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Diplomovka/aya_300k_lr_experiments/logs/lr_0p00018.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|2026-10-16 21:37:27] 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] 2026-10-16 21:37:28,006 >> 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] 2026-10-16 21:37:28,016 >> 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] 2026-10-16 21:37:29,746 >> 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] 2026-10-16 21:37:29,750 >> 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] 2026-10-16 21:37:29,892 >> 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] 2026-10-16 21:37:29,896 >> 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|2026-10-16 21:37:30] llamafactory.data.loader:144 >> Loading dataset aya_slovak_300k_fixed.jsonl...
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tokenizer on dataset: 100%|██████████| 299000/299000 [01:49<00:00, 2733.72 examples/s]
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>
Running tokenizer on dataset: 0%| | 0/1000 [00:00<?, ? examples/s] Running tokenizer on dataset: 100%|██████████| 1000/1000 [00:00<00:00, 2844.47 examples/s] Running tokenizer on dataset: 100%|██████████| 1000/1000 [00:00<00:00, 2767.69 examples/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] 2026-10-16 21:39:31,231 >> 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] 2026-10-16 21:39:31,234 >> 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|2026-10-16 21:39:31] 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|2026-10-16 21:39:48] llamafactory.model.model_utils.checkpointing:144 >> Gradient checkpointing enabled.
[INFO|2026-10-16 21:39:48] llamafactory.model.adapter:144 >> Upcasting trainable params to float32.
[INFO|2026-10-16 21:39:48] 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|2026-10-16 21:39:51] llamafactory.model.loader:144 >> trainable params: 41,943,040 || all params: 7,283,675,136 || trainable%: 0.5758
The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'pad_token_id': 2}.
==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1
\\ /| Num examples = 299,000 | Num Epochs = 1 | Total steps = 18,688
O^O/ \_/ \ Batch size per device = 1 | Gradient accumulation steps = 16
\ / Data Parallel GPUs = 1 | Total batch size (1 x 16 x 1) = 16
"-____-" Trainable parameters = 41,943,040 of 7,283,675,136 (0.58% trained)
0%| | 0/18688 [00:00<?, ?it/s]`use_return_dict` is deprecated! Use `return_dict` instead!
Unsloth: Double buffering enabled (parallel H2D + compute) for backward pass.
Unsloth: Will smartly offload gradients to save VRAM!
0%| | 1/18688 [00:06<33:01:15, 6.36s/it] 0%| | 2/18688 [00:09<24:30:31, 4.72s/it] 0%| | 3/18688 [00:13<22:38:07, 4.36s/it] 0%| | 4/18688 [00:17<20:49:22, 4.01s/it] 0%| | 5/18688 [00:21<20:38:06, 3.98s/it] 0%| | 6/18688 [00:25<20:50:46, 4.02s/it] 0%| | 7/18688 [00:30<22:59:03, 4.43s/it] 0%| | 8/18688 [00:34<21:37:08, 4.17s/it] 0%| | 9/18688 [00:39<22:56:09, 4.42s/it] 0%| | 10/18688 [00:42<21:16:04, 4.10s/it] {'loss': '2.105', 'grad_norm': '5.808', 'learning_rate': '1.08e-06', 'epoch': '0.0005351', 'num_input_tokens_seen': 35216, 'train_runtime': '42.6', 'train_tokens_per_second': '826.7'}
0%| | 10/18688 [00:42<21:16:04, 4.10s/it] 0%| | 11/18688 [00:46<20:35:39, 3.97s/it] 0%| | 12/18688 [00:51<22:51:41, 4.41s/it] 0%| | 13/18688 [00:55<22:21:54, 4.31s/it] 0%| | 14/18688 [00:59<21:50:46, 4.21s/it] 0%| | 15/18688 [01:04<21:59:56, 4.24s/it] 0%| | 16/18688 [01:07<21:10:42, 4.08s/it] 0%| | 17/18688 [01:11<20:07:25, 3.88s/it] 0%| | 18/18688 [01:14<19:48:03, 3.82s/it] 0%| | 19/18688 [01:18<19:19:35, 3.73s/it] 0%| | 20/18688 [01:21<19:04:04, 3.68s/it] {'loss': '2.029', 'grad_norm': '4.273', 'learning_rate': '2.28e-06', 'epoch': '0.00107', 'num_input_tokens_seen': 66488, 'train_runtime': '81.94', 'train_tokens_per_second': '811.5'}
0%| | 20/18688 [01:21<19:04:04, 3.68s/it] 0%| | 21/18688 [01:26<20:01:58, 3.86s/it] 0%| | 22/18688 [01:30<20:36:12, 3.97s/it] 0%| | 23/18688 [01:34<20:59:59, 4.05s/it] 0%| | 24/18688 [01:38<21:18:06, 4.11s/it] 0%| | 25/18688 [01:43<21:18:50, 4.11s/it] 0%| | 26/18688 [01:47<21:15:24, 4.10s/it] 0%| | 27/18688 [01:51<21:05:34, 4.07s/it] 0%| | 28/18688 [01:54<20:06:53, 3.88s/it] 0%| | 29/18688 [01:58<20:18:51, 3.92s/it] 0%| | 30/18688 [02:02<19:48:40, 3.82s/it] {'loss': '1.881', 'grad_norm': '6.558', 'learning_rate': '3.48e-06', 'epoch': '0.001605', 'num_input_tokens_seen': 97280, 'train_runtime': '122.2', 'train_tokens_per_second': '796.2'}
0%| | 30/18688 [02:02<19:48:40, 3.82s/it] 0%| | 31/18688 [02:06<20:07:36, 3.88s/it] 0%| | 32/18688 [02:09<19:54:47, 3.84s/it] 0%| | 33/18688 [02:13<20:01:42, 3.87s/it] 0%| | 34/18688 [02:17<20:09:51, 3.89s/it] 0%| | 35/18688 [02:21<19:26:25, 3.75s/it] 0%| | 36/18688 [02:24<19:02:43, 3.68s/it] 0%| | 37/18688 [02:28<19:53:51, 3.84s/it] 0%| | 38/18688 [02:33<20:58:44, 4.05s/it] 0%| | 39/18688 [02:37<21:35:34, 4.17s/it] 0%| | 40/18688 [02:41<20:36:05, 3.98s/it] {'loss': '1.781', 'grad_norm': '2.598', 'learning_rate': '4.68e-06', 'epoch': '0.00214', 'num_input_tokens_seen': 129200, 'train_runtime': '161.5', 'train_tokens_per_second': '800.1'}
0%| | 40/18688 [02:41<20:36:05, 3.98s/it] 0%| | 41/18688 [02:45<20:05:28, 3.88s/it] 0%| | 42/18688 [02:48<19:44:53, 3.81s/it] 0%| | 43/18688 [02:52<19:31:29, 3.77s/it] 0%| | 44/18688 [02:57<20:46:21, 4.01s/it] 0%| | 45/18688 [03:01<21:17:02, 4.11s/it] 0%| | 46/18688 [03:05<21:44:32, 4.20s/it] 0%| | 47/18688 [03:09<20:35:17, 3.98s/it] 0%| | 48/18688 [03:12<20:02:28, 3.87s/it] 0%| | 49/18688 [03:16<20:14:14, 3.91s/it] 0%| | 50/18688 [03:22<22:09:27, 4.28s/it] {'loss': '1.605', 'grad_norm': '1.97', 'learning_rate': '5.88e-06', 'epoch': '0.002676', 'num_input_tokens_seen': 163208, 'train_runtime': '202', 'train_tokens_per_second': '807.9'}
0%| | 50/18688 [03:22<22:09:27, 4.28s/it] 0%| | 51/18688 [03:25<20:48:34, 4.02s/it] 0%| | 52/18688 [03:30<22:05:45, 4.27s/it] 0%| | 53/18688 [03:34<22:00:12, 4.25s/it] 0%| | 54/18688 [03:38<20:53:35, 4.04s/it] 0%| | 55/18688 [03:42<20:57:49, 4.05s/it] 0%| | 56/18688 [03:46<21:09:41, 4.09s/it] 0%| | 57/18688 [03:50<20:39:30, 3.99s/it] 0%| | 58/18688 [03:53<19:48:19, 3.83s/it] 0%| | 59/18688 [03:57<20:27:36, 3.95s/it] 0%| | 60/18688 [04:02<21:26:16, 4.14s/it] {'loss': '1.469', 'grad_norm': '1.632', 'learning_rate': '7.08e-06', 'epoch': '0.003211', 'num_input_tokens_seen': 197328, 'train_runtime': '242.3', 'train_tokens_per_second': '814.3'}
0%| | 60/18688 [04:02<21:26:16, 4.14s/it] 0%| | 61/18688 [04:06<21:40:14, 4.19s/it] 0%| | 62/18688 [04:10<21:38:33, 4.18s/it] 0%| | 63/18688 [04:16<23:29:00, 4.54s/it] 0%| | 64/18688 [04:19<22:00:04, 4.25s/it] 0%| | 65/18688 [04:24<22:56:36, 4.44s/it] 0%| | 66/18688 [04:28<21:27:19, 4.15s/it] 0%| | 67/18688 [04:31<20:36:19, 3.98s/it] 0%| | 68/18688 [04:36<21:36:33, 4.18s/it] 0%| | 69/18688 [04:40<21:41:35, 4.19s/it] 0%| | 70/18688 [04:44<21:36:47, 4.18s/it] {'loss': '1.357', 'grad_norm': '1.648', 'learning_rate': '8.28e-06', 'epoch': '0.003746', 'num_input_tokens_seen': 234064, 'train_runtime': '284.7', 'train_tokens_per_second': '822.2'}
0%| | 70/18688 [04:44<21:36:47, 4.18s/it] 0%| | 71/18688 [04:48<21:48:56, 4.22s/it] 0%| | 72/18688 [04:52<21:22:09, 4.13s/it] 0%| | 73/18688 [04:56<20:27:02, 3.95s/it] 0%| | 74/18688 [05:00<20:39:34, 4.00s/it] 0%| | 75/18688 [05:04<20:42:20, 4.00s/it] 0%| | 76/18688 [05:08<20:30:33, 3.97s/it] 0%| | 77/18688 [05:12<21:08:02, 4.09s/it] 0%| | 78/18688 [05:16<20:08:23, 3.90s/it] 0%| | 79/18688 [05:21<21:39:53, 4.19s/it] 0%| | 80/18688 [05:25<21:27:25, 4.15s/it] {'loss': '1.284', 'grad_norm': '2.319', 'learning_rate': '9.48e-06', 'epoch': '0.004281', 'num_input_tokens_seen': 266768, 'train_runtime': '325.2', 'train_tokens_per_second': '820.3'}
0%| | 80/18688 [05:25<21:27:25, 4.15s/it] 0%| | 81/18688 [05:29<21:58:09, 4.25s/it] 0%| | 82/18688 [05:33<21:38:26, 4.19s/it] 0%| | 83/18688 [05:37<21:04:53, 4.08s/it] 0%| | 84/18688 [05:41<21:15:56, 4.12s/it] 0%| | 85/18688 [05:45<21:04:26, 4.08s/it] 0%| | 86/18688 [05:50<21:33:17, 4.17s/it] 0%| | 87/18688 [05:53<20:24:29, 3.95s/it] 0%| | 88/18688 [05:58<21:31:20, 4.17s/it] 0%| | 89/18688 [06:01<20:45:49, 4.02s/it] 0%| | 90/18688 [06:06<20:52:14, 4.04s/it] {'loss': '1.166', 'grad_norm': '2.128', 'learning_rate': '1.068e-05', 'epoch': '0.004816', 'num_input_tokens_seen': 299720, 'train_runtime': '366', 'train_tokens_per_second': '818.9'}
0%| | 90/18688 [06:06<20:52:14, 4.04s/it] 0%| | 91/18688 [06:10<22:02:19, 4.27s/it] 0%| | 92/18688 [06:14<21:54:45, 4.24s/it] 0%| | 93/18688 [06:18<21:27:52, 4.16s/it] 1%| | 94/18688 [06:23<22:13:30, 4.30s/it] 1%| | 95/18688 [06:28<22:36:43, 4.38s/it] 1%| | 96/18688 [06:32<23:02:57, 4.46s/it] 1%| | 97/18688 [06:36<21:58:40, 4.26s/it] 1%| | 98/18688 [06:40<21:11:47, 4.10s/it] 1%| | 99/18688 [06:44<21:24:12, 4.15s/it] 1%| | 100/18688 [06:48<21:01:09, 4.07s/it] {'loss': '1.158', 'grad_norm': '2.071', 'learning_rate': '1.188e-05', 'epoch': '0.005351', 'num_input_tokens_seen': 333368, 'train_runtime': '408.5', 'train_tokens_per_second': '816.1'}
1%| | 100/18688 [06:48<21:01:09, 4.07s/it] 1%| | 101/18688 [06:53<22:56:28, 4.44s/it] 1%| | 102/18688 [06:58<23:01:28, 4.46s/it] 1%| | 103/18688 [07:01<21:32:06, 4.17s/it] 1%| | 104/18688 [07:06<21:41:14, 4.20s/it] 1%| | 105/18688 [07:10<21:44:03, 4.21s/it] 1%| | 106/18688 [07:14<22:26:27, 4.35s/it] 1%| | 107/18688 [07:18<21:32:18, 4.17s/it] 1%| | 108/18688 [07:22<21:26:42, 4.16s/it] 1%| | 109/18688 [07:26<21:28:22, 4.16s/it] 1%| | 110/18688 [07:30<20:36:22, 3.99s/it] {'loss': '1.168', 'grad_norm': '1.658', 'learning_rate': '1.308e-05', 'epoch': '0.005886', 'num_input_tokens_seen': 370256, 'train_runtime': '450.6', 'train_tokens_per_second': '821.7'}
1%| | 110/18688 [07:30<20:36:22, 3.99s/it] 1%| | 111/18688 [07:34<20:35:26, 3.99s/it] 1%| | 112/18688 [07:38<20:00:54, 3.88s/it] 1%| | 113/18688 [07:41<19:48:57, 3.84s/it] 1%| | 114/18688 [07:46<21:08:54, 4.10s/it] 1%| | 115/18688 [07:50<20:57:13, 4.06s/it] 1%| | 116/18688 [07:54<19:55:16, 3.86s/it] 1%| | 117/18688 [07:57<19:36:05, 3.80s/it] 1%| | 118/18688 [08:01<20:15:36, 3.93s/it] 1%| | 119/18688 [08:06<21:29:38, 4.17s/it] 1%| | 120/18688 [08:10<20:52:09, 4.05s/it] {'loss': '1.062', 'grad_norm': '1.806', 'learning_rate': '1.428e-05', 'epoch': '0.006421', 'num_input_tokens_seen': 402568, 'train_runtime': '490.4', 'train_tokens_per_second': '820.9'}
1%| | 120/18688 [08:10<20:52:09, 4.05s/it] 1%| | 121/18688 [08:14<21:39:27, 4.20s/it] 1%| | 122/18688 [08:18<20:27:59, 3.97s/it] 1%| | 123/18688 [08:22<20:05:47, 3.90s/it] 1%| | 124/18688 [08:26<20:17:01, 3.93s/it] 1%| | 125/18688 [08:30<21:24:02, 4.15s/it] 1%| | 126/18688 [08:34<20:50:53, 4.04s/it] 1%| | 127/18688 [08:38<21:12:42, 4.11s/it] 1%| | 128/18688 [08:42<21:07:04, 4.10s/it] 1%| | 129/18688 [08:47<22:02:07, 4.27s/it] 1%| | 130/18688 [08:53<23:51:15, 4.63s/it] {'loss': '1.101', 'grad_norm': '1.455', 'learning_rate': '1.548e-05', 'epoch': '0.006957', 'num_input_tokens_seen': 434592, 'train_runtime': '533.1', 'train_tokens_per_second': '815.3'}
1%| | 130/18688 [08:53<23:51:15, 4.63s/it] 1%| | 131/18688 [08:57<23:09:38, 4.49s/it] 1%| | 132/18688 [09:02<24:55:52, 4.84s/it] 1%| | 133/18688 [09:06<23:48:38, 4.62s/it] 1%| | 134/18688 [09:10<22:25:01, 4.35s/it] 1%| | 135/18688 [09:14<21:24:19, 4.15s/it] 1%| | 136/18688 [09:20<23:46:21, 4.61s/it] 1%| | 137/18688 [09:23<22:17:18, 4.33s/it] 1%| | 138/18688 [09:27<21:53:31, 4.25s/it] 1%| | 139/18688 [09:31<21:15:33, 4.13s/it] 1%| | 140/18688 [09:35<21:14:11, 4.12s/it] {'loss': '1.064', 'grad_norm': '1.484', 'learning_rate': '1.668e-05', 'epoch': '0.007492', 'num_input_tokens_seen': 471752, 'train_runtime': '575.8', 'train_tokens_per_second': '819.3'}
1%| | 140/18688 [09:35<21:14:11, 4.12s/it] 1%| | 141/18688 [09:39<20:19:15, 3.94s/it] 1%| | 142/18688 [09:43<20:44:48, 4.03s/it] 1%| | 143/18688 [09:47<20:18:22, 3.94s/it] 1%| | 144/18688 [09:50<19:33:35, 3.80s/it] 1%| | 145/18688 [09:54<19:12:34, 3.73s/it] 1%| | 146/18688 [09:58<20:16:47, 3.94s/it] 1%| | 147/18688 [10:03<20:59:03, 4.07s/it] 1%| | 148/18688 [10:06<20:21:40, 3.95s/it] 1%| | 149/18688 [10:10<19:34:33, 3.80s/it] 1%| | 150/18688 [10:14<19:56:28, 3.87s/it] {'loss': '1.069', 'grad_norm': '1.631', 'learning_rate': '1.788e-05', 'epoch': '0.008027', 'num_input_tokens_seen': 501528, 'train_runtime': '614.3', 'train_tokens_per_second': '816.5'}
1%| | 150/18688 [10:14<19:56:28, 3.87s/it] 1%| | 151/18688 [10:18<20:33:16, 3.99s/it] 1%| | 152/18688 [10:22<19:56:04, 3.87s/it] 1%| | 153/18688 [10:25<19:21:17, 3.76s/it] 1%| | 154/18688 [10:29<19:18:47, 3.75s/it] 1%| | 155/18688 [10:33<19:15:05, 3.74s/it] 1%| | 156/18688 [10:37<19:48:23, 3.85s/it] 1%| | 157/18688 [10:40<19:35:00, 3.80s/it] 1%| | 158/18688 [10:44<19:26:06, 3.78s/it] 1%| | 159/18688 [10:49<20:50:33, 4.05s/it] 1%| | 160/18688 [10:52<20:00:22, 3.89s/it] {'loss': '1.031', 'grad_norm': '1.519', 'learning_rate': '1.908e-05', 'epoch': '0.008562', 'num_input_tokens_seen': 532056, 'train_runtime': '652.8', 'train_tokens_per_second': '815.1'}
1%| | 160/18688 [10:52<20:00:22, 3.89s/it] 1%| | 161/18688 [10:57<20:39:59, 4.02s/it] 1%| | 162/18688 [11:00<19:59:59, 3.89s/it] 1%| | 163/18688 [11:05<20:48:07, 4.04s/it] 1%| | 164/18688 [11:08<20:27:10, 3.97s/it] 1%| | 165/18688 [11:13<21:59:28, 4.27s/it] 1%| | 166/18688 [11:17<20:56:31, 4.07s/it] 1%| | 167/18688 [11:20<20:04:06, 3.90s/it] 1%| | 168/18688 [11:24<20:05:30, 3.91s/it] 1%| | 169/18688 [11:28<19:34:54, 3.81s/it] 1%| | 170/18688 [11:32<19:16:34, 3.75s/it] {'loss': '1.072', 'grad_norm': '1.641', 'learning_rate': '2.028e-05', 'epoch': '0.009097', 'num_input_tokens_seen': 564304, 'train_runtime': '692.1', 'train_tokens_per_second': '815.4'}
1%| | 170/18688 [11:32<19:16:34, 3.75s/it] 1%| | 171/18688 [11:35<19:25:13, 3.78s/it] 1%| | 172/18688 [11:39<19:38:59, 3.82s/it] 1%| | 173/18688 [11:44<20:32:05, 3.99s/it] 1%| | 174/18688 [11:47<19:38:36, 3.82s/it] 1%| | 175/18688 [11:51<20:27:46, 3.98s/it] 1%| | 176/18688 [11:56<21:01:35, 4.09s/it] 1%| | 177/18688 [11:59<20:15:25, 3.94s/it] 1%| | 178/18688 [12:03<20:24:16, 3.97s/it] 1%| | 179/18688 [12:07<19:55:26, 3.88s/it] 1%| | 180/18688 [12:11<20:03:21, 3.90s/it] {'loss': '1.119', 'grad_norm': '1.585', 'learning_rate': '2.148e-05', 'epoch': '0.009632', 'num_input_tokens_seen': 595424, 'train_runtime': '731.6', 'train_tokens_per_second': '813.9'}
1%| | 180/18688 [12:11<20:03:21, 3.90s/it] 1%| | 181/18688 [12:15<19:29:16, 3.79s/it] 1%| | 182/18688 [12:18<19:07:44, 3.72s/it] 1%| | 183/18688 [12:23<20:44:32, 4.04s/it] 1%| | 184/18688 [12:27<20:35:02, 4.00s/it] 1%| | 185/18688 [12:31<20:58:32, 4.08s/it] 1%| | 186/18688 [12:35<21:21:15, 4.15s/it] 1%| | 187/18688 [12:40<21:09:46, 4.12s/it] 1%| | 188/18688 [12:43<20:51:49, 4.06s/it] 1%| | 189/18688 [12:47<20:24:21, 3.97s/it] 1%| | 190/18688 [12:52<21:06:55, 4.11s/it] {'loss': '1.042', 'grad_norm': '1.754', 'learning_rate': '2.268e-05', 'epoch': '0.01017', 'num_input_tokens_seen': 628248, 'train_runtime': '772.1', 'train_tokens_per_second': '813.7'}
1%| | 190/18688 [12:52<21:06:55, 4.11s/it] 1%| | 191/18688 [12:56<21:47:08, 4.24s/it] 1%| | 192/18688 [13:00<21:24:06, 4.17s/it] 1%| | 193/18688 [13:04<20:46:13, 4.04s/it] 1%| | 194/18688 [13:08<20:26:57, 3.98s/it] 1%| | 195/18688 [13:12<20:54:40, 4.07s/it] 1%| | 196/18688 [13:16<21:25:19, 4.17s/it] 1%| | 197/18688 [13:20<21:10:42, 4.12s/it] 1%| | 198/18688 [13:24<20:40:35, 4.03s/it] 1%| | 199/18688 [13:28<20:44:52, 4.04s/it] 1%| | 200/18688 [13:33<21:20:40, 4.16s/it] {'loss': '1.054', 'grad_norm': '1.431', 'learning_rate': '2.388e-05', 'epoch': '0.0107', 'num_input_tokens_seen': 661320, 'train_runtime': '813.3', 'train_tokens_per_second': '813.2'}
1%| | 200/18688 [13:33<21:20:40, 4.16s/it] 1%| | 201/18688 [13:37<20:43:21, 4.04s/it] 1%| | 202/18688 [13:40<20:00:12, 3.90s/it] 1%| | 203/18688 [13:45<21:02:55, 4.10s/it] 1%| | 204/18688 [13:49<21:08:23, 4.12s/it] 1%| | 205/18688 [13:53<21:21:22, 4.16s/it] 1%| | 206/18688 [13:57<21:38:09, 4.21s/it] 1%| | 207/18688 [14:02<21:53:28, 4.26s/it] 1%| | 208/18688 [14:06<21:09:46, 4.12s/it] 1%| | 209/18688 [14:10<22:18:35, 4.35s/it] 1%| | 210/18688 [14:14<21:07:06, 4.11s/it] {'loss': '1.162', 'grad_norm': '2.086', 'learning_rate': '2.508e-05', 'epoch': '0.01124', 'num_input_tokens_seen': 696656, 'train_runtime': '854.5', 'train_tokens_per_second': '815.3'}
1%| | 210/18688 [14:14<21:07:06, 4.11s/it] 1%| | 211/18688 [14:17<20:07:09, 3.92s/it] 1%| | 212/18688 [14:22<20:43:31, 4.04s/it] 1%| | 213/18688 [14:25<20:00:07, 3.90s/it] 1%| | 214/18688 [14:29<20:09:30, 3.93s/it] 1%| | 215/18688 [14:33<19:47:44, 3.86s/it] 1%| | 216/18688 [14:37<19:55:11, 3.88s/it] 1%| | 217/18688 [14:41<19:26:39, 3.79s/it] 1%| | 218/18688 [14:44<19:34:41, 3.82s/it] 1%| | 219/18688 [14:48<19:51:20, 3.87s/it] 1%| | 220/18688 [14:53<21:07:49, 4.12s/it] {'loss': '1.077', 'grad_norm': '0.9608', 'learning_rate': '2.628e-05', 'epoch': '0.01177', 'num_input_tokens_seen': 727368, 'train_runtime': '893.7', 'train_tokens_per_second': '813.9'}
1%| | 220/18688 [14:53<21:07:49, 4.12s/it] 1%| | 221/18688 [14:57<21:03:09, 4.10s/it] 1%| | 222/18688 [15:01<21:04:51, 4.11s/it] 1%| | 223/18688 [15:05<21:03:00, 4.10s/it] 1%| | 224/18688 [15:09<20:46:49, 4.05s/it] 1%| | 225/18688 [15:14<21:19:53, 4.16s/it] 1%| | 226/18688 [15:18<21:06:05, 4.11s/it] 1%| | 227/18688 [15:22<21:36:41, 4.21s/it] 1%| | 228/18688 [15:27<23:09:59, 4.52s/it] 1%| | 229/18688 [15:32<22:37:57, 4.41s/it] 1%| | 230/18688 [15:36<22:13:00, 4.33s/it] {'loss': '1.06', 'grad_norm': '2.199', 'learning_rate': '2.748e-05', 'epoch': '0.01231', 'num_input_tokens_seen': 761544, 'train_runtime': '936.3', 'train_tokens_per_second': '813.4'}
1%| | 230/18688 [15:36<22:13:00, 4.33s/it] 1%| | 231/18688 [15:40<22:26:49, 4.38s/it] 1%| | 232/18688 [15:45<23:42:12, 4.62s/it] 1%| | 233/18688 [15:50<23:33:01, 4.59s/it] 1%|▏ | 234/18688 [15:55<23:34:29, 4.60s/it] 1%|▏ | 235/18688 [15:58<22:09:42, 4.32s/it] 1%|▏ | 236/18688 [16:04<23:51:42, 4.66s/it] 1%|▏ | 237/18688 [16:07<22:08:41, 4.32s/it] 1%|▏ | 238/18688 [16:11<21:51:42, 4.27s/it] 1%|▏ | 239/18688 [16:16<21:46:50, 4.25s/it] 1%|▏ | 240/18688 [16:19<21:14:12, 4.14s/it] {'loss': '1.072', 'grad_norm': '1.699', 'learning_rate': '2.868e-05', 'epoch': '0.01284', 'num_input_tokens_seen': 797960, 'train_runtime': '980', 'train_tokens_per_second': '814.3'}
1%|▏ | 240/18688 [16:19<21:14:12, 4.14s/it] 1%|▏ | 241/18688 [16:24<22:04:40, 4.31s/it] 1%|▏ | 242/18688 [16:29<22:15:31, 4.34s/it] 1%|▏ | 243/18688 [16:33<21:43:14, 4.24s/it] 1%|▏ | 244/18688 [16:36<20:52:07, 4.07s/it] 1%|▏ | 245/18688 [16:40<20:18:45, 3.96s/it] 1%|▏ | 246/18688 [16:44<20:15:39, 3.96s/it] 1%|▏ | 247/18688 [16:48<19:43:59, 3.85s/it] 1%|▏ | 248/18688 [16:51<19:32:56, 3.82s/it] 1%|▏ | 249/18688 [16:56<20:21:36, 3.98s/it] 1%|▏ | 250/18688 [17:00<20:24:07, 3.98s/it] {'loss': '1.075', 'grad_norm': '1.322', 'learning_rate': '2.988e-05', 'epoch': '0.01338', 'num_input_tokens_seen': 831064, 'train_runtime': '1020', 'train_tokens_per_second': '814.7'}
1%|▏ | 250/18688 [17:00<20:24:07, 3.98s/it] 1%|▏ | 251/18688 [17:04<20:27:05, 3.99s/it] 1%|▏ | 252/18688 [17:07<19:56:07, 3.89s/it] 1%|▏ | 253/18688 [17:11<19:25:19, 3.79s/it] 1%|▏ | 254/18688 [17:15<19:21:07, 3.78s/it] 1%|▏ | 255/18688 [17:18<19:05:34, 3.73s/it] 1%|▏ | 256/18688 [17:22<19:40:35, 3.84s/it] 1%|▏ | 257/18688 [17:27<20:32:33, 4.01s/it] 1%|▏ | 258/18688 [17:31<20:50:20, 4.07s/it] 1%|▏ | 259/18688 [17:35<20:43:40, 4.05s/it] 1%|▏ | 260/18688 [17:40<21:41:54, 4.24s/it] {'loss': '1.011', 'grad_norm': '1.65', 'learning_rate': '3.108e-05', 'epoch': '0.01391', 'num_input_tokens_seen': 862936, 'train_runtime': '1060', 'train_tokens_per_second': '814'}
1%|▏ | 260/18688 [17:40<21:41:54, 4.24s/it] 1%|▏ | 261/18688 [17:44<21:55:05, 4.28s/it] 1%|▏ | 262/18688 [17:48<21:45:20, 4.25s/it] 1%|▏ | 263/18688 [17:52<21:46:23, 4.25s/it] 1%|▏ | 264/18688 [17:56<21:00:58, 4.11s/it] 1%|▏ | 265/18688 [18:00<20:06:25, 3.93s/it] 1%|▏ | 266/18688 [18:03<19:18:10, 3.77s/it] 1%|▏ | 267/18688 [18:08<20:34:32, 4.02s/it] 1%|▏ | 268/18688 [18:11<19:47:42, 3.87s/it] 1%|▏ | 269/18688 [18:15<19:16:36, 3.77s/it] 1%|▏ | 270/18688 [18:18<18:49:27, 3.68s/it] {'loss': '1.009', 'grad_norm': '1.409', 'learning_rate': '3.228e-05', 'epoch': '0.01445', 'num_input_tokens_seen': 893208, 'train_runtime': '1099', 'train_tokens_per_second': '812.9'}
1%|▏ | 270/18688 [18:18<18:49:27, 3.68s/it] 1%|▏ | 271/18688 [18:22<18:48:47, 3.68s/it] 1%|▏ | 272/18688 [18:26<19:08:16, 3.74s/it] 1%|▏ | 273/18688 [18:30<19:47:37, 3.87s/it] 1%|▏ | 274/18688 [18:34<20:27:21, 4.00s/it] 1%|▏ | 275/18688 [18:38<20:00:00, 3.91s/it] 1%|▏ | 276/18688 [18:42<19:51:00, 3.88s/it] 1%|▏ | 277/18688 [18:45<19:31:05, 3.82s/it] 1%|▏ | 278/18688 [18:49<19:23:38, 3.79s/it] 1%|▏ | 279/18688 [18:54<21:30:12, 4.21s/it] 1%|▏ | 280/18688 [18:59<21:43:24, 4.25s/it] {'loss': '1.085', 'grad_norm': '1.447', 'learning_rate': '3.348e-05', 'epoch': '0.01498', 'num_input_tokens_seen': 925664, 'train_runtime': '1139', 'train_tokens_per_second': '812.5'}
1%|▏ | 280/18688 [18:59<21:43:24, 4.25s/it] 2%|▏ | 281/18688 [19:03<21:05:22, 4.12s/it] 2%|▏ | 282/18688 [19:08<22:33:05, 4.41s/it] 2%|▏ | 283/18688 [19:13<23:38:16, 4.62s/it] 2%|▏ | 284/18688 [19:17<23:31:15, 4.60s/it] 2%|▏ | 285/18688 [19:21<22:24:03, 4.38s/it] 2%|▏ | 286/18688 [19:26<23:10:35, 4.53s/it] 2%|▏ | 287/18688 [19:30<22:14:06, 4.35s/it] 2%|▏ | 288/18688 [19:34<22:23:51, 4.38s/it] 2%|▏ | 289/18688 [19:38<21:44:38, 4.25s/it] 2%|▏ | 290/18688 [19:42<21:27:29, 4.20s/it] {'loss': '1.073', 'grad_norm': '1.402', 'learning_rate': '3.468e-05', 'epoch': '0.01552', 'num_input_tokens_seen': 960824, 'train_runtime': '1183', 'train_tokens_per_second': '812.2'}
2%|▏ | 290/18688 [19:42<21:27:29, 4.20s/it] 2%|▏ | 291/18688 [19:47<21:33:41, 4.22s/it] 2%|▏ | 292/18688 [19:51<21:04:42, 4.12s/it] 2%|▏ | 293/18688 [19:55<20:59:50, 4.11s/it] 2%|▏ | 294/18688 [19:59<20:55:26, 4.10s/it] 2%|▏ | 295/18688 [20:03<20:59:46, 4.11s/it] 2%|▏ | 296/18688 [20:08<21:45:13, 4.26s/it] 2%|▏ | 297/18688 [20:12<21:39:17, 4.24s/it] 2%|▏ | 298/18688 [20:16<22:06:24, 4.33s/it] 2%|▏ | 299/18688 [20:21<22:08:15, 4.33s/it] 2%|▏ | 300/18688 [20:25<21:39:54, 4.24s/it] {'loss': '1.054', 'grad_norm': '1.4', 'learning_rate': '3.588e-05', 'epoch': '0.01605', 'num_input_tokens_seen': 993184, 'train_runtime': '1225', 'train_tokens_per_second': '810.7'}
2%|▏ | 300/18688 [20:25<21:39:54, 4.24s/it] 2%|▏ | 301/18688 [20:28<20:37:05, 4.04s/it] 2%|▏ | 302/18688 [20:33<21:42:46, 4.25s/it] 2%|▏ | 303/18688 [20:37<21:42:20, 4.25s/it] 2%|▏ | 304/18688 [20:41<20:53:36, 4.09s/it] 2%|▏ | 305/18688 [20:44<19:51:21, 3.89s/it] 2%|▏ | 306/18688 [20:48<19:28:27, 3.81s/it] 2%|▏ | 307/18688 [20:51<18:59:22, 3.72s/it] 2%|▏ | 308/18688 [20:55<19:22:39, 3.80s/it] 2%|▏ | 309/18688 [20:59<19:44:08, 3.87s/it] 2%|▏ | 310/18688 [21:03<19:30:49, 3.82s/it] {'loss': '1.054', 'grad_norm': '1.172', 'learning_rate': '3.708e-05', 'epoch': '0.01659', 'num_input_tokens_seen': 1022656, 'train_runtime': '1264', 'train_tokens_per_second': '809.3'}
