2025년 3월 19일 수요일

LoRA Fine-Tuning: Efficiently Adapt Large Language Models


 

You can absolutely fine-tune existing ML models using LoRA (Low-Rank Adaptation). LoRA is an efficient method for fine-tuning large, pre-trained models, offering several key advantages:

  • Reduced Memory Usage: LoRA adds and trains low-rank matrices instead of updating the entire model, significantly reducing the memory requirements.
  • Faster Training Speed: Fewer parameters to update translate to faster training times.
  • Preservation of Original Model: LoRA doesn't modify the original model's weights; it only learns the added low-rank matrices, preserving the original model.
  • Modularity & Reusability: LoRA modules are small and task-specific, making them easy to reuse or combine with other models.

LoRA Training Process:

  1. Load Pre-trained Model: Load the existing ML model you want to fine-tune (e.g., BERT, GPT-3).
  2. Add LoRA Layers: Add LoRA layers to specific layers of the model. These LoRA layers consist of two small matrices.
  3. Train Only LoRA Layers: Freeze the weights of the original model and train only the weights of the added LoRA layers.
  4. Resulting Model: After training, you can either merge the LoRA layer weights into the original model or store them separately for use.

Libraries Supporting LoRA:

LoRA is a very useful technique for fine-tuning large models and is widely used in many research and application areas. It's a great way to adapt powerful pre-trained models to your specific tasks without requiring massive computational resources.

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