:14:53, 4.00s/it] 51%|█████ | 9469/18688 [11:06:00<10:45:31, 4.20s/it] 51%|█████ | 9470/18688 [11:06:04<10:46:06, 4.21s/it] {'loss': '0.9118', 'grad_norm': '1.499', 'learning_rate': '5.57e-05', 'epoch': '0.5068', 'num_input_tokens_seen': 31834432, 'train_runtime': '3.996e+04', 'train_tokens_per_second': '796.6'}
51%|█████ | 9470/18688 [11:06:04<10:46:06, 4.21s/it] 51%|█████ | 9471/18688 [11:06:08<10:31:09, 4.11s/it] 51%|█████ | 9472/18688 [11:06:13<10:58:52, 4.29s/it] 51%|█████ | 9473/18688 [11:06:17<10:27:44, 4.09s/it] 51%|█████ | 9474/18688 [11:06:20<10:09:26, 3.97s/it] 51%|█████ | 9475/18688 [11:06:25<10:37:57, 4.15s/it] 51%|█████ | 9476/18688 [11:06:29<10:23:04, 4.06s/it] 51%|█████ | 9477/18688 [11:06:33<10:32:31, 4.12s/it] 51%|█████ | 9478/18688 [11:06:37<10:39:03, 4.16s/it] 51%|█████ | 9479/18688 [11:06:42<11:28:19, 4.48s/it] 51%|█████ | 9480/18688 [11:06:46<11:00:25, 4.30s/it] {'loss': '0.9768', 'grad_norm': '1.532', 'learning_rate': '5.561e-05', 'epoch': '0.5073', 'num_input_tokens_seen': 31867696, 'train_runtime': '4.001e+04', 'train_tokens_per_second': '796.6'}
51%|█████ | 9480/18688 [11:06:46<11:00:25, 4.30s/it] 51%|█████ | 9481/18688 [11:06:52<11:43:55, 4.59s/it] 51%|█████ | 9482/18688 [11:06:56<11:19:25, 4.43s/it] 51%|█████ | 9483/18688 [11:07:00<11:34:32, 4.53s/it] 51%|█████ | 9484/18688 [11:07:05<11:17:43, 4.42s/it] 51%|█████ | 9485/18688 [11:07:08<10:54:23, 4.27s/it] 51%|█████ | 9486/18688 [11:07:12<10:23:22, 4.06s/it] 51%|█████ | 9487/18688 [11:07:16<10:23:49, 4.07s/it] 51%|█████ | 9488/18688 [11:07:21<10:54:27, 4.27s/it] 51%|█████ | 9489/18688 [11:07:25<11:08:00, 4.36s/it] 51%|█████ | 9490/18688 [11:07:30<10:55:20, 4.27s/it] {'loss': '0.9279', 'grad_norm': '1.56', 'learning_rate': '5.552e-05', 'epoch': '0.5078', 'num_input_tokens_seen': 31903608, 'train_runtime': '4.005e+04', 'train_tokens_per_second': '796.6'}
51%|█████ | 9490/18688 [11:07:30<10:55:20, 4.27s/it] 51%|█████ | 9491/18688 [11:07:34<11:10:06, 4.37s/it] 51%|█████ | 9492/18688 [11:07:38<10:33:52, 4.14s/it] 51%|█████ | 9493/18688 [11:07:42<10:59:20, 4.30s/it] 51%|█████ | 9494/18688 [11:07:47<11:00:36, 4.31s/it] 51%|█████ | 9495/18688 [11:07:51<11:17:01, 4.42s/it] 51%|█████ | 9496/18688 [11:07:55<10:33:53, 4.14s/it] 51%|█████ | 9497/18688 [11:08:00<11:32:08, 4.52s/it] 51%|█████ | 9498/18688 [11:08:04<10:54:54, 4.28s/it] 51%|█████ | 9499/18688 [11:08:08<10:51:11, 4.25s/it] 51%|█████ | 9500/18688 [11:08:13<10:57:48, 4.30s/it] {'loss': '0.8811', 'grad_norm': '1.147', 'learning_rate': '5.543e-05', 'epoch': '0.5084', 'num_input_tokens_seen': 31939816, 'train_runtime': '4.009e+04', 'train_tokens_per_second': '796.6'}
51%|█████ | 9500/18688 [11:08:13<10:57:48, 4.30s/it] 51%|█████ | 9501/18688 [11:08:16<10:20:54, 4.06s/it] 51%|█████ | 9502/18688 [11:08:20<10:16:23, 4.03s/it] 51%|█████ | 9503/18688 [11:08:24<9:59:34, 3.92s/it] 51%|█████ | 9504/18688 [11:08:27<9:40:04, 3.79s/it] 51%|█████ | 9505/18688 [11:08:31<9:50:19, 3.86s/it] 51%|█████ | 9506/18688 [11:08:35<9:51:53, 3.87s/it] 51%|█████ | 9507/18688 [11:08:39<9:55:12, 3.89s/it] 51%|█████ | 9508/18688 [11:08:43<10:20:38, 4.06s/it] 51%|█████ | 9509/18688 [11:08:48<10:45:54, 4.22s/it] 51%|█████ | 9510/18688 [11:08:52<10:23:33, 4.08s/it] {'loss': '0.8847', 'grad_norm': '1.349', 'learning_rate': '5.534e-05', 'epoch': '0.5089', 'num_input_tokens_seen': 31969848, 'train_runtime': '4.013e+04', 'train_tokens_per_second': '796.6'}
51%|█████ | 9510/18688 [11:08:52<10:23:33, 4.08s/it] 51%|█████ | 9511/18688 [11:08:56<10:35:59, 4.16s/it] 51%|█████ | 9512/18688 [11:09:01<10:43:58, 4.21s/it] 51%|█████ | 9513/18688 [11:09:05<10:49:48, 4.25s/it] 51%|█████ | 9514/18688 [11:09:09<10:37:39, 4.17s/it] 51%|█████ | 9515/18688 [11:09:13<10:43:52, 4.21s/it] 51%|█████ | 9516/18688 [11:09:17<10:36:50, 4.17s/it] 51%|█████ | 9517/18688 [11:09:22<11:02:45, 4.34s/it] 51%|█████ | 9518/18688 [11:09:25<10:23:28, 4.08s/it] 51%|█████ | 9519/18688 [11:09:30<10:37:04, 4.17s/it] 51%|█████ | 9520/18688 [11:09:34<10:40:38, 4.19s/it] {'loss': '0.8983', 'grad_norm': '1.303', 'learning_rate': '5.525e-05', 'epoch': '0.5094', 'num_input_tokens_seen': 32004080, 'train_runtime': '4.017e+04', 'train_tokens_per_second': '796.6'}
51%|█████ | 9520/18688 [11:09:34<10:40:38, 4.19s/it] 51%|█████ | 9521/18688 [11:09:39<11:06:48, 4.36s/it] 51%|█████ | 9522/18688 [11:09:43<10:46:02, 4.23s/it] 51%|█████ | 9523/18688 [11:09:48<11:11:58, 4.40s/it] 51%|█████ | 9524/18688 [11:09:52<11:10:00, 4.39s/it] 51%|█████ | 9525/18688 [11:09:56<10:39:45, 4.19s/it] 51%|█████ | 9526/18688 [11:09:59<10:07:27, 3.98s/it] 51%|█████ | 9527/18688 [11:10:03<10:20:41, 4.07s/it] 51%|█████ | 9528/18688 [11:10:08<10:42:35, 4.21s/it] 51%|█████ | 9529/18688 [11:10:12<10:28:27, 4.12s/it] 51%|█████ | 9530/18688 [11:10:16<10:38:03, 4.18s/it] {'loss': '0.9917', 'grad_norm': '1.288', 'learning_rate': '5.515e-05', 'epoch': '0.51', 'num_input_tokens_seen': 32038736, 'train_runtime': '4.022e+04', 'train_tokens_per_second': '796.7'}
51%|█████ | 9530/18688 [11:10:16<10:38:03, 4.18s/it] 51%|█████ | 9531/18688 [11:10:20<10:37:52, 4.18s/it] 51%|█████ | 9532/18688 [11:10:24<10:27:02, 4.11s/it] 51%|█████ | 9533/18688 [11:10:28<10:14:58, 4.03s/it] 51%|█████ | 9534/18688 [11:10:33<10:36:46, 4.17s/it] 51%|█████ | 9535/18688 [11:10:36<10:20:06, 4.06s/it] 51%|█████ | 9536/18688 [11:10:41<10:45:54, 4.23s/it] 51%|█████ | 9537/18688 [11:10:45<10:45:35, 4.23s/it] 51%|█████ | 9538/18688 [11:10:49<10:28:03, 4.12s/it] 51%|█████ | 9539/18688 [11:10:54<10:45:48, 4.24s/it] 51%|█████ | 9540/18688 [11:10:58<10:35:46, 4.17s/it] {'loss': '0.9143', 'grad_norm': '1.083', 'learning_rate': '5.506e-05', 'epoch': '0.5105', 'num_input_tokens_seen': 32072448, 'train_runtime': '4.026e+04', 'train_tokens_per_second': '796.7'}
51%|█████ | 9540/18688 [11:10:58<10:35:46, 4.17s/it] 51%|█████ | 9541/18688 [11:11:02<10:26:45, 4.11s/it] 51%|█████ | 9542/18688 [11:11:05<10:00:14, 3.94s/it] 51%|█████ | 9543/18688 [11:11:09<10:11:19, 4.01s/it] 51%|█████ | 9544/18688 [11:11:14<10:26:47, 4.11s/it] 51%|█████ | 9545/18688 [11:11:19<11:12:03, 4.41s/it] 51%|█████ | 9546/18688 [11:11:23<10:50:01, 4.27s/it] 51%|█████ | 9547/18688 [11:11:27<10:46:05, 4.24s/it] 51%|█████ | 9548/18688 [11:11:32<11:20:59, 4.47s/it] 51%|█████ | 9549/18688 [11:11:36<10:49:39, 4.27s/it] 51%|█████ | 9550/18688 [11:11:40<10:30:15, 4.14s/it] {'loss': '0.9858', 'grad_norm': '1.178', 'learning_rate': '5.497e-05', 'epoch': '0.511', 'num_input_tokens_seen': 32105416, 'train_runtime': '4.03e+04', 'train_tokens_per_second': '796.7'}
51%|█████ | 9550/18688 [11:11:40<10:30:15, 4.14s/it] 51%|█████ | 9551/18688 [11:11:43<10:13:58, 4.03s/it] 51%|█████ | 9552/18688 [11:11:48<10:34:15, 4.17s/it] 51%|█████ | 9553/18688 [11:11:51<10:10:45, 4.01s/it] 51%|█████ | 9554/18688 [11:11:55<9:56:02, 3.92s/it] 51%|█████ | 9555/18688 [11:12:00<10:29:03, 4.13s/it] 51%|█████ | 9556/18688 [11:12:04<10:19:15, 4.07s/it] 51%|█████ | 9557/18688 [11:12:08<10:31:22, 4.15s/it] 51%|█████ | 9558/18688 [11:12:13<10:48:51, 4.26s/it] 51%|█████ | 9559/18688 [11:12:17<11:00:21, 4.34s/it] 51%|█████ | 9560/18688 [11:12:21<11:00:26, 4.34s/it] {'loss': '0.9037', 'grad_norm': '1.126', 'learning_rate': '5.488e-05', 'epoch': '0.5116', 'num_input_tokens_seen': 32140936, 'train_runtime': '4.034e+04', 'train_tokens_per_second': '796.7'}
51%|█████ | 9560/18688 [11:12:21<11:00:26, 4.34s/it] 51%|█████ | 9561/18688 [11:12:25<10:34:00, 4.17s/it] 51%|█████ | 9562/18688 [11:12:29<10:11:04, 4.02s/it] 51%|█████ | 9563/18688 [11:12:32<9:50:31, 3.88s/it] 51%|█████ | 9564/18688 [11:12:37<10:20:32, 4.08s/it] 51%|█████ | 9565/18688 [11:12:41<9:59:44, 3.94s/it] 51%|█████ | 9566/18688 [11:12:45<10:02:40, 3.96s/it] 51%|█████ | 9567/18688 [11:12:48<9:57:01, 3.93s/it] 51%|█████ | 9568/18688 [11:12:52<9:58:21, 3.94s/it] 51%|█████ | 9569/18688 [11:12:56<9:54:37, 3.91s/it] 51%|█████ | 9570/18688 [11:13:00<10:05:02, 3.98s/it] {'loss': '0.9011', 'grad_norm': '1.289', 'learning_rate': '5.479e-05', 'epoch': '0.5121', 'num_input_tokens_seen': 32172424, 'train_runtime': '4.038e+04', 'train_tokens_per_second': '796.7'}
51%|█████ | 9570/18688 [11:13:00<10:05:02, 3.98s/it] 51%|█████ | 9571/18688 [11:13:05<10:28:15, 4.13s/it] 51%|█████ | 9572/18688 [11:13:09<10:38:28, 4.20s/it] 51%|█████ | 9573/18688 [11:13:13<10:25:11, 4.12s/it] 51%|█████ | 9574/18688 [11:13:17<10:04:13, 3.98s/it] 51%|█████ | 9575/18688 [11:13:21<9:59:07, 3.94s/it] 51%|█████ | 9576/18688 [11:13:24<9:41:28, 3.83s/it] 51%|█████ | 9577/18688 [11:13:28<9:30:20, 3.76s/it] 51%|█████▏ | 9578/18688 [11:13:32<10:07:45, 4.00s/it] 51%|█████▏ | 9579/18688 [11:13:37<10:49:05, 4.28s/it] 51%|█████▏ | 9580/18688 [11:13:41<10:28:43, 4.14s/it] {'loss': '0.9145', 'grad_norm': '1.44', 'learning_rate': '5.47e-05', 'epoch': '0.5126', 'num_input_tokens_seen': 32203856, 'train_runtime': '4.042e+04', 'train_tokens_per_second': '796.7'}
51%|█████▏ | 9580/18688 [11:13:41<10:28:43, 4.14s/it] 51%|█████▏ | 9581/18688 [11:13:46<10:48:03, 4.27s/it] 51%|█████▏ | 9582/18688 [11:13:50<11:03:53, 4.37s/it] 51%|█████▏ | 9583/18688 [11:13:54<10:34:31, 4.18s/it] 51%|█████▏ | 9584/18688 [11:13:58<10:11:42, 4.03s/it] 51%|█████▏ | 9585/18688 [11:14:01<9:52:15, 3.90s/it] 51%|█████▏ | 9586/18688 [11:14:06<10:07:55, 4.01s/it] 51%|█████▏ | 9587/18688 [11:14:10<10:28:13, 4.14s/it] 51%|█████▏ | 9588/18688 [11:14:15<10:45:30, 4.26s/it] 51%|█████▏ | 9589/18688 [11:14:18<10:24:34, 4.12s/it] 51%|█████▏ | 9590/18688 [11:14:22<10:14:22, 4.05s/it] {'loss': '0.957', 'grad_norm': '1.774', 'learning_rate': '5.461e-05', 'epoch': '0.5132', 'num_input_tokens_seen': 32236288, 'train_runtime': '4.046e+04', 'train_tokens_per_second': '796.7'}
51%|█████▏ | 9590/18688 [11:14:22<10:14:22, 4.05s/it] 51%|█████▏ | 9591/18688 [11:14:27<10:50:02, 4.29s/it] 51%|█████▏ | 9592/18688 [11:14:31<10:42:59, 4.24s/it] 51%|█████▏ | 9593/18688 [11:14:35<10:10:42, 4.03s/it] 51%|█████▏ | 9594/18688 [11:14:39<10:01:42, 3.97s/it] 51%|█████▏ | 9595/18688 [11:14:43<10:19:21, 4.09s/it] 51%|█████▏ | 9596/18688 [11:14:47<10:33:18, 4.18s/it] 51%|█████▏ | 9597/18688 [11:14:52<10:42:20, 4.24s/it] 51%|█████▏ | 9598/18688 [11:14:56<10:38:02, 4.21s/it] 51%|█████▏ | 9599/18688 [11:15:00<10:28:20, 4.15s/it] 51%|█████▏ | 9600/18688 [11:15:04<10:29:54, 4.16s/it] {'loss': '0.961', 'grad_norm': '1.116', 'learning_rate': '5.452e-05', 'epoch': '0.5137', 'num_input_tokens_seen': 32271544, 'train_runtime': '4.05e+04', 'train_tokens_per_second': '796.7'}
51%|█████▏ | 9600/18688 [11:15:04<10:29:54, 4.16s/it] 51%|█████▏ | 9601/18688 [11:15:08<10:40:05, 4.23s/it] 51%|█████▏ | 9602/18688 [11:15:13<10:40:18, 4.23s/it] 51%|█████▏ | 9603/18688 [11:15:17<10:26:54, 4.14s/it] 51%|█████▏ | 9604/18688 [11:15:21<10:37:35, 4.21s/it] 51%|█████▏ | 9605/18688 [11:15:25<10:28:06, 4.15s/it] 51%|█████▏ | 9606/18688 [11:15:29<10:06:51, 4.01s/it] 51%|█████▏ | 9607/18688 [11:15:33<10:07:43, 4.02s/it] 51%|█████▏ | 9608/18688 [11:15:38<10:55:56, 4.33s/it] 51%|█████▏ | 9609/18688 [11:15:42<10:52:45, 4.31s/it] 51%|█████▏ | 9610/18688 [11:15:47<11:24:19, 4.52s/it] {'loss': '0.9579', 'grad_norm': '1.389', 'learning_rate': '5.443e-05', 'epoch': '0.5142', 'num_input_tokens_seen': 32307752, 'train_runtime': '4.055e+04', 'train_tokens_per_second': '796.8'}
51%|█████▏ | 9610/18688 [11:15:47<11:24:19, 4.52s/it] 51%|█████▏ | 9611/18688 [11:15:51<10:52:52, 4.32s/it] 51%|█████▏ | 9612/18688 [11:15:56<11:45:41, 4.67s/it] 51%|█████▏ | 9613/18688 [11:16:01<11:59:27, 4.76s/it] 51%|█████▏ | 9614/18688 [11:16:06<11:38:52, 4.62s/it] 51%|█████▏ | 9615/18688 [11:16:11<11:50:35, 4.70s/it] 51%|█████▏ | 9616/18688 [11:16:15<11:30:09, 4.56s/it] 51%|█████▏ | 9617/18688 [11:16:19<11:25:16, 4.53s/it] 51%|█████▏ | 9618/18688 [11:16:23<11:08:49, 4.42s/it] 51%|█████▏ | 9619/18688 [11:16:27<10:39:50, 4.23s/it] 51%|█████▏ | 9620/18688 [11:16:31<10:19:45, 4.10s/it] {'loss': '0.9543', 'grad_norm': '1.236', 'learning_rate': '5.434e-05', 'epoch': '0.5148', 'num_input_tokens_seen': 32345512, 'train_runtime': '4.059e+04', 'train_tokens_per_second': '796.9'}
51%|█████▏ | 9620/18688 [11:16:31<10:19:45, 4.10s/it] 51%|█████▏ | 9621/18688 [11:16:35<10:05:08, 4.00s/it] 51%|█████▏ | 9622/18688 [11:16:38<9:49:43, 3.90s/it] 51%|█████▏ | 9623/18688 [11:16:42<9:46:46, 3.88s/it] 51%|█████▏ | 9624/18688 [11:16:46<9:55:30, 3.94s/it] 52%|█████▏ | 9625/18688 [11:16:50<9:39:25, 3.84s/it] 52%|█████▏ | 9626/18688 [11:16:53<9:23:38, 3.73s/it] 52%|█████▏ | 9627/18688 [11:16:58<10:05:49, 4.01s/it] 52%|█████▏ | 9628/18688 [11:17:02<10:07:28, 4.02s/it] 52%|█████▏ | 9629/18688 [11:17:07<10:41:30, 4.25s/it] 52%|█████▏ | 9630/18688 [11:17:11<10:48:22, 4.29s/it] {'loss': '0.8733', 'grad_norm': '1.169', 'learning_rate': '5.424e-05', 'epoch': '0.5153', 'num_input_tokens_seen': 32377936, 'train_runtime': '4.063e+04', 'train_tokens_per_second': '796.9'}
52%|█████▏ | 9630/18688 [11:17:11<10:48:22, 4.29s/it] 52%|█████▏ | 9631/18688 [11:17:16<10:48:23, 4.30s/it] 52%|█████▏ | 9632/18688 [11:17:21<11:43:56, 4.66s/it] 52%|█████▏ | 9633/18688 [11:17:26<11:36:56, 4.62s/it] 52%|█████▏ | 9634/18688 [11:17:30<11:14:09, 4.47s/it] 52%|█████▏ | 9635/18688 [11:17:34<11:22:08, 4.52s/it] 52%|█████▏ | 9636/18688 [11:17:38<10:59:36, 4.37s/it] 52%|█████▏ | 9637/18688 [11:17:43<10:45:21, 4.28s/it] 52%|█████▏ | 9638/18688 [11:17:47<10:35:11, 4.21s/it] 52%|█████▏ | 9639/18688 [11:17:51<10:49:29, 4.31s/it] 52%|█████▏ | 9640/18688 [11:17:55<10:40:38, 4.25s/it] {'loss': '0.9146', 'grad_norm': '0.9746', 'learning_rate': '5.415e-05', 'epoch': '0.5159', 'num_input_tokens_seen': 32415512, 'train_runtime': '4.068e+04', 'train_tokens_per_second': '796.9'}
52%|█████▏ | 9640/18688 [11:17:55<10:40:38, 4.25s/it] 52%|█████▏ | 9641/18688 [11:17:59<10:40:52, 4.25s/it] 52%|█████▏ | 9642/18688 [11:18:03<10:30:12, 4.18s/it] 52%|█████▏ | 9643/18688 [11:18:10<11:56:42, 4.75s/it] 52%|█████▏ | 9644/18688 [11:18:15<12:19:58, 4.91s/it] 52%|█████▏ | 9645/18688 [11:18:19<11:50:27, 4.71s/it] 52%|█████▏ | 9646/18688 [11:18:23<10:58:30, 4.37s/it] 52%|█████▏ | 9647/18688 [11:18:26<10:19:38, 4.11s/it] 52%|█████▏ | 9648/18688 [11:18:31<10:31:08, 4.19s/it] 52%|█████▏ | 9649/18688 [11:18:34<9:57:47, 3.97s/it] 52%|█████▏ | 9650/18688 [11:18:38<9:40:49, 3.86s/it] {'loss': '0.923', 'grad_norm': '1.277', 'learning_rate': '5.406e-05', 'epoch': '0.5164', 'num_input_tokens_seen': 32449768, 'train_runtime': '4.072e+04', 'train_tokens_per_second': '796.9'}
52%|█████▏ | 9650/18688 [11:18:38<9:40:49, 3.86s/it] 52%|█████▏ | 9651/18688 [11:18:41<9:28:04, 3.77s/it] 52%|█████▏ | 9652/18688 [11:18:46<9:56:38, 3.96s/it] 52%|█████▏ | 9653/18688 [11:18:49<9:37:37, 3.84s/it] 52%|█████▏ | 9654/18688 [11:18:54<10:30:48, 4.19s/it] 52%|█████▏ | 9655/18688 [11:18:59<11:01:34, 4.39s/it] 52%|█████▏ | 9656/18688 [11:19:03<10:31:37, 4.20s/it] 52%|█████▏ | 9657/18688 [11:19:07<10:15:28, 4.09s/it] 52%|█████▏ | 9658/18688 [11:19:10<9:58:20, 3.98s/it] 52%|█████▏ | 9659/18688 [11:19:14<9:50:01, 3.92s/it] 52%|█████▏ | 9660/18688 [11:19:19<10:24:13, 4.15s/it] {'loss': '0.937', 'grad_norm': '1.322', 'learning_rate': '5.397e-05', 'epoch': '0.5169', 'num_input_tokens_seen': 32483488, 'train_runtime': '4.076e+04', 'train_tokens_per_second': '797'}
52%|█████▏ | 9660/18688 [11:19:19<10:24:13, 4.15s/it] 52%|█████▏ | 9661/18688 [11:19:23<10:12:37, 4.07s/it] 52%|█████▏ | 9662/18688 [11:19:27<10:30:19, 4.19s/it] 52%|█████▏ | 9663/18688 [11:19:31<10:23:54, 4.15s/it] 52%|█████▏ | 9664/18688 [11:19:35<10:19:09, 4.12s/it] 52%|█████▏ | 9665/18688 [11:19:39<10:19:33, 4.12s/it] 52%|█████▏ | 9666/18688 [11:19:44<10:33:54, 4.22s/it] 52%|█████▏ | 9667/18688 [11:19:48<10:16:42, 4.10s/it] 52%|█████▏ | 9668/18688 [11:19:52<10:32:55, 4.21s/it] 52%|█████▏ | 9669/18688 [11:19:56<10:20:48, 4.13s/it] 52%|█████▏ | 9670/18688 [11:20:00<10:35:36, 4.23s/it] {'loss': '0.9631', 'grad_norm': '1.176', 'learning_rate': '5.388e-05', 'epoch': '0.5175', 'num_input_tokens_seen': 32517792, 'train_runtime': '4.08e+04', 'train_tokens_per_second': '797'}
52%|█████▏ | 9670/18688 [11:20:00<10:35:36, 4.23s/it] 52%|█████▏ | 9671/18688 [11:20:04<10:24:31, 4.16s/it] 52%|█████▏ | 9672/18688 [11:20:09<10:31:43, 4.20s/it] 52%|█████▏ | 9673/18688 [11:20:14<11:17:18, 4.51s/it] 52%|█████▏ | 9674/18688 [11:20:18<10:40:15, 4.26s/it] 52%|█████▏ | 9675/18688 [11:20:22<10:27:41, 4.18s/it] 52%|█████▏ | 9676/18688 [11:20:25<9:59:09, 3.99s/it] 52%|█████▏ | 9677/18688 [11:20:29<9:58:00, 3.98s/it] 52%|█████▏ | 9678/18688 [11:20:34<10:24:08, 4.16s/it] 52%|█████▏ | 9679/18688 [11:20:38<10:27:43, 4.18s/it] 52%|█████▏ | 9680/18688 [11:20:42<10:30:37, 4.20s/it] {'loss': '0.9611', 'grad_norm': '0.9618', 'learning_rate': '5.379e-05', 'epoch': '0.518', 'num_input_tokens_seen': 32552808, 'train_runtime': '4.084e+04', 'train_tokens_per_second': '797'}
52%|█████▏ | 9680/18688 [11:20:42<10:30:37, 4.20s/it] 52%|█████▏ | 9681/18688 [11:20:47<10:42:45, 4.28s/it] 52%|█████▏ | 9682/18688 [11:20:52<11:15:46, 4.50s/it] 52%|█████▏ | 9683/18688 [11:20:55<10:34:03, 4.22s/it] 52%|█████▏ | 9684/18688 [11:20:59<10:26:07, 4.17s/it] 52%|█████▏ | 9685/18688 [11:21:03<9:53:40, 3.96s/it] 52%|█████▏ | 9686/18688 [11:21:07<10:02:27, 4.02s/it] 52%|█████▏ | 9687/18688 [11:21:11<9:48:23, 3.92s/it] 52%|█████▏ | 9688/18688 [11:21:15<9:47:33, 3.92s/it] 52%|█████▏ | 9689/18688 [11:21:19<10:13:03, 4.09s/it] 52%|█████▏ | 9690/18688 [11:21:24<10:30:12, 4.20s/it] {'loss': '0.9556', 'grad_norm': '1.202', 'learning_rate': '5.37e-05', 'epoch': '0.5185', 'num_input_tokens_seen': 32587184, 'train_runtime': '4.088e+04', 'train_tokens_per_second': '797.1'}
52%|█████▏ | 9690/18688 [11:21:24<10:30:12, 4.20s/it] 52%|█████▏ | 9691/18688 [11:21:29<11:06:26, 4.44s/it] 52%|█████▏ | 9692/18688 [11:21:33<10:53:40, 4.36s/it] 52%|█████▏ | 9693/18688 [11:21:37<10:56:34, 4.38s/it] 52%|█████▏ | 9694/18688 [11:21:41<10:49:46, 4.33s/it] 52%|█████▏ | 9695/18688 [11:21:45<10:40:43, 4.27s/it] 52%|█████▏ | 9696/18688 [11:21:50<11:12:22, 4.49s/it] 52%|█████▏ | 9697/18688 [11:21:55<11:17:57, 4.52s/it] 52%|█████▏ | 9698/18688 [11:21:59<10:39:59, 4.27s/it] 52%|█████▏ | 9699/18688 [11:22:02<10:12:53, 4.09s/it] 52%|█████▏ | 9700/18688 [11:22:06<9:48:36, 3.93s/it] {'loss': '0.8932', 'grad_norm': '1.355', 'learning_rate': '5.361e-05', 'epoch': '0.5191', 'num_input_tokens_seen': 32621480, 'train_runtime': '4.093e+04', 'train_tokens_per_second': '797.1'}
52%|█████▏ | 9700/18688 [11:22:06<9:48:36, 3.93s/it] 52%|█████▏ | 9701/18688 [11:22:11<10:19:24, 4.14s/it] 52%|█████▏ | 9702/18688 [11:22:14<10:04:11, 4.03s/it] 52%|█████▏ | 9703/18688 [11:22:19<10:26:52, 4.19s/it] 52%|█████▏ | 9704/18688 [11:22:23<10:21:54, 4.15s/it] 52%|█████▏ | 9705/18688 [11:22:28<10:37:03, 4.26s/it] 52%|█████▏ | 9706/18688 [11:22:32<10:42:26, 4.29s/it] 52%|█████▏ | 9707/18688 [11:22:36<10:17:38, 4.13s/it] 52%|█████▏ | 9708/18688 [11:22:39<9:47:54, 3.93s/it] 52%|█████▏ | 9709/18688 [11:22:44<10:16:48, 4.12s/it] 52%|█████▏ | 9710/18688 [11:22:48<10:07:49, 4.06s/it] {'loss': '0.9567', 'grad_norm': '1.179', 'learning_rate': '5.352e-05', 'epoch': '0.5196', 'num_input_tokens_seen': 32654592, 'train_runtime': '4.097e+04', 'train_tokens_per_second': '797.1'}
52%|█████▏ | 9710/18688 [11:22:48<10:07:49, 4.06s/it] 52%|█████▏ | 9711/18688 [11:22:51<9:58:51, 4.00s/it] 52%|█████▏ | 9712/18688 [11:22:55<9:33:33, 3.83s/it] 52%|█████▏ | 9713/18688 [11:22:59<9:42:25, 3.89s/it] 52%|█████▏ | 9714/18688 [11:23:03<10:07:42, 4.06s/it] 52%|█████▏ | 9715/18688 [11:23:08<10:43:00, 4.30s/it] 52%|█████▏ | 9716/18688 [11:23:12<10:15:05, 4.11s/it] 52%|█████▏ | 9717/18688 [11:23:16<10:29:17, 4.21s/it] 52%|█████▏ | 9718/18688 [11:23:21<10:50:06, 4.35s/it] 52%|█████▏ | 9719/18688 [11:23:26<11:08:53, 4.47s/it] 52%|█████▏ | 9720/18688 [11:23:30<11:09:54, 4.48s/it] {'loss': '0.8988', 'grad_norm': '1.193', 'learning_rate': '5.342e-05', 'epoch': '0.5201', 'num_input_tokens_seen': 32690224, 'train_runtime': '4.101e+04', 'train_tokens_per_second': '797.1'}
52%|█████▏ | 9720/18688 [11:23:30<11:09:54, 4.48s/it] 52%|█████▏ | 9721/18688 [11:23:34<10:41:35, 4.29s/it] 52%|█████▏ | 9722/18688 [11:23:38<10:25:03, 4.18s/it] 52%|█████▏ | 9723/18688 [11:23:42<10:19:32, 4.15s/it] 52%|█████▏ | 9724/18688 [11:23:46<10:26:06, 4.19s/it] 52%|█████▏ | 9725/18688 [11:23:50<10:14:36, 4.11s/it] 52%|█████▏ | 9726/18688 [11:23:54<9:54:56, 3.98s/it] 52%|█████▏ | 9727/18688 [11:23:58<10:04:17, 4.05s/it] 52%|█████▏ | 9728/18688 [11:24:02<9:46:29, 3.93s/it] 52%|█████▏ | 9729/18688 [11:24:06<9:35:15, 3.85s/it] 52%|█████▏ | 9730/18688 [11:24:09<9:30:56, 3.82s/it] {'loss': '0.8504', 'grad_norm': '1.488', 'learning_rate': '5.333e-05', 'epoch': '0.5207', 'num_input_tokens_seen': 32719632, 'train_runtime': '4.105e+04', 'train_tokens_per_second': '797.1'}
52%|█████▏ | 9730/18688 [11:24:09<9:30:56, 3.82s/it] 52%|█████▏ | 9731/18688 [11:24:14<10:27:09, 4.20s/it] 52%|█████▏ | 9732/18688 [11:24:18<10:13:29, 4.11s/it] 52%|█████▏ | 9733/18688 [11:24:23<10:40:32, 4.29s/it] 52%|█████▏ | 9734/18688 [11:24:27<10:22:57, 4.17s/it] 52%|█████▏ | 9735/18688 [11:24:31<9:56:34, 4.00s/it] 52%|█████▏ | 9736/18688 [11:24:34<9:36:34, 3.86s/it] 52%|█████▏ | 9737/18688 [11:24:38<9:54:00, 3.98s/it] 52%|█████▏ | 9738/18688 [11:24:43<10:09:37, 4.09s/it] 52%|█████▏ | 9739/18688 [11:24:47<10:29:46, 4.22s/it] 52%|█████▏ | 9740/18688 [11:24:51<10:31:41, 4.24s/it] {'loss': '0.941', 'grad_norm': '1.25', 'learning_rate': '5.324e-05', 'epoch': '0.5212', 'num_input_tokens_seen': 32754296, 'train_runtime': '4.109e+04', 'train_tokens_per_second': '797.1'}
52%|█████▏ | 9740/18688 [11:24:51<10:31:41, 4.24s/it] 52%|█████▏ | 9741/18688 [11:24:56<11:00:41, 4.43s/it] 52%|█████▏ | 9742/18688 [11:25:00<10:17:44, 4.14s/it] 52%|█████▏ | 9743/18688 [11:25:03<9:56:11, 4.00s/it] 52%|█████▏ | 9744/18688 [11:25:07<9:42:01, 3.90s/it] 52%|█████▏ | 9745/18688 [11:25:11<9:34:42, 3.86s/it] 52%|█████▏ | 9746/18688 [11:25:15<9:33:41, 3.85s/it] 52%|█████▏ | 9747/18688 [11:25:19<9:42:42, 3.91s/it] 52%|█████▏ | 9748/18688 [11:25:23<9:43:20, 3.92s/it] 52%|█████▏ | 9749/18688 [11:25:27<10:03:02, 4.05s/it] 52%|█████▏ | 9750/18688 [11:25:32<10:46:41, 4.34s/it] {'loss': '0.9536', 'grad_norm': '1.402', 'learning_rate': '5.315e-05', 'epoch': '0.5217', 'num_input_tokens_seen': 32786872, 'train_runtime': '4.113e+04', 'train_tokens_per_second': '797.1'}
52%|█████▏ | 9750/18688 [11:25:32<10:46:41, 4.34s/it] 52%|█████▏ | 9751/18688 [11:25:37<10:58:10, 4.42s/it] 52%|█████▏ | 9752/18688 [11:25:41<11:02:00, 4.44s/it] 52%|█████▏ | 9753/18688 [11:25:46<11:12:18, 4.51s/it] 52%|█████▏ | 9754/18688 [11:25:51<11:34:09, 4.66s/it] 52%|█████▏ | 9755/18688 [11:25:56<11:33:22, 4.66s/it] 52%|█████▏ | 9756/18688 [11:26:00<11:20:39, 4.57s/it] 52%|█████▏ | 9757/18688 [11:26:04<10:56:20, 4.41s/it] 52%|█████▏ | 9758/18688 [11:26:09<11:08:49, 4.49s/it] 52%|█████▏ | 9759/18688 [11:26:13<10:44:10, 4.33s/it] 52%|█████▏ | 9760/18688 [11:26:17<10:58:03, 4.42s/it] {'loss': '0.9081', 'grad_norm': '1.909', 'learning_rate': '5.306e-05', 'epoch': '0.5223', 'num_input_tokens_seen': 32824744, 'train_runtime': '4.118e+04', 'train_tokens_per_second': '797.1'}
52%|█████▏ | 9760/18688 [11:26:17<10:58:03, 4.42s/it] 52%|█████▏ | 9761/18688 [11:26:21<10:49:26, 4.37s/it] 52%|█████▏ | 9762/18688 [11:26:25<10:23:52, 4.19s/it] 52%|█████▏ | 9763/18688 [11:26:30<10:42:24, 4.32s/it] 52%|█████▏ | 9764/18688 [11:26:33<10:05:56, 4.07s/it] 52%|█████▏ | 9765/18688 [11:26:37<10:03:45, 4.06s/it] 52%|█████▏ | 9766/18688 [11:26:42<10:12:41, 4.12s/it] 52%|█████▏ | 9767/18688 [11:26:46<10:29:05, 4.23s/it] 52%|█████▏ | 9768/18688 [11:26:51<11:05:57, 4.48s/it] 52%|█████▏ | 9769/18688 [11:26:55<10:49:12, 4.37s/it] 52%|█████▏ | 9770/18688 [11:26:59<10:15:36, 4.14s/it] {'loss': '0.9356', 'grad_norm': '1.257', 'learning_rate': '5.297e-05', 'epoch': '0.5228', 'num_input_tokens_seen': 32858176, 'train_runtime': '4.122e+04', 'train_tokens_per_second': '797.2'}
52%|█████▏ | 9770/18688 [11:26:59<10:15:36, 4.14s/it] 52%|█████▏ | 9771/18688 [11:27:03<10:19:21, 4.17s/it] 52%|█████▏ | 9772/18688 [11:27:07<10:11:13, 4.11s/it] 52%|█████▏ | 9773/18688 [11:27:11<10:03:33, 4.06s/it] 52%|█████▏ | 9774/18688 [11:27:15<10:07:48, 4.09s/it] 52%|█████▏ | 9775/18688 [11:27:21<11:03:58, 4.47s/it] 52%|█████▏ | 9776/18688 [11:27:25<10:54:44, 4.41s/it] 52%|█████▏ | 9777/18688 [11:27:29<11:04:01, 4.47s/it] 52%|█████▏ | 9778/18688 [11:27:33<10:33:09, 4.26s/it] 52%|█████▏ | 9779/18688 [11:27:37<10:18:37, 4.17s/it] 52%|█████▏ | 9780/18688 [11:27:42<10:42:22, 4.33s/it] {'loss': '0.9093', 'grad_norm': '1.239', 'learning_rate': '5.288e-05', 'epoch': '0.5233', 'num_input_tokens_seen': 32894288, 'train_runtime': '4.126e+04', 'train_tokens_per_second': '797.2'}
52%|█████▏ | 9780/18688 [11:27:42<10:42:22, 4.33s/it] 52%|█████▏ | 9781/18688 [11:27:46<10:20:49, 4.18s/it] 52%|█████▏ | 9782/18688 [11:27:49<9:48:35, 3.97s/it] 52%|█████▏ | 9783/18688 [11:27:54<10:08:52, 4.10s/it] 52%|█████▏ | 9784/18688 [11:27:58<10:39:52, 4.31s/it] 52%|█████▏ | 9785/18688 [11:28:03<10:48:49, 4.37s/it] 52%|█████▏ | 9786/18688 [11:28:07<10:43:38, 4.34s/it] 52%|█████▏ | 9787/18688 [11:28:11<10:32:36, 4.26s/it] 52%|█████▏ | 9788/18688 [11:28:15<10:06:09, 4.09s/it] 52%|█████▏ | 9789/18688 [11:28:19<10:05:17, 4.08s/it] 52%|█████▏ | 9790/18688 [11:28:23<9:44:15, 3.94s/it] {'loss': '0.8944', 'grad_norm': '1.171', 'learning_rate': '5.279e-05', 'epoch': '0.5239', 'num_input_tokens_seen': 32928536, 'train_runtime': '4.13e+04', 'train_tokens_per_second': '797.2'}
52%|█████▏ | 9790/18688 [11:28:23<9:44:15, 3.94s/it] 52%|█████▏ | 9791/18688 [11:28:27<10:11:49, 4.13s/it] 52%|█████▏ | 9792/18688 [11:28:31<10:02:07, 4.06s/it] 52%|█████▏ | 9793/18688 [11:28:36<10:22:37, 4.20s/it] 52%|█████▏ | 9794/18688 [11:28:40<10:17:46, 4.17s/it] 52%|█████▏ | 9795/18688 [11:28:44<10:43:21, 4.34s/it] 52%|█████▏ | 9796/18688 [11:28:48<10:22:28, 4.20s/it] 52%|█████▏ | 9797/18688 [11:28:52<9:49:03, 3.98s/it] 52%|█████▏ | 9798/18688 [11:28:56<10:02:17, 4.06s/it] 52%|█████▏ | 9799/18688 [11:29:00<9:51:31, 3.99s/it] 52%|█████▏ | 9800/18688 [11:29:04<9:52:09, 4.00s/it] {'loss': '0.9132', 'grad_norm': '1.716', 'learning_rate': '5.269e-05', 'epoch': '0.5244', 'num_input_tokens_seen': 32960776, 'train_runtime': '4.134e+04', 'train_tokens_per_second': '797.2'}
52%|█████▏ | 9800/18688 [11:29:04<9:52:09, 4.00s/it] 52%|█████▏ | 9801/18688 [11:29:08<10:11:24, 4.13s/it] 52%|█████▏ | 9802/18688 [11:29:12<10:00:46, 4.06s/it] 52%|█████▏ | 9803/18688 [11:29:17<10:30:23, 4.26s/it] 52%|█████▏ | 9804/18688 [11:29:21<10:03:26, 4.08s/it] 52%|█████▏ | 9805/18688 [11:29:25<10:21:33, 4.20s/it] 52%|█████▏ | 9806/18688 [11:29:30<10:42:57, 4.34s/it] 52%|█████▏ | 9807/18688 [11:29:34<10:40:28, 4.33s/it] 52%|█████▏ | 9808/18688 [11:29:39<11:03:31, 4.48s/it] 52%|█████▏ | 9809/18688 [11:29:44<11:11:45, 4.54s/it] 52%|█████▏ | 9810/18688 [11:29:48<10:47:17, 4.37s/it] {'loss': '0.9474', 'grad_norm': '1.559', 'learning_rate': '5.26e-05', 'epoch': '0.5249', 'num_input_tokens_seen': 32996760, 'train_runtime': '4.139e+04', 'train_tokens_per_second': '797.3'}
52%|█████▏ | 9810/18688 [11:29:48<10:47:17, 4.37s/it] 52%|█████▏ | 9811/18688 [11:29:51<10:11:21, 4.13s/it] 53%|█████▎ | 9812/18688 [11:29:55<10:10:33, 4.13s/it] 53%|█████▎ | 9813/18688 [11:29:59<9:52:37, 4.01s/it] 53%|█████▎ | 9814/18688 [11:30:03<9:40:38, 3.93s/it] 53%|█████▎ | 9815/18688 [11:30:08<10:24:03, 4.22s/it] 53%|█████▎ | 9816/18688 [11:30:12<10:20:35, 4.20s/it] 53%|█████▎ | 9817/18688 [11:30:17<10:58:14, 4.45s/it] 53%|█████▎ | 9818/18688 [11:30:20<10:20:16, 4.20s/it] 53%|█████▎ | 9819/18688 [11:30:26<11:13:28, 4.56s/it] 53%|█████▎ | 9820/18688 [11:30:30<10:54:21, 4.43s/it] {'loss': '0.8651', 'grad_norm': '1.346', 'learning_rate': '5.251e-05', 'epoch': '0.5255', 'num_input_tokens_seen': 33030624, 'train_runtime': '4.143e+04', 'train_tokens_per_second': '797.3'}
53%|█████▎ | 9820/18688 [11:30:30<10:54:21, 4.43s/it] 53%|█████▎ | 9821/18688 [11:30:34<10:53:16, 4.42s/it] 53%|█████▎ | 9822/18688 [11:30:38<10:26:56, 4.24s/it] 53%|█████▎ | 9823/18688 [11:30:42<9:58:17, 4.05s/it] 53%|█████▎ | 9824/18688 [11:30:46<10:08:38, 4.12s/it] 53%|█████▎ | 9825/18688 [11:30:50<10:15:23, 4.17s/it] 53%|█████▎ | 9826/18688 [11:30:54<10:02:11, 4.08s/it] 53%|█████▎ | 9827/18688 [11:30:59<10:56:00, 4.44s/it] 53%|█████▎ | 9828/18688 [11:31:03<10:24:21, 4.23s/it] 53%|█████▎ | 9829/18688 [11:31:07<10:04:42, 4.10s/it] 53%|█████▎ | 9830/18688 [11:31:11<9:39:56, 3.93s/it] {'loss': '0.9535', 'grad_norm': '1.264', 'learning_rate': '5.242e-05', 'epoch': '0.526', 'num_input_tokens_seen': 33062328, 'train_runtime': '4.147e+04', 'train_tokens_per_second': '797.2'}
53%|█████▎ | 9830/18688 [11:31:11<9:39:56, 3.93s/it] 53%|█████▎ | 9831/18688 [11:31:14<9:41:01, 3.94s/it] 53%|█████▎ | 9832/18688 [11:31:19<10:05:15, 4.10s/it] 53%|█████▎ | 9833/18688 [11:31:23<10:01:18, 4.07s/it] 53%|█████▎ | 9834/18688 [11:31:28<10:37:49, 4.32s/it] 53%|█████▎ | 9835/18688 [11:31:32<10:12:40, 4.15s/it] 53%|█████▎ | 9836/18688 [11:31:36<10:09:15, 4.13s/it] 53%|█████▎ | 9837/18688 [11:31:40<10:09:50, 4.13s/it] 53%|█████▎ | 9838/18688 [11:31:44<9:58:37, 4.06s/it] 53%|█████▎ | 9839/18688 [11:31:48<10:05:25, 4.11s/it] 53%|█████▎ | 9840/18688 [11:31:51<9:39:04, 3.93s/it] {'loss': '0.9303', 'grad_norm': '1.265', 'learning_rate': '5.233e-05', 'epoch': '0.5266', 'num_input_tokens_seen': 33094656, 'train_runtime': '4.151e+04', 'train_tokens_per_second': '797.2'}
53%|█████▎ | 9840/18688 [11:31:51<9:39:04, 3.93s/it] 53%|█████▎ | 9841/18688 [11:31:56<9:48:09, 3.99s/it] 53%|█████▎ | 9842/18688 [11:32:00<9:45:51, 3.97s/it] 53%|█████▎ | 9843/18688 [11:32:06<11:30:18, 4.68s/it] 53%|█████▎ | 9844/18688 [11:32:09<10:39:59, 4.34s/it] 53%|█████▎ | 9845/18688 [11:32:14<10:34:05, 4.30s/it] 53%|█████▎ | 9846/18688 [11:32:19<11:07:10, 4.53s/it] 53%|█████▎ | 9847/18688 [11:32:23<10:47:39, 4.40s/it] 53%|█████▎ | 9848/18688 [11:32:26<10:06:51, 4.12s/it] 53%|█████▎ | 9849/18688 [11:32:31<10:18:18, 4.20s/it] 53%|█████▎ | 9850/18688 [11:32:35<10:33:49, 4.30s/it] {'loss': '0.9463', 'grad_norm': '1.369', 'learning_rate': '5.224e-05', 'epoch': '0.5271', 'num_input_tokens_seen': 33131424, 'train_runtime': '4.156e+04', 'train_tokens_per_second': '797.3'}
53%|█████▎ | 9850/18688 [11:32:35<10:33:49, 4.30s/it] 53%|█████▎ | 9851/18688 [11:32:39<10:28:25, 4.27s/it] 53%|█████▎ | 9852/18688 [11:32:43<10:23:41, 4.24s/it] 53%|█████▎ | 9853/18688 [11:32:48<10:17:23, 4.19s/it] 53%|█████▎ | 9854/18688 [11:32:51<9:58:55, 4.07s/it] 53%|█████▎ | 9855/18688 [11:32:55<9:39:56, 3.94s/it] 53%|█████▎ | 9856/18688 [11:32:59<10:00:46, 4.08s/it] 53%|█████▎ | 9857/18688 [11:33:04<10:19:38, 4.21s/it] 53%|█████▎ | 9858/18688 [11:33:08<10:04:11, 4.11s/it] 53%|█████▎ | 9859/18688 [11:33:12<10:11:55, 4.16s/it] 53%|█████▎ | 9860/18688 [11:33:17<10:24:51, 4.25s/it] {'loss': '0.9589', 'grad_norm': '1.491', 'learning_rate': '5.215e-05', 'epoch': '0.5276', 'num_input_tokens_seen': 33164080, 'train_runtime': '4.16e+04', 'train_tokens_per_second': '797.3'}
53%|█████▎ | 9860/18688 [11:33:17<10:24:51, 4.25s/it] 53%|█████▎ | 9861/18688 [11:33:20<9:52:43, 4.03s/it] 53%|█████▎ | 9862/18688 [11:33:24<10:04:21, 4.11s/it] 53%|█████▎ | 9863/18688 [11:33:29<10:21:22, 4.22s/it] 53%|█████▎ | 9864/18688 [11:33:33<10:18:24, 4.20s/it] 53%|█████▎ | 9865/18688 [11:33:37<10:19:37, 4.21s/it] 53%|█████▎ | 9866/18688 [11:33:42<10:36:26, 4.33s/it] 53%|█████▎ | 9867/18688 [11:33:47<11:22:03, 4.64s/it] 53%|█████▎ | 9868/18688 [11:33:51<10:46:11, 4.40s/it] 53%|█████▎ | 9869/18688 [11:33:56<11:25:18, 4.66s/it] 53%|█████▎ | 9870/18688 [11:34:00<10:39:54, 4.35s/it] {'loss': '0.8814', 'grad_norm': '1.357', 'learning_rate': '5.206e-05', 'epoch': '0.5282', 'num_input_tokens_seen': 33200528, 'train_runtime': '4.164e+04', 'train_tokens_per_second': '797.3'}
53%|█████▎ | 9870/18688 [11:34:00<10:39:54, 4.35s/it] 53%|█████▎ | 9871/18688 [11:34:04<10:45:07, 4.39s/it] 53%|█████▎ | 9872/18688 [11:34:08<10:09:17, 4.15s/it] 53%|█████▎ | 9873/18688 [11:34:12<10:15:05, 4.19s/it] 53%|█████▎ | 9874/18688 [11:34:16<9:44:30, 3.98s/it] 53%|█████▎ | 9875/18688 [11:34:20<10:02:45, 4.10s/it] 53%|█████▎ | 9876/18688 [11:34:24<9:42:44, 3.97s/it] 53%|█████▎ | 9877/18688 [11:34:27<9:29:37, 3.88s/it] 53%|█████▎ | 9878/18688 [11:34:32<9:45:16, 3.99s/it] 53%|█████▎ | 9879/18688 [11:34:35<9:20:59, 3.82s/it] 53%|█████▎ | 9880/18688 [11:34:40<10:05:10, 4.12s/it] {'loss': '0.8996', 'grad_norm': '1.451', 'learning_rate': '5.196e-05', 'epoch': '0.5287', 'num_input_tokens_seen': 33231392, 'train_runtime': '4.168e+04', 'train_tokens_per_second': '797.3'}
53%|█████▎ | 9880/18688 [11:34:40<10:05:10, 4.12s/it] 53%|█████▎ | 9881/18688 [11:34:44<9:43:39, 3.98s/it] 53%|█████▎ | 9882/18688 [11:34:49<10:24:56, 4.26s/it] 53%|█████▎ | 9883/18688 [11:34:53<10:25:38, 4.26s/it] 53%|█████▎ | 9884/18688 [11:34:57<10:06:20, 4.13s/it] 53%|█████▎ | 9885/18688 [11:35:01<10:11:48, 4.17s/it] 53%|█████▎ | 9886/18688 [11:35:06<10:52:57, 4.45s/it] 53%|█████▎ | 9887/18688 [11:35:10<10:41:45, 4.38s/it] 53%|█████▎ | 9888/18688 [11:35:15<11:20:27, 4.64s/it] 53%|█████▎ | 9889/18688 [11:35:19<10:46:04, 4.41s/it] 53%|█████▎ | 9890/18688 [11:35:24<10:42:11, 4.38s/it] {'loss': '0.9084', 'grad_norm': '1.172', 'learning_rate': '5.187e-05', 'epoch': '0.5292', 'num_input_tokens_seen': 33267288, 'train_runtime': '4.172e+04', 'train_tokens_per_second': '797.3'}
53%|█████▎ | 9890/18688 [11:35:24<10:42:11, 4.38s/it] 53%|█████▎ | 9891/18688 [11:35:29<11:10:57, 4.58s/it] 53%|█████▎ | 9892/18688 [11:35:34<11:39:07, 4.77s/it] 53%|█████▎ | 9893/18688 [11:35:38<11:12:08, 4.59s/it] 53%|█████▎ | 9894/18688 [11:35:42<10:44:23, 4.40s/it] 53%|█████▎ | 9895/18688 [11:35:46<10:33:49, 4.33s/it] 53%|█████▎ | 9896/18688 [11:35:50<10:22:28, 4.25s/it] 53%|█████▎ | 9897/18688 [11:35:55<10:46:41, 4.41s/it] 53%|█████▎ | 9898/18688 [11:35:59<10:41:23, 4.38s/it] 53%|█████▎ | 9899/18688 [11:36:03<10:20:22, 4.24s/it] 53%|█████▎ | 9900/18688 [11:36:08<10:32:18, 4.32s/it] {'loss': '0.9588', 'grad_norm': '1.527', 'learning_rate': '5.178e-05', 'epoch': '0.5298', 'num_input_tokens_seen': 33301776, 'train_runtime': '4.177e+04', 'train_tokens_per_second': '797.3'}
53%|█████▎ | 9900/18688 [11:36:08<10:32:18, 4.32s/it] 53%|█████▎ | 9901/18688 [11:36:12<10:21:36, 4.24s/it] 53%|█████▎ | 9902/18688 [11:36:16<10:37:29, 4.35s/it] 53%|█████▎ | 9903/18688 [11:36:20<10:18:11, 4.22s/it] 53%|█████▎ | 9904/18688 [11:36:24<9:59:02, 4.09s/it] 53%|█████▎ | 9905/18688 [11:36:29<10:16:40, 4.21s/it] 53%|█████▎ | 9906/18688 [11:36:33<10:20:24, 4.24s/it] 53%|█████▎ | 9907/18688 [11:36:37<10:30:12, 4.31s/it] 53%|█████▎ | 9908/18688 [11:36:42<10:46:54, 4.42s/it] 53%|█████▎ | 9909/18688 [11:36:46<10:30:08, 4.31s/it] 53%|█████▎ | 9910/18688 [11:36:52<11:37:41, 4.77s/it] {'loss': '0.9152', 'grad_norm': '1.172', 'learning_rate': '5.169e-05', 'epoch': '0.5303', 'num_input_tokens_seen': 33337120, 'train_runtime': '4.181e+04', 'train_tokens_per_second': '797.3'}
53%|█████▎ | 9910/18688 [11:36:52<11:37:41, 4.77s/it] 53%|█████▎ | 9911/18688 [11:36:56<11:07:45, 4.56s/it] 53%|█████▎ | 9912/18688 [11:37:01<11:10:39, 4.59s/it] 53%|█████▎ | 9913/18688 [11:37:04<10:24:29, 4.27s/it] 53%|█████▎ | 9914/18688 [11:37:09<10:48:46, 4.44s/it] 53%|█████▎ | 9915/18688 [11:37:14<10:57:25, 4.50s/it] 53%|█████▎ | 9916/18688 [11:37:18<11:08:59, 4.58s/it] 53%|█████▎ | 9917/18688 [11:37:23<10:49:10, 4.44s/it] 53%|█████▎ | 9918/18688 [11:37:27<10:36:16, 4.35s/it] 53%|█████▎ | 9919/18688 [11:37:31<10:27:39, 4.29s/it] 53%|█████▎ | 9920/18688 [11:37:35<10:20:20, 4.25s/it] {'loss': '0.8877', 'grad_norm': '1.39', 'learning_rate': '5.16e-05', 'epoch': '0.5308', 'num_input_tokens_seen': 33371000, 'train_runtime': '4.186e+04', 'train_tokens_per_second': '797.3'}
53%|█████▎ | 9920/18688 [11:37:35<10:20:20, 4.25s/it] 53%|█████▎ | 9921/18688 [11:37:40<10:44:28, 4.41s/it] 53%|█████▎ | 9922/18688 [11:37:44<10:43:00, 4.40s/it] 53%|█████▎ | 9923/18688 [11:37:48<10:11:55, 4.19s/it] 53%|█████▎ | 9924/18688 [11:37:52<10:20:45, 4.25s/it] 53%|█████▎ | 9925/18688 [11:37:57<10:41:34, 4.39s/it] 53%|█████▎ | 9926/18688 [11:38:01<10:26:00, 4.29s/it] 53%|█████▎ | 9927/18688 [11:38:05<10:02:45, 4.13s/it] 53%|█████▎ | 9928/18688 [11:38:08<9:45:01, 4.01s/it] 53%|█████▎ | 9929/18688 [11:38:12<9:43:14, 4.00s/it] 53%|█████▎ | 9930/18688 [11:38:16<9:37:10, 3.95s/it] {'loss': '0.9318', 'grad_norm': '1.205', 'learning_rate': '5.151e-05', 'epoch': '0.5314', 'num_input_tokens_seen': 33404656, 'train_runtime': '4.19e+04', 'train_tokens_per_second': '797.3'}
53%|█████▎ | 9930/18688 [11:38:16<9:37:10, 3.95s/it] 53%|█████▎ | 9931/18688 [11:38:20<9:39:48, 3.97s/it] 53%|█████▎ | 9932/18688 [11:38:24<9:35:38, 3.94s/it] 53%|█████▎ | 9933/18688 [11:38:28<9:31:09, 3.91s/it] 53%|█████▎ | 9934/18688 [11:38:33<9:58:49, 4.10s/it] 53%|█████▎ | 9935/18688 [11:38:38<10:55:56, 4.50s/it] 53%|█████▎ | 9936/18688 [11:38:42<10:31:27, 4.33s/it] 53%|█████▎ | 9937/18688 [11:38:47<11:08:38, 4.58s/it] 53%|█████▎ | 9938/18688 [11:38:51<10:31:15, 4.33s/it] 53%|█████▎ | 9939/18688 [11:38:55<10:18:37, 4.24s/it] 53%|█████▎ | 9940/18688 [11:38:59<10:26:59, 4.30s/it] {'loss': '0.8959', 'grad_norm': '1.527', 'learning_rate': '5.142e-05', 'epoch': '0.5319', 'num_input_tokens_seen': 33439232, 'train_runtime': '4.194e+04', 'train_tokens_per_second': '797.3'}
53%|█████▎ | 9940/18688 [11:38:59<10:26:59, 4.30s/it] 53%|█████▎ | 9941/18688 [11:39:03<9:52:48, 4.07s/it] 53%|█████▎ | 9942/18688 [11:39:07<10:10:24, 4.19s/it] 53%|█████▎ | 9943/18688 [11:39:11<9:58:20, 4.11s/it] 53%|█████▎ | 9944/18688 [11:39:15<9:38:52, 3.97s/it] 53%|█████▎ | 9945/18688 [11:39:18<9:19:01, 3.84s/it] 53%|█████▎ | 9946/18688 [11:39:23<9:31:28, 3.92s/it] 53%|█████▎ | 9947/18688 [11:39:27<9:42:50, 4.00s/it] 53%|█████▎ | 9948/18688 [11:39:32<10:19:34, 4.25s/it] 53%|█████▎ | 9949/18688 [11:39:36<10:35:50, 4.37s/it] 53%|█████▎ | 9950/18688 [11:39:40<10:03:17, 4.14s/it] {'loss': '0.8843', 'grad_norm': '1.46', 'learning_rate': '5.132e-05', 'epoch': '0.5324', 'num_input_tokens_seen': 33470712, 'train_runtime': '4.198e+04', 'train_tokens_per_second': '797.3'}
53%|█████▎ | 9950/18688 [11:39:40<10:03:17, 4.14s/it] 53%|█████▎ | 9951/18688 [11:39:44<9:45:19, 4.02s/it] 53%|█████▎ | 9952/18688 [11:39:48<9:57:11, 4.10s/it] 53%|█████▎ | 9953/18688 [11:39:52<9:44:42, 4.02s/it] 53%|█████▎ | 9954/18688 [11:39:56<9:44:03, 4.01s/it] 53%|█████▎ | 9955/18688 [11:40:00<10:05:48, 4.16s/it] 53%|█████▎ | 9956/18688 [11:40:04<9:41:01, 3.99s/it] 53%|█████▎ | 9957/18688 [11:40:07<9:26:57, 3.90s/it] 53%|█████▎ | 9958/18688 [11:40:12<10:07:15, 4.17s/it] 53%|█████▎ | 9959/18688 [11:40:17<10:09:41, 4.19s/it] 53%|█████▎ | 9960/18688 [11:40:21<10:12:58, 4.21s/it] {'loss': '0.9601', 'grad_norm': '1.547', 'learning_rate': '5.123e-05', 'epoch': '0.533', 'num_input_tokens_seen': 33504040, 'train_runtime': '4.202e+04', 'train_tokens_per_second': '797.3'}
53%|█████▎ | 9960/18688 [11:40:21<10:12:58, 4.21s/it] 53%|█████▎ | 9961/18688 [11:40:24<9:50:29, 4.06s/it] 53%|█████▎ | 9962/18688 [11:40:28<9:39:55, 3.99s/it] 53%|█████▎ | 9963/18688 [11:40:32<9:18:35, 3.84s/it] 53%|█████▎ | 9964/18688 [11:40:36<9:13:48, 3.81s/it] 53%|█████▎ | 9965/18688 [11:40:40<9:22:36, 3.87s/it] 53%|█████▎ | 9966/18688 [11:40:44<10:09:21, 4.19s/it] 53%|█████▎ | 9967/18688 [11:40:49<10:23:12, 4.29s/it] 53%|█████▎ | 9968/18688 [11:40:53<10:06:37, 4.17s/it] 53%|█████▎ | 9969/18688 [11:40:57<10:14:34, 4.23s/it] 53%|█████▎ | 9970/18688 [11:41:02<10:25:46, 4.31s/it] {'loss': '0.9996', 'grad_norm': '1.148', 'learning_rate': '5.114e-05', 'epoch': '0.5335', 'num_input_tokens_seen': 33536184, 'train_runtime': '4.206e+04', 'train_tokens_per_second': '797.3'}
53%|█████▎ | 9970/18688 [11:41:02<10:25:46, 4.31s/it] 53%|█████▎ | 9971/18688 [11:41:06<10:14:32, 4.23s/it] 53%|█████▎ | 9972/18688 [11:41:10<10:20:17, 4.27s/it] 53%|█████▎ | 9973/18688 [11:41:14<10:15:28, 4.24s/it] 53%|█████▎ | 9974/18688 [11:41:19<10:29:26, 4.33s/it] 53%|█████▎ | 9975/18688 [11:41:24<10:44:03, 4.44s/it] 53%|█████▎ | 9976/18688 [11:41:28<10:54:21, 4.51s/it] 53%|█████▎ | 9977/18688 [11:41:32<10:18:44, 4.26s/it] 53%|█████▎ | 9978/18688 [11:41:36<9:55:45, 4.10s/it] 53%|█████▎ | 9979/18688 [11:41:40<10:22:33, 4.29s/it] 53%|█████▎ | 9980/18688 [11:41:45<10:52:36, 4.50s/it] {'loss': '0.8899', 'grad_norm': '1.303', 'learning_rate': '5.105e-05', 'epoch': '0.534', 'num_input_tokens_seen': 33573168, 'train_runtime': '4.211e+04', 'train_tokens_per_second': '797.4'}
53%|█████▎ | 9980/18688 [11:41:45<10:52:36, 4.50s/it] 53%|█████▎ | 9981/18688 [11:41:49<10:34:23, 4.37s/it] 53%|█████▎ | 9982/18688 [11:41:53<9:55:24, 4.10s/it] 53%|█████▎ | 9983/18688 [11:41:57<9:37:37, 3.98s/it] 53%|█████▎ | 9984/18688 [11:42:01<10:12:09, 4.22s/it] 53%|█████▎ | 9985/18688 [11:42:06<10:43:32, 4.44s/it] 53%|█████▎ | 9986/18688 [11:42:10<10:05:28, 4.17s/it] 53%|█████▎ | 9987/18688 [11:42:14<9:57:36, 4.12s/it] 53%|█████▎ | 9988/18688 [11:42:19<10:34:43, 4.38s/it] 53%|█████▎ | 9989/18688 [11:42:23<10:18:33, 4.27s/it] 53%|█████▎ | 9990/18688 [11:42:27<10:20:44, 4.28s/it] {'loss': '0.8952', 'grad_norm': '1.365', 'learning_rate': '5.096e-05', 'epoch': '0.5346', 'num_input_tokens_seen': 33608320, 'train_runtime': '4.215e+04', 'train_tokens_per_second': '797.4'}
53%|█████▎ | 9990/18688 [11:42:27<10:20:44, 4.28s/it] 53%|█████▎ | 9991/18688 [11:42:32<10:29:32, 4.34s/it] 53%|█████▎ | 9992/18688 [11:42:36<10:19:20, 4.27s/it] 53%|█████▎ | 9993/18688 [11:42:40<10:12:35, 4.23s/it] 53%|█████▎ | 9994/18688 [11:42:44<9:51:24, 4.08s/it] 53%|█████▎ | 9995/18688 [11:42:48<10:19:32, 4.28s/it] 53%|█████▎ | 9996/18688 [11:42:53<10:52:56, 4.51s/it] 53%|█████▎ | 9997/18688 [11:42:57<10:33:44, 4.38s/it] 53%|█████▎ | 9998/18688 [11:43:02<10:19:12, 4.28s/it] 54%|█████▎ | 9999/18688 [11:43:08<11:58:25, 4.96s/it] 54%|█████▎ | 10000/18688 [11:43:12<11:19:13, 4.69s/it] {'loss': '1.009', 'grad_norm': '1.702', 'learning_rate': '5.087e-05', 'epoch': '0.5351', 'num_input_tokens_seen': 33646792, 'train_runtime': '4.219e+04', 'train_tokens_per_second': '797.5'}
54%|█████▎ | 10000/18688 [11:43:12<11:19:13, 4.69s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
0%| | 3/1000 [00:00<00:38, 25.86it/s]
1%| | 6/1000 [00:00<00:54, 18.28it/s]
1%| | 8/1000 [00:00<00:58, 16.90it/s]
1%| | 10/1000 [00:00<01:02, 15.88it/s]
1%| | 12/1000 [00:00<01:03, 15.46it/s]
1%|▏ | 14/1000 [00:00<01:07, 14.67it/s]
2%|▏ | 16/1000 [00:01<01:06, 14.76it/s]
2%|▏ | 18/1000 [00:01<01:41, 9.67it/s]
2%|▏ | 20/1000 [00:01<01:38, 9.97it/s]
2%|▏ | 22/1000 [00:01<01:55, 8.46it/s]
2%|▏ | 24/1000 [00:02<01:42, 9.55it/s]
3%|▎ | 26/1000 [00:02<01:46, 9.13it/s]
3%|▎ | 28/1000 [00:02<01:37, 9.97it/s]
3%|▎ | 30/1000 [00:02<01:29, 10.88it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.10it/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, 11.32it/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, 10.48it/s]
5%|▍ | 46/1000 [00:04<01:21, 11.66it/s]
5%|▍ | 48/1000 [00:04<01:29, 10.63it/s]
5%|▌ | 50/1000 [00:04<01:21, 11.66it/s]
5%|▌ | 52/1000 [00:04<01:16, 12.46it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.15it/s]
6%|▌ | 56/1000 [00:04<01:08, 13.77it/s]
6%|▌ | 58/1000 [00:05<01:48, 8.69it/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<01:23, 11.25it/s]
7%|▋ | 66/1000 [00:05<01:20, 11.54it/s]
7%|▋ | 68/1000 [00:06<01:19, 11.74it/s]
7%|▋ | 70/1000 [00:06<01:18, 11.84it/s]
7%|▋ | 72/1000 [00:06<01:17, 12.01it/s]
7%|▋ | 74/1000 [00:06<01:17, 12.02it/s]
8%|▊ | 76/1000 [00:06<01:12, 12.72it/s]
8%|▊ | 78/1000 [00:06<01:09, 13.29it/s]
8%|▊ | 80/1000 [00:06<01:12, 12.65it/s]
8%|▊ | 82/1000 [00:07<01:08, 13.44it/s]
8%|▊ | 84/1000 [00:07<01:05, 14.07it/s]
9%|▊ | 86/1000 [00:07<01:03, 14.36it/s]
9%|▉ | 88/1000 [00:07<01:03, 14.28it/s]
9%|▉ | 90/1000 [00:07<01:03, 14.36it/s]
9%|▉ | 92/1000 [00:07<01:04, 14.09it/s]
9%|▉ | 94/1000 [00:07<01:04, 13.94it/s]
10%|▉ | 96/1000 [00:08<01:03, 14.27it/s]
10%|▉ | 98/1000 [00:08<01:03, 14.21it/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, 7.81it/s]
11%|█ | 106/1000 [00:09<02:02, 7.33it/s]
11%|█ | 108/1000 [00:09<01:42, 8.67it/s]
11%|█ | 110/1000 [00:09<01:28, 10.11it/s]
11%|█ | 112/1000 [00:09<01:19, 11.22it/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, 13.30it/s]
12%|█▏ | 120/1000 [00:10<01:09, 12.74it/s]
12%|█▏ | 122/1000 [00:10<01:22, 10.60it/s]
12%|█▏ | 124/1000 [00:10<01:30, 9.66it/s]
13%|█▎ | 126/1000 [00:11<01:22, 10.63it/s]
13%|█▎ | 128/1000 [00:11<01:15, 11.55it/s]
13%|█▎ | 130/1000 [00:11<01:11, 12.21it/s]
13%|█▎ | 132/1000 [00:11<01:05, 13.19it/s]
13%|█▎ | 134/1000 [00:11<01:09, 12.40it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.34it/s]
14%|█▍ | 138/1000 [00:11<01:05, 13.25it/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, 10.60it/s]
15%|█▍ | 148/1000 [00:12<01:14, 11.51it/s]
15%|█▌ | 150/1000 [00:13<01:07, 12.59it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.11it/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, 13.77it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.69it/s]
16%|█▌ | 162/1000 [00:13<01:01, 13.57it/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<00:59, 13.97it/s]
17%|█▋ | 170/1000 [00:14<01:23, 9.89it/s]
17%|█▋ | 172/1000 [00:14<01:20, 10.22it/s]
17%|█▋ | 174/1000 [00:15<01:35, 8.61it/s]
18%|█▊ | 175/1000 [00:15<02:11, 6.28it/s]
18%|█▊ | 177/1000 [00:15<01:44, 7.91it/s]
18%|█▊ | 179/1000 [00:15<01:40, 8.16it/s]
18%|█▊ | 181/1000 [00:16<01:43, 7.89it/s]
18%|█▊ | 183/1000 [00:16<01:27, 9.29it/s]
18%|█▊ | 185/1000 [00:16<01:39, 8.20it/s]
19%|█▊ | 187/1000 [00:16<01:27, 9.29it/s]
19%|█▉ | 189/1000 [00:16<01:17, 10.49it/s]
19%|█▉ | 191/1000 [00:17<01:10, 11.44it/s]
19%|█▉ | 193/1000 [00:17<01:17, 10.39it/s]
20%|█▉ | 195/1000 [00:17<01:09, 11.64it/s]
20%|█▉ | 197/1000 [00:17<01:24, 9.47it/s]
20%|█▉ | 199/1000 [00:17<01:19, 10.10it/s]
20%|██ | 201/1000 [00:18<01:14, 10.77it/s]
20%|██ | 203/1000 [00:18<01:06, 11.98it/s]
20%|██ | 205/1000 [00:18<01:01, 12.89it/s]
21%|██ | 207/1000 [00:18<00:59, 13.33it/s]
21%|██ | 209/1000 [00:18<00:56, 14.07it/s]
21%|██ | 211/1000 [00:18<00:53, 14.79it/s]
21%|██▏ | 213/1000 [00:18<00:52, 14.94it/s]
22%|██▏ | 215/1000 [00:18<00:55, 14.27it/s]
22%|██▏ | 217/1000 [00:19<00:52, 14.86it/s]
22%|██▏ | 219/1000 [00:19<01:27, 8.88it/s]
22%|██▏ | 221/1000 [00:19<01:23, 9.34it/s]
22%|██▏ | 223/1000 [00:19<01:20, 9.63it/s]
22%|██▎ | 225/1000 [00:20<01:13, 10.54it/s]
23%|██▎ | 227/1000 [00:20<01:06, 11.64it/s]
23%|██▎ | 229/1000 [00:20<01:03, 12.10it/s]
23%|██▎ | 231/1000 [00:20<01:01, 12.56it/s]
23%|██▎ | 233/1000 [00:20<00:59, 12.85it/s]
24%|██▎ | 235/1000 [00:20<00:57, 13.38it/s]
24%|██▎ | 237/1000 [00:20<00:53, 14.36it/s]
24%|██▍ | 239/1000 [00:21<00:53, 14.21it/s]
24%|██▍ | 241/1000 [00:21<00:52, 14.57it/s]
24%|██▍ | 243/1000 [00:21<00:51, 14.81it/s]
24%|██▍ | 245/1000 [00:21<00:51, 14.79it/s]
25%|██▍ | 247/1000 [00:21<00:49, 15.18it/s]
25%|██▍ | 249/1000 [00:21<00:57, 12.98it/s]
25%|██▌ | 251/1000 [00:21<01:02, 11.95it/s]
25%|██▌ | 253/1000 [00:22<00:58, 12.78it/s]
26%|██▌ | 255/1000 [00:22<00:54, 13.74it/s]
26%|██▌ | 257/1000 [00:22<00:53, 13.92it/s]
26%|██▌ | 259/1000 [00:22<00:52, 14.15it/s]
26%|██▌ | 261/1000 [00:22<00:58, 12.64it/s]
26%|██▋ | 263/1000 [00:22<00:55, 13.35it/s]
26%|██▋ | 265/1000 [00:22<00:53, 13.78it/s]
27%|██▋ | 267/1000 [00:23<00:52, 13.94it/s]
27%|██▋ | 269/1000 [00:23<00:49, 14.64it/s]
27%|██▋ | 271/1000 [00:23<00:55, 13.18it/s]
27%|██▋ | 273/1000 [00:23<01:03, 11.46it/s]
28%|██▊ | 275/1000 [00:23<01:01, 11.74it/s]
28%|██▊ | 277/1000 [00:23<00:57, 12.65it/s]
28%|██▊ | 279/1000 [00:24<00:56, 12.84it/s]
28%|██▊ | 281/1000 [00:24<00:51, 13.93it/s]
28%|██▊ | 283/1000 [00:24<00:50, 14.20it/s]
28%|██▊ | 285/1000 [00:24<00:49, 14.55it/s]
29%|██▊ | 287/1000 [00:24<00:48, 14.77it/s]
29%|██▉ | 289/1000 [00:24<00:47, 14.90it/s]
29%|██▉ | 291/1000 [00:24<00:46, 15.14it/s]
29%|██▉ | 293/1000 [00:24<00:46, 15.15it/s]
30%|██▉ | 295/1000 [00:25<00:45, 15.34it/s]
30%|██▉ | 297/1000 [00:25<00:45, 15.48it/s]
30%|██▉ | 299/1000 [00:25<00:46, 15.23it/s]
30%|███ | 301/1000 [00:25<00:46, 14.94it/s]
30%|███ | 303/1000 [00:25<00:51, 13.60it/s]
30%|███ | 305/1000 [00:25<00:52, 13.19it/s]
31%|███ | 307/1000 [00:25<00:53, 13.02it/s]
31%|███ | 309/1000 [00:26<00:54, 12.77it/s]
31%|███ | 311/1000 [00:26<00:54, 12.54it/s]
31%|███▏ | 313/1000 [00:26<00:54, 12.53it/s]
32%|███▏ | 315/1000 [00:26<00:54, 12.55it/s]
32%|███▏ | 317/1000 [00:26<00:54, 12.56it/s]
32%|███▏ | 319/1000 [00:26<00:54, 12.54it/s]
32%|███▏ | 321/1000 [00:27<01:22, 8.26it/s]
32%|███▏ | 323/1000 [00:27<01:13, 9.17it/s]
32%|███▎ | 325/1000 [00:27<01:07, 9.95it/s]
33%|███▎ | 327/1000 [00:27<01:06, 10.16it/s]
33%|███▎ | 329/1000 [00:28<01:06, 10.12it/s]
33%|███▎ | 331/1000 [00:28<01:02, 10.68it/s]
33%|███▎ | 333/1000 [00:28<01:00, 11.03it/s]
34%|███▎ | 335/1000 [00:28<00:56, 11.74it/s]
34%|███▎ | 337/1000 [00:28<01:10, 9.46it/s]
34%|███▍ | 339/1000 [00:29<01:02, 10.63it/s]
34%|███▍ | 341/1000 [00:29<00:56, 11.61it/s]
34%|███▍ | 343/1000 [00:29<00:52, 12.60it/s]
34%|███▍ | 345/1000 [00:29<00:49, 13.14it/s]
35%|███▍ | 347/1000 [00:29<00:46, 14.10it/s]
35%|███▍ | 349/1000 [00:29<00:45, 14.41it/s]
35%|███▌ | 351/1000 [00:29<00:44, 14.57it/s]
35%|███▌ | 353/1000 [00:29<00:42, 15.10it/s]
36%|███▌ | 355/1000 [00:30<00:42, 15.15it/s]
36%|███▌ | 357/1000 [00:30<00:42, 14.97it/s]
36%|███▌ | 359/1000 [00:30<00:43, 14.91it/s]
36%|███▌ | 361/1000 [00:30<00:47, 13.33it/s]
36%|███▋ | 363/1000 [00:30<00:46, 13.61it/s]
36%|███▋ | 365/1000 [00:30<00:44, 14.38it/s]
37%|███▋ | 367/1000 [00:30<00:53, 11.88it/s]
37%|███▋ | 369/1000 [00:31<00:50, 12.53it/s]
37%|███▋ | 371/1000 [00:31<00:48, 13.06it/s]
37%|███▋ | 373/1000 [00:31<00:55, 11.33it/s]
38%|███▊ | 375/1000 [00:31<00:57, 10.81it/s]
38%|███▊ | 377/1000 [00:31<00:53, 11.64it/s]
38%|███▊ | 379/1000 [00:31<00:51, 12.14it/s]
38%|███▊ | 381/1000 [00:32<00:48, 12.88it/s]
38%|███▊ | 383/1000 [00:32<00:46, 13.38it/s]
38%|███▊ | 385/1000 [00:32<00:45, 13.60it/s]
39%|███▊ | 387/1000 [00:32<00:44, 13.77it/s]
39%|███▉ | 389/1000 [00:32<00:44, 13.81it/s]
39%|███▉ | 391/1000 [00:32<00:43, 13.93it/s]
39%|███▉ | 393/1000 [00:32<00:43, 13.81it/s]
40%|███▉ | 395/1000 [00:33<00:44, 13.61it/s]
40%|███▉ | 397/1000 [00:33<00:43, 13.76it/s]
40%|███▉ | 399/1000 [00:33<00:47, 12.60it/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, 14.75it/s]
41%|████ | 407/1000 [00:33<00:38, 15.23it/s]
41%|████ | 409/1000 [00:34<00:57, 10.22it/s]
41%|████ | 411/1000 [00:34<00:52, 11.12it/s]
41%|████▏ | 413/1000 [00:34<01:04, 9.11it/s]
42%|████▏ | 415/1000 [00:34<01:01, 9.57it/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<00:57, 10.00it/s]
42%|████▏ | 423/1000 [00:36<01:17, 7.45it/s]
42%|████▎ | 425/1000 [00:36<01:06, 8.71it/s]
43%|████▎ | 427/1000 [00:36<00:58, 9.75it/s]
43%|████▎ | 429/1000 [00:36<01:02, 9.11it/s]
43%|████▎ | 431/1000 [00:36<00:55, 10.17it/s]
43%|████▎ | 433/1000 [00:37<01:15, 7.48it/s]
44%|████▎ | 435/1000 [00:37<01:05, 8.60it/s]
44%|████▎ | 437/1000 [00:37<01:06, 8.51it/s]
44%|████▍ | 439/1000 [00:37<00:58, 9.56it/s]
44%|████▍ | 441/1000 [00:37<00:56, 9.90it/s]
44%|████▍ | 443/1000 [00:38<00:51, 10.89it/s]
44%|████▍ | 445/1000 [00:38<00:46, 11.91it/s]
45%|████▍ | 447/1000 [00:38<00:51, 10.68it/s]
45%|████▍ | 449/1000 [00:38<00:47, 11.55it/s]
45%|████▌ | 451/1000 [00:38<00:43, 12.48it/s]
45%|████▌ | 453/1000 [00:38<00:41, 13.25it/s]
46%|████▌ | 455/1000 [00:38<00:44, 12.37it/s]
46%|████▌ | 457/1000 [00:39<00:41, 13.20it/s]
46%|████▌ | 459/1000 [00:39<00:39, 13.77it/s]
46%|████▌ | 461/1000 [00:39<00:37, 14.21it/s]
46%|████▋ | 463/1000 [00:39<00:35, 15.01it/s]
46%|████▋ | 465/1000 [00:39<00:36, 14.86it/s]
47%|████▋ | 467/1000 [00:39<00:40, 13.17it/s]
47%|████▋ | 469/1000 [00:39<00:38, 13.63it/s]
47%|████▋ | 471/1000 [00:40<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 [00:41<00:40, 12.77it/s]
48%|████▊ | 483/1000 [00:41<00:37, 13.66it/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, 11.15it/s]
49%|████▉ | 491/1000 [00:41<00:42, 12.05it/s]
49%|████▉ | 493/1000 [00:41<00:38, 13.06it/s]
50%|████▉ | 495/1000 [00:42<00:35, 14.06it/s]
50%|████▉ | 497/1000 [00:42<00:34, 14.68it/s]
50%|████▉ | 499/1000 [00:42<00:33, 14.83it/s]
50%|█████ | 501/1000 [00:42<00:43, 11.43it/s]
50%|█████ | 503/1000 [00:42<00:41, 11.98it/s]
50%|█████ | 505/1000 [00:42<00:39, 12.65it/s]
51%|█████ | 507/1000 [00:43<00:37, 13.13it/s]
51%|█████ | 509/1000 [00:43<00:48, 10.08it/s]
51%|█████ | 511/1000 [00:43<00:43, 11.21it/s]
51%|█████▏ | 513/1000 [00:43<00:41, 11.81it/s]
52%|█████▏ | 515/1000 [00:43<00:37, 12.80it/s]
52%|█████▏ | 517/1000 [00:43<00:36, 13.18it/s]
52%|█████▏ | 519/1000 [00:44<00:36, 13.28it/s]
52%|█████▏ | 521/1000 [00:44<00:34, 14.01it/s]
52%|█████▏ | 523/1000 [00:44<00:33, 14.17it/s]
52%|█████▎ | 525/1000 [00:44<00:33, 14.26it/s]
53%|█████▎ | 527/1000 [00:44<00:39, 12.02it/s]
53%|█████▎ | 529/1000 [00:44<00:37, 12.59it/s]
53%|█████▎ | 531/1000 [00:44<00:34, 13.76it/s]
53%|█████▎ | 533/1000 [00:45<00:33, 14.15it/s]
54%|█████▎ | 535/1000 [00:45<00:32, 14.43it/s]
54%|█████▎ | 537/1000 [00:45<00:32, 14.10it/s]
54%|█████▍ | 539/1000 [00:45<00:31, 14.47it/s]
54%|█████▍ | 541/1000 [00:45<00:31, 14.58it/s]
54%|█████▍ | 543/1000 [00:45<00:30, 14.83it/s]
55%|█████▍ | 545/1000 [00:45<00:31, 14.31it/s]
55%|█████▍ | 547/1000 [00:45<00:32, 14.15it/s]
55%|█████▍ | 549/1000 [00:46<00:31, 14.12it/s]
55%|█████▌ | 551/1000 [00:46<00:32, 13.66it/s]
55%|█████▌ | 553/1000 [00:46<00:32, 13.74it/s]
56%|█████▌ | 555/1000 [00:46<00:32, 13.49it/s]
56%|█████▌ | 557/1000 [00:46<00:32, 13.43it/s]
56%|█████▌ | 559/1000 [00:46<00:32, 13.65it/s]
56%|█████▌ | 561/1000 [00:47<00:31, 14.02it/s]
56%|█████▋ | 563/1000 [00:47<00:41, 10.48it/s]
56%|█████▋ | 565/1000 [00:47<00:38, 11.26it/s]
57%|█████▋ | 567/1000 [00:47<00:35, 12.13it/s]
57%|█████▋ | 569/1000 [00:47<00:34, 12.54it/s]
57%|█████▋ | 571/1000 [00:47<00:34, 12.32it/s]
57%|█████▋ | 573/1000 [00:48<00:32, 13.00it/s]
57%|█████▊ | 575/1000 [00:48<00:31, 13.35it/s]
58%|█████▊ | 577/1000 [00:48<00:31, 13.55it/s]
58%|█████▊ | 579/1000 [00:48<00:35, 11.86it/s]
58%|█████▊ | 581/1000 [00:48<00:32, 13.06it/s]
58%|█████▊ | 583/1000 [00:48<00:30, 13.57it/s]
58%|█████▊ | 585/1000 [00:48<00:30, 13.80it/s]
59%|█████▊ | 587/1000 [00:49<00:28, 14.44it/s]
59%|█████▉ | 589/1000 [00:49<00:27, 15.09it/s]
59%|█████▉ | 591/1000 [00:49<00:28, 14.46it/s]
59%|█████▉ | 593/1000 [00:49<00:30, 13.36it/s]
60%|█████▉ | 595/1000 [00:49<00:29, 13.77it/s]
60%|█████▉ | 597/1000 [00:49<00:29, 13.67it/s]
60%|█████▉ | 599/1000 [00:49<00:27, 14.61it/s]
60%|██████ | 601/1000 [00:50<00:28, 14.22it/s]
60%|██████ | 603/1000 [00:50<00:26, 14.88it/s]
60%|██████ | 605/1000 [00:50<00:26, 14.92it/s]
61%|██████ | 607/1000 [00:50<00:26, 15.01it/s]
61%|██████ | 609/1000 [00:50<00:25, 15.15it/s]
61%|██████ | 611/1000 [00:50<00:25, 15.38it/s]
61%|██████▏ | 613/1000 [00:50<00:26, 14.84it/s]
62%|██████▏ | 615/1000 [00:50<00:26, 14.46it/s]
62%|██████▏ | 617/1000 [00:51<00:26, 14.53it/s]
62%|██████▏ | 619/1000 [00:51<00:32, 11.88it/s]
62%|██████▏ | 621/1000 [00:51<00:39, 9.53it/s]
62%|██████▏ | 623/1000 [00:51<00:35, 10.70it/s]
62%|██████▎ | 625/1000 [00:51<00:31, 11.93it/s]
63%|██████▎ | 627/1000 [00:52<00:29, 12.70it/s]
63%|██████▎ | 629/1000 [00:52<00:27, 13.60it/s]
63%|██████▎ | 631/1000 [00:52<00:29, 12.60it/s]
63%|██████▎ | 633/1000 [00:52<00:28, 13.01it/s]
64%|██████▎ | 635/1000 [00:52<00:33, 10.84it/s]
64%|██████▎ | 637/1000 [00:52<00:31, 11.67it/s]
64%|██████▍ | 639/1000 [00:53<00:32, 11.11it/s]
64%|██████▍ | 641/1000 [00:53<00:29, 12.08it/s]
64%|██████▍ | 643/1000 [00:53<00:28, 12.63it/s]
64%|██████▍ | 645/1000 [00:53<00:27, 12.90it/s]
65%|██████▍ | 647/1000 [00:53<00:26, 13.10it/s]
65%|██████▍ | 649/1000 [00:53<00:27, 12.59it/s]
65%|██████▌ | 651/1000 [00:53<00:26, 13.29it/s]
65%|██████▌ | 653/1000 [00:54<00:28, 12.13it/s]
66%|██████▌ | 655/1000 [00:54<00:35, 9.66it/s]
66%|██████▌ | 657/1000 [00:54<00:31, 10.76it/s]
66%|██████▌ | 659/1000 [00:54<00:34, 9.77it/s]
66%|██████▌ | 661/1000 [00:55<00:31, 10.82it/s]
66%|██████▋ | 663/1000 [00:55<00:29, 11.45it/s]
66%|██████▋ | 665/1000 [00:55<00:28, 11.76it/s]
67%|██████▋ | 667/1000 [00:55<00:31, 10.53it/s]
67%|██████▋ | 669/1000 [00:55<00:31, 10.50it/s]
67%|██████▋ | 671/1000 [00:55<00:29, 10.99it/s]
67%|██████▋ | 673/1000 [00:56<00:28, 11.37it/s]
68%|██████▊ | 675/1000 [00:56<00:29, 10.90it/s]
68%|██████▊ | 677/1000 [00:56<00:28, 11.28it/s]
68%|██████▊ | 679/1000 [00:56<00:27, 11.56it/s]
68%|██████▊ | 681/1000 [00:56<00:26, 11.85it/s]
68%|██████▊ | 683/1000 [00:56<00:27, 11.38it/s]
68%|██████▊ | 685/1000 [00:57<00:26, 11.72it/s]
69%|██████▊ | 687/1000 [00:57<00:26, 12.01it/s]
69%|██████▉ | 689/1000 [00:57<00:24, 12.45it/s]
69%|██████▉ | 691/1000 [00:57<00:23, 13.10it/s]
69%|██████▉ | 693/1000 [00:57<00:22, 13.61it/s]
70%|██████▉ | 695/1000 [00:57<00:22, 13.59it/s]
70%|██████▉ | 697/1000 [00:57<00:21, 14.04it/s]
70%|██████▉ | 699/1000 [00:58<00:21, 14.13it/s]
70%|███████ | 701/1000 [00:58<00:24, 12.10it/s]
70%|███████ | 703/1000 [00:58<00:23, 12.76it/s]
70%|███████ | 705/1000 [00:58<00:30, 9.77it/s]
71%|███████ | 707/1000 [00:59<00:32, 8.96it/s]
71%|███████ | 709/1000 [00:59<00:28, 10.22it/s]
71%|███████ | 711/1000 [00:59<00:25, 11.27it/s]
71%|███████▏ | 713/1000 [00:59<00:23, 12.44it/s]
72%|███████▏ | 715/1000 [00:59<00:21, 13.16it/s]
72%|███████▏ | 717/1000 [00:59<00:23, 12.18it/s]
72%|███████▏ | 719/1000 [00:59<00:21, 13.12it/s]
72%|███████▏ | 721/1000 [00:59<00:19, 14.09it/s]
72%|███████▏ | 723/1000 [01:00<00:19, 13.86it/s]
72%|███████▎ | 725/1000 [01:00<00:19, 14.08it/s]
73%|███████▎ | 727/1000 [01:00<00:23, 11.72it/s]
73%|███████▎ | 729/1000 [01:00<00:33, 8.20it/s]
73%|███████▎ | 731/1000 [01:01<00:28, 9.41it/s]
73%|███████▎ | 733/1000 [01:01<00:28, 9.33it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.53it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.81it/s]
74%|███████▍ | 739/1000 [01:01<00:24, 10.84it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.52it/s]
74%|███████▍ | 743/1000 [01:02<00:21, 12.16it/s]
74%|███████▍ | 745/1000 [01:02<00:25, 10.16it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.59it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.76it/s]
75%|███████▌ | 751/1000 [01:02<00:21, 11.86it/s]
75%|███████▌ | 753/1000 [01:03<00:19, 12.61it/s]
76%|███████▌ | 755/1000 [01:03<00:18, 13.32it/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, 12.32it/s]
76%|███████▋ | 763/1000 [01:04<00:23, 10.27it/s]
76%|███████▋ | 765/1000 [01:04<00:27, 8.62it/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, 11.86it/s]
77%|███████▋ | 773/1000 [01:04<00:18, 12.60it/s]
78%|███████▊ | 775/1000 [01:05<00:17, 13.15it/s]
78%|███████▊ | 777/1000 [01:05<00:15, 14.07it/s]
78%|███████▊ | 779/1000 [01:05<00:21, 10.47it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.39it/s]
78%|███████▊ | 783/1000 [01:06<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<00:24, 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 [01:07<00:17, 11.94it/s]
80%|███████▉ | 796/1000 [01:07<00:16, 12.39it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 13.14it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.32it/s]
80%|████████ | 802/1000 [01:07<00:23, 8.26it/s]
80%|████████ | 804/1000 [01:08<00:23, 8.31it/s]
81%|████████ | 806/1000 [01:08<00:21, 8.99it/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, 11.72it/s]
81%|████████▏ | 814/1000 [01:08<00:14, 12.61it/s]
82%|████████▏ | 816/1000 [01:09<00:16, 11.04it/s]
82%|████████▏ | 818/1000 [01:09<00:15, 11.99it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.48it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 13.04it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.85it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.24it/s]
83%|████████▎ | 828/1000 [01:09<00:11, 14.51it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.95it/s]
83%|████████▎ | 832/1000 [01:10<00:11, 14.29it/s]
83%|████████▎ | 834/1000 [01:10<00:11, 14.02it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.40it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.33it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.45it/s]
84%|████████▍ | 842/1000 [01:10<00:10, 14.48it/s]
84%|████████▍ | 844/1000 [01:11<00:11, 14.00it/s]
85%|████████▍ | 846/1000 [01:11<00:11, 13.96it/s]
85%|████████▍ | 848/1000 [01:11<00:11, 13.46it/s]
85%|████████▌ | 850/1000 [01:11<00:11, 13.46it/s]
85%|████████▌ | 852/1000 [01:11<00:11, 12.83it/s]
85%|████████▌ | 854/1000 [01:11<00:11, 13.15it/s]
86%|████████▌ | 856/1000 [01:12<00:10, 13.33it/s]
86%|████████▌ | 858/1000 [01:12<00:10, 13.94it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 14.79it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.01it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 13.67it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.58it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.43it/s]
87%|████████▋ | 870/1000 [01:13<00:09, 13.50it/s]
87%|████████▋ | 872/1000 [01:13<00:08, 14.25it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 12.20it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 10.92it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.82it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.48it/s]
88%|████████▊ | 882/1000 [01:14<00:10, 11.35it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 12.09it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.89it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.87it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.31it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.07it/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, 13.33it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.23it/s]
90%|█████████ | 902/1000 [01:15<00:07, 12.96it/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, 12.08it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.30it/s]
91%|█████████▏| 914/1000 [01:17<00:12, 6.82it/s]
92%|█████████▏| 916/1000 [01:17<00:10, 8.14it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.40it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.81it/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, 11.83it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 12.43it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.91it/s]
94%|█████████▎| 936/1000 [01:18<00:04, 13.47it/s]
94%|█████████▍| 938/1000 [01:18<00:04, 14.32it/s]
94%|█████████▍| 940/1000 [01:19<00:04, 14.59it/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, 11.89it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 12.70it/s]
95%|█████████▌| 950/1000 [01:19<00:03, 13.48it/s]
95%|█████████▌| 952/1000 [01:20<00:03, 14.17it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 14.28it/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, 13.70it/s]
96%|█████████▋| 964/1000 [01:21<00:03, 10.21it/s]
97%|█████████▋| 966/1000 [01:21<00:03, 10.72it/s]
97%|█████████▋| 968/1000 [01:21<00:02, 11.15it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 11.46it/s]
97%|█████████▋| 972/1000 [01:21<00:03, 9.10it/s]
97%|█████████▋| 974/1000 [01:22<00:02, 9.95it/s]
98%|█████████▊| 976/1000 [01:22<00:02, 10.55it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.32it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.49it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 9.90it/s]
98%|█████████▊| 984/1000 [01:23<00:01, 10.08it/s]
99%|█████████▊| 986/1000 [01:23<00:01, 10.22it/s]
99%|█████████▉| 988/1000 [01:23<00:01, 10.48it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 10.98it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 11.48it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 12.37it/s]
100%|█████████▉| 996/1000 [01:24<00:00, 13.40it/s]
100%|█████████▉| 998/1000 [01:24<00:00, 13.72it/s]
100%|██████████| 1000/1000 [01:24<00:00, 14.49it/s]
{'eval_loss': '0.902', 'eval_runtime': '84.33', 'eval_samples_per_second': '11.86', 'eval_steps_per_second': '11.86', 'epoch': '0.5351', 'num_input_tokens_seen': 33646792}
54%|█████▎ | 10000/18688 [11:44:36<11:19:13, 4.69s/it]
100%|██████████| 1000/1000 [01:24<00:00, 14.49it/s]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00010/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 [11:44:41<72:19:41, 29.97s/it] 54%|█████▎ | 10002/18688 [11:44:45<53:17:05, 22.08s/it] 54%|█████▎ | 10003/18688 [11:44:49<40:05:48, 16.62s/it] 54%|█████▎ | 10004/18688 [11:44:53<31:05:02, 12.89s/it] 54%|█████▎ | 10005/18688 [11:44:57<24:44:54, 10.26s/it] 54%|█████▎ | 10006/18688 [11:45:01<20:20:59, 8.44s/it] 54%|█████▎ | 10007/18688 [11:45:06<17:38:30, 7.32s/it] 54%|█████▎ | 10008/18688 [11:45:10<15:08:54, 6.28s/it] 54%|█████▎ | 10009/18688 [11:45:14<13:55:15, 5.77s/it] 54%|█████▎ | 10010/18688 [11:45:18<12:32:24, 5.20s/it] {'loss': '0.953', 'grad_norm': '1.694', 'learning_rate': '5.078e-05', 'epoch': '0.5357', 'num_input_tokens_seen': 33679768, 'train_runtime': '4.232e+04', 'train_tokens_per_second': '795.9'}
54%|█████▎ | 10010/18688 [11:45:18<12:32:24, 5.20s/it] 54%|█████▎ | 10011/18688 [11:45:22<11:35:14, 4.81s/it] 54%|█████▎ | 10012/18688 [11:45:26<10:50:32, 4.50s/it] 54%|█████▎ | 10013/18688 [11:45:30<10:45:11, 4.46s/it] 54%|█████▎ | 10014/18688 [11:45:36<11:29:29, 4.77s/it] 54%|█████▎ | 10015/18688 [11:45:40<10:57:06, 4.55s/it] 54%|█████▎ | 10016/18688 [11:45:44<10:47:13, 4.48s/it] 54%|█████▎ | 10017/18688 [11:45:49<10:57:17, 4.55s/it] 54%|█████▎ | 10018/18688 [11:45:53<10:34:39, 4.39s/it] 54%|█████▎ | 10019/18688 [11:45:58<10:49:34, 4.50s/it] 54%|█████▎ | 10020/18688 [11:46:01<10:09:54, 4.22s/it] {'loss': '0.9743', 'grad_norm': '1.212', 'learning_rate': '5.069e-05', 'epoch': '0.5362', 'num_input_tokens_seen': 33715552, 'train_runtime': '4.236e+04', 'train_tokens_per_second': '795.9'}
54%|█████▎ | 10020/18688 [11:46:01<10:09:54, 4.22s/it] 54%|█████▎ | 10021/18688 [11:46:06<10:26:44, 4.34s/it] 54%|█████▎ | 10022/18688 [11:46:10<10:24:08, 4.32s/it] 54%|█████▎ | 10023/18688 [11:46:14<10:26:59, 4.34s/it] 54%|█████▎ | 10024/18688 [11:46:19<10:39:44, 4.43s/it] 54%|█████▎ | 10025/18688 [11:46:24<11:04:25, 4.60s/it] 54%|█████▎ | 10026/18688 [11:46:28<10:37:42, 4.42s/it] 54%|█████▎ | 10027/18688 [11:46:32<10:21:31, 4.31s/it] 54%|█████▎ | 10028/18688 [11:46:36<10:17:41, 4.28s/it] 54%|█████▎ | 10029/18688 [11:46:41<10:17:21, 4.28s/it] 54%|█████▎ | 10030/18688 [11:46:44<9:46:28, 4.06s/it] {'loss': '1.014', 'grad_norm': '1.451', 'learning_rate': '5.059e-05', 'epoch': '0.5367', 'num_input_tokens_seen': 33752960, 'train_runtime': '4.24e+04', 'train_tokens_per_second': '796'}
54%|█████▎ | 10030/18688 [11:46:44<9:46:28, 4.06s/it] 54%|█████▎ | 10031/18688 [11:46:48<9:22:33, 3.90s/it] 54%|█████▎ | 10032/18688 [11:46:54<10:51:10, 4.51s/it] 54%|█████▎ | 10033/18688 [11:46:58<10:31:19, 4.38s/it] 54%|█████▎ | 10034/18688 [11:47:02<10:13:19, 4.25s/it] 54%|█████▎ | 10035/18688 [11:47:06<10:02:11, 4.18s/it] 54%|█████▎ | 10036/18688 [11:47:10<9:57:16, 4.14s/it] 54%|█████▎ | 10037/18688 [11:47:14<10:22:18, 4.32s/it] 54%|█████▎ | 10038/18688 [11:47:18<10:11:24, 4.24s/it] 54%|█████▎ | 10039/18688 [11:47:22<9:48:21, 4.08s/it] 54%|█████▎ | 10040/18688 [11:47:27<10:11:56, 4.25s/it] {'loss': '0.9881', 'grad_norm': '1.255', 'learning_rate': '5.05e-05', 'epoch': '0.5373', 'num_input_tokens_seen': 33787960, 'train_runtime': '4.245e+04', 'train_tokens_per_second': '796'}
54%|█████▎ | 10040/18688 [11:47:27<10:11:56, 4.25s/it] 54%|█████▎ | 10041/18688 [11:47:33<11:24:49, 4.75s/it] 54%|█████▎ | 10042/18688 [11:47:36<10:37:00, 4.42s/it] 54%|█████▎ | 10043/18688 [11:47:40<10:04:42, 4.20s/it] 54%|█████▎ | 10044/18688 [11:47:44<9:35:17, 3.99s/it] 54%|█████▍ | 10045/18688 [11:47:47<9:21:22, 3.90s/it] 54%|█████▍ | 10046/18688 [11:47:52<9:51:51, 4.11s/it] 54%|█████▍ | 10047/18688 [11:47:55<9:24:47, 3.92s/it] 54%|█████▍ | 10048/18688 [11:48:01<10:29:44, 4.37s/it] 54%|█████▍ | 10049/18688 [11:48:05<10:38:44, 4.44s/it] 54%|█████▍ | 10050/18688 [11:48:10<10:56:18, 4.56s/it] {'loss': '0.8702', 'grad_norm': '1.239', 'learning_rate': '5.041e-05', 'epoch': '0.5378', 'num_input_tokens_seen': 33824672, 'train_runtime': '4.249e+04', 'train_tokens_per_second': '796'}
54%|█████▍ | 10050/18688 [11:48:10<10:56:18, 4.56s/it] 54%|█████▍ | 10051/18688 [11:48:14<10:15:08, 4.27s/it] 54%|█████▍ | 10052/18688 [11:48:18<10:11:59, 4.25s/it] 54%|█████▍ | 10053/18688 [11:48:22<9:52:10, 4.11s/it] 54%|█████▍ | 10054/18688 [11:48:26<9:37:48, 4.02s/it] 54%|█████▍ | 10055/18688 [11:48:30<10:00:15, 4.17s/it] 54%|█████▍ | 10056/18688 [11:48:34<10:00:57, 4.18s/it] 54%|█████▍ | 10057/18688 [11:48:39<10:22:15, 4.33s/it] 54%|█████▍ | 10058/18688 [11:48:43<10:22:21, 4.33s/it] 54%|█████▍ | 10059/18688 [11:48:47<10:01:53, 4.19s/it] 54%|█████▍ | 10060/18688 [11:48:51<10:02:08, 4.19s/it] {'loss': '0.9753', 'grad_norm': '1.633', 'learning_rate': '5.032e-05', 'epoch': '0.5383', 'num_input_tokens_seen': 33856824, 'train_runtime': '4.253e+04', 'train_tokens_per_second': '796'}
54%|█████▍ | 10060/18688 [11:48:51<10:02:08, 4.19s/it] 54%|█████▍ | 10061/18688 [11:48:55<9:43:25, 4.06s/it] 54%|█████▍ | 10062/18688 [11:48:59<9:31:28, 3.97s/it] 54%|█████▍ | 10063/18688 [11:49:03<9:26:10, 3.94s/it] 54%|█████▍ | 10064/18688 [11:49:07<9:50:26, 4.11s/it] 54%|█████▍ | 10065/18688 [11:49:11<9:41:49, 4.05s/it] 54%|█████▍ | 10066/18688 [11:49:15<9:53:03, 4.13s/it] 54%|█████▍ | 10067/18688 [11:49:19<9:27:47, 3.95s/it] 54%|█████▍ | 10068/18688 [11:49:23<9:33:34, 3.99s/it] 54%|█████▍ | 10069/18688 [11:49:27<9:29:54, 3.97s/it] 54%|█████▍ | 10070/18688 [11:49:32<9:54:06, 4.14s/it] {'loss': '0.9621', 'grad_norm': '1.045', 'learning_rate': '5.023e-05', 'epoch': '0.5389', 'num_input_tokens_seen': 33890200, 'train_runtime': '4.257e+04', 'train_tokens_per_second': '796.1'}
54%|█████▍ | 10070/18688 [11:49:32<9:54:06, 4.14s/it] 54%|█████▍ | 10071/18688 [11:49:37<10:31:14, 4.40s/it] 54%|█████▍ | 10072/18688 [11:49:40<9:56:20, 4.15s/it] 54%|█████▍ | 10073/18688 [11:49:44<9:26:37, 3.95s/it] 54%|█████▍ | 10074/18688 [11:49:47<9:11:53, 3.84s/it] 54%|█████▍ | 10075/18688 [11:49:51<9:25:42, 3.94s/it] 54%|█████▍ | 10076/18688 [11:49:55<9:33:36, 4.00s/it] 54%|█████▍ | 10077/18688 [11:49:59<9:23:28, 3.93s/it] 54%|█████▍ | 10078/18688 [11:50:03<9:25:19, 3.94s/it] 54%|█████▍ | 10079/18688 [11:50:07<9:38:19, 4.03s/it] 54%|█████▍ | 10080/18688 [11:50:11<9:22:30, 3.92s/it] {'loss': '0.9639', 'grad_norm': '1.32', 'learning_rate': '5.014e-05', 'epoch': '0.5394', 'num_input_tokens_seen': 33920600, 'train_runtime': '4.261e+04', 'train_tokens_per_second': '796'}
54%|█████▍ | 10080/18688 [11:50:11<9:22:30, 3.92s/it] 54%|█████▍ | 10081/18688 [11:50:16<9:58:21, 4.17s/it] 54%|█████▍ | 10082/18688 [11:50:21<10:40:09, 4.46s/it] 54%|█████▍ | 10083/18688 [11:50:25<10:06:12, 4.23s/it] 54%|█████▍ | 10084/18688 [11:50:28<9:44:45, 4.08s/it] 54%|█████▍ | 10085/18688 [11:50:33<10:07:58, 4.24s/it] 54%|█████▍ | 10086/18688 [11:50:37<9:48:07, 4.10s/it] 54%|█████▍ | 10087/18688 [11:50:40<9:27:58, 3.96s/it] 54%|█████▍ | 10088/18688 [11:50:44<9:05:45, 3.81s/it] 54%|█████▍ | 10089/18688 [11:50:47<8:53:15, 3.72s/it] 54%|█████▍ | 10090/18688 [11:50:52<9:19:37, 3.91s/it] {'loss': '0.8461', 'grad_norm': '1.261', 'learning_rate': '5.005e-05', 'epoch': '0.5399', 'num_input_tokens_seen': 33953920, 'train_runtime': '4.265e+04', 'train_tokens_per_second': '796.1'}
54%|█████▍ | 10090/18688 [11:50:52<9:19:37, 3.91s/it] 54%|█████▍ | 10091/18688 [11:50:56<9:45:12, 4.08s/it] 54%|█████▍ | 10092/18688 [11:51:01<10:24:12, 4.36s/it] 54%|█████▍ | 10093/18688 [11:51:05<10:15:19, 4.30s/it] 54%|█████▍ | 10094/18688 [11:51:09<9:59:47, 4.19s/it] 54%|█████▍ | 10095/18688 [11:51:14<10:17:48, 4.31s/it] 54%|█████▍ | 10096/18688 [11:51:18<10:21:29, 4.34s/it] 54%|█████▍ | 10097/18688 [11:51:23<10:15:10, 4.30s/it] 54%|█████▍ | 10098/18688 [11:51:28<11:08:15, 4.67s/it] 54%|█████▍ | 10099/18688 [11:51:32<10:46:04, 4.51s/it] 54%|█████▍ | 10100/18688 [11:51:37<10:38:12, 4.46s/it] {'loss': '0.9438', 'grad_norm': '1.396', 'learning_rate': '4.995e-05', 'epoch': '0.5405', 'num_input_tokens_seen': 33993312, 'train_runtime': '4.27e+04', 'train_tokens_per_second': '796.2'}
54%|█████▍ | 10100/18688 [11:51:37<10:38:12, 4.46s/it] 54%|█████▍ | 10101/18688 [11:51:40<10:00:05, 4.19s/it] 54%|█████▍ | 10102/18688 [11:51:44<9:41:41, 4.06s/it] 54%|█████▍ | 10103/18688 [11:51:48<9:34:32, 4.02s/it] 54%|█████▍ | 10104/18688 [11:51:52<10:01:50, 4.21s/it] 54%|█████▍ | 10105/18688 [11:51:57<10:09:24, 4.26s/it] 54%|█████▍ | 10106/18688 [11:52:00<9:39:34, 4.05s/it] 54%|█████▍ | 10107/18688 [11:52:04<9:23:00, 3.94s/it] 54%|█████▍ | 10108/18688 [11:52:09<9:49:42, 4.12s/it] 54%|█████▍ | 10109/18688 [11:52:12<9:29:57, 3.99s/it] 54%|█████▍ | 10110/18688 [11:52:16<9:17:59, 3.90s/it] {'loss': '0.9008', 'grad_norm': '1.322', 'learning_rate': '4.986e-05', 'epoch': '0.541', 'num_input_tokens_seen': 34024272, 'train_runtime': '4.274e+04', 'train_tokens_per_second': '796.1'}
54%|█████▍ | 10110/18688 [11:52:16<9:17:59, 3.90s/it] 54%|█████▍ | 10111/18688 [11:52:20<9:42:07, 4.07s/it] 54%|█████▍ | 10112/18688 [11:52:25<9:44:25, 4.09s/it] 54%|█████▍ | 10113/18688 [11:52:28<9:19:14, 3.91s/it] 54%|█████▍ | 10114/18688 [11:52:32<9:09:02, 3.84s/it] 54%|█████▍ | 10115/18688 [11:52:35<8:59:12, 3.77s/it] 54%|█████▍ | 10116/18688 [11:52:39<8:47:08, 3.69s/it] 54%|█████▍ | 10117/18688 [11:52:43<9:06:28, 3.83s/it] 54%|█████▍ | 10118/18688 [11:52:47<9:30:36, 3.99s/it] 54%|█████▍ | 10119/18688 [11:52:51<9:33:08, 4.01s/it] 54%|█████▍ | 10120/18688 [11:52:56<9:47:31, 4.11s/it] {'loss': '0.9502', 'grad_norm': '1.338', 'learning_rate': '4.977e-05', 'epoch': '0.5415', 'num_input_tokens_seen': 34057008, 'train_runtime': '4.278e+04', 'train_tokens_per_second': '796.2'}
54%|█████▍ | 10120/18688 [11:52:56<9:47:31, 4.11s/it] 54%|█████▍ | 10121/18688 [11:53:00<9:57:34, 4.19s/it] 54%|█████▍ | 10122/18688 [11:53:05<10:14:06, 4.30s/it] 54%|█████▍ | 10123/18688 [11:53:09<10:19:12, 4.34s/it] 54%|█████▍ | 10124/18688 [11:53:13<10:07:33, 4.26s/it] 54%|█████▍ | 10125/18688 [11:53:17<9:47:50, 4.12s/it] 54%|█████▍ | 10126/18688 [11:53:21<9:22:38, 3.94s/it] 54%|█████▍ | 10127/18688 [11:53:24<9:14:04, 3.88s/it] 54%|█████▍ | 10128/18688 [11:53:28<9:11:08, 3.86s/it] 54%|█████▍ | 10129/18688 [11:53:32<8:58:59, 3.78s/it] 54%|█████▍ | 10130/18688 [11:53:36<9:23:12, 3.95s/it] {'loss': '0.8455', 'grad_norm': '1.223', 'learning_rate': '4.968e-05', 'epoch': '0.5421', 'num_input_tokens_seen': 34089432, 'train_runtime': '4.282e+04', 'train_tokens_per_second': '796.2'}
54%|█████▍ | 10130/18688 [11:53:36<9:23:12, 3.95s/it] 54%|█████▍ | 10131/18688 [11:53:41<9:47:01, 4.12s/it] 54%|█████▍ | 10132/18688 [11:53:45<10:01:37, 4.22s/it] 54%|█████▍ | 10133/18688 [11:53:49<9:44:39, 4.10s/it] 54%|█████▍ | 10134/18688 [11:53:53<9:32:48, 4.02s/it] 54%|█████▍ | 10135/18688 [11:53:57<9:49:13, 4.13s/it] 54%|█████▍ | 10136/18688 [11:54:01<9:49:55, 4.14s/it] 54%|█████▍ | 10137/18688 [11:54:05<9:54:47, 4.17s/it] 54%|█████▍ | 10138/18688 [11:54:09<9:42:13, 4.09s/it] 54%|█████▍ | 10139/18688 [11:54:14<9:53:05, 4.16s/it] 54%|█████▍ | 10140/18688 [11:54:19<10:24:54, 4.39s/it] {'loss': '0.9751', 'grad_norm': '1.208', 'learning_rate': '4.959e-05', 'epoch': '0.5426', 'num_input_tokens_seen': 34125208, 'train_runtime': '4.286e+04', 'train_tokens_per_second': '796.2'}
54%|█████▍ | 10140/18688 [11:54:19<10:24:54, 4.39s/it] 54%|█████▍ | 10141/18688 [11:54:24<10:47:58, 4.55s/it] 54%|█████▍ | 10142/18688 [11:54:28<10:37:33, 4.48s/it] 54%|█████▍ | 10143/18688 [11:54:33<11:20:28, 4.78s/it] 54%|█████▍ | 10144/18688 [11:54:38<11:29:28, 4.84s/it] 54%|█████▍ | 10145/18688 [11:54:42<10:44:52, 4.53s/it] 54%|█████▍ | 10146/18688 [11:54:47<11:19:18, 4.77s/it] 54%|█████▍ | 10147/18688 [11:54:51<10:24:52, 4.39s/it] 54%|█████▍ | 10148/18688 [11:54:56<10:48:41, 4.56s/it] 54%|█████▍ | 10149/18688 [11:54:59<10:03:50, 4.24s/it] 54%|█████▍ | 10150/18688 [11:55:03<9:40:22, 4.08s/it] {'loss': '0.9267', 'grad_norm': '1.376', 'learning_rate': '4.95e-05', 'epoch': '0.5431', 'num_input_tokens_seen': 34164360, 'train_runtime': '4.29e+04', 'train_tokens_per_second': '796.3'}
54%|█████▍ | 10150/18688 [11:55:03<9:40:22, 4.08s/it] 54%|█████▍ | 10151/18688 [11:55:07<9:45:49, 4.12s/it] 54%|█████▍ | 10152/18688 [11:55:11<9:23:17, 3.96s/it] 54%|█████▍ | 10153/18688 [11:55:15<9:26:40, 3.98s/it] 54%|█████▍ | 10154/18688 [11:55:20<10:03:54, 4.25s/it] 54%|█████▍ | 10155/18688 [11:55:24<9:43:22, 4.10s/it] 54%|█████▍ | 10156/18688 [11:55:27<9:26:21, 3.98s/it] 54%|█████▍ | 10157/18688 [11:55:31<9:20:31, 3.94s/it] 54%|█████▍ | 10158/18688 [11:55:35<9:18:40, 3.93s/it] 54%|█████▍ | 10159/18688 [11:55:39<9:12:34, 3.89s/it] 54%|█████▍ | 10160/18688 [11:55:44<10:17:51, 4.35s/it] {'loss': '0.8992', 'grad_norm': '1.459', 'learning_rate': '4.941e-05', 'epoch': '0.5437', 'num_input_tokens_seen': 34198360, 'train_runtime': '4.294e+04', 'train_tokens_per_second': '796.3'}
54%|█████▍ | 10160/18688 [11:55:44<10:17:51, 4.35s/it] 54%|█████▍ | 10161/18688 [11:55:48<10:10:15, 4.29s/it] 54%|█████▍ | 10162/18688 [11:55:52<9:53:18, 4.18s/it] 54%|█████▍ | 10163/18688 [11:55:56<9:46:12, 4.13s/it] 54%|█████▍ | 10164/18688 [11:56:00<9:45:16, 4.12s/it] 54%|█████▍ | 10165/18688 [11:56:05<9:45:10, 4.12s/it] 54%|█████▍ | 10166/18688 [11:56:09<9:40:21, 4.09s/it] 54%|█████▍ | 10167/18688 [11:56:12<9:28:38, 4.00s/it] 54%|█████▍ | 10168/18688 [11:56:18<10:17:08, 4.35s/it] 54%|█████▍ | 10169/18688 [11:56:23<10:55:35, 4.62s/it] 54%|█████▍ | 10170/18688 [11:56:27<10:30:50, 4.44s/it] {'loss': '0.9276', 'grad_norm': '1.584', 'learning_rate': '4.931e-05', 'epoch': '0.5442', 'num_input_tokens_seen': 34233192, 'train_runtime': '4.299e+04', 'train_tokens_per_second': '796.4'}
54%|█████▍ | 10170/18688 [11:56:27<10:30:50, 4.44s/it] 54%|█████▍ | 10171/18688 [11:56:31<10:14:30, 4.33s/it] 54%|█████▍ | 10172/18688 [11:56:35<10:03:54, 4.25s/it] 54%|█████▍ | 10173/18688 [11:56:40<10:41:00, 4.52s/it] 54%|█████▍ | 10174/18688 [11:56:45<10:39:20, 4.51s/it] 54%|█████▍ | 10175/18688 [11:56:48<10:02:44, 4.25s/it] 54%|█████▍ | 10176/18688 [11:56:53<10:07:42, 4.28s/it] 54%|█████▍ | 10177/18688 [11:56:56<9:36:38, 4.07s/it] 54%|█████▍ | 10178/18688 [11:57:01<9:59:42, 4.23s/it] 54%|█████▍ | 10179/18688 [11:57:06<10:27:36, 4.43s/it] 54%|█████▍ | 10180/18688 [11:57:10<10:24:30, 4.40s/it] {'loss': '0.9675', 'grad_norm': '1.305', 'learning_rate': '4.922e-05', 'epoch': '0.5447', 'num_input_tokens_seen': 34269328, 'train_runtime': '4.303e+04', 'train_tokens_per_second': '796.4'}
54%|█████▍ | 10180/18688 [11:57:10<10:24:30, 4.40s/it] 54%|█████▍ | 10181/18688 [11:57:14<9:50:40, 4.17s/it] 54%|█████▍ | 10182/18688 [11:57:18<9:49:21, 4.16s/it] 54%|█████▍ | 10183/18688 [11:57:22<10:08:49, 4.30s/it] 54%|█████▍ | 10184/18688 [11:57:26<9:41:01, 4.10s/it] 55%|█████▍ | 10185/18688 [11:57:30<9:39:08, 4.09s/it] 55%|█████▍ | 10186/18688 [11:57:35<10:08:11, 4.29s/it] 55%|█████▍ | 10187/18688 [11:57:38<9:42:14, 4.11s/it] 55%|█████▍ | 10188/18688 [11:57:42<9:15:25, 3.92s/it] 55%|█████▍ | 10189/18688 [11:57:46<9:37:19, 4.08s/it] 55%|█████▍ | 10190/18688 [11:57:50<9:26:01, 4.00s/it] {'loss': '0.898', 'grad_norm': '1.5', 'learning_rate': '4.913e-05', 'epoch': '0.5453', 'num_input_tokens_seen': 34302080, 'train_runtime': '4.307e+04', 'train_tokens_per_second': '796.4'}
55%|█████▍ | 10190/18688 [11:57:50<9:26:01, 4.00s/it] 55%|█████▍ | 10191/18688 [11:57:55<9:45:31, 4.13s/it] 55%|█████▍ | 10192/18688 [11:57:59<9:39:07, 4.09s/it] 55%|█████▍ | 10193/18688 [11:58:03<9:52:48, 4.19s/it] 55%|█████▍ | 10194/18688 [11:58:07<9:45:04, 4.13s/it] 55%|█████▍ | 10195/18688 [11:58:11<9:20:59, 3.96s/it] 55%|█████▍ | 10196/18688 [11:58:14<9:14:38, 3.92s/it] 55%|█████▍ | 10197/18688 [11:58:19<9:50:34, 4.17s/it] 55%|█████▍ | 10198/18688 [11:58:24<9:55:09, 4.21s/it] 55%|█████▍ | 10199/18688 [11:58:27<9:32:32, 4.05s/it] 55%|█████▍ | 10200/18688 [11:58:31<9:24:37, 3.99s/it] {'loss': '0.9', 'grad_norm': '1.372', 'learning_rate': '4.904e-05', 'epoch': '0.5458', 'num_input_tokens_seen': 34335616, 'train_runtime': '4.311e+04', 'train_tokens_per_second': '796.4'}
55%|█████▍ | 10200/18688 [11:58:31<9:24:37, 3.99s/it] 55%|█████▍ | 10201/18688 [11:58:34<9:01:25, 3.83s/it] 55%|█████▍ | 10202/18688 [11:58:38<9:02:12, 3.83s/it] 55%|█████▍ | 10203/18688 [11:58:42<9:14:41, 3.92s/it] 55%|█████▍ | 10204/18688 [11:58:46<8:59:23, 3.81s/it] 55%|█████▍ | 10205/18688 [11:58:50<8:58:19, 3.81s/it] 55%|█████▍ | 10206/18688 [11:58:54<9:32:30, 4.05s/it] 55%|█████▍ | 10207/18688 [11:58:59<9:49:23, 4.17s/it] 55%|█████▍ | 10208/18688 [11:59:03<9:29:20, 4.03s/it] 55%|█████▍ | 10209/18688 [11:59:06<9:06:05, 3.86s/it] 55%|█████▍ | 10210/18688 [11:59:11<9:42:33, 4.12s/it] {'loss': '0.8662', 'grad_norm': '1.072', 'learning_rate': '4.895e-05', 'epoch': '0.5464', 'num_input_tokens_seen': 34367872, 'train_runtime': '4.315e+04', 'train_tokens_per_second': '796.5'}
55%|█████▍ | 10210/18688 [11:59:11<9:42:33, 4.12s/it] 55%|█████▍ | 10211/18688 [11:59:16<10:17:44, 4.37s/it] 55%|█████▍ | 10212/18688 [11:59:20<9:52:30, 4.19s/it] 55%|█████▍ | 10213/18688 [11:59:24<10:22:10, 4.40s/it] 55%|█████▍ | 10214/18688 [11:59:30<10:50:51, 4.61s/it] 55%|█████▍ | 10215/18688 [11:59:34<10:46:28, 4.58s/it] 55%|█████▍ | 10216/18688 [11:59:38<10:02:01, 4.26s/it] 55%|█████▍ | 10217/18688 [11:59:42<10:14:43, 4.35s/it] 55%|█████▍ | 10218/18688 [11:59:46<9:55:12, 4.22s/it] 55%|█████▍ | 10219/18688 [11:59:50<9:54:11, 4.21s/it] 55%|█████▍ | 10220/18688 [11:59:54<9:44:36, 4.14s/it] {'loss': '0.9662', 'grad_norm': '1.67', 'learning_rate': '4.886e-05', 'epoch': '0.5469', 'num_input_tokens_seen': 34405496, 'train_runtime': '4.319e+04', 'train_tokens_per_second': '796.5'}
55%|█████▍ | 10220/18688 [11:59:54<9:44:36, 4.14s/it] 55%|█████▍ | 10221/18688 [11:59:58<9:16:25, 3.94s/it] 55%|█████▍ | 10222/18688 [12:00:02<9:40:43, 4.12s/it] 55%|█████▍ | 10223/18688 [12:00:06<9:17:23, 3.95s/it] 55%|█████▍ | 10224/18688 [12:00:10<9:16:33, 3.95s/it] 55%|█████▍ | 10225/18688 [12:00:13<9:01:16, 3.84s/it] 55%|█████▍ | 10226/18688 [12:00:18<9:38:16, 4.10s/it] 55%|█████▍ | 10227/18688 [12:00:22<9:24:54, 4.01s/it] 55%|█████▍ | 10228/18688 [12:00:26<9:17:30, 3.95s/it] 55%|█████▍ | 10229/18688 [12:00:29<9:10:14, 3.90s/it] 55%|█████▍ | 10230/18688 [12:00:34<9:21:03, 3.98s/it] {'loss': '0.841', 'grad_norm': '1.155', 'learning_rate': '4.877e-05', 'epoch': '0.5474', 'num_input_tokens_seen': 34436792, 'train_runtime': '4.323e+04', 'train_tokens_per_second': '796.5'}
55%|█████▍ | 10230/18688 [12:00:34<9:21:03, 3.98s/it] 55%|█████▍ | 10231/18688 [12:00:38<9:28:21, 4.03s/it] 55%|█████▍ | 10232/18688 [12:00:41<9:08:22, 3.89s/it] 55%|█████▍ | 10233/18688 [12:00:45<9:14:45, 3.94s/it] 55%|█████▍ | 10234/18688 [12:00:49<9:17:32, 3.96s/it] 55%|█████▍ | 10235/18688 [12:00:54<9:44:36, 4.15s/it] 55%|█████▍ | 10236/18688 [12:00:59<10:30:09, 4.47s/it] 55%|█████▍ | 10237/18688 [12:01:03<9:59:51, 4.26s/it] 55%|█████▍ | 10238/18688 [12:01:07<9:44:26, 4.15s/it] 55%|█████▍ | 10239/18688 [12:01:11<10:07:58, 4.32s/it] 55%|█████▍ | 10240/18688 [12:01:15<9:42:46, 4.14s/it] {'loss': '0.8944', 'grad_norm': '1.245', 'learning_rate': '4.868e-05', 'epoch': '0.548', 'num_input_tokens_seen': 34471432, 'train_runtime': '4.328e+04', 'train_tokens_per_second': '796.6'}
55%|█████▍ | 10240/18688 [12:01:15<9:42:46, 4.14s/it] 55%|█████▍ | 10241/18688 [12:01:20<9:58:35, 4.25s/it] 55%|█████▍ | 10242/18688 [12:01:24<10:15:57, 4.38s/it] 55%|█████▍ | 10243/18688 [12:01:29<10:17:29, 4.39s/it] 55%|█████▍ | 10244/18688 [12:01:32<9:43:53, 4.15s/it] 55%|█████▍ | 10245/18688 [12:01:36<9:31:06, 4.06s/it] 55%|█████▍ | 10246/18688 [12:01:40<9:32:36, 4.07s/it] 55%|█████▍ | 10247/18688 [12:01:45<9:48:42, 4.18s/it] 55%|█████▍ | 10248/18688 [12:01:49<10:00:11, 4.27s/it] 55%|█████▍ | 10249/18688 [12:01:53<9:50:25, 4.20s/it] 55%|█████▍ | 10250/18688 [12:01:57<9:47:36, 4.18s/it] {'loss': '0.907', 'grad_norm': '1.458', 'learning_rate': '4.858e-05', 'epoch': '0.5485', 'num_input_tokens_seen': 34506080, 'train_runtime': '4.332e+04', 'train_tokens_per_second': '796.6'}
55%|█████▍ | 10250/18688 [12:01:57<9:47:36, 4.18s/it] 55%|█████▍ | 10251/18688 [12:02:01<9:29:50, 4.05s/it] 55%|█████▍ | 10252/18688 [12:02:05<9:08:14, 3.90s/it] 55%|█████▍ | 10253/18688 [12:02:08<8:49:48, 3.77s/it] 55%|█████▍ | 10254/18688 [12:02:13<9:53:52, 4.22s/it] 55%|█████▍ | 10255/18688 [12:02:18<9:58:45, 4.26s/it] 55%|█████▍ | 10256/18688 [12:02:22<9:37:09, 4.11s/it] 55%|█████▍ | 10257/18688 [12:02:26<10:05:10, 4.31s/it] 55%|█████▍ | 10258/18688 [12:02:30<9:42:05, 4.14s/it] 55%|█████▍ | 10259/18688 [12:02:34<9:42:36, 4.15s/it] 55%|█████▍ | 10260/18688 [12:02:38<9:14:35, 3.95s/it] {'loss': '0.9268', 'grad_norm': '1.418', 'learning_rate': '4.849e-05', 'epoch': '0.549', 'num_input_tokens_seen': 34539184, 'train_runtime': '4.336e+04', 'train_tokens_per_second': '796.6'}
55%|█████▍ | 10260/18688 [12:02:38<9:14:35, 3.95s/it] 55%|█████▍ | 10261/18688 [12:02:42<9:42:31, 4.15s/it] 55%|█████▍ | 10262/18688 [12:02:47<9:49:00, 4.19s/it] 55%|█████▍ | 10263/18688 [12:02:51<9:34:47, 4.09s/it] 55%|█████▍ | 10264/18688 [12:02:55<10:05:07, 4.31s/it] 55%|█████▍ | 10265/18688 [12:03:00<10:24:00, 4.44s/it] 55%|█████▍ | 10266/18688 [12:03:04<9:50:32, 4.21s/it] 55%|█████▍ | 10267/18688 [12:03:08<9:37:02, 4.11s/it] 55%|█████▍ | 10268/18688 [12:03:13<10:16:56, 4.40s/it] 55%|█████▍ | 10269/18688 [12:03:17<10:06:02, 4.32s/it] 55%|█████▍ | 10270/18688 [12:03:20<9:34:34, 4.10s/it] {'loss': '0.8707', 'grad_norm': '1.246', 'learning_rate': '4.84e-05', 'epoch': '0.5496', 'num_input_tokens_seen': 34575560, 'train_runtime': '4.34e+04', 'train_tokens_per_second': '796.7'}
55%|█████▍ | 10270/18688 [12:03:20<9:34:34, 4.10s/it] 55%|█████▍ | 10271/18688 [12:03:25<9:53:25, 4.23s/it] 55%|█████▍ | 10272/18688 [12:03:29<9:28:37, 4.05s/it] 55%|█████▍ | 10273/18688 [12:03:34<10:20:32, 4.42s/it] 55%|█████▍ | 10274/18688 [12:03:38<9:46:36, 4.18s/it] 55%|█████▍ | 10275/18688 [12:03:42<9:40:25, 4.14s/it] 55%|█████▍ | 10276/18688 [12:03:46<9:37:33, 4.12s/it] 55%|█████▍ | 10277/18688 [12:03:49<9:19:20, 3.99s/it] 55%|█████▍ | 10278/18688 [12:03:54<9:43:06, 4.16s/it] 55%|█████▌ | 10279/18688 [12:03:58<9:27:05, 4.05s/it] 55%|█████▌ | 10280/18688 [12:04:01<9:02:43, 3.87s/it] {'loss': '0.9', 'grad_norm': '1.158', 'learning_rate': '4.831e-05', 'epoch': '0.5501', 'num_input_tokens_seen': 34607320, 'train_runtime': '4.344e+04', 'train_tokens_per_second': '796.6'}
55%|█████▌ | 10280/18688 [12:04:01<9:02:43, 3.87s/it] 55%|█████▌ | 10281/18688 [12:04:05<9:21:19, 4.01s/it] 55%|█████▌ | 10282/18688 [12:04:10<9:36:17, 4.11s/it] 55%|█████▌ | 10283/18688 [12:04:14<9:19:18, 3.99s/it] 55%|█████▌ | 10284/18688 [12:04:18<9:41:42, 4.15s/it] 55%|█████▌ | 10285/18688 [12:04:22<9:20:53, 4.00s/it] 55%|█████▌ | 10286/18688 [12:04:25<9:03:28, 3.88s/it] 55%|█████▌ | 10287/18688 [12:04:29<9:07:38, 3.91s/it] 55%|█████▌ | 10288/18688 [12:04:33<9:08:41, 3.92s/it] 55%|█████▌ | 10289/18688 [12:04:37<9:08:27, 3.92s/it] 55%|█████▌ | 10290/18688 [12:04:42<9:40:54, 4.15s/it] {'loss': '0.9169', 'grad_norm': '1.165', 'learning_rate': '4.822e-05', 'epoch': '0.5506', 'num_input_tokens_seen': 34639288, 'train_runtime': '4.348e+04', 'train_tokens_per_second': '796.6'}
55%|█████▌ | 10290/18688 [12:04:42<9:40:54, 4.15s/it] 55%|█████▌ | 10291/18688 [12:04:46<9:22:12, 4.02s/it] 55%|█████▌ | 10292/18688 [12:04:50<9:44:08, 4.17s/it] 55%|█████▌ | 10293/18688 [12:04:55<10:10:13, 4.36s/it] 55%|█████▌ | 10294/18688 [12:04:58<9:35:30, 4.11s/it] 55%|█████▌ | 10295/18688 [12:05:02<9:27:22, 4.06s/it] 55%|█████▌ | 10296/18688 [12:05:07<9:49:41, 4.22s/it] 55%|█████▌ | 10297/18688 [12:05:12<10:09:21, 4.36s/it] 55%|█████▌ | 10298/18688 [12:05:15<9:41:50, 4.16s/it] 55%|█████▌ | 10299/18688 [12:05:19<9:34:29, 4.11s/it] 55%|█████▌ | 10300/18688 [12:05:23<9:21:43, 4.02s/it] {'loss': '0.8614', 'grad_norm': '1.258', 'learning_rate': '4.813e-05', 'epoch': '0.5512', 'num_input_tokens_seen': 34673832, 'train_runtime': '4.352e+04', 'train_tokens_per_second': '796.7'}
55%|█████▌ | 10300/18688 [12:05:23<9:21:43, 4.02s/it] 55%|█████▌ | 10301/18688 [12:05:27<9:35:14, 4.12s/it] 55%|█████▌ | 10302/18688 [12:05:32<9:44:26, 4.18s/it] 55%|█████▌ | 10303/18688 [12:05:36<9:46:33, 4.20s/it] 55%|█████▌ | 10304/18688 [12:05:40<9:43:18, 4.17s/it] 55%|█████▌ | 10305/18688 [12:05:44<9:50:35, 4.23s/it] 55%|█████▌ | 10306/18688 [12:05:48<9:37:58, 4.14s/it] 55%|█████▌ | 10307/18688 [12:05:52<9:27:53, 4.07s/it] 55%|█████▌ | 10308/18688 [12:05:56<9:02:19, 3.88s/it] 55%|█████▌ | 10309/18688 [12:05:59<8:54:06, 3.82s/it] 55%|█████▌ | 10310/18688 [12:06:04<9:05:33, 3.91s/it] {'loss': '0.8858', 'grad_norm': '1.425', 'learning_rate': '4.804e-05', 'epoch': '0.5517', 'num_input_tokens_seen': 34705512, 'train_runtime': '4.356e+04', 'train_tokens_per_second': '796.7'}
55%|█████▌ | 10310/18688 [12:06:04<9:05:33, 3.91s/it] 55%|█████▌ | 10311/18688 [12:06:08<9:21:29, 4.02s/it] 55%|█████▌ | 10312/18688 [12:06:12<9:12:21, 3.96s/it] 55%|█████▌ | 10313/18688 [12:06:15<8:58:04, 3.85s/it] 55%|█████▌ | 10314/18688 [12:06:20<9:42:00, 4.17s/it] 55%|█████▌ | 10315/18688 [12:06:24<9:47:18, 4.21s/it] 55%|█████▌ | 10316/18688 [12:06:28<9:36:52, 4.13s/it] 55%|█████▌ | 10317/18688 [12:06:32<9:22:18, 4.03s/it] 55%|█████▌ | 10318/18688 [12:06:36<9:09:31, 3.94s/it] 55%|█████▌ | 10319/18688 [12:06:40<8:56:09, 3.84s/it] 55%|█████▌ | 10320/18688 [12:06:45<9:44:35, 4.19s/it] {'loss': '0.9598', 'grad_norm': '1.08', 'learning_rate': '4.794e-05', 'epoch': '0.5522', 'num_input_tokens_seen': 34739040, 'train_runtime': '4.361e+04', 'train_tokens_per_second': '796.7'}
55%|█████▌ | 10320/18688 [12:06:45<9:44:35, 4.19s/it] 55%|█████▌ | 10321/18688 [12:06:48<9:32:57, 4.11s/it] 55%|█████▌ | 10322/18688 [12:06:53<9:49:29, 4.23s/it] 55%|█████▌ | 10323/18688 [12:06:57<9:27:25, 4.07s/it] 55%|█████▌ | 10324/18688 [12:07:00<9:10:36, 3.95s/it] 55%|█████▌ | 10325/18688 [12:07:05<9:38:05, 4.15s/it] 55%|█████▌ | 10326/18688 [12:07:09<9:13:44, 3.97s/it] 55%|█████▌ | 10327/18688 [12:07:12<9:11:14, 3.96s/it] 55%|█████▌ | 10328/18688 [12:07:17<9:29:45, 4.09s/it] 55%|█████▌ | 10329/18688 [12:07:21<9:43:55, 4.19s/it] 55%|█████▌ | 10330/18688 [12:07:25<9:30:06, 4.09s/it] {'loss': '0.917', 'grad_norm': '1.29', 'learning_rate': '4.785e-05', 'epoch': '0.5528', 'num_input_tokens_seen': 34772400, 'train_runtime': '4.365e+04', 'train_tokens_per_second': '796.7'}
55%|█████▌ | 10330/18688 [12:07:25<9:30:06, 4.09s/it] 55%|█████▌ | 10331/18688 [12:07:29<9:39:56, 4.16s/it] 55%|█████▌ | 10332/18688 [12:07:33<9:23:36, 4.05s/it] 55%|█████▌ | 10333/18688 [12:07:37<9:13:49, 3.98s/it] 55%|█████▌ | 10334/18688 [12:07:41<9:29:48, 4.09s/it] 55%|█████▌ | 10335/18688 [12:07:45<9:14:17, 3.98s/it] 55%|█████▌ | 10336/18688 [12:07:49<9:04:06, 3.91s/it] 55%|█████▌ | 10337/18688 [12:07:52<8:48:20, 3.80s/it] 55%|█████▌ | 10338/18688 [12:07:56<8:41:22, 3.75s/it] 55%|█████▌ | 10339/18688 [12:08:00<8:44:00, 3.77s/it] 55%|█████▌ | 10340/18688 [12:08:03<8:31:11, 3.67s/it] {'loss': '0.8763', 'grad_norm': '1.43', 'learning_rate': '4.776e-05', 'epoch': '0.5533', 'num_input_tokens_seen': 34800776, 'train_runtime': '4.368e+04', 'train_tokens_per_second': '796.7'}
55%|█████▌ | 10340/18688 [12:08:03<8:31:11, 3.67s/it] 55%|█████▌ | 10341/18688 [12:08:07<8:26:38, 3.64s/it] 55%|█████▌ | 10342/18688 [12:08:11<8:53:40, 3.84s/it] 55%|█████▌ | 10343/18688 [12:08:16<9:27:39, 4.08s/it] 55%|█████▌ | 10344/18688 [12:08:20<9:33:42, 4.13s/it] 55%|█████▌ | 10345/18688 [12:08:24<9:23:35, 4.05s/it] 55%|█████▌ | 10346/18688 [12:08:28<9:32:16, 4.12s/it] 55%|█████▌ | 10347/18688 [12:08:32<9:29:07, 4.09s/it] 55%|█████▌ | 10348/18688 [12:08:36<9:30:23, 4.10s/it] 55%|█████▌ | 10349/18688 [12:08:41<9:38:42, 4.16s/it] 55%|█████▌ | 10350/18688 [12:08:45<10:05:57, 4.36s/it] {'loss': '0.9136', 'grad_norm': '1.056', 'learning_rate': '4.767e-05', 'epoch': '0.5538', 'num_input_tokens_seen': 34835592, 'train_runtime': '4.373e+04', 'train_tokens_per_second': '796.7'}
55%|█████▌ | 10350/18688 [12:08:45<10:05:57, 4.36s/it] 55%|█████▌ | 10351/18688 [12:08:49<9:42:34, 4.19s/it] 55%|█████▌ | 10352/18688 [12:08:54<9:45:23, 4.21s/it] 55%|█████▌ | 10353/18688 [12:08:57<9:23:29, 4.06s/it] 55%|█████▌ | 10354/18688 [12:09:02<9:32:54, 4.12s/it] 55%|█████▌ | 10355/18688 [12:09:06<10:00:26, 4.32s/it] 55%|█████▌ | 10356/18688 [12:09:11<10:20:00, 4.46s/it] 55%|█████▌ | 10357/18688 [12:09:16<10:19:02, 4.46s/it] 55%|█████▌ | 10358/18688 [12:09:20<10:33:05, 4.56s/it] 55%|█████▌ | 10359/18688 [12:09:25<10:42:57, 4.63s/it] 55%|█████▌ | 10360/18688 [12:09:29<10:02:28, 4.34s/it] {'loss': '0.9424', 'grad_norm': '1.619', 'learning_rate': '4.758e-05', 'epoch': '0.5544', 'num_input_tokens_seen': 34872128, 'train_runtime': '4.377e+04', 'train_tokens_per_second': '796.7'}
55%|█████▌ | 10360/18688 [12:09:29<10:02:28, 4.34s/it] 55%|█████▌ | 10361/18688 [12:09:33<9:49:22, 4.25s/it] 55%|█████▌ | 10362/18688 [12:09:37<9:33:53, 4.14s/it] 55%|█████▌ | 10363/18688 [12:09:41<9:53:24, 4.28s/it] 55%|█████▌ | 10364/18688 [12:09:45<9:36:07, 4.15s/it] 55%|█████▌ | 10365/18688 [12:09:49<9:07:11, 3.94s/it] 55%|█████▌ | 10366/18688 [12:09:53<9:28:58, 4.10s/it] 55%|█████▌ | 10367/18688 [12:09:57<9:38:38, 4.17s/it] 55%|█████▌ | 10368/18688 [12:10:02<9:37:56, 4.17s/it] 55%|█████▌ | 10369/18688 [12:10:06<9:29:03, 4.10s/it] 55%|█████▌ | 10370/18688 [12:10:09<9:12:35, 3.99s/it] {'loss': '0.8527', 'grad_norm': '1.308', 'learning_rate': '4.749e-05', 'epoch': '0.5549', 'num_input_tokens_seen': 34904264, 'train_runtime': '4.381e+04', 'train_tokens_per_second': '796.7'}
55%|█████▌ | 10370/18688 [12:10:09<9:12:35, 3.99s/it] 55%|█████▌ | 10371/18688 [12:10:13<9:00:56, 3.90s/it] 56%|█████▌ | 10372/18688 [12:10:17<9:08:26, 3.96s/it] 56%|█████▌ | 10373/18688 [12:10:21<9:09:25, 3.96s/it] 56%|█████▌ | 10374/18688 [12:10:25<8:58:25, 3.89s/it] 56%|█████▌ | 10375/18688 [12:10:29<9:27:11, 4.09s/it] 56%|█████▌ | 10376/18688 [12:10:34<9:33:58, 4.14s/it] 56%|█████▌ | 10377/18688 [12:10:38<9:53:27, 4.28s/it] 56%|█████▌ | 10378/18688 [12:10:42<9:43:45, 4.21s/it] 56%|█████▌ | 10379/18688 [12:10:46<9:41:38, 4.20s/it] 56%|█████▌ | 10380/18688 [12:10:50<9:29:46, 4.11s/it] {'loss': '0.9899', 'grad_norm': '1.254', 'learning_rate': '4.74e-05', 'epoch': '0.5555', 'num_input_tokens_seen': 34938328, 'train_runtime': '4.385e+04', 'train_tokens_per_second': '796.8'}
56%|█████▌ | 10380/18688 [12:10:50<9:29:46, 4.11s/it] 56%|█████▌ | 10381/18688 [12:10:55<10:11:24, 4.42s/it] 56%|█████▌ | 10382/18688 [12:11:00<10:04:09, 4.36s/it] 56%|█████▌ | 10383/18688 [12:11:03<9:31:21, 4.13s/it] 56%|█████▌ | 10384/18688 [12:11:07<9:26:20, 4.09s/it] 56%|█████▌ | 10385/18688 [12:11:11<9:04:59, 3.94s/it] 56%|█████▌ | 10386/18688 [12:11:16<9:40:14, 4.19s/it] 56%|█████▌ | 10387/18688 [12:11:19<9:10:30, 3.98s/it] 56%|█████▌ | 10388/18688 [12:11:24<9:36:27, 4.17s/it] 56%|█████▌ | 10389/18688 [12:11:28<9:27:29, 4.10s/it] 56%|█████▌ | 10390/18688 [12:11:33<9:58:19, 4.33s/it] {'loss': '0.8954', 'grad_norm': '1.396', 'learning_rate': '4.731e-05', 'epoch': '0.556', 'num_input_tokens_seen': 34974160, 'train_runtime': '4.389e+04', 'train_tokens_per_second': '796.8'}
56%|█████▌ | 10390/18688 [12:11:33<9:58:19, 4.33s/it] 56%|█████▌ | 10391/18688 [12:11:36<9:27:22, 4.10s/it] 56%|█████▌ | 10392/18688 [12:11:40<9:36:30, 4.17s/it] 56%|█████▌ | 10393/18688 [12:11:44<9:18:20, 4.04s/it] 56%|█████▌ | 10394/18688 [12:11:48<9:00:05, 3.91s/it] 56%|█████▌ | 10395/18688 [12:11:52<9:19:51, 4.05s/it] 56%|█████▌ | 10396/18688 [12:11:56<9:05:39, 3.95s/it] 56%|█████▌ | 10397/18688 [12:12:00<8:54:14, 3.87s/it] 56%|█████▌ | 10398/18688 [12:12:04<9:10:17, 3.98s/it] 56%|█████▌ | 10399/18688 [12:12:08<9:20:11, 4.05s/it] 56%|█████▌ | 10400/18688 [12:12:12<9:12:05, 4.00s/it] {'loss': '0.92', 'grad_norm': '1.285', 'learning_rate': '4.721e-05', 'epoch': '0.5565', 'num_input_tokens_seen': 35006040, 'train_runtime': '4.393e+04', 'train_tokens_per_second': '796.8'}
56%|█████▌ | 10400/18688 [12:12:12<9:12:05, 4.00s/it] 56%|█████▌ | 10401/18688 [12:12:16<9:00:52, 3.92s/it] 56%|█████▌ | 10402/18688 [12:12:19<8:52:13, 3.85s/it] 56%|█████▌ | 10403/18688 [12:12:23<8:48:28, 3.83s/it] 56%|█████▌ | 10404/18688 [12:12:27<8:39:04, 3.76s/it] 56%|█████▌ | 10405/18688 [12:12:31<9:20:35, 4.06s/it] 56%|█████▌ | 10406/18688 [12:12:35<9:02:34, 3.93s/it] 56%|█████▌ | 10407/18688 [12:12:39<9:20:24, 4.06s/it] 56%|█████▌ | 10408/18688 [12:12:44<9:41:12, 4.21s/it] 56%|█████▌ | 10409/18688 [12:12:48<9:20:04, 4.06s/it] 56%|█████▌ | 10410/18688 [12:12:52<9:12:57, 4.01s/it] {'loss': '0.9224', 'grad_norm': '1.392', 'learning_rate': '4.712e-05', 'epoch': '0.5571', 'num_input_tokens_seen': 35037200, 'train_runtime': '4.397e+04', 'train_tokens_per_second': '796.8'}
56%|█████▌ | 10410/18688 [12:12:52<9:12:57, 4.01s/it] 56%|█████▌ | 10411/18688 [12:12:55<9:02:33, 3.93s/it] 56%|█████▌ | 10412/18688 [12:13:00<9:18:32, 4.05s/it] 56%|█████▌ | 10413/18688 [12:13:04<9:22:14, 4.08s/it] 56%|█████▌ | 10414/18688 [12:13:08<9:13:38, 4.01s/it] 56%|█████▌ | 10415/18688 [12:13:12<9:13:20, 4.01s/it] 56%|█████▌ | 10416/18688 [12:13:16<9:18:26, 4.05s/it] 56%|█████▌ | 10417/18688 [12:13:20<9:03:41, 3.94s/it] 56%|█████▌ | 10418/18688 [12:13:24<9:26:52, 4.11s/it] 56%|█████▌ | 10419/18688 [12:13:28<9:25:09, 4.10s/it] 56%|█████▌ | 10420/18688 [12:13:32<9:32:09, 4.15s/it] {'loss': '0.9863', 'grad_norm': '1.607', 'learning_rate': '4.703e-05', 'epoch': '0.5576', 'num_input_tokens_seen': 35068880, 'train_runtime': '4.401e+04', 'train_tokens_per_second': '796.8'}
56%|█████▌ | 10420/18688 [12:13:32<9:32:09, 4.15s/it] 56%|█████▌ | 10421/18688 [12:13:36<9:21:00, 4.07s/it] 56%|█████▌ | 10422/18688 [12:13:40<9:20:44, 4.07s/it] 56%|█████▌ | 10423/18688 [12:13:44<9:16:00, 4.04s/it] 56%|█████▌ | 10424/18688 [12:13:49<9:48:56, 4.28s/it] 56%|█████▌ | 10425/18688 [12:13:53<9:28:51, 4.13s/it] 56%|█████▌ | 10426/18688 [12:13:56<9:05:36, 3.96s/it] 56%|█████▌ | 10427/18688 [12:14:00<8:59:55, 3.92s/it] 56%|█████▌ | 10428/18688 [12:14:04<8:58:18, 3.91s/it] 56%|█████▌ | 10429/18688 [12:14:08<8:47:12, 3.83s/it] 56%|█████▌ | 10430/18688 [12:14:12<8:44:21, 3.81s/it] {'loss': '0.9334', 'grad_norm': '1.239', 'learning_rate': '4.694e-05', 'epoch': '0.5581', 'num_input_tokens_seen': 35098904, 'train_runtime': '4.405e+04', 'train_tokens_per_second': '796.8'}
56%|█████▌ | 10430/18688 [12:14:12<8:44:21, 3.81s/it] 56%|█████▌ | 10431/18688 [12:14:16<9:02:04, 3.94s/it] 56%|█████▌ | 10432/18688 [12:14:20<8:56:05, 3.90s/it] 56%|█████▌ | 10433/18688 [12:14:24<9:27:44, 4.13s/it] 56%|█████▌ | 10434/18688 [12:14:28<9:01:41, 3.94s/it] 56%|█████▌ | 10435/18688 [12:14:32<8:57:29, 3.91s/it] 56%|█████▌ | 10436/18688 [12:14:36<9:00:38, 3.93s/it] 56%|█████▌ | 10437/18688 [12:14:40<9:22:24, 4.09s/it] 56%|█████▌ | 10438/18688 [12:14:44<8:58:35, 3.92s/it] 56%|█████▌ | 10439/18688 [12:14:48<9:17:56, 4.06s/it] 56%|█████▌ | 10440/18688 [12:14:53<9:48:03, 4.28s/it] {'loss': '0.956', 'grad_norm': '1.643', 'learning_rate': '4.685e-05', 'epoch': '0.5587', 'num_input_tokens_seen': 35131432, 'train_runtime': '4.409e+04', 'train_tokens_per_second': '796.8'}
56%|█████▌ | 10440/18688 [12:14:53<9:48:03, 4.28s/it] 56%|█████▌ | 10441/18688 [12:14:57<9:30:14, 4.15s/it] 56%|█████▌ | 10442/18688 [12:15:00<9:05:44, 3.97s/it] 56%|█████▌ | 10443/18688 [12:15:04<9:16:49, 4.05s/it] 56%|█████▌ | 10444/18688 [12:15:08<8:56:03, 3.90s/it] 56%|█████▌ | 10445/18688 [12:15:12<9:00:02, 3.93s/it] 56%|█████▌ | 10446/18688 [12:15:16<8:48:29, 3.85s/it] 56%|█████▌ | 10447/18688 [12:15:20<8:55:43, 3.90s/it] 56%|█████▌ | 10448/18688 [12:15:23<8:38:15, 3.77s/it] 56%|█████▌ | 10449/18688 [12:15:27<8:34:04, 3.74s/it] 56%|█████▌ | 10450/18688 [12:15:30<8:23:05, 3.66s/it] {'loss': '0.8917', 'grad_norm': '1.424', 'learning_rate': '4.676e-05', 'epoch': '0.5592', 'num_input_tokens_seen': 35160576, 'train_runtime': '4.413e+04', 'train_tokens_per_second': '796.7'}
56%|█████▌ | 10450/18688 [12:15:30<8:23:05, 3.66s/it] 56%|█████▌ | 10451/18688 [12:15:34<8:25:22, 3.68s/it] 56%|█████▌ | 10452/18688 [12:15:38<8:50:19, 3.86s/it] 56%|█████▌ | 10453/18688 [12:15:42<8:54:23, 3.89s/it] 56%|█████▌ | 10454/18688 [12:15:46<8:38:55, 3.78s/it] 56%|█████▌ | 10455/18688 [12:15:49<8:34:55, 3.75s/it] 56%|█████▌ | 10456/18688 [12:15:53<8:27:01, 3.70s/it] 56%|█████▌ | 10457/18688 [12:15:57<8:46:58, 3.84s/it] 56%|█████▌ | 10458/18688 [12:16:02<9:19:18, 4.08s/it] 56%|█████▌ | 10459/18688 [12:16:06<9:07:15, 3.99s/it] 56%|█████▌ | 10460/18688 [12:16:09<8:59:18, 3.93s/it] {'loss': '0.9109', 'grad_norm': '1.258', 'learning_rate': '4.667e-05', 'epoch': '0.5597', 'num_input_tokens_seen': 35190992, 'train_runtime': '4.417e+04', 'train_tokens_per_second': '796.7'}
56%|█████▌ | 10460/18688 [12:16:09<8:59:18, 3.93s/it] 56%|█████▌ | 10461/18688 [12:16:14<9:12:45, 4.03s/it] 56%|█████▌ | 10462/18688 [12:16:18<9:05:21, 3.98s/it] 56%|█████▌ | 10463/18688 [12:16:21<8:52:34, 3.89s/it] 56%|█████▌ | 10464/18688 [12:16:25<8:35:27, 3.76s/it] 56%|█████▌ | 10465/18688 [12:16:29<9:10:57, 4.02s/it] 56%|█████▌ | 10466/18688 [12:16:33<9:07:04, 3.99s/it] 56%|█████▌ | 10467/18688 [12:16:38<9:35:00, 4.20s/it] 56%|█████▌ | 10468/18688 [12:16:42<9:52:05, 4.32s/it] 56%|█████▌ | 10469/18688 [12:16:47<9:49:54, 4.31s/it] 56%|█████▌ | 10470/18688 [12:16:51<9:39:02, 4.23s/it] {'loss': '0.9831', 'grad_norm': '1.481', 'learning_rate': '4.658e-05', 'epoch': '0.5603', 'num_input_tokens_seen': 35226248, 'train_runtime': '4.421e+04', 'train_tokens_per_second': '796.8'}
56%|█████▌ | 10470/18688 [12:16:51<9:39:02, 4.23s/it] 56%|█████▌ | 10471/18688 [12:16:55<9:57:24, 4.36s/it] 56%|█████▌ | 10472/18688 [12:16:59<9:34:23, 4.19s/it] 56%|█████▌ | 10473/18688 [12:17:04<9:35:16, 4.20s/it] 56%|█████▌ | 10474/18688 [12:17:08<9:32:27, 4.18s/it] 56%|█████▌ | 10475/18688 [12:17:11<9:11:58, 4.03s/it] 56%|█████▌ | 10476/18688 [12:17:16<9:35:20, 4.20s/it] 56%|█████▌ | 10477/18688 [12:17:20<9:45:36, 4.28s/it] 56%|█████▌ | 10478/18688 [12:17:24<9:16:33, 4.07s/it] 56%|█████▌ | 10479/18688 [12:17:29<9:40:25, 4.24s/it] 56%|█████▌ | 10480/18688 [12:17:33<9:34:59, 4.20s/it] {'loss': '0.8876', 'grad_norm': '1.417', 'learning_rate': '4.648e-05', 'epoch': '0.5608', 'num_input_tokens_seen': 35261360, 'train_runtime': '4.425e+04', 'train_tokens_per_second': '796.8'}
56%|█████▌ | 10480/18688 [12:17:33<9:34:59, 4.20s/it] 56%|█████▌ | 10481/18688 [12:17:36<9:12:32, 4.04s/it] 56%|█████▌ | 10482/18688 [12:17:41<9:42:31, 4.26s/it] 56%|█████▌ | 10483/18688 [12:17:45<9:31:45, 4.18s/it] 56%|█████▌ | 10484/18688 [12:17:50<9:40:31, 4.25s/it] 56%|█████▌ | 10485/18688 [12:17:53<9:16:53, 4.07s/it] 56%|█████▌ | 10486/18688 [12:17:57<9:09:52, 4.02s/it] 56%|█████▌ | 10487/18688 [12:18:01<9:18:36, 4.09s/it] 56%|█████▌ | 10488/18688 [12:18:07<10:09:30, 4.46s/it] 56%|█████▌ | 10489/18688 [12:18:10<9:35:18, 4.21s/it] 56%|█████▌ | 10490/18688 [12:18:14<9:21:36, 4.11s/it] {'loss': '0.9191', 'grad_norm': '2.214', 'learning_rate': '4.639e-05', 'epoch': '0.5613', 'num_input_tokens_seen': 35295720, 'train_runtime': '4.429e+04', 'train_tokens_per_second': '796.8'}
56%|█████▌ | 10490/18688 [12:18:14<9:21:36, 4.11s/it] 56%|█████▌ | 10491/18688 [12:18:19<9:57:20, 4.37s/it] 56%|█████▌ | 10492/18688 [12:18:23<9:40:01, 4.25s/it] 56%|█████▌ | 10493/18688 [12:18:27<9:22:07, 4.12s/it] 56%|█████▌ | 10494/18688 [12:18:31<9:09:07, 4.02s/it] 56%|█████▌ | 10495/18688 [12:18:35<9:19:48, 4.10s/it] 56%|█████▌ | 10496/18688 [12:18:38<8:53:19, 3.91s/it] 56%|█████▌ | 10497/18688 [12:18:43<9:14:25, 4.06s/it] 56%|█████▌ | 10498/18688 [12:18:47<9:00:06, 3.96s/it] 56%|█████▌ | 10499/18688 [12:18:51<9:32:35, 4.20s/it] 56%|█████▌ | 10500/18688 [12:18:55<9:08:50, 4.02s/it] {'loss': '0.898', 'grad_norm': '1.238', 'learning_rate': '4.63e-05', 'epoch': '0.5619', 'num_input_tokens_seen': 35329448, 'train_runtime': '4.434e+04', 'train_tokens_per_second': '796.9'}
56%|█████▌ | 10500/18688 [12:18:55<9:08:50, 4.02s/it] 56%|█████▌ | 10501/18688 [12:18:59<9:19:01, 4.10s/it] 56%|█████▌ | 10502/18688 [12:19:03<9:03:01, 3.98s/it] 56%|█████▌ | 10503/18688 [12:19:08<9:38:47, 4.24s/it] 56%|█████▌ | 10504/18688 [12:19:12<9:46:40, 4.30s/it] 56%|█████▌ | 10505/18688 [12:19:16<9:26:45, 4.16s/it] 56%|█████▌ | 10506/18688 [12:19:20<9:28:16, 4.17s/it] 56%|█████▌ | 10507/18688 [12:19:24<9:02:21, 3.98s/it] 56%|█████▌ | 10508/18688 [12:19:28<9:05:10, 4.00s/it] 56%|█████▌ | 10509/18688 [12:19:32<8:52:54, 3.91s/it] 56%|█████▌ | 10510/18688 [12:19:36<9:21:45, 4.12s/it] {'loss': '0.9498', 'grad_norm': '1.084', 'learning_rate': '4.621e-05', 'epoch': '0.5624', 'num_input_tokens_seen': 35363256, 'train_runtime': '4.438e+04', 'train_tokens_per_second': '796.9'}
56%|█████▌ | 10510/18688 [12:19:36<9:21:45, 4.12s/it] 56%|█████▌ | 10511/18688 [12:19:40<9:05:18, 4.00s/it] 56%|█████▋ | 10512/18688 [12:19:44<9:14:30, 4.07s/it] 56%|█████▋ | 10513/18688 [12:19:48<9:14:52, 4.07s/it] 56%|█████▋ | 10514/18688 [12:19:53<9:32:22, 4.20s/it] 56%|█████▋ | 10515/18688 [12:19:58<10:04:44, 4.44s/it] 56%|█████▋ | 10516/18688 [12:20:02<9:44:58, 4.29s/it] 56%|█████▋ | 10517/18688 [12:20:05<9:15:47, 4.08s/it] 56%|█████▋ | 10518/18688 [12:20:09<9:02:45, 3.99s/it] 56%|█████▋ | 10519/18688 [12:20:13<9:04:54, 4.00s/it] 56%|█████▋ | 10520/18688 [12:20:18<9:35:34, 4.23s/it] {'loss': '0.916', 'grad_norm': '1.377', 'learning_rate': '4.612e-05', 'epoch': '0.5629', 'num_input_tokens_seen': 35396824, 'train_runtime': '4.442e+04', 'train_tokens_per_second': '796.9'}
56%|█████▋ | 10520/18688 [12:20:18<9:35:34, 4.23s/it] 56%|█████▋ | 10521/18688 [12:20:21<9:10:26, 4.04s/it] 56%|█████▋ | 10522/18688 [12:20:25<9:01:46, 3.98s/it] 56%|█████▋ | 10523/18688 [12:20:31<10:32:03, 4.64s/it] 56%|█████▋ | 10524/18688 [12:20:35<9:48:58, 4.33s/it] 56%|█████▋ | 10525/18688 [12:20:39<9:42:23, 4.28s/it] 56%|█████▋ | 10526/18688 [12:20:43<9:23:32, 4.14s/it] 56%|█████▋ | 10527/18688 [12:20:47<9:05:58, 4.01s/it] 56%|█████▋ | 10528/18688 [12:20:52<9:42:45, 4.28s/it] 56%|█████▋ | 10529/18688 [12:20:55<9:19:44, 4.12s/it] 56%|█████▋ | 10530/18688 [12:20:59<9:08:57, 4.04s/it] {'loss': '0.9093', 'grad_norm': '1.351', 'learning_rate': '4.603e-05', 'epoch': '0.5635', 'num_input_tokens_seen': 35431456, 'train_runtime': '4.446e+04', 'train_tokens_per_second': '796.9'}
56%|█████▋ | 10530/18688 [12:20:59<9:08:57, 4.04s/it] 56%|█████▋ | 10531/18688 [12:21:03<8:52:55, 3.92s/it] 56%|█████▋ | 10532/18688 [12:21:06<8:40:48, 3.83s/it] 56%|█████▋ | 10533/18688 [12:21:10<8:45:42, 3.87s/it] 56%|█████▋ | 10534/18688 [12:21:14<8:51:25, 3.91s/it] 56%|█████▋ | 10535/18688 [12:21:18<8:47:42, 3.88s/it] 56%|█████▋ | 10536/18688 [12:21:22<8:54:42, 3.94s/it] 56%|█████▋ | 10537/18688 [12:21:26<8:59:36, 3.97s/it] 56%|█████▋ | 10538/18688 [12:21:31<9:38:17, 4.26s/it] 56%|█████▋ | 10539/18688 [12:21:35<9:11:21, 4.06s/it] 56%|█████▋ | 10540/18688 [12:21:39<9:17:27, 4.11s/it] {'loss': '0.9284', 'grad_norm': '1.429', 'learning_rate': '4.594e-05', 'epoch': '0.564', 'num_input_tokens_seen': 35463280, 'train_runtime': '4.45e+04', 'train_tokens_per_second': '796.9'}
56%|█████▋ | 10540/18688 [12:21:39<9:17:27, 4.11s/it] 56%|█████▋ | 10541/18688 [12:21:43<8:52:05, 3.92s/it] 56%|█████▋ | 10542/18688 [12:21:47<9:19:13, 4.12s/it] 56%|█████▋ | 10543/18688 [12:21:53<10:14:51, 4.53s/it] 56%|█████▋ | 10544/18688 [12:21:57<10:15:39, 4.54s/it] 56%|█████▋ | 10545/18688 [12:22:01<9:46:03, 4.32s/it] 56%|█████▋ | 10546/18688 [12:22:05<9:38:53, 4.27s/it] 56%|█████▋ | 10547/18688 [12:22:09<9:37:18, 4.25s/it] 56%|█████▋ | 10548/18688 [12:22:13<9:10:57, 4.06s/it] 56%|█████▋ | 10549/18688 [12:22:18<9:46:44, 4.33s/it] 56%|█████▋ | 10550/18688 [12:22:22<9:17:31, 4.11s/it] {'loss': '0.9491', 'grad_norm': '1.154', 'learning_rate': '4.585e-05', 'epoch': '0.5645', 'num_input_tokens_seen': 35497984, 'train_runtime': '4.454e+04', 'train_tokens_per_second': '797'}
56%|█████▋ | 10550/18688 [12:22:22<9:17:31, 4.11s/it] 56%|█████▋ | 10551/18688 [12:22:26<9:19:18, 4.12s/it] 56%|█████▋ | 10552/18688 [12:22:30<9:20:53, 4.14s/it] 56%|█████▋ | 10553/18688 [12:22:35<9:40:43, 4.28s/it] 56%|█████▋ | 10554/18688 [12:22:39<9:52:00, 4.37s/it] 56%|█████▋ | 10555/18688 [12:22:43<9:14:32, 4.09s/it] 56%|█████▋ | 10556/18688 [12:22:47<9:32:43, 4.23s/it] 56%|█████▋ | 10557/18688 [12:22:52<9:51:24, 4.36s/it] 56%|█████▋ | 10558/18688 [12:22:55<9:20:59, 4.14s/it] 57%|█████▋ | 10559/18688 [12:23:00<9:44:57, 4.32s/it] 57%|█████▋ | 10560/18688 [12:23:05<10:21:33, 4.59s/it] {'loss': '0.9108', 'grad_norm': '1.223', 'learning_rate': '4.576e-05', 'epoch': '0.5651', 'num_input_tokens_seen': 35533728, 'train_runtime': '4.459e+04', 'train_tokens_per_second': '797'}
57%|█████▋ | 10560/18688 [12:23:05<10:21:33, 4.59s/it] 57%|█████▋ | 10561/18688 [12:23:09<9:41:40, 4.29s/it] 57%|█████▋ | 10562/18688 [12:23:13<9:25:15, 4.17s/it] 57%|█████▋ | 10563/18688 [12:23:16<8:55:41, 3.96s/it] 57%|█████▋ | 10564/18688 [12:23:21<9:08:35, 4.05s/it] 57%|█████▋ | 10565/18688 [12:23:24<8:56:20, 3.96s/it] 57%|█████▋ | 10566/18688 [12:23:28<8:57:44, 3.97s/it] 57%|█████▋ | 10567/18688 [12:23:32<8:40:56, 3.85s/it] 57%|█████▋ | 10568/18688 [12:23:37<9:16:10, 4.11s/it] 57%|█████▋ | 10569/18688 [12:23:41<9:16:10, 4.11s/it] 57%|█████▋ | 10570/18688 [12:23:44<8:57:11, 3.97s/it] {'loss': '0.9363', 'grad_norm': '1.31', 'learning_rate': '4.566e-05', 'epoch': '0.5656', 'num_input_tokens_seen': 35565456, 'train_runtime': '4.462e+04', 'train_tokens_per_second': '797'}
57%|█████▋ | 10570/18688 [12:23:44<8:57:11, 3.97s/it] 57%|█████▋ | 10571/18688 [12:23:48<9:04:36, 4.03s/it] 57%|█████▋ | 10572/18688 [12:23:52<8:45:16, 3.88s/it] 57%|█████▋ | 10573/18688 [12:23:56<8:28:25, 3.76s/it] 57%|█████▋ | 10574/18688 [12:23:59<8:35:25, 3.81s/it] 57%|█████▋ | 10575/18688 [12:24:04<9:10:47, 4.07s/it] 57%|█████▋ | 10576/18688 [12:24:08<9:02:13, 4.01s/it] 57%|█████▋ | 10577/18688 [12:24:13<9:26:16, 4.19s/it] 57%|█████▋ | 10578/18688 [12:24:17<9:49:10, 4.36s/it] 57%|█████▋ | 10579/18688 [12:24:22<9:56:32, 4.41s/it] 57%|█████▋ | 10580/18688 [12:24:25<9:22:10, 4.16s/it] {'loss': '0.8709', 'grad_norm': '1.525', 'learning_rate': '4.557e-05', 'epoch': '0.5662', 'num_input_tokens_seen': 35596224, 'train_runtime': '4.467e+04', 'train_tokens_per_second': '796.9'}
57%|█████▋ | 10580/18688 [12:24:25<9:22:10, 4.16s/it] 57%|█████▋ | 10581/18688 [12:24:29<8:59:15, 3.99s/it] 57%|█████▋ | 10582/18688 [12:24:33<8:40:15, 3.85s/it] 57%|█████▋ | 10583/18688 [12:24:36<8:33:42, 3.80s/it] 57%|█████▋ | 10584/18688 [12:24:40<8:24:29, 3.74s/it] 57%|█████▋ | 10585/18688 [12:24:44<8:44:27, 3.88s/it] 57%|█████▋ | 10586/18688 [12:24:48<8:35:20, 3.82s/it] 57%|█████▋ | 10587/18688 [12:24:52<9:04:14, 4.03s/it] 57%|█████▋ | 10588/18688 [12:24:57<9:17:07, 4.13s/it] 57%|█████▋ | 10589/18688 [12:25:01<9:17:22, 4.13s/it] 57%|█████▋ | 10590/18688 [12:25:05<9:33:22, 4.25s/it] {'loss': '0.8621', 'grad_norm': '1.134', 'learning_rate': '4.548e-05', 'epoch': '0.5667', 'num_input_tokens_seen': 35629064, 'train_runtime': '4.471e+04', 'train_tokens_per_second': '797'}
57%|█████▋ | 10590/18688 [12:25:05<9:33:22, 4.25s/it] 57%|█████▋ | 10591/18688 [12:25:10<9:56:38, 4.42s/it] 57%|█████▋ | 10592/18688 [12:25:15<10:14:13, 4.55s/it] 57%|█████▋ | 10593/18688 [12:25:19<9:39:51, 4.30s/it] 57%|█████▋ | 10594/18688 [12:25:23<9:40:49, 4.31s/it] 57%|█████▋ | 10595/18688 [12:25:27<9:38:34, 4.29s/it] 57%|█████▋ | 10596/18688 [12:25:32<9:37:43, 4.28s/it] 57%|█████▋ | 10597/18688 [12:25:36<9:44:48, 4.34s/it] 57%|█████▋ | 10598/18688 [12:25:40<9:50:14, 4.38s/it] 57%|█████▋ | 10599/18688 [12:25:44<9:28:18, 4.22s/it] 57%|█████▋ | 10600/18688 [12:25:49<9:42:40, 4.32s/it] {'loss': '0.9626', 'grad_norm': '1.373', 'learning_rate': '4.539e-05', 'epoch': '0.5672', 'num_input_tokens_seen': 35664616, 'train_runtime': '4.475e+04', 'train_tokens_per_second': '797'}
57%|█████▋ | 10600/18688 [12:25:49<9:42:40, 4.32s/it] 57%|█████▋ | 10601/18688 [12:25:54<9:59:18, 4.45s/it] 57%|█████▋ | 10602/18688 [12:25:57<9:29:42, 4.23s/it] 57%|█████▋ | 10603/18688 [12:26:01<9:25:42, 4.20s/it] 57%|█████▋ | 10604/18688 [12:26:07<10:02:35, 4.47s/it] 57%|█████▋ | 10605/18688 [12:26:11<9:53:45, 4.41s/it] 57%|█████▋ | 10606/18688 [12:26:15<9:42:56, 4.33s/it] 57%|█████▋ | 10607/18688 [12:26:19<9:19:00, 4.15s/it] 57%|█████▋ | 10608/18688 [12:26:23<9:17:36, 4.14s/it] 57%|█████▋ | 10609/18688 [12:26:27<9:05:33, 4.05s/it] 57%|█████▋ | 10610/18688 [12:26:31<9:08:09, 4.07s/it] {'loss': '0.8936', 'grad_norm': '1.095', 'learning_rate': '4.53e-05', 'epoch': '0.5678', 'num_input_tokens_seen': 35698272, 'train_runtime': '4.479e+04', 'train_tokens_per_second': '797'}
57%|█████▋ | 10610/18688 [12:26:31<9:08:09, 4.07s/it] 57%|█████▋ | 10611/18688 [12:26:35<9:29:14, 4.23s/it] 57%|█████▋ | 10612/18688 [12:26:40<9:55:42, 4.43s/it] 57%|█████▋ | 10613/18688 [12:26:44<9:28:17, 4.22s/it] 57%|█████▋ | 10614/18688 [12:26:48<9:09:36, 4.08s/it] 57%|█████▋ | 10615/18688 [12:26:51<8:48:27, 3.93s/it] 57%|█████▋ | 10616/18688 [12:26:56<9:20:43, 4.17s/it] 57%|█████▋ | 10617/18688 [12:27:00<9:17:52, 4.15s/it] 57%|█████▋ | 10618/18688 [12:27:05<9:34:43, 4.27s/it] 57%|█████▋ | 10619/18688 [12:27:08<9:12:55, 4.11s/it] 57%|█████▋ | 10620/18688 [12:27:13<9:44:16, 4.35s/it] {'loss': '0.8814', 'grad_norm': '1.196', 'learning_rate': '4.521e-05', 'epoch': '0.5683', 'num_input_tokens_seen': 35734088, 'train_runtime': '4.483e+04', 'train_tokens_per_second': '797'}
57%|█████▋ | 10620/18688 [12:27:13<9:44:16, 4.35s/it] 57%|█████▋ | 10621/18688 [12:27:19<10:18:14, 4.60s/it] 57%|█████▋ | 10622/18688 [12:27:22<9:38:46, 4.31s/it] 57%|█████▋ | 10623/18688 [12:27:27<9:42:01, 4.33s/it] 57%|█████▋ | 10624/18688 [12:27:31<9:44:57, 4.35s/it] 57%|█████▋ | 10625/18688 [12:27:35<9:49:11, 4.38s/it] 57%|█████▋ | 10626/18688 [12:27:40<9:49:58, 4.39s/it] 57%|█████▋ | 10627/18688 [12:27:43<9:20:50, 4.17s/it] 57%|█████▋ | 10628/18688 [12:27:48<9:48:25, 4.38s/it] 57%|█████▋ | 10629/18688 [12:27:52<9:30:53, 4.25s/it] 57%|█████▋ | 10630/18688 [12:27:57<9:32:17, 4.26s/it] {'loss': '0.9254', 'grad_norm': '1.473', 'learning_rate': '4.512e-05', 'epoch': '0.5688', 'num_input_tokens_seen': 35770040, 'train_runtime': '4.488e+04', 'train_tokens_per_second': '797.1'}
57%|█████▋ | 10630/18688 [12:27:57<9:32:17, 4.26s/it] 57%|█████▋ | 10631/18688 [12:28:01<9:58:39, 4.46s/it] 57%|█████▋ | 10632/18688 [12:28:06<9:42:17, 4.34s/it] 57%|█████▋ | 10633/18688 [12:28:11<10:12:35, 4.56s/it] 57%|█████▋ | 10634/18688 [12:28:15<9:47:37, 4.38s/it] 57%|█████▋ | 10635/18688 [12:28:19<9:36:49, 4.30s/it] 57%|█████▋ | 10636/18688 [12:28:22<9:02:05, 4.04s/it] 57%|█████▋ | 10637/18688 [12:28:27<9:32:00, 4.26s/it] 57%|█████▋ | 10638/18688 [12:28:32<9:53:12, 4.42s/it] 57%|█████▋ | 10639/18688 [12:28:35<9:19:07, 4.17s/it] 57%|█████▋ | 10640/18688 [12:28:39<9:04:59, 4.06s/it] {'loss': '0.9296', 'grad_norm': '1.165', 'learning_rate': '4.503e-05', 'epoch': '0.5694', 'num_input_tokens_seen': 35805136, 'train_runtime': '4.492e+04', 'train_tokens_per_second': '797.1'}
57%|█████▋ | 10640/18688 [12:28:39<9:04:59, 4.06s/it] 57%|█████▋ | 10641/18688 [12:28:43<9:15:19, 4.14s/it] 57%|█████▋ | 10642/18688 [12:28:47<8:58:28, 4.02s/it] 57%|█████▋ | 10643/18688 [12:28:52<9:17:19, 4.16s/it] 57%|█████▋ | 10644/18688 [12:28:56<9:10:48, 4.11s/it] 57%|█████▋ | 10645/18688 [12:29:00<9:16:32, 4.15s/it] 57%|█████▋ | 10646/18688 [12:29:04<8:57:39, 4.01s/it] 57%|█████▋ | 10647/18688 [12:29:08<9:14:13, 4.14s/it] 57%|█████▋ | 10648/18688 [12:29:11<8:48:05, 3.94s/it] 57%|█████▋ | 10649/18688 [12:29:16<9:03:36, 4.06s/it] 57%|█████▋ | 10650/18688 [12:29:19<8:44:56, 3.92s/it] {'loss': '0.8682', 'grad_norm': '1.488', 'learning_rate': '4.494e-05', 'epoch': '0.5699', 'num_input_tokens_seen': 35838216, 'train_runtime': '4.496e+04', 'train_tokens_per_second': '797.1'}
57%|█████▋ | 10650/18688 [12:29:19<8:44:56, 3.92s/it] 57%|█████▋ | 10651/18688 [12:29:23<8:42:52, 3.90s/it] 57%|█████▋ | 10652/18688 [12:29:28<9:22:56, 4.20s/it] 57%|█████▋ | 10653/18688 [12:29:33<9:29:19, 4.25s/it] 57%|█████▋ | 10654/18688 [12:29:37<9:32:49, 4.28s/it] 57%|█████▋ | 10655/18688 [12:29:41<9:09:31, 4.10s/it] 57%|█████▋ | 10656/18688 [12:29:45<9:13:03, 4.13s/it] 57%|█████▋ | 10657/18688 [12:29:48<8:55:14, 4.00s/it] 57%|█████▋ | 10658/18688 [12:29:54<9:48:54, 4.40s/it] 57%|█████▋ | 10659/18688 [12:29:59<10:21:26, 4.64s/it] 57%|█████▋ | 10660/18688 [12:30:03<9:52:00, 4.42s/it] {'loss': '0.9855', 'grad_norm': '1.376', 'learning_rate': '4.485e-05', 'epoch': '0.5704', 'num_input_tokens_seen': 35875008, 'train_runtime': '4.5e+04', 'train_tokens_per_second': '797.2'}
57%|█████▋ | 10660/18688 [12:30:03<9:52:00, 4.42s/it] 57%|█████▋ | 10661/18688 [12:30:07<9:54:56, 4.45s/it] 57%|█████▋ | 10662/18688 [12:30:11<9:16:48, 4.16s/it] 57%|█████▋ | 10663/18688 [12:30:15<9:13:48, 4.14s/it] 57%|█████▋ | 10664/18688 [12:30:20<9:52:28, 4.43s/it] 57%|█████▋ | 10665/18688 [12:30:25<9:59:30, 4.48s/it] 57%|█████▋ | 10666/18688 [12:30:28<9:26:24, 4.24s/it] 57%|█████▋ | 10667/18688 [12:30:33<9:29:26, 4.26s/it] 57%|█████▋ | 10668/18688 [12:30:36<8:57:47, 4.02s/it] 57%|█████▋ | 10669/18688 [12:30:40<8:42:11, 3.91s/it] 57%|█████▋ | 10670/18688 [12:30:44<8:52:24, 3.98s/it] {'loss': '0.877', 'grad_norm': '1.344', 'learning_rate': '4.475e-05', 'epoch': '0.571', 'num_input_tokens_seen': 35907104, 'train_runtime': '4.504e+04', 'train_tokens_per_second': '797.1'}
57%|█████▋ | 10670/18688 [12:30:44<8:52:24, 3.98s/it] 57%|█████▋ | 10671/18688 [12:30:48<8:57:27, 4.02s/it] 57%|█████▋ | 10672/18688 [12:30:52<8:54:42, 4.00s/it] 57%|█████▋ | 10673/18688 [12:30:56<9:10:43, 4.12s/it] 57%|█████▋ | 10674/18688 [12:31:01<9:33:47, 4.30s/it] 57%|█████▋ | 10675/18688 [12:31:05<9:24:21, 4.23s/it] 57%|█████▋ | 10676/18688 [12:31:10<9:53:13, 4.44s/it] 57%|█████▋ | 10677/18688 [12:31:14<9:20:52, 4.20s/it] 57%|█████▋ | 10678/18688 [12:31:18<9:35:18, 4.31s/it] 57%|█████▋ | 10679/18688 [12:31:22<9:21:44, 4.21s/it] 57%|█████▋ | 10680/18688 [12:31:27<10:00:07, 4.50s/it] {'loss': '0.9418', 'grad_norm': '1.599', 'learning_rate': '4.466e-05', 'epoch': '0.5715', 'num_input_tokens_seen': 35941312, 'train_runtime': '4.509e+04', 'train_tokens_per_second': '797.1'}
57%|█████▋ | 10680/18688 [12:31:27<10:00:07, 4.50s/it] 57%|█████▋ | 10681/18688 [12:31:31<9:23:06, 4.22s/it] 57%|█████▋ | 10682/18688 [12:31:35<9:13:14, 4.15s/it] 57%|█████▋ | 10683/18688 [12:31:39<8:57:41, 4.03s/it] 57%|█████▋ | 10684/18688 [12:31:42<8:44:38, 3.93s/it] 57%|█████▋ | 10685/18688 [12:31:46<8:40:39, 3.90s/it] 57%|█████▋ | 10686/18688 [12:31:50<8:32:00, 3.84s/it] 57%|█████▋ | 10687/18688 [12:31:55<9:09:51, 4.12s/it] 57%|█████▋ | 10688/18688 [12:31:58<8:43:47, 3.93s/it] 57%|█████▋ | 10689/18688 [12:32:02<8:48:47, 3.97s/it] 57%|█████▋ | 10690/18688 [12:32:06<8:38:48, 3.89s/it] {'loss': '0.9479', 'grad_norm': '1.633', 'learning_rate': '4.457e-05', 'epoch': '0.572', 'num_input_tokens_seen': 35971848, 'train_runtime': '4.513e+04', 'train_tokens_per_second': '797.1'}
57%|█████▋ | 10690/18688 [12:32:06<8:38:48, 3.89s/it] 57%|█████▋ | 10691/18688 [12:32:10<8:46:27, 3.95s/it] 57%|█████▋ | 10692/18688 [12:32:15<9:04:23, 4.09s/it] 57%|█████▋ | 10693/18688 [12:32:20<9:42:30, 4.37s/it] 57%|█████▋ | 10694/18688 [12:32:24<9:26:18, 4.25s/it] 57%|█████▋ | 10695/18688 [12:32:28<9:17:05, 4.18s/it] 57%|█████▋ | 10696/18688 [12:32:31<8:56:43, 4.03s/it] 57%|█████▋ | 10697/18688 [12:32:36<9:16:48, 4.18s/it] 57%|█████▋ | 10698/18688 [12:32:40<9:31:24, 4.29s/it] 57%|█████▋ | 10699/18688 [12:32:45<9:31:37, 4.29s/it] 57%|█████▋ | 10700/18688 [12:32:48<8:59:03, 4.05s/it] {'loss': '0.9209', 'grad_norm': '1.199', 'learning_rate': '4.448e-05', 'epoch': '0.5726', 'num_input_tokens_seen': 36006368, 'train_runtime': '4.517e+04', 'train_tokens_per_second': '797.2'}
57%|█████▋ | 10700/18688 [12:32:48<8:59:03, 4.05s/it] 57%|█████▋ | 10701/18688 [12:32:52<8:36:54, 3.88s/it] 57%|█████▋ | 10702/18688 [12:32:55<8:32:17, 3.85s/it] 57%|█████▋ | 10703/18688 [12:32:59<8:27:29, 3.81s/it] 57%|█████▋ | 10704/18688 [12:33:04<8:55:21, 4.02s/it] 57%|█████▋ | 10705/18688 [12:33:08<8:58:19, 4.05s/it] 57%|█████▋ | 10706/18688 [12:33:11<8:34:36, 3.87s/it] 57%|█████▋ | 10707/18688 [12:33:16<8:56:02, 4.03s/it] 57%|█████▋ | 10708/18688 [12:33:20<8:59:37, 4.06s/it] 57%|█████▋ | 10709/18688 [12:33:24<8:59:12, 4.05s/it] 57%|█████▋ | 10710/18688 [12:33:29<9:59:33, 4.51s/it] {'loss': '0.8499', 'grad_norm': '1.1', 'learning_rate': '4.439e-05', 'epoch': '0.5731', 'num_input_tokens_seen': 36041184, 'train_runtime': '4.521e+04', 'train_tokens_per_second': '797.2'}
57%|█████▋ | 10710/18688 [12:33:29<9:59:33, 4.51s/it] 57%|█████▋ | 10711/18688 [12:33:33<9:37:24, 4.34s/it] 57%|█████▋ | 10712/18688 [12:33:37<9:10:21, 4.14s/it] 57%|█████▋ | 10713/18688 [12:33:40<8:47:13, 3.97s/it] 57%|█████▋ | 10714/18688 [12:33:44<8:31:02, 3.85s/it] 57%|█████▋ | 10715/18688 [12:33:48<8:24:17, 3.79s/it] 57%|█████▋ | 10716/18688 [12:33:52<8:25:15, 3.80s/it] 57%|█████▋ | 10717/18688 [12:33:55<8:14:14, 3.72s/it] 57%|█████▋ | 10718/18688 [12:34:00<9:09:14, 4.13s/it] 57%|█████▋ | 10719/18688 [12:34:04<8:58:34, 4.06s/it] 57%|█████▋ | 10720/18688 [12:34:07<8:33:45, 3.87s/it] {'loss': '0.9223', 'grad_norm': '1.469', 'learning_rate': '4.43e-05', 'epoch': '0.5736', 'num_input_tokens_seen': 36070336, 'train_runtime': '4.525e+04', 'train_tokens_per_second': '797.2'}
57%|█████▋ | 10720/18688 [12:34:07<8:33:45, 3.87s/it] 57%|█████▋ | 10721/18688 [12:34:12<8:51:51, 4.01s/it] 57%|█████▋ | 10722/18688 [12:34:15<8:29:12, 3.84s/it] 57%|█████▋ | 10723/18688 [12:34:20<9:05:30, 4.11s/it] 57%|█████▋ | 10724/18688 [12:34:24<9:02:21, 4.09s/it] 57%|█████▋ | 10725/18688 [12:34:28<9:14:13, 4.18s/it] 57%|█████▋ | 10726/18688 [12:34:32<9:05:49, 4.11s/it] 57%|█████▋ | 10727/18688 [12:34:36<9:04:03, 4.10s/it] 57%|█████▋ | 10728/18688 [12:34:40<8:37:54, 3.90s/it] 57%|█████▋ | 10729/18688 [12:34:44<8:39:16, 3.91s/it] 57%|█████▋ | 10730/18688 [12:34:48<8:32:46, 3.87s/it] {'loss': '0.8855', 'grad_norm': '1.035', 'learning_rate': '4.421e-05', 'epoch': '0.5742', 'num_input_tokens_seen': 36102064, 'train_runtime': '4.529e+04', 'train_tokens_per_second': '797.2'}
57%|█████▋ | 10730/18688 [12:34:48<8:32:46, 3.87s/it] 57%|█████▋ | 10731/18688 [12:34:52<8:38:10, 3.91s/it] 57%|█████▋ | 10732/18688 [12:34:56<9:13:57, 4.18s/it] 57%|█████▋ | 10733/18688 [12:35:00<8:53:54, 4.03s/it] 57%|█████▋ | 10734/18688 [12:35:04<8:39:00, 3.92s/it] 57%|█████▋ | 10735/18688 [12:35:08<8:55:58, 4.04s/it] 57%|█████▋ | 10736/18688 [12:35:12<8:35:14, 3.89s/it] 57%|█████▋ | 10737/18688 [12:35:17<9:35:07, 4.34s/it] 57%|█████▋ | 10738/18688 [12:35:21<9:33:12, 4.33s/it] 57%|█████▋ | 10739/18688 [12:35:26<9:47:19, 4.43s/it] 57%|█████▋ | 10740/18688 [12:35:30<9:41:59, 4.39s/it] {'loss': '0.9621', 'grad_norm': '1.287', 'learning_rate': '4.412e-05', 'epoch': '0.5747', 'num_input_tokens_seen': 36135392, 'train_runtime': '4.533e+04', 'train_tokens_per_second': '797.1'}
