running_log.txt

[INFO|2025-05-29 19:59:18] tokenization_utils_base.py:2034 >> loading file vocab.json from cache at /home/kiho/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-7B-Instruct/snapshots/c03e6d358207e414f1eca0bb1891e29f1db0e242/vocab.json

[INFO|2025-05-29 19:59:18] tokenization_utils_base.py:2034 >> loading file merges.txt from cache at /home/kiho/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-7B-Instruct/snapshots/c03e6d358207e414f1eca0bb1891e29f1db0e242/merges.txt

[INFO|2025-05-29 19:59:18] tokenization_utils_base.py:2034 >> loading file tokenizer.json from cache at /home/kiho/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-7B-Instruct/snapshots/c03e6d358207e414f1eca0bb1891e29f1db0e242/tokenizer.json

[INFO|2025-05-29 19:59:18] tokenization_utils_base.py:2034 >> loading file added_tokens.json from cache at None

[INFO|2025-05-29 19:59:18] tokenization_utils_base.py:2034 >> loading file special_tokens_map.json from cache at None

[INFO|2025-05-29 19:59:18] tokenization_utils_base.py:2034 >> loading file tokenizer_config.json from cache at /home/kiho/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-7B-Instruct/snapshots/c03e6d358207e414f1eca0bb1891e29f1db0e242/tokenizer_config.json

[INFO|2025-05-29 19:59:18] tokenization_utils_base.py:2034 >> loading file chat_template.jinja from cache at None

[INFO|2025-05-29 19:59:19] tokenization_utils_base.py:2304 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.

[INFO|2025-05-29 19:59:20] configuration_utils.py:696 >> loading configuration file config.json from cache at /home/kiho/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-7B-Instruct/snapshots/c03e6d358207e414f1eca0bb1891e29f1db0e242/config.json

[INFO|2025-05-29 19:59:20] configuration_utils.py:768 >> Model config Qwen2Config {
  "_name_or_path": "Qwen/Qwen2.5-Coder-7B-Instruct",
  "architectures": [
    "Qwen2ForCausalLM"
  ],
  "attention_dropout": 0.0,
  "bos_token_id": 151643,
  "eos_token_id": 151645,
  "hidden_act": "silu",
  "hidden_size": 3584,
  "initializer_range": 0.02,
  "intermediate_size": 18944,
  "max_position_embeddings": 32768,
  "max_window_layers": 28,
  "model_type": "qwen2",
  "num_attention_heads": 28,
  "num_hidden_layers": 28,
  "num_key_value_heads": 4,
  "rms_norm_eps": 1e-06,
  "rope_scaling": null,
  "rope_theta": 1000000.0,
  "sliding_window": null,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.48.2",
  "use_cache": true,
  "use_sliding_window": false,
  "vocab_size": 152064
}


[INFO|2025-05-29 19:59:20] tokenization_utils_base.py:2034 >> loading file vocab.json from cache at /home/kiho/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-7B-Instruct/snapshots/c03e6d358207e414f1eca0bb1891e29f1db0e242/vocab.json

[INFO|2025-05-29 19:59:20] tokenization_utils_base.py:2034 >> loading file merges.txt from cache at /home/kiho/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-7B-Instruct/snapshots/c03e6d358207e414f1eca0bb1891e29f1db0e242/merges.txt

[INFO|2025-05-29 19:59:20] tokenization_utils_base.py:2034 >> loading file tokenizer.json from cache at /home/kiho/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-7B-Instruct/snapshots/c03e6d358207e414f1eca0bb1891e29f1db0e242/tokenizer.json

[INFO|2025-05-29 19:59:20] tokenization_utils_base.py:2034 >> loading file added_tokens.json from cache at None

[INFO|2025-05-29 19:59:20] tokenization_utils_base.py:2034 >> loading file special_tokens_map.json from cache at None

[INFO|2025-05-29 19:59:20] tokenization_utils_base.py:2034 >> loading file tokenizer_config.json from cache at /home/kiho/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-7B-Instruct/snapshots/c03e6d358207e414f1eca0bb1891e29f1db0e242/tokenizer_config.json

[INFO|2025-05-29 19:59:20] tokenization_utils_base.py:2034 >> loading file chat_template.jinja from cache at None

[INFO|2025-05-29 19:59:21] tokenization_utils_base.py:2304 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.

[INFO|2025-05-29 19:59:21] logging.py:157 >> Add <|im_end|> to stop words.

[INFO|2025-05-29 19:59:21] logging.py:157 >> Loading dataset Codes_query_filtered_330k_ns_over8_1.json...

