49 lines
1.8 KiB
Python
49 lines
1.8 KiB
Python
"""
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Usage:
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python3 apply_delta.py --base /path/to/model_weights/llama-13b --target stable-vicuna-13b --delta pvduy/stable-vicuna-13b-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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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 = AutoModelForCausalLM.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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DEFAULT_PAD_TOKEN = "[PAD]"
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base_tokenizer = AutoTokenizer.from_pretrained(base_model_path, use_fast=False)
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num_new_tokens = base_tokenizer.add_special_tokens(dict(pad_token=DEFAULT_PAD_TOKEN))
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base.resize_token_embeddings(len(base_tokenizer))
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input_embeddings = base.get_input_embeddings().weight.data
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output_embeddings = base.get_output_embeddings().weight.data
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input_embeddings[-num_new_tokens:] = 0
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output_embeddings[-num_new_tokens:] = 0
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print("Applying delta")
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for name, param in tqdm(base.state_dict().items(), desc="Applying delta"):
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assert name in delta.state_dict()
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param.data += delta.state_dict()[name]
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print("Saving target model")
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base.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) |