57%|█████▋ | 10740/18688 [12:35:30<9:41:59, 4.39s/it] 57%|█████▋ | 10741/18688 [12:35:34<9:04:24, 4.11s/it] 57%|█████▋ | 10742/18688 [12:35:38<8:51:34, 4.01s/it] 57%|█████▋ | 10743/18688 [12:35:42<9:19:18, 4.22s/it] 57%|█████▋ | 10744/18688 [12:35:46<9:15:34, 4.20s/it] 57%|█████▋ | 10745/18688 [12:35:51<9:19:54, 4.23s/it] 58%|█████▊ | 10746/18688 [12:35:54<8:58:08, 4.07s/it] 58%|█████▊ | 10747/18688 [12:35:59<9:02:14, 4.10s/it] 58%|█████▊ | 10748/18688 [12:36:03<8:59:40, 4.08s/it] 58%|█████▊ | 10749/18688 [12:36:06<8:49:16, 4.00s/it] 58%|█████▊ | 10750/18688 [12:36:11<9:03:09, 4.11s/it] {'loss': '0.9813', 'grad_norm': '1.708', 'learning_rate': '4.403e-05', 'epoch': '0.5753', 'num_input_tokens_seen': 36167592, 'train_runtime': '4.537e+04', 'train_tokens_per_second': '797.1'}
58%|█████▊ | 10750/18688 [12:36:11<9:03:09, 4.11s/it] 58%|█████▊ | 10751/18688 [12:36:15<9:26:57, 4.29s/it] 58%|█████▊ | 10752/18688 [12:36:20<9:18:37, 4.22s/it] 58%|█████▊ | 10753/18688 [12:36:23<9:03:58, 4.11s/it] 58%|█████▊ | 10754/18688 [12:36:28<9:05:50, 4.13s/it] 58%|█████▊ | 10755/18688 [12:36:31<8:53:10, 4.03s/it] 58%|█████▊ | 10756/18688 [12:36:37<9:40:05, 4.39s/it] 58%|█████▊ | 10757/18688 [12:36:42<10:36:05, 4.81s/it] 58%|█████▊ | 10758/18688 [12:36:46<9:49:44, 4.46s/it] 58%|█████▊ | 10759/18688 [12:36:51<10:07:59, 4.60s/it] 58%|█████▊ | 10760/18688 [12:36:55<10:00:04, 4.54s/it] {'loss': '0.917', 'grad_norm': '1.265', 'learning_rate': '4.394e-05', 'epoch': '0.5758', 'num_input_tokens_seen': 36207584, 'train_runtime': '4.542e+04', 'train_tokens_per_second': '797.2'}
58%|█████▊ | 10760/18688 [12:36:55<10:00:04, 4.54s/it] 58%|█████▊ | 10761/18688 [12:36:59<9:39:40, 4.39s/it] 58%|█████▊ | 10762/18688 [12:37:03<9:13:29, 4.19s/it] 58%|█████▊ | 10763/18688 [12:37:08<9:23:39, 4.27s/it] 58%|█████▊ | 10764/18688 [12:37:12<9:26:42, 4.29s/it] 58%|█████▊ | 10765/18688 [12:37:15<8:54:47, 4.05s/it] 58%|█████▊ | 10766/18688 [12:37:19<8:35:16, 3.90s/it] 58%|█████▊ | 10767/18688 [12:37:23<8:33:54, 3.89s/it] 58%|█████▊ | 10768/18688 [12:37:26<8:16:56, 3.76s/it] 58%|█████▊ | 10769/18688 [12:37:30<8:22:07, 3.80s/it] 58%|█████▊ | 10770/18688 [12:37:34<8:37:42, 3.92s/it] {'loss': '0.841', 'grad_norm': '1.411', 'learning_rate': '4.385e-05', 'epoch': '0.5763', 'num_input_tokens_seen': 36238112, 'train_runtime': '4.545e+04', 'train_tokens_per_second': '797.2'}
58%|█████▊ | 10770/18688 [12:37:34<8:37:42, 3.92s/it] 58%|█████▊ | 10771/18688 [12:37:38<8:25:06, 3.83s/it] 58%|█████▊ | 10772/18688 [12:37:42<8:39:02, 3.93s/it] 58%|█████▊ | 10773/18688 [12:37:47<9:08:17, 4.16s/it] 58%|█████▊ | 10774/18688 [12:37:51<9:00:12, 4.10s/it] 58%|█████▊ | 10775/18688 [12:37:54<8:42:50, 3.96s/it] 58%|█████▊ | 10776/18688 [12:37:58<8:29:13, 3.86s/it] 58%|█████▊ | 10777/18688 [12:38:02<8:28:03, 3.85s/it] 58%|█████▊ | 10778/18688 [12:38:06<8:23:02, 3.82s/it] 58%|█████▊ | 10779/18688 [12:38:10<8:43:41, 3.97s/it] 58%|█████▊ | 10780/18688 [12:38:14<8:54:30, 4.06s/it] {'loss': '0.9409', 'grad_norm': '1.542', 'learning_rate': '4.376e-05', 'epoch': '0.5769', 'num_input_tokens_seen': 36269040, 'train_runtime': '4.549e+04', 'train_tokens_per_second': '797.2'}
58%|█████▊ | 10780/18688 [12:38:14<8:54:30, 4.06s/it] 58%|█████▊ | 10781/18688 [12:38:18<8:32:17, 3.89s/it] 58%|█████▊ | 10782/18688 [12:38:22<8:33:32, 3.90s/it] 58%|█████▊ | 10783/18688 [12:38:25<8:24:48, 3.83s/it] 58%|█████▊ | 10784/18688 [12:38:29<8:19:44, 3.79s/it] 58%|█████▊ | 10785/18688 [12:38:33<8:32:43, 3.89s/it] 58%|█████▊ | 10786/18688 [12:38:37<8:24:17, 3.83s/it] 58%|█████▊ | 10787/18688 [12:38:41<8:45:28, 3.99s/it] 58%|█████▊ | 10788/18688 [12:38:45<8:37:50, 3.93s/it] 58%|█████▊ | 10789/18688 [12:38:51<10:03:00, 4.58s/it] 58%|█████▊ | 10790/18688 [12:38:56<10:21:52, 4.72s/it] {'loss': '0.9065', 'grad_norm': '0.9973', 'learning_rate': '4.367e-05', 'epoch': '0.5774', 'num_input_tokens_seen': 36302864, 'train_runtime': '4.554e+04', 'train_tokens_per_second': '797.2'}
58%|█████▊ | 10790/18688 [12:38:56<10:21:52, 4.72s/it] 58%|█████▊ | 10791/18688 [12:39:00<9:38:58, 4.40s/it] 58%|█████▊ | 10792/18688 [12:39:04<9:29:58, 4.33s/it] 58%|█████▊ | 10793/18688 [12:39:07<8:57:05, 4.08s/it] 58%|█████▊ | 10794/18688 [12:39:11<8:46:27, 4.00s/it] 58%|█████▊ | 10795/18688 [12:39:16<9:02:38, 4.12s/it] 58%|█████▊ | 10796/18688 [12:39:19<8:42:04, 3.97s/it] 58%|█████▊ | 10797/18688 [12:39:24<8:58:16, 4.09s/it] 58%|█████▊ | 10798/18688 [12:39:29<9:42:40, 4.43s/it] 58%|█████▊ | 10799/18688 [12:39:32<9:05:16, 4.15s/it] 58%|█████▊ | 10800/18688 [12:39:37<9:16:25, 4.23s/it] {'loss': '0.9051', 'grad_norm': '2.008', 'learning_rate': '4.357e-05', 'epoch': '0.5779', 'num_input_tokens_seen': 36335984, 'train_runtime': '4.558e+04', 'train_tokens_per_second': '797.2'}
58%|█████▊ | 10800/18688 [12:39:37<9:16:25, 4.23s/it] 58%|█████▊ | 10801/18688 [12:39:40<8:46:11, 4.00s/it] 58%|█████▊ | 10802/18688 [12:39:45<9:17:25, 4.24s/it] 58%|█████▊ | 10803/18688 [12:39:50<9:40:41, 4.42s/it] 58%|█████▊ | 10804/18688 [12:39:54<9:45:38, 4.46s/it] 58%|█████▊ | 10805/18688 [12:39:59<9:30:37, 4.34s/it] 58%|█████▊ | 10806/18688 [12:40:04<10:33:02, 4.82s/it] 58%|█████▊ | 10807/18688 [12:40:09<10:34:00, 4.83s/it] 58%|█████▊ | 10808/18688 [12:40:13<9:40:33, 4.42s/it] 58%|█████▊ | 10809/18688 [12:40:16<9:06:18, 4.16s/it] 58%|█████▊ | 10810/18688 [12:40:21<9:19:59, 4.26s/it] {'loss': '0.9076', 'grad_norm': '1.222', 'learning_rate': '4.348e-05', 'epoch': '0.5785', 'num_input_tokens_seen': 36372664, 'train_runtime': '4.562e+04', 'train_tokens_per_second': '797.3'}
58%|█████▊ | 10810/18688 [12:40:21<9:19:59, 4.26s/it] 58%|█████▊ | 10811/18688 [12:40:25<9:05:05, 4.15s/it] 58%|█████▊ | 10812/18688 [12:40:28<8:42:37, 3.98s/it] 58%|█████▊ | 10813/18688 [12:40:33<9:11:04, 4.20s/it] 58%|█████▊ | 10814/18688 [12:40:37<9:14:47, 4.23s/it] 58%|█████▊ | 10815/18688 [12:40:41<9:00:47, 4.12s/it] 58%|█████▊ | 10816/18688 [12:40:45<9:04:46, 4.15s/it] 58%|█████▊ | 10817/18688 [12:40:50<9:26:37, 4.32s/it] 58%|█████▊ | 10818/18688 [12:40:54<9:05:39, 4.16s/it] 58%|█████▊ | 10819/18688 [12:40:58<9:16:19, 4.24s/it] 58%|█████▊ | 10820/18688 [12:41:03<9:19:15, 4.26s/it] {'loss': '0.8873', 'grad_norm': '1.462', 'learning_rate': '4.339e-05', 'epoch': '0.579', 'num_input_tokens_seen': 36405504, 'train_runtime': '4.566e+04', 'train_tokens_per_second': '797.3'}
58%|█████▊ | 10820/18688 [12:41:03<9:19:15, 4.26s/it] 58%|█████▊ | 10821/18688 [12:41:06<8:50:30, 4.05s/it] 58%|█████▊ | 10822/18688 [12:41:10<8:54:01, 4.07s/it] 58%|█████▊ | 10823/18688 [12:41:14<8:43:28, 3.99s/it] 58%|█████▊ | 10824/18688 [12:41:18<8:27:48, 3.87s/it] 58%|█████▊ | 10825/18688 [12:41:22<9:00:32, 4.12s/it] 58%|█████▊ | 10826/18688 [12:41:26<8:35:04, 3.93s/it] 58%|█████▊ | 10827/18688 [12:41:30<8:53:43, 4.07s/it] 58%|█████▊ | 10828/18688 [12:41:36<9:39:22, 4.42s/it] 58%|█████▊ | 10829/18688 [12:41:40<9:34:25, 4.39s/it] 58%|█████▊ | 10830/18688 [12:41:43<8:59:06, 4.12s/it] {'loss': '0.9424', 'grad_norm': '1.255', 'learning_rate': '4.33e-05', 'epoch': '0.5795', 'num_input_tokens_seen': 36439368, 'train_runtime': '4.57e+04', 'train_tokens_per_second': '797.3'}
58%|█████▊ | 10830/18688 [12:41:43<8:59:06, 4.12s/it] 58%|█████▊ | 10831/18688 [12:41:47<8:49:00, 4.04s/it] 58%|█████▊ | 10832/18688 [12:41:51<8:35:31, 3.94s/it] 58%|█████▊ | 10833/18688 [12:41:55<8:53:00, 4.07s/it] 58%|█████▊ | 10834/18688 [12:41:59<8:38:56, 3.96s/it] 58%|█████▊ | 10835/18688 [12:42:03<8:27:24, 3.88s/it] 58%|█████▊ | 10836/18688 [12:42:07<8:26:46, 3.87s/it] 58%|█████▊ | 10837/18688 [12:42:11<8:37:37, 3.96s/it] 58%|█████▊ | 10838/18688 [12:42:16<9:28:17, 4.34s/it] 58%|█████▊ | 10839/18688 [12:42:21<9:54:24, 4.54s/it] 58%|█████▊ | 10840/18688 [12:42:25<9:43:57, 4.46s/it] {'loss': '0.8723', 'grad_norm': '1.352', 'learning_rate': '4.321e-05', 'epoch': '0.5801', 'num_input_tokens_seen': 36471744, 'train_runtime': '4.575e+04', 'train_tokens_per_second': '797.3'}
58%|█████▊ | 10840/18688 [12:42:25<9:43:57, 4.46s/it] 58%|█████▊ | 10841/18688 [12:42:30<9:42:53, 4.46s/it] 58%|█████▊ | 10842/18688 [12:42:35<10:08:49, 4.66s/it] 58%|█████▊ | 10843/18688 [12:42:40<10:15:59, 4.71s/it] 58%|█████▊ | 10844/18688 [12:42:43<9:38:43, 4.43s/it] 58%|█████▊ | 10845/18688 [12:42:47<9:05:10, 4.17s/it] 58%|█████▊ | 10846/18688 [12:42:51<8:50:12, 4.06s/it] 58%|█████▊ | 10847/18688 [12:42:55<9:10:06, 4.21s/it] 58%|█████▊ | 10848/18688 [12:43:00<9:47:47, 4.50s/it] 58%|█████▊ | 10849/18688 [12:43:04<9:07:47, 4.19s/it] 58%|█████▊ | 10850/18688 [12:43:08<8:50:18, 4.06s/it] {'loss': '0.9183', 'grad_norm': '1.422', 'learning_rate': '4.312e-05', 'epoch': '0.5806', 'num_input_tokens_seen': 36507216, 'train_runtime': '4.579e+04', 'train_tokens_per_second': '797.3'}
58%|█████▊ | 10850/18688 [12:43:08<8:50:18, 4.06s/it] 58%|█████▊ | 10851/18688 [12:43:12<9:05:17, 4.17s/it] 58%|█████▊ | 10852/18688 [12:43:16<8:49:12, 4.05s/it] 58%|█████▊ | 10853/18688 [12:43:20<8:51:43, 4.07s/it] 58%|█████▊ | 10854/18688 [12:43:24<9:01:48, 4.15s/it] 58%|█████▊ | 10855/18688 [12:43:28<8:58:15, 4.12s/it] 58%|█████▊ | 10856/18688 [12:43:33<9:17:36, 4.27s/it] 58%|█████▊ | 10857/18688 [12:43:37<9:20:16, 4.29s/it] 58%|█████▊ | 10858/18688 [12:43:41<8:55:43, 4.11s/it] 58%|█████▊ | 10859/18688 [12:43:45<8:38:16, 3.97s/it] 58%|█████▊ | 10860/18688 [12:43:49<8:44:40, 4.02s/it] {'loss': '0.9414', 'grad_norm': '1.363', 'learning_rate': '4.303e-05', 'epoch': '0.5811', 'num_input_tokens_seen': 36540224, 'train_runtime': '4.583e+04', 'train_tokens_per_second': '797.3'}
58%|█████▊ | 10860/18688 [12:43:49<8:44:40, 4.02s/it] 58%|█████▊ | 10861/18688 [12:43:53<8:36:50, 3.96s/it] 58%|█████▊ | 10862/18688 [12:43:57<8:44:26, 4.02s/it] 58%|█████▊ | 10863/18688 [12:44:01<8:39:38, 3.98s/it] 58%|█████▊ | 10864/18688 [12:44:05<8:39:21, 3.98s/it] 58%|█████▊ | 10865/18688 [12:44:09<9:02:54, 4.16s/it] 58%|█████▊ | 10866/18688 [12:44:14<9:20:51, 4.30s/it] 58%|█████▊ | 10867/18688 [12:44:19<9:33:24, 4.40s/it] 58%|█████▊ | 10868/18688 [12:44:22<9:01:19, 4.15s/it] 58%|█████▊ | 10869/18688 [12:44:26<8:41:05, 4.00s/it] 58%|█████▊ | 10870/18688 [12:44:30<8:34:58, 3.95s/it] {'loss': '0.9179', 'grad_norm': '1.191', 'learning_rate': '4.294e-05', 'epoch': '0.5817', 'num_input_tokens_seen': 36572264, 'train_runtime': '4.587e+04', 'train_tokens_per_second': '797.3'}
58%|█████▊ | 10870/18688 [12:44:30<8:34:58, 3.95s/it] 58%|█████▊ | 10871/18688 [12:44:34<8:47:16, 4.05s/it] 58%|█████▊ | 10872/18688 [12:44:38<9:04:04, 4.18s/it] 58%|█████▊ | 10873/18688 [12:44:42<8:52:58, 4.09s/it] 58%|█████▊ | 10874/18688 [12:44:47<9:02:25, 4.17s/it] 58%|█████▊ | 10875/18688 [12:44:51<9:31:15, 4.39s/it] 58%|█████▊ | 10876/18688 [12:44:56<9:29:47, 4.38s/it] 58%|█████▊ | 10877/18688 [12:45:00<9:16:34, 4.28s/it] 58%|█████▊ | 10878/18688 [12:45:03<8:45:50, 4.04s/it] 58%|█████▊ | 10879/18688 [12:45:07<8:28:09, 3.90s/it] 58%|█████▊ | 10880/18688 [12:45:10<8:12:00, 3.78s/it] {'loss': '0.9743', 'grad_norm': '1.308', 'learning_rate': '4.285e-05', 'epoch': '0.5822', 'num_input_tokens_seen': 36605224, 'train_runtime': '4.591e+04', 'train_tokens_per_second': '797.3'}
58%|█████▊ | 10880/18688 [12:45:10<8:12:00, 3.78s/it] 58%|█████▊ | 10881/18688 [12:45:15<8:23:34, 3.87s/it] 58%|█████▊ | 10882/18688 [12:45:18<8:12:50, 3.79s/it] 58%|█████▊ | 10883/18688 [12:45:22<8:15:51, 3.81s/it] 58%|█████▊ | 10884/18688 [12:45:27<8:44:14, 4.03s/it] 58%|█████▊ | 10885/18688 [12:45:31<8:59:30, 4.15s/it] 58%|█████▊ | 10886/18688 [12:45:36<9:24:56, 4.34s/it] 58%|█████▊ | 10887/18688 [12:45:41<9:43:17, 4.49s/it] 58%|█████▊ | 10888/18688 [12:45:45<9:27:37, 4.37s/it] 58%|█████▊ | 10889/18688 [12:45:49<9:21:15, 4.32s/it] 58%|█████▊ | 10890/18688 [12:45:52<8:53:24, 4.10s/it] {'loss': '0.9036', 'grad_norm': '1.173', 'learning_rate': '4.276e-05', 'epoch': '0.5827', 'num_input_tokens_seen': 36638776, 'train_runtime': '4.595e+04', 'train_tokens_per_second': '797.3'}
58%|█████▊ | 10890/18688 [12:45:52<8:53:24, 4.10s/it] 58%|█████▊ | 10891/18688 [12:45:56<8:37:06, 3.98s/it] 58%|█████▊ | 10892/18688 [12:46:00<8:42:38, 4.02s/it] 58%|█████▊ | 10893/18688 [12:46:05<9:00:35, 4.16s/it] 58%|█████▊ | 10894/18688 [12:46:09<8:53:57, 4.11s/it] 58%|█████▊ | 10895/18688 [12:46:13<8:44:48, 4.04s/it] 58%|█████▊ | 10896/18688 [12:46:16<8:27:42, 3.91s/it] 58%|█████▊ | 10897/18688 [12:46:21<8:45:19, 4.05s/it] 58%|█████▊ | 10898/18688 [12:46:24<8:34:28, 3.96s/it] 58%|█████▊ | 10899/18688 [12:46:29<8:57:46, 4.14s/it] 58%|█████▊ | 10900/18688 [12:46:33<8:36:59, 3.98s/it] {'loss': '0.9225', 'grad_norm': '1.273', 'learning_rate': '4.267e-05', 'epoch': '0.5833', 'num_input_tokens_seen': 36669608, 'train_runtime': '4.599e+04', 'train_tokens_per_second': '797.3'}
58%|█████▊ | 10900/18688 [12:46:33<8:36:59, 3.98s/it] 58%|█████▊ | 10901/18688 [12:46:36<8:21:58, 3.87s/it] 58%|█████▊ | 10902/18688 [12:46:40<8:26:51, 3.91s/it] 58%|█████▊ | 10903/18688 [12:46:44<8:39:36, 4.00s/it] 58%|█████▊ | 10904/18688 [12:46:48<8:39:25, 4.00s/it] 58%|█████▊ | 10905/18688 [12:46:52<8:32:41, 3.95s/it] 58%|█████▊ | 10906/18688 [12:46:56<8:16:30, 3.83s/it] 58%|█████▊ | 10907/18688 [12:47:00<8:15:32, 3.82s/it] 58%|█████▊ | 10908/18688 [12:47:03<8:06:18, 3.75s/it] 58%|█████▊ | 10909/18688 [12:47:07<8:27:31, 3.91s/it] 58%|█████▊ | 10910/18688 [12:47:12<8:43:55, 4.04s/it] {'loss': '0.8757', 'grad_norm': '1.194', 'learning_rate': '4.258e-05', 'epoch': '0.5838', 'num_input_tokens_seen': 36699808, 'train_runtime': '4.603e+04', 'train_tokens_per_second': '797.3'}
58%|█████▊ | 10910/18688 [12:47:12<8:43:55, 4.04s/it] 58%|█████▊ | 10911/18688 [12:47:16<9:07:33, 4.22s/it] 58%|█████▊ | 10912/18688 [12:47:21<9:34:13, 4.43s/it] 58%|█████▊ | 10913/18688 [12:47:26<9:45:37, 4.52s/it] 58%|█████▊ | 10914/18688 [12:47:30<9:16:59, 4.30s/it] 58%|█████▊ | 10915/18688 [12:47:34<9:02:41, 4.19s/it] 58%|█████▊ | 10916/18688 [12:47:38<9:16:37, 4.30s/it] 58%|█████▊ | 10917/18688 [12:47:43<9:22:11, 4.34s/it] 58%|█████▊ | 10918/18688 [12:47:46<8:57:10, 4.15s/it] 58%|█████▊ | 10919/18688 [12:47:51<8:57:25, 4.15s/it] 58%|█████▊ | 10920/18688 [12:47:55<9:18:11, 4.31s/it] {'loss': '0.9146', 'grad_norm': '1.278', 'learning_rate': '4.249e-05', 'epoch': '0.5843', 'num_input_tokens_seen': 36734448, 'train_runtime': '4.608e+04', 'train_tokens_per_second': '797.3'}
58%|█████▊ | 10920/18688 [12:47:55<9:18:11, 4.31s/it] 58%|█████▊ | 10921/18688 [12:48:00<9:21:37, 4.34s/it] 58%|█████▊ | 10922/18688 [12:48:04<9:01:33, 4.18s/it] 58%|█████▊ | 10923/18688 [12:48:08<9:25:44, 4.37s/it] 58%|█████▊ | 10924/18688 [12:48:13<9:24:25, 4.36s/it] 58%|█████▊ | 10925/18688 [12:48:16<8:51:54, 4.11s/it] 58%|█████▊ | 10926/18688 [12:48:20<8:34:56, 3.98s/it] 58%|█████▊ | 10927/18688 [12:48:24<8:23:18, 3.89s/it] 58%|█████▊ | 10928/18688 [12:48:28<8:35:09, 3.98s/it] 58%|█████▊ | 10929/18688 [12:48:32<8:47:20, 4.08s/it] 58%|█████▊ | 10930/18688 [12:48:36<8:49:37, 4.10s/it] {'loss': '0.9453', 'grad_norm': '1.325', 'learning_rate': '4.24e-05', 'epoch': '0.5849', 'num_input_tokens_seen': 36767736, 'train_runtime': '4.612e+04', 'train_tokens_per_second': '797.3'}
58%|█████▊ | 10930/18688 [12:48:36<8:49:37, 4.10s/it] 58%|█████▊ | 10931/18688 [12:48:42<9:41:33, 4.50s/it] 58%|█████▊ | 10932/18688 [12:48:45<9:09:10, 4.25s/it] 59%|█████▊ | 10933/18688 [12:48:50<9:28:20, 4.40s/it] 59%|█████▊ | 10934/18688 [12:48:54<9:22:04, 4.35s/it] 59%|█████▊ | 10935/18688 [12:49:00<10:08:34, 4.71s/it] 59%|█████▊ | 10936/18688 [12:49:04<9:30:53, 4.42s/it] 59%|█████▊ | 10937/18688 [12:49:09<10:24:18, 4.83s/it] 59%|█████▊ | 10938/18688 [12:49:14<10:07:57, 4.71s/it] 59%|█████▊ | 10939/18688 [12:49:18<9:48:49, 4.56s/it] 59%|█████▊ | 10940/18688 [12:49:22<9:18:53, 4.33s/it] {'loss': '0.9222', 'grad_norm': '1.416', 'learning_rate': '4.231e-05', 'epoch': '0.5854', 'num_input_tokens_seen': 36806376, 'train_runtime': '4.616e+04', 'train_tokens_per_second': '797.3'}
59%|█████▊ | 10940/18688 [12:49:22<9:18:53, 4.33s/it] 59%|█████▊ | 10941/18688 [12:49:26<9:23:23, 4.36s/it] 59%|█████▊ | 10942/18688 [12:49:30<9:12:45, 4.28s/it] 59%|█████▊ | 10943/18688 [12:49:34<8:49:39, 4.10s/it] 59%|█████▊ | 10944/18688 [12:49:38<8:56:22, 4.16s/it] 59%|█████▊ | 10945/18688 [12:49:42<8:53:11, 4.13s/it] 59%|█████▊ | 10946/18688 [12:49:47<9:03:56, 4.22s/it] 59%|█████▊ | 10947/18688 [12:49:51<8:51:10, 4.12s/it] 59%|█████▊ | 10948/18688 [12:49:55<9:03:36, 4.21s/it] 59%|█████▊ | 10949/18688 [12:50:00<9:47:08, 4.55s/it] 59%|█████▊ | 10950/18688 [12:50:05<9:36:53, 4.47s/it] {'loss': '0.9289', 'grad_norm': '1.034', 'learning_rate': '4.222e-05', 'epoch': '0.586', 'num_input_tokens_seen': 36843568, 'train_runtime': '4.621e+04', 'train_tokens_per_second': '797.4'}
59%|█████▊ | 10950/18688 [12:50:05<9:36:53, 4.47s/it] 59%|█████▊ | 10951/18688 [12:50:09<9:39:31, 4.49s/it] 59%|█████▊ | 10952/18688 [12:50:13<8:59:37, 4.19s/it] 59%|█████▊ | 10953/18688 [12:50:16<8:33:46, 3.99s/it] 59%|█████▊ | 10954/18688 [12:50:21<8:48:02, 4.10s/it] 59%|█████▊ | 10955/18688 [12:50:24<8:30:11, 3.96s/it] 59%|█████▊ | 10956/18688 [12:50:28<8:18:23, 3.87s/it] 59%|█████▊ | 10957/18688 [12:50:32<8:24:39, 3.92s/it] 59%|█████▊ | 10958/18688 [12:50:36<8:26:43, 3.93s/it] 59%|█████▊ | 10959/18688 [12:50:40<8:30:57, 3.97s/it] 59%|█████▊ | 10960/18688 [12:50:43<8:12:15, 3.82s/it] {'loss': '0.9594', 'grad_norm': '1.253', 'learning_rate': '4.213e-05', 'epoch': '0.5865', 'num_input_tokens_seen': 36873216, 'train_runtime': '4.624e+04', 'train_tokens_per_second': '797.4'}
59%|█████▊ | 10960/18688 [12:50:43<8:12:15, 3.82s/it] 59%|█████▊ | 10961/18688 [12:50:47<8:08:08, 3.79s/it] 59%|█████▊ | 10962/18688 [12:50:51<8:20:32, 3.89s/it] 59%|█████▊ | 10963/18688 [12:50:56<8:43:03, 4.06s/it] 59%|█████▊ | 10964/18688 [12:51:00<8:49:30, 4.11s/it] 59%|█████▊ | 10965/18688 [12:51:04<8:34:29, 4.00s/it] 59%|█████▊ | 10966/18688 [12:51:07<8:18:03, 3.87s/it] 59%|█████▊ | 10967/18688 [12:51:11<8:19:29, 3.88s/it] 59%|█████▊ | 10968/18688 [12:51:15<8:08:49, 3.80s/it] 59%|█████▊ | 10969/18688 [12:51:20<8:57:34, 4.18s/it] 59%|█████▊ | 10970/18688 [12:51:24<9:13:31, 4.30s/it] {'loss': '0.9545', 'grad_norm': '1.552', 'learning_rate': '4.204e-05', 'epoch': '0.587', 'num_input_tokens_seen': 36905696, 'train_runtime': '4.628e+04', 'train_tokens_per_second': '797.4'}
59%|█████▊ | 10970/18688 [12:51:24<9:13:31, 4.30s/it] 59%|█████▊ | 10971/18688 [12:51:29<9:23:58, 4.38s/it] 59%|█████▊ | 10972/18688 [12:51:33<8:59:52, 4.20s/it] 59%|█████▊ | 10973/18688 [12:51:37<8:54:57, 4.16s/it] 59%|█████▊ | 10974/18688 [12:51:41<8:58:44, 4.19s/it] 59%|█████▊ | 10975/18688 [12:51:45<8:34:00, 4.00s/it] 59%|█████▊ | 10976/18688 [12:51:49<8:38:10, 4.03s/it] 59%|█████▊ | 10977/18688 [12:51:53<8:54:56, 4.16s/it] 59%|█████▊ | 10978/18688 [12:51:57<8:27:44, 3.95s/it] 59%|█████▊ | 10979/18688 [12:52:01<8:23:32, 3.92s/it] 59%|█████▉ | 10980/18688 [12:52:05<8:33:19, 4.00s/it] {'loss': '0.931', 'grad_norm': '1.356', 'learning_rate': '4.195e-05', 'epoch': '0.5876', 'num_input_tokens_seen': 36937392, 'train_runtime': '4.633e+04', 'train_tokens_per_second': '797.3'}
59%|█████▉ | 10980/18688 [12:52:05<8:33:19, 4.00s/it] 59%|█████▉ | 10981/18688 [12:52:08<8:11:14, 3.82s/it] 59%|█████▉ | 10982/18688 [12:52:12<8:05:18, 3.78s/it] 59%|█████▉ | 10983/18688 [12:52:16<8:25:58, 3.94s/it] 59%|█████▉ | 10984/18688 [12:52:20<8:36:35, 4.02s/it] 59%|█████▉ | 10985/18688 [12:52:24<8:30:15, 3.97s/it] 59%|█████▉ | 10986/18688 [12:52:28<8:41:38, 4.06s/it] 59%|█████▉ | 10987/18688 [12:52:32<8:17:51, 3.88s/it] 59%|█████▉ | 10988/18688 [12:52:36<8:05:44, 3.79s/it] 59%|█████▉ | 10989/18688 [12:52:40<8:43:35, 4.08s/it] 59%|█████▉ | 10990/18688 [12:52:45<9:18:08, 4.35s/it] {'loss': '0.866', 'grad_norm': '1.089', 'learning_rate': '4.186e-05', 'epoch': '0.5881', 'num_input_tokens_seen': 36969240, 'train_runtime': '4.637e+04', 'train_tokens_per_second': '797.3'}
59%|█████▉ | 10990/18688 [12:52:45<9:18:08, 4.35s/it] 59%|█████▉ | 10991/18688 [12:52:50<9:18:09, 4.35s/it] 59%|█████▉ | 10992/18688 [12:52:54<9:10:00, 4.29s/it] 59%|█████▉ | 10993/18688 [12:52:58<9:15:37, 4.33s/it] 59%|█████▉ | 10994/18688 [12:53:02<8:54:22, 4.17s/it] 59%|█████▉ | 10995/18688 [12:53:06<9:03:51, 4.24s/it] 59%|█████▉ | 10996/18688 [12:53:10<8:55:20, 4.18s/it] 59%|█████▉ | 10997/18688 [12:53:15<8:54:41, 4.17s/it] 59%|█████▉ | 10998/18688 [12:53:18<8:28:30, 3.97s/it] 59%|█████▉ | 10999/18688 [12:53:23<9:15:37, 4.34s/it] 59%|█████▉ | 11000/18688 [12:53:28<9:27:16, 4.43s/it] {'loss': '0.8879', 'grad_norm': '1.31', 'learning_rate': '4.177e-05', 'epoch': '0.5886', 'num_input_tokens_seen': 37004632, 'train_runtime': '4.641e+04', 'train_tokens_per_second': '797.4'}
59%|█████▉ | 11000/18688 [12:53:28<9:27:16, 4.43s/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, 25.80it/s]
1%| | 6/1000 [00:00<00:54, 18.24it/s]
1%| | 8/1000 [00:00<00:56, 17.51it/s]
1%| | 10/1000 [00:00<00:59, 16.60it/s]
1%| | 12/1000 [00:00<01:00, 16.38it/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<01:51, 8.75it/s]
2%|▏ | 24/1000 [00:01<01:39, 9.79it/s]
3%|▎ | 26/1000 [00:02<01:45, 9.27it/s]
3%|▎ | 28/1000 [00:02<01:36, 10.11it/s]
3%|▎ | 30/1000 [00:02<01:27, 11.02it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.17it/s]
3%|▎ | 34/1000 [00:02<01:25, 11.29it/s]
4%|▎ | 36/1000 [00:03<01:32, 10.45it/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, 9.03it/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, 10.68it/s]
5%|▌ | 50/1000 [00:04<01:21, 11.71it/s]
5%|▌ | 52/1000 [00:04<01:15, 12.55it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.29it/s]
6%|▌ | 56/1000 [00:04<01:07, 13.96it/s]
6%|▌ | 58/1000 [00:05<01:47, 8.73it/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, 12.32it/s]
7%|▋ | 68/1000 [00:05<01:12, 12.90it/s]
7%|▋ | 70/1000 [00:05<01:08, 13.50it/s]
7%|▋ | 72/1000 [00:06<01:06, 13.94it/s]
7%|▋ | 74/1000 [00:06<01:07, 13.71it/s]
8%|▊ | 76/1000 [00:06<01:04, 14.32it/s]
8%|▊ | 78/1000 [00:06<01:03, 14.55it/s]
8%|▊ | 80/1000 [00:06<01:08, 13.50it/s]
8%|▊ | 82/1000 [00:06<01:05, 14.09it/s]
8%|▊ | 84/1000 [00:06<01:02, 14.56it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.68it/s]
9%|▉ | 88/1000 [00:07<01:02, 14.49it/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, 14.34it/s]
10%|▉ | 98/1000 [00:07<01:03, 14.27it/s]
10%|█ | 100/1000 [00:08<01:31, 9.82it/s]
10%|█ | 102/1000 [00:08<01:23, 10.77it/s]
10%|█ | 104/1000 [00:08<01:54, 7.82it/s]
11%|█ | 106/1000 [00:09<02:01, 7.33it/s]
11%|█ | 108/1000 [00:09<01:43, 8.65it/s]
11%|█ | 110/1000 [00:09<01:28, 10.08it/s]
11%|█ | 112/1000 [00:09<01:19, 11.20it/s]
11%|█▏ | 114/1000 [00:09<01:14, 11.89it/s]
12%|█▏ | 116/1000 [00:09<01:10, 12.53it/s]
12%|█▏ | 118/1000 [00:09<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, 9.65it/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, 12.23it/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, 13.32it/s]
14%|█▍ | 138/1000 [00:11<01:05, 13.24it/s]
14%|█▍ | 140/1000 [00:11<01:25, 10.10it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.56it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.59it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.60it/s]
15%|█▍ | 148/1000 [00:12<01:13, 11.51it/s]
15%|█▌ | 150/1000 [00:12<01:07, 12.60it/s]
15%|█▌ | 152/1000 [00:12<01:04, 13.15it/s]
15%|█▌ | 154/1000 [00:13<01:03, 13.42it/s]
16%|█▌ | 156/1000 [00:13<01:03, 13.29it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.78it/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, 13.63it/s]
17%|█▋ | 166/1000 [00:13<01:00, 13.78it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.28it/s]
17%|█▋ | 170/1000 [00:14<01:19, 10.47it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.45it/s]
17%|█▋ | 174/1000 [00:14<01:29, 9.19it/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, 8.78it/s]
18%|█▊ | 182/1000 [00:15<01:35, 8.55it/s]
18%|█▊ | 184/1000 [00:16<01:23, 9.79it/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 [00:16<01:11, 11.35it/s]
19%|█▉ | 194/1000 [00:16<01:16, 10.57it/s]
20%|█▉ | 196/1000 [00:17<01:29, 8.99it/s]
20%|█▉ | 198/1000 [00:17<01:18, 10.23it/s]
20%|██ | 200/1000 [00:17<01:15, 10.54it/s]
20%|██ | 202/1000 [00:17<01:09, 11.50it/s]
20%|██ | 204/1000 [00:17<01:03, 12.62it/s]
21%|██ | 206/1000 [00:17<00:59, 13.23it/s]
21%|██ | 208/1000 [00:18<00:58, 13.63it/s]
21%|██ | 210/1000 [00:18<00:54, 14.50it/s]
21%|██ | 212/1000 [00:18<00:53, 14.77it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.77it/s]
22%|██▏ | 216/1000 [00:18<00:53, 14.52it/s]
22%|██▏ | 218/1000 [00:18<00:52, 14.92it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.91it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.19it/s]
22%|██▏ | 224/1000 [00:19<01:20, 9.66it/s]
23%|██▎ | 226/1000 [00:19<01:12, 10.66it/s]
23%|██▎ | 228/1000 [00:19<01:06, 11.57it/s]
23%|██▎ | 230/1000 [00:20<01:03, 12.08it/s]
23%|██▎ | 232/1000 [00:20<01:01, 12.44it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.14it/s]
24%|██▎ | 236/1000 [00:20<00:55, 13.65it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.29it/s]
24%|██▍ | 240/1000 [00:20<00:53, 14.26it/s]
24%|██▍ | 242/1000 [00:20<00:51, 14.80it/s]
24%|██▍ | 244/1000 [00:20<00:50, 14.99it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.91it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.65it/s]
25%|██▌ | 250/1000 [00:21<00:55, 13.40it/s]
25%|██▌ | 252/1000 [00:21<01:01, 12.14it/s]
25%|██▌ | 254/1000 [00:21<00:56, 13.16it/s]
26%|██▌ | 256/1000 [00:21<00:54, 13.72it/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, 13.06it/s]
26%|██▋ | 264/1000 [00:22<00:53, 13.64it/s]
27%|██▋ | 266/1000 [00:22<00:52, 13.96it/s]
27%|██▋ | 268/1000 [00:22<00:50, 14.54it/s]
27%|██▋ | 270/1000 [00:22<00:54, 13.27it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.65it/s]
27%|██▋ | 274/1000 [00:23<01:01, 11.74it/s]
28%|██▊ | 276/1000 [00:23<00:56, 12.76it/s]
28%|██▊ | 278/1000 [00:23<00:55, 12.99it/s]
28%|██▊ | 280/1000 [00:23<00:53, 13.54it/s]
28%|██▊ | 282/1000 [00:23<00:49, 14.49it/s]
28%|██▊ | 284/1000 [00:23<00:49, 14.43it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.72it/s]
29%|██▉ | 288/1000 [00:24<00:47, 15.07it/s]
29%|██▉ | 290/1000 [00:24<00:45, 15.52it/s]
29%|██▉ | 292/1000 [00:24<00:45, 15.48it/s]
29%|██▉ | 294/1000 [00:24<00:45, 15.38it/s]
30%|██▉ | 296/1000 [00:24<00:45, 15.41it/s]
30%|██▉ | 298/1000 [00:24<00:45, 15.55it/s]
30%|███ | 300/1000 [00:25<00:46, 15.18it/s]
30%|███ | 302/1000 [00:25<00:45, 15.20it/s]
30%|███ | 304/1000 [00:25<00:50, 13.79it/s]
31%|███ | 306/1000 [00:25<00:47, 14.54it/s]
31%|███ | 308/1000 [00:25<00:47, 14.50it/s]
31%|███ | 310/1000 [00:25<00:47, 14.66it/s]
31%|███ | 312/1000 [00:25<00:46, 14.66it/s]
31%|███▏ | 314/1000 [00:25<00:44, 15.37it/s]
32%|███▏ | 316/1000 [00:26<00:43, 15.67it/s]
32%|███▏ | 318/1000 [00:26<00:42, 16.03it/s]
32%|███▏ | 320/1000 [00:26<01:12, 9.38it/s]
32%|███▏ | 322/1000 [00:26<01:03, 10.74it/s]
32%|███▏ | 324/1000 [00:26<00:58, 11.51it/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, 12.45it/s]
33%|███▎ | 332/1000 [00:27<00:52, 12.71it/s]
33%|███▎ | 334/1000 [00:27<00:50, 13.21it/s]
34%|███▎ | 336/1000 [00:27<00:48, 13.61it/s]
34%|███▍ | 338/1000 [00:28<01:05, 10.09it/s]
34%|███▍ | 340/1000 [00:28<00:59, 11.15it/s]
34%|███▍ | 342/1000 [00:28<00:52, 12.42it/s]
34%|███▍ | 344/1000 [00:28<00:50, 13.05it/s]
35%|███▍ | 346/1000 [00:28<00:48, 13.41it/s]
35%|███▍ | 348/1000 [00:28<00:47, 13.86it/s]
35%|███▌ | 350/1000 [00:28<00:45, 14.21it/s]
35%|███▌ | 352/1000 [00:29<00:46, 13.83it/s]
35%|███▌ | 354/1000 [00:29<00:45, 14.15it/s]
36%|███▌ | 356/1000 [00:29<00:44, 14.41it/s]
36%|███▌ | 358/1000 [00:29<00:45, 14.26it/s]
36%|███▌ | 360/1000 [00:29<00:45, 13.98it/s]
36%|███▌ | 362/1000 [00:29<00:50, 12.74it/s]
36%|███▋ | 364/1000 [00:29<00:47, 13.35it/s]
37%|███▋ | 366/1000 [00:30<00:54, 11.70it/s]
37%|███▋ | 368/1000 [00:30<00:52, 12.15it/s]
37%|███▋ | 370/1000 [00:30<00:48, 13.03it/s]
37%|███▋ | 372/1000 [00:30<00:46, 13.40it/s]
37%|███▋ | 374/1000 [00:30<00:55, 11.33it/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, 12.45it/s]
38%|███▊ | 382/1000 [00:31<00:48, 12.65it/s]
38%|███▊ | 384/1000 [00:31<00:46, 13.36it/s]
39%|███▊ | 386/1000 [00:31<00:44, 13.67it/s]
39%|███▉ | 388/1000 [00:31<00:44, 13.66it/s]
39%|███▉ | 390/1000 [00:32<00:45, 13.55it/s]
39%|███▉ | 392/1000 [00:32<00:43, 13.89it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.67it/s]
40%|███▉ | 396/1000 [00:32<00:43, 13.78it/s]
40%|███▉ | 398/1000 [00:32<00:47, 12.58it/s]
40%|████ | 400/1000 [00:32<00:45, 13.22it/s]
40%|████ | 402/1000 [00:32<00:43, 13.88it/s]
40%|████ | 404/1000 [00:33<00:41, 14.51it/s]
41%|████ | 406/1000 [00:33<00:39, 15.03it/s]
41%|████ | 408/1000 [00:33<00:47, 12.58it/s]
41%|████ | 410/1000 [00:33<00:56, 10.50it/s]
41%|████ | 412/1000 [00:33<00:50, 11.58it/s]
41%|████▏ | 414/1000 [00:34<01:07, 8.66it/s]
42%|████▏ | 416/1000 [00:34<01:23, 6.97it/s]
42%|████▏ | 418/1000 [00:34<01:10, 8.29it/s]
42%|████▏ | 420/1000 [00:34<01:00, 9.56it/s]
42%|████▏ | 422/1000 [00:34<00:52, 10.91it/s]
42%|████▏ | 424/1000 [00:35<01:14, 7.69it/s]
43%|████▎ | 426/1000 [00:35<01:04, 8.96it/s]
43%|████▎ | 428/1000 [00:35<00:56, 10.17it/s]
43%|████▎ | 430/1000 [00:35<01:02, 9.13it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.06it/s]
43%|████▎ | 434/1000 [00:36<01:08, 8.29it/s]
44%|████▎ | 436/1000 [00:36<01:00, 9.33it/s]
44%|████▍ | 438/1000 [00:36<01:02, 9.01it/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<00:48, 11.41it/s]
45%|████▍ | 446/1000 [00:37<00:45, 12.17it/s]
45%|████▍ | 448/1000 [00:37<00:50, 10.98it/s]
45%|████▌ | 450/1000 [00:37<00:46, 11.80it/s]
45%|████▌ | 452/1000 [00:37<00:42, 12.83it/s]
45%|████▌ | 454/1000 [00:38<00:44, 12.31it/s]
46%|████▌ | 456/1000 [00:38<00:42, 12.77it/s]
46%|████▌ | 458/1000 [00:38<00:39, 13.79it/s]
46%|████▌ | 460/1000 [00:38<00:38, 13.93it/s]
46%|████▌ | 462/1000 [00:38<00:37, 14.52it/s]
46%|████▋ | 464/1000 [00:38<00:35, 15.03it/s]
47%|████▋ | 466/1000 [00:38<00:35, 14.85it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.90it/s]
47%|████▋ | 470/1000 [00:39<00:52, 10.05it/s]
47%|████▋ | 472/1000 [00:39<00:51, 10.24it/s]
47%|████▋ | 474/1000 [00:39<00:45, 11.52it/s]
48%|████▊ | 476/1000 [00:39<00:43, 12.19it/s]
48%|████▊ | 478/1000 [00:40<00:43, 12.02it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.95it/s]
48%|████▊ | 482/1000 [00:40<00:38, 13.36it/s]
48%|████▊ | 484/1000 [00:40<00:37, 13.86it/s]
49%|████▊ | 486/1000 [00:40<00:36, 14.05it/s]
49%|████▉ | 488/1000 [00:40<00:35, 14.32it/s]
49%|████▉ | 490/1000 [00:41<00:45, 11.11it/s]
49%|████▉ | 492/1000 [00:41<00:41, 12.26it/s]
49%|████▉ | 494/1000 [00:41<00:40, 12.36it/s]
50%|████▉ | 496/1000 [00:41<00:40, 12.42it/s]
50%|████▉ | 498/1000 [00:41<00:40, 12.37it/s]
50%|█████ | 500/1000 [00:41<00:48, 10.35it/s]
50%|█████ | 502/1000 [00:42<00:45, 10.87it/s]
50%|█████ | 504/1000 [00:42<00:44, 11.25it/s]
51%|█████ | 506/1000 [00:42<00:43, 11.49it/s]
51%|█████ | 508/1000 [00:42<00:41, 11.79it/s]
51%|█████ | 510/1000 [00:42<00:52, 9.33it/s]
51%|█████ | 512/1000 [00:43<00:48, 10.09it/s]
51%|█████▏ | 514/1000 [00:43<00:44, 10.82it/s]
52%|█████▏ | 516/1000 [00:43<00:42, 11.41it/s]
52%|█████▏ | 518/1000 [00:43<00:40, 11.76it/s]
52%|█████▏ | 520/1000 [00:43<00:40, 11.87it/s]
52%|█████▏ | 522/1000 [00:43<00:39, 11.98it/s]
52%|█████▏ | 524/1000 [00:43<00:39, 12.16it/s]
53%|█████▎ | 526/1000 [00:44<00:37, 12.55it/s]
53%|█████▎ | 528/1000 [00:44<00:43, 10.88it/s]
53%|█████▎ | 530/1000 [00:44<00:41, 11.28it/s]
53%|█████▎ | 532/1000 [00:44<00:40, 11.61it/s]
53%|█████▎ | 534/1000 [00:44<00:38, 12.17it/s]
54%|█████▎ | 536/1000 [00:44<00:36, 12.81it/s]
54%|█████▍ | 538/1000 [00:45<00:35, 13.01it/s]
54%|█████▍ | 540/1000 [00:45<00:33, 13.69it/s]
54%|█████▍ | 542/1000 [00:45<00:32, 14.17it/s]
54%|█████▍ | 544/1000 [00:45<00:32, 14.17it/s]
55%|█████▍ | 546/1000 [00:45<00:32, 13.94it/s]
55%|█████▍ | 548/1000 [00:45<00:32, 13.75it/s]
55%|█████▌ | 550/1000 [00:45<00:32, 14.04it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.20it/s]
55%|█████▌ | 554/1000 [00:46<00:32, 13.90it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.07it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.47it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 15.03it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 15.00it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.81it/s]
57%|█████▋ | 566/1000 [00:47<00:37, 11.62it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.18it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.22it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.77it/s]
57%|█████▋ | 574/1000 [00:47<00:31, 13.66it/s]
58%|█████▊ | 576/1000 [00:47<00:31, 13.60it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.63it/s]
58%|█████▊ | 580/1000 [00:48<00:32, 12.88it/s]
58%|█████▊ | 582/1000 [00:48<00:30, 13.63it/s]
58%|█████▊ | 584/1000 [00:48<00:30, 13.86it/s]
59%|█████▊ | 586/1000 [00:48<00:29, 14.25it/s]
59%|█████▉ | 588/1000 [00:48<00:27, 15.07it/s]
59%|█████▉ | 590/1000 [00:48<00:27, 15.03it/s]
59%|█████▉ | 592/1000 [00:49<00:27, 14.78it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.32it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.79it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 14.05it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.43it/s]
60%|██████ | 602/1000 [00:49<00:27, 14.54it/s]
60%|██████ | 604/1000 [00:49<00:26, 15.09it/s]
61%|██████ | 606/1000 [00:50<00:26, 14.86it/s]
61%|██████ | 608/1000 [00:50<00:25, 15.15it/s]
61%|██████ | 610/1000 [00:50<00:26, 14.99it/s]
61%|██████ | 612/1000 [00:50<00:25, 15.09it/s]
61%|██████▏ | 614/1000 [00:50<00:26, 14.37it/s]
62%|██████▏ | 616/1000 [00:50<00:25, 14.78it/s]
62%|██████▏ | 618/1000 [00:50<00:26, 14.21it/s]
62%|██████▏ | 620/1000 [00:51<00:38, 9.78it/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, 12.10it/s]
63%|██████▎ | 628/1000 [00:51<00:27, 13.34it/s]
63%|██████▎ | 630/1000 [00:51<00:26, 13.83it/s]
63%|██████▎ | 632/1000 [00:52<00:29, 12.50it/s]
63%|██████▎ | 634/1000 [00:52<00:27, 13.22it/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, 11.45it/s]
64%|██████▍ | 642/1000 [00:52<00:29, 12.33it/s]
64%|██████▍ | 644/1000 [00:53<00:28, 12.59it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.87it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.36it/s]
65%|██████▌ | 650/1000 [00:53<00:27, 12.57it/s]
65%|██████▌ | 652/1000 [00:53<00:29, 11.96it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.45it/s]
66%|██████▌ | 656/1000 [00:54<00:31, 10.79it/s]
66%|██████▌ | 658/1000 [00:54<00:28, 11.87it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.15it/s]
66%|██████▌ | 662/1000 [00:54<00:29, 11.28it/s]
66%|██████▋ | 664/1000 [00:54<00:28, 11.78it/s]
67%|██████▋ | 666/1000 [00:54<00:26, 12.81it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.35it/s]
67%|██████▋ | 670/1000 [00:55<00:29, 11.12it/s]
67%|██████▋ | 672/1000 [00:55<00:27, 11.89it/s]
67%|██████▋ | 674/1000 [00:55<00:26, 12.48it/s]
68%|██████▊ | 676/1000 [00:55<00:27, 11.74it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.23it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 13.00it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.14it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.35it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.42it/s]
69%|██████▉ | 688/1000 [00:56<00:22, 13.73it/s]
69%|██████▉ | 690/1000 [00:56<00:22, 13.81it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.29it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.38it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 14.10it/s]
70%|██████▉ | 698/1000 [00:57<00:20, 14.62it/s]
70%|███████ | 700/1000 [00:57<00:24, 12.19it/s]
70%|███████ | 702/1000 [00:57<00:22, 13.00it/s]
70%|███████ | 704/1000 [00:58<00:29, 9.88it/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, 10.93it/s]
71%|███████ | 712/1000 [00:58<00:24, 11.90it/s]
71%|███████▏ | 714/1000 [00:58<00:22, 12.98it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 13.05it/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<00:19, 13.91it/s]
72%|███████▏ | 724/1000 [00:59<00:20, 13.76it/s]
73%|███████▎ | 726/1000 [00:59<00:19, 14.00it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.92it/s]
73%|███████▎ | 730/1000 [01:00<00:32, 8.19it/s]
73%|███████▎ | 732/1000 [01:00<00:32, 8.31it/s]
73%|███████▎ | 734/1000 [01:00<00:30, 8.62it/s]
74%|███████▎ | 735/1000 [01:00<00:31, 8.47it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.92it/s]
74%|███████▍ | 739/1000 [01:01<00:23, 11.02it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.71it/s]
74%|███████▍ | 743/1000 [01:01<00:20, 12.35it/s]
74%|███████▍ | 745/1000 [01:01<00:25, 10.20it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.58it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.76it/s]
75%|███████▌ | 751/1000 [01:02<00:20, 11.87it/s]
75%|███████▌ | 753/1000 [01:02<00:19, 12.64it/s]
76%|███████▌ | 755/1000 [01:02<00:18, 13.31it/s]
76%|███████▌ | 757/1000 [01:02<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, 10.24it/s]
76%|███████▋ | 765/1000 [01:03<00:27, 8.60it/s]
77%|███████▋ | 767/1000 [01:03<00:24, 9.56it/s]
77%|███████▋ | 769/1000 [01:04<00:21, 10.57it/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, 13.16it/s]
78%|███████▊ | 777/1000 [01:04<00:15, 14.16it/s]
78%|███████▊ | 779/1000 [01:04<00:20, 10.53it/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, 7.07it/s]
78%|███████▊ | 785/1000 [01:05<00:29, 7.39it/s]
79%|███████▊ | 786/1000 [01:05<00:30, 7.00it/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, 11.31it/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, 13.15it/s]
80%|████████ | 800/1000 [01:06<00:16, 12.33it/s]
80%|████████ | 802/1000 [01:07<00:23, 8.26it/s]
80%|████████ | 804/1000 [01:07<00:23, 8.30it/s]
81%|████████ | 806/1000 [01:07<00:21, 8.98it/s]
81%|████████ | 808/1000 [01:07<00:18, 10.36it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.49it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.70it/s]
81%|████████▏ | 814/1000 [01:08<00:14, 12.60it/s]
82%|████████▏ | 816/1000 [01:08<00:16, 11.05it/s]
82%|████████▏ | 818/1000 [01:08<00:15, 11.99it/s]
82%|████████▏ | 820/1000 [01:08<00:14, 12.48it/s]
82%|████████▏ | 822/1000 [01:09<00:14, 12.62it/s]
82%|████████▏ | 824/1000 [01:09<00:13, 13.08it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 13.40it/s]
83%|████████▎ | 828/1000 [01:09<00:13, 13.19it/s]
83%|████████▎ | 830/1000 [01:09<00:13, 12.71it/s]
83%|████████▎ | 832/1000 [01:09<00:12, 13.34it/s]
83%|████████▎ | 834/1000 [01:09<00:12, 13.36it/s]
84%|████████▎ | 836/1000 [01:10<00:12, 13.04it/s]
84%|████████▍ | 838/1000 [01:10<00:12, 13.30it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 13.64it/s]
84%|████████▍ | 842/1000 [01:10<00:11, 13.87it/s]
84%|████████▍ | 844/1000 [01:10<00:11, 13.61it/s]
85%|████████▍ | 846/1000 [01:10<00:11, 13.69it/s]
85%|████████▍ | 848/1000 [01:10<00:11, 13.71it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 13.97it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.62it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.21it/s]
86%|████████▌ | 856/1000 [01:11<00:10, 14.08it/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, 13.53it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 14.06it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.87it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.73it/s]
87%|████████▋ | 870/1000 [01:12<00:09, 14.12it/s]
87%|████████▋ | 872/1000 [01:12<00:08, 14.68it/s]
87%|████████▋ | 874/1000 [01:12<00:10, 12.41it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.05it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.95it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.59it/s]
88%|████████▊ | 882/1000 [01:13<00:10, 11.42it/s]
88%|████████▊ | 884/1000 [01:13<00:09, 12.15it/s]
89%|████████▊ | 886/1000 [01:13<00:08, 12.86it/s]
89%|████████▉ | 888/1000 [01:13<00:08, 13.80it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.21it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.00it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 14.15it/s]
90%|████████▉ | 896/1000 [01:14<00:07, 13.92it/s]
90%|████████▉ | 898/1000 [01:14<00:07, 13.33it/s]
90%|█████████ | 900/1000 [01:14<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, 10.88it/s]
91%|█████████ | 908/1000 [01:15<00:07, 11.85it/s]
91%|█████████ | 910/1000 [01:15<00:07, 12.11it/s]
91%|█████████ | 912/1000 [01:15<00:07, 12.33it/s]
91%|█████████▏| 914/1000 [01:16<00:12, 6.82it/s]
92%|█████████▏| 916/1000 [01:16<00:10, 8.15it/s]
92%|█████████▏| 918/1000 [01:16<00:11, 7.41it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.82it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 10.27it/s]
92%|█████████▏| 924/1000 [01:17<00:06, 10.96it/s]
93%|█████████▎| 926/1000 [01:17<00:06, 11.80it/s]
93%|█████████▎| 928/1000 [01:17<00:06, 10.62it/s]
93%|█████████▎| 930/1000 [01:17<00:06, 11.08it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 11.40it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 11.73it/s]
94%|█████████▎| 936/1000 [01:18<00:05, 11.85it/s]
94%|█████████▍| 938/1000 [01:18<00:05, 11.95it/s]
94%|█████████▍| 940/1000 [01:18<00:04, 12.03it/s]
94%|█████████▍| 942/1000 [01:18<00:05, 10.65it/s]
94%|█████████▍| 944/1000 [01:19<00:05, 11.04it/s]
95%|█████████▍| 946/1000 [01:19<00:05, 10.70it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 11.14it/s]
95%|█████████▌| 950/1000 [01:19<00:04, 11.81it/s]
95%|█████████▌| 952/1000 [01:19<00:03, 12.86it/s]
95%|█████████▌| 954/1000 [01:19<00:03, 13.31it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 13.58it/s]
96%|█████████▌| 958/1000 [01:20<00:02, 14.28it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 12.85it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.52it/s]
96%|█████████▋| 964/1000 [01:20<00:03, 10.15it/s]
97%|█████████▋| 966/1000 [01:20<00:02, 11.39it/s]
97%|█████████▋| 968/1000 [01:21<00:02, 12.06it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 12.69it/s]
97%|█████████▋| 972/1000 [01:21<00:02, 9.70it/s]
97%|█████████▋| 974/1000 [01:21<00:02, 10.89it/s]
98%|█████████▊| 976/1000 [01:21<00:02, 11.68it/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, 10.46it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 10.46it/s]
99%|█████████▊| 986/1000 [01:22<00:01, 10.71it/s]
99%|█████████▉| 988/1000 [01:22<00:01, 10.93it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 11.41it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 12.32it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 13.05it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 13.98it/s]
100%|█████████▉| 998/1000 [01:23<00:00, 14.14it/s]
100%|██████████| 1000/1000 [01:23<00:00, 14.81it/s]
{'eval_loss': '0.8942', 'eval_runtime': '83.86', 'eval_samples_per_second': '11.92', 'eval_steps_per_second': '11.92', 'epoch': '0.5886', 'num_input_tokens_seen': 37004632}
59%|█████▉ | 11000/18688 [12:54:52<9:27:16, 4.43s/it]
100%|██████████| 1000/1000 [01:23<00:00, 14.81it/s]
 59%|█████▉ | 11001/18688 [12:54:56<63:05:12, 29.55s/it] 59%|█████▉ | 11002/18688 [12:55:01<47:10:36, 22.10s/it] 59%|█████▉ | 11003/18688 [12:55:05<35:44:48, 16.75s/it] 59%|█████▉ | 11004/18688 [12:55:09<27:38:14, 12.95s/it] 59%|█████▉ | 11005/18688 [12:55:13<21:52:48, 10.25s/it] 59%|█████▉ | 11006/18688 [12:55:18<18:24:23, 8.63s/it] 59%|█████▉ | 11007/18688 [12:55:22<15:26:13, 7.24s/it] 59%|█████▉ | 11008/18688 [12:55:27<13:44:56, 6.44s/it] 59%|█████▉ | 11009/18688 [12:55:31<12:31:57, 5.88s/it] 59%|█████▉ | 11010/18688 [12:55:35<11:13:22, 5.26s/it] {'loss': '0.9112', 'grad_norm': '1.131', 'learning_rate': '4.168e-05', 'epoch': '0.5892', 'num_input_tokens_seen': 37041936, 'train_runtime': '4.654e+04', 'train_tokens_per_second': '796'}
59%|█████▉ | 11010/18688 [12:55:35<11:13:22, 5.26s/it] 59%|█████▉ | 11011/18688 [12:55:40<11:01:31, 5.17s/it] 59%|█████▉ | 11012/18688 [12:55:45<10:44:02, 5.03s/it] 59%|█████▉ | 11013/18688 [12:55:49<10:10:03, 4.77s/it] 59%|█████▉ | 11014/18688 [12:55:52<9:28:30, 4.44s/it] 59%|█████▉ | 11015/18688 [12:55:58<9:54:11, 4.65s/it] 59%|█████▉ | 11016/18688 [12:56:01<9:19:26, 4.38s/it] 59%|█████▉ | 11017/18688 [12:56:05<8:58:32, 4.21s/it] 59%|█████▉ | 11018/18688 [12:56:10<9:10:31, 4.31s/it] 59%|█████▉ | 11019/18688 [12:56:14<9:22:33, 4.40s/it] 59%|█████▉ | 11020/18688 [12:56:19<9:35:53, 4.51s/it] {'loss': '0.9452', 'grad_norm': '1.224', 'learning_rate': '4.159e-05', 'epoch': '0.5897', 'num_input_tokens_seen': 37078200, 'train_runtime': '4.658e+04', 'train_tokens_per_second': '796'}
59%|█████▉ | 11020/18688 [12:56:19<9:35:53, 4.51s/it] 59%|█████▉ | 11021/18688 [12:56:23<9:20:37, 4.39s/it] 59%|█████▉ | 11022/18688 [12:56:27<8:53:59, 4.18s/it] 59%|█████▉ | 11023/18688 [12:56:31<8:37:40, 4.05s/it] 59%|█████▉ | 11024/18688 [12:56:35<8:46:52, 4.12s/it] 59%|█████▉ | 11025/18688 [12:56:40<9:33:07, 4.49s/it] 59%|█████▉ | 11026/18688 [12:56:45<9:28:40, 4.45s/it] 59%|█████▉ | 11027/18688 [12:56:49<9:25:59, 4.43s/it] 59%|█████▉ | 11028/18688 [12:56:52<8:52:04, 4.17s/it] 59%|█████▉ | 11029/18688 [12:56:57<8:50:19, 4.15s/it] 59%|█████▉ | 11030/18688 [12:57:00<8:35:01, 4.04s/it] {'loss': '0.8685', 'grad_norm': '1.305', 'learning_rate': '4.15e-05', 'epoch': '0.5902', 'num_input_tokens_seen': 37111288, 'train_runtime': '4.662e+04', 'train_tokens_per_second': '796'}
59%|█████▉ | 11030/18688 [12:57:00<8:35:01, 4.04s/it] 59%|█████▉ | 11031/18688 [12:57:05<9:06:31, 4.28s/it] 59%|█████▉ | 11032/18688 [12:57:09<8:48:31, 4.14s/it] 59%|█████▉ | 11033/18688 [12:57:13<8:28:19, 3.98s/it] 59%|█████▉ | 11034/18688 [12:57:17<8:59:19, 4.23s/it] 59%|█████▉ | 11035/18688 [12:57:21<8:43:31, 4.10s/it] 59%|█████▉ | 11036/18688 [12:57:26<9:15:18, 4.35s/it] 59%|█████▉ | 11037/18688 [12:57:30<8:41:39, 4.09s/it] 59%|█████▉ | 11038/18688 [12:57:33<8:30:28, 4.00s/it] 59%|█████▉ | 11039/18688 [12:57:37<8:19:01, 3.91s/it] 59%|█████▉ | 11040/18688 [12:57:41<8:24:49, 3.96s/it] {'loss': '0.9456', 'grad_norm': '1.08', 'learning_rate': '4.141e-05', 'epoch': '0.5908', 'num_input_tokens_seen': 37144120, 'train_runtime': '4.666e+04', 'train_tokens_per_second': '796'}
59%|█████▉ | 11040/18688 [12:57:41<8:24:49, 3.96s/it] 59%|█████▉ | 11041/18688 [12:57:45<8:21:24, 3.93s/it] 59%|█████▉ | 11042/18688 [12:57:49<8:11:41, 3.86s/it] 59%|█████▉ | 11043/18688 [12:57:54<9:11:03, 4.32s/it] 59%|█████▉ | 11044/18688 [12:57:59<9:13:59, 4.35s/it] 59%|█████▉ | 11045/18688 [12:58:02<8:39:16, 4.08s/it] 59%|█████▉ | 11046/18688 [12:58:06<8:37:14, 4.06s/it] 59%|█████▉ | 11047/18688 [12:58:10<8:23:12, 3.95s/it] 59%|█████▉ | 11048/18688 [12:58:13<8:08:03, 3.83s/it] 59%|█████▉ | 11049/18688 [12:58:18<8:28:32, 3.99s/it] 59%|█████▉ | 11050/18688 [12:58:22<8:30:54, 4.01s/it] {'loss': '0.9159', 'grad_norm': '1.415', 'learning_rate': '4.132e-05', 'epoch': '0.5913', 'num_input_tokens_seen': 37176384, 'train_runtime': '4.67e+04', 'train_tokens_per_second': '796'}
59%|█████▉ | 11050/18688 [12:58:22<8:30:54, 4.01s/it] 59%|█████▉ | 11051/18688 [12:58:26<8:34:28, 4.04s/it] 59%|█████▉ | 11052/18688 [12:58:30<8:29:14, 4.00s/it] 59%|█████▉ | 11053/18688 [12:58:34<8:34:16, 4.04s/it] 59%|█████▉ | 11054/18688 [12:58:38<8:30:17, 4.01s/it] 59%|█████▉ | 11055/18688 [12:58:43<8:58:12, 4.23s/it] 59%|█████▉ | 11056/18688 [12:58:48<9:31:55, 4.50s/it] 59%|█████▉ | 11057/18688 [12:58:51<8:55:55, 4.21s/it] 59%|█████▉ | 11058/18688 [12:58:56<9:02:14, 4.26s/it] 59%|█████▉ | 11059/18688 [12:58:59<8:35:19, 4.05s/it] 59%|█████▉ | 11060/18688 [12:59:03<8:40:56, 4.10s/it] {'loss': '0.9627', 'grad_norm': '1.084', 'learning_rate': '4.123e-05', 'epoch': '0.5918', 'num_input_tokens_seen': 37209768, 'train_runtime': '4.674e+04', 'train_tokens_per_second': '796'}
59%|█████▉ | 11060/18688 [12:59:03<8:40:56, 4.10s/it] 59%|█████▉ | 11061/18688 [12:59:08<8:50:10, 4.17s/it] 59%|█████▉ | 11062/18688 [12:59:11<8:27:38, 3.99s/it] 59%|█████▉ | 11063/18688 [12:59:16<8:41:00, 4.10s/it] 59%|█████▉ | 11064/18688 [12:59:19<8:17:26, 3.91s/it] 59%|█████▉ | 11065/18688 [12:59:25<9:20:54, 4.41s/it] 59%|█████▉ | 11066/18688 [12:59:29<9:20:33, 4.41s/it] 59%|█████▉ | 11067/18688 [12:59:33<9:02:35, 4.27s/it] 59%|█████▉ | 11068/18688 [12:59:37<8:47:55, 4.16s/it] 59%|█████▉ | 11069/18688 [12:59:41<8:32:01, 4.03s/it] 59%|█████▉ | 11070/18688 [12:59:45<8:33:02, 4.04s/it] {'loss': '0.8772', 'grad_norm': '1.482', 'learning_rate': '4.114e-05', 'epoch': '0.5924', 'num_input_tokens_seen': 37243352, 'train_runtime': '4.679e+04', 'train_tokens_per_second': '796'}
59%|█████▉ | 11070/18688 [12:59:45<8:33:02, 4.04s/it] 59%|█████▉ | 11071/18688 [12:59:49<8:40:24, 4.10s/it] 59%|█████▉ | 11072/18688 [12:59:53<8:49:46, 4.17s/it] 59%|█████▉ | 11073/18688 [12:59:58<8:51:25, 4.19s/it] 59%|█████▉ | 11074/18688 [13:00:02<9:03:02, 4.28s/it] 59%|█████▉ | 11075/18688 [13:00:06<8:57:17, 4.23s/it] 59%|█████▉ | 11076/18688 [13:00:11<9:00:52, 4.26s/it] 59%|█████▉ | 11077/18688 [13:00:14<8:42:36, 4.12s/it] 59%|█████▉ | 11078/18688 [13:00:19<9:16:17, 4.39s/it] 59%|█████▉ | 11079/18688 [13:00:23<8:42:09, 4.12s/it] 59%|█████▉ | 11080/18688 [13:00:26<8:22:06, 3.96s/it] {'loss': '0.8788', 'grad_norm': '1.251', 'learning_rate': '4.105e-05', 'epoch': '0.5929', 'num_input_tokens_seen': 37276464, 'train_runtime': '4.683e+04', 'train_tokens_per_second': '796'}
59%|█████▉ | 11080/18688 [13:00:26<8:22:06, 3.96s/it] 59%|█████▉ | 11081/18688 [13:00:30<8:14:02, 3.90s/it] 59%|█████▉ | 11082/18688 [13:00:35<8:55:05, 4.22s/it] 59%|█████▉ | 11083/18688 [13:00:40<9:13:52, 4.37s/it] 59%|█████▉ | 11084/18688 [13:00:44<9:08:06, 4.32s/it] 59%|█████▉ | 11085/18688 [13:00:49<9:30:14, 4.50s/it] 59%|█████▉ | 11086/18688 [13:00:53<9:22:28, 4.44s/it] 59%|█████▉ | 11087/18688 [13:00:57<9:08:24, 4.33s/it] 59%|█████▉ | 11088/18688 [13:01:01<8:46:29, 4.16s/it] 59%|█████▉ | 11089/18688 [13:01:05<8:50:12, 4.19s/it] 59%|█████▉ | 11090/18688 [13:01:09<8:38:33, 4.09s/it] {'loss': '0.9086', 'grad_norm': '1.24', 'learning_rate': '4.096e-05', 'epoch': '0.5934', 'num_input_tokens_seen': 37311592, 'train_runtime': '4.687e+04', 'train_tokens_per_second': '796.1'}
59%|█████▉ | 11090/18688 [13:01:09<8:38:33, 4.09s/it] 59%|█████▉ | 11091/18688 [13:01:13<8:34:46, 4.07s/it] 59%|█████▉ | 11092/18688 [13:01:18<9:06:19, 4.32s/it] 59%|█████▉ | 11093/18688 [13:01:22<8:59:29, 4.26s/it] 59%|█████▉ | 11094/18688 [13:01:27<9:08:39, 4.33s/it] 59%|█████▉ | 11095/18688 [13:01:31<9:13:19, 4.37s/it] 59%|█████▉ | 11096/18688 [13:01:35<9:08:06, 4.33s/it] 59%|█████▉ | 11097/18688 [13:01:40<8:59:45, 4.27s/it] 59%|█████▉ | 11098/18688 [13:01:43<8:28:24, 4.02s/it] 59%|█████▉ | 11099/18688 [13:01:47<8:41:15, 4.12s/it] 59%|█████▉ | 11100/18688 [13:01:52<8:51:27, 4.20s/it] {'loss': '0.8973', 'grad_norm': '1.408', 'learning_rate': '4.087e-05', 'epoch': '0.594', 'num_input_tokens_seen': 37346912, 'train_runtime': '4.691e+04', 'train_tokens_per_second': '796.1'}
59%|█████▉ | 11100/18688 [13:01:52<8:51:27, 4.20s/it] 59%|█████▉ | 11101/18688 [13:01:56<8:58:52, 4.26s/it] 59%|█████▉ | 11102/18688 [13:02:01<9:08:40, 4.34s/it] 59%|█████▉ | 11103/18688 [13:02:04<8:35:19, 4.08s/it] 59%|█████▉ | 11104/18688 [13:02:08<8:39:20, 4.11s/it] 59%|█████▉ | 11105/18688 [13:02:12<8:16:08, 3.93s/it] 59%|█████▉ | 11106/18688 [13:02:16<8:19:25, 3.95s/it] 59%|█████▉ | 11107/18688 [13:02:20<8:26:40, 4.01s/it] 59%|█████▉ | 11108/18688 [13:02:24<8:18:34, 3.95s/it] 59%|█████▉ | 11109/18688 [13:02:29<8:47:11, 4.17s/it] 59%|█████▉ | 11110/18688 [13:02:33<8:48:32, 4.18s/it] {'loss': '0.9412', 'grad_norm': '1.477', 'learning_rate': '4.078e-05', 'epoch': '0.5945', 'num_input_tokens_seen': 37378160, 'train_runtime': '4.695e+04', 'train_tokens_per_second': '796.1'}
59%|█████▉ | 11110/18688 [13:02:33<8:48:32, 4.18s/it] 59%|█████▉ | 11111/18688 [13:02:37<8:33:17, 4.06s/it] 59%|█████▉ | 11112/18688 [13:02:42<9:15:47, 4.40s/it] 59%|█████▉ | 11113/18688 [13:02:46<9:15:45, 4.40s/it] 59%|█████▉ | 11114/18688 [13:02:50<8:53:44, 4.23s/it] 59%|█████▉ | 11115/18688 [13:02:54<9:03:33, 4.31s/it] 59%|█████▉ | 11116/18688 [13:02:58<8:34:22, 4.08s/it] 59%|█████▉ | 11117/18688 [13:03:02<8:19:46, 3.96s/it] 59%|█████▉ | 11118/18688 [13:03:06<8:38:09, 4.11s/it] 59%|█████▉ | 11119/18688 [13:03:10<8:28:02, 4.03s/it] 60%|█████▉ | 11120/18688 [13:03:14<8:25:21, 4.01s/it] {'loss': '0.8868', 'grad_norm': '1.371', 'learning_rate': '4.069e-05', 'epoch': '0.5951', 'num_input_tokens_seen': 37411824, 'train_runtime': '4.699e+04', 'train_tokens_per_second': '796.1'}
60%|█████▉ | 11120/18688 [13:03:14<8:25:21, 4.01s/it] 60%|█████▉ | 11121/18688 [13:03:18<8:31:45, 4.06s/it] 60%|█████▉ | 11122/18688 [13:03:23<8:46:51, 4.18s/it] 60%|█████▉ | 11123/18688 [13:03:27<8:56:28, 4.25s/it] 60%|█████▉ | 11124/18688 [13:03:32<9:28:39, 4.51s/it] 60%|█████▉ | 11125/18688 [13:03:36<9:19:29, 4.44s/it] 60%|█████▉ | 11126/18688 [13:03:40<8:53:17, 4.23s/it] 60%|█████▉ | 11127/18688 [13:03:45<9:02:38, 4.31s/it] 60%|█████▉ | 11128/18688 [13:03:49<8:55:28, 4.25s/it] 60%|█████▉ | 11129/18688 [13:03:53<8:51:24, 4.22s/it] 60%|█████▉ | 11130/18688 [13:03:56<8:28:41, 4.04s/it] {'loss': '0.9127', 'grad_norm': '1.406', 'learning_rate': '4.06e-05', 'epoch': '0.5956', 'num_input_tokens_seen': 37445888, 'train_runtime': '4.704e+04', 'train_tokens_per_second': '796.1'}
60%|█████▉ | 11130/18688 [13:03:56<8:28:41, 4.04s/it] 60%|█████▉ | 11131/18688 [13:04:01<8:37:43, 4.11s/it] 60%|█████▉ | 11132/18688 [13:04:06<9:04:24, 4.32s/it] 60%|█████▉ | 11133/18688 [13:04:10<8:52:12, 4.23s/it] 60%|█████▉ | 11134/18688 [13:04:15<9:29:09, 4.52s/it] 60%|█████▉ | 11135/18688 [13:04:19<9:06:24, 4.34s/it] 60%|█████▉ | 11136/18688 [13:04:22<8:44:22, 4.17s/it] 60%|█████▉ | 11137/18688 [13:04:27<8:47:29, 4.19s/it] 60%|█████▉ | 11138/18688 [13:04:30<8:24:47, 4.01s/it] 60%|█████▉ | 11139/18688 [13:04:34<8:13:10, 3.92s/it] 60%|█████▉ | 11140/18688 [13:04:37<7:55:49, 3.78s/it] {'loss': '0.8486', 'grad_norm': '1.27', 'learning_rate': '4.051e-05', 'epoch': '0.5961', 'num_input_tokens_seen': 37479048, 'train_runtime': '4.708e+04', 'train_tokens_per_second': '796.1'}
60%|█████▉ | 11140/18688 [13:04:37<7:55:49, 3.78s/it] 60%|█████▉ | 11141/18688 [13:04:41<7:56:20, 3.79s/it] 60%|█████▉ | 11142/18688 [13:04:45<8:05:24, 3.86s/it] 60%|█████▉ | 11143/18688 [13:04:51<9:02:10, 4.31s/it] 60%|█████▉ | 11144/18688 [13:04:55<8:46:33, 4.19s/it] 60%|█████▉ | 11145/18688 [13:04:59<8:45:02, 4.18s/it] 60%|█████▉ | 11146/18688 [13:05:02<8:20:07, 3.98s/it] 60%|█████▉ | 11147/18688 [13:05:06<8:15:31, 3.94s/it] 60%|█████▉ | 11148/18688 [13:05:10<8:05:26, 3.86s/it] 60%|█████▉ | 11149/18688 [13:05:14<8:23:21, 4.01s/it] 60%|█████▉ | 11150/18688 [13:05:18<8:33:13, 4.09s/it] {'loss': '0.8979', 'grad_norm': '1.299', 'learning_rate': '4.042e-05', 'epoch': '0.5967', 'num_input_tokens_seen': 37512280, 'train_runtime': '4.712e+04', 'train_tokens_per_second': '796.1'}
60%|█████▉ | 11150/18688 [13:05:18<8:33:13, 4.09s/it] 60%|█████▉ | 11151/18688 [13:05:23<8:51:59, 4.24s/it] 60%|█████▉ | 11152/18688 [13:05:27<8:42:52, 4.16s/it] 60%|█████▉ | 11153/18688 [13:05:31<8:23:45, 4.01s/it] 60%|█████▉ | 11154/18688 [13:05:35<8:31:39, 4.07s/it] 60%|█████▉ | 11155/18688 [13:05:39<8:34:09, 4.10s/it] 60%|█████▉ | 11156/18688 [13:05:43<8:20:07, 3.98s/it] 60%|█████▉ | 11157/18688 [13:05:47<8:15:04, 3.94s/it] 60%|█████▉ | 11158/18688 [13:05:51<8:27:51, 4.05s/it] 60%|█████▉ | 11159/18688 [13:05:55<8:39:09, 4.14s/it] 60%|█████▉ | 11160/18688 [13:05:59<8:22:17, 4.00s/it] {'loss': '0.9257', 'grad_norm': '1.165', 'learning_rate': '4.033e-05', 'epoch': '0.5972', 'num_input_tokens_seen': 37544480, 'train_runtime': '4.716e+04', 'train_tokens_per_second': '796.1'}
60%|█████▉ | 11160/18688 [13:05:59<8:22:17, 4.00s/it] 60%|█████▉ | 11161/18688 [13:06:03<8:15:43, 3.95s/it] 60%|█████▉ | 11162/18688 [13:06:07<8:10:34, 3.91s/it] 60%|█████▉ | 11163/18688 [13:06:11<8:13:40, 3.94s/it] 60%|█████▉ | 11164/18688 [13:06:15<8:24:27, 4.02s/it] 60%|█████▉ | 11165/18688 [13:06:19<8:35:31, 4.11s/it] 60%|█████▉ | 11166/18688 [13:06:23<8:35:19, 4.11s/it] 60%|█████▉ | 11167/18688 [13:06:27<8:12:10, 3.93s/it] 60%|█████▉ | 11168/18688 [13:06:31<8:32:32, 4.09s/it] 60%|█████▉ | 11169/18688 [13:06:36<9:01:29, 4.32s/it] 60%|█████▉ | 11170/18688 [13:06:39<8:29:15, 4.06s/it] {'loss': '0.9679', 'grad_norm': '1.239', 'learning_rate': '4.024e-05', 'epoch': '0.5977', 'num_input_tokens_seen': 37577344, 'train_runtime': '4.72e+04', 'train_tokens_per_second': '796.1'}
60%|█████▉ | 11170/18688 [13:06:39<8:29:15, 4.06s/it] 60%|█████▉ | 11171/18688 [13:06:43<8:09:13, 3.90s/it] 60%|█████▉ | 11172/18688 [13:06:47<7:59:11, 3.83s/it] 60%|█████▉ | 11173/18688 [13:06:51<8:07:27, 3.89s/it] 60%|█████▉ | 11174/18688 [13:06:55<8:09:56, 3.91s/it] 60%|█████▉ | 11175/18688 [13:06:59<8:21:39, 4.01s/it] 60%|█████▉ | 11176/18688 [13:07:04<8:54:37, 4.27s/it] 60%|█████▉ | 11177/18688 [13:07:07<8:28:02, 4.06s/it] 60%|█████▉ | 11178/18688 [13:07:12<8:38:33, 4.14s/it] 60%|█████▉ | 11179/18688 [13:07:15<8:18:37, 3.98s/it] 60%|█████▉ | 11180/18688 [13:07:20<8:28:21, 4.06s/it] {'loss': '0.9164', 'grad_norm': '1.532', 'learning_rate': '4.015e-05', 'epoch': '0.5983', 'num_input_tokens_seen': 37609192, 'train_runtime': '4.724e+04', 'train_tokens_per_second': '796.1'}
60%|█████▉ | 11180/18688 [13:07:20<8:28:21, 4.06s/it] 60%|█████▉ | 11181/18688 [13:07:24<8:35:49, 4.12s/it] 60%|█████▉ | 11182/18688 [13:07:27<8:19:19, 3.99s/it] 60%|█████▉ | 11183/18688 [13:07:32<8:21:38, 4.01s/it] 60%|█████▉ | 11184/18688 [13:07:35<8:14:20, 3.95s/it] 60%|█████▉ | 11185/18688 [13:07:40<8:53:47, 4.27s/it] 60%|█████▉ | 11186/18688 [13:07:44<8:39:49, 4.16s/it] 60%|█████▉ | 11187/18688 [13:07:48<8:43:25, 4.19s/it] 60%|█████▉ | 11188/18688 [13:07:53<8:42:32, 4.18s/it] 60%|█████▉ | 11189/18688 [13:07:57<9:00:00, 4.32s/it] 60%|█████▉ | 11190/18688 [13:08:02<9:08:50, 4.39s/it] {'loss': '0.8899', 'grad_norm': '1.521', 'learning_rate': '4.006e-05', 'epoch': '0.5988', 'num_input_tokens_seen': 37643704, 'train_runtime': '4.728e+04', 'train_tokens_per_second': '796.1'}
60%|█████▉ | 11190/18688 [13:08:02<9:08:50, 4.39s/it] 60%|█████▉ | 11191/18688 [13:08:06<9:02:05, 4.34s/it] 60%|█████▉ | 11192/18688 [13:08:10<8:34:01, 4.11s/it] 60%|█████▉ | 11193/18688 [13:08:14<9:01:04, 4.33s/it] 60%|█████▉ | 11194/18688 [13:08:20<9:33:23, 4.59s/it] 60%|█████▉ | 11195/18688 [13:08:25<10:04:24, 4.84s/it] 60%|█████▉ | 11196/18688 [13:08:29<9:28:00, 4.55s/it] 60%|█████▉ | 11197/18688 [13:08:33<9:22:12, 4.50s/it] 60%|█████▉ | 11198/18688 [13:08:37<8:50:05, 4.25s/it] 60%|█████▉ | 11199/18688 [13:08:42<8:59:55, 4.33s/it] 60%|█████▉ | 11200/18688 [13:08:46<8:51:42, 4.26s/it] {'loss': '0.8611', 'grad_norm': '1.175', 'learning_rate': '3.997e-05', 'epoch': '0.5993', 'num_input_tokens_seen': 37678432, 'train_runtime': '4.733e+04', 'train_tokens_per_second': '796.1'}
60%|█████▉ | 11200/18688 [13:08:46<8:51:42, 4.26s/it] 60%|█████▉ | 11201/18688 [13:08:49<8:28:06, 4.07s/it] 60%|█████▉ | 11202/18688 [13:08:53<8:12:30, 3.95s/it] 60%|█████▉ | 11203/18688 [13:08:57<8:22:26, 4.03s/it] 60%|█████▉ | 11204/18688 [13:09:01<8:28:58, 4.08s/it] 60%|█████▉ | 11205/18688 [13:09:05<8:22:42, 4.03s/it] 60%|█████▉ | 11206/18688 [13:09:09<8:28:33, 4.08s/it] 60%|█████▉ | 11207/18688 [13:09:15<9:20:31, 4.50s/it] 60%|█████▉ | 11208/18688 [13:09:19<8:57:59, 4.32s/it] 60%|█████▉ | 11209/18688 [13:09:23<9:02:20, 4.35s/it] 60%|█████▉ | 11210/18688 [13:09:27<8:34:40, 4.13s/it] {'loss': '0.8794', 'grad_norm': '2.041', 'learning_rate': '3.988e-05', 'epoch': '0.5999', 'num_input_tokens_seen': 37711928, 'train_runtime': '4.737e+04', 'train_tokens_per_second': '796.2'}
60%|█████▉ | 11210/18688 [13:09:27<8:34:40, 4.13s/it] 60%|█████▉ | 11211/18688 [13:09:31<8:39:00, 4.16s/it] 60%|█████▉ | 11212/18688 [13:09:35<8:17:54, 4.00s/it] 60%|██████ | 11213/18688 [13:09:39<8:29:20, 4.09s/it] 60%|██████ | 11214/18688 [13:09:45<9:28:05, 4.56s/it] 60%|██████ | 11215/18688 [13:09:48<8:56:49, 4.31s/it] 60%|██████ | 11216/18688 [13:09:53<8:55:26, 4.30s/it] 60%|██████ | 11217/18688 [13:09:56<8:26:00, 4.06s/it] 60%|██████ | 11218/18688 [13:10:01<8:37:25, 4.16s/it] 60%|██████ | 11219/18688 [13:10:04<8:21:13, 4.03s/it] 60%|██████ | 11220/18688 [13:10:08<8:09:01, 3.93s/it] {'loss': '0.9177', 'grad_norm': '1.344', 'learning_rate': '3.979e-05', 'epoch': '0.6004', 'num_input_tokens_seen': 37746016, 'train_runtime': '4.741e+04', 'train_tokens_per_second': '796.2'}
60%|██████ | 11220/18688 [13:10:08<8:09:01, 3.93s/it] 60%|██████ | 11221/18688 [13:10:13<9:00:04, 4.34s/it] 60%|██████ | 11222/18688 [13:10:17<8:46:42, 4.23s/it] 60%|██████ | 11223/18688 [13:10:21<8:33:49, 4.13s/it] 60%|██████ | 11224/18688 [13:10:26<9:02:05, 4.36s/it] 60%|██████ | 11225/18688 [13:10:30<8:59:00, 4.33s/it] 60%|██████ | 11226/18688 [13:10:34<8:48:09, 4.25s/it] 60%|██████ | 11227/18688 [13:10:38<8:32:58, 4.13s/it] 60%|██████ | 11228/18688 [13:10:43<8:44:47, 4.22s/it] 60%|██████ | 11229/18688 [13:10:47<8:56:06, 4.31s/it] 60%|██████ | 11230/18688 [13:10:52<9:21:18, 4.52s/it] {'loss': '0.9333', 'grad_norm': '1.692', 'learning_rate': '3.97e-05', 'epoch': '0.6009', 'num_input_tokens_seen': 37781880, 'train_runtime': '4.745e+04', 'train_tokens_per_second': '796.2'}
60%|██████ | 11230/18688 [13:10:52<9:21:18, 4.52s/it] 60%|██████ | 11231/18688 [13:10:56<8:46:36, 4.24s/it] 60%|██████ | 11232/18688 [13:11:00<8:52:29, 4.29s/it] 60%|██████ | 11233/18688 [13:11:04<8:44:21, 4.22s/it] 60%|██████ | 11234/18688 [13:11:08<8:33:21, 4.13s/it] 60%|██████ | 11235/18688 [13:11:12<8:37:51, 4.17s/it] 60%|██████ | 11236/18688 [13:11:17<8:53:57, 4.30s/it] 60%|██████ | 11237/18688 [13:11:22<9:04:51, 4.39s/it] 60%|██████ | 11238/18688 [13:11:27<9:46:52, 4.73s/it] 60%|██████ | 11239/18688 [13:11:31<9:09:41, 4.43s/it] 60%|██████ | 11240/18688 [13:11:36<9:30:38, 4.60s/it] {'loss': '0.9213', 'grad_norm': '1.182', 'learning_rate': '3.961e-05', 'epoch': '0.6015', 'num_input_tokens_seen': 37819928, 'train_runtime': '4.75e+04', 'train_tokens_per_second': '796.3'}
60%|██████ | 11240/18688 [13:11:36<9:30:38, 4.60s/it] 60%|██████ | 11241/18688 [13:11:41<9:37:52, 4.66s/it] 60%|██████ | 11242/18688 [13:11:45<9:27:16, 4.57s/it] 60%|██████ | 11243/18688 [13:11:49<9:11:35, 4.45s/it] 60%|██████ | 11244/18688 [13:11:53<9:03:34, 4.38s/it] 60%|██████ | 11245/18688 [13:11:58<9:09:29, 4.43s/it] 60%|██████ | 11246/18688 [13:12:02<8:39:55, 4.19s/it] 60%|██████ | 11247/18688 [13:12:05<8:27:14, 4.09s/it] 60%|██████ | 11248/18688 [13:12:10<8:28:48, 4.10s/it] 60%|██████ | 11249/18688 [13:12:14<8:56:35, 4.33s/it] 60%|██████ | 11250/18688 [13:12:20<9:28:54, 4.59s/it] {'loss': '0.9695', 'grad_norm': '1.405', 'learning_rate': '3.952e-05', 'epoch': '0.602', 'num_input_tokens_seen': 37855248, 'train_runtime': '4.754e+04', 'train_tokens_per_second': '796.3'}
60%|██████ | 11250/18688 [13:12:20<9:28:54, 4.59s/it] 60%|██████ | 11251/18688 [13:12:23<8:47:00, 4.25s/it] 60%|██████ | 11252/18688 [13:12:27<8:20:34, 4.04s/it] 60%|██████ | 11253/18688 [13:12:30<8:06:23, 3.93s/it] 60%|██████ | 11254/18688 [13:12:34<7:56:22, 3.84s/it] 60%|██████ | 11255/18688 [13:12:38<7:47:44, 3.78s/it] 60%|██████ | 11256/18688 [13:12:42<8:09:48, 3.95s/it] 60%|██████ | 11257/18688 [13:12:46<8:17:54, 4.02s/it] 60%|██████ | 11258/18688 [13:12:50<8:24:28, 4.07s/it] 60%|██████ | 11259/18688 [13:12:54<8:23:58, 4.07s/it] 60%|██████ | 11260/18688 [13:13:00<9:08:50, 4.43s/it] {'loss': '0.8957', 'grad_norm': '1.356', 'learning_rate': '3.943e-05', 'epoch': '0.6025', 'num_input_tokens_seen': 37886672, 'train_runtime': '4.758e+04', 'train_tokens_per_second': '796.3'}
60%|██████ | 11260/18688 [13:13:00<9:08:50, 4.43s/it] 60%|██████ | 11261/18688 [13:13:04<8:48:23, 4.27s/it] 60%|██████ | 11262/18688 [13:13:08<8:42:07, 4.22s/it] 60%|██████ | 11263/18688 [13:13:11<8:23:00, 4.06s/it] 60%|██████ | 11264/18688 [13:13:15<8:10:04, 3.96s/it] 60%|██████ | 11265/18688 [13:13:19<8:19:24, 4.04s/it] 60%|██████ | 11266/18688 [13:13:24<8:32:44, 4.14s/it] 60%|██████ | 11267/18688 [13:13:28<8:28:20, 4.11s/it] 60%|██████ | 11268/18688 [13:13:31<8:14:33, 4.00s/it] 60%|██████ | 11269/18688 [13:13:36<8:33:16, 4.15s/it] 60%|██████ | 11270/18688 [13:13:39<8:09:24, 3.96s/it] {'loss': '0.9423', 'grad_norm': '1.207', 'learning_rate': '3.934e-05', 'epoch': '0.6031', 'num_input_tokens_seen': 37917320, 'train_runtime': '4.762e+04', 'train_tokens_per_second': '796.2'}
60%|██████ | 11270/18688 [13:13:39<8:09:24, 3.96s/it] 60%|██████ | 11271/18688 [13:13:44<8:13:35, 3.99s/it] 60%|██████ | 11272/18688 [13:13:48<8:44:08, 4.24s/it] 60%|██████ | 11273/18688 [13:13:52<8:25:39, 4.09s/it] 60%|██████ | 11274/18688 [13:13:56<8:18:03, 4.03s/it] 60%|██████ | 11275/18688 [13:14:00<8:29:46, 4.13s/it] 60%|██████ | 11276/18688 [13:14:06<9:20:26, 4.54s/it] 60%|██████ | 11277/18688 [13:14:10<8:50:29, 4.29s/it] 60%|██████ | 11278/18688 [13:14:14<8:51:33, 4.30s/it] 60%|██████ | 11279/18688 [13:14:18<8:41:52, 4.23s/it] 60%|██████ | 11280/18688 [13:14:22<8:40:22, 4.21s/it] {'loss': '0.9228', 'grad_norm': '1.301', 'learning_rate': '3.925e-05', 'epoch': '0.6036', 'num_input_tokens_seen': 37953256, 'train_runtime': '4.766e+04', 'train_tokens_per_second': '796.3'}
60%|██████ | 11280/18688 [13:14:22<8:40:22, 4.21s/it] 60%|██████ | 11281/18688 [13:14:26<8:31:51, 4.15s/it] 60%|██████ | 11282/18688 [13:14:30<8:22:01, 4.07s/it] 60%|██████ | 11283/18688 [13:14:35<9:10:46, 4.46s/it] 60%|██████ | 11284/18688 [13:14:40<8:59:59, 4.38s/it] 60%|██████ | 11285/18688 [13:14:44<9:00:13, 4.38s/it] 60%|██████ | 11286/18688 [13:14:47<8:25:47, 4.10s/it] 60%|██████ | 11287/18688 [13:14:51<8:05:26, 3.94s/it] 60%|██████ | 11288/18688 [13:14:56<8:33:42, 4.17s/it] 60%|██████ | 11289/18688 [13:15:00<8:49:06, 4.29s/it] 60%|██████ | 11290/18688 [13:15:04<8:23:11, 4.08s/it] {'loss': '0.8584', 'grad_norm': '1.288', 'learning_rate': '3.917e-05', 'epoch': '0.6041', 'num_input_tokens_seen': 37986632, 'train_runtime': '4.77e+04', 'train_tokens_per_second': '796.3'}
60%|██████ | 11290/18688 [13:15:04<8:23:11, 4.08s/it] 60%|██████ | 11291/18688 [13:15:08<8:33:56, 4.17s/it] 60%|██████ | 11292/18688 [13:15:12<8:18:33, 4.04s/it] 60%|██████ | 11293/18688 [13:15:16<8:27:36, 4.12s/it] 60%|██████ | 11294/18688 [13:15:21<9:09:13, 4.46s/it] 60%|██████ | 11295/18688 [13:15:25<8:52:54, 4.32s/it] 60%|██████ | 11296/18688 [13:15:29<8:23:42, 4.09s/it] 60%|██████ | 11297/18688 [13:15:33<8:14:50, 4.02s/it] 60%|██████ | 11298/18688 [13:15:37<8:21:05, 4.07s/it] 60%|██████ | 11299/18688 [13:15:41<8:33:54, 4.17s/it] 60%|██████ | 11300/18688 [13:15:46<8:29:03, 4.13s/it] {'loss': '0.9424', 'grad_norm': '1.43', 'learning_rate': '3.908e-05', 'epoch': '0.6047', 'num_input_tokens_seen': 38021816, 'train_runtime': '4.775e+04', 'train_tokens_per_second': '796.3'}
60%|██████ | 11300/18688 [13:15:46<8:29:03, 4.13s/it] 60%|██████ | 11301/18688 [13:15:49<8:21:17, 4.07s/it] 60%|██████ | 11302/18688 [13:15:54<8:31:10, 4.15s/it] 60%|██████ | 11303/18688 [13:15:57<8:06:24, 3.95s/it] 60%|██████ | 11304/18688 [13:16:02<8:35:31, 4.19s/it] 60%|██████ | 11305/18688 [13:16:06<8:15:52, 4.03s/it] 60%|██████ | 11306/18688 [13:16:10<8:17:38, 4.04s/it] 61%|██████ | 11307/18688 [13:16:15<8:53:42, 4.34s/it] 61%|██████ | 11308/18688 [13:16:19<8:49:35, 4.31s/it] 61%|██████ | 11309/18688 [13:16:23<8:30:57, 4.15s/it] 61%|██████ | 11310/18688 [13:16:27<8:32:28, 4.17s/it] {'loss': '0.882', 'grad_norm': '1.201', 'learning_rate': '3.899e-05', 'epoch': '0.6052', 'num_input_tokens_seen': 38053976, 'train_runtime': '4.779e+04', 'train_tokens_per_second': '796.3'}
61%|██████ | 11310/18688 [13:16:27<8:32:28, 4.17s/it] 61%|██████ | 11311/18688 [13:16:32<9:08:48, 4.46s/it] 61%|██████ | 11312/18688 [13:16:36<8:35:16, 4.19s/it] 61%|██████ | 11313/18688 [13:16:39<8:17:44, 4.05s/it] 61%|██████ | 11314/18688 [13:16:43<8:12:38, 4.01s/it] 61%|██████ | 11315/18688 [13:16:48<8:22:48, 4.09s/it] 61%|██████ | 11316/18688 [13:16:51<8:05:59, 3.96s/it] 61%|██████ | 11317/18688 [13:16:55<7:52:11, 3.84s/it] 61%|██████ | 11318/18688 [13:16:59<7:55:28, 3.87s/it] 61%|██████ | 11319/18688 [13:17:03<8:18:31, 4.06s/it] 61%|██████ | 11320/18688 [13:17:08<8:35:05, 4.19s/it] {'loss': '0.8998', 'grad_norm': '1.235', 'learning_rate': '3.89e-05', 'epoch': '0.6058', 'num_input_tokens_seen': 38086360, 'train_runtime': '4.783e+04', 'train_tokens_per_second': '796.3'}
61%|██████ | 11320/18688 [13:17:08<8:35:05, 4.19s/it] 61%|██████ | 11321/18688 [13:17:12<8:47:01, 4.29s/it] 61%|██████ | 11322/18688 [13:17:17<8:43:23, 4.26s/it] 61%|██████ | 11323/18688 [13:17:21<8:35:59, 4.20s/it] 61%|██████ | 11324/18688 [13:17:24<8:08:49, 3.98s/it] 61%|██████ | 11325/18688 [13:17:28<8:01:47, 3.93s/it] 61%|██████ | 11326/18688 [13:17:32<8:20:09, 4.08s/it] 61%|██████ | 11327/18688 [13:17:36<8:25:02, 4.12s/it] 61%|██████ | 11328/18688 [13:17:40<8:13:13, 4.02s/it] 61%|██████ | 11329/18688 [13:17:44<8:03:54, 3.95s/it] 61%|██████ | 11330/18688 [13:17:47<7:45:25, 3.80s/it] {'loss': '0.8685', 'grad_norm': '1.488', 'learning_rate': '3.881e-05', 'epoch': '0.6063', 'num_input_tokens_seen': 38117032, 'train_runtime': '4.787e+04', 'train_tokens_per_second': '796.3'}
61%|██████ | 11330/18688 [13:17:47<7:45:25, 3.80s/it] 61%|██████ | 11331/18688 [13:17:53<8:56:23, 4.37s/it] 61%|██████ | 11332/18688 [13:17:58<9:06:38, 4.46s/it] 61%|██████ | 11333/18688 [13:18:02<8:48:36, 4.31s/it] 61%|██████ | 11334/18688 [13:18:07<9:10:20, 4.49s/it] 61%|██████ | 11335/18688 [13:18:12<9:23:17, 4.60s/it] 61%|██████ | 11336/18688 [13:18:15<8:52:18, 4.34s/it] 61%|██████ | 11337/18688 [13:18:20<9:14:16, 4.52s/it] 61%|██████ | 11338/18688 [13:18:24<8:46:56, 4.30s/it] 61%|██████ | 11339/18688 [13:18:28<8:28:25, 4.15s/it] 61%|██████ | 11340/18688 [13:18:31<8:08:03, 3.99s/it] {'loss': '0.9904', 'grad_norm': '1.362', 'learning_rate': '3.872e-05', 'epoch': '0.6068', 'num_input_tokens_seen': 38154872, 'train_runtime': '4.791e+04', 'train_tokens_per_second': '796.4'}
61%|██████ | 11340/18688 [13:18:31<8:08:03, 3.99s/it] 61%|██████ | 11341/18688 [13:18:36<8:31:22, 4.18s/it] 61%|██████ | 11342/18688 [13:18:40<8:15:48, 4.05s/it] 61%|██████ | 11343/18688 [13:18:44<8:08:19, 3.99s/it] 61%|██████ | 11344/18688 [13:18:48<8:06:49, 3.98s/it] 61%|██████ | 11345/18688 [13:18:52<8:16:42, 4.06s/it] 61%|██████ | 11346/18688 [13:18:57<8:52:14, 4.35s/it] 61%|██████ | 11347/18688 [13:19:01<8:37:50, 4.23s/it] 61%|██████ | 11348/18688 [13:19:06<9:10:06, 4.50s/it] 61%|██████ | 11349/18688 [13:19:11<9:34:45, 4.70s/it] 61%|██████ | 11350/18688 [13:19:15<9:11:22, 4.51s/it] {'loss': '0.911', 'grad_norm': '1.361', 'learning_rate': '3.863e-05', 'epoch': '0.6074', 'num_input_tokens_seen': 38191624, 'train_runtime': '4.796e+04', 'train_tokens_per_second': '796.4'}
61%|██████ | 11350/18688 [13:19:15<9:11:22, 4.51s/it] 61%|██████ | 11351/18688 [13:19:19<8:40:46, 4.26s/it] 61%|██████ | 11352/18688 [13:19:23<8:36:27, 4.22s/it] 61%|██████ | 11353/18688 [13:19:27<8:24:03, 4.12s/it] 61%|██████ | 11354/18688 [13:19:31<8:21:44, 4.10s/it] 61%|██████ | 11355/18688 [13:19:35<8:26:30, 4.14s/it] 61%|██████ | 11356/18688 [13:19:40<8:41:12, 4.27s/it] 61%|██████ | 11357/18688 [13:19:43<8:18:07, 4.08s/it] 61%|██████ | 11358/18688 [13:19:48<8:34:08, 4.21s/it] 61%|██████ | 11359/18688 [13:19:53<9:19:33, 4.58s/it] 61%|██████ | 11360/18688 [13:19:57<8:37:52, 4.24s/it] {'loss': '0.9992', 'grad_norm': '1.723', 'learning_rate': '3.854e-05', 'epoch': '0.6079', 'num_input_tokens_seen': 38224632, 'train_runtime': '4.8e+04', 'train_tokens_per_second': '796.4'}
61%|██████ | 11360/18688 [13:19:57<8:37:52, 4.24s/it] 61%|██████ | 11361/18688 [13:20:01<8:30:41, 4.18s/it] 61%|██████ | 11362/18688 [13:20:05<8:24:53, 4.14s/it] 61%|██████ | 11363/18688 [13:20:08<8:01:54, 3.95s/it] 61%|██████ | 11364/18688 [13:20:13<8:26:48, 4.15s/it] 61%|██████ | 11365/18688 [13:20:17<8:32:02, 4.20s/it] 61%|██████ | 11366/18688 [13:20:23<9:30:25, 4.67s/it] 61%|██████ | 11367/18688 [13:20:27<9:00:28, 4.43s/it] 61%|██████ | 11368/18688 [13:20:30<8:25:08, 4.14s/it] 61%|██████ | 11369/18688 [13:20:34<8:15:11, 4.06s/it] 61%|██████ | 11370/18688 [13:20:38<8:02:38, 3.96s/it] {'loss': '0.9858', 'grad_norm': '1.439', 'learning_rate': '3.845e-05', 'epoch': '0.6084', 'num_input_tokens_seen': 38257520, 'train_runtime': '4.804e+04', 'train_tokens_per_second': '796.4'}
61%|██████ | 11370/18688 [13:20:38<8:02:38, 3.96s/it] 61%|██████ | 11371/18688 [13:20:42<8:08:02, 4.00s/it] 61%|██████ | 11372/18688 [13:20:47<8:23:48, 4.13s/it] 61%|██████ | 11373/18688 [13:20:50<8:02:41, 3.96s/it] 61%|██████ | 11374/18688 [13:20:54<8:13:35, 4.05s/it] 61%|██████ | 11375/18688 [13:20:58<8:15:03, 4.06s/it] 61%|██████ | 11376/18688 [13:21:03<8:35:11, 4.23s/it] 61%|██████ | 11377/18688 [13:21:08<8:46:27, 4.32s/it] 61%|██████ | 11378/18688 [13:21:12<8:40:45, 4.27s/it] 61%|██████ | 11379/18688 [13:21:15<8:19:16, 4.10s/it] 61%|██████ | 11380/18688 [13:21:20<8:32:42, 4.21s/it] {'loss': '0.9029', 'grad_norm': '1.448', 'learning_rate': '3.836e-05', 'epoch': '0.609', 'num_input_tokens_seen': 38292432, 'train_runtime': '4.808e+04', 'train_tokens_per_second': '796.4'}
61%|██████ | 11380/18688 [13:21:20<8:32:42, 4.21s/it] 61%|██████ | 11381/18688 [13:21:23<8:03:43, 3.97s/it] 61%|██████ | 11382/18688 [13:21:28<8:11:32, 4.04s/it] 61%|██████ | 11383/18688 [13:21:33<8:47:42, 4.33s/it] 61%|██████ | 11384/18688 [13:21:37<9:07:03, 4.49s/it] 61%|██████ | 11385/18688 [13:21:41<8:49:38, 4.35s/it] 61%|██████ | 11386/18688 [13:21:46<9:06:06, 4.49s/it] 61%|██████ | 11387/18688 [13:21:51<8:58:09, 4.42s/it] 61%|██████ | 11388/18688 [13:21:55<8:49:55, 4.36s/it] 61%|██████ | 11389/18688 [13:21:58<8:23:47, 4.14s/it] 61%|██████ | 11390/18688 [13:22:02<8:17:21, 4.09s/it] {'loss': '0.9122', 'grad_norm': '1.434', 'learning_rate': '3.828e-05', 'epoch': '0.6095', 'num_input_tokens_seen': 38326040, 'train_runtime': '4.812e+04', 'train_tokens_per_second': '796.4'}
61%|██████ | 11390/18688 [13:22:02<8:17:21, 4.09s/it] 61%|██████ | 11391/18688 [13:22:07<8:23:46, 4.14s/it] 61%|██████ | 11392/18688 [13:22:10<8:09:14, 4.02s/it] 61%|██████ | 11393/18688 [13:22:14<8:06:26, 4.00s/it] 61%|██████ | 11394/18688 [13:22:18<8:05:03, 3.99s/it] 61%|██████ | 11395/18688 [13:22:22<8:02:14, 3.97s/it] 61%|██████ | 11396/18688 [13:22:27<8:18:07, 4.10s/it] 61%|██████ | 11397/18688 [13:22:31<8:18:40, 4.10s/it] 61%|██████ | 11398/18688 [13:22:35<8:14:21, 4.07s/it] 61%|██████ | 11399/18688 [13:22:38<8:00:21, 3.95s/it] 61%|██████ | 11400/18688 [13:22:42<8:04:15, 3.99s/it] {'loss': '0.945', 'grad_norm': '1.395', 'learning_rate': '3.819e-05', 'epoch': '0.61', 'num_input_tokens_seen': 38355688, 'train_runtime': '4.816e+04', 'train_tokens_per_second': '796.4'}
61%|██████ | 11400/18688 [13:22:42<8:04:15, 3.99s/it] 61%|██████ | 11401/18688 [13:22:48<8:48:53, 4.35s/it] 61%|██████ | 11402/18688 [13:22:52<8:46:33, 4.34s/it] 61%|██████ | 11403/18688 [13:22:55<8:16:20, 4.09s/it] 61%|██████ | 11404/18688 [13:22:59<8:04:33, 3.99s/it] 61%|██████ | 11405/18688 [13:23:04<8:17:18, 4.10s/it] 61%|██████ | 11406/18688 [13:23:07<7:55:10, 3.92s/it] 61%|██████ | 11407/18688 [13:23:11<7:48:14, 3.86s/it] 61%|██████ | 11408/18688 [13:23:16<8:43:14, 4.31s/it] 61%|██████ | 11409/18688 [13:23:20<8:40:28, 4.29s/it] 61%|██████ | 11410/18688 [13:23:26<9:18:07, 4.60s/it] {'loss': '0.8972', 'grad_norm': '1.76', 'learning_rate': '3.81e-05', 'epoch': '0.6106', 'num_input_tokens_seen': 38391136, 'train_runtime': '4.821e+04', 'train_tokens_per_second': '796.4'}
61%|██████ | 11410/18688 [13:23:26<9:18:07, 4.60s/it] 61%|██████ | 11411/18688 [13:23:31<9:26:33, 4.67s/it] 61%|██████ | 11412/18688 [13:23:34<8:46:09, 4.34s/it] 61%|██████ | 11413/18688 [13:23:38<8:18:32, 4.11s/it] 61%|██████ | 11414/18688 [13:23:43<8:55:01, 4.41s/it] 61%|██████ | 11415/18688 [13:23:47<8:31:46, 4.22s/it] 61%|██████ | 11416/18688 [13:23:51<8:39:29, 4.29s/it] 61%|██████ | 11417/18688 [13:23:55<8:16:59, 4.10s/it] 61%|██████ | 11418/18688 [13:23:58<7:53:23, 3.91s/it] 61%|██████ | 11419/18688 [13:24:02<7:49:18, 3.87s/it] 61%|██████ | 11420/18688 [13:24:07<8:19:39, 4.12s/it] {'loss': '0.9405', 'grad_norm': '1.423', 'learning_rate': '3.801e-05', 'epoch': '0.6111', 'num_input_tokens_seen': 38424824, 'train_runtime': '4.825e+04', 'train_tokens_per_second': '796.4'}
61%|██████ | 11420/18688 [13:24:07<8:19:39, 4.12s/it] 61%|██████ | 11421/18688 [13:24:11<8:19:25, 4.12s/it] 61%|██████ | 11422/18688 [13:24:15<8:04:51, 4.00s/it] 61%|██████ | 11423/18688 [13:24:20<8:44:18, 4.33s/it] 61%|██████ | 11424/18688 [13:24:24<8:37:00, 4.27s/it] 61%|██████ | 11425/18688 [13:24:28<8:21:57, 4.15s/it] 61%|██████ | 11426/18688 [13:24:32<8:34:37, 4.25s/it] 61%|██████ | 11427/18688 [13:24:36<8:10:38, 4.05s/it] 61%|██████ | 11428/18688 [13:24:40<8:16:02, 4.10s/it] 61%|██████ | 11429/18688 [13:24:45<8:53:16, 4.41s/it] 61%|██████ | 11430/18688 [13:24:49<8:26:55, 4.19s/it] {'loss': '0.9113', 'grad_norm': '1.278', 'learning_rate': '3.792e-05', 'epoch': '0.6116', 'num_input_tokens_seen': 38460160, 'train_runtime': '4.829e+04', 'train_tokens_per_second': '796.5'}
61%|██████ | 11430/18688 [13:24:49<8:26:55, 4.19s/it] 61%|██████ | 11431/18688 [13:24:53<8:26:27, 4.19s/it] 61%|██████ | 11432/18688 [13:24:58<8:51:38, 4.40s/it] 61%|██████ | 11433/18688 [13:25:02<8:58:35, 4.45s/it] 61%|██████ | 11434/18688 [13:25:06<8:44:48, 4.34s/it] 61%|██████ | 11435/18688 [13:25:10<8:17:25, 4.11s/it] 61%|██████ | 11436/18688 [13:25:14<7:57:46, 3.95s/it] 61%|██████ | 11437/18688 [13:25:17<7:45:49, 3.85s/it] 61%|██████ | 11438/18688 [13:25:22<8:11:04, 4.06s/it] 61%|██████ | 11439/18688 [13:25:26<8:27:23, 4.20s/it] 61%|██████ | 11440/18688 [13:25:31<8:29:58, 4.22s/it] {'loss': '0.9126', 'grad_norm': '1.429', 'learning_rate': '3.783e-05', 'epoch': '0.6122', 'num_input_tokens_seen': 38493712, 'train_runtime': '4.833e+04', 'train_tokens_per_second': '796.5'}
61%|██████ | 11440/18688 [13:25:31<8:29:58, 4.22s/it] 61%|██████ | 11441/18688 [13:25:35<8:30:41, 4.23s/it] 61%|██████ | 11442/18688 [13:25:38<8:05:40, 4.02s/it] 61%|██████ | 11443/18688 [13:25:43<8:30:37, 4.23s/it] 61%|██████ | 11444/18688 [13:25:47<8:34:45, 4.26s/it] 61%|██████ | 11445/18688 [13:25:52<8:47:39, 4.37s/it] 61%|██████ | 11446/18688 [13:25:56<8:38:43, 4.30s/it] 61%|██████▏ | 11447/18688 [13:26:01<8:43:11, 4.34s/it] 61%|██████▏ | 11448/18688 [13:26:04<8:25:20, 4.19s/it] 61%|██████▏ | 11449/18688 [13:26:09<8:48:03, 4.38s/it] 61%|██████▏ | 11450/18688 [13:26:13<8:34:26, 4.26s/it] {'loss': '0.863', 'grad_norm': '1.138', 'learning_rate': '3.774e-05', 'epoch': '0.6127', 'num_input_tokens_seen': 38529016, 'train_runtime': '4.837e+04', 'train_tokens_per_second': '796.5'}
61%|██████▏ | 11450/18688 [13:26:13<8:34:26, 4.26s/it] 61%|██████▏ | 11451/18688 [13:26:17<8:10:20, 4.07s/it] 61%|██████▏ | 11452/18688 [13:26:22<8:32:25, 4.25s/it] 61%|██████▏ | 11453/18688 [13:26:27<9:20:57, 4.65s/it] 61%|██████▏ | 11454/18688 [13:26:31<8:50:12, 4.40s/it] 61%|██████▏ | 11455/18688 [13:26:35<8:22:16, 4.17s/it] 61%|██████▏ | 11456/18688 [13:26:39<8:38:26, 4.30s/it] 61%|██████▏ | 11457/18688 [13:26:43<8:10:44, 4.07s/it] 61%|██████▏ | 11458/18688 [13:26:46<7:52:01, 3.92s/it] 61%|██████▏ | 11459/18688 [13:26:50<7:47:46, 3.88s/it] 61%|██████▏ | 11460/18688 [13:26:54<7:48:03, 3.89s/it] {'loss': '0.8858', 'grad_norm': '1.328', 'learning_rate': '3.765e-05', 'epoch': '0.6132', 'num_input_tokens_seen': 38560368, 'train_runtime': '4.841e+04', 'train_tokens_per_second': '796.5'}
61%|██████▏ | 11460/18688 [13:26:54<7:48:03, 3.89s/it] 61%|██████▏ | 11461/18688 [13:26:58<7:59:22, 3.98s/it] 61%|██████▏ | 11462/18688 [13:27:02<8:04:38, 4.02s/it] 61%|██████▏ | 11463/18688 [13:27:06<8:06:40, 4.04s/it] 61%|██████▏ | 11464/18688 [13:27:11<8:11:26, 4.08s/it] 61%|██████▏ | 11465/18688 [13:27:15<8:09:30, 4.07s/it] 61%|██████▏ | 11466/18688 [13:27:19<8:16:15, 4.12s/it] 61%|██████▏ | 11467/18688 [13:27:24<8:47:35, 4.38s/it] 61%|██████▏ | 11468/18688 [13:27:28<8:48:07, 4.39s/it] 61%|██████▏ | 11469/18688 [13:27:34<9:24:35, 4.69s/it] 61%|██████▏ | 11470/18688 [13:27:38<9:16:00, 4.62s/it] {'loss': '0.9597', 'grad_norm': '1.035', 'learning_rate': '3.757e-05', 'epoch': '0.6138', 'num_input_tokens_seen': 38596616, 'train_runtime': '4.846e+04', 'train_tokens_per_second': '796.5'}
61%|██████▏ | 11470/18688 [13:27:38<9:16:00, 4.62s/it] 61%|██████▏ | 11471/18688 [13:27:42<8:39:27, 4.32s/it] 61%|██████▏ | 11472/18688 [13:27:46<8:22:09, 4.18s/it] 61%|██████▏ | 11473/18688 [13:27:49<7:56:41, 3.96s/it] 61%|██████▏ | 11474/18688 [13:27:53<8:06:25, 4.05s/it] 61%|██████▏ | 11475/18688 [13:27:57<8:10:20, 4.08s/it] 61%|██████▏ | 11476/18688 [13:28:02<8:30:09, 4.24s/it] 61%|██████▏ | 11477/18688 [13:28:06<8:10:12, 4.08s/it] 61%|██████▏ | 11478/18688 [13:28:10<8:06:03, 4.04s/it] 61%|██████▏ | 11479/18688 [13:28:15<8:39:37, 4.32s/it] 61%|██████▏ | 11480/18688 [13:28:20<9:03:26, 4.52s/it] {'loss': '0.9037', 'grad_norm': '1.272', 'learning_rate': '3.748e-05', 'epoch': '0.6143', 'num_input_tokens_seen': 38625808, 'train_runtime': '4.85e+04', 'train_tokens_per_second': '796.4'}
61%|██████▏ | 11480/18688 [13:28:20<9:03:26, 4.52s/it] 61%|██████▏ | 11481/18688 [13:28:24<9:07:25, 4.56s/it] 61%|██████▏ | 11482/18688 [13:28:30<9:36:57, 4.80s/it] 61%|██████▏ | 11483/18688 [13:28:34<9:07:12, 4.56s/it] 61%|██████▏ | 11484/18688 [13:28:37<8:38:36, 4.32s/it] 61%|██████▏ | 11485/18688 [13:28:42<9:03:02, 4.52s/it] 61%|██████▏ | 11486/18688 [13:28:46<8:32:09, 4.27s/it] 61%|██████▏ | 11487/18688 [13:28:50<8:18:59, 4.16s/it] 61%|██████▏ | 11488/18688 [13:28:54<8:21:38, 4.18s/it] 61%|██████▏ | 11489/18688 [13:29:00<9:21:21, 4.68s/it] 61%|██████▏ | 11490/18688 [13:29:05<9:39:27, 4.83s/it] {'loss': '0.9302', 'grad_norm': '1.36', 'learning_rate': '3.739e-05', 'epoch': '0.6148', 'num_input_tokens_seen': 38659568, 'train_runtime': '4.855e+04', 'train_tokens_per_second': '796.4'}
61%|██████▏ | 11490/18688 [13:29:05<9:39:27, 4.83s/it] 61%|██████▏ | 11491/18688 [13:29:09<9:00:33, 4.51s/it] 61%|██████▏ | 11492/18688 [13:29:13<8:57:33, 4.48s/it] 61%|██████▏ | 11493/18688 [13:29:17<8:33:54, 4.29s/it] 62%|██████▏ | 11494/18688 [13:29:21<8:28:55, 4.24s/it] 62%|██████▏ | 11495/18688 [13:29:25<8:11:53, 4.10s/it] 62%|██████▏ | 11496/18688 [13:29:29<8:09:41, 4.09s/it] 62%|██████▏ | 11497/18688 [13:29:33<7:52:13, 3.94s/it] 62%|██████▏ | 11498/18688 [13:29:37<8:11:51, 4.10s/it] 62%|██████▏ | 11499/18688 [13:29:42<8:31:34, 4.27s/it] 62%|██████▏ | 11500/18688 [13:29:46<8:33:44, 4.29s/it] {'loss': '0.9695', 'grad_norm': '1.513', 'learning_rate': '3.73e-05', 'epoch': '0.6154', 'num_input_tokens_seen': 38691136, 'train_runtime': '4.859e+04', 'train_tokens_per_second': '796.3'}
62%|██████▏ | 11500/18688 [13:29:46<8:33:44, 4.29s/it] 62%|██████▏ | 11501/18688 [13:29:51<9:00:15, 4.51s/it] 62%|██████▏ | 11502/18688 [13:29:55<8:44:50, 4.38s/it] 62%|██████▏ | 11503/18688 [13:29:59<8:32:25, 4.28s/it] 62%|██████▏ | 11504/18688 [13:30:04<8:26:33, 4.23s/it] 62%|██████▏ | 11505/18688 [13:30:08<8:48:09, 4.41s/it] 62%|██████▏ | 11506/18688 [13:30:13<8:52:43, 4.45s/it] 62%|██████▏ | 11507/18688 [13:30:17<8:32:06, 4.28s/it] 62%|██████▏ | 11508/18688 [13:30:20<8:07:35, 4.07s/it] 62%|██████▏ | 11509/18688 [13:30:25<8:28:34, 4.25s/it] 62%|██████▏ | 11510/18688 [13:30:29<8:00:32, 4.02s/it] {'loss': '0.943', 'grad_norm': '1.453', 'learning_rate': '3.721e-05', 'epoch': '0.6159', 'num_input_tokens_seen': 38724736, 'train_runtime': '4.863e+04', 'train_tokens_per_second': '796.3'}
62%|██████▏ | 11510/18688 [13:30:29<8:00:32, 4.02s/it] 62%|██████▏ | 11511/18688 [13:30:33<8:02:30, 4.03s/it] 62%|██████▏ | 11512/18688 [13:30:36<7:43:27, 3.88s/it] 62%|██████▏ | 11513/18688 [13:30:40<7:30:58, 3.77s/it] 62%|██████▏ | 11514/18688 [13:30:44<7:46:03, 3.90s/it] 62%|██████▏ | 11515/18688 [13:30:48<7:46:48, 3.90s/it] 62%|██████▏ | 11516/18688 [13:30:52<8:05:29, 4.06s/it] 62%|██████▏ | 11517/18688 [13:30:56<7:47:27, 3.91s/it] 62%|██████▏ | 11518/18688 [13:30:59<7:39:25, 3.84s/it] 62%|██████▏ | 11519/18688 [13:31:04<8:21:28, 4.20s/it] 62%|██████▏ | 11520/18688 [13:31:10<8:59:04, 4.51s/it] {'loss': '0.9061', 'grad_norm': '1.195', 'learning_rate': '3.712e-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<8:59:04, 4.51s/it] 62%|██████▏ | 11521/18688 [13:31:14<9:02:01, 4.54s/it] 62%|██████▏ | 11522/18688 [13:31:19<9:20:14, 4.69s/it] 62%|██████▏ | 11523/18688 [13:31:25<9:38:37, 4.85s/it] 62%|██████▏ | 11524/18688 [13:31:28<9:00:14, 4.52s/it] 62%|██████▏ | 11525/18688 [13:31:32<8:29:13, 4.27s/it] 62%|██████▏ | 11526/18688 [13:31:36<8:08:40, 4.09s/it] 62%|██████▏ | 11527/18688 [13:31:40<8:11:33, 4.12s/it] 62%|██████▏ | 11528/18688 [13:31:44<8:03:00, 4.05s/it] 62%|██████▏ | 11529/18688 [13:31:48<8:10:02, 4.11s/it] 62%|██████▏ | 11530/18688 [13:31:52<7:52:09, 3.96s/it] {'loss': '0.8948', 'grad_norm': '1.315', 'learning_rate': '3.704e-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<7:52:09, 3.96s/it] 62%|██████▏ | 11531/18688 [13:31:56<8:14:05, 4.14s/it] 62%|██████▏ | 11532/18688 [13:32:00<8:04:06, 4.06s/it] 62%|██████▏ | 11533/18688 [13:32:04<7:47:59, 3.92s/it] 62%|██████▏ | 11534/18688 [13:32:09<8:52:55, 4.47s/it] 62%|██████▏ | 11535/18688 [13:32:13<8:28:44, 4.27s/it] 62%|██████▏ | 11536/18688 [13:32:17<8:04:32, 4.06s/it] 62%|██████▏ | 11537/18688 [13:32:20<7:43:41, 3.89s/it] 62%|██████▏ | 11538/18688 [13:32:24<7:31:31, 3.79s/it] 62%|██████▏ | 11539/18688 [13:32:28<7:41:27, 3.87s/it] 62%|██████▏ | 11540/18688 [13:32:32<7:46:00, 3.91s/it] {'loss': '0.9212', 'grad_norm': '1.437', 'learning_rate': '3.695e-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<7:46:00, 3.91s/it] 62%|██████▏ | 11541/18688 [13:32:36<7:40:04, 3.86s/it] 62%|██████▏ | 11542/18688 [13:32:40<7:50:31, 3.95s/it] 62%|██████▏ | 11543/18688 [13:32:44<7:47:47, 3.93s/it] 62%|██████▏ | 11544/18688 [13:32:47<7:32:55, 3.80s/it] 62%|██████▏ | 11545/18688 [13:32:52<8:23:37, 4.23s/it] 62%|██████▏ | 11546/18688 [13:32:57<8:30:25, 4.29s/it] 62%|██████▏ | 11547/18688 [13:33:01<8:12:06, 4.13s/it] 62%|██████▏ | 11548/18688 [13:33:04<7:50:02, 3.95s/it] 62%|██████▏ | 11549/18688 [13:33:08<7:41:41, 3.88s/it] 62%|██████▏ | 11550/18688 [13:33:12<7:49:45, 3.95s/it] {'loss': '0.8846', 'grad_norm': '1.485', 'learning_rate': '3.686e-05', 'epoch': '0.6181', 'num_input_tokens_seen': 38856632, 'train_runtime': '4.879e+04', 'train_tokens_per_second': '796.4'}
62%|██████▏ | 11550/18688 [13:33:12<7:49:45, 3.95s/it] 62%|██████▏ | 11551/18688 [13:33:16<7:57:15, 4.01s/it] 62%|██████▏ | 11552/18688 [13:33:20<7:59:07, 4.03s/it] 62%|██████▏ | 11553/18688 [13:33:24<7:40:58, 3.88s/it] 62%|██████▏ | 11554/18688 [13:33:27<7:36:00, 3.84s/it] 62%|██████▏ | 11555/18688 [13:33:32<7:51:50, 3.97s/it] 62%|██████▏ | 11556/18688 [13:33:35<7:44:13, 3.91s/it] 62%|██████▏ | 11557/18688 [13:33:39<7:36:16, 3.84s/it] 62%|██████▏ | 11558/18688 [13:33:44<8:09:14, 4.12s/it] 62%|██████▏ | 11559/18688 [13:33:49<8:28:14, 4.28s/it] 62%|██████▏ | 11560/18688 [13:33:53<8:39:24, 4.37s/it] {'loss': '0.9251', 'grad_norm': '1.577', 'learning_rate': '3.677e-05', 'epoch': '0.6186', 'num_input_tokens_seen': 38889464, 'train_runtime': '4.883e+04', 'train_tokens_per_second': '796.4'}
62%|██████▏ | 11560/18688 [13:33:53<8:39:24, 4.37s/it] 62%|██████▏ | 11561/18688 [13:33:57<8:11:04, 4.13s/it] 62%|██████▏ | 11562/18688 [13:34:01<8:07:41, 4.11s/it] 62%|██████▏ | 11563/18688 [13:34:05<8:07:11, 4.10s/it] 62%|██████▏ | 11564/18688 [13:34:10<8:28:16, 4.28s/it] 62%|██████▏ | 11565/18688 [13:34:15<9:00:13, 4.55s/it] 62%|██████▏ | 11566/18688 [13:34:18<8:22:32, 4.23s/it] 62%|██████▏ | 11567/18688 [13:34:22<8:18:59, 4.20s/it] 62%|██████▏ | 11568/18688 [13:34:27<8:19:51, 4.21s/it] 62%|██████▏ | 11569/18688 [13:34:31<8:41:32, 4.40s/it] 62%|██████▏ | 11570/18688 [13:34:35<8:15:51, 4.18s/it] {'loss': '0.905', 'grad_norm': '1.51', 'learning_rate': '3.668e-05', 'epoch': '0.6191', 'num_input_tokens_seen': 38923384, 'train_runtime': '4.888e+04', 'train_tokens_per_second': '796.4'}
62%|██████▏ | 11570/18688 [13:34:35<8:15:51, 4.18s/it] 62%|██████▏ | 11571/18688 [13:34:39<8:18:40, 4.20s/it] 62%|██████▏ | 11572/18688 [13:34:43<8:12:28, 4.15s/it] 62%|██████▏ | 11573/18688 [13:34:48<8:15:55, 4.18s/it] 62%|██████▏ | 11574/18688 [13:34:52<8:11:16, 4.14s/it] 62%|██████▏ | 11575/18688 [13:34:57<8:57:10, 4.53s/it] 62%|██████▏ | 11576/18688 [13:35:01<8:29:26, 4.30s/it] 62%|██████▏ | 11577/18688 [13:35:06<8:41:23, 4.40s/it] 62%|██████▏ | 11578/18688 [13:35:11<9:08:03, 4.62s/it] 62%|██████▏ | 11579/18688 [13:35:15<9:00:37, 4.56s/it] 62%|██████▏ | 11580/18688 [13:35:20<9:01:02, 4.57s/it] {'loss': '0.9652', 'grad_norm': '1.201', 'learning_rate': '3.659e-05', 'epoch': '0.6197', 'num_input_tokens_seen': 38959992, 'train_runtime': '4.892e+04', 'train_tokens_per_second': '796.4'}
62%|██████▏ | 11580/18688 [13:35:20<9:01:02, 4.57s/it] 62%|██████▏ | 11581/18688 [13:35:24<8:47:13, 4.45s/it] 62%|██████▏ | 11582/18688 [13:35:28<8:47:12, 4.45s/it] 62%|██████▏ | 11583/18688 [13:35:33<9:01:59, 4.58s/it] 62%|██████▏ | 11584/18688 [13:35:38<9:27:13, 4.79s/it] 62%|██████▏ | 11585/18688 [13:35:43<9:25:14, 4.77s/it] 62%|██████▏ | 11586/18688 [13:35:48<9:31:19, 4.83s/it] 62%|██████▏ | 11587/18688 [13:35:55<10:26:30, 5.29s/it] 62%|██████▏ | 11588/18688 [13:35:59<10:07:36, 5.13s/it] 62%|██████▏ | 11589/18688 [13:36:04<10:09:04, 5.15s/it] 62%|██████▏ | 11590/18688 [13:36:10<10:31:47, 5.34s/it] {'loss': '0.9044', 'grad_norm': '1.256', 'learning_rate': '3.651e-05', 'epoch': '0.6202', 'num_input_tokens_seen': 38992168, 'train_runtime': '4.897e+04', 'train_tokens_per_second': '796.2'}
62%|██████▏ | 11590/18688 [13:36:10<10:31:47, 5.34s/it] 62%|██████▏ | 11591/18688 [13:36:15<10:20:59, 5.25s/it] 62%|██████▏ | 11592/18688 [13:36:20<10:05:02, 5.12s/it] 62%|██████▏ | 11593/18688 [13:36:25<9:55:01, 5.03s/it] 62%|██████▏ | 11594/18688 [13:36:29<9:25:33, 4.78s/it] 62%|██████▏ | 11595/18688 [13:36:34<9:28:15, 4.81s/it] 62%|██████▏ | 11596/18688 [13:36:38<9:09:58, 4.65s/it] 62%|██████▏ | 11597/18688 [13:36:42<8:32:06, 4.33s/it] 62%|██████▏ | 11598/18688 [13:36:46<8:21:24, 4.24s/it] 62%|██████▏ | 11599/18688 [13:36:50<8:11:35, 4.16s/it] 62%|██████▏ | 11600/18688 [13:36:54<8:10:28, 4.15s/it] {'loss': '0.9137', 'grad_norm': '1.535', 'learning_rate': '3.642e-05', 'epoch': '0.6207', 'num_input_tokens_seen': 39022800, 'train_runtime': '4.901e+04', 'train_tokens_per_second': '796.1'}
62%|██████▏ | 11600/18688 [13:36:54<8:10:28, 4.15s/it] 62%|██████▏ | 11601/18688 [13:36:59<8:32:01, 4.33s/it] 62%|██████▏ | 11602/18688 [13:37:03<8:26:40, 4.29s/it] 62%|██████▏ | 11603/18688 [13:37:07<8:20:26, 4.24s/it] 62%|██████▏ | 11604/18688 [13:37:11<8:06:15, 4.12s/it] 62%|██████▏ | 11605/18688 [13:37:15<7:59:36, 4.06s/it] 62%|██████▏ | 11606/18688 [13:37:18<7:42:15, 3.92s/it] 62%|██████▏ | 11607/18688 [13:37:23<7:54:46, 4.02s/it] 62%|██████▏ | 11608/18688 [13:37:28<8:32:29, 4.34s/it] 62%|██████▏ | 11609/18688 [13:37:32<8:19:27, 4.23s/it] 62%|██████▏ | 11610/18688 [13:37:36<8:06:55, 4.13s/it] {'loss': '0.8321', 'grad_norm': '1.402', 'learning_rate': '3.633e-05', 'epoch': '0.6213', 'num_input_tokens_seen': 39053456, 'train_runtime': '4.906e+04', 'train_tokens_per_second': '796.1'}
62%|██████▏ | 11610/18688 [13:37:36<8:06:55, 4.13s/it] 62%|██████▏ | 11611/18688 [13:37:39<7:49:12, 3.98s/it] 62%|██████▏ | 11612/18688 [13:37:44<8:11:57, 4.17s/it] 62%|██████▏ | 11613/18688 [13:37:48<8:23:30, 4.27s/it] 62%|██████▏ | 11614/18688 [13:37:52<8:04:14, 4.11s/it] 62%|██████▏ | 11615/18688 [13:37:56<8:11:06, 4.17s/it] 62%|██████▏ | 11616/18688 [13:38:01<8:24:23, 4.28s/it] 62%|██████▏ | 11617/18688 [13:38:05<8:26:38, 4.30s/it] 62%|██████▏ | 11618/18688 [13:38:09<8:09:43, 4.16s/it] 62%|██████▏ | 11619/18688 [13:38:13<7:55:37, 4.04s/it] 62%|██████▏ | 11620/18688 [13:38:17<7:57:31, 4.05s/it] {'loss': '0.888', 'grad_norm': '1.154', 'learning_rate': '3.624e-05', 'epoch': '0.6218', 'num_input_tokens_seen': 39087064, 'train_runtime': '4.91e+04', 'train_tokens_per_second': '796.1'}
62%|██████▏ | 11620/18688 [13:38:17<7:57:31, 4.05s/it] 62%|██████▏ | 11621/18688 [13:38:21<7:42:41, 3.93s/it] 62%|██████▏ | 11622/18688 [13:38:25<7:48:22, 3.98s/it] 62%|██████▏ | 11623/18688 [13:38:29<7:54:07, 4.03s/it] 62%|██████▏ | 11624/18688 [13:38:33<7:54:00, 4.03s/it] 62%|██████▏ | 11625/18688 [13:38:38<8:18:40, 4.24s/it] 62%|██████▏ | 11626/18688 [13:38:42<8:33:36, 4.36s/it] 62%|██████▏ | 11627/18688 [13:38:48<9:10:39, 4.68s/it] 62%|██████▏ | 11628/18688 [13:38:52<8:59:36, 4.59s/it] 62%|██████▏ | 11629/18688 [13:38:56<8:25:07, 4.29s/it] 62%|██████▏ | 11630/18688 [13:38:59<8:05:44, 4.13s/it] {'loss': '0.924', 'grad_norm': '1.43', 'learning_rate': '3.616e-05', 'epoch': '0.6223', 'num_input_tokens_seen': 39121248, 'train_runtime': '4.914e+04', 'train_tokens_per_second': '796.1'}
62%|██████▏ | 11630/18688 [13:38:59<8:05:44, 4.13s/it] 62%|██████▏ | 11631/18688 [13:39:04<8:13:03, 4.19s/it] 62%|██████▏ | 11632/18688 [13:39:08<8:04:47, 4.12s/it] 62%|██████▏ | 11633/18688 [13:39:11<7:44:13, 3.95s/it] 62%|██████▏ | 11634/18688 [13:39:15<7:36:55, 3.89s/it] 62%|██████▏ | 11635/18688 [13:39:19<7:33:06, 3.85s/it] 62%|██████▏ | 11636/18688 [13:39:23<7:28:45, 3.82s/it] 62%|██████▏ | 11637/18688 [13:39:27<8:05:39, 4.13s/it] 62%|██████▏ | 11638/18688 [13:39:32<8:28:43, 4.33s/it] 62%|██████▏ | 11639/18688 [13:39:36<8:19:21, 4.25s/it] 62%|██████▏ | 11640/18688 [13:39:40<7:52:24, 4.02s/it] {'loss': '0.8608', 'grad_norm': '1.388', 'learning_rate': '3.607e-05', 'epoch': '0.6229', 'num_input_tokens_seen': 39152528, 'train_runtime': '4.918e+04', 'train_tokens_per_second': '796.1'}
62%|██████▏ | 11640/18688 [13:39:40<7:52:24, 4.02s/it] 62%|██████▏ | 11641/18688 [13:39:43<7:36:48, 3.89s/it] 62%|██████▏ | 11642/18688 [13:39:48<7:49:56, 4.00s/it] 62%|██████▏ | 11643/18688 [13:39:51<7:45:32, 3.96s/it] 62%|██████▏ | 11644/18688 [13:39:55<7:28:43, 3.82s/it] 62%|██████▏ | 11645/18688 [13:40:00<8:00:34, 4.09s/it] 62%|██████▏ | 11646/18688 [13:40:04<7:59:57, 4.09s/it] 62%|██████▏ | 11647/18688 [13:40:08<8:01:01, 4.10s/it] 62%|██████▏ | 11648/18688 [13:40:13<8:29:31, 4.34s/it] 62%|██████▏ | 11649/18688 [13:40:18<9:06:27, 4.66s/it] 62%|██████▏ | 11650/18688 [13:40:23<9:14:20, 4.73s/it] {'loss': '0.9636', 'grad_norm': '1.292', 'learning_rate': '3.598e-05', 'epoch': '0.6234', 'num_input_tokens_seen': 39188112, 'train_runtime': '4.922e+04', 'train_tokens_per_second': '796.1'}
62%|██████▏ | 11650/18688 [13:40:23<9:14:20, 4.73s/it] 62%|██████▏ | 11651/18688 [13:40:27<8:43:14, 4.46s/it] 62%|██████▏ | 11652/18688 [13:40:31<8:19:10, 4.26s/it] 62%|██████▏ | 11653/18688 [13:40:36<8:58:42, 4.59s/it] 62%|██████▏ | 11654/18688 [13:40:40<8:38:35, 4.42s/it] 62%|██████▏ | 11655/18688 [13:40:44<8:16:45, 4.24s/it] 62%|██████▏ | 11656/18688 [13:40:48<8:08:33, 4.17s/it] 62%|██████▏ | 11657/18688 [13:40:52<8:02:58, 4.12s/it] 62%|██████▏ | 11658/18688 [13:40:57<8:24:05, 4.30s/it] 62%|██████▏ | 11659/18688 [13:41:00<8:07:28, 4.16s/it] 62%|██████▏ | 11660/18688 [13:41:05<8:15:04, 4.23s/it] {'loss': '0.8961', 'grad_norm': '1.403', 'learning_rate': '3.589e-05', 'epoch': '0.6239', 'num_input_tokens_seen': 39219224, 'train_runtime': '4.927e+04', 'train_tokens_per_second': '796.1'}
62%|██████▏ | 11660/18688 [13:41:05<8:15:04, 4.23s/it] 62%|██████▏ | 11661/18688 [13:41:09<8:14:55, 4.23s/it] 62%|██████▏ | 11662/18688 [13:41:14<8:27:25, 4.33s/it] 62%|██████▏ | 11663/18688 [13:41:18<8:33:39, 4.39s/it] 62%|██████▏ | 11664/18688 [13:41:23<8:32:35, 4.38s/it] 62%|██████▏ | 11665/18688 [13:41:28<9:23:34, 4.81s/it] 62%|██████▏ | 11666/18688 [13:41:33<9:27:15, 4.85s/it] 62%|██████▏ | 11667/18688 [13:41:38<9:13:07, 4.73s/it] 62%|██████▏ | 11668/18688 [13:41:43<9:32:31, 4.89s/it] 62%|██████▏ | 11669/18688 [13:41:48<9:44:22, 5.00s/it] 62%|██████▏ | 11670/18688 [13:41:53<9:35:13, 4.92s/it] {'loss': '0.8953', 'grad_norm': '1.478', 'learning_rate': '3.58e-05', 'epoch': '0.6245', 'num_input_tokens_seen': 39250448, 'train_runtime': '4.931e+04', 'train_tokens_per_second': '795.9'}
62%|██████▏ | 11670/18688 [13:41:53<9:35:13, 4.92s/it] 62%|██████▏ | 11671/18688 [13:41:58<9:33:56, 4.91s/it] 62%|██████▏ | 11672/18688 [13:42:04<10:33:31, 5.42s/it] 62%|██████▏ | 11673/18688 [13:42:09<10:15:23, 5.26s/it] 62%|██████▏ | 11674/18688 [13:42:15<10:15:59, 5.27s/it] 62%|██████▏ | 11675/18688 [13:42:19<9:53:26, 5.08s/it] 62%|██████▏ | 11676/18688 [13:42:24<9:42:40, 4.99s/it] 62%|██████▏ | 11677/18688 [13:42:30<10:07:12, 5.20s/it] 62%|██████▏ | 11678/18688 [13:42:35<9:58:46, 5.13s/it] 62%|██████▏ | 11679/18688 [13:42:40<10:04:56, 5.18s/it] 62%|██████▎ | 11680/18688 [13:42:45<9:56:51, 5.11s/it] {'loss': '0.8928', 'grad_norm': '1.359', 'learning_rate': '3.572e-05', 'epoch': '0.625', 'num_input_tokens_seen': 39284104, 'train_runtime': '4.937e+04', 'train_tokens_per_second': '795.8'}
62%|██████▎ | 11680/18688 [13:42:45<9:56:51, 5.11s/it] 63%|██████▎ | 11681/18688 [13:42:50<10:07:28, 5.20s/it] 63%|██████▎ | 11682/18688 [13:42:55<9:42:31, 4.99s/it] 63%|██████▎ | 11683/18688 [13:42:59<9:22:09, 4.82s/it] 63%|██████▎ | 11684/18688 [13:43:03<9:00:36, 4.63s/it] 63%|██████▎ | 11685/18688 [13:43:08<8:59:08, 4.62s/it] 63%|██████▎ | 11686/18688 [13:43:12<8:51:10, 4.55s/it] 63%|██████▎ | 11687/18688 [13:43:16<8:18:26, 4.27s/it] 63%|██████▎ | 11688/18688 [13:43:20<8:12:36, 4.22s/it] 63%|██████▎ | 11689/18688 [13:43:24<8:14:27, 4.24s/it] 63%|██████▎ | 11690/18688 [13:43:28<7:56:45, 4.09s/it] {'loss': '0.9058', 'grad_norm': '1.136', 'learning_rate': '3.563e-05', 'epoch': '0.6256', 'num_input_tokens_seen': 39317120, 'train_runtime': '4.941e+04', 'train_tokens_per_second': '795.8'}
63%|██████▎ | 11690/18688 [13:43:28<7:56:45, 4.09s/it] 63%|██████▎ | 11691/18688 [13:43:32<7:36:07, 3.91s/it] 63%|██████▎ | 11692/18688 [13:43:37<8:15:13, 4.25s/it] 63%|██████▎ | 11693/18688 [13:43:41<8:19:26, 4.28s/it] 63%|██████▎ | 11694/18688 [13:43:45<8:18:20, 4.28s/it] 63%|██████▎ | 11695/18688 [13:43:50<8:27:14, 4.35s/it] 63%|██████▎ | 11696/18688 [13:43:54<8:04:46, 4.16s/it] 63%|██████▎ | 11697/18688 [13:43:57<7:49:21, 4.03s/it] 63%|██████▎ | 11698/18688 [13:44:01<7:43:26, 3.98s/it] 63%|██████▎ | 11699/18688 [13:44:05<7:54:03, 4.07s/it] 63%|██████▎ | 11700/18688 [13:44:10<8:19:00, 4.28s/it] {'loss': '0.8759', 'grad_norm': '1.361', 'learning_rate': '3.554e-05', 'epoch': '0.6261', 'num_input_tokens_seen': 39351192, 'train_runtime': '4.945e+04', 'train_tokens_per_second': '795.8'}
63%|██████▎ | 11700/18688 [13:44:10<8:19:00, 4.28s/it] 63%|██████▎ | 11701/18688 [13:44:15<8:18:52, 4.28s/it] 63%|██████▎ | 11702/18688 [13:44:18<8:01:01, 4.13s/it] 63%|██████▎ | 11703/18688 [13:44:23<8:08:06, 4.19s/it] 63%|██████▎ | 11704/18688 [13:44:27<8:21:19, 4.31s/it] 63%|██████▎ | 11705/18688 [13:44:31<8:17:10, 4.27s/it] 63%|██████▎ | 11706/18688 [13:44:35<7:48:09, 4.02s/it] 63%|██████▎ | 11707/18688 [13:44:38<7:33:52, 3.90s/it] 63%|██████▎ | 11708/18688 [13:44:43<7:43:52, 3.99s/it] 63%|██████▎ | 11709/18688 [13:44:47<7:51:33, 4.05s/it] 63%|██████▎ | 11710/18688 [13:44:50<7:32:57, 3.89s/it] {'loss': '0.9131', 'grad_norm': '1.112', 'learning_rate': '3.545e-05', 'epoch': '0.6266', 'num_input_tokens_seen': 39382248, 'train_runtime': '4.949e+04', 'train_tokens_per_second': '795.7'}
63%|██████▎ | 11710/18688 [13:44:50<7:32:57, 3.89s/it] 63%|██████▎ | 11711/18688 [13:44:54<7:37:45, 3.94s/it] 63%|██████▎ | 11712/18688 [13:45:00<8:42:57, 4.50s/it] 63%|██████▎ | 11713/18688 [13:45:04<8:20:45, 4.31s/it] 63%|██████▎ | 11714/18688 [13:45:08<7:55:07, 4.09s/it] 63%|██████▎ | 11715/18688 [13:45:12<8:02:09, 4.15s/it] 63%|██████▎ | 11716/18688 [13:45:16<7:49:46, 4.04s/it] 63%|██████▎ | 11717/18688 [13:45:20<8:05:57, 4.18s/it] 63%|██████▎ | 11718/18688 [13:45:24<7:44:38, 4.00s/it] 63%|██████▎ | 11719/18688 [13:45:28<7:54:08, 4.08s/it] 63%|██████▎ | 11720/18688 [13:45:32<7:58:28, 4.12s/it] {'loss': '0.9532', 'grad_norm': '1.876', 'learning_rate': '3.537e-05', 'epoch': '0.6272', 'num_input_tokens_seen': 39416704, 'train_runtime': '4.953e+04', 'train_tokens_per_second': '795.8'}
63%|██████▎ | 11720/18688 [13:45:32<7:58:28, 4.12s/it] 63%|██████▎ | 11721/18688 [13:45:37<8:08:58, 4.21s/it] 63%|██████▎ | 11722/18688 [13:45:41<8:00:08, 4.14s/it] 63%|██████▎ | 11723/18688 [13:45:46<8:30:09, 4.39s/it] 63%|██████▎ | 11724/18688 [13:45:49<7:59:21, 4.13s/it] 63%|██████▎ | 11725/18688 [13:45:53<8:02:35, 4.16s/it] 63%|██████▎ | 11726/18688 [13:45:58<8:03:03, 4.16s/it] 63%|██████▎ | 11727/18688 [13:46:02<8:16:15, 4.28s/it] 63%|██████▎ | 11728/18688 [13:46:06<8:06:32, 4.19s/it] 63%|██████▎ | 11729/18688 [13:46:11<8:11:55, 4.24s/it] 63%|██████▎ | 11730/18688 [13:46:14<7:58:50, 4.13s/it] {'loss': '0.8691', 'grad_norm': '1.413', 'learning_rate': '3.528e-05', 'epoch': '0.6277', 'num_input_tokens_seen': 39450600, 'train_runtime': '4.957e+04', 'train_tokens_per_second': '795.8'}
63%|██████▎ | 11730/18688 [13:46:14<7:58:50, 4.13s/it] 63%|██████▎ | 11731/18688 [13:46:18<7:54:01, 4.09s/it] 63%|██████▎ | 11732/18688 [13:46:23<8:19:48, 4.31s/it] 63%|██████▎ | 11733/18688 [13:46:28<8:48:56, 4.56s/it] 63%|██████▎ | 11734/18688 [13:46:33<8:38:28, 4.47s/it] 63%|██████▎ | 11735/18688 [13:46:37<8:20:40, 4.32s/it] 63%|██████▎ | 11736/18688 [13:46:40<8:03:57, 4.18s/it] 63%|██████▎ | 11737/18688 [13:46:44<7:55:30, 4.10s/it] 63%|██████▎ | 11738/18688 [13:46:49<8:12:51, 4.25s/it] 63%|██████▎ | 11739/18688 [13:46:53<7:49:43, 4.06s/it] 63%|██████▎ | 11740/18688 [13:46:57<7:49:18, 4.05s/it] {'loss': '0.9342', 'grad_norm': '1.361', 'learning_rate': '3.519e-05', 'epoch': '0.6282', 'num_input_tokens_seen': 39485416, 'train_runtime': '4.962e+04', 'train_tokens_per_second': '795.8'}
63%|██████▎ | 11740/18688 [13:46:57<7:49:18, 4.05s/it] 63%|██████▎ | 11741/18688 [13:47:00<7:43:44, 4.01s/it] 63%|██████▎ | 11742/18688 [13:47:05<7:52:12, 4.08s/it] 63%|██████▎ | 11743/18688 [13:47:09<8:01:14, 4.16s/it] 63%|██████▎ | 11744/18688 [13:47:13<7:40:14, 3.98s/it] 63%|██████▎ | 11745/18688 [13:47:16<7:23:40, 3.83s/it] 63%|██████▎ | 11746/18688 [13:47:20<7:30:30, 3.89s/it] 63%|██████▎ | 11747/18688 [13:47:25<7:55:57, 4.11s/it] 63%|██████▎ | 11748/18688 [13:47:30<8:16:46, 4.29s/it] 63%|██████▎ | 11749/18688 [13:47:34<8:29:54, 4.41s/it] 63%|██████▎ | 11750/18688 [13:47:39<8:32:45, 4.43s/it] {'loss': '0.918', 'grad_norm': '1.196', 'learning_rate': '3.51e-05', 'epoch': '0.6288', 'num_input_tokens_seen': 39518704, 'train_runtime': '4.966e+04', 'train_tokens_per_second': '795.8'}
63%|██████▎ | 11750/18688 [13:47:39<8:32:45, 4.43s/it] 63%|██████▎ | 11751/18688 [13:47:42<8:05:27, 4.20s/it] 63%|██████▎ | 11752/18688 [13:47:46<7:47:16, 4.04s/it] 63%|██████▎ | 11753/18688 [13:47:51<8:12:03, 4.26s/it] 63%|██████▎ | 11754/18688 [13:47:55<8:02:51, 4.18s/it] 63%|██████▎ | 11755/18688 [13:47:58<7:45:59, 4.03s/it] 63%|██████▎ | 11756/18688 [13:48:03<8:00:06, 4.16s/it] 63%|██████▎ | 11757/18688 [13:48:07<7:49:46, 4.07s/it] 63%|██████▎ | 11758/18688 [13:48:12<8:18:40, 4.32s/it] 63%|██████▎ | 11759/18688 [13:48:16<8:34:34, 4.46s/it] 63%|██████▎ | 11760/18688 [13:48:21<8:21:25, 4.34s/it] {'loss': '0.9354', 'grad_norm': '1.206', 'learning_rate': '3.502e-05', 'epoch': '0.6293', 'num_input_tokens_seen': 39552712, 'train_runtime': '4.97e+04', 'train_tokens_per_second': '795.8'}
63%|██████▎ | 11760/18688 [13:48:21<8:21:25, 4.34s/it] 63%|██████▎ | 11761/18688 [13:48:24<7:51:00, 4.08s/it] 63%|██████▎ | 11762/18688 [13:48:28<7:49:27, 4.07s/it] 63%|██████▎ | 11763/18688 [13:48:32<7:54:05, 4.11s/it] 63%|██████▎ | 11764/18688 [13:48:37<8:08:39, 4.23s/it] 63%|██████▎ | 11765/18688 [13:48:41<8:05:46, 4.21s/it] 63%|██████▎ | 11766/18688 [13:48:45<7:45:16, 4.03s/it] 63%|██████▎ | 11767/18688 [13:48:48<7:37:19, 3.96s/it] 63%|██████▎ | 11768/18688 [13:48:52<7:40:04, 3.99s/it] 63%|██████▎ | 11769/18688 [13:48:56<7:31:48, 3.92s/it] 63%|██████▎ | 11770/18688 [13:49:00<7:25:45, 3.87s/it] {'loss': '0.8864', 'grad_norm': '1.119', 'learning_rate': '3.493e-05', 'epoch': '0.6298', 'num_input_tokens_seen': 39582008, 'train_runtime': '4.974e+04', 'train_tokens_per_second': '795.8'}
63%|██████▎ | 11770/18688 [13:49:00<7:25:45, 3.87s/it] 63%|██████▎ | 11771/18688 [13:49:04<7:45:04, 4.03s/it] 63%|██████▎ | 11772/18688 [13:49:09<8:05:50, 4.21s/it] 63%|██████▎ | 11773/18688 [13:49:13<7:57:39, 4.14s/it] 63%|██████▎ | 11774/18688 [13:49:17<8:09:24, 4.25s/it] 63%|██████▎ | 11775/18688 [13:49:22<8:19:10, 4.33s/it] 63%|██████▎ | 11776/18688 [13:49:26<8:24:13, 4.38s/it] 63%|██████▎ | 11777/18688 [13:49:31<8:19:05, 4.33s/it] 63%|██████▎ | 11778/18688 [13:49:35<8:10:15, 4.26s/it] 63%|██████▎ | 11779/18688 [13:49:39<7:55:12, 4.13s/it] 63%|██████▎ | 11780/18688 [13:49:42<7:46:34, 4.05s/it] {'loss': '0.9436', 'grad_norm': '1.406', 'learning_rate': '3.484e-05', 'epoch': '0.6304', 'num_input_tokens_seen': 39615360, 'train_runtime': '4.978e+04', 'train_tokens_per_second': '795.8'}
63%|██████▎ | 11780/18688 [13:49:42<7:46:34, 4.05s/it] 63%|██████▎ | 11781/18688 [13:49:46<7:37:24, 3.97s/it] 63%|██████▎ | 11782/18688 [13:49:50<7:33:41, 3.94s/it] 63%|██████▎ | 11783/18688 [13:49:54<7:25:24, 3.87s/it] 63%|██████▎ | 11784/18688 [13:49:58<7:23:11, 3.85s/it] 63%|██████▎ | 11785/18688 [13:50:03<8:10:34, 4.26s/it] 63%|██████▎ | 11786/18688 [13:50:07<8:20:58, 4.36s/it] 63%|██████▎ | 11787/18688 [13:50:12<8:45:56, 4.57s/it] 63%|██████▎ | 11788/18688 [13:50:16<8:14:59, 4.30s/it] 63%|██████▎ | 11789/18688 [13:50:21<8:18:52, 4.34s/it] 63%|██████▎ | 11790/18688 [13:50:25<8:10:57, 4.27s/it] {'loss': '0.876', 'grad_norm': '1.278', 'learning_rate': '3.476e-05', 'epoch': '0.6309', 'num_input_tokens_seen': 39650600, 'train_runtime': '4.983e+04', 'train_tokens_per_second': '795.8'}
63%|██████▎ | 11790/18688 [13:50:25<8:10:57, 4.27s/it] 63%|██████▎ | 11791/18688 [13:50:29<7:55:46, 4.14s/it] 63%|██████▎ | 11792/18688 [13:50:32<7:37:08, 3.98s/it] 63%|██████▎ | 11793/18688 [13:50:38<8:32:55, 4.46s/it] 63%|██████▎ | 11794/18688 [13:50:42<8:35:40, 4.49s/it] 63%|██████▎ | 11795/18688 [13:50:46<8:04:58, 4.22s/it] 63%|██████▎ | 11796/18688 [13:50:50<7:50:34, 4.10s/it] 63%|██████▎ | 11797/18688 [13:50:53<7:41:06, 4.01s/it] 63%|██████▎ | 11798/18688 [13:50:58<7:45:56, 4.06s/it] 63%|██████▎ | 11799/18688 [13:51:02<7:40:47, 4.01s/it] 63%|██████▎ | 11800/18688 [13:51:06<7:39:05, 4.00s/it] {'loss': '0.8895', 'grad_norm': '1.467', 'learning_rate': '3.467e-05', 'epoch': '0.6314', 'num_input_tokens_seen': 39682520, 'train_runtime': '4.987e+04', 'train_tokens_per_second': '795.8'}
63%|██████▎ | 11800/18688 [13:51:06<7:39:05, 4.00s/it] 63%|██████▎ | 11801/18688 [13:51:09<7:24:34, 3.87s/it] 63%|██████▎ | 11802/18688 [13:51:13<7:35:51, 3.97s/it] 63%|██████▎ | 11803/18688 [13:51:17<7:18:06, 3.82s/it] 63%|██████▎ | 11804/18688 [13:51:20<7:05:23, 3.71s/it] 63%|██████▎ | 11805/18688 [13:51:24<7:01:13, 3.67s/it] 63%|██████▎ | 11806/18688 [13:51:28<7:33:47, 3.96s/it] 63%|██████▎ | 11807/18688 [13:51:33<7:50:13, 4.10s/it] 63%|██████▎ | 11808/18688 [13:51:37<8:02:55, 4.21s/it] 63%|██████▎ | 11809/18688 [13:51:41<7:43:39, 4.04s/it] 63%|██████▎ | 11810/18688 [13:51:45<7:26:30, 3.90s/it] {'loss': '0.8964', 'grad_norm': '1.284', 'learning_rate': '3.458e-05', 'epoch': '0.632', 'num_input_tokens_seen': 39710928, 'train_runtime': '4.991e+04', 'train_tokens_per_second': '795.7'}
63%|██████▎ | 11810/18688 [13:51:45<7:26:30, 3.90s/it] 63%|██████▎ | 11811/18688 [13:51:49<7:47:02, 4.07s/it] 63%|██████▎ | 11812/18688 [13:51:53<7:57:11, 4.16s/it] 63%|██████▎ | 11813/18688 [13:51:58<8:04:03, 4.22s/it] 63%|██████▎ | 11814/18688 [13:52:02<8:13:04, 4.30s/it] 63%|██████▎ | 11815/18688 [13:52:06<7:47:12, 4.08s/it] 63%|██████▎ | 11816/18688 [13:52:09<7:31:37, 3.94s/it] 63%|██████▎ | 11817/18688 [13:52:14<8:01:26, 4.20s/it] 63%|██████▎ | 11818/18688 [13:52:19<8:10:23, 4.28s/it] 63%|██████▎ | 11819/18688 [13:52:23<7:58:44, 4.18s/it] 63%|██████▎ | 11820/18688 [13:52:26<7:42:24, 4.04s/it] {'loss': '0.9297', 'grad_norm': '1.688', 'learning_rate': '3.45e-05', 'epoch': '0.6325', 'num_input_tokens_seen': 39743904, 'train_runtime': '4.995e+04', 'train_tokens_per_second': '795.7'}
63%|██████▎ | 11820/18688 [13:52:26<7:42:24, 4.04s/it] 63%|██████▎ | 11821/18688 [13:52:31<8:05:09, 4.24s/it] 63%|██████▎ | 11822/18688 [13:52:35<8:01:52, 4.21s/it] 63%|██████▎ | 11823/18688 [13:52:39<7:39:06, 4.01s/it] 63%|██████▎ | 11824/18688 [13:52:43<7:42:46, 4.05s/it] 63%|██████▎ | 11825/18688 [13:52:47<7:46:26, 4.08s/it] 63%|██████▎ | 11826/18688 [13:52:52<8:00:25, 4.20s/it] 63%|██████▎ | 11827/18688 [13:52:55<7:35:23, 3.98s/it] 63%|██████▎ | 11828/18688 [13:52:59<7:35:18, 3.98s/it] 63%|██████▎ | 11829/18688 [13:53:04<8:10:01, 4.29s/it] 63%|██████▎ | 11830/18688 [13:53:08<7:50:52, 4.12s/it] {'loss': '0.9076', 'grad_norm': '1.526', 'learning_rate': '3.441e-05', 'epoch': '0.633', 'num_input_tokens_seen': 39778240, 'train_runtime': '4.999e+04', 'train_tokens_per_second': '795.8'}
63%|██████▎ | 11830/18688 [13:53:08<7:50:52, 4.12s/it] 63%|██████▎ | 11831/18688 [13:53:13<8:16:38, 4.35s/it] 63%|██████▎ | 11832/18688 [13:53:18<8:48:23, 4.62s/it] 63%|██████▎ | 11833/18688 [13:53:23<8:52:46, 4.66s/it] 63%|██████▎ | 11834/18688 [13:53:27<8:34:55, 4.51s/it] 63%|██████▎ | 11835/18688 [13:53:32<9:03:58, 4.76s/it] 63%|██████▎ | 11836/18688 [13:53:37<9:01:32, 4.74s/it] 63%|██████▎ | 11837/18688 [13:53:41<8:49:05, 4.63s/it] 63%|██████▎ | 11838/18688 [13:53:46<8:40:22, 4.56s/it] 63%|██████▎ | 11839/18688 [13:53:50<8:34:17, 4.51s/it] 63%|██████▎ | 11840/18688 [13:53:55<8:41:55, 4.57s/it] {'loss': '0.9722', 'grad_norm': '1.184', 'learning_rate': '3.432e-05', 'epoch': '0.6336', 'num_input_tokens_seen': 39816056, 'train_runtime': '5.004e+04', 'train_tokens_per_second': '795.8'}
63%|██████▎ | 11840/18688 [13:53:55<8:41:55, 4.57s/it] 63%|██████▎ | 11841/18688 [13:53:59<8:48:49, 4.63s/it] 63%|██████▎ | 11842/18688 [13:54:04<8:38:53, 4.55s/it] 63%|██████▎ | 11843/18688 [13:54:07<8:04:15, 4.24s/it] 63%|██████▎ | 11844/18688 [13:54:11<7:43:40, 4.06s/it] 63%|██████▎ | 11845/18688 [13:54:16<8:03:55, 4.24s/it] 63%|██████▎ | 11846/18688 [13:54:22<9:10:15, 4.83s/it] 63%|██████▎ | 11847/18688 [13:54:28<9:39:10, 5.08s/it] 63%|██████▎ | 11848/18688 [13:54:32<9:09:23, 4.82s/it] 63%|██████▎ | 11849/18688 [13:54:36<8:57:43, 4.72s/it] 63%|██████▎ | 11850/18688 [13:54:42<9:28:46, 4.99s/it] {'loss': '0.8482', 'grad_norm': '1.553', 'learning_rate': '3.423e-05', 'epoch': '0.6341', 'num_input_tokens_seen': 39851384, 'train_runtime': '5.008e+04', 'train_tokens_per_second': '795.7'}
63%|██████▎ | 11850/18688 [13:54:42<9:28:46, 4.99s/it] 63%|██████▎ | 11851/18688 [13:54:47<9:38:58, 5.08s/it] 63%|██████▎ | 11852/18688 [13:54:52<9:47:47, 5.16s/it] 63%|██████▎ | 11853/18688 [13:54:57<9:39:26, 5.09s/it] 63%|██████▎ | 11854/18688 [13:55:02<9:16:45, 4.89s/it] 63%|██████▎ | 11855/18688 [13:55:07<9:12:52, 4.85s/it] 63%|██████▎ | 11856/18688 [13:55:11<9:08:15, 4.81s/it] 63%|██████▎ | 11857/18688 [13:55:17<9:24:16, 4.96s/it] 63%|██████▎ | 11858/18688 [13:55:22<9:36:51, 5.07s/it] 63%|██████▎ | 11859/18688 [13:55:26<9:13:55, 4.87s/it] 63%|██████▎ | 11860/18688 [13:55:31<9:17:21, 4.90s/it] {'loss': '0.8789', 'grad_norm': '1.069', 'learning_rate': '3.415e-05', 'epoch': '0.6346', 'num_input_tokens_seen': 39884640, 'train_runtime': '5.013e+04', 'train_tokens_per_second': '795.6'}
63%|██████▎ | 11860/18688 [13:55:31<9:17:21, 4.90s/it] 63%|██████▎ | 11861/18688 [13:55:36<9:16:28, 4.89s/it] 63%|██████▎ | 11862/18688 [13:55:40<8:56:12, 4.71s/it] 63%|██████▎ | 11863/18688 [13:55:44<8:26:57, 4.46s/it] 63%|██████▎ | 11864/18688 [13:55:48<8:06:04, 4.27s/it] 63%|██████▎ | 11865/18688 [13:55:53<8:20:11, 4.40s/it] 63%|██████▎ | 11866/18688 [13:55:57<7:57:23, 4.20s/it] 64%|██████▎ | 11867/18688 [13:56:01<8:20:17, 4.40s/it] 64%|██████▎ | 11868/18688 [13:56:05<7:57:46, 4.20s/it] 64%|██████▎ | 11869/18688 [13:56:09<7:49:17, 4.13s/it] 64%|██████▎ | 11870/18688 [13:56:14<8:15:03, 4.36s/it] {'loss': '0.9193', 'grad_norm': '1.635', 'learning_rate': '3.406e-05', 'epoch': '0.6352', 'num_input_tokens_seen': 39916648, 'train_runtime': '5.017e+04', 'train_tokens_per_second': '795.6'}
64%|██████▎ | 11870/18688 [13:56:14<8:15:03, 4.36s/it] 64%|██████▎ | 11871/18688 [13:56:19<8:26:11, 4.46s/it] 64%|██████▎ | 11872/18688 [13:56:23<8:20:59, 4.41s/it] 64%|██████▎ | 11873/18688 [13:56:26<7:47:50, 4.12s/it] 64%|██████▎ | 11874/18688 [13:56:31<7:46:38, 4.11s/it] 64%|██████▎ | 11875/18688 [13:56:35<7:56:06, 4.19s/it] 64%|██████▎ | 11876/18688 [13:56:39<7:36:55, 4.02s/it] 64%|██████▎ | 11877/18688 [13:56:43<7:48:26, 4.13s/it] 64%|██████▎ | 11878/18688 [13:56:47<7:49:09, 4.13s/it] 64%|██████▎ | 11879/18688 [13:56:53<8:42:19, 4.60s/it] 64%|██████▎ | 11880/18688 [13:56:56<8:08:24, 4.30s/it] {'loss': '0.9238', 'grad_norm': '1.473', 'learning_rate': '3.397e-05', 'epoch': '0.6357', 'num_input_tokens_seen': 39950848, 'train_runtime': '5.022e+04', 'train_tokens_per_second': '795.6'}
64%|██████▎ | 11880/18688 [13:56:56<8:08:24, 4.30s/it] 64%|██████▎ | 11881/18688 [13:57:01<8:17:45, 4.39s/it] 64%|██████▎ | 11882/18688 [13:57:05<8:06:40, 4.29s/it] 64%|██████▎ | 11883/18688 [13:57:09<8:10:24, 4.32s/it] 64%|██████▎ | 11884/18688 [13:57:14<8:17:24, 4.39s/it] 64%|██████▎ | 11885/18688 [13:57:18<8:04:14, 4.27s/it] 64%|██████▎ | 11886/18688 [13:57:22<7:52:58, 4.17s/it] 64%|██████▎ | 11887/18688 [13:57:26<7:33:40, 4.00s/it] 64%|██████▎ | 11888/18688 [13:57:30<7:59:13, 4.23s/it] 64%|██████▎ | 11889/18688 [13:57:35<8:07:27, 4.30s/it] 64%|██████▎ | 11890/18688 [13:57:39<8:06:08, 4.29s/it] {'loss': '0.9667', 'grad_norm': '1.388', 'learning_rate': '3.389e-05', 'epoch': '0.6363', 'num_input_tokens_seen': 39985264, 'train_runtime': '5.026e+04', 'train_tokens_per_second': '795.6'}
64%|██████▎ | 11890/18688 [13:57:39<8:06:08, 4.29s/it] 64%|██████▎ | 11891/18688 [13:57:43<8:04:31, 4.28s/it] 64%|██████▎ | 11892/18688 [13:57:47<7:54:22, 4.19s/it] 64%|██████▎ | 11893/18688 [13:57:52<8:09:32, 4.32s/it] 64%|██████▎ | 11894/18688 [13:57:56<7:56:45, 4.21s/it] 64%|██████▎ | 11895/18688 [13:57:59<7:37:39, 4.04s/it] 64%|██████▎ | 11896/18688 [13:58:03<7:20:22, 3.89s/it] 64%|██████▎ | 11897/18688 [13:58:07<7:20:14, 3.89s/it] 64%|██████▎ | 11898/18688 [13:58:11<7:22:37, 3.91s/it] 64%|██████▎ | 11899/18688 [13:58:15<7:22:50, 3.91s/it] 64%|██████▎ | 11900/18688 [13:58:20<7:50:36, 4.16s/it] {'loss': '0.9181', 'grad_norm': '1.458', 'learning_rate': '3.38e-05', 'epoch': '0.6368', 'num_input_tokens_seen': 40015912, 'train_runtime': '5.03e+04', 'train_tokens_per_second': '795.5'}
64%|██████▎ | 11900/18688 [13:58:20<7:50:36, 4.16s/it] 64%|██████▎ | 11901/18688 [13:58:23<7:30:20, 3.98s/it] 64%|██████▎ | 11902/18688 [13:58:27<7:37:26, 4.04s/it] 64%|██████▎ | 11903/18688 [13:58:32<7:56:19, 4.21s/it] 64%|██████▎ | 11904/18688 [13:58:37<8:20:59, 4.43s/it] 64%|██████▎ | 11905/18688 [13:58:41<8:07:09, 4.31s/it] 64%|██████▎ | 11906/18688 [13:58:45<7:51:51, 4.17s/it] 64%|██████▎ | 11907/18688 [13:58:48<7:37:35, 4.05s/it] 64%|██████▎ | 11908/18688 [13:58:54<8:20:29, 4.43s/it] 64%|██████▎ | 11909/18688 [13:58:57<7:47:35, 4.14s/it] 64%|██████▎ | 11910/18688 [13:59:01<7:27:52, 3.96s/it] {'loss': '0.9256', 'grad_norm': '1.46', 'learning_rate': '3.372e-05', 'epoch': '0.6373', 'num_input_tokens_seen': 40049264, 'train_runtime': '5.034e+04', 'train_tokens_per_second': '795.6'}
64%|██████▎ | 11910/18688 [13:59:01<7:27:52, 3.96s/it] 64%|██████▎ | 11911/18688 [13:59:05<7:38:11, 4.06s/it] 64%|██████▎ | 11912/18688 [13:59:09<7:29:47, 3.98s/it] 64%|██████▎ | 11913/18688 [13:59:13<7:22:21, 3.92s/it] 64%|██████▍ | 11914/18688 [13:59:16<7:10:47, 3.82s/it] 64%|██████▍ | 11915/18688 [13:59:21<7:55:31, 4.21s/it] 64%|██████▍ | 11916/18688 [13:59:25<7:41:02, 4.08s/it] 64%|██████▍ | 11917/18688 [13:59:30<8:10:09, 4.34s/it] 64%|██████▍ | 11918/18688 [13:59:35<8:20:53, 4.44s/it] 64%|██████▍ | 11919/18688 [13:59:39<8:09:03, 4.33s/it] 64%|██████▍ | 11920/18688 [13:59:43<8:07:20, 4.32s/it] {'loss': '0.9599', 'grad_norm': '1.125', 'learning_rate': '3.363e-05', 'epoch': '0.6379', 'num_input_tokens_seen': 40083792, 'train_runtime': '5.038e+04', 'train_tokens_per_second': '795.6'}
64%|██████▍ | 11920/18688 [13:59:43<8:07:20, 4.32s/it] 64%|██████▍ | 11921/18688 [13:59:47<7:44:23, 4.12s/it] 64%|██████▍ | 11922/18688 [13:59:51<7:31:50, 4.01s/it] 64%|██████▍ | 11923/18688 [13:59:55<7:35:27, 4.04s/it] 64%|██████▍ | 11924/18688 [13:59:59<7:34:54, 4.04s/it] 64%|██████▍ | 11925/18688 [14:00:02<7:18:49, 3.89s/it] 64%|██████▍ | 11926/18688 [14:00:06<7:13:18, 3.84s/it] 64%|██████▍ | 11927/18688 [14:00:12<8:23:45, 4.47s/it] 64%|██████▍ | 11928/18688 [14:00:17<8:32:32, 4.55s/it] 64%|██████▍ | 11929/18688 [14:00:21<8:15:02, 4.39s/it] 64%|██████▍ | 11930/18688 [14:00:24<7:49:01, 4.16s/it] {'loss': '0.877', 'grad_norm': '1.269', 'learning_rate': '3.354e-05', 'epoch': '0.6384', 'num_input_tokens_seen': 40116656, 'train_runtime': '5.042e+04', 'train_tokens_per_second': '795.6'}
64%|██████▍ | 11930/18688 [14:00:24<7:49:01, 4.16s/it] 64%|██████▍ | 11931/18688 [14:00:28<7:38:31, 4.07s/it] 64%|██████▍ | 11932/18688 [14:00:32<7:33:58, 4.03s/it] 64%|██████▍ | 11933/18688 [14:00:36<7:27:21, 3.97s/it] 64%|██████▍ | 11934/18688 [14:00:41<8:17:57, 4.42s/it] 64%|██████▍ | 11935/18688 [14:00:46<8:36:20, 4.59s/it] 64%|██████▍ | 11936/18688 [14:00:51<8:51:02, 4.72s/it] 64%|██████▍ | 11937/18688 [14:00:56<8:50:53, 4.72s/it] 64%|██████▍ | 11938/18688 [14:01:01<8:42:30, 4.64s/it] 64%|██████▍ | 11939/18688 [14:01:04<8:04:15, 4.31s/it] 64%|██████▍ | 11940/18688 [14:01:09<8:23:18, 4.48s/it] {'loss': '0.8928', 'grad_norm': '1.217', 'learning_rate': '3.346e-05', 'epoch': '0.6389', 'num_input_tokens_seen': 40154472, 'train_runtime': '5.047e+04', 'train_tokens_per_second': '795.6'}
64%|██████▍ | 11940/18688 [14:01:09<8:23:18, 4.48s/it] 64%|██████▍ | 11941/18688 [14:01:13<8:15:59, 4.41s/it] 64%|██████▍ | 11942/18688 [14:01:18<8:26:54, 4.51s/it] 64%|██████▍ | 11943/18688 [14:01:22<8:19:47, 4.45s/it] 64%|██████▍ | 11944/18688 [14:01:26<7:58:51, 4.26s/it] 64%|██████▍ | 11945/18688 [14:01:30<7:44:43, 4.14s/it] 64%|██████▍ | 11946/18688 [14:01:34<7:45:55, 4.15s/it] 64%|██████▍ | 11947/18688 [14:01:38<7:40:45, 4.10s/it] 64%|██████▍ | 11948/18688 [14:01:42<7:39:30, 4.09s/it] 64%|██████▍ | 11949/18688 [14:01:47<7:52:59, 4.21s/it] 64%|██████▍ | 11950/18688 [14:01:50<7:30:03, 4.01s/it] {'loss': '0.9252', 'grad_norm': '1.438', 'learning_rate': '3.337e-05', 'epoch': '0.6395', 'num_input_tokens_seen': 40187520, 'train_runtime': '5.051e+04', 'train_tokens_per_second': '795.6'}
64%|██████▍ | 11950/18688 [14:01:50<7:30:03, 4.01s/it] 64%|██████▍ | 11951/18688 [14:01:54<7:12:36, 3.85s/it] 64%|██████▍ | 11952/18688 [14:01:57<7:10:13, 3.83s/it] 64%|██████▍ | 11953/18688 [14:02:02<7:18:32, 3.91s/it] 64%|██████▍ | 11954/18688 [14:02:06<7:49:33, 4.18s/it] 64%|██████▍ | 11955/18688 [14:02:12<8:27:20, 4.52s/it] 64%|██████▍ | 11956/18688 [14:02:15<7:53:02, 4.22s/it] 64%|██████▍ | 11957/18688 [14:02:20<8:21:23, 4.47s/it] 64%|██████▍ | 11958/18688 [14:02:25<8:25:28, 4.51s/it] 64%|██████▍ | 11959/18688 [14:02:29<8:03:43, 4.31s/it] 64%|██████▍ | 11960/18688 [14:02:33<8:03:31, 4.31s/it] {'loss': '0.8854', 'grad_norm': '1.158', 'learning_rate': '3.328e-05', 'epoch': '0.64', 'num_input_tokens_seen': 40222440, 'train_runtime': '5.055e+04', 'train_tokens_per_second': '795.6'}
64%|██████▍ | 11960/18688 [14:02:33<8:03:31, 4.31s/it] 64%|██████▍ | 11961/18688 [14:02:38<8:41:22, 4.65s/it] 64%|██████▍ | 11962/18688 [14:02:43<8:32:05, 4.57s/it] 64%|██████▍ | 11963/18688 [14:02:48<8:37:42, 4.62s/it] 64%|██████▍ | 11964/18688 [14:02:52<8:17:19, 4.44s/it] 64%|██████▍ | 11965/18688 [14:02:55<7:46:04, 4.16s/it] 64%|██████▍ | 11966/18688 [14:03:00<8:11:03, 4.38s/it] 64%|██████▍ | 11967/18688 [14:03:04<8:02:36, 4.31s/it] 64%|██████▍ | 11968/18688 [14:03:08<8:00:44, 4.29s/it] 64%|██████▍ | 11969/18688 [14:03:14<8:41:41, 4.66s/it] 64%|██████▍ | 11970/18688 [14:03:18<8:31:42, 4.57s/it] {'loss': '0.9313', 'grad_norm': '1.67', 'learning_rate': '3.32e-05', 'epoch': '0.6405', 'num_input_tokens_seen': 40259536, 'train_runtime': '5.06e+04', 'train_tokens_per_second': '795.7'}
64%|██████▍ | 11970/18688 [14:03:18<8:31:42, 4.57s/it] 64%|██████▍ | 11971/18688 [14:03:24<8:55:59, 4.79s/it] 64%|██████▍ | 11972/18688 [14:03:27<8:22:20, 4.49s/it] 64%|██████▍ | 11973/18688 [14:03:31<8:00:34, 4.29s/it] 64%|██████▍ | 11974/18688 [14:03:35<7:40:53, 4.12s/it] 64%|██████▍ | 11975/18688 [14:03:39<7:37:18, 4.09s/it] 64%|██████▍ | 11976/18688 [14:03:44<8:06:03, 4.34s/it] 64%|██████▍ | 11977/18688 [14:03:48<7:55:15, 4.25s/it] 64%|██████▍ | 11978/18688 [14:03:52<7:48:12, 4.19s/it] 64%|██████▍ | 11979/18688 [14:03:56<7:49:16, 4.20s/it] 64%|██████▍ | 11980/18688 [14:04:00<7:43:07, 4.14s/it] {'loss': '1.001', 'grad_norm': '1.448', 'learning_rate': '3.311e-05', 'epoch': '0.6411', 'num_input_tokens_seen': 40293704, 'train_runtime': '5.064e+04', 'train_tokens_per_second': '795.7'}
64%|██████▍ | 11980/18688 [14:04:00<7:43:07, 4.14s/it] 64%|██████▍ | 11981/18688 [14:04:04<7:24:18, 3.97s/it] 64%|██████▍ | 11982/18688 [14:04:08<7:41:57, 4.13s/it] 64%|██████▍ | 11983/18688 [14:04:13<8:11:17, 4.40s/it] 64%|██████▍ | 11984/18688 [14:04:18<8:13:57, 4.42s/it] 64%|██████▍ | 11985/18688 [14:04:22<8:02:26, 4.32s/it] 64%|██████▍ | 11986/18688 [14:04:26<7:51:12, 4.22s/it] 64%|██████▍ | 11987/18688 [14:04:30<7:38:06, 4.10s/it] 64%|██████▍ | 11988/18688 [14:04:35<8:03:50, 4.33s/it] 64%|██████▍ | 11989/18688 [14:04:38<7:42:09, 4.14s/it] 64%|██████▍ | 11990/18688 [14:04:43<7:47:50, 4.19s/it] {'loss': '0.9139', 'grad_norm': '1.183', 'learning_rate': '3.303e-05', 'epoch': '0.6416', 'num_input_tokens_seen': 40325872, 'train_runtime': '5.068e+04', 'train_tokens_per_second': '795.6'}
64%|██████▍ | 11990/18688 [14:04:43<7:47:50, 4.19s/it] 64%|██████▍ | 11991/18688 [14:04:47<8:02:50, 4.33s/it] 64%|██████▍ | 11992/18688 [14:04:52<8:08:06, 4.37s/it] 64%|██████▍ | 11993/18688 [14:04:56<8:16:22, 4.45s/it] 64%|██████▍ | 11994/18688 [14:05:00<7:46:18, 4.18s/it] 64%|██████▍ | 11995/18688 [14:05:04<7:34:47, 4.08s/it] 64%|██████▍ | 11996/18688 [14:05:08<7:39:00, 4.12s/it] 64%|██████▍ | 11997/18688 [14:05:12<7:51:51, 4.23s/it] 64%|██████▍ | 11998/18688 [14:05:17<8:11:40, 4.41s/it] 64%|██████▍ | 11999/18688 [14:05:21<8:00:53, 4.31s/it] 64%|██████▍ | 12000/18688 [14:05:25<7:47:27, 4.19s/it] {'loss': '0.9111', 'grad_norm': '1.354', 'learning_rate': '3.294e-05', 'epoch': '0.6421', 'num_input_tokens_seen': 40360224, 'train_runtime': '5.073e+04', 'train_tokens_per_second': '795.7'}
64%|██████▍ | 12000/18688 [14:05:25<7:47:27, 4.19s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
0%| | 3/1000 [00:00<00:39, 25.42it/s]
1%| | 6/1000 [00:00<00:54, 18.09it/s]
1%| | 8/1000 [00:00<00:57, 17.36it/s]
1%| | 10/1000 [00:00<01:00, 16.42it/s]
1%| | 12/1000 [00:00<01:00, 16.21it/s]
1%|▏ | 14/1000 [00:00<01:02, 15.77it/s]
2%|▏ | 16/1000 [00:00<01:03, 15.56it/s]
2%|▏ | 18/1000 [00:01<01:38, 9.97it/s]
2%|▏ | 20/1000 [00:01<01:33, 10.52it/s]
2%|▏ | 22/1000 [00:01<01:52, 8.69it/s]
2%|▏ | 24/1000 [00:01<01:40, 9.72it/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, 10.99it/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, 10.43it/s]
4%|▍ | 38/1000 [00:03<01:24, 11.33it/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, 10.47it/s]
5%|▍ | 46/1000 [00:03<01:22, 11.62it/s]
5%|▍ | 48/1000 [00:04<01:29, 10.58it/s]
5%|▌ | 50/1000 [00:04<01:21, 11.61it/s]
5%|▌ | 52/1000 [00:04<01:16, 12.43it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.14it/s]
6%|▌ | 56/1000 [00:04<01:08, 13.80it/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<01:28, 10.56it/s]
