Diplomovka/aya_300k_lr_experiments/logs/lr_0p00023.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

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[INFO|2026-10-18 18:03:23] llamafactory.hparams.parser:651 >> Process rank: 0, world size: 1, device: cuda:0, distributed training: False, compute dtype: torch.bfloat16
[INFO|configuration_utils.py:765] 2026-10-18 18:03:24,138 >> 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] 2026-10-18 18:03:24,147 >> 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
}
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] 2026-10-18 18:03:26,022 >> 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] 2026-10-18 18:03:26,025 >> 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] 2026-10-18 18:03:26,175 >> 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] 2026-10-18 18:03:26,178 >> 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|2026-10-18 18:03:26] 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] 2026-10-18 18:03:27,681 >> 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] 2026-10-18 18:03:27,684 >> 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|2026-10-18 18:03:27] 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
}
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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|2026-10-18 18:03:47] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled.
[INFO|2026-10-18 18:03:47] llamafactory.model.adapter:144 >> Upcasting trainable params to float32.
[INFO|2026-10-18 18:03:47] 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|2026-10-18 18:03:50] llamafactory.model.loader:144 >> trainable params: 41,943,040 || all params: 7,283,675,136 || trainable%: 0.5758
The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'pad_token_id': 2}.
==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1
\\ /| Num examples = 299,000 | Num Epochs = 1 | Total steps = 18,688
O^O/ \_/ \ Batch size per device = 1 | Gradient accumulation steps = 16
\ / Data Parallel GPUs = 1 | Total batch size (1 x 16 x 1) = 16
"-____-" Trainable parameters = 41,943,040 of 7,283,675,136 (0.58% trained)
0%| | 0/18688 [00:00<?, ?it/s]`use_return_dict` is deprecated! Use `return_dict` instead!
Unsloth: Double buffering enabled (parallel H2D + compute) for backward pass.
Unsloth: Will smartly offload gradients to save VRAM!
0%| | 1/18688 [00:06<32:25:30, 6.25s/it] 0%| | 2/18688 [00:09<24:24:44, 4.70s/it] 0%| | 3/18688 [00:13<22:56:19, 4.42s/it] 0%| | 4/18688 [00:17<21:12:22, 4.09s/it] 0%| | 5/18688 [00:21<20:56:37, 4.04s/it] 0%| | 6/18688 [00:25<20:51:11, 4.02s/it] 0%| | 7/18688 [00:30<23:10:38, 4.47s/it] 0%| | 8/18688 [00:34<21:30:15, 4.14s/it] 0%| | 9/18688 [00:39<22:36:38, 4.36s/it] 0%| | 10/18688 [00:42<21:06:32, 4.07s/it] {'loss': '2.105', 'grad_norm': '5.808', 'learning_rate': '1.38e-06', 'epoch': '0.0005351', 'num_input_tokens_seen': 35216, 'train_runtime': '42.55', 'train_tokens_per_second': '827.6'}
0%| | 10/18688 [00:42<21:06:32, 4.07s/it] 0%| | 11/18688 [00:46<20:34:53, 3.97s/it] 0%| | 12/18688 [00:51<22:32:23, 4.34s/it] 0%| | 13/18688 [00:55<21:37:27, 4.17s/it] 0%| | 14/18688 [00:58<20:50:07, 4.02s/it] 0%| | 15/18688 [01:02<20:42:47, 3.99s/it] 0%| | 16/18688 [01:06<19:54:37, 3.84s/it] 0%| | 17/18688 [01:09<19:17:29, 3.72s/it] 0%| | 18/18688 [01:13<19:45:59, 3.81s/it] 0%| | 19/18688 [01:17<19:43:32, 3.80s/it] 0%| | 20/18688 [01:21<19:29:01, 3.76s/it] {'loss': '2.018', 'grad_norm': '4.631', 'learning_rate': '2.913e-06', 'epoch': '0.00107', 'num_input_tokens_seen': 66488, 'train_runtime': '81.25', 'train_tokens_per_second': '818.3'}
0%| | 20/18688 [01:21<19:29:01, 3.76s/it] 0%| | 21/18688 [01:25<20:40:26, 3.99s/it] 0%| | 22/18688 [01:30<21:11:02, 4.09s/it] 0%| | 23/18688 [01:34<21:31:46, 4.15s/it] 0%| | 24/18688 [01:38<21:27:18, 4.14s/it] 0%| | 25/18688 [01:42<21:33:43, 4.16s/it] 0%| | 26/18688 [01:46<21:14:56, 4.10s/it] 0%| | 27/18688 [01:50<21:01:08, 4.05s/it] 0%| | 28/18688 [01:54<20:05:06, 3.87s/it] 0%| | 29/18688 [01:58<21:04:57, 4.07s/it] 0%| | 30/18688 [02:02<21:04:22, 4.07s/it] {'loss': '1.8', 'grad_norm': '2.474', 'learning_rate': '4.447e-06', 'epoch': '0.001605', 'num_input_tokens_seen': 97280, 'train_runtime': '122.7', 'train_tokens_per_second': '793.1'}
0%| | 30/18688 [02:02<21:04:22, 4.07s/it] 0%| | 31/18688 [02:07<21:43:33, 4.19s/it] 0%| | 32/18688 [02:11<21:22:52, 4.13s/it] 0%| | 33/18688 [02:15<21:04:04, 4.07s/it] 0%| | 34/18688 [02:19<21:12:52, 4.09s/it] 0%| | 35/18688 [02:22<20:12:51, 3.90s/it] 0%| | 36/18688 [02:26<19:38:24, 3.79s/it] 0%| | 37/18688 [02:30<20:21:00, 3.93s/it] 0%| | 38/18688 [02:35<21:40:40, 4.18s/it] 0%| | 39/18688 [02:39<22:12:14, 4.29s/it] 0%| | 40/18688 [02:43<21:01:46, 4.06s/it] {'loss': '1.738', 'grad_norm': '2.486', 'learning_rate': '5.98e-06', 'epoch': '0.00214', 'num_input_tokens_seen': 129200, 'train_runtime': '163.3', 'train_tokens_per_second': '791.3'}
0%| | 40/18688 [02:43<21:01:46, 4.06s/it] 0%| | 41/18688 [02:46<20:25:28, 3.94s/it] 0%| | 42/18688 [02:50<19:59:47, 3.86s/it] 0%| | 43/18688 [02:54<19:22:06, 3.74s/it] 0%| | 44/18688 [02:58<21:07:45, 4.08s/it] 0%| | 45/18688 [03:03<21:57:41, 4.24s/it] 0%| | 46/18688 [03:08<22:47:42, 4.40s/it] 0%| | 47/18688 [03:11<21:35:27, 4.17s/it] 0%| | 48/18688 [03:15<20:49:30, 4.02s/it] 0%| | 49/18688 [03:20<21:27:49, 4.15s/it] 0%| | 50/18688 [03:25<23:05:44, 4.46s/it] {'loss': '1.547', 'grad_norm': '1.606', 'learning_rate': '7.513e-06', 'epoch': '0.002676', 'num_input_tokens_seen': 163208, 'train_runtime': '205.3', 'train_tokens_per_second': '795.1'}
0%| | 50/18688 [03:25<23:05:44, 4.46s/it] 0%| | 51/18688 [03:28<21:26:02, 4.14s/it] 0%| | 52/18688 [03:33<22:30:31, 4.35s/it] 0%| | 53/18688 [03:37<22:18:07, 4.31s/it] 0%| | 54/18688 [03:41<21:30:56, 4.16s/it] 0%| | 55/18688 [03:45<21:55:53, 4.24s/it] 0%| | 56/18688 [03:50<21:57:55, 4.24s/it] 0%| | 57/18688 [03:54<21:53:56, 4.23s/it] 0%| | 58/18688 [03:58<21:25:08, 4.14s/it] 0%| | 59/18688 [04:02<22:12:02, 4.29s/it] 0%| | 60/18688 [04:08<23:26:07, 4.53s/it] {'loss': '1.396', 'grad_norm': '1.633', 'learning_rate': '9.047e-06', 'epoch': '0.003211', 'num_input_tokens_seen': 197328, 'train_runtime': '248.1', 'train_tokens_per_second': '795.5'}
0%| | 60/18688 [04:08<23:26:07, 4.53s/it] 0%| | 61/18688 [04:12<23:12:42, 4.49s/it] 0%| | 62/18688 [04:16<22:42:57, 4.39s/it] 0%| | 63/18688 [04:21<24:16:01, 4.69s/it] 0%| | 64/18688 [04:25<22:32:48, 4.36s/it] 0%| | 65/18688 [04:30<23:19:16, 4.51s/it] 0%| | 66/18688 [04:33<21:42:20, 4.20s/it] 0%| | 67/18688 [04:37<20:26:57, 3.95s/it] 0%| | 68/18688 [04:41<21:05:51, 4.08s/it] 0%| | 69/18688 [04:45<21:18:47, 4.12s/it] 0%| | 70/18688 [04:50<21:21:24, 4.13s/it] {'loss': '1.282', 'grad_norm': '1.716', 'learning_rate': '1.058e-05', 'epoch': '0.003746', 'num_input_tokens_seen': 234064, 'train_runtime': '290', 'train_tokens_per_second': '807'}
0%| | 70/18688 [04:50<21:21:24, 4.13s/it] 0%| | 71/18688 [04:54<21:38:52, 4.19s/it] 0%| | 72/18688 [04:58<21:14:35, 4.11s/it] 0%| | 73/18688 [05:01<20:21:14, 3.94s/it] 0%| | 74/18688 [05:05<20:30:16, 3.97s/it] 0%| | 75/18688 [05:09<20:37:14, 3.99s/it] 0%| | 76/18688 [05:13<20:23:43, 3.94s/it] 0%| | 77/18688 [05:18<21:30:33, 4.16s/it] 0%| | 78/18688 [05:21<20:38:25, 3.99s/it] 0%| | 79/18688 [05:27<22:33:21, 4.36s/it] 0%| | 80/18688 [05:31<22:31:48, 4.36s/it] {'loss': '1.206', 'grad_norm': '2.293', 'learning_rate': '1.211e-05', 'epoch': '0.004281', 'num_input_tokens_seen': 266768, 'train_runtime': '331.6', 'train_tokens_per_second': '804.6'}
0%| | 80/18688 [05:31<22:31:48, 4.36s/it] 0%| | 81/18688 [05:36<22:40:26, 4.39s/it] 0%| | 82/18688 [05:39<21:47:24, 4.22s/it] 0%| | 83/18688 [05:43<20:36:27, 3.99s/it] 0%| | 84/18688 [05:47<20:14:29, 3.92s/it] 0%| | 85/18688 [05:51<20:34:47, 3.98s/it] 0%| | 86/18688 [05:55<21:23:17, 4.14s/it] 0%| | 87/18688 [05:59<20:18:04, 3.93s/it] 0%| | 88/18688 [06:03<21:28:23, 4.16s/it] 0%| | 89/18688 [06:07<20:52:50, 4.04s/it] 0%| | 90/18688 [06:11<21:11:51, 4.10s/it] {'loss': '1.124', 'grad_norm': '1.988', 'learning_rate': '1.365e-05', 'epoch': '0.004816', 'num_input_tokens_seen': 299720, 'train_runtime': '371.8', 'train_tokens_per_second': '806.1'}
0%| | 90/18688 [06:11<21:11:51, 4.10s/it] 0%| | 91/18688 [06:16<22:47:19, 4.41s/it] 0%| | 92/18688 [06:21<22:34:30, 4.37s/it] 0%| | 93/18688 [06:25<22:46:29, 4.41s/it] 1%| | 94/18688 [06:30<23:30:14, 4.55s/it] 1%| | 95/18688 [06:35<23:49:36, 4.61s/it] 1%| | 96/18688 [06:39<23:21:46, 4.52s/it] 1%| | 97/18688 [06:43<21:50:54, 4.23s/it] 1%| | 98/18688 [06:46<20:54:57, 4.05s/it] 1%| | 99/18688 [06:50<20:35:18, 3.99s/it] 1%| | 100/18688 [06:54<20:33:43, 3.98s/it] {'loss': '1.139', 'grad_norm': '1.957', 'learning_rate': '1.518e-05', 'epoch': '0.005351', 'num_input_tokens_seen': 333368, 'train_runtime': '414.7', 'train_tokens_per_second': '803.9'}
1%| | 100/18688 [06:54<20:33:43, 3.98s/it] 1%| | 101/18688 [07:00<23:01:53, 4.46s/it] 1%| | 102/18688 [07:05<23:32:52, 4.56s/it] 1%| | 103/18688 [07:08<22:31:10, 4.36s/it] 1%| | 104/18688 [07:13<22:54:52, 4.44s/it] 1%| | 105/18688 [07:17<22:43:01, 4.40s/it] 1%| | 106/18688 [07:22<23:13:21, 4.50s/it] 1%| | 107/18688 [07:26<22:44:34, 4.41s/it] 1%| | 108/18688 [07:31<22:53:44, 4.44s/it] 1%| | 109/18688 [07:35<23:02:13, 4.46s/it] 1%| | 110/18688 [07:39<22:22:56, 4.34s/it] {'loss': '1.152', 'grad_norm': '1.601', 'learning_rate': '1.671e-05', 'epoch': '0.005886', 'num_input_tokens_seen': 370256, 'train_runtime': '459.9', 'train_tokens_per_second': '805.1'}
1%| | 110/18688 [07:39<22:22:56, 4.34s/it] 1%| | 111/18688 [07:44<22:13:45, 4.31s/it] 1%| | 112/18688 [07:48<21:57:05, 4.25s/it] 1%| | 113/18688 [07:52<21:55:59, 4.25s/it] 1%| | 114/18688 [07:57<22:41:36, 4.40s/it] 1%| | 115/18688 [08:01<22:11:08, 4.30s/it] 1%| | 116/18688 [08:05<21:15:45, 4.12s/it] 1%| | 117/18688 [08:08<20:31:22, 3.98s/it] 1%| | 118/18688 [08:12<20:54:33, 4.05s/it] 1%| | 119/18688 [08:17<21:57:09, 4.26s/it] 1%| | 120/18688 [08:21<21:01:16, 4.08s/it] {'loss': '1.053', 'grad_norm': '1.696', 'learning_rate': '1.825e-05', 'epoch': '0.006421', 'num_input_tokens_seen': 402568, 'train_runtime': '501.3', 'train_tokens_per_second': '803.1'}
1%| | 120/18688 [08:21<21:01:16, 4.08s/it] 1%| | 121/18688 [08:25<21:04:13, 4.09s/it] 1%| | 122/18688 [08:29<20:26:04, 3.96s/it] 1%| | 123/18688 [08:32<19:39:19, 3.81s/it] 1%| | 124/18688 [08:35<19:04:12, 3.70s/it] 1%| | 125/18688 [08:40<20:23:46, 3.96s/it] 1%| | 126/18688 [08:43<19:33:03, 3.79s/it] 1%| | 127/18688 [08:47<19:38:39, 3.81s/it] 1%| | 128/18688 [08:51<19:23:15, 3.76s/it] 1%| | 129/18688 [08:55<20:25:08, 3.96s/it] 1%| | 130/18688 [09:00<22:10:14, 4.30s/it] {'loss': '1.092', 'grad_norm': '1.336', 'learning_rate': '1.978e-05', 'epoch': '0.006957', 'num_input_tokens_seen': 434592, 'train_runtime': '540.9', 'train_tokens_per_second': '803.4'}
1%| | 130/18688 [09:00<22:10:14, 4.30s/it] 1%| | 131/18688 [09:04<21:30:46, 4.17s/it] 1%| | 132/18688 [09:10<23:32:37, 4.57s/it] 1%| | 133/18688 [09:14<22:47:50, 4.42s/it] 1%| | 134/18688 [09:18<22:16:51, 4.32s/it] 1%| | 135/18688 [09:22<21:16:36, 4.13s/it] 1%| | 136/18688 [09:28<24:13:29, 4.70s/it] 1%| | 137/18688 [09:31<22:45:27, 4.42s/it] 1%| | 138/18688 [09:35<22:13:12, 4.31s/it] 1%| | 139/18688 [09:40<21:52:28, 4.25s/it] 1%| | 140/18688 [09:44<21:39:00, 4.20s/it] {'loss': '1.056', 'grad_norm': '1.432', 'learning_rate': '2.131e-05', 'epoch': '0.007492', 'num_input_tokens_seen': 471752, 'train_runtime': '584.2', 'train_tokens_per_second': '807.5'}
1%| | 140/18688 [09:44<21:39:00, 4.20s/it] 1%| | 141/18688 [09:47<20:36:06, 4.00s/it] 1%| | 142/18688 [09:52<21:16:36, 4.13s/it] 1%| | 143/18688 [09:55<20:46:14, 4.03s/it] 1%| | 144/18688 [09:59<19:50:16, 3.85s/it] 1%| | 145/18688 [10:02<19:20:56, 3.76s/it] 1%| | 146/18688 [10:07<20:39:30, 4.01s/it] 1%| | 147/18688 [10:11<21:11:16, 4.11s/it] 1%| | 148/18688 [10:15<20:39:17, 4.01s/it] 1%| | 149/18688 [10:19<20:04:07, 3.90s/it] 1%| | 150/18688 [10:23<19:49:36, 3.85s/it] {'loss': '1.063', 'grad_norm': '1.512', 'learning_rate': '2.285e-05', 'epoch': '0.008027', 'num_input_tokens_seen': 501528, 'train_runtime': '623', 'train_tokens_per_second': '805'}
1%| | 150/18688 [10:23<19:49:36, 3.85s/it] 1%| | 151/18688 [10:27<20:07:30, 3.91s/it] 1%| | 152/18688 [10:30<19:36:30, 3.81s/it] 1%| | 153/18688 [10:34<19:11:33, 3.73s/it] 1%| | 154/18688 [10:37<18:59:04, 3.69s/it] 1%| | 155/18688 [10:41<19:18:27, 3.75s/it] 1%| | 156/18688 [10:45<19:57:11, 3.88s/it] 1%| | 157/18688 [10:49<19:37:54, 3.81s/it] 1%| | 158/18688 [10:53<19:53:41, 3.87s/it] 1%| | 159/18688 [10:58<21:15:10, 4.13s/it] 1%| | 160/18688 [11:01<20:17:22, 3.94s/it] {'loss': '1.022', 'grad_norm': '1.454', 'learning_rate': '2.438e-05', 'epoch': '0.008562', 'num_input_tokens_seen': 532056, 'train_runtime': '661.7', 'train_tokens_per_second': '804'}
1%| | 160/18688 [11:01<20:17:22, 3.94s/it] 1%| | 161/18688 [11:06<20:51:42, 4.05s/it] 1%| | 162/18688 [11:09<20:05:29, 3.90s/it] 1%| | 163/18688 [11:14<20:54:11, 4.06s/it] 1%| | 164/18688 [11:17<20:29:58, 3.98s/it] 1%| | 165/18688 [11:22<22:01:12, 4.28s/it] 1%| | 166/18688 [11:26<20:54:00, 4.06s/it] 1%| | 167/18688 [11:29<19:59:16, 3.89s/it] 1%| | 168/18688 [11:33<19:57:46, 3.88s/it] 1%| | 169/18688 [11:37<19:27:13, 3.78s/it] 1%| | 170/18688 [11:40<19:01:52, 3.70s/it] {'loss': '1.064', 'grad_norm': '1.564', 'learning_rate': '2.591e-05', 'epoch': '0.009097', 'num_input_tokens_seen': 564304, 'train_runtime': '700.8', 'train_tokens_per_second': '805.3'}
1%| | 170/18688 [11:40<19:01:52, 3.70s/it] 1%| | 171/18688 [11:44<18:59:57, 3.69s/it] 1%| | 172/18688 [11:48<19:15:55, 3.75s/it] 1%| | 173/18688 [11:52<20:14:47, 3.94s/it] 1%| | 174/18688 [11:56<19:23:53, 3.77s/it] 1%| | 175/18688 [12:00<20:44:32, 4.03s/it] 1%| | 176/18688 [12:05<21:21:03, 4.15s/it] 1%| | 177/18688 [12:08<20:26:21, 3.98s/it] 1%| | 178/18688 [12:12<20:42:25, 4.03s/it] 1%| | 179/18688 [12:16<19:45:25, 3.84s/it] 1%| | 180/18688 [12:20<19:52:04, 3.86s/it] {'loss': '1.112', 'grad_norm': '1.412', 'learning_rate': '2.745e-05', 'epoch': '0.009632', 'num_input_tokens_seen': 595424, 'train_runtime': '740.2', 'train_tokens_per_second': '804.4'}
1%| | 180/18688 [12:20<19:52:04, 3.86s/it] 1%| | 181/18688 [12:23<19:37:51, 3.82s/it] 1%| | 182/18688 [12:27<19:08:26, 3.72s/it] 1%| | 183/18688 [12:32<20:43:51, 4.03s/it] 1%| | 184/18688 [12:35<20:11:10, 3.93s/it] 1%| | 185/18688 [12:39<19:57:35, 3.88s/it] 1%| | 186/18688 [12:43<20:01:46, 3.90s/it] 1%| | 187/18688 [12:47<20:51:29, 4.06s/it] 1%| | 188/18688 [12:52<21:14:15, 4.13s/it] 1%| | 189/18688 [12:56<21:10:38, 4.12s/it] 1%| | 190/18688 [13:00<21:36:08, 4.20s/it] {'loss': '1.036', 'grad_norm': '1.566', 'learning_rate': '2.898e-05', 'epoch': '0.01017', 'num_input_tokens_seen': 628248, 'train_runtime': '780.8', 'train_tokens_per_second': '804.6'}
1%| | 190/18688 [13:00<21:36:08, 4.20s/it] 1%| | 191/18688 [13:05<22:03:21, 4.29s/it] 1%| | 192/18688 [13:09<21:10:04, 4.12s/it] 1%| | 193/18688 [13:12<20:08:38, 3.92s/it] 1%| | 194/18688 [13:16<20:28:07, 3.98s/it] 1%| | 195/18688 [13:21<21:24:26, 4.17s/it] 1%| | 196/18688 [13:26<22:26:48, 4.37s/it] 1%| | 197/18688 [13:30<22:10:24, 4.32s/it] 1%| | 198/18688 [13:33<21:00:15, 4.09s/it] 1%| | 199/18688 [13:37<20:48:43, 4.05s/it] 1%| | 200/18688 [13:42<21:14:59, 4.14s/it] {'loss': '1.048', 'grad_norm': '1.362', 'learning_rate': '3.051e-05', 'epoch': '0.0107', 'num_input_tokens_seen': 661320, 'train_runtime': '822.1', 'train_tokens_per_second': '804.4'}
1%| | 200/18688 [13:42<21:14:59, 4.14s/it] 1%| | 201/18688 [13:45<20:33:51, 4.00s/it] 1%| | 202/18688 [13:49<19:56:54, 3.88s/it] 1%| | 203/18688 [13:53<20:54:18, 4.07s/it] 1%| | 204/18688 [13:58<21:00:43, 4.09s/it] 1%| | 205/18688 [14:02<21:16:04, 4.14s/it] 1%| | 206/18688 [14:06<21:47:43, 4.25s/it] 1%| | 207/18688 [14:11<22:22:43, 4.36s/it] 1%| | 208/18688 [14:15<21:29:15, 4.19s/it] 1%| | 209/18688 [14:20<22:45:21, 4.43s/it] 1%| | 210/18688 [14:23<21:20:23, 4.16s/it] {'loss': '1.157', 'grad_norm': '1.791', 'learning_rate': '3.205e-05', 'epoch': '0.01124', 'num_input_tokens_seen': 696656, 'train_runtime': '863.7', 'train_tokens_per_second': '806.6'}
1%| | 210/18688 [14:23<21:20:23, 4.16s/it] 1%| | 211/18688 [14:27<20:14:31, 3.94s/it] 1%| | 212/18688 [14:31<20:48:08, 4.05s/it] 1%| | 213/18688 [14:35<20:50:34, 4.06s/it] 1%| | 214/18688 [14:39<21:06:17, 4.11s/it] 1%| | 215/18688 [14:43<21:06:06, 4.11s/it] 1%| | 216/18688 [14:48<21:22:37, 4.17s/it] 1%| | 217/18688 [14:52<21:10:59, 4.13s/it] 1%| | 218/18688 [14:56<21:23:18, 4.17s/it] 1%| | 219/18688 [15:00<21:14:05, 4.14s/it] 1%| | 220/18688 [15:05<22:14:48, 4.34s/it] {'loss': '1.071', 'grad_norm': '0.9305', 'learning_rate': '3.358e-05', 'epoch': '0.01177', 'num_input_tokens_seen': 727368, 'train_runtime': '905.4', 'train_tokens_per_second': '803.4'}
1%| | 220/18688 [15:05<22:14:48, 4.34s/it] 1%| | 221/18688 [15:09<22:24:12, 4.37s/it] 1%| | 222/18688 [15:13<21:59:51, 4.29s/it] 1%| | 223/18688 [15:18<21:56:51, 4.28s/it] 1%| | 224/18688 [15:22<21:39:05, 4.22s/it] 1%| | 225/18688 [15:26<21:57:18, 4.28s/it] 1%| | 226/18688 [15:30<21:09:42, 4.13s/it] 1%| | 227/18688 [15:34<21:02:49, 4.10s/it] 1%| | 228/18688 [15:39<22:14:45, 4.34s/it] 1%| | 229/18688 [15:43<21:17:48, 4.15s/it] 1%| | 230/18688 [15:46<20:42:47, 4.04s/it] {'loss': '1.054', 'grad_norm': '2.228', 'learning_rate': '3.511e-05', 'epoch': '0.01231', 'num_input_tokens_seen': 761544, 'train_runtime': '946.9', 'train_tokens_per_second': '804.3'}
1%| | 230/18688 [15:46<20:42:47, 4.04s/it] 1%| | 231/18688 [15:50<20:43:07, 4.04s/it] 1%| | 232/18688 [15:55<22:15:04, 4.34s/it] 1%| | 233/18688 [16:00<21:56:38, 4.28s/it] 1%|▏ | 234/18688 [16:04<21:50:45, 4.26s/it] 1%|▏ | 235/18688 [16:08<21:24:38, 4.18s/it] 1%|▏ | 236/18688 [16:14<24:01:54, 4.69s/it] 1%|▏ | 237/18688 [16:18<22:49:22, 4.45s/it] 1%|▏ | 238/18688 [16:22<22:20:35, 4.36s/it] 1%|▏ | 239/18688 [16:26<22:07:14, 4.32s/it] 1%|▏ | 240/18688 [16:30<21:11:38, 4.14s/it] {'loss': '1.066', 'grad_norm': '1.485', 'learning_rate': '3.665e-05', 'epoch': '0.01284', 'num_input_tokens_seen': 797960, 'train_runtime': '990.1', 'train_tokens_per_second': '805.9'}
1%|▏ | 240/18688 [16:30<21:11:38, 4.14s/it] 1%|▏ | 241/18688 [16:34<22:13:11, 4.34s/it] 1%|▏ | 242/18688 [16:39<22:08:55, 4.32s/it] 1%|▏ | 243/18688 [16:43<22:24:11, 4.37s/it] 1%|▏ | 244/18688 [16:47<22:07:27, 4.32s/it] 1%|▏ | 245/18688 [16:51<21:29:39, 4.20s/it] 1%|▏ | 246/18688 [16:56<21:38:42, 4.23s/it] 1%|▏ | 247/18688 [17:00<21:21:33, 4.17s/it] 1%|▏ | 248/18688 [17:04<21:25:13, 4.18s/it] 1%|▏ | 249/18688 [17:09<22:10:29, 4.33s/it] 1%|▏ | 250/18688 [17:13<22:17:44, 4.35s/it] {'loss': '1.071', 'grad_norm': '1.145', 'learning_rate': '3.818e-05', 'epoch': '0.01338', 'num_input_tokens_seen': 831064, 'train_runtime': '1033', 'train_tokens_per_second': '804.2'}
1%|▏ | 250/18688 [17:13<22:17:44, 4.35s/it] 1%|▏ | 251/18688 [17:17<22:30:26, 4.39s/it] 1%|▏ | 252/18688 [17:21<21:43:21, 4.24s/it] 1%|▏ | 253/18688 [17:25<20:42:56, 4.05s/it] 1%|▏ | 254/18688 [17:29<20:40:37, 4.04s/it] 1%|▏ | 255/18688 [17:33<20:36:17, 4.02s/it] 1%|▏ | 256/18688 [17:37<20:45:54, 4.06s/it] 1%|▏ | 257/18688 [17:41<21:20:43, 4.17s/it] 1%|▏ | 258/18688 [17:45<20:53:34, 4.08s/it] 1%|▏ | 259/18688 [17:49<20:08:25, 3.93s/it] 1%|▏ | 260/18688 [17:53<20:37:42, 4.03s/it] {'loss': '1.007', 'grad_norm': '1.561', 'learning_rate': '3.971e-05', 'epoch': '0.01391', 'num_input_tokens_seen': 862936, 'train_runtime': '1074', 'train_tokens_per_second': '803.7'}
1%|▏ | 260/18688 [17:53<20:37:42, 4.03s/it] 1%|▏ | 261/18688 [17:58<21:23:28, 4.18s/it] 1%|▏ | 262/18688 [18:02<21:09:43, 4.13s/it] 1%|▏ | 263/18688 [18:06<21:24:41, 4.18s/it] 1%|▏ | 264/18688 [18:10<20:46:12, 4.06s/it] 1%|▏ | 265/18688 [18:13<19:56:19, 3.90s/it] 1%|▏ | 266/18688 [18:17<19:13:01, 3.76s/it] 1%|▏ | 267/18688 [18:21<20:37:21, 4.03s/it] 1%|▏ | 268/18688 [18:25<19:50:01, 3.88s/it] 1%|▏ | 269/18688 [18:29<19:47:50, 3.87s/it] 1%|▏ | 270/18688 [18:32<19:14:42, 3.76s/it] {'loss': '1.004', 'grad_norm': '1.379', 'learning_rate': '4.125e-05', 'epoch': '0.01445', 'num_input_tokens_seen': 893208, 'train_runtime': '1113', 'train_tokens_per_second': '802.6'}
1%|▏ | 270/18688 [18:32<19:14:42, 3.76s/it] 1%|▏ | 271/18688 [18:36<19:07:11, 3.74s/it] 1%|▏ | 272/18688 [18:40<19:22:39, 3.79s/it] 1%|▏ | 273/18688 [18:44<19:12:46, 3.76s/it] 1%|▏ | 274/18688 [18:48<20:28:56, 4.00s/it] 1%|▏ | 275/18688 [18:52<20:33:55, 4.02s/it] 1%|▏ | 276/18688 [18:57<21:01:39, 4.11s/it] 1%|▏ | 277/18688 [19:00<20:37:45, 4.03s/it] 1%|▏ | 278/18688 [19:04<20:08:07, 3.94s/it] 1%|▏ | 279/18688 [19:09<22:01:10, 4.31s/it] 1%|▏ | 280/18688 [19:14<22:27:25, 4.39s/it] {'loss': '1.08', 'grad_norm': '1.499', 'learning_rate': '4.278e-05', 'epoch': '0.01498', 'num_input_tokens_seen': 925664, 'train_runtime': '1154', 'train_tokens_per_second': '801.9'}
1%|▏ | 280/18688 [19:14<22:27:25, 4.39s/it] 2%|▏ | 281/18688 [19:18<21:44:33, 4.25s/it] 2%|▏ | 282/18688 [19:23<22:27:56, 4.39s/it] 2%|▏ | 283/18688 [19:27<22:58:54, 4.50s/it] 2%|▏ | 284/18688 [19:32<22:50:46, 4.47s/it] 2%|▏ | 285/18688 [19:35<21:37:09, 4.23s/it] 2%|▏ | 286/18688 [19:40<22:46:17, 4.45s/it] 2%|▏ | 287/18688 [19:44<21:35:42, 4.22s/it] 2%|▏ | 288/18688 [19:49<22:03:20, 4.32s/it] 2%|▏ | 289/18688 [19:53<22:17:55, 4.36s/it] 2%|▏ | 290/18688 [19:57<22:18:09, 4.36s/it] {'loss': '1.071', 'grad_norm': '1.312', 'learning_rate': '4.431e-05', 'epoch': '0.01552', 'num_input_tokens_seen': 960824, 'train_runtime': '1198', 'train_tokens_per_second': '802.1'}
2%|▏ | 290/18688 [19:57<22:18:09, 4.36s/it] 2%|▏ | 291/18688 [20:02<22:12:27, 4.35s/it] 2%|▏ | 292/18688 [20:05<21:02:56, 4.12s/it] 2%|▏ | 293/18688 [20:09<20:39:07, 4.04s/it] 2%|▏ | 294/18688 [20:13<20:29:37, 4.01s/it] 2%|▏ | 295/18688 [20:17<20:41:49, 4.05s/it] 2%|▏ | 296/18688 [20:22<21:22:01, 4.18s/it] 2%|▏ | 297/18688 [20:26<21:43:13, 4.25s/it] 2%|▏ | 298/18688 [20:31<22:41:01, 4.44s/it] 2%|▏ | 299/18688 [20:36<23:07:16, 4.53s/it] 2%|▏ | 300/18688 [20:40<23:11:21, 4.54s/it] {'loss': '1.052', 'grad_norm': '1.387', 'learning_rate': '4.585e-05', 'epoch': '0.01605', 'num_input_tokens_seen': 993184, 'train_runtime': '1241', 'train_tokens_per_second': '800.4'}
2%|▏ | 300/18688 [20:40<23:11:21, 4.54s/it] 2%|▏ | 301/18688 [20:44<21:31:37, 4.21s/it] 2%|▏ | 302/18688 [20:48<21:46:42, 4.26s/it] 2%|▏ | 303/18688 [20:52<21:12:42, 4.15s/it] 2%|▏ | 304/18688 [20:56<20:14:46, 3.96s/it] 2%|▏ | 305/18688 [20:59<19:24:22, 3.80s/it] 2%|▏ | 306/18688 [21:03<19:09:30, 3.75s/it] 2%|▏ | 307/18688 [21:06<18:44:43, 3.67s/it] 2%|▏ | 308/18688 [21:10<19:21:40, 3.79s/it] 2%|▏ | 309/18688 [21:14<19:57:58, 3.91s/it] 2%|▏ | 310/18688 [21:18<19:40:08, 3.85s/it] {'loss': '1.048', 'grad_norm': '1.117', 'learning_rate': '4.738e-05', 'epoch': '0.01659', 'num_input_tokens_seen': 1022656, 'train_runtime': '1279', 'train_tokens_per_second': '799.8'}
2%|▏ | 310/18688 [21:18<19:40:08, 3.85s/it] 2%|▏ | 311/18688 [21:22<19:12:34, 3.76s/it] 2%|▏ | 312/18688 [21:25<18:39:30, 3.66s/it] 2%|▏ | 313/18688 [21:29<19:29:15, 3.82s/it] 2%|▏ | 314/18688 [21:33<19:12:23, 3.76s/it] 2%|▏ | 315/18688 [21:37<19:13:23, 3.77s/it] 2%|▏ | 316/18688 [21:41<19:47:23, 3.88s/it] 2%|▏ | 317/18688 [21:45<20:10:30, 3.95s/it] 2%|▏ | 318/18688 [21:48<19:34:33, 3.84s/it] 2%|▏ | 319/18688 [21:52<19:10:32, 3.76s/it] 2%|▏ | 320/18688 [21:56<19:42:23, 3.86s/it] {'loss': '1.019', 'grad_norm': '1.22', 'learning_rate': '4.891e-05', 'epoch': '0.01712', 'num_input_tokens_seen': 1053904, 'train_runtime': '1317', 'train_tokens_per_second': '800.4'}
2%|▏ | 320/18688 [21:56<19:42:23, 3.86s/it] 2%|▏ | 321/18688 [22:00<20:04:53, 3.94s/it] 2%|▏ | 322/18688 [22:04<20:04:00, 3.93s/it] 2%|▏ | 323/18688 [22:10<22:11:39, 4.35s/it] 2%|▏ | 324/18688 [22:13<21:08:16, 4.14s/it] 2%|▏ | 325/18688 [22:17<20:06:12, 3.94s/it] 2%|▏ | 326/18688 [22:20<19:39:23, 3.85s/it] 2%|▏ | 327/18688 [22:25<21:04:11, 4.13s/it] 2%|▏ | 328/18688 [22:29<21:04:59, 4.13s/it] 2%|▏ | 329/18688 [22:34<22:18:41, 4.38s/it] 2%|▏ | 330/18688 [22:38<21:45:29, 4.27s/it] {'loss': '1.072', 'grad_norm': '1.187', 'learning_rate': '5.045e-05', 'epoch': '0.01766', 'num_input_tokens_seen': 1090264, 'train_runtime': '1359', 'train_tokens_per_second': '802.5'}
