LoRA

LoRA for Parameter-Efficient Fine-Tuning

Fine-tuning a large model does not always require updating every model parameter. Low-Rank Adaptation (LoRA) takes advantage of this idea by keeping the original model weights frozen and learning much smaller matrices that modify selected layers. For developers, LoRA changes what must be trained, stored, and moved between experiments—not just the size of the fine-tuning job. That distinction determines when it is useful, what it does not save, and how adapter choices affect model behavior.