Adapter Modules and Prefix Tuning

Fully fine-tuning a large language model for every new task means storing and updating a full copy of all its weights, billions of parameters, per task, which is expensive and makes it hard to serve many tasks from one base model. Adapter modules and prefix tuning are two answers to the same problem: freeze the pretrained model entirely, and add a small number of new, trainable parameters that steer its behavior.

An adapter is a tiny extra layer (often a bottleneck: shrink the dimension, then expand it back) inserted inside each transformer block; only these small layers get trained, often under 5% of the original parameter count. Prefix tuning takes a different route: instead of adding layers, it prepends a set of trainable "virtual tokens" to the input at every attention layer, which the frozen model attends to as if they were real context, effectively learning a task-specific prompt the model can't ignore, without writing any English words at all.

Worked example. A 7-billion-parameter model fully fine-tuned for 10 different tasks would require storing 10 full 7B-parameter copies. Using adapters with a bottleneck of a few million trainable parameters each, the same 10 tasks need one shared 7B base plus 10 small adapter files, cutting total storage by well over 99% while reaching accuracy close to full fine-tuning on many benchmarks.