LoRA, or Low-Rank Adaptation, is a parameter-efficient method for fine-tuning large models by training a small set of additional low-rank parameters while leaving most base model weights unchanged. This reduces training cost and storage requirements while allowing a model to adapt to specific tasks or domains.
LoRA stands for Low-Rank Adaptation. It is a method used to fine-tune large models by updating only a small, targeted part of the model. This makes it quicker and less resource-intensive to adapt the model to specific tasks or new datasets.
Abbreviation for Low-Rank Adaptability.