VeRA: Vector-based Random Matrix Adaptation
Authors: name: Dawid Jan Kopiczko, name: Tijmen Blankevoort, name: Yuki Markus Asano

Low-rank adapation (LoRA) is a popular method that reduces the number of
trainable parameters when finetuning large language models, but still faces
acute storage challenges when scaling to even larger models or deploying
numerous per-user or per-task adapted models. In this work, we present
Vector-based Random Matrix Adaptation (VeRA), which reduces the number of
trainable parameters by 10x compared to LoRA, yet maintains the same
performance. It achieves this by using a single pair of low-rank matrices
shared across all layers and learning small scaling vectors instead. We
demonstrate its effectiveness on the GLUE and E2E benchmarks, and show its
application in instruction-following with just 1.4M parameters using the Llama2
7B model.