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# Mistral

[Mistral](https://huggingface.co/papers/2310.06825) is a 7B parameter language model, available as a pretrained and instruction-tuned variant, focused on balancing 
the scaling costs of large models with performance and efficient inference. This model uses sliding window attention (SWA) trained with a 8K context length and a fixed cache size to handle longer sequences more effectively. Grouped-query attention (GQA) speeds up inference and reduces memory requirements. Mistral also features a byte-fallback BPE tokenizer to improve token handling and efficiency by ensuring characters are never mapped to out-of-vocabulary tokens.

You can find all the original Mistral checkpoints under the [Mistral AI_](https://huggingface.co/mistralai) organization.

> [!TIP]
> Click on the Mistral models in the right sidebar for more examples of how to apply Mistral to different language tasks.

The example below demonstrates how to chat with [`Pipeline`] or the [`AutoModel`], and from the command line.

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

```python
>>> import torch
>>> from transformers import pipeline

>>> messages = [
...     {"role": "user", "content": "What is your favourite condiment?"},
...     {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
...     {"role": "user", "content": "Do you have mayonnaise recipes?"}
... ]

>>> chatbot = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.3", torch_dtype=torch.bfloat16, device=0)
>>> chatbot(messages)
```

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

```python
>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer

>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3", torch_dtype=torch.bfloat16, attn_implementation="sdpa", device_map="auto")
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")

>>> messages = [
...     {"role": "user", "content": "What is your favourite condiment?"},
...     {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
...     {"role": "user", "content": "Do you have mayonnaise recipes?"}
... ]

>>> model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")

>>> generated_ids = model.generate(model_inputs, max_new_tokens=100, do_sample=True)
>>> tokenizer.batch_decode(generated_ids)[0]
"Mayonnaise can be made as follows: (...)"
```

</hfoption>
<hfoption id="transformers-cli">

```python
echo -e "My favorite condiment is" | transformers-cli chat --model_name_or_path mistralai/Mistral-7B-v0.3 --torch_dtype auto --device 0 --attn_implementation flash_attention_2
```

</hfoption>
</hfoptions>


Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.

The example below uses [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to 4-bits.

```python
>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

>>> # specify how to quantize the model
>>> quantization_config = BitsAndBytesConfig(
...         load_in_4bit=True,
...         bnb_4bit_quant_type="nf4",
...         bnb_4bit_compute_dtype="torch.float16",
... )

>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3", quantization_config=True, torch_dtype=torch.bfloat16, device_map="auto")
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")

>>> prompt = "My favourite condiment is"

>>> messages = [
...     {"role": "user", "content": "What is your favourite condiment?"},
...     {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
...     {"role": "user", "content": "Do you have mayonnaise recipes?"}
... ]

>>> model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")

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

Use the [AttentionMaskVisualizer](https://github.com/huggingface/transformers/blob/beb9b5b02246b9b7ee81ddf938f93f44cfeaad19/src/transformers/utils/attention_visualizer.py#L139) to better understand what tokens the model can and cannot attend to.

```py
>>> from transformers.utils.attention_visualizer import AttentionMaskVisualizer

>>> visualizer = AttentionMaskVisualizer("mistralai/Mistral-7B-Instruct-v0.3")
>>> visualizer("Do you have mayonnaise recipes?")
```

<div class="flex justify-center">
    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/mistral-attn-mask.png"/>
</div>

## MistralConfig

[[autodoc]] MistralConfig

## MistralModel

[[autodoc]] MistralModel
    - forward

## MistralForCausalLM

[[autodoc]] MistralForCausalLM
    - forward

## MistralForSequenceClassification

[[autodoc]] MistralForSequenceClassification
    - forward

## MistralForTokenClassification

[[autodoc]] MistralForTokenClassification
    - forward

## MistralForQuestionAnswering

[[autodoc]] MistralForQuestionAnswering
- forward

## FlaxMistralModel

[[autodoc]] FlaxMistralModel
    - __call__

## FlaxMistralForCausalLM

[[autodoc]] FlaxMistralForCausalLM
    - __call__

## TFMistralModel

[[autodoc]] TFMistralModel
    - call

## TFMistralForCausalLM

[[autodoc]] TFMistralForCausalLM
    - call

## TFMistralForSequenceClassification

[[autodoc]] TFMistralForSequenceClassification
    - call
