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*This model was released on 2023-12-11 and added to Hugging Face Transformers on 2023-12-11 and contributed by [ybelkada](https://huggingface.co/ybelkada) and [ArthurZ](https://huggingface.co/ArthurZ).*

# Mixtral

<div style="float: right;">
    <div class="flex flex-wrap space-x-1">
        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
    </div>
</div>

[Mixtral](https://huggingface.co/papers/2401.04088) is a Sparse Mixture of Experts (SMoE) model based on the Mistral 7B architecture, where each layer contains 8 experts but only 2 are activated per token via a router network. This design allows tokens to access a total of 47B parameters while only using 13B active parameters during inference, improving efficiency. The model was trained with a 32k token context window and outperforms or matches larger models like Llama 2 70B and GPT-3.5, with especially strong results in mathematics, coding, and multilingual tasks. A fine-tuned instruction-following version, Mixtral 8x7B-Instruct, also surpasses GPT-3.5 Turbo, Claude-2.1, Gemini Pro, and Llama 2 70B-chat, with both versions released under Apache 2.0.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="text-generation", model="mistralai/Mixtral-8x7B-v0.1", dtype="auto",)
pipeline("Plants create energy through a process known as photosynthesis.")
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-v0.1")
model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1", dtype="auto",)

inputs = tokenizer("Plants create energy through a process known as photosynthesis.", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))
```

</hfoption>
</hfoptions>

## MixtralConfig

[[autodoc]] MixtralConfig

## MixtralModel

[[autodoc]] MixtralModel
    - forward

## MixtralForCausalLM

[[autodoc]] MixtralForCausalLM
    - forward

## MixtralForSequenceClassification

[[autodoc]] MixtralForSequenceClassification
    - forward

## MixtralForTokenClassification

[[autodoc]] MixtralForTokenClassification
    - forward

## MixtralForQuestionAnswering
[[autodoc]] MixtralForQuestionAnswering
    - forward