lora_adapter 模型和原模型合并成一个模型

lora 部分合并到原模型参数上

import torch
from peft import PeftModel
from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaTokenizer
from transformers.generation.utils import GenerationConfig
 
 
def apply_lora(model_name_or_path, output_path, lora_path):
    print(f"Loading the base model from {model_name_or_path}")
    base_tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=False, trust_remote_code=True)
    base = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="cuda:0", torch_dtype=torch.bfloat16, trust_remote_code=True)
    # base.generation_config = GenerationConfig.from_pretrained(model_name_or_path)
 
    print(f"Loading the LoRA adapter from {lora_path}")
 
    lora_model = PeftModel.from_pretrained(
        base,
        lora_path,
        torch_dtype=torch.float16,
    )
 
    print("Applying the LoRA")
    model = lora_model.merge_and_unload()
 
    print(f"Saving the target model to {output_path}")
    model.save_pretrained(output_path)
    base_tokenizer.save_pretrained(output_path)
 
 
if __name__ == "__main__":
    lora_path = "/data2/xinyuuliu/LLaMA-Factory/saves/qwen/lora/orpo"
    model_path = "/data2/xinyuuliu/Qwen1.5-7B-Chat"
    output = "/data2/xinyuuliu/LLaMA-Factory/saves/qwen/lora/orpo/lora_merge"
 
    apply_lora(model_path,output,lora_path)