convert_checkpoint.py

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import os

# --- 1. 配置你的路径 ---

# 你微调时使用的“基础模型”的Hugging Face Hub ID
# ！！！这一步至关重要，必须是你开始训练时用的那个模型！！！
base_model_id = "Qwen/Qwen1.5-7B" 

# 你保存的训练检查点 .pth 文件的路径
checkpoint_path = "/path/to/your/checkpoint.pth"  # <--- 修改这里：你的.pth文件路径

# 你想将转换后的、可供评测的模型保存到哪里？
# 这会是一个新的目录，lm-evaluation-harness将从这里加载
output_dir = "../my_pretrain_for_eval" # <--- 修改这里：定义你的输出目录名

# --- 2. 加载基础模型和Tokenizer ---
# 这一步是获取模型的“骨架”（架构）和分词器

print(f"Loading base model architecture from: {base_model_id}")
# trust_remote_code=True 对于Qwen等模型是必需的
model = AutoModelForCausalLM.from_pretrained(base_model_id, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)

# --- 3. 加载你的检查点并提取模型权重 ---

print(f"Loading training checkpoint from: {checkpoint_path}")
# 加载你保存的字典
checkpoint = torch.load(checkpoint_path, map_location='cpu') # 使用map_location='cpu'以避免GPU内存问题

# 从字典中提取模型的 state_dict
# 根据你保存的代码，权重在 'model' 这个键下
model_weights = checkpoint['model']

# --- 4. 将你的权重加载到模型“骨架”中 ---

print("Loading fine-tuned weights into the model...")
model.load_state_dict(model_weights)
print("Weights loaded successfully.")

# --- 5. 将模型和Tokenizer保存为标准的Hugging Face格式 ---

print(f"Saving model and tokenizer to: {output_dir}")
os.makedirs(output_dir, exist_ok=True)

# model.save_pretrained() 会自动创建 config.json, pytorch_model.bin 等文件
model.save_pretrained(output_dir)

# tokenizer.save_pretrained() 会自动创建 tokenizer.json 等文件
tokenizer.save_pretrained(output_dir)

print("Conversion complete!")
print(f"Your model is now ready for evaluation at: {output_dir}")