Локальные LLM
FastChat
An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
★ 39,6 тыс. звёздApache-2.0
Смотреть руководство →llm — проект с открытым исходным кодом из категории «Локальные LLM» от simonw (simonw/llm на GitHub). У него 12 583 звезды на GitHub и 1 014 форков, он распространяется по лицензии Apache-2.0 и последний раз обновлялся на GitHub 22 сентября 2026.
Access large language models from the command-line
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.
Existing tools plug in by changing one URL.
Ask questions over your PDFs, notes, and codebases.
Pay nothing per token, ever.
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.
Download the source and move into the project folder.
git clone https://github.com/simonw/llm.git
cd llmEvery 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 simonw/llm with 1,014 forks. Check the repo's contributors and recent commits to see how active maintenance is.
Apache 2.0 licensed — commercial use allowed with attribution.
8B models run on 16GB RAM; bigger models want a real GPU.
A recent 8–32B instruct model is the sweet spot for most machines.
Yes — with no network calls, there's nothing to leak.
An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
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Соседние проекты из категории «Локальные LLM» в каталоге, ближайшие по числу звёзд к llm — продолжайте изучать полку по обе стороны от него.
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Fine-tune LLMs from one YAML. Layer streaming trains an 8B model on a 4 GB laptop GPU.
Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.