RAG et connaissances
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Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
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Voir le guide →llmfit est un projet de LLM locaux open source, créé par AlexsJones (AlexsJones/llmfit sur GitHub). Il compte 37 547 étoiles sur GitHub et 2 406 forks, il est publié sous la licence MIT et a été mis à jour pour la dernière fois sur GitHub le 3 octobre 2026.
Hundreds of models & providers. One command to find what runs on your hardware.
One binary or app gets you a working local model setup.
Pick a size that fits your RAM/VRAM; smaller models run on laptops.
Use the built-in UI, or point OpenAI-compatible clients at it.
Airplane mode works — your prompts never leave the device.
Large language models drive its smartest features, locally or via API.
Prompts and documents stay on your hardware, always.
RAG, tools, and agents build on the same base.
Smart compression runs big models on modest hardware.
Works on planes, in basements, behind firewalls.
Swap between hundreds of open models freely.
Download the source and move into the project folder.
git clone https://github.com/AlexsJones/llmfit.git
cd llmfitEvery project documents its own setup; start with install requirements.
cat README.md | head -60Follow the project's launch command — Docker, pip, npm, or a binary release.
# see README for the exact launch commandIt is an open-source project at AlexsJones/llmfit with 2,406 forks. Check the repo's contributors and recent commits to see how active maintenance is.
MIT licensed — use it commercially, no strings attached.
A recent 8–32B instruct model is the sweet spot for most machines.
Yes — with no network calls, there's nothing to leak.
Close on many tasks now; frontier cloud models still lead on hard reasoning.
Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
⚡ Pure-Rust WebGPU inference engine — OpenAI-API compatible, GGUF native, runs on any GPU. No Python. No llama.cpp. Single binary.
动手学Ollama，CPU玩转大模型部署，在线阅读地址：https://datawhalechina.github.io/handy-ollama/
Find the best local models for your hardware — and how to run them.
Strata — one-click Qwen3.8-Flash-Next inference on consumer hardware: chat, code and images, locally, no account, MIT
One-command installer for a full self-hosted AI stack on your server.
Les autres projets de la catégorie LLM locaux avec le plus d'étoiles du catalogue LibreAI — la sélection réaliste d'alternatives si llmfit ne vous convient pas.
The simplest way to run open language models on your own machine.
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
LLM inference in C/C++
A high-throughput and memory-efficient inference and serving engine for LLMs
🤯 LobeHub is your Chief Agent Operator, organizing your agents into 7×24 operations by hiring, scheduling, and reporting on your entire AI team.
Projets de la catégorie LLM locaux proches dans le catalogue, les plus proches en étoiles de llmfit — continuez à explorer de part et d'autre.
An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
Run a 70B LLM on a single 4GB GPU — layer-by-layer inference with almost no memory tricks skipped
BitNet — Microsoft's official inference framework for 1-bit large language models, running fast on ordinary CPUs, MIT
Strata — one-click Qwen3.8-Flash-Next inference on consumer hardware: chat, code and images, locally, no account, MIT
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Gratuit et ouvert : récupérez le code source, mettez une étoile au dépôt et commencez à créer.