---
license: apache-2.0
language:
- en
tags:
- finetuned
pipeline_tag: text-generation
inference: true
widget:
- messages:
  - role: user
    content: What is your favorite condiment?
---

# Model Card for Mistral-7B-Instruct-v0.2

The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.2.

Mistral-7B-Instruct-v0.2 has the following changes compared to [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
- 32k context window (vs 8k context in v0.1)
- Rope-theta = 1e6
- No Sliding-Window Attention

## How to use

It is recommended to use `mistralai/Mistral-7B-Instruct-v0.2` with [mistral_inference](https://github.com/mistralai/mistral-inference) and [mistral_common](https://github.com/mistralai/mistral-common). For HF `transformers` code snippets, please keep scrolling.

## Generate with `mistral_inference` and `mistral_common`

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

### Download model

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

mistral_models_path = Path.home().joinpath('mistral_models', '7B-Instruct-v0.2')
mistral_models_path.mkdir(parents=True, exist_ok=True)

snapshot_download(repo_id="mistralai/Mistral-7B-Instruct-v0.2", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model"], local_dir=mistral_models_path)
```

### Chat

After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. You can chat with the model using

```
mistral-chat $HOME/mistral_models/7B-Instruct-v0.2 --instruct --max_tokens 256
```

### Instruct following

```py
from mistral_inference.model import Transformer
from mistral_inference.generate import generate

from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest


tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tokenizer.model")
# tokenizer = MistralTokenizer.v1()

model = Transformer.from_folder(mistral_models_path)

completion_request = ChatCompletionRequest(messages=[UserMessage(content="Explain Machine Learning to me in a nutshell.")])

tokens = tokenizer.encode_chat_completion(completion_request).tokens

out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])

print(result)
```

## Generate with `transformers`

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

### Instruct following

```py
from transformers import AutoModelForCausalLM, AutoTokenizer
 
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
model.to("cuda")

tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]

messages_prompt = tokenizer.apply_chat_template(
            messages,
            tokenize=False,
            add_generation_prompt=True,
)

inputs = tokenizer(tool_use_prompt, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=1000)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(result)
```
Or:
```py
from transformers import pipeline

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]
chatbot = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.2")
result = chatbot(messages)

print(result)
```

## Limitations

The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. 
It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.

## The Mistral AI Team

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