[INFO|2025-05-29 19:59:33] configuration_utils.py:696 >> loading configuration file config.json from cache at /home/kiho/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-7B-Instruct/snapshots/c03e6d358207e414f1eca0bb1891e29f1db0e242/config.json

[INFO|2025-05-29 19:59:33] configuration_utils.py:768 >> Model config Qwen2Config {
  "_name_or_path": "Qwen/Qwen2.5-Coder-7B-Instruct",
  "architectures": [
    "Qwen2ForCausalLM"
  ],
  "attention_dropout": 0.0,
  "bos_token_id": 151643,
  "eos_token_id": 151645,
  "hidden_act": "silu",
  "hidden_size": 3584,
  "initializer_range": 0.02,
  "intermediate_size": 18944,
  "max_position_embeddings": 32768,
  "max_window_layers": 28,
  "model_type": "qwen2",
  "num_attention_heads": 28,
  "num_hidden_layers": 28,
  "num_key_value_heads": 4,
  "rms_norm_eps": 1e-06,
  "rope_scaling": null,
  "rope_theta": 1000000.0,
  "sliding_window": null,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.48.2",
  "use_cache": true,
  "use_sliding_window": false,
  "vocab_size": 152064
}


[WARNING|2025-05-29 19:59:33] logging.py:162 >> Input length is smaller than max length. Consider increase input length.

[INFO|2025-05-29 19:59:33] logging.py:157 >> Using llama3 scaling strategy and setting scaling factor to 1.0.

[INFO|2025-05-29 19:59:33] logging.py:157 >> Using block diagonal attention for sequence packing without cross-attention.

[INFO|2025-05-29 19:59:33] logging.py:157 >> Liger kernel has been applied to the model.

[INFO|2025-05-29 19:59:33] modeling_utils.py:3904 >> loading weights file model.safetensors from cache at /home/kiho/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-7B-Instruct/snapshots/c03e6d358207e414f1eca0bb1891e29f1db0e242/model.safetensors.index.json

[INFO|2025-05-29 19:59:33] modeling_utils.py:1582 >> Instantiating Qwen2ForCausalLM model under default dtype torch.bfloat16.

[INFO|2025-05-29 19:59:33] configuration_utils.py:1140 >> Generate config GenerationConfig {
  "bos_token_id": 151643,
  "eos_token_id": 151645
}


[INFO|2025-05-29 19:59:37] modeling_utils.py:4888 >> All model checkpoint weights were used when initializing Qwen2ForCausalLM.


[INFO|2025-05-29 19:59:37] modeling_utils.py:4896 >> All the weights of Qwen2ForCausalLM were initialized from the model checkpoint at Qwen/Qwen2.5-Coder-7B-Instruct.
If your task is similar to the task the model of the checkpoint was trained on, you can already use Qwen2ForCausalLM for predictions without further training.

[INFO|2025-05-29 19:59:37] configuration_utils.py:1095 >> loading configuration file generation_config.json from cache at /home/kiho/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-7B-Instruct/snapshots/c03e6d358207e414f1eca0bb1891e29f1db0e242/generation_config.json

[INFO|2025-05-29 19:59:37] configuration_utils.py:1140 >> Generate config GenerationConfig {
  "bos_token_id": 151643,
  "do_sample": true,
  "eos_token_id": [
    151645,
    151643
  ],
  "pad_token_id": 151643,
  "repetition_penalty": 1.1,
  "temperature": 0.7,
  "top_k": 20,
  "top_p": 0.8
}


[INFO|2025-05-29 19:59:37] logging.py:157 >> Gradient checkpointing enabled.

[INFO|2025-05-29 19:59:37] logging.py:157 >> Using torch SDPA for faster training and inference.

[INFO|2025-05-29 19:59:37] logging.py:157 >> Upcasting trainable params to float32.

[INFO|2025-05-29 19:59:37] logging.py:157 >> Fine-tuning method: Freeze

[INFO|2025-05-29 19:59:37] logging.py:157 >> Set trainable layers: .26.,.27.

[INFO|2025-05-29 19:59:37] logging.py:157 >> trainable params: 466,115,584 || all params: 7,615,616,512 || trainable%: 6.1205

[INFO|2025-05-29 19:59:37] trainer.py:741 >> Using auto half precision backend

[INFO|2025-05-29 19:59:37] logging.py:157 >> Found linear modules: q_proj,gate_proj,up_proj,o_proj,v_proj,k_proj,down_proj

[INFO|2025-05-29 19:59:37] logging.py:157 >> Using APOLLO optimizer with args: {'rank': 256, 'proj': 'random', 'proj_type': 'std', 'update_proj_gap': 200, 'scale': 1, 'scale_type': 'channel', 'scale_front': False}.

