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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)
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