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""" | |
Apply the LoRA weights on top of a base model. | |
Usage: | |
python3 -m fastchat.model.apply_lora --base ~/model_weights/llama-7b --target ~/model_weights/baize-7b --lora project-baize/baize-lora-7B | |
Dependency: | |
pip3 install git+https://github.com/huggingface/peft.git@2822398fbe896f25d4dac5e468624dc5fd65a51b | |
""" | |
import argparse | |
import torch | |
from peft import PeftModel | |
from transformers import AutoTokenizer, AutoModelForCausalLM | |
def apply_lora(base_model_path, target_model_path, lora_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 | |
) | |
base_tokenizer = AutoTokenizer.from_pretrained(base_model_path, use_fast=False) | |
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 {target_model_path}") | |
model.save_pretrained(target_model_path) | |
base_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("--lora-path", type=str, required=True) | |
args = parser.parse_args() | |
apply_lora(args.base_model_path, args.target_model_path, args.lora_path) | |