VeRA: Vector-based Random Matrix Adaptation

Dawid, Tijmen Blankevoort, Yuki Asano

VeRA: Vector-based Random Matrix Adaptation: 31 upvotes on Hugging Face Daily Papers, #3 of 10 papers on 2023-10-18. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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