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"""
Usage:
python3 -m fastchat.model.apply_delta --base ~/model_weights/llama-7b --target ~/model_weights/vicuna-7b --delta lmsys/vicuna-7b-delta
"""
import argparse
import torch
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForCausalLM
from LLAVA_Biovil.llava import LlavaLlamaForCausalLM
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 = LlavaLlamaForCausalLM.from_pretrained(delta_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
delta_tokenizer = AutoTokenizer.from_pretrained(delta_path)
print("Applying delta")
for name, param in tqdm(delta.state_dict().items(), desc="Applying delta"):
if name not in base.state_dict():
assert name in ['model.mm_projector.weight', 'model.mm_projector.bias'], f'{name} not in base model'
continue
if param.data.shape == base.state_dict()[name].shape:
param.data += base.state_dict()[name]
else:
assert name in ['model.embed_tokens.weight', 'lm_head.weight'], \
f'{name} dimension mismatch: {param.data.shape} vs {base.state_dict()[name].shape}'
bparam = base.state_dict()[name]
param.data[:bparam.shape[0], :bparam.shape[1]] += bparam
print("Saving target model")
delta.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)
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