Adapter (PEFT) — AI Glossary

Reference details

Related terms

• LoRA (Low-Rank Adaptation)
• Prefix Tuning
• Supervised Fine-Tuning (SFT)

Example: One base model, many behaviours

Keep one frozen base in memory and load a small adapter per task — support tone, code review, summarisation. Swapping tasks means swapping a few megabytes rather than loading a new multi-gigabyte model. That is the operational argument for adapters, independent of training cost.

Commonly confused with

Adapters and LoRA are related but not identical. Classic adapters insert new bottleneck layers into each block; LoRA adds low-rank updates to existing weight matrices and can be merged back into the base at inference. LoRA is the more common form today, and adapter is often used loosely to mean both.

When to use it

Reach for it when:

• Several task variants must share one base model in memory
• Adapters need independent versioning and rollback
• Full fine-tuning is not affordable

Reach for something else when:

• The change is broad and general rather than task-specific
• You have not tried prompting or retrieval first
• There is no evaluation to catch a quietly worse adapter

Primary source

• Houlsby et al., Parameter-Efficient Transfer Learning for NLP (arXiv)

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