Comprehensive Guide to Using Mistral AI’s Large Language Models
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.
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.
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.
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.
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.