running_log.txt

[INFO|2025-05-29 23:14:31] tokenization_utils_base.py:2034 >> loading file tokenizer.model from cache at None

[INFO|2025-05-29 23:14:31] tokenization_utils_base.py:2034 >> loading file tokenizer.json from cache at /home/kiho/.cache/huggingface/hub/models--deepseek-ai--deepseek-coder-7b-instruct-v1.5/snapshots/2a050a4c59d687a85324d32e147517992117ed30/tokenizer.json

[INFO|2025-05-29 23:14:31] tokenization_utils_base.py:2034 >> loading file added_tokens.json from cache at None

[INFO|2025-05-29 23:14:31] tokenization_utils_base.py:2034 >> loading file special_tokens_map.json from cache at None

[INFO|2025-05-29 23:14:31] tokenization_utils_base.py:2034 >> loading file tokenizer_config.json from cache at /home/kiho/.cache/huggingface/hub/models--deepseek-ai--deepseek-coder-7b-instruct-v1.5/snapshots/2a050a4c59d687a85324d32e147517992117ed30/tokenizer_config.json

[INFO|2025-05-29 23:14:31] tokenization_utils_base.py:2034 >> loading file chat_template.jinja from cache at None

[INFO|2025-05-29 23:14:32] 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 23:14:32] logging.py:157 >> Loading dataset Codes_query_filtered_330k_ns_over8_1.json...

[INFO|2025-05-29 23:14:48] configuration_utils.py:696 >> loading configuration file config.json from cache at /home/kiho/.cache/huggingface/hub/models--deepseek-ai--deepseek-coder-7b-instruct-v1.5/snapshots/2a050a4c59d687a85324d32e147517992117ed30/config.json

[INFO|2025-05-29 23:14:48] configuration_utils.py:768 >> Model config LlamaConfig {
  "_name_or_path": "deepseek-ai/deepseek-coder-7b-instruct-v1.5",
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 100000,
  "eos_token_id": 100015,
  "head_dim": 128,
  "hidden_act": "silu",
  "hidden_size": 4096,
  "initializer_range": 0.02,
  "intermediate_size": 11008,
  "max_position_embeddings": 4096,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 32,
  "num_hidden_layers": 30,
  "num_key_value_heads": 32,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-06,
  "rope_scaling": null,
  "rope_theta": 10000.0,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.48.2",
  "use_cache": true,
  "vocab_size": 102400
}


[WARNING|2025-05-29 23:14:48] logging.py:162 >> Input length is smaller than max length. Consider increase input length.

[INFO|2025-05-29 23:14:48] logging.py:157 >> Using llama3 scaling strategy and setting scaling factor to 1.0.

[INFO|2025-05-29 23:14:48] logging.py:157 >> Using block diagonal attention for sequence packing without cross-attention.

[INFO|2025-05-29 23:14:48] logging.py:157 >> Liger kernel has been applied to the model.

[INFO|2025-05-29 23:14:48] modeling_utils.py:3904 >> loading weights file model.safetensors from cache at /home/kiho/.cache/huggingface/hub/models--deepseek-ai--deepseek-coder-7b-instruct-v1.5/snapshots/2a050a4c59d687a85324d32e147517992117ed30/model.safetensors.index.json

[INFO|2025-05-29 23:14:48] modeling_utils.py:1582 >> Instantiating LlamaForCausalLM model under default dtype torch.bfloat16.

[INFO|2025-05-29 23:14:48] configuration_utils.py:1140 >> Generate config GenerationConfig {
  "bos_token_id": 100000,
  "eos_token_id": 100015
}


[INFO|2025-05-29 23:14:52] modeling_utils.py:4888 >> All model checkpoint weights were used when initializing LlamaForCausalLM.


[INFO|2025-05-29 23:14:52] modeling_utils.py:4896 >> All the weights of LlamaForCausalLM were initialized from the model checkpoint at deepseek-ai/deepseek-coder-7b-instruct-v1.5.
If your task is similar to the task the model of the checkpoint was trained on, you can already use LlamaForCausalLM for predictions without further training.