2%|▏ | 310/18688 [21:03<19:30:49, 3.82s/it] 2%|▏ | 311/18688 [21:07<19:06:54, 3.74s/it] 2%|▏ | 312/18688 [21:11<19:08:38, 3.75s/it] 2%|▏ | 313/18688 [21:15<19:49:38, 3.88s/it] 2%|▏ | 314/18688 [21:18<19:34:18, 3.83s/it] 2%|▏ | 315/18688 [21:22<19:30:13, 3.82s/it] 2%|▏ | 316/18688 [21:26<19:50:41, 3.89s/it] 2%|▏ | 317/18688 [21:30<20:12:22, 3.96s/it] 2%|▏ | 318/18688 [21:34<19:36:08, 3.84s/it] 2%|▏ | 319/18688 [21:38<19:10:55, 3.76s/it] 2%|▏ | 320/18688 [21:42<20:07:43, 3.95s/it] {'loss': '1.023', 'grad_norm': '1.312', 'learning_rate': '3.828e-05', 'epoch': '0.01712', 'num_input_tokens_seen': 1053904, 'train_runtime': '1302', 'train_tokens_per_second': '809.2'}
2%|▏ | 320/18688 [21:42<20:07:43, 3.95s/it] 2%|▏ | 321/18688 [21:46<20:22:48, 3.99s/it] 2%|▏ | 322/18688 [21:50<20:17:00, 3.98s/it] 2%|▏ | 323/18688 [21:56<22:44:22, 4.46s/it] 2%|▏ | 324/18688 [21:59<21:31:57, 4.22s/it] 2%|▏ | 325/18688 [22:03<20:28:02, 4.01s/it] 2%|▏ | 326/18688 [22:06<19:57:38, 3.91s/it] 2%|▏ | 327/18688 [22:12<21:48:16, 4.28s/it] 2%|▏ | 328/18688 [22:16<22:02:34, 4.32s/it] 2%|▏ | 329/18688 [22:21<23:00:32, 4.51s/it] 2%|▏ | 330/18688 [22:25<21:53:46, 4.29s/it] {'loss': '1.075', 'grad_norm': '1.264', 'learning_rate': '3.948e-05', 'epoch': '0.01766', 'num_input_tokens_seen': 1090264, 'train_runtime': '1345', 'train_tokens_per_second': '810.5'}
2%|▏ | 330/18688 [22:25<21:53:46, 4.29s/it] 2%|▏ | 331/18688 [22:29<21:38:21, 4.24s/it] 2%|▏ | 332/18688 [22:33<21:13:59, 4.16s/it] 2%|▏ | 333/18688 [22:36<20:29:13, 4.02s/it] 2%|▏ | 334/18688 [22:40<19:41:36, 3.86s/it] 2%|▏ | 335/18688 [22:44<19:31:01, 3.83s/it] 2%|▏ | 336/18688 [22:47<19:06:03, 3.75s/it] 2%|▏ | 337/18688 [22:51<19:29:08, 3.82s/it] 2%|▏ | 338/18688 [22:56<20:35:26, 4.04s/it] 2%|▏ | 339/18688 [23:00<21:16:05, 4.17s/it] 2%|▏ | 340/18688 [23:06<22:55:22, 4.50s/it] {'loss': '0.9713', 'grad_norm': '1.55', 'learning_rate': '4.068e-05', 'epoch': '0.01819', 'num_input_tokens_seen': 1121696, 'train_runtime': '1386', 'train_tokens_per_second': '809.3'}
2%|▏ | 340/18688 [23:06<22:55:22, 4.50s/it] 2%|▏ | 341/18688 [23:09<22:03:08, 4.33s/it] 2%|▏ | 342/18688 [23:14<21:46:08, 4.27s/it] 2%|▏ | 343/18688 [23:18<21:57:03, 4.31s/it] 2%|▏ | 344/18688 [23:22<21:36:41, 4.24s/it] 2%|▏ | 345/18688 [23:26<21:37:18, 4.24s/it] 2%|▏ | 346/18688 [23:31<22:07:27, 4.34s/it] 2%|▏ | 347/18688 [23:36<22:55:29, 4.50s/it] 2%|▏ | 348/18688 [23:40<22:30:49, 4.42s/it] 2%|▏ | 349/18688 [23:45<23:03:26, 4.53s/it] 2%|▏ | 350/18688 [23:49<22:08:34, 4.35s/it] {'loss': '1.061', 'grad_norm': '1.414', 'learning_rate': '4.188e-05', 'epoch': '0.01873', 'num_input_tokens_seen': 1153216, 'train_runtime': '1429', 'train_tokens_per_second': '806.9'}
2%|▏ | 350/18688 [23:49<22:08:34, 4.35s/it] 2%|▏ | 351/18688 [23:53<21:56:06, 4.31s/it] 2%|▏ | 352/18688 [23:57<22:14:15, 4.37s/it] 2%|▏ | 353/18688 [24:02<22:06:50, 4.34s/it] 2%|▏ | 354/18688 [24:06<21:42:07, 4.26s/it] 2%|▏ | 355/18688 [24:09<20:44:58, 4.07s/it] 2%|▏ | 356/18688 [24:15<23:35:16, 4.63s/it] 2%|▏ | 357/18688 [24:20<23:09:48, 4.55s/it] 2%|▏ | 358/18688 [24:24<22:52:26, 4.49s/it] 2%|▏ | 359/18688 [24:28<22:18:06, 4.38s/it] 2%|▏ | 360/18688 [24:32<21:13:59, 4.17s/it] {'loss': '1.046', 'grad_norm': '1.186', 'learning_rate': '4.308e-05', 'epoch': '0.01926', 'num_input_tokens_seen': 1188456, 'train_runtime': '1472', 'train_tokens_per_second': '807.2'}
2%|▏ | 360/18688 [24:32<21:13:59, 4.17s/it] 2%|▏ | 361/18688 [24:35<20:08:10, 3.96s/it] 2%|▏ | 362/18688 [24:40<20:52:21, 4.10s/it] 2%|▏ | 363/18688 [24:44<20:52:36, 4.10s/it] 2%|▏ | 364/18688 [24:48<21:38:08, 4.25s/it] 2%|▏ | 365/18688 [24:53<22:08:20, 4.35s/it] 2%|▏ | 366/18688 [24:57<20:52:47, 4.10s/it] 2%|▏ | 367/18688 [25:01<22:04:32, 4.34s/it] 2%|▏ | 368/18688 [25:05<21:31:15, 4.23s/it] 2%|▏ | 369/18688 [25:09<20:34:50, 4.04s/it] 2%|▏ | 370/18688 [25:14<21:24:36, 4.21s/it] {'loss': '1.074', 'grad_norm': '1.3', 'learning_rate': '4.428e-05', 'epoch': '0.0198', 'num_input_tokens_seen': 1222640, 'train_runtime': '1514', 'train_tokens_per_second': '807.5'}
2%|▏ | 370/18688 [25:14<21:24:36, 4.21s/it] 2%|▏ | 371/18688 [25:17<20:31:47, 4.03s/it] 2%|▏ | 372/18688 [25:21<20:01:38, 3.94s/it] 2%|▏ | 373/18688 [25:25<19:47:18, 3.89s/it] 2%|▏ | 374/18688 [25:29<20:46:39, 4.08s/it] 2%|▏ | 375/18688 [25:33<20:42:05, 4.07s/it] 2%|▏ | 376/18688 [25:37<20:20:44, 4.00s/it] 2%|▏ | 377/18688 [25:41<20:37:15, 4.05s/it] 2%|▏ | 378/18688 [25:45<20:27:34, 4.02s/it] 2%|▏ | 379/18688 [25:49<19:42:26, 3.87s/it] 2%|▏ | 380/18688 [25:53<20:01:03, 3.94s/it] {'loss': '1.082', 'grad_norm': '1.401', 'learning_rate': '4.548e-05', 'epoch': '0.02033', 'num_input_tokens_seen': 1253888, 'train_runtime': '1553', 'train_tokens_per_second': '807.2'}
2%|▏ | 380/18688 [25:53<20:01:03, 3.94s/it] 2%|▏ | 381/18688 [25:57<19:34:58, 3.85s/it] 2%|▏ | 382/18688 [26:00<18:55:18, 3.72s/it] 2%|▏ | 383/18688 [26:04<19:41:59, 3.87s/it] 2%|▏ | 384/18688 [26:09<20:32:39, 4.04s/it] 2%|▏ | 385/18688 [26:13<20:43:32, 4.08s/it] 2%|▏ | 386/18688 [26:18<21:58:07, 4.32s/it] 2%|▏ | 387/18688 [26:22<22:35:20, 4.44s/it] 2%|▏ | 388/18688 [26:26<21:32:27, 4.24s/it] 2%|▏ | 389/18688 [26:30<20:19:20, 4.00s/it] 2%|▏ | 390/18688 [26:35<21:44:33, 4.28s/it] {'loss': '1.007', 'grad_norm': '1.122', 'learning_rate': '4.668e-05', 'epoch': '0.02087', 'num_input_tokens_seen': 1286896, 'train_runtime': '1595', 'train_tokens_per_second': '806.8'}
2%|▏ | 390/18688 [26:35<21:44:33, 4.28s/it] 2%|▏ | 391/18688 [26:39<21:39:05, 4.26s/it] 2%|▏ | 392/18688 [26:43<21:19:54, 4.20s/it] 2%|▏ | 393/18688 [26:47<20:31:11, 4.04s/it] 2%|▏ | 394/18688 [26:50<20:23:21, 4.01s/it] 2%|▏ | 395/18688 [26:55<21:46:54, 4.29s/it] 2%|▏ | 396/18688 [26:59<21:18:50, 4.19s/it] 2%|▏ | 397/18688 [27:03<20:13:14, 3.98s/it] 2%|▏ | 398/18688 [27:06<19:30:50, 3.84s/it] 2%|▏ | 399/18688 [27:11<20:30:01, 4.04s/it] 2%|▏ | 400/18688 [27:15<21:00:24, 4.14s/it] {'loss': '1.029', 'grad_norm': '1.445', 'learning_rate': '4.788e-05', 'epoch': '0.0214', 'num_input_tokens_seen': 1319712, 'train_runtime': '1636', 'train_tokens_per_second': '806.8'}
2%|▏ | 400/18688 [27:15<21:00:24, 4.14s/it] 2%|▏ | 401/18688 [27:20<21:37:08, 4.26s/it] 2%|▏ | 402/18688 [27:23<20:33:56, 4.05s/it] 2%|▏ | 403/18688 [27:28<21:17:19, 4.19s/it] 2%|▏ | 404/18688 [27:32<21:24:31, 4.22s/it] 2%|▏ | 405/18688 [27:36<21:21:46, 4.21s/it] 2%|▏ | 406/18688 [27:42<23:52:56, 4.70s/it] 2%|▏ | 407/18688 [27:47<23:24:57, 4.61s/it] 2%|▏ | 408/18688 [27:51<22:31:43, 4.44s/it] 2%|▏ | 409/18688 [27:55<22:12:02, 4.37s/it] 2%|▏ | 410/18688 [27:59<22:02:52, 4.34s/it] {'loss': '1.078', 'grad_norm': '1.076', 'learning_rate': '4.908e-05', 'epoch': '0.02194', 'num_input_tokens_seen': 1352936, 'train_runtime': '1680', 'train_tokens_per_second': '805.5'}
2%|▏ | 410/18688 [27:59<22:02:52, 4.34s/it] 2%|▏ | 411/18688 [28:04<22:10:00, 4.37s/it] 2%|▏ | 412/18688 [28:08<21:37:41, 4.26s/it] 2%|▏ | 413/18688 [28:12<21:46:14, 4.29s/it] 2%|▏ | 414/18688 [28:16<21:18:38, 4.20s/it] 2%|▏ | 415/18688 [28:20<20:53:48, 4.12s/it] 2%|▏ | 416/18688 [28:24<21:44:58, 4.29s/it] 2%|▏ | 417/18688 [28:28<21:16:55, 4.19s/it] 2%|▏ | 418/18688 [28:32<20:20:01, 4.01s/it] 2%|▏ | 419/18688 [28:36<20:33:15, 4.05s/it] 2%|▏ | 420/18688 [28:40<20:43:03, 4.08s/it] {'loss': '1.074', 'grad_norm': '1.385', 'learning_rate': '5.028e-05', 'epoch': '0.02247', 'num_input_tokens_seen': 1385008, 'train_runtime': '1721', 'train_tokens_per_second': '804.9'}
2%|▏ | 420/18688 [28:40<20:43:03, 4.08s/it] 2%|▏ | 421/18688 [28:45<22:13:43, 4.38s/it] 2%|▏ | 422/18688 [28:51<23:22:17, 4.61s/it] 2%|▏ | 423/18688 [28:55<22:32:48, 4.44s/it] 2%|▏ | 424/18688 [28:59<22:22:00, 4.41s/it] 2%|▏ | 425/18688 [29:03<21:40:32, 4.27s/it] 2%|▏ | 426/18688 [29:07<20:52:47, 4.12s/it] 2%|▏ | 427/18688 [29:11<20:51:30, 4.11s/it] 2%|▏ | 428/18688 [29:16<22:28:29, 4.43s/it] 2%|▏ | 429/18688 [29:20<22:02:25, 4.35s/it] 2%|▏ | 430/18688 [29:24<20:40:54, 4.08s/it] {'loss': '1.029', 'grad_norm': '1.286', 'learning_rate': '5.148e-05', 'epoch': '0.02301', 'num_input_tokens_seen': 1420952, 'train_runtime': '1764', 'train_tokens_per_second': '805.5'}
2%|▏ | 430/18688 [29:24<20:40:54, 4.08s/it] 2%|▏ | 431/18688 [29:27<20:15:34, 3.99s/it] 2%|▏ | 432/18688 [29:31<20:15:13, 3.99s/it] 2%|▏ | 433/18688 [29:35<20:20:44, 4.01s/it] 2%|▏ | 434/18688 [29:40<21:17:08, 4.20s/it] 2%|▏ | 435/18688 [29:44<21:27:40, 4.23s/it] 2%|▏ | 436/18688 [29:48<21:10:41, 4.18s/it] 2%|▏ | 437/18688 [29:53<21:59:48, 4.34s/it] 2%|▏ | 438/18688 [29:57<21:28:49, 4.24s/it] 2%|▏ | 439/18688 [30:01<20:56:23, 4.13s/it] 2%|▏ | 440/18688 [30:05<20:48:25, 4.10s/it] {'loss': '1.073', 'grad_norm': '1.121', 'learning_rate': '5.268e-05', 'epoch': '0.02355', 'num_input_tokens_seen': 1452280, 'train_runtime': '1805', 'train_tokens_per_second': '804.4'}
2%|▏ | 440/18688 [30:05<20:48:25, 4.10s/it] 2%|▏ | 441/18688 [30:09<20:28:18, 4.04s/it] 2%|▏ | 442/18688 [30:13<20:29:58, 4.04s/it] 2%|▏ | 443/18688 [30:17<20:26:20, 4.03s/it] 2%|▏ | 444/18688 [30:22<22:23:38, 4.42s/it] 2%|▏ | 445/18688 [30:27<23:05:36, 4.56s/it] 2%|▏ | 446/18688 [30:31<21:49:57, 4.31s/it] 2%|▏ | 447/18688 [30:35<21:09:52, 4.18s/it] 2%|▏ | 448/18688 [30:39<21:08:22, 4.17s/it] 2%|▏ | 449/18688 [30:43<21:41:53, 4.28s/it] 2%|▏ | 450/18688 [30:48<22:05:52, 4.36s/it] {'loss': '1.035', 'grad_norm': '1.347', 'learning_rate': '5.388e-05', 'epoch': '0.02408', 'num_input_tokens_seen': 1487680, 'train_runtime': '1848', 'train_tokens_per_second': '804.8'}
2%|▏ | 450/18688 [30:48<22:05:52, 4.36s/it] 2%|▏ | 451/18688 [30:51<20:42:52, 4.09s/it] 2%|▏ | 452/18688 [30:56<21:11:20, 4.18s/it] 2%|▏ | 453/18688 [31:00<21:09:23, 4.18s/it] 2%|▏ | 454/18688 [31:05<22:35:51, 4.46s/it] 2%|▏ | 455/18688 [31:10<23:56:11, 4.73s/it] 2%|▏ | 456/18688 [31:15<23:02:48, 4.55s/it] 2%|▏ | 457/18688 [31:18<21:43:24, 4.29s/it] 2%|▏ | 458/18688 [31:23<21:45:05, 4.30s/it] 2%|▏ | 459/18688 [31:26<20:46:53, 4.10s/it] 2%|▏ | 460/18688 [31:30<20:09:32, 3.98s/it] {'loss': '0.9811', 'grad_norm': '1.217', 'learning_rate': '5.508e-05', 'epoch': '0.02462', 'num_input_tokens_seen': 1521584, 'train_runtime': '1890', 'train_tokens_per_second': '804.9'}
2%|▏ | 460/18688 [31:30<20:09:32, 3.98s/it] 2%|▏ | 461/18688 [31:34<19:57:31, 3.94s/it] 2%|▏ | 462/18688 [31:37<19:30:45, 3.85s/it] 2%|▏ | 463/18688 [31:41<19:46:11, 3.91s/it] 2%|▏ | 464/18688 [31:45<19:12:23, 3.79s/it] 2%|▏ | 465/18688 [31:49<18:50:13, 3.72s/it] 2%|▏ | 466/18688 [31:53<19:31:44, 3.86s/it] 2%|▏ | 467/18688 [31:57<19:28:52, 3.85s/it] 3%|▎ | 468/18688 [32:01<20:27:58, 4.04s/it] 3%|▎ | 469/18688 [32:05<19:34:13, 3.87s/it] 3%|▎ | 470/18688 [32:09<21:13:01, 4.19s/it] {'loss': '1.042', 'grad_norm': '1.034', 'learning_rate': '5.628e-05', 'epoch': '0.02515', 'num_input_tokens_seen': 1553928, 'train_runtime': '1930', 'train_tokens_per_second': '805.2'}
3%|▎ | 470/18688 [32:09<21:13:01, 4.19s/it] 3%|▎ | 471/18688 [32:14<21:46:10, 4.30s/it] 3%|▎ | 472/18688 [32:18<21:46:02, 4.30s/it] 3%|▎ | 473/18688 [32:22<21:19:27, 4.21s/it] 3%|▎ | 474/18688 [32:26<20:49:46, 4.12s/it] 3%|▎ | 475/18688 [32:30<20:03:59, 3.97s/it] 3%|▎ | 476/18688 [32:34<20:22:18, 4.03s/it] 3%|▎ | 477/18688 [32:38<20:12:45, 4.00s/it] 3%|▎ | 478/18688 [32:42<20:31:46, 4.06s/it] 3%|▎ | 479/18688 [32:47<21:29:04, 4.25s/it] 3%|▎ | 480/18688 [32:51<20:52:26, 4.13s/it] {'loss': '1.112', 'grad_norm': '1.242', 'learning_rate': '5.748e-05', 'epoch': '0.02569', 'num_input_tokens_seen': 1588296, 'train_runtime': '1971', 'train_tokens_per_second': '805.8'}
3%|▎ | 480/18688 [32:51<20:52:26, 4.13s/it] 3%|▎ | 481/18688 [32:55<21:30:57, 4.25s/it] 3%|▎ | 482/18688 [32:59<20:45:59, 4.11s/it] 3%|▎ | 483/18688 [33:03<20:04:50, 3.97s/it] 3%|▎ | 484/18688 [33:06<19:17:55, 3.82s/it] 3%|▎ | 485/18688 [33:11<20:11:34, 3.99s/it] 3%|▎ | 486/18688 [33:14<19:25:24, 3.84s/it] 3%|▎ | 487/18688 [33:19<20:31:16, 4.06s/it] 3%|▎ | 488/18688 [33:23<20:59:43, 4.15s/it] 3%|▎ | 489/18688 [33:28<21:44:14, 4.30s/it] 3%|▎ | 490/18688 [33:32<21:16:01, 4.21s/it] {'loss': '1.012', 'grad_norm': '1.458', 'learning_rate': '5.868e-05', 'epoch': '0.02622', 'num_input_tokens_seen': 1620376, 'train_runtime': '2012', 'train_tokens_per_second': '805.3'}
3%|▎ | 490/18688 [33:32<21:16:01, 4.21s/it] 3%|▎ | 491/18688 [33:36<20:57:04, 4.14s/it] 3%|▎ | 492/18688 [33:40<21:41:08, 4.29s/it] 3%|▎ | 493/18688 [33:44<21:22:14, 4.23s/it] 3%|▎ | 494/18688 [33:49<21:52:56, 4.33s/it] 3%|▎ | 495/18688 [33:53<21:58:29, 4.35s/it] 3%|▎ | 496/18688 [33:57<20:46:02, 4.11s/it] 3%|▎ | 497/18688 [34:00<19:51:16, 3.93s/it] 3%|▎ | 498/18688 [34:04<19:03:05, 3.77s/it] 3%|▎ | 499/18688 [34:08<19:15:16, 3.81s/it] 3%|▎ | 500/18688 [34:13<21:38:32, 4.28s/it] {'loss': '1.015', 'grad_norm': '1.025', 'learning_rate': '5.988e-05', 'epoch': '0.02676', 'num_input_tokens_seen': 1653200, 'train_runtime': '2053', 'train_tokens_per_second': '805.1'}
3%|▎ | 500/18688 [34:13<21:38:32, 4.28s/it] 3%|▎ | 501/18688 [34:17<20:53:35, 4.14s/it] 3%|▎ | 502/18688 [34:21<20:39:37, 4.09s/it] 3%|▎ | 503/18688 [34:25<20:33:26, 4.07s/it] 3%|▎ | 504/18688 [34:29<20:36:44, 4.08s/it] 3%|▎ | 505/18688 [34:33<21:20:18, 4.22s/it] 3%|▎ | 506/18688 [34:38<21:44:52, 4.31s/it] 3%|▎ | 507/18688 [34:42<20:57:30, 4.15s/it] 3%|▎ | 508/18688 [34:46<20:27:13, 4.05s/it] 3%|▎ | 509/18688 [34:50<21:18:51, 4.22s/it] 3%|▎ | 510/18688 [34:54<20:26:02, 4.05s/it] {'loss': '1.07', 'grad_norm': '1.245', 'learning_rate': '6.108e-05', 'epoch': '0.02729', 'num_input_tokens_seen': 1682936, 'train_runtime': '2094', 'train_tokens_per_second': '803.6'}
3%|▎ | 510/18688 [34:54<20:26:02, 4.05s/it] 3%|▎ | 511/18688 [34:57<19:50:20, 3.93s/it] 3%|▎ | 512/18688 [35:03<22:27:25, 4.45s/it] 3%|▎ | 513/18688 [35:08<22:30:56, 4.46s/it] 3%|▎ | 514/18688 [35:12<21:49:53, 4.32s/it] 3%|▎ | 515/18688 [35:15<20:39:40, 4.09s/it] 3%|▎ | 516/18688 [35:19<20:41:02, 4.10s/it] 3%|▎ | 517/18688 [35:23<20:01:47, 3.97s/it] 3%|▎ | 518/18688 [35:27<20:42:39, 4.10s/it] 3%|▎ | 519/18688 [35:32<21:47:08, 4.32s/it] 3%|▎ | 520/18688 [35:37<22:15:47, 4.41s/it] {'loss': '1.012', 'grad_norm': '1.147', 'learning_rate': '6.228e-05', 'epoch': '0.02783', 'num_input_tokens_seen': 1718448, 'train_runtime': '2137', 'train_tokens_per_second': '804'}
3%|▎ | 520/18688 [35:37<22:15:47, 4.41s/it] 3%|▎ | 521/18688 [35:41<21:16:45, 4.22s/it] 3%|▎ | 522/18688 [35:45<21:50:34, 4.33s/it] 3%|▎ | 523/18688 [35:49<21:08:26, 4.19s/it] 3%|▎ | 524/18688 [35:53<20:40:59, 4.10s/it] 3%|▎ | 525/18688 [35:57<20:56:40, 4.15s/it] 3%|▎ | 526/18688 [36:01<20:13:09, 4.01s/it] 3%|▎ | 527/18688 [36:04<19:25:06, 3.85s/it] 3%|▎ | 528/18688 [36:08<19:33:12, 3.88s/it] 3%|▎ | 529/18688 [36:12<19:22:29, 3.84s/it] 3%|▎ | 530/18688 [36:16<20:13:46, 4.01s/it] {'loss': '0.9731', 'grad_norm': '0.9704', 'learning_rate': '6.348e-05', 'epoch': '0.02836', 'num_input_tokens_seen': 1750696, 'train_runtime': '2177', 'train_tokens_per_second': '804.2'}
3%|▎ | 530/18688 [36:16<20:13:46, 4.01s/it] 3%|▎ | 531/18688 [36:20<19:34:43, 3.88s/it] 3%|▎ | 532/18688 [36:23<18:54:29, 3.75s/it] 3%|▎ | 533/18688 [36:27<18:53:13, 3.75s/it] 3%|▎ | 534/18688 [36:31<19:23:49, 3.85s/it] 3%|▎ | 535/18688 [36:35<19:50:01, 3.93s/it] 3%|▎ | 536/18688 [36:39<19:15:28, 3.82s/it] 3%|▎ | 537/18688 [36:44<20:36:06, 4.09s/it] 3%|▎ | 538/18688 [36:47<20:00:27, 3.97s/it] 3%|▎ | 539/18688 [36:51<19:41:46, 3.91s/it] 3%|▎ | 540/18688 [36:55<19:14:35, 3.82s/it] {'loss': '1.073', 'grad_norm': '0.9989', 'learning_rate': '6.468e-05', 'epoch': '0.0289', 'num_input_tokens_seen': 1780496, 'train_runtime': '2215', 'train_tokens_per_second': '803.7'}
3%|▎ | 540/18688 [36:55<19:14:35, 3.82s/it] 3%|▎ | 541/18688 [36:59<20:03:17, 3.98s/it] 3%|▎ | 542/18688 [37:03<19:54:25, 3.95s/it] 3%|▎ | 543/18688 [37:07<19:45:32, 3.92s/it] 3%|▎ | 544/18688 [37:11<20:24:24, 4.05s/it] 3%|▎ | 545/18688 [37:16<21:51:29, 4.34s/it] 3%|▎ | 546/18688 [37:20<21:25:32, 4.25s/it] 3%|▎ | 547/18688 [37:24<20:28:43, 4.06s/it] 3%|▎ | 548/18688 [37:28<20:27:52, 4.06s/it] 3%|▎ | 549/18688 [37:32<20:23:09, 4.05s/it] 3%|▎ | 550/18688 [37:37<22:00:48, 4.37s/it] {'loss': '0.9776', 'grad_norm': '1.103', 'learning_rate': '6.588e-05', 'epoch': '0.02943', 'num_input_tokens_seen': 1814136, 'train_runtime': '2258', 'train_tokens_per_second': '803.6'}
3%|▎ | 550/18688 [37:37<22:00:48, 4.37s/it] 3%|▎ | 551/18688 [37:41<21:28:56, 4.26s/it] 3%|▎ | 552/18688 [37:45<21:14:46, 4.22s/it] 3%|▎ | 553/18688 [37:49<19:59:53, 3.97s/it] 3%|▎ | 554/18688 [37:53<20:02:27, 3.98s/it] 3%|▎ | 555/18688 [37:56<19:35:00, 3.89s/it] 3%|▎ | 556/18688 [38:01<20:40:33, 4.11s/it] 3%|▎ | 557/18688 [38:05<20:30:14, 4.07s/it] 3%|▎ | 558/18688 [38:09<20:51:26, 4.14s/it] 3%|▎ | 559/18688 [38:14<21:42:22, 4.31s/it] 3%|▎ | 560/18688 [38:18<21:15:45, 4.22s/it] {'loss': '0.9152', 'grad_norm': '1.03', 'learning_rate': '6.708e-05', 'epoch': '0.02997', 'num_input_tokens_seen': 1844456, 'train_runtime': '2298', 'train_tokens_per_second': '802.5'}
3%|▎ | 560/18688 [38:18<21:15:45, 4.22s/it] 3%|▎ | 561/18688 [38:22<21:39:49, 4.30s/it] 3%|▎ | 562/18688 [38:27<22:28:48, 4.46s/it] 3%|▎ | 563/18688 [38:31<21:46:16, 4.32s/it] 3%|▎ | 564/18688 [38:36<22:12:56, 4.41s/it] 3%|▎ | 565/18688 [38:41<22:35:27, 4.49s/it] 3%|▎ | 566/18688 [38:45<22:19:02, 4.43s/it] 3%|▎ | 567/18688 [38:49<21:53:38, 4.35s/it] 3%|▎ | 568/18688 [38:54<22:13:27, 4.42s/it] 3%|▎ | 569/18688 [38:58<21:51:17, 4.34s/it] 3%|▎ | 570/18688 [39:02<21:19:04, 4.24s/it] {'loss': '1.043', 'grad_norm': '0.9924', 'learning_rate': '6.828e-05', 'epoch': '0.0305', 'num_input_tokens_seen': 1877632, 'train_runtime': '2342', 'train_tokens_per_second': '801.7'}
3%|▎ | 570/18688 [39:02<21:19:04, 4.24s/it] 3%|▎ | 571/18688 [39:07<22:19:47, 4.44s/it] 3%|▎ | 572/18688 [39:11<22:06:45, 4.39s/it] 3%|▎ | 573/18688 [39:16<23:04:40, 4.59s/it] 3%|▎ | 574/18688 [39:20<22:35:38, 4.49s/it] 3%|▎ | 575/18688 [39:24<21:32:58, 4.28s/it] 3%|▎ | 576/18688 [39:28<21:08:46, 4.20s/it] 3%|▎ | 577/18688 [39:32<21:22:27, 4.25s/it] 3%|▎ | 578/18688 [39:36<20:50:30, 4.14s/it] 3%|▎ | 579/18688 [39:40<19:43:41, 3.92s/it] 3%|▎ | 580/18688 [39:44<20:55:52, 4.16s/it] {'loss': '0.958', 'grad_norm': '1.101', 'learning_rate': '6.948e-05', 'epoch': '0.03104', 'num_input_tokens_seen': 1911192, 'train_runtime': '2385', 'train_tokens_per_second': '801.4'}
3%|▎ | 580/18688 [39:44<20:55:52, 4.16s/it] 3%|▎ | 581/18688 [39:48<20:41:37, 4.11s/it] 3%|▎ | 582/18688 [39:52<19:47:46, 3.94s/it] 3%|▎ | 583/18688 [39:56<19:53:58, 3.96s/it] 3%|▎ | 584/18688 [40:00<19:55:20, 3.96s/it] 3%|▎ | 585/18688 [40:04<19:23:22, 3.86s/it] 3%|▎ | 586/18688 [40:08<20:46:00, 4.13s/it] 3%|▎ | 587/18688 [40:12<20:03:31, 3.99s/it] 3%|▎ | 588/18688 [40:16<20:28:29, 4.07s/it] 3%|▎ | 589/18688 [40:20<20:48:52, 4.14s/it] 3%|▎ | 590/18688 [40:25<20:55:52, 4.16s/it] {'loss': '0.9969', 'grad_norm': '0.9132', 'learning_rate': '7.068e-05', 'epoch': '0.03157', 'num_input_tokens_seen': 1943152, 'train_runtime': '2425', 'train_tokens_per_second': '801.2'}
3%|▎ | 590/18688 [40:25<20:55:52, 4.16s/it] 3%|▎ | 591/18688 [40:28<20:02:33, 3.99s/it] 3%|▎ | 592/18688 [40:32<20:14:46, 4.03s/it] 3%|▎ | 593/18688 [40:36<19:47:38, 3.94s/it] 3%|▎ | 594/18688 [40:40<19:35:17, 3.90s/it] 3%|▎ | 595/18688 [40:44<20:08:43, 4.01s/it] 3%|▎ | 596/18688 [40:48<20:15:38, 4.03s/it] 3%|▎ | 597/18688 [40:52<19:25:23, 3.87s/it] 3%|▎ | 598/18688 [40:56<19:13:51, 3.83s/it] 3%|▎ | 599/18688 [41:01<21:08:22, 4.21s/it] 3%|▎ | 600/18688 [41:05<21:11:37, 4.22s/it] {'loss': '1.001', 'grad_norm': '1.029', 'learning_rate': '7.188e-05', 'epoch': '0.03211', 'num_input_tokens_seen': 1976328, 'train_runtime': '2465', 'train_tokens_per_second': '801.6'}
3%|▎ | 600/18688 [41:05<21:11:37, 4.22s/it] 3%|▎ | 601/18688 [41:09<21:14:36, 4.23s/it] 3%|▎ | 602/18688 [41:13<21:21:41, 4.25s/it] 3%|▎ | 603/18688 [41:17<21:01:56, 4.19s/it] 3%|▎ | 604/18688 [41:21<20:47:47, 4.14s/it] 3%|▎ | 605/18688 [41:27<22:32:23, 4.49s/it] 3%|▎ | 606/18688 [41:31<22:13:52, 4.43s/it] 3%|▎ | 607/18688 [41:35<21:36:14, 4.30s/it] 3%|▎ | 608/18688 [41:39<21:05:58, 4.20s/it] 3%|▎ | 609/18688 [41:44<21:40:58, 4.32s/it] 3%|▎ | 610/18688 [41:47<20:44:57, 4.13s/it] {'loss': '0.9793', 'grad_norm': '1.156', 'learning_rate': '7.308e-05', 'epoch': '0.03264', 'num_input_tokens_seen': 2008480, 'train_runtime': '2508', 'train_tokens_per_second': '800.9'}
3%|▎ | 610/18688 [41:47<20:44:57, 4.13s/it] 3%|▎ | 611/18688 [41:51<20:06:35, 4.00s/it] 3%|▎ | 612/18688 [41:56<21:18:39, 4.24s/it] 3%|▎ | 613/18688 [42:00<21:04:42, 4.20s/it] 3%|▎ | 614/18688 [42:04<20:47:43, 4.14s/it] 3%|▎ | 615/18688 [42:08<20:12:53, 4.03s/it] 3%|▎ | 616/18688 [42:13<22:37:14, 4.51s/it] 3%|▎ | 617/18688 [42:18<22:26:45, 4.47s/it] 3%|▎ | 618/18688 [42:21<21:06:54, 4.21s/it] 3%|▎ | 619/18688 [42:26<21:32:30, 4.29s/it] 3%|▎ | 620/18688 [42:29<20:18:04, 4.04s/it] {'loss': '1.006', 'grad_norm': '1.373', 'learning_rate': '7.428e-05', 'epoch': '0.03318', 'num_input_tokens_seen': 2043576, 'train_runtime': '2550', 'train_tokens_per_second': '801.5'}
3%|▎ | 620/18688 [42:29<20:18:04, 4.04s/it] 3%|▎ | 621/18688 [42:34<20:41:25, 4.12s/it] 3%|▎ | 622/18688 [42:37<19:41:33, 3.92s/it] 3%|▎ | 623/18688 [42:42<20:57:09, 4.18s/it] 3%|▎ | 624/18688 [42:46<21:02:32, 4.19s/it] 3%|▎ | 625/18688 [42:50<20:42:45, 4.13s/it] 3%|▎ | 626/18688 [42:54<20:51:36, 4.16s/it] 3%|▎ | 627/18688 [42:59<21:28:09, 4.28s/it] 3%|▎ | 628/18688 [43:04<22:57:24, 4.58s/it] 3%|▎ | 629/18688 [43:08<21:20:53, 4.26s/it] 3%|▎ | 630/18688 [43:12<21:01:29, 4.19s/it] {'loss': '1.026', 'grad_norm': '0.9501', 'learning_rate': '7.548e-05', 'epoch': '0.03371', 'num_input_tokens_seen': 2077600, 'train_runtime': '2592', 'train_tokens_per_second': '801.5'}
3%|▎ | 630/18688 [43:12<21:01:29, 4.19s/it] 3%|▎ | 631/18688 [43:16<21:20:55, 4.26s/it] 3%|▎ | 632/18688 [43:20<20:50:36, 4.16s/it] 3%|▎ | 633/18688 [43:24<19:58:06, 3.98s/it] 3%|▎ | 634/18688 [43:28<20:53:05, 4.16s/it] 3%|▎ | 635/18688 [43:33<21:21:38, 4.26s/it] 3%|▎ | 636/18688 [43:37<21:01:15, 4.19s/it] 3%|▎ | 637/18688 [43:41<21:13:17, 4.23s/it] 3%|▎ | 638/18688 [43:45<21:11:47, 4.23s/it] 3%|▎ | 639/18688 [43:49<20:39:50, 4.12s/it] 3%|▎ | 640/18688 [43:53<20:21:12, 4.06s/it] {'loss': '1.065', 'grad_norm': '1.073', 'learning_rate': '7.668e-05', 'epoch': '0.03425', 'num_input_tokens_seen': 2110816, 'train_runtime': '2633', 'train_tokens_per_second': '801.5'}