2%|▏ | 330/18688 [22:38<21:45:29, 4.27s/it] 2%|▏ | 331/18688 [22:42<20:42:57, 4.06s/it] 2%|▏ | 332/18688 [22:45<20:07:29, 3.95s/it] 2%|▏ | 333/18688 [22:49<19:15:21, 3.78s/it] 2%|▏ | 334/18688 [22:52<18:49:14, 3.69s/it] 2%|▏ | 335/18688 [22:56<18:53:51, 3.71s/it] 2%|▏ | 336/18688 [23:00<18:36:41, 3.65s/it] 2%|▏ | 337/18688 [23:03<18:56:44, 3.72s/it] 2%|▏ | 338/18688 [23:08<20:32:45, 4.03s/it] 2%|▏ | 339/18688 [23:13<21:20:17, 4.19s/it] 2%|▏ | 340/18688 [23:18<22:36:52, 4.44s/it] {'loss': '0.9692', 'grad_norm': '1.423', 'learning_rate': '5.198e-05', 'epoch': '0.01819', 'num_input_tokens_seen': 1121696, 'train_runtime': '1398', 'train_tokens_per_second': '802.2'}
2%|▏ | 340/18688 [23:18<22:36:52, 4.44s/it] 2%|▏ | 341/18688 [23:21<21:06:56, 4.14s/it] 2%|▏ | 342/18688 [23:25<20:22:06, 4.00s/it] 2%|▏ | 343/18688 [23:29<20:30:54, 4.03s/it] 2%|▏ | 344/18688 [23:33<19:46:41, 3.88s/it] 2%|▏ | 345/18688 [23:37<20:09:10, 3.96s/it] 2%|▏ | 346/18688 [23:41<21:06:43, 4.14s/it] 2%|▏ | 347/18688 [23:46<22:04:34, 4.33s/it] 2%|▏ | 348/18688 [23:50<21:54:01, 4.30s/it] 2%|▏ | 349/18688 [23:55<22:23:33, 4.40s/it] 2%|▏ | 350/18688 [23:58<21:05:19, 4.14s/it] {'loss': '1.058', 'grad_norm': '1.457', 'learning_rate': '5.351e-05', 'epoch': '0.01873', 'num_input_tokens_seen': 1153216, 'train_runtime': '1439', 'train_tokens_per_second': '801.5'}
2%|▏ | 350/18688 [23:58<21:05:19, 4.14s/it] 2%|▏ | 351/18688 [24:02<20:19:00, 3.99s/it] 2%|▏ | 352/18688 [24:06<20:46:32, 4.08s/it] 2%|▏ | 353/18688 [24:11<21:01:01, 4.13s/it] 2%|▏ | 354/18688 [24:14<20:19:20, 3.99s/it] 2%|▏ | 355/18688 [24:18<19:36:05, 3.85s/it] 2%|▏ | 356/18688 [24:24<22:32:48, 4.43s/it] 2%|▏ | 357/18688 [24:27<21:40:55, 4.26s/it] 2%|▏ | 358/18688 [24:32<21:36:47, 4.24s/it] 2%|▏ | 359/18688 [24:36<21:22:54, 4.20s/it] 2%|▏ | 360/18688 [24:40<21:02:43, 4.13s/it] {'loss': '1.041', 'grad_norm': '1.16', 'learning_rate': '5.505e-05', 'epoch': '0.01926', 'num_input_tokens_seen': 1188456, 'train_runtime': '1480', 'train_tokens_per_second': '802.9'}
2%|▏ | 360/18688 [24:40<21:02:43, 4.13s/it] 2%|▏ | 361/18688 [24:43<20:16:36, 3.98s/it] 2%|▏ | 362/18688 [24:48<21:06:08, 4.15s/it] 2%|▏ | 363/18688 [24:52<21:45:15, 4.27s/it] 2%|▏ | 364/18688 [24:57<22:21:08, 4.39s/it] 2%|▏ | 365/18688 [25:02<22:37:36, 4.45s/it] 2%|▏ | 366/18688 [25:05<21:11:11, 4.16s/it] 2%|▏ | 367/18688 [25:10<21:44:15, 4.27s/it] 2%|▏ | 368/18688 [25:14<21:06:48, 4.15s/it] 2%|▏ | 369/18688 [25:17<20:23:09, 4.01s/it] 2%|▏ | 370/18688 [25:22<21:35:01, 4.24s/it] {'loss': '1.071', 'grad_norm': '1.302', 'learning_rate': '5.658e-05', 'epoch': '0.0198', 'num_input_tokens_seen': 1222640, 'train_runtime': '1522', 'train_tokens_per_second': '803.1'}
2%|▏ | 370/18688 [25:22<21:35:01, 4.24s/it] 2%|▏ | 371/18688 [25:26<20:35:44, 4.05s/it] 2%|▏ | 372/18688 [25:29<19:57:50, 3.92s/it] 2%|▏ | 373/18688 [25:33<19:43:34, 3.88s/it] 2%|▏ | 374/18688 [25:37<20:39:11, 4.06s/it] 2%|▏ | 375/18688 [25:42<21:23:37, 4.21s/it] 2%|▏ | 376/18688 [25:46<21:07:22, 4.15s/it] 2%|▏ | 377/18688 [25:50<21:09:34, 4.16s/it] 2%|▏ | 378/18688 [25:54<20:56:33, 4.12s/it] 2%|▏ | 379/18688 [25:58<20:43:59, 4.08s/it] 2%|▏ | 380/18688 [26:02<20:43:05, 4.07s/it] {'loss': '1.075', 'grad_norm': '1.293', 'learning_rate': '5.811e-05', 'epoch': '0.02033', 'num_input_tokens_seen': 1253888, 'train_runtime': '1563', 'train_tokens_per_second': '802.3'}
2%|▏ | 380/18688 [26:02<20:43:05, 4.07s/it] 2%|▏ | 381/18688 [26:06<20:03:23, 3.94s/it] 2%|▏ | 382/18688 [26:09<19:14:04, 3.78s/it] 2%|▏ | 383/18688 [26:14<19:48:44, 3.90s/it] 2%|▏ | 384/18688 [26:18<20:02:07, 3.94s/it] 2%|▏ | 385/18688 [26:22<20:25:52, 4.02s/it] 2%|▏ | 386/18688 [26:26<20:57:10, 4.12s/it] 2%|▏ | 387/18688 [26:31<21:32:02, 4.24s/it] 2%|▏ | 388/18688 [26:34<20:52:11, 4.11s/it] 2%|▏ | 389/18688 [26:38<19:50:31, 3.90s/it] 2%|▏ | 390/18688 [26:43<21:05:01, 4.15s/it] {'loss': '1.005', 'grad_norm': '1.041', 'learning_rate': '5.965e-05', 'epoch': '0.02087', 'num_input_tokens_seen': 1286896, 'train_runtime': '1603', 'train_tokens_per_second': '802.8'}
2%|▏ | 390/18688 [26:43<21:05:01, 4.15s/it] 2%|▏ | 391/18688 [26:47<21:14:09, 4.18s/it] 2%|▏ | 392/18688 [26:51<20:59:28, 4.13s/it] 2%|▏ | 393/18688 [26:55<20:47:11, 4.09s/it] 2%|▏ | 394/18688 [26:59<21:09:44, 4.16s/it] 2%|▏ | 395/18688 [27:04<22:32:09, 4.43s/it] 2%|▏ | 396/18688 [27:08<22:03:12, 4.34s/it] 2%|▏ | 397/18688 [27:12<21:29:42, 4.23s/it] 2%|▏ | 398/18688 [27:16<21:15:12, 4.18s/it] 2%|▏ | 399/18688 [27:21<22:06:41, 4.35s/it] 2%|▏ | 400/18688 [27:25<21:38:57, 4.26s/it] {'loss': '1.025', 'grad_norm': '1.194', 'learning_rate': '6.118e-05', 'epoch': '0.0214', 'num_input_tokens_seen': 1319712, 'train_runtime': '1646', 'train_tokens_per_second': '801.9'}
2%|▏ | 400/18688 [27:25<21:38:57, 4.26s/it] 2%|▏ | 401/18688 [27:30<22:05:46, 4.35s/it] 2%|▏ | 402/18688 [27:33<20:53:21, 4.11s/it] 2%|▏ | 403/18688 [27:37<20:45:46, 4.09s/it] 2%|▏ | 404/18688 [27:41<20:14:53, 3.99s/it] 2%|▏ | 405/18688 [27:45<19:58:14, 3.93s/it] 2%|▏ | 406/18688 [27:50<22:20:43, 4.40s/it] 2%|▏ | 407/18688 [27:54<21:39:26, 4.26s/it] 2%|▏ | 408/18688 [27:58<20:26:26, 4.03s/it] 2%|▏ | 409/18688 [28:02<20:03:55, 3.95s/it] 2%|▏ | 410/18688 [28:05<19:36:50, 3.86s/it] {'loss': '1.076', 'grad_norm': '0.9803', 'learning_rate': '6.271e-05', 'epoch': '0.02194', 'num_input_tokens_seen': 1352936, 'train_runtime': '1686', 'train_tokens_per_second': '802.6'}
2%|▏ | 410/18688 [28:05<19:36:50, 3.86s/it] 2%|▏ | 411/18688 [28:09<19:55:43, 3.93s/it] 2%|▏ | 412/18688 [28:13<19:16:27, 3.80s/it] 2%|▏ | 413/18688 [28:17<19:56:14, 3.93s/it] 2%|▏ | 414/18688 [28:21<20:01:57, 3.95s/it] 2%|▏ | 415/18688 [28:25<20:02:20, 3.95s/it] 2%|▏ | 416/18688 [28:30<20:57:40, 4.13s/it] 2%|▏ | 417/18688 [28:33<19:56:16, 3.93s/it] 2%|▏ | 418/18688 [28:36<19:11:17, 3.78s/it] 2%|▏ | 419/18688 [28:41<20:05:29, 3.96s/it] 2%|▏ | 420/18688 [28:45<19:42:57, 3.89s/it] {'loss': '1.07', 'grad_norm': '1.154', 'learning_rate': '6.425e-05', 'epoch': '0.02247', 'num_input_tokens_seen': 1385008, 'train_runtime': '1725', 'train_tokens_per_second': '802.9'}
2%|▏ | 420/18688 [28:45<19:42:57, 3.89s/it] 2%|▏ | 421/18688 [28:50<21:30:04, 4.24s/it] 2%|▏ | 422/18688 [28:55<23:00:31, 4.53s/it] 2%|▏ | 423/18688 [28:59<22:15:20, 4.39s/it] 2%|▏ | 424/18688 [29:03<22:17:41, 4.39s/it] 2%|▏ | 425/18688 [29:08<22:15:47, 4.39s/it] 2%|▏ | 426/18688 [29:12<21:43:05, 4.28s/it] 2%|▏ | 427/18688 [29:16<21:19:17, 4.20s/it] 2%|▏ | 428/18688 [29:21<22:46:51, 4.49s/it] 2%|▏ | 429/18688 [29:25<22:48:46, 4.50s/it] 2%|▏ | 430/18688 [29:29<22:12:12, 4.38s/it] {'loss': '1.026', 'grad_norm': '1.158', 'learning_rate': '6.578e-05', 'epoch': '0.02301', 'num_input_tokens_seen': 1420952, 'train_runtime': '1770', 'train_tokens_per_second': '802.8'}
2%|▏ | 430/18688 [29:29<22:12:12, 4.38s/it] 2%|▏ | 431/18688 [29:34<22:10:23, 4.37s/it] 2%|▏ | 432/18688 [29:38<22:26:30, 4.43s/it] 2%|▏ | 433/18688 [29:43<22:23:12, 4.41s/it] 2%|▏ | 434/18688 [29:47<22:15:54, 4.39s/it] 2%|▏ | 435/18688 [29:51<21:30:27, 4.24s/it] 2%|▏ | 436/18688 [29:55<21:06:58, 4.16s/it] 2%|▏ | 437/18688 [29:59<21:22:23, 4.22s/it] 2%|▏ | 438/18688 [30:03<20:30:20, 4.04s/it] 2%|▏ | 439/18688 [30:07<19:47:35, 3.90s/it] 2%|▏ | 440/18688 [30:10<19:51:52, 3.92s/it] {'loss': '1.067', 'grad_norm': '1.078', 'learning_rate': '6.731e-05', 'epoch': '0.02355', 'num_input_tokens_seen': 1452280, 'train_runtime': '1811', 'train_tokens_per_second': '801.9'}
2%|▏ | 440/18688 [30:10<19:51:52, 3.92s/it] 2%|▏ | 441/18688 [30:14<19:47:10, 3.90s/it] 2%|▏ | 442/18688 [30:19<20:33:31, 4.06s/it] 2%|▏ | 443/18688 [30:23<21:01:34, 4.15s/it] 2%|▏ | 444/18688 [30:29<23:24:20, 4.62s/it] 2%|▏ | 445/18688 [30:34<23:47:32, 4.70s/it] 2%|▏ | 446/18688 [30:38<22:28:00, 4.43s/it] 2%|▏ | 447/18688 [30:41<21:13:45, 4.19s/it] 2%|▏ | 448/18688 [30:45<20:32:54, 4.06s/it] 2%|▏ | 449/18688 [30:49<20:51:24, 4.12s/it] 2%|▏ | 450/18688 [30:53<20:47:13, 4.10s/it] {'loss': '1.031', 'grad_norm': '1.253', 'learning_rate': '6.885e-05', 'epoch': '0.02408', 'num_input_tokens_seen': 1487680, 'train_runtime': '1854', 'train_tokens_per_second': '802.5'}
2%|▏ | 450/18688 [30:53<20:47:13, 4.10s/it] 2%|▏ | 451/18688 [30:57<20:45:55, 4.10s/it] 2%|▏ | 452/18688 [31:02<21:23:16, 4.22s/it] 2%|▏ | 453/18688 [31:06<20:54:54, 4.13s/it] 2%|▏ | 454/18688 [31:11<22:24:50, 4.43s/it] 2%|▏ | 455/18688 [31:16<23:56:46, 4.73s/it] 2%|▏ | 456/18688 [31:20<23:03:53, 4.55s/it] 2%|▏ | 457/18688 [31:24<21:32:15, 4.25s/it] 2%|▏ | 458/18688 [31:28<21:24:10, 4.23s/it] 2%|▏ | 459/18688 [31:32<20:12:21, 3.99s/it] 2%|▏ | 460/18688 [31:35<19:43:01, 3.89s/it] {'loss': '0.9789', 'grad_norm': '1.12', 'learning_rate': '7.038e-05', 'epoch': '0.02462', 'num_input_tokens_seen': 1521584, 'train_runtime': '1896', 'train_tokens_per_second': '802.6'}
2%|▏ | 460/18688 [31:35<19:43:01, 3.89s/it] 2%|▏ | 461/18688 [31:39<19:36:35, 3.87s/it] 2%|▏ | 462/18688 [31:43<19:10:49, 3.79s/it] 2%|▏ | 463/18688 [31:47<19:28:28, 3.85s/it] 2%|▏ | 464/18688 [31:50<18:57:03, 3.74s/it] 2%|▏ | 465/18688 [31:54<18:38:10, 3.68s/it] 2%|▏ | 466/18688 [31:58<19:22:48, 3.83s/it] 2%|▏ | 467/18688 [32:02<19:29:10, 3.85s/it] 3%|▎ | 468/18688 [32:06<20:25:42, 4.04s/it] 3%|▎ | 469/18688 [32:10<19:29:37, 3.85s/it] 3%|▎ | 470/18688 [32:15<21:17:29, 4.21s/it] {'loss': '1.039', 'grad_norm': '0.9518', 'learning_rate': '7.191e-05', 'epoch': '0.02515', 'num_input_tokens_seen': 1553928, 'train_runtime': '1935', 'train_tokens_per_second': '803'}
3%|▎ | 470/18688 [32:15<21:17:29, 4.21s/it] 3%|▎ | 471/18688 [32:19<21:47:15, 4.31s/it] 3%|▎ | 472/18688 [32:24<22:00:02, 4.35s/it] 3%|▎ | 473/18688 [32:28<21:59:54, 4.35s/it] 3%|▎ | 474/18688 [32:32<21:17:32, 4.21s/it] 3%|▎ | 475/18688 [32:35<20:15:10, 4.00s/it] 3%|▎ | 476/18688 [32:40<20:51:27, 4.12s/it] 3%|▎ | 477/18688 [32:44<20:25:09, 4.04s/it] 3%|▎ | 478/18688 [32:47<19:53:02, 3.93s/it] 3%|▎ | 479/18688 [32:52<20:55:50, 4.14s/it] 3%|▎ | 480/18688 [32:56<20:29:09, 4.05s/it] {'loss': '1.108', 'grad_norm': '1.097', 'learning_rate': '7.345e-05', 'epoch': '0.02569', 'num_input_tokens_seen': 1588296, 'train_runtime': '1976', 'train_tokens_per_second': '803.7'}
3%|▎ | 480/18688 [32:56<20:29:09, 4.05s/it] 3%|▎ | 481/18688 [33:00<21:15:24, 4.20s/it] 3%|▎ | 482/18688 [33:04<20:34:23, 4.07s/it] 3%|▎ | 483/18688 [33:08<20:02:23, 3.96s/it] 3%|▎ | 484/18688 [33:12<19:39:04, 3.89s/it] 3%|▎ | 485/18688 [33:16<20:52:48, 4.13s/it] 3%|▎ | 486/18688 [33:20<20:45:23, 4.11s/it] 3%|▎ | 487/18688 [33:24<20:39:10, 4.08s/it] 3%|▎ | 488/18688 [33:28<20:22:48, 4.03s/it] 3%|▎ | 489/18688 [33:32<20:30:04, 4.06s/it] 3%|▎ | 490/18688 [33:36<19:42:30, 3.90s/it] {'loss': '1.01', 'grad_norm': '1.293', 'learning_rate': '7.498e-05', 'epoch': '0.02622', 'num_input_tokens_seen': 1620376, 'train_runtime': '2016', 'train_tokens_per_second': '803.6'}
3%|▎ | 490/18688 [33:36<19:42:30, 3.90s/it] 3%|▎ | 491/18688 [33:39<19:00:18, 3.76s/it] 3%|▎ | 492/18688 [33:44<19:40:49, 3.89s/it] 3%|▎ | 493/18688 [33:47<19:34:14, 3.87s/it] 3%|▎ | 494/18688 [33:52<20:37:37, 4.08s/it] 3%|▎ | 495/18688 [33:56<21:11:18, 4.19s/it] 3%|▎ | 496/18688 [34:00<20:35:43, 4.08s/it] 3%|▎ | 497/18688 [34:04<19:43:30, 3.90s/it] 3%|▎ | 498/18688 [34:07<18:57:10, 3.75s/it] 3%|▎ | 499/18688 [34:11<19:09:38, 3.79s/it] 3%|▎ | 500/18688 [34:16<21:29:38, 4.25s/it] {'loss': '1.012', 'grad_norm': '0.9711', 'learning_rate': '7.651e-05', 'epoch': '0.02676', 'num_input_tokens_seen': 1653200, 'train_runtime': '2057', 'train_tokens_per_second': '803.8'}
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3%|▎ | 510/18688 [34:55<20:02:57, 3.97s/it] 3%|▎ | 511/18688 [34:58<19:43:47, 3.91s/it] 3%|▎ | 512/18688 [35:04<22:31:02, 4.46s/it] 3%|▎ | 513/18688 [35:09<23:07:20, 4.58s/it] 3%|▎ | 514/18688 [35:14<22:54:55, 4.54s/it] 3%|▎ | 515/18688 [35:18<22:13:07, 4.40s/it] 3%|▎ | 516/18688 [35:22<22:19:16, 4.42s/it] 3%|▎ | 517/18688 [35:26<21:09:44, 4.19s/it] 3%|▎ | 518/18688 [35:30<21:12:07, 4.20s/it] 3%|▎ | 519/18688 [35:34<21:39:20, 4.29s/it] 3%|▎ | 520/18688 [35:39<21:53:00, 4.34s/it] {'loss': '1.008', 'grad_norm': '1.086', 'learning_rate': '7.958e-05', 'epoch': '0.02783', 'num_input_tokens_seen': 1718448, 'train_runtime': '2139', 'train_tokens_per_second': '803.2'}
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3%|▎ | 530/18688 [36:21<22:22:00, 4.43s/it] 3%|▎ | 531/18688 [36:25<21:37:00, 4.29s/it] 3%|▎ | 532/18688 [36:29<21:10:52, 4.20s/it] 3%|▎ | 533/18688 [36:33<20:52:45, 4.14s/it] 3%|▎ | 534/18688 [36:37<20:32:03, 4.07s/it] 3%|▎ | 535/18688 [36:42<21:19:29, 4.23s/it] 3%|▎ | 536/18688 [36:46<21:01:34, 4.17s/it] 3%|▎ | 537/18688 [36:51<21:51:29, 4.34s/it] 3%|▎ | 538/18688 [36:54<20:53:26, 4.14s/it] 3%|▎ | 539/18688 [36:58<19:51:35, 3.94s/it] 3%|▎ | 540/18688 [37:01<19:13:46, 3.81s/it] {'loss': '1.073', 'grad_norm': '0.9578', 'learning_rate': '8.265e-05', 'epoch': '0.0289', 'num_input_tokens_seen': 1780496, 'train_runtime': '2222', 'train_tokens_per_second': '801.4'}
3%|▎ | 540/18688 [37:01<19:13:46, 3.81s/it] 3%|▎ | 541/18688 [37:06<19:58:28, 3.96s/it] 3%|▎ | 542/18688 [37:09<19:15:15, 3.82s/it] 3%|▎ | 543/18688 [37:13<18:50:59, 3.74s/it] 3%|▎ | 544/18688 [37:17<19:24:36, 3.85s/it] 3%|▎ | 545/18688 [37:22<21:09:43, 4.20s/it] 3%|▎ | 546/18688 [37:26<20:57:02, 4.16s/it] 3%|▎ | 547/18688 [37:29<20:07:35, 3.99s/it] 3%|▎ | 548/18688 [37:33<19:45:05, 3.92s/it] 3%|▎ | 549/18688 [37:37<19:10:52, 3.81s/it] 3%|▎ | 550/18688 [37:41<20:28:53, 4.07s/it] {'loss': '0.9756', 'grad_norm': '1.055', 'learning_rate': '8.418e-05', 'epoch': '0.02943', 'num_input_tokens_seen': 1814136, 'train_runtime': '2262', 'train_tokens_per_second': '802'}
3%|▎ | 550/18688 [37:41<20:28:53, 4.07s/it] 3%|▎ | 551/18688 [37:45<20:09:56, 4.00s/it] 3%|▎ | 552/18688 [37:49<20:17:48, 4.03s/it] 3%|▎ | 553/18688 [37:53<19:19:34, 3.84s/it] 3%|▎ | 554/18688 [37:57<19:33:57, 3.88s/it] 3%|▎ | 555/18688 [38:00<18:53:11, 3.75s/it] 3%|▎ | 556/18688 [38:04<19:37:36, 3.90s/it] 3%|▎ | 557/18688 [38:08<19:24:08, 3.85s/it] 3%|▎ | 558/18688 [38:12<19:21:06, 3.84s/it] 3%|▎ | 559/18688 [38:16<19:54:46, 3.95s/it] 3%|▎ | 560/18688 [38:20<19:09:03, 3.80s/it] {'loss': '0.9136', 'grad_norm': '0.9609', 'learning_rate': '8.571e-05', 'epoch': '0.02997', 'num_input_tokens_seen': 1844456, 'train_runtime': '2300', 'train_tokens_per_second': '801.9'}
3%|▎ | 560/18688 [38:20<19:09:03, 3.80s/it] 3%|▎ | 561/18688 [38:24<19:53:39, 3.95s/it] 3%|▎ | 562/18688 [38:28<20:48:20, 4.13s/it] 3%|▎ | 563/18688 [38:32<19:43:29, 3.92s/it] 3%|▎ | 564/18688 [38:36<20:10:57, 4.01s/it] 3%|▎ | 565/18688 [38:41<20:43:17, 4.12s/it] 3%|▎ | 566/18688 [38:45<21:00:34, 4.17s/it] 3%|▎ | 567/18688 [38:49<20:48:19, 4.13s/it] 3%|▎ | 568/18688 [38:53<21:23:40, 4.25s/it] 3%|▎ | 569/18688 [38:57<20:54:49, 4.16s/it] 3%|▎ | 570/18688 [39:01<20:29:01, 4.07s/it] {'loss': '1.042', 'grad_norm': '0.9506', 'learning_rate': '8.725e-05', 'epoch': '0.0305', 'num_input_tokens_seen': 1877632, 'train_runtime': '2342', 'train_tokens_per_second': '801.8'}
3%|▎ | 570/18688 [39:01<20:29:01, 4.07s/it] 3%|▎ | 571/18688 [39:06<21:10:54, 4.21s/it] 3%|▎ | 572/18688 [39:09<20:31:10, 4.08s/it] 3%|▎ | 573/18688 [39:14<21:16:46, 4.23s/it] 3%|▎ | 574/18688 [39:18<20:51:17, 4.14s/it] 3%|▎ | 575/18688 [39:22<19:55:29, 3.96s/it] 3%|▎ | 576/18688 [39:25<19:18:59, 3.84s/it] 3%|▎ | 577/18688 [39:29<19:58:42, 3.97s/it] 3%|▎ | 578/18688 [39:33<19:45:34, 3.93s/it] 3%|▎ | 579/18688 [39:37<18:56:53, 3.77s/it] 3%|▎ | 580/18688 [39:41<20:22:36, 4.05s/it] {'loss': '0.9562', 'grad_norm': '1.035', 'learning_rate': '8.878e-05', 'epoch': '0.03104', 'num_input_tokens_seen': 1911192, 'train_runtime': '2382', 'train_tokens_per_second': '802.4'}
3%|▎ | 580/18688 [39:41<20:22:36, 4.05s/it] 3%|▎ | 581/18688 [39:46<20:38:36, 4.10s/it] 3%|▎ | 582/18688 [39:50<20:40:37, 4.11s/it] 3%|▎ | 583/18688 [39:54<21:24:42, 4.26s/it] 3%|▎ | 584/18688 [39:59<21:41:54, 4.31s/it] 3%|▎ | 585/18688 [40:03<21:13:24, 4.22s/it] 3%|▎ | 586/18688 [40:07<22:01:40, 4.38s/it] 3%|▎ | 587/18688 [40:12<21:39:17, 4.31s/it] 3%|▎ | 588/18688 [40:16<22:15:09, 4.43s/it] 3%|▎ | 589/18688 [40:21<22:08:01, 4.40s/it] 3%|▎ | 590/18688 [40:24<21:07:59, 4.20s/it] {'loss': '0.9975', 'grad_norm': '0.8844', 'learning_rate': '9.031e-05', 'epoch': '0.03157', 'num_input_tokens_seen': 1943152, 'train_runtime': '2425', 'train_tokens_per_second': '801.3'}
3%|▎ | 590/18688 [40:24<21:07:59, 4.20s/it] 3%|▎ | 591/18688 [40:28<20:28:50, 4.07s/it] 3%|▎ | 592/18688 [40:33<21:02:26, 4.19s/it] 3%|▎ | 593/18688 [40:36<20:18:54, 4.04s/it] 3%|▎ | 594/18688 [40:40<19:53:19, 3.96s/it] 3%|▎ | 595/18688 [40:44<20:28:37, 4.07s/it] 3%|▎ | 596/18688 [40:49<21:05:06, 4.20s/it] 3%|▎ | 597/18688 [40:53<20:49:34, 4.14s/it] 3%|▎ | 598/18688 [40:57<20:26:38, 4.07s/it] 3%|▎ | 599/18688 [41:02<22:32:29, 4.49s/it] 3%|▎ | 600/18688 [41:07<22:34:08, 4.49s/it] {'loss': '0.9981', 'grad_norm': '1.01', 'learning_rate': '9.185e-05', 'epoch': '0.03211', 'num_input_tokens_seen': 1976328, 'train_runtime': '2467', 'train_tokens_per_second': '801'}
3%|▎ | 600/18688 [41:07<22:34:08, 4.49s/it] 3%|▎ | 601/18688 [41:11<22:07:54, 4.41s/it] 3%|▎ | 602/18688 [41:15<21:42:27, 4.32s/it] 3%|▎ | 603/18688 [41:19<20:30:36, 4.08s/it] 3%|▎ | 604/18688 [41:22<20:06:23, 4.00s/it] 3%|▎ | 605/18688 [41:27<21:32:24, 4.29s/it] 3%|▎ | 606/18688 [41:31<21:10:50, 4.22s/it] 3%|▎ | 607/18688 [41:35<20:47:15, 4.14s/it] 3%|▎ | 608/18688 [41:39<20:25:12, 4.07s/it] 3%|▎ | 609/18688 [41:44<21:32:14, 4.29s/it] 3%|▎ | 610/18688 [41:48<20:47:57, 4.14s/it] {'loss': '0.9792', 'grad_norm': '1.105', 'learning_rate': '9.338e-05', 'epoch': '0.03264', 'num_input_tokens_seen': 2008480, 'train_runtime': '2508', 'train_tokens_per_second': '800.7'}
3%|▎ | 610/18688 [41:48<20:47:57, 4.14s/it] 3%|▎ | 611/18688 [41:52<20:48:39, 4.14s/it] 3%|▎ | 612/18688 [41:57<22:03:17, 4.39s/it] 3%|▎ | 613/18688 [42:01<21:35:38, 4.30s/it] 3%|▎ | 614/18688 [42:05<20:47:06, 4.14s/it] 3%|▎ | 615/18688 [42:09<20:02:56, 3.99s/it] 3%|▎ | 616/18688 [42:14<22:40:57, 4.52s/it] 3%|▎ | 617/18688 [42:19<22:54:12, 4.56s/it] 3%|▎ | 618/18688 [42:23<22:04:12, 4.40s/it] 3%|▎ | 619/18688 [42:28<22:20:25, 4.45s/it] 3%|▎ | 620/18688 [42:31<21:19:00, 4.25s/it] {'loss': '1.005', 'grad_norm': '1.255', 'learning_rate': '9.491e-05', 'epoch': '0.03318', 'num_input_tokens_seen': 2043576, 'train_runtime': '2552', 'train_tokens_per_second': '800.8'}
3%|▎ | 620/18688 [42:31<21:19:00, 4.25s/it] 3%|▎ | 621/18688 [42:36<21:41:20, 4.32s/it] 3%|▎ | 622/18688 [42:39<20:26:43, 4.07s/it] 3%|▎ | 623/18688 [42:44<21:37:21, 4.31s/it] 3%|▎ | 624/18688 [42:48<21:24:03, 4.27s/it] 3%|▎ | 625/18688 [42:52<20:57:31, 4.18s/it] 3%|▎ | 626/18688 [42:56<20:48:46, 4.15s/it] 3%|▎ | 627/18688 [43:01<21:12:00, 4.23s/it] 3%|▎ | 628/18688 [43:06<23:07:45, 4.61s/it] 3%|▎ | 629/18688 [43:10<22:12:24, 4.43s/it] 3%|▎ | 630/18688 [43:14<21:45:00, 4.34s/it] {'loss': '1.025', 'grad_norm': '0.9182', 'learning_rate': '9.645e-05', 'epoch': '0.03371', 'num_input_tokens_seen': 2077600, 'train_runtime': '2595', 'train_tokens_per_second': '800.6'}
3%|▎ | 630/18688 [43:14<21:45:00, 4.34s/it] 3%|▎ | 631/18688 [43:19<21:51:09, 4.36s/it] 3%|▎ | 632/18688 [43:23<21:12:01, 4.23s/it] 3%|▎ | 633/18688 [43:26<20:12:13, 4.03s/it] 3%|▎ | 634/18688 [43:30<20:23:18, 4.07s/it] 3%|▎ | 635/18688 [43:35<20:33:42, 4.10s/it] 3%|▎ | 636/18688 [43:39<20:41:10, 4.13s/it] 3%|▎ | 637/18688 [43:44<21:44:40, 4.34s/it] 3%|▎ | 638/18688 [43:48<21:36:55, 4.31s/it] 3%|▎ | 639/18688 [43:52<21:00:59, 4.19s/it] 3%|▎ | 640/18688 [43:56<20:20:12, 4.06s/it] {'loss': '1.063', 'grad_norm': '1.062', 'learning_rate': '9.798e-05', 'epoch': '0.03425', 'num_input_tokens_seen': 2110816, 'train_runtime': '2636', 'train_tokens_per_second': '800.7'}
3%|▎ | 640/18688 [43:56<20:20:12, 4.06s/it] 3%|▎ | 641/18688 [44:00<21:04:52, 4.21s/it] 3%|▎ | 642/18688 [44:05<21:46:26, 4.34s/it] 3%|▎ | 643/18688 [44:09<21:30:23, 4.29s/it] 3%|▎ | 644/18688 [44:13<21:04:09, 4.20s/it] 3%|▎ | 645/18688 [44:17<21:11:16, 4.23s/it] 3%|▎ | 646/18688 [44:22<22:30:17, 4.49s/it] 3%|▎ | 647/18688 [44:26<21:47:10, 4.35s/it] 3%|▎ | 648/18688 [44:31<22:22:10, 4.46s/it] 3%|▎ | 649/18688 [44:35<21:39:07, 4.32s/it] 3%|▎ | 650/18688 [44:39<21:08:08, 4.22s/it] {'loss': '0.9743', 'grad_norm': '0.9258', 'learning_rate': '9.951e-05', 'epoch': '0.03478', 'num_input_tokens_seen': 2146864, 'train_runtime': '2680', 'train_tokens_per_second': '801.2'}
3%|▎ | 650/18688 [44:39<21:08:08, 4.22s/it] 3%|▎ | 651/18688 [44:43<21:21:36, 4.26s/it] 3%|▎ | 652/18688 [44:48<22:19:57, 4.46s/it] 3%|▎ | 653/18688 [44:52<21:42:27, 4.33s/it] 3%|▎ | 654/18688 [44:56<21:19:55, 4.26s/it] 4%|▎ | 655/18688 [45:00<20:36:36, 4.11s/it] 4%|▎ | 656/18688 [45:05<21:04:53, 4.21s/it] 4%|▎ | 657/18688 [45:09<20:56:19, 4.18s/it] 4%|▎ | 658/18688 [45:12<20:02:31, 4.00s/it] 4%|▎ | 659/18688 [45:16<20:13:37, 4.04s/it] 4%|▎ | 660/18688 [45:21<20:12:15, 4.03s/it] {'loss': '1.061', 'grad_norm': '2.052', 'learning_rate': '0.000101', 'epoch': '0.03532', 'num_input_tokens_seen': 2179224, 'train_runtime': '2721', 'train_tokens_per_second': '800.9'}
4%|▎ | 660/18688 [45:21<20:12:15, 4.03s/it] 4%|▎ | 661/18688 [45:24<19:27:06, 3.88s/it] 4%|▎ | 662/18688 [45:28<19:33:39, 3.91s/it] 4%|▎ | 663/18688 [45:32<19:09:44, 3.83s/it] 4%|▎ | 664/18688 [45:37<21:44:16, 4.34s/it] 4%|▎ | 665/18688 [45:41<21:00:41, 4.20s/it] 4%|▎ | 666/18688 [45:45<20:15:46, 4.05s/it] 4%|▎ | 667/18688 [45:49<20:35:46, 4.11s/it] 4%|▎ | 668/18688 [45:53<19:51:08, 3.97s/it] 4%|▎ | 669/18688 [45:56<19:30:30, 3.90s/it] 4%|▎ | 670/18688 [46:01<20:19:47, 4.06s/it] {'loss': '1.067', 'grad_norm': '1.166', 'learning_rate': '0.0001026', 'epoch': '0.03585', 'num_input_tokens_seen': 2211824, 'train_runtime': '2761', 'train_tokens_per_second': '801'}
4%|▎ | 670/18688 [46:01<20:19:47, 4.06s/it] 4%|▎ | 671/18688 [46:05<20:55:48, 4.18s/it] 4%|▎ | 672/18688 [46:10<21:42:42, 4.34s/it] 4%|▎ | 673/18688 [46:15<22:48:45, 4.56s/it] 4%|▎ | 674/18688 [46:20<22:56:01, 4.58s/it] 4%|▎ | 675/18688 [46:24<22:39:48, 4.53s/it] 4%|▎ | 676/18688 [46:29<22:35:24, 4.52s/it] 4%|▎ | 677/18688 [46:33<21:58:59, 4.39s/it] 4%|▎ | 678/18688 [46:37<21:24:18, 4.28s/it] 4%|▎ | 679/18688 [46:41<21:21:27, 4.27s/it] 4%|▎ | 680/18688 [46:44<20:14:33, 4.05s/it] {'loss': '1.042', 'grad_norm': '1.275', 'learning_rate': '0.0001041', 'epoch': '0.03639', 'num_input_tokens_seen': 2246768, 'train_runtime': '2805', 'train_tokens_per_second': '801'}
4%|▎ | 680/18688 [46:44<20:14:33, 4.05s/it] 4%|▎ | 681/18688 [46:49<20:27:17, 4.09s/it] 4%|▎ | 682/18688 [46:53<21:12:44, 4.24s/it] 4%|▎ | 683/18688 [46:57<21:08:24, 4.23s/it] 4%|▎ | 684/18688 [47:02<21:57:58, 4.39s/it] 4%|▎ | 685/18688 [47:06<21:23:22, 4.28s/it] 4%|▎ | 686/18688 [47:10<21:04:45, 4.22s/it] 4%|▎ | 687/18688 [47:15<21:45:28, 4.35s/it] 4%|▎ | 688/18688 [47:20<22:10:12, 4.43s/it] 4%|▎ | 689/18688 [47:24<22:22:33, 4.48s/it] 4%|▎ | 690/18688 [47:29<22:36:41, 4.52s/it] {'loss': '1.025', 'grad_norm': '0.9699', 'learning_rate': '0.0001056', 'epoch': '0.03692', 'num_input_tokens_seen': 2280616, 'train_runtime': '2849', 'train_tokens_per_second': '800.4'}
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5%|▌ | 950/18688 [1:05:31<19:33:29, 3.97s/it] 5%|▌ | 951/18688 [1:05:36<20:08:06, 4.09s/it] 5%|▌ | 952/18688 [1:05:40<20:12:35, 4.10s/it] 5%|▌ | 953/18688 [1:05:44<19:56:38, 4.05s/it] 5%|▌ | 954/18688 [1:05:47<19:15:54, 3.91s/it] 5%|▌ | 955/18688 [1:05:51<19:49:07, 4.02s/it] 5%|▌ | 956/18688 [1:05:56<20:03:02, 4.07s/it] 5%|▌ | 957/18688 [1:06:01<22:35:07, 4.59s/it] 5%|▌ | 958/18688 [1:06:05<21:33:14, 4.38s/it] 5%|▌ | 959/18688 [1:06:09<20:42:36, 4.21s/it] 5%|▌ | 960/18688 [1:06:13<19:41:34, 4.00s/it] {'loss': '1.066', 'grad_norm': '1.135', 'learning_rate': '0.000147', 'epoch': '0.05137', 'num_input_tokens_seen': 3189048, 'train_runtime': '3973', 'train_tokens_per_second': '802.7'}
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***** 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)
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{'eval_loss': '1', 'eval_runtime': '82.81', 'eval_samples_per_second': '12.08', 'eval_steps_per_second': '12.08', 'epoch': '0.05351', 'num_input_tokens_seen': 3324984}
