mistral-common 1.4.4

No project description provided

Mistral Common

What is it?

mistral-common is a set of tools to help you work with Mistral models.

Our first release contains tokenization. Our tokenizers go beyond the usual text <-> tokens, adding parsing of tools and structured conversation. We also release the validation and normalization code that is used in our API.

We are releasing three versions of our tokenizer powering different sets of models.

Open Model Tokenizer Mistral 7B Instruct v0.1 v1 Mistral 7B Instruct v0.2 v1 Mistral 7B Instruct v0.3 v3 Mixtral 8x7B Instruct v0.1 v1 Mixtral 8x22B Instruct v0.1 v3 Mixtral 8x22B Instruct v0.3 v3 Codestral 22B v0.1 v3 Codestral Mamba 7B v0.1 v3 Mathstral 7B v0.1 v3 Nemo 12B 2407 v3 - Tekken Large 123B 2407 v3

Endpoint Model Tokenizer mistral-embed v1 open-mistral-7b v3 open-mixtral-8x7b v1 open-mixtral-8x22b v3 mistral-small-latest v2 mistral-large-latest v3 codestral-22b v3 open-codestral-mamba v3 open-mistral-nemo v3 - Tekken

Installation

pip

You can install mistral-common via pip:

pip install mistral-common

From Source

Alternatively, you can install from source directly. This repo uses poetry as a dependency and virtual environment manager.

You can install poetry with

pip install poetry

poetry will set up a virtual environment and install dependencies with the following command:

poetry install

Examples

# Import needed packages:
from mistral_common.protocol.instruct.messages import (
    UserMessage,
)
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from mistral_common.protocol.instruct.tool_calls import (
    Function,
    Tool,
)
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer

# Load Mistral tokenizer

model_name = "open-mixtral-8x22b"

tokenizer = MistralTokenizer.from_model(model_name)

# Tokenize a list of messages
tokenized = tokenizer.encode_chat_completion(
    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"),
        ],
        model=model_name,
    )
)
tokens, text = tokenized.tokens, tokenized.text

# Count the number of tokens
print(len(tokens))