In this paper, we introduce a subspace-inspired Low-Rank Adaptation (LoRA)method, which is computationally efficient, easy to implement, and readilyapplicable to large language, multimodal, and diffusion models. Initially, weequivalently decompose the weights of LoRA into two subspaces, and find thatsimply mixing them can enhance performance. To study such a phenomenon, werevisit it through a fine-grained subspace lens, showing that such modificationis equivalent to employing a fixed mixer to fuse the subspaces. To be moreflexible, we jointly learn the mixer with the original LoRA weights, and termthe method Mixture-of-Subspaces LoRA (MoSLoRA). MoSLoRA consistentlyoutperforms LoRA on tasks in different modalities, including commonsensereasoning, visual instruction tuning, and subject-driven text-to-imagegeneration, demonstrating its effectiveness and robustness. Codes are availableat https://github.com/wutaiqiang/MoSLoRA{github}.