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qwen-test

PEFT
Safetensors
llama-factory
lora
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qwen-test
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[INFO|parser.py:344] 2024-07-16 19:25:29,726 >> Process rank: 0, device: cuda:0, n_gpu: 1, distributed training: False, compute dtype: torch.float16

[INFO|tokenization_utils_base.py:2108] 2024-07-16 19:25:32,413 >> loading file qwen.tiktoken from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen-1_8B-Chat/snapshots/1d0f68de57b88cfde81f3c3e537f24464d889081/qwen.tiktoken

[INFO|tokenization_utils_base.py:2108] 2024-07-16 19:25:32,413 >> loading file added_tokens.json from cache at None

[INFO|tokenization_utils_base.py:2108] 2024-07-16 19:25:32,413 >> loading file special_tokens_map.json from cache at None

[INFO|tokenization_utils_base.py:2108] 2024-07-16 19:25:32,413 >> loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen-1_8B-Chat/snapshots/1d0f68de57b88cfde81f3c3e537f24464d889081/tokenizer_config.json

[INFO|tokenization_utils_base.py:2108] 2024-07-16 19:25:32,414 >> loading file tokenizer.json from cache at None

[INFO|template.py:268] 2024-07-16 19:25:32,773 >> Add eos token: <|im_end|>

[INFO|template.py:372] 2024-07-16 19:25:32,773 >> Add pad token: <|im_end|>

[INFO|loader.py:52] 2024-07-16 19:25:32,774 >> Loading dataset glaive_toolcall_en_demo.json...

[INFO|configuration_utils.py:733] 2024-07-16 19:26:23,755 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen-1_8B-Chat/snapshots/1d0f68de57b88cfde81f3c3e537f24464d889081/config.json

[INFO|configuration_utils.py:733] 2024-07-16 19:26:24,494 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen-1_8B-Chat/snapshots/1d0f68de57b88cfde81f3c3e537f24464d889081/config.json

[INFO|configuration_utils.py:796] 2024-07-16 19:26:24,495 >> Model config QWenConfig {
  "_name_or_path": "Qwen/Qwen-1_8B-Chat",
  "architectures": [
    "QWenLMHeadModel"
  ],
  "attn_dropout_prob": 0.0,
  "auto_map": {
    "AutoConfig": "Qwen/Qwen-1_8B-Chat--configuration_qwen.QWenConfig",
    "AutoModelForCausalLM": "Qwen/Qwen-1_8B-Chat--modeling_qwen.QWenLMHeadModel"
  },
  "bf16": false,
  "emb_dropout_prob": 0.0,
  "fp16": false,
  "fp32": false,
  "hidden_size": 2048,
  "initializer_range": 0.02,
  "intermediate_size": 11008,
  "kv_channels": 128,
  "layer_norm_epsilon": 1e-06,
  "max_position_embeddings": 8192,
  "model_type": "qwen",
  "no_bias": true,
  "num_attention_heads": 16,
  "num_hidden_layers": 24,
  "onnx_safe": null,
  "rotary_emb_base": 10000,
  "rotary_pct": 1.0,
  "scale_attn_weights": true,
  "seq_length": 8192,
  "softmax_in_fp32": false,
  "tie_word_embeddings": false,
  "tokenizer_class": "QWenTokenizer",
  "transformers_version": "4.41.2",
  "use_cache": true,
  "use_cache_kernel": false,
  "use_cache_quantization": false,
  "use_dynamic_ntk": true,
  "use_flash_attn": "auto",
  "use_logn_attn": true,
  "vocab_size": 151936
}


[INFO|modeling_utils.py:3474] 2024-07-16 19:26:26,974 >> loading weights file model.safetensors from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen-1_8B-Chat/snapshots/1d0f68de57b88cfde81f3c3e537f24464d889081/model.safetensors.index.json

[INFO|modeling_utils.py:1519] 2024-07-16 19:26:45,032 >> Instantiating QWenLMHeadModel model under default dtype torch.float16.