6%|▋ | 64/1000 [00:05<01:24, 11.02it/s]
7%|▋ | 66/1000 [00:05<01:22, 11.27it/s]
7%|▋ | 68/1000 [00:05<01:20, 11.52it/s]
7%|▋ | 70/1000 [00:06<01:17, 11.93it/s]
7%|▋ | 72/1000 [00:06<01:15, 12.32it/s]
7%|▋ | 74/1000 [00:06<01:14, 12.48it/s]
8%|▊ | 76/1000 [00:06<01:14, 12.38it/s]
8%|▊ | 78/1000 [00:06<01:14, 12.34it/s]
8%|▊ | 80/1000 [00:06<01:18, 11.72it/s]
8%|▊ | 82/1000 [00:07<01:16, 12.00it/s]
8%|▊ | 84/1000 [00:07<01:15, 12.17it/s]
9%|▊ | 86/1000 [00:07<01:14, 12.35it/s]
9%|▉ | 88/1000 [00:07<01:14, 12.32it/s]
9%|▉ | 90/1000 [00:07<01:13, 12.30it/s]
9%|▉ | 92/1000 [00:07<01:13, 12.27it/s]
9%|▉ | 94/1000 [00:08<01:12, 12.51it/s]
10%|▉ | 96/1000 [00:08<01:08, 13.21it/s]
10%|▉ | 98/1000 [00:08<01:07, 13.45it/s]
10%|█ | 100/1000 [00:08<01:34, 9.52it/s]
10%|█ | 102/1000 [00:08<01:25, 10.53it/s]
10%|█ | 104/1000 [00:09<01:55, 7.73it/s]
10%|█ | 105/1000 [00:09<02:05, 7.13it/s]
11%|█ | 106/1000 [00:09<02:02, 7.31it/s]
11%|█ | 108/1000 [00:09<01:39, 9.00it/s]
11%|█ | 110/1000 [00:09<01:23, 10.65it/s]
11%|█ | 112/1000 [00:09<01:15, 11.77it/s]
11%|█▏ | 114/1000 [00:10<01:11, 12.36it/s]
12%|█▏ | 116/1000 [00:10<01:08, 12.91it/s]
12%|█▏ | 118/1000 [00:10<01:04, 13.59it/s]
12%|█▏ | 120/1000 [00:10<01:08, 12.90it/s]
12%|█▏ | 122/1000 [00:10<01:22, 10.62it/s]
12%|█▏ | 124/1000 [00:11<01:30, 9.66it/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, 13.16it/s]
13%|█▎ | 134/1000 [00:11<01:10, 12.35it/s]
14%|█▎ | 136/1000 [00:11<01:05, 13.18it/s]
14%|█▍ | 138/1000 [00:12<01:05, 13.14it/s]
14%|█▍ | 140/1000 [00:12<01:25, 10.04it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.50it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.52it/s]
15%|█▍ | 146/1000 [00:13<01:20, 10.56it/s]
15%|█▍ | 148/1000 [00:13<01:14, 11.46it/s]
15%|█▌ | 150/1000 [00:13<01:07, 12.51it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.08it/s]
15%|█▌ | 154/1000 [00:13<01:03, 13.35it/s]
16%|█▌ | 156/1000 [00:13<01:03, 13.21it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.71it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.61it/s]
16%|█▌ | 162/1000 [00:14<01:02, 13.47it/s]
16%|█▋ | 164/1000 [00:14<01:01, 13.59it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.76it/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, 11.44it/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, 8.63it/s]
18%|█▊ | 180/1000 [00:16<01:33, 8.76it/s]
18%|█▊ | 182/1000 [00:16<01:35, 8.54it/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<01:24, 9.63it/s]
19%|█▉ | 190/1000 [00:17<01:15, 10.67it/s]
19%|█▉ | 192/1000 [00:17<01:11, 11.31it/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, 10.14it/s]
20%|██ | 200/1000 [00:18<01:17, 10.31it/s]
20%|██ | 202/1000 [00:18<01:12, 11.07it/s]
20%|██ | 204/1000 [00:18<01:05, 12.19it/s]
21%|██ | 206/1000 [00:18<01:05, 12.13it/s]
21%|██ | 208/1000 [00:18<01:02, 12.76it/s]
21%|██ | 210/1000 [00:18<00:57, 13.70it/s]
21%|██ | 212/1000 [00:18<00:56, 14.07it/s]
21%|██▏ | 214/1000 [00:19<00:57, 13.56it/s]
22%|██▏ | 216/1000 [00:19<00:58, 13.42it/s]
22%|██▏ | 218/1000 [00:19<00:58, 13.32it/s]
22%|██▏ | 220/1000 [00:19<01:33, 8.35it/s]
22%|██▏ | 222/1000 [00:20<01:28, 8.78it/s]
22%|██▏ | 224/1000 [00:20<01:23, 9.31it/s]
23%|██▎ | 226/1000 [00:20<01:14, 10.36it/s]
23%|██▎ | 228/1000 [00:20<01:08, 11.32it/s]
23%|██▎ | 230/1000 [00:20<01:04, 11.87it/s]
23%|██▎ | 232/1000 [00:20<01:02, 12.27it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.04it/s]
24%|██▎ | 236/1000 [00:21<00:56, 13.57it/s]
24%|██▍ | 238/1000 [00:21<00:53, 14.22it/s]
24%|██▍ | 240/1000 [00:21<00:53, 14.24it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.79it/s]
24%|██▍ | 244/1000 [00:21<00:50, 15.00it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.92it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.63it/s]
25%|██▌ | 250/1000 [00:22<00:56, 13.32it/s]
25%|██▌ | 252/1000 [00:22<01:02, 11.94it/s]
25%|██▌ | 254/1000 [00:22<01:01, 12.14it/s]
26%|██▌ | 256/1000 [00:22<01:00, 12.29it/s]
26%|██▌ | 258/1000 [00:22<01:00, 12.34it/s]
26%|██▌ | 260/1000 [00:22<01:04, 11.53it/s]
26%|██▌ | 262/1000 [00:23<01:02, 11.75it/s]
26%|██▋ | 264/1000 [00:23<01:00, 12.25it/s]
27%|██▋ | 266/1000 [00:23<00:56, 12.92it/s]
27%|██▋ | 268/1000 [00:23<00:53, 13.70it/s]
27%|██▋ | 270/1000 [00:23<00:57, 12.74it/s]
27%|██▋ | 272/1000 [00:23<01:04, 11.34it/s]
27%|██▋ | 274/1000 [00:24<01:03, 11.48it/s]
28%|██▊ | 276/1000 [00:24<00:57, 12.51it/s]
28%|██▊ | 278/1000 [00:24<00:56, 12.83it/s]
28%|██▊ | 280/1000 [00:24<00:53, 13.41it/s]
28%|██▊ | 282/1000 [00:24<00:50, 14.35it/s]
28%|██▊ | 284/1000 [00:24<00:49, 14.33it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.63it/s]
29%|██▉ | 288/1000 [00:24<00:47, 14.98it/s]
29%|██▉ | 290/1000 [00:25<00:46, 15.41it/s]
29%|██▉ | 292/1000 [00:25<00:46, 15.39it/s]
29%|██▉ | 294/1000 [00:25<00:46, 15.29it/s]
30%|██▉ | 296/1000 [00:25<00:45, 15.32it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.44it/s]
30%|███ | 300/1000 [00:25<00:46, 15.11it/s]
30%|███ | 302/1000 [00:25<00:46, 15.15it/s]
30%|███ | 304/1000 [00:26<00:50, 13.74it/s]
31%|███ | 306/1000 [00:26<00:47, 14.48it/s]
31%|███ | 308/1000 [00:26<00:47, 14.43it/s]
31%|███ | 310/1000 [00:26<00:47, 14.62it/s]
31%|███ | 312/1000 [00:26<00:47, 14.60it/s]
31%|███▏ | 314/1000 [00:26<00:44, 15.30it/s]
32%|███▏ | 316/1000 [00:26<00:43, 15.56it/s]
32%|███▏ | 318/1000 [00:26<00:42, 16.00it/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, 11.45it/s]
33%|███▎ | 328/1000 [00:27<00:58, 11.48it/s]
33%|███▎ | 330/1000 [00:28<00:55, 12.10it/s]
33%|███▎ | 332/1000 [00:28<00:53, 12.45it/s]
33%|███▎ | 334/1000 [00:28<00:53, 12.55it/s]
34%|███▎ | 336/1000 [00:28<00:54, 12.22it/s]
34%|███▍ | 338/1000 [00:28<01:14, 8.84it/s]
34%|███▍ | 340/1000 [00:29<01:05, 10.08it/s]
34%|███▍ | 342/1000 [00:29<00:57, 11.37it/s]
34%|███▍ | 344/1000 [00:29<00:53, 12.19it/s]
35%|███▍ | 346/1000 [00:29<00:50, 12.87it/s]
35%|███▍ | 348/1000 [00:29<00:48, 13.42it/s]
35%|███▌ | 350/1000 [00:29<00:48, 13.41it/s]
35%|███▌ | 352/1000 [00:29<00:48, 13.40it/s]
35%|███▌ | 354/1000 [00:30<00:47, 13.52it/s]
36%|███▌ | 356/1000 [00:30<00:46, 13.99it/s]
36%|███▌ | 358/1000 [00:30<00:45, 14.00it/s]
36%|███▌ | 360/1000 [00:30<00:44, 14.52it/s]
36%|███▌ | 362/1000 [00:30<00:48, 13.06it/s]
36%|███▋ | 364/1000 [00:30<00:46, 13.60it/s]
37%|███▋ | 366/1000 [00:31<00:53, 11.81it/s]
37%|███▋ | 368/1000 [00:31<00:51, 12.24it/s]
37%|███▋ | 370/1000 [00:31<00:48, 13.12it/s]
37%|███▋ | 372/1000 [00:31<00:46, 13.47it/s]
37%|███▋ | 374/1000 [00:31<00:55, 11.34it/s]
38%|███▊ | 376/1000 [00:31<00:57, 10.92it/s]
38%|███▊ | 378/1000 [00:32<00:53, 11.60it/s]
38%|███▊ | 380/1000 [00:32<00:49, 12.41it/s]
38%|███▊ | 382/1000 [00:32<00:49, 12.60it/s]
38%|███▊ | 384/1000 [00:32<00:46, 13.35it/s]
39%|███▊ | 386/1000 [00:32<00:44, 13.66it/s]
39%|███▉ | 388/1000 [00:32<00:44, 13.61it/s]
39%|███▉ | 390/1000 [00:32<00:45, 13.50it/s]
39%|███▉ | 392/1000 [00:33<00:43, 13.84it/s]
39%|███▉ | 394/1000 [00:33<00:44, 13.63it/s]
40%|███▉ | 396/1000 [00:33<00:43, 13.76it/s]
40%|███▉ | 398/1000 [00:33<00:48, 12.51it/s]
40%|████ | 400/1000 [00:33<00:45, 13.11it/s]
40%|████ | 402/1000 [00:33<00:44, 13.46it/s]
40%|████ | 404/1000 [00:33<00:42, 14.16it/s]
41%|████ | 406/1000 [00:34<00:40, 14.74it/s]
41%|████ | 408/1000 [00:34<00:47, 12.41it/s]
41%|████ | 410/1000 [00:34<00:56, 10.40it/s]
41%|████ | 412/1000 [00:34<00:51, 11.50it/s]
41%|████▏ | 414/1000 [00:35<01:08, 8.61it/s]
42%|████▏ | 416/1000 [00:35<01:24, 6.95it/s]
42%|████▏ | 418/1000 [00:35<01:10, 8.26it/s]
42%|████▏ | 420/1000 [00:35<01:01, 9.51it/s]
42%|████▏ | 422/1000 [00:35<00:53, 10.83it/s]
42%|████▏ | 424/1000 [00:36<01:15, 7.67it/s]
43%|████▎ | 426/1000 [00:36<01:04, 8.92it/s]
43%|████▎ | 428/1000 [00:36<00:56, 10.12it/s]
43%|████▎ | 430/1000 [00:36<01:02, 9.09it/s]
43%|████▎ | 432/1000 [00:37<01:20, 7.03it/s]
43%|████▎ | 434/1000 [00:37<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 [00:38<00:52, 10.55it/s]
44%|████▍ | 444/1000 [00:38<00:48, 11.40it/s]
45%|████▍ | 446/1000 [00:38<00:45, 12.16it/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<00:44, 12.29it/s]
45%|████▌ | 454/1000 [00:39<00:46, 11.65it/s]
46%|████▌ | 456/1000 [00:39<00:46, 11.82it/s]
46%|████▌ | 458/1000 [00:39<00:44, 12.26it/s]
46%|████▌ | 460/1000 [00:39<00:42, 12.57it/s]
46%|████▌ | 462/1000 [00:39<00:42, 12.51it/s]
46%|████▋ | 464/1000 [00:39<00:42, 12.59it/s]
47%|████▋ | 466/1000 [00:40<00:41, 12.73it/s]
47%|████▋ | 468/1000 [00:40<00:45, 11.70it/s]
47%|████▋ | 470/1000 [00:40<00:56, 9.32it/s]
47%|████▋ | 472/1000 [00:40<00:55, 9.55it/s]
47%|████▋ | 474/1000 [00:40<00:51, 10.23it/s]
48%|████▊ | 476/1000 [00:41<00:48, 10.75it/s]
48%|████▊ | 478/1000 [00:41<00:48, 10.66it/s]
48%|████▊ | 480/1000 [00:41<00:46, 11.16it/s]
48%|████▊ | 482/1000 [00:41<00:45, 11.49it/s]
48%|████▊ | 484/1000 [00:41<00:43, 11.74it/s]
49%|████▊ | 486/1000 [00:41<00:42, 11.96it/s]
49%|████▉ | 488/1000 [00:42<00:42, 11.99it/s]
49%|████▉ | 490/1000 [00:42<00:50, 10.05it/s]
49%|████▉ | 492/1000 [00:42<00:44, 11.45it/s]
49%|████▉ | 494/1000 [00:42<00:40, 12.61it/s]
50%|████▉ | 496/1000 [00:42<00:37, 13.54it/s]
50%|████▉ | 498/1000 [00:42<00:35, 14.10it/s]
50%|█████ | 500/1000 [00:43<00:43, 11.37it/s]
50%|█████ | 502/1000 [00:43<00:41, 11.86it/s]
50%|█████ | 504/1000 [00:43<00:39, 12.50it/s]
51%|█████ | 506/1000 [00:43<00:38, 12.70it/s]
51%|█████ | 508/1000 [00:43<00:36, 13.62it/s]
51%|█████ | 510/1000 [00:43<00:47, 10.33it/s]
51%|█████ | 512/1000 [00:44<00:43, 11.10it/s]
51%|█████▏ | 514/1000 [00:44<00:40, 11.93it/s]
52%|█████▏ | 516/1000 [00:44<00:38, 12.51it/s]
52%|█████▏ | 518/1000 [00:44<00:36, 13.04it/s]
52%|█████▏ | 520/1000 [00:44<00:35, 13.41it/s]
52%|█████▏ | 522/1000 [00:44<00:33, 14.14it/s]
52%|█████▏ | 524/1000 [00:44<00:33, 14.17it/s]
53%|█████▎ | 526/1000 [00:45<00:33, 14.34it/s]
53%|█████▎ | 528/1000 [00:45<00:39, 11.93it/s]
53%|█████▎ | 530/1000 [00:45<00:36, 12.77it/s]
53%|█████▎ | 532/1000 [00:45<00:34, 13.72it/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, 13.99it/s]
54%|█████▍ | 540/1000 [00:46<00:31, 14.41it/s]
54%|█████▍ | 542/1000 [00:46<00:31, 14.67it/s]
54%|█████▍ | 544/1000 [00:46<00:31, 14.57it/s]
55%|█████▍ | 546/1000 [00:46<00:31, 14.19it/s]
55%|█████▍ | 548/1000 [00:46<00:32, 13.88it/s]
55%|█████▌ | 550/1000 [00:46<00:31, 14.12it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.24it/s]
55%|█████▌ | 554/1000 [00:47<00:32, 13.92it/s]
56%|█████▌ | 556/1000 [00:47<00:31, 14.07it/s]
56%|█████▌ | 558/1000 [00:47<00:30, 14.44it/s]
56%|█████▌ | 560/1000 [00:47<00:29, 14.94it/s]
56%|█████▌ | 562/1000 [00:47<00:29, 14.90it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.75it/s]
57%|█████▋ | 566/1000 [00:48<00:37, 11.64it/s]
57%|█████▋ | 568/1000 [00:48<00:35, 12.20it/s]
57%|█████▋ | 570/1000 [00:48<00:32, 13.24it/s]
57%|█████▋ | 572/1000 [00:48<00:33, 12.77it/s]
57%|█████▋ | 574/1000 [00:48<00:31, 13.66it/s]
58%|█████▊ | 576/1000 [00:48<00:31, 13.57it/s]
58%|█████▊ | 578/1000 [00:49<00:36, 11.61it/s]
58%|█████▊ | 580/1000 [00:49<00:32, 12.84it/s]
58%|█████▊ | 582/1000 [00:49<00:30, 13.59it/s]
58%|█████▊ | 584/1000 [00:49<00:30, 13.80it/s]
59%|█████▊ | 586/1000 [00:49<00:29, 14.16it/s]
59%|█████▉ | 588/1000 [00:49<00:27, 14.78it/s]
59%|█████▉ | 590/1000 [00:49<00:27, 14.82it/s]
59%|█████▉ | 592/1000 [00:49<00:27, 14.63it/s]
59%|█████▉ | 594/1000 [00:50<00:30, 13.24it/s]
60%|█████▉ | 596/1000 [00:50<00:29, 13.78it/s]
60%|█████▉ | 598/1000 [00:50<00:28, 14.01it/s]
60%|██████ | 600/1000 [00:50<00:27, 14.37it/s]
60%|██████ | 602/1000 [00:50<00:27, 14.48it/s]
60%|██████ | 604/1000 [00:50<00:26, 15.03it/s]
61%|██████ | 606/1000 [00:50<00:26, 14.81it/s]
61%|██████ | 608/1000 [00:51<00:25, 15.10it/s]
61%|██████ | 610/1000 [00:51<00:26, 14.96it/s]
61%|██████ | 612/1000 [00:51<00:25, 15.11it/s]
61%|██████▏ | 614/1000 [00:51<00:26, 14.36it/s]
62%|██████▏ | 616/1000 [00:51<00:25, 14.77it/s]
62%|██████▏ | 618/1000 [00:51<00:26, 14.16it/s]
62%|██████▏ | 620/1000 [00:52<00:39, 9.73it/s]
62%|██████▏ | 622/1000 [00:52<00:38, 9.94it/s]
62%|██████▏ | 624/1000 [00:52<00:33, 11.36it/s]
63%|██████▎ | 626/1000 [00:52<00:31, 12.05it/s]
63%|██████▎ | 628/1000 [00:52<00:28, 13.23it/s]
63%|██████▎ | 630/1000 [00:52<00:26, 13.74it/s]
63%|██████▎ | 632/1000 [00:52<00:29, 12.43it/s]
63%|██████▎ | 634/1000 [00:53<00:27, 13.13it/s]
64%|██████▎ | 636/1000 [00:53<00:33, 10.93it/s]
64%|██████▍ | 638/1000 [00:53<00:33, 10.67it/s]
64%|██████▍ | 640/1000 [00:53<00:31, 11.46it/s]
64%|██████▍ | 642/1000 [00:53<00:29, 12.33it/s]
64%|██████▍ | 644/1000 [00:54<00:28, 12.58it/s]
65%|██████▍ | 646/1000 [00:54<00:27, 12.86it/s]
65%|██████▍ | 648/1000 [00:54<00:26, 13.33it/s]
65%|██████▌ | 650/1000 [00:54<00:27, 12.54it/s]
65%|██████▌ | 652/1000 [00:54<00:29, 11.92it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.41it/s]
66%|██████▌ | 656/1000 [00:55<00:32, 10.74it/s]
66%|██████▌ | 658/1000 [00:55<00:29, 11.79it/s]
66%|██████▌ | 660/1000 [00:55<00:33, 10.12it/s]
66%|██████▌ | 662/1000 [00:55<00:30, 11.25it/s]
66%|██████▋ | 664/1000 [00:55<00:28, 11.74it/s]
67%|██████▋ | 666/1000 [00:55<00:26, 12.76it/s]
67%|██████▋ | 668/1000 [00:56<00:29, 11.31it/s]
67%|██████▋ | 670/1000 [00:56<00:29, 11.09it/s]
67%|██████▋ | 672/1000 [00:56<00:27, 11.86it/s]
67%|██████▋ | 674/1000 [00:56<00:26, 12.44it/s]
68%|██████▊ | 676/1000 [00:56<00:27, 11.70it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.20it/s]
68%|██████▊ | 680/1000 [00:57<00:24, 12.96it/s]
68%|██████▊ | 682/1000 [00:57<00:26, 12.09it/s]
68%|██████▊ | 684/1000 [00:57<00:25, 12.29it/s]
69%|██████▊ | 686/1000 [00:57<00:23, 13.34it/s]
69%|██████▉ | 688/1000 [00:57<00:22, 13.66it/s]
69%|██████▉ | 690/1000 [00:57<00:22, 13.73it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.18it/s]
69%|██████▉ | 694/1000 [00:58<00:21, 14.29it/s]
70%|██████▉ | 696/1000 [00:58<00:21, 14.04it/s]
70%|██████▉ | 698/1000 [00:58<00:20, 14.59it/s]
70%|███████ | 700/1000 [00:58<00:24, 12.18it/s]
70%|███████ | 702/1000 [00:58<00:22, 12.99it/s]
70%|███████ | 704/1000 [00:59<00:30, 9.85it/s]
71%|███████ | 706/1000 [00:59<00:28, 10.48it/s]
71%|███████ | 708/1000 [00:59<00:31, 9.19it/s]
71%|███████ | 710/1000 [00:59<00:28, 10.34it/s]
71%|███████ | 712/1000 [00:59<00:26, 11.00it/s]
71%|███████▏ | 714/1000 [00:59<00:23, 12.16it/s]
72%|███████▏ | 716/1000 [01:00<00:22, 12.45it/s]
72%|███████▏ | 718/1000 [01:00<00:24, 11.70it/s]
72%|███████▏ | 720/1000 [01:00<00:23, 12.04it/s]
72%|███████▏ | 722/1000 [01:00<00:21, 12.66it/s]
72%|███████▏ | 724/1000 [01:00<00:21, 12.55it/s]
73%|███████▎ | 726/1000 [01:00<00:22, 12.42it/s]
73%|███████▎ | 728/1000 [01:01<00:24, 11.09it/s]
73%|███████▎ | 730/1000 [01:01<00:34, 7.91it/s]
73%|███████▎ | 732/1000 [01:01<00:33, 8.09it/s]
73%|███████▎ | 734/1000 [01:01<00:31, 8.42it/s]
74%|███████▎ | 735/1000 [01:02<00:31, 8.30it/s]
74%|███████▎ | 737/1000 [01:02<00:26, 9.77it/s]
74%|███████▍ | 739/1000 [01:02<00:23, 10.89it/s]
74%|███████▍ | 741/1000 [01:02<00:22, 11.60it/s]
74%|███████▍ | 743/1000 [01:02<00:20, 12.25it/s]
74%|███████▍ | 745/1000 [01:02<00:25, 10.14it/s]
75%|███████▍ | 747/1000 [01:03<00:26, 9.54it/s]
75%|███████▍ | 749/1000 [01:03<00:23, 10.71it/s]
75%|███████▌ | 751/1000 [01:03<00:21, 11.81it/s]
75%|███████▌ | 753/1000 [01:03<00:19, 12.57it/s]
76%|███████▌ | 755/1000 [01:03<00:18, 13.30it/s]
76%|███████▌ | 757/1000 [01:03<00:23, 10.28it/s]
76%|███████▌ | 759/1000 [01:04<00:21, 11.31it/s]
76%|███████▌ | 761/1000 [01:04<00:19, 12.29it/s]
76%|███████▋ | 763/1000 [01:04<00:23, 10.24it/s]
76%|███████▋ | 765/1000 [01:04<00:27, 8.59it/s]
77%|███████▋ | 767/1000 [01:04<00:24, 9.54it/s]
77%|███████▋ | 769/1000 [01:05<00:21, 10.57it/s]
77%|███████▋ | 771/1000 [01:05<00:19, 11.81it/s]
77%|███████▋ | 773/1000 [01:05<00:18, 12.57it/s]
78%|███████▊ | 775/1000 [01:05<00:17, 13.12it/s]
78%|███████▊ | 777/1000 [01:05<00:15, 14.08it/s]
78%|███████▊ | 779/1000 [01:05<00:21, 10.48it/s]
78%|███████▊ | 781/1000 [01:06<00:23, 9.39it/s]
78%|███████▊ | 783/1000 [01:06<00:26, 8.29it/s]
78%|███████▊ | 784/1000 [01:06<00:30, 7.06it/s]
78%|███████▊ | 785/1000 [01:06<00:29, 7.36it/s]
79%|███████▊ | 786/1000 [01:07<00:30, 6.97it/s]
79%|███████▉ | 788/1000 [01:07<00:24, 8.81it/s]
79%|███████▉ | 790/1000 [01:07<00:20, 10.12it/s]
79%|███████▉ | 792/1000 [01:07<00:18, 11.27it/s]
79%|███████▉ | 794/1000 [01:07<00:17, 11.69it/s]
80%|███████▉ | 796/1000 [01:07<00:17, 11.83it/s]
80%|███████▉ | 798/1000 [01:07<00:16, 12.00it/s]
80%|████████ | 800/1000 [01:08<00:17, 11.42it/s]
80%|████████ | 802/1000 [01:08<00:25, 7.83it/s]
80%|████████ | 803/1000 [01:08<00:26, 7.43it/s]
80%|████████ | 805/1000 [01:08<00:22, 8.58it/s]
81%|████████ | 806/1000 [01:09<00:22, 8.61it/s]
81%|████████ | 808/1000 [01:09<00:19, 9.63it/s]
81%|████████ | 810/1000 [01:09<00:19, 9.88it/s]
81%|████████ | 812/1000 [01:09<00:17, 10.59it/s]
81%|████████▏ | 814/1000 [01:09<00:16, 11.07it/s]
82%|████████▏ | 816/1000 [01:09<00:18, 9.99it/s]
82%|████████▏ | 818/1000 [01:10<00:16, 11.11it/s]
82%|████████▏ | 820/1000 [01:10<00:15, 11.84it/s]
82%|████████▏ | 822/1000 [01:10<00:14, 12.54it/s]
82%|████████▏ | 824/1000 [01:10<00:13, 13.34it/s]
83%|████████▎ | 826/1000 [01:10<00:12, 13.85it/s]
83%|████████▎ | 828/1000 [01:10<00:12, 14.20it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.73it/s]
83%|████████▎ | 832/1000 [01:11<00:11, 14.15it/s]
83%|████████▎ | 834/1000 [01:11<00:11, 13.93it/s]
84%|████████▎ | 836/1000 [01:11<00:11, 14.29it/s]
84%|████████▍ | 838/1000 [01:11<00:11, 14.23it/s]
84%|████████▍ | 840/1000 [01:11<00:11, 14.31it/s]
84%|████████▍ | 842/1000 [01:11<00:11, 14.34it/s]
84%|████████▍ | 844/1000 [01:11<00:11, 13.90it/s]
85%|████████▍ | 846/1000 [01:12<00:11, 13.85it/s]
85%|████████▍ | 848/1000 [01:12<00:10, 13.84it/s]
85%|████████▌ | 850/1000 [01:12<00:10, 14.07it/s]
85%|████████▌ | 852/1000 [01:12<00:10, 13.67it/s]
85%|████████▌ | 854/1000 [01:12<00:10, 14.24it/s]
86%|████████▌ | 856/1000 [01:12<00:10, 14.08it/s]
86%|████████▌ | 858/1000 [01:12<00:09, 14.56it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 15.30it/s]
86%|████████▌ | 862/1000 [01:13<00:10, 13.47it/s]
86%|████████▋ | 864/1000 [01:13<00:09, 13.98it/s]
87%|████████▋ | 866/1000 [01:13<00:09, 13.81it/s]
87%|████████▋ | 868/1000 [01:13<00:09, 13.68it/s]
87%|████████▋ | 870/1000 [01:13<00:09, 14.05it/s]
87%|████████▋ | 872/1000 [01:13<00:08, 14.65it/s]
87%|████████▋ | 874/1000 [01:14<00:10, 12.40it/s]
88%|████████▊ | 876/1000 [01:14<00:11, 11.03it/s]
88%|████████▊ | 878/1000 [01:14<00:10, 11.91it/s]
88%|████████▊ | 880/1000 [01:14<00:09, 12.55it/s]
88%|████████▊ | 882/1000 [01:14<00:10, 11.35it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 12.09it/s]
89%|████████▊ | 886/1000 [01:15<00:08, 12.86it/s]
89%|████████▉ | 888/1000 [01:15<00:08, 13.81it/s]
89%|████████▉ | 890/1000 [01:15<00:07, 14.21it/s]
89%|████████▉ | 892/1000 [01:15<00:07, 13.99it/s]
89%|████████▉ | 894/1000 [01:15<00:07, 14.14it/s]
90%|████████▉ | 896/1000 [01:15<00:07, 13.88it/s]
90%|████████▉ | 898/1000 [01:15<00:07, 13.23it/s]
90%|█████████ | 900/1000 [01:16<00:08, 12.22it/s]
90%|█████████ | 902/1000 [01:16<00:07, 12.96it/s]
90%|█████████ | 904/1000 [01:16<00:09, 9.78it/s]
91%|█████████ | 906/1000 [01:16<00:08, 10.84it/s]
91%|█████████ | 908/1000 [01:16<00:07, 11.82it/s]
91%|█████████ | 910/1000 [01:17<00:07, 12.08it/s]
91%|█████████ | 912/1000 [01:17<00:07, 12.26it/s]
91%|█████████▏| 914/1000 [01:17<00:12, 6.80it/s]
92%|█████████▏| 916/1000 [01:17<00:10, 8.13it/s]
92%|█████████▏| 918/1000 [01:18<00:11, 7.39it/s]
92%|█████████▏| 920/1000 [01:18<00:09, 8.80it/s]
92%|█████████▏| 922/1000 [01:18<00:07, 10.24it/s]
92%|█████████▏| 924/1000 [01:18<00:06, 10.93it/s]
93%|█████████▎| 926/1000 [01:18<00:06, 11.78it/s]
93%|█████████▎| 928/1000 [01:19<00:06, 10.62it/s]
93%|█████████▎| 930/1000 [01:19<00:05, 11.82it/s]
93%|█████████▎| 932/1000 [01:19<00:05, 12.42it/s]
93%|█████████▎| 934/1000 [01:19<00:05, 12.90it/s]
94%|█████████▎| 936/1000 [01:19<00:04, 13.42it/s]
94%|█████████▍| 938/1000 [01:19<00:04, 14.31it/s]
94%|█████████▍| 940/1000 [01:19<00:04, 14.62it/s]
94%|█████████▍| 942/1000 [01:20<00:04, 12.30it/s]
94%|█████████▍| 944/1000 [01:20<00:04, 12.82it/s]
95%|█████████▍| 946/1000 [01:20<00:04, 11.87it/s]
95%|█████████▍| 948/1000 [01:20<00:04, 12.66it/s]
95%|█████████▌| 950/1000 [01:20<00:03, 13.46it/s]
95%|█████████▌| 952/1000 [01:20<00:03, 14.15it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 14.17it/s]
96%|█████████▌| 956/1000 [01:21<00:03, 14.16it/s]
96%|█████████▌| 958/1000 [01:21<00:02, 14.65it/s]
96%|█████████▌| 960/1000 [01:21<00:03, 13.04it/s]
96%|█████████▌| 962/1000 [01:21<00:02, 13.66it/s]
96%|█████████▋| 964/1000 [01:21<00:03, 10.19it/s]
97%|█████████▋| 966/1000 [01:21<00:03, 11.29it/s]
97%|█████████▋| 968/1000 [01:22<00:02, 11.57it/s]
97%|█████████▋| 970/1000 [01:22<00:02, 12.03it/s]
97%|█████████▋| 972/1000 [01:22<00:03, 8.80it/s]
97%|█████████▋| 974/1000 [01:22<00:02, 10.05it/s]
98%|█████████▊| 976/1000 [01:22<00:02, 10.97it/s]
98%|█████████▊| 978/1000 [01:23<00:02, 9.42it/s]
98%|█████████▊| 980/1000 [01:23<00:02, 9.62it/s]
98%|█████████▊| 982/1000 [01:23<00:01, 9.83it/s]
98%|█████████▊| 984/1000 [01:23<00:01, 9.96it/s]
99%|█████████▊| 986/1000 [01:23<00:01, 10.32it/s]
99%|█████████▉| 988/1000 [01:24<00:01, 10.64it/s]
99%|█████████▉| 990/1000 [01:24<00:00, 11.18it/s]
99%|█████████▉| 992/1000 [01:24<00:00, 12.10it/s]
99%|█████████▉| 994/1000 [01:24<00:00, 12.82it/s]
100%|█████████▉| 996/1000 [01:24<00:00, 13.75it/s]
100%|█████████▉| 998/1000 [01:24<00:00, 13.95it/s]
100%|██████████| 1000/1000 [01:24<00:00, 14.65it/s]
{'eval_loss': '0.8896', 'eval_runtime': '84.99', 'eval_samples_per_second': '11.77', 'eval_steps_per_second': '11.77', 'epoch': '0.6421', 'num_input_tokens_seen': 40360224}
64%|██████▍ | 12000/18688 [14:06:50<7:47:27, 4.19s/it]
100%|██████████| 1000/1000 [01:24<00:00, 14.65it/s]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00010/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 [14:06:55<55:22:46, 29.81s/it] 64%|██████▍ | 12002/18688 [14:06:59<41:01:20, 22.09s/it] 64%|██████▍ | 12003/18688 [14:07:03<31:12:06, 16.80s/it] 64%|██████▍ | 12004/18688 [14:07:07<24:00:15, 12.93s/it] 64%|██████▍ | 12005/18688 [14:07:12<19:25:41, 10.47s/it] 64%|██████▍ | 12006/18688 [14:07:16<15:44:45, 8.48s/it] 64%|██████▍ | 12007/18688 [14:07:20<13:27:37, 7.25s/it] 64%|██████▍ | 12008/18688 [14:07:25<11:56:42, 6.44s/it] 64%|██████▍ | 12009/18688 [14:07:29<10:35:07, 5.71s/it] 64%|██████▍ | 12010/18688 [14:07:33<10:02:07, 5.41s/it] {'loss': '0.8803', 'grad_norm': '1.358', 'learning_rate': '3.285e-05', 'epoch': '0.6427', 'num_input_tokens_seen': 40393544, 'train_runtime': '5.085e+04', 'train_tokens_per_second': '794.3'}
64%|██████▍ | 12010/18688 [14:07:33<10:02:07, 5.41s/it] 64%|██████▍ | 12011/18688 [14:07:38<9:27:04, 5.10s/it] 64%|██████▍ | 12012/18688 [14:07:42<8:53:31, 4.80s/it] 64%|██████▍ | 12013/18688 [14:07:46<8:47:33, 4.74s/it] 64%|██████▍ | 12014/18688 [14:07:52<8:59:24, 4.85s/it] 64%|██████▍ | 12015/18688 [14:07:55<8:14:02, 4.44s/it] 64%|██████▍ | 12016/18688 [14:07:59<8:01:40, 4.33s/it] 64%|██████▍ | 12017/18688 [14:08:03<7:33:41, 4.08s/it] 64%|██████▍ | 12018/18688 [14:08:06<7:20:00, 3.96s/it] 64%|██████▍ | 12019/18688 [14:08:10<7:10:21, 3.87s/it] 64%|██████▍ | 12020/18688 [14:08:14<7:12:03, 3.89s/it] {'loss': '0.8722', 'grad_norm': '1.215', 'learning_rate': '3.277e-05', 'epoch': '0.6432', 'num_input_tokens_seen': 40423536, 'train_runtime': '5.089e+04', 'train_tokens_per_second': '794.3'}
64%|██████▍ | 12020/18688 [14:08:14<7:12:03, 3.89s/it] 64%|██████▍ | 12021/18688 [14:08:19<7:40:27, 4.14s/it] 64%|██████▍ | 12022/18688 [14:08:22<7:22:23, 3.98s/it] 64%|██████▍ | 12023/18688 [14:08:26<7:05:29, 3.83s/it] 64%|██████▍ | 12024/18688 [14:08:30<7:26:09, 4.02s/it] 64%|██████▍ | 12025/18688 [14:08:35<7:52:04, 4.25s/it] 64%|██████▍ | 12026/18688 [14:08:40<8:23:46, 4.54s/it] 64%|██████▍ | 12027/18688 [14:08:44<8:11:00, 4.42s/it] 64%|██████▍ | 12028/18688 [14:08:48<7:56:15, 4.29s/it] 64%|██████▍ | 12029/18688 [14:08:52<7:33:04, 4.08s/it] 64%|██████▍ | 12030/18688 [14:08:56<7:18:08, 3.95s/it] {'loss': '0.8852', 'grad_norm': '1.218', 'learning_rate': '3.268e-05', 'epoch': '0.6437', 'num_input_tokens_seen': 40455808, 'train_runtime': '5.094e+04', 'train_tokens_per_second': '794.2'}
64%|██████▍ | 12030/18688 [14:08:56<7:18:08, 3.95s/it] 64%|██████▍ | 12031/18688 [14:09:00<7:33:30, 4.09s/it] 64%|██████▍ | 12032/18688 [14:09:04<7:21:23, 3.98s/it] 64%|██████▍ | 12033/18688 [14:09:08<7:31:15, 4.07s/it] 64%|██████▍ | 12034/18688 [14:09:12<7:36:36, 4.12s/it] 64%|██████▍ | 12035/18688 [14:09:16<7:29:36, 4.05s/it] 64%|██████▍ | 12036/18688 [14:09:20<7:29:43, 4.06s/it] 64%|██████▍ | 12037/18688 [14:09:24<7:26:26, 4.03s/it] 64%|██████▍ | 12038/18688 [14:09:28<7:37:06, 4.12s/it] 64%|██████▍ | 12039/18688 [14:09:34<8:12:58, 4.45s/it] 64%|██████▍ | 12040/18688 [14:09:38<8:18:05, 4.50s/it] {'loss': '0.869', 'grad_norm': '1.236', 'learning_rate': '3.26e-05', 'epoch': '0.6443', 'num_input_tokens_seen': 40487512, 'train_runtime': '5.098e+04', 'train_tokens_per_second': '794.2'}
64%|██████▍ | 12040/18688 [14:09:38<8:18:05, 4.50s/it] 64%|██████▍ | 12041/18688 [14:09:42<7:55:35, 4.29s/it] 64%|██████▍ | 12042/18688 [14:09:47<8:00:19, 4.34s/it] 64%|██████▍ | 12043/18688 [14:09:51<7:48:54, 4.23s/it] 64%|██████▍ | 12044/18688 [14:09:56<8:23:11, 4.54s/it] 64%|██████▍ | 12045/18688 [14:10:00<8:00:51, 4.34s/it] 64%|██████▍ | 12046/18688 [14:10:04<8:12:00, 4.44s/it] 64%|██████▍ | 12047/18688 [14:10:09<8:07:28, 4.40s/it] 64%|██████▍ | 12048/18688 [14:10:14<8:27:51, 4.59s/it] 64%|██████▍ | 12049/18688 [14:10:17<8:00:40, 4.34s/it] 64%|██████▍ | 12050/18688 [14:10:22<7:54:02, 4.28s/it] {'loss': '0.9287', 'grad_norm': '1.47', 'learning_rate': '3.251e-05', 'epoch': '0.6448', 'num_input_tokens_seen': 40520616, 'train_runtime': '5.102e+04', 'train_tokens_per_second': '794.2'}
64%|██████▍ | 12050/18688 [14:10:22<7:54:02, 4.28s/it] 64%|██████▍ | 12051/18688 [14:10:26<8:12:06, 4.45s/it] 64%|██████▍ | 12052/18688 [14:10:31<8:13:24, 4.46s/it] 64%|██████▍ | 12053/18688 [14:10:35<8:02:11, 4.36s/it] 65%|██████▍ | 12054/18688 [14:10:40<8:25:27, 4.57s/it] 65%|██████▍ | 12055/18688 [14:10:45<8:50:29, 4.80s/it] 65%|██████▍ | 12056/18688 [14:10:50<8:26:47, 4.58s/it] 65%|██████▍ | 12057/18688 [14:10:53<7:53:47, 4.29s/it] 65%|██████▍ | 12058/18688 [14:10:58<8:21:56, 4.54s/it] 65%|██████▍ | 12059/18688 [14:11:02<8:07:21, 4.41s/it] 65%|██████▍ | 12060/18688 [14:11:06<7:57:35, 4.32s/it] {'loss': '0.9817', 'grad_norm': '1.166', 'learning_rate': '3.243e-05', 'epoch': '0.6454', 'num_input_tokens_seen': 40558168, 'train_runtime': '5.107e+04', 'train_tokens_per_second': '794.2'}
65%|██████▍ | 12060/18688 [14:11:06<7:57:35, 4.32s/it] 65%|██████▍ | 12061/18688 [14:11:11<8:01:25, 4.36s/it] 65%|██████▍ | 12062/18688 [14:11:15<7:52:24, 4.28s/it] 65%|██████▍ | 12063/18688 [14:11:19<7:48:50, 4.25s/it] 65%|██████▍ | 12064/18688 [14:11:25<8:30:01, 4.62s/it] 65%|██████▍ | 12065/18688 [14:11:30<8:42:03, 4.73s/it] 65%|██████▍ | 12066/18688 [14:11:35<9:08:20, 4.97s/it] 65%|██████▍ | 12067/18688 [14:11:39<8:34:51, 4.67s/it] 65%|██████▍ | 12068/18688 [14:11:43<8:02:58, 4.38s/it] 65%|██████▍ | 12069/18688 [14:11:47<7:47:43, 4.24s/it] 65%|██████▍ | 12070/18688 [14:11:52<8:17:40, 4.51s/it] {'loss': '0.9248', 'grad_norm': '1.281', 'learning_rate': '3.234e-05', 'epoch': '0.6459', 'num_input_tokens_seen': 40595384, 'train_runtime': '5.111e+04', 'train_tokens_per_second': '794.2'}
65%|██████▍ | 12070/18688 [14:11:52<8:17:40, 4.51s/it] 65%|██████▍ | 12071/18688 [14:11:56<8:05:01, 4.40s/it] 65%|██████▍ | 12072/18688 [14:12:00<7:35:34, 4.13s/it] 65%|██████▍ | 12073/18688 [14:12:04<7:53:13, 4.29s/it] 65%|██████▍ | 12074/18688 [14:12:09<8:04:08, 4.39s/it] 65%|██████▍ | 12075/18688 [14:12:13<8:04:09, 4.39s/it] 65%|██████▍ | 12076/18688 [14:12:17<7:34:16, 4.12s/it] 65%|██████▍ | 12077/18688 [14:12:22<7:59:49, 4.35s/it] 65%|██████▍ | 12078/18688 [14:12:25<7:39:36, 4.17s/it] 65%|██████▍ | 12079/18688 [14:12:29<7:21:40, 4.01s/it] 65%|██████▍ | 12080/18688 [14:12:33<7:22:13, 4.02s/it] {'loss': '0.9419', 'grad_norm': '1.208', 'learning_rate': '3.225e-05', 'epoch': '0.6464', 'num_input_tokens_seen': 40628992, 'train_runtime': '5.115e+04', 'train_tokens_per_second': '794.3'}
65%|██████▍ | 12080/18688 [14:12:33<7:22:13, 4.02s/it] 65%|██████▍ | 12081/18688 [14:12:37<7:19:43, 3.99s/it] 65%|██████▍ | 12082/18688 [14:12:41<7:20:31, 4.00s/it] 65%|██████▍ | 12083/18688 [14:12:46<7:40:32, 4.18s/it] 65%|██████▍ | 12084/18688 [14:12:50<8:02:03, 4.38s/it] 65%|██████▍ | 12085/18688 [14:12:55<8:01:35, 4.38s/it] 65%|██████▍ | 12086/18688 [14:12:59<8:02:44, 4.39s/it] 65%|██████▍ | 12087/18688 [14:13:03<7:55:18, 4.32s/it] 65%|██████▍ | 12088/18688 [14:13:08<8:00:47, 4.37s/it] 65%|██████▍ | 12089/18688 [14:13:12<7:44:14, 4.22s/it] 65%|██████▍ | 12090/18688 [14:13:16<7:36:36, 4.15s/it] {'loss': '0.9079', 'grad_norm': '1.201', 'learning_rate': '3.217e-05', 'epoch': '0.647', 'num_input_tokens_seen': 40663528, 'train_runtime': '5.12e+04', 'train_tokens_per_second': '794.3'}
65%|██████▍ | 12090/18688 [14:13:16<7:36:36, 4.15s/it] 65%|██████▍ | 12091/18688 [14:13:20<7:40:25, 4.19s/it] 65%|██████▍ | 12092/18688 [14:13:24<7:20:45, 4.01s/it] 65%|██████▍ | 12093/18688 [14:13:28<7:20:46, 4.01s/it] 65%|██████▍ | 12094/18688 [14:13:33<7:54:26, 4.32s/it] 65%|██████▍ | 12095/18688 [14:13:38<8:16:10, 4.52s/it] 65%|██████▍ | 12096/18688 [14:13:42<8:03:17, 4.40s/it] 65%|██████▍ | 12097/18688 [14:13:47<8:17:38, 4.53s/it] 65%|██████▍ | 12098/18688 [14:13:51<8:01:52, 4.39s/it] 65%|██████▍ | 12099/18688 [14:13:55<7:43:39, 4.22s/it] 65%|██████▍ | 12100/18688 [14:13:58<7:18:49, 4.00s/it] {'loss': '0.9282', 'grad_norm': '1.253', 'learning_rate': '3.208e-05', 'epoch': '0.6475', 'num_input_tokens_seen': 40696336, 'train_runtime': '5.124e+04', 'train_tokens_per_second': '794.3'}
65%|██████▍ | 12100/18688 [14:13:58<7:18:49, 4.00s/it] 65%|██████▍ | 12101/18688 [14:14:02<7:30:25, 4.10s/it] 65%|██████▍ | 12102/18688 [14:14:06<7:17:29, 3.99s/it] 65%|██████▍ | 12103/18688 [14:14:10<7:06:35, 3.89s/it] 65%|██████▍ | 12104/18688 [14:14:13<6:56:52, 3.80s/it] 65%|██████▍ | 12105/18688 [14:14:18<7:42:50, 4.22s/it] 65%|██████▍ | 12106/18688 [14:14:24<8:14:27, 4.51s/it] 65%|██████▍ | 12107/18688 [14:14:28<8:22:25, 4.58s/it] 65%|██████▍ | 12108/18688 [14:14:33<8:12:06, 4.49s/it] 65%|██████▍ | 12109/18688 [14:14:37<7:57:05, 4.35s/it] 65%|██████▍ | 12110/18688 [14:14:40<7:31:25, 4.12s/it] {'loss': '0.919', 'grad_norm': '1.223', 'learning_rate': '3.2e-05', 'epoch': '0.648', 'num_input_tokens_seen': 40729808, 'train_runtime': '5.128e+04', 'train_tokens_per_second': '794.3'}
65%|██████▍ | 12110/18688 [14:14:40<7:31:25, 4.12s/it] 65%|██████▍ | 12111/18688 [14:14:44<7:24:49, 4.06s/it] 65%|██████▍ | 12112/18688 [14:14:49<7:33:38, 4.14s/it] 65%|██████▍ | 12113/18688 [14:14:53<7:33:28, 4.14s/it] 65%|██████▍ | 12114/18688 [14:14:57<7:34:06, 4.14s/it] 65%|██████▍ | 12115/18688 [14:15:01<7:33:15, 4.14s/it] 65%|██████▍ | 12116/18688 [14:15:05<7:44:09, 4.24s/it] 65%|██████▍ | 12117/18688 [14:15:10<7:40:45, 4.21s/it] 65%|██████▍ | 12118/18688 [14:15:14<8:01:11, 4.39s/it] 65%|██████▍ | 12119/18688 [14:15:19<7:57:28, 4.36s/it] 65%|██████▍ | 12120/18688 [14:15:23<7:46:47, 4.26s/it] {'loss': '0.8716', 'grad_norm': '1.247', 'learning_rate': '3.191e-05', 'epoch': '0.6486', 'num_input_tokens_seen': 40763240, 'train_runtime': '5.132e+04', 'train_tokens_per_second': '794.2'}
65%|██████▍ | 12120/18688 [14:15:23<7:46:47, 4.26s/it] 65%|██████▍ | 12121/18688 [14:15:27<7:41:06, 4.21s/it] 65%|██████▍ | 12122/18688 [14:15:32<8:05:09, 4.43s/it] 65%|██████▍ | 12123/18688 [14:15:36<8:12:16, 4.50s/it] 65%|██████▍ | 12124/18688 [14:15:41<8:23:54, 4.61s/it] 65%|██████▍ | 12125/18688 [14:15:46<8:37:34, 4.73s/it] 65%|██████▍ | 12126/18688 [14:15:50<8:09:52, 4.48s/it] 65%|██████▍ | 12127/18688 [14:15:54<7:51:40, 4.31s/it] 65%|██████▍ | 12128/18688 [14:15:58<7:35:22, 4.17s/it] 65%|██████▍ | 12129/18688 [14:16:03<8:10:01, 4.48s/it] 65%|██████▍ | 12130/18688 [14:16:07<7:41:58, 4.23s/it] {'loss': '0.8657', 'grad_norm': '1.157', 'learning_rate': '3.183e-05', 'epoch': '0.6491', 'num_input_tokens_seen': 40795904, 'train_runtime': '5.137e+04', 'train_tokens_per_second': '794.2'}
65%|██████▍ | 12130/18688 [14:16:07<7:41:58, 4.23s/it] 65%|██████▍ | 12131/18688 [14:16:10<7:19:59, 4.03s/it] 65%|██████▍ | 12132/18688 [14:16:14<7:04:32, 3.89s/it] 65%|██████▍ | 12133/18688 [14:16:18<7:17:34, 4.01s/it] 65%|██████▍ | 12134/18688 [14:16:22<7:06:06, 3.90s/it] 65%|██████▍ | 12135/18688 [14:16:26<7:00:33, 3.85s/it] 65%|██████▍ | 12136/18688 [14:16:30<7:18:34, 4.02s/it] 65%|██████▍ | 12137/18688 [14:16:33<7:01:15, 3.86s/it] 65%|██████▍ | 12138/18688 [14:16:37<6:59:08, 3.84s/it] 65%|██████▍ | 12139/18688 [14:16:42<7:19:22, 4.03s/it] 65%|██████▍ | 12140/18688 [14:16:46<7:21:17, 4.04s/it] {'loss': '0.8991', 'grad_norm': '1.631', 'learning_rate': '3.174e-05', 'epoch': '0.6496', 'num_input_tokens_seen': 40827344, 'train_runtime': '5.141e+04', 'train_tokens_per_second': '794.2'}
65%|██████▍ | 12140/18688 [14:16:46<7:21:17, 4.04s/it] 65%|██████▍ | 12141/18688 [14:16:50<7:26:17, 4.09s/it] 65%|██████▍ | 12142/18688 [14:16:54<7:31:01, 4.13s/it] 65%|██████▍ | 12143/18688 [14:16:59<7:44:55, 4.26s/it] 65%|██████▍ | 12144/18688 [14:17:03<7:47:21, 4.29s/it] 65%|██████▍ | 12145/18688 [14:17:07<7:41:39, 4.23s/it] 65%|██████▍ | 12146/18688 [14:17:11<7:39:56, 4.22s/it] 65%|██████▍ | 12147/18688 [14:17:16<7:46:51, 4.28s/it] 65%|██████▌ | 12148/18688 [14:17:20<7:43:59, 4.26s/it] 65%|██████▌ | 12149/18688 [14:17:25<7:53:16, 4.34s/it] 65%|██████▌ | 12150/18688 [14:17:29<7:39:11, 4.21s/it] {'loss': '0.9071', 'grad_norm': '1.446', 'learning_rate': '3.166e-05', 'epoch': '0.6502', 'num_input_tokens_seen': 40858048, 'train_runtime': '5.145e+04', 'train_tokens_per_second': '794.1'}
65%|██████▌ | 12150/18688 [14:17:29<7:39:11, 4.21s/it] 65%|██████▌ | 12151/18688 [14:17:33<7:34:36, 4.17s/it] 65%|██████▌ | 12152/18688 [14:17:36<7:19:54, 4.04s/it] 65%|██████▌ | 12153/18688 [14:17:41<7:44:52, 4.27s/it] 65%|██████▌ | 12154/18688 [14:17:46<8:08:48, 4.49s/it] 65%|██████▌ | 12155/18688 [14:17:50<8:01:37, 4.42s/it] 65%|██████▌ | 12156/18688 [14:17:54<7:33:55, 4.17s/it] 65%|██████▌ | 12157/18688 [14:17:58<7:17:41, 4.02s/it] 65%|██████▌ | 12158/18688 [14:18:02<7:17:59, 4.02s/it] 65%|██████▌ | 12159/18688 [14:18:06<7:16:10, 4.01s/it] 65%|██████▌ | 12160/18688 [14:18:10<7:28:18, 4.12s/it] {'loss': '0.95', 'grad_norm': '1.332', 'learning_rate': '3.157e-05', 'epoch': '0.6507', 'num_input_tokens_seen': 40889384, 'train_runtime': '5.149e+04', 'train_tokens_per_second': '794.1'}
65%|██████▌ | 12160/18688 [14:18:10<7:28:18, 4.12s/it] 65%|██████▌ | 12161/18688 [14:18:14<7:24:37, 4.09s/it] 65%|██████▌ | 12162/18688 [14:18:18<7:29:46, 4.14s/it] 65%|██████▌ | 12163/18688 [14:18:22<7:10:28, 3.96s/it] 65%|██████▌ | 12164/18688 [14:18:26<7:22:12, 4.07s/it] 65%|██████▌ | 12165/18688 [14:18:31<8:01:52, 4.43s/it] 65%|██████▌ | 12166/18688 [14:18:35<7:38:19, 4.22s/it] 65%|██████▌ | 12167/18688 [14:18:39<7:32:26, 4.16s/it] 65%|██████▌ | 12168/18688 [14:18:43<7:28:18, 4.13s/it] 65%|██████▌ | 12169/18688 [14:18:47<7:21:21, 4.06s/it] 65%|██████▌ | 12170/18688 [14:18:51<7:24:19, 4.09s/it] {'loss': '0.841', 'grad_norm': '1.547', 'learning_rate': '3.149e-05', 'epoch': '0.6512', 'num_input_tokens_seen': 40921552, 'train_runtime': '5.153e+04', 'train_tokens_per_second': '794.1'}
65%|██████▌ | 12170/18688 [14:18:51<7:24:19, 4.09s/it] 65%|██████▌ | 12171/18688 [14:18:56<7:35:31, 4.19s/it] 65%|██████▌ | 12172/18688 [14:19:00<7:32:35, 4.17s/it] 65%|██████▌ | 12173/18688 [14:19:05<7:50:43, 4.34s/it] 65%|██████▌ | 12174/18688 [14:19:08<7:34:20, 4.18s/it] 65%|██████▌ | 12175/18688 [14:19:12<7:25:42, 4.11s/it] 65%|██████▌ | 12176/18688 [14:19:17<7:58:35, 4.41s/it] 65%|██████▌ | 12177/18688 [14:19:22<7:57:01, 4.40s/it] 65%|██████▌ | 12178/18688 [14:19:26<8:04:59, 4.47s/it] 65%|██████▌ | 12179/18688 [14:19:31<8:03:36, 4.46s/it] 65%|██████▌ | 12180/18688 [14:19:36<8:13:14, 4.55s/it] {'loss': '0.9159', 'grad_norm': '1.246', 'learning_rate': '3.14e-05', 'epoch': '0.6518', 'num_input_tokens_seen': 40953680, 'train_runtime': '5.158e+04', 'train_tokens_per_second': '794'}
65%|██████▌ | 12180/18688 [14:19:36<8:13:14, 4.55s/it] 65%|██████▌ | 12181/18688 [14:19:41<8:30:00, 4.70s/it] 65%|██████▌ | 12182/18688 [14:19:45<8:01:34, 4.44s/it] 65%|██████▌ | 12183/18688 [14:19:49<8:15:23, 4.57s/it] 65%|██████▌ | 12184/18688 [14:19:54<8:31:29, 4.72s/it] 65%|██████▌ | 12185/18688 [14:19:58<8:05:38, 4.48s/it] 65%|██████▌ | 12186/18688 [14:20:02<7:40:47, 4.25s/it] 65%|██████▌ | 12187/18688 [14:20:06<7:40:24, 4.25s/it] 65%|██████▌ | 12188/18688 [14:20:11<7:39:13, 4.24s/it] 65%|██████▌ | 12189/18688 [14:20:15<7:50:03, 4.34s/it] 65%|██████▌ | 12190/18688 [14:20:19<7:42:08, 4.27s/it] {'loss': '0.9289', 'grad_norm': '1.389', 'learning_rate': '3.132e-05', 'epoch': '0.6523', 'num_input_tokens_seen': 40986968, 'train_runtime': '5.162e+04', 'train_tokens_per_second': '794'}
65%|██████▌ | 12190/18688 [14:20:19<7:42:08, 4.27s/it] 65%|██████▌ | 12191/18688 [14:20:23<7:36:50, 4.22s/it] 65%|██████▌ | 12192/18688 [14:20:27<7:30:12, 4.16s/it] 65%|██████▌ | 12193/18688 [14:20:32<7:49:40, 4.34s/it] 65%|██████▌ | 12194/18688 [14:20:38<8:30:11, 4.71s/it] 65%|██████▌ | 12195/18688 [14:20:42<8:12:17, 4.55s/it] 65%|██████▌ | 12196/18688 [14:20:47<8:31:30, 4.73s/it] 65%|██████▌ | 12197/18688 [14:20:52<8:25:05, 4.67s/it] 65%|██████▌ | 12198/18688 [14:20:56<8:11:11, 4.54s/it] 65%|██████▌ | 12199/18688 [14:21:00<8:05:39, 4.49s/it] 65%|██████▌ | 12200/18688 [14:21:04<7:56:07, 4.40s/it] {'loss': '0.9222', 'grad_norm': '1.285', 'learning_rate': '3.123e-05', 'epoch': '0.6528', 'num_input_tokens_seen': 41021120, 'train_runtime': '5.166e+04', 'train_tokens_per_second': '794'}
65%|██████▌ | 12200/18688 [14:21:04<7:56:07, 4.40s/it] 65%|██████▌ | 12201/18688 [14:21:08<7:34:52, 4.21s/it] 65%|██████▌ | 12202/18688 [14:21:12<7:34:50, 4.21s/it] 65%|██████▌ | 12203/18688 [14:21:16<7:25:36, 4.12s/it] 65%|██████▌ | 12204/18688 [14:21:20<7:27:47, 4.14s/it] 65%|██████▌ | 12205/18688 [14:21:26<8:04:44, 4.49s/it] 65%|██████▌ | 12206/18688 [14:21:30<7:47:54, 4.33s/it] 65%|██████▌ | 12207/18688 [14:21:34<7:34:31, 4.21s/it] 65%|██████▌ | 12208/18688 [14:21:38<7:31:15, 4.18s/it] 65%|██████▌ | 12209/18688 [14:21:43<7:50:54, 4.36s/it] 65%|██████▌ | 12210/18688 [14:21:46<7:37:48, 4.24s/it] {'loss': '0.9565', 'grad_norm': '1.175', 'learning_rate': '3.115e-05', 'epoch': '0.6534', 'num_input_tokens_seen': 41051400, 'train_runtime': '5.171e+04', 'train_tokens_per_second': '793.9'}
65%|██████▌ | 12210/18688 [14:21:46<7:37:48, 4.24s/it] 65%|██████▌ | 12211/18688 [14:21:50<7:28:59, 4.16s/it] 65%|██████▌ | 12212/18688 [14:21:54<7:21:19, 4.09s/it] 65%|██████▌ | 12213/18688 [14:21:59<7:25:32, 4.13s/it] 65%|██████▌ | 12214/18688 [14:22:04<7:52:34, 4.38s/it] 65%|██████▌ | 12215/18688 [14:22:09<8:25:51, 4.69s/it] 65%|██████▌ | 12216/18688 [14:22:13<8:20:34, 4.64s/it] 65%|██████▌ | 12217/18688 [14:22:18<8:13:30, 4.58s/it] 65%|██████▌ | 12218/18688 [14:22:23<8:18:05, 4.62s/it] 65%|██████▌ | 12219/18688 [14:22:27<8:07:03, 4.52s/it] 65%|██████▌ | 12220/18688 [14:22:32<8:14:44, 4.59s/it] {'loss': '0.9558', 'grad_norm': '1.423', 'learning_rate': '3.106e-05', 'epoch': '0.6539', 'num_input_tokens_seen': 41087720, 'train_runtime': '5.175e+04', 'train_tokens_per_second': '793.9'}
65%|██████▌ | 12220/18688 [14:22:32<8:14:44, 4.59s/it] 65%|██████▌ | 12221/18688 [14:22:36<8:13:44, 4.58s/it] 65%|██████▌ | 12222/18688 [14:22:41<8:07:03, 4.52s/it] 65%|██████▌ | 12223/18688 [14:22:45<7:58:53, 4.44s/it] 65%|██████▌ | 12224/18688 [14:22:49<7:48:50, 4.35s/it] 65%|██████▌ | 12225/18688 [14:22:53<7:46:02, 4.33s/it] 65%|██████▌ | 12226/18688 [14:22:58<8:04:52, 4.50s/it] 65%|██████▌ | 12227/18688 [14:23:02<7:57:41, 4.44s/it] 65%|██████▌ | 12228/18688 [14:23:07<8:08:20, 4.54s/it] 65%|██████▌ | 12229/18688 [14:23:12<8:06:12, 4.52s/it] 65%|██████▌ | 12230/18688 [14:23:16<7:43:28, 4.31s/it] {'loss': '0.9422', 'grad_norm': '1.249', 'learning_rate': '3.098e-05', 'epoch': '0.6544', 'num_input_tokens_seen': 41119864, 'train_runtime': '5.18e+04', 'train_tokens_per_second': '793.9'}
65%|██████▌ | 12230/18688 [14:23:16<7:43:28, 4.31s/it] 65%|██████▌ | 12231/18688 [14:23:20<7:34:22, 4.22s/it] 65%|██████▌ | 12232/18688 [14:23:23<7:23:49, 4.12s/it] 65%|██████▌ | 12233/18688 [14:23:29<7:57:05, 4.43s/it] 65%|██████▌ | 12234/18688 [14:23:33<7:50:24, 4.37s/it] 65%|██████▌ | 12235/18688 [14:23:37<7:54:49, 4.41s/it] 65%|██████▌ | 12236/18688 [14:23:42<7:46:28, 4.34s/it] 65%|██████▌ | 12237/18688 [14:23:46<7:53:08, 4.40s/it] 65%|██████▌ | 12238/18688 [14:23:51<7:56:38, 4.43s/it] 65%|██████▌ | 12239/18688 [14:23:55<7:47:27, 4.35s/it] 65%|██████▌ | 12240/18688 [14:23:59<7:38:33, 4.27s/it] {'loss': '0.9322', 'grad_norm': '1.241', 'learning_rate': '3.09e-05', 'epoch': '0.655', 'num_input_tokens_seen': 41151696, 'train_runtime': '5.184e+04', 'train_tokens_per_second': '793.8'}
65%|██████▌ | 12240/18688 [14:23:59<7:38:33, 4.27s/it] 66%|██████▌ | 12241/18688 [14:24:03<7:30:54, 4.20s/it] 66%|██████▌ | 12242/18688 [14:24:07<7:30:33, 4.19s/it] 66%|██████▌ | 12243/18688 [14:24:12<7:47:11, 4.35s/it] 66%|██████▌ | 12244/18688 [14:24:15<7:27:03, 4.16s/it] 66%|██████▌ | 12245/18688 [14:24:19<7:12:39, 4.03s/it] 66%|██████▌ | 12246/18688 [14:24:23<6:55:43, 3.87s/it] 66%|██████▌ | 12247/18688 [14:24:27<7:11:06, 4.02s/it] 66%|██████▌ | 12248/18688 [14:24:33<8:01:18, 4.48s/it] 66%|██████▌ | 12249/18688 [14:24:37<7:49:56, 4.38s/it] 66%|██████▌ | 12250/18688 [14:24:41<7:38:46, 4.28s/it] {'loss': '0.9245', 'grad_norm': '1.298', 'learning_rate': '3.081e-05', 'epoch': '0.6555', 'num_input_tokens_seen': 41181688, 'train_runtime': '5.188e+04', 'train_tokens_per_second': '793.8'}
66%|██████▌ | 12250/18688 [14:24:41<7:38:46, 4.28s/it] 66%|██████▌ | 12251/18688 [14:24:45<7:27:16, 4.17s/it] 66%|██████▌ | 12252/18688 [14:24:49<7:38:39, 4.28s/it] 66%|██████▌ | 12253/18688 [14:24:53<7:18:42, 4.09s/it] 66%|██████▌ | 12254/18688 [14:24:57<7:28:00, 4.18s/it] 66%|██████▌ | 12255/18688 [14:25:02<7:30:45, 4.20s/it] 66%|██████▌ | 12256/18688 [14:25:07<8:08:25, 4.56s/it] 66%|██████▌ | 12257/18688 [14:25:11<8:02:50, 4.50s/it] 66%|██████▌ | 12258/18688 [14:25:16<8:04:27, 4.52s/it] 66%|██████▌ | 12259/18688 [14:25:20<7:58:54, 4.47s/it] 66%|██████▌ | 12260/18688 [14:25:24<7:49:32, 4.38s/it] {'loss': '0.915', 'grad_norm': '1.306', 'learning_rate': '3.073e-05', 'epoch': '0.6561', 'num_input_tokens_seen': 41215008, 'train_runtime': '5.192e+04', 'train_tokens_per_second': '793.7'}
66%|██████▌ | 12260/18688 [14:25:24<7:49:32, 4.38s/it] 66%|██████▌ | 12261/18688 [14:25:30<8:25:20, 4.72s/it] 66%|██████▌ | 12262/18688 [14:25:34<8:16:19, 4.63s/it] 66%|██████▌ | 12263/18688 [14:25:38<7:55:27, 4.44s/it] 66%|██████▌ | 12264/18688 [14:25:43<8:02:37, 4.51s/it] 66%|██████▌ | 12265/18688 [14:25:47<7:36:16, 4.26s/it] 66%|██████▌ | 12266/18688 [14:25:51<7:46:17, 4.36s/it] 66%|██████▌ | 12267/18688 [14:25:55<7:26:25, 4.17s/it] 66%|██████▌ | 12268/18688 [14:26:00<7:52:58, 4.42s/it] 66%|██████▌ | 12269/18688 [14:26:05<8:02:31, 4.51s/it] 66%|██████▌ | 12270/18688 [14:26:10<8:13:15, 4.61s/it] {'loss': '0.9114', 'grad_norm': '1.222', 'learning_rate': '3.064e-05', 'epoch': '0.6566', 'num_input_tokens_seen': 41250544, 'train_runtime': '5.197e+04', 'train_tokens_per_second': '793.7'}
66%|██████▌ | 12270/18688 [14:26:10<8:13:15, 4.61s/it] 66%|██████▌ | 12271/18688 [14:26:13<7:42:09, 4.32s/it] 66%|██████▌ | 12272/18688 [14:26:18<8:03:16, 4.52s/it] 66%|██████▌ | 12273/18688 [14:26:23<8:12:10, 4.60s/it] 66%|██████▌ | 12274/18688 [14:26:27<8:02:32, 4.51s/it] 66%|██████▌ | 12275/18688 [14:26:31<7:45:15, 4.35s/it] 66%|██████▌ | 12276/18688 [14:26:35<7:40:59, 4.31s/it] 66%|██████▌ | 12277/18688 [14:26:39<7:27:28, 4.19s/it] 66%|██████▌ | 12278/18688 [14:26:44<7:28:41, 4.20s/it] 66%|██████▌ | 12279/18688 [14:26:48<7:40:29, 4.31s/it] 66%|██████▌ | 12280/18688 [14:26:52<7:17:26, 4.10s/it] {'loss': '0.9207', 'grad_norm': '1.558', 'learning_rate': '3.056e-05', 'epoch': '0.6571', 'num_input_tokens_seen': 41284480, 'train_runtime': '5.201e+04', 'train_tokens_per_second': '793.7'}
66%|██████▌ | 12280/18688 [14:26:52<7:17:26, 4.10s/it] 66%|██████▌ | 12281/18688 [14:26:56<7:10:51, 4.03s/it] 66%|██████▌ | 12282/18688 [14:26:59<6:53:29, 3.87s/it] 66%|██████▌ | 12283/18688 [14:27:04<7:12:41, 4.05s/it] 66%|██████▌ | 12284/18688 [14:27:07<6:54:44, 3.89s/it] 66%|██████▌ | 12285/18688 [14:27:11<7:05:20, 3.99s/it] 66%|██████▌ | 12286/18688 [14:27:15<6:53:19, 3.87s/it] 66%|██████▌ | 12287/18688 [14:27:19<7:10:35, 4.04s/it] 66%|██████▌ | 12288/18688 [14:27:24<7:25:42, 4.18s/it] 66%|██████▌ | 12289/18688 [14:27:28<7:28:52, 4.21s/it] 66%|██████▌ | 12290/18688 [14:27:32<7:31:44, 4.24s/it] {'loss': '0.9256', 'grad_norm': '1.58', 'learning_rate': '3.047e-05', 'epoch': '0.6577', 'num_input_tokens_seen': 41315560, 'train_runtime': '5.205e+04', 'train_tokens_per_second': '793.7'}
66%|██████▌ | 12290/18688 [14:27:32<7:31:44, 4.24s/it] 66%|██████▌ | 12291/18688 [14:27:37<7:53:51, 4.44s/it] 66%|██████▌ | 12292/18688 [14:27:42<7:43:11, 4.35s/it] 66%|██████▌ | 12293/18688 [14:27:46<8:02:07, 4.52s/it] 66%|██████▌ | 12294/18688 [14:27:50<7:41:31, 4.33s/it] 66%|██████▌ | 12295/18688 [14:27:54<7:31:47, 4.24s/it] 66%|██████▌ | 12296/18688 [14:27:59<7:53:45, 4.45s/it] 66%|██████▌ | 12297/18688 [14:28:04<7:55:40, 4.47s/it] 66%|██████▌ | 12298/18688 [14:28:08<7:45:22, 4.37s/it] 66%|██████▌ | 12299/18688 [14:28:12<7:24:27, 4.17s/it] 66%|██████▌ | 12300/18688 [14:28:16<7:20:49, 4.14s/it] {'loss': '0.9229', 'grad_norm': '1.119', 'learning_rate': '3.039e-05', 'epoch': '0.6582', 'num_input_tokens_seen': 41349464, 'train_runtime': '5.21e+04', 'train_tokens_per_second': '793.7'}
66%|██████▌ | 12300/18688 [14:28:16<7:20:49, 4.14s/it] 66%|██████▌ | 12301/18688 [14:28:20<7:12:43, 4.07s/it] 66%|██████▌ | 12302/18688 [14:28:24<7:07:31, 4.02s/it] 66%|██████▌ | 12303/18688 [14:28:28<7:14:11, 4.08s/it] 66%|██████▌ | 12304/18688 [14:28:32<7:04:35, 3.99s/it] 66%|██████▌ | 12305/18688 [14:28:36<7:22:30, 4.16s/it] 66%|██████▌ | 12306/18688 [14:28:40<7:26:12, 4.19s/it] 66%|██████▌ | 12307/18688 [14:28:44<7:23:22, 4.17s/it] 66%|██████▌ | 12308/18688 [14:28:48<7:07:49, 4.02s/it] 66%|██████▌ | 12309/18688 [14:28:52<7:16:51, 4.11s/it] 66%|██████▌ | 12310/18688 [14:28:57<7:18:08, 4.12s/it] {'loss': '0.9468', 'grad_norm': '1.373', 'learning_rate': '3.031e-05', 'epoch': '0.6587', 'num_input_tokens_seen': 41381760, 'train_runtime': '5.214e+04', 'train_tokens_per_second': '793.7'}
66%|██████▌ | 12310/18688 [14:28:57<7:18:08, 4.12s/it] 66%|██████▌ | 12311/18688 [14:29:00<6:57:58, 3.93s/it] 66%|██████▌ | 12312/18688 [14:29:05<7:19:44, 4.14s/it] 66%|██████▌ | 12313/18688 [14:29:09<7:13:49, 4.08s/it] 66%|██████▌ | 12314/18688 [14:29:12<7:02:38, 3.98s/it] 66%|██████▌ | 12315/18688 [14:29:17<7:23:17, 4.17s/it] 66%|██████▌ | 12316/18688 [14:29:21<7:07:19, 4.02s/it] 66%|██████▌ | 12317/18688 [14:29:25<7:10:52, 4.06s/it] 66%|██████▌ | 12318/18688 [14:29:28<6:53:46, 3.90s/it] 66%|██████▌ | 12319/18688 [14:29:32<7:00:58, 3.97s/it] 66%|██████▌ | 12320/18688 [14:29:37<7:09:56, 4.05s/it] {'loss': '0.8766', 'grad_norm': '1.127', 'learning_rate': '3.022e-05', 'epoch': '0.6593', 'num_input_tokens_seen': 41413256, 'train_runtime': '5.218e+04', 'train_tokens_per_second': '793.7'}
66%|██████▌ | 12320/18688 [14:29:37<7:09:56, 4.05s/it] 66%|██████▌ | 12321/18688 [14:29:41<7:13:25, 4.08s/it] 66%|██████▌ | 12322/18688 [14:29:45<7:13:52, 4.09s/it] 66%|██████▌ | 12323/18688 [14:29:49<7:10:36, 4.06s/it] 66%|██████▌ | 12324/18688 [14:29:54<7:26:51, 4.21s/it] 66%|██████▌ | 12325/18688 [14:29:58<7:46:00, 4.39s/it] 66%|██████▌ | 12326/18688 [14:30:03<7:39:19, 4.33s/it] 66%|██████▌ | 12327/18688 [14:30:06<7:18:00, 4.13s/it] 66%|██████▌ | 12328/18688 [14:30:11<7:41:21, 4.35s/it] 66%|██████▌ | 12329/18688 [14:30:15<7:17:10, 4.12s/it] 66%|██████▌ | 12330/18688 [14:30:19<7:26:51, 4.22s/it] {'loss': '0.9715', 'grad_norm': '1.258', 'learning_rate': '3.014e-05', 'epoch': '0.6598', 'num_input_tokens_seen': 41445536, 'train_runtime': '5.222e+04', 'train_tokens_per_second': '793.7'}
66%|██████▌ | 12330/18688 [14:30:19<7:26:51, 4.22s/it] 66%|██████▌ | 12331/18688 [14:30:23<7:27:12, 4.22s/it] 66%|██████▌ | 12332/18688 [14:30:28<7:33:45, 4.28s/it] 66%|██████▌ | 12333/18688 [14:30:31<7:15:10, 4.11s/it] 66%|██████▌ | 12334/18688 [14:30:35<6:56:06, 3.93s/it] 66%|██████▌ | 12335/18688 [14:30:39<6:57:19, 3.94s/it] 66%|██████▌ | 12336/18688 [14:30:43<6:47:10, 3.85s/it] 66%|██████▌ | 12337/18688 [14:30:46<6:38:38, 3.77s/it] 66%|██████▌ | 12338/18688 [14:30:50<6:50:48, 3.88s/it] 66%|██████▌ | 12339/18688 [14:30:55<7:12:25, 4.09s/it] 66%|██████▌ | 12340/18688 [14:31:00<7:34:13, 4.29s/it] {'loss': '0.9397', 'grad_norm': '1.258', 'learning_rate': '3.005e-05', 'epoch': '0.6603', 'num_input_tokens_seen': 41476336, 'train_runtime': '5.226e+04', 'train_tokens_per_second': '793.7'}
66%|██████▌ | 12340/18688 [14:31:00<7:34:13, 4.29s/it] 66%|██████▌ | 12341/18688 [14:31:04<7:44:28, 4.39s/it] 66%|██████▌ | 12342/18688 [14:31:09<8:06:20, 4.60s/it] 66%|██████▌ | 12343/18688 [14:31:13<7:34:31, 4.30s/it] 66%|██████▌ | 12344/18688 [14:31:17<7:35:52, 4.31s/it] 66%|██████▌ | 12345/18688 [14:31:21<7:18:28, 4.15s/it] 66%|██████▌ | 12346/18688 [14:31:26<7:31:02, 4.27s/it] 66%|██████▌ | 12347/18688 [14:31:31<7:51:25, 4.46s/it] 66%|██████▌ | 12348/18688 [14:31:35<7:56:53, 4.51s/it] 66%|██████▌ | 12349/18688 [14:31:40<7:53:48, 4.48s/it] 66%|██████▌ | 12350/18688 [14:31:44<8:05:47, 4.60s/it] {'loss': '0.9665', 'grad_norm': '1.42', 'learning_rate': '2.997e-05', 'epoch': '0.6609', 'num_input_tokens_seen': 41513968, 'train_runtime': '5.23e+04', 'train_tokens_per_second': '793.7'}
66%|██████▌ | 12350/18688 [14:31:44<8:05:47, 4.60s/it] 66%|██████▌ | 12351/18688 [14:31:48<7:48:03, 4.43s/it] 66%|██████▌ | 12352/18688 [14:31:53<7:42:31, 4.38s/it] 66%|██████▌ | 12353/18688 [14:31:57<7:43:00, 4.39s/it] 66%|██████▌ | 12354/18688 [14:32:01<7:39:50, 4.36s/it] 66%|██████▌ | 12355/18688 [14:32:06<7:41:18, 4.37s/it] 66%|██████▌ | 12356/18688 [14:32:09<7:15:22, 4.13s/it] 66%|██████▌ | 12357/18688 [14:32:13<7:08:22, 4.06s/it] 66%|██████▌ | 12358/18688 [14:32:17<7:11:29, 4.09s/it] 66%|██████▌ | 12359/18688 [14:32:22<7:17:02, 4.14s/it] 66%|██████▌ | 12360/18688 [14:32:26<7:28:26, 4.25s/it] {'loss': '0.9542', 'grad_norm': '1.239', 'learning_rate': '2.989e-05', 'epoch': '0.6614', 'num_input_tokens_seen': 41545912, 'train_runtime': '5.235e+04', 'train_tokens_per_second': '793.7'}
66%|██████▌ | 12360/18688 [14:32:26<7:28:26, 4.25s/it] 66%|██████▌ | 12361/18688 [14:32:30<7:04:02, 4.02s/it] 66%|██████▌ | 12362/18688 [14:32:33<6:48:09, 3.87s/it] 66%|██████▌ | 12363/18688 [14:32:37<6:36:31, 3.76s/it] 66%|██████▌ | 12364/18688 [14:32:41<6:44:34, 3.84s/it] 66%|██████▌ | 12365/18688 [14:32:45<7:06:20, 4.05s/it] 66%|██████▌ | 12366/18688 [14:32:50<7:30:56, 4.28s/it] 66%|██████▌ | 12367/18688 [14:32:54<7:28:41, 4.26s/it] 66%|██████▌ | 12368/18688 [14:32:59<7:36:07, 4.33s/it] 66%|██████▌ | 12369/18688 [14:33:02<7:10:16, 4.09s/it] 66%|██████▌ | 12370/18688 [14:33:06<7:02:26, 4.01s/it] {'loss': '0.9084', 'grad_norm': '1.249', 'learning_rate': '2.98e-05', 'epoch': '0.6619', 'num_input_tokens_seen': 41577536, 'train_runtime': '5.239e+04', 'train_tokens_per_second': '793.7'}
66%|██████▌ | 12370/18688 [14:33:06<7:02:26, 4.01s/it] 66%|██████▌ | 12371/18688 [14:33:11<7:22:12, 4.20s/it] 66%|██████▌ | 12372/18688 [14:33:15<7:15:57, 4.14s/it] 66%|██████▌ | 12373/18688 [14:33:19<7:25:51, 4.24s/it] 66%|██████▌ | 12374/18688 [14:33:24<7:30:48, 4.28s/it] 66%|██████▌ | 12375/18688 [14:33:28<7:24:47, 4.23s/it] 66%|██████▌ | 12376/18688 [14:33:32<7:30:38, 4.28s/it] 66%|██████▌ | 12377/18688 [14:33:36<7:25:28, 4.24s/it] 66%|██████▌ | 12378/18688 [14:33:41<7:38:36, 4.36s/it] 66%|██████▌ | 12379/18688 [14:33:45<7:21:08, 4.20s/it] 66%|██████▌ | 12380/18688 [14:33:49<7:13:03, 4.12s/it] {'loss': '0.8773', 'grad_norm': '1.422', 'learning_rate': '2.972e-05', 'epoch': '0.6625', 'num_input_tokens_seen': 41611592, 'train_runtime': '5.243e+04', 'train_tokens_per_second': '793.7'}
66%|██████▌ | 12380/18688 [14:33:49<7:13:03, 4.12s/it] 66%|██████▋ | 12381/18688 [14:33:53<7:02:59, 4.02s/it] 66%|██████▋ | 12382/18688 [14:33:57<7:08:15, 4.07s/it] 66%|██████▋ | 12383/18688 [14:34:02<7:46:43, 4.44s/it] 66%|██████▋ | 12384/18688 [14:34:06<7:42:59, 4.41s/it] 66%|██████▋ | 12385/18688 [14:34:11<7:48:15, 4.46s/it] 66%|██████▋ | 12386/18688 [14:34:15<7:45:39, 4.43s/it] 66%|██████▋ | 12387/18688 [14:34:19<7:27:47, 4.26s/it] 66%|██████▋ | 12388/18688 [14:34:23<7:16:54, 4.16s/it] 66%|██████▋ | 12389/18688 [14:34:27<7:00:54, 4.01s/it] 66%|██████▋ | 12390/18688 [14:34:30<6:52:18, 3.93s/it] {'loss': '0.8842', 'grad_norm': '1.357', 'learning_rate': '2.964e-05', 'epoch': '0.663', 'num_input_tokens_seen': 41642264, 'train_runtime': '5.247e+04', 'train_tokens_per_second': '793.6'}
66%|██████▋ | 12390/18688 [14:34:30<6:52:18, 3.93s/it] 66%|██████▋ | 12391/18688 [14:34:34<6:49:49, 3.90s/it] 66%|██████▋ | 12392/18688 [14:34:38<6:39:50, 3.81s/it] 66%|██████▋ | 12393/18688 [14:34:43<7:16:02, 4.16s/it] 66%|██████▋ | 12394/18688 [14:34:47<7:24:17, 4.24s/it] 66%|██████▋ | 12395/18688 [14:34:51<7:23:00, 4.22s/it] 66%|██████▋ | 12396/18688 [14:34:55<7:02:29, 4.03s/it] 66%|██████▋ | 12397/18688 [14:35:00<7:17:48, 4.18s/it] 66%|██████▋ | 12398/18688 [14:35:04<7:22:46, 4.22s/it] 66%|██████▋ | 12399/18688 [14:35:08<7:19:00, 4.19s/it] 66%|██████▋ | 12400/18688 [14:35:13<7:38:30, 4.38s/it] {'loss': '0.9214', 'grad_norm': '1.213', 'learning_rate': '2.955e-05', 'epoch': '0.6635', 'num_input_tokens_seen': 41676704, 'train_runtime': '5.251e+04', 'train_tokens_per_second': '793.6'}
66%|██████▋ | 12400/18688 [14:35:13<7:38:30, 4.38s/it] 66%|██████▋ | 12401/18688 [14:35:18<8:04:17, 4.62s/it] 66%|██████▋ | 12402/18688 [14:35:23<8:00:04, 4.58s/it] 66%|██████▋ | 12403/18688 [14:35:26<7:35:21, 4.35s/it] 66%|██████▋ | 12404/18688 [14:35:30<7:17:33, 4.18s/it] 66%|██████▋ | 12405/18688 [14:35:34<6:55:45, 3.97s/it] 66%|██████▋ | 12406/18688 [14:35:38<7:19:37, 4.20s/it] 66%|██████▋ | 12407/18688 [14:35:42<7:02:24, 4.04s/it] 66%|██████▋ | 12408/18688 [14:35:46<7:15:54, 4.16s/it] 66%|██████▋ | 12409/18688 [14:35:50<7:10:25, 4.11s/it] 66%|██████▋ | 12410/18688 [14:35:56<7:44:29, 4.44s/it] {'loss': '0.9736', 'grad_norm': '1.337', 'learning_rate': '2.947e-05', 'epoch': '0.6641', 'num_input_tokens_seen': 41709352, 'train_runtime': '5.256e+04', 'train_tokens_per_second': '793.6'}
66%|██████▋ | 12410/18688 [14:35:56<7:44:29, 4.44s/it] 66%|██████▋ | 12411/18688 [14:36:00<7:52:29, 4.52s/it] 66%|██████▋ | 12412/18688 [14:36:04<7:35:27, 4.35s/it] 66%|██████▋ | 12413/18688 [14:36:09<7:42:22, 4.42s/it] 66%|██████▋ | 12414/18688 [14:36:12<7:13:25, 4.15s/it] 66%|██████▋ | 12415/18688 [14:36:16<7:08:21, 4.10s/it] 66%|██████▋ | 12416/18688 [14:36:20<6:53:06, 3.95s/it] 66%|██████▋ | 12417/18688 [14:36:25<7:20:21, 4.21s/it] 66%|██████▋ | 12418/18688 [14:36:29<7:18:31, 4.20s/it] 66%|██████▋ | 12419/18688 [14:36:33<6:59:03, 4.01s/it] 66%|██████▋ | 12420/18688 [14:36:37<7:10:32, 4.12s/it] {'loss': '0.9076', 'grad_norm': '1.353', 'learning_rate': '2.939e-05', 'epoch': '0.6646', 'num_input_tokens_seen': 41742408, 'train_runtime': '5.26e+04', 'train_tokens_per_second': '793.6'}
66%|██████▋ | 12420/18688 [14:36:37<7:10:32, 4.12s/it] 66%|██████▋ | 12421/18688 [14:36:40<6:52:49, 3.95s/it] 66%|██████▋ | 12422/18688 [14:36:45<7:21:31, 4.23s/it] 66%|██████▋ | 12423/18688 [14:36:51<8:10:42, 4.70s/it] 66%|██████▋ | 12424/18688 [14:36:56<8:05:27, 4.65s/it] 66%|██████▋ | 12425/18688 [14:37:00<7:44:01, 4.45s/it] 66%|██████▋ | 12426/18688 [14:37:04<7:31:45, 4.33s/it] 66%|██████▋ | 12427/18688 [14:37:08<7:15:44, 4.18s/it] 67%|██████▋ | 12428/18688 [14:37:11<7:02:55, 4.05s/it] 67%|██████▋ | 12429/18688 [14:37:15<6:53:31, 3.96s/it] 67%|██████▋ | 12430/18688 [14:37:19<6:54:10, 3.97s/it] {'loss': '0.9099', 'grad_norm': '1.666', 'learning_rate': '2.93e-05', 'epoch': '0.6652', 'num_input_tokens_seen': 41776160, 'train_runtime': '5.264e+04', 'train_tokens_per_second': '793.6'}
67%|██████▋ | 12430/18688 [14:37:19<6:54:10, 3.97s/it] 67%|██████▋ | 12431/18688 [14:37:23<7:05:31, 4.08s/it] 67%|██████▋ | 12432/18688 [14:37:28<7:15:15, 4.17s/it] 67%|██████▋ | 12433/18688 [14:37:31<6:53:37, 3.97s/it] 67%|██████▋ | 12434/18688 [14:37:35<6:44:52, 3.88s/it] 67%|██████▋ | 12435/18688 [14:37:40<7:14:27, 4.17s/it] 67%|██████▋ | 12436/18688 [14:37:44<7:26:05, 4.28s/it] 67%|██████▋ | 12437/18688 [14:37:49<7:31:31, 4.33s/it] 67%|██████▋ | 12438/18688 [14:37:54<7:49:31, 4.51s/it] 67%|██████▋ | 12439/18688 [14:37:58<7:38:49, 4.41s/it] 67%|██████▋ | 12440/18688 [14:38:02<7:35:20, 4.37s/it] {'loss': '0.9772', 'grad_norm': '1.657', 'learning_rate': '2.922e-05', 'epoch': '0.6657', 'num_input_tokens_seen': 41808800, 'train_runtime': '5.268e+04', 'train_tokens_per_second': '793.6'}
67%|██████▋ | 12440/18688 [14:38:02<7:35:20, 4.37s/it] 67%|██████▋ | 12441/18688 [14:38:07<7:34:57, 4.37s/it] 67%|██████▋ | 12442/18688 [14:38:10<7:09:53, 4.13s/it] 67%|██████▋ | 12443/18688 [14:38:14<7:16:57, 4.20s/it] 67%|██████▋ | 12444/18688 [14:38:19<7:22:12, 4.25s/it] 67%|██████▋ | 12445/18688 [14:38:23<7:20:31, 4.23s/it] 67%|██████▋ | 12446/18688 [14:38:27<7:21:45, 4.25s/it] 67%|██████▋ | 12447/18688 [14:38:31<7:10:40, 4.14s/it] 67%|██████▋ | 12448/18688 [14:38:36<7:27:34, 4.30s/it] 67%|██████▋ | 12449/18688 [14:38:41<7:45:59, 4.48s/it] 67%|██████▋ | 12450/18688 [14:38:45<7:23:55, 4.27s/it] {'loss': '0.8911', 'grad_norm': '1.392', 'learning_rate': '2.914e-05', 'epoch': '0.6662', 'num_input_tokens_seen': 41843544, 'train_runtime': '5.273e+04', 'train_tokens_per_second': '793.6'}
67%|██████▋ | 12450/18688 [14:38:45<7:23:55, 4.27s/it] 67%|██████▋ | 12451/18688 [14:38:49<7:36:00, 4.39s/it] 67%|██████▋ | 12452/18688 [14:38:54<7:35:54, 4.39s/it] 67%|██████▋ | 12453/18688 [14:38:57<7:18:14, 4.22s/it] 67%|██████▋ | 12454/18688 [14:39:01<7:13:35, 4.17s/it] 67%|██████▋ | 12455/18688 [14:39:05<7:01:06, 4.05s/it] 67%|██████▋ | 12456/18688 [14:39:09<6:51:57, 3.97s/it] 67%|██████▋ | 12457/18688 [14:39:13<7:02:41, 4.07s/it] 67%|██████▋ | 12458/18688 [14:39:17<7:00:55, 4.05s/it] 67%|██████▋ | 12459/18688 [14:39:21<7:01:46, 4.06s/it] 67%|██████▋ | 12460/18688 [14:39:26<7:07:09, 4.12s/it] {'loss': '0.8989', 'grad_norm': '1.412', 'learning_rate': '2.905e-05', 'epoch': '0.6668', 'num_input_tokens_seen': 41874896, 'train_runtime': '5.277e+04', 'train_tokens_per_second': '793.6'}
67%|██████▋ | 12460/18688 [14:39:26<7:07:09, 4.12s/it] 67%|██████▋ | 12461/18688 [14:39:31<7:44:05, 4.47s/it] 67%|██████▋ | 12462/18688 [14:39:36<8:09:57, 4.72s/it] 67%|██████▋ | 12463/18688 [14:39:40<7:37:58, 4.41s/it] 67%|██████▋ | 12464/18688 [14:39:45<8:08:57, 4.71s/it] 67%|██████▋ | 12465/18688 [14:39:50<7:56:19, 4.59s/it] 67%|██████▋ | 12466/18688 [14:39:54<7:43:31, 4.47s/it] 67%|██████▋ | 12467/18688 [14:39:58<7:45:49, 4.49s/it] 67%|██████▋ | 12468/18688 [14:40:04<8:07:39, 4.70s/it] 67%|██████▋ | 12469/18688 [14:40:08<7:45:33, 4.49s/it] 67%|██████▋ | 12470/18688 [14:40:11<7:21:35, 4.26s/it] {'loss': '0.9523', 'grad_norm': '1.466', 'learning_rate': '2.897e-05', 'epoch': '0.6673', 'num_input_tokens_seen': 41912360, 'train_runtime': '5.281e+04', 'train_tokens_per_second': '793.6'}
67%|██████▋ | 12470/18688 [14:40:11<7:21:35, 4.26s/it] 67%|██████▋ | 12471/18688 [14:40:16<7:31:13, 4.35s/it] 67%|██████▋ | 12472/18688 [14:40:22<8:12:43, 4.76s/it] 67%|██████▋ | 12473/18688 [14:40:27<8:39:32, 5.02s/it] 67%|██████▋ | 12474/18688 [14:40:31<8:07:02, 4.70s/it] 67%|██████▋ | 12475/18688 [14:40:35<7:35:42, 4.40s/it] 67%|██████▋ | 12476/18688 [14:40:38<7:09:06, 4.14s/it] 67%|██████▋ | 12477/18688 [14:40:43<7:13:47, 4.19s/it] 67%|██████▋ | 12478/18688 [14:40:48<7:34:32, 4.39s/it] 67%|██████▋ | 12479/18688 [14:40:51<7:09:11, 4.15s/it] 67%|██████▋ | 12480/18688 [14:40:56<7:24:30, 4.30s/it] {'loss': '0.8558', 'grad_norm': '1.313', 'learning_rate': '2.889e-05', 'epoch': '0.6678', 'num_input_tokens_seen': 41946928, 'train_runtime': '5.286e+04', 'train_tokens_per_second': '793.6'}
67%|██████▋ | 12480/18688 [14:40:56<7:24:30, 4.30s/it] 67%|██████▋ | 12481/18688 [14:41:00<7:35:13, 4.40s/it] 67%|██████▋ | 12482/18688 [14:41:04<7:19:38, 4.25s/it] 67%|██████▋ | 12483/18688 [14:41:08<7:07:26, 4.13s/it] 67%|██████▋ | 12484/18688 [14:41:13<7:27:13, 4.33s/it] 67%|██████▋ | 12485/18688 [14:41:18<7:35:09, 4.40s/it] 67%|██████▋ | 12486/18688 [14:41:23<8:13:16, 4.77s/it] 67%|██████▋ | 12487/18688 [14:41:27<7:55:36, 4.60s/it] 67%|██████▋ | 12488/18688 [14:41:31<7:38:51, 4.44s/it] 67%|██████▋ | 12489/18688 [14:41:35<7:19:58, 4.26s/it] 67%|██████▋ | 12490/18688 [14:41:40<7:33:08, 4.39s/it] {'loss': '0.9473', 'grad_norm': '1.243', 'learning_rate': '2.88e-05', 'epoch': '0.6684', 'num_input_tokens_seen': 41981944, 'train_runtime': '5.29e+04', 'train_tokens_per_second': '793.6'}
67%|██████▋ | 12490/18688 [14:41:40<7:33:08, 4.39s/it] 67%|██████▋ | 12491/18688 [14:41:45<7:58:47, 4.64s/it] 67%|██████▋ | 12492/18688 [14:41:50<7:48:23, 4.54s/it] 67%|██████▋ | 12493/18688 [14:41:53<7:29:18, 4.35s/it] 67%|██████▋ | 12494/18688 [14:41:58<7:24:57, 4.31s/it] 67%|██████▋ | 12495/18688 [14:42:02<7:12:07, 4.19s/it] 67%|██████▋ | 12496/18688 [14:42:06<7:09:20, 4.16s/it] 67%|██████▋ | 12497/18688 [14:42:10<7:10:27, 4.17s/it] 67%|██████▋ | 12498/18688 [14:42:14<7:22:30, 4.29s/it] 67%|██████▋ | 12499/18688 [14:42:20<7:53:53, 4.59s/it] 67%|██████▋ | 12500/18688 [14:42:24<7:42:07, 4.48s/it] {'loss': '0.8945', 'grad_norm': '1.283', 'learning_rate': '2.872e-05', 'epoch': '0.6689', 'num_input_tokens_seen': 42016024, 'train_runtime': '5.294e+04', 'train_tokens_per_second': '793.6'}
67%|██████▋ | 12500/18688 [14:42:24<7:42:07, 4.48s/it] 67%|██████▋ | 12501/18688 [14:42:28<7:29:53, 4.36s/it] 67%|██████▋ | 12502/18688 [14:42:32<7:17:41, 4.25s/it] 67%|██████▋ | 12503/18688 [14:42:37<7:42:20, 4.49s/it] 67%|██████▋ | 12504/18688 [14:42:41<7:31:47, 4.38s/it] 67%|██████▋ | 12505/18688 [14:42:46<7:56:23, 4.62s/it] 67%|██████▋ | 12506/18688 [14:42:50<7:33:35, 4.40s/it] 67%|██████▋ | 12507/18688 [14:42:54<7:25:50, 4.33s/it] 67%|██████▋ | 12508/18688 [14:42:59<7:43:50, 4.50s/it] 67%|██████▋ | 12509/18688 [14:43:04<7:54:47, 4.61s/it] 67%|██████▋ | 12510/18688 [14:43:08<7:43:02, 4.50s/it] {'loss': '0.9306', 'grad_norm': '1.155', 'learning_rate': '2.864e-05', 'epoch': '0.6694', 'num_input_tokens_seen': 42055312, 'train_runtime': '5.299e+04', 'train_tokens_per_second': '793.7'}
67%|██████▋ | 12510/18688 [14:43:08<7:43:02, 4.50s/it] 67%|██████▋ | 12511/18688 [14:43:13<7:58:20, 4.65s/it] 67%|██████▋ | 12512/18688 [14:43:19<8:32:32, 4.98s/it] 67%|██████▋ | 12513/18688 [14:43:23<7:50:44, 4.57s/it] 67%|██████▋ | 12514/18688 [14:43:26<7:20:40, 4.28s/it] 67%|██████▋ | 12515/18688 [14:43:30<6:59:31, 4.08s/it] 67%|██████▋ | 12516/18688 [14:43:35<7:16:21, 4.24s/it] 67%|██████▋ | 12517/18688 [14:43:39<7:18:43, 4.27s/it] 67%|██████▋ | 12518/18688 [14:43:44<7:56:58, 4.64s/it] 67%|██████▋ | 12519/18688 [14:43:49<8:09:21, 4.76s/it] 67%|██████▋ | 12520/18688 [14:43:53<7:40:13, 4.48s/it] {'loss': '0.8966', 'grad_norm': '1.335', 'learning_rate': '2.856e-05', 'epoch': '0.67', 'num_input_tokens_seen': 42091352, 'train_runtime': '5.303e+04', 'train_tokens_per_second': '793.7'}
67%|██████▋ | 12520/18688 [14:43:53<7:40:13, 4.48s/it] 67%|██████▋ | 12521/18688 [14:43:58<7:49:49, 4.57s/it] 67%|██████▋ | 12522/18688 [14:44:02<7:42:07, 4.50s/it] 67%|██████▋ | 12523/18688 [14:44:08<8:01:26, 4.69s/it] 67%|██████▋ | 12524/18688 [14:44:12<7:57:38, 4.65s/it] 67%|██████▋ | 12525/18688 [14:44:16<7:48:22, 4.56s/it] 67%|██████▋ | 12526/18688 [14:44:21<7:33:07, 4.41s/it] 67%|██████▋ | 12527/18688 [14:44:26<7:58:38, 4.66s/it] 67%|██████▋ | 12528/18688 [14:44:30<7:42:11, 4.50s/it] 67%|██████▋ | 12529/18688 [14:44:34<7:19:24, 4.28s/it] 67%|██████▋ | 12530/18688 [14:44:37<6:57:44, 4.07s/it] {'loss': '0.9295', 'grad_norm': '1.29', 'learning_rate': '2.847e-05', 'epoch': '0.6705', 'num_input_tokens_seen': 42125880, 'train_runtime': '5.308e+04', 'train_tokens_per_second': '793.7'}
67%|██████▋ | 12530/18688 [14:44:37<6:57:44, 4.07s/it] 67%|██████▋ | 12531/18688 [14:44:41<6:41:09, 3.91s/it] 67%|██████▋ | 12532/18688 [14:44:44<6:27:49, 3.78s/it] 67%|██████▋ | 12533/18688 [14:44:48<6:17:20, 3.68s/it] 67%|██████▋ | 12534/18688 [14:44:52<6:42:33, 3.92s/it] 67%|██████▋ | 12535/18688 [14:44:56<6:45:32, 3.95s/it] 67%|██████▋ | 12536/18688 [14:45:00<6:40:27, 3.91s/it] 67%|██████▋ | 12537/18688 [14:45:04<6:37:48, 3.88s/it] 67%|██████▋ | 12538/18688 [14:45:08<6:42:05, 3.92s/it] 67%|██████▋ | 12539/18688 [14:45:13<7:16:21, 4.26s/it] 67%|██████▋ | 12540/18688 [14:45:16<6:54:01, 4.04s/it] {'loss': '0.9085', 'grad_norm': '1.15', 'learning_rate': '2.839e-05', 'epoch': '0.671', 'num_input_tokens_seen': 42154864, 'train_runtime': '5.312e+04', 'train_tokens_per_second': '793.6'}
67%|██████▋ | 12540/18688 [14:45:16<6:54:01, 4.04s/it] 67%|██████▋ | 12541/18688 [14:45:20<6:37:29, 3.88s/it] 67%|██████▋ | 12542/18688 [14:45:24<6:33:19, 3.84s/it] 67%|██████▋ | 12543/18688 [14:45:28<6:40:30, 3.91s/it] 67%|██████▋ | 12544/18688 [14:45:32<6:53:22, 4.04s/it] 67%|██████▋ | 12545/18688 [14:45:37<7:05:34, 4.16s/it] 67%|██████▋ | 12546/18688 [14:45:41<7:08:15, 4.18s/it] 67%|██████▋ | 12547/18688 [14:45:45<7:04:35, 4.15s/it] 67%|██████▋ | 12548/18688 [14:45:50<7:23:07, 4.33s/it] 67%|██████▋ | 12549/18688 [14:45:54<7:17:28, 4.28s/it] 67%|██████▋ | 12550/18688 [14:45:58<7:17:33, 4.28s/it] {'loss': '0.9451', 'grad_norm': '1.148', 'learning_rate': '2.831e-05', 'epoch': '0.6716', 'num_input_tokens_seen': 42189640, 'train_runtime': '5.316e+04', 'train_tokens_per_second': '793.7'}
67%|██████▋ | 12550/18688 [14:45:58<7:17:33, 4.28s/it] 67%|██████▋ | 12551/18688 [14:46:04<8:12:19, 4.81s/it] 67%|██████▋ | 12552/18688 [14:46:08<7:46:53, 4.57s/it] 67%|██████▋ | 12553/18688 [14:46:13<7:51:22, 4.61s/it] 67%|██████▋ | 12554/18688 [14:46:17<7:35:10, 4.45s/it] 67%|██████▋ | 12555/18688 [14:46:21<7:27:57, 4.38s/it] 67%|██████▋ | 12556/18688 [14:46:25<7:14:24, 4.25s/it] 67%|██████▋ | 12557/18688 [14:46:29<7:18:19, 4.29s/it] 67%|██████▋ | 12558/18688 [14:46:34<7:41:48, 4.52s/it] 67%|██████▋ | 12559/18688 [14:46:38<7:16:48, 4.28s/it] 67%|██████▋ | 12560/18688 [14:46:42<7:15:11, 4.26s/it] {'loss': '0.9463', 'grad_norm': '1.424', 'learning_rate': '2.823e-05', 'epoch': '0.6721', 'num_input_tokens_seen': 42222776, 'train_runtime': '5.32e+04', 'train_tokens_per_second': '793.6'}
67%|██████▋ | 12560/18688 [14:46:42<7:15:11, 4.26s/it] 67%|██████▋ | 12561/18688 [14:46:46<7:04:05, 4.15s/it] 67%|██████▋ | 12562/18688 [14:46:50<6:55:32, 4.07s/it] 67%|██████▋ | 12563/18688 [14:46:54<6:55:52, 4.07s/it] 67%|██████▋ | 12564/18688 [14:46:58<6:44:27, 3.96s/it] 67%|██████▋ | 12565/18688 [14:47:03<7:10:07, 4.21s/it] 67%|██████▋ | 12566/18688 [14:47:07<7:21:44, 4.33s/it] 67%|██████▋ | 12567/18688 [14:47:12<7:40:47, 4.52s/it] 67%|██████▋ | 12568/18688 [14:47:16<7:18:44, 4.30s/it] 67%|██████▋ | 12569/18688 [14:47:20<6:59:17, 4.11s/it] 67%|██████▋ | 12570/18688 [14:47:23<6:44:10, 3.96s/it] {'loss': '0.8638', 'grad_norm': '1.299', 'learning_rate': '2.814e-05', 'epoch': '0.6726', 'num_input_tokens_seen': 42252760, 'train_runtime': '5.324e+04', 'train_tokens_per_second': '793.6'}
67%|██████▋ | 12570/18688 [14:47:23<6:44:10, 3.96s/it] 67%|██████▋ | 12571/18688 [14:47:28<7:13:36, 4.25s/it] 67%|██████▋ | 12572/18688 [14:47:33<7:19:41, 4.31s/it] 67%|██████▋ | 12573/18688 [14:47:37<7:24:35, 4.36s/it] 67%|██████▋ | 12574/18688 [14:47:41<7:08:19, 4.20s/it] 67%|██████▋ | 12575/18688 [14:47:46<7:28:33, 4.40s/it] 67%|██████▋ | 12576/18688 [14:47:50<7:29:53, 4.42s/it] 67%|██████▋ | 12577/18688 [14:47:54<7:00:46, 4.13s/it] 67%|██████▋ | 12578/18688 [14:47:58<6:50:23, 4.03s/it] 67%|██████▋ | 12579/18688 [14:48:02<6:58:40, 4.11s/it] 67%|██████▋ | 12580/18688 [14:48:05<6:38:59, 3.92s/it] {'loss': '0.9261', 'grad_norm': '1.505', 'learning_rate': '2.806e-05', 'epoch': '0.6732', 'num_input_tokens_seen': 42287328, 'train_runtime': '5.329e+04', 'train_tokens_per_second': '793.6'}
67%|██████▋ | 12580/18688 [14:48:05<6:38:59, 3.92s/it] 67%|██████▋ | 12581/18688 [14:48:09<6:34:34, 3.88s/it] 67%|██████▋ | 12582/18688 [14:48:14<6:57:17, 4.10s/it] 67%|██████▋ | 12583/18688 [14:48:18<6:59:52, 4.13s/it] 67%|██████▋ | 12584/18688 [14:48:22<6:43:24, 3.97s/it] 67%|██████▋ | 12585/18688 [14:48:25<6:37:09, 3.90s/it] 67%|██████▋ | 12586/18688 [14:48:30<6:47:08, 4.00s/it] 67%|██████▋ | 12587/18688 [14:48:34<6:48:16, 4.02s/it] 67%|██████▋ | 12588/18688 [14:48:37<6:35:04, 3.89s/it] 67%|██████▋ | 12589/18688 [14:48:41<6:21:55, 3.76s/it] 67%|██████▋ | 12590/18688 [14:48:45<6:35:05, 3.89s/it] {'loss': '0.8976', 'grad_norm': '1.408', 'learning_rate': '2.798e-05', 'epoch': '0.6737', 'num_input_tokens_seen': 42319456, 'train_runtime': '5.333e+04', 'train_tokens_per_second': '793.6'}
67%|██████▋ | 12590/18688 [14:48:45<6:35:05, 3.89s/it] 67%|██████▋ | 12591/18688 [14:48:49<6:55:57, 4.09s/it] 67%|██████▋ | 12592/18688 [14:48:55<7:34:48, 4.48s/it] 67%|██████▋ | 12593/18688 [14:48:58<7:08:53, 4.22s/it] 67%|██████▋ | 12594/18688 [14:49:02<6:53:44, 4.07s/it] 67%|██████▋ | 12595/18688 [14:49:06<6:59:08, 4.13s/it] 67%|██████▋ | 12596/18688 [14:49:10<6:55:10, 4.09s/it] 67%|██████▋ | 12597/18688 [14:49:16<7:27:59, 4.41s/it] 67%|██████▋ | 12598/18688 [14:49:20<7:34:44, 4.48s/it] 67%|██████▋ | 12599/18688 [14:49:25<7:31:45, 4.45s/it] 67%|██████▋ | 12600/18688 [14:49:29<7:38:28, 4.52s/it] {'loss': '0.8812', 'grad_norm': '1.537', 'learning_rate': '2.79e-05', 'epoch': '0.6742', 'num_input_tokens_seen': 42357016, 'train_runtime': '5.337e+04', 'train_tokens_per_second': '793.7'}
67%|██████▋ | 12600/18688 [14:49:29<7:38:28, 4.52s/it] 67%|██████▋ | 12601/18688 [14:49:34<7:38:33, 4.52s/it] 67%|██████▋ | 12602/18688 [14:49:37<7:12:04, 4.26s/it] 67%|██████▋ | 12603/18688 [14:49:41<6:51:15, 4.06s/it] 67%|██████▋ | 12604/18688 [14:49:46<7:03:13, 4.17s/it] 67%|██████▋ | 12605/18688 [14:49:50<7:05:48, 4.20s/it] 67%|██████▋ | 12606/18688 [14:49:54<7:15:41, 4.30s/it] 67%|██████▋ | 12607/18688 [14:49:58<6:58:21, 4.13s/it] 67%|██████▋ | 12608/18688 [14:50:03<7:16:41, 4.31s/it] 67%|██████▋ | 12609/18688 [14:50:07<7:11:36, 4.26s/it] 67%|██████▋ | 12610/18688 [14:50:11<6:59:17, 4.14s/it] {'loss': '0.9166', 'grad_norm': '1.311', 'learning_rate': '2.782e-05', 'epoch': '0.6748', 'num_input_tokens_seen': 42389648, 'train_runtime': '5.341e+04', 'train_tokens_per_second': '793.6'}
67%|██████▋ | 12610/18688 [14:50:11<6:59:17, 4.14s/it] 67%|██████▋ | 12611/18688 [14:50:16<7:20:58, 4.35s/it] 67%|██████▋ | 12612/18688 [14:50:19<7:03:35, 4.18s/it] 67%|██████▋ | 12613/18688 [14:50:23<6:41:53, 3.97s/it] 67%|██████▋ | 12614/18688 [14:50:27<6:49:29, 4.04s/it] 68%|██████▊ | 12615/18688 [14:50:31<6:34:33, 3.90s/it] 68%|██████▊ | 12616/18688 [14:50:34<6:27:49, 3.83s/it] 68%|██████▊ | 12617/18688 [14:50:38<6:18:43, 3.74s/it] 68%|██████▊ | 12618/18688 [14:50:43<7:01:24, 4.17s/it] 68%|██████▊ | 12619/18688 [14:50:47<7:07:01, 4.22s/it] 68%|██████▊ | 12620/18688 [14:50:51<7:00:04, 4.15s/it] {'loss': '0.9535', 'grad_norm': '1.248', 'learning_rate': '2.773e-05', 'epoch': '0.6753', 'num_input_tokens_seen': 42420432, 'train_runtime': '5.345e+04', 'train_tokens_per_second': '793.6'}
68%|██████▊ | 12620/18688 [14:50:51<7:00:04, 4.15s/it] 68%|██████▊ | 12621/18688 [14:50:55<6:49:22, 4.05s/it] 68%|██████▊ | 12622/18688 [14:51:00<7:00:23, 4.16s/it] 68%|██████▊ | 12623/18688 [14:51:05<7:29:06, 4.44s/it] 68%|██████▊ | 12624/18688 [14:51:09<7:20:21, 4.36s/it] 68%|██████▊ | 12625/18688 [14:51:12<6:52:50, 4.09s/it] 68%|██████▊ | 12626/18688 [14:51:16<6:35:25, 3.91s/it] 68%|██████▊ | 12627/18688 [14:51:20<6:53:37, 4.09s/it] 68%|██████▊ | 12628/18688 [14:51:24<6:52:12, 4.08s/it] 68%|██████▊ | 12629/18688 [14:51:28<6:50:19, 4.06s/it] 68%|██████▊ | 12630/18688 [14:51:32<6:41:56, 3.98s/it] {'loss': '0.8777', 'grad_norm': '1.101', 'learning_rate': '2.765e-05', 'epoch': '0.6759', 'num_input_tokens_seen': 42452464, 'train_runtime': '5.349e+04', 'train_tokens_per_second': '793.6'}
68%|██████▊ | 12630/18688 [14:51:32<6:41:56, 3.98s/it] 68%|██████▊ | 12631/18688 [14:51:36<6:28:02, 3.84s/it] 68%|██████▊ | 12632/18688 [14:51:39<6:22:16, 3.79s/it] 68%|██████▊ | 12633/18688 [14:51:43<6:15:22, 3.72s/it] 68%|██████▊ | 12634/18688 [14:51:47<6:23:09, 3.80s/it] 68%|██████▊ | 12635/18688 [14:51:51<6:36:57, 3.93s/it] 68%|██████▊ | 12636/18688 [14:51:55<6:40:19, 3.97s/it] 68%|██████▊ | 12637/18688 [14:51:59<6:43:14, 4.00s/it] 68%|██████▊ | 12638/18688 [14:52:04<7:11:21, 4.28s/it] 68%|██████▊ | 12639/18688 [14:52:09<7:18:54, 4.35s/it] 68%|██████▊ | 12640/18688 [14:52:13<7:12:21, 4.29s/it] {'loss': '0.8581', 'grad_norm': '1.219', 'learning_rate': '2.757e-05', 'epoch': '0.6764', 'num_input_tokens_seen': 42483392, 'train_runtime': '5.353e+04', 'train_tokens_per_second': '793.6'}
68%|██████▊ | 12640/18688 [14:52:13<7:12:21, 4.29s/it] 68%|██████▊ | 12641/18688 [14:52:17<7:05:17, 4.22s/it] 68%|██████▊ | 12642/18688 [14:52:21<6:55:18, 4.12s/it] 68%|██████▊ | 12643/18688 [14:52:25<6:55:38, 4.13s/it] 68%|██████▊ | 12644/18688 [14:52:29<6:44:20, 4.01s/it] 68%|██████▊ | 12645/18688 [14:52:33<7:01:53, 4.19s/it] 68%|██████▊ | 12646/18688 [14:52:37<6:43:31, 4.01s/it] 68%|██████▊ | 12647/18688 [14:52:41<6:52:46, 4.10s/it] 68%|██████▊ | 12648/18688 [14:52:47<7:29:10, 4.46s/it] 68%|██████▊ | 12649/18688 [14:52:50<7:07:30, 4.25s/it] 68%|██████▊ | 12650/18688 [14:52:55<7:07:37, 4.25s/it] {'loss': '0.8936', 'grad_norm': '1.474', 'learning_rate': '2.749e-05', 'epoch': '0.6769', 'num_input_tokens_seen': 42515232, 'train_runtime': '5.358e+04', 'train_tokens_per_second': '793.6'}
68%|██████▊ | 12650/18688 [14:52:55<7:07:37, 4.25s/it] 68%|██████▊ | 12651/18688 [14:52:58<6:43:46, 4.01s/it] 68%|██████▊ | 12652/18688 [14:53:02<6:33:43, 3.91s/it] 68%|██████▊ | 12653/18688 [14:53:07<7:02:09, 4.20s/it] 68%|██████▊ | 12654/18688 [14:53:11<6:56:16, 4.14s/it] 68%|██████▊ | 12655/18688 [14:53:14<6:45:08, 4.03s/it] 68%|██████▊ | 12656/18688 [14:53:18<6:30:42, 3.89s/it] 68%|██████▊ | 12657/18688 [14:53:23<6:55:44, 4.14s/it] 68%|██████▊ | 12658/18688 [14:53:26<6:35:52, 3.94s/it] 68%|██████▊ | 12659/18688 [14:53:30<6:27:08, 3.85s/it] 68%|██████▊ | 12660/18688 [14:53:33<6:18:59, 3.77s/it] {'loss': '0.8742', 'grad_norm': '1.342', 'learning_rate': '2.741e-05', 'epoch': '0.6775', 'num_input_tokens_seen': 42545840, 'train_runtime': '5.361e+04', 'train_tokens_per_second': '793.6'}
68%|██████▊ | 12660/18688 [14:53:33<6:18:59, 3.77s/it] 68%|██████▊ | 12661/18688 [14:53:37<6:27:38, 3.86s/it] 68%|██████▊ | 12662/18688 [14:53:42<6:45:24, 4.04s/it] 68%|██████▊ | 12663/18688 [14:53:46<6:53:11, 4.11s/it] 68%|██████▊ | 12664/18688 [14:53:50<6:43:23, 4.02s/it] 68%|██████▊ | 12665/18688 [14:53:54<6:42:38, 4.01s/it] 68%|██████▊ | 12666/18688 [14:53:58<6:59:30, 4.18s/it] 68%|██████▊ | 12667/18688 [14:54:03<6:59:16, 4.18s/it] 68%|██████▊ | 12668/18688 [14:54:07<6:52:23, 4.11s/it] 68%|██████▊ | 12669/18688 [14:54:11<6:58:52, 4.18s/it] 68%|██████▊ | 12670/18688 [14:54:15<6:58:18, 4.17s/it] {'loss': '0.927', 'grad_norm': '1.168', 'learning_rate': '2.733e-05', 'epoch': '0.678', 'num_input_tokens_seen': 42579744, 'train_runtime': '5.366e+04', 'train_tokens_per_second': '793.6'}
68%|██████▊ | 12670/18688 [14:54:15<6:58:18, 4.17s/it] 68%|██████▊ | 12671/18688 [14:54:20<7:22:42, 4.41s/it] 68%|██████▊ | 12672/18688 [14:54:24<6:58:03, 4.17s/it] 68%|██████▊ | 12673/18688 [14:54:28<7:06:55, 4.26s/it] 68%|██████▊ | 12674/18688 [14:54:33<7:23:26, 4.42s/it] 68%|██████▊ | 12675/18688 [14:54:37<7:20:14, 4.39s/it] 68%|██████▊ | 12676/18688 [14:54:41<6:58:49, 4.18s/it] 68%|██████▊ | 12677/18688 [14:54:46<7:14:30, 4.34s/it] 68%|██████▊ | 12678/18688 [14:54:50<7:16:20, 4.36s/it] 68%|██████▊ | 12679/18688 [14:54:54<7:16:49, 4.36s/it] 68%|██████▊ | 12680/18688 [14:54:58<6:53:33, 4.13s/it] {'loss': '0.9842', 'grad_norm': '1.291', 'learning_rate': '2.725e-05', 'epoch': '0.6785', 'num_input_tokens_seen': 42615848, 'train_runtime': '5.37e+04', 'train_tokens_per_second': '793.6'}
68%|██████▊ | 12680/18688 [14:54:58<6:53:33, 4.13s/it] 68%|██████▊ | 12681/18688 [14:55:02<6:58:33, 4.18s/it] 68%|██████▊ | 12682/18688 [14:55:06<6:55:40, 4.15s/it] 68%|██████▊ | 12683/18688 [14:55:10<6:52:25, 4.12s/it] 68%|██████▊ | 12684/18688 [14:55:14<6:36:51, 3.97s/it] 68%|██████▊ | 12685/18688 [14:55:19<7:01:25, 4.21s/it] 68%|██████▊ | 12686/18688 [14:55:24<7:25:28, 4.45s/it] 68%|██████▊ | 12687/18688 [14:55:28<7:03:38, 4.24s/it] 68%|██████▊ | 12688/18688 [14:55:31<6:50:37, 4.11s/it] 68%|██████▊ | 12689/18688 [14:55:37<7:23:45, 4.44s/it] 68%|██████▊ | 12690/18688 [14:55:41<7:25:35, 4.46s/it] {'loss': '0.9271', 'grad_norm': '1.37', 'learning_rate': '2.716e-05', 'epoch': '0.6791', 'num_input_tokens_seen': 42649984, 'train_runtime': '5.374e+04', 'train_tokens_per_second': '793.6'}
68%|██████▊ | 12690/18688 [14:55:41<7:25:35, 4.46s/it] 68%|██████▊ | 12691/18688 [14:55:45<7:02:57, 4.23s/it] 68%|██████▊ | 12692/18688 [14:55:49<7:08:45, 4.29s/it] 68%|██████▊ | 12693/18688 [14:55:54<7:17:22, 4.38s/it] 68%|██████▊ | 12694/18688 [14:55:59<7:29:31, 4.50s/it] 68%|██████▊ | 12695/18688 [14:56:04<7:53:52, 4.74s/it] 68%|██████▊ | 12696/18688 [14:56:09<7:49:22, 4.70s/it] 68%|██████▊ | 12697/18688 [14:56:14<8:07:12, 4.88s/it] 68%|██████▊ | 12698/18688 [14:56:18<7:56:49, 4.78s/it] 68%|██████▊ | 12699/18688 [14:56:24<8:09:39, 4.91s/it] 68%|██████▊ | 12700/18688 [14:56:29<8:13:02, 4.94s/it] {'loss': '0.937', 'grad_norm': '1.255', 'learning_rate': '2.708e-05', 'epoch': '0.6796', 'num_input_tokens_seen': 42681544, 'train_runtime': '5.379e+04', 'train_tokens_per_second': '793.5'}
68%|██████▊ | 12700/18688 [14:56:29<8:13:02, 4.94s/it] 68%|██████▊ | 12701/18688 [14:56:35<8:45:07, 5.26s/it] 68%|██████▊ | 12702/18688 [14:56:40<8:48:12, 5.29s/it] 68%|██████▊ | 12703/18688 [14:56:45<8:26:07, 5.07s/it] 68%|██████▊ | 12704/18688 [14:56:49<8:23:28, 5.05s/it] 68%|██████▊ | 12705/18688 [14:56:55<8:28:40, 5.10s/it] 68%|██████▊ | 12706/18688 [14:56:59<8:12:55, 4.94s/it] 68%|██████▊ | 12707/18688 [14:57:05<8:43:32, 5.25s/it] 68%|██████▊ | 12708/18688 [14:57:10<8:27:58, 5.10s/it] 68%|██████▊ | 12709/18688 [14:57:15<8:22:36, 5.04s/it] 68%|██████▊ | 12710/18688 [14:57:20<8:22:45, 5.05s/it] {'loss': '0.8563', 'grad_norm': '1.198', 'learning_rate': '2.7e-05', 'epoch': '0.6801', 'num_input_tokens_seen': 42713200, 'train_runtime': '5.384e+04', 'train_tokens_per_second': '793.3'}
68%|██████▊ | 12710/18688 [14:57:20<8:22:45, 5.05s/it] 68%|██████▊ | 12711/18688 [14:57:25<8:14:51, 4.97s/it] 68%|██████▊ | 12712/18688 [14:57:30<8:26:03, 5.08s/it] 68%|██████▊ | 12713/18688 [14:57:35<8:20:19, 5.02s/it] 68%|██████▊ | 12714/18688 [14:57:39<8:02:16, 4.84s/it] 68%|██████▊ | 12715/18688 [14:57:45<8:19:51, 5.02s/it] 68%|██████▊ | 12716/18688 [14:57:50<8:14:57, 4.97s/it] 68%|██████▊ | 12717/18688 [14:57:55<8:12:25, 4.95s/it] 68%|██████▊ | 12718/18688 [14:57:58<7:30:37, 4.53s/it] 68%|██████▊ | 12719/18688 [14:58:03<7:26:09, 4.48s/it] 68%|██████▊ | 12720/18688 [14:58:07<7:24:29, 4.47s/it] {'loss': '0.9036', 'grad_norm': '1.517', 'learning_rate': '2.692e-05', 'epoch': '0.6807', 'num_input_tokens_seen': 42746688, 'train_runtime': '5.389e+04', 'train_tokens_per_second': '793.3'}
68%|██████▊ | 12720/18688 [14:58:07<7:24:29, 4.47s/it] 68%|██████▊ | 12721/18688 [14:58:12<7:29:22, 4.52s/it] 68%|██████▊ | 12722/18688 [14:58:17<7:43:20, 4.66s/it] 68%|██████▊ | 12723/18688 [14:58:21<7:35:11, 4.58s/it] 68%|██████▊ | 12724/18688 [14:58:25<7:08:25, 4.31s/it] 68%|██████▊ | 12725/18688 [14:58:29<6:54:30, 4.17s/it] 68%|██████▊ | 12726/18688 [14:58:33<7:10:08, 4.33s/it] 68%|██████▊ | 12727/18688 [14:58:37<6:47:50, 4.11s/it] 68%|██████▊ | 12728/18688 [14:58:40<6:35:06, 3.98s/it] 68%|██████▊ | 12729/18688 [14:58:45<6:37:49, 4.01s/it] 68%|██████▊ | 12730/18688 [14:58:49<6:48:16, 4.11s/it] {'loss': '0.8926', 'grad_norm': '1.445', 'learning_rate': '2.684e-05', 'epoch': '0.6812', 'num_input_tokens_seen': 42779592, 'train_runtime': '5.393e+04', 'train_tokens_per_second': '793.3'}
68%|██████▊ | 12730/18688 [14:58:49<6:48:16, 4.11s/it] 68%|██████▊ | 12731/18688 [14:58:53<6:37:56, 4.01s/it] 68%|██████▊ | 12732/18688 [14:58:56<6:29:32, 3.92s/it] 68%|██████▊ | 12733/18688 [14:59:01<6:45:05, 4.08s/it] 68%|██████▊ | 12734/18688 [14:59:06<7:06:23, 4.30s/it] 68%|██████▊ | 12735/18688 [14:59:09<6:43:56, 4.07s/it] 68%|██████▊ | 12736/18688 [14:59:13<6:27:13, 3.90s/it] 68%|██████▊ | 12737/18688 [14:59:17<6:33:02, 3.96s/it] 68%|██████▊ | 12738/18688 [14:59:21<6:38:04, 4.01s/it] 68%|██████▊ | 12739/18688 [14:59:26<7:09:44, 4.33s/it] 68%|██████▊ | 12740/18688 [14:59:30<7:01:50, 4.26s/it] {'loss': '0.9123', 'grad_norm': '1.112', 'learning_rate': '2.676e-05', 'epoch': '0.6817', 'num_input_tokens_seen': 42812264, 'train_runtime': '5.397e+04', 'train_tokens_per_second': '793.3'}
68%|██████▊ | 12740/18688 [14:59:30<7:01:50, 4.26s/it] 68%|██████▊ | 12741/18688 [14:59:34<6:47:16, 4.11s/it] 68%|██████▊ | 12742/18688 [14:59:38<6:34:43, 3.98s/it] 68%|██████▊ | 12743/18688 [14:59:41<6:31:44, 3.95s/it] 68%|██████▊ | 12744/18688 [14:59:45<6:23:42, 3.87s/it] 68%|██████▊ | 12745/18688 [14:59:50<6:48:06, 4.12s/it] 68%|██████▊ | 12746/18688 [14:59:55<7:07:24, 4.32s/it] 68%|██████▊ | 12747/18688 [14:59:58<6:43:19, 4.07s/it] 68%|██████▊ | 12748/18688 [15:00:02<6:35:12, 3.99s/it] 68%|██████▊ | 12749/18688 [15:00:06<6:40:18, 4.04s/it] 68%|██████▊ | 12750/18688 [15:00:10<6:35:38, 4.00s/it] {'loss': '0.8889', 'grad_norm': '1.294', 'learning_rate': '2.668e-05', 'epoch': '0.6823', 'num_input_tokens_seen': 42843488, 'train_runtime': '5.401e+04', 'train_tokens_per_second': '793.2'}
68%|██████▊ | 12750/18688 [15:00:10<6:35:38, 4.00s/it] 68%|██████▊ | 12751/18688 [15:00:14<6:40:06, 4.04s/it] 68%|██████▊ | 12752/18688 [15:00:18<6:43:47, 4.08s/it] 68%|██████▊ | 12753/18688 [15:00:23<7:08:12, 4.33s/it] 68%|██████▊ | 12754/18688 [15:00:27<6:43:33, 4.08s/it] 68%|██████▊ | 12755/18688 [15:00:31<6:54:07, 4.19s/it] 68%|██████▊ | 12756/18688 [15:00:36<7:02:23, 4.27s/it] 68%|██████▊ | 12757/18688 [15:00:40<6:56:16, 4.21s/it] 68%|██████▊ | 12758/18688 [15:00:44<6:53:57, 4.19s/it] 68%|██████▊ | 12759/18688 [15:00:48<6:40:54, 4.06s/it] 68%|██████▊ | 12760/18688 [15:00:51<6:36:30, 4.01s/it] {'loss': '0.9189', 'grad_norm': '1.329', 'learning_rate': '2.66e-05', 'epoch': '0.6828', 'num_input_tokens_seen': 42876112, 'train_runtime': '5.405e+04', 'train_tokens_per_second': '793.2'}
68%|██████▊ | 12760/18688 [15:00:51<6:36:30, 4.01s/it] 68%|██████▊ | 12761/18688 [15:00:56<6:54:39, 4.20s/it] 68%|██████▊ | 12762/18688 [15:01:01<7:25:04, 4.51s/it] 68%|██████▊ | 12763/18688 [15:01:05<7:14:46, 4.40s/it] 68%|██████▊ | 12764/18688 [15:01:10<7:17:11, 4.43s/it] 68%|██████▊ | 12765/18688 [15:01:14<6:58:43, 4.24s/it] 68%|██████▊ | 12766/18688 [15:01:18<7:11:06, 4.37s/it] 68%|██████▊ | 12767/18688 [15:01:23<7:14:09, 4.40s/it] 68%|██████▊ | 12768/18688 [15:01:26<6:47:17, 4.13s/it] 68%|██████▊ | 12769/18688 [15:01:31<7:04:28, 4.30s/it] 68%|██████▊ | 12770/18688 [15:01:36<7:08:46, 4.35s/it] {'loss': '0.9661', 'grad_norm': '1.275', 'learning_rate': '2.652e-05', 'epoch': '0.6833', 'num_input_tokens_seen': 42912456, 'train_runtime': '5.41e+04', 'train_tokens_per_second': '793.3'}
68%|██████▊ | 12770/18688 [15:01:36<7:08:46, 4.35s/it] 68%|██████▊ | 12771/18688 [15:01:39<6:45:50, 4.12s/it] 68%|██████▊ | 12772/18688 [15:01:43<6:40:31, 4.06s/it] 68%|██████▊ | 12773/18688 [15:01:47<6:47:55, 4.14s/it] 68%|██████▊ | 12774/18688 [15:01:52<7:10:36, 4.37s/it] 68%|██████▊ | 12775/18688 [15:01:57<7:18:03, 4.45s/it] 68%|██████▊ | 12776/18688 [15:02:02<7:48:57, 4.76s/it] 68%|██████▊ | 12777/18688 [15:02:06<7:28:37, 4.55s/it] 68%|██████▊ | 12778/18688 [15:02:10<7:01:51, 4.28s/it] 68%|██████▊ | 12779/18688 [15:02:15<7:19:40, 4.46s/it] 68%|██████▊ | 12780/18688 [15:02:19<7:01:45, 4.28s/it] {'loss': '0.9342', 'grad_norm': '1.104', 'learning_rate': '2.644e-05', 'epoch': '0.6839', 'num_input_tokens_seen': 42946800, 'train_runtime': '5.414e+04', 'train_tokens_per_second': '793.3'}
68%|██████▊ | 12780/18688 [15:02:19<7:01:45, 4.28s/it] 68%|██████▊ | 12781/18688 [15:02:23<6:50:50, 4.17s/it] 68%|██████▊ | 12782/18688 [15:02:27<6:53:41, 4.20s/it] 68%|██████▊ | 12783/18688 [15:02:31<6:52:59, 4.20s/it] 68%|██████▊ | 12784/18688 [15:02:36<7:15:48, 4.43s/it] 68%|██████▊ | 12785/18688 [15:02:40<7:03:22, 4.30s/it] 68%|██████▊ | 12786/18688 [15:02:44<6:53:58, 4.21s/it] 68%|██████▊ | 12787/18688 [15:02:48<6:38:28, 4.05s/it] 68%|██████▊ | 12788/18688 [15:02:52<6:36:18, 4.03s/it] 68%|██████▊ | 12789/18688 [15:02:56<6:24:48, 3.91s/it] 68%|██████▊ | 12790/18688 [15:03:00<6:30:25, 3.97s/it] {'loss': '0.9568', 'grad_norm': '1.612', 'learning_rate': '2.635e-05', 'epoch': '0.6844', 'num_input_tokens_seen': 42977776, 'train_runtime': '5.418e+04', 'train_tokens_per_second': '793.2'}
68%|██████▊ | 12790/18688 [15:03:00<6:30:25, 3.97s/it] 68%|██████▊ | 12791/18688 [15:03:04<6:38:54, 4.06s/it] 68%|██████▊ | 12792/18688 [15:03:08<6:27:30, 3.94s/it] 68%|██████▊ | 12793/18688 [15:03:12<6:28:44, 3.96s/it] 68%|██████▊ | 12794/18688 [15:03:16<6:43:35, 4.11s/it] 68%|██████▊ | 12795/18688 [15:03:20<6:38:36, 4.06s/it] 68%|██████▊ | 12796/18688 [15:03:24<6:47:57, 4.15s/it] 68%|██████▊ | 12797/18688 [15:03:29<6:50:22, 4.18s/it] 68%|██████▊ | 12798/18688 [15:03:34<7:21:33, 4.50s/it] 68%|██████▊ | 12799/18688 [15:03:38<7:22:35, 4.51s/it] 68%|██████▊ | 12800/18688 [15:03:42<6:57:54, 4.26s/it] {'loss': '0.905', 'grad_norm': '1.116', 'learning_rate': '2.627e-05', 'epoch': '0.6849', 'num_input_tokens_seen': 43011528, 'train_runtime': '5.422e+04', 'train_tokens_per_second': '793.2'}
68%|██████▊ | 12800/18688 [15:03:42<6:57:54, 4.26s/it] 68%|██████▊ | 12801/18688 [15:03:46<6:46:16, 4.14s/it] 69%|██████▊ | 12802/18688 [15:03:50<6:35:45, 4.03s/it] 69%|██████▊ | 12803/18688 [15:03:54<6:45:57, 4.14s/it] 69%|██████▊ | 12804/18688 [15:03:58<6:39:34, 4.07s/it] 69%|██████▊ | 12805/18688 [15:04:02<6:36:01, 4.04s/it] 69%|██████▊ | 12806/18688 [15:04:06<6:23:03, 3.91s/it] 69%|██████▊ | 12807/18688 [15:04:10<6:48:10, 4.16s/it] 69%|██████▊ | 12808/18688 [15:04:14<6:47:19, 4.16s/it] 69%|██████▊ | 12809/18688 [15:04:18<6:42:59, 4.11s/it] 69%|██████▊ | 12810/18688 [15:04:23<6:41:35, 4.10s/it] {'loss': '0.9174', 'grad_norm': '1.517', 'learning_rate': '2.619e-05', 'epoch': '0.6855', 'num_input_tokens_seen': 43043032, 'train_runtime': '5.426e+04', 'train_tokens_per_second': '793.2'}
69%|██████▊ | 12810/18688 [15:04:23<6:41:35, 4.10s/it] 69%|██████▊ | 12811/18688 [15:04:27<7:04:17, 4.33s/it] 69%|██████▊ | 12812/18688 [15:04:32<7:06:24, 4.35s/it] 69%|██████▊ | 12813/18688 [15:04:36<7:09:59, 4.39s/it] 69%|██████▊ | 12814/18688 [15:04:40<6:44:28, 4.13s/it] 69%|██████▊ | 12815/18688 [15:04:44<6:39:48, 4.08s/it] 69%|██████▊ | 12816/18688 [15:04:48<6:30:28, 3.99s/it] 69%|██████▊ | 12817/18688 [15:04:52<6:46:47, 4.16s/it] 69%|██████▊ | 12818/18688 [15:04:56<6:47:24, 4.16s/it] 69%|██████▊ | 12819/18688 [15:05:00<6:37:57, 4.07s/it] 69%|██████▊ | 12820/18688 [15:05:05<7:03:38, 4.33s/it] {'loss': '0.9202', 'grad_norm': '1.448', 'learning_rate': '2.611e-05', 'epoch': '0.686', 'num_input_tokens_seen': 43077904, 'train_runtime': '5.431e+04', 'train_tokens_per_second': '793.2'}
69%|██████▊ | 12820/18688 [15:05:05<7:03:38, 4.33s/it] 69%|██████▊ | 12821/18688 [15:05:10<7:22:50, 4.53s/it] 69%|██████▊ | 12822/18688 [15:05:14<7:10:46, 4.41s/it] 69%|██████▊ | 12823/18688 [15:05:18<7:03:04, 4.33s/it] 69%|██████▊ | 12824/18688 [15:05:22<6:48:31, 4.18s/it] 69%|██████▊ | 12825/18688 [15:05:26<6:34:13, 4.03s/it] 69%|██████▊ | 12826/18688 [15:05:30<6:51:52, 4.22s/it] 69%|██████▊ | 12827/18688 [15:05:35<6:48:25, 4.18s/it] 69%|██████▊ | 12828/18688 [15:05:39<6:44:10, 4.14s/it] 69%|██████▊ | 12829/18688 [15:05:42<6:30:51, 4.00s/it] 69%|██████▊ | 12830/18688 [15:05:46<6:33:08, 4.03s/it] {'loss': '0.8637', 'grad_norm': '1.285', 'learning_rate': '2.603e-05', 'epoch': '0.6866', 'num_input_tokens_seen': 43110640, 'train_runtime': '5.435e+04', 'train_tokens_per_second': '793.2'}
69%|██████▊ | 12830/18688 [15:05:46<6:33:08, 4.03s/it] 69%|██████▊ | 12831/18688 [15:05:51<6:41:41, 4.11s/it] 69%|██████▊ | 12832/18688 [15:05:56<7:01:23, 4.32s/it] 69%|██████▊ | 12833/18688 [15:06:00<6:58:46, 4.29s/it] 69%|██████▊ | 12834/18688 [15:06:03<6:34:55, 4.05s/it] 69%|██████▊ | 12835/18688 [15:06:07<6:26:46, 3.96s/it] 69%|██████▊ | 12836/18688 [15:06:11<6:34:01, 4.04s/it] 69%|██████▊ | 12837/18688 [15:06:15<6:20:49, 3.91s/it] 69%|██████▊ | 12838/18688 [15:06:19<6:33:18, 4.03s/it] 69%|██████▊ | 12839/18688 [15:06:23<6:24:44, 3.95s/it] 69%|██████▊ | 12840/18688 [15:06:27<6:34:25, 4.05s/it] {'loss': '0.8941', 'grad_norm': '1.19', 'learning_rate': '2.595e-05', 'epoch': '0.6871', 'num_input_tokens_seen': 43141536, 'train_runtime': '5.439e+04', 'train_tokens_per_second': '793.2'}
69%|██████▊ | 12840/18688 [15:06:27<6:34:25, 4.05s/it] 69%|██████▊ | 12841/18688 [15:06:31<6:31:24, 4.02s/it] 69%|██████▊ | 12842/18688 [15:06:36<6:53:20, 4.24s/it] 69%|██████▊ | 12843/18688 [15:06:40<6:38:45, 4.09s/it] 69%|██████▊ | 12844/18688 [15:06:44<6:46:41, 4.18s/it] 69%|██████▊ | 12845/18688 [15:06:49<7:00:50, 4.32s/it] 69%|██████▊ | 12846/18688 [15:06:52<6:42:26, 4.13s/it] 69%|██████▊ | 12847/18688 [15:06:57<7:00:30, 4.32s/it] 69%|██████▉ | 12848/18688 [15:07:02<7:10:25, 4.42s/it] 69%|██████▉ | 12849/18688 [15:07:06<7:05:23, 4.37s/it] 69%|██████▉ | 12850/18688 [15:07:11<7:16:42, 4.49s/it] {'loss': '0.8947', 'grad_norm': '1.146', 'learning_rate': '2.587e-05', 'epoch': '0.6876', 'num_input_tokens_seen': 43176720, 'train_runtime': '5.443e+04', 'train_tokens_per_second': '793.2'}
69%|██████▉ | 12850/18688 [15:07:11<7:16:42, 4.49s/it] 69%|██████▉ | 12851/18688 [15:07:14<6:53:04, 4.25s/it] 69%|██████▉ | 12852/18688 [15:07:19<6:52:53, 4.24s/it] 69%|██████▉ | 12853/18688 [15:07:23<6:50:36, 4.22s/it] 69%|██████▉ | 12854/18688 [15:07:27<6:39:51, 4.11s/it] 69%|██████▉ | 12855/18688 [15:07:31<6:33:14, 4.04s/it] 69%|██████▉ | 12856/18688 [15:07:34<6:24:53, 3.96s/it] 69%|██████▉ | 12857/18688 [15:07:39<6:32:01, 4.03s/it] 69%|██████▉ | 12858/18688 [15:07:43<6:30:56, 4.02s/it] 69%|██████▉ | 12859/18688 [15:07:48<7:03:58, 4.36s/it] 69%|██████▉ | 12860/18688 [15:07:52<6:47:57, 4.20s/it] {'loss': '0.9512', 'grad_norm': '1.465', 'learning_rate': '2.579e-05', 'epoch': '0.6882', 'num_input_tokens_seen': 43208576, 'train_runtime': '5.447e+04', 'train_tokens_per_second': '793.2'}
69%|██████▉ | 12860/18688 [15:07:52<6:47:57, 4.20s/it] 69%|██████▉ | 12861/18688 [15:07:55<6:39:25, 4.11s/it] 69%|██████▉ | 12862/18688 [15:07:59<6:26:38, 3.98s/it] 69%|██████▉ | 12863/18688 [15:08:03<6:19:26, 3.91s/it] 69%|██████▉ | 12864/18688 [15:08:07<6:14:23, 3.86s/it] 69%|██████▉ | 12865/18688 [15:08:10<6:06:17, 3.77s/it] 69%|██████▉ | 12866/18688 [15:08:14<6:17:15, 3.89s/it] 69%|██████▉ | 12867/18688 [15:08:19<6:34:19, 4.06s/it] 69%|██████▉ | 12868/18688 [15:08:22<6:22:45, 3.95s/it] 69%|██████▉ | 12869/18688 [15:08:27<6:27:30, 4.00s/it] 69%|██████▉ | 12870/18688 [15:08:31<6:36:52, 4.09s/it] {'loss': '0.8923', 'grad_norm': '1.219', 'learning_rate': '2.571e-05', 'epoch': '0.6887', 'num_input_tokens_seen': 43239616, 'train_runtime': '5.451e+04', 'train_tokens_per_second': '793.2'}
69%|██████▉ | 12870/18688 [15:08:31<6:36:52, 4.09s/it] 69%|██████▉ | 12871/18688 [15:08:35<6:25:03, 3.97s/it] 69%|██████▉ | 12872/18688 [15:08:39<6:41:26, 4.14s/it] 69%|██████▉ | 12873/18688 [15:08:44<6:49:23, 4.22s/it] 69%|██████▉ | 12874/18688 [15:08:48<6:48:03, 4.21s/it] 69%|██████▉ | 12875/18688 [15:08:52<6:39:05, 4.12s/it] 69%|██████▉ | 12876/18688 [15:08:55<6:26:40, 3.99s/it] 69%|██████▉ | 12877/18688 [15:08:59<6:26:29, 3.99s/it] 69%|██████▉ | 12878/18688 [15:09:03<6:28:52, 4.02s/it] 69%|██████▉ | 12879/18688 [15:09:09<7:00:38, 4.34s/it] 69%|██████▉ | 12880/18688 [15:09:13<7:11:33, 4.46s/it] {'loss': '0.9268', 'grad_norm': '1.21', 'learning_rate': '2.563e-05', 'epoch': '0.6892', 'num_input_tokens_seen': 43273640, 'train_runtime': '5.455e+04', 'train_tokens_per_second': '793.2'}
69%|██████▉ | 12880/18688 [15:09:13<7:11:33, 4.46s/it] 69%|██████▉ | 12881/18688 [15:09:17<6:45:12, 4.19s/it] 69%|██████▉ | 12882/18688 [15:09:21<6:31:21, 4.04s/it] 69%|██████▉ | 12883/18688 [15:09:25<6:35:46, 4.09s/it] 69%|██████▉ | 12884/18688 [15:09:29<6:34:44, 4.08s/it] 69%|██████▉ | 12885/18688 [15:09:32<6:17:50, 3.91s/it] 69%|██████▉ | 12886/18688 [15:09:36<6:22:38, 3.96s/it] 69%|██████▉ | 12887/18688 [15:09:41<6:47:26, 4.21s/it] 69%|██████▉ | 12888/18688 [15:09:46<6:58:01, 4.32s/it] 69%|██████▉ | 12889/18688 [15:09:50<6:59:40, 4.34s/it] 69%|██████▉ | 12890/18688 [15:09:55<7:20:04, 4.55s/it] {'loss': '0.969', 'grad_norm': '1.259', 'learning_rate': '2.555e-05', 'epoch': '0.6898', 'num_input_tokens_seen': 43307600, 'train_runtime': '5.46e+04', 'train_tokens_per_second': '793.2'}
69%|██████▉ | 12890/18688 [15:09:55<7:20:04, 4.55s/it] 69%|██████▉ | 12891/18688 [15:09:59<7:00:14, 4.35s/it] 69%|██████▉ | 12892/18688 [15:10:03<6:47:11, 4.22s/it] 69%|██████▉ | 12893/18688 [15:10:07<6:29:04, 4.03s/it] 69%|██████▉ | 12894/18688 [15:10:11<6:47:44, 4.22s/it] 69%|██████▉ | 12895/18688 [15:10:15<6:33:38, 4.08s/it] 69%|██████▉ | 12896/18688 [15:10:19<6:29:57, 4.04s/it] 69%|██████▉ | 12897/18688 [15:10:23<6:19:19, 3.93s/it] 69%|██████▉ | 12898/18688 [15:10:28<6:58:53, 4.34s/it] 69%|██████▉ | 12899/18688 [15:10:34<7:52:43, 4.90s/it] 69%|██████▉ | 12900/18688 [15:10:38<7:28:19, 4.65s/it] {'loss': '0.9098', 'grad_norm': '1.387', 'learning_rate': '2.547e-05', 'epoch': '0.6903', 'num_input_tokens_seen': 43342592, 'train_runtime': '5.464e+04', 'train_tokens_per_second': '793.3'}
69%|██████▉ | 12900/18688 [15:10:38<7:28:19, 4.65s/it] 69%|██████▉ | 12901/18688 [15:10:42<7:09:04, 4.45s/it] 69%|██████▉ | 12902/18688 [15:10:46<6:52:18, 4.28s/it] 69%|██████▉ | 12903/18688 [15:10:50<6:30:55, 4.05s/it] 69%|██████▉ | 12904/18688 [15:10:54<6:46:23, 4.22s/it] 69%|██████▉ | 12905/18688 [15:10:58<6:37:32, 4.12s/it] 69%|██████▉ | 12906/18688 [15:11:02<6:20:37, 3.95s/it] 69%|██████▉ | 12907/18688 [15:11:06<6:20:28, 3.95s/it] 69%|██████▉ | 12908/18688 [15:11:09<6:20:13, 3.95s/it] 69%|██████▉ | 12909/18688 [15:11:13<6:07:18, 3.81s/it] 69%|██████▉ | 12910/18688 [15:11:18<6:28:15, 4.03s/it] {'loss': '0.8493', 'grad_norm': '1.338', 'learning_rate': '2.539e-05', 'epoch': '0.6908', 'num_input_tokens_seen': 43373352, 'train_runtime': '5.468e+04', 'train_tokens_per_second': '793.2'}
69%|██████▉ | 12910/18688 [15:11:18<6:28:15, 4.03s/it] 69%|██████▉ | 12911/18688 [15:11:22<6:37:29, 4.13s/it] 69%|██████▉ | 12912/18688 [15:11:25<6:20:33, 3.95s/it] 69%|██████▉ | 12913/18688 [15:11:29<6:06:12, 3.80s/it] 69%|██████▉ | 12914/18688 [15:11:32<5:56:25, 3.70s/it] 69%|██████▉ | 12915/18688 [15:11:37<6:14:29, 3.89s/it] 69%|██████▉ | 12916/18688 [15:11:40<6:11:35, 3.86s/it] 69%|██████▉ | 12917/18688 [15:11:45<6:23:44, 3.99s/it] 69%|██████▉ | 12918/18688 [15:11:49<6:44:46, 4.21s/it] 69%|██████▉ | 12919/18688 [15:11:53<6:34:35, 4.10s/it] 69%|██████▉ | 12920/18688 [15:11:57<6:19:46, 3.95s/it] {'loss': '0.8911', 'grad_norm': '1.143', 'learning_rate': '2.531e-05', 'epoch': '0.6914', 'num_input_tokens_seen': 43402664, 'train_runtime': '5.472e+04', 'train_tokens_per_second': '793.2'}
69%|██████▉ | 12920/18688 [15:11:57<6:19:46, 3.95s/it] 69%|██████▉ | 12921/18688 [15:12:01<6:11:16, 3.86s/it] 69%|██████▉ | 12922/18688 [15:12:04<6:02:48, 3.78s/it] 69%|██████▉ | 12923/18688 [15:12:08<5:56:32, 3.71s/it] 69%|██████▉ | 12924/18688 [15:12:12<6:09:17, 3.84s/it] 69%|██████▉ | 12925/18688 [15:12:16<6:24:16, 4.00s/it] 69%|██████▉ | 12926/18688 [15:12:20<6:15:52, 3.91s/it] 69%|██████▉ | 12927/18688 [15:12:25<6:46:22, 4.23s/it] 69%|██████▉ | 12928/18688 [15:12:30<7:00:19, 4.38s/it] 69%|██████▉ | 12929/18688 [15:12:33<6:39:44, 4.16s/it] 69%|██████▉ | 12930/18688 [15:12:38<6:42:55, 4.20s/it] {'loss': '0.9031', 'grad_norm': '1.151', 'learning_rate': '2.524e-05', 'epoch': '0.6919', 'num_input_tokens_seen': 43435184, 'train_runtime': '5.476e+04', 'train_tokens_per_second': '793.2'}
69%|██████▉ | 12930/18688 [15:12:38<6:42:55, 4.20s/it] 69%|██████▉ | 12931/18688 [15:12:42<6:50:13, 4.28s/it] 69%|██████▉ | 12932/18688 [15:12:46<6:38:25, 4.15s/it] 69%|██████▉ | 12933/18688 [15:12:50<6:50:05, 4.28s/it] 69%|██████▉ | 12934/18688 [15:12:55<7:00:07, 4.38s/it] 69%|██████▉ | 12935/18688 [15:13:01<7:32:44, 4.72s/it] 69%|██████▉ | 12936/18688 [15:13:04<7:03:12, 4.41s/it] 69%|██████▉ | 12937/18688 [15:13:08<6:43:46, 4.21s/it] 69%|██████▉ | 12938/18688 [15:13:12<6:31:08, 4.08s/it] 69%|██████▉ | 12939/18688 [15:13:16<6:24:17, 4.01s/it] 69%|██████▉ | 12940/18688 [15:13:20<6:30:31, 4.08s/it] {'loss': '0.9289', 'grad_norm': '1.09', 'learning_rate': '2.516e-05', 'epoch': '0.6924', 'num_input_tokens_seen': 43470608, 'train_runtime': '5.48e+04', 'train_tokens_per_second': '793.3'}
69%|██████▉ | 12940/18688 [15:13:20<6:30:31, 4.08s/it] 69%|██████▉ | 12941/18688 [15:13:24<6:28:31, 4.06s/it] 69%|██████▉ | 12942/18688 [15:13:28<6:37:34, 4.15s/it] 69%|██████▉ | 12943/18688 [15:13:33<6:39:45, 4.17s/it] 69%|██████▉ | 12944/18688 [15:13:37<6:42:04, 4.20s/it] 69%|██████▉ | 12945/18688 [15:13:41<6:37:49, 4.16s/it] 69%|██████▉ | 12946/18688 [15:13:44<6:20:50, 3.98s/it] 69%|██████▉ | 12947/18688 [15:13:49<6:33:24, 4.11s/it] 69%|██████▉ | 12948/18688 [15:13:53<6:33:04, 4.11s/it] 69%|██████▉ | 12949/18688 [15:13:56<6:14:29, 3.92s/it] 69%|██████▉ | 12950/18688 [15:14:00<6:15:27, 3.93s/it] {'loss': '0.9082', 'grad_norm': '1.18', 'learning_rate': '2.508e-05', 'epoch': '0.693', 'num_input_tokens_seen': 43503288, 'train_runtime': '5.484e+04', 'train_tokens_per_second': '793.3'}
69%|██████▉ | 12950/18688 [15:14:00<6:15:27, 3.93s/it] 69%|██████▉ | 12951/18688 [15:14:06<7:06:04, 4.46s/it] 69%|██████▉ | 12952/18688 [15:14:10<6:40:36, 4.19s/it] 69%|██████▉ | 12953/18688 [15:14:13<6:27:56, 4.06s/it] 69%|██████▉ | 12954/18688 [15:14:18<6:57:17, 4.37s/it] 69%|██████▉ | 12955/18688 [15:14:22<6:45:52, 4.25s/it] 69%|██████▉ | 12956/18688 [15:14:27<7:02:09, 4.42s/it] 69%|██████▉ | 12957/18688 [15:14:31<6:52:56, 4.32s/it] 69%|██████▉ | 12958/18688 [15:14:35<6:41:48, 4.21s/it] 69%|██████▉ | 12959/18688 [15:14:39<6:25:37, 4.04s/it] 69%|██████▉ | 12960/18688 [15:14:43<6:29:35, 4.08s/it] {'loss': '0.9377', 'grad_norm': '1.108', 'learning_rate': '2.5e-05', 'epoch': '0.6935', 'num_input_tokens_seen': 43539128, 'train_runtime': '5.488e+04', 'train_tokens_per_second': '793.3'}
69%|██████▉ | 12960/18688 [15:14:43<6:29:35, 4.08s/it] 69%|██████▉ | 12961/18688 [15:14:47<6:16:59, 3.95s/it] 69%|██████▉ | 12962/18688 [15:14:52<6:44:20, 4.24s/it] 69%|██████▉ | 12963/18688 [15:14:57<7:04:19, 4.45s/it] 69%|██████▉ | 12964/18688 [15:15:00<6:36:39, 4.16s/it] 69%|██████▉ | 12965/18688 [15:15:04<6:37:45, 4.17s/it] 69%|██████▉ | 12966/18688 [15:15:08<6:35:43, 4.15s/it] 69%|██████▉ | 12967/18688 [15:15:14<7:07:45, 4.49s/it] 69%|██████▉ | 12968/18688 [15:15:17<6:49:51, 4.30s/it] 69%|██████▉ | 12969/18688 [15:15:22<6:51:59, 4.32s/it] 69%|██████▉ | 12970/18688 [15:15:25<6:30:51, 4.10s/it] {'loss': '0.8522', 'grad_norm': '1.189', 'learning_rate': '2.492e-05', 'epoch': '0.694', 'num_input_tokens_seen': 43573992, 'train_runtime': '5.493e+04', 'train_tokens_per_second': '793.3'}
69%|██████▉ | 12970/18688 [15:15:25<6:30:51, 4.10s/it] 69%|██████▉ | 12971/18688 [15:15:30<6:30:01, 4.09s/it] 69%|██████▉ | 12972/18688 [15:15:34<6:41:13, 4.21s/it] 69%|██████▉ | 12973/18688 [15:15:37<6:20:40, 4.00s/it] 69%|██████▉ | 12974/18688 [15:15:42<6:42:24, 4.23s/it] 69%|██████▉ | 12975/18688 [15:15:46<6:31:59, 4.12s/it] 69%|██████▉ | 12976/18688 [15:15:50<6:36:23, 4.16s/it] 69%|██████▉ | 12977/18688 [15:15:55<6:47:56, 4.29s/it] 69%|██████▉ | 12978/18688 [15:15:59<6:38:29, 4.19s/it] 69%|██████▉ | 12979/18688 [15:16:03<6:25:20, 4.05s/it] 69%|██████▉ | 12980/18688 [15:16:08<6:49:31, 4.30s/it] {'loss': '0.8447', 'grad_norm': '1.509', 'learning_rate': '2.484e-05', 'epoch': '0.6946', 'num_input_tokens_seen': 43608880, 'train_runtime': '5.497e+04', 'train_tokens_per_second': '793.4'}
69%|██████▉ | 12980/18688 [15:16:08<6:49:31, 4.30s/it] 69%|██████▉ | 12981/18688 [15:16:11<6:32:41, 4.13s/it] 69%|██████▉ | 12982/18688 [15:16:15<6:33:30, 4.14s/it] 69%|██████▉ | 12983/18688 [15:16:19<6:17:26, 3.97s/it] 69%|██████▉ | 12984/18688 [15:16:23<6:14:23, 3.94s/it] 69%|██████▉ | 12985/18688 [15:16:28<6:42:52, 4.24s/it] 69%|██████▉ | 12986/18688 [15:16:31<6:24:42, 4.05s/it] 69%|██████▉ | 12987/18688 [15:16:35<6:10:28, 3.90s/it] 69%|██████▉ | 12988/18688 [15:16:39<6:08:18, 3.88s/it] 70%|██████▉ | 12989/18688 [15:16:42<5:59:25, 3.78s/it] 70%|██████▉ | 12990/18688 [15:16:47<6:12:39, 3.92s/it] {'loss': '0.8845', 'grad_norm': '1.151', 'learning_rate': '2.476e-05', 'epoch': '0.6951', 'num_input_tokens_seen': 43640872, 'train_runtime': '5.501e+04', 'train_tokens_per_second': '793.4'}
70%|██████▉ | 12990/18688 [15:16:47<6:12:39, 3.92s/it] 70%|██████▉ | 12991/18688 [15:16:51<6:18:44, 3.99s/it] 70%|██████▉ | 12992/18688 [15:16:55<6:24:12, 4.05s/it] 70%|██████▉ | 12993/18688 [15:17:00<6:40:53, 4.22s/it] 70%|██████▉ | 12994/18688 [15:17:03<6:31:09, 4.12s/it] 70%|██████▉ | 12995/18688 [15:17:07<6:21:58, 4.03s/it] 70%|██████▉ | 12996/18688 [15:17:12<6:31:39, 4.13s/it] 70%|██████▉ | 12997/18688 [15:17:15<6:24:27, 4.05s/it] 70%|██████▉ | 12998/18688 [15:17:20<6:27:51, 4.09s/it] 70%|██████▉ | 12999/18688 [15:17:23<6:16:44, 3.97s/it] 70%|██████▉ | 13000/18688 [15:17:27<6:03:58, 3.84s/it] {'loss': '0.9363', 'grad_norm': '1.327', 'learning_rate': '2.468e-05', 'epoch': '0.6957', 'num_input_tokens_seen': 43672984, 'train_runtime': '5.505e+04', 'train_tokens_per_second': '793.4'}
70%|██████▉ | 13000/18688 [15:17:27<6:03:58, 3.84s/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, 18.23it/s]
1%| | 8/1000 [00:00<00:56, 17.48it/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<01:01, 15.95it/s]
2%|▏ | 16/1000 [00:00<01:02, 15.76it/s]
2%|▏ | 18/1000 [00:01<01:38, 9.99it/s]
2%|▏ | 20/1000 [00:01<01:33, 10.50it/s]
2%|▏ | 22/1000 [00:01<01:52, 8.71it/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, 10.15it/s]
3%|▎ | 34/1000 [00:02<01:25, 11.27it/s]
4%|▎ | 36/1000 [00:03<01:34, 10.20it/s]
4%|▍ | 38/1000 [00:03<01:28, 10.88it/s]
4%|▍ | 40/1000 [00:03<01:32, 10.33it/s]
4%|▍ | 42/1000 [00:03<01:54, 8.39it/s]
4%|▍ | 44/1000 [00:03<01:36, 9.87it/s]
5%|▍ | 46/1000 [00:04<01:27, 10.95it/s]
5%|▍ | 48/1000 [00:04<01:35, 9.93it/s]
5%|▌ | 50/1000 [00:04<01:27, 10.88it/s]
5%|▌ | 52/1000 [00:04<01:23, 11.30it/s]
5%|▌ | 54/1000 [00:04<01:18, 12.13it/s]
6%|▌ | 56/1000 [00:04<01:12, 13.03it/s]
6%|▌ | 58/1000 [00:05<01:51, 8.47it/s]
6%|▌ | 60/1000 [00:05<01:38, 9.54it/s]
6%|▌ | 62/1000 [00:05<01:29, 10.50it/s]
6%|▋ | 64/1000 [00:05<01:20, 11.61it/s]
7%|▋ | 66/1000 [00:05<01:16, 12.17it/s]
7%|▋ | 68/1000 [00:06<01:12, 12.78it/s]
7%|▋ | 70/1000 [00:06<01:09, 13.40it/s]
7%|▋ | 72/1000 [00:06<01:06, 13.87it/s]
7%|▋ | 74/1000 [00:06<01:07, 13.67it/s]
8%|▊ | 76/1000 [00:06<01:04, 14.27it/s]
8%|▊ | 78/1000 [00:06<01:03, 14.49it/s]
8%|▊ | 80/1000 [00:06<01:08, 13.48it/s]
8%|▊ | 82/1000 [00:06<01:05, 14.07it/s]
8%|▊ | 84/1000 [00:07<01:02, 14.55it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.68it/s]
9%|▉ | 88/1000 [00:07<01:02, 14.51it/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, 14.00it/s]
10%|▉ | 96/1000 [00:07<01:03, 14.31it/s]
10%|▉ | 98/1000 [00:08<01:03, 14.22it/s]
10%|█ | 100/1000 [00:08<01:31, 9.81it/s]
10%|█ | 102/1000 [00:08<01:26, 10.43it/s]
10%|█ | 104/1000 [00:09<01:58, 7.56it/s]
10%|█ | 105/1000 [00:09<02:07, 7.01it/s]
11%|█ | 106/1000 [00:09<02:04, 7.20it/s]
11%|█ | 108/1000 [00:09<01:40, 8.92it/s]
11%|█ | 110/1000 [00:09<01:23, 10.60it/s]
11%|█ | 112/1000 [00:09<01:15, 11.74it/s]
11%|█▏ | 114/1000 [00:09<01:11, 12.31it/s]
12%|█▏ | 116/1000 [00:10<01:08, 12.85it/s]
12%|█▏ | 118/1000 [00:10<01:05, 13.57it/s]
12%|█▏ | 120/1000 [00:10<01:08, 12.87it/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, 10.65it/s]
13%|█▎ | 128/1000 [00:11<01:15, 11.57it/s]
13%|█▎ | 130/1000 [00:11<01:11, 12.22it/s]
13%|█▎ | 132/1000 [00:11<01:05, 13.17it/s]
13%|█▎ | 134/1000 [00:11<01:09, 12.38it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.35it/s]
14%|█▍ | 138/1000 [00:11<01:04, 13.28it/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, 9.57it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.60it/s]
15%|█▍ | 148/1000 [00:12<01:14, 11.51it/s]
15%|█▌ | 150/1000 [00:13<01:07, 12.61it/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, 13.76it/s]
16%|█▌ | 160/1000 [00:13<01:01, 13.66it/s]
16%|█▌ | 162/1000 [00:13<01:01, 13.54it/s]
16%|█▋ | 164/1000 [00:14<01:01, 13.63it/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, 10.51it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.48it/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, 8.58it/s]
18%|█▊ | 184/1000 [00:16<01:23, 9.82it/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, 10.72it/s]
19%|█▉ | 192/1000 [00:16<01:11, 11.35it/s]
19%|█▉ | 194/1000 [00:17<01:18, 10.28it/s]
20%|█▉ | 196/1000 [00:17<01:34, 8.49it/s]
20%|█▉ | 198/1000 [00:17<01:23, 9.59it/s]
20%|██ | 200/1000 [00:17<01:19, 10.09it/s]
20%|██ | 202/1000 [00:18<01:12, 11.08it/s]
20%|██ | 204/1000 [00:18<01:07, 11.82it/s]
21%|██ | 206/1000 [00:18<01:02, 12.60it/s]
21%|██ | 208/1000 [00:18<01:00, 13.14it/s]
21%|██ | 210/1000 [00:18<00:56, 14.11it/s]
21%|██ | 212/1000 [00:18<00:54, 14.49it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.58it/s]
22%|██▏ | 216/1000 [00:18<00:54, 14.36it/s]
22%|██▏ | 218/1000 [00:19<00:52, 14.77it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.88it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.18it/s]
22%|██▏ | 224/1000 [00:19<01:20, 9.65it/s]
23%|██▎ | 226/1000 [00:20<01:12, 10.64it/s]
23%|██▎ | 228/1000 [00:20<01:06, 11.55it/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, 13.11it/s]
24%|██▎ | 236/1000 [00:20<00:56, 13.61it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.26it/s]
24%|██▍ | 240/1000 [00:20<00:53, 14.25it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.82it/s]
24%|██▍ | 244/1000 [00:21<00:50, 15.02it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.97it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.68it/s]
25%|██▌ | 250/1000 [00:21<00:55, 13.42it/s]
25%|██▌ | 252/1000 [00:21<01:01, 12.17it/s]
25%|██▌ | 254/1000 [00:22<00:56, 13.17it/s]
26%|██▌ | 256/1000 [00:22<00:54, 13.75it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.80it/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 [00:22<00:52, 14.00it/s]
27%|██▋ | 268/1000 [00:23<00:50, 14.57it/s]
27%|██▋ | 270/1000 [00:23<00:54, 13.29it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.66it/s]
27%|██▋ | 274/1000 [00:23<01:01, 11.71it/s]
28%|██▊ | 276/1000 [00:23<00:56, 12.73it/s]
28%|██▊ | 278/1000 [00:23<00:55, 12.94it/s]
28%|██▊ | 280/1000 [00:24<00:53, 13.52it/s]
28%|██▊ | 282/1000 [00:24<00:49, 14.49it/s]
28%|██▊ | 284/1000 [00:24<00:49, 14.43it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.71it/s]
29%|██▉ | 288/1000 [00:24<00:47, 15.05it/s]
29%|██▉ | 290/1000 [00:24<00:45, 15.51it/s]
29%|██▉ | 292/1000 [00:24<00:48, 14.66it/s]
29%|██▉ | 294/1000 [00:24<00:50, 13.92it/s]
30%|██▉ | 296/1000 [00:25<00:52, 13.43it/s]
30%|██▉ | 298/1000 [00:25<00:53, 13.19it/s]
30%|███ | 300/1000 [00:25<00:54, 12.95it/s]
30%|███ | 302/1000 [00:25<00:54, 12.79it/s]
30%|███ | 304/1000 [00:25<00:58, 11.98it/s]
31%|███ | 306/1000 [00:25<00:57, 12.16it/s]
31%|███ | 308/1000 [00:26<00:56, 12.23it/s]
31%|███ | 310/1000 [00:26<00:56, 12.25it/s]
31%|███ | 312/1000 [00:26<00:55, 12.34it/s]
31%|███▏ | 314/1000 [00:26<00:54, 12.69it/s]
32%|███▏ | 316/1000 [00:26<00:50, 13.64it/s]
32%|███▏ | 318/1000 [00:26<00:46, 14.57it/s]
32%|███▏ | 320/1000 [00:27<01:15, 8.98it/s]
32%|███▏ | 322/1000 [00:27<01:05, 10.36it/s]
32%|███▏ | 324/1000 [00:27<01:00, 11.19it/s]
33%|███▎ | 326/1000 [00:27<00:59, 11.26it/s]
33%|███▎ | 328/1000 [00:27<00:59, 11.37it/s]
33%|███▎ | 330/1000 [00:27<00:54, 12.36it/s]
33%|███▎ | 332/1000 [00:28<00:52, 12.64it/s]
33%|███▎ | 334/1000 [00:28<00:50, 13.14it/s]
34%|███▎ | 336/1000 [00:28<00:49, 13.55it/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, 12.40it/s]
34%|███▍ | 344/1000 [00:29<00:50, 13.05it/s]
35%|███▍ | 346/1000 [00:29<00:47, 13.66it/s]
35%|███▍ | 348/1000 [00:29<00:46, 14.09it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.73it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.92it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.91it/s]
36%|███▌ | 356/1000 [00:29<00:42, 15.00it/s]
36%|███▌ | 358/1000 [00:30<00:43, 14.67it/s]
36%|███▌ | 360/1000 [00:30<00:42, 15.04it/s]
36%|███▌ | 362/1000 [00:30<00:47, 13.37it/s]
36%|███▋ | 364/1000 [00:30<00:45, 13.88it/s]
37%|███▋ | 366/1000 [00:30<00:52, 11.97it/s]
37%|███▋ | 368/1000 [00:30<00:51, 12.36it/s]
37%|███▋ | 370/1000 [00:30<00:47, 13.20it/s]
37%|███▋ | 372/1000 [00:31<00:46, 13.53it/s]
37%|███▋ | 374/1000 [00:31<00:55, 11.35it/s]
38%|███▊ | 376/1000 [00:31<00:57, 10.95it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.63it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.45it/s]
38%|███▊ | 382/1000 [00:31<00:48, 12.65it/s]
38%|███▊ | 384/1000 [00:32<00:45, 13.42it/s]
39%|███▊ | 386/1000 [00:32<00:44, 13.73it/s]
39%|███▉ | 388/1000 [00:32<00:44, 13.67it/s]
39%|███▉ | 390/1000 [00:32<00:44, 13.56it/s]
39%|███▉ | 392/1000 [00:32<00:43, 13.89it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.68it/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, 13.25it/s]
40%|████ | 402/1000 [00:33<00:43, 13.90it/s]
40%|████ | 404/1000 [00:33<00:41, 14.53it/s]
41%|████ | 406/1000 [00:33<00:39, 15.00it/s]
41%|████ | 408/1000 [00:33<00:47, 12.56it/s]
41%|████ | 410/1000 [00:34<00:56, 10.47it/s]
41%|████ | 412/1000 [00:34<00:50, 11.56it/s]
41%|████▏ | 414/1000 [00:34<01:07, 8.65it/s]
42%|████▏ | 416/1000 [00:35<01:23, 6.98it/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, 10.89it/s]
42%|████▏ | 424/1000 [00:35<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, 9.09it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.03it/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<00:59, 9.49it/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, 12.16it/s]
45%|████▍ | 448/1000 [00:38<00:52, 10.46it/s]
45%|████▌ | 450/1000 [00:38<00:48, 11.24it/s]
45%|████▌ | 452/1000 [00:38<00:45, 12.05it/s]
45%|████▌ | 454/1000 [00:38<00:47, 11.57it/s]
46%|████▌ | 456/1000 [00:38<00:44, 12.16it/s]
46%|████▌ | 458/1000 [00:39<00:41, 13.15it/s]
46%|████▌ | 460/1000 [00:39<00:40, 13.33it/s]
46%|████▌ | 462/1000 [00:39<00:38, 14.03it/s]
46%|████▋ | 464/1000 [00:39<00:36, 14.62it/s]
47%|████▋ | 466/1000 [00:39<00:36, 14.56it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.75it/s]
47%|████▋ | 470/1000 [00:40<00:53, 9.99it/s]
47%|████▋ | 472/1000 [00:40<00:51, 10.17it/s]
47%|████▋ | 474/1000 [00:40<00:46, 11.43it/s]
48%|████▊ | 476/1000 [00:40<00:43, 12.10it/s]
48%|████▊ | 478/1000 [00:40<00:43, 11.99it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.93it/s]
48%|████▊ | 482/1000 [00:40<00:38, 13.32it/s]
48%|████▊ | 484/1000 [00:41<00:37, 13.84it/s]
49%|████▊ | 486/1000 [00:41<00:36, 14.01it/s]
49%|████▉ | 488/1000 [00:41<00:35, 14.24it/s]
49%|████▉ | 490/1000 [00:41<00:46, 11.08it/s]
49%|████▉ | 492/1000 [00:41<00:41, 12.34it/s]
49%|████▉ | 494/1000 [00:41<00:37, 13.32it/s]
50%|████▉ | 496/1000 [00:42<00:37, 13.55it/s]
50%|████▉ | 498/1000 [00:42<00:38, 13.20it/s]
50%|█████ | 500/1000 [00:42<00:46, 10.73it/s]
50%|█████ | 502/1000 [00:42<00:44, 11.22it/s]
50%|█████ | 504/1000 [00:42<00:41, 11.98it/s]
51%|█████ | 506/1000 [00:42<00:40, 12.34it/s]
51%|█████ | 508/1000 [00:43<00:36, 13.35it/s]
51%|█████ | 510/1000 [00:43<00:47, 10.25it/s]
51%|█████ | 512/1000 [00:43<00:44, 11.04it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 11.92it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.52it/s]
52%|█████▏ | 518/1000 [00:43<00:36, 13.06it/s]
52%|█████▏ | 520/1000 [00:44<00:35, 13.44it/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, 11.96it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.83it/s]
53%|█████▎ | 532/1000 [00:44<00:33, 13.79it/s]
53%|█████▎ | 534/1000 [00:45<00:32, 14.15it/s]
54%|█████▎ | 536/1000 [00:45<00:32, 14.27it/s]
54%|█████▍ | 538/1000 [00:45<00:32, 14.03it/s]
54%|█████▍ | 540/1000 [00:45<00:31, 14.47it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.74it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.60it/s]
55%|█████▍ | 546/1000 [00:45<00:31, 14.23it/s]
55%|█████▍ | 548/1000 [00:46<00:32, 13.92it/s]
55%|█████▌ | 550/1000 [00:46<00:31, 14.15it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.29it/s]
55%|█████▌ | 554/1000 [00:46<00:31, 13.97it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.12it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.49it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 15.00it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 14.93it/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, 12.23it/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<00:31, 13.71it/s]
58%|█████▊ | 576/1000 [00:48<00:31, 13.33it/s]
58%|█████▊ | 578/1000 [00:48<00:37, 11.15it/s]
58%|█████▊ | 580/1000 [00:48<00:34, 12.08it/s]
58%|█████▊ | 582/1000 [00:48<00:32, 12.72it/s]
58%|█████▊ | 584/1000 [00:48<00:31, 13.16it/s]
59%|█████▊ | 586/1000 [00:48<00:30, 13.69it/s]
59%|█████▉ | 588/1000 [00:49<00:28, 14.45it/s]
59%|█████▉ | 590/1000 [00:49<00:28, 14.57it/s]
59%|█████▉ | 592/1000 [00:49<00:28, 14.46it/s]
59%|█████▉ | 594/1000 [00:49<00:31, 13.08it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.65it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 13.96it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.33it/s]
60%|██████ | 602/1000 [00:50<00:27, 14.43it/s]
60%|██████ | 604/1000 [00:50<00:26, 14.97it/s]
61%|██████ | 606/1000 [00:50<00:26, 14.72it/s]
61%|██████ | 608/1000 [00:50<00:26, 15.04it/s]
61%|██████ | 610/1000 [00:50<00:26, 14.91it/s]
61%|██████ | 612/1000 [00:50<00:25, 15.10it/s]
61%|██████▏ | 614/1000 [00:50<00:26, 14.36it/s]
62%|██████▏ | 616/1000 [00:50<00:25, 14.77it/s]
62%|██████▏ | 618/1000 [00:51<00:26, 14.20it/s]
62%|██████▏ | 620/1000 [00:51<00:38, 9.76it/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 [00:52<00:27, 13.31it/s]
63%|██████▎ | 630/1000 [00:52<00:26, 13.81it/s]
63%|██████▎ | 632/1000 [00:52<00:29, 12.45it/s]
63%|██████▎ | 634/1000 [00:52<00:27, 13.15it/s]
64%|██████▎ | 636/1000 [00:52<00:33, 10.93it/s]
64%|██████▍ | 638/1000 [00:52<00:33, 10.65it/s]
64%|██████▍ | 640/1000 [00:53<00:31, 11.45it/s]
64%|██████▍ | 642/1000 [00:53<00:29, 12.33it/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, 13.35it/s]
65%|██████▌ | 650/1000 [00:53<00:27, 12.55it/s]
65%|██████▌ | 652/1000 [00:54<00:29, 11.97it/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, 11.84it/s]
66%|██████▌ | 660/1000 [00:54<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, 12.80it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.34it/s]
67%|██████▋ | 670/1000 [00:55<00:29, 11.10it/s]
67%|██████▋ | 672/1000 [00:55<00:27, 11.86it/s]
67%|██████▋ | 674/1000 [00:55<00:26, 12.46it/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, 12.98it/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<00:25, 12.31it/s]
69%|██████▉ | 688/1000 [00:57<00:25, 12.33it/s]
69%|██████▉ | 690/1000 [00:57<00:24, 12.43it/s]
69%|██████▉ | 692/1000 [00:57<00:24, 12.41it/s]
69%|██████▉ | 694/1000 [00:57<00:24, 12.54it/s]
70%|██████▉ | 696/1000 [00:57<00:24, 12.41it/s]
70%|██████▉ | 698/1000 [00:57<00:24, 12.38it/s]
70%|███████ | 700/1000 [00:58<00:27, 10.90it/s]
70%|███████ | 702/1000 [00:58<00:26, 11.31it/s]
70%|███████ | 704/1000 [00:58<00:32, 9.08it/s]
71%|███████ | 706/1000 [00:58<00:29, 9.89it/s]
71%|███████ | 708/1000 [00:59<00:32, 9.08it/s]
71%|███████ | 710/1000 [00:59<00:27, 10.40it/s]
71%|███████ | 712/1000 [00:59<00:25, 11.45it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.61it/s]
72%|███████▏ | 716/1000 [00:59<00:22, 12.80it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.41it/s]
72%|███████▏ | 720/1000 [00:59<00:20, 13.38it/s]
72%|███████▏ | 722/1000 [01:00<00:20, 13.80it/s]
72%|███████▏ | 724/1000 [01:00<00:20, 13.66it/s]
73%|███████▎ | 726/1000 [01:00<00:19, 13.90it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.84it/s]
73%|███████▎ | 730/1000 [01:00<00:33, 8.17it/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, 8.43it/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 [01:02<00:20, 12.31it/s]
74%|███████▍ | 745/1000 [01:02<00:25, 10.17it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.58it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.73it/s]
75%|███████▌ | 751/1000 [01:02<00:21, 11.82it/s]
75%|███████▌ | 753/1000 [01:03<00:19, 12.56it/s]
76%|███████▌ | 755/1000 [01:03<00:18, 13.30it/s]
76%|███████▌ | 757/1000 [01:03<00:23, 10.28it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.30it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.28it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.25it/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, 10.56it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.83it/s]
77%|███████▋ | 773/1000 [01:04<00:17, 12.64it/s]
78%|███████▊ | 775/1000 [01:04<00:17, 13.18it/s]
78%|███████▊ | 777/1000 [01:05<00:15, 14.20it/s]
78%|███████▊ | 779/1000 [01:05<00:20, 10.54it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.41it/s]
78%|███████▊ | 783/1000 [01:05<00:26, 8.32it/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, 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, 11.92it/s]
80%|███████▉ | 796/1000 [01:07<00:16, 12.37it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 13.12it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.31it/s]
80%|████████ | 802/1000 [01:07<00:23, 8.25it/s]
80%|████████ | 804/1000 [01:08<00:23, 8.29it/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, 11.70it/s]
81%|████████▏ | 814/1000 [01:08<00:14, 12.60it/s]
82%|████████▏ | 816/1000 [01:09<00:16, 11.04it/s]
82%|████████▏ | 818/1000 [01:09<00:15, 11.98it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.48it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 13.02it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.83it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.25it/s]
83%|████████▎ | 828/1000 [01:09<00:11, 14.50it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.90it/s]
83%|████████▎ | 832/1000 [01:10<00:11, 14.23it/s]
83%|████████▎ | 834/1000 [01:10<00:11, 13.99it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.36it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.28it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.43it/s]
84%|████████▍ | 842/1000 [01:10<00:10, 14.43it/s]
84%|████████▍ | 844/1000 [01:11<00:11, 13.97it/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, 14.10it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.70it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.29it/s]
86%|████████▌ | 856/1000 [01:11<00:10, 14.11it/s]
86%|████████▌ | 858/1000 [01:12<00:09, 14.61it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 15.38it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.53it/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, 14.13it/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, 11.90it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.54it/s]
88%|████████▊ | 882/1000 [01:14<00:10, 11.38it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 12.12it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.93it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.91it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.35it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.09it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 14.24it/s]
90%|████████▉ | 896/1000 [01:14<00:07, 13.64it/s]
90%|████████▉ | 898/1000 [01:15<00:07, 12.97it/s]
90%|█████████ | 900/1000 [01:15<00:08, 11.54it/s]
90%|█████████ | 902/1000 [01:15<00:08, 11.90it/s]
90%|█████████ | 904/1000 [01:15<00:10, 9.35it/s]
91%|█████████ | 906/1000 [01:15<00:09, 10.41it/s]
91%|█████████ | 908/1000 [01:16<00:08, 11.44it/s]
91%|█████████ | 910/1000 [01:16<00:07, 11.75it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.00it/s]
91%|█████████▏| 914/1000 [01:17<00:12, 6.75it/s]
92%|█████████▏| 916/1000 [01:17<00:10, 8.07it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.34it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.76it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 10.21it/s]
92%|█████████▏| 924/1000 [01:17<00:06, 10.91it/s]
93%|█████████▎| 926/1000 [01:18<00:06, 11.75it/s]
93%|█████████▎| 928/1000 [01:18<00:06, 10.61it/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, 12.89it/s]
94%|█████████▎| 936/1000 [01:18<00:04, 13.41it/s]
94%|█████████▍| 938/1000 [01:18<00:04, 14.19it/s]
94%|█████████▍| 940/1000 [01:19<00:04, 14.52it/s]
94%|█████████▍| 942/1000 [01:19<00:04, 12.24it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.78it/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, 13.47it/s]
95%|█████████▌| 952/1000 [01:20<00:03, 14.07it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 14.18it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 14.16it/s]
96%|█████████▌| 958/1000 [01:20<00:02, 14.70it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 13.09it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.70it/s]
96%|█████████▋| 964/1000 [01:21<00:03, 10.19it/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, 12.70it/s]
97%|█████████▋| 972/1000 [01:21<00:02, 9.68it/s]
97%|█████████▋| 974/1000 [01:21<00:02, 10.33it/s]
98%|█████████▊| 976/1000 [01:22<00:02, 10.79it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.38it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.52it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 9.91it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 10.11it/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, 11.07it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 12.00it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 12.79it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 13.77it/s]
100%|█████████▉| 998/1000 [01:24<00:00, 13.96it/s]
100%|██████████| 1000/1000 [01:24<00:00, 14.64it/s]
{'eval_loss': '0.8851', 'eval_runtime': '84.21', 'eval_samples_per_second': '11.88', 'eval_steps_per_second': '11.88', 'epoch': '0.6957', 'num_input_tokens_seen': 43672984}
70%|██████▉ | 13000/18688 [15:18:51<6:03:58, 3.84s/it]
100%|██████████| 1000/1000 [01:24<00:00, 14.64it/s]