3%|▎ | 640/18688 [43:53<20:21:12, 4.06s/it] 3%|▎ | 641/18688 [43:57<20:50:30, 4.16s/it] 3%|▎ | 642/18688 [44:02<21:24:09, 4.27s/it] 3%|▎ | 643/18688 [44:06<20:48:10, 4.15s/it] 3%|▎ | 644/18688 [44:10<20:23:09, 4.07s/it] 3%|▎ | 645/18688 [44:14<21:11:38, 4.23s/it] 3%|▎ | 646/18688 [44:19<22:37:26, 4.51s/it] 3%|▎ | 647/18688 [44:23<21:54:47, 4.37s/it] 3%|▎ | 648/18688 [44:28<22:46:06, 4.54s/it] 3%|▎ | 649/18688 [44:32<21:53:22, 4.37s/it] 3%|▎ | 650/18688 [44:36<21:18:10, 4.25s/it] {'loss': '0.9755', 'grad_norm': '0.9366', 'learning_rate': '7.788e-05', 'epoch': '0.03478', 'num_input_tokens_seen': 2146864, 'train_runtime': '2677', 'train_tokens_per_second': '802'}
3%|▎ | 650/18688 [44:36<21:18:10, 4.25s/it] 3%|▎ | 651/18688 [44:41<21:25:14, 4.28s/it] 3%|▎ | 652/18688 [44:45<22:04:07, 4.40s/it] 3%|▎ | 653/18688 [44:49<21:16:32, 4.25s/it] 3%|▎ | 654/18688 [44:53<20:28:19, 4.09s/it] 4%|▎ | 655/18688 [44:57<19:43:09, 3.94s/it] 4%|▎ | 656/18688 [45:01<19:58:44, 3.99s/it] 4%|▎ | 657/18688 [45:05<20:05:17, 4.01s/it] 4%|▎ | 658/18688 [45:08<19:14:31, 3.84s/it] 4%|▎ | 659/18688 [45:12<19:32:52, 3.90s/it] 4%|▎ | 660/18688 [45:16<19:42:19, 3.93s/it] {'loss': '1.061', 'grad_norm': '1.266', 'learning_rate': '7.908e-05', 'epoch': '0.03532', 'num_input_tokens_seen': 2179224, 'train_runtime': '2717', 'train_tokens_per_second': '802.2'}
4%|▎ | 660/18688 [45:16<19:42:19, 3.93s/it] 4%|▎ | 661/18688 [45:20<19:17:18, 3.85s/it] 4%|▎ | 662/18688 [45:24<19:27:03, 3.88s/it] 4%|▎ | 663/18688 [45:27<19:05:41, 3.81s/it] 4%|▎ | 664/18688 [45:33<21:48:42, 4.36s/it] 4%|▎ | 665/18688 [45:37<21:36:13, 4.32s/it] 4%|▎ | 666/18688 [45:41<20:27:44, 4.09s/it] 4%|▎ | 667/18688 [45:45<20:11:04, 4.03s/it] 4%|▎ | 668/18688 [45:48<19:20:55, 3.87s/it] 4%|▎ | 669/18688 [45:52<19:04:22, 3.81s/it] 4%|▎ | 670/18688 [45:56<19:46:55, 3.95s/it] {'loss': '1.068', 'grad_norm': '1.255', 'learning_rate': '8.028e-05', 'epoch': '0.03585', 'num_input_tokens_seen': 2211824, 'train_runtime': '2757', 'train_tokens_per_second': '802.3'}
4%|▎ | 670/18688 [45:56<19:46:55, 3.95s/it] 4%|▎ | 671/18688 [46:01<20:37:23, 4.12s/it] 4%|▎ | 672/18688 [46:05<21:17:17, 4.25s/it] 4%|▎ | 673/18688 [46:10<21:57:09, 4.39s/it] 4%|▎ | 674/18688 [46:14<22:06:38, 4.42s/it] 4%|▎ | 675/18688 [46:19<21:57:42, 4.39s/it] 4%|▎ | 676/18688 [46:23<22:10:38, 4.43s/it] 4%|▎ | 677/18688 [46:28<21:56:46, 4.39s/it] 4%|▎ | 678/18688 [46:32<21:28:34, 4.29s/it] 4%|▎ | 679/18688 [46:36<21:23:30, 4.28s/it] 4%|▎ | 680/18688 [46:39<20:19:03, 4.06s/it] {'loss': '1.043', 'grad_norm': '1.305', 'learning_rate': '8.148e-05', 'epoch': '0.03639', 'num_input_tokens_seen': 2246768, 'train_runtime': '2800', 'train_tokens_per_second': '802.4'}
4%|▎ | 680/18688 [46:39<20:19:03, 4.06s/it] 4%|▎ | 681/18688 [46:44<20:48:52, 4.16s/it] 4%|▎ | 682/18688 [46:48<21:25:29, 4.28s/it] 4%|▎ | 683/18688 [46:53<21:09:50, 4.23s/it] 4%|▎ | 684/18688 [46:57<21:15:45, 4.25s/it] 4%|▎ | 685/18688 [47:00<20:07:04, 4.02s/it] 4%|▎ | 686/18688 [47:04<19:42:02, 3.94s/it] 4%|▎ | 687/18688 [47:08<20:14:05, 4.05s/it] 4%|▎ | 688/18688 [47:13<20:30:03, 4.10s/it] 4%|▎ | 689/18688 [47:17<21:17:11, 4.26s/it] 4%|▎ | 690/18688 [47:22<22:00:00, 4.40s/it] {'loss': '1.025', 'grad_norm': '0.9871', 'learning_rate': '8.268e-05', 'epoch': '0.03692', 'num_input_tokens_seen': 2280616, 'train_runtime': '2842', 'train_tokens_per_second': '802.3'}
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5%|▌ | 980/18688 [1:07:39<20:26:03, 4.15s/it] 5%|▌ | 981/18688 [1:07:42<19:25:25, 3.95s/it] 5%|▌ | 982/18688 [1:07:47<20:31:33, 4.17s/it] 5%|▌ | 983/18688 [1:07:52<21:02:03, 4.28s/it] 5%|▌ | 984/18688 [1:07:56<20:46:17, 4.22s/it] 5%|▌ | 985/18688 [1:08:00<20:35:53, 4.19s/it] 5%|▌ | 986/18688 [1:08:03<19:40:07, 4.00s/it] 5%|▌ | 987/18688 [1:08:07<19:42:48, 4.01s/it] 5%|▌ | 988/18688 [1:08:11<19:01:52, 3.87s/it] 5%|▌ | 989/18688 [1:08:15<19:00:45, 3.87s/it] 5%|▌ | 990/18688 [1:08:19<19:10:55, 3.90s/it] {'loss': '0.9867', 'grad_norm': '0.9594', 'learning_rate': '0.0001187', 'epoch': '0.05298', 'num_input_tokens_seen': 3289808, 'train_runtime': '4099', 'train_tokens_per_second': '802.5'}
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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': '0.9959', 'eval_runtime': '83.01', 'eval_samples_per_second': '12.05', 'eval_steps_per_second': '12.05', 'epoch': '0.05351', 'num_input_tokens_seen': 3324984}
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5%|▌ | 1010/18688 [1:11:07<27:11:55, 5.54s/it] 5%|▌ | 1011/18688 [1:11:11<24:08:55, 4.92s/it] 5%|▌ | 1012/18688 [1:11:15<23:14:37, 4.73s/it] 5%|▌ | 1013/18688 [1:11:20<23:27:33, 4.78s/it] 5%|▌ | 1014/18688 [1:11:25<23:17:22, 4.74s/it] 5%|▌ | 1015/18688 [1:11:29<23:20:44, 4.76s/it] 5%|▌ | 1016/18688 [1:11:34<22:22:02, 4.56s/it] 5%|▌ | 1017/18688 [1:11:37<21:06:41, 4.30s/it] 5%|▌ | 1018/18688 [1:11:41<20:51:31, 4.25s/it] 5%|▌ | 1019/18688 [1:11:46<21:40:22, 4.42s/it] 5%|▌ | 1020/18688 [1:11:50<20:31:29, 4.18s/it] {'loss': '1.024', 'grad_norm': '1.461', 'learning_rate': '0.0001223', 'epoch': '0.05458', 'num_input_tokens_seen': 3400000, 'train_runtime': '4310', 'train_tokens_per_second': '788.8'}
5%|▌ | 1020/18688 [1:11:50<20:31:29, 4.18s/it] 5%|▌ | 1021/18688 [1:11:55<21:30:05, 4.38s/it] 5%|▌ | 1022/18688 [1:11:59<21:23:21, 4.36s/it] 5%|▌ | 1023/18688 [1:12:03<21:32:34, 4.39s/it] 5%|▌ | 1024/18688 [1:12:07<20:58:41, 4.28s/it] 5%|▌ | 1025/18688 [1:12:13<22:58:48, 4.68s/it] 5%|▌ | 1026/18688 [1:12:17<21:43:47, 4.43s/it] 5%|▌ | 1027/18688 [1:12:20<20:30:08, 4.18s/it] 6%|▌ | 1028/18688 [1:12:25<20:26:28, 4.17s/it] 6%|▌ | 1029/18688 [1:12:28<19:22:13, 3.95s/it] 6%|▌ | 1030/18688 [1:12:31<18:34:27, 3.79s/it] {'loss': '1.018', 'grad_norm': '1.169', 'learning_rate': '0.0001235', 'epoch': '0.05512', 'num_input_tokens_seen': 3435320, 'train_runtime': '4352', 'train_tokens_per_second': '789.4'}
6%|▌ | 1030/18688 [1:12:31<18:34:27, 3.79s/it] 6%|▌ | 1031/18688 [1:12:36<19:01:04, 3.88s/it] 6%|▌ | 1032/18688 [1:12:40<19:29:58, 3.98s/it] 6%|▌ | 1033/18688 [1:12:44<19:40:01, 4.01s/it] 6%|▌ | 1034/18688 [1:12:49<20:54:53, 4.26s/it] 6%|▌ | 1035/18688 [1:12:53<20:59:10, 4.28s/it] 6%|▌ | 1036/18688 [1:12:57<20:55:48, 4.27s/it] 6%|▌ | 1037/18688 [1:13:02<21:02:26, 4.29s/it] 6%|▌ | 1038/18688 [1:13:05<19:55:33, 4.06s/it] 6%|▌ | 1039/18688 [1:13:09<19:00:09, 3.88s/it] 6%|▌ | 1040/18688 [1:13:13<19:25:08, 3.96s/it] {'loss': '0.9954', 'grad_norm': '1.071', 'learning_rate': '0.0001247', 'epoch': '0.05565', 'num_input_tokens_seen': 3469792, 'train_runtime': '4393', 'train_tokens_per_second': '789.8'}
6%|▌ | 1040/18688 [1:13:13<19:25:08, 3.96s/it] 6%|▌ | 1041/18688 [1:13:17<19:35:00, 4.00s/it] 6%|▌ | 1042/18688 [1:13:20<18:50:23, 3.84s/it] 6%|▌ | 1043/18688 [1:13:24<19:15:38, 3.93s/it] 6%|▌ | 1044/18688 [1:13:29<19:36:55, 4.00s/it] 6%|▌ | 1045/18688 [1:13:33<19:27:19, 3.97s/it] 6%|▌ | 1046/18688 [1:13:38<21:20:19, 4.35s/it] 6%|▌ | 1047/18688 [1:13:41<20:13:26, 4.13s/it] 6%|▌ | 1048/18688 [1:13:45<20:14:38, 4.13s/it] 6%|▌ | 1049/18688 [1:13:49<19:48:34, 4.04s/it] 6%|▌ | 1050/18688 [1:13:53<19:24:32, 3.96s/it] {'loss': '0.9975', 'grad_norm': '1.027', 'learning_rate': '0.0001259', 'epoch': '0.05619', 'num_input_tokens_seen': 3502400, 'train_runtime': '4434', 'train_tokens_per_second': '790'}
6%|▌ | 1050/18688 [1:13:53<19:24:32, 3.96s/it] 6%|▌ | 1051/18688 [1:13:58<20:21:05, 4.15s/it] 6%|▌ | 1052/18688 [1:14:01<19:27:10, 3.97s/it] 6%|▌ | 1053/18688 [1:14:05<19:27:01, 3.97s/it] 6%|▌ | 1054/18688 [1:14:09<18:41:59, 3.82s/it] 6%|▌ | 1055/18688 [1:14:13<19:39:05, 4.01s/it] 6%|▌ | 1056/18688 [1:14:17<19:03:36, 3.89s/it] 6%|▌ | 1057/18688 [1:14:21<19:58:25, 4.08s/it] 6%|▌ | 1058/18688 [1:14:26<20:34:05, 4.20s/it] 6%|▌ | 1059/18688 [1:14:30<20:12:37, 4.13s/it] 6%|▌ | 1060/18688 [1:14:34<20:41:15, 4.22s/it] {'loss': '1.012', 'grad_norm': '1.058', 'learning_rate': '0.0001271', 'epoch': '0.05672', 'num_input_tokens_seen': 3536816, 'train_runtime': '4475', 'train_tokens_per_second': '790.4'}
6%|▌ | 1060/18688 [1:14:34<20:41:15, 4.22s/it] 6%|▌ | 1061/18688 [1:14:38<20:40:32, 4.22s/it] 6%|▌ | 1062/18688 [1:14:43<20:58:50, 4.29s/it] 6%|▌ | 1063/18688 [1:14:47<20:27:58, 4.18s/it] 6%|▌ | 1064/18688 [1:14:51<21:09:38, 4.32s/it] 6%|▌ | 1065/18688 [1:14:55<20:02:01, 4.09s/it] 6%|▌ | 1066/18688 [1:14:59<19:50:27, 4.05s/it] 6%|▌ | 1067/18688 [1:15:03<19:44:49, 4.03s/it] 6%|▌ | 1068/18688 [1:15:07<19:20:13, 3.95s/it] 6%|▌ | 1069/18688 [1:15:11<20:18:21, 4.15s/it] 6%|▌ | 1070/18688 [1:15:16<20:52:04, 4.26s/it] {'loss': '1.008', 'grad_norm': '1.065', 'learning_rate': '0.0001283', 'epoch': '0.05726', 'num_input_tokens_seen': 3571048, 'train_runtime': '4516', 'train_tokens_per_second': '790.7'}
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6%|▌ | 1080/18688 [1:15:57<18:47:16, 3.84s/it] 6%|▌ | 1081/18688 [1:16:01<18:43:19, 3.83s/it] 6%|▌ | 1082/18688 [1:16:05<19:59:13, 4.09s/it] 6%|▌ | 1083/18688 [1:16:09<19:07:02, 3.91s/it] 6%|▌ | 1084/18688 [1:16:13<19:16:34, 3.94s/it] 6%|▌ | 1085/18688 [1:16:16<18:30:28, 3.79s/it] 6%|▌ | 1086/18688 [1:16:21<19:48:02, 4.05s/it] 6%|▌ | 1087/18688 [1:16:26<21:16:56, 4.35s/it] 6%|▌ | 1088/18688 [1:16:30<21:12:06, 4.34s/it] 6%|▌ | 1089/18688 [1:16:35<21:01:33, 4.30s/it] 6%|▌ | 1090/18688 [1:16:39<21:04:40, 4.31s/it] {'loss': '1.02', 'grad_norm': '0.9699', 'learning_rate': '0.0001307', 'epoch': '0.05833', 'num_input_tokens_seen': 3639504, 'train_runtime': '4599', 'train_tokens_per_second': '791.3'}
6%|▌ | 1090/18688 [1:16:39<21:04:40, 4.31s/it] 6%|▌ | 1091/18688 [1:16:43<20:19:10, 4.16s/it] 6%|▌ | 1092/18688 [1:16:47<20:24:42, 4.18s/it] 6%|▌ | 1093/18688 [1:16:51<20:44:47, 4.24s/it] 6%|▌ | 1094/18688 [1:16:55<20:33:53, 4.21s/it] 6%|▌ | 1095/18688 [1:17:00<20:39:22, 4.23s/it] 6%|▌ | 1096/18688 [1:17:04<21:21:30, 4.37s/it] 6%|▌ | 1097/18688 [1:17:08<20:11:46, 4.13s/it] 6%|▌ | 1098/18688 [1:17:12<20:03:08, 4.10s/it] 6%|▌ | 1099/18688 [1:17:16<19:10:38, 3.93s/it] 6%|▌ | 1100/18688 [1:17:20<19:45:56, 4.05s/it] {'loss': '1.058', 'grad_norm': '0.8615', 'learning_rate': '0.0001319', 'epoch': '0.05886', 'num_input_tokens_seen': 3673560, 'train_runtime': '4640', 'train_tokens_per_second': '791.6'}
6%|▌ | 1100/18688 [1:17:20<19:45:56, 4.05s/it] 6%|▌ | 1101/18688 [1:17:24<20:32:21, 4.20s/it] 6%|▌ | 1102/18688 [1:17:28<19:52:50, 4.07s/it] 6%|▌ | 1103/18688 [1:17:32<19:26:00, 3.98s/it] 6%|▌ | 1104/18688 [1:17:36<19:28:40, 3.99s/it] 6%|▌ | 1105/18688 [1:17:40<19:10:06, 3.92s/it] 6%|▌ | 1106/18688 [1:17:43<18:44:43, 3.84s/it] 6%|▌ | 1107/18688 [1:17:48<19:34:24, 4.01s/it] 6%|▌ | 1108/18688 [1:17:52<19:17:12, 3.95s/it] 6%|▌ | 1109/18688 [1:17:55<19:05:52, 3.91s/it] 6%|▌ | 1110/18688 [1:18:00<19:53:11, 4.07s/it] {'loss': '0.9991', 'grad_norm': '0.9785', 'learning_rate': '0.0001331', 'epoch': '0.0594', 'num_input_tokens_seen': 3706976, 'train_runtime': '4680', 'train_tokens_per_second': '792'}
6%|▌ | 1110/18688 [1:18:00<19:53:11, 4.07s/it] 6%|▌ | 1111/18688 [1:18:05<20:50:49, 4.27s/it] 6%|▌ | 1112/18688 [1:18:09<20:41:16, 4.24s/it] 6%|▌ | 1113/18688 [1:18:12<19:40:30, 4.03s/it] 6%|▌ | 1114/18688 [1:18:16<18:52:34, 3.87s/it] 6%|▌ | 1115/18688 [1:18:20<19:35:06, 4.01s/it] 6%|▌ | 1116/18688 [1:18:25<20:10:05, 4.13s/it] 6%|▌ | 1117/18688 [1:18:28<19:47:47, 4.06s/it] 6%|▌ | 1118/18688 [1:18:32<19:33:48, 4.01s/it] 6%|▌ | 1119/18688 [1:18:36<19:11:58, 3.93s/it] 6%|▌ | 1120/18688 [1:18:41<20:11:40, 4.14s/it] {'loss': '0.9786', 'grad_norm': '0.9003', 'learning_rate': '0.0001343', 'epoch': '0.05993', 'num_input_tokens_seen': 3740912, 'train_runtime': '4721', 'train_tokens_per_second': '792.4'}
6%|▌ | 1120/18688 [1:18:41<20:11:40, 4.14s/it] 6%|▌ | 1121/18688 [1:18:45<20:00:12, 4.10s/it] 6%|▌ | 1122/18688 [1:18:49<20:37:59, 4.23s/it] 6%|▌ | 1123/18688 [1:18:53<20:16:23, 4.16s/it] 6%|▌ | 1124/18688 [1:18:57<20:20:26, 4.17s/it] 6%|▌ | 1125/18688 [1:19:02<20:11:22, 4.14s/it] 6%|▌ | 1126/18688 [1:19:05<19:52:06, 4.07s/it] 6%|▌ | 1127/18688 [1:19:09<19:42:23, 4.04s/it] 6%|▌ | 1128/18688 [1:19:14<20:15:54, 4.15s/it] 6%|▌ | 1129/18688 [1:19:17<19:14:48, 3.95s/it] 6%|▌ | 1130/18688 [1:19:21<18:31:06, 3.80s/it] {'loss': '0.9317', 'grad_norm': '0.9143', 'learning_rate': '0.0001355', 'epoch': '0.06047', 'num_input_tokens_seen': 3772344, 'train_runtime': '4761', 'train_tokens_per_second': '792.3'}
6%|▌ | 1130/18688 [1:19:21<18:31:06, 3.80s/it] 6%|▌ | 1131/18688 [1:19:26<20:12:29, 4.14s/it] 6%|▌ | 1132/18688 [1:19:29<19:10:36, 3.93s/it] 6%|▌ | 1133/18688 [1:19:33<19:24:14, 3.98s/it] 6%|▌ | 1134/18688 [1:19:37<18:59:23, 3.89s/it] 6%|▌ | 1135/18688 [1:19:41<18:34:57, 3.81s/it] 6%|▌ | 1136/18688 [1:19:44<18:05:45, 3.71s/it] 6%|▌ | 1137/18688 [1:19:48<17:51:55, 3.66s/it] 6%|▌ | 1138/18688 [1:19:53<20:35:59, 4.23s/it] 6%|▌ | 1139/18688 [1:19:57<20:30:09, 4.21s/it] 6%|▌ | 1140/18688 [1:20:01<19:32:24, 4.01s/it] {'loss': '0.975', 'grad_norm': '1.115', 'learning_rate': '0.0001367', 'epoch': '0.061', 'num_input_tokens_seen': 3806288, 'train_runtime': '4801', 'train_tokens_per_second': '792.8'}
6%|▌ | 1140/18688 [1:20:01<19:32:24, 4.01s/it] 6%|▌ | 1141/18688 [1:20:06<20:36:42, 4.23s/it] 6%|▌ | 1142/18688 [1:20:10<20:14:20, 4.15s/it] 6%|▌ | 1143/18688 [1:20:13<19:35:24, 4.02s/it] 6%|▌ | 1144/18688 [1:20:17<19:32:28, 4.01s/it] 6%|▌ | 1145/18688 [1:20:21<19:40:37, 4.04s/it] 6%|▌ | 1146/18688 [1:20:25<19:29:01, 4.00s/it] 6%|▌ | 1147/18688 [1:20:29<18:49:01, 3.86s/it] 6%|▌ | 1148/18688 [1:20:33<18:41:31, 3.84s/it] 6%|▌ | 1149/18688 [1:20:37<19:27:05, 3.99s/it] 6%|▌ | 1150/18688 [1:20:41<20:03:30, 4.12s/it] {'loss': '1.062', 'grad_norm': '1.157', 'learning_rate': '0.0001379', 'epoch': '0.06154', 'num_input_tokens_seen': 3839712, 'train_runtime': '4842', 'train_tokens_per_second': '793'}
6%|▌ | 1150/18688 [1:20:41<20:03:30, 4.12s/it] 6%|▌ | 1151/18688 [1:20:46<20:39:12, 4.24s/it] 6%|▌ | 1152/18688 [1:20:50<20:32:16, 4.22s/it] 6%|▌ | 1153/18688 [1:20:54<20:49:30, 4.28s/it] 6%|▌ | 1154/18688 [1:20:58<20:01:36, 4.11s/it] 6%|▌ | 1155/18688 [1:21:02<19:42:07, 4.05s/it] 6%|▌ | 1156/18688 [1:21:08<22:22:50, 4.60s/it] 6%|▌ | 1157/18688 [1:21:12<21:45:52, 4.47s/it] 6%|▌ | 1158/18688 [1:21:17<21:40:39, 4.45s/it] 6%|▌ | 1159/18688 [1:21:22<22:35:40, 4.64s/it] 6%|▌ | 1160/18688 [1:21:26<21:46:19, 4.47s/it] {'loss': '1.071', 'grad_norm': '1.174', 'learning_rate': '0.0001391', 'epoch': '0.06207', 'num_input_tokens_seen': 3876000, 'train_runtime': '4886', 'train_tokens_per_second': '793.3'}
6%|▌ | 1160/18688 [1:21:26<21:46:19, 4.47s/it] 6%|▌ | 1161/18688 [1:21:29<20:28:01, 4.20s/it] 6%|▌ | 1162/18688 [1:21:34<21:21:32, 4.39s/it] 6%|▌ | 1163/18688 [1:21:39<22:39:00, 4.65s/it] 6%|▌ | 1164/18688 [1:21:43<21:11:37, 4.35s/it] 6%|▌ | 1165/18688 [1:21:47<20:38:16, 4.24s/it] 6%|▌ | 1166/18688 [1:21:51<19:53:32, 4.09s/it] 6%|▌ | 1167/18688 [1:21:54<18:54:43, 3.89s/it] 6%|▋ | 1168/18688 [1:21:58<18:50:21, 3.87s/it] 6%|▋ | 1169/18688 [1:22:02<19:31:08, 4.01s/it] 6%|▋ | 1170/18688 [1:22:06<19:36:37, 4.03s/it] {'loss': '0.9817', 'grad_norm': '1.036', 'learning_rate': '0.0001403', 'epoch': '0.06261', 'num_input_tokens_seen': 3908440, 'train_runtime': '4927', 'train_tokens_per_second': '793.3'}
6%|▋ | 1170/18688 [1:22:06<19:36:37, 4.03s/it] 6%|▋ | 1171/18688 [1:22:10<19:16:11, 3.96s/it] 6%|▋ | 1172/18688 [1:22:14<19:38:51, 4.04s/it] 6%|▋ | 1173/18688 [1:22:19<20:26:07, 4.20s/it] 6%|▋ | 1174/18688 [1:22:22<19:24:51, 3.99s/it] 6%|▋ | 1175/18688 [1:22:26<18:59:55, 3.91s/it] 6%|▋ | 1176/18688 [1:22:31<19:35:38, 4.03s/it] 6%|▋ | 1177/18688 [1:22:35<19:39:41, 4.04s/it] 6%|▋ | 1178/18688 [1:22:38<19:16:29, 3.96s/it] 6%|▋ | 1179/18688 [1:22:43<19:38:37, 4.04s/it] 6%|▋ | 1180/18688 [1:22:47<19:45:01, 4.06s/it] {'loss': '0.9849', 'grad_norm': '1.021', 'learning_rate': '0.0001415', 'epoch': '0.06314', 'num_input_tokens_seen': 3941608, 'train_runtime': '4967', 'train_tokens_per_second': '793.5'}
6%|▋ | 1180/18688 [1:22:47<19:45:01, 4.06s/it] 6%|▋ | 1181/18688 [1:22:51<20:35:52, 4.24s/it] 6%|▋ | 1182/18688 [1:22:55<20:26:56, 4.21s/it] 6%|▋ | 1183/18688 [1:23:00<21:27:45, 4.41s/it] 6%|▋ | 1184/18688 [1:23:04<20:12:07, 4.15s/it] 6%|▋ | 1185/18688 [1:23:07<19:21:27, 3.98s/it] 6%|▋ | 1186/18688 [1:23:12<20:19:21, 4.18s/it] 6%|▋ | 1187/18688 [1:23:17<20:59:08, 4.32s/it] 6%|▋ | 1188/18688 [1:23:20<19:57:53, 4.11s/it] 6%|▋ | 1189/18688 [1:23:24<19:02:07, 3.92s/it] 6%|▋ | 1190/18688 [1:23:28<19:25:07, 4.00s/it] {'loss': '1.02', 'grad_norm': '1.189', 'learning_rate': '0.0001427', 'epoch': '0.06368', 'num_input_tokens_seen': 3974776, 'train_runtime': '5009', 'train_tokens_per_second': '793.6'}
6%|▋ | 1190/18688 [1:23:28<19:25:07, 4.00s/it] 6%|▋ | 1191/18688 [1:23:32<19:27:35, 4.00s/it] 6%|▋ | 1192/18688 [1:23:36<19:30:07, 4.01s/it] 6%|▋ | 1193/18688 [1:23:40<20:00:54, 4.12s/it] 6%|▋ | 1194/18688 [1:23:44<19:12:14, 3.95s/it] 6%|▋ | 1195/18688 [1:23:48<18:53:16, 3.89s/it] 6%|▋ | 1196/18688 [1:23:52<19:05:37, 3.93s/it] 6%|▋ | 1197/18688 [1:23:56<19:26:45, 4.00s/it] 6%|▋ | 1198/18688 [1:24:00<19:07:04, 3.94s/it] 6%|▋ | 1199/18688 [1:24:04<18:52:41, 3.89s/it] 6%|▋ | 1200/18688 [1:24:08<19:13:37, 3.96s/it] {'loss': '0.9722', 'grad_norm': '1.182', 'learning_rate': '0.0001439', 'epoch': '0.06421', 'num_input_tokens_seen': 4008136, 'train_runtime': '5048', 'train_tokens_per_second': '794'}
6%|▋ | 1200/18688 [1:24:08<19:13:37, 3.96s/it] 6%|▋ | 1201/18688 [1:24:11<18:37:37, 3.83s/it] 6%|▋ | 1202/18688 [1:24:15<18:54:05, 3.89s/it] 6%|▋ | 1203/18688 [1:24:20<20:23:28, 4.20s/it] 6%|▋ | 1204/18688 [1:24:25<21:21:14, 4.40s/it] 6%|▋ | 1205/18688 [1:24:29<20:52:56, 4.30s/it] 6%|▋ | 1206/18688 [1:24:33<20:25:51, 4.21s/it] 6%|▋ | 1207/18688 [1:24:38<21:26:11, 4.41s/it] 6%|▋ | 1208/18688 [1:24:42<20:39:43, 4.26s/it] 6%|▋ | 1209/18688 [1:24:46<21:13:55, 4.37s/it] 6%|▋ | 1210/18688 [1:24:51<20:51:33, 4.30s/it] {'loss': '1.056', 'grad_norm': '1.056', 'learning_rate': '0.0001451', 'epoch': '0.06475', 'num_input_tokens_seen': 4042776, 'train_runtime': '5091', 'train_tokens_per_second': '794.1'}
6%|▋ | 1210/18688 [1:24:51<20:51:33, 4.30s/it] 6%|▋ | 1211/18688 [1:24:54<19:37:29, 4.04s/it] 6%|▋ | 1212/18688 [1:24:58<19:38:23, 4.05s/it] 6%|▋ | 1213/18688 [1:25:02<19:19:23, 3.98s/it] 6%|▋ | 1214/18688 [1:25:06<19:45:59, 4.07s/it] 7%|▋ | 1215/18688 [1:25:10<19:50:50, 4.09s/it] 7%|▋ | 1216/18688 [1:25:14<19:00:04, 3.92s/it] 7%|▋ | 1217/18688 [1:25:18<19:25:44, 4.00s/it] 7%|▋ | 1218/18688 [1:25:22<19:34:02, 4.03s/it] 7%|▋ | 1219/18688 [1:25:27<20:46:54, 4.28s/it] 7%|▋ | 1220/18688 [1:25:31<20:00:22, 4.12s/it] {'loss': '0.992', 'grad_norm': '0.9601', 'learning_rate': '0.0001463', 'epoch': '0.06528', 'num_input_tokens_seen': 4074336, 'train_runtime': '5131', 'train_tokens_per_second': '794'}
7%|▋ | 1220/18688 [1:25:31<20:00:22, 4.12s/it] 7%|▋ | 1221/18688 [1:25:35<20:21:15, 4.20s/it] 7%|▋ | 1222/18688 [1:25:39<19:15:17, 3.97s/it] 7%|▋ | 1223/18688 [1:25:42<19:05:28, 3.94s/it] 7%|▋ | 1224/18688 [1:25:46<18:27:32, 3.81s/it] 7%|▋ | 1225/18688 [1:25:50<19:06:59, 3.94s/it] 7%|▋ | 1226/18688 [1:25:55<20:22:58, 4.20s/it] 7%|▋ | 1227/18688 [1:25:59<19:22:42, 4.00s/it] 7%|▋ | 1228/18688 [1:26:03<19:42:38, 4.06s/it] 7%|▋ | 1229/18688 [1:26:07<20:04:15, 4.14s/it] 7%|▋ | 1230/18688 [1:26:11<19:49:15, 4.09s/it] {'loss': '1.036', 'grad_norm': '1.117', 'learning_rate': '0.0001475', 'epoch': '0.06582', 'num_input_tokens_seen': 4107808, 'train_runtime': '5172', 'train_tokens_per_second': '794.3'}
7%|▋ | 1230/18688 [1:26:11<19:49:15, 4.09s/it] 7%|▋ | 1231/18688 [1:26:16<20:26:01, 4.21s/it] 7%|▋ | 1232/18688 [1:26:20<20:14:29, 4.17s/it] 7%|▋ | 1233/18688 [1:26:24<20:08:44, 4.15s/it] 7%|▋ | 1234/18688 [1:26:28<20:22:31, 4.20s/it] 7%|▋ | 1235/18688 [1:26:32<20:27:03, 4.22s/it] 7%|▋ | 1236/18688 [1:26:37<20:35:42, 4.25s/it] 7%|▋ | 1237/18688 [1:26:40<20:02:34, 4.13s/it] 7%|▋ | 1238/18688 [1:26:44<19:48:01, 4.08s/it] 7%|▋ | 1239/18688 [1:26:49<19:55:34, 4.11s/it] 7%|▋ | 1240/18688 [1:26:52<19:23:27, 4.00s/it] {'loss': '0.9428', 'grad_norm': '0.9334', 'learning_rate': '0.0001487', 'epoch': '0.06635', 'num_input_tokens_seen': 4141480, 'train_runtime': '5213', 'train_tokens_per_second': '794.5'}
7%|▋ | 1240/18688 [1:26:52<19:23:27, 4.00s/it] 7%|▋ | 1241/18688 [1:26:56<18:54:50, 3.90s/it] 7%|▋ | 1242/18688 [1:27:01<21:09:22, 4.37s/it] 7%|▋ | 1243/18688 [1:27:06<20:45:33, 4.28s/it] 7%|▋ | 1244/18688 [1:27:10<21:02:02, 4.34s/it] 7%|▋ | 1245/18688 [1:27:14<21:04:49, 4.35s/it] 7%|▋ | 1246/18688 [1:27:19<20:46:45, 4.29s/it] 7%|▋ | 1247/18688 [1:27:22<20:07:56, 4.16s/it] 7%|▋ | 1248/18688 [1:27:27<20:07:00, 4.15s/it] 7%|▋ | 1249/18688 [1:27:31<19:51:53, 4.10s/it] 7%|▋ | 1250/18688 [1:27:35<20:31:26, 4.24s/it] {'loss': '1.04', 'grad_norm': '0.9771', 'learning_rate': '0.0001499', 'epoch': '0.06689', 'num_input_tokens_seen': 4175648, 'train_runtime': '5256', 'train_tokens_per_second': '794.5'}
7%|▋ | 1250/18688 [1:27:35<20:31:26, 4.24s/it] 7%|▋ | 1251/18688 [1:27:39<20:03:04, 4.14s/it] 7%|▋ | 1252/18688 [1:27:43<20:05:14, 4.15s/it] 7%|▋ | 1253/18688 [1:27:47<19:53:04, 4.11s/it] 7%|▋ | 1254/18688 [1:27:52<21:00:06, 4.34s/it] 7%|▋ | 1255/18688 [1:27:57<21:37:55, 4.47s/it] 7%|▋ | 1256/18688 [1:28:01<21:52:56, 4.52s/it] 7%|▋ | 1257/18688 [1:28:06<22:30:05, 4.65s/it] 7%|▋ | 1258/18688 [1:28:10<21:30:25, 4.44s/it] 7%|▋ | 1259/18688 [1:28:14<20:33:15, 4.25s/it] 7%|▋ | 1260/18688 [1:28:18<19:25:31, 4.01s/it] {'loss': '0.9825', 'grad_norm': '1.346', 'learning_rate': '0.0001511', 'epoch': '0.06742', 'num_input_tokens_seen': 4207024, 'train_runtime': '5298', 'train_tokens_per_second': '794.1'}
7%|▋ | 1260/18688 [1:28:18<19:25:31, 4.01s/it] 7%|▋ | 1261/18688 [1:28:21<18:41:16, 3.86s/it] 7%|▋ | 1262/18688 [1:28:25<18:30:45, 3.82s/it] 7%|▋ | 1263/18688 [1:28:29<18:18:15, 3.78s/it] 7%|▋ | 1264/18688 [1:28:32<17:50:50, 3.69s/it] 7%|▋ | 1265/18688 [1:28:36<17:42:21, 3.66s/it] 7%|▋ | 1266/18688 [1:28:40<18:21:16, 3.79s/it] 7%|▋ | 1267/18688 [1:28:44<18:43:48, 3.87s/it] 7%|▋ | 1268/18688 [1:28:47<18:07:48, 3.75s/it] 7%|▋ | 1269/18688 [1:28:51<17:47:39, 3.68s/it] 7%|▋ | 1270/18688 [1:28:55<18:51:42, 3.90s/it] {'loss': '1.011', 'grad_norm': '1.178', 'learning_rate': '0.0001523', 'epoch': '0.06796', 'num_input_tokens_seen': 4236392, 'train_runtime': '5336', 'train_tokens_per_second': '794'}
7%|▋ | 1270/18688 [1:28:55<18:51:42, 3.90s/it] 7%|▋ | 1271/18688 [1:28:59<18:55:47, 3.91s/it] 7%|▋ | 1272/18688 [1:29:03<18:23:27, 3.80s/it] 7%|▋ | 1273/18688 [1:29:06<18:01:23, 3.73s/it] 7%|▋ | 1274/18688 [1:29:10<17:57:37, 3.71s/it] 7%|▋ | 1275/18688 [1:29:14<19:10:00, 3.96s/it] 7%|▋ | 1276/18688 [1:29:19<19:19:22, 4.00s/it] 7%|▋ | 1277/18688 [1:29:22<18:58:51, 3.92s/it] 7%|▋ | 1278/18688 [1:29:26<18:25:38, 3.81s/it] 7%|▋ | 1279/18688 [1:29:30<18:44:15, 3.87s/it] 7%|▋ | 1280/18688 [1:29:33<18:05:13, 3.74s/it] {'loss': '0.9809', 'grad_norm': '1.165', 'learning_rate': '0.0001535', 'epoch': '0.06849', 'num_input_tokens_seen': 4265784, 'train_runtime': '5374', 'train_tokens_per_second': '793.8'}
