LoRA: Low-Rank Adaptation of Large Language Models

A starting point.

LoRA adapts large models with low-rank updates, reducing the number of trainable parameters during fine-tuning.

What to keep in mind

Read the original paper for the experimental setting, baselines, and limitations; a result in one setting is not a guarantee of performance elsewhere.

This work is included in a researcher’s reading path. A detailed editorial explanation is still being prepared. The complete manuscript is available in Full paper.

Source: LoRA: Low-Rank Adaptation of Large Language Models. The original manuscript contains the methods, experiments, figures, and references. An arXiv posting date may follow an earlier conference publication. Read the linked record for version history.