 70%|██████▉ | 13001/18688 [15:18:56<46:15:05, 29.28s/it] 70%|██████▉ | 13002/18688 [15:18:59<34:05:43, 21.59s/it] 70%|██████▉ | 13003/18688 [15:19:04<25:56:29, 16.43s/it] 70%|██████▉ | 13004/18688 [15:19:07<19:55:24, 12.62s/it] 70%|██████▉ | 13005/18688 [15:19:11<15:51:53, 10.05s/it] 70%|██████▉ | 13006/18688 [15:19:16<13:24:46, 8.50s/it] 70%|██████▉ | 13007/18688 [15:19:20<11:02:49, 7.00s/it] 70%|██████▉ | 13008/18688 [15:19:23<9:24:45, 5.97s/it] 70%|██████▉ | 13009/18688 [15:19:28<8:42:04, 5.52s/it] 70%|██████▉ | 13010/18688 [15:19:32<8:08:06, 5.16s/it] {'loss': '0.888', 'grad_norm': '1.203', 'learning_rate': '2.46e-05', 'epoch': '0.6962', 'num_input_tokens_seen': 43704864, 'train_runtime': '5.517e+04', 'train_tokens_per_second': '792.1'}
70%|██████▉ | 13010/18688 [15:19:32<8:08:06, 5.16s/it] 70%|██████▉ | 13011/18688 [15:19:36<7:20:14, 4.65s/it] 70%|██████▉ | 13012/18688 [15:19:40<7:08:33, 4.53s/it] 70%|██████▉ | 13013/18688 [15:19:44<6:58:22, 4.42s/it] 70%|██████▉ | 13014/18688 [15:19:48<6:39:07, 4.22s/it] 70%|██████▉ | 13015/18688 [15:19:52<6:32:51, 4.16s/it] 70%|██████▉ | 13016/18688 [15:19:56<6:39:03, 4.22s/it] 70%|██████▉ | 13017/18688 [15:20:02<7:27:22, 4.73s/it] 70%|██████▉ | 13018/18688 [15:20:07<7:31:09, 4.77s/it] 70%|██████▉ | 13019/18688 [15:20:11<7:07:51, 4.53s/it] 70%|██████▉ | 13020/18688 [15:20:16<7:24:45, 4.71s/it] {'loss': '0.905', 'grad_norm': '1.504', 'learning_rate': '2.452e-05', 'epoch': '0.6967', 'num_input_tokens_seen': 43739792, 'train_runtime': '5.522e+04', 'train_tokens_per_second': '792.2'}
70%|██████▉ | 13020/18688 [15:20:16<7:24:45, 4.71s/it] 70%|██████▉ | 13021/18688 [15:20:20<7:12:45, 4.58s/it] 70%|██████▉ | 13022/18688 [15:20:25<7:18:46, 4.65s/it] 70%|██████▉ | 13023/18688 [15:20:29<7:06:44, 4.52s/it] 70%|██████▉ | 13024/18688 [15:20:33<6:38:53, 4.23s/it] 70%|██████▉ | 13025/18688 [15:20:37<6:35:31, 4.19s/it] 70%|██████▉ | 13026/18688 [15:20:41<6:43:37, 4.28s/it] 70%|██████▉ | 13027/18688 [15:20:45<6:31:31, 4.15s/it] 70%|██████▉ | 13028/18688 [15:20:50<6:37:35, 4.21s/it] 70%|██████▉ | 13029/18688 [15:20:55<7:16:16, 4.63s/it] 70%|██████▉ | 13030/18688 [15:21:00<7:11:00, 4.57s/it] {'loss': '0.9503', 'grad_norm': '1.095', 'learning_rate': '2.445e-05', 'epoch': '0.6973', 'num_input_tokens_seen': 43774432, 'train_runtime': '5.526e+04', 'train_tokens_per_second': '792.2'}
70%|██████▉ | 13030/18688 [15:21:00<7:11:00, 4.57s/it] 70%|██████▉ | 13031/18688 [15:21:04<6:59:08, 4.45s/it] 70%|██████▉ | 13032/18688 [15:21:08<7:03:00, 4.49s/it] 70%|██████▉ | 13033/18688 [15:21:12<6:43:06, 4.28s/it] 70%|██████▉ | 13034/18688 [15:21:16<6:22:03, 4.05s/it] 70%|██████▉ | 13035/18688 [15:21:20<6:17:43, 4.01s/it] 70%|██████▉ | 13036/18688 [15:21:24<6:27:46, 4.12s/it] 70%|██████▉ | 13037/18688 [15:21:29<6:44:33, 4.30s/it] 70%|██████▉ | 13038/18688 [15:21:34<7:02:56, 4.49s/it] 70%|██████▉ | 13039/18688 [15:21:39<7:14:15, 4.61s/it] 70%|██████▉ | 13040/18688 [15:21:43<7:15:04, 4.62s/it] {'loss': '0.9588', 'grad_norm': '1.742', 'learning_rate': '2.437e-05', 'epoch': '0.6978', 'num_input_tokens_seen': 43807728, 'train_runtime': '5.53e+04', 'train_tokens_per_second': '792.1'}
70%|██████▉ | 13040/18688 [15:21:43<7:15:04, 4.62s/it] 70%|██████▉ | 13041/18688 [15:21:48<7:15:53, 4.63s/it] 70%|██████▉ | 13042/18688 [15:21:52<6:59:16, 4.46s/it] 70%|██████▉ | 13043/18688 [15:21:56<6:40:07, 4.25s/it] 70%|██████▉ | 13044/18688 [15:22:01<7:09:32, 4.57s/it] 70%|██████▉ | 13045/18688 [15:22:05<6:48:53, 4.35s/it] 70%|██████▉ | 13046/18688 [15:22:09<6:51:10, 4.37s/it] 70%|██████▉ | 13047/18688 [15:22:14<6:49:14, 4.35s/it] 70%|██████▉ | 13048/18688 [15:22:18<6:53:22, 4.40s/it] 70%|██████▉ | 13049/18688 [15:22:23<6:58:53, 4.46s/it] 70%|██████▉ | 13050/18688 [15:22:27<7:08:43, 4.56s/it] {'loss': '0.911', 'grad_norm': '1.398', 'learning_rate': '2.429e-05', 'epoch': '0.6983', 'num_input_tokens_seen': 43842104, 'train_runtime': '5.535e+04', 'train_tokens_per_second': '792.1'}
70%|██████▉ | 13050/18688 [15:22:27<7:08:43, 4.56s/it] 70%|██████▉ | 13051/18688 [15:22:32<7:16:09, 4.64s/it] 70%|██████▉ | 13052/18688 [15:22:36<6:54:12, 4.41s/it] 70%|██████▉ | 13053/18688 [15:22:40<6:46:45, 4.33s/it] 70%|██████▉ | 13054/18688 [15:22:46<7:27:52, 4.77s/it] 70%|██████▉ | 13055/18688 [15:22:50<7:06:48, 4.55s/it] 70%|██████▉ | 13056/18688 [15:22:55<7:07:23, 4.55s/it] 70%|██████▉ | 13057/18688 [15:22:59<7:12:37, 4.61s/it] 70%|██████▉ | 13058/18688 [15:23:04<7:07:33, 4.56s/it] 70%|██████▉ | 13059/18688 [15:23:09<7:21:15, 4.70s/it] 70%|██████▉ | 13060/18688 [15:23:13<7:03:47, 4.52s/it] {'loss': '0.9269', 'grad_norm': '1.372', 'learning_rate': '2.421e-05', 'epoch': '0.6989', 'num_input_tokens_seen': 43879256, 'train_runtime': '5.539e+04', 'train_tokens_per_second': '792.1'}
70%|██████▉ | 13060/18688 [15:23:13<7:03:47, 4.52s/it] 70%|██████▉ | 13061/18688 [15:23:17<6:50:10, 4.37s/it] 70%|██████▉ | 13062/18688 [15:23:21<6:46:22, 4.33s/it] 70%|██████▉ | 13063/18688 [15:23:25<6:40:53, 4.28s/it] 70%|██████▉ | 13064/18688 [15:23:29<6:19:31, 4.05s/it] 70%|██████▉ | 13065/18688 [15:23:33<6:30:50, 4.17s/it] 70%|██████▉ | 13066/18688 [15:23:38<6:52:50, 4.41s/it] 70%|██████▉ | 13067/18688 [15:23:42<6:36:41, 4.23s/it] 70%|██████▉ | 13068/18688 [15:23:46<6:36:40, 4.23s/it] 70%|██████▉ | 13069/18688 [15:23:52<7:06:47, 4.56s/it] 70%|██████▉ | 13070/18688 [15:23:56<6:54:08, 4.42s/it] {'loss': '0.8754', 'grad_norm': '1.443', 'learning_rate': '2.413e-05', 'epoch': '0.6994', 'num_input_tokens_seen': 43914584, 'train_runtime': '5.544e+04', 'train_tokens_per_second': '792.2'}
70%|██████▉ | 13070/18688 [15:23:56<6:54:08, 4.42s/it] 70%|██████▉ | 13071/18688 [15:24:00<6:44:45, 4.32s/it] 70%|██████▉ | 13072/18688 [15:24:04<6:50:31, 4.39s/it] 70%|██████▉ | 13073/18688 [15:24:08<6:34:36, 4.22s/it] 70%|██████▉ | 13074/18688 [15:24:13<6:43:49, 4.32s/it] 70%|██████▉ | 13075/18688 [15:24:18<6:56:53, 4.46s/it] 70%|██████▉ | 13076/18688 [15:24:22<6:45:52, 4.34s/it] 70%|██████▉ | 13077/18688 [15:24:26<6:35:59, 4.23s/it] 70%|██████▉ | 13078/18688 [15:24:29<6:15:45, 4.02s/it] 70%|██████▉ | 13079/18688 [15:24:33<6:14:22, 4.00s/it] 70%|██████▉ | 13080/18688 [15:24:37<6:03:28, 3.89s/it] {'loss': '0.8947', 'grad_norm': '1.398', 'learning_rate': '2.405e-05', 'epoch': '0.6999', 'num_input_tokens_seen': 43945088, 'train_runtime': '5.548e+04', 'train_tokens_per_second': '792.1'}
70%|██████▉ | 13080/18688 [15:24:37<6:03:28, 3.89s/it] 70%|██████▉ | 13081/18688 [15:24:41<6:13:17, 3.99s/it] 70%|███████ | 13082/18688 [15:24:45<6:00:16, 3.86s/it] 70%|███████ | 13083/18688 [15:24:49<6:27:49, 4.15s/it] 70%|███████ | 13084/18688 [15:24:55<6:57:56, 4.47s/it] 70%|███████ | 13085/18688 [15:24:59<6:55:41, 4.45s/it] 70%|███████ | 13086/18688 [15:25:04<7:07:43, 4.58s/it] 70%|███████ | 13087/18688 [15:25:08<6:49:38, 4.39s/it] 70%|███████ | 13088/18688 [15:25:12<6:41:19, 4.30s/it] 70%|███████ | 13089/18688 [15:25:16<6:46:05, 4.35s/it] 70%|███████ | 13090/18688 [15:25:21<7:02:26, 4.53s/it] {'loss': '0.865', 'grad_norm': '1.094', 'learning_rate': '2.398e-05', 'epoch': '0.7005', 'num_input_tokens_seen': 43980344, 'train_runtime': '5.552e+04', 'train_tokens_per_second': '792.1'}
70%|███████ | 13090/18688 [15:25:21<7:02:26, 4.53s/it] 70%|███████ | 13091/18688 [15:25:26<7:01:49, 4.52s/it] 70%|███████ | 13092/18688 [15:25:30<6:47:41, 4.37s/it] 70%|███████ | 13093/18688 [15:25:34<6:50:21, 4.40s/it] 70%|███████ | 13094/18688 [15:25:38<6:42:55, 4.32s/it] 70%|███████ | 13095/18688 [15:25:44<7:03:54, 4.55s/it] 70%|███████ | 13096/18688 [15:25:47<6:46:07, 4.36s/it] 70%|███████ | 13097/18688 [15:25:52<6:54:19, 4.45s/it] 70%|███████ | 13098/18688 [15:25:56<6:33:00, 4.22s/it] 70%|███████ | 13099/18688 [15:26:00<6:41:42, 4.31s/it] 70%|███████ | 13100/18688 [15:26:05<6:58:49, 4.50s/it] {'loss': '0.8498', 'grad_norm': '1.104', 'learning_rate': '2.39e-05', 'epoch': '0.701', 'num_input_tokens_seen': 44015280, 'train_runtime': '5.557e+04', 'train_tokens_per_second': '792.1'}
70%|███████ | 13100/18688 [15:26:05<6:58:49, 4.50s/it] 70%|███████ | 13101/18688 [15:26:10<6:56:58, 4.48s/it] 70%|███████ | 13102/18688 [15:26:15<7:20:51, 4.74s/it] 70%|███████ | 13103/18688 [15:26:19<7:09:38, 4.62s/it] 70%|███████ | 13104/18688 [15:26:24<6:56:41, 4.48s/it] 70%|███████ | 13105/18688 [15:26:28<6:46:23, 4.37s/it] 70%|███████ | 13106/18688 [15:26:32<6:34:27, 4.24s/it] 70%|███████ | 13107/18688 [15:26:37<6:57:22, 4.49s/it] 70%|███████ | 13108/18688 [15:26:41<6:43:14, 4.34s/it] 70%|███████ | 13109/18688 [15:26:44<6:26:28, 4.16s/it] 70%|███████ | 13110/18688 [15:26:48<6:12:49, 4.01s/it] {'loss': '0.87', 'grad_norm': '1.245', 'learning_rate': '2.382e-05', 'epoch': '0.7015', 'num_input_tokens_seen': 44048352, 'train_runtime': '5.561e+04', 'train_tokens_per_second': '792.1'}
70%|███████ | 13110/18688 [15:26:48<6:12:49, 4.01s/it] 70%|███████ | 13111/18688 [15:26:52<6:24:41, 4.14s/it] 70%|███████ | 13112/18688 [15:26:56<6:18:27, 4.07s/it] 70%|███████ | 13113/18688 [15:27:01<6:41:05, 4.32s/it] 70%|███████ | 13114/18688 [15:27:05<6:23:41, 4.13s/it] 70%|███████ | 13115/18688 [15:27:09<6:19:24, 4.08s/it] 70%|███████ | 13116/18688 [15:27:13<6:13:42, 4.02s/it] 70%|███████ | 13117/18688 [15:27:16<5:59:33, 3.87s/it] 70%|███████ | 13118/18688 [15:27:20<5:55:09, 3.83s/it] 70%|███████ | 13119/18688 [15:27:24<6:04:51, 3.93s/it] 70%|███████ | 13120/18688 [15:27:29<6:35:19, 4.26s/it] {'loss': '0.8593', 'grad_norm': '1.339', 'learning_rate': '2.374e-05', 'epoch': '0.7021', 'num_input_tokens_seen': 44080432, 'train_runtime': '5.565e+04', 'train_tokens_per_second': '792.1'}
70%|███████ | 13120/18688 [15:27:29<6:35:19, 4.26s/it] 70%|███████ | 13121/18688 [15:27:33<6:31:04, 4.21s/it] 70%|███████ | 13122/18688 [15:27:38<6:52:59, 4.45s/it] 70%|███████ | 13123/18688 [15:27:43<6:48:08, 4.40s/it] 70%|███████ | 13124/18688 [15:27:47<6:52:40, 4.45s/it] 70%|███████ | 13125/18688 [15:27:51<6:40:45, 4.32s/it] 70%|███████ | 13126/18688 [15:27:55<6:31:10, 4.22s/it] 70%|███████ | 13127/18688 [15:27:59<6:16:31, 4.06s/it] 70%|███████ | 13128/18688 [15:28:03<6:28:05, 4.19s/it] 70%|███████ | 13129/18688 [15:28:07<6:17:57, 4.08s/it] 70%|███████ | 13130/18688 [15:28:12<6:33:45, 4.25s/it] {'loss': '0.9557', 'grad_norm': '1.371', 'learning_rate': '2.366e-05', 'epoch': '0.7026', 'num_input_tokens_seen': 44113840, 'train_runtime': '5.569e+04', 'train_tokens_per_second': '792.1'}
70%|███████ | 13130/18688 [15:28:12<6:33:45, 4.25s/it] 70%|███████ | 13131/18688 [15:28:16<6:18:52, 4.09s/it] 70%|███████ | 13132/18688 [15:28:20<6:20:05, 4.10s/it] 70%|███████ | 13133/18688 [15:28:24<6:13:22, 4.03s/it] 70%|███████ | 13134/18688 [15:28:27<6:01:48, 3.91s/it] 70%|███████ | 13135/18688 [15:28:32<6:26:00, 4.17s/it] 70%|███████ | 13136/18688 [15:28:37<6:40:35, 4.33s/it] 70%|███████ | 13137/18688 [15:28:41<6:26:25, 4.18s/it] 70%|███████ | 13138/18688 [15:28:45<6:39:34, 4.32s/it] 70%|███████ | 13139/18688 [15:28:49<6:37:15, 4.30s/it] 70%|███████ | 13140/18688 [15:28:54<6:43:19, 4.36s/it] {'loss': '0.8669', 'grad_norm': '1.282', 'learning_rate': '2.359e-05', 'epoch': '0.7031', 'num_input_tokens_seen': 44147016, 'train_runtime': '5.573e+04', 'train_tokens_per_second': '792.1'}
70%|███████ | 13140/18688 [15:28:54<6:43:19, 4.36s/it] 70%|███████ | 13141/18688 [15:28:58<6:47:59, 4.41s/it] 70%|███████ | 13142/18688 [15:29:02<6:35:24, 4.28s/it] 70%|███████ | 13143/18688 [15:29:06<6:21:59, 4.13s/it] 70%|███████ | 13144/18688 [15:29:11<6:40:26, 4.33s/it] 70%|███████ | 13145/18688 [15:29:16<7:04:23, 4.59s/it] 70%|███████ | 13146/18688 [15:29:21<7:14:32, 4.70s/it] 70%|███████ | 13147/18688 [15:29:25<6:56:36, 4.51s/it] 70%|███████ | 13148/18688 [15:29:29<6:40:40, 4.34s/it] 70%|███████ | 13149/18688 [15:29:33<6:30:52, 4.23s/it] 70%|███████ | 13150/18688 [15:29:37<6:29:34, 4.22s/it] {'loss': '1', 'grad_norm': '1.591', 'learning_rate': '2.351e-05', 'epoch': '0.7037', 'num_input_tokens_seen': 44178912, 'train_runtime': '5.578e+04', 'train_tokens_per_second': '792.1'}
70%|███████ | 13150/18688 [15:29:37<6:29:34, 4.22s/it] 70%|███████ | 13151/18688 [15:29:41<6:25:41, 4.18s/it] 70%|███████ | 13152/18688 [15:29:45<6:06:45, 3.97s/it] 70%|███████ | 13153/18688 [15:29:49<6:05:01, 3.96s/it] 70%|███████ | 13154/18688 [15:29:53<6:07:40, 3.99s/it] 70%|███████ | 13155/18688 [15:29:57<6:05:20, 3.96s/it] 70%|███████ | 13156/18688 [15:30:01<6:16:40, 4.09s/it] 70%|███████ | 13157/18688 [15:30:06<6:27:01, 4.20s/it] 70%|███████ | 13158/18688 [15:30:10<6:34:54, 4.28s/it] 70%|███████ | 13159/18688 [15:30:15<7:02:02, 4.58s/it] 70%|███████ | 13160/18688 [15:30:20<6:52:00, 4.47s/it] {'loss': '0.9055', 'grad_norm': '1.311', 'learning_rate': '2.343e-05', 'epoch': '0.7042', 'num_input_tokens_seen': 44212928, 'train_runtime': '5.582e+04', 'train_tokens_per_second': '792.1'}
70%|███████ | 13160/18688 [15:30:20<6:52:00, 4.47s/it] 70%|███████ | 13161/18688 [15:30:25<7:12:21, 4.69s/it] 70%|███████ | 13162/18688 [15:30:31<7:44:37, 5.04s/it] 70%|███████ | 13163/18688 [15:30:35<7:17:18, 4.75s/it] 70%|███████ | 13164/18688 [15:30:40<7:18:46, 4.77s/it] 70%|███████ | 13165/18688 [15:30:44<7:06:18, 4.63s/it] 70%|███████ | 13166/18688 [15:30:48<6:50:36, 4.46s/it] 70%|███████ | 13167/18688 [15:30:52<6:29:06, 4.23s/it] 70%|███████ | 13168/18688 [15:30:56<6:27:07, 4.21s/it] 70%|███████ | 13169/18688 [15:31:01<6:41:44, 4.37s/it] 70%|███████ | 13170/18688 [15:31:05<6:54:33, 4.51s/it] {'loss': '0.9419', 'grad_norm': '1.512', 'learning_rate': '2.335e-05', 'epoch': '0.7047', 'num_input_tokens_seen': 44252128, 'train_runtime': '5.587e+04', 'train_tokens_per_second': '792.1'}
70%|███████ | 13170/18688 [15:31:05<6:54:33, 4.51s/it] 70%|███████ | 13171/18688 [15:31:10<6:59:32, 4.56s/it] 70%|███████ | 13172/18688 [15:31:14<6:47:52, 4.44s/it] 70%|███████ | 13173/18688 [15:31:18<6:24:19, 4.18s/it] 70%|███████ | 13174/18688 [15:31:22<6:14:27, 4.07s/it] 70%|███████ | 13175/18688 [15:31:25<6:01:09, 3.93s/it] 71%|███████ | 13176/18688 [15:31:29<6:06:45, 3.99s/it] 71%|███████ | 13177/18688 [15:31:33<6:07:01, 4.00s/it] 71%|███████ | 13178/18688 [15:31:38<6:28:01, 4.23s/it] 71%|███████ | 13179/18688 [15:31:42<6:18:05, 4.12s/it] 71%|███████ | 13180/18688 [15:31:46<6:23:10, 4.17s/it] {'loss': '0.8347', 'grad_norm': '1.4', 'learning_rate': '2.328e-05', 'epoch': '0.7053', 'num_input_tokens_seen': 44284016, 'train_runtime': '5.591e+04', 'train_tokens_per_second': '792.1'}
71%|███████ | 13180/18688 [15:31:46<6:23:10, 4.17s/it] 71%|███████ | 13181/18688 [15:31:51<6:35:29, 4.31s/it] 71%|███████ | 13182/18688 [15:31:55<6:42:48, 4.39s/it] 71%|███████ | 13183/18688 [15:32:00<6:37:01, 4.33s/it] 71%|███████ | 13184/18688 [15:32:04<6:31:59, 4.27s/it] 71%|███████ | 13185/18688 [15:32:08<6:34:29, 4.30s/it] 71%|███████ | 13186/18688 [15:32:12<6:19:14, 4.14s/it] 71%|███████ | 13187/18688 [15:32:16<6:21:43, 4.16s/it] 71%|███████ | 13188/18688 [15:32:20<6:06:19, 4.00s/it] 71%|███████ | 13189/18688 [15:32:24<6:17:30, 4.12s/it] 71%|███████ | 13190/18688 [15:32:29<6:46:39, 4.44s/it] {'loss': '1.062', 'grad_norm': '1.419', 'learning_rate': '2.32e-05', 'epoch': '0.7058', 'num_input_tokens_seen': 44319968, 'train_runtime': '5.595e+04', 'train_tokens_per_second': '792.1'}
71%|███████ | 13190/18688 [15:32:29<6:46:39, 4.44s/it] 71%|███████ | 13191/18688 [15:32:35<7:19:00, 4.79s/it] 71%|███████ | 13192/18688 [15:32:39<7:03:33, 4.62s/it] 71%|███████ | 13193/18688 [15:32:43<6:30:58, 4.27s/it] 71%|███████ | 13194/18688 [15:32:46<6:10:53, 4.05s/it] 71%|███████ | 13195/18688 [15:32:50<5:54:48, 3.88s/it] 71%|███████ | 13196/18688 [15:32:53<5:49:18, 3.82s/it] 71%|███████ | 13197/18688 [15:32:57<5:46:26, 3.79s/it] 71%|███████ | 13198/18688 [15:33:01<5:47:26, 3.80s/it] 71%|███████ | 13199/18688 [15:33:05<6:05:14, 3.99s/it] 71%|███████ | 13200/18688 [15:33:10<6:36:44, 4.34s/it] {'loss': '0.9393', 'grad_norm': '1.539', 'learning_rate': '2.312e-05', 'epoch': '0.7064', 'num_input_tokens_seen': 44349832, 'train_runtime': '5.599e+04', 'train_tokens_per_second': '792.1'}
71%|███████ | 13200/18688 [15:33:10<6:36:44, 4.34s/it] 71%|███████ | 13201/18688 [15:33:15<6:45:37, 4.44s/it] 71%|███████ | 13202/18688 [15:33:19<6:43:19, 4.41s/it] 71%|███████ | 13203/18688 [15:33:24<6:45:26, 4.44s/it] 71%|███████ | 13204/18688 [15:33:28<6:36:49, 4.34s/it] 71%|███████ | 13205/18688 [15:33:33<6:52:25, 4.51s/it] 71%|███████ | 13206/18688 [15:33:37<6:41:44, 4.40s/it] 71%|███████ | 13207/18688 [15:33:41<6:27:11, 4.24s/it] 71%|███████ | 13208/18688 [15:33:45<6:20:55, 4.17s/it] 71%|███████ | 13209/18688 [15:33:49<6:07:34, 4.03s/it] 71%|███████ | 13210/18688 [15:33:53<6:07:47, 4.03s/it] {'loss': '0.9117', 'grad_norm': '1.739', 'learning_rate': '2.305e-05', 'epoch': '0.7069', 'num_input_tokens_seen': 44380488, 'train_runtime': '5.603e+04', 'train_tokens_per_second': '792'}
71%|███████ | 13210/18688 [15:33:53<6:07:47, 4.03s/it] 71%|███████ | 13211/18688 [15:33:57<6:18:06, 4.14s/it] 71%|███████ | 13212/18688 [15:34:02<6:25:11, 4.22s/it] 71%|███████ | 13213/18688 [15:34:06<6:27:19, 4.24s/it] 71%|███████ | 13214/18688 [15:34:10<6:34:28, 4.32s/it] 71%|███████ | 13215/18688 [15:34:15<6:31:21, 4.29s/it] 71%|███████ | 13216/18688 [15:34:19<6:37:03, 4.35s/it] 71%|███████ | 13217/18688 [15:34:23<6:27:43, 4.25s/it] 71%|███████ | 13218/18688 [15:34:28<6:37:37, 4.36s/it] 71%|███████ | 13219/18688 [15:34:31<6:16:13, 4.13s/it] 71%|███████ | 13220/18688 [15:34:36<6:27:53, 4.26s/it] {'loss': '0.9222', 'grad_norm': '1.469', 'learning_rate': '2.297e-05', 'epoch': '0.7074', 'num_input_tokens_seen': 44411288, 'train_runtime': '5.608e+04', 'train_tokens_per_second': '792'}
71%|███████ | 13220/18688 [15:34:36<6:27:53, 4.26s/it] 71%|███████ | 13221/18688 [15:34:41<6:40:09, 4.39s/it] 71%|███████ | 13222/18688 [15:34:45<6:42:19, 4.42s/it] 71%|███████ | 13223/18688 [15:34:49<6:20:50, 4.18s/it] 71%|███████ | 13224/18688 [15:34:53<6:15:52, 4.13s/it] 71%|███████ | 13225/18688 [15:34:57<6:29:58, 4.28s/it] 71%|███████ | 13226/18688 [15:35:02<6:28:08, 4.26s/it] 71%|███████ | 13227/18688 [15:35:06<6:39:31, 4.39s/it] 71%|███████ | 13228/18688 [15:35:10<6:28:54, 4.27s/it] 71%|███████ | 13229/18688 [15:35:15<6:55:58, 4.57s/it] 71%|███████ | 13230/18688 [15:35:21<7:14:17, 4.77s/it] {'loss': '1.041', 'grad_norm': '1.458', 'learning_rate': '2.289e-05', 'epoch': '0.708', 'num_input_tokens_seen': 44448992, 'train_runtime': '5.612e+04', 'train_tokens_per_second': '792'}
71%|███████ | 13230/18688 [15:35:21<7:14:17, 4.77s/it] 71%|███████ | 13231/18688 [15:35:25<6:50:41, 4.52s/it] 71%|███████ | 13232/18688 [15:35:29<6:40:42, 4.41s/it] 71%|███████ | 13233/18688 [15:35:33<6:24:57, 4.23s/it] 71%|███████ | 13234/18688 [15:35:36<6:13:02, 4.10s/it] 71%|███████ | 13235/18688 [15:35:42<6:41:58, 4.42s/it] 71%|███████ | 13236/18688 [15:35:45<6:24:13, 4.23s/it] 71%|███████ | 13237/18688 [15:35:49<6:16:21, 4.14s/it] 71%|███████ | 13238/18688 [15:35:54<6:23:39, 4.22s/it] 71%|███████ | 13239/18688 [15:35:58<6:25:44, 4.25s/it] 71%|███████ | 13240/18688 [15:36:03<6:37:18, 4.38s/it] {'loss': '0.8719', 'grad_norm': '1.483', 'learning_rate': '2.282e-05', 'epoch': '0.7085', 'num_input_tokens_seen': 44482184, 'train_runtime': '5.616e+04', 'train_tokens_per_second': '792'}
71%|███████ | 13240/18688 [15:36:03<6:37:18, 4.38s/it] 71%|███████ | 13241/18688 [15:36:07<6:27:10, 4.26s/it] 71%|███████ | 13242/18688 [15:36:11<6:27:17, 4.27s/it] 71%|███████ | 13243/18688 [15:36:15<6:31:51, 4.32s/it] 71%|███████ | 13244/18688 [15:36:19<6:24:08, 4.23s/it] 71%|███████ | 13245/18688 [15:36:25<6:53:32, 4.56s/it] 71%|███████ | 13246/18688 [15:36:29<6:40:17, 4.41s/it] 71%|███████ | 13247/18688 [15:36:33<6:26:31, 4.26s/it] 71%|███████ | 13248/18688 [15:36:38<6:50:42, 4.53s/it] 71%|███████ | 13249/18688 [15:36:42<6:52:02, 4.55s/it] 71%|███████ | 13250/18688 [15:36:47<6:38:21, 4.40s/it] {'loss': '0.9154', 'grad_norm': '1.508', 'learning_rate': '2.274e-05', 'epoch': '0.709', 'num_input_tokens_seen': 44517360, 'train_runtime': '5.621e+04', 'train_tokens_per_second': '792'}
71%|███████ | 13250/18688 [15:36:47<6:38:21, 4.40s/it] 71%|███████ | 13251/18688 [15:36:50<6:20:27, 4.20s/it] 71%|███████ | 13252/18688 [15:36:54<6:04:46, 4.03s/it] 71%|███████ | 13253/18688 [15:36:58<6:16:13, 4.15s/it] 71%|███████ | 13254/18688 [15:37:02<6:08:05, 4.06s/it] 71%|███████ | 13255/18688 [15:37:07<6:37:57, 4.39s/it] 71%|███████ | 13256/18688 [15:37:11<6:17:22, 4.17s/it] 71%|███████ | 13257/18688 [15:37:15<6:14:29, 4.14s/it] 71%|███████ | 13258/18688 [15:37:19<6:05:24, 4.04s/it] 71%|███████ | 13259/18688 [15:37:23<6:14:54, 4.14s/it] 71%|███████ | 13260/18688 [15:37:27<6:12:40, 4.12s/it] {'loss': '0.8663', 'grad_norm': '1.455', 'learning_rate': '2.266e-05', 'epoch': '0.7096', 'num_input_tokens_seen': 44547576, 'train_runtime': '5.625e+04', 'train_tokens_per_second': '792'}
71%|███████ | 13260/18688 [15:37:27<6:12:40, 4.12s/it] 71%|███████ | 13261/18688 [15:37:32<6:20:04, 4.20s/it] 71%|███████ | 13262/18688 [15:37:36<6:10:28, 4.10s/it] 71%|███████ | 13263/18688 [15:37:40<6:19:20, 4.20s/it] 71%|███████ | 13264/18688 [15:37:45<6:39:15, 4.42s/it] 71%|███████ | 13265/18688 [15:37:49<6:27:12, 4.28s/it] 71%|███████ | 13266/18688 [15:37:53<6:12:06, 4.12s/it] 71%|███████ | 13267/18688 [15:37:57<6:06:21, 4.05s/it] 71%|███████ | 13268/18688 [15:38:01<6:25:44, 4.27s/it] 71%|███████ | 13269/18688 [15:38:06<6:38:13, 4.41s/it] 71%|███████ | 13270/18688 [15:38:10<6:23:09, 4.24s/it] {'loss': '0.9018', 'grad_norm': '1.419', 'learning_rate': '2.259e-05', 'epoch': '0.7101', 'num_input_tokens_seen': 44581544, 'train_runtime': '5.629e+04', 'train_tokens_per_second': '792'}
71%|███████ | 13270/18688 [15:38:10<6:23:09, 4.24s/it] 71%|███████ | 13271/18688 [15:38:15<6:33:03, 4.35s/it] 71%|███████ | 13272/18688 [15:38:19<6:33:16, 4.36s/it] 71%|███████ | 13273/18688 [15:38:23<6:16:47, 4.17s/it] 71%|███████ | 13274/18688 [15:38:26<6:04:28, 4.04s/it] 71%|███████ | 13275/18688 [15:38:30<5:57:10, 3.96s/it] 71%|███████ | 13276/18688 [15:38:34<5:58:52, 3.98s/it] 71%|███████ | 13277/18688 [15:38:38<6:01:06, 4.00s/it] 71%|███████ | 13278/18688 [15:38:42<6:00:21, 4.00s/it] 71%|███████ | 13279/18688 [15:38:47<6:22:41, 4.24s/it] 71%|███████ | 13280/18688 [15:38:51<6:20:58, 4.23s/it] {'loss': '0.9057', 'grad_norm': '1.173', 'learning_rate': '2.251e-05', 'epoch': '0.7106', 'num_input_tokens_seen': 44612840, 'train_runtime': '5.633e+04', 'train_tokens_per_second': '792'}
71%|███████ | 13280/18688 [15:38:51<6:20:58, 4.23s/it] 71%|███████ | 13281/18688 [15:38:55<6:07:31, 4.08s/it] 71%|███████ | 13282/18688 [15:38:59<6:17:18, 4.19s/it] 71%|███████ | 13283/18688 [15:39:03<5:59:28, 3.99s/it] 71%|███████ | 13284/18688 [15:39:07<5:52:10, 3.91s/it] 71%|███████ | 13285/18688 [15:39:11<6:06:21, 4.07s/it] 71%|███████ | 13286/18688 [15:39:15<6:12:41, 4.14s/it] 71%|███████ | 13287/18688 [15:39:20<6:34:55, 4.39s/it] 71%|███████ | 13288/18688 [15:39:24<6:24:07, 4.27s/it] 71%|███████ | 13289/18688 [15:39:28<6:19:27, 4.22s/it] 71%|███████ | 13290/18688 [15:39:32<6:02:57, 4.03s/it] {'loss': '0.9309', 'grad_norm': '1.358', 'learning_rate': '2.243e-05', 'epoch': '0.7112', 'num_input_tokens_seen': 44645984, 'train_runtime': '5.637e+04', 'train_tokens_per_second': '792'}
71%|███████ | 13290/18688 [15:39:32<6:02:57, 4.03s/it] 71%|███████ | 13291/18688 [15:39:36<5:57:10, 3.97s/it] 71%|███████ | 13292/18688 [15:39:40<6:13:39, 4.15s/it] 71%|███████ | 13293/18688 [15:39:45<6:27:24, 4.31s/it] 71%|███████ | 13294/18688 [15:39:49<6:21:55, 4.25s/it] 71%|███████ | 13295/18688 [15:39:53<6:12:29, 4.14s/it] 71%|███████ | 13296/18688 [15:39:57<6:08:18, 4.10s/it] 71%|███████ | 13297/18688 [15:40:02<6:23:22, 4.27s/it] 71%|███████ | 13298/18688 [15:40:06<6:17:40, 4.20s/it] 71%|███████ | 13299/18688 [15:40:10<6:10:06, 4.12s/it] 71%|███████ | 13300/18688 [15:40:14<6:00:58, 4.02s/it] {'loss': '0.9342', 'grad_norm': '1.61', 'learning_rate': '2.236e-05', 'epoch': '0.7117', 'num_input_tokens_seen': 44678200, 'train_runtime': '5.641e+04', 'train_tokens_per_second': '792'}
71%|███████ | 13300/18688 [15:40:14<6:00:58, 4.02s/it] 71%|███████ | 13301/18688 [15:40:17<5:54:50, 3.95s/it] 71%|███████ | 13302/18688 [15:40:22<6:04:56, 4.07s/it] 71%|███████ | 13303/18688 [15:40:26<6:09:29, 4.12s/it] 71%|███████ | 13304/18688 [15:40:30<6:20:55, 4.25s/it] 71%|███████ | 13305/18688 [15:40:34<6:15:01, 4.18s/it] 71%|███████ | 13306/18688 [15:40:39<6:28:21, 4.33s/it] 71%|███████ | 13307/18688 [15:40:43<6:09:36, 4.12s/it] 71%|███████ | 13308/18688 [15:40:47<6:03:11, 4.05s/it] 71%|███████ | 13309/18688 [15:40:51<6:11:26, 4.14s/it] 71%|███████ | 13310/18688 [15:40:56<6:27:10, 4.32s/it] {'loss': '0.9538', 'grad_norm': '1.403', 'learning_rate': '2.228e-05', 'epoch': '0.7122', 'num_input_tokens_seen': 44712528, 'train_runtime': '5.646e+04', 'train_tokens_per_second': '792'}
71%|███████ | 13310/18688 [15:40:56<6:27:10, 4.32s/it] 71%|███████ | 13311/18688 [15:41:00<6:17:30, 4.21s/it] 71%|███████ | 13312/18688 [15:41:04<6:28:37, 4.34s/it] 71%|███████ | 13313/18688 [15:41:09<6:24:02, 4.29s/it] 71%|███████ | 13314/18688 [15:41:13<6:17:45, 4.22s/it] 71%|███████ | 13315/18688 [15:41:17<6:16:35, 4.21s/it] 71%|███████▏ | 13316/18688 [15:41:21<6:27:35, 4.33s/it] 71%|███████▏ | 13317/18688 [15:41:26<6:24:44, 4.30s/it] 71%|███████▏ | 13318/18688 [15:41:30<6:39:25, 4.46s/it] 71%|███████▏ | 13319/18688 [15:41:35<6:36:59, 4.44s/it] 71%|███████▏ | 13320/18688 [15:41:38<6:12:19, 4.16s/it] {'loss': '0.8843', 'grad_norm': '1.03', 'learning_rate': '2.22e-05', 'epoch': '0.7128', 'num_input_tokens_seen': 44745936, 'train_runtime': '5.65e+04', 'train_tokens_per_second': '792'}
71%|███████▏ | 13320/18688 [15:41:38<6:12:19, 4.16s/it] 71%|███████▏ | 13321/18688 [15:41:43<6:32:49, 4.39s/it] 71%|███████▏ | 13322/18688 [15:41:48<6:40:40, 4.48s/it] 71%|███████▏ | 13323/18688 [15:41:52<6:25:02, 4.31s/it] 71%|███████▏ | 13324/18688 [15:41:56<6:32:18, 4.39s/it] 71%|███████▏ | 13325/18688 [15:42:00<6:15:42, 4.20s/it] 71%|███████▏ | 13326/18688 [15:42:05<6:24:32, 4.30s/it] 71%|███████▏ | 13327/18688 [15:42:09<6:22:36, 4.28s/it] 71%|███████▏ | 13328/18688 [15:42:14<6:49:31, 4.58s/it] 71%|███████▏ | 13329/18688 [15:42:18<6:37:27, 4.45s/it] 71%|███████▏ | 13330/18688 [15:42:23<6:49:33, 4.59s/it] {'loss': '0.936', 'grad_norm': '1.353', 'learning_rate': '2.213e-05', 'epoch': '0.7133', 'num_input_tokens_seen': 44781648, 'train_runtime': '5.654e+04', 'train_tokens_per_second': '792'}
71%|███████▏ | 13330/18688 [15:42:23<6:49:33, 4.59s/it] 71%|███████▏ | 13331/18688 [15:42:28<6:54:57, 4.65s/it] 71%|███████▏ | 13332/18688 [15:42:33<7:05:15, 4.76s/it] 71%|███████▏ | 13333/18688 [15:42:37<6:47:51, 4.57s/it] 71%|███████▏ | 13334/18688 [15:42:41<6:33:29, 4.41s/it] 71%|███████▏ | 13335/18688 [15:42:45<6:23:22, 4.30s/it] 71%|███████▏ | 13336/18688 [15:42:51<6:53:12, 4.63s/it] 71%|███████▏ | 13337/18688 [15:42:56<7:09:27, 4.82s/it] 71%|███████▏ | 13338/18688 [15:42:59<6:34:29, 4.42s/it] 71%|███████▏ | 13339/18688 [15:43:03<6:17:00, 4.23s/it] 71%|███████▏ | 13340/18688 [15:43:08<6:26:45, 4.34s/it] {'loss': '0.9837', 'grad_norm': '1.273', 'learning_rate': '2.205e-05', 'epoch': '0.7138', 'num_input_tokens_seen': 44817080, 'train_runtime': '5.659e+04', 'train_tokens_per_second': '792'}
71%|███████▏ | 13340/18688 [15:43:08<6:26:45, 4.34s/it] 71%|███████▏ | 13341/18688 [15:43:11<6:07:32, 4.12s/it] 71%|███████▏ | 13342/18688 [15:43:15<5:56:58, 4.01s/it] 71%|███████▏ | 13343/18688 [15:43:19<6:00:36, 4.05s/it] 71%|███████▏ | 13344/18688 [15:43:23<6:02:36, 4.07s/it] 71%|███████▏ | 13345/18688 [15:43:28<6:15:54, 4.22s/it] 71%|███████▏ | 13346/18688 [15:43:33<6:28:19, 4.36s/it] 71%|███████▏ | 13347/18688 [15:43:36<6:04:20, 4.09s/it] 71%|███████▏ | 13348/18688 [15:43:40<5:50:26, 3.94s/it] 71%|███████▏ | 13349/18688 [15:43:43<5:43:07, 3.86s/it] 71%|███████▏ | 13350/18688 [15:43:48<5:57:31, 4.02s/it] {'loss': '0.9247', 'grad_norm': '1.162', 'learning_rate': '2.198e-05', 'epoch': '0.7144', 'num_input_tokens_seen': 44846864, 'train_runtime': '5.663e+04', 'train_tokens_per_second': '792'}
71%|███████▏ | 13350/18688 [15:43:48<5:57:31, 4.02s/it] 71%|███████▏ | 13351/18688 [15:43:52<5:57:24, 4.02s/it] 71%|███████▏ | 13352/18688 [15:43:56<6:01:49, 4.07s/it] 71%|███████▏ | 13353/18688 [15:44:02<6:40:25, 4.50s/it] 71%|███████▏ | 13354/18688 [15:44:06<6:30:09, 4.39s/it] 71%|███████▏ | 13355/18688 [15:44:09<6:12:56, 4.20s/it] 71%|███████▏ | 13356/18688 [15:44:14<6:12:41, 4.19s/it] 71%|███████▏ | 13357/18688 [15:44:19<6:39:29, 4.50s/it] 71%|███████▏ | 13358/18688 [15:44:24<6:55:21, 4.68s/it] 71%|███████▏ | 13359/18688 [15:44:28<6:38:47, 4.49s/it] 71%|███████▏ | 13360/18688 [15:44:32<6:15:31, 4.23s/it] {'loss': '0.9576', 'grad_norm': '1.349', 'learning_rate': '2.19e-05', 'epoch': '0.7149', 'num_input_tokens_seen': 44883600, 'train_runtime': '5.667e+04', 'train_tokens_per_second': '792'}
71%|███████▏ | 13360/18688 [15:44:32<6:15:31, 4.23s/it] 71%|███████▏ | 13361/18688 [15:44:36<6:13:45, 4.21s/it] 72%|███████▏ | 13362/18688 [15:44:40<6:08:05, 4.15s/it] 72%|███████▏ | 13363/18688 [15:44:44<6:12:47, 4.20s/it] 72%|███████▏ | 13364/18688 [15:44:48<6:01:58, 4.08s/it] 72%|███████▏ | 13365/18688 [15:44:52<6:06:09, 4.13s/it] 72%|███████▏ | 13366/18688 [15:44:56<6:02:07, 4.08s/it] 72%|███████▏ | 13367/18688 [15:45:00<5:54:03, 3.99s/it] 72%|███████▏ | 13368/18688 [15:45:04<5:49:01, 3.94s/it] 72%|███████▏ | 13369/18688 [15:45:08<6:10:09, 4.18s/it] 72%|███████▏ | 13370/18688 [15:45:13<6:33:45, 4.44s/it] {'loss': '0.8812', 'grad_norm': '1.242', 'learning_rate': '2.183e-05', 'epoch': '0.7155', 'num_input_tokens_seen': 44916888, 'train_runtime': '5.671e+04', 'train_tokens_per_second': '792'}
72%|███████▏ | 13370/18688 [15:45:13<6:33:45, 4.44s/it] 72%|███████▏ | 13371/18688 [15:45:17<6:16:42, 4.25s/it] 72%|███████▏ | 13372/18688 [15:45:21<6:04:58, 4.12s/it] 72%|███████▏ | 13373/18688 [15:45:25<5:48:38, 3.94s/it] 72%|███████▏ | 13374/18688 [15:45:29<5:47:49, 3.93s/it] 72%|███████▏ | 13375/18688 [15:45:32<5:45:39, 3.90s/it] 72%|███████▏ | 13376/18688 [15:45:36<5:38:19, 3.82s/it] 72%|███████▏ | 13377/18688 [15:45:41<6:07:27, 4.15s/it] 72%|███████▏ | 13378/18688 [15:45:45<6:17:55, 4.27s/it] 72%|███████▏ | 13379/18688 [15:45:50<6:15:41, 4.25s/it] 72%|███████▏ | 13380/18688 [15:45:53<6:01:17, 4.08s/it] {'loss': '0.8863', 'grad_norm': '1.365', 'learning_rate': '2.175e-05', 'epoch': '0.716', 'num_input_tokens_seen': 44946248, 'train_runtime': '5.675e+04', 'train_tokens_per_second': '792'}
72%|███████▏ | 13380/18688 [15:45:53<6:01:17, 4.08s/it] 72%|███████▏ | 13381/18688 [15:45:58<6:28:53, 4.40s/it] 72%|███████▏ | 13382/18688 [15:46:03<6:24:21, 4.35s/it] 72%|███████▏ | 13383/18688 [15:46:07<6:27:55, 4.39s/it] 72%|███████▏ | 13384/18688 [15:46:11<6:19:38, 4.29s/it] 72%|███████▏ | 13385/18688 [15:46:17<6:53:20, 4.68s/it] 72%|███████▏ | 13386/18688 [15:46:21<6:30:48, 4.42s/it] 72%|███████▏ | 13387/18688 [15:46:24<6:09:39, 4.18s/it] 72%|███████▏ | 13388/18688 [15:46:28<6:00:26, 4.08s/it] 72%|███████▏ | 13389/18688 [15:46:32<5:49:11, 3.95s/it] 72%|███████▏ | 13390/18688 [15:46:36<5:53:27, 4.00s/it] {'loss': '0.8429', 'grad_norm': '1.287', 'learning_rate': '2.167e-05', 'epoch': '0.7165', 'num_input_tokens_seen': 44979392, 'train_runtime': '5.68e+04', 'train_tokens_per_second': '791.9'}
72%|███████▏ | 13390/18688 [15:46:36<5:53:27, 4.00s/it] 72%|███████▏ | 13391/18688 [15:46:40<5:56:22, 4.04s/it] 72%|███████▏ | 13392/18688 [15:46:44<5:51:58, 3.99s/it] 72%|███████▏ | 13393/18688 [15:46:48<5:50:37, 3.97s/it] 72%|███████▏ | 13394/18688 [15:46:52<5:57:51, 4.06s/it] 72%|███████▏ | 13395/18688 [15:46:57<6:22:24, 4.33s/it] 72%|███████▏ | 13396/18688 [15:47:02<6:28:46, 4.41s/it] 72%|███████▏ | 13397/18688 [15:47:06<6:24:50, 4.36s/it] 72%|███████▏ | 13398/18688 [15:47:10<6:05:52, 4.15s/it] 72%|███████▏ | 13399/18688 [15:47:14<6:09:10, 4.19s/it] 72%|███████▏ | 13400/18688 [15:47:18<6:19:55, 4.31s/it] {'loss': '0.9006', 'grad_norm': '1.371', 'learning_rate': '2.16e-05', 'epoch': '0.7171', 'num_input_tokens_seen': 45012672, 'train_runtime': '5.684e+04', 'train_tokens_per_second': '791.9'}
72%|███████▏ | 13400/18688 [15:47:18<6:19:55, 4.31s/it] 72%|███████▏ | 13401/18688 [15:47:22<6:06:27, 4.16s/it] 72%|███████▏ | 13402/18688 [15:47:28<6:36:09, 4.50s/it] 72%|███████▏ | 13403/18688 [15:47:32<6:26:08, 4.38s/it] 72%|███████▏ | 13404/18688 [15:47:36<6:28:05, 4.41s/it] 72%|███████▏ | 13405/18688 [15:47:40<6:09:06, 4.19s/it] 72%|███████▏ | 13406/18688 [15:47:44<6:13:46, 4.25s/it] 72%|███████▏ | 13407/18688 [15:47:49<6:20:22, 4.32s/it] 72%|███████▏ | 13408/18688 [15:47:53<6:27:17, 4.40s/it] 72%|███████▏ | 13409/18688 [15:47:58<6:23:25, 4.36s/it] 72%|███████▏ | 13410/18688 [15:48:02<6:33:54, 4.48s/it] {'loss': '0.9081', 'grad_norm': '1.396', 'learning_rate': '2.152e-05', 'epoch': '0.7176', 'num_input_tokens_seen': 45047216, 'train_runtime': '5.688e+04', 'train_tokens_per_second': '791.9'}
72%|███████▏ | 13410/18688 [15:48:02<6:33:54, 4.48s/it] 72%|███████▏ | 13411/18688 [15:48:06<6:17:23, 4.29s/it] 72%|███████▏ | 13412/18688 [15:48:10<5:56:15, 4.05s/it] 72%|███████▏ | 13413/18688 [15:48:15<6:32:46, 4.47s/it] 72%|███████▏ | 13414/18688 [15:48:19<6:29:24, 4.43s/it] 72%|███████▏ | 13415/18688 [15:48:23<6:09:40, 4.21s/it] 72%|███████▏ | 13416/18688 [15:48:27<6:03:14, 4.13s/it] 72%|███████▏ | 13417/18688 [15:48:31<5:53:40, 4.03s/it] 72%|███████▏ | 13418/18688 [15:48:35<5:47:45, 3.96s/it] 72%|███████▏ | 13419/18688 [15:48:38<5:44:37, 3.92s/it] 72%|███████▏ | 13420/18688 [15:48:43<6:09:03, 4.20s/it] {'loss': '0.9143', 'grad_norm': '1.285', 'learning_rate': '2.145e-05', 'epoch': '0.7181', 'num_input_tokens_seen': 45079448, 'train_runtime': '5.692e+04', 'train_tokens_per_second': '791.9'}
72%|███████▏ | 13420/18688 [15:48:43<6:09:03, 4.20s/it] 72%|███████▏ | 13421/18688 [15:48:48<6:15:26, 4.28s/it] 72%|███████▏ | 13422/18688 [15:48:51<5:58:08, 4.08s/it] 72%|███████▏ | 13423/18688 [15:48:56<5:59:51, 4.10s/it] 72%|███████▏ | 13424/18688 [15:49:00<6:02:41, 4.13s/it] 72%|███████▏ | 13425/18688 [15:49:03<5:50:32, 4.00s/it] 72%|███████▏ | 13426/18688 [15:49:08<6:13:03, 4.25s/it] 72%|███████▏ | 13427/18688 [15:49:12<5:58:05, 4.08s/it] 72%|███████▏ | 13428/18688 [15:49:16<5:51:58, 4.01s/it] 72%|███████▏ | 13429/18688 [15:49:21<6:17:09, 4.30s/it] 72%|███████▏ | 13430/18688 [15:49:25<6:25:30, 4.40s/it] {'loss': '0.9209', 'grad_norm': '1.715', 'learning_rate': '2.137e-05', 'epoch': '0.7187', 'num_input_tokens_seen': 45113168, 'train_runtime': '5.697e+04', 'train_tokens_per_second': '791.9'}
72%|███████▏ | 13430/18688 [15:49:25<6:25:30, 4.40s/it] 72%|███████▏ | 13431/18688 [15:49:30<6:23:59, 4.38s/it] 72%|███████▏ | 13432/18688 [15:49:35<6:42:53, 4.60s/it] 72%|███████▏ | 13433/18688 [15:49:39<6:43:19, 4.61s/it] 72%|███████▏ | 13434/18688 [15:49:43<6:21:43, 4.36s/it] 72%|███████▏ | 13435/18688 [15:49:48<6:40:43, 4.58s/it] 72%|███████▏ | 13436/18688 [15:49:53<6:32:15, 4.48s/it] 72%|███████▏ | 13437/18688 [15:49:57<6:37:22, 4.54s/it] 72%|███████▏ | 13438/18688 [15:50:04<7:44:32, 5.31s/it] 72%|███████▏ | 13439/18688 [15:50:10<7:41:59, 5.28s/it] 72%|███████▏ | 13440/18688 [15:50:14<7:11:55, 4.94s/it] {'loss': '0.8939', 'grad_norm': '1.033', 'learning_rate': '2.13e-05', 'epoch': '0.7192', 'num_input_tokens_seen': 45155632, 'train_runtime': '5.701e+04', 'train_tokens_per_second': '792'}
72%|███████▏ | 13440/18688 [15:50:14<7:11:55, 4.94s/it] 72%|███████▏ | 13441/18688 [15:50:18<6:41:46, 4.59s/it] 72%|███████▏ | 13442/18688 [15:50:22<6:42:22, 4.60s/it] 72%|███████▏ | 13443/18688 [15:50:26<6:29:41, 4.46s/it] 72%|███████▏ | 13444/18688 [15:50:30<6:15:38, 4.30s/it] 72%|███████▏ | 13445/18688 [15:50:34<6:12:28, 4.26s/it] 72%|███████▏ | 13446/18688 [15:50:38<5:55:46, 4.07s/it] 72%|███████▏ | 13447/18688 [15:50:42<6:02:48, 4.15s/it] 72%|███████▏ | 13448/18688 [15:50:46<5:56:55, 4.09s/it] 72%|███████▏ | 13449/18688 [15:50:51<6:14:01, 4.28s/it] 72%|███████▏ | 13450/18688 [15:50:55<6:04:22, 4.17s/it] {'loss': '0.9615', 'grad_norm': '1.714', 'learning_rate': '2.122e-05', 'epoch': '0.7197', 'num_input_tokens_seen': 45188776, 'train_runtime': '5.706e+04', 'train_tokens_per_second': '792'}
72%|███████▏ | 13450/18688 [15:50:55<6:04:22, 4.17s/it] 72%|███████▏ | 13451/18688 [15:51:00<6:18:00, 4.33s/it] 72%|███████▏ | 13452/18688 [15:51:04<6:19:00, 4.34s/it] 72%|███████▏ | 13453/18688 [15:51:08<6:17:04, 4.32s/it] 72%|███████▏ | 13454/18688 [15:51:12<5:58:43, 4.11s/it] 72%|███████▏ | 13455/18688 [15:51:16<5:57:23, 4.10s/it] 72%|███████▏ | 13456/18688 [15:51:20<5:53:44, 4.06s/it] 72%|███████▏ | 13457/18688 [15:51:26<6:50:36, 4.71s/it] 72%|███████▏ | 13458/18688 [15:51:31<6:56:33, 4.78s/it] 72%|███████▏ | 13459/18688 [15:51:35<6:32:35, 4.50s/it] 72%|███████▏ | 13460/18688 [15:51:39<6:25:24, 4.42s/it] {'loss': '0.9353', 'grad_norm': '1.526', 'learning_rate': '2.115e-05', 'epoch': '0.7203', 'num_input_tokens_seen': 45225200, 'train_runtime': '5.71e+04', 'train_tokens_per_second': '792'}
72%|███████▏ | 13460/18688 [15:51:39<6:25:24, 4.42s/it] 72%|███████▏ | 13461/18688 [15:51:43<6:10:02, 4.25s/it] 72%|███████▏ | 13462/18688 [15:51:47<6:15:08, 4.31s/it] 72%|███████▏ | 13463/18688 [15:51:53<6:34:20, 4.53s/it] 72%|███████▏ | 13464/18688 [15:51:56<6:09:48, 4.25s/it] 72%|███████▏ | 13465/18688 [15:52:00<6:01:57, 4.16s/it] 72%|███████▏ | 13466/18688 [15:52:04<6:02:28, 4.16s/it] 72%|███████▏ | 13467/18688 [15:52:09<6:20:46, 4.38s/it] 72%|███████▏ | 13468/18688 [15:52:13<6:15:03, 4.31s/it] 72%|███████▏ | 13469/18688 [15:52:17<6:00:47, 4.15s/it] 72%|███████▏ | 13470/18688 [15:52:21<5:50:41, 4.03s/it] {'loss': '0.8791', 'grad_norm': '1.499', 'learning_rate': '2.108e-05', 'epoch': '0.7208', 'num_input_tokens_seen': 45260024, 'train_runtime': '5.714e+04', 'train_tokens_per_second': '792.1'}
72%|███████▏ | 13470/18688 [15:52:21<5:50:41, 4.03s/it] 72%|███████▏ | 13471/18688 [15:52:25<5:51:15, 4.04s/it] 72%|███████▏ | 13472/18688 [15:52:29<5:47:12, 3.99s/it] 72%|███████▏ | 13473/18688 [15:52:33<5:45:18, 3.97s/it] 72%|███████▏ | 13474/18688 [15:52:38<6:14:38, 4.31s/it] 72%|███████▏ | 13475/18688 [15:52:42<6:11:47, 4.28s/it] 72%|███████▏ | 13476/18688 [15:52:46<6:00:31, 4.15s/it] 72%|███████▏ | 13477/18688 [15:52:50<5:55:29, 4.09s/it] 72%|███████▏ | 13478/18688 [15:52:55<6:25:23, 4.44s/it] 72%|███████▏ | 13479/18688 [15:52:59<6:14:32, 4.31s/it] 72%|███████▏ | 13480/18688 [15:53:03<6:13:36, 4.30s/it] {'loss': '0.9405', 'grad_norm': '1.185', 'learning_rate': '2.1e-05', 'epoch': '0.7213', 'num_input_tokens_seen': 45293200, 'train_runtime': '5.718e+04', 'train_tokens_per_second': '792.1'}
72%|███████▏ | 13480/18688 [15:53:03<6:13:36, 4.30s/it] 72%|███████▏ | 13481/18688 [15:53:07<6:03:37, 4.19s/it] 72%|███████▏ | 13482/18688 [15:53:11<6:03:57, 4.19s/it] 72%|███████▏ | 13483/18688 [15:53:16<6:05:07, 4.21s/it] 72%|███████▏ | 13484/18688 [15:53:19<5:51:19, 4.05s/it] 72%|███████▏ | 13485/18688 [15:53:23<5:42:59, 3.96s/it] 72%|███████▏ | 13486/18688 [15:53:28<6:01:47, 4.17s/it] 72%|███████▏ | 13487/18688 [15:53:32<6:02:47, 4.19s/it] 72%|███████▏ | 13488/18688 [15:53:36<6:05:04, 4.21s/it] 72%|███████▏ | 13489/18688 [15:53:40<6:01:58, 4.18s/it] 72%|███████▏ | 13490/18688 [15:53:45<6:07:02, 4.24s/it] {'loss': '0.8565', 'grad_norm': '1.393', 'learning_rate': '2.093e-05', 'epoch': '0.7219', 'num_input_tokens_seen': 45323704, 'train_runtime': '5.723e+04', 'train_tokens_per_second': '792'}
72%|███████▏ | 13490/18688 [15:53:45<6:07:02, 4.24s/it] 72%|███████▏ | 13491/18688 [15:53:49<6:15:08, 4.33s/it] 72%|███████▏ | 13492/18688 [15:53:54<6:30:19, 4.51s/it] 72%|███████▏ | 13493/18688 [15:53:58<6:21:42, 4.41s/it] 72%|███████▏ | 13494/18688 [15:54:03<6:21:29, 4.41s/it] 72%|███████▏ | 13495/18688 [15:54:07<6:20:22, 4.39s/it] 72%|███████▏ | 13496/18688 [15:54:11<6:03:00, 4.19s/it] 72%|███████▏ | 13497/18688 [15:54:16<6:14:25, 4.33s/it] 72%|███████▏ | 13498/18688 [15:54:19<6:01:43, 4.18s/it] 72%|███████▏ | 13499/18688 [15:54:24<5:59:37, 4.16s/it] 72%|███████▏ | 13500/18688 [15:54:28<6:01:48, 4.18s/it] {'loss': '0.9286', 'grad_norm': '1.302', 'learning_rate': '2.085e-05', 'epoch': '0.7224', 'num_input_tokens_seen': 45357712, 'train_runtime': '5.727e+04', 'train_tokens_per_second': '792'}
72%|███████▏ | 13500/18688 [15:54:28<6:01:48, 4.18s/it] 72%|███████▏ | 13501/18688 [15:54:31<5:49:35, 4.04s/it] 72%|███████▏ | 13502/18688 [15:54:35<5:38:32, 3.92s/it] 72%|███████▏ | 13503/18688 [15:54:39<5:51:16, 4.06s/it] 72%|███████▏ | 13504/18688 [15:54:43<5:48:17, 4.03s/it] 72%|███████▏ | 13505/18688 [15:54:48<5:54:23, 4.10s/it] 72%|███████▏ | 13506/18688 [15:54:53<6:21:36, 4.42s/it] 72%|███████▏ | 13507/18688 [15:54:57<6:03:50, 4.21s/it] 72%|███████▏ | 13508/18688 [15:55:01<6:01:02, 4.18s/it] 72%|███████▏ | 13509/18688 [15:55:05<6:16:08, 4.36s/it] 72%|███████▏ | 13510/18688 [15:55:10<6:13:35, 4.33s/it] {'loss': '0.9336', 'grad_norm': '1.227', 'learning_rate': '2.078e-05', 'epoch': '0.7229', 'num_input_tokens_seen': 45390832, 'train_runtime': '5.731e+04', 'train_tokens_per_second': '792'}
72%|███████▏ | 13510/18688 [15:55:10<6:13:35, 4.33s/it] 72%|███████▏ | 13511/18688 [15:55:14<6:13:04, 4.32s/it] 72%|███████▏ | 13512/18688 [15:55:19<6:17:23, 4.37s/it] 72%|███████▏ | 13513/18688 [15:55:23<6:27:48, 4.50s/it] 72%|███████▏ | 13514/18688 [15:55:27<6:06:22, 4.25s/it] 72%|███████▏ | 13515/18688 [15:55:31<5:55:18, 4.12s/it] 72%|███████▏ | 13516/18688 [15:55:34<5:41:36, 3.96s/it] 72%|███████▏ | 13517/18688 [15:55:39<6:08:26, 4.28s/it] 72%|███████▏ | 13518/18688 [15:55:44<6:10:16, 4.30s/it] 72%|███████▏ | 13519/18688 [15:55:47<5:52:41, 4.09s/it] 72%|███████▏ | 13520/18688 [15:55:52<6:10:53, 4.31s/it] {'loss': '0.9432', 'grad_norm': '0.9556', 'learning_rate': '2.07e-05', 'epoch': '0.7235', 'num_input_tokens_seen': 45426936, 'train_runtime': '5.735e+04', 'train_tokens_per_second': '792.1'}
72%|███████▏ | 13520/18688 [15:55:52<6:10:53, 4.31s/it] 72%|███████▏ | 13521/18688 [15:55:57<6:11:30, 4.31s/it] 72%|███████▏ | 13522/18688 [15:56:00<5:58:15, 4.16s/it] 72%|███████▏ | 13523/18688 [15:56:05<6:00:30, 4.19s/it] 72%|███████▏ | 13524/18688 [15:56:09<5:57:05, 4.15s/it] 72%|███████▏ | 13525/18688 [15:56:12<5:40:44, 3.96s/it] 72%|███████▏ | 13526/18688 [15:56:16<5:43:57, 4.00s/it] 72%|███████▏ | 13527/18688 [15:56:20<5:49:19, 4.06s/it] 72%|███████▏ | 13528/18688 [15:56:25<6:07:16, 4.27s/it] 72%|███████▏ | 13529/18688 [15:56:29<5:59:06, 4.18s/it] 72%|███████▏ | 13530/18688 [15:56:33<5:49:41, 4.07s/it] {'loss': '0.9086', 'grad_norm': '1.45', 'learning_rate': '2.063e-05', 'epoch': '0.724', 'num_input_tokens_seen': 45459144, 'train_runtime': '5.739e+04', 'train_tokens_per_second': '792.1'}
72%|███████▏ | 13530/18688 [15:56:33<5:49:41, 4.07s/it] 72%|███████▏ | 13531/18688 [15:56:37<5:59:07, 4.18s/it] 72%|███████▏ | 13532/18688 [15:56:42<6:01:50, 4.21s/it] 72%|███████▏ | 13533/18688 [15:56:46<5:58:40, 4.17s/it] 72%|███████▏ | 13534/18688 [15:56:50<6:11:17, 4.32s/it] 72%|███████▏ | 13535/18688 [15:56:55<6:09:15, 4.30s/it] 72%|███████▏ | 13536/18688 [15:57:00<6:23:50, 4.47s/it] 72%|███████▏ | 13537/18688 [15:57:05<6:44:48, 4.72s/it] 72%|███████▏ | 13538/18688 [15:57:10<6:48:54, 4.76s/it] 72%|███████▏ | 13539/18688 [15:57:14<6:33:52, 4.59s/it] 72%|███████▏ | 13540/18688 [15:57:19<6:34:30, 4.60s/it] {'loss': '1.002', 'grad_norm': '1.393', 'learning_rate': '2.056e-05', 'epoch': '0.7245', 'num_input_tokens_seen': 45495864, 'train_runtime': '5.744e+04', 'train_tokens_per_second': '792.1'}
72%|███████▏ | 13540/18688 [15:57:19<6:34:30, 4.60s/it] 72%|███████▏ | 13541/18688 [15:57:23<6:20:55, 4.44s/it] 72%|███████▏ | 13542/18688 [15:57:27<6:08:57, 4.30s/it] 72%|███████▏ | 13543/18688 [15:57:31<6:03:56, 4.24s/it] 72%|███████▏ | 13544/18688 [15:57:35<6:12:30, 4.34s/it] 72%|███████▏ | 13545/18688 [15:57:39<6:08:36, 4.30s/it] 72%|███████▏ | 13546/18688 [15:57:44<6:15:21, 4.38s/it] 72%|███████▏ | 13547/18688 [15:57:48<6:07:44, 4.29s/it] 72%|███████▏ | 13548/18688 [15:57:53<6:19:30, 4.43s/it] 73%|███████▎ | 13549/18688 [15:57:57<6:22:21, 4.46s/it] 73%|███████▎ | 13550/18688 [15:58:02<6:22:29, 4.47s/it] {'loss': '0.81', 'grad_norm': '1.207', 'learning_rate': '2.048e-05', 'epoch': '0.7251', 'num_input_tokens_seen': 45528392, 'train_runtime': '5.748e+04', 'train_tokens_per_second': '792'}
73%|███████▎ | 13550/18688 [15:58:02<6:22:29, 4.47s/it] 73%|███████▎ | 13551/18688 [15:58:06<6:16:07, 4.39s/it] 73%|███████▎ | 13552/18688 [15:58:10<6:08:35, 4.31s/it] 73%|███████▎ | 13553/18688 [15:58:14<6:04:25, 4.26s/it] 73%|███████▎ | 13554/18688 [15:58:18<5:56:18, 4.16s/it] 73%|███████▎ | 13555/18688 [15:58:24<6:25:14, 4.50s/it] 73%|███████▎ | 13556/18688 [15:58:27<6:05:09, 4.27s/it] 73%|███████▎ | 13557/18688 [15:58:32<6:04:45, 4.27s/it] 73%|███████▎ | 13558/18688 [15:58:36<6:12:17, 4.35s/it] 73%|███████▎ | 13559/18688 [15:58:41<6:16:51, 4.41s/it] 73%|███████▎ | 13560/18688 [15:58:46<6:50:38, 4.80s/it] {'loss': '0.8967', 'grad_norm': '1.392', 'learning_rate': '2.041e-05', 'epoch': '0.7256', 'num_input_tokens_seen': 45563968, 'train_runtime': '5.753e+04', 'train_tokens_per_second': '792'}
73%|███████▎ | 13560/18688 [15:58:46<6:50:38, 4.80s/it] 73%|███████▎ | 13561/18688 [15:58:50<6:19:36, 4.44s/it] 73%|███████▎ | 13562/18688 [15:58:54<6:12:36, 4.36s/it] 73%|███████▎ | 13563/18688 [15:58:58<6:03:55, 4.26s/it] 73%|███████▎ | 13564/18688 [15:59:02<5:53:40, 4.14s/it] 73%|███████▎ | 13565/18688 [15:59:06<5:47:56, 4.08s/it] 73%|███████▎ | 13566/18688 [15:59:11<6:08:03, 4.31s/it] 73%|███████▎ | 13567/18688 [15:59:15<6:05:09, 4.28s/it] 73%|███████▎ | 13568/18688 [15:59:19<6:00:38, 4.23s/it] 73%|███████▎ | 13569/18688 [15:59:23<5:43:20, 4.02s/it] 73%|███████▎ | 13570/18688 [15:59:27<5:46:14, 4.06s/it] {'loss': '0.8279', 'grad_norm': '1.376', 'learning_rate': '2.033e-05', 'epoch': '0.7262', 'num_input_tokens_seen': 45596000, 'train_runtime': '5.757e+04', 'train_tokens_per_second': '792'}
73%|███████▎ | 13570/18688 [15:59:27<5:46:14, 4.06s/it] 73%|███████▎ | 13571/18688 [15:59:31<5:56:52, 4.18s/it] 73%|███████▎ | 13572/18688 [15:59:37<6:28:11, 4.55s/it] 73%|███████▎ | 13573/18688 [15:59:42<6:39:14, 4.68s/it] 73%|███████▎ | 13574/18688 [15:59:45<6:07:11, 4.31s/it] 73%|███████▎ | 13575/18688 [15:59:50<6:10:51, 4.35s/it] 73%|███████▎ | 13576/18688 [15:59:53<5:55:03, 4.17s/it] 73%|███████▎ | 13577/18688 [15:59:57<5:41:30, 4.01s/it] 73%|███████▎ | 13578/18688 [16:00:01<5:47:02, 4.07s/it] 73%|███████▎ | 13579/18688 [16:00:06<6:10:24, 4.35s/it] 73%|███████▎ | 13580/18688 [16:00:11<6:12:43, 4.38s/it] {'loss': '0.9022', 'grad_norm': '1.432', 'learning_rate': '2.026e-05', 'epoch': '0.7267', 'num_input_tokens_seen': 45630320, 'train_runtime': '5.761e+04', 'train_tokens_per_second': '792'}
73%|███████▎ | 13580/18688 [16:00:11<6:12:43, 4.38s/it] 73%|███████▎ | 13581/18688 [16:00:15<6:23:46, 4.51s/it] 73%|███████▎ | 13582/18688 [16:00:19<6:10:25, 4.35s/it] 73%|███████▎ | 13583/18688 [16:00:23<6:01:35, 4.25s/it] 73%|███████▎ | 13584/18688 [16:00:27<5:51:34, 4.13s/it] 73%|███████▎ | 13585/18688 [16:00:31<5:42:20, 4.03s/it] 73%|███████▎ | 13586/18688 [16:00:35<5:28:39, 3.87s/it] 73%|███████▎ | 13587/18688 [16:00:39<5:54:28, 4.17s/it] 73%|███████▎ | 13588/18688 [16:00:44<5:56:08, 4.19s/it] 73%|███████▎ | 13589/18688 [16:00:48<5:53:03, 4.15s/it] 73%|███████▎ | 13590/18688 [16:00:51<5:36:24, 3.96s/it] {'loss': '0.9217', 'grad_norm': '1.197', 'learning_rate': '2.019e-05', 'epoch': '0.7272', 'num_input_tokens_seen': 45661896, 'train_runtime': '5.765e+04', 'train_tokens_per_second': '792'}
73%|███████▎ | 13590/18688 [16:00:51<5:36:24, 3.96s/it] 73%|███████▎ | 13591/18688 [16:00:55<5:33:52, 3.93s/it] 73%|███████▎ | 13592/18688 [16:00:59<5:39:57, 4.00s/it] 73%|███████▎ | 13593/18688 [16:01:04<5:46:43, 4.08s/it] 73%|███████▎ | 13594/18688 [16:01:07<5:41:18, 4.02s/it] 73%|███████▎ | 13595/18688 [16:01:11<5:40:30, 4.01s/it] 73%|███████▎ | 13596/18688 [16:01:16<5:44:53, 4.06s/it] 73%|███████▎ | 13597/18688 [16:01:20<5:50:08, 4.13s/it] 73%|███████▎ | 13598/18688 [16:01:24<5:57:22, 4.21s/it] 73%|███████▎ | 13599/18688 [16:01:28<5:43:41, 4.05s/it] 73%|███████▎ | 13600/18688 [16:01:32<5:38:56, 4.00s/it] {'loss': '0.9573', 'grad_norm': '1.294', 'learning_rate': '2.011e-05', 'epoch': '0.7278', 'num_input_tokens_seen': 45693664, 'train_runtime': '5.769e+04', 'train_tokens_per_second': '792'}
73%|███████▎ | 13600/18688 [16:01:32<5:38:56, 4.00s/it] 73%|███████▎ | 13601/18688 [16:01:36<5:29:43, 3.89s/it] 73%|███████▎ | 13602/18688 [16:01:40<5:52:53, 4.16s/it] 73%|███████▎ | 13603/18688 [16:01:45<5:58:32, 4.23s/it] 73%|███████▎ | 13604/18688 [16:01:49<6:09:14, 4.36s/it] 73%|███████▎ | 13605/18688 [16:01:53<6:02:01, 4.27s/it] 73%|███████▎ | 13606/18688 [16:01:58<6:14:32, 4.42s/it] 73%|███████▎ | 13607/18688 [16:02:03<6:33:58, 4.65s/it] 73%|███████▎ | 13608/18688 [16:02:07<6:16:36, 4.45s/it] 73%|███████▎ | 13609/18688 [16:02:11<5:53:51, 4.18s/it] 73%|███████▎ | 13610/18688 [16:02:15<6:01:52, 4.28s/it] {'loss': '1', 'grad_norm': '1.426', 'learning_rate': '2.004e-05', 'epoch': '0.7283', 'num_input_tokens_seen': 45730440, 'train_runtime': '5.774e+04', 'train_tokens_per_second': '792.1'}
73%|███████▎ | 13610/18688 [16:02:15<6:01:52, 4.28s/it] 73%|███████▎ | 13611/18688 [16:02:20<6:17:01, 4.46s/it] 73%|███████▎ | 13612/18688 [16:02:25<6:18:48, 4.48s/it] 73%|███████▎ | 13613/18688 [16:02:30<6:46:57, 4.81s/it] 73%|███████▎ | 13614/18688 [16:02:34<6:20:00, 4.49s/it] 73%|███████▎ | 13615/18688 [16:02:38<6:02:20, 4.29s/it] 73%|███████▎ | 13616/18688 [16:02:42<5:53:01, 4.18s/it] 73%|███████▎ | 13617/18688 [16:02:47<6:05:41, 4.33s/it] 73%|███████▎ | 13618/18688 [16:02:52<6:28:42, 4.60s/it] 73%|███████▎ | 13619/18688 [16:02:57<6:48:34, 4.84s/it] 73%|███████▎ | 13620/18688 [16:03:02<6:35:09, 4.68s/it] {'loss': '0.8832', 'grad_norm': '1.539', 'learning_rate': '1.997e-05', 'epoch': '0.7288', 'num_input_tokens_seen': 45768744, 'train_runtime': '5.778e+04', 'train_tokens_per_second': '792.1'}
73%|███████▎ | 13620/18688 [16:03:02<6:35:09, 4.68s/it] 73%|███████▎ | 13621/18688 [16:03:05<6:16:28, 4.46s/it] 73%|███████▎ | 13622/18688 [16:03:10<6:23:25, 4.54s/it] 73%|███████▎ | 13623/18688 [16:03:14<6:11:30, 4.40s/it] 73%|███████▎ | 13624/18688 [16:03:19<6:19:44, 4.50s/it] 73%|███████▎ | 13625/18688 [16:03:23<6:18:35, 4.49s/it] 73%|███████▎ | 13626/18688 [16:03:29<6:33:10, 4.66s/it] 73%|███████▎ | 13627/18688 [16:03:33<6:23:57, 4.55s/it] 73%|███████▎ | 13628/18688 [16:03:37<6:05:04, 4.33s/it] 73%|███████▎ | 13629/18688 [16:03:40<5:51:33, 4.17s/it] 73%|███████▎ | 13630/18688 [16:03:44<5:40:43, 4.04s/it] {'loss': '0.9612', 'grad_norm': '1.551', 'learning_rate': '1.99e-05', 'epoch': '0.7294', 'num_input_tokens_seen': 45803008, 'train_runtime': '5.782e+04', 'train_tokens_per_second': '792.1'}
73%|███████▎ | 13630/18688 [16:03:44<5:40:43, 4.04s/it] 73%|███████▎ | 13631/18688 [16:03:48<5:28:53, 3.90s/it] 73%|███████▎ | 13632/18688 [16:03:52<5:34:57, 3.98s/it] 73%|███████▎ | 13633/18688 [16:03:57<5:56:33, 4.23s/it] 73%|███████▎ | 13634/18688 [16:04:00<5:42:44, 4.07s/it] 73%|███████▎ | 13635/18688 [16:04:04<5:42:41, 4.07s/it] 73%|███████▎ | 13636/18688 [16:04:10<6:07:57, 4.37s/it] 73%|███████▎ | 13637/18688 [16:04:14<6:02:17, 4.30s/it] 73%|███████▎ | 13638/18688 [16:04:18<5:57:13, 4.24s/it] 73%|███████▎ | 13639/18688 [16:04:23<6:10:08, 4.40s/it] 73%|███████▎ | 13640/18688 [16:04:27<6:00:43, 4.29s/it] {'loss': '0.9343', 'grad_norm': '1.354', 'learning_rate': '1.982e-05', 'epoch': '0.7299', 'num_input_tokens_seen': 45837280, 'train_runtime': '5.787e+04', 'train_tokens_per_second': '792.1'}
73%|███████▎ | 13640/18688 [16:04:27<6:00:43, 4.29s/it] 73%|███████▎ | 13641/18688 [16:04:30<5:47:56, 4.14s/it] 73%|███████▎ | 13642/18688 [16:04:34<5:39:10, 4.03s/it] 73%|███████▎ | 13643/18688 [16:04:38<5:37:04, 4.01s/it] 73%|███████▎ | 13644/18688 [16:04:42<5:33:57, 3.97s/it] 73%|███████▎ | 13645/18688 [16:04:46<5:39:02, 4.03s/it] 73%|███████▎ | 13646/18688 [16:04:50<5:28:48, 3.91s/it] 73%|███████▎ | 13647/18688 [16:04:54<5:24:35, 3.86s/it] 73%|███████▎ | 13648/18688 [16:04:57<5:17:30, 3.78s/it] 73%|███████▎ | 13649/18688 [16:05:02<5:36:35, 4.01s/it] 73%|███████▎ | 13650/18688 [16:05:06<5:53:26, 4.21s/it] {'loss': '0.9166', 'grad_norm': '0.9932', 'learning_rate': '1.975e-05', 'epoch': '0.7304', 'num_input_tokens_seen': 45867312, 'train_runtime': '5.791e+04', 'train_tokens_per_second': '792.1'}
73%|███████▎ | 13650/18688 [16:05:06<5:53:26, 4.21s/it] 73%|███████▎ | 13651/18688 [16:05:10<5:42:37, 4.08s/it] 73%|███████▎ | 13652/18688 [16:05:15<5:51:42, 4.19s/it] 73%|███████▎ | 13653/18688 [16:05:19<6:06:25, 4.37s/it] 73%|███████▎ | 13654/18688 [16:05:23<5:49:18, 4.16s/it] 73%|███████▎ | 13655/18688 [16:05:27<5:48:11, 4.15s/it] 73%|███████▎ | 13656/18688 [16:05:31<5:46:14, 4.13s/it] 73%|███████▎ | 13657/18688 [16:05:35<5:37:37, 4.03s/it] 73%|███████▎ | 13658/18688 [16:05:39<5:45:26, 4.12s/it] 73%|███████▎ | 13659/18688 [16:05:43<5:44:00, 4.10s/it] 73%|███████▎ | 13660/18688 [16:05:47<5:34:12, 3.99s/it] {'loss': '0.9712', 'grad_norm': '1.194', 'learning_rate': '1.968e-05', 'epoch': '0.731', 'num_input_tokens_seen': 45898472, 'train_runtime': '5.795e+04', 'train_tokens_per_second': '792.1'}
73%|███████▎ | 13660/18688 [16:05:47<5:34:12, 3.99s/it] 73%|███████▎ | 13661/18688 [16:05:51<5:38:39, 4.04s/it] 73%|███████▎ | 13662/18688 [16:05:56<5:56:25, 4.26s/it] 73%|███████▎ | 13663/18688 [16:06:02<6:29:35, 4.65s/it] 73%|███████▎ | 13664/18688 [16:06:05<6:00:21, 4.30s/it] 73%|███████▎ | 13665/18688 [16:06:09<5:42:52, 4.10s/it] 73%|███████▎ | 13666/18688 [16:06:13<5:34:15, 3.99s/it] 73%|███████▎ | 13667/18688 [16:06:17<5:50:22, 4.19s/it] 73%|███████▎ | 13668/18688 [16:06:21<5:38:03, 4.04s/it] 73%|███████▎ | 13669/18688 [16:06:26<6:04:13, 4.35s/it] 73%|███████▎ | 13670/18688 [16:06:30<5:52:55, 4.22s/it] {'loss': '0.9304', 'grad_norm': '1.237', 'learning_rate': '1.96e-05', 'epoch': '0.7315', 'num_input_tokens_seen': 45934840, 'train_runtime': '5.799e+04', 'train_tokens_per_second': '792.1'}
73%|███████▎ | 13670/18688 [16:06:30<5:52:55, 4.22s/it] 73%|███████▎ | 13671/18688 [16:06:34<5:41:28, 4.08s/it] 73%|███████▎ | 13672/18688 [16:06:38<5:55:30, 4.25s/it] 73%|███████▎ | 13673/18688 [16:06:43<5:59:16, 4.30s/it] 73%|███████▎ | 13674/18688 [16:06:46<5:38:06, 4.05s/it] 73%|███████▎ | 13675/18688 [16:06:50<5:30:37, 3.96s/it] 73%|███████▎ | 13676/18688 [16:06:53<5:19:03, 3.82s/it] 73%|███████▎ | 13677/18688 [16:06:57<5:18:35, 3.81s/it] 73%|███████▎ | 13678/18688 [16:07:02<5:36:10, 4.03s/it] 73%|███████▎ | 13679/18688 [16:07:06<5:31:15, 3.97s/it] 73%|███████▎ | 13680/18688 [16:07:12<6:25:45, 4.62s/it] {'loss': '0.9018', 'grad_norm': '1.166', 'learning_rate': '1.953e-05', 'epoch': '0.732', 'num_input_tokens_seen': 45967560, 'train_runtime': '5.803e+04', 'train_tokens_per_second': '792.1'}
73%|███████▎ | 13680/18688 [16:07:12<6:25:45, 4.62s/it] 73%|███████▎ | 13681/18688 [16:07:17<6:34:15, 4.72s/it] 73%|███████▎ | 13682/18688 [16:07:21<6:19:08, 4.54s/it] 73%|███████▎ | 13683/18688 [16:07:25<6:07:39, 4.41s/it] 73%|███████▎ | 13684/18688 [16:07:28<5:47:44, 4.17s/it] 73%|███████▎ | 13685/18688 [16:07:32<5:36:49, 4.04s/it] 73%|███████▎ | 13686/18688 [16:07:36<5:34:23, 4.01s/it] 73%|███████▎ | 13687/18688 [16:07:40<5:27:14, 3.93s/it] 73%|███████▎ | 13688/18688 [16:07:45<5:45:00, 4.14s/it] 73%|███████▎ | 13689/18688 [16:07:49<6:04:31, 4.38s/it] 73%|███████▎ | 13690/18688 [16:07:53<5:47:17, 4.17s/it] {'loss': '0.933', 'grad_norm': '1.171', 'learning_rate': '1.946e-05', 'epoch': '0.7326', 'num_input_tokens_seen': 46000784, 'train_runtime': '5.807e+04', 'train_tokens_per_second': '792.1'}
73%|███████▎ | 13690/18688 [16:07:53<5:47:17, 4.17s/it] 73%|███████▎ | 13691/18688 [16:07:58<5:55:03, 4.26s/it] 73%|███████▎ | 13692/18688 [16:08:02<5:51:25, 4.22s/it] 73%|███████▎ | 13693/18688 [16:08:06<5:51:53, 4.23s/it] 73%|███████▎ | 13694/18688 [16:08:11<6:10:05, 4.45s/it] 73%|███████▎ | 13695/18688 [16:08:16<6:24:03, 4.62s/it] 73%|███████▎ | 13696/18688 [16:08:20<5:58:32, 4.31s/it] 73%|███████▎ | 13697/18688 [16:08:23<5:43:28, 4.13s/it] 73%|███████▎ | 13698/18688 [16:08:27<5:32:52, 4.00s/it] 73%|███████▎ | 13699/18688 [16:08:31<5:26:03, 3.92s/it] 73%|███████▎ | 13700/18688 [16:08:35<5:38:49, 4.08s/it] {'loss': '0.9248', 'grad_norm': '1.199', 'learning_rate': '1.939e-05', 'epoch': '0.7331', 'num_input_tokens_seen': 46031872, 'train_runtime': '5.812e+04', 'train_tokens_per_second': '792.1'}
73%|███████▎ | 13700/18688 [16:08:35<5:38:49, 4.08s/it] 73%|███████▎ | 13701/18688 [16:08:39<5:37:10, 4.06s/it] 73%|███████▎ | 13702/18688 [16:08:43<5:35:52, 4.04s/it] 73%|███████▎ | 13703/18688 [16:08:47<5:33:55, 4.02s/it] 73%|███████▎ | 13704/18688 [16:08:51<5:28:18, 3.95s/it] 73%|███████▎ | 13705/18688 [16:08:55<5:33:16, 4.01s/it] 73%|███████▎ | 13706/18688 [16:08:59<5:31:45, 4.00s/it] 73%|███████▎ | 13707/18688 [16:09:03<5:26:51, 3.94s/it] 73%|███████▎ | 13708/18688 [16:09:08<5:48:01, 4.19s/it] 73%|███████▎ | 13709/18688 [16:09:12<6:01:06, 4.35s/it] 73%|███████▎ | 13710/18688 [16:09:16<5:40:59, 4.11s/it] {'loss': '0.9107', 'grad_norm': '1.454', 'learning_rate': '1.931e-05', 'epoch': '0.7336', 'num_input_tokens_seen': 46062608, 'train_runtime': '5.816e+04', 'train_tokens_per_second': '792'}
73%|███████▎ | 13710/18688 [16:09:16<5:40:59, 4.11s/it] 73%|███████▎ | 13711/18688 [16:09:20<5:32:48, 4.01s/it] 73%|███████▎ | 13712/18688 [16:09:23<5:27:48, 3.95s/it] 73%|███████▎ | 13713/18688 [16:09:28<5:51:57, 4.24s/it] 73%|███████▎ | 13714/18688 [16:09:33<6:00:12, 4.35s/it] 73%|███████▎ | 13715/18688 [16:09:36<5:39:08, 4.09s/it] 73%|███████▎ | 13716/18688 [16:09:41<5:50:25, 4.23s/it] 73%|███████▎ | 13717/18688 [16:09:47<6:24:22, 4.64s/it] 73%|███████▎ | 13718/18688 [16:09:51<6:08:02, 4.44s/it] 73%|███████▎ | 13719/18688 [16:09:56<6:25:16, 4.65s/it] 73%|███████▎ | 13720/18688 [16:10:00<6:22:06, 4.61s/it] {'loss': '0.9246', 'grad_norm': '1.357', 'learning_rate': '1.924e-05', 'epoch': '0.7342', 'num_input_tokens_seen': 46098736, 'train_runtime': '5.82e+04', 'train_tokens_per_second': '792.1'}
73%|███████▎ | 13720/18688 [16:10:00<6:22:06, 4.61s/it] 73%|███████▎ | 13721/18688 [16:10:05<6:22:25, 4.62s/it] 73%|███████▎ | 13722/18688 [16:10:08<5:56:50, 4.31s/it] 73%|███████▎ | 13723/18688 [16:10:13<6:02:58, 4.39s/it] 73%|███████▎ | 13724/18688 [16:10:17<5:53:09, 4.27s/it] 73%|███████▎ | 13725/18688 [16:10:22<6:00:47, 4.36s/it] 73%|███████▎ | 13726/18688 [16:10:26<5:56:19, 4.31s/it] 73%|███████▎ | 13727/18688 [16:10:31<6:10:44, 4.48s/it] 73%|███████▎ | 13728/18688 [16:10:35<6:11:35, 4.50s/it] 73%|███████▎ | 13729/18688 [16:10:40<6:30:11, 4.72s/it] 73%|███████▎ | 13730/18688 [16:10:44<6:11:26, 4.50s/it] {'loss': '0.919', 'grad_norm': '1.474', 'learning_rate': '1.917e-05', 'epoch': '0.7347', 'num_input_tokens_seen': 46134672, 'train_runtime': '5.824e+04', 'train_tokens_per_second': '792.1'}
73%|███████▎ | 13730/18688 [16:10:44<6:11:26, 4.50s/it] 73%|███████▎ | 13731/18688 [16:10:48<5:48:50, 4.22s/it] 73%|███████▎ | 13732/18688 [16:10:52<5:34:14, 4.05s/it] 73%|███████▎ | 13733/18688 [16:10:55<5:22:33, 3.91s/it] 73%|███████▎ | 13734/18688 [16:10:59<5:20:16, 3.88s/it] 73%|███████▎ | 13735/18688 [16:11:03<5:16:52, 3.84s/it] 74%|███████▎ | 13736/18688 [16:11:07<5:26:15, 3.95s/it] 74%|███████▎ | 13737/18688 [16:11:11<5:37:06, 4.09s/it] 74%|███████▎ | 13738/18688 [16:11:16<5:51:10, 4.26s/it] 74%|███████▎ | 13739/18688 [16:11:21<6:03:08, 4.40s/it] 74%|███████▎ | 13740/18688 [16:11:25<5:58:04, 4.34s/it] {'loss': '0.918', 'grad_norm': '1.355', 'learning_rate': '1.91e-05', 'epoch': '0.7353', 'num_input_tokens_seen': 46165248, 'train_runtime': '5.829e+04', 'train_tokens_per_second': '792.1'}
74%|███████▎ | 13740/18688 [16:11:25<5:58:04, 4.34s/it] 74%|███████▎ | 13741/18688 [16:11:29<5:44:16, 4.18s/it] 74%|███████▎ | 13742/18688 [16:11:33<5:33:42, 4.05s/it] 74%|███████▎ | 13743/18688 [16:11:36<5:26:58, 3.97s/it] 74%|███████▎ | 13744/18688 [16:11:40<5:17:23, 3.85s/it] 74%|███████▎ | 13745/18688 [16:11:44<5:34:28, 4.06s/it] 74%|███████▎ | 13746/18688 [16:11:49<5:44:57, 4.19s/it] 74%|███████▎ | 13747/18688 [16:11:54<6:00:03, 4.37s/it] 74%|███████▎ | 13748/18688 [16:11:59<6:13:33, 4.54s/it] 74%|███████▎ | 13749/18688 [16:12:03<6:08:13, 4.47s/it] 74%|███████▎ | 13750/18688 [16:12:07<5:53:09, 4.29s/it] {'loss': '0.8831', 'grad_norm': '1.222', 'learning_rate': '1.903e-05', 'epoch': '0.7358', 'num_input_tokens_seen': 46200256, 'train_runtime': '5.833e+04', 'train_tokens_per_second': '792.1'}
74%|███████▎ | 13750/18688 [16:12:07<5:53:09, 4.29s/it] 74%|███████▎ | 13751/18688 [16:12:11<5:43:08, 4.17s/it] 74%|███████▎ | 13752/18688 [16:12:14<5:30:57, 4.02s/it] 74%|███████▎ | 13753/18688 [16:12:19<5:36:21, 4.09s/it] 74%|███████▎ | 13754/18688 [16:12:23<5:48:25, 4.24s/it] 74%|███████▎ | 13755/18688 [16:12:28<5:53:32, 4.30s/it] 74%|███████▎ | 13756/18688 [16:12:33<6:17:07, 4.59s/it] 74%|███████▎ | 13757/18688 [16:12:37<6:04:43, 4.44s/it] 74%|███████▎ | 13758/18688 [16:12:41<5:47:42, 4.23s/it] 74%|███████▎ | 13759/18688 [16:12:45<5:46:40, 4.22s/it] 74%|███████▎ | 13760/18688 [16:12:48<5:28:51, 4.00s/it] {'loss': '0.8731', 'grad_norm': '1.215', 'learning_rate': '1.896e-05', 'epoch': '0.7363', 'num_input_tokens_seen': 46232192, 'train_runtime': '5.837e+04', 'train_tokens_per_second': '792.1'}
74%|███████▎ | 13760/18688 [16:12:48<5:28:51, 4.00s/it] 74%|███████▎ | 13761/18688 [16:12:53<5:39:33, 4.14s/it] 74%|███████▎ | 13762/18688 [16:12:58<5:59:19, 4.38s/it] 74%|███████▎ | 13763/18688 [16:13:02<5:43:44, 4.19s/it] 74%|███████▎ | 13764/18688 [16:13:05<5:33:41, 4.07s/it] 74%|███████▎ | 13765/18688 [16:13:09<5:28:55, 4.01s/it] 74%|███████▎ | 13766/18688 [16:13:13<5:23:47, 3.95s/it] 74%|███████▎ | 13767/18688 [16:13:19<6:11:11, 4.53s/it] 74%|███████▎ | 13768/18688 [16:13:24<6:13:26, 4.55s/it] 74%|███████▎ | 13769/18688 [16:13:28<5:59:22, 4.38s/it] 74%|███████▎ | 13770/18688 [16:13:31<5:46:31, 4.23s/it] {'loss': '0.8659', 'grad_norm': '1.307', 'learning_rate': '1.888e-05', 'epoch': '0.7369', 'num_input_tokens_seen': 46266584, 'train_runtime': '5.841e+04', 'train_tokens_per_second': '792.1'}
74%|███████▎ | 13770/18688 [16:13:31<5:46:31, 4.23s/it] 74%|███████▎ | 13771/18688 [16:13:37<6:16:34, 4.60s/it] 74%|███████▎ | 13772/18688 [16:13:41<6:06:41, 4.48s/it] 74%|███████▎ | 13773/18688 [16:13:45<5:58:53, 4.38s/it] 74%|███████▎ | 13774/18688 [16:13:49<5:37:23, 4.12s/it] 74%|███████▎ | 13775/18688 [16:13:54<5:53:12, 4.31s/it] 74%|███████▎ | 13776/18688 [16:13:57<5:34:56, 4.09s/it] 74%|███████▎ | 13777/18688 [16:14:02<5:47:58, 4.25s/it] 74%|███████▎ | 13778/18688 [16:14:06<5:40:38, 4.16s/it] 74%|███████▎ | 13779/18688 [16:14:10<5:45:01, 4.22s/it] 74%|███████▎ | 13780/18688 [16:14:14<5:37:04, 4.12s/it] {'loss': '0.9505', 'grad_norm': '1.303', 'learning_rate': '1.881e-05', 'epoch': '0.7374', 'num_input_tokens_seen': 46302480, 'train_runtime': '5.845e+04', 'train_tokens_per_second': '792.1'}
74%|███████▎ | 13780/18688 [16:14:14<5:37:04, 4.12s/it] 74%|███████▎ | 13781/18688 [16:14:18<5:32:37, 4.07s/it] 74%|███████▎ | 13782/18688 [16:14:22<5:31:17, 4.05s/it] 74%|███████▍ | 13783/18688 [16:14:26<5:41:13, 4.17s/it] 74%|███████▍ | 13784/18688 [16:14:30<5:35:31, 4.11s/it] 74%|███████▍ | 13785/18688 [16:14:34<5:30:00, 4.04s/it] 74%|███████▍ | 13786/18688 [16:14:39<5:40:22, 4.17s/it] 74%|███████▍ | 13787/18688 [16:14:43<5:38:24, 4.14s/it] 74%|███████▍ | 13788/18688 [16:14:47<5:49:58, 4.29s/it] 74%|███████▍ | 13789/18688 [16:14:51<5:32:40, 4.07s/it] 74%|███████▍ | 13790/18688 [16:14:56<5:46:14, 4.24s/it] {'loss': '0.9585', 'grad_norm': '1.459', 'learning_rate': '1.874e-05', 'epoch': '0.7379', 'num_input_tokens_seen': 46333888, 'train_runtime': '5.85e+04', 'train_tokens_per_second': '792.1'}
74%|███████▍ | 13790/18688 [16:14:56<5:46:14, 4.24s/it] 74%|███████▍ | 13791/18688 [16:14:59<5:34:54, 4.10s/it] 74%|███████▍ | 13792/18688 [16:15:03<5:28:08, 4.02s/it] 74%|███████▍ | 13793/18688 [16:15:07<5:32:14, 4.07s/it] 74%|███████▍ | 13794/18688 [16:15:11<5:33:44, 4.09s/it] 74%|███████▍ | 13795/18688 [16:15:15<5:31:28, 4.06s/it] 74%|███████▍ | 13796/18688 [16:15:19<5:25:42, 3.99s/it] 74%|███████▍ | 13797/18688 [16:15:24<5:33:27, 4.09s/it] 74%|███████▍ | 13798/18688 [16:15:28<5:47:36, 4.27s/it] 74%|███████▍ | 13799/18688 [16:15:34<6:10:43, 4.55s/it] 74%|███████▍ | 13800/18688 [16:15:38<5:57:25, 4.39s/it] {'loss': '0.8828', 'grad_norm': '1.106', 'learning_rate': '1.867e-05', 'epoch': '0.7385', 'num_input_tokens_seen': 46365576, 'train_runtime': '5.854e+04', 'train_tokens_per_second': '792.1'}
74%|███████▍ | 13800/18688 [16:15:38<5:57:25, 4.39s/it] 74%|███████▍ | 13801/18688 [16:15:42<6:03:53, 4.47s/it] 74%|███████▍ | 13802/18688 [16:15:47<6:23:11, 4.71s/it] 74%|███████▍ | 13803/18688 [16:15:52<6:13:55, 4.59s/it] 74%|███████▍ | 13804/18688 [16:15:56<6:13:08, 4.58s/it] 74%|███████▍ | 13805/18688 [16:16:01<6:05:44, 4.49s/it] 74%|███████▍ | 13806/18688 [16:16:05<6:02:20, 4.45s/it] 74%|███████▍ | 13807/18688 [16:16:09<5:50:57, 4.31s/it] 74%|███████▍ | 13808/18688 [16:16:13<5:43:58, 4.23s/it] 74%|███████▍ | 13809/18688 [16:16:17<5:45:13, 4.25s/it] 74%|███████▍ | 13810/18688 [16:16:21<5:32:03, 4.08s/it] {'loss': '0.9025', 'grad_norm': '1.546', 'learning_rate': '1.86e-05', 'epoch': '0.739', 'num_input_tokens_seen': 46402096, 'train_runtime': '5.858e+04', 'train_tokens_per_second': '792.1'}
74%|███████▍ | 13810/18688 [16:16:21<5:32:03, 4.08s/it] 74%|███████▍ | 13811/18688 [16:16:27<6:24:12, 4.73s/it] 74%|███████▍ | 13812/18688 [16:16:31<6:08:31, 4.53s/it] 74%|███████▍ | 13813/18688 [16:16:36<6:11:38, 4.57s/it] 74%|███████▍ | 13814/18688 [16:16:40<5:50:03, 4.31s/it] 74%|███████▍ | 13815/18688 [16:16:44<5:48:49, 4.29s/it] 74%|███████▍ | 13816/18688 [16:16:48<5:32:50, 4.10s/it] 74%|███████▍ | 13817/18688 [16:16:52<5:52:38, 4.34s/it] 74%|███████▍ | 13818/18688 [16:16:57<5:47:28, 4.28s/it] 74%|███████▍ | 13819/18688 [16:17:01<5:48:32, 4.30s/it] 74%|███████▍ | 13820/18688 [16:17:05<5:33:00, 4.10s/it] {'loss': '0.9472', 'grad_norm': '1.591', 'learning_rate': '1.853e-05', 'epoch': '0.7395', 'num_input_tokens_seen': 46438560, 'train_runtime': '5.863e+04', 'train_tokens_per_second': '792.1'}
74%|███████▍ | 13820/18688 [16:17:05<5:33:00, 4.10s/it] 74%|███████▍ | 13821/18688 [16:17:09<5:37:01, 4.15s/it] 74%|███████▍ | 13822/18688 [16:17:13<5:47:02, 4.28s/it] 74%|███████▍ | 13823/18688 [16:17:18<6:00:34, 4.45s/it] 74%|███████▍ | 13824/18688 [16:17:22<5:49:15, 4.31s/it] 74%|███████▍ | 13825/18688 [16:17:27<6:05:57, 4.52s/it] 74%|███████▍ | 13826/18688 [16:17:32<6:01:43, 4.46s/it] 74%|███████▍ | 13827/18688 [16:17:36<5:50:20, 4.32s/it] 74%|███████▍ | 13828/18688 [16:17:40<6:02:06, 4.47s/it] 74%|███████▍ | 13829/18688 [16:17:44<5:39:07, 4.19s/it] 74%|███████▍ | 13830/18688 [16:17:49<5:59:10, 4.44s/it] {'loss': '0.9703', 'grad_norm': '1.193', 'learning_rate': '1.846e-05', 'epoch': '0.7401', 'num_input_tokens_seen': 46476488, 'train_runtime': '5.867e+04', 'train_tokens_per_second': '792.2'}
74%|███████▍ | 13830/18688 [16:17:49<5:59:10, 4.44s/it] 74%|███████▍ | 13831/18688 [16:17:53<6:00:00, 4.45s/it] 74%|███████▍ | 13832/18688 [16:17:58<5:56:45, 4.41s/it] 74%|███████▍ | 13833/18688 [16:18:03<6:06:49, 4.53s/it] 74%|███████▍ | 13834/18688 [16:18:08<6:19:14, 4.69s/it] 74%|███████▍ | 13835/18688 [16:18:12<6:00:17, 4.45s/it] 74%|███████▍ | 13836/18688 [16:18:16<5:58:22, 4.43s/it] 74%|███████▍ | 13837/18688 [16:18:20<5:48:06, 4.31s/it] 74%|███████▍ | 13838/18688 [16:18:24<5:52:09, 4.36s/it] 74%|███████▍ | 13839/18688 [16:18:29<5:50:17, 4.33s/it] 74%|███████▍ | 13840/18688 [16:18:32<5:35:51, 4.16s/it] {'loss': '0.9462', 'grad_norm': '0.99', 'learning_rate': '1.839e-05', 'epoch': '0.7406', 'num_input_tokens_seen': 46512696, 'train_runtime': '5.871e+04', 'train_tokens_per_second': '792.2'}
74%|███████▍ | 13840/18688 [16:18:32<5:35:51, 4.16s/it] 74%|███████▍ | 13841/18688 [16:18:36<5:32:09, 4.11s/it] 74%|███████▍ | 13842/18688 [16:18:41<5:34:07, 4.14s/it] 74%|███████▍ | 13843/18688 [16:18:45<5:42:48, 4.25s/it] 74%|███████▍ | 13844/18688 [16:18:50<5:46:03, 4.29s/it] 74%|███████▍ | 13845/18688 [16:18:54<5:53:51, 4.38s/it] 74%|███████▍ | 13846/18688 [16:18:59<6:11:16, 4.60s/it] 74%|███████▍ | 13847/18688 [16:19:03<5:59:32, 4.46s/it] 74%|███████▍ | 13848/18688 [16:19:08<5:56:57, 4.43s/it] 74%|███████▍ | 13849/18688 [16:19:12<5:49:12, 4.33s/it] 74%|███████▍ | 13850/18688 [16:19:16<5:41:10, 4.23s/it] {'loss': '0.9244', 'grad_norm': '1.211', 'learning_rate': '1.831e-05', 'epoch': '0.7411', 'num_input_tokens_seen': 46546440, 'train_runtime': '5.876e+04', 'train_tokens_per_second': '792.2'}
74%|███████▍ | 13850/18688 [16:19:16<5:41:10, 4.23s/it] 74%|███████▍ | 13851/18688 [16:19:20<5:45:04, 4.28s/it] 74%|███████▍ | 13852/18688 [16:19:24<5:26:01, 4.05s/it] 74%|███████▍ | 13853/18688 [16:19:27<5:12:21, 3.88s/it] 74%|███████▍ | 13854/18688 [16:19:31<5:17:50, 3.95s/it] 74%|███████▍ | 13855/18688 [16:19:36<5:36:01, 4.17s/it] 74%|███████▍ | 13856/18688 [16:19:40<5:29:25, 4.09s/it] 74%|███████▍ | 13857/18688 [16:19:44<5:26:47, 4.06s/it] 74%|███████▍ | 13858/18688 [16:19:48<5:34:42, 4.16s/it] 74%|███████▍ | 13859/18688 [16:19:53<5:45:58, 4.30s/it] 74%|███████▍ | 13860/18688 [16:19:56<5:28:04, 4.08s/it] {'loss': '0.9052', 'grad_norm': '1.39', 'learning_rate': '1.824e-05', 'epoch': '0.7417', 'num_input_tokens_seen': 46577360, 'train_runtime': '5.88e+04', 'train_tokens_per_second': '792.2'}
74%|███████▍ | 13860/18688 [16:19:56<5:28:04, 4.08s/it] 74%|███████▍ | 13861/18688 [16:20:01<5:29:35, 4.10s/it] 74%|███████▍ | 13862/18688 [16:20:04<5:23:04, 4.02s/it] 74%|███████▍ | 13863/18688 [16:20:09<5:38:32, 4.21s/it] 74%|███████▍ | 13864/18688 [16:20:13<5:33:45, 4.15s/it] 74%|███████▍ | 13865/18688 [16:20:18<5:41:29, 4.25s/it] 74%|███████▍ | 13866/18688 [16:20:22<5:43:24, 4.27s/it] 74%|███████▍ | 13867/18688 [16:20:26<5:37:09, 4.20s/it] 74%|███████▍ | 13868/18688 [16:20:31<5:46:51, 4.32s/it] 74%|███████▍ | 13869/18688 [16:20:35<5:58:20, 4.46s/it] 74%|███████▍ | 13870/18688 [16:20:39<5:49:49, 4.36s/it] {'loss': '0.921', 'grad_norm': '1.33', 'learning_rate': '1.817e-05', 'epoch': '0.7422', 'num_input_tokens_seen': 46609400, 'train_runtime': '5.884e+04', 'train_tokens_per_second': '792.1'}
74%|███████▍ | 13870/18688 [16:20:39<5:49:49, 4.36s/it] 74%|███████▍ | 13871/18688 [16:20:44<6:01:38, 4.50s/it] 74%|███████▍ | 13872/18688 [16:20:49<6:08:04, 4.59s/it] 74%|███████▍ | 13873/18688 [16:20:53<6:03:45, 4.53s/it] 74%|███████▍ | 13874/18688 [16:20:59<6:21:43, 4.76s/it] 74%|███████▍ | 13875/18688 [16:21:03<6:07:59, 4.59s/it] 74%|███████▍ | 13876/18688 [16:21:07<5:57:59, 4.46s/it] 74%|███████▍ | 13877/18688 [16:21:12<6:02:26, 4.52s/it] 74%|███████▍ | 13878/18688 [16:21:16<6:01:48, 4.51s/it] 74%|███████▍ | 13879/18688 [16:21:22<6:25:06, 4.80s/it] 74%|███████▍ | 13880/18688 [16:21:26<6:21:15, 4.76s/it] {'loss': '1.019', 'grad_norm': '1.133', 'learning_rate': '1.81e-05', 'epoch': '0.7427', 'num_input_tokens_seen': 46648576, 'train_runtime': '5.889e+04', 'train_tokens_per_second': '792.2'}
74%|███████▍ | 13880/18688 [16:21:26<6:21:15, 4.76s/it] 74%|███████▍ | 13881/18688 [16:21:31<6:07:11, 4.58s/it] 74%|███████▍ | 13882/18688 [16:21:35<5:56:55, 4.46s/it] 74%|███████▍ | 13883/18688 [16:21:39<5:45:06, 4.31s/it] 74%|███████▍ | 13884/18688 [16:21:42<5:28:11, 4.10s/it] 74%|███████▍ | 13885/18688 [16:21:46<5:18:42, 3.98s/it] 74%|███████▍ | 13886/18688 [16:21:50<5:28:21, 4.10s/it] 74%|███████▍ | 13887/18688 [16:21:54<5:21:00, 4.01s/it] 74%|███████▍ | 13888/18688 [16:21:58<5:23:02, 4.04s/it] 74%|███████▍ | 13889/18688 [16:22:02<5:19:26, 3.99s/it] 74%|███████▍ | 13890/18688 [16:22:06<5:15:17, 3.94s/it] {'loss': '0.8512', 'grad_norm': '1.32', 'learning_rate': '1.803e-05', 'epoch': '0.7433', 'num_input_tokens_seen': 46679336, 'train_runtime': '5.893e+04', 'train_tokens_per_second': '792.2'}
74%|███████▍ | 13890/18688 [16:22:06<5:15:17, 3.94s/it] 74%|███████▍ | 13891/18688 [16:22:10<5:04:45, 3.81s/it] 74%|███████▍ | 13892/18688 [16:22:13<5:08:41, 3.86s/it] 74%|███████▍ | 13893/18688 [16:22:18<5:17:13, 3.97s/it] 74%|███████▍ | 13894/18688 [16:22:22<5:24:28, 4.06s/it] 74%|███████▍ | 13895/18688 [16:22:26<5:31:46, 4.15s/it] 74%|███████▍ | 13896/18688 [16:22:32<5:55:37, 4.45s/it] 74%|███████▍ | 13897/18688 [16:22:36<5:49:21, 4.38s/it] 74%|███████▍ | 13898/18688 [16:22:39<5:31:41, 4.15s/it] 74%|███████▍ | 13899/18688 [16:22:43<5:14:56, 3.95s/it] 74%|███████▍ | 13900/18688 [16:22:47<5:22:42, 4.04s/it] {'loss': '0.84', 'grad_norm': '1.48', 'learning_rate': '1.796e-05', 'epoch': '0.7438', 'num_input_tokens_seen': 46710680, 'train_runtime': '5.897e+04', 'train_tokens_per_second': '792.1'}
74%|███████▍ | 13900/18688 [16:22:47<5:22:42, 4.04s/it] 74%|███████▍ | 13901/18688 [16:22:51<5:16:23, 3.97s/it] 74%|███████▍ | 13902/18688 [16:22:55<5:12:01, 3.91s/it] 74%|███████▍ | 13903/18688 [16:22:59<5:16:52, 3.97s/it] 74%|███████▍ | 13904/18688 [16:23:03<5:28:47, 4.12s/it] 74%|███████▍ | 13905/18688 [16:23:08<5:32:36, 4.17s/it] 74%|███████▍ | 13906/18688 [16:23:12<5:43:30, 4.31s/it] 74%|███████▍ | 13907/18688 [16:23:18<6:12:52, 4.68s/it] 74%|███████▍ | 13908/18688 [16:23:22<6:04:16, 4.57s/it] 74%|███████▍ | 13909/18688 [16:23:26<5:49:31, 4.39s/it] 74%|███████▍ | 13910/18688 [16:23:31<6:02:02, 4.55s/it] {'loss': '0.9098', 'grad_norm': '1.506', 'learning_rate': '1.789e-05', 'epoch': '0.7443', 'num_input_tokens_seen': 46747080, 'train_runtime': '5.901e+04', 'train_tokens_per_second': '792.2'}
74%|███████▍ | 13910/18688 [16:23:31<6:02:02, 4.55s/it] 74%|███████▍ | 13911/18688 [16:23:36<6:08:28, 4.63s/it] 74%|███████▍ | 13912/18688 [16:23:39<5:48:12, 4.37s/it] 74%|███████▍ | 13913/18688 [16:23:45<6:14:19, 4.70s/it] 74%|███████▍ | 13914/18688 [16:23:49<5:49:48, 4.40s/it] 74%|███████▍ | 13915/18688 [16:23:53<5:43:08, 4.31s/it] 74%|███████▍ | 13916/18688 [16:23:57<5:34:29, 4.21s/it] 74%|███████▍ | 13917/18688 [16:24:02<5:55:18, 4.47s/it] 74%|███████▍ | 13918/18688 [16:24:06<5:46:12, 4.35s/it] 74%|███████▍ | 13919/18688 [16:24:09<5:28:04, 4.13s/it] 74%|███████▍ | 13920/18688 [16:24:14<5:31:48, 4.18s/it] {'loss': '0.8913', 'grad_norm': '1.202', 'learning_rate': '1.782e-05', 'epoch': '0.7449', 'num_input_tokens_seen': 46782264, 'train_runtime': '5.905e+04', 'train_tokens_per_second': '792.2'}
74%|███████▍ | 13920/18688 [16:24:14<5:31:48, 4.18s/it] 74%|███████▍ | 13921/18688 [16:24:18<5:32:08, 4.18s/it] 74%|███████▍ | 13922/18688 [16:24:23<5:43:16, 4.32s/it] 75%|███████▍ | 13923/18688 [16:24:26<5:28:42, 4.14s/it] 75%|███████▍ | 13924/18688 [16:24:30<5:26:09, 4.11s/it] 75%|███████▍ | 13925/18688 [16:24:34<5:20:39, 4.04s/it] 75%|███████▍ | 13926/18688 [16:24:40<5:53:03, 4.45s/it] 75%|███████▍ | 13927/18688 [16:24:45<6:09:37, 4.66s/it] 75%|███████▍ | 13928/18688 [16:24:49<5:48:46, 4.40s/it] 75%|███████▍ | 13929/18688 [16:24:53<5:54:21, 4.47s/it] 75%|███████▍ | 13930/18688 [16:24:58<5:57:52, 4.51s/it] {'loss': '0.8652', 'grad_norm': '1.22', 'learning_rate': '1.775e-05', 'epoch': '0.7454', 'num_input_tokens_seen': 46817480, 'train_runtime': '5.91e+04', 'train_tokens_per_second': '792.2'}
75%|███████▍ | 13930/18688 [16:24:58<5:57:52, 4.51s/it] 75%|███████▍ | 13931/18688 [16:25:02<5:54:24, 4.47s/it] 75%|███████▍ | 13932/18688 [16:25:06<5:36:47, 4.25s/it] 75%|███████▍ | 13933/18688 [16:25:10<5:21:37, 4.06s/it] 75%|███████▍ | 13934/18688 [16:25:14<5:19:09, 4.03s/it] 75%|███████▍ | 13935/18688 [16:25:18<5:18:55, 4.03s/it] 75%|███████▍ | 13936/18688 [16:25:21<5:08:58, 3.90s/it] 75%|███████▍ | 13937/18688 [16:25:25<5:15:16, 3.98s/it] 75%|███████▍ | 13938/18688 [16:25:30<5:42:51, 4.33s/it] 75%|███████▍ | 13939/18688 [16:25:34<5:31:09, 4.18s/it] 75%|███████▍ | 13940/18688 [16:25:39<5:32:50, 4.21s/it] {'loss': '0.9329', 'grad_norm': '1.172', 'learning_rate': '1.768e-05', 'epoch': '0.746', 'num_input_tokens_seen': 46847224, 'train_runtime': '5.914e+04', 'train_tokens_per_second': '792.2'}
75%|███████▍ | 13940/18688 [16:25:39<5:32:50, 4.21s/it] 75%|███████▍ | 13941/18688 [16:25:42<5:23:32, 4.09s/it] 75%|███████▍ | 13942/18688 [16:25:47<5:35:04, 4.24s/it] 75%|███████▍ | 13943/18688 [16:25:51<5:20:22, 4.05s/it] 75%|███████▍ | 13944/18688 [16:25:55<5:23:20, 4.09s/it] 75%|███████▍ | 13945/18688 [16:25:59<5:34:19, 4.23s/it] 75%|███████▍ | 13946/18688 [16:26:03<5:22:15, 4.08s/it] 75%|███████▍ | 13947/18688 [16:26:08<5:42:11, 4.33s/it] 75%|███████▍ | 13948/18688 [16:26:13<5:59:20, 4.55s/it] 75%|███████▍ | 13949/18688 [16:26:18<6:14:28, 4.74s/it] 75%|███████▍ | 13950/18688 [16:26:23<6:07:54, 4.66s/it] {'loss': '0.8581', 'grad_norm': '1.507', 'learning_rate': '1.761e-05', 'epoch': '0.7465', 'num_input_tokens_seen': 46881720, 'train_runtime': '5.918e+04', 'train_tokens_per_second': '792.1'}
75%|███████▍ | 13950/18688 [16:26:23<6:07:54, 4.66s/it] 75%|███████▍ | 13951/18688 [16:26:27<5:53:41, 4.48s/it] 75%|███████▍ | 13952/18688 [16:26:32<6:03:29, 4.61s/it] 75%|███████▍ | 13953/18688 [16:26:36<6:00:45, 4.57s/it] 75%|███████▍ | 13954/18688 [16:26:40<5:45:33, 4.38s/it] 75%|███████▍ | 13955/18688 [16:26:44<5:27:06, 4.15s/it] 75%|███████▍ | 13956/18688 [16:26:48<5:36:26, 4.27s/it] 75%|███████▍ | 13957/18688 [16:26:52<5:19:04, 4.05s/it] 75%|███████▍ | 13958/18688 [16:26:56<5:22:27, 4.09s/it] 75%|███████▍ | 13959/18688 [16:27:00<5:26:07, 4.14s/it] 75%|███████▍ | 13960/18688 [16:27:04<5:24:19, 4.12s/it] {'loss': '0.8984', 'grad_norm': '1.193', 'learning_rate': '1.754e-05', 'epoch': '0.747', 'num_input_tokens_seen': 46914040, 'train_runtime': '5.922e+04', 'train_tokens_per_second': '792.1'}
75%|███████▍ | 13960/18688 [16:27:04<5:24:19, 4.12s/it] 75%|███████▍ | 13961/18688 [16:27:08<5:18:13, 4.04s/it] 75%|███████▍ | 13962/18688 [16:27:13<5:33:54, 4.24s/it] 75%|███████▍ | 13963/18688 [16:27:17<5:44:47, 4.38s/it] 75%|███████▍ | 13964/18688 [16:27:21<5:35:36, 4.26s/it] 75%|███████▍ | 13965/18688 [16:27:26<5:34:47, 4.25s/it] 75%|███████▍ | 13966/18688 [16:27:30<5:23:59, 4.12s/it] 75%|███████▍ | 13967/18688 [16:27:34<5:27:13, 4.16s/it] 75%|███████▍ | 13968/18688 [16:27:37<5:16:03, 4.02s/it] 75%|███████▍ | 13969/18688 [16:27:41<5:15:10, 4.01s/it] 75%|███████▍ | 13970/18688 [16:27:45<5:15:47, 4.02s/it] {'loss': '0.9013', 'grad_norm': '1.379', 'learning_rate': '1.747e-05', 'epoch': '0.7476', 'num_input_tokens_seen': 46945168, 'train_runtime': '5.927e+04', 'train_tokens_per_second': '792.1'}
75%|███████▍ | 13970/18688 [16:27:45<5:15:47, 4.02s/it] 75%|███████▍ | 13971/18688 [16:27:49<5:09:00, 3.93s/it] 75%|███████▍ | 13972/18688 [16:27:54<5:25:13, 4.14s/it] 75%|███████▍ | 13973/18688 [16:27:58<5:31:34, 4.22s/it] 75%|███████▍ | 13974/18688 [16:28:02<5:31:39, 4.22s/it] 75%|███████▍ | 13975/18688 [16:28:06<5:22:17, 4.10s/it] 75%|███████▍ | 13976/18688 [16:28:10<5:10:24, 3.95s/it] 75%|███████▍ | 13977/18688 [16:28:13<4:59:25, 3.81s/it] 75%|███████▍ | 13978/18688 [16:28:18<5:07:27, 3.92s/it] 75%|███████▍ | 13979/18688 [16:28:22<5:19:32, 4.07s/it] 75%|███████▍ | 13980/18688 [16:28:26<5:21:46, 4.10s/it] {'loss': '0.9257', 'grad_norm': '1.623', 'learning_rate': '1.74e-05', 'epoch': '0.7481', 'num_input_tokens_seen': 46975808, 'train_runtime': '5.931e+04', 'train_tokens_per_second': '792.1'}
75%|███████▍ | 13980/18688 [16:28:26<5:21:46, 4.10s/it] 75%|███████▍ | 13981/18688 [16:28:30<5:16:51, 4.04s/it] 75%|███████▍ | 13982/18688 [16:28:34<5:14:54, 4.01s/it] 75%|███████▍ | 13983/18688 [16:28:38<5:16:38, 4.04s/it] 75%|███████▍ | 13984/18688 [16:28:42<5:11:54, 3.98s/it] 75%|███████▍ | 13985/18688 [16:28:46<5:10:41, 3.96s/it] 75%|███████▍ | 13986/18688 [16:28:50<5:17:46, 4.05s/it] 75%|███████▍ | 13987/18688 [16:28:54<5:10:15, 3.96s/it] 75%|███████▍ | 13988/18688 [16:28:59<5:31:02, 4.23s/it] 75%|███████▍ | 13989/18688 [16:29:02<5:14:43, 4.02s/it] 75%|███████▍ | 13990/18688 [16:29:06<5:19:54, 4.09s/it] {'loss': '0.8822', 'grad_norm': '1.901', 'learning_rate': '1.734e-05', 'epoch': '0.7486', 'num_input_tokens_seen': 47007592, 'train_runtime': '5.935e+04', 'train_tokens_per_second': '792.1'}
75%|███████▍ | 13990/18688 [16:29:06<5:19:54, 4.09s/it] 75%|███████▍ | 13991/18688 [16:29:10<5:15:18, 4.03s/it] 75%|███████▍ | 13992/18688 [16:29:15<5:30:18, 4.22s/it] 75%|███████▍ | 13993/18688 [16:29:19<5:17:53, 4.06s/it] 75%|███████▍ | 13994/18688 [16:29:23<5:31:27, 4.24s/it] 75%|███████▍ | 13995/18688 [16:29:27<5:19:17, 4.08s/it] 75%|███████▍ | 13996/18688 [16:29:31<5:07:12, 3.93s/it] 75%|███████▍ | 13997/18688 [16:29:35<5:15:16, 4.03s/it] 75%|███████▍ | 13998/18688 [16:29:39<5:11:01, 3.98s/it] 75%|███████▍ | 13999/18688 [16:29:43<5:06:15, 3.92s/it] 75%|███████▍ | 14000/18688 [16:29:47<5:27:49, 4.20s/it] {'loss': '0.9352', 'grad_norm': '1.441', 'learning_rate': '1.727e-05', 'epoch': '0.7492', 'num_input_tokens_seen': 47040648, 'train_runtime': '5.939e+04', 'train_tokens_per_second': '792.1'}
75%|███████▍ | 14000/18688 [16:29:47<5:27:49, 4.20s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
0%| | 3/1000 [00:00<00:38, 25.77it/s]
1%| | 6/1000 [00:00<00:54, 18.25it/s]
1%| | 8/1000 [00:00<00:56, 17.51it/s]
1%| | 10/1000 [00:00<00:59, 16.60it/s]
1%| | 12/1000 [00:00<01:00, 16.34it/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, 9.97it/s]
2%|▏ | 20/1000 [00:01<01:33, 10.52it/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, 9.28it/s]
3%|▎ | 28/1000 [00:02<01:36, 10.11it/s]
3%|▎ | 30/1000 [00:02<01:28, 11.01it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.16it/s]
3%|▎ | 34/1000 [00:02<01:25, 11.27it/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<01:28, 10.84it/s]
4%|▍ | 42/1000 [00:03<01:48, 8.80it/s]
4%|▍ | 44/1000 [00:03<01:38, 9.67it/s]
5%|▍ | 46/1000 [00:04<01:31, 10.40it/s]
5%|▍ | 48/1000 [00:04<01:38, 9.69it/s]
5%|▌ | 50/1000 [00:04<01:31, 10.37it/s]
5%|▌ | 52/1000 [00:04<01:26, 10.95it/s]
5%|▌ | 54/1000 [00:04<01:20, 11.70it/s]
6%|▌ | 56/1000 [00:04<01:14, 12.67it/s]
6%|▌ | 58/1000 [00:05<01:52, 8.36it/s]
6%|▌ | 60/1000 [00:05<01:39, 9.46it/s]
6%|▌ | 62/1000 [00:05<01:29, 10.46it/s]
6%|▋ | 64/1000 [00:05<01:20, 11.58it/s]
7%|▋ | 66/1000 [00:05<01:16, 12.13it/s]
7%|▋ | 68/1000 [00:06<01:13, 12.77it/s]
7%|▋ | 70/1000 [00:06<01:09, 13.38it/s]
7%|▋ | 72/1000 [00:06<01:07, 13.85it/s]
7%|▋ | 74/1000 [00:06<01:07, 13.64it/s]
8%|▊ | 76/1000 [00:06<01:04, 14.24it/s]
8%|▊ | 78/1000 [00:06<01:03, 14.47it/s]
8%|▊ | 80/1000 [00:06<01:08, 13.43it/s]
8%|▊ | 82/1000 [00:07<01:05, 14.05it/s]
8%|▊ | 84/1000 [00:07<01:03, 14.54it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.68it/s]
9%|▉ | 88/1000 [00:07<01:02, 14.50it/s]
9%|▉ | 90/1000 [00:07<01:02, 14.51it/s]
9%|▉ | 92/1000 [00:07<01:03, 14.19it/s]
9%|▉ | 94/1000 [00:07<01:04, 14.02it/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, 9.81it/s]
10%|█ | 102/1000 [00:08<01:23, 10.78it/s]
10%|█ | 104/1000 [00:09<01:54, 7.82it/s]
11%|█ | 106/1000 [00:09<02:01, 7.33it/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, 11.17it/s]
11%|█▏ | 114/1000 [00:09<01:14, 11.85it/s]
12%|█▏ | 116/1000 [00:10<01:10, 12.50it/s]
12%|█▏ | 118/1000 [00:10<01:06, 13.26it/s]
12%|█▏ | 120/1000 [00:10<01:09, 12.68it/s]
12%|█▏ | 122/1000 [00:10<01:23, 10.56it/s]
12%|█▏ | 124/1000 [00:10<01:30, 9.67it/s]
13%|█▎ | 126/1000 [00:10<01:22, 10.64it/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<01:05, 13.16it/s]
13%|█▎ | 134/1000 [00:11<01:10, 12.35it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.32it/s]
14%|█▍ | 138/1000 [00:11<01:05, 13.25it/s]
14%|█▍ | 140/1000 [00:12<01:24, 10.12it/s]
14%|█▍ | 142/1000 [00:12<01:40, 8.56it/s]
14%|█▍ | 144/1000 [00:12<01:29, 9.58it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.61it/s]
15%|█▍ | 148/1000 [00:12<01:13, 11.52it/s]
15%|█▌ | 150/1000 [00:13<01:07, 12.55it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.10it/s]
15%|█▌ | 154/1000 [00:13<01:03, 13.37it/s]
16%|█▌ | 156/1000 [00:13<01:03, 13.22it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.76it/s]
16%|█▌ | 160/1000 [00:13<01:03, 13.32it/s]
16%|█▌ | 162/1000 [00:13<01:05, 12.86it/s]
16%|█▋ | 164/1000 [00:14<01:05, 12.84it/s]
17%|█▋ | 166/1000 [00:14<01:07, 12.40it/s]
17%|█▋ | 168/1000 [00:14<01:03, 13.17it/s]
17%|█▋ | 170/1000 [00:14<01:22, 10.00it/s]
17%|█▋ | 172/1000 [00:14<01:15, 11.01it/s]
17%|█▋ | 174/1000 [00:15<01:32, 8.95it/s]
18%|█▊ | 176/1000 [00:15<01:56, 7.09it/s]
18%|█▊ | 178/1000 [00:15<01:36, 8.53it/s]
18%|█▊ | 180/1000 [00:15<01:34, 8.68it/s]
18%|█▊ | 182/1000 [00:16<01:37, 8.43it/s]
18%|█▊ | 184/1000 [00:16<01:24, 9.69it/s]
19%|█▊ | 186/1000 [00:16<01:38, 8.30it/s]
19%|█▉ | 188/1000 [00:16<01:24, 9.62it/s]
19%|█▉ | 190/1000 [00:16<01:15, 10.67it/s]
19%|█▉ | 192/1000 [00:17<01:11, 11.30it/s]
19%|█▉ | 194/1000 [00:17<01:16, 10.53it/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, 10.55it/s]
20%|██ | 202/1000 [00:18<01:09, 11.50it/s]
20%|██ | 204/1000 [00:18<01:03, 12.60it/s]
21%|██ | 206/1000 [00:18<01:00, 13.20it/s]
21%|██ | 208/1000 [00:18<00:58, 13.58it/s]
21%|██ | 210/1000 [00:18<00:54, 14.44it/s]
21%|██ | 212/1000 [00:18<00:53, 14.70it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.70it/s]
22%|██▏ | 216/1000 [00:18<00:54, 14.42it/s]
22%|██▏ | 218/1000 [00:19<00:52, 14.77it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.88it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.18it/s]
22%|██▏ | 224/1000 [00:19<01:20, 9.65it/s]
23%|██▎ | 226/1000 [00:20<01:12, 10.64it/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, 12.41it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.15it/s]
24%|██▎ | 236/1000 [00:20<00:56, 13.56it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.18it/s]
24%|██▍ | 240/1000 [00:21<00:53, 14.16it/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, 14.92it/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, 12.16it/s]
25%|██▌ | 254/1000 [00:22<00:56, 13.17it/s]
26%|██▌ | 256/1000 [00:22<00:54, 13.73it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.78it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.89it/s]
26%|██▌ | 262/1000 [00:22<00:57, 12.74it/s]
26%|██▋ | 264/1000 [00:22<00:58, 12.67it/s]
27%|██▋ | 266/1000 [00:23<00:58, 12.53it/s]
27%|██▋ | 268/1000 [00:23<00:58, 12.42it/s]
27%|██▋ | 270/1000 [00:23<01:02, 11.68it/s]
27%|██▋ | 272/1000 [00:23<01:09, 10.53it/s]
27%|██▋ | 274/1000 [00:23<01:07, 10.76it/s]
28%|██▊ | 276/1000 [00:23<01:04, 11.18it/s]
28%|██▊ | 278/1000 [00:24<01:02, 11.54it/s]
28%|██▊ | 280/1000 [00:24<01:00, 11.86it/s]
28%|██▊ | 282/1000 [00:24<00:58, 12.28it/s]
28%|██▊ | 284/1000 [00:24<00:57, 12.48it/s]
29%|██▊ | 286/1000 [00:24<00:57, 12.44it/s]
29%|██▉ | 288/1000 [00:24<00:57, 12.34it/s]
29%|██▉ | 290/1000 [00:25<00:57, 12.38it/s]
29%|██▉ | 292/1000 [00:25<00:57, 12.24it/s]
29%|██▉ | 294/1000 [00:25<00:55, 12.67it/s]
30%|██▉ | 296/1000 [00:25<00:52, 13.40it/s]
30%|██▉ | 298/1000 [00:25<00:50, 14.03it/s]
30%|███ | 300/1000 [00:25<00:49, 14.14it/s]
30%|███ | 302/1000 [00:25<00:48, 14.42it/s]
30%|███ | 304/1000 [00:26<00:52, 13.32it/s]
31%|███ | 306/1000 [00:26<00:49, 14.12it/s]
31%|███ | 308/1000 [00:26<00:48, 14.18it/s]
31%|███ | 310/1000 [00:26<00:48, 14.37it/s]
31%|███ | 312/1000 [00:26<00:47, 14.43it/s]
31%|███▏ | 314/1000 [00:26<00:45, 15.17it/s]
32%|███▏ | 316/1000 [00:26<00:44, 15.48it/s]
32%|███▏ | 318/1000 [00:26<00:42, 15.95it/s]
32%|███▏ | 320/1000 [00:27<01:12, 9.36it/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, 11.46it/s]
33%|███▎ | 328/1000 [00:27<00:58, 11.49it/s]
33%|███▎ | 330/1000 [00:28<00:53, 12.43it/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, 10.08it/s]
34%|███▍ | 340/1000 [00:28<00:59, 11.14it/s]
34%|███▍ | 342/1000 [00:29<00:53, 12.38it/s]
34%|███▍ | 344/1000 [00:29<00:50, 13.02it/s]
35%|███▍ | 346/1000 [00:29<00:48, 13.55it/s]
35%|███▍ | 348/1000 [00:29<00:46, 13.91it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.55it/s]
35%|███▌ | 352/1000 [00:29<00:44, 14.71it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.79it/s]
36%|███▌ | 356/1000 [00:30<00:43, 14.93it/s]
36%|███▌ | 358/1000 [00:30<00:43, 14.63it/s]
36%|███▌ | 360/1000 [00:30<00:42, 15.03it/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, 11.96it/s]
37%|███▋ | 368/1000 [00:30<00:51, 12.37it/s]
37%|███▋ | 370/1000 [00:31<00:47, 13.22it/s]
37%|███▋ | 372/1000 [00:31<00:46, 13.53it/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, 12.47it/s]
38%|███▊ | 382/1000 [00:32<00:48, 12.66it/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, 13.68it/s]
39%|███▉ | 390/1000 [00:32<00:44, 13.57it/s]
39%|███▉ | 392/1000 [00:32<00:43, 13.92it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.70it/s]
40%|███▉ | 396/1000 [00:33<00:43, 13.81it/s]
40%|███▉ | 398/1000 [00:33<00:47, 12.56it/s]
40%|████ | 400/1000 [00:33<00:45, 13.24it/s]
40%|████ | 402/1000 [00:33<00:42, 13.91it/s]
40%|████ | 404/1000 [00:33<00:40, 14.55it/s]
41%|████ | 406/1000 [00:33<00:39, 15.07it/s]
41%|████ | 408/1000 [00:34<00:46, 12.62it/s]
41%|████ | 410/1000 [00:34<00:56, 10.52it/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, 6.98it/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, 10.89it/s]
42%|████▏ | 424/1000 [00:36<01:14, 7.70it/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, 9.12it/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, 9.32it/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, 10.55it/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, 11.81it/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, 12.73it/s]
46%|████▌ | 458/1000 [00:39<00:39, 13.73it/s]
46%|████▌ | 460/1000 [00:39<00:38, 13.85it/s]
46%|████▌ | 462/1000 [00:39<00:37, 14.38it/s]
46%|████▋ | 464/1000 [00:39<00:36, 14.87it/s]
47%|████▋ | 466/1000 [00:39<00:36, 14.76it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.87it/s]
47%|████▋ | 470/1000 [00:40<00:55, 9.52it/s]
47%|████▋ | 472/1000 [00:40<00:54, 9.68it/s]
47%|████▋ | 474/1000 [00:40<00:49, 10.64it/s]
48%|████▊ | 476/1000 [00:40<00:45, 11.48it/s]
48%|████▊ | 478/1000 [00:40<00:45, 11.47it/s]
48%|████▊ | 480/1000 [00:40<00:43, 12.07it/s]
48%|████▊ | 482/1000 [00:41<00:42, 12.24it/s]
48%|████▊ | 484/1000 [00:41<00:40, 12.88it/s]
49%|████▊ | 486/1000 [00:41<00:38, 13.31it/s]
49%|████▉ | 488/1000 [00:41<00:38, 13.26it/s]
49%|████▉ | 490/1000 [00:41<00:47, 10.66it/s]
49%|████▉ | 492/1000 [00:41<00:42, 12.02it/s]
49%|████▉ | 494/1000 [00:42<00:38, 13.08it/s]
50%|████▉ | 496/1000 [00:42<00:36, 13.88it/s]
50%|████▉ | 498/1000 [00:42<00:35, 14.31it/s]
50%|█████ | 500/1000 [00:42<00:43, 11.48it/s]
50%|█████ | 502/1000 [00:42<00:41, 11.95it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.56it/s]
51%|█████ | 506/1000 [00:43<00:38, 12.75it/s]
51%|█████ | 508/1000 [00:43<00:36, 13.64it/s]
51%|█████ | 510/1000 [00:43<00:47, 10.35it/s]
51%|█████ | 512/1000 [00:43<00:43, 11.12it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 12.00it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.57it/s]
52%|█████▏ | 518/1000 [00:44<00:36, 13.09it/s]
52%|█████▏ | 520/1000 [00:44<00:35, 13.46it/s]
52%|█████▏ | 522/1000 [00:44<00:33, 14.18it/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, 11.98it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.82it/s]
53%|█████▎ | 532/1000 [00:45<00:34, 13.76it/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<00:32, 14.02it/s]
54%|█████▍ | 540/1000 [00:45<00:31, 14.44it/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, 14.22it/s]
55%|█████▍ | 548/1000 [00:46<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<00:31, 13.95it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.11it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.49it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 15.05it/s]
56%|█████▌ | 562/1000 [00:47<00:29, 15.01it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.82it/s]
57%|█████▋ | 566/1000 [00:47<00:37, 11.68it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.20it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.31it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.83it/s]
57%|█████▋ | 574/1000 [00:48<00:31, 13.71it/s]
58%|█████▊ | 576/1000 [00:48<00:31, 13.64it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.66it/s]
58%|█████▊ | 580/1000 [00:48<00:32, 12.88it/s]
58%|█████▊ | 582/1000 [00:48<00:30, 13.63it/s]
58%|█████▊ | 584/1000 [00:48<00:29, 13.87it/s]
59%|█████▊ | 586/1000 [00:48<00:29, 14.22it/s]
59%|█████▉ | 588/1000 [00:49<00:27, 15.02it/s]
59%|█████▉ | 590/1000 [00:49<00:27, 14.99it/s]
59%|█████▉ | 592/1000 [00:49<00:27, 14.75it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.29it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.77it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 14.00it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.38it/s]
60%|██████ | 602/1000 [00:50<00:27, 14.50it/s]
60%|██████ | 604/1000 [00:50<00:26, 15.04it/s]
61%|██████ | 606/1000 [00:50<00:27, 14.31it/s]
61%|██████ | 608/1000 [00:50<00:28, 13.63it/s]
61%|██████ | 610/1000 [00:50<00:29, 13.17it/s]
61%|██████ | 612/1000 [00:50<00:30, 12.84it/s]
61%|██████▏ | 614/1000 [00:51<00:30, 12.64it/s]
62%|██████▏ | 616/1000 [00:51<00:30, 12.69it/s]
62%|██████▏ | 618/1000 [00:51<00:30, 12.52it/s]
62%|██████▏ | 620/1000 [00:51<00:41, 9.13it/s]
62%|██████▏ | 622/1000 [00:51<00:40, 9.39it/s]
62%|██████▏ | 624/1000 [00:52<00:37, 10.15it/s]
63%|██████▎ | 626/1000 [00:52<00:34, 10.74it/s]
63%|██████▎ | 628/1000 [00:52<00:32, 11.27it/s]
63%|██████▎ | 630/1000 [00:52<00:32, 11.56it/s]
63%|██████▎ | 632/1000 [00:52<00:33, 11.03it/s]
63%|██████▎ | 634/1000 [00:52<00:32, 11.37it/s]
64%|██████▎ | 636/1000 [00:53<00:36, 10.02it/s]
64%|██████▍ | 638/1000 [00:53<00:36, 10.04it/s]
64%|██████▍ | 640/1000 [00:53<00:32, 10.94it/s]
64%|██████▍ | 642/1000 [00:53<00:30, 11.92it/s]
64%|██████▍ | 644/1000 [00:53<00:28, 12.28it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.65it/s]
65%|██████▍ | 648/1000 [00:54<00:26, 13.20it/s]
65%|██████▌ | 650/1000 [00:54<00:28, 12.44it/s]
65%|██████▌ | 652/1000 [00:54<00:29, 11.86it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.41it/s]
66%|██████▌ | 656/1000 [00:54<00:32, 10.73it/s]
66%|██████▌ | 658/1000 [00:55<00:29, 11.79it/s]
66%|██████▌ | 660/1000 [00:55<00:33, 10.12it/s]
66%|██████▌ | 662/1000 [00:55<00:30, 11.25it/s]
66%|██████▋ | 664/1000 [00:55<00:28, 11.74it/s]
67%|██████▋ | 666/1000 [00:55<00:26, 12.76it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.31it/s]
67%|██████▋ | 670/1000 [00:56<00:29, 11.10it/s]
67%|██████▋ | 672/1000 [00:56<00:27, 11.85it/s]
67%|██████▋ | 674/1000 [00:56<00:26, 12.45it/s]
68%|██████▊ | 676/1000 [00:56<00:27, 11.72it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.20it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 12.97it/s]
68%|██████▊ | 682/1000 [00:57<00:26, 12.14it/s]
68%|██████▊ | 684/1000 [00:57<00:25, 12.33it/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, 13.75it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.20it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.30it/s]
70%|██████▉ | 696/1000 [00:58<00:21, 14.05it/s]
70%|██████▉ | 698/1000 [00:58<00:20, 14.56it/s]
70%|███████ | 700/1000 [00:58<00:24, 12.17it/s]
70%|███████ | 702/1000 [00:58<00:22, 12.98it/s]
70%|███████ | 704/1000 [00:58<00:30, 9.86it/s]
71%|███████ | 706/1000 [00:58<00:27, 10.67it/s]
71%|███████ | 708/1000 [00:59<00:30, 9.63it/s]
71%|███████ | 710/1000 [00:59<00:26, 10.89it/s]
71%|███████ | 712/1000 [00:59<00:24, 11.83it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.90it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 12.99it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.50it/s]
72%|███████▏ | 720/1000 [01:00<00:20, 13.48it/s]
72%|███████▏ | 722/1000 [01:00<00:19, 13.90it/s]
72%|███████▏ | 724/1000 [01:00<00:20, 13.73it/s]
73%|███████▎ | 726/1000 [01:00<00:19, 13.97it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.91it/s]
73%|███████▎ | 730/1000 [01:01<00:32, 8.20it/s]
73%|███████▎ | 732/1000 [01:01<00:32, 8.33it/s]
73%|███████▎ | 734/1000 [01:01<00:30, 8.62it/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 [01:02<00:22, 11.70it/s]
74%|███████▍ | 743/1000 [01:02<00:20, 12.35it/s]
74%|███████▍ | 745/1000 [01:02<00:24, 10.21it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.60it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.78it/s]
75%|███████▌ | 751/1000 [01:03<00:20, 11.89it/s]
75%|███████▌ | 753/1000 [01:03<00:19, 12.63it/s]
76%|███████▌ | 755/1000 [01:03<00:18, 13.30it/s]
76%|███████▌ | 757/1000 [01:03<00:23, 10.27it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.30it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.26it/s]
76%|███████▋ | 763/1000 [01:04<00:23, 10.25it/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, 11.85it/s]
77%|███████▋ | 773/1000 [01:05<00:17, 12.63it/s]
78%|███████▊ | 775/1000 [01:05<00:17, 13.17it/s]
78%|███████▊ | 777/1000 [01:05<00:15, 14.13it/s]
78%|███████▊ | 779/1000 [01:05<00:21, 10.51it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.41it/s]
78%|███████▊ | 783/1000 [01:06<00:26, 8.30it/s]
78%|███████▊ | 784/1000 [01:06<00:30, 7.07it/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, 10.14it/s]
79%|███████▉ | 792/1000 [01:07<00:18, 11.29it/s]
79%|███████▉ | 794/1000 [01:07<00:17, 11.91it/s]
80%|███████▉ | 796/1000 [01:07<00:16, 12.35it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 13.12it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.32it/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, 8.99it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.33it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.46it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.68it/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, 12.47it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 12.99it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.76it/s]
83%|████████▎ | 826/1000 [01:10<00:12, 14.17it/s]
83%|████████▎ | 828/1000 [01:10<00:11, 14.45it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.93it/s]
83%|████████▎ | 832/1000 [01:10<00:11, 14.30it/s]
83%|████████▎ | 834/1000 [01:10<00:11, 14.05it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.40it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 13.94it/s]
84%|████████▍ | 840/1000 [01:11<00:11, 13.85it/s]
84%|████████▍ | 842/1000 [01:11<00:11, 13.83it/s]
84%|████████▍ | 844/1000 [01:11<00:12, 12.79it/s]
85%|████████▍ | 846/1000 [01:11<00:12, 12.76it/s]
85%|████████▍ | 848/1000 [01:11<00:11, 13.06it/s]
85%|████████▌ | 850/1000 [01:11<00:11, 13.47it/s]
85%|████████▌ | 852/1000 [01:11<00:11, 13.19it/s]
85%|████████▌ | 854/1000 [01:12<00:11, 13.06it/s]
86%|████████▌ | 856/1000 [01:12<00:10, 13.20it/s]
86%|████████▌ | 858/1000 [01:12<00:10, 13.46it/s]
86%|████████▌ | 860/1000 [01:12<00:10, 13.62it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 12.60it/s]
86%|████████▋ | 864/1000 [01:12<00:10, 13.31it/s]
87%|████████▋ | 866/1000 [01:12<00:10, 13.30it/s]
87%|████████▋ | 868/1000 [01:13<00:09, 13.36it/s]
87%|████████▋ | 870/1000 [01:13<00:09, 13.85it/s]
87%|████████▋ | 872/1000 [01:13<00:08, 14.48it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 12.35it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.01it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.88it/s]
88%|████████▊ | 880/1000 [01:14<00:09, 12.52it/s]
88%|████████▊ | 882/1000 [01:14<00:10, 11.36it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 12.09it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.85it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.82it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.24it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.03it/s]
89%|████████▉ | 894/1000 [01:15<00:07, 14.19it/s]
90%|████████▉ | 896/1000 [01:15<00:07, 13.94it/s]
90%|████████▉ | 898/1000 [01:15<00:07, 13.31it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.26it/s]
90%|█████████ | 902/1000 [01:15<00:07, 13.01it/s]
90%|█████████ | 904/1000 [01:16<00:09, 9.81it/s]
91%|█████████ | 906/1000 [01:16<00:08, 10.87it/s]
91%|█████████ | 908/1000 [01:16<00:07, 11.83it/s]
91%|█████████ | 910/1000 [01:16<00:07, 12.09it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.31it/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, 7.40it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.79it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 10.23it/s]
92%|█████████▏| 924/1000 [01:18<00:06, 10.93it/s]
93%|█████████▎| 926/1000 [01:18<00:06, 11.78it/s]
93%|█████████▎| 928/1000 [01:18<00:06, 10.63it/s]
93%|█████████▎| 930/1000 [01:18<00:05, 11.83it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 12.45it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.92it/s]
94%|█████████▎| 936/1000 [01:19<00:04, 13.47it/s]
94%|█████████▍| 938/1000 [01:19<00:04, 14.20it/s]
94%|█████████▍| 940/1000 [01:19<00:04, 14.47it/s]
94%|█████████▍| 942/1000 [01:19<00:04, 12.20it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.75it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.81it/s]
95%|█████████▍| 948/1000 [01:20<00:04, 12.62it/s]
95%|█████████▌| 950/1000 [01:20<00:03, 13.42it/s]
95%|█████████▌| 952/1000 [01:20<00:03, 14.11it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 14.21it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 14.20it/s]
96%|█████████▌| 958/1000 [01:20<00:02, 14.71it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 13.07it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.68it/s]
96%|█████████▋| 964/1000 [01:21<00:03, 10.20it/s]
97%|█████████▋| 966/1000 [01:21<00:03, 11.20it/s]
97%|█████████▋| 968/1000 [01:21<00:02, 11.48it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 11.69it/s]
97%|█████████▋| 972/1000 [01:22<00:03, 9.21it/s]
97%|█████████▋| 974/1000 [01:22<00:02, 9.91it/s]
98%|█████████▊| 976/1000 [01:22<00:02, 10.46it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.20it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.40it/s]
98%|█████████▊| 982/1000 [01:23<00:01, 9.72it/s]
98%|█████████▊| 984/1000 [01:23<00:01, 9.92it/s]
99%|█████████▊| 986/1000 [01:23<00:01, 10.07it/s]
99%|█████████▉| 988/1000 [01:23<00:01, 10.35it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 10.84it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 11.75it/s]
99%|█████████▉| 994/1000 [01:24<00:00, 12.58it/s]
100%|█████████▉| 996/1000 [01:24<00:00, 13.54it/s]
100%|█████████▉| 998/1000 [01:24<00:00, 13.82it/s]
100%|██████████| 1000/1000 [01:24<00:00, 14.53it/s]
{'eval_loss': '0.8796', 'eval_runtime': '84.54', 'eval_samples_per_second': '11.83', 'eval_steps_per_second': '11.83', 'epoch': '0.7492', 'num_input_tokens_seen': 47040648}
75%|███████▍ | 14000/18688 [16:31:12<5:27:49, 4.20s/it]
100%|██████████| 1000/1000 [01:24<00:00, 14.53it/s]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00010/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 [16:31:17<38:51:12, 29.84s/it] 75%|███████▍ | 14002/18688 [16:31:22<28:55:43, 22.22s/it] 75%|███████▍ | 14003/18688 [16:31:26<21:59:43, 16.90s/it] 75%|███████▍ | 14004/18688 [16:31:30<17:04:30, 13.12s/it] 75%|███████▍ | 14005/18688 [16:31:35<13:42:54, 10.54s/it] 75%|███████▍ | 14006/18688 [16:31:39<11:16:01, 8.66s/it] 75%|███████▍ | 14007/18688 [16:31:43<9:31:14, 7.32s/it] 75%|███████▍ | 14008/18688 [16:31:48<8:19:59, 6.41s/it] 75%|███████▍ | 14009/18688 [16:31:53<8:02:54, 6.19s/it] 75%|███████▍ | 14010/18688 [16:31:57<7:14:46, 5.58s/it] {'loss': '1', 'grad_norm': '1.826', 'learning_rate': '1.72e-05', 'epoch': '0.7497', 'num_input_tokens_seen': 47078616, 'train_runtime': '5.952e+04', 'train_tokens_per_second': '791'}
75%|███████▍ | 14010/18688 [16:31:57<7:14:46, 5.58s/it] 75%|███████▍ | 14011/18688 [16:32:01<6:30:50, 5.01s/it] 75%|███████▍ | 14012/18688 [16:32:05<5:59:53, 4.62s/it] 75%|███████▍ | 14013/18688 [16:32:10<6:12:32, 4.78s/it] 75%|███████▍ | 14014/18688 [16:32:14<6:04:35, 4.68s/it] 75%|███████▍ | 14015/18688 [16:32:19<5:59:43, 4.62s/it] 75%|███████▌ | 14016/18688 [16:32:23<5:37:16, 4.33s/it] 75%|███████▌ | 14017/18688 [16:32:27<5:33:51, 4.29s/it] 75%|███████▌ | 14018/18688 [16:32:30<5:16:53, 4.07s/it] 75%|███████▌ | 14019/18688 [16:32:34<5:07:14, 3.95s/it] 75%|███████▌ | 14020/18688 [16:32:39<5:28:43, 4.23s/it] {'loss': '0.9494', 'grad_norm': '1.315', 'learning_rate': '1.713e-05', 'epoch': '0.7502', 'num_input_tokens_seen': 47112024, 'train_runtime': '5.956e+04', 'train_tokens_per_second': '791'}
75%|███████▌ | 14020/18688 [16:32:39<5:28:43, 4.23s/it] 75%|███████▌ | 14021/18688 [16:32:43<5:15:37, 4.06s/it] 75%|███████▌ | 14022/18688 [16:32:46<5:12:08, 4.01s/it] 75%|███████▌ | 14023/18688 [16:32:50<5:08:25, 3.97s/it] 75%|███████▌ | 14024/18688 [16:32:55<5:27:50, 4.22s/it] 75%|███████▌ | 14025/18688 [16:32:59<5:15:45, 4.06s/it] 75%|███████▌ | 14026/18688 [16:33:03<5:08:44, 3.97s/it] 75%|███████▌ | 14027/18688 [16:33:08<5:51:28, 4.52s/it] 75%|███████▌ | 14028/18688 [16:33:13<5:46:52, 4.47s/it] 75%|███████▌ | 14029/18688 [16:33:16<5:26:02, 4.20s/it] 75%|███████▌ | 14030/18688 [16:33:21<5:36:30, 4.33s/it] {'loss': '0.8974', 'grad_norm': '1.212', 'learning_rate': '1.706e-05', 'epoch': '0.7508', 'num_input_tokens_seen': 47146904, 'train_runtime': '5.96e+04', 'train_tokens_per_second': '791'}
75%|███████▌ | 14030/18688 [16:33:21<5:36:30, 4.33s/it] 75%|███████▌ | 14031/18688 [16:33:25<5:22:55, 4.16s/it] 75%|███████▌ | 14032/18688 [16:33:30<5:43:33, 4.43s/it] 75%|███████▌ | 14033/18688 [16:33:34<5:35:39, 4.33s/it] 75%|███████▌ | 14034/18688 [16:33:38<5:37:34, 4.35s/it] 75%|███████▌ | 14035/18688 [16:33:43<5:41:17, 4.40s/it] 75%|███████▌ | 14036/18688 [16:33:47<5:42:23, 4.42s/it] 75%|███████▌ | 14037/18688 [16:33:52<5:49:42, 4.51s/it] 75%|███████▌ | 14038/18688 [16:33:56<5:39:39, 4.38s/it] 75%|███████▌ | 14039/18688 [16:34:00<5:21:04, 4.14s/it] 75%|███████▌ | 14040/18688 [16:34:05<5:50:20, 4.52s/it] {'loss': '0.8874', 'grad_norm': '1.42', 'learning_rate': '1.699e-05', 'epoch': '0.7513', 'num_input_tokens_seen': 47184824, 'train_runtime': '5.965e+04', 'train_tokens_per_second': '791.1'}
75%|███████▌ | 14040/18688 [16:34:05<5:50:20, 4.52s/it] 75%|███████▌ | 14041/18688 [16:34:10<5:50:19, 4.52s/it] 75%|███████▌ | 14042/18688 [16:34:14<5:40:33, 4.40s/it] 75%|███████▌ | 14043/18688 [16:34:18<5:35:54, 4.34s/it] 75%|███████▌ | 14044/18688 [16:34:22<5:41:24, 4.41s/it] 75%|███████▌ | 14045/18688 [16:34:27<5:40:53, 4.41s/it] 75%|███████▌ | 14046/18688 [16:34:31<5:45:18, 4.46s/it] 75%|███████▌ | 14047/18688 [16:34:36<5:56:33, 4.61s/it] 75%|███████▌ | 14048/18688 [16:34:40<5:39:40, 4.39s/it] 75%|███████▌ | 14049/18688 [16:34:45<5:51:03, 4.54s/it] 75%|███████▌ | 14050/18688 [16:34:49<5:38:10, 4.37s/it] {'loss': '0.9264', 'grad_norm': '1.254', 'learning_rate': '1.692e-05', 'epoch': '0.7518', 'num_input_tokens_seen': 47218264, 'train_runtime': '5.969e+04', 'train_tokens_per_second': '791.1'}
75%|███████▌ | 14050/18688 [16:34:49<5:38:10, 4.37s/it] 75%|███████▌ | 14051/18688 [16:34:54<5:40:25, 4.40s/it] 75%|███████▌ | 14052/18688 [16:34:58<5:49:33, 4.52s/it] 75%|███████▌ | 14053/18688 [16:35:02<5:33:48, 4.32s/it] 75%|███████▌ | 14054/18688 [16:35:06<5:29:35, 4.27s/it] 75%|███████▌ | 14055/18688 [16:35:11<5:36:41, 4.36s/it] 75%|███████▌ | 14056/18688 [16:35:15<5:36:25, 4.36s/it] 75%|███████▌ | 14057/18688 [16:35:19<5:27:23, 4.24s/it] 75%|███████▌ | 14058/18688 [16:35:24<5:28:07, 4.25s/it] 75%|███████▌ | 14059/18688 [16:35:28<5:29:05, 4.27s/it] 75%|███████▌ | 14060/18688 [16:35:32<5:17:41, 4.12s/it] {'loss': '0.907', 'grad_norm': '1.392', 'learning_rate': '1.685e-05', 'epoch': '0.7524', 'num_input_tokens_seen': 47251056, 'train_runtime': '5.973e+04', 'train_tokens_per_second': '791'}
75%|███████▌ | 14060/18688 [16:35:32<5:17:41, 4.12s/it] 75%|███████▌ | 14061/18688 [16:35:36<5:24:17, 4.21s/it] 75%|███████▌ | 14062/18688 [16:35:42<6:01:36, 4.69s/it] 75%|███████▌ | 14063/18688 [16:35:46<5:44:12, 4.47s/it] 75%|███████▌ | 14064/18688 [16:35:50<5:30:16, 4.29s/it] 75%|███████▌ | 14065/18688 [16:35:54<5:36:51, 4.37s/it] 75%|███████▌ | 14066/18688 [16:35:59<5:47:28, 4.51s/it] 75%|███████▌ | 14067/18688 [16:36:04<6:03:21, 4.72s/it] 75%|███████▌ | 14068/18688 [16:36:10<6:14:44, 4.87s/it] 75%|███████▌ | 14069/18688 [16:36:14<6:07:09, 4.77s/it] 75%|███████▌ | 14070/18688 [16:36:18<5:50:18, 4.55s/it] {'loss': '0.9418', 'grad_norm': '1.35', 'learning_rate': '1.679e-05', 'epoch': '0.7529', 'num_input_tokens_seen': 47289704, 'train_runtime': '5.978e+04', 'train_tokens_per_second': '791.1'}
75%|███████▌ | 14070/18688 [16:36:18<5:50:18, 4.55s/it] 75%|███████▌ | 14071/18688 [16:36:23<5:49:08, 4.54s/it] 75%|███████▌ | 14072/18688 [16:36:26<5:31:10, 4.30s/it] 75%|███████▌ | 14073/18688 [16:36:30<5:26:24, 4.24s/it] 75%|███████▌ | 14074/18688 [16:36:35<5:22:40, 4.20s/it] 75%|███████▌ | 14075/18688 [16:36:39<5:21:26, 4.18s/it] 75%|███████▌ | 14076/18688 [16:36:43<5:17:30, 4.13s/it] 75%|███████▌ | 14077/18688 [16:36:47<5:18:08, 4.14s/it] 75%|███████▌ | 14078/18688 [16:36:51<5:23:40, 4.21s/it] 75%|███████▌ | 14079/18688 [16:36:56<5:28:07, 4.27s/it] 75%|███████▌ | 14080/18688 [16:37:00<5:33:33, 4.34s/it] {'loss': '0.9774', 'grad_norm': '1.602', 'learning_rate': '1.672e-05', 'epoch': '0.7534', 'num_input_tokens_seen': 47323048, 'train_runtime': '5.982e+04', 'train_tokens_per_second': '791.1'}
75%|███████▌ | 14080/18688 [16:37:00<5:33:33, 4.34s/it] 75%|███████▌ | 14081/18688 [16:37:04<5:25:40, 4.24s/it] 75%|███████▌ | 14082/18688 [16:37:09<5:30:27, 4.30s/it] 75%|███████▌ | 14083/18688 [16:37:14<5:48:01, 4.53s/it] 75%|███████▌ | 14084/18688 [16:37:18<5:42:06, 4.46s/it] 75%|███████▌ | 14085/18688 [16:37:22<5:31:32, 4.32s/it] 75%|███████▌ | 14086/18688 [16:37:26<5:26:19, 4.25s/it] 75%|███████▌ | 14087/18688 [16:37:30<5:18:35, 4.15s/it] 75%|███████▌ | 14088/18688 [16:37:35<5:37:20, 4.40s/it] 75%|███████▌ | 14089/18688 [16:37:41<6:05:54, 4.77s/it] 75%|███████▌ | 14090/18688 [16:37:45<5:58:27, 4.68s/it] {'loss': '0.9472', 'grad_norm': '1.375', 'learning_rate': '1.665e-05', 'epoch': '0.754', 'num_input_tokens_seen': 47358160, 'train_runtime': '5.987e+04', 'train_tokens_per_second': '791.1'}
75%|███████▌ | 14090/18688 [16:37:45<5:58:27, 4.68s/it] 75%|███████▌ | 14091/18688 [16:37:50<6:12:49, 4.87s/it] 75%|███████▌ | 14092/18688 [16:37:55<6:08:21, 4.81s/it] 75%|███████▌ | 14093/18688 [16:38:00<6:08:34, 4.81s/it] 75%|███████▌ | 14094/18688 [16:38:04<5:51:01, 4.58s/it] 75%|███████▌ | 14095/18688 [16:38:08<5:45:37, 4.51s/it] 75%|███████▌ | 14096/18688 [16:38:13<5:59:44, 4.70s/it] 75%|███████▌ | 14097/18688 [16:38:17<5:41:50, 4.47s/it] 75%|███████▌ | 14098/18688 [16:38:23<6:01:25, 4.72s/it] 75%|███████▌ | 14099/18688 [16:38:26<5:38:14, 4.42s/it] 75%|███████▌ | 14100/18688 [16:38:30<5:21:11, 4.20s/it] {'loss': '0.8547', 'grad_norm': '1.439', 'learning_rate': '1.658e-05', 'epoch': '0.7545', 'num_input_tokens_seen': 47394824, 'train_runtime': '5.991e+04', 'train_tokens_per_second': '791.1'}
75%|███████▌ | 14100/18688 [16:38:30<5:21:11, 4.20s/it] 75%|███████▌ | 14101/18688 [16:38:34<5:16:06, 4.13s/it] 75%|███████▌ | 14102/18688 [16:38:39<5:26:33, 4.27s/it] 75%|███████▌ | 14103/18688 [16:38:43<5:31:26, 4.34s/it] 75%|███████▌ | 14104/18688 [16:38:47<5:27:10, 4.28s/it] 75%|███████▌ | 14105/18688 [16:38:51<5:17:17, 4.15s/it] 75%|███████▌ | 14106/18688 [16:38:55<5:15:52, 4.14s/it] 75%|███████▌ | 14107/18688 [16:38:59<5:10:37, 4.07s/it] 75%|███████▌ | 14108/18688 [16:39:04<5:27:41, 4.29s/it] 75%|███████▌ | 14109/18688 [16:39:08<5:27:20, 4.29s/it] 76%|███████▌ | 14110/18688 [16:39:12<5:21:26, 4.21s/it] {'loss': '0.9081', 'grad_norm': '1.497', 'learning_rate': '1.651e-05', 'epoch': '0.7551', 'num_input_tokens_seen': 47428384, 'train_runtime': '5.995e+04', 'train_tokens_per_second': '791.1'}
76%|███████▌ | 14110/18688 [16:39:12<5:21:26, 4.21s/it] 76%|███████▌ | 14111/18688 [16:39:16<5:19:56, 4.19s/it] 76%|███████▌ | 14112/18688 [16:39:20<5:16:57, 4.16s/it] 76%|███████▌ | 14113/18688 [16:39:25<5:14:58, 4.13s/it] 76%|███████▌ | 14114/18688 [16:39:29<5:17:11, 4.16s/it] 76%|███████▌ | 14115/18688 [16:39:34<5:37:02, 4.42s/it] 76%|███████▌ | 14116/18688 [16:39:38<5:35:49, 4.41s/it] 76%|███████▌ | 14117/18688 [16:39:42<5:25:34, 4.27s/it] 76%|███████▌ | 14118/18688 [16:39:46<5:16:40, 4.16s/it] 76%|███████▌ | 14119/18688 [16:39:51<5:38:03, 4.44s/it] 76%|███████▌ | 14120/18688 [16:39:56<5:45:42, 4.54s/it] {'loss': '0.8281', 'grad_norm': '1.22', 'learning_rate': '1.645e-05', 'epoch': '0.7556', 'num_input_tokens_seen': 47462432, 'train_runtime': '6e+04', 'train_tokens_per_second': '791.1'}
76%|███████▌ | 14120/18688 [16:39:56<5:45:42, 4.54s/it] 76%|███████▌ | 14121/18688 [16:40:00<5:37:10, 4.43s/it] 76%|███████▌ | 14122/18688 [16:40:04<5:30:59, 4.35s/it] 76%|███████▌ | 14123/18688 [16:40:09<5:38:07, 4.44s/it] 76%|███████▌ | 14124/18688 [16:40:13<5:19:56, 4.21s/it] 76%|███████▌ | 14125/18688 [16:40:17<5:18:07, 4.18s/it] 76%|███████▌ | 14126/18688 [16:40:21<5:20:03, 4.21s/it] 76%|███████▌ | 14127/18688 [16:40:25<5:16:55, 4.17s/it] 76%|███████▌ | 14128/18688 [16:40:29<5:04:54, 4.01s/it] 76%|███████▌ | 14129/18688 [16:40:33<5:20:15, 4.21s/it] 76%|███████▌ | 14130/18688 [16:40:37<5:05:29, 4.02s/it] {'loss': '0.9203', 'grad_norm': '1.432', 'learning_rate': '1.638e-05', 'epoch': '0.7561', 'num_input_tokens_seen': 47493992, 'train_runtime': '6.004e+04', 'train_tokens_per_second': '791.1'}
76%|███████▌ | 14130/18688 [16:40:37<5:05:29, 4.02s/it] 76%|███████▌ | 14131/18688 [16:40:41<5:06:36, 4.04s/it] 76%|███████▌ | 14132/18688 [16:40:45<4:58:58, 3.94s/it] 76%|███████▌ | 14133/18688 [16:40:48<4:48:37, 3.80s/it] 76%|███████▌ | 14134/18688 [16:40:52<4:55:38, 3.90s/it] 76%|███████▌ | 14135/18688 [16:40:56<4:49:46, 3.82s/it] 76%|███████▌ | 14136/18688 [16:41:01<5:24:25, 4.28s/it] 76%|███████▌ | 14137/18688 [16:41:05<5:21:07, 4.23s/it] 76%|███████▌ | 14138/18688 [16:41:10<5:24:06, 4.27s/it] 76%|███████▌ | 14139/18688 [16:41:14<5:14:05, 4.14s/it] 76%|███████▌ | 14140/18688 [16:41:17<5:02:13, 3.99s/it] {'loss': '0.8952', 'grad_norm': '1.562', 'learning_rate': '1.631e-05', 'epoch': '0.7567', 'num_input_tokens_seen': 47526208, 'train_runtime': '6.008e+04', 'train_tokens_per_second': '791.1'}
76%|███████▌ | 14140/18688 [16:41:17<5:02:13, 3.99s/it] 76%|███████▌ | 14141/18688 [16:41:21<5:07:21, 4.06s/it] 76%|███████▌ | 14142/18688 [16:41:26<5:22:47, 4.26s/it] 76%|███████▌ | 14143/18688 [16:41:31<5:40:01, 4.49s/it] 76%|███████▌ | 14144/18688 [16:41:35<5:31:53, 4.38s/it] 76%|███████▌ | 14145/18688 [16:41:40<5:26:36, 4.31s/it] 76%|███████▌ | 14146/18688 [16:41:43<5:17:59, 4.20s/it] 76%|███████▌ | 14147/18688 [16:41:48<5:16:07, 4.18s/it] 76%|███████▌ | 14148/18688 [16:41:51<5:09:46, 4.09s/it] 76%|███████▌ | 14149/18688 [16:41:57<5:44:13, 4.55s/it] 76%|███████▌ | 14150/18688 [16:42:01<5:26:41, 4.32s/it] {'loss': '0.8968', 'grad_norm': '1.146', 'learning_rate': '1.624e-05', 'epoch': '0.7572', 'num_input_tokens_seen': 47562168, 'train_runtime': '6.012e+04', 'train_tokens_per_second': '791.1'}
76%|███████▌ | 14150/18688 [16:42:01<5:26:41, 4.32s/it] 76%|███████▌ | 14151/18688 [16:42:05<5:21:02, 4.25s/it] 76%|███████▌ | 14152/18688 [16:42:09<5:25:07, 4.30s/it] 76%|███████▌ | 14153/18688 [16:42:14<5:26:08, 4.31s/it] 76%|███████▌ | 14154/18688 [16:42:18<5:17:14, 4.20s/it] 76%|███████▌ | 14155/18688 [16:42:22<5:14:31, 4.16s/it] 76%|███████▌ | 14156/18688 [16:42:26<5:16:02, 4.18s/it] 76%|███████▌ | 14157/18688 [16:42:30<5:05:34, 4.05s/it] 76%|███████▌ | 14158/18688 [16:42:34<4:59:57, 3.97s/it] 76%|███████▌ | 14159/18688 [16:42:37<4:59:40, 3.97s/it] 76%|███████▌ | 14160/18688 [16:42:42<5:04:23, 4.03s/it] {'loss': '0.9373', 'grad_norm': '1.52', 'learning_rate': '1.618e-05', 'epoch': '0.7577', 'num_input_tokens_seen': 47594240, 'train_runtime': '6.016e+04', 'train_tokens_per_second': '791.1'}
76%|███████▌ | 14160/18688 [16:42:42<5:04:23, 4.03s/it] 76%|███████▌ | 14161/18688 [16:42:46<5:15:22, 4.18s/it] 76%|███████▌ | 14162/18688 [16:42:50<5:05:57, 4.06s/it] 76%|███████▌ | 14163/18688 [16:42:54<5:04:30, 4.04s/it] 76%|███████▌ | 14164/18688 [16:42:59<5:37:31, 4.48s/it] 76%|███████▌ | 14165/18688 [16:43:04<5:32:22, 4.41s/it] 76%|███████▌ | 14166/18688 [16:43:08<5:30:37, 4.39s/it] 76%|███████▌ | 14167/18688 [16:43:12<5:17:28, 4.21s/it] 76%|███████▌ | 14168/18688 [16:43:15<5:03:48, 4.03s/it] 76%|███████▌ | 14169/18688 [16:43:20<5:10:14, 4.12s/it] 76%|███████▌ | 14170/18688 [16:43:24<5:09:11, 4.11s/it] {'loss': '0.8781', 'grad_norm': '1.486', 'learning_rate': '1.611e-05', 'epoch': '0.7583', 'num_input_tokens_seen': 47625832, 'train_runtime': '6.02e+04', 'train_tokens_per_second': '791.1'}
76%|███████▌ | 14170/18688 [16:43:24<5:09:11, 4.11s/it] 76%|███████▌ | 14171/18688 [16:43:28<5:07:33, 4.09s/it] 76%|███████▌ | 14172/18688 [16:43:32<5:16:09, 4.20s/it] 76%|███████▌ | 14173/18688 [16:43:37<5:25:09, 4.32s/it] 76%|███████▌ | 14174/18688 [16:43:41<5:16:51, 4.21s/it] 76%|███████▌ | 14175/18688 [16:43:45<5:08:26, 4.10s/it] 76%|███████▌ | 14176/18688 [16:43:49<5:19:03, 4.24s/it] 76%|███████▌ | 14177/18688 [16:43:54<5:21:40, 4.28s/it] 76%|███████▌ | 14178/18688 [16:43:58<5:30:22, 4.40s/it] 76%|███████▌ | 14179/18688 [16:44:03<5:30:37, 4.40s/it] 76%|███████▌ | 14180/18688 [16:44:07<5:26:07, 4.34s/it] {'loss': '0.976', 'grad_norm': '1.103', 'learning_rate': '1.604e-05', 'epoch': '0.7588', 'num_input_tokens_seen': 47660328, 'train_runtime': '6.025e+04', 'train_tokens_per_second': '791.1'}
76%|███████▌ | 14180/18688 [16:44:07<5:26:07, 4.34s/it] 76%|███████▌ | 14181/18688 [16:44:11<5:24:24, 4.32s/it] 76%|███████▌ | 14182/18688 [16:44:15<5:18:19, 4.24s/it] 76%|███████▌ | 14183/18688 [16:44:20<5:33:10, 4.44s/it] 76%|███████▌ | 14184/18688 [16:44:25<5:34:16, 4.45s/it] 76%|███████▌ | 14185/18688 [16:44:28<5:19:27, 4.26s/it] 76%|███████▌ | 14186/18688 [16:44:32<5:06:39, 4.09s/it] 76%|███████▌ | 14187/18688 [16:44:38<5:44:00, 4.59s/it] 76%|███████▌ | 14188/18688 [16:44:42<5:30:21, 4.40s/it] 76%|███████▌ | 14189/18688 [16:44:46<5:20:50, 4.28s/it] 76%|███████▌ | 14190/18688 [16:44:50<5:09:29, 4.13s/it] {'loss': '0.9308', 'grad_norm': '1.393', 'learning_rate': '1.597e-05', 'epoch': '0.7593', 'num_input_tokens_seen': 47694656, 'train_runtime': '6.029e+04', 'train_tokens_per_second': '791.1'}
76%|███████▌ | 14190/18688 [16:44:50<5:09:29, 4.13s/it] 76%|███████▌ | 14191/18688 [16:44:54<5:22:15, 4.30s/it] 76%|███████▌ | 14192/18688 [16:44:58<5:10:15, 4.14s/it] 76%|███████▌ | 14193/18688 [16:45:02<5:00:21, 4.01s/it] 76%|███████▌ | 14194/18688 [16:45:06<5:12:59, 4.18s/it] 76%|███████▌ | 14195/18688 [16:45:11<5:23:05, 4.31s/it] 76%|███████▌ | 14196/18688 [16:45:16<5:29:06, 4.40s/it] 76%|███████▌ | 14197/18688 [16:45:20<5:25:56, 4.35s/it] 76%|███████▌ | 14198/18688 [16:45:24<5:18:23, 4.25s/it] 76%|███████▌ | 14199/18688 [16:45:28<5:03:39, 4.06s/it] 76%|███████▌ | 14200/18688 [16:45:32<5:08:36, 4.13s/it] {'loss': '0.8722', 'grad_norm': '1.294', 'learning_rate': '1.591e-05', 'epoch': '0.7599', 'num_input_tokens_seen': 47727040, 'train_runtime': '6.033e+04', 'train_tokens_per_second': '791.1'}
76%|███████▌ | 14200/18688 [16:45:32<5:08:36, 4.13s/it] 76%|███████▌ | 14201/18688 [16:45:35<4:56:18, 3.96s/it] 76%|███████▌ | 14202/18688 [16:45:39<4:57:54, 3.98s/it] 76%|███████▌ | 14203/18688 [16:45:43<4:48:24, 3.86s/it] 76%|███████▌ | 14204/18688 [16:45:48<5:09:40, 4.14s/it] 76%|███████▌ | 14205/18688 [16:45:53<5:30:17, 4.42s/it] 76%|███████▌ | 14206/18688 [16:45:57<5:31:33, 4.44s/it] 76%|███████▌ | 14207/18688 [16:46:02<5:31:34, 4.44s/it] 76%|███████▌ | 14208/18688 [16:46:05<5:14:07, 4.21s/it] 76%|███████▌ | 14209/18688 [16:46:11<5:37:01, 4.51s/it] 76%|███████▌ | 14210/18688 [16:46:15<5:30:26, 4.43s/it] {'loss': '0.8476', 'grad_norm': '1.261', 'learning_rate': '1.584e-05', 'epoch': '0.7604', 'num_input_tokens_seen': 47759160, 'train_runtime': '6.038e+04', 'train_tokens_per_second': '791'}
76%|███████▌ | 14210/18688 [16:46:15<5:30:26, 4.43s/it] 76%|███████▌ | 14211/18688 [16:46:20<5:39:25, 4.55s/it] 76%|███████▌ | 14212/18688 [16:46:24<5:39:40, 4.55s/it] 76%|███████▌ | 14213/18688 [16:46:29<5:46:54, 4.65s/it] 76%|███████▌ | 14214/18688 [16:46:33<5:30:28, 4.43s/it] 76%|███████▌ | 14215/18688 [16:46:37<5:09:37, 4.15s/it] 76%|███████▌ | 14216/18688 [16:46:42<5:39:14, 4.55s/it] 76%|███████▌ | 14217/18688 [16:46:47<5:52:50, 4.74s/it] 76%|███████▌ | 14218/18688 [16:46:51<5:24:57, 4.36s/it] 76%|███████▌ | 14219/18688 [16:46:55<5:24:08, 4.35s/it] 76%|███████▌ | 14220/18688 [16:46:59<5:22:29, 4.33s/it] {'loss': '0.9367', 'grad_norm': '1.082', 'learning_rate': '1.577e-05', 'epoch': '0.7609', 'num_input_tokens_seen': 47797224, 'train_runtime': '6.042e+04', 'train_tokens_per_second': '791.1'}
76%|███████▌ | 14220/18688 [16:46:59<5:22:29, 4.33s/it] 76%|███████▌ | 14221/18688 [16:47:05<6:02:09, 4.86s/it] 76%|███████▌ | 14222/18688 [16:47:10<5:56:12, 4.79s/it] 76%|███████▌ | 14223/18688 [16:47:14<5:46:55, 4.66s/it] 76%|███████▌ | 14224/18688 [16:47:18<5:28:07, 4.41s/it] 76%|███████▌ | 14225/18688 [16:47:22<5:15:51, 4.25s/it] 76%|███████▌ | 14226/18688 [16:47:27<5:24:55, 4.37s/it] 76%|███████▌ | 14227/18688 [16:47:31<5:22:17, 4.33s/it] 76%|███████▌ | 14228/18688 [16:47:35<5:05:03, 4.10s/it] 76%|███████▌ | 14229/18688 [16:47:39<5:01:09, 4.05s/it] 76%|███████▌ | 14230/18688 [16:47:43<5:18:12, 4.28s/it] {'loss': '0.8631', 'grad_norm': '1.373', 'learning_rate': '1.571e-05', 'epoch': '0.7615', 'num_input_tokens_seen': 47833792, 'train_runtime': '6.046e+04', 'train_tokens_per_second': '791.1'}
76%|███████▌ | 14230/18688 [16:47:43<5:18:12, 4.28s/it] 76%|███████▌ | 14231/18688 [16:47:48<5:30:02, 4.44s/it] 76%|███████▌ | 14232/18688 [16:47:52<5:17:12, 4.27s/it] 76%|███████▌ | 14233/18688 [16:47:56<5:05:45, 4.12s/it] 76%|███████▌ | 14234/18688 [16:48:00<5:02:20, 4.07s/it] 76%|███████▌ | 14235/18688 [16:48:04<4:59:44, 4.04s/it] 76%|███████▌ | 14236/18688 [16:48:08<5:10:30, 4.18s/it] 76%|███████▌ | 14237/18688 [16:48:13<5:30:58, 4.46s/it] 76%|███████▌ | 14238/18688 [16:48:19<5:54:51, 4.78s/it] 76%|███████▌ | 14239/18688 [16:48:23<5:41:44, 4.61s/it] 76%|███████▌ | 14240/18688 [16:48:27<5:35:59, 4.53s/it] {'loss': '0.9705', 'grad_norm': '1.554', 'learning_rate': '1.564e-05', 'epoch': '0.762', 'num_input_tokens_seen': 47870392, 'train_runtime': '6.051e+04', 'train_tokens_per_second': '791.1'}
76%|███████▌ | 14240/18688 [16:48:27<5:35:59, 4.53s/it] 76%|███████▌ | 14241/18688 [16:48:32<5:27:08, 4.41s/it] 76%|███████▌ | 14242/18688 [16:48:36<5:16:54, 4.28s/it] 76%|███████▌ | 14243/18688 [16:48:41<5:33:18, 4.50s/it] 76%|███████▌ | 14244/18688 [16:48:44<5:18:27, 4.30s/it] 76%|███████▌ | 14245/18688 [16:48:49<5:33:24, 4.50s/it] 76%|███████▌ | 14246/18688 [16:48:53<5:19:39, 4.32s/it] 76%|███████▌ | 14247/18688 [16:48:57<5:07:09, 4.15s/it] 76%|███████▌ | 14248/18688 [16:49:02<5:21:21, 4.34s/it] 76%|███████▌ | 14249/18688 [16:49:06<5:10:42, 4.20s/it] 76%|███████▋ | 14250/18688 [16:49:09<4:56:48, 4.01s/it] {'loss': '0.9072', 'grad_norm': '1.879', 'learning_rate': '1.557e-05', 'epoch': '0.7625', 'num_input_tokens_seen': 47903680, 'train_runtime': '6.055e+04', 'train_tokens_per_second': '791.1'}
