---
language:
- en
license: apache-2.0
tags:
- pretrained
pipeline_tag: text-generation
inference:
  parameters:
    temperature: 0.7
---

# Model Card for Mistral-7B-v0.1

The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. 
Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested.

For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/).

Mistral-7B-v0.1 has the following characteristics:
- 7.3B parameters
- Byte-fallback BPE tokenizer
- Grouped-Query Attention
- 8k context window
- 4k Sliding-Window Attention
- 32000 vocab size

## How to use

It is recommended to use `mistralai/Mistral-7B-v0.1` with [mistral_inference](https://github.com/mistralai/mistral-inference). For HF `transformers` code snippets, please keep scrolling.

## Generate with `mistral_inference`

### Install dependencies
```
pip install mistral_inference
```

### Download model

```py
from huggingface_hub import snapshot_download
from pathlib import Path

mistral_models_path = Path.home().joinpath('mistral_models', '7B-v0.1')
mistral_models_path.mkdir(parents=True, exist_ok=True)

snapshot_download(repo_id="mistralai/Mistral-7B-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model"], local_dir=mistral_models_path)
```

### Demo

After installing `mistral_inference`, a `mistral-demo` CLI command should be available in your environment.

```
mistral-demo $HOME/mistral_models/7B-v0.1
```

Should give something along the following lines:

```
This is a test of the emergency broadcast system. This is only a test.

If this were a real emergency, you would be told what to do.

This is a test
=====================
This is another test of the new blogging software. I’m not sure if I’m going to keep it or not. I’m not sure if I’m going to keep
=====================
This is a third test, mistral AI is very good at testing. 🙂

This is a third test, mistral AI is very good at testing. 🙂

This
=====================
```

## Generate with `transformers`

### Install dependencies
```
pip install transformers
```

### Text completion

```py
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "mistralai/Mistral-7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(model_id)
inputs = tokenizer("Hello my name is", return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Notice
Mistral-7B is a pretrained base model and therefore does not have any moderation mechanisms.

## The Mistral AI 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.