Mixtral#

Mixtral 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

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

Mixtral 8x7B Instruct v0.1

mistralai/Mixtral-8x7B-Instruct-v0.1

Example Recipes#

Recipe

Description

mixtral-8x7b-v0-1_squad.yaml

SFT — Mixtral 8x7B on SQuAD

mixtral-8x7b-v0-1_squad_peft.yaml

LoRA — Mixtral 8x7B on SQuAD

Try with NeMo AutoModel#

1. Install (full instructions):

pip install nemo-automodel

2. Clone the repo to get the example recipes:

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

3. Run the recipe from inside the repo:

automodel --nproc-per-node=8 examples/llm_finetune/mistral/mixtral-8x7b-v0-1_squad.yaml
Run with Docker

1. Pull the container and mount a checkpoint directory:

docker run --gpus all -it --rm \
  --shm-size=8g \
  -v $(pwd)/checkpoints:/opt/Automodel/checkpoints \
  nvcr.io/nvidia/nemo-automodel:26.02.00

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

cd /opt/Automodel

3. Run the recipe:

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

See the Installation Guide and LLM Fine-Tuning Guide.

Fine-Tuning#

See the LLM Fine-Tuning Guide and the Large MoE Fine-Tuning Guide.

Hugging Face Model Cards#