ModelRefs / Parameter-Efficient Fine-Tuning (PEFT) — AI Glossary
Parameter-Efficient Fine-Tuning (PEFT) — AI Glossary
A family of techniques that adapt a pretrained model by updating only a small fraction of its parameters. Also called PEFT.
Overview
PEFT methods — LoRA, QLoRA, Prefix Tuning, Adapters, IA³ — reduce fine-tuning cost by orders of magnitude while achieving results close to full fine-tuning. The Hugging Face PEFT library is the standard implementation.
Reference details
| Topic | training |
|---|---|
| Also known as | PEFT |
| Last reviewed | 2026-06-24 |
Related terms
Commonly confused with
PEFT is the family; LoRA is one member of it, alongside adapters, prefix tuning and prompt tuning. Using the two words interchangeably obscures a real choice, since the methods differ in where they insert parameters and whether they can be merged back into the base weights. All of them sidestep the memory problem that makes full fine-tuning expensive.
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Frequently asked questions
What is Parameter-Efficient Fine-Tuning (PEFT)?
A family of techniques that adapt a pretrained model by updating only a small fraction of its parameters.
Is Parameter-Efficient Fine-Tuning (PEFT) the same as PEFT?
Yes — PEFT are common aliases for Parameter-Efficient Fine-Tuning (PEFT).
What concepts are related to Parameter-Efficient Fine-Tuning (PEFT)?
Closely related concepts include lora, fine tuning.