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5%|▌ | 1010/18688 [1:11:07<27:28:48, 5.60s/it] 5%|▌ | 1011/18688 [1:11:11<25:05:08, 5.11s/it] 5%|▌ | 1012/18688 [1:11:16<24:34:18, 5.00s/it] 5%|▌ | 1013/18688 [1:11:21<24:47:16, 5.05s/it] 5%|▌ | 1014/18688 [1:11:26<24:52:19, 5.07s/it] 5%|▌ | 1015/18688 [1:11:31<25:08:23, 5.12s/it] 5%|▌ | 1016/18688 [1:11:36<24:18:20, 4.95s/it] 5%|▌ | 1017/18688 [1:11:40<22:41:49, 4.62s/it] 5%|▌ | 1018/18688 [1:11:44<21:56:53, 4.47s/it] 5%|▌ | 1019/18688 [1:11:49<22:25:10, 4.57s/it] 5%|▌ | 1020/18688 [1:11:52<21:01:31, 4.28s/it] {'loss': '1.029', 'grad_norm': '1.643', 'learning_rate': '0.0001562', 'epoch': '0.05458', 'num_input_tokens_seen': 3400000, 'train_runtime': '4313', 'train_tokens_per_second': '788.4'}
5%|▌ | 1020/18688 [1:11:52<21:01:31, 4.28s/it] 5%|▌ | 1021/18688 [1:11:57<21:49:24, 4.45s/it] 5%|▌ | 1022/18688 [1:12:01<21:37:13, 4.41s/it] 5%|▌ | 1023/18688 [1:12:06<21:30:12, 4.38s/it] 5%|▌ | 1024/18688 [1:12:10<20:52:35, 4.25s/it] 5%|▌ | 1025/18688 [1:12:15<22:54:55, 4.67s/it] 5%|▌ | 1026/18688 [1:12:19<21:38:06, 4.41s/it] 5%|▌ | 1027/18688 [1:12:23<20:25:22, 4.16s/it] 6%|▌ | 1028/18688 [1:12:27<20:22:51, 4.15s/it] 6%|▌ | 1029/18688 [1:12:30<19:18:19, 3.94s/it] 6%|▌ | 1030/18688 [1:12:34<18:30:08, 3.77s/it] {'loss': '1.023', 'grad_norm': '1.242', 'learning_rate': '0.0001578', 'epoch': '0.05512', 'num_input_tokens_seen': 3435320, 'train_runtime': '4354', 'train_tokens_per_second': '789'}
6%|▌ | 1030/18688 [1:12:34<18:30:08, 3.77s/it] 6%|▌ | 1031/18688 [1:12:38<19:19:45, 3.94s/it] 6%|▌ | 1032/18688 [1:12:43<20:16:14, 4.13s/it] 6%|▌ | 1033/18688 [1:12:47<20:34:09, 4.19s/it] 6%|▌ | 1034/18688 [1:12:52<21:47:46, 4.44s/it] 6%|▌ | 1035/18688 [1:12:56<21:18:09, 4.34s/it] 6%|▌ | 1036/18688 [1:13:00<21:08:35, 4.31s/it] 6%|▌ | 1037/18688 [1:13:05<21:13:40, 4.33s/it] 6%|▌ | 1038/18688 [1:13:08<20:16:58, 4.14s/it] 6%|▌ | 1039/18688 [1:13:12<19:14:26, 3.92s/it] 6%|▌ | 1040/18688 [1:13:16<19:21:41, 3.95s/it] {'loss': '0.9977', 'grad_norm': '1.155', 'learning_rate': '0.0001593', 'epoch': '0.05565', 'num_input_tokens_seen': 3469792, 'train_runtime': '4396', 'train_tokens_per_second': '789.2'}
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6%|▌ | 1060/18688 [1:14:36<19:42:33, 4.03s/it] 6%|▌ | 1061/18688 [1:14:40<19:20:47, 3.95s/it] 6%|▌ | 1062/18688 [1:14:44<19:48:01, 4.04s/it] 6%|▌ | 1063/18688 [1:14:48<19:39:47, 4.02s/it] 6%|▌ | 1064/18688 [1:14:53<20:49:12, 4.25s/it] 6%|▌ | 1065/18688 [1:14:57<19:45:11, 4.04s/it] 6%|▌ | 1066/18688 [1:15:01<19:36:22, 4.01s/it] 6%|▌ | 1067/18688 [1:15:05<19:33:00, 3.99s/it] 6%|▌ | 1068/18688 [1:15:08<19:10:06, 3.92s/it] 6%|▌ | 1069/18688 [1:15:13<20:41:17, 4.23s/it] 6%|▌ | 1070/18688 [1:15:18<21:21:43, 4.37s/it] {'loss': '1.008', 'grad_norm': '1.18', 'learning_rate': '0.0001639', 'epoch': '0.05726', 'num_input_tokens_seen': 3571048, 'train_runtime': '4518', 'train_tokens_per_second': '790.3'}
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6%|▌ | 1080/18688 [1:16:00<20:17:10, 4.15s/it] 6%|▌ | 1081/18688 [1:16:04<19:53:32, 4.07s/it] 6%|▌ | 1082/18688 [1:16:09<20:48:35, 4.26s/it] 6%|▌ | 1083/18688 [1:16:12<19:42:13, 4.03s/it] 6%|▌ | 1084/18688 [1:16:16<19:42:04, 4.03s/it] 6%|▌ | 1085/18688 [1:16:19<18:47:36, 3.84s/it] 6%|▌ | 1086/18688 [1:16:24<20:02:36, 4.10s/it] 6%|▌ | 1087/18688 [1:16:29<20:54:00, 4.27s/it] 6%|▌ | 1088/18688 [1:16:33<21:05:06, 4.31s/it] 6%|▌ | 1089/18688 [1:16:38<21:29:37, 4.40s/it] 6%|▌ | 1090/18688 [1:16:43<22:00:50, 4.50s/it] {'loss': '1.025', 'grad_norm': '1.078', 'learning_rate': '0.000167', 'epoch': '0.05833', 'num_input_tokens_seen': 3639504, 'train_runtime': '4603', 'train_tokens_per_second': '790.7'}
6%|▌ | 1090/18688 [1:16:43<22:00:50, 4.50s/it] 6%|▌ | 1091/18688 [1:16:46<20:57:52, 4.29s/it] 6%|▌ | 1092/18688 [1:16:51<20:51:31, 4.27s/it] 6%|▌ | 1093/18688 [1:16:55<21:02:40, 4.31s/it] 6%|▌ | 1094/18688 [1:16:59<20:46:15, 4.25s/it] 6%|▌ | 1095/18688 [1:17:03<20:39:04, 4.23s/it] 6%|▌ | 1096/18688 [1:17:08<20:42:55, 4.24s/it] 6%|▌ | 1097/18688 [1:17:11<19:44:08, 4.04s/it] 6%|▌ | 1098/18688 [1:17:16<20:18:37, 4.16s/it] 6%|▌ | 1099/18688 [1:17:19<19:28:45, 3.99s/it] 6%|▌ | 1100/18688 [1:17:23<19:57:58, 4.09s/it] {'loss': '1.067', 'grad_norm': '0.9387', 'learning_rate': '0.0001685', 'epoch': '0.05886', 'num_input_tokens_seen': 3673560, 'train_runtime': '4644', 'train_tokens_per_second': '791'}
6%|▌ | 1100/18688 [1:17:23<19:57:58, 4.09s/it] 6%|▌ | 1101/18688 [1:17:28<20:39:19, 4.23s/it] 6%|▌ | 1102/18688 [1:17:32<19:57:24, 4.09s/it] 6%|▌ | 1103/18688 [1:17:36<19:29:21, 3.99s/it] 6%|▌ | 1104/18688 [1:17:40<19:29:41, 3.99s/it] 6%|▌ | 1105/18688 [1:17:43<19:11:06, 3.93s/it] 6%|▌ | 1106/18688 [1:17:47<18:44:22, 3.84s/it] 6%|▌ | 1107/18688 [1:17:51<19:31:47, 4.00s/it] 6%|▌ | 1108/18688 [1:17:55<19:16:00, 3.95s/it] 6%|▌ | 1109/18688 [1:17:59<19:23:56, 3.97s/it] 6%|▌ | 1110/18688 [1:18:04<20:43:08, 4.24s/it] {'loss': '1.004', 'grad_norm': '1.054', 'learning_rate': '0.00017', 'epoch': '0.0594', 'num_input_tokens_seen': 3706976, 'train_runtime': '4685', 'train_tokens_per_second': '791.3'}
6%|▌ | 1110/18688 [1:18:04<20:43:08, 4.24s/it] 6%|▌ | 1111/18688 [1:18:09<22:02:49, 4.52s/it] 6%|▌ | 1112/18688 [1:18:14<22:10:33, 4.54s/it] 6%|▌ | 1113/18688 [1:18:18<21:21:19, 4.37s/it] 6%|▌ | 1114/18688 [1:18:22<20:27:46, 4.19s/it] 6%|▌ | 1115/18688 [1:18:26<20:32:08, 4.21s/it] 6%|▌ | 1116/18688 [1:18:30<20:50:56, 4.27s/it] 6%|▌ | 1117/18688 [1:18:34<20:17:39, 4.16s/it] 6%|▌ | 1118/18688 [1:18:38<20:28:53, 4.20s/it] 6%|▌ | 1119/18688 [1:18:42<20:09:18, 4.13s/it] 6%|▌ | 1120/18688 [1:18:47<20:51:19, 4.27s/it] {'loss': '0.9791', 'grad_norm': '0.9588', 'learning_rate': '0.0001716', 'epoch': '0.05993', 'num_input_tokens_seen': 3740912, 'train_runtime': '4727', 'train_tokens_per_second': '791.3'}
6%|▌ | 1120/18688 [1:18:47<20:51:19, 4.27s/it] 6%|▌ | 1121/18688 [1:18:51<20:35:36, 4.22s/it] 6%|▌ | 1122/18688 [1:18:55<20:52:20, 4.28s/it] 6%|▌ | 1123/18688 [1:18:59<19:35:34, 4.02s/it] 6%|▌ | 1124/18688 [1:19:03<19:51:28, 4.07s/it] 6%|▌ | 1125/18688 [1:19:07<19:50:19, 4.07s/it] 6%|▌ | 1126/18688 [1:19:11<19:36:47, 4.02s/it] 6%|▌ | 1127/18688 [1:19:15<18:51:34, 3.87s/it] 6%|▌ | 1128/18688 [1:19:19<19:17:38, 3.96s/it] 6%|▌ | 1129/18688 [1:19:22<18:32:24, 3.80s/it] 6%|▌ | 1130/18688 [1:19:26<18:00:21, 3.69s/it] {'loss': '0.9355', 'grad_norm': '1.006', 'learning_rate': '0.0001731', 'epoch': '0.06047', 'num_input_tokens_seen': 3772344, 'train_runtime': '4766', 'train_tokens_per_second': '791.5'}
6%|▌ | 1130/18688 [1:19:26<18:00:21, 3.69s/it] 6%|▌ | 1131/18688 [1:19:31<19:47:43, 4.06s/it] 6%|▌ | 1132/18688 [1:19:34<18:53:08, 3.87s/it] 6%|▌ | 1133/18688 [1:19:38<19:10:50, 3.93s/it] 6%|▌ | 1134/18688 [1:19:42<18:49:24, 3.86s/it] 6%|▌ | 1135/18688 [1:19:45<18:27:24, 3.79s/it] 6%|▌ | 1136/18688 [1:19:49<17:59:47, 3.69s/it] 6%|▌ | 1137/18688 [1:19:52<17:53:16, 3.67s/it] 6%|▌ | 1138/18688 [1:19:58<21:06:14, 4.33s/it] 6%|▌ | 1139/18688 [1:20:02<20:49:26, 4.27s/it] 6%|▌ | 1140/18688 [1:20:06<19:44:32, 4.05s/it] {'loss': '0.9796', 'grad_norm': '1.173', 'learning_rate': '0.0001746', 'epoch': '0.061', 'num_input_tokens_seen': 3806288, 'train_runtime': '4806', 'train_tokens_per_second': '791.9'}
6%|▌ | 1140/18688 [1:20:06<19:44:32, 4.05s/it] 6%|▌ | 1141/18688 [1:20:11<20:44:14, 4.25s/it] 6%|▌ | 1142/18688 [1:20:15<20:04:59, 4.12s/it] 6%|▌ | 1143/18688 [1:20:18<19:28:16, 4.00s/it] 6%|▌ | 1144/18688 [1:20:22<19:27:22, 3.99s/it] 6%|▌ | 1145/18688 [1:20:26<19:35:25, 4.02s/it] 6%|▌ | 1146/18688 [1:20:30<19:24:59, 3.98s/it] 6%|▌ | 1147/18688 [1:20:34<18:38:13, 3.82s/it] 6%|▌ | 1148/18688 [1:20:38<19:05:41, 3.92s/it] 6%|▌ | 1149/18688 [1:20:42<19:44:48, 4.05s/it] 6%|▌ | 1150/18688 [1:20:46<19:56:22, 4.09s/it] {'loss': '1.066', 'grad_norm': '1.191', 'learning_rate': '0.0001762', 'epoch': '0.06154', 'num_input_tokens_seen': 3839712, 'train_runtime': '4847', 'train_tokens_per_second': '792.2'}
6%|▌ | 1150/18688 [1:20:46<19:56:22, 4.09s/it] 6%|▌ | 1151/18688 [1:20:51<20:13:40, 4.15s/it] 6%|▌ | 1152/18688 [1:20:55<20:06:16, 4.13s/it] 6%|▌ | 1153/18688 [1:20:59<20:31:34, 4.21s/it] 6%|▌ | 1154/18688 [1:21:03<19:48:22, 4.07s/it] 6%|▌ | 1155/18688 [1:21:06<19:05:37, 3.92s/it] 6%|▌ | 1156/18688 [1:21:12<21:55:46, 4.50s/it] 6%|▌ | 1157/18688 [1:21:17<21:36:54, 4.44s/it] 6%|▌ | 1158/18688 [1:21:21<21:50:19, 4.48s/it] 6%|▌ | 1159/18688 [1:21:26<23:04:53, 4.74s/it] 6%|▌ | 1160/18688 [1:21:31<22:05:45, 4.54s/it] {'loss': '1.074', 'grad_norm': '1.348', 'learning_rate': '0.0001777', 'epoch': '0.06207', 'num_input_tokens_seen': 3876000, 'train_runtime': '4891', 'train_tokens_per_second': '792.5'}
6%|▌ | 1160/18688 [1:21:31<22:05:45, 4.54s/it] 6%|▌ | 1161/18688 [1:21:35<21:19:29, 4.38s/it] 6%|▌ | 1162/18688 [1:21:39<21:53:42, 4.50s/it] 6%|▌ | 1163/18688 [1:21:45<23:13:43, 4.77s/it] 6%|▌ | 1164/18688 [1:21:49<22:18:01, 4.58s/it] 6%|▌ | 1165/18688 [1:21:53<21:24:23, 4.40s/it] 6%|▌ | 1166/18688 [1:21:56<20:05:12, 4.13s/it] 6%|▌ | 1167/18688 [1:22:00<19:52:48, 4.08s/it] 6%|▋ | 1168/18688 [1:22:05<20:05:07, 4.13s/it] 6%|▋ | 1169/18688 [1:22:09<20:16:58, 4.17s/it] 6%|▋ | 1170/18688 [1:22:13<19:44:29, 4.06s/it] {'loss': '0.9886', 'grad_norm': '1.191', 'learning_rate': '0.0001792', 'epoch': '0.06261', 'num_input_tokens_seen': 3908440, 'train_runtime': '4933', 'train_tokens_per_second': '792.3'}
6%|▋ | 1170/18688 [1:22:13<19:44:29, 4.06s/it] 6%|▋ | 1171/18688 [1:22:17<19:37:47, 4.03s/it] 6%|▋ | 1172/18688 [1:22:21<20:29:18, 4.21s/it] 6%|▋ | 1173/18688 [1:22:26<21:44:24, 4.47s/it] 6%|▋ | 1174/18688 [1:22:30<20:49:25, 4.28s/it] 6%|▋ | 1175/18688 [1:22:34<20:08:39, 4.14s/it] 6%|▋ | 1176/18688 [1:22:38<19:53:17, 4.09s/it] 6%|▋ | 1177/18688 [1:22:42<19:53:40, 4.09s/it] 6%|▋ | 1178/18688 [1:22:46<19:25:38, 3.99s/it] 6%|▋ | 1179/18688 [1:22:50<19:43:32, 4.06s/it] 6%|▋ | 1180/18688 [1:22:54<19:27:19, 4.00s/it] {'loss': '0.9906', 'grad_norm': '1.165', 'learning_rate': '0.0001808', 'epoch': '0.06314', 'num_input_tokens_seen': 3941608, 'train_runtime': '4974', 'train_tokens_per_second': '792.4'}
6%|▋ | 1180/18688 [1:22:54<19:27:19, 4.00s/it] 6%|▋ | 1181/18688 [1:22:58<19:59:56, 4.11s/it] 6%|▋ | 1182/18688 [1:23:02<20:02:25, 4.12s/it] 6%|▋ | 1183/18688 [1:23:08<21:37:07, 4.45s/it] 6%|▋ | 1184/18688 [1:23:11<20:35:36, 4.24s/it] 6%|▋ | 1185/18688 [1:23:15<19:23:47, 3.99s/it] 6%|▋ | 1186/18688 [1:23:19<19:55:42, 4.10s/it] 6%|▋ | 1187/18688 [1:23:24<20:33:59, 4.23s/it] 6%|▋ | 1188/18688 [1:23:28<20:08:37, 4.14s/it] 6%|▋ | 1189/18688 [1:23:31<19:11:57, 3.95s/it] 6%|▋ | 1190/18688 [1:23:36<20:04:01, 4.13s/it] {'loss': '1.026', 'grad_norm': '1.32', 'learning_rate': '0.0001823', 'epoch': '0.06368', 'num_input_tokens_seen': 3974776, 'train_runtime': '5016', 'train_tokens_per_second': '792.4'}
6%|▋ | 1190/18688 [1:23:36<20:04:01, 4.13s/it] 6%|▋ | 1191/18688 [1:23:40<20:15:40, 4.17s/it] 6%|▋ | 1192/18688 [1:23:44<20:32:34, 4.23s/it] 6%|▋ | 1193/18688 [1:23:49<20:37:46, 4.25s/it] 6%|▋ | 1194/18688 [1:23:52<19:37:58, 4.04s/it] 6%|▋ | 1195/18688 [1:23:56<19:11:10, 3.95s/it] 6%|▋ | 1196/18688 [1:24:00<19:17:11, 3.97s/it] 6%|▋ | 1197/18688 [1:24:04<19:34:32, 4.03s/it] 6%|▋ | 1198/18688 [1:24:08<19:11:23, 3.95s/it] 6%|▋ | 1199/18688 [1:24:12<18:56:25, 3.90s/it] 6%|▋ | 1200/18688 [1:24:16<19:16:29, 3.97s/it] {'loss': '0.9775', 'grad_norm': '1.365', 'learning_rate': '0.0001838', 'epoch': '0.06421', 'num_input_tokens_seen': 4008136, 'train_runtime': '5056', 'train_tokens_per_second': '792.7'}
6%|▋ | 1200/18688 [1:24:16<19:16:29, 3.97s/it] 6%|▋ | 1201/18688 [1:24:20<19:12:11, 3.95s/it] 6%|▋ | 1202/18688 [1:24:24<19:38:14, 4.04s/it] 6%|▋ | 1203/18688 [1:24:28<20:17:44, 4.18s/it] 6%|▋ | 1204/18688 [1:24:33<20:55:16, 4.31s/it] 6%|▋ | 1205/18688 [1:24:37<20:31:46, 4.23s/it] 6%|▋ | 1206/18688 [1:24:41<20:40:41, 4.26s/it] 6%|▋ | 1207/18688 [1:24:46<21:32:08, 4.44s/it] 6%|▋ | 1208/18688 [1:24:50<20:44:54, 4.27s/it] 6%|▋ | 1209/18688 [1:24:55<21:11:29, 4.36s/it] 6%|▋ | 1210/18688 [1:24:59<20:50:23, 4.29s/it] {'loss': '1.061', 'grad_norm': '1.224', 'learning_rate': '0.0001854', 'epoch': '0.06475', 'num_input_tokens_seen': 4042776, 'train_runtime': '5099', 'train_tokens_per_second': '792.8'}
6%|▋ | 1210/18688 [1:24:59<20:50:23, 4.29s/it] 6%|▋ | 1211/18688 [1:25:02<19:37:46, 4.04s/it] 6%|▋ | 1212/18688 [1:25:06<19:43:00, 4.06s/it] 6%|▋ | 1213/18688 [1:25:10<19:11:16, 3.95s/it] 6%|▋ | 1214/18688 [1:25:14<19:42:36, 4.06s/it] 7%|▋ | 1215/18688 [1:25:18<19:05:16, 3.93s/it] 7%|▋ | 1216/18688 [1:25:21<18:28:22, 3.81s/it] 7%|▋ | 1217/18688 [1:25:26<18:47:19, 3.87s/it] 7%|▋ | 1218/18688 [1:25:29<18:37:19, 3.84s/it] 7%|▋ | 1219/18688 [1:25:34<19:49:23, 4.09s/it] 7%|▋ | 1220/18688 [1:25:38<19:19:28, 3.98s/it] {'loss': '0.9986', 'grad_norm': '1.007', 'learning_rate': '0.0001869', 'epoch': '0.06528', 'num_input_tokens_seen': 4074336, 'train_runtime': '5138', 'train_tokens_per_second': '793'}
7%|▋ | 1220/18688 [1:25:38<19:19:28, 3.98s/it] 7%|▋ | 1221/18688 [1:25:42<19:53:04, 4.10s/it] 7%|▋ | 1222/18688 [1:25:45<18:51:38, 3.89s/it] 7%|▋ | 1223/18688 [1:25:49<18:48:56, 3.88s/it] 7%|▋ | 1224/18688 [1:25:53<18:12:10, 3.75s/it] 7%|▋ | 1225/18688 [1:25:57<19:01:33, 3.92s/it] 7%|▋ | 1226/18688 [1:26:02<20:49:11, 4.29s/it] 7%|▋ | 1227/18688 [1:26:06<19:39:04, 4.05s/it] 7%|▋ | 1228/18688 [1:26:10<19:48:18, 4.08s/it] 7%|▋ | 1229/18688 [1:26:14<20:07:28, 4.15s/it] 7%|▋ | 1230/18688 [1:26:18<19:46:42, 4.08s/it] {'loss': '1.039', 'grad_norm': '1.277', 'learning_rate': '0.0001884', 'epoch': '0.06582', 'num_input_tokens_seen': 4107808, 'train_runtime': '5179', 'train_tokens_per_second': '793.2'}
7%|▋ | 1230/18688 [1:26:18<19:46:42, 4.08s/it] 7%|▋ | 1231/18688 [1:26:23<20:24:28, 4.21s/it] 7%|▋ | 1232/18688 [1:26:27<20:38:02, 4.26s/it] 7%|▋ | 1233/18688 [1:26:31<20:12:11, 4.17s/it] 7%|▋ | 1234/18688 [1:26:35<19:52:19, 4.10s/it] 7%|▋ | 1235/18688 [1:26:39<19:44:29, 4.07s/it] 7%|▋ | 1236/18688 [1:26:43<20:17:39, 4.19s/it] 7%|▋ | 1237/18688 [1:26:47<19:41:28, 4.06s/it] 7%|▋ | 1238/18688 [1:26:51<19:07:13, 3.94s/it] 7%|▋ | 1239/18688 [1:26:55<19:13:07, 3.97s/it] 7%|▋ | 1240/18688 [1:26:59<18:52:12, 3.89s/it] {'loss': '0.948', 'grad_norm': '1.009', 'learning_rate': '0.00019', 'epoch': '0.06635', 'num_input_tokens_seen': 4141480, 'train_runtime': '5219', 'train_tokens_per_second': '793.5'}
7%|▋ | 1240/18688 [1:26:59<18:52:12, 3.89s/it] 7%|▋ | 1241/18688 [1:27:02<18:32:35, 3.83s/it] 7%|▋ | 1242/18688 [1:27:07<20:36:23, 4.25s/it] 7%|▋ | 1243/18688 [1:27:11<19:49:28, 4.09s/it] 7%|▋ | 1244/18688 [1:27:15<19:41:27, 4.06s/it] 7%|▋ | 1245/18688 [1:27:19<19:35:58, 4.05s/it] 7%|▋ | 1246/18688 [1:27:23<19:20:29, 3.99s/it] 7%|▋ | 1247/18688 [1:27:27<18:48:53, 3.88s/it] 7%|▋ | 1248/18688 [1:27:31<19:11:27, 3.96s/it] 7%|▋ | 1249/18688 [1:27:35<19:11:49, 3.96s/it] 7%|▋ | 1250/18688 [1:27:39<20:16:13, 4.18s/it] {'loss': '1.047', 'grad_norm': '1.049', 'learning_rate': '0.0001915', 'epoch': '0.06689', 'num_input_tokens_seen': 4175648, 'train_runtime': '5260', 'train_tokens_per_second': '793.9'}
7%|▋ | 1250/18688 [1:27:39<20:16:13, 4.18s/it] 7%|▋ | 1251/18688 [1:27:43<19:59:45, 4.13s/it] 7%|▋ | 1252/18688 [1:27:47<19:16:09, 3.98s/it] 7%|▋ | 1253/18688 [1:27:51<18:50:29, 3.89s/it] 7%|▋ | 1254/18688 [1:27:55<19:29:44, 4.03s/it] 7%|▋ | 1255/18688 [1:27:59<19:54:56, 4.11s/it] 7%|▋ | 1256/18688 [1:28:04<19:52:13, 4.10s/it] 7%|▋ | 1257/18688 [1:28:08<20:25:54, 4.22s/it] 7%|▋ | 1258/18688 [1:28:11<19:19:00, 3.99s/it] 7%|▋ | 1259/18688 [1:28:15<18:59:59, 3.92s/it] 7%|▋ | 1260/18688 [1:28:19<18:16:22, 3.77s/it] {'loss': '0.9923', 'grad_norm': '1.363', 'learning_rate': '0.000193', 'epoch': '0.06742', 'num_input_tokens_seen': 4207024, 'train_runtime': '5299', 'train_tokens_per_second': '793.9'}
7%|▋ | 1260/18688 [1:28:19<18:16:22, 3.77s/it] 7%|▋ | 1261/18688 [1:28:22<17:49:46, 3.68s/it] 7%|▋ | 1262/18688 [1:28:26<17:52:36, 3.69s/it] 7%|▋ | 1263/18688 [1:28:29<17:49:13, 3.68s/it] 7%|▋ | 1264/18688 [1:28:33<17:28:29, 3.61s/it] 7%|▋ | 1265/18688 [1:28:37<17:25:22, 3.60s/it] 7%|▋ | 1266/18688 [1:28:41<18:03:08, 3.73s/it] 7%|▋ | 1267/18688 [1:28:44<17:49:52, 3.68s/it] 7%|▋ | 1268/18688 [1:28:48<17:27:45, 3.61s/it] 7%|▋ | 1269/18688 [1:28:52<17:59:49, 3.72s/it] 7%|▋ | 1270/18688 [1:28:56<18:59:20, 3.92s/it] {'loss': '1.017', 'grad_norm': '1.285', 'learning_rate': '0.0001946', 'epoch': '0.06796', 'num_input_tokens_seen': 4236392, 'train_runtime': '5336', 'train_tokens_per_second': '793.9'}
7%|▋ | 1270/18688 [1:28:56<18:59:20, 3.92s/it] 7%|▋ | 1271/18688 [1:29:00<19:06:36, 3.95s/it] 7%|▋ | 1272/18688 [1:29:03<18:30:59, 3.83s/it] 7%|▋ | 1273/18688 [1:29:07<18:05:24, 3.74s/it] 7%|▋ | 1274/18688 [1:29:11<17:44:37, 3.67s/it] 7%|▋ | 1275/18688 [1:29:15<18:42:20, 3.87s/it] 7%|▋ | 1276/18688 [1:29:19<18:38:55, 3.86s/it] 7%|▋ | 1277/18688 [1:29:22<18:29:05, 3.82s/it] 7%|▋ | 1278/18688 [1:29:26<18:02:53, 3.73s/it] 7%|▋ | 1279/18688 [1:29:30<18:27:45, 3.82s/it] 7%|▋ | 1280/18688 [1:29:33<17:53:27, 3.70s/it] {'loss': '0.9908', 'grad_norm': '1.232', 'learning_rate': '0.0001961', 'epoch': '0.06849', 'num_input_tokens_seen': 4265784, 'train_runtime': '5374', 'train_tokens_per_second': '793.8'}
7%|▋ | 1280/18688 [1:29:33<17:53:27, 3.70s/it] 7%|▋ | 1281/18688 [1:29:37<17:42:41, 3.66s/it] 7%|▋ | 1282/18688 [1:29:41<18:19:45, 3.79s/it] 7%|▋ | 1283/18688 [1:29:46<19:57:44, 4.13s/it] 7%|▋ | 1284/18688 [1:29:50<19:57:03, 4.13s/it] 7%|▋ | 1285/18688 [1:29:55<20:45:25, 4.29s/it] 7%|▋ | 1286/18688 [1:29:59<21:13:56, 4.39s/it] 7%|▋ | 1287/18688 [1:30:03<20:38:06, 4.27s/it] 7%|▋ | 1288/18688 [1:30:07<20:16:21, 4.19s/it] 7%|▋ | 1289/18688 [1:30:12<20:21:31, 4.21s/it] 7%|▋ | 1290/18688 [1:30:16<20:43:09, 4.29s/it] {'loss': '1.049', 'grad_norm': '1.46', 'learning_rate': '0.0001976', 'epoch': '0.06903', 'num_input_tokens_seen': 4298040, 'train_runtime': '5417', 'train_tokens_per_second': '793.5'}
7%|▋ | 1290/18688 [1:30:16<20:43:09, 4.29s/it] 7%|▋ | 1291/18688 [1:30:20<19:34:12, 4.05s/it] 7%|▋ | 1292/18688 [1:30:24<20:14:01, 4.19s/it] 7%|▋ | 1293/18688 [1:30:29<20:32:21, 4.25s/it] 7%|▋ | 1294/18688 [1:30:33<21:33:02, 4.46s/it] 7%|▋ | 1295/18688 [1:30:37<20:10:29, 4.18s/it] 7%|▋ | 1296/18688 [1:30:41<19:27:46, 4.03s/it] 7%|▋ | 1297/18688 [1:30:44<18:38:25, 3.86s/it] 7%|▋ | 1298/18688 [1:30:48<18:51:46, 3.90s/it] 7%|▋ | 1299/18688 [1:30:53<20:17:18, 4.20s/it] 7%|▋ | 1300/18688 [1:30:57<19:34:12, 4.05s/it] {'loss': '1.068', 'grad_norm': '1.072', 'learning_rate': '0.0001992', 'epoch': '0.06957', 'num_input_tokens_seen': 4332752, 'train_runtime': '5457', 'train_tokens_per_second': '793.9'}
7%|▋ | 1300/18688 [1:30:57<19:34:12, 4.05s/it] 7%|▋ | 1301/18688 [1:31:00<18:36:59, 3.85s/it] 7%|▋ | 1302/18688 [1:31:04<18:52:38, 3.91s/it] 7%|▋ | 1303/18688 [1:31:08<19:03:02, 3.94s/it] 7%|▋ | 1304/18688 [1:31:12<18:29:09, 3.83s/it] 7%|▋ | 1305/18688 [1:31:16<19:11:42, 3.98s/it] 7%|▋ | 1306/18688 [1:31:21<19:52:28, 4.12s/it] 7%|▋ | 1307/18688 [1:31:25<20:35:50, 4.27s/it] 7%|▋ | 1308/18688 [1:31:29<20:00:43, 4.15s/it] 7%|▋ | 1309/18688 [1:31:32<19:05:05, 3.95s/it] 7%|▋ | 1310/18688 [1:31:37<19:13:00, 3.98s/it] {'loss': '1.075', 'grad_norm': '1.113', 'learning_rate': '0.0002007', 'epoch': '0.0701', 'num_input_tokens_seen': 4365560, 'train_runtime': '5497', 'train_tokens_per_second': '794.2'}
7%|▋ | 1310/18688 [1:31:37<19:13:00, 3.98s/it] 7%|▋ | 1311/18688 [1:31:40<18:38:45, 3.86s/it] 7%|▋ | 1312/18688 [1:31:44<18:18:27, 3.79s/it] 7%|▋ | 1313/18688 [1:31:48<18:49:13, 3.90s/it] 7%|▋ | 1314/18688 [1:31:52<19:02:37, 3.95s/it] 7%|▋ | 1315/18688 [1:31:56<19:14:43, 3.99s/it] 7%|▋ | 1316/18688 [1:32:00<18:47:14, 3.89s/it] 7%|▋ | 1317/18688 [1:32:04<18:57:15, 3.93s/it] 7%|▋ | 1318/18688 [1:32:07<18:39:56, 3.87s/it] 7%|▋ | 1319/18688 [1:32:12<19:09:54, 3.97s/it] 7%|▋ | 1320/18688 [1:32:16<19:26:23, 4.03s/it] {'loss': '0.995', 'grad_norm': '1.441', 'learning_rate': '0.0002022', 'epoch': '0.07064', 'num_input_tokens_seen': 4398144, 'train_runtime': '5536', 'train_tokens_per_second': '794.4'}
7%|▋ | 1320/18688 [1:32:16<19:26:23, 4.03s/it] 7%|▋ | 1321/18688 [1:32:20<19:37:07, 4.07s/it] 7%|▋ | 1322/18688 [1:32:25<20:16:38, 4.20s/it] 7%|▋ | 1323/18688 [1:32:29<20:01:50, 4.15s/it] 7%|▋ | 1324/18688 [1:32:33<20:43:33, 4.30s/it] 7%|▋ | 1325/18688 [1:32:37<20:33:00, 4.26s/it] 7%|▋ | 1326/18688 [1:32:42<20:45:46, 4.31s/it] 7%|▋ | 1327/18688 [1:32:46<20:28:57, 4.25s/it] 7%|▋ | 1328/18688 [1:32:50<20:12:30, 4.19s/it] 7%|▋ | 1329/18688 [1:32:55<21:13:50, 4.40s/it] 7%|▋ | 1330/18688 [1:32:59<20:40:49, 4.29s/it] {'loss': '1.058', 'grad_norm': '1.44', 'learning_rate': '0.0002038', 'epoch': '0.07117', 'num_input_tokens_seen': 4428848, 'train_runtime': '5579', 'train_tokens_per_second': '793.8'}
7%|▋ | 1330/18688 [1:32:59<20:40:49, 4.29s/it] 7%|▋ | 1331/18688 [1:33:04<21:16:54, 4.41s/it] 7%|▋ | 1332/18688 [1:33:07<20:28:19, 4.25s/it] 7%|▋ | 1333/18688 [1:33:11<19:24:23, 4.03s/it] 7%|▋ | 1334/18688 [1:33:14<18:32:06, 3.85s/it] 7%|▋ | 1335/18688 [1:33:19<20:15:28, 4.20s/it] 7%|▋ | 1336/18688 [1:33:25<21:54:47, 4.55s/it] 7%|▋ | 1337/18688 [1:33:28<20:18:05, 4.21s/it] 7%|▋ | 1338/18688 [1:33:32<20:21:59, 4.23s/it] 7%|▋ | 1339/18688 [1:33:36<19:10:47, 3.98s/it] 7%|▋ | 1340/18688 [1:33:40<18:44:03, 3.89s/it] {'loss': '1.038', 'grad_norm': '1.313', 'learning_rate': '0.0002053', 'epoch': '0.07171', 'num_input_tokens_seen': 4462248, 'train_runtime': '5620', 'train_tokens_per_second': '794'}
7%|▋ | 1340/18688 [1:33:40<18:44:03, 3.89s/it] 7%|▋ | 1341/18688 [1:33:44<19:32:24, 4.06s/it] 7%|▋ | 1342/18688 [1:33:49<20:29:31, 4.25s/it] 7%|▋ | 1343/18688 [1:33:53<20:12:15, 4.19s/it] 7%|▋ | 1344/18688 [1:33:57<20:33:48, 4.27s/it] 7%|▋ | 1345/18688 [1:34:03<23:17:04, 4.83s/it] 7%|▋ | 1346/18688 [1:34:08<22:34:05, 4.68s/it] 7%|▋ | 1347/18688 [1:34:12<21:46:39, 4.52s/it] 7%|▋ | 1348/18688 [1:34:16<21:40:25, 4.50s/it] 7%|▋ | 1349/18688 [1:34:20<20:50:13, 4.33s/it] 7%|▋ | 1350/18688 [1:34:25<20:53:36, 4.34s/it] {'loss': '1.084', 'grad_norm': '1.506', 'learning_rate': '0.0002068', 'epoch': '0.07224', 'num_input_tokens_seen': 4497528, 'train_runtime': '5665', 'train_tokens_per_second': '793.9'}
7%|▋ | 1350/18688 [1:34:25<20:53:36, 4.34s/it] 7%|▋ | 1351/18688 [1:34:29<21:39:57, 4.50s/it] 7%|▋ | 1352/18688 [1:34:34<22:03:03, 4.58s/it] 7%|▋ | 1353/18688 [1:34:39<22:32:24, 4.68s/it] 7%|▋ | 1354/18688 [1:34:43<21:25:24, 4.45s/it] 7%|▋ | 1355/18688 [1:34:48<22:10:49, 4.61s/it] 7%|▋ | 1356/18688 [1:34:52<21:37:52, 4.49s/it] 7%|▋ | 1357/18688 [1:34:56<20:16:42, 4.21s/it] 7%|▋ | 1358/18688 [1:35:00<19:58:56, 4.15s/it] 7%|▋ | 1359/18688 [1:35:04<19:46:25, 4.11s/it] 7%|▋ | 1360/18688 [1:35:07<19:07:59, 3.98s/it] {'loss': '1.047', 'grad_norm': '1.386', 'learning_rate': '0.0002084', 'epoch': '0.07278', 'num_input_tokens_seen': 4534280, 'train_runtime': '5708', 'train_tokens_per_second': '794.4'}
7%|▋ | 1360/18688 [1:35:07<19:07:59, 3.98s/it] 7%|▋ | 1361/18688 [1:35:12<19:29:48, 4.05s/it] 7%|▋ | 1362/18688 [1:35:16<19:24:58, 4.03s/it] 7%|▋ | 1363/18688 [1:35:20<19:43:18, 4.10s/it] 7%|▋ | 1364/18688 [1:35:24<19:44:21, 4.10s/it] 7%|▋ | 1365/18688 [1:35:27<18:46:17, 3.90s/it] 7%|▋ | 1366/18688 [1:35:31<18:35:47, 3.86s/it] 7%|▋ | 1367/18688 [1:35:35<18:46:00, 3.90s/it] 7%|▋ | 1368/18688 [1:35:40<20:40:40, 4.30s/it] 7%|▋ | 1369/18688 [1:35:44<19:31:19, 4.06s/it] 7%|▋ | 1370/18688 [1:35:48<18:56:43, 3.94s/it] {'loss': '1.045', 'grad_norm': '1.106', 'learning_rate': '0.0002099', 'epoch': '0.07331', 'num_input_tokens_seen': 4565296, 'train_runtime': '5748', 'train_tokens_per_second': '794.2'}
7%|▋ | 1370/18688 [1:35:48<18:56:43, 3.94s/it] 7%|▋ | 1371/18688 [1:35:52<20:06:46, 4.18s/it] 7%|▋ | 1372/18688 [1:35:56<20:02:30, 4.17s/it] 7%|▋ | 1373/18688 [1:36:00<19:34:05, 4.07s/it] 7%|▋ | 1374/18688 [1:36:05<20:25:40, 4.25s/it] 7%|▋ | 1375/18688 [1:36:09<20:09:58, 4.19s/it] 7%|▋ | 1376/18688 [1:36:13<20:02:44, 4.17s/it] 7%|▋ | 1377/18688 [1:36:17<19:16:44, 4.01s/it] 7%|▋ | 1378/18688 [1:36:21<19:47:57, 4.12s/it] 7%|▋ | 1379/18688 [1:36:25<20:05:58, 4.18s/it] 7%|▋ | 1380/18688 [1:36:29<19:49:27, 4.12s/it] {'loss': '0.9745', 'grad_norm': '1.54', 'learning_rate': '0.0002114', 'epoch': '0.07385', 'num_input_tokens_seen': 4596896, 'train_runtime': '5790', 'train_tokens_per_second': '793.9'}
7%|▋ | 1380/18688 [1:36:29<19:49:27, 4.12s/it] 7%|▋ | 1381/18688 [1:36:34<19:50:19, 4.13s/it] 7%|▋ | 1382/18688 [1:36:38<20:22:47, 4.24s/it] 7%|▋ | 1383/18688 [1:36:43<20:47:20, 4.32s/it] 7%|▋ | 1384/18688 [1:36:47<21:01:32, 4.37s/it] 7%|▋ | 1385/18688 [1:36:51<20:53:01, 4.35s/it] 7%|▋ | 1386/18688 [1:36:56<21:55:39, 4.56s/it] 7%|▋ | 1387/18688 [1:37:01<21:24:25, 4.45s/it] 7%|▋ | 1388/18688 [1:37:05<20:33:04, 4.28s/it] 7%|▋ | 1389/18688 [1:37:09<20:11:17, 4.20s/it] 7%|▋ | 1390/18688 [1:37:13<20:00:59, 4.17s/it] {'loss': '0.9968', 'grad_norm': '1.09', 'learning_rate': '0.000213', 'epoch': '0.07438', 'num_input_tokens_seen': 4631296, 'train_runtime': '5833', 'train_tokens_per_second': '794'}
