# Low-Rank Adaptation

> Low-rank adaptation (LoRA) fine-tunes a language model by training small low-rank matrices added to its weight layers while freezing the original weights, drastically reducing the number of trainable parameters.

- Identifier: PTL-0078
- Category: Prompt Optimization
- Canonical URL: https://protologue.com/t/low-rank-adaptation/
- Also known as: LoRA
- Introduced: 2021

## Description

LoRA is a common alternative when prompting alone cannot reach the required behavior, and adapters can be swapped per task on one base model.

## Related terms

- [Prompt Tuning](https://protologue.com/t/prompt-tuning/)
- [Prefix Tuning](https://protologue.com/t/prefix-tuning/)
- [Instruction Tuning](https://protologue.com/t/instruction-tuning/)

## Sources

- Hu et al. (2021). LoRA: Low-Rank Adaptation of Large Language Models. https://arxiv.org/abs/2106.09685

## Cite this entry

Protologue. (2026). Low-Rank Adaptation. In Protologue: A Taxonomy of Prompting and LLM Techniques (v1.0.0, PTL-0078). https://protologue.com/t/low-rank-adaptation/

License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)