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* feat: move to uv first * fix: update doc to uv first * fix: merge dev/tests into uv pyproject * fix: update docker docs to match current config * fix: migrate examples to readme * fix: add llmcompressor to conflict * feat: rec uv sync with lockfile for dev/ci * fix: update docker docs to clarify how to use uv images * chore: docs * fix: use system python, no venv * fix: set backend cpu * fix: only set for installing pytorch step * fix: remove unsloth kernel and installs * fix: remove U in tests * fix: set backend in deps too * chore: test * chore: comments * fix: attempt to lock torch * fix: workaround torch cuda and not upgraded * fix: forgot to push * fix: missed source * fix: nightly upstream loralinear config * fix: nightly phi3 long rope not work * fix: forgot commit * fix: test phi3 template change * fix: no more requirements * fix: carry over changes from new requirements to pyproject * chore: remove lockfile per discussion * fix: set match-runtime * fix: remove unneeded hf hub buildtime * fix: duplicate cache delete on nightly * fix: torchvision being overridden * fix: migrate to uv images * fix: leftover from merge * fix: simplify base readme * fix: update assertion message to be clearer * chore: docs * fix: change fallback for cicd script * fix: match against main exactly * fix: peft 0.19.1 change * fix: e2e test * fix: ci * fix: e2e test
Finetune Ministral3 with Axolotl
Ministral3 is a family of open-weight models from MistralAI found on HuggingFace. This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
Please see Thinking and Vision for their respective fine-tuning.
Thanks to the team at MistralAI for giving us early access to prepare for these releases.
Note: This is still experimental given it is based on transformers v5 RC.
Getting started
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Install Axolotl from source following the installation guide.
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Install Cut Cross Entropy to reduce training VRAM usage.
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Swap to the Axolotl transformers v5 branch
cp examples/ministral3/ministral3-3b-qlora.yaml ministral3-3b-qlora.yaml git fetch git checkout transformers-v5 # Install packages for transformers v5 uv pip install -e . -
Run the fine-tuning:
axolotl train ministral3-3b-qlora.yaml
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: qloraandload_in_4bit: truefrom 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.
Thinking
Ministral3 2512 model supports thinking capabilities, enabling Chain-of-Thought reasoning with explicit thinking steps.
📚 See the Thinking fine-tuning guide →
Vision
Ministral3 2512 model also supports vision capabilities.
📚 See the Vision fine-tuning guide →
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.
Related Resources
Future Work
- Add parity to Preference Tuning, RL, etc.
- Add parity to other tokenizer configs like overriding tokens.