7%|▋ | 1390/18688 [1:37:13<20:00:59, 4.17s/it] 7%|▋ | 1391/18688 [1:37:16<19:08:34, 3.98s/it] 7%|▋ | 1392/18688 [1:37:21<20:48:23, 4.33s/it] 7%|▋ | 1393/18688 [1:37:25<20:23:30, 4.24s/it] 7%|▋ | 1394/18688 [1:37:30<20:12:54, 4.21s/it] 7%|▋ | 1395/18688 [1:37:35<21:28:45, 4.47s/it] 7%|▋ | 1396/18688 [1:37:39<21:19:00, 4.44s/it] 7%|▋ | 1397/18688 [1:37:42<19:50:12, 4.13s/it] 7%|▋ | 1398/18688 [1:37:47<20:21:30, 4.24s/it] 7%|▋ | 1399/18688 [1:37:51<20:09:55, 4.20s/it] 7%|▋ | 1400/18688 [1:37:56<20:48:42, 4.33s/it] {'loss': '1.076', 'grad_norm': '1.271', 'learning_rate': '0.0002145', 'epoch': '0.07492', 'num_input_tokens_seen': 4668640, 'train_runtime': '5876', 'train_tokens_per_second': '794.5'}
7%|▋ | 1400/18688 [1:37:56<20:48:42, 4.33s/it] 7%|▋ | 1401/18688 [1:38:00<20:59:09, 4.37s/it] 8%|▊ | 1402/18688 [1:38:04<20:01:32, 4.17s/it] 8%|▊ | 1403/18688 [1:38:08<19:34:33, 4.08s/it] 8%|▊ | 1404/18688 [1:38:12<19:39:31, 4.09s/it] 8%|▊ | 1405/18688 [1:38:15<18:50:01, 3.92s/it] 8%|▊ | 1406/18688 [1:38:19<18:15:33, 3.80s/it] 8%|▊ | 1407/18688 [1:38:23<18:20:06, 3.82s/it] 8%|▊ | 1408/18688 [1:38:27<19:04:03, 3.97s/it] 8%|▊ | 1409/18688 [1:38:31<18:31:32, 3.86s/it] 8%|▊ | 1410/18688 [1:38:36<20:16:11, 4.22s/it] {'loss': '1.02', 'grad_norm': '1.468', 'learning_rate': '0.000216', 'epoch': '0.07545', 'num_input_tokens_seen': 4701160, 'train_runtime': '5916', 'train_tokens_per_second': '794.6'}
8%|▊ | 1410/18688 [1:38:36<20:16:11, 4.22s/it] 8%|▊ | 1411/18688 [1:38:40<19:46:04, 4.12s/it] 8%|▊ | 1412/18688 [1:38:44<19:59:02, 4.16s/it] 8%|▊ | 1413/18688 [1:38:48<20:09:37, 4.20s/it] 8%|▊ | 1414/18688 [1:38:53<21:33:28, 4.49s/it] 8%|▊ | 1415/18688 [1:38:58<21:23:56, 4.46s/it] 8%|▊ | 1416/18688 [1:39:02<20:44:29, 4.32s/it] 8%|▊ | 1417/18688 [1:39:07<22:05:04, 4.60s/it] 8%|▊ | 1418/18688 [1:39:11<21:23:15, 4.46s/it] 8%|▊ | 1419/18688 [1:39:15<20:05:10, 4.19s/it] 8%|▊ | 1420/18688 [1:39:18<19:20:27, 4.03s/it] {'loss': '1.035', 'grad_norm': '1.543', 'learning_rate': '0.0002176', 'epoch': '0.07599', 'num_input_tokens_seen': 4735568, 'train_runtime': '5959', 'train_tokens_per_second': '794.7'}
8%|▊ | 1420/18688 [1:39:18<19:20:27, 4.03s/it] 8%|▊ | 1421/18688 [1:39:22<19:24:24, 4.05s/it] 8%|▊ | 1422/18688 [1:39:27<20:12:44, 4.21s/it] 8%|▊ | 1423/18688 [1:39:32<21:50:25, 4.55s/it] 8%|▊ | 1424/18688 [1:39:36<20:34:37, 4.29s/it] 8%|▊ | 1425/18688 [1:39:40<19:30:52, 4.07s/it] 8%|▊ | 1426/18688 [1:39:43<18:54:00, 3.94s/it] 8%|▊ | 1427/18688 [1:39:47<19:24:31, 4.05s/it] 8%|▊ | 1428/18688 [1:39:51<18:57:40, 3.95s/it] 8%|▊ | 1429/18688 [1:39:56<20:03:09, 4.18s/it] 8%|▊ | 1430/18688 [1:40:00<20:08:50, 4.20s/it] {'loss': '1.051', 'grad_norm': '1.46', 'learning_rate': '0.0002191', 'epoch': '0.07652', 'num_input_tokens_seen': 4771024, 'train_runtime': '6001', 'train_tokens_per_second': '795.1'}
8%|▊ | 1430/18688 [1:40:00<20:08:50, 4.20s/it] 8%|▊ | 1431/18688 [1:40:04<19:40:30, 4.10s/it] 8%|▊ | 1432/18688 [1:40:08<19:18:55, 4.03s/it] 8%|▊ | 1433/18688 [1:40:12<19:47:15, 4.13s/it] 8%|▊ | 1434/18688 [1:40:16<19:49:01, 4.13s/it] 8%|▊ | 1435/18688 [1:40:21<20:19:24, 4.24s/it] 8%|▊ | 1436/18688 [1:40:24<19:12:12, 4.01s/it] 8%|▊ | 1437/18688 [1:40:29<19:37:26, 4.10s/it] 8%|▊ | 1438/18688 [1:40:32<18:53:04, 3.94s/it] 8%|▊ | 1439/18688 [1:40:36<19:01:31, 3.97s/it] 8%|▊ | 1440/18688 [1:40:41<19:35:11, 4.09s/it] {'loss': '0.9536', 'grad_norm': '1.276', 'learning_rate': '0.0002206', 'epoch': '0.07706', 'num_input_tokens_seen': 4800712, 'train_runtime': '6041', 'train_tokens_per_second': '794.7'}
8%|▊ | 1440/18688 [1:40:41<19:35:11, 4.09s/it] 8%|▊ | 1441/18688 [1:40:45<19:56:51, 4.16s/it] 8%|▊ | 1442/18688 [1:40:50<20:40:08, 4.31s/it] 8%|▊ | 1443/18688 [1:40:55<21:54:42, 4.57s/it] 8%|▊ | 1444/18688 [1:40:59<20:49:35, 4.35s/it] 8%|▊ | 1445/18688 [1:41:03<20:31:55, 4.29s/it] 8%|▊ | 1446/18688 [1:41:07<19:45:34, 4.13s/it] 8%|▊ | 1447/18688 [1:41:11<20:25:09, 4.26s/it] 8%|▊ | 1448/18688 [1:41:15<20:15:22, 4.23s/it] 8%|▊ | 1449/18688 [1:41:20<20:15:41, 4.23s/it] 8%|▊ | 1450/18688 [1:41:24<20:23:00, 4.26s/it] {'loss': '1.057', 'grad_norm': '1.308', 'learning_rate': '0.0002222', 'epoch': '0.07759', 'num_input_tokens_seen': 4835448, 'train_runtime': '6084', 'train_tokens_per_second': '794.7'}
8%|▊ | 1450/18688 [1:41:24<20:23:00, 4.26s/it] 8%|▊ | 1451/18688 [1:41:28<20:14:58, 4.23s/it] 8%|▊ | 1452/18688 [1:41:32<19:28:19, 4.07s/it] 8%|▊ | 1453/18688 [1:41:35<19:01:54, 3.98s/it] 8%|▊ | 1454/18688 [1:41:40<19:55:31, 4.16s/it] 8%|▊ | 1455/18688 [1:41:45<20:40:28, 4.32s/it] 8%|▊ | 1456/18688 [1:41:48<19:43:16, 4.12s/it] 8%|▊ | 1457/18688 [1:41:53<20:13:51, 4.23s/it] 8%|▊ | 1458/18688 [1:41:56<19:17:53, 4.03s/it] 8%|▊ | 1459/18688 [1:42:00<18:24:28, 3.85s/it] 8%|▊ | 1460/18688 [1:42:05<20:17:02, 4.24s/it] {'loss': '1.006', 'grad_norm': '1.172', 'learning_rate': '0.0002237', 'epoch': '0.07813', 'num_input_tokens_seen': 4868640, 'train_runtime': '6126', 'train_tokens_per_second': '794.8'}
8%|▊ | 1460/18688 [1:42:05<20:17:02, 4.24s/it] 8%|▊ | 1461/18688 [1:42:09<20:04:20, 4.19s/it] 8%|▊ | 1462/18688 [1:42:14<20:37:51, 4.31s/it] 8%|▊ | 1463/18688 [1:42:18<19:57:28, 4.17s/it] 8%|▊ | 1464/18688 [1:42:23<21:15:29, 4.44s/it] 8%|▊ | 1465/18688 [1:42:27<21:17:52, 4.45s/it] 8%|▊ | 1466/18688 [1:42:31<21:00:00, 4.39s/it] 8%|▊ | 1467/18688 [1:42:36<21:37:02, 4.52s/it] 8%|▊ | 1468/18688 [1:42:40<21:01:57, 4.40s/it] 8%|▊ | 1469/18688 [1:42:45<21:02:35, 4.40s/it] 8%|▊ | 1470/18688 [1:42:49<20:47:29, 4.35s/it] {'loss': '1.064', 'grad_norm': '1.217', 'learning_rate': '0.0002252', 'epoch': '0.07866', 'num_input_tokens_seen': 4907240, 'train_runtime': '6169', 'train_tokens_per_second': '795.4'}
8%|▊ | 1470/18688 [1:42:49<20:47:29, 4.35s/it] 8%|▊ | 1471/18688 [1:42:53<21:00:17, 4.39s/it] 8%|▊ | 1472/18688 [1:42:57<19:38:32, 4.11s/it] 8%|▊ | 1473/18688 [1:43:02<20:32:48, 4.30s/it] 8%|▊ | 1474/18688 [1:43:06<20:59:11, 4.39s/it] 8%|▊ | 1475/18688 [1:43:10<20:36:27, 4.31s/it] 8%|▊ | 1476/18688 [1:43:14<19:39:51, 4.11s/it] 8%|▊ | 1477/18688 [1:43:18<19:40:01, 4.11s/it] 8%|▊ | 1478/18688 [1:43:22<18:53:39, 3.95s/it] 8%|▊ | 1479/18688 [1:43:25<18:28:04, 3.86s/it] 8%|▊ | 1480/18688 [1:43:29<18:49:11, 3.94s/it] {'loss': '0.9878', 'grad_norm': '1.174', 'learning_rate': '0.0002268', 'epoch': '0.0792', 'num_input_tokens_seen': 4940128, 'train_runtime': '6210', 'train_tokens_per_second': '795.5'}
8%|▊ | 1480/18688 [1:43:29<18:49:11, 3.94s/it] 8%|▊ | 1481/18688 [1:43:33<18:26:28, 3.86s/it] 8%|▊ | 1482/18688 [1:43:37<18:12:26, 3.81s/it] 8%|▊ | 1483/18688 [1:43:41<19:23:30, 4.06s/it] 8%|▊ | 1484/18688 [1:43:45<19:05:30, 4.00s/it] 8%|▊ | 1485/18688 [1:43:49<19:06:10, 4.00s/it] 8%|▊ | 1486/18688 [1:43:54<20:18:48, 4.25s/it] 8%|▊ | 1487/18688 [1:43:58<19:08:43, 4.01s/it] 8%|▊ | 1488/18688 [1:44:01<18:20:54, 3.84s/it] 8%|▊ | 1489/18688 [1:44:04<17:45:50, 3.72s/it] 8%|▊ | 1490/18688 [1:44:09<18:28:34, 3.87s/it] {'loss': '1.028', 'grad_norm': '1.45', 'learning_rate': '0.0002283', 'epoch': '0.07973', 'num_input_tokens_seen': 4971208, 'train_runtime': '6249', 'train_tokens_per_second': '795.5'}
8%|▊ | 1490/18688 [1:44:09<18:28:34, 3.87s/it] 8%|▊ | 1491/18688 [1:44:12<18:23:10, 3.85s/it] 8%|▊ | 1492/18688 [1:44:17<19:22:02, 4.05s/it] 8%|▊ | 1493/18688 [1:44:21<19:36:49, 4.11s/it] 8%|▊ | 1494/18688 [1:44:26<20:06:07, 4.21s/it] 8%|▊ | 1495/18688 [1:44:30<19:57:43, 4.18s/it] 8%|▊ | 1496/18688 [1:44:34<20:40:35, 4.33s/it] 8%|▊ | 1497/18688 [1:44:39<21:41:19, 4.54s/it] 8%|▊ | 1498/18688 [1:44:43<20:12:19, 4.23s/it] 8%|▊ | 1499/18688 [1:44:47<20:22:56, 4.27s/it] 8%|▊ | 1500/18688 [1:44:51<19:26:33, 4.07s/it] {'loss': '1.053', 'grad_norm': '1.533', 'learning_rate': '0.0002298', 'epoch': '0.08027', 'num_input_tokens_seen': 5004640, 'train_runtime': '6291', 'train_tokens_per_second': '795.5'}
8%|▊ | 1500/18688 [1:44:51<19:26:33, 4.07s/it] 8%|▊ | 1501/18688 [1:44:55<19:21:21, 4.05s/it] 8%|▊ | 1502/18688 [1:44:59<19:18:26, 4.04s/it] 8%|▊ | 1503/18688 [1:45:03<19:12:10, 4.02s/it] 8%|▊ | 1504/18688 [1:45:07<18:57:18, 3.97s/it] 8%|▊ | 1505/18688 [1:45:11<19:18:17, 4.04s/it] 8%|▊ | 1506/18688 [1:45:15<18:35:16, 3.89s/it] 8%|▊ | 1507/18688 [1:45:19<18:46:10, 3.93s/it] 8%|▊ | 1508/18688 [1:45:23<19:41:02, 4.12s/it] 8%|▊ | 1509/18688 [1:45:27<19:54:46, 4.17s/it] 8%|▊ | 1510/18688 [1:45:31<19:32:09, 4.09s/it] {'loss': '1.04', 'grad_norm': '2.171', 'learning_rate': '0.00023', 'epoch': '0.0808', 'num_input_tokens_seen': 5038496, 'train_runtime': '6332', 'train_tokens_per_second': '795.7'}
8%|▊ | 1510/18688 [1:45:31<19:32:09, 4.09s/it] 8%|▊ | 1511/18688 [1:45:35<18:50:23, 3.95s/it] 8%|▊ | 1512/18688 [1:45:39<19:16:25, 4.04s/it] 8%|▊ | 1513/18688 [1:45:43<19:00:32, 3.98s/it] 8%|▊ | 1514/18688 [1:45:47<19:26:39, 4.08s/it] 8%|▊ | 1515/18688 [1:45:52<20:39:10, 4.33s/it] 8%|▊ | 1516/18688 [1:45:56<19:42:26, 4.13s/it] 8%|▊ | 1517/18688 [1:46:00<19:00:46, 3.99s/it] 8%|▊ | 1518/18688 [1:46:04<18:55:37, 3.97s/it] 8%|▊ | 1519/18688 [1:46:08<19:50:38, 4.16s/it] 8%|▊ | 1520/18688 [1:46:13<20:07:25, 4.22s/it] {'loss': '1.004', 'grad_norm': '1.448', 'learning_rate': '0.00023', 'epoch': '0.08134', 'num_input_tokens_seen': 5072456, 'train_runtime': '6373', 'train_tokens_per_second': '795.9'}
8%|▊ | 1520/18688 [1:46:13<20:07:25, 4.22s/it] 8%|▊ | 1521/18688 [1:46:17<20:13:50, 4.24s/it] 8%|▊ | 1522/18688 [1:46:21<20:34:22, 4.31s/it] 8%|▊ | 1523/18688 [1:46:26<21:20:30, 4.48s/it] 8%|▊ | 1524/18688 [1:46:30<19:47:32, 4.15s/it] 8%|▊ | 1525/18688 [1:46:34<19:37:04, 4.11s/it] 8%|▊ | 1526/18688 [1:46:38<20:13:26, 4.24s/it] 8%|▊ | 1527/18688 [1:46:43<21:05:06, 4.42s/it] 8%|▊ | 1528/18688 [1:46:47<20:32:26, 4.31s/it] 8%|▊ | 1529/18688 [1:46:53<22:30:21, 4.72s/it] 8%|▊ | 1530/18688 [1:46:57<21:59:52, 4.62s/it] {'loss': '0.9995', 'grad_norm': '1.528', 'learning_rate': '0.00023', 'epoch': '0.08187', 'num_input_tokens_seen': 5109856, 'train_runtime': '6418', 'train_tokens_per_second': '796.2'}
8%|▊ | 1530/18688 [1:46:57<21:59:52, 4.62s/it] 8%|▊ | 1531/18688 [1:47:01<21:23:11, 4.49s/it] 8%|▊ | 1532/18688 [1:47:06<21:54:23, 4.60s/it] 8%|▊ | 1533/18688 [1:47:10<20:59:29, 4.41s/it] 8%|▊ | 1534/18688 [1:47:15<22:11:34, 4.66s/it] 8%|▊ | 1535/18688 [1:47:19<21:12:34, 4.45s/it] 8%|▊ | 1536/18688 [1:47:24<21:43:01, 4.56s/it] 8%|▊ | 1537/18688 [1:47:30<23:07:29, 4.85s/it] 8%|▊ | 1538/18688 [1:47:34<21:55:13, 4.60s/it] 8%|▊ | 1539/18688 [1:47:37<20:43:06, 4.35s/it] 8%|▊ | 1540/18688 [1:47:42<21:05:12, 4.43s/it] {'loss': '1.021', 'grad_norm': '1.437', 'learning_rate': '0.00023', 'epoch': '0.08241', 'num_input_tokens_seen': 5146272, 'train_runtime': '6463', 'train_tokens_per_second': '796.3'}
8%|▊ | 1540/18688 [1:47:42<21:05:12, 4.43s/it] 8%|▊ | 1541/18688 [1:47:46<20:49:34, 4.37s/it] 8%|▊ | 1542/18688 [1:47:51<21:10:21, 4.45s/it] 8%|▊ | 1543/18688 [1:47:55<21:08:13, 4.44s/it] 8%|▊ | 1544/18688 [1:47:59<20:14:28, 4.25s/it] 8%|▊ | 1545/18688 [1:48:03<19:15:22, 4.04s/it] 8%|▊ | 1546/18688 [1:48:06<18:43:41, 3.93s/it] 8%|▊ | 1547/18688 [1:48:10<18:29:44, 3.88s/it] 8%|▊ | 1548/18688 [1:48:15<19:59:12, 4.20s/it] 8%|▊ | 1549/18688 [1:48:19<19:55:47, 4.19s/it] 8%|▊ | 1550/18688 [1:48:23<19:11:14, 4.03s/it] {'loss': '0.9325', 'grad_norm': '1.293', 'learning_rate': '0.00023', 'epoch': '0.08294', 'num_input_tokens_seen': 5178800, 'train_runtime': '6503', 'train_tokens_per_second': '796.3'}
8%|▊ | 1550/18688 [1:48:23<19:11:14, 4.03s/it] 8%|▊ | 1551/18688 [1:48:27<19:31:21, 4.10s/it] 8%|▊ | 1552/18688 [1:48:32<20:18:57, 4.27s/it] 8%|▊ | 1553/18688 [1:48:37<21:18:52, 4.48s/it] 8%|▊ | 1554/18688 [1:48:41<21:16:00, 4.47s/it] 8%|▊ | 1555/18688 [1:48:45<20:51:04, 4.38s/it] 8%|▊ | 1556/18688 [1:48:50<20:38:06, 4.34s/it] 8%|▊ | 1557/18688 [1:48:55<21:46:30, 4.58s/it] 8%|▊ | 1558/18688 [1:48:59<21:15:41, 4.47s/it] 8%|▊ | 1559/18688 [1:49:03<20:48:49, 4.37s/it] 8%|▊ | 1560/18688 [1:49:08<21:36:35, 4.54s/it] {'loss': '0.9655', 'grad_norm': '1.499', 'learning_rate': '0.00023', 'epoch': '0.08348', 'num_input_tokens_seen': 5214832, 'train_runtime': '6549', 'train_tokens_per_second': '796.3'}
8%|▊ | 1560/18688 [1:49:08<21:36:35, 4.54s/it] 8%|▊ | 1561/18688 [1:49:13<21:59:35, 4.62s/it] 8%|▊ | 1562/18688 [1:49:17<21:51:39, 4.60s/it] 8%|▊ | 1563/18688 [1:49:22<21:18:43, 4.48s/it] 8%|▊ | 1564/18688 [1:49:26<20:44:13, 4.36s/it] 8%|▊ | 1565/18688 [1:49:30<21:07:21, 4.44s/it] 8%|▊ | 1566/18688 [1:49:34<20:08:40, 4.24s/it] 8%|▊ | 1567/18688 [1:49:38<19:50:51, 4.17s/it] 8%|▊ | 1568/18688 [1:49:42<19:39:01, 4.13s/it] 8%|▊ | 1569/18688 [1:49:47<20:36:16, 4.33s/it] 8%|▊ | 1570/18688 [1:49:52<21:10:19, 4.45s/it] {'loss': '1.023', 'grad_norm': '1.485', 'learning_rate': '0.00023', 'epoch': '0.08401', 'num_input_tokens_seen': 5248400, 'train_runtime': '6592', 'train_tokens_per_second': '796.2'}
8%|▊ | 1570/18688 [1:49:52<21:10:19, 4.45s/it] 8%|▊ | 1571/18688 [1:49:56<21:11:21, 4.46s/it] 8%|▊ | 1572/18688 [1:50:01<21:23:50, 4.50s/it] 8%|▊ | 1573/18688 [1:50:05<21:02:47, 4.43s/it] 8%|▊ | 1574/18688 [1:50:09<20:41:37, 4.35s/it] 8%|▊ | 1575/18688 [1:50:14<20:49:21, 4.38s/it] 8%|▊ | 1576/18688 [1:50:18<21:15:02, 4.47s/it] 8%|▊ | 1577/18688 [1:50:23<20:55:32, 4.40s/it] 8%|▊ | 1578/18688 [1:50:27<20:59:37, 4.42s/it] 8%|▊ | 1579/18688 [1:50:31<20:23:35, 4.29s/it] 8%|▊ | 1580/18688 [1:50:35<20:15:07, 4.26s/it] {'loss': '1.027', 'grad_norm': '1.117', 'learning_rate': '0.00023', 'epoch': '0.08455', 'num_input_tokens_seen': 5280136, 'train_runtime': '6636', 'train_tokens_per_second': '795.7'}
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10%|▉ | 1800/18688 [2:05:57<19:28:28, 4.15s/it] 10%|▉ | 1801/18688 [2:06:02<20:01:21, 4.27s/it] 10%|▉ | 1802/18688 [2:06:06<19:40:11, 4.19s/it] 10%|▉ | 1803/18688 [2:06:10<19:37:23, 4.18s/it] 10%|▉ | 1804/18688 [2:06:14<19:58:32, 4.26s/it] 10%|▉ | 1805/18688 [2:06:19<20:34:49, 4.39s/it] 10%|▉ | 1806/18688 [2:06:23<20:32:47, 4.38s/it] 10%|▉ | 1807/18688 [2:06:27<19:55:46, 4.25s/it] 10%|▉ | 1808/18688 [2:06:31<19:32:45, 4.17s/it] 10%|▉ | 1809/18688 [2:06:36<20:22:50, 4.35s/it] 10%|▉ | 1810/18688 [2:06:40<19:54:14, 4.25s/it] {'loss': '0.9955', 'grad_norm': '1.7', 'learning_rate': '0.0002298', 'epoch': '0.09686', 'num_input_tokens_seen': 6039224, 'train_runtime': '7601', 'train_tokens_per_second': '794.6'}
10%|▉ | 1810/18688 [2:06:40<19:54:14, 4.25s/it] 10%|▉ | 1811/18688 [2:06:45<20:20:56, 4.34s/it] 10%|▉ | 1812/18688 [2:06:49<20:21:58, 4.34s/it] 10%|▉ | 1813/18688 [2:06:54<20:31:49, 4.38s/it] 10%|▉ | 1814/18688 [2:06:58<19:58:55, 4.26s/it] 10%|▉ | 1815/18688 [2:07:02<20:42:09, 4.42s/it] 10%|▉ | 1816/18688 [2:07:07<21:15:10, 4.53s/it] 10%|▉ | 1817/18688 [2:07:11<20:54:32, 4.46s/it] 10%|▉ | 1818/18688 [2:07:16<20:41:33, 4.42s/it] 10%|▉ | 1819/18688 [2:07:20<20:21:37, 4.35s/it] 10%|▉ | 1820/18688 [2:07:24<19:53:33, 4.25s/it] {'loss': '1.05', 'grad_norm': '1.382', 'learning_rate': '0.0002298', 'epoch': '0.09739', 'num_input_tokens_seen': 6071920, 'train_runtime': '7644', 'train_tokens_per_second': '794.3'}
10%|▉ | 1820/18688 [2:07:24<19:53:33, 4.25s/it] 10%|▉ | 1821/18688 [2:07:28<19:52:31, 4.24s/it] 10%|▉ | 1822/18688 [2:07:32<18:55:48, 4.04s/it] 10%|▉ | 1823/18688 [2:07:36<19:49:32, 4.23s/it] 10%|▉ | 1824/18688 [2:07:41<20:02:06, 4.28s/it] 10%|▉ | 1825/18688 [2:07:45<20:21:17, 4.35s/it] 10%|▉ | 1826/18688 [2:07:50<20:11:37, 4.31s/it] 10%|▉ | 1827/18688 [2:07:54<20:24:24, 4.36s/it] 10%|▉ | 1828/18688 [2:07:59<21:12:44, 4.53s/it] 10%|▉ | 1829/18688 [2:08:04<21:38:42, 4.62s/it] 10%|▉ | 1830/18688 [2:08:09<21:57:09, 4.69s/it] {'loss': '1.092', 'grad_norm': '1.353', 'learning_rate': '0.0002298', 'epoch': '0.09793', 'num_input_tokens_seen': 6107064, 'train_runtime': '7689', 'train_tokens_per_second': '794.3'}
10%|▉ | 1830/18688 [2:08:09<21:57:09, 4.69s/it] 10%|▉ | 1831/18688 [2:08:13<21:13:35, 4.53s/it] 10%|▉ | 1832/18688 [2:08:16<19:48:32, 4.23s/it] 10%|▉ | 1833/18688 [2:08:20<19:32:54, 4.18s/it] 10%|▉ | 1834/18688 [2:08:24<19:25:07, 4.15s/it] 10%|▉ | 1835/18688 [2:08:29<19:55:35, 4.26s/it] 10%|▉ | 1836/18688 [2:08:33<20:22:34, 4.35s/it] 10%|▉ | 1837/18688 [2:08:37<19:38:09, 4.19s/it] 10%|▉ | 1838/18688 [2:08:41<18:53:23, 4.04s/it] 10%|▉ | 1839/18688 [2:08:45<19:13:16, 4.11s/it] 10%|▉ | 1840/18688 [2:08:51<21:10:16, 4.52s/it] {'loss': '1.016', 'grad_norm': '1.403', 'learning_rate': '0.0002298', 'epoch': '0.09846', 'num_input_tokens_seen': 6144296, 'train_runtime': '7731', 'train_tokens_per_second': '794.7'}
10%|▉ | 1840/18688 [2:08:51<21:10:16, 4.52s/it] 10%|▉ | 1841/18688 [2:08:54<20:03:53, 4.29s/it] 10%|▉ | 1842/18688 [2:08:58<18:49:42, 4.02s/it] 10%|▉ | 1843/18688 [2:09:02<18:43:05, 4.00s/it] 10%|▉ | 1844/18688 [2:09:06<19:06:42, 4.08s/it] 10%|▉ | 1845/18688 [2:09:11<19:48:34, 4.23s/it] 10%|▉ | 1846/18688 [2:09:15<20:20:38, 4.35s/it] 10%|▉ | 1847/18688 [2:09:20<20:55:05, 4.47s/it] 10%|▉ | 1848/18688 [2:09:25<21:06:02, 4.51s/it] 10%|▉ | 1849/18688 [2:09:28<19:48:30, 4.23s/it] 10%|▉ | 1850/18688 [2:09:33<19:55:59, 4.26s/it] {'loss': '1.055', 'grad_norm': '1.641', 'learning_rate': '0.0002298', 'epoch': '0.099', 'num_input_tokens_seen': 6179064, 'train_runtime': '7773', 'train_tokens_per_second': '794.9'}
10%|▉ | 1850/18688 [2:09:33<19:55:59, 4.26s/it] 10%|▉ | 1851/18688 [2:09:37<20:29:34, 4.38s/it] 10%|▉ | 1852/18688 [2:09:42<20:19:57, 4.35s/it] 10%|▉ | 1853/18688 [2:09:45<19:42:23, 4.21s/it] 10%|▉ | 1854/18688 [2:09:50<19:47:09, 4.23s/it] 10%|▉ | 1855/18688 [2:09:54<20:12:48, 4.32s/it] 10%|▉ | 1856/18688 [2:09:59<20:43:31, 4.43s/it] 10%|▉ | 1857/18688 [2:10:03<20:08:09, 4.31s/it] 10%|▉ | 1858/18688 [2:10:07<20:10:28, 4.32s/it] 10%|▉ | 1859/18688 [2:10:11<19:50:00, 4.24s/it] 10%|▉ | 1860/18688 [2:10:16<20:33:57, 4.40s/it] {'loss': '1.043', 'grad_norm': '2.537', 'learning_rate': '0.0002298', 'epoch': '0.09953', 'num_input_tokens_seen': 6213504, 'train_runtime': '7817', 'train_tokens_per_second': '794.9'}
10%|▉ | 1860/18688 [2:10:16<20:33:57, 4.40s/it] 10%|▉ | 1861/18688 [2:10:20<20:02:34, 4.29s/it] 10%|▉ | 1862/18688 [2:10:25<20:20:44, 4.35s/it] 10%|▉ | 1863/18688 [2:10:29<20:41:06, 4.43s/it] 10%|▉ | 1864/18688 [2:10:33<20:00:59, 4.28s/it] 10%|▉ | 1865/18688 [2:10:38<20:39:13, 4.42s/it] 10%|▉ | 1866/18688 [2:10:42<20:48:58, 4.45s/it] 10%|▉ | 1867/18688 [2:10:47<20:26:50, 4.38s/it] 10%|▉ | 1868/18688 [2:10:51<20:54:56, 4.48s/it] 10%|█ | 1869/18688 [2:10:56<21:45:12, 4.66s/it] 10%|█ | 1870/18688 [2:11:00<20:49:08, 4.46s/it] {'loss': '1.019', 'grad_norm': '1.867', 'learning_rate': '0.0002297', 'epoch': '0.1001', 'num_input_tokens_seen': 6248336, 'train_runtime': '7861', 'train_tokens_per_second': '794.9'}
10%|█ | 1870/18688 [2:11:00<20:49:08, 4.46s/it] 10%|█ | 1871/18688 [2:11:05<20:16:45, 4.34s/it] 10%|█ | 1872/18688 [2:11:09<19:56:28, 4.27s/it] 10%|█ | 1873/18688 [2:11:12<19:05:41, 4.09s/it] 10%|█ | 1874/18688 [2:11:17<19:56:39, 4.27s/it] 10%|█ | 1875/18688 [2:11:21<19:53:46, 4.26s/it] 10%|█ | 1876/18688 [2:11:26<21:10:01, 4.53s/it] 10%|█ | 1877/18688 [2:11:30<20:23:45, 4.37s/it] 10%|█ | 1878/18688 [2:11:35<20:04:13, 4.30s/it] 10%|█ | 1879/18688 [2:11:39<19:50:23, 4.25s/it] 10%|█ | 1880/18688 [2:11:42<18:40:15, 4.00s/it] {'loss': '1.006', 'grad_norm': '1.739', 'learning_rate': '0.0002297', 'epoch': '0.1006', 'num_input_tokens_seen': 6282464, 'train_runtime': '7903', 'train_tokens_per_second': '795'}
10%|█ | 1880/18688 [2:11:42<18:40:15, 4.00s/it] 10%|█ | 1881/18688 [2:11:46<18:35:12, 3.98s/it] 10%|█ | 1882/18688 [2:11:50<18:27:17, 3.95s/it] 10%|█ | 1883/18688 [2:11:56<21:17:53, 4.56s/it] 10%|█ | 1884/18688 [2:11:59<19:59:35, 4.28s/it] 10%|█ | 1885/18688 [2:12:03<19:31:09, 4.18s/it] 10%|█ | 1886/18688 [2:12:08<19:50:20, 4.25s/it] 10%|█ | 1887/18688 [2:12:12<20:21:23, 4.36s/it] 10%|█ | 1888/18688 [2:12:17<20:03:32, 4.30s/it] 10%|█ | 1889/18688 [2:12:20<19:19:13, 4.14s/it] 10%|█ | 1890/18688 [2:12:24<19:02:38, 4.08s/it] {'loss': '0.9937', 'grad_norm': '1.432', 'learning_rate': '0.0002297', 'epoch': '0.1011', 'num_input_tokens_seen': 6317720, 'train_runtime': '7945', 'train_tokens_per_second': '795.2'}
10%|█ | 1890/18688 [2:12:24<19:02:38, 4.08s/it] 10%|█ | 1891/18688 [2:12:28<18:02:49, 3.87s/it] 10%|█ | 1892/18688 [2:12:32<18:59:15, 4.07s/it] 10%|█ | 1893/18688 [2:12:36<19:01:29, 4.08s/it] 10%|█ | 1894/18688 [2:12:40<18:19:39, 3.93s/it] 10%|█ | 1895/18688 [2:12:45<19:17:11, 4.13s/it] 10%|█ | 1896/18688 [2:12:49<19:39:40, 4.22s/it] 10%|█ | 1897/18688 [2:12:54<20:12:24, 4.33s/it] 10%|█ | 1898/18688 [2:12:58<20:07:13, 4.31s/it] 10%|█ | 1899/18688 [2:13:03<21:23:05, 4.59s/it] 10%|█ | 1900/18688 [2:13:07<20:28:04, 4.39s/it] {'loss': '0.968', 'grad_norm': '1.814', 'learning_rate': '0.0002297', 'epoch': '0.1017', 'num_input_tokens_seen': 6351128, 'train_runtime': '7987', 'train_tokens_per_second': '795.1'}
10%|█ | 1900/18688 [2:13:07<20:28:04, 4.39s/it] 10%|█ | 1901/18688 [2:13:11<19:18:36, 4.14s/it] 10%|█ | 1902/18688 [2:13:15<19:41:48, 4.22s/it] 10%|█ | 1903/18688 [2:13:19<19:43:56, 4.23s/it] 10%|█ | 1904/18688 [2:13:24<20:07:49, 4.32s/it] 10%|█ | 1905/18688 [2:13:29<21:21:50, 4.58s/it] 10%|█ | 1906/18688 [2:13:33<21:16:13, 4.56s/it] 10%|█ | 1907/18688 [2:13:38<21:08:30, 4.54s/it] 10%|█ | 1908/18688 [2:13:42<20:05:20, 4.31s/it] 10%|█ | 1909/18688 [2:13:46<19:25:15, 4.17s/it] 10%|█ | 1910/18688 [2:13:50<20:14:29, 4.34s/it] {'loss': '1.049', 'grad_norm': '1.532', 'learning_rate': '0.0002297', 'epoch': '0.1022', 'num_input_tokens_seen': 6385648, 'train_runtime': '8031', 'train_tokens_per_second': '795.1'}
10%|█ | 1910/18688 [2:13:50<20:14:29, 4.34s/it] 10%|█ | 1911/18688 [2:13:54<19:09:28, 4.11s/it] 10%|█ | 1912/18688 [2:13:57<18:14:36, 3.91s/it] 10%|█ | 1913/18688 [2:14:01<18:02:26, 3.87s/it] 10%|█ | 1914/18688 [2:14:05<17:37:13, 3.78s/it] 10%|█ | 1915/18688 [2:14:08<17:15:48, 3.71s/it] 10%|█ | 1916/18688 [2:14:12<16:50:53, 3.62s/it] 10%|█ | 1917/18688 [2:14:15<16:50:48, 3.62s/it] 10%|█ | 1918/18688 [2:14:19<17:09:23, 3.68s/it] 10%|█ | 1919/18688 [2:14:24<18:37:22, 4.00s/it] 10%|█ | 1920/18688 [2:14:28<18:38:18, 4.00s/it] {'loss': '1.023', 'grad_norm': '1.937', 'learning_rate': '0.0002297', 'epoch': '0.1027', 'num_input_tokens_seen': 6414704, 'train_runtime': '8068', 'train_tokens_per_second': '795.1'}
10%|█ | 1920/18688 [2:14:28<18:38:18, 4.00s/it] 10%|█ | 1921/18688 [2:14:32<19:34:32, 4.20s/it] 10%|█ | 1922/18688 [2:14:37<19:30:59, 4.19s/it] 10%|█ | 1923/18688 [2:14:42<20:37:30, 4.43s/it] 10%|█ | 1924/18688 [2:14:45<19:36:26, 4.21s/it] 10%|█ | 1925/18688 [2:14:49<19:29:36, 4.19s/it] 10%|█ | 1926/18688 [2:14:54<19:51:09, 4.26s/it] 10%|█ | 1927/18688 [2:14:58<18:57:27, 4.07s/it] 10%|█ | 1928/18688 [2:15:01<18:43:57, 4.02s/it] 10%|█ | 1929/18688 [2:15:06<19:41:02, 4.23s/it] 10%|█ | 1930/18688 [2:15:10<18:53:52, 4.06s/it] {'loss': '1.048', 'grad_norm': '1.581', 'learning_rate': '0.0002296', 'epoch': '0.1033', 'num_input_tokens_seen': 6449208, 'train_runtime': '8110', 'train_tokens_per_second': '795.2'}
10%|█ | 1930/18688 [2:15:10<18:53:52, 4.06s/it] 10%|█ | 1931/18688 [2:15:13<18:03:24, 3.88s/it] 10%|█ | 1932/18688 [2:15:17<17:30:06, 3.76s/it] 10%|█ | 1933/18688 [2:15:20<17:01:58, 3.66s/it] 10%|█ | 1934/18688 [2:15:24<17:50:35, 3.83s/it] 10%|█ | 1935/18688 [2:15:29<19:07:52, 4.11s/it] 10%|█ | 1936/18688 [2:15:33<18:44:50, 4.03s/it] 10%|█ | 1937/18688 [2:15:37<18:10:57, 3.91s/it] 10%|█ | 1938/18688 [2:15:41<19:27:12, 4.18s/it] 10%|█ | 1939/18688 [2:15:45<19:04:52, 4.10s/it] 10%|█ | 1940/18688 [2:15:50<19:50:58, 4.27s/it] {'loss': '1.038', 'grad_norm': '1.505', 'learning_rate': '0.0002296', 'epoch': '0.1038', 'num_input_tokens_seen': 6482200, 'train_runtime': '8151', 'train_tokens_per_second': '795.3'}
10%|█ | 1940/18688 [2:15:50<19:50:58, 4.27s/it] 10%|█ | 1941/18688 [2:15:54<19:40:11, 4.23s/it] 10%|█ | 1942/18688 [2:15:58<18:58:41, 4.08s/it] 10%|█ | 1943/18688 [2:16:02<19:02:35, 4.09s/it] 10%|█ | 1944/18688 [2:16:06<18:18:39, 3.94s/it] 10%|█ | 1945/18688 [2:16:09<18:03:08, 3.88s/it] 10%|█ | 1946/18688 [2:16:13<18:07:35, 3.90s/it] 10%|█ | 1947/18688 [2:16:18<18:42:30, 4.02s/it] 10%|█ | 1948/18688 [2:16:22<19:44:12, 4.24s/it] 10%|█ | 1949/18688 [2:16:26<18:55:05, 4.07s/it] 10%|█ | 1950/18688 [2:16:30<18:27:48, 3.97s/it] {'loss': '1.093', 'grad_norm': '1.616', 'learning_rate': '0.0002296', 'epoch': '0.1043', 'num_input_tokens_seen': 6514736, 'train_runtime': '8190', 'train_tokens_per_second': '795.4'}
