Ministral

Ministral is a family of openweight models from MistralAI found on HuggingFace. 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/ministral/ministral-small-qlora.yaml

This config uses about 8.76 GiB VRAM.

Let us know how it goes. Happy finetuning! 🚀

Tips

  • We recommend adding the same/similar SystemPrompt that the model is tuned for. You can find this within the repo’s files titled SYSTEM_PROMPT.txt.
  • 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 text dataset format follows the OpenAI Messages format as seen here.

Optimization Guides

Please check the Optimizations doc.

Limitations

We only support the mistral-common tokenizer for Supervised Fine-tuning at the moment and for type: chat_template only.

In addition, we do not support overriding tokens yet.

Future Work

  • Add parity to Preference Tuning, RL, etc.
  • Add parity to other tokenizer configs like overriding tokens.