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""" | |
Make the delta weights by subtracting base weights. | |
Usage: | |
python3 -m fastchat.model.make_delta --base ~/model_weights/llama-13b --target ~/model_weights/vicuna-13b --delta ~/model_weights/vicuna-13b-delta --hub-repo-id lmsys/vicuna-13b-delta-v1.1 | |
""" | |
import argparse | |
import torch | |
from tqdm import tqdm | |
from transformers import AutoTokenizer, AutoModelForCausalLM | |
def make_delta(base_model_path, target_model_path, delta_path): | |
print(f"Loading the base model from {base_model_path}") | |
base = AutoModelForCausalLM.from_pretrained( | |
base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True | |
) | |
print(f"Loading the target model from {target_model_path}") | |
target = AutoModelForCausalLM.from_pretrained( | |
target_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True | |
) | |
print("Calculating the delta") | |
for name, param in tqdm(target.state_dict().items(), desc="Calculating delta"): | |
assert name in base.state_dict() | |
param.data -= base.state_dict()[name] | |
print(f"Saving the delta to {delta_path}") | |
if args.hub_repo_id: | |
kwargs = {"push_to_hub": True, "repo_id": args.hub_repo_id} | |
else: | |
kwargs = {} | |
target.save_pretrained(delta_path, **kwargs) | |
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) | |
parser.add_argument("--hub-repo-id", type=str) | |
args = parser.parse_args() | |
make_delta(args.base_model_path, args.target_model_path, args.delta_path) | |