10%|█ | 1950/18688 [2:16:30<18:27:48, 3.97s/it] 10%|█ | 1951/18688 [2:16:33<17:48:51, 3.83s/it] 10%|█ | 1952/18688 [2:16:37<18:22:38, 3.95s/it] 10%|█ | 1953/18688 [2:16:42<19:28:20, 4.19s/it] 10%|█ | 1954/18688 [2:16:46<18:55:21, 4.07s/it] 10%|█ | 1955/18688 [2:16:50<19:00:10, 4.09s/it] 10%|█ | 1956/18688 [2:16:55<19:39:06, 4.23s/it] 10%|█ | 1957/18688 [2:16:59<19:57:23, 4.29s/it] 10%|█ | 1958/18688 [2:17:04<20:36:39, 4.44s/it] 10%|█ | 1959/18688 [2:17:08<19:53:37, 4.28s/it] 10%|█ | 1960/18688 [2:17:12<19:03:20, 4.10s/it] {'loss': '1.057', 'grad_norm': '1.583', 'learning_rate': '0.0002296', 'epoch': '0.1049', 'num_input_tokens_seen': 6548928, 'train_runtime': '8232', 'train_tokens_per_second': '795.5'}
10%|█ | 1960/18688 [2:17:12<19:03:20, 4.10s/it] 10%|█ | 1961/18688 [2:17:15<18:06:02, 3.90s/it] 10%|█ | 1962/18688 [2:17:20<19:29:08, 4.19s/it] 11%|█ | 1963/18688 [2:17:24<19:15:05, 4.14s/it] 11%|█ | 1964/18688 [2:17:29<20:37:37, 4.44s/it] 11%|█ | 1965/18688 [2:17:33<19:51:11, 4.27s/it] 11%|█ | 1966/18688 [2:17:37<19:33:17, 4.21s/it] 11%|█ | 1967/18688 [2:17:41<18:45:58, 4.04s/it] 11%|█ | 1968/18688 [2:17:45<18:54:24, 4.07s/it] 11%|█ | 1969/18688 [2:17:49<19:02:37, 4.10s/it] 11%|█ | 1970/18688 [2:17:53<18:49:54, 4.06s/it] {'loss': '0.997', 'grad_norm': '1.273', 'learning_rate': '0.0002296', 'epoch': '0.1054', 'num_input_tokens_seen': 6583256, 'train_runtime': '8273', 'train_tokens_per_second': '795.7'}
11%|█ | 1970/18688 [2:17:53<18:49:54, 4.06s/it] 11%|█ | 1971/18688 [2:17:56<18:15:45, 3.93s/it] 11%|█ | 1972/18688 [2:18:01<18:39:48, 4.02s/it] 11%|█ | 1973/18688 [2:18:05<19:11:41, 4.13s/it] 11%|█ | 1974/18688 [2:18:10<19:48:58, 4.27s/it] 11%|█ | 1975/18688 [2:18:14<20:30:31, 4.42s/it] 11%|█ | 1976/18688 [2:18:18<19:49:53, 4.27s/it] 11%|█ | 1977/18688 [2:18:23<20:43:48, 4.47s/it] 11%|█ | 1978/18688 [2:18:27<19:33:52, 4.21s/it] 11%|█ | 1979/18688 [2:18:32<20:13:11, 4.36s/it] 11%|█ | 1980/18688 [2:18:37<21:25:56, 4.62s/it] {'loss': '1.08', 'grad_norm': '2.093', 'learning_rate': '0.0002296', 'epoch': '0.106', 'num_input_tokens_seen': 6619632, 'train_runtime': '8317', 'train_tokens_per_second': '795.9'}
11%|█ | 1980/18688 [2:18:37<21:25:56, 4.62s/it] 11%|█ | 1981/18688 [2:18:41<20:49:12, 4.49s/it] 11%|█ | 1982/18688 [2:18:46<21:17:13, 4.59s/it] 11%|█ | 1983/18688 [2:18:50<20:29:13, 4.42s/it] 11%|█ | 1984/18688 [2:18:54<19:43:12, 4.25s/it] 11%|█ | 1985/18688 [2:18:58<19:29:47, 4.20s/it] 11%|█ | 1986/18688 [2:19:02<19:48:27, 4.27s/it] 11%|█ | 1987/18688 [2:19:06<19:16:43, 4.16s/it] 11%|█ | 1988/18688 [2:19:10<19:28:46, 4.20s/it] 11%|█ | 1989/18688 [2:19:15<20:14:23, 4.36s/it] 11%|█ | 1990/18688 [2:19:19<19:24:40, 4.18s/it] {'loss': '1.019', 'grad_norm': '1.304', 'learning_rate': '0.0002295', 'epoch': '0.1065', 'num_input_tokens_seen': 6654592, 'train_runtime': '8359', 'train_tokens_per_second': '796.1'}
11%|█ | 1990/18688 [2:19:19<19:24:40, 4.18s/it] 11%|█ | 1991/18688 [2:19:24<20:25:30, 4.40s/it] 11%|█ | 1992/18688 [2:19:28<20:42:34, 4.47s/it] 11%|█ | 1993/18688 [2:19:32<19:47:51, 4.27s/it] 11%|█ | 1994/18688 [2:19:37<19:47:45, 4.27s/it] 11%|█ | 1995/18688 [2:19:42<20:47:40, 4.48s/it] 11%|█ | 1996/18688 [2:19:46<20:49:56, 4.49s/it] 11%|█ | 1997/18688 [2:19:50<19:42:47, 4.25s/it] 11%|█ | 1998/18688 [2:19:54<19:27:35, 4.20s/it] 11%|█ | 1999/18688 [2:19:58<19:27:37, 4.20s/it] 11%|█ | 2000/18688 [2:20:02<18:53:59, 4.08s/it] {'loss': '1.037', 'grad_norm': '1.626', 'learning_rate': '0.0002295', 'epoch': '0.107', 'num_input_tokens_seen': 6689560, 'train_runtime': '8402', 'train_tokens_per_second': '796.2'}
11%|█ | 2000/18688 [2:20:02<18:53:59, 4.08s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
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{'eval_loss': '1.015', 'eval_runtime': '83.12', 'eval_samples_per_second': '12.03', 'eval_steps_per_second': '12.03', 'epoch': '0.107', 'num_input_tokens_seen': 6689560}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00023/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 [2:21:31<137:43:52, 29.71s/it] 11%|█ | 2002/18688 [2:21:35<101:09:50, 21.83s/it] 11%|█ | 2003/18688 [2:21:39<76:43:26, 16.55s/it] 11%|█ | 2004/18688 [2:21:43<59:45:57, 12.90s/it] 11%|█ | 2005/18688 [2:21:48<47:36:23, 10.27s/it] 11%|█ | 2006/18688 [2:21:52<39:36:00, 8.55s/it] 11%|█ | 2007/18688 [2:21:57<34:07:05, 7.36s/it] 11%|█ | 2008/18688 [2:22:01<30:21:18, 6.55s/it] 11%|█ | 2009/18688 [2:22:05<26:11:58, 5.65s/it] 11%|█ | 2010/18688 [2:22:08<23:22:17, 5.04s/it] {'loss': '0.9686', 'grad_norm': '1.405', 'learning_rate': '0.0002295', 'epoch': '0.1076', 'num_input_tokens_seen': 6727088, 'train_runtime': '8529', 'train_tokens_per_second': '788.7'}
11%|█ | 2010/18688 [2:22:08<23:22:17, 5.04s/it] 11%|█ | 2011/18688 [2:22:13<22:53:20, 4.94s/it] 11%|█ | 2012/18688 [2:22:18<22:03:53, 4.76s/it] 11%|█ | 2013/18688 [2:22:22<21:08:35, 4.56s/it] 11%|█ | 2014/18688 [2:22:25<19:37:54, 4.24s/it] 11%|█ | 2015/18688 [2:22:30<20:22:22, 4.40s/it] 11%|█ | 2016/18688 [2:22:34<19:50:32, 4.28s/it] 11%|█ | 2017/18688 [2:22:38<19:37:35, 4.24s/it] 11%|█ | 2018/18688 [2:22:42<19:11:15, 4.14s/it] 11%|█ | 2019/18688 [2:22:46<18:55:53, 4.09s/it] 11%|█ | 2020/18688 [2:22:50<18:37:31, 4.02s/it] {'loss': '1.041', 'grad_norm': '1.633', 'learning_rate': '0.0002295', 'epoch': '0.1081', 'num_input_tokens_seen': 6760040, 'train_runtime': '8570', 'train_tokens_per_second': '788.8'}
11%|█ | 2020/18688 [2:22:50<18:37:31, 4.02s/it] 11%|█ | 2021/18688 [2:22:53<18:05:36, 3.91s/it] 11%|█ | 2022/18688 [2:22:58<18:55:08, 4.09s/it] 11%|█ | 2023/18688 [2:23:02<19:02:00, 4.11s/it] 11%|█ | 2024/18688 [2:23:06<19:07:40, 4.13s/it] 11%|█ | 2025/18688 [2:23:10<18:41:12, 4.04s/it] 11%|█ | 2026/18688 [2:23:14<18:36:25, 4.02s/it] 11%|█ | 2027/18688 [2:23:18<17:54:16, 3.87s/it] 11%|█ | 2028/18688 [2:23:21<17:37:57, 3.81s/it] 11%|█ | 2029/18688 [2:23:25<18:06:57, 3.91s/it] 11%|█ | 2030/18688 [2:23:30<18:48:52, 4.07s/it] {'loss': '1.054', 'grad_norm': '2.126', 'learning_rate': '0.0002295', 'epoch': '0.1086', 'num_input_tokens_seen': 6791920, 'train_runtime': '8610', 'train_tokens_per_second': '788.8'}
11%|█ | 2030/18688 [2:23:30<18:48:52, 4.07s/it] 11%|█ | 2031/18688 [2:23:34<19:29:36, 4.21s/it] 11%|█ | 2032/18688 [2:23:38<19:09:36, 4.14s/it] 11%|█ | 2033/18688 [2:23:43<19:29:29, 4.21s/it] 11%|█ | 2034/18688 [2:23:47<19:01:48, 4.11s/it] 11%|█ | 2035/18688 [2:23:51<19:14:14, 4.16s/it] 11%|█ | 2036/18688 [2:23:55<19:22:58, 4.19s/it] 11%|█ | 2037/18688 [2:23:59<18:27:26, 3.99s/it] 11%|█ | 2038/18688 [2:24:03<18:37:03, 4.03s/it] 11%|█ | 2039/18688 [2:24:07<18:53:27, 4.08s/it] 11%|█ | 2040/18688 [2:24:11<18:47:38, 4.06s/it] {'loss': '1.054', 'grad_norm': '2.004', 'learning_rate': '0.0002294', 'epoch': '0.1092', 'num_input_tokens_seen': 6827176, 'train_runtime': '8652', 'train_tokens_per_second': '789.1'}
11%|█ | 2040/18688 [2:24:11<18:47:38, 4.06s/it] 11%|█ | 2041/18688 [2:24:15<19:01:13, 4.11s/it] 11%|█ | 2042/18688 [2:24:19<18:04:06, 3.91s/it] 11%|█ | 2043/18688 [2:24:23<18:10:16, 3.93s/it] 11%|█ | 2044/18688 [2:24:27<19:08:26, 4.14s/it] 11%|█ | 2045/18688 [2:24:32<20:00:08, 4.33s/it] 11%|█ | 2046/18688 [2:24:36<19:07:10, 4.14s/it] 11%|█ | 2047/18688 [2:24:39<18:25:03, 3.98s/it] 11%|█ | 2048/18688 [2:24:43<18:22:41, 3.98s/it] 11%|█ | 2049/18688 [2:24:47<18:03:35, 3.91s/it] 11%|█ | 2050/18688 [2:24:51<18:01:46, 3.90s/it] {'loss': '1.005', 'grad_norm': '1.435', 'learning_rate': '0.0002294', 'epoch': '0.1097', 'num_input_tokens_seen': 6859736, 'train_runtime': '8691', 'train_tokens_per_second': '789.2'}
11%|█ | 2050/18688 [2:24:51<18:01:46, 3.90s/it] 11%|█ | 2051/18688 [2:24:55<17:39:54, 3.82s/it] 11%|█ | 2052/18688 [2:24:59<19:02:33, 4.12s/it] 11%|█ | 2053/18688 [2:25:04<19:23:31, 4.20s/it] 11%|█ | 2054/18688 [2:25:08<19:27:01, 4.21s/it] 11%|█ | 2055/18688 [2:25:11<18:23:44, 3.98s/it] 11%|█ | 2056/18688 [2:25:15<17:36:19, 3.81s/it] 11%|█ | 2057/18688 [2:25:18<17:16:26, 3.74s/it] 11%|█ | 2058/18688 [2:25:22<17:10:47, 3.72s/it] 11%|█ | 2059/18688 [2:25:26<17:57:49, 3.89s/it] 11%|█ | 2060/18688 [2:25:31<18:29:13, 4.00s/it] {'loss': '0.9787', 'grad_norm': '1.338', 'learning_rate': '0.0002294', 'epoch': '0.1102', 'num_input_tokens_seen': 6893248, 'train_runtime': '8731', 'train_tokens_per_second': '789.5'}
11%|█ | 2060/18688 [2:25:31<18:29:13, 4.00s/it] 11%|█ | 2061/18688 [2:25:35<19:04:43, 4.13s/it] 11%|█ | 2062/18688 [2:25:39<19:06:59, 4.14s/it] 11%|█ | 2063/18688 [2:25:43<18:40:37, 4.04s/it] 11%|█ | 2064/18688 [2:25:48<19:19:42, 4.19s/it] 11%|█ | 2065/18688 [2:25:51<18:41:18, 4.05s/it] 11%|█ | 2066/18688 [2:25:55<18:12:14, 3.94s/it] 11%|█ | 2067/18688 [2:25:59<18:53:06, 4.09s/it] 11%|█ | 2068/18688 [2:26:03<18:17:30, 3.96s/it] 11%|█ | 2069/18688 [2:26:08<18:58:57, 4.11s/it] 11%|█ | 2070/18688 [2:26:11<18:23:37, 3.98s/it] {'loss': '1.058', 'grad_norm': '1.57', 'learning_rate': '0.0002294', 'epoch': '0.1108', 'num_input_tokens_seen': 6926840, 'train_runtime': '8772', 'train_tokens_per_second': '789.7'}
11%|█ | 2070/18688 [2:26:11<18:23:37, 3.98s/it] 11%|█ | 2071/18688 [2:26:16<19:58:14, 4.33s/it] 11%|█ | 2072/18688 [2:26:21<19:59:30, 4.33s/it] 11%|█ | 2073/18688 [2:26:26<21:38:37, 4.69s/it] 11%|█ | 2074/18688 [2:26:30<20:14:53, 4.39s/it] 11%|█ | 2075/18688 [2:26:34<19:30:22, 4.23s/it] 11%|█ | 2076/18688 [2:26:38<18:57:33, 4.11s/it] 11%|█ | 2077/18688 [2:26:42<19:00:14, 4.12s/it] 11%|█ | 2078/18688 [2:26:46<18:52:12, 4.09s/it] 11%|█ | 2079/18688 [2:26:50<18:34:07, 4.02s/it] 11%|█ | 2080/18688 [2:26:54<19:02:13, 4.13s/it] {'loss': '1.001', 'grad_norm': '1.787', 'learning_rate': '0.0002294', 'epoch': '0.1113', 'num_input_tokens_seen': 6960856, 'train_runtime': '8815', 'train_tokens_per_second': '789.7'}
11%|█ | 2080/18688 [2:26:54<19:02:13, 4.13s/it] 11%|█ | 2081/18688 [2:26:57<18:03:41, 3.92s/it] 11%|█ | 2082/18688 [2:27:01<17:55:10, 3.88s/it] 11%|█ | 2083/18688 [2:27:06<18:52:31, 4.09s/it] 11%|█ | 2084/18688 [2:27:10<19:10:07, 4.16s/it] 11%|█ | 2085/18688 [2:27:14<18:13:13, 3.95s/it] 11%|█ | 2086/18688 [2:27:18<18:33:28, 4.02s/it] 11%|█ | 2087/18688 [2:27:22<18:24:41, 3.99s/it] 11%|█ | 2088/18688 [2:27:26<18:44:24, 4.06s/it] 11%|█ | 2089/18688 [2:27:29<17:57:11, 3.89s/it] 11%|█ | 2090/18688 [2:27:34<18:27:38, 4.00s/it] {'loss': '0.9659', 'grad_norm': '1.787', 'learning_rate': '0.0002293', 'epoch': '0.1118', 'num_input_tokens_seen': 6993128, 'train_runtime': '8854', 'train_tokens_per_second': '789.8'}
11%|█ | 2090/18688 [2:27:34<18:27:38, 4.00s/it] 11%|█ | 2091/18688 [2:27:38<18:40:32, 4.05s/it] 11%|█ | 2092/18688 [2:27:42<19:03:56, 4.14s/it] 11%|█ | 2093/18688 [2:27:46<18:10:01, 3.94s/it] 11%|█ | 2094/18688 [2:27:50<18:12:35, 3.95s/it] 11%|█ | 2095/18688 [2:27:54<18:35:30, 4.03s/it] 11%|█ | 2096/18688 [2:27:58<19:17:08, 4.18s/it] 11%|█ | 2097/18688 [2:28:03<19:14:54, 4.18s/it] 11%|█ | 2098/18688 [2:28:08<20:17:08, 4.40s/it] 11%|█ | 2099/18688 [2:28:11<19:08:40, 4.15s/it] 11%|█ | 2100/18688 [2:28:15<18:10:12, 3.94s/it] {'loss': '1.045', 'grad_norm': '1.654', 'learning_rate': '0.0002293', 'epoch': '0.1124', 'num_input_tokens_seen': 7027808, 'train_runtime': '8895', 'train_tokens_per_second': '790.1'}
11%|█ | 2100/18688 [2:28:15<18:10:12, 3.94s/it] 11%|█ | 2101/18688 [2:28:19<18:55:05, 4.11s/it] 11%|█ | 2102/18688 [2:28:23<18:09:39, 3.94s/it] 11%|█▏ | 2103/18688 [2:28:27<18:31:33, 4.02s/it] 11%|█▏ | 2104/18688 [2:28:31<18:18:27, 3.97s/it] 11%|█▏ | 2105/18688 [2:28:35<18:53:56, 4.10s/it] 11%|█▏ | 2106/18688 [2:28:39<18:46:56, 4.08s/it] 11%|█▏ | 2107/18688 [2:28:43<18:58:00, 4.12s/it] 11%|█▏ | 2108/18688 [2:28:47<18:31:23, 4.02s/it] 11%|█▏ | 2109/18688 [2:28:51<18:28:29, 4.01s/it] 11%|█▏ | 2110/18688 [2:28:56<19:14:04, 4.18s/it] {'loss': '0.9537', 'grad_norm': '1.598', 'learning_rate': '0.0002293', 'epoch': '0.1129', 'num_input_tokens_seen': 7060536, 'train_runtime': '8936', 'train_tokens_per_second': '790.1'}
11%|█▏ | 2110/18688 [2:28:56<19:14:04, 4.18s/it] 11%|█▏ | 2111/18688 [2:28:59<18:36:57, 4.04s/it] 11%|█▏ | 2112/18688 [2:29:04<18:49:53, 4.09s/it] 11%|█▏ | 2113/18688 [2:29:08<18:56:39, 4.11s/it] 11%|█▏ | 2114/18688 [2:29:13<20:20:28, 4.42s/it] 11%|█▏ | 2115/18688 [2:29:18<20:43:20, 4.50s/it] 11%|█▏ | 2116/18688 [2:29:22<19:57:07, 4.33s/it] 11%|█▏ | 2117/18688 [2:29:26<20:44:29, 4.51s/it] 11%|█▏ | 2118/18688 [2:29:31<21:01:30, 4.57s/it] 11%|█▏ | 2119/18688 [2:29:35<20:28:03, 4.45s/it] 11%|█▏ | 2120/18688 [2:29:40<20:14:30, 4.40s/it] {'loss': '1.089', 'grad_norm': '1.417', 'learning_rate': '0.0002293', 'epoch': '0.1134', 'num_input_tokens_seen': 7095224, 'train_runtime': '8980', 'train_tokens_per_second': '790.1'}
11%|█▏ | 2120/18688 [2:29:40<20:14:30, 4.40s/it] 11%|█▏ | 2121/18688 [2:29:44<20:00:18, 4.35s/it] 11%|█▏ | 2122/18688 [2:29:49<20:34:03, 4.47s/it] 11%|█▏ | 2123/18688 [2:29:53<21:08:20, 4.59s/it] 11%|█▏ | 2124/18688 [2:29:57<19:44:55, 4.29s/it] 11%|█▏ | 2125/18688 [2:30:01<18:46:26, 4.08s/it] 11%|█▏ | 2126/18688 [2:30:04<18:10:23, 3.95s/it] 11%|█▏ | 2127/18688 [2:30:08<17:46:34, 3.86s/it] 11%|█▏ | 2128/18688 [2:30:12<17:41:27, 3.85s/it] 11%|█▏ | 2129/18688 [2:30:17<19:18:35, 4.20s/it] 11%|█▏ | 2130/18688 [2:30:20<18:17:20, 3.98s/it] {'loss': '1.035', 'grad_norm': '1.725', 'learning_rate': '0.0002292', 'epoch': '0.114', 'num_input_tokens_seen': 7128176, 'train_runtime': '9021', 'train_tokens_per_second': '790.2'}
11%|█▏ | 2130/18688 [2:30:20<18:17:20, 3.98s/it] 11%|█▏ | 2131/18688 [2:30:24<18:05:04, 3.93s/it] 11%|█▏ | 2132/18688 [2:30:28<17:32:45, 3.82s/it] 11%|█▏ | 2133/18688 [2:30:32<18:53:16, 4.11s/it] 11%|█▏ | 2134/18688 [2:30:37<19:39:00, 4.27s/it] 11%|█▏ | 2135/18688 [2:30:41<18:38:16, 4.05s/it] 11%|█▏ | 2136/18688 [2:30:45<18:51:42, 4.10s/it] 11%|█▏ | 2137/18688 [2:30:49<18:40:46, 4.06s/it] 11%|█▏ | 2138/18688 [2:30:53<18:16:19, 3.97s/it] 11%|█▏ | 2139/18688 [2:30:56<17:56:42, 3.90s/it] 11%|█▏ | 2140/18688 [2:31:00<17:19:00, 3.77s/it] {'loss': '1.036', 'grad_norm': '1.508', 'learning_rate': '0.0002292', 'epoch': '0.1145', 'num_input_tokens_seen': 7160536, 'train_runtime': '9060', 'train_tokens_per_second': '790.3'}
11%|█▏ | 2140/18688 [2:31:00<17:19:00, 3.77s/it] 11%|█▏ | 2141/18688 [2:31:03<16:50:41, 3.66s/it] 11%|█▏ | 2142/18688 [2:31:07<17:03:30, 3.71s/it] 11%|█▏ | 2143/18688 [2:31:12<18:54:30, 4.11s/it] 11%|█▏ | 2144/18688 [2:31:16<19:00:33, 4.14s/it] 11%|█▏ | 2145/18688 [2:31:21<19:16:14, 4.19s/it] 11%|█▏ | 2146/18688 [2:31:24<18:34:07, 4.04s/it] 11%|█▏ | 2147/18688 [2:31:29<19:17:36, 4.20s/it] 11%|█▏ | 2148/18688 [2:31:33<19:41:44, 4.29s/it] 11%|█▏ | 2149/18688 [2:31:38<19:49:48, 4.32s/it] 12%|█▏ | 2150/18688 [2:31:42<19:20:02, 4.21s/it] {'loss': '1.021', 'grad_norm': '1.47', 'learning_rate': '0.0002292', 'epoch': '0.1151', 'num_input_tokens_seen': 7194240, 'train_runtime': '9102', 'train_tokens_per_second': '790.4'}
12%|█▏ | 2150/18688 [2:31:42<19:20:02, 4.21s/it] 12%|█▏ | 2151/18688 [2:31:46<19:27:31, 4.24s/it] 12%|█▏ | 2152/18688 [2:31:51<20:06:34, 4.38s/it] 12%|█▏ | 2153/18688 [2:31:55<19:42:50, 4.29s/it] 12%|█▏ | 2154/18688 [2:31:59<18:58:51, 4.13s/it] 12%|█▏ | 2155/18688 [2:32:03<18:53:01, 4.11s/it] 12%|█▏ | 2156/18688 [2:32:06<18:24:14, 4.01s/it] 12%|█▏ | 2157/18688 [2:32:11<19:09:03, 4.17s/it] 12%|█▏ | 2158/18688 [2:32:15<19:03:27, 4.15s/it] 12%|█▏ | 2159/18688 [2:32:19<19:05:10, 4.16s/it] 12%|█▏ | 2160/18688 [2:32:24<19:20:16, 4.21s/it] {'loss': '1.033', 'grad_norm': '1.608', 'learning_rate': '0.0002292', 'epoch': '0.1156', 'num_input_tokens_seen': 7224368, 'train_runtime': '9144', 'train_tokens_per_second': '790.1'}
12%|█▏ | 2160/18688 [2:32:24<19:20:16, 4.21s/it] 12%|█▏ | 2161/18688 [2:32:29<20:38:14, 4.50s/it] 12%|█▏ | 2162/18688 [2:32:33<19:58:34, 4.35s/it] 12%|█▏ | 2163/18688 [2:32:37<19:19:42, 4.21s/it] 12%|█▏ | 2164/18688 [2:32:41<19:50:38, 4.32s/it] 12%|█▏ | 2165/18688 [2:32:45<19:21:20, 4.22s/it] 12%|█▏ | 2166/18688 [2:32:49<19:22:43, 4.22s/it] 12%|█▏ | 2167/18688 [2:32:54<19:39:41, 4.28s/it] 12%|█▏ | 2168/18688 [2:32:58<20:09:02, 4.39s/it] 12%|█▏ | 2169/18688 [2:33:03<20:57:32, 4.57s/it] 12%|█▏ | 2170/18688 [2:33:07<19:43:19, 4.30s/it] {'loss': '1.024', 'grad_norm': '1.381', 'learning_rate': '0.0002291', 'epoch': '0.1161', 'num_input_tokens_seen': 7257872, 'train_runtime': '9188', 'train_tokens_per_second': '790'}
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***** 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)
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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)
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{'eval_loss': '1.023', 'eval_runtime': '82.76', 'eval_samples_per_second': '12.08', 'eval_steps_per_second': '12.08', 'epoch': '0.1605', 'num_input_tokens_seen': 10063432}
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20%|██ | 3820/18688 [4:28:57<15:47:48, 3.82s/it] 20%|██ | 3821/18688 [4:29:01<16:29:47, 3.99s/it] 20%|██ | 3822/18688 [4:29:05<16:21:38, 3.96s/it] 20%|██ | 3823/18688 [4:29:09<15:46:46, 3.82s/it] 20%|██ | 3824/18688 [4:29:12<15:23:30, 3.73s/it] 20%|██ | 3825/18688 [4:29:16<15:24:49, 3.73s/it] 20%|██ | 3826/18688 [4:29:20<15:47:31, 3.83s/it] 20%|██ | 3827/18688 [4:29:24<16:24:21, 3.97s/it] 20%|██ | 3828/18688 [4:29:30<18:14:10, 4.42s/it] 20%|██ | 3829/18688 [4:29:34<17:35:43, 4.26s/it] 20%|██ | 3830/18688 [4:29:38<17:16:23, 4.19s/it] {'loss': '1.005', 'grad_norm': '1.707', 'learning_rate': '0.0002197', 'epoch': '0.2049', 'num_input_tokens_seen': 12862056, 'train_runtime': '1.618e+04', 'train_tokens_per_second': '795'}
20%|██ | 3830/18688 [4:29:38<17:16:23, 4.19s/it] 20%|██ | 3831/18688 [4:29:41<16:29:13, 3.99s/it] 21%|██ | 3832/18688 [4:29:46<17:19:17, 4.20s/it] 21%|██ | 3833/18688 [4:29:50<16:54:57, 4.10s/it] 21%|██ | 3834/18688 [4:29:55<18:08:53, 4.40s/it] 21%|██ | 3835/18688 [4:29:58<17:15:01, 4.18s/it] 21%|██ | 3836/18688 [4:30:02<16:40:02, 4.04s/it] 21%|██ | 3837/18688 [4:30:06<16:41:23, 4.05s/it] 21%|██ | 3838/18688 [4:30:11<17:27:43, 4.23s/it] 21%|██ | 3839/18688 [4:30:16<18:17:08, 4.43s/it] 21%|██ | 3840/18688 [4:30:20<17:50:55, 4.33s/it] {'loss': '1.013', 'grad_norm': '2.504', 'learning_rate': '0.0002196', 'epoch': '0.2055', 'num_input_tokens_seen': 12895912, 'train_runtime': '1.622e+04', 'train_tokens_per_second': '795'}
21%|██ | 3840/18688 [4:30:20<17:50:55, 4.33s/it] 21%|██ | 3841/18688 [4:30:24<17:08:53, 4.16s/it] 21%|██ | 3842/18688 [4:30:28<16:53:18, 4.10s/it] 21%|██ | 3843/18688 [4:30:31<16:20:17, 3.96s/it] 21%|██ | 3844/18688 [4:30:37<19:06:42, 4.64s/it] 21%|██ | 3845/18688 [4:30:42<18:35:26, 4.51s/it] 21%|██ | 3846/18688 [4:30:46<18:13:09, 4.42s/it] 21%|██ | 3847/18688 [4:30:50<17:28:14, 4.24s/it] 21%|██ | 3848/18688 [4:30:54<17:25:56, 4.23s/it] 21%|██ | 3849/18688 [4:30:58<17:14:41, 4.18s/it] 21%|██ | 3850/18688 [4:31:03<17:45:49, 4.31s/it] {'loss': '1.047', 'grad_norm': '2.07', 'learning_rate': '0.0002196', 'epoch': '0.206', 'num_input_tokens_seen': 12932544, 'train_runtime': '1.626e+04', 'train_tokens_per_second': '795.2'}
21%|██ | 3850/18688 [4:31:03<17:45:49, 4.31s/it] 21%|██ | 3851/18688 [4:31:06<16:38:48, 4.04s/it] 21%|██ | 3852/18688 [4:31:10<16:03:40, 3.90s/it] 21%|██ | 3853/18688 [4:31:13<16:00:59, 3.89s/it] 21%|██ | 3854/18688 [4:31:18<17:04:13, 4.14s/it] 21%|██ | 3855/18688 [4:31:22<16:24:49, 3.98s/it] 21%|██ | 3856/18688 [4:31:26<17:02:00, 4.13s/it] 21%|██ | 3857/18688 [4:31:30<16:31:02, 4.01s/it] 21%|██ | 3858/18688 [4:31:34<16:37:50, 4.04s/it] 21%|██ | 3859/18688 [4:31:38<16:15:12, 3.95s/it] 21%|██ | 3860/18688 [4:31:42<16:28:03, 4.00s/it] {'loss': '1.043', 'grad_norm': '2.021', 'learning_rate': '0.0002195', 'epoch': '0.2066', 'num_input_tokens_seen': 12965064, 'train_runtime': '1.63e+04', 'train_tokens_per_second': '795.3'}
21%|██ | 3860/18688 [4:31:42<16:28:03, 4.00s/it] 21%|██ | 3861/18688 [4:31:47<18:15:06, 4.43s/it] 21%|██ | 3862/18688 [4:31:51<17:21:52, 4.22s/it] 21%|██ | 3863/18688 [4:31:56<17:56:56, 4.36s/it] 21%|██ | 3864/18688 [4:32:00<17:33:44, 4.26s/it] 21%|██ | 3865/18688 [4:32:04<18:03:45, 4.39s/it] 21%|██ | 3866/18688 [4:32:09<17:54:53, 4.35s/it] 21%|██ | 3867/18688 [4:32:13<17:52:05, 4.34s/it] 21%|██ | 3868/18688 [4:32:17<16:51:09, 4.09s/it] 21%|██ | 3869/18688 [4:32:21<17:03:55, 4.15s/it] 21%|██ | 3870/18688 [4:32:25<16:51:59, 4.10s/it] {'loss': '1.08', 'grad_norm': '2.123', 'learning_rate': '0.0002194', 'epoch': '0.2071', 'num_input_tokens_seen': 12999672, 'train_runtime': '1.635e+04', 'train_tokens_per_second': '795.3'}
21%|██ | 3870/18688 [4:32:25<16:51:59, 4.10s/it] 21%|██ | 3871/18688 [4:32:29<16:30:12, 4.01s/it] 21%|██ | 3872/18688 [4:32:33<16:33:56, 4.03s/it] 21%|██ | 3873/18688 [4:32:37<16:58:50, 4.13s/it] 21%|██ | 3874/18688 [4:32:41<16:58:13, 4.12s/it] 21%|██ | 3875/18688 [4:32:45<16:43:51, 4.07s/it] 21%|██ | 3876/18688 [4:32:49<17:03:59, 4.15s/it] 21%|██ | 3877/18688 [4:32:54<17:15:06, 4.19s/it] 21%|██ | 3878/18688 [4:32:58<17:56:22, 4.36s/it] 21%|██ | 3879/18688 [4:33:03<17:51:07, 4.34s/it] 21%|██ | 3880/18688 [4:33:07<17:19:41, 4.21s/it] {'loss': '1.044', 'grad_norm': '2.629', 'learning_rate': '0.0002193', 'epoch': '0.2076', 'num_input_tokens_seen': 13032520, 'train_runtime': '1.639e+04', 'train_tokens_per_second': '795.3'}
21%|██ | 3880/18688 [4:33:07<17:19:41, 4.21s/it] 21%|██ | 3881/18688 [4:33:11<17:01:26, 4.14s/it] 21%|██ | 3882/18688 [4:33:15<16:38:37, 4.05s/it] 21%|██ | 3883/18688 [4:33:18<16:15:49, 3.95s/it] 21%|██ | 3884/18688 [4:33:22<16:28:19, 4.01s/it] 21%|██ | 3885/18688 [4:33:26<16:20:42, 3.98s/it] 21%|██ | 3886/18688 [4:33:30<15:40:55, 3.81s/it] 21%|██ | 3887/18688 [4:33:34<16:04:19, 3.91s/it] 21%|██ | 3888/18688 [4:33:38<16:41:30, 4.06s/it] 21%|██ | 3889/18688 [4:33:42<16:17:43, 3.96s/it] 21%|██ | 3890/18688 [4:33:46<15:54:28, 3.87s/it] {'loss': '0.984', 'grad_norm': '1.839', 'learning_rate': '0.0002192', 'epoch': '0.2082', 'num_input_tokens_seen': 13063912, 'train_runtime': '1.643e+04', 'train_tokens_per_second': '795.3'}
21%|██ | 3890/18688 [4:33:46<15:54:28, 3.87s/it] 21%|██ | 3891/18688 [4:33:50<16:01:24, 3.90s/it] 21%|██ | 3892/18688 [4:33:55<18:11:15, 4.43s/it] 21%|██ | 3893/18688 [4:34:00<18:14:38, 4.44s/it] 21%|██ | 3894/18688 [4:34:03<17:06:50, 4.16s/it] 21%|██ | 3895/18688 [4:34:07<16:32:15, 4.02s/it] 21%|██ | 3896/18688 [4:34:11<16:06:02, 3.92s/it] 21%|██ | 3897/18688 [4:34:15<17:11:10, 4.18s/it] 21%|██ | 3898/18688 [4:34:20<17:12:30, 4.19s/it] 21%|██ | 3899/18688 [4:34:25<18:11:59, 4.43s/it] 21%|██ | 3900/18688 [4:34:29<18:05:42, 4.41s/it] {'loss': '1.066', 'grad_norm': '2.126', 'learning_rate': '0.0002191', 'epoch': '0.2087', 'num_input_tokens_seen': 13097456, 'train_runtime': '1.647e+04', 'train_tokens_per_second': '795.3'}
21%|██ | 3900/18688 [4:34:29<18:05:42, 4.41s/it] 21%|██ | 3901/18688 [4:34:34<19:10:35, 4.67s/it] 21%|██ | 3902/18688 [4:34:39<18:54:40, 4.60s/it] 21%|██ | 3903/18688 [4:34:43<18:30:56, 4.51s/it] 21%|██ | 3904/18688 [4:34:47<17:54:32, 4.36s/it] 21%|██ | 3905/18688 [4:34:51<17:09:31, 4.18s/it] 21%|██ | 3906/18688 [4:34:56<18:00:47, 4.39s/it] 21%|██ | 3907/18688 [4:35:01<19:07:34, 4.66s/it] 21%|██ | 3908/18688 [4:35:06<19:12:53, 4.68s/it] 21%|██ | 3909/18688 [4:35:10<18:18:26, 4.46s/it] 21%|██ | 3910/18688 [4:35:13<17:13:54, 4.20s/it] {'loss': '1.089', 'grad_norm': '1.761', 'learning_rate': '0.000219', 'epoch': '0.2092', 'num_input_tokens_seen': 13135768, 'train_runtime': '1.651e+04', 'train_tokens_per_second': '795.4'}
21%|██ | 3910/18688 [4:35:13<17:13:54, 4.20s/it] 21%|██ | 3911/18688 [4:35:17<16:33:31, 4.03s/it] 21%|██ | 3912/18688 [4:35:21<16:37:38, 4.05s/it] 21%|██ | 3913/18688 [4:35:25<16:38:07, 4.05s/it] 21%|██ | 3914/18688 [4:35:30<17:32:01, 4.27s/it] 21%|██ | 3915/18688 [4:35:34<17:05:51, 4.17s/it] 21%|██ | 3916/18688 [4:35:37<16:17:47, 3.97s/it] 21%|██ | 3917/18688 [4:35:41<15:58:15, 3.89s/it] 21%|██ | 3918/18688 [4:35:45<15:44:50, 3.84s/it] 21%|██ | 3919/18688 [4:35:49<16:07:39, 3.93s/it] 21%|██ | 3920/18688 [4:35:53<16:12:47, 3.95s/it] {'loss': '0.9988', 'grad_norm': '2.078', 'learning_rate': '0.0002189', 'epoch': '0.2098', 'num_input_tokens_seen': 13165712, 'train_runtime': '1.655e+04', 'train_tokens_per_second': '795.4'}
