# Optimizers – Axolotl

# Optimizers

Configuring optimizers

## Overview

Axolotl supports all optimizers supported by transformers OptimizerNames

Here is a list of optimizers supported by transformers as of `v4.54.0`:

- `adamw_torch`
- `adamw_torch_fused`
- `adamw_torch_xla`
- `adamw_torch_npu_fused`
- `adamw_apex_fused`
- `adafactor`
- `adamw_anyprecision`
- `adamw_torch_4bit`
- `adamw_torch_8bit`
- `ademamix`
- `sgd`
- `adagrad`
- `adamw_bnb_8bit`
- `adamw_8bit` # alias for adamw_bnb_8bit
- `ademamix_8bit`
- `lion_8bit`
- `lion_32bit`
- `paged_adamw_32bit`
- `paged_adamw_8bit`
- `paged_ademamix_32bit`
- `paged_ademamix_8bit`
- `paged_lion_32bit`
- `paged_lion_8bit`
- `rmsprop`
- `rmsprop_bnb`
- `rmsprop_bnb_8bit`
- `rmsprop_bnb_32bit`
- `galore_adamw`
- `galore_adamw_8bit`
- `galore_adafactor`
- `galore_adamw_layerwise`
- `galore_adamw_8bit_layerwise`
- `galore_adafactor_layerwise`
- `lomo`
- `adalomo`
- `grokadamw`
- `schedule_free_radam`
- `schedule_free_adamw`
- `schedule_free_sgd`
- `apollo_adamw`
- `apollo_adamw_layerwise`
- `stable_adamw`

## Custom Optimizers

Enable custom optimizers by passing a string to the `optimizer` argument. Each optimizer will receive beta and epsilon args, however, some may accept additional args which are detailed below.

### optimi_adamw

    optimizer: optimi_adamw

### ao_adamw_4bit

Deprecated: Please use `adamw_torch_4bit`.

### ao_adamw_8bit

Deprecated: Please use `adamw_torch_8bit`.

### ao_adamw_fp8

    optimizer: ao_adamw_fp8

### adopt_adamw

GitHub: https://github.com/iShohei220/adopt Paper: https://arxiv.org/abs/2411.02853

    optimizer: adopt_adamw

### came_pytorch

GitHub: https://github.com/yangluo7/CAME/tree/master Paper: https://arxiv.org/abs/2307.02047

    optimizer: came_pytorch

    # optional args (defaults below)
    adam_beta1: 0.9
    adam_beta2: 0.999
    adam_beta3: 0.9999
    adam_epsilon: 1e-30
    adam_epsilon2: 1e-16

### muon

Blog: https://kellerjordan.github.io/posts/muon/ Paper: https://arxiv.org/abs/2502.16982v1

    optimizer: muon

### dion

Microsoft’s Dion (DIstributed OrthoNormalization) optimizer is a scalable and communication-efficient orthonormalizing optimizer that uses low-rank approximations to reduce gradient communication.

GitHub: https://github.com/microsoft/dion Paper: https://arxiv.org/pdf/2504.05295 Note: Implementation written for PyTorch 2.7+ for DTensor

    optimizer: dion
    dion_lr: 0.01
    dion_momentum: 0.95
    lr: 0.00001  # learning rate for embeddings and parameters that fallback to AdamW