Diplomovka/aya_300k_lr_experiments/logs/lr_0p00020.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|2026-10-17 19:52:56] llamafactory.hparams.parser:651 >> Process rank: 0, world size: 1, device: cuda:0, distributed training: False, compute dtype: torch.bfloat16
Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.
[INFO|configuration_utils.py:765] 2026-10-17 19:52:56,675 >> 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] 2026-10-17 19:52:56,685 >> 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] 2026-10-17 19:52:58,322 >> 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] 2026-10-17 19:52:58,325 >> 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] 2026-10-17 19:52:58,466 >> 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] 2026-10-17 19:52:58,469 >> 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|2026-10-17 19:52:59] 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] 2026-10-17 19:52:59,960 >> 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] 2026-10-17 19:52:59,964 >> 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|2026-10-17 19:52:59] 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|2026-10-17 19:53:17] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled.
[INFO|2026-10-17 19:53:17] llamafactory.model.adapter:144 >> Upcasting trainable params to float32.
[INFO|2026-10-17 19:53:17] 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|2026-10-17 19:53:20] 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<32:41:29, 6.30s/it] 0%| | 2/18688 [00:09<24:30:45, 4.72s/it] 0%| | 3/18688 [00:14<23:30:40, 4.53s/it] 0%| | 4/18688 [00:18<22:33:05, 4.35s/it] 0%| | 5/18688 [00:22<22:21:13, 4.31s/it] 0%| | 6/18688 [00:26<22:33:17, 4.35s/it] 0%| | 7/18688 [00:32<24:59:27, 4.82s/it] 0%| | 8/18688 [00:36<23:27:33, 4.52s/it] 0%| | 9/18688 [00:41<24:36:00, 4.74s/it] 0%| | 10/18688 [00:45<23:20:32, 4.50s/it] {'loss': '2.105', 'grad_norm': '5.814', 'learning_rate': '1.2e-06', 'epoch': '0.0005351', 'num_input_tokens_seen': 35216, 'train_runtime': '45.8', 'train_tokens_per_second': '768.9'}
0%| | 10/18688 [00:45<23:20:32, 4.50s/it] 0%| | 11/18688 [00:49<22:49:16, 4.40s/it] 0%| | 12/18688 [00:55<24:35:04, 4.74s/it] 0%| | 13/18688 [00:59<23:37:49, 4.56s/it] 0%| | 14/18688 [01:03<22:53:48, 4.41s/it] 0%| | 15/18688 [01:08<22:52:00, 4.41s/it] 0%| | 16/18688 [01:12<22:11:07, 4.28s/it] 0%| | 17/18688 [01:16<21:40:11, 4.18s/it] 0%| | 18/18688 [01:20<21:44:10, 4.19s/it] 0%| | 19/18688 [01:24<21:23:58, 4.13s/it] 0%| | 20/18688 [01:28<21:13:33, 4.09s/it] {'loss': '2.025', 'grad_norm': '4.394', 'learning_rate': '2.533e-06', 'epoch': '0.00107', 'num_input_tokens_seen': 66488, 'train_runtime': '88.24', 'train_tokens_per_second': '753.5'}
0%| | 20/18688 [01:28<21:13:33, 4.09s/it] 0%| | 21/18688 [01:32<21:53:09, 4.22s/it] 0%| | 22/18688 [01:37<21:58:02, 4.24s/it] 0%| | 23/18688 [01:41<21:57:48, 4.24s/it] 0%| | 24/18688 [01:45<21:15:48, 4.10s/it] 0%| | 25/18688 [01:49<21:15:33, 4.10s/it] 0%| | 26/18688 [01:53<21:04:54, 4.07s/it] 0%| | 27/18688 [01:57<21:19:50, 4.12s/it] 0%| | 28/18688 [02:01<21:05:02, 4.07s/it] 0%| | 29/18688 [02:05<21:24:53, 4.13s/it] 0%| | 30/18688 [02:09<20:54:09, 4.03s/it] {'loss': '1.846', 'grad_norm': '3.843', 'learning_rate': '3.867e-06', 'epoch': '0.001605', 'num_input_tokens_seen': 97280, 'train_runtime': '129.4', 'train_tokens_per_second': '751.7'}
0%| | 30/18688 [02:09<20:54:09, 4.03s/it] 0%| | 31/18688 [02:13<20:52:33, 4.03s/it] 0%| | 32/18688 [02:17<20:25:15, 3.94s/it] 0%| | 33/18688 [02:21<20:40:23, 3.99s/it] 0%| | 34/18688 [02:25<21:09:26, 4.08s/it] 0%| | 35/18688 [02:29<20:52:28, 4.03s/it] 0%| | 36/18688 [02:33<20:50:44, 4.02s/it] 0%| | 37/18688 [02:38<21:52:01, 4.22s/it] 0%| | 38/18688 [02:43<23:09:24, 4.47s/it] 0%| | 39/18688 [02:48<23:41:05, 4.57s/it] 0%| | 40/18688 [02:52<22:56:54, 4.43s/it] {'loss': '1.762', 'grad_norm': '2.59', 'learning_rate': '5.2e-06', 'epoch': '0.00214', 'num_input_tokens_seen': 129200, 'train_runtime': '172.1', 'train_tokens_per_second': '750.6'}
0%| | 40/18688 [02:52<22:56:54, 4.43s/it] 0%| | 41/18688 [02:56<22:24:06, 4.32s/it] 0%| | 42/18688 [03:00<22:07:47, 4.27s/it] 0%| | 43/18688 [03:04<21:36:20, 4.17s/it] 0%| | 44/18688 [03:08<22:06:33, 4.27s/it] 0%| | 45/18688 [03:13<22:13:40, 4.29s/it] 0%| | 46/18688 [03:17<22:37:16, 4.37s/it] 0%| | 47/18688 [03:21<21:51:49, 4.22s/it] 0%| | 48/18688 [03:25<21:15:34, 4.11s/it] 0%| | 49/18688 [03:29<21:15:06, 4.10s/it] 0%| | 50/18688 [03:34<22:52:34, 4.42s/it] {'loss': '1.582', 'grad_norm': '1.748', 'learning_rate': '6.533e-06', 'epoch': '0.002676', 'num_input_tokens_seen': 163208, 'train_runtime': '214.6', 'train_tokens_per_second': '760.4'}
0%| | 50/18688 [03:34<22:52:34, 4.42s/it] 0%| | 51/18688 [03:38<21:16:55, 4.11s/it] 0%| | 52/18688 [03:42<22:24:01, 4.33s/it] 0%| | 53/18688 [03:47<22:13:48, 4.29s/it] 0%| | 54/18688 [03:50<21:01:42, 4.06s/it] 0%| | 55/18688 [03:54<21:02:51, 4.07s/it] 0%| | 56/18688 [03:58<21:16:25, 4.11s/it] 0%| | 57/18688 [04:02<20:46:59, 4.02s/it] 0%| | 58/18688 [04:06<19:53:00, 3.84s/it] 0%| | 59/18688 [04:10<20:42:56, 4.00s/it] 0%| | 60/18688 [04:15<22:16:01, 4.30s/it] {'loss': '1.439', 'grad_norm': '1.632', 'learning_rate': '7.867e-06', 'epoch': '0.003211', 'num_input_tokens_seen': 197328, 'train_runtime': '255.5', 'train_tokens_per_second': '772.3'}
0%| | 60/18688 [04:15<22:16:01, 4.30s/it] 0%| | 61/18688 [04:20<22:41:50, 4.39s/it] 0%| | 62/18688 [04:24<22:52:02, 4.42s/it] 0%| | 63/18688 [04:30<24:55:42, 4.82s/it] 0%| | 64/18688 [04:34<23:46:54, 4.60s/it] 0%| | 65/18688 [04:39<24:57:07, 4.82s/it] 0%| | 66/18688 [04:43<23:41:23, 4.58s/it] 0%| | 67/18688 [04:47<22:46:25, 4.40s/it] 0%| | 68/18688 [04:52<23:24:15, 4.53s/it] 0%| | 69/18688 [04:56<22:56:38, 4.44s/it] 0%| | 70/18688 [05:01<23:10:22, 4.48s/it] {'loss': '1.324', 'grad_norm': '1.652', 'learning_rate': '9.2e-06', 'epoch': '0.003746', 'num_input_tokens_seen': 234064, 'train_runtime': '301.4', 'train_tokens_per_second': '776.6'}
0%| | 70/18688 [05:01<23:10:22, 4.48s/it] 0%| | 71/18688 [05:06<23:29:25, 4.54s/it] 0%| | 72/18688 [05:10<23:10:11, 4.48s/it] 0%| | 73/18688 [05:14<22:22:51, 4.33s/it] 0%| | 74/18688 [05:18<21:40:48, 4.19s/it] 0%| | 75/18688 [05:22<21:36:59, 4.18s/it] 0%| | 76/18688 [05:26<20:51:42, 4.04s/it] 0%| | 77/18688 [05:30<21:23:47, 4.14s/it] 0%| | 78/18688 [05:34<20:48:17, 4.02s/it] 0%| | 79/18688 [05:39<22:08:48, 4.28s/it] 0%| | 80/18688 [05:43<21:59:49, 4.26s/it] {'loss': '1.249', 'grad_norm': '2.35', 'learning_rate': '1.053e-05', 'epoch': '0.004281', 'num_input_tokens_seen': 266768, 'train_runtime': '343.3', 'train_tokens_per_second': '777'}
0%| | 80/18688 [05:43<21:59:49, 4.26s/it] 0%| | 81/18688 [05:48<22:53:17, 4.43s/it] 0%| | 82/18688 [05:52<22:28:25, 4.35s/it] 0%| | 83/18688 [05:56<21:55:13, 4.24s/it] 0%| | 84/18688 [06:00<21:48:19, 4.22s/it] 0%| | 85/18688 [06:04<22:04:50, 4.27s/it] 0%| | 86/18688 [06:09<22:54:59, 4.43s/it] 0%| | 87/18688 [06:13<22:06:05, 4.28s/it] 0%| | 88/18688 [06:18<23:05:06, 4.47s/it] 0%| | 89/18688 [06:22<22:40:09, 4.39s/it] 0%| | 90/18688 [06:26<21:49:58, 4.23s/it] {'loss': '1.147', 'grad_norm': '1.994', 'learning_rate': '1.187e-05', 'epoch': '0.004816', 'num_input_tokens_seen': 299720, 'train_runtime': '386.6', 'train_tokens_per_second': '775.3'}
0%| | 90/18688 [06:26<21:49:58, 4.23s/it] 0%| | 91/18688 [06:31<22:44:34, 4.40s/it] 0%| | 92/18688 [06:35<22:24:32, 4.34s/it] 0%| | 93/18688 [06:39<21:49:31, 4.23s/it] 1%| | 94/18688 [06:44<22:14:56, 4.31s/it] 1%| | 95/18688 [06:48<22:26:49, 4.35s/it] 1%| | 96/18688 [06:52<22:26:06, 4.34s/it] 1%| | 97/18688 [06:56<21:11:59, 4.11s/it] 1%| | 98/18688 [07:00<20:29:51, 3.97s/it] 1%| | 99/18688 [07:03<20:13:41, 3.92s/it] 1%| | 100/18688 [07:07<19:34:21, 3.79s/it] {'loss': '1.15', 'grad_norm': '2.19', 'learning_rate': '1.32e-05', 'epoch': '0.005351', 'num_input_tokens_seen': 333368, 'train_runtime': '427.3', 'train_tokens_per_second': '780.2'}
1%| | 100/18688 [07:07<19:34:21, 3.79s/it] 1%| | 101/18688 [07:12<22:18:46, 4.32s/it] 1%| | 102/18688 [07:17<22:50:29, 4.42s/it] 1%| | 103/18688 [07:21<21:56:53, 4.25s/it] 1%| | 104/18688 [07:25<22:31:41, 4.36s/it] 1%| | 105/18688 [07:30<22:50:58, 4.43s/it] 1%| | 106/18688 [07:35<23:23:28, 4.53s/it] 1%| | 107/18688 [07:39<22:42:55, 4.40s/it] 1%| | 108/18688 [07:43<22:26:27, 4.35s/it] 1%| | 109/18688 [07:47<22:12:06, 4.30s/it] 1%| | 110/18688 [07:51<21:20:50, 4.14s/it] {'loss': '1.16', 'grad_norm': '1.69', 'learning_rate': '1.453e-05', 'epoch': '0.005886', 'num_input_tokens_seen': 370256, 'train_runtime': '471.6', 'train_tokens_per_second': '785.1'}
1%| | 110/18688 [07:51<21:20:50, 4.14s/it] 1%| | 111/18688 [07:56<21:56:50, 4.25s/it] 1%| | 112/18688 [08:00<21:46:18, 4.22s/it] 1%| | 113/18688 [08:04<21:38:03, 4.19s/it] 1%| | 114/18688 [08:09<22:36:49, 4.38s/it] 1%| | 115/18688 [08:13<21:45:34, 4.22s/it] 1%| | 116/18688 [08:17<21:23:37, 4.15s/it] 1%| | 117/18688 [08:21<21:22:16, 4.14s/it] 1%| | 118/18688 [08:25<21:44:52, 4.22s/it] 1%| | 119/18688 [08:30<22:49:40, 4.43s/it] 1%| | 120/18688 [08:34<22:20:01, 4.33s/it] {'loss': '1.058', 'grad_norm': '1.777', 'learning_rate': '1.587e-05', 'epoch': '0.006421', 'num_input_tokens_seen': 402568, 'train_runtime': '514.6', 'train_tokens_per_second': '782.3'}
1%| | 120/18688 [08:34<22:20:01, 4.33s/it] 1%| | 121/18688 [08:39<22:42:40, 4.40s/it] 1%| | 122/18688 [08:43<21:52:04, 4.24s/it] 1%| | 123/18688 [08:46<21:13:23, 4.12s/it] 1%| | 124/18688 [08:50<20:18:10, 3.94s/it] 1%| | 125/18688 [08:55<22:00:13, 4.27s/it] 1%| | 126/18688 [08:59<21:30:27, 4.17s/it] 1%| | 127/18688 [09:03<21:26:22, 4.16s/it] 1%| | 128/18688 [09:07<20:43:46, 4.02s/it] 1%| | 129/18688 [09:11<21:55:18, 4.25s/it] 1%| | 130/18688 [09:17<23:42:08, 4.60s/it] {'loss': '1.097', 'grad_norm': '1.443', 'learning_rate': '1.72e-05', 'epoch': '0.006957', 'num_input_tokens_seen': 434592, 'train_runtime': '557.4', 'train_tokens_per_second': '779.7'}
1%| | 130/18688 [09:17<23:42:08, 4.60s/it] 1%| | 131/18688 [09:21<23:06:46, 4.48s/it] 1%| | 132/18688 [09:27<25:07:45, 4.88s/it] 1%| | 133/18688 [09:31<24:30:51, 4.76s/it] 1%| | 134/18688 [09:35<23:26:44, 4.55s/it] 1%| | 135/18688 [09:40<22:45:50, 4.42s/it] 1%| | 136/18688 [09:46<25:17:17, 4.91s/it] 1%| | 137/18688 [09:50<24:05:24, 4.67s/it] 1%| | 138/18688 [09:54<23:47:16, 4.62s/it] 1%| | 139/18688 [09:58<23:10:15, 4.50s/it] 1%| | 140/18688 [10:03<23:11:04, 4.50s/it] {'loss': '1.061', 'grad_norm': '1.477', 'learning_rate': '1.853e-05', 'epoch': '0.007492', 'num_input_tokens_seen': 471752, 'train_runtime': '603.4', 'train_tokens_per_second': '781.8'}
1%| | 140/18688 [10:03<23:11:04, 4.50s/it] 1%| | 141/18688 [10:07<22:19:45, 4.33s/it] 1%| | 142/18688 [10:11<22:07:56, 4.30s/it] 1%| | 143/18688 [10:15<21:13:56, 4.12s/it] 1%| | 144/18688 [10:18<20:09:37, 3.91s/it] 1%| | 145/18688 [10:22<19:27:25, 3.78s/it] 1%| | 146/18688 [10:26<20:05:50, 3.90s/it] 1%| | 147/18688 [10:30<20:49:19, 4.04s/it] 1%| | 148/18688 [10:34<20:41:23, 4.02s/it] 1%| | 149/18688 [10:38<20:09:44, 3.92s/it] 1%| | 150/18688 [10:42<20:22:29, 3.96s/it] {'loss': '1.067', 'grad_norm': '1.579', 'learning_rate': '1.987e-05', 'epoch': '0.008027', 'num_input_tokens_seen': 501528, 'train_runtime': '642.4', 'train_tokens_per_second': '780.7'}
1%| | 150/18688 [10:42<20:22:29, 3.96s/it] 1%| | 151/18688 [10:46<21:16:50, 4.13s/it] 1%| | 152/18688 [10:50<20:52:23, 4.05s/it] 1%| | 153/18688 [10:54<20:20:51, 3.95s/it] 1%| | 154/18688 [10:58<19:46:47, 3.84s/it] 1%| | 155/18688 [11:01<19:22:03, 3.76s/it] 1%| | 156/18688 [11:06<20:13:33, 3.93s/it] 1%| | 157/18688 [11:09<19:49:27, 3.85s/it] 1%| | 158/18688 [11:13<19:48:52, 3.85s/it] 1%| | 159/18688 [11:18<21:37:51, 4.20s/it] 1%| | 160/18688 [11:22<21:11:38, 4.12s/it] {'loss': '1.027', 'grad_norm': '1.487', 'learning_rate': '2.12e-05', 'epoch': '0.008562', 'num_input_tokens_seen': 532056, 'train_runtime': '682.5', 'train_tokens_per_second': '779.6'}
1%| | 160/18688 [11:22<21:11:38, 4.12s/it] 1%| | 161/18688 [11:27<22:05:44, 4.29s/it] 1%| | 162/18688 [11:31<21:42:37, 4.22s/it] 1%| | 163/18688 [11:36<22:49:12, 4.43s/it] 1%| | 164/18688 [11:40<22:21:39, 4.35s/it] 1%| | 165/18688 [11:45<24:00:16, 4.67s/it] 1%| | 166/18688 [11:49<23:03:51, 4.48s/it] 1%| | 167/18688 [11:53<22:23:00, 4.35s/it] 1%| | 168/18688 [11:58<22:20:20, 4.34s/it] 1%| | 169/18688 [12:02<21:48:37, 4.24s/it] 1%| | 170/18688 [12:05<20:43:08, 4.03s/it] {'loss': '1.069', 'grad_norm': '1.599', 'learning_rate': '2.253e-05', 'epoch': '0.009097', 'num_input_tokens_seen': 564304, 'train_runtime': '725.7', 'train_tokens_per_second': '777.6'}
1%| | 170/18688 [12:05<20:43:08, 4.03s/it] 1%| | 171/18688 [12:09<20:59:30, 4.08s/it] 1%| | 172/18688 [12:14<21:24:49, 4.16s/it] 1%| | 173/18688 [12:19<22:20:10, 4.34s/it] 1%| | 174/18688 [12:22<21:42:43, 4.22s/it] 1%| | 175/18688 [12:27<21:57:52, 4.27s/it] 1%| | 176/18688 [12:31<22:29:22, 4.37s/it] 1%| | 177/18688 [12:35<21:26:37, 4.17s/it] 1%| | 178/18688 [12:39<21:12:32, 4.12s/it] 1%| | 179/18688 [12:43<20:07:09, 3.91s/it] 1%| | 180/18688 [12:46<20:01:22, 3.89s/it] {'loss': '1.116', 'grad_norm': '1.496', 'learning_rate': '2.387e-05', 'epoch': '0.009632', 'num_input_tokens_seen': 595424, 'train_runtime': '767', 'train_tokens_per_second': '776.4'}
1%| | 180/18688 [12:46<20:01:22, 3.89s/it] 1%| | 181/18688 [12:50<19:46:55, 3.85s/it] 1%| | 182/18688 [12:54<19:40:11, 3.83s/it] 1%| | 183/18688 [12:59<21:30:45, 4.19s/it] 1%| | 184/18688 [13:03<20:50:53, 4.06s/it] 1%| | 185/18688 [13:07<20:27:57, 3.98s/it] 1%| | 186/18688 [13:10<20:20:36, 3.96s/it] 1%| | 187/18688 [13:15<20:38:46, 4.02s/it] 1%| | 188/18688 [13:19<21:10:33, 4.12s/it] 1%| | 189/18688 [13:23<21:15:24, 4.14s/it] 1%| | 190/18688 [13:28<22:20:38, 4.35s/it] {'loss': '1.039', 'grad_norm': '1.644', 'learning_rate': '2.52e-05', 'epoch': '0.01017', 'num_input_tokens_seen': 628248, 'train_runtime': '808.5', 'train_tokens_per_second': '777.1'}
1%| | 190/18688 [13:28<22:20:38, 4.35s/it] 1%| | 191/18688 [13:33<23:26:35, 4.56s/it] 1%| | 192/18688 [13:37<22:23:05, 4.36s/it] 1%| | 193/18688 [13:40<20:55:22, 4.07s/it] 1%| | 194/18688 [13:44<20:35:18, 4.01s/it] 1%| | 195/18688 [13:49<21:35:07, 4.20s/it] 1%| | 196/18688 [13:54<22:25:07, 4.36s/it] 1%| | 197/18688 [13:58<21:50:43, 4.25s/it] 1%| | 198/18688 [14:01<20:48:15, 4.05s/it] 1%| | 199/18688 [14:05<20:42:13, 4.03s/it] 1%| | 200/18688 [14:10<21:41:11, 4.22s/it] {'loss': '1.051', 'grad_norm': '1.412', 'learning_rate': '2.653e-05', 'epoch': '0.0107', 'num_input_tokens_seen': 661320, 'train_runtime': '850.3', 'train_tokens_per_second': '777.7'}
1%| | 200/18688 [14:10<21:41:11, 4.22s/it] 1%| | 201/18688 [14:14<21:26:36, 4.18s/it] 1%| | 202/18688 [14:17<20:32:55, 4.00s/it] 1%| | 203/18688 [14:22<21:56:19, 4.27s/it] 1%| | 204/18688 [14:27<22:23:23, 4.36s/it] 1%| | 205/18688 [14:32<22:41:35, 4.42s/it] 1%| | 206/18688 [14:36<23:07:07, 4.50s/it] 1%| | 207/18688 [14:41<23:18:17, 4.54s/it] 1%| | 208/18688 [14:45<22:37:05, 4.41s/it] 1%| | 209/18688 [14:50<23:52:07, 4.65s/it] 1%| | 210/18688 [14:54<22:09:06, 4.32s/it] {'loss': '1.159', 'grad_norm': '2.034', 'learning_rate': '2.787e-05', 'epoch': '0.01124', 'num_input_tokens_seen': 696656, 'train_runtime': '894.2', 'train_tokens_per_second': '779.1'}
1%| | 210/18688 [14:54<22:09:06, 4.32s/it] 1%| | 211/18688 [14:57<21:03:24, 4.10s/it] 1%| | 212/18688 [15:02<21:32:30, 4.20s/it] 1%| | 213/18688 [15:05<20:45:23, 4.04s/it] 1%| | 214/18688 [15:09<20:33:17, 4.01s/it] 1%| | 215/18688 [15:13<20:43:44, 4.04s/it] 1%| | 216/18688 [15:18<21:02:06, 4.10s/it] 1%| | 217/18688 [15:21<20:11:10, 3.93s/it] 1%| | 218/18688 [15:25<20:42:19, 4.04s/it] 1%| | 219/18688 [15:29<20:16:03, 3.95s/it] 1%| | 220/18688 [15:34<21:37:41, 4.22s/it] {'loss': '1.074', 'grad_norm': '0.9515', 'learning_rate': '2.92e-05', 'epoch': '0.01177', 'num_input_tokens_seen': 727368, 'train_runtime': '934.6', 'train_tokens_per_second': '778.3'}
1%| | 220/18688 [15:34<21:37:41, 4.22s/it] 1%| | 221/18688 [15:38<21:43:38, 4.24s/it] 1%| | 222/18688 [15:42<21:30:35, 4.19s/it] 1%| | 223/18688 [15:47<21:50:08, 4.26s/it] 1%| | 224/18688 [15:51<21:54:44, 4.27s/it] 1%| | 225/18688 [15:56<22:43:10, 4.43s/it] 1%| | 226/18688 [16:00<22:07:10, 4.31s/it] 1%| | 227/18688 [16:04<22:05:00, 4.31s/it] 1%| | 228/18688 [16:10<23:31:53, 4.59s/it] 1%| | 229/18688 [16:14<22:46:12, 4.44s/it] 1%| | 230/18688 [16:18<22:10:35, 4.33s/it] {'loss': '1.057', 'grad_norm': '2.239', 'learning_rate': '3.053e-05', 'epoch': '0.01231', 'num_input_tokens_seen': 761544, 'train_runtime': '978.2', 'train_tokens_per_second': '778.5'}
1%| | 230/18688 [16:18<22:10:35, 4.33s/it] 1%| | 231/18688 [16:22<22:12:20, 4.33s/it] 1%| | 232/18688 [16:27<23:39:06, 4.61s/it] 1%| | 233/18688 [16:32<23:12:46, 4.53s/it] 1%|▏ | 234/18688 [16:36<22:36:25, 4.41s/it] 1%|▏ | 235/18688 [16:39<21:13:13, 4.14s/it] 1%|▏ | 236/18688 [16:45<23:13:50, 4.53s/it] 1%|▏ | 237/18688 [16:48<21:41:50, 4.23s/it] 1%|▏ | 238/18688 [16:52<21:32:49, 4.20s/it] 1%|▏ | 239/18688 [16:57<21:35:19, 4.21s/it] 1%|▏ | 240/18688 [17:00<20:23:12, 3.98s/it] {'loss': '1.07', 'grad_norm': '1.606', 'learning_rate': '3.187e-05', 'epoch': '0.01284', 'num_input_tokens_seen': 797960, 'train_runtime': '1021', 'train_tokens_per_second': '781.9'}
1%|▏ | 240/18688 [17:00<20:23:12, 3.98s/it] 1%|▏ | 241/18688 [17:04<21:02:26, 4.11s/it] 1%|▏ | 242/18688 [17:09<21:14:01, 4.14s/it] 1%|▏ | 243/18688 [17:13<21:00:09, 4.10s/it] 1%|▏ | 244/18688 [17:16<20:21:23, 3.97s/it] 1%|▏ | 245/18688 [17:20<20:15:56, 3.96s/it] 1%|▏ | 246/18688 [17:24<20:15:10, 3.95s/it] 1%|▏ | 247/18688 [17:28<20:27:18, 3.99s/it] 1%|▏ | 248/18688 [17:33<20:51:54, 4.07s/it] 1%|▏ | 249/18688 [17:37<21:49:55, 4.26s/it] 1%|▏ | 250/18688 [17:42<22:07:07, 4.32s/it] {'loss': '1.073', 'grad_norm': '1.178', 'learning_rate': '3.32e-05', 'epoch': '0.01338', 'num_input_tokens_seen': 831064, 'train_runtime': '1062', 'train_tokens_per_second': '782.4'}
1%|▏ | 250/18688 [17:42<22:07:07, 4.32s/it] 1%|▏ | 251/18688 [17:46<21:51:23, 4.27s/it] 1%|▏ | 252/18688 [17:50<21:00:23, 4.10s/it] 1%|▏ | 253/18688 [17:53<20:37:45, 4.03s/it] 1%|▏ | 254/18688 [17:57<20:26:58, 3.99s/it] 1%|▏ | 255/18688 [18:01<19:50:39, 3.88s/it] 1%|▏ | 256/18688 [18:05<20:12:46, 3.95s/it] 1%|▏ | 257/18688 [18:09<20:54:30, 4.08s/it] 1%|▏ | 258/18688 [18:13<20:28:18, 4.00s/it] 1%|▏ | 259/18688 [18:17<19:49:54, 3.87s/it] 1%|▏ | 260/18688 [18:21<20:25:35, 3.99s/it] {'loss': '1.009', 'grad_norm': '1.58', 'learning_rate': '3.453e-05', 'epoch': '0.01391', 'num_input_tokens_seen': 862936, 'train_runtime': '1102', 'train_tokens_per_second': '783.3'}
1%|▏ | 260/18688 [18:21<20:25:35, 3.99s/it] 1%|▏ | 261/18688 [18:25<20:36:46, 4.03s/it] 1%|▏ | 262/18688 [18:29<20:28:40, 4.00s/it] 1%|▏ | 263/18688 [18:33<20:55:15, 4.09s/it] 1%|▏ | 264/18688 [18:37<20:24:53, 3.99s/it] 1%|▏ | 265/18688 [18:41<19:41:42, 3.85s/it] 1%|▏ | 266/18688 [18:44<19:31:26, 3.82s/it] 1%|▏ | 267/18688 [18:49<21:14:50, 4.15s/it] 1%|▏ | 268/18688 [18:54<21:11:42, 4.14s/it] 1%|▏ | 269/18688 [18:57<20:35:32, 4.02s/it] 1%|▏ | 270/18688 [19:01<19:53:33, 3.89s/it] {'loss': '1.007', 'grad_norm': '1.435', 'learning_rate': '3.587e-05', 'epoch': '0.01445', 'num_input_tokens_seen': 893208, 'train_runtime': '1141', 'train_tokens_per_second': '782.6'}
1%|▏ | 270/18688 [19:01<19:53:33, 3.89s/it] 1%|▏ | 271/18688 [19:05<20:23:28, 3.99s/it] 1%|▏ | 272/18688 [19:09<20:58:33, 4.10s/it] 1%|▏ | 273/18688 [19:14<20:54:33, 4.09s/it] 1%|▏ | 274/18688 [19:18<21:18:32, 4.17s/it] 1%|▏ | 275/18688 [19:22<20:45:41, 4.06s/it] 1%|▏ | 276/18688 [19:26<21:09:03, 4.14s/it] 1%|▏ | 277/18688 [19:30<21:04:04, 4.12s/it] 1%|▏ | 278/18688 [19:34<20:27:43, 4.00s/it] 1%|▏ | 279/18688 [19:39<22:13:40, 4.35s/it] 1%|▏ | 280/18688 [19:43<22:09:12, 4.33s/it] {'loss': '1.083', 'grad_norm': '1.42', 'learning_rate': '3.72e-05', 'epoch': '0.01498', 'num_input_tokens_seen': 925664, 'train_runtime': '1184', 'train_tokens_per_second': '782'}
1%|▏ | 280/18688 [19:43<22:09:12, 4.33s/it] 2%|▏ | 281/18688 [19:47<21:04:48, 4.12s/it] 2%|▏ | 282/18688 [19:52<22:04:32, 4.32s/it] 2%|▏ | 283/18688 [19:57<23:20:32, 4.57s/it] 2%|▏ | 284/18688 [20:01<23:25:37, 4.58s/it] 2%|▏ | 285/18688 [20:06<22:50:20, 4.47s/it] 2%|▏ | 286/18688 [20:11<23:43:51, 4.64s/it] 2%|▏ | 287/18688 [20:14<21:54:22, 4.29s/it] 2%|▏ | 288/18688 [20:18<21:56:15, 4.29s/it] 2%|▏ | 289/18688 [20:23<22:08:44, 4.33s/it] 2%|▏ | 290/18688 [20:27<22:05:26, 4.32s/it] {'loss': '1.072', 'grad_norm': '1.356', 'learning_rate': '3.853e-05', 'epoch': '0.01552', 'num_input_tokens_seen': 960824, 'train_runtime': '1228', 'train_tokens_per_second': '782.6'}
2%|▏ | 290/18688 [20:27<22:05:26, 4.32s/it] 2%|▏ | 291/18688 [20:32<22:20:43, 4.37s/it] 2%|▏ | 292/18688 [20:36<21:39:43, 4.24s/it] 2%|▏ | 293/18688 [20:40<21:23:02, 4.18s/it] 2%|▏ | 294/18688 [20:44<21:09:25, 4.14s/it] 2%|▏ | 295/18688 [20:48<21:08:19, 4.14s/it] 2%|▏ | 296/18688 [20:52<21:40:58, 4.24s/it] 2%|▏ | 297/18688 [20:56<21:35:28, 4.23s/it] 2%|▏ | 298/18688 [21:01<22:21:55, 4.38s/it] 2%|▏ | 299/18688 [21:06<22:19:40, 4.37s/it] 2%|▏ | 300/18688 [21:10<21:48:20, 4.27s/it] {'loss': '1.052', 'grad_norm': '1.321', 'learning_rate': '3.987e-05', 'epoch': '0.01605', 'num_input_tokens_seen': 993184, 'train_runtime': '1270', 'train_tokens_per_second': '782'}
2%|▏ | 300/18688 [21:10<21:48:20, 4.27s/it] 2%|▏ | 301/18688 [21:13<20:31:20, 4.02s/it] 2%|▏ | 302/18688 [21:17<21:06:55, 4.13s/it] 2%|▏ | 303/18688 [21:21<20:44:46, 4.06s/it] 2%|▏ | 304/18688 [21:25<19:56:21, 3.90s/it] 2%|▏ | 305/18688 [21:28<19:11:18, 3.76s/it] 2%|▏ | 306/18688 [21:32<19:02:39, 3.73s/it] 2%|▏ | 307/18688 [21:35<18:41:32, 3.66s/it] 2%|▏ | 308/18688 [21:39<19:15:26, 3.77s/it] 2%|▏ | 309/18688 [21:44<19:52:29, 3.89s/it] 2%|▏ | 310/18688 [21:48<20:11:17, 3.95s/it] {'loss': '1.05', 'grad_norm': '1.131', 'learning_rate': '4.12e-05', 'epoch': '0.01659', 'num_input_tokens_seen': 1022656, 'train_runtime': '1308', 'train_tokens_per_second': '781.7'}
2%|▏ | 310/18688 [21:48<20:11:17, 3.95s/it] 2%|▏ | 311/18688 [21:51<19:33:35, 3.83s/it] 2%|▏ | 312/18688 [21:55<18:54:59, 3.71s/it] 2%|▏ | 313/18688 [21:59<19:41:54, 3.86s/it] 2%|▏ | 314/18688 [22:03<19:22:12, 3.80s/it] 2%|▏ | 315/18688 [22:06<19:21:16, 3.79s/it] 2%|▏ | 316/18688 [22:10<19:48:26, 3.88s/it] 2%|▏ | 317/18688 [22:15<20:50:44, 4.08s/it] 2%|▏ | 318/18688 [22:19<20:22:02, 3.99s/it] 2%|▏ | 319/18688 [22:22<19:52:53, 3.90s/it] 2%|▏ | 320/18688 [22:27<20:12:40, 3.96s/it] {'loss': '1.021', 'grad_norm': '1.15', 'learning_rate': '4.253e-05', 'epoch': '0.01712', 'num_input_tokens_seen': 1053904, 'train_runtime': '1347', 'train_tokens_per_second': '782.4'}
2%|▏ | 320/18688 [22:27<20:12:40, 3.96s/it] 2%|▏ | 321/18688 [22:31<20:26:51, 4.01s/it] 2%|▏ | 322/18688 [22:35<20:18:54, 3.98s/it] 2%|▏ | 323/18688 [22:40<22:21:23, 4.38s/it] 2%|▏ | 324/18688 [22:44<21:15:21, 4.17s/it] 2%|▏ | 325/18688 [22:47<20:17:40, 3.98s/it] 2%|▏ | 326/18688 [22:51<20:19:19, 3.98s/it] 2%|▏ | 327/18688 [22:56<21:30:50, 4.22s/it] 2%|▏ | 328/18688 [23:00<21:27:37, 4.21s/it] 2%|▏ | 329/18688 [23:05<22:48:21, 4.47s/it] 2%|▏ | 330/18688 [23:09<21:51:21, 4.29s/it] {'loss': '1.074', 'grad_norm': '1.235', 'learning_rate': '4.387e-05', 'epoch': '0.01766', 'num_input_tokens_seen': 1090264, 'train_runtime': '1390', 'train_tokens_per_second': '784.6'}
