In this paper, we address the challenges associated with merging low-rank adaptations of large neural networks. With the rise of parameter-efficient adaptation techniques, such as Low-Rank Adaptation (LoRA), model fine-tuning has become more accessible. While fine-tuning models with LoRA is highly efficient, existing merging methods often sacrifice this efficiency by merging fully-sized weight matrices. We propose the Core Space merging framework, which enables the merging of LoRA-adapted models within a common alignment basis, thereby preserving the efficiency of low-rank adaptation while substantially improving accuracy across tasks. We further provide a formal proof that projection into Core Space ensures no loss of information and provide a complexity analysis showing the efficiency gains. Extensive empirical results demonstrate that Core Space significantly improves existing merging techniques and achieves state-of-the-art results on both vision and language tasks while utilizing a fraction of the computational resources. Codebase is available at https://github.com/apanariello4/core-space-merging.

翻译：本文针对大型神经网络低秩适配模型的融合挑战展开研究。随着参数高效适配技术（如低秩适配LoRA）的兴起，模型微调已变得更加便捷。尽管基于LoRA的模型微调具有极高效率，现有融合方法往往通过合并完整尺寸的权重矩阵而牺牲了这一优势。我们提出核心空间融合框架，该框架能够在公共对齐基中实现LoRA适配模型的融合，从而在保持低秩适配效率的同时，显著提升跨任务准确率。我们进一步给出形式化证明，表明向核心空间的投影能确保信息无损，并通过复杂度分析展示其效率增益。大量实验结果表明，核心空间方法显著改进了现有融合技术，在视觉与语言任务上均达到最先进性能，且仅需消耗少量计算资源。代码库已发布于https://github.com/apanariello4/core-space-merging。