76%|███████▋ | 14250/18688 [16:49:09<4:56:48, 4.01s/it] 76%|███████▋ | 14251/18688 [16:49:13<4:49:57, 3.92s/it] 76%|███████▋ | 14252/18688 [16:49:17<4:50:35, 3.93s/it] 76%|███████▋ | 14253/18688 [16:49:21<5:01:52, 4.08s/it] 76%|███████▋ | 14254/18688 [16:49:25<4:57:01, 4.02s/it] 76%|███████▋ | 14255/18688 [16:49:29<4:46:50, 3.88s/it] 76%|███████▋ | 14256/18688 [16:49:33<4:51:06, 3.94s/it] 76%|███████▋ | 14257/18688 [16:49:37<4:56:16, 4.01s/it] 76%|███████▋ | 14258/18688 [16:49:42<5:07:48, 4.17s/it] 76%|███████▋ | 14259/18688 [16:49:46<5:07:18, 4.16s/it] 76%|███████▋ | 14260/18688 [16:49:49<4:51:11, 3.95s/it] {'loss': '0.8655', 'grad_norm': '1.322', 'learning_rate': '1.551e-05', 'epoch': '0.7631', 'num_input_tokens_seen': 47935776, 'train_runtime': '6.059e+04', 'train_tokens_per_second': '791.2'}
76%|███████▋ | 14260/18688 [16:49:49<4:51:11, 3.95s/it] 76%|███████▋ | 14261/18688 [16:49:54<5:14:49, 4.27s/it] 76%|███████▋ | 14262/18688 [16:49:58<5:05:30, 4.14s/it] 76%|███████▋ | 14263/18688 [16:50:02<5:07:44, 4.17s/it] 76%|███████▋ | 14264/18688 [16:50:07<5:30:00, 4.48s/it] 76%|███████▋ | 14265/18688 [16:50:13<5:44:22, 4.67s/it] 76%|███████▋ | 14266/18688 [16:50:17<5:39:36, 4.61s/it] 76%|███████▋ | 14267/18688 [16:50:22<5:44:25, 4.67s/it] 76%|███████▋ | 14268/18688 [16:50:25<5:19:34, 4.34s/it] 76%|███████▋ | 14269/18688 [16:50:29<5:05:11, 4.14s/it] 76%|███████▋ | 14270/18688 [16:50:33<4:49:57, 3.94s/it] {'loss': '0.8425', 'grad_norm': '1.54', 'learning_rate': '1.544e-05', 'epoch': '0.7636', 'num_input_tokens_seen': 47971408, 'train_runtime': '6.063e+04', 'train_tokens_per_second': '791.2'}
76%|███████▋ | 14270/18688 [16:50:33<4:49:57, 3.94s/it] 76%|███████▋ | 14271/18688 [16:50:38<5:13:03, 4.25s/it] 76%|███████▋ | 14272/18688 [16:50:41<5:03:08, 4.12s/it] 76%|███████▋ | 14273/18688 [16:50:45<4:58:05, 4.05s/it] 76%|███████▋ | 14274/18688 [16:50:50<5:18:57, 4.34s/it] 76%|███████▋ | 14275/18688 [16:50:54<5:06:49, 4.17s/it] 76%|███████▋ | 14276/18688 [16:50:58<5:04:06, 4.14s/it] 76%|███████▋ | 14277/18688 [16:51:02<4:52:52, 3.98s/it] 76%|███████▋ | 14278/18688 [16:51:07<5:11:41, 4.24s/it] 76%|███████▋ | 14279/18688 [16:51:10<5:02:00, 4.11s/it] 76%|███████▋ | 14280/18688 [16:51:15<5:08:01, 4.19s/it] {'loss': '0.9226', 'grad_norm': '1.404', 'learning_rate': '1.538e-05', 'epoch': '0.7641', 'num_input_tokens_seen': 48006968, 'train_runtime': '6.068e+04', 'train_tokens_per_second': '791.2'}
76%|███████▋ | 14280/18688 [16:51:15<5:08:01, 4.19s/it] 76%|███████▋ | 14281/18688 [16:51:20<5:26:00, 4.44s/it] 76%|███████▋ | 14282/18688 [16:51:24<5:24:12, 4.42s/it] 76%|███████▋ | 14283/18688 [16:51:28<5:10:00, 4.22s/it] 76%|███████▋ | 14284/18688 [16:51:32<5:11:35, 4.25s/it] 76%|███████▋ | 14285/18688 [16:51:36<5:00:25, 4.09s/it] 76%|███████▋ | 14286/18688 [16:51:40<5:05:03, 4.16s/it] 76%|███████▋ | 14287/18688 [16:51:44<5:02:01, 4.12s/it] 76%|███████▋ | 14288/18688 [16:51:48<4:56:52, 4.05s/it] 76%|███████▋ | 14289/18688 [16:51:52<4:48:40, 3.94s/it] 76%|███████▋ | 14290/18688 [16:51:56<4:54:55, 4.02s/it] {'loss': '0.8967', 'grad_norm': '1.736', 'learning_rate': '1.531e-05', 'epoch': '0.7647', 'num_input_tokens_seen': 48040176, 'train_runtime': '6.072e+04', 'train_tokens_per_second': '791.2'}
76%|███████▋ | 14290/18688 [16:51:56<4:54:55, 4.02s/it] 76%|███████▋ | 14291/18688 [16:52:00<4:46:56, 3.92s/it] 76%|███████▋ | 14292/18688 [16:52:04<4:48:36, 3.94s/it] 76%|███████▋ | 14293/18688 [16:52:08<4:50:36, 3.97s/it] 76%|███████▋ | 14294/18688 [16:52:14<5:33:08, 4.55s/it] 76%|███████▋ | 14295/18688 [16:52:17<5:15:32, 4.31s/it] 76%|███████▋ | 14296/18688 [16:52:22<5:14:56, 4.30s/it] 77%|███████▋ | 14297/18688 [16:52:26<5:20:02, 4.37s/it] 77%|███████▋ | 14298/18688 [16:52:30<5:07:50, 4.21s/it] 77%|███████▋ | 14299/18688 [16:52:34<5:00:52, 4.11s/it] 77%|███████▋ | 14300/18688 [16:52:38<4:57:47, 4.07s/it] {'loss': '0.9504', 'grad_norm': '1.288', 'learning_rate': '1.524e-05', 'epoch': '0.7652', 'num_input_tokens_seen': 48074240, 'train_runtime': '6.076e+04', 'train_tokens_per_second': '791.2'}
77%|███████▋ | 14300/18688 [16:52:38<4:57:47, 4.07s/it] 77%|███████▋ | 14301/18688 [16:52:42<4:49:00, 3.95s/it] 77%|███████▋ | 14302/18688 [16:52:47<5:17:07, 4.34s/it] 77%|███████▋ | 14303/18688 [16:52:50<5:02:02, 4.13s/it] 77%|███████▋ | 14304/18688 [16:52:55<5:15:11, 4.31s/it] 77%|███████▋ | 14305/18688 [16:53:00<5:18:03, 4.35s/it] 77%|███████▋ | 14306/18688 [16:53:04<5:14:40, 4.31s/it] 77%|███████▋ | 14307/18688 [16:53:08<5:05:18, 4.18s/it] 77%|███████▋ | 14308/18688 [16:53:12<4:59:05, 4.10s/it] 77%|███████▋ | 14309/18688 [16:53:15<4:51:28, 3.99s/it] 77%|███████▋ | 14310/18688 [16:53:19<4:44:00, 3.89s/it] {'loss': '0.8541', 'grad_norm': '1.155', 'learning_rate': '1.518e-05', 'epoch': '0.7658', 'num_input_tokens_seen': 48106104, 'train_runtime': '6.08e+04', 'train_tokens_per_second': '791.2'}
77%|███████▋ | 14310/18688 [16:53:19<4:44:00, 3.89s/it] 77%|███████▋ | 14311/18688 [16:53:23<4:51:50, 4.00s/it] 77%|███████▋ | 14312/18688 [16:53:28<5:07:35, 4.22s/it] 77%|███████▋ | 14313/18688 [16:53:32<5:00:21, 4.12s/it] 77%|███████▋ | 14314/18688 [16:53:36<5:00:59, 4.13s/it] 77%|███████▋ | 14315/18688 [16:53:40<5:06:59, 4.21s/it] 77%|███████▋ | 14316/18688 [16:53:44<4:53:07, 4.02s/it] 77%|███████▋ | 14317/18688 [16:53:48<4:51:58, 4.01s/it] 77%|███████▋ | 14318/18688 [16:53:52<4:56:59, 4.08s/it] 77%|███████▋ | 14319/18688 [16:53:56<4:48:43, 3.97s/it] 77%|███████▋ | 14320/18688 [16:54:01<5:13:10, 4.30s/it] {'loss': '0.8553', 'grad_norm': '1.434', 'learning_rate': '1.511e-05', 'epoch': '0.7663', 'num_input_tokens_seen': 48137712, 'train_runtime': '6.084e+04', 'train_tokens_per_second': '791.2'}
77%|███████▋ | 14320/18688 [16:54:01<5:13:10, 4.30s/it] 77%|███████▋ | 14321/18688 [16:54:06<5:23:32, 4.45s/it] 77%|███████▋ | 14322/18688 [16:54:10<5:25:34, 4.47s/it] 77%|███████▋ | 14323/18688 [16:54:15<5:19:18, 4.39s/it] 77%|███████▋ | 14324/18688 [16:54:18<5:02:31, 4.16s/it] 77%|███████▋ | 14325/18688 [16:54:23<5:11:26, 4.28s/it] 77%|███████▋ | 14326/18688 [16:54:27<5:09:18, 4.25s/it] 77%|███████▋ | 14327/18688 [16:54:32<5:32:50, 4.58s/it] 77%|███████▋ | 14328/18688 [16:54:37<5:26:15, 4.49s/it] 77%|███████▋ | 14329/18688 [16:54:40<5:13:45, 4.32s/it] 77%|███████▋ | 14330/18688 [16:54:45<5:13:13, 4.31s/it] {'loss': '0.9101', 'grad_norm': '1.177', 'learning_rate': '1.505e-05', 'epoch': '0.7668', 'num_input_tokens_seen': 48174248, 'train_runtime': '6.089e+04', 'train_tokens_per_second': '791.2'}
77%|███████▋ | 14330/18688 [16:54:45<5:13:13, 4.31s/it] 77%|███████▋ | 14331/18688 [16:54:49<5:15:11, 4.34s/it] 77%|███████▋ | 14332/18688 [16:54:53<5:07:03, 4.23s/it] 77%|███████▋ | 14333/18688 [16:54:58<5:24:07, 4.47s/it] 77%|███████▋ | 14334/18688 [16:55:02<5:14:22, 4.33s/it] 77%|███████▋ | 14335/18688 [16:55:07<5:16:09, 4.36s/it] 77%|███████▋ | 14336/18688 [16:55:11<5:23:35, 4.46s/it] 77%|███████▋ | 14337/18688 [16:55:15<5:11:44, 4.30s/it] 77%|███████▋ | 14338/18688 [16:55:20<5:12:42, 4.31s/it] 77%|███████▋ | 14339/18688 [16:55:25<5:32:55, 4.59s/it] 77%|███████▋ | 14340/18688 [16:55:29<5:23:47, 4.47s/it] {'loss': '0.9251', 'grad_norm': '1.374', 'learning_rate': '1.498e-05', 'epoch': '0.7674', 'num_input_tokens_seen': 48211768, 'train_runtime': '6.093e+04', 'train_tokens_per_second': '791.3'}
77%|███████▋ | 14340/18688 [16:55:29<5:23:47, 4.47s/it] 77%|███████▋ | 14341/18688 [16:55:33<5:03:37, 4.19s/it] 77%|███████▋ | 14342/18688 [16:55:36<4:48:16, 3.98s/it] 77%|███████▋ | 14343/18688 [16:55:40<4:40:02, 3.87s/it] 77%|███████▋ | 14344/18688 [16:55:43<4:33:35, 3.78s/it] 77%|███████▋ | 14345/18688 [16:55:47<4:27:52, 3.70s/it] 77%|███████▋ | 14346/18688 [16:55:51<4:33:14, 3.78s/it] 77%|███████▋ | 14347/18688 [16:55:56<4:55:40, 4.09s/it] 77%|███████▋ | 14348/18688 [16:56:00<5:08:42, 4.27s/it] 77%|███████▋ | 14349/18688 [16:56:04<5:03:53, 4.20s/it] 77%|███████▋ | 14350/18688 [16:56:08<5:02:51, 4.19s/it] {'loss': '0.8932', 'grad_norm': '1.519', 'learning_rate': '1.492e-05', 'epoch': '0.7679', 'num_input_tokens_seen': 48242496, 'train_runtime': '6.097e+04', 'train_tokens_per_second': '791.3'}
77%|███████▋ | 14350/18688 [16:56:08<5:02:51, 4.19s/it] 77%|███████▋ | 14351/18688 [16:56:13<5:10:19, 4.29s/it] 77%|███████▋ | 14352/18688 [16:56:17<5:02:03, 4.18s/it] 77%|███████▋ | 14353/18688 [16:56:21<5:00:59, 4.17s/it] 77%|███████▋ | 14354/18688 [16:56:25<4:46:57, 3.97s/it] 77%|███████▋ | 14355/18688 [16:56:29<4:55:58, 4.10s/it] 77%|███████▋ | 14356/18688 [16:56:34<5:12:58, 4.33s/it] 77%|███████▋ | 14357/18688 [16:56:38<5:19:09, 4.42s/it] 77%|███████▋ | 14358/18688 [16:56:42<5:07:52, 4.27s/it] 77%|███████▋ | 14359/18688 [16:56:47<5:12:39, 4.33s/it] 77%|███████▋ | 14360/18688 [16:56:50<4:58:38, 4.14s/it] {'loss': '0.9068', 'grad_norm': '1.621', 'learning_rate': '1.485e-05', 'epoch': '0.7684', 'num_input_tokens_seen': 48278152, 'train_runtime': '6.101e+04', 'train_tokens_per_second': '791.3'}
77%|███████▋ | 14360/18688 [16:56:50<4:58:38, 4.14s/it] 77%|███████▋ | 14361/18688 [16:56:54<4:48:22, 4.00s/it] 77%|███████▋ | 14362/18688 [16:56:58<4:40:29, 3.89s/it] 77%|███████▋ | 14363/18688 [16:57:02<4:41:49, 3.91s/it] 77%|███████▋ | 14364/18688 [16:57:06<4:56:57, 4.12s/it] 77%|███████▋ | 14365/18688 [16:57:10<4:47:29, 3.99s/it] 77%|███████▋ | 14366/18688 [16:57:14<4:44:26, 3.95s/it] 77%|███████▋ | 14367/18688 [16:57:18<4:56:55, 4.12s/it] 77%|███████▋ | 14368/18688 [16:57:22<4:49:20, 4.02s/it] 77%|███████▋ | 14369/18688 [16:57:26<4:54:38, 4.09s/it] 77%|███████▋ | 14370/18688 [16:57:31<5:05:21, 4.24s/it] {'loss': '0.8652', 'grad_norm': '1.293', 'learning_rate': '1.479e-05', 'epoch': '0.769', 'num_input_tokens_seen': 48311496, 'train_runtime': '6.105e+04', 'train_tokens_per_second': '791.3'}
77%|███████▋ | 14370/18688 [16:57:31<5:05:21, 4.24s/it] 77%|███████▋ | 14371/18688 [16:57:36<5:23:23, 4.49s/it] 77%|███████▋ | 14372/18688 [16:57:40<5:09:03, 4.30s/it] 77%|███████▋ | 14373/18688 [16:57:44<4:59:59, 4.17s/it] 77%|███████▋ | 14374/18688 [16:57:47<4:46:08, 3.98s/it] 77%|███████▋ | 14375/18688 [16:57:51<4:43:17, 3.94s/it] 77%|███████▋ | 14376/18688 [16:57:55<4:33:38, 3.81s/it] 77%|███████▋ | 14377/18688 [16:57:59<4:45:22, 3.97s/it] 77%|███████▋ | 14378/18688 [16:58:03<4:53:31, 4.09s/it] 77%|███████▋ | 14379/18688 [16:58:07<4:43:04, 3.94s/it] 77%|███████▋ | 14380/18688 [16:58:11<4:44:21, 3.96s/it] {'loss': '0.8777', 'grad_norm': '1.3', 'learning_rate': '1.472e-05', 'epoch': '0.7695', 'num_input_tokens_seen': 48343040, 'train_runtime': '6.109e+04', 'train_tokens_per_second': '791.3'}
77%|███████▋ | 14380/18688 [16:58:11<4:44:21, 3.96s/it] 77%|███████▋ | 14381/18688 [16:58:15<4:36:31, 3.85s/it] 77%|███████▋ | 14382/18688 [16:58:19<4:36:58, 3.86s/it] 77%|███████▋ | 14383/18688 [16:58:23<4:57:04, 4.14s/it] 77%|███████▋ | 14384/18688 [16:58:27<4:48:41, 4.02s/it] 77%|███████▋ | 14385/18688 [16:58:31<4:47:07, 4.00s/it] 77%|███████▋ | 14386/18688 [16:58:36<5:08:09, 4.30s/it] 77%|███████▋ | 14387/18688 [16:58:40<5:00:22, 4.19s/it] 77%|███████▋ | 14388/18688 [16:58:44<4:59:49, 4.18s/it] 77%|███████▋ | 14389/18688 [16:58:48<4:57:30, 4.15s/it] 77%|███████▋ | 14390/18688 [16:58:52<4:46:12, 4.00s/it] {'loss': '0.8518', 'grad_norm': '1.349', 'learning_rate': '1.466e-05', 'epoch': '0.77', 'num_input_tokens_seen': 48375824, 'train_runtime': '6.113e+04', 'train_tokens_per_second': '791.3'}
77%|███████▋ | 14390/18688 [16:58:52<4:46:12, 4.00s/it] 77%|███████▋ | 14391/18688 [16:58:56<4:52:44, 4.09s/it] 77%|███████▋ | 14392/18688 [16:59:01<5:06:41, 4.28s/it] 77%|███████▋ | 14393/18688 [16:59:05<4:52:17, 4.08s/it] 77%|███████▋ | 14394/18688 [16:59:08<4:39:14, 3.90s/it] 77%|███████▋ | 14395/18688 [16:59:12<4:42:40, 3.95s/it] 77%|███████▋ | 14396/18688 [16:59:16<4:35:25, 3.85s/it] 77%|███████▋ | 14397/18688 [16:59:20<4:53:18, 4.10s/it] 77%|███████▋ | 14398/18688 [16:59:25<5:00:21, 4.20s/it] 77%|███████▋ | 14399/18688 [16:59:29<4:50:45, 4.07s/it] 77%|███████▋ | 14400/18688 [16:59:32<4:46:06, 4.00s/it] {'loss': '0.8885', 'grad_norm': '1.2', 'learning_rate': '1.459e-05', 'epoch': '0.7706', 'num_input_tokens_seen': 48407544, 'train_runtime': '6.117e+04', 'train_tokens_per_second': '791.3'}
77%|███████▋ | 14400/18688 [16:59:32<4:46:06, 4.00s/it] 77%|███████▋ | 14401/18688 [16:59:37<5:06:05, 4.28s/it] 77%|███████▋ | 14402/18688 [16:59:42<5:07:32, 4.31s/it] 77%|███████▋ | 14403/18688 [16:59:46<5:09:56, 4.34s/it] 77%|███████▋ | 14404/18688 [16:59:50<4:58:04, 4.17s/it] 77%|███████▋ | 14405/18688 [16:59:54<4:57:25, 4.17s/it] 77%|███████▋ | 14406/18688 [16:59:58<4:58:42, 4.19s/it] 77%|███████▋ | 14407/18688 [17:00:02<4:47:25, 4.03s/it] 77%|███████▋ | 14408/18688 [17:00:06<4:38:28, 3.90s/it] 77%|███████▋ | 14409/18688 [17:00:09<4:30:31, 3.79s/it] 77%|███████▋ | 14410/18688 [17:00:14<4:46:57, 4.02s/it] {'loss': '0.9581', 'grad_norm': '1.318', 'learning_rate': '1.453e-05', 'epoch': '0.7711', 'num_input_tokens_seen': 48441672, 'train_runtime': '6.121e+04', 'train_tokens_per_second': '791.3'}
77%|███████▋ | 14410/18688 [17:00:14<4:46:57, 4.02s/it] 77%|███████▋ | 14411/18688 [17:00:18<4:44:58, 4.00s/it] 77%|███████▋ | 14412/18688 [17:00:22<4:50:22, 4.07s/it] 77%|███████▋ | 14413/18688 [17:00:27<5:07:09, 4.31s/it] 77%|███████▋ | 14414/18688 [17:00:31<5:05:18, 4.29s/it] 77%|███████▋ | 14415/18688 [17:00:35<5:07:43, 4.32s/it] 77%|███████▋ | 14416/18688 [17:00:39<4:51:18, 4.09s/it] 77%|███████▋ | 14417/18688 [17:00:43<4:59:02, 4.20s/it] 77%|███████▋ | 14418/18688 [17:00:47<4:53:56, 4.13s/it] 77%|███████▋ | 14419/18688 [17:00:51<4:52:19, 4.11s/it] 77%|███████▋ | 14420/18688 [17:00:55<4:51:29, 4.10s/it] {'loss': '0.9103', 'grad_norm': '1.28', 'learning_rate': '1.446e-05', 'epoch': '0.7716', 'num_input_tokens_seen': 48474872, 'train_runtime': '6.126e+04', 'train_tokens_per_second': '791.4'}
77%|███████▋ | 14420/18688 [17:00:55<4:51:29, 4.10s/it] 77%|███████▋ | 14421/18688 [17:00:59<4:46:41, 4.03s/it] 77%|███████▋ | 14422/18688 [17:01:03<4:36:52, 3.89s/it] 77%|███████▋ | 14423/18688 [17:01:07<4:31:40, 3.82s/it] 77%|███████▋ | 14424/18688 [17:01:11<4:40:39, 3.95s/it] 77%|███████▋ | 14425/18688 [17:01:17<5:19:49, 4.50s/it] 77%|███████▋ | 14426/18688 [17:01:20<5:05:43, 4.30s/it] 77%|███████▋ | 14427/18688 [17:01:25<5:01:14, 4.24s/it] 77%|███████▋ | 14428/18688 [17:01:29<4:58:47, 4.21s/it] 77%|███████▋ | 14429/18688 [17:01:33<4:55:34, 4.16s/it] 77%|███████▋ | 14430/18688 [17:01:37<4:47:47, 4.06s/it] {'loss': '0.897', 'grad_norm': '1.369', 'learning_rate': '1.44e-05', 'epoch': '0.7722', 'num_input_tokens_seen': 48508912, 'train_runtime': '6.13e+04', 'train_tokens_per_second': '791.4'}
77%|███████▋ | 14430/18688 [17:01:37<4:47:47, 4.06s/it] 77%|███████▋ | 14431/18688 [17:01:40<4:40:24, 3.95s/it] 77%|███████▋ | 14432/18688 [17:01:45<4:50:03, 4.09s/it] 77%|███████▋ | 14433/18688 [17:01:51<5:27:50, 4.62s/it] 77%|███████▋ | 14434/18688 [17:01:55<5:16:22, 4.46s/it] 77%|███████▋ | 14435/18688 [17:01:59<5:21:29, 4.54s/it] 77%|███████▋ | 14436/18688 [17:02:03<5:08:32, 4.35s/it] 77%|███████▋ | 14437/18688 [17:02:08<5:13:46, 4.43s/it] 77%|███████▋ | 14438/18688 [17:02:11<4:54:42, 4.16s/it] 77%|███████▋ | 14439/18688 [17:02:16<5:07:14, 4.34s/it] 77%|███████▋ | 14440/18688 [17:02:20<5:07:27, 4.34s/it] {'loss': '0.8629', 'grad_norm': '1.244', 'learning_rate': '1.434e-05', 'epoch': '0.7727', 'num_input_tokens_seen': 48545432, 'train_runtime': '6.134e+04', 'train_tokens_per_second': '791.4'}
77%|███████▋ | 14440/18688 [17:02:20<5:07:27, 4.34s/it] 77%|███████▋ | 14441/18688 [17:02:25<5:14:23, 4.44s/it] 77%|███████▋ | 14442/18688 [17:02:29<5:07:08, 4.34s/it] 77%|███████▋ | 14443/18688 [17:02:33<4:56:47, 4.19s/it] 77%|███████▋ | 14444/18688 [17:02:37<4:47:10, 4.06s/it] 77%|███████▋ | 14445/18688 [17:02:41<4:58:04, 4.22s/it] 77%|███████▋ | 14446/18688 [17:02:45<4:49:20, 4.09s/it] 77%|███████▋ | 14447/18688 [17:02:49<4:51:43, 4.13s/it] 77%|███████▋ | 14448/18688 [17:02:54<4:58:35, 4.23s/it] 77%|███████▋ | 14449/18688 [17:02:57<4:42:55, 4.00s/it] 77%|███████▋ | 14450/18688 [17:03:02<5:01:10, 4.26s/it] {'loss': '0.9206', 'grad_norm': '1.273', 'learning_rate': '1.427e-05', 'epoch': '0.7732', 'num_input_tokens_seen': 48579560, 'train_runtime': '6.138e+04', 'train_tokens_per_second': '791.4'}
77%|███████▋ | 14450/18688 [17:03:02<5:01:10, 4.26s/it] 77%|███████▋ | 14451/18688 [17:03:07<5:06:19, 4.34s/it] 77%|███████▋ | 14452/18688 [17:03:12<5:19:51, 4.53s/it] 77%|███████▋ | 14453/18688 [17:03:16<5:13:57, 4.45s/it] 77%|███████▋ | 14454/18688 [17:03:20<5:02:57, 4.29s/it] 77%|███████▋ | 14455/18688 [17:03:25<5:10:22, 4.40s/it] 77%|███████▋ | 14456/18688 [17:03:28<4:56:54, 4.21s/it] 77%|███████▋ | 14457/18688 [17:03:32<4:54:22, 4.17s/it] 77%|███████▋ | 14458/18688 [17:03:36<4:47:50, 4.08s/it] 77%|███████▋ | 14459/18688 [17:03:41<4:50:09, 4.12s/it] 77%|███████▋ | 14460/18688 [17:03:45<5:01:41, 4.28s/it] {'loss': '0.8914', 'grad_norm': '1.326', 'learning_rate': '1.421e-05', 'epoch': '0.7738', 'num_input_tokens_seen': 48615864, 'train_runtime': '6.143e+04', 'train_tokens_per_second': '791.5'}
77%|███████▋ | 14460/18688 [17:03:45<5:01:41, 4.28s/it] 77%|███████▋ | 14461/18688 [17:03:49<4:52:41, 4.15s/it] 77%|███████▋ | 14462/18688 [17:03:53<4:42:33, 4.01s/it] 77%|███████▋ | 14463/18688 [17:03:56<4:33:29, 3.88s/it] 77%|███████▋ | 14464/18688 [17:04:01<4:43:55, 4.03s/it] 77%|███████▋ | 14465/18688 [17:04:06<5:11:55, 4.43s/it] 77%|███████▋ | 14466/18688 [17:04:11<5:16:24, 4.50s/it] 77%|███████▋ | 14467/18688 [17:04:14<4:56:02, 4.21s/it] 77%|███████▋ | 14468/18688 [17:04:19<4:58:30, 4.24s/it] 77%|███████▋ | 14469/18688 [17:04:23<4:53:59, 4.18s/it] 77%|███████▋ | 14470/18688 [17:04:26<4:38:53, 3.97s/it] {'loss': '0.9327', 'grad_norm': '1.521', 'learning_rate': '1.414e-05', 'epoch': '0.7743', 'num_input_tokens_seen': 48648616, 'train_runtime': '6.147e+04', 'train_tokens_per_second': '791.5'}
77%|███████▋ | 14470/18688 [17:04:26<4:38:53, 3.97s/it] 77%|███████▋ | 14471/18688 [17:04:30<4:34:07, 3.90s/it] 77%|███████▋ | 14472/18688 [17:04:33<4:26:34, 3.79s/it] 77%|███████▋ | 14473/18688 [17:04:37<4:24:46, 3.77s/it] 77%|███████▋ | 14474/18688 [17:04:42<4:41:04, 4.00s/it] 77%|███████▋ | 14475/18688 [17:04:46<4:46:12, 4.08s/it] 77%|███████▋ | 14476/18688 [17:04:50<4:44:36, 4.05s/it] 77%|███████▋ | 14477/18688 [17:04:54<4:44:35, 4.05s/it] 77%|███████▋ | 14478/18688 [17:04:57<4:33:46, 3.90s/it] 77%|███████▋ | 14479/18688 [17:05:01<4:29:41, 3.84s/it] 77%|███████▋ | 14480/18688 [17:05:05<4:28:39, 3.83s/it] {'loss': '0.8916', 'grad_norm': '1.257', 'learning_rate': '1.408e-05', 'epoch': '0.7748', 'num_input_tokens_seen': 48678584, 'train_runtime': '6.151e+04', 'train_tokens_per_second': '791.5'}
77%|███████▋ | 14480/18688 [17:05:05<4:28:39, 3.83s/it] 77%|███████▋ | 14481/18688 [17:05:08<4:22:23, 3.74s/it] 77%|███████▋ | 14482/18688 [17:05:12<4:17:09, 3.67s/it] 77%|███████▋ | 14483/18688 [17:05:17<4:39:25, 3.99s/it] 78%|███████▊ | 14484/18688 [17:05:21<4:45:22, 4.07s/it] 78%|███████▊ | 14485/18688 [17:05:25<4:46:29, 4.09s/it] 78%|███████▊ | 14486/18688 [17:05:29<4:48:49, 4.12s/it] 78%|███████▊ | 14487/18688 [17:05:34<4:54:44, 4.21s/it] 78%|███████▊ | 14488/18688 [17:05:38<5:03:25, 4.33s/it] 78%|███████▊ | 14489/18688 [17:05:43<5:09:48, 4.43s/it] 78%|███████▊ | 14490/18688 [17:05:48<5:16:18, 4.52s/it] {'loss': '0.8618', 'grad_norm': '1.312', 'learning_rate': '1.402e-05', 'epoch': '0.7754', 'num_input_tokens_seen': 48715240, 'train_runtime': '6.155e+04', 'train_tokens_per_second': '791.5'}
78%|███████▊ | 14490/18688 [17:05:48<5:16:18, 4.52s/it] 78%|███████▊ | 14491/18688 [17:05:52<5:17:54, 4.54s/it] 78%|███████▊ | 14492/18688 [17:05:56<4:55:59, 4.23s/it] 78%|███████▊ | 14493/18688 [17:06:00<4:56:33, 4.24s/it] 78%|███████▊ | 14494/18688 [17:06:04<4:52:21, 4.18s/it] 78%|███████▊ | 14495/18688 [17:06:08<4:54:29, 4.21s/it] 78%|███████▊ | 14496/18688 [17:06:12<4:50:02, 4.15s/it] 78%|███████▊ | 14497/18688 [17:06:17<4:56:43, 4.25s/it] 78%|███████▊ | 14498/18688 [17:06:21<4:56:31, 4.25s/it] 78%|███████▊ | 14499/18688 [17:06:25<4:41:13, 4.03s/it] 78%|███████▊ | 14500/18688 [17:06:29<4:46:46, 4.11s/it] {'loss': '0.9042', 'grad_norm': '1.201', 'learning_rate': '1.395e-05', 'epoch': '0.7759', 'num_input_tokens_seen': 48748360, 'train_runtime': '6.159e+04', 'train_tokens_per_second': '791.5'}
78%|███████▊ | 14500/18688 [17:06:29<4:46:46, 4.11s/it] 78%|███████▊ | 14501/18688 [17:06:33<4:50:13, 4.16s/it] 78%|███████▊ | 14502/18688 [17:06:37<4:41:49, 4.04s/it] 78%|███████▊ | 14503/18688 [17:06:41<4:40:41, 4.02s/it] 78%|███████▊ | 14504/18688 [17:06:45<4:34:03, 3.93s/it] 78%|███████▊ | 14505/18688 [17:06:50<4:59:48, 4.30s/it] 78%|███████▊ | 14506/18688 [17:06:55<5:14:01, 4.51s/it] 78%|███████▊ | 14507/18688 [17:06:59<5:15:49, 4.53s/it] 78%|███████▊ | 14508/18688 [17:07:04<5:07:31, 4.41s/it] 78%|███████▊ | 14509/18688 [17:07:08<5:00:42, 4.32s/it] 78%|███████▊ | 14510/18688 [17:07:12<5:08:14, 4.43s/it] {'loss': '0.9462', 'grad_norm': '1.304', 'learning_rate': '1.389e-05', 'epoch': '0.7765', 'num_input_tokens_seen': 48784912, 'train_runtime': '6.163e+04', 'train_tokens_per_second': '791.5'}
78%|███████▊ | 14510/18688 [17:07:12<5:08:14, 4.43s/it] 78%|███████▊ | 14511/18688 [17:07:16<4:59:52, 4.31s/it] 78%|███████▊ | 14512/18688 [17:07:20<4:46:51, 4.12s/it] 78%|███████▊ | 14513/18688 [17:07:24<4:38:07, 4.00s/it] 78%|███████▊ | 14514/18688 [17:07:27<4:26:48, 3.84s/it] 78%|███████▊ | 14515/18688 [17:07:32<4:47:25, 4.13s/it] 78%|███████▊ | 14516/18688 [17:07:36<4:40:59, 4.04s/it] 78%|███████▊ | 14517/18688 [17:07:39<4:31:16, 3.90s/it] 78%|███████▊ | 14518/18688 [17:07:44<4:53:12, 4.22s/it] 78%|███████▊ | 14519/18688 [17:07:49<4:58:52, 4.30s/it] 78%|███████▊ | 14520/18688 [17:07:53<4:55:14, 4.25s/it] {'loss': '0.9006', 'grad_norm': '1.259', 'learning_rate': '1.383e-05', 'epoch': '0.777', 'num_input_tokens_seen': 48818280, 'train_runtime': '6.167e+04', 'train_tokens_per_second': '791.6'}
78%|███████▊ | 14520/18688 [17:07:53<4:55:14, 4.25s/it] 78%|███████▊ | 14521/18688 [17:07:57<4:43:40, 4.08s/it] 78%|███████▊ | 14522/18688 [17:08:01<4:46:34, 4.13s/it] 78%|███████▊ | 14523/18688 [17:08:05<4:48:45, 4.16s/it] 78%|███████▊ | 14524/18688 [17:08:10<5:04:43, 4.39s/it] 78%|███████▊ | 14525/18688 [17:08:14<5:00:21, 4.33s/it] 78%|███████▊ | 14526/18688 [17:08:18<4:52:17, 4.21s/it] 78%|███████▊ | 14527/18688 [17:08:22<4:38:25, 4.01s/it] 78%|███████▊ | 14528/18688 [17:08:26<4:46:00, 4.13s/it] 78%|███████▊ | 14529/18688 [17:08:31<4:49:33, 4.18s/it] 78%|███████▊ | 14530/18688 [17:08:34<4:41:10, 4.06s/it] {'loss': '0.929', 'grad_norm': '1.454', 'learning_rate': '1.376e-05', 'epoch': '0.7775', 'num_input_tokens_seen': 48852544, 'train_runtime': '6.171e+04', 'train_tokens_per_second': '791.6'}
78%|███████▊ | 14530/18688 [17:08:34<4:41:10, 4.06s/it] 78%|███████▊ | 14531/18688 [17:08:38<4:42:53, 4.08s/it] 78%|███████▊ | 14532/18688 [17:08:43<4:44:50, 4.11s/it] 78%|███████▊ | 14533/18688 [17:08:47<4:48:15, 4.16s/it] 78%|███████▊ | 14534/18688 [17:08:52<5:01:30, 4.35s/it] 78%|███████▊ | 14535/18688 [17:08:55<4:45:15, 4.12s/it] 78%|███████▊ | 14536/18688 [17:09:00<4:54:22, 4.25s/it] 78%|███████▊ | 14537/18688 [17:09:05<5:03:16, 4.38s/it] 78%|███████▊ | 14538/18688 [17:09:08<4:51:52, 4.22s/it] 78%|███████▊ | 14539/18688 [17:09:13<4:56:41, 4.29s/it] 78%|███████▊ | 14540/18688 [17:09:17<4:53:54, 4.25s/it] {'loss': '0.9861', 'grad_norm': '1.32', 'learning_rate': '1.37e-05', 'epoch': '0.7781', 'num_input_tokens_seen': 48889080, 'train_runtime': '6.176e+04', 'train_tokens_per_second': '791.6'}
78%|███████▊ | 14540/18688 [17:09:17<4:53:54, 4.25s/it] 78%|███████▊ | 14541/18688 [17:09:21<4:45:12, 4.13s/it] 78%|███████▊ | 14542/18688 [17:09:25<4:51:07, 4.21s/it] 78%|███████▊ | 14543/18688 [17:09:30<4:52:37, 4.24s/it] 78%|███████▊ | 14544/18688 [17:09:34<4:49:56, 4.20s/it] 78%|███████▊ | 14545/18688 [17:09:37<4:34:33, 3.98s/it] 78%|███████▊ | 14546/18688 [17:09:41<4:25:15, 3.84s/it] 78%|███████▊ | 14547/18688 [17:09:44<4:24:39, 3.83s/it] 78%|███████▊ | 14548/18688 [17:09:48<4:26:34, 3.86s/it] 78%|███████▊ | 14549/18688 [17:09:53<4:32:47, 3.95s/it] 78%|███████▊ | 14550/18688 [17:09:58<4:55:55, 4.29s/it] {'loss': '0.9334', 'grad_norm': '1.227', 'learning_rate': '1.364e-05', 'epoch': '0.7786', 'num_input_tokens_seen': 48921000, 'train_runtime': '6.18e+04', 'train_tokens_per_second': '791.6'}
78%|███████▊ | 14550/18688 [17:09:58<4:55:55, 4.29s/it] 78%|███████▊ | 14551/18688 [17:10:01<4:39:01, 4.05s/it] 78%|███████▊ | 14552/18688 [17:10:05<4:42:56, 4.10s/it] 78%|███████▊ | 14553/18688 [17:10:09<4:40:08, 4.06s/it] 78%|███████▊ | 14554/18688 [17:10:13<4:28:06, 3.89s/it] 78%|███████▊ | 14555/18688 [17:10:16<4:24:05, 3.83s/it] 78%|███████▊ | 14556/18688 [17:10:21<4:32:31, 3.96s/it] 78%|███████▊ | 14557/18688 [17:10:25<4:38:50, 4.05s/it] 78%|███████▊ | 14558/18688 [17:10:29<4:28:20, 3.90s/it] 78%|███████▊ | 14559/18688 [17:10:33<4:32:46, 3.96s/it] 78%|███████▊ | 14560/18688 [17:10:36<4:24:53, 3.85s/it] {'loss': '0.9001', 'grad_norm': '1.518', 'learning_rate': '1.358e-05', 'epoch': '0.7791', 'num_input_tokens_seen': 48950784, 'train_runtime': '6.184e+04', 'train_tokens_per_second': '791.6'}
78%|███████▊ | 14560/18688 [17:10:36<4:24:53, 3.85s/it] 78%|███████▊ | 14561/18688 [17:10:41<4:47:37, 4.18s/it] 78%|███████▊ | 14562/18688 [17:10:45<4:36:20, 4.02s/it] 78%|███████▊ | 14563/18688 [17:10:49<4:31:18, 3.95s/it] 78%|███████▊ | 14564/18688 [17:10:53<4:41:44, 4.10s/it] 78%|███████▊ | 14565/18688 [17:10:57<4:36:35, 4.03s/it] 78%|███████▊ | 14566/18688 [17:11:01<4:30:31, 3.94s/it] 78%|███████▊ | 14567/18688 [17:11:05<4:37:20, 4.04s/it] 78%|███████▊ | 14568/18688 [17:11:09<4:45:08, 4.15s/it] 78%|███████▊ | 14569/18688 [17:11:13<4:34:37, 4.00s/it] 78%|███████▊ | 14570/18688 [17:11:17<4:31:28, 3.96s/it] {'loss': '0.8938', 'grad_norm': '1.297', 'learning_rate': '1.351e-05', 'epoch': '0.7797', 'num_input_tokens_seen': 48981576, 'train_runtime': '6.188e+04', 'train_tokens_per_second': '791.6'}
78%|███████▊ | 14570/18688 [17:11:17<4:31:28, 3.96s/it] 78%|███████▊ | 14571/18688 [17:11:21<4:41:27, 4.10s/it] 78%|███████▊ | 14572/18688 [17:11:25<4:37:45, 4.05s/it] 78%|███████▊ | 14573/18688 [17:11:30<4:45:22, 4.16s/it] 78%|███████▊ | 14574/18688 [17:11:34<4:58:06, 4.35s/it] 78%|███████▊ | 14575/18688 [17:11:38<4:40:13, 4.09s/it] 78%|███████▊ | 14576/18688 [17:11:43<5:01:19, 4.40s/it] 78%|███████▊ | 14577/18688 [17:11:47<4:54:42, 4.30s/it] 78%|███████▊ | 14578/18688 [17:11:51<4:47:31, 4.20s/it] 78%|███████▊ | 14579/18688 [17:11:56<4:58:09, 4.35s/it] 78%|███████▊ | 14580/18688 [17:12:00<4:55:42, 4.32s/it] {'loss': '0.9065', 'grad_norm': '1.604', 'learning_rate': '1.345e-05', 'epoch': '0.7802', 'num_input_tokens_seen': 49016560, 'train_runtime': '6.192e+04', 'train_tokens_per_second': '791.6'}
78%|███████▊ | 14580/18688 [17:12:00<4:55:42, 4.32s/it] 78%|███████▊ | 14581/18688 [17:12:04<4:56:55, 4.34s/it] 78%|███████▊ | 14582/18688 [17:12:08<4:51:31, 4.26s/it] 78%|███████▊ | 14583/18688 [17:12:13<4:52:45, 4.28s/it] 78%|███████▊ | 14584/18688 [17:12:17<4:58:41, 4.37s/it] 78%|███████▊ | 14585/18688 [17:12:21<4:39:34, 4.09s/it] 78%|███████▊ | 14586/18688 [17:12:24<4:31:22, 3.97s/it] 78%|███████▊ | 14587/18688 [17:12:28<4:30:05, 3.95s/it] 78%|███████▊ | 14588/18688 [17:12:32<4:27:49, 3.92s/it] 78%|███████▊ | 14589/18688 [17:12:37<4:43:28, 4.15s/it] 78%|███████▊ | 14590/18688 [17:12:41<4:35:27, 4.03s/it] {'loss': '0.8284', 'grad_norm': '1.313', 'learning_rate': '1.339e-05', 'epoch': '0.7807', 'num_input_tokens_seen': 49048816, 'train_runtime': '6.196e+04', 'train_tokens_per_second': '791.6'}
78%|███████▊ | 14590/18688 [17:12:41<4:35:27, 4.03s/it] 78%|███████▊ | 14591/18688 [17:12:44<4:29:06, 3.94s/it] 78%|███████▊ | 14592/18688 [17:12:49<4:35:15, 4.03s/it] 78%|███████▊ | 14593/18688 [17:12:53<4:49:42, 4.24s/it] 78%|███████▊ | 14594/18688 [17:12:57<4:33:41, 4.01s/it] 78%|███████▊ | 14595/18688 [17:13:01<4:35:39, 4.04s/it] 78%|███████▊ | 14596/18688 [17:13:06<4:47:10, 4.21s/it] 78%|███████▊ | 14597/18688 [17:13:10<4:58:14, 4.37s/it] 78%|███████▊ | 14598/18688 [17:13:14<4:45:09, 4.18s/it] 78%|███████▊ | 14599/18688 [17:13:18<4:45:12, 4.19s/it] 78%|███████▊ | 14600/18688 [17:13:23<4:47:27, 4.22s/it] {'loss': '0.8831', 'grad_norm': '1.232', 'learning_rate': '1.333e-05', 'epoch': '0.7813', 'num_input_tokens_seen': 49082728, 'train_runtime': '6.2e+04', 'train_tokens_per_second': '791.6'}
78%|███████▊ | 14600/18688 [17:13:23<4:47:27, 4.22s/it] 78%|███████▊ | 14601/18688 [17:13:26<4:36:22, 4.06s/it] 78%|███████▊ | 14602/18688 [17:13:31<4:57:46, 4.37s/it] 78%|███████▊ | 14603/18688 [17:13:35<4:40:20, 4.12s/it] 78%|███████▊ | 14604/18688 [17:13:38<4:29:04, 3.95s/it] 78%|███████▊ | 14605/18688 [17:13:42<4:30:43, 3.98s/it] 78%|███████▊ | 14606/18688 [17:13:47<4:41:37, 4.14s/it] 78%|███████▊ | 14607/18688 [17:13:51<4:37:54, 4.09s/it] 78%|███████▊ | 14608/18688 [17:13:55<4:45:54, 4.20s/it] 78%|███████▊ | 14609/18688 [17:13:59<4:39:54, 4.12s/it] 78%|███████▊ | 14610/18688 [17:14:03<4:27:18, 3.93s/it] {'loss': '0.9047', 'grad_norm': '1.314', 'learning_rate': '1.326e-05', 'epoch': '0.7818', 'num_input_tokens_seen': 49115320, 'train_runtime': '6.204e+04', 'train_tokens_per_second': '791.6'}
78%|███████▊ | 14610/18688 [17:14:03<4:27:18, 3.93s/it] 78%|███████▊ | 14611/18688 [17:14:07<4:30:02, 3.97s/it] 78%|███████▊ | 14612/18688 [17:14:10<4:19:08, 3.81s/it] 78%|███████▊ | 14613/18688 [17:14:14<4:18:38, 3.81s/it] 78%|███████▊ | 14614/18688 [17:14:19<4:31:15, 3.99s/it] 78%|███████▊ | 14615/18688 [17:14:22<4:20:57, 3.84s/it] 78%|███████▊ | 14616/18688 [17:14:27<4:40:51, 4.14s/it] 78%|███████▊ | 14617/18688 [17:14:31<4:43:28, 4.18s/it] 78%|███████▊ | 14618/18688 [17:14:35<4:37:44, 4.09s/it] 78%|███████▊ | 14619/18688 [17:14:39<4:29:24, 3.97s/it] 78%|███████▊ | 14620/18688 [17:14:43<4:44:22, 4.19s/it] {'loss': '0.8535', 'grad_norm': '1.72', 'learning_rate': '1.32e-05', 'epoch': '0.7823', 'num_input_tokens_seen': 49146664, 'train_runtime': '6.208e+04', 'train_tokens_per_second': '791.6'}
78%|███████▊ | 14620/18688 [17:14:43<4:44:22, 4.19s/it] 78%|███████▊ | 14621/18688 [17:14:48<4:46:13, 4.22s/it] 78%|███████▊ | 14622/18688 [17:14:52<4:48:34, 4.26s/it] 78%|███████▊ | 14623/18688 [17:14:56<4:43:05, 4.18s/it] 78%|███████▊ | 14624/18688 [17:15:00<4:41:52, 4.16s/it] 78%|███████▊ | 14625/18688 [17:15:05<4:44:37, 4.20s/it] 78%|███████▊ | 14626/18688 [17:15:09<4:44:32, 4.20s/it] 78%|███████▊ | 14627/18688 [17:15:12<4:31:49, 4.02s/it] 78%|███████▊ | 14628/18688 [17:15:16<4:35:11, 4.07s/it] 78%|███████▊ | 14629/18688 [17:15:21<4:48:04, 4.26s/it] 78%|███████▊ | 14630/18688 [17:15:26<4:53:20, 4.34s/it] {'loss': '0.9505', 'grad_norm': '1.252', 'learning_rate': '1.314e-05', 'epoch': '0.7829', 'num_input_tokens_seen': 49181440, 'train_runtime': '6.213e+04', 'train_tokens_per_second': '791.6'}
78%|███████▊ | 14630/18688 [17:15:26<4:53:20, 4.34s/it] 78%|███████▊ | 14631/18688 [17:15:30<4:42:45, 4.18s/it] 78%|███████▊ | 14632/18688 [17:15:34<4:41:23, 4.16s/it] 78%|███████▊ | 14633/18688 [17:15:38<4:54:44, 4.36s/it] 78%|███████▊ | 14634/18688 [17:15:42<4:40:44, 4.15s/it] 78%|███████▊ | 14635/18688 [17:15:47<4:49:56, 4.29s/it] 78%|███████▊ | 14636/18688 [17:15:50<4:33:54, 4.06s/it] 78%|███████▊ | 14637/18688 [17:15:54<4:37:13, 4.11s/it] 78%|███████▊ | 14638/18688 [17:15:58<4:32:01, 4.03s/it] 78%|███████▊ | 14639/18688 [17:16:02<4:27:17, 3.96s/it] 78%|███████▊ | 14640/18688 [17:16:06<4:30:45, 4.01s/it] {'loss': '0.9044', 'grad_norm': '1.153', 'learning_rate': '1.308e-05', 'epoch': '0.7834', 'num_input_tokens_seen': 49213704, 'train_runtime': '6.217e+04', 'train_tokens_per_second': '791.6'}
78%|███████▊ | 14640/18688 [17:16:06<4:30:45, 4.01s/it] 78%|███████▊ | 14641/18688 [17:16:11<4:51:12, 4.32s/it] 78%|███████▊ | 14642/18688 [17:16:15<4:45:07, 4.23s/it] 78%|███████▊ | 14643/18688 [17:16:19<4:42:27, 4.19s/it] 78%|███████▊ | 14644/18688 [17:16:24<4:46:47, 4.26s/it] 78%|███████▊ | 14645/18688 [17:16:27<4:33:56, 4.07s/it] 78%|███████▊ | 14646/18688 [17:16:32<4:47:31, 4.27s/it] 78%|███████▊ | 14647/18688 [17:16:37<4:53:22, 4.36s/it] 78%|███████▊ | 14648/18688 [17:16:42<5:09:22, 4.59s/it] 78%|███████▊ | 14649/18688 [17:16:46<4:53:08, 4.35s/it] 78%|███████▊ | 14650/18688 [17:16:50<4:55:44, 4.39s/it] {'loss': '0.9518', 'grad_norm': '1.31', 'learning_rate': '1.302e-05', 'epoch': '0.7839', 'num_input_tokens_seen': 49251928, 'train_runtime': '6.221e+04', 'train_tokens_per_second': '791.7'}
78%|███████▊ | 14650/18688 [17:16:50<4:55:44, 4.39s/it] 78%|███████▊ | 14651/18688 [17:16:55<4:56:29, 4.41s/it] 78%|███████▊ | 14652/18688 [17:16:59<5:01:12, 4.48s/it] 78%|███████▊ | 14653/18688 [17:17:03<4:47:17, 4.27s/it] 78%|███████▊ | 14654/18688 [17:17:07<4:39:14, 4.15s/it] 78%|███████▊ | 14655/18688 [17:17:12<4:56:58, 4.42s/it] 78%|███████▊ | 14656/18688 [17:17:16<4:42:21, 4.20s/it] 78%|███████▊ | 14657/18688 [17:17:19<4:30:54, 4.03s/it] 78%|███████▊ | 14658/18688 [17:17:23<4:22:56, 3.91s/it] 78%|███████▊ | 14659/18688 [17:17:27<4:18:15, 3.85s/it] 78%|███████▊ | 14660/18688 [17:17:32<4:42:14, 4.20s/it] {'loss': '0.872', 'grad_norm': '1.366', 'learning_rate': '1.296e-05', 'epoch': '0.7845', 'num_input_tokens_seen': 49285664, 'train_runtime': '6.225e+04', 'train_tokens_per_second': '791.7'}
78%|███████▊ | 14660/18688 [17:17:32<4:42:14, 4.20s/it] 78%|███████▊ | 14661/18688 [17:17:36<4:35:42, 4.11s/it] 78%|███████▊ | 14662/18688 [17:17:39<4:30:59, 4.04s/it] 78%|███████▊ | 14663/18688 [17:17:44<4:48:27, 4.30s/it] 78%|███████▊ | 14664/18688 [17:17:48<4:38:51, 4.16s/it] 78%|███████▊ | 14665/18688 [17:17:53<4:47:24, 4.29s/it] 78%|███████▊ | 14666/18688 [17:17:57<4:43:40, 4.23s/it] 78%|███████▊ | 14667/18688 [17:18:01<4:36:09, 4.12s/it] 78%|███████▊ | 14668/18688 [17:18:05<4:29:10, 4.02s/it] 78%|███████▊ | 14669/18688 [17:18:09<4:44:38, 4.25s/it] 78%|███████▊ | 14670/18688 [17:18:13<4:34:22, 4.10s/it] {'loss': '0.9335', 'grad_norm': '1.171', 'learning_rate': '1.289e-05', 'epoch': '0.785', 'num_input_tokens_seen': 49319416, 'train_runtime': '6.229e+04', 'train_tokens_per_second': '791.7'}
78%|███████▊ | 14670/18688 [17:18:13<4:34:22, 4.10s/it] 79%|███████▊ | 14671/18688 [17:18:18<4:46:04, 4.27s/it] 79%|███████▊ | 14672/18688 [17:18:22<4:47:16, 4.29s/it] 79%|███████▊ | 14673/18688 [17:18:26<4:36:35, 4.13s/it] 79%|███████▊ | 14674/18688 [17:18:29<4:21:59, 3.92s/it] 79%|███████▊ | 14675/18688 [17:18:35<4:49:26, 4.33s/it] 79%|███████▊ | 14676/18688 [17:18:38<4:35:47, 4.12s/it] 79%|███████▊ | 14677/18688 [17:18:42<4:30:39, 4.05s/it] 79%|███████▊ | 14678/18688 [17:18:46<4:27:36, 4.00s/it] 79%|███████▊ | 14679/18688 [17:18:51<4:42:46, 4.23s/it] 79%|███████▊ | 14680/18688 [17:18:54<4:27:44, 4.01s/it] {'loss': '0.8968', 'grad_norm': '1.163', 'learning_rate': '1.283e-05', 'epoch': '0.7856', 'num_input_tokens_seen': 49353336, 'train_runtime': '6.233e+04', 'train_tokens_per_second': '791.7'}
79%|███████▊ | 14680/18688 [17:18:54<4:27:44, 4.01s/it] 79%|███████▊ | 14681/18688 [17:18:58<4:33:14, 4.09s/it] 79%|███████▊ | 14682/18688 [17:19:04<4:52:46, 4.39s/it] 79%|███████▊ | 14683/18688 [17:19:07<4:43:49, 4.25s/it] 79%|███████▊ | 14684/18688 [17:19:11<4:28:29, 4.02s/it] 79%|███████▊ | 14685/18688 [17:19:16<4:48:18, 4.32s/it] 79%|███████▊ | 14686/18688 [17:19:20<4:32:46, 4.09s/it] 79%|███████▊ | 14687/18688 [17:19:23<4:21:08, 3.92s/it] 79%|███████▊ | 14688/18688 [17:19:27<4:25:19, 3.98s/it] 79%|███████▊ | 14689/18688 [17:19:31<4:16:01, 3.84s/it] 79%|███████▊ | 14690/18688 [17:19:36<4:39:54, 4.20s/it] {'loss': '0.9041', 'grad_norm': '1.064', 'learning_rate': '1.277e-05', 'epoch': '0.7861', 'num_input_tokens_seen': 49387728, 'train_runtime': '6.238e+04', 'train_tokens_per_second': '791.8'}
79%|███████▊ | 14690/18688 [17:19:36<4:39:54, 4.20s/it] 79%|███████▊ | 14691/18688 [17:19:42<5:12:19, 4.69s/it] 79%|███████▊ | 14692/18688 [17:19:46<5:02:25, 4.54s/it] 79%|███████▊ | 14693/18688 [17:19:49<4:45:20, 4.29s/it] 79%|███████▊ | 14694/18688 [17:19:53<4:33:31, 4.11s/it] 79%|███████▊ | 14695/18688 [17:19:57<4:28:14, 4.03s/it] 79%|███████▊ | 14696/18688 [17:20:02<4:41:44, 4.23s/it] 79%|███████▊ | 14697/18688 [17:20:06<4:44:42, 4.28s/it] 79%|███████▊ | 14698/18688 [17:20:10<4:28:06, 4.03s/it] 79%|███████▊ | 14699/18688 [17:20:14<4:30:46, 4.07s/it] 79%|███████▊ | 14700/18688 [17:20:18<4:38:01, 4.18s/it] {'loss': '0.9055', 'grad_norm': '1.235', 'learning_rate': '1.271e-05', 'epoch': '0.7866', 'num_input_tokens_seen': 49423416, 'train_runtime': '6.242e+04', 'train_tokens_per_second': '791.8'}
79%|███████▊ | 14700/18688 [17:20:18<4:38:01, 4.18s/it] 79%|███████▊ | 14701/18688 [17:20:22<4:38:18, 4.19s/it] 79%|███████▊ | 14702/18688 [17:20:27<4:46:23, 4.31s/it] 79%|███████▊ | 14703/18688 [17:20:31<4:34:30, 4.13s/it] 79%|███████▊ | 14704/18688 [17:20:36<4:50:07, 4.37s/it] 79%|███████▊ | 14705/18688 [17:20:39<4:37:48, 4.18s/it] 79%|███████▊ | 14706/18688 [17:20:44<4:46:37, 4.32s/it] 79%|███████▊ | 14707/18688 [17:20:49<4:54:52, 4.44s/it] 79%|███████▊ | 14708/18688 [17:20:53<4:49:05, 4.36s/it] 79%|███████▊ | 14709/18688 [17:20:58<4:54:58, 4.45s/it] 79%|███████▊ | 14710/18688 [17:21:01<4:36:51, 4.18s/it] {'loss': '0.9004', 'grad_norm': '1.435', 'learning_rate': '1.265e-05', 'epoch': '0.7872', 'num_input_tokens_seen': 49458488, 'train_runtime': '6.246e+04', 'train_tokens_per_second': '791.8'}
79%|███████▊ | 14710/18688 [17:21:01<4:36:51, 4.18s/it] 79%|███████▊ | 14711/18688 [17:21:05<4:23:14, 3.97s/it] 79%|███████▊ | 14712/18688 [17:21:09<4:28:48, 4.06s/it] 79%|███████▊ | 14713/18688 [17:21:12<4:17:00, 3.88s/it] 79%|███████▊ | 14714/18688 [17:21:16<4:23:01, 3.97s/it] 79%|███████▊ | 14715/18688 [17:21:21<4:24:26, 3.99s/it] 79%|███████▊ | 14716/18688 [17:21:25<4:31:36, 4.10s/it] 79%|███████▉ | 14717/18688 [17:21:29<4:38:04, 4.20s/it] 79%|███████▉ | 14718/18688 [17:21:33<4:37:08, 4.19s/it] 79%|███████▉ | 14719/18688 [17:21:38<4:38:56, 4.22s/it] 79%|███████▉ | 14720/18688 [17:21:42<4:36:13, 4.18s/it] {'loss': '0.9254', 'grad_norm': '1.425', 'learning_rate': '1.259e-05', 'epoch': '0.7877', 'num_input_tokens_seen': 49490616, 'train_runtime': '6.25e+04', 'train_tokens_per_second': '791.8'}
79%|███████▉ | 14720/18688 [17:21:42<4:36:13, 4.18s/it] 79%|███████▉ | 14721/18688 [17:21:46<4:39:09, 4.22s/it] 79%|███████▉ | 14722/18688 [17:21:51<4:47:16, 4.35s/it] 79%|███████▉ | 14723/18688 [17:21:54<4:34:16, 4.15s/it] 79%|███████▉ | 14724/18688 [17:21:58<4:30:52, 4.10s/it] 79%|███████▉ | 14725/18688 [17:22:03<4:41:29, 4.26s/it] 79%|███████▉ | 14726/18688 [17:22:07<4:31:00, 4.10s/it] 79%|███████▉ | 14727/18688 [17:22:11<4:23:10, 3.99s/it] 79%|███████▉ | 14728/18688 [17:22:16<4:44:43, 4.31s/it] 79%|███████▉ | 14729/18688 [17:22:20<4:46:07, 4.34s/it] 79%|███████▉ | 14730/18688 [17:22:24<4:34:46, 4.17s/it] {'loss': '0.9441', 'grad_norm': '1.65', 'learning_rate': '1.253e-05', 'epoch': '0.7882', 'num_input_tokens_seen': 49523712, 'train_runtime': '6.254e+04', 'train_tokens_per_second': '791.8'}
79%|███████▉ | 14730/18688 [17:22:24<4:34:46, 4.17s/it] 79%|███████▉ | 14731/18688 [17:22:28<4:36:28, 4.19s/it] 79%|███████▉ | 14732/18688 [17:22:32<4:37:52, 4.21s/it] 79%|███████▉ | 14733/18688 [17:22:37<4:42:37, 4.29s/it] 79%|███████▉ | 14734/18688 [17:22:42<5:00:47, 4.56s/it] 79%|███████▉ | 14735/18688 [17:22:47<5:08:57, 4.69s/it] 79%|███████▉ | 14736/18688 [17:22:51<4:56:46, 4.51s/it] 79%|███████▉ | 14737/18688 [17:22:55<4:53:29, 4.46s/it] 79%|███████▉ | 14738/18688 [17:22:59<4:42:08, 4.29s/it] 79%|███████▉ | 14739/18688 [17:23:04<4:51:46, 4.43s/it] 79%|███████▉ | 14740/18688 [17:23:08<4:35:53, 4.19s/it] {'loss': '0.8538', 'grad_norm': '1.281', 'learning_rate': '1.247e-05', 'epoch': '0.7888', 'num_input_tokens_seen': 49562040, 'train_runtime': '6.259e+04', 'train_tokens_per_second': '791.9'}
79%|███████▉ | 14740/18688 [17:23:08<4:35:53, 4.19s/it] 79%|███████▉ | 14741/18688 [17:23:12<4:32:24, 4.14s/it] 79%|███████▉ | 14742/18688 [17:23:16<4:32:52, 4.15s/it] 79%|███████▉ | 14743/18688 [17:23:20<4:35:37, 4.19s/it] 79%|███████▉ | 14744/18688 [17:23:24<4:36:03, 4.20s/it] 79%|███████▉ | 14745/18688 [17:23:28<4:27:06, 4.06s/it] 79%|███████▉ | 14746/18688 [17:23:32<4:26:18, 4.05s/it] 79%|███████▉ | 14747/18688 [17:23:36<4:25:12, 4.04s/it] 79%|███████▉ | 14748/18688 [17:23:40<4:26:03, 4.05s/it] 79%|███████▉ | 14749/18688 [17:23:45<4:33:42, 4.17s/it] 79%|███████▉ | 14750/18688 [17:23:49<4:37:56, 4.23s/it] {'loss': '0.9257', 'grad_norm': '1.376', 'learning_rate': '1.241e-05', 'epoch': '0.7893', 'num_input_tokens_seen': 49594568, 'train_runtime': '6.263e+04', 'train_tokens_per_second': '791.9'}
79%|███████▉ | 14750/18688 [17:23:49<4:37:56, 4.23s/it] 79%|███████▉ | 14751/18688 [17:23:53<4:32:16, 4.15s/it] 79%|███████▉ | 14752/18688 [17:23:59<5:02:02, 4.60s/it] 79%|███████▉ | 14753/18688 [17:24:02<4:45:24, 4.35s/it] 79%|███████▉ | 14754/18688 [17:24:06<4:37:56, 4.24s/it] 79%|███████▉ | 14755/18688 [17:24:10<4:23:01, 4.01s/it] 79%|███████▉ | 14756/18688 [17:24:14<4:32:02, 4.15s/it] 79%|███████▉ | 14757/18688 [17:24:18<4:23:42, 4.02s/it] 79%|███████▉ | 14758/18688 [17:24:23<4:48:14, 4.40s/it] 79%|███████▉ | 14759/18688 [17:24:27<4:41:41, 4.30s/it] 79%|███████▉ | 14760/18688 [17:24:32<4:40:04, 4.28s/it] {'loss': '0.9302', 'grad_norm': '1.247', 'learning_rate': '1.235e-05', 'epoch': '0.7898', 'num_input_tokens_seen': 49628960, 'train_runtime': '6.267e+04', 'train_tokens_per_second': '791.9'}
79%|███████▉ | 14760/18688 [17:24:32<4:40:04, 4.28s/it] 79%|███████▉ | 14761/18688 [17:24:35<4:30:33, 4.13s/it] 79%|███████▉ | 14762/18688 [17:24:40<4:30:47, 4.14s/it] 79%|███████▉ | 14763/18688 [17:24:43<4:22:36, 4.01s/it] 79%|███████▉ | 14764/18688 [17:24:47<4:21:28, 4.00s/it] 79%|███████▉ | 14765/18688 [17:24:51<4:10:57, 3.84s/it] 79%|███████▉ | 14766/18688 [17:24:55<4:13:40, 3.88s/it] 79%|███████▉ | 14767/18688 [17:24:59<4:11:53, 3.85s/it] 79%|███████▉ | 14768/18688 [17:25:03<4:19:35, 3.97s/it] 79%|███████▉ | 14769/18688 [17:25:06<4:09:21, 3.82s/it] 79%|███████▉ | 14770/18688 [17:25:10<4:13:00, 3.87s/it] {'loss': '0.9327', 'grad_norm': '1.176', 'learning_rate': '1.229e-05', 'epoch': '0.7904', 'num_input_tokens_seen': 49658712, 'train_runtime': '6.271e+04', 'train_tokens_per_second': '791.9'}
79%|███████▉ | 14770/18688 [17:25:10<4:13:00, 3.87s/it] 79%|███████▉ | 14771/18688 [17:25:15<4:24:13, 4.05s/it] 79%|███████▉ | 14772/18688 [17:25:19<4:27:16, 4.10s/it] 79%|███████▉ | 14773/18688 [17:25:24<4:39:05, 4.28s/it] 79%|███████▉ | 14774/18688 [17:25:27<4:23:06, 4.03s/it] 79%|███████▉ | 14775/18688 [17:25:31<4:23:16, 4.04s/it] 79%|███████▉ | 14776/18688 [17:25:35<4:16:31, 3.93s/it] 79%|███████▉ | 14777/18688 [17:25:40<4:39:57, 4.30s/it] 79%|███████▉ | 14778/18688 [17:25:44<4:37:05, 4.25s/it] 79%|███████▉ | 14779/18688 [17:25:48<4:36:22, 4.24s/it] 79%|███████▉ | 14780/18688 [17:25:53<4:49:07, 4.44s/it] {'loss': '0.907', 'grad_norm': '1.329', 'learning_rate': '1.223e-05', 'epoch': '0.7909', 'num_input_tokens_seen': 49694280, 'train_runtime': '6.275e+04', 'train_tokens_per_second': '791.9'}
79%|███████▉ | 14780/18688 [17:25:53<4:49:07, 4.44s/it] 79%|███████▉ | 14781/18688 [17:25:57<4:43:41, 4.36s/it] 79%|███████▉ | 14782/18688 [17:26:01<4:28:44, 4.13s/it] 79%|███████▉ | 14783/18688 [17:26:05<4:21:39, 4.02s/it] 79%|███████▉ | 14784/18688 [17:26:09<4:19:05, 3.98s/it] 79%|███████▉ | 14785/18688 [17:26:13<4:22:53, 4.04s/it] 79%|███████▉ | 14786/18688 [17:26:16<4:14:22, 3.91s/it] 79%|███████▉ | 14787/18688 [17:26:20<4:07:18, 3.80s/it] 79%|███████▉ | 14788/18688 [17:26:24<4:02:46, 3.74s/it] 79%|███████▉ | 14789/18688 [17:26:27<4:05:50, 3.78s/it] 79%|███████▉ | 14790/18688 [17:26:32<4:16:01, 3.94s/it] {'loss': '0.9029', 'grad_norm': '1.511', 'learning_rate': '1.217e-05', 'epoch': '0.7914', 'num_input_tokens_seen': 49723480, 'train_runtime': '6.279e+04', 'train_tokens_per_second': '791.9'}
79%|███████▉ | 14790/18688 [17:26:32<4:16:01, 3.94s/it] 79%|███████▉ | 14791/18688 [17:26:36<4:20:18, 4.01s/it] 79%|███████▉ | 14792/18688 [17:26:40<4:11:55, 3.88s/it] 79%|███████▉ | 14793/18688 [17:26:44<4:26:49, 4.11s/it] 79%|███████▉ | 14794/18688 [17:26:48<4:20:37, 4.02s/it] 79%|███████▉ | 14795/18688 [17:26:53<4:49:32, 4.46s/it] 79%|███████▉ | 14796/18688 [17:26:58<4:44:34, 4.39s/it] 79%|███████▉ | 14797/18688 [17:27:02<4:41:14, 4.34s/it] 79%|███████▉ | 14798/18688 [17:27:06<4:28:28, 4.14s/it] 79%|███████▉ | 14799/18688 [17:27:10<4:28:00, 4.13s/it] 79%|███████▉ | 14800/18688 [17:27:14<4:34:31, 4.24s/it] {'loss': '0.8963', 'grad_norm': '1.077', 'learning_rate': '1.211e-05', 'epoch': '0.792', 'num_input_tokens_seen': 49759216, 'train_runtime': '6.283e+04', 'train_tokens_per_second': '791.9'}
79%|███████▉ | 14800/18688 [17:27:14<4:34:31, 4.24s/it] 79%|███████▉ | 14801/18688 [17:27:19<4:52:23, 4.51s/it] 79%|███████▉ | 14802/18688 [17:27:23<4:44:06, 4.39s/it] 79%|███████▉ | 14803/18688 [17:27:28<4:47:57, 4.45s/it] 79%|███████▉ | 14804/18688 [17:27:32<4:45:14, 4.41s/it] 79%|███████▉ | 14805/18688 [17:27:36<4:33:55, 4.23s/it] 79%|███████▉ | 14806/18688 [17:27:40<4:19:04, 4.00s/it] 79%|███████▉ | 14807/18688 [17:27:43<4:14:47, 3.94s/it] 79%|███████▉ | 14808/18688 [17:27:47<4:16:45, 3.97s/it] 79%|███████▉ | 14809/18688 [17:27:51<4:16:24, 3.97s/it] 79%|███████▉ | 14810/18688 [17:27:55<4:13:19, 3.92s/it] {'loss': '0.8896', 'grad_norm': '1.278', 'learning_rate': '1.205e-05', 'epoch': '0.7925', 'num_input_tokens_seen': 49792744, 'train_runtime': '6.288e+04', 'train_tokens_per_second': '791.9'}
79%|███████▉ | 14810/18688 [17:27:55<4:13:19, 3.92s/it] 79%|███████▉ | 14811/18688 [17:27:59<4:18:33, 4.00s/it] 79%|███████▉ | 14812/18688 [17:28:04<4:29:24, 4.17s/it] 79%|███████▉ | 14813/18688 [17:28:08<4:25:12, 4.11s/it] 79%|███████▉ | 14814/18688 [17:28:12<4:19:40, 4.02s/it] 79%|███████▉ | 14815/18688 [17:28:15<4:10:38, 3.88s/it] 79%|███████▉ | 14816/18688 [17:28:20<4:17:57, 4.00s/it] 79%|███████▉ | 14817/18688 [17:28:24<4:28:25, 4.16s/it] 79%|███████▉ | 14818/18688 [17:28:29<4:33:23, 4.24s/it] 79%|███████▉ | 14819/18688 [17:28:32<4:20:45, 4.04s/it] 79%|███████▉ | 14820/18688 [17:28:36<4:26:41, 4.14s/it] {'loss': '0.9219', 'grad_norm': '0.998', 'learning_rate': '1.199e-05', 'epoch': '0.793', 'num_input_tokens_seen': 49826648, 'train_runtime': '6.292e+04', 'train_tokens_per_second': '791.9'}
79%|███████▉ | 14820/18688 [17:28:36<4:26:41, 4.14s/it] 79%|███████▉ | 14821/18688 [17:28:40<4:14:52, 3.95s/it] 79%|███████▉ | 14822/18688 [17:28:44<4:10:19, 3.89s/it] 79%|███████▉ | 14823/18688 [17:28:48<4:15:45, 3.97s/it] 79%|███████▉ | 14824/18688 [17:28:52<4:25:56, 4.13s/it] 79%|███████▉ | 14825/18688 [17:28:56<4:14:51, 3.96s/it] 79%|███████▉ | 14826/18688 [17:29:00<4:13:53, 3.94s/it] 79%|███████▉ | 14827/18688 [17:29:03<4:06:26, 3.83s/it] 79%|███████▉ | 14828/18688 [17:29:08<4:18:19, 4.02s/it] 79%|███████▉ | 14829/18688 [17:29:12<4:12:43, 3.93s/it] 79%|███████▉ | 14830/18688 [17:29:16<4:19:23, 4.03s/it] {'loss': '0.9413', 'grad_norm': '1.263', 'learning_rate': '1.193e-05', 'epoch': '0.7936', 'num_input_tokens_seen': 49857272, 'train_runtime': '6.296e+04', 'train_tokens_per_second': '791.9'}
79%|███████▉ | 14830/18688 [17:29:16<4:19:23, 4.03s/it] 79%|███████▉ | 14831/18688 [17:29:21<4:33:50, 4.26s/it] 79%|███████▉ | 14832/18688 [17:29:26<4:48:30, 4.49s/it] 79%|███████▉ | 14833/18688 [17:29:30<4:36:29, 4.30s/it] 79%|███████▉ | 14834/18688 [17:29:34<4:33:55, 4.26s/it] 79%|███████▉ | 14835/18688 [17:29:38<4:33:17, 4.26s/it] 79%|███████▉ | 14836/18688 [17:29:42<4:20:41, 4.06s/it] 79%|███████▉ | 14837/18688 [17:29:47<4:39:07, 4.35s/it] 79%|███████▉ | 14838/18688 [17:29:51<4:43:19, 4.42s/it] 79%|███████▉ | 14839/18688 [17:29:56<4:48:34, 4.50s/it] 79%|███████▉ | 14840/18688 [17:30:00<4:42:53, 4.41s/it] {'loss': '0.9468', 'grad_norm': '1.824', 'learning_rate': '1.187e-05', 'epoch': '0.7941', 'num_input_tokens_seen': 49892864, 'train_runtime': '6.3e+04', 'train_tokens_per_second': '791.9'}
79%|███████▉ | 14840/18688 [17:30:00<4:42:53, 4.41s/it] 79%|███████▉ | 14841/18688 [17:30:04<4:30:15, 4.22s/it] 79%|███████▉ | 14842/18688 [17:30:08<4:22:25, 4.09s/it] 79%|███████▉ | 14843/18688 [17:30:12<4:24:24, 4.13s/it] 79%|███████▉ | 14844/18688 [17:30:17<4:43:36, 4.43s/it] 79%|███████▉ | 14845/18688 [17:30:22<4:45:46, 4.46s/it] 79%|███████▉ | 14846/18688 [17:30:25<4:27:24, 4.18s/it] 79%|███████▉ | 14847/18688 [17:30:29<4:29:12, 4.21s/it] 79%|███████▉ | 14848/18688 [17:30:34<4:40:47, 4.39s/it] 79%|███████▉ | 14849/18688 [17:30:38<4:40:14, 4.38s/it] 79%|███████▉ | 14850/18688 [17:30:43<4:39:26, 4.37s/it] {'loss': '0.9133', 'grad_norm': '1.324', 'learning_rate': '1.181e-05', 'epoch': '0.7946', 'num_input_tokens_seen': 49928696, 'train_runtime': '6.304e+04', 'train_tokens_per_second': '792'}
79%|███████▉ | 14850/18688 [17:30:43<4:39:26, 4.37s/it] 79%|███████▉ | 14851/18688 [17:30:48<4:46:46, 4.48s/it] 79%|███████▉ | 14852/18688 [17:30:53<5:06:31, 4.79s/it] 79%|███████▉ | 14853/18688 [17:30:57<4:41:08, 4.40s/it] 79%|███████▉ | 14854/18688 [17:31:01<4:45:20, 4.47s/it] 79%|███████▉ | 14855/18688 [17:31:05<4:27:14, 4.18s/it] 79%|███████▉ | 14856/18688 [17:31:08<4:13:44, 3.97s/it] 80%|███████▉ | 14857/18688 [17:31:12<4:04:32, 3.83s/it] 80%|███████▉ | 14858/18688 [17:31:16<4:04:59, 3.84s/it] 80%|███████▉ | 14859/18688 [17:31:20<4:08:49, 3.90s/it] 80%|███████▉ | 14860/18688 [17:31:24<4:12:51, 3.96s/it] {'loss': '0.8894', 'grad_norm': '1.19', 'learning_rate': '1.175e-05', 'epoch': '0.7952', 'num_input_tokens_seen': 49962016, 'train_runtime': '6.308e+04', 'train_tokens_per_second': '792'}
80%|███████▉ | 14860/18688 [17:31:24<4:12:51, 3.96s/it] 80%|███████▉ | 14861/18688 [17:31:28<4:27:04, 4.19s/it] 80%|███████▉ | 14862/18688 [17:31:32<4:19:38, 4.07s/it] 80%|███████▉ | 14863/18688 [17:31:36<4:12:10, 3.96s/it] 80%|███████▉ | 14864/18688 [17:31:39<4:04:36, 3.84s/it] 80%|███████▉ | 14865/18688 [17:31:43<4:07:17, 3.88s/it] 80%|███████▉ | 14866/18688 [17:31:48<4:11:17, 3.94s/it] 80%|███████▉ | 14867/18688 [17:31:52<4:13:11, 3.98s/it] 80%|███████▉ | 14868/18688 [17:31:56<4:13:17, 3.98s/it] 80%|███████▉ | 14869/18688 [17:32:00<4:15:03, 4.01s/it] 80%|███████▉ | 14870/18688 [17:32:03<4:07:05, 3.88s/it] {'loss': '0.8703', 'grad_norm': '1.359', 'learning_rate': '1.169e-05', 'epoch': '0.7957', 'num_input_tokens_seen': 49992136, 'train_runtime': '6.312e+04', 'train_tokens_per_second': '792'}
80%|███████▉ | 14870/18688 [17:32:03<4:07:05, 3.88s/it] 80%|███████▉ | 14871/18688 [17:32:08<4:23:10, 4.14s/it] 80%|███████▉ | 14872/18688 [17:32:12<4:15:24, 4.02s/it] 80%|███████▉ | 14873/18688 [17:32:16<4:23:41, 4.15s/it] 80%|███████▉ | 14874/18688 [17:32:20<4:24:51, 4.17s/it] 80%|███████▉ | 14875/18688 [17:32:25<4:26:21, 4.19s/it] 80%|███████▉ | 14876/18688 [17:32:28<4:18:26, 4.07s/it] 80%|███████▉ | 14877/18688 [17:32:33<4:20:04, 4.09s/it] 80%|███████▉ | 14878/18688 [17:32:36<4:12:20, 3.97s/it] 80%|███████▉ | 14879/18688 [17:32:40<4:05:12, 3.86s/it] 80%|███████▉ | 14880/18688 [17:32:44<4:07:07, 3.89s/it] {'loss': '0.9009', 'grad_norm': '1.265', 'learning_rate': '1.164e-05', 'epoch': '0.7963', 'num_input_tokens_seen': 50025056, 'train_runtime': '6.316e+04', 'train_tokens_per_second': '792'}
80%|███████▉ | 14880/18688 [17:32:44<4:07:07, 3.89s/it] 80%|███████▉ | 14881/18688 [17:32:49<4:22:57, 4.14s/it] 80%|███████▉ | 14882/18688 [17:32:53<4:19:31, 4.09s/it] 80%|███████▉ | 14883/18688 [17:32:56<4:12:59, 3.99s/it] 80%|███████▉ | 14884/18688 [17:33:00<4:09:06, 3.93s/it] 80%|███████▉ | 14885/18688 [17:33:04<4:10:33, 3.95s/it] 80%|███████▉ | 14886/18688 [17:33:09<4:20:11, 4.11s/it] 80%|███████▉ | 14887/18688 [17:33:14<4:39:45, 4.42s/it] 80%|███████▉ | 14888/18688 [17:33:17<4:27:22, 4.22s/it] 80%|███████▉ | 14889/18688 [17:33:22<4:26:04, 4.20s/it] 80%|███████▉ | 14890/18688 [17:33:27<4:40:07, 4.43s/it] {'loss': '0.9048', 'grad_norm': '1.165', 'learning_rate': '1.158e-05', 'epoch': '0.7968', 'num_input_tokens_seen': 50060816, 'train_runtime': '6.321e+04', 'train_tokens_per_second': '792'}
80%|███████▉ | 14890/18688 [17:33:27<4:40:07, 4.43s/it] 80%|███████▉ | 14891/18688 [17:33:31<4:43:10, 4.47s/it] 80%|███████▉ | 14892/18688 [17:33:35<4:24:44, 4.18s/it] 80%|███████▉ | 14893/18688 [17:33:39<4:25:22, 4.20s/it] 80%|███████▉ | 14894/18688 [17:33:43<4:21:57, 4.14s/it] 80%|███████▉ | 14895/18688 [17:33:47<4:21:13, 4.13s/it] 80%|███████▉ | 14896/18688 [17:33:51<4:18:37, 4.09s/it] 80%|███████▉ | 14897/18688 [17:33:56<4:28:46, 4.25s/it] 80%|███████▉ | 14898/18688 [17:33:59<4:15:51, 4.05s/it] 80%|███████▉ | 14899/18688 [17:34:03<4:11:20, 3.98s/it] 80%|███████▉ | 14900/18688 [17:34:07<4:15:16, 4.04s/it] {'loss': '0.899', 'grad_norm': '1.443', 'learning_rate': '1.152e-05', 'epoch': '0.7973', 'num_input_tokens_seen': 50093656, 'train_runtime': '6.325e+04', 'train_tokens_per_second': '792'}
80%|███████▉ | 14900/18688 [17:34:07<4:15:16, 4.04s/it] 80%|███████▉ | 14901/18688 [17:34:11<4:07:06, 3.92s/it] 80%|███████▉ | 14902/18688 [17:34:15<4:11:05, 3.98s/it] 80%|███████▉ | 14903/18688 [17:34:19<4:20:14, 4.13s/it] 80%|███████▉ | 14904/18688 [17:34:23<4:12:59, 4.01s/it] 80%|███████▉ | 14905/18688 [17:34:27<4:11:53, 4.00s/it] 80%|███████▉ | 14906/18688 [17:34:32<4:23:28, 4.18s/it] 80%|███████▉ | 14907/18688 [17:34:36<4:18:55, 4.11s/it] 80%|███████▉ | 14908/18688 [17:34:40<4:27:06, 4.24s/it] 80%|███████▉ | 14909/18688 [17:34:44<4:25:36, 4.22s/it] 80%|███████▉ | 14910/18688 [17:34:48<4:13:11, 4.02s/it] {'loss': '0.9413', 'grad_norm': '1.435', 'learning_rate': '1.146e-05', 'epoch': '0.7979', 'num_input_tokens_seen': 50125760, 'train_runtime': '6.329e+04', 'train_tokens_per_second': '792'}
80%|███████▉ | 14910/18688 [17:34:48<4:13:11, 4.02s/it] 80%|███████▉ | 14911/18688 [17:34:53<4:33:47, 4.35s/it] 80%|███████▉ | 14912/18688 [17:34:57<4:17:29, 4.09s/it] 80%|███████▉ | 14913/18688 [17:35:01<4:20:05, 4.13s/it] 80%|███████▉ | 14914/18688 [17:35:06<4:32:41, 4.34s/it] 80%|███████▉ | 14915/18688 [17:35:09<4:20:36, 4.14s/it] 80%|███████▉ | 14916/18688 [17:35:14<4:24:32, 4.21s/it] 80%|███████▉ | 14917/18688 [17:35:18<4:20:14, 4.14s/it] 80%|███████▉ | 14918/18688 [17:35:22<4:31:03, 4.31s/it] 80%|███████▉ | 14919/18688 [17:35:26<4:21:40, 4.17s/it] 80%|███████▉ | 14920/18688 [17:35:31<4:36:47, 4.41s/it] {'loss': '0.9863', 'grad_norm': '1.247', 'learning_rate': '1.14e-05', 'epoch': '0.7984', 'num_input_tokens_seen': 50161592, 'train_runtime': '6.333e+04', 'train_tokens_per_second': '792'}
80%|███████▉ | 14920/18688 [17:35:31<4:36:47, 4.41s/it] 80%|███████▉ | 14921/18688 [17:35:35<4:21:08, 4.16s/it] 80%|███████▉ | 14922/18688 [17:35:39<4:25:34, 4.23s/it] 80%|███████▉ | 14923/18688 [17:35:43<4:15:08, 4.07s/it] 80%|███████▉ | 14924/18688 [17:35:49<4:48:47, 4.60s/it] 80%|███████▉ | 14925/18688 [17:35:53<4:38:07, 4.43s/it] 80%|███████▉ | 14926/18688 [17:35:56<4:19:13, 4.13s/it] 80%|███████▉ | 14927/18688 [17:36:00<4:22:29, 4.19s/it] 80%|███████▉ | 14928/18688 [17:36:04<4:19:46, 4.15s/it] 80%|███████▉ | 14929/18688 [17:36:09<4:24:50, 4.23s/it] 80%|███████▉ | 14930/18688 [17:36:13<4:26:33, 4.26s/it] {'loss': '0.8816', 'grad_norm': '1.44', 'learning_rate': '1.134e-05', 'epoch': '0.7989', 'num_input_tokens_seen': 50195760, 'train_runtime': '6.337e+04', 'train_tokens_per_second': '792.1'}
80%|███████▉ | 14930/18688 [17:36:13<4:26:33, 4.26s/it] 80%|███████▉ | 14931/18688 [17:36:17<4:23:50, 4.21s/it] 80%|███████▉ | 14932/18688 [17:36:22<4:34:04, 4.38s/it] 80%|███████▉ | 14933/18688 [17:36:26<4:18:27, 4.13s/it] 80%|███████▉ | 14934/18688 [17:36:30<4:27:25, 4.27s/it] 80%|███████▉ | 14935/18688 [17:36:35<4:27:42, 4.28s/it] 80%|███████▉ | 14936/18688 [17:36:39<4:33:01, 4.37s/it] 80%|███████▉ | 14937/18688 [17:36:43<4:25:59, 4.25s/it] 80%|███████▉ | 14938/18688 [17:36:47<4:13:37, 4.06s/it] 80%|███████▉ | 14939/18688 [17:36:51<4:18:15, 4.13s/it] 80%|███████▉ | 14940/18688 [17:36:55<4:05:59, 3.94s/it] {'loss': '0.8796', 'grad_norm': '1.43', 'learning_rate': '1.129e-05', 'epoch': '0.7995', 'num_input_tokens_seen': 50228848, 'train_runtime': '6.342e+04', 'train_tokens_per_second': '792.1'}
80%|███████▉ | 14940/18688 [17:36:55<4:05:59, 3.94s/it] 80%|███████▉ | 14941/18688 [17:36:58<3:59:23, 3.83s/it] 80%|███████▉ | 14942/18688 [17:37:02<3:52:59, 3.73s/it] 80%|███████▉ | 14943/18688 [17:37:06<3:56:51, 3.79s/it] 80%|███████▉ | 14944/18688 [17:37:09<3:53:37, 3.74s/it] 80%|███████▉ | 14945/18688 [17:37:13<3:52:19, 3.72s/it] 80%|███████▉ | 14946/18688 [17:37:17<4:08:54, 3.99s/it] 80%|███████▉ | 14947/18688 [17:37:21<4:09:41, 4.00s/it] 80%|███████▉ | 14948/18688 [17:37:26<4:09:38, 4.00s/it] 80%|███████▉ | 14949/18688 [17:37:29<3:59:41, 3.85s/it] 80%|███████▉ | 14950/18688 [17:37:33<4:11:35, 4.04s/it] {'loss': '0.8486', 'grad_norm': '1.201', 'learning_rate': '1.123e-05', 'epoch': '0.8', 'num_input_tokens_seen': 50260192, 'train_runtime': '6.345e+04', 'train_tokens_per_second': '792.1'}
80%|███████▉ | 14950/18688 [17:37:33<4:11:35, 4.04s/it] 80%|████████ | 14951/18688 [17:37:37<4:08:19, 3.99s/it] 80%|████████ | 14952/18688 [17:37:41<3:58:15, 3.83s/it] 80%|████████ | 14953/18688 [17:37:46<4:17:26, 4.14s/it] 80%|████████ | 14954/18688 [17:37:50<4:15:35, 4.11s/it] 80%|████████ | 14955/18688 [17:37:53<4:08:03, 3.99s/it] 80%|████████ | 14956/18688 [17:37:57<4:04:58, 3.94s/it] 80%|████████ | 14957/18688 [17:38:02<4:18:56, 4.16s/it] 80%|████████ | 14958/18688 [17:38:06<4:13:56, 4.08s/it] 80%|████████ | 14959/18688 [17:38:10<4:08:42, 4.00s/it] 80%|████████ | 14960/18688 [17:38:14<4:19:39, 4.18s/it] {'loss': '0.9205', 'grad_norm': '1.505', 'learning_rate': '1.117e-05', 'epoch': '0.8005', 'num_input_tokens_seen': 50293264, 'train_runtime': '6.349e+04', 'train_tokens_per_second': '792.1'}
80%|████████ | 14960/18688 [17:38:14<4:19:39, 4.18s/it] 80%|████████ | 14961/18688 [17:38:18<4:14:18, 4.09s/it] 80%|████████ | 14962/18688 [17:38:22<4:06:11, 3.96s/it] 80%|████████ | 14963/18688 [17:38:26<4:03:30, 3.92s/it] 80%|████████ | 14964/18688 [17:38:29<3:58:59, 3.85s/it] 80%|████████ | 14965/18688 [17:38:34<4:08:43, 4.01s/it] 80%|████████ | 14966/18688 [17:38:38<4:10:33, 4.04s/it] 80%|████████ | 14967/18688 [17:38:41<3:59:44, 3.87s/it] 80%|████████ | 14968/18688 [17:38:45<4:06:59, 3.98s/it] 80%|████████ | 14969/18688 [17:38:49<4:06:32, 3.98s/it] 80%|████████ | 14970/18688 [17:38:55<4:27:36, 4.32s/it] {'loss': '0.9063', 'grad_norm': '1.429', 'learning_rate': '1.111e-05', 'epoch': '0.8011', 'num_input_tokens_seen': 50325944, 'train_runtime': '6.354e+04', 'train_tokens_per_second': '792.1'}
80%|████████ | 14970/18688 [17:38:55<4:27:36, 4.32s/it] 80%|████████ | 14971/18688 [17:38:59<4:34:00, 4.42s/it] 80%|████████ | 14972/18688 [17:39:03<4:22:53, 4.24s/it] 80%|████████ | 14973/18688 [17:39:07<4:21:04, 4.22s/it] 80%|████████ | 14974/18688 [17:39:11<4:09:14, 4.03s/it] 80%|████████ | 14975/18688 [17:39:15<4:09:27, 4.03s/it] 80%|████████ | 14976/18688 [17:39:19<4:03:45, 3.94s/it] 80%|████████ | 14977/18688 [17:39:22<4:02:08, 3.91s/it] 80%|████████ | 14978/18688 [17:39:27<4:17:06, 4.16s/it] 80%|████████ | 14979/18688 [17:39:31<4:18:16, 4.18s/it] 80%|████████ | 14980/18688 [17:39:37<4:38:24, 4.51s/it] {'loss': '0.9485', 'grad_norm': '1.23', 'learning_rate': '1.106e-05', 'epoch': '0.8016', 'num_input_tokens_seen': 50361112, 'train_runtime': '6.358e+04', 'train_tokens_per_second': '792.1'}
80%|████████ | 14980/18688 [17:39:37<4:38:24, 4.51s/it] 80%|████████ | 14981/18688 [17:39:41<4:32:39, 4.41s/it] 80%|████████ | 14982/18688 [17:39:45<4:27:30, 4.33s/it] 80%|████████ | 14983/18688 [17:39:50<4:32:18, 4.41s/it] 80%|████████ | 14984/18688 [17:39:53<4:20:11, 4.21s/it] 80%|████████ | 14985/18688 [17:39:59<4:40:13, 4.54s/it] 80%|████████ | 14986/18688 [17:40:03<4:34:33, 4.45s/it] 80%|████████ | 14987/18688 [17:40:07<4:29:50, 4.37s/it] 80%|████████ | 14988/18688 [17:40:11<4:17:44, 4.18s/it] 80%|████████ | 14989/18688 [17:40:15<4:20:12, 4.22s/it] 80%|████████ | 14990/18688 [17:40:19<4:09:48, 4.05s/it] {'loss': '0.9184', 'grad_norm': '1.324', 'learning_rate': '1.1e-05', 'epoch': '0.8021', 'num_input_tokens_seen': 50396304, 'train_runtime': '6.362e+04', 'train_tokens_per_second': '792.2'}
80%|████████ | 14990/18688 [17:40:19<4:09:48, 4.05s/it] 80%|████████ | 14991/18688 [17:40:22<4:00:16, 3.90s/it] 80%|████████ | 14992/18688 [17:40:26<3:54:43, 3.81s/it] 80%|████████ | 14993/18688 [17:40:31<4:13:26, 4.12s/it] 80%|████████ | 14994/18688 [17:40:36<4:32:32, 4.43s/it] 80%|████████ | 14995/18688 [17:40:39<4:16:25, 4.17s/it] 80%|████████ | 14996/18688 [17:40:44<4:15:38, 4.15s/it] 80%|████████ | 14997/18688 [17:40:48<4:19:36, 4.22s/it] 80%|████████ | 14998/18688 [17:40:52<4:07:23, 4.02s/it] 80%|████████ | 14999/18688 [17:40:55<3:59:06, 3.89s/it] 80%|████████ | 15000/18688 [17:40:59<3:59:39, 3.90s/it] {'loss': '0.8813', 'grad_norm': '1.512', 'learning_rate': '1.094e-05', 'epoch': '0.8027', 'num_input_tokens_seen': 50429344, 'train_runtime': '6.366e+04', 'train_tokens_per_second': '792.2'}
80%|████████ | 15000/18688 [17:40:59<3:59:39, 3.90s/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, 25.69it/s]
1%| | 6/1000 [00:00<00:54, 18.26it/s]
1%| | 8/1000 [00:00<00:56, 17.57it/s]
1%| | 10/1000 [00:00<00:59, 16.57it/s]
1%| | 12/1000 [00:00<01:00, 16.38it/s]
1%|▏ | 14/1000 [00:00<01:01, 15.96it/s]
2%|▏ | 16/1000 [00:00<01:02, 15.76it/s]
2%|▏ | 18/1000 [00:01<01:38, 10.00it/s]
2%|▏ | 20/1000 [00:01<01:33, 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, 11.02it/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, 10.44it/s]
4%|▍ | 38/1000 [00:03<01:24, 11.35it/s]
4%|▍ | 40/1000 [00:03<01:27, 10.97it/s]
4%|▍ | 42/1000 [00:03<01:46, 9.03it/s]
4%|▍ | 44/1000 [00:03<01:30, 10.54it/s]
5%|▍ | 46/1000 [00:03<01:21, 11.72it/s]
5%|▍ | 48/1000 [00:04<01:29, 10.65it/s]
5%|▌ | 50/1000 [00:04<01:21, 11.69it/s]
5%|▌ | 52/1000 [00:04<01:15, 12.48it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.21it/s]
6%|▌ | 56/1000 [00:04<01:07, 13.90it/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<01:15, 12.29it/s]
7%|▋ | 68/1000 [00:05<01:12, 12.87it/s]
7%|▋ | 70/1000 [00:05<01:09, 13.45it/s]
7%|▋ | 72/1000 [00:06<01:06, 13.89it/s]
7%|▋ | 74/1000 [00:06<01:07, 13.67it/s]
8%|▊ | 76/1000 [00:06<01:04, 14.28it/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, 14.57it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.69it/s]
9%|▉ | 88/1000 [00:07<01:02, 14.52it/s]
9%|▉ | 90/1000 [00:07<01:02, 14.52it/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, 14.29it/s]
10%|▉ | 98/1000 [00:07<01:03, 14.22it/s]
10%|█ | 100/1000 [00:08<01:31, 9.80it/s]
10%|█ | 102/1000 [00:08<01:23, 10.77it/s]
10%|█ | 104/1000 [00:08<01:54, 7.81it/s]
11%|█ | 106/1000 [00:09<02:02, 7.33it/s]
11%|█ | 108/1000 [00:09<01:42, 8.68it/s]
11%|█ | 110/1000 [00:09<01:27, 10.13it/s]
11%|█ | 112/1000 [00:09<01:18, 11.26it/s]
11%|█▏ | 114/1000 [00:09<01:14, 11.92it/s]
12%|█▏ | 116/1000 [00:09<01:10, 12.55it/s]
12%|█▏ | 118/1000 [00:09<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, 9.64it/s]
13%|█▎ | 126/1000 [00:10<01:22, 10.61it/s]
13%|█▎ | 128/1000 [00:10<01:15, 11.54it/s]
13%|█▎ | 130/1000 [00:11<01:11, 12.20it/s]
13%|█▎ | 132/1000 [00:11<01:05, 13.20it/s]
13%|█▎ | 134/1000 [00:11<01:09, 12.41it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.32it/s]
14%|█▍ | 138/1000 [00:11<01:05, 13.26it/s]
14%|█▍ | 140/1000 [00:11<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, 10.60it/s]
15%|█▍ | 148/1000 [00:12<01:13, 11.53it/s]
15%|█▌ | 150/1000 [00:12<01:07, 12.63it/s]
15%|█▌ | 152/1000 [00:12<01:04, 13.19it/s]
15%|█▌ | 154/1000 [00:13<01:04, 13.14it/s]
16%|█▌ | 156/1000 [00:13<01:06, 12.73it/s]
16%|█▌ | 158/1000 [00:13<01:04, 13.11it/s]
16%|█▌ | 160/1000 [00:13<01:06, 12.57it/s]
16%|█▌ | 162/1000 [00:13<01:07, 12.45it/s]
16%|█▋ | 164/1000 [00:13<01:05, 12.84it/s]
17%|█▋ | 166/1000 [00:14<01:03, 13.21it/s]
17%|█▋ | 168/1000 [00:14<01:00, 13.74it/s]
17%|█▋ | 170/1000 [00:14<01:21, 10.22it/s]
17%|█▋ | 172/1000 [00:14<01:13, 11.23it/s]
17%|█▋ | 174/1000 [00:14<01:31, 9.06it/s]
18%|█▊ | 176/1000 [00:15<01:55, 7.13it/s]
18%|█▊ | 178/1000 [00:15<01:35, 8.59it/s]
18%|█▊ | 180/1000 [00:15<01:35, 8.62it/s]
18%|█▊ | 182/1000 [00:15<01:37, 8.40it/s]
18%|█▊ | 184/1000 [00:16<01:24, 9.65it/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, 10.63it/s]
19%|█▉ | 192/1000 [00:16<01:11, 11.28it/s]
19%|█▉ | 194/1000 [00:17<01:16, 10.52it/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, 10.55it/s]
20%|██ | 202/1000 [00:17<01:09, 11.50it/s]
20%|██ | 204/1000 [00:17<01:03, 12.62it/s]
21%|██ | 206/1000 [00:18<01:00, 13.23it/s]
21%|██ | 208/1000 [00:18<00:58, 13.61it/s]
21%|██ | 210/1000 [00:18<00:54, 14.47it/s]
21%|██ | 212/1000 [00:18<00:53, 14.70it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.71it/s]
22%|██▏ | 216/1000 [00:18<00:54, 14.46it/s]
22%|██▏ | 218/1000 [00:18<00:52, 14.86it/s]
22%|██▏ | 220/1000 [00:19<01:29, 8.72it/s]
22%|██▏ | 222/1000 [00:19<01:26, 9.03it/s]
22%|██▏ | 224/1000 [00:19<01:22, 9.39it/s]
23%|██▎ | 226/1000 [00:19<01:15, 10.24it/s]
23%|██▎ | 228/1000 [00:20<01:08, 11.23it/s]
23%|██▎ | 230/1000 [00:20<01:05, 11.80it/s]
23%|██▎ | 232/1000 [00:20<01:02, 12.22it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.04it/s]
24%|██▎ | 236/1000 [00:20<00:56, 13.57it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.24it/s]
24%|██▍ | 240/1000 [00:20<00:53, 14.24it/s]
24%|██▍ | 242/1000 [00:20<00:51, 14.74it/s]
24%|██▍ | 244/1000 [00:21<00:50, 14.93it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.87it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.63it/s]
25%|██▌ | 250/1000 [00:21<00:56, 13.39it/s]
25%|██▌ | 252/1000 [00:21<01:01, 12.11it/s]
25%|██▌ | 254/1000 [00:21<00:56, 13.13it/s]
26%|██▌ | 256/1000 [00:22<00:54, 13.73it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.76it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.89it/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, 14.02it/s]
27%|██▋ | 268/1000 [00:22<00:50, 14.61it/s]
27%|██▋ | 270/1000 [00:23<00:54, 13.32it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.65it/s]
27%|██▋ | 274/1000 [00:23<01:01, 11.72it/s]
28%|██▊ | 276/1000 [00:23<00:56, 12.71it/s]
28%|██▊ | 278/1000 [00:23<00:55, 12.94it/s]
28%|██▊ | 280/1000 [00:23<00:53, 13.54it/s]
28%|██▊ | 282/1000 [00:23<00:49, 14.54it/s]
28%|██▊ | 284/1000 [00:24<00:49, 14.47it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.75it/s]
29%|██▉ | 288/1000 [00:24<00:47, 15.04it/s]
29%|██▉ | 290/1000 [00:24<00:46, 15.41it/s]
29%|██▉ | 292/1000 [00:24<00:45, 15.39it/s]
29%|██▉ | 294/1000 [00:24<00:46, 15.29it/s]
30%|██▉ | 296/1000 [00:24<00:45, 15.37it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.51it/s]
30%|███ | 300/1000 [00:25<00:46, 15.14it/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, 14.52it/s]
31%|███ | 308/1000 [00:25<00:47, 14.47it/s]
31%|███ | 310/1000 [00:25<00:47, 14.62it/s]
31%|███ | 312/1000 [00:26<00:47, 14.57it/s]
31%|███▏ | 314/1000 [00:26<00:44, 15.33it/s]
32%|███▏ | 316/1000 [00:26<00:43, 15.56it/s]
32%|███▏ | 318/1000 [00:26<00:42, 15.90it/s]
32%|███▏ | 320/1000 [00:26<01:13, 9.31it/s]
32%|███▏ | 322/1000 [00:26<01:03, 10.68it/s]
32%|███▏ | 324/1000 [00:27<00:58, 11.49it/s]
33%|███▎ | 326/1000 [00:27<00:58, 11.46it/s]
33%|███▎ | 328/1000 [00:27<00:58, 11.50it/s]
33%|███▎ | 330/1000 [00:27<00:53, 12.44it/s]
33%|███▎ | 332/1000 [00:27<00:52, 12.69it/s]
33%|███▎ | 334/1000 [00:27<00:50, 13.18it/s]
34%|███▎ | 336/1000 [00:27<00:48, 13.57it/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, 12.39it/s]
34%|███▍ | 344/1000 [00:28<00:50, 13.05it/s]
35%|███▍ | 346/1000 [00:28<00:47, 13.63it/s]
35%|███▍ | 348/1000 [00:28<00:46, 14.07it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.71it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.91it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.92it/s]
36%|███▌ | 356/1000 [00:29<00:42, 15.01it/s]
36%|███▌ | 358/1000 [00:29<00:43, 14.70it/s]
36%|███▌ | 360/1000 [00:29<00:42, 15.07it/s]
36%|███▌ | 362/1000 [00:29<00:47, 13.37it/s]
36%|███▋ | 364/1000 [00:30<00:45, 13.86it/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, 13.23it/s]
37%|███▋ | 372/1000 [00:30<00:46, 13.56it/s]
37%|███▋ | 374/1000 [00:30<00:54, 11.42it/s]
38%|███▊ | 376/1000 [00:31<00:56, 11.00it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.67it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.49it/s]
38%|███▊ | 382/1000 [00:31<00:48, 12.68it/s]
38%|███▊ | 384/1000 [00:31<00:46, 13.39it/s]
39%|███▊ | 386/1000 [00:31<00:44, 13.67it/s]
39%|███▉ | 388/1000 [00:31<00:44, 13.62it/s]
39%|███▉ | 390/1000 [00:32<00:45, 13.51it/s]
39%|███▉ | 392/1000 [00:32<00:43, 13.85it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.64it/s]
40%|███▉ | 396/1000 [00:32<00:43, 13.76it/s]
40%|███▉ | 398/1000 [00:32<00:47, 12.58it/s]
40%|████ | 400/1000 [00:32<00:45, 13.24it/s]
40%|████ | 402/1000 [00:32<00:43, 13.90it/s]
40%|████ | 404/1000 [00:33<00:41, 14.32it/s]
41%|████ | 406/1000 [00:33<00:41, 14.47it/s]
41%|████ | 408/1000 [00:33<00:49, 12.02it/s]
41%|████ | 410/1000 [00:33<01:01, 9.60it/s]
41%|████ | 412/1000 [00:33<00:54, 10.78it/s]
41%|████▏ | 414/1000 [00:34<01:10, 8.30it/s]
42%|████▏ | 416/1000 [00:34<01:25, 6.79it/s]
42%|████▏ | 418/1000 [00:34<01:11, 8.11it/s]
42%|████▏ | 420/1000 [00:34<01:01, 9.40it/s]
42%|████▏ | 422/1000 [00:35<00:53, 10.73it/s]
42%|████▏ | 424/1000 [00:35<01:15, 7.63it/s]
43%|████▎ | 426/1000 [00:35<01:04, 8.88it/s]
43%|████▎ | 428/1000 [00:35<00:56, 10.06it/s]
43%|████▎ | 430/1000 [00:36<01:02, 9.07it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.03it/s]
43%|████▎ | 434/1000 [00:36<01:08, 8.25it/s]
44%|████▎ | 436/1000 [00:36<01:00, 9.30it/s]
44%|████▍ | 438/1000 [00:37<01:02, 8.96it/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, 12.15it/s]
45%|████▍ | 448/1000 [00:37<00:50, 10.97it/s]
45%|████▌ | 450/1000 [00:38<00:46, 11.77it/s]
45%|████▌ | 452/1000 [00:38<00:42, 12.82it/s]
45%|████▌ | 454/1000 [00:38<00:44, 12.30it/s]
46%|████▌ | 456/1000 [00:38<00:42, 12.72it/s]
46%|████▌ | 458/1000 [00:38<00:39, 13.74it/s]
46%|████▌ | 460/1000 [00:38<00:38, 13.87it/s]
46%|████▌ | 462/1000 [00:38<00:37, 14.45it/s]
46%|████▋ | 464/1000 [00:38<00:35, 14.96it/s]
47%|████▋ | 466/1000 [00:39<00:36, 14.82it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.91it/s]
47%|████▋ | 470/1000 [00:39<00:52, 10.07it/s]
47%|████▋ | 472/1000 [00:39<00:51, 10.24it/s]
47%|████▋ | 474/1000 [00:39<00:45, 11.51it/s]
48%|████▊ | 476/1000 [00:40<00:43, 12.16it/s]
48%|████▊ | 478/1000 [00:40<00:43, 12.03it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.94it/s]
48%|████▊ | 482/1000 [00:40<00:38, 13.34it/s]
48%|████▊ | 484/1000 [00:40<00:37, 13.85it/s]
49%|████▊ | 486/1000 [00:40<00:36, 14.01it/s]
49%|████▉ | 488/1000 [00:40<00:35, 14.30it/s]
49%|████▉ | 490/1000 [00:41<00:46, 10.97it/s]
49%|████▉ | 492/1000 [00:41<00:44, 11.40it/s]
49%|████▉ | 494/1000 [00:41<00:43, 11.69it/s]
50%|████▉ | 496/1000 [00:41<00:42, 11.93it/s]
50%|████▉ | 498/1000 [00:41<00:42, 11.93it/s]
50%|█████ | 500/1000 [00:42<00:49, 10.13it/s]
50%|█████ | 502/1000 [00:42<00:45, 10.87it/s]
50%|█████ | 504/1000 [00:42<00:42, 11.70it/s]
51%|█████ | 506/1000 [00:42<00:40, 12.09it/s]
51%|█████ | 508/1000 [00:42<00:37, 13.13it/s]
51%|█████ | 510/1000 [00:42<00:48, 10.16it/s]
51%|█████ | 512/1000 [00:43<00:44, 10.99it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 11.91it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.52it/s]
52%|█████▏ | 518/1000 [00:43<00:36, 13.05it/s]
52%|█████▏ | 520/1000 [00:43<00:35, 13.44it/s]
52%|█████▏ | 522/1000 [00:43<00:33, 14.20it/s]
52%|█████▏ | 524/1000 [00:43<00:33, 14.21it/s]
53%|█████▎ | 526/1000 [00:44<00:32, 14.38it/s]
53%|█████▎ | 528/1000 [00:44<00:39, 11.99it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.81it/s]
53%|█████▎ | 532/1000 [00:44<00:34, 13.72it/s]
53%|█████▎ | 534/1000 [00:44<00:33, 14.10it/s]
54%|█████▎ | 536/1000 [00:44<00:32, 14.24it/s]
54%|█████▍ | 538/1000 [00:44<00:32, 14.01it/s]
54%|█████▍ | 540/1000 [00:45<00:31, 14.43it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.68it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.57it/s]
55%|█████▍ | 546/1000 [00:45<00:31, 14.22it/s]
55%|█████▍ | 548/1000 [00:45<00:32, 13.91it/s]
55%|█████▌ | 550/1000 [00:45<00:31, 14.14it/s]
55%|█████▌ | 552/1000 [00:45<00:31, 14.24it/s]
55%|█████▌ | 554/1000 [00:46<00:32, 13.89it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.08it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.47it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 14.99it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 14.96it/s]
56%|█████▋ | 564/1000 [00:46<00:40, 10.79it/s]
57%|█████▋ | 566/1000 [00:47<00:37, 11.71it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.25it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.33it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.83it/s]
57%|█████▋ | 574/1000 [00:47<00:31, 13.68it/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, 12.89it/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, 14.23it/s]
59%|█████▉ | 588/1000 [00:48<00:27, 15.08it/s]
59%|█████▉ | 590/1000 [00:48<00:27, 15.07it/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, 13.86it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 14.10it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.49it/s]
60%|██████ | 602/1000 [00:49<00:27, 14.62it/s]
60%|██████ | 604/1000 [00:49<00:26, 15.17it/s]
61%|██████ | 606/1000 [00:49<00:26, 14.89it/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, 14.81it/s]
62%|██████▏ | 618/1000 [00:50<00:26, 14.23it/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, 11.43it/s]
63%|██████▎ | 626/1000 [00:51<00:30, 12.11it/s]
63%|██████▎ | 628/1000 [00:51<00:27, 13.37it/s]
63%|██████▎ | 630/1000 [00:51<00:26, 13.85it/s]
63%|██████▎ | 632/1000 [00:51<00:29, 12.49it/s]
63%|██████▎ | 634/1000 [00:52<00:27, 13.20it/s]
64%|██████▎ | 636/1000 [00:52<00:33, 10.94it/s]
64%|██████▍ | 638/1000 [00:52<00:33, 10.68it/s]
64%|██████▍ | 640/1000 [00:52<00:31, 11.46it/s]
64%|██████▍ | 642/1000 [00:52<00:29, 12.33it/s]
64%|██████▍ | 644/1000 [00:52<00:28, 12.56it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.85it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.34it/s]
65%|██████▌ | 650/1000 [00:53<00:27, 12.57it/s]
65%|██████▌ | 652/1000 [00:53<00:29, 11.96it/s]
65%|██████▌ | 654/1000 [00:53<00:36, 9.43it/s]
66%|██████▌ | 656/1000 [00:54<00:31, 10.78it/s]
66%|██████▌ | 658/1000 [00:54<00:28, 11.87it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.15it/s]
66%|██████▌ | 662/1000 [00:54<00:29, 11.28it/s]
66%|██████▋ | 664/1000 [00:54<00:28, 11.77it/s]
67%|██████▋ | 666/1000 [00:54<00:26, 12.81it/s]
67%|██████▋ | 668/1000 [00:55<00:29, 11.30it/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, 12.44it/s]
68%|██████▊ | 676/1000 [00:55<00:27, 11.72it/s]
68%|██████▊ | 678/1000 [00:55<00:26, 12.20it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 12.98it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.12it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.33it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.43it/s]
69%|██████▉ | 688/1000 [00:56<00:22, 13.72it/s]
69%|██████▉ | 690/1000 [00:56<00:22, 13.78it/s]
69%|██████▉ | 692/1000 [00:56<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, 14.66it/s]
70%|███████ | 700/1000 [00:57<00:24, 12.25it/s]
70%|███████ | 702/1000 [00:57<00:22, 13.05it/s]
70%|███████ | 704/1000 [00:57<00:30, 9.86it/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, 10.92it/s]
71%|███████ | 712/1000 [00:58<00:24, 11.88it/s]
71%|███████▏ | 714/1000 [00:58<00:22, 12.98it/s]
72%|███████▏ | 716/1000 [00:58<00:21, 13.07it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.57it/s]
72%|███████▏ | 720/1000 [00:59<00:20, 13.53it/s]
72%|███████▏ | 722/1000 [00:59<00:19, 13.96it/s]
72%|███████▏ | 724/1000 [00:59<00:20, 13.78it/s]
73%|███████▎ | 726/1000 [00:59<00:19, 14.00it/s]
73%|███████▎ | 728/1000 [00:59<00:22, 11.84it/s]
73%|███████▎ | 730/1000 [01:00<00:34, 7.78it/s]
73%|███████▎ | 732/1000 [01:00<00:35, 7.63it/s]
73%|███████▎ | 734/1000 [01:00<00:33, 8.06it/s]
74%|███████▎ | 735/1000 [01:00<00:33, 8.00it/s]
74%|███████▎ | 737/1000 [01:01<00:27, 9.45it/s]
74%|███████▍ | 739/1000 [01:01<00:24, 10.57it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.34it/s]
74%|███████▍ | 743/1000 [01:01<00:21, 11.89it/s]
74%|███████▍ | 745/1000 [01:01<00:25, 9.96it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.44it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.62it/s]
75%|███████▌ | 751/1000 [01:02<00:21, 11.73it/s]
75%|███████▌ | 753/1000 [01:02<00:19, 12.51it/s]
76%|███████▌ | 755/1000 [01:02<00:18, 13.26it/s]
76%|███████▌ | 757/1000 [01:02<00:23, 10.27it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.30it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.29it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.24it/s]
76%|███████▋ | 765/1000 [01:03<00:27, 8.59it/s]
77%|███████▋ | 767/1000 [01:03<00:24, 9.55it/s]
77%|███████▋ | 769/1000 [01:04<00:21, 10.57it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.84it/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, 14.06it/s]
78%|███████▊ | 779/1000 [01:04<00:21, 10.48it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.39it/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 [01:05<00:31, 6.88it/s]
79%|███████▉ | 788/1000 [01:06<00:25, 8.36it/s]
79%|███████▉ | 790/1000 [01:06<00:21, 9.74it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 10.95it/s]
79%|███████▉ | 794/1000 [01:06<00:17, 11.67it/s]
80%|███████▉ | 796/1000 [01:06<00:16, 12.19it/s]
80%|███████▉ | 798/1000 [01:06<00:15, 12.99it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.19it/s]
80%|████████ | 802/1000 [01:07<00:24, 8.22it/s]
80%|████████ | 804/1000 [01:07<00:23, 8.29it/s]
81%|████████ | 806/1000 [01:07<00:21, 8.97it/s]
81%|████████ | 808/1000 [01:07<00:18, 10.36it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.49it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.71it/s]
81%|████████▏ | 814/1000 [01:08<00:14, 12.61it/s]
82%|████████▏ | 816/1000 [01:08<00:16, 11.04it/s]
82%|████████▏ | 818/1000 [01:08<00:15, 11.97it/s]
82%|████████▏ | 820/1000 [01:08<00:14, 12.48it/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, 14.24it/s]
83%|████████▎ | 828/1000 [01:09<00:11, 14.50it/s]
83%|████████▎ | 830/1000 [01:09<00:12, 13.94it/s]
83%|████████▎ | 832/1000 [01:09<00:11, 14.35it/s]
83%|████████▎ | 834/1000 [01:09<00:11, 14.08it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.38it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.30it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.42it/s]
84%|████████▍ | 842/1000 [01:10<00:10, 14.45it/s]
84%|████████▍ | 844/1000 [01:10<00:11, 13.99it/s]
85%|████████▍ | 846/1000 [01:10<00:11, 13.94it/s]
85%|████████▍ | 848/1000 [01:10<00:10, 13.89it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.10it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.71it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.30it/s]
86%|████████▌ | 856/1000 [01:11<00:10, 14.15it/s]
86%|████████▌ | 858/1000 [01:11<00:09, 14.66it/s]
86%|████████▌ | 860/1000 [01:11<00:09, 15.40it/s]
86%|████████▌ | 862/1000 [01:11<00:10, 13.55it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 14.07it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.87it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.76it/s]
87%|████████▋ | 870/1000 [01:12<00:09, 14.13it/s]
87%|████████▋ | 872/1000 [01:12<00:08, 14.74it/s]
87%|████████▋ | 874/1000 [01:12<00:10, 12.47it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.06it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.93it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.57it/s]
88%|████████▊ | 882/1000 [01:13<00:10, 11.40it/s]
88%|████████▊ | 884/1000 [01:13<00:09, 12.15it/s]
89%|████████▊ | 886/1000 [01:13<00:08, 12.94it/s]
89%|████████▉ | 888/1000 [01:13<00:08, 13.88it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.28it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.05it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 14.20it/s]
90%|████████▉ | 896/1000 [01:14<00:07, 13.93it/s]
90%|████████▉ | 898/1000 [01:14<00:07, 13.29it/s]
90%|█████████ | 900/1000 [01:14<00:08, 12.23it/s]
90%|█████████ | 902/1000 [01:14<00:07, 12.99it/s]
90%|█████████ | 904/1000 [01:15<00:09, 9.81it/s]
91%|█████████ | 906/1000 [01:15<00:08, 10.87it/s]
91%|█████████ | 908/1000 [01:15<00:07, 11.86it/s]
91%|█████████ | 910/1000 [01:15<00:07, 12.11it/s]
91%|█████████ | 912/1000 [01:15<00:07, 12.30it/s]
91%|█████████▏| 914/1000 [01:16<00:12, 6.81it/s]
92%|█████████▏| 916/1000 [01:16<00:10, 8.14it/s]
92%|█████████▏| 918/1000 [01:16<00:11, 7.30it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.63it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 9.96it/s]
92%|█████████▏| 924/1000 [01:17<00:07, 10.23it/s]
93%|█████████▎| 926/1000 [01:17<00:06, 11.09it/s]
93%|█████████▎| 928/1000 [01:17<00:07, 10.22it/s]
93%|█████████▎| 930/1000 [01:17<00:06, 11.40it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 12.06it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.65it/s]
94%|█████████▎| 936/1000 [01:18<00:04, 12.83it/s]
94%|█████████▍| 938/1000 [01:18<00:04, 13.03it/s]
94%|█████████▍| 940/1000 [01:18<00:04, 13.16it/s]
94%|█████████▍| 942/1000 [01:18<00:05, 11.56it/s]
94%|█████████▍| 944/1000 [01:18<00:04, 12.25it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.54it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 12.41it/s]
95%|█████████▌| 950/1000 [01:19<00:03, 13.22it/s]
95%|█████████▌| 952/1000 [01:19<00:03, 13.96it/s]
95%|█████████▌| 954/1000 [01:19<00:03, 14.12it/s]
96%|█████████▌| 956/1000 [01:19<00:03, 14.16it/s]
96%|█████████▌| 958/1000 [01:19<00:02, 14.66it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 13.04it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.67it/s]
96%|█████████▋| 964/1000 [01:20<00:03, 10.19it/s]
97%|█████████▋| 966/1000 [01:20<00:02, 11.43it/s]
97%|█████████▋| 968/1000 [01:20<00:02, 12.10it/s]
97%|█████████▋| 970/1000 [01:20<00:02, 12.72it/s]
97%|█████████▋| 972/1000 [01:21<00:02, 9.68it/s]
97%|█████████▋| 974/1000 [01:21<00:02, 10.87it/s]
98%|█████████▊| 976/1000 [01:21<00:02, 11.67it/s]
98%|█████████▊| 978/1000 [01:21<00:02, 9.88it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.97it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.46it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 10.46it/s]
99%|█████████▊| 986/1000 [01:22<00:01, 10.69it/s]
99%|█████████▉| 988/1000 [01:22<00:01, 10.91it/s]
99%|█████████▉| 990/1000 [01:22<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 [01:23<00:00, 13.94it/s]
100%|█████████▉| 998/1000 [01:23<00:00, 14.11it/s]
100%|██████████| 1000/1000 [01:23<00:00, 14.77it/s]
{'eval_loss': '0.877', 'eval_runtime': '83.64', 'eval_samples_per_second': '11.96', 'eval_steps_per_second': '11.96', 'epoch': '0.8027', 'num_input_tokens_seen': 50429344}
80%|████████ | 15000/18688 [17:42:23<3:59:39, 3.90s/it]
100%|██████████| 1000/1000 [01:23<00:00, 14.77it/s]
 80%|████████ | 15001/18688 [17:42:27<29:48:44, 29.11s/it] 80%|████████ | 15002/18688 [17:42:31<22:12:39, 21.69s/it] 80%|████████ | 15003/18688 [17:42:35<16:44:23, 16.35s/it] 80%|████████ | 15004/18688 [17:42:39<12:57:27, 12.66s/it] 80%|████████ | 15005/18688 [17:42:43<10:19:53, 10.10s/it] 80%|████████ | 15006/18688 [17:42:48<8:31:31, 8.34s/it] 80%|████████ | 15007/18688 [17:42:52<7:14:43, 7.09s/it] 80%|████████ | 15008/18688 [17:42:56<6:18:16, 6.17s/it] 80%|████████ | 15009/18688 [17:42:59<5:32:26, 5.42s/it] 80%|████████ | 15010/18688 [17:43:03<4:58:50, 4.88s/it] {'loss': '0.8347', 'grad_norm': '1.335', 'learning_rate': '1.088e-05', 'epoch': '0.8032', 'num_input_tokens_seen': 50461320, 'train_runtime': '6.378e+04', 'train_tokens_per_second': '791.1'}
80%|████████ | 15010/18688 [17:43:03<4:58:50, 4.88s/it] 80%|████████ | 15011/18688 [17:43:07<4:41:53, 4.60s/it] 80%|████████ | 15012/18688 [17:43:11<4:26:01, 4.34s/it] 80%|████████ | 15013/18688 [17:43:15<4:30:56, 4.42s/it] 80%|████████ | 15014/18688 [17:43:20<4:29:02, 4.39s/it] 80%|████████ | 15015/18688 [17:43:25<4:41:40, 4.60s/it] 80%|████████ | 15016/18688 [17:43:28<4:21:19, 4.27s/it] 80%|████████ | 15017/18688 [17:43:33<4:31:20, 4.43s/it] 80%|████████ | 15018/18688 [17:43:37<4:21:21, 4.27s/it] 80%|████████ | 15019/18688 [17:43:40<4:06:34, 4.03s/it] 80%|████████ | 15020/18688 [17:43:45<4:23:01, 4.30s/it] {'loss': '0.95', 'grad_norm': '1.132', 'learning_rate': '1.083e-05', 'epoch': '0.8037', 'num_input_tokens_seen': 50497304, 'train_runtime': '6.383e+04', 'train_tokens_per_second': '791.2'}
80%|████████ | 15020/18688 [17:43:45<4:23:01, 4.30s/it] 80%|████████ | 15021/18688 [17:43:50<4:28:53, 4.40s/it] 80%|████████ | 15022/18688 [17:43:54<4:27:48, 4.38s/it] 80%|████████ | 15023/18688 [17:43:58<4:17:41, 4.22s/it] 80%|████████ | 15024/18688 [17:44:02<4:13:39, 4.15s/it] 80%|████████ | 15025/18688 [17:44:06<4:10:30, 4.10s/it] 80%|████████ | 15026/18688 [17:44:10<4:01:05, 3.95s/it] 80%|████████ | 15027/18688 [17:44:14<4:08:54, 4.08s/it] 80%|████████ | 15028/18688 [17:44:19<4:18:46, 4.24s/it] 80%|████████ | 15029/18688 [17:44:23<4:19:00, 4.25s/it] 80%|████████ | 15030/18688 [17:44:28<4:30:02, 4.43s/it] {'loss': '0.8869', 'grad_norm': '1.369', 'learning_rate': '1.077e-05', 'epoch': '0.8043', 'num_input_tokens_seen': 50533168, 'train_runtime': '6.387e+04', 'train_tokens_per_second': '791.2'}
80%|████████ | 15030/18688 [17:44:28<4:30:02, 4.43s/it] 80%|████████ | 15031/18688 [17:44:32<4:16:47, 4.21s/it] 80%|████████ | 15032/18688 [17:44:36<4:15:47, 4.20s/it] 80%|████████ | 15033/18688 [17:44:39<4:05:42, 4.03s/it] 80%|████████ | 15034/18688 [17:44:43<3:57:58, 3.91s/it] 80%|████████ | 15035/18688 [17:44:48<4:11:13, 4.13s/it] 80%|████████ | 15036/18688 [17:44:51<4:04:15, 4.01s/it] 80%|████████ | 15037/18688 [17:44:56<4:06:54, 4.06s/it] 80%|████████ | 15038/18688 [17:45:00<4:07:39, 4.07s/it] 80%|████████ | 15039/18688 [17:45:04<4:07:47, 4.07s/it] 80%|████████ | 15040/18688 [17:45:08<4:12:25, 4.15s/it] {'loss': '0.9212', 'grad_norm': '1.066', 'learning_rate': '1.071e-05', 'epoch': '0.8048', 'num_input_tokens_seen': 50565640, 'train_runtime': '6.391e+04', 'train_tokens_per_second': '791.2'}
80%|████████ | 15040/18688 [17:45:08<4:12:25, 4.15s/it] 80%|████████ | 15041/18688 [17:45:12<4:07:34, 4.07s/it] 80%|████████ | 15042/18688 [17:45:16<4:00:52, 3.96s/it] 80%|████████ | 15043/18688 [17:45:20<4:03:43, 4.01s/it] 81%|████████ | 15044/18688 [17:45:23<3:55:49, 3.88s/it] 81%|████████ | 15045/18688 [17:45:28<4:11:08, 4.14s/it] 81%|████████ | 15046/18688 [17:45:32<4:13:14, 4.17s/it] 81%|████████ | 15047/18688 [17:45:36<4:08:47, 4.10s/it] 81%|████████ | 15048/18688 [17:45:40<4:01:44, 3.98s/it] 81%|████████ | 15049/18688 [17:45:44<4:02:08, 3.99s/it] 81%|████████ | 15050/18688 [17:45:48<4:09:08, 4.11s/it] {'loss': '0.8527', 'grad_norm': '1.355', 'learning_rate': '1.066e-05', 'epoch': '0.8054', 'num_input_tokens_seen': 50599192, 'train_runtime': '6.395e+04', 'train_tokens_per_second': '791.2'}
81%|████████ | 15050/18688 [17:45:48<4:09:08, 4.11s/it] 81%|████████ | 15051/18688 [17:45:53<4:22:49, 4.34s/it] 81%|████████ | 15052/18688 [17:45:57<4:15:58, 4.22s/it] 81%|████████ | 15053/18688 [17:46:01<4:10:15, 4.13s/it] 81%|████████ | 15054/18688 [17:46:05<4:11:37, 4.15s/it] 81%|████████ | 15055/18688 [17:46:09<4:03:20, 4.02s/it] 81%|████████ | 15056/18688 [17:46:13<3:55:39, 3.89s/it] 81%|████████ | 15057/18688 [17:46:17<4:09:23, 4.12s/it] 81%|████████ | 15058/18688 [17:46:21<4:02:08, 4.00s/it] 81%|████████ | 15059/18688 [17:46:25<4:09:00, 4.12s/it] 81%|████████ | 15060/18688 [17:46:29<4:02:42, 4.01s/it] {'loss': '0.9088', 'grad_norm': '1.172', 'learning_rate': '1.06e-05', 'epoch': '0.8059', 'num_input_tokens_seen': 50632520, 'train_runtime': '6.399e+04', 'train_tokens_per_second': '791.3'}
81%|████████ | 15060/18688 [17:46:29<4:02:42, 4.01s/it] 81%|████████ | 15061/18688 [17:46:33<3:52:32, 3.85s/it] 81%|████████ | 15062/18688 [17:46:36<3:45:07, 3.73s/it] 81%|████████ | 15063/18688 [17:46:40<3:50:58, 3.82s/it] 81%|████████ | 15064/18688 [17:46:44<3:56:30, 3.92s/it] 81%|████████ | 15065/18688 [17:46:48<3:58:46, 3.95s/it] 81%|████████ | 15066/18688 [17:46:53<4:06:46, 4.09s/it] 81%|████████ | 15067/18688 [17:46:57<4:09:45, 4.14s/it] 81%|████████ | 15068/18688 [17:47:01<4:09:27, 4.13s/it] 81%|████████ | 15069/18688 [17:47:05<3:57:18, 3.93s/it] 81%|████████ | 15070/18688 [17:47:09<3:59:39, 3.97s/it] {'loss': '0.8846', 'grad_norm': '1.29', 'learning_rate': '1.055e-05', 'epoch': '0.8064', 'num_input_tokens_seen': 50663544, 'train_runtime': '6.403e+04', 'train_tokens_per_second': '791.3'}
81%|████████ | 15070/18688 [17:47:09<3:59:39, 3.97s/it] 81%|████████ | 15071/18688 [17:47:14<4:17:04, 4.26s/it] 81%|████████ | 15072/18688 [17:47:18<4:19:06, 4.30s/it] 81%|████████ | 15073/18688 [17:47:22<4:14:11, 4.22s/it] 81%|████████ | 15074/18688 [17:47:26<4:01:43, 4.01s/it] 81%|████████ | 15075/18688 [17:47:29<3:53:08, 3.87s/it] 81%|████████ | 15076/18688 [17:47:34<4:05:46, 4.08s/it] 81%|████████ | 15077/18688 [17:47:38<4:06:30, 4.10s/it] 81%|████████ | 15078/18688 [17:47:42<4:07:58, 4.12s/it] 81%|████████ | 15079/18688 [17:47:46<4:02:20, 4.03s/it] 81%|████████ | 15080/18688 [17:47:50<4:07:48, 4.12s/it] {'loss': '0.9045', 'grad_norm': '1.25', 'learning_rate': '1.049e-05', 'epoch': '0.807', 'num_input_tokens_seen': 50698608, 'train_runtime': '6.407e+04', 'train_tokens_per_second': '791.3'}
81%|████████ | 15080/18688 [17:47:50<4:07:48, 4.12s/it] 81%|████████ | 15081/18688 [17:47:54<4:05:42, 4.09s/it] 81%|████████ | 15082/18688 [17:47:59<4:20:27, 4.33s/it] 81%|████████ | 15083/18688 [17:48:03<4:15:03, 4.25s/it] 81%|████████ | 15084/18688 [17:48:07<4:14:24, 4.24s/it] 81%|████████ | 15085/18688 [17:48:11<4:10:35, 4.17s/it] 81%|████████ | 15086/18688 [17:48:15<4:01:08, 4.02s/it] 81%|████████ | 15087/18688 [17:48:20<4:10:19, 4.17s/it] 81%|████████ | 15088/18688 [17:48:25<4:38:08, 4.64s/it] 81%|████████ | 15089/18688 [17:48:29<4:19:27, 4.33s/it] 81%|████████ | 15090/18688 [17:48:33<4:08:34, 4.15s/it] {'loss': '0.9018', 'grad_norm': '1.611', 'learning_rate': '1.043e-05', 'epoch': '0.8075', 'num_input_tokens_seen': 50734696, 'train_runtime': '6.411e+04', 'train_tokens_per_second': '791.3'}
81%|████████ | 15090/18688 [17:48:33<4:08:34, 4.15s/it] 81%|████████ | 15091/18688 [17:48:36<3:57:40, 3.96s/it] 81%|████████ | 15092/18688 [17:48:40<3:48:19, 3.81s/it] 81%|████████ | 15093/18688 [17:48:44<4:04:46, 4.09s/it] 81%|████████ | 15094/18688 [17:48:48<3:59:20, 4.00s/it] 81%|████████ | 15095/18688 [17:48:52<4:05:27, 4.10s/it] 81%|████████ | 15096/18688 [17:48:56<4:02:11, 4.05s/it] 81%|████████ | 15097/18688 [17:49:00<3:52:13, 3.88s/it] 81%|████████ | 15098/18688 [17:49:04<3:58:28, 3.99s/it] 81%|████████ | 15099/18688 [17:49:08<4:02:59, 4.06s/it] 81%|████████ | 15100/18688 [17:49:12<3:54:44, 3.93s/it] {'loss': '0.8938', 'grad_norm': '1.17', 'learning_rate': '1.038e-05', 'epoch': '0.808', 'num_input_tokens_seen': 50764760, 'train_runtime': '6.415e+04', 'train_tokens_per_second': '791.3'}
81%|████████ | 15100/18688 [17:49:12<3:54:44, 3.93s/it] 81%|████████ | 15101/18688 [17:49:16<3:50:52, 3.86s/it] 81%|████████ | 15102/18688 [17:49:19<3:49:13, 3.84s/it] 81%|████████ | 15103/18688 [17:49:24<4:03:57, 4.08s/it] 81%|████████ | 15104/18688 [17:49:29<4:11:01, 4.20s/it] 81%|████████ | 15105/18688 [17:49:33<4:07:41, 4.15s/it] 81%|████████ | 15106/18688 [17:49:37<4:06:40, 4.13s/it] 81%|████████ | 15107/18688 [17:49:40<3:55:34, 3.95s/it] 81%|████████ | 15108/18688 [17:49:44<3:52:28, 3.90s/it] 81%|████████ | 15109/18688 [17:49:49<4:10:27, 4.20s/it] 81%|████████ | 15110/18688 [17:49:53<4:02:09, 4.06s/it] {'loss': '0.88', 'grad_norm': '1.117', 'learning_rate': '1.032e-05', 'epoch': '0.8086', 'num_input_tokens_seen': 50798992, 'train_runtime': '6.419e+04', 'train_tokens_per_second': '791.3'}
81%|████████ | 15110/18688 [17:49:53<4:02:09, 4.06s/it] 81%|████████ | 15111/18688 [17:49:57<4:05:39, 4.12s/it] 81%|████████ | 15112/18688 [17:50:00<3:57:04, 3.98s/it] 81%|████████ | 15113/18688 [17:50:05<4:03:12, 4.08s/it] 81%|████████ | 15114/18688 [17:50:10<4:25:37, 4.46s/it] 81%|████████ | 15115/18688 [17:50:15<4:25:27, 4.46s/it] 81%|████████ | 15116/18688 [17:50:19<4:29:57, 4.53s/it] 81%|████████ | 15117/18688 [17:50:23<4:19:18, 4.36s/it] 81%|████████ | 15118/18688 [17:50:27<4:03:55, 4.10s/it] 81%|████████ | 15119/18688 [17:50:30<3:55:30, 3.96s/it] 81%|████████ | 15120/18688 [17:50:35<4:13:43, 4.27s/it] {'loss': '0.9364', 'grad_norm': '1.356', 'learning_rate': '1.027e-05', 'epoch': '0.8091', 'num_input_tokens_seen': 50834728, 'train_runtime': '6.424e+04', 'train_tokens_per_second': '791.4'}
81%|████████ | 15120/18688 [17:50:35<4:13:43, 4.27s/it] 81%|████████ | 15121/18688 [17:50:40<4:15:37, 4.30s/it] 81%|████████ | 15122/18688 [17:50:43<4:04:04, 4.11s/it] 81%|████████ | 15123/18688 [17:50:47<3:53:09, 3.92s/it] 81%|████████ | 15124/18688 [17:50:52<4:06:07, 4.14s/it] 81%|████████ | 15125/18688 [17:50:56<4:07:50, 4.17s/it] 81%|████████ | 15126/18688 [17:51:00<4:02:23, 4.08s/it] 81%|████████ | 15127/18688 [17:51:03<3:55:58, 3.98s/it] 81%|████████ | 15128/18688 [17:51:08<3:58:03, 4.01s/it] 81%|████████ | 15129/18688 [17:51:11<3:47:49, 3.84s/it] 81%|████████ | 15130/18688 [17:51:15<3:48:08, 3.85s/it] {'loss': '0.9159', 'grad_norm': '1.375', 'learning_rate': '1.021e-05', 'epoch': '0.8096', 'num_input_tokens_seen': 50865640, 'train_runtime': '6.428e+04', 'train_tokens_per_second': '791.4'}
81%|████████ | 15130/18688 [17:51:15<3:48:08, 3.85s/it] 81%|████████ | 15131/18688 [17:51:18<3:41:30, 3.74s/it] 81%|████████ | 15132/18688 [17:51:23<4:00:29, 4.06s/it] 81%|████████ | 15133/18688 [17:51:27<4:05:43, 4.15s/it] 81%|████████ | 15134/18688 [17:51:32<4:10:06, 4.22s/it] 81%|████████ | 15135/18688 [17:51:35<3:58:42, 4.03s/it] 81%|████████ | 15136/18688 [17:51:39<3:59:11, 4.04s/it] 81%|████████ | 15137/18688 [17:51:44<4:06:48, 4.17s/it] 81%|████████ | 15138/18688 [17:51:48<4:06:08, 4.16s/it] 81%|████████ | 15139/18688 [17:51:52<4:09:13, 4.21s/it] 81%|████████ | 15140/18688 [17:51:57<4:15:16, 4.32s/it] {'loss': '0.8876', 'grad_norm': '1.481', 'learning_rate': '1.016e-05', 'epoch': '0.8102', 'num_input_tokens_seen': 50901552, 'train_runtime': '6.432e+04', 'train_tokens_per_second': '791.4'}
81%|████████ | 15140/18688 [17:51:57<4:15:16, 4.32s/it] 81%|████████ | 15141/18688 [17:52:01<4:05:28, 4.15s/it] 81%|████████ | 15142/18688 [17:52:04<3:52:49, 3.94s/it] 81%|████████ | 15143/18688 [17:52:08<3:53:27, 3.95s/it] 81%|████████ | 15144/18688 [17:52:13<4:16:22, 4.34s/it] 81%|████████ | 15145/18688 [17:52:17<4:03:16, 4.12s/it] 81%|████████ | 15146/18688 [17:52:22<4:12:32, 4.28s/it] 81%|████████ | 15147/18688 [17:52:27<4:22:37, 4.45s/it] 81%|████████ | 15148/18688 [17:52:31<4:23:48, 4.47s/it] 81%|████████ | 15149/18688 [17:52:37<4:40:52, 4.76s/it] 81%|████████ | 15150/18688 [17:52:42<4:47:47, 4.88s/it] {'loss': '0.9577', 'grad_norm': '1.752', 'learning_rate': '1.01e-05', 'epoch': '0.8107', 'num_input_tokens_seen': 50940912, 'train_runtime': '6.436e+04', 'train_tokens_per_second': '791.5'}
81%|████████ | 15150/18688 [17:52:42<4:47:47, 4.88s/it] 81%|████████ | 15151/18688 [17:52:46<4:38:00, 4.72s/it] 81%|████████ | 15152/18688 [17:52:50<4:28:09, 4.55s/it] 81%|████████ | 15153/18688 [17:52:55<4:29:19, 4.57s/it] 81%|████████ | 15154/18688 [17:53:00<4:35:42, 4.68s/it] 81%|████████ | 15155/18688 [17:53:04<4:23:08, 4.47s/it] 81%|████████ | 15156/18688 [17:53:08<4:11:51, 4.28s/it] 81%|████████ | 15157/18688 [17:53:13<4:29:33, 4.58s/it] 81%|████████ | 15158/18688 [17:53:18<4:33:46, 4.65s/it] 81%|████████ | 15159/18688 [17:53:23<4:39:42, 4.76s/it] 81%|████████ | 15160/18688 [17:53:27<4:28:05, 4.56s/it] {'loss': '0.9247', 'grad_norm': '1.801', 'learning_rate': '1.005e-05', 'epoch': '0.8112', 'num_input_tokens_seen': 50980088, 'train_runtime': '6.441e+04', 'train_tokens_per_second': '791.5'}
81%|████████ | 15160/18688 [17:53:27<4:28:05, 4.56s/it] 81%|████████ | 15161/18688 [17:53:31<4:17:48, 4.39s/it] 81%|████████ | 15162/18688 [17:53:34<4:01:37, 4.11s/it] 81%|████████ | 15163/18688 [17:53:38<3:57:08, 4.04s/it] 81%|████████ | 15164/18688 [17:53:43<4:07:23, 4.21s/it] 81%|████████ | 15165/18688 [17:53:46<3:58:20, 4.06s/it] 81%|████████ | 15166/18688 [17:53:50<3:53:36, 3.98s/it] 81%|████████ | 15167/18688 [17:53:54<3:54:54, 4.00s/it] 81%|████████ | 15168/18688 [17:54:01<4:48:27, 4.92s/it] 81%|████████ | 15169/18688 [17:54:05<4:31:44, 4.63s/it] 81%|████████ | 15170/18688 [17:54:10<4:26:41, 4.55s/it] {'loss': '0.8764', 'grad_norm': '1.272', 'learning_rate': '9.991e-06', 'epoch': '0.8118', 'num_input_tokens_seen': 51016928, 'train_runtime': '6.445e+04', 'train_tokens_per_second': '791.6'}
81%|████████ | 15170/18688 [17:54:10<4:26:41, 4.55s/it] 81%|████████ | 15171/18688 [17:54:14<4:23:55, 4.50s/it] 81%|████████ | 15172/18688 [17:54:19<4:29:07, 4.59s/it] 81%|████████ | 15173/18688 [17:54:23<4:26:52, 4.56s/it] 81%|████████ | 15174/18688 [17:54:27<4:19:46, 4.44s/it] 81%|████████ | 15175/18688 [17:54:31<4:04:04, 4.17s/it] 81%|████████ | 15176/18688 [17:54:35<4:01:14, 4.12s/it] 81%|████████ | 15177/18688 [17:54:38<3:50:12, 3.93s/it] 81%|████████ | 15178/18688 [17:54:43<3:54:01, 4.00s/it] 81%|████████ | 15179/18688 [17:54:46<3:51:04, 3.95s/it] 81%|████████ | 15180/18688 [17:54:52<4:12:02, 4.31s/it] {'loss': '0.8801', 'grad_norm': '1.585', 'learning_rate': '9.936e-06', 'epoch': '0.8123', 'num_input_tokens_seen': 51052032, 'train_runtime': '6.449e+04', 'train_tokens_per_second': '791.6'}
81%|████████ | 15180/18688 [17:54:52<4:12:02, 4.31s/it] 81%|████████ | 15181/18688 [17:54:56<4:15:50, 4.38s/it] 81%|████████ | 15182/18688 [17:55:01<4:20:00, 4.45s/it] 81%|████████ | 15183/18688 [17:55:05<4:16:59, 4.40s/it] 81%|████████▏ | 15184/18688 [17:55:09<4:16:01, 4.38s/it] 81%|████████▏ | 15185/18688 [17:55:13<4:02:19, 4.15s/it] 81%|████████▏ | 15186/18688 [17:55:17<4:04:08, 4.18s/it] 81%|████████▏ | 15187/18688 [17:55:21<4:04:55, 4.20s/it] 81%|████████▏ | 15188/18688 [17:55:26<4:09:28, 4.28s/it] 81%|████████▏ | 15189/18688 [17:55:30<4:03:47, 4.18s/it] 81%|████████▏ | 15190/18688 [17:55:34<3:56:26, 4.06s/it] {'loss': '0.9113', 'grad_norm': '1.512', 'learning_rate': '9.882e-06', 'epoch': '0.8128', 'num_input_tokens_seen': 51087368, 'train_runtime': '6.453e+04', 'train_tokens_per_second': '791.6'}
81%|████████▏ | 15190/18688 [17:55:34<3:56:26, 4.06s/it] 81%|████████▏ | 15191/18688 [17:55:38<4:02:48, 4.17s/it] 81%|████████▏ | 15192/18688 [17:55:42<3:54:18, 4.02s/it] 81%|████████▏ | 15193/18688 [17:55:45<3:46:55, 3.90s/it] 81%|████████▏ | 15194/18688 [17:55:49<3:41:54, 3.81s/it] 81%|████████▏ | 15195/18688 [17:55:54<3:54:54, 4.04s/it] 81%|████████▏ | 15196/18688 [17:55:57<3:52:10, 3.99s/it] 81%|████████▏ | 15197/18688 [17:56:01<3:50:52, 3.97s/it] 81%|████████▏ | 15198/18688 [17:56:05<3:50:53, 3.97s/it] 81%|████████▏ | 15199/18688 [17:56:09<3:47:33, 3.91s/it] 81%|████████▏ | 15200/18688 [17:56:13<3:41:27, 3.81s/it] {'loss': '0.8993', 'grad_norm': '1.253', 'learning_rate': '9.827e-06', 'epoch': '0.8134', 'num_input_tokens_seen': 51118880, 'train_runtime': '6.457e+04', 'train_tokens_per_second': '791.6'}
81%|████████▏ | 15200/18688 [17:56:13<3:41:27, 3.81s/it] 81%|████████▏ | 15201/18688 [17:56:17<3:56:58, 4.08s/it] 81%|████████▏ | 15202/18688 [17:56:21<3:50:22, 3.97s/it] 81%|████████▏ | 15203/18688 [17:56:25<3:51:42, 3.99s/it] 81%|████████▏ | 15204/18688 [17:56:29<3:52:39, 4.01s/it] 81%|████████▏ | 15205/18688 [17:56:33<3:54:07, 4.03s/it] 81%|████████▏ | 15206/18688 [17:56:38<4:09:41, 4.30s/it] 81%|████████▏ | 15207/18688 [17:56:42<4:09:34, 4.30s/it] 81%|████████▏ | 15208/18688 [17:56:47<4:05:04, 4.23s/it] 81%|████████▏ | 15209/18688 [17:56:50<3:57:04, 4.09s/it] 81%|████████▏ | 15210/18688 [17:56:54<3:51:11, 3.99s/it] {'loss': '0.8217', 'grad_norm': '1.036', 'learning_rate': '9.773e-06', 'epoch': '0.8139', 'num_input_tokens_seen': 51153248, 'train_runtime': '6.461e+04', 'train_tokens_per_second': '791.7'}
81%|████████▏ | 15210/18688 [17:56:54<3:51:11, 3.99s/it] 81%|████████▏ | 15211/18688 [17:56:58<3:54:08, 4.04s/it] 81%|████████▏ | 15212/18688 [17:57:02<3:50:15, 3.97s/it] 81%|████████▏ | 15213/18688 [17:57:06<3:43:36, 3.86s/it] 81%|████████▏ | 15214/18688 [17:57:09<3:39:15, 3.79s/it] 81%|████████▏ | 15215/18688 [17:57:14<3:48:54, 3.95s/it] 81%|████████▏ | 15216/18688 [17:57:18<4:01:01, 4.17s/it] 81%|████████▏ | 15217/18688 [17:57:22<3:57:30, 4.11s/it] 81%|████████▏ | 15218/18688 [17:57:26<3:55:02, 4.06s/it] 81%|████████▏ | 15219/18688 [17:57:30<3:58:12, 4.12s/it] 81%|████████▏ | 15220/18688 [17:57:34<3:48:57, 3.96s/it] {'loss': '0.9185', 'grad_norm': '1.275', 'learning_rate': '9.718e-06', 'epoch': '0.8144', 'num_input_tokens_seen': 51184888, 'train_runtime': '6.465e+04', 'train_tokens_per_second': '791.7'}
81%|████████▏ | 15220/18688 [17:57:34<3:48:57, 3.96s/it] 81%|████████▏ | 15221/18688 [17:57:38<3:54:44, 4.06s/it] 81%|████████▏ | 15222/18688 [17:57:42<3:47:37, 3.94s/it] 81%|████████▏ | 15223/18688 [17:57:47<3:58:22, 4.13s/it] 81%|████████▏ | 15224/18688 [17:57:50<3:53:51, 4.05s/it] 81%|████████▏ | 15225/18688 [17:57:54<3:53:07, 4.04s/it] 81%|████████▏ | 15226/18688 [17:57:58<3:51:33, 4.01s/it] 81%|████████▏ | 15227/18688 [17:58:02<3:51:45, 4.02s/it] 81%|████████▏ | 15228/18688 [17:58:07<4:03:25, 4.22s/it] 81%|████████▏ | 15229/18688 [17:58:12<4:12:36, 4.38s/it] 81%|████████▏ | 15230/18688 [17:58:16<4:16:00, 4.44s/it] {'loss': '0.8668', 'grad_norm': '1.274', 'learning_rate': '9.664e-06', 'epoch': '0.815', 'num_input_tokens_seen': 51219576, 'train_runtime': '6.47e+04', 'train_tokens_per_second': '791.7'}
81%|████████▏ | 15230/18688 [17:58:16<4:16:00, 4.44s/it] 82%|████████▏ | 15231/18688 [17:58:21<4:12:39, 4.39s/it] 82%|████████▏ | 15232/18688 [17:58:25<4:06:08, 4.27s/it] 82%|████████▏ | 15233/18688 [17:58:29<4:02:06, 4.20s/it] 82%|████████▏ | 15234/18688 [17:58:33<4:08:29, 4.32s/it] 82%|████████▏ | 15235/18688 [17:58:37<4:01:07, 4.19s/it] 82%|████████▏ | 15236/18688 [17:58:41<3:50:21, 4.00s/it] 82%|████████▏ | 15237/18688 [17:58:44<3:41:57, 3.86s/it] 82%|████████▏ | 15238/18688 [17:58:48<3:38:21, 3.80s/it] 82%|████████▏ | 15239/18688 [17:58:52<3:34:13, 3.73s/it] 82%|████████▏ | 15240/18688 [17:58:56<3:39:46, 3.82s/it] {'loss': '0.9039', 'grad_norm': '1.234', 'learning_rate': '9.61e-06', 'epoch': '0.8155', 'num_input_tokens_seen': 51250712, 'train_runtime': '6.474e+04', 'train_tokens_per_second': '791.7'}
82%|████████▏ | 15240/18688 [17:58:56<3:39:46, 3.82s/it] 82%|████████▏ | 15241/18688 [17:58:59<3:36:12, 3.76s/it] 82%|████████▏ | 15242/18688 [17:59:03<3:42:54, 3.88s/it] 82%|████████▏ | 15243/18688 [17:59:08<3:59:33, 4.17s/it] 82%|████████▏ | 15244/18688 [17:59:13<4:03:02, 4.23s/it] 82%|████████▏ | 15245/18688 [17:59:17<4:03:01, 4.24s/it] 82%|████████▏ | 15246/18688 [17:59:20<3:49:28, 4.00s/it] 82%|████████▏ | 15247/18688 [17:59:25<3:57:38, 4.14s/it] 82%|████████▏ | 15248/18688 [17:59:29<3:58:06, 4.15s/it] 82%|████████▏ | 15249/18688 [17:59:33<3:48:42, 3.99s/it] 82%|████████▏ | 15250/18688 [17:59:36<3:43:07, 3.89s/it] {'loss': '0.9484', 'grad_norm': '1.492', 'learning_rate': '9.557e-06', 'epoch': '0.8161', 'num_input_tokens_seen': 51283456, 'train_runtime': '6.478e+04', 'train_tokens_per_second': '791.7'}
82%|████████▏ | 15250/18688 [17:59:36<3:43:07, 3.89s/it] 82%|████████▏ | 15251/18688 [17:59:41<3:52:09, 4.05s/it] 82%|████████▏ | 15252/18688 [17:59:45<3:52:18, 4.06s/it] 82%|████████▏ | 15253/18688 [17:59:48<3:43:20, 3.90s/it] 82%|████████▏ | 15254/18688 [17:59:52<3:41:11, 3.86s/it] 82%|████████▏ | 15255/18688 [17:59:55<3:34:05, 3.74s/it] 82%|████████▏ | 15256/18688 [17:59:59<3:38:07, 3.81s/it] 82%|████████▏ | 15257/18688 [18:00:04<3:44:30, 3.93s/it] 82%|████████▏ | 15258/18688 [18:00:08<3:50:50, 4.04s/it] 82%|████████▏ | 15259/18688 [18:00:13<4:02:52, 4.25s/it] 82%|████████▏ | 15260/18688 [18:00:17<3:56:21, 4.14s/it] {'loss': '0.9177', 'grad_norm': '1.309', 'learning_rate': '9.503e-06', 'epoch': '0.8166', 'num_input_tokens_seen': 51316544, 'train_runtime': '6.482e+04', 'train_tokens_per_second': '791.7'}
82%|████████▏ | 15260/18688 [18:00:17<3:56:21, 4.14s/it] 82%|████████▏ | 15261/18688 [18:00:22<4:21:52, 4.58s/it] 82%|████████▏ | 15262/18688 [18:00:27<4:17:37, 4.51s/it] 82%|████████▏ | 15263/18688 [18:00:30<3:59:45, 4.20s/it] 82%|████████▏ | 15264/18688 [18:00:34<4:00:15, 4.21s/it] 82%|████████▏ | 15265/18688 [18:00:38<3:58:45, 4.19s/it] 82%|████████▏ | 15266/18688 [18:00:42<3:46:25, 3.97s/it] 82%|████████▏ | 15267/18688 [18:00:46<3:53:22, 4.09s/it] 82%|████████▏ | 15268/18688 [18:00:51<4:01:00, 4.23s/it] 82%|████████▏ | 15269/18688 [18:00:55<3:54:40, 4.12s/it] 82%|████████▏ | 15270/18688 [18:00:59<3:56:39, 4.15s/it] {'loss': '0.8953', 'grad_norm': '1.222', 'learning_rate': '9.449e-06', 'epoch': '0.8171', 'num_input_tokens_seen': 51352344, 'train_runtime': '6.486e+04', 'train_tokens_per_second': '791.7'}
82%|████████▏ | 15270/18688 [18:00:59<3:56:39, 4.15s/it] 82%|████████▏ | 15271/18688 [18:01:02<3:46:18, 3.97s/it] 82%|████████▏ | 15272/18688 [18:01:06<3:42:08, 3.90s/it] 82%|████████▏ | 15273/18688 [18:01:10<3:40:36, 3.88s/it] 82%|████████▏ | 15274/18688 [18:01:14<3:36:53, 3.81s/it] 82%|████████▏ | 15275/18688 [18:01:18<3:47:48, 4.00s/it] 82%|████████▏ | 15276/18688 [18:01:22<3:42:58, 3.92s/it] 82%|████████▏ | 15277/18688 [18:01:26<3:47:15, 4.00s/it] 82%|████████▏ | 15278/18688 [18:01:30<3:52:38, 4.09s/it] 82%|████████▏ | 15279/18688 [18:01:35<3:54:57, 4.14s/it] 82%|████████▏ | 15280/18688 [18:01:38<3:50:40, 4.06s/it] {'loss': '0.905', 'grad_norm': '1.385', 'learning_rate': '9.396e-06', 'epoch': '0.8177', 'num_input_tokens_seen': 51383304, 'train_runtime': '6.49e+04', 'train_tokens_per_second': '791.7'}
82%|████████▏ | 15280/18688 [18:01:38<3:50:40, 4.06s/it] 82%|████████▏ | 15281/18688 [18:01:42<3:44:16, 3.95s/it] 82%|████████▏ | 15282/18688 [18:01:47<3:53:18, 4.11s/it] 82%|████████▏ | 15283/18688 [18:01:50<3:48:03, 4.02s/it] 82%|████████▏ | 15284/18688 [18:01:54<3:43:46, 3.94s/it] 82%|████████▏ | 15285/18688 [18:01:58<3:48:05, 4.02s/it] 82%|████████▏ | 15286/18688 [18:02:03<3:49:54, 4.05s/it] 82%|████████▏ | 15287/18688 [18:02:07<3:52:19, 4.10s/it] 82%|████████▏ | 15288/18688 [18:02:12<4:04:39, 4.32s/it] 82%|████████▏ | 15289/18688 [18:02:16<4:05:14, 4.33s/it] 82%|████████▏ | 15290/18688 [18:02:19<3:52:42, 4.11s/it] {'loss': '0.9241', 'grad_norm': '1.453', 'learning_rate': '9.343e-06', 'epoch': '0.8182', 'num_input_tokens_seen': 51417800, 'train_runtime': '6.494e+04', 'train_tokens_per_second': '791.8'}
82%|████████▏ | 15290/18688 [18:02:19<3:52:42, 4.11s/it] 82%|████████▏ | 15291/18688 [18:02:25<4:16:41, 4.53s/it] 82%|████████▏ | 15292/18688 [18:02:29<4:11:59, 4.45s/it] 82%|████████▏ | 15293/18688 [18:02:33<4:05:28, 4.34s/it] 82%|████████▏ | 15294/18688 [18:02:37<4:01:30, 4.27s/it] 82%|████████▏ | 15295/18688 [18:02:42<4:03:38, 4.31s/it] 82%|████████▏ | 15296/18688 [18:02:47<4:09:33, 4.41s/it] 82%|████████▏ | 15297/18688 [18:02:51<4:03:29, 4.31s/it] 82%|████████▏ | 15298/18688 [18:02:55<4:09:02, 4.41s/it] 82%|████████▏ | 15299/18688 [18:02:59<3:59:29, 4.24s/it] 82%|████████▏ | 15300/18688 [18:03:03<3:48:06, 4.04s/it] {'loss': '0.9117', 'grad_norm': '1.233', 'learning_rate': '9.29e-06', 'epoch': '0.8187', 'num_input_tokens_seen': 51452920, 'train_runtime': '6.498e+04', 'train_tokens_per_second': '791.8'}
82%|████████▏ | 15300/18688 [18:03:03<3:48:06, 4.04s/it] 82%|████████▏ | 15301/18688 [18:03:06<3:40:15, 3.90s/it] 82%|████████▏ | 15302/18688 [18:03:10<3:41:52, 3.93s/it] 82%|████████▏ | 15303/18688 [18:03:14<3:46:44, 4.02s/it] 82%|████████▏ | 15304/18688 [18:03:19<3:57:05, 4.20s/it] 82%|████████▏ | 15305/18688 [18:03:23<3:52:13, 4.12s/it] 82%|████████▏ | 15306/18688 [18:03:28<4:06:25, 4.37s/it] 82%|████████▏ | 15307/18688 [18:03:32<3:59:52, 4.26s/it] 82%|████████▏ | 15308/18688 [18:03:36<3:50:05, 4.08s/it] 82%|████████▏ | 15309/18688 [18:03:39<3:43:54, 3.98s/it] 82%|████████▏ | 15310/18688 [18:03:44<3:46:48, 4.03s/it] {'loss': '0.9608', 'grad_norm': '1.223', 'learning_rate': '9.237e-06', 'epoch': '0.8193', 'num_input_tokens_seen': 51486696, 'train_runtime': '6.502e+04', 'train_tokens_per_second': '791.8'}
82%|████████▏ | 15310/18688 [18:03:44<3:46:48, 4.03s/it] 82%|████████▏ | 15311/18688 [18:03:48<3:56:15, 4.20s/it] 82%|████████▏ | 15312/18688 [18:03:53<4:01:35, 4.29s/it] 82%|████████▏ | 15313/18688 [18:03:57<3:59:49, 4.26s/it] 82%|████████▏ | 15314/18688 [18:04:01<3:55:06, 4.18s/it] 82%|████████▏ | 15315/18688 [18:04:05<3:57:19, 4.22s/it] 82%|████████▏ | 15316/18688 [18:04:09<3:53:50, 4.16s/it] 82%|████████▏ | 15317/18688 [18:04:13<3:52:22, 4.14s/it] 82%|████████▏ | 15318/18688 [18:04:17<3:45:01, 4.01s/it] 82%|████████▏ | 15319/18688 [18:04:21<3:41:08, 3.94s/it] 82%|████████▏ | 15320/18688 [18:04:24<3:34:53, 3.83s/it] {'loss': '0.8877', 'grad_norm': '1.26', 'learning_rate': '9.184e-06', 'epoch': '0.8198', 'num_input_tokens_seen': 51520216, 'train_runtime': '6.506e+04', 'train_tokens_per_second': '791.8'}
82%|████████▏ | 15320/18688 [18:04:24<3:34:53, 3.83s/it] 82%|████████▏ | 15321/18688 [18:04:28<3:33:54, 3.81s/it] 82%|████████▏ | 15322/18688 [18:04:32<3:38:12, 3.89s/it] 82%|████████▏ | 15323/18688 [18:04:36<3:37:50, 3.88s/it] 82%|████████▏ | 15324/18688 [18:04:40<3:44:08, 4.00s/it] 82%|████████▏ | 15325/18688 [18:04:45<3:57:12, 4.23s/it] 82%|████████▏ | 15326/18688 [18:04:49<3:44:35, 4.01s/it] 82%|████████▏ | 15327/18688 [18:04:52<3:36:01, 3.86s/it] 82%|████████▏ | 15328/18688 [18:04:56<3:33:37, 3.81s/it] 82%|████████▏ | 15329/18688 [18:05:00<3:33:00, 3.80s/it] 82%|████████▏ | 15330/18688 [18:05:04<3:37:33, 3.89s/it] {'loss': '0.8748', 'grad_norm': '1.194', 'learning_rate': '9.131e-06', 'epoch': '0.8203', 'num_input_tokens_seen': 51551488, 'train_runtime': '6.51e+04', 'train_tokens_per_second': '791.8'}
82%|████████▏ | 15330/18688 [18:05:04<3:37:33, 3.89s/it] 82%|████████▏ | 15331/18688 [18:05:07<3:35:47, 3.86s/it] 82%|████████▏ | 15332/18688 [18:05:12<3:54:49, 4.20s/it] 82%|████████▏ | 15333/18688 [18:05:16<3:51:54, 4.15s/it] 82%|████████▏ | 15334/18688 [18:05:20<3:45:57, 4.04s/it] 82%|████████▏ | 15335/18688 [18:05:24<3:42:08, 3.98s/it] 82%|████████▏ | 15336/18688 [18:05:28<3:42:16, 3.98s/it] 82%|████████▏ | 15337/18688 [18:05:33<3:51:52, 4.15s/it] 82%|████████▏ | 15338/18688 [18:05:38<4:08:19, 4.45s/it] 82%|████████▏ | 15339/18688 [18:05:41<3:53:18, 4.18s/it] 82%|████████▏ | 15340/18688 [18:05:46<3:59:19, 4.29s/it] {'loss': '0.9432', 'grad_norm': '1.132', 'learning_rate': '9.079e-06', 'epoch': '0.8209', 'num_input_tokens_seen': 51585536, 'train_runtime': '6.515e+04', 'train_tokens_per_second': '791.8'}
82%|████████▏ | 15340/18688 [18:05:46<3:59:19, 4.29s/it] 82%|████████▏ | 15341/18688 [18:05:49<3:49:05, 4.11s/it] 82%|████████▏ | 15342/18688 [18:05:54<3:48:43, 4.10s/it] 82%|████████▏ | 15343/18688 [18:05:57<3:41:20, 3.97s/it] 82%|████████▏ | 15344/18688 [18:06:01<3:36:49, 3.89s/it] 82%|████████▏ | 15345/18688 [18:06:04<3:29:55, 3.77s/it] 82%|████████▏ | 15346/18688 [18:06:08<3:34:02, 3.84s/it] 82%|████████▏ | 15347/18688 [18:06:13<3:47:23, 4.08s/it] 82%|████████▏ | 15348/18688 [18:06:17<3:46:55, 4.08s/it] 82%|████████▏ | 15349/18688 [18:06:21<3:45:25, 4.05s/it] 82%|████████▏ | 15350/18688 [18:06:25<3:35:23, 3.87s/it] {'loss': '0.9092', 'grad_norm': '1.434', 'learning_rate': '9.026e-06', 'epoch': '0.8214', 'num_input_tokens_seen': 51615568, 'train_runtime': '6.519e+04', 'train_tokens_per_second': '791.8'}
82%|████████▏ | 15350/18688 [18:06:25<3:35:23, 3.87s/it] 82%|████████▏ | 15351/18688 [18:06:29<3:39:01, 3.94s/it] 82%|████████▏ | 15352/18688 [18:06:32<3:30:23, 3.78s/it] 82%|████████▏ | 15353/18688 [18:06:36<3:36:13, 3.89s/it] 82%|████████▏ | 15354/18688 [18:06:40<3:37:38, 3.92s/it] 82%|████████▏ | 15355/18688 [18:06:44<3:32:37, 3.83s/it] 82%|████████▏ | 15356/18688 [18:06:48<3:44:31, 4.04s/it] 82%|████████▏ | 15357/18688 [18:06:52<3:38:16, 3.93s/it] 82%|████████▏ | 15358/18688 [18:06:56<3:43:22, 4.02s/it] 82%|████████▏ | 15359/18688 [18:07:00<3:39:03, 3.95s/it] 82%|████████▏ | 15360/18688 [18:07:04<3:42:50, 4.02s/it] {'loss': '0.9155', 'grad_norm': '1.212', 'learning_rate': '8.974e-06', 'epoch': '0.8219', 'num_input_tokens_seen': 51647704, 'train_runtime': '6.522e+04', 'train_tokens_per_second': '791.8'}
82%|████████▏ | 15360/18688 [18:07:04<3:42:50, 4.02s/it] 82%|████████▏ | 15361/18688 [18:07:08<3:40:20, 3.97s/it] 82%|████████▏ | 15362/18688 [18:07:12<3:32:07, 3.83s/it] 82%|████████▏ | 15363/18688 [18:07:16<3:37:57, 3.93s/it] 82%|████████▏ | 15364/18688 [18:07:21<3:53:57, 4.22s/it] 82%|████████▏ | 15365/18688 [18:07:24<3:44:32, 4.05s/it] 82%|████████▏ | 15366/18688 [18:07:29<3:55:05, 4.25s/it] 82%|████████▏ | 15367/18688 [18:07:33<3:51:56, 4.19s/it] 82%|████████▏ | 15368/18688 [18:07:39<4:13:04, 4.57s/it] 82%|████████▏ | 15369/18688 [18:07:43<4:03:53, 4.41s/it] 82%|████████▏ | 15370/18688 [18:07:47<3:55:55, 4.27s/it] {'loss': '0.8928', 'grad_norm': '1.443', 'learning_rate': '8.922e-06', 'epoch': '0.8225', 'num_input_tokens_seen': 51683864, 'train_runtime': '6.527e+04', 'train_tokens_per_second': '791.9'}
82%|████████▏ | 15370/18688 [18:07:47<3:55:55, 4.27s/it] 82%|████████▏ | 15371/18688 [18:07:50<3:46:31, 4.10s/it] 82%|████████▏ | 15372/18688 [18:07:54<3:47:42, 4.12s/it] 82%|████████▏ | 15373/18688 [18:07:58<3:41:11, 4.00s/it] 82%|████████▏ | 15374/18688 [18:08:02<3:34:06, 3.88s/it] 82%|████████▏ | 15375/18688 [18:08:05<3:27:31, 3.76s/it] 82%|████████▏ | 15376/18688 [18:08:10<3:36:53, 3.93s/it] 82%|████████▏ | 15377/18688 [18:08:13<3:29:28, 3.80s/it] 82%|████████▏ | 15378/18688 [18:08:17<3:25:53, 3.73s/it] 82%|████████▏ | 15379/18688 [18:08:21<3:35:45, 3.91s/it] 82%|████████▏ | 15380/18688 [18:08:25<3:41:03, 4.01s/it] {'loss': '0.9241', 'grad_norm': '1.724', 'learning_rate': '8.87e-06', 'epoch': '0.823', 'num_input_tokens_seen': 51713808, 'train_runtime': '6.531e+04', 'train_tokens_per_second': '791.9'}
82%|████████▏ | 15380/18688 [18:08:25<3:41:03, 4.01s/it] 82%|████████▏ | 15381/18688 [18:08:29<3:41:02, 4.01s/it] 82%|████████▏ | 15382/18688 [18:08:33<3:37:42, 3.95s/it] 82%|████████▏ | 15383/18688 [18:08:38<3:57:38, 4.31s/it] 82%|████████▏ | 15384/18688 [18:08:42<3:55:13, 4.27s/it] 82%|████████▏ | 15385/18688 [18:08:47<3:56:59, 4.31s/it] 82%|████████▏ | 15386/18688 [18:08:51<3:48:53, 4.16s/it] 82%|████████▏ | 15387/18688 [18:08:54<3:39:06, 3.98s/it] 82%|████████▏ | 15388/18688 [18:08:59<3:50:13, 4.19s/it] 82%|████████▏ | 15389/18688 [18:09:03<3:46:55, 4.13s/it] 82%|████████▏ | 15390/18688 [18:09:06<3:36:16, 3.93s/it] {'loss': '0.8925', 'grad_norm': '1.564', 'learning_rate': '8.818e-06', 'epoch': '0.8235', 'num_input_tokens_seen': 51747464, 'train_runtime': '6.535e+04', 'train_tokens_per_second': '791.9'}
82%|████████▏ | 15390/18688 [18:09:06<3:36:16, 3.93s/it] 82%|████████▏ | 15391/18688 [18:09:10<3:28:11, 3.79s/it] 82%|████████▏ | 15392/18688 [18:09:13<3:22:17, 3.68s/it] 82%|████████▏ | 15393/18688 [18:09:17<3:23:12, 3.70s/it] 82%|████████▏ | 15394/18688 [18:09:20<3:21:18, 3.67s/it] 82%|████████▏ | 15395/18688 [18:09:24<3:19:36, 3.64s/it] 82%|████████▏ | 15396/18688 [18:09:28<3:20:11, 3.65s/it] 82%|████████▏ | 15397/18688 [18:09:32<3:32:24, 3.87s/it] 82%|████████▏ | 15398/18688 [18:09:36<3:33:13, 3.89s/it] 82%|████████▏ | 15399/18688 [18:09:40<3:38:10, 3.98s/it] 82%|████████▏ | 15400/18688 [18:09:45<3:44:58, 4.11s/it] {'loss': '0.9026', 'grad_norm': '1.429', 'learning_rate': '8.766e-06', 'epoch': '0.8241', 'num_input_tokens_seen': 51777744, 'train_runtime': '6.539e+04', 'train_tokens_per_second': '791.9'}
82%|████████▏ | 15400/18688 [18:09:45<3:44:58, 4.11s/it] 82%|████████▏ | 15401/18688 [18:09:49<3:47:00, 4.14s/it] 82%|████████▏ | 15402/18688 [18:09:53<3:39:08, 4.00s/it] 82%|████████▏ | 15403/18688 [18:09:57<3:53:37, 4.27s/it] 82%|████████▏ | 15404/18688 [18:10:01<3:39:51, 4.02s/it] 82%|████████▏ | 15405/18688 [18:10:05<3:49:56, 4.20s/it] 82%|████████▏ | 15406/18688 [18:10:10<3:54:15, 4.28s/it] 82%|████████▏ | 15407/18688 [18:10:14<3:55:20, 4.30s/it] 82%|████████▏ | 15408/18688 [18:10:18<3:51:24, 4.23s/it] 82%|████████▏ | 15409/18688 [18:10:23<3:53:43, 4.28s/it] 82%|████████▏ | 15410/18688 [18:10:27<3:52:19, 4.25s/it] {'loss': '0.9167', 'grad_norm': '1.384', 'learning_rate': '8.714e-06', 'epoch': '0.8246', 'num_input_tokens_seen': 51813432, 'train_runtime': '6.543e+04', 'train_tokens_per_second': '791.9'}
82%|████████▏ | 15410/18688 [18:10:27<3:52:19, 4.25s/it] 82%|████████▏ | 15411/18688 [18:10:32<4:10:59, 4.60s/it] 82%|████████▏ | 15412/18688 [18:10:36<4:03:17, 4.46s/it] 82%|████████▏ | 15413/18688 [18:10:40<3:46:53, 4.16s/it] 82%|████████▏ | 15414/18688 [18:10:44<3:47:51, 4.18s/it] 82%|████████▏ | 15415/18688 [18:10:49<4:01:45, 4.43s/it] 82%|████████▏ | 15416/18688 [18:10:54<4:11:32, 4.61s/it] 82%|████████▏ | 15417/18688 [18:10:58<3:56:12, 4.33s/it] 83%|████████▎ | 15418/18688 [18:11:01<3:44:25, 4.12s/it] 83%|████████▎ | 15419/18688 [18:11:06<3:45:17, 4.13s/it] 83%|████████▎ | 15420/18688 [18:11:10<3:45:10, 4.13s/it] {'loss': '0.9161', 'grad_norm': '1.163', 'learning_rate': '8.663e-06', 'epoch': '0.8252', 'num_input_tokens_seen': 51850784, 'train_runtime': '6.547e+04', 'train_tokens_per_second': '792'}
83%|████████▎ | 15420/18688 [18:11:10<3:45:10, 4.13s/it] 83%|████████▎ | 15421/18688 [18:11:14<3:52:47, 4.28s/it] 83%|████████▎ | 15422/18688 [18:11:19<3:53:55, 4.30s/it] 83%|████████▎ | 15423/18688 [18:11:22<3:42:25, 4.09s/it] 83%|████████▎ | 15424/18688 [18:11:26<3:40:43, 4.06s/it] 83%|████████▎ | 15425/18688 [18:11:31<3:51:11, 4.25s/it] 83%|████████▎ | 15426/18688 [18:11:35<3:41:43, 4.08s/it] 83%|████████▎ | 15427/18688 [18:11:38<3:36:31, 3.98s/it] 83%|████████▎ | 15428/18688 [18:11:42<3:34:01, 3.94s/it] 83%|████████▎ | 15429/18688 [18:11:47<3:39:06, 4.03s/it] 83%|████████▎ | 15430/18688 [18:11:50<3:33:21, 3.93s/it] {'loss': '0.9246', 'grad_norm': '1.381', 'learning_rate': '8.611e-06', 'epoch': '0.8257', 'num_input_tokens_seen': 51883152, 'train_runtime': '6.551e+04', 'train_tokens_per_second': '792'}
83%|████████▎ | 15430/18688 [18:11:50<3:33:21, 3.93s/it] 83%|████████▎ | 15431/18688 [18:11:54<3:30:05, 3.87s/it] 83%|████████▎ | 15432/18688 [18:11:58<3:26:46, 3.81s/it] 83%|████████▎ | 15433/18688 [18:12:02<3:39:50, 4.05s/it] 83%|████████▎ | 15434/18688 [18:12:06<3:41:59, 4.09s/it] 83%|████████▎ | 15435/18688 [18:12:10<3:38:10, 4.02s/it] 83%|████████▎ | 15436/18688 [18:12:14<3:28:28, 3.85s/it] 83%|████████▎ | 15437/18688 [18:12:18<3:34:57, 3.97s/it] 83%|████████▎ | 15438/18688 [18:12:22<3:36:02, 3.99s/it] 83%|████████▎ | 15439/18688 [18:12:26<3:29:40, 3.87s/it] 83%|████████▎ | 15440/18688 [18:12:29<3:25:23, 3.79s/it] {'loss': '0.8378', 'grad_norm': '1.228', 'learning_rate': '8.56e-06', 'epoch': '0.8262', 'num_input_tokens_seen': 51914592, 'train_runtime': '6.555e+04', 'train_tokens_per_second': '792'}
83%|████████▎ | 15440/18688 [18:12:29<3:25:23, 3.79s/it] 83%|████████▎ | 15441/18688 [18:12:33<3:29:18, 3.87s/it] 83%|████████▎ | 15442/18688 [18:12:39<3:52:05, 4.29s/it] 83%|████████▎ | 15443/18688 [18:12:43<3:51:57, 4.29s/it] 83%|████████▎ | 15444/18688 [18:12:48<4:00:44, 4.45s/it] 83%|████████▎ | 15445/18688 [18:12:51<3:48:20, 4.22s/it] 83%|████████▎ | 15446/18688 [18:12:56<3:53:47, 4.33s/it] 83%|████████▎ | 15447/18688 [18:13:00<3:52:28, 4.30s/it] 83%|████████▎ | 15448/18688 [18:13:04<3:41:31, 4.10s/it] 83%|████████▎ | 15449/18688 [18:13:08<3:44:02, 4.15s/it] 83%|████████▎ | 15450/18688 [18:13:12<3:47:11, 4.21s/it] {'loss': '0.9453', 'grad_norm': '1.196', 'learning_rate': '8.509e-06', 'epoch': '0.8268', 'num_input_tokens_seen': 51950680, 'train_runtime': '6.559e+04', 'train_tokens_per_second': '792'}
83%|████████▎ | 15450/18688 [18:13:12<3:47:11, 4.21s/it] 83%|████████▎ | 15451/18688 [18:13:17<3:51:54, 4.30s/it] 83%|████████▎ | 15452/18688 [18:13:21<3:55:43, 4.37s/it] 83%|████████▎ | 15453/18688 [18:13:26<3:50:04, 4.27s/it] 83%|████████▎ | 15454/18688 [18:13:30<3:52:53, 4.32s/it] 83%|████████▎ | 15455/18688 [18:13:34<3:42:37, 4.13s/it] 83%|████████▎ | 15456/18688 [18:13:38<3:41:51, 4.12s/it] 83%|████████▎ | 15457/18688 [18:13:41<3:34:04, 3.98s/it] 83%|████████▎ | 15458/18688 [18:13:46<3:37:33, 4.04s/it] 83%|████████▎ | 15459/18688 [18:13:49<3:31:59, 3.94s/it] 83%|████████▎ | 15460/18688 [18:13:54<3:42:25, 4.13s/it] {'loss': '0.9676', 'grad_norm': '1.163', 'learning_rate': '8.458e-06', 'epoch': '0.8273', 'num_input_tokens_seen': 51985304, 'train_runtime': '6.563e+04', 'train_tokens_per_second': '792'}
83%|████████▎ | 15460/18688 [18:13:54<3:42:25, 4.13s/it] 83%|████████▎ | 15461/18688 [18:13:58<3:37:25, 4.04s/it] 83%|████████▎ | 15462/18688 [18:14:02<3:42:40, 4.14s/it] 83%|████████▎ | 15463/18688 [18:14:06<3:33:18, 3.97s/it] 83%|████████▎ | 15464/18688 [18:14:10<3:40:09, 4.10s/it] 83%|████████▎ | 15465/18688 [18:14:14<3:36:06, 4.02s/it] 83%|████████▎ | 15466/18688 [18:14:19<3:48:36, 4.26s/it] 83%|████████▎ | 15467/18688 [18:14:23<3:52:19, 4.33s/it] 83%|████████▎ | 15468/18688 [18:14:27<3:51:12, 4.31s/it] 83%|████████▎ | 15469/18688 [18:14:31<3:47:08, 4.23s/it] 83%|████████▎ | 15470/18688 [18:14:36<3:43:57, 4.18s/it] {'loss': '0.8988', 'grad_norm': '1.338', 'learning_rate': '8.407e-06', 'epoch': '0.8278', 'num_input_tokens_seen': 52018432, 'train_runtime': '6.568e+04', 'train_tokens_per_second': '792'}
83%|████████▎ | 15470/18688 [18:14:36<3:43:57, 4.18s/it] 83%|████████▎ | 15471/18688 [18:14:40<3:47:17, 4.24s/it] 83%|████████▎ | 15472/18688 [18:14:44<3:51:49, 4.33s/it] 83%|████████▎ | 15473/18688 [18:14:48<3:42:54, 4.16s/it] 83%|████████▎ | 15474/18688 [18:14:52<3:33:09, 3.98s/it] 83%|████████▎ | 15475/18688 [18:14:56<3:30:41, 3.93s/it] 83%|████████▎ | 15476/18688 [18:15:00<3:31:33, 3.95s/it] 83%|████████▎ | 15477/18688 [18:15:03<3:26:27, 3.86s/it] 83%|████████▎ | 15478/18688 [18:15:07<3:26:50, 3.87s/it] 83%|████████▎ | 15479/18688 [18:15:11<3:33:01, 3.98s/it] 83%|████████▎ | 15480/18688 [18:15:16<3:39:44, 4.11s/it] {'loss': '0.9379', 'grad_norm': '1.389', 'learning_rate': '8.357e-06', 'epoch': '0.8284', 'num_input_tokens_seen': 52050600, 'train_runtime': '6.572e+04', 'train_tokens_per_second': '792'}
83%|████████▎ | 15480/18688 [18:15:16<3:39:44, 4.11s/it] 83%|████████▎ | 15481/18688 [18:15:21<3:54:55, 4.40s/it] 83%|████████▎ | 15482/18688 [18:15:25<3:50:28, 4.31s/it] 83%|████████▎ | 15483/18688 [18:15:29<3:38:52, 4.10s/it] 83%|████████▎ | 15484/18688 [18:15:33<3:44:00, 4.19s/it] 83%|████████▎ | 15485/18688 [18:15:38<4:01:06, 4.52s/it] 83%|████████▎ | 15486/18688 [18:15:42<3:52:07, 4.35s/it] 83%|████████▎ | 15487/18688 [18:15:47<3:58:28, 4.47s/it] 83%|████████▎ | 15488/18688 [18:15:51<3:52:36, 4.36s/it] 83%|████████▎ | 15489/18688 [18:15:55<3:51:29, 4.34s/it] 83%|████████▎ | 15490/18688 [18:15:59<3:38:20, 4.10s/it] {'loss': '0.93', 'grad_norm': '1.286', 'learning_rate': '8.306e-06', 'epoch': '0.8289', 'num_input_tokens_seen': 52087808, 'train_runtime': '6.576e+04', 'train_tokens_per_second': '792.1'}
