mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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185c76f6ad
* preserve image_sizes * preserve image_sizes * init plugin * support audio-text2text lora * nit * support image/video-text2text, audio-text2text * remove args * remove lines * add docs && nit * remove some comments * fix && add merge part script * add license
91 lines
3.7 KiB
Python
91 lines
3.7 KiB
Python
# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
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#
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# This code is based on the HuggingFace's PEFT library.
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# https://github.com/huggingface/peft/blob/v0.10.0/examples/loftq_finetuning/quantize_save_load.py
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import shutil
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import fire
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from peft import PeftModel
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from transformers import AutoModel, AutoProcessor, AutoTokenizer
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def merge_lora(
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base_model_path: str,
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lora_checkpoint_path: str,
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extra_file: str = "spk_dict.pt",
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submodule_name: str = "thinker",
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save_path: str = "./merged_model_checkpoint",
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):
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"""Load the original model, tokenizer, and processor configuration, merge the LoRA weights.
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for a specified submodule, and save the final merged model along with its configurations.
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Args:
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base_model_path (str): Path to the original model directory.
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lora_checkpoint_path (str): Path to the directory containing LoRA weights.
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extra_file (str): Name of the extra file to be copied (default: "spk_dict.pt").
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submodule_name (str): Name of the submodule to merge (default: "thinker").
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save_path (str): Directory where the merged model and configurations will be saved.
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"""
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# 1. Load the original model, tokenizer, and processor
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model = AutoModel.from_pretrained(base_model_path)
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tokenizer = AutoTokenizer.from_pretrained(base_model_path)
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try:
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processor = AutoProcessor.from_pretrained(base_model_path)
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except Exception:
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print("Processor configuration not found, skipping processor load.")
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processor = None
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print("Successfully loaded the original model, tokenizer, and processor (if available).")
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# 2. Extract the submodule to be merged (e.g., model.thinker)
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if not hasattr(model, submodule_name):
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raise AttributeError(f"The model does not have a submodule named '{submodule_name}'.")
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base_submodule = getattr(model, submodule_name)
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print(f"Successfully extracted submodule: {submodule_name}.")
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# 3. Load the LoRA weights onto the extracted submodule
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lora_model = PeftModel.from_pretrained(base_submodule, lora_checkpoint_path)
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print("LoRA weights loaded successfully.")
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# 4. Merge the LoRA weights into the submodule and unload the LoRA modules
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merged_submodule = lora_model.merge_and_unload()
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print("LoRA weights merged successfully.")
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# 5. Replace the original submodule with the merged submodule in the model
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setattr(model, submodule_name, merged_submodule)
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# 6. Save the final merged model along with the tokenizer and processor configuration
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model.save_pretrained(save_path)
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tokenizer.save_pretrained(save_path)
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if processor is not None:
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processor.save_pretrained(save_path)
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print(f"Merged model and configuration saved to {save_path}.")
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source_file = os.path.join(base_model_path, extra_file)
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target_file = os.path.join(save_path, extra_file)
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if os.path.exists(source_file):
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shutil.copy(source_file, target_file)
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print(f"File '{extra_file}' copied from {base_model_path} to {save_path}.")
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else:
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print(f"File '{extra_file}' not found in {base_model_path}, skipping copy.")
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if __name__ == "__main__":
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fire.Fire(merge_lora)
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