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""" |
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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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) |
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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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