> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/automodel/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/automodel/_mcp/server.

# Mixtral

[Mixtral](https://mistral.ai/news/mixtral-of-experts/) is Mistral AI's Mixture-of-Experts model series. Each token is processed by a subset of experts, enabling a large total parameter count with efficient per-token compute.

|                  |                                               |
| ---------------- | --------------------------------------------- |
| **Task**         | Text Generation (MoE)                         |
| **Architecture** | `MixtralForCausalLM`                          |
| **Parameters**   | 47B total / 13B active                        |
| **HF Org**       | [mistralai](https://huggingface.co/mistralai) |

## Available Models

* **Mixtral-8x7B**: 8 experts, 2 active per token (\~13B active)
* **Mixtral-8x7B-Instruct**: instruction-tuned variant
* **Mixtral-8x22B**: 8 experts, 2 active per token (\~39B active)

## Architecture

* `MixtralForCausalLM`

## Example HF Models

| Model                      | HF ID                                                                                                 |
| -------------------------- | ----------------------------------------------------------------------------------------------------- |
| Mixtral 8x7B v0.1          | [`mistralai/Mixtral-8x7B-v0.1`](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)                   |
| Mixtral 8x7B Instruct v0.1 | [`mistralai/Mixtral-8x7B-Instruct-v0.1`](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) |

## Example Recipes

| Recipe                                                                                                                                                    | Description                  |
| --------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------- |
| [mixtral-8x7b-v0-1\_squad.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/mistral/mixtral-8x7b-v0-1_squad.yaml)            | SFT — Mixtral 8x7B on SQuAD  |
| [mixtral-8x7b-v0-1\_squad\_peft.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/mistral/mixtral-8x7b-v0-1_squad_peft.yaml) | LoRA — Mixtral 8x7B on SQuAD |

## Try with NeMo AutoModel

**1. Install** ([full instructions](/get-started/installation)):

```bash
pip install nemo-automodel
```

**2. Clone the repo** to get the example recipes:

```bash
git clone https://github.com/NVIDIA-NeMo/Automodel.git
cd Automodel
```

**3. Run the recipe** from inside the repo:

```bash
automodel --nproc-per-node=8 examples/llm_finetune/mistral/mixtral-8x7b-v0-1_squad.yaml
```

**1. Pull the container** and mount a checkpoint directory:

```bash
docker run --gpus all -it --rm \
  --shm-size=8g \
  -v $(pwd)/checkpoints:/opt/Automodel/checkpoints \
  nvcr.io/nvidia/nemo-automodel:26.04.00
```

**2.** Navigate to the AutoModel directory (where the recipes are):

```bash
cd /opt/Automodel
```

**3. Run the recipe**:

```bash
automodel --nproc-per-node=8 examples/llm_finetune/mistral/mixtral-8x7b-v0-1_squad.yaml
```

See the [Installation Guide](/get-started/installation) and [LLM Fine-Tuning Guide](/recipes-e2e-examples/sft-peft).

## Fine-Tuning

See the [LLM Fine-Tuning Guide](/recipes-e2e-examples/sft-peft) and the [Large MoE Fine-Tuning Guide](/recipes-e2e-examples/large-moe-fine-tuning).

## Hugging Face Model Cards

* [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)
* [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)