Adapter Based

Adapter-based fine-tuning is a parameter-efficient method for adapting large pre-trained models (like LLMs and vision transformers) to new tasks, minimizing computational costs and memory usage compared to full model retraining. Current research focuses on developing efficient adapter architectures (e.g., parallel convolutional adapters, multi-head routing) and applying them across diverse domains, including image segmentation, speech processing, and machine translation. This approach offers significant advantages by enabling rapid adaptation to specific tasks while preserving the knowledge embedded in the original model, leading to improved performance and resource efficiency in various applications.