LLM plugin providing access to Mistral models using the Mistral API

Install this plugin in the same environment as LLM:

llm install llm-mistral

First, obtain an API key for the Mistral API.

Configure the key using the llm keys set mistral command:

llm keys set mistral

<paste key here>

You can now access the three Mistral hosted models: mistral-tiny, mistral-small and mistral-medium.

To run a prompt through mistral-tiny:

llm -m mistral-tiny 'A sassy name for a pet sasquatch'

To start an interactive chat session with mistral-small:

llm chat -m mistral-small

Chatting with mistral-small
Type 'exit' or 'quit' to exit
Type '!multi' to enter multiple lines, then '!end' to finish
> three proud names for a pet walrus
1. "Nanuq," the Inuit word for walrus, which symbolizes strength and resilience.
2. "Sir Tuskalot," a playful and regal name that highlights the walrus' distinctive tusks.
3. "Glacier," a name that reflects the walrus' icy Arctic habitat and majestic presence.

To use a system prompt with mistral-medium to explain some code:

cat example.py | llm -m mistral-medium -s 'explain this code'

All three models accept the following options, using -o name value syntax:

• -o temperature 0.7: The sampling temperature, between 0 and 1. Higher increases randomness, lower values are more focused and deterministic.
• -o top_p 0.1: 0.1 means consider only tokens in the top 10% probability mass. Use this or temperature but not both.
• -o max_tokens 20: Maximum number of tokens to generate in the completion.
• -o safe_mode 1: Turns on safe mode, which adds a system prompt to add guardrails to the model output.
• -o random_seed 123: Set an integer random seed to generate deterministic results.

The Mistral Embeddings API can be used to generate 1,024 dimensional embeddings for any text.

To embed a single string:

llm embed -m mistral-embed -c 'this is text'

This will return a JSON array of 1,024 floating point numbers.

The LLM documentation has more, including how to embed in bulk and store the results in a SQLite database.

See LLM now provides tools for working with embeddings and Embeddings: What they are and why they matter for more about embeddings.

To set up this plugin locally, first checkout the code. Then create a new virtual environment:

cd llm-mistral
python3 -m venv venv
source venv/bin/activate

Now install the dependencies and test dependencies:

llm install -e '.[test]'

To run the tests:

pytest