Comprehensive Guide to Using Mistral AI’s Large Language Models

The Data Beast
2 min readNov 13

Introduction to Mistral AI’s LLM

Mistral AI’s first Large Language Model (LLM), Mistral 7B v0.1, is a robust AI algorithm trained on vast datasets to generate coherent text and undertake various natural language processing tasks. This model is accessible for download, and a Docker image is available to quickly set up a completion API on any major cloud provider with NVIDIA GPUs​​.

Getting Started

To use Mistral AI LLM on your infrastructure, you can refer to their Quickstart guide. If you prefer using the API of a deployed instance, the Interacting with the model or the API specification pages provide detailed guidance. For local deployment on consumer-grade hardware, Mistral AI suggests the llama.cpp project or Ollama​​.

Seeking Help

Mistral AI maintains a Discord community for discussing their models and interacting with engineers. They also have a sales team for enterprise needs and additional product information​​. Furthermore, the platform is open to contributions from the community, inviting external contributions through PRs​​.

Quickstart Guide

Docker Setup

Mistral AI provides Docker images on Github. These images require a cloud VM with at least 24GB of vRAM for optimal performance. The model weights are distributed separately from the Docker images​​.

Running an Inference Server

To run an inference server using Docker and vLLM on a GPU-enabled host, you can use a specific command line, which involves downloading the model from Hugging Face​​. For GPUs with CUDA capabilities below 8.0, a specific parameter (--dtype half) is required to avoid errors​​.

Alternative Setup

For a direct setup on a GPU-enabled host with CUDA 11.8, you can install vLLM using pip and login to the Hugging Face hub. After these steps, you can start the server with a simple Python command​​. Once the server is operational, any OpenAI-compatible client can interact with it​​.

API Documentation

Model Availability