---
title: Learning Rate Groups
description: "Setting different learning rates by module name"
---

## Background

Inspired by LoRA+, Axolotl allows practitioners to specify separate learning rates for each module or groups of
modules in a model.

## Example

```yaml
lr_groups:
  - name: o_proj
    modules:
      - self_attn.o_proj.weight
    lr: 1e-6
  - name: q_proj
    modules:
      - model.layers.2.self_attn.q_proj.weight
    lr: 1e-5

learning_rate: 2e-5
```

In this example, we have a default learning rate of 2e-5 across the entire model, but we have a separate learning rate
of 1e-6 for all the self attention `o_proj` modules across all layers, and a learning are of 1e-5 to the 3rd layer's
self attention `q_proj` module.

::: {.callout-note}

We currently only support varying `lr` for now. If you're interested in adding support for others (`weight_decay`), we welcome PRs. See https://github.com/axolotl-ai-cloud/axolotl/blob/613bcf90e58f3ab81d3827e7fc572319908db9fb/src/axolotl/core/trainers/mixins/optimizer.py#L17

:::