Low-rank Adaption (LoRA) has been the de-facto parameter-efficient fine-tuning technique for large language models. We present HeteroLoRA, a light-weight search algorithm that leverages zero-cost proxies to allocate the limited LoRA trainable parameters across the model for better fine-tuned performance. In addition to the allocation for the standard LoRA-adapted models, we also demonstrate the efficacy of HeteroLoRA by performing the allocation in a more challenging search space that includes LoRA modules and LoRA-adapted shortcut connections. Experiments show that HeteroLoRA enables improvements in model performance given the same parameter budge. For example, on MRPC, we see an improvement of 1.6% in accuracy with similar training parameter budget. We will open-source our algorithm once the paper is accepted.
@article{arxiv.2406.14956,
title = {Unlocking the Global Synergies in Low-Rank Adapters},
author = {Zixi Zhang and Cheng Zhang and Xitong Gao and Robert D. Mullins and George A. Constantinides and Yiren Zhao},
journal= {arXiv preprint arXiv:2406.14956},
year = {2024}
}