7%|▋ | 1280/18688 [1:29:33<18:05:13, 3.74s/it] 7%|▋ | 1281/18688 [1:29:37<17:50:12, 3.69s/it] 7%|▋ | 1282/18688 [1:29:41<17:57:31, 3.71s/it] 7%|▋ | 1283/18688 [1:29:45<19:00:24, 3.93s/it] 7%|▋ | 1284/18688 [1:29:49<18:26:37, 3.82s/it] 7%|▋ | 1285/18688 [1:29:53<19:06:00, 3.95s/it] 7%|▋ | 1286/18688 [1:29:57<19:21:35, 4.01s/it] 7%|▋ | 1287/18688 [1:30:01<19:18:10, 3.99s/it] 7%|▋ | 1288/18688 [1:30:05<19:21:51, 4.01s/it] 7%|▋ | 1289/18688 [1:30:10<20:06:24, 4.16s/it] 7%|▋ | 1290/18688 [1:30:14<20:58:49, 4.34s/it] {'loss': '1.04', 'grad_norm': '1.461', 'learning_rate': '0.0001547', 'epoch': '0.06903', 'num_input_tokens_seen': 4298040, 'train_runtime': '5415', 'train_tokens_per_second': '793.8'}
7%|▋ | 1290/18688 [1:30:14<20:58:49, 4.34s/it] 7%|▋ | 1291/18688 [1:30:18<19:48:56, 4.10s/it] 7%|▋ | 1292/18688 [1:30:22<20:30:05, 4.24s/it] 7%|▋ | 1293/18688 [1:30:27<20:50:27, 4.31s/it] 7%|▋ | 1294/18688 [1:30:32<21:47:22, 4.51s/it] 7%|▋ | 1295/18688 [1:30:36<20:37:20, 4.27s/it] 7%|▋ | 1296/18688 [1:30:40<20:13:49, 4.19s/it] 7%|▋ | 1297/18688 [1:30:43<19:10:56, 3.97s/it] 7%|▋ | 1298/18688 [1:30:47<19:17:49, 3.99s/it] 7%|▋ | 1299/18688 [1:30:52<21:06:08, 4.37s/it] 7%|▋ | 1300/18688 [1:30:56<20:17:31, 4.20s/it] {'loss': '1.055', 'grad_norm': '0.9423', 'learning_rate': '0.0001559', 'epoch': '0.06957', 'num_input_tokens_seen': 4332752, 'train_runtime': '5457', 'train_tokens_per_second': '794'}
7%|▋ | 1300/18688 [1:30:56<20:17:31, 4.20s/it] 7%|▋ | 1301/18688 [1:31:00<19:09:46, 3.97s/it] 7%|▋ | 1302/18688 [1:31:04<19:16:06, 3.99s/it] 7%|▋ | 1303/18688 [1:31:08<19:23:38, 4.02s/it] 7%|▋ | 1304/18688 [1:31:12<19:19:30, 4.00s/it] 7%|▋ | 1305/18688 [1:31:16<20:18:28, 4.21s/it] 7%|▋ | 1306/18688 [1:31:21<21:14:13, 4.40s/it] 7%|▋ | 1307/18688 [1:31:26<21:55:20, 4.54s/it] 7%|▋ | 1308/18688 [1:31:30<21:19:18, 4.42s/it] 7%|▋ | 1309/18688 [1:31:34<20:28:28, 4.24s/it] 7%|▋ | 1310/18688 [1:31:38<20:12:29, 4.19s/it] {'loss': '1.071', 'grad_norm': '1.065', 'learning_rate': '0.0001571', 'epoch': '0.0701', 'num_input_tokens_seen': 4365560, 'train_runtime': '5499', 'train_tokens_per_second': '793.9'}
7%|▋ | 1310/18688 [1:31:38<20:12:29, 4.19s/it] 7%|▋ | 1311/18688 [1:31:42<19:37:55, 4.07s/it] 7%|▋ | 1312/18688 [1:31:46<19:08:38, 3.97s/it] 7%|▋ | 1313/18688 [1:31:50<19:24:18, 4.02s/it] 7%|▋ | 1314/18688 [1:31:54<19:26:12, 4.03s/it] 7%|▋ | 1315/18688 [1:31:58<19:32:42, 4.05s/it] 7%|▋ | 1316/18688 [1:32:02<19:35:04, 4.06s/it] 7%|▋ | 1317/18688 [1:32:06<19:46:47, 4.10s/it] 7%|▋ | 1318/18688 [1:32:10<19:16:26, 3.99s/it] 7%|▋ | 1319/18688 [1:32:14<19:17:12, 4.00s/it] 7%|▋ | 1320/18688 [1:32:18<19:47:41, 4.10s/it] {'loss': '0.9882', 'grad_norm': '1.226', 'learning_rate': '0.0001583', 'epoch': '0.07064', 'num_input_tokens_seen': 4398144, 'train_runtime': '5539', 'train_tokens_per_second': '794.1'}
7%|▋ | 1320/18688 [1:32:18<19:47:41, 4.10s/it] 7%|▋ | 1321/18688 [1:32:22<19:46:40, 4.10s/it] 7%|▋ | 1322/18688 [1:32:27<20:02:09, 4.15s/it] 7%|▋ | 1323/18688 [1:32:30<19:00:36, 3.94s/it] 7%|▋ | 1324/18688 [1:32:34<19:18:22, 4.00s/it] 7%|▋ | 1325/18688 [1:32:38<18:47:51, 3.90s/it] 7%|▋ | 1326/18688 [1:32:42<18:57:23, 3.93s/it] 7%|▋ | 1327/18688 [1:32:46<19:01:35, 3.95s/it] 7%|▋ | 1328/18688 [1:32:49<18:33:12, 3.85s/it] 7%|▋ | 1329/18688 [1:32:54<19:29:46, 4.04s/it] 7%|▋ | 1330/18688 [1:32:57<18:45:58, 3.89s/it] {'loss': '1.046', 'grad_norm': '1.276', 'learning_rate': '0.0001595', 'epoch': '0.07117', 'num_input_tokens_seen': 4428848, 'train_runtime': '5578', 'train_tokens_per_second': '794'}
7%|▋ | 1330/18688 [1:32:57<18:45:58, 3.89s/it] 7%|▋ | 1331/18688 [1:33:02<19:36:09, 4.07s/it] 7%|▋ | 1332/18688 [1:33:06<19:22:22, 4.02s/it] 7%|▋ | 1333/18688 [1:33:10<19:16:44, 4.00s/it] 7%|▋ | 1334/18688 [1:33:14<19:02:27, 3.95s/it] 7%|▋ | 1335/18688 [1:33:19<20:20:19, 4.22s/it] 7%|▋ | 1336/18688 [1:33:24<21:40:03, 4.50s/it] 7%|▋ | 1337/18688 [1:33:27<20:09:57, 4.18s/it] 7%|▋ | 1338/18688 [1:33:31<20:16:09, 4.21s/it] 7%|▋ | 1339/18688 [1:33:35<19:07:35, 3.97s/it] 7%|▋ | 1340/18688 [1:33:38<18:42:37, 3.88s/it] {'loss': '1.03', 'grad_norm': '1.151', 'learning_rate': '0.0001607', 'epoch': '0.07171', 'num_input_tokens_seen': 4462248, 'train_runtime': '5619', 'train_tokens_per_second': '794.1'}
7%|▋ | 1340/18688 [1:33:38<18:42:37, 3.88s/it] 7%|▋ | 1341/18688 [1:33:43<19:17:56, 4.01s/it] 7%|▋ | 1342/18688 [1:33:47<19:38:06, 4.08s/it] 7%|▋ | 1343/18688 [1:33:50<18:41:22, 3.88s/it] 7%|▋ | 1344/18688 [1:33:54<18:48:45, 3.90s/it] 7%|▋ | 1345/18688 [1:34:00<21:25:42, 4.45s/it] 7%|▋ | 1346/18688 [1:34:04<21:08:40, 4.39s/it] 7%|▋ | 1347/18688 [1:34:09<20:56:54, 4.35s/it] 7%|▋ | 1348/18688 [1:34:14<21:46:15, 4.52s/it] 7%|▋ | 1349/18688 [1:34:18<21:39:28, 4.50s/it] 7%|▋ | 1350/18688 [1:34:23<21:43:46, 4.51s/it] {'loss': '1.071', 'grad_norm': '1.264', 'learning_rate': '0.0001619', 'epoch': '0.07224', 'num_input_tokens_seen': 4497528, 'train_runtime': '5663', 'train_tokens_per_second': '794.2'}
7%|▋ | 1350/18688 [1:34:23<21:43:46, 4.51s/it] 7%|▋ | 1351/18688 [1:34:27<21:39:02, 4.50s/it] 7%|▋ | 1352/18688 [1:34:31<21:27:58, 4.46s/it] 7%|▋ | 1353/18688 [1:34:36<21:51:06, 4.54s/it] 7%|▋ | 1354/18688 [1:34:40<20:57:02, 4.35s/it] 7%|▋ | 1355/18688 [1:34:45<21:50:57, 4.54s/it] 7%|▋ | 1356/18688 [1:34:50<21:53:12, 4.55s/it] 7%|▋ | 1357/18688 [1:34:54<21:09:54, 4.40s/it] 7%|▋ | 1358/18688 [1:34:58<20:36:02, 4.28s/it] 7%|▋ | 1359/18688 [1:35:02<20:09:24, 4.19s/it] 7%|▋ | 1360/18688 [1:35:05<19:24:55, 4.03s/it] {'loss': '1.034', 'grad_norm': '1.241', 'learning_rate': '0.0001631', 'epoch': '0.07278', 'num_input_tokens_seen': 4534280, 'train_runtime': '5706', 'train_tokens_per_second': '794.7'}
7%|▋ | 1360/18688 [1:35:05<19:24:55, 4.03s/it] 7%|▋ | 1361/18688 [1:35:09<19:40:05, 4.09s/it] 7%|▋ | 1362/18688 [1:35:13<19:18:07, 4.01s/it] 7%|▋ | 1363/18688 [1:35:17<18:54:35, 3.93s/it] 7%|▋ | 1364/18688 [1:35:21<18:23:55, 3.82s/it] 7%|▋ | 1365/18688 [1:35:24<17:47:34, 3.70s/it] 7%|▋ | 1366/18688 [1:35:28<17:54:19, 3.72s/it] 7%|▋ | 1367/18688 [1:35:32<18:02:53, 3.75s/it] 7%|▋ | 1368/18688 [1:35:36<19:44:19, 4.10s/it] 7%|▋ | 1369/18688 [1:35:41<19:37:12, 4.08s/it] 7%|▋ | 1370/18688 [1:35:45<19:37:15, 4.08s/it] {'loss': '1.036', 'grad_norm': '1.014', 'learning_rate': '0.0001643', 'epoch': '0.07331', 'num_input_tokens_seen': 4565296, 'train_runtime': '5745', 'train_tokens_per_second': '794.6'}
7%|▋ | 1370/18688 [1:35:45<19:37:15, 4.08s/it] 7%|▋ | 1371/18688 [1:35:49<20:31:13, 4.27s/it] 7%|▋ | 1372/18688 [1:35:53<20:18:22, 4.22s/it] 7%|▋ | 1373/18688 [1:35:57<20:01:11, 4.16s/it] 7%|▋ | 1374/18688 [1:36:02<20:42:25, 4.31s/it] 7%|▋ | 1375/18688 [1:36:06<19:46:21, 4.11s/it] 7%|▋ | 1376/18688 [1:36:09<19:03:03, 3.96s/it] 7%|▋ | 1377/18688 [1:36:13<18:35:19, 3.87s/it] 7%|▋ | 1378/18688 [1:36:17<19:19:03, 4.02s/it] 7%|▋ | 1379/18688 [1:36:22<19:46:05, 4.11s/it] 7%|▋ | 1380/18688 [1:36:25<19:01:51, 3.96s/it] {'loss': '0.9657', 'grad_norm': '1.307', 'learning_rate': '0.0001655', 'epoch': '0.07385', 'num_input_tokens_seen': 4596896, 'train_runtime': '5786', 'train_tokens_per_second': '794.5'}
7%|▋ | 1380/18688 [1:36:25<19:01:51, 3.96s/it] 7%|▋ | 1381/18688 [1:36:29<19:18:25, 4.02s/it] 7%|▋ | 1382/18688 [1:36:34<20:07:29, 4.19s/it] 7%|▋ | 1383/18688 [1:36:39<20:42:42, 4.31s/it] 7%|▋ | 1384/18688 [1:36:43<20:53:16, 4.35s/it] 7%|▋ | 1385/18688 [1:36:47<20:47:45, 4.33s/it] 7%|▋ | 1386/18688 [1:36:52<21:27:27, 4.46s/it] 7%|▋ | 1387/18688 [1:36:56<21:04:38, 4.39s/it] 7%|▋ | 1388/18688 [1:37:00<20:17:53, 4.22s/it] 7%|▋ | 1389/18688 [1:37:04<20:00:30, 4.16s/it] 7%|▋ | 1390/18688 [1:37:08<19:43:10, 4.10s/it] {'loss': '0.9922', 'grad_norm': '0.9738', 'learning_rate': '0.0001667', 'epoch': '0.07438', 'num_input_tokens_seen': 4631296, 'train_runtime': '5829', 'train_tokens_per_second': '794.6'}
7%|▋ | 1390/18688 [1:37:08<19:43:10, 4.10s/it] 7%|▋ | 1391/18688 [1:37:12<18:46:08, 3.91s/it] 7%|▋ | 1392/18688 [1:37:17<20:33:02, 4.28s/it] 7%|▋ | 1393/18688 [1:37:21<20:13:40, 4.21s/it] 7%|▋ | 1394/18688 [1:37:25<20:08:14, 4.19s/it] 7%|▋ | 1395/18688 [1:37:30<21:41:57, 4.52s/it] 7%|▋ | 1396/18688 [1:37:35<21:40:37, 4.51s/it] 7%|▋ | 1397/18688 [1:37:39<21:01:15, 4.38s/it] 7%|▋ | 1398/18688 [1:37:44<21:49:37, 4.54s/it] 7%|▋ | 1399/18688 [1:37:48<21:46:30, 4.53s/it] 7%|▋ | 1400/18688 [1:37:53<22:22:20, 4.66s/it] {'loss': '1.06', 'grad_norm': '1.046', 'learning_rate': '0.0001679', 'epoch': '0.07492', 'num_input_tokens_seen': 4668640, 'train_runtime': '5874', 'train_tokens_per_second': '794.8'}
7%|▋ | 1400/18688 [1:37:53<22:22:20, 4.66s/it] 7%|▋ | 1401/18688 [1:37:58<21:53:38, 4.56s/it] 8%|▊ | 1402/18688 [1:38:01<20:38:59, 4.30s/it] 8%|▊ | 1403/18688 [1:38:05<20:02:49, 4.18s/it] 8%|▊ | 1404/18688 [1:38:09<19:59:25, 4.16s/it] 8%|▊ | 1405/18688 [1:38:13<19:07:11, 3.98s/it] 8%|▊ | 1406/18688 [1:38:16<18:28:56, 3.85s/it] 8%|▊ | 1407/18688 [1:38:20<18:29:35, 3.85s/it] 8%|▊ | 1408/18688 [1:38:24<18:53:19, 3.94s/it] 8%|▊ | 1409/18688 [1:38:28<18:13:54, 3.80s/it] 8%|▊ | 1410/18688 [1:38:32<19:28:59, 4.06s/it] {'loss': '1.009', 'grad_norm': '1.169', 'learning_rate': '0.0001691', 'epoch': '0.07545', 'num_input_tokens_seen': 4701160, 'train_runtime': '5913', 'train_tokens_per_second': '795.1'}
8%|▊ | 1410/18688 [1:38:32<19:28:59, 4.06s/it] 8%|▊ | 1411/18688 [1:38:36<19:14:40, 4.01s/it] 8%|▊ | 1412/18688 [1:38:41<19:36:20, 4.09s/it] 8%|▊ | 1413/18688 [1:38:45<19:18:43, 4.02s/it] 8%|▊ | 1414/18688 [1:38:49<20:25:03, 4.26s/it] 8%|▊ | 1415/18688 [1:38:53<20:02:46, 4.18s/it] 8%|▊ | 1416/18688 [1:38:57<19:20:25, 4.03s/it] 8%|▊ | 1417/18688 [1:39:02<20:26:11, 4.26s/it] 8%|▊ | 1418/18688 [1:39:05<19:34:42, 4.08s/it] 8%|▊ | 1419/18688 [1:39:09<18:41:08, 3.90s/it] 8%|▊ | 1420/18688 [1:39:13<18:21:49, 3.83s/it] {'loss': '1.022', 'grad_norm': '1.26', 'learning_rate': '0.0001703', 'epoch': '0.07599', 'num_input_tokens_seen': 4735568, 'train_runtime': '5953', 'train_tokens_per_second': '795.5'}
8%|▊ | 1420/18688 [1:39:13<18:21:49, 3.83s/it] 8%|▊ | 1421/18688 [1:39:17<18:41:54, 3.90s/it] 8%|▊ | 1422/18688 [1:39:21<19:48:38, 4.13s/it] 8%|▊ | 1423/18688 [1:39:27<21:44:50, 4.53s/it] 8%|▊ | 1424/18688 [1:39:30<20:28:57, 4.27s/it] 8%|▊ | 1425/18688 [1:39:34<19:26:01, 4.05s/it] 8%|▊ | 1426/18688 [1:39:38<19:10:00, 4.00s/it] 8%|▊ | 1427/18688 [1:39:42<19:34:09, 4.08s/it] 8%|▊ | 1428/18688 [1:39:46<19:02:54, 3.97s/it] 8%|▊ | 1429/18688 [1:39:50<20:00:37, 4.17s/it] 8%|▊ | 1430/18688 [1:39:55<19:59:24, 4.17s/it] {'loss': '1.037', 'grad_norm': '1.304', 'learning_rate': '0.0001715', 'epoch': '0.07652', 'num_input_tokens_seen': 4771024, 'train_runtime': '5995', 'train_tokens_per_second': '795.8'}
8%|▊ | 1430/18688 [1:39:55<19:59:24, 4.17s/it] 8%|▊ | 1431/18688 [1:39:58<19:02:28, 3.97s/it] 8%|▊ | 1432/18688 [1:40:02<18:28:03, 3.85s/it] 8%|▊ | 1433/18688 [1:40:06<19:00:38, 3.97s/it] 8%|▊ | 1434/18688 [1:40:10<19:13:15, 4.01s/it] 8%|▊ | 1435/18688 [1:40:14<19:40:35, 4.11s/it] 8%|▊ | 1436/18688 [1:40:18<18:46:38, 3.92s/it] 8%|▊ | 1437/18688 [1:40:22<19:13:04, 4.01s/it] 8%|▊ | 1438/18688 [1:40:26<18:21:06, 3.83s/it] 8%|▊ | 1439/18688 [1:40:29<18:12:34, 3.80s/it] 8%|▊ | 1440/18688 [1:40:33<18:21:29, 3.83s/it] {'loss': '0.9473', 'grad_norm': '1.125', 'learning_rate': '0.0001727', 'epoch': '0.07706', 'num_input_tokens_seen': 4800712, 'train_runtime': '6034', 'train_tokens_per_second': '795.7'}
8%|▊ | 1440/18688 [1:40:33<18:21:29, 3.83s/it] 8%|▊ | 1441/18688 [1:40:37<18:27:04, 3.85s/it] 8%|▊ | 1442/18688 [1:40:41<18:53:10, 3.94s/it] 8%|▊ | 1443/18688 [1:40:46<20:40:48, 4.32s/it] 8%|▊ | 1444/18688 [1:40:50<19:57:45, 4.17s/it] 8%|▊ | 1445/18688 [1:40:54<19:56:07, 4.16s/it] 8%|▊ | 1446/18688 [1:40:58<19:06:00, 3.99s/it] 8%|▊ | 1447/18688 [1:41:02<19:20:25, 4.04s/it] 8%|▊ | 1448/18688 [1:41:06<18:47:40, 3.92s/it] 8%|▊ | 1449/18688 [1:41:10<18:57:56, 3.96s/it] 8%|▊ | 1450/18688 [1:41:14<18:55:13, 3.95s/it] {'loss': '1.044', 'grad_norm': '1.092', 'learning_rate': '0.0001739', 'epoch': '0.07759', 'num_input_tokens_seen': 4835448, 'train_runtime': '6074', 'train_tokens_per_second': '796.1'}
8%|▊ | 1450/18688 [1:41:14<18:55:13, 3.95s/it] 8%|▊ | 1451/18688 [1:41:18<18:44:44, 3.92s/it] 8%|▊ | 1452/18688 [1:41:21<18:03:15, 3.77s/it] 8%|▊ | 1453/18688 [1:41:25<18:33:27, 3.88s/it] 8%|▊ | 1454/18688 [1:41:29<19:00:50, 3.97s/it] 8%|▊ | 1455/18688 [1:41:34<20:02:40, 4.19s/it] 8%|▊ | 1456/18688 [1:41:38<19:29:28, 4.07s/it] 8%|▊ | 1457/18688 [1:41:42<20:12:50, 4.22s/it] 8%|▊ | 1458/18688 [1:41:46<19:19:01, 4.04s/it] 8%|▊ | 1459/18688 [1:41:49<18:26:37, 3.85s/it] 8%|▊ | 1460/18688 [1:41:55<20:19:00, 4.25s/it] {'loss': '0.9937', 'grad_norm': '1.011', 'learning_rate': '0.0001751', 'epoch': '0.07813', 'num_input_tokens_seen': 4868640, 'train_runtime': '6115', 'train_tokens_per_second': '796.2'}
8%|▊ | 1460/18688 [1:41:55<20:19:00, 4.25s/it] 8%|▊ | 1461/18688 [1:41:59<20:06:55, 4.20s/it] 8%|▊ | 1462/18688 [1:42:04<21:02:22, 4.40s/it] 8%|▊ | 1463/18688 [1:42:07<19:48:03, 4.14s/it] 8%|▊ | 1464/18688 [1:42:12<21:28:59, 4.49s/it] 8%|▊ | 1465/18688 [1:42:17<21:33:34, 4.51s/it] 8%|▊ | 1466/18688 [1:42:21<21:11:11, 4.43s/it] 8%|▊ | 1467/18688 [1:42:26<22:02:48, 4.61s/it] 8%|▊ | 1468/18688 [1:42:31<22:01:29, 4.60s/it] 8%|▊ | 1469/18688 [1:42:35<22:02:13, 4.61s/it] 8%|▊ | 1470/18688 [1:42:40<21:44:39, 4.55s/it] {'loss': '1.052', 'grad_norm': '1.095', 'learning_rate': '0.0001763', 'epoch': '0.07866', 'num_input_tokens_seen': 4907240, 'train_runtime': '6160', 'train_tokens_per_second': '796.6'}
8%|▊ | 1470/18688 [1:42:40<21:44:39, 4.55s/it] 8%|▊ | 1471/18688 [1:42:44<21:42:18, 4.54s/it] 8%|▊ | 1472/18688 [1:42:48<20:36:45, 4.31s/it] 8%|▊ | 1473/18688 [1:42:53<21:26:36, 4.48s/it] 8%|▊ | 1474/18688 [1:42:57<21:19:25, 4.46s/it] 8%|▊ | 1475/18688 [1:43:02<21:31:54, 4.50s/it] 8%|▊ | 1476/18688 [1:43:06<20:30:10, 4.29s/it] 8%|▊ | 1477/18688 [1:43:10<20:15:07, 4.24s/it] 8%|▊ | 1478/18688 [1:43:13<19:06:57, 4.00s/it] 8%|▊ | 1479/18688 [1:43:17<18:31:56, 3.88s/it] 8%|▊ | 1480/18688 [1:43:21<18:50:09, 3.94s/it] {'loss': '0.9754', 'grad_norm': '1.06', 'learning_rate': '0.0001775', 'epoch': '0.0792', 'num_input_tokens_seen': 4940128, 'train_runtime': '6202', 'train_tokens_per_second': '796.6'}
8%|▊ | 1480/18688 [1:43:21<18:50:09, 3.94s/it] 8%|▊ | 1481/18688 [1:43:25<18:25:09, 3.85s/it] 8%|▊ | 1482/18688 [1:43:28<18:08:37, 3.80s/it] 8%|▊ | 1483/18688 [1:43:33<19:37:45, 4.11s/it] 8%|▊ | 1484/18688 [1:43:37<19:47:18, 4.14s/it] 8%|▊ | 1485/18688 [1:43:42<20:01:44, 4.19s/it] 8%|▊ | 1486/18688 [1:43:47<21:14:28, 4.45s/it] 8%|▊ | 1487/18688 [1:43:50<19:53:56, 4.16s/it] 8%|▊ | 1488/18688 [1:43:54<19:11:16, 4.02s/it] 8%|▊ | 1489/18688 [1:43:57<18:20:32, 3.84s/it] 8%|▊ | 1490/18688 [1:44:02<18:53:00, 3.95s/it] {'loss': '1.024', 'grad_norm': '1.359', 'learning_rate': '0.0001787', 'epoch': '0.07973', 'num_input_tokens_seen': 4971208, 'train_runtime': '6242', 'train_tokens_per_second': '796.4'}
8%|▊ | 1490/18688 [1:44:02<18:53:00, 3.95s/it] 8%|▊ | 1491/18688 [1:44:05<18:40:44, 3.91s/it] 8%|▊ | 1492/18688 [1:44:10<19:57:04, 4.18s/it] 8%|▊ | 1493/18688 [1:44:15<20:24:47, 4.27s/it] 8%|▊ | 1494/18688 [1:44:19<20:04:02, 4.20s/it] 8%|▊ | 1495/18688 [1:44:22<19:14:35, 4.03s/it] 8%|▊ | 1496/18688 [1:44:27<19:55:52, 4.17s/it] 8%|▊ | 1497/18688 [1:44:32<20:50:14, 4.36s/it] 8%|▊ | 1498/18688 [1:44:36<20:28:30, 4.29s/it] 8%|▊ | 1499/18688 [1:44:40<20:22:19, 4.27s/it] 8%|▊ | 1500/18688 [1:44:44<19:26:10, 4.07s/it] {'loss': '1.042', 'grad_norm': '1.354', 'learning_rate': '0.0001799', 'epoch': '0.08027', 'num_input_tokens_seen': 5004640, 'train_runtime': '6284', 'train_tokens_per_second': '796.4'}
8%|▊ | 1500/18688 [1:44:44<19:26:10, 4.07s/it] 8%|▊ | 1501/18688 [1:44:48<19:22:06, 4.06s/it] 8%|▊ | 1502/18688 [1:44:52<19:19:03, 4.05s/it] 8%|▊ | 1503/18688 [1:44:56<19:14:40, 4.03s/it] 8%|▊ | 1504/18688 [1:45:00<18:59:27, 3.98s/it] 8%|▊ | 1505/18688 [1:45:04<19:20:17, 4.05s/it] 8%|▊ | 1506/18688 [1:45:07<18:33:53, 3.89s/it] 8%|▊ | 1507/18688 [1:45:11<18:12:54, 3.82s/it] 8%|▊ | 1508/18688 [1:45:15<19:18:43, 4.05s/it] 8%|▊ | 1509/18688 [1:45:20<19:41:15, 4.13s/it] 8%|▊ | 1510/18688 [1:45:24<19:22:16, 4.06s/it] {'loss': '1.021', 'grad_norm': '3.014', 'learning_rate': '0.00018', 'epoch': '0.0808', 'num_input_tokens_seen': 5038496, 'train_runtime': '6324', 'train_tokens_per_second': '796.7'}
8%|▊ | 1510/18688 [1:45:24<19:22:16, 4.06s/it] 8%|▊ | 1511/18688 [1:45:27<18:48:35, 3.94s/it] 8%|▊ | 1512/18688 [1:45:32<19:46:21, 4.14s/it] 8%|▊ | 1513/18688 [1:45:36<19:40:53, 4.13s/it] 8%|▊ | 1514/18688 [1:45:40<19:33:34, 4.10s/it] 8%|▊ | 1515/18688 [1:45:45<20:16:57, 4.25s/it] 8%|▊ | 1516/18688 [1:45:48<19:25:40, 4.07s/it] 8%|▊ | 1517/18688 [1:45:52<18:50:10, 3.95s/it] 8%|▊ | 1518/18688 [1:45:56<18:49:45, 3.95s/it] 8%|▊ | 1519/18688 [1:46:00<19:26:36, 4.08s/it] 8%|▊ | 1520/18688 [1:46:05<20:06:47, 4.22s/it] {'loss': '0.9908', 'grad_norm': '1.17', 'learning_rate': '0.00018', 'epoch': '0.08134', 'num_input_tokens_seen': 5072456, 'train_runtime': '6365', 'train_tokens_per_second': '796.9'}
8%|▊ | 1520/18688 [1:46:05<20:06:47, 4.22s/it] 8%|▊ | 1521/18688 [1:46:10<20:48:46, 4.36s/it] 8%|▊ | 1522/18688 [1:46:14<21:22:58, 4.48s/it] 8%|▊ | 1523/18688 [1:46:20<22:32:01, 4.73s/it] 8%|▊ | 1524/18688 [1:46:23<21:09:15, 4.44s/it] 8%|▊ | 1525/18688 [1:46:28<21:03:40, 4.42s/it] 8%|▊ | 1526/18688 [1:46:33<21:34:06, 4.52s/it] 8%|▊ | 1527/18688 [1:46:37<21:59:20, 4.61s/it] 8%|▊ | 1528/18688 [1:46:41<21:05:06, 4.42s/it] 8%|▊ | 1529/18688 [1:46:47<22:48:47, 4.79s/it] 8%|▊ | 1530/18688 [1:46:52<22:36:57, 4.75s/it] {'loss': '0.9876', 'grad_norm': '1.327', 'learning_rate': '0.00018', 'epoch': '0.08187', 'num_input_tokens_seen': 5109856, 'train_runtime': '6412', 'train_tokens_per_second': '796.9'}
8%|▊ | 1530/18688 [1:46:52<22:36:57, 4.75s/it] 8%|▊ | 1531/18688 [1:46:56<22:38:17, 4.75s/it] 8%|▊ | 1532/18688 [1:47:02<23:13:58, 4.88s/it] 8%|▊ | 1533/18688 [1:47:05<21:53:15, 4.59s/it] 8%|▊ | 1534/18688 [1:47:11<22:44:32, 4.77s/it] 8%|▊ | 1535/18688 [1:47:14<21:19:38, 4.48s/it] 8%|▊ | 1536/18688 [1:47:19<21:07:14, 4.43s/it] 8%|▊ | 1537/18688 [1:47:24<22:09:23, 4.65s/it] 8%|▊ | 1538/18688 [1:47:28<20:39:47, 4.34s/it] 8%|▊ | 1539/18688 [1:47:31<19:32:59, 4.10s/it] 8%|▊ | 1540/18688 [1:47:36<20:23:05, 4.28s/it] {'loss': '1.012', 'grad_norm': '1.274', 'learning_rate': '0.00018', 'epoch': '0.08241', 'num_input_tokens_seen': 5146272, 'train_runtime': '6456', 'train_tokens_per_second': '797.1'}
8%|▊ | 1540/18688 [1:47:36<20:23:05, 4.28s/it] 8%|▊ | 1541/18688 [1:47:40<20:30:38, 4.31s/it] 8%|▊ | 1542/18688 [1:47:45<21:04:54, 4.43s/it] 8%|▊ | 1543/18688 [1:47:49<20:36:39, 4.33s/it] 8%|▊ | 1544/18688 [1:47:53<19:36:36, 4.12s/it] 8%|▊ | 1545/18688 [1:47:56<18:39:56, 3.92s/it] 8%|▊ | 1546/18688 [1:48:00<18:35:58, 3.91s/it] 8%|▊ | 1547/18688 [1:48:04<18:33:42, 3.90s/it] 8%|▊ | 1548/18688 [1:48:09<20:28:21, 4.30s/it] 8%|▊ | 1549/18688 [1:48:13<20:25:16, 4.29s/it] 8%|▊ | 1550/18688 [1:48:17<19:54:41, 4.18s/it] {'loss': '0.918', 'grad_norm': '1.115', 'learning_rate': '0.00018', 'epoch': '0.08294', 'num_input_tokens_seen': 5178800, 'train_runtime': '6498', 'train_tokens_per_second': '797'}
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8%|▊ | 1570/18688 [1:49:44<20:52:14, 4.39s/it] 8%|▊ | 1571/18688 [1:49:49<21:13:59, 4.47s/it] 8%|▊ | 1572/18688 [1:49:53<21:00:27, 4.42s/it] 8%|▊ | 1573/18688 [1:49:57<20:00:18, 4.21s/it] 8%|▊ | 1574/18688 [1:50:01<19:23:00, 4.08s/it] 8%|▊ | 1575/18688 [1:50:05<19:13:25, 4.04s/it] 8%|▊ | 1576/18688 [1:50:09<19:51:50, 4.18s/it] 8%|▊ | 1577/18688 [1:50:13<19:36:44, 4.13s/it] 8%|▊ | 1578/18688 [1:50:18<19:58:51, 4.20s/it] 8%|▊ | 1579/18688 [1:50:22<19:40:12, 4.14s/it] 8%|▊ | 1580/18688 [1:50:26<19:58:31, 4.20s/it] {'loss': '1.014', 'grad_norm': '0.9914', 'learning_rate': '0.00018', 'epoch': '0.08455', 'num_input_tokens_seen': 5280136, 'train_runtime': '6626', 'train_tokens_per_second': '796.8'}
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10%|▉ | 1820/18688 [2:06:54<18:35:10, 3.97s/it] 10%|▉ | 1821/18688 [2:06:58<18:20:36, 3.92s/it] 10%|▉ | 1822/18688 [2:07:02<18:16:02, 3.90s/it] 10%|▉ | 1823/18688 [2:07:06<18:48:34, 4.02s/it] 10%|▉ | 1824/18688 [2:07:10<19:02:58, 4.07s/it] 10%|▉ | 1825/18688 [2:07:14<19:01:28, 4.06s/it] 10%|▉ | 1826/18688 [2:07:18<18:33:09, 3.96s/it] 10%|▉ | 1827/18688 [2:07:22<18:41:24, 3.99s/it] 10%|▉ | 1828/18688 [2:07:27<19:48:26, 4.23s/it] 10%|▉ | 1829/18688 [2:07:31<20:00:06, 4.27s/it] 10%|▉ | 1830/18688 [2:07:36<20:36:12, 4.40s/it] {'loss': '1.068', 'grad_norm': '1.116', 'learning_rate': '0.0001798', 'epoch': '0.09793', 'num_input_tokens_seen': 6107064, 'train_runtime': '7656', 'train_tokens_per_second': '797.7'}
10%|▉ | 1830/18688 [2:07:36<20:36:12, 4.40s/it] 10%|▉ | 1831/18688 [2:07:40<21:07:53, 4.51s/it] 10%|▉ | 1832/18688 [2:07:44<20:21:47, 4.35s/it] 10%|▉ | 1833/18688 [2:07:49<20:30:09, 4.38s/it] 10%|▉ | 1834/18688 [2:07:53<20:46:57, 4.44s/it] 10%|▉ | 1835/18688 [2:07:58<21:15:13, 4.54s/it] 10%|▉ | 1836/18688 [2:08:03<21:16:58, 4.55s/it] 10%|▉ | 1837/18688 [2:08:07<20:15:05, 4.33s/it] 10%|▉ | 1838/18688 [2:08:10<19:17:35, 4.12s/it] 10%|▉ | 1839/18688 [2:08:15<19:29:28, 4.16s/it] 10%|▉ | 1840/18688 [2:08:20<21:17:40, 4.55s/it] {'loss': '0.9973', 'grad_norm': '1.311', 'learning_rate': '0.0001798', 'epoch': '0.09846', 'num_input_tokens_seen': 6144296, 'train_runtime': '7700', 'train_tokens_per_second': '797.9'}
10%|▉ | 1840/18688 [2:08:20<21:17:40, 4.55s/it] 10%|▉ | 1841/18688 [2:08:23<19:45:58, 4.22s/it] 10%|▉ | 1842/18688 [2:08:27<18:49:22, 4.02s/it] 10%|▉ | 1843/18688 [2:08:31<19:16:32, 4.12s/it] 10%|▉ | 1844/18688 [2:08:36<19:30:01, 4.17s/it] 10%|▉ | 1845/18688 [2:08:40<20:03:56, 4.29s/it] 10%|▉ | 1846/18688 [2:08:45<20:35:19, 4.40s/it] 10%|▉ | 1847/18688 [2:08:50<21:34:55, 4.61s/it] 10%|▉ | 1848/18688 [2:08:55<21:59:42, 4.70s/it] 10%|▉ | 1849/18688 [2:08:58<20:26:34, 4.37s/it] 10%|▉ | 1850/18688 [2:09:03<20:20:37, 4.35s/it] {'loss': '1.031', 'grad_norm': '1.288', 'learning_rate': '0.0001798', 'epoch': '0.099', 'num_input_tokens_seen': 6179064, 'train_runtime': '7743', 'train_tokens_per_second': '798'}
10%|▉ | 1850/18688 [2:09:03<20:20:37, 4.35s/it] 10%|▉ | 1851/18688 [2:09:07<20:45:06, 4.44s/it] 10%|▉ | 1852/18688 [2:09:12<20:28:17, 4.38s/it] 10%|▉ | 1853/18688 [2:09:16<19:48:19, 4.24s/it] 10%|▉ | 1854/18688 [2:09:20<20:19:50, 4.35s/it] 10%|▉ | 1855/18688 [2:09:25<20:35:55, 4.41s/it] 10%|▉ | 1856/18688 [2:09:29<20:55:55, 4.48s/it] 10%|▉ | 1857/18688 [2:09:33<20:13:10, 4.32s/it] 10%|▉ | 1858/18688 [2:09:37<19:43:10, 4.22s/it] 10%|▉ | 1859/18688 [2:09:41<19:18:17, 4.13s/it] 10%|▉ | 1860/18688 [2:09:46<20:12:35, 4.32s/it] {'loss': '1.015', 'grad_norm': '2.019', 'learning_rate': '0.0001798', 'epoch': '0.09953', 'num_input_tokens_seen': 6213504, 'train_runtime': '7787', 'train_tokens_per_second': '798'}