21%|██ | 3920/18688 [4:35:53<16:12:47, 3.95s/it] 21%|██ | 3921/18688 [4:35:56<15:47:01, 3.85s/it] 21%|██ | 3922/18688 [4:36:00<15:39:25, 3.82s/it] 21%|██ | 3923/18688 [4:36:04<15:46:25, 3.85s/it] 21%|██ | 3924/18688 [4:36:09<17:18:08, 4.22s/it] 21%|██ | 3925/18688 [4:36:13<16:54:57, 4.13s/it] 21%|██ | 3926/18688 [4:36:17<16:23:46, 4.00s/it] 21%|██ | 3927/18688 [4:36:21<16:49:11, 4.10s/it] 21%|██ | 3928/18688 [4:36:26<17:24:29, 4.25s/it] 21%|██ | 3929/18688 [4:36:30<17:40:00, 4.31s/it] 21%|██ | 3930/18688 [4:36:34<17:18:59, 4.22s/it] {'loss': '1.083', 'grad_norm': '1.59', 'learning_rate': '0.0002189', 'epoch': '0.2103', 'num_input_tokens_seen': 13200840, 'train_runtime': '1.659e+04', 'train_tokens_per_second': '795.5'}
21%|██ | 3930/18688 [4:36:34<17:18:59, 4.22s/it] 21%|██ | 3931/18688 [4:36:38<17:26:34, 4.26s/it] 21%|██ | 3932/18688 [4:36:43<17:21:37, 4.24s/it] 21%|██ | 3933/18688 [4:36:46<16:27:54, 4.02s/it] 21%|██ | 3934/18688 [4:36:50<16:49:18, 4.10s/it] 21%|██ | 3935/18688 [4:36:54<16:30:57, 4.03s/it] 21%|██ | 3936/18688 [4:36:58<16:24:03, 4.00s/it] 21%|██ | 3937/18688 [4:37:02<15:52:36, 3.87s/it] 21%|██ | 3938/18688 [4:37:06<16:16:36, 3.97s/it] 21%|██ | 3939/18688 [4:37:10<16:02:20, 3.91s/it] 21%|██ | 3940/18688 [4:37:14<16:36:16, 4.05s/it] {'loss': '0.9976', 'grad_norm': '2.059', 'learning_rate': '0.0002188', 'epoch': '0.2108', 'num_input_tokens_seen': 13232512, 'train_runtime': '1.663e+04', 'train_tokens_per_second': '795.5'}
21%|██ | 3940/18688 [4:37:14<16:36:16, 4.05s/it] 21%|██ | 3941/18688 [4:37:19<16:56:45, 4.14s/it] 21%|██ | 3942/18688 [4:37:23<17:57:01, 4.38s/it] 21%|██ | 3943/18688 [4:37:28<17:33:10, 4.29s/it] 21%|██ | 3944/18688 [4:37:32<17:34:57, 4.29s/it] 21%|██ | 3945/18688 [4:37:36<17:46:36, 4.34s/it] 21%|██ | 3946/18688 [4:37:41<18:17:00, 4.46s/it] 21%|██ | 3947/18688 [4:37:45<17:49:23, 4.35s/it] 21%|██ | 3948/18688 [4:37:50<17:59:27, 4.39s/it] 21%|██ | 3949/18688 [4:37:54<17:41:33, 4.32s/it] 21%|██ | 3950/18688 [4:37:58<17:11:38, 4.20s/it] {'loss': '1.018', 'grad_norm': '1.74', 'learning_rate': '0.0002187', 'epoch': '0.2114', 'num_input_tokens_seen': 13265784, 'train_runtime': '1.668e+04', 'train_tokens_per_second': '795.4'}
21%|██ | 3950/18688 [4:37:58<17:11:38, 4.20s/it] 21%|██ | 3951/18688 [4:38:02<17:06:43, 4.18s/it] 21%|██ | 3952/18688 [4:38:07<18:54:12, 4.62s/it] 21%|██ | 3953/18688 [4:38:12<18:35:15, 4.54s/it] 21%|██ | 3954/18688 [4:38:16<18:33:49, 4.54s/it] 21%|██ | 3955/18688 [4:38:21<18:15:39, 4.46s/it] 21%|██ | 3956/18688 [4:38:25<17:42:24, 4.33s/it] 21%|██ | 3957/18688 [4:38:28<17:03:18, 4.17s/it] 21%|██ | 3958/18688 [4:38:33<17:28:05, 4.27s/it] 21%|██ | 3959/18688 [4:38:37<17:16:25, 4.22s/it] 21%|██ | 3960/18688 [4:38:41<17:01:18, 4.16s/it] {'loss': '1.054', 'grad_norm': '2.249', 'learning_rate': '0.0002186', 'epoch': '0.2119', 'num_input_tokens_seen': 13299808, 'train_runtime': '1.672e+04', 'train_tokens_per_second': '795.4'}
21%|██ | 3960/18688 [4:38:41<17:01:18, 4.16s/it] 21%|██ | 3961/18688 [4:38:46<17:57:18, 4.39s/it] 21%|██ | 3962/18688 [4:38:50<17:47:56, 4.35s/it] 21%|██ | 3963/18688 [4:38:55<17:45:39, 4.34s/it] 21%|██ | 3964/18688 [4:38:59<18:02:10, 4.41s/it] 21%|██ | 3965/18688 [4:39:03<17:48:18, 4.35s/it] 21%|██ | 3966/18688 [4:39:08<18:26:39, 4.51s/it] 21%|██ | 3967/18688 [4:39:12<17:19:29, 4.24s/it] 21%|██ | 3968/18688 [4:39:15<16:26:17, 4.02s/it] 21%|██ | 3969/18688 [4:39:19<16:29:24, 4.03s/it] 21%|██ | 3970/18688 [4:39:24<16:50:56, 4.12s/it] {'loss': '0.9788', 'grad_norm': '2.288', 'learning_rate': '0.0002185', 'epoch': '0.2124', 'num_input_tokens_seen': 13334904, 'train_runtime': '1.676e+04', 'train_tokens_per_second': '795.4'}
21%|██ | 3970/18688 [4:39:24<16:50:56, 4.12s/it] 21%|██ | 3971/18688 [4:39:28<16:52:16, 4.13s/it] 21%|██▏ | 3972/18688 [4:39:33<18:36:01, 4.55s/it] 21%|██▏ | 3973/18688 [4:39:38<18:01:11, 4.41s/it] 21%|██▏ | 3974/18688 [4:39:41<17:04:58, 4.18s/it] 21%|██▏ | 3975/18688 [4:39:45<17:04:03, 4.18s/it] 21%|██▏ | 3976/18688 [4:39:49<16:10:24, 3.96s/it] 21%|██▏ | 3977/18688 [4:39:53<16:40:43, 4.08s/it] 21%|██▏ | 3978/18688 [4:39:57<16:09:47, 3.96s/it] 21%|██▏ | 3979/18688 [4:40:03<18:16:11, 4.47s/it] 21%|██▏ | 3980/18688 [4:40:06<17:38:48, 4.32s/it] {'loss': '1.028', 'grad_norm': '2.617', 'learning_rate': '0.0002184', 'epoch': '0.213', 'num_input_tokens_seen': 13370056, 'train_runtime': '1.681e+04', 'train_tokens_per_second': '795.5'}
21%|██▏ | 3980/18688 [4:40:06<17:38:48, 4.32s/it] 21%|██▏ | 3981/18688 [4:40:10<16:33:10, 4.05s/it] 21%|██▏ | 3982/18688 [4:40:14<16:27:43, 4.03s/it] 21%|██▏ | 3983/18688 [4:40:18<16:14:32, 3.98s/it] 21%|██▏ | 3984/18688 [4:40:22<16:28:34, 4.03s/it] 21%|██▏ | 3985/18688 [4:40:27<17:11:56, 4.21s/it] 21%|██▏ | 3986/18688 [4:40:31<17:02:13, 4.17s/it] 21%|██▏ | 3987/18688 [4:40:34<16:09:17, 3.96s/it] 21%|██▏ | 3988/18688 [4:40:38<16:20:03, 4.00s/it] 21%|██▏ | 3989/18688 [4:40:42<16:35:52, 4.07s/it] 21%|██▏ | 3990/18688 [4:40:46<15:57:35, 3.91s/it] {'loss': '1.119', 'grad_norm': '1.901', 'learning_rate': '0.0002183', 'epoch': '0.2135', 'num_input_tokens_seen': 13401352, 'train_runtime': '1.685e+04', 'train_tokens_per_second': '795.5'}
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21%|██▏ | 4000/18688 [4:41:26<16:48:22, 4.12s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
0%| | 3/1000 [00:00<00:37, 26.38it/s]
1%| | 6/1000 [00:00<00:53, 18.44it/s]
1%| | 8/1000 [00:00<00:55, 17.72it/s]
1%| | 10/1000 [00:00<00:59, 16.75it/s]
1%| | 12/1000 [00:00<01:00, 16.41it/s]
1%|▏ | 14/1000 [00:00<01:01, 15.91it/s]
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{'eval_loss': '1.032', 'eval_runtime': '82.9', 'eval_samples_per_second': '12.06', 'eval_steps_per_second': '12.06', 'epoch': '0.214', 'num_input_tokens_seen': 13433360}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00023/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
}
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***** 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)
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{'eval_loss': '1.035', 'eval_runtime': '82.87', 'eval_samples_per_second': '12.07', 'eval_steps_per_second': '12.07', 'epoch': '0.2676', 'num_input_tokens_seen': 16794000}
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32%|███▏ | 5980/18688 [7:01:06<14:42:51, 4.17s/it] 32%|███▏ | 5981/18688 [7:01:09<14:10:18, 4.01s/it] 32%|███▏ | 5982/18688 [7:01:13<13:46:53, 3.90s/it] 32%|███▏ | 5983/18688 [7:01:17<13:51:52, 3.93s/it] 32%|███▏ | 5984/18688 [7:01:21<13:59:11, 3.96s/it] 32%|███▏ | 5985/18688 [7:01:26<15:24:26, 4.37s/it] 32%|███▏ | 5986/18688 [7:01:30<14:36:45, 4.14s/it] 32%|███▏ | 5987/18688 [7:01:34<14:28:26, 4.10s/it] 32%|███▏ | 5988/18688 [7:01:38<14:41:27, 4.16s/it] 32%|███▏ | 5989/18688 [7:01:42<14:48:34, 4.20s/it] 32%|███▏ | 5990/18688 [7:01:46<14:09:38, 4.01s/it] {'loss': '1.008', 'grad_norm': '1.97', 'learning_rate': '0.0001934', 'epoch': '0.3205', 'num_input_tokens_seen': 20106224, 'train_runtime': '2.531e+04', 'train_tokens_per_second': '794.5'}
32%|███▏ | 5990/18688 [7:01:46<14:09:38, 4.01s/it] 32%|███▏ | 5991/18688 [7:01:50<13:48:53, 3.92s/it] 32%|███▏ | 5992/18688 [7:01:54<13:55:27, 3.95s/it] 32%|███▏ | 5993/18688 [7:01:58<14:17:21, 4.05s/it] 32%|███▏ | 5994/18688 [7:02:03<14:50:07, 4.21s/it] 32%|███▏ | 5995/18688 [7:02:06<13:59:21, 3.97s/it] 32%|███▏ | 5996/18688 [7:02:10<14:31:03, 4.12s/it] 32%|███▏ | 5997/18688 [7:02:14<14:22:01, 4.08s/it] 32%|███▏ | 5998/18688 [7:02:19<14:34:45, 4.14s/it] 32%|███▏ | 5999/18688 [7:02:23<14:26:17, 4.10s/it] 32%|███▏ | 6000/18688 [7:02:27<14:57:46, 4.25s/it] {'loss': '1.068', 'grad_norm': '1.876', 'learning_rate': '0.0001933', 'epoch': '0.3211', 'num_input_tokens_seen': 20140120, 'train_runtime': '2.535e+04', 'train_tokens_per_second': '794.5'}
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***** Running Evaluation *****
Num examples = 1000
Batch size = 1
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{'eval_loss': '1.025', 'eval_runtime': '82.7', 'eval_samples_per_second': '12.09', 'eval_steps_per_second': '12.09', 'epoch': '0.3211', 'num_input_tokens_seen': 20140120}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00023/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
}
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***** 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)
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{'eval_loss': '1.016', 'eval_runtime': '82.82', 'eval_samples_per_second': '12.07', 'eval_steps_per_second': '12.07', 'epoch': '0.3746', 'num_input_tokens_seen': 23480632}
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42%|████▏ | 7940/18688 [9:19:28<12:33:56, 4.21s/it] 42%|████▏ | 7941/18688 [9:19:32<13:03:02, 4.37s/it] 42%|████▏ | 7942/18688 [9:19:37<13:14:28, 4.44s/it] 43%|████▎ | 7943/18688 [9:19:41<12:48:46, 4.29s/it] 43%|████▎ | 7944/18688 [9:19:46<13:14:54, 4.44s/it] 43%|████▎ | 7945/18688 [9:19:49<12:35:12, 4.22s/it] 43%|████▎ | 7946/18688 [9:19:54<12:55:56, 4.33s/it] 43%|████▎ | 7947/18688 [9:19:59<13:07:15, 4.40s/it] 43%|████▎ | 7948/18688 [9:20:03<13:01:01, 4.36s/it] 43%|████▎ | 7949/18688 [9:20:07<12:52:51, 4.32s/it] 43%|████▎ | 7950/18688 [9:20:12<13:48:25, 4.63s/it] {'loss': '0.9925', 'grad_norm': '1.869', 'learning_rate': '0.0001589', 'epoch': '0.4254', 'num_input_tokens_seen': 26698152, 'train_runtime': '3.361e+04', 'train_tokens_per_second': '794.3'}
43%|████▎ | 7950/18688 [9:20:12<13:48:25, 4.63s/it] 43%|████▎ | 7951/18688 [9:20:17<13:55:02, 4.67s/it] 43%|████▎ | 7952/18688 [9:20:22<13:58:43, 4.69s/it] 43%|████▎ | 7953/18688 [9:20:26<13:26:27, 4.51s/it] 43%|████▎ | 7954/18688 [9:20:30<12:49:35, 4.30s/it] 43%|████▎ | 7955/18688 [9:20:34<12:51:08, 4.31s/it] 43%|████▎ | 7956/18688 [9:20:38<12:38:34, 4.24s/it] 43%|████▎ | 7957/18688 [9:20:43<12:40:44, 4.25s/it] 43%|████▎ | 7958/18688 [9:20:48<13:45:53, 4.62s/it] 43%|████▎ | 7959/18688 [9:20:52<13:20:47, 4.48s/it] 43%|████▎ | 7960/18688 [9:20:58<14:54:18, 5.00s/it] {'loss': '0.9793', 'grad_norm': '2.379', 'learning_rate': '0.0001587', 'epoch': '0.426', 'num_input_tokens_seen': 26732688, 'train_runtime': '3.366e+04', 'train_tokens_per_second': '794.2'}
43%|████▎ | 7960/18688 [9:20:58<14:54:18, 5.00s/it] 43%|████▎ | 7961/18688 [9:21:02<14:05:45, 4.73s/it] 43%|████▎ | 7962/18688 [9:21:06<13:21:18, 4.48s/it] 43%|████▎ | 7963/18688 [9:21:11<13:27:46, 4.52s/it] 43%|████▎ | 7964/18688 [9:21:15<12:56:05, 4.34s/it] 43%|████▎ | 7965/18688 [9:21:19<12:38:49, 4.25s/it] 43%|████▎ | 7966/18688 [9:21:23<12:40:52, 4.26s/it] 43%|████▎ | 7967/18688 [9:21:27<12:27:40, 4.18s/it] 43%|████▎ | 7968/18688 [9:21:31<12:13:59, 4.11s/it] 43%|████▎ | 7969/18688 [9:21:36<12:56:50, 4.35s/it] 43%|████▎ | 7970/18688 [9:21:40<12:53:31, 4.33s/it] {'loss': '1.044', 'grad_norm': '2.156', 'learning_rate': '0.0001586', 'epoch': '0.4265', 'num_input_tokens_seen': 26762544, 'train_runtime': '3.37e+04', 'train_tokens_per_second': '794.1'}
43%|████▎ | 7970/18688 [9:21:40<12:53:31, 4.33s/it] 43%|████▎ | 7971/18688 [9:21:44<12:30:12, 4.20s/it] 43%|████▎ | 7972/18688 [9:21:48<12:01:38, 4.04s/it] 43%|████▎ | 7973/18688 [9:21:52<11:41:44, 3.93s/it] 43%|████▎ | 7974/18688 [9:21:56<11:46:47, 3.96s/it] 43%|████▎ | 7975/18688 [9:21:59<11:24:20, 3.83s/it] 43%|████▎ | 7976/18688 [9:22:04<11:58:42, 4.03s/it] 43%|████▎ | 7977/18688 [9:22:08<12:19:57, 4.15s/it] 43%|████▎ | 7978/18688 [9:22:13<13:17:10, 4.47s/it] 43%|████▎ | 7979/18688 [9:22:18<13:08:43, 4.42s/it] 43%|████▎ | 7980/18688 [9:22:21<12:40:29, 4.26s/it] {'loss': '1.049', 'grad_norm': '2.222', 'learning_rate': '0.0001584', 'epoch': '0.427', 'num_input_tokens_seen': 26796120, 'train_runtime': '3.374e+04', 'train_tokens_per_second': '794.1'}
43%|████▎ | 7980/18688 [9:22:21<12:40:29, 4.26s/it] 43%|████▎ | 7981/18688 [9:22:27<13:39:35, 4.59s/it] 43%|████▎ | 7982/18688 [9:22:30<12:47:39, 4.30s/it] 43%|████▎ | 7983/18688 [9:22:35<13:00:48, 4.38s/it] 43%|████▎ | 7984/18688 [9:22:39<12:48:43, 4.31s/it] 43%|████▎ | 7985/18688 [9:22:44<13:25:24, 4.52s/it] 43%|████▎ | 7986/18688 [9:22:48<13:04:30, 4.40s/it] 43%|████▎ | 7987/18688 [9:22:52<12:23:58, 4.17s/it] 43%|████▎ | 7988/18688 [9:22:56<12:32:01, 4.22s/it] 43%|████▎ | 7989/18688 [9:23:01<12:46:29, 4.30s/it] 43%|████▎ | 7990/18688 [9:23:05<12:32:18, 4.22s/it] {'loss': '1.012', 'grad_norm': '2.39', 'learning_rate': '0.0001582', 'epoch': '0.4276', 'num_input_tokens_seen': 26828872, 'train_runtime': '3.379e+04', 'train_tokens_per_second': '794.1'}
43%|████▎ | 7990/18688 [9:23:05<12:32:18, 4.22s/it] 43%|████▎ | 7991/18688 [9:23:09<12:52:33, 4.33s/it] 43%|████▎ | 7992/18688 [9:23:14<13:30:16, 4.55s/it] 43%|████▎ | 7993/18688 [9:23:18<12:37:06, 4.25s/it] 43%|████▎ | 7994/18688 [9:23:22<12:30:33, 4.21s/it] 43%|████▎ | 7995/18688 [9:23:26<11:51:10, 3.99s/it] 43%|████▎ | 7996/18688 [9:23:30<11:50:17, 3.99s/it] 43%|████▎ | 7997/18688 [9:23:34<12:24:29, 4.18s/it] 43%|████▎ | 7998/18688 [9:23:38<12:02:53, 4.06s/it] 43%|████▎ | 7999/18688 [9:23:42<11:48:38, 3.98s/it] 43%|████▎ | 8000/18688 [9:23:45<11:20:39, 3.82s/it] {'loss': '1.035', 'grad_norm': '2.014', 'learning_rate': '0.000158', 'epoch': '0.4281', 'num_input_tokens_seen': 26861912, 'train_runtime': '3.383e+04', 'train_tokens_per_second': '794.1'}
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***** Running Evaluation *****
Num examples = 1000
Batch size = 1
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{'eval_loss': '1.011', 'eval_runtime': '82.66', 'eval_samples_per_second': '12.1', 'eval_steps_per_second': '12.1', 'epoch': '0.4281', 'num_input_tokens_seen': 26861912}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00023/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
}
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***** 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)
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{'eval_loss': '0.9934', 'eval_runtime': '82.82', 'eval_samples_per_second': '12.07', 'eval_steps_per_second': '12.07', 'epoch': '0.4816', 'num_input_tokens_seen': 30248528}
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53%|█████▎ | 9880/18688 [11:37:39<10:15:14, 4.19s/it] 53%|█████▎ | 9881/18688 [11:37:43<10:13:30, 4.18s/it] 53%|█████▎ | 9882/18688 [11:37:48<10:46:11, 4.40s/it] 53%|█████▎ | 9883/18688 [11:37:51<10:05:51, 4.13s/it] 53%|█████▎ | 9884/18688 [11:37:55<9:49:39, 4.02s/it] 53%|█████▎ | 9885/18688 [11:37:59<9:50:22, 4.02s/it] 53%|█████▎ | 9886/18688 [11:38:04<10:32:26, 4.31s/it] 53%|█████▎ | 9887/18688 [11:38:08<10:18:16, 4.22s/it] 53%|█████▎ | 9888/18688 [11:38:13<10:52:52, 4.45s/it] 53%|█████▎ | 9889/18688 [11:38:17<10:13:27, 4.18s/it] 53%|█████▎ | 9890/18688 [11:38:21<10:14:17, 4.19s/it] {'loss': '0.9782', 'grad_norm': '1.818', 'learning_rate': '0.0001193', 'epoch': '0.5292', 'num_input_tokens_seen': 33267288, 'train_runtime': '4.19e+04', 'train_tokens_per_second': '793.9'}
53%|█████▎ | 9890/18688 [11:38:21<10:14:17, 4.19s/it] 53%|█████▎ | 9891/18688 [11:38:25<10:19:55, 4.23s/it] 53%|█████▎ | 9892/18688 [11:38:30<11:01:15, 4.51s/it] 53%|█████▎ | 9893/18688 [11:38:35<10:46:01, 4.41s/it] 53%|█████▎ | 9894/18688 [11:38:39<10:27:22, 4.28s/it] 53%|█████▎ | 9895/18688 [11:38:43<10:19:53, 4.23s/it] 53%|█████▎ | 9896/18688 [11:38:47<10:04:07, 4.12s/it] 53%|█████▎ | 9897/18688 [11:38:51<10:33:02, 4.32s/it] 53%|█████▎ | 9898/18688 [11:38:56<10:43:26, 4.39s/it] 53%|█████▎ | 9899/18688 [11:39:00<10:29:35, 4.30s/it] 53%|█████▎ | 9900/18688 [11:39:04<10:17:39, 4.22s/it] {'loss': '1.04', 'grad_norm': '2.035', 'learning_rate': '0.0001191', 'epoch': '0.5298', 'num_input_tokens_seen': 33301776, 'train_runtime': '4.194e+04', 'train_tokens_per_second': '793.9'}
53%|█████▎ | 9900/18688 [11:39:04<10:17:39, 4.22s/it] 53%|█████▎ | 9901/18688 [11:39:09<10:30:19, 4.30s/it] 53%|█████▎ | 9902/18688 [11:39:13<10:56:43, 4.48s/it] 53%|█████▎ | 9903/18688 [11:39:17<10:36:55, 4.35s/it] 53%|█████▎ | 9904/18688 [11:39:21<10:12:29, 4.18s/it] 53%|█████▎ | 9905/18688 [11:39:26<10:20:00, 4.24s/it] 53%|█████▎ | 9906/18688 [11:39:30<10:09:47, 4.17s/it] 53%|█████▎ | 9907/18688 [11:39:34<10:07:53, 4.15s/it] 53%|█████▎ | 9908/18688 [11:39:38<10:23:48, 4.26s/it] 53%|█████▎ | 9909/18688 [11:39:42<10:04:28, 4.13s/it] 53%|█████▎ | 9910/18688 [11:39:47<10:36:02, 4.35s/it] {'loss': '0.9867', 'grad_norm': '1.93', 'learning_rate': '0.0001189', 'epoch': '0.5303', 'num_input_tokens_seen': 33337120, 'train_runtime': '4.199e+04', 'train_tokens_per_second': '794'}
53%|█████▎ | 9910/18688 [11:39:47<10:36:02, 4.35s/it] 53%|█████▎ | 9911/18688 [11:39:50<9:57:44, 4.09s/it] 53%|█████▎ | 9912/18688 [11:39:55<10:08:02, 4.16s/it] 53%|█████▎ | 9913/18688 [11:39:58<9:33:23, 3.92s/it] 53%|█████▎ | 9914/18688 [11:40:03<10:10:12, 4.17s/it] 53%|█████▎ | 9915/18688 [11:40:08<10:38:36, 4.37s/it] 53%|█████▎ | 9916/18688 [11:40:13<11:11:45, 4.59s/it] 53%|█████▎ | 9917/18688 [11:40:17<11:02:30, 4.53s/it] 53%|█████▎ | 9918/18688 [11:40:21<10:29:53, 4.31s/it] 53%|█████▎ | 9919/18688 [11:40:25<10:27:59, 4.30s/it] 53%|█████▎ | 9920/18688 [11:40:29<10:18:00, 4.23s/it] {'loss': '0.9666', 'grad_norm': '2.144', 'learning_rate': '0.0001187', 'epoch': '0.5308', 'num_input_tokens_seen': 33371000, 'train_runtime': '4.203e+04', 'train_tokens_per_second': '794'}
53%|█████▎ | 9920/18688 [11:40:29<10:18:00, 4.23s/it] 53%|█████▎ | 9921/18688 [11:40:35<11:00:52, 4.52s/it] 53%|█████▎ | 9922/18688 [11:40:39<11:03:41, 4.54s/it] 53%|█████▎ | 9923/18688 [11:40:43<10:22:15, 4.26s/it] 53%|█████▎ | 9924/18688 [11:40:47<10:20:11, 4.25s/it] 53%|█████▎ | 9925/18688 [11:40:51<10:26:31, 4.29s/it] 53%|█████▎ | 9926/18688 [11:40:55<9:54:07, 4.07s/it] 53%|█████▎ | 9927/18688 [11:40:59<9:38:41, 3.96s/it] 53%|█████▎ | 9928/18688 [11:41:02<9:26:28, 3.88s/it] 53%|█████▎ | 9929/18688 [11:41:06<9:31:25, 3.91s/it] 53%|█████▎ | 9930/18688 [11:41:10<9:25:35, 3.87s/it] {'loss': '1.003', 'grad_norm': '1.844', 'learning_rate': '0.0001185', 'epoch': '0.5314', 'num_input_tokens_seen': 33404656, 'train_runtime': '4.207e+04', 'train_tokens_per_second': '794'}
53%|█████▎ | 9930/18688 [11:41:10<9:25:35, 3.87s/it] 53%|█████▎ | 9931/18688 [11:41:14<9:06:24, 3.74s/it] 53%|█████▎ | 9932/18688 [11:41:17<9:06:39, 3.75s/it] 53%|█████▎ | 9933/18688 [11:41:21<9:00:05, 3.70s/it] 53%|█████▎ | 9934/18688 [11:41:25<9:28:22, 3.90s/it] 53%|█████▎ | 9935/18688 [11:41:30<10:27:44, 4.30s/it] 53%|█████▎ | 9936/18688 [11:41:34<9:50:28, 4.05s/it] 53%|█████▎ | 9937/18688 [11:41:38<9:58:05, 4.10s/it] 53%|█████▎ | 9938/18688 [11:41:42<9:40:13, 3.98s/it] 53%|█████▎ | 9939/18688 [11:41:46<9:39:10, 3.97s/it] 53%|█████▎ | 9940/18688 [11:41:50<10:00:30, 4.12s/it] {'loss': '0.9691', 'grad_norm': '2.915', 'learning_rate': '0.0001183', 'epoch': '0.5319', 'num_input_tokens_seen': 33439232, 'train_runtime': '4.211e+04', 'train_tokens_per_second': '794.1'}
53%|█████▎ | 9940/18688 [11:41:50<10:00:30, 4.12s/it] 53%|█████▎ | 9941/18688 [11:41:54<9:32:49, 3.93s/it] 53%|█████▎ | 9942/18688 [11:41:58<9:54:12, 4.08s/it] 53%|█████▎ | 9943/18688 [11:42:02<9:29:17, 3.91s/it] 53%|█████▎ | 9944/18688 [11:42:05<9:15:22, 3.81s/it] 53%|█████▎ | 9945/18688 [11:42:09<9:00:05, 3.71s/it] 53%|█████▎ | 9946/18688 [11:42:13<9:12:33, 3.79s/it] 53%|█████▎ | 9947/18688 [11:42:16<9:01:46, 3.72s/it] 53%|█████▎ | 9948/18688 [11:42:20<9:19:17, 3.84s/it] 53%|█████▎ | 9949/18688 [11:42:25<9:52:42, 4.07s/it] 53%|█████▎ | 9950/18688 [11:42:29<9:31:12, 3.92s/it] {'loss': '0.9502', 'grad_norm': '2.209', 'learning_rate': '0.000118', 'epoch': '0.5324', 'num_input_tokens_seen': 33470712, 'train_runtime': '4.215e+04', 'train_tokens_per_second': '794.1'}
53%|█████▎ | 9950/18688 [11:42:29<9:31:12, 3.92s/it] 53%|█████▎ | 9951/18688 [11:42:32<9:21:26, 3.86s/it] 53%|█████▎ | 9952/18688 [11:42:37<9:40:02, 3.98s/it] 53%|█████▎ | 9953/18688 [11:42:40<9:31:42, 3.93s/it] 53%|█████▎ | 9954/18688 [11:42:44<9:36:34, 3.96s/it] 53%|█████▎ | 9955/18688 [11:42:49<9:57:56, 4.11s/it] 53%|█████▎ | 9956/18688 [11:42:52<9:33:56, 3.94s/it] 53%|█████▎ | 9957/18688 [11:42:56<9:19:13, 3.84s/it] 53%|█████▎ | 9958/18688 [11:43:00<9:38:18, 3.97s/it] 53%|█████▎ | 9959/18688 [11:43:04<9:37:30, 3.97s/it] 53%|█████▎ | 9960/18688 [11:43:08<9:49:24, 4.05s/it] {'loss': '1.033', 'grad_norm': '2.335', 'learning_rate': '0.0001178', 'epoch': '0.533', 'num_input_tokens_seen': 33504040, 'train_runtime': '4.219e+04', 'train_tokens_per_second': '794.1'}
53%|█████▎ | 9960/18688 [11:43:08<9:49:24, 4.05s/it] 53%|█████▎ | 9961/18688 [11:43:12<9:38:18, 3.98s/it] 53%|█████▎ | 9962/18688 [11:43:17<9:50:55, 4.06s/it] 53%|█████▎ | 9963/18688 [11:43:21<9:47:21, 4.04s/it] 53%|█████▎ | 9964/18688 [11:43:25<9:51:45, 4.07s/it] 53%|█████▎ | 9965/18688 [11:43:29<10:01:19, 4.14s/it] 53%|█████▎ | 9966/18688 [11:43:34<10:44:08, 4.43s/it] 53%|█████▎ | 9967/18688 [11:43:39<10:48:55, 4.46s/it] 53%|█████▎ | 9968/18688 [11:43:43<10:39:56, 4.40s/it] 53%|█████▎ | 9969/18688 [11:43:48<10:54:42, 4.51s/it] 53%|█████▎ | 9970/18688 [11:43:52<11:10:14, 4.61s/it] {'loss': '1.075', 'grad_norm': '1.784', 'learning_rate': '0.0001176', 'epoch': '0.5335', 'num_input_tokens_seen': 33536184, 'train_runtime': '4.223e+04', 'train_tokens_per_second': '794.1'}
53%|█████▎ | 9970/18688 [11:43:52<11:10:14, 4.61s/it] 53%|█████▎ | 9971/18688 [11:43:56<10:41:37, 4.42s/it] 53%|█████▎ | 9972/18688 [11:44:01<10:29:14, 4.33s/it] 53%|█████▎ | 9973/18688 [11:44:05<10:36:51, 4.38s/it] 53%|█████▎ | 9974/18688 [11:44:10<10:42:50, 4.43s/it] 53%|█████▎ | 9975/18688 [11:44:14<10:50:57, 4.48s/it] 53%|█████▎ | 9976/18688 [11:44:19<11:08:57, 4.61s/it] 53%|█████▎ | 9977/18688 [11:44:23<10:38:05, 4.40s/it] 53%|█████▎ | 9978/18688 [11:44:27<10:02:25, 4.15s/it] 53%|█████▎ | 9979/18688 [11:44:31<10:13:24, 4.23s/it] 53%|█████▎ | 9980/18688 [11:44:36<10:59:58, 4.55s/it] {'loss': '0.9697', 'grad_norm': '2.01', 'learning_rate': '0.0001174', 'epoch': '0.534', 'num_input_tokens_seen': 33573168, 'train_runtime': '4.228e+04', 'train_tokens_per_second': '794.1'}
53%|█████▎ | 9980/18688 [11:44:36<10:59:58, 4.55s/it] 53%|█████▎ | 9981/18688 [11:44:40<10:38:08, 4.40s/it] 53%|█████▎ | 9982/18688 [11:44:44<9:56:55, 4.11s/it] 53%|█████▎ | 9983/18688 [11:44:48<9:53:31, 4.09s/it] 53%|█████▎ | 9984/18688 [11:44:53<10:32:18, 4.36s/it] 53%|█████▎ | 9985/18688 [11:44:58<10:52:32, 4.50s/it] 53%|█████▎ | 9986/18688 [11:45:01<10:08:59, 4.20s/it] 53%|█████▎ | 9987/18688 [11:45:05<9:42:25, 4.02s/it] 53%|█████▎ | 9988/18688 [11:45:10<10:20:35, 4.28s/it] 53%|█████▎ | 9989/18688 [11:45:14<10:04:55, 4.17s/it] 53%|█████▎ | 9990/18688 [11:45:18<10:01:57, 4.15s/it] {'loss': '0.9725', 'grad_norm': '1.845', 'learning_rate': '0.0001172', 'epoch': '0.5346', 'num_input_tokens_seen': 33608320, 'train_runtime': '4.232e+04', 'train_tokens_per_second': '794.2'}
53%|█████▎ | 9990/18688 [11:45:18<10:01:57, 4.15s/it] 53%|█████▎ | 9991/18688 [11:45:22<10:14:55, 4.24s/it] 53%|█████▎ | 9992/18688 [11:45:26<9:45:58, 4.04s/it] 53%|█████▎ | 9993/18688 [11:45:30<9:47:20, 4.05s/it] 53%|█████▎ | 9994/18688 [11:45:34<9:42:35, 4.02s/it] 53%|█████▎ | 9995/18688 [11:45:39<10:18:30, 4.27s/it] 53%|█████▎ | 9996/18688 [11:45:44<10:50:49, 4.49s/it] 53%|█████▎ | 9997/18688 [11:45:47<10:12:06, 4.23s/it] 53%|█████▎ | 9998/18688 [11:45:51<9:53:34, 4.10s/it] 54%|█████▎ | 9999/18688 [11:45:58<11:39:33, 4.83s/it] 54%|█████▎ | 10000/18688 [11:46:02<11:09:42, 4.63s/it] {'loss': '1.095', 'grad_norm': '2.441', 'learning_rate': '0.000117', 'epoch': '0.5351', 'num_input_tokens_seen': 33646792, 'train_runtime': '4.236e+04', 'train_tokens_per_second': '794.3'}
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***** Running Evaluation *****
Num examples = 1000
Batch size = 1
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{'eval_loss': '0.9806', 'eval_runtime': '82.87', 'eval_samples_per_second': '12.07', 'eval_steps_per_second': '12.07', 'epoch': '0.5351', 'num_input_tokens_seen': 33646792}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00023/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
}
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***** 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)
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{'eval_loss': '0.9636', 'eval_runtime': '82.94', 'eval_samples_per_second': '12.06', 'eval_steps_per_second': '12.06', 'epoch': '0.5886', 'num_input_tokens_seen': 37004632}
