Model Merging
After training, the LoRA adapters are saved as a separate file. The run_model_merge function in train_pipeline.py performs the final step: it loads the base model in its original precision (bfloat16) and merges the trained adapter weights into it. The result is a single, standalone fine-tuned model that can be deployed without requiring the PEFT library.