# OLMo 3

Olmo 3 are a family of 7B and 32B models open source models trained by The Allen Institute for Artificial Intelligence.

This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.

## Getting started

1. Install Axolotl following the installation guide.

2. Install Cut Cross Entropy to reduce training VRAM usage.

3. Run the finetuning example:

       axolotl train examples/olmo3/olmo3-7b-qlora.yaml

This uses about 11.3 GiB VRAM. Let us know how it goes. Happy finetuning! 🚀

### TIPS

- The example config can be re-used for Olmo and Olmo 2.
- You can run a full finetuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
- Read more on how to load your own dataset at docs.
- The dataset format follows the OpenAI Messages format as seen here.

## Optimization Guides

Please check the Optimizations doc.

## Related Resources

- Olmo 3 Blog
- Axolotl Docs
- Axolotl Website
- Axolotl GitHub
- Axolotl Discord