2%|▏ | 330/18688 [23:09<21:51:21, 4.29s/it] 2%|▏ | 331/18688 [23:13<20:45:42, 4.07s/it] 2%|▏ | 332/18688 [23:16<20:30:48, 4.02s/it] 2%|▏ | 333/18688 [23:20<20:25:04, 4.00s/it] 2%|▏ | 334/18688 [23:24<20:26:34, 4.01s/it] 2%|▏ | 335/18688 [23:29<20:43:20, 4.06s/it] 2%|▏ | 336/18688 [23:33<20:28:27, 4.02s/it] 2%|▏ | 337/18688 [23:37<21:03:46, 4.13s/it] 2%|▏ | 338/18688 [23:42<22:08:51, 4.35s/it] 2%|▏ | 339/18688 [23:46<22:37:12, 4.44s/it] 2%|▏ | 340/18688 [23:52<23:39:40, 4.64s/it] {'loss': '0.9702', 'grad_norm': '1.484', 'learning_rate': '4.52e-05', 'epoch': '0.01819', 'num_input_tokens_seen': 1121696, 'train_runtime': '1432', 'train_tokens_per_second': '783.3'}
2%|▏ | 340/18688 [23:52<23:39:40, 4.64s/it] 2%|▏ | 341/18688 [23:56<22:34:32, 4.43s/it] 2%|▏ | 342/18688 [24:00<22:07:32, 4.34s/it] 2%|▏ | 343/18688 [24:04<22:16:23, 4.37s/it] 2%|▏ | 344/18688 [24:08<21:50:24, 4.29s/it] 2%|▏ | 345/18688 [24:12<21:36:10, 4.24s/it] 2%|▏ | 346/18688 [24:17<22:06:15, 4.34s/it] 2%|▏ | 347/18688 [24:22<22:41:45, 4.45s/it] 2%|▏ | 348/18688 [24:26<22:11:53, 4.36s/it] 2%|▏ | 349/18688 [24:30<22:30:43, 4.42s/it] 2%|▏ | 350/18688 [24:34<21:11:37, 4.16s/it] {'loss': '1.06', 'grad_norm': '1.551', 'learning_rate': '4.653e-05', 'epoch': '0.01873', 'num_input_tokens_seen': 1153216, 'train_runtime': '1474', 'train_tokens_per_second': '782.2'}
2%|▏ | 350/18688 [24:34<21:11:37, 4.16s/it] 2%|▏ | 351/18688 [24:38<20:24:30, 4.01s/it] 2%|▏ | 352/18688 [24:42<20:41:07, 4.06s/it] 2%|▏ | 353/18688 [24:46<21:03:28, 4.13s/it] 2%|▏ | 354/18688 [24:50<20:21:48, 4.00s/it] 2%|▏ | 355/18688 [24:54<20:17:16, 3.98s/it] 2%|▏ | 356/18688 [25:00<23:15:17, 4.57s/it] 2%|▏ | 357/18688 [25:04<22:47:55, 4.48s/it] 2%|▏ | 358/18688 [25:08<23:01:21, 4.52s/it] 2%|▏ | 359/18688 [25:13<23:07:15, 4.54s/it] 2%|▏ | 360/18688 [25:17<22:07:41, 4.35s/it] {'loss': '1.045', 'grad_norm': '1.164', 'learning_rate': '4.787e-05', 'epoch': '0.01926', 'num_input_tokens_seen': 1188456, 'train_runtime': '1517', 'train_tokens_per_second': '783.2'}
2%|▏ | 360/18688 [25:17<22:07:41, 4.35s/it] 2%|▏ | 361/18688 [25:21<21:32:22, 4.23s/it] 2%|▏ | 362/18688 [25:25<22:04:10, 4.34s/it] 2%|▏ | 363/18688 [25:30<21:59:00, 4.32s/it] 2%|▏ | 364/18688 [25:34<22:36:11, 4.44s/it] 2%|▏ | 365/18688 [25:39<22:48:13, 4.48s/it] 2%|▏ | 366/18688 [25:43<21:19:52, 4.19s/it] 2%|▏ | 367/18688 [25:47<22:23:31, 4.40s/it] 2%|▏ | 368/18688 [25:51<21:28:48, 4.22s/it] 2%|▏ | 369/18688 [25:55<20:33:30, 4.04s/it] 2%|▏ | 370/18688 [26:00<21:34:44, 4.24s/it] {'loss': '1.072', 'grad_norm': '1.244', 'learning_rate': '4.92e-05', 'epoch': '0.0198', 'num_input_tokens_seen': 1222640, 'train_runtime': '1560', 'train_tokens_per_second': '783.7'}
2%|▏ | 370/18688 [26:00<21:34:44, 4.24s/it] 2%|▏ | 371/18688 [26:03<20:35:08, 4.05s/it] 2%|▏ | 372/18688 [26:07<19:59:18, 3.93s/it] 2%|▏ | 373/18688 [26:11<19:52:20, 3.91s/it] 2%|▏ | 374/18688 [26:15<20:29:33, 4.03s/it] 2%|▏ | 375/18688 [26:19<20:30:58, 4.03s/it] 2%|▏ | 376/18688 [26:23<20:13:56, 3.98s/it] 2%|▏ | 377/18688 [26:27<20:32:41, 4.04s/it] 2%|▏ | 378/18688 [26:31<20:24:30, 4.01s/it] 2%|▏ | 379/18688 [26:35<19:42:30, 3.88s/it] 2%|▏ | 380/18688 [26:39<20:01:24, 3.94s/it] {'loss': '1.081', 'grad_norm': '1.333', 'learning_rate': '5.053e-05', 'epoch': '0.02033', 'num_input_tokens_seen': 1253888, 'train_runtime': '1599', 'train_tokens_per_second': '784.1'}
2%|▏ | 380/18688 [26:39<20:01:24, 3.94s/it] 2%|▏ | 381/18688 [26:42<19:35:29, 3.85s/it] 2%|▏ | 382/18688 [26:46<18:55:06, 3.72s/it] 2%|▏ | 383/18688 [26:50<19:39:07, 3.86s/it] 2%|▏ | 384/18688 [26:54<19:57:18, 3.92s/it] 2%|▏ | 385/18688 [26:58<19:35:30, 3.85s/it] 2%|▏ | 386/18688 [27:02<20:24:51, 4.02s/it] 2%|▏ | 387/18688 [27:07<21:07:39, 4.16s/it] 2%|▏ | 388/18688 [27:10<20:15:23, 3.98s/it] 2%|▏ | 389/18688 [27:14<19:27:04, 3.83s/it] 2%|▏ | 390/18688 [27:18<20:49:23, 4.10s/it] {'loss': '1.005', 'grad_norm': '1.082', 'learning_rate': '5.187e-05', 'epoch': '0.02087', 'num_input_tokens_seen': 1286896, 'train_runtime': '1639', 'train_tokens_per_second': '785.3'}
2%|▏ | 390/18688 [27:18<20:49:23, 4.10s/it] 2%|▏ | 391/18688 [27:22<20:39:13, 4.06s/it] 2%|▏ | 392/18688 [27:26<19:51:23, 3.91s/it] 2%|▏ | 393/18688 [27:29<19:19:39, 3.80s/it] 2%|▏ | 394/18688 [27:33<19:35:30, 3.86s/it] 2%|▏ | 395/18688 [27:38<20:53:43, 4.11s/it] 2%|▏ | 396/18688 [27:42<21:11:35, 4.17s/it] 2%|▏ | 397/18688 [27:46<20:59:22, 4.13s/it] 2%|▏ | 398/18688 [27:50<20:34:31, 4.05s/it] 2%|▏ | 399/18688 [27:55<21:39:35, 4.26s/it] 2%|▏ | 400/18688 [27:59<21:22:13, 4.21s/it] {'loss': '1.027', 'grad_norm': '1.391', 'learning_rate': '5.32e-05', 'epoch': '0.0214', 'num_input_tokens_seen': 1319712, 'train_runtime': '1680', 'train_tokens_per_second': '785.7'}
2%|▏ | 400/18688 [27:59<21:22:13, 4.21s/it] 2%|▏ | 401/18688 [28:04<21:52:24, 4.31s/it] 2%|▏ | 402/18688 [28:07<20:42:45, 4.08s/it] 2%|▏ | 403/18688 [28:12<21:07:37, 4.16s/it] 2%|▏ | 404/18688 [28:16<20:58:47, 4.13s/it] 2%|▏ | 405/18688 [28:20<21:13:29, 4.18s/it] 2%|▏ | 406/18688 [28:25<23:16:32, 4.58s/it] 2%|▏ | 407/18688 [28:30<22:33:38, 4.44s/it] 2%|▏ | 408/18688 [28:33<21:14:03, 4.18s/it] 2%|▏ | 409/18688 [28:37<20:34:29, 4.05s/it] 2%|▏ | 410/18688 [28:41<19:55:41, 3.93s/it] {'loss': '1.076', 'grad_norm': '1.058', 'learning_rate': '5.453e-05', 'epoch': '0.02194', 'num_input_tokens_seen': 1352936, 'train_runtime': '1721', 'train_tokens_per_second': '786.1'}
2%|▏ | 410/18688 [28:41<19:55:41, 3.93s/it] 2%|▏ | 411/18688 [28:45<20:18:50, 4.00s/it] 2%|▏ | 412/18688 [28:48<19:36:33, 3.86s/it] 2%|▏ | 413/18688 [28:52<20:09:08, 3.97s/it] 2%|▏ | 414/18688 [28:57<20:35:24, 4.06s/it] 2%|▏ | 415/18688 [29:01<20:21:27, 4.01s/it] 2%|▏ | 416/18688 [29:05<21:09:10, 4.17s/it] 2%|▏ | 417/18688 [29:09<20:01:18, 3.94s/it] 2%|▏ | 418/18688 [29:12<19:13:02, 3.79s/it] 2%|▏ | 419/18688 [29:17<20:24:34, 4.02s/it] 2%|▏ | 420/18688 [29:21<20:31:32, 4.04s/it] {'loss': '1.072', 'grad_norm': '1.274', 'learning_rate': '5.587e-05', 'epoch': '0.02247', 'num_input_tokens_seen': 1385008, 'train_runtime': '1761', 'train_tokens_per_second': '786.4'}
2%|▏ | 420/18688 [29:21<20:31:32, 4.04s/it] 2%|▏ | 421/18688 [29:25<21:22:02, 4.21s/it] 2%|▏ | 422/18688 [29:30<22:45:49, 4.49s/it] 2%|▏ | 423/18688 [29:34<21:33:30, 4.25s/it] 2%|▏ | 424/18688 [29:38<21:15:30, 4.19s/it] 2%|▏ | 425/18688 [29:42<21:22:39, 4.21s/it] 2%|▏ | 426/18688 [29:46<20:35:21, 4.06s/it] 2%|▏ | 427/18688 [29:50<20:44:19, 4.09s/it] 2%|▏ | 428/18688 [29:56<22:56:17, 4.52s/it] 2%|▏ | 429/18688 [30:00<22:20:06, 4.40s/it] 2%|▏ | 430/18688 [30:03<20:54:49, 4.12s/it] {'loss': '1.028', 'grad_norm': '1.271', 'learning_rate': '5.72e-05', 'epoch': '0.02301', 'num_input_tokens_seen': 1420952, 'train_runtime': '1804', 'train_tokens_per_second': '787.7'}
2%|▏ | 430/18688 [30:03<20:54:49, 4.12s/it] 2%|▏ | 431/18688 [30:08<21:07:13, 4.16s/it] 2%|▏ | 432/18688 [30:12<20:46:43, 4.10s/it] 2%|▏ | 433/18688 [30:16<20:29:04, 4.04s/it] 2%|▏ | 434/18688 [30:20<20:46:42, 4.10s/it] 2%|▏ | 435/18688 [30:24<20:23:21, 4.02s/it] 2%|▏ | 436/18688 [30:27<19:39:13, 3.88s/it] 2%|▏ | 437/18688 [30:31<20:16:46, 4.00s/it] 2%|▏ | 438/18688 [30:35<19:40:03, 3.88s/it] 2%|▏ | 439/18688 [30:39<19:21:13, 3.82s/it] 2%|▏ | 440/18688 [30:43<19:31:09, 3.85s/it] {'loss': '1.069', 'grad_norm': '1.132', 'learning_rate': '5.853e-05', 'epoch': '0.02355', 'num_input_tokens_seen': 1452280, 'train_runtime': '1843', 'train_tokens_per_second': '787.9'}
2%|▏ | 440/18688 [30:43<19:31:09, 3.85s/it] 2%|▏ | 441/18688 [30:46<19:30:44, 3.85s/it] 2%|▏ | 442/18688 [30:50<19:46:28, 3.90s/it] 2%|▏ | 443/18688 [30:54<19:54:37, 3.93s/it] 2%|▏ | 444/18688 [31:00<22:01:25, 4.35s/it] 2%|▏ | 445/18688 [31:05<22:48:16, 4.50s/it] 2%|▏ | 446/18688 [31:08<21:39:21, 4.27s/it] 2%|▏ | 447/18688 [31:12<21:01:34, 4.15s/it] 2%|▏ | 448/18688 [31:16<21:00:47, 4.15s/it] 2%|▏ | 449/18688 [31:21<21:10:11, 4.18s/it] 2%|▏ | 450/18688 [31:25<20:52:24, 4.12s/it] {'loss': '1.032', 'grad_norm': '1.283', 'learning_rate': '5.987e-05', 'epoch': '0.02408', 'num_input_tokens_seen': 1487680, 'train_runtime': '1885', 'train_tokens_per_second': '789.2'}
2%|▏ | 450/18688 [31:25<20:52:24, 4.12s/it] 2%|▏ | 451/18688 [31:28<20:04:54, 3.96s/it] 2%|▏ | 452/18688 [31:32<20:25:12, 4.03s/it] 2%|▏ | 453/18688 [31:36<19:44:47, 3.90s/it] 2%|▏ | 454/18688 [31:41<20:56:04, 4.13s/it] 2%|▏ | 455/18688 [31:46<22:40:09, 4.48s/it] 2%|▏ | 456/18688 [31:50<22:08:50, 4.37s/it] 2%|▏ | 457/18688 [31:54<20:51:58, 4.12s/it] 2%|▏ | 458/18688 [31:58<21:20:36, 4.21s/it] 2%|▏ | 459/18688 [32:02<20:31:25, 4.05s/it] 2%|▏ | 460/18688 [32:05<19:57:21, 3.94s/it] {'loss': '0.98', 'grad_norm': '1.148', 'learning_rate': '6.12e-05', 'epoch': '0.02462', 'num_input_tokens_seen': 1521584, 'train_runtime': '1926', 'train_tokens_per_second': '790'}
2%|▏ | 460/18688 [32:05<19:57:21, 3.94s/it] 2%|▏ | 461/18688 [32:09<19:45:35, 3.90s/it] 2%|▏ | 462/18688 [32:13<19:14:09, 3.80s/it] 2%|▏ | 463/18688 [32:17<20:14:28, 4.00s/it] 2%|▏ | 464/18688 [32:21<20:01:13, 3.95s/it] 2%|▏ | 465/18688 [32:25<19:26:26, 3.84s/it] 2%|▏ | 466/18688 [32:29<19:56:38, 3.94s/it] 2%|▏ | 467/18688 [32:33<19:59:17, 3.95s/it] 3%|▎ | 468/18688 [32:37<20:49:09, 4.11s/it] 3%|▎ | 469/18688 [32:41<20:01:18, 3.96s/it] 3%|▎ | 470/18688 [32:46<22:02:32, 4.36s/it] {'loss': '1.04', 'grad_norm': '0.9741', 'learning_rate': '6.253e-05', 'epoch': '0.02515', 'num_input_tokens_seen': 1553928, 'train_runtime': '1967', 'train_tokens_per_second': '790.1'}
3%|▎ | 470/18688 [32:46<22:02:32, 4.36s/it] 3%|▎ | 471/18688 [32:51<22:30:26, 4.45s/it] 3%|▎ | 472/18688 [32:55<22:14:54, 4.40s/it] 3%|▎ | 473/18688 [32:59<21:38:48, 4.28s/it] 3%|▎ | 474/18688 [33:03<21:28:03, 4.24s/it] 3%|▎ | 475/18688 [33:07<20:43:43, 4.10s/it] 3%|▎ | 476/18688 [33:11<20:48:54, 4.11s/it] 3%|▎ | 477/18688 [33:15<20:16:28, 4.01s/it] 3%|▎ | 478/18688 [33:19<19:49:12, 3.92s/it] 3%|▎ | 479/18688 [33:23<20:52:21, 4.13s/it] 3%|▎ | 480/18688 [33:28<21:07:02, 4.18s/it] {'loss': '1.109', 'grad_norm': '1.153', 'learning_rate': '6.387e-05', 'epoch': '0.02569', 'num_input_tokens_seen': 1588296, 'train_runtime': '2008', 'train_tokens_per_second': '790.9'}
3%|▎ | 480/18688 [33:28<21:07:02, 4.18s/it] 3%|▎ | 481/18688 [33:33<22:16:16, 4.40s/it] 3%|▎ | 482/18688 [33:37<22:03:03, 4.36s/it] 3%|▎ | 483/18688 [33:41<21:38:50, 4.28s/it] 3%|▎ | 484/18688 [33:45<20:59:51, 4.15s/it] 3%|▎ | 485/18688 [33:50<21:56:48, 4.34s/it] 3%|▎ | 486/18688 [33:53<21:22:27, 4.23s/it] 3%|▎ | 487/18688 [33:58<21:34:42, 4.27s/it] 3%|▎ | 488/18688 [34:02<21:04:08, 4.17s/it] 3%|▎ | 489/18688 [34:06<21:25:30, 4.24s/it] 3%|▎ | 490/18688 [34:10<21:00:24, 4.16s/it] {'loss': '1.012', 'grad_norm': '1.456', 'learning_rate': '6.52e-05', 'epoch': '0.02622', 'num_input_tokens_seen': 1620376, 'train_runtime': '2051', 'train_tokens_per_second': '790.2'}
3%|▎ | 490/18688 [34:10<21:00:24, 4.16s/it] 3%|▎ | 491/18688 [34:14<19:56:11, 3.94s/it] 3%|▎ | 492/18688 [34:18<20:13:14, 4.00s/it] 3%|▎ | 493/18688 [34:22<19:58:15, 3.95s/it] 3%|▎ | 494/18688 [34:26<20:20:30, 4.02s/it] 3%|▎ | 495/18688 [34:30<20:34:54, 4.07s/it] 3%|▎ | 496/18688 [34:34<19:48:37, 3.92s/it] 3%|▎ | 497/18688 [34:37<19:10:31, 3.79s/it] 3%|▎ | 498/18688 [34:40<18:36:10, 3.68s/it] 3%|▎ | 499/18688 [34:44<19:02:02, 3.77s/it] 3%|▎ | 500/18688 [34:50<21:53:04, 4.33s/it] {'loss': '1.014', 'grad_norm': '1.018', 'learning_rate': '6.653e-05', 'epoch': '0.02676', 'num_input_tokens_seen': 1653200, 'train_runtime': '2091', 'train_tokens_per_second': '790.8'}
3%|▎ | 500/18688 [34:50<21:53:04, 4.33s/it] 3%|▎ | 501/18688 [34:53<20:29:58, 4.06s/it] 3%|▎ | 502/18688 [34:57<19:53:55, 3.94s/it] 3%|▎ | 503/18688 [35:01<19:20:43, 3.83s/it] 3%|▎ | 504/18688 [35:04<18:56:24, 3.75s/it] 3%|▎ | 505/18688 [35:08<19:26:03, 3.85s/it] 3%|▎ | 506/18688 [35:13<20:16:01, 4.01s/it] 3%|▎ | 507/18688 [35:16<19:45:36, 3.91s/it] 3%|▎ | 508/18688 [35:20<19:26:27, 3.85s/it] 3%|▎ | 509/18688 [35:24<19:56:53, 3.95s/it] 3%|▎ | 510/18688 [35:28<19:29:16, 3.86s/it] {'loss': '1.069', 'grad_norm': '1.201', 'learning_rate': '6.787e-05', 'epoch': '0.02729', 'num_input_tokens_seen': 1682936, 'train_runtime': '2128', 'train_tokens_per_second': '790.7'}
3%|▎ | 510/18688 [35:28<19:29:16, 3.86s/it] 3%|▎ | 511/18688 [35:31<18:48:24, 3.72s/it] 3%|▎ | 512/18688 [35:37<21:23:17, 4.24s/it] 3%|▎ | 513/18688 [35:41<21:46:39, 4.31s/it] 3%|▎ | 514/18688 [35:45<21:16:59, 4.22s/it] 3%|▎ | 515/18688 [35:49<20:15:25, 4.01s/it] 3%|▎ | 516/18688 [35:53<20:22:00, 4.03s/it] 3%|▎ | 517/18688 [35:57<19:49:27, 3.93s/it] 3%|▎ | 518/18688 [36:01<20:17:24, 4.02s/it] 3%|▎ | 519/18688 [36:05<21:00:54, 4.16s/it] 3%|▎ | 520/18688 [36:10<21:15:20, 4.21s/it] {'loss': '1.009', 'grad_norm': '1.12', 'learning_rate': '6.92e-05', 'epoch': '0.02783', 'num_input_tokens_seen': 1718448, 'train_runtime': '2170', 'train_tokens_per_second': '791.9'}
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3%|▎ | 530/18688 [36:49<20:32:42, 4.07s/it] 3%|▎ | 531/18688 [36:53<20:09:09, 4.00s/it] 3%|▎ | 532/18688 [36:57<20:17:02, 4.02s/it] 3%|▎ | 533/18688 [37:01<20:20:52, 4.03s/it] 3%|▎ | 534/18688 [37:05<20:30:26, 4.07s/it] 3%|▎ | 535/18688 [37:10<21:12:40, 4.21s/it] 3%|▎ | 536/18688 [37:14<20:56:53, 4.15s/it] 3%|▎ | 537/18688 [37:19<22:06:45, 4.39s/it] 3%|▎ | 538/18688 [37:23<21:23:58, 4.24s/it] 3%|▎ | 539/18688 [37:26<20:14:54, 4.02s/it] 3%|▎ | 540/18688 [37:30<19:30:16, 3.87s/it] {'loss': '1.072', 'grad_norm': '0.9847', 'learning_rate': '7.187e-05', 'epoch': '0.0289', 'num_input_tokens_seen': 1780496, 'train_runtime': '2250', 'train_tokens_per_second': '791.3'}
3%|▎ | 540/18688 [37:30<19:30:16, 3.87s/it] 3%|▎ | 541/18688 [37:34<20:10:33, 4.00s/it] 3%|▎ | 542/18688 [37:37<19:24:16, 3.85s/it] 3%|▎ | 543/18688 [37:41<18:57:55, 3.76s/it] 3%|▎ | 544/18688 [37:45<19:30:55, 3.87s/it] 3%|▎ | 545/18688 [37:50<21:03:41, 4.18s/it] 3%|▎ | 546/18688 [37:54<20:53:33, 4.15s/it] 3%|▎ | 547/18688 [37:58<20:08:02, 4.00s/it] 3%|▎ | 548/18688 [38:02<19:45:59, 3.92s/it] 3%|▎ | 549/18688 [38:05<19:11:55, 3.81s/it] 3%|▎ | 550/18688 [38:10<20:41:29, 4.11s/it] {'loss': '0.9769', 'grad_norm': '1.099', 'learning_rate': '7.32e-05', 'epoch': '0.02943', 'num_input_tokens_seen': 1814136, 'train_runtime': '2290', 'train_tokens_per_second': '792.1'}
3%|▎ | 550/18688 [38:10<20:41:29, 4.11s/it] 3%|▎ | 551/18688 [38:14<20:20:08, 4.04s/it] 3%|▎ | 552/18688 [38:18<20:38:45, 4.10s/it] 3%|▎ | 553/18688 [38:22<20:28:48, 4.07s/it] 3%|▎ | 554/18688 [38:26<21:09:00, 4.20s/it] 3%|▎ | 555/18688 [38:30<20:48:39, 4.13s/it] 3%|▎ | 556/18688 [38:35<21:28:28, 4.26s/it] 3%|▎ | 557/18688 [38:39<21:02:04, 4.18s/it] 3%|▎ | 558/18688 [38:43<20:45:45, 4.12s/it] 3%|▎ | 559/18688 [38:48<21:29:31, 4.27s/it] 3%|▎ | 560/18688 [38:52<21:01:51, 4.18s/it] {'loss': '0.9143', 'grad_norm': '1.014', 'learning_rate': '7.453e-05', 'epoch': '0.02997', 'num_input_tokens_seen': 1844456, 'train_runtime': '2332', 'train_tokens_per_second': '790.9'}
3%|▎ | 560/18688 [38:52<21:01:51, 4.18s/it] 3%|▎ | 561/18688 [38:56<21:51:30, 4.34s/it] 3%|▎ | 562/18688 [39:01<22:23:04, 4.45s/it] 3%|▎ | 563/18688 [39:04<20:50:30, 4.14s/it] 3%|▎ | 564/18688 [39:09<20:56:19, 4.16s/it] 3%|▎ | 565/18688 [39:13<20:58:24, 4.17s/it] 3%|▎ | 566/18688 [39:17<20:29:16, 4.07s/it] 3%|▎ | 567/18688 [39:20<19:41:15, 3.91s/it] 3%|▎ | 568/18688 [39:24<20:01:32, 3.98s/it] 3%|▎ | 569/18688 [39:28<19:31:09, 3.88s/it] 3%|▎ | 570/18688 [39:32<19:31:24, 3.88s/it] {'loss': '1.042', 'grad_norm': '0.964', 'learning_rate': '7.587e-05', 'epoch': '0.0305', 'num_input_tokens_seen': 1877632, 'train_runtime': '2372', 'train_tokens_per_second': '791.5'}
3%|▎ | 570/18688 [39:32<19:31:24, 3.88s/it] 3%|▎ | 571/18688 [39:36<20:37:59, 4.10s/it] 3%|▎ | 572/18688 [39:40<20:07:28, 4.00s/it] 3%|▎ | 573/18688 [39:45<20:59:07, 4.17s/it] 3%|▎ | 574/18688 [39:49<20:38:42, 4.10s/it] 3%|▎ | 575/18688 [39:52<19:45:17, 3.93s/it] 3%|▎ | 576/18688 [39:56<19:10:20, 3.81s/it] 3%|▎ | 577/18688 [40:00<19:53:39, 3.95s/it] 3%|▎ | 578/18688 [40:04<19:41:57, 3.92s/it] 3%|▎ | 579/18688 [40:08<19:41:42, 3.92s/it] 3%|▎ | 580/18688 [40:13<21:09:34, 4.21s/it] {'loss': '0.9572', 'grad_norm': '1.042', 'learning_rate': '7.72e-05', 'epoch': '0.03104', 'num_input_tokens_seen': 1911192, 'train_runtime': '2413', 'train_tokens_per_second': '792'}
3%|▎ | 580/18688 [40:13<21:09:34, 4.21s/it] 3%|▎ | 581/18688 [40:17<21:14:00, 4.22s/it] 3%|▎ | 582/18688 [40:21<20:29:37, 4.07s/it] 3%|▎ | 583/18688 [40:25<21:01:06, 4.18s/it] 3%|▎ | 584/18688 [40:30<21:29:10, 4.27s/it] 3%|▎ | 585/18688 [40:34<21:03:28, 4.19s/it] 3%|▎ | 586/18688 [40:38<21:54:48, 4.36s/it] 3%|▎ | 587/18688 [40:42<21:20:26, 4.24s/it] 3%|▎ | 588/18688 [40:47<21:55:58, 4.36s/it] 3%|▎ | 589/18688 [40:52<22:21:43, 4.45s/it] 3%|▎ | 590/18688 [40:56<21:54:54, 4.36s/it] {'loss': '0.9967', 'grad_norm': '0.9022', 'learning_rate': '7.853e-05', 'epoch': '0.03157', 'num_input_tokens_seen': 1943152, 'train_runtime': '2456', 'train_tokens_per_second': '791.1'}
3%|▎ | 590/18688 [40:56<21:54:54, 4.36s/it] 3%|▎ | 591/18688 [40:59<20:36:08, 4.10s/it] 3%|▎ | 592/18688 [41:04<20:49:48, 4.14s/it] 3%|▎ | 593/18688 [41:08<20:47:25, 4.14s/it] 3%|▎ | 594/18688 [41:12<20:55:16, 4.16s/it] 3%|▎ | 595/18688 [41:16<21:31:45, 4.28s/it] 3%|▎ | 596/18688 [41:20<21:10:47, 4.21s/it] 3%|▎ | 597/18688 [41:24<20:02:41, 3.99s/it] 3%|▎ | 598/18688 [41:27<19:15:39, 3.83s/it] 3%|▎ | 599/18688 [41:32<21:08:14, 4.21s/it] 3%|▎ | 600/18688 [41:37<21:40:42, 4.31s/it] {'loss': '0.9994', 'grad_norm': '1.03', 'learning_rate': '7.987e-05', 'epoch': '0.03211', 'num_input_tokens_seen': 1976328, 'train_runtime': '2498', 'train_tokens_per_second': '791.3'}
3%|▎ | 600/18688 [41:37<21:40:42, 4.31s/it] 3%|▎ | 601/18688 [41:41<21:22:53, 4.26s/it] 3%|▎ | 602/18688 [41:45<20:56:19, 4.17s/it] 3%|▎ | 603/18688 [41:49<20:04:39, 4.00s/it] 3%|▎ | 604/18688 [41:53<19:47:36, 3.94s/it] 3%|▎ | 605/18688 [41:58<21:24:39, 4.26s/it] 3%|▎ | 606/18688 [42:02<21:29:01, 4.28s/it] 3%|▎ | 607/18688 [42:06<21:04:42, 4.20s/it] 3%|▎ | 608/18688 [42:10<21:12:56, 4.22s/it] 3%|▎ | 609/18688 [42:15<21:22:53, 4.26s/it] 3%|▎ | 610/18688 [42:18<20:14:27, 4.03s/it] {'loss': '0.9783', 'grad_norm': '1.137', 'learning_rate': '8.12e-05', 'epoch': '0.03264', 'num_input_tokens_seen': 2008480, 'train_runtime': '2539', 'train_tokens_per_second': '791.2'}
3%|▎ | 610/18688 [42:18<20:14:27, 4.03s/it] 3%|▎ | 611/18688 [42:22<19:45:39, 3.94s/it] 3%|▎ | 612/18688 [42:27<21:23:39, 4.26s/it] 3%|▎ | 613/18688 [42:31<21:58:34, 4.38s/it] 3%|▎ | 614/18688 [42:36<21:49:12, 4.35s/it] 3%|▎ | 615/18688 [42:40<21:10:34, 4.22s/it] 3%|▎ | 616/18688 [42:45<23:38:57, 4.71s/it] 3%|▎ | 617/18688 [42:50<23:35:45, 4.70s/it] 3%|▎ | 618/18688 [42:54<22:12:00, 4.42s/it] 3%|▎ | 619/18688 [42:58<21:43:14, 4.33s/it] 3%|▎ | 620/18688 [43:01<20:22:54, 4.06s/it] {'loss': '1.006', 'grad_norm': '1.31', 'learning_rate': '8.253e-05', 'epoch': '0.03318', 'num_input_tokens_seen': 2043576, 'train_runtime': '2582', 'train_tokens_per_second': '791.5'}
3%|▎ | 620/18688 [43:01<20:22:54, 4.06s/it] 3%|▎ | 621/18688 [43:06<20:45:38, 4.14s/it] 3%|▎ | 622/18688 [43:09<19:44:42, 3.93s/it] 3%|▎ | 623/18688 [43:14<20:31:08, 4.09s/it] 3%|▎ | 624/18688 [43:17<19:59:13, 3.98s/it] 3%|▎ | 625/18688 [43:21<20:07:08, 4.01s/it] 3%|▎ | 626/18688 [43:26<20:22:44, 4.06s/it] 3%|▎ | 627/18688 [43:30<20:30:02, 4.09s/it] 3%|▎ | 628/18688 [43:35<22:02:19, 4.39s/it] 3%|▎ | 629/18688 [43:39<21:22:46, 4.26s/it] 3%|▎ | 630/18688 [43:43<21:49:06, 4.35s/it] {'loss': '1.026', 'grad_norm': '0.9282', 'learning_rate': '8.387e-05', 'epoch': '0.03371', 'num_input_tokens_seen': 2077600, 'train_runtime': '2624', 'train_tokens_per_second': '791.8'}
3%|▎ | 630/18688 [43:43<21:49:06, 4.35s/it] 3%|▎ | 631/18688 [43:48<22:26:39, 4.47s/it] 3%|▎ | 632/18688 [43:53<22:15:20, 4.44s/it] 3%|▎ | 633/18688 [43:57<21:41:36, 4.33s/it] 3%|▎ | 634/18688 [44:01<22:09:14, 4.42s/it] 3%|▎ | 635/18688 [44:06<22:03:06, 4.40s/it] 3%|▎ | 636/18688 [44:09<21:15:13, 4.24s/it] 3%|▎ | 637/18688 [44:14<21:52:30, 4.36s/it] 3%|▎ | 638/18688 [44:19<22:18:21, 4.45s/it] 3%|▎ | 639/18688 [44:23<22:10:42, 4.42s/it] 3%|▎ | 640/18688 [44:27<21:47:54, 4.35s/it] {'loss': '1.064', 'grad_norm': '1.063', 'learning_rate': '8.52e-05', 'epoch': '0.03425', 'num_input_tokens_seen': 2110816, 'train_runtime': '2668', 'train_tokens_per_second': '791.2'}
3%|▎ | 640/18688 [44:27<21:47:54, 4.35s/it] 3%|▎ | 641/18688 [44:32<22:17:44, 4.45s/it] 3%|▎ | 642/18688 [44:37<23:06:58, 4.61s/it] 3%|▎ | 643/18688 [44:41<22:45:16, 4.54s/it] 3%|▎ | 644/18688 [44:45<22:06:19, 4.41s/it] 3%|▎ | 645/18688 [44:50<21:55:02, 4.37s/it] 3%|▎ | 646/18688 [44:55<22:39:46, 4.52s/it] 3%|▎ | 647/18688 [44:58<21:39:58, 4.32s/it] 3%|▎ | 648/18688 [45:03<22:29:06, 4.49s/it] 3%|▎ | 649/18688 [45:08<22:08:44, 4.42s/it] 3%|▎ | 650/18688 [45:12<21:48:21, 4.35s/it] {'loss': '0.9758', 'grad_norm': '0.9247', 'learning_rate': '8.653e-05', 'epoch': '0.03478', 'num_input_tokens_seen': 2146864, 'train_runtime': '2712', 'train_tokens_per_second': '791.5'}
3%|▎ | 650/18688 [45:12<21:48:21, 4.35s/it] 3%|▎ | 651/18688 [45:16<21:51:11, 4.36s/it] 3%|▎ | 652/18688 [45:21<22:25:55, 4.48s/it] 3%|▎ | 653/18688 [45:25<21:39:26, 4.32s/it] 3%|▎ | 654/18688 [45:29<21:26:56, 4.28s/it] 4%|▎ | 655/18688 [45:33<20:43:48, 4.14s/it] 4%|▎ | 656/18688 [45:37<20:42:38, 4.13s/it] 4%|▎ | 657/18688 [45:41<20:36:09, 4.11s/it] 4%|▎ | 658/18688 [45:44<19:34:42, 3.91s/it] 4%|▎ | 659/18688 [45:48<19:19:41, 3.86s/it] 4%|▎ | 660/18688 [45:52<19:33:31, 3.91s/it] {'loss': '1.062', 'grad_norm': '2.144', 'learning_rate': '8.787e-05', 'epoch': '0.03532', 'num_input_tokens_seen': 2179224, 'train_runtime': '2753', 'train_tokens_per_second': '791.7'}
4%|▎ | 660/18688 [45:52<19:33:31, 3.91s/it] 4%|▎ | 661/18688 [45:56<18:59:41, 3.79s/it] 4%|▎ | 662/18688 [46:00<19:14:40, 3.84s/it] 4%|▎ | 663/18688 [46:03<18:59:19, 3.79s/it] 4%|▎ | 664/18688 [46:09<21:30:03, 4.29s/it] 4%|▎ | 665/18688 [46:13<20:50:42, 4.16s/it] 4%|▎ | 666/18688 [46:16<19:53:49, 3.97s/it] 4%|▎ | 667/18688 [46:20<19:48:33, 3.96s/it] 4%|▎ | 668/18688 [46:24<19:25:59, 3.88s/it] 4%|▎ | 669/18688 [46:28<19:50:52, 3.97s/it] 4%|▎ | 670/18688 [46:32<20:32:04, 4.10s/it] {'loss': '1.068', 'grad_norm': '1.228', 'learning_rate': '8.92e-05', 'epoch': '0.03585', 'num_input_tokens_seen': 2211824, 'train_runtime': '2793', 'train_tokens_per_second': '791.9'}
4%|▎ | 670/18688 [46:32<20:32:04, 4.10s/it] 4%|▎ | 671/18688 [46:37<21:09:13, 4.23s/it] 4%|▎ | 672/18688 [46:42<21:39:55, 4.33s/it] 4%|▎ | 673/18688 [46:46<22:13:30, 4.44s/it] 4%|▎ | 674/18688 [46:51<21:55:52, 4.38s/it] 4%|▎ | 675/18688 [46:54<21:14:57, 4.25s/it] 4%|▎ | 676/18688 [46:59<21:02:25, 4.21s/it] 4%|▎ | 677/18688 [47:03<20:49:07, 4.16s/it] 4%|▎ | 678/18688 [47:07<20:39:33, 4.13s/it] 4%|▎ | 679/18688 [47:11<20:52:49, 4.17s/it] 4%|▎ | 680/18688 [47:14<19:51:52, 3.97s/it] {'loss': '1.042', 'grad_norm': '1.278', 'learning_rate': '9.053e-05', 'epoch': '0.03639', 'num_input_tokens_seen': 2246768, 'train_runtime': '2835', 'train_tokens_per_second': '792.5'}
4%|▎ | 680/18688 [47:14<19:51:52, 3.97s/it] 4%|▎ | 681/18688 [47:18<19:42:22, 3.94s/it] 4%|▎ | 682/18688 [47:23<20:31:33, 4.10s/it] 4%|▎ | 683/18688 [47:27<20:15:26, 4.05s/it] 4%|▎ | 684/18688 [47:31<20:54:57, 4.18s/it] 4%|▎ | 685/18688 [47:35<19:52:38, 3.97s/it] 4%|▎ | 686/18688 [47:38<19:14:10, 3.85s/it] 4%|▎ | 687/18688 [47:42<19:47:44, 3.96s/it] 4%|▎ | 688/18688 [47:47<20:11:56, 4.04s/it] 4%|▎ | 689/18688 [47:51<20:31:00, 4.10s/it] 4%|▎ | 690/18688 [47:56<21:24:11, 4.28s/it] {'loss': '1.025', 'grad_norm': '0.9733', 'learning_rate': '9.187e-05', 'epoch': '0.03692', 'num_input_tokens_seen': 2280616, 'train_runtime': '2876', 'train_tokens_per_second': '792.9'}
