Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. 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.

• 05/22/2024 Tabby VSCode 1.6 comes with multiple choices in inline completion, and the auto-generated commit messages🐱💻!
• 05/11/2024 v0.11.0 brings significant enterprise upgrades, including 📊storage usage stats, 🔗GitHub & GitLab integration, 📋Activities page, and the long-awaited 🤖Ask Tabby feature!
• 04/22/2024 v0.10.0 released, featuring the latest Reports tab with team-wise analytics for Tabby usage.

You can find our documentation here.

• 📚 Installation
• 💻 IDE/Editor Extensions
• ⚙️ Configuration

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/StarCoder-1B --device cuda

For additional options (e.g inference type, parallelism), please refer to the documentation page.

Full guide at CONTRIBUTING.md;

git clone --recurse-submodules https://github.com/TabbyML/tabby
cd tabby

If you have already cloned the repository, you could run the git submodule update --recursive --init command to fetch all submodules.

1. Set up the Rust environment by following this tutorial.

2. Install the required dependencies:

# For MacOS
brew install protobuf

# For Ubuntu / Debian
apt-get install protobuf-compiler libopenblas-dev

3. Install useful tools:

# For Ubuntu
sudo apt install make sqlite3 graphviz

4. Now, you can build Tabby by running the command cargo build.

... and don't forget to submit a Pull Request

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