# This supports merging as many adapters as you want. # python merge_adapters.py --base_model_name_or_path --peft_model_paths --output_dir from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel import torch import os import argparse def get_args(): parser = argparse.ArgumentParser() parser.add_argument("--base_model_name_or_path", type=str) parser.add_argument("--peft_model_paths", type=str, nargs='+', help="List of paths to PEFT models") parser.add_argument("--output_dir", type=str) parser.add_argument("--device", type=str, default="cpu") parser.add_argument("--push_to_hub", action="store_true") parser.add_argument("--trust_remote_code", action="store_true") return parser.parse_args() def main(): args = get_args() if args.device == 'auto': device_arg = {'device_map': 'auto'} else: device_arg = {'device_map': {"": args.device}} print(f"Loading base model: {args.base_model_name_or_path}") base_model = AutoModelForCausalLM.from_pretrained( args.base_model_name_or_path, return_dict=True, torch_dtype=torch.float16, trust_remote_code=args.trust_remote_code, **device_arg ) model = base_model for peft_model_path in args.peft_model_paths: print(f"Loading PEFT: {peft_model_path}") model = PeftModel.from_pretrained(model, peft_model_path, **device_arg) print(f"Running merge_and_unload for {peft_model_path}") model = model.merge_and_unload() tokenizer = AutoTokenizer.from_pretrained(args.base_model_name_or_path) if args.push_to_hub: print(f"Saving to hub ...") model.push_to_hub(f"{args.output_dir}", use_temp_dir=False) tokenizer.push_to_hub(f"{args.output_dir}", use_temp_dir=False) else: model.save_pretrained(f"{args.output_dir}") tokenizer.save_pretrained(f"{args.output_dir}") print(f"Model saved to {args.output_dir}") if __name__ == "__main__": main()