83%|████████▎ | 15490/18688 [18:15:59<3:38:20, 4.10s/it] 83%|████████▎ | 15491/18688 [18:16:03<3:39:55, 4.13s/it] 83%|████████▎ | 15492/18688 [18:16:07<3:33:24, 4.01s/it] 83%|████████▎ | 15493/18688 [18:16:11<3:28:20, 3.91s/it] 83%|████████▎ | 15494/18688 [18:16:14<3:20:51, 3.77s/it] 83%|████████▎ | 15495/18688 [18:16:18<3:18:11, 3.72s/it] 83%|████████▎ | 15496/18688 [18:16:22<3:27:47, 3.91s/it] 83%|████████▎ | 15497/18688 [18:16:26<3:37:14, 4.08s/it] 83%|████████▎ | 15498/18688 [18:16:31<3:48:35, 4.30s/it] 83%|████████▎ | 15499/18688 [18:16:36<3:59:30, 4.51s/it] 83%|████████▎ | 15500/18688 [18:16:40<3:52:19, 4.37s/it] {'loss': '0.8915', 'grad_norm': '1.459', 'learning_rate': '8.256e-06', 'epoch': '0.8294', 'num_input_tokens_seen': 52121240, 'train_runtime': '6.58e+04', 'train_tokens_per_second': '792.1'}
83%|████████▎ | 15500/18688 [18:16:40<3:52:19, 4.37s/it] 83%|████████▎ | 15501/18688 [18:16:46<4:14:15, 4.79s/it] 83%|████████▎ | 15502/18688 [18:16:50<4:04:34, 4.61s/it] 83%|████████▎ | 15503/18688 [18:16:55<4:06:23, 4.64s/it] 83%|████████▎ | 15504/18688 [18:16:59<3:50:53, 4.35s/it] 83%|████████▎ | 15505/18688 [18:17:03<3:51:54, 4.37s/it] 83%|████████▎ | 15506/18688 [18:17:08<3:56:29, 4.46s/it] 83%|████████▎ | 15507/18688 [18:17:12<3:47:50, 4.30s/it] 83%|████████▎ | 15508/18688 [18:17:16<3:51:13, 4.36s/it] 83%|████████▎ | 15509/18688 [18:17:21<3:54:30, 4.43s/it] 83%|████████▎ | 15510/18688 [18:17:24<3:44:31, 4.24s/it] {'loss': '0.9036', 'grad_norm': '1.184', 'learning_rate': '8.206e-06', 'epoch': '0.83', 'num_input_tokens_seen': 52158440, 'train_runtime': '6.584e+04', 'train_tokens_per_second': '792.1'}
83%|████████▎ | 15510/18688 [18:17:24<3:44:31, 4.24s/it] 83%|████████▎ | 15511/18688 [18:17:29<3:41:30, 4.18s/it] 83%|████████▎ | 15512/18688 [18:17:33<3:39:35, 4.15s/it] 83%|████████▎ | 15513/18688 [18:17:36<3:34:24, 4.05s/it] 83%|████████▎ | 15514/18688 [18:17:41<3:35:35, 4.08s/it] 83%|████████▎ | 15515/18688 [18:17:44<3:24:58, 3.88s/it] 83%|████████▎ | 15516/18688 [18:17:49<3:45:57, 4.27s/it] 83%|████████▎ | 15517/18688 [18:17:53<3:41:18, 4.19s/it] 83%|████████▎ | 15518/18688 [18:17:58<3:44:17, 4.25s/it] 83%|████████▎ | 15519/18688 [18:18:01<3:38:31, 4.14s/it] 83%|████████▎ | 15520/18688 [18:18:06<3:43:33, 4.23s/it] {'loss': '0.8737', 'grad_norm': '1.572', 'learning_rate': '8.156e-06', 'epoch': '0.8305', 'num_input_tokens_seen': 52193000, 'train_runtime': '6.589e+04', 'train_tokens_per_second': '792.2'}
83%|████████▎ | 15520/18688 [18:18:06<3:43:33, 4.23s/it] 83%|████████▎ | 15521/18688 [18:18:10<3:40:53, 4.18s/it] 83%|████████▎ | 15522/18688 [18:18:15<3:46:47, 4.30s/it] 83%|████████▎ | 15523/18688 [18:18:18<3:39:29, 4.16s/it] 83%|████████▎ | 15524/18688 [18:18:23<3:47:44, 4.32s/it] 83%|████████▎ | 15525/18688 [18:18:27<3:44:52, 4.27s/it] 83%|████████▎ | 15526/18688 [18:18:31<3:43:39, 4.24s/it] 83%|████████▎ | 15527/18688 [18:18:35<3:40:05, 4.18s/it] 83%|████████▎ | 15528/18688 [18:18:39<3:29:53, 3.99s/it] 83%|████████▎ | 15529/18688 [18:18:43<3:23:55, 3.87s/it] 83%|████████▎ | 15530/18688 [18:18:47<3:28:59, 3.97s/it] {'loss': '0.8693', 'grad_norm': '1.205', 'learning_rate': '8.106e-06', 'epoch': '0.831', 'num_input_tokens_seen': 52226032, 'train_runtime': '6.593e+04', 'train_tokens_per_second': '792.2'}
83%|████████▎ | 15530/18688 [18:18:47<3:28:59, 3.97s/it] 83%|████████▎ | 15531/18688 [18:18:51<3:25:41, 3.91s/it] 83%|████████▎ | 15532/18688 [18:18:55<3:30:43, 4.01s/it] 83%|████████▎ | 15533/18688 [18:18:58<3:22:22, 3.85s/it] 83%|████████▎ | 15534/18688 [18:19:03<3:29:49, 3.99s/it] 83%|████████▎ | 15535/18688 [18:19:06<3:26:41, 3.93s/it] 83%|████████▎ | 15536/18688 [18:19:11<3:34:32, 4.08s/it] 83%|████████▎ | 15537/18688 [18:19:15<3:43:37, 4.26s/it] 83%|████████▎ | 15538/18688 [18:19:19<3:34:44, 4.09s/it] 83%|████████▎ | 15539/18688 [18:19:23<3:25:17, 3.91s/it] 83%|████████▎ | 15540/18688 [18:19:26<3:23:43, 3.88s/it] {'loss': '0.9114', 'grad_norm': '1.367', 'learning_rate': '8.056e-06', 'epoch': '0.8316', 'num_input_tokens_seen': 52258680, 'train_runtime': '6.597e+04', 'train_tokens_per_second': '792.2'}
83%|████████▎ | 15540/18688 [18:19:26<3:23:43, 3.88s/it] 83%|████████▎ | 15541/18688 [18:19:31<3:29:23, 3.99s/it] 83%|████████▎ | 15542/18688 [18:19:36<3:52:58, 4.44s/it] 83%|████████▎ | 15543/18688 [18:19:40<3:40:37, 4.21s/it] 83%|████████▎ | 15544/18688 [18:19:43<3:28:42, 3.98s/it] 83%|████████▎ | 15545/18688 [18:19:48<3:36:19, 4.13s/it] 83%|████████▎ | 15546/18688 [18:19:52<3:37:02, 4.14s/it] 83%|████████▎ | 15547/18688 [18:19:57<3:43:45, 4.27s/it] 83%|████████▎ | 15548/18688 [18:20:01<3:40:45, 4.22s/it] 83%|████████▎ | 15549/18688 [18:20:05<3:38:50, 4.18s/it] 83%|████████▎ | 15550/18688 [18:20:09<3:45:08, 4.30s/it] {'loss': '0.8958', 'grad_norm': '1.323', 'learning_rate': '8.006e-06', 'epoch': '0.8321', 'num_input_tokens_seen': 52295024, 'train_runtime': '6.601e+04', 'train_tokens_per_second': '792.2'}
83%|████████▎ | 15550/18688 [18:20:09<3:45:08, 4.30s/it] 83%|████████▎ | 15551/18688 [18:20:13<3:39:58, 4.21s/it] 83%|████████▎ | 15552/18688 [18:20:18<3:45:58, 4.32s/it] 83%|████████▎ | 15553/18688 [18:20:21<3:34:09, 4.10s/it] 83%|████████▎ | 15554/18688 [18:20:26<3:35:28, 4.13s/it] 83%|████████▎ | 15555/18688 [18:20:29<3:30:51, 4.04s/it] 83%|████████▎ | 15556/18688 [18:20:35<3:48:30, 4.38s/it] 83%|████████▎ | 15557/18688 [18:20:38<3:36:09, 4.14s/it] 83%|████████▎ | 15558/18688 [18:20:42<3:28:30, 4.00s/it] 83%|████████▎ | 15559/18688 [18:20:46<3:27:33, 3.98s/it] 83%|████████▎ | 15560/18688 [18:20:50<3:29:58, 4.03s/it] {'loss': '0.8123', 'grad_norm': '1.129', 'learning_rate': '7.957e-06', 'epoch': '0.8326', 'num_input_tokens_seen': 52328728, 'train_runtime': '6.605e+04', 'train_tokens_per_second': '792.3'}
83%|████████▎ | 15560/18688 [18:20:50<3:29:58, 4.03s/it] 83%|████████▎ | 15561/18688 [18:20:54<3:33:56, 4.11s/it] 83%|████████▎ | 15562/18688 [18:20:58<3:23:28, 3.91s/it] 83%|████████▎ | 15563/18688 [18:21:01<3:21:03, 3.86s/it] 83%|████████▎ | 15564/18688 [18:21:05<3:16:41, 3.78s/it] 83%|████████▎ | 15565/18688 [18:21:09<3:12:57, 3.71s/it] 83%|████████▎ | 15566/18688 [18:21:12<3:10:40, 3.66s/it] 83%|████████▎ | 15567/18688 [18:21:16<3:15:22, 3.76s/it] 83%|████████▎ | 15568/18688 [18:21:20<3:14:19, 3.74s/it] 83%|████████▎ | 15569/18688 [18:21:24<3:22:59, 3.90s/it] 83%|████████▎ | 15570/18688 [18:21:28<3:17:01, 3.79s/it] {'loss': '0.9046', 'grad_norm': '1.681', 'learning_rate': '7.907e-06', 'epoch': '0.8332', 'num_input_tokens_seen': 52357432, 'train_runtime': '6.609e+04', 'train_tokens_per_second': '792.2'}
83%|████████▎ | 15570/18688 [18:21:28<3:17:01, 3.79s/it] 83%|████████▎ | 15571/18688 [18:21:32<3:18:07, 3.81s/it] 83%|████████▎ | 15572/18688 [18:21:36<3:28:47, 4.02s/it] 83%|████████▎ | 15573/18688 [18:21:40<3:23:02, 3.91s/it] 83%|████████▎ | 15574/18688 [18:21:44<3:25:23, 3.96s/it] 83%|████████▎ | 15575/18688 [18:21:48<3:27:08, 3.99s/it] 83%|████████▎ | 15576/18688 [18:21:52<3:35:41, 4.16s/it] 83%|████████▎ | 15577/18688 [18:21:56<3:27:10, 4.00s/it] 83%|████████▎ | 15578/18688 [18:22:00<3:29:28, 4.04s/it] 83%|████████▎ | 15579/18688 [18:22:04<3:30:07, 4.06s/it] 83%|████████▎ | 15580/18688 [18:22:08<3:21:04, 3.88s/it] {'loss': '0.9575', 'grad_norm': '1.355', 'learning_rate': '7.858e-06', 'epoch': '0.8337', 'num_input_tokens_seen': 52389688, 'train_runtime': '6.613e+04', 'train_tokens_per_second': '792.2'}
83%|████████▎ | 15580/18688 [18:22:08<3:21:04, 3.88s/it] 83%|████████▎ | 15581/18688 [18:22:11<3:18:48, 3.84s/it] 83%|████████▎ | 15582/18688 [18:22:15<3:17:32, 3.82s/it] 83%|████████▎ | 15583/18688 [18:22:19<3:22:13, 3.91s/it] 83%|████████▎ | 15584/18688 [18:22:23<3:25:00, 3.96s/it] 83%|████████▎ | 15585/18688 [18:22:27<3:26:10, 3.99s/it] 83%|████████▎ | 15586/18688 [18:22:31<3:17:45, 3.83s/it] 83%|████████▎ | 15587/18688 [18:22:36<3:32:21, 4.11s/it] 83%|████████▎ | 15588/18688 [18:22:41<3:45:35, 4.37s/it] 83%|████████▎ | 15589/18688 [18:22:45<3:49:22, 4.44s/it] 83%|████████▎ | 15590/18688 [18:22:49<3:40:25, 4.27s/it] {'loss': '0.9704', 'grad_norm': '1.201', 'learning_rate': '7.809e-06', 'epoch': '0.8342', 'num_input_tokens_seen': 52424536, 'train_runtime': '6.617e+04', 'train_tokens_per_second': '792.3'}
83%|████████▎ | 15590/18688 [18:22:49<3:40:25, 4.27s/it] 83%|████████▎ | 15591/18688 [18:22:54<3:42:09, 4.30s/it] 83%|████████▎ | 15592/18688 [18:22:57<3:32:53, 4.13s/it] 83%|████████▎ | 15593/18688 [18:23:01<3:24:45, 3.97s/it] 83%|████████▎ | 15594/18688 [18:23:05<3:32:24, 4.12s/it] 83%|████████▎ | 15595/18688 [18:23:09<3:31:58, 4.11s/it] 83%|████████▎ | 15596/18688 [18:23:13<3:30:59, 4.09s/it] 83%|████████▎ | 15597/18688 [18:23:17<3:22:46, 3.94s/it] 83%|████████▎ | 15598/18688 [18:23:21<3:28:38, 4.05s/it] 83%|████████▎ | 15599/18688 [18:23:25<3:23:25, 3.95s/it] 83%|████████▎ | 15600/18688 [18:23:30<3:35:25, 4.19s/it] {'loss': '0.9302', 'grad_norm': '1.233', 'learning_rate': '7.76e-06', 'epoch': '0.8348', 'num_input_tokens_seen': 52458416, 'train_runtime': '6.621e+04', 'train_tokens_per_second': '792.3'}
83%|████████▎ | 15600/18688 [18:23:30<3:35:25, 4.19s/it] 83%|████████▎ | 15601/18688 [18:23:33<3:28:08, 4.05s/it] 83%|████████▎ | 15602/18688 [18:23:37<3:23:20, 3.95s/it] 83%|████████▎ | 15603/18688 [18:23:41<3:22:18, 3.93s/it] 83%|████████▎ | 15604/18688 [18:23:45<3:21:51, 3.93s/it] 84%|████████▎ | 15605/18688 [18:23:48<3:14:06, 3.78s/it] 84%|████████▎ | 15606/18688 [18:23:52<3:16:17, 3.82s/it] 84%|████████▎ | 15607/18688 [18:23:56<3:12:12, 3.74s/it] 84%|████████▎ | 15608/18688 [18:24:00<3:14:39, 3.79s/it] 84%|████████▎ | 15609/18688 [18:24:04<3:17:59, 3.86s/it] 84%|████████▎ | 15610/18688 [18:24:08<3:28:37, 4.07s/it] {'loss': '0.9118', 'grad_norm': '1.128', 'learning_rate': '7.711e-06', 'epoch': '0.8353', 'num_input_tokens_seen': 52488808, 'train_runtime': '6.625e+04', 'train_tokens_per_second': '792.3'}
84%|████████▎ | 15610/18688 [18:24:08<3:28:37, 4.07s/it] 84%|████████▎ | 15611/18688 [18:24:13<3:31:24, 4.12s/it] 84%|████████▎ | 15612/18688 [18:24:17<3:30:15, 4.10s/it] 84%|████████▎ | 15613/18688 [18:24:21<3:30:12, 4.10s/it] 84%|████████▎ | 15614/18688 [18:24:25<3:37:52, 4.25s/it] 84%|████████▎ | 15615/18688 [18:24:29<3:34:56, 4.20s/it] 84%|████████▎ | 15616/18688 [18:24:33<3:27:50, 4.06s/it] 84%|████████▎ | 15617/18688 [18:24:37<3:29:20, 4.09s/it] 84%|████████▎ | 15618/18688 [18:24:42<3:31:03, 4.12s/it] 84%|████████▎ | 15619/18688 [18:24:46<3:40:19, 4.31s/it] 84%|████████▎ | 15620/18688 [18:24:50<3:36:24, 4.23s/it] {'loss': '0.8862', 'grad_norm': '1.1', 'learning_rate': '7.662e-06', 'epoch': '0.8359', 'num_input_tokens_seen': 52523424, 'train_runtime': '6.629e+04', 'train_tokens_per_second': '792.3'}
84%|████████▎ | 15620/18688 [18:24:50<3:36:24, 4.23s/it] 84%|████████▎ | 15621/18688 [18:24:54<3:26:23, 4.04s/it] 84%|████████▎ | 15622/18688 [18:24:58<3:27:20, 4.06s/it] 84%|████████▎ | 15623/18688 [18:25:02<3:28:37, 4.08s/it] 84%|████████▎ | 15624/18688 [18:25:06<3:26:16, 4.04s/it] 84%|████████▎ | 15625/18688 [18:25:10<3:19:36, 3.91s/it] 84%|████████▎ | 15626/18688 [18:25:13<3:13:16, 3.79s/it] 84%|████████▎ | 15627/18688 [18:25:17<3:12:16, 3.77s/it] 84%|████████▎ | 15628/18688 [18:25:22<3:25:21, 4.03s/it] 84%|████████▎ | 15629/18688 [18:25:27<3:44:19, 4.40s/it] 84%|████████▎ | 15630/18688 [18:25:31<3:36:48, 4.25s/it] {'loss': '0.8636', 'grad_norm': '1.303', 'learning_rate': '7.614e-06', 'epoch': '0.8364', 'num_input_tokens_seen': 52556456, 'train_runtime': '6.633e+04', 'train_tokens_per_second': '792.3'}
84%|████████▎ | 15630/18688 [18:25:31<3:36:48, 4.25s/it] 84%|████████▎ | 15631/18688 [18:25:35<3:37:53, 4.28s/it] 84%|████████▎ | 15632/18688 [18:25:39<3:30:56, 4.14s/it] 84%|████████▎ | 15633/18688 [18:25:43<3:23:45, 4.00s/it] 84%|████████▎ | 15634/18688 [18:25:46<3:17:37, 3.88s/it] 84%|████████▎ | 15635/18688 [18:25:50<3:13:10, 3.80s/it] 84%|████████▎ | 15636/18688 [18:25:53<3:08:18, 3.70s/it] 84%|████████▎ | 15637/18688 [18:25:58<3:18:15, 3.90s/it] 84%|████████▎ | 15638/18688 [18:26:01<3:13:49, 3.81s/it] 84%|████████▎ | 15639/18688 [18:26:05<3:08:20, 3.71s/it] 84%|████████▎ | 15640/18688 [18:26:09<3:13:23, 3.81s/it] {'loss': '0.8968', 'grad_norm': '1.336', 'learning_rate': '7.565e-06', 'epoch': '0.8369', 'num_input_tokens_seen': 52586576, 'train_runtime': '6.637e+04', 'train_tokens_per_second': '792.3'}
84%|████████▎ | 15640/18688 [18:26:09<3:13:23, 3.81s/it] 84%|████████▎ | 15641/18688 [18:26:14<3:37:17, 4.28s/it] 84%|████████▎ | 15642/18688 [18:26:19<3:40:53, 4.35s/it] 84%|████████▎ | 15643/18688 [18:26:22<3:32:34, 4.19s/it] 84%|████████▎ | 15644/18688 [18:26:27<3:39:12, 4.32s/it] 84%|████████▎ | 15645/18688 [18:26:31<3:29:10, 4.12s/it] 84%|████████▎ | 15646/18688 [18:26:35<3:28:14, 4.11s/it] 84%|████████▎ | 15647/18688 [18:26:39<3:28:34, 4.12s/it] 84%|████████▎ | 15648/18688 [18:26:43<3:21:26, 3.98s/it] 84%|████████▎ | 15649/18688 [18:26:48<3:40:45, 4.36s/it] 84%|████████▎ | 15650/18688 [18:26:52<3:43:15, 4.41s/it] {'loss': '0.9444', 'grad_norm': '0.8505', 'learning_rate': '7.517e-06', 'epoch': '0.8375', 'num_input_tokens_seen': 52623808, 'train_runtime': '6.641e+04', 'train_tokens_per_second': '792.4'}
84%|████████▎ | 15650/18688 [18:26:52<3:43:15, 4.41s/it] 84%|████████▎ | 15651/18688 [18:26:57<3:39:48, 4.34s/it] 84%|████████▍ | 15652/18688 [18:27:00<3:29:57, 4.15s/it] 84%|████████▍ | 15653/18688 [18:27:04<3:30:06, 4.15s/it] 84%|████████▍ | 15654/18688 [18:27:08<3:25:30, 4.06s/it] 84%|████████▍ | 15655/18688 [18:27:12<3:21:59, 4.00s/it] 84%|████████▍ | 15656/18688 [18:27:16<3:22:09, 4.00s/it] 84%|████████▍ | 15657/18688 [18:27:21<3:41:49, 4.39s/it] 84%|████████▍ | 15658/18688 [18:27:25<3:32:37, 4.21s/it] 84%|████████▍ | 15659/18688 [18:27:29<3:29:52, 4.16s/it] 84%|████████▍ | 15660/18688 [18:27:34<3:32:09, 4.20s/it] {'loss': '0.9182', 'grad_norm': '1.478', 'learning_rate': '7.469e-06', 'epoch': '0.838', 'num_input_tokens_seen': 52657304, 'train_runtime': '6.645e+04', 'train_tokens_per_second': '792.4'}
84%|████████▍ | 15660/18688 [18:27:34<3:32:09, 4.20s/it] 84%|████████▍ | 15661/18688 [18:27:38<3:37:33, 4.31s/it] 84%|████████▍ | 15662/18688 [18:27:43<3:41:52, 4.40s/it] 84%|████████▍ | 15663/18688 [18:27:47<3:39:10, 4.35s/it] 84%|████████▍ | 15664/18688 [18:27:52<3:46:34, 4.50s/it] 84%|████████▍ | 15665/18688 [18:27:56<3:41:20, 4.39s/it] 84%|████████▍ | 15666/18688 [18:28:01<3:56:55, 4.70s/it] 84%|████████▍ | 15667/18688 [18:28:06<3:54:41, 4.66s/it] 84%|████████▍ | 15668/18688 [18:28:10<3:51:54, 4.61s/it] 84%|████████▍ | 15669/18688 [18:28:14<3:40:51, 4.39s/it] 84%|████████▍ | 15670/18688 [18:28:19<3:39:32, 4.36s/it] {'loss': '0.8983', 'grad_norm': '1.236', 'learning_rate': '7.421e-06', 'epoch': '0.8385', 'num_input_tokens_seen': 52696808, 'train_runtime': '6.65e+04', 'train_tokens_per_second': '792.4'}
84%|████████▍ | 15670/18688 [18:28:19<3:39:32, 4.36s/it] 84%|████████▍ | 15671/18688 [18:28:22<3:29:19, 4.16s/it] 84%|████████▍ | 15672/18688 [18:28:27<3:31:31, 4.21s/it] 84%|████████▍ | 15673/18688 [18:28:31<3:27:03, 4.12s/it] 84%|████████▍ | 15674/18688 [18:28:34<3:23:04, 4.04s/it] 84%|████████▍ | 15675/18688 [18:28:39<3:26:23, 4.11s/it] 84%|████████▍ | 15676/18688 [18:28:43<3:31:04, 4.20s/it] 84%|████████▍ | 15677/18688 [18:28:48<3:38:02, 4.34s/it] 84%|████████▍ | 15678/18688 [18:28:52<3:37:08, 4.33s/it] 84%|████████▍ | 15679/18688 [18:28:57<3:42:54, 4.44s/it] 84%|████████▍ | 15680/18688 [18:29:00<3:31:12, 4.21s/it] {'loss': '0.995', 'grad_norm': '1.432', 'learning_rate': '7.373e-06', 'epoch': '0.8391', 'num_input_tokens_seen': 52731144, 'train_runtime': '6.654e+04', 'train_tokens_per_second': '792.5'}
84%|████████▍ | 15680/18688 [18:29:00<3:31:12, 4.21s/it] 84%|████████▍ | 15681/18688 [18:29:05<3:28:09, 4.15s/it] 84%|████████▍ | 15682/18688 [18:29:10<3:41:20, 4.42s/it] 84%|████████▍ | 15683/18688 [18:29:14<3:41:20, 4.42s/it] 84%|████████▍ | 15684/18688 [18:29:18<3:38:01, 4.35s/it] 84%|████████▍ | 15685/18688 [18:29:22<3:30:28, 4.21s/it] 84%|████████▍ | 15686/18688 [18:29:26<3:22:41, 4.05s/it] 84%|████████▍ | 15687/18688 [18:29:30<3:27:23, 4.15s/it] 84%|████████▍ | 15688/18688 [18:29:35<3:33:32, 4.27s/it] 84%|████████▍ | 15689/18688 [18:29:39<3:32:57, 4.26s/it] 84%|████████▍ | 15690/18688 [18:29:42<3:20:34, 4.01s/it] {'loss': '0.9818', 'grad_norm': '1.487', 'learning_rate': '7.326e-06', 'epoch': '0.8396', 'num_input_tokens_seen': 52764968, 'train_runtime': '6.658e+04', 'train_tokens_per_second': '792.5'}
84%|████████▍ | 15690/18688 [18:29:42<3:20:34, 4.01s/it] 84%|████████▍ | 15691/18688 [18:29:46<3:12:29, 3.85s/it] 84%|████████▍ | 15692/18688 [18:29:49<3:08:53, 3.78s/it] 84%|████████▍ | 15693/18688 [18:29:54<3:16:00, 3.93s/it] 84%|████████▍ | 15694/18688 [18:29:57<3:09:45, 3.80s/it] 84%|████████▍ | 15695/18688 [18:30:01<3:12:58, 3.87s/it] 84%|████████▍ | 15696/18688 [18:30:05<3:11:38, 3.84s/it] 84%|████████▍ | 15697/18688 [18:30:08<3:05:57, 3.73s/it] 84%|████████▍ | 15698/18688 [18:30:13<3:11:13, 3.84s/it] 84%|████████▍ | 15699/18688 [18:30:17<3:19:15, 4.00s/it] 84%|████████▍ | 15700/18688 [18:30:20<3:10:44, 3.83s/it] {'loss': '0.8841', 'grad_norm': '1.301', 'learning_rate': '7.278e-06', 'epoch': '0.8401', 'num_input_tokens_seen': 52795136, 'train_runtime': '6.662e+04', 'train_tokens_per_second': '792.5'}
84%|████████▍ | 15700/18688 [18:30:20<3:10:44, 3.83s/it] 84%|████████▍ | 15701/18688 [18:30:24<3:09:14, 3.80s/it] 84%|████████▍ | 15702/18688 [18:30:29<3:25:16, 4.12s/it] 84%|████████▍ | 15703/18688 [18:30:33<3:16:18, 3.95s/it] 84%|████████▍ | 15704/18688 [18:30:36<3:14:13, 3.91s/it] 84%|████████▍ | 15705/18688 [18:30:40<3:16:42, 3.96s/it] 84%|████████▍ | 15706/18688 [18:30:44<3:15:22, 3.93s/it] 84%|████████▍ | 15707/18688 [18:30:48<3:15:40, 3.94s/it] 84%|████████▍ | 15708/18688 [18:30:52<3:17:19, 3.97s/it] 84%|████████▍ | 15709/18688 [18:30:56<3:20:24, 4.04s/it] 84%|████████▍ | 15710/18688 [18:31:00<3:15:22, 3.94s/it] {'loss': '0.9395', 'grad_norm': '1.344', 'learning_rate': '7.231e-06', 'epoch': '0.8407', 'num_input_tokens_seen': 52827016, 'train_runtime': '6.666e+04', 'train_tokens_per_second': '792.5'}
84%|████████▍ | 15710/18688 [18:31:00<3:15:22, 3.94s/it] 84%|████████▍ | 15711/18688 [18:31:05<3:25:15, 4.14s/it] 84%|████████▍ | 15712/18688 [18:31:09<3:27:49, 4.19s/it] 84%|████████▍ | 15713/18688 [18:31:13<3:23:33, 4.11s/it] 84%|████████▍ | 15714/18688 [18:31:17<3:22:50, 4.09s/it] 84%|████████▍ | 15715/18688 [18:31:22<3:31:27, 4.27s/it] 84%|████████▍ | 15716/18688 [18:31:26<3:30:29, 4.25s/it] 84%|████████▍ | 15717/18688 [18:31:30<3:23:53, 4.12s/it] 84%|████████▍ | 15718/18688 [18:31:34<3:23:38, 4.11s/it] 84%|████████▍ | 15719/18688 [18:31:38<3:23:16, 4.11s/it] 84%|████████▍ | 15720/18688 [18:31:42<3:29:00, 4.23s/it] {'loss': '0.9201', 'grad_norm': '1.51', 'learning_rate': '7.183e-06', 'epoch': '0.8412', 'num_input_tokens_seen': 52860536, 'train_runtime': '6.67e+04', 'train_tokens_per_second': '792.5'}
84%|████████▍ | 15720/18688 [18:31:42<3:29:00, 4.23s/it] 84%|████████▍ | 15721/18688 [18:31:46<3:18:57, 4.02s/it] 84%|████████▍ | 15722/18688 [18:31:50<3:15:03, 3.95s/it] 84%|████████▍ | 15723/18688 [18:31:53<3:07:48, 3.80s/it] 84%|████████▍ | 15724/18688 [18:31:58<3:20:06, 4.05s/it] 84%|████████▍ | 15725/18688 [18:32:02<3:24:21, 4.14s/it] 84%|████████▍ | 15726/18688 [18:32:06<3:21:21, 4.08s/it] 84%|████████▍ | 15727/18688 [18:32:10<3:19:07, 4.03s/it] 84%|████████▍ | 15728/18688 [18:32:14<3:11:22, 3.88s/it] 84%|████████▍ | 15729/18688 [18:32:19<3:26:37, 4.19s/it] 84%|████████▍ | 15730/18688 [18:32:23<3:26:43, 4.19s/it] {'loss': '0.8352', 'grad_norm': '1.193', 'learning_rate': '7.136e-06', 'epoch': '0.8417', 'num_input_tokens_seen': 52893392, 'train_runtime': '6.674e+04', 'train_tokens_per_second': '792.5'}
84%|████████▍ | 15730/18688 [18:32:23<3:26:43, 4.19s/it] 84%|████████▍ | 15731/18688 [18:32:27<3:29:10, 4.24s/it] 84%|████████▍ | 15732/18688 [18:32:31<3:17:55, 4.02s/it] 84%|████████▍ | 15733/18688 [18:32:35<3:16:54, 4.00s/it] 84%|████████▍ | 15734/18688 [18:32:39<3:17:13, 4.01s/it] 84%|████████▍ | 15735/18688 [18:32:42<3:13:37, 3.93s/it] 84%|████████▍ | 15736/18688 [18:32:46<3:12:21, 3.91s/it] 84%|████████▍ | 15737/18688 [18:32:50<3:14:25, 3.95s/it] 84%|████████▍ | 15738/18688 [18:32:55<3:27:06, 4.21s/it] 84%|████████▍ | 15739/18688 [18:33:00<3:31:39, 4.31s/it] 84%|████████▍ | 15740/18688 [18:33:03<3:20:57, 4.09s/it] {'loss': '0.8856', 'grad_norm': '1.233', 'learning_rate': '7.089e-06', 'epoch': '0.8423', 'num_input_tokens_seen': 52924496, 'train_runtime': '6.678e+04', 'train_tokens_per_second': '792.5'}
84%|████████▍ | 15740/18688 [18:33:03<3:20:57, 4.09s/it] 84%|████████▍ | 15741/18688 [18:33:07<3:21:47, 4.11s/it] 84%|████████▍ | 15742/18688 [18:33:12<3:24:56, 4.17s/it] 84%|████████▍ | 15743/18688 [18:33:16<3:22:59, 4.14s/it] 84%|████████▍ | 15744/18688 [18:33:19<3:13:05, 3.94s/it] 84%|████████▍ | 15745/18688 [18:33:23<3:09:00, 3.85s/it] 84%|████████▍ | 15746/18688 [18:33:26<3:05:56, 3.79s/it] 84%|████████▍ | 15747/18688 [18:33:30<3:07:28, 3.82s/it] 84%|████████▍ | 15748/18688 [18:33:35<3:26:48, 4.22s/it] 84%|████████▍ | 15749/18688 [18:33:40<3:28:35, 4.26s/it] 84%|████████▍ | 15750/18688 [18:33:43<3:18:04, 4.05s/it] {'loss': '0.8847', 'grad_norm': '1.452', 'learning_rate': '7.042e-06', 'epoch': '0.8428', 'num_input_tokens_seen': 52957624, 'train_runtime': '6.682e+04', 'train_tokens_per_second': '792.5'}
84%|████████▍ | 15750/18688 [18:33:43<3:18:04, 4.05s/it] 84%|████████▍ | 15751/18688 [18:33:48<3:21:55, 4.13s/it] 84%|████████▍ | 15752/18688 [18:33:53<3:37:10, 4.44s/it] 84%|████████▍ | 15753/18688 [18:33:56<3:24:34, 4.18s/it] 84%|████████▍ | 15754/18688 [18:34:00<3:14:16, 3.97s/it] 84%|████████▍ | 15755/18688 [18:34:05<3:27:09, 4.24s/it] 84%|████████▍ | 15756/18688 [18:34:10<3:40:18, 4.51s/it] 84%|████████▍ | 15757/18688 [18:34:14<3:37:48, 4.46s/it] 84%|████████▍ | 15758/18688 [18:34:18<3:25:11, 4.20s/it] 84%|████████▍ | 15759/18688 [18:34:22<3:18:16, 4.06s/it] 84%|████████▍ | 15760/18688 [18:34:26<3:22:15, 4.14s/it] {'loss': '0.8925', 'grad_norm': '1.632', 'learning_rate': '6.996e-06', 'epoch': '0.8433', 'num_input_tokens_seen': 52993224, 'train_runtime': '6.687e+04', 'train_tokens_per_second': '792.5'}
84%|████████▍ | 15760/18688 [18:34:26<3:22:15, 4.14s/it] 84%|████████▍ | 15761/18688 [18:34:30<3:25:01, 4.20s/it] 84%|████████▍ | 15762/18688 [18:34:35<3:26:09, 4.23s/it] 84%|████████▍ | 15763/18688 [18:34:39<3:26:20, 4.23s/it] 84%|████████▍ | 15764/18688 [18:34:43<3:21:29, 4.13s/it] 84%|████████▍ | 15765/18688 [18:34:48<3:34:19, 4.40s/it] 84%|████████▍ | 15766/18688 [18:34:51<3:21:17, 4.13s/it] 84%|████████▍ | 15767/18688 [18:34:55<3:13:21, 3.97s/it] 84%|████████▍ | 15768/18688 [18:35:00<3:24:43, 4.21s/it] 84%|████████▍ | 15769/18688 [18:35:04<3:33:17, 4.38s/it] 84%|████████▍ | 15770/18688 [18:35:08<3:28:57, 4.30s/it] {'loss': '0.8941', 'grad_norm': '1.422', 'learning_rate': '6.949e-06', 'epoch': '0.8439', 'num_input_tokens_seen': 53029128, 'train_runtime': '6.691e+04', 'train_tokens_per_second': '792.6'}
84%|████████▍ | 15770/18688 [18:35:08<3:28:57, 4.30s/it] 84%|████████▍ | 15771/18688 [18:35:13<3:26:02, 4.24s/it] 84%|████████▍ | 15772/18688 [18:35:17<3:27:55, 4.28s/it] 84%|████████▍ | 15773/18688 [18:35:21<3:29:14, 4.31s/it] 84%|████████▍ | 15774/18688 [18:35:25<3:24:32, 4.21s/it] 84%|████████▍ | 15775/18688 [18:35:30<3:27:37, 4.28s/it] 84%|████████▍ | 15776/18688 [18:35:34<3:22:43, 4.18s/it] 84%|████████▍ | 15777/18688 [18:35:38<3:18:33, 4.09s/it] 84%|████████▍ | 15778/18688 [18:35:41<3:13:00, 3.98s/it] 84%|████████▍ | 15779/18688 [18:35:45<3:13:45, 4.00s/it] 84%|████████▍ | 15780/18688 [18:35:50<3:20:29, 4.14s/it] {'loss': '0.9251', 'grad_norm': '1.644', 'learning_rate': '6.903e-06', 'epoch': '0.8444', 'num_input_tokens_seen': 53062584, 'train_runtime': '6.695e+04', 'train_tokens_per_second': '792.6'}
84%|████████▍ | 15780/18688 [18:35:50<3:20:29, 4.14s/it] 84%|████████▍ | 15781/18688 [18:35:54<3:17:20, 4.07s/it] 84%|████████▍ | 15782/18688 [18:35:58<3:19:19, 4.12s/it] 84%|████████▍ | 15783/18688 [18:36:02<3:16:33, 4.06s/it] 84%|████████▍ | 15784/18688 [18:36:06<3:11:17, 3.95s/it] 84%|████████▍ | 15785/18688 [18:36:10<3:16:59, 4.07s/it] 84%|████████▍ | 15786/18688 [18:36:14<3:20:17, 4.14s/it] 84%|████████▍ | 15787/18688 [18:36:18<3:21:58, 4.18s/it] 84%|████████▍ | 15788/18688 [18:36:22<3:14:54, 4.03s/it] 84%|████████▍ | 15789/18688 [18:36:27<3:30:35, 4.36s/it] 84%|████████▍ | 15790/18688 [18:36:31<3:21:31, 4.17s/it] {'loss': '0.8775', 'grad_norm': '1.059', 'learning_rate': '6.856e-06', 'epoch': '0.8449', 'num_input_tokens_seen': 53095576, 'train_runtime': '6.699e+04', 'train_tokens_per_second': '792.6'}
84%|████████▍ | 15790/18688 [18:36:31<3:21:31, 4.17s/it] 84%|████████▍ | 15791/18688 [18:36:35<3:12:05, 3.98s/it] 85%|████████▍ | 15792/18688 [18:36:38<3:06:46, 3.87s/it] 85%|████████▍ | 15793/18688 [18:36:42<3:04:58, 3.83s/it] 85%|████████▍ | 15794/18688 [18:36:46<3:12:24, 3.99s/it] 85%|████████▍ | 15795/18688 [18:36:52<3:31:22, 4.38s/it] 85%|████████▍ | 15796/18688 [18:36:55<3:21:33, 4.18s/it] 85%|████████▍ | 15797/18688 [18:37:00<3:26:27, 4.28s/it] 85%|████████▍ | 15798/18688 [18:37:04<3:25:42, 4.27s/it] 85%|████████▍ | 15799/18688 [18:37:08<3:15:36, 4.06s/it] 85%|████████▍ | 15800/18688 [18:37:12<3:21:42, 4.19s/it] {'loss': '0.8422', 'grad_norm': '1.267', 'learning_rate': '6.81e-06', 'epoch': '0.8455', 'num_input_tokens_seen': 53128888, 'train_runtime': '6.703e+04', 'train_tokens_per_second': '792.6'}
85%|████████▍ | 15800/18688 [18:37:12<3:21:42, 4.19s/it] 85%|████████▍ | 15801/18688 [18:37:17<3:27:44, 4.32s/it] 85%|████████▍ | 15802/18688 [18:37:21<3:26:09, 4.29s/it] 85%|████████▍ | 15803/18688 [18:37:25<3:26:23, 4.29s/it] 85%|████████▍ | 15804/18688 [18:37:29<3:19:52, 4.16s/it] 85%|████████▍ | 15805/18688 [18:37:34<3:25:44, 4.28s/it] 85%|████████▍ | 15806/18688 [18:37:38<3:28:51, 4.35s/it] 85%|████████▍ | 15807/18688 [18:37:42<3:18:31, 4.13s/it] 85%|████████▍ | 15808/18688 [18:37:47<3:28:55, 4.35s/it] 85%|████████▍ | 15809/18688 [18:37:51<3:22:10, 4.21s/it] 85%|████████▍ | 15810/18688 [18:37:55<3:19:52, 4.17s/it] {'loss': '0.9103', 'grad_norm': '1.425', 'learning_rate': '6.764e-06', 'epoch': '0.846', 'num_input_tokens_seen': 53163248, 'train_runtime': '6.708e+04', 'train_tokens_per_second': '792.6'}
85%|████████▍ | 15810/18688 [18:37:55<3:19:52, 4.17s/it] 85%|████████▍ | 15811/18688 [18:37:59<3:17:36, 4.12s/it] 85%|████████▍ | 15812/18688 [18:38:03<3:14:54, 4.07s/it] 85%|████████▍ | 15813/18688 [18:38:07<3:17:34, 4.12s/it] 85%|████████▍ | 15814/18688 [18:38:12<3:32:43, 4.44s/it] 85%|████████▍ | 15815/18688 [18:38:17<3:36:36, 4.52s/it] 85%|████████▍ | 15816/18688 [18:38:20<3:21:34, 4.21s/it] 85%|████████▍ | 15817/18688 [18:38:24<3:12:44, 4.03s/it] 85%|████████▍ | 15818/18688 [18:38:28<3:10:19, 3.98s/it] 85%|████████▍ | 15819/18688 [18:38:32<3:16:37, 4.11s/it] 85%|████████▍ | 15820/18688 [18:38:36<3:15:07, 4.08s/it] {'loss': '0.9037', 'grad_norm': '1.318', 'learning_rate': '6.719e-06', 'epoch': '0.8466', 'num_input_tokens_seen': 53196960, 'train_runtime': '6.712e+04', 'train_tokens_per_second': '792.6'}
85%|████████▍ | 15820/18688 [18:38:36<3:15:07, 4.08s/it] 85%|████████▍ | 15821/18688 [18:38:40<3:06:02, 3.89s/it] 85%|████████▍ | 15822/18688 [18:38:43<3:03:51, 3.85s/it] 85%|████████▍ | 15823/18688 [18:38:47<3:03:08, 3.84s/it] 85%|████████▍ | 15824/18688 [18:38:51<2:59:55, 3.77s/it] 85%|████████▍ | 15825/18688 [18:38:54<2:58:45, 3.75s/it] 85%|████████▍ | 15826/18688 [18:38:58<3:01:06, 3.80s/it] 85%|████████▍ | 15827/18688 [18:39:02<2:58:15, 3.74s/it] 85%|████████▍ | 15828/18688 [18:39:06<3:01:21, 3.80s/it] 85%|████████▍ | 15829/18688 [18:39:10<3:04:32, 3.87s/it] 85%|████████▍ | 15830/18688 [18:39:14<3:07:16, 3.93s/it] {'loss': '0.9153', 'grad_norm': '1.056', 'learning_rate': '6.673e-06', 'epoch': '0.8471', 'num_input_tokens_seen': 53225760, 'train_runtime': '6.715e+04', 'train_tokens_per_second': '792.6'}
85%|████████▍ | 15830/18688 [18:39:14<3:07:16, 3.93s/it] 85%|████████▍ | 15831/18688 [18:39:18<3:01:48, 3.82s/it] 85%|████████▍ | 15832/18688 [18:39:22<3:06:26, 3.92s/it] 85%|████████▍ | 15833/18688 [18:39:26<3:13:06, 4.06s/it] 85%|████████▍ | 15834/18688 [18:39:30<3:13:00, 4.06s/it] 85%|████████▍ | 15835/18688 [18:39:35<3:24:45, 4.31s/it] 85%|████████▍ | 15836/18688 [18:39:39<3:21:14, 4.23s/it] 85%|████████▍ | 15837/18688 [18:39:44<3:25:50, 4.33s/it] 85%|████████▍ | 15838/18688 [18:39:48<3:25:53, 4.33s/it] 85%|████████▍ | 15839/18688 [18:39:52<3:25:10, 4.32s/it] 85%|████████▍ | 15840/18688 [18:39:56<3:21:49, 4.25s/it] {'loss': '0.9643', 'grad_norm': '1.686', 'learning_rate': '6.627e-06', 'epoch': '0.8476', 'num_input_tokens_seen': 53261736, 'train_runtime': '6.72e+04', 'train_tokens_per_second': '792.6'}
85%|████████▍ | 15840/18688 [18:39:56<3:21:49, 4.25s/it] 85%|████████▍ | 15841/18688 [18:40:01<3:21:26, 4.25s/it] 85%|████████▍ | 15842/18688 [18:40:05<3:16:49, 4.15s/it] 85%|████████▍ | 15843/18688 [18:40:08<3:11:46, 4.04s/it] 85%|████████▍ | 15844/18688 [18:40:12<3:08:01, 3.97s/it] 85%|████████▍ | 15845/18688 [18:40:16<3:09:25, 4.00s/it] 85%|████████▍ | 15846/18688 [18:40:21<3:17:40, 4.17s/it] 85%|████████▍ | 15847/18688 [18:40:25<3:18:17, 4.19s/it] 85%|████████▍ | 15848/18688 [18:40:29<3:18:14, 4.19s/it] 85%|████████▍ | 15849/18688 [18:40:33<3:17:36, 4.18s/it] 85%|████████▍ | 15850/18688 [18:40:38<3:22:38, 4.28s/it] {'loss': '0.8948', 'grad_norm': '1.559', 'learning_rate': '6.582e-06', 'epoch': '0.8482', 'num_input_tokens_seen': 53296088, 'train_runtime': '6.724e+04', 'train_tokens_per_second': '792.6'}
85%|████████▍ | 15850/18688 [18:40:38<3:22:38, 4.28s/it] 85%|████████▍ | 15851/18688 [18:40:41<3:11:20, 4.05s/it] 85%|████████▍ | 15852/18688 [18:40:47<3:28:18, 4.41s/it] 85%|████████▍ | 15853/18688 [18:40:50<3:18:51, 4.21s/it] 85%|████████▍ | 15854/18688 [18:40:54<3:08:25, 3.99s/it] 85%|████████▍ | 15855/18688 [18:40:58<3:11:37, 4.06s/it] 85%|████████▍ | 15856/18688 [18:41:02<3:04:39, 3.91s/it] 85%|████████▍ | 15857/18688 [18:41:06<3:10:20, 4.03s/it] 85%|████████▍ | 15858/18688 [18:41:10<3:07:40, 3.98s/it] 85%|████████▍ | 15859/18688 [18:41:15<3:19:29, 4.23s/it] 85%|████████▍ | 15860/18688 [18:41:19<3:16:34, 4.17s/it] {'loss': '0.8824', 'grad_norm': '1.657', 'learning_rate': '6.537e-06', 'epoch': '0.8487', 'num_input_tokens_seen': 53329832, 'train_runtime': '6.728e+04', 'train_tokens_per_second': '792.7'}
85%|████████▍ | 15860/18688 [18:41:19<3:16:34, 4.17s/it] 85%|████████▍ | 15861/18688 [18:41:22<3:08:02, 3.99s/it] 85%|████████▍ | 15862/18688 [18:41:26<3:09:52, 4.03s/it] 85%|████████▍ | 15863/18688 [18:41:30<3:09:12, 4.02s/it] 85%|████████▍ | 15864/18688 [18:41:34<3:09:27, 4.03s/it] 85%|████████▍ | 15865/18688 [18:41:39<3:14:32, 4.13s/it] 85%|████████▍ | 15866/18688 [18:41:43<3:14:50, 4.14s/it] 85%|████████▍ | 15867/18688 [18:41:47<3:15:34, 4.16s/it] 85%|████████▍ | 15868/18688 [18:41:51<3:16:15, 4.18s/it] 85%|████████▍ | 15869/18688 [18:41:55<3:14:53, 4.15s/it] 85%|████████▍ | 15870/18688 [18:41:59<3:08:36, 4.02s/it] {'loss': '0.884', 'grad_norm': '1.377', 'learning_rate': '6.492e-06', 'epoch': '0.8492', 'num_input_tokens_seen': 53362512, 'train_runtime': '6.732e+04', 'train_tokens_per_second': '792.7'}
85%|████████▍ | 15870/18688 [18:41:59<3:08:36, 4.02s/it] 85%|████████▍ | 15871/18688 [18:42:03<3:08:12, 4.01s/it] 85%|████████▍ | 15872/18688 [18:42:08<3:24:04, 4.35s/it] 85%|████████▍ | 15873/18688 [18:42:13<3:26:42, 4.41s/it] 85%|████████▍ | 15874/18688 [18:42:17<3:24:15, 4.36s/it] 85%|████████▍ | 15875/18688 [18:42:21<3:18:11, 4.23s/it] 85%|████████▍ | 15876/18688 [18:42:25<3:11:21, 4.08s/it] 85%|████████▍ | 15877/18688 [18:42:29<3:16:22, 4.19s/it] 85%|████████▍ | 15878/18688 [18:42:34<3:20:56, 4.29s/it] 85%|████████▍ | 15879/18688 [18:42:38<3:17:52, 4.23s/it] 85%|████████▍ | 15880/18688 [18:42:42<3:17:23, 4.22s/it] {'loss': '0.9478', 'grad_norm': '1.159', 'learning_rate': '6.447e-06', 'epoch': '0.8498', 'num_input_tokens_seen': 53397128, 'train_runtime': '6.736e+04', 'train_tokens_per_second': '792.7'}
85%|████████▍ | 15880/18688 [18:42:42<3:17:23, 4.22s/it] 85%|████████▍ | 15881/18688 [18:42:45<3:07:38, 4.01s/it] 85%|████████▍ | 15882/18688 [18:42:50<3:10:50, 4.08s/it] 85%|████████▍ | 15883/18688 [18:42:54<3:06:47, 4.00s/it] 85%|████████▍ | 15884/18688 [18:42:58<3:08:46, 4.04s/it] 85%|████████▌ | 15885/18688 [18:43:01<3:04:31, 3.95s/it] 85%|████████▌ | 15886/18688 [18:43:05<3:02:58, 3.92s/it] 85%|████████▌ | 15887/18688 [18:43:09<2:56:22, 3.78s/it] 85%|████████▌ | 15888/18688 [18:43:13<2:57:37, 3.81s/it] 85%|████████▌ | 15889/18688 [18:43:16<2:53:03, 3.71s/it] 85%|████████▌ | 15890/18688 [18:43:20<2:51:17, 3.67s/it] {'loss': '0.8754', 'grad_norm': '1.542', 'learning_rate': '6.402e-06', 'epoch': '0.8503', 'num_input_tokens_seen': 53425792, 'train_runtime': '6.74e+04', 'train_tokens_per_second': '792.7'}
85%|████████▌ | 15890/18688 [18:43:20<2:51:17, 3.67s/it] 85%|████████▌ | 15891/18688 [18:43:23<2:51:07, 3.67s/it] 85%|████████▌ | 15892/18688 [18:43:27<2:47:45, 3.60s/it] 85%|████████▌ | 15893/18688 [18:43:30<2:48:58, 3.63s/it] 85%|████████▌ | 15894/18688 [18:43:35<3:00:42, 3.88s/it] 85%|████████▌ | 15895/18688 [18:43:39<3:00:52, 3.89s/it] 85%|████████▌ | 15896/18688 [18:43:43<3:05:17, 3.98s/it] 85%|████████▌ | 15897/18688 [18:43:47<3:06:38, 4.01s/it] 85%|████████▌ | 15898/18688 [18:43:51<2:59:31, 3.86s/it] 85%|████████▌ | 15899/18688 [18:43:54<2:56:16, 3.79s/it] 85%|████████▌ | 15900/18688 [18:43:58<2:59:14, 3.86s/it] {'loss': '0.8784', 'grad_norm': '1.311', 'learning_rate': '6.357e-06', 'epoch': '0.8508', 'num_input_tokens_seen': 53455944, 'train_runtime': '6.744e+04', 'train_tokens_per_second': '792.7'}
85%|████████▌ | 15900/18688 [18:43:58<2:59:14, 3.86s/it] 85%|████████▌ | 15901/18688 [18:44:02<2:55:17, 3.77s/it] 85%|████████▌ | 15902/18688 [18:44:05<2:53:07, 3.73s/it] 85%|████████▌ | 15903/18688 [18:44:10<3:08:36, 4.06s/it] 85%|████████▌ | 15904/18688 [18:44:15<3:10:39, 4.11s/it] 85%|████████▌ | 15905/18688 [18:44:18<3:01:53, 3.92s/it] 85%|████████▌ | 15906/18688 [18:44:23<3:19:57, 4.31s/it] 85%|████████▌ | 15907/18688 [18:44:27<3:11:15, 4.13s/it] 85%|████████▌ | 15908/18688 [18:44:32<3:22:16, 4.37s/it] 85%|████████▌ | 15909/18688 [18:44:37<3:28:38, 4.50s/it] 85%|████████▌ | 15910/18688 [18:44:40<3:16:02, 4.23s/it] {'loss': '0.8974', 'grad_norm': '1.332', 'learning_rate': '6.313e-06', 'epoch': '0.8514', 'num_input_tokens_seen': 53491384, 'train_runtime': '6.748e+04', 'train_tokens_per_second': '792.7'}
85%|████████▌ | 15910/18688 [18:44:40<3:16:02, 4.23s/it] 85%|████████▌ | 15911/18688 [18:44:44<3:11:36, 4.14s/it] 85%|████████▌ | 15912/18688 [18:44:48<3:08:35, 4.08s/it] 85%|████████▌ | 15913/18688 [18:44:52<3:11:52, 4.15s/it] 85%|████████▌ | 15914/18688 [18:44:56<3:05:52, 4.02s/it] 85%|████████▌ | 15915/18688 [18:45:00<3:08:08, 4.07s/it] 85%|████████▌ | 15916/18688 [18:45:04<3:02:58, 3.96s/it] 85%|████████▌ | 15917/18688 [18:45:08<3:07:44, 4.07s/it] 85%|████████▌ | 15918/18688 [18:45:13<3:18:47, 4.31s/it] 85%|████████▌ | 15919/18688 [18:45:17<3:13:07, 4.18s/it] 85%|████████▌ | 15920/18688 [18:45:21<3:14:12, 4.21s/it] {'loss': '0.9539', 'grad_norm': '1.354', 'learning_rate': '6.268e-06', 'epoch': '0.8519', 'num_input_tokens_seen': 53525408, 'train_runtime': '6.752e+04', 'train_tokens_per_second': '792.7'}
85%|████████▌ | 15920/18688 [18:45:21<3:14:12, 4.21s/it] 85%|████████▌ | 15921/18688 [18:45:26<3:14:30, 4.22s/it] 85%|████████▌ | 15922/18688 [18:45:29<3:07:12, 4.06s/it] 85%|████████▌ | 15923/18688 [18:45:34<3:16:38, 4.27s/it] 85%|████████▌ | 15924/18688 [18:45:38<3:15:29, 4.24s/it] 85%|████████▌ | 15925/18688 [18:45:44<3:33:09, 4.63s/it] 85%|████████▌ | 15926/18688 [18:45:47<3:18:26, 4.31s/it] 85%|████████▌ | 15927/18688 [18:45:53<3:30:09, 4.57s/it] 85%|████████▌ | 15928/18688 [18:45:57<3:28:25, 4.53s/it] 85%|████████▌ | 15929/18688 [18:46:01<3:20:33, 4.36s/it] 85%|████████▌ | 15930/18688 [18:46:05<3:14:57, 4.24s/it] {'loss': '0.9358', 'grad_norm': '1.143', 'learning_rate': '6.224e-06', 'epoch': '0.8524', 'num_input_tokens_seen': 53562496, 'train_runtime': '6.757e+04', 'train_tokens_per_second': '792.8'}
85%|████████▌ | 15930/18688 [18:46:05<3:14:57, 4.24s/it] 85%|████████▌ | 15931/18688 [18:46:10<3:27:21, 4.51s/it] 85%|████████▌ | 15932/18688 [18:46:15<3:27:24, 4.52s/it] 85%|████████▌ | 15933/18688 [18:46:20<3:34:43, 4.68s/it] 85%|████████▌ | 15934/18688 [18:46:24<3:29:03, 4.55s/it] 85%|████████▌ | 15935/18688 [18:46:30<3:47:50, 4.97s/it] 85%|████████▌ | 15936/18688 [18:46:34<3:35:50, 4.71s/it] 85%|████████▌ | 15937/18688 [18:46:38<3:25:06, 4.47s/it] 85%|████████▌ | 15938/18688 [18:46:42<3:22:16, 4.41s/it] 85%|████████▌ | 15939/18688 [18:46:46<3:16:16, 4.28s/it] 85%|████████▌ | 15940/18688 [18:46:50<3:08:32, 4.12s/it] {'loss': '0.863', 'grad_norm': '1.129', 'learning_rate': '6.18e-06', 'epoch': '0.853', 'num_input_tokens_seen': 53601520, 'train_runtime': '6.761e+04', 'train_tokens_per_second': '792.8'}
85%|████████▌ | 15940/18688 [18:46:50<3:08:32, 4.12s/it] 85%|████████▌ | 15941/18688 [18:46:54<3:08:28, 4.12s/it] 85%|████████▌ | 15942/18688 [18:46:58<3:04:12, 4.02s/it] 85%|████████▌ | 15943/18688 [18:47:02<3:07:01, 4.09s/it] 85%|████████▌ | 15944/18688 [18:47:06<3:02:07, 3.98s/it] 85%|████████▌ | 15945/18688 [18:47:10<3:08:27, 4.12s/it] 85%|████████▌ | 15946/18688 [18:47:14<3:03:12, 4.01s/it] 85%|████████▌ | 15947/18688 [18:47:18<3:10:25, 4.17s/it] 85%|████████▌ | 15948/18688 [18:47:23<3:18:03, 4.34s/it] 85%|████████▌ | 15949/18688 [18:47:29<3:38:28, 4.79s/it] 85%|████████▌ | 15950/18688 [18:47:33<3:27:44, 4.55s/it] {'loss': '0.9332', 'grad_norm': '1.106', 'learning_rate': '6.136e-06', 'epoch': '0.8535', 'num_input_tokens_seen': 53638512, 'train_runtime': '6.765e+04', 'train_tokens_per_second': '792.8'}
85%|████████▌ | 15950/18688 [18:47:33<3:27:44, 4.55s/it] 85%|████████▌ | 15951/18688 [18:47:38<3:32:53, 4.67s/it] 85%|████████▌ | 15952/18688 [18:47:42<3:21:56, 4.43s/it] 85%|████████▌ | 15953/18688 [18:47:46<3:18:18, 4.35s/it] 85%|████████▌ | 15954/18688 [18:47:50<3:10:25, 4.18s/it] 85%|████████▌ | 15955/18688 [18:47:54<3:07:09, 4.11s/it] 85%|████████▌ | 15956/18688 [18:47:58<3:12:09, 4.22s/it] 85%|████████▌ | 15957/18688 [18:48:02<3:03:34, 4.03s/it] 85%|████████▌ | 15958/18688 [18:48:05<2:58:09, 3.92s/it] 85%|████████▌ | 15959/18688 [18:48:09<2:55:45, 3.86s/it] 85%|████████▌ | 15960/18688 [18:48:14<3:04:32, 4.06s/it] {'loss': '0.9314', 'grad_norm': '1.587', 'learning_rate': '6.092e-06', 'epoch': '0.854', 'num_input_tokens_seen': 53671096, 'train_runtime': '6.769e+04', 'train_tokens_per_second': '792.8'}
85%|████████▌ | 15960/18688 [18:48:14<3:04:32, 4.06s/it] 85%|████████▌ | 15961/18688 [18:48:18<3:07:16, 4.12s/it] 85%|████████▌ | 15962/18688 [18:48:22<2:59:33, 3.95s/it] 85%|████████▌ | 15963/18688 [18:48:26<3:02:27, 4.02s/it] 85%|████████▌ | 15964/18688 [18:48:30<3:09:23, 4.17s/it] 85%|████████▌ | 15965/18688 [18:48:34<3:05:20, 4.08s/it] 85%|████████▌ | 15966/18688 [18:48:38<3:03:43, 4.05s/it] 85%|████████▌ | 15967/18688 [18:48:43<3:18:58, 4.39s/it] 85%|████████▌ | 15968/18688 [18:48:48<3:18:25, 4.38s/it] 85%|████████▌ | 15969/18688 [18:48:52<3:16:59, 4.35s/it] 85%|████████▌ | 15970/18688 [18:48:56<3:09:11, 4.18s/it] {'loss': '0.926', 'grad_norm': '1.177', 'learning_rate': '6.049e-06', 'epoch': '0.8546', 'num_input_tokens_seen': 53706720, 'train_runtime': '6.774e+04', 'train_tokens_per_second': '792.9'}
85%|████████▌ | 15970/18688 [18:48:56<3:09:11, 4.18s/it] 85%|████████▌ | 15971/18688 [18:49:00<3:08:28, 4.16s/it] 85%|████████▌ | 15972/18688 [18:49:03<3:00:20, 3.98s/it] 85%|████████▌ | 15973/18688 [18:49:08<3:12:44, 4.26s/it] 85%|████████▌ | 15974/18688 [18:49:12<3:11:04, 4.22s/it] 85%|████████▌ | 15975/18688 [18:49:16<3:05:06, 4.09s/it] 85%|████████▌ | 15976/18688 [18:49:20<3:04:24, 4.08s/it] 85%|████████▌ | 15977/18688 [18:49:25<3:07:48, 4.16s/it] 85%|████████▌ | 15978/18688 [18:49:29<3:06:15, 4.12s/it] 86%|████████▌ | 15979/18688 [18:49:33<3:06:10, 4.12s/it] 86%|████████▌ | 15980/18688 [18:49:37<3:02:35, 4.05s/it] {'loss': '0.8775', 'grad_norm': '1.159', 'learning_rate': '6.005e-06', 'epoch': '0.8551', 'num_input_tokens_seen': 53740784, 'train_runtime': '6.778e+04', 'train_tokens_per_second': '792.9'}
86%|████████▌ | 15980/18688 [18:49:37<3:02:35, 4.05s/it] 86%|████████▌ | 15981/18688 [18:49:41<3:00:57, 4.01s/it] 86%|████████▌ | 15982/18688 [18:49:44<2:57:09, 3.93s/it] 86%|████████▌ | 15983/18688 [18:49:48<3:00:44, 4.01s/it] 86%|████████▌ | 15984/18688 [18:49:53<3:13:32, 4.29s/it] 86%|████████▌ | 15985/18688 [18:49:58<3:12:20, 4.27s/it] 86%|████████▌ | 15986/18688 [18:50:02<3:11:28, 4.25s/it] 86%|████████▌ | 15987/18688 [18:50:06<3:09:50, 4.22s/it] 86%|████████▌ | 15988/18688 [18:50:10<3:12:52, 4.29s/it] 86%|████████▌ | 15989/18688 [18:50:14<3:03:00, 4.07s/it] 86%|████████▌ | 15990/18688 [18:50:19<3:13:18, 4.30s/it] {'loss': '0.9353', 'grad_norm': '1.115', 'learning_rate': '5.962e-06', 'epoch': '0.8557', 'num_input_tokens_seen': 53775136, 'train_runtime': '6.782e+04', 'train_tokens_per_second': '792.9'}
86%|████████▌ | 15990/18688 [18:50:19<3:13:18, 4.30s/it] 86%|████████▌ | 15991/18688 [18:50:24<3:19:36, 4.44s/it] 86%|████████▌ | 15992/18688 [18:50:28<3:17:21, 4.39s/it] 86%|████████▌ | 15993/18688 [18:50:32<3:10:32, 4.24s/it] 86%|████████▌ | 15994/18688 [18:50:35<3:02:39, 4.07s/it] 86%|████████▌ | 15995/18688 [18:50:40<3:05:59, 4.14s/it] 86%|████████▌ | 15996/18688 [18:50:44<3:04:21, 4.11s/it] 86%|████████▌ | 15997/18688 [18:50:48<3:02:21, 4.07s/it] 86%|████████▌ | 15998/18688 [18:50:52<3:07:11, 4.18s/it] 86%|████████▌ | 15999/18688 [18:50:56<2:58:56, 3.99s/it] 86%|████████▌ | 16000/18688 [18:51:00<3:01:51, 4.06s/it] {'loss': '0.903', 'grad_norm': '1.122', 'learning_rate': '5.918e-06', 'epoch': '0.8562', 'num_input_tokens_seen': 53808416, 'train_runtime': '6.786e+04', 'train_tokens_per_second': '792.9'}
86%|████████▌ | 16000/18688 [18:51:00<3:01:51, 4.06s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
0%| | 3/1000 [00:00<00:38, 25.84it/s]
1%| | 6/1000 [00:00<00:54, 18.26it/s]
1%| | 8/1000 [00:00<00:56, 17.48it/s]
1%| | 10/1000 [00:00<00:59, 16.57it/s]
1%| | 12/1000 [00:00<01:00, 16.34it/s]
1%|▏ | 14/1000 [00:00<01:03, 15.46it/s]
2%|▏ | 16/1000 [00:01<01:09, 14.20it/s]
2%|▏ | 18/1000 [00:01<01:44, 9.40it/s]
2%|▏ | 20/1000 [00:01<01:37, 10.08it/s]
2%|▏ | 22/1000 [00:01<01:55, 8.49it/s]
2%|▏ | 24/1000 [00:02<01:42, 9.56it/s]
3%|▎ | 26/1000 [00:02<01:46, 9.14it/s]
3%|▎ | 28/1000 [00:02<01:37, 10.00it/s]
3%|▎ | 30/1000 [00:02<01:28, 10.91it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.11it/s]
3%|▎ | 34/1000 [00:02<01:29, 10.83it/s]
4%|▎ | 36/1000 [00:03<01:34, 10.17it/s]
4%|▍ | 38/1000 [00:03<01:26, 11.14it/s]
4%|▍ | 40/1000 [00:03<01:28, 10.83it/s]
4%|▍ | 42/1000 [00:03<01:47, 8.94it/s]
4%|▍ | 44/1000 [00:03<01:31, 10.40it/s]
5%|▍ | 46/1000 [00:04<01:22, 11.54it/s]
5%|▍ | 48/1000 [00:04<01:30, 10.54it/s]
5%|▌ | 50/1000 [00:04<01:22, 11.57it/s]
5%|▌ | 52/1000 [00:04<01:16, 12.43it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.19it/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, 10.73it/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, 12.89it/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, 13.70it/s]
8%|▊ | 76/1000 [00:06<01:04, 14.29it/s]
8%|▊ | 78/1000 [00:06<01:03, 14.49it/s]
8%|▊ | 80/1000 [00:06<01:08, 13.46it/s]
8%|▊ | 82/1000 [00:06<01:05, 14.06it/s]
8%|▊ | 84/1000 [00:07<01:03, 14.52it/s]
9%|▊ | 86/1000 [00:07<01:02, 14.66it/s]
9%|▉ | 88/1000 [00:07<01:03, 14.48it/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, 14.27it/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, 7.30it/s]
11%|█ | 108/1000 [00:09<01:43, 8.62it/s]
11%|█ | 110/1000 [00:09<01:28, 10.05it/s]
11%|█ | 112/1000 [00:09<01:19, 11.17it/s]
11%|█▏ | 114/1000 [00:09<01:14, 11.85it/s]
12%|█▏ | 116/1000 [00:09<01:10, 12.49it/s]
12%|█▏ | 118/1000 [00:10<01:06, 13.27it/s]
12%|█▏ | 120/1000 [00:10<01:09, 12.71it/s]
12%|█▏ | 122/1000 [00:10<01:22, 10.58it/s]
12%|█▏ | 124/1000 [00:10<01:30, 9.65it/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, 12.20it/s]
13%|█▎ | 132/1000 [00:11<01:05, 13.17it/s]
13%|█▎ | 134/1000 [00:11<01:09, 12.38it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.32it/s]
14%|█▍ | 138/1000 [00:11<01:05, 13.25it/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, 9.57it/s]
15%|█▍ | 146/1000 [00:12<01:20, 10.60it/s]
15%|█▍ | 148/1000 [00:12<01:13, 11.52it/s]
15%|█▌ | 150/1000 [00:12<01:07, 12.61it/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<01:04, 13.06it/s]
16%|█▌ | 158/1000 [00:13<01:04, 13.00it/s]
16%|█▌ | 160/1000 [00:13<01:05, 12.87it/s]
16%|█▌ | 162/1000 [00:13<01:08, 12.17it/s]
16%|█▋ | 164/1000 [00:14<01:09, 12.10it/s]
17%|█▋ | 166/1000 [00:14<01:05, 12.64it/s]
17%|█▋ | 168/1000 [00:14<01:02, 13.36it/s]
17%|█▋ | 170/1000 [00:14<01:22, 10.00it/s]
17%|█▋ | 172/1000 [00:14<01:16, 10.86it/s]
17%|█▋ | 174/1000 [00:15<01:32, 8.90it/s]
18%|█▊ | 176/1000 [00:15<01:56, 7.07it/s]
18%|█▊ | 178/1000 [00:15<01:36, 8.53it/s]
18%|█▊ | 180/1000 [00:15<01:34, 8.70it/s]
18%|█▊ | 182/1000 [00:16<01:35, 8.52it/s]
18%|█▊ | 184/1000 [00:16<01:23, 9.77it/s]
19%|█▊ | 186/1000 [00:16<01:37, 8.31it/s]
19%|█▉ | 188/1000 [00:16<01:24, 9.64it/s]
19%|█▉ | 190/1000 [00:16<01:15, 10.68it/s]
19%|█▉ | 192/1000 [00:16<01:11, 11.33it/s]
19%|█▉ | 194/1000 [00:17<01:16, 10.56it/s]
20%|█▉ | 196/1000 [00:17<01:29, 8.96it/s]
20%|█▉ | 198/1000 [00:17<01:23, 9.66it/s]
20%|██ | 200/1000 [00:17<01:18, 10.15it/s]
20%|██ | 202/1000 [00:18<01:11, 11.17it/s]
20%|██ | 204/1000 [00:18<01:04, 12.36it/s]
21%|██ | 206/1000 [00:18<01:00, 13.02it/s]
21%|██ | 208/1000 [00:18<00:58, 13.46it/s]
21%|██ | 210/1000 [00:18<00:54, 14.38it/s]
21%|██ | 212/1000 [00:18<00:53, 14.67it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.70it/s]
22%|██▏ | 216/1000 [00:18<00:54, 14.45it/s]
22%|██▏ | 218/1000 [00:19<00:52, 14.83it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.90it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.15it/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, 11.97it/s]
23%|██▎ | 232/1000 [00:20<01:02, 12.36it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.14it/s]
24%|██▎ | 236/1000 [00:20<00:56, 13.62it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.27it/s]
24%|██▍ | 240/1000 [00:20<00:53, 14.25it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.77it/s]
24%|██▍ | 244/1000 [00:21<00:50, 14.99it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.98it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.70it/s]
25%|██▌ | 250/1000 [00:21<00:55, 13.43it/s]
25%|██▌ | 252/1000 [00:21<01:01, 12.17it/s]
25%|██▌ | 254/1000 [00:22<00:56, 13.21it/s]
26%|██▌ | 256/1000 [00:22<00:53, 13.79it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.83it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.94it/s]
26%|██▌ | 262/1000 [00:22<00:56, 13.07it/s]
26%|██▋ | 264/1000 [00:22<00:53, 13.64it/s]
27%|██▋ | 266/1000 [00:22<00:52, 14.01it/s]
27%|██▋ | 268/1000 [00:23<00:50, 14.59it/s]
27%|██▋ | 270/1000 [00:23<00:54, 13.29it/s]
27%|██▋ | 272/1000 [00:23<01:03, 11.50it/s]
27%|██▋ | 274/1000 [00:23<01:02, 11.61it/s]
28%|██▊ | 276/1000 [00:23<00:57, 12.64it/s]
28%|██▊ | 278/1000 [00:23<00:56, 12.89it/s]
28%|██▊ | 280/1000 [00:24<00:53, 13.48it/s]
28%|██▊ | 282/1000 [00:24<00:49, 14.40it/s]
28%|██▊ | 284/1000 [00:24<00:49, 14.36it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.65it/s]
29%|██▉ | 288/1000 [00:24<00:47, 15.05it/s]
29%|██▉ | 290/1000 [00:24<00:45, 15.52it/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<00:47, 14.91it/s]
30%|██▉ | 298/1000 [00:25<00:46, 15.10it/s]
30%|███ | 300/1000 [00:25<00:48, 14.41it/s]
30%|███ | 302/1000 [00:25<00:47, 14.54it/s]
30%|███ | 304/1000 [00:25<00:54, 12.85it/s]
31%|███ | 306/1000 [00:25<00:51, 13.56it/s]
31%|███ | 308/1000 [00:25<00:52, 13.31it/s]
31%|███ | 310/1000 [00:26<00:51, 13.38it/s]
31%|███ | 312/1000 [00:26<00:50, 13.69it/s]
31%|███▏ | 314/1000 [00:26<00:47, 14.50it/s]
32%|███▏ | 316/1000 [00:26<00:46, 14.69it/s]
32%|███▏ | 318/1000 [00:26<00:46, 14.65it/s]
32%|███▏ | 320/1000 [00:27<01:15, 9.01it/s]
32%|███▏ | 322/1000 [00:27<01:07, 10.00it/s]
32%|███▏ | 324/1000 [00:27<01:02, 10.80it/s]
33%|███▎ | 326/1000 [00:27<01:01, 10.93it/s]
33%|███▎ | 328/1000 [00:27<01:00, 11.11it/s]
33%|███▎ | 330/1000 [00:27<00:55, 12.13it/s]
33%|███▎ | 332/1000 [00:27<00:53, 12.47it/s]
33%|███▎ | 334/1000 [00:28<00:51, 13.01it/s]
34%|███▎ | 336/1000 [00:28<00:49, 13.46it/s]
34%|███▍ | 338/1000 [00:28<01:06, 10.01it/s]
34%|███▍ | 340/1000 [00:28<00:59, 11.09it/s]
34%|███▍ | 342/1000 [00:28<00:53, 12.36it/s]
34%|███▍ | 344/1000 [00:28<00:50, 13.03it/s]
35%|███▍ | 346/1000 [00:29<00:49, 13.20it/s]
35%|███▍ | 348/1000 [00:29<00:47, 13.71it/s]
35%|███▌ | 350/1000 [00:29<00:45, 14.39it/s]
35%|███▌ | 352/1000 [00:29<00:44, 14.65it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.71it/s]
36%|███▌ | 356/1000 [00:29<00:43, 14.86it/s]
36%|███▌ | 358/1000 [00:29<00:43, 14.60it/s]
36%|███▌ | 360/1000 [00:30<00:42, 14.99it/s]
36%|███▌ | 362/1000 [00:30<00:47, 13.37it/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, 12.34it/s]
37%|███▋ | 370/1000 [00:30<00:47, 13.22it/s]
37%|███▋ | 372/1000 [00:30<00:46, 13.56it/s]
37%|███▋ | 374/1000 [00:31<00:54, 11.42it/s]
38%|███▊ | 376/1000 [00:31<00:56, 10.99it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.67it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.49it/s]
38%|███▊ | 382/1000 [00:31<00:48, 12.68it/s]
38%|███▊ | 384/1000 [00:31<00:45, 13.45it/s]
39%|███▊ | 386/1000 [00:32<00:44, 13.76it/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, 13.91it/s]
39%|███▉ | 394/1000 [00:32<00:44, 13.69it/s]
40%|███▉ | 396/1000 [00:32<00:43, 13.78it/s]
40%|███▉ | 398/1000 [00:33<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, 14.50it/s]
41%|████ | 406/1000 [00:33<00:39, 14.94it/s]
41%|████ | 408/1000 [00:33<00:47, 12.51it/s]
41%|████ | 410/1000 [00:34<00:56, 10.47it/s]
41%|████ | 412/1000 [00:34<00:50, 11.54it/s]
41%|████▏ | 414/1000 [00:34<01:07, 8.64it/s]
42%|████▏ | 416/1000 [00:34<01:25, 6.81it/s]
42%|████▏ | 418/1000 [00:35<01:14, 7.85it/s]
42%|████▏ | 420/1000 [00:35<01:06, 8.79it/s]
42%|████▏ | 422/1000 [00:35<00:59, 9.68it/s]
42%|████▏ | 424/1000 [00:35<01:20, 7.16it/s]
43%|████▎ | 426/1000 [00:36<01:10, 8.18it/s]
43%|████▎ | 428/1000 [00:36<01:02, 9.21it/s]
43%|████▎ | 430/1000 [00:36<01:06, 8.60it/s]
43%|████▎ | 432/1000 [00:36<01:23, 6.83it/s]
43%|████▎ | 434/1000 [00:37<01:10, 8.06it/s]
44%|████▎ | 436/1000 [00:37<01:01, 9.11it/s]
44%|████▍ | 438/1000 [00:37<01:03, 8.89it/s]
44%|████▍ | 440/1000 [00:37<00:59, 9.42it/s]
44%|████▍ | 442/1000 [00:37<00:53, 10.49it/s]
44%|████▍ | 444/1000 [00:37<00:48, 11.36it/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, 11.78it/s]
45%|████▌ | 452/1000 [00:38<00:42, 12.84it/s]
45%|████▌ | 454/1000 [00:38<00:44, 12.31it/s]
46%|████▌ | 456/1000 [00:38<00:42, 12.77it/s]
46%|████▌ | 458/1000 [00:38<00:39, 13.76it/s]
46%|████▌ | 460/1000 [00:39<00:38, 13.86it/s]
46%|████▌ | 462/1000 [00:39<00:37, 14.48it/s]
46%|████▋ | 464/1000 [00:39<00:35, 15.00it/s]
47%|████▋ | 466/1000 [00:39<00:35, 14.84it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.91it/s]
47%|████▋ | 470/1000 [00:40<00:52, 10.07it/s]
47%|████▋ | 472/1000 [00:40<00:51, 10.24it/s]
47%|████▋ | 474/1000 [00:40<00:45, 11.51it/s]
48%|████▊ | 476/1000 [00:40<00:43, 12.16it/s]
48%|████▊ | 478/1000 [00:40<00:43, 12.04it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.97it/s]
48%|████▊ | 482/1000 [00:40<00:38, 13.35it/s]
48%|████▊ | 484/1000 [00:41<00:37, 13.84it/s]
49%|████▊ | 486/1000 [00:41<00:36, 14.00it/s]
49%|████▉ | 488/1000 [00:41<00:37, 13.61it/s]
49%|████▉ | 490/1000 [00:41<00:47, 10.83it/s]
49%|████▉ | 492/1000 [00:41<00:41, 12.17it/s]
49%|████▉ | 494/1000 [00:41<00:38, 13.20it/s]
50%|████▉ | 496/1000 [00:41<00:35, 14.03it/s]
50%|████▉ | 498/1000 [00:42<00:34, 14.49it/s]
50%|█████ | 500/1000 [00:42<00:43, 11.56it/s]
50%|█████ | 502/1000 [00:42<00:41, 12.02it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.63it/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, 10.42it/s]
51%|█████ | 512/1000 [00:43<00:43, 11.19it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 12.02it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.57it/s]
52%|█████▏ | 518/1000 [00:43<00:36, 13.08it/s]
52%|█████▏ | 520/1000 [00:43<00:35, 13.47it/s]
52%|█████▏ | 522/1000 [00:44<00:33, 14.23it/s]
52%|█████▏ | 524/1000 [00:44<00:33, 14.23it/s]
53%|█████▎ | 526/1000 [00:44<00:32, 14.38it/s]
53%|█████▎ | 528/1000 [00:44<00:39, 11.98it/s]
53%|█████▎ | 530/1000 [00:44<00:36, 12.86it/s]
53%|█████▎ | 532/1000 [00:44<00:33, 13.83it/s]
53%|█████▎ | 534/1000 [00:44<00:32, 14.17it/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, 14.48it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.74it/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, 14.16it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.25it/s]
55%|█████▌ | 554/1000 [00:46<00:32, 13.90it/s]
56%|█████▌ | 556/1000 [00:46<00:33, 13.25it/s]
56%|█████▌ | 558/1000 [00:46<00:31, 13.83it/s]
56%|█████▌ | 560/1000 [00:46<00:30, 14.56it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 14.68it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.72it/s]
57%|█████▋ | 566/1000 [00:47<00:37, 11.65it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.21it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.30it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.71it/s]
57%|█████▋ | 574/1000 [00:47<00:31, 13.60it/s]
58%|█████▊ | 576/1000 [00:48<00:31, 13.54it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.60it/s]
58%|█████▊ | 580/1000 [00:48<00:32, 12.87it/s]
58%|█████▊ | 582/1000 [00:48<00:30, 13.66it/s]
58%|█████▊ | 584/1000 [00:48<00:29, 13.89it/s]
59%|█████▊ | 586/1000 [00:48<00:29, 14.26it/s]
59%|█████▉ | 588/1000 [00:48<00:27, 15.07it/s]
59%|█████▉ | 590/1000 [00:49<00:27, 15.03it/s]
59%|█████▉ | 592/1000 [00:49<00:28, 14.34it/s]
59%|█████▉ | 594/1000 [00:49<00:31, 13.09it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.68it/s]
60%|█████▉ | 598/1000 [00:49<00:31, 12.81it/s]
60%|██████ | 600/1000 [00:49<00:29, 13.49it/s]
60%|██████ | 602/1000 [00:49<00:28, 13.88it/s]
60%|██████ | 604/1000 [00:50<00:27, 14.58it/s]
61%|██████ | 606/1000 [00:50<00:27, 14.48it/s]
61%|██████ | 608/1000 [00:50<00:26, 14.89it/s]
61%|██████ | 610/1000 [00:50<00:26, 14.83it/s]
61%|██████ | 612/1000 [00:50<00:25, 15.05it/s]
61%|██████▏ | 614/1000 [00:50<00:26, 14.33it/s]
62%|██████▏ | 616/1000 [00:50<00:26, 14.77it/s]
62%|██████▏ | 618/1000 [00:51<00:26, 14.20it/s]
62%|██████▏ | 620/1000 [00:51<00:38, 9.78it/s]
62%|██████▏ | 622/1000 [00:51<00:37, 9.96it/s]
62%|██████▏ | 624/1000 [00:51<00:33, 11.20it/s]
63%|██████▎ | 626/1000 [00:51<00:31, 11.95it/s]
63%|██████▎ | 628/1000 [00:51<00:28, 13.23it/s]
63%|██████▎ | 630/1000 [00:52<00:26, 13.74it/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<00:33, 10.95it/s]
64%|██████▍ | 638/1000 [00:52<00:33, 10.70it/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, 12.63it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.90it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.18it/s]
65%|██████▌ | 650/1000 [00:53<00:28, 12.48it/s]
65%|██████▌ | 652/1000 [00:53<00:29, 11.91it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.43it/s]
66%|██████▌ | 656/1000 [00:54<00:31, 10.75it/s]
66%|██████▌ | 658/1000 [00:54<00:28, 11.84it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.15it/s]
66%|██████▌ | 662/1000 [00:54<00:29, 11.28it/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, 11.34it/s]
67%|██████▋ | 670/1000 [00:55<00:29, 11.11it/s]
67%|██████▋ | 672/1000 [00:55<00:27, 11.87it/s]
67%|██████▋ | 674/1000 [00:55<00:26, 12.47it/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, 13.00it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.16it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.35it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.37it/s]
69%|██████▉ | 688/1000 [00:56<00:23, 13.41it/s]
69%|██████▉ | 690/1000 [00:57<00:23, 13.05it/s]
69%|██████▉ | 692/1000 [00:57<00:25, 12.21it/s]
69%|██████▉ | 694/1000 [00:57<00:25, 12.19it/s]
70%|██████▉ | 696/1000 [00:57<00:24, 12.55it/s]
70%|██████▉ | 698/1000 [00:57<00:22, 13.36it/s]
70%|███████ | 700/1000 [00:57<00:26, 11.48it/s]
70%|███████ | 702/1000 [00:58<00:25, 11.76it/s]
70%|███████ | 704/1000 [00:58<00:31, 9.35it/s]
71%|███████ | 706/1000 [00:58<00:29, 9.96it/s]
71%|███████ | 708/1000 [00:58<00:31, 9.23it/s]
71%|███████ | 710/1000 [00:59<00:27, 10.53it/s]
71%|███████ | 712/1000 [00:59<00:24, 11.55it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.70it/s]
72%|███████▏ | 716/1000 [00:59<00:22, 12.86it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.44it/s]
72%|███████▏ | 720/1000 [00:59<00:20, 13.42it/s]
72%|███████▏ | 722/1000 [00:59<00:20, 13.86it/s]
72%|███████▏ | 724/1000 [01:00<00:20, 13.71it/s]
73%|███████▎ | 726/1000 [01:00<00:19, 13.95it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.91it/s]
73%|███████▎ | 730/1000 [01:00<00:32, 8.20it/s]
73%|███████▎ | 732/1000 [01:01<00:32, 8.32it/s]
73%|███████▎ | 734/1000 [01:01<00:30, 8.62it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.46it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.92it/s]
74%|███████▍ | 739/1000 [01:01<00:23, 11.03it/s]
74%|███████▍ | 741/1000 [01:01<00:22, 11.70it/s]
74%|███████▍ | 743/1000 [01:01<00:20, 12.33it/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<00:20, 11.86it/s]
75%|███████▌ | 753/1000 [01:02<00:19, 12.60it/s]
76%|███████▌ | 755/1000 [01:02<00:18, 13.30it/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, 12.28it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 9.96it/s]
76%|███████▋ | 765/1000 [01:04<00:27, 8.40it/s]
77%|███████▋ | 767/1000 [01:04<00:25, 9.19it/s]
77%|███████▋ | 769/1000 [01:04<00:23, 9.86it/s]
77%|███████▋ | 771/1000 [01:04<00:21, 10.87it/s]
77%|███████▋ | 773/1000 [01:04<00:19, 11.85it/s]
78%|███████▊ | 775/1000 [01:04<00:17, 12.56it/s]
78%|███████▊ | 777/1000 [01:05<00:16, 13.59it/s]
78%|███████▊ | 779/1000 [01:05<00:21, 10.31it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.30it/s]
78%|███████▊ | 783/1000 [01:05<00:26, 8.24it/s]
78%|███████▊ | 784/1000 [01:06<00:30, 7.03it/s]
78%|███████▊ | 785/1000 [01:06<00:29, 7.35it/s]
79%|███████▊ | 786/1000 [01:06<00:30, 6.97it/s]
79%|███████▉ | 788/1000 [01:06<00:24, 8.81it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.14it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 11.29it/s]
79%|███████▉ | 794/1000 [01:06<00:17, 11.91it/s]
80%|███████▉ | 796/1000 [01:07<00:16, 12.38it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 13.14it/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, 8.30it/s]
81%|████████ | 806/1000 [01:08<00:21, 8.98it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.36it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.47it/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, 11.06it/s]
82%|████████▏ | 818/1000 [01:09<00:15, 12.00it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.51it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 13.07it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.86it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.28it/s]
83%|████████▎ | 828/1000 [01:09<00:11, 14.53it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.96it/s]
83%|████████▎ | 832/1000 [01:10<00:11, 14.34it/s]
83%|████████▎ | 834/1000 [01:10<00:11, 14.07it/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, 14.46it/s]
84%|████████▍ | 842/1000 [01:10<00:10, 14.46it/s]
84%|████████▍ | 844/1000 [01:11<00:11, 13.99it/s]
85%|████████▍ | 846/1000 [01:11<00:11, 13.96it/s]
85%|████████▍ | 848/1000 [01:11<00:10, 13.93it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.15it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.72it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.31it/s]
86%|████████▌ | 856/1000 [01:11<00:10, 14.12it/s]
86%|████████▌ | 858/1000 [01:11<00:09, 14.61it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 15.38it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.53it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 14.05it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.86it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.70it/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, 12.39it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.04it/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, 11.37it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 12.10it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.86it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.84it/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, 14.20it/s]
90%|████████▉ | 896/1000 [01:14<00:07, 13.94it/s]
90%|████████▉ | 898/1000 [01:15<00:07, 13.28it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.25it/s]
90%|█████████ | 902/1000 [01:15<00:07, 13.00it/s]
90%|█████████ | 904/1000 [01:15<00:09, 9.81it/s]
91%|█████████ | 906/1000 [01:15<00:08, 10.86it/s]
91%|█████████ | 908/1000 [01:15<00:07, 11.83it/s]
91%|█████████ | 910/1000 [01:16<00:07, 12.09it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.32it/s]
91%|█████████▏| 914/1000 [01:16<00:12, 6.82it/s]
92%|█████████▏| 916/1000 [01:16<00:10, 8.14it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.40it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.82it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 10.28it/s]
92%|█████████▏| 924/1000 [01:17<00:06, 10.97it/s]
93%|█████████▎| 926/1000 [01:17<00:06, 11.82it/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, 12.42it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.89it/s]
94%|█████████▎| 936/1000 [01:18<00:04, 13.46it/s]
94%|█████████▍| 938/1000 [01:18<00:04, 14.36it/s]
94%|█████████▍| 940/1000 [01:18<00:04, 14.67it/s]
94%|█████████▍| 942/1000 [01:19<00:04, 12.32it/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, 12.29it/s]
95%|█████████▌| 950/1000 [01:19<00:03, 12.84it/s]
95%|█████████▌| 952/1000 [01:19<00:03, 13.44it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 13.17it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 12.99it/s]
96%|█████████▌| 958/1000 [01:20<00:03, 13.75it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 12.49it/s]
96%|█████████▌| 962/1000 [01:20<00:03, 12.66it/s]
96%|█████████▋| 964/1000 [01:20<00:03, 9.67it/s]
97%|█████████▋| 966/1000 [01:21<00:03, 10.76it/s]
97%|█████████▋| 968/1000 [01:21<00:02, 11.07it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 11.32it/s]
97%|█████████▋| 972/1000 [01:21<00:03, 9.03it/s]
97%|█████████▋| 974/1000 [01:21<00:02, 10.29it/s]
98%|█████████▊| 976/1000 [01:22<00:02, 11.18it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.62it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.76it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.31it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 10.37it/s]
99%|█████████▊| 986/1000 [01:23<00:01, 10.65it/s]
99%|█████████▉| 988/1000 [01:23<00:01, 10.88it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 11.36it/s]
99%|█████████▉| 992/1000 [01:23<00:00, 12.25it/s]
99%|█████████▉| 994/1000 [01:23<00:00, 12.99it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 13.91it/s]
100%|█████████▉| 998/1000 [01:23<00:00, 14.10it/s]
100%|██████████| 1000/1000 [01:24<00:00, 14.77it/s]
{'eval_loss': '0.8748', 'eval_runtime': '84.1', 'eval_samples_per_second': '11.89', 'eval_steps_per_second': '11.89', 'epoch': '0.8562', 'num_input_tokens_seen': 53808416}
86%|████████▌ | 16000/18688 [18:52:24<3:01:51, 4.06s/it]
100%|██████████| 1000/1000 [01:24<00:00, 14.77it/s]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00010/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 [18:52:29<22:06:52, 29.63s/it] 86%|████████▌ | 16002/18688 [18:52:34<16:28:02, 22.07s/it] 86%|████████▌ | 16003/18688 [18:52:37<12:18:25, 16.50s/it] 86%|████████▌ | 16004/18688 [18:52:41<9:30:49, 12.76s/it] 86%|████████▌ | 16005/18688 [18:52:46<7:46:39, 10.44s/it] 86%|████████▌ | 16006/18688 [18:52:51<6:25:51, 8.63s/it] 86%|████████▌ | 16007/18688 [18:52:55<5:24:20, 7.26s/it] 86%|████████▌ | 16008/18688 [18:52:59<4:50:05, 6.49s/it] 86%|████████▌ | 16009/18688 [18:53:04<4:20:26, 5.83s/it] 86%|████████▌ | 16010/18688 [18:53:07<3:51:48, 5.19s/it] {'loss': '0.9657', 'grad_norm': '1.763', 'learning_rate': '5.875e-06', 'epoch': '0.8567', 'num_input_tokens_seen': 53843200, 'train_runtime': '6.799e+04', 'train_tokens_per_second': '792'}
86%|████████▌ | 16010/18688 [18:53:07<3:51:48, 5.19s/it] 86%|████████▌ | 16011/18688 [18:53:12<3:50:13, 5.16s/it] 86%|████████▌ | 16012/18688 [18:53:17<3:36:07, 4.85s/it] 86%|████████▌ | 16013/18688 [18:53:20<3:17:48, 4.44s/it] 86%|████████▌ | 16014/18688 [18:53:24<3:14:08, 4.36s/it] 86%|████████▌ | 16015/18688 [18:53:28<3:03:34, 4.12s/it] 86%|████████▌ | 16016/18688 [18:53:32<3:03:38, 4.12s/it] 86%|████████▌ | 16017/18688 [18:53:36<2:56:32, 3.97s/it] 86%|████████▌ | 16018/18688 [18:53:39<2:50:04, 3.82s/it] 86%|████████▌ | 16019/18688 [18:53:43<2:48:31, 3.79s/it] 86%|████████▌ | 16020/18688 [18:53:48<3:05:43, 4.18s/it] {'loss': '0.9016', 'grad_norm': '1.119', 'learning_rate': '5.833e-06', 'epoch': '0.8573', 'num_input_tokens_seen': 53874888, 'train_runtime': '6.803e+04', 'train_tokens_per_second': '791.9'}
86%|████████▌ | 16020/18688 [18:53:48<3:05:43, 4.18s/it] 86%|████████▌ | 16021/18688 [18:53:51<2:58:46, 4.02s/it] 86%|████████▌ | 16022/18688 [18:53:56<2:59:44, 4.05s/it] 86%|████████▌ | 16023/18688 [18:54:00<3:08:34, 4.25s/it] 86%|████████▌ | 16024/18688 [18:54:04<3:07:39, 4.23s/it] 86%|████████▌ | 16025/18688 [18:54:08<3:02:43, 4.12s/it] 86%|████████▌ | 16026/18688 [18:54:14<3:18:39, 4.48s/it] 86%|████████▌ | 16027/18688 [18:54:18<3:15:44, 4.41s/it] 86%|████████▌ | 16028/18688 [18:54:22<3:07:22, 4.23s/it] 86%|████████▌ | 16029/18688 [18:54:26<3:12:16, 4.34s/it] 86%|████████▌ | 16030/18688 [18:54:30<3:08:34, 4.26s/it] {'loss': '0.9053', 'grad_norm': '1.214', 'learning_rate': '5.79e-06', 'epoch': '0.8578', 'num_input_tokens_seen': 53910296, 'train_runtime': '6.807e+04', 'train_tokens_per_second': '792'}
86%|████████▌ | 16030/18688 [18:54:30<3:08:34, 4.26s/it] 86%|████████▌ | 16031/18688 [18:54:34<2:58:32, 4.03s/it] 86%|████████▌ | 16032/18688 [18:54:39<3:09:29, 4.28s/it] 86%|████████▌ | 16033/18688 [18:54:42<2:59:58, 4.07s/it] 86%|████████▌ | 16034/18688 [18:54:46<3:00:26, 4.08s/it] 86%|████████▌ | 16035/18688 [18:54:51<3:10:54, 4.32s/it] 86%|████████▌ | 16036/18688 [18:54:55<2:59:50, 4.07s/it] 86%|████████▌ | 16037/18688 [18:54:59<3:04:01, 4.16s/it] 86%|████████▌ | 16038/18688 [18:55:04<3:09:13, 4.28s/it] 86%|████████▌ | 16039/18688 [18:55:09<3:17:18, 4.47s/it] 86%|████████▌ | 16040/18688 [18:55:13<3:14:27, 4.41s/it] {'loss': '0.8556', 'grad_norm': '1.637', 'learning_rate': '5.747e-06', 'epoch': '0.8583', 'num_input_tokens_seen': 53946480, 'train_runtime': '6.811e+04', 'train_tokens_per_second': '792'}
86%|████████▌ | 16040/18688 [18:55:13<3:14:27, 4.41s/it] 86%|████████▌ | 16041/18688 [18:55:18<3:17:51, 4.48s/it] 86%|████████▌ | 16042/18688 [18:55:23<3:28:46, 4.73s/it] 86%|████████▌ | 16043/18688 [18:55:27<3:15:23, 4.43s/it] 86%|████████▌ | 16044/18688 [18:55:31<3:09:59, 4.31s/it] 86%|████████▌ | 16045/18688 [18:55:35<3:11:32, 4.35s/it] 86%|████████▌ | 16046/18688 [18:55:39<3:04:28, 4.19s/it] 86%|████████▌ | 16047/18688 [18:55:43<3:06:02, 4.23s/it] 86%|████████▌ | 16048/18688 [18:55:48<3:10:50, 4.34s/it] 86%|████████▌ | 16049/18688 [18:55:52<3:14:13, 4.42s/it] 86%|████████▌ | 16050/18688 [18:55:56<3:04:05, 4.19s/it] {'loss': '0.9071', 'grad_norm': '1.218', 'learning_rate': '5.705e-06', 'epoch': '0.8589', 'num_input_tokens_seen': 53982744, 'train_runtime': '6.816e+04', 'train_tokens_per_second': '792'}
86%|████████▌ | 16050/18688 [18:55:56<3:04:05, 4.19s/it] 86%|████████▌ | 16051/18688 [18:56:00<2:58:43, 4.07s/it] 86%|████████▌ | 16052/18688 [18:56:05<3:15:25, 4.45s/it] 86%|████████▌ | 16053/18688 [18:56:10<3:14:13, 4.42s/it] 86%|████████▌ | 16054/18688 [18:56:14<3:13:14, 4.40s/it] 86%|████████▌ | 16055/18688 [18:56:18<3:11:49, 4.37s/it] 86%|████████▌ | 16056/18688 [18:56:23<3:16:48, 4.49s/it] 86%|████████▌ | 16057/18688 [18:56:27<3:10:42, 4.35s/it] 86%|████████▌ | 16058/18688 [18:56:32<3:18:47, 4.54s/it] 86%|████████▌ | 16059/18688 [18:56:36<3:10:01, 4.34s/it] 86%|████████▌ | 16060/18688 [18:56:40<3:05:46, 4.24s/it] {'loss': '0.9151', 'grad_norm': '1.274', 'learning_rate': '5.662e-06', 'epoch': '0.8594', 'num_input_tokens_seen': 54018688, 'train_runtime': '6.82e+04', 'train_tokens_per_second': '792.1'}
86%|████████▌ | 16060/18688 [18:56:40<3:05:46, 4.24s/it] 86%|████████▌ | 16061/18688 [18:56:44<3:05:26, 4.24s/it] 86%|████████▌ | 16062/18688 [18:56:48<2:58:29, 4.08s/it] 86%|████████▌ | 16063/18688 [18:56:52<2:59:02, 4.09s/it] 86%|████████▌ | 16064/18688 [18:56:56<2:59:10, 4.10s/it] 86%|████████▌ | 16065/18688 [18:57:00<3:02:39, 4.18s/it] 86%|████████▌ | 16066/18688 [18:57:05<3:02:31, 4.18s/it] 86%|████████▌ | 16067/18688 [18:57:08<2:56:52, 4.05s/it] 86%|████████▌ | 16068/18688 [18:57:12<2:52:31, 3.95s/it] 86%|████████▌ | 16069/18688 [18:57:16<2:47:54, 3.85s/it] 86%|████████▌ | 16070/18688 [18:57:20<2:49:39, 3.89s/it] {'loss': '0.9332', 'grad_norm': '1.36', 'learning_rate': '5.62e-06', 'epoch': '0.8599', 'num_input_tokens_seen': 54050688, 'train_runtime': '6.824e+04', 'train_tokens_per_second': '792.1'}
86%|████████▌ | 16070/18688 [18:57:20<2:49:39, 3.89s/it] 86%|████████▌ | 16071/18688 [18:57:24<2:55:06, 4.01s/it] 86%|████████▌ | 16072/18688 [18:57:29<3:11:15, 4.39s/it] 86%|████████▌ | 16073/18688 [18:57:33<3:09:29, 4.35s/it] 86%|████████▌ | 16074/18688 [18:57:38<3:18:00, 4.54s/it] 86%|████████▌ | 16075/18688 [18:57:44<3:26:58, 4.75s/it] 86%|████████▌ | 16076/18688 [18:57:47<3:12:04, 4.41s/it] 86%|████████▌ | 16077/18688 [18:57:52<3:19:59, 4.60s/it] 86%|████████▌ | 16078/18688 [18:57:57<3:23:53, 4.69s/it] 86%|████████▌ | 16079/18688 [18:58:01<3:17:15, 4.54s/it] 86%|████████▌ | 16080/18688 [18:58:05<3:09:28, 4.36s/it] {'loss': '0.9281', 'grad_norm': '1.136', 'learning_rate': '5.578e-06', 'epoch': '0.8605', 'num_input_tokens_seen': 54091488, 'train_runtime': '6.829e+04', 'train_tokens_per_second': '792.1'}
86%|████████▌ | 16080/18688 [18:58:05<3:09:28, 4.36s/it] 86%|████████▌ | 16081/18688 [18:58:09<3:06:18, 4.29s/it] 86%|████████▌ | 16082/18688 [18:58:13<3:01:33, 4.18s/it] 86%|████████▌ | 16083/18688 [18:58:17<2:59:06, 4.13s/it] 86%|████████▌ | 16084/18688 [18:58:22<3:00:22, 4.16s/it] 86%|████████▌ | 16085/18688 [18:58:26<3:07:22, 4.32s/it] 86%|████████▌ | 16086/18688 [18:58:31<3:11:35, 4.42s/it] 86%|████████▌ | 16087/18688 [18:58:36<3:13:41, 4.47s/it] 86%|████████▌ | 16088/18688 [18:58:40<3:14:13, 4.48s/it] 86%|████████▌ | 16089/18688 [18:58:44<3:03:02, 4.23s/it] 86%|████████▌ | 16090/18688 [18:58:48<3:06:23, 4.30s/it] {'loss': '0.9686', 'grad_norm': '1.333', 'learning_rate': '5.536e-06', 'epoch': '0.861', 'num_input_tokens_seen': 54126304, 'train_runtime': '6.833e+04', 'train_tokens_per_second': '792.1'}
86%|████████▌ | 16090/18688 [18:58:48<3:06:23, 4.30s/it] 86%|████████▌ | 16091/18688 [18:58:53<3:07:26, 4.33s/it] 86%|████████▌ | 16092/18688 [18:58:56<2:59:50, 4.16s/it] 86%|████████▌ | 16093/18688 [18:59:00<2:52:19, 3.98s/it] 86%|████████▌ | 16094/18688 [18:59:04<2:53:45, 4.02s/it] 86%|████████▌ | 16095/18688 [18:59:08<2:48:37, 3.90s/it] 86%|████████▌ | 16096/18688 [18:59:11<2:42:57, 3.77s/it] 86%|████████▌ | 16097/18688 [18:59:16<2:54:20, 4.04s/it] 86%|████████▌ | 16098/18688 [18:59:20<3:00:24, 4.18s/it] 86%|████████▌ | 16099/18688 [18:59:24<2:52:00, 3.99s/it] 86%|████████▌ | 16100/18688 [18:59:28<2:55:23, 4.07s/it] {'loss': '0.8954', 'grad_norm': '1.333', 'learning_rate': '5.495e-06', 'epoch': '0.8615', 'num_input_tokens_seen': 54157960, 'train_runtime': '6.837e+04', 'train_tokens_per_second': '792.1'}
86%|████████▌ | 16100/18688 [18:59:28<2:55:23, 4.07s/it] 86%|████████▌ | 16101/18688 [18:59:32<2:49:38, 3.93s/it] 86%|████████▌ | 16102/18688 [18:59:35<2:45:58, 3.85s/it] 86%|████████▌ | 16103/18688 [18:59:39<2:49:20, 3.93s/it] 86%|████████▌ | 16104/18688 [18:59:44<2:54:26, 4.05s/it] 86%|████████▌ | 16105/18688 [18:59:49<3:03:25, 4.26s/it] 86%|████████▌ | 16106/18688 [18:59:53<3:07:37, 4.36s/it] 86%|████████▌ | 16107/18688 [18:59:57<3:05:23, 4.31s/it] 86%|████████▌ | 16108/18688 [19:00:02<3:06:28, 4.34s/it] 86%|████████▌ | 16109/18688 [19:00:06<3:04:53, 4.30s/it] 86%|████████▌ | 16110/18688 [19:00:10<3:06:57, 4.35s/it] {'loss': '0.9664', 'grad_norm': '1.2', 'learning_rate': '5.453e-06', 'epoch': '0.8621', 'num_input_tokens_seen': 54190576, 'train_runtime': '6.841e+04', 'train_tokens_per_second': '792.1'}
86%|████████▌ | 16110/18688 [19:00:10<3:06:57, 4.35s/it] 86%|████████▌ | 16111/18688 [19:00:16<3:20:06, 4.66s/it] 86%|████████▌ | 16112/18688 [19:00:20<3:16:18, 4.57s/it] 86%|████████▌ | 16113/18688 [19:00:24<3:06:54, 4.36s/it] 86%|████████▌ | 16114/18688 [19:00:29<3:11:01, 4.45s/it] 86%|████████▌ | 16115/18688 [19:00:33<3:10:33, 4.44s/it] 86%|████████▌ | 16116/18688 [19:00:37<3:05:18, 4.32s/it] 86%|████████▌ | 16117/18688 [19:00:41<3:02:09, 4.25s/it] 86%|████████▌ | 16118/18688 [19:00:46<3:05:09, 4.32s/it] 86%|████████▋ | 16119/18688 [19:00:49<2:55:49, 4.11s/it] 86%|████████▋ | 16120/18688 [19:00:53<2:48:35, 3.94s/it] {'loss': '0.8742', 'grad_norm': '1.324', 'learning_rate': '5.412e-06', 'epoch': '0.8626', 'num_input_tokens_seen': 54224232, 'train_runtime': '6.845e+04', 'train_tokens_per_second': '792.1'}
86%|████████▋ | 16120/18688 [19:00:53<2:48:35, 3.94s/it] 86%|████████▋ | 16121/18688 [19:00:57<2:53:06, 4.05s/it] 86%|████████▋ | 16122/18688 [19:01:02<2:59:48, 4.20s/it] 86%|████████▋ | 16123/18688 [19:01:05<2:53:41, 4.06s/it] 86%|████████▋ | 16124/18688 [19:01:09<2:48:16, 3.94s/it] 86%|████████▋ | 16125/18688 [19:01:13<2:46:00, 3.89s/it] 86%|████████▋ | 16126/18688 [19:01:17<2:44:03, 3.84s/it] 86%|████████▋ | 16127/18688 [19:01:21<2:47:38, 3.93s/it] 86%|████████▋ | 16128/18688 [19:01:25<2:54:41, 4.09s/it] 86%|████████▋ | 16129/18688 [19:01:29<2:55:25, 4.11s/it] 86%|████████▋ | 16130/18688 [19:01:33<2:51:30, 4.02s/it] {'loss': '0.8682', 'grad_norm': '1.353', 'learning_rate': '5.37e-06', 'epoch': '0.8631', 'num_input_tokens_seen': 54255880, 'train_runtime': '6.849e+04', 'train_tokens_per_second': '792.1'}
86%|████████▋ | 16130/18688 [19:01:33<2:51:30, 4.02s/it] 86%|████████▋ | 16131/18688 [19:01:37<2:49:35, 3.98s/it] 86%|████████▋ | 16132/18688 [19:01:41<2:52:49, 4.06s/it] 86%|████████▋ | 16133/18688 [19:01:45<2:48:28, 3.96s/it] 86%|████████▋ | 16134/18688 [19:01:49<2:46:57, 3.92s/it] 86%|████████▋ | 16135/18688 [19:01:53<2:50:15, 4.00s/it] 86%|████████▋ | 16136/18688 [19:01:57<2:45:16, 3.89s/it] 86%|████████▋ | 16137/18688 [19:02:02<3:03:12, 4.31s/it] 86%|████████▋ | 16138/18688 [19:02:07<3:12:49, 4.54s/it] 86%|████████▋ | 16139/18688 [19:02:12<3:16:10, 4.62s/it] 86%|████████▋ | 16140/18688 [19:02:17<3:18:26, 4.67s/it] {'loss': '0.9024', 'grad_norm': '1.395', 'learning_rate': '5.329e-06', 'epoch': '0.8637', 'num_input_tokens_seen': 54291480, 'train_runtime': '6.854e+04', 'train_tokens_per_second': '792.1'}
86%|████████▋ | 16140/18688 [19:02:17<3:18:26, 4.67s/it] 86%|████████▋ | 16141/18688 [19:02:21<3:17:31, 4.65s/it] 86%|████████▋ | 16142/18688 [19:02:25<3:11:32, 4.51s/it] 86%|████████▋ | 16143/18688 [19:02:30<3:11:09, 4.51s/it] 86%|████████▋ | 16144/18688 [19:02:34<3:03:00, 4.32s/it] 86%|████████▋ | 16145/18688 [19:02:38<3:01:40, 4.29s/it] 86%|████████▋ | 16146/18688 [19:02:43<3:06:41, 4.41s/it] 86%|████████▋ | 16147/18688 [19:02:47<3:03:18, 4.33s/it] 86%|████████▋ | 16148/18688 [19:02:51<3:01:38, 4.29s/it] 86%|████████▋ | 16149/18688 [19:02:55<2:51:11, 4.05s/it] 86%|████████▋ | 16150/18688 [19:02:59<3:01:11, 4.28s/it] {'loss': '0.9228', 'grad_norm': '1.409', 'learning_rate': '5.288e-06', 'epoch': '0.8642', 'num_input_tokens_seen': 54325880, 'train_runtime': '6.858e+04', 'train_tokens_per_second': '792.2'}
86%|████████▋ | 16150/18688 [19:02:59<3:01:11, 4.28s/it] 86%|████████▋ | 16151/18688 [19:03:03<2:53:56, 4.11s/it] 86%|████████▋ | 16152/18688 [19:03:07<2:54:12, 4.12s/it] 86%|████████▋ | 16153/18688 [19:03:11<2:50:15, 4.03s/it] 86%|████████▋ | 16154/18688 [19:03:15<2:47:24, 3.96s/it] 86%|████████▋ | 16155/18688 [19:03:19<2:46:45, 3.95s/it] 86%|████████▋ | 16156/18688 [19:03:24<3:04:35, 4.37s/it] 86%|████████▋ | 16157/18688 [19:03:29<3:04:19, 4.37s/it] 86%|████████▋ | 16158/18688 [19:03:33<3:06:52, 4.43s/it] 86%|████████▋ | 16159/18688 [19:03:39<3:21:09, 4.77s/it] 86%|████████▋ | 16160/18688 [19:03:43<3:16:22, 4.66s/it] {'loss': '0.9203', 'grad_norm': '1.312', 'learning_rate': '5.247e-06', 'epoch': '0.8647', 'num_input_tokens_seen': 54363088, 'train_runtime': '6.862e+04', 'train_tokens_per_second': '792.2'}
86%|████████▋ | 16160/18688 [19:03:43<3:16:22, 4.66s/it] 86%|████████▋ | 16161/18688 [19:03:47<3:09:51, 4.51s/it] 86%|████████▋ | 16162/18688 [19:03:51<3:00:28, 4.29s/it] 86%|████████▋ | 16163/18688 [19:03:56<3:10:05, 4.52s/it] 86%|████████▋ | 16164/18688 [19:04:00<3:08:56, 4.49s/it] 86%|████████▋ | 16165/18688 [19:04:05<3:09:24, 4.50s/it] 87%|████████▋ | 16166/18688 [19:04:10<3:09:51, 4.52s/it] 87%|████████▋ | 16167/18688 [19:04:14<3:08:57, 4.50s/it] 87%|████████▋ | 16168/18688 [19:04:18<3:03:12, 4.36s/it] 87%|████████▋ | 16169/18688 [19:04:22<2:54:32, 4.16s/it] 87%|████████▋ | 16170/18688 [19:04:25<2:47:43, 4.00s/it] {'loss': '0.9502', 'grad_norm': '1.81', 'learning_rate': '5.207e-06', 'epoch': '0.8653', 'num_input_tokens_seen': 54395472, 'train_runtime': '6.867e+04', 'train_tokens_per_second': '792.2'}
87%|████████▋ | 16170/18688 [19:04:25<2:47:43, 4.00s/it] 87%|████████▋ | 16171/18688 [19:04:29<2:45:21, 3.94s/it] 87%|████████▋ | 16172/18688 [19:04:33<2:49:40, 4.05s/it] 87%|████████▋ | 16173/18688 [19:04:37<2:45:31, 3.95s/it] 87%|████████▋ | 16174/18688 [19:04:43<3:03:10, 4.37s/it] 87%|████████▋ | 16175/18688 [19:04:46<2:55:49, 4.20s/it] 87%|████████▋ | 16176/18688 [19:04:50<2:53:54, 4.15s/it] 87%|████████▋ | 16177/18688 [19:04:54<2:52:42, 4.13s/it] 87%|████████▋ | 16178/18688 [19:04:59<3:03:20, 4.38s/it] 87%|████████▋ | 16179/18688 [19:05:06<3:27:04, 4.95s/it] 87%|████████▋ | 16180/18688 [19:05:10<3:19:06, 4.76s/it] {'loss': '0.8995', 'grad_norm': '1.312', 'learning_rate': '5.166e-06', 'epoch': '0.8658', 'num_input_tokens_seen': 54432584, 'train_runtime': '6.871e+04', 'train_tokens_per_second': '792.2'}
87%|████████▋ | 16180/18688 [19:05:10<3:19:06, 4.76s/it] 87%|████████▋ | 16181/18688 [19:05:14<3:03:18, 4.39s/it] 87%|████████▋ | 16182/18688 [19:05:19<3:12:52, 4.62s/it] 87%|████████▋ | 16183/18688 [19:05:23<3:06:25, 4.47s/it] 87%|████████▋ | 16184/18688 [19:05:28<3:16:31, 4.71s/it] 87%|████████▋ | 16185/18688 [19:05:32<3:06:11, 4.46s/it] 87%|████████▋ | 16186/18688 [19:05:37<3:13:37, 4.64s/it] 87%|████████▋ | 16187/18688 [19:05:41<3:10:23, 4.57s/it] 87%|████████▋ | 16188/18688 [19:05:45<3:01:56, 4.37s/it] 87%|████████▋ | 16189/18688 [19:05:49<2:55:24, 4.21s/it] 87%|████████▋ | 16190/18688 [19:05:54<3:04:54, 4.44s/it] {'loss': '0.9185', 'grad_norm': '1.552', 'learning_rate': '5.126e-06', 'epoch': '0.8664', 'num_input_tokens_seen': 54467968, 'train_runtime': '6.875e+04', 'train_tokens_per_second': '792.2'}
87%|████████▋ | 16190/18688 [19:05:54<3:04:54, 4.44s/it] 87%|████████▋ | 16191/18688 [19:05:58<2:56:25, 4.24s/it] 87%|████████▋ | 16192/18688 [19:06:02<2:57:20, 4.26s/it] 87%|████████▋ | 16193/18688 [19:06:07<3:08:06, 4.52s/it] 87%|████████▋ | 16194/18688 [19:06:11<2:55:32, 4.22s/it] 87%|████████▋ | 16195/18688 [19:06:14<2:46:02, 4.00s/it] 87%|████████▋ | 16196/18688 [19:06:18<2:44:16, 3.96s/it] 87%|████████▋ | 16197/18688 [19:06:22<2:42:04, 3.90s/it] 87%|████████▋ | 16198/18688 [19:06:26<2:47:57, 4.05s/it] 87%|████████▋ | 16199/18688 [19:06:31<2:52:55, 4.17s/it] 87%|████████▋ | 16200/18688 [19:06:35<2:49:40, 4.09s/it] {'loss': '0.9004', 'grad_norm': '1.189', 'learning_rate': '5.086e-06', 'epoch': '0.8669', 'num_input_tokens_seen': 54499824, 'train_runtime': '6.88e+04', 'train_tokens_per_second': '792.2'}
87%|████████▋ | 16200/18688 [19:06:35<2:49:40, 4.09s/it] 87%|████████▋ | 16201/18688 [19:06:38<2:42:34, 3.92s/it] 87%|████████▋ | 16202/18688 [19:06:42<2:40:48, 3.88s/it] 87%|████████▋ | 16203/18688 [19:06:46<2:42:00, 3.91s/it] 87%|████████▋ | 16204/18688 [19:06:51<2:50:31, 4.12s/it] 87%|████████▋ | 16205/18688 [19:06:55<2:52:23, 4.17s/it] 87%|████████▋ | 16206/18688 [19:06:59<2:54:11, 4.21s/it] 87%|████████▋ | 16207/18688 [19:07:03<2:49:44, 4.10s/it] 87%|████████▋ | 16208/18688 [19:07:07<2:51:10, 4.14s/it] 87%|████████▋ | 16209/18688 [19:07:12<2:53:12, 4.19s/it] 87%|████████▋ | 16210/18688 [19:07:15<2:44:06, 3.97s/it] {'loss': '0.9324', 'grad_norm': '1.383', 'learning_rate': '5.045e-06', 'epoch': '0.8674', 'num_input_tokens_seen': 54531792, 'train_runtime': '6.884e+04', 'train_tokens_per_second': '792.2'}
87%|████████▋ | 16210/18688 [19:07:15<2:44:06, 3.97s/it] 87%|████████▋ | 16211/18688 [19:07:19<2:45:38, 4.01s/it] 87%|████████▋ | 16212/18688 [19:07:23<2:47:33, 4.06s/it] 87%|████████▋ | 16213/18688 [19:07:27<2:46:41, 4.04s/it] 87%|████████▋ | 16214/18688 [19:07:31<2:45:24, 4.01s/it] 87%|████████▋ | 16215/18688 [19:07:35<2:44:54, 4.00s/it] 87%|████████▋ | 16216/18688 [19:07:40<2:48:02, 4.08s/it] 87%|████████▋ | 16217/18688 [19:07:44<2:57:06, 4.30s/it] 87%|████████▋ | 16218/18688 [19:07:48<2:51:57, 4.18s/it] 87%|████████▋ | 16219/18688 [19:07:53<2:57:30, 4.31s/it] 87%|████████▋ | 16220/18688 [19:07:57<2:58:02, 4.33s/it] {'loss': '0.9017', 'grad_norm': '1.242', 'learning_rate': '5.006e-06', 'epoch': '0.868', 'num_input_tokens_seen': 54562368, 'train_runtime': '6.888e+04', 'train_tokens_per_second': '792.2'}
87%|████████▋ | 16220/18688 [19:07:57<2:58:02, 4.33s/it] 87%|████████▋ | 16221/18688 [19:08:01<2:56:17, 4.29s/it] 87%|████████▋ | 16222/18688 [19:08:05<2:49:41, 4.13s/it] 87%|████████▋ | 16223/18688 [19:08:10<2:52:17, 4.19s/it] 87%|████████▋ | 16224/18688 [19:08:14<2:59:11, 4.36s/it] 87%|████████▋ | 16225/18688 [19:08:18<2:53:20, 4.22s/it] 87%|████████▋ | 16226/18688 [19:08:22<2:50:49, 4.16s/it] 87%|████████▋ | 16227/18688 [19:08:27<2:55:15, 4.27s/it] 87%|████████▋ | 16228/18688 [19:08:31<2:53:47, 4.24s/it] 87%|████████▋ | 16229/18688 [19:08:35<2:53:42, 4.24s/it] 87%|████████▋ | 16230/18688 [19:08:39<2:54:43, 4.26s/it] {'loss': '0.8903', 'grad_norm': '1.428', 'learning_rate': '4.966e-06', 'epoch': '0.8685', 'num_input_tokens_seen': 54595720, 'train_runtime': '6.892e+04', 'train_tokens_per_second': '792.2'}
87%|████████▋ | 16230/18688 [19:08:39<2:54:43, 4.26s/it] 87%|████████▋ | 16231/18688 [19:08:44<2:54:57, 4.27s/it] 87%|████████▋ | 16232/18688 [19:08:47<2:46:59, 4.08s/it] 87%|████████▋ | 16233/18688 [19:08:51<2:39:39, 3.90s/it] 87%|████████▋ | 16234/18688 [19:08:55<2:42:54, 3.98s/it] 87%|████████▋ | 16235/18688 [19:08:59<2:45:55, 4.06s/it] 87%|████████▋ | 16236/18688 [19:09:03<2:43:04, 3.99s/it] 87%|████████▋ | 16237/18688 [19:09:07<2:44:58, 4.04s/it] 87%|████████▋ | 16238/18688 [19:09:12<2:51:15, 4.19s/it] 87%|████████▋ | 16239/18688 [19:09:16<2:46:45, 4.09s/it] 87%|████████▋ | 16240/18688 [19:09:20<2:46:42, 4.09s/it] {'loss': '0.8717', 'grad_norm': '1.155', 'learning_rate': '4.926e-06', 'epoch': '0.869', 'num_input_tokens_seen': 54626424, 'train_runtime': '6.896e+04', 'train_tokens_per_second': '792.1'}
87%|████████▋ | 16240/18688 [19:09:20<2:46:42, 4.09s/it] 87%|████████▋ | 16241/18688 [19:09:24<2:43:46, 4.02s/it] 87%|████████▋ | 16242/18688 [19:09:28<2:54:01, 4.27s/it] 87%|████████▋ | 16243/18688 [19:09:32<2:44:15, 4.03s/it] 87%|████████▋ | 16244/18688 [19:09:35<2:38:11, 3.88s/it] 87%|████████▋ | 16245/18688 [19:09:40<2:42:51, 4.00s/it] 87%|████████▋ | 16246/18688 [19:09:45<2:53:28, 4.26s/it] 87%|████████▋ | 16247/18688 [19:09:49<2:52:54, 4.25s/it] 87%|████████▋ | 16248/18688 [19:09:53<2:49:50, 4.18s/it] 87%|████████▋ | 16249/18688 [19:09:57<2:50:32, 4.20s/it] 87%|████████▋ | 16250/18688 [19:10:01<2:49:31, 4.17s/it] {'loss': '0.8871', 'grad_norm': '1.25', 'learning_rate': '4.887e-06', 'epoch': '0.8696', 'num_input_tokens_seen': 54660032, 'train_runtime': '6.9e+04', 'train_tokens_per_second': '792.2'}
87%|████████▋ | 16250/18688 [19:10:01<2:49:31, 4.17s/it] 87%|████████▋ | 16251/18688 [19:10:05<2:42:03, 3.99s/it] 87%|████████▋ | 16252/18688 [19:10:09<2:43:26, 4.03s/it] 87%|████████▋ | 16253/18688 [19:10:13<2:43:40, 4.03s/it] 87%|████████▋ | 16254/18688 [19:10:17<2:44:09, 4.05s/it] 87%|████████▋ | 16255/18688 [19:10:21<2:49:17, 4.17s/it] 87%|████████▋ | 16256/18688 [19:10:25<2:45:47, 4.09s/it] 87%|████████▋ | 16257/18688 [19:10:30<2:47:55, 4.14s/it] 87%|████████▋ | 16258/18688 [19:10:34<2:49:47, 4.19s/it] 87%|████████▋ | 16259/18688 [19:10:38<2:53:58, 4.30s/it] 87%|████████▋ | 16260/18688 [19:10:43<2:53:11, 4.28s/it] {'loss': '0.8959', 'grad_norm': '1.218', 'learning_rate': '4.847e-06', 'epoch': '0.8701', 'num_input_tokens_seen': 54694552, 'train_runtime': '6.904e+04', 'train_tokens_per_second': '792.2'}
87%|████████▋ | 16260/18688 [19:10:43<2:53:11, 4.28s/it] 87%|████████▋ | 16261/18688 [19:10:47<2:57:16, 4.38s/it] 87%|████████▋ | 16262/18688 [19:10:52<2:56:56, 4.38s/it] 87%|████████▋ | 16263/18688 [19:10:57<3:03:01, 4.53s/it] 87%|████████▋ | 16264/18688 [19:11:01<2:59:08, 4.43s/it] 87%|████████▋ | 16265/18688 [19:11:05<2:53:27, 4.30s/it] 87%|████████▋ | 16266/18688 [19:11:09<2:52:12, 4.27s/it] 87%|████████▋ | 16267/18688 [19:11:13<2:47:41, 4.16s/it] 87%|████████▋ | 16268/18688 [19:11:17<2:46:58, 4.14s/it] 87%|████████▋ | 16269/18688 [19:11:21<2:46:53, 4.14s/it] 87%|████████▋ | 16270/18688 [19:11:25<2:46:58, 4.14s/it] {'loss': '0.894', 'grad_norm': '1.539', 'learning_rate': '4.808e-06', 'epoch': '0.8706', 'num_input_tokens_seen': 54730696, 'train_runtime': '6.909e+04', 'train_tokens_per_second': '792.2'}
87%|████████▋ | 16270/18688 [19:11:25<2:46:58, 4.14s/it] 87%|████████▋ | 16271/18688 [19:11:30<2:54:00, 4.32s/it] 87%|████████▋ | 16272/18688 [19:11:34<2:48:01, 4.17s/it] 87%|████████▋ | 16273/18688 [19:11:39<2:57:05, 4.40s/it] 87%|████████▋ | 16274/18688 [19:11:42<2:45:56, 4.12s/it] 87%|████████▋ | 16275/18688 [19:11:47<2:47:58, 4.18s/it] 87%|████████▋ | 16276/18688 [19:11:51<2:46:30, 4.14s/it] 87%|████████▋ | 16277/18688 [19:11:55<2:54:13, 4.34s/it] 87%|████████▋ | 16278/18688 [19:11:59<2:51:12, 4.26s/it] 87%|████████▋ | 16279/18688 [19:12:04<2:50:04, 4.24s/it] 87%|████████▋ | 16280/18688 [19:12:07<2:44:39, 4.10s/it] {'loss': '0.9103', 'grad_norm': '1.266', 'learning_rate': '4.769e-06', 'epoch': '0.8712', 'num_input_tokens_seen': 54765720, 'train_runtime': '6.913e+04', 'train_tokens_per_second': '792.2'}
87%|████████▋ | 16280/18688 [19:12:07<2:44:39, 4.10s/it] 87%|████████▋ | 16281/18688 [19:12:11<2:37:48, 3.93s/it] 87%|████████▋ | 16282/18688 [19:12:15<2:33:16, 3.82s/it] 87%|████████▋ | 16283/18688 [19:12:19<2:39:25, 3.98s/it] 87%|████████▋ | 16284/18688 [19:12:23<2:39:57, 3.99s/it] 87%|████████▋ | 16285/18688 [19:12:27<2:37:34, 3.93s/it] 87%|████████▋ | 16286/18688 [19:12:31<2:41:52, 4.04s/it] 87%|████████▋ | 16287/18688 [19:12:35<2:45:48, 4.14s/it] 87%|████████▋ | 16288/18688 [19:12:40<2:47:09, 4.18s/it] 87%|████████▋ | 16289/18688 [19:12:44<2:47:33, 4.19s/it] 87%|████████▋ | 16290/18688 [19:12:48<2:48:11, 4.21s/it] {'loss': '0.9416', 'grad_norm': '1.526', 'learning_rate': '4.73e-06', 'epoch': '0.8717', 'num_input_tokens_seen': 54797360, 'train_runtime': '6.917e+04', 'train_tokens_per_second': '792.2'}
87%|████████▋ | 16290/18688 [19:12:48<2:48:11, 4.21s/it] 87%|████████▋ | 16291/18688 [19:12:53<2:50:57, 4.28s/it] 87%|████████▋ | 16292/18688 [19:12:58<3:04:41, 4.63s/it] 87%|████████▋ | 16293/18688 [19:13:03<3:04:49, 4.63s/it] 87%|████████▋ | 16294/18688 [19:13:07<3:07:18, 4.69s/it] 87%|████████▋ | 16295/18688 [19:13:12<3:02:21, 4.57s/it] 87%|████████▋ | 16296/18688 [19:13:16<3:01:43, 4.56s/it] 87%|████████▋ | 16297/18688 [19:13:21<3:03:10, 4.60s/it] 87%|████████▋ | 16298/18688 [19:13:25<2:55:24, 4.40s/it] 87%|████████▋ | 16299/18688 [19:13:29<2:48:36, 4.23s/it] 87%|████████▋ | 16300/18688 [19:13:33<2:44:10, 4.13s/it] {'loss': '0.8934', 'grad_norm': '1.413', 'learning_rate': '4.691e-06', 'epoch': '0.8722', 'num_input_tokens_seen': 54835080, 'train_runtime': '6.921e+04', 'train_tokens_per_second': '792.3'}
87%|████████▋ | 16300/18688 [19:13:33<2:44:10, 4.13s/it] 87%|████████▋ | 16301/18688 [19:13:37<2:49:23, 4.26s/it] 87%|████████▋ | 16302/18688 [19:13:41<2:44:48, 4.14s/it] 87%|████████▋ | 16303/18688 [19:13:46<2:57:52, 4.47s/it] 87%|████████▋ | 16304/18688 [19:13:50<2:53:08, 4.36s/it] 87%|████████▋ | 16305/18688 [19:13:54<2:46:52, 4.20s/it] 87%|████████▋ | 16306/18688 [19:13:58<2:38:46, 4.00s/it] 87%|████████▋ | 16307/18688 [19:14:01<2:34:44, 3.90s/it] 87%|████████▋ | 16308/18688 [19:14:05<2:34:01, 3.88s/it] 87%|████████▋ | 16309/18688 [19:14:10<2:38:59, 4.01s/it] 87%|████████▋ | 16310/18688 [19:14:14<2:43:16, 4.12s/it] {'loss': '0.9308', 'grad_norm': '1.444', 'learning_rate': '4.653e-06', 'epoch': '0.8728', 'num_input_tokens_seen': 54868472, 'train_runtime': '6.925e+04', 'train_tokens_per_second': '792.3'}
87%|████████▋ | 16310/18688 [19:14:14<2:43:16, 4.12s/it] 87%|████████▋ | 16311/18688 [19:14:18<2:43:31, 4.13s/it] 87%|████████▋ | 16312/18688 [19:14:22<2:36:58, 3.96s/it] 87%|████████▋ | 16313/18688 [19:14:26<2:41:28, 4.08s/it] 87%|████████▋ | 16314/18688 [19:14:30<2:43:53, 4.14s/it] 87%|████████▋ | 16315/18688 [19:14:34<2:42:56, 4.12s/it] 87%|████████▋ | 16316/18688 [19:14:39<2:49:49, 4.30s/it] 87%|████████▋ | 16317/18688 [19:14:44<2:54:25, 4.41s/it] 87%|████████▋ | 16318/18688 [19:14:48<2:52:26, 4.37s/it] 87%|████████▋ | 16319/18688 [19:14:52<2:48:11, 4.26s/it] 87%|████████▋ | 16320/18688 [19:14:56<2:47:22, 4.24s/it] {'loss': '0.9595', 'grad_norm': '1.356', 'learning_rate': '4.614e-06', 'epoch': '0.8733', 'num_input_tokens_seen': 54904520, 'train_runtime': '6.93e+04', 'train_tokens_per_second': '792.3'}
87%|████████▋ | 16320/18688 [19:14:56<2:47:22, 4.24s/it] 87%|████████▋ | 16321/18688 [19:15:01<2:48:43, 4.28s/it] 87%|████████▋ | 16322/18688 [19:15:05<2:49:46, 4.31s/it] 87%|████████▋ | 16323/18688 [19:15:09<2:43:05, 4.14s/it] 87%|████████▋ | 16324/18688 [19:15:13<2:42:55, 4.14s/it] 87%|████████▋ | 16325/18688 [19:15:17<2:42:50, 4.13s/it] 87%|████████▋ | 16326/18688 [19:15:21<2:36:34, 3.98s/it] 87%|████████▋ | 16327/18688 [19:15:25<2:36:31, 3.98s/it] 87%|████████▋ | 16328/18688 [19:15:29<2:38:16, 4.02s/it] 87%|████████▋ | 16329/18688 [19:15:34<2:48:17, 4.28s/it] 87%|████████▋ | 16330/18688 [19:15:37<2:41:43, 4.11s/it] {'loss': '0.8775', 'grad_norm': '1.466', 'learning_rate': '4.576e-06', 'epoch': '0.8738', 'num_input_tokens_seen': 54938024, 'train_runtime': '6.934e+04', 'train_tokens_per_second': '792.3'}
87%|████████▋ | 16330/18688 [19:15:37<2:41:43, 4.11s/it] 87%|████████▋ | 16331/18688 [19:15:42<2:44:20, 4.18s/it] 87%|████████▋ | 16332/18688 [19:15:46<2:41:09, 4.10s/it] 87%|████████▋ | 16333/18688 [19:15:49<2:38:04, 4.03s/it] 87%|████████▋ | 16334/18688 [19:15:54<2:38:59, 4.05s/it] 87%|████████▋ | 16335/18688 [19:15:58<2:40:26, 4.09s/it] 87%|████████▋ | 16336/18688 [19:16:01<2:33:07, 3.91s/it] 87%|████████▋ | 16337/18688 [19:16:05<2:30:56, 3.85s/it] 87%|████████▋ | 16338/18688 [19:16:09<2:29:52, 3.83s/it] 87%|████████▋ | 16339/18688 [19:16:13<2:39:33, 4.08s/it] 87%|████████▋ | 16340/18688 [19:16:17<2:38:03, 4.04s/it] {'loss': '0.9054', 'grad_norm': '1.462', 'learning_rate': '4.538e-06', 'epoch': '0.8744', 'num_input_tokens_seen': 54970040, 'train_runtime': '6.938e+04', 'train_tokens_per_second': '792.3'}
87%|████████▋ | 16340/18688 [19:16:17<2:38:03, 4.04s/it] 87%|████████▋ | 16341/18688 [19:16:22<2:43:44, 4.19s/it] 87%|████████▋ | 16342/18688 [19:16:27<2:52:38, 4.42s/it] 87%|████████▋ | 16343/18688 [19:16:31<2:54:04, 4.45s/it] 87%|████████▋ | 16344/18688 [19:16:37<3:03:09, 4.69s/it] 87%|████████▋ | 16345/18688 [19:16:41<2:56:40, 4.52s/it] 87%|████████▋ | 16346/18688 [19:16:45<2:52:55, 4.43s/it] 87%|████████▋ | 16347/18688 [19:16:49<2:45:06, 4.23s/it] 87%|████████▋ | 16348/18688 [19:16:52<2:37:12, 4.03s/it] 87%|████████▋ | 16349/18688 [19:16:57<2:41:31, 4.14s/it] 87%|████████▋ | 16350/18688 [19:17:01<2:41:59, 4.16s/it] {'loss': '0.9254', 'grad_norm': '1.212', 'learning_rate': '4.5e-06', 'epoch': '0.8749', 'num_input_tokens_seen': 55006632, 'train_runtime': '6.942e+04', 'train_tokens_per_second': '792.4'}
87%|████████▋ | 16350/18688 [19:17:01<2:41:59, 4.16s/it] 87%|████████▋ | 16351/18688 [19:17:04<2:35:30, 3.99s/it] 88%|████████▊ | 16352/18688 [19:17:09<2:36:51, 4.03s/it] 88%|████████▊ | 16353/18688 [19:17:13<2:39:01, 4.09s/it] 88%|████████▊ | 16354/18688 [19:17:17<2:34:35, 3.97s/it] 88%|████████▊ | 16355/18688 [19:17:21<2:36:36, 4.03s/it] 88%|████████▊ | 16356/18688 [19:17:24<2:32:35, 3.93s/it] 88%|████████▊ | 16357/18688 [19:17:29<2:38:17, 4.07s/it] 88%|████████▊ | 16358/18688 [19:17:32<2:33:18, 3.95s/it] 88%|████████▊ | 16359/18688 [19:17:37<2:34:56, 3.99s/it] 88%|████████▊ | 16360/18688 [19:17:41<2:38:10, 4.08s/it] {'loss': '0.8915', 'grad_norm': '1.339', 'learning_rate': '4.462e-06', 'epoch': '0.8755', 'num_input_tokens_seen': 55037856, 'train_runtime': '6.946e+04', 'train_tokens_per_second': '792.4'}
88%|████████▊ | 16360/18688 [19:17:41<2:38:10, 4.08s/it] 88%|████████▊ | 16361/18688 [19:17:45<2:37:16, 4.06s/it] 88%|████████▊ | 16362/18688 [19:17:49<2:33:44, 3.97s/it] 88%|████████▊ | 16363/18688 [19:17:53<2:40:58, 4.15s/it] 88%|████████▊ | 16364/18688 [19:17:57<2:34:46, 4.00s/it] 88%|████████▊ | 16365/18688 [19:18:01<2:36:47, 4.05s/it] 88%|████████▊ | 16366/18688 [19:18:05<2:39:51, 4.13s/it] 88%|████████▊ | 16367/18688 [19:18:09<2:38:44, 4.10s/it] 88%|████████▊ | 16368/18688 [19:18:13<2:38:05, 4.09s/it] 88%|████████▊ | 16369/18688 [19:18:18<2:47:05, 4.32s/it] 88%|████████▊ | 16370/18688 [19:18:23<2:49:14, 4.38s/it] {'loss': '0.9091', 'grad_norm': '1.203', 'learning_rate': '4.425e-06', 'epoch': '0.876', 'num_input_tokens_seen': 55071192, 'train_runtime': '6.95e+04', 'train_tokens_per_second': '792.4'}
88%|████████▊ | 16370/18688 [19:18:23<2:49:14, 4.38s/it] 88%|████████▊ | 16371/18688 [19:18:27<2:42:32, 4.21s/it] 88%|████████▊ | 16372/18688 [19:18:30<2:35:02, 4.02s/it] 88%|████████▊ | 16373/18688 [19:18:35<2:39:46, 4.14s/it] 88%|████████▊ | 16374/18688 [19:18:38<2:37:12, 4.08s/it] 88%|████████▊ | 16375/18688 [19:18:43<2:41:10, 4.18s/it] 88%|████████▊ | 16376/18688 [19:18:47<2:37:50, 4.10s/it] 88%|████████▊ | 16377/18688 [19:18:51<2:38:36, 4.12s/it] 88%|████████▊ | 16378/18688 [19:18:55<2:37:30, 4.09s/it] 88%|████████▊ | 16379/18688 [19:18:59<2:36:15, 4.06s/it] 88%|████████▊ | 16380/18688 [19:19:04<2:41:53, 4.21s/it] {'loss': '0.9085', 'grad_norm': '1.094', 'learning_rate': '4.387e-06', 'epoch': '0.8765', 'num_input_tokens_seen': 55103616, 'train_runtime': '6.954e+04', 'train_tokens_per_second': '792.4'}
88%|████████▊ | 16380/18688 [19:19:04<2:41:53, 4.21s/it] 88%|████████▊ | 16381/18688 [19:19:09<2:54:27, 4.54s/it] 88%|████████▊ | 16382/18688 [19:19:13<2:47:40, 4.36s/it] 88%|████████▊ | 16383/18688 [19:19:17<2:51:07, 4.45s/it] 88%|████████▊ | 16384/18688 [19:19:23<2:57:47, 4.63s/it] 88%|████████▊ | 16385/18688 [19:19:27<2:53:43, 4.53s/it] 88%|████████▊ | 16386/18688 [19:19:31<2:47:40, 4.37s/it] 88%|████████▊ | 16387/18688 [19:19:35<2:40:53, 4.20s/it] 88%|████████▊ | 16388/18688 [19:19:38<2:32:29, 3.98s/it] 88%|████████▊ | 16389/18688 [19:19:43<2:48:42, 4.40s/it] 88%|████████▊ | 16390/18688 [19:19:48<2:52:36, 4.51s/it] {'loss': '0.8785', 'grad_norm': '1.422', 'learning_rate': '4.35e-06', 'epoch': '0.8771', 'num_input_tokens_seen': 55142560, 'train_runtime': '6.959e+04', 'train_tokens_per_second': '792.4'}
88%|████████▊ | 16390/18688 [19:19:48<2:52:36, 4.51s/it] 88%|████████▊ | 16391/18688 [19:19:52<2:44:07, 4.29s/it] 88%|████████▊ | 16392/18688 [19:19:56<2:44:09, 4.29s/it] 88%|████████▊ | 16393/18688 [19:20:00<2:39:34, 4.17s/it] 88%|████████▊ | 16394/18688 [19:20:04<2:32:09, 3.98s/it] 88%|████████▊ | 16395/18688 [19:20:08<2:38:19, 4.14s/it] 88%|████████▊ | 16396/18688 [19:20:13<2:47:56, 4.40s/it] 88%|████████▊ | 16397/18688 [19:20:17<2:38:50, 4.16s/it] 88%|████████▊ | 16398/18688 [19:20:21<2:43:56, 4.30s/it] 88%|████████▊ | 16399/18688 [19:20:26<2:41:56, 4.24s/it] 88%|████████▊ | 16400/18688 [19:20:30<2:39:44, 4.19s/it] {'loss': '0.8549', 'grad_norm': '1.128', 'learning_rate': '4.313e-06', 'epoch': '0.8776', 'num_input_tokens_seen': 55176144, 'train_runtime': '6.963e+04', 'train_tokens_per_second': '792.4'}
88%|████████▊ | 16400/18688 [19:20:30<2:39:44, 4.19s/it] 88%|████████▊ | 16401/18688 [19:20:33<2:34:39, 4.06s/it] 88%|████████▊ | 16402/18688 [19:20:37<2:33:27, 4.03s/it] 88%|████████▊ | 16403/18688 [19:20:41<2:27:15, 3.87s/it] 88%|████████▊ | 16404/18688 [19:20:45<2:28:40, 3.91s/it] 88%|████████▊ | 16405/18688 [19:20:48<2:25:45, 3.83s/it] 88%|████████▊ | 16406/18688 [19:20:53<2:30:40, 3.96s/it] 88%|████████▊ | 16407/18688 [19:20:58<2:44:45, 4.33s/it] 88%|████████▊ | 16408/18688 [19:21:02<2:43:38, 4.31s/it] 88%|████████▊ | 16409/18688 [19:21:06<2:33:29, 4.04s/it] 88%|████████▊ | 16410/18688 [19:21:10<2:34:46, 4.08s/it] {'loss': '0.8804', 'grad_norm': '1.298', 'learning_rate': '4.276e-06', 'epoch': '0.8781', 'num_input_tokens_seen': 55208216, 'train_runtime': '6.967e+04', 'train_tokens_per_second': '792.4'}
88%|████████▊ | 16410/18688 [19:21:10<2:34:46, 4.08s/it] 88%|████████▊ | 16411/18688 [19:21:14<2:39:50, 4.21s/it] 88%|████████▊ | 16412/18688 [19:21:18<2:31:42, 4.00s/it] 88%|████████▊ | 16413/18688 [19:21:21<2:25:44, 3.84s/it] 88%|████████▊ | 16414/18688 [19:21:26<2:34:48, 4.08s/it] 88%|████████▊ | 16415/18688 [19:21:29<2:27:53, 3.90s/it] 88%|████████▊ | 16416/18688 [19:21:33<2:23:47, 3.80s/it] 88%|████████▊ | 16417/18688 [19:21:37<2:27:46, 3.90s/it] 88%|████████▊ | 16418/18688 [19:21:41<2:32:06, 4.02s/it] 88%|████████▊ | 16419/18688 [19:21:45<2:27:16, 3.89s/it] 88%|████████▊ | 16420/18688 [19:21:49<2:28:57, 3.94s/it] {'loss': '0.9115', 'grad_norm': '1.322', 'learning_rate': '4.239e-06', 'epoch': '0.8787', 'num_input_tokens_seen': 55239688, 'train_runtime': '6.971e+04', 'train_tokens_per_second': '792.4'}
88%|████████▊ | 16420/18688 [19:21:49<2:28:57, 3.94s/it] 88%|████████▊ | 16421/18688 [19:21:53<2:25:20, 3.85s/it] 88%|████████▊ | 16422/18688 [19:21:57<2:27:37, 3.91s/it] 88%|████████▊ | 16423/18688 [19:22:02<2:38:44, 4.21s/it] 88%|████████▊ | 16424/18688 [19:22:05<2:32:15, 4.04s/it] 88%|████████▊ | 16425/18688 [19:22:09<2:27:14, 3.90s/it] 88%|████████▊ | 16426/18688 [19:22:13<2:28:55, 3.95s/it] 88%|████████▊ | 16427/18688 [19:22:17<2:24:48, 3.84s/it] 88%|████████▊ | 16428/18688 [19:22:20<2:22:46, 3.79s/it] 88%|████████▊ | 16429/18688 [19:22:24<2:22:59, 3.80s/it] 88%|████████▊ | 16430/18688 [19:22:28<2:27:48, 3.93s/it] {'loss': '0.8726', 'grad_norm': '1.261', 'learning_rate': '4.202e-06', 'epoch': '0.8792', 'num_input_tokens_seen': 55271168, 'train_runtime': '6.975e+04', 'train_tokens_per_second': '792.4'}
88%|████████▊ | 16430/18688 [19:22:28<2:27:48, 3.93s/it] 88%|████████▊ | 16431/18688 [19:22:32<2:29:57, 3.99s/it] 88%|████████▊ | 16432/18688 [19:22:36<2:25:27, 3.87s/it] 88%|████████▊ | 16433/18688 [19:22:41<2:33:23, 4.08s/it] 88%|████████▊ | 16434/18688 [19:22:45<2:33:27, 4.08s/it] 88%|████████▊ | 16435/18688 [19:22:49<2:31:53, 4.05s/it] 88%|████████▊ | 16436/18688 [19:22:53<2:33:07, 4.08s/it] 88%|████████▊ | 16437/18688 [19:22:56<2:26:28, 3.90s/it] 88%|████████▊ | 16438/18688 [19:23:01<2:30:29, 4.01s/it] 88%|████████▊ | 16439/18688 [19:23:05<2:39:20, 4.25s/it] 88%|████████▊ | 16440/18688 [19:23:09<2:32:03, 4.06s/it] {'loss': '0.9298', 'grad_norm': '1.622', 'learning_rate': '4.165e-06', 'epoch': '0.8797', 'num_input_tokens_seen': 55304424, 'train_runtime': '6.979e+04', 'train_tokens_per_second': '792.4'}
88%|████████▊ | 16440/18688 [19:23:09<2:32:03, 4.06s/it] 88%|████████▊ | 16441/18688 [19:23:13<2:29:47, 4.00s/it] 88%|████████▊ | 16442/18688 [19:23:18<2:38:06, 4.22s/it] 88%|████████▊ | 16443/18688 [19:23:21<2:33:54, 4.11s/it] 88%|████████▊ | 16444/18688 [19:23:25<2:31:19, 4.05s/it] 88%|████████▊ | 16445/18688 [19:23:29<2:31:36, 4.06s/it] 88%|████████▊ | 16446/18688 [19:23:33<2:28:40, 3.98s/it] 88%|████████▊ | 16447/18688 [19:23:37<2:29:05, 3.99s/it] 88%|████████▊ | 16448/18688 [19:23:41<2:26:15, 3.92s/it] 88%|████████▊ | 16449/18688 [19:23:46<2:34:42, 4.15s/it] 88%|████████▊ | 16450/18688 [19:23:50<2:40:07, 4.29s/it] {'loss': '0.9483', 'grad_norm': '1.25', 'learning_rate': '4.129e-06', 'epoch': '0.8803', 'num_input_tokens_seen': 55338856, 'train_runtime': '6.983e+04', 'train_tokens_per_second': '792.5'}
88%|████████▊ | 16450/18688 [19:23:50<2:40:07, 4.29s/it] 88%|████████▊ | 16451/18688 [19:23:55<2:45:11, 4.43s/it] 88%|████████▊ | 16452/18688 [19:23:59<2:39:24, 4.28s/it] 88%|████████▊ | 16453/18688 [19:24:03<2:36:27, 4.20s/it] 88%|████████▊ | 16454/18688 [19:24:07<2:39:45, 4.29s/it] 88%|████████▊ | 16455/18688 [19:24:12<2:45:45, 4.45s/it] 88%|████████▊ | 16456/18688 [19:24:16<2:34:30, 4.15s/it] 88%|████████▊ | 16457/18688 [19:24:20<2:37:25, 4.23s/it] 88%|████████▊ | 16458/18688 [19:24:24<2:32:44, 4.11s/it] 88%|████████▊ | 16459/18688 [19:24:29<2:46:20, 4.48s/it] 88%|████████▊ | 16460/18688 [19:24:34<2:44:03, 4.42s/it] {'loss': '0.9083', 'grad_norm': '1.484', 'learning_rate': '4.093e-06', 'epoch': '0.8808', 'num_input_tokens_seen': 55375240, 'train_runtime': '6.987e+04', 'train_tokens_per_second': '792.5'}
88%|████████▊ | 16460/18688 [19:24:34<2:44:03, 4.42s/it] 88%|████████▊ | 16461/18688 [19:24:37<2:35:41, 4.19s/it] 88%|████████▊ | 16462/18688 [19:24:41<2:29:13, 4.02s/it] 88%|████████▊ | 16463/18688 [19:24:46<2:40:38, 4.33s/it] 88%|████████▊ | 16464/18688 [19:24:50<2:34:02, 4.16s/it] 88%|████████▊ | 16465/18688 [19:24:54<2:34:48, 4.18s/it] 88%|████████▊ | 16466/18688 [19:24:58<2:36:42, 4.23s/it] 88%|████████▊ | 16467/18688 [19:25:02<2:33:55, 4.16s/it] 88%|████████▊ | 16468/18688 [19:25:07<2:36:25, 4.23s/it] 88%|████████▊ | 16469/18688 [19:25:13<2:56:48, 4.78s/it] 88%|████████▊ | 16470/18688 [19:25:17<2:53:23, 4.69s/it] {'loss': '0.9389', 'grad_norm': '1.497', 'learning_rate': '4.056e-06', 'epoch': '0.8813', 'num_input_tokens_seen': 55411336, 'train_runtime': '6.992e+04', 'train_tokens_per_second': '792.5'}
88%|████████▊ | 16470/18688 [19:25:17<2:53:23, 4.69s/it] 88%|████████▊ | 16471/18688 [19:25:21<2:44:24, 4.45s/it] 88%|████████▊ | 16472/18688 [19:25:25<2:39:51, 4.33s/it] 88%|████████▊ | 16473/18688 [19:25:29<2:31:52, 4.11s/it] 88%|████████▊ | 16474/18688 [19:25:32<2:28:04, 4.01s/it] 88%|████████▊ | 16475/18688 [19:25:37<2:31:04, 4.10s/it] 88%|████████▊ | 16476/18688 [19:25:42<2:38:05, 4.29s/it] 88%|████████▊ | 16477/18688 [19:25:45<2:31:02, 4.10s/it] 88%|████████▊ | 16478/18688 [19:25:49<2:25:31, 3.95s/it] 88%|████████▊ | 16479/18688 [19:25:53<2:23:46, 3.91s/it] 88%|████████▊ | 16480/18688 [19:25:57<2:27:47, 4.02s/it] {'loss': '0.912', 'grad_norm': '1.362', 'learning_rate': '4.02e-06', 'epoch': '0.8819', 'num_input_tokens_seen': 55443536, 'train_runtime': '6.996e+04', 'train_tokens_per_second': '792.5'}
88%|████████▊ | 16480/18688 [19:25:57<2:27:47, 4.02s/it] 88%|████████▊ | 16481/18688 [19:26:02<2:38:34, 4.31s/it] 88%|████████▊ | 16482/18688 [19:26:05<2:29:44, 4.07s/it] 88%|████████▊ | 16483/18688 [19:26:10<2:39:07, 4.33s/it] 88%|████████▊ | 16484/18688 [19:26:14<2:29:28, 4.07s/it] 88%|████████▊ | 16485/18688 [19:26:17<2:25:06, 3.95s/it] 88%|████████▊ | 16486/18688 [19:26:21<2:19:34, 3.80s/it] 88%|████████▊ | 16487/18688 [19:26:25<2:23:53, 3.92s/it] 88%|████████▊ | 16488/18688 [19:26:29<2:21:51, 3.87s/it] 88%|████████▊ | 16489/18688 [19:26:33<2:21:37, 3.86s/it] 88%|████████▊ | 16490/18688 [19:26:37<2:22:43, 3.90s/it] {'loss': '0.8697', 'grad_norm': '1.282', 'learning_rate': '3.985e-06', 'epoch': '0.8824', 'num_input_tokens_seen': 55474704, 'train_runtime': '7e+04', 'train_tokens_per_second': '792.5'}
88%|████████▊ | 16490/18688 [19:26:37<2:22:43, 3.90s/it] 88%|████████▊ | 16491/18688 [19:26:41<2:28:32, 4.06s/it] 88%|████████▊ | 16492/18688 [19:26:46<2:42:37, 4.44s/it] 88%|████████▊ | 16493/18688 [19:26:51<2:42:40, 4.45s/it] 88%|████████▊ | 16494/18688 [19:26:55<2:40:39, 4.39s/it] 88%|████████▊ | 16495/18688 [19:27:00<2:44:05, 4.49s/it] 88%|████████▊ | 16496/18688 [19:27:03<2:33:40, 4.21s/it] 88%|████████▊ | 16497/18688 [19:27:08<2:34:21, 4.23s/it] 88%|████████▊ | 16498/18688 [19:27:12<2:29:57, 4.11s/it] 88%|████████▊ | 16499/18688 [19:27:16<2:35:43, 4.27s/it] 88%|████████▊ | 16500/18688 [19:27:21<2:37:27, 4.32s/it] {'loss': '0.9602', 'grad_norm': '1.014', 'learning_rate': '3.949e-06', 'epoch': '0.8829', 'num_input_tokens_seen': 55512608, 'train_runtime': '7.004e+04', 'train_tokens_per_second': '792.6'}
88%|████████▊ | 16500/18688 [19:27:21<2:37:27, 4.32s/it] 88%|████████▊ | 16501/18688 [19:27:25<2:34:10, 4.23s/it] 88%|████████▊ | 16502/18688 [19:27:29<2:33:07, 4.20s/it] 88%|████████▊ | 16503/18688 [19:27:32<2:27:14, 4.04s/it] 88%|████████▊ | 16504/18688 [19:27:36<2:26:45, 4.03s/it] 88%|████████▊ | 16505/18688 [19:27:41<2:26:53, 4.04s/it] 88%|████████▊ | 16506/18688 [19:27:44<2:22:59, 3.93s/it] 88%|████████▊ | 16507/18688 [19:27:48<2:21:17, 3.89s/it] 88%|████████▊ | 16508/18688 [19:27:52<2:21:40, 3.90s/it] 88%|████████▊ | 16509/18688 [19:27:57<2:31:51, 4.18s/it] 88%|████████▊ | 16510/18688 [19:28:01<2:33:47, 4.24s/it] {'loss': '0.9255', 'grad_norm': '1.367', 'learning_rate': '3.913e-06', 'epoch': '0.8835', 'num_input_tokens_seen': 55544984, 'train_runtime': '7.008e+04', 'train_tokens_per_second': '792.6'}
88%|████████▊ | 16510/18688 [19:28:01<2:33:47, 4.24s/it] 88%|████████▊ | 16511/18688 [19:28:05<2:34:48, 4.27s/it] 88%|████████▊ | 16512/18688 [19:28:10<2:34:37, 4.26s/it] 88%|████████▊ | 16513/18688 [19:28:13<2:28:04, 4.08s/it] 88%|████████▊ | 16514/18688 [19:28:17<2:22:48, 3.94s/it] 88%|████████▊ | 16515/18688 [19:28:20<2:17:47, 3.80s/it] 88%|████████▊ | 16516/18688 [19:28:24<2:18:49, 3.83s/it] 88%|████████▊ | 16517/18688 [19:28:29<2:27:52, 4.09s/it] 88%|████████▊ | 16518/18688 [19:28:33<2:21:41, 3.92s/it] 88%|████████▊ | 16519/18688 [19:28:37<2:23:13, 3.96s/it] 88%|████████▊ | 16520/18688 [19:28:40<2:17:27, 3.80s/it] {'loss': '0.8757', 'grad_norm': '1.636', 'learning_rate': '3.878e-06', 'epoch': '0.884', 'num_input_tokens_seen': 55575064, 'train_runtime': '7.012e+04', 'train_tokens_per_second': '792.6'}
88%|████████▊ | 16520/18688 [19:28:40<2:17:27, 3.80s/it] 88%|████████▊ | 16521/18688 [19:28:44<2:18:19, 3.83s/it] 88%|████████▊ | 16522/18688 [19:28:48<2:20:21, 3.89s/it] 88%|████████▊ | 16523/18688 [19:28:52<2:17:56, 3.82s/it] 88%|████████▊ | 16524/18688 [19:28:56<2:21:42, 3.93s/it] 88%|████████▊ | 16525/18688 [19:29:00<2:19:38, 3.87s/it] 88%|████████▊ | 16526/18688 [19:29:05<2:31:03, 4.19s/it] 88%|████████▊ | 16527/18688 [19:29:09<2:34:18, 4.28s/it] 88%|████████▊ | 16528/18688 [19:29:13<2:36:23, 4.34s/it] 88%|████████▊ | 16529/18688 [19:29:17<2:30:51, 4.19s/it] 88%|████████▊ | 16530/18688 [19:29:21<2:24:48, 4.03s/it] {'loss': '0.9506', 'grad_norm': '1.404', 'learning_rate': '3.843e-06', 'epoch': '0.8845', 'num_input_tokens_seen': 55609112, 'train_runtime': '7.016e+04', 'train_tokens_per_second': '792.6'}
88%|████████▊ | 16530/18688 [19:29:21<2:24:48, 4.03s/it] 88%|████████▊ | 16531/18688 [19:29:25<2:21:01, 3.92s/it] 88%|████████▊ | 16532/18688 [19:29:28<2:19:09, 3.87s/it] 88%|████████▊ | 16533/18688 [19:29:33<2:24:48, 4.03s/it] 88%|████████▊ | 16534/18688 [19:29:36<2:20:42, 3.92s/it] 88%|████████▊ | 16535/18688 [19:29:41<2:23:24, 4.00s/it] 88%|████████▊ | 16536/18688 [19:29:44<2:17:22, 3.83s/it] 88%|████████▊ | 16537/18688 [19:29:50<2:38:55, 4.43s/it] 88%|████████▊ | 16538/18688 [19:29:54<2:31:55, 4.24s/it] 89%|████████▊ | 16539/18688 [19:29:58<2:36:07, 4.36s/it] 89%|████████▊ | 16540/18688 [19:30:02<2:27:52, 4.13s/it] {'loss': '0.8847', 'grad_norm': '1.252', 'learning_rate': '3.808e-06', 'epoch': '0.8851', 'num_input_tokens_seen': 55642976, 'train_runtime': '7.02e+04', 'train_tokens_per_second': '792.6'}
89%|████████▊ | 16540/18688 [19:30:02<2:27:52, 4.13s/it] 89%|████████▊ | 16541/18688 [19:30:06<2:31:32, 4.24s/it] 89%|████████▊ | 16542/18688 [19:30:11<2:30:51, 4.22s/it] 89%|████████▊ | 16543/18688 [19:30:15<2:34:03, 4.31s/it] 89%|████████▊ | 16544/18688 [19:30:19<2:28:40, 4.16s/it] 89%|████████▊ | 16545/18688 [19:30:23<2:32:37, 4.27s/it] 89%|████████▊ | 16546/18688 [19:30:28<2:31:41, 4.25s/it] 89%|████████▊ | 16547/18688 [19:30:32<2:35:16, 4.35s/it] 89%|████████▊ | 16548/18688 [19:30:36<2:31:51, 4.26s/it] 89%|████████▊ | 16549/18688 [19:30:41<2:32:45, 4.28s/it] 89%|████████▊ | 16550/18688 [19:30:44<2:25:11, 4.07s/it] {'loss': '0.9203', 'grad_norm': '1.972', 'learning_rate': '3.773e-06', 'epoch': '0.8856', 'num_input_tokens_seen': 55678856, 'train_runtime': '7.024e+04', 'train_tokens_per_second': '792.6'}
89%|████████▊ | 16550/18688 [19:30:44<2:25:11, 4.07s/it] 89%|████████▊ | 16551/18688 [19:30:48<2:19:26, 3.92s/it] 89%|████████▊ | 16552/18688 [19:30:52<2:21:24, 3.97s/it] 89%|████████▊ | 16553/18688 [19:30:55<2:16:50, 3.85s/it] 89%|████████▊ | 16554/18688 [19:31:00<2:27:57, 4.16s/it] 89%|████████▊ | 16555/18688 [19:31:04<2:20:31, 3.95s/it] 89%|████████▊ | 16556/18688 [19:31:08<2:19:26, 3.92s/it] 89%|████████▊ | 16557/18688 [19:31:11<2:14:37, 3.79s/it] 89%|████████▊ | 16558/18688 [19:31:15<2:18:58, 3.91s/it] 89%|████████▊ | 16559/18688 [19:31:19<2:17:08, 3.86s/it] 89%|████████▊ | 16560/18688 [19:31:23<2:22:31, 4.02s/it] {'loss': '0.8579', 'grad_norm': '1.489', 'learning_rate': '3.738e-06', 'epoch': '0.8862', 'num_input_tokens_seen': 55709232, 'train_runtime': '7.028e+04', 'train_tokens_per_second': '792.6'}
89%|████████▊ | 16560/18688 [19:31:23<2:22:31, 4.02s/it] 89%|████████▊ | 16561/18688 [19:31:28<2:27:33, 4.16s/it] 89%|████████▊ | 16562/18688 [19:31:32<2:29:45, 4.23s/it] 89%|████████▊ | 16563/18688 [19:31:36<2:25:20, 4.10s/it] 89%|████████▊ | 16564/18688 [19:31:41<2:29:32, 4.22s/it] 89%|████████▊ | 16565/18688 [19:31:45<2:32:36, 4.31s/it] 89%|████████▊ | 16566/18688 [19:31:50<2:35:09, 4.39s/it] 89%|████████▊ | 16567/18688 [19:31:54<2:32:04, 4.30s/it] 89%|████████▊ | 16568/18688 [19:31:58<2:31:12, 4.28s/it] 89%|████████▊ | 16569/18688 [19:32:02<2:27:31, 4.18s/it] 89%|████████▊ | 16570/18688 [19:32:06<2:25:45, 4.13s/it] {'loss': '0.9547', 'grad_norm': '1.152', 'learning_rate': '3.704e-06', 'epoch': '0.8867', 'num_input_tokens_seen': 55743240, 'train_runtime': '7.033e+04', 'train_tokens_per_second': '792.6'}
89%|████████▊ | 16570/18688 [19:32:06<2:25:45, 4.13s/it] 89%|████████▊ | 16571/18688 [19:32:10<2:25:16, 4.12s/it] 89%|████████▊ | 16572/18688 [19:32:14<2:20:38, 3.99s/it] 89%|████████▊ | 16573/18688 [19:32:18<2:23:34, 4.07s/it] 89%|████████▊ | 16574/18688 [19:32:22<2:25:45, 4.14s/it] 89%|████████▊ | 16575/18688 [19:32:27<2:32:15, 4.32s/it] 89%|████████▊ | 16576/18688 [19:32:32<2:33:10, 4.35s/it] 89%|████████▊ | 16577/18688 [19:32:36<2:31:00, 4.29s/it] 89%|████████▊ | 16578/18688 [19:32:39<2:24:37, 4.11s/it] 89%|████████▊ | 16579/18688 [19:32:44<2:33:51, 4.38s/it] 89%|████████▊ | 16580/18688 [19:32:49<2:31:35, 4.31s/it] {'loss': '0.8809', 'grad_norm': '1.637', 'learning_rate': '3.669e-06', 'epoch': '0.8872', 'num_input_tokens_seen': 55779448, 'train_runtime': '7.037e+04', 'train_tokens_per_second': '792.7'}
89%|████████▊ | 16580/18688 [19:32:49<2:31:35, 4.31s/it] 89%|████████▊ | 16581/18688 [19:32:53<2:33:06, 4.36s/it] 89%|████████▊ | 16582/18688 [19:32:57<2:25:46, 4.15s/it] 89%|████████▊ | 16583/18688 [19:33:00<2:18:30, 3.95s/it] 89%|████████▊ | 16584/18688 [19:33:05<2:27:16, 4.20s/it] 89%|████████▊ | 16585/18688 [19:33:09<2:24:03, 4.11s/it] 89%|████████▉ | 16586/18688 [19:33:14<2:30:56, 4.31s/it] 89%|████████▉ | 16587/18688 [19:33:18<2:31:36, 4.33s/it] 89%|████████▉ | 16588/18688 [19:33:22<2:31:59, 4.34s/it] 89%|████████▉ | 16589/18688 [19:33:27<2:32:32, 4.36s/it] 89%|████████▉ | 16590/18688 [19:33:30<2:25:18, 4.16s/it] {'loss': '0.9027', 'grad_norm': '1.382', 'learning_rate': '3.635e-06', 'epoch': '0.8878', 'num_input_tokens_seen': 55814120, 'train_runtime': '7.041e+04', 'train_tokens_per_second': '792.7'}
89%|████████▉ | 16590/18688 [19:33:30<2:25:18, 4.16s/it] 89%|████████▉ | 16591/18688 [19:33:36<2:36:59, 4.49s/it] 89%|████████▉ | 16592/18688 [19:33:40<2:36:11, 4.47s/it] 89%|████████▉ | 16593/18688 [19:33:45<2:37:51, 4.52s/it] 89%|████████▉ | 16594/18688 [19:33:49<2:37:47, 4.52s/it] 89%|████████▉ | 16595/18688 [19:33:53<2:29:58, 4.30s/it] 89%|████████▉ | 16596/18688 [19:33:57<2:26:37, 4.21s/it] 89%|████████▉ | 16597/18688 [19:34:01<2:26:28, 4.20s/it] 89%|████████▉ | 16598/18688 [19:34:06<2:31:39, 4.35s/it] 89%|████████▉ | 16599/18688 [19:34:10<2:27:43, 4.24s/it] 89%|████████▉ | 16600/18688 [19:34:14<2:27:51, 4.25s/it] {'loss': '0.9486', 'grad_norm': '1.227', 'learning_rate': '3.601e-06', 'epoch': '0.8883', 'num_input_tokens_seen': 55851920, 'train_runtime': '7.045e+04', 'train_tokens_per_second': '792.7'}
89%|████████▉ | 16600/18688 [19:34:14<2:27:51, 4.25s/it] 89%|████████▉ | 16601/18688 [19:34:18<2:23:54, 4.14s/it] 89%|████████▉ | 16602/18688 [19:34:23<2:30:16, 4.32s/it] 89%|████████▉ | 16603/18688 [19:34:26<2:22:44, 4.11s/it] 89%|████████▉ | 16604/18688 [19:34:30<2:17:33, 3.96s/it] 89%|████████▉ | 16605/18688 [19:34:34<2:15:27, 3.90s/it] 89%|████████▉ | 16606/18688 [19:34:39<2:25:22, 4.19s/it] 89%|████████▉ | 16607/18688 [19:34:42<2:20:31, 4.05s/it] 89%|████████▉ | 16608/18688 [19:34:47<2:25:34, 4.20s/it] 89%|████████▉ | 16609/18688 [19:34:51<2:19:20, 4.02s/it] 89%|████████▉ | 16610/18688 [19:34:55<2:22:22, 4.11s/it] {'loss': '0.9097', 'grad_norm': '1.78', 'learning_rate': '3.567e-06', 'epoch': '0.8888', 'num_input_tokens_seen': 55883696, 'train_runtime': '7.05e+04', 'train_tokens_per_second': '792.7'}
89%|████████▉ | 16610/18688 [19:34:55<2:22:22, 4.11s/it] 89%|████████▉ | 16611/18688 [19:35:00<2:29:18, 4.31s/it] 89%|████████▉ | 16612/18688 [19:35:04<2:33:36, 4.44s/it] 89%|████████▉ | 16613/18688 [19:35:08<2:25:57, 4.22s/it] 89%|████████▉ | 16614/18688 [19:35:12<2:21:05, 4.08s/it] 89%|████████▉ | 16615/18688 [19:35:16<2:20:40, 4.07s/it] 89%|████████▉ | 16616/18688 [19:35:20<2:17:20, 3.98s/it] 89%|████████▉ | 16617/18688 [19:35:23<2:14:36, 3.90s/it] 89%|████████▉ | 16618/18688 [19:35:28<2:17:40, 3.99s/it] 89%|████████▉ | 16619/18688 [19:35:32<2:18:04, 4.00s/it] 89%|████████▉ | 16620/18688 [19:35:36<2:21:53, 4.12s/it] {'loss': '0.8972', 'grad_norm': '1.532', 'learning_rate': '3.533e-06', 'epoch': '0.8894', 'num_input_tokens_seen': 55917848, 'train_runtime': '7.054e+04', 'train_tokens_per_second': '792.8'}
89%|████████▉ | 16620/18688 [19:35:36<2:21:53, 4.12s/it] 89%|████████▉ | 16621/18688 [19:35:40<2:24:54, 4.21s/it] 89%|████████▉ | 16622/18688 [19:35:45<2:30:28, 4.37s/it] 89%|████████▉ | 16623/18688 [19:35:50<2:30:47, 4.38s/it] 89%|████████▉ | 16624/18688 [19:35:53<2:23:00, 4.16s/it] 89%|████████▉ | 16625/18688 [19:35:57<2:23:03, 4.16s/it] 89%|████████▉ | 16626/18688 [19:36:01<2:18:17, 4.02s/it] 89%|████████▉ | 16627/18688 [19:36:05<2:13:30, 3.89s/it] 89%|████████▉ | 16628/18688 [19:36:08<2:11:40, 3.84s/it] 89%|████████▉ | 16629/18688 [19:36:12<2:13:30, 3.89s/it] 89%|████████▉ | 16630/18688 [19:36:16<2:15:17, 3.94s/it] {'loss': '0.9049', 'grad_norm': '1.275', 'learning_rate': '3.499e-06', 'epoch': '0.8899', 'num_input_tokens_seen': 55949896, 'train_runtime': '7.058e+04', 'train_tokens_per_second': '792.8'}
89%|████████▉ | 16630/18688 [19:36:16<2:15:17, 3.94s/it] 89%|████████▉ | 16631/18688 [19:36:21<2:16:06, 3.97s/it] 89%|████████▉ | 16632/18688 [19:36:25<2:20:53, 4.11s/it] 89%|████████▉ | 16633/18688 [19:36:29<2:16:47, 3.99s/it] 89%|████████▉ | 16634/18688 [19:36:34<2:26:04, 4.27s/it] 89%|████████▉ | 16635/18688 [19:36:38<2:22:46, 4.17s/it] 89%|████████▉ | 16636/18688 [19:36:41<2:18:14, 4.04s/it] 89%|████████▉ | 16637/18688 [19:36:45<2:15:15, 3.96s/it] 89%|████████▉ | 16638/18688 [19:36:49<2:17:23, 4.02s/it] 89%|████████▉ | 16639/18688 [19:36:53<2:14:55, 3.95s/it] 89%|████████▉ | 16640/18688 [19:36:57<2:11:25, 3.85s/it] {'loss': '0.8812', 'grad_norm': '1.206', 'learning_rate': '3.466e-06', 'epoch': '0.8904', 'num_input_tokens_seen': 55983352, 'train_runtime': '7.062e+04', 'train_tokens_per_second': '792.8'}
89%|████████▉ | 16640/18688 [19:36:57<2:11:25, 3.85s/it] 89%|████████▉ | 16641/18688 [19:37:01<2:16:40, 4.01s/it] 89%|████████▉ | 16642/18688 [19:37:06<2:25:08, 4.26s/it] 89%|████████▉ | 16643/18688 [19:37:10<2:26:10, 4.29s/it] 89%|████████▉ | 16644/18688 [19:37:14<2:21:51, 4.16s/it] 89%|████████▉ | 16645/18688 [19:37:19<2:34:22, 4.53s/it] 89%|████████▉ | 16646/18688 [19:37:24<2:39:27, 4.69s/it] 89%|████████▉ | 16647/18688 [19:37:28<2:32:11, 4.47s/it] 89%|████████▉ | 16648/18688 [19:37:32<2:22:04, 4.18s/it] 89%|████████▉ | 16649/18688 [19:37:37<2:26:10, 4.30s/it] 89%|████████▉ | 16650/18688 [19:37:41<2:28:04, 4.36s/it] {'loss': '0.8811', 'grad_norm': '1.227', 'learning_rate': '3.432e-06', 'epoch': '0.891', 'num_input_tokens_seen': 56021224, 'train_runtime': '7.066e+04', 'train_tokens_per_second': '792.8'}
89%|████████▉ | 16650/18688 [19:37:41<2:28:04, 4.36s/it] 89%|████████▉ | 16651/18688 [19:37:45<2:24:12, 4.25s/it] 89%|████████▉ | 16652/18688 [19:37:49<2:23:30, 4.23s/it] 89%|████████▉ | 16653/18688 [19:37:54<2:28:25, 4.38s/it] 89%|████████▉ | 16654/18688 [19:37:58<2:28:36, 4.38s/it] 89%|████████▉ | 16655/18688 [19:38:02<2:21:42, 4.18s/it] 89%|████████▉ | 16656/18688 [19:38:06<2:16:12, 4.02s/it] 89%|████████▉ | 16657/18688 [19:38:11<2:31:50, 4.49s/it] 89%|████████▉ | 16658/18688 [19:38:15<2:25:05, 4.29s/it] 89%|████████▉ | 16659/18688 [19:38:19<2:19:10, 4.12s/it] 89%|████████▉ | 16660/18688 [19:38:23<2:17:34, 4.07s/it] {'loss': '0.9545', 'grad_norm': '1.54', 'learning_rate': '3.399e-06', 'epoch': '0.8915', 'num_input_tokens_seen': 56055168, 'train_runtime': '7.07e+04', 'train_tokens_per_second': '792.8'}
89%|████████▉ | 16660/18688 [19:38:23<2:17:34, 4.07s/it] 89%|████████▉ | 16661/18688 [19:38:28<2:28:08, 4.38s/it] 89%|████████▉ | 16662/18688 [19:38:33<2:31:50, 4.50s/it] 89%|████████▉ | 16663/18688 [19:38:36<2:21:59, 4.21s/it] 89%|████████▉ | 16664/18688 [19:38:41<2:23:39, 4.26s/it] 89%|████████▉ | 16665/18688 [19:38:45<2:25:21, 4.31s/it] 89%|████████▉ | 16666/18688 [19:38:49<2:24:04, 4.27s/it] 89%|████████▉ | 16667/18688 [19:38:53<2:21:01, 4.19s/it] 89%|████████▉ | 16668/18688 [19:38:57<2:18:00, 4.10s/it] 89%|████████▉ | 16669/18688 [19:39:02<2:24:00, 4.28s/it] 89%|████████▉ | 16670/18688 [19:39:06<2:26:23, 4.35s/it] {'loss': '0.9667', 'grad_norm': '1.341', 'learning_rate': '3.366e-06', 'epoch': '0.892', 'num_input_tokens_seen': 56091040, 'train_runtime': '7.075e+04', 'train_tokens_per_second': '792.8'}
89%|████████▉ | 16670/18688 [19:39:06<2:26:23, 4.35s/it] 89%|████████▉ | 16671/18688 [19:39:10<2:20:52, 4.19s/it] 89%|████████▉ | 16672/18688 [19:39:14<2:19:22, 4.15s/it] 89%|████████▉ | 16673/18688 [19:39:18<2:17:01, 4.08s/it] 89%|████████▉ | 16674/18688 [19:39:22<2:12:54, 3.96s/it] 89%|████████▉ | 16675/18688 [19:39:25<2:10:08, 3.88s/it] 89%|████████▉ | 16676/18688 [19:39:29<2:07:51, 3.81s/it] 89%|████████▉ | 16677/18688 [19:39:33<2:08:14, 3.83s/it] 89%|████████▉ | 16678/18688 [19:39:37<2:09:37, 3.87s/it] 89%|████████▉ | 16679/18688 [19:39:42<2:19:38, 4.17s/it] 89%|████████▉ | 16680/18688 [19:39:46<2:20:02, 4.18s/it] {'loss': '0.9374', 'grad_norm': '1.25', 'learning_rate': '3.333e-06', 'epoch': '0.8926', 'num_input_tokens_seen': 56121448, 'train_runtime': '7.079e+04', 'train_tokens_per_second': '792.8'}
89%|████████▉ | 16680/18688 [19:39:46<2:20:02, 4.18s/it] 89%|████████▉ | 16681/18688 [19:39:50<2:17:17, 4.10s/it] 89%|████████▉ | 16682/18688 [19:39:54<2:14:59, 4.04s/it] 89%|████████▉ | 16683/18688 [19:39:58<2:16:21, 4.08s/it] 89%|████████▉ | 16684/18688 [19:40:02<2:10:54, 3.92s/it] 89%|████████▉ | 16685/18688 [19:40:06<2:15:05, 4.05s/it] 89%|████████▉ | 16686/18688 [19:40:10<2:18:54, 4.16s/it] 89%|████████▉ | 16687/18688 [19:40:14<2:17:02, 4.11s/it] 89%|████████▉ | 16688/18688 [19:40:18<2:16:06, 4.08s/it] 89%|████████▉ | 16689/18688 [19:40:22<2:15:44, 4.07s/it] 89%|████████▉ | 16690/18688 [19:40:26<2:13:38, 4.01s/it] {'loss': '0.932', 'grad_norm': '1.277', 'learning_rate': '3.3e-06', 'epoch': '0.8931', 'num_input_tokens_seen': 56152576, 'train_runtime': '7.083e+04', 'train_tokens_per_second': '792.8'}
89%|████████▉ | 16690/18688 [19:40:26<2:13:38, 4.01s/it] 89%|████████▉ | 16691/18688 [19:40:30<2:12:41, 3.99s/it] 89%|████████▉ | 16692/18688 [19:40:34<2:14:09, 4.03s/it] 89%|████████▉ | 16693/18688 [19:40:39<2:20:44, 4.23s/it] 89%|████████▉ | 16694/18688 [19:40:43<2:19:23, 4.19s/it] 89%|████████▉ | 16695/18688 [19:40:47<2:16:56, 4.12s/it] 89%|████████▉ | 16696/18688 [19:40:51<2:17:56, 4.15s/it] 89%|████████▉ | 16697/18688 [19:40:55<2:18:24, 4.17s/it] 89%|████████▉ | 16698/18688 [19:41:00<2:26:39, 4.42s/it] 89%|████████▉ | 16699/18688 [19:41:04<2:19:35, 4.21s/it] 89%|████████▉ | 16700/18688 [19:41:10<2:35:12, 4.68s/it] {'loss': '0.908', 'grad_norm': '1.353', 'learning_rate': '3.268e-06', 'epoch': '0.8936', 'num_input_tokens_seen': 56189952, 'train_runtime': '7.087e+04', 'train_tokens_per_second': '792.9'}
89%|████████▉ | 16700/18688 [19:41:10<2:35:12, 4.68s/it] 89%|████████▉ | 16701/18688 [19:41:14<2:29:23, 4.51s/it] 89%|████████▉ | 16702/18688 [19:41:19<2:28:14, 4.48s/it] 89%|████████▉ | 16703/18688 [19:41:23<2:29:58, 4.53s/it] 89%|████████▉ | 16704/18688 [19:41:28<2:30:36, 4.55s/it] 89%|████████▉ | 16705/18688 [19:41:32<2:26:30, 4.43s/it] 89%|████████▉ | 16706/18688 [19:41:36<2:22:40, 4.32s/it] 89%|████████▉ | 16707/18688 [19:41:41<2:28:23, 4.49s/it] 89%|████████▉ | 16708/18688 [19:41:45<2:26:57, 4.45s/it] 89%|████████▉ | 16709/18688 [19:41:49<2:21:08, 4.28s/it] 89%|████████▉ | 16710/18688 [19:41:53<2:19:54, 4.24s/it] {'loss': '0.8842', 'grad_norm': '1.116', 'learning_rate': '3.235e-06', 'epoch': '0.8942', 'num_input_tokens_seen': 56226176, 'train_runtime': '7.091e+04', 'train_tokens_per_second': '792.9'}
89%|████████▉ | 16710/18688 [19:41:53<2:19:54, 4.24s/it] 89%|████████▉ | 16711/18688 [19:41:57<2:16:57, 4.16s/it] 89%|████████▉ | 16712/18688 [19:42:01<2:11:37, 4.00s/it] 89%|████████▉ | 16713/18688 [19:42:05<2:10:33, 3.97s/it] 89%|████████▉ | 16714/18688 [19:42:08<2:05:21, 3.81s/it] 89%|████████▉ | 16715/18688 [19:42:13<2:11:05, 3.99s/it] 89%|████████▉ | 16716/18688 [19:42:17<2:12:52, 4.04s/it] 89%|████████▉ | 16717/18688 [19:42:21<2:17:58, 4.20s/it] 89%|████████▉ | 16718/18688 [19:42:25<2:12:05, 4.02s/it] 89%|████████▉ | 16719/18688 [19:42:29<2:08:52, 3.93s/it] 89%|████████▉ | 16720/18688 [19:42:33<2:10:39, 3.98s/it] {'loss': '0.8803', 'grad_norm': '1.313', 'learning_rate': '3.203e-06', 'epoch': '0.8947', 'num_input_tokens_seen': 56258272, 'train_runtime': '7.095e+04', 'train_tokens_per_second': '792.9'}
