LoRA and parameter-efficient finetuning
**Parameter-efficient finetuning (PEFT)** methods like **LoRA** adapt large models by training small adapter matrices instead of all weights.
What it is
Parameter-efficient finetuning (PEFT) methods like LoRA adapt large models by training small adapter matrices instead of all weights.
Why it matters
Lets builders specialize models on modest hardware—still need eval, license checks, and privacy care.
How it works (plain)
Freeze most weights → learn low-rank updates on selected layers → merge or swap adapters. Data quality still dominates.
Try it
Before finetuning, write your eval set and success bar—then pick PEFT vs full finetune.
Myths
- ⚠️ Myth: LoRA removes overfitting risk.
- ✓ Reality: Small data still overfits—validate.
- ⚠️ Myth: Adapters inherit no license duties.
- ✓ Reality: Base model licenses still apply.
Sources
- Course 07 pretraining/instruction tuning; Course 25 licenses
- Hu et al. LoRA paper (cite): https://arxiv.org/abs/2106.09685 ↗