axolotl/examples/olmo3/README.md

Finetune Allenai's Olmo 3 with Axolotl

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.

   Here is an example of how to install from pip:

   # Ensure you have a compatible version of Pytorch installed
   pip3 install packaging setuptools wheel ninja
   pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
   
   # Install Cut Cross Entropy
   python scripts/cutcrossentropy_install.py | sh
   

2. Run the finetuning example:

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

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