9de5b76336
* 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
83 lines
2.9 KiB
Markdown
83 lines
2.9 KiB
Markdown
# Finetune Mistral Small 4 with Axolotl
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Mistral Small 4 is a 119B parameter (6.5B active) multimodal MoE model from MistralAI that unifies instruct, reasoning, and coding capabilities into a single model. It is available on HuggingFace at [Mistral-Small-4-119B-2603](https://huggingface.co/mistralai/Mistral-Small-4-119B-2603).
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Thanks to the team at MistralAI for giving us early access to prepare for this release.
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## Getting started
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1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
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2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage
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3. Install transformers from main
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```bash
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uv pip install git+https://github.com/huggingface/transformers.git
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```
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4. Run one of the example configs:
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```bash
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# text-only
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axolotl train examples/mistral4/qlora-text.yml # no experts ~69 GiB, experts ~93 GiB
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axolotl train examples/mistral4/fft-text.yml
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# text + vision
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# run: wget https://huggingface.co/datasets/Nanobit/text-vision-2k-test/resolve/main/African_elephant.jpg
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axolotl train examples/mistral4/qlora-vision.yml # no experts ~68 GiB
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axolotl train examples/mistral4/fft-vision.yml
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```
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Note: FFT configs provided as reference. Please adjust hyperparameters as needed.
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## Reasoning Effort
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The chat template supports a `reasoning_effort` variable to control the model's reasoning depth:
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- `"none"` — instruct mode (default)
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- `"high"` — reasoning mode with explicit thinking steps
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Pass it via `chat_template_kwargs` under your dataset config:
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```yaml
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datasets:
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- path: your/dataset
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type: chat_template
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chat_template_kwargs:
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reasoning_effort: high
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```
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## Thinking Support
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The chat template supports a `thinking` content type in assistant messages for training on reasoning traces (rendered as `[THINK]...[/THINK]` blocks).
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To use thinking datasets, add the `thinking` mapping via `message_property_mappings`:
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```yaml
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datasets:
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- path: your/thinking-dataset
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type: chat_template
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message_property_mappings:
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role: role
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content: content
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thinking: thinking
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chat_template_kwargs:
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reasoning_effort: high
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```
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See the [Magistral thinking guide](../magistral/think/README.md) for dataset format details.
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## Tips
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- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
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- The text dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
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- The vision model requires multi-modal dataset format as documented [here](https://docs.axolotl.ai/docs/multimodal.html#dataset-format).
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## Related Resources
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- [MistralAI Mistral Small 4 Blog](https://mistral.ai/news/mistral-small-4)
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- [Axolotl Docs](https://docs.axolotl.ai)
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- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
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- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)
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