10%|▉ | 1860/18688 [2:09:46<20:12:35, 4.32s/it] 10%|▉ | 1861/18688 [2:09:50<19:57:06, 4.27s/it] 10%|▉ | 1862/18688 [2:09:55<20:31:01, 4.39s/it] 10%|▉ | 1863/18688 [2:09:59<20:46:06, 4.44s/it] 10%|▉ | 1864/18688 [2:10:04<20:43:08, 4.43s/it] 10%|▉ | 1865/18688 [2:10:09<21:18:22, 4.56s/it] 10%|▉ | 1866/18688 [2:10:13<20:58:52, 4.49s/it] 10%|▉ | 1867/18688 [2:10:17<20:16:06, 4.34s/it] 10%|▉ | 1868/18688 [2:10:22<20:47:34, 4.45s/it] 10%|█ | 1869/18688 [2:10:27<21:31:54, 4.61s/it] 10%|█ | 1870/18688 [2:10:31<20:39:45, 4.42s/it] {'loss': '1.001', 'grad_norm': '1.66', 'learning_rate': '0.0001798', 'epoch': '0.1001', 'num_input_tokens_seen': 6248336, 'train_runtime': '7831', 'train_tokens_per_second': '797.9'}
10%|█ | 1870/18688 [2:10:31<20:39:45, 4.42s/it] 10%|█ | 1871/18688 [2:10:35<20:12:36, 4.33s/it] 10%|█ | 1872/18688 [2:10:39<20:04:33, 4.30s/it] 10%|█ | 1873/18688 [2:10:43<19:38:13, 4.20s/it] 10%|█ | 1874/18688 [2:10:48<20:12:40, 4.33s/it] 10%|█ | 1875/18688 [2:10:52<20:38:54, 4.42s/it] 10%|█ | 1876/18688 [2:10:58<22:15:41, 4.77s/it] 10%|█ | 1877/18688 [2:11:02<21:11:30, 4.54s/it] 10%|█ | 1878/18688 [2:11:06<20:45:26, 4.45s/it] 10%|█ | 1879/18688 [2:11:10<20:18:11, 4.35s/it] 10%|█ | 1880/18688 [2:11:14<18:57:09, 4.06s/it] {'loss': '0.986', 'grad_norm': '1.312', 'learning_rate': '0.0001798', 'epoch': '0.1006', 'num_input_tokens_seen': 6282464, 'train_runtime': '7874', 'train_tokens_per_second': '797.9'}
10%|█ | 1880/18688 [2:11:14<18:57:09, 4.06s/it] 10%|█ | 1881/18688 [2:11:17<18:47:47, 4.03s/it] 10%|█ | 1882/18688 [2:11:21<18:14:50, 3.91s/it] 10%|█ | 1883/18688 [2:11:27<20:34:22, 4.41s/it] 10%|█ | 1884/18688 [2:11:30<19:21:39, 4.15s/it] 10%|█ | 1885/18688 [2:11:34<19:04:36, 4.09s/it] 10%|█ | 1886/18688 [2:11:39<19:32:34, 4.19s/it] 10%|█ | 1887/18688 [2:11:43<19:40:56, 4.22s/it] 10%|█ | 1888/18688 [2:11:47<20:03:29, 4.30s/it] 10%|█ | 1889/18688 [2:11:52<19:57:36, 4.28s/it] 10%|█ | 1890/18688 [2:11:56<19:43:22, 4.23s/it] {'loss': '0.9768', 'grad_norm': '1.17', 'learning_rate': '0.0001798', 'epoch': '0.1011', 'num_input_tokens_seen': 6317720, 'train_runtime': '7916', 'train_tokens_per_second': '798.1'}
10%|█ | 1890/18688 [2:11:56<19:43:22, 4.23s/it] 10%|█ | 1891/18688 [2:11:59<18:32:43, 3.97s/it] 10%|█ | 1892/18688 [2:12:03<18:41:13, 4.01s/it] 10%|█ | 1893/18688 [2:12:07<18:16:24, 3.92s/it] 10%|█ | 1894/18688 [2:12:10<17:45:02, 3.81s/it] 10%|█ | 1895/18688 [2:12:15<18:46:17, 4.02s/it] 10%|█ | 1896/18688 [2:12:19<18:48:30, 4.03s/it] 10%|█ | 1897/18688 [2:12:23<18:51:08, 4.04s/it] 10%|█ | 1898/18688 [2:12:27<18:45:07, 4.02s/it] 10%|█ | 1899/18688 [2:12:32<20:04:56, 4.31s/it] 10%|█ | 1900/18688 [2:12:36<18:59:18, 4.07s/it] {'loss': '0.9499', 'grad_norm': '1.641', 'learning_rate': '0.0001798', 'epoch': '0.1017', 'num_input_tokens_seen': 6351128, 'train_runtime': '7956', 'train_tokens_per_second': '798.3'}
10%|█ | 1900/18688 [2:12:36<18:59:18, 4.07s/it] 10%|█ | 1901/18688 [2:12:39<18:19:16, 3.93s/it] 10%|█ | 1902/18688 [2:12:43<18:23:24, 3.94s/it] 10%|█ | 1903/18688 [2:12:47<18:10:25, 3.90s/it] 10%|█ | 1904/18688 [2:12:51<18:50:15, 4.04s/it] 10%|█ | 1905/18688 [2:12:56<20:28:41, 4.39s/it] 10%|█ | 1906/18688 [2:13:01<20:42:07, 4.44s/it] 10%|█ | 1907/18688 [2:13:06<20:49:27, 4.47s/it] 10%|█ | 1908/18688 [2:13:09<19:25:22, 4.17s/it] 10%|█ | 1909/18688 [2:13:13<18:56:16, 4.06s/it] 10%|█ | 1910/18688 [2:13:18<19:55:20, 4.27s/it] {'loss': '1.025', 'grad_norm': '1.203', 'learning_rate': '0.0001797', 'epoch': '0.1022', 'num_input_tokens_seen': 6385648, 'train_runtime': '7998', 'train_tokens_per_second': '798.4'}
10%|█ | 1910/18688 [2:13:18<19:55:20, 4.27s/it] 10%|█ | 1911/18688 [2:13:21<18:56:24, 4.06s/it] 10%|█ | 1912/18688 [2:13:25<18:06:33, 3.89s/it] 10%|█ | 1913/18688 [2:13:28<17:56:59, 3.85s/it] 10%|█ | 1914/18688 [2:13:32<17:34:25, 3.77s/it] 10%|█ | 1915/18688 [2:13:36<17:15:11, 3.70s/it] 10%|█ | 1916/18688 [2:13:39<16:50:15, 3.61s/it] 10%|█ | 1917/18688 [2:13:43<17:02:28, 3.66s/it] 10%|█ | 1918/18688 [2:13:47<17:11:31, 3.69s/it] 10%|█ | 1919/18688 [2:13:51<18:26:58, 3.96s/it] 10%|█ | 1920/18688 [2:13:55<18:09:56, 3.90s/it] {'loss': '1.003', 'grad_norm': '1.283', 'learning_rate': '0.0001797', 'epoch': '0.1027', 'num_input_tokens_seen': 6414704, 'train_runtime': '8035', 'train_tokens_per_second': '798.3'}
10%|█ | 1920/18688 [2:13:55<18:09:56, 3.90s/it] 10%|█ | 1921/18688 [2:13:59<18:48:18, 4.04s/it] 10%|█ | 1922/18688 [2:14:03<18:53:17, 4.06s/it] 10%|█ | 1923/18688 [2:14:08<19:43:28, 4.24s/it] 10%|█ | 1924/18688 [2:14:11<18:35:23, 3.99s/it] 10%|█ | 1925/18688 [2:14:15<18:07:06, 3.89s/it] 10%|█ | 1926/18688 [2:14:19<18:39:19, 4.01s/it] 10%|█ | 1927/18688 [2:14:23<18:08:24, 3.90s/it] 10%|█ | 1928/18688 [2:14:27<18:10:07, 3.90s/it] 10%|█ | 1929/18688 [2:14:32<19:13:22, 4.13s/it] 10%|█ | 1930/18688 [2:14:35<18:31:18, 3.98s/it] {'loss': '1.025', 'grad_norm': '1.283', 'learning_rate': '0.0001797', 'epoch': '0.1033', 'num_input_tokens_seen': 6449208, 'train_runtime': '8076', 'train_tokens_per_second': '798.6'}
10%|█ | 1930/18688 [2:14:35<18:31:18, 3.98s/it] 10%|█ | 1931/18688 [2:14:39<18:16:01, 3.92s/it] 10%|█ | 1932/18688 [2:14:42<17:38:40, 3.79s/it] 10%|█ | 1933/18688 [2:14:46<17:08:40, 3.68s/it] 10%|█ | 1934/18688 [2:14:50<17:55:00, 3.85s/it] 10%|█ | 1935/18688 [2:14:55<19:11:36, 4.12s/it] 10%|█ | 1936/18688 [2:14:59<18:47:37, 4.04s/it] 10%|█ | 1937/18688 [2:15:03<18:26:37, 3.96s/it] 10%|█ | 1938/18688 [2:15:07<19:39:39, 4.23s/it] 10%|█ | 1939/18688 [2:15:11<18:45:04, 4.03s/it] 10%|█ | 1940/18688 [2:15:15<19:11:50, 4.13s/it] {'loss': '1.011', 'grad_norm': '1.342', 'learning_rate': '0.0001797', 'epoch': '0.1038', 'num_input_tokens_seen': 6482200, 'train_runtime': '8116', 'train_tokens_per_second': '798.7'}
10%|█ | 1940/18688 [2:15:15<19:11:50, 4.13s/it] 10%|█ | 1941/18688 [2:15:19<19:08:47, 4.12s/it] 10%|█ | 1942/18688 [2:15:23<18:55:26, 4.07s/it] 10%|█ | 1943/18688 [2:15:27<19:03:26, 4.10s/it] 10%|█ | 1944/18688 [2:15:31<18:19:59, 3.94s/it] 10%|█ | 1945/18688 [2:15:35<17:56:17, 3.86s/it] 10%|█ | 1946/18688 [2:15:39<18:05:18, 3.89s/it] 10%|█ | 1947/18688 [2:15:43<18:43:33, 4.03s/it] 10%|█ | 1948/18688 [2:15:48<19:46:47, 4.25s/it] 10%|█ | 1949/18688 [2:15:52<19:16:33, 4.15s/it] 10%|█ | 1950/18688 [2:15:56<19:15:55, 4.14s/it] {'loss': '1.071', 'grad_norm': '1.332', 'learning_rate': '0.0001797', 'epoch': '0.1043', 'num_input_tokens_seen': 6514736, 'train_runtime': '8156', 'train_tokens_per_second': '798.7'}
10%|█ | 1950/18688 [2:15:56<19:15:55, 4.14s/it] 10%|█ | 1951/18688 [2:16:00<19:17:10, 4.15s/it] 10%|█ | 1952/18688 [2:16:05<19:48:30, 4.26s/it] 10%|█ | 1953/18688 [2:16:09<20:31:28, 4.42s/it] 10%|█ | 1954/18688 [2:16:13<19:59:10, 4.30s/it] 10%|█ | 1955/18688 [2:16:18<19:58:57, 4.30s/it] 10%|█ | 1956/18688 [2:16:22<20:17:49, 4.37s/it] 10%|█ | 1957/18688 [2:16:27<20:30:43, 4.41s/it] 10%|█ | 1958/18688 [2:16:32<21:29:07, 4.62s/it] 10%|█ | 1959/18688 [2:16:36<21:05:40, 4.54s/it] 10%|█ | 1960/18688 [2:16:40<20:27:21, 4.40s/it] {'loss': '1.037', 'grad_norm': '1.374', 'learning_rate': '0.0001797', 'epoch': '0.1049', 'num_input_tokens_seen': 6548928, 'train_runtime': '8201', 'train_tokens_per_second': '798.6'}
10%|█ | 1960/18688 [2:16:40<20:27:21, 4.40s/it] 10%|█ | 1961/18688 [2:16:44<19:28:47, 4.19s/it] 10%|█ | 1962/18688 [2:16:49<20:53:47, 4.50s/it] 11%|█ | 1963/18688 [2:16:53<20:15:12, 4.36s/it] 11%|█ | 1964/18688 [2:16:58<21:26:22, 4.62s/it] 11%|█ | 1965/18688 [2:17:02<20:03:58, 4.32s/it] 11%|█ | 1966/18688 [2:17:07<20:24:26, 4.39s/it] 11%|█ | 1967/18688 [2:17:10<19:32:53, 4.21s/it] 11%|█ | 1968/18688 [2:17:15<19:28:20, 4.19s/it] 11%|█ | 1969/18688 [2:17:19<19:18:52, 4.16s/it] 11%|█ | 1970/18688 [2:17:22<18:18:59, 3.94s/it] {'loss': '0.979', 'grad_norm': '1.157', 'learning_rate': '0.0001797', 'epoch': '0.1054', 'num_input_tokens_seen': 6583256, 'train_runtime': '8243', 'train_tokens_per_second': '798.7'}
11%|█ | 1970/18688 [2:17:22<18:18:59, 3.94s/it] 11%|█ | 1971/18688 [2:17:26<17:47:03, 3.83s/it] 11%|█ | 1972/18688 [2:17:30<18:20:54, 3.95s/it] 11%|█ | 1973/18688 [2:17:34<18:42:19, 4.03s/it] 11%|█ | 1974/18688 [2:17:38<18:52:25, 4.07s/it] 11%|█ | 1975/18688 [2:17:43<19:46:02, 4.26s/it] 11%|█ | 1976/18688 [2:17:47<19:01:18, 4.10s/it] 11%|█ | 1977/18688 [2:17:51<19:34:09, 4.22s/it] 11%|█ | 1978/18688 [2:17:55<18:50:10, 4.06s/it] 11%|█ | 1979/18688 [2:18:00<19:53:38, 4.29s/it] 11%|█ | 1980/18688 [2:18:05<20:55:55, 4.51s/it] {'loss': '1.05', 'grad_norm': '1.439', 'learning_rate': '0.0001797', 'epoch': '0.106', 'num_input_tokens_seen': 6619632, 'train_runtime': '8285', 'train_tokens_per_second': '799'}
11%|█ | 1980/18688 [2:18:05<20:55:55, 4.51s/it] 11%|█ | 1981/18688 [2:18:09<20:13:54, 4.36s/it] 11%|█ | 1982/18688 [2:18:13<20:24:08, 4.40s/it] 11%|█ | 1983/18688 [2:18:17<19:37:42, 4.23s/it] 11%|█ | 1984/18688 [2:18:21<19:15:40, 4.15s/it] 11%|█ | 1985/18688 [2:18:25<19:22:49, 4.18s/it] 11%|█ | 1986/18688 [2:18:29<19:23:44, 4.18s/it] 11%|█ | 1987/18688 [2:18:33<18:25:12, 3.97s/it] 11%|█ | 1988/18688 [2:18:38<19:22:36, 4.18s/it] 11%|█ | 1989/18688 [2:18:42<20:10:18, 4.35s/it] 11%|█ | 1990/18688 [2:18:46<19:53:02, 4.29s/it] {'loss': '0.9976', 'grad_norm': '1.102', 'learning_rate': '0.0001796', 'epoch': '0.1065', 'num_input_tokens_seen': 6654592, 'train_runtime': '8327', 'train_tokens_per_second': '799.2'}
11%|█ | 1990/18688 [2:18:46<19:53:02, 4.29s/it] 11%|█ | 1991/18688 [2:18:51<20:29:11, 4.42s/it] 11%|█ | 1992/18688 [2:18:56<20:42:51, 4.47s/it] 11%|█ | 1993/18688 [2:19:00<20:15:39, 4.37s/it] 11%|█ | 1994/18688 [2:19:04<20:12:31, 4.36s/it] 11%|█ | 1995/18688 [2:19:09<21:05:33, 4.55s/it] 11%|█ | 1996/18688 [2:19:13<20:36:01, 4.44s/it] 11%|█ | 1997/18688 [2:19:17<19:40:21, 4.24s/it] 11%|█ | 1998/18688 [2:19:21<19:13:40, 4.15s/it] 11%|█ | 1999/18688 [2:19:25<18:47:22, 4.05s/it] 11%|█ | 2000/18688 [2:19:29<18:22:39, 3.96s/it] {'loss': '1.014', 'grad_norm': '1.274', 'learning_rate': '0.0001796', 'epoch': '0.107', 'num_input_tokens_seen': 6689560, 'train_runtime': '8369', 'train_tokens_per_second': '799.3'}
11%|█ | 2000/18688 [2:19:29<18:22:39, 3.96s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
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{'eval_loss': '0.9905', 'eval_runtime': '82.9', 'eval_samples_per_second': '12.06', 'eval_steps_per_second': '12.06', 'epoch': '0.107', 'num_input_tokens_seen': 6689560}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00018/checkpoint-2000
loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
11%|█ | 2001/18688 [2:20:58<136:33:58, 29.46s/it] 11%|█ | 2002/18688 [2:21:01<100:29:28, 21.68s/it] 11%|█ | 2003/18688 [2:21:05<76:14:53, 16.45s/it] 11%|█ | 2004/18688 [2:21:10<59:48:35, 12.91s/it] 11%|█ | 2005/18688 [2:21:15<48:07:16, 10.38s/it] 11%|█ | 2006/18688 [2:21:19<40:23:01, 8.71s/it] 11%|█ | 2007/18688 [2:21:24<35:08:58, 7.59s/it] 11%|█ | 2008/18688 [2:21:29<31:03:12, 6.70s/it] 11%|█ | 2009/18688 [2:21:32<26:40:42, 5.76s/it] 11%|█ | 2010/18688 [2:21:36<23:42:42, 5.12s/it] {'loss': '0.9452', 'grad_norm': '1.083', 'learning_rate': '0.0001796', 'epoch': '0.1076', 'num_input_tokens_seen': 6727088, 'train_runtime': '8497', 'train_tokens_per_second': '791.7'}
11%|█ | 2010/18688 [2:21:36<23:42:42, 5.12s/it] 11%|█ | 2011/18688 [2:21:40<22:32:34, 4.87s/it] 11%|█ | 2012/18688 [2:21:44<21:14:42, 4.59s/it] 11%|█ | 2013/18688 [2:21:48<20:10:02, 4.35s/it] 11%|█ | 2014/18688 [2:21:52<19:05:03, 4.12s/it] 11%|█ | 2015/18688 [2:21:56<19:54:46, 4.30s/it] 11%|█ | 2016/18688 [2:22:00<18:48:05, 4.06s/it] 11%|█ | 2017/18688 [2:22:04<18:16:43, 3.95s/it] 11%|█ | 2018/18688 [2:22:07<18:00:02, 3.89s/it] 11%|█ | 2019/18688 [2:22:11<18:08:29, 3.92s/it] 11%|█ | 2020/18688 [2:22:15<18:07:29, 3.91s/it] {'loss': '1.016', 'grad_norm': '1.287', 'learning_rate': '0.0001796', 'epoch': '0.1081', 'num_input_tokens_seen': 6760040, 'train_runtime': '8536', 'train_tokens_per_second': '792'}
11%|█ | 2020/18688 [2:22:15<18:07:29, 3.91s/it] 11%|█ | 2021/18688 [2:22:19<17:45:26, 3.84s/it] 11%|█ | 2022/18688 [2:22:23<18:45:47, 4.05s/it] 11%|█ | 2023/18688 [2:22:28<19:20:26, 4.18s/it] 11%|█ | 2024/18688 [2:22:33<19:54:20, 4.30s/it] 11%|█ | 2025/18688 [2:22:37<19:34:36, 4.23s/it] 11%|█ | 2026/18688 [2:22:41<19:15:42, 4.16s/it] 11%|█ | 2027/18688 [2:22:44<18:22:52, 3.97s/it] 11%|█ | 2028/18688 [2:22:48<18:08:02, 3.92s/it] 11%|█ | 2029/18688 [2:22:52<17:49:03, 3.85s/it] 11%|█ | 2030/18688 [2:22:56<17:52:02, 3.86s/it] {'loss': '1.032', 'grad_norm': '1.71', 'learning_rate': '0.0001796', 'epoch': '0.1086', 'num_input_tokens_seen': 6791920, 'train_runtime': '8576', 'train_tokens_per_second': '792'}
11%|█ | 2030/18688 [2:22:56<17:52:02, 3.86s/it] 11%|█ | 2031/18688 [2:23:00<18:27:37, 3.99s/it] 11%|█ | 2032/18688 [2:23:04<18:34:08, 4.01s/it] 11%|█ | 2033/18688 [2:23:08<19:05:59, 4.13s/it] 11%|█ | 2034/18688 [2:23:12<18:33:48, 4.01s/it] 11%|█ | 2035/18688 [2:23:16<18:56:13, 4.09s/it] 11%|█ | 2036/18688 [2:23:21<19:21:06, 4.18s/it] 11%|█ | 2037/18688 [2:23:24<18:27:26, 3.99s/it] 11%|█ | 2038/18688 [2:23:28<18:37:52, 4.03s/it] 11%|█ | 2039/18688 [2:23:33<18:54:04, 4.09s/it] 11%|█ | 2040/18688 [2:23:37<18:49:48, 4.07s/it] {'loss': '1.028', 'grad_norm': '1.515', 'learning_rate': '0.0001796', 'epoch': '0.1092', 'num_input_tokens_seen': 6827176, 'train_runtime': '8617', 'train_tokens_per_second': '792.3'}
11%|█ | 2040/18688 [2:23:37<18:49:48, 4.07s/it] 11%|█ | 2041/18688 [2:23:41<19:03:41, 4.12s/it] 11%|█ | 2042/18688 [2:23:44<18:07:37, 3.92s/it] 11%|█ | 2043/18688 [2:23:48<18:26:52, 3.99s/it] 11%|█ | 2044/18688 [2:23:53<19:26:16, 4.20s/it] 11%|█ | 2045/18688 [2:23:58<20:43:02, 4.48s/it] 11%|█ | 2046/18688 [2:24:02<20:21:12, 4.40s/it] 11%|█ | 2047/18688 [2:24:06<19:25:11, 4.20s/it] 11%|█ | 2048/18688 [2:24:10<19:26:58, 4.21s/it] 11%|█ | 2049/18688 [2:24:14<18:50:37, 4.08s/it] 11%|█ | 2050/18688 [2:24:18<18:41:13, 4.04s/it] {'loss': '0.9808', 'grad_norm': '1.162', 'learning_rate': '0.0001795', 'epoch': '0.1097', 'num_input_tokens_seen': 6859736, 'train_runtime': '8659', 'train_tokens_per_second': '792.2'}
11%|█ | 2050/18688 [2:24:18<18:41:13, 4.04s/it] 11%|█ | 2051/18688 [2:24:22<18:07:08, 3.92s/it] 11%|█ | 2052/18688 [2:24:27<19:22:08, 4.19s/it] 11%|█ | 2053/18688 [2:24:31<19:55:42, 4.31s/it] 11%|█ | 2054/18688 [2:24:36<19:52:06, 4.30s/it] 11%|█ | 2055/18688 [2:24:39<18:42:34, 4.05s/it] 11%|█ | 2056/18688 [2:24:43<18:06:21, 3.92s/it] 11%|█ | 2057/18688 [2:24:46<17:50:47, 3.86s/it] 11%|█ | 2058/18688 [2:24:50<17:36:50, 3.81s/it] 11%|█ | 2059/18688 [2:24:54<18:16:55, 3.96s/it] 11%|█ | 2060/18688 [2:24:59<18:40:08, 4.04s/it] {'loss': '0.9537', 'grad_norm': '0.9506', 'learning_rate': '0.0001795', 'epoch': '0.1102', 'num_input_tokens_seen': 6893248, 'train_runtime': '8699', 'train_tokens_per_second': '792.4'}
11%|█ | 2060/18688 [2:24:59<18:40:08, 4.04s/it] 11%|█ | 2061/18688 [2:25:03<19:16:13, 4.17s/it] 11%|█ | 2062/18688 [2:25:07<19:19:43, 4.19s/it] 11%|█ | 2063/18688 [2:25:11<18:57:46, 4.11s/it] 11%|█ | 2064/18688 [2:25:16<19:59:01, 4.33s/it] 11%|█ | 2065/18688 [2:25:20<19:34:15, 4.24s/it] 11%|█ | 2066/18688 [2:25:24<18:50:09, 4.08s/it] 11%|█ | 2067/18688 [2:25:28<19:19:05, 4.18s/it] 11%|█ | 2068/18688 [2:25:32<18:36:10, 4.03s/it] 11%|█ | 2069/18688 [2:25:36<19:18:13, 4.18s/it] 11%|█ | 2070/18688 [2:25:40<18:58:41, 4.11s/it] {'loss': '1.033', 'grad_norm': '1.152', 'learning_rate': '0.0001795', 'epoch': '0.1108', 'num_input_tokens_seen': 6926840, 'train_runtime': '8741', 'train_tokens_per_second': '792.5'}
11%|█ | 2070/18688 [2:25:40<18:58:41, 4.11s/it] 11%|█ | 2071/18688 [2:25:46<20:54:50, 4.53s/it] 11%|█ | 2072/18688 [2:25:50<20:12:37, 4.38s/it] 11%|█ | 2073/18688 [2:25:55<21:45:24, 4.71s/it] 11%|█ | 2074/18688 [2:25:59<20:28:54, 4.44s/it] 11%|█ | 2075/18688 [2:26:03<20:03:04, 4.35s/it] 11%|█ | 2076/18688 [2:26:07<19:36:57, 4.25s/it] 11%|█ | 2077/18688 [2:26:11<19:30:07, 4.23s/it] 11%|█ | 2078/18688 [2:26:16<19:16:25, 4.18s/it] 11%|█ | 2079/18688 [2:26:20<19:14:22, 4.17s/it] 11%|█ | 2080/18688 [2:26:24<20:02:54, 4.35s/it] {'loss': '0.975', 'grad_norm': '1.496', 'learning_rate': '0.0001795', 'epoch': '0.1113', 'num_input_tokens_seen': 6960856, 'train_runtime': '8785', 'train_tokens_per_second': '792.4'}
11%|█ | 2080/18688 [2:26:24<20:02:54, 4.35s/it] 11%|█ | 2081/18688 [2:26:28<19:28:07, 4.22s/it] 11%|█ | 2082/18688 [2:26:33<19:37:18, 4.25s/it] 11%|█ | 2083/18688 [2:26:38<20:22:53, 4.42s/it] 11%|█ | 2084/18688 [2:26:42<20:43:01, 4.49s/it] 11%|█ | 2085/18688 [2:26:46<19:25:40, 4.21s/it] 11%|█ | 2086/18688 [2:26:50<19:24:25, 4.21s/it] 11%|█ | 2087/18688 [2:26:54<18:45:51, 4.07s/it] 11%|█ | 2088/18688 [2:26:58<19:01:27, 4.13s/it] 11%|█ | 2089/18688 [2:27:01<18:10:11, 3.94s/it] 11%|█ | 2090/18688 [2:27:06<18:27:21, 4.00s/it] {'loss': '0.9411', 'grad_norm': '1.307', 'learning_rate': '0.0001795', 'epoch': '0.1118', 'num_input_tokens_seen': 6993128, 'train_runtime': '8826', 'train_tokens_per_second': '792.3'}
11%|█ | 2090/18688 [2:27:06<18:27:21, 4.00s/it] 11%|█ | 2091/18688 [2:27:10<18:42:25, 4.06s/it] 11%|█ | 2092/18688 [2:27:14<19:29:56, 4.23s/it] 11%|█ | 2093/18688 [2:27:18<18:52:23, 4.09s/it] 11%|█ | 2094/18688 [2:27:23<19:20:53, 4.20s/it] 11%|█ | 2095/18688 [2:27:27<20:07:02, 4.36s/it] 11%|█ | 2096/18688 [2:27:32<20:22:37, 4.42s/it] 11%|█ | 2097/18688 [2:27:36<20:25:22, 4.43s/it] 11%|█ | 2098/18688 [2:27:42<21:43:49, 4.72s/it] 11%|█ | 2099/18688 [2:27:46<20:58:36, 4.55s/it] 11%|█ | 2100/18688 [2:27:50<20:11:28, 4.38s/it] {'loss': '1.017', 'grad_norm': '1.455', 'learning_rate': '0.0001795', 'epoch': '0.1124', 'num_input_tokens_seen': 7027808, 'train_runtime': '8870', 'train_tokens_per_second': '792.3'}
11%|█ | 2100/18688 [2:27:50<20:11:28, 4.38s/it] 11%|█ | 2101/18688 [2:27:55<20:47:36, 4.51s/it] 11%|█ | 2102/18688 [2:27:59<20:09:55, 4.38s/it] 11%|█▏ | 2103/18688 [2:28:03<20:32:47, 4.46s/it] 11%|█▏ | 2104/18688 [2:28:08<20:00:37, 4.34s/it] 11%|█▏ | 2105/18688 [2:28:12<20:15:51, 4.40s/it] 11%|█▏ | 2106/18688 [2:28:16<19:36:45, 4.26s/it] 11%|█▏ | 2107/18688 [2:28:20<19:43:06, 4.28s/it] 11%|█▏ | 2108/18688 [2:28:24<19:26:56, 4.22s/it] 11%|█▏ | 2109/18688 [2:28:29<19:51:54, 4.31s/it] 11%|█▏ | 2110/18688 [2:28:34<20:46:50, 4.51s/it] {'loss': '0.9348', 'grad_norm': '1.456', 'learning_rate': '0.0001794', 'epoch': '0.1129', 'num_input_tokens_seen': 7060536, 'train_runtime': '8914', 'train_tokens_per_second': '792'}
11%|█▏ | 2110/18688 [2:28:34<20:46:50, 4.51s/it] 11%|█▏ | 2111/18688 [2:28:38<20:27:31, 4.44s/it] 11%|█▏ | 2112/18688 [2:28:42<20:06:37, 4.37s/it] 11%|█▏ | 2113/18688 [2:28:47<19:49:14, 4.30s/it] 11%|█▏ | 2114/18688 [2:28:52<20:56:15, 4.55s/it] 11%|█▏ | 2115/18688 [2:28:56<21:13:14, 4.61s/it] 11%|█▏ | 2116/18688 [2:29:00<19:56:03, 4.33s/it] 11%|█▏ | 2117/18688 [2:29:05<20:48:15, 4.52s/it] 11%|█▏ | 2118/18688 [2:29:10<21:00:27, 4.56s/it] 11%|█▏ | 2119/18688 [2:29:14<20:30:51, 4.46s/it] 11%|█▏ | 2120/18688 [2:29:19<20:42:20, 4.50s/it] {'loss': '1.075', 'grad_norm': '1.251', 'learning_rate': '0.0001794', 'epoch': '0.1134', 'num_input_tokens_seen': 7095224, 'train_runtime': '8959', 'train_tokens_per_second': '792'}
11%|█▏ | 2120/18688 [2:29:19<20:42:20, 4.50s/it] 11%|█▏ | 2121/18688 [2:29:23<20:18:39, 4.41s/it] 11%|█▏ | 2122/18688 [2:29:27<20:21:07, 4.42s/it] 11%|█▏ | 2123/18688 [2:29:32<20:17:09, 4.41s/it] 11%|█▏ | 2124/18688 [2:29:35<19:07:30, 4.16s/it] 11%|█▏ | 2125/18688 [2:29:39<18:19:57, 3.98s/it] 11%|█▏ | 2126/18688 [2:29:42<17:53:12, 3.89s/it] 11%|█▏ | 2127/18688 [2:29:46<17:38:07, 3.83s/it] 11%|█▏ | 2128/18688 [2:29:50<17:34:41, 3.82s/it] 11%|█▏ | 2129/18688 [2:29:55<19:14:35, 4.18s/it] 11%|█▏ | 2130/18688 [2:29:58<18:16:29, 3.97s/it] {'loss': '1.014', 'grad_norm': '1.69', 'learning_rate': '0.0001794', 'epoch': '0.114', 'num_input_tokens_seen': 7128176, 'train_runtime': '8999', 'train_tokens_per_second': '792.1'}
11%|█▏ | 2130/18688 [2:29:58<18:16:29, 3.97s/it] 11%|█▏ | 2131/18688 [2:30:02<18:04:03, 3.93s/it] 11%|█▏ | 2132/18688 [2:30:06<17:38:11, 3.83s/it] 11%|█▏ | 2133/18688 [2:30:11<19:02:00, 4.14s/it] 11%|█▏ | 2134/18688 [2:30:15<19:46:56, 4.30s/it] 11%|█▏ | 2135/18688 [2:30:19<18:47:53, 4.09s/it] 11%|█▏ | 2136/18688 [2:30:23<18:59:57, 4.13s/it] 11%|█▏ | 2137/18688 [2:30:27<18:49:18, 4.09s/it] 11%|█▏ | 2138/18688 [2:30:31<18:12:35, 3.96s/it] 11%|█▏ | 2139/18688 [2:30:35<18:02:03, 3.92s/it] 11%|█▏ | 2140/18688 [2:30:38<17:24:16, 3.79s/it] {'loss': '1.014', 'grad_norm': '1.306', 'learning_rate': '0.0001794', 'epoch': '0.1145', 'num_input_tokens_seen': 7160536, 'train_runtime': '9039', 'train_tokens_per_second': '792.2'}
11%|█▏ | 2140/18688 [2:30:38<17:24:16, 3.79s/it] 11%|█▏ | 2141/18688 [2:30:42<17:24:12, 3.79s/it] 11%|█▏ | 2142/18688 [2:30:45<16:54:30, 3.68s/it] 11%|█▏ | 2143/18688 [2:30:50<18:18:20, 3.98s/it] 11%|█▏ | 2144/18688 [2:30:54<18:02:26, 3.93s/it] 11%|█▏ | 2145/18688 [2:30:58<19:01:34, 4.14s/it] 11%|█▏ | 2146/18688 [2:31:03<18:55:47, 4.12s/it] 11%|█▏ | 2147/18688 [2:31:07<19:28:05, 4.24s/it] 11%|█▏ | 2148/18688 [2:31:11<19:24:36, 4.22s/it] 11%|█▏ | 2149/18688 [2:31:16<19:36:11, 4.27s/it] 12%|█▏ | 2150/18688 [2:31:19<18:31:28, 4.03s/it] {'loss': '0.9907', 'grad_norm': '1.365', 'learning_rate': '0.0001794', 'epoch': '0.1151', 'num_input_tokens_seen': 7194240, 'train_runtime': '9080', 'train_tokens_per_second': '792.4'}
12%|█▏ | 2150/18688 [2:31:19<18:31:28, 4.03s/it] 12%|█▏ | 2151/18688 [2:31:23<18:07:09, 3.94s/it] 12%|█▏ | 2152/18688 [2:31:27<18:28:46, 4.02s/it] 12%|█▏ | 2153/18688 [2:31:31<18:43:31, 4.08s/it] 12%|█▏ | 2154/18688 [2:31:35<18:02:06, 3.93s/it] 12%|█▏ | 2155/18688 [2:31:39<17:52:51, 3.89s/it] 12%|█▏ | 2156/18688 [2:31:42<17:39:40, 3.85s/it] 12%|█▏ | 2157/18688 [2:31:46<17:52:55, 3.89s/it] 12%|█▏ | 2158/18688 [2:31:50<17:43:25, 3.86s/it] 12%|█▏ | 2159/18688 [2:31:54<17:55:37, 3.90s/it] 12%|█▏ | 2160/18688 [2:31:58<18:11:08, 3.96s/it] {'loss': '1.009', 'grad_norm': '1.313', 'learning_rate': '0.0001793', 'epoch': '0.1156', 'num_input_tokens_seen': 7224368, 'train_runtime': '9119', 'train_tokens_per_second': '792.3'}
12%|█▏ | 2160/18688 [2:31:58<18:11:08, 3.96s/it] 12%|█▏ | 2161/18688 [2:32:03<19:19:56, 4.21s/it] 12%|█▏ | 2162/18688 [2:32:07<18:38:12, 4.06s/it] 12%|█▏ | 2163/18688 [2:32:11<18:37:18, 4.06s/it] 12%|█▏ | 2164/18688 [2:32:15<19:19:53, 4.21s/it] 12%|█▏ | 2165/18688 [2:32:19<18:21:47, 4.00s/it] 12%|█▏ | 2166/18688 [2:32:24<19:12:54, 4.19s/it] 12%|█▏ | 2167/18688 [2:32:28<19:43:42, 4.30s/it] 12%|█▏ | 2168/18688 [2:32:32<19:48:18, 4.32s/it] 12%|█▏ | 2169/18688 [2:32:37<20:14:06, 4.41s/it] 12%|█▏ | 2170/18688 [2:32:41<20:00:55, 4.36s/it] {'loss': '1.003', 'grad_norm': '1.181', 'learning_rate': '0.0001793', 'epoch': '0.1161', 'num_input_tokens_seen': 7257872, 'train_runtime': '9162', 'train_tokens_per_second': '792.2'}
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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)
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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': '0.9867', 'eval_runtime': '82.96', 'eval_samples_per_second': '12.05', 'eval_steps_per_second': '12.05', 'epoch': '0.1605', 'num_input_tokens_seen': 10063432}
