TabPFN v2: A Tabular Foundation Model

TabPFN is a transformer-based foundation model for tabular data that leverages prior-data based learning to achieve strong performance on small tabular regression tasks without requiring task-specific training.

Installation

pip install tabpfn

Model Details

Developed by: Prior Labs
Model type: Transformer-based foundation model for tabular data
License: Prior Labs License (Apache 2.0 with additional attribution requirement)
Paper: Published in Nature (January 2025)
Repository: GitHub - priorlabs/tabpfn

📚 Citation

@article{hollmann2025tabpfn,
 title={Accurate predictions on small data with a tabular foundation model},
 author={Hollmann, Noah and M{\"u}ller, Samuel and Purucker, Lennart and
         Krishnakumar, Arjun and K{\"o}rfer, Max and Hoo, Shi Bin and
         Schirrmeister, Robin Tibor and Hutter, Frank},
 journal={Nature},
 year={2025},
 month={01},
 day={09},
 doi={10.1038/s41586-024-08328-6},
 publisher={Springer Nature},
 url={https://www.nature.com/articles/s41586-024-08328-6},
}

Quick Start

📚 For detailed usage examples and best practices, check out:

Interactive Colab Tutorial

Technical Requirements

Python ≥ 3.9
PyTorch ≥ 2.1
scikit-learn ≥ 1.0
Hardware: 16GB+ RAM, CPU (GPU optional)

Limitations

Not designed for very large datasets
Not suitable for non-tabular data formats

Resources

Documentation: https://priorlabs.ai/docs
Source: https://github.com/priorlabs/tabpfn
Paper: https://www.nature.com/articles/s41586-024-08328-6

Team

Noah Hollmann
Samuel Müller
Lennart Purucker
Arjun Krishnakumar
Max Körfer
Shi Bin Hoo
Robin Tibor Schirrmeister
Frank Hutter
Eddie Bergman
Léo Grinsztajn