Overview
LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning method that uses low-rank matrices to adapt pre-trained models. It significantly reduces the number of trainable parameters while maintaining performance.
Key Characteristics
Advantages
- Drastically reduced parameter count
- Lower memory requirements
- Faster training times
- Easy to switch between tasks
Limitations
- May have slightly lower performance
- Requires careful rank selection
- Limited to certain layer types
- May need task-specific tuning
Implementation Steps
- Select target layers for adaptation
- Choose appropriate rank
- Initialize LoRA matrices
- Train with frozen base model
- Merge weights for inference
Best Practices
- Start with small rank values
- Monitor adaptation quality
- Use appropriate learning rates
- Consider layer-wise adaptation
- Validate on multiple tasks