---
title: "LoRA (Low-Rank Adaptation)"
description: "LoRA, or Low-Rank Adaptation, is a parameter-efficient fine-tuning method for large neural networks. Instead of updating all model weights, LoRA freezes the base model and trains small low-rank matrices that modify selected layers."
canonical_url: "https://promptmetheus.com/resources/llm-knowledge-base/lora-low-rank-adaptation"
---

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LoRA, or Low-Rank Adaptation, is a parameter-efficient fine-tuning method for large neural networks. Instead of updating all model weights, LoRA freezes the base model and trains small low-rank matrices that modify selected layers.

In [Large Language Models (LLMs)](https://promptmetheus.com/resources/llm-knowledge-base/large-language-model-llm), LoRA is often used to adapt a model to a task, domain, style, or product behavior with much less training memory than full [fine-tuning](https://promptmetheus.com/resources/llm-knowledge-base/fine-tuning).

The practical advantages include:

- fewer trainable [model parameters](https://promptmetheus.com/resources/llm-knowledge-base/model-parameter);
- lower GPU memory requirements;
- smaller task-specific adapter files;
- easier switching between adaptations; and
- less operational overhead than storing a full copy of each fine-tuned model.

LoRA is not a general replacement for high-quality data or evaluation. A poor dataset can still degrade behavior, and an adapter can conflict with safety policies or domain expectations. Teams should evaluate LoRA adapters against held-out tasks, regression suites, and production constraints.

The original paper, [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685), introduced the method for adapting large language models with far fewer trainable parameters than full fine-tuning.

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