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 16%|█▌ | 3001/18688 [3:32:00<126:34:31, 29.05s/it] 16%|█▌ | 3002/18688 [3:32:04<94:24:00, 21.67s/it] 16%|█▌ | 3003/18688 [3:32:08<70:38:51, 16.21s/it] 16%|█▌ | 3004/18688 [3:32:12<54:55:10, 12.61s/it] 16%|█▌ | 3005/18688 [3:32:16<43:51:48, 10.07s/it] 16%|█▌ | 3006/18688 [3:32:20<36:04:26, 8.28s/it] 16%|█▌ | 3007/18688 [3:32:24<30:07:07, 6.91s/it] 16%|█▌ | 3008/18688 [3:32:28<26:25:43, 6.07s/it] 16%|█▌ | 3009/18688 [3:32:32<23:32:08, 5.40s/it] 16%|█▌ | 3010/18688 [3:32:36<21:46:59, 5.00s/it] {'loss': '1.01', 'grad_norm': '1.527', 'learning_rate': '0.0001766', 'epoch': '0.1611', 'num_input_tokens_seen': 10094960, 'train_runtime': '1.276e+04', 'train_tokens_per_second': '791.4'}
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20%|██ | 3830/18688 [4:29:30<16:12:32, 3.93s/it] 20%|██ | 3831/18688 [4:29:34<15:45:48, 3.82s/it] 21%|██ | 3832/18688 [4:29:39<17:07:31, 4.15s/it] 21%|██ | 3833/18688 [4:29:43<17:18:30, 4.19s/it] 21%|██ | 3834/18688 [4:29:48<18:33:10, 4.50s/it] 21%|██ | 3835/18688 [4:29:52<17:31:36, 4.25s/it] 21%|██ | 3836/18688 [4:29:55<16:48:00, 4.07s/it] 21%|██ | 3837/18688 [4:29:59<16:06:35, 3.91s/it] 21%|██ | 3838/18688 [4:30:03<16:30:38, 4.00s/it] 21%|██ | 3839/18688 [4:30:08<16:59:37, 4.12s/it] 21%|██ | 3840/18688 [4:30:11<16:20:14, 3.96s/it] {'loss': '0.9839', 'grad_norm': '1.938', 'learning_rate': '0.0001719', 'epoch': '0.2055', 'num_input_tokens_seen': 12895912, 'train_runtime': '1.621e+04', 'train_tokens_per_second': '795.5'}
21%|██ | 3840/18688 [4:30:11<16:20:14, 3.96s/it] 21%|██ | 3841/18688 [4:30:15<16:01:38, 3.89s/it] 21%|██ | 3842/18688 [4:30:19<16:23:51, 3.98s/it] 21%|██ | 3843/18688 [4:30:23<15:42:12, 3.81s/it] 21%|██ | 3844/18688 [4:30:29<18:33:23, 4.50s/it] 21%|██ | 3845/18688 [4:30:33<18:14:10, 4.42s/it] 21%|██ | 3846/18688 [4:30:37<17:58:25, 4.36s/it] 21%|██ | 3847/18688 [4:30:41<17:19:05, 4.20s/it] 21%|██ | 3848/18688 [4:30:45<17:19:07, 4.20s/it] 21%|██ | 3849/18688 [4:30:49<17:21:09, 4.21s/it] 21%|██ | 3850/18688 [4:30:54<17:50:08, 4.33s/it] {'loss': '1.002', 'grad_norm': '1.413', 'learning_rate': '0.0001718', 'epoch': '0.206', 'num_input_tokens_seen': 12932544, 'train_runtime': '1.625e+04', 'train_tokens_per_second': '795.6'}
21%|██ | 3850/18688 [4:30:54<17:50:08, 4.33s/it] 21%|██ | 3851/18688 [4:30:57<16:43:23, 4.06s/it] 21%|██ | 3852/18688 [4:31:01<16:07:40, 3.91s/it] 21%|██ | 3853/18688 [4:31:05<16:03:05, 3.90s/it] 21%|██ | 3854/18688 [4:31:10<17:06:26, 4.15s/it] 21%|██ | 3855/18688 [4:31:13<16:26:46, 3.99s/it] 21%|██ | 3856/18688 [4:31:18<17:04:07, 4.14s/it] 21%|██ | 3857/18688 [4:31:21<16:32:53, 4.02s/it] 21%|██ | 3858/18688 [4:31:25<16:38:27, 4.04s/it] 21%|██ | 3859/18688 [4:31:29<16:17:52, 3.96s/it] 21%|██ | 3860/18688 [4:31:34<16:49:12, 4.08s/it] {'loss': '1.005', 'grad_norm': '1.734', 'learning_rate': '0.0001718', 'epoch': '0.2066', 'num_input_tokens_seen': 12965064, 'train_runtime': '1.629e+04', 'train_tokens_per_second': '795.7'}
21%|██ | 3860/18688 [4:31:34<16:49:12, 4.08s/it] 21%|██ | 3861/18688 [4:31:39<18:46:33, 4.56s/it] 21%|██ | 3862/18688 [4:31:43<17:23:56, 4.22s/it] 21%|██ | 3863/18688 [4:31:47<17:25:49, 4.23s/it] 21%|██ | 3864/18688 [4:31:51<16:46:18, 4.07s/it] 21%|██ | 3865/18688 [4:31:56<17:41:53, 4.30s/it] 21%|██ | 3866/18688 [4:32:00<17:46:43, 4.32s/it] 21%|██ | 3867/18688 [4:32:04<17:52:00, 4.34s/it] 21%|██ | 3868/18688 [4:32:08<17:26:39, 4.24s/it] 21%|██ | 3869/18688 [4:32:13<17:29:58, 4.25s/it] 21%|██ | 3870/18688 [4:32:16<16:53:29, 4.10s/it] {'loss': '1.039', 'grad_norm': '1.742', 'learning_rate': '0.0001717', 'epoch': '0.2071', 'num_input_tokens_seen': 12999672, 'train_runtime': '1.634e+04', 'train_tokens_per_second': '795.7'}
21%|██ | 3870/18688 [4:32:16<16:53:29, 4.10s/it] 21%|██ | 3871/18688 [4:32:20<16:50:59, 4.09s/it] 21%|██ | 3872/18688 [4:32:25<16:53:37, 4.10s/it] 21%|██ | 3873/18688 [4:32:29<17:23:40, 4.23s/it] 21%|██ | 3874/18688 [4:32:33<17:15:18, 4.19s/it] 21%|██ | 3875/18688 [4:32:37<16:26:44, 4.00s/it] 21%|██ | 3876/18688 [4:32:41<17:00:06, 4.13s/it] 21%|██ | 3877/18688 [4:32:46<17:21:59, 4.22s/it] 21%|██ | 3878/18688 [4:32:50<18:02:36, 4.39s/it] 21%|██ | 3879/18688 [4:32:55<18:15:03, 4.44s/it] 21%|██ | 3880/18688 [4:32:59<17:57:29, 4.37s/it] {'loss': '1.001', 'grad_norm': '2.123', 'learning_rate': '0.0001716', 'epoch': '0.2076', 'num_input_tokens_seen': 13032520, 'train_runtime': '1.638e+04', 'train_tokens_per_second': '795.7'}
21%|██ | 3880/18688 [4:32:59<17:57:29, 4.37s/it] 21%|██ | 3881/18688 [4:33:03<17:47:27, 4.33s/it] 21%|██ | 3882/18688 [4:33:08<17:47:05, 4.32s/it] 21%|██ | 3883/18688 [4:33:12<17:24:05, 4.23s/it] 21%|██ | 3884/18688 [4:33:16<17:43:26, 4.31s/it] 21%|██ | 3885/18688 [4:33:20<17:33:41, 4.27s/it] 21%|██ | 3886/18688 [4:33:24<17:05:48, 4.16s/it] 21%|██ | 3887/18688 [4:33:29<17:16:40, 4.20s/it] 21%|██ | 3888/18688 [4:33:33<17:46:58, 4.33s/it] 21%|██ | 3889/18688 [4:33:37<17:24:15, 4.23s/it] 21%|██ | 3890/18688 [4:33:41<17:10:56, 4.18s/it] {'loss': '0.9563', 'grad_norm': '1.739', 'learning_rate': '0.0001716', 'epoch': '0.2082', 'num_input_tokens_seen': 13063912, 'train_runtime': '1.642e+04', 'train_tokens_per_second': '795.5'}
21%|██ | 3890/18688 [4:33:41<17:10:56, 4.18s/it] 21%|██ | 3891/18688 [4:33:46<17:25:22, 4.24s/it] 21%|██ | 3892/18688 [4:33:51<18:39:19, 4.54s/it] 21%|██ | 3893/18688 [4:33:55<18:28:04, 4.49s/it] 21%|██ | 3894/18688 [4:33:59<17:15:30, 4.20s/it] 21%|██ | 3895/18688 [4:34:02<16:42:41, 4.07s/it] 21%|██ | 3896/18688 [4:34:07<16:45:10, 4.08s/it] 21%|██ | 3897/18688 [4:34:11<17:38:53, 4.30s/it] 21%|██ | 3898/18688 [4:34:15<17:11:32, 4.18s/it] 21%|██ | 3899/18688 [4:34:20<17:58:45, 4.38s/it] 21%|██ | 3900/18688 [4:34:24<17:36:57, 4.29s/it] {'loss': '1.024', 'grad_norm': '1.669', 'learning_rate': '0.0001715', 'epoch': '0.2087', 'num_input_tokens_seen': 13097456, 'train_runtime': '1.646e+04', 'train_tokens_per_second': '795.5'}
21%|██ | 3900/18688 [4:34:24<17:36:57, 4.29s/it] 21%|██ | 3901/18688 [4:34:30<18:52:05, 4.59s/it] 21%|██ | 3902/18688 [4:34:34<18:37:23, 4.53s/it] 21%|██ | 3903/18688 [4:34:38<18:10:45, 4.43s/it] 21%|██ | 3904/18688 [4:34:42<17:26:20, 4.25s/it] 21%|██ | 3905/18688 [4:34:45<16:35:12, 4.04s/it] 21%|██ | 3906/18688 [4:34:50<17:38:07, 4.29s/it] 21%|██ | 3907/18688 [4:34:56<19:01:42, 4.63s/it] 21%|██ | 3908/18688 [4:35:01<19:42:02, 4.80s/it] 21%|██ | 3909/18688 [4:35:05<18:58:51, 4.62s/it] 21%|██ | 3910/18688 [4:35:09<18:00:58, 4.39s/it] {'loss': '1.044', 'grad_norm': '1.437', 'learning_rate': '0.0001714', 'epoch': '0.2092', 'num_input_tokens_seen': 13135768, 'train_runtime': '1.651e+04', 'train_tokens_per_second': '795.6'}
21%|██ | 3910/18688 [4:35:09<18:00:58, 4.39s/it] 21%|██ | 3911/18688 [4:35:13<16:56:05, 4.13s/it] 21%|██ | 3912/18688 [4:35:16<16:15:47, 3.96s/it] 21%|██ | 3913/18688 [4:35:20<15:46:26, 3.84s/it] 21%|██ | 3914/18688 [4:35:25<17:24:20, 4.24s/it] 21%|██ | 3915/18688 [4:35:29<17:20:40, 4.23s/it] 21%|██ | 3916/18688 [4:35:33<16:58:04, 4.14s/it] 21%|██ | 3917/18688 [4:35:37<16:38:08, 4.05s/it] 21%|██ | 3918/18688 [4:35:41<16:30:22, 4.02s/it] 21%|██ | 3919/18688 [4:35:45<16:34:46, 4.04s/it] 21%|██ | 3920/18688 [4:35:49<16:40:15, 4.06s/it] {'loss': '0.9594', 'grad_norm': '1.773', 'learning_rate': '0.0001713', 'epoch': '0.2098', 'num_input_tokens_seen': 13165712, 'train_runtime': '1.655e+04', 'train_tokens_per_second': '795.5'}
21%|██ | 3920/18688 [4:35:49<16:40:15, 4.06s/it] 21%|██ | 3921/18688 [4:35:53<16:49:43, 4.10s/it] 21%|██ | 3922/18688 [4:35:57<16:34:43, 4.04s/it] 21%|██ | 3923/18688 [4:36:01<16:25:24, 4.00s/it] 21%|██ | 3924/18688 [4:36:06<17:43:35, 4.32s/it] 21%|██ | 3925/18688 [4:36:10<17:13:57, 4.20s/it] 21%|██ | 3926/18688 [4:36:14<16:36:30, 4.05s/it] 21%|██ | 3927/18688 [4:36:18<16:49:18, 4.10s/it] 21%|██ | 3928/18688 [4:36:22<17:19:09, 4.22s/it] 21%|██ | 3929/18688 [4:36:27<17:11:51, 4.19s/it] 21%|██ | 3930/18688 [4:36:31<17:00:52, 4.15s/it] {'loss': '1.041', 'grad_norm': '1.547', 'learning_rate': '0.0001713', 'epoch': '0.2103', 'num_input_tokens_seen': 13200840, 'train_runtime': '1.659e+04', 'train_tokens_per_second': '795.7'}
21%|██ | 3930/18688 [4:36:31<17:00:52, 4.15s/it] 21%|██ | 3931/18688 [4:36:35<17:05:15, 4.17s/it] 21%|██ | 3932/18688 [4:36:39<16:40:36, 4.07s/it] 21%|██ | 3933/18688 [4:36:42<15:58:50, 3.90s/it] 21%|██ | 3934/18688 [4:36:46<16:30:52, 4.03s/it] 21%|██ | 3935/18688 [4:36:51<16:32:26, 4.04s/it] 21%|██ | 3936/18688 [4:36:55<16:30:35, 4.03s/it] 21%|██ | 3937/18688 [4:36:58<15:58:49, 3.90s/it] 21%|██ | 3938/18688 [4:37:02<16:06:36, 3.93s/it] 21%|██ | 3939/18688 [4:37:06<15:28:32, 3.78s/it] 21%|██ | 3940/18688 [4:37:10<16:00:35, 3.91s/it] {'loss': '0.9596', 'grad_norm': '1.549', 'learning_rate': '0.0001712', 'epoch': '0.2108', 'num_input_tokens_seen': 13232512, 'train_runtime': '1.663e+04', 'train_tokens_per_second': '795.7'}
21%|██ | 3940/18688 [4:37:10<16:00:35, 3.91s/it] 21%|██ | 3941/18688 [4:37:14<16:27:13, 4.02s/it] 21%|██ | 3942/18688 [4:37:19<17:36:33, 4.30s/it] 21%|██ | 3943/18688 [4:37:23<16:57:33, 4.14s/it] 21%|██ | 3944/18688 [4:37:27<17:14:19, 4.21s/it] 21%|██ | 3945/18688 [4:37:32<17:30:48, 4.28s/it] 21%|██ | 3946/18688 [4:37:36<18:09:01, 4.43s/it] 21%|██ | 3947/18688 [4:37:40<17:36:04, 4.30s/it] 21%|██ | 3948/18688 [4:37:45<17:25:15, 4.25s/it] 21%|██ | 3949/18688 [4:37:48<16:40:52, 4.07s/it] 21%|██ | 3950/18688 [4:37:52<16:34:28, 4.05s/it] {'loss': '0.9848', 'grad_norm': '1.493', 'learning_rate': '0.0001711', 'epoch': '0.2114', 'num_input_tokens_seen': 13265784, 'train_runtime': '1.667e+04', 'train_tokens_per_second': '795.7'}
21%|██ | 3950/18688 [4:37:52<16:34:28, 4.05s/it] 21%|██ | 3951/18688 [4:37:56<16:46:56, 4.10s/it] 21%|██ | 3952/18688 [4:38:02<18:27:33, 4.51s/it] 21%|██ | 3953/18688 [4:38:06<17:47:21, 4.35s/it] 21%|██ | 3954/18688 [4:38:10<17:41:07, 4.32s/it] 21%|██ | 3955/18688 [4:38:14<17:07:07, 4.18s/it] 21%|██ | 3956/18688 [4:38:17<16:14:15, 3.97s/it] 21%|██ | 3957/18688 [4:38:21<15:44:58, 3.85s/it] 21%|██ | 3958/18688 [4:38:25<16:33:57, 4.05s/it] 21%|██ | 3959/18688 [4:38:30<16:38:34, 4.07s/it] 21%|██ | 3960/18688 [4:38:33<16:18:37, 3.99s/it] {'loss': '1.01', 'grad_norm': '1.95', 'learning_rate': '0.0001711', 'epoch': '0.2119', 'num_input_tokens_seen': 13299808, 'train_runtime': '1.671e+04', 'train_tokens_per_second': '795.7'}
21%|██ | 3960/18688 [4:38:33<16:18:37, 3.99s/it] 21%|██ | 3961/18688 [4:38:38<17:35:27, 4.30s/it] 21%|██ | 3962/18688 [4:38:43<17:23:59, 4.25s/it] 21%|██ | 3963/18688 [4:38:47<17:25:04, 4.26s/it] 21%|██ | 3964/18688 [4:38:51<17:48:59, 4.36s/it] 21%|██ | 3965/18688 [4:38:56<18:00:38, 4.40s/it] 21%|██ | 3966/18688 [4:39:01<19:05:25, 4.67s/it] 21%|██ | 3967/18688 [4:39:05<18:23:09, 4.50s/it] 21%|██ | 3968/18688 [4:39:09<17:39:04, 4.32s/it] 21%|██ | 3969/18688 [4:39:13<17:23:59, 4.26s/it] 21%|██ | 3970/18688 [4:39:17<17:06:21, 4.18s/it] {'loss': '0.9455', 'grad_norm': '1.556', 'learning_rate': '0.000171', 'epoch': '0.2124', 'num_input_tokens_seen': 13334904, 'train_runtime': '1.676e+04', 'train_tokens_per_second': '795.7'}
21%|██ | 3970/18688 [4:39:17<17:06:21, 4.18s/it] 21%|██ | 3971/18688 [4:39:22<17:05:53, 4.18s/it] 21%|██▏ | 3972/18688 [4:39:27<18:32:36, 4.54s/it] 21%|██▏ | 3973/18688 [4:39:31<17:51:45, 4.37s/it] 21%|██▏ | 3974/18688 [4:39:35<17:27:49, 4.27s/it] 21%|██▏ | 3975/18688 [4:39:39<17:27:15, 4.27s/it] 21%|██▏ | 3976/18688 [4:39:43<16:25:09, 4.02s/it] 21%|██▏ | 3977/18688 [4:39:47<16:50:58, 4.12s/it] 21%|██▏ | 3978/18688 [4:39:51<16:17:11, 3.99s/it] 21%|██▏ | 3979/18688 [4:39:56<18:19:50, 4.49s/it] 21%|██▏ | 3980/18688 [4:40:01<18:02:34, 4.42s/it] {'loss': '0.9889', 'grad_norm': '1.965', 'learning_rate': '0.0001709', 'epoch': '0.213', 'num_input_tokens_seen': 13370056, 'train_runtime': '1.68e+04', 'train_tokens_per_second': '795.8'}
21%|██▏ | 3980/18688 [4:40:01<18:02:34, 4.42s/it] 21%|██▏ | 3981/18688 [4:40:05<17:33:52, 4.30s/it] 21%|██▏ | 3982/18688 [4:40:09<17:13:49, 4.22s/it] 21%|██▏ | 3983/18688 [4:40:13<17:23:04, 4.26s/it] 21%|██▏ | 3984/18688 [4:40:17<17:19:35, 4.24s/it] 21%|██▏ | 3985/18688 [4:40:22<18:04:59, 4.43s/it] 21%|██▏ | 3986/18688 [4:40:27<18:11:25, 4.45s/it] 21%|██▏ | 3987/18688 [4:40:30<17:28:18, 4.28s/it] 21%|██▏ | 3988/18688 [4:40:35<17:53:30, 4.38s/it] 21%|██▏ | 3989/18688 [4:40:40<18:19:04, 4.49s/it] 21%|██▏ | 3990/18688 [4:40:44<17:45:03, 4.35s/it] {'loss': '1.078', 'grad_norm': '1.701', 'learning_rate': '0.0001708', 'epoch': '0.2135', 'num_input_tokens_seen': 13401352, 'train_runtime': '1.684e+04', 'train_tokens_per_second': '795.6'}
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21%|██▏ | 4000/18688 [4:41:27<18:18:24, 4.49s/it]
***** Running Evaluation *****
Num examples = 1000
Batch size = 1
0%| | 0/1000 [00:00<?, ?it/s]
0%| | 3/1000 [00:00<00:38, 26.23it/s]
1%| | 6/1000 [00:00<00:53, 18.45it/s]
1%| | 8/1000 [00:00<00:56, 17.65it/s]
1%| | 10/1000 [00:00<00:59, 16.69it/s]
1%| | 12/1000 [00:00<00:59, 16.48it/s]
1%|▏ | 14/1000 [00:00<01:01, 16.02it/s]
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{'eval_loss': '0.9833', 'eval_runtime': '83.08', 'eval_samples_per_second': '12.04', 'eval_steps_per_second': '12.04', 'epoch': '0.214', 'num_input_tokens_seen': 13433360}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00018/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': '0.9868', 'eval_runtime': '82.99', 'eval_samples_per_second': '12.05', 'eval_steps_per_second': '12.05', 'epoch': '0.2676', 'num_input_tokens_seen': 16794000}
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32%|███▏ | 5970/18688 [7:01:15<15:17:16, 4.33s/it] 32%|███▏ | 5971/18688 [7:01:19<15:30:46, 4.39s/it] 32%|███▏ | 5972/18688 [7:01:24<15:45:27, 4.46s/it] 32%|███▏ | 5973/18688 [7:01:28<15:26:51, 4.37s/it] 32%|███▏ | 5974/18688 [7:01:32<14:43:07, 4.17s/it] 32%|███▏ | 5975/18688 [7:01:36<14:48:51, 4.20s/it] 32%|███▏ | 5976/18688 [7:01:40<14:29:56, 4.11s/it] 32%|███▏ | 5977/18688 [7:01:44<14:40:44, 4.16s/it] 32%|███▏ | 5978/18688 [7:01:48<14:02:26, 3.98s/it] 32%|███▏ | 5979/18688 [7:01:53<14:47:09, 4.19s/it] 32%|███▏ | 5980/18688 [7:01:57<15:06:14, 4.28s/it] {'loss': '1.017', 'grad_norm': '1.77', 'learning_rate': '0.0001515', 'epoch': '0.32', 'num_input_tokens_seen': 20073584, 'train_runtime': '2.532e+04', 'train_tokens_per_second': '792.9'}
32%|███▏ | 5980/18688 [7:01:57<15:06:14, 4.28s/it] 32%|███▏ | 5981/18688 [7:02:01<14:47:42, 4.19s/it] 32%|███▏ | 5982/18688 [7:02:05<14:34:05, 4.13s/it] 32%|███▏ | 5983/18688 [7:02:09<14:39:37, 4.15s/it] 32%|███▏ | 5984/18688 [7:02:14<14:58:06, 4.24s/it] 32%|███▏ | 5985/18688 [7:02:19<16:10:34, 4.58s/it] 32%|███▏ | 5986/18688 [7:02:23<15:16:19, 4.33s/it] 32%|███▏ | 5987/18688 [7:02:27<15:22:24, 4.36s/it] 32%|███▏ | 5988/18688 [7:02:32<15:26:54, 4.38s/it] 32%|███▏ | 5989/18688 [7:02:36<15:44:30, 4.46s/it] 32%|███▏ | 5990/18688 [7:02:40<15:21:57, 4.36s/it] {'loss': '0.9677', 'grad_norm': '1.723', 'learning_rate': '0.0001514', 'epoch': '0.3205', 'num_input_tokens_seen': 20106224, 'train_runtime': '2.536e+04', 'train_tokens_per_second': '792.8'}
32%|███▏ | 5990/18688 [7:02:40<15:21:57, 4.36s/it] 32%|███▏ | 5991/18688 [7:02:45<15:16:28, 4.33s/it] 32%|███▏ | 5992/18688 [7:02:49<15:24:49, 4.37s/it] 32%|███▏ | 5993/18688 [7:02:54<15:46:28, 4.47s/it] 32%|███▏ | 5994/18688 [7:02:58<15:56:45, 4.52s/it] 32%|███▏ | 5995/18688 [7:03:02<14:47:24, 4.19s/it] 32%|███▏ | 5996/18688 [7:03:06<15:06:14, 4.28s/it] 32%|███▏ | 5997/18688 [7:03:10<14:48:28, 4.20s/it] 32%|███▏ | 5998/18688 [7:03:15<14:46:31, 4.19s/it] 32%|███▏ | 5999/18688 [7:03:18<14:00:50, 3.98s/it] 32%|███▏ | 6000/18688 [7:03:22<14:17:42, 4.06s/it] {'loss': '1.018', 'grad_norm': '1.426', 'learning_rate': '0.0001512', 'epoch': '0.3211', 'num_input_tokens_seen': 20140120, 'train_runtime': '2.54e+04', 'train_tokens_per_second': '792.8'}
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***** Running Evaluation *****
Num examples = 1000
Batch size = 1
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{'eval_loss': '0.9756', 'eval_runtime': '83.09', 'eval_samples_per_second': '12.04', 'eval_steps_per_second': '12.04', 'epoch': '0.3211', 'num_input_tokens_seen': 20140120}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00018/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': '0.9769', 'eval_runtime': '83.18', 'eval_samples_per_second': '12.02', 'eval_steps_per_second': '12.02', 'epoch': '0.3746', 'num_input_tokens_seen': 23480632}
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42%|████▏ | 7930/18688 [9:20:15<12:01:11, 4.02s/it] 42%|████▏ | 7931/18688 [9:20:19<11:58:38, 4.01s/it] 42%|████▏ | 7932/18688 [9:20:23<12:04:12, 4.04s/it] 42%|████▏ | 7933/18688 [9:20:28<12:48:55, 4.29s/it] 42%|████▏ | 7934/18688 [9:20:33<13:31:52, 4.53s/it] 42%|████▏ | 7935/18688 [9:20:36<12:35:22, 4.21s/it] 42%|████▏ | 7936/18688 [9:20:40<12:22:25, 4.14s/it] 42%|████▏ | 7937/18688 [9:20:45<12:49:31, 4.29s/it] 42%|████▏ | 7938/18688 [9:20:49<12:08:35, 4.07s/it] 42%|████▏ | 7939/18688 [9:20:53<12:05:09, 4.05s/it] 42%|████▏ | 7940/18688 [9:20:56<11:41:36, 3.92s/it] {'loss': '0.9261', 'grad_norm': '2.133', 'learning_rate': '0.0001245', 'epoch': '0.4249', 'num_input_tokens_seen': 26662440, 'train_runtime': '3.366e+04', 'train_tokens_per_second': '792.2'}
42%|████▏ | 7940/18688 [9:20:56<11:41:36, 3.92s/it] 42%|████▏ | 7941/18688 [9:21:01<12:13:48, 4.10s/it] 42%|████▏ | 7942/18688 [9:21:05<12:36:40, 4.22s/it] 43%|████▎ | 7943/18688 [9:21:10<12:40:49, 4.25s/it] 43%|████▎ | 7944/18688 [9:21:15<13:27:05, 4.51s/it] 43%|████▎ | 7945/18688 [9:21:19<13:04:50, 4.38s/it] 43%|████▎ | 7946/18688 [9:21:24<13:29:33, 4.52s/it] 43%|████▎ | 7947/18688 [9:21:28<13:26:14, 4.50s/it] 43%|████▎ | 7948/18688 [9:21:32<12:46:40, 4.28s/it] 43%|████▎ | 7949/18688 [9:21:36<12:24:54, 4.16s/it] 43%|████▎ | 7950/18688 [9:21:41<13:42:49, 4.60s/it] {'loss': '0.9502', 'grad_norm': '1.736', 'learning_rate': '0.0001244', 'epoch': '0.4254', 'num_input_tokens_seen': 26698152, 'train_runtime': '3.37e+04', 'train_tokens_per_second': '792.2'}
43%|████▎ | 7950/18688 [9:21:41<13:42:49, 4.60s/it] 43%|████▎ | 7951/18688 [9:21:46<13:25:36, 4.50s/it] 43%|████▎ | 7952/18688 [9:21:50<13:22:27, 4.48s/it] 43%|████▎ | 7953/18688 [9:21:54<12:59:15, 4.36s/it] 43%|████▎ | 7954/18688 [9:21:58<12:27:29, 4.18s/it] 43%|████▎ | 7955/18688 [9:22:02<12:22:51, 4.15s/it] 43%|████▎ | 7956/18688 [9:22:06<12:21:55, 4.15s/it] 43%|████▎ | 7957/18688 [9:22:11<12:35:07, 4.22s/it] 43%|████▎ | 7958/18688 [9:22:16<13:39:38, 4.58s/it] 43%|████▎ | 7959/18688 [9:22:20<13:06:29, 4.40s/it] 43%|████▎ | 7960/18688 [9:22:26<14:46:05, 4.96s/it] {'loss': '0.9366', 'grad_norm': '2.299', 'learning_rate': '0.0001242', 'epoch': '0.426', 'num_input_tokens_seen': 26732688, 'train_runtime': '3.375e+04', 'train_tokens_per_second': '792.2'}
43%|████▎ | 7960/18688 [9:22:26<14:46:05, 4.96s/it] 43%|████▎ | 7961/18688 [9:22:30<13:56:21, 4.68s/it] 43%|████▎ | 7962/18688 [9:22:34<13:18:19, 4.47s/it] 43%|████▎ | 7963/18688 [9:22:39<13:18:36, 4.47s/it] 43%|████▎ | 7964/18688 [9:22:42<12:36:20, 4.23s/it] 43%|████▎ | 7965/18688 [9:22:46<11:54:52, 4.00s/it] 43%|████▎ | 7966/18688 [9:22:50<11:52:51, 3.99s/it] 43%|████▎ | 7967/18688 [9:22:53<11:31:42, 3.87s/it] 43%|████▎ | 7968/18688 [9:22:57<11:09:15, 3.75s/it] 43%|████▎ | 7969/18688 [9:23:01<11:57:23, 4.02s/it] 43%|████▎ | 7970/18688 [9:23:05<11:53:32, 3.99s/it] {'loss': '0.9997', 'grad_norm': '2.151', 'learning_rate': '0.0001241', 'epoch': '0.4265', 'num_input_tokens_seen': 26762544, 'train_runtime': '3.379e+04', 'train_tokens_per_second': '792.1'}
43%|████▎ | 7970/18688 [9:23:05<11:53:32, 3.99s/it] 43%|████▎ | 7971/18688 [9:23:09<11:51:46, 3.98s/it] 43%|████▎ | 7972/18688 [9:23:13<11:55:33, 4.01s/it] 43%|████▎ | 7973/18688 [9:23:17<11:42:37, 3.93s/it] 43%|████▎ | 7974/18688 [9:23:22<12:14:12, 4.11s/it] 43%|████▎ | 7975/18688 [9:23:26<12:10:22, 4.09s/it] 43%|████▎ | 7976/18688 [9:23:30<12:36:38, 4.24s/it] 43%|████▎ | 7977/18688 [9:23:35<13:02:51, 4.39s/it] 43%|████▎ | 7978/18688 [9:23:40<13:59:14, 4.70s/it] 43%|████▎ | 7979/18688 [9:23:45<13:38:24, 4.59s/it] 43%|████▎ | 7980/18688 [9:23:49<13:15:08, 4.46s/it] {'loss': '1.009', 'grad_norm': '1.784', 'learning_rate': '0.0001239', 'epoch': '0.427', 'num_input_tokens_seen': 26796120, 'train_runtime': '3.383e+04', 'train_tokens_per_second': '792.1'}
43%|████▎ | 7980/18688 [9:23:49<13:15:08, 4.46s/it] 43%|████▎ | 7981/18688 [9:23:54<13:45:16, 4.62s/it] 43%|████▎ | 7982/18688 [9:23:57<12:40:33, 4.26s/it] 43%|████▎ | 7983/18688 [9:24:02<12:45:47, 4.29s/it] 43%|████▎ | 7984/18688 [9:24:05<12:11:30, 4.10s/it] 43%|████▎ | 7985/18688 [9:24:10<12:44:19, 4.28s/it] 43%|████▎ | 7986/18688 [9:24:14<12:35:12, 4.23s/it] 43%|████▎ | 7987/18688 [9:24:18<11:55:04, 4.01s/it] 43%|████▎ | 7988/18688 [9:24:22<11:43:41, 3.95s/it] 43%|████▎ | 7989/18688 [9:24:26<11:49:49, 3.98s/it] 43%|████▎ | 7990/18688 [9:24:29<11:22:32, 3.83s/it] {'loss': '0.9756', 'grad_norm': '2.141', 'learning_rate': '0.0001238', 'epoch': '0.4276', 'num_input_tokens_seen': 26828872, 'train_runtime': '3.387e+04', 'train_tokens_per_second': '792.1'}
43%|████▎ | 7990/18688 [9:24:29<11:22:32, 3.83s/it] 43%|████▎ | 7991/18688 [9:24:33<11:52:51, 4.00s/it] 43%|████▎ | 7992/18688 [9:24:39<12:55:22, 4.35s/it] 43%|████▎ | 7993/18688 [9:24:42<12:16:57, 4.13s/it] 43%|████▎ | 7994/18688 [9:24:46<12:18:25, 4.14s/it] 43%|████▎ | 7995/18688 [9:24:50<11:43:28, 3.95s/it] 43%|████▎ | 7996/18688 [9:24:54<11:45:24, 3.96s/it] 43%|████▎ | 7997/18688 [9:24:59<12:21:45, 4.16s/it] 43%|████▎ | 7998/18688 [9:25:02<12:02:19, 4.05s/it] 43%|████▎ | 7999/18688 [9:25:06<11:49:01, 3.98s/it] 43%|████▎ | 8000/18688 [9:25:10<11:24:08, 3.84s/it] {'loss': '0.9898', 'grad_norm': '1.65', 'learning_rate': '0.0001236', 'epoch': '0.4281', 'num_input_tokens_seen': 26861912, 'train_runtime': '3.391e+04', 'train_tokens_per_second': '792.1'}
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***** Running Evaluation *****
Num examples = 1000
Batch size = 1
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{'eval_loss': '0.9688', 'eval_runtime': '83', 'eval_samples_per_second': '12.05', 'eval_steps_per_second': '12.05', 'epoch': '0.4281', 'num_input_tokens_seen': 26861912}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00018/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': '0.9563', 'eval_runtime': '83.42', 'eval_samples_per_second': '11.99', 'eval_steps_per_second': '11.99', 'epoch': '0.4816', 'num_input_tokens_seen': 30248528}
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53%|█████▎ | 9870/18688 [11:39:08<10:54:13, 4.45s/it] 53%|█████▎ | 9871/18688 [11:39:12<10:42:47, 4.37s/it] 53%|█████▎ | 9872/18688 [11:39:15<10:03:23, 4.11s/it] 53%|█████▎ | 9873/18688 [11:39:20<10:06:50, 4.13s/it] 53%|█████▎ | 9874/18688 [11:39:23<9:51:10, 4.02s/it] 53%|█████▎ | 9875/18688 [11:39:28<9:55:45, 4.06s/it] 53%|█████▎ | 9876/18688 [11:39:31<9:41:38, 3.96s/it] 53%|█████▎ | 9877/18688 [11:39:35<9:49:28, 4.01s/it] 53%|█████▎ | 9878/18688 [11:39:40<10:16:03, 4.20s/it] 53%|█████▎ | 9879/18688 [11:39:44<10:08:41, 4.15s/it] 53%|█████▎ | 9880/18688 [11:39:49<10:44:48, 4.39s/it] {'loss': '0.9416', 'grad_norm': '1.924', 'learning_rate': '9.354e-05', 'epoch': '0.5287', 'num_input_tokens_seen': 33231392, 'train_runtime': '4.199e+04', 'train_tokens_per_second': '791.4'}
