feat: add Mistral Small 4 (#3502)

* feat: add mistral small 4

* fix: update mistral common

* fix: deepcopy when passing in tokenizer

* feat: add doc on reasoning and thinking section

* fix: don't use custom tokenizer and quantize experts

* chore: update docs and configs

* chore: update doc to follow official name

* feat: update cce to include mistral4

* chore: move

* fix: naming

* fix: test mock breaking get_text_config check

* fix: enable CCE and add expert block targetting to configs

* chore: docs

* fix: use act checkpointing

* chore: doc

* chore: docs

* chore: docs

examples/mistral4/README.md

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+# Finetune Mistral Small 4 with Axolotl
+
+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).
+
+Thanks to the team at MistralAI for giving us early access to prepare for this release.
+
+## Getting started
+
+Note: Training this model requires weights in BF16 which we will link to later.
+Users interested in training can convert / descale the existing FP8 weights.
+
+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. Install transformers from main
+
+  ```bash
+  pip install git+https://github.com/huggingface/transformers.git
+  ```
+
+4. Run one of the example configs:
+
+  ```bash
+  # text-only
+  axolotl train examples/mistral4/qlora-text.yml  # no experts ~69 GiB, experts ~93 GiB
+  axolotl train examples/mistral4/fft-text.yml
+
+  # text + vision
+  # run: wget https://huggingface.co/datasets/Nanobit/text-vision-2k-test/resolve/main/African_elephant.jpg
+  axolotl train examples/mistral4/qlora-vision.yml  # no experts ~68 GiB
+  axolotl train examples/mistral4/fft-vision.yml
+  ```
+
+Note: FFT configs provided as reference. Please adjust hyperparameters as needed.
+
+## 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
+
+- 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
+
+- [MistralAI Mistral Small 4 Blog](https://mistral.ai/news/mistral-small-4)
+- [Axolotl Docs](https://docs.axolotl.ai)
+- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
+- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)