89%|████████▉ | 16720/18688 [19:42:33<2:10:39, 3.98s/it] 89%|████████▉ | 16721/18688 [19:42:37<2:17:34, 4.20s/it] 89%|████████▉ | 16722/18688 [19:42:41<2:15:38, 4.14s/it] 89%|████████▉ | 16723/18688 [19:42:45<2:13:14, 4.07s/it] 89%|████████▉ | 16724/18688 [19:42:49<2:12:52, 4.06s/it] 89%|████████▉ | 16725/18688 [19:42:53<2:12:40, 4.06s/it] 90%|████████▉ | 16726/18688 [19:42:58<2:13:37, 4.09s/it] 90%|████████▉ | 16727/18688 [19:43:02<2:13:14, 4.08s/it] 90%|████████▉ | 16728/18688 [19:43:05<2:08:24, 3.93s/it] 90%|████████▉ | 16729/18688 [19:43:09<2:08:38, 3.94s/it] 90%|████████▉ | 16730/18688 [19:43:14<2:16:47, 4.19s/it] {'loss': '0.8211', 'grad_norm': '1.296', 'learning_rate': '3.171e-06', 'epoch': '0.8953', 'num_input_tokens_seen': 56290536, 'train_runtime': '7.099e+04', 'train_tokens_per_second': '792.9'}
90%|████████▉ | 16730/18688 [19:43:14<2:16:47, 4.19s/it] 90%|████████▉ | 16731/18688 [19:43:18<2:14:11, 4.11s/it] 90%|████████▉ | 16732/18688 [19:43:22<2:14:56, 4.14s/it] 90%|████████▉ | 16733/18688 [19:43:27<2:19:46, 4.29s/it] 90%|████████▉ | 16734/18688 [19:43:31<2:17:49, 4.23s/it] 90%|████████▉ | 16735/18688 [19:43:35<2:15:19, 4.16s/it] 90%|████████▉ | 16736/18688 [19:43:39<2:14:22, 4.13s/it] 90%|████████▉ | 16737/18688 [19:43:44<2:27:33, 4.54s/it] 90%|████████▉ | 16738/18688 [19:43:48<2:19:32, 4.29s/it] 90%|████████▉ | 16739/18688 [19:43:54<2:30:34, 4.64s/it] 90%|████████▉ | 16740/18688 [19:43:58<2:27:06, 4.53s/it] {'loss': '0.8802', 'grad_norm': '1.382', 'learning_rate': '3.139e-06', 'epoch': '0.8958', 'num_input_tokens_seen': 56329184, 'train_runtime': '7.104e+04', 'train_tokens_per_second': '792.9'}
90%|████████▉ | 16740/18688 [19:43:58<2:27:06, 4.53s/it] 90%|████████▉ | 16741/18688 [19:44:02<2:27:06, 4.53s/it] 90%|████████▉ | 16742/18688 [19:44:08<2:35:56, 4.81s/it] 90%|████████▉ | 16743/18688 [19:44:12<2:29:30, 4.61s/it] 90%|████████▉ | 16744/18688 [19:44:16<2:20:38, 4.34s/it] 90%|████████▉ | 16745/18688 [19:44:19<2:13:24, 4.12s/it] 90%|████████▉ | 16746/18688 [19:44:23<2:07:55, 3.95s/it] 90%|████████▉ | 16747/18688 [19:44:27<2:08:47, 3.98s/it] 90%|████████▉ | 16748/18688 [19:44:32<2:15:22, 4.19s/it] 90%|████████▉ | 16749/18688 [19:44:36<2:21:53, 4.39s/it] 90%|████████▉ | 16750/18688 [19:44:41<2:25:00, 4.49s/it] {'loss': '0.9678', 'grad_norm': '1.207', 'learning_rate': '3.107e-06', 'epoch': '0.8963', 'num_input_tokens_seen': 56364320, 'train_runtime': '7.108e+04', 'train_tokens_per_second': '793'}
90%|████████▉ | 16750/18688 [19:44:41<2:25:00, 4.49s/it] 90%|████████▉ | 16751/18688 [19:44:45<2:23:15, 4.44s/it] 90%|████████▉ | 16752/18688 [19:44:49<2:19:06, 4.31s/it] 90%|████████▉ | 16753/18688 [19:44:54<2:23:41, 4.46s/it] 90%|████████▉ | 16754/18688 [19:44:58<2:14:06, 4.16s/it] 90%|████████▉ | 16755/18688 [19:45:01<2:08:57, 4.00s/it] 90%|████████▉ | 16756/18688 [19:45:06<2:11:48, 4.09s/it] 90%|████████▉ | 16757/18688 [19:45:10<2:09:02, 4.01s/it] 90%|████████▉ | 16758/18688 [19:45:13<2:07:51, 3.97s/it] 90%|████████▉ | 16759/18688 [19:45:18<2:17:37, 4.28s/it] 90%|████████▉ | 16760/18688 [19:45:22<2:15:05, 4.20s/it] {'loss': '0.9633', 'grad_norm': '1.355', 'learning_rate': '3.076e-06', 'epoch': '0.8969', 'num_input_tokens_seen': 56398024, 'train_runtime': '7.112e+04', 'train_tokens_per_second': '793'}
90%|████████▉ | 16760/18688 [19:45:22<2:15:05, 4.20s/it] 90%|████████▉ | 16761/18688 [19:45:27<2:17:46, 4.29s/it] 90%|████████▉ | 16762/18688 [19:45:31<2:15:37, 4.23s/it] 90%|████████▉ | 16763/18688 [19:45:35<2:15:02, 4.21s/it] 90%|████████▉ | 16764/18688 [19:45:40<2:20:27, 4.38s/it] 90%|████████▉ | 16765/18688 [19:45:44<2:17:35, 4.29s/it] 90%|████████▉ | 16766/18688 [19:45:49<2:28:05, 4.62s/it] 90%|████████▉ | 16767/18688 [19:45:53<2:21:51, 4.43s/it] 90%|████████▉ | 16768/18688 [19:45:57<2:17:48, 4.31s/it] 90%|████████▉ | 16769/18688 [19:46:01<2:15:12, 4.23s/it] 90%|████████▉ | 16770/18688 [19:46:07<2:23:56, 4.50s/it] {'loss': '0.8999', 'grad_norm': '1.19', 'learning_rate': '3.044e-06', 'epoch': '0.8974', 'num_input_tokens_seen': 56433360, 'train_runtime': '7.117e+04', 'train_tokens_per_second': '793'}
90%|████████▉ | 16770/18688 [19:46:07<2:23:56, 4.50s/it] 90%|████████▉ | 16771/18688 [19:46:11<2:26:27, 4.58s/it] 90%|████████▉ | 16772/18688 [19:46:15<2:20:36, 4.40s/it] 90%|████████▉ | 16773/18688 [19:46:20<2:24:42, 4.53s/it] 90%|████████▉ | 16774/18688 [19:46:25<2:27:31, 4.62s/it] 90%|████████▉ | 16775/18688 [19:46:29<2:21:22, 4.43s/it] 90%|████████▉ | 16776/18688 [19:46:33<2:20:49, 4.42s/it] 90%|████████▉ | 16777/18688 [19:46:38<2:20:37, 4.42s/it] 90%|████████▉ | 16778/18688 [19:46:42<2:15:18, 4.25s/it] 90%|████████▉ | 16779/18688 [19:46:47<2:20:42, 4.42s/it] 90%|████████▉ | 16780/18688 [19:46:51<2:22:10, 4.47s/it] {'loss': '0.8964', 'grad_norm': '1.375', 'learning_rate': '3.013e-06', 'epoch': '0.8979', 'num_input_tokens_seen': 56466600, 'train_runtime': '7.121e+04', 'train_tokens_per_second': '792.9'}
90%|████████▉ | 16780/18688 [19:46:51<2:22:10, 4.47s/it] 90%|████████▉ | 16781/18688 [19:46:55<2:16:22, 4.29s/it] 90%|████████▉ | 16782/18688 [19:46:59<2:13:42, 4.21s/it] 90%|████████▉ | 16783/18688 [19:47:04<2:20:01, 4.41s/it] 90%|████████▉ | 16784/18688 [19:47:08<2:15:16, 4.26s/it] 90%|████████▉ | 16785/18688 [19:47:11<2:07:12, 4.01s/it] 90%|████████▉ | 16786/18688 [19:47:16<2:12:02, 4.17s/it] 90%|████████▉ | 16787/18688 [19:47:20<2:17:38, 4.34s/it] 90%|████████▉ | 16788/18688 [19:47:24<2:11:01, 4.14s/it] 90%|████████▉ | 16789/18688 [19:47:28<2:12:41, 4.19s/it] 90%|████████▉ | 16790/18688 [19:47:33<2:13:47, 4.23s/it] {'loss': '0.846', 'grad_norm': '1.205', 'learning_rate': '2.982e-06', 'epoch': '0.8985', 'num_input_tokens_seen': 56497552, 'train_runtime': '7.125e+04', 'train_tokens_per_second': '792.9'}
90%|████████▉ | 16790/18688 [19:47:33<2:13:47, 4.23s/it] 90%|████████▉ | 16791/18688 [19:47:37<2:18:03, 4.37s/it] 90%|████████▉ | 16792/18688 [19:47:42<2:17:47, 4.36s/it] 90%|████████▉ | 16793/18688 [19:47:46<2:19:42, 4.42s/it] 90%|████████▉ | 16794/18688 [19:47:50<2:13:16, 4.22s/it] 90%|████████▉ | 16795/18688 [19:47:54<2:08:18, 4.07s/it] 90%|████████▉ | 16796/18688 [19:47:58<2:04:54, 3.96s/it] 90%|████████▉ | 16797/18688 [19:48:02<2:07:41, 4.05s/it] 90%|████████▉ | 16798/18688 [19:48:06<2:05:16, 3.98s/it] 90%|████████▉ | 16799/18688 [19:48:10<2:05:17, 3.98s/it] 90%|████████▉ | 16800/18688 [19:48:15<2:14:59, 4.29s/it] {'loss': '0.8389', 'grad_norm': '1.425', 'learning_rate': '2.951e-06', 'epoch': '0.899', 'num_input_tokens_seen': 56530520, 'train_runtime': '7.13e+04', 'train_tokens_per_second': '792.9'}
90%|████████▉ | 16800/18688 [19:48:15<2:14:59, 4.29s/it] 90%|████████▉ | 16801/18688 [19:48:19<2:17:22, 4.37s/it] 90%|████████▉ | 16802/18688 [19:48:24<2:21:03, 4.49s/it] 90%|████████▉ | 16803/18688 [19:48:29<2:21:43, 4.51s/it] 90%|████████▉ | 16804/18688 [19:48:33<2:22:05, 4.53s/it] 90%|████████▉ | 16805/18688 [19:48:37<2:13:59, 4.27s/it] 90%|████████▉ | 16806/18688 [19:48:40<2:06:34, 4.04s/it] 90%|████████▉ | 16807/18688 [19:48:44<2:06:23, 4.03s/it] 90%|████████▉ | 16808/18688 [19:48:48<2:01:06, 3.87s/it] 90%|████████▉ | 16809/18688 [19:48:51<1:59:29, 3.82s/it] 90%|████████▉ | 16810/18688 [19:48:57<2:16:59, 4.38s/it] {'loss': '0.8998', 'grad_norm': '1.151', 'learning_rate': '2.92e-06', 'epoch': '0.8995', 'num_input_tokens_seen': 56564096, 'train_runtime': '7.134e+04', 'train_tokens_per_second': '792.9'}
90%|████████▉ | 16810/18688 [19:48:57<2:16:59, 4.38s/it] 90%|████████▉ | 16811/18688 [19:49:01<2:15:04, 4.32s/it] 90%|████████▉ | 16812/18688 [19:49:06<2:15:12, 4.32s/it] 90%|████████▉ | 16813/18688 [19:49:10<2:19:40, 4.47s/it] 90%|████████▉ | 16814/18688 [19:49:15<2:23:05, 4.58s/it] 90%|████████▉ | 16815/18688 [19:49:19<2:16:37, 4.38s/it] 90%|████████▉ | 16816/18688 [19:49:23<2:11:01, 4.20s/it] 90%|████████▉ | 16817/18688 [19:49:28<2:19:02, 4.46s/it] 90%|████████▉ | 16818/18688 [19:49:33<2:20:01, 4.49s/it] 90%|████████▉ | 16819/18688 [19:49:37<2:17:22, 4.41s/it] 90%|█████████ | 16820/18688 [19:49:42<2:25:19, 4.67s/it] {'loss': '0.996', 'grad_norm': '1.077', 'learning_rate': '2.889e-06', 'epoch': '0.9001', 'num_input_tokens_seen': 56602408, 'train_runtime': '7.138e+04', 'train_tokens_per_second': '792.9'}
90%|█████████ | 16820/18688 [19:49:42<2:25:19, 4.67s/it] 90%|█████████ | 16821/18688 [19:49:47<2:24:32, 4.65s/it] 90%|█████████ | 16822/18688 [19:49:51<2:24:10, 4.64s/it] 90%|█████████ | 16823/18688 [19:49:56<2:24:27, 4.65s/it] 90%|█████████ | 16824/18688 [19:50:01<2:24:25, 4.65s/it] 90%|█████████ | 16825/18688 [19:50:05<2:23:12, 4.61s/it] 90%|█████████ | 16826/18688 [19:50:09<2:18:10, 4.45s/it] 90%|█████████ | 16827/18688 [19:50:13<2:12:02, 4.26s/it] 90%|█████████ | 16828/18688 [19:50:17<2:11:17, 4.24s/it] 90%|█████████ | 16829/18688 [19:50:21<2:08:54, 4.16s/it] 90%|█████████ | 16830/18688 [19:50:25<2:09:32, 4.18s/it] {'loss': '0.9371', 'grad_norm': '1.15', 'learning_rate': '2.859e-06', 'epoch': '0.9006', 'num_input_tokens_seen': 56636688, 'train_runtime': '7.143e+04', 'train_tokens_per_second': '792.9'}
90%|█████████ | 16830/18688 [19:50:25<2:09:32, 4.18s/it] 90%|█████████ | 16831/18688 [19:50:30<2:08:49, 4.16s/it] 90%|█████████ | 16832/18688 [19:50:33<2:06:14, 4.08s/it] 90%|█████████ | 16833/18688 [19:50:37<2:03:25, 3.99s/it] 90%|█████████ | 16834/18688 [19:50:43<2:19:45, 4.52s/it] 90%|█████████ | 16835/18688 [19:50:47<2:13:02, 4.31s/it] 90%|█████████ | 16836/18688 [19:50:52<2:20:31, 4.55s/it] 90%|█████████ | 16837/18688 [19:50:56<2:18:46, 4.50s/it] 90%|█████████ | 16838/18688 [19:51:00<2:10:22, 4.23s/it] 90%|█████████ | 16839/18688 [19:51:04<2:11:24, 4.26s/it] 90%|█████████ | 16840/18688 [19:51:08<2:09:28, 4.20s/it] {'loss': '0.9137', 'grad_norm': '1.154', 'learning_rate': '2.828e-06', 'epoch': '0.9011', 'num_input_tokens_seen': 56673048, 'train_runtime': '7.147e+04', 'train_tokens_per_second': '793'}
90%|█████████ | 16840/18688 [19:51:08<2:09:28, 4.20s/it] 90%|█████████ | 16841/18688 [19:51:12<2:04:25, 4.04s/it] 90%|█████████ | 16842/18688 [19:51:16<2:08:51, 4.19s/it] 90%|█████████ | 16843/18688 [19:51:21<2:07:24, 4.14s/it] 90%|█████████ | 16844/18688 [19:51:25<2:11:26, 4.28s/it] 90%|█████████ | 16845/18688 [19:51:29<2:07:53, 4.16s/it] 90%|█████████ | 16846/18688 [19:51:33<2:06:08, 4.11s/it] 90%|█████████ | 16847/18688 [19:51:37<2:06:16, 4.12s/it] 90%|█████████ | 16848/18688 [19:51:42<2:10:09, 4.24s/it] 90%|█████████ | 16849/18688 [19:51:46<2:09:04, 4.21s/it] 90%|█████████ | 16850/18688 [19:51:50<2:12:40, 4.33s/it] {'loss': '0.9238', 'grad_norm': '1.269', 'learning_rate': '2.798e-06', 'epoch': '0.9017', 'num_input_tokens_seen': 56705840, 'train_runtime': '7.151e+04', 'train_tokens_per_second': '793'}
90%|█████████ | 16850/18688 [19:51:50<2:12:40, 4.33s/it] 90%|█████████ | 16851/18688 [19:51:55<2:17:14, 4.48s/it] 90%|█████████ | 16852/18688 [19:52:00<2:18:59, 4.54s/it] 90%|█████████ | 16853/18688 [19:52:04<2:17:43, 4.50s/it] 90%|█████████ | 16854/18688 [19:52:08<2:11:55, 4.32s/it] 90%|█████████ | 16855/18688 [19:52:12<2:09:02, 4.22s/it] 90%|█████████ | 16856/18688 [19:52:16<2:06:42, 4.15s/it] 90%|█████████ | 16857/18688 [19:52:20<2:00:50, 3.96s/it] 90%|█████████ | 16858/18688 [19:52:24<1:59:09, 3.91s/it] 90%|█████████ | 16859/18688 [19:52:29<2:10:13, 4.27s/it] 90%|█████████ | 16860/18688 [19:52:33<2:12:37, 4.35s/it] {'loss': '0.9416', 'grad_norm': '1.057', 'learning_rate': '2.768e-06', 'epoch': '0.9022', 'num_input_tokens_seen': 56741496, 'train_runtime': '7.155e+04', 'train_tokens_per_second': '793'}
90%|█████████ | 16860/18688 [19:52:33<2:12:37, 4.35s/it] 90%|█████████ | 16861/18688 [19:52:37<2:11:20, 4.31s/it] 90%|█████████ | 16862/18688 [19:52:42<2:11:13, 4.31s/it] 90%|█████████ | 16863/18688 [19:52:45<2:05:06, 4.11s/it] 90%|█████████ | 16864/18688 [19:52:50<2:07:16, 4.19s/it] 90%|█████████ | 16865/18688 [19:52:53<2:03:16, 4.06s/it] 90%|█████████ | 16866/18688 [19:52:59<2:12:10, 4.35s/it] 90%|█████████ | 16867/18688 [19:53:02<2:07:26, 4.20s/it] 90%|█████████ | 16868/18688 [19:53:07<2:11:33, 4.34s/it] 90%|█████████ | 16869/18688 [19:53:11<2:04:27, 4.11s/it] 90%|█████████ | 16870/18688 [19:53:15<2:03:39, 4.08s/it] {'loss': '0.8622', 'grad_norm': '1.146', 'learning_rate': '2.738e-06', 'epoch': '0.9027', 'num_input_tokens_seen': 56775752, 'train_runtime': '7.16e+04', 'train_tokens_per_second': '793'}
90%|█████████ | 16870/18688 [19:53:15<2:03:39, 4.08s/it] 90%|█████████ | 16871/18688 [19:53:19<2:06:36, 4.18s/it] 90%|█████████ | 16872/18688 [19:53:23<2:00:57, 4.00s/it] 90%|█████████ | 16873/18688 [19:53:27<2:05:28, 4.15s/it] 90%|█████████ | 16874/18688 [19:53:31<2:00:02, 3.97s/it] 90%|█████████ | 16875/18688 [19:53:34<1:58:12, 3.91s/it] 90%|█████████ | 16876/18688 [19:53:39<2:00:30, 3.99s/it] 90%|█████████ | 16877/18688 [19:53:43<1:59:48, 3.97s/it] 90%|█████████ | 16878/18688 [19:53:47<2:07:12, 4.22s/it] 90%|█████████ | 16879/18688 [19:53:52<2:12:16, 4.39s/it] 90%|█████████ | 16880/18688 [19:53:56<2:08:44, 4.27s/it] {'loss': '0.8894', 'grad_norm': '1.255', 'learning_rate': '2.708e-06', 'epoch': '0.9033', 'num_input_tokens_seen': 56810064, 'train_runtime': '7.164e+04', 'train_tokens_per_second': '793'}
90%|█████████ | 16880/18688 [19:53:56<2:08:44, 4.27s/it] 90%|█████████ | 16881/18688 [19:54:00<2:02:10, 4.06s/it] 90%|█████████ | 16882/18688 [19:54:03<1:57:26, 3.90s/it] 90%|█████████ | 16883/18688 [19:54:07<1:54:28, 3.81s/it] 90%|█████████ | 16884/18688 [19:54:12<2:06:46, 4.22s/it] 90%|█████████ | 16885/18688 [19:54:16<2:05:05, 4.16s/it] 90%|█████████ | 16886/18688 [19:54:21<2:10:09, 4.33s/it] 90%|█████████ | 16887/18688 [19:54:25<2:09:58, 4.33s/it] 90%|█████████ | 16888/18688 [19:54:29<2:05:47, 4.19s/it] 90%|█████████ | 16889/18688 [19:54:34<2:10:49, 4.36s/it] 90%|█████████ | 16890/18688 [19:54:38<2:11:08, 4.38s/it] {'loss': '0.9047', 'grad_norm': '1.276', 'learning_rate': '2.679e-06', 'epoch': '0.9038', 'num_input_tokens_seen': 56845384, 'train_runtime': '7.168e+04', 'train_tokens_per_second': '793.1'}
90%|█████████ | 16890/18688 [19:54:38<2:11:08, 4.38s/it] 90%|█████████ | 16891/18688 [19:54:42<2:06:13, 4.21s/it] 90%|█████████ | 16892/18688 [19:54:45<2:00:02, 4.01s/it] 90%|█████████ | 16893/18688 [19:54:49<1:54:45, 3.84s/it] 90%|█████████ | 16894/18688 [19:54:52<1:51:08, 3.72s/it] 90%|█████████ | 16895/18688 [19:54:56<1:53:25, 3.80s/it] 90%|█████████ | 16896/18688 [19:55:00<1:52:10, 3.76s/it] 90%|█████████ | 16897/18688 [19:55:05<2:07:19, 4.27s/it] 90%|█████████ | 16898/18688 [19:55:09<2:01:27, 4.07s/it] 90%|█████████ | 16899/18688 [19:55:14<2:05:35, 4.21s/it] 90%|█████████ | 16900/18688 [19:55:17<1:59:34, 4.01s/it] {'loss': '0.8753', 'grad_norm': '1.502', 'learning_rate': '2.649e-06', 'epoch': '0.9043', 'num_input_tokens_seen': 56875712, 'train_runtime': '7.172e+04', 'train_tokens_per_second': '793.1'}
90%|█████████ | 16900/18688 [19:55:17<1:59:34, 4.01s/it] 90%|█████████ | 16901/18688 [19:55:21<2:01:44, 4.09s/it] 90%|█████████ | 16902/18688 [19:55:26<2:06:51, 4.26s/it] 90%|█████████ | 16903/18688 [19:55:30<2:06:47, 4.26s/it] 90%|█████████ | 16904/18688 [19:55:34<2:03:53, 4.17s/it] 90%|█████████ | 16905/18688 [19:55:39<2:05:53, 4.24s/it] 90%|█████████ | 16906/18688 [19:55:44<2:12:23, 4.46s/it] 90%|█████████ | 16907/18688 [19:55:47<2:06:51, 4.27s/it] 90%|█████████ | 16908/18688 [19:55:51<2:02:53, 4.14s/it] 90%|█████████ | 16909/18688 [19:55:55<2:00:52, 4.08s/it] 90%|█████████ | 16910/18688 [19:55:59<1:56:07, 3.92s/it] {'loss': '0.871', 'grad_norm': '1.665', 'learning_rate': '2.62e-06', 'epoch': '0.9049', 'num_input_tokens_seen': 56910152, 'train_runtime': '7.176e+04', 'train_tokens_per_second': '793.1'}
90%|█████████ | 16910/18688 [19:55:59<1:56:07, 3.92s/it] 90%|█████████ | 16911/18688 [19:56:02<1:53:06, 3.82s/it] 90%|█████████ | 16912/18688 [19:56:06<1:54:05, 3.85s/it] 91%|█████████ | 16913/18688 [19:56:11<2:02:17, 4.13s/it] 91%|█████████ | 16914/18688 [19:56:15<1:57:14, 3.97s/it] 91%|█████████ | 16915/18688 [19:56:18<1:55:31, 3.91s/it] 91%|█████████ | 16916/18688 [19:56:23<1:57:07, 3.97s/it] 91%|█████████ | 16917/18688 [19:56:26<1:52:53, 3.82s/it] 91%|█████████ | 16918/18688 [19:56:31<2:03:25, 4.18s/it] 91%|█████████ | 16919/18688 [19:56:35<2:00:40, 4.09s/it] 91%|█████████ | 16920/18688 [19:56:39<1:56:54, 3.97s/it] {'loss': '0.8585', 'grad_norm': '1.419', 'learning_rate': '2.591e-06', 'epoch': '0.9054', 'num_input_tokens_seen': 56940672, 'train_runtime': '7.18e+04', 'train_tokens_per_second': '793.1'}
91%|█████████ | 16920/18688 [19:56:39<1:56:54, 3.97s/it] 91%|█████████ | 16921/18688 [19:56:42<1:54:47, 3.90s/it] 91%|█████████ | 16922/18688 [19:56:47<1:58:27, 4.02s/it] 91%|█████████ | 16923/18688 [19:56:50<1:56:01, 3.94s/it] 91%|█████████ | 16924/18688 [19:56:54<1:53:10, 3.85s/it] 91%|█████████ | 16925/18688 [19:56:58<1:56:44, 3.97s/it] 91%|█████████ | 16926/18688 [19:57:03<2:00:44, 4.11s/it] 91%|█████████ | 16927/18688 [19:57:07<2:02:30, 4.17s/it] 91%|█████████ | 16928/18688 [19:57:11<1:57:19, 4.00s/it] 91%|█████████ | 16929/18688 [19:57:15<1:56:57, 3.99s/it] 91%|█████████ | 16930/18688 [19:57:19<1:56:31, 3.98s/it] {'loss': '0.8814', 'grad_norm': '1.273', 'learning_rate': '2.562e-06', 'epoch': '0.906', 'num_input_tokens_seen': 56972728, 'train_runtime': '7.184e+04', 'train_tokens_per_second': '793.1'}
91%|█████████ | 16930/18688 [19:57:19<1:56:31, 3.98s/it] 91%|█████████ | 16931/18688 [19:57:22<1:53:48, 3.89s/it] 91%|█████████ | 16932/18688 [19:57:27<2:00:22, 4.11s/it] 91%|█████████ | 16933/18688 [19:57:30<1:55:48, 3.96s/it] 91%|█████████ | 16934/18688 [19:57:36<2:05:19, 4.29s/it] 91%|█████████ | 16935/18688 [19:57:40<2:03:31, 4.23s/it] 91%|█████████ | 16936/18688 [19:57:43<1:57:47, 4.03s/it] 91%|█████████ | 16937/18688 [19:57:47<1:53:11, 3.88s/it] 91%|█████████ | 16938/18688 [19:57:51<2:00:22, 4.13s/it] 91%|█████████ | 16939/18688 [19:57:56<2:06:50, 4.35s/it] 91%|█████████ | 16940/18688 [19:58:00<2:00:19, 4.13s/it] {'loss': '0.8414', 'grad_norm': '1.726', 'learning_rate': '2.533e-06', 'epoch': '0.9065', 'num_input_tokens_seen': 57006328, 'train_runtime': '7.188e+04', 'train_tokens_per_second': '793.1'}
91%|█████████ | 16940/18688 [19:58:00<2:00:19, 4.13s/it] 91%|█████████ | 16941/18688 [19:58:05<2:04:22, 4.27s/it] 91%|█████████ | 16942/18688 [19:58:09<2:04:55, 4.29s/it] 91%|█████████ | 16943/18688 [19:58:13<1:59:31, 4.11s/it] 91%|█████████ | 16944/18688 [19:58:16<1:54:15, 3.93s/it] 91%|█████████ | 16945/18688 [19:58:20<1:52:56, 3.89s/it] 91%|█████████ | 16946/18688 [19:58:25<2:00:51, 4.16s/it] 91%|█████████ | 16947/18688 [19:58:29<2:04:49, 4.30s/it] 91%|█████████ | 16948/18688 [19:58:34<2:07:21, 4.39s/it] 91%|█████████ | 16949/18688 [19:58:39<2:13:17, 4.60s/it] 91%|█████████ | 16950/18688 [19:58:43<2:05:09, 4.32s/it] {'loss': '0.9273', 'grad_norm': '1.432', 'learning_rate': '2.505e-06', 'epoch': '0.907', 'num_input_tokens_seen': 57042000, 'train_runtime': '7.192e+04', 'train_tokens_per_second': '793.1'}
91%|█████████ | 16950/18688 [19:58:43<2:05:09, 4.32s/it] 91%|█████████ | 16951/18688 [19:58:46<1:58:03, 4.08s/it] 91%|█████████ | 16952/18688 [19:58:50<1:56:45, 4.04s/it] 91%|█████████ | 16953/18688 [19:58:54<1:53:18, 3.92s/it] 91%|█████████ | 16954/18688 [19:58:57<1:50:38, 3.83s/it] 91%|█████████ | 16955/18688 [19:59:01<1:50:18, 3.82s/it] 91%|█████████ | 16956/18688 [19:59:05<1:54:25, 3.96s/it] 91%|█████████ | 16957/18688 [19:59:10<2:00:55, 4.19s/it] 91%|█████████ | 16958/18688 [19:59:14<1:56:50, 4.05s/it] 91%|█████████ | 16959/18688 [19:59:18<1:56:03, 4.03s/it] 91%|█████████ | 16960/18688 [19:59:22<1:57:06, 4.07s/it] {'loss': '0.8836', 'grad_norm': '1.261', 'learning_rate': '2.476e-06', 'epoch': '0.9076', 'num_input_tokens_seen': 57074336, 'train_runtime': '7.196e+04', 'train_tokens_per_second': '793.1'}
91%|█████████ | 16960/18688 [19:59:22<1:57:06, 4.07s/it] 91%|█████████ | 16961/18688 [19:59:26<2:00:09, 4.17s/it] 91%|█████████ | 16962/18688 [19:59:30<1:58:47, 4.13s/it] 91%|█████████ | 16963/18688 [19:59:34<1:54:45, 3.99s/it] 91%|█████████ | 16964/18688 [19:59:39<2:01:22, 4.22s/it] 91%|█████████ | 16965/18688 [19:59:43<1:59:39, 4.17s/it] 91%|█████████ | 16966/18688 [19:59:48<2:08:35, 4.48s/it] 91%|█████████ | 16967/18688 [19:59:52<2:06:39, 4.42s/it] 91%|█████████ | 16968/18688 [19:59:56<1:58:35, 4.14s/it] 91%|█████████ | 16969/18688 [20:00:00<2:00:54, 4.22s/it] 91%|█████████ | 16970/18688 [20:00:05<2:00:48, 4.22s/it] {'loss': '0.9711', 'grad_norm': '1.401', 'learning_rate': '2.448e-06', 'epoch': '0.9081', 'num_input_tokens_seen': 57108760, 'train_runtime': '7.201e+04', 'train_tokens_per_second': '793.1'}
91%|█████████ | 16970/18688 [20:00:05<2:00:48, 4.22s/it] 91%|█████████ | 16971/18688 [20:00:08<1:54:50, 4.01s/it] 91%|█████████ | 16972/18688 [20:00:12<1:50:48, 3.87s/it] 91%|█████████ | 16973/18688 [20:00:16<1:55:41, 4.05s/it] 91%|█████████ | 16974/18688 [20:00:20<1:52:33, 3.94s/it] 91%|█████████ | 16975/18688 [20:00:25<2:04:18, 4.35s/it] 91%|█████████ | 16976/18688 [20:00:29<1:57:59, 4.14s/it] 91%|█████████ | 16977/18688 [20:00:32<1:54:07, 4.00s/it] 91%|█████████ | 16978/18688 [20:00:36<1:49:49, 3.85s/it] 91%|█████████ | 16979/18688 [20:00:41<1:56:41, 4.10s/it] 91%|█████████ | 16980/18688 [20:00:45<1:55:31, 4.06s/it] {'loss': '0.9277', 'grad_norm': '1.346', 'learning_rate': '2.42e-06', 'epoch': '0.9086', 'num_input_tokens_seen': 57140504, 'train_runtime': '7.205e+04', 'train_tokens_per_second': '793.1'}
91%|█████████ | 16980/18688 [20:00:45<1:55:31, 4.06s/it] 91%|█████████ | 16981/18688 [20:00:48<1:50:17, 3.88s/it] 91%|█████████ | 16982/18688 [20:00:52<1:48:38, 3.82s/it] 91%|█████████ | 16983/18688 [20:00:56<1:55:07, 4.05s/it] 91%|█████████ | 16984/18688 [20:01:00<1:52:28, 3.96s/it] 91%|█████████ | 16985/18688 [20:01:05<1:56:54, 4.12s/it] 91%|█████████ | 16986/18688 [20:01:09<1:59:08, 4.20s/it] 91%|█████████ | 16987/18688 [20:01:15<2:11:12, 4.63s/it] 91%|█████████ | 16988/18688 [20:01:18<2:04:46, 4.40s/it] 91%|█████████ | 16989/18688 [20:01:22<1:58:29, 4.18s/it] 91%|█████████ | 16990/18688 [20:01:26<1:57:43, 4.16s/it] {'loss': '0.9073', 'grad_norm': '1.213', 'learning_rate': '2.392e-06', 'epoch': '0.9092', 'num_input_tokens_seen': 57174824, 'train_runtime': '7.209e+04', 'train_tokens_per_second': '793.1'}
91%|█████████ | 16990/18688 [20:01:26<1:57:43, 4.16s/it] 91%|█████████ | 16991/18688 [20:01:30<1:55:07, 4.07s/it] 91%|█████████ | 16992/18688 [20:01:34<1:51:57, 3.96s/it] 91%|█████████ | 16993/18688 [20:01:38<1:50:55, 3.93s/it] 91%|█████████ | 16994/18688 [20:01:42<1:52:01, 3.97s/it] 91%|█████████ | 16995/18688 [20:01:46<1:52:30, 3.99s/it] 91%|█████████ | 16996/18688 [20:01:50<1:52:41, 4.00s/it] 91%|█████████ | 16997/18688 [20:01:54<1:52:05, 3.98s/it] 91%|█████████ | 16998/18688 [20:01:58<1:57:27, 4.17s/it] 91%|█████████ | 16999/18688 [20:02:03<1:58:58, 4.23s/it] 91%|█████████ | 17000/18688 [20:02:06<1:55:19, 4.10s/it] {'loss': '0.9214', 'grad_norm': '1.256', 'learning_rate': '2.364e-06', 'epoch': '0.9097', 'num_input_tokens_seen': 57206512, 'train_runtime': '7.213e+04', 'train_tokens_per_second': '793.1'}
91%|█████████ | 17000/18688 [20:02:06<1:55:19, 4.10s/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, 25.86it/s]
1%| | 6/1000 [00:00<00:54, 18.34it/s]
1%| | 8/1000 [00:00<00:56, 17.57it/s]
1%| | 10/1000 [00:00<00:59, 16.65it/s]
1%| | 12/1000 [00:00<01:00, 16.42it/s]
1%|▏ | 14/1000 [00:00<01:01, 15.92it/s]
2%|▏ | 16/1000 [00:00<01:02, 15.68it/s]
2%|▏ | 18/1000 [00:01<01:38, 9.97it/s]
2%|▏ | 20/1000 [00:01<01:33, 10.51it/s]
2%|▏ | 22/1000 [00:01<01:52, 8.71it/s]
2%|▏ | 24/1000 [00:01<01:39, 9.76it/s]
3%|▎ | 26/1000 [00:02<01:45, 9.27it/s]
3%|▎ | 28/1000 [00:02<01:36, 10.08it/s]
3%|▎ | 30/1000 [00:02<01:28, 10.98it/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, 11.33it/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, 10.48it/s]
5%|▍ | 46/1000 [00:03<01:21, 11.65it/s]
5%|▍ | 48/1000 [00:04<01:29, 10.61it/s]
5%|▌ | 50/1000 [00:04<01:21, 11.64it/s]
5%|▌ | 52/1000 [00:04<01:16, 12.47it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.21it/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, 9.74it/s]
6%|▌ | 62/1000 [00:05<01:27, 10.70it/s]
6%|▋ | 64/1000 [00:05<01:19, 11.77it/s]
7%|▋ | 66/1000 [00:05<01:16, 12.13it/s]
7%|▋ | 68/1000 [00:05<01:13, 12.76it/s]
7%|▋ | 70/1000 [00:06<01:12, 12.78it/s]
7%|▋ | 72/1000 [00:06<01:13, 12.67it/s]
7%|▋ | 74/1000 [00:06<01:13, 12.65it/s]
8%|▊ | 76/1000 [00:06<01:12, 12.68it/s]
8%|▊ | 78/1000 [00:06<01:13, 12.54it/s]
8%|▊ | 80/1000 [00:06<01:18, 11.78it/s]
8%|▊ | 82/1000 [00:07<01:15, 12.08it/s]
8%|▊ | 84/1000 [00:07<01:14, 12.25it/s]
9%|▊ | 86/1000 [00:07<01:12, 12.60it/s]
9%|▉ | 88/1000 [00:07<01:13, 12.44it/s]
9%|▉ | 90/1000 [00:07<01:13, 12.31it/s]
9%|▉ | 92/1000 [00:07<01:14, 12.15it/s]
9%|▉ | 94/1000 [00:08<01:14, 12.11it/s]
10%|▉ | 96/1000 [00:08<01:14, 12.11it/s]
10%|▉ | 98/1000 [00:08<01:14, 12.17it/s]
10%|█ | 100/1000 [00:08<01:41, 8.84it/s]
10%|█ | 102/1000 [00:08<01:30, 9.92it/s]
10%|█ | 104/1000 [00:09<01:59, 7.49it/s]
10%|█ | 105/1000 [00:09<02:08, 6.97it/s]
11%|█ | 106/1000 [00:09<02:04, 7.17it/s]
11%|█ | 108/1000 [00:09<01:40, 8.88it/s]
11%|█ | 110/1000 [00:09<01:24, 10.54it/s]
11%|█ | 112/1000 [00:09<01:15, 11.70it/s]
11%|█▏ | 114/1000 [00:10<01:12, 12.28it/s]
12%|█▏ | 116/1000 [00:10<01:08, 12.85it/s]
12%|█▏ | 118/1000 [00:10<01:05, 13.52it/s]
12%|█▏ | 120/1000 [00:10<01:08, 12.84it/s]
12%|█▏ | 122/1000 [00:10<01:22, 10.61it/s]
12%|█▏ | 124/1000 [00:11<01:30, 9.68it/s]
13%|█▎ | 126/1000 [00:11<01:22, 10.64it/s]
13%|█▎ | 128/1000 [00:11<01:15, 11.55it/s]
13%|█▎ | 130/1000 [00:11<01:11, 12.23it/s]
13%|█▎ | 132/1000 [00:11<01:05, 13.19it/s]
13%|█▎ | 134/1000 [00:11<01:09, 12.38it/s]
14%|█▎ | 136/1000 [00:11<01:04, 13.33it/s]
14%|█▍ | 138/1000 [00:12<01:05, 13.26it/s]
14%|█▍ | 140/1000 [00:12<01:25, 10.11it/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, 11.50it/s]
15%|█▌ | 150/1000 [00:13<01:07, 12.59it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.14it/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, 13.74it/s]
16%|█▌ | 160/1000 [00:13<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, 13.76it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.25it/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<01:55, 7.16it/s]
18%|█▊ | 178/1000 [00:15<01:35, 8.61it/s]
18%|█▊ | 180/1000 [00:16<01:33, 8.76it/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, 8.34it/s]
19%|█▉ | 188/1000 [00:16<01:24, 9.66it/s]
19%|█▉ | 190/1000 [00:17<01:15, 10.67it/s]
19%|█▉ | 192/1000 [00:17<01:11, 11.31it/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, 10.15it/s]
20%|██ | 200/1000 [00:18<01:16, 10.52it/s]
20%|██ | 202/1000 [00:18<01:09, 11.49it/s]
20%|██ | 204/1000 [00:18<01:03, 12.60it/s]
21%|██ | 206/1000 [00:18<01:00, 13.13it/s]
21%|██ | 208/1000 [00:18<00:58, 13.53it/s]
21%|██ | 210/1000 [00:18<00:54, 14.40it/s]
21%|██ | 212/1000 [00:18<00:53, 14.67it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.69it/s]
22%|██▏ | 216/1000 [00:19<00:54, 14.45it/s]
22%|██▏ | 218/1000 [00:19<00:52, 14.83it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.89it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.16it/s]
22%|██▏ | 224/1000 [00:20<01:20, 9.65it/s]
23%|██▎ | 226/1000 [00:20<01:12, 10.64it/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, 12.41it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.14it/s]
24%|██▎ | 236/1000 [00:20<00:56, 13.56it/s]
24%|██▍ | 238/1000 [00:21<00:53, 14.15it/s]
24%|██▍ | 240/1000 [00:21<00:53, 14.16it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.72it/s]
24%|██▍ | 244/1000 [00:21<00:50, 14.94it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.92it/s]
25%|██▍ | 248/1000 [00:21<00:59, 12.65it/s]
25%|██▌ | 250/1000 [00:21<00:56, 13.38it/s]
25%|██▌ | 252/1000 [00:22<01:01, 12.13it/s]
25%|██▌ | 254/1000 [00:22<00:56, 13.15it/s]
26%|██▌ | 256/1000 [00:22<00:54, 13.73it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.76it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.82it/s]
26%|██▌ | 262/1000 [00:22<00:56, 12.99it/s]
26%|██▋ | 264/1000 [00:22<00:54, 13.57it/s]
27%|██▋ | 266/1000 [00:23<00:52, 13.94it/s]
27%|██▋ | 268/1000 [00:23<00:50, 14.51it/s]
27%|██▋ | 270/1000 [00:23<00:55, 13.25it/s]
27%|██▋ | 272/1000 [00:23<01:02, 11.63it/s]
27%|██▋ | 274/1000 [00:23<01:02, 11.70it/s]
28%|██▊ | 276/1000 [00:23<00:56, 12.70it/s]
28%|██▊ | 278/1000 [00:24<00:55, 12.94it/s]
28%|██▊ | 280/1000 [00:24<00:53, 13.52it/s]
28%|██▊ | 282/1000 [00:24<00:49, 14.48it/s]
28%|██▊ | 284/1000 [00:24<00:49, 14.44it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.71it/s]
29%|██▉ | 288/1000 [00:24<00:47, 15.08it/s]
29%|██▉ | 290/1000 [00:24<00:45, 15.49it/s]
29%|██▉ | 292/1000 [00:24<00:45, 15.44it/s]
29%|██▉ | 294/1000 [00:25<00:46, 15.34it/s]
30%|██▉ | 296/1000 [00:25<00:45, 15.38it/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, 13.72it/s]
31%|███ | 306/1000 [00:25<00:47, 14.46it/s]
31%|███ | 308/1000 [00:26<00:47, 14.43it/s]
31%|███ | 310/1000 [00:26<00:47, 14.54it/s]
31%|███ | 312/1000 [00:26<00:47, 14.54it/s]
31%|███▏ | 314/1000 [00:26<00:45, 15.21it/s]
32%|███▏ | 316/1000 [00:26<00:44, 15.51it/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, 10.69it/s]
32%|███▏ | 324/1000 [00:27<00:58, 11.47it/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<00:54, 12.39it/s]
33%|███▎ | 332/1000 [00:27<00:52, 12.66it/s]
33%|███▎ | 334/1000 [00:28<00:50, 13.16it/s]
34%|███▎ | 336/1000 [00:28<00:48, 13.56it/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, 12.34it/s]
34%|███▍ | 344/1000 [00:28<00:50, 13.01it/s]
35%|███▍ | 346/1000 [00:29<00:48, 13.59it/s]
35%|███▍ | 348/1000 [00:29<00:46, 14.01it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.61it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.80it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.84it/s]
36%|███▌ | 356/1000 [00:29<00:43, 14.96it/s]
36%|███▌ | 358/1000 [00:29<00:43, 14.65it/s]
36%|███▌ | 360/1000 [00:30<00:42, 15.03it/s]
36%|███▌ | 362/1000 [00:30<00:47, 13.35it/s]
36%|███▋ | 364/1000 [00:30<00:45, 13.86it/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, 13.22it/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, 10.96it/s]
38%|███▊ | 378/1000 [00:31<00:53, 11.62it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.42it/s]
38%|███▊ | 382/1000 [00:31<00:49, 12.60it/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, 13.52it/s]
39%|███▉ | 392/1000 [00:32<00:43, 13.86it/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, 12.55it/s]
40%|████ | 400/1000 [00:33<00:45, 13.20it/s]
40%|████ | 402/1000 [00:33<00:43, 13.84it/s]
40%|████ | 404/1000 [00:33<00:41, 14.47it/s]
41%|████ | 406/1000 [00:33<00:39, 14.98it/s]
41%|████ | 408/1000 [00:33<00:47, 12.55it/s]
41%|████ | 410/1000 [00:34<00:56, 10.47it/s]
41%|████ | 412/1000 [00:34<00:50, 11.55it/s]
41%|████▏ | 414/1000 [00:34<01:07, 8.64it/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<01:14, 7.68it/s]
43%|████▎ | 426/1000 [00:35<01:04, 8.94it/s]
43%|████▎ | 428/1000 [00:36<00:56, 10.13it/s]
43%|████▎ | 430/1000 [00:36<01:02, 9.10it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.05it/s]
43%|████▎ | 434/1000 [00:36<01:08, 8.26it/s]
44%|████▎ | 436/1000 [00:37<01:00, 9.29it/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<00:52, 10.54it/s]
44%|████▍ | 444/1000 [00:37<00:48, 11.39it/s]
45%|████▍ | 446/1000 [00:37<00:45, 12.15it/s]
45%|████▍ | 448/1000 [00:38<00:50, 10.94it/s]
45%|████▌ | 450/1000 [00:38<00:46, 11.77it/s]
45%|████▌ | 452/1000 [00:38<00:44, 12.42it/s]
45%|████▌ | 454/1000 [00:38<00:47, 11.60it/s]
46%|████▌ | 456/1000 [00:38<00:45, 11.88it/s]
46%|████▌ | 458/1000 [00:38<00:43, 12.45it/s]
46%|████▌ | 460/1000 [00:39<00:41, 12.92it/s]
46%|████▌ | 462/1000 [00:39<00:39, 13.66it/s]
46%|████▋ | 464/1000 [00:39<00:37, 14.32it/s]
47%|████▋ | 466/1000 [00:39<00:37, 14.29it/s]
47%|████▋ | 468/1000 [00:39<00:42, 12.50it/s]
47%|████▋ | 470/1000 [00:39<00:53, 9.85it/s]
47%|████▋ | 472/1000 [00:40<00:52, 10.08it/s]
47%|████▋ | 474/1000 [00:40<00:46, 11.38it/s]
48%|████▊ | 476/1000 [00:40<00:43, 12.05it/s]
48%|████▊ | 478/1000 [00:40<00:43, 11.95it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.89it/s]
48%|████▊ | 482/1000 [00:40<00:38, 13.29it/s]
48%|████▊ | 484/1000 [00:40<00:37, 13.80it/s]
49%|████▊ | 486/1000 [00:41<00:36, 13.97it/s]
49%|████▉ | 488/1000 [00:41<00:35, 14.26it/s]
49%|████▉ | 490/1000 [00:41<00:45, 11.12it/s]
49%|████▉ | 492/1000 [00:41<00:40, 12.43it/s]
49%|████▉ | 494/1000 [00:41<00:37, 13.43it/s]
50%|████▉ | 496/1000 [00:41<00:35, 14.20it/s]
50%|████▉ | 498/1000 [00:42<00:34, 14.52it/s]
50%|█████ | 500/1000 [00:42<00:43, 11.56it/s]
50%|█████ | 502/1000 [00:42<00:41, 11.99it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.59it/s]
51%|█████ | 506/1000 [00:42<00:38, 12.77it/s]
51%|█████ | 508/1000 [00:42<00:35, 13.70it/s]
51%|█████ | 510/1000 [00:43<00:47, 10.39it/s]
51%|█████ | 512/1000 [00:43<00:43, 11.15it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 12.02it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.59it/s]
52%|█████▏ | 518/1000 [00:43<00:38, 12.48it/s]
52%|█████▏ | 520/1000 [00:43<00:38, 12.41it/s]
52%|█████▏ | 522/1000 [00:44<00:38, 12.35it/s]
52%|█████▏ | 524/1000 [00:44<00:38, 12.25it/s]
53%|█████▎ | 526/1000 [00:44<00:37, 12.56it/s]
53%|█████▎ | 528/1000 [00:44<00:42, 11.09it/s]
53%|█████▎ | 530/1000 [00:44<00:38, 12.12it/s]
53%|█████▎ | 532/1000 [00:44<00:35, 13.20it/s]
53%|█████▎ | 534/1000 [00:44<00:34, 13.65it/s]
54%|█████▎ | 536/1000 [00:45<00:33, 13.90it/s]
54%|█████▍ | 538/1000 [00:45<00:33, 13.79it/s]
54%|█████▍ | 540/1000 [00:45<00:32, 14.30it/s]
54%|█████▍ | 542/1000 [00:45<00:31, 14.61it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.56it/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, 14.26it/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, 14.49it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 15.07it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 14.99it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.78it/s]
57%|█████▋ | 566/1000 [00:47<00:37, 11.65it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.19it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.20it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.74it/s]
57%|█████▋ | 574/1000 [00:47<00:31, 13.64it/s]
58%|█████▊ | 576/1000 [00:48<00:31, 13.56it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.61it/s]
58%|█████▊ | 580/1000 [00:48<00:32, 12.81it/s]
58%|█████▊ | 582/1000 [00:48<00:30, 13.54it/s]
58%|█████▊ | 584/1000 [00:48<00:30, 13.78it/s]
59%|█████▊ | 586/1000 [00:48<00:29, 14.18it/s]
59%|█████▉ | 588/1000 [00:48<00:27, 14.77it/s]
59%|█████▉ | 590/1000 [00:49<00:27, 14.79it/s]
59%|█████▉ | 592/1000 [00:49<00:27, 14.62it/s]
59%|█████▉ | 594/1000 [00:49<00:30, 13.23it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.79it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 14.05it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.42it/s]
60%|██████ | 602/1000 [00:49<00:27, 14.56it/s]
60%|██████ | 604/1000 [00:50<00:26, 15.08it/s]
61%|██████ | 606/1000 [00:50<00:26, 14.82it/s]
61%|██████ | 608/1000 [00:50<00:25, 15.14it/s]
61%|██████ | 610/1000 [00:50<00:26, 14.98it/s]
61%|██████ | 612/1000 [00:50<00:25, 15.15it/s]
61%|██████▏ | 614/1000 [00:50<00:26, 14.40it/s]
62%|██████▏ | 616/1000 [00:50<00:25, 14.79it/s]
62%|██████▏ | 618/1000 [00:51<00:26, 14.21it/s]
62%|██████▏ | 620/1000 [00:51<00:38, 9.76it/s]
62%|██████▏ | 622/1000 [00:51<00:37, 9.96it/s]
62%|██████▏ | 624/1000 [00:51<00:32, 11.42it/s]
63%|██████▎ | 626/1000 [00:51<00:30, 12.09it/s]
63%|██████▎ | 628/1000 [00:51<00:27, 13.33it/s]
63%|██████▎ | 630/1000 [00:52<00:26, 13.81it/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, 10.94it/s]
64%|██████▍ | 638/1000 [00:52<00:33, 10.65it/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, 12.56it/s]
65%|██████▍ | 646/1000 [00:53<00:27, 12.85it/s]
65%|██████▍ | 648/1000 [00:53<00:27, 12.99it/s]
65%|██████▌ | 650/1000 [00:53<00:29, 12.03it/s]
65%|██████▌ | 652/1000 [00:53<00:31, 10.88it/s]
65%|██████▌ | 654/1000 [00:54<00:38, 8.99it/s]
66%|██████▌ | 656/1000 [00:54<00:34, 10.11it/s]
66%|██████▌ | 658/1000 [00:54<00:30, 11.04it/s]
66%|██████▌ | 660/1000 [00:54<00:35, 9.54it/s]
66%|██████▌ | 662/1000 [00:55<00:32, 10.25it/s]
66%|██████▋ | 664/1000 [00:55<00:30, 10.97it/s]
67%|██████▋ | 666/1000 [00:55<00:27, 12.13it/s]
67%|██████▋ | 668/1000 [00:55<00:30, 10.94it/s]
67%|██████▋ | 670/1000 [00:55<00:30, 10.85it/s]
67%|██████▋ | 672/1000 [00:55<00:28, 11.64it/s]
67%|██████▋ | 674/1000 [00:56<00:26, 12.27it/s]
68%|██████▊ | 676/1000 [00:56<00:27, 11.60it/s]
68%|██████▊ | 678/1000 [00:56<00:26, 12.11it/s]
68%|██████▊ | 680/1000 [00:56<00:24, 12.91it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.08it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.30it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.35it/s]
69%|██████▉ | 688/1000 [00:57<00:22, 13.64it/s]
69%|██████▉ | 690/1000 [00:57<00:22, 13.73it/s]
69%|██████▉ | 692/1000 [00:57<00:21, 14.22it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.31it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 14.05it/s]
70%|██████▉ | 698/1000 [00:57<00:20, 14.58it/s]
70%|███████ | 700/1000 [00:57<00:24, 12.16it/s]
70%|███████ | 702/1000 [00:58<00:23, 12.94it/s]
70%|███████ | 704/1000 [00:58<00:30, 9.83it/s]
71%|███████ | 706/1000 [00:58<00:27, 10.64it/s]
71%|███████ | 708/1000 [00:58<00:30, 9.61it/s]
71%|███████ | 710/1000 [00:58<00:26, 10.88it/s]
71%|███████ | 712/1000 [00:59<00:24, 11.83it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.91it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 13.00it/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, 13.92it/s]
72%|███████▏ | 724/1000 [00:59<00:20, 13.76it/s]
73%|███████▎ | 726/1000 [01:00<00:19, 13.97it/s]
73%|███████▎ | 728/1000 [01:00<00:22, 11.89it/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, 8.61it/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, 11.00it/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, 10.15it/s]
75%|███████▍ | 747/1000 [01:02<00:26, 9.44it/s]
75%|███████▍ | 749/1000 [01:02<00:24, 10.12it/s]
75%|███████▌ | 751/1000 [01:02<00:23, 10.72it/s]
75%|███████▌ | 753/1000 [01:02<00:21, 11.25it/s]
76%|███████▌ | 755/1000 [01:03<00:21, 11.53it/s]
76%|███████▌ | 757/1000 [01:03<00:26, 9.19it/s]
76%|███████▌ | 759/1000 [01:03<00:24, 9.88it/s]
76%|███████▌ | 761/1000 [01:03<00:22, 10.48it/s]
76%|███████▋ | 763/1000 [01:03<00:25, 9.32it/s]
76%|███████▋ | 765/1000 [01:04<00:28, 8.11it/s]
77%|███████▋ | 767/1000 [01:04<00:25, 9.12it/s]
77%|███████▋ | 769/1000 [01:04<00:22, 10.18it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.49it/s]
77%|███████▋ | 773/1000 [01:04<00:18, 12.35it/s]
78%|███████▊ | 775/1000 [01:04<00:17, 12.96it/s]
78%|███████▊ | 777/1000 [01:05<00:16, 13.51it/s]
78%|███████▊ | 779/1000 [01:05<00:21, 10.26it/s]
78%|███████▊ | 781/1000 [01:05<00:23, 9.27it/s]
78%|███████▊ | 783/1000 [01:05<00:26, 8.25it/s]
78%|███████▊ | 784/1000 [01:06<00:30, 7.03it/s]
78%|███████▊ | 785/1000 [01:06<00:29, 7.33it/s]
79%|███████▊ | 786/1000 [01:06<00:30, 6.94it/s]
79%|███████▉ | 788/1000 [01:06<00:24, 8.79it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.12it/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, 12.34it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 13.12it/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, 8.28it/s]
81%|████████ | 806/1000 [01:08<00:21, 8.96it/s]
81%|████████ | 808/1000 [01:08<00:18, 10.28it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.41it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.64it/s]
81%|████████▏ | 814/1000 [01:08<00:14, 12.55it/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, 12.45it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 13.00it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.80it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.21it/s]
83%|████████▎ | 828/1000 [01:10<00:11, 14.48it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.93it/s]
83%|████████▎ | 832/1000 [01:10<00:11, 14.36it/s]
83%|████████▎ | 834/1000 [01:10<00:11, 14.06it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.43it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.33it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.46it/s]
84%|████████▍ | 842/1000 [01:10<00:10, 14.46it/s]
84%|████████▍ | 844/1000 [01:11<00:11, 13.98it/s]
85%|████████▍ | 846/1000 [01:11<00:11, 13.94it/s]
85%|████████▍ | 848/1000 [01:11<00:10, 13.89it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.10it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.72it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.31it/s]
86%|████████▌ | 856/1000 [01:11<00:10, 14.13it/s]
86%|████████▌ | 858/1000 [01:12<00:09, 14.64it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 15.40it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.51it/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, 13.72it/s]
87%|████████▋ | 870/1000 [01:12<00:09, 14.14it/s]
87%|████████▋ | 872/1000 [01:13<00:08, 14.75it/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, 11.93it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.55it/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, 12.87it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.84it/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, 14.20it/s]
90%|████████▉ | 896/1000 [01:14<00:07, 13.93it/s]
90%|████████▉ | 898/1000 [01:15<00:07, 13.34it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.28it/s]
90%|█████████ | 902/1000 [01:15<00:07, 13.02it/s]
90%|█████████ | 904/1000 [01:15<00:09, 9.81it/s]
91%|█████████ | 906/1000 [01:15<00:08, 10.88it/s]
91%|█████████ | 908/1000 [01:16<00:07, 11.87it/s]
91%|█████████ | 910/1000 [01:16<00:07, 12.12it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.31it/s]
91%|█████████▏| 914/1000 [01:16<00:12, 6.81it/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, 8.81it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 10.23it/s]
92%|█████████▏| 924/1000 [01:17<00:06, 10.93it/s]
93%|█████████▎| 926/1000 [01:17<00:06, 11.78it/s]
93%|█████████▎| 928/1000 [01:18<00:06, 10.62it/s]
93%|█████████▎| 930/1000 [01:18<00:05, 11.84it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 12.41it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.89it/s]
94%|█████████▎| 936/1000 [01:18<00:04, 13.47it/s]
94%|█████████▍| 938/1000 [01:18<00:04, 14.35it/s]
94%|█████████▍| 940/1000 [01:19<00:04, 14.64it/s]
94%|█████████▍| 942/1000 [01:19<00:04, 12.27it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.78it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.84it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 12.65it/s]
95%|█████████▌| 950/1000 [01:19<00:03, 13.44it/s]
95%|█████████▌| 952/1000 [01:19<00:03, 14.17it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 14.26it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 14.24it/s]
96%|█████████▌| 958/1000 [01:20<00:02, 14.78it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 13.11it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.73it/s]
96%|█████████▋| 964/1000 [01:20<00:03, 10.23it/s]
97%|█████████▋| 966/1000 [01:21<00:03, 11.27it/s]
97%|█████████▋| 968/1000 [01:21<00:02, 11.62it/s]
97%|█████████▋| 970/1000 [01:21<00:02, 12.06it/s]
97%|█████████▋| 972/1000 [01:21<00:03, 9.03it/s]
97%|█████████▋| 974/1000 [01:21<00:02, 10.25it/s]
98%|█████████▊| 976/1000 [01:22<00:02, 10.73it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.35it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.53it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.11it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 10.23it/s]
99%|█████████▊| 986/1000 [01:23<00:01, 10.53it/s]
99%|█████████▉| 988/1000 [01:23<00:01, 10.80it/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, 12.99it/s]
100%|█████████▉| 996/1000 [01:23<00:00, 13.89it/s]
100%|█████████▉| 998/1000 [01:23<00:00, 14.07it/s]
100%|██████████| 1000/1000 [01:24<00:00, 14.73it/s]
{'eval_loss': '0.8732', 'eval_runtime': '84.16', 'eval_samples_per_second': '11.88', 'eval_steps_per_second': '11.88', 'epoch': '0.9097', 'num_input_tokens_seen': 57206512}
91%|█████████ | 17000/18688 [20:03:31<1:55:19, 4.10s/it]
100%|██████████| 1000/1000 [01:24<00:00, 14.73it/s]
 91%|█████████ | 17001/18688 [20:03:35<13:48:28, 29.47s/it] 91%|█████████ | 17002/18688 [20:03:39<10:14:04, 21.85s/it] 91%|█████████ | 17003/18688 [20:03:44<7:48:39, 16.69s/it] 91%|█████████ | 17004/18688 [20:03:48<5:59:24, 12.81s/it] 91%|█████████ | 17005/18688 [20:03:51<4:42:11, 10.06s/it] 91%|█████████ | 17006/18688 [20:03:55<3:47:02, 8.10s/it] 91%|█████████ | 17007/18688 [20:04:00<3:20:17, 7.15s/it] 91%|█████████ | 17008/18688 [20:04:05<3:02:53, 6.53s/it] 91%|█████████ | 17009/18688 [20:04:09<2:43:36, 5.85s/it] 91%|█████████ | 17010/18688 [20:04:13<2:27:18, 5.27s/it] {'loss': '0.9117', 'grad_norm': '1.334', 'learning_rate': '2.336e-06', 'epoch': '0.9102', 'num_input_tokens_seen': 57241360, 'train_runtime': '7.225e+04', 'train_tokens_per_second': '792.2'}
91%|█████████ | 17010/18688 [20:04:13<2:27:18, 5.27s/it] 91%|█████████ | 17011/18688 [20:04:17<2:20:10, 5.02s/it] 91%|█████████ | 17012/18688 [20:04:21<2:11:53, 4.72s/it] 91%|█████████ | 17013/18688 [20:04:26<2:09:28, 4.64s/it] 91%|█████████ | 17014/18688 [20:04:31<2:11:31, 4.71s/it] 91%|█████████ | 17015/18688 [20:04:35<2:04:57, 4.48s/it] 91%|█████████ | 17016/18688 [20:04:39<2:07:47, 4.59s/it] 91%|█████████ | 17017/18688 [20:04:45<2:12:10, 4.75s/it] 91%|█████████ | 17018/18688 [20:04:48<2:03:03, 4.42s/it] 91%|█████████ | 17019/18688 [20:04:53<2:05:41, 4.52s/it] 91%|█████████ | 17020/18688 [20:04:57<2:00:03, 4.32s/it] {'loss': '0.9445', 'grad_norm': '1.289', 'learning_rate': '2.309e-06', 'epoch': '0.9108', 'num_input_tokens_seen': 57278416, 'train_runtime': '7.23e+04', 'train_tokens_per_second': '792.3'}
91%|█████████ | 17020/18688 [20:04:57<2:00:03, 4.32s/it] 91%|█████████ | 17021/18688 [20:05:02<2:03:48, 4.46s/it] 91%|█████████ | 17022/18688 [20:05:06<2:00:57, 4.36s/it] 91%|█████████ | 17023/18688 [20:05:09<1:54:53, 4.14s/it] 91%|█████████ | 17024/18688 [20:05:13<1:54:18, 4.12s/it] 91%|█████████ | 17025/18688 [20:05:18<1:53:55, 4.11s/it] 91%|█████████ | 17026/18688 [20:05:22<1:54:20, 4.13s/it] 91%|█████████ | 17027/18688 [20:05:25<1:50:59, 4.01s/it] 91%|█████████ | 17028/18688 [20:05:29<1:49:32, 3.96s/it] 91%|█████████ | 17029/18688 [20:05:34<1:55:22, 4.17s/it] 91%|█████████ | 17030/18688 [20:05:38<1:53:40, 4.11s/it] {'loss': '0.8777', 'grad_norm': '1.737', 'learning_rate': '2.281e-06', 'epoch': '0.9113', 'num_input_tokens_seen': 57311688, 'train_runtime': '7.234e+04', 'train_tokens_per_second': '792.3'}
91%|█████████ | 17030/18688 [20:05:38<1:53:40, 4.11s/it] 91%|█████████ | 17031/18688 [20:05:43<1:57:35, 4.26s/it] 91%|█████████ | 17032/18688 [20:05:47<1:56:17, 4.21s/it] 91%|█████████ | 17033/18688 [20:05:50<1:51:45, 4.05s/it] 91%|█████████ | 17034/18688 [20:05:55<1:55:30, 4.19s/it] 91%|█████████ | 17035/18688 [20:05:59<1:55:01, 4.17s/it] 91%|█████████ | 17036/18688 [20:06:03<1:51:14, 4.04s/it] 91%|█████████ | 17037/18688 [20:06:06<1:47:44, 3.92s/it] 91%|█████████ | 17038/18688 [20:06:10<1:44:48, 3.81s/it] 91%|█████████ | 17039/18688 [20:06:13<1:42:48, 3.74s/it] 91%|█████████ | 17040/18688 [20:06:18<1:46:38, 3.88s/it] {'loss': '0.9572', 'grad_norm': '1.597', 'learning_rate': '2.254e-06', 'epoch': '0.9118', 'num_input_tokens_seen': 57342880, 'train_runtime': '7.238e+04', 'train_tokens_per_second': '792.3'}
91%|█████████ | 17040/18688 [20:06:18<1:46:38, 3.88s/it] 91%|█████████ | 17041/18688 [20:06:22<1:48:02, 3.94s/it] 91%|█████████ | 17042/18688 [20:06:26<1:49:16, 3.98s/it] 91%|█████████ | 17043/18688 [20:06:30<1:52:31, 4.10s/it] 91%|█████████ | 17044/18688 [20:06:34<1:52:16, 4.10s/it] 91%|█████████ | 17045/18688 [20:06:39<1:58:52, 4.34s/it] 91%|█████████ | 17046/18688 [20:06:43<1:57:05, 4.28s/it] 91%|█████████ | 17047/18688 [20:06:47<1:54:23, 4.18s/it] 91%|█████████ | 17048/18688 [20:06:52<1:55:44, 4.23s/it] 91%|█████████ | 17049/18688 [20:06:56<1:58:35, 4.34s/it] 91%|█████████ | 17050/18688 [20:07:01<1:58:01, 4.32s/it] {'loss': '0.9211', 'grad_norm': '1.038', 'learning_rate': '2.227e-06', 'epoch': '0.9124', 'num_input_tokens_seen': 57378656, 'train_runtime': '7.242e+04', 'train_tokens_per_second': '792.3'}
91%|█████████ | 17050/18688 [20:07:01<1:58:01, 4.32s/it] 91%|█████████ | 17051/18688 [20:07:05<1:57:11, 4.30s/it] 91%|█████████ | 17052/18688 [20:07:10<2:01:24, 4.45s/it] 91%|█████████▏| 17053/18688 [20:07:15<2:08:13, 4.71s/it] 91%|█████████▏| 17054/18688 [20:07:19<2:00:10, 4.41s/it] 91%|█████████▏| 17055/18688 [20:07:23<2:01:01, 4.45s/it] 91%|█████████▏| 17056/18688 [20:07:28<2:04:33, 4.58s/it] 91%|█████████▏| 17057/18688 [20:07:32<1:57:58, 4.34s/it] 91%|█████████▏| 17058/18688 [20:07:36<1:58:42, 4.37s/it] 91%|█████████▏| 17059/18688 [20:07:40<1:55:49, 4.27s/it] 91%|█████████▏| 17060/18688 [20:07:44<1:50:16, 4.06s/it] {'loss': '0.9745', 'grad_norm': '1.431', 'learning_rate': '2.2e-06', 'epoch': '0.9129', 'num_input_tokens_seen': 57414632, 'train_runtime': '7.246e+04', 'train_tokens_per_second': '792.3'}
91%|█████████▏| 17060/18688 [20:07:44<1:50:16, 4.06s/it] 91%|█████████▏| 17061/18688 [20:07:47<1:45:18, 3.88s/it] 91%|█████████▏| 17062/18688 [20:07:52<1:47:55, 3.98s/it] 91%|█████████▏| 17063/18688 [20:07:56<1:49:22, 4.04s/it] 91%|█████████▏| 17064/18688 [20:08:00<1:54:15, 4.22s/it] 91%|█████████▏| 17065/18688 [20:08:04<1:51:37, 4.13s/it] 91%|█████████▏| 17066/18688 [20:08:09<1:59:12, 4.41s/it] 91%|█████████▏| 17067/18688 [20:08:13<1:52:03, 4.15s/it] 91%|█████████▏| 17068/18688 [20:08:17<1:50:32, 4.09s/it] 91%|█████████▏| 17069/18688 [20:08:22<1:55:17, 4.27s/it] 91%|█████████▏| 17070/18688 [20:08:26<1:53:42, 4.22s/it] {'loss': '0.9127', 'grad_norm': '1.366', 'learning_rate': '2.173e-06', 'epoch': '0.9134', 'num_input_tokens_seen': 57449088, 'train_runtime': '7.251e+04', 'train_tokens_per_second': '792.3'}
91%|█████████▏| 17070/18688 [20:08:26<1:53:42, 4.22s/it] 91%|█████████▏| 17071/18688 [20:08:30<1:57:41, 4.37s/it] 91%|█████████▏| 17072/18688 [20:08:35<1:56:48, 4.34s/it] 91%|█████████▏| 17073/18688 [20:08:39<1:59:35, 4.44s/it] 91%|█████████▏| 17074/18688 [20:08:45<2:08:19, 4.77s/it] 91%|█████████▏| 17075/18688 [20:08:49<2:01:54, 4.53s/it] 91%|█████████▏| 17076/18688 [20:08:54<2:05:09, 4.66s/it] 91%|█████████▏| 17077/18688 [20:08:58<2:00:58, 4.51s/it] 91%|█████████▏| 17078/18688 [20:09:02<1:55:07, 4.29s/it] 91%|█████████▏| 17079/18688 [20:09:06<1:56:40, 4.35s/it] 91%|█████████▏| 17080/18688 [20:09:10<1:52:23, 4.19s/it] {'loss': '0.9468', 'grad_norm': '1.232', 'learning_rate': '2.147e-06', 'epoch': '0.914', 'num_input_tokens_seen': 57488120, 'train_runtime': '7.255e+04', 'train_tokens_per_second': '792.4'}
91%|█████████▏| 17080/18688 [20:09:10<1:52:23, 4.19s/it] 91%|█████████▏| 17081/18688 [20:09:14<1:48:04, 4.03s/it] 91%|█████████▏| 17082/18688 [20:09:18<1:51:56, 4.18s/it] 91%|█████████▏| 17083/18688 [20:09:22<1:48:17, 4.05s/it] 91%|█████████▏| 17084/18688 [20:09:26<1:45:52, 3.96s/it] 91%|█████████▏| 17085/18688 [20:09:30<1:46:43, 3.99s/it] 91%|█████████▏| 17086/18688 [20:09:35<1:53:24, 4.25s/it] 91%|█████████▏| 17087/18688 [20:09:39<1:54:29, 4.29s/it] 91%|█████████▏| 17088/18688 [20:09:44<2:00:36, 4.52s/it] 91%|█████████▏| 17089/18688 [20:09:48<1:52:42, 4.23s/it] 91%|█████████▏| 17090/18688 [20:09:51<1:48:11, 4.06s/it] {'loss': '0.935', 'grad_norm': '1.196', 'learning_rate': '2.12e-06', 'epoch': '0.9145', 'num_input_tokens_seen': 57522368, 'train_runtime': '7.259e+04', 'train_tokens_per_second': '792.4'}
91%|█████████▏| 17090/18688 [20:09:51<1:48:11, 4.06s/it] 91%|█████████▏| 17091/18688 [20:09:55<1:44:40, 3.93s/it] 91%|█████████▏| 17092/18688 [20:10:00<1:50:58, 4.17s/it] 91%|█████████▏| 17093/18688 [20:10:04<1:48:52, 4.10s/it] 91%|█████████▏| 17094/18688 [20:10:07<1:46:13, 4.00s/it] 91%|█████████▏| 17095/18688 [20:10:11<1:43:35, 3.90s/it] 91%|█████████▏| 17096/18688 [20:10:15<1:41:48, 3.84s/it] 91%|█████████▏| 17097/18688 [20:10:18<1:40:23, 3.79s/it] 91%|█████████▏| 17098/18688 [20:10:22<1:38:00, 3.70s/it] 91%|█████████▏| 17099/18688 [20:10:26<1:41:18, 3.83s/it] 92%|█████████▏| 17100/18688 [20:10:30<1:40:13, 3.79s/it] {'loss': '0.8784', 'grad_norm': '1.257', 'learning_rate': '2.094e-06', 'epoch': '0.9151', 'num_input_tokens_seen': 57552504, 'train_runtime': '7.263e+04', 'train_tokens_per_second': '792.4'}
92%|█████████▏| 17100/18688 [20:10:30<1:40:13, 3.79s/it] 92%|█████████▏| 17101/18688 [20:10:33<1:37:53, 3.70s/it] 92%|█████████▏| 17102/18688 [20:10:37<1:41:25, 3.84s/it] 92%|█████████▏| 17103/18688 [20:10:42<1:44:01, 3.94s/it] 92%|█████████▏| 17104/18688 [20:10:46<1:47:20, 4.07s/it] 92%|█████████▏| 17105/18688 [20:10:50<1:50:23, 4.18s/it] 92%|█████████▏| 17106/18688 [20:10:55<1:50:57, 4.21s/it] 92%|█████████▏| 17107/18688 [20:10:59<1:51:46, 4.24s/it] 92%|█████████▏| 17108/18688 [20:11:02<1:46:17, 4.04s/it] 92%|█████████▏| 17109/18688 [20:11:07<1:46:53, 4.06s/it] 92%|█████████▏| 17110/18688 [20:11:10<1:44:08, 3.96s/it] {'loss': '0.8467', 'grad_norm': '1.338', 'learning_rate': '2.068e-06', 'epoch': '0.9156', 'num_input_tokens_seen': 57586208, 'train_runtime': '7.267e+04', 'train_tokens_per_second': '792.4'}
92%|█████████▏| 17110/18688 [20:11:10<1:44:08, 3.96s/it] 92%|█████████▏| 17111/18688 [20:11:14<1:41:41, 3.87s/it] 92%|█████████▏| 17112/18688 [20:11:18<1:44:50, 3.99s/it] 92%|█████████▏| 17113/18688 [20:11:23<1:46:46, 4.07s/it] 92%|█████████▏| 17114/18688 [20:11:26<1:45:34, 4.02s/it] 92%|█████████▏| 17115/18688 [20:11:30<1:43:46, 3.96s/it] 92%|█████████▏| 17116/18688 [20:11:35<1:47:19, 4.10s/it] 92%|█████████▏| 17117/18688 [20:11:38<1:42:52, 3.93s/it] 92%|█████████▏| 17118/18688 [20:11:42<1:41:51, 3.89s/it] 92%|█████████▏| 17119/18688 [20:11:47<1:52:26, 4.30s/it] 92%|█████████▏| 17120/18688 [20:11:52<1:52:38, 4.31s/it] {'loss': '0.9742', 'grad_norm': '1.695', 'learning_rate': '2.042e-06', 'epoch': '0.9161', 'num_input_tokens_seen': 57620560, 'train_runtime': '7.271e+04', 'train_tokens_per_second': '792.4'}
92%|█████████▏| 17120/18688 [20:11:52<1:52:38, 4.31s/it] 92%|█████████▏| 17121/18688 [20:11:57<1:58:18, 4.53s/it] 92%|█████████▏| 17122/18688 [20:12:01<1:53:27, 4.35s/it] 92%|█████████▏| 17123/18688 [20:12:04<1:48:05, 4.14s/it] 92%|█████████▏| 17124/18688 [20:12:08<1:47:27, 4.12s/it] 92%|█████████▏| 17125/18688 [20:12:12<1:43:56, 3.99s/it] 92%|█████████▏| 17126/18688 [20:12:16<1:41:14, 3.89s/it] 92%|█████████▏| 17127/18688 [20:12:19<1:40:41, 3.87s/it] 92%|█████████▏| 17128/18688 [20:12:24<1:44:29, 4.02s/it] 92%|█████████▏| 17129/18688 [20:12:28<1:47:12, 4.13s/it] 92%|█████████▏| 17130/18688 [20:12:32<1:45:27, 4.06s/it] {'loss': '0.9409', 'grad_norm': '1.081', 'learning_rate': '2.016e-06', 'epoch': '0.9167', 'num_input_tokens_seen': 57653336, 'train_runtime': '7.275e+04', 'train_tokens_per_second': '792.5'}
92%|█████████▏| 17130/18688 [20:12:32<1:45:27, 4.06s/it] 92%|█████████▏| 17131/18688 [20:12:36<1:41:50, 3.92s/it] 92%|█████████▏| 17132/18688 [20:12:40<1:40:56, 3.89s/it] 92%|█████████▏| 17133/18688 [20:12:44<1:43:48, 4.01s/it] 92%|█████████▏| 17134/18688 [20:12:49<1:51:08, 4.29s/it] 92%|█████████▏| 17135/18688 [20:12:52<1:46:45, 4.12s/it] 92%|█████████▏| 17136/18688 [20:12:57<1:52:31, 4.35s/it] 92%|█████████▏| 17137/18688 [20:13:01<1:46:28, 4.12s/it] 92%|█████████▏| 17138/18688 [20:13:05<1:46:37, 4.13s/it] 92%|█████████▏| 17139/18688 [20:13:09<1:44:06, 4.03s/it] 92%|█████████▏| 17140/18688 [20:13:12<1:40:20, 3.89s/it] {'loss': '0.9453', 'grad_norm': '1.389', 'learning_rate': '1.991e-06', 'epoch': '0.9172', 'num_input_tokens_seen': 57685760, 'train_runtime': '7.279e+04', 'train_tokens_per_second': '792.5'}
92%|█████████▏| 17140/18688 [20:13:12<1:40:20, 3.89s/it] 92%|█████████▏| 17141/18688 [20:13:16<1:40:49, 3.91s/it] 92%|█████████▏| 17142/18688 [20:13:21<1:42:45, 3.99s/it] 92%|█████████▏| 17143/18688 [20:13:25<1:43:21, 4.01s/it] 92%|█████████▏| 17144/18688 [20:13:30<1:50:03, 4.28s/it] 92%|█████████▏| 17145/18688 [20:13:33<1:44:44, 4.07s/it] 92%|█████████▏| 17146/18688 [20:13:37<1:41:30, 3.95s/it] 92%|█████████▏| 17147/18688 [20:13:41<1:44:09, 4.06s/it] 92%|█████████▏| 17148/18688 [20:13:45<1:43:44, 4.04s/it] 92%|█████████▏| 17149/18688 [20:13:50<1:53:16, 4.42s/it] 92%|█████████▏| 17150/18688 [20:13:54<1:49:36, 4.28s/it] {'loss': '0.8796', 'grad_norm': '1.183', 'learning_rate': '1.965e-06', 'epoch': '0.9177', 'num_input_tokens_seen': 57719592, 'train_runtime': '7.283e+04', 'train_tokens_per_second': '792.5'}
92%|█████████▏| 17150/18688 [20:13:54<1:49:36, 4.28s/it] 92%|█████████▏| 17151/18688 [20:13:59<1:48:39, 4.24s/it] 92%|█████████▏| 17152/18688 [20:14:02<1:46:19, 4.15s/it] 92%|█████████▏| 17153/18688 [20:14:07<1:47:00, 4.18s/it] 92%|█████████▏| 17154/18688 [20:14:10<1:42:34, 4.01s/it] 92%|█████████▏| 17155/18688 [20:14:14<1:43:00, 4.03s/it] 92%|█████████▏| 17156/18688 [20:14:19<1:49:35, 4.29s/it] 92%|█████████▏| 17157/18688 [20:14:23<1:48:32, 4.25s/it] 92%|█████████▏| 17158/18688 [20:14:28<1:50:54, 4.35s/it] 92%|█████████▏| 17159/18688 [20:14:32<1:45:07, 4.13s/it] 92%|█████████▏| 17160/18688 [20:14:35<1:40:58, 3.97s/it] {'loss': '0.8259', 'grad_norm': '1.32', 'learning_rate': '1.94e-06', 'epoch': '0.9183', 'num_input_tokens_seen': 57752144, 'train_runtime': '7.288e+04', 'train_tokens_per_second': '792.5'}
92%|█████████▏| 17160/18688 [20:14:35<1:40:58, 3.97s/it] 92%|█████████▏| 17161/18688 [20:14:39<1:36:46, 3.80s/it] 92%|█████████▏| 17162/18688 [20:14:42<1:35:57, 3.77s/it] 92%|█████████▏| 17163/18688 [20:14:47<1:39:16, 3.91s/it] 92%|█████████▏| 17164/18688 [20:14:50<1:38:45, 3.89s/it] 92%|█████████▏| 17165/18688 [20:14:55<1:41:38, 4.00s/it] 92%|█████████▏| 17166/18688 [20:14:59<1:43:58, 4.10s/it] 92%|█████████▏| 17167/18688 [20:15:03<1:41:48, 4.02s/it] 92%|█████████▏| 17168/18688 [20:15:08<1:48:15, 4.27s/it] 92%|█████████▏| 17169/18688 [20:15:12<1:46:42, 4.22s/it] 92%|█████████▏| 17170/18688 [20:15:16<1:50:04, 4.35s/it] {'loss': '0.8334', 'grad_norm': '1.265', 'learning_rate': '1.915e-06', 'epoch': '0.9188', 'num_input_tokens_seen': 57786352, 'train_runtime': '7.292e+04', 'train_tokens_per_second': '792.5'}
92%|█████████▏| 17170/18688 [20:15:16<1:50:04, 4.35s/it] 92%|█████████▏| 17171/18688 [20:15:22<1:55:47, 4.58s/it] 92%|█████████▏| 17172/18688 [20:15:26<1:53:48, 4.50s/it] 92%|█████████▏| 17173/18688 [20:15:31<1:55:30, 4.57s/it] 92%|█████████▏| 17174/18688 [20:15:35<1:50:11, 4.37s/it] 92%|█████████▏| 17175/18688 [20:15:39<1:50:50, 4.40s/it] 92%|█████████▏| 17176/18688 [20:15:42<1:43:59, 4.13s/it] 92%|█████████▏| 17177/18688 [20:15:46<1:39:10, 3.94s/it] 92%|█████████▏| 17178/18688 [20:15:50<1:41:09, 4.02s/it] 92%|█████████▏| 17179/18688 [20:15:54<1:41:22, 4.03s/it] 92%|█████████▏| 17180/18688 [20:15:58<1:39:51, 3.97s/it] {'loss': '0.9028', 'grad_norm': '1.343', 'learning_rate': '1.89e-06', 'epoch': '0.9193', 'num_input_tokens_seen': 57820360, 'train_runtime': '7.296e+04', 'train_tokens_per_second': '792.5'}
92%|█████████▏| 17180/18688 [20:15:58<1:39:51, 3.97s/it] 92%|█████████▏| 17181/18688 [20:16:02<1:41:05, 4.02s/it] 92%|█████████▏| 17182/18688 [20:16:06<1:38:41, 3.93s/it] 92%|█████████▏| 17183/18688 [20:16:10<1:42:24, 4.08s/it] 92%|█████████▏| 17184/18688 [20:16:15<1:43:36, 4.13s/it] 92%|█████████▏| 17185/18688 [20:16:19<1:42:02, 4.07s/it] 92%|█████████▏| 17186/18688 [20:16:23<1:43:33, 4.14s/it] 92%|█████████▏| 17187/18688 [20:16:27<1:40:31, 4.02s/it] 92%|█████████▏| 17188/18688 [20:16:30<1:38:55, 3.96s/it] 92%|█████████▏| 17189/18688 [20:16:35<1:43:05, 4.13s/it] 92%|█████████▏| 17190/18688 [20:16:38<1:38:16, 3.94s/it] {'loss': '0.8901', 'grad_norm': '1.257', 'learning_rate': '1.865e-06', 'epoch': '0.9199', 'num_input_tokens_seen': 57852184, 'train_runtime': '7.3e+04', 'train_tokens_per_second': '792.5'}
92%|█████████▏| 17190/18688 [20:16:38<1:38:16, 3.94s/it] 92%|█████████▏| 17191/18688 [20:16:43<1:40:00, 4.01s/it] 92%|█████████▏| 17192/18688 [20:16:46<1:37:49, 3.92s/it] 92%|█████████▏| 17193/18688 [20:16:50<1:35:56, 3.85s/it] 92%|█████████▏| 17194/18688 [20:16:54<1:36:17, 3.87s/it] 92%|█████████▏| 17195/18688 [20:16:58<1:39:08, 3.98s/it] 92%|█████████▏| 17196/18688 [20:17:02<1:38:00, 3.94s/it] 92%|█████████▏| 17197/18688 [20:17:06<1:38:42, 3.97s/it] 92%|█████████▏| 17198/18688 [20:17:10<1:39:46, 4.02s/it] 92%|█████████▏| 17199/18688 [20:17:15<1:42:56, 4.15s/it] 92%|█████████▏| 17200/18688 [20:17:18<1:40:12, 4.04s/it] {'loss': '0.9144', 'grad_norm': '1.195', 'learning_rate': '1.84e-06', 'epoch': '0.9204', 'num_input_tokens_seen': 57883952, 'train_runtime': '7.304e+04', 'train_tokens_per_second': '792.5'}
92%|█████████▏| 17200/18688 [20:17:18<1:40:12, 4.04s/it] 92%|█████████▏| 17201/18688 [20:17:23<1:40:36, 4.06s/it] 92%|█████████▏| 17202/18688 [20:17:27<1:45:11, 4.25s/it] 92%|█████████▏| 17203/18688 [20:17:33<1:53:14, 4.58s/it] 92%|█████████▏| 17204/18688 [20:17:37<1:52:58, 4.57s/it] 92%|█████████▏| 17205/18688 [20:17:42<1:53:08, 4.58s/it] 92%|█████████▏| 17206/18688 [20:17:46<1:47:41, 4.36s/it] 92%|█████████▏| 17207/18688 [20:17:49<1:41:40, 4.12s/it] 92%|█████████▏| 17208/18688 [20:17:54<1:48:22, 4.39s/it] 92%|█████████▏| 17209/18688 [20:17:58<1:43:10, 4.19s/it] 92%|█████████▏| 17210/18688 [20:18:02<1:42:31, 4.16s/it] {'loss': '0.9204', 'grad_norm': '1.248', 'learning_rate': '1.816e-06', 'epoch': '0.9209', 'num_input_tokens_seen': 57920480, 'train_runtime': '7.308e+04', 'train_tokens_per_second': '792.5'}
92%|█████████▏| 17210/18688 [20:18:02<1:42:31, 4.16s/it] 92%|█████████▏| 17211/18688 [20:18:06<1:44:21, 4.24s/it] 92%|█████████▏| 17212/18688 [20:18:11<1:48:24, 4.41s/it] 92%|█████████▏| 17213/18688 [20:18:16<1:48:39, 4.42s/it] 92%|█████████▏| 17214/18688 [20:18:21<1:54:46, 4.67s/it] 92%|█████████▏| 17215/18688 [20:18:25<1:48:45, 4.43s/it] 92%|█████████▏| 17216/18688 [20:18:29<1:46:38, 4.35s/it] 92%|█████████▏| 17217/18688 [20:18:33<1:43:48, 4.23s/it] 92%|█████████▏| 17218/18688 [20:18:36<1:38:28, 4.02s/it] 92%|█████████▏| 17219/18688 [20:18:41<1:40:39, 4.11s/it] 92%|█████████▏| 17220/18688 [20:18:44<1:35:44, 3.91s/it] {'loss': '0.8743', 'grad_norm': '1.24', 'learning_rate': '1.792e-06', 'epoch': '0.9215', 'num_input_tokens_seen': 57956232, 'train_runtime': '7.312e+04', 'train_tokens_per_second': '792.6'}
92%|█████████▏| 17220/18688 [20:18:44<1:35:44, 3.91s/it] 92%|█████████▏| 17221/18688 [20:18:48<1:37:35, 3.99s/it] 92%|█████████▏| 17222/18688 [20:18:53<1:40:10, 4.10s/it] 92%|█████████▏| 17223/18688 [20:18:56<1:35:19, 3.90s/it] 92%|█████████▏| 17224/18688 [20:19:00<1:33:43, 3.84s/it] 92%|█████████▏| 17225/18688 [20:19:04<1:36:53, 3.97s/it] 92%|█████████▏| 17226/18688 [20:19:08<1:36:09, 3.95s/it] 92%|█████████▏| 17227/18688 [20:19:12<1:39:44, 4.10s/it] 92%|█████████▏| 17228/18688 [20:19:16<1:37:01, 3.99s/it] 92%|█████████▏| 17229/18688 [20:19:21<1:41:20, 4.17s/it] 92%|█████████▏| 17230/18688 [20:19:25<1:38:20, 4.05s/it] {'loss': '0.8996', 'grad_norm': '1.857', 'learning_rate': '1.767e-06', 'epoch': '0.922', 'num_input_tokens_seen': 57989112, 'train_runtime': '7.317e+04', 'train_tokens_per_second': '792.6'}
92%|█████████▏| 17230/18688 [20:19:25<1:38:20, 4.05s/it] 92%|█████████▏| 17231/18688 [20:19:28<1:37:28, 4.01s/it] 92%|█████████▏| 17232/18688 [20:19:34<1:46:23, 4.38s/it] 92%|█████████▏| 17233/18688 [20:19:38<1:46:16, 4.38s/it] 92%|█████████▏| 17234/18688 [20:19:42<1:42:59, 4.25s/it] 92%|█████████▏| 17235/18688 [20:19:48<1:51:53, 4.62s/it] 92%|█████████▏| 17236/18688 [20:19:51<1:45:14, 4.35s/it] 92%|█████████▏| 17237/18688 [20:19:55<1:43:00, 4.26s/it] 92%|█████████▏| 17238/18688 [20:19:59<1:39:14, 4.11s/it] 92%|█████████▏| 17239/18688 [20:20:03<1:40:46, 4.17s/it] 92%|█████████▏| 17240/18688 [20:20:08<1:41:44, 4.22s/it] {'loss': '0.8641', 'grad_norm': '1.202', 'learning_rate': '1.743e-06', 'epoch': '0.9225', 'num_input_tokens_seen': 58025944, 'train_runtime': '7.321e+04', 'train_tokens_per_second': '792.6'}
92%|█████████▏| 17240/18688 [20:20:08<1:41:44, 4.22s/it] 92%|█████████▏| 17241/18688 [20:20:13<1:47:10, 4.44s/it] 92%|█████████▏| 17242/18688 [20:20:17<1:42:54, 4.27s/it] 92%|█████████▏| 17243/18688 [20:20:21<1:43:58, 4.32s/it] 92%|█████████▏| 17244/18688 [20:20:25<1:41:15, 4.21s/it] 92%|█████████▏| 17245/18688 [20:20:29<1:37:38, 4.06s/it] 92%|█████████▏| 17246/18688 [20:20:33<1:37:30, 4.06s/it] 92%|█████████▏| 17247/18688 [20:20:37<1:36:05, 4.00s/it] 92%|█████████▏| 17248/18688 [20:20:41<1:37:50, 4.08s/it] 92%|█████████▏| 17249/18688 [20:20:44<1:33:14, 3.89s/it] 92%|█████████▏| 17250/18688 [20:20:49<1:39:01, 4.13s/it] {'loss': '0.9392', 'grad_norm': '1.473', 'learning_rate': '1.72e-06', 'epoch': '0.9231', 'num_input_tokens_seen': 58059080, 'train_runtime': '7.325e+04', 'train_tokens_per_second': '792.6'}
92%|█████████▏| 17250/18688 [20:20:49<1:39:01, 4.13s/it] 92%|█████████▏| 17251/18688 [20:20:53<1:40:01, 4.18s/it] 92%|█████████▏| 17252/18688 [20:20:58<1:40:49, 4.21s/it] 92%|█████████▏| 17253/18688 [20:21:02<1:41:37, 4.25s/it] 92%|█████████▏| 17254/18688 [20:21:06<1:42:53, 4.30s/it] 92%|█████████▏| 17255/18688 [20:21:10<1:37:02, 4.06s/it] 92%|█████████▏| 17256/18688 [20:21:14<1:41:12, 4.24s/it] 92%|█████████▏| 17257/18688 [20:21:18<1:38:07, 4.11s/it] 92%|█████████▏| 17258/18688 [20:21:22<1:35:23, 4.00s/it] 92%|█████████▏| 17259/18688 [20:21:26<1:36:06, 4.04s/it] 92%|█████████▏| 17260/18688 [20:21:31<1:40:17, 4.21s/it] {'loss': '0.8613', 'grad_norm': '1.345', 'learning_rate': '1.696e-06', 'epoch': '0.9236', 'num_input_tokens_seen': 58093128, 'train_runtime': '7.329e+04', 'train_tokens_per_second': '792.6'}
92%|█████████▏| 17260/18688 [20:21:31<1:40:17, 4.21s/it] 92%|█████████▏| 17261/18688 [20:21:35<1:39:53, 4.20s/it] 92%|█████████▏| 17262/18688 [20:21:39<1:38:55, 4.16s/it] 92%|█████████▏| 17263/18688 [20:21:43<1:34:50, 3.99s/it] 92%|█████████▏| 17264/18688 [20:21:47<1:36:07, 4.05s/it] 92%|█████████▏| 17265/18688 [20:21:51<1:34:47, 4.00s/it] 92%|█████████▏| 17266/18688 [20:21:54<1:31:07, 3.85s/it] 92%|█████████▏| 17267/18688 [20:21:58<1:29:16, 3.77s/it] 92%|█████████▏| 17268/18688 [20:22:02<1:31:14, 3.86s/it] 92%|█████████▏| 17269/18688 [20:22:05<1:28:35, 3.75s/it] 92%|█████████▏| 17270/18688 [20:22:10<1:32:05, 3.90s/it] {'loss': '0.899', 'grad_norm': '1.507', 'learning_rate': '1.672e-06', 'epoch': '0.9241', 'num_input_tokens_seen': 58122328, 'train_runtime': '7.333e+04', 'train_tokens_per_second': '792.6'}
92%|█████████▏| 17270/18688 [20:22:10<1:32:05, 3.90s/it] 92%|█████████▏| 17271/18688 [20:22:13<1:31:32, 3.88s/it] 92%|█████████▏| 17272/18688 [20:22:17<1:31:13, 3.87s/it] 92%|█████████▏| 17273/18688 [20:22:21<1:33:01, 3.94s/it] 92%|█████████▏| 17274/18688 [20:22:26<1:37:45, 4.15s/it] 92%|█████████▏| 17275/18688 [20:22:30<1:33:45, 3.98s/it] 92%|█████████▏| 17276/18688 [20:22:33<1:32:25, 3.93s/it] 92%|█████████▏| 17277/18688 [20:22:38<1:37:57, 4.17s/it] 92%|█████████▏| 17278/18688 [20:22:42<1:32:52, 3.95s/it] 92%|█████████▏| 17279/18688 [20:22:45<1:31:09, 3.88s/it] 92%|█████████▏| 17280/18688 [20:22:50<1:34:13, 4.02s/it] {'loss': '0.8701', 'grad_norm': '1.145', 'learning_rate': '1.649e-06', 'epoch': '0.9247', 'num_input_tokens_seen': 58154592, 'train_runtime': '7.337e+04', 'train_tokens_per_second': '792.6'}
92%|█████████▏| 17280/18688 [20:22:50<1:34:13, 4.02s/it] 92%|█████████▏| 17281/18688 [20:22:53<1:31:25, 3.90s/it] 92%|█████████▏| 17282/18688 [20:22:57<1:29:58, 3.84s/it] 92%|█████████▏| 17283/18688 [20:23:01<1:33:47, 4.01s/it] 92%|█████████▏| 17284/18688 [20:23:05<1:34:58, 4.06s/it] 92%|█████████▏| 17285/18688 [20:23:10<1:40:04, 4.28s/it] 92%|█████████▏| 17286/18688 [20:23:14<1:36:03, 4.11s/it] 93%|█████████▎| 17287/18688 [20:23:18<1:38:01, 4.20s/it] 93%|█████████▎| 17288/18688 [20:23:23<1:43:50, 4.45s/it] 93%|█████████▎| 17289/18688 [20:23:27<1:38:33, 4.23s/it] 93%|█████████▎| 17290/18688 [20:23:31<1:38:18, 4.22s/it] {'loss': '0.9493', 'grad_norm': '1.263', 'learning_rate': '1.626e-06', 'epoch': '0.9252', 'num_input_tokens_seen': 58188728, 'train_runtime': '7.341e+04', 'train_tokens_per_second': '792.6'}
93%|█████████▎| 17290/18688 [20:23:31<1:38:18, 4.22s/it] 93%|█████████▎| 17291/18688 [20:23:35<1:33:07, 4.00s/it] 93%|█████████▎| 17292/18688 [20:23:39<1:32:29, 3.98s/it] 93%|█████████▎| 17293/18688 [20:23:43<1:31:31, 3.94s/it] 93%|█████████▎| 17294/18688 [20:23:48<1:39:41, 4.29s/it] 93%|█████████▎| 17295/18688 [20:23:52<1:37:32, 4.20s/it] 93%|█████████▎| 17296/18688 [20:23:56<1:34:51, 4.09s/it] 93%|█████████▎| 17297/18688 [20:24:00<1:35:59, 4.14s/it] 93%|█████████▎| 17298/18688 [20:24:04<1:33:24, 4.03s/it] 93%|█████████▎| 17299/18688 [20:24:08<1:36:14, 4.16s/it] 93%|█████████▎| 17300/18688 [20:24:12<1:34:48, 4.10s/it] {'loss': '0.9087', 'grad_norm': '1.208', 'learning_rate': '1.603e-06', 'epoch': '0.9258', 'num_input_tokens_seen': 58221144, 'train_runtime': '7.345e+04', 'train_tokens_per_second': '792.6'}
93%|█████████▎| 17300/18688 [20:24:12<1:34:48, 4.10s/it] 93%|█████████▎| 17301/18688 [20:24:16<1:35:52, 4.15s/it] 93%|█████████▎| 17302/18688 [20:24:21<1:38:07, 4.25s/it] 93%|█████████▎| 17303/18688 [20:24:25<1:39:09, 4.30s/it] 93%|█████████▎| 17304/18688 [20:24:29<1:38:09, 4.26s/it] 93%|█████████▎| 17305/18688 [20:24:33<1:36:54, 4.20s/it] 93%|█████████▎| 17306/18688 [20:24:38<1:39:05, 4.30s/it] 93%|█████████▎| 17307/18688 [20:24:42<1:34:55, 4.12s/it] 93%|█████████▎| 17308/18688 [20:24:46<1:36:38, 4.20s/it] 93%|█████████▎| 17309/18688 [20:24:50<1:37:37, 4.25s/it] 93%|█████████▎| 17310/18688 [20:24:54<1:34:31, 4.12s/it] {'loss': '0.9633', 'grad_norm': '1.439', 'learning_rate': '1.58e-06', 'epoch': '0.9263', 'num_input_tokens_seen': 58256976, 'train_runtime': '7.349e+04', 'train_tokens_per_second': '792.7'}
93%|█████████▎| 17310/18688 [20:24:54<1:34:31, 4.12s/it] 93%|█████████▎| 17311/18688 [20:24:58<1:35:19, 4.15s/it] 93%|█████████▎| 17312/18688 [20:25:03<1:36:36, 4.21s/it] 93%|█████████▎| 17313/18688 [20:25:08<1:41:33, 4.43s/it] 93%|█████████▎| 17314/18688 [20:25:13<1:44:44, 4.57s/it] 93%|█████████▎| 17315/18688 [20:25:17<1:42:30, 4.48s/it] 93%|█████████▎| 17316/18688 [20:25:21<1:39:28, 4.35s/it] 93%|█████████▎| 17317/18688 [20:25:25<1:36:26, 4.22s/it] 93%|█████████▎| 17318/18688 [20:25:28<1:32:30, 4.05s/it] 93%|█████████▎| 17319/18688 [20:25:32<1:30:02, 3.95s/it] 93%|█████████▎| 17320/18688 [20:25:37<1:38:07, 4.30s/it] {'loss': '0.9483', 'grad_norm': '1.314', 'learning_rate': '1.557e-06', 'epoch': '0.9268', 'num_input_tokens_seen': 58293144, 'train_runtime': '7.354e+04', 'train_tokens_per_second': '792.7'}
93%|█████████▎| 17320/18688 [20:25:37<1:38:07, 4.30s/it] 93%|█████████▎| 17321/18688 [20:25:41<1:33:06, 4.09s/it] 93%|█████████▎| 17322/18688 [20:25:46<1:41:32, 4.46s/it] 93%|█████████▎| 17323/18688 [20:25:50<1:36:34, 4.24s/it] 93%|█████████▎| 17324/18688 [20:25:54<1:34:57, 4.18s/it] 93%|█████████▎| 17325/18688 [20:25:58<1:30:51, 4.00s/it] 93%|█████████▎| 17326/18688 [20:26:03<1:38:36, 4.34s/it] 93%|█████████▎| 17327/18688 [20:26:06<1:34:33, 4.17s/it] 93%|█████████▎| 17328/18688 [20:26:10<1:33:21, 4.12s/it] 93%|█████████▎| 17329/18688 [20:26:14<1:31:20, 4.03s/it] 93%|█████████▎| 17330/18688 [20:26:18<1:29:09, 3.94s/it] {'loss': '0.8322', 'grad_norm': '1.241', 'learning_rate': '1.535e-06', 'epoch': '0.9274', 'num_input_tokens_seen': 58325712, 'train_runtime': '7.358e+04', 'train_tokens_per_second': '792.7'}
93%|█████████▎| 17330/18688 [20:26:18<1:29:09, 3.94s/it] 93%|█████████▎| 17331/18688 [20:26:22<1:32:41, 4.10s/it] 93%|█████████▎| 17332/18688 [20:26:26<1:30:07, 3.99s/it] 93%|█████████▎| 17333/18688 [20:26:31<1:32:04, 4.08s/it] 93%|█████████▎| 17334/18688 [20:26:35<1:36:05, 4.26s/it] 93%|█████████▎| 17335/18688 [20:26:39<1:32:48, 4.12s/it] 93%|█████████▎| 17336/18688 [20:26:44<1:39:42, 4.42s/it] 93%|█████████▎| 17337/18688 [20:26:48<1:35:07, 4.22s/it] 93%|█████████▎| 17338/18688 [20:26:52<1:35:12, 4.23s/it] 93%|█████████▎| 17339/18688 [20:26:56<1:34:44, 4.21s/it] 93%|█████████▎| 17340/18688 [20:27:00<1:30:31, 4.03s/it] {'loss': '0.9151', 'grad_norm': '1.291', 'learning_rate': '1.512e-06', 'epoch': '0.9279', 'num_input_tokens_seen': 58359832, 'train_runtime': '7.362e+04', 'train_tokens_per_second': '792.7'}
93%|█████████▎| 17340/18688 [20:27:00<1:30:31, 4.03s/it] 93%|█████████▎| 17341/18688 [20:27:03<1:27:33, 3.90s/it] 93%|█████████▎| 17342/18688 [20:27:07<1:26:48, 3.87s/it] 93%|█████████▎| 17343/18688 [20:27:11<1:26:03, 3.84s/it] 93%|█████████▎| 17344/18688 [20:27:15<1:29:04, 3.98s/it] 93%|█████████▎| 17345/18688 [20:27:20<1:32:11, 4.12s/it] 93%|█████████▎| 17346/18688 [20:27:24<1:33:51, 4.20s/it] 93%|█████████▎| 17347/18688 [20:27:28<1:29:40, 4.01s/it] 93%|█████████▎| 17348/18688 [20:27:32<1:27:56, 3.94s/it] 93%|█████████▎| 17349/18688 [20:27:35<1:27:18, 3.91s/it] 93%|█████████▎| 17350/18688 [20:27:39<1:26:27, 3.88s/it] {'loss': '0.9591', 'grad_norm': '1.176', 'learning_rate': '1.49e-06', 'epoch': '0.9284', 'num_input_tokens_seen': 58390064, 'train_runtime': '7.366e+04', 'train_tokens_per_second': '792.7'}
93%|█████████▎| 17350/18688 [20:27:39<1:26:27, 3.88s/it] 93%|█████████▎| 17351/18688 [20:27:43<1:25:45, 3.85s/it] 93%|█████████▎| 17352/18688 [20:27:48<1:30:20, 4.06s/it] 93%|█████████▎| 17353/18688 [20:27:51<1:27:48, 3.95s/it] 93%|█████████▎| 17354/18688 [20:27:55<1:25:19, 3.84s/it] 93%|█████████▎| 17355/18688 [20:27:59<1:26:56, 3.91s/it] 93%|█████████▎| 17356/18688 [20:28:04<1:33:14, 4.20s/it] 93%|█████████▎| 17357/18688 [20:28:08<1:31:25, 4.12s/it] 93%|█████████▎| 17358/18688 [20:28:12<1:29:50, 4.05s/it] 93%|█████████▎| 17359/18688 [20:28:15<1:27:27, 3.95s/it] 93%|█████████▎| 17360/18688 [20:28:19<1:24:49, 3.83s/it] {'loss': '0.8711', 'grad_norm': '1.274', 'learning_rate': '1.468e-06', 'epoch': '0.929', 'num_input_tokens_seen': 58420528, 'train_runtime': '7.37e+04', 'train_tokens_per_second': '792.7'}
93%|█████████▎| 17360/18688 [20:28:19<1:24:49, 3.83s/it] 93%|█████████▎| 17361/18688 [20:28:23<1:27:39, 3.96s/it] 93%|█████████▎| 17362/18688 [20:28:27<1:26:26, 3.91s/it] 93%|█████████▎| 17363/18688 [20:28:32<1:31:36, 4.15s/it] 93%|█████████▎| 17364/18688 [20:28:36<1:30:31, 4.10s/it] 93%|█████████▎| 17365/18688 [20:28:39<1:29:03, 4.04s/it] 93%|█████████▎| 17366/18688 [20:28:44<1:32:50, 4.21s/it] 93%|█████████▎| 17367/18688 [20:28:49<1:38:13, 4.46s/it] 93%|█████████▎| 17368/18688 [20:28:53<1:32:23, 4.20s/it] 93%|█████████▎| 17369/18688 [20:28:57<1:31:59, 4.18s/it] 93%|█████████▎| 17370/18688 [20:29:00<1:28:02, 4.01s/it] {'loss': '0.9327', 'grad_norm': '1.195', 'learning_rate': '1.446e-06', 'epoch': '0.9295', 'num_input_tokens_seen': 58455184, 'train_runtime': '7.374e+04', 'train_tokens_per_second': '792.7'}
93%|█████████▎| 17370/18688 [20:29:00<1:28:02, 4.01s/it] 93%|█████████▎| 17371/18688 [20:29:05<1:32:48, 4.23s/it] 93%|█████████▎| 17372/18688 [20:29:09<1:30:16, 4.12s/it] 93%|█████████▎| 17373/18688 [20:29:13<1:29:40, 4.09s/it] 93%|█████████▎| 17374/18688 [20:29:18<1:31:49, 4.19s/it] 93%|█████████▎| 17375/18688 [20:29:22<1:31:08, 4.17s/it] 93%|█████████▎| 17376/18688 [20:29:26<1:30:27, 4.14s/it] 93%|█████████▎| 17377/18688 [20:29:30<1:29:56, 4.12s/it] 93%|█████████▎| 17378/18688 [20:29:34<1:33:48, 4.30s/it] 93%|█████████▎| 17379/18688 [20:29:39<1:32:54, 4.26s/it] 93%|█████████▎| 17380/18688 [20:29:44<1:38:22, 4.51s/it] {'loss': '0.978', 'grad_norm': '1.509', 'learning_rate': '1.424e-06', 'epoch': '0.93', 'num_input_tokens_seen': 58491344, 'train_runtime': '7.378e+04', 'train_tokens_per_second': '792.7'}
93%|█████████▎| 17380/18688 [20:29:44<1:38:22, 4.51s/it] 93%|█████████▎| 17381/18688 [20:29:48<1:36:29, 4.43s/it] 93%|█████████▎| 17382/18688 [20:29:53<1:39:01, 4.55s/it] 93%|█████████▎| 17383/18688 [20:29:57<1:39:19, 4.57s/it] 93%|█████████▎| 17384/18688 [20:30:02<1:39:18, 4.57s/it] 93%|█████████▎| 17385/18688 [20:30:06<1:37:24, 4.49s/it] 93%|█████████▎| 17386/18688 [20:30:10<1:30:32, 4.17s/it] 93%|█████████▎| 17387/18688 [20:30:13<1:25:36, 3.95s/it] 93%|█████████▎| 17388/18688 [20:30:17<1:25:22, 3.94s/it] 93%|█████████▎| 17389/18688 [20:30:21<1:27:22, 4.04s/it] 93%|█████████▎| 17390/18688 [20:30:25<1:25:34, 3.96s/it] {'loss': '0.9087', 'grad_norm': '1.289', 'learning_rate': '1.403e-06', 'epoch': '0.9306', 'num_input_tokens_seen': 58524768, 'train_runtime': '7.383e+04', 'train_tokens_per_second': '792.7'}
93%|█████████▎| 17390/18688 [20:30:25<1:25:34, 3.96s/it] 93%|█████████▎| 17391/18688 [20:30:29<1:25:51, 3.97s/it] 93%|█████████▎| 17392/18688 [20:30:33<1:26:03, 3.98s/it] 93%|█████████▎| 17393/18688 [20:30:37<1:23:27, 3.87s/it] 93%|█████████▎| 17394/18688 [20:30:41<1:24:52, 3.94s/it] 93%|█████████▎| 17395/18688 [20:30:45<1:23:45, 3.89s/it] 93%|█████████▎| 17396/18688 [20:30:50<1:31:05, 4.23s/it] 93%|█████████▎| 17397/18688 [20:30:53<1:26:45, 4.03s/it] 93%|█████████▎| 17398/18688 [20:30:58<1:28:31, 4.12s/it] 93%|█████████▎| 17399/18688 [20:31:02<1:28:50, 4.14s/it] 93%|█████████▎| 17400/18688 [20:31:05<1:25:11, 3.97s/it] {'loss': '0.8835', 'grad_norm': '1.383', 'learning_rate': '1.381e-06', 'epoch': '0.9311', 'num_input_tokens_seen': 58556856, 'train_runtime': '7.387e+04', 'train_tokens_per_second': '792.7'}
93%|█████████▎| 17400/18688 [20:31:05<1:25:11, 3.97s/it] 93%|█████████▎| 17401/18688 [20:31:09<1:22:15, 3.84s/it] 93%|█████████▎| 17402/18688 [20:31:13<1:22:09, 3.83s/it] 93%|█████████▎| 17403/18688 [20:31:17<1:27:22, 4.08s/it] 93%|█████████▎| 17404/18688 [20:31:22<1:28:26, 4.13s/it] 93%|█████████▎| 17405/18688 [20:31:26<1:27:25, 4.09s/it] 93%|█████████▎| 17406/18688 [20:31:29<1:26:35, 4.05s/it] 93%|█████████▎| 17407/18688 [20:31:34<1:26:12, 4.04s/it] 93%|█████████▎| 17408/18688 [20:31:38<1:31:47, 4.30s/it] 93%|█████████▎| 17409/18688 [20:31:43<1:35:35, 4.48s/it] 93%|█████████▎| 17410/18688 [20:31:47<1:31:47, 4.31s/it] {'loss': '0.954', 'grad_norm': '1.288', 'learning_rate': '1.36e-06', 'epoch': '0.9316', 'num_input_tokens_seen': 58591696, 'train_runtime': '7.391e+04', 'train_tokens_per_second': '792.8'}
93%|█████████▎| 17410/18688 [20:31:47<1:31:47, 4.31s/it] 93%|█████████▎| 17411/18688 [20:31:52<1:34:11, 4.43s/it] 93%|█████████▎| 17412/18688 [20:31:56<1:33:06, 4.38s/it] 93%|█████████▎| 17413/18688 [20:32:00<1:28:52, 4.18s/it] 93%|█████████▎| 17414/18688 [20:32:03<1:24:53, 4.00s/it] 93%|█████████▎| 17415/18688 [20:32:08<1:30:24, 4.26s/it] 93%|█████████▎| 17416/18688 [20:32:12<1:26:44, 4.09s/it] 93%|█████████▎| 17417/18688 [20:32:16<1:28:09, 4.16s/it] 93%|█████████▎| 17418/18688 [20:32:20<1:27:21, 4.13s/it] 93%|█████████▎| 17419/18688 [20:32:24<1:25:29, 4.04s/it] 93%|█████████▎| 17420/18688 [20:32:28<1:25:32, 4.05s/it] {'loss': '0.9275', 'grad_norm': '1.372', 'learning_rate': '1.339e-06', 'epoch': '0.9322', 'num_input_tokens_seen': 58623808, 'train_runtime': '7.395e+04', 'train_tokens_per_second': '792.8'}
93%|█████████▎| 17420/18688 [20:32:28<1:25:32, 4.05s/it] 93%|█████████▎| 17421/18688 [20:32:32<1:25:02, 4.03s/it] 93%|█████████▎| 17422/18688 [20:32:37<1:26:54, 4.12s/it] 93%|█████████▎| 17423/18688 [20:32:41<1:30:20, 4.29s/it] 93%|█████████▎| 17424/18688 [20:32:45<1:25:46, 4.07s/it] 93%|█████████▎| 17425/18688 [20:32:50<1:29:09, 4.24s/it] 93%|█████████▎| 17426/18688 [20:32:54<1:28:01, 4.18s/it] 93%|█████████▎| 17427/18688 [20:32:57<1:24:01, 4.00s/it] 93%|█████████▎| 17428/18688 [20:33:02<1:26:43, 4.13s/it] 93%|█████████▎| 17429/18688 [20:33:06<1:25:27, 4.07s/it] 93%|█████████▎| 17430/18688 [20:33:10<1:25:41, 4.09s/it] {'loss': '0.8827', 'grad_norm': '1.127', 'learning_rate': '1.318e-06', 'epoch': '0.9327', 'num_input_tokens_seen': 58656896, 'train_runtime': '7.399e+04', 'train_tokens_per_second': '792.8'}
93%|█████████▎| 17430/18688 [20:33:10<1:25:41, 4.09s/it] 93%|█████████▎| 17431/18688 [20:33:14<1:29:01, 4.25s/it] 93%|█████████▎| 17432/18688 [20:33:18<1:28:25, 4.22s/it] 93%|█████████▎| 17433/18688 [20:33:23<1:30:01, 4.30s/it] 93%|█████████▎| 17434/18688 [20:33:27<1:26:42, 4.15s/it] 93%|█████████▎| 17435/18688 [20:33:31<1:27:45, 4.20s/it] 93%|█████████▎| 17436/18688 [20:33:37<1:35:39, 4.58s/it] 93%|█████████▎| 17437/18688 [20:33:40<1:29:09, 4.28s/it] 93%|█████████▎| 17438/18688 [20:33:44<1:26:29, 4.15s/it] 93%|█████████▎| 17439/18688 [20:33:48<1:26:23, 4.15s/it] 93%|█████████▎| 17440/18688 [20:33:53<1:32:41, 4.46s/it] {'loss': '0.9789', 'grad_norm': '1.188', 'learning_rate': '1.297e-06', 'epoch': '0.9332', 'num_input_tokens_seen': 58693384, 'train_runtime': '7.403e+04', 'train_tokens_per_second': '792.8'}
93%|█████████▎| 17440/18688 [20:33:53<1:32:41, 4.46s/it] 93%|█████████▎| 17441/18688 [20:33:57<1:28:03, 4.24s/it] 93%|█████████▎| 17442/18688 [20:34:01<1:26:49, 4.18s/it] 93%|█████████▎| 17443/18688 [20:34:05<1:23:35, 4.03s/it] 93%|█████████▎| 17444/18688 [20:34:09<1:24:32, 4.08s/it] 93%|█████████▎| 17445/18688 [20:34:14<1:28:19, 4.26s/it] 93%|█████████▎| 17446/18688 [20:34:17<1:24:46, 4.10s/it] 93%|█████████▎| 17447/18688 [20:34:21<1:24:54, 4.10s/it] 93%|█████████▎| 17448/18688 [20:34:25<1:22:46, 4.01s/it] 93%|█████████▎| 17449/18688 [20:34:29<1:22:48, 4.01s/it] 93%|█████████▎| 17450/18688 [20:34:33<1:22:56, 4.02s/it] {'loss': '0.9061', 'grad_norm': '1.589', 'learning_rate': '1.277e-06', 'epoch': '0.9338', 'num_input_tokens_seen': 58724152, 'train_runtime': '7.407e+04', 'train_tokens_per_second': '792.8'}
93%|█████████▎| 17450/18688 [20:34:33<1:22:56, 4.02s/it] 93%|█████████▎| 17451/18688 [20:34:37<1:21:12, 3.94s/it] 93%|█████████▎| 17452/18688 [20:34:41<1:19:11, 3.84s/it] 93%|█████████▎| 17453/18688 [20:34:45<1:23:08, 4.04s/it] 93%|█████████▎| 17454/18688 [20:34:49<1:24:40, 4.12s/it] 93%|█████████▎| 17455/18688 [20:34:54<1:29:11, 4.34s/it] 93%|█████████▎| 17456/18688 [20:34:58<1:27:20, 4.25s/it] 93%|█████████▎| 17457/18688 [20:35:02<1:25:14, 4.16s/it] 93%|█████████▎| 17458/18688 [20:35:07<1:26:09, 4.20s/it] 93%|█████████▎| 17459/18688 [20:35:11<1:28:16, 4.31s/it] 93%|█████████▎| 17460/18688 [20:35:16<1:32:15, 4.51s/it] {'loss': '0.9019', 'grad_norm': '1.361', 'learning_rate': '1.256e-06', 'epoch': '0.9343', 'num_input_tokens_seen': 58760104, 'train_runtime': '7.412e+04', 'train_tokens_per_second': '792.8'}
93%|█████████▎| 17460/18688 [20:35:16<1:32:15, 4.51s/it] 93%|█████████▎| 17461/18688 [20:35:20<1:27:48, 4.29s/it] 93%|█████████▎| 17462/18688 [20:35:23<1:23:17, 4.08s/it] 93%|█████████▎| 17463/18688 [20:35:27<1:20:18, 3.93s/it] 93%|█████████▎| 17464/18688 [20:35:31<1:18:42, 3.86s/it] 93%|█████████▎| 17465/18688 [20:35:36<1:26:53, 4.26s/it] 93%|█████████▎| 17466/18688 [20:35:39<1:22:02, 4.03s/it] 93%|█████████▎| 17467/18688 [20:35:44<1:24:13, 4.14s/it] 93%|█████████▎| 17468/18688 [20:35:48<1:23:37, 4.11s/it] 93%|█████████▎| 17469/18688 [20:35:52<1:22:27, 4.06s/it] 93%|█████████▎| 17470/18688 [20:35:56<1:24:44, 4.17s/it] {'loss': '0.8797', 'grad_norm': '1.349', 'learning_rate': '1.236e-06', 'epoch': '0.9348', 'num_input_tokens_seen': 58792120, 'train_runtime': '7.416e+04', 'train_tokens_per_second': '792.8'}
93%|█████████▎| 17470/18688 [20:35:56<1:24:44, 4.17s/it] 93%|█████████▎| 17471/18688 [20:36:00<1:21:05, 4.00s/it] 93%|█████████▎| 17472/18688 [20:36:04<1:23:56, 4.14s/it] 93%|█████████▎| 17473/18688 [20:36:08<1:21:37, 4.03s/it] 94%|█████████▎| 17474/18688 [20:36:12<1:19:32, 3.93s/it] 94%|█████████▎| 17475/18688 [20:36:17<1:25:44, 4.24s/it] 94%|█████████▎| 17476/18688 [20:36:21<1:23:45, 4.15s/it] 94%|█████████▎| 17477/18688 [20:36:25<1:22:47, 4.10s/it] 94%|█████████▎| 17478/18688 [20:36:30<1:27:31, 4.34s/it] 94%|█████████▎| 17479/18688 [20:36:34<1:26:56, 4.31s/it] 94%|█████████▎| 17480/18688 [20:36:38<1:24:15, 4.19s/it] {'loss': '0.8587', 'grad_norm': '1.279', 'learning_rate': '1.216e-06', 'epoch': '0.9354', 'num_input_tokens_seen': 58825248, 'train_runtime': '7.42e+04', 'train_tokens_per_second': '792.8'}
94%|█████████▎| 17480/18688 [20:36:38<1:24:15, 4.19s/it] 94%|█████████▎| 17481/18688 [20:36:42<1:25:53, 4.27s/it] 94%|█████████▎| 17482/18688 [20:36:46<1:25:27, 4.25s/it] 94%|█████████▎| 17483/18688 [20:36:51<1:27:49, 4.37s/it] 94%|█████████▎| 17484/18688 [20:36:55<1:26:48, 4.33s/it] 94%|█████████▎| 17485/18688 [20:36:59<1:23:01, 4.14s/it] 94%|█████████▎| 17486/18688 [20:37:03<1:20:03, 4.00s/it] 94%|█████████▎| 17487/18688 [20:37:06<1:18:34, 3.93s/it] 94%|█████████▎| 17488/18688 [20:37:10<1:18:16, 3.91s/it] 94%|█████████▎| 17489/18688 [20:37:14<1:18:53, 3.95s/it] 94%|█████████▎| 17490/18688 [20:37:19<1:24:01, 4.21s/it] {'loss': '0.9098', 'grad_norm': '1.305', 'learning_rate': '1.196e-06', 'epoch': '0.9359', 'num_input_tokens_seen': 58858952, 'train_runtime': '7.424e+04', 'train_tokens_per_second': '792.8'}
94%|█████████▎| 17490/18688 [20:37:19<1:24:01, 4.21s/it] 94%|█████████▎| 17491/18688 [20:37:23<1:21:20, 4.08s/it] 94%|█████████▎| 17492/18688 [20:37:27<1:23:29, 4.19s/it] 94%|█████████▎| 17493/18688 [20:37:31<1:23:12, 4.18s/it] 94%|█████████▎| 17494/18688 [20:37:36<1:25:14, 4.28s/it] 94%|█████████▎| 17495/18688 [20:37:40<1:21:34, 4.10s/it] 94%|█████████▎| 17496/18688 [20:37:45<1:28:00, 4.43s/it] 94%|█████████▎| 17497/18688 [20:37:49<1:23:28, 4.21s/it] 94%|█████████▎| 17498/18688 [20:37:53<1:22:40, 4.17s/it] 94%|█████████▎| 17499/18688 [20:37:56<1:18:27, 3.96s/it] 94%|█████████▎| 17500/18688 [20:38:00<1:18:41, 3.97s/it] {'loss': '0.9758', 'grad_norm': '1.504', 'learning_rate': '1.176e-06', 'epoch': '0.9365', 'num_input_tokens_seen': 58892912, 'train_runtime': '7.428e+04', 'train_tokens_per_second': '792.8'}
94%|█████████▎| 17500/18688 [20:38:00<1:18:41, 3.97s/it] 94%|█████████▎| 17501/18688 [20:38:04<1:15:52, 3.84s/it] 94%|█████████▎| 17502/18688 [20:38:08<1:20:51, 4.09s/it] 94%|█████████▎| 17503/18688 [20:38:13<1:26:27, 4.38s/it] 94%|█████████▎| 17504/18688 [20:38:17<1:24:34, 4.29s/it] 94%|█████████▎| 17505/18688 [20:38:21<1:22:08, 4.17s/it] 94%|█████████▎| 17506/18688 [20:38:25<1:18:36, 3.99s/it] 94%|█████████▎| 17507/18688 [20:38:30<1:25:20, 4.34s/it] 94%|█████████▎| 17508/18688 [20:38:34<1:22:30, 4.20s/it] 94%|█████████▎| 17509/18688 [20:38:38<1:20:56, 4.12s/it] 94%|█████████▎| 17510/18688 [20:38:42<1:18:39, 4.01s/it] {'loss': '0.8796', 'grad_norm': '1.287', 'learning_rate': '1.156e-06', 'epoch': '0.937', 'num_input_tokens_seen': 58928048, 'train_runtime': '7.432e+04', 'train_tokens_per_second': '792.9'}
94%|█████████▎| 17510/18688 [20:38:42<1:18:39, 4.01s/it] 94%|█████████▎| 17511/18688 [20:38:46<1:18:57, 4.02s/it] 94%|█████████▎| 17512/18688 [20:38:51<1:29:17, 4.56s/it] 94%|█████████▎| 17513/18688 [20:38:56<1:27:10, 4.45s/it] 94%|█████████▎| 17514/18688 [20:38:59<1:21:35, 4.17s/it] 94%|█████████▎| 17515/18688 [20:39:03<1:21:46, 4.18s/it] 94%|█████████▎| 17516/18688 [20:39:08<1:26:11, 4.41s/it] 94%|█████████▎| 17517/18688 [20:39:13<1:27:23, 4.48s/it] 94%|█████████▎| 17518/18688 [20:39:17<1:26:05, 4.42s/it] 94%|█████████▎| 17519/18688 [20:39:21<1:21:15, 4.17s/it] 94%|█████████▍| 17520/18688 [20:39:24<1:16:52, 3.95s/it] {'loss': '0.9294', 'grad_norm': '1.57', 'learning_rate': '1.137e-06', 'epoch': '0.9375', 'num_input_tokens_seen': 58963264, 'train_runtime': '7.436e+04', 'train_tokens_per_second': '792.9'}
94%|█████████▍| 17520/18688 [20:39:24<1:16:52, 3.95s/it] 94%|█████████▍| 17521/18688 [20:39:28<1:16:29, 3.93s/it] 94%|█████████▍| 17522/18688 [20:39:32<1:15:45, 3.90s/it] 94%|█████████▍| 17523/18688 [20:39:36<1:14:44, 3.85s/it] 94%|█████████▍| 17524/18688 [20:39:40<1:16:56, 3.97s/it] 94%|█████████▍| 17525/18688 [20:39:44<1:14:59, 3.87s/it] 94%|█████████▍| 17526/18688 [20:39:48<1:18:24, 4.05s/it] 94%|█████████▍| 17527/18688 [20:39:52<1:18:43, 4.07s/it] 94%|█████████▍| 17528/18688 [20:39:57<1:20:32, 4.17s/it] 94%|█████████▍| 17529/18688 [20:40:01<1:22:36, 4.28s/it] 94%|█████████▍| 17530/18688 [20:40:05<1:19:31, 4.12s/it] {'loss': '0.9012', 'grad_norm': '1.227', 'learning_rate': '1.118e-06', 'epoch': '0.9381', 'num_input_tokens_seen': 58996040, 'train_runtime': '7.441e+04', 'train_tokens_per_second': '792.9'}
94%|█████████▍| 17530/18688 [20:40:05<1:19:31, 4.12s/it] 94%|█████████▍| 17531/18688 [20:40:09<1:16:42, 3.98s/it] 94%|█████████▍| 17532/18688 [20:40:12<1:13:41, 3.82s/it] 94%|█████████▍| 17533/18688 [20:40:15<1:11:26, 3.71s/it] 94%|█████████▍| 17534/18688 [20:40:20<1:18:08, 4.06s/it] 94%|█████████▍| 17535/18688 [20:40:25<1:19:51, 4.16s/it] 94%|█████████▍| 17536/18688 [20:40:29<1:19:43, 4.15s/it] 94%|█████████▍| 17537/18688 [20:40:33<1:19:39, 4.15s/it] 94%|█████████▍| 17538/18688 [20:40:38<1:25:20, 4.45s/it] 94%|█████████▍| 17539/18688 [20:40:42<1:20:05, 4.18s/it] 94%|█████████▍| 17540/18688 [20:40:45<1:15:52, 3.97s/it] {'loss': '0.8379', 'grad_norm': '1.476', 'learning_rate': '1.099e-06', 'epoch': '0.9386', 'num_input_tokens_seen': 59029056, 'train_runtime': '7.445e+04', 'train_tokens_per_second': '792.9'}
94%|█████████▍| 17540/18688 [20:40:45<1:15:52, 3.97s/it] 94%|█████████▍| 17541/18688 [20:40:49<1:12:55, 3.82s/it] 94%|█████████▍| 17542/18688 [20:40:53<1:16:30, 4.01s/it] 94%|█████████▍| 17543/18688 [20:40:57<1:14:01, 3.88s/it] 94%|█████████▍| 17544/18688 [20:41:01<1:17:16, 4.05s/it] 94%|█████████▍| 17545/18688 [20:41:06<1:20:29, 4.23s/it] 94%|█████████▍| 17546/18688 [20:41:09<1:17:37, 4.08s/it] 94%|█████████▍| 17547/18688 [20:41:13<1:14:32, 3.92s/it] 94%|█████████▍| 17548/18688 [20:41:17<1:14:07, 3.90s/it] 94%|█████████▍| 17549/18688 [20:41:20<1:11:41, 3.78s/it] 94%|█████████▍| 17550/18688 [20:41:25<1:15:50, 4.00s/it] {'loss': '0.9439', 'grad_norm': '1.064', 'learning_rate': '1.08e-06', 'epoch': '0.9391', 'num_input_tokens_seen': 59060984, 'train_runtime': '7.449e+04', 'train_tokens_per_second': '792.9'}
94%|█████████▍| 17550/18688 [20:41:25<1:15:50, 4.00s/it] 94%|█████████▍| 17551/18688 [20:41:29<1:14:55, 3.95s/it] 94%|█████████▍| 17552/18688 [20:41:34<1:19:51, 4.22s/it] 94%|█████████▍| 17553/18688 [20:41:38<1:19:12, 4.19s/it] 94%|█████████▍| 17554/18688 [20:41:42<1:17:19, 4.09s/it] 94%|█████████▍| 17555/18688 [20:41:46<1:18:12, 4.14s/it] 94%|█████████▍| 17556/18688 [20:41:50<1:19:55, 4.24s/it] 94%|█████████▍| 17557/18688 [20:41:55<1:20:19, 4.26s/it] 94%|█████████▍| 17558/18688 [20:41:59<1:21:22, 4.32s/it] 94%|█████████▍| 17559/18688 [20:42:03<1:16:56, 4.09s/it] 94%|█████████▍| 17560/18688 [20:42:06<1:13:59, 3.94s/it] {'loss': '0.9546', 'grad_norm': '1.454', 'learning_rate': '1.061e-06', 'epoch': '0.9397', 'num_input_tokens_seen': 59094352, 'train_runtime': '7.453e+04', 'train_tokens_per_second': '792.9'}
94%|█████████▍| 17560/18688 [20:42:06<1:13:59, 3.94s/it] 94%|█████████▍| 17561/18688 [20:42:10<1:11:40, 3.82s/it] 94%|█████████▍| 17562/18688 [20:42:13<1:10:00, 3.73s/it] 94%|█████████▍| 17563/18688 [20:42:18<1:12:54, 3.89s/it] 94%|█████████▍| 17564/18688 [20:42:22<1:18:51, 4.21s/it] 94%|█████████▍| 17565/18688 [20:42:27<1:19:35, 4.25s/it] 94%|█████████▍| 17566/18688 [20:42:31<1:17:54, 4.17s/it] 94%|█████████▍| 17567/18688 [20:42:35<1:17:30, 4.15s/it] 94%|█████████▍| 17568/18688 [20:42:39<1:16:44, 4.11s/it] 94%|█████████▍| 17569/18688 [20:42:44<1:23:10, 4.46s/it] 94%|█████████▍| 17570/18688 [20:42:48<1:21:51, 4.39s/it] {'loss': '0.9638', 'grad_norm': '1.541', 'learning_rate': '1.042e-06', 'epoch': '0.9402', 'num_input_tokens_seen': 59129360, 'train_runtime': '7.457e+04', 'train_tokens_per_second': '792.9'}
94%|█████████▍| 17570/18688 [20:42:48<1:21:51, 4.39s/it] 94%|█████████▍| 17571/18688 [20:42:53<1:20:31, 4.33s/it] 94%|█████████▍| 17572/18688 [20:42:56<1:17:37, 4.17s/it] 94%|█████████▍| 17573/18688 [20:43:00<1:16:01, 4.09s/it] 94%|█████████▍| 17574/18688 [20:43:04<1:16:05, 4.10s/it] 94%|█████████▍| 17575/18688 [20:43:08<1:14:54, 4.04s/it] 94%|█████████▍| 17576/18688 [20:43:12<1:12:16, 3.90s/it] 94%|█████████▍| 17577/18688 [20:43:16<1:15:53, 4.10s/it] 94%|█████████▍| 17578/18688 [20:43:21<1:17:00, 4.16s/it] 94%|█████████▍| 17579/18688 [20:43:25<1:19:39, 4.31s/it] 94%|█████████▍| 17580/18688 [20:43:29<1:17:34, 4.20s/it] {'loss': '0.8549', 'grad_norm': '1.028', 'learning_rate': '1.024e-06', 'epoch': '0.9407', 'num_input_tokens_seen': 59163280, 'train_runtime': '7.461e+04', 'train_tokens_per_second': '793'}
94%|█████████▍| 17580/18688 [20:43:29<1:17:34, 4.20s/it] 94%|█████████▍| 17581/18688 [20:43:34<1:19:07, 4.29s/it] 94%|█████████▍| 17582/18688 [20:43:38<1:19:58, 4.34s/it] 94%|█████████▍| 17583/18688 [20:43:43<1:22:21, 4.47s/it] 94%|█████████▍| 17584/18688 [20:43:48<1:26:18, 4.69s/it] 94%|█████████▍| 17585/18688 [20:43:52<1:21:16, 4.42s/it] 94%|█████████▍| 17586/18688 [20:43:56<1:17:40, 4.23s/it] 94%|█████████▍| 17587/18688 [20:44:00<1:16:34, 4.17s/it] 94%|█████████▍| 17588/18688 [20:44:04<1:14:51, 4.08s/it] 94%|█████████▍| 17589/18688 [20:44:07<1:12:34, 3.96s/it] 94%|█████████▍| 17590/18688 [20:44:11<1:12:41, 3.97s/it] {'loss': '0.9401', 'grad_norm': '1.358', 'learning_rate': '1.005e-06', 'epoch': '0.9413', 'num_input_tokens_seen': 59198392, 'train_runtime': '7.465e+04', 'train_tokens_per_second': '793'}
94%|█████████▍| 17590/18688 [20:44:11<1:12:41, 3.97s/it] 94%|█████████▍| 17591/18688 [20:44:16<1:14:56, 4.10s/it] 94%|█████████▍| 17592/18688 [20:44:21<1:17:48, 4.26s/it] 94%|█████████▍| 17593/18688 [20:44:24<1:14:06, 4.06s/it] 94%|█████████▍| 17594/18688 [20:44:28<1:15:06, 4.12s/it] 94%|█████████▍| 17595/18688 [20:44:33<1:17:53, 4.28s/it] 94%|█████████▍| 17596/18688 [20:44:38<1:19:35, 4.37s/it] 94%|█████████▍| 17597/18688 [20:44:41<1:16:47, 4.22s/it] 94%|█████████▍| 17598/18688 [20:44:45<1:13:41, 4.06s/it] 94%|█████████▍| 17599/18688 [20:44:50<1:17:18, 4.26s/it] 94%|█████████▍| 17600/18688 [20:44:54<1:14:50, 4.13s/it] {'loss': '0.9435', 'grad_norm': '1.35', 'learning_rate': '9.872e-07', 'epoch': '0.9418', 'num_input_tokens_seen': 59233504, 'train_runtime': '7.469e+04', 'train_tokens_per_second': '793'}
94%|█████████▍| 17600/18688 [20:44:54<1:14:50, 4.13s/it] 94%|█████████▍| 17601/18688 [20:44:57<1:12:01, 3.98s/it] 94%|█████████▍| 17602/18688 [20:45:03<1:19:00, 4.36s/it] 94%|█████████▍| 17603/18688 [20:45:07<1:16:38, 4.24s/it] 94%|█████████▍| 17604/18688 [20:45:11<1:16:06, 4.21s/it] 94%|█████████▍| 17605/18688 [20:45:14<1:13:09, 4.05s/it] 94%|█████████▍| 17606/18688 [20:45:18<1:11:48, 3.98s/it] 94%|█████████▍| 17607/18688 [20:45:22<1:10:03, 3.89s/it] 94%|█████████▍| 17608/18688 [20:45:26<1:09:13, 3.85s/it] 94%|█████████▍| 17609/18688 [20:45:30<1:11:26, 3.97s/it] 94%|█████████▍| 17610/18688 [20:45:34<1:13:44, 4.10s/it] {'loss': '0.9207', 'grad_norm': '1.319', 'learning_rate': '9.692e-07', 'epoch': '0.9423', 'num_input_tokens_seen': 59266768, 'train_runtime': '7.473e+04', 'train_tokens_per_second': '793'}
94%|█████████▍| 17610/18688 [20:45:34<1:13:44, 4.10s/it] 94%|█████████▍| 17611/18688 [20:45:39<1:16:42, 4.27s/it] 94%|█████████▍| 17612/18688 [20:45:43<1:13:12, 4.08s/it] 94%|█████████▍| 17613/18688 [20:45:47<1:15:30, 4.21s/it] 94%|█████████▍| 17614/18688 [20:45:52<1:18:13, 4.37s/it] 94%|█████████▍| 17615/18688 [20:45:56<1:15:31, 4.22s/it] 94%|█████████▍| 17616/18688 [20:45:59<1:12:42, 4.07s/it] 94%|█████████▍| 17617/18688 [20:46:03<1:11:52, 4.03s/it] 94%|█████████▍| 17618/18688 [20:46:07<1:10:14, 3.94s/it] 94%|█████████▍| 17619/18688 [20:46:12<1:15:59, 4.26s/it] 94%|█████████▍| 17620/18688 [20:46:16<1:15:01, 4.22s/it] {'loss': '0.9361', 'grad_norm': '1.167', 'learning_rate': '9.514e-07', 'epoch': '0.9429', 'num_input_tokens_seen': 59301600, 'train_runtime': '7.478e+04', 'train_tokens_per_second': '793'}
94%|█████████▍| 17620/18688 [20:46:16<1:15:01, 4.22s/it] 94%|█████████▍| 17621/18688 [20:46:21<1:15:22, 4.24s/it] 94%|█████████▍| 17622/18688 [20:46:24<1:13:17, 4.13s/it] 94%|█████████▍| 17623/18688 [20:46:30<1:21:12, 4.57s/it] 94%|█████████▍| 17624/18688 [20:46:34<1:18:50, 4.45s/it] 94%|█████████▍| 17625/18688 [20:46:39<1:18:24, 4.43s/it] 94%|█████████▍| 17626/18688 [20:46:42<1:15:09, 4.25s/it] 94%|█████████▍| 17627/18688 [20:46:46<1:11:12, 4.03s/it] 94%|█████████▍| 17628/18688 [20:46:49<1:08:41, 3.89s/it] 94%|█████████▍| 17629/18688 [20:46:54<1:11:13, 4.04s/it] 94%|█████████▍| 17630/18688 [20:46:59<1:15:29, 4.28s/it] {'loss': '0.8912', 'grad_norm': '1.199', 'learning_rate': '9.337e-07', 'epoch': '0.9434', 'num_input_tokens_seen': 59336528, 'train_runtime': '7.482e+04', 'train_tokens_per_second': '793.1'}
94%|█████████▍| 17630/18688 [20:46:59<1:15:29, 4.28s/it] 94%|█████████▍| 17631/18688 [20:47:02<1:12:10, 4.10s/it] 94%|█████████▍| 17632/18688 [20:47:07<1:14:08, 4.21s/it] 94%|█████████▍| 17633/18688 [20:47:11<1:14:10, 4.22s/it] 94%|█████████▍| 17634/18688 [20:47:15<1:12:13, 4.11s/it] 94%|█████████▍| 17635/18688 [20:47:20<1:18:38, 4.48s/it] 94%|█████████▍| 17636/18688 [20:47:25<1:19:43, 4.55s/it] 94%|█████████▍| 17637/18688 [20:47:30<1:21:44, 4.67s/it] 94%|█████████▍| 17638/18688 [20:47:34<1:18:48, 4.50s/it] 94%|█████████▍| 17639/18688 [20:47:39<1:18:49, 4.51s/it] 94%|█████████▍| 17640/18688 [20:47:43<1:19:05, 4.53s/it] {'loss': '0.912', 'grad_norm': '1.291', 'learning_rate': '9.162e-07', 'epoch': '0.9439', 'num_input_tokens_seen': 59374240, 'train_runtime': '7.486e+04', 'train_tokens_per_second': '793.1'}
94%|█████████▍| 17640/18688 [20:47:43<1:19:05, 4.53s/it] 94%|█████████▍| 17641/18688 [20:47:48<1:20:29, 4.61s/it] 94%|█████████▍| 17642/18688 [20:47:52<1:19:59, 4.59s/it] 94%|█████████▍| 17643/18688 [20:47:56<1:13:49, 4.24s/it] 94%|█████████▍| 17644/18688 [20:48:00<1:10:41, 4.06s/it] 94%|█████████▍| 17645/18688 [20:48:04<1:13:03, 4.20s/it] 94%|█████████▍| 17646/18688 [20:48:09<1:14:19, 4.28s/it] 94%|█████████▍| 17647/18688 [20:48:13<1:15:45, 4.37s/it] 94%|█████████▍| 17648/18688 [20:48:17<1:13:06, 4.22s/it] 94%|█████████▍| 17649/18688 [20:48:21<1:09:52, 4.03s/it] 94%|█████████▍| 17650/18688 [20:48:25<1:10:49, 4.09s/it] {'loss': '0.9466', 'grad_norm': '1.658', 'learning_rate': '8.989e-07', 'epoch': '0.9445', 'num_input_tokens_seen': 59408248, 'train_runtime': '7.491e+04', 'train_tokens_per_second': '793.1'}
94%|█████████▍| 17650/18688 [20:48:25<1:10:49, 4.09s/it] 94%|█████████▍| 17651/18688 [20:48:29<1:11:08, 4.12s/it] 94%|█████████▍| 17652/18688 [20:48:34<1:13:49, 4.28s/it] 94%|█████████▍| 17653/18688 [20:48:38<1:13:29, 4.26s/it] 94%|█████████▍| 17654/18688 [20:48:41<1:10:00, 4.06s/it] 94%|█████████▍| 17655/18688 [20:48:45<1:07:59, 3.95s/it] 94%|█████████▍| 17656/18688 [20:48:50<1:12:25, 4.21s/it] 94%|█████████▍| 17657/18688 [20:48:54<1:14:05, 4.31s/it] 94%|█████████▍| 17658/18688 [20:48:58<1:11:49, 4.18s/it] 94%|█████████▍| 17659/18688 [20:49:02<1:07:58, 3.96s/it] 94%|█████████▍| 17660/18688 [20:49:06<1:08:40, 4.01s/it] {'loss': '0.9862', 'grad_norm': '1.401', 'learning_rate': '8.817e-07', 'epoch': '0.945', 'num_input_tokens_seen': 59441992, 'train_runtime': '7.495e+04', 'train_tokens_per_second': '793.1'}
94%|█████████▍| 17660/18688 [20:49:06<1:08:40, 4.01s/it] 95%|█████████▍| 17661/18688 [20:49:09<1:06:01, 3.86s/it] 95%|█████████▍| 17662/18688 [20:49:14<1:09:39, 4.07s/it] 95%|█████████▍| 17663/18688 [20:49:18<1:09:59, 4.10s/it] 95%|█████████▍| 17664/18688 [20:49:23<1:13:47, 4.32s/it] 95%|█████████▍| 17665/18688 [20:49:27<1:10:08, 4.11s/it] 95%|█████████▍| 17666/18688 [20:49:30<1:07:11, 3.95s/it] 95%|█████████▍| 17667/18688 [20:49:34<1:07:26, 3.96s/it] 95%|█████████▍| 17668/18688 [20:49:38<1:06:33, 3.92s/it] 95%|█████████▍| 17669/18688 [20:49:43<1:10:08, 4.13s/it] 95%|█████████▍| 17670/18688 [20:49:47<1:10:02, 4.13s/it] {'loss': '0.8579', 'grad_norm': '1.349', 'learning_rate': '8.647e-07', 'epoch': '0.9456', 'num_input_tokens_seen': 59474960, 'train_runtime': '7.499e+04', 'train_tokens_per_second': '793.1'}
95%|█████████▍| 17670/18688 [20:49:47<1:10:02, 4.13s/it] 95%|█████████▍| 17671/18688 [20:49:51<1:08:05, 4.02s/it] 95%|█████████▍| 17672/18688 [20:49:55<1:10:17, 4.15s/it] 95%|█████████▍| 17673/18688 [20:50:00<1:13:09, 4.32s/it] 95%|█████████▍| 17674/18688 [20:50:03<1:09:56, 4.14s/it] 95%|█████████▍| 17675/18688 [20:50:08<1:10:32, 4.18s/it] 95%|█████████▍| 17676/18688 [20:50:11<1:07:37, 4.01s/it] 95%|█████████▍| 17677/18688 [20:50:16<1:10:06, 4.16s/it] 95%|█████████▍| 17678/18688 [20:50:20<1:09:53, 4.15s/it] 95%|█████████▍| 17679/18688 [20:50:24<1:07:15, 4.00s/it] 95%|█████████▍| 17680/18688 [20:50:27<1:06:21, 3.95s/it] {'loss': '0.8665', 'grad_norm': '1.179', 'learning_rate': '8.479e-07', 'epoch': '0.9461', 'num_input_tokens_seen': 59507024, 'train_runtime': '7.503e+04', 'train_tokens_per_second': '793.1'}
95%|█████████▍| 17680/18688 [20:50:27<1:06:21, 3.95s/it] 95%|█████████▍| 17681/18688 [20:50:31<1:06:49, 3.98s/it] 95%|█████████▍| 17682/18688 [20:50:36<1:08:04, 4.06s/it] 95%|█████████▍| 17683/18688 [20:50:40<1:07:39, 4.04s/it] 95%|█████████▍| 17684/18688 [20:50:44<1:10:39, 4.22s/it] 95%|█████████▍| 17685/18688 [20:50:48<1:08:22, 4.09s/it] 95%|█████████▍| 17686/18688 [20:50:52<1:08:26, 4.10s/it] 95%|█████████▍| 17687/18688 [20:50:56<1:05:56, 3.95s/it] 95%|█████████▍| 17688/18688 [20:51:00<1:07:40, 4.06s/it] 95%|█████████▍| 17689/18688 [20:51:05<1:09:31, 4.18s/it] 95%|█████████▍| 17690/18688 [20:51:09<1:09:05, 4.15s/it] {'loss': '0.8825', 'grad_norm': '1.515', 'learning_rate': '8.312e-07', 'epoch': '0.9466', 'num_input_tokens_seen': 59540920, 'train_runtime': '7.507e+04', 'train_tokens_per_second': '793.1'}
95%|█████████▍| 17690/18688 [20:51:09<1:09:05, 4.15s/it] 95%|█████████▍| 17691/18688 [20:51:13<1:08:10, 4.10s/it] 95%|█████████▍| 17692/18688 [20:51:16<1:05:31, 3.95s/it] 95%|█████████▍| 17693/18688 [20:51:21<1:09:25, 4.19s/it] 95%|█████████▍| 17694/18688 [20:51:25<1:08:48, 4.15s/it] 95%|█████████▍| 17695/18688 [20:51:29<1:08:16, 4.12s/it] 95%|█████████▍| 17696/18688 [20:51:33<1:04:59, 3.93s/it] 95%|█████████▍| 17697/18688 [20:51:37<1:04:45, 3.92s/it] 95%|█████████▍| 17698/18688 [20:51:40<1:03:23, 3.84s/it] 95%|█████████▍| 17699/18688 [20:51:45<1:05:30, 3.97s/it] 95%|█████████▍| 17700/18688 [20:51:50<1:10:58, 4.31s/it] {'loss': '0.886', 'grad_norm': '1.17', 'learning_rate': '8.147e-07', 'epoch': '0.9472', 'num_input_tokens_seen': 59574736, 'train_runtime': '7.511e+04', 'train_tokens_per_second': '793.2'}
95%|█████████▍| 17700/18688 [20:51:50<1:10:58, 4.31s/it] 95%|█████████▍| 17701/18688 [20:51:53<1:07:19, 4.09s/it] 95%|█████████▍| 17702/18688 [20:51:57<1:04:03, 3.90s/it] 95%|█████████▍| 17703/18688 [20:52:01<1:05:46, 4.01s/it] 95%|█████████▍| 17704/18688 [20:52:05<1:04:23, 3.93s/it] 95%|█████████▍| 17705/18688 [20:52:08<1:02:56, 3.84s/it] 95%|█████████▍| 17706/18688 [20:52:12<1:01:54, 3.78s/it] 95%|█████████▍| 17707/18688 [20:52:16<1:01:26, 3.76s/it] 95%|█████████▍| 17708/18688 [20:52:20<1:04:49, 3.97s/it] 95%|█████████▍| 17709/18688 [20:52:24<1:03:19, 3.88s/it] 95%|█████████▍| 17710/18688 [20:52:28<1:07:10, 4.12s/it] {'loss': '0.8296', 'grad_norm': '1.286', 'learning_rate': '7.984e-07', 'epoch': '0.9477', 'num_input_tokens_seen': 59605200, 'train_runtime': '7.515e+04', 'train_tokens_per_second': '793.2'}
95%|█████████▍| 17710/18688 [20:52:28<1:07:10, 4.12s/it] 95%|█████████▍| 17711/18688 [20:52:33<1:11:00, 4.36s/it] 95%|█████████▍| 17712/18688 [20:52:37<1:09:13, 4.26s/it] 95%|█████████▍| 17713/18688 [20:52:41<1:05:57, 4.06s/it] 95%|█████████▍| 17714/18688 [20:52:45<1:05:43, 4.05s/it] 95%|█████████▍| 17715/18688 [20:52:49<1:03:19, 3.91s/it] 95%|█████████▍| 17716/18688 [20:52:52<1:01:51, 3.82s/it] 95%|█████████▍| 17717/18688 [20:52:57<1:06:46, 4.13s/it] 95%|█████████▍| 17718/18688 [20:53:01<1:04:34, 3.99s/it] 95%|█████████▍| 17719/18688 [20:53:04<1:02:59, 3.90s/it] 95%|█████████▍| 17720/18688 [20:53:09<1:05:19, 4.05s/it] {'loss': '0.8758', 'grad_norm': '1.507', 'learning_rate': '7.822e-07', 'epoch': '0.9482', 'num_input_tokens_seen': 59637096, 'train_runtime': '7.519e+04', 'train_tokens_per_second': '793.2'}
95%|█████████▍| 17720/18688 [20:53:09<1:05:19, 4.05s/it] 95%|█████████▍| 17721/18688 [20:53:13<1:05:37, 4.07s/it] 95%|█████████▍| 17722/18688 [20:53:17<1:03:26, 3.94s/it] 95%|█████████▍| 17723/18688 [20:53:20<1:02:56, 3.91s/it] 95%|█████████▍| 17724/18688 [20:53:25<1:04:13, 4.00s/it] 95%|█████████▍| 17725/18688 [20:53:28<1:03:36, 3.96s/it] 95%|█████████▍| 17726/18688 [20:53:32<1:01:18, 3.82s/it] 95%|█████████▍| 17727/18688 [20:53:36<1:02:59, 3.93s/it] 95%|█████████▍| 17728/18688 [20:53:40<1:00:57, 3.81s/it] 95%|█████████▍| 17729/18688 [20:53:44<1:02:50, 3.93s/it] 95%|█████████▍| 17730/18688 [20:53:48<1:05:40, 4.11s/it] {'loss': '0.9187', 'grad_norm': '1.176', 'learning_rate': '7.662e-07', 'epoch': '0.9488', 'num_input_tokens_seen': 59667800, 'train_runtime': '7.523e+04', 'train_tokens_per_second': '793.1'}
95%|█████████▍| 17730/18688 [20:53:48<1:05:40, 4.11s/it] 95%|█████████▍| 17731/18688 [20:53:52<1:02:25, 3.91s/it] 95%|█████████▍| 17732/18688 [20:53:56<1:03:28, 3.98s/it] 95%|█████████▍| 17733/18688 [20:54:00<1:03:32, 3.99s/it] 95%|█████████▍| 17734/18688 [20:54:04<1:02:38, 3.94s/it] 95%|█████████▍| 17735/18688 [20:54:08<1:03:19, 3.99s/it] 95%|█████████▍| 17736/18688 [20:54:13<1:06:00, 4.16s/it] 95%|█████████▍| 17737/18688 [20:54:16<1:03:16, 3.99s/it] 95%|█████████▍| 17738/18688 [20:54:20<1:03:07, 3.99s/it] 95%|█████████▍| 17739/18688 [20:54:24<1:02:33, 3.96s/it] 95%|█████████▍| 17740/18688 [20:54:30<1:11:39, 4.54s/it] {'loss': '0.8448', 'grad_norm': '1.112', 'learning_rate': '7.503e-07', 'epoch': '0.9493', 'num_input_tokens_seen': 59700792, 'train_runtime': '7.527e+04', 'train_tokens_per_second': '793.2'}
95%|█████████▍| 17740/18688 [20:54:30<1:11:39, 4.54s/it] 95%|█████████▍| 17741/18688 [20:54:34<1:11:58, 4.56s/it] 95%|█████████▍| 17742/18688 [20:54:38<1:08:22, 4.34s/it] 95%|█████████▍| 17743/18688 [20:54:43<1:07:59, 4.32s/it] 95%|█████████▍| 17744/18688 [20:54:47<1:08:03, 4.33s/it] 95%|█████████▍| 17745/18688 [20:54:52<1:11:12, 4.53s/it] 95%|█████████▍| 17746/18688 [20:54:57<1:14:22, 4.74s/it] 95%|█████████▍| 17747/18688 [20:55:01<1:12:24, 4.62s/it] 95%|█████████▍| 17748/18688 [20:55:06<1:10:43, 4.51s/it] 95%|█████████▍| 17749/18688 [20:55:09<1:06:43, 4.26s/it] 95%|█████████▍| 17750/18688 [20:55:13<1:04:07, 4.10s/it] {'loss': '0.9305', 'grad_norm': '1.329', 'learning_rate': '7.346e-07', 'epoch': '0.9498', 'num_input_tokens_seen': 59735976, 'train_runtime': '7.531e+04', 'train_tokens_per_second': '793.2'}
95%|█████████▍| 17750/18688 [20:55:13<1:04:07, 4.10s/it] 95%|█████████▍| 17751/18688 [20:55:17<1:04:15, 4.11s/it] 95%|█████████▍| 17752/18688 [20:55:22<1:05:44, 4.21s/it] 95%|█████████▍| 17753/18688 [20:55:25<1:03:20, 4.07s/it] 95%|█████████▌| 17754/18688 [20:55:29<1:01:36, 3.96s/it] 95%|█████████▌| 17755/18688 [20:55:33<1:01:59, 3.99s/it] 95%|█████████▌| 17756/18688 [20:55:37<1:01:04, 3.93s/it] 95%|█████████▌| 17757/18688 [20:55:41<1:02:03, 4.00s/it] 95%|█████████▌| 17758/18688 [20:55:45<1:00:08, 3.88s/it] 95%|█████████▌| 17759/18688 [20:55:49<59:39, 3.85s/it] 95%|█████████▌| 17760/18688 [20:55:53<1:01:27, 3.97s/it] {'loss': '0.8653', 'grad_norm': '1.418', 'learning_rate': '7.191e-07', 'epoch': '0.9504', 'num_input_tokens_seen': 59767968, 'train_runtime': '7.535e+04', 'train_tokens_per_second': '793.2'}
95%|█████████▌| 17760/18688 [20:55:53<1:01:27, 3.97s/it] 95%|█████████▌| 17761/18688 [20:55:57<1:00:49, 3.94s/it] 95%|█████████▌| 17762/18688 [20:56:00<59:15, 3.84s/it] 95%|█████████▌| 17763/18688 [20:56:04<58:23, 3.79s/it] 95%|█████████▌| 17764/18688 [20:56:09<1:02:35, 4.06s/it] 95%|█████████▌| 17765/18688 [20:56:12<1:00:48, 3.95s/it] 95%|█████████▌| 17766/18688 [20:56:16<1:01:21, 3.99s/it] 95%|█████████▌| 17767/18688 [20:56:20<1:00:29, 3.94s/it] 95%|█████████▌| 17768/18688 [20:56:24<59:12, 3.86s/it] 95%|█████████▌| 17769/18688 [20:56:28<1:00:03, 3.92s/it] 95%|█████████▌| 17770/18688 [20:56:32<1:00:08, 3.93s/it] {'loss': '0.9251', 'grad_norm': '1.909', 'learning_rate': '7.037e-07', 'epoch': '0.9509', 'num_input_tokens_seen': 59799216, 'train_runtime': '7.539e+04', 'train_tokens_per_second': '793.2'}
95%|█████████▌| 17770/18688 [20:56:32<1:00:08, 3.93s/it] 95%|█████████▌| 17771/18688 [20:56:36<58:49, 3.85s/it] 95%|█████████▌| 17772/18688 [20:56:40<1:02:39, 4.10s/it] 95%|█████████▌| 17773/18688 [20:56:44<1:01:44, 4.05s/it] 95%|█████████▌| 17774/18688 [20:56:49<1:05:27, 4.30s/it] 95%|█████████▌| 17775/18688 [20:56:53<1:04:50, 4.26s/it] 95%|█████████▌| 17776/18688 [20:56:57<1:02:05, 4.09s/it] 95%|█████████▌| 17777/18688 [20:57:01<59:45, 3.94s/it] 95%|█████████▌| 17778/18688 [20:57:04<58:01, 3.83s/it] 95%|█████████▌| 17779/18688 [20:57:09<1:04:21, 4.25s/it] 95%|█████████▌| 17780/18688 [20:57:14<1:07:59, 4.49s/it] {'loss': '0.93', 'grad_norm': '1.416', 'learning_rate': '6.885e-07', 'epoch': '0.9514', 'num_input_tokens_seen': 59834888, 'train_runtime': '7.543e+04', 'train_tokens_per_second': '793.2'}
95%|█████████▌| 17780/18688 [20:57:14<1:07:59, 4.49s/it] 95%|█████████▌| 17781/18688 [20:57:19<1:06:52, 4.42s/it] 95%|█████████▌| 17782/18688 [20:57:22<1:03:36, 4.21s/it] 95%|█████████▌| 17783/18688 [20:57:27<1:03:30, 4.21s/it] 95%|█████████▌| 17784/18688 [20:57:31<1:05:45, 4.36s/it] 95%|█████████▌| 17785/18688 [20:57:35<1:02:36, 4.16s/it] 95%|█████████▌| 17786/18688 [20:57:39<1:00:31, 4.03s/it] 95%|█████████▌| 17787/18688 [20:57:43<1:03:32, 4.23s/it] 95%|█████████▌| 17788/18688 [20:57:47<1:01:34, 4.11s/it] 95%|█████████▌| 17789/18688 [20:57:51<1:01:12, 4.09s/it] 95%|█████████▌| 17790/18688 [20:57:55<59:15, 3.96s/it] {'loss': '0.8786', 'grad_norm': '1.46', 'learning_rate': '6.735e-07', 'epoch': '0.952', 'num_input_tokens_seen': 59866856, 'train_runtime': '7.548e+04', 'train_tokens_per_second': '793.2'}
95%|█████████▌| 17790/18688 [20:57:55<59:15, 3.96s/it] 95%|█████████▌| 17791/18688 [20:57:59<58:07, 3.89s/it] 95%|█████████▌| 17792/18688 [20:58:02<56:45, 3.80s/it] 95%|█████████▌| 17793/18688 [20:58:06<57:45, 3.87s/it] 95%|█████████▌| 17794/18688 [20:58:11<59:42, 4.01s/it] 95%|█████████▌| 17795/18688 [20:58:14<58:47, 3.95s/it] 95%|█████████▌| 17796/18688 [20:58:18<57:43, 3.88s/it] 95%|█████████▌| 17797/18688 [20:58:22<58:16, 3.92s/it] 95%|█████████▌| 17798/18688 [20:58:26<58:25, 3.94s/it] 95%|█████████▌| 17799/18688 [20:58:31<1:01:08, 4.13s/it] 95%|█████████▌| 17800/18688 [20:58:34<59:09, 4.00s/it] {'loss': '0.8815', 'grad_norm': '1.297', 'learning_rate': '6.586e-07', 'epoch': '0.9525', 'num_input_tokens_seen': 59898904, 'train_runtime': '7.551e+04', 'train_tokens_per_second': '793.2'}
95%|█████████▌| 17800/18688 [20:58:34<59:09, 4.00s/it] 95%|█████████▌| 17801/18688 [20:58:38<57:11, 3.87s/it] 95%|█████████▌| 17802/18688 [20:58:42<58:09, 3.94s/it] 95%|█████████▌| 17803/18688 [20:58:46<57:15, 3.88s/it] 95%|█████████▌| 17804/18688 [20:58:50<57:07, 3.88s/it] 95%|█████████▌| 17805/18688 [20:58:54<57:22, 3.90s/it] 95%|█████████▌| 17806/18688 [20:58:57<56:53, 3.87s/it] 95%|█████████▌| 17807/18688 [20:59:01<55:51, 3.80s/it] 95%|█████████▌| 17808/18688 [20:59:05<56:53, 3.88s/it] 95%|█████████▌| 17809/18688 [20:59:09<56:12, 3.84s/it] 95%|█████████▌| 17810/18688 [20:59:13<55:31, 3.79s/it] {'loss': '0.9026', 'grad_norm': '1.306', 'learning_rate': '6.439e-07', 'epoch': '0.953', 'num_input_tokens_seen': 59928400, 'train_runtime': '7.555e+04', 'train_tokens_per_second': '793.2'}
95%|█████████▌| 17810/18688 [20:59:13<55:31, 3.79s/it] 95%|█████████▌| 17811/18688 [20:59:17<58:10, 3.98s/it] 95%|█████████▌| 17812/18688 [20:59:21<58:45, 4.02s/it] 95%|█████████▌| 17813/18688 [20:59:25<58:04, 3.98s/it] 95%|█████████▌| 17814/18688 [20:59:30<1:02:03, 4.26s/it] 95%|█████████▌| 17815/18688 [20:59:35<1:04:33, 4.44s/it] 95%|█████████▌| 17816/18688 [20:59:40<1:06:07, 4.55s/it] 95%|█████████▌| 17817/18688 [20:59:44<1:04:36, 4.45s/it] 95%|█████████▌| 17818/18688 [20:59:48<1:01:55, 4.27s/it] 95%|█████████▌| 17819/18688 [20:59:51<58:47, 4.06s/it] 95%|█████████▌| 17820/18688 [20:59:55<57:42, 3.99s/it] {'loss': '0.9252', 'grad_norm': '1.35', 'learning_rate': '6.294e-07', 'epoch': '0.9536', 'num_input_tokens_seen': 59963056, 'train_runtime': '7.56e+04', 'train_tokens_per_second': '793.2'}
95%|█████████▌| 17820/18688 [20:59:55<57:42, 3.99s/it] 95%|█████████▌| 17821/18688 [20:59:59<56:15, 3.89s/it] 95%|█████████▌| 17822/18688 [21:00:03<55:40, 3.86s/it] 95%|█████████▌| 17823/18688 [21:00:07<58:03, 4.03s/it] 95%|█████████▌| 17824/18688 [21:00:10<55:55, 3.88s/it] 95%|█████████▌| 17825/18688 [21:00:14<56:08, 3.90s/it] 95%|█████████▌| 17826/18688 [21:00:18<54:16, 3.78s/it] 95%|█████████▌| 17827/18688 [21:00:22<54:04, 3.77s/it] 95%|█████████▌| 17828/18688 [21:00:25<52:37, 3.67s/it] 95%|█████████▌| 17829/18688 [21:00:29<51:42, 3.61s/it] 95%|█████████▌| 17830/18688 [21:00:32<52:15, 3.65s/it] {'loss': '0.808', 'grad_norm': '1.293', 'learning_rate': '6.15e-07', 'epoch': '0.9541', 'num_input_tokens_seen': 59990576, 'train_runtime': '7.563e+04', 'train_tokens_per_second': '793.2'}
95%|█████████▌| 17830/18688 [21:00:32<52:15, 3.65s/it] 95%|█████████▌| 17831/18688 [21:00:36<53:11, 3.72s/it] 95%|█████████▌| 17832/18688 [21:00:40<54:33, 3.82s/it] 95%|█████████▌| 17833/18688 [21:00:44<55:25, 3.89s/it] 95%|█████████▌| 17834/18688 [21:00:48<56:34, 3.98s/it] 95%|█████████▌| 17835/18688 [21:00:52<55:16, 3.89s/it] 95%|█████████▌| 17836/18688 [21:00:56<56:25, 3.97s/it] 95%|█████████▌| 17837/18688 [21:01:01<58:04, 4.09s/it] 95%|█████████▌| 17838/18688 [21:01:05<56:48, 4.01s/it] 95%|█████████▌| 17839/18688 [21:01:09<57:53, 4.09s/it] 95%|█████████▌| 17840/18688 [21:01:13<1:00:08, 4.26s/it] {'loss': '0.9495', 'grad_norm': '1.485', 'learning_rate': '6.008e-07', 'epoch': '0.9546', 'num_input_tokens_seen': 60023656, 'train_runtime': '7.567e+04', 'train_tokens_per_second': '793.2'}
95%|█████████▌| 17840/18688 [21:01:13<1:00:08, 4.26s/it] 95%|█████████▌| 17841/18688 [21:01:18<1:00:58, 4.32s/it] 95%|█████████▌| 17842/18688 [21:01:23<1:03:08, 4.48s/it] 95%|█████████▌| 17843/18688 [21:01:27<1:02:27, 4.43s/it] 95%|█████████▌| 17844/18688 [21:01:31<1:00:07, 4.27s/it] 95%|█████████▌| 17845/18688 [21:01:34<56:41, 4.03s/it] 95%|█████████▌| 17846/18688 [21:01:39<57:19, 4.08s/it] 95%|█████████▌| 17847/18688 [21:01:42<55:20, 3.95s/it] 96%|█████████▌| 17848/18688 [21:01:46<53:40, 3.83s/it] 96%|█████████▌| 17849/18688 [21:01:50<55:45, 3.99s/it] 96%|█████████▌| 17850/18688 [21:01:54<55:18, 3.96s/it] {'loss': '0.9074', 'grad_norm': '1.227', 'learning_rate': '5.868e-07', 'epoch': '0.9552', 'num_input_tokens_seen': 60057400, 'train_runtime': '7.571e+04', 'train_tokens_per_second': '793.2'}
96%|█████████▌| 17850/18688 [21:01:54<55:18, 3.96s/it] 96%|█████████▌| 17851/18688 [21:01:58<55:16, 3.96s/it] 96%|█████████▌| 17852/18688 [21:02:02<53:33, 3.84s/it] 96%|█████████▌| 17853/18688 [21:02:06<56:01, 4.03s/it] 96%|█████████▌| 17854/18688 [21:02:10<55:00, 3.96s/it] 96%|█████████▌| 17855/18688 [21:02:14<55:12, 3.98s/it] 96%|█████████▌| 17856/18688 [21:02:18<53:56, 3.89s/it] 96%|█████████▌| 17857/18688 [21:02:21<53:13, 3.84s/it] 96%|█████████▌| 17858/18688 [21:02:25<52:27, 3.79s/it] 96%|█████████▌| 17859/18688 [21:02:29<52:29, 3.80s/it] 96%|█████████▌| 17860/18688 [21:02:33<52:20, 3.79s/it] {'loss': '0.9513', 'grad_norm': '1.422', 'learning_rate': '5.729e-07', 'epoch': '0.9557', 'num_input_tokens_seen': 60086904, 'train_runtime': '7.575e+04', 'train_tokens_per_second': '793.2'}
96%|█████████▌| 17860/18688 [21:02:33<52:20, 3.79s/it] 96%|█████████▌| 17861/18688 [21:02:37<53:59, 3.92s/it] 96%|█████████▌| 17862/18688 [21:02:41<56:35, 4.11s/it] 96%|█████████▌| 17863/18688 [21:02:46<56:50, 4.13s/it] 96%|█████████▌| 17864/18688 [21:02:50<56:20, 4.10s/it] 96%|█████████▌| 17865/18688 [21:02:53<54:51, 4.00s/it] 96%|█████████▌| 17866/18688 [21:02:57<54:58, 4.01s/it] 96%|█████████▌| 17867/18688 [21:03:01<52:54, 3.87s/it] 96%|█████████▌| 17868/18688 [21:03:05<53:07, 3.89s/it] 96%|█████████▌| 17869/18688 [21:03:09<54:31, 4.00s/it] 96%|█████████▌| 17870/18688 [21:03:13<55:42, 4.09s/it] {'loss': '0.8718', 'grad_norm': '1.217', 'learning_rate': '5.592e-07', 'epoch': '0.9563', 'num_input_tokens_seen': 60119624, 'train_runtime': '7.579e+04', 'train_tokens_per_second': '793.2'}
96%|█████████▌| 17870/18688 [21:03:13<55:42, 4.09s/it] 96%|█████████▌| 17871/18688 [21:03:18<59:15, 4.35s/it] 96%|█████████▌| 17872/18688 [21:03:23<1:00:20, 4.44s/it] 96%|█████████▌| 17873/18688 [21:03:27<56:30, 4.16s/it] 96%|█████████▌| 17874/18688 [21:03:31<59:31, 4.39s/it] 96%|█████████▌| 17875/18688 [21:03:37<1:03:00, 4.65s/it] 96%|█████████▌| 17876/18688 [21:03:41<1:01:49, 4.57s/it] 96%|█████████▌| 17877/18688 [21:03:46<1:01:35, 4.56s/it] 96%|█████████▌| 17878/18688 [21:03:50<59:45, 4.43s/it] 96%|█████████▌| 17879/18688 [21:03:55<1:03:12, 4.69s/it] 96%|█████████▌| 17880/18688 [21:04:01<1:08:06, 5.06s/it] {'loss': '0.9495', 'grad_norm': '1.089', 'learning_rate': '5.456e-07', 'epoch': '0.9568', 'num_input_tokens_seen': 60161744, 'train_runtime': '7.584e+04', 'train_tokens_per_second': '793.3'}
96%|█████████▌| 17880/18688 [21:04:01<1:08:06, 5.06s/it] 96%|█████████▌| 17881/18688 [21:04:05<1:01:58, 4.61s/it] 96%|█████████▌| 17882/18688 [21:04:09<59:37, 4.44s/it] 96%|█████████▌| 17883/18688 [21:04:12<56:28, 4.21s/it] 96%|█████████▌| 17884/18688 [21:04:16<53:24, 3.99s/it] 96%|█████████▌| 17885/18688 [21:04:20<55:28, 4.15s/it] 96%|█████████▌| 17886/18688 [21:04:24<53:03, 3.97s/it] 96%|█████████▌| 17887/18688 [21:04:28<53:16, 3.99s/it] 96%|█████████▌| 17888/18688 [21:04:32<53:05, 3.98s/it] 96%|█████████▌| 17889/18688 [21:04:36<52:22, 3.93s/it] 96%|█████████▌| 17890/18688 [21:04:39<51:58, 3.91s/it] {'loss': '0.9176', 'grad_norm': '1.408', 'learning_rate': '5.322e-07', 'epoch': '0.9573', 'num_input_tokens_seen': 60191768, 'train_runtime': '7.588e+04', 'train_tokens_per_second': '793.2'}
96%|█████████▌| 17890/18688 [21:04:39<51:58, 3.91s/it] 96%|█████████▌| 17891/18688 [21:04:43<51:24, 3.87s/it] 96%|█████████▌| 17892/18688 [21:04:47<51:53, 3.91s/it] 96%|█████████▌| 17893/18688 [21:04:51<50:28, 3.81s/it] 96%|█████████▌| 17894/18688 [21:04:55<50:30, 3.82s/it] 96%|█████████▌| 17895/18688 [21:04:59<52:17, 3.96s/it] 96%|█████████▌| 17896/18688 [21:05:02<50:11, 3.80s/it] 96%|█████████▌| 17897/18688 [21:05:07<54:15, 4.12s/it] 96%|█████████▌| 17898/18688 [21:05:12<55:30, 4.22s/it] 96%|█████████▌| 17899/18688 [21:05:16<55:30, 4.22s/it] 96%|█████████▌| 17900/18688 [21:05:20<56:16, 4.28s/it] {'loss': '0.9258', 'grad_norm': '1.357', 'learning_rate': '5.19e-07', 'epoch': '0.9579', 'num_input_tokens_seen': 60225864, 'train_runtime': '7.592e+04', 'train_tokens_per_second': '793.3'}
96%|█████████▌| 17900/18688 [21:05:20<56:16, 4.28s/it] 96%|█████████▌| 17901/18688 [21:05:25<55:48, 4.25s/it] 96%|█████████▌| 17902/18688 [21:05:29<55:48, 4.26s/it] 96%|█████████▌| 17903/18688 [21:05:33<56:29, 4.32s/it] 96%|█████████▌| 17904/18688 [21:05:38<58:34, 4.48s/it] 96%|█████████▌| 17905/18688 [21:05:42<56:45, 4.35s/it] 96%|█████████▌| 17906/18688 [21:05:46<53:26, 4.10s/it] 96%|█████████▌| 17907/18688 [21:05:50<52:58, 4.07s/it] 96%|█████████▌| 17908/18688 [21:05:54<53:15, 4.10s/it] 96%|█████████▌| 17909/18688 [21:05:58<52:19, 4.03s/it] 96%|█████████▌| 17910/18688 [21:06:02<52:43, 4.07s/it] {'loss': '0.8804', 'grad_norm': '1.376', 'learning_rate': '5.06e-07', 'epoch': '0.9584', 'num_input_tokens_seen': 60261128, 'train_runtime': '7.596e+04', 'train_tokens_per_second': '793.3'}
96%|█████████▌| 17910/18688 [21:06:02<52:43, 4.07s/it] 96%|█████████▌| 17911/18688 [21:06:05<50:55, 3.93s/it] 96%|█████████▌| 17912/18688 [21:06:09<50:04, 3.87s/it] 96%|█████████▌| 17913/18688 [21:06:13<50:49, 3.93s/it] 96%|█████████▌| 17914/18688 [21:06:18<53:18, 4.13s/it] 96%|█████████▌| 17915/18688 [21:06:22<52:26, 4.07s/it] 96%|█████████▌| 17916/18688 [21:06:26<51:02, 3.97s/it] 96%|█████████▌| 17917/18688 [21:06:30<53:08, 4.14s/it] 96%|█████████▌| 17918/18688 [21:06:36<59:41, 4.65s/it] 96%|█████████▌| 17919/18688 [21:06:40<58:07, 4.54s/it] 96%|█████████▌| 17920/18688 [21:06:44<56:01, 4.38s/it] {'loss': '0.9245', 'grad_norm': '1.354', 'learning_rate': '4.931e-07', 'epoch': '0.9589', 'num_input_tokens_seen': 60296848, 'train_runtime': '7.6e+04', 'train_tokens_per_second': '793.3'}
96%|█████████▌| 17920/18688 [21:06:44<56:01, 4.38s/it] 96%|█████████▌| 17921/18688 [21:06:48<55:19, 4.33s/it] 96%|█████████▌| 17922/18688 [21:06:53<57:07, 4.47s/it] 96%|█████████▌| 17923/18688 [21:06:58<58:24, 4.58s/it] 96%|█████████▌| 17924/18688 [21:07:02<54:08, 4.25s/it] 96%|█████████▌| 17925/18688 [21:07:06<53:14, 4.19s/it] 96%|█████████▌| 17926/18688 [21:07:09<50:44, 4.00s/it] 96%|█████████▌| 17927/18688 [21:07:13<51:30, 4.06s/it] 96%|█████████▌| 17928/18688 [21:07:17<49:38, 3.92s/it] 96%|█████████▌| 17929/18688 [21:07:21<50:54, 4.02s/it] 96%|█████████▌| 17930/18688 [21:07:25<51:52, 4.11s/it] {'loss': '0.8886', 'grad_norm': '1.61', 'learning_rate': '4.804e-07', 'epoch': '0.9595', 'num_input_tokens_seen': 60330792, 'train_runtime': '7.605e+04', 'train_tokens_per_second': '793.3'}
96%|█████████▌| 17930/18688 [21:07:25<51:52, 4.11s/it] 96%|█████████▌| 17931/18688 [21:07:31<55:15, 4.38s/it] 96%|█████████▌| 17932/18688 [21:07:34<51:58, 4.13s/it] 96%|█████████▌| 17933/18688 [21:07:38<53:10, 4.23s/it] 96%|█████████▌| 17934/18688 [21:07:42<50:33, 4.02s/it] 96%|█████████▌| 17935/18688 [21:07:47<54:45, 4.36s/it] 96%|█████████▌| 17936/18688 [21:07:51<52:04, 4.16s/it] 96%|█████████▌| 17937/18688 [21:07:55<51:44, 4.13s/it] 96%|█████████▌| 17938/18688 [21:07:59<50:38, 4.05s/it] 96%|█████████▌| 17939/18688 [21:08:03<50:08, 4.02s/it] 96%|█████████▌| 17940/18688 [21:08:09<56:39, 4.55s/it] {'loss': '0.884', 'grad_norm': '1.225', 'learning_rate': '4.678e-07', 'epoch': '0.96', 'num_input_tokens_seen': 60367736, 'train_runtime': '7.609e+04', 'train_tokens_per_second': '793.4'}
96%|█████████▌| 17940/18688 [21:08:09<56:39, 4.55s/it] 96%|█████████▌| 17941/18688 [21:08:13<56:59, 4.58s/it] 96%|█████████▌| 17942/18688 [21:08:17<53:02, 4.27s/it] 96%|█████████▌| 17943/18688 [21:08:21<52:14, 4.21s/it] 96%|█████████▌| 17944/18688 [21:08:24<49:44, 4.01s/it] 96%|█████████▌| 17945/18688 [21:08:28<49:04, 3.96s/it] 96%|█████████▌| 17946/18688 [21:08:32<47:37, 3.85s/it] 96%|█████████▌| 17947/18688 [21:08:36<49:56, 4.04s/it] 96%|█████████▌| 17948/18688 [21:08:40<48:34, 3.94s/it] 96%|█████████▌| 17949/18688 [21:08:45<52:35, 4.27s/it] 96%|█████████▌| 17950/18688 [21:08:49<49:58, 4.06s/it] {'loss': '0.9129', 'grad_norm': '1.358', 'learning_rate': '4.554e-07', 'epoch': '0.9605', 'num_input_tokens_seen': 60399488, 'train_runtime': '7.613e+04', 'train_tokens_per_second': '793.4'}
96%|█████████▌| 17950/18688 [21:08:49<49:58, 4.06s/it] 96%|█████████▌| 17951/18688 [21:08:53<51:48, 4.22s/it] 96%|█████████▌| 17952/18688 [21:08:58<54:18, 4.43s/it] 96%|█████████▌| 17953/18688 [21:09:02<51:55, 4.24s/it] 96%|█████████▌| 17954/18688 [21:09:06<50:04, 4.09s/it] 96%|█████████▌| 17955/18688 [21:09:11<52:54, 4.33s/it] 96%|█████████▌| 17956/18688 [21:09:15<54:07, 4.44s/it] 96%|█████████▌| 17957/18688 [21:09:19<53:15, 4.37s/it] 96%|█████████▌| 17958/18688 [21:09:23<51:49, 4.26s/it] 96%|█████████▌| 17959/18688 [21:09:28<51:45, 4.26s/it] 96%|█████████▌| 17960/18688 [21:09:32<50:29, 4.16s/it] {'loss': '0.9036', 'grad_norm': '1.294', 'learning_rate': '4.432e-07', 'epoch': '0.9611', 'num_input_tokens_seen': 60435944, 'train_runtime': '7.617e+04', 'train_tokens_per_second': '793.4'}
