You now understand the range of PEFT methods beyond standard LoRA.
Takeaways:
- DoRA improves on LoRA by separating magnitude and direction
- rsLoRA and LoRA+ optimize training dynamics
- Spectrum identifies which layers need adaptation
- Adapters, prefix tuning, and IA3 offer alternative approaches
- TIES and DARE enable sophisticated adapter merging
- Choose methods based on task complexity and resource constraints
Next, I'll teach you preference alignment methods like DPO.