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PoLoRA: A Preconditioned Orthogonalized LoRA Optimizer
Nikhil Ghosh, Tetiana Parshakova, Robert M. Gower
Low-rank adaptation (LoRA) makes finetuning large language models cheaper by adding to each weight matrix a trainable low-rank update parameterized as the product of two matrices.…
PLoP: Precise LoRA Placement for Efficient Finetuning of Large Models
Soufiane Hayou, Nikhil Ghosh, Bin Yu
Low-Rank Adaptation (LoRA) is a widely used finetuning method for large models. Its small memory footprint allows practitioners to adapt large models to specific tasks at a fractio…
The Impact of Initialization on LoRA Finetuning Dynamics
Soufiane Hayou, Nikhil Ghosh, Bin Yu
In this paper, we study the role of initialization in Low Rank Adaptation (LoRA) as originally introduced in Hu et al. (2021). Essentially, to start from the pretrained model as in…
LoRA+: Efficient Low Rank Adaptation of Large Models
Soufiane Hayou, Nikhil Ghosh, Bin Yu
In this paper, we show that Low Rank Adaptation (LoRA) as originally introduced in Hu et al. (2021) leads to suboptimal finetuning of models with large width (embedding dimension).…