SuperLoRA: Parameter-Efficient Unified Adaptation of Multi-Layer
  Attention Modules
Xiangyu Chen Jing Liu Ye Wang Pu Perry Wang Matthew Brand Guanghui Wang Toshiaki Koike-Akino
Low-rank adaptation (LoRA) and its variants are widely employed in
fine-tuning large models, including large language models for natural language
processing and diffusion models for computer vision. This paper proposes a
generalized framework called SuperLoRA that unifies and extends different LoRA
variants, which can be realized under different hyper-parameter settings.
Introducing grouping, folding, shuffling, projecting, and tensor factoring,
SuperLoRA offers high flexibility compared with other LoRA variants and
demonstrates superior performance for transfer learning tasks especially in the
extremely few-parameter regimes.