Get Started

Install REE, launch the TUI, and run your first reproducible generation.

Quickstart Guide

Everything you need to go from zero to your first receipt: [1] prerequisites, [2] installation, and [3] a guided first run.

Prerequisites

• Docker installed and running.

• Python 3 installed on your machine.

• Disk Space Requirements: The compressed REE container image is roughly 7 GB. When uncompressed, it occupies approximately 12 GB on disk.

Model weights are downloaded separately and their size can vary significantly depending on the model.

Installing REE

Clone the GitHub repository and navigate into it:

git clone https://github.com/gensyn-ai/ree.git
cd ree

No additional installation or dependency management is required. The TUI handles pulling the REE container image automatically on your first run.

The repository also includes ree.sh, a lower-level shell script that ree.py calls under the hood. You shouldn't need to use ree.sh directly unless you're debugging or working on an advanced integration.

Launching the TUI

The TUI opens with an interactive form where you can configure and launch generations entirely from within the interface without the need to manually assemble CLI commands.

From the ree directory, run:

python3 ree.py

If you prefer the command line, REE can also be driven directly via ree.sh or the gensyn-sdk CLI without the TUI. This may be preferable if you're scripting, working in a CI pipeline, or using a coding agent like Claude Code. See the Advanced Usage & CLI Reference for the full CLI documentation.

Your First Run

When the TUI launches, you'll see this form:

To run your first generation:

1. Use the arrow keys to navigate to Model Name from this list and press Enter to edit it.

2. Navigate to Prompt Text and press Enter. Type a simple prompt like Hello world.

3. Set a Max New Tokens count.

4. Press r to run.

REE will pull the container image, prepare the model, run inference, and display a progress checklist:

Once complete, you'll see the REE Output section showing the receipt file path and the model's generated text.

If you used a Hugging Face test model or small-parameter model, the output may be nonsensical. This is expected, since these models either have random, untrained weights or consume too few tokens to produce a polished output. The important thing is that the pipeline ran successfully.

From here you can press e to reset and configure another run, r to re-run with the same settings, or q to quit.