Large language models (LLMs) like ChatGPT, Copilot, and Gemini are not just limited to chatbots but are increasingly being integrated into IDEs and office productivity suites. Using engines like Llama.cpp or vLLM, developers can easily add language-based analysis to their applications. Mistral.rs, a new LLM engine written in Rust, supports popular models and can be integrated into projects using Python, Rust, or OpenAI-compatible APIs.

To deploy Mistral.rs, developers need to ensure they meet hardware and software requirements, such as having a GPU with at least 8GB of vRAM or 16GB of system memory if running on a CPU. Installation involves getting dependencies like libssl-dev and pkg-config, setting up Rust, and installing Hugging Face to fetch models. Mistral.rs can be tested by running models like Mistral-7b-Instruct in interactive mode, with options for quantization to optimize performance.

Mistral.rs can be integrated into projects using Rust or Python APIs, or via an OpenAI API-compatible HTTP server. Developers can interact with Mistral.rs using curl commands or the openAI Python library. The HTTP server can be accessed programmatically, while Rust APIs can be used to pass queries to Mistral-7B-Instruct and generate responses. A step-by-step guide is provided for setting up a Rust project to test Mistral.rs integration.

Overall, Mistral.rs offers a versatile solution for integrating LLMs into applications, with support for various models and easy integration options for developers using Rust or Python. The potential applications of Mistral.rs in building AI-enabled apps are vast, making it a valuable tool for developers exploring the capabilities of LLMs.

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Source link: https://www.theregister.com/AMP/2024/06/22/llm_rust_ai/