[INFO|configuration_utils.py:962] 2024-07-16 19:26:45,034 >> Generate config GenerationConfig {}


[INFO|modeling_utils.py:4280] 2024-07-16 19:26:51,937 >> All model checkpoint weights were used when initializing QWenLMHeadModel.


[INFO|modeling_utils.py:4288] 2024-07-16 19:26:51,937 >> All the weights of QWenLMHeadModel were initialized from the model checkpoint at Qwen/Qwen-1_8B-Chat.
If your task is similar to the task the model of the checkpoint was trained on, you can already use QWenLMHeadModel for predictions without further training.

[INFO|configuration_utils.py:917] 2024-07-16 19:26:52,423 >> loading configuration file generation_config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen-1_8B-Chat/snapshots/1d0f68de57b88cfde81f3c3e537f24464d889081/generation_config.json

[INFO|configuration_utils.py:962] 2024-07-16 19:26:52,424 >> Generate config GenerationConfig {
  "chat_format": "chatml",
  "do_sample": true,
  "eos_token_id": 151643,
  "max_new_tokens": 512,
  "max_window_size": 6144,
  "pad_token_id": 151643,
  "repetition_penalty": 1.1,
  "top_k": 0,
  "top_p": 0.8
}


[WARNING|checkpointing.py:70] 2024-07-16 19:26:52,440 >> You are using the old GC format, some features (e.g. BAdam) will be invalid.

[INFO|checkpointing.py:103] 2024-07-16 19:26:52,440 >> Gradient checkpointing enabled.

[INFO|attention.py:86] 2024-07-16 19:26:52,440 >> Using vanilla attention implementation.

[INFO|adapter.py:302] 2024-07-16 19:26:52,441 >> Upcasting trainable params to float32.

[INFO|adapter.py:158] 2024-07-16 19:26:52,441 >> Fine-tuning method: LoRA

[INFO|misc.py:51] 2024-07-16 19:26:52,442 >> Found linear modules: c_attn,c_proj,w1,w2

[INFO|loader.py:196] 2024-07-16 19:26:53,145 >> trainable params: 6,709,248 || all params: 1,843,537,920 || trainable%: 0.3639

[INFO|trainer.py:641] 2024-07-16 19:26:53,161 >> Using auto half precision backend

[INFO|trainer.py:2078] 2024-07-16 19:26:54,481 >> ***** Running training *****

[INFO|trainer.py:2079] 2024-07-16 19:26:54,481 >>   Num examples = 300

[INFO|trainer.py:2080] 2024-07-16 19:26:54,481 >>   Num Epochs = 3

[INFO|trainer.py:2081] 2024-07-16 19:26:54,481 >>   Instantaneous batch size per device = 2

[INFO|trainer.py:2084] 2024-07-16 19:26:54,481 >>   Total train batch size (w. parallel, distributed & accumulation) = 16

[INFO|trainer.py:2085] 2024-07-16 19:26:54,481 >>   Gradient Accumulation steps = 8

[INFO|trainer.py:2086] 2024-07-16 19:26:54,482 >>   Total optimization steps = 54

[INFO|trainer.py:2087] 2024-07-16 19:26:54,484 >>   Number of trainable parameters = 6,709,248

[INFO|callbacks.py:310] 2024-07-16 19:27:37,133 >> {'loss': 0.6756, 'learning_rate': 4.8950e-05, 'epoch': 0.27, 'throughput': 1193.09}

[INFO|callbacks.py:310] 2024-07-16 19:28:17,112 >> {'loss': 0.6799, 'learning_rate': 4.5887e-05, 'epoch': 0.53, 'throughput': 1200.41}

[INFO|callbacks.py:310] 2024-07-16 19:28:56,484 >> {'loss': 0.6995, 'learning_rate': 4.1070e-05, 'epoch': 0.80, 'throughput': 1208.30}

[INFO|callbacks.py:310] 2024-07-16 19:29:36,117 >> {'loss': 0.6313, 'learning_rate': 3.4902e-05, 'epoch': 1.07, 'throughput': 1209.98}

[INFO|callbacks.py:310] 2024-07-16 19:30:17,804 >> {'loss': 0.5683, 'learning_rate': 2.7902e-05, 'epoch': 1.33, 'throughput': 1211.98}

