This repo contains implementations of heavily-requested papers on alphaXiv. The goal of this repo is to provide well-documented, easy-to-follow implementations of popular research paper codebases.

Request implementations: Open an issue or click 'implement' on any paper on alphaXiv.

Each implementation must include:

1. README with specs: GPU count/type required, runtime estimates, dataset instructions, reproduction results
2. Standard structure: Use pyproject.toml for dependencies and src/ layout for code
3. Speedrun.sh: Each project must have a clear Nanochat-style speedrun.sh script that sets up the environment and runs relevant scripts for training and evaluation.

paper-name/
├── README.md
├── pyproject.toml
└── src/
    └── paper_name/
        ├── train.py
        └── eval.py