Block Circulant Adapter for Large Language Models

Xinyu Ding; Meiqi Wang; Siyu Liao; Zhongfeng Wang

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arXiv · 2505.00582

Block Circulant Adapter for Large Language Models

Xinyu Ding·Meiqi Wang·Siyu Liao·Zhongfeng Wang

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Abstract

Fine-tuning large language models (LLMs) is difficult due to their huge model size. Recent Fourier domain-based methods show potential for reducing fine-tuning costs. We propose a block circulant matrix-based fine-tuning method with a stable training heuristic to leverage the properties of circulant matrices and one-dimensional Fourier transforms to reduce storage and computation costs. Experiments show that our method uses $14\times$ less number of parameters than VeRA, $16\times$ smaller than LoRA and $32\times$ less FLOPs than FourierFT, while maintaining close or better task performance. Our approach presents a promising way in frequency domain to fine-tune large models on downstream tasks.

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BibTeX RIS

Xinyu Ding, Meiqi Wang, Siyu Liao, Zhongfeng Wang. 2025-05-01. Block Circulant Adapter for Large Language Models. https://arxiv.org/abs/2505.00582

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