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4%|▎ | 700/18688 [48:37<20:20:44, 4.07s/it] 4%|▍ | 701/18688 [48:41<20:45:03, 4.15s/it] 4%|▍ | 702/18688 [48:45<20:37:58, 4.13s/it] 4%|▍ | 703/18688 [48:49<20:53:24, 4.18s/it] 4%|▍ | 704/18688 [48:54<20:56:14, 4.19s/it] 4%|▍ | 705/18688 [48:57<20:11:35, 4.04s/it] 4%|▍ | 706/18688 [49:01<20:14:26, 4.05s/it] 4%|▍ | 707/18688 [49:06<20:37:44, 4.13s/it] 4%|▍ | 708/18688 [49:09<20:13:29, 4.05s/it] 4%|▍ | 709/18688 [49:14<20:27:28, 4.10s/it] 4%|▍ | 710/18688 [49:17<19:25:43, 3.89s/it] {'loss': '1.096', 'grad_norm': '1.016', 'learning_rate': '9.453e-05', 'epoch': '0.03799', 'num_input_tokens_seen': 2347584, 'train_runtime': '2958', 'train_tokens_per_second': '793.7'}
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4%|▍ | 750/18688 [51:57<20:38:10, 4.14s/it] 4%|▍ | 751/18688 [52:01<20:08:47, 4.04s/it] 4%|▍ | 752/18688 [52:05<20:22:28, 4.09s/it] 4%|▍ | 753/18688 [52:09<20:28:15, 4.11s/it] 4%|▍ | 754/18688 [52:13<20:19:51, 4.08s/it] 4%|▍ | 755/18688 [52:18<21:52:16, 4.39s/it] 4%|▍ | 756/18688 [52:23<22:54:26, 4.60s/it] 4%|▍ | 757/18688 [52:27<22:04:48, 4.43s/it] 4%|▍ | 758/18688 [52:32<21:54:10, 4.40s/it] 4%|▍ | 759/18688 [52:36<22:13:54, 4.46s/it] 4%|▍ | 760/18688 [52:41<22:58:27, 4.61s/it] {'loss': '1.041', 'grad_norm': '1.152', 'learning_rate': '0.0001012', 'epoch': '0.04067', 'num_input_tokens_seen': 2512440, 'train_runtime': '3162', 'train_tokens_per_second': '794.6'}
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5%|▌ | 950/18688 [1:05:54<20:45:35, 4.21s/it] 5%|▌ | 951/18688 [1:05:58<21:39:23, 4.40s/it] 5%|▌ | 952/18688 [1:06:03<21:49:32, 4.43s/it] 5%|▌ | 953/18688 [1:06:07<21:35:19, 4.38s/it] 5%|▌ | 954/18688 [1:06:11<20:23:11, 4.14s/it] 5%|▌ | 955/18688 [1:06:15<21:09:48, 4.30s/it] 5%|▌ | 956/18688 [1:06:20<21:12:00, 4.30s/it] 5%|▌ | 957/18688 [1:06:26<23:22:37, 4.75s/it] 5%|▌ | 958/18688 [1:06:30<22:19:09, 4.53s/it] 5%|▌ | 959/18688 [1:06:34<21:53:33, 4.45s/it] 5%|▌ | 960/18688 [1:06:38<21:07:22, 4.29s/it] {'loss': '1.063', 'grad_norm': '1.095', 'learning_rate': '0.0001279', 'epoch': '0.05137', 'num_input_tokens_seen': 3189048, 'train_runtime': '3998', 'train_tokens_per_second': '797.6'}
5%|▌ | 960/18688 [1:06:38<21:07:22, 4.29s/it] 5%|▌ | 961/18688 [1:06:41<20:17:05, 4.12s/it] 5%|▌ | 962/18688 [1:06:45<19:39:38, 3.99s/it] 5%|▌ | 963/18688 [1:06:49<19:36:51, 3.98s/it] 5%|▌ | 964/18688 [1:06:53<19:32:29, 3.97s/it] 5%|▌ | 965/18688 [1:06:57<19:33:38, 3.97s/it] 5%|▌ | 966/18688 [1:07:01<20:04:22, 4.08s/it] 5%|▌ | 967/18688 [1:07:06<21:05:57, 4.29s/it] 5%|▌ | 968/18688 [1:07:11<21:14:21, 4.31s/it] 5%|▌ | 969/18688 [1:07:15<20:47:44, 4.23s/it] 5%|▌ | 970/18688 [1:07:19<21:28:02, 4.36s/it] {'loss': '0.9585', 'grad_norm': '0.9934', 'learning_rate': '0.0001292', 'epoch': '0.05191', 'num_input_tokens_seen': 3219536, 'train_runtime': '4040', 'train_tokens_per_second': '797'}
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5%|▌ | 980/18688 [1:08:04<21:03:06, 4.28s/it] 5%|▌ | 981/18688 [1:08:07<19:53:07, 4.04s/it] 5%|▌ | 982/18688 [1:08:12<20:50:47, 4.24s/it] 5%|▌ | 983/18688 [1:08:16<21:16:29, 4.33s/it] 5%|▌ | 984/18688 [1:08:21<20:57:16, 4.26s/it] 5%|▌ | 985/18688 [1:08:25<20:42:40, 4.21s/it] 5%|▌ | 986/18688 [1:08:28<19:44:45, 4.02s/it] 5%|▌ | 987/18688 [1:08:32<19:46:21, 4.02s/it] 5%|▌ | 988/18688 [1:08:36<19:04:44, 3.88s/it] 5%|▌ | 989/18688 [1:08:40<19:22:58, 3.94s/it] 5%|▌ | 990/18688 [1:08:44<19:49:21, 4.03s/it] {'loss': '0.9883', 'grad_norm': '0.9673', 'learning_rate': '0.0001319', 'epoch': '0.05298', 'num_input_tokens_seen': 3289808, 'train_runtime': '4125', 'train_tokens_per_second': '797.6'}
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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': '0.9974', 'eval_runtime': '82.86', 'eval_samples_per_second': '12.07', 'eval_steps_per_second': '12.07', 'epoch': '0.05351', 'num_input_tokens_seen': 3324984}
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5%|▌ | 1010/18688 [1:11:35<27:41:32, 5.64s/it] 5%|▌ | 1011/18688 [1:11:38<24:28:11, 4.98s/it] 5%|▌ | 1012/18688 [1:11:43<23:26:09, 4.77s/it] 5%|▌ | 1013/18688 [1:11:48<23:46:56, 4.84s/it] 5%|▌ | 1014/18688 [1:11:52<23:30:48, 4.79s/it] 5%|▌ | 1015/18688 [1:11:57<23:26:57, 4.78s/it] 5%|▌ | 1016/18688 [1:12:01<22:25:48, 4.57s/it] 5%|▌ | 1017/18688 [1:12:05<21:09:54, 4.31s/it] 5%|▌ | 1018/18688 [1:12:09<20:53:16, 4.26s/it] 5%|▌ | 1019/18688 [1:12:14<21:40:33, 4.42s/it] 5%|▌ | 1020/18688 [1:12:17<20:27:39, 4.17s/it] {'loss': '1.027', 'grad_norm': '1.502', 'learning_rate': '0.0001359', 'epoch': '0.05458', 'num_input_tokens_seen': 3400000, 'train_runtime': '4338', 'train_tokens_per_second': '783.8'}
5%|▌ | 1020/18688 [1:12:17<20:27:39, 4.17s/it] 5%|▌ | 1021/18688 [1:12:22<21:27:44, 4.37s/it] 5%|▌ | 1022/18688 [1:12:27<21:21:55, 4.35s/it] 5%|▌ | 1023/18688 [1:12:31<21:35:35, 4.40s/it] 5%|▌ | 1024/18688 [1:12:35<21:15:32, 4.33s/it] 5%|▌ | 1025/18688 [1:12:41<23:33:55, 4.80s/it] 5%|▌ | 1026/18688 [1:12:46<22:55:05, 4.67s/it] 5%|▌ | 1027/18688 [1:12:49<21:39:07, 4.41s/it] 6%|▌ | 1028/18688 [1:12:54<21:15:45, 4.33s/it] 6%|▌ | 1029/18688 [1:12:57<19:55:46, 4.06s/it] 6%|▌ | 1030/18688 [1:13:00<18:57:15, 3.86s/it] {'loss': '1.018', 'grad_norm': '1.207', 'learning_rate': '0.0001372', 'epoch': '0.05512', 'num_input_tokens_seen': 3435320, 'train_runtime': '4381', 'train_tokens_per_second': '784.2'}
6%|▌ | 1030/18688 [1:13:00<18:57:15, 3.86s/it] 6%|▌ | 1031/18688 [1:13:05<19:47:05, 4.03s/it] 6%|▌ | 1032/18688 [1:13:10<20:45:32, 4.23s/it] 6%|▌ | 1033/18688 [1:13:14<20:47:53, 4.24s/it] 6%|▌ | 1034/18688 [1:13:19<21:40:31, 4.42s/it] 6%|▌ | 1035/18688 [1:13:23<21:56:44, 4.48s/it] 6%|▌ | 1036/18688 [1:13:28<22:08:58, 4.52s/it] 6%|▌ | 1037/18688 [1:13:33<22:26:21, 4.58s/it] 6%|▌ | 1038/18688 [1:13:36<20:58:03, 4.28s/it] 6%|▌ | 1039/18688 [1:13:40<20:15:20, 4.13s/it] 6%|▌ | 1040/18688 [1:13:44<20:12:34, 4.12s/it] {'loss': '0.9966', 'grad_norm': '1.102', 'learning_rate': '0.0001385', 'epoch': '0.05565', 'num_input_tokens_seen': 3469792, 'train_runtime': '4425', 'train_tokens_per_second': '784.2'}
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6%|▌ | 1080/18688 [1:16:34<18:50:02, 3.85s/it] 6%|▌ | 1081/18688 [1:16:38<18:46:21, 3.84s/it] 6%|▌ | 1082/18688 [1:16:43<20:31:36, 4.20s/it] 6%|▌ | 1083/18688 [1:16:47<20:15:20, 4.14s/it] 6%|▌ | 1084/18688 [1:16:51<20:48:25, 4.26s/it] 6%|▌ | 1085/18688 [1:16:55<20:17:26, 4.15s/it] 6%|▌ | 1086/18688 [1:17:00<21:14:40, 4.34s/it] 6%|▌ | 1087/18688 [1:17:05<22:07:08, 4.52s/it] 6%|▌ | 1088/18688 [1:17:10<22:11:18, 4.54s/it] 6%|▌ | 1089/18688 [1:17:14<22:28:41, 4.60s/it] 6%|▌ | 1090/18688 [1:17:19<22:36:15, 4.62s/it] {'loss': '1.02', 'grad_norm': '1.01', 'learning_rate': '0.0001452', 'epoch': '0.05833', 'num_input_tokens_seen': 3639504, 'train_runtime': '4640', 'train_tokens_per_second': '784.4'}
6%|▌ | 1090/18688 [1:17:19<22:36:15, 4.62s/it] 6%|▌ | 1091/18688 [1:17:23<21:41:20, 4.44s/it] 6%|▌ | 1092/18688 [1:17:27<21:24:39, 4.38s/it] 6%|▌ | 1093/18688 [1:17:32<21:38:51, 4.43s/it] 6%|▌ | 1094/18688 [1:17:36<21:47:38, 4.46s/it] 6%|▌ | 1095/18688 [1:17:41<22:04:59, 4.52s/it] 6%|▌ | 1096/18688 [1:17:46<22:28:11, 4.60s/it] 6%|▌ | 1097/18688 [1:17:50<21:34:10, 4.41s/it] 6%|▌ | 1098/18688 [1:17:54<21:38:48, 4.43s/it] 6%|▌ | 1099/18688 [1:17:58<20:58:09, 4.29s/it] 6%|▌ | 1100/18688 [1:18:03<21:41:12, 4.44s/it] {'loss': '1.059', 'grad_norm': '0.918', 'learning_rate': '0.0001465', 'epoch': '0.05886', 'num_input_tokens_seen': 3673560, 'train_runtime': '4684', 'train_tokens_per_second': '784.3'}
6%|▌ | 1100/18688 [1:18:03<21:41:12, 4.44s/it] 6%|▌ | 1101/18688 [1:18:08<22:45:03, 4.66s/it] 6%|▌ | 1102/18688 [1:18:13<22:08:07, 4.53s/it] 6%|▌ | 1103/18688 [1:18:17<21:45:47, 4.46s/it] 6%|▌ | 1104/18688 [1:18:21<21:51:43, 4.48s/it] 6%|▌ | 1105/18688 [1:18:26<21:30:07, 4.40s/it] 6%|▌ | 1106/18688 [1:18:29<20:22:31, 4.17s/it] 6%|▌ | 1107/18688 [1:18:34<20:41:06, 4.24s/it] 6%|▌ | 1108/18688 [1:18:38<20:30:34, 4.20s/it] 6%|▌ | 1109/18688 [1:18:42<20:10:20, 4.13s/it] 6%|▌ | 1110/18688 [1:18:46<20:38:08, 4.23s/it] {'loss': '0.9994', 'grad_norm': '1.016', 'learning_rate': '0.0001479', 'epoch': '0.0594', 'num_input_tokens_seen': 3706976, 'train_runtime': '4727', 'train_tokens_per_second': '784.3'}
6%|▌ | 1110/18688 [1:18:46<20:38:08, 4.23s/it] 6%|▌ | 1111/18688 [1:18:51<21:21:22, 4.37s/it] 6%|▌ | 1112/18688 [1:18:55<20:55:11, 4.28s/it] 6%|▌ | 1113/18688 [1:18:59<20:29:07, 4.20s/it] 6%|▌ | 1114/18688 [1:19:03<20:05:26, 4.12s/it] 6%|▌ | 1115/18688 [1:19:07<20:52:33, 4.28s/it] 6%|▌ | 1116/18688 [1:19:12<21:37:21, 4.43s/it] 6%|▌ | 1117/18688 [1:19:17<21:31:21, 4.41s/it] 6%|▌ | 1118/18688 [1:19:21<21:22:14, 4.38s/it] 6%|▌ | 1119/18688 [1:19:25<21:18:07, 4.36s/it] 6%|▌ | 1120/18688 [1:19:30<22:23:40, 4.59s/it] {'loss': '0.9767', 'grad_norm': '0.9384', 'learning_rate': '0.0001492', 'epoch': '0.05993', 'num_input_tokens_seen': 3740912, 'train_runtime': '4771', 'train_tokens_per_second': '784.1'}
6%|▌ | 1120/18688 [1:19:30<22:23:40, 4.59s/it] 6%|▌ | 1121/18688 [1:19:35<22:20:20, 4.58s/it] 6%|▌ | 1122/18688 [1:19:40<22:39:30, 4.64s/it] 6%|▌ | 1123/18688 [1:19:44<21:30:10, 4.41s/it] 6%|▌ | 1124/18688 [1:19:48<21:11:54, 4.34s/it] 6%|▌ | 1125/18688 [1:19:52<21:21:23, 4.38s/it] 6%|▌ | 1126/18688 [1:19:57<21:28:09, 4.40s/it] 6%|▌ | 1127/18688 [1:20:01<20:50:55, 4.27s/it] 6%|▌ | 1128/18688 [1:20:05<21:20:19, 4.37s/it] 6%|▌ | 1129/18688 [1:20:09<20:36:24, 4.22s/it] 6%|▌ | 1130/18688 [1:20:13<20:07:51, 4.13s/it] {'loss': '0.9338', 'grad_norm': '0.9328', 'learning_rate': '0.0001505', 'epoch': '0.06047', 'num_input_tokens_seen': 3772344, 'train_runtime': '4814', 'train_tokens_per_second': '783.7'}
6%|▌ | 1130/18688 [1:20:13<20:07:51, 4.13s/it] 6%|▌ | 1131/18688 [1:20:18<21:48:42, 4.47s/it] 6%|▌ | 1132/18688 [1:20:22<21:09:58, 4.34s/it] 6%|▌ | 1133/18688 [1:20:27<21:20:52, 4.38s/it] 6%|▌ | 1134/18688 [1:20:31<21:07:09, 4.33s/it] 6%|▌ | 1135/18688 [1:20:35<20:33:30, 4.22s/it] 6%|▌ | 1136/18688 [1:20:39<20:05:28, 4.12s/it] 6%|▌ | 1137/18688 [1:20:43<19:58:06, 4.10s/it] 6%|▌ | 1138/18688 [1:20:49<22:32:53, 4.63s/it] 6%|▌ | 1139/18688 [1:20:53<22:25:06, 4.60s/it] 6%|▌ | 1140/18688 [1:20:57<21:31:45, 4.42s/it] {'loss': '0.9755', 'grad_norm': '1.111', 'learning_rate': '0.0001519', 'epoch': '0.061', 'num_input_tokens_seen': 3806288, 'train_runtime': '4858', 'train_tokens_per_second': '783.5'}
6%|▌ | 1140/18688 [1:20:57<21:31:45, 4.42s/it] 6%|▌ | 1141/18688 [1:21:02<22:29:42, 4.62s/it] 6%|▌ | 1142/18688 [1:21:06<21:22:14, 4.38s/it] 6%|▌ | 1143/18688 [1:21:10<20:50:06, 4.28s/it] 6%|▌ | 1144/18688 [1:21:14<20:37:48, 4.23s/it] 6%|▌ | 1145/18688 [1:21:18<20:25:51, 4.19s/it] 6%|▌ | 1146/18688 [1:21:22<19:59:51, 4.10s/it] 6%|▌ | 1147/18688 [1:21:26<19:03:01, 3.91s/it] 6%|▌ | 1148/18688 [1:21:30<18:52:23, 3.87s/it] 6%|▌ | 1149/18688 [1:21:34<19:33:07, 4.01s/it] 6%|▌ | 1150/18688 [1:21:38<19:50:23, 4.07s/it] {'loss': '1.063', 'grad_norm': '1.152', 'learning_rate': '0.0001532', 'epoch': '0.06154', 'num_input_tokens_seen': 3839712, 'train_runtime': '4899', 'train_tokens_per_second': '783.8'}
6%|▌ | 1150/18688 [1:21:38<19:50:23, 4.07s/it] 6%|▌ | 1151/18688 [1:21:42<20:08:55, 4.14s/it] 6%|▌ | 1152/18688 [1:21:47<20:03:53, 4.12s/it] 6%|▌ | 1153/18688 [1:21:51<20:30:58, 4.21s/it] 6%|▌ | 1154/18688 [1:21:55<19:48:52, 4.07s/it] 6%|▌ | 1155/18688 [1:21:58<18:57:43, 3.89s/it] 6%|▌ | 1156/18688 [1:22:04<21:21:01, 4.38s/it] 6%|▌ | 1157/18688 [1:22:07<20:24:41, 4.19s/it] 6%|▌ | 1158/18688 [1:22:12<20:20:04, 4.18s/it] 6%|▌ | 1159/18688 [1:22:17<22:09:09, 4.55s/it] 6%|▌ | 1160/18688 [1:22:21<21:00:38, 4.32s/it] {'loss': '1.069', 'grad_norm': '1.208', 'learning_rate': '0.0001545', 'epoch': '0.06207', 'num_input_tokens_seen': 3876000, 'train_runtime': '4941', 'train_tokens_per_second': '784.4'}
6%|▌ | 1160/18688 [1:22:21<21:00:38, 4.32s/it] 6%|▌ | 1161/18688 [1:22:24<19:42:06, 4.05s/it] 6%|▌ | 1162/18688 [1:22:29<20:13:49, 4.16s/it] 6%|▌ | 1163/18688 [1:22:34<21:43:31, 4.46s/it] 6%|▌ | 1164/18688 [1:22:37<20:32:13, 4.22s/it] 6%|▌ | 1165/18688 [1:22:41<20:03:55, 4.12s/it] 6%|▌ | 1166/18688 [1:22:45<19:03:49, 3.92s/it] 6%|▌ | 1167/18688 [1:22:48<18:20:15, 3.77s/it] 6%|▋ | 1168/18688 [1:22:52<18:27:56, 3.79s/it] 6%|▋ | 1169/18688 [1:22:56<19:07:23, 3.93s/it] 6%|▋ | 1170/18688 [1:23:00<19:04:06, 3.92s/it] {'loss': '0.9872', 'grad_norm': '1.109', 'learning_rate': '0.0001559', 'epoch': '0.06261', 'num_input_tokens_seen': 3908440, 'train_runtime': '4981', 'train_tokens_per_second': '784.7'}
6%|▋ | 1170/18688 [1:23:00<19:04:06, 3.92s/it] 6%|▋ | 1171/18688 [1:23:04<18:53:04, 3.88s/it] 6%|▋ | 1172/18688 [1:23:08<19:23:11, 3.98s/it] 6%|▋ | 1173/18688 [1:23:13<20:16:13, 4.17s/it] 6%|▋ | 1174/18688 [1:23:16<19:19:10, 3.97s/it] 6%|▋ | 1175/18688 [1:23:20<18:38:02, 3.83s/it] 6%|▋ | 1176/18688 [1:23:24<19:28:35, 4.00s/it] 6%|▋ | 1177/18688 [1:23:29<20:10:09, 4.15s/it] 6%|▋ | 1178/18688 [1:23:33<20:15:10, 4.16s/it] 6%|▋ | 1179/18688 [1:23:37<20:47:59, 4.28s/it] 6%|▋ | 1180/18688 [1:23:42<20:51:47, 4.29s/it] {'loss': '0.985', 'grad_norm': '1.064', 'learning_rate': '0.0001572', 'epoch': '0.06314', 'num_input_tokens_seen': 3941608, 'train_runtime': '5022', 'train_tokens_per_second': '784.8'}
6%|▋ | 1180/18688 [1:23:42<20:51:47, 4.29s/it] 6%|▋ | 1181/18688 [1:23:46<21:23:50, 4.40s/it] 6%|▋ | 1182/18688 [1:23:51<21:02:08, 4.33s/it] 6%|▋ | 1183/18688 [1:23:56<22:21:46, 4.60s/it] 6%|▋ | 1184/18688 [1:24:00<21:28:45, 4.42s/it] 6%|▋ | 1185/18688 [1:24:04<20:54:29, 4.30s/it] 6%|▋ | 1186/18688 [1:24:09<21:30:10, 4.42s/it] 6%|▋ | 1187/18688 [1:24:13<21:58:41, 4.52s/it] 6%|▋ | 1188/18688 [1:24:17<21:17:34, 4.38s/it] 6%|▋ | 1189/18688 [1:24:21<20:37:37, 4.24s/it] 6%|▋ | 1190/18688 [1:24:26<21:13:30, 4.37s/it] {'loss': '1.02', 'grad_norm': '1.249', 'learning_rate': '0.0001585', 'epoch': '0.06368', 'num_input_tokens_seen': 3974776, 'train_runtime': '5066', 'train_tokens_per_second': '784.5'}
6%|▋ | 1190/18688 [1:24:26<21:13:30, 4.37s/it] 6%|▋ | 1191/18688 [1:24:30<21:00:59, 4.32s/it] 6%|▋ | 1192/18688 [1:24:34<20:59:32, 4.32s/it] 6%|▋ | 1193/18688 [1:24:39<21:28:12, 4.42s/it] 6%|▋ | 1194/18688 [1:24:43<20:27:59, 4.21s/it] 6%|▋ | 1195/18688 [1:24:47<20:12:32, 4.16s/it] 6%|▋ | 1196/18688 [1:24:51<20:31:01, 4.22s/it] 6%|▋ | 1197/18688 [1:24:56<20:56:29, 4.31s/it] 6%|▋ | 1198/18688 [1:25:00<20:22:08, 4.19s/it] 6%|▋ | 1199/18688 [1:25:04<20:12:04, 4.16s/it] 6%|▋ | 1200/18688 [1:25:08<20:48:43, 4.28s/it] {'loss': '0.9717', 'grad_norm': '1.348', 'learning_rate': '0.0001599', 'epoch': '0.06421', 'num_input_tokens_seen': 4008136, 'train_runtime': '5109', 'train_tokens_per_second': '784.5'}
6%|▋ | 1200/18688 [1:25:08<20:48:43, 4.28s/it] 6%|▋ | 1201/18688 [1:25:12<20:21:01, 4.19s/it] 6%|▋ | 1202/18688 [1:25:16<20:15:58, 4.17s/it] 6%|▋ | 1203/18688 [1:25:21<21:13:31, 4.37s/it] 6%|▋ | 1204/18688 [1:25:26<21:46:14, 4.48s/it] 6%|▋ | 1205/18688 [1:25:30<21:08:30, 4.35s/it] 6%|▋ | 1206/18688 [1:25:34<20:32:37, 4.23s/it] 6%|▋ | 1207/18688 [1:25:39<21:19:53, 4.39s/it] 6%|▋ | 1208/18688 [1:25:43<20:36:50, 4.25s/it] 6%|▋ | 1209/18688 [1:25:47<21:04:13, 4.34s/it] 6%|▋ | 1210/18688 [1:25:51<20:44:15, 4.27s/it] {'loss': '1.058', 'grad_norm': '1.136', 'learning_rate': '0.0001612', 'epoch': '0.06475', 'num_input_tokens_seen': 4042776, 'train_runtime': '5152', 'train_tokens_per_second': '784.7'}
6%|▋ | 1210/18688 [1:25:51<20:44:15, 4.27s/it] 6%|▋ | 1211/18688 [1:25:55<19:33:26, 4.03s/it] 6%|▋ | 1212/18688 [1:25:59<19:36:32, 4.04s/it] 6%|▋ | 1213/18688 [1:26:03<19:32:32, 4.03s/it] 6%|▋ | 1214/18688 [1:26:08<20:34:01, 4.24s/it] 7%|▋ | 1215/18688 [1:26:12<20:29:43, 4.22s/it] 7%|▋ | 1216/18688 [1:26:16<19:57:05, 4.11s/it] 7%|▋ | 1217/18688 [1:26:20<20:28:35, 4.22s/it] 7%|▋ | 1218/18688 [1:26:24<20:26:15, 4.21s/it] 7%|▋ | 1219/18688 [1:26:29<21:41:47, 4.47s/it] 7%|▋ | 1220/18688 [1:26:34<21:10:42, 4.36s/it] {'loss': '0.9945', 'grad_norm': '0.9629', 'learning_rate': '0.0001625', 'epoch': '0.06528', 'num_input_tokens_seen': 4074336, 'train_runtime': '5194', 'train_tokens_per_second': '784.4'}
7%|▋ | 1220/18688 [1:26:34<21:10:42, 4.36s/it] 7%|▋ | 1221/18688 [1:26:38<21:50:34, 4.50s/it] 7%|▋ | 1222/18688 [1:26:42<20:59:55, 4.33s/it] 7%|▋ | 1223/18688 [1:26:47<20:55:04, 4.31s/it] 7%|▋ | 1224/18688 [1:26:51<20:26:08, 4.21s/it] 7%|▋ | 1225/18688 [1:26:55<20:42:54, 4.27s/it] 7%|▋ | 1226/18688 [1:27:00<21:51:55, 4.51s/it] 7%|▋ | 1227/18688 [1:27:04<21:06:43, 4.35s/it] 7%|▋ | 1228/18688 [1:27:08<21:10:20, 4.37s/it] 7%|▋ | 1229/18688 [1:27:13<21:05:19, 4.35s/it] 7%|▋ | 1230/18688 [1:27:17<20:25:56, 4.21s/it] {'loss': '1.037', 'grad_norm': '1.172', 'learning_rate': '0.0001639', 'epoch': '0.06582', 'num_input_tokens_seen': 4107808, 'train_runtime': '5237', 'train_tokens_per_second': '784.4'}
7%|▋ | 1230/18688 [1:27:17<20:25:56, 4.21s/it] 7%|▋ | 1231/18688 [1:27:21<20:50:55, 4.30s/it] 7%|▋ | 1232/18688 [1:27:25<20:29:56, 4.23s/it] 7%|▋ | 1233/18688 [1:27:29<20:06:01, 4.15s/it] 7%|▋ | 1234/18688 [1:27:33<19:47:43, 4.08s/it] 7%|▋ | 1235/18688 [1:27:37<19:38:04, 4.05s/it] 7%|▋ | 1236/18688 [1:27:41<19:35:55, 4.04s/it] 7%|▋ | 1237/18688 [1:27:44<18:45:58, 3.87s/it] 7%|▋ | 1238/18688 [1:27:48<18:29:27, 3.81s/it] 7%|▋ | 1239/18688 [1:27:52<18:47:43, 3.88s/it] 7%|▋ | 1240/18688 [1:27:56<18:35:51, 3.84s/it] {'loss': '0.9436', 'grad_norm': '0.9806', 'learning_rate': '0.0001652', 'epoch': '0.06635', 'num_input_tokens_seen': 4141480, 'train_runtime': '5276', 'train_tokens_per_second': '784.9'}
7%|▋ | 1240/18688 [1:27:56<18:35:51, 3.84s/it] 7%|▋ | 1241/18688 [1:28:00<18:21:07, 3.79s/it] 7%|▋ | 1242/18688 [1:28:05<20:17:26, 4.19s/it] 7%|▋ | 1243/18688 [1:28:08<19:19:41, 3.99s/it] 7%|▋ | 1244/18688 [1:28:12<19:22:54, 4.00s/it] 7%|▋ | 1245/18688 [1:28:16<19:23:35, 4.00s/it] 7%|▋ | 1246/18688 [1:28:20<19:13:26, 3.97s/it] 7%|▋ | 1247/18688 [1:28:24<18:46:27, 3.88s/it] 7%|▋ | 1248/18688 [1:28:28<19:10:38, 3.96s/it] 7%|▋ | 1249/18688 [1:28:32<19:12:39, 3.97s/it] 7%|▋ | 1250/18688 [1:28:37<20:04:32, 4.14s/it] {'loss': '1.041', 'grad_norm': '1.013', 'learning_rate': '0.0001665', 'epoch': '0.06689', 'num_input_tokens_seen': 4175648, 'train_runtime': '5317', 'train_tokens_per_second': '785.3'}
7%|▋ | 1250/18688 [1:28:37<20:04:32, 4.14s/it] 7%|▋ | 1251/18688 [1:28:40<19:42:39, 4.07s/it] 7%|▋ | 1252/18688 [1:28:44<19:06:28, 3.95s/it] 7%|▋ | 1253/18688 [1:28:48<18:51:07, 3.89s/it] 7%|▋ | 1254/18688 [1:28:53<20:00:03, 4.13s/it] 7%|▋ | 1255/18688 [1:28:57<20:56:50, 4.33s/it] 7%|▋ | 1256/18688 [1:29:02<21:09:12, 4.37s/it] 7%|▋ | 1257/18688 [1:29:07<21:49:15, 4.51s/it] 7%|▋ | 1258/18688 [1:29:10<20:27:30, 4.23s/it] 7%|▋ | 1259/18688 [1:29:14<19:48:30, 4.09s/it] 7%|▋ | 1260/18688 [1:29:17<18:53:03, 3.90s/it] {'loss': '0.9845', 'grad_norm': '1.337', 'learning_rate': '0.0001679', 'epoch': '0.06742', 'num_input_tokens_seen': 4207024, 'train_runtime': '5358', 'train_tokens_per_second': '785.2'}
7%|▋ | 1260/18688 [1:29:17<18:53:03, 3.90s/it] 7%|▋ | 1261/18688 [1:29:21<18:16:46, 3.78s/it] 7%|▋ | 1262/18688 [1:29:25<18:13:44, 3.77s/it] 7%|▋ | 1263/18688 [1:29:29<18:29:31, 3.82s/it] 7%|▋ | 1264/18688 [1:29:33<18:44:24, 3.87s/it] 7%|▋ | 1265/18688 [1:29:36<18:33:34, 3.83s/it] 7%|▋ | 1266/18688 [1:29:41<19:08:13, 3.95s/it] 7%|▋ | 1267/18688 [1:29:44<18:35:26, 3.84s/it] 7%|▋ | 1268/18688 [1:29:48<18:11:21, 3.76s/it] 7%|▋ | 1269/18688 [1:29:52<18:37:30, 3.85s/it] 7%|▋ | 1270/18688 [1:29:57<20:01:27, 4.14s/it] {'loss': '1.014', 'grad_norm': '1.228', 'learning_rate': '0.0001692', 'epoch': '0.06796', 'num_input_tokens_seen': 4236392, 'train_runtime': '5397', 'train_tokens_per_second': '784.9'}
7%|▋ | 1270/18688 [1:29:57<20:01:27, 4.14s/it] 7%|▋ | 1271/18688 [1:30:01<20:01:35, 4.14s/it] 7%|▋ | 1272/18688 [1:30:05<19:34:26, 4.05s/it] 7%|▋ | 1273/18688 [1:30:08<19:03:35, 3.94s/it] 7%|▋ | 1274/18688 [1:30:12<18:25:53, 3.81s/it] 7%|▋ | 1275/18688 [1:30:16<19:11:14, 3.97s/it] 7%|▋ | 1276/18688 [1:30:20<19:00:01, 3.93s/it] 7%|▋ | 1277/18688 [1:30:24<18:44:43, 3.88s/it] 7%|▋ | 1278/18688 [1:30:27<18:15:15, 3.77s/it] 7%|▋ | 1279/18688 [1:30:31<18:38:09, 3.85s/it] 7%|▋ | 1280/18688 [1:30:35<18:01:22, 3.73s/it] {'loss': '0.9844', 'grad_norm': '1.136', 'learning_rate': '0.0001705', 'epoch': '0.06849', 'num_input_tokens_seen': 4265784, 'train_runtime': '5435', 'train_tokens_per_second': '784.8'}
7%|▋ | 1280/18688 [1:30:35<18:01:22, 3.73s/it] 7%|▋ | 1281/18688 [1:30:38<18:02:21, 3.73s/it] 7%|▋ | 1282/18688 [1:30:42<18:31:05, 3.83s/it] 7%|▋ | 1283/18688 [1:30:47<19:41:36, 4.07s/it] 7%|▋ | 1284/18688 [1:30:51<18:52:40, 3.90s/it] 7%|▋ | 1285/18688 [1:30:55<19:22:15, 4.01s/it] 7%|▋ | 1286/18688 [1:30:59<19:27:06, 4.02s/it] 7%|▋ | 1287/18688 [1:31:02<18:45:04, 3.88s/it] 7%|▋ | 1288/18688 [1:31:07<18:58:39, 3.93s/it] 7%|▋ | 1289/18688 [1:31:11<19:49:57, 4.10s/it] 7%|▋ | 1290/18688 [1:31:16<20:45:16, 4.29s/it] {'loss': '1.041', 'grad_norm': '1.119', 'learning_rate': '0.0001719', 'epoch': '0.06903', 'num_input_tokens_seen': 4298040, 'train_runtime': '5476', 'train_tokens_per_second': '784.8'}
7%|▋ | 1290/18688 [1:31:16<20:45:16, 4.29s/it] 7%|▋ | 1291/18688 [1:31:20<20:22:22, 4.22s/it] 7%|▋ | 1292/18688 [1:31:25<21:25:18, 4.43s/it] 7%|▋ | 1293/18688 [1:31:30<21:57:09, 4.54s/it] 7%|▋ | 1294/18688 [1:31:35<23:13:08, 4.81s/it] 7%|▋ | 1295/18688 [1:31:39<22:01:44, 4.56s/it] 7%|▋ | 1296/18688 [1:31:43<21:16:37, 4.40s/it] 7%|▋ | 1297/18688 [1:31:47<20:01:23, 4.14s/it] 7%|▋ | 1298/18688 [1:31:51<19:51:50, 4.11s/it] 7%|▋ | 1299/18688 [1:31:55<21:00:25, 4.35s/it] 7%|▋ | 1300/18688 [1:31:59<20:04:39, 4.16s/it] {'loss': '1.056', 'grad_norm': '0.9965', 'learning_rate': '0.0001732', 'epoch': '0.06957', 'num_input_tokens_seen': 4332752, 'train_runtime': '5520', 'train_tokens_per_second': '785'}
7%|▋ | 1300/18688 [1:31:59<20:04:39, 4.16s/it] 7%|▋ | 1301/18688 [1:32:03<18:59:05, 3.93s/it] 7%|▋ | 1302/18688 [1:32:07<19:08:00, 3.96s/it] 7%|▋ | 1303/18688 [1:32:11<19:14:47, 3.99s/it] 7%|▋ | 1304/18688 [1:32:14<18:36:54, 3.85s/it] 7%|▋ | 1305/18688 [1:32:19<19:18:21, 4.00s/it] 7%|▋ | 1306/18688 [1:32:23<19:59:30, 4.14s/it] 7%|▋ | 1307/18688 [1:32:27<20:27:32, 4.24s/it] 7%|▋ | 1308/18688 [1:32:31<19:38:34, 4.07s/it] 7%|▋ | 1309/18688 [1:32:35<19:18:15, 4.00s/it] 7%|▋ | 1310/18688 [1:32:40<20:14:51, 4.19s/it] {'loss': '1.072', 'grad_norm': '1.09', 'learning_rate': '0.0001745', 'epoch': '0.0701', 'num_input_tokens_seen': 4365560, 'train_runtime': '5560', 'train_tokens_per_second': '785.2'}
7%|▋ | 1310/18688 [1:32:40<20:14:51, 4.19s/it] 7%|▋ | 1311/18688 [1:32:44<20:01:14, 4.15s/it] 7%|▋ | 1312/18688 [1:32:48<19:35:39, 4.06s/it] 7%|▋ | 1313/18688 [1:32:52<19:44:11, 4.09s/it] 7%|▋ | 1314/18688 [1:32:56<19:41:22, 4.08s/it] 7%|▋ | 1315/18688 [1:33:00<19:52:14, 4.12s/it] 7%|▋ | 1316/18688 [1:33:04<19:51:33, 4.12s/it] 7%|▋ | 1317/18688 [1:33:08<20:17:41, 4.21s/it] 7%|▋ | 1318/18688 [1:33:13<20:19:57, 4.21s/it] 7%|▋ | 1319/18688 [1:33:17<20:47:38, 4.31s/it] 7%|▋ | 1320/18688 [1:33:22<21:10:43, 4.39s/it] {'loss': '0.9935', 'grad_norm': '1.637', 'learning_rate': '0.0001759', 'epoch': '0.07064', 'num_input_tokens_seen': 4398144, 'train_runtime': '5602', 'train_tokens_per_second': '785.1'}
