Mistral Inference

This repository contains minimal code to run our 7B, 8x7B and 8x22B models.

Blog 7B: https://mistral.ai/news/announcing-mistral-7b/
Blog 8x7B: https://mistral.ai/news/mixtral-of-experts/
Blog 8x22B: https://mistral.ai/news/mixtral-8x22b/
Blog Codestral 22B: https://mistral.ai/news/codestral
Blog Codestral Mamba 7B: https://mistral.ai/news/codestral-mamba/
Blog Mathstral 7B: https://mistral.ai/news/mathstral/
Blog Nemo: https://mistral.ai/news/mistral-nemo/
Blog Mistral Large 2: https://mistral.ai/news/mistral-large-2407/

Discord: https://discord.com/invite/mistralai
Documentation: https://docs.mistral.ai/
Guardrailing: https://docs.mistral.ai/usage/guardrailing

Installation

Note: You will use a GPU to install mistral-inference, as it currently requires xformers to be installed and xformers itself needs a GPU for installation.

PyPI

pip install mistral-inference

Local

cd $HOME && git clone https://github.com/mistralai/mistral-inference
cd $HOME/mistral-inference && poetry install .

Model download

Name	Download	md5sum
7B Instruct	https://models.mistralcdn.com/mistral-7b-v0-3/mistral-7B-Instruct-v0.3.tar	`80b71fcb6416085bcb4efad86dfb4d52`
8x7B Instruct	https://models.mistralcdn.com/mixtral-8x7b-v0-1/Mixtral-8x7B-v0.1-Instruct.tar (Updated model coming soon!)	`8e2d3930145dc43d3084396f49d38a3f`
8x22 Instruct	https://models.mistralcdn.com/mixtral-8x22b-v0-3/mixtral-8x22B-Instruct-v0.3.tar	`471a02a6902706a2f1e44a693813855b`
7B Base	https://models.mistralcdn.com/mistral-7b-v0-3/mistral-7B-v0.3.tar	`0663b293810d7571dad25dae2f2a5806`
8x7B	Updated model coming soon!	-
8x22B	https://models.mistralcdn.com/mixtral-8x22b-v0-3/mixtral-8x22B-v0.3.tar	`a2fa75117174f87d1197e3a4eb50371a`
Codestral 22B	https://models.mistralcdn.com/codestral-22b-v0-1/codestral-22B-v0.1.tar	`1ea95d474a1d374b1d1b20a8e0159de3`
Mathstral 7B	https://models.mistralcdn.com/mathstral-7b-v0-1/mathstral-7B-v0.1.tar	`5f05443e94489c261462794b1016f10b`
Codestral-Mamba 7B	https://models.mistralcdn.com/codestral-mamba-7b-v0-1/codestral-mamba-7B-v0.1.tar	`d3993e4024d1395910c55db0d11db163`
Nemo Base	https://models.mistralcdn.com/mistral-nemo-2407/mistral-nemo-base-2407.tar	`c5d079ac4b55fc1ae35f51f0a3c0eb83`
Nemo Instruct	https://models.mistralcdn.com/mistral-nemo-2407/mistral-nemo-instruct-2407.tar	`296fbdf911cb88e6f0be74cd04827fe7`
Mistral Large 2	https://models.mistralcdn.com/mistral-large-2407/mistral-large-instruct-2407.tar	`fc602155f9e39151fba81fcaab2fa7c4`
Note:

Important:
- mixtral-8x22B-Instruct-v0.3.tar is exactly the same as Mixtral-8x22B-Instruct-v0.1, only stored in .safetensors format
- mixtral-8x22B-v0.3.tar is the same as Mixtral-8x22B-v0.1, but has an extended vocabulary of 32768 tokens.
- codestral-22B-v0.1.tar has a custom non-commercial license, called Mistral AI Non-Production (MNPL) License
- mistral-large-instruct-2407.tar has a custom non-commercial license, called Mistral AI Research (MRL) License
All of the listed models above support function calling. For example, Mistral 7B Base/Instruct v3 is a minor update to Mistral 7B Base/Instruct v2, with the addition of function calling capabilities.
The "coming soon" models will include function calling as well.
You can download the previous versions of our models from our docs.

Usage

News!!!: Mistral Large 2 is out. Read more about its capabilities here.

Create a local folder to store models

export MISTRAL_MODEL=$HOME/mistral_models
mkdir -p $MISTRAL_MODEL

Download any of the above links and extract the content, e.g.:

export 12B_DIR=$MISTRAL_MODEL/12B_Nemo
wget https://models.mistralcdn.com/mistral-nemo-2407/mistral-nemo-instruct-2407.tar
mkdir -p $12B_DIR
tar -xf mistral-nemo-instruct-2407.tar -C $12B_DIR

or

export M8x7B_DIR=$MISTRAL_MODEL/8x7b_instruct
wget https://models.mistralcdn.com/mixtral-8x7b-v0-1/Mixtral-8x7B-v0.1-Instruct.tar
mkdir -p $M8x7B_DIR
tar -xf Mixtral-8x7B-v0.1-Instruct.tar -C $M8x7B_DIR

Usage

The following sections give an overview of how to run the model from the Command-line interface (CLI) or directly within Python.

CLI

Demo

To test that a model works in your setup, you can run the mistral-demo command. E.g. the 12B Mistral-Nemo model can be tested on a single GPU as follows:

mistral-demo $12B_DIR

Large models, such 8x7B and 8x22B have to be run in a multi-GPU setup. For these models, you can use the following command:

torchrun --nproc-per-node 2 --no-python mistral-demo $M8x7B_DIR

Note: Change --nproc-per-node to more GPUs if available.

