| """ | |
| Usage: | |
| python3 apply_delta.py --base /path/to/model_weights/llama-13b --target stable-vicuna-13b --delta pvduy/stable-vicuna-13b-delta | |
| """ | |
| import argparse | |
| import torch | |
| from tqdm import tqdm | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| def apply_delta(base_model_path, target_model_path, delta_path): | |
| print("Loading base model") | |
| base = AutoModelForCausalLM.from_pretrained( | |
| base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True) | |
| print("Loading delta") | |
| delta = AutoModelForCausalLM.from_pretrained(delta_path, torch_dtype=torch.float16, low_cpu_mem_usage=True) | |
| delta_tokenizer = AutoTokenizer.from_pretrained(delta_path) | |
| DEFAULT_PAD_TOKEN = "[PAD]" | |
| base_tokenizer = AutoTokenizer.from_pretrained(base_model_path, use_fast=False) | |
| num_new_tokens = base_tokenizer.add_special_tokens(dict(pad_token=DEFAULT_PAD_TOKEN)) | |
| base.resize_token_embeddings(len(base_tokenizer)) | |
| input_embeddings = base.get_input_embeddings().weight.data | |
| output_embeddings = base.get_output_embeddings().weight.data | |
| input_embeddings[-num_new_tokens:] = 0 | |
| output_embeddings[-num_new_tokens:] = 0 | |
| print("Applying delta") | |
| for name, param in tqdm(base.state_dict().items(), desc="Applying delta"): | |
| assert name in delta.state_dict() | |
| param.data += delta.state_dict()[name] | |
| print("Saving target model") | |
| base.save_pretrained(target_model_path) | |
| delta_tokenizer.save_pretrained(target_model_path) | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--base-model-path", type=str, required=True) | |
| parser.add_argument("--target-model-path", type=str, required=True) | |
| parser.add_argument("--delta-path", type=str, required=True) | |
| args = parser.parse_args() | |
| apply_delta(args.base_model_path, args.target_model_path, args.delta_path) |