7%|▋ | 1320/18688 [1:33:22<21:10:43, 4.39s/it] 7%|▋ | 1321/18688 [1:33:26<20:59:36, 4.35s/it] 7%|▋ | 1322/18688 [1:33:30<20:39:58, 4.28s/it] 7%|▋ | 1323/18688 [1:33:34<19:26:07, 4.03s/it] 7%|▋ | 1324/18688 [1:33:38<19:37:58, 4.07s/it] 7%|▋ | 1325/18688 [1:33:41<19:01:39, 3.95s/it] 7%|▋ | 1326/18688 [1:33:46<19:19:38, 4.01s/it] 7%|▋ | 1327/18688 [1:33:49<18:46:23, 3.89s/it] 7%|▋ | 1328/18688 [1:33:53<18:16:23, 3.79s/it] 7%|▋ | 1329/18688 [1:33:57<19:19:02, 4.01s/it] 7%|▋ | 1330/18688 [1:34:01<18:37:20, 3.86s/it] {'loss': '1.051', 'grad_norm': '1.253', 'learning_rate': '0.0001772', 'epoch': '0.07117', 'num_input_tokens_seen': 4428848, 'train_runtime': '5641', 'train_tokens_per_second': '785.1'}
7%|▋ | 1330/18688 [1:34:01<18:37:20, 3.86s/it] 7%|▋ | 1331/18688 [1:34:05<19:26:56, 4.03s/it] 7%|▋ | 1332/18688 [1:34:09<19:06:40, 3.96s/it] 7%|▋ | 1333/18688 [1:34:13<18:41:00, 3.88s/it] 7%|▋ | 1334/18688 [1:34:17<18:34:47, 3.85s/it] 7%|▋ | 1335/18688 [1:34:21<19:57:04, 4.14s/it] 7%|▋ | 1336/18688 [1:34:27<21:34:04, 4.47s/it] 7%|▋ | 1337/18688 [1:34:31<20:54:36, 4.34s/it] 7%|▋ | 1338/18688 [1:34:35<21:14:05, 4.41s/it] 7%|▋ | 1339/18688 [1:34:39<20:35:13, 4.27s/it] 7%|▋ | 1340/18688 [1:34:43<20:08:34, 4.18s/it] {'loss': '1.031', 'grad_norm': '1.252', 'learning_rate': '0.0001785', 'epoch': '0.07171', 'num_input_tokens_seen': 4462248, 'train_runtime': '5684', 'train_tokens_per_second': '785.1'}
7%|▋ | 1340/18688 [1:34:43<20:08:34, 4.18s/it] 7%|▋ | 1341/18688 [1:34:48<21:05:28, 4.38s/it] 7%|▋ | 1342/18688 [1:34:53<21:26:19, 4.45s/it] 7%|▋ | 1343/18688 [1:34:57<20:45:43, 4.31s/it] 7%|▋ | 1344/18688 [1:35:01<20:45:26, 4.31s/it] 7%|▋ | 1345/18688 [1:35:07<23:20:08, 4.84s/it] 7%|▋ | 1346/18688 [1:35:11<22:40:32, 4.71s/it] 7%|▋ | 1347/18688 [1:35:16<22:19:23, 4.63s/it] 7%|▋ | 1348/18688 [1:35:21<22:28:41, 4.67s/it] 7%|▋ | 1349/18688 [1:35:25<22:05:36, 4.59s/it] 7%|▋ | 1350/18688 [1:35:29<21:25:08, 4.45s/it] {'loss': '1.073', 'grad_norm': '1.326', 'learning_rate': '0.0001799', 'epoch': '0.07224', 'num_input_tokens_seen': 4497528, 'train_runtime': '5730', 'train_tokens_per_second': '785'}
7%|▋ | 1350/18688 [1:35:29<21:25:08, 4.45s/it] 7%|▋ | 1351/18688 [1:35:34<21:27:22, 4.46s/it] 7%|▋ | 1352/18688 [1:35:38<21:20:02, 4.43s/it] 7%|▋ | 1353/18688 [1:35:43<21:46:54, 4.52s/it] 7%|▋ | 1354/18688 [1:35:47<20:53:40, 4.34s/it] 7%|▋ | 1355/18688 [1:35:52<22:03:45, 4.58s/it] 7%|▋ | 1356/18688 [1:35:56<22:03:53, 4.58s/it] 7%|▋ | 1357/18688 [1:36:00<21:08:15, 4.39s/it] 7%|▋ | 1358/18688 [1:36:04<20:48:11, 4.32s/it] 7%|▋ | 1359/18688 [1:36:09<21:02:41, 4.37s/it] 7%|▋ | 1360/18688 [1:36:13<20:46:12, 4.32s/it] {'loss': '1.039', 'grad_norm': '1.286', 'learning_rate': '0.0001812', 'epoch': '0.07278', 'num_input_tokens_seen': 4534280, 'train_runtime': '5774', 'train_tokens_per_second': '785.3'}
7%|▋ | 1360/18688 [1:36:13<20:46:12, 4.32s/it] 7%|▋ | 1361/18688 [1:36:18<21:04:33, 4.38s/it] 7%|▋ | 1362/18688 [1:36:22<20:55:15, 4.35s/it] 7%|▋ | 1363/18688 [1:36:26<20:43:04, 4.31s/it] 7%|▋ | 1364/18688 [1:36:30<20:31:24, 4.26s/it] 7%|▋ | 1365/18688 [1:36:34<20:06:57, 4.18s/it] 7%|▋ | 1366/18688 [1:36:38<20:02:40, 4.17s/it] 7%|▋ | 1367/18688 [1:36:42<19:54:07, 4.14s/it] 7%|▋ | 1368/18688 [1:36:47<20:58:13, 4.36s/it] 7%|▋ | 1369/18688 [1:36:51<19:42:43, 4.10s/it] 7%|▋ | 1370/18688 [1:36:55<19:07:29, 3.98s/it] {'loss': '1.04', 'grad_norm': '1.053', 'learning_rate': '0.0001825', 'epoch': '0.07331', 'num_input_tokens_seen': 4565296, 'train_runtime': '5815', 'train_tokens_per_second': '785.1'}
7%|▋ | 1370/18688 [1:36:55<19:07:29, 3.98s/it] 7%|▋ | 1371/18688 [1:36:59<20:14:37, 4.21s/it] 7%|▋ | 1372/18688 [1:37:03<20:06:32, 4.18s/it] 7%|▋ | 1373/18688 [1:37:07<19:10:58, 3.99s/it] 7%|▋ | 1374/18688 [1:37:11<19:41:37, 4.09s/it] 7%|▋ | 1375/18688 [1:37:15<19:32:44, 4.06s/it] 7%|▋ | 1376/18688 [1:37:19<18:57:24, 3.94s/it] 7%|▋ | 1377/18688 [1:37:23<18:38:20, 3.88s/it] 7%|▋ | 1378/18688 [1:37:27<19:22:02, 4.03s/it] 7%|▋ | 1379/18688 [1:37:32<20:02:49, 4.17s/it] 7%|▋ | 1380/18688 [1:37:35<19:13:50, 4.00s/it] {'loss': '0.9689', 'grad_norm': '1.44', 'learning_rate': '0.0001839', 'epoch': '0.07385', 'num_input_tokens_seen': 4596896, 'train_runtime': '5856', 'train_tokens_per_second': '785'}
7%|▋ | 1380/18688 [1:37:35<19:13:50, 4.00s/it] 7%|▋ | 1381/18688 [1:37:39<19:19:36, 4.02s/it] 7%|▋ | 1382/18688 [1:37:44<20:02:59, 4.17s/it] 7%|▋ | 1383/18688 [1:37:48<20:02:01, 4.17s/it] 7%|▋ | 1384/18688 [1:37:52<20:01:25, 4.17s/it] 7%|▋ | 1385/18688 [1:37:56<19:50:42, 4.13s/it] 7%|▋ | 1386/18688 [1:38:01<20:27:10, 4.26s/it] 7%|▋ | 1387/18688 [1:38:05<20:21:28, 4.24s/it] 7%|▋ | 1388/18688 [1:38:09<19:45:22, 4.11s/it] 7%|▋ | 1389/18688 [1:38:13<19:37:47, 4.09s/it] 7%|▋ | 1390/18688 [1:38:17<19:26:27, 4.05s/it] {'loss': '0.9927', 'grad_norm': '1.043', 'learning_rate': '0.0001852', 'epoch': '0.07438', 'num_input_tokens_seen': 4631296, 'train_runtime': '5897', 'train_tokens_per_second': '785.4'}
7%|▋ | 1390/18688 [1:38:17<19:26:27, 4.05s/it] 7%|▋ | 1391/18688 [1:38:21<19:28:49, 4.05s/it] 7%|▋ | 1392/18688 [1:38:26<21:32:21, 4.48s/it] 7%|▋ | 1393/18688 [1:38:31<21:22:13, 4.45s/it] 7%|▋ | 1394/18688 [1:38:35<21:05:52, 4.39s/it] 7%|▋ | 1395/18688 [1:38:40<22:27:31, 4.68s/it] 7%|▋ | 1396/18688 [1:38:45<22:03:35, 4.59s/it] 7%|▋ | 1397/18688 [1:38:49<21:12:25, 4.42s/it] 7%|▋ | 1398/18688 [1:38:53<21:58:14, 4.57s/it] 7%|▋ | 1399/18688 [1:38:58<21:59:42, 4.58s/it] 7%|▋ | 1400/18688 [1:39:03<22:36:26, 4.71s/it] {'loss': '1.065', 'grad_norm': '1.112', 'learning_rate': '0.0001865', 'epoch': '0.07492', 'num_input_tokens_seen': 4668640, 'train_runtime': '5944', 'train_tokens_per_second': '785.5'}
7%|▋ | 1400/18688 [1:39:03<22:36:26, 4.71s/it] 7%|▋ | 1401/18688 [1:39:08<22:42:04, 4.73s/it] 8%|▊ | 1402/18688 [1:39:12<21:27:12, 4.47s/it] 8%|▊ | 1403/18688 [1:39:16<20:44:53, 4.32s/it] 8%|▊ | 1404/18688 [1:39:20<20:28:03, 4.26s/it] 8%|▊ | 1405/18688 [1:39:23<19:25:11, 4.05s/it] 8%|▊ | 1406/18688 [1:39:27<18:40:50, 3.89s/it] 8%|▊ | 1407/18688 [1:39:31<18:38:21, 3.88s/it] 8%|▊ | 1408/18688 [1:39:35<19:26:22, 4.05s/it] 8%|▊ | 1409/18688 [1:39:39<19:03:20, 3.97s/it] 8%|▊ | 1410/18688 [1:39:44<20:31:15, 4.28s/it] {'loss': '1.014', 'grad_norm': '1.296', 'learning_rate': '0.0001879', 'epoch': '0.07545', 'num_input_tokens_seen': 4701160, 'train_runtime': '5984', 'train_tokens_per_second': '785.6'}
8%|▊ | 1410/18688 [1:39:44<20:31:15, 4.28s/it] 8%|▊ | 1411/18688 [1:39:48<20:03:30, 4.18s/it] 8%|▊ | 1412/18688 [1:39:52<20:11:39, 4.21s/it] 8%|▊ | 1413/18688 [1:39:56<19:42:42, 4.11s/it] 8%|▊ | 1414/18688 [1:40:01<20:42:07, 4.31s/it] 8%|▊ | 1415/18688 [1:40:05<20:16:50, 4.23s/it] 8%|▊ | 1416/18688 [1:40:09<19:24:27, 4.05s/it] 8%|▊ | 1417/18688 [1:40:13<20:30:56, 4.28s/it] 8%|▊ | 1418/18688 [1:40:17<20:09:27, 4.20s/it] 8%|▊ | 1419/18688 [1:40:21<19:11:01, 4.00s/it] 8%|▊ | 1420/18688 [1:40:25<18:55:38, 3.95s/it] {'loss': '1.03', 'grad_norm': '1.358', 'learning_rate': '0.0001892', 'epoch': '0.07599', 'num_input_tokens_seen': 4735568, 'train_runtime': '6025', 'train_tokens_per_second': '786'}
8%|▊ | 1420/18688 [1:40:25<18:55:38, 3.95s/it] 8%|▊ | 1421/18688 [1:40:29<19:28:25, 4.06s/it] 8%|▊ | 1422/18688 [1:40:34<20:34:41, 4.29s/it] 8%|▊ | 1423/18688 [1:40:39<22:06:50, 4.61s/it] 8%|▊ | 1424/18688 [1:40:43<20:52:32, 4.35s/it] 8%|▊ | 1425/18688 [1:40:47<19:46:57, 4.13s/it] 8%|▊ | 1426/18688 [1:40:50<19:05:19, 3.98s/it] 8%|▊ | 1427/18688 [1:40:54<19:31:50, 4.07s/it] 8%|▊ | 1428/18688 [1:40:58<19:02:02, 3.97s/it] 8%|▊ | 1429/18688 [1:41:03<19:39:10, 4.10s/it] 8%|▊ | 1430/18688 [1:41:06<19:07:45, 3.99s/it] {'loss': '1.039', 'grad_norm': '1.253', 'learning_rate': '0.0001905', 'epoch': '0.07652', 'num_input_tokens_seen': 4771024, 'train_runtime': '6067', 'train_tokens_per_second': '786.4'}
8%|▊ | 1430/18688 [1:41:06<19:07:45, 3.99s/it] 8%|▊ | 1431/18688 [1:41:10<18:21:24, 3.83s/it] 8%|▊ | 1432/18688 [1:41:13<18:00:36, 3.76s/it] 8%|▊ | 1433/18688 [1:41:17<18:09:53, 3.79s/it] 8%|▊ | 1434/18688 [1:41:21<18:01:48, 3.76s/it] 8%|▊ | 1435/18688 [1:41:25<18:42:22, 3.90s/it] 8%|▊ | 1436/18688 [1:41:29<18:06:40, 3.78s/it] 8%|▊ | 1437/18688 [1:41:33<18:45:49, 3.92s/it] 8%|▊ | 1438/18688 [1:41:36<18:01:42, 3.76s/it] 8%|▊ | 1439/18688 [1:41:40<17:44:53, 3.70s/it] 8%|▊ | 1440/18688 [1:41:44<18:02:40, 3.77s/it] {'loss': '0.9476', 'grad_norm': '1.127', 'learning_rate': '0.0001919', 'epoch': '0.07706', 'num_input_tokens_seen': 4800712, 'train_runtime': '6104', 'train_tokens_per_second': '786.4'}
8%|▊ | 1440/18688 [1:41:44<18:02:40, 3.77s/it] 8%|▊ | 1441/18688 [1:41:48<18:14:40, 3.81s/it] 8%|▊ | 1442/18688 [1:41:52<18:45:23, 3.92s/it] 8%|▊ | 1443/18688 [1:41:57<20:35:47, 4.30s/it] 8%|▊ | 1444/18688 [1:42:01<19:54:41, 4.16s/it] 8%|▊ | 1445/18688 [1:42:05<19:55:00, 4.16s/it] 8%|▊ | 1446/18688 [1:42:09<19:05:05, 3.98s/it] 8%|▊ | 1447/18688 [1:42:13<19:22:54, 4.05s/it] 8%|▊ | 1448/18688 [1:42:17<18:53:08, 3.94s/it] 8%|▊ | 1449/18688 [1:42:21<19:01:56, 3.97s/it] 8%|▊ | 1450/18688 [1:42:25<18:58:43, 3.96s/it] {'loss': '1.048', 'grad_norm': '1.191', 'learning_rate': '0.0001932', 'epoch': '0.07759', 'num_input_tokens_seen': 4835448, 'train_runtime': '6145', 'train_tokens_per_second': '786.9'}
8%|▊ | 1450/18688 [1:42:25<18:58:43, 3.96s/it] 8%|▊ | 1451/18688 [1:42:28<18:35:04, 3.88s/it] 8%|▊ | 1452/18688 [1:42:32<17:56:16, 3.75s/it] 8%|▊ | 1453/18688 [1:42:35<17:58:00, 3.75s/it] 8%|▊ | 1454/18688 [1:42:40<18:35:56, 3.89s/it] 8%|▊ | 1455/18688 [1:42:44<19:46:21, 4.13s/it] 8%|▊ | 1456/18688 [1:42:48<19:06:35, 3.99s/it] 8%|▊ | 1457/18688 [1:42:52<19:47:50, 4.14s/it] 8%|▊ | 1458/18688 [1:42:56<18:59:59, 3.97s/it] 8%|▊ | 1459/18688 [1:42:59<18:14:14, 3.81s/it] 8%|▊ | 1460/18688 [1:43:05<20:10:44, 4.22s/it] {'loss': '0.9992', 'grad_norm': '1.132', 'learning_rate': '0.0001945', 'epoch': '0.07813', 'num_input_tokens_seen': 4868640, 'train_runtime': '6185', 'train_tokens_per_second': '787.2'}
8%|▊ | 1460/18688 [1:43:05<20:10:44, 4.22s/it] 8%|▊ | 1461/18688 [1:43:09<20:01:45, 4.19s/it] 8%|▊ | 1462/18688 [1:43:13<20:27:32, 4.28s/it] 8%|▊ | 1463/18688 [1:43:17<19:52:05, 4.15s/it] 8%|▊ | 1464/18688 [1:43:22<21:25:52, 4.48s/it] 8%|▊ | 1465/18688 [1:43:27<21:24:17, 4.47s/it] 8%|▊ | 1466/18688 [1:43:31<21:05:06, 4.41s/it] 8%|▊ | 1467/18688 [1:43:36<21:42:02, 4.54s/it] 8%|▊ | 1468/18688 [1:43:40<21:07:05, 4.41s/it] 8%|▊ | 1469/18688 [1:43:44<20:46:23, 4.34s/it] 8%|▊ | 1470/18688 [1:43:48<20:32:07, 4.29s/it] {'loss': '1.055', 'grad_norm': '1.215', 'learning_rate': '0.0001959', 'epoch': '0.07866', 'num_input_tokens_seen': 4907240, 'train_runtime': '6229', 'train_tokens_per_second': '787.8'}
8%|▊ | 1470/18688 [1:43:48<20:32:07, 4.29s/it] 8%|▊ | 1471/18688 [1:43:53<20:53:30, 4.37s/it] 8%|▊ | 1472/18688 [1:43:56<19:34:11, 4.09s/it] 8%|▊ | 1473/18688 [1:44:01<20:11:10, 4.22s/it] 8%|▊ | 1474/18688 [1:44:05<19:56:42, 4.17s/it] 8%|▊ | 1475/18688 [1:44:09<19:52:44, 4.16s/it] 8%|▊ | 1476/18688 [1:44:13<19:05:58, 3.99s/it] 8%|▊ | 1477/18688 [1:44:17<19:29:42, 4.08s/it] 8%|▊ | 1478/18688 [1:44:21<19:16:45, 4.03s/it] 8%|▊ | 1479/18688 [1:44:25<19:11:27, 4.01s/it] 8%|▊ | 1480/18688 [1:44:29<19:32:53, 4.09s/it] {'loss': '0.9808', 'grad_norm': '1.137', 'learning_rate': '0.0001972', 'epoch': '0.0792', 'num_input_tokens_seen': 4940128, 'train_runtime': '6270', 'train_tokens_per_second': '787.9'}
8%|▊ | 1480/18688 [1:44:29<19:32:53, 4.09s/it] 8%|▊ | 1481/18688 [1:44:33<18:59:18, 3.97s/it] 8%|▊ | 1482/18688 [1:44:36<18:12:52, 3.81s/it] 8%|▊ | 1483/18688 [1:44:41<19:20:55, 4.05s/it] 8%|▊ | 1484/18688 [1:44:45<19:09:09, 4.01s/it] 8%|▊ | 1485/18688 [1:44:49<18:57:41, 3.97s/it] 8%|▊ | 1486/18688 [1:44:54<20:33:03, 4.30s/it] 8%|▊ | 1487/18688 [1:44:57<19:33:45, 4.09s/it] 8%|▊ | 1488/18688 [1:45:01<19:12:05, 4.02s/it] 8%|▊ | 1489/18688 [1:45:05<19:09:16, 4.01s/it] 8%|▊ | 1490/18688 [1:45:10<20:01:39, 4.19s/it] {'loss': '1.047', 'grad_norm': '1.387', 'learning_rate': '0.0001985', 'epoch': '0.07973', 'num_input_tokens_seen': 4971208, 'train_runtime': '6310', 'train_tokens_per_second': '787.8'}
8%|▊ | 1490/18688 [1:45:10<20:01:39, 4.19s/it] 8%|▊ | 1491/18688 [1:45:14<19:28:23, 4.08s/it] 8%|▊ | 1492/18688 [1:45:18<20:07:15, 4.21s/it] 8%|▊ | 1493/18688 [1:45:22<19:47:42, 4.14s/it] 8%|▊ | 1494/18688 [1:45:26<19:46:50, 4.14s/it] 8%|▊ | 1495/18688 [1:45:30<19:48:09, 4.15s/it] 8%|▊ | 1496/18688 [1:45:35<20:22:58, 4.27s/it] 8%|▊ | 1497/18688 [1:45:40<21:25:51, 4.49s/it] 8%|▊ | 1498/18688 [1:45:44<20:48:24, 4.36s/it] 8%|▊ | 1499/18688 [1:45:49<21:02:01, 4.41s/it] 8%|▊ | 1500/18688 [1:45:52<20:25:46, 4.28s/it] {'loss': '1.045', 'grad_norm': '1.396', 'learning_rate': '0.0001999', 'epoch': '0.08027', 'num_input_tokens_seen': 5004640, 'train_runtime': '6353', 'train_tokens_per_second': '787.8'}
8%|▊ | 1500/18688 [1:45:52<20:25:46, 4.28s/it] 8%|▊ | 1501/18688 [1:45:57<20:03:48, 4.20s/it] 8%|▊ | 1502/18688 [1:46:01<19:51:14, 4.16s/it] 8%|▊ | 1503/18688 [1:46:05<20:20:12, 4.26s/it] 8%|▊ | 1504/18688 [1:46:09<20:07:11, 4.22s/it] 8%|▊ | 1505/18688 [1:46:14<20:19:28, 4.26s/it] 8%|▊ | 1506/18688 [1:46:17<19:20:12, 4.05s/it] 8%|▊ | 1507/18688 [1:46:21<18:46:35, 3.93s/it] 8%|▊ | 1508/18688 [1:46:25<19:42:26, 4.13s/it] 8%|▊ | 1509/18688 [1:46:30<19:57:39, 4.18s/it] 8%|▊ | 1510/18688 [1:46:34<19:47:31, 4.15s/it] {'loss': '1.028', 'grad_norm': '1.414', 'learning_rate': '0.0002', 'epoch': '0.0808', 'num_input_tokens_seen': 5038496, 'train_runtime': '6394', 'train_tokens_per_second': '788'}
8%|▊ | 1510/18688 [1:46:34<19:47:31, 4.15s/it] 8%|▊ | 1511/18688 [1:46:37<19:00:27, 3.98s/it] 8%|▊ | 1512/18688 [1:46:42<19:32:52, 4.10s/it] 8%|▊ | 1513/18688 [1:46:46<19:55:29, 4.18s/it] 8%|▊ | 1514/18688 [1:46:51<20:21:08, 4.27s/it] 8%|▊ | 1515/18688 [1:46:56<21:27:46, 4.50s/it] 8%|▊ | 1516/18688 [1:47:00<20:58:19, 4.40s/it] 8%|▊ | 1517/18688 [1:47:04<20:15:15, 4.25s/it] 8%|▊ | 1518/18688 [1:47:08<19:48:42, 4.15s/it] 8%|▊ | 1519/18688 [1:47:12<20:39:05, 4.33s/it] 8%|▊ | 1520/18688 [1:47:17<20:59:21, 4.40s/it] {'loss': '0.9977', 'grad_norm': '1.269', 'learning_rate': '0.0002', 'epoch': '0.08134', 'num_input_tokens_seen': 5072456, 'train_runtime': '6437', 'train_tokens_per_second': '788'}
8%|▊ | 1520/18688 [1:47:17<20:59:21, 4.40s/it] 8%|▊ | 1521/18688 [1:47:21<21:14:42, 4.46s/it] 8%|▊ | 1522/18688 [1:47:26<21:54:43, 4.60s/it] 8%|▊ | 1523/18688 [1:47:32<22:41:00, 4.76s/it] 8%|▊ | 1524/18688 [1:47:35<20:50:28, 4.37s/it] 8%|▊ | 1525/18688 [1:47:39<20:20:59, 4.27s/it] 8%|▊ | 1526/18688 [1:47:43<20:23:08, 4.28s/it] 8%|▊ | 1527/18688 [1:47:48<20:31:50, 4.31s/it] 8%|▊ | 1528/18688 [1:47:51<19:29:19, 4.09s/it] 8%|▊ | 1529/18688 [1:47:57<21:38:33, 4.54s/it] 8%|▊ | 1530/18688 [1:48:02<22:00:34, 4.62s/it] {'loss': '0.986', 'grad_norm': '1.279', 'learning_rate': '0.0002', 'epoch': '0.08187', 'num_input_tokens_seen': 5109856, 'train_runtime': '6482', 'train_tokens_per_second': '788.3'}
8%|▊ | 1530/18688 [1:48:02<22:00:34, 4.62s/it] 8%|▊ | 1531/18688 [1:48:06<22:10:45, 4.65s/it] 8%|▊ | 1532/18688 [1:48:11<22:31:28, 4.73s/it] 8%|▊ | 1533/18688 [1:48:15<20:38:14, 4.33s/it] 8%|▊ | 1534/18688 [1:48:20<21:27:54, 4.50s/it] 8%|▊ | 1535/18688 [1:48:23<20:26:47, 4.29s/it] 8%|▊ | 1536/18688 [1:48:28<20:29:09, 4.30s/it] 8%|▊ | 1537/18688 [1:48:33<21:41:34, 4.55s/it] 8%|▊ | 1538/18688 [1:48:36<20:19:12, 4.27s/it] 8%|▊ | 1539/18688 [1:48:40<19:18:04, 4.05s/it] 8%|▊ | 1540/18688 [1:48:44<19:53:14, 4.18s/it] {'loss': '1.017', 'grad_norm': '1.312', 'learning_rate': '0.0002', 'epoch': '0.08241', 'num_input_tokens_seen': 5146272, 'train_runtime': '6525', 'train_tokens_per_second': '788.7'}
8%|▊ | 1540/18688 [1:48:44<19:53:14, 4.18s/it] 8%|▊ | 1541/18688 [1:48:48<19:14:46, 4.04s/it] 8%|▊ | 1542/18688 [1:48:53<20:01:16, 4.20s/it] 8%|▊ | 1543/18688 [1:48:57<20:00:08, 4.20s/it] 8%|▊ | 1544/18688 [1:49:01<19:21:46, 4.07s/it] 8%|▊ | 1545/18688 [1:49:04<18:28:12, 3.88s/it] 8%|▊ | 1546/18688 [1:49:08<18:16:10, 3.84s/it] 8%|▊ | 1547/18688 [1:49:12<18:12:03, 3.82s/it] 8%|▊ | 1548/18688 [1:49:17<19:44:25, 4.15s/it] 8%|▊ | 1549/18688 [1:49:21<19:46:45, 4.15s/it] 8%|▊ | 1550/18688 [1:49:25<19:32:14, 4.10s/it] {'loss': '0.9249', 'grad_norm': '1.168', 'learning_rate': '0.0002', 'epoch': '0.08294', 'num_input_tokens_seen': 5178800, 'train_runtime': '6565', 'train_tokens_per_second': '788.8'}
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8%|▊ | 1560/18688 [1:50:06<19:22:16, 4.07s/it] 8%|▊ | 1561/18688 [1:50:10<19:37:12, 4.12s/it] 8%|▊ | 1562/18688 [1:50:14<19:31:32, 4.10s/it] 8%|▊ | 1563/18688 [1:50:18<19:02:37, 4.00s/it] 8%|▊ | 1564/18688 [1:50:22<18:54:38, 3.98s/it] 8%|▊ | 1565/18688 [1:50:26<19:46:48, 4.16s/it] 8%|▊ | 1566/18688 [1:50:30<19:14:08, 4.04s/it] 8%|▊ | 1567/18688 [1:50:34<19:03:48, 4.01s/it] 8%|▊ | 1568/18688 [1:50:38<19:04:33, 4.01s/it] 8%|▊ | 1569/18688 [1:50:43<20:17:31, 4.27s/it] 8%|▊ | 1570/18688 [1:50:47<20:29:48, 4.31s/it] {'loss': '1.014', 'grad_norm': '1.508', 'learning_rate': '0.0002', 'epoch': '0.08401', 'num_input_tokens_seen': 5248400, 'train_runtime': '6648', 'train_tokens_per_second': '789.5'}
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10%|▉ | 1800/18688 [2:06:39<19:40:33, 4.19s/it] 10%|▉ | 1801/18688 [2:06:43<20:08:33, 4.29s/it] 10%|▉ | 1802/18688 [2:06:48<20:30:04, 4.37s/it] 10%|▉ | 1803/18688 [2:06:52<20:47:00, 4.43s/it] 10%|▉ | 1804/18688 [2:06:57<21:00:07, 4.48s/it] 10%|▉ | 1805/18688 [2:07:02<21:20:14, 4.55s/it] 10%|▉ | 1806/18688 [2:07:06<21:02:41, 4.49s/it] 10%|▉ | 1807/18688 [2:07:10<20:18:22, 4.33s/it] 10%|▉ | 1808/18688 [2:07:14<19:44:22, 4.21s/it] 10%|▉ | 1809/18688 [2:07:19<20:41:50, 4.41s/it] 10%|▉ | 1810/18688 [2:07:23<20:43:43, 4.42s/it] {'loss': '0.9886', 'grad_norm': '1.45', 'learning_rate': '0.0001998', 'epoch': '0.09686', 'num_input_tokens_seen': 6039224, 'train_runtime': '7644', 'train_tokens_per_second': '790.1'}
10%|▉ | 1810/18688 [2:07:23<20:43:43, 4.42s/it] 10%|▉ | 1811/18688 [2:07:28<20:49:20, 4.44s/it] 10%|▉ | 1812/18688 [2:07:32<20:44:26, 4.42s/it] 10%|▉ | 1813/18688 [2:07:37<20:40:24, 4.41s/it] 10%|▉ | 1814/18688 [2:07:40<19:46:48, 4.22s/it] 10%|▉ | 1815/18688 [2:07:45<20:05:41, 4.29s/it] 10%|▉ | 1816/18688 [2:07:49<20:14:20, 4.32s/it] 10%|▉ | 1817/18688 [2:07:53<19:44:52, 4.21s/it] 10%|▉ | 1818/18688 [2:07:57<19:51:10, 4.24s/it] 10%|▉ | 1819/18688 [2:08:01<19:08:47, 4.09s/it] 10%|▉ | 1820/18688 [2:08:05<18:35:43, 3.97s/it] {'loss': '1.039', 'grad_norm': '1.139', 'learning_rate': '0.0001998', 'epoch': '0.09739', 'num_input_tokens_seen': 6071920, 'train_runtime': '7685', 'train_tokens_per_second': '790.1'}
10%|▉ | 1820/18688 [2:08:05<18:35:43, 3.97s/it] 10%|▉ | 1821/18688 [2:08:09<18:56:00, 4.04s/it] 10%|▉ | 1822/18688 [2:08:13<18:50:40, 4.02s/it] 10%|▉ | 1823/18688 [2:08:18<19:43:30, 4.21s/it] 10%|▉ | 1824/18688 [2:08:22<20:03:08, 4.28s/it] 10%|▉ | 1825/18688 [2:08:27<20:22:57, 4.35s/it] 10%|▉ | 1826/18688 [2:08:31<20:09:25, 4.30s/it] 10%|▉ | 1827/18688 [2:08:35<20:20:06, 4.34s/it] 10%|▉ | 1828/18688 [2:08:40<20:58:08, 4.48s/it] 10%|▉ | 1829/18688 [2:08:45<21:23:33, 4.57s/it] 10%|▉ | 1830/18688 [2:08:50<22:09:50, 4.73s/it] {'loss': '1.075', 'grad_norm': '1.13', 'learning_rate': '0.0001998', 'epoch': '0.09793', 'num_input_tokens_seen': 6107064, 'train_runtime': '7730', 'train_tokens_per_second': '790'}
10%|▉ | 1830/18688 [2:08:50<22:09:50, 4.73s/it] 10%|▉ | 1831/18688 [2:08:54<21:35:16, 4.61s/it] 10%|▉ | 1832/18688 [2:08:58<20:24:37, 4.36s/it] 10%|▉ | 1833/18688 [2:09:02<19:55:59, 4.26s/it] 10%|▉ | 1834/18688 [2:09:06<20:02:24, 4.28s/it] 10%|▉ | 1835/18688 [2:09:11<20:52:20, 4.46s/it] 10%|▉ | 1836/18688 [2:09:16<21:19:31, 4.56s/it] 10%|▉ | 1837/18688 [2:09:20<20:14:25, 4.32s/it] 10%|▉ | 1838/18688 [2:09:23<19:17:25, 4.12s/it] 10%|▉ | 1839/18688 [2:09:28<19:29:15, 4.16s/it] 10%|▉ | 1840/18688 [2:09:33<21:17:33, 4.55s/it] {'loss': '1.01', 'grad_norm': '1.389', 'learning_rate': '0.0001998', 'epoch': '0.09846', 'num_input_tokens_seen': 6144296, 'train_runtime': '7774', 'train_tokens_per_second': '790.4'}
10%|▉ | 1840/18688 [2:09:33<21:17:33, 4.55s/it] 10%|▉ | 1841/18688 [2:09:37<19:46:16, 4.22s/it] 10%|▉ | 1842/18688 [2:09:40<18:38:50, 3.98s/it] 10%|▉ | 1843/18688 [2:09:44<18:32:48, 3.96s/it] 10%|▉ | 1844/18688 [2:09:49<19:19:42, 4.13s/it] 10%|▉ | 1845/18688 [2:09:53<20:01:19, 4.28s/it] 10%|▉ | 1846/18688 [2:09:58<20:29:53, 4.38s/it] 10%|▉ | 1847/18688 [2:10:03<21:08:49, 4.52s/it] 10%|▉ | 1848/18688 [2:10:08<21:41:06, 4.64s/it] 10%|▉ | 1849/18688 [2:10:11<20:14:08, 4.33s/it] 10%|▉ | 1850/18688 [2:10:15<20:11:24, 4.32s/it] {'loss': '1.044', 'grad_norm': '1.336', 'learning_rate': '0.0001998', 'epoch': '0.099', 'num_input_tokens_seen': 6179064, 'train_runtime': '7816', 'train_tokens_per_second': '790.6'}
10%|▉ | 1850/18688 [2:10:15<20:11:24, 4.32s/it] 10%|▉ | 1851/18688 [2:10:20<21:05:45, 4.51s/it] 10%|▉ | 1852/18688 [2:10:25<21:06:35, 4.51s/it] 10%|▉ | 1853/18688 [2:10:29<20:57:11, 4.48s/it] 10%|▉ | 1854/18688 [2:10:34<21:16:35, 4.55s/it] 10%|▉ | 1855/18688 [2:10:39<21:14:45, 4.54s/it] 10%|▉ | 1856/18688 [2:10:43<21:22:46, 4.57s/it] 10%|▉ | 1857/18688 [2:10:47<20:46:14, 4.44s/it] 10%|▉ | 1858/18688 [2:10:52<20:33:07, 4.40s/it] 10%|▉ | 1859/18688 [2:10:56<20:15:04, 4.33s/it] 10%|▉ | 1860/18688 [2:11:00<20:31:20, 4.39s/it] {'loss': '1.025', 'grad_norm': '1.208', 'learning_rate': '0.0001998', 'epoch': '0.09953', 'num_input_tokens_seen': 6213504, 'train_runtime': '7861', 'train_tokens_per_second': '790.4'}
10%|▉ | 1860/18688 [2:11:00<20:31:20, 4.39s/it] 10%|▉ | 1861/18688 [2:11:04<19:26:07, 4.16s/it] 10%|▉ | 1862/18688 [2:11:08<19:30:46, 4.17s/it] 10%|▉ | 1863/18688 [2:11:13<19:49:46, 4.24s/it] 10%|▉ | 1864/18688 [2:11:17<20:02:05, 4.29s/it] 10%|▉ | 1865/18688 [2:11:22<20:54:39, 4.47s/it] 10%|▉ | 1866/18688 [2:11:26<20:54:52, 4.48s/it] 10%|▉ | 1867/18688 [2:11:31<20:33:59, 4.40s/it] 10%|▉ | 1868/18688 [2:11:35<21:04:02, 4.51s/it] 10%|█ | 1869/18688 [2:11:40<21:52:09, 4.68s/it] 10%|█ | 1870/18688 [2:11:44<20:46:51, 4.45s/it] {'loss': '1.009', 'grad_norm': '1.663', 'learning_rate': '0.0001998', 'epoch': '0.1001', 'num_input_tokens_seen': 6248336, 'train_runtime': '7905', 'train_tokens_per_second': '790.4'}
10%|█ | 1870/18688 [2:11:44<20:46:51, 4.45s/it] 10%|█ | 1871/18688 [2:11:48<20:12:46, 4.33s/it] 10%|█ | 1872/18688 [2:11:53<20:02:26, 4.29s/it] 10%|█ | 1873/18688 [2:11:57<19:35:42, 4.20s/it] 10%|█ | 1874/18688 [2:12:01<20:11:12, 4.32s/it] 10%|█ | 1875/18688 [2:12:06<20:32:24, 4.40s/it] 10%|█ | 1876/18688 [2:12:11<21:58:38, 4.71s/it] 10%|█ | 1877/18688 [2:12:16<21:42:30, 4.65s/it] 10%|█ | 1878/18688 [2:12:20<21:23:14, 4.58s/it] 10%|█ | 1879/18688 [2:12:24<20:45:07, 4.44s/it] 10%|█ | 1880/18688 [2:12:28<19:48:50, 4.24s/it] {'loss': '0.9915', 'grad_norm': '1.369', 'learning_rate': '0.0001998', 'epoch': '0.1006', 'num_input_tokens_seen': 6282464, 'train_runtime': '7949', 'train_tokens_per_second': '790.4'}
10%|█ | 1880/18688 [2:12:28<19:48:50, 4.24s/it] 10%|█ | 1881/18688 [2:12:32<19:36:26, 4.20s/it] 10%|█ | 1882/18688 [2:12:36<18:49:33, 4.03s/it] 10%|█ | 1883/18688 [2:12:41<21:00:22, 4.50s/it] 10%|█ | 1884/18688 [2:12:45<19:49:17, 4.25s/it] 10%|█ | 1885/18688 [2:12:49<19:24:15, 4.16s/it] 10%|█ | 1886/18688 [2:12:53<19:47:14, 4.24s/it] 10%|█ | 1887/18688 [2:12:58<19:54:08, 4.26s/it] 10%|█ | 1888/18688 [2:13:02<19:40:00, 4.21s/it] 10%|█ | 1889/18688 [2:13:06<18:59:23, 4.07s/it] 10%|█ | 1890/18688 [2:13:09<18:49:58, 4.04s/it] {'loss': '0.9837', 'grad_norm': '1.219', 'learning_rate': '0.0001997', 'epoch': '0.1011', 'num_input_tokens_seen': 6317720, 'train_runtime': '7990', 'train_tokens_per_second': '790.7'}
