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.