An adapter is a small trainable module inserted into a frozen pre-trained model, enabling task-specific fine-tuning with far fewer parameters than full fine-tuning. Adapters typically consist of a down-projection, nonlinearity, and up-projection added alongside existing layers, and are a foundational technique in parameter-efficient fine-tuning (PEFT) methods including LoRA.