48 lines
1.9 KiB
Python
48 lines
1.9 KiB
Python
"""
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Usage:
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python3 -m fastchat.model.apply_delta --base ~/model_weights/llama-7b --target ~/model_weights/vicuna-7b --delta lmsys/vicuna-7b-delta
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"""
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import argparse
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import torch
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from tqdm import tqdm
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from llava import LlavaLlamaForCausalLM
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def apply_delta(base_model_path, target_model_path, delta_path):
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print("Loading base model")
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base = AutoModelForCausalLM.from_pretrained(
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base_model_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
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print("Loading delta")
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delta = LlavaLlamaForCausalLM.from_pretrained(delta_path, torch_dtype=torch.float16, low_cpu_mem_usage=True)
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delta_tokenizer = AutoTokenizer.from_pretrained(delta_path)
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print("Applying delta")
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for name, param in tqdm(delta.state_dict().items(), desc="Applying delta"):
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if name not in base.state_dict():
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assert name in ['model.mm_projector.weight', 'model.mm_projector.bias'], f'{name} not in base model'
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continue
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if param.data.shape == base.state_dict()[name].shape:
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param.data += base.state_dict()[name]
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else:
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assert name in ['model.embed_tokens.weight', 'lm_head.weight'], \
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f'{name} dimension mismatch: {param.data.shape} vs {base.state_dict()[name].shape}'
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bparam = base.state_dict()[name]
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param.data[:bparam.shape[0], :bparam.shape[1]] += bparam
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print("Saving target model")
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delta.save_pretrained(target_model_path)
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delta_tokenizer.save_pretrained(target_model_path)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--base-model-path", type=str, required=True)
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parser.add_argument("--target-model-path", type=str, required=True)
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parser.add_argument("--delta-path", type=str, required=True)
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args = parser.parse_args()
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apply_delta(args.base_model_path, args.target_model_path, args.delta_path)
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