Produktivität
MyBrain
All-in-one productivity app and AI assistant with Tasks, Notes, Calendar, Diary and Bookmarks.
★ 2,2 Tsd. SterneGPL-3.0
Anleitung ansehen →Soup ist ein Open-Source-Projekt aus der Kategorie Lokale LLMs von MakazhanAlpamys (MakazhanAlpamys/Soup auf GitHub). Es hat 8.063 Sterne auf GitHub und 1.297 Forks, wird unter der Lizenz Apache-2.0 veröffentlicht und wurde zuletzt am 2. Oktober 2026 auf GitHub aktualisiert.
Fine-tune LLMs from one YAML. Layer streaming trains an 8B model on a 4 GB laptop GPU.
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.
Scriptable from the terminal, so it drops straight into existing workflows.
Large language models drive its smartest features, locally or via API.
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/MakazhanAlpamys/Soup.git
cd SoupEvery 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 MakazhanAlpamys/Soup with 1,297 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.
All-in-one productivity app and AI assistant with Tasks, Notes, Calendar, Diary and Bookmarks.
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Check if your GPU can fine-tune an LLM — memory estimates and LoRA recipes.
Run and fine-tune LLMs and diffusion models locally — faster training, smaller memory footprint.
Compile and run LLMs anywhere - phones, laptops, browsers and GPUs - with one engine
Die anderen Projekte der Kategorie Lokale LLMs mit den meisten Sternen im LibreAI-Katalog — die realistische Auswahl, falls Soup nicht das Richtige ist.
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.
Verwandte Projekte der Kategorie Lokale LLMs im Katalog, die Soup bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.
KoboldCpp - Run GGUF AI models locally with a KoboldAI UI. One File. Zero Install
vLLM-Omni — efficient inference for omni-modality models: image, audio, video, diffusion and policies.
Typescript/React Library for AI Chat 💬🚀
DeepSeek-Coder-V2 — DeepSeek's open code model family that challenged closed-source code models.
Access large language models from the command-line
Quantization, kernels, runtime and inference engine for mobiles, wearables, smart home and robots
Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.