Local large language models (LLMs) often struggle with hallucinations because their knowledge is limited to their static training data. To combat this, the author integrated the Brave Search MCP (Model Context Protocol) into their local setup using LM Studio. This tool acts as a bridge, allowing the LLM to query the Brave Search API for real-time information and current web results. By combining pretrained data with live web access, the model provides more accurate and up-to-date responses. While the technical setup is relatively straightforward, the author emphasizes that mastering specific prompting techniques is essential to prevent the model from getting stuck in tool-calling loops and to ensure it uses its new search capabilities effectively.
The author explores the common frustration of running local Large Language Models (LLMs), where the gap between potential and usability is often caused by slow inference speeds. Instead of upgrading to larger, more complex models, the author discovered that implementing speculative decoding significantly improved the experience. This technique uses a smaller "draft" model to quickly predict tokens, which a larger "verification" model then checks. This process drastically increases speed and creates a smoother conversational flow without sacrificing the model's intelligence. By focusing on how models are run rather than just which models are used, users can make their self-hosted AI tools much more practical for daily use.
This article discusses how to effectively prompt local Large Language Models (LLMs) like those run with LM Studio or Ollama. It explains that local LLMs behave differently than cloud-based models and require more explicit and structured prompts for optimal results. The article provides guidance on how to craft better prompts, including using clear language, breaking down tasks into steps, and providing examples.
The article discusses the increasing usefulness of running AI models locally, highlighting benefits like latency, privacy, cost, and control. It explores practical applications such as data processing, note-taking, voice assistance, and self-sufficiency, while acknowledging the limitations compared to cloud-based models.
A user shares their optimal settings for running the gpt-oss-120b model on a system with dual RTX 3090 GPUs and 128GB of RAM, aiming for a balance between performance and quality.
This article details how the author uses a local LLM to summarize Docker logs and other home lab logs, providing proactive insights into their self-hosted setup and improving maintenance.
This article details 7 lessons the author learned while self-hosting Large Language Models (LLMs), covering topics like the importance of memory bandwidth, quantization, electricity costs, hardware choices beyond Nvidia, prompt engineering, Mixture of Experts models, and starting with simpler tools like LM Studio.
LM Studio has released lms, a command-line interface (CLI) tool to load/unload models, start/stop the API server, and inspect raw LLM input. It is developed on GitHub and is MIT Licensed.
This is a GitHub repository for a Discord bot named discord-llm-chatbot. This bot allows you to chat with Large Language Models (LLMs) directly in your Discord server. It supports various LLMs, including those from OpenAI API, Mistral API, Anthropic API, and local models like ollama, oobabooga, Jan, LM Studio, etc. The bot offers a reply-based chat system, customizable system prompt, and seamless threading of conversations. It also supports image and text file attachments, and streamed responses.
