Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Co-pilot. It boasts several key features:
- Self-contained, with no need for a DBMS or cloud service.
- OpenAPI interface, easy to integrate with existing infrastructure (e.g, Cloud IDE).
- Supports consumer-grade GPUs.
- 10/24/2023 ⛳️ Major updates for Tabby IDE plugins across -VSCode/Vim/IntelliJ!
- 10/15/2023 RAG-based code completion is enabled by detail in v0.3.0🎉! Check out the blogpost explaining how Tabby utilizes repo-level context to get even smarter!
- 10/04/2023 Check out the model directory for the latest models supported by Tabby.
Archived
- 09/21/2023 We've hit 10K stars 🌟 on GitHub! 🚀🎉👏
- 09/18/2023 Apple's M1/M2 Metal inference support has landed in v0.1.1!
- 08/31/2023 Tabby's first stable release v0.0.1 🥳.
- 08/28/2023 Experimental support for the CodeLlama 7B.
- 08/24/2023 Tabby is now on JetBrains Marketplace!
You can find our documentation here.
The easiest way to start a Tabby server is by using the following Docker command:
docker run -it \
--gpus all -p 8080:8080 -v $HOME/.tabby:/data \
tabbyml/tabby \
serve --model TabbyML/SantaCoder-1B --device cudaFor additional options (e.g inference type, parallelism), please refer to the documentation page.
git clone --recurse-submodules https://github.com/TabbyML/tabby
cd tabbyIf you have already cloned the repository, you could run the git submodule update --recursive --init command to fetch all submodules.
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Set up the Rust environment by following this tutorial.
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Install the required dependencies:
# For MacOS
brew install protobuf
# For Ubuntu / Debian
apt-get install protobuf-compiler libopenblas-dev- Now, you can build Tabby by running the command
cargo build.
... and don't forget to submit a Pull Request
- #️⃣ Slack - connect with the TabbyML community
- 🎤 Twitter / X - engage with TabbyML for all things possible
- 📚 LinkedIn - follow for the latest from the community
- 💌 Newsletter - subscribe to unlock Tabby insights and secrets