38 lines
1.5 KiB
Python
38 lines
1.5 KiB
Python
import torch
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from peft import PeftModel
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
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# --- 1. Define your model paths ---
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base_model_path = "Qwen/Qwen2.5-VL-3B-Instruct" # The original student model
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adapter_path = "/home/azureuser/finetuned_models/qwen2.5_vl/lora/Qwen2.5-VL-3B_distill_all_nolabel" # The folder where your LoRA adapter was saved
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merged_model_path = "/home/azureuser/finetuned_models/qwen2.5_vl/Qwen2.5-VL-3B_distill_merged_all_nolabel" # Where to save the new, merged model
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print("Loading base model...")
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# --- 2. Load the base model ---
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# Loading on the CPU
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base_model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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base_model_path,
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torch_dtype=torch.bfloat16,
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trust_remote_code=True,
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device_map="cpu",
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)
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print("Loading LoRA adapter...")
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# --- 3. Load the LoRA adapter onto the base model ---
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model = PeftModel.from_pretrained(base_model, adapter_path)
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print("Merging adapter into the base model...")
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# --- 4. Merge the weights ---
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# Combines the LoRA weights into the base model's layers.
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model = model.merge_and_unload()
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print(f"Saving merged model to {merged_model_path}...")
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# --- 5. Save the new, standalone model ---
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# The saved model is a standard Hugging Face model.
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model.save_pretrained(merged_model_path)
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# --- 6. Save the processor for easy use later ---
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processor = AutoProcessor.from_pretrained(base_model_path, trust_remote_code=True)
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processor.save_pretrained(merged_model_path)
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print("Merge complete!") |