Lokale LLMs
llama.cpp
LLM inference in C/C++
★ 130,2 Tsd. SterneMIT
Anleitung ansehen →kalosm ist ein Open-Source-Projekt aus der Kategorie Lokale LLMs von floneum (floneum/kalosm auf GitHub). Es hat 2.229 Sterne auf GitHub und 135 Forks, wird unter der Lizenz Apache-2.0 veröffentlicht und wurde zuletzt am 2. Oktober 2026 auf GitHub aktualisiert.
Run controllable pre-trained AI models locally, written in Rust.
Pull it into your Rust project.
Pick a pre-trained model for your task.
Define the structure generations must follow.
Infer entirely on the user's machine.
Models run on-device, no cloud.
Outputs follow your required structure.
Embeds directly in Rust applications.
Start from working models fast.
Designed for quick model loading.
Permissive for commercial embedding.
Download the source and move into the project folder.
git clone https://github.com/floneum/kalosm.git
cd kalosmEvery 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 commandThe model is forced to output valid structures like JSON schemas, not free text.
Apache 2.0 licensed — commercial use allowed with attribution.
Pre-trained models including Llama-family options; check the docs.
It is a Rust-native framework with built-in structured generation.
Local inference benefits from one, but CPU options exist.
LLM inference in C/C++
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Die anderen Projekte der Kategorie Lokale LLMs mit den meisten Sternen im LibreAI-Katalog — die realistische Auswahl, falls kalosm nicht das Richtige ist.
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LLM inference in C/C++
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Verwandte Projekte der Kategorie Lokale LLMs im Katalog, die kalosm bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.
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Let any LLM actually watch a video — frames plus transcript, run locally.
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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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Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.