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48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
"""
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Apply the LoRA weights on top of a base model.
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Usage:
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python3 -m fastchat.model.apply_lora --base ~/model_weights/llama-7b --target ~/model_weights/baize-7b --lora project-baize/baize-lora-7B
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Dependency:
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pip3 install git+https://github.com/huggingface/peft.git@2822398fbe896f25d4dac5e468624dc5fd65a51b
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"""
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import argparse
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import torch
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from peft import PeftModel
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from transformers import AutoTokenizer, AutoModelForCausalLM
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def apply_lora(base_model_path, target_model_path, lora_path):
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print(f"Loading the base model from {base_model_path}")
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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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)
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base_tokenizer = AutoTokenizer.from_pretrained(base_model_path, use_fast=False)
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print(f"Loading the LoRA adapter from {lora_path}")
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lora_model = PeftModel.from_pretrained(
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base,
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lora_path,
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# torch_dtype=torch.float16
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)
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print("Applying the LoRA")
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model = lora_model.merge_and_unload()
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print(f"Saving the target model to {target_model_path}")
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model.save_pretrained(target_model_path)
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base_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("--lora-path", type=str, required=True)
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args = parser.parse_args()
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apply_lora(args.base_model_path, args.target_model_path, args.lora_path)
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