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64%|██████▎ | 11890/18688 [13:59:17<8:17:46, 4.39s/it] 64%|██████▎ | 11891/18688 [13:59:21<8:05:00, 4.28s/it] 64%|██████▎ | 11892/18688 [13:59:25<7:58:06, 4.22s/it] 64%|██████▎ | 11893/18688 [13:59:30<8:22:50, 4.44s/it] 64%|██████▎ | 11894/18688 [13:59:34<8:18:20, 4.40s/it] 64%|██████▎ | 11895/18688 [13:59:38<7:50:54, 4.16s/it] 64%|██████▎ | 11896/18688 [13:59:41<7:25:35, 3.94s/it] 64%|██████▎ | 11897/18688 [13:59:45<7:22:23, 3.91s/it] 64%|██████▎ | 11898/18688 [13:59:49<7:25:33, 3.94s/it] 64%|██████▎ | 11899/18688 [13:59:53<7:19:12, 3.88s/it] 64%|██████▎ | 11900/18688 [13:59:57<7:28:31, 3.96s/it] {'loss': '0.9777', 'grad_norm': '1.819', 'learning_rate': '7.774e-05', 'epoch': '0.6368', 'num_input_tokens_seen': 40015912, 'train_runtime': '5.04e+04', 'train_tokens_per_second': '794'}
64%|██████▎ | 11900/18688 [13:59:57<7:28:31, 3.96s/it] 64%|██████▎ | 11901/18688 [14:00:01<7:12:59, 3.83s/it] 64%|██████▎ | 11902/18688 [14:00:05<7:26:46, 3.95s/it] 64%|██████▎ | 11903/18688 [14:00:09<7:48:01, 4.14s/it] 64%|██████▎ | 11904/18688 [14:00:14<8:13:40, 4.37s/it] 64%|██████▎ | 11905/18688 [14:00:18<8:02:08, 4.26s/it] 64%|██████▎ | 11906/18688 [14:00:22<7:45:49, 4.12s/it] 64%|██████▎ | 11907/18688 [14:00:26<7:44:50, 4.11s/it] 64%|██████▎ | 11908/18688 [14:00:31<8:15:28, 4.38s/it] 64%|██████▎ | 11909/18688 [14:00:35<8:04:35, 4.29s/it] 64%|██████▎ | 11910/18688 [14:00:39<7:52:55, 4.19s/it] {'loss': '0.9761', 'grad_norm': '2.063', 'learning_rate': '7.755e-05', 'epoch': '0.6373', 'num_input_tokens_seen': 40049264, 'train_runtime': '5.044e+04', 'train_tokens_per_second': '794'}
64%|██████▎ | 11910/18688 [14:00:39<7:52:55, 4.19s/it] 64%|██████▎ | 11911/18688 [14:00:44<8:02:20, 4.27s/it] 64%|██████▎ | 11912/18688 [14:00:48<7:57:36, 4.23s/it] 64%|██████▎ | 11913/18688 [14:00:52<7:48:53, 4.15s/it] 64%|██████▍ | 11914/18688 [14:00:56<7:47:06, 4.14s/it] 64%|██████▍ | 11915/18688 [14:01:01<8:32:31, 4.54s/it] 64%|██████▍ | 11916/18688 [14:01:06<8:20:02, 4.43s/it] 64%|██████▍ | 11917/18688 [14:01:10<8:15:44, 4.39s/it] 64%|██████▍ | 11918/18688 [14:01:15<8:37:57, 4.59s/it] 64%|██████▍ | 11919/18688 [14:01:19<8:34:57, 4.56s/it] 64%|██████▍ | 11920/18688 [14:01:24<8:39:18, 4.60s/it] {'loss': '1.015', 'grad_norm': '1.593', 'learning_rate': '7.735e-05', 'epoch': '0.6379', 'num_input_tokens_seen': 40083792, 'train_runtime': '5.048e+04', 'train_tokens_per_second': '794'}
64%|██████▍ | 11920/18688 [14:01:24<8:39:18, 4.60s/it] 64%|██████▍ | 11921/18688 [14:01:28<8:18:47, 4.42s/it] 64%|██████▍ | 11922/18688 [14:01:32<7:54:03, 4.20s/it] 64%|██████▍ | 11923/18688 [14:01:36<8:04:10, 4.29s/it] 64%|██████▍ | 11924/18688 [14:01:41<8:05:29, 4.31s/it] 64%|██████▍ | 11925/18688 [14:01:45<7:55:06, 4.22s/it] 64%|██████▍ | 11926/18688 [14:01:49<7:55:23, 4.22s/it] 64%|██████▍ | 11927/18688 [14:01:54<8:26:45, 4.50s/it] 64%|██████▍ | 11928/18688 [14:01:59<8:33:17, 4.56s/it] 64%|██████▍ | 11929/18688 [14:02:03<8:09:15, 4.34s/it] 64%|██████▍ | 11930/18688 [14:02:06<7:40:44, 4.09s/it] {'loss': '0.9298', 'grad_norm': '1.912', 'learning_rate': '7.715e-05', 'epoch': '0.6384', 'num_input_tokens_seen': 40116656, 'train_runtime': '5.053e+04', 'train_tokens_per_second': '794'}
64%|██████▍ | 11930/18688 [14:02:06<7:40:44, 4.09s/it] 64%|██████▍ | 11931/18688 [14:02:10<7:42:37, 4.11s/it] 64%|██████▍ | 11932/18688 [14:02:14<7:43:20, 4.11s/it] 64%|██████▍ | 11933/18688 [14:02:19<7:46:28, 4.14s/it] 64%|██████▍ | 11934/18688 [14:02:24<8:31:33, 4.54s/it] 64%|██████▍ | 11935/18688 [14:02:29<8:31:26, 4.54s/it] 64%|██████▍ | 11936/18688 [14:02:34<8:47:36, 4.69s/it] 64%|██████▍ | 11937/18688 [14:02:38<8:47:48, 4.69s/it] 64%|██████▍ | 11938/18688 [14:02:43<8:38:40, 4.61s/it] 64%|██████▍ | 11939/18688 [14:02:46<8:04:44, 4.31s/it] 64%|██████▍ | 11940/18688 [14:02:51<8:26:06, 4.50s/it] {'loss': '0.9458', 'grad_norm': '1.82', 'learning_rate': '7.695e-05', 'epoch': '0.6389', 'num_input_tokens_seen': 40154472, 'train_runtime': '5.057e+04', 'train_tokens_per_second': '794'}
64%|██████▍ | 11940/18688 [14:02:51<8:26:06, 4.50s/it] 64%|██████▍ | 11941/18688 [14:02:55<8:13:16, 4.39s/it] 64%|██████▍ | 11942/18688 [14:03:00<8:27:07, 4.51s/it] 64%|██████▍ | 11943/18688 [14:03:05<8:22:24, 4.47s/it] 64%|██████▍ | 11944/18688 [14:03:08<7:57:28, 4.25s/it] 64%|██████▍ | 11945/18688 [14:03:12<7:42:44, 4.12s/it] 64%|██████▍ | 11946/18688 [14:03:16<7:25:14, 3.96s/it] 64%|██████▍ | 11947/18688 [14:03:20<7:26:18, 3.97s/it] 64%|██████▍ | 11948/18688 [14:03:24<7:27:53, 3.99s/it] 64%|██████▍ | 11949/18688 [14:03:28<7:41:55, 4.11s/it] 64%|██████▍ | 11950/18688 [14:03:32<7:20:12, 3.92s/it] {'loss': '0.9873', 'grad_norm': '2.009', 'learning_rate': '7.675e-05', 'epoch': '0.6395', 'num_input_tokens_seen': 40187520, 'train_runtime': '5.061e+04', 'train_tokens_per_second': '794'}
64%|██████▍ | 11950/18688 [14:03:32<7:20:12, 3.92s/it] 64%|██████▍ | 11951/18688 [14:03:35<7:05:11, 3.79s/it] 64%|██████▍ | 11952/18688 [14:03:39<7:04:03, 3.78s/it] 64%|██████▍ | 11953/18688 [14:03:43<6:59:28, 3.74s/it] 64%|██████▍ | 11954/18688 [14:03:47<7:18:45, 3.91s/it] 64%|██████▍ | 11955/18688 [14:03:52<8:16:43, 4.43s/it] 64%|██████▍ | 11956/18688 [14:03:56<8:00:42, 4.28s/it] 64%|██████▍ | 11957/18688 [14:04:01<8:25:43, 4.51s/it] 64%|██████▍ | 11958/18688 [14:04:06<8:27:24, 4.52s/it] 64%|██████▍ | 11959/18688 [14:04:10<8:02:36, 4.30s/it] 64%|██████▍ | 11960/18688 [14:04:14<7:58:55, 4.27s/it] {'loss': '0.9464', 'grad_norm': '1.749', 'learning_rate': '7.655e-05', 'epoch': '0.64', 'num_input_tokens_seen': 40222440, 'train_runtime': '5.065e+04', 'train_tokens_per_second': '794.1'}
64%|██████▍ | 11960/18688 [14:04:14<7:58:55, 4.27s/it] 64%|██████▍ | 11961/18688 [14:04:19<8:33:33, 4.58s/it] 64%|██████▍ | 11962/18688 [14:04:24<8:21:39, 4.48s/it] 64%|██████▍ | 11963/18688 [14:04:28<8:25:41, 4.51s/it] 64%|██████▍ | 11964/18688 [14:04:32<8:02:08, 4.30s/it] 64%|██████▍ | 11965/18688 [14:04:36<7:43:14, 4.13s/it] 64%|██████▍ | 11966/18688 [14:04:40<8:05:18, 4.33s/it] 64%|██████▍ | 11967/18688 [14:04:44<7:54:17, 4.23s/it] 64%|██████▍ | 11968/18688 [14:04:49<7:48:05, 4.18s/it] 64%|██████▍ | 11969/18688 [14:04:53<8:05:14, 4.33s/it] 64%|██████▍ | 11970/18688 [14:04:57<7:52:04, 4.22s/it] {'loss': '0.9964', 'grad_norm': '2.147', 'learning_rate': '7.636e-05', 'epoch': '0.6405', 'num_input_tokens_seen': 40259536, 'train_runtime': '5.07e+04', 'train_tokens_per_second': '794.1'}
64%|██████▍ | 11970/18688 [14:04:57<7:52:04, 4.22s/it] 64%|██████▍ | 11971/18688 [14:05:02<8:25:10, 4.51s/it] 64%|██████▍ | 11972/18688 [14:05:06<7:59:30, 4.28s/it] 64%|██████▍ | 11973/18688 [14:05:10<7:47:08, 4.17s/it] 64%|██████▍ | 11974/18688 [14:05:14<7:42:16, 4.13s/it] 64%|██████▍ | 11975/18688 [14:05:18<7:42:27, 4.13s/it] 64%|██████▍ | 11976/18688 [14:05:23<8:10:07, 4.38s/it] 64%|██████▍ | 11977/18688 [14:05:27<7:47:59, 4.18s/it] 64%|██████▍ | 11978/18688 [14:05:31<7:41:37, 4.13s/it] 64%|██████▍ | 11979/18688 [14:05:35<7:41:20, 4.13s/it] 64%|██████▍ | 11980/18688 [14:05:39<7:37:12, 4.09s/it] {'loss': '1.065', 'grad_norm': '2.161', 'learning_rate': '7.616e-05', 'epoch': '0.6411', 'num_input_tokens_seen': 40293704, 'train_runtime': '5.074e+04', 'train_tokens_per_second': '794.1'}
64%|██████▍ | 11980/18688 [14:05:39<7:37:12, 4.09s/it] 64%|██████▍ | 11981/18688 [14:05:43<7:18:54, 3.93s/it] 64%|██████▍ | 11982/18688 [14:05:46<7:17:59, 3.92s/it] 64%|██████▍ | 11983/18688 [14:05:50<7:14:45, 3.89s/it] 64%|██████▍ | 11984/18688 [14:05:54<7:19:13, 3.93s/it] 64%|██████▍ | 11985/18688 [14:05:58<7:23:13, 3.97s/it] 64%|██████▍ | 11986/18688 [14:06:02<7:22:49, 3.96s/it] 64%|██████▍ | 11987/18688 [14:06:06<7:15:58, 3.90s/it] 64%|██████▍ | 11988/18688 [14:06:11<7:39:11, 4.11s/it] 64%|██████▍ | 11989/18688 [14:06:14<7:28:10, 4.01s/it] 64%|██████▍ | 11990/18688 [14:06:19<7:35:19, 4.08s/it] {'loss': '0.9814', 'grad_norm': '1.714', 'learning_rate': '7.596e-05', 'epoch': '0.6416', 'num_input_tokens_seen': 40325872, 'train_runtime': '5.078e+04', 'train_tokens_per_second': '794.1'}
64%|██████▍ | 11990/18688 [14:06:19<7:35:19, 4.08s/it] 64%|██████▍ | 11991/18688 [14:06:23<7:51:39, 4.23s/it] 64%|██████▍ | 11992/18688 [14:06:27<7:50:24, 4.22s/it] 64%|██████▍ | 11993/18688 [14:06:31<7:44:27, 4.16s/it] 64%|██████▍ | 11994/18688 [14:06:35<7:22:58, 3.97s/it] 64%|██████▍ | 11995/18688 [14:06:39<7:09:14, 3.85s/it] 64%|██████▍ | 11996/18688 [14:06:43<7:20:14, 3.95s/it] 64%|██████▍ | 11997/18688 [14:06:47<7:14:21, 3.89s/it] 64%|██████▍ | 11998/18688 [14:06:51<7:44:31, 4.17s/it] 64%|██████▍ | 11999/18688 [14:06:55<7:40:56, 4.13s/it] 64%|██████▍ | 12000/18688 [14:06:59<7:25:03, 3.99s/it] {'loss': '0.9608', 'grad_norm': '1.932', 'learning_rate': '7.576e-05', 'epoch': '0.6421', 'num_input_tokens_seen': 40360224, 'train_runtime': '5.082e+04', 'train_tokens_per_second': '794.2'}
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***** Running Evaluation *****
Num examples = 1000
Batch size = 1
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{'eval_loss': '0.9514', 'eval_runtime': '83.12', 'eval_samples_per_second': '12.03', 'eval_steps_per_second': '12.03', 'epoch': '0.6421', 'num_input_tokens_seen': 40360224}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00023/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
}
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***** 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)
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{'eval_loss': '0.9341', 'eval_runtime': '82.96', 'eval_samples_per_second': '12.05', 'eval_steps_per_second': '12.05', 'epoch': '0.6957', 'num_input_tokens_seen': 43672984}
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72%|███████▏ | 13440/18688 [15:50:33<6:50:45, 4.70s/it] 72%|███████▏ | 13441/18688 [15:50:37<6:25:51, 4.41s/it] 72%|███████▏ | 13442/18688 [15:50:42<6:33:22, 4.50s/it] 72%|███████▏ | 13443/18688 [15:50:46<6:26:04, 4.42s/it] 72%|███████▏ | 13444/18688 [15:50:50<6:10:52, 4.24s/it] 72%|███████▏ | 13445/18688 [15:50:54<6:07:57, 4.21s/it] 72%|███████▏ | 13446/18688 [15:50:58<5:51:41, 4.03s/it] 72%|███████▏ | 13447/18688 [15:51:02<5:59:25, 4.11s/it] 72%|███████▏ | 13448/18688 [15:51:06<6:05:20, 4.18s/it] 72%|███████▏ | 13449/18688 [15:51:11<6:26:11, 4.42s/it] 72%|███████▏ | 13450/18688 [15:51:16<6:21:13, 4.37s/it] {'loss': '1.011', 'grad_norm': '2.392', 'learning_rate': '4.882e-05', 'epoch': '0.7197', 'num_input_tokens_seen': 45188776, 'train_runtime': '5.708e+04', 'train_tokens_per_second': '791.7'}
72%|███████▏ | 13450/18688 [15:51:16<6:21:13, 4.37s/it] 72%|███████▏ | 13451/18688 [15:51:20<6:31:48, 4.49s/it] 72%|███████▏ | 13452/18688 [15:51:25<6:33:11, 4.51s/it] 72%|███████▏ | 13453/18688 [15:51:30<6:36:11, 4.54s/it] 72%|███████▏ | 13454/18688 [15:51:33<6:19:43, 4.35s/it] 72%|███████▏ | 13455/18688 [15:51:37<6:09:32, 4.24s/it] 72%|███████▏ | 13456/18688 [15:51:41<5:59:50, 4.13s/it] 72%|███████▏ | 13457/18688 [15:51:47<6:28:33, 4.46s/it] 72%|███████▏ | 13458/18688 [15:51:51<6:26:45, 4.44s/it] 72%|███████▏ | 13459/18688 [15:51:55<6:10:35, 4.25s/it] 72%|███████▏ | 13460/18688 [15:51:59<6:11:55, 4.27s/it] {'loss': '0.9963', 'grad_norm': '2.01', 'learning_rate': '4.865e-05', 'epoch': '0.7203', 'num_input_tokens_seen': 45225200, 'train_runtime': '5.712e+04', 'train_tokens_per_second': '791.8'}
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74%|███████▍ | 13870/18688 [16:21:00<5:39:42, 4.23s/it] 74%|███████▍ | 13871/18688 [16:21:05<5:49:55, 4.36s/it] 74%|███████▍ | 13872/18688 [16:21:09<5:54:49, 4.42s/it] 74%|███████▍ | 13873/18688 [16:21:13<5:44:13, 4.29s/it] 74%|███████▍ | 13874/18688 [16:21:18<6:04:45, 4.55s/it] 74%|███████▍ | 13875/18688 [16:21:23<5:54:39, 4.42s/it] 74%|███████▍ | 13876/18688 [16:21:27<5:53:11, 4.40s/it] 74%|███████▍ | 13877/18688 [16:21:31<5:54:29, 4.42s/it] 74%|███████▍ | 13878/18688 [16:21:36<5:50:21, 4.37s/it] 74%|███████▍ | 13879/18688 [16:21:41<6:22:20, 4.77s/it] 74%|███████▍ | 13880/18688 [16:21:46<6:30:15, 4.87s/it] {'loss': '1.071', 'grad_norm': '1.628', 'learning_rate': '4.164e-05', 'epoch': '0.7427', 'num_input_tokens_seen': 46648576, 'train_runtime': '5.891e+04', 'train_tokens_per_second': '791.9'}
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74%|███████▍ | 13890/18688 [16:22:25<5:07:46, 3.85s/it] 74%|███████▍ | 13891/18688 [16:22:29<4:58:39, 3.74s/it] 74%|███████▍ | 13892/18688 [16:22:33<4:56:27, 3.71s/it] 74%|███████▍ | 13893/18688 [16:22:37<5:07:20, 3.85s/it] 74%|███████▍ | 13894/18688 [16:22:41<5:20:50, 4.02s/it] 74%|███████▍ | 13895/18688 [16:22:46<5:34:33, 4.19s/it] 74%|███████▍ | 13896/18688 [16:22:50<5:42:31, 4.29s/it] 74%|███████▍ | 13897/18688 [16:22:55<5:44:22, 4.31s/it] 74%|███████▍ | 13898/18688 [16:22:59<5:35:51, 4.21s/it] 74%|███████▍ | 13899/18688 [16:23:03<5:30:30, 4.14s/it] 74%|███████▍ | 13900/18688 [16:23:07<5:44:36, 4.32s/it] {'loss': '0.8837', 'grad_norm': '1.84', 'learning_rate': '4.131e-05', 'epoch': '0.7438', 'num_input_tokens_seen': 46710680, 'train_runtime': '5.899e+04', 'train_tokens_per_second': '791.9'}
74%|███████▍ | 13900/18688 [16:23:07<5:44:36, 4.32s/it] 74%|███████▍ | 13901/18688 [16:23:11<5:40:08, 4.26s/it] 74%|███████▍ | 13902/18688 [16:23:15<5:27:41, 4.11s/it] 74%|███████▍ | 13903/18688 [16:23:19<5:25:29, 4.08s/it] 74%|███████▍ | 13904/18688 [16:23:23<5:29:12, 4.13s/it] 74%|███████▍ | 13905/18688 [16:23:28<5:31:52, 4.16s/it] 74%|███████▍ | 13906/18688 [16:23:32<5:31:12, 4.16s/it] 74%|███████▍ | 13907/18688 [16:23:37<5:59:12, 4.51s/it] 74%|███████▍ | 13908/18688 [16:23:41<5:52:09, 4.42s/it] 74%|███████▍ | 13909/18688 [16:23:45<5:42:22, 4.30s/it] 74%|███████▍ | 13910/18688 [16:23:51<6:02:46, 4.56s/it] {'loss': '0.9525', 'grad_norm': '1.853', 'learning_rate': '4.115e-05', 'epoch': '0.7443', 'num_input_tokens_seen': 46747080, 'train_runtime': '5.903e+04', 'train_tokens_per_second': '791.9'}
74%|███████▍ | 13910/18688 [16:23:51<6:02:46, 4.56s/it] 74%|███████▍ | 13911/18688 [16:23:55<5:55:53, 4.47s/it] 74%|███████▍ | 13912/18688 [16:23:59<5:37:59, 4.25s/it] 74%|███████▍ | 13913/18688 [16:24:04<6:05:20, 4.59s/it] 74%|███████▍ | 13914/18688 [16:24:08<5:41:43, 4.29s/it] 74%|███████▍ | 13915/18688 [16:24:12<5:36:52, 4.23s/it] 74%|███████▍ | 13916/18688 [16:24:16<5:29:36, 4.14s/it] 74%|███████▍ | 13917/18688 [16:24:20<5:30:59, 4.16s/it] 74%|███████▍ | 13918/18688 [16:24:24<5:22:00, 4.05s/it] 74%|███████▍ | 13919/18688 [16:24:27<5:10:11, 3.90s/it] 74%|███████▍ | 13920/18688 [16:24:32<5:29:01, 4.14s/it] {'loss': '0.9198', 'grad_norm': '1.69', 'learning_rate': '4.099e-05', 'epoch': '0.7449', 'num_input_tokens_seen': 46782264, 'train_runtime': '5.907e+04', 'train_tokens_per_second': '791.9'}
74%|███████▍ | 13920/18688 [16:24:32<5:29:01, 4.14s/it] 74%|███████▍ | 13921/18688 [16:24:36<5:38:17, 4.26s/it] 74%|███████▍ | 13922/18688 [16:24:41<5:47:10, 4.37s/it] 75%|███████▍ | 13923/18688 [16:24:45<5:31:04, 4.17s/it] 75%|███████▍ | 13924/18688 [16:24:48<5:16:40, 3.99s/it] 75%|███████▍ | 13925/18688 [16:24:52<5:12:19, 3.93s/it] 75%|███████▍ | 13926/18688 [16:24:57<5:44:00, 4.33s/it] 75%|███████▍ | 13927/18688 [16:25:02<5:42:05, 4.31s/it] 75%|███████▍ | 13928/18688 [16:25:05<5:21:39, 4.05s/it] 75%|███████▍ | 13929/18688 [16:25:09<5:30:45, 4.17s/it] 75%|███████▍ | 13930/18688 [16:25:14<5:33:57, 4.21s/it] {'loss': '0.9036', 'grad_norm': '1.558', 'learning_rate': '4.083e-05', 'epoch': '0.7454', 'num_input_tokens_seen': 46817480, 'train_runtime': '5.911e+04', 'train_tokens_per_second': '792'}
75%|███████▍ | 13930/18688 [16:25:14<5:33:57, 4.21s/it] 75%|███████▍ | 13931/18688 [16:25:18<5:33:38, 4.21s/it] 75%|███████▍ | 13932/18688 [16:25:21<5:16:12, 3.99s/it] 75%|███████▍ | 13933/18688 [16:25:25<5:07:41, 3.88s/it] 75%|███████▍ | 13934/18688 [16:25:29<5:04:40, 3.85s/it] 75%|███████▍ | 13935/18688 [16:25:32<4:56:31, 3.74s/it] 75%|███████▍ | 13936/18688 [16:25:36<4:50:04, 3.66s/it] 75%|███████▍ | 13937/18688 [16:25:40<4:52:32, 3.69s/it] 75%|███████▍ | 13938/18688 [16:25:44<5:05:43, 3.86s/it] 75%|███████▍ | 13939/18688 [16:25:47<4:56:41, 3.75s/it] 75%|███████▍ | 13940/18688 [16:25:51<5:03:42, 3.84s/it] {'loss': '0.9785', 'grad_norm': '1.523', 'learning_rate': '4.067e-05', 'epoch': '0.746', 'num_input_tokens_seen': 46847224, 'train_runtime': '5.915e+04', 'train_tokens_per_second': '792'}
75%|███████▍ | 13940/18688 [16:25:51<5:03:42, 3.84s/it] 75%|███████▍ | 13941/18688 [16:25:55<5:01:48, 3.81s/it] 75%|███████▍ | 13942/18688 [16:26:00<5:19:15, 4.04s/it] 75%|███████▍ | 13943/18688 [16:26:03<5:04:42, 3.85s/it] 75%|███████▍ | 13944/18688 [16:26:07<4:59:21, 3.79s/it] 75%|███████▍ | 13945/18688 [16:26:11<5:12:54, 3.96s/it] 75%|███████▍ | 13946/18688 [16:26:15<5:03:45, 3.84s/it] 75%|███████▍ | 13947/18688 [16:26:19<5:22:15, 4.08s/it] 75%|███████▍ | 13948/18688 [16:26:24<5:41:32, 4.32s/it] 75%|███████▍ | 13949/18688 [16:26:29<5:50:36, 4.44s/it] 75%|███████▍ | 13950/18688 [16:26:33<5:36:35, 4.26s/it] {'loss': '0.9004', 'grad_norm': '1.864', 'learning_rate': '4.051e-05', 'epoch': '0.7465', 'num_input_tokens_seen': 46881720, 'train_runtime': '5.919e+04', 'train_tokens_per_second': '792'}
75%|███████▍ | 13950/18688 [16:26:33<5:36:35, 4.26s/it] 75%|███████▍ | 13951/18688 [16:26:36<5:22:53, 4.09s/it] 75%|███████▍ | 13952/18688 [16:26:41<5:41:27, 4.33s/it] 75%|███████▍ | 13953/18688 [16:26:45<5:37:08, 4.27s/it] 75%|███████▍ | 13954/18688 [16:26:49<5:22:21, 4.09s/it] 75%|███████▍ | 13955/18688 [16:26:53<5:14:31, 3.99s/it] 75%|███████▍ | 13956/18688 [16:26:57<5:18:45, 4.04s/it] 75%|███████▍ | 13957/18688 [16:27:01<5:15:42, 4.00s/it] 75%|███████▍ | 13958/18688 [16:27:05<5:26:11, 4.14s/it] 75%|███████▍ | 13959/18688 [16:27:10<5:25:28, 4.13s/it] 75%|███████▍ | 13960/18688 [16:27:14<5:28:46, 4.17s/it] {'loss': '0.9429', 'grad_norm': '1.566', 'learning_rate': '4.035e-05', 'epoch': '0.747', 'num_input_tokens_seen': 46914040, 'train_runtime': '5.923e+04', 'train_tokens_per_second': '792'}
75%|███████▍ | 13960/18688 [16:27:14<5:28:46, 4.17s/it] 75%|███████▍ | 13961/18688 [16:27:18<5:30:13, 4.19s/it] 75%|███████▍ | 13962/18688 [16:27:22<5:35:49, 4.26s/it] 75%|███████▍ | 13963/18688 [16:27:27<5:53:00, 4.48s/it] 75%|███████▍ | 13964/18688 [16:27:32<5:46:31, 4.40s/it] 75%|███████▍ | 13965/18688 [16:27:36<5:48:23, 4.43s/it] 75%|███████▍ | 13966/18688 [16:27:40<5:37:41, 4.29s/it] 75%|███████▍ | 13967/18688 [16:27:44<5:32:01, 4.22s/it] 75%|███████▍ | 13968/18688 [16:27:48<5:26:06, 4.15s/it] 75%|███████▍ | 13969/18688 [16:27:52<5:26:56, 4.16s/it] 75%|███████▍ | 13970/18688 [16:27:56<5:22:36, 4.10s/it] {'loss': '0.9441', 'grad_norm': '1.717', 'learning_rate': '4.019e-05', 'epoch': '0.7476', 'num_input_tokens_seen': 46945168, 'train_runtime': '5.928e+04', 'train_tokens_per_second': '792'}
75%|███████▍ | 13970/18688 [16:27:56<5:22:36, 4.10s/it] 75%|███████▍ | 13971/18688 [16:28:00<5:09:53, 3.94s/it] 75%|███████▍ | 13972/18688 [16:28:04<5:14:12, 4.00s/it] 75%|███████▍ | 13973/18688 [16:28:08<5:10:22, 3.95s/it] 75%|███████▍ | 13974/18688 [16:28:12<5:22:31, 4.11s/it] 75%|███████▍ | 13975/18688 [16:28:17<5:24:48, 4.13s/it] 75%|███████▍ | 13976/18688 [16:28:20<5:20:02, 4.08s/it] 75%|███████▍ | 13977/18688 [16:28:24<5:07:57, 3.92s/it] 75%|███████▍ | 13978/18688 [16:28:29<5:23:51, 4.13s/it] 75%|███████▍ | 13979/18688 [16:28:34<5:41:30, 4.35s/it] 75%|███████▍ | 13980/18688 [16:28:38<5:33:56, 4.26s/it] {'loss': '0.9674', 'grad_norm': '2.213', 'learning_rate': '4.003e-05', 'epoch': '0.7481', 'num_input_tokens_seen': 46975808, 'train_runtime': '5.932e+04', 'train_tokens_per_second': '791.9'}
75%|███████▍ | 13980/18688 [16:28:38<5:33:56, 4.26s/it] 75%|███████▍ | 13981/18688 [16:28:42<5:35:30, 4.28s/it] 75%|███████▍ | 13982/18688 [16:28:46<5:38:10, 4.31s/it] 75%|███████▍ | 13983/18688 [16:28:51<5:39:05, 4.32s/it] 75%|███████▍ | 13984/18688 [16:28:54<5:25:04, 4.15s/it] 75%|███████▍ | 13985/18688 [16:28:59<5:28:16, 4.19s/it] 75%|███████▍ | 13986/18688 [16:29:03<5:33:13, 4.25s/it] 75%|███████▍ | 13987/18688 [16:29:07<5:18:13, 4.06s/it] 75%|███████▍ | 13988/18688 [16:29:12<5:43:58, 4.39s/it] 75%|███████▍ | 13989/18688 [16:29:16<5:33:27, 4.26s/it] 75%|███████▍ | 13990/18688 [16:29:20<5:35:11, 4.28s/it] {'loss': '0.924', 'grad_norm': '2.151', 'learning_rate': '3.987e-05', 'epoch': '0.7486', 'num_input_tokens_seen': 47007592, 'train_runtime': '5.936e+04', 'train_tokens_per_second': '791.9'}
75%|███████▍ | 13990/18688 [16:29:20<5:35:11, 4.28s/it] 75%|███████▍ | 13991/18688 [16:29:24<5:18:24, 4.07s/it] 75%|███████▍ | 13992/18688 [16:29:28<5:33:37, 4.26s/it] 75%|███████▍ | 13993/18688 [16:29:33<5:31:25, 4.24s/it] 75%|███████▍ | 13994/18688 [16:29:37<5:29:15, 4.21s/it] 75%|███████▍ | 13995/18688 [16:29:41<5:24:58, 4.15s/it] 75%|███████▍ | 13996/18688 [16:29:45<5:16:29, 4.05s/it] 75%|███████▍ | 13997/18688 [16:29:49<5:25:18, 4.16s/it] 75%|███████▍ | 13998/18688 [16:29:53<5:25:58, 4.17s/it] 75%|███████▍ | 13999/18688 [16:29:57<5:26:16, 4.17s/it] 75%|███████▍ | 14000/18688 [16:30:02<5:47:23, 4.45s/it] {'loss': '0.9784', 'grad_norm': '1.904', 'learning_rate': '3.971e-05', 'epoch': '0.7492', 'num_input_tokens_seen': 47040648, 'train_runtime': '5.94e+04', 'train_tokens_per_second': '791.9'}
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***** Running Evaluation *****
Num examples = 1000
Batch size = 1
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{'eval_loss': '0.9228', 'eval_runtime': '82.83', 'eval_samples_per_second': '12.07', 'eval_steps_per_second': '12.07', 'epoch': '0.7492', 'num_input_tokens_seen': 47040648}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00023/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 [16:31:31<38:30:14, 29.57s/it] 75%|███████▍ | 14002/18688 [16:31:35<28:40:52, 22.03s/it] 75%|███████▍ | 14003/18688 [16:31:40<21:49:14, 16.77s/it] 75%|███████▍ | 14004/18688 [16:31:44<17:05:46, 13.14s/it] 75%|███████▍ | 14005/18688 [16:31:49<13:54:05, 10.69s/it] 75%|███████▍ | 14006/18688 [16:31:54<11:32:54, 8.88s/it] 75%|███████▍ | 14007/18688 [16:31:58<9:46:20, 7.52s/it] 75%|███████▍ | 14008/18688 [16:32:02<8:24:07, 6.46s/it] 75%|███████▍ | 14009/18688 [16:32:08<8:12:20, 6.31s/it] 75%|███████▍ | 14010/18688 [16:32:12<7:25:16, 5.71s/it] {'loss': '1.048', 'grad_norm': '1.879', 'learning_rate': '3.955e-05', 'epoch': '0.7497', 'num_input_tokens_seen': 47078616, 'train_runtime': '5.953e+04', 'train_tokens_per_second': '790.8'}
75%|███████▍ | 14010/18688 [16:32:12<7:25:16, 5.71s/it] 75%|███████▍ | 14011/18688 [16:32:17<6:49:44, 5.26s/it] 75%|███████▍ | 14012/18688 [16:32:21<6:20:16, 4.88s/it] 75%|███████▍ | 14013/18688 [16:32:26<6:22:55, 4.91s/it] 75%|███████▍ | 14014/18688 [16:32:30<6:04:11, 4.68s/it] 75%|███████▍ | 14015/18688 [16:32:35<6:08:45, 4.73s/it] 75%|███████▌ | 14016/18688 [16:32:39<5:52:27, 4.53s/it] 75%|███████▌ | 14017/18688 [16:32:43<5:52:57, 4.53s/it] 75%|███████▌ | 14018/18688 [16:32:47<5:39:48, 4.37s/it] 75%|███████▌ | 14019/18688 [16:32:51<5:32:33, 4.27s/it] 75%|███████▌ | 14020/18688 [16:32:55<5:30:58, 4.25s/it] {'loss': '0.9889', 'grad_norm': '1.683', 'learning_rate': '3.94e-05', 'epoch': '0.7502', 'num_input_tokens_seen': 47112024, 'train_runtime': '5.958e+04', 'train_tokens_per_second': '790.8'}
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***** 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)
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{'eval_loss': '0.9159', 'eval_runtime': '82.94', 'eval_samples_per_second': '12.06', 'eval_steps_per_second': '12.06', 'epoch': '0.8027', 'num_input_tokens_seen': 50429344}
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85%|████████▍ | 15880/18688 [18:45:00<3:22:36, 4.33s/it] 85%|████████▍ | 15881/18688 [18:45:04<3:15:56, 4.19s/it] 85%|████████▍ | 15882/18688 [18:45:08<3:21:25, 4.31s/it] 85%|████████▍ | 15883/18688 [18:45:12<3:20:41, 4.29s/it] 85%|████████▍ | 15884/18688 [18:45:17<3:24:02, 4.37s/it] 85%|████████▌ | 15885/18688 [18:45:21<3:21:20, 4.31s/it] 85%|████████▌ | 15886/18688 [18:45:25<3:15:57, 4.20s/it] 85%|████████▌ | 15887/18688 [18:45:29<3:09:08, 4.05s/it] 85%|████████▌ | 15888/18688 [18:45:33<3:09:08, 4.05s/it] 85%|████████▌ | 15889/18688 [18:45:37<3:07:34, 4.02s/it] 85%|████████▌ | 15890/18688 [18:45:41<3:07:27, 4.02s/it] {'loss': '0.9035', 'grad_norm': '2.022', 'learning_rate': '1.472e-05', 'epoch': '0.8503', 'num_input_tokens_seen': 53425792, 'train_runtime': '6.754e+04', 'train_tokens_per_second': '791'}
