Adapter

Also known as: adapter layer, adapter module

An adapter is a small trainable module inserted between or alongside frozen layers of a pretrained model to enable task-specific adaptation without modifying the original weights. Classic adapters add two linear projections with a bottleneck dimension inside each transformer layer. During fine-tuning only adapter weights are updated; at inference, the base model and adapter run together. LoRA is a modern variant that applies a rank-decomposed adapter to the weight matrices directly, achieving similar efficiency.