exporting model after lora

easydistill/mmkd/exporting.py

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