<!--Copyright 2023 Mistral AI and The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.

-->

# Mixtral

## Overview

Mixtral-8x7B is Mistral AI's second Large Language Model (LLM). 

The Mixtral model was proposed in the by the [Mistral AI](https://mistral.ai/) team.

It was introduced in the [Mixtral of Experts blogpost](https://mistral.ai/news/mixtral-of-experts/) with the following introduction:

*Today, the team is proud to release Mixtral 8x7B, a high-quality sparse mixture of experts models (SMoE) with open weights. Licensed under Apache 2.0. Mixtral outperforms Llama 2 70B on most benchmarks with 6x faster inference. It is the strongest open-weight model with a permissive license and the best model overall regarding cost/performance trade-offs. In particular, it matches or outperforms GPT3.5 on most standard benchmarks.*

Tips:


- The model needs to be converted using the [conversion script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/mixtral/convert_mixtral_weights_to_hf.py).
- If the model is quantized to 4bits, a single A100 is enough to fit the entire 45B model.

This model was contributed by [Younes Belkada](https://huggingface.co/ybelkada) and [Arthur Zucker](https://huggingface.co/ArthurZ) .
The original code can be found [here](https://github.com/mistralai/mistral-src).


### Model Details

Mixtral-45B is a decoder-based LM with the following architectural choices:

* Mixtral is a Mixture of Expert (MOE) model with 8 experts per MLP, with a total of 45B paramateres but the compute required is the same as a 14B model. This is because even though each experts have to be loaded in RAM (70B like ram requirement) each token from the hidden states are dipatched twice (top 2 routing) and thus the compute (the operation required at each foward computation) is just 2 X sequence_length. 

The following implementation details are shared with Mistral AI's first model [mistral](~models/doc/mistral):
* Sliding Window Attention - Trained with 8k context length and fixed cache size, with a theoretical attention span of 128K tokens
* GQA (Grouped Query Attention) - allowing faster inference and lower cache size.
* Byte-fallback BPE tokenizer - ensures that characters are never mapped to out of vocabulary tokens.

They also provide an instruction fine-tuned model: `mistralai/Mixtral-8x7B-v0.1` which can be used for chat-based inference.

For more details please read our [release blog post](https://mistral.ai/news/mixtral-of-experts/)

### License

`Mixtral-8x7B` is released under the Apache 2.0 license.

## Usage tips

`Mixtral-8x7B` can be found on the [Huggingface Hub](https://huggingface.co/mistralai)

These ready-to-use checkpoints can be downloaded and used via the HuggingFace Hub:

```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> device = "cuda" # the device to load the model onto

>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1")
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-8x7B")

>>> prompt = "My favourite condiment is"

>>> model_inputs = tokenizer([prompt], return_tensors="pt").to(device)
>>> model.to(device)

>>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
>>> tokenizer.batch_decode(generated_ids)[0]
"The expected output"
```

To use the raw checkpoints with HuggingFace you can use the `convert_mixtral_weights_to_hf.py` script to convert them to the HuggingFace format:

```bash
python src/transformers/models/mixtral/convert_mixtral_weights_to_hf.py \
    --input_dir /path/to/downloaded/mistral/weights --output_dir /output/path
```

You can then load the converted model from the `output/path`:

```python
from transformers import MixtralForCausalLM, LlamaTokenizer

tokenizer = LlamaTokenizer.from_pretrained("/output/path")
model = MixtralForCausalLM.from_pretrained("/output/path")
```

## Combining Mixtral and Flash Attention 2

First, make sure to install the latest version of Flash Attention 2 to include the sliding window attention feature.

```bash
pip install -U flash-attn --no-build-isolation
```

Make also sure that you have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of [`flash-attn`](https://github.com/Dao-AILab/flash-attention) repository. Make also sure to load your model in half-precision (e.g. `torch.float16`)

To load and run a model using Flash Attention 2, refer to the snippet below:

```python
>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> device = "cuda" # the device to load the model onto

>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1", torch_dtype=torch.float16, attn_implementation="flash_attention_2")
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-v0.1")

>>> prompt = "My favourite condiment is"

>>> model_inputs = tokenizer([prompt], return_tensors="pt").to(device)
>>> model.to(device)

>>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
>>> tokenizer.batch_decode(generated_ids)[0]
"The expected output"
```

### Expected speedups

Below is a expected speedup diagram that compares pure inference time between the native implementation in transformers using `mistralai/Mixtral-8x7B-v0.1` checkpoint and the Flash Attention 2 version of the model.

<div style="text-align: center">
<img src="https://huggingface.co/datasets/ybelkada/documentation-images/resolve/main/mixtral-7b-inference-large-seqlen.png">
</div>

### Sliding window Attention

The current implementation supports the sliding window attention mechanism and memory efficient cache management. 
To enable sliding window attention, just make sure to have a `flash-attn` version that is compatible with sliding window attention (`>=2.3.0`). 

The Flash Attention-2 model uses also a more memory efficient cache slicing mechanism - as recommended per the official implementation of Mistral model that use rolling cache mechanism we keep the cache size fixed (`self.config.sliding_window`), support batched generation only for `padding_side="left"` and use the absolute position of the current token to compute the positional embedding.

## The Mistral Team

Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.

## MixtralConfig

[[autodoc]] MixtralConfig

## MixtralModel

[[autodoc]] MixtralModel
    - forward

## MixtralForCausalLM

[[autodoc]] MixtralForCausalLM
    - forward

## MixtralForSequenceClassification

[[autodoc]] MixtralForSequenceClassification
    - forward
