Pruning Adapter

Pruning adapters are a technique for making large pre-trained models more efficient, focusing on reducing the size and computational cost of adapter modules used for fine-tuning on new tasks. Current research explores various pruning strategies, including those based on magnitude, tropical geometry, and structured approaches like channel or block pruning, often within the context of ONNX-compatible frameworks for broader applicability. This work aims to improve the efficiency of both training and inference, balancing accuracy with reduced memory footprint and faster processing, ultimately impacting the feasibility of deploying large models in resource-constrained environments.

Papers