LLM local
node-llama-cpp
Run AI models locally on your machine with node.js bindings for llama.cpp. Enforce a JSON schema on the model output on the generation level
★ 2,2k estrellasMIT
Ver la guía →maid es un proyecto de LLM local de código abierto de Mobile-Artificial-Intelligence (Mobile-Artificial-Intelligence/maid en GitHub). Tiene 2.713 estrellas en GitHub y 296 bifurcaciones, se publica bajo la licencia MIT y se actualizó por última vez en GitHub el 30 de septiembre de 2026.
Maid is a free and open source application for interfacing with llama.cpp models locally, and with Anthropic, DeepSeek, Ollama, Mistral and OpenAI models rem…
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.
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.
Existing tools plug in by changing one URL.
Ask questions over your PDFs, notes, and codebases.
Download the source and move into the project folder.
git clone https://github.com/Mobile-Artificial-Intelligence/maid.git
cd maidEvery 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 Mobile-Artificial-Intelligence/maid with 296 forks. Check the repo's contributors and recent commits to see how active maintenance is.
MIT licensed — use it commercially, no strings attached.
Yes — with no network calls, there's nothing to leak.
Close on many tasks now; frontier cloud models still lead on hard reasoning.
8B models run on 16GB RAM; bigger models want a real GPU.
Run AI models locally on your machine with node.js bindings for llama.cpp. Enforce a JSON schema on the model output on the generation level
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动手学Ollama，CPU玩转大模型部署，在线阅读地址：https://datawhalechina.github.io/handy-ollama/
Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.