Mistral Small 3.2 is a minor update to the Mistral Small 3.1 model, offering improvements in instruction following, repetition errors, and function calling. The article details the author's experience running the model locally using Ollama and GGUF quantizations, including generating an SVG image and describing it with the model itself.
A summary of a workshop presented at PyCon US on building software with LLMs, covering setup, prompting, building tools (text-to-SQL, structured data extraction, semantic search/RAG), tool usage, and security considerations like prompt injection. It also discusses the current LLM landscape, including models from OpenAI, Gemini, Anthropic, and open-weight alternatives.
This article details a new plugin, llm-video-frames, that allows users to feed video files into long context vision LLMs (like GPT-4.1) by converting them into a sequence of JPEG frames. It showcases how to install and use the plugin, provides examples with the Cleo video, and discusses the cost and technical details of the process. It also covers the development of the plugin using an LLM and highlights other features in LLM 0.25.
A review of the Qwen2.5-VL-32B large language model, noting its performance, capabilities, and how it runs on a 64GB Mac. Includes a demonstration with a map image and performance statistics.
Simon Willison explains how to use the mistral.rs library in Rust to run the Llama Vision model on a Mac M2 laptop. He provides a detailed example and discusses the memory usage and GPU utilization.