10%|█ | 1890/18688 [2:13:09<18:49:58, 4.04s/it] 10%|█ | 1891/18688 [2:13:13<17:56:53, 3.85s/it] 10%|█ | 1892/18688 [2:13:17<18:17:25, 3.92s/it] 10%|█ | 1893/18688 [2:13:21<18:01:09, 3.86s/it] 10%|█ | 1894/18688 [2:13:24<17:34:56, 3.77s/it] 10%|█ | 1895/18688 [2:13:29<18:19:23, 3.93s/it] 10%|█ | 1896/18688 [2:13:33<18:29:44, 3.97s/it] 10%|█ | 1897/18688 [2:13:37<18:37:50, 3.99s/it] 10%|█ | 1898/18688 [2:13:41<18:36:37, 3.99s/it] 10%|█ | 1899/18688 [2:13:46<19:59:02, 4.29s/it] 10%|█ | 1900/18688 [2:13:49<18:55:52, 4.06s/it] {'loss': '0.9579', 'grad_norm': '1.517', 'learning_rate': '0.0001997', 'epoch': '0.1017', 'num_input_tokens_seen': 6351128, 'train_runtime': '8030', 'train_tokens_per_second': '791'}
10%|█ | 1900/18688 [2:13:49<18:55:52, 4.06s/it] 10%|█ | 1901/18688 [2:13:53<18:08:26, 3.89s/it] 10%|█ | 1902/18688 [2:13:57<18:10:19, 3.90s/it] 10%|█ | 1903/18688 [2:14:01<18:13:54, 3.91s/it] 10%|█ | 1904/18688 [2:14:05<18:25:39, 3.95s/it] 10%|█ | 1905/18688 [2:14:09<19:46:08, 4.24s/it] 10%|█ | 1906/18688 [2:14:14<19:50:44, 4.26s/it] 10%|█ | 1907/18688 [2:14:18<20:00:36, 4.29s/it] 10%|█ | 1908/18688 [2:14:22<18:55:27, 4.06s/it] 10%|█ | 1909/18688 [2:14:26<19:14:14, 4.13s/it] 10%|█ | 1910/18688 [2:14:31<20:40:51, 4.44s/it] {'loss': '1.035', 'grad_norm': '1.755', 'learning_rate': '0.0001997', 'epoch': '0.1022', 'num_input_tokens_seen': 6385648, 'train_runtime': '8072', 'train_tokens_per_second': '791.1'}
10%|█ | 1910/18688 [2:14:31<20:40:51, 4.44s/it] 10%|█ | 1911/18688 [2:14:35<20:10:38, 4.33s/it] 10%|█ | 1912/18688 [2:14:39<19:29:59, 4.18s/it] 10%|█ | 1913/18688 [2:14:43<18:56:27, 4.06s/it] 10%|█ | 1914/18688 [2:14:46<18:15:45, 3.92s/it] 10%|█ | 1915/18688 [2:14:50<17:44:19, 3.81s/it] 10%|█ | 1916/18688 [2:14:53<17:11:55, 3.69s/it] 10%|█ | 1917/18688 [2:14:57<17:07:25, 3.68s/it] 10%|█ | 1918/18688 [2:15:01<17:13:15, 3.70s/it] 10%|█ | 1919/18688 [2:15:05<18:29:56, 3.97s/it] 10%|█ | 1920/18688 [2:15:09<18:11:57, 3.91s/it] {'loss': '1.008', 'grad_norm': '1.461', 'learning_rate': '0.0001997', 'epoch': '0.1027', 'num_input_tokens_seen': 6414704, 'train_runtime': '8110', 'train_tokens_per_second': '791'}
10%|█ | 1920/18688 [2:15:09<18:11:57, 3.91s/it] 10%|█ | 1921/18688 [2:15:13<18:50:34, 4.05s/it] 10%|█ | 1922/18688 [2:15:18<19:05:39, 4.10s/it] 10%|█ | 1923/18688 [2:15:22<19:51:36, 4.26s/it] 10%|█ | 1924/18688 [2:15:26<18:41:12, 4.01s/it] 10%|█ | 1925/18688 [2:15:29<18:11:57, 3.91s/it] 10%|█ | 1926/18688 [2:15:34<18:42:57, 4.02s/it] 10%|█ | 1927/18688 [2:15:37<18:12:29, 3.91s/it] 10%|█ | 1928/18688 [2:15:41<18:12:36, 3.91s/it] 10%|█ | 1929/18688 [2:15:46<19:12:06, 4.12s/it] 10%|█ | 1930/18688 [2:15:50<18:41:30, 4.02s/it] {'loss': '1.035', 'grad_norm': '1.342', 'learning_rate': '0.0001997', 'epoch': '0.1033', 'num_input_tokens_seen': 6449208, 'train_runtime': '8150', 'train_tokens_per_second': '791.3'}
10%|█ | 1930/18688 [2:15:50<18:41:30, 4.02s/it] 10%|█ | 1931/18688 [2:15:53<17:56:00, 3.85s/it] 10%|█ | 1932/18688 [2:15:57<17:26:42, 3.75s/it] 10%|█ | 1933/18688 [2:16:00<17:00:39, 3.66s/it] 10%|█ | 1934/18688 [2:16:04<17:57:56, 3.86s/it] 10%|█ | 1935/18688 [2:16:10<19:40:11, 4.23s/it] 10%|█ | 1936/18688 [2:16:14<19:21:24, 4.16s/it] 10%|█ | 1937/18688 [2:16:18<19:20:55, 4.16s/it] 10%|█ | 1938/18688 [2:16:23<20:45:42, 4.46s/it] 10%|█ | 1939/18688 [2:16:27<19:59:05, 4.30s/it] 10%|█ | 1940/18688 [2:16:31<20:32:20, 4.41s/it] {'loss': '1.025', 'grad_norm': '1.416', 'learning_rate': '0.0001997', 'epoch': '0.1038', 'num_input_tokens_seen': 6482200, 'train_runtime': '8192', 'train_tokens_per_second': '791.3'}
10%|█ | 1940/18688 [2:16:31<20:32:20, 4.41s/it] 10%|█ | 1941/18688 [2:16:36<20:06:36, 4.32s/it] 10%|█ | 1942/18688 [2:16:40<19:56:40, 4.29s/it] 10%|█ | 1943/18688 [2:16:44<20:01:54, 4.31s/it] 10%|█ | 1944/18688 [2:16:48<19:19:12, 4.15s/it] 10%|█ | 1945/18688 [2:16:52<18:34:38, 3.99s/it] 10%|█ | 1946/18688 [2:16:56<18:39:55, 4.01s/it] 10%|█ | 1947/18688 [2:17:00<19:22:52, 4.17s/it] 10%|█ | 1948/18688 [2:17:05<20:17:22, 4.36s/it] 10%|█ | 1949/18688 [2:17:09<19:45:27, 4.25s/it] 10%|█ | 1950/18688 [2:17:13<19:37:49, 4.22s/it] {'loss': '1.084', 'grad_norm': '1.517', 'learning_rate': '0.0001997', 'epoch': '0.1043', 'num_input_tokens_seen': 6514736, 'train_runtime': '8234', 'train_tokens_per_second': '791.2'}
10%|█ | 1950/18688 [2:17:13<19:37:49, 4.22s/it] 10%|█ | 1951/18688 [2:17:17<19:21:09, 4.16s/it] 10%|█ | 1952/18688 [2:17:21<19:20:42, 4.16s/it] 10%|█ | 1953/18688 [2:17:26<19:33:20, 4.21s/it] 10%|█ | 1954/18688 [2:17:29<18:34:55, 4.00s/it] 10%|█ | 1955/18688 [2:17:33<18:46:13, 4.04s/it] 10%|█ | 1956/18688 [2:17:38<19:32:32, 4.20s/it] 10%|█ | 1957/18688 [2:17:42<19:38:19, 4.23s/it] 10%|█ | 1958/18688 [2:17:47<20:24:13, 4.39s/it] 10%|█ | 1959/18688 [2:17:51<19:49:28, 4.27s/it] 10%|█ | 1960/18688 [2:17:55<19:00:31, 4.09s/it] {'loss': '1.046', 'grad_norm': '1.607', 'learning_rate': '0.0001996', 'epoch': '0.1049', 'num_input_tokens_seen': 6548928, 'train_runtime': '8275', 'train_tokens_per_second': '791.4'}
10%|█ | 1960/18688 [2:17:55<19:00:31, 4.09s/it] 10%|█ | 1961/18688 [2:17:58<18:33:21, 3.99s/it] 10%|█ | 1962/18688 [2:18:04<20:24:17, 4.39s/it] 11%|█ | 1963/18688 [2:18:08<20:14:55, 4.36s/it] 11%|█ | 1964/18688 [2:18:13<21:23:19, 4.60s/it] 11%|█ | 1965/18688 [2:18:17<20:17:37, 4.37s/it] 11%|█ | 1966/18688 [2:18:21<20:23:28, 4.39s/it] 11%|█ | 1967/18688 [2:18:25<19:37:15, 4.22s/it] 11%|█ | 1968/18688 [2:18:30<20:04:06, 4.32s/it] 11%|█ | 1969/18688 [2:18:34<20:12:17, 4.35s/it] 11%|█ | 1970/18688 [2:18:38<19:36:21, 4.22s/it] {'loss': '0.9873', 'grad_norm': '1.123', 'learning_rate': '0.0001996', 'epoch': '0.1054', 'num_input_tokens_seen': 6583256, 'train_runtime': '8319', 'train_tokens_per_second': '791.4'}
11%|█ | 1970/18688 [2:18:38<19:36:21, 4.22s/it] 11%|█ | 1971/18688 [2:18:42<19:25:53, 4.18s/it] 11%|█ | 1972/18688 [2:18:47<19:59:54, 4.31s/it] 11%|█ | 1973/18688 [2:18:51<20:30:04, 4.42s/it] 11%|█ | 1974/18688 [2:18:56<20:40:36, 4.45s/it] 11%|█ | 1975/18688 [2:19:01<21:39:18, 4.66s/it] 11%|█ | 1976/18688 [2:19:05<20:57:30, 4.51s/it] 11%|█ | 1977/18688 [2:19:10<21:29:45, 4.63s/it] 11%|█ | 1978/18688 [2:19:14<20:33:57, 4.43s/it] 11%|█ | 1979/18688 [2:19:19<21:12:00, 4.57s/it] 11%|█ | 1980/18688 [2:19:24<22:06:58, 4.77s/it] {'loss': '1.057', 'grad_norm': '1.654', 'learning_rate': '0.0001996', 'epoch': '0.106', 'num_input_tokens_seen': 6619632, 'train_runtime': '8365', 'train_tokens_per_second': '791.4'}
11%|█ | 1980/18688 [2:19:24<22:06:58, 4.77s/it] 11%|█ | 1981/18688 [2:19:29<21:47:36, 4.70s/it] 11%|█ | 1982/18688 [2:19:34<22:04:09, 4.76s/it] 11%|█ | 1983/18688 [2:19:38<21:01:33, 4.53s/it] 11%|█ | 1984/18688 [2:19:42<20:27:06, 4.41s/it] 11%|█ | 1985/18688 [2:19:46<20:24:15, 4.40s/it] 11%|█ | 1986/18688 [2:19:51<20:25:15, 4.40s/it] 11%|█ | 1987/18688 [2:19:54<19:05:49, 4.12s/it] 11%|█ | 1988/18688 [2:19:58<19:19:25, 4.17s/it] 11%|█ | 1989/18688 [2:20:03<20:11:00, 4.35s/it] 11%|█ | 1990/18688 [2:20:07<19:29:47, 4.20s/it] {'loss': '1.004', 'grad_norm': '1.199', 'learning_rate': '0.0001996', 'epoch': '0.1065', 'num_input_tokens_seen': 6654592, 'train_runtime': '8407', 'train_tokens_per_second': '791.5'}
11%|█ | 1990/18688 [2:20:07<19:29:47, 4.20s/it] 11%|█ | 1991/18688 [2:20:12<20:36:30, 4.44s/it] 11%|█ | 1992/18688 [2:20:17<20:47:17, 4.48s/it] 11%|█ | 1993/18688 [2:20:20<19:58:40, 4.31s/it] 11%|█ | 1994/18688 [2:20:25<20:28:38, 4.42s/it] 11%|█ | 1995/18688 [2:20:30<21:16:50, 4.59s/it] 11%|█ | 1996/18688 [2:20:34<20:44:16, 4.47s/it] 11%|█ | 1997/18688 [2:20:38<19:44:39, 4.26s/it] 11%|█ | 1998/18688 [2:20:42<19:24:13, 4.19s/it] 11%|█ | 1999/18688 [2:20:46<19:26:47, 4.19s/it] 11%|█ | 2000/18688 [2:20:50<19:23:11, 4.18s/it] {'loss': '1.023', 'grad_norm': '1.356', 'learning_rate': '0.0001996', 'epoch': '0.107', 'num_input_tokens_seen': 6689560, 'train_runtime': '8451', 'train_tokens_per_second': '791.6'}
11%|█ | 2000/18688 [2:20:50<19:23:11, 4.18s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
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{'eval_loss': '0.9974', 'eval_runtime': '83.11', '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_0p00020/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 [2:22:20<137:46:33, 29.72s/it] 11%|█ | 2002/18688 [2:22:23<101:08:49, 21.82s/it] 11%|█ | 2003/18688 [2:22:27<76:40:39, 16.54s/it] 11%|█ | 2004/18688 [2:22:32<59:42:46, 12.88s/it] 11%|█ | 2005/18688 [2:22:36<47:56:40, 10.35s/it] 11%|█ | 2006/18688 [2:22:41<40:17:44, 8.70s/it] 11%|█ | 2007/18688 [2:22:46<35:05:06, 7.57s/it] 11%|█ | 2008/18688 [2:22:51<31:33:09, 6.81s/it] 11%|█ | 2009/18688 [2:22:55<27:44:09, 5.99s/it] 11%|█ | 2010/18688 [2:22:59<24:39:00, 5.32s/it] {'loss': '0.9538', 'grad_norm': '1.212', 'learning_rate': '0.0001996', 'epoch': '0.1076', 'num_input_tokens_seen': 6727088, 'train_runtime': '8579', 'train_tokens_per_second': '784.1'}
11%|█ | 2010/18688 [2:22:59<24:39:00, 5.32s/it] 11%|█ | 2011/18688 [2:23:03<23:25:54, 5.06s/it] 11%|█ | 2012/18688 [2:23:08<22:23:21, 4.83s/it] 11%|█ | 2013/18688 [2:23:12<21:38:16, 4.67s/it] 11%|█ | 2014/18688 [2:23:16<20:34:11, 4.44s/it] 11%|█ | 2015/18688 [2:23:21<21:26:43, 4.63s/it] 11%|█ | 2016/18688 [2:23:25<20:22:57, 4.40s/it] 11%|█ | 2017/18688 [2:23:29<20:06:23, 4.34s/it] 11%|█ | 2018/18688 [2:23:33<19:58:02, 4.31s/it] 11%|█ | 2019/18688 [2:23:38<20:04:36, 4.34s/it] 11%|█ | 2020/18688 [2:23:41<19:30:35, 4.21s/it] {'loss': '1.03', 'grad_norm': '1.403', 'learning_rate': '0.0001996', 'epoch': '0.1081', 'num_input_tokens_seen': 6760040, 'train_runtime': '8622', 'train_tokens_per_second': '784'}
11%|█ | 2020/18688 [2:23:41<19:30:35, 4.21s/it] 11%|█ | 2021/18688 [2:23:45<19:15:13, 4.16s/it] 11%|█ | 2022/18688 [2:23:50<19:57:24, 4.31s/it] 11%|█ | 2023/18688 [2:23:55<20:00:47, 4.32s/it] 11%|█ | 2024/18688 [2:23:59<20:24:59, 4.41s/it] 11%|█ | 2025/18688 [2:24:03<20:18:17, 4.39s/it] 11%|█ | 2026/18688 [2:24:08<20:27:41, 4.42s/it] 11%|█ | 2027/18688 [2:24:12<19:42:24, 4.26s/it] 11%|█ | 2028/18688 [2:24:16<19:25:47, 4.20s/it] 11%|█ | 2029/18688 [2:24:20<19:23:16, 4.19s/it] 11%|█ | 2030/18688 [2:24:24<19:35:06, 4.23s/it] {'loss': '1.04', 'grad_norm': '1.795', 'learning_rate': '0.0001995', 'epoch': '0.1086', 'num_input_tokens_seen': 6791920, 'train_runtime': '8665', 'train_tokens_per_second': '783.8'}
11%|█ | 2030/18688 [2:24:24<19:35:06, 4.23s/it] 11%|█ | 2031/18688 [2:24:29<20:19:36, 4.39s/it] 11%|█ | 2032/18688 [2:24:33<20:09:30, 4.36s/it] 11%|█ | 2033/18688 [2:24:38<20:10:26, 4.36s/it] 11%|█ | 2034/18688 [2:24:42<19:17:59, 4.17s/it] 11%|█ | 2035/18688 [2:24:46<19:25:33, 4.20s/it] 11%|█ | 2036/18688 [2:24:50<19:31:41, 4.22s/it] 11%|█ | 2037/18688 [2:24:54<18:47:40, 4.06s/it] 11%|█ | 2038/18688 [2:24:58<19:14:00, 4.16s/it] 11%|█ | 2039/18688 [2:25:02<19:26:50, 4.21s/it] 11%|█ | 2040/18688 [2:25:06<19:08:37, 4.14s/it] {'loss': '1.037', 'grad_norm': '1.519', 'learning_rate': '0.0001995', 'epoch': '0.1092', 'num_input_tokens_seen': 6827176, 'train_runtime': '8707', 'train_tokens_per_second': '784.1'}
11%|█ | 2040/18688 [2:25:06<19:08:37, 4.14s/it] 11%|█ | 2041/18688 [2:25:11<19:21:14, 4.19s/it] 11%|█ | 2042/18688 [2:25:14<18:18:20, 3.96s/it] 11%|█ | 2043/18688 [2:25:18<18:19:35, 3.96s/it] 11%|█ | 2044/18688 [2:25:23<19:00:49, 4.11s/it] 11%|█ | 2045/18688 [2:25:28<20:05:52, 4.35s/it] 11%|█ | 2046/18688 [2:25:31<19:12:21, 4.15s/it] 11%|█ | 2047/18688 [2:25:35<19:02:50, 4.12s/it] 11%|█ | 2048/18688 [2:25:40<19:23:26, 4.20s/it] 11%|█ | 2049/18688 [2:25:44<19:28:08, 4.21s/it] 11%|█ | 2050/18688 [2:25:48<19:22:23, 4.19s/it] {'loss': '0.9926', 'grad_norm': '1.254', 'learning_rate': '0.0001995', 'epoch': '0.1097', 'num_input_tokens_seen': 6859736, 'train_runtime': '8749', 'train_tokens_per_second': '784.1'}
11%|█ | 2050/18688 [2:25:48<19:22:23, 4.19s/it] 11%|█ | 2051/18688 [2:25:52<19:13:41, 4.16s/it] 11%|█ | 2052/18688 [2:25:57<20:28:25, 4.43s/it] 11%|█ | 2053/18688 [2:26:02<21:05:38, 4.57s/it] 11%|█ | 2054/18688 [2:26:06<20:53:56, 4.52s/it] 11%|█ | 2055/18688 [2:26:10<19:56:21, 4.32s/it] 11%|█ | 2056/18688 [2:26:14<19:35:43, 4.24s/it] 11%|█ | 2057/18688 [2:26:18<19:25:48, 4.21s/it] 11%|█ | 2058/18688 [2:26:22<18:43:51, 4.05s/it] 11%|█ | 2059/18688 [2:26:27<19:14:53, 4.17s/it] 11%|█ | 2060/18688 [2:26:31<19:33:30, 4.23s/it] {'loss': '0.9584', 'grad_norm': '1.247', 'learning_rate': '0.0001995', 'epoch': '0.1102', 'num_input_tokens_seen': 6893248, 'train_runtime': '8792', 'train_tokens_per_second': '784.1'}
11%|█ | 2060/18688 [2:26:31<19:33:30, 4.23s/it] 11%|█ | 2061/18688 [2:26:35<19:43:04, 4.27s/it] 11%|█ | 2062/18688 [2:26:40<20:10:55, 4.37s/it] 11%|█ | 2063/18688 [2:26:44<19:49:00, 4.29s/it] 11%|█ | 2064/18688 [2:26:49<20:38:49, 4.47s/it] 11%|█ | 2065/18688 [2:26:53<20:18:08, 4.40s/it] 11%|█ | 2066/18688 [2:26:57<20:00:12, 4.33s/it] 11%|█ | 2067/18688 [2:27:02<20:42:39, 4.49s/it] 11%|█ | 2068/18688 [2:27:06<19:52:58, 4.31s/it] 11%|█ | 2069/18688 [2:27:11<20:25:15, 4.42s/it] 11%|█ | 2070/18688 [2:27:15<20:00:41, 4.34s/it] {'loss': '1.043', 'grad_norm': '1.297', 'learning_rate': '0.0001995', 'epoch': '0.1108', 'num_input_tokens_seen': 6926840, 'train_runtime': '8835', 'train_tokens_per_second': '784'}
11%|█ | 2070/18688 [2:27:15<20:00:41, 4.34s/it] 11%|█ | 2071/18688 [2:27:20<21:35:24, 4.68s/it] 11%|█ | 2072/18688 [2:27:25<21:06:00, 4.57s/it] 11%|█ | 2073/18688 [2:27:30<22:36:11, 4.90s/it] 11%|█ | 2074/18688 [2:27:34<21:13:50, 4.60s/it] 11%|█ | 2075/18688 [2:27:38<20:14:30, 4.39s/it] 11%|█ | 2076/18688 [2:27:42<18:58:12, 4.11s/it] 11%|█ | 2077/18688 [2:27:45<18:21:21, 3.98s/it] 11%|█ | 2078/18688 [2:27:49<17:38:26, 3.82s/it] 11%|█ | 2079/18688 [2:27:53<17:51:33, 3.87s/it] 11%|█ | 2080/18688 [2:27:57<18:54:55, 4.10s/it] {'loss': '0.9855', 'grad_norm': '1.423', 'learning_rate': '0.0001994', 'epoch': '0.1113', 'num_input_tokens_seen': 6960856, 'train_runtime': '8878', 'train_tokens_per_second': '784.1'}
11%|█ | 2080/18688 [2:27:57<18:54:55, 4.10s/it] 11%|█ | 2081/18688 [2:28:01<18:39:31, 4.04s/it] 11%|█ | 2082/18688 [2:28:05<18:20:44, 3.98s/it] 11%|█ | 2083/18688 [2:28:10<18:55:18, 4.10s/it] 11%|█ | 2084/18688 [2:28:14<18:57:31, 4.11s/it] 11%|█ | 2085/18688 [2:28:17<18:03:04, 3.91s/it] 11%|█ | 2086/18688 [2:28:21<18:28:15, 4.01s/it] 11%|█ | 2087/18688 [2:28:25<18:05:59, 3.93s/it] 11%|█ | 2088/18688 [2:28:29<18:32:18, 4.02s/it] 11%|█ | 2089/18688 [2:28:33<18:20:54, 3.98s/it] 11%|█ | 2090/18688 [2:28:37<18:43:10, 4.06s/it] {'loss': '0.9521', 'grad_norm': '1.534', 'learning_rate': '0.0001994', 'epoch': '0.1118', 'num_input_tokens_seen': 6993128, 'train_runtime': '8918', 'train_tokens_per_second': '784.2'}
11%|█ | 2090/18688 [2:28:37<18:43:10, 4.06s/it] 11%|█ | 2091/18688 [2:28:42<19:20:01, 4.19s/it] 11%|█ | 2092/18688 [2:28:46<19:35:52, 4.25s/it] 11%|█ | 2093/18688 [2:28:50<18:33:21, 4.03s/it] 11%|█ | 2094/18688 [2:28:54<18:47:23, 4.08s/it] 11%|█ | 2095/18688 [2:28:59<19:32:27, 4.24s/it] 11%|█ | 2096/18688 [2:29:04<20:25:04, 4.43s/it] 11%|█ | 2097/18688 [2:29:08<20:40:16, 4.49s/it] 11%|█ | 2098/18688 [2:29:13<21:51:30, 4.74s/it] 11%|█ | 2099/18688 [2:29:17<20:32:40, 4.46s/it] 11%|█ | 2100/18688 [2:29:21<19:55:14, 4.32s/it] {'loss': '1.026', 'grad_norm': '1.696', 'learning_rate': '0.0001994', 'epoch': '0.1124', 'num_input_tokens_seen': 7027808, 'train_runtime': '8962', 'train_tokens_per_second': '784.2'}
11%|█ | 2100/18688 [2:29:21<19:55:14, 4.32s/it] 11%|█ | 2101/18688 [2:29:26<20:28:00, 4.44s/it] 11%|█ | 2102/18688 [2:29:30<19:36:37, 4.26s/it] 11%|█▏ | 2103/18688 [2:29:34<20:10:43, 4.38s/it] 11%|█▏ | 2104/18688 [2:29:38<19:30:19, 4.23s/it] 11%|█▏ | 2105/18688 [2:29:43<20:04:20, 4.36s/it] 11%|█▏ | 2106/18688 [2:29:47<19:17:23, 4.19s/it] 11%|█▏ | 2107/18688 [2:29:51<19:12:59, 4.17s/it] 11%|█▏ | 2108/18688 [2:29:55<18:49:40, 4.09s/it] 11%|█▏ | 2109/18688 [2:29:59<18:54:33, 4.11s/it] 11%|█▏ | 2110/18688 [2:30:04<19:54:16, 4.32s/it] {'loss': '0.9414', 'grad_norm': '1.54', 'learning_rate': '0.0001994', 'epoch': '0.1129', 'num_input_tokens_seen': 7060536, 'train_runtime': '9004', 'train_tokens_per_second': '784.1'}
11%|█▏ | 2110/18688 [2:30:04<19:54:16, 4.32s/it] 11%|█▏ | 2111/18688 [2:30:07<18:57:09, 4.12s/it] 11%|█▏ | 2112/18688 [2:30:12<18:51:00, 4.09s/it] 11%|█▏ | 2113/18688 [2:30:16<19:12:31, 4.17s/it] 11%|█▏ | 2114/18688 [2:30:21<20:28:08, 4.45s/it] 11%|█▏ | 2115/18688 [2:30:26<20:53:28, 4.54s/it] 11%|█▏ | 2116/18688 [2:30:30<20:08:37, 4.38s/it] 11%|█▏ | 2117/18688 [2:30:35<20:51:09, 4.53s/it] 11%|█▏ | 2118/18688 [2:30:39<21:17:05, 4.62s/it] 11%|█▏ | 2119/18688 [2:30:44<20:38:42, 4.49s/it] 11%|█▏ | 2120/18688 [2:30:48<21:02:36, 4.57s/it] {'loss': '1.077', 'grad_norm': '1.318', 'learning_rate': '0.0001994', 'epoch': '0.1134', 'num_input_tokens_seen': 7095224, 'train_runtime': '9049', 'train_tokens_per_second': '784.1'}
11%|█▏ | 2120/18688 [2:30:48<21:02:36, 4.57s/it] 11%|█▏ | 2121/18688 [2:30:53<20:44:42, 4.51s/it] 11%|█▏ | 2122/18688 [2:30:57<20:57:08, 4.55s/it] 11%|█▏ | 2123/18688 [2:31:02<21:20:53, 4.64s/it] 11%|█▏ | 2124/18688 [2:31:06<20:35:23, 4.47s/it] 11%|█▏ | 2125/18688 [2:31:10<19:40:19, 4.28s/it] 11%|█▏ | 2126/18688 [2:31:14<19:21:33, 4.21s/it] 11%|█▏ | 2127/18688 [2:31:18<19:15:42, 4.19s/it] 11%|█▏ | 2128/18688 [2:31:22<18:58:30, 4.13s/it] 11%|█▏ | 2129/18688 [2:31:28<20:42:11, 4.50s/it] 11%|█▏ | 2130/18688 [2:31:32<19:58:28, 4.34s/it] {'loss': '1.026', 'grad_norm': '1.526', 'learning_rate': '0.0001993', 'epoch': '0.114', 'num_input_tokens_seen': 7128176, 'train_runtime': '9092', 'train_tokens_per_second': '784'}
11%|█▏ | 2130/18688 [2:31:32<19:58:28, 4.34s/it] 11%|█▏ | 2131/18688 [2:31:36<19:50:52, 4.32s/it] 11%|█▏ | 2132/18688 [2:31:40<19:14:42, 4.18s/it] 11%|█▏ | 2133/18688 [2:31:45<20:27:03, 4.45s/it] 11%|█▏ | 2134/18688 [2:31:50<21:05:57, 4.59s/it] 11%|█▏ | 2135/18688 [2:31:54<20:25:11, 4.44s/it] 11%|█▏ | 2136/18688 [2:31:58<20:28:50, 4.45s/it] 11%|█▏ | 2137/18688 [2:32:02<19:48:16, 4.31s/it] 11%|█▏ | 2138/18688 [2:32:06<18:44:08, 4.08s/it] 11%|█▏ | 2139/18688 [2:32:09<18:00:03, 3.92s/it] 11%|█▏ | 2140/18688 [2:32:13<17:23:19, 3.78s/it] {'loss': '1.02', 'grad_norm': '1.477', 'learning_rate': '0.0001993', 'epoch': '0.1145', 'num_input_tokens_seen': 7160536, 'train_runtime': '9133', 'train_tokens_per_second': '784'}
11%|█▏ | 2140/18688 [2:32:13<17:23:19, 3.78s/it] 11%|█▏ | 2141/18688 [2:32:16<16:55:32, 3.68s/it] 11%|█▏ | 2142/18688 [2:32:20<16:34:05, 3.60s/it] 11%|█▏ | 2143/18688 [2:32:24<17:57:45, 3.91s/it] 11%|█▏ | 2144/18688 [2:32:28<17:39:01, 3.84s/it] 11%|█▏ | 2145/18688 [2:32:32<18:20:27, 3.99s/it] 11%|█▏ | 2146/18688 [2:32:36<17:55:49, 3.90s/it] 11%|█▏ | 2147/18688 [2:32:41<18:55:43, 4.12s/it] 11%|█▏ | 2148/18688 [2:32:45<18:49:51, 4.10s/it] 11%|█▏ | 2149/18688 [2:32:49<19:00:13, 4.14s/it] 12%|█▏ | 2150/18688 [2:32:53<18:45:22, 4.08s/it] {'loss': '1.001', 'grad_norm': '1.346', 'learning_rate': '0.0001993', 'epoch': '0.1151', 'num_input_tokens_seen': 7194240, 'train_runtime': '9173', 'train_tokens_per_second': '784.2'}
12%|█▏ | 2150/18688 [2:32:53<18:45:22, 4.08s/it] 12%|█▏ | 2151/18688 [2:32:57<18:37:56, 4.06s/it] 12%|█▏ | 2152/18688 [2:33:01<18:49:11, 4.10s/it] 12%|█▏ | 2153/18688 [2:33:05<19:11:35, 4.18s/it] 12%|█▏ | 2154/18688 [2:33:09<18:49:53, 4.10s/it] 12%|█▏ | 2155/18688 [2:33:13<18:28:42, 4.02s/it] 12%|█▏ | 2156/18688 [2:33:17<18:38:04, 4.06s/it] 12%|█▏ | 2157/18688 [2:33:22<18:53:03, 4.11s/it] 12%|█▏ | 2158/18688 [2:33:25<18:25:35, 4.01s/it] 12%|█▏ | 2159/18688 [2:33:29<17:59:14, 3.92s/it] 12%|█▏ | 2160/18688 [2:33:33<18:13:29, 3.97s/it] {'loss': '1.016', 'grad_norm': '1.661', 'learning_rate': '0.0001993', 'epoch': '0.1156', 'num_input_tokens_seen': 7224368, 'train_runtime': '9214', 'train_tokens_per_second': '784.1'}
12%|█▏ | 2160/18688 [2:33:33<18:13:29, 3.97s/it] 12%|█▏ | 2161/18688 [2:33:38<19:21:29, 4.22s/it] 12%|█▏ | 2162/18688 [2:33:41<18:13:03, 3.97s/it] 12%|█▏ | 2163/18688 [2:33:45<17:30:45, 3.82s/it] 12%|█▏ | 2164/18688 [2:33:49<17:59:00, 3.92s/it] 12%|█▏ | 2165/18688 [2:33:52<17:21:52, 3.78s/it] 12%|█▏ | 2166/18688 [2:33:57<17:54:37, 3.90s/it] 12%|█▏ | 2167/18688 [2:34:01<18:09:08, 3.96s/it] 12%|█▏ | 2168/18688 [2:34:05<18:37:41, 4.06s/it] 12%|█▏ | 2169/18688 [2:34:10<19:19:42, 4.21s/it] 12%|█▏ | 2170/18688 [2:34:13<18:35:29, 4.05s/it] {'loss': '1.011', 'grad_norm': '1.28', 'learning_rate': '0.0001993', 'epoch': '0.1161', 'num_input_tokens_seen': 7257872, 'train_runtime': '9254', 'train_tokens_per_second': '784.3'}
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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': '1.006', 'eval_runtime': '82.83', 'eval_samples_per_second': '12.07', 'eval_steps_per_second': '12.07', 'epoch': '0.1605', 'num_input_tokens_seen': 10063432}
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20%|██ | 3820/18688 [4:32:04<16:56:18, 4.10s/it] 20%|██ | 3821/18688 [4:32:08<17:33:35, 4.25s/it] 20%|██ | 3822/18688 [4:32:12<17:28:04, 4.23s/it] 20%|██ | 3823/18688 [4:32:17<17:22:04, 4.21s/it] 20%|██ | 3824/18688 [4:32:21<17:10:35, 4.16s/it] 20%|██ | 3825/18688 [4:32:25<16:56:49, 4.10s/it] 20%|██ | 3826/18688 [4:32:28<16:33:28, 4.01s/it] 20%|██ | 3827/18688 [4:32:33<16:51:43, 4.08s/it] 20%|██ | 3828/18688 [4:32:38<18:12:14, 4.41s/it] 20%|██ | 3829/18688 [4:32:41<17:14:01, 4.18s/it] 20%|██ | 3830/18688 [4:32:46<17:11:54, 4.17s/it] {'loss': '0.9841', 'grad_norm': '1.664', 'learning_rate': '0.0001911', 'epoch': '0.2049', 'num_input_tokens_seen': 12862056, 'train_runtime': '1.637e+04', 'train_tokens_per_second': '785.9'}
20%|██ | 3830/18688 [4:32:46<17:11:54, 4.17s/it] 20%|██ | 3831/18688 [4:32:50<17:04:36, 4.14s/it] 21%|██ | 3832/18688 [4:32:55<18:12:01, 4.41s/it] 21%|██ | 3833/18688 [4:32:59<17:44:06, 4.30s/it] 21%|██ | 3834/18688 [4:33:04<19:14:59, 4.67s/it] 21%|██ | 3835/18688 [4:33:08<18:32:16, 4.49s/it] 21%|██ | 3836/18688 [4:33:12<18:02:26, 4.37s/it] 21%|██ | 3837/18688 [4:33:16<17:34:19, 4.26s/it] 21%|██ | 3838/18688 [4:33:21<18:01:54, 4.37s/it] 21%|██ | 3839/18688 [4:33:26<18:27:16, 4.47s/it] 21%|██ | 3840/18688 [4:33:29<17:17:46, 4.19s/it] {'loss': '0.9924', 'grad_norm': '2.041', 'learning_rate': '0.000191', 'epoch': '0.2055', 'num_input_tokens_seen': 12895912, 'train_runtime': '1.641e+04', 'train_tokens_per_second': '785.9'}
21%|██ | 3840/18688 [4:33:29<17:17:46, 4.19s/it] 21%|██ | 3841/18688 [4:33:33<16:25:53, 3.98s/it] 21%|██ | 3842/18688 [4:33:37<16:58:54, 4.12s/it] 21%|██ | 3843/18688 [4:33:41<16:44:11, 4.06s/it] 21%|██ | 3844/18688 [4:33:47<19:13:40, 4.66s/it] 21%|██ | 3845/18688 [4:33:52<18:50:26, 4.57s/it] 21%|██ | 3846/18688 [4:33:56<18:24:46, 4.47s/it] 21%|██ | 3847/18688 [4:34:00<17:43:59, 4.30s/it] 21%|██ | 3848/18688 [4:34:04<18:12:06, 4.42s/it] 21%|██ | 3849/18688 [4:34:09<18:24:35, 4.47s/it] 21%|██ | 3850/18688 [4:34:14<19:12:40, 4.66s/it] {'loss': '1.017', 'grad_norm': '1.758', 'learning_rate': '0.0001909', 'epoch': '0.206', 'num_input_tokens_seen': 12932544, 'train_runtime': '1.645e+04', 'train_tokens_per_second': '786'}
21%|██ | 3850/18688 [4:34:14<19:12:40, 4.66s/it] 21%|██ | 3851/18688 [4:34:18<18:26:30, 4.47s/it] 21%|██ | 3852/18688 [4:34:22<17:53:26, 4.34s/it] 21%|██ | 3853/18688 [4:34:27<17:50:11, 4.33s/it] 21%|██ | 3854/18688 [4:34:32<18:57:10, 4.60s/it] 21%|██ | 3855/18688 [4:34:36<18:04:45, 4.39s/it] 21%|██ | 3856/18688 [4:34:40<18:24:49, 4.47s/it] 21%|██ | 3857/18688 [4:34:44<17:50:12, 4.33s/it] 21%|██ | 3858/18688 [4:34:49<17:59:39, 4.37s/it] 21%|██ | 3859/18688 [4:34:53<17:53:07, 4.34s/it] 21%|██ | 3860/18688 [4:34:58<18:07:15, 4.40s/it] {'loss': '1.02', 'grad_norm': '1.747', 'learning_rate': '0.0001908', 'epoch': '0.2066', 'num_input_tokens_seen': 12965064, 'train_runtime': '1.65e+04', 'train_tokens_per_second': '785.9'}