[INFO|2025-05-29 23:14:52] configuration_utils.py:1095 >> loading configuration file generation_config.json from cache at /home/kiho/.cache/huggingface/hub/models--deepseek-ai--deepseek-coder-7b-instruct-v1.5/snapshots/2a050a4c59d687a85324d32e147517992117ed30/generation_config.json

[INFO|2025-05-29 23:14:52] configuration_utils.py:1140 >> Generate config GenerationConfig {
  "bos_token_id": 100000,
  "eos_token_id": 100015
}


[INFO|2025-05-29 23:14:52] logging.py:157 >> Gradient checkpointing enabled.

[INFO|2025-05-29 23:14:52] logging.py:157 >> Using torch SDPA for faster training and inference.

[INFO|2025-05-29 23:14:52] logging.py:157 >> Upcasting trainable params to float32.

[INFO|2025-05-29 23:14:52] logging.py:157 >> Fine-tuning method: Freeze

[INFO|2025-05-29 23:14:52] logging.py:157 >> Set trainable layers: .28.,.29.

[INFO|2025-05-29 23:14:52] logging.py:157 >> trainable params: 404,766,720 || all params: 6,910,365,696 || trainable%: 5.8574

[INFO|2025-05-29 23:14:52] trainer.py:741 >> Using auto half precision backend

[INFO|2025-05-29 23:14:52] logging.py:157 >> Found linear modules: down_proj,up_proj,q_proj,v_proj,gate_proj,o_proj,k_proj

[INFO|2025-05-29 23:14:52] 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 23:14:52] trainer.py:2369 >> ***** Running training *****

[INFO|2025-05-29 23:14:52] trainer.py:2370 >>   Num examples = 7,871

[INFO|2025-05-29 23:14:52] trainer.py:2371 >>   Num Epochs = 1

[INFO|2025-05-29 23:14:52] trainer.py:2372 >>   Instantaneous batch size per device = 16

[INFO|2025-05-29 23:14:52] trainer.py:2375 >>   Total train batch size (w. parallel, distributed & accumulation) = 512

[INFO|2025-05-29 23:14:52] trainer.py:2376 >>   Gradient Accumulation steps = 8

[INFO|2025-05-29 23:14:52] trainer.py:2377 >>   Total optimization steps = 15

[INFO|2025-05-29 23:14:52] trainer.py:2378 >>   Number of trainable parameters = 404,766,720

[INFO|2025-05-29 23:16:39] logging.py:157 >> {'loss': 0.7344, 'learning_rate': 4.9454e-05, 'epoch': 0.07, 'throughput': 19884.80}

[INFO|2025-05-29 23:18:18] logging.py:157 >> {'loss': 0.7113, 'learning_rate': 4.7839e-05, 'epoch': 0.13, 'throughput': 20489.52}

[INFO|2025-05-29 23:19:58] logging.py:157 >> {'loss': 0.7009, 'learning_rate': 4.5225e-05, 'epoch': 0.20, 'throughput': 20677.48}

[INFO|2025-05-29 23:21:37] logging.py:157 >> {'loss': 0.6704, 'learning_rate': 4.1728e-05, 'epoch': 0.26, 'throughput': 20771.66}

[INFO|2025-05-29 23:23:17] logging.py:157 >> {'loss': 0.6504, 'learning_rate': 3.7500e-05, 'epoch': 0.33, 'throughput': 20829.21}

[INFO|2025-05-29 23:24:56] logging.py:157 >> {'loss': 0.6217, 'learning_rate': 3.2725e-05, 'epoch': 0.39, 'throughput': 20867.75}

[INFO|2025-05-29 23:26:36] logging.py:157 >> {'loss': 0.6052, 'learning_rate': 2.7613e-05, 'epoch': 0.46, 'throughput': 20897.01}

[INFO|2025-05-29 23:28:15] logging.py:157 >> {'loss': 0.6302, 'learning_rate': 2.2387e-05, 'epoch': 0.52, 'throughput': 20915.80}

[INFO|2025-05-29 23:29:55] logging.py:157 >> {'loss': 0.5964, 'learning_rate': 1.7275e-05, 'epoch': 0.59, 'throughput': 20930.91}

[INFO|2025-05-29 23:31:35] logging.py:157 >> {'loss': 0.6233, 'learning_rate': 1.2500e-05, 'epoch': 0.65, 'throughput': 20943.31}

[INFO|2025-05-29 23:33:14] logging.py:157 >> {'loss': 0.5964, 'learning_rate': 8.2717e-06, 'epoch': 0.72, 'throughput': 20954.49}