53%|█████▎ | 9880/18688 [11:39:49<10:44:48, 4.39s/it] 53%|█████▎ | 9881/18688 [11:39:53<10:33:59, 4.32s/it] 53%|█████▎ | 9882/18688 [11:39:58<11:04:15, 4.53s/it] 53%|█████▎ | 9883/18688 [11:40:02<10:38:17, 4.35s/it] 53%|█████▎ | 9884/18688 [11:40:06<10:35:51, 4.33s/it] 53%|█████▎ | 9885/18688 [11:40:11<10:46:20, 4.41s/it] 53%|█████▎ | 9886/18688 [11:40:16<11:14:43, 4.60s/it] 53%|█████▎ | 9887/18688 [11:40:20<10:50:51, 4.44s/it] 53%|█████▎ | 9888/18688 [11:40:25<11:17:02, 4.62s/it] 53%|█████▎ | 9889/18688 [11:40:29<10:31:05, 4.30s/it] 53%|█████▎ | 9890/18688 [11:40:33<10:31:04, 4.30s/it] {'loss': '0.9403', 'grad_norm': '1.675', 'learning_rate': '9.337e-05', 'epoch': '0.5292', 'num_input_tokens_seen': 33267288, 'train_runtime': '4.203e+04', 'train_tokens_per_second': '791.4'}
53%|█████▎ | 9890/18688 [11:40:33<10:31:04, 4.30s/it] 53%|█████▎ | 9891/18688 [11:40:37<10:32:33, 4.31s/it] 53%|█████▎ | 9892/18688 [11:40:43<11:23:22, 4.66s/it] 53%|█████▎ | 9893/18688 [11:40:47<11:19:53, 4.64s/it] 53%|█████▎ | 9894/18688 [11:40:51<10:48:50, 4.43s/it] 53%|█████▎ | 9895/18688 [11:40:55<10:36:18, 4.34s/it] 53%|█████▎ | 9896/18688 [11:40:59<10:16:42, 4.21s/it] 53%|█████▎ | 9897/18688 [11:41:04<10:34:00, 4.33s/it] 53%|█████▎ | 9898/18688 [11:41:08<10:38:15, 4.36s/it] 53%|█████▎ | 9899/18688 [11:41:13<10:30:56, 4.31s/it] 53%|█████▎ | 9900/18688 [11:41:16<9:53:51, 4.05s/it] {'loss': '1.008', 'grad_norm': '1.941', 'learning_rate': '9.321e-05', 'epoch': '0.5298', 'num_input_tokens_seen': 33301776, 'train_runtime': '4.208e+04', 'train_tokens_per_second': '791.5'}
53%|█████▎ | 9900/18688 [11:41:16<9:53:51, 4.05s/it] 53%|█████▎ | 9901/18688 [11:41:20<9:52:46, 4.05s/it] 53%|█████▎ | 9902/18688 [11:41:25<10:08:59, 4.16s/it] 53%|█████▎ | 9903/18688 [11:41:28<9:53:08, 4.05s/it] 53%|█████▎ | 9904/18688 [11:41:33<10:00:03, 4.10s/it] 53%|█████▎ | 9905/18688 [11:41:37<10:35:04, 4.34s/it] 53%|█████▎ | 9906/18688 [11:41:42<10:42:46, 4.39s/it] 53%|█████▎ | 9907/18688 [11:41:46<10:48:36, 4.43s/it] 53%|█████▎ | 9908/18688 [11:41:51<10:43:12, 4.40s/it] 53%|█████▎ | 9909/18688 [11:41:55<10:18:46, 4.23s/it] 53%|█████▎ | 9910/18688 [11:41:59<10:47:15, 4.42s/it] {'loss': '0.9548', 'grad_norm': '1.591', 'learning_rate': '9.304e-05', 'epoch': '0.5303', 'num_input_tokens_seen': 33337120, 'train_runtime': '4.212e+04', 'train_tokens_per_second': '791.5'}
53%|█████▎ | 9910/18688 [11:41:59<10:47:15, 4.42s/it] 53%|█████▎ | 9911/18688 [11:42:03<10:06:02, 4.14s/it] 53%|█████▎ | 9912/18688 [11:42:08<10:34:16, 4.34s/it] 53%|█████▎ | 9913/18688 [11:42:12<10:15:46, 4.21s/it] 53%|█████▎ | 9914/18688 [11:42:17<10:50:40, 4.45s/it] 53%|█████▎ | 9915/18688 [11:42:22<11:14:05, 4.61s/it] 53%|█████▎ | 9916/18688 [11:42:27<11:26:03, 4.69s/it] 53%|█████▎ | 9917/18688 [11:42:31<11:12:04, 4.60s/it] 53%|█████▎ | 9918/18688 [11:42:35<10:58:46, 4.51s/it] 53%|█████▎ | 9919/18688 [11:42:40<11:01:13, 4.52s/it] 53%|█████▎ | 9920/18688 [11:42:44<10:29:24, 4.31s/it] {'loss': '0.9307', 'grad_norm': '1.951', 'learning_rate': '9.288e-05', 'epoch': '0.5308', 'num_input_tokens_seen': 33371000, 'train_runtime': '4.216e+04', 'train_tokens_per_second': '791.5'}
53%|█████▎ | 9920/18688 [11:42:44<10:29:24, 4.31s/it] 53%|█████▎ | 9921/18688 [11:42:48<10:50:28, 4.45s/it] 53%|█████▎ | 9922/18688 [11:42:53<10:56:20, 4.49s/it] 53%|█████▎ | 9923/18688 [11:42:57<10:32:45, 4.33s/it] 53%|█████▎ | 9924/18688 [11:43:01<10:28:13, 4.30s/it] 53%|█████▎ | 9925/18688 [11:43:06<10:37:22, 4.36s/it] 53%|█████▎ | 9926/18688 [11:43:10<10:26:57, 4.29s/it] 53%|█████▎ | 9927/18688 [11:43:14<10:18:00, 4.23s/it] 53%|█████▎ | 9928/18688 [11:43:18<10:14:11, 4.21s/it] 53%|█████▎ | 9929/18688 [11:43:22<10:15:59, 4.22s/it] 53%|█████▎ | 9930/18688 [11:43:26<10:11:50, 4.19s/it] {'loss': '0.9749', 'grad_norm': '1.705', 'learning_rate': '9.271e-05', 'epoch': '0.5314', 'num_input_tokens_seen': 33404656, 'train_runtime': '4.221e+04', 'train_tokens_per_second': '791.5'}
53%|█████▎ | 9930/18688 [11:43:26<10:11:50, 4.19s/it] 53%|█████▎ | 9931/18688 [11:43:30<10:04:02, 4.14s/it] 53%|█████▎ | 9932/18688 [11:43:35<10:09:41, 4.18s/it] 53%|█████▎ | 9933/18688 [11:43:39<10:06:50, 4.16s/it] 53%|█████▎ | 9934/18688 [11:43:43<10:23:18, 4.27s/it] 53%|█████▎ | 9935/18688 [11:43:49<11:15:42, 4.63s/it] 53%|█████▎ | 9936/18688 [11:43:53<10:39:42, 4.39s/it] 53%|█████▎ | 9937/18688 [11:43:57<10:48:38, 4.45s/it] 53%|█████▎ | 9938/18688 [11:44:01<10:16:00, 4.22s/it] 53%|█████▎ | 9939/18688 [11:44:05<10:13:23, 4.21s/it] 53%|█████▎ | 9940/18688 [11:44:10<10:24:02, 4.28s/it] {'loss': '0.9325', 'grad_norm': '2.449', 'learning_rate': '9.255e-05', 'epoch': '0.5319', 'num_input_tokens_seen': 33439232, 'train_runtime': '4.225e+04', 'train_tokens_per_second': '791.5'}
53%|█████▎ | 9940/18688 [11:44:10<10:24:02, 4.28s/it] 53%|█████▎ | 9941/18688 [11:44:14<10:13:10, 4.21s/it] 53%|█████▎ | 9942/18688 [11:44:18<10:32:51, 4.34s/it] 53%|█████▎ | 9943/18688 [11:44:22<9:57:01, 4.10s/it] 53%|█████▎ | 9944/18688 [11:44:25<9:34:59, 3.95s/it] 53%|█████▎ | 9945/18688 [11:44:29<9:26:35, 3.89s/it] 53%|█████▎ | 9946/18688 [11:44:33<9:32:29, 3.93s/it] 53%|█████▎ | 9947/18688 [11:44:37<9:17:06, 3.82s/it] 53%|█████▎ | 9948/18688 [11:44:41<9:31:37, 3.92s/it] 53%|█████▎ | 9949/18688 [11:44:46<10:02:35, 4.14s/it] 53%|█████▎ | 9950/18688 [11:44:49<9:39:51, 3.98s/it] {'loss': '0.9077', 'grad_norm': '1.808', 'learning_rate': '9.238e-05', 'epoch': '0.5324', 'num_input_tokens_seen': 33470712, 'train_runtime': '4.229e+04', 'train_tokens_per_second': '791.5'}
53%|█████▎ | 9950/18688 [11:44:49<9:39:51, 3.98s/it] 53%|█████▎ | 9951/18688 [11:44:53<9:44:49, 4.02s/it] 53%|█████▎ | 9952/18688 [11:44:58<10:00:16, 4.12s/it] 53%|█████▎ | 9953/18688 [11:45:01<9:45:54, 4.02s/it] 53%|█████▎ | 9954/18688 [11:45:05<9:43:33, 4.01s/it] 53%|█████▎ | 9955/18688 [11:45:10<10:02:16, 4.14s/it] 53%|█████▎ | 9956/18688 [11:45:13<9:36:26, 3.96s/it] 53%|█████▎ | 9957/18688 [11:45:17<9:22:35, 3.87s/it] 53%|█████▎ | 9958/18688 [11:45:21<9:41:16, 3.99s/it] 53%|█████▎ | 9959/18688 [11:45:25<9:26:28, 3.89s/it] 53%|█████▎ | 9960/18688 [11:45:29<9:41:42, 4.00s/it] {'loss': '1.007', 'grad_norm': '2.115', 'learning_rate': '9.222e-05', 'epoch': '0.533', 'num_input_tokens_seen': 33504040, 'train_runtime': '4.233e+04', 'train_tokens_per_second': '791.5'}
53%|█████▎ | 9960/18688 [11:45:29<9:41:42, 4.00s/it] 53%|█████▎ | 9961/18688 [11:45:33<9:29:34, 3.92s/it] 53%|█████▎ | 9962/18688 [11:45:37<9:28:21, 3.91s/it] 53%|█████▎ | 9963/18688 [11:45:40<9:06:54, 3.76s/it] 53%|█████▎ | 9964/18688 [11:45:44<9:16:40, 3.83s/it] 53%|█████▎ | 9965/18688 [11:45:48<9:30:49, 3.93s/it] 53%|█████▎ | 9966/18688 [11:45:53<10:04:36, 4.16s/it] 53%|█████▎ | 9967/18688 [11:45:58<10:17:10, 4.25s/it] 53%|█████▎ | 9968/18688 [11:46:02<10:18:04, 4.25s/it] 53%|█████▎ | 9969/18688 [11:46:06<10:37:00, 4.38s/it] 53%|█████▎ | 9970/18688 [11:46:11<10:42:42, 4.42s/it] {'loss': '1.05', 'grad_norm': '1.663', 'learning_rate': '9.206e-05', 'epoch': '0.5335', 'num_input_tokens_seen': 33536184, 'train_runtime': '4.237e+04', 'train_tokens_per_second': '791.5'}
53%|█████▎ | 9970/18688 [11:46:11<10:42:42, 4.42s/it] 53%|█████▎ | 9971/18688 [11:46:15<10:15:01, 4.23s/it] 53%|█████▎ | 9972/18688 [11:46:19<10:06:25, 4.17s/it] 53%|█████▎ | 9973/18688 [11:46:23<10:09:20, 4.20s/it] 53%|█████▎ | 9974/18688 [11:46:28<10:45:00, 4.44s/it] 53%|█████▎ | 9975/18688 [11:46:33<10:59:01, 4.54s/it] 53%|█████▎ | 9976/18688 [11:46:38<11:21:35, 4.69s/it] 53%|█████▎ | 9977/18688 [11:46:42<10:58:35, 4.54s/it] 53%|█████▎ | 9978/18688 [11:46:46<10:36:07, 4.38s/it] 53%|█████▎ | 9979/18688 [11:46:50<10:36:39, 4.39s/it] 53%|█████▎ | 9980/18688 [11:46:56<11:23:09, 4.71s/it] {'loss': '0.923', 'grad_norm': '1.714', 'learning_rate': '9.189e-05', 'epoch': '0.534', 'num_input_tokens_seen': 33573168, 'train_runtime': '4.242e+04', 'train_tokens_per_second': '791.5'}
53%|█████▎ | 9980/18688 [11:46:56<11:23:09, 4.71s/it] 53%|█████▎ | 9981/18688 [11:47:01<11:17:02, 4.67s/it] 53%|█████▎ | 9982/18688 [11:47:05<10:48:33, 4.47s/it] 53%|█████▎ | 9983/18688 [11:47:09<10:34:22, 4.37s/it] 53%|█████▎ | 9984/18688 [11:47:14<11:07:40, 4.60s/it] 53%|█████▎ | 9985/18688 [11:47:19<11:19:41, 4.69s/it] 53%|█████▎ | 9986/18688 [11:47:22<10:28:08, 4.33s/it] 53%|█████▎ | 9987/18688 [11:47:26<9:55:38, 4.11s/it] 53%|█████▎ | 9988/18688 [11:47:31<10:35:56, 4.39s/it] 53%|█████▎ | 9989/18688 [11:47:35<10:35:36, 4.38s/it] 53%|█████▎ | 9990/18688 [11:47:39<10:29:31, 4.34s/it] {'loss': '0.9361', 'grad_norm': '1.674', 'learning_rate': '9.173e-05', 'epoch': '0.5346', 'num_input_tokens_seen': 33608320, 'train_runtime': '4.246e+04', 'train_tokens_per_second': '791.5'}
53%|█████▎ | 9990/18688 [11:47:39<10:29:31, 4.34s/it] 53%|█████▎ | 9991/18688 [11:47:44<10:35:20, 4.38s/it] 53%|█████▎ | 9992/18688 [11:47:48<10:01:21, 4.15s/it] 53%|█████▎ | 9993/18688 [11:47:52<10:01:22, 4.15s/it] 53%|█████▎ | 9994/18688 [11:47:55<9:41:57, 4.02s/it] 53%|█████▎ | 9995/18688 [11:48:00<10:15:46, 4.25s/it] 53%|█████▎ | 9996/18688 [11:48:06<11:04:41, 4.59s/it] 53%|█████▎ | 9997/18688 [11:48:09<10:26:14, 4.32s/it] 53%|█████▎ | 9998/18688 [11:48:13<10:03:20, 4.17s/it] 54%|█████▎ | 9999/18688 [11:48:20<11:49:33, 4.90s/it] 54%|█████▎ | 10000/18688 [11:48:24<11:22:33, 4.71s/it] {'loss': '1.058', 'grad_norm': '2.477', 'learning_rate': '9.156e-05', 'epoch': '0.5351', 'num_input_tokens_seen': 33646792, 'train_runtime': '4.25e+04', 'train_tokens_per_second': '791.6'}
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***** Running Evaluation *****
Num examples = 1000
Batch size = 1
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{'eval_loss': '0.9435', 'eval_runtime': '83.11', 'eval_samples_per_second': '12.03', 'eval_steps_per_second': '12.03', 'epoch': '0.5351', 'num_input_tokens_seen': 33646792}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00018/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 [11:49:52<71:34:12, 29.66s/it] 54%|█████▎ | 10002/18688 [11:49:55<52:42:58, 21.85s/it] 54%|█████▎ | 10003/18688 [11:49:59<39:50:06, 16.51s/it] 54%|█████▎ | 10004/18688 [11:50:04<31:02:49, 12.87s/it] 54%|█████▎ | 10005/18688 [11:50:08<24:41:04, 10.23s/it] 54%|█████▎ | 10006/18688 [11:50:12<20:17:57, 8.42s/it] 54%|█████▎ | 10007/18688 [11:50:16<17:22:32, 7.21s/it] 54%|█████▎ | 10008/18688 [11:50:20<14:54:12, 6.18s/it] 54%|█████▎ | 10009/18688 [11:50:25<14:04:17, 5.84s/it] 54%|█████▎ | 10010/18688 [11:50:30<12:55:30, 5.36s/it] {'loss': '0.9966', 'grad_norm': '2.386', 'learning_rate': '9.14e-05', 'epoch': '0.5357', 'num_input_tokens_seen': 33679768, 'train_runtime': '4.263e+04', 'train_tokens_per_second': '790'}
54%|█████▎ | 10010/18688 [11:50:30<12:55:30, 5.36s/it] 54%|█████▎ | 10011/18688 [11:50:34<12:02:56, 5.00s/it] 54%|█████▎ | 10012/18688 [11:50:38<11:30:19, 4.77s/it] 54%|█████▎ | 10013/18688 [11:50:43<11:24:06, 4.73s/it] 54%|█████▎ | 10014/18688 [11:50:48<12:01:26, 4.99s/it] 54%|█████▎ | 10015/18688 [11:50:52<11:18:45, 4.70s/it] 54%|█████▎ | 10016/18688 [11:50:56<11:00:16, 4.57s/it] 54%|█████▎ | 10017/18688 [11:51:01<10:58:35, 4.56s/it] 54%|█████▎ | 10018/18688 [11:51:05<10:24:17, 4.32s/it] 54%|█████▎ | 10019/18688 [11:51:09<10:31:17, 4.37s/it] 54%|█████▎ | 10020/18688 [11:51:13<10:14:56, 4.26s/it] {'loss': '1.017', 'grad_norm': '1.702', 'learning_rate': '9.123e-05', 'epoch': '0.5362', 'num_input_tokens_seen': 33715552, 'train_runtime': '4.267e+04', 'train_tokens_per_second': '790.1'}
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***** Running Evaluation *****
Num examples = 1000
Batch size = 1
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:71: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:281: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 0/1000 [00:00<?, ?it/s]/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:172: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:202: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:254: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
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{'eval_loss': '0.9343', 'eval_runtime': '83.07', '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%|██████▎ | 11890/18688 [14:02:27<7:57:27, 4.21s/it] 64%|██████▎ | 11891/18688 [14:02:30<7:31:51, 3.99s/it] 64%|██████▎ | 11892/18688 [14:02:34<7:15:55, 3.85s/it] 64%|██████▎ | 11893/18688 [14:02:38<7:41:52, 4.08s/it] 64%|██████▎ | 11894/18688 [14:02:42<7:36:33, 4.03s/it] 64%|██████▎ | 11895/18688 [14:02:46<7:21:44, 3.90s/it] 64%|██████▎ | 11896/18688 [14:02:49<7:06:11, 3.76s/it] 64%|██████▎ | 11897/18688 [14:02:53<7:09:28, 3.79s/it] 64%|██████▎ | 11898/18688 [14:02:57<7:13:02, 3.83s/it] 64%|██████▎ | 11899/18688 [14:03:01<7:18:06, 3.87s/it] 64%|██████▎ | 11900/18688 [14:03:05<7:18:09, 3.87s/it] {'loss': '0.95', 'grad_norm': '1.635', 'learning_rate': '6.084e-05', 'epoch': '0.6368', 'num_input_tokens_seen': 40015912, 'train_runtime': '5.059e+04', 'train_tokens_per_second': '791.1'}
64%|██████▎ | 11900/18688 [14:03:05<7:18:09, 3.87s/it] 64%|██████▎ | 11901/18688 [14:03:09<7:06:57, 3.77s/it] 64%|██████▎ | 11902/18688 [14:03:13<7:30:08, 3.98s/it] 64%|██████▎ | 11903/18688 [14:03:18<8:00:21, 4.25s/it] 64%|██████▎ | 11904/18688 [14:03:23<8:36:36, 4.57s/it] 64%|██████▎ | 11905/18688 [14:03:27<8:25:00, 4.47s/it] 64%|██████▎ | 11906/18688 [14:03:31<8:09:05, 4.33s/it] 64%|██████▎ | 11907/18688 [14:03:35<7:47:20, 4.14s/it] 64%|██████▎ | 11908/18688 [14:03:40<8:01:23, 4.26s/it] 64%|██████▎ | 11909/18688 [14:03:43<7:32:41, 4.01s/it] 64%|██████▎ | 11910/18688 [14:03:47<7:30:35, 3.99s/it] {'loss': '0.9548', 'grad_norm': '1.715', 'learning_rate': '6.069e-05', 'epoch': '0.6373', 'num_input_tokens_seen': 40049264, 'train_runtime': '5.063e+04', 'train_tokens_per_second': '791.1'}
64%|██████▎ | 11910/18688 [14:03:47<7:30:35, 3.99s/it] 64%|██████▎ | 11911/18688 [14:03:52<7:54:30, 4.20s/it] 64%|██████▎ | 11912/18688 [14:03:56<7:54:38, 4.20s/it] 64%|██████▎ | 11913/18688 [14:04:00<7:32:57, 4.01s/it] 64%|██████▍ | 11914/18688 [14:04:03<7:16:42, 3.87s/it] 64%|██████▍ | 11915/18688 [14:04:08<7:58:33, 4.24s/it] 64%|██████▍ | 11916/18688 [14:04:12<7:46:29, 4.13s/it] 64%|██████▍ | 11917/18688 [14:04:16<7:36:47, 4.05s/it] 64%|██████▍ | 11918/18688 [14:04:21<7:58:16, 4.24s/it] 64%|██████▍ | 11919/18688 [14:04:25<7:50:55, 4.17s/it] 64%|██████▍ | 11920/18688 [14:04:29<7:59:47, 4.25s/it] {'loss': '0.9859', 'grad_norm': '1.352', 'learning_rate': '6.053e-05', 'epoch': '0.6379', 'num_input_tokens_seen': 40083792, 'train_runtime': '5.067e+04', 'train_tokens_per_second': '791.1'}
64%|██████▍ | 11920/18688 [14:04:29<7:59:47, 4.25s/it] 64%|██████▍ | 11921/18688 [14:04:33<7:40:49, 4.09s/it] 64%|██████▍ | 11922/18688 [14:04:36<7:27:49, 3.97s/it] 64%|██████▍ | 11923/18688 [14:04:41<7:44:46, 4.12s/it] 64%|██████▍ | 11924/18688 [14:04:45<7:53:42, 4.20s/it] 64%|██████▍ | 11925/18688 [14:04:49<7:45:49, 4.13s/it] 64%|██████▍ | 11926/18688 [14:04:54<7:50:07, 4.17s/it] 64%|██████▍ | 11927/18688 [14:04:59<8:33:09, 4.55s/it] 64%|██████▍ | 11928/18688 [14:05:04<8:53:57, 4.74s/it] 64%|██████▍ | 11929/18688 [14:05:08<8:39:14, 4.61s/it] 64%|██████▍ | 11930/18688 [14:05:12<8:06:19, 4.32s/it] {'loss': '0.9029', 'grad_norm': '1.614', 'learning_rate': '6.038e-05', 'epoch': '0.6384', 'num_input_tokens_seen': 40116656, 'train_runtime': '5.071e+04', 'train_tokens_per_second': '791.1'}
64%|██████▍ | 11930/18688 [14:05:12<8:06:19, 4.32s/it] 64%|██████▍ | 11931/18688 [14:05:16<7:47:03, 4.15s/it] 64%|██████▍ | 11932/18688 [14:05:20<7:35:29, 4.05s/it] 64%|██████▍ | 11933/18688 [14:05:24<7:29:20, 3.99s/it] 64%|██████▍ | 11934/18688 [14:05:29<8:22:16, 4.46s/it] 64%|██████▍ | 11935/18688 [14:05:34<8:22:13, 4.46s/it] 64%|██████▍ | 11936/18688 [14:05:39<8:51:07, 4.72s/it] 64%|██████▍ | 11937/18688 [14:05:44<9:07:21, 4.86s/it] 64%|██████▍ | 11938/18688 [14:05:49<9:06:40, 4.86s/it] 64%|██████▍ | 11939/18688 [14:05:53<8:27:06, 4.51s/it] 64%|██████▍ | 11940/18688 [14:05:57<8:36:26, 4.59s/it] {'loss': '0.9212', 'grad_norm': '1.471', 'learning_rate': '6.022e-05', 'epoch': '0.6389', 'num_input_tokens_seen': 40154472, 'train_runtime': '5.076e+04', 'train_tokens_per_second': '791.1'}
64%|██████▍ | 11940/18688 [14:05:57<8:36:26, 4.59s/it] 64%|██████▍ | 11941/18688 [14:06:02<8:19:17, 4.44s/it] 64%|██████▍ | 11942/18688 [14:06:07<8:41:12, 4.64s/it] 64%|██████▍ | 11943/18688 [14:06:11<8:38:02, 4.61s/it] 64%|██████▍ | 11944/18688 [14:06:15<8:20:33, 4.45s/it] 64%|██████▍ | 11945/18688 [14:06:19<8:02:56, 4.30s/it] 64%|██████▍ | 11946/18688 [14:06:23<7:39:00, 4.08s/it] 64%|██████▍ | 11947/18688 [14:06:27<7:39:34, 4.09s/it] 64%|██████▍ | 11948/18688 [14:06:31<7:37:14, 4.07s/it] 64%|██████▍ | 11949/18688 [14:06:35<7:48:29, 4.17s/it] 64%|██████▍ | 11950/18688 [14:06:39<7:43:32, 4.13s/it] {'loss': '0.9555', 'grad_norm': '1.983', 'learning_rate': '6.007e-05', 'epoch': '0.6395', 'num_input_tokens_seen': 40187520, 'train_runtime': '5.08e+04', 'train_tokens_per_second': '791.1'}
64%|██████▍ | 11950/18688 [14:06:39<7:43:32, 4.13s/it] 64%|██████▍ | 11951/18688 [14:06:43<7:26:17, 3.97s/it] 64%|██████▍ | 11952/18688 [14:06:47<7:28:22, 3.99s/it] 64%|██████▍ | 11953/18688 [14:06:51<7:26:57, 3.98s/it] 64%|██████▍ | 11954/18688 [14:06:55<7:31:52, 4.03s/it] 64%|██████▍ | 11955/18688 [14:07:00<8:19:27, 4.45s/it] 64%|██████▍ | 11956/18688 [14:07:04<7:49:29, 4.18s/it] 64%|██████▍ | 11957/18688 [14:07:10<8:31:55, 4.56s/it] 64%|██████▍ | 11958/18688 [14:07:14<8:40:24, 4.64s/it] 64%|██████▍ | 11959/18688 [14:07:18<8:11:31, 4.38s/it] 64%|██████▍ | 11960/18688 [14:07:22<8:09:02, 4.36s/it] {'loss': '0.9143', 'grad_norm': '1.7', 'learning_rate': '5.991e-05', 'epoch': '0.64', 'num_input_tokens_seen': 40222440, 'train_runtime': '5.084e+04', 'train_tokens_per_second': '791.1'}
64%|██████▍ | 11960/18688 [14:07:22<8:09:02, 4.36s/it] 64%|██████▍ | 11961/18688 [14:07:28<8:47:21, 4.70s/it] 64%|██████▍ | 11962/18688 [14:07:32<8:31:32, 4.56s/it] 64%|██████▍ | 11963/18688 [14:07:37<8:32:19, 4.57s/it] 64%|██████▍ | 11964/18688 [14:07:41<8:08:48, 4.36s/it] 64%|██████▍ | 11965/18688 [14:07:45<7:55:38, 4.24s/it] 64%|██████▍ | 11966/18688 [14:07:50<8:26:41, 4.52s/it] 64%|██████▍ | 11967/18688 [14:07:54<8:26:05, 4.52s/it] 64%|██████▍ | 11968/18688 [14:07:59<8:22:34, 4.49s/it] 64%|██████▍ | 11969/18688 [14:08:03<8:29:58, 4.55s/it] 64%|██████▍ | 11970/18688 [14:08:08<8:15:12, 4.42s/it] {'loss': '0.9664', 'grad_norm': '1.87', 'learning_rate': '5.976e-05', 'epoch': '0.6405', 'num_input_tokens_seen': 40259536, 'train_runtime': '5.089e+04', 'train_tokens_per_second': '791.1'}
64%|██████▍ | 11970/18688 [14:08:08<8:15:12, 4.42s/it] 64%|██████▍ | 11971/18688 [14:08:13<8:40:52, 4.65s/it] 64%|██████▍ | 11972/18688 [14:08:17<8:12:37, 4.40s/it] 64%|██████▍ | 11973/18688 [14:08:20<7:53:14, 4.23s/it] 64%|██████▍ | 11974/18688 [14:08:24<7:32:17, 4.04s/it] 64%|██████▍ | 11975/18688 [14:08:28<7:23:43, 3.97s/it] 64%|██████▍ | 11976/18688 [14:08:33<8:00:51, 4.30s/it] 64%|██████▍ | 11977/18688 [14:08:37<7:41:35, 4.13s/it] 64%|██████▍ | 11978/18688 [14:08:41<7:37:21, 4.09s/it] 64%|██████▍ | 11979/18688 [14:08:45<7:50:52, 4.21s/it] 64%|██████▍ | 11980/18688 [14:08:49<7:56:05, 4.26s/it] {'loss': '1.035', 'grad_norm': '1.851', 'learning_rate': '5.96e-05', 'epoch': '0.6411', 'num_input_tokens_seen': 40293704, 'train_runtime': '5.093e+04', 'train_tokens_per_second': '791.2'}
64%|██████▍ | 11980/18688 [14:08:49<7:56:05, 4.26s/it] 64%|██████▍ | 11981/18688 [14:08:53<7:32:31, 4.05s/it] 64%|██████▍ | 11982/18688 [14:08:57<7:28:18, 4.01s/it] 64%|██████▍ | 11983/18688 [14:09:01<7:22:28, 3.96s/it] 64%|██████▍ | 11984/18688 [14:09:05<7:35:49, 4.08s/it] 64%|██████▍ | 11985/18688 [14:09:10<7:47:15, 4.18s/it] 64%|██████▍ | 11986/18688 [14:09:14<7:53:25, 4.24s/it] 64%|██████▍ | 11987/18688 [14:09:18<7:52:56, 4.23s/it] 64%|██████▍ | 11988/18688 [14:09:23<8:01:19, 4.31s/it] 64%|██████▍ | 11989/18688 [14:09:26<7:40:08, 4.12s/it] 64%|██████▍ | 11990/18688 [14:09:31<7:58:37, 4.29s/it] {'loss': '0.9494', 'grad_norm': '1.643', 'learning_rate': '5.945e-05', 'epoch': '0.6416', 'num_input_tokens_seen': 40325872, 'train_runtime': '5.097e+04', 'train_tokens_per_second': '791.1'}
64%|██████▍ | 11990/18688 [14:09:31<7:58:37, 4.29s/it] 64%|██████▍ | 11991/18688 [14:09:36<8:14:28, 4.43s/it] 64%|██████▍ | 11992/18688 [14:09:40<8:06:05, 4.36s/it] 64%|██████▍ | 11993/18688 [14:09:44<7:54:52, 4.26s/it] 64%|██████▍ | 11994/18688 [14:09:47<7:30:26, 4.04s/it] 64%|██████▍ | 11995/18688 [14:09:51<7:14:12, 3.89s/it] 64%|██████▍ | 11996/18688 [14:09:55<7:23:06, 3.97s/it] 64%|██████▍ | 11997/18688 [14:09:59<7:17:19, 3.92s/it] 64%|██████▍ | 11998/18688 [14:10:04<7:45:43, 4.18s/it] 64%|██████▍ | 11999/18688 [14:10:08<7:41:49, 4.14s/it] 64%|██████▍ | 12000/18688 [14:10:12<7:39:22, 4.12s/it] {'loss': '0.941', 'grad_norm': '1.975', 'learning_rate': '5.929e-05', 'epoch': '0.6421', 'num_input_tokens_seen': 40360224, 'train_runtime': '5.101e+04', 'train_tokens_per_second': '791.2'}
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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': '0.9226', 'eval_runtime': '83.26', '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_0p00018/checkpoint-12000
loading configuration file config.json from cache at /home/schwarc/.cache/huggingface/hub/models--slovak-nlp--mistral-sk-7b/snapshots/089497ae0d72018e9591895c54f774bf0b4a83a4/config.json
Model config MistralConfig {
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"sliding_window": 4096,
"tie_word_embeddings": false,
"transformers_version": "5.5.0",
"use_cache": true,
"vocab_size": 32000
}
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***** Running Evaluation *****
Num examples = 1000
Batch size = 1
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:71: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:281: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
0%| | 0/1000 [00:00<?, ?it/s]/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:172: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:202: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
/home/schwarc/miniconda3/envs/llama_factory_unsloth/lib/python3.11/site-packages/transformers/modeling_attn_mask_utils.py:254: FutureWarning: The attention mask API under `transformers.modeling_attn_mask_utils` (`AttentionMaskConverter`) is deprecated and will be removed in Transformers v5.10. Please use the new API in `transformers.masking_utils`.