21%|██ | 3860/18688 [4:34:58<18:07:15, 4.40s/it] 21%|██ | 3861/18688 [4:35:03<19:56:30, 4.84s/it] 21%|██ | 3862/18688 [4:35:07<18:53:19, 4.59s/it] 21%|██ | 3863/18688 [4:35:12<19:04:32, 4.63s/it] 21%|██ | 3864/18688 [4:35:16<18:24:33, 4.47s/it] 21%|██ | 3865/18688 [4:35:21<19:05:41, 4.64s/it] 21%|██ | 3866/18688 [4:35:25<18:17:25, 4.44s/it] 21%|██ | 3867/18688 [4:35:29<17:34:34, 4.27s/it] 21%|██ | 3868/18688 [4:35:33<16:33:47, 4.02s/it] 21%|██ | 3869/18688 [4:35:37<16:34:16, 4.03s/it] 21%|██ | 3870/18688 [4:35:40<16:18:59, 3.96s/it] {'loss': '1.054', 'grad_norm': '1.911', 'learning_rate': '0.0001908', 'epoch': '0.2071', 'num_input_tokens_seen': 12999672, 'train_runtime': '1.654e+04', 'train_tokens_per_second': '785.9'}
21%|██ | 3870/18688 [4:35:40<16:18:59, 3.96s/it] 21%|██ | 3871/18688 [4:35:45<16:30:26, 4.01s/it] 21%|██ | 3872/18688 [4:35:49<16:35:36, 4.03s/it] 21%|██ | 3873/18688 [4:35:53<17:19:22, 4.21s/it] 21%|██ | 3874/18688 [4:35:58<17:43:54, 4.31s/it] 21%|██ | 3875/18688 [4:36:02<17:31:42, 4.26s/it] 21%|██ | 3876/18688 [4:36:06<17:39:46, 4.29s/it] 21%|██ | 3877/18688 [4:36:11<17:51:16, 4.34s/it] 21%|██ | 3878/18688 [4:36:16<18:22:00, 4.46s/it] 21%|██ | 3879/18688 [4:36:20<18:22:49, 4.47s/it] 21%|██ | 3880/18688 [4:36:24<17:58:20, 4.37s/it] {'loss': '1.012', 'grad_norm': '2.62', 'learning_rate': '0.0001907', 'epoch': '0.2076', 'num_input_tokens_seen': 13032520, 'train_runtime': '1.658e+04', 'train_tokens_per_second': '785.8'}
21%|██ | 3880/18688 [4:36:24<17:58:20, 4.37s/it] 21%|██ | 3881/18688 [4:36:28<17:48:02, 4.33s/it] 21%|██ | 3882/18688 [4:36:33<17:43:13, 4.31s/it] 21%|██ | 3883/18688 [4:36:37<17:25:44, 4.24s/it] 21%|██ | 3884/18688 [4:36:41<17:47:50, 4.33s/it] 21%|██ | 3885/18688 [4:36:46<17:42:14, 4.31s/it] 21%|██ | 3886/18688 [4:36:49<16:49:44, 4.09s/it] 21%|██ | 3887/18688 [4:36:54<17:22:51, 4.23s/it] 21%|██ | 3888/18688 [4:36:58<17:57:48, 4.37s/it] 21%|██ | 3889/18688 [4:37:03<17:44:38, 4.32s/it] 21%|██ | 3890/18688 [4:37:07<17:28:27, 4.25s/it] {'loss': '0.9686', 'grad_norm': '1.888', 'learning_rate': '0.0001906', 'epoch': '0.2082', 'num_input_tokens_seen': 13063912, 'train_runtime': '1.663e+04', 'train_tokens_per_second': '785.7'}
21%|██ | 3890/18688 [4:37:07<17:28:27, 4.25s/it] 21%|██ | 3891/18688 [4:37:11<17:39:14, 4.30s/it] 21%|██ | 3892/18688 [4:37:17<19:16:46, 4.69s/it] 21%|██ | 3893/18688 [4:37:22<19:28:34, 4.74s/it] 21%|██ | 3894/18688 [4:37:25<18:16:03, 4.45s/it] 21%|██ | 3895/18688 [4:37:29<17:35:58, 4.28s/it] 21%|██ | 3896/18688 [4:37:33<16:40:09, 4.06s/it] 21%|██ | 3897/18688 [4:37:37<16:59:28, 4.14s/it] 21%|██ | 3898/18688 [4:37:41<16:26:27, 4.00s/it] 21%|██ | 3899/18688 [4:37:45<17:01:21, 4.14s/it] 21%|██ | 3900/18688 [4:37:49<16:44:13, 4.07s/it] {'loss': '1.043', 'grad_norm': '1.618', 'learning_rate': '0.0001905', 'epoch': '0.2087', 'num_input_tokens_seen': 13097456, 'train_runtime': '1.667e+04', 'train_tokens_per_second': '785.7'}
21%|██ | 3900/18688 [4:37:49<16:44:13, 4.07s/it] 21%|██ | 3901/18688 [4:37:54<17:45:33, 4.32s/it] 21%|██ | 3902/18688 [4:37:58<17:51:56, 4.35s/it] 21%|██ | 3903/18688 [4:38:03<18:02:32, 4.39s/it] 21%|██ | 3904/18688 [4:38:07<17:48:11, 4.34s/it] 21%|██ | 3905/18688 [4:38:11<17:26:36, 4.25s/it] 21%|██ | 3906/18688 [4:38:16<18:38:28, 4.54s/it] 21%|██ | 3907/18688 [4:38:22<19:56:47, 4.86s/it] 21%|██ | 3908/18688 [4:38:27<20:17:13, 4.94s/it] 21%|██ | 3909/18688 [4:38:31<19:22:15, 4.72s/it] 21%|██ | 3910/18688 [4:38:35<18:09:55, 4.43s/it] {'loss': '1.058', 'grad_norm': '1.496', 'learning_rate': '0.0001905', 'epoch': '0.2092', 'num_input_tokens_seen': 13135768, 'train_runtime': '1.672e+04', 'train_tokens_per_second': '785.8'}
21%|██ | 3910/18688 [4:38:35<18:09:55, 4.43s/it] 21%|██ | 3911/18688 [4:38:39<16:58:53, 4.14s/it] 21%|██ | 3912/18688 [4:38:42<16:17:37, 3.97s/it] 21%|██ | 3913/18688 [4:38:46<16:04:22, 3.92s/it] 21%|██ | 3914/18688 [4:38:51<17:05:57, 4.17s/it] 21%|██ | 3915/18688 [4:38:55<16:57:14, 4.13s/it] 21%|██ | 3916/18688 [4:38:58<16:22:14, 3.99s/it] 21%|██ | 3917/18688 [4:39:02<16:03:33, 3.91s/it] 21%|██ | 3918/18688 [4:39:06<15:32:20, 3.79s/it] 21%|██ | 3919/18688 [4:39:09<15:40:17, 3.82s/it] 21%|██ | 3920/18688 [4:39:13<15:52:42, 3.87s/it] {'loss': '0.9682', 'grad_norm': '1.65', 'learning_rate': '0.0001904', 'epoch': '0.2098', 'num_input_tokens_seen': 13165712, 'train_runtime': '1.675e+04', 'train_tokens_per_second': '785.8'}
21%|██ | 3920/18688 [4:39:13<15:52:42, 3.87s/it] 21%|██ | 3921/18688 [4:39:17<15:44:36, 3.84s/it] 21%|██ | 3922/18688 [4:39:21<16:06:29, 3.93s/it] 21%|██ | 3923/18688 [4:39:25<16:15:28, 3.96s/it] 21%|██ | 3924/18688 [4:39:31<17:50:35, 4.35s/it] 21%|██ | 3925/18688 [4:39:35<17:18:23, 4.22s/it] 21%|██ | 3926/18688 [4:39:38<16:53:44, 4.12s/it] 21%|██ | 3927/18688 [4:39:43<17:03:29, 4.16s/it] 21%|██ | 3928/18688 [4:39:47<17:29:32, 4.27s/it] 21%|██ | 3929/18688 [4:39:51<17:18:50, 4.22s/it] 21%|██ | 3930/18688 [4:39:55<17:05:26, 4.17s/it] {'loss': '1.053', 'grad_norm': '1.778', 'learning_rate': '0.0001903', 'epoch': '0.2103', 'num_input_tokens_seen': 13200840, 'train_runtime': '1.68e+04', 'train_tokens_per_second': '786'}
21%|██ | 3930/18688 [4:39:55<17:05:26, 4.17s/it] 21%|██ | 3931/18688 [4:40:00<17:09:28, 4.19s/it] 21%|██ | 3932/18688 [4:40:03<16:43:48, 4.08s/it] 21%|██ | 3933/18688 [4:40:07<16:06:03, 3.93s/it] 21%|██ | 3934/18688 [4:40:11<16:40:26, 4.07s/it] 21%|██ | 3935/18688 [4:40:15<16:25:18, 4.01s/it] 21%|██ | 3936/18688 [4:40:20<16:44:26, 4.09s/it] 21%|██ | 3937/18688 [4:40:24<16:41:23, 4.07s/it] 21%|██ | 3938/18688 [4:40:28<17:10:14, 4.19s/it] 21%|██ | 3939/18688 [4:40:32<16:37:03, 4.06s/it] 21%|██ | 3940/18688 [4:40:36<17:16:36, 4.22s/it] {'loss': '0.9748', 'grad_norm': '1.831', 'learning_rate': '0.0001902', 'epoch': '0.2108', 'num_input_tokens_seen': 13232512, 'train_runtime': '1.684e+04', 'train_tokens_per_second': '785.9'}
21%|██ | 3940/18688 [4:40:36<17:16:36, 4.22s/it] 21%|██ | 3941/18688 [4:40:41<17:43:40, 4.33s/it] 21%|██ | 3942/18688 [4:40:46<18:35:15, 4.54s/it] 21%|██ | 3943/18688 [4:40:50<18:01:16, 4.40s/it] 21%|██ | 3944/18688 [4:40:54<17:59:49, 4.39s/it] 21%|██ | 3945/18688 [4:40:59<18:06:34, 4.42s/it] 21%|██ | 3946/18688 [4:41:04<18:31:37, 4.52s/it] 21%|██ | 3947/18688 [4:41:08<17:50:50, 4.36s/it] 21%|██ | 3948/18688 [4:41:12<17:30:59, 4.28s/it] 21%|██ | 3949/18688 [4:41:15<16:37:56, 4.06s/it] 21%|██ | 3950/18688 [4:41:19<16:18:49, 3.98s/it] {'loss': '0.995', 'grad_norm': '1.525', 'learning_rate': '0.0001901', 'epoch': '0.2114', 'num_input_tokens_seen': 13265784, 'train_runtime': '1.688e+04', 'train_tokens_per_second': '785.9'}
21%|██ | 3950/18688 [4:41:19<16:18:49, 3.98s/it] 21%|██ | 3951/18688 [4:41:23<16:30:19, 4.03s/it] 21%|██ | 3952/18688 [4:41:29<18:23:03, 4.49s/it] 21%|██ | 3953/18688 [4:41:33<17:58:51, 4.39s/it] 21%|██ | 3954/18688 [4:41:37<17:56:49, 4.39s/it] 21%|██ | 3955/18688 [4:41:42<17:42:46, 4.33s/it] 21%|██ | 3956/18688 [4:41:46<17:14:14, 4.21s/it] 21%|██ | 3957/18688 [4:41:49<16:46:01, 4.10s/it] 21%|██ | 3958/18688 [4:41:54<17:42:04, 4.33s/it] 21%|██ | 3959/18688 [4:41:59<17:55:16, 4.38s/it] 21%|██ | 3960/18688 [4:42:03<17:30:37, 4.28s/it] {'loss': '1.024', 'grad_norm': '2.119', 'learning_rate': '0.0001901', 'epoch': '0.2119', 'num_input_tokens_seen': 13299808, 'train_runtime': '1.692e+04', 'train_tokens_per_second': '785.9'}
21%|██ | 3960/18688 [4:42:03<17:30:37, 4.28s/it] 21%|██ | 3961/18688 [4:42:08<18:20:28, 4.48s/it] 21%|██ | 3962/18688 [4:42:12<18:09:03, 4.44s/it] 21%|██ | 3963/18688 [4:42:16<18:05:01, 4.42s/it] 21%|██ | 3964/18688 [4:42:21<18:16:17, 4.47s/it] 21%|██ | 3965/18688 [4:42:25<17:49:37, 4.36s/it] 21%|██ | 3966/18688 [4:42:30<18:41:14, 4.57s/it] 21%|██ | 3967/18688 [4:42:34<17:45:50, 4.34s/it] 21%|██ | 3968/18688 [4:42:38<17:11:52, 4.21s/it] 21%|██ | 3969/18688 [4:42:42<16:59:27, 4.16s/it] 21%|██ | 3970/18688 [4:42:46<16:48:37, 4.11s/it] {'loss': '0.9583', 'grad_norm': '2.044', 'learning_rate': '0.00019', 'epoch': '0.2124', 'num_input_tokens_seen': 13334904, 'train_runtime': '1.697e+04', 'train_tokens_per_second': '786'}
21%|██ | 3970/18688 [4:42:46<16:48:37, 4.11s/it] 21%|██ | 3971/18688 [4:42:50<16:15:31, 3.98s/it] 21%|██▏ | 3972/18688 [4:42:55<17:45:39, 4.34s/it] 21%|██▏ | 3973/18688 [4:42:59<17:11:51, 4.21s/it] 21%|██▏ | 3974/18688 [4:43:03<16:54:38, 4.14s/it] 21%|██▏ | 3975/18688 [4:43:07<17:19:31, 4.24s/it] 21%|██▏ | 3976/18688 [4:43:11<17:02:56, 4.17s/it] 21%|██▏ | 3977/18688 [4:43:16<17:47:51, 4.36s/it] 21%|██▏ | 3978/18688 [4:43:20<17:23:23, 4.26s/it] 21%|██▏ | 3979/18688 [4:43:26<19:17:59, 4.72s/it] 21%|██▏ | 3980/18688 [4:43:30<18:45:16, 4.59s/it] {'loss': '1.005', 'grad_norm': '2.174', 'learning_rate': '0.0001899', 'epoch': '0.213', 'num_input_tokens_seen': 13370056, 'train_runtime': '1.701e+04', 'train_tokens_per_second': '786'}
21%|██▏ | 3980/18688 [4:43:30<18:45:16, 4.59s/it] 21%|██▏ | 3981/18688 [4:43:34<17:32:11, 4.29s/it] 21%|██▏ | 3982/18688 [4:43:37<16:53:11, 4.13s/it] 21%|██▏ | 3983/18688 [4:43:42<16:55:42, 4.14s/it] 21%|██▏ | 3984/18688 [4:43:46<16:55:16, 4.14s/it] 21%|██▏ | 3985/18688 [4:43:51<17:52:04, 4.37s/it] 21%|██▏ | 3986/18688 [4:43:55<17:36:16, 4.31s/it] 21%|██▏ | 3987/18688 [4:43:59<17:12:04, 4.21s/it] 21%|██▏ | 3988/18688 [4:44:03<17:16:47, 4.23s/it] 21%|██▏ | 3989/18688 [4:44:07<17:14:29, 4.22s/it] 21%|██▏ | 3990/18688 [4:44:11<17:00:36, 4.17s/it] {'loss': '1.091', 'grad_norm': '1.858', 'learning_rate': '0.0001898', 'epoch': '0.2135', 'num_input_tokens_seen': 13401352, 'train_runtime': '1.705e+04', 'train_tokens_per_second': '785.9'}
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21%|██▏ | 4000/18688 [4:44:54<18:04:18, 4.43s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
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1%| | 6/1000 [00:00<00:59, 16.64it/s]
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1%| | 12/1000 [00:00<01:01, 16.11it/s]
1%|▏ | 14/1000 [00:00<01:02, 15.83it/s]
2%|▏ | 16/1000 [00:01<01:06, 14.85it/s]
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{'eval_loss': '0.9974', 'eval_runtime': '82.96', 'eval_samples_per_second': '12.05', 'eval_steps_per_second': '12.05', 'epoch': '0.214', 'num_input_tokens_seen': 13433360}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00020/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': '1.001', 'eval_runtime': '83.1', 'eval_samples_per_second': '12.03', 'eval_steps_per_second': '12.03', 'epoch': '0.2676', 'num_input_tokens_seen': 16794000}
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32%|███▏ | 5970/18688 [7:05:41<16:14:47, 4.60s/it] 32%|███▏ | 5971/18688 [7:05:45<16:30:43, 4.67s/it] 32%|███▏ | 5972/18688 [7:05:50<16:47:17, 4.75s/it] 32%|███▏ | 5973/18688 [7:05:55<16:36:49, 4.70s/it] 32%|███▏ | 5974/18688 [7:05:59<15:46:20, 4.47s/it] 32%|███▏ | 5975/18688 [7:06:03<15:44:33, 4.46s/it] 32%|███▏ | 5976/18688 [7:06:07<15:12:22, 4.31s/it] 32%|███▏ | 5977/18688 [7:06:12<15:18:28, 4.34s/it] 32%|███▏ | 5978/18688 [7:06:16<15:02:32, 4.26s/it] 32%|███▏ | 5979/18688 [7:06:21<15:56:07, 4.51s/it] 32%|███▏ | 5980/18688 [7:06:25<15:59:48, 4.53s/it] {'loss': '1.032', 'grad_norm': '1.818', 'learning_rate': '0.0001683', 'epoch': '0.32', 'num_input_tokens_seen': 20073584, 'train_runtime': '2.559e+04', 'train_tokens_per_second': '784.6'}
32%|███▏ | 5980/18688 [7:06:25<15:59:48, 4.53s/it] 32%|███▏ | 5981/18688 [7:06:29<15:27:52, 4.38s/it] 32%|███▏ | 5982/18688 [7:06:33<15:06:20, 4.28s/it] 32%|███▏ | 5983/18688 [7:06:37<14:45:51, 4.18s/it] 32%|███▏ | 5984/18688 [7:06:41<14:37:11, 4.14s/it] 32%|███▏ | 5985/18688 [7:06:47<15:58:49, 4.53s/it] 32%|███▏ | 5986/18688 [7:06:51<15:33:31, 4.41s/it] 32%|███▏ | 5987/18688 [7:06:55<15:27:17, 4.38s/it] 32%|███▏ | 5988/18688 [7:07:00<15:46:15, 4.47s/it] 32%|███▏ | 5989/18688 [7:07:05<15:59:49, 4.53s/it] 32%|███▏ | 5990/18688 [7:07:09<15:33:04, 4.41s/it] {'loss': '0.9834', 'grad_norm': '1.836', 'learning_rate': '0.0001682', 'epoch': '0.3205', 'num_input_tokens_seen': 20106224, 'train_runtime': '2.563e+04', 'train_tokens_per_second': '784.5'}
32%|███▏ | 5990/18688 [7:07:09<15:33:04, 4.41s/it] 32%|███▏ | 5991/18688 [7:07:13<15:24:44, 4.37s/it] 32%|███▏ | 5992/18688 [7:07:18<15:31:43, 4.40s/it] 32%|███▏ | 5993/18688 [7:07:22<15:51:32, 4.50s/it] 32%|███▏ | 5994/18688 [7:07:27<15:53:28, 4.51s/it] 32%|███▏ | 5995/18688 [7:07:30<14:44:48, 4.18s/it] 32%|███▏ | 5996/18688 [7:07:35<15:07:47, 4.29s/it] 32%|███▏ | 5997/18688 [7:07:39<14:48:44, 4.20s/it] 32%|███▏ | 5998/18688 [7:07:43<14:46:12, 4.19s/it] 32%|███▏ | 5999/18688 [7:07:47<14:26:07, 4.10s/it] 32%|███▏ | 6000/18688 [7:07:52<15:04:17, 4.28s/it] {'loss': '1.036', 'grad_norm': '1.764', 'learning_rate': '0.0001681', 'epoch': '0.3211', 'num_input_tokens_seen': 20140120, 'train_runtime': '2.567e+04', 'train_tokens_per_second': '784.5'}
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***** Running Evaluation *****
Num examples = 1000
Batch size = 1
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{'eval_loss': '1', 'eval_runtime': '82.97', 'eval_samples_per_second': '12.05', 'eval_steps_per_second': '12.05', 'epoch': '0.3211', 'num_input_tokens_seen': 20140120}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00020/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': '0.9866', 'eval_runtime': '83.29', 'eval_samples_per_second': '12.01', 'eval_steps_per_second': '12.01', 'epoch': '0.3746', 'num_input_tokens_seen': 23480632}
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42%|████▏ | 7930/18688 [9:25:35<12:22:04, 4.14s/it] 42%|████▏ | 7931/18688 [9:25:38<11:43:23, 3.92s/it] 42%|████▏ | 7932/18688 [9:25:42<11:25:50, 3.83s/it] 42%|████▏ | 7933/18688 [9:25:46<11:55:44, 3.99s/it] 42%|████▏ | 7934/18688 [9:25:51<12:52:58, 4.31s/it] 42%|████▏ | 7935/18688 [9:25:55<12:06:03, 4.05s/it] 42%|████▏ | 7936/18688 [9:25:59<11:58:01, 4.01s/it] 42%|████▏ | 7937/18688 [9:26:03<12:18:02, 4.12s/it] 42%|████▏ | 7938/18688 [9:26:07<11:47:06, 3.95s/it] 42%|████▏ | 7939/18688 [9:26:11<11:50:40, 3.97s/it] 42%|████▏ | 7940/18688 [9:26:14<11:31:41, 3.86s/it] {'loss': '0.9462', 'grad_norm': '2.515', 'learning_rate': '0.0001384', 'epoch': '0.4249', 'num_input_tokens_seen': 26662440, 'train_runtime': '3.397e+04', 'train_tokens_per_second': '784.8'}
42%|████▏ | 7940/18688 [9:26:14<11:31:41, 3.86s/it] 42%|████▏ | 7941/18688 [9:26:19<12:09:55, 4.08s/it] 42%|████▏ | 7942/18688 [9:26:23<12:18:00, 4.12s/it] 43%|████▎ | 7943/18688 [9:26:27<11:57:48, 4.01s/it] 43%|████▎ | 7944/18688 [9:26:32<12:48:32, 4.29s/it] 43%|████▎ | 7945/18688 [9:26:36<12:25:55, 4.17s/it] 43%|████▎ | 7946/18688 [9:26:40<12:41:49, 4.26s/it] 43%|████▎ | 7947/18688 [9:26:44<12:28:28, 4.18s/it] 43%|████▎ | 7948/18688 [9:26:48<12:04:29, 4.05s/it] 43%|████▎ | 7949/18688 [9:26:51<11:42:20, 3.92s/it] 43%|████▎ | 7950/18688 [9:26:57<12:51:33, 4.31s/it] {'loss': '0.9668', 'grad_norm': '1.96', 'learning_rate': '0.0001382', 'epoch': '0.4254', 'num_input_tokens_seen': 26698152, 'train_runtime': '3.402e+04', 'train_tokens_per_second': '784.8'}
43%|████▎ | 7950/18688 [9:26:57<12:51:33, 4.31s/it] 43%|████▎ | 7951/18688 [9:27:01<12:44:51, 4.27s/it] 43%|████▎ | 7952/18688 [9:27:05<12:41:01, 4.25s/it] 43%|████▎ | 7953/18688 [9:27:09<12:01:51, 4.03s/it] 43%|████▎ | 7954/18688 [9:27:12<11:30:15, 3.86s/it] 43%|████▎ | 7955/18688 [9:27:16<11:26:41, 3.84s/it] 43%|████▎ | 7956/18688 [9:27:19<11:04:26, 3.71s/it] 43%|████▎ | 7957/18688 [9:27:23<11:29:19, 3.85s/it] 43%|████▎ | 7958/18688 [9:27:29<12:50:59, 4.31s/it] 43%|████▎ | 7959/18688 [9:27:33<12:36:21, 4.23s/it] 43%|████▎ | 7960/18688 [9:27:39<14:25:37, 4.84s/it] {'loss': '0.9439', 'grad_norm': '2.22', 'learning_rate': '0.000138', 'epoch': '0.426', 'num_input_tokens_seen': 26732688, 'train_runtime': '3.406e+04', 'train_tokens_per_second': '784.9'}
43%|████▎ | 7960/18688 [9:27:39<14:25:37, 4.84s/it] 43%|████▎ | 7961/18688 [9:27:43<13:41:09, 4.59s/it] 43%|████▎ | 7962/18688 [9:27:47<13:07:02, 4.40s/it] 43%|████▎ | 7963/18688 [9:27:52<13:16:39, 4.46s/it] 43%|████▎ | 7964/18688 [9:27:56<12:57:09, 4.35s/it] 43%|████▎ | 7965/18688 [9:28:00<12:28:04, 4.19s/it] 43%|████▎ | 7966/18688 [9:28:03<12:16:15, 4.12s/it] 43%|████▎ | 7967/18688 [9:28:07<11:54:29, 4.00s/it] 43%|████▎ | 7968/18688 [9:28:11<11:48:58, 3.97s/it] 43%|████▎ | 7969/18688 [9:28:16<12:32:11, 4.21s/it] 43%|████▎ | 7970/18688 [9:28:20<12:28:55, 4.19s/it] {'loss': '1.015', 'grad_norm': '2.169', 'learning_rate': '0.0001379', 'epoch': '0.4265', 'num_input_tokens_seen': 26762544, 'train_runtime': '3.41e+04', 'train_tokens_per_second': '784.8'}
43%|████▎ | 7970/18688 [9:28:20<12:28:55, 4.19s/it] 43%|████▎ | 7971/18688 [9:28:24<12:24:48, 4.17s/it] 43%|████▎ | 7972/18688 [9:28:28<12:21:21, 4.15s/it] 43%|████▎ | 7973/18688 [9:28:32<12:04:34, 4.06s/it] 43%|████▎ | 7974/18688 [9:28:36<12:01:38, 4.04s/it] 43%|████▎ | 7975/18688 [9:28:40<11:41:09, 3.93s/it] 43%|████▎ | 7976/18688 [9:28:44<11:59:40, 4.03s/it] 43%|████▎ | 7977/18688 [9:28:48<12:09:45, 4.09s/it] 43%|████▎ | 7978/18688 [9:28:53<13:09:02, 4.42s/it] 43%|████▎ | 7979/18688 [9:28:58<13:03:42, 4.39s/it] 43%|████▎ | 7980/18688 [9:29:02<12:38:50, 4.25s/it] {'loss': '1.024', 'grad_norm': '2.034', 'learning_rate': '0.0001377', 'epoch': '0.427', 'num_input_tokens_seen': 26796120, 'train_runtime': '3.414e+04', 'train_tokens_per_second': '784.8'}
43%|████▎ | 7980/18688 [9:29:02<12:38:50, 4.25s/it] 43%|████▎ | 7981/18688 [9:29:07<13:23:57, 4.51s/it] 43%|████▎ | 7982/18688 [9:29:10<12:28:56, 4.20s/it] 43%|████▎ | 7983/18688 [9:29:14<12:20:08, 4.15s/it] 43%|████▎ | 7984/18688 [9:29:18<11:54:10, 4.00s/it] 43%|████▎ | 7985/18688 [9:29:23<12:40:50, 4.27s/it] 43%|████▎ | 7986/18688 [9:29:27<12:33:55, 4.23s/it] 43%|████▎ | 7987/18688 [9:29:30<11:54:45, 4.01s/it] 43%|████▎ | 7988/18688 [9:29:34<11:49:34, 3.98s/it] 43%|████▎ | 7989/18688 [9:29:39<12:01:40, 4.05s/it] 43%|████▎ | 7990/18688 [9:29:42<11:41:28, 3.93s/it] {'loss': '0.9848', 'grad_norm': '2.17', 'learning_rate': '0.0001375', 'epoch': '0.4276', 'num_input_tokens_seen': 26828872, 'train_runtime': '3.418e+04', 'train_tokens_per_second': '784.9'}
43%|████▎ | 7990/18688 [9:29:42<11:41:28, 3.93s/it] 43%|████▎ | 7991/18688 [9:29:46<11:48:42, 3.98s/it] 43%|████▎ | 7992/18688 [9:29:51<12:41:00, 4.27s/it] 43%|████▎ | 7993/18688 [9:29:55<12:08:28, 4.09s/it] 43%|████▎ | 7994/18688 [9:29:59<12:11:10, 4.10s/it] 43%|████▎ | 7995/18688 [9:30:03<11:38:59, 3.92s/it] 43%|████▎ | 7996/18688 [9:30:07<11:44:04, 3.95s/it] 43%|████▎ | 7997/18688 [9:30:11<12:21:10, 4.16s/it] 43%|████▎ | 7998/18688 [9:30:15<12:19:25, 4.15s/it] 43%|████▎ | 7999/18688 [9:30:20<12:23:58, 4.18s/it] 43%|████▎ | 8000/18688 [9:30:23<11:54:05, 4.01s/it] {'loss': '1.007', 'grad_norm': '1.743', 'learning_rate': '0.0001374', 'epoch': '0.4281', 'num_input_tokens_seen': 26861912, 'train_runtime': '3.422e+04', 'train_tokens_per_second': '784.9'}
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***** Running Evaluation *****
Num examples = 1000
Batch size = 1
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{'eval_loss': '0.9825', 'eval_runtime': '83.04', 'eval_samples_per_second': '12.04', 'eval_steps_per_second': '12.04', 'epoch': '0.4281', 'num_input_tokens_seen': 26861912}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00020/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': '0.9666', 'eval_runtime': '82.91', 'eval_samples_per_second': '12.06', 'eval_steps_per_second': '12.06', 'epoch': '0.4816', 'num_input_tokens_seen': 30248528}
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53%|█████▎ | 9870/18688 [11:42:49<10:19:34, 4.22s/it] 53%|█████▎ | 9871/18688 [11:42:53<10:28:55, 4.28s/it] 53%|█████▎ | 9872/18688 [11:42:57<9:52:04, 4.03s/it] 53%|█████▎ | 9873/18688 [11:43:01<9:58:04, 4.07s/it] 53%|█████▎ | 9874/18688 [11:43:05<9:46:32, 3.99s/it] 53%|█████▎ | 9875/18688 [11:43:09<9:55:43, 4.06s/it] 53%|█████▎ | 9876/18688 [11:43:13<9:48:11, 4.00s/it] 53%|█████▎ | 9877/18688 [11:43:16<9:32:47, 3.90s/it] 53%|█████▎ | 9878/18688 [11:43:20<9:41:52, 3.96s/it] 53%|█████▎ | 9879/18688 [11:43:24<9:13:47, 3.77s/it] 53%|█████▎ | 9880/18688 [11:43:28<9:49:48, 4.02s/it] {'loss': '0.954', 'grad_norm': '2.004', 'learning_rate': '0.0001039', 'epoch': '0.5287', 'num_input_tokens_seen': 33231392, 'train_runtime': '4.221e+04', 'train_tokens_per_second': '787.3'}
53%|█████▎ | 9880/18688 [11:43:28<9:49:48, 4.02s/it] 53%|█████▎ | 9881/18688 [11:43:32<9:31:44, 3.90s/it] 53%|█████▎ | 9882/18688 [11:43:37<9:59:52, 4.09s/it] 53%|█████▎ | 9883/18688 [11:43:40<9:31:18, 3.89s/it] 53%|█████▎ | 9884/18688 [11:43:44<9:24:59, 3.85s/it] 53%|█████▎ | 9885/18688 [11:43:48<9:38:17, 3.94s/it] 53%|█████▎ | 9886/18688 [11:43:53<10:41:35, 4.37s/it] 53%|█████▎ | 9887/18688 [11:43:57<10:32:46, 4.31s/it] 53%|█████▎ | 9888/18688 [11:44:02<11:03:05, 4.52s/it] 53%|█████▎ | 9889/18688 [11:44:06<10:20:06, 4.23s/it] 53%|█████▎ | 9890/18688 [11:44:11<10:34:08, 4.32s/it] {'loss': '0.9599', 'grad_norm': '1.713', 'learning_rate': '0.0001037', 'epoch': '0.5292', 'num_input_tokens_seen': 33267288, 'train_runtime': '4.225e+04', 'train_tokens_per_second': '787.4'}
53%|█████▎ | 9890/18688 [11:44:11<10:34:08, 4.32s/it] 53%|█████▎ | 9891/18688 [11:44:15<10:50:45, 4.44s/it] 53%|█████▎ | 9892/18688 [11:44:20<11:25:04, 4.67s/it] 53%|█████▎ | 9893/18688 [11:44:25<11:00:02, 4.50s/it] 53%|█████▎ | 9894/18688 [11:44:28<10:34:55, 4.33s/it] 53%|█████▎ | 9895/18688 [11:44:33<10:25:57, 4.27s/it] 53%|█████▎ | 9896/18688 [11:44:37<10:16:05, 4.20s/it] 53%|█████▎ | 9897/18688 [11:44:42<10:50:25, 4.44s/it] 53%|█████▎ | 9898/18688 [11:44:46<10:52:30, 4.45s/it] 53%|█████▎ | 9899/18688 [11:44:50<10:36:51, 4.35s/it] 53%|█████▎ | 9900/18688 [11:44:54<10:18:35, 4.22s/it] {'loss': '1.012', 'grad_norm': '1.739', 'learning_rate': '0.0001036', 'epoch': '0.5298', 'num_input_tokens_seen': 33301776, 'train_runtime': '4.229e+04', 'train_tokens_per_second': '787.4'}
53%|█████▎ | 9900/18688 [11:44:54<10:18:35, 4.22s/it] 53%|█████▎ | 9901/18688 [11:44:59<10:31:39, 4.31s/it] 53%|█████▎ | 9902/18688 [11:45:04<10:57:12, 4.49s/it] 53%|█████▎ | 9903/18688 [11:45:07<10:30:56, 4.31s/it] 53%|█████▎ | 9904/18688 [11:45:11<10:08:54, 4.16s/it] 53%|█████▎ | 9905/18688 [11:45:16<10:21:35, 4.25s/it] 53%|█████▎ | 9906/18688 [11:45:20<10:29:12, 4.30s/it] 53%|█████▎ | 9907/18688 [11:45:25<10:40:29, 4.38s/it] 53%|█████▎ | 9908/18688 [11:45:30<10:57:53, 4.50s/it] 53%|█████▎ | 9909/18688 [11:45:34<10:40:38, 4.38s/it] 53%|█████▎ | 9910/18688 [11:45:39<11:03:32, 4.54s/it] {'loss': '0.9656', 'grad_norm': '1.633', 'learning_rate': '0.0001034', 'epoch': '0.5303', 'num_input_tokens_seen': 33337120, 'train_runtime': '4.234e+04', 'train_tokens_per_second': '787.4'}
53%|█████▎ | 9910/18688 [11:45:39<11:03:32, 4.54s/it] 53%|█████▎ | 9911/18688 [11:45:42<10:21:40, 4.25s/it] 53%|█████▎ | 9912/18688 [11:45:47<10:29:50, 4.31s/it] 53%|█████▎ | 9913/18688 [11:45:50<9:48:39, 4.03s/it] 53%|█████▎ | 9914/18688 [11:45:55<10:27:19, 4.29s/it] 53%|█████▎ | 9915/18688 [11:45:59<10:38:50, 4.37s/it] 53%|█████▎ | 9916/18688 [11:46:04<10:59:14, 4.51s/it] 53%|█████▎ | 9917/18688 [11:46:09<10:56:49, 4.49s/it] 53%|█████▎ | 9918/18688 [11:46:13<10:46:10, 4.42s/it] 53%|█████▎ | 9919/18688 [11:46:17<10:50:49, 4.45s/it] 53%|█████▎ | 9920/18688 [11:46:21<10:24:52, 4.28s/it] {'loss': '0.944', 'grad_norm': '1.903', 'learning_rate': '0.0001032', 'epoch': '0.5308', 'num_input_tokens_seen': 33371000, 'train_runtime': '4.238e+04', 'train_tokens_per_second': '787.4'}