96%|█████████▌| 17960/18688 [21:09:32<50:29, 4.16s/it] 96%|█████████▌| 17961/18688 [21:09:35<48:07, 3.97s/it] 96%|█████████▌| 17962/18688 [21:09:39<47:46, 3.95s/it] 96%|█████████▌| 17963/18688 [21:09:43<47:39, 3.94s/it] 96%|█████████▌| 17964/18688 [21:09:47<46:36, 3.86s/it] 96%|█████████▌| 17965/18688 [21:09:50<45:48, 3.80s/it] 96%|█████████▌| 17966/18688 [21:09:56<51:36, 4.29s/it] 96%|█████████▌| 17967/18688 [21:10:00<53:01, 4.41s/it] 96%|█████████▌| 17968/18688 [21:10:05<52:55, 4.41s/it] 96%|█████████▌| 17969/18688 [21:10:09<51:45, 4.32s/it] 96%|█████████▌| 17970/18688 [21:10:13<49:49, 4.16s/it] {'loss': '0.8967', 'grad_norm': '1.604', 'learning_rate': '4.311e-07', 'epoch': '0.9616', 'num_input_tokens_seen': 60469000, 'train_runtime': '7.621e+04', 'train_tokens_per_second': '793.4'}
96%|█████████▌| 17970/18688 [21:10:13<49:49, 4.16s/it] 96%|█████████▌| 17971/18688 [21:10:17<49:36, 4.15s/it] 96%|█████████▌| 17972/18688 [21:10:22<51:28, 4.31s/it] 96%|█████████▌| 17973/18688 [21:10:26<51:12, 4.30s/it] 96%|█████████▌| 17974/18688 [21:10:29<48:42, 4.09s/it] 96%|█████████▌| 17975/18688 [21:10:33<46:24, 3.91s/it] 96%|█████████▌| 17976/18688 [21:10:37<46:13, 3.90s/it] 96%|█████████▌| 17977/18688 [21:10:40<44:59, 3.80s/it] 96%|█████████▌| 17978/18688 [21:10:44<46:00, 3.89s/it] 96%|█████████▌| 17979/18688 [21:10:49<46:34, 3.94s/it] 96%|█████████▌| 17980/18688 [21:10:53<47:33, 4.03s/it] {'loss': '0.8872', 'grad_norm': '1.648', 'learning_rate': '4.193e-07', 'epoch': '0.9621', 'num_input_tokens_seen': 60501200, 'train_runtime': '7.625e+04', 'train_tokens_per_second': '793.4'}
96%|█████████▌| 17980/18688 [21:10:53<47:33, 4.03s/it] 96%|█████████▌| 17981/18688 [21:10:57<47:28, 4.03s/it] 96%|█████████▌| 17982/18688 [21:11:01<46:41, 3.97s/it] 96%|█████████▌| 17983/18688 [21:11:04<44:57, 3.83s/it] 96%|█████████▌| 17984/18688 [21:11:08<44:41, 3.81s/it] 96%|█████████▌| 17985/18688 [21:11:12<44:08, 3.77s/it] 96%|█████████▌| 17986/18688 [21:11:15<43:10, 3.69s/it] 96%|█████████▌| 17987/18688 [21:11:20<46:51, 4.01s/it] 96%|█████████▋| 17988/18688 [21:11:25<49:45, 4.27s/it] 96%|█████████▋| 17989/18688 [21:11:29<49:32, 4.25s/it] 96%|█████████▋| 17990/18688 [21:11:33<49:31, 4.26s/it] {'loss': '0.9338', 'grad_norm': '1.607', 'learning_rate': '4.075e-07', 'epoch': '0.9627', 'num_input_tokens_seen': 60533800, 'train_runtime': '7.629e+04', 'train_tokens_per_second': '793.4'}
96%|█████████▋| 17990/18688 [21:11:33<49:31, 4.26s/it] 96%|█████████▋| 17991/18688 [21:11:37<46:59, 4.05s/it] 96%|█████████▋| 17992/18688 [21:11:41<46:46, 4.03s/it] 96%|█████████▋| 17993/18688 [21:11:45<47:10, 4.07s/it] 96%|█████████▋| 17994/18688 [21:11:48<45:21, 3.92s/it] 96%|█████████▋| 17995/18688 [21:11:52<45:17, 3.92s/it] 96%|█████████▋| 17996/18688 [21:11:58<52:03, 4.51s/it] 96%|█████████▋| 17997/18688 [21:12:02<49:42, 4.32s/it] 96%|█████████▋| 17998/18688 [21:12:08<53:54, 4.69s/it] 96%|█████████▋| 17999/18688 [21:12:12<53:42, 4.68s/it] 96%|█████████▋| 18000/18688 [21:12:16<49:36, 4.33s/it] {'loss': '0.8786', 'grad_norm': '1.432', 'learning_rate': '3.96e-07', 'epoch': '0.9632', 'num_input_tokens_seen': 60569592, 'train_runtime': '7.634e+04', 'train_tokens_per_second': '793.5'}
96%|█████████▋| 18000/18688 [21:12:16<49:36, 4.33s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
0%| | 3/1000 [00:00<00:48, 20.51it/s]
1%| | 6/1000 [00:00<00:58, 16.93it/s]
1%| | 8/1000 [00:00<00:59, 16.73it/s]
1%| | 10/1000 [00:00<01:01, 16.14it/s]
1%| | 12/1000 [00:00<01:01, 16.09it/s]
1%|▏ | 14/1000 [00:00<01:02, 15.76it/s]
2%|▏ | 16/1000 [00:00<01:02, 15.63it/s]
2%|▏ | 18/1000 [00:01<01:38, 9.98it/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, 9.77it/s]
3%|▎ | 26/1000 [00:02<01:44, 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, 10.16it/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<01:25, 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, 10.51it/s]
5%|▍ | 46/1000 [00:04<01:21, 11.71it/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, 12.52it/s]
5%|▌ | 54/1000 [00:04<01:11, 13.26it/s]
6%|▌ | 56/1000 [00:04<01:11, 13.28it/s]
6%|▌ | 58/1000 [00:05<02:00, 7.81it/s]
6%|▌ | 60/1000 [00:05<01:44, 8.97it/s]
6%|▌ | 62/1000 [00:05<01:33, 10.04it/s]
6%|▋ | 64/1000 [00:05<01:23, 11.18it/s]
7%|▋ | 66/1000 [00:05<01:20, 11.55it/s]
7%|▋ | 68/1000 [00:06<01:17, 12.09it/s]
7%|▋ | 70/1000 [00:06<01:14, 12.40it/s]
7%|▋ | 72/1000 [00:06<01:10, 13.08it/s]
7%|▋ | 74/1000 [00:06<01:10, 13.11it/s]
8%|▊ | 76/1000 [00:06<01:06, 13.83it/s]
8%|▊ | 78/1000 [00:06<01:07, 13.71it/s]
8%|▊ | 80/1000 [00:06<01:10, 12.99it/s]
8%|▊ | 82/1000 [00:07<01:06, 13.73it/s]
8%|▊ | 84/1000 [00:07<01:04, 14.27it/s]
9%|▊ | 86/1000 [00:07<01:03, 14.47it/s]
9%|▉ | 88/1000 [00:07<01:03, 14.35it/s]
9%|▉ | 90/1000 [00:07<01:03, 14.41it/s]
9%|▉ | 92/1000 [00:07<01:04, 14.08it/s]
9%|▉ | 94/1000 [00:07<01:05, 13.91it/s]
10%|▉ | 96/1000 [00:07<01:03, 14.29it/s]
10%|▉ | 98/1000 [00:08<01:04, 13.93it/s]
10%|█ | 100/1000 [00:08<01:38, 9.16it/s]
10%|█ | 102/1000 [00:08<01:31, 9.80it/s]
10%|█ | 104/1000 [00:09<02:00, 7.41it/s]
10%|█ | 105/1000 [00:09<02:09, 6.90it/s]
11%|█ | 106/1000 [00:09<02:05, 7.11it/s]
11%|█ | 108/1000 [00:09<01:44, 8.54it/s]
11%|█ | 110/1000 [00:09<01:26, 10.25it/s]
11%|█ | 112/1000 [00:09<01:17, 11.49it/s]
11%|█▏ | 114/1000 [00:09<01:13, 12.13it/s]
12%|█▏ | 116/1000 [00:10<01:09, 12.73it/s]
12%|█▏ | 118/1000 [00:10<01:05, 13.50it/s]
12%|█▏ | 120/1000 [00:10<01:08, 12.85it/s]
12%|█▏ | 122/1000 [00:10<01:22, 10.60it/s]
12%|█▏ | 124/1000 [00:10<01:30, 9.66it/s]
13%|█▎ | 126/1000 [00:11<01:22, 10.63it/s]
13%|█▎ | 128/1000 [00:11<01:15, 11.55it/s]
13%|█▎ | 130/1000 [00:11<01:11, 12.23it/s]
13%|█▎ | 132/1000 [00:11<01:05, 13.19it/s]
13%|█▎ | 134/1000 [00:11<01:10, 12.37it/s]
14%|█▎ | 136/1000 [00:11<01:06, 13.09it/s]
14%|█▍ | 138/1000 [00:11<01:06, 13.06it/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<01:20, 10.56it/s]
15%|█▍ | 148/1000 [00:13<01:14, 11.49it/s]
15%|█▌ | 150/1000 [00:13<01:07, 12.53it/s]
15%|█▌ | 152/1000 [00:13<01:04, 13.09it/s]
15%|█▌ | 154/1000 [00:13<01:03, 13.36it/s]
16%|█▌ | 156/1000 [00:13<01:03, 13.22it/s]
16%|█▌ | 158/1000 [00:13<01:01, 13.74it/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, 13.62it/s]
17%|█▋ | 166/1000 [00:14<01:00, 13.78it/s]
17%|█▋ | 168/1000 [00:14<00:58, 14.32it/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<01:29, 9.19it/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<01:33, 8.78it/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<01:24, 9.65it/s]
19%|█▉ | 190/1000 [00:16<01:15, 10.68it/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, 8.97it/s]
20%|█▉ | 198/1000 [00:17<01:18, 10.17it/s]
20%|██ | 200/1000 [00:17<01:16, 10.52it/s]
20%|██ | 202/1000 [00:18<01:09, 11.48it/s]
20%|██ | 204/1000 [00:18<01:03, 12.61it/s]
21%|██ | 206/1000 [00:18<01:00, 13.20it/s]
21%|██ | 208/1000 [00:18<00:59, 13.25it/s]
21%|██ | 210/1000 [00:18<00:56, 13.93it/s]
21%|██ | 212/1000 [00:18<00:55, 14.12it/s]
21%|██▏ | 214/1000 [00:18<00:58, 13.54it/s]
22%|██▏ | 216/1000 [00:19<00:57, 13.58it/s]
22%|██▏ | 218/1000 [00:19<00:55, 14.10it/s]
22%|██▏ | 220/1000 [00:19<01:30, 8.66it/s]
22%|██▏ | 222/1000 [00:19<01:26, 8.95it/s]
22%|██▏ | 224/1000 [00:19<01:22, 9.46it/s]
23%|██▎ | 226/1000 [00:20<01:13, 10.48it/s]
23%|██▎ | 228/1000 [00:20<01:07, 11.42it/s]
23%|██▎ | 230/1000 [00:20<01:04, 11.94it/s]
23%|██▎ | 232/1000 [00:20<01:02, 12.34it/s]
23%|██▎ | 234/1000 [00:20<00:58, 13.10it/s]
24%|██▎ | 236/1000 [00:20<00:56, 13.61it/s]
24%|██▍ | 238/1000 [00:20<00:53, 14.27it/s]
24%|██▍ | 240/1000 [00:21<00:53, 14.25it/s]
24%|██▍ | 242/1000 [00:21<00:51, 14.75it/s]
24%|██▍ | 244/1000 [00:21<00:50, 14.95it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.91it/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, 12.15it/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, 13.79it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.91it/s]
26%|██▌ | 262/1000 [00:22<00:56, 13.04it/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, 14.51it/s]
27%|██▋ | 270/1000 [00:23<00:55, 13.26it/s]
27%|██▋ | 272/1000 [00:23<01:03, 11.52it/s]
27%|██▋ | 274/1000 [00:23<01:02, 11.61it/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, 13.50it/s]
28%|██▊ | 282/1000 [00:24<00:49, 14.50it/s]
28%|██▊ | 284/1000 [00:24<00:49, 14.45it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.75it/s]
29%|██▉ | 288/1000 [00:24<00:47, 15.09it/s]
29%|██▉ | 290/1000 [00:24<00:45, 15.51it/s]
29%|██▉ | 292/1000 [00:24<00:45, 15.48it/s]
29%|██▉ | 294/1000 [00:25<00:45, 15.36it/s]
30%|██▉ | 296/1000 [00:25<00:45, 15.41it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.54it/s]
30%|███ | 300/1000 [00:25<00:46, 15.19it/s]
30%|███ | 302/1000 [00:25<00:45, 15.18it/s]
30%|███ | 304/1000 [00:25<00:50, 13.78it/s]
31%|███ | 306/1000 [00:25<00:47, 14.54it/s]
31%|███ | 308/1000 [00:25<00:47, 14.50it/s]
31%|███ | 310/1000 [00:26<00:46, 14.70it/s]
31%|███ | 312/1000 [00:26<00:46, 14.68it/s]
31%|███▏ | 314/1000 [00:26<00:44, 15.39it/s]
32%|███▏ | 316/1000 [00:26<00:43, 15.59it/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, 11.51it/s]
33%|███▎ | 326/1000 [00:27<00:58, 11.47it/s]
33%|███▎ | 328/1000 [00:27<00:58, 11.49it/s]
33%|███▎ | 330/1000 [00:27<00:54, 12.38it/s]
33%|███▎ | 332/1000 [00:27<00:52, 12.67it/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, 10.08it/s]
34%|███▍ | 340/1000 [00:28<00:59, 11.14it/s]
34%|███▍ | 342/1000 [00:28<00:53, 12.39it/s]
34%|███▍ | 344/1000 [00:28<00:50, 13.04it/s]
35%|███▍ | 346/1000 [00:29<00:48, 13.59it/s]
35%|███▍ | 348/1000 [00:29<00:46, 13.99it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.64it/s]
35%|███▌ | 352/1000 [00:29<00:43, 14.85it/s]
35%|███▌ | 354/1000 [00:29<00:43, 14.90it/s]
36%|███▌ | 356/1000 [00:29<00:42, 14.99it/s]
36%|███▌ | 358/1000 [00:29<00:43, 14.70it/s]
36%|███▌ | 360/1000 [00:29<00:42, 15.07it/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 [00:30<00:47, 13.23it/s]
37%|███▋ | 372/1000 [00:30<00:46, 13.56it/s]
37%|███▋ | 374/1000 [00:31<00:54, 11.40it/s]
38%|███▊ | 376/1000 [00:31<00:56, 10.99it/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<00:46, 13.37it/s]
39%|███▊ | 386/1000 [00:32<00:44, 13.68it/s]
39%|███▉ | 388/1000 [00:32<00:44, 13.64it/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, 13.67it/s]
40%|███▉ | 396/1000 [00:32<00:43, 13.77it/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, 13.85it/s]
40%|████ | 404/1000 [00:33<00:41, 14.46it/s]
41%|████ | 406/1000 [00:33<00:39, 14.97it/s]
41%|████ | 408/1000 [00:33<00:47, 12.55it/s]
41%|████ | 410/1000 [00:33<00:56, 10.49it/s]
41%|████ | 412/1000 [00:34<00:50, 11.57it/s]
41%|████▏ | 414/1000 [00:34<01:09, 8.48it/s]
42%|████▏ | 416/1000 [00:34<01:30, 6.44it/s]
42%|████▏ | 418/1000 [00:35<01:15, 7.75it/s]
42%|████▏ | 420/1000 [00:35<01:04, 9.01it/s]
42%|████▏ | 422/1000 [00:35<00:55, 10.36it/s]
42%|████▏ | 424/1000 [00:35<01:17, 7.45it/s]
43%|████▎ | 426/1000 [00:35<01:05, 8.71it/s]
43%|████▎ | 428/1000 [00:36<00:57, 9.95it/s]
43%|████▎ | 430/1000 [00:36<01:03, 9.01it/s]
43%|████▎ | 432/1000 [00:36<01:21, 7.01it/s]
43%|████▎ | 434/1000 [00:36<01:08, 8.23it/s]
44%|████▎ | 436/1000 [00:37<01:00, 9.25it/s]
44%|████▍ | 438/1000 [00:37<01:02, 8.95it/s]
44%|████▍ | 440/1000 [00:37<00:59, 9.45it/s]
44%|████▍ | 442/1000 [00:37<00:53, 10.50it/s]
44%|████▍ | 444/1000 [00:37<00:49, 11.23it/s]
45%|████▍ | 446/1000 [00:37<00:47, 11.58it/s]
45%|████▍ | 448/1000 [00:38<00:51, 10.64it/s]
45%|████▌ | 450/1000 [00:38<00:47, 11.54it/s]
45%|████▌ | 452/1000 [00:38<00:43, 12.62it/s]
45%|████▌ | 454/1000 [00:38<00:44, 12.16it/s]
46%|████▌ | 456/1000 [00:38<00:43, 12.63it/s]
46%|████▌ | 458/1000 [00:38<00:39, 13.63it/s]
46%|████▌ | 460/1000 [00:39<00:39, 13.79it/s]
46%|████▌ | 462/1000 [00:39<00:37, 14.37it/s]
46%|████▋ | 464/1000 [00:39<00:35, 14.91it/s]
47%|████▋ | 466/1000 [00:39<00:36, 14.80it/s]
47%|████▋ | 468/1000 [00:39<00:41, 12.88it/s]
47%|████▋ | 470/1000 [00:39<00:52, 10.05it/s]
47%|████▋ | 472/1000 [00:40<00:51, 10.23it/s]
47%|████▋ | 474/1000 [00:40<00:45, 11.51it/s]
48%|████▊ | 476/1000 [00:40<00:43, 12.17it/s]
48%|████▊ | 478/1000 [00:40<00:43, 12.04it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.97it/s]
48%|████▊ | 482/1000 [00:40<00:38, 13.36it/s]
48%|████▊ | 484/1000 [00:40<00:37, 13.86it/s]
49%|████▊ | 486/1000 [00:41<00:36, 14.02it/s]
49%|████▉ | 488/1000 [00:41<00:35, 14.31it/s]
49%|████▉ | 490/1000 [00:41<00:45, 11.15it/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, 14.23it/s]
50%|████▉ | 498/1000 [00:41<00:34, 14.63it/s]
50%|█████ | 500/1000 [00:42<00:43, 11.61it/s]
50%|█████ | 502/1000 [00:42<00:41, 12.04it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.62it/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, 10.41it/s]
51%|█████ | 512/1000 [00:43<00:43, 11.16it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 12.03it/s]
52%|█████▏ | 516/1000 [00:43<00:38, 12.58it/s]
52%|█████▏ | 518/1000 [00:43<00:38, 12.66it/s]
52%|█████▏ | 520/1000 [00:43<00:37, 12.85it/s]
52%|█████▏ | 522/1000 [00:43<00:35, 13.44it/s]
52%|█████▏ | 524/1000 [00:44<00:37, 12.83it/s]
53%|█████▎ | 526/1000 [00:44<00:35, 13.27it/s]
53%|█████▎ | 528/1000 [00:44<00:41, 11.44it/s]
53%|█████▎ | 530/1000 [00:44<00:39, 11.92it/s]
53%|█████▎ | 532/1000 [00:44<00:36, 12.84it/s]
53%|█████▎ | 534/1000 [00:44<00:34, 13.43it/s]
54%|█████▎ | 536/1000 [00:45<00:33, 13.74it/s]
54%|█████▍ | 538/1000 [00:45<00:33, 13.67it/s]
54%|█████▍ | 540/1000 [00:45<00:32, 14.20it/s]
54%|█████▍ | 542/1000 [00:45<00:32, 13.97it/s]
54%|█████▍ | 544/1000 [00:45<00:32, 14.07it/s]
55%|█████▍ | 546/1000 [00:45<00:32, 13.89it/s]
55%|█████▍ | 548/1000 [00:45<00:33, 13.69it/s]
55%|█████▌ | 550/1000 [00:46<00:32, 13.99it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.18it/s]
55%|█████▌ | 554/1000 [00:46<00:32, 13.88it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.05it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.44it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 14.96it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 14.91it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.78it/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<00:34, 12.35it/s]
57%|█████▋ | 572/1000 [00:47<00:35, 12.15it/s]
57%|█████▋ | 574/1000 [00:47<00:32, 13.14it/s]
58%|█████▊ | 576/1000 [00:48<00:32, 13.24it/s]
58%|█████▊ | 578/1000 [00:48<00:36, 11.44it/s]
58%|█████▊ | 580/1000 [00:48<00:33, 12.68it/s]
58%|█████▊ | 582/1000 [00:48<00:31, 13.43it/s]
58%|█████▊ | 584/1000 [00:48<00:30, 13.70it/s]
59%|█████▊ | 586/1000 [00:48<00:29, 14.13it/s]
59%|█████▉ | 588/1000 [00:48<00:27, 14.97it/s]
59%|█████▉ | 590/1000 [00:49<00:27, 14.94it/s]
59%|█████▉ | 592/1000 [00:49<00:28, 14.28it/s]
59%|█████▉ | 594/1000 [00:49<00:31, 13.04it/s]
60%|█████▉ | 596/1000 [00:49<00:29, 13.64it/s]
60%|█████▉ | 598/1000 [00:49<00:28, 13.95it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.36it/s]
60%|██████ | 602/1000 [00:49<00:27, 14.53it/s]
60%|██████ | 604/1000 [00:50<00:26, 14.98it/s]
61%|██████ | 606/1000 [00:50<00:27, 14.21it/s]
61%|██████ | 608/1000 [00:50<00:26, 14.70it/s]
61%|██████ | 610/1000 [00:50<00:26, 14.70it/s]
61%|██████ | 612/1000 [00:50<00:25, 14.96it/s]
61%|██████▏ | 614/1000 [00:50<00:27, 14.28it/s]
62%|██████▏ | 616/1000 [00:50<00:26, 14.72it/s]
62%|██████▏ | 618/1000 [00:51<00:27, 13.97it/s]
62%|██████▏ | 620/1000 [00:51<00:39, 9.69it/s]
62%|██████▏ | 622/1000 [00:51<00:38, 9.92it/s]
62%|██████▏ | 624/1000 [00:51<00:33, 11.37it/s]
63%|██████▎ | 626/1000 [00:51<00:31, 12.06it/s]
63%|██████▎ | 628/1000 [00:51<00:27, 13.32it/s]
63%|██████▎ | 630/1000 [00:52<00:26, 13.81it/s]
63%|██████▎ | 632/1000 [00:52<00:29, 12.47it/s]
63%|██████▎ | 634/1000 [00:52<00:27, 13.20it/s]
64%|██████▎ | 636/1000 [00:52<00:33, 10.96it/s]
64%|██████▍ | 638/1000 [00:52<00:33, 10.67it/s]
64%|██████▍ | 640/1000 [00:53<00:31, 11.34it/s]
64%|██████▍ | 642/1000 [00:53<00:30, 11.73it/s]
64%|██████▍ | 644/1000 [00:53<00:29, 12.15it/s]
65%|██████▍ | 646/1000 [00:53<00:28, 12.54it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.10it/s]
65%|██████▌ | 650/1000 [00:53<00:28, 12.41it/s]
65%|██████▌ | 652/1000 [00:53<00:29, 11.78it/s]
65%|██████▌ | 654/1000 [00:54<00:36, 9.36it/s]
66%|██████▌ | 656/1000 [00:54<00:32, 10.72it/s]
66%|██████▌ | 658/1000 [00:54<00:28, 11.81it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.15it/s]
66%|██████▌ | 662/1000 [00:54<00:30, 10.94it/s]
66%|██████▋ | 664/1000 [00:55<00:29, 11.30it/s]
67%|██████▋ | 666/1000 [00:55<00:27, 12.24it/s]
67%|██████▋ | 668/1000 [00:55<00:31, 10.45it/s]
67%|██████▋ | 670/1000 [00:55<00:31, 10.48it/s]
67%|██████▋ | 672/1000 [00:55<00:28, 11.35it/s]
67%|██████▋ | 674/1000 [00:55<00:27, 11.67it/s]
68%|██████▊ | 676/1000 [00:56<00:28, 11.23it/s]
68%|██████▊ | 678/1000 [00:56<00:27, 11.78it/s]
68%|██████▊ | 680/1000 [00:56<00:25, 12.62it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 11.92it/s]
68%|██████▊ | 684/1000 [00:56<00:26, 12.15it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.22it/s]
69%|██████▉ | 688/1000 [00:57<00:22, 13.58it/s]
69%|██████▉ | 690/1000 [00:57<00:22, 13.69it/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, 14.56it/s]
70%|███████ | 700/1000 [00:57<00:24, 12.15it/s]
70%|███████ | 702/1000 [00:58<00:22, 12.97it/s]
70%|███████ | 704/1000 [00:58<00:30, 9.85it/s]
71%|███████ | 706/1000 [00:58<00:27, 10.59it/s]
71%|███████ | 708/1000 [00:58<00:30, 9.57it/s]
71%|███████ | 710/1000 [00:58<00:26, 10.88it/s]
71%|███████ | 712/1000 [00:59<00:24, 11.79it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.90it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 13.01it/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, 13.94it/s]
72%|███████▏ | 724/1000 [00:59<00:20, 13.75it/s]
73%|███████▎ | 726/1000 [01:00<00:19, 13.97it/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 [01:01<00:30, 8.61it/s]
74%|███████▎ | 735/1000 [01:01<00:31, 8.46it/s]
74%|███████▎ | 737/1000 [01:01<00:26, 9.91it/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, 12.31it/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, 10.76it/s]
75%|███████▌ | 751/1000 [01:02<00:20, 11.88it/s]
75%|███████▌ | 753/1000 [01:02<00:19, 12.65it/s]
76%|███████▌ | 755/1000 [01:02<00:18, 13.37it/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, 12.10it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.17it/s]
76%|███████▋ | 765/1000 [01:04<00:27, 8.56it/s]
77%|███████▋ | 767/1000 [01:04<00:24, 9.52it/s]
77%|███████▋ | 769/1000 [01:04<00:21, 10.54it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.80it/s]
77%|███████▋ | 773/1000 [01:04<00:17, 12.62it/s]
78%|███████▊ | 775/1000 [01:04<00:17, 13.16it/s]
78%|███████▊ | 777/1000 [01:04<00:15, 14.16it/s]
78%|███████▊ | 779/1000 [01:05<00:21, 10.20it/s]
78%|███████▊ | 781/1000 [01:05<00:24, 8.92it/s]
78%|███████▊ | 783/1000 [01:05<00:27, 7.79it/s]
78%|███████▊ | 784/1000 [01:06<00:32, 6.73it/s]
78%|███████▊ | 785/1000 [01:06<00:30, 7.07it/s]
79%|███████▊ | 786/1000 [01:06<00:31, 6.75it/s]
79%|███████▉ | 788/1000 [01:06<00:24, 8.59it/s]
79%|███████▉ | 790/1000 [01:06<00:21, 9.95it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 11.14it/s]
79%|███████▉ | 794/1000 [01:06<00:17, 11.81it/s]
80%|███████▉ | 796/1000 [01:07<00:16, 12.27it/s]
80%|███████▉ | 798/1000 [01:07<00:15, 13.04it/s]
80%|████████ | 800/1000 [01:07<00:16, 12.25it/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, 10.32it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.46it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.68it/s]
81%|████████▏ | 814/1000 [01:08<00:14, 12.57it/s]
82%|████████▏ | 816/1000 [01:09<00:16, 11.00it/s]
82%|████████▏ | 818/1000 [01:09<00:15, 11.93it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.42it/s]
82%|████████▏ | 822/1000 [01:09<00:14, 12.57it/s]
82%|████████▏ | 824/1000 [01:09<00:13, 12.73it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 13.40it/s]
83%|████████▎ | 828/1000 [01:09<00:12, 13.88it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.54it/s]
83%|████████▎ | 832/1000 [01:10<00:12, 14.00it/s]
83%|████████▎ | 834/1000 [01:10<00:12, 13.83it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.25it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.14it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.32it/s]
84%|████████▍ | 842/1000 [01:10<00:10, 14.37it/s]
84%|████████▍ | 844/1000 [01:11<00:11, 13.94it/s]
85%|████████▍ | 846/1000 [01:11<00:11, 13.90it/s]
85%|████████▍ | 848/1000 [01:11<00:10, 13.91it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.13it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.68it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.27it/s]
86%|████████▌ | 856/1000 [01:11<00:10, 14.11it/s]
86%|████████▌ | 858/1000 [01:11<00:09, 14.63it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 15.39it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.53it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 14.07it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.85it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.73it/s]
87%|████████▋ | 870/1000 [01:12<00:09, 14.11it/s]
87%|████████▋ | 872/1000 [01:12<00:08, 14.74it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 12.42it/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, 12.56it/s]
88%|████████▊ | 882/1000 [01:13<00:10, 11.38it/s]
88%|████████▊ | 884/1000 [01:14<00:09, 12.09it/s]
89%|████████▊ | 886/1000 [01:14<00:08, 12.82it/s]
89%|████████▉ | 888/1000 [01:14<00:08, 13.76it/s]
89%|████████▉ | 890/1000 [01:14<00:07, 14.20it/s]
89%|████████▉ | 892/1000 [01:14<00:07, 14.00it/s]
89%|████████▉ | 894/1000 [01:14<00:07, 14.15it/s]
90%|████████▉ | 896/1000 [01:14<00:07, 13.91it/s]
90%|████████▉ | 898/1000 [01:15<00:07, 13.33it/s]
90%|█████████ | 900/1000 [01:15<00:08, 12.21it/s]
90%|█████████ | 902/1000 [01:15<00:07, 12.96it/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, 11.85it/s]
91%|█████████ | 910/1000 [01:16<00:07, 12.10it/s]
91%|█████████ | 912/1000 [01:16<00:07, 12.29it/s]
91%|█████████▏| 914/1000 [01:16<00:12, 6.81it/s]
92%|█████████▏| 916/1000 [01:17<00:10, 8.14it/s]
92%|█████████▏| 918/1000 [01:17<00:11, 7.40it/s]
92%|█████████▏| 920/1000 [01:17<00:09, 8.81it/s]
92%|█████████▏| 922/1000 [01:17<00:07, 10.25it/s]
92%|█████████▏| 924/1000 [01:17<00:06, 10.94it/s]
93%|█████████▎| 926/1000 [01:17<00:06, 11.79it/s]
93%|█████████▎| 928/1000 [01:18<00:06, 10.61it/s]
93%|█████████▎| 930/1000 [01:18<00:06, 11.56it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 12.07it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.16it/s]
94%|█████████▎| 936/1000 [01:18<00:05, 12.58it/s]
94%|█████████▍| 938/1000 [01:18<00:04, 13.56it/s]
94%|█████████▍| 940/1000 [01:18<00:04, 13.99it/s]
94%|█████████▍| 942/1000 [01:19<00:04, 11.91it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.29it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.52it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 12.40it/s]
95%|█████████▌| 950/1000 [01:19<00:03, 13.25it/s]
95%|█████████▌| 952/1000 [01:19<00:03, 14.01it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 14.15it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 14.16it/s]
96%|█████████▌| 958/1000 [01:20<00:02, 14.69it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 13.07it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.71it/s]
96%|█████████▋| 964/1000 [01:20<00:03, 10.21it/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, 12.72it/s]
97%|█████████▋| 972/1000 [01:21<00:02, 9.69it/s]
97%|█████████▋| 974/1000 [01:21<00:02, 10.71it/s]
98%|█████████▊| 976/1000 [01:21<00:02, 11.36it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.74it/s]
98%|█████████▊| 980/1000 [01:22<00:02, 9.86it/s]
98%|█████████▊| 982/1000 [01:22<00:01, 10.37it/s]
98%|█████████▊| 984/1000 [01:22<00:01, 10.40it/s]
99%|█████████▊| 986/1000 [01:22<00:01, 10.65it/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, 13.97it/s]
100%|█████████▉| 998/1000 [01:23<00:00, 14.14it/s]
100%|██████████| 1000/1000 [01:23<00:00, 14.80it/s]
{'eval_loss': '0.8727', 'eval_runtime': '84.02', 'eval_samples_per_second': '11.9', 'eval_steps_per_second': '11.9', 'epoch': '0.9632', 'num_input_tokens_seen': 60569592}
96%|█████████▋| 18000/18688 [21:13:40<49:36, 4.33s/it]
100%|██████████| 1000/1000 [01:23<00:00, 14.80it/s]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00010/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 [21:13:45<5:41:30, 29.83s/it] 96%|█████████▋| 18002/18688 [21:13:49<4:11:22, 21.99s/it] 96%|█████████▋| 18003/18688 [21:13:53<3:08:56, 16.55s/it] 96%|█████████▋| 18004/18688 [21:13:56<2:24:23, 12.67s/it] 96%|█████████▋| 18005/18688 [21:14:00<1:53:40, 9.99s/it] 96%|█████████▋| 18006/18688 [21:14:04<1:32:19, 8.12s/it] 96%|█████████▋| 18007/18688 [21:14:09<1:21:16, 7.16s/it] 96%|█████████▋| 18008/18688 [21:14:13<1:09:59, 6.18s/it] 96%|█████████▋| 18009/18688 [21:14:16<1:01:19, 5.42s/it] 96%|█████████▋| 18010/18688 [21:14:21<57:31, 5.09s/it] {'loss': '0.8977', 'grad_norm': '1.484', 'learning_rate': '3.846e-07', 'epoch': '0.9637', 'num_input_tokens_seen': 60601344, 'train_runtime': '7.646e+04', 'train_tokens_per_second': '792.6'}
96%|█████████▋| 18010/18688 [21:14:21<57:31, 5.09s/it] 96%|█████████▋| 18011/18688 [21:14:25<53:57, 4.78s/it] 96%|█████████▋| 18012/18688 [21:14:29<51:39, 4.59s/it] 96%|█████████▋| 18013/18688 [21:14:33<51:19, 4.56s/it] 96%|█████████▋| 18014/18688 [21:14:37<49:14, 4.38s/it] 96%|█████████▋| 18015/18688 [21:14:42<48:48, 4.35s/it] 96%|█████████▋| 18016/18688 [21:14:45<46:04, 4.11s/it] 96%|█████████▋| 18017/18688 [21:14:49<44:49, 4.01s/it] 96%|█████████▋| 18018/18688 [21:14:54<47:52, 4.29s/it] 96%|█████████▋| 18019/18688 [21:14:58<47:07, 4.23s/it] 96%|█████████▋| 18020/18688 [21:15:02<46:25, 4.17s/it] {'loss': '0.883', 'grad_norm': '1.248', 'learning_rate': '3.733e-07', 'epoch': '0.9643', 'num_input_tokens_seen': 60636728, 'train_runtime': '7.65e+04', 'train_tokens_per_second': '792.6'}
96%|█████████▋| 18020/18688 [21:15:02<46:25, 4.17s/it] 96%|█████████▋| 18021/18688 [21:15:06<47:01, 4.23s/it] 96%|█████████▋| 18022/18688 [21:15:11<47:57, 4.32s/it] 96%|█████████▋| 18023/18688 [21:15:16<51:37, 4.66s/it] 96%|█████████▋| 18024/18688 [21:15:21<50:56, 4.60s/it] 96%|█████████▋| 18025/18688 [21:15:25<51:13, 4.64s/it] 96%|█████████▋| 18026/18688 [21:15:29<48:18, 4.38s/it] 96%|█████████▋| 18027/18688 [21:15:33<45:55, 4.17s/it] 96%|█████████▋| 18028/18688 [21:15:37<45:41, 4.15s/it] 96%|█████████▋| 18029/18688 [21:15:41<43:51, 3.99s/it] 96%|█████████▋| 18030/18688 [21:15:45<44:21, 4.05s/it] {'loss': '0.9', 'grad_norm': '1.424', 'learning_rate': '3.623e-07', 'epoch': '0.9648', 'num_input_tokens_seen': 60673048, 'train_runtime': '7.655e+04', 'train_tokens_per_second': '792.6'}
96%|█████████▋| 18030/18688 [21:15:45<44:21, 4.05s/it] 96%|█████████▋| 18031/18688 [21:15:48<42:42, 3.90s/it] 96%|█████████▋| 18032/18688 [21:15:52<43:00, 3.93s/it] 96%|█████████▋| 18033/18688 [21:15:57<44:02, 4.03s/it] 97%|█████████▋| 18034/18688 [21:16:00<42:47, 3.93s/it] 97%|█████████▋| 18035/18688 [21:16:04<42:18, 3.89s/it] 97%|█████████▋| 18036/18688 [21:16:08<42:12, 3.88s/it] 97%|█████████▋| 18037/18688 [21:16:12<41:39, 3.84s/it] 97%|█████████▋| 18038/18688 [21:16:16<41:24, 3.82s/it] 97%|█████████▋| 18039/18688 [21:16:20<45:01, 4.16s/it] 97%|█████████▋| 18040/18688 [21:16:25<44:30, 4.12s/it] {'loss': '0.9001', 'grad_norm': '1.288', 'learning_rate': '3.514e-07', 'epoch': '0.9654', 'num_input_tokens_seen': 60705056, 'train_runtime': '7.659e+04', 'train_tokens_per_second': '792.6'}
97%|█████████▋| 18040/18688 [21:16:25<44:30, 4.12s/it] 97%|█████████▋| 18041/18688 [21:16:28<42:19, 3.93s/it] 97%|█████████▋| 18042/18688 [21:16:32<44:09, 4.10s/it] 97%|█████████▋| 18043/18688 [21:16:37<44:32, 4.14s/it] 97%|█████████▋| 18044/18688 [21:16:41<43:57, 4.10s/it] 97%|█████████▋| 18045/18688 [21:16:44<41:43, 3.89s/it] 97%|█████████▋| 18046/18688 [21:16:48<42:31, 3.97s/it] 97%|█████████▋| 18047/18688 [21:16:52<42:23, 3.97s/it] 97%|█████████▋| 18048/18688 [21:16:56<41:22, 3.88s/it] 97%|█████████▋| 18049/18688 [21:17:00<41:17, 3.88s/it] 97%|█████████▋| 18050/18688 [21:17:04<42:34, 4.00s/it] {'loss': '0.9519', 'grad_norm': '2.079', 'learning_rate': '3.406e-07', 'epoch': '0.9659', 'num_input_tokens_seen': 60736776, 'train_runtime': '7.662e+04', 'train_tokens_per_second': '792.7'}
97%|█████████▋| 18050/18688 [21:17:04<42:34, 4.00s/it] 97%|█████████▋| 18051/18688 [21:17:09<43:57, 4.14s/it] 97%|█████████▋| 18052/18688 [21:17:12<41:46, 3.94s/it] 97%|█████████▋| 18053/18688 [21:17:18<48:14, 4.56s/it] 97%|█████████▋| 18054/18688 [21:17:22<44:46, 4.24s/it] 97%|█████████▋| 18055/18688 [21:17:25<43:33, 4.13s/it] 97%|█████████▋| 18056/18688 [21:17:30<44:03, 4.18s/it] 97%|█████████▋| 18057/18688 [21:17:34<43:21, 4.12s/it] 97%|█████████▋| 18058/18688 [21:17:37<42:10, 4.02s/it] 97%|█████████▋| 18059/18688 [21:17:43<46:09, 4.40s/it] 97%|█████████▋| 18060/18688 [21:17:46<43:48, 4.19s/it] {'loss': '0.9142', 'grad_norm': '1.768', 'learning_rate': '3.301e-07', 'epoch': '0.9664', 'num_input_tokens_seen': 60772824, 'train_runtime': '7.667e+04', 'train_tokens_per_second': '792.7'}
97%|█████████▋| 18060/18688 [21:17:46<43:48, 4.19s/it] 97%|█████████▋| 18061/18688 [21:17:50<42:31, 4.07s/it] 97%|█████████▋| 18062/18688 [21:17:54<40:57, 3.93s/it] 97%|█████████▋| 18063/18688 [21:17:57<39:48, 3.82s/it] 97%|█████████▋| 18064/18688 [21:18:03<45:03, 4.33s/it] 97%|█████████▋| 18065/18688 [21:18:08<47:07, 4.54s/it] 97%|█████████▋| 18066/18688 [21:18:13<47:28, 4.58s/it] 97%|█████████▋| 18067/18688 [21:18:17<45:57, 4.44s/it] 97%|█████████▋| 18068/18688 [21:18:22<47:28, 4.59s/it] 97%|█████████▋| 18069/18688 [21:18:25<43:56, 4.26s/it] 97%|█████████▋| 18070/18688 [21:18:30<44:46, 4.35s/it] {'loss': '0.9182', 'grad_norm': '1.397', 'learning_rate': '3.197e-07', 'epoch': '0.967', 'num_input_tokens_seen': 60809920, 'train_runtime': '7.671e+04', 'train_tokens_per_second': '792.7'}
97%|█████████▋| 18070/18688 [21:18:30<44:46, 4.35s/it] 97%|█████████▋| 18071/18688 [21:18:34<44:43, 4.35s/it] 97%|█████████▋| 18072/18688 [21:18:38<44:28, 4.33s/it] 97%|█████████▋| 18073/18688 [21:18:42<42:16, 4.13s/it] 97%|█████████▋| 18074/18688 [21:18:46<40:56, 4.00s/it] 97%|█████████▋| 18075/18688 [21:18:50<42:11, 4.13s/it] 97%|█████████▋| 18076/18688 [21:18:54<40:02, 3.93s/it] 97%|█████████▋| 18077/18688 [21:18:57<38:52, 3.82s/it] 97%|█████████▋| 18078/18688 [21:19:03<43:29, 4.28s/it] 97%|█████████▋| 18079/18688 [21:19:06<41:24, 4.08s/it] 97%|█████████▋| 18080/18688 [21:19:10<40:26, 3.99s/it] {'loss': '0.8865', 'grad_norm': '1.508', 'learning_rate': '3.094e-07', 'epoch': '0.9675', 'num_input_tokens_seen': 60842784, 'train_runtime': '7.675e+04', 'train_tokens_per_second': '792.7'}
97%|█████████▋| 18080/18688 [21:19:10<40:26, 3.99s/it] 97%|█████████▋| 18081/18688 [21:19:14<40:50, 4.04s/it] 97%|█████████▋| 18082/18688 [21:19:18<41:40, 4.13s/it] 97%|█████████▋| 18083/18688 [21:19:22<39:51, 3.95s/it] 97%|█████████▋| 18084/18688 [21:19:26<38:57, 3.87s/it] 97%|█████████▋| 18085/18688 [21:19:29<38:17, 3.81s/it] 97%|█████████▋| 18086/18688 [21:19:33<37:05, 3.70s/it] 97%|█████████▋| 18087/18688 [21:19:38<41:09, 4.11s/it] 97%|█████████▋| 18088/18688 [21:19:41<39:27, 3.95s/it] 97%|█████████▋| 18089/18688 [21:19:45<38:27, 3.85s/it] 97%|█████████▋| 18090/18688 [21:19:49<39:26, 3.96s/it] {'loss': '0.8657', 'grad_norm': '1.457', 'learning_rate': '2.994e-07', 'epoch': '0.968', 'num_input_tokens_seen': 60874792, 'train_runtime': '7.679e+04', 'train_tokens_per_second': '792.7'}
97%|█████████▋| 18090/18688 [21:19:49<39:26, 3.96s/it] 97%|█████████▋| 18091/18688 [21:19:53<39:38, 3.98s/it] 97%|█████████▋| 18092/18688 [21:19:57<38:37, 3.89s/it] 97%|█████████▋| 18093/18688 [21:20:01<39:41, 4.00s/it] 97%|█████████▋| 18094/18688 [21:20:06<41:01, 4.14s/it] 97%|█████████▋| 18095/18688 [21:20:10<41:31, 4.20s/it] 97%|█████████▋| 18096/18688 [21:20:14<41:33, 4.21s/it] 97%|█████████▋| 18097/18688 [21:20:18<41:24, 4.20s/it] 97%|█████████▋| 18098/18688 [21:20:22<39:11, 3.99s/it] 97%|█████████▋| 18099/18688 [21:20:25<37:54, 3.86s/it] 97%|█████████▋| 18100/18688 [21:20:30<38:38, 3.94s/it] {'loss': '0.9299', 'grad_norm': '1.11', 'learning_rate': '2.895e-07', 'epoch': '0.9686', 'num_input_tokens_seen': 60908184, 'train_runtime': '7.683e+04', 'train_tokens_per_second': '792.8'}
97%|█████████▋| 18100/18688 [21:20:30<38:38, 3.94s/it] 97%|█████████▋| 18101/18688 [21:20:33<37:10, 3.80s/it] 97%|█████████▋| 18102/18688 [21:20:37<37:52, 3.88s/it] 97%|█████████▋| 18103/18688 [21:20:42<39:24, 4.04s/it] 97%|█████████▋| 18104/18688 [21:20:46<40:24, 4.15s/it] 97%|█████████▋| 18105/18688 [21:20:51<43:33, 4.48s/it] 97%|█████████▋| 18106/18688 [21:20:56<43:55, 4.53s/it] 97%|█████████▋| 18107/18688 [21:21:00<41:41, 4.31s/it] 97%|█████████▋| 18108/18688 [21:21:04<41:30, 4.29s/it] 97%|█████████▋| 18109/18688 [21:21:08<41:44, 4.33s/it] 97%|█████████▋| 18110/18688 [21:21:13<41:51, 4.35s/it] {'loss': '0.9234', 'grad_norm': '1.053', 'learning_rate': '2.797e-07', 'epoch': '0.9691', 'num_input_tokens_seen': 60945736, 'train_runtime': '7.687e+04', 'train_tokens_per_second': '792.8'}
97%|█████████▋| 18110/18688 [21:21:13<41:51, 4.35s/it] 97%|█████████▋| 18111/18688 [21:21:16<39:24, 4.10s/it] 97%|█████████▋| 18112/18688 [21:21:21<41:07, 4.28s/it] 97%|█████████▋| 18113/18688 [21:21:25<39:24, 4.11s/it] 97%|█████████▋| 18114/18688 [21:21:28<37:51, 3.96s/it] 97%|█████████▋| 18115/18688 [21:21:32<36:49, 3.86s/it] 97%|█████████▋| 18116/18688 [21:21:36<36:42, 3.85s/it] 97%|█████████▋| 18117/18688 [21:21:39<36:10, 3.80s/it] 97%|█████████▋| 18118/18688 [21:21:43<36:26, 3.84s/it] 97%|█████████▋| 18119/18688 [21:21:47<35:37, 3.76s/it] 97%|█████████▋| 18120/18688 [21:21:52<38:35, 4.08s/it] {'loss': '0.8931', 'grad_norm': '1.188', 'learning_rate': '2.702e-07', 'epoch': '0.9696', 'num_input_tokens_seen': 60976584, 'train_runtime': '7.691e+04', 'train_tokens_per_second': '792.8'}
97%|█████████▋| 18120/18688 [21:21:52<38:35, 4.08s/it] 97%|█████████▋| 18121/18688 [21:21:56<38:16, 4.05s/it] 97%|█████████▋| 18122/18688 [21:22:00<38:23, 4.07s/it] 97%|█████████▋| 18123/18688 [21:22:04<37:28, 3.98s/it] 97%|█████████▋| 18124/18688 [21:22:07<36:35, 3.89s/it] 97%|█████████▋| 18125/18688 [21:22:11<35:55, 3.83s/it] 97%|█████████▋| 18126/18688 [21:22:15<37:14, 3.98s/it] 97%|█████████▋| 18127/18688 [21:22:19<35:42, 3.82s/it] 97%|█████████▋| 18128/18688 [21:22:23<35:40, 3.82s/it] 97%|█████████▋| 18129/18688 [21:22:26<35:08, 3.77s/it] 97%|█████████▋| 18130/18688 [21:22:30<35:09, 3.78s/it] {'loss': '0.8676', 'grad_norm': '1.564', 'learning_rate': '2.608e-07', 'epoch': '0.9702', 'num_input_tokens_seen': 61006880, 'train_runtime': '7.695e+04', 'train_tokens_per_second': '792.8'}
97%|█████████▋| 18130/18688 [21:22:30<35:09, 3.78s/it] 97%|█████████▋| 18131/18688 [21:22:34<35:11, 3.79s/it] 97%|█████████▋| 18132/18688 [21:22:37<34:17, 3.70s/it] 97%|█████████▋| 18133/18688 [21:22:41<34:07, 3.69s/it] 97%|█████████▋| 18134/18688 [21:22:45<35:09, 3.81s/it] 97%|█████████▋| 18135/18688 [21:22:49<34:40, 3.76s/it] 97%|█████████▋| 18136/18688 [21:22:53<36:11, 3.93s/it] 97%|█████████▋| 18137/18688 [21:22:57<35:43, 3.89s/it] 97%|█████████▋| 18138/18688 [21:23:01<36:11, 3.95s/it] 97%|█████████▋| 18139/18688 [21:23:05<35:28, 3.88s/it] 97%|█████████▋| 18140/18688 [21:23:08<34:28, 3.77s/it] {'loss': '0.8628', 'grad_norm': '0.9893', 'learning_rate': '2.515e-07', 'epoch': '0.9707', 'num_input_tokens_seen': 61037216, 'train_runtime': '7.699e+04', 'train_tokens_per_second': '792.8'}
97%|█████████▋| 18140/18688 [21:23:08<34:28, 3.77s/it] 97%|█████████▋| 18141/18688 [21:23:12<33:36, 3.69s/it] 97%|█████████▋| 18142/18688 [21:23:17<36:59, 4.07s/it] 97%|█████████▋| 18143/18688 [21:23:20<35:56, 3.96s/it] 97%|█████████▋| 18144/18688 [21:23:24<36:03, 3.98s/it] 97%|█████████▋| 18145/18688 [21:23:28<34:40, 3.83s/it] 97%|█████████▋| 18146/18688 [21:23:31<33:59, 3.76s/it] 97%|█████████▋| 18147/18688 [21:23:36<35:20, 3.92s/it] 97%|█████████▋| 18148/18688 [21:23:39<34:57, 3.89s/it] 97%|█████████▋| 18149/18688 [21:23:44<35:56, 4.00s/it] 97%|█████████▋| 18150/18688 [21:23:47<34:53, 3.89s/it] {'loss': '0.9455', 'grad_norm': '1.734', 'learning_rate': '2.424e-07', 'epoch': '0.9712', 'num_input_tokens_seen': 61067976, 'train_runtime': '7.703e+04', 'train_tokens_per_second': '792.8'}
97%|█████████▋| 18150/18688 [21:23:47<34:53, 3.89s/it] 97%|█████████▋| 18151/18688 [21:23:51<35:22, 3.95s/it] 97%|█████████▋| 18152/18688 [21:23:55<34:38, 3.88s/it] 97%|█████████▋| 18153/18688 [21:24:00<37:16, 4.18s/it] 97%|█████████▋| 18154/18688 [21:24:04<35:30, 3.99s/it] 97%|█████████▋| 18155/18688 [21:24:08<35:25, 3.99s/it] 97%|█████████▋| 18156/18688 [21:24:12<36:50, 4.16s/it] 97%|█████████▋| 18157/18688 [21:24:16<37:16, 4.21s/it] 97%|█████████▋| 18158/18688 [21:24:20<35:31, 4.02s/it] 97%|█████████▋| 18159/18688 [21:24:24<34:29, 3.91s/it] 97%|█████████▋| 18160/18688 [21:24:28<36:27, 4.14s/it] {'loss': '0.8803', 'grad_norm': '1.315', 'learning_rate': '2.335e-07', 'epoch': '0.9718', 'num_input_tokens_seen': 61101848, 'train_runtime': '7.707e+04', 'train_tokens_per_second': '792.8'}
97%|█████████▋| 18160/18688 [21:24:28<36:27, 4.14s/it] 97%|█████████▋| 18161/18688 [21:24:33<36:40, 4.18s/it] 97%|█████████▋| 18162/18688 [21:24:37<36:21, 4.15s/it] 97%|█████████▋| 18163/18688 [21:24:41<35:26, 4.05s/it] 97%|█████████▋| 18164/18688 [21:24:45<35:16, 4.04s/it] 97%|█████████▋| 18165/18688 [21:24:50<37:34, 4.31s/it] 97%|█████████▋| 18166/18688 [21:24:54<38:06, 4.38s/it] 97%|█████████▋| 18167/18688 [21:24:58<37:13, 4.29s/it] 97%|█████████▋| 18168/18688 [21:25:02<35:24, 4.08s/it] 97%|█████████▋| 18169/18688 [21:25:06<35:01, 4.05s/it] 97%|█████████▋| 18170/18688 [21:25:10<35:07, 4.07s/it] {'loss': '0.956', 'grad_norm': '1.417', 'learning_rate': '2.248e-07', 'epoch': '0.9723', 'num_input_tokens_seen': 61136040, 'train_runtime': '7.711e+04', 'train_tokens_per_second': '792.8'}
97%|█████████▋| 18170/18688 [21:25:10<35:07, 4.07s/it] 97%|█████████▋| 18171/18688 [21:25:14<34:08, 3.96s/it] 97%|█████████▋| 18172/18688 [21:25:17<33:12, 3.86s/it] 97%|█████████▋| 18173/18688 [21:25:21<32:37, 3.80s/it] 97%|█████████▋| 18174/18688 [21:25:25<33:30, 3.91s/it] 97%|█████████▋| 18175/18688 [21:25:29<33:56, 3.97s/it] 97%|█████████▋| 18176/18688 [21:25:33<33:50, 3.97s/it] 97%|█████████▋| 18177/18688 [21:25:37<34:55, 4.10s/it] 97%|█████████▋| 18178/18688 [21:25:41<33:53, 3.99s/it] 97%|█████████▋| 18179/18688 [21:25:45<33:23, 3.94s/it] 97%|█████████▋| 18180/18688 [21:25:49<33:04, 3.91s/it] {'loss': '0.9009', 'grad_norm': '1.272', 'learning_rate': '2.162e-07', 'epoch': '0.9728', 'num_input_tokens_seen': 61166064, 'train_runtime': '7.715e+04', 'train_tokens_per_second': '792.8'}
97%|█████████▋| 18180/18688 [21:25:49<33:04, 3.91s/it] 97%|█████████▋| 18181/18688 [21:25:53<32:55, 3.90s/it] 97%|█████████▋| 18182/18688 [21:25:57<32:38, 3.87s/it] 97%|█████████▋| 18183/18688 [21:26:00<32:39, 3.88s/it] 97%|█████████▋| 18184/18688 [21:26:05<34:21, 4.09s/it] 97%|█████████▋| 18185/18688 [21:26:09<32:59, 3.94s/it] 97%|█████████▋| 18186/18688 [21:26:13<33:08, 3.96s/it] 97%|█████████▋| 18187/18688 [21:26:17<34:00, 4.07s/it] 97%|█████████▋| 18188/18688 [21:26:22<35:30, 4.26s/it] 97%|█████████▋| 18189/18688 [21:26:25<34:00, 4.09s/it] 97%|█████████▋| 18190/18688 [21:26:29<32:26, 3.91s/it] {'loss': '0.9081', 'grad_norm': '1.574', 'learning_rate': '2.078e-07', 'epoch': '0.9734', 'num_input_tokens_seen': 61198296, 'train_runtime': '7.719e+04', 'train_tokens_per_second': '792.8'}
97%|█████████▋| 18190/18688 [21:26:29<32:26, 3.91s/it] 97%|█████████▋| 18191/18688 [21:26:33<32:10, 3.88s/it] 97%|█████████▋| 18192/18688 [21:26:36<31:04, 3.76s/it] 97%|█████████▋| 18193/18688 [21:26:40<30:41, 3.72s/it] 97%|█████████▋| 18194/18688 [21:26:44<31:40, 3.85s/it] 97%|█████████▋| 18195/18688 [21:26:48<32:06, 3.91s/it] 97%|█████████▋| 18196/18688 [21:26:52<31:48, 3.88s/it] 97%|█████████▋| 18197/18688 [21:26:56<32:10, 3.93s/it] 97%|█████████▋| 18198/18688 [21:27:00<33:14, 4.07s/it] 97%|█████████▋| 18199/18688 [21:27:05<34:59, 4.29s/it] 97%|█████████▋| 18200/18688 [21:27:09<33:00, 4.06s/it] {'loss': '0.9009', 'grad_norm': '1.415', 'learning_rate': '1.996e-07', 'epoch': '0.9739', 'num_input_tokens_seen': 61229808, 'train_runtime': '7.723e+04', 'train_tokens_per_second': '792.8'}
97%|█████████▋| 18200/18688 [21:27:09<33:00, 4.06s/it] 97%|█████████▋| 18201/18688 [21:27:13<34:09, 4.21s/it] 97%|█████████▋| 18202/18688 [21:27:17<33:46, 4.17s/it] 97%|█████████▋| 18203/18688 [21:27:21<32:52, 4.07s/it] 97%|█████████▋| 18204/18688 [21:27:25<32:09, 3.99s/it] 97%|█████████▋| 18205/18688 [21:27:29<32:15, 4.01s/it] 97%|█████████▋| 18206/18688 [21:27:33<33:02, 4.11s/it] 97%|█████████▋| 18207/18688 [21:27:37<33:19, 4.16s/it] 97%|█████████▋| 18208/18688 [21:27:42<33:14, 4.16s/it] 97%|█████████▋| 18209/18688 [21:27:45<32:08, 4.03s/it] 97%|█████████▋| 18210/18688 [21:27:51<35:11, 4.42s/it] {'loss': '0.9376', 'grad_norm': '1.466', 'learning_rate': '1.915e-07', 'epoch': '0.9744', 'num_input_tokens_seen': 61264992, 'train_runtime': '7.727e+04', 'train_tokens_per_second': '792.9'}
97%|█████████▋| 18210/18688 [21:27:51<35:11, 4.42s/it] 97%|█████████▋| 18211/18688 [21:27:55<34:25, 4.33s/it] 97%|█████████▋| 18212/18688 [21:27:59<33:15, 4.19s/it] 97%|█████████▋| 18213/18688 [21:28:02<31:38, 4.00s/it] 97%|█████████▋| 18214/18688 [21:28:07<33:15, 4.21s/it] 97%|█████████▋| 18215/18688 [21:28:11<32:16, 4.09s/it] 97%|█████████▋| 18216/18688 [21:28:15<31:39, 4.02s/it] 97%|█████████▋| 18217/18688 [21:28:18<31:09, 3.97s/it] 97%|█████████▋| 18218/18688 [21:28:22<29:52, 3.81s/it] 97%|█████████▋| 18219/18688 [21:28:25<29:05, 3.72s/it] 97%|█████████▋| 18220/18688 [21:28:29<28:52, 3.70s/it] {'loss': '0.8833', 'grad_norm': '1.2', 'learning_rate': '1.836e-07', 'epoch': '0.975', 'num_input_tokens_seen': 61295160, 'train_runtime': '7.731e+04', 'train_tokens_per_second': '792.9'}
97%|█████████▋| 18220/18688 [21:28:29<28:52, 3.70s/it] 98%|█████████▊| 18221/18688 [21:28:34<32:30, 4.18s/it] 98%|█████████▊| 18222/18688 [21:28:38<31:08, 4.01s/it] 98%|█████████▊| 18223/18688 [21:28:42<30:38, 3.95s/it] 98%|█████████▊| 18224/18688 [21:28:46<30:41, 3.97s/it] 98%|█████████▊| 18225/18688 [21:28:49<30:01, 3.89s/it] 98%|█████████▊| 18226/18688 [21:28:54<30:47, 4.00s/it] 98%|█████████▊| 18227/18688 [21:28:58<31:41, 4.13s/it] 98%|█████████▊| 18228/18688 [21:29:02<31:06, 4.06s/it] 98%|█████████▊| 18229/18688 [21:29:06<29:51, 3.90s/it] 98%|█████████▊| 18230/18688 [21:29:09<29:25, 3.85s/it] {'loss': '0.8756', 'grad_norm': '1.425', 'learning_rate': '1.759e-07', 'epoch': '0.9755', 'num_input_tokens_seen': 61327592, 'train_runtime': '7.735e+04', 'train_tokens_per_second': '792.9'}
98%|█████████▊| 18230/18688 [21:29:09<29:25, 3.85s/it] 98%|█████████▊| 18231/18688 [21:29:14<30:20, 3.98s/it] 98%|█████████▊| 18232/18688 [21:29:18<31:26, 4.14s/it] 98%|█████████▊| 18233/18688 [21:29:22<31:27, 4.15s/it] 98%|█████████▊| 18234/18688 [21:29:26<31:17, 4.14s/it] 98%|█████████▊| 18235/18688 [21:29:30<30:12, 4.00s/it] 98%|█████████▊| 18236/18688 [21:29:34<29:54, 3.97s/it] 98%|█████████▊| 18237/18688 [21:29:38<29:36, 3.94s/it] 98%|█████████▊| 18238/18688 [21:29:43<32:24, 4.32s/it] 98%|█████████▊| 18239/18688 [21:29:47<32:10, 4.30s/it] 98%|█████████▊| 18240/18688 [21:29:51<31:12, 4.18s/it] {'loss': '0.9045', 'grad_norm': '1.249', 'learning_rate': '1.683e-07', 'epoch': '0.9761', 'num_input_tokens_seen': 61363024, 'train_runtime': '7.739e+04', 'train_tokens_per_second': '792.9'}
98%|█████████▊| 18240/18688 [21:29:51<31:12, 4.18s/it] 98%|█████████▊| 18241/18688 [21:29:55<29:46, 4.00s/it] 98%|█████████▊| 18242/18688 [21:29:59<29:30, 3.97s/it] 98%|█████████▊| 18243/18688 [21:30:03<29:47, 4.02s/it] 98%|█████████▊| 18244/18688 [21:30:07<29:08, 3.94s/it] 98%|█████████▊| 18245/18688 [21:30:11<29:34, 4.01s/it] 98%|█████████▊| 18246/18688 [21:30:15<31:05, 4.22s/it] 98%|█████████▊| 18247/18688 [21:30:19<30:27, 4.14s/it] 98%|█████████▊| 18248/18688 [21:30:23<29:14, 3.99s/it] 98%|█████████▊| 18249/18688 [21:30:28<30:46, 4.21s/it] 98%|█████████▊| 18250/18688 [21:30:31<29:26, 4.03s/it] {'loss': '0.8348', 'grad_norm': '1.152', 'learning_rate': '1.609e-07', 'epoch': '0.9766', 'num_input_tokens_seen': 61395528, 'train_runtime': '7.743e+04', 'train_tokens_per_second': '792.9'}
98%|█████████▊| 18250/18688 [21:30:31<29:26, 4.03s/it] 98%|█████████▊| 18251/18688 [21:30:35<28:23, 3.90s/it] 98%|█████████▊| 18252/18688 [21:30:39<29:09, 4.01s/it] 98%|█████████▊| 18253/18688 [21:30:44<30:39, 4.23s/it] 98%|█████████▊| 18254/18688 [21:30:48<30:03, 4.16s/it] 98%|█████████▊| 18255/18688 [21:30:51<28:29, 3.95s/it] 98%|█████████▊| 18256/18688 [21:30:56<29:23, 4.08s/it] 98%|█████████▊| 18257/18688 [21:31:00<29:11, 4.06s/it] 98%|█████████▊| 18258/18688 [21:31:04<28:42, 4.01s/it] 98%|█████████▊| 18259/18688 [21:31:09<30:23, 4.25s/it] 98%|█████████▊| 18260/18688 [21:31:13<30:15, 4.24s/it] {'loss': '0.8937', 'grad_norm': '1.391', 'learning_rate': '1.536e-07', 'epoch': '0.9771', 'num_input_tokens_seen': 61428936, 'train_runtime': '7.747e+04', 'train_tokens_per_second': '792.9'}
98%|█████████▊| 18260/18688 [21:31:13<30:15, 4.24s/it] 98%|█████████▊| 18261/18688 [21:31:17<29:55, 4.20s/it] 98%|█████████▊| 18262/18688 [21:31:21<29:48, 4.20s/it] 98%|█████████▊| 18263/18688 [21:31:25<28:56, 4.09s/it] 98%|█████████▊| 18264/18688 [21:31:29<27:50, 3.94s/it] 98%|█████████▊| 18265/18688 [21:31:32<27:19, 3.87s/it] 98%|█████████▊| 18266/18688 [21:31:37<28:15, 4.02s/it] 98%|█████████▊| 18267/18688 [21:31:40<27:02, 3.85s/it] 98%|█████████▊| 18268/18688 [21:31:44<26:31, 3.79s/it] 98%|█████████▊| 18269/18688 [21:31:48<27:38, 3.96s/it] 98%|█████████▊| 18270/18688 [21:31:52<27:22, 3.93s/it] {'loss': '0.8934', 'grad_norm': '1.309', 'learning_rate': '1.466e-07', 'epoch': '0.9777', 'num_input_tokens_seen': 61458864, 'train_runtime': '7.751e+04', 'train_tokens_per_second': '792.9'}
98%|█████████▊| 18270/18688 [21:31:52<27:22, 3.93s/it] 98%|█████████▊| 18271/18688 [21:31:56<26:41, 3.84s/it] 98%|█████████▊| 18272/18688 [21:31:59<26:27, 3.82s/it] 98%|█████████▊| 18273/18688 [21:32:04<28:31, 4.12s/it] 98%|█████████▊| 18274/18688 [21:32:08<28:51, 4.18s/it] 98%|█████████▊| 18275/18688 [21:32:12<28:15, 4.11s/it] 98%|█████████▊| 18276/18688 [21:32:16<27:55, 4.07s/it] 98%|█████████▊| 18277/18688 [21:32:21<28:52, 4.21s/it] 98%|█████████▊| 18278/18688 [21:32:25<27:37, 4.04s/it] 98%|█████████▊| 18279/18688 [21:32:29<28:47, 4.22s/it] 98%|█████████▊| 18280/18688 [21:32:33<27:09, 3.99s/it] {'loss': '0.9123', 'grad_norm': '1.266', 'learning_rate': '1.396e-07', 'epoch': '0.9782', 'num_input_tokens_seen': 61492032, 'train_runtime': '7.755e+04', 'train_tokens_per_second': '792.9'}
98%|█████████▊| 18280/18688 [21:32:33<27:09, 3.99s/it] 98%|█████████▊| 18281/18688 [21:32:37<27:42, 4.08s/it] 98%|█████████▊| 18282/18688 [21:32:41<28:14, 4.17s/it] 98%|█████████▊| 18283/18688 [21:32:45<27:35, 4.09s/it] 98%|█████████▊| 18284/18688 [21:32:49<26:46, 3.98s/it] 98%|█████████▊| 18285/18688 [21:32:53<26:52, 4.00s/it] 98%|█████████▊| 18286/18688 [21:32:57<26:50, 4.01s/it] 98%|█████████▊| 18287/18688 [21:33:01<25:46, 3.86s/it] 98%|█████████▊| 18288/18688 [21:33:05<26:17, 3.94s/it] 98%|█████████▊| 18289/18688 [21:33:08<25:40, 3.86s/it] 98%|█████████▊| 18290/18688 [21:33:13<27:53, 4.21s/it] {'loss': '0.9539', 'grad_norm': '1.549', 'learning_rate': '1.329e-07', 'epoch': '0.9787', 'num_input_tokens_seen': 61525008, 'train_runtime': '7.759e+04', 'train_tokens_per_second': '792.9'}
98%|█████████▊| 18290/18688 [21:33:13<27:53, 4.21s/it] 98%|█████████▊| 18291/18688 [21:33:18<28:00, 4.23s/it] 98%|█████████▊| 18292/18688 [21:33:21<26:30, 4.02s/it] 98%|█████████▊| 18293/18688 [21:33:26<28:11, 4.28s/it] 98%|█████████▊| 18294/18688 [21:33:30<27:18, 4.16s/it] 98%|█████████▊| 18295/18688 [21:33:35<28:10, 4.30s/it] 98%|█████████▊| 18296/18688 [21:33:39<28:16, 4.33s/it] 98%|█████████▊| 18297/18688 [21:33:43<27:55, 4.29s/it] 98%|█████████▊| 18298/18688 [21:33:47<26:26, 4.07s/it] 98%|█████████▊| 18299/18688 [21:33:51<27:26, 4.23s/it] 98%|█████████▊| 18300/18688 [21:33:55<25:55, 4.01s/it] {'loss': '0.8992', 'grad_norm': '1.347', 'learning_rate': '1.263e-07', 'epoch': '0.9793', 'num_input_tokens_seen': 61559520, 'train_runtime': '7.764e+04', 'train_tokens_per_second': '792.9'}
98%|█████████▊| 18300/18688 [21:33:55<25:55, 4.01s/it] 98%|█████████▊| 18301/18688 [21:33:58<25:09, 3.90s/it] 98%|█████████▊| 18302/18688 [21:34:03<25:38, 3.98s/it] 98%|█████████▊| 18303/18688 [21:34:07<27:12, 4.24s/it] 98%|█████████▊| 18304/18688 [21:34:12<27:29, 4.29s/it] 98%|█████████▊| 18305/18688 [21:34:16<26:59, 4.23s/it] 98%|█████████▊| 18306/18688 [21:34:20<26:32, 4.17s/it] 98%|█████████▊| 18307/18688 [21:34:24<26:38, 4.20s/it] 98%|█████████▊| 18308/18688 [21:34:28<25:29, 4.02s/it] 98%|█████████▊| 18309/18688 [21:34:32<24:57, 3.95s/it] 98%|█████████▊| 18310/18688 [21:34:36<25:10, 4.00s/it] {'loss': '0.8483', 'grad_norm': '1.324', 'learning_rate': '1.199e-07', 'epoch': '0.9798', 'num_input_tokens_seen': 61592872, 'train_runtime': '7.768e+04', 'train_tokens_per_second': '792.9'}
98%|█████████▊| 18310/18688 [21:34:36<25:10, 4.00s/it] 98%|█████████▊| 18311/18688 [21:34:39<24:18, 3.87s/it] 98%|█████████▊| 18312/18688 [21:34:43<23:49, 3.80s/it] 98%|█████████▊| 18313/18688 [21:34:47<23:39, 3.79s/it] 98%|█████████▊| 18314/18688 [21:34:50<23:05, 3.70s/it] 98%|█████████▊| 18315/18688 [21:34:54<23:19, 3.75s/it] 98%|█████████▊| 18316/18688 [21:34:58<23:35, 3.81s/it] 98%|█████████▊| 18317/18688 [21:35:02<23:36, 3.82s/it] 98%|█████████▊| 18318/18688 [21:35:07<25:14, 4.09s/it] 98%|█████████▊| 18319/18688 [21:35:11<25:02, 4.07s/it] 98%|█████████▊| 18320/18688 [21:35:16<26:26, 4.31s/it] {'loss': '0.8767', 'grad_norm': '1.241', 'learning_rate': '1.137e-07', 'epoch': '0.9803', 'num_input_tokens_seen': 61625152, 'train_runtime': '7.772e+04', 'train_tokens_per_second': '793'}
98%|█████████▊| 18320/18688 [21:35:16<26:26, 4.31s/it] 98%|█████████▊| 18321/18688 [21:35:19<25:43, 4.21s/it] 98%|█████████▊| 18322/18688 [21:35:23<25:12, 4.13s/it] 98%|█████████▊| 18323/18688 [21:35:27<24:37, 4.05s/it] 98%|█████████▊| 18324/18688 [21:35:31<24:45, 4.08s/it] 98%|█████████▊| 18325/18688 [21:35:35<24:15, 4.01s/it] 98%|█████████▊| 18326/18688 [21:35:40<24:55, 4.13s/it] 98%|█████████▊| 18327/18688 [21:35:44<25:00, 4.16s/it] 98%|█████████▊| 18328/18688 [21:35:48<24:31, 4.09s/it] 98%|█████████▊| 18329/18688 [21:35:52<24:12, 4.04s/it] 98%|█████████▊| 18330/18688 [21:35:57<25:31, 4.28s/it] {'loss': '0.8846', 'grad_norm': '1.138', 'learning_rate': '1.076e-07', 'epoch': '0.9809', 'num_input_tokens_seen': 61659880, 'train_runtime': '7.776e+04', 'train_tokens_per_second': '793'}
98%|█████████▊| 18330/18688 [21:35:57<25:31, 4.28s/it] 98%|█████████▊| 18331/18688 [21:36:02<27:30, 4.62s/it] 98%|█████████▊| 18332/18688 [21:36:06<25:23, 4.28s/it] 98%|█████████▊| 18333/18688 [21:36:09<23:51, 4.03s/it] 98%|█████████▊| 18334/18688 [21:36:13<23:18, 3.95s/it] 98%|█████████▊| 18335/18688 [21:36:17<23:20, 3.97s/it] 98%|█████████▊| 18336/18688 [21:36:21<23:54, 4.07s/it] 98%|█████████▊| 18337/18688 [21:36:25<24:20, 4.16s/it] 98%|█████████▊| 18338/18688 [21:36:29<23:15, 3.99s/it] 98%|█████████▊| 18339/18688 [21:36:33<23:00, 3.95s/it] 98%|█████████▊| 18340/18688 [21:36:37<23:17, 4.02s/it] {'loss': '0.8502', 'grad_norm': '1.348', 'learning_rate': '1.017e-07', 'epoch': '0.9814', 'num_input_tokens_seen': 61693224, 'train_runtime': '7.78e+04', 'train_tokens_per_second': '793'}
98%|█████████▊| 18340/18688 [21:36:37<23:17, 4.02s/it] 98%|█████████▊| 18341/18688 [21:36:41<23:45, 4.11s/it] 98%|█████████▊| 18342/18688 [21:36:46<23:47, 4.12s/it] 98%|█████████▊| 18343/18688 [21:36:50<24:42, 4.30s/it] 98%|█████████▊| 18344/18688 [21:36:54<24:24, 4.26s/it] 98%|█████████▊| 18345/18688 [21:36:59<24:14, 4.24s/it] 98%|█████████▊| 18346/18688 [21:37:03<23:50, 4.18s/it] 98%|█████████▊| 18347/18688 [21:37:07<23:14, 4.09s/it] 98%|█████████▊| 18348/18688 [21:37:11<23:14, 4.10s/it] 98%|█████████▊| 18349/18688 [21:37:15<23:50, 4.22s/it] 98%|█████████▊| 18350/18688 [21:37:19<22:42, 4.03s/it] {'loss': '0.8909', 'grad_norm': '1.314', 'learning_rate': '9.595e-08', 'epoch': '0.9819', 'num_input_tokens_seen': 61728552, 'train_runtime': '7.784e+04', 'train_tokens_per_second': '793'}
98%|█████████▊| 18350/18688 [21:37:19<22:42, 4.03s/it] 98%|█████████▊| 18351/18688 [21:37:23<22:50, 4.07s/it] 98%|█████████▊| 18352/18688 [21:37:27<23:06, 4.13s/it] 98%|█████████▊| 18353/18688 [21:37:32<23:57, 4.29s/it] 98%|█████████▊| 18354/18688 [21:37:35<22:39, 4.07s/it] 98%|█████████▊| 18355/18688 [21:37:40<23:48, 4.29s/it] 98%|█████████▊| 18356/18688 [21:37:44<23:04, 4.17s/it] 98%|█████████▊| 18357/18688 [21:37:48<23:25, 4.25s/it] 98%|█████████▊| 18358/18688 [21:37:53<23:09, 4.21s/it] 98%|█████████▊| 18359/18688 [21:37:57<23:15, 4.24s/it] 98%|█████████▊| 18360/18688 [21:38:01<23:10, 4.24s/it] {'loss': '0.8948', 'grad_norm': '1.355', 'learning_rate': '9.038e-08', 'epoch': '0.9825', 'num_input_tokens_seen': 61763968, 'train_runtime': '7.788e+04', 'train_tokens_per_second': '793'}
98%|█████████▊| 18360/18688 [21:38:01<23:10, 4.24s/it] 98%|█████████▊| 18361/18688 [21:38:05<22:11, 4.07s/it] 98%|█████████▊| 18362/18688 [21:38:10<24:38, 4.54s/it] 98%|█████████▊| 18363/18688 [21:38:15<25:09, 4.64s/it] 98%|█████████▊| 18364/18688 [21:38:19<23:28, 4.35s/it] 98%|█████████▊| 18365/18688 [21:38:23<22:29, 4.18s/it] 98%|█████████▊| 18366/18688 [21:38:29<24:59, 4.66s/it] 98%|█████████▊| 18367/18688 [21:38:32<23:41, 4.43s/it] 98%|█████████▊| 18368/18688 [21:38:37<23:56, 4.49s/it] 98%|█████████▊| 18369/18688 [21:38:41<23:19, 4.39s/it] 98%|█████████▊| 18370/18688 [21:38:45<22:43, 4.29s/it] {'loss': '0.8865', 'grad_norm': '1.214', 'learning_rate': '8.497e-08', 'epoch': '0.983', 'num_input_tokens_seen': 61801088, 'train_runtime': '7.793e+04', 'train_tokens_per_second': '793.1'}
98%|█████████▊| 18370/18688 [21:38:45<22:43, 4.29s/it] 98%|█████████▊| 18371/18688 [21:38:50<22:41, 4.30s/it] 98%|█████████▊| 18372/18688 [21:38:53<21:53, 4.16s/it] 98%|█████████▊| 18373/18688 [21:38:57<20:45, 3.95s/it] 98%|█████████▊| 18374/18688 [21:39:01<21:17, 4.07s/it] 98%|█████████▊| 18375/18688 [21:39:05<21:23, 4.10s/it] 98%|█████████▊| 18376/18688 [21:39:10<21:54, 4.21s/it] 98%|█████████▊| 18377/18688 [21:39:14<21:46, 4.20s/it] 98%|█████████▊| 18378/18688 [21:39:19<23:15, 4.50s/it] 98%|█████████▊| 18379/18688 [21:39:24<22:48, 4.43s/it] 98%|█████████▊| 18380/18688 [21:39:29<23:36, 4.60s/it] {'loss': '0.9758', 'grad_norm': '1.494', 'learning_rate': '7.972e-08', 'epoch': '0.9835', 'num_input_tokens_seen': 61837960, 'train_runtime': '7.797e+04', 'train_tokens_per_second': '793.1'}
98%|█████████▊| 18380/18688 [21:39:29<23:36, 4.60s/it] 98%|█████████▊| 18381/18688 [21:39:33<22:51, 4.47s/it] 98%|█████████▊| 18382/18688 [21:39:38<23:22, 4.58s/it] 98%|█████████▊| 18383/18688 [21:39:43<24:41, 4.86s/it] 98%|█████████▊| 18384/18688 [21:39:47<23:49, 4.70s/it] 98%|█████████▊| 18385/18688 [21:39:52<22:54, 4.54s/it] 98%|█████████▊| 18386/18688 [21:39:56<23:10, 4.61s/it] 98%|█████████▊| 18387/18688 [21:40:00<22:03, 4.40s/it] 98%|█████████▊| 18388/18688 [21:40:05<22:49, 4.57s/it] 98%|█████████▊| 18389/18688 [21:40:10<22:44, 4.56s/it] 98%|█████████▊| 18390/18688 [21:40:14<22:32, 4.54s/it] {'loss': '0.948', 'grad_norm': '1.231', 'learning_rate': '7.465e-08', 'epoch': '0.9841', 'num_input_tokens_seen': 61878304, 'train_runtime': '7.801e+04', 'train_tokens_per_second': '793.2'}
98%|█████████▊| 18390/18688 [21:40:14<22:32, 4.54s/it] 98%|█████████▊| 18391/18688 [21:40:18<21:22, 4.32s/it] 98%|█████████▊| 18392/18688 [21:40:22<20:39, 4.19s/it] 98%|█████████▊| 18393/18688 [21:40:26<20:45, 4.22s/it] 98%|█████████▊| 18394/18688 [21:40:30<20:04, 4.10s/it] 98%|█████████▊| 18395/18688 [21:40:34<19:54, 4.08s/it] 98%|█████████▊| 18396/18688 [21:40:38<20:19, 4.18s/it] 98%|█████████▊| 18397/18688 [21:40:43<20:27, 4.22s/it] 98%|█████████▊| 18398/18688 [21:40:46<19:36, 4.06s/it] 98%|█████████▊| 18399/18688 [21:40:50<18:59, 3.94s/it] 98%|█████████▊| 18400/18688 [21:40:54<18:53, 3.93s/it] {'loss': '0.883', 'grad_norm': '1.457', 'learning_rate': '6.974e-08', 'epoch': '0.9846', 'num_input_tokens_seen': 61909184, 'train_runtime': '7.805e+04', 'train_tokens_per_second': '793.2'}
98%|█████████▊| 18400/18688 [21:40:54<18:53, 3.93s/it] 98%|█████████▊| 18401/18688 [21:40:58<19:13, 4.02s/it] 98%|█████████▊| 18402/18688 [21:41:03<20:02, 4.21s/it] 98%|█████████▊| 18403/18688 [21:41:07<19:51, 4.18s/it] 98%|█████████▊| 18404/18688 [21:41:11<19:19, 4.08s/it] 98%|█████████▊| 18405/18688 [21:41:15<19:35, 4.15s/it] 98%|█████████▊| 18406/18688 [21:41:20<19:44, 4.20s/it] 98%|█████████▊| 18407/18688 [21:41:23<18:59, 4.06s/it] 99%|█████████▊| 18408/18688 [21:41:28<19:36, 4.20s/it] 99%|█████████▊| 18409/18688 [21:41:31<18:46, 4.04s/it] 99%|█████████▊| 18410/18688 [21:41:36<19:39, 4.24s/it] {'loss': '0.8982', 'grad_norm': '1.481', 'learning_rate': '6.5e-08', 'epoch': '0.9852', 'num_input_tokens_seen': 61945000, 'train_runtime': '7.81e+04', 'train_tokens_per_second': '793.2'}
99%|█████████▊| 18410/18688 [21:41:36<19:39, 4.24s/it] 99%|█████████▊| 18411/18688 [21:41:40<19:03, 4.13s/it] 99%|█████████▊| 18412/18688 [21:41:45<19:39, 4.28s/it] 99%|█████████▊| 18413/18688 [21:41:49<19:48, 4.32s/it] 99%|█████████▊| 18414/18688 [21:41:55<21:48, 4.78s/it] 99%|█████████▊| 18415/18688 [21:42:00<21:57, 4.83s/it] 99%|█████████▊| 18416/18688 [21:42:04<20:49, 4.59s/it] 99%|█████████▊| 18417/18688 [21:42:09<21:13, 4.70s/it] 99%|█████████▊| 18418/18688 [21:42:13<19:53, 4.42s/it] 99%|█████████▊| 18419/18688 [21:42:17<19:18, 4.31s/it] 99%|█████████▊| 18420/18688 [21:42:20<18:04, 4.05s/it] {'loss': '0.8741', 'grad_norm': '1.438', 'learning_rate': '6.042e-08', 'epoch': '0.9857', 'num_input_tokens_seen': 61982512, 'train_runtime': '7.814e+04', 'train_tokens_per_second': '793.2'}
99%|█████████▊| 18420/18688 [21:42:20<18:04, 4.05s/it] 99%|█████████▊| 18421/18688 [21:42:25<18:34, 4.17s/it] 99%|█████████▊| 18422/18688 [21:42:28<17:56, 4.05s/it] 99%|█████████▊| 18423/18688 [21:42:33<18:36, 4.21s/it] 99%|█████████▊| 18424/18688 [21:42:37<18:04, 4.11s/it] 99%|█████████▊| 18425/18688 [21:42:41<18:26, 4.21s/it] 99%|█████████▊| 18426/18688 [21:42:46<19:04, 4.37s/it] 99%|█████████▊| 18427/18688 [21:42:50<18:46, 4.31s/it] 99%|█████████▊| 18428/18688 [21:42:54<18:38, 4.30s/it] 99%|█████████▊| 18429/18688 [21:42:58<17:47, 4.12s/it] 99%|█████████▊| 18430/18688 [21:43:02<17:26, 4.06s/it] {'loss': '0.9368', 'grad_norm': '1.474', 'learning_rate': '5.602e-08', 'epoch': '0.9862', 'num_input_tokens_seen': 62016416, 'train_runtime': '7.818e+04', 'train_tokens_per_second': '793.2'}
99%|█████████▊| 18430/18688 [21:43:02<17:26, 4.06s/it] 99%|█████████▊| 18431/18688 [21:43:06<16:48, 3.92s/it] 99%|█████████▊| 18432/18688 [21:43:10<17:19, 4.06s/it] 99%|█████████▊| 18433/18688 [21:43:14<17:05, 4.02s/it] 99%|█████████▊| 18434/18688 [21:43:18<16:58, 4.01s/it] 99%|█████████▊| 18435/18688 [21:43:22<17:14, 4.09s/it] 99%|█████████▊| 18436/18688 [21:43:26<16:42, 3.98s/it] 99%|█████████▊| 18437/18688 [21:43:29<16:00, 3.83s/it] 99%|█████████▊| 18438/18688 [21:43:33<16:17, 3.91s/it] 99%|█████████▊| 18439/18688 [21:43:37<15:51, 3.82s/it] 99%|█████████▊| 18440/18688 [21:43:41<15:25, 3.73s/it] {'loss': '0.8805', 'grad_norm': '1.266', 'learning_rate': '5.177e-08', 'epoch': '0.9868', 'num_input_tokens_seen': 62046720, 'train_runtime': '7.822e+04', 'train_tokens_per_second': '793.2'}
99%|█████████▊| 18440/18688 [21:43:41<15:25, 3.73s/it] 99%|█████████▊| 18441/18688 [21:43:44<15:18, 3.72s/it] 99%|█████████▊| 18442/18688 [21:43:49<16:24, 4.00s/it] 99%|█████████▊| 18443/18688 [21:43:53<16:55, 4.14s/it] 99%|█████████▊| 18444/18688 [21:43:57<16:02, 3.94s/it] 99%|█████████▊| 18445/18688 [21:44:01<15:33, 3.84s/it] 99%|█████████▊| 18446/18688 [21:44:05<16:11, 4.02s/it] 99%|█████████▊| 18447/18688 [21:44:09<16:16, 4.05s/it] 99%|█████████▊| 18448/18688 [21:44:14<16:40, 4.17s/it] 99%|█████████▊| 18449/18688 [21:44:18<17:04, 4.29s/it] 99%|█████████▊| 18450/18688 [21:44:22<16:52, 4.26s/it] {'loss': '0.9312', 'grad_norm': '1.59', 'learning_rate': '4.77e-08', 'epoch': '0.9873', 'num_input_tokens_seen': 62079696, 'train_runtime': '7.826e+04', 'train_tokens_per_second': '793.2'}
99%|█████████▊| 18450/18688 [21:44:22<16:52, 4.26s/it] 99%|█████████▊| 18451/18688 [21:44:27<16:50, 4.26s/it] 99%|█████████▊| 18452/18688 [21:44:30<16:20, 4.16s/it] 99%|█████████▊| 18453/18688 [21:44:35<16:59, 4.34s/it] 99%|█████████▊| 18454/18688 [21:44:39<16:41, 4.28s/it] 99%|█████████▉| 18455/18688 [21:44:44<17:28, 4.50s/it] 99%|█████████▉| 18456/18688 [21:44:48<16:37, 4.30s/it] 99%|█████████▉| 18457/18688 [21:44:53<16:46, 4.36s/it] 99%|█████████▉| 18458/18688 [21:44:57<16:41, 4.35s/it] 99%|█████████▉| 18459/18688 [21:45:01<16:32, 4.33s/it] 99%|█████████▉| 18460/18688 [21:45:06<16:24, 4.32s/it] {'loss': '0.9848', 'grad_norm': '1.533', 'learning_rate': '4.379e-08', 'epoch': '0.9878', 'num_input_tokens_seen': 62114832, 'train_runtime': '7.831e+04', 'train_tokens_per_second': '793.2'}
99%|█████████▉| 18460/18688 [21:45:06<16:24, 4.32s/it] 99%|█████████▉| 18461/18688 [21:45:10<16:51, 4.45s/it] 99%|█████████▉| 18462/18688 [21:45:14<16:06, 4.28s/it] 99%|█████████▉| 18463/18688 [21:45:18<15:42, 4.19s/it] 99%|█████████▉| 18464/18688 [21:45:22<15:26, 4.14s/it] 99%|█████████▉| 18465/18688 [21:45:26<15:27, 4.16s/it] 99%|█████████▉| 18466/18688 [21:45:31<16:19, 4.41s/it] 99%|█████████▉| 18467/18688 [21:45:36<16:31, 4.49s/it] 99%|█████████▉| 18468/18688 [21:45:41<16:27, 4.49s/it] 99%|█████████▉| 18469/18688 [21:45:44<15:33, 4.26s/it] 99%|█████████▉| 18470/18688 [21:45:48<14:36, 4.02s/it] {'loss': '0.9443', 'grad_norm': '1.716', 'learning_rate': '4.005e-08', 'epoch': '0.9884', 'num_input_tokens_seen': 62149288, 'train_runtime': '7.835e+04', 'train_tokens_per_second': '793.2'}
99%|█████████▉| 18470/18688 [21:45:48<14:36, 4.02s/it] 99%|█████████▉| 18471/18688 [21:45:52<14:18, 3.96s/it] 99%|█████████▉| 18472/18688 [21:45:57<15:58, 4.44s/it] 99%|█████████▉| 18473/18688 [21:46:02<16:18, 4.55s/it] 99%|█████████▉| 18474/18688 [21:46:06<15:46, 4.42s/it] 99%|█████████▉| 18475/18688 [21:46:10<14:47, 4.17s/it] 99%|█████████▉| 18476/18688 [21:46:13<14:09, 4.01s/it] 99%|█████████▉| 18477/18688 [21:46:17<13:31, 3.85s/it] 99%|█████████▉| 18478/18688 [21:46:20<13:14, 3.78s/it] 99%|█████████▉| 18479/18688 [21:46:25<14:04, 4.04s/it] 99%|█████████▉| 18480/18688 [21:46:30<15:07, 4.36s/it] {'loss': '0.9449', 'grad_norm': '1.156', 'learning_rate': '3.648e-08', 'epoch': '0.9889', 'num_input_tokens_seen': 62184432, 'train_runtime': '7.839e+04', 'train_tokens_per_second': '793.3'}
99%|█████████▉| 18480/18688 [21:46:30<15:07, 4.36s/it] 99%|█████████▉| 18481/18688 [21:46:34<14:54, 4.32s/it] 99%|█████████▉| 18482/18688 [21:46:38<14:26, 4.21s/it] 99%|█████████▉| 18483/18688 [21:46:42<13:53, 4.07s/it] 99%|█████████▉| 18484/18688 [21:46:46<13:32, 3.98s/it] 99%|█████████▉| 18485/18688 [21:46:50<13:29, 3.99s/it] 99%|█████████▉| 18486/18688 [21:46:55<14:08, 4.20s/it] 99%|█████████▉| 18487/18688 [21:46:59<14:38, 4.37s/it] 99%|█████████▉| 18488/18688 [21:47:04<14:21, 4.31s/it] 99%|█████████▉| 18489/18688 [21:47:08<14:08, 4.26s/it] 99%|█████████▉| 18490/18688 [21:47:13<15:10, 4.60s/it] {'loss': '0.8959', 'grad_norm': '0.9901', 'learning_rate': '3.307e-08', 'epoch': '0.9894', 'num_input_tokens_seen': 62220832, 'train_runtime': '7.843e+04', 'train_tokens_per_second': '793.3'}
99%|█████████▉| 18490/18688 [21:47:13<15:10, 4.60s/it] 99%|█████████▉| 18491/18688 [21:47:17<14:00, 4.27s/it] 99%|█████████▉| 18492/18688 [21:47:20<13:22, 4.09s/it] 99%|█████████▉| 18493/18688 [21:47:25<13:40, 4.21s/it] 99%|█████████▉| 18494/18688 [21:47:29<13:30, 4.18s/it] 99%|█████████▉| 18495/18688 [21:47:33<13:50, 4.30s/it] 99%|█████████▉| 18496/18688 [21:47:37<13:22, 4.18s/it] 99%|█████████▉| 18497/18688 [21:47:43<14:36, 4.59s/it] 99%|█████████▉| 18498/18688 [21:47:47<13:53, 4.38s/it] 99%|█████████▉| 18499/18688 [21:47:52<14:09, 4.50s/it] 99%|█████████▉| 18500/18688 [21:47:56<13:46, 4.39s/it] {'loss': '0.8545', 'grad_norm': '1.089', 'learning_rate': '2.983e-08', 'epoch': '0.99', 'num_input_tokens_seen': 62254984, 'train_runtime': '7.848e+04', 'train_tokens_per_second': '793.3'}
99%|█████████▉| 18500/18688 [21:47:56<13:46, 4.39s/it] 99%|█████████▉| 18501/18688 [21:47:59<12:51, 4.13s/it] 99%|█████████▉| 18502/18688 [21:48:03<12:27, 4.02s/it] 99%|█████████▉| 18503/18688 [21:48:06<11:54, 3.86s/it] 99%|█████████▉| 18504/18688 [21:48:10<11:43, 3.82s/it] 99%|█████████▉| 18505/18688 [21:48:14<11:38, 3.82s/it] 99%|█████████▉| 18506/18688 [21:48:19<12:19, 4.06s/it] 99%|█████████▉| 18507/18688 [21:48:23<12:36, 4.18s/it] 99%|█████████▉| 18508/18688 [21:48:27<12:08, 4.04s/it] 99%|█████████▉| 18509/18688 [21:48:30<11:44, 3.93s/it] 99%|█████████▉| 18510/18688 [21:48:35<11:47, 3.98s/it] {'loss': '0.9247', 'grad_norm': '1.492', 'learning_rate': '2.676e-08', 'epoch': '0.9905', 'num_input_tokens_seen': 62283384, 'train_runtime': '7.852e+04', 'train_tokens_per_second': '793.3'}
99%|█████████▉| 18510/18688 [21:48:35<11:47, 3.98s/it] 99%|█████████▉| 18511/18688 [21:48:38<11:34, 3.93s/it] 99%|█████████▉| 18512/18688 [21:48:42<11:20, 3.87s/it] 99%|█████████▉| 18513/18688 [21:48:46<11:30, 3.95s/it] 99%|█████████▉| 18514/18688 [21:48:52<12:42, 4.38s/it] 99%|█████████▉| 18515/18688 [21:48:55<12:05, 4.20s/it] 99%|█████████▉| 18516/18688 [21:49:00<12:37, 4.41s/it] 99%|█████████▉| 18517/18688 [21:49:04<11:50, 4.16s/it] 99%|█████████▉| 18518/18688 [21:49:08<11:22, 4.01s/it] 99%|█████████▉| 18519/18688 [21:49:12<11:33, 4.10s/it] 99%|█████████▉| 18520/18688 [21:49:16<11:08, 3.98s/it] {'loss': '0.9387', 'grad_norm': '1.237', 'learning_rate': '2.385e-08', 'epoch': '0.991', 'num_input_tokens_seen': 62315792, 'train_runtime': '7.856e+04', 'train_tokens_per_second': '793.3'}
99%|█████████▉| 18520/18688 [21:49:16<11:08, 3.98s/it] 99%|█████████▉| 18521/18688 [21:49:21<12:16, 4.41s/it] 99%|█████████▉| 18522/18688 [21:49:25<11:56, 4.31s/it] 99%|█████████▉| 18523/18688 [21:49:30<12:41, 4.61s/it] 99%|█████████▉| 18524/18688 [21:49:34<12:12, 4.47s/it] 99%|█████████▉| 18525/18688 [21:49:39<12:12, 4.50s/it] 99%|█████████▉| 18526/18688 [21:49:44<12:25, 4.60s/it] 99%|█████████▉| 18527/18688 [21:49:48<11:40, 4.35s/it] 99%|█████████▉| 18528/18688 [21:49:52<11:49, 4.44s/it] 99%|█████████▉| 18529/18688 [21:49:56<11:29, 4.33s/it] 99%|█████████▉| 18530/18688 [21:50:01<11:24, 4.33s/it] {'loss': '0.896', 'grad_norm': '1.128', 'learning_rate': '2.111e-08', 'epoch': '0.9916', 'num_input_tokens_seen': 62354968, 'train_runtime': '7.86e+04', 'train_tokens_per_second': '793.3'}
99%|█████████▉| 18530/18688 [21:50:01<11:24, 4.33s/it] 99%|█████████▉| 18531/18688 [21:50:05<11:41, 4.47s/it] 99%|█████████▉| 18532/18688 [21:50:10<11:15, 4.33s/it] 99%|█████████▉| 18533/18688 [21:50:14<11:07, 4.31s/it] 99%|█████████▉| 18534/18688 [21:50:19<11:24, 4.44s/it] 99%|█████████▉| 18535/18688 [21:50:22<10:40, 4.19s/it] 99%|█████████▉| 18536/18688 [21:50:26<10:38, 4.20s/it] 99%|█████████▉| 18537/18688 [21:50:30<10:01, 3.98s/it] 99%|█████████▉| 18538/18688 [21:50:34<10:09, 4.06s/it] 99%|█████████▉| 18539/18688 [21:50:38<10:09, 4.09s/it] 99%|█████████▉| 18540/18688 [21:50:44<10:59, 4.45s/it] {'loss': '0.8929', 'grad_norm': '1.115', 'learning_rate': '1.854e-08', 'epoch': '0.9921', 'num_input_tokens_seen': 62390624, 'train_runtime': '7.864e+04', 'train_tokens_per_second': '793.3'}
99%|█████████▉| 18540/18688 [21:50:44<10:59, 4.45s/it] 99%|█████████▉| 18541/18688 [21:50:49<11:40, 4.77s/it] 99%|█████████▉| 18542/18688 [21:50:55<12:16, 5.04s/it] 99%|█████████▉| 18543/18688 [21:50:59<11:59, 4.96s/it] 99%|█████████▉| 18544/18688 [21:51:03<10:59, 4.58s/it] 99%|█████████▉| 18545/18688 [21:51:08<10:58, 4.60s/it] 99%|█████████▉| 18546/18688 [21:51:11<10:04, 4.26s/it] 99%|█████████▉| 18547/18688 [21:51:16<10:00, 4.26s/it] 99%|█████████▉| 18548/18688 [21:51:19<09:39, 4.14s/it] 99%|█████████▉| 18549/18688 [21:51:23<09:18, 4.02s/it] 99%|█████████▉| 18550/18688 [21:51:27<09:00, 3.92s/it] {'loss': '0.9431', 'grad_norm': '1.077', 'learning_rate': '1.614e-08', 'epoch': '0.9926', 'num_input_tokens_seen': 62425328, 'train_runtime': '7.869e+04', 'train_tokens_per_second': '793.3'}
99%|█████████▉| 18550/18688 [21:51:27<09:00, 3.92s/it] 99%|█████████▉| 18551/18688 [21:51:31<08:58, 3.93s/it] 99%|█████████▉| 18552/18688 [21:51:35<09:04, 4.00s/it] 99%|█████████▉| 18553/18688 [21:51:39<08:52, 3.95s/it] 99%|█████████▉| 18554/18688 [21:51:43<08:55, 4.00s/it] 99%|█████████▉| 18555/18688 [21:51:47<08:47, 3.96s/it] 99%|█████████▉| 18556/18688 [21:51:51<08:47, 3.99s/it] 99%|█████████▉| 18557/18688 [21:51:55<08:36, 3.94s/it] 99%|█████████▉| 18558/18688 [21:51:58<08:13, 3.79s/it] 99%|█████████▉| 18559/18688 [21:52:02<08:17, 3.86s/it] 99%|█████████▉| 18560/18688 [21:52:06<08:10, 3.83s/it] {'loss': '0.8593', 'grad_norm': '1.217', 'learning_rate': '1.39e-08', 'epoch': '0.9932', 'num_input_tokens_seen': 62456656, 'train_runtime': '7.873e+04', 'train_tokens_per_second': '793.3'}
99%|█████████▉| 18560/18688 [21:52:06<08:10, 3.83s/it] 99%|█████████▉| 18561/18688 [21:52:09<07:57, 3.76s/it] 99%|█████████▉| 18562/18688 [21:52:14<08:39, 4.12s/it] 99%|█████████▉| 18563/18688 [21:52:18<08:12, 3.94s/it] 99%|█████████▉| 18564/18688 [21:52:22<08:10, 3.96s/it] 99%|█████████▉| 18565/18688 [21:52:26<08:14, 4.02s/it] 99%|█████████▉| 18566/18688 [21:52:30<08:02, 3.96s/it] 99%|█████████▉| 18567/18688 [21:52:34<07:46, 3.86s/it] 99%|█████████▉| 18568/18688 [21:52:37<07:39, 3.83s/it] 99%|█████████▉| 18569/18688 [21:52:41<07:46, 3.92s/it] 99%|█████████▉| 18570/18688 [21:52:45<07:29, 3.81s/it] {'loss': '0.8952', 'grad_norm': '1.229', 'learning_rate': '1.183e-08', 'epoch': '0.9937', 'num_input_tokens_seen': 62488152, 'train_runtime': '7.877e+04', 'train_tokens_per_second': '793.3'}
99%|█████████▉| 18570/18688 [21:52:45<07:29, 3.81s/it] 99%|█████████▉| 18571/18688 [21:52:49<07:20, 3.77s/it] 99%|█████████▉| 18572/18688 [21:52:54<08:08, 4.21s/it] 99%|█████████▉| 18573/18688 [21:52:59<08:21, 4.36s/it] 99%|█████████▉| 18574/18688 [21:53:03<08:20, 4.39s/it] 99%|█████████▉| 18575/18688 [21:53:08<08:34, 4.55s/it] 99%|█████████▉| 18576/18688 [21:53:12<08:09, 4.37s/it] 99%|█████████▉| 18577/18688 [21:53:17<08:13, 4.45s/it] 99%|█████████▉| 18578/18688 [21:53:21<07:53, 4.30s/it] 99%|█████████▉| 18579/18688 [21:53:25<07:45, 4.27s/it] 99%|█████████▉| 18580/18688 [21:53:29<07:35, 4.22s/it] {'loss': '0.9736', 'grad_norm': '1.534', 'learning_rate': '9.923e-09', 'epoch': '0.9942', 'num_input_tokens_seen': 62524560, 'train_runtime': '7.881e+04', 'train_tokens_per_second': '793.4'}
99%|█████████▉| 18580/18688 [21:53:29<07:35, 4.22s/it] 99%|█████████▉| 18581/18688 [21:53:33<07:28, 4.19s/it] 99%|█████████▉| 18582/18688 [21:53:38<07:44, 4.38s/it] 99%|█████████▉| 18583/18688 [21:53:42<07:46, 4.44s/it] 99%|█████████▉| 18584/18688 [21:53:47<07:35, 4.38s/it] 99%|█████████▉| 18585/18688 [21:53:51<07:31, 4.38s/it] 99%|█████████▉| 18586/18688 [21:53:56<07:53, 4.65s/it] 99%|█████████▉| 18587/18688 [21:54:00<07:33, 4.49s/it] 99%|█████████▉| 18588/18688 [21:54:06<07:51, 4.71s/it] 99%|█████████▉| 18589/18688 [21:54:10<07:31, 4.56s/it] 99%|█████████▉| 18590/18688 [21:54:13<06:58, 4.27s/it] {'loss': '0.8633', 'grad_norm': '1.548', 'learning_rate': '8.186e-09', 'epoch': '0.9948', 'num_input_tokens_seen': 62562056, 'train_runtime': '7.885e+04', 'train_tokens_per_second': '793.4'}
99%|█████████▉| 18590/18688 [21:54:13<06:58, 4.27s/it] 99%|█████████▉| 18591/18688 [21:54:18<06:58, 4.32s/it] 99%|█████████▉| 18592/18688 [21:54:21<06:29, 4.06s/it] 99%|█████████▉| 18593/18688 [21:54:25<06:20, 4.01s/it] 99%|█████████▉| 18594/18688 [21:54:29<06:09, 3.93s/it] 100%|█████████▉| 18595/18688 [21:54:33<05:57, 3.85s/it] 100%|█████████▉| 18596/18688 [21:54:38<06:34, 4.28s/it] 100%|█████████▉| 18597/18688 [21:54:41<06:09, 4.06s/it] 100%|█████████▉| 18598/18688 [21:54:45<06:04, 4.05s/it] 100%|█████████▉| 18599/18688 [21:54:50<06:06, 4.12s/it] 100%|█████████▉| 18600/18688 [21:54:54<06:15, 4.27s/it] {'loss': '0.9387', 'grad_norm': '1.281', 'learning_rate': '6.615e-09', 'epoch': '0.9953', 'num_input_tokens_seen': 62593856, 'train_runtime': '7.889e+04', 'train_tokens_per_second': '793.4'}
100%|█████████▉| 18600/18688 [21:54:54<06:15, 4.27s/it] 100%|█████████▉| 18601/18688 [21:54:58<06:05, 4.20s/it] 100%|█████████▉| 18602/18688 [21:55:02<05:58, 4.16s/it] 100%|█████████▉| 18603/18688 [21:55:07<06:00, 4.24s/it] 100%|█████████▉| 18604/18688 [21:55:12<06:09, 4.39s/it] 100%|█████████▉| 18605/18688 [21:55:17<06:16, 4.54s/it] 100%|█████████▉| 18606/18688 [21:55:21<06:10, 4.52s/it] 100%|█████████▉| 18607/18688 [21:55:26<06:14, 4.62s/it] 100%|█████████▉| 18608/18688 [21:55:30<06:00, 4.50s/it] 100%|█████████▉| 18609/18688 [21:55:35<05:56, 4.51s/it] 100%|█████████▉| 18610/18688 [21:55:39<05:43, 4.40s/it] {'loss': '0.8809', 'grad_norm': '1.189', 'learning_rate': '5.212e-09', 'epoch': '0.9959', 'num_input_tokens_seen': 62632432, 'train_runtime': '7.894e+04', 'train_tokens_per_second': '793.4'}
100%|█████████▉| 18610/18688 [21:55:39<05:43, 4.40s/it] 100%|█████████▉| 18611/18688 [21:55:43<05:40, 4.42s/it] 100%|█████████▉| 18612/18688 [21:55:48<05:35, 4.41s/it] 100%|█████████▉| 18613/18688 [21:55:52<05:38, 4.52s/it] 100%|█████████▉| 18614/18688 [21:55:56<05:24, 4.38s/it] 100%|█████████▉| 18615/18688 [21:56:02<05:36, 4.62s/it] 100%|█████████▉| 18616/18688 [21:56:05<05:12, 4.34s/it] 100%|█████████▉| 18617/18688 [21:56:09<05:05, 4.30s/it] 100%|█████████▉| 18618/18688 [21:56:14<05:03, 4.34s/it] 100%|█████████▉| 18619/18688 [21:56:18<04:49, 4.19s/it] 100%|█████████▉| 18620/18688 [21:56:22<04:37, 4.09s/it] {'loss': '0.9432', 'grad_norm': '1.111', 'learning_rate': '3.976e-09', 'epoch': '0.9964', 'num_input_tokens_seen': 62668504, 'train_runtime': '7.898e+04', 'train_tokens_per_second': '793.5'}
100%|█████████▉| 18620/18688 [21:56:22<04:37, 4.09s/it] 100%|█████████▉| 18621/18688 [21:56:25<04:26, 3.98s/it] 100%|█████████▉| 18622/18688 [21:56:30<04:30, 4.10s/it] 100%|█████████▉| 18623/18688 [21:56:33<04:13, 3.91s/it] 100%|█████████▉| 18624/18688 [21:56:37<04:01, 3.77s/it] 100%|█████████▉| 18625/18688 [21:56:41<04:03, 3.86s/it] 100%|█████████▉| 18626/18688 [21:56:44<03:56, 3.82s/it] 100%|█████████▉| 18627/18688 [21:56:48<03:53, 3.82s/it] 100%|█████████▉| 18628/18688 [21:56:53<03:57, 3.95s/it] 100%|█████████▉| 18629/18688 [21:56:57<03:57, 4.03s/it] 100%|█████████▉| 18630/18688 [21:57:01<03:56, 4.08s/it] {'loss': '0.936', 'grad_norm': '1.036', 'learning_rate': '2.907e-09', 'epoch': '0.9969', 'num_input_tokens_seen': 62700024, 'train_runtime': '7.902e+04', 'train_tokens_per_second': '793.5'}
100%|█████████▉| 18630/18688 [21:57:01<03:56, 4.08s/it] 100%|█████████▉| 18631/18688 [21:57:05<03:57, 4.16s/it] 100%|█████████▉| 18632/18688 [21:57:10<04:08, 4.43s/it] 100%|█████████▉| 18633/18688 [21:57:14<03:55, 4.28s/it] 100%|█████████▉| 18634/18688 [21:57:18<03:43, 4.14s/it] 100%|█████████▉| 18635/18688 [21:57:22<03:39, 4.13s/it] 100%|█████████▉| 18636/18688 [21:57:26<03:36, 4.17s/it] 100%|█████████▉| 18637/18688 [21:57:30<03:26, 4.05s/it] 100%|█████████▉| 18638/18688 [21:57:35<03:26, 4.13s/it] 100%|█████████▉| 18639/18688 [21:57:38<03:18, 4.04s/it] 100%|█████████▉| 18640/18688 [21:57:43<03:25, 4.29s/it] {'loss': '0.9133', 'grad_norm': '1.383', 'learning_rate': '2.005e-09', 'epoch': '0.9975', 'num_input_tokens_seen': 62736400, 'train_runtime': '7.906e+04', 'train_tokens_per_second': '793.5'}
100%|█████████▉| 18640/18688 [21:57:43<03:25, 4.29s/it] 100%|█████████▉| 18641/18688 [21:57:48<03:22, 4.30s/it] 100%|█████████▉| 18642/18688 [21:57:52<03:18, 4.32s/it] 100%|█████████▉| 18643/18688 [21:57:56<03:15, 4.36s/it] 100%|█████████▉| 18644/18688 [21:58:01<03:17, 4.49s/it] 100%|█████████▉| 18645/18688 [21:58:05<03:07, 4.36s/it] 100%|█████████▉| 18646/18688 [21:58:09<03:01, 4.32s/it] 100%|█████████▉| 18647/18688 [21:58:13<02:52, 4.22s/it] 100%|█████████▉| 18648/18688 [21:58:17<02:45, 4.14s/it] 100%|█████████▉| 18649/18688 [21:58:21<02:38, 4.06s/it] 100%|█████████▉| 18650/18688 [21:58:25<02:31, 3.99s/it] {'loss': '0.9506', 'grad_norm': '1.31', 'learning_rate': '1.27e-09', 'epoch': '0.998', 'num_input_tokens_seen': 62770736, 'train_runtime': '7.911e+04', 'train_tokens_per_second': '793.5'}
100%|█████████▉| 18650/18688 [21:58:25<02:31, 3.99s/it] 100%|█████████▉| 18651/18688 [21:58:29<02:22, 3.84s/it] 100%|█████████▉| 18652/18688 [21:58:33<02:22, 3.97s/it] 100%|█████████▉| 18653/18688 [21:58:38<02:27, 4.20s/it] 100%|█████████▉| 18654/18688 [21:58:41<02:17, 4.05s/it] 100%|█████████▉| 18655/18688 [21:58:45<02:15, 4.09s/it] 100%|█████████▉| 18656/18688 [21:58:50<02:15, 4.24s/it] 100%|█████████▉| 18657/18688 [21:58:54<02:08, 4.14s/it] 100%|█████████▉| 18658/18688 [21:58:58<02:05, 4.19s/it] 100%|█████████▉| 18659/18688 [21:59:02<01:59, 4.11s/it] 100%|█████████▉| 18660/18688 [21:59:06<01:51, 3.98s/it] {'loss': '0.947', 'grad_norm': '1.438', 'learning_rate': '7.024e-10', 'epoch': '0.9985', 'num_input_tokens_seen': 62801816, 'train_runtime': '7.915e+04', 'train_tokens_per_second': '793.5'}
100%|█████████▉| 18660/18688 [21:59:06<01:51, 3.98s/it] 100%|█████████▉| 18661/18688 [21:59:10<01:49, 4.05s/it] 100%|█████████▉| 18662/18688 [21:59:14<01:44, 4.03s/it] 100%|█████████▉| 18663/18688 [21:59:19<01:45, 4.22s/it] 100%|█████████▉| 18664/18688 [21:59:23<01:44, 4.34s/it] 100%|█████████▉| 18665/18688 [21:59:27<01:36, 4.21s/it] 100%|█████████▉| 18666/18688 [21:59:31<01:29, 4.09s/it] 100%|█████████▉| 18667/18688 [21:59:35<01:28, 4.19s/it] 100%|█████████▉| 18668/18688 [21:59:39<01:19, 3.98s/it] 100%|█████████▉| 18669/18688 [21:59:44<01:21, 4.30s/it] 100%|█████████▉| 18670/18688 [21:59:48<01:14, 4.11s/it] {'loss': '0.8628', 'grad_norm': '1.32', 'learning_rate': '3.015e-10', 'epoch': '0.9991', 'num_input_tokens_seen': 62835552, 'train_runtime': '7.919e+04', 'train_tokens_per_second': '793.5'}
100%|█████████▉| 18670/18688 [21:59:48<01:14, 4.11s/it] 100%|█████████▉| 18671/18688 [21:59:51<01:07, 3.96s/it] 100%|█████████▉| 18672/18688 [21:59:56<01:05, 4.07s/it] 100%|█████████▉| 18673/18688 [21:59:59<01:00, 4.00s/it] 100%|█████████▉| 18674/18688 [22:00:03<00:55, 3.93s/it] 100%|█████████▉| 18675/18688 [22:00:07<00:49, 3.83s/it] 100%|█████████▉| 18676/18688 [22:00:11<00:45, 3.82s/it] 100%|█████████▉| 18677/18688 [22:00:15<00:42, 3.90s/it] 100%|█████████▉| 18678/18688 [22:00:19<00:40, 4.10s/it] 100%|█████████▉| 18679/18688 [22:00:24<00:37, 4.16s/it] 100%|█████████▉| 18680/18688 [22:00:28<00:33, 4.25s/it] {'loss': '0.8469', 'grad_norm': '1.215', 'learning_rate': '6.765e-11', 'epoch': '0.9996', 'num_input_tokens_seen': 62866392, 'train_runtime': '7.923e+04', 'train_tokens_per_second': '793.5'}
100%|█████████▉| 18680/18688 [22:00:28<00:33, 4.25s/it] 100%|█████████▉| 18681/18688 [22:00:32<00:29, 4.16s/it] 100%|█████████▉| 18682/18688 [22:00:36<00:24, 4.03s/it] 100%|█████████▉| 18683/18688 [22:00:40<00:20, 4.01s/it] 100%|█████████▉| 18684/18688 [22:00:44<00:16, 4.10s/it] 100%|█████████▉| 18685/18688 [22:00:48<00:12, 4.21s/it] 100%|█████████▉| 18686/18688 [22:00:52<00:08, 4.12s/it] 100%|█████████▉| 18687/18688 [22:00:57<00:04, 4.30s/it] 100%|██████████| 18688/18688 [22:00:59<00:00, 3.55s/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, 25.62it/s]
1%| | 6/1000 [00:00<00:54, 18.26it/s]
1%| | 8/1000 [00:00<00:56, 17.59it/s]
1%| | 10/1000 [00:00<00:59, 16.69it/s]
1%| | 12/1000 [00:00<01:00, 16.37it/s]
1%|▏ | 14/1000 [00:00<01:02, 15.84it/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, 10.53it/s]
2%|▏ | 22/1000 [00:01<01:51, 8.73it/s]
2%|▏ | 24/1000 [00:01<01:39, 9.78it/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, 11.01it/s]
3%|▎ | 32/1000 [00:02<01:35, 10.17it/s]
3%|▎ | 34/1000 [00:02<01:25, 11.27it/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, 10.94it/s]
4%|▍ | 42/1000 [00:03<01:47, 8.88it/s]
4%|▍ | 44/1000 [00:03<01:32, 10.30it/s]
5%|▍ | 46/1000 [00:04<01:23, 11.45it/s]
5%|▍ | 48/1000 [00:04<01:36, 9.89it/s]
5%|▌ | 50/1000 [00:04<01:26, 11.00it/s]
5%|▌ | 52/1000 [00:04<01:19, 11.90it/s]
5%|▌ | 54/1000 [00:04<01:14, 12.70it/s]
6%|▌ | 56/1000 [00:04<01:10, 13.33it/s]
6%|▌ | 58/1000 [00:05<01:50, 8.53it/s]
6%|▌ | 60/1000 [00:05<01:37, 9.60it/s]
6%|▌ | 62/1000 [00:05<01:28, 10.58it/s]
6%|▋ | 64/1000 [00:05<01:20, 11.66it/s]
7%|▋ | 66/1000 [00:05<01:16, 12.19it/s]
7%|▋ | 68/1000 [00:05<01:12, 12.80it/s]
7%|▋ | 70/1000 [00:06<01:09, 13.38it/s]
7%|▋ | 72/1000 [00:06<01:07, 13.83it/s]
7%|▋ | 74/1000 [00:06<01:07, 13.65it/s]
8%|▊ | 76/1000 [00:06<01:04, 14.26it/s]
8%|▊ | 78/1000 [00:06<01:03, 14.45it/s]
8%|▊ | 80/1000 [00:06<01:08, 13.44it/s]
8%|▊ | 82/1000 [00:06<01:05, 14.06it/s]
8%|▊ | 84/1000 [00:07<01:02, 14.54it/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, 14.52it/s]
9%|▉ | 92/1000 [00:07<01:04, 14.18it/s]
9%|▉ | 94/1000 [00:07<01:04, 14.00it/s]
10%|▉ | 96/1000 [00:07<01:03, 14.30it/s]
10%|▉ | 98/1000 [00:08<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<01:42, 8.67it/s]
11%|█ | 110/1000 [00:09<01:27, 10.12it/s]
11%|█ | 112/1000 [00:09<01:18, 11.24it/s]
11%|█▏ | 114/1000 [00:09<01:14, 11.89it/s]
12%|█▏ | 116/1000 [00:09<01:10, 12.51it/s]
12%|█▏ | 118/1000 [00:10<01:06, 13.24it/s]
12%|█▏ | 120/1000 [00:10<01:09, 12.68it/s]
12%|█▏ | 122/1000 [00:10<01:23, 10.57it/s]
12%|█▏ | 124/1000 [00:10<01:30, 9.66it/s]
13%|█▎ | 126/1000 [00:10<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<01:10, 12.24it/s]
13%|█▎ | 134/1000 [00:11<01:13, 11.73it/s]
14%|█▎ | 136/1000 [00:11<01:12, 11.91it/s]
14%|█▍ | 138/1000 [00:11<01:11, 12.11it/s]
14%|█▍ | 140/1000 [00:12<01:30, 9.47it/s]
14%|█▍ | 142/1000 [00:12<01:44, 8.18it/s]
14%|█▍ | 144/1000 [00:12<01:33, 9.12it/s]
15%|█▍ | 146/1000 [00:12<01:26, 9.87it/s]
15%|█▍ | 148/1000 [00:12<01:20, 10.59it/s]
15%|█▌ | 150/1000 [00:13<01:16, 11.16it/s]
15%|█▌ | 152/1000 [00:13<01:14, 11.41it/s]
15%|█▌ | 154/1000 [00:13<01:11, 11.91it/s]
16%|█▌ | 156/1000 [00:13<01:09, 12.22it/s]
16%|█▌ | 158/1000 [00:13<01:04, 12.97it/s]
16%|█▌ | 160/1000 [00:13<01:04, 13.11it/s]
16%|█▌ | 162/1000 [00:14<01:03, 13.11it/s]
16%|█▋ | 164/1000 [00:14<01:02, 13.30it/s]
17%|█▋ | 166/1000 [00:14<01:01, 13.54it/s]
17%|█▋ | 168/1000 [00:14<00:59, 14.09it/s]
17%|█▋ | 170/1000 [00:14<01:19, 10.40it/s]
17%|█▋ | 172/1000 [00:14<01:12, 11.39it/s]
17%|█▋ | 174/1000 [00:15<01:30, 9.13it/s]
18%|█▊ | 176/1000 [00:15<01:54, 7.17it/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, 8.55it/s]
18%|█▊ | 184/1000 [00:16<01:23, 9.79it/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, 11.34it/s]
19%|█▉ | 194/1000 [00:17<01:16, 10.56it/s]
20%|█▉ | 196/1000 [00:17<01:29, 8.97it/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<01:03, 12.63it/s]
21%|██ | 206/1000 [00:18<01:00, 13.22it/s]
21%|██ | 208/1000 [00:18<00:58, 13.60it/s]
21%|██ | 210/1000 [00:18<00:54, 14.49it/s]
21%|██ | 212/1000 [00:18<00:53, 14.76it/s]
21%|██▏ | 214/1000 [00:18<00:53, 14.77it/s]
22%|██▏ | 216/1000 [00:18<00:54, 14.51it/s]
22%|██▏ | 218/1000 [00:19<00:52, 14.90it/s]
22%|██▏ | 220/1000 [00:19<01:27, 8.92it/s]
22%|██▏ | 222/1000 [00:19<01:24, 9.18it/s]
22%|██▏ | 224/1000 [00:19<01:20, 9.67it/s]
23%|██▎ | 226/1000 [00:20<01:12, 10.68it/s]
23%|██▎ | 228/1000 [00:20<01:06, 11.57it/s]
23%|██▎ | 230/1000 [00:20<01:03, 12.04it/s]
23%|██▎ | 232/1000 [00:20<01:01, 12.39it/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, 14.24it/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, 15.01it/s]
25%|██▍ | 246/1000 [00:21<00:50, 14.98it/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, 12.18it/s]
25%|██▌ | 254/1000 [00:22<00:56, 13.20it/s]
26%|██▌ | 256/1000 [00:22<00:54, 13.73it/s]
26%|██▌ | 258/1000 [00:22<00:53, 13.77it/s]
26%|██▌ | 260/1000 [00:22<00:57, 12.89it/s]
26%|██▌ | 262/1000 [00:22<00:56, 13.06it/s]
26%|██▋ | 264/1000 [00:22<00:54, 13.63it/s]
27%|██▋ | 266/1000 [00:22<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, 12.75it/s]
28%|██▊ | 278/1000 [00:23<00:55, 12.98it/s]
28%|██▊ | 280/1000 [00:24<00:53, 13.55it/s]
28%|██▊ | 282/1000 [00:24<00:49, 14.50it/s]
28%|██▊ | 284/1000 [00:24<00:49, 14.46it/s]
29%|██▊ | 286/1000 [00:24<00:48, 14.73it/s]
29%|██▉ | 288/1000 [00:24<00:47, 15.04it/s]
29%|██▉ | 290/1000 [00:24<00:46, 15.41it/s]
29%|██▉ | 292/1000 [00:24<00:46, 15.38it/s]
29%|██▉ | 294/1000 [00:24<00:46, 15.29it/s]
30%|██▉ | 296/1000 [00:25<00:45, 15.36it/s]
30%|██▉ | 298/1000 [00:25<00:45, 15.51it/s]
30%|███ | 300/1000 [00:25<00:46, 15.13it/s]
30%|███ | 302/1000 [00:25<00:46, 15.14it/s]
30%|███ | 304/1000 [00:25<00:50, 13.74it/s]
31%|███ | 306/1000 [00:25<00:48, 14.44it/s]
31%|███ | 308/1000 [00:25<00:48, 14.41it/s]
31%|███ | 310/1000 [00:26<00:47, 14.55it/s]
31%|███ | 312/1000 [00:26<00:47, 14.55it/s]
31%|███▏ | 314/1000 [00:26<00:44, 15.26it/s]
32%|███▏ | 316/1000 [00:26<00:43, 15.56it/s]
32%|███▏ | 318/1000 [00:26<00:44, 15.45it/s]
32%|███▏ | 320/1000 [00:27<01:20, 8.49it/s]
32%|███▏ | 322/1000 [00:27<01:08, 9.88it/s]
32%|███▏ | 324/1000 [00:27<01:02, 10.80it/s]
33%|███▎ | 326/1000 [00:27<01:01, 10.91it/s]
33%|███▎ | 328/1000 [00:27<01:00, 11.08it/s]
33%|███▎ | 330/1000 [00:27<00:55, 12.07it/s]
33%|███▎ | 332/1000 [00:27<00:53, 12.42it/s]
33%|███▎ | 334/1000 [00:28<00:51, 12.97it/s]
34%|███▎ | 336/1000 [00:28<00:49, 13.41it/s]
34%|███▍ | 338/1000 [00:28<01:06, 10.00it/s]
34%|███▍ | 340/1000 [00:28<00:59, 11.09it/s]
34%|███▍ | 342/1000 [00:28<00:53, 12.35it/s]
34%|███▍ | 344/1000 [00:28<00:50, 13.03it/s]
35%|███▍ | 346/1000 [00:29<00:48, 13.62it/s]
35%|███▍ | 348/1000 [00:29<00:46, 14.04it/s]
35%|███▌ | 350/1000 [00:29<00:44, 14.65it/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, 14.96it/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<00:46, 13.82it/s]
37%|███▋ | 366/1000 [00:30<00:53, 11.93it/s]
37%|███▋ | 368/1000 [00:30<00:51, 12.34it/s]
37%|███▋ | 370/1000 [00:30<00:47, 13.19it/s]
37%|███▋ | 372/1000 [00:30<00:46, 13.51it/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, 11.63it/s]
38%|███▊ | 380/1000 [00:31<00:49, 12.45it/s]
38%|███▊ | 382/1000 [00:31<00:48, 12.66it/s]
38%|███▊ | 384/1000 [00:31<00:45, 13.42it/s]
39%|███▊ | 386/1000 [00:32<00:44, 13.73it/s]
39%|███▉ | 388/1000 [00:32<00:44, 13.68it/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, 13.48it/s]
40%|███▉ | 396/1000 [00:32<00:46, 13.09it/s]
40%|███▉ | 398/1000 [00:33<00:49, 12.04it/s]
40%|████ | 400/1000 [00:33<00:49, 12.14it/s]
40%|████ | 402/1000 [00:33<00:48, 12.39it/s]
40%|████ | 404/1000 [00:33<00:48, 12.34it/s]
41%|████ | 406/1000 [00:33<00:47, 12.51it/s]
41%|████ | 408/1000 [00:33<00:54, 10.84it/s]
41%|████ | 410/1000 [00:34<01:01, 9.57it/s]
41%|████ | 412/1000 [00:34<00:54, 10.78it/s]
41%|████▏ | 414/1000 [00:34<01:10, 8.32it/s]
42%|████▏ | 416/1000 [00:35<01:25, 6.82it/s]
42%|████▏ | 418/1000 [00:35<01:14, 7.86it/s]
42%|████▏ | 420/1000 [00:35<01:03, 9.12it/s]
42%|████▏ | 422/1000 [00:35<00:55, 10.49it/s]
42%|████▏ | 424/1000 [00:35<01:16, 7.56it/s]
43%|████▎ | 426/1000 [00:36<01:05, 8.81it/s]
43%|████▎ | 428/1000 [00:36<00:57, 10.02it/s]
43%|████▎ | 430/1000 [00:36<01:03, 9.04it/s]
43%|████▎ | 432/1000 [00:36<01:20, 7.02it/s]
43%|████▎ | 434/1000 [00:37<01:08, 8.24it/s]
44%|████▎ | 436/1000 [00:37<01:00, 9.28it/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, 11.39it/s]
45%|████▍ | 446/1000 [00:38<00:45, 12.16it/s]
45%|████▍ | 448/1000 [00:38<00:50, 10.97it/s]
45%|████▌ | 450/1000 [00:38<00:46, 11.80it/s]
45%|████▌ | 452/1000 [00:38<00:42, 12.82it/s]
45%|████▌ | 454/1000 [00:38<00:44, 12.31it/s]
46%|████▌ | 456/1000 [00:38<00:42, 12.73it/s]
46%|████▌ | 458/1000 [00:38<00:39, 13.74it/s]
46%|████▌ | 460/1000 [00:39<00:38, 13.87it/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<00:35, 14.85it/s]
47%|████▋ | 468/1000 [00:39<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, 12.18it/s]
48%|████▊ | 478/1000 [00:40<00:43, 12.05it/s]
48%|████▊ | 480/1000 [00:40<00:40, 12.98it/s]
48%|████▊ | 482/1000 [00:40<00:38, 13.35it/s]
48%|████▊ | 484/1000 [00:41<00:37, 13.87it/s]
49%|████▊ | 486/1000 [00:41<00:36, 14.02it/s]
49%|████▉ | 488/1000 [00:41<00:35, 14.31it/s]
49%|████▉ | 490/1000 [00:41<00:45, 11.15it/s]
49%|████▉ | 492/1000 [00:41<00:40, 12.40it/s]
49%|████▉ | 494/1000 [00:41<00:37, 13.36it/s]
50%|████▉ | 496/1000 [00:41<00:35, 14.11it/s]
50%|████▉ | 498/1000 [00:42<00:34, 14.53it/s]
50%|█████ | 500/1000 [00:42<00:43, 11.59it/s]
50%|█████ | 502/1000 [00:42<00:41, 12.04it/s]
50%|█████ | 504/1000 [00:42<00:39, 12.63it/s]
51%|█████ | 506/1000 [00:42<00:38, 12.80it/s]
51%|█████ | 508/1000 [00:42<00:35, 13.72it/s]
51%|█████ | 510/1000 [00:43<00:47, 10.42it/s]
51%|█████ | 512/1000 [00:43<00:43, 11.17it/s]
51%|█████▏ | 514/1000 [00:43<00:40, 12.03it/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, 13.49it/s]
52%|█████▏ | 522/1000 [00:44<00:33, 14.23it/s]
52%|█████▏ | 524/1000 [00:44<00:33, 14.25it/s]
53%|█████▎ | 526/1000 [00:44<00:32, 14.39it/s]
53%|█████▎ | 528/1000 [00:44<00:41, 11.31it/s]
53%|█████▎ | 530/1000 [00:44<00:38, 12.28it/s]
53%|█████▎ | 532/1000 [00:44<00:36, 12.98it/s]
53%|█████▎ | 534/1000 [00:44<00:35, 12.98it/s]
54%|█████▎ | 536/1000 [00:45<00:34, 13.40it/s]
54%|█████▍ | 538/1000 [00:45<00:34, 13.39it/s]
54%|█████▍ | 540/1000 [00:45<00:33, 13.88it/s]
54%|█████▍ | 542/1000 [00:45<00:32, 14.20it/s]
54%|█████▍ | 544/1000 [00:45<00:31, 14.27it/s]
55%|█████▍ | 546/1000 [00:45<00:32, 14.01it/s]
55%|█████▍ | 548/1000 [00:45<00:32, 13.78it/s]
55%|█████▌ | 550/1000 [00:46<00:32, 14.01it/s]
55%|█████▌ | 552/1000 [00:46<00:31, 14.15it/s]
55%|█████▌ | 554/1000 [00:46<00:32, 13.85it/s]
56%|█████▌ | 556/1000 [00:46<00:31, 14.05it/s]
56%|█████▌ | 558/1000 [00:46<00:30, 14.44it/s]
56%|█████▌ | 560/1000 [00:46<00:29, 14.99it/s]
56%|█████▌ | 562/1000 [00:46<00:29, 14.90it/s]
56%|█████▋ | 564/1000 [00:47<00:40, 10.77it/s]
57%|█████▋ | 566/1000 [00:47<00:37, 11.67it/s]
57%|█████▋ | 568/1000 [00:47<00:35, 12.23it/s]
57%|█████▋ | 570/1000 [00:47<00:32, 13.34it/s]
57%|█████▋ | 572/1000 [00:47<00:33, 12.86it/s]
57%|█████▋ | 574/1000 [00:47<00:30, 13.74it/s]
58%|█████▊ | 576/1000 [00:48<00:31, 13.65it/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, 13.65it/s]
58%|█████▊ | 584/1000 [00:48<00:29, 13.87it/s]
59%|█████▊ | 586/1000 [00:48<00:29, 14.24it/s]
59%|█████▉ | 588/1000 [00:48<00:27, 15.05it/s]
59%|█████▉ | 590/1000 [00:49<00:27, 15.02it/s]
59%|█████▉ | 592/1000 [00:49<00:27, 14.78it/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, 14.10it/s]
60%|██████ | 600/1000 [00:49<00:27, 14.47it/s]
60%|██████ | 602/1000 [00:49<00:27, 14.59it/s]
60%|██████ | 604/1000 [00:50<00:26, 15.14it/s]
61%|██████ | 606/1000 [00:50<00:26, 14.87it/s]
61%|██████ | 608/1000 [00:50<00:25, 15.17it/s]
61%|██████ | 610/1000 [00:50<00:25, 15.02it/s]
61%|██████ | 612/1000 [00:50<00:25, 15.13it/s]
61%|██████▏ | 614/1000 [00:50<00:26, 14.37it/s]
62%|██████▏ | 616/1000 [00:50<00:26, 14.76it/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<00:38, 9.84it/s]
62%|██████▏ | 624/1000 [00:51<00:35, 10.49it/s]
63%|██████▎ | 626/1000 [00:51<00:34, 10.95it/s]
63%|██████▎ | 628/1000 [00:52<00:32, 11.53it/s]
63%|██████▎ | 630/1000 [00:52<00:31, 11.90it/s]
63%|██████▎ | 632/1000 [00:52<00:32, 11.21it/s]
63%|██████▎ | 634/1000 [00:52<00:32, 11.41it/s]
64%|██████▎ | 636/1000 [00:52<00:36, 10.07it/s]
64%|██████▍ | 638/1000 [00:53<00:35, 10.11it/s]
64%|██████▍ | 640/1000 [00:53<00:32, 11.01it/s]
64%|██████▍ | 642/1000 [00:53<00:29, 11.98it/s]
64%|██████▍ | 644/1000 [00:53<00:28, 12.29it/s]
65%|██████▍ | 646/1000 [00:53<00:28, 12.62it/s]
65%|██████▍ | 648/1000 [00:53<00:26, 13.15it/s]
65%|██████▌ | 650/1000 [00:53<00:28, 12.44it/s]
65%|██████▌ | 652/1000 [00:54<00:29, 11.89it/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, 11.82it/s]
66%|██████▌ | 660/1000 [00:54<00:33, 10.14it/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, 12.77it/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, 11.87it/s]
67%|██████▋ | 674/1000 [00:56<00:26, 12.46it/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, 12.97it/s]
68%|██████▊ | 682/1000 [00:56<00:26, 12.11it/s]
68%|██████▊ | 684/1000 [00:56<00:25, 12.30it/s]
69%|██████▊ | 686/1000 [00:56<00:23, 13.41it/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, 14.26it/s]
69%|██████▉ | 694/1000 [00:57<00:21, 14.33it/s]
70%|██████▉ | 696/1000 [00:57<00:21, 14.05it/s]
70%|██████▉ | 698/1000 [00:57<00:20, 14.60it/s]
70%|███████ | 700/1000 [00:58<00:24, 12.20it/s]
70%|███████ | 702/1000 [00:58<00:22, 13.02it/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<00:30, 9.63it/s]
71%|███████ | 710/1000 [00:58<00:26, 10.93it/s]
71%|███████ | 712/1000 [00:59<00:24, 11.89it/s]
71%|███████▏ | 714/1000 [00:59<00:22, 12.97it/s]
72%|███████▏ | 716/1000 [00:59<00:21, 13.05it/s]
72%|███████▏ | 718/1000 [00:59<00:22, 12.57it/s]
72%|███████▏ | 720/1000 [00:59<00:20, 13.53it/s]
72%|███████▏ | 722/1000 [00:59<00:19, 13.98it/s]
72%|███████▏ | 724/1000 [00:59<00:20, 13.80it/s]
73%|███████▎ | 726/1000 [01:00<00:19, 14.01it/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, 8.45it/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, 11.69it/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, 9.58it/s]
75%|███████▍ | 749/1000 [01:02<00:23, 10.76it/s]
75%|███████▌ | 751/1000 [01:02<00:20, 11.88it/s]
75%|███████▌ | 753/1000 [01:02<00:19, 12.60it/s]
76%|███████▌ | 755/1000 [01:02<00:18, 13.27it/s]
76%|███████▌ | 757/1000 [01:03<00:23, 10.27it/s]
76%|███████▌ | 759/1000 [01:03<00:21, 11.30it/s]
76%|███████▌ | 761/1000 [01:03<00:19, 12.30it/s]
76%|███████▋ | 763/1000 [01:03<00:23, 10.25it/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, 10.59it/s]
77%|███████▋ | 771/1000 [01:04<00:19, 11.58it/s]
77%|███████▋ | 773/1000 [01:04<00:18, 12.17it/s]
78%|███████▊ | 775/1000 [01:04<00:17, 12.76it/s]
78%|███████▊ | 777/1000 [01:04<00:16, 13.60it/s]
78%|███████▊ | 779/1000 [01:05<00:22, 9.85it/s]
78%|███████▊ | 781/1000 [01:05<00:24, 8.98it/s]
78%|███████▊ | 783/1000 [01:05<00:26, 8.06it/s]
78%|███████▊ | 784/1000 [01:06<00:31, 6.91it/s]
78%|███████▊ | 785/1000 [01:06<00:29, 7.23it/s]
79%|███████▊ | 786/1000 [01:06<00:31, 6.88it/s]
79%|███████▉ | 788/1000 [01:06<00:24, 8.72it/s]
79%|███████▉ | 790/1000 [01:06<00:20, 10.06it/s]
79%|███████▉ | 792/1000 [01:06<00:18, 11.22it/s]
79%|███████▉ | 794/1000 [01:06<00:17, 11.87it/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, 12.29it/s]
80%|████████ | 802/1000 [01:07<00:24, 8.25it/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, 10.31it/s]
81%|████████ | 810/1000 [01:08<00:18, 10.46it/s]
81%|████████ | 812/1000 [01:08<00:16, 11.68it/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, 11.96it/s]
82%|████████▏ | 820/1000 [01:09<00:14, 12.46it/s]
82%|████████▏ | 822/1000 [01:09<00:13, 13.01it/s]
82%|████████▏ | 824/1000 [01:09<00:12, 13.79it/s]
83%|████████▎ | 826/1000 [01:09<00:12, 14.22it/s]
83%|████████▎ | 828/1000 [01:09<00:11, 14.49it/s]
83%|████████▎ | 830/1000 [01:10<00:12, 13.93it/s]
83%|████████▎ | 832/1000 [01:10<00:11, 14.29it/s]
83%|████████▎ | 834/1000 [01:10<00:11, 14.03it/s]
84%|████████▎ | 836/1000 [01:10<00:11, 14.39it/s]
84%|████████▍ | 838/1000 [01:10<00:11, 14.30it/s]
84%|████████▍ | 840/1000 [01:10<00:11, 14.41it/s]
84%|████████▍ | 842/1000 [01:10<00:10, 14.41it/s]
84%|████████▍ | 844/1000 [01:10<00:11, 13.96it/s]
85%|████████▍ | 846/1000 [01:11<00:11, 13.92it/s]
85%|████████▍ | 848/1000 [01:11<00:10, 13.91it/s]
85%|████████▌ | 850/1000 [01:11<00:10, 14.13it/s]
85%|████████▌ | 852/1000 [01:11<00:10, 13.72it/s]
85%|████████▌ | 854/1000 [01:11<00:10, 14.29it/s]
86%|████████▌ | 856/1000 [01:11<00:10, 14.12it/s]
86%|████████▌ | 858/1000 [01:11<00:09, 14.59it/s]
86%|████████▌ | 860/1000 [01:12<00:09, 15.33it/s]
86%|████████▌ | 862/1000 [01:12<00:10, 13.51it/s]
86%|████████▋ | 864/1000 [01:12<00:09, 14.04it/s]
87%|████████▋ | 866/1000 [01:12<00:09, 13.84it/s]
87%|████████▋ | 868/1000 [01:12<00:09, 13.74it/s]
87%|████████▋ | 870/1000 [01:12<00:09, 14.11it/s]
87%|████████▋ | 872/1000 [01:12<00:08, 14.70it/s]
87%|████████▋ | 874/1000 [01:13<00:10, 12.45it/s]
88%|████████▊ | 876/1000 [01:13<00:11, 11.06it/s]
88%|████████▊ | 878/1000 [01:13<00:10, 11.91it/s]
88%|████████▊ | 880/1000 [01:13<00:09, 12.54it/s]
88%|████████▊ | 882/1000 [01:13<00:10, 10.93it/s]
88%|████████▊ | 884/1000 [01:14<00:10, 11.27it/s]
89%|████████▊ | 886/1000 [01:14<00:09, 11.58it/s]
89%|████████▉ | 888/1000 [01:14<00:09, 11.83it/s]
89%|████████▉ | 890/1000 [01:14<00:09, 11.95it/s]
89%|████████▉ | 892/1000 [01:14<00:08, 12.04it/s]
89%|████████▉ | 894/1000 [01:14<00:08, 12.20it/s]
90%|████████▉ | 896/1000 [01:15<00:08, 12.39it/s]
90%|████████▉ | 898/1000 [01:15<00:08, 11.87it/s]
90%|█████████ | 900/1000 [01:15<00:08, 11.27it/s]
90%|█████████ | 902/1000 [01:15<00:08, 11.56it/s]
90%|█████████ | 904/1000 [01:15<00:10, 9.17it/s]
91%|█████████ | 906/1000 [01:16<00:09, 9.93it/s]
91%|█████████ | 908/1000 [01:16<00:08, 10.67it/s]
91%|█████████ | 910/1000 [01:16<00:08, 11.17it/s]
91%|█████████ | 912/1000 [01:16<00:07, 11.60it/s]
91%|█████████▏| 914/1000 [01:17<00:12, 6.65it/s]
92%|█████████▏| 916/1000 [01:17<00:10, 7.97it/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, 10.19it/s]
92%|█████████▏| 924/1000 [01:18<00:06, 10.89it/s]
93%|█████████▎| 926/1000 [01:18<00:06, 11.75it/s]
93%|█████████▎| 928/1000 [01:18<00:06, 10.61it/s]
93%|█████████▎| 930/1000 [01:18<00:05, 11.84it/s]
93%|█████████▎| 932/1000 [01:18<00:05, 12.44it/s]
93%|█████████▎| 934/1000 [01:18<00:05, 12.87it/s]
94%|█████████▎| 936/1000 [01:18<00:04, 13.40it/s]
94%|█████████▍| 938/1000 [01:19<00:04, 14.26it/s]
94%|█████████▍| 940/1000 [01:19<00:04, 14.59it/s]
94%|█████████▍| 942/1000 [01:19<00:04, 12.28it/s]
94%|█████████▍| 944/1000 [01:19<00:04, 12.81it/s]
95%|█████████▍| 946/1000 [01:19<00:04, 11.84it/s]
95%|█████████▍| 948/1000 [01:19<00:04, 12.67it/s]
95%|█████████▌| 950/1000 [01:19<00:03, 13.47it/s]
95%|█████████▌| 952/1000 [01:20<00:03, 14.16it/s]
95%|█████████▌| 954/1000 [01:20<00:03, 14.24it/s]
96%|█████████▌| 956/1000 [01:20<00:03, 14.24it/s]
96%|█████████▌| 958/1000 [01:20<00:02, 14.77it/s]
96%|█████████▌| 960/1000 [01:20<00:03, 13.12it/s]
96%|█████████▌| 962/1000 [01:20<00:02, 13.75it/s]
96%|█████████▋| 964/1000 [01:21<00:03, 10.21it/s]
97%|█████████▋| 966/1000 [01:21<00:02, 11.41it/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, 9.71it/s]
97%|█████████▋| 974/1000 [01:22<00:02, 10.90it/s]
98%|█████████▊| 976/1000 [01:22<00:02, 11.68it/s]
98%|█████████▊| 978/1000 [01:22<00:02, 9.88it/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, 10.92it/s]
99%|█████████▉| 990/1000 [01:23<00:00, 11.40it/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, 13.91it/s]
100%|█████████▉| 998/1000 [01:24<00:00, 14.07it/s]
100%|██████████| 1000/1000 [01:24<00:00, 14.71it/s]
{'eval_loss': '0.8726', 'eval_runtime': '84.2', 'eval_samples_per_second': '11.88', 'eval_steps_per_second': '11.88', 'epoch': '1', 'num_input_tokens_seen': 62891360}
100%|██████████| 18688/18688 [22:02:23<00:00, 3.55s/it]
100%|██████████| 1000/1000 [01:24<00:00, 14.71it/s]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00010/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': '7.934e+04', 'train_samples_per_second': '3.768', 'train_steps_per_second': '0.236', 'train_loss': '0.9424', 'epoch': '1', 'num_input_tokens_seen': 62891360}
100%|██████████| 18688/18688 [22:02:24<00:00, 3.55s/it] 100%|██████████| 18688/18688 [22:02:24<00:00, 4.25s/it]
Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00010
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 = 0.9424
train_runtime = 22:02:24.58
train_samples_per_second = 3.768
train_steps_per_second = 0.236
Figure saved at: /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00010/training_loss.png
Figure saved at: /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00010/training_eval_loss.png
[WARNING|2026-10-21 19:09:59] 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, 25.60it/s] 1%| | 6/1000 [00:00<00:54, 18.27it/s] 1%| | 8/1000 [00:00<00:56, 17.55it/s] 1%| | 10/1000 [00:00<00:59, 16.66it/s] 1%| | 12/1000 [00:00<01:00, 16.43it/s] 1%|▏ | 14/1000 [00:00<01:01, 15.97it/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<01:51, 8.75it/s] 2%|▏ | 24/1000 [00:01<01:39, 9.79it/s] 3%|▎ | 26/1000 [00:02<01:44, 9.30it/s] 3%|▎ | 28/1000 [00:02<01:35, 10.13it/s] 3%|▎ | 30/1000 [00:02<01:27, 11.04it/s] 3%|▎ | 32/1000 [00:02<01:35, 10.18it/s] 3%|▎ | 34/1000 [00:02<01:25, 11.29it/s] 4%|▎ | 36/1000 [00:03<01:32, 10.45it/s] 4%|▍ | 38/1000 [00:03<01:24, 11.34it/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, 11.72it/s] 5%|▍ | 48/1000 [00:04<01:29, 10.65it/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, 13.23it/s] 6%|▌ | 56/1000 [00:04<01:07, 13.91it/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, 12.91it/s] 7%|▋ | 70/1000 [00:05<01:08, 13.50it/s] 7%|▋ | 72/1000 [00:06<01:06, 13.94it/s] 7%|▋ | 74/1000 [00:06<01:07, 13.70it/s] 8%|▊ | 76/1000 [00:06<01:04, 14.30it/s] 8%|▊ | 78/1000 [00:06<01:03, 14.53it/s] 8%|▊ | 80/1000 [00:06<01:08, 13.48it/s] 8%|▊ | 82/1000 [00:06<01:05, 14.11it/s] 8%|▊ | 84/1000 [00:06<01:02, 14.55it/s] 9%|▊ | 86/1000 [00:07<01:02, 14.62it/s] 9%|▉ | 88/1000 [00:07<01:05, 13.87it/s] 9%|▉ | 90/1000 [00:07<01:06, 13.76it/s] 9%|▉ | 92/1000 [00:07<01:08, 13.25it/s] 9%|▉ | 94/1000 [00:07<01:12, 12.55it/s] 10%|▉ | 96/1000 [00:07<01:08, 13.23it/s] 10%|▉ | 98/1000 [00:08<01:07, 13.45it/s] 10%|█ | 100/1000 [00:08<01:35, 9.46it/s] 10%|█ | 102/1000 [00:08<01:26, 10.44it/s] 10%|█ | 104/1000 [00:08<01:58, 7.58it/s] 10%|█ | 105/1000 [00:09<02:07, 7.03it/s] 11%|█ | 106/1000 [00:09<02:03, 7.23it/s] 11%|█ | 108/1000 [00:09<01:39, 8.93it/s] 11%|█ | 110/1000 [00:09<01:24, 10.59it/s] 11%|█ | 112/1000 [00:09<01:15, 11.75it/s] 11%|█▏ | 114/1000 [00:09<01:11, 12.35it/s] 12%|█▏ | 116/1000 [00:09<01:08, 12.89it/s] 12%|█▏ | 118/1000 [00:10<01:05, 13.53it/s] 12%|█▏ | 120/1000 [00:10<01:08, 12.87it/s] 12%|█▏ | 122/1000 [00:10<01:22, 10.62it/s] 12%|█▏ | 124/1000 [00:10<01:30, 9.68it/s] 13%|█▎ | 126/1000 [00:10<01:22, 10.64it/s] 13%|█▎ | 128/1000 [00:11<01:15, 11.55it/s] 13%|█▎ | 130/1000 [00:11<01:11, 12.23it/s] 13%|█▎ | 132/1000 [00:11<01:05, 13.18it/s] 13%|█▎ | 134/1000 [00:11<01:10, 12.37it/s] 14%|█▎ | 136/1000 [00:11<01:04, 13.33it/s] 14%|█▍ | 138/1000 [00:11<01:04, 13.26it/s] 14%|█▍ | 140/1000 [00:12<01:24, 10.12it/s] 14%|█▍ | 142/1000 [00:12<01:40, 8.55it/s] 14%|█▍ | 144/1000 [00:12<01:29, 9.58it/s] 15%|█▍ | 146/1000 [00:12<01:20, 10.60it/s] 15%|█▍ | 148/1000 [00:12<01:14, 11.51it/s] 15%|█▌ | 150/1000 [00:12<01:07, 12.58it/s] 15%|█▌ | 152/1000 [00:13<01:04, 13.11it/s] 15%|█▌ | 154/1000 [00:13<01:03, 13.37it/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<01:02, 13.50it/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, 14.23it/s] 17%|█▋ | 170/1000 [00:14<01:19, 10.47it/s] 17%|█▋ | 172/1000 [00:14<01:12, 11.44it/s] 17%|█▋ | 174/1000 [00:14<01:29, 9.18it/s] 18%|█▊ | 176/1000 [00:15<01:57, 7.01it/s] 18%|█▊ | 178/1000 [00:15<01:41, 8.07it/s] 18%|█▊ | 179/1000 [00:15<01:44, 7.86it/s] 18%|█▊ | 181/1000 [00:16<01:46, 7.71it/s] 18%|█▊ | 183/1000 [00:16<01:33, 8.75it/s] 18%|█▊ | 185/1000 [00:16<01:45, 7.74it/s] 19%|█▊ | 187/1000 [00:16<01:32, 8.81it/s] 19%|█▉ | 189/1000 [00:16<01:23, 9.70it/s] 19%|█▉ | 191/1000 [00:16<01:16, 10.57it/s] 19%|█▉ | 193/1000 [00:17<01:21, 9.86it/s] 20%|█▉ | 195/1000 [00:17<01:16, 10.48it/s] 20%|█▉ | 197/1000 [00:17<01:29, 8.95it/s] 20%|█▉ | 199/1000 [00:17<01:22, 9.69it/s] 20%|██ | 201/1000 [00:17<01:16, 10.44it/s] 20%|██ | 203/1000 [00:18<01:08, 11.72it/s] 20%|██ | 205/1000 [00:18<01:02, 12.66it/s] 21%|██ | 207/1000 [00:18<01:00, 13.14it/s] 21%|██ | 209/1000 [00:18<00:56, 13.93it/s] 21%|██ | 211/1000 [00:18<00:54, 14.61it/s] 21%|██▏ | 213/1000 [00:18<00:53, 14.81it/s] 22%|██▏ | 215/1000 [00:18<00:55, 14.21it/s] 22%|██▏ | 217/1000 [00:19<00:53, 14.74it/s] 22%|██▏ | 219/1000 [00:19<01:28, 8.85it/s] 22%|██▏ | 221/1000 [00:19<01:23, 9.30it/s] 22%|██▏ | 223/1000 [00:19<01:20, 9.59it/s] 22%|██▎ | 225/1000 [00:19<01:13, 10.50it/s] 23%|██▎ | 227/1000 [00:20<01:06, 11.60it/s] 23%|██▎ | 229/1000 [00:20<01:03, 12.06it/s] 23%|██▎ | 231/1000 [00:20<01:01, 12.53it/s] 23%|██▎ | 233/1000 [00:20<00:59, 12.82it/s] 24%|██▎ | 235/1000 [00:20<00:57, 13.39it/s] 24%|██▎ | 237/1000 [00:20<00:53, 14.37it/s] 24%|██▍ | 239/1000 [00:20<00:53, 14.22it/s] 24%|██▍ | 241/1000 [00:21<00:52, 14.59it/s] 24%|██▍ | 243/1000 [00:21<00:51, 14.82it/s] 24%|██▍ | 245/1000 [00:21<00:51, 14.77it/s] 25%|██▍ | 247/1000 [00:21<00:49, 15.15it/s] 25%|██▍ | 249/1000 [00:21<00:57, 12.95it/s] 25%|██▌ | 251/1000 [00:21<01:02, 11.95it/s] 25%|██▌ | 253/1000 [00:22<00:58, 12.78it/s] 26%|██▌ | 255/1000 [00:22<00:54, 13.71it/s] 26%|██▌ | 257/1000 [00:22<00:53, 13.86it/s] 26%|██▌ | 259/1000 [00:22<00:52, 14.13it/s] 26%|██▌ | 261/1000 [00:22<00:58, 12.66it/s] 26%|██▋ | 263/1000 [00:22<00:54, 13.40it/s] 26%|██▋ | 265/1000 [00:22<00:53, 13.86it/s] 27%|██▋ | 267/1000 [00:23<00:52, 13.99it/s] 27%|██▋ | 269/1000 [00:23<00:49, 14.70it/s] 27%|██▋ | 271/1000 [00:23<00:55, 13.21it/s] 27%|██▋ | 273/1000 [00:23<01:03, 11.46it/s] 28%|██▊ | 275/1000 [00:23<01:01, 11.75it/s] 28%|██▊ | 277/1000 [00:23<00:57, 12.67it/s] 28%|██▊ | 279/1000 [00:23<00:56, 12.86it/s] 28%|██▊ | 281/1000 [00:24<00:51, 13.85it/s] 28%|██▊ | 283/1000 [00:24<00:50, 14.16it/s] 28%|██▊ | 285/1000 [00:24<00:49, 14.50it/s] 29%|██▊ | 287/1000 [00:24<00:48, 14.69it/s] 29%|██▉ | 289/1000 [00:24<00:46, 15.25it/s] 29%|██▉ | 291/1000 [00:24<00:46, 15.40it/s] 29%|██▉ | 293/1000 [00:24<00:46, 15.35it/s] 30%|██▉ | 295/1000 [00:25<00:45, 15.55it/s] 30%|██▉ | 297/1000 [00:25<00:44, 15.65it/s] 30%|██▉ | 299/1000 [00:25<00:45, 15.38it/s] 30%|███ | 301/1000 [00:25<00:46, 15.04it/s] 30%|███ | 303/1000 [00:25<00:50, 13.72it/s] 30%|███ | 305/1000 [00:25<00:49, 14.17it/s] 31%|███ | 307/1000 [00:25<00:47, 14.55it/s] 31%|███ | 309/1000 [00:25<00:46, 14.95it/s] 31%|███ | 311/1000 [00:26<00:47, 14.55it/s] 31%|███▏ | 313/1000 [00:26<00:45, 15.04it/s] 32%|███▏ | 315/1000 [00:26<00:44, 15.53it/s] 32%|███▏ | 317/1000 [00:26<00:42, 16.00it/s] 32%|███▏ | 319/1000 [00:26<00:42, 16.08it/s] 32%|███▏ | 321/1000 [00:27<01:11, 9.45it/s] 32%|███▏ | 323/1000 [00:27<01:03, 10.69it/s] 32%|███▎ | 325/1000 [00:27<00:58, 11.53it/s] 33%|███▎ | 327/1000 [00:27<00:57, 11.61it/s] 33%|███▎ | 329/1000 [00:27<00:58, 11.56it/s] 33%|███▎ | 331/1000 [00:27<00:55, 12.11it/s] 33%|███▎ | 333/1000 [00:27<00:53, 12.58it/s] 34%|███▎ | 335/1000 [00:28<00:50, 13.07it/s] 34%|███▎ | 337/1000 [00:28<01:06, 10.01it/s] 34%|███▍ | 339/1000 [00:28<00:59, 11.08it/s] 34%|███▍ | 341/1000 [00:28<00:55, 11.96it/s] 34%|███▍ | 343/1000 [00:28<00:51, 12.87it/s] 34%|███▍ | 345/1000 [00:28<00:49, 13.35it/s] 35%|███▍ | 347/1000 [00:29<00:45, 14.25it/s] 35%|███▍ | 349/1000 [00:29<00:44, 14.48it/s] 35%|███▌ | 351/1000 [00:29<00:44, 14.61it/s] 35%|███▌ | 353/1000 [00:29<00:42, 15.11it/s] 36%|███▌ | 355/1000 [00:29<00:42, 15.14it/s] 36%|███▌ | 357/1000 [00:29<00:43, 14.95it/s] 36%|███▌ | 359/1000 [00:29<00:43, 14.82it/s] 36%|███▌ | 361/1000 [00:30<00:48, 13.27it/s] 36%|███▋ | 363/1000 [00:30<00:46, 13.57it/s] 36%|███▋ | 365/1000 [00:30<00:44, 14.33it/s] 37%|███▋ | 367/1000 [00:30<00:53, 11.85it/s] 37%|███▋ | 369/1000 [00:30<00:50, 12.50it/s] 37%|███▋ | 371/1000 [00:30<00:47, 13.17it/s] 37%|███▋ | 373/1000 [00:31<00:55, 11.39it/s] 38%|███▊ | 375/1000 [00:31<00:57, 10.82it/s] 38%|███▊ | 377/1000 [00:31<00:53, 11.64it/s] 38%|███▊ | 379/1000 [00:31<00:51, 12.13it/s] 38%|███▊ | 381/1000 [00:31<00:48, 12.87it/s] 38%|███▊ | 383/1000 [00:31<00:46, 13.39it/s] 38%|███▊ | 385/1000 [00:31<00:45, 13.59it/s] 39%|███▊ | 387/1000 [00:32<00:46, 13.23it/s] 39%|███▉ | 389/1000 [00:32<00:46, 13.14it/s] 39%|███▉ | 391/1000 [00:32<00:47, 12.96it/s] 39%|███▉ | 393/1000 [00:32<00:48, 12.41it/s] 40%|███▉ | 395/1000 [00:32<00:47, 12.65it/s] 40%|███▉ | 397/1000 [00:32<00:46, 13.07it/s] 40%|███▉ | 399/1000 [00:33<00:50, 11.98it/s] 40%|████ | 401/1000 [00:33<00:45, 13.09it/s] 40%|████ | 403/1000 [00:33<00:43, 13.76it/s] 40%|████ | 405/1000 [00:33<00:41, 14.34it/s] 41%|████ | 407/1000 [00:33<00:39, 14.89it/s] 41%|████ | 409/1000 [00:33<00:58, 10.08it/s] 41%|████ | 411/1000 [00:34<00:53, 11.01it/s] 41%|████▏ | 413/1000 [00:34<01:04, 9.07it/s] 42%|████▏ | 415/1000 [00:34<01:01, 9.56it/s] 42%|████▏ | 417/1000 [00:34<01:19, 7.35it/s] 42%|████▏ | 419/1000 [00:35<01:08, 8.48it/s] 42%|████▏ | 421/1000 [00:35<00:58, 9.95it/s] 42%|████▏ | 423/1000 [00:35<01:17, 7.42it/s] 42%|████▎ | 425/1000 [00:35<01:06, 8.67it/s] 43%|████▎ | 427/1000 [00:35<00:58, 9.71it/s] 43%|████▎ | 429/1000 [00:36<01:02, 9.08it/s] 43%|████▎ | 431/1000 [00:36<00:56, 10.14it/s] 43%|████▎ | 433/1000 [00:36<01:15, 7.46it/s] 44%|████▎ | 435/1000 [00:36<01:05, 8.59it/s] 44%|████▎ | 437/1000 [00:37<01:06, 8.49it/s] 44%|████▍ | 439/1000 [00:37<00:58, 9.55it/s] 44%|████▍ | 441/1000 [00:37<00:56, 9.87it/s] 44%|████▍ | 443/1000 [00:37<00:51, 10.85it/s] 44%|████▍ | 445/1000 [00:37<00:46, 11.88it/s] 45%|████▍ | 447/1000 [00:38<00:51, 10.66it/s] 45%|████▍ | 449/1000 [00:38<00:47, 11.55it/s] 45%|████▌ | 451/1000 [00:38<00:43, 12.48it/s] 45%|████▌ | 453/1000 [00:38<00:41, 13.24it/s] 46%|████▌ | 455/1000 [00:38<00:44, 12.35it/s] 46%|████▌ | 457/1000 [00:38<00:41, 13.17it/s] 46%|████▌ | 459/1000 [00:38<00:39, 13.71it/s] 46%|████▌ | 461/1000 [00:38<00:38, 14.15it/s] 46%|████▋ | 463/1000 [00:39<00:35, 14.95it/s] 46%|████▋ | 465/1000 [00:39<00:36, 14.83it/s] 47%|████▋ | 467/1000 [00:39<00:41, 12.74it/s] 47%|████▋ | 469/1000 [00:39<00:42, 12.52it/s] 47%|████▋ | 471/1000 [00:39<00:54, 9.62it/s] 47%|████▋ | 473/1000 [00:40<00:53, 9.80it/s] 48%|████▊ | 475/1000 [00:40<00:50, 10.48it/s] 48%|████▊ | 477/1000 [00:40<00:47, 10.95it/s] 48%|████▊ | 479/1000 [00:40<00:48, 10.76it/s] 48%|████▊ | 481/1000 [00:40<00:46, 11.13it/s] 48%|████▊ | 483/1000 [00:40<00:44, 11.65it/s] 48%|████▊ | 485/1000 [00:41<00:41, 12.40it/s] 49%|████▊ | 487/1000 [00:41<00:39, 13.12it/s] 49%|████▉ | 489/1000 [00:41<00:47, 10.66it/s] 49%|████▉ | 491/1000 [00:41<00:43, 11.60it/s] 49%|████▉ | 493/1000 [00:41<00:40, 12.67it/s] 50%|████▉ | 495/1000 [00:41<00:36, 13.74it/s] 50%|████▉ | 497/1000 [00:41<00:34, 14.42it/s] 50%|████▉ | 499/1000 [00:42<00:34, 14.62it/s] 50%|█████ | 501/1000 [00:42<00:43, 11.35it/s] 50%|█████ | 503/1000 [00:42<00:41, 11.92it/s] 50%|█████ | 505/1000 [00:42<00:39, 12.59it/s] 51%|█████ | 507/1000 [00:42<00:37, 13.10it/s] 51%|█████ | 509/1000 [00:43<00:48, 10.06it/s] 51%|█████ | 511/1000 [00:43<00:43, 11.18it/s] 51%|█████▏ | 513/1000 [00:43<00:41, 11.78it/s] 52%|█████▏ | 515/1000 [00:43<00:37, 12.82it/s] 52%|█████▏ | 517/1000 [00:43<00:36, 13.21it/s] 52%|█████▏ | 519/1000 [00:43<00:36, 13.32it/s] 52%|█████▏ | 521/1000 [00:43<00:34, 14.05it/s] 52%|█████▏ | 523/1000 [00:44<00:33, 14.17it/s] 52%|█████▎ | 525/1000 [00:44<00:33, 14.25it/s] 53%|█████▎ | 527/1000 [00:44<00:39, 11.99it/s] 53%|█████▎ | 529/1000 [00:44<00:37, 12.57it/s] 53%|█████▎ | 531/1000 [00:44<00:33, 13.80it/s] 53%|█████▎ | 533/1000 [00:44<00:32, 14.21it/s] 54%|█████▎ | 535/1000 [00:44<00:32, 14.47it/s] 54%|█████▎ | 537/1000 [00:45<00:32, 14.14it/s] 54%|█████▍ | 539/1000 [00:45<00:31, 14.50it/s] 54%|█████▍ | 541/1000 [00:45<00:31, 14.58it/s] 54%|█████▍ | 543/1000 [00:45<00:30, 14.80it/s] 55%|█████▍ | 545/1000 [00:45<00:31, 14.28it/s] 55%|█████▍ | 547/1000 [00:45<00:32, 14.12it/s] 55%|█████▍ | 549/1000 [00:45<00:31, 14.10it/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, 14.16it/s] 56%|█████▌ | 557/1000 [00:46<00:30, 14.57it/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, 10.83it/s] 56%|█████▋ | 565/1000 [00:47<00:37, 11.59it/s] 57%|█████▋ | 567/1000 [00:47<00:34, 12.46it/s] 57%|█████▋ | 569/1000 [00:47<00:32, 13.17it/s] 57%|█████▋ | 571/1000 [00:47<00:32, 13.14it/s] 57%|█████▋ | 573/1000 [00:47<00:31, 13.63it/s] 57%|█████▊ | 575/1000 [00:47<00:30, 13.78it/s] 58%|█████▊ | 577/1000 [00:48<00:30, 13.87it/s] 58%|█████▊ | 579/1000 [00:48<00:35, 12.02it/s] 58%|█████▊ | 581/1000 [00:48<00:31, 13.20it/s] 58%|█████▊ | 583/1000 [00:48<00:30, 13.68it/s] 58%|█████▊ | 585/1000 [00:48<00:29, 13.86it/s] 59%|█████▊ | 587/1000 [00:48<00:28, 14.51it/s] 59%|█████▉ | 589/1000 [00:48<00:26, 15.23it/s] 59%|█████▉ | 591/1000 [00:49<00:28, 14.53it/s] 59%|█████▉ | 593/1000 [00:49<00:30, 13.41it/s] 60%|█████▉ | 595/1000 [00:49<00:29, 13.83it/s] 60%|█████▉ | 597/1000 [00:49<00:29, 13.72it/s] 60%|█████▉ | 599/1000 [00:49<00:27, 14.64it/s] 60%|██████ | 601/1000 [00:49<00:28, 14.23it/s] 60%|██████ | 603/1000 [00:49<00:26, 14.86it/s] 60%|██████ | 605/1000 [00:50<00:26, 14.82it/s] 61%|██████ | 607/1000 [00:50<00:26, 14.90it/s] 61%|██████ | 609/1000 [00:50<00:25, 15.06it/s] 61%|██████ | 611/1000 [00:50<00:25, 15.31it/s] 61%|██████▏ | 613/1000 [00:50<00:26, 14.81it/s] 62%|██████▏ | 615/1000 [00:50<00:26, 14.43it/s] 62%|██████▏ | 617/1000 [00:50<00:26, 14.46it/s] 62%|██████▏ | 619/1000 [00:51<00:32, 11.82it/s] 62%|██████▏ | 621/1000 [00:51<00:39, 9.51it/s] 62%|██████▏ | 623/1000 [00:51<00:35, 10.68it/s] 62%|██████▎ | 625/1000 [00:51<00:31, 11.91it/s] 63%|██████▎ | 627/1000 [00:51<00:29, 12.65it/s] 63%|██████▎ | 629/1000 [00:51<00:27, 13.57it/s] 63%|██████▎ | 631/1000 [00:52<00:29, 12.59it/s] 63%|██████▎ | 633/1000 [00:52<00:28, 13.01it/s] 64%|██████▎ | 635/1000 [00:52<00:34, 10.56it/s] 64%|██████▎ | 637/1000 [00:52<00:32, 11.15it/s] 64%|██████▍ | 639/1000 [00:52<00:34, 10.54it/s] 64%|██████▍ | 641/1000 [00:53<00:32, 11.03it/s] 64%|██████▍ | 643/1000 [00:53<00:30, 11.80it/s] 64%|██████▍ | 645/1000 [00:53<00:28, 12.25it/s] 65%|██████▍ | 647/1000 [00:53<00:28, 12.38it/s] 65%|██████▍ | 649/1000 [00:53<00:29, 11.97it/s] 65%|██████▌ | 651/1000 [00:53<00:27, 12.79it/s] 65%|██████▌ | 653/1000 [00:54<00:29, 11.78it/s] 66%|██████▌ | 655/1000 [00:54<00:36, 9.52it/s] 66%|██████▌ | 657/1000 [00:54<00:32, 10.64it/s] 66%|██████▌ | 659/1000 [00:54<00:35, 9.72it/s] 66%|██████▌ | 661/1000 [00:54<00:31, 10.77it/s] 66%|██████▋ | 663/1000 [00:54<00:28, 11.65it/s] 66%|██████▋ | 665/1000 [00:55<00:28, 11.82it/s] 67%|██████▋ | 667/1000 [00:55<00:31, 10.59it/s] 67%|██████▋ | 669/1000 [00:55<00:30, 10.74it/s] 67%|██████▋ | 671/1000 [00:55<00:28, 11.54it/s] 67%|██████▋ | 673/1000 [00:55<00:27, 11.98it/s] 68%|██████▊ | 675/1000 [00:56<00:28, 11.59it/s] 68%|██████▊ | 677/1000 [00:56<00:26, 12.04it/s] 68%|██████▊ | 679/1000 [00:56<00:25, 12.46it/s] 68%|██████▊ | 681/1000 [00:56<00:24, 13.00it/s] 68%|██████▊ | 683/1000 [00:56<00:26, 12.17it/s] 68%|██████▊ | 685/1000 [00:56<00:24, 12.78it/s] 69%|██████▊ | 687/1000 [00:56<00:23, 13.39it/s] 69%|██████▉ | 689/1000 [00:57<00:22, 13.87it/s] 69%|██████▉ | 691/1000 [00:57<00:21, 14.12it/s] 69%|██████▉ | 693/1000 [00:57<00:21, 14.32it/s] 70%|██████▉ | 695/1000 [00:57<00:21, 14.06it/s] 70%|██████▉ | 697/1000 [00:57<00:21, 14.38it/s] 70%|██████▉ | 699/1000 [00:57<00:20, 14.43it/s] 70%|███████ | 701/1000 [00:57<00:25, 11.72it/s] 70%|███████ | 703/1000 [00:58<00:24, 12.35it/s] 70%|███████ | 705/1000 [00:58<00:32, 8.98it/s] 71%|███████ | 707/1000 [00:58<00:34, 8.48it/s] 71%|███████ | 709/1000 [00:58<00:30, 9.70it/s] 71%|███████ | 711/1000 [00:59<00:26, 10.83it/s] 71%|███████▏ | 713/1000 [00:59<00:23, 12.03it/s] 72%|███████▏ | 715/1000 [00:59<00:22, 12.81it/s] 72%|███████▏ | 717/1000 [00:59<00:23, 11.99it/s] 72%|███████▏ | 719/1000 [00:59<00:21, 12.82it/s] 72%|███████▏ | 721/1000 [00:59<00:20, 13.84it/s] 72%|███████▏ | 723/1000 [00:59<00:20, 13.68it/s] 72%|███████▎ | 725/1000 [01:00<00:19, 13.89it/s] 73%|███████▎ | 727/1000 [01:00<00:23, 11.62it/s] 73%|███████▎ | 729/1000 [01:00<00:33, 8.16it/s] 73%|███████▎ | 731/1000 [01:00<00:28, 9.38it/s] 73%|███████▎ | 733/1000 [01:01<00:28, 9.31it/s] 74%|███████▎ | 735/1000 [01:01<00:31, 8.50it/s] 74%|███████▎ | 737/1000 [01:01<00:28, 9.38it/s] 74%|███████▍ | 739/1000 [01:01<00:25, 10.08it/s] 74%|███████▍ | 741/1000 [01:01<00:24, 10.64it/s] 74%|███████▍ | 743/1000 [01:01<00:23, 11.06it/s] 74%|███████▍ | 745/1000 [01:02<00:26, 9.50it/s] 75%|███████▍ | 747/1000 [01:02<00:27, 9.10it/s] 75%|███████▍ | 749/1000 [01:02<00:24, 10.31it/s] 75%|███████▌ | 751/1000 [01:02<00:21, 11.47it/s] 75%|███████▌ | 753/1000 [01:02<00:20, 12.31it/s] 76%|███████▌ | 755/1000 [01:03<00:18, 13.08it/s] 76%|███████▌ | 757/1000 [01:03<00:23, 10.19it/s] 76%|███████▌ | 759/1000 [01:03<00:21, 11.24it/s] 76%|███████▌ | 761/1000 [01:03<00:19, 12.24it/s] 76%|███████▋ | 763/1000 [01:03<00:23, 10.23it/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, 10.56it/s] 77%|███████▋ | 771/1000 [01:04<00:19, 11.84it/s] 77%|███████▋ | 773/1000 [01:04<00:17, 12.63it/s] 78%|███████▊ | 775/1000 [01:04<00:17, 13.18it/s] 78%|███████▊ | 777/1000 [01:04<00:15, 14.17it/s] 78%|███████▊ | 779/1000 [01:05<00:21, 10.50it/s] 78%|███████▊ | 781/1000 [01:05<00:23, 9.41it/s] 78%|███████▊ | 783/1000 [01:05<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, 7.00it/s] 79%|███████▉ | 788/1000 [01:06<00:24, 8.82it/s] 79%|███████▉ | 790/1000 [01:06<00:21, 9.91it/s] 79%|███████▉ | 792/1000 [01:06<00:18, 11.06it/s] 79%|███████▉ | 794/1000 [01:06<00:17, 11.59it/s] 80%|███████▉ | 796/1000 [01:07<00:16, 12.13it/s] 80%|███████▉ | 798/1000 [01:07<00:15, 12.95it/s] 80%|████████ | 800/1000 [01:07<00:16, 11.99it/s] 80%|████████ | 802/1000 [01:07<00:26, 7.57it/s] 80%|████████ | 803/1000 [01:08<00:27, 7.21it/s] 80%|████████ | 805/1000 [01:08<00:22, 8.65it/s] 81%|████████ | 807/1000 [01:08<00:20, 9.22it/s] 81%|████████ | 809/1000 [01:08<00:18, 10.38it/s] 81%|████████ | 811/1000 [01:08<00:17, 10.68it/s] 81%|████████▏ | 813/1000 [01:08<00:15, 11.90it/s] 82%|████████▏ | 815/1000 [01:08<00:14, 12.70it/s] 82%|████████▏ | 817/1000 [01:09<00:16, 11.05it/s] 82%|████████▏ | 819/1000 [01:09<00:15, 11.75it/s] 82%|████████▏ | 821/1000 [01:09<00:14, 12.48it/s] 82%|████████▏ | 823/1000 [01:09<00:13, 13.20it/s] 82%|████████▎ | 825/1000 [01:09<00:12, 14.00it/s] 83%|████████▎ | 827/1000 [01:09<00:12, 14.21it/s] 83%|████████▎ | 829/1000 [01:10<00:12, 14.06it/s] 83%|████████▎ | 831/1000 [01:10<00:11, 14.20it/s] 83%|████████▎ | 833/1000 [01:10<00:11, 14.00it/s] 84%|████████▎ | 835/1000 [01:10<00:11, 14.32it/s] 84%|████████▎ | 837/1000 [01:10<00:11, 14.40it/s] 84%|████████▍ | 839/1000 [01:10<00:11, 14.00it/s] 84%|████████▍ | 841/1000 [01:10<00:11, 14.29it/s] 84%|████████▍ | 843/1000 [01:11<00:11, 14.23it/s] 84%|████████▍ | 845/1000 [01:11<00:11, 13.92it/s] 85%|████████▍ | 847/1000 [01:11<00:10, 13.95it/s] 85%|████████▍ | 849/1000 [01:11<00:10, 14.08it/s] 85%|████████▌ | 851/1000 [01:11<00:10, 13.89it/s] 85%|████████▌ | 853/1000 [01:11<00:10, 13.84it/s] 86%|████████▌ | 855/1000 [01:11<00:10, 14.13it/s] 86%|████████▌ | 857/1000 [01:12<00:10, 14.22it/s] 86%|████████▌ | 859/1000 [01:12<00:09, 14.85it/s] 86%|████████▌ | 861/1000 [01:12<00:10, 13.80it/s] 86%|████████▋ | 863/1000 [01:12<00:09, 13.81it/s] 86%|████████▋ | 865/1000 [01:12<00:09, 13.92it/s] 87%|████████▋ | 867/1000 [01:12<00:09, 13.75it/s] 87%|████████▋ | 869/1000 [01:12<00:09, 13.71it/s] 87%|████████▋ | 871/1000 [01:13<00:08, 14.51it/s] 87%|████████▋ | 873/1000 [01:13<00:09, 13.56it/s] 88%|████████▊ | 875/1000 [01:13<00:09, 12.65it/s] 88%|████████▊ | 877/1000 [01:13<00:10, 11.36it/s] 88%|████████▊ | 879/1000 [01:13<00:10, 11.99it/s] 88%|████████▊ | 881/1000 [01:13<00:10, 10.86it/s] 88%|████████▊ | 883/1000 [01:14<00:10, 11.31it/s] 88%|████████▊ | 885/1000 [01:14<00:09, 11.52it/s] 89%|████████▊ | 887/1000 [01:14<00:09, 11.77it/s] 89%|████████▉ | 889/1000 [01:14<00:09, 11.91it/s] 89%|████████▉ | 891/1000 [01:14<00:09, 11.97it/s] 89%|████████▉ | 893/1000 [01:14<00:08, 12.03it/s] 90%|████████▉ | 895/1000 [01:15<00:08, 12.12it/s] 90%|████████▉ | 897/1000 [01:15<00:08, 12.21it/s] 90%|████████▉ | 899/1000 [01:15<00:09, 11.03it/s] 90%|█████████ | 901/1000 [01:15<00:08, 11.76it/s] 90%|█████████ | 903/1000 [01:15<00:07, 12.30it/s] 90%|█████████ | 905/1000 [01:16<00:09, 9.52it/s] 91%|█████████ | 907/1000 [01:16<00:08, 10.69it/s] 91%|█████████ | 909/1000 [01:16<00:07, 12.04it/s] 91%|█████████ | 911/1000 [01:16<00:07, 11.75it/s] 91%|█████████▏| 913/1000 [01:16<00:09, 9.31it/s] 92%|█████████▏| 915/1000 [01:17<00:11, 7.19it/s] 92%|█████████▏| 917/1000 [01:17<00:12, 6.87it/s] 92%|█████████▏| 919/1000 [01:17<00:09, 8.14it/s] 92%|█████████▏| 921/1000 [01:17<00:08, 9.66it/s] 92%|█████████▏| 923/1000 [01:17<00:07, 10.69it/s] 92%|█████████▎| 925/1000 [01:18<00:06, 11.47it/s] 93%|█████████▎| 927/1000 [01:18<00:06, 12.06it/s] 93%|█████████▎| 929/1000 [01:18<00:06, 10.93it/s] 93%|█████████▎| 931/1000 [01:18<00:05, 12.08it/s] 93%|█████████▎| 933/1000 [01:18<00:05, 12.35it/s] 94%|█████████▎| 935/1000 [01:18<00:04, 13.26it/s] 94%|█████████▎| 937/1000 [01:19<00:04, 13.65it/s] 94%|█████████▍| 939/1000 [01:19<00:04, 14.33it/s] 94%|█████████▍| 941/1000 [01:19<00:04, 14.41it/s] 94%|█████████▍| 943/1000 [01:19<00:04, 12.15it/s] 94%|█████████▍| 945/1000 [01:19<00:04, 12.45it/s] 95%|█████████▍| 947/1000 [01:19<00:04, 11.87it/s] 95%|█████████▍| 949/1000 [01:19<00:04, 12.70it/s] 95%|█████████▌| 951/1000 [01:20<00:03, 13.62it/s] 95%|█████████▌| 953/1000 [01:20<00:03, 14.12it/s] 96%|█████████▌| 955/1000 [01:20<00:03, 13.92it/s] 96%|█████████▌| 957/1000 [01:20<00:02, 14.34it/s] 96%|█████████▌| 959/1000 [01:20<00:03, 13.22it/s] 96%|█████████▌| 961/1000 [01:20<00:02, 13.31it/s] 96%|█████████▋| 963/1000 [01:20<00:02, 13.98it/s] 96%|█████████▋| 965/1000 [01:21<00:03, 10.36it/s] 97%|█████████▋| 967/1000 [01:21<00:02, 11.59it/s] 97%|█████████▋| 969/1000 [01:21<00:02, 12.04it/s] 97%|█████████▋| 971/1000 [01:21<00:02, 12.58it/s] 97%|█████████▋| 973/1000 [01:22<00:02, 9.88it/s] 98%|█████████▊| 975/1000 [01:22<00:02, 10.74it/s] 98%|█████████▊| 977/1000 [01:22<00:02, 9.54it/s] 98%|█████████▊| 979/1000 [01:22<00:02, 9.68it/s] 98%|█████████▊| 981/1000 [01:22<00:01, 9.97it/s] 98%|█████████▊| 983/1000 [01:22<00:01, 10.97it/s] 98%|█████████▊| 985/1000 [01:23<00:01, 10.34it/s] 99%|█████████▊| 987/1000 [01:23<00:01, 11.23it/s] 99%|█████████▉| 989/1000 [01:23<00:00, 11.21it/s] 99%|█████████▉| 991/1000 [01:23<00:00, 11.92it/s] 99%|█████████▉| 993/1000 [01:23<00:00, 12.56it/s] 100%|█████████▉| 995/1000 [01:23<00:00, 13.46it/s] 100%|█████████▉| 997/1000 [01:24<00:00, 14.03it/s] 100%|█████████▉| 999/1000 [01:24<00:00, 14.59it/s] 100%|██████████| 1000/1000 [01:24<00:00, 11.88it/s]
***** eval metrics *****
epoch = 1.0
eval_loss = 0.8726
eval_runtime = 0:01:24.27
eval_samples_per_second = 11.866
eval_steps_per_second = 11.866
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'}}