Chat

To interactively chat with the models, you can make use of the mistral-chat command.

mistral-chat $12B_DIR --instruct --max_tokens 1024 --temperature 0.35

For large models, you can make use of torchrun.

torchrun --nproc-per-node 2 --no-python mistral-chat $M8x7B_DIR --instruct

Note: Change --nproc-per-node to more GPUs if necessary (e.g. for 8x22B).

Chat with Codestral

To use Codestral as a coding assistant you can run the following command using mistral-chat. Make sure $M22B_CODESTRAL is set to a valid path to the downloaded codestral folder, e.g. $HOME/mistral_models/Codestral-22B-v0.1

mistral-chat $M22B_CODESTRAL --instruct --max_tokens 256

If you prompt it with "Write me a function that computes fibonacci in Rust", the model should generate something along the following lines:

Sure, here's a simple implementation of a function that computes the Fibonacci sequence in Rust. This function takes an integer `n` as an argument and returns the `n`th Fibonacci number.

fn fibonacci(n: u32) -> u32 {
    match n {
        0 => 0,
        1 => 1,
        _ => fibonacci(n - 1) + fibonacci(n - 2),
    }
}

fn main() {
    let n = 10;
    println!("The {}th Fibonacci number is: {}", n, fibonacci(n));
}

This function uses recursion to calculate the Fibonacci number. However, it's not the most efficient solution because it performs a lot of redundant calculations. A more efficient solution would use a loop to iteratively calculate the Fibonacci numbers.

You can continue chatting afterwards, e.g. with "Translate it to Python".

Chat with Codestral-Mamba

To use Codestral-Mamba as a coding assistant you can run the following command using mistral-chat. Make sure $7B_CODESTRAL_MAMBA is set to a valid path to the downloaded codestral-mamba folder, e.g. $HOME/mistral_models/mamba-codestral-7B-v0.1.

You then need to additionally install the following packages:

pip install packaging mamba-ssm causal-conv1d transformers

before you can start chatting:

mistral-chat $7B_CODESTRAL_MAMBA --instruct --max_tokens 256

Chat with Mathstral

To use Mathstral as an assistant you can run the following command using mistral-chat. Make sure $7B_MATHSTRAL is set to a valid path to the downloaded codestral folder, e.g. $HOME/mistral_models/mathstral-7B-v0.1

mistral-chat $7B_MATHSTRAL --instruct --max_tokens 256

If you prompt it with "Albert likes to surf every week. Each surfing session lasts for 4 hours and costs $20 per hour. How much would Albert spend in 5 weeks?", the model should answer with the correct calculation.

You can then continue chatting afterwards, e.g. with "How much would he spend in a year?".

Python

Instruction Following:

from mistral_inference.transformer 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("./mistral-nemo-instruct-v0.1/tekken.json")  # change to extracted tokenizer file
model = Transformer.from_folder("./mistral-nemo-instruct-v0.1")  # change to extracted model dir

prompt = "How expensive would it be to ask a window cleaner to clean all windows in Paris. Make a reasonable guess in US Dollar."

completion_request = ChatCompletionRequest(messages=[UserMessage(content=prompt)])

tokens = tokenizer.encode_chat_completion(completion_request).tokens

out_tokens, _ = generate([tokens], model, max_tokens=1024, temperature=0.35, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])

print(result)

Function Calling:

from mistral_common.protocol.instruct.tool_calls import Function, Tool

completion_request = ChatCompletionRequest(
    tools=[
        Tool(
            function=Function(
                name="get_current_weather",
                description="Get the current weather",
                parameters={
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g. San Francisco, CA",
                        },
                        "format": {
                            "type": "string",
                            "enum": ["celsius", "fahrenheit"],
                            "description": "The temperature unit to use. Infer this from the users location.",
                        },
                    },
                    "required": ["location", "format"],
                },
            )
        )
    ],
    messages=[
        UserMessage(content="What's the weather like today in Paris?"),
        ],
)

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)

Fill-in-the-middle (FIM):

Make sure to have mistral-common >= 1.2.0 installed:

pip install --upgrade mistral-common

You can simulate a code completion in-filling as follows.

from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.tokens.instruct.request import FIMRequest

tokenizer = MistralTokenizer.from_model("codestral-22b")
model = Transformer.from_folder("./mistral_22b_codestral")

prefix = """def add("""
suffix = """    return sum"""

request = FIMRequest(prompt=prefix, suffix=suffix)

tokens = tokenizer.encode_fim(request).tokens

out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.decode(out_tokens[0])

middle = result.split(suffix)[0].strip()
print(middle)

One-file-ref

If you want a self-contained implementation, look at one_file_ref.py, or run it with

python -m one_file_ref $M7B_DIR

which 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
=====================

Note: To run self-contained implementations, you need to do a local installation.

Test

To run logits equivalence:

python -m pytest tests

Deployment

The deploy folder contains code to build a vLLM image with the required dependencies to serve the Mistral AI model. In the image, the transformers library is used instead of the reference implementation. To build it:

docker build deploy --build-arg MAX_JOBS=8

Instructions to run the image can be found in the official documentation.

Model platforms

Use Mistral models on Mistral AI official API (La Plateforme)
Use Mistral models via cloud providers

References

[1]: LoRA: Low-Rank Adaptation of Large Language Models, Hu et al. 2021

License

githubnext/mistral-inference

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