85%|████████▌ | 15890/18688 [18:45:41<3:07:27, 4.02s/it] 85%|████████▌ | 15891/18688 [18:45:45<3:04:41, 3.96s/it] 85%|████████▌ | 15892/18688 [18:45:49<3:06:06, 3.99s/it] 85%|████████▌ | 15893/18688 [18:45:53<3:06:51, 4.01s/it] 85%|████████▌ | 15894/18688 [18:45:58<3:18:12, 4.26s/it] 85%|████████▌ | 15895/18688 [18:46:02<3:16:28, 4.22s/it] 85%|████████▌ | 15896/18688 [18:46:06<3:17:42, 4.25s/it] 85%|████████▌ | 15897/18688 [18:46:10<3:14:47, 4.19s/it] 85%|████████▌ | 15898/18688 [18:46:14<3:04:59, 3.98s/it] 85%|████████▌ | 15899/18688 [18:46:17<2:59:48, 3.87s/it] 85%|████████▌ | 15900/18688 [18:46:21<3:01:27, 3.91s/it] {'loss': '0.9138', 'grad_norm': '1.631', 'learning_rate': '1.462e-05', 'epoch': '0.8508', 'num_input_tokens_seen': 53455944, 'train_runtime': '6.758e+04', 'train_tokens_per_second': '791'}
85%|████████▌ | 15900/18688 [18:46:21<3:01:27, 3.91s/it] 85%|████████▌ | 15901/18688 [18:46:25<2:56:29, 3.80s/it] 85%|████████▌ | 15902/18688 [18:46:28<2:52:09, 3.71s/it] 85%|████████▌ | 15903/18688 [18:46:33<3:08:28, 4.06s/it] 85%|████████▌ | 15904/18688 [18:46:37<3:05:13, 3.99s/it] 85%|████████▌ | 15905/18688 [18:46:40<2:59:13, 3.86s/it] 85%|████████▌ | 15906/18688 [18:46:46<3:17:33, 4.26s/it] 85%|████████▌ | 15907/18688 [18:46:50<3:15:36, 4.22s/it] 85%|████████▌ | 15908/18688 [18:46:55<3:29:42, 4.53s/it] 85%|████████▌ | 15909/18688 [18:47:00<3:37:56, 4.71s/it] 85%|████████▌ | 15910/18688 [18:47:04<3:29:22, 4.52s/it] {'loss': '0.9306', 'grad_norm': '1.65', 'learning_rate': '1.452e-05', 'epoch': '0.8514', 'num_input_tokens_seen': 53491384, 'train_runtime': '6.762e+04', 'train_tokens_per_second': '791'}
85%|████████▌ | 15910/18688 [18:47:04<3:29:22, 4.52s/it] 85%|████████▌ | 15911/18688 [18:47:08<3:25:31, 4.44s/it] 85%|████████▌ | 15912/18688 [18:47:13<3:23:04, 4.39s/it] 85%|████████▌ | 15913/18688 [18:47:18<3:29:22, 4.53s/it] 85%|████████▌ | 15914/18688 [18:47:21<3:19:18, 4.31s/it] 85%|████████▌ | 15915/18688 [18:47:26<3:20:41, 4.34s/it] 85%|████████▌ | 15916/18688 [18:47:30<3:17:37, 4.28s/it] 85%|████████▌ | 15917/18688 [18:47:34<3:17:39, 4.28s/it] 85%|████████▌ | 15918/18688 [18:47:39<3:29:42, 4.54s/it] 85%|████████▌ | 15919/18688 [18:47:43<3:21:38, 4.37s/it] 85%|████████▌ | 15920/18688 [18:47:48<3:26:23, 4.47s/it] {'loss': '0.9932', 'grad_norm': '2.317', 'learning_rate': '1.442e-05', 'epoch': '0.8519', 'num_input_tokens_seen': 53525408, 'train_runtime': '6.767e+04', 'train_tokens_per_second': '791'}
85%|████████▌ | 15920/18688 [18:47:48<3:26:23, 4.47s/it] 85%|████████▌ | 15921/18688 [18:47:53<3:27:01, 4.49s/it] 85%|████████▌ | 15922/18688 [18:47:56<3:14:09, 4.21s/it] 85%|████████▌ | 15923/18688 [18:48:01<3:20:52, 4.36s/it] 85%|████████▌ | 15924/18688 [18:48:05<3:17:57, 4.30s/it] 85%|████████▌ | 15925/18688 [18:48:10<3:32:42, 4.62s/it] 85%|████████▌ | 15926/18688 [18:48:14<3:16:08, 4.26s/it] 85%|████████▌ | 15927/18688 [18:48:19<3:27:08, 4.50s/it] 85%|████████▌ | 15928/18688 [18:48:24<3:29:05, 4.55s/it] 85%|████████▌ | 15929/18688 [18:48:28<3:27:43, 4.52s/it] 85%|████████▌ | 15930/18688 [18:48:32<3:23:42, 4.43s/it] {'loss': '0.9698', 'grad_norm': '1.686', 'learning_rate': '1.432e-05', 'epoch': '0.8524', 'num_input_tokens_seen': 53562496, 'train_runtime': '6.771e+04', 'train_tokens_per_second': '791'}
85%|████████▌ | 15930/18688 [18:48:32<3:23:42, 4.43s/it] 85%|████████▌ | 15931/18688 [18:48:38<3:37:42, 4.74s/it] 85%|████████▌ | 15932/18688 [18:48:42<3:38:50, 4.76s/it] 85%|████████▌ | 15933/18688 [18:48:48<3:48:36, 4.98s/it] 85%|████████▌ | 15934/18688 [18:48:53<3:45:01, 4.90s/it] 85%|████████▌ | 15935/18688 [18:48:58<3:55:10, 5.13s/it] 85%|████████▌ | 15936/18688 [18:49:03<3:44:20, 4.89s/it] 85%|████████▌ | 15937/18688 [18:49:07<3:35:25, 4.70s/it] 85%|████████▌ | 15938/18688 [18:49:12<3:35:35, 4.70s/it] 85%|████████▌ | 15939/18688 [18:49:16<3:31:37, 4.62s/it] 85%|████████▌ | 15940/18688 [18:49:20<3:26:53, 4.52s/it] {'loss': '0.896', 'grad_norm': '1.436', 'learning_rate': '1.421e-05', 'epoch': '0.853', 'num_input_tokens_seen': 53601520, 'train_runtime': '6.776e+04', 'train_tokens_per_second': '791'}
85%|████████▌ | 15940/18688 [18:49:20<3:26:53, 4.52s/it] 85%|████████▌ | 15941/18688 [18:49:25<3:28:26, 4.55s/it] 85%|████████▌ | 15942/18688 [18:49:29<3:23:00, 4.44s/it] 85%|████████▌ | 15943/18688 [18:49:34<3:23:49, 4.46s/it] 85%|████████▌ | 15944/18688 [18:49:38<3:18:13, 4.33s/it] 85%|████████▌ | 15945/18688 [18:49:42<3:23:30, 4.45s/it] 85%|████████▌ | 15946/18688 [18:49:46<3:13:18, 4.23s/it] 85%|████████▌ | 15947/18688 [18:49:51<3:18:32, 4.35s/it] 85%|████████▌ | 15948/18688 [18:49:56<3:26:26, 4.52s/it] 85%|████████▌ | 15949/18688 [18:50:01<3:42:50, 4.88s/it] 85%|████████▌ | 15950/18688 [18:50:05<3:30:33, 4.61s/it] {'loss': '0.9705', 'grad_norm': '1.44', 'learning_rate': '1.411e-05', 'epoch': '0.8535', 'num_input_tokens_seen': 53638512, 'train_runtime': '6.781e+04', 'train_tokens_per_second': '791.1'}
85%|████████▌ | 15950/18688 [18:50:05<3:30:33, 4.61s/it] 85%|████████▌ | 15951/18688 [18:50:10<3:33:04, 4.67s/it] 85%|████████▌ | 15952/18688 [18:50:14<3:20:11, 4.39s/it] 85%|████████▌ | 15953/18688 [18:50:18<3:17:24, 4.33s/it] 85%|████████▌ | 15954/18688 [18:50:22<3:12:15, 4.22s/it] 85%|████████▌ | 15955/18688 [18:50:26<3:10:26, 4.18s/it] 85%|████████▌ | 15956/18688 [18:50:31<3:20:47, 4.41s/it] 85%|████████▌ | 15957/18688 [18:50:35<3:15:24, 4.29s/it] 85%|████████▌ | 15958/18688 [18:50:39<3:09:19, 4.16s/it] 85%|████████▌ | 15959/18688 [18:50:43<3:03:25, 4.03s/it] 85%|████████▌ | 15960/18688 [18:50:47<3:11:58, 4.22s/it] {'loss': '0.9669', 'grad_norm': '1.842', 'learning_rate': '1.401e-05', 'epoch': '0.854', 'num_input_tokens_seen': 53671096, 'train_runtime': '6.785e+04', 'train_tokens_per_second': '791'}
85%|████████▌ | 15960/18688 [18:50:47<3:11:58, 4.22s/it] 85%|████████▌ | 15961/18688 [18:50:52<3:16:13, 4.32s/it] 85%|████████▌ | 15962/18688 [18:50:56<3:06:48, 4.11s/it] 85%|████████▌ | 15963/18688 [18:51:00<3:07:59, 4.14s/it] 85%|████████▌ | 15964/18688 [18:51:04<3:11:15, 4.21s/it] 85%|████████▌ | 15965/18688 [18:51:08<3:04:12, 4.06s/it] 85%|████████▌ | 15966/18688 [18:51:12<3:03:00, 4.03s/it] 85%|████████▌ | 15967/18688 [18:51:17<3:21:23, 4.44s/it] 85%|████████▌ | 15968/18688 [18:51:22<3:21:51, 4.45s/it] 85%|████████▌ | 15969/18688 [18:51:26<3:19:31, 4.40s/it] 85%|████████▌ | 15970/18688 [18:51:30<3:09:49, 4.19s/it] {'loss': '0.958', 'grad_norm': '1.395', 'learning_rate': '1.391e-05', 'epoch': '0.8546', 'num_input_tokens_seen': 53706720, 'train_runtime': '6.789e+04', 'train_tokens_per_second': '791.1'}
85%|████████▌ | 15970/18688 [18:51:30<3:09:49, 4.19s/it] 85%|████████▌ | 15971/18688 [18:51:34<3:13:31, 4.27s/it] 85%|████████▌ | 15972/18688 [18:51:38<3:10:41, 4.21s/it] 85%|████████▌ | 15973/18688 [18:51:43<3:24:36, 4.52s/it] 85%|████████▌ | 15974/18688 [18:51:48<3:21:52, 4.46s/it] 85%|████████▌ | 15975/18688 [18:51:52<3:16:52, 4.35s/it] 85%|████████▌ | 15976/18688 [18:51:56<3:16:10, 4.34s/it] 85%|████████▌ | 15977/18688 [18:52:01<3:20:27, 4.44s/it] 85%|████████▌ | 15978/18688 [18:52:05<3:20:37, 4.44s/it] 86%|████████▌ | 15979/18688 [18:52:10<3:22:35, 4.49s/it] 86%|████████▌ | 15980/18688 [18:52:14<3:17:30, 4.38s/it] {'loss': '0.9078', 'grad_norm': '1.387', 'learning_rate': '1.381e-05', 'epoch': '0.8551', 'num_input_tokens_seen': 53740784, 'train_runtime': '6.793e+04', 'train_tokens_per_second': '791.1'}
86%|████████▌ | 15980/18688 [18:52:14<3:17:30, 4.38s/it] 86%|████████▌ | 15981/18688 [18:52:18<3:15:47, 4.34s/it] 86%|████████▌ | 15982/18688 [18:52:22<3:13:05, 4.28s/it] 86%|████████▌ | 15983/18688 [18:52:27<3:15:08, 4.33s/it] 86%|████████▌ | 15984/18688 [18:52:32<3:27:54, 4.61s/it] 86%|████████▌ | 15985/18688 [18:52:36<3:18:04, 4.40s/it] 86%|████████▌ | 15986/18688 [18:52:40<3:12:56, 4.28s/it] 86%|████████▌ | 15987/18688 [18:52:44<3:08:09, 4.18s/it] 86%|████████▌ | 15988/18688 [18:52:49<3:16:39, 4.37s/it] 86%|████████▌ | 15989/18688 [18:52:53<3:12:25, 4.28s/it] 86%|████████▌ | 15990/18688 [18:52:58<3:24:09, 4.54s/it] {'loss': '0.969', 'grad_norm': '1.499', 'learning_rate': '1.371e-05', 'epoch': '0.8557', 'num_input_tokens_seen': 53775136, 'train_runtime': '6.798e+04', 'train_tokens_per_second': '791.1'}
86%|████████▌ | 15990/18688 [18:52:58<3:24:09, 4.54s/it] 86%|████████▌ | 15991/18688 [18:53:03<3:28:36, 4.64s/it] 86%|████████▌ | 15992/18688 [18:53:07<3:26:40, 4.60s/it] 86%|████████▌ | 15993/18688 [18:53:12<3:20:53, 4.47s/it] 86%|████████▌ | 15994/18688 [18:53:16<3:13:54, 4.32s/it] 86%|████████▌ | 15995/18688 [18:53:20<3:18:15, 4.42s/it] 86%|████████▌ | 15996/18688 [18:53:24<3:16:09, 4.37s/it] 86%|████████▌ | 15997/18688 [18:53:29<3:14:51, 4.34s/it] 86%|████████▌ | 15998/18688 [18:53:33<3:16:28, 4.38s/it] 86%|████████▌ | 15999/18688 [18:53:37<3:04:25, 4.12s/it] 86%|████████▌ | 16000/18688 [18:53:41<3:04:27, 4.12s/it] {'loss': '0.9312', 'grad_norm': '1.362', 'learning_rate': '1.361e-05', 'epoch': '0.8562', 'num_input_tokens_seen': 53808416, 'train_runtime': '6.802e+04', 'train_tokens_per_second': '791.1'}
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***** Running Evaluation *****
Num examples = 1000
Batch size = 1
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{'eval_loss': '0.9089', 'eval_runtime': '83.1', 'eval_samples_per_second': '12.03', 'eval_steps_per_second': '12.03', 'epoch': '0.8562', 'num_input_tokens_seen': 53808416}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00023/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 [18:55:09<21:56:04, 29.39s/it] 86%|████████▌ | 16002/18688 [18:55:13<16:16:39, 21.82s/it] 86%|████████▌ | 16003/18688 [18:55:17<12:09:24, 16.30s/it] 86%|████████▌ | 16004/18688 [18:55:21<9:24:12, 12.61s/it] 86%|████████▌ | 16005/18688 [18:55:26<7:41:38, 10.32s/it] 86%|████████▌ | 16006/18688 [18:55:30<6:22:09, 8.55s/it] 86%|████████▌ | 16007/18688 [18:55:34<5:21:26, 7.19s/it] 86%|████████▌ | 16008/18688 [18:55:39<4:47:51, 6.44s/it] 86%|████████▌ | 16009/18688 [18:55:43<4:16:03, 5.73s/it] 86%|████████▌ | 16010/18688 [18:55:47<3:53:19, 5.23s/it] {'loss': '1.001', 'grad_norm': '2.401', 'learning_rate': '1.351e-05', 'epoch': '0.8567', 'num_input_tokens_seen': 53843200, 'train_runtime': '6.815e+04', 'train_tokens_per_second': '790.1'}
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***** 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)
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{'eval_loss': '0.9047', 'eval_runtime': '82.93', 'eval_samples_per_second': '12.06', 'eval_steps_per_second': '12.06', 'epoch': '0.9097', 'num_input_tokens_seen': 57206512}
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96%|█████████▌| 17860/18688 [21:08:08<57:03, 4.13s/it] 96%|█████████▌| 17861/18688 [21:08:12<57:42, 4.19s/it] 96%|█████████▌| 17862/18688 [21:08:17<1:01:01, 4.43s/it] 96%|█████████▌| 17863/18688 [21:08:22<1:00:51, 4.43s/it] 96%|█████████▌| 17864/18688 [21:08:26<1:01:14, 4.46s/it] 96%|█████████▌| 17865/18688 [21:08:30<59:54, 4.37s/it] 96%|█████████▌| 17866/18688 [21:08:35<1:00:03, 4.38s/it] 96%|█████████▌| 17867/18688 [21:08:39<57:50, 4.23s/it] 96%|█████████▌| 17868/18688 [21:08:43<58:02, 4.25s/it] 96%|█████████▌| 17869/18688 [21:08:48<59:17, 4.34s/it] 96%|█████████▌| 17870/18688 [21:08:52<58:57, 4.32s/it] {'loss': '0.9044', 'grad_norm': '1.71', 'learning_rate': '1.286e-06', 'epoch': '0.9563', 'num_input_tokens_seen': 60119624, 'train_runtime': '7.613e+04', 'train_tokens_per_second': '789.7'}
96%|█████████▌| 17870/18688 [21:08:52<58:57, 4.32s/it] 96%|█████████▌| 17871/18688 [21:08:57<1:03:00, 4.63s/it] 96%|█████████▌| 17872/18688 [21:09:02<1:03:11, 4.65s/it] 96%|█████████▌| 17873/18688 [21:09:05<58:21, 4.30s/it] 96%|█████████▌| 17874/18688 [21:09:10<1:00:48, 4.48s/it] 96%|█████████▌| 17875/18688 [21:09:15<1:03:12, 4.66s/it] 96%|█████████▌| 17876/18688 [21:09:20<1:01:08, 4.52s/it] 96%|█████████▌| 17877/18688 [21:09:24<59:51, 4.43s/it] 96%|█████████▌| 17878/18688 [21:09:28<57:16, 4.24s/it] 96%|█████████▌| 17879/18688 [21:09:33<1:02:08, 4.61s/it] 96%|█████████▌| 17880/18688 [21:09:39<1:08:18, 5.07s/it] {'loss': '0.9844', 'grad_norm': '1.505', 'learning_rate': '1.255e-06', 'epoch': '0.9568', 'num_input_tokens_seen': 60161744, 'train_runtime': '7.618e+04', 'train_tokens_per_second': '789.7'}
96%|█████████▌| 17880/18688 [21:09:39<1:08:18, 5.07s/it] 96%|█████████▌| 17881/18688 [21:09:43<1:04:30, 4.80s/it] 96%|█████████▌| 17882/18688 [21:09:48<1:03:25, 4.72s/it] 96%|█████████▌| 17883/18688 [21:09:52<1:00:43, 4.53s/it] 96%|█████████▌| 17884/18688 [21:09:56<58:23, 4.36s/it] 96%|█████████▌| 17885/18688 [21:10:01<59:57, 4.48s/it] 96%|█████████▌| 17886/18688 [21:10:04<57:08, 4.27s/it] 96%|█████████▌| 17887/18688 [21:10:08<55:48, 4.18s/it] 96%|█████████▌| 17888/18688 [21:10:12<54:50, 4.11s/it] 96%|█████████▌| 17889/18688 [21:10:17<54:58, 4.13s/it] 96%|█████████▌| 17890/18688 [21:10:20<53:54, 4.05s/it] {'loss': '0.952', 'grad_norm': '1.858', 'learning_rate': '1.224e-06', 'epoch': '0.9573', 'num_input_tokens_seen': 60191768, 'train_runtime': '7.622e+04', 'train_tokens_per_second': '789.7'}
96%|█████████▌| 17890/18688 [21:10:20<53:54, 4.05s/it] 96%|█████████▌| 17891/18688 [21:10:24<53:16, 4.01s/it] 96%|█████████▌| 17892/18688 [21:10:29<54:04, 4.08s/it] 96%|█████████▌| 17893/18688 [21:10:32<52:52, 3.99s/it] 96%|█████████▌| 17894/18688 [21:10:36<52:04, 3.93s/it] 96%|█████████▌| 17895/18688 [21:10:40<53:11, 4.02s/it] 96%|█████████▌| 17896/18688 [21:10:44<52:49, 4.00s/it] 96%|█████████▌| 17897/18688 [21:10:49<57:02, 4.33s/it] 96%|█████████▌| 17898/18688 [21:10:54<58:21, 4.43s/it] 96%|█████████▌| 17899/18688 [21:10:59<58:28, 4.45s/it] 96%|█████████▌| 17900/18688 [21:11:03<59:41, 4.54s/it] {'loss': '0.9593', 'grad_norm': '1.669', 'learning_rate': '1.194e-06', 'epoch': '0.9579', 'num_input_tokens_seen': 60225864, 'train_runtime': '7.626e+04', 'train_tokens_per_second': '789.7'}
96%|█████████▌| 17900/18688 [21:11:03<59:41, 4.54s/it] 96%|█████████▌| 17901/18688 [21:11:08<59:34, 4.54s/it] 96%|█████████▌| 17902/18688 [21:11:12<59:08, 4.51s/it] 96%|█████████▌| 17903/18688 [21:11:17<59:22, 4.54s/it] 96%|█████████▌| 17904/18688 [21:11:22<1:02:03, 4.75s/it] 96%|█████████▌| 17905/18688 [21:11:27<1:01:04, 4.68s/it] 96%|█████████▌| 17906/18688 [21:11:31<58:27, 4.48s/it] 96%|█████████▌| 17907/18688 [21:11:35<58:25, 4.49s/it] 96%|█████████▌| 17908/18688 [21:11:40<58:42, 4.52s/it] 96%|█████████▌| 17909/18688 [21:11:44<57:30, 4.43s/it] 96%|█████████▌| 17910/18688 [21:11:49<57:48, 4.46s/it] {'loss': '0.9078', 'grad_norm': '1.856', 'learning_rate': '1.164e-06', 'epoch': '0.9584', 'num_input_tokens_seen': 60261128, 'train_runtime': '7.631e+04', 'train_tokens_per_second': '789.7'}
96%|█████████▌| 17910/18688 [21:11:49<57:48, 4.46s/it] 96%|█████████▌| 17911/18688 [21:11:53<56:02, 4.33s/it] 96%|█████████▌| 17912/18688 [21:11:56<53:44, 4.16s/it] 96%|█████████▌| 17913/18688 [21:12:00<53:00, 4.10s/it] 96%|█████████▌| 17914/18688 [21:12:05<54:53, 4.25s/it] 96%|█████████▌| 17915/18688 [21:12:09<54:57, 4.27s/it] 96%|█████████▌| 17916/18688 [21:12:13<54:23, 4.23s/it] 96%|█████████▌| 17917/18688 [21:12:18<55:53, 4.35s/it] 96%|█████████▌| 17918/18688 [21:12:24<1:02:53, 4.90s/it] 96%|█████████▌| 17919/18688 [21:12:28<1:00:14, 4.70s/it] 96%|█████████▌| 17920/18688 [21:12:32<57:30, 4.49s/it] {'loss': '0.9526', 'grad_norm': '1.665', 'learning_rate': '1.134e-06', 'epoch': '0.9589', 'num_input_tokens_seen': 60296848, 'train_runtime': '7.635e+04', 'train_tokens_per_second': '789.7'}
96%|█████████▌| 17920/18688 [21:12:32<57:30, 4.49s/it] 96%|█████████▌| 17921/18688 [21:12:37<56:22, 4.41s/it] 96%|█████████▌| 17922/18688 [21:12:42<58:21, 4.57s/it] 96%|█████████▌| 17923/18688 [21:12:47<1:00:31, 4.75s/it] 96%|█████████▌| 17924/18688 [21:12:51<57:58, 4.55s/it] 96%|█████████▌| 17925/18688 [21:12:55<57:44, 4.54s/it] 96%|█████████▌| 17926/18688 [21:12:59<56:02, 4.41s/it] 96%|█████████▌| 17927/18688 [21:13:04<55:57, 4.41s/it] 96%|█████████▌| 17928/18688 [21:13:08<54:52, 4.33s/it] 96%|█████████▌| 17929/18688 [21:13:13<56:04, 4.43s/it] 96%|█████████▌| 17930/18688 [21:13:17<56:50, 4.50s/it] {'loss': '0.9157', 'grad_norm': '1.889', 'learning_rate': '1.105e-06', 'epoch': '0.9595', 'num_input_tokens_seen': 60330792, 'train_runtime': '7.64e+04', 'train_tokens_per_second': '789.7'}
96%|█████████▌| 17930/18688 [21:13:17<56:50, 4.50s/it] 96%|█████████▌| 17931/18688 [21:13:23<59:59, 4.75s/it] 96%|█████████▌| 17932/18688 [21:13:26<55:41, 4.42s/it] 96%|█████████▌| 17933/18688 [21:13:31<55:41, 4.43s/it] 96%|█████████▌| 17934/18688 [21:13:34<52:19, 4.16s/it] 96%|█████████▌| 17935/18688 [21:13:39<54:53, 4.37s/it] 96%|█████████▌| 17936/18688 [21:13:43<51:56, 4.14s/it] 96%|█████████▌| 17937/18688 [21:13:47<51:07, 4.08s/it] 96%|█████████▌| 17938/18688 [21:13:51<50:07, 4.01s/it] 96%|█████████▌| 17939/18688 [21:13:54<49:12, 3.94s/it] 96%|█████████▌| 17940/18688 [21:14:00<55:27, 4.45s/it] {'loss': '0.9163', 'grad_norm': '1.524', 'learning_rate': '1.076e-06', 'epoch': '0.96', 'num_input_tokens_seen': 60367736, 'train_runtime': '7.644e+04', 'train_tokens_per_second': '789.7'}
96%|█████████▌| 17940/18688 [21:14:00<55:27, 4.45s/it] 96%|█████████▌| 17941/18688 [21:14:05<55:40, 4.47s/it] 96%|█████████▌| 17942/18688 [21:14:08<51:49, 4.17s/it] 96%|█████████▌| 17943/18688 [21:14:12<51:18, 4.13s/it] 96%|█████████▌| 17944/18688 [21:14:15<48:44, 3.93s/it] 96%|█████████▌| 17945/18688 [21:14:19<47:45, 3.86s/it] 96%|█████████▌| 17946/18688 [21:14:23<46:35, 3.77s/it] 96%|█████████▌| 17947/18688 [21:14:27<48:43, 3.94s/it] 96%|█████████▌| 17948/18688 [21:14:31<47:58, 3.89s/it] 96%|█████████▌| 17949/18688 [21:14:36<52:22, 4.25s/it] 96%|█████████▌| 17950/18688 [21:14:40<50:18, 4.09s/it] {'loss': '0.939', 'grad_norm': '1.748', 'learning_rate': '1.047e-06', 'epoch': '0.9605', 'num_input_tokens_seen': 60399488, 'train_runtime': '7.648e+04', 'train_tokens_per_second': '789.7'}
96%|█████████▌| 17950/18688 [21:14:40<50:18, 4.09s/it] 96%|█████████▌| 17951/18688 [21:14:44<52:44, 4.29s/it] 96%|█████████▌| 17952/18688 [21:14:49<54:14, 4.42s/it] 96%|█████████▌| 17953/18688 [21:14:53<51:30, 4.20s/it] 96%|█████████▌| 17954/18688 [21:14:57<49:29, 4.05s/it] 96%|█████████▌| 17955/18688 [21:15:01<52:16, 4.28s/it] 96%|█████████▌| 17956/18688 [21:15:06<53:36, 4.39s/it] 96%|█████████▌| 17957/18688 [21:15:10<53:05, 4.36s/it] 96%|█████████▌| 17958/18688 [21:15:14<52:05, 4.28s/it] 96%|█████████▌| 17959/18688 [21:15:19<52:41, 4.34s/it] 96%|█████████▌| 17960/18688 [21:15:23<50:52, 4.19s/it] {'loss': '0.9528', 'grad_norm': '2.055', 'learning_rate': '1.019e-06', 'epoch': '0.9611', 'num_input_tokens_seen': 60435944, 'train_runtime': '7.652e+04', 'train_tokens_per_second': '789.8'}
96%|█████████▌| 17960/18688 [21:15:23<50:52, 4.19s/it] 96%|█████████▌| 17961/18688 [21:15:26<48:08, 3.97s/it] 96%|█████████▌| 17962/18688 [21:15:30<47:17, 3.91s/it] 96%|█████████▌| 17963/18688 [21:15:34<47:21, 3.92s/it] 96%|█████████▌| 17964/18688 [21:15:38<47:43, 3.95s/it] 96%|█████████▌| 17965/18688 [21:15:42<47:55, 3.98s/it] 96%|█████████▌| 17966/18688 [21:15:48<53:49, 4.47s/it] 96%|█████████▌| 17967/18688 [21:15:52<55:20, 4.60s/it] 96%|█████████▌| 17968/18688 [21:15:57<55:09, 4.60s/it] 96%|█████████▌| 17969/18688 [21:16:01<54:10, 4.52s/it] 96%|█████████▌| 17970/18688 [21:16:05<51:36, 4.31s/it] {'loss': '0.9295', 'grad_norm': '2.016', 'learning_rate': '9.916e-07', 'epoch': '0.9616', 'num_input_tokens_seen': 60469000, 'train_runtime': '7.657e+04', 'train_tokens_per_second': '789.8'}
96%|█████████▌| 17970/18688 [21:16:05<51:36, 4.31s/it] 96%|█████████▌| 17971/18688 [21:16:10<52:28, 4.39s/it] 96%|█████████▌| 17972/18688 [21:16:15<54:12, 4.54s/it] 96%|█████████▌| 17973/18688 [21:16:19<54:10, 4.55s/it] 96%|█████████▌| 17974/18688 [21:16:23<52:23, 4.40s/it] 96%|█████████▌| 17975/18688 [21:16:27<51:06, 4.30s/it] 96%|█████████▌| 17976/18688 [21:16:32<50:37, 4.27s/it] 96%|█████████▌| 17977/18688 [21:16:35<49:04, 4.14s/it] 96%|█████████▌| 17978/18688 [21:16:40<50:13, 4.24s/it] 96%|█████████▌| 17979/18688 [21:16:44<50:40, 4.29s/it] 96%|█████████▌| 17980/18688 [21:16:49<51:34, 4.37s/it] {'loss': '0.9142', 'grad_norm': '2.166', 'learning_rate': '9.643e-07', 'epoch': '0.9621', 'num_input_tokens_seen': 60501200, 'train_runtime': '7.661e+04', 'train_tokens_per_second': '789.7'}
96%|█████████▌| 17980/18688 [21:16:49<51:34, 4.37s/it] 96%|█████████▌| 17981/18688 [21:16:53<51:02, 4.33s/it] 96%|█████████▌| 17982/18688 [21:16:57<50:12, 4.27s/it] 96%|█████████▌| 17983/18688 [21:17:01<49:01, 4.17s/it] 96%|█████████▌| 17984/18688 [21:17:05<47:06, 4.01s/it] 96%|█████████▌| 17985/18688 [21:17:09<46:17, 3.95s/it] 96%|█████████▌| 17986/18688 [21:17:13<46:05, 3.94s/it] 96%|█████████▌| 17987/18688 [21:17:17<49:00, 4.20s/it] 96%|█████████▋| 17988/18688 [21:17:22<51:14, 4.39s/it] 96%|█████████▋| 17989/18688 [21:17:26<49:39, 4.26s/it] 96%|█████████▋| 17990/18688 [21:17:30<49:31, 4.26s/it] {'loss': '0.9665', 'grad_norm': '2.034', 'learning_rate': '9.373e-07', 'epoch': '0.9627', 'num_input_tokens_seen': 60533800, 'train_runtime': '7.665e+04', 'train_tokens_per_second': '789.7'}
96%|█████████▋| 17990/18688 [21:17:30<49:31, 4.26s/it] 96%|█████████▋| 17991/18688 [21:17:34<47:43, 4.11s/it] 96%|█████████▋| 17992/18688 [21:17:38<46:38, 4.02s/it] 96%|█████████▋| 17993/18688 [21:17:42<46:58, 4.06s/it] 96%|█████████▋| 17994/18688 [21:17:46<45:11, 3.91s/it] 96%|█████████▋| 17995/18688 [21:17:49<44:51, 3.88s/it] 96%|█████████▋| 17996/18688 [21:17:55<51:23, 4.46s/it] 96%|█████████▋| 17997/18688 [21:17:59<48:33, 4.22s/it] 96%|█████████▋| 17998/18688 [21:18:04<53:05, 4.62s/it] 96%|█████████▋| 17999/18688 [21:18:09<52:45, 4.59s/it] 96%|█████████▋| 18000/18688 [21:18:13<49:10, 4.29s/it] {'loss': '0.9108', 'grad_norm': '1.862', 'learning_rate': '9.107e-07', 'epoch': '0.9632', 'num_input_tokens_seen': 60569592, 'train_runtime': '7.669e+04', 'train_tokens_per_second': '789.8'}
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***** Running Evaluation *****
Num examples = 1000
Batch size = 1
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{'eval_loss': '0.9032', 'eval_runtime': '83.04', 'eval_samples_per_second': '12.04', 'eval_steps_per_second': '12.04', 'epoch': '0.9632', 'num_input_tokens_seen': 60569592}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00023/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
}
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***** 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)
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{'eval_loss': '0.9032', 'eval_runtime': '82.94', 'eval_samples_per_second': '12.06', 'eval_steps_per_second': '12.06', 'epoch': '1', 'num_input_tokens_seen': 62891360}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00023/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': '7.979e+04', 'train_samples_per_second': '3.747', 'train_steps_per_second': '0.234', 'train_loss': '0.998', 'epoch': '1', 'num_input_tokens_seen': 62891360}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00023
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 = 0.998
train_runtime = 22:09:49.35
train_samples_per_second = 3.747
train_steps_per_second = 0.234
Figure saved at: /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00023/training_loss.png
Figure saved at: /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00023/training_eval_loss.png
[WARNING|2026-10-19 16:13:41] llamafactory.extras.ploting:149 >> No metric eval_accuracy to plot.
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:71: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:281: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 0/1000 [00:00<?, ?it/s]/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:172: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:202: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:254: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
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***** eval metrics *****
epoch = 1.0
eval_loss = 0.9032
eval_runtime = 0:01:22.80
eval_samples_per_second = 12.076
eval_steps_per_second = 12.076
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'}}