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fine_tune_improved.py

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+import torch
+from datasets import load_dataset
+from peft import LoraConfig, get_peft_model
+from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments
+from trl import SFTTrainer
+
+# 1. 加载模型和分词器
+# 使用Qwen2.5-0.5B模型，小尺寸适合CPU训练
+model_name = "Qwen/Qwen2.5-0.5B"  # 使用小尺寸的Qwen模型
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float32,
+    low_cpu_mem_usage=True,
+    trust_remote_code=True,
+)
+model.config.use_cache = False
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+# 为Qwen添加pad token
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+# 2. 加载并准备数据集
+dataset = load_dataset("json", data_files="data.json", split="train")
+
+# 将数据集转换为文本格式，添加结束标记
+def convert_to_text(examples):
+    texts = []
+    for i in range(len(examples['instruction'])):
+        text = f"### Instruction:\n{examples['instruction'][i]}\n\n### Input:\n{examples['input'][i]}\n\n### Response:\n{examples['output'][i]}{tokenizer.eos_token}"
+        texts.append(text)
+    return {"text": texts}
+
+dataset = dataset.map(convert_to_text, batched=True, remove_columns=dataset.column_names)
+
+# 3. 配置LoRA参数
+lora_config = LoraConfig(
+    r=16,  # 增加rank
+    lora_alpha=32,
+    lora_dropout=0.1,
+    bias="none",
+    task_type="CAUSAL_LM",
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],  # Qwen的注意力和MLP模块
+)
+
+# 4. 创建PEFT模型
+model = get_peft_model(model, lora_config)
+
+# 5. 配置训练参数
+output_dir = "./qwen2.5-0.5b-lora"
+training_args = TrainingArguments(
+    output_dir=output_dir,
+    per_device_train_batch_size=2,  # 稍微增加批次大小
+    gradient_accumulation_steps=4,   # 减少梯度累积
+    learning_rate=2e-4,             # 调整学习率
+    logging_steps=10,
+    max_steps=200,                  # 增加训练步数
+    save_strategy="steps",
+    save_steps=50,
+    dataloader_num_workers=0,
+    fp16=False,
+    report_to=[],
+    remove_unused_columns=False,
+    warmup_steps=20,                # 添加预热步数
+    weight_decay=0.01,              # 添加权重衰减
+)
+
+# 6. 创建SFTTrainer
+trainer = SFTTrainer(
+    model=model,
+    train_dataset=dataset,
+    args=training_args,
+    peft_config=lora_config,
+    max_seq_length=256,  # 限制序列长度
+)
+
+# 7. 开始训练
+print("开始训练Qwen2.5-0.5B模型...")
+trainer.train()
+
+# 8. 保存模型
+print("保存Qwen2.5-0.5B LoRA适配器...")
+trainer.save_model(output_dir)
+print(f"Qwen2.5-0.5B LoRA适配器已保存到 {output_dir}")