72 lines
2.2 KiB
Python
72 lines
2.2 KiB
Python
import os
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os.environ['CUDA_VISIBLE_DEVICES'] = '0'
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import torch
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from transformers import TrainingArguments, AutoModelForCausalLM, AutoTokenizer
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from trl import DPOTrainer
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from peft import get_peft_model, LoraConfig, TaskType
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from datasets import load_dataset
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def find_all_linear_names(model):
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cls = torch.nn.Linear
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lora_module_names = set()
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for name, module in model.named_modules():
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if isinstance(module, cls):
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names = name.split('.')
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lora_module_names.add(names[0] if len(names) == 1 else names[-1])
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if 'lm_head' in lora_module_names:
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lora_module_names.remove('lm_head')
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return list(lora_module_names)
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def init_model():
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device = 'cuda:0'
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# Do model patching and add fast LoRA weights
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model_name_or_path = "minimind"
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tokenizer_name_or_path = "minimind"
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model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True)
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path, trust_remote_code=True, use_fast=False)
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tokenizer.pad_token = tokenizer.eos_token
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target_modules = find_all_linear_names(model)
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peft_config = LoraConfig(
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task_type=TaskType.CAUSAL_LM,
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r=8,
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lora_alpha=16,
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lora_dropout=0.1,
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inference_mode=False,
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target_modules=target_modules
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)
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model = get_peft_model(model, peft_config)
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model.print_trainable_parameters()
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model = model.to(device)
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return model, tokenizer
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if __name__ == '__main__':
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model, tokenizer = init_model()
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training_args = TrainingArguments(output_dir="./minimind_dpo",
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per_device_train_batch_size=1,
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remove_unused_columns=False)
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################
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# Dataset
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################
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# 确保路径正确，文件存在
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dataset_path = './dataset/dpo/train_data.json'
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# 加载数据集
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train_dataset = load_dataset('json', data_files=dataset_path)
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dpo_trainer = DPOTrainer(
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model,
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ref_model=None,
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args=training_args,
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beta=0.1,
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train_dataset=train_dataset['train'],
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tokenizer=tokenizer,
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max_length=512,
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max_prompt_length=512
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)
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dpo_trainer.train()
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