ebbd7fa847
* fix: clarify incompat * fix: transformers api change upstream * fix: add pre prop * feat: add examples * chore: cleanup * chore: update readme
79 lines
3.0 KiB
Markdown
79 lines
3.0 KiB
Markdown
# Finetune Mistral Medium 3.5 with Axolotl
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[Mistral Medium 3.5](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B) is a 128B parameter dense multimodal model from MistralAI that unifies instruct, reasoning, and agentic capabilities into a single model.
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It shares the `mistral3` architecture (dense, YaRN RoPE, 256k context) with Ministral 3 and supports the same `reasoning_effort` toggle as Mistral Small 4.
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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. (Text config only) Install [Flash Attention 4](https://docs.axolotl.ai/docs/attention.html#flash-attention-4) on Hopper/Blackwell.
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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/mistral-medium-3_5/qlora-text.yml # ~83.1 GiB
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# text + vision
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# wget https://huggingface.co/datasets/Nanobit/text-vision-2k-test/resolve/main/African_elephant.jpg
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axolotl train examples/mistral-medium-3_5/qlora-vision.yml # ~80.3 GiB
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```
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Note: vision training does not currently work with Flash Attention 4.
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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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- For smaller experiments on the same architecture, see [`examples/ministral3`](../ministral3/README.md) (Ministral 3, 3B).
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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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- [Mistral Medium 3.5 Blog](https://mistral.ai/news/vibe-remote-agents-mistral-medium-3-5)
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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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