Adapter Layers

Adapter Layers are small, trainable neural network modules inserted between existing layers of pre-trained models to enable task-specific adaptation without modifying the original model parameters. These lightweight components typically consist of down-projection and up-projection linear layers with a bottleneck architecture that reduces dimensionality before expanding back to the original size. Adapter layers allow multiple tasks to share the same base model while maintaining separate, specialized parameters for each application, enabling efficient multi-task deployment scenarios. The technique achieves performance comparable to full fine-tuning while using only 1-4% of the original model parameters, significantly reducing storage requirements and training costs. Advanced adapter implementations incorporate techniques like parallel adapter insertion, hierarchical adapter structures, and task-specific routing mechanisms to optimize performance across diverse applications.

Adapter layers enable modular AI systems where specialized capabilities can be added, removed, or updated independently without affecting the core model functionality.