53%|█████▎ | 9920/18688 [11:46:21<10:24:52, 4.28s/it] 53%|█████▎ | 9921/18688 [11:46:26<10:46:57, 4.43s/it] 53%|█████▎ | 9922/18688 [11:46:30<10:43:31, 4.40s/it] 53%|█████▎ | 9923/18688 [11:46:34<10:09:08, 4.17s/it] 53%|█████▎ | 9924/18688 [11:46:38<10:16:41, 4.22s/it] 53%|█████▎ | 9925/18688 [11:46:43<10:30:44, 4.32s/it] 53%|█████▎ | 9926/18688 [11:46:47<9:57:38, 4.09s/it] 53%|█████▎ | 9927/18688 [11:46:50<9:41:31, 3.98s/it] 53%|█████▎ | 9928/18688 [11:46:54<9:29:49, 3.90s/it] 53%|█████▎ | 9929/18688 [11:46:58<9:29:47, 3.90s/it] 53%|█████▎ | 9930/18688 [11:47:02<9:25:28, 3.87s/it] {'loss': '0.9849', 'grad_norm': '1.719', 'learning_rate': '0.000103', 'epoch': '0.5314', 'num_input_tokens_seen': 33404656, 'train_runtime': '4.242e+04', 'train_tokens_per_second': '787.4'}
53%|█████▎ | 9930/18688 [11:47:02<9:25:28, 3.87s/it] 53%|█████▎ | 9931/18688 [11:47:05<9:09:05, 3.76s/it] 53%|█████▎ | 9932/18688 [11:47:09<9:15:35, 3.81s/it] 53%|█████▎ | 9933/18688 [11:47:13<9:16:54, 3.82s/it] 53%|█████▎ | 9934/18688 [11:47:18<9:52:59, 4.06s/it] 53%|█████▎ | 9935/18688 [11:47:23<10:57:08, 4.50s/it] 53%|█████▎ | 9936/18688 [11:47:27<10:33:39, 4.34s/it] 53%|█████▎ | 9937/18688 [11:47:32<10:47:21, 4.44s/it] 53%|█████▎ | 9938/18688 [11:47:36<10:37:23, 4.37s/it] 53%|█████▎ | 9939/18688 [11:47:40<10:41:38, 4.40s/it] 53%|█████▎ | 9940/18688 [11:47:45<10:59:34, 4.52s/it] {'loss': '0.9467', 'grad_norm': '2.092', 'learning_rate': '0.0001028', 'epoch': '0.5319', 'num_input_tokens_seen': 33439232, 'train_runtime': '4.247e+04', 'train_tokens_per_second': '787.4'}
53%|█████▎ | 9940/18688 [11:47:45<10:59:34, 4.52s/it] 53%|█████▎ | 9941/18688 [11:47:49<10:35:58, 4.36s/it] 53%|█████▎ | 9942/18688 [11:47:54<10:56:55, 4.51s/it] 53%|█████▎ | 9943/18688 [11:47:58<10:31:02, 4.33s/it] 53%|█████▎ | 9944/18688 [11:48:02<10:04:07, 4.15s/it] 53%|█████▎ | 9945/18688 [11:48:05<9:32:15, 3.93s/it] 53%|█████▎ | 9946/18688 [11:48:09<9:43:33, 4.01s/it] 53%|█████▎ | 9947/18688 [11:48:13<9:43:08, 4.00s/it] 53%|█████▎ | 9948/18688 [11:48:17<9:49:52, 4.05s/it] 53%|█████▎ | 9949/18688 [11:48:22<10:13:52, 4.21s/it] 53%|█████▎ | 9950/18688 [11:48:26<9:46:00, 4.02s/it] {'loss': '0.9243', 'grad_norm': '2.074', 'learning_rate': '0.0001026', 'epoch': '0.5324', 'num_input_tokens_seen': 33470712, 'train_runtime': '4.251e+04', 'train_tokens_per_second': '787.4'}
53%|█████▎ | 9950/18688 [11:48:26<9:46:00, 4.02s/it] 53%|█████▎ | 9951/18688 [11:48:29<9:31:24, 3.92s/it] 53%|█████▎ | 9952/18688 [11:48:34<9:45:06, 4.02s/it] 53%|█████▎ | 9953/18688 [11:48:37<9:34:42, 3.95s/it] 53%|█████▎ | 9954/18688 [11:48:41<9:39:21, 3.98s/it] 53%|█████▎ | 9955/18688 [11:48:46<10:05:42, 4.16s/it] 53%|█████▎ | 9956/18688 [11:48:50<9:38:24, 3.97s/it] 53%|█████▎ | 9957/18688 [11:48:53<9:22:25, 3.86s/it] 53%|█████▎ | 9958/18688 [11:48:57<9:40:16, 3.99s/it] 53%|█████▎ | 9959/18688 [11:49:01<9:25:11, 3.88s/it] 53%|█████▎ | 9960/18688 [11:49:06<9:57:52, 4.11s/it] {'loss': '1.011', 'grad_norm': '2.119', 'learning_rate': '0.0001025', 'epoch': '0.533', 'num_input_tokens_seen': 33504040, 'train_runtime': '4.255e+04', 'train_tokens_per_second': '787.5'}
53%|█████▎ | 9960/18688 [11:49:06<9:57:52, 4.11s/it] 53%|█████▎ | 9961/18688 [11:49:09<9:45:37, 4.03s/it] 53%|█████▎ | 9962/18688 [11:49:13<9:35:23, 3.96s/it] 53%|█████▎ | 9963/18688 [11:49:17<9:12:39, 3.80s/it] 53%|█████▎ | 9964/18688 [11:49:20<9:08:31, 3.77s/it] 53%|█████▎ | 9965/18688 [11:49:24<9:06:25, 3.76s/it] 53%|█████▎ | 9966/18688 [11:49:29<10:01:48, 4.14s/it] 53%|█████▎ | 9967/18688 [11:49:34<10:12:56, 4.22s/it] 53%|█████▎ | 9968/18688 [11:49:38<10:00:26, 4.13s/it] 53%|█████▎ | 9969/18688 [11:49:42<10:02:38, 4.15s/it] 53%|█████▎ | 9970/18688 [11:49:46<10:15:32, 4.24s/it] {'loss': '1.055', 'grad_norm': '1.668', 'learning_rate': '0.0001023', 'epoch': '0.5335', 'num_input_tokens_seen': 33536184, 'train_runtime': '4.259e+04', 'train_tokens_per_second': '787.5'}
53%|█████▎ | 9970/18688 [11:49:46<10:15:32, 4.24s/it] 53%|█████▎ | 9971/18688 [11:49:50<9:54:50, 4.09s/it] 53%|█████▎ | 9972/18688 [11:49:54<9:51:27, 4.07s/it] 53%|█████▎ | 9973/18688 [11:49:58<10:02:00, 4.14s/it] 53%|█████▎ | 9974/18688 [11:50:03<10:18:13, 4.26s/it] 53%|█████▎ | 9975/18688 [11:50:07<10:33:00, 4.36s/it] 53%|█████▎ | 9976/18688 [11:50:12<10:57:41, 4.53s/it] 53%|█████▎ | 9977/18688 [11:50:16<10:38:57, 4.40s/it] 53%|█████▎ | 9978/18688 [11:50:20<10:22:22, 4.29s/it] 53%|█████▎ | 9979/18688 [11:50:25<10:28:47, 4.33s/it] 53%|█████▎ | 9980/18688 [11:50:30<11:07:02, 4.60s/it] {'loss': '0.9458', 'grad_norm': '1.746', 'learning_rate': '0.0001021', 'epoch': '0.534', 'num_input_tokens_seen': 33573168, 'train_runtime': '4.263e+04', 'train_tokens_per_second': '787.5'}
53%|█████▎ | 9980/18688 [11:50:30<11:07:02, 4.60s/it] 53%|█████▎ | 9981/18688 [11:50:34<10:42:23, 4.43s/it] 53%|█████▎ | 9982/18688 [11:50:38<9:59:04, 4.13s/it] 53%|█████▎ | 9983/18688 [11:50:41<9:38:15, 3.99s/it] 53%|█████▎ | 9984/18688 [11:50:46<10:20:33, 4.28s/it] 53%|█████▎ | 9985/18688 [11:50:51<10:46:06, 4.45s/it] 53%|█████▎ | 9986/18688 [11:50:54<10:03:33, 4.16s/it] 53%|█████▎ | 9987/18688 [11:50:58<9:51:22, 4.08s/it] 53%|█████▎ | 9988/18688 [11:51:03<10:26:59, 4.32s/it] 53%|█████▎ | 9989/18688 [11:51:07<10:09:39, 4.21s/it] 53%|█████▎ | 9990/18688 [11:51:12<10:17:56, 4.26s/it] {'loss': '0.9425', 'grad_norm': '1.671', 'learning_rate': '0.0001019', 'epoch': '0.5346', 'num_input_tokens_seen': 33608320, 'train_runtime': '4.267e+04', 'train_tokens_per_second': '787.6'}
53%|█████▎ | 9990/18688 [11:51:12<10:17:56, 4.26s/it] 53%|█████▎ | 9991/18688 [11:51:16<10:26:05, 4.32s/it] 53%|█████▎ | 9992/18688 [11:51:20<9:56:03, 4.11s/it] 53%|█████▎ | 9993/18688 [11:51:24<9:56:24, 4.12s/it] 53%|█████▎ | 9994/18688 [11:51:27<9:37:36, 3.99s/it] 53%|█████▎ | 9995/18688 [11:51:32<9:58:45, 4.13s/it] 53%|█████▎ | 9996/18688 [11:51:37<10:46:57, 4.47s/it] 53%|█████▎ | 9997/18688 [11:51:41<10:09:07, 4.21s/it] 53%|█████▎ | 9998/18688 [11:51:45<9:50:36, 4.08s/it] 54%|█████▎ | 9999/18688 [11:51:51<11:37:29, 4.82s/it] 54%|█████▎ | 10000/18688 [11:51:55<11:03:09, 4.58s/it] {'loss': '1.073', 'grad_norm': '2.339', 'learning_rate': '0.0001017', 'epoch': '0.5351', 'num_input_tokens_seen': 33646792, 'train_runtime': '4.272e+04', 'train_tokens_per_second': '787.7'}
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***** Running Evaluation *****
Num examples = 1000
Batch size = 1
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{'eval_loss': '0.9573', 'eval_runtime': '82.92', 'eval_samples_per_second': '12.06', 'eval_steps_per_second': '12.06', 'epoch': '0.5351', 'num_input_tokens_seen': 33646792}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00020/checkpoint-10000
loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
54%|█████▎ | 10001/18688 [11:53:23<71:07:47, 29.48s/it] 54%|█████▎ | 10002/18688 [11:53:26<52:23:59, 21.72s/it] 54%|█████▎ | 10003/18688 [11:53:30<39:26:55, 16.35s/it] 54%|█████▎ | 10004/18688 [11:53:34<30:28:39, 12.63s/it] 54%|█████▎ | 10005/18688 [11:53:38<24:24:30, 10.12s/it] 54%|█████▎ | 10006/18688 [11:53:43<20:08:35, 8.35s/it] 54%|█████▎ | 10007/18688 [11:53:47<17:11:56, 7.13s/it] 54%|█████▎ | 10008/18688 [11:53:50<14:36:39, 6.06s/it] 54%|█████▎ | 10009/18688 [11:53:55<13:31:53, 5.61s/it] 54%|█████▎ | 10010/18688 [11:53:59<12:15:05, 5.08s/it] {'loss': '1.011', 'grad_norm': '2.494', 'learning_rate': '0.0001016', 'epoch': '0.5357', 'num_input_tokens_seen': 33679768, 'train_runtime': '4.284e+04', 'train_tokens_per_second': '786.2'}
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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': '0.9433', 'eval_runtime': '83.03', 'eval_samples_per_second': '12.04', 'eval_steps_per_second': '12.04', 'epoch': '0.5886', 'num_input_tokens_seen': 37004632}
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64%|██████▎ | 11880/18688 [14:04:41<7:50:05, 4.14s/it] 64%|██████▎ | 11881/18688 [14:04:45<7:53:02, 4.17s/it] 64%|██████▎ | 11882/18688 [14:04:49<7:47:49, 4.12s/it] 64%|██████▎ | 11883/18688 [14:04:53<7:44:15, 4.09s/it] 64%|██████▎ | 11884/18688 [14:04:57<7:42:20, 4.08s/it] 64%|██████▎ | 11885/18688 [14:05:01<7:38:11, 4.04s/it] 64%|██████▎ | 11886/18688 [14:05:05<7:32:57, 4.00s/it] 64%|██████▎ | 11887/18688 [14:05:09<7:18:50, 3.87s/it] 64%|██████▎ | 11888/18688 [14:05:13<7:47:24, 4.12s/it] 64%|██████▎ | 11889/18688 [14:05:18<7:59:18, 4.23s/it] 64%|██████▎ | 11890/18688 [14:05:22<7:55:15, 4.19s/it] {'loss': '1.012', 'grad_norm': '1.827', 'learning_rate': '6.778e-05', 'epoch': '0.6363', 'num_input_tokens_seen': 39985264, 'train_runtime': '5.072e+04', 'train_tokens_per_second': '788.3'}
64%|██████▎ | 11890/18688 [14:05:22<7:55:15, 4.19s/it] 64%|██████▎ | 11891/18688 [14:05:25<7:29:30, 3.97s/it] 64%|██████▎ | 11892/18688 [14:05:29<7:24:18, 3.92s/it] 64%|██████▎ | 11893/18688 [14:05:34<8:01:33, 4.25s/it] 64%|██████▎ | 11894/18688 [14:05:38<7:49:09, 4.14s/it] 64%|██████▎ | 11895/18688 [14:05:42<7:29:59, 3.97s/it] 64%|██████▎ | 11896/18688 [14:05:45<7:19:14, 3.88s/it] 64%|██████▎ | 11897/18688 [14:05:49<7:27:08, 3.95s/it] 64%|██████▎ | 11898/18688 [14:05:53<7:25:17, 3.93s/it] 64%|██████▎ | 11899/18688 [14:05:57<7:31:47, 3.99s/it] 64%|██████▎ | 11900/18688 [14:06:02<7:41:52, 4.08s/it] {'loss': '0.9627', 'grad_norm': '1.747', 'learning_rate': '6.76e-05', 'epoch': '0.6368', 'num_input_tokens_seen': 40015912, 'train_runtime': '5.076e+04', 'train_tokens_per_second': '788.3'}
64%|██████▎ | 11900/18688 [14:06:02<7:41:52, 4.08s/it] 64%|██████▎ | 11901/18688 [14:06:06<7:41:43, 4.08s/it] 64%|██████▎ | 11902/18688 [14:06:10<7:55:42, 4.21s/it] 64%|██████▎ | 11903/18688 [14:06:15<8:21:30, 4.43s/it] 64%|██████▎ | 11904/18688 [14:06:21<8:49:54, 4.69s/it] 64%|██████▎ | 11905/18688 [14:06:25<8:31:53, 4.53s/it] 64%|██████▎ | 11906/18688 [14:06:29<8:18:35, 4.41s/it] 64%|██████▎ | 11907/18688 [14:06:32<7:53:31, 4.19s/it] 64%|██████▎ | 11908/18688 [14:06:37<8:12:16, 4.36s/it] 64%|██████▎ | 11909/18688 [14:06:41<7:48:34, 4.15s/it] 64%|██████▎ | 11910/18688 [14:06:44<7:26:54, 3.96s/it] {'loss': '0.9683', 'grad_norm': '1.851', 'learning_rate': '6.743e-05', 'epoch': '0.6373', 'num_input_tokens_seen': 40049264, 'train_runtime': '5.08e+04', 'train_tokens_per_second': '788.3'}
64%|██████▎ | 11910/18688 [14:06:44<7:26:54, 3.96s/it] 64%|██████▎ | 11911/18688 [14:06:49<7:36:04, 4.04s/it] 64%|██████▎ | 11912/18688 [14:06:52<7:26:24, 3.95s/it] 64%|██████▎ | 11913/18688 [14:06:56<7:12:30, 3.83s/it] 64%|██████▍ | 11914/18688 [14:06:59<7:02:35, 3.74s/it] 64%|██████▍ | 11915/18688 [14:07:05<7:50:40, 4.17s/it] 64%|██████▍ | 11916/18688 [14:07:08<7:33:45, 4.02s/it] 64%|██████▍ | 11917/18688 [14:07:12<7:37:23, 4.05s/it] 64%|██████▍ | 11918/18688 [14:07:17<7:56:11, 4.22s/it] 64%|██████▍ | 11919/18688 [14:07:21<7:48:54, 4.16s/it] 64%|██████▍ | 11920/18688 [14:07:25<7:52:31, 4.19s/it] {'loss': '1.001', 'grad_norm': '1.566', 'learning_rate': '6.726e-05', 'epoch': '0.6379', 'num_input_tokens_seen': 40083792, 'train_runtime': '5.085e+04', 'train_tokens_per_second': '788.3'}
64%|██████▍ | 11920/18688 [14:07:25<7:52:31, 4.19s/it] 64%|██████▍ | 11921/18688 [14:07:29<7:32:07, 4.01s/it] 64%|██████▍ | 11922/18688 [14:07:33<7:22:41, 3.93s/it] 64%|██████▍ | 11923/18688 [14:07:37<7:29:26, 3.99s/it] 64%|██████▍ | 11924/18688 [14:07:41<7:26:49, 3.96s/it] 64%|██████▍ | 11925/18688 [14:07:44<7:08:22, 3.80s/it] 64%|██████▍ | 11926/18688 [14:07:48<7:05:40, 3.78s/it] 64%|██████▍ | 11927/18688 [14:07:53<7:49:14, 4.16s/it] 64%|██████▍ | 11928/18688 [14:07:58<8:07:49, 4.33s/it] 64%|██████▍ | 11929/18688 [14:08:02<8:01:54, 4.28s/it] 64%|██████▍ | 11930/18688 [14:08:06<7:54:06, 4.21s/it] {'loss': '0.9124', 'grad_norm': '1.831', 'learning_rate': '6.709e-05', 'epoch': '0.6384', 'num_input_tokens_seen': 40116656, 'train_runtime': '5.089e+04', 'train_tokens_per_second': '788.4'}
64%|██████▍ | 11930/18688 [14:08:06<7:54:06, 4.21s/it] 64%|██████▍ | 11931/18688 [14:08:10<7:54:49, 4.22s/it] 64%|██████▍ | 11932/18688 [14:08:14<7:40:28, 4.09s/it] 64%|██████▍ | 11933/18688 [14:08:18<7:28:47, 3.99s/it] 64%|██████▍ | 11934/18688 [14:08:23<8:07:56, 4.33s/it] 64%|██████▍ | 11935/18688 [14:08:27<7:57:54, 4.25s/it] 64%|██████▍ | 11936/18688 [14:08:32<8:28:16, 4.52s/it] 64%|██████▍ | 11937/18688 [14:08:37<8:43:01, 4.65s/it] 64%|██████▍ | 11938/18688 [14:08:42<8:49:14, 4.70s/it] 64%|██████▍ | 11939/18688 [14:08:46<8:24:00, 4.48s/it] 64%|██████▍ | 11940/18688 [14:08:51<8:39:16, 4.62s/it] {'loss': '0.9325', 'grad_norm': '1.537', 'learning_rate': '6.691e-05', 'epoch': '0.6389', 'num_input_tokens_seen': 40154472, 'train_runtime': '5.093e+04', 'train_tokens_per_second': '788.4'}
64%|██████▍ | 11940/18688 [14:08:51<8:39:16, 4.62s/it] 64%|██████▍ | 11941/18688 [14:08:55<8:19:07, 4.44s/it] 64%|██████▍ | 11942/18688 [14:08:59<8:26:41, 4.51s/it] 64%|██████▍ | 11943/18688 [14:09:03<8:13:54, 4.39s/it] 64%|██████▍ | 11944/18688 [14:09:07<7:58:00, 4.25s/it] 64%|██████▍ | 11945/18688 [14:09:11<7:50:22, 4.19s/it] 64%|██████▍ | 11946/18688 [14:09:15<7:43:21, 4.12s/it] 64%|██████▍ | 11947/18688 [14:09:20<7:47:42, 4.16s/it] 64%|██████▍ | 11948/18688 [14:09:24<8:00:05, 4.27s/it] 64%|██████▍ | 11949/18688 [14:09:29<8:08:56, 4.35s/it] 64%|██████▍ | 11950/18688 [14:09:32<7:41:51, 4.11s/it] {'loss': '0.9673', 'grad_norm': '2.125', 'learning_rate': '6.674e-05', 'epoch': '0.6395', 'num_input_tokens_seen': 40187520, 'train_runtime': '5.097e+04', 'train_tokens_per_second': '788.4'}
64%|██████▍ | 11950/18688 [14:09:32<7:41:51, 4.11s/it] 64%|██████▍ | 11951/18688 [14:09:36<7:31:24, 4.02s/it] 64%|██████▍ | 11952/18688 [14:09:40<7:22:00, 3.94s/it] 64%|██████▍ | 11953/18688 [14:09:43<7:06:42, 3.80s/it] 64%|██████▍ | 11954/18688 [14:09:47<7:16:56, 3.89s/it] 64%|██████▍ | 11955/18688 [14:09:53<8:16:21, 4.42s/it] 64%|██████▍ | 11956/18688 [14:09:57<8:00:08, 4.28s/it] 64%|██████▍ | 11957/18688 [14:10:02<8:38:24, 4.62s/it] 64%|██████▍ | 11958/18688 [14:10:07<8:47:47, 4.71s/it] 64%|██████▍ | 11959/18688 [14:10:12<8:32:42, 4.57s/it] 64%|██████▍ | 11960/18688 [14:10:16<8:30:01, 4.55s/it] {'loss': '0.9258', 'grad_norm': '1.576', 'learning_rate': '6.657e-05', 'epoch': '0.64', 'num_input_tokens_seen': 40222440, 'train_runtime': '5.102e+04', 'train_tokens_per_second': '788.4'}
64%|██████▍ | 11960/18688 [14:10:16<8:30:01, 4.55s/it] 64%|██████▍ | 11961/18688 [14:10:22<9:05:49, 4.87s/it] 64%|██████▍ | 11962/18688 [14:10:26<8:59:23, 4.81s/it] 64%|██████▍ | 11963/18688 [14:10:31<8:56:48, 4.79s/it] 64%|██████▍ | 11964/18688 [14:10:35<8:36:31, 4.61s/it] 64%|██████▍ | 11965/18688 [14:10:39<8:11:21, 4.39s/it] 64%|██████▍ | 11966/18688 [14:10:44<8:38:17, 4.63s/it] 64%|██████▍ | 11967/18688 [14:10:49<8:29:32, 4.55s/it] 64%|██████▍ | 11968/18688 [14:10:53<8:27:14, 4.53s/it] 64%|██████▍ | 11969/18688 [14:10:58<8:45:54, 4.70s/it] 64%|██████▍ | 11970/18688 [14:11:02<8:19:48, 4.46s/it] {'loss': '0.9852', 'grad_norm': '1.825', 'learning_rate': '6.64e-05', 'epoch': '0.6405', 'num_input_tokens_seen': 40259536, 'train_runtime': '5.106e+04', 'train_tokens_per_second': '788.4'}
64%|██████▍ | 11970/18688 [14:11:02<8:19:48, 4.46s/it] 64%|██████▍ | 11971/18688 [14:11:08<8:49:24, 4.73s/it] 64%|██████▍ | 11972/18688 [14:11:11<8:15:16, 4.42s/it] 64%|██████▍ | 11973/18688 [14:11:15<7:54:26, 4.24s/it] 64%|██████▍ | 11974/18688 [14:11:19<7:32:50, 4.05s/it] 64%|██████▍ | 11975/18688 [14:11:22<7:20:32, 3.94s/it] 64%|██████▍ | 11976/18688 [14:11:27<7:50:04, 4.20s/it] 64%|██████▍ | 11977/18688 [14:11:31<7:33:38, 4.06s/it] 64%|██████▍ | 11978/18688 [14:11:35<7:31:18, 4.04s/it] 64%|██████▍ | 11979/18688 [14:11:39<7:33:31, 4.06s/it] 64%|██████▍ | 11980/18688 [14:11:43<7:29:48, 4.02s/it] {'loss': '1.051', 'grad_norm': '1.863', 'learning_rate': '6.622e-05', 'epoch': '0.6411', 'num_input_tokens_seen': 40293704, 'train_runtime': '5.11e+04', 'train_tokens_per_second': '788.5'}
64%|██████▍ | 11980/18688 [14:11:43<7:29:48, 4.02s/it] 64%|██████▍ | 11981/18688 [14:11:46<7:12:43, 3.87s/it] 64%|██████▍ | 11982/18688 [14:11:50<7:14:44, 3.89s/it] 64%|██████▍ | 11983/18688 [14:11:54<7:11:48, 3.86s/it] 64%|██████▍ | 11984/18688 [14:11:58<7:16:32, 3.91s/it] 64%|██████▍ | 11985/18688 [14:12:02<7:27:40, 4.01s/it] 64%|██████▍ | 11986/18688 [14:12:07<7:36:49, 4.09s/it] 64%|██████▍ | 11987/18688 [14:12:11<7:41:56, 4.14s/it] 64%|██████▍ | 11988/18688 [14:12:16<8:03:27, 4.33s/it] 64%|██████▍ | 11989/18688 [14:12:20<7:52:16, 4.23s/it] 64%|██████▍ | 11990/18688 [14:12:24<8:05:11, 4.35s/it] {'loss': '0.9599', 'grad_norm': '1.685', 'learning_rate': '6.605e-05', 'epoch': '0.6416', 'num_input_tokens_seen': 40325872, 'train_runtime': '5.114e+04', 'train_tokens_per_second': '788.5'}
64%|██████▍ | 11990/18688 [14:12:24<8:05:11, 4.35s/it] 64%|██████▍ | 11991/18688 [14:12:29<8:23:01, 4.51s/it] 64%|██████▍ | 11992/18688 [14:12:33<8:11:20, 4.40s/it] 64%|██████▍ | 11993/18688 [14:12:38<8:13:55, 4.43s/it] 64%|██████▍ | 11994/18688 [14:12:42<7:57:57, 4.28s/it] 64%|██████▍ | 11995/18688 [14:12:45<7:38:54, 4.11s/it] 64%|██████▍ | 11996/18688 [14:12:50<7:55:15, 4.26s/it] 64%|██████▍ | 11997/18688 [14:12:54<7:56:17, 4.27s/it] 64%|██████▍ | 11998/18688 [14:13:00<8:29:04, 4.57s/it] 64%|██████▍ | 11999/18688 [14:13:04<8:28:18, 4.56s/it] 64%|██████▍ | 12000/18688 [14:13:08<8:14:43, 4.44s/it] {'loss': '0.9542', 'grad_norm': '2.028', 'learning_rate': '6.588e-05', 'epoch': '0.6421', 'num_input_tokens_seen': 40360224, 'train_runtime': '5.119e+04', 'train_tokens_per_second': '788.5'}
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***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
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{'eval_loss': '0.9326', 'eval_runtime': '83.27', 'eval_samples_per_second': '12.01', 'eval_steps_per_second': '12.01', 'epoch': '0.6421', 'num_input_tokens_seen': 40360224}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00020/checkpoint-12000
loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
64%|██████▍ | 12001/18688 [14:14:36<54:48:27, 29.51s/it] 64%|██████▍ | 12002/18688 [14:14:40<40:34:27, 21.85s/it] 64%|██████▍ | 12003/18688 [14:14:44<30:26:35, 16.39s/it] 64%|██████▍ | 12004/18688 [14:14:48<23:30:18, 12.66s/it] 64%|██████▍ | 12005/18688 [14:14:52<18:51:05, 10.15s/it] 64%|██████▍ | 12006/18688 [14:14:56<15:12:36, 8.19s/it] 64%|██████▍ | 12007/18688 [14:15:00<12:59:22, 7.00s/it] 64%|██████▍ | 12008/18688 [14:15:04<11:28:07, 6.18s/it] 64%|██████▍ | 12009/18688 [14:15:08<10:02:12, 5.41s/it] 64%|██████▍ | 12010/18688 [14:15:13<9:52:28, 5.32s/it] {'loss': '0.9232', 'grad_norm': '1.914', 'learning_rate': '6.571e-05', 'epoch': '0.6427', 'num_input_tokens_seen': 40393544, 'train_runtime': '5.131e+04', 'train_tokens_per_second': '787.2'}
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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': '0.9202', 'eval_runtime': '82.97', 'eval_samples_per_second': '12.05', 'eval_steps_per_second': '12.05', 'epoch': '0.6957', 'num_input_tokens_seen': 43672984}
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74%|███████▍ | 13860/18688 [16:24:21<5:12:59, 3.89s/it] 74%|███████▍ | 13861/18688 [16:24:25<5:11:26, 3.87s/it] 74%|███████▍ | 13862/18688 [16:24:29<5:07:05, 3.82s/it] 74%|███████▍ | 13863/18688 [16:24:33<5:19:20, 3.97s/it] 74%|███████▍ | 13864/18688 [16:24:37<5:21:17, 4.00s/it] 74%|███████▍ | 13865/18688 [16:24:41<5:27:51, 4.08s/it] 74%|███████▍ | 13866/18688 [16:24:46<5:35:37, 4.18s/it] 74%|███████▍ | 13867/18688 [16:24:50<5:32:10, 4.13s/it] 74%|███████▍ | 13868/18688 [16:24:54<5:27:31, 4.08s/it] 74%|███████▍ | 13869/18688 [16:24:57<5:13:54, 3.91s/it] 74%|███████▍ | 13870/18688 [16:25:01<5:07:38, 3.83s/it] {'loss': '0.9504', 'grad_norm': '1.924', 'learning_rate': '3.635e-05', 'epoch': '0.7422', 'num_input_tokens_seen': 46609400, 'train_runtime': '5.91e+04', 'train_tokens_per_second': '788.6'}
74%|███████▍ | 13870/18688 [16:25:01<5:07:38, 3.83s/it] 74%|███████▍ | 13871/18688 [16:25:05<5:24:54, 4.05s/it] 74%|███████▍ | 13872/18688 [16:25:10<5:37:50, 4.21s/it] 74%|███████▍ | 13873/18688 [16:25:14<5:39:15, 4.23s/it] 74%|███████▍ | 13874/18688 [16:25:20<6:06:42, 4.57s/it] 74%|███████▍ | 13875/18688 [16:25:24<5:55:32, 4.43s/it] 74%|███████▍ | 13876/18688 [16:25:28<5:52:52, 4.40s/it] 74%|███████▍ | 13877/18688 [16:25:33<5:56:10, 4.44s/it] 74%|███████▍ | 13878/18688 [16:25:37<5:59:26, 4.48s/it] 74%|███████▍ | 13879/18688 [16:25:43<6:22:30, 4.77s/it] 74%|███████▍ | 13880/18688 [16:25:47<6:18:53, 4.73s/it] {'loss': '1.057', 'grad_norm': '1.563', 'learning_rate': '3.621e-05', 'epoch': '0.7427', 'num_input_tokens_seen': 46648576, 'train_runtime': '5.915e+04', 'train_tokens_per_second': '788.7'}
74%|███████▍ | 13880/18688 [16:25:47<6:18:53, 4.73s/it] 74%|███████▍ | 13881/18688 [16:25:52<6:06:20, 4.57s/it] 74%|███████▍ | 13882/18688 [16:25:56<6:05:33, 4.56s/it] 74%|███████▍ | 13883/18688 [16:26:00<5:54:35, 4.43s/it] 74%|███████▍ | 13884/18688 [16:26:04<5:45:28, 4.31s/it] 74%|███████▍ | 13885/18688 [16:26:08<5:37:22, 4.21s/it] 74%|███████▍ | 13886/18688 [16:26:12<5:34:47, 4.18s/it] 74%|███████▍ | 13887/18688 [16:26:17<5:36:16, 4.20s/it] 74%|███████▍ | 13888/18688 [16:26:21<5:32:12, 4.15s/it] 74%|███████▍ | 13889/18688 [16:26:24<5:22:09, 4.03s/it] 74%|███████▍ | 13890/18688 [16:26:28<5:22:29, 4.03s/it] {'loss': '0.8808', 'grad_norm': '1.875', 'learning_rate': '3.607e-05', 'epoch': '0.7433', 'num_input_tokens_seen': 46679336, 'train_runtime': '5.919e+04', 'train_tokens_per_second': '788.6'}
74%|███████▍ | 13890/18688 [16:26:28<5:22:29, 4.03s/it] 74%|███████▍ | 13891/18688 [16:26:32<5:19:26, 4.00s/it] 74%|███████▍ | 13892/18688 [16:26:36<5:09:57, 3.88s/it] 74%|███████▍ | 13893/18688 [16:26:40<5:16:11, 3.96s/it] 74%|███████▍ | 13894/18688 [16:26:44<5:22:46, 4.04s/it] 74%|███████▍ | 13895/18688 [16:26:49<5:34:40, 4.19s/it] 74%|███████▍ | 13896/18688 [16:26:53<5:32:07, 4.16s/it] 74%|███████▍ | 13897/18688 [16:26:57<5:25:40, 4.08s/it] 74%|███████▍ | 13898/18688 [16:27:00<5:16:01, 3.96s/it] 74%|███████▍ | 13899/18688 [16:27:04<5:02:02, 3.78s/it] 74%|███████▍ | 13900/18688 [16:27:08<5:12:00, 3.91s/it] {'loss': '0.8668', 'grad_norm': '1.829', 'learning_rate': '3.593e-05', 'epoch': '0.7438', 'num_input_tokens_seen': 46710680, 'train_runtime': '5.923e+04', 'train_tokens_per_second': '788.7'}
74%|███████▍ | 13900/18688 [16:27:08<5:12:00, 3.91s/it] 74%|███████▍ | 13901/18688 [16:27:12<5:05:50, 3.83s/it] 74%|███████▍ | 13902/18688 [16:27:16<5:07:40, 3.86s/it] 74%|███████▍ | 13903/18688 [16:27:20<5:10:59, 3.90s/it] 74%|███████▍ | 13904/18688 [16:27:24<5:18:33, 4.00s/it] 74%|███████▍ | 13905/18688 [16:27:28<5:24:25, 4.07s/it] 74%|███████▍ | 13906/18688 [16:27:32<5:25:55, 4.09s/it] 74%|███████▍ | 13907/18688 [16:27:38<5:55:15, 4.46s/it] 74%|███████▍ | 13908/18688 [16:27:42<5:49:04, 4.38s/it] 74%|███████▍ | 13909/18688 [16:27:46<5:37:44, 4.24s/it] 74%|███████▍ | 13910/18688 [16:27:50<5:48:05, 4.37s/it] {'loss': '0.9367', 'grad_norm': '1.812', 'learning_rate': '3.579e-05', 'epoch': '0.7443', 'num_input_tokens_seen': 46747080, 'train_runtime': '5.927e+04', 'train_tokens_per_second': '788.7'}
74%|███████▍ | 13910/18688 [16:27:50<5:48:05, 4.37s/it] 74%|███████▍ | 13911/18688 [16:27:55<5:46:41, 4.35s/it] 74%|███████▍ | 13912/18688 [16:27:58<5:32:00, 4.17s/it] 74%|███████▍ | 13913/18688 [16:28:04<6:01:08, 4.54s/it] 74%|███████▍ | 13914/18688 [16:28:07<5:38:05, 4.25s/it] 74%|███████▍ | 13915/18688 [16:28:11<5:34:01, 4.20s/it] 74%|███████▍ | 13916/18688 [16:28:15<5:27:32, 4.12s/it] 74%|███████▍ | 13917/18688 [16:28:20<5:29:13, 4.14s/it] 74%|███████▍ | 13918/18688 [16:28:23<5:20:32, 4.03s/it] 74%|███████▍ | 13919/18688 [16:28:27<5:08:35, 3.88s/it] 74%|███████▍ | 13920/18688 [16:28:31<5:20:29, 4.03s/it] {'loss': '0.915', 'grad_norm': '1.505', 'learning_rate': '3.565e-05', 'epoch': '0.7449', 'num_input_tokens_seen': 46782264, 'train_runtime': '5.931e+04', 'train_tokens_per_second': '788.8'}
