You now understand LoRA and QLoRA, the most practical fine-tuning methods for most practitioners.
Takeaways:
- LoRA trains small adapter matrices while freezing the base model
- Rank controls capacity. Start with .
- QLoRA adds -bit quantization for consumer GPU training
- NF4 and double quantization minimize quality loss
- Adapters can be merged or kept separate for multi-task use
Next, I'll show you advanced PEFT methods that build on these foundations.