lauguage_model_fine_tuning/merge_teacher_model.py

#!/usr/bin/env python3
"""
模型合并脚本 - 将LoRA权重合并到基础模型中
用于推理和部署
"""

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import argparse

def merge_lora_model(base_model_path, lora_model_path, output_path):
    """
    合并LoRA权重到基础模型
    
    Args:
        base_model_path: 基础模型路径
        lora_model_path: LoRA模型路径（训练输出）
        output_path: 合并后模型保存路径
    """
    print("📥 Loading base model...")
    
    # 加载基础模型（不使用量化）
    base_model = AutoModelForCausalLM.from_pretrained(
        base_model_path,
        torch_dtype=torch.float16,
        device_map="auto",
        trust_remote_code=True,
    )
    
    print("📥 Loading LoRA model...")
    
    # 加载LoRA模型
    model = PeftModel.from_pretrained(base_model, lora_model_path)
    
    print("🔄 Merging LoRA weights...")
    
    # 合并权重
    model = model.merge_and_unload()
    
    print("💾 Saving merged model...")
    
    # 保存合并后的模型
    model.save_pretrained(output_path, safe_serialization=True)
    
    # 复制tokenizer
    tokenizer = AutoTokenizer.from_pretrained(base_model_path)
    tokenizer.save_pretrained(output_path)
    
    print(f"✅ Model merged and saved to {output_path}")

def test_merged_model(model_path):
    """测试合并后的模型"""
    print("🧪 Testing merged model...")
    
    # 加载模型和tokenizer
    model = AutoModelForCausalLM.from_pretrained(
        model_path,
        torch_dtype=torch.float16,
        device_map="auto",
    )
    tokenizer = AutoTokenizer.from_pretrained(model_path)
    
    # 测试提示
    test_prompt = "### Human: Create an advertisement for a revolutionary AI-powered smartwatch\n### Assistant:"
    
    inputs = tokenizer(test_prompt, return_tensors="pt").to(model.device)
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=200,
            do_sample=True,
            temperature=0.7,
            top_p=0.9,
            pad_token_id=tokenizer.eos_token_id,
        )
    
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    generated_text = response[len(test_prompt):].strip()
    
    print(f"\n📝 Test Prompt: Create an advertisement for a revolutionary AI-powered smartwatch")
    print(f"📄 Generated Response:\n{generated_text}")

def main():
    parser = argparse.ArgumentParser(description="Merge LoRA weights with base model")
    parser.add_argument("--base_model", required=True, help="Path to base model")
    parser.add_argument("--lora_model", required=True, help="Path to LoRA model (training output)")
    parser.add_argument("--output", required=True, help="Output path for merged model")
    parser.add_argument("--test", action="store_true", help="Test the merged model")
    
    args = parser.parse_args()
    
    # 合并模型
    merge_lora_model(args.base_model, args.lora_model, args.output)
    
    # 测试模型（可选）
    if args.test:
        test_merged_model(args.output)

if __name__ == "__main__":
    # 示例用法
    print("📋 Merge LoRA Model Script")
    print("\n使用方法:")
    print("python merge_model.py --base_model microsoft/DialoGPT-medium --lora_model ./results --output ./merged_model --test")
    print("\n或者直接运行默认配置:")
    
    # 默认配置
    merge_lora_model(
        base_model_path="microsoft/DialoGPT-medium",  # 替换为实际的OpenAI OSS 120B模型
        lora_model_path="./results",
        output_path="./merged_model"
    )
    
    # 测试合并后的模型
    test_merged_model("./merged_model")