74%|███████▍ | 13920/18688 [16:28:31<5:20:29, 4.03s/it] 74%|███████▍ | 13921/18688 [16:28:35<5:23:24, 4.07s/it] 74%|███████▍ | 13922/18688 [16:28:40<5:36:40, 4.24s/it] 75%|███████▍ | 13923/18688 [16:28:44<5:23:11, 4.07s/it] 75%|███████▍ | 13924/18688 [16:28:47<5:10:47, 3.91s/it] 75%|███████▍ | 13925/18688 [16:28:51<5:07:58, 3.88s/it] 75%|███████▍ | 13926/18688 [16:28:56<5:40:32, 4.29s/it] 75%|███████▍ | 13927/18688 [16:29:01<5:39:40, 4.28s/it] 75%|███████▍ | 13928/18688 [16:29:04<5:19:10, 4.02s/it] 75%|███████▍ | 13929/18688 [16:29:09<5:32:11, 4.19s/it] 75%|███████▍ | 13930/18688 [16:29:13<5:34:47, 4.22s/it] {'loss': '0.8876', 'grad_norm': '1.497', 'learning_rate': '3.551e-05', 'epoch': '0.7454', 'num_input_tokens_seen': 46817480, 'train_runtime': '5.935e+04', 'train_tokens_per_second': '788.8'}
75%|███████▍ | 13930/18688 [16:29:13<5:34:47, 4.22s/it] 75%|███████▍ | 13931/18688 [16:29:17<5:31:59, 4.19s/it] 75%|███████▍ | 13932/18688 [16:29:21<5:20:17, 4.04s/it] 75%|███████▍ | 13933/18688 [16:29:24<5:11:14, 3.93s/it] 75%|███████▍ | 13934/18688 [16:29:29<5:17:59, 4.01s/it] 75%|███████▍ | 13935/18688 [16:29:33<5:16:49, 4.00s/it] 75%|███████▍ | 13936/18688 [16:29:36<5:08:23, 3.89s/it] 75%|███████▍ | 13937/18688 [16:29:40<5:05:15, 3.86s/it] 75%|███████▍ | 13938/18688 [16:29:44<5:18:26, 4.02s/it] 75%|███████▍ | 13939/18688 [16:29:48<5:14:54, 3.98s/it] 75%|███████▍ | 13940/18688 [16:29:52<5:15:59, 3.99s/it] {'loss': '0.9639', 'grad_norm': '1.388', 'learning_rate': '3.537e-05', 'epoch': '0.746', 'num_input_tokens_seen': 46847224, 'train_runtime': '5.939e+04', 'train_tokens_per_second': '788.8'}
75%|███████▍ | 13940/18688 [16:29:52<5:15:59, 3.99s/it] 75%|███████▍ | 13941/18688 [16:29:56<5:09:47, 3.92s/it] 75%|███████▍ | 13942/18688 [16:30:01<5:27:17, 4.14s/it] 75%|███████▍ | 13943/18688 [16:30:04<5:20:18, 4.05s/it] 75%|███████▍ | 13944/18688 [16:30:08<5:07:42, 3.89s/it] 75%|███████▍ | 13945/18688 [16:30:12<5:16:38, 4.01s/it] 75%|███████▍ | 13946/18688 [16:30:16<5:05:32, 3.87s/it] 75%|███████▍ | 13947/18688 [16:30:20<5:22:49, 4.09s/it] 75%|███████▍ | 13948/18688 [16:30:25<5:41:30, 4.32s/it] 75%|███████▍ | 13949/18688 [16:30:30<5:50:02, 4.43s/it] 75%|███████▍ | 13950/18688 [16:30:34<5:36:14, 4.26s/it] {'loss': '0.8899', 'grad_norm': '1.716', 'learning_rate': '3.523e-05', 'epoch': '0.7465', 'num_input_tokens_seen': 46881720, 'train_runtime': '5.943e+04', 'train_tokens_per_second': '788.8'}
75%|███████▍ | 13950/18688 [16:30:34<5:36:14, 4.26s/it] 75%|███████▍ | 13951/18688 [16:30:38<5:34:07, 4.23s/it] 75%|███████▍ | 13952/18688 [16:30:43<5:57:44, 4.53s/it] 75%|███████▍ | 13953/18688 [16:30:47<5:47:55, 4.41s/it] 75%|███████▍ | 13954/18688 [16:30:51<5:35:27, 4.25s/it] 75%|███████▍ | 13955/18688 [16:30:55<5:25:23, 4.12s/it] 75%|███████▍ | 13956/18688 [16:30:59<5:29:32, 4.18s/it] 75%|███████▍ | 13957/18688 [16:31:03<5:12:29, 3.96s/it] 75%|███████▍ | 13958/18688 [16:31:07<5:15:52, 4.01s/it] 75%|███████▍ | 13959/18688 [16:31:11<5:15:29, 4.00s/it] 75%|███████▍ | 13960/18688 [16:31:15<5:11:51, 3.96s/it] {'loss': '0.9352', 'grad_norm': '1.605', 'learning_rate': '3.509e-05', 'epoch': '0.747', 'num_input_tokens_seen': 46914040, 'train_runtime': '5.948e+04', 'train_tokens_per_second': '788.8'}
75%|███████▍ | 13960/18688 [16:31:15<5:11:51, 3.96s/it] 75%|███████▍ | 13961/18688 [16:31:19<5:08:51, 3.92s/it] 75%|███████▍ | 13962/18688 [16:31:23<5:11:22, 3.95s/it] 75%|███████▍ | 13963/18688 [16:31:28<5:36:59, 4.28s/it] 75%|███████▍ | 13964/18688 [16:31:32<5:38:25, 4.30s/it] 75%|███████▍ | 13965/18688 [16:31:36<5:41:27, 4.34s/it] 75%|███████▍ | 13966/18688 [16:31:40<5:28:04, 4.17s/it] 75%|███████▍ | 13967/18688 [16:31:44<5:14:46, 4.00s/it] 75%|███████▍ | 13968/18688 [16:31:47<5:02:36, 3.85s/it] 75%|███████▍ | 13969/18688 [16:31:51<5:00:39, 3.82s/it] 75%|███████▍ | 13970/18688 [16:31:55<5:04:58, 3.88s/it] {'loss': '0.9252', 'grad_norm': '1.653', 'learning_rate': '3.495e-05', 'epoch': '0.7476', 'num_input_tokens_seen': 46945168, 'train_runtime': '5.952e+04', 'train_tokens_per_second': '788.8'}
75%|███████▍ | 13970/18688 [16:31:55<5:04:58, 3.88s/it] 75%|███████▍ | 13971/18688 [16:31:59<5:05:58, 3.89s/it] 75%|███████▍ | 13972/18688 [16:32:03<5:11:38, 3.96s/it] 75%|███████▍ | 13973/18688 [16:32:07<5:14:58, 4.01s/it] 75%|███████▍ | 13974/18688 [16:32:11<5:17:36, 4.04s/it] 75%|███████▍ | 13975/18688 [16:32:15<5:09:02, 3.93s/it] 75%|███████▍ | 13976/18688 [16:32:19<4:57:42, 3.79s/it] 75%|███████▍ | 13977/18688 [16:32:22<4:48:01, 3.67s/it] 75%|███████▍ | 13978/18688 [16:32:26<4:59:24, 3.81s/it] 75%|███████▍ | 13979/18688 [16:32:30<5:13:09, 3.99s/it] 75%|███████▍ | 13980/18688 [16:32:34<5:03:02, 3.86s/it] {'loss': '0.9542', 'grad_norm': '1.939', 'learning_rate': '3.481e-05', 'epoch': '0.7481', 'num_input_tokens_seen': 46975808, 'train_runtime': '5.955e+04', 'train_tokens_per_second': '788.8'}
75%|███████▍ | 13980/18688 [16:32:34<5:03:02, 3.86s/it] 75%|███████▍ | 13981/18688 [16:32:38<5:02:52, 3.86s/it] 75%|███████▍ | 13982/18688 [16:32:42<5:04:09, 3.88s/it] 75%|███████▍ | 13983/18688 [16:32:46<5:08:19, 3.93s/it] 75%|███████▍ | 13984/18688 [16:32:50<5:01:39, 3.85s/it] 75%|███████▍ | 13985/18688 [16:32:53<5:03:58, 3.88s/it] 75%|███████▍ | 13986/18688 [16:32:58<5:16:47, 4.04s/it] 75%|███████▍ | 13987/18688 [16:33:01<5:05:37, 3.90s/it] 75%|███████▍ | 13988/18688 [16:33:07<5:33:09, 4.25s/it] 75%|███████▍ | 13989/18688 [16:33:10<5:15:48, 4.03s/it] 75%|███████▍ | 13990/18688 [16:33:14<5:19:53, 4.09s/it] {'loss': '0.9239', 'grad_norm': '2.229', 'learning_rate': '3.467e-05', 'epoch': '0.7486', 'num_input_tokens_seen': 47007592, 'train_runtime': '5.959e+04', 'train_tokens_per_second': '788.8'}
75%|███████▍ | 13990/18688 [16:33:14<5:19:53, 4.09s/it] 75%|███████▍ | 13991/18688 [16:33:18<5:11:06, 3.97s/it] 75%|███████▍ | 13992/18688 [16:33:23<5:26:42, 4.17s/it] 75%|███████▍ | 13993/18688 [16:33:27<5:22:07, 4.12s/it] 75%|███████▍ | 13994/18688 [16:33:30<5:15:42, 4.04s/it] 75%|███████▍ | 13995/18688 [16:33:34<5:07:39, 3.93s/it] 75%|███████▍ | 13996/18688 [16:33:38<5:00:03, 3.84s/it] 75%|███████▍ | 13997/18688 [16:33:42<5:09:56, 3.96s/it] 75%|███████▍ | 13998/18688 [16:33:46<5:06:40, 3.92s/it] 75%|███████▍ | 13999/18688 [16:33:50<5:02:14, 3.87s/it] 75%|███████▍ | 14000/18688 [16:33:54<5:22:05, 4.12s/it] {'loss': '0.9698', 'grad_norm': '1.883', 'learning_rate': '3.453e-05', 'epoch': '0.7492', 'num_input_tokens_seen': 47040648, 'train_runtime': '5.963e+04', 'train_tokens_per_second': '788.8'}
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***** Running Evaluation *****
Num examples = 1000
Batch size = 1
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{'eval_loss': '0.9127', 'eval_runtime': '82.87', '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_0p00020/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 [16:35:22<38:07:41, 29.29s/it] 75%|███████▍ | 14002/18688 [16:35:27<28:31:12, 21.91s/it] 75%|███████▍ | 14003/18688 [16:35:32<21:49:43, 16.77s/it] 75%|███████▍ | 14004/18688 [16:35:36<17:00:55, 13.08s/it] 75%|███████▍ | 14005/18688 [16:35:41<13:49:40, 10.63s/it] 75%|███████▍ | 14006/18688 [16:35:46<11:24:23, 8.77s/it] 75%|███████▍ | 14007/18688 [16:35:50<9:30:53, 7.32s/it] 75%|███████▍ | 14008/18688 [16:35:53<8:01:21, 6.17s/it] 75%|███████▍ | 14009/18688 [16:35:59<7:48:43, 6.01s/it] 75%|███████▍ | 14010/18688 [16:36:03<7:03:36, 5.43s/it] {'loss': '1.039', 'grad_norm': '2.122', 'learning_rate': '3.439e-05', 'epoch': '0.7497', 'num_input_tokens_seen': 47078616, 'train_runtime': '5.976e+04', 'train_tokens_per_second': '787.8'}
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75%|███████▌ | 14020/18688 [16:36:44<5:12:49, 4.02s/it] 75%|███████▌ | 14021/18688 [16:36:48<5:02:13, 3.89s/it] 75%|███████▌ | 14022/18688 [16:36:52<5:02:02, 3.88s/it] 75%|███████▌ | 14023/18688 [16:36:55<5:01:01, 3.87s/it] 75%|███████▌ | 14024/18688 [16:37:00<5:22:13, 4.15s/it] 75%|███████▌ | 14025/18688 [16:37:04<5:10:39, 4.00s/it] 75%|███████▌ | 14026/18688 [16:37:07<5:00:26, 3.87s/it] 75%|███████▌ | 14027/18688 [16:37:14<5:50:47, 4.52s/it] 75%|███████▌ | 14028/18688 [16:37:18<5:51:28, 4.53s/it] 75%|███████▌ | 14029/18688 [16:37:22<5:40:55, 4.39s/it] 75%|███████▌ | 14030/18688 [16:37:26<5:37:45, 4.35s/it] {'loss': '0.9246', 'grad_norm': '1.511', 'learning_rate': '3.412e-05', 'epoch': '0.7508', 'num_input_tokens_seen': 47146904, 'train_runtime': '5.985e+04', 'train_tokens_per_second': '787.8'}
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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': '0.9071', 'eval_runtime': '83.3', 'eval_samples_per_second': '12.01', 'eval_steps_per_second': '12.01', 'epoch': '0.8027', 'num_input_tokens_seen': 50429344}
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85%|████████▍ | 15870/18688 [18:47:07<3:23:45, 4.34s/it] 85%|████████▍ | 15871/18688 [18:47:12<3:24:03, 4.35s/it] 85%|████████▍ | 15872/18688 [18:47:17<3:40:04, 4.69s/it] 85%|████████▍ | 15873/18688 [18:47:22<3:37:37, 4.64s/it] 85%|████████▍ | 15874/18688 [18:47:26<3:36:13, 4.61s/it] 85%|████████▍ | 15875/18688 [18:47:30<3:32:04, 4.52s/it] 85%|████████▍ | 15876/18688 [18:47:35<3:26:19, 4.40s/it] 85%|████████▍ | 15877/18688 [18:47:39<3:28:24, 4.45s/it] 85%|████████▍ | 15878/18688 [18:47:44<3:29:31, 4.47s/it] 85%|████████▍ | 15879/18688 [18:47:47<3:18:24, 4.24s/it] 85%|████████▍ | 15880/18688 [18:47:51<3:14:07, 4.15s/it] {'loss': '0.9688', 'grad_norm': '1.557', 'learning_rate': '1.289e-05', 'epoch': '0.8498', 'num_input_tokens_seen': 53397128, 'train_runtime': '6.767e+04', 'train_tokens_per_second': '789.1'}
85%|████████▍ | 15880/18688 [18:47:51<3:14:07, 4.15s/it] 85%|████████▍ | 15881/18688 [18:47:55<3:04:17, 3.94s/it] 85%|████████▍ | 15882/18688 [18:47:59<3:12:00, 4.11s/it] 85%|████████▍ | 15883/18688 [18:48:03<3:09:46, 4.06s/it] 85%|████████▍ | 15884/18688 [18:48:07<3:09:24, 4.05s/it] 85%|████████▌ | 15885/18688 [18:48:11<3:03:26, 3.93s/it] 85%|████████▌ | 15886/18688 [18:48:14<2:58:27, 3.82s/it] 85%|████████▌ | 15887/18688 [18:48:18<2:54:20, 3.73s/it] 85%|████████▌ | 15888/18688 [18:48:22<2:58:29, 3.82s/it] 85%|████████▌ | 15889/18688 [18:48:26<2:57:18, 3.80s/it] 85%|████████▌ | 15890/18688 [18:48:30<2:59:42, 3.85s/it] {'loss': '0.9006', 'grad_norm': '1.921', 'learning_rate': '1.28e-05', 'epoch': '0.8503', 'num_input_tokens_seen': 53425792, 'train_runtime': '6.771e+04', 'train_tokens_per_second': '789'}
85%|████████▌ | 15890/18688 [18:48:30<2:59:42, 3.85s/it] 85%|████████▌ | 15891/18688 [18:48:34<2:59:16, 3.85s/it] 85%|████████▌ | 15892/18688 [18:48:37<2:52:56, 3.71s/it] 85%|████████▌ | 15893/18688 [18:48:41<2:51:03, 3.67s/it] 85%|████████▌ | 15894/18688 [18:48:45<3:01:41, 3.90s/it] 85%|████████▌ | 15895/18688 [18:48:49<2:57:36, 3.82s/it] 85%|████████▌ | 15896/18688 [18:48:52<2:57:27, 3.81s/it] 85%|████████▌ | 15897/18688 [18:48:56<2:59:02, 3.85s/it] 85%|████████▌ | 15898/18688 [18:49:00<3:01:28, 3.90s/it] 85%|████████▌ | 15899/18688 [18:49:04<3:03:12, 3.94s/it] 85%|████████▌ | 15900/18688 [18:49:09<3:06:55, 4.02s/it] {'loss': '0.9079', 'grad_norm': '1.475', 'learning_rate': '1.271e-05', 'epoch': '0.8508', 'num_input_tokens_seen': 53455944, 'train_runtime': '6.775e+04', 'train_tokens_per_second': '789'}
85%|████████▌ | 15900/18688 [18:49:09<3:06:55, 4.02s/it] 85%|████████▌ | 15901/18688 [18:49:12<3:00:04, 3.88s/it] 85%|████████▌ | 15902/18688 [18:49:16<3:01:10, 3.90s/it] 85%|████████▌ | 15903/18688 [18:49:21<3:18:47, 4.28s/it] 85%|████████▌ | 15904/18688 [18:49:25<3:10:20, 4.10s/it] 85%|████████▌ | 15905/18688 [18:49:29<3:08:13, 4.06s/it] 85%|████████▌ | 15906/18688 [18:49:35<3:29:11, 4.51s/it] 85%|████████▌ | 15907/18688 [18:49:39<3:23:19, 4.39s/it] 85%|████████▌ | 15908/18688 [18:49:44<3:34:17, 4.63s/it] 85%|████████▌ | 15909/18688 [18:49:49<3:37:20, 4.69s/it] 85%|████████▌ | 15910/18688 [18:49:52<3:25:43, 4.44s/it] {'loss': '0.9167', 'grad_norm': '1.765', 'learning_rate': '1.263e-05', 'epoch': '0.8514', 'num_input_tokens_seen': 53491384, 'train_runtime': '6.779e+04', 'train_tokens_per_second': '789'}
85%|████████▌ | 15910/18688 [18:49:52<3:25:43, 4.44s/it] 85%|████████▌ | 15911/18688 [18:49:57<3:22:28, 4.37s/it] 85%|████████▌ | 15912/18688 [18:50:01<3:18:50, 4.30s/it] 85%|████████▌ | 15913/18688 [18:50:05<3:21:38, 4.36s/it] 85%|████████▌ | 15914/18688 [18:50:09<3:12:19, 4.16s/it] 85%|████████▌ | 15915/18688 [18:50:13<3:10:48, 4.13s/it] 85%|████████▌ | 15916/18688 [18:50:17<3:04:21, 3.99s/it] 85%|████████▌ | 15917/18688 [18:50:21<3:03:22, 3.97s/it] 85%|████████▌ | 15918/18688 [18:50:26<3:16:32, 4.26s/it] 85%|████████▌ | 15919/18688 [18:50:29<3:11:04, 4.14s/it] 85%|████████▌ | 15920/18688 [18:50:34<3:12:30, 4.17s/it] {'loss': '0.9823', 'grad_norm': '1.738', 'learning_rate': '1.254e-05', 'epoch': '0.8519', 'num_input_tokens_seen': 53525408, 'train_runtime': '6.783e+04', 'train_tokens_per_second': '789.1'}
85%|████████▌ | 15920/18688 [18:50:34<3:12:30, 4.17s/it] 85%|████████▌ | 15921/18688 [18:50:38<3:12:51, 4.18s/it] 85%|████████▌ | 15922/18688 [18:50:42<3:04:45, 4.01s/it] 85%|████████▌ | 15923/18688 [18:50:46<3:14:30, 4.22s/it] 85%|████████▌ | 15924/18688 [18:50:51<3:15:46, 4.25s/it] 85%|████████▌ | 15925/18688 [18:50:56<3:31:46, 4.60s/it] 85%|████████▌ | 15926/18688 [18:50:59<3:15:50, 4.25s/it] 85%|████████▌ | 15927/18688 [18:51:05<3:29:58, 4.56s/it] 85%|████████▌ | 15928/18688 [18:51:09<3:32:21, 4.62s/it] 85%|████████▌ | 15929/18688 [18:51:14<3:29:02, 4.55s/it] 85%|████████▌ | 15930/18688 [18:51:18<3:25:54, 4.48s/it] {'loss': '0.9522', 'grad_norm': '1.498', 'learning_rate': '1.245e-05', 'epoch': '0.8524', 'num_input_tokens_seen': 53562496, 'train_runtime': '6.788e+04', 'train_tokens_per_second': '789.1'}
85%|████████▌ | 15930/18688 [18:51:18<3:25:54, 4.48s/it] 85%|████████▌ | 15931/18688 [18:51:23<3:34:45, 4.67s/it] 85%|████████▌ | 15932/18688 [18:51:28<3:31:35, 4.61s/it] 85%|████████▌ | 15933/18688 [18:51:33<3:39:30, 4.78s/it] 85%|████████▌ | 15934/18688 [18:51:38<3:37:47, 4.74s/it] 85%|████████▌ | 15935/18688 [18:51:43<3:50:14, 5.02s/it] 85%|████████▌ | 15936/18688 [18:51:47<3:36:54, 4.73s/it] 85%|████████▌ | 15937/18688 [18:51:51<3:24:41, 4.46s/it] 85%|████████▌ | 15938/18688 [18:51:55<3:21:12, 4.39s/it] 85%|████████▌ | 15939/18688 [18:51:59<3:14:58, 4.26s/it] 85%|████████▌ | 15940/18688 [18:52:03<3:07:15, 4.09s/it] {'loss': '0.8783', 'grad_norm': '1.243', 'learning_rate': '1.236e-05', 'epoch': '0.853', 'num_input_tokens_seen': 53601520, 'train_runtime': '6.792e+04', 'train_tokens_per_second': '789.1'}
85%|████████▌ | 15940/18688 [18:52:03<3:07:15, 4.09s/it] 85%|████████▌ | 15941/18688 [18:52:07<3:08:37, 4.12s/it] 85%|████████▌ | 15942/18688 [18:52:11<3:04:01, 4.02s/it] 85%|████████▌ | 15943/18688 [18:52:15<3:07:16, 4.09s/it] 85%|████████▌ | 15944/18688 [18:52:19<3:06:19, 4.07s/it] 85%|████████▌ | 15945/18688 [18:52:24<3:12:44, 4.22s/it] 85%|████████▌ | 15946/18688 [18:52:28<3:07:46, 4.11s/it] 85%|████████▌ | 15947/18688 [18:52:32<3:17:01, 4.31s/it] 85%|████████▌ | 15948/18688 [18:52:37<3:24:11, 4.47s/it] 85%|████████▌ | 15949/18688 [18:52:43<3:47:20, 4.98s/it] 85%|████████▌ | 15950/18688 [18:52:48<3:39:16, 4.81s/it] {'loss': '0.9614', 'grad_norm': '1.385', 'learning_rate': '1.227e-05', 'epoch': '0.8535', 'num_input_tokens_seen': 53638512, 'train_runtime': '6.797e+04', 'train_tokens_per_second': '789.2'}
85%|████████▌ | 15950/18688 [18:52:48<3:39:16, 4.81s/it] 85%|████████▌ | 15951/18688 [18:52:53<3:42:27, 4.88s/it] 85%|████████▌ | 15952/18688 [18:52:57<3:26:33, 4.53s/it] 85%|████████▌ | 15953/18688 [18:53:01<3:23:19, 4.46s/it] 85%|████████▌ | 15954/18688 [18:53:05<3:14:20, 4.27s/it] 85%|████████▌ | 15955/18688 [18:53:08<3:06:27, 4.09s/it] 85%|████████▌ | 15956/18688 [18:53:13<3:10:55, 4.19s/it] 85%|████████▌ | 15957/18688 [18:53:16<3:02:20, 4.01s/it] 85%|████████▌ | 15958/18688 [18:53:20<2:56:38, 3.88s/it] 85%|████████▌ | 15959/18688 [18:53:23<2:50:29, 3.75s/it] 85%|████████▌ | 15960/18688 [18:53:28<3:00:45, 3.98s/it] {'loss': '0.9543', 'grad_norm': '1.728', 'learning_rate': '1.218e-05', 'epoch': '0.854', 'num_input_tokens_seen': 53671096, 'train_runtime': '6.801e+04', 'train_tokens_per_second': '789.2'}
85%|████████▌ | 15960/18688 [18:53:28<3:00:45, 3.98s/it] 85%|████████▌ | 15961/18688 [18:53:32<3:05:13, 4.08s/it] 85%|████████▌ | 15962/18688 [18:53:36<3:00:07, 3.96s/it] 85%|████████▌ | 15963/18688 [18:53:40<3:02:17, 4.01s/it] 85%|████████▌ | 15964/18688 [18:53:45<3:11:06, 4.21s/it] 85%|████████▌ | 15965/18688 [18:53:48<3:04:20, 4.06s/it] 85%|████████▌ | 15966/18688 [18:53:53<3:05:19, 4.09s/it] 85%|████████▌ | 15967/18688 [18:53:58<3:21:49, 4.45s/it] 85%|████████▌ | 15968/18688 [18:54:03<3:24:16, 4.51s/it] 85%|████████▌ | 15969/18688 [18:54:07<3:25:01, 4.52s/it] 85%|████████▌ | 15970/18688 [18:54:11<3:17:35, 4.36s/it] {'loss': '0.9454', 'grad_norm': '1.376', 'learning_rate': '1.21e-05', 'epoch': '0.8546', 'num_input_tokens_seen': 53706720, 'train_runtime': '6.805e+04', 'train_tokens_per_second': '789.2'}
85%|████████▌ | 15970/18688 [18:54:11<3:17:35, 4.36s/it] 85%|████████▌ | 15971/18688 [18:54:16<3:18:13, 4.38s/it] 85%|████████▌ | 15972/18688 [18:54:19<3:07:13, 4.14s/it] 85%|████████▌ | 15973/18688 [18:54:24<3:17:43, 4.37s/it] 85%|████████▌ | 15974/18688 [18:54:28<3:18:44, 4.39s/it] 85%|████████▌ | 15975/18688 [18:54:33<3:14:27, 4.30s/it] 85%|████████▌ | 15976/18688 [18:54:37<3:14:24, 4.30s/it] 85%|████████▌ | 15977/18688 [18:54:42<3:20:13, 4.43s/it] 85%|████████▌ | 15978/18688 [18:54:46<3:20:04, 4.43s/it] 86%|████████▌ | 15979/18688 [18:54:51<3:21:26, 4.46s/it] 86%|████████▌ | 15980/18688 [18:54:54<3:10:14, 4.21s/it] {'loss': '0.9012', 'grad_norm': '1.296', 'learning_rate': '1.201e-05', 'epoch': '0.8551', 'num_input_tokens_seen': 53740784, 'train_runtime': '6.809e+04', 'train_tokens_per_second': '789.2'}
86%|████████▌ | 15980/18688 [18:54:54<3:10:14, 4.21s/it] 86%|████████▌ | 15981/18688 [18:54:58<3:05:44, 4.12s/it] 86%|████████▌ | 15982/18688 [18:55:02<3:00:00, 3.99s/it] 86%|████████▌ | 15983/18688 [18:55:06<3:02:35, 4.05s/it] 86%|████████▌ | 15984/18688 [18:55:11<3:14:43, 4.32s/it] 86%|████████▌ | 15985/18688 [18:55:15<3:07:02, 4.15s/it] 86%|████████▌ | 15986/18688 [18:55:19<3:04:49, 4.10s/it] 86%|████████▌ | 15987/18688 [18:55:22<2:59:57, 4.00s/it] 86%|████████▌ | 15988/18688 [18:55:27<3:05:02, 4.11s/it] 86%|████████▌ | 15989/18688 [18:55:30<2:56:52, 3.93s/it] 86%|████████▌ | 15990/18688 [18:55:35<3:07:27, 4.17s/it] {'loss': '0.9619', 'grad_norm': '1.582', 'learning_rate': '1.192e-05', 'epoch': '0.8557', 'num_input_tokens_seen': 53775136, 'train_runtime': '6.814e+04', 'train_tokens_per_second': '789.2'}
86%|████████▌ | 15990/18688 [18:55:35<3:07:27, 4.17s/it] 86%|████████▌ | 15991/18688 [18:55:40<3:16:18, 4.37s/it] 86%|████████▌ | 15992/18688 [18:55:44<3:18:01, 4.41s/it] 86%|████████▌ | 15993/18688 [18:55:48<3:11:14, 4.26s/it] 86%|████████▌ | 15994/18688 [18:55:52<3:05:33, 4.13s/it] 86%|████████▌ | 15995/18688 [18:55:57<3:12:59, 4.30s/it] 86%|████████▌ | 15996/18688 [18:56:01<3:12:48, 4.30s/it] 86%|████████▌ | 15997/18688 [18:56:05<3:12:57, 4.30s/it] 86%|████████▌ | 15998/18688 [18:56:10<3:17:47, 4.41s/it] 86%|████████▌ | 15999/18688 [18:56:14<3:05:20, 4.14s/it] 86%|████████▌ | 16000/18688 [18:56:18<3:08:48, 4.21s/it] {'loss': '0.921', 'grad_norm': '1.402', 'learning_rate': '1.184e-05', 'epoch': '0.8562', 'num_input_tokens_seen': 53808416, 'train_runtime': '6.818e+04', 'train_tokens_per_second': '789.2'}
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***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
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{'eval_loss': '0.9014', 'eval_runtime': '83.22', 'eval_samples_per_second': '12.02', 'eval_steps_per_second': '12.02', 'epoch': '0.8562', 'num_input_tokens_seen': 53808416}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00020/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 [18:57:46<22:00:26, 29.49s/it] 86%|████████▌ | 16002/18688 [18:57:51<16:22:57, 21.96s/it] 86%|████████▌ | 16003/18688 [18:57:54<12:14:06, 16.40s/it] 86%|████████▌ | 16004/18688 [18:57:58<9:27:50, 12.69s/it] 86%|████████▌ | 16005/18688 [18:58:03<7:44:34, 10.39s/it] 86%|████████▌ | 16006/18688 [18:58:08<6:24:15, 8.60s/it] 86%|████████▌ | 16007/18688 [18:58:12<5:23:10, 7.23s/it] 86%|████████▌ | 16008/18688 [18:58:16<4:49:09, 6.47s/it] 86%|████████▌ | 16009/18688 [18:58:20<4:16:00, 5.73s/it] 86%|████████▌ | 16010/18688 [18:58:24<3:46:33, 5.08s/it] {'loss': '0.9852', 'grad_norm': '2.076', 'learning_rate': '1.175e-05', 'epoch': '0.8567', 'num_input_tokens_seen': 53843200, 'train_runtime': '6.83e+04', 'train_tokens_per_second': '788.3'}
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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': '0.8987', 'eval_runtime': '83.09', 'eval_samples_per_second': '12.04', 'eval_steps_per_second': '12.04', 'epoch': '0.9097', 'num_input_tokens_seen': 57206512}
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96%|█████████▋| 17990/18688 [21:17:18<48:18, 4.15s/it] 96%|█████████▋| 17991/18688 [21:17:21<45:52, 3.95s/it] 96%|█████████▋| 17992/18688 [21:17:25<45:34, 3.93s/it] 96%|█████████▋| 17993/18688 [21:17:29<46:14, 3.99s/it] 96%|█████████▋| 17994/18688 [21:17:33<44:43, 3.87s/it] 96%|█████████▋| 17995/18688 [21:17:37<44:30, 3.85s/it] 96%|█████████▋| 17996/18688 [21:17:43<51:13, 4.44s/it] 96%|█████████▋| 17997/18688 [21:17:47<50:06, 4.35s/it] 96%|█████████▋| 17998/18688 [21:17:53<55:17, 4.81s/it] 96%|█████████▋| 17999/18688 [21:17:57<55:16, 4.81s/it] 96%|█████████▋| 18000/18688 [21:18:01<52:11, 4.55s/it] {'loss': '0.9004', 'grad_norm': '1.84', 'learning_rate': '7.919e-07', 'epoch': '0.9632', 'num_input_tokens_seen': 60569592, 'train_runtime': '7.668e+04', 'train_tokens_per_second': '789.9'}
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***** Running Evaluation *****
Num examples = 1000
Batch size = 1
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{'eval_loss': '0.8975', 'eval_runtime': '83.03', '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_0p00020/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': '0.8977', 'eval_runtime': '82.88', '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_0p00020/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': '7.971e+04', 'train_samples_per_second': '3.751', 'train_steps_per_second': '0.234', 'train_loss': '0.9813', 'epoch': '1', 'num_input_tokens_seen': 62891360}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00020
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 = 0.9813
train_runtime = 22:08:30.32
train_samples_per_second = 3.751
train_steps_per_second = 0.234
Figure saved at: /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00020/training_loss.png
Figure saved at: /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00020/training_eval_loss.png
[WARNING|2026-10-18 18:01:52] 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 = 0.8977
eval_runtime = 0:01:22.97
eval_samples_per_second = 12.052
eval_steps_per_second = 12.052
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'}}