[INFO|2025-05-29 23:34:54] logging.py:157 >> {'loss': 0.6106, 'learning_rate': 4.7746e-06, 'epoch': 0.78, 'throughput': 20962.99}

[INFO|2025-05-29 23:36:33] logging.py:157 >> {'loss': 0.5916, 'learning_rate': 2.1614e-06, 'epoch': 0.85, 'throughput': 20970.47}

[INFO|2025-05-29 23:38:13] logging.py:157 >> {'loss': 0.5893, 'learning_rate': 5.4631e-07, 'epoch': 0.91, 'throughput': 20976.44}

[INFO|2025-05-29 23:39:52] logging.py:157 >> {'loss': 0.6298, 'learning_rate': 0.0000e+00, 'epoch': 0.98, 'throughput': 20983.82}

[INFO|2025-05-29 23:39:52] trainer.py:3910 >> Saving model checkpoint to saves/DeepSeek-Coder-7B-Instruct/freeze/deepseek_nlx_8_1/checkpoint-15

[INFO|2025-05-29 23:39:52] configuration_utils.py:420 >> Configuration saved in saves/DeepSeek-Coder-7B-Instruct/freeze/deepseek_nlx_8_1/checkpoint-15/config.json

[INFO|2025-05-29 23:39:52] configuration_utils.py:909 >> Configuration saved in saves/DeepSeek-Coder-7B-Instruct/freeze/deepseek_nlx_8_1/checkpoint-15/generation_config.json

[INFO|2025-05-29 23:40:13] modeling_utils.py:2996 >> The model is bigger than the maximum size per checkpoint (5GB) and is going to be split in 3 checkpoint shards. You can find where each parameters has been saved in the index located at saves/DeepSeek-Coder-7B-Instruct/freeze/deepseek_nlx_8_1/checkpoint-15/model.safetensors.index.json.

[INFO|2025-05-29 23:40:13] tokenization_utils_base.py:2491 >> tokenizer config file saved in saves/DeepSeek-Coder-7B-Instruct/freeze/deepseek_nlx_8_1/checkpoint-15/tokenizer_config.json

[INFO|2025-05-29 23:40:13] tokenization_utils_base.py:2500 >> Special tokens file saved in saves/DeepSeek-Coder-7B-Instruct/freeze/deepseek_nlx_8_1/checkpoint-15/special_tokens_map.json

[INFO|2025-05-29 23:40:13] trainer.py:2643 >> 

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



[INFO|2025-05-29 23:40:13] trainer.py:3910 >> Saving model checkpoint to saves/DeepSeek-Coder-7B-Instruct/freeze/deepseek_nlx_8_1

[INFO|2025-05-29 23:40:13] configuration_utils.py:420 >> Configuration saved in saves/DeepSeek-Coder-7B-Instruct/freeze/deepseek_nlx_8_1/config.json

[INFO|2025-05-29 23:40:13] configuration_utils.py:909 >> Configuration saved in saves/DeepSeek-Coder-7B-Instruct/freeze/deepseek_nlx_8_1/generation_config.json

[INFO|2025-05-29 23:40:34] modeling_utils.py:2996 >> The model is bigger than the maximum size per checkpoint (5GB) and is going to be split in 3 checkpoint shards. You can find where each parameters has been saved in the index located at saves/DeepSeek-Coder-7B-Instruct/freeze/deepseek_nlx_8_1/model.safetensors.index.json.

[INFO|2025-05-29 23:40:34] tokenization_utils_base.py:2491 >> tokenizer config file saved in saves/DeepSeek-Coder-7B-Instruct/freeze/deepseek_nlx_8_1/tokenizer_config.json

[INFO|2025-05-29 23:40:34] tokenization_utils_base.py:2500 >> Special tokens file saved in saves/DeepSeek-Coder-7B-Instruct/freeze/deepseek_nlx_8_1/special_tokens_map.json

[WARNING|2025-05-29 23:40:34] logging.py:162 >> No metric eval_loss to plot.

[WARNING|2025-05-29 23:40:34] logging.py:162 >> No metric eval_accuracy to plot.

[INFO|2025-05-29 23:40:34] modelcard.py:449 >> Dropping the following result as it does not have all the necessary fields:
{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}}