FLoRA: Low-Rank Core Space for N-dimension

The official implementation of paper: "FLoRA: Low-Rank Core Space for N-dimension".

Links: Arxiv

Update log

• 2024.07.22: For NLP task (GLUE benchmark), please refer to Subspace-Tuning.
• 2024.07.04: We refactor the core code of FLoRA for simplicity and release the code (the core implementations are as same as the original implementations). Please open an issue if you encounter bugs. You can integrate our code into your project easily (see example). Currently, we provide FLoRA layers for linear(2 dims), conv2d(4 dims), conv3d(5 dims), embeddings(1 dims). We also provide a base N-dims-FLoRA layer for high dimensions of weights. You can refer to our implementations of class Linear, Conv2D in layers.py for more details and then customize your class.

TODO

• [x] Refactor the code and release it
• [ ] Release the PEFT-style code
• [ ] Release the task-specific code in our paper.

Reference

@article{si2024flora,
  title={FLoRA: Low-Rank Core Space for N-dimension},
  author={Si, Chongjie* and Wang, Xuehui* and Yang, Xue and Xu, Zhengqin and Li, Qingyun and Dai, Jifeng and Qiao, Yu and Yang, Xiaokang and Shen, Wei},
  journal={arXiv preprint arXiv:2405.14739},
  year={2024}
}