feat: Add Mistral Medium 3.5 (#3633)

* fix: clarify incompat

* fix: transformers api change upstream

* fix: add pre prop

* feat: add examples

* chore: cleanup

* chore: update readme

examples/mistral-medium-3_5/README.md

@@ -0,0 +1,78 @@
+# Finetune Mistral Medium 3.5 with Axolotl
+
+[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.
+It shares the `mistral3` architecture (dense, YaRN RoPE, 256k context) with Ministral 3 and supports the same `reasoning_effort` toggle as Mistral Small 4.
+
+Thanks to the team at MistralAI for giving us early access to prepare for this release.
+
+## Getting started
+
+1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
+
+2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
+
+3. (Text config only) Install [Flash Attention 4](https://docs.axolotl.ai/docs/attention.html#flash-attention-4) on Hopper/Blackwell.
+
+4. Run one of the example configs:
+
+    ```bash
+    # text-only
+    axolotl train examples/mistral-medium-3_5/qlora-text.yml  # ~83.1 GiB
+
+    # text + vision
+    # wget https://huggingface.co/datasets/Nanobit/text-vision-2k-test/resolve/main/African_elephant.jpg
+    axolotl train examples/mistral-medium-3_5/qlora-vision.yml  # ~80.3 GiB
+    ```
+
+Note: vision training does not currently work with Flash Attention 4.
+
+## Reasoning Effort
+
+The chat template supports a `reasoning_effort` variable to control the model's reasoning depth:
+
+- `"none"` — instruct mode (default)
+- `"high"` — reasoning mode with explicit thinking steps
+
+Pass it via `chat_template_kwargs` under your dataset config:
+
+```yaml
+datasets:
+  - path: your/dataset
+    type: chat_template
+    chat_template_kwargs:
+      reasoning_effort: high
+```
+
+## Thinking Support
+
+The chat template supports a `thinking` content type in assistant messages for training on reasoning traces (rendered as `[THINK]...[/THINK]` blocks).
+
+To use thinking datasets, add the `thinking` mapping via `message_property_mappings`:
+
+```yaml
+datasets:
+  - path: your/thinking-dataset
+    type: chat_template
+    message_property_mappings:
+      role: role
+      content: content
+      thinking: thinking
+    chat_template_kwargs:
+      reasoning_effort: high
+```
+
+See the [Magistral thinking guide](../magistral/think/README.md) for dataset format details.
+
+## Tips
+
+- For smaller experiments on the same architecture, see [`examples/ministral3`](../ministral3/README.md) (Ministral 3, 3B).
+- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
+- The text dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
+- The vision model requires multi-modal dataset format as documented [here](https://docs.axolotl.ai/docs/multimodal.html#dataset-format).
+
+## Related Resources
+
+- [Mistral Medium 3.5 Blog](https://mistral.ai/news/vibe-remote-agents-mistral-medium-3-5)
+- [Axolotl Docs](https://docs.axolotl.ai)
+- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
+- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)