[INFO|callbacks.py:310] 2024-07-16 19:30:57,971 >> {'loss': 0.4988, 'learning_rate': 2.0659e-05, 'epoch': 1.60, 'throughput': 1214.64}

[INFO|callbacks.py:310] 2024-07-16 19:31:38,903 >> {'loss': 0.5748, 'learning_rate': 1.3780e-05, 'epoch': 1.87, 'throughput': 1215.63}

[INFO|callbacks.py:310] 2024-07-16 19:32:15,869 >> {'loss': 0.5793, 'learning_rate': 7.8440e-06, 'epoch': 2.13, 'throughput': 1214.06}

[INFO|callbacks.py:310] 2024-07-16 19:32:55,941 >> {'loss': 0.5500, 'learning_rate': 3.3494e-06, 'epoch': 2.40, 'throughput': 1214.25}

[INFO|callbacks.py:310] 2024-07-16 19:33:38,528 >> {'loss': 0.5715, 'learning_rate': 6.7388e-07, 'epoch': 2.67, 'throughput': 1214.09}

[INFO|trainer.py:2329] 2024-07-16 19:34:11,515 >> 

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



[INFO|trainer.py:3410] 2024-07-16 19:34:11,517 >> Saving model checkpoint to saves/Qwen-1.8B-Chat/lora/train_2024-07-16-18-56-15

[INFO|configuration_utils.py:733] 2024-07-16 19:34:12,032 >> loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--Qwen--Qwen-1_8B-Chat/snapshots/1d0f68de57b88cfde81f3c3e537f24464d889081/config.json

[INFO|configuration_utils.py:796] 2024-07-16 19:34:12,033 >> Model config QWenConfig {
  "architectures": [
    "QWenLMHeadModel"
  ],
  "attn_dropout_prob": 0.0,
  "auto_map": {
    "AutoConfig": "Qwen/Qwen-1_8B-Chat--configuration_qwen.QWenConfig",
    "AutoModelForCausalLM": "Qwen/Qwen-1_8B-Chat--modeling_qwen.QWenLMHeadModel"
  },
  "bf16": false,
  "emb_dropout_prob": 0.0,
  "fp16": false,
  "fp32": false,
  "hidden_size": 2048,
  "initializer_range": 0.02,
  "intermediate_size": 11008,
  "kv_channels": 128,
  "layer_norm_epsilon": 1e-06,
  "max_position_embeddings": 8192,
  "model_type": "qwen",
  "no_bias": true,
  "num_attention_heads": 16,
  "num_hidden_layers": 24,
  "onnx_safe": null,
  "rotary_emb_base": 10000,
  "rotary_pct": 1.0,
  "scale_attn_weights": true,
  "seq_length": 8192,
  "softmax_in_fp32": false,
  "tie_word_embeddings": false,
  "tokenizer_class": "QWenTokenizer",
  "transformers_version": "4.41.2",
  "use_cache": true,
  "use_cache_kernel": false,
  "use_cache_quantization": false,
  "use_dynamic_ntk": true,
  "use_flash_attn": "auto",
  "use_logn_attn": true,
  "vocab_size": 151936
}


[INFO|tokenization_utils_base.py:2513] 2024-07-16 19:34:12,174 >> tokenizer config file saved in saves/Qwen-1.8B-Chat/lora/train_2024-07-16-18-56-15/tokenizer_config.json

[INFO|tokenization_utils_base.py:2522] 2024-07-16 19:34:12,174 >> Special tokens file saved in saves/Qwen-1.8B-Chat/lora/train_2024-07-16-18-56-15/special_tokens_map.json

[WARNING|ploting.py:89] 2024-07-16 19:34:12,511 >> No metric eval_loss to plot.

[WARNING|ploting.py:89] 2024-07-16 19:34:12,512 >> No metric eval_accuracy to plot.

[INFO|modelcard.py:450] 2024-07-16 19:34:12,513 >> Dropping the following result as it does not have all the necessary fields:
{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}}