warnings.warn(DEPRECATION_MESSAGE, FutureWarning)
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{'eval_loss': '0.913', 'eval_runtime': '83.31', 'eval_samples_per_second': '12', 'eval_steps_per_second': '12', 'epoch': '0.6957', 'num_input_tokens_seen': 43672984}
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74%|███████▍ | 13870/18688 [16:23:05<5:35:13, 4.17s/it] 74%|███████▍ | 13871/18688 [16:23:10<5:53:25, 4.40s/it] 74%|███████▍ | 13872/18688 [16:23:15<6:07:43, 4.58s/it] 74%|███████▍ | 13873/18688 [16:23:20<6:05:54, 4.56s/it] 74%|███████▍ | 13874/18688 [16:23:25<6:22:35, 4.77s/it] 74%|███████▍ | 13875/18688 [16:23:29<5:55:06, 4.43s/it] 74%|███████▍ | 13876/18688 [16:23:33<5:47:26, 4.33s/it] 74%|███████▍ | 13877/18688 [16:23:37<5:46:52, 4.33s/it] 74%|███████▍ | 13878/18688 [16:23:42<5:53:13, 4.41s/it] 74%|███████▍ | 13879/18688 [16:23:47<6:19:03, 4.73s/it] 74%|███████▍ | 13880/18688 [16:23:52<6:17:06, 4.71s/it] {'loss': '1.052', 'grad_norm': '1.366', 'learning_rate': '3.259e-05', 'epoch': '0.7427', 'num_input_tokens_seen': 46648576, 'train_runtime': '5.903e+04', 'train_tokens_per_second': '790.2'}
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74%|███████▍ | 13890/18688 [16:24:30<5:15:55, 3.95s/it] 74%|███████▍ | 13891/18688 [16:24:35<5:18:08, 3.98s/it] 74%|███████▍ | 13892/18688 [16:24:39<5:20:46, 4.01s/it] 74%|███████▍ | 13893/18688 [16:24:43<5:24:16, 4.06s/it] 74%|███████▍ | 13894/18688 [16:24:47<5:25:21, 4.07s/it] 74%|███████▍ | 13895/18688 [16:24:51<5:27:31, 4.10s/it] 74%|███████▍ | 13896/18688 [16:24:55<5:27:30, 4.10s/it] 74%|███████▍ | 13897/18688 [16:24:59<5:23:05, 4.05s/it] 74%|███████▍ | 13898/18688 [16:25:03<5:12:11, 3.91s/it] 74%|███████▍ | 13899/18688 [16:25:06<5:05:32, 3.83s/it] 74%|███████▍ | 13900/18688 [16:25:11<5:16:24, 3.96s/it] {'loss': '0.8555', 'grad_norm': '1.842', 'learning_rate': '3.233e-05', 'epoch': '0.7438', 'num_input_tokens_seen': 46710680, 'train_runtime': '5.911e+04', 'train_tokens_per_second': '790.2'}
74%|███████▍ | 13900/18688 [16:25:11<5:16:24, 3.96s/it] 74%|███████▍ | 13901/18688 [16:25:14<5:09:34, 3.88s/it] 74%|███████▍ | 13902/18688 [16:25:18<5:06:47, 3.85s/it] 74%|███████▍ | 13903/18688 [16:25:22<5:17:58, 3.99s/it] 74%|███████▍ | 13904/18688 [16:25:27<5:24:14, 4.07s/it] 74%|███████▍ | 13905/18688 [16:25:31<5:28:35, 4.12s/it] 74%|███████▍ | 13906/18688 [16:25:35<5:38:10, 4.24s/it] 74%|███████▍ | 13907/18688 [16:25:41<6:05:13, 4.58s/it] 74%|███████▍ | 13908/18688 [16:25:45<6:02:43, 4.55s/it] 74%|███████▍ | 13909/18688 [16:25:49<5:53:02, 4.43s/it] 74%|███████▍ | 13910/18688 [16:25:54<5:59:05, 4.51s/it] {'loss': '0.931', 'grad_norm': '1.699', 'learning_rate': '3.221e-05', 'epoch': '0.7443', 'num_input_tokens_seen': 46747080, 'train_runtime': '5.915e+04', 'train_tokens_per_second': '790.3'}
74%|███████▍ | 13910/18688 [16:25:54<5:59:05, 4.51s/it] 74%|███████▍ | 13911/18688 [16:25:58<5:54:06, 4.45s/it] 74%|███████▍ | 13912/18688 [16:26:02<5:43:46, 4.32s/it] 74%|███████▍ | 13913/18688 [16:26:08<6:11:07, 4.66s/it] 74%|███████▍ | 13914/18688 [16:26:11<5:45:19, 4.34s/it] 74%|███████▍ | 13915/18688 [16:26:16<5:39:23, 4.27s/it] 74%|███████▍ | 13916/18688 [16:26:20<5:31:41, 4.17s/it] 74%|███████▍ | 13917/18688 [16:26:24<5:32:16, 4.18s/it] 74%|███████▍ | 13918/18688 [16:26:28<5:29:30, 4.14s/it] 74%|███████▍ | 13919/18688 [16:26:32<5:27:40, 4.12s/it] 74%|███████▍ | 13920/18688 [16:26:36<5:37:56, 4.25s/it] {'loss': '0.9089', 'grad_norm': '1.387', 'learning_rate': '3.208e-05', 'epoch': '0.7449', 'num_input_tokens_seen': 46782264, 'train_runtime': '5.92e+04', 'train_tokens_per_second': '790.3'}
74%|███████▍ | 13920/18688 [16:26:36<5:37:56, 4.25s/it] 74%|███████▍ | 13921/18688 [16:26:41<5:37:27, 4.25s/it] 74%|███████▍ | 13922/18688 [16:26:45<5:50:44, 4.42s/it] 75%|███████▍ | 13923/18688 [16:26:49<5:34:01, 4.21s/it] 75%|███████▍ | 13924/18688 [16:26:53<5:24:55, 4.09s/it] 75%|███████▍ | 13925/18688 [16:26:57<5:20:59, 4.04s/it] 75%|███████▍ | 13926/18688 [16:27:03<6:00:09, 4.54s/it] 75%|███████▍ | 13927/18688 [16:27:07<6:03:29, 4.58s/it] 75%|███████▍ | 13928/18688 [16:27:11<5:41:57, 4.31s/it] 75%|███████▍ | 13929/18688 [16:27:15<5:42:39, 4.32s/it] 75%|███████▍ | 13930/18688 [16:27:20<5:42:25, 4.32s/it] {'loss': '0.8847', 'grad_norm': '1.456', 'learning_rate': '3.195e-05', 'epoch': '0.7454', 'num_input_tokens_seen': 46817480, 'train_runtime': '5.924e+04', 'train_tokens_per_second': '790.3'}
75%|███████▍ | 13930/18688 [16:27:20<5:42:25, 4.32s/it] 75%|███████▍ | 13931/18688 [16:27:24<5:35:02, 4.23s/it] 75%|███████▍ | 13932/18688 [16:27:27<5:17:07, 4.00s/it] 75%|███████▍ | 13933/18688 [16:27:31<5:07:28, 3.88s/it] 75%|███████▍ | 13934/18688 [16:27:35<5:13:00, 3.95s/it] 75%|███████▍ | 13935/18688 [16:27:38<5:02:13, 3.82s/it] 75%|███████▍ | 13936/18688 [16:27:42<4:55:07, 3.73s/it] 75%|███████▍ | 13937/18688 [16:27:46<4:56:18, 3.74s/it] 75%|███████▍ | 13938/18688 [16:27:50<5:09:35, 3.91s/it] 75%|███████▍ | 13939/18688 [16:27:53<4:58:22, 3.77s/it] 75%|███████▍ | 13940/18688 [16:27:57<5:05:17, 3.86s/it] {'loss': '0.9595', 'grad_norm': '1.432', 'learning_rate': '3.183e-05', 'epoch': '0.746', 'num_input_tokens_seen': 46847224, 'train_runtime': '5.928e+04', 'train_tokens_per_second': '790.3'}
75%|███████▍ | 13940/18688 [16:27:57<5:05:17, 3.86s/it] 75%|███████▍ | 13941/18688 [16:28:01<5:03:07, 3.83s/it] 75%|███████▍ | 13942/18688 [16:28:06<5:22:37, 4.08s/it] 75%|███████▍ | 13943/18688 [16:28:10<5:18:21, 4.03s/it] 75%|███████▍ | 13944/18688 [16:28:14<5:14:18, 3.98s/it] 75%|███████▍ | 13945/18688 [16:28:18<5:32:21, 4.20s/it] 75%|███████▍ | 13946/18688 [16:28:22<5:26:38, 4.13s/it] 75%|███████▍ | 13947/18688 [16:28:27<5:46:32, 4.39s/it] 75%|███████▍ | 13948/18688 [16:28:33<6:06:32, 4.64s/it] 75%|███████▍ | 13949/18688 [16:28:38<6:17:38, 4.78s/it] 75%|███████▍ | 13950/18688 [16:28:42<6:01:55, 4.58s/it] {'loss': '0.8792', 'grad_norm': '1.87', 'learning_rate': '3.17e-05', 'epoch': '0.7465', 'num_input_tokens_seen': 46881720, 'train_runtime': '5.932e+04', 'train_tokens_per_second': '790.3'}
75%|███████▍ | 13950/18688 [16:28:42<6:01:55, 4.58s/it] 75%|███████▍ | 13951/18688 [16:28:46<5:49:53, 4.43s/it] 75%|███████▍ | 13952/18688 [16:28:51<6:03:36, 4.61s/it] 75%|███████▍ | 13953/18688 [16:28:55<5:52:50, 4.47s/it] 75%|███████▍ | 13954/18688 [16:28:59<5:33:43, 4.23s/it] 75%|███████▍ | 13955/18688 [16:29:02<5:17:48, 4.03s/it] 75%|███████▍ | 13956/18688 [16:29:06<5:18:21, 4.04s/it] 75%|███████▍ | 13957/18688 [16:29:10<5:05:33, 3.88s/it] 75%|███████▍ | 13958/18688 [16:29:14<5:11:55, 3.96s/it] 75%|███████▍ | 13959/18688 [16:29:18<5:16:11, 4.01s/it] 75%|███████▍ | 13960/18688 [16:29:22<5:17:03, 4.02s/it] {'loss': '0.9211', 'grad_norm': '1.525', 'learning_rate': '3.158e-05', 'epoch': '0.747', 'num_input_tokens_seen': 46914040, 'train_runtime': '5.936e+04', 'train_tokens_per_second': '790.3'}
75%|███████▍ | 13960/18688 [16:29:22<5:17:03, 4.02s/it] 75%|███████▍ | 13961/18688 [16:29:26<5:16:16, 4.01s/it] 75%|███████▍ | 13962/18688 [16:29:30<5:14:54, 4.00s/it] 75%|███████▍ | 13963/18688 [16:29:35<5:29:53, 4.19s/it] 75%|███████▍ | 13964/18688 [16:29:39<5:25:34, 4.14s/it] 75%|███████▍ | 13965/18688 [16:29:43<5:25:55, 4.14s/it] 75%|███████▍ | 13966/18688 [16:29:47<5:14:57, 4.00s/it] 75%|███████▍ | 13967/18688 [16:29:50<5:02:55, 3.85s/it] 75%|███████▍ | 13968/18688 [16:29:54<4:54:47, 3.75s/it] 75%|███████▍ | 13969/18688 [16:29:57<4:55:51, 3.76s/it] 75%|███████▍ | 13970/18688 [16:30:01<4:56:15, 3.77s/it] {'loss': '0.9216', 'grad_norm': '1.684', 'learning_rate': '3.145e-05', 'epoch': '0.7476', 'num_input_tokens_seen': 46945168, 'train_runtime': '5.94e+04', 'train_tokens_per_second': '790.3'}
75%|███████▍ | 13970/18688 [16:30:01<4:56:15, 3.77s/it] 75%|███████▍ | 13971/18688 [16:30:05<4:51:26, 3.71s/it] 75%|███████▍ | 13972/18688 [16:30:09<5:01:06, 3.83s/it] 75%|███████▍ | 13973/18688 [16:30:13<4:57:35, 3.79s/it] 75%|███████▍ | 13974/18688 [16:30:17<5:03:51, 3.87s/it] 75%|███████▍ | 13975/18688 [16:30:20<5:00:14, 3.82s/it] 75%|███████▍ | 13976/18688 [16:30:24<4:51:57, 3.72s/it] 75%|███████▍ | 13977/18688 [16:30:27<4:44:31, 3.62s/it] 75%|███████▍ | 13978/18688 [16:30:31<4:58:21, 3.80s/it] 75%|███████▍ | 13979/18688 [16:30:36<5:12:57, 3.99s/it] 75%|███████▍ | 13980/18688 [16:30:39<5:03:29, 3.87s/it] {'loss': '0.9445', 'grad_norm': '1.798', 'learning_rate': '3.133e-05', 'epoch': '0.7481', 'num_input_tokens_seen': 46975808, 'train_runtime': '5.944e+04', 'train_tokens_per_second': '790.3'}
75%|███████▍ | 13980/18688 [16:30:39<5:03:29, 3.87s/it] 75%|███████▍ | 13981/18688 [16:30:43<5:08:03, 3.93s/it] 75%|███████▍ | 13982/18688 [16:30:48<5:20:38, 4.09s/it] 75%|███████▍ | 13983/18688 [16:30:52<5:24:19, 4.14s/it] 75%|███████▍ | 13984/18688 [16:30:56<5:13:11, 3.99s/it] 75%|███████▍ | 13985/18688 [16:31:00<5:10:15, 3.96s/it] 75%|███████▍ | 13986/18688 [16:31:04<5:07:46, 3.93s/it] 75%|███████▍ | 13987/18688 [16:31:07<4:56:36, 3.79s/it] 75%|███████▍ | 13988/18688 [16:31:12<5:23:44, 4.13s/it] 75%|███████▍ | 13989/18688 [16:31:16<5:21:49, 4.11s/it] 75%|███████▍ | 13990/18688 [16:31:20<5:26:42, 4.17s/it] {'loss': '0.911', 'grad_norm': '2.307', 'learning_rate': '3.12e-05', 'epoch': '0.7486', 'num_input_tokens_seen': 47007592, 'train_runtime': '5.948e+04', 'train_tokens_per_second': '790.3'}
75%|███████▍ | 13990/18688 [16:31:20<5:26:42, 4.17s/it] 75%|███████▍ | 13991/18688 [16:31:24<5:12:26, 3.99s/it] 75%|███████▍ | 13992/18688 [16:31:29<5:28:07, 4.19s/it] 75%|███████▍ | 13993/18688 [16:31:32<5:15:30, 4.03s/it] 75%|███████▍ | 13994/18688 [16:31:36<5:09:50, 3.96s/it] 75%|███████▍ | 13995/18688 [16:31:40<5:03:52, 3.89s/it] 75%|███████▍ | 13996/18688 [16:31:43<4:55:41, 3.78s/it] 75%|███████▍ | 13997/18688 [16:31:48<5:07:18, 3.93s/it] 75%|███████▍ | 13998/18688 [16:31:51<5:04:54, 3.90s/it] 75%|███████▍ | 13999/18688 [16:31:55<5:01:26, 3.86s/it] 75%|███████▍ | 14000/18688 [16:32:00<5:21:04, 4.11s/it] {'loss': '0.9577', 'grad_norm': '1.806', 'learning_rate': '3.108e-05', 'epoch': '0.7492', 'num_input_tokens_seen': 47040648, 'train_runtime': '5.952e+04', 'train_tokens_per_second': '790.3'}
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***** Running Evaluation *****
Num examples = 1000
Batch size = 1
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{'eval_loss': '0.905', 'eval_runtime': '83.04', 'eval_samples_per_second': '12.04', 'eval_steps_per_second': '12.04', 'epoch': '0.7492', 'num_input_tokens_seen': 47040648}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00018/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 [16:33:28<38:11:37, 29.34s/it] 75%|███████▍ | 14002/18688 [16:33:32<28:27:49, 21.87s/it] 75%|███████▍ | 14003/18688 [16:33:37<21:40:02, 16.65s/it] 75%|███████▍ | 14004/18688 [16:33:41<16:55:41, 13.01s/it] 75%|███████▍ | 14005/18688 [16:33:46<13:40:34, 10.51s/it] 75%|███████▍ | 14006/18688 [16:33:51<11:19:11, 8.70s/it] 75%|███████▍ | 14007/18688 [16:33:55<9:26:59, 7.27s/it] 75%|███████▍ | 14008/18688 [16:33:58<7:58:52, 6.14s/it] 75%|███████▍ | 14009/18688 [16:34:04<7:47:03, 5.99s/it] 75%|███████▍ | 14010/18688 [16:34:08<7:03:23, 5.43s/it] {'loss': '1.027', 'grad_norm': '2.214', 'learning_rate': '3.096e-05', 'epoch': '0.7497', 'num_input_tokens_seen': 47078616, 'train_runtime': '5.965e+04', 'train_tokens_per_second': '789.3'}
75%|███████▍ | 14010/18688 [16:34:08<7:03:23, 5.43s/it] 75%|███████▍ | 14011/18688 [16:34:11<6:22:29, 4.91s/it] 75%|███████▍ | 14012/18688 [16:34:15<5:53:44, 4.54s/it] 75%|███████▍ | 14013/18688 [16:34:20<6:07:44, 4.72s/it] 75%|███████▍ | 14014/18688 [16:34:24<5:54:39, 4.55s/it] 75%|███████▍ | 14015/18688 [16:34:29<5:58:47, 4.61s/it] 75%|███████▌ | 14016/18688 [16:34:33<5:33:57, 4.29s/it] 75%|███████▌ | 14017/18688 [16:34:37<5:40:02, 4.37s/it] 75%|███████▌ | 14018/18688 [16:34:41<5:20:26, 4.12s/it] 75%|███████▌ | 14019/18688 [16:34:44<5:08:54, 3.97s/it] 75%|███████▌ | 14020/18688 [16:34:48<5:04:52, 3.92s/it] {'loss': '0.9734', 'grad_norm': '1.604', 'learning_rate': '3.083e-05', 'epoch': '0.7502', 'num_input_tokens_seen': 47112024, 'train_runtime': '5.969e+04', 'train_tokens_per_second': '789.3'}
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75%|███████▌ | 14050/18688 [16:36:57<5:38:46, 4.38s/it] 75%|███████▌ | 14051/18688 [16:37:02<5:32:47, 4.31s/it] 75%|███████▌ | 14052/18688 [16:37:06<5:28:46, 4.26s/it] 75%|███████▌ | 14053/18688 [16:37:10<5:23:58, 4.19s/it] 75%|███████▌ | 14054/18688 [16:37:14<5:23:15, 4.19s/it] 75%|███████▌ | 14055/18688 [16:37:19<5:37:23, 4.37s/it] 75%|███████▌ | 14056/18688 [16:37:23<5:25:09, 4.21s/it] 75%|███████▌ | 14057/18688 [16:37:26<5:18:11, 4.12s/it] 75%|███████▌ | 14058/18688 [16:37:31<5:19:58, 4.15s/it] 75%|███████▌ | 14059/18688 [16:37:35<5:20:52, 4.16s/it] 75%|███████▌ | 14060/18688 [16:37:39<5:10:14, 4.02s/it] {'loss': '0.929', 'grad_norm': '1.806', 'learning_rate': '3.034e-05', 'epoch': '0.7524', 'num_input_tokens_seen': 47251056, 'train_runtime': '5.986e+04', 'train_tokens_per_second': '789.4'}
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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': '0.8995', 'eval_runtime': '83.47', 'eval_samples_per_second': '11.98', 'eval_steps_per_second': '11.98', 'epoch': '0.8027', 'num_input_tokens_seen': 50429344}
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85%|████████▍ | 15880/18688 [18:46:59<3:19:11, 4.26s/it] 85%|████████▍ | 15881/18688 [18:47:03<3:15:41, 4.18s/it] 85%|████████▍ | 15882/18688 [18:47:07<3:21:53, 4.32s/it] 85%|████████▍ | 15883/18688 [18:47:11<3:16:42, 4.21s/it] 85%|████████▍ | 15884/18688 [18:47:16<3:18:17, 4.24s/it] 85%|████████▌ | 15885/18688 [18:47:20<3:13:31, 4.14s/it] 85%|████████▌ | 15886/18688 [18:47:23<3:06:59, 4.00s/it] 85%|████████▌ | 15887/18688 [18:47:27<3:01:06, 3.88s/it] 85%|████████▌ | 15888/18688 [18:47:31<2:58:53, 3.83s/it] 85%|████████▌ | 15889/18688 [18:47:34<2:55:43, 3.77s/it] 85%|████████▌ | 15890/18688 [18:47:38<2:57:17, 3.80s/it] {'loss': '0.8878', 'grad_norm': '1.663', 'learning_rate': '1.152e-05', 'epoch': '0.8503', 'num_input_tokens_seen': 53425792, 'train_runtime': '6.766e+04', 'train_tokens_per_second': '789.6'}
85%|████████▌ | 15890/18688 [18:47:38<2:57:17, 3.80s/it] 85%|████████▌ | 15891/18688 [18:47:42<2:58:26, 3.83s/it] 85%|████████▌ | 15892/18688 [18:47:46<2:56:12, 3.78s/it] 85%|████████▌ | 15893/18688 [18:47:49<2:54:45, 3.75s/it] 85%|████████▌ | 15894/18688 [18:47:54<3:04:44, 3.97s/it] 85%|████████▌ | 15895/18688 [18:47:57<3:00:33, 3.88s/it] 85%|████████▌ | 15896/18688 [18:48:01<3:00:05, 3.87s/it] 85%|████████▌ | 15897/18688 [18:48:05<3:01:20, 3.90s/it] 85%|████████▌ | 15898/18688 [18:48:09<2:59:02, 3.85s/it] 85%|████████▌ | 15899/18688 [18:48:13<2:58:53, 3.85s/it] 85%|████████▌ | 15900/18688 [18:48:17<3:02:10, 3.92s/it] {'loss': '0.8941', 'grad_norm': '1.593', 'learning_rate': '1.144e-05', 'epoch': '0.8508', 'num_input_tokens_seen': 53455944, 'train_runtime': '6.77e+04', 'train_tokens_per_second': '789.6'}
85%|████████▌ | 15900/18688 [18:48:17<3:02:10, 3.92s/it] 85%|████████▌ | 15901/18688 [18:48:21<3:00:31, 3.89s/it] 85%|████████▌ | 15902/18688 [18:48:24<2:57:12, 3.82s/it] 85%|████████▌ | 15903/18688 [18:48:29<3:12:50, 4.15s/it] 85%|████████▌ | 15904/18688 [18:48:33<3:08:02, 4.05s/it] 85%|████████▌ | 15905/18688 [18:48:37<3:00:03, 3.88s/it] 85%|████████▌ | 15906/18688 [18:48:42<3:19:00, 4.29s/it] 85%|████████▌ | 15907/18688 [18:48:46<3:10:50, 4.12s/it] 85%|████████▌ | 15908/18688 [18:48:51<3:22:10, 4.36s/it] 85%|████████▌ | 15909/18688 [18:48:55<3:29:05, 4.51s/it] 85%|████████▌ | 15910/18688 [18:48:59<3:15:57, 4.23s/it] {'loss': '0.9129', 'grad_norm': '1.652', 'learning_rate': '1.136e-05', 'epoch': '0.8514', 'num_input_tokens_seen': 53491384, 'train_runtime': '6.774e+04', 'train_tokens_per_second': '789.7'}
85%|████████▌ | 15910/18688 [18:48:59<3:15:57, 4.23s/it] 85%|████████▌ | 15911/18688 [18:49:03<3:11:34, 4.14s/it] 85%|████████▌ | 15912/18688 [18:49:07<3:07:45, 4.06s/it] 85%|████████▌ | 15913/18688 [18:49:11<3:11:44, 4.15s/it] 85%|████████▌ | 15914/18688 [18:49:15<3:05:36, 4.01s/it] 85%|████████▌ | 15915/18688 [18:49:19<3:06:30, 4.04s/it] 85%|████████▌ | 15916/18688 [18:49:23<3:01:34, 3.93s/it] 85%|████████▌ | 15917/18688 [18:49:26<3:01:32, 3.93s/it] 85%|████████▌ | 15918/18688 [18:49:31<3:14:11, 4.21s/it] 85%|████████▌ | 15919/18688 [18:49:35<3:09:45, 4.11s/it] 85%|████████▌ | 15920/18688 [18:49:40<3:11:56, 4.16s/it] {'loss': '0.9742', 'grad_norm': '1.896', 'learning_rate': '1.128e-05', 'epoch': '0.8519', 'num_input_tokens_seen': 53525408, 'train_runtime': '6.778e+04', 'train_tokens_per_second': '789.7'}
85%|████████▌ | 15920/18688 [18:49:40<3:11:56, 4.16s/it] 85%|████████▌ | 15921/18688 [18:49:44<3:12:59, 4.18s/it] 85%|████████▌ | 15922/18688 [18:49:47<3:05:15, 4.02s/it] 85%|████████▌ | 15923/18688 [18:49:52<3:15:02, 4.23s/it] 85%|████████▌ | 15924/18688 [18:49:56<3:14:23, 4.22s/it] 85%|████████▌ | 15925/18688 [18:50:02<3:34:10, 4.65s/it] 85%|████████▌ | 15926/18688 [18:50:06<3:20:05, 4.35s/it] 85%|████████▌ | 15927/18688 [18:50:11<3:33:21, 4.64s/it] 85%|████████▌ | 15928/18688 [18:50:15<3:29:13, 4.55s/it] 85%|████████▌ | 15929/18688 [18:50:19<3:20:59, 4.37s/it] 85%|████████▌ | 15930/18688 [18:50:23<3:16:05, 4.27s/it] {'loss': '0.9538', 'grad_norm': '1.425', 'learning_rate': '1.12e-05', 'epoch': '0.8524', 'num_input_tokens_seen': 53562496, 'train_runtime': '6.782e+04', 'train_tokens_per_second': '789.7'}
85%|████████▌ | 15930/18688 [18:50:23<3:16:05, 4.27s/it] 85%|████████▌ | 15931/18688 [18:50:29<3:32:34, 4.63s/it] 85%|████████▌ | 15932/18688 [18:50:33<3:34:48, 4.68s/it] 85%|████████▌ | 15933/18688 [18:50:39<3:39:59, 4.79s/it] 85%|████████▌ | 15934/18688 [18:50:43<3:35:02, 4.68s/it] 85%|████████▌ | 15935/18688 [18:50:48<3:44:20, 4.89s/it] 85%|████████▌ | 15936/18688 [18:50:53<3:34:25, 4.67s/it] 85%|████████▌ | 15937/18688 [18:50:56<3:24:17, 4.46s/it] 85%|████████▌ | 15938/18688 [18:51:01<3:22:01, 4.41s/it] 85%|████████▌ | 15939/18688 [18:51:05<3:17:38, 4.31s/it] 85%|████████▌ | 15940/18688 [18:51:09<3:10:48, 4.17s/it] {'loss': '0.8783', 'grad_norm': '1.303', 'learning_rate': '1.112e-05', 'epoch': '0.853', 'num_input_tokens_seen': 53601520, 'train_runtime': '6.787e+04', 'train_tokens_per_second': '789.8'}
85%|████████▌ | 15940/18688 [18:51:09<3:10:48, 4.17s/it] 85%|████████▌ | 15941/18688 [18:51:13<3:10:22, 4.16s/it] 85%|████████▌ | 15942/18688 [18:51:17<3:05:54, 4.06s/it] 85%|████████▌ | 15943/18688 [18:51:21<3:11:36, 4.19s/it] 85%|████████▌ | 15944/18688 [18:51:25<3:03:41, 4.02s/it] 85%|████████▌ | 15945/18688 [18:51:29<3:08:30, 4.12s/it] 85%|████████▌ | 15946/18688 [18:51:33<3:03:22, 4.01s/it] 85%|████████▌ | 15947/18688 [18:51:37<3:09:59, 4.16s/it] 85%|████████▌ | 15948/18688 [18:51:42<3:16:41, 4.31s/it] 85%|████████▌ | 15949/18688 [18:51:48<3:36:48, 4.75s/it] 85%|████████▌ | 15950/18688 [18:51:52<3:26:51, 4.53s/it] {'loss': '0.9541', 'grad_norm': '1.405', 'learning_rate': '1.104e-05', 'epoch': '0.8535', 'num_input_tokens_seen': 53638512, 'train_runtime': '6.791e+04', 'train_tokens_per_second': '789.8'}
85%|████████▌ | 15950/18688 [18:51:52<3:26:51, 4.53s/it] 85%|████████▌ | 15951/18688 [18:51:57<3:31:18, 4.63s/it] 85%|████████▌ | 15952/18688 [18:52:01<3:19:44, 4.38s/it] 85%|████████▌ | 15953/18688 [18:52:05<3:15:17, 4.28s/it] 85%|████████▌ | 15954/18688 [18:52:08<3:04:34, 4.05s/it] 85%|████████▌ | 15955/18688 [18:52:12<3:00:13, 3.96s/it] 85%|████████▌ | 15956/18688 [18:52:16<3:07:33, 4.12s/it] 85%|████████▌ | 15957/18688 [18:52:20<3:00:38, 3.97s/it] 85%|████████▌ | 15958/18688 [18:52:24<2:56:10, 3.87s/it] 85%|████████▌ | 15959/18688 [18:52:27<2:51:10, 3.76s/it] 85%|████████▌ | 15960/18688 [18:52:31<2:59:43, 3.95s/it] {'loss': '0.9474', 'grad_norm': '1.84', 'learning_rate': '1.097e-05', 'epoch': '0.854', 'num_input_tokens_seen': 53671096, 'train_runtime': '6.795e+04', 'train_tokens_per_second': '789.8'}
85%|████████▌ | 15960/18688 [18:52:31<2:59:43, 3.95s/it] 85%|████████▌ | 15961/18688 [18:52:36<3:04:36, 4.06s/it] 85%|████████▌ | 15962/18688 [18:52:39<2:56:57, 3.89s/it] 85%|████████▌ | 15963/18688 [18:52:44<3:01:08, 3.99s/it] 85%|████████▌ | 15964/18688 [18:52:48<3:08:08, 4.14s/it] 85%|████████▌ | 15965/18688 [18:52:52<3:03:13, 4.04s/it] 85%|████████▌ | 15966/18688 [18:52:56<3:03:07, 4.04s/it] 85%|████████▌ | 15967/18688 [18:53:01<3:19:43, 4.40s/it] 85%|████████▌ | 15968/18688 [18:53:05<3:18:57, 4.39s/it] 85%|████████▌ | 15969/18688 [18:53:10<3:16:43, 4.34s/it] 85%|████████▌ | 15970/18688 [18:53:13<3:06:17, 4.11s/it] {'loss': '0.9444', 'grad_norm': '1.387', 'learning_rate': '1.089e-05', 'epoch': '0.8546', 'num_input_tokens_seen': 53706720, 'train_runtime': '6.799e+04', 'train_tokens_per_second': '789.9'}
85%|████████▌ | 15970/18688 [18:53:13<3:06:17, 4.11s/it] 85%|████████▌ | 15971/18688 [18:53:17<3:05:24, 4.09s/it] 85%|████████▌ | 15972/18688 [18:53:21<2:57:56, 3.93s/it] 85%|████████▌ | 15973/18688 [18:53:26<3:11:01, 4.22s/it] 85%|████████▌ | 15974/18688 [18:53:30<3:08:53, 4.18s/it] 85%|████████▌ | 15975/18688 [18:53:33<3:00:50, 4.00s/it] 85%|████████▌ | 15976/18688 [18:53:37<3:01:16, 4.01s/it] 85%|████████▌ | 15977/18688 [18:53:42<3:05:55, 4.11s/it] 85%|████████▌ | 15978/18688 [18:53:46<3:05:01, 4.10s/it] 86%|████████▌ | 15979/18688 [18:53:50<3:05:21, 4.11s/it] 86%|████████▌ | 15980/18688 [18:53:54<3:00:05, 3.99s/it] {'loss': '0.8984', 'grad_norm': '1.433', 'learning_rate': '1.081e-05', 'epoch': '0.8551', 'num_input_tokens_seen': 53740784, 'train_runtime': '6.803e+04', 'train_tokens_per_second': '789.9'}
86%|████████▌ | 15980/18688 [18:53:54<3:00:05, 3.99s/it] 86%|████████▌ | 15981/18688 [18:53:58<2:57:18, 3.93s/it] 86%|████████▌ | 15982/18688 [18:54:01<2:54:37, 3.87s/it] 86%|████████▌ | 15983/18688 [18:54:05<2:59:04, 3.97s/it] 86%|████████▌ | 15984/18688 [18:54:10<3:12:31, 4.27s/it] 86%|████████▌ | 15985/18688 [18:54:15<3:12:53, 4.28s/it] 86%|████████▌ | 15986/18688 [18:54:19<3:16:35, 4.37s/it] 86%|████████▌ | 15987/18688 [18:54:23<3:08:23, 4.18s/it] 86%|████████▌ | 15988/18688 [18:54:27<3:11:16, 4.25s/it] 86%|████████▌ | 15989/18688 [18:54:31<3:05:13, 4.12s/it] 86%|████████▌ | 15990/18688 [18:54:36<3:19:37, 4.44s/it] {'loss': '0.9557', 'grad_norm': '1.325', 'learning_rate': '1.073e-05', 'epoch': '0.8557', 'num_input_tokens_seen': 53775136, 'train_runtime': '6.808e+04', 'train_tokens_per_second': '789.9'}
86%|████████▌ | 15990/18688 [18:54:36<3:19:37, 4.44s/it] 86%|████████▌ | 15991/18688 [18:54:41<3:24:32, 4.55s/it] 86%|████████▌ | 15992/18688 [18:54:45<3:19:37, 4.44s/it] 86%|████████▌ | 15993/18688 [18:54:49<3:11:42, 4.27s/it] 86%|████████▌ | 15994/18688 [18:54:53<3:09:02, 4.21s/it] 86%|████████▌ | 15995/18688 [18:54:58<3:11:01, 4.26s/it] 86%|████████▌ | 15996/18688 [18:55:02<3:11:17, 4.26s/it] 86%|████████▌ | 15997/18688 [18:55:06<3:12:18, 4.29s/it] 86%|████████▌ | 15998/18688 [18:55:11<3:20:15, 4.47s/it] 86%|████████▌ | 15999/18688 [18:55:15<3:09:36, 4.23s/it] 86%|████████▌ | 16000/18688 [18:55:19<3:09:14, 4.22s/it] {'loss': '0.9175', 'grad_norm': '1.491', 'learning_rate': '1.065e-05', 'epoch': '0.8562', 'num_input_tokens_seen': 53808416, 'train_runtime': '6.812e+04', 'train_tokens_per_second': '789.9'}
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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': '0.8954', 'eval_runtime': '83.12', 'eval_samples_per_second': '12.03', 'eval_steps_per_second': '12.03', 'epoch': '0.8562', 'num_input_tokens_seen': 53808416}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00018/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 [18:56:47<21:57:20, 29.42s/it] 86%|████████▌ | 16002/18688 [18:56:52<16:18:22, 21.86s/it] 86%|████████▌ | 16003/18688 [18:56:55<12:11:00, 16.34s/it] 86%|████████▌ | 16004/18688 [18:56:59<9:25:34, 12.64s/it] 86%|████████▌ | 16005/18688 [18:57:04<7:42:56, 10.35s/it] 86%|████████▌ | 16006/18688 [18:57:09<6:23:57, 8.59s/it] 86%|████████▌ | 16007/18688 [18:57:13<5:29:12, 7.37s/it] 86%|████████▌ | 16008/18688 [18:57:18<4:58:40, 6.69s/it] 86%|████████▌ | 16009/18688 [18:57:23<4:28:51, 6.02s/it] 86%|████████▌ | 16010/18688 [18:57:26<3:59:14, 5.36s/it] {'loss': '0.9859', 'grad_norm': '2.192', 'learning_rate': '1.058e-05', 'epoch': '0.8567', 'num_input_tokens_seen': 53843200, 'train_runtime': '6.825e+04', 'train_tokens_per_second': '788.9'}
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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': '0.8925', 'eval_runtime': '83.24', 'eval_samples_per_second': '12.01', 'eval_steps_per_second': '12.01', 'epoch': '0.9097', 'num_input_tokens_seen': 57206512}
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96%|█████████▋| 17990/18688 [21:19:14<48:46, 4.19s/it] 96%|█████████▋| 17991/18688 [21:19:18<47:55, 4.13s/it] 96%|█████████▋| 17992/18688 [21:19:22<46:49, 4.04s/it] 96%|█████████▋| 17993/18688 [21:19:26<48:32, 4.19s/it] 96%|█████████▋| 17994/18688 [21:19:30<48:05, 4.16s/it] 96%|█████████▋| 17995/18688 [21:19:35<47:46, 4.14s/it] 96%|█████████▋| 17996/18688 [21:19:41<54:33, 4.73s/it] 96%|█████████▋| 17997/18688 [21:19:45<51:47, 4.50s/it] 96%|█████████▋| 17998/18688 [21:19:50<56:13, 4.89s/it] 96%|█████████▋| 17999/18688 [21:19:55<56:20, 4.91s/it] 96%|█████████▋| 18000/18688 [21:19:59<53:04, 4.63s/it] {'loss': '0.8975', 'grad_norm': '1.843', 'learning_rate': '7.127e-07', 'epoch': '0.9632', 'num_input_tokens_seen': 60569592, 'train_runtime': '7.68e+04', 'train_tokens_per_second': '788.7'}
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***** Running Evaluation *****
Num examples = 1000
Batch size = 1
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{'eval_loss': '0.8914', 'eval_runtime': '83.55', 'eval_samples_per_second': '11.97', 'eval_steps_per_second': '11.97', 'epoch': '0.9632', 'num_input_tokens_seen': 60569592}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00018/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': '0.8915', 'eval_runtime': '83.42', 'eval_samples_per_second': '11.99', 'eval_steps_per_second': '11.99', 'epoch': '1', 'num_input_tokens_seen': 62891360}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00018/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': '7.989e+04', 'train_samples_per_second': '3.743', 'train_steps_per_second': '0.234', 'train_loss': '0.9712', 'epoch': '1', 'num_input_tokens_seen': 62891360}
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Saving model checkpoint to /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00018
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 = 0.9712
train_runtime = 22:11:32.00
train_samples_per_second = 3.743
train_steps_per_second = 0.234
Figure saved at: /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00018/training_loss.png
Figure saved at: /home/schwarc/diplomovka/aya_300k_lr_validation/lr_0p00018/training_eval_loss.png
[WARNING|2026-10-17 19:51:24] 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 = 0.8915
eval_runtime = 0:01:23.25
eval_samples_per_second = 12.011
eval_steps_per_second = 12.011
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'}}
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