[INFO|2025-05-29 19:59:38] trainer.py:2369 >> ***** Running training *****

[INFO|2025-05-29 19:59:38] trainer.py:2370 >>   Num examples = 6,260

[INFO|2025-05-29 19:59:38] trainer.py:2371 >>   Num Epochs = 1

[INFO|2025-05-29 19:59:38] trainer.py:2372 >>   Instantaneous batch size per device = 16

[INFO|2025-05-29 19:59:38] trainer.py:2375 >>   Total train batch size (w. parallel, distributed & accumulation) = 512

[INFO|2025-05-29 19:59:38] trainer.py:2376 >>   Gradient Accumulation steps = 8

[INFO|2025-05-29 19:59:38] trainer.py:2377 >>   Total optimization steps = 12

[INFO|2025-05-29 19:59:38] trainer.py:2378 >>   Number of trainable parameters = 466,115,584

[INFO|2025-05-29 20:01:27] logging.py:157 >> {'loss': 0.6759, 'learning_rate': 4.9148e-05, 'epoch': 0.08, 'throughput': 19284.83}

[INFO|2025-05-29 20:03:09] logging.py:157 >> {'loss': 0.6698, 'learning_rate': 4.6651e-05, 'epoch': 0.16, 'throughput': 19910.87}

[INFO|2025-05-29 20:04:51] logging.py:157 >> {'loss': 0.6228, 'learning_rate': 4.2678e-05, 'epoch': 0.24, 'throughput': 20123.38}

[INFO|2025-05-29 20:06:33] logging.py:157 >> {'loss': 0.6228, 'learning_rate': 3.7500e-05, 'epoch': 0.33, 'throughput': 20244.72}

[INFO|2025-05-29 20:08:15] logging.py:157 >> {'loss': 0.6104, 'learning_rate': 3.1470e-05, 'epoch': 0.41, 'throughput': 20320.48}

[INFO|2025-05-29 20:09:57] logging.py:157 >> {'loss': 0.6063, 'learning_rate': 2.5000e-05, 'epoch': 0.49, 'throughput': 20365.11}

[INFO|2025-05-29 20:11:38] logging.py:157 >> {'loss': 0.6236, 'learning_rate': 1.8530e-05, 'epoch': 0.57, 'throughput': 20397.20}

[INFO|2025-05-29 20:13:20] logging.py:157 >> {'loss': 0.6225, 'learning_rate': 1.2500e-05, 'epoch': 0.65, 'throughput': 20419.47}

[INFO|2025-05-29 20:15:02] logging.py:157 >> {'loss': 0.5952, 'learning_rate': 7.3223e-06, 'epoch': 0.73, 'throughput': 20439.93}

[INFO|2025-05-29 20:16:45] logging.py:157 >> {'loss': 0.6016, 'learning_rate': 3.3494e-06, 'epoch': 0.82, 'throughput': 20429.94}

[INFO|2025-05-29 20:18:27] logging.py:157 >> {'loss': 0.5926, 'learning_rate': 8.5185e-07, 'epoch': 0.90, 'throughput': 20439.47}

[INFO|2025-05-29 20:20:09] logging.py:157 >> {'loss': 0.6137, 'learning_rate': 0.0000e+00, 'epoch': 0.98, 'throughput': 20449.96}

[INFO|2025-05-29 20:20:09] trainer.py:3910 >> Saving model checkpoint to saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/checkpoint-12

[INFO|2025-05-29 20:20:09] configuration_utils.py:420 >> Configuration saved in saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/checkpoint-12/config.json

[INFO|2025-05-29 20:20:09] configuration_utils.py:909 >> Configuration saved in saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/checkpoint-12/generation_config.json

[INFO|2025-05-29 20:20:32] modeling_utils.py:2996 >> The model is bigger than the maximum size per checkpoint (5GB) and is going to be split in 4 checkpoint shards. You can find where each parameters has been saved in the index located at saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/checkpoint-12/model.safetensors.index.json.

[INFO|2025-05-29 20:20:32] tokenization_utils_base.py:2491 >> tokenizer config file saved in saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/checkpoint-12/tokenizer_config.json

[INFO|2025-05-29 20:20:32] tokenization_utils_base.py:2500 >> Special tokens file saved in saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/checkpoint-12/special_tokens_map.json

[INFO|2025-05-29 20:20:33] trainer.py:2643 >> 

Training completed. Do not forget to share your model on huggingface.co/models =)



[INFO|2025-05-29 20:20:33] trainer.py:3910 >> Saving model checkpoint to saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1

[INFO|2025-05-29 20:20:33] configuration_utils.py:420 >> Configuration saved in saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/config.json

[INFO|2025-05-29 20:20:33] configuration_utils.py:909 >> Configuration saved in saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/generation_config.json

[INFO|2025-05-29 20:20:56] modeling_utils.py:2996 >> The model is bigger than the maximum size per checkpoint (5GB) and is going to be split in 4 checkpoint shards. You can find where each parameters has been saved in the index located at saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/model.safetensors.index.json.

[INFO|2025-05-29 20:20:56] tokenization_utils_base.py:2491 >> tokenizer config file saved in saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/tokenizer_config.json

[INFO|2025-05-29 20:20:56] tokenization_utils_base.py:2500 >> Special tokens file saved in saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/special_tokens_map.json

[WARNING|2025-05-29 20:20:56] logging.py:162 >> No metric eval_loss to plot.

[WARNING|2025-05-29 20:20:56] logging.py:162 >> No metric eval_accuracy to plot.

[INFO|2025-05-29 20:20:56] modelcard.py:449 >> Dropping the following result as it does not have all the necessary fields:
{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}}