Adapters

Adapters are lightweight neural network modules inserted into pre-trained models to enable efficient task-specific fine-tuning without modifying the original model parameters, allowing rapid customization while preserving base model capabilities. These parameter-efficient techniques add small trainable layers between existing model components, typically reducing trainable parameters by over 95% compared to full fine-tuning. Common adapter architectures include bottleneck adapters with down-projection and up-projection layers, Low-Rank Adaptation (LoRA) that decomposes weight updates into low-rank matrices, and prefix tuning approaches. Adapters enable multi-task learning where different modules handle specialized capabilities, prevent catastrophic forgetting, and support modular system design. Benefits include reduced computational costs, faster training times, and memory efficiency for deploying multiple task-specific variants. For AI agents, adapters provide cost-effective personalization, domain adaptation, and skill acquisition without expensive retraining.