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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Create Custom LLMs
| Date | Stars |
|---|---|
| 2026-07-31 | 1858 |
| 2026-08-06 | 1858 |
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# Augmentoolkit - Data for Domain-expert AI Augmentoolkit creates domain-expert datasets that update an AI's brain (basically, its knowledge cutoff), so that the AI becomes an expert in an area of your choosing. You upload documents, and press a button. And get a fully trained custom LLM. Now every aspect of your AI's behavior and understanding is under your control. Better still, Augmentoolkit **optionally works offline on your computer** -- no external API key required* for datagen† on most hardware. Maybe you want AI to know the latest research papers in your field, or perhaps you want an LLM that understands your passion deeply and has learned from the same sources as you. Possibly, you dream of creating a lore expert for your favorite obscure fictional universe. Whatever the application is, Augmentoolkit lets you take text and make an LLM's brain inherently learn the information contained within. It also automatically creates a RAG-ready dataset (and can start up an inference server) if you want some traditional grounding as well. Get started now (the interface will guide you through generating your first dataset): (Be sure to use Python 3.11 when creating the virtual environment to be sure this'll work) ### MacOS (interface) ```bash git clone https://github.com/e-p-armstrong/augmentoolkit.git cd augmentoolkit # Python == 3.11 bash macos.sh # NOTE: Will attempt to install valkey via brew if not found. # bash local_macos.sh # use this command if doing local dataset generation ``` ### Linux (interface) ```bash git clone https://github.com/e-p-armstrong/augmentoolkit.git cd augmentoolkit # Python == 3.11 bash linux.sh # NOTE: will build Valkey from source if a Redis/Valkey server is not running ``` **Or for local inference** ```bash git clone https://github.com/e-p-armstrong/augmentoolkit.git cd augmentoolkit # Python == 3.11 bash local_linux.sh normal # or you can write "small" or a custom model name to serve the quantized version (for more consumer hardware) or a model of your choice, respectively. See the quickstart page linked just a bit farther down for a full reference here. ``` If you have multiple GPUs, run `local_linux.sh` with the `--tensor-parallelism N` argument. N == number of GPUs you have (even). So: 1, 2, 4, 8... etc. > [!IMPORTANT] > > Please star the repo. ### Windows (interface) > [!NOTE] > > If you're on windows, your best bet is to use [WSL](https://learn.microsoft.com/en-us/windows/wsl/install). [The CLI is easier to get running on windows honestly.](docs/quickstart.md#windows-cli) <sub>*Note that datagen can take a while on a lot of hardware however, don't expect fast datagen on an old mac for instance. And for training you will need either a powerful machine of your own, or to rent (latter is done automatically for you if you so choose).</sub> <sub>†If you want data to generate faster you *can* use an open-source LLM API, and the quickstart encourages you to. In addition to its custom dataset generation model, Augmentoolkit is optimized for open source LLMs like Deepseek or Llama.</sub>  Augmentoolkit, now that it is on its 3.0 version, has been refined and improved through over a year of professional application and experimentation. It is now the best way in the world to create domain expert LLMs, and it's MIT licensed. If you use this project and like it, please consider starring the repo! It's also designed to be extremely customizable so consider **forking** Augmentoolkit! > [!IMPORTANT] > > The below links contain very useful information. There is a table of contents, that links to extensive documentation pages for any conceivable part of the project, a bit further down. [Help Videos](#video-tutorials) I walk through how to do all the cool stuff in this project starting from scratch, including training LLMs with the data and configs you get (takes 10 minutes). Check out the help videos if you want further guidance! [Community](https://discord.gg
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Ikko Eltociear Ashimine · Japan
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Emmanuel Ferdman · Israel
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9cef1a965cc7fc92, llm:topics: ai, dataset-generation, finetuning-llms; description: Create Custom LLMs
matched fp:9cef1a965cc7fc92, llm:topics: ai, dataset-generation, finetuning-llms; description: Create Custom LLMs
matched fp:9cef1a965cc7fc92, llm:topics: ai, dataset-generation, finetuning-llms; description: Create Custom LLMs