What is Parameter-Efficient Fine-Tuning (PEFT)?
Parameter-Efficient Fine-Tuning (PEFT) — Techniques that adapt large models by only training a small number of extra parameters, saving compute costs.
PEFT methods like LoRA and QLoRA freeze most model weights and only train small adapter layers. This reduces fine-tuning costs by 90%+ and allows customization of large models on consumer hardware. The resulting adapters are small files that can be swapped in and out of the base model.
Frequently Asked Questions
How does PEFT reduce costs?
Instead of training all model parameters (billions), PEFT only trains a few million adapter parameters. This requires dramatically less GPU memory and compute time.
Does PEFT produce worse results than full fine-tuning?
For most tasks, PEFT results are comparable to full fine-tuning. The quality gap is minimal, making the 90%+ cost savings well worth the trade-off.
What is the most popular PEFT method?
LoRA (Low-Rank Adaptation) is the most widely used. QLoRA combines LoRA with quantization for even greater memory savings, enabling fine-tuning of 70B parameter models on a single GPU.