TabPFN

TabPFN is a foundation model for tabular data that outperforms traditional methods while being dramatically faster. This repository contains the core PyTorch implementation with CUDA optimization.

⚠️ Major Update: Version 2.0: Complete codebase overhaul with new architecture and features. Previous version available at v1.0.0 and pip install tabpfn<2.

📚 For detailed usage examples and best practices, check out Interactive Colab Tutorial

🌐 TabPFN Ecosystem

Choose the right TabPFN implementation for your needs:

• TabPFN Client: Easy-to-use API client for cloud-based inference
• TabPFN Extensions: Community extensions and integrations
• TabPFN (this repo): Core implementation for local deployment and research
• TabPFN UX: No-code TabPFN usage

Try our Interactive Colab Tutorial to get started quickly.

🏁 Quick Start

Installation

# Simple installation
pip install tabpfn

# Local development installation
git clone https://github.com/PriorLabs/TabPFN.git
pip install -e "TabPFN[dev]"

Basic Usage

from sklearn.datasets import load_breast_cancer
from sklearn.metrics import accuracy_score, roc_auc_score
from sklearn.model_selection import train_test_split

from tabpfn import TabPFNClassifier

# Load data
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

# Initialize a classifier
clf = TabPFNClassifier()
clf.fit(X_train, y_train)

# Predict probabilities
prediction_probabilities = clf.predict_proba(X_test)
print("ROC AUC:", roc_auc_score(y_test, prediction_probabilities[:, 1]))

# Predict labels
predictions = clf.predict(X_test)
print("Accuracy", accuracy_score(y_test, predictions))

Best Results

For optimal performance, use the AutoTabPFNClassifier or AutoTabPFNRegressor for post-hoc ensembling. These can be found in the TabPFN Extensions repository. Post-hoc ensembling combines multiple TabPFN models into an ensemble.

Steps for Best Results:

1. Install the extensions:

   git clone https://github.com/priorlabs/tabpfn-extensions.git
   pip install -e tabpfn-extensions

2. from tabpfn_extensions.post_hoc_ensembles.sklearn_interface import AutoTabPFNClassifier
   
   clf = AutoTabPFNClassifier(max_time=120) # 120 seconds tuning time
   clf.fit(X_train, y_train)
   predictions = clf.predict(X_test)

See our Colab

🤝 Join Our Community

We're building the future of tabular machine learning and would love your involvement:

1. Connect & Learn:

   • Join our Discord Community
   • Read our Documentation
   • Check out GitHub Issues

2. Contribute:

   • Report bugs or request features
   • Submit pull requests
   • Share your research and use cases

3. Stay Updated: Star the repo and join Discord for the latest updates

📜 License

Prior Labs License (Apache 2.0 with additional attribution requirement): here

Attribution clause (cropped):
If You distribute or make available the Work or any Derivative
Work thereof relating to any part of the source or model weights,
or a product or service (including another AI model) that contains
any source or model weights, You shall (A) provide a copy of this
License with any such materials; and (B) prominently display
“Built with TabPFN” on each related website, user interface, blogpost,
about page, or product documentation. If You use the source or model
weights or model outputs to create, train, fine tune, distil, or
otherwise improve an AI model, which is distributed or made available,
you shall also include “TabPFN” at the beginning of any such AI model name.
To clarify, internal benchmarking and testing without external
communication shall not qualify as distribution or making available
pursuant to this Section 10 and no attribution under this Section 10
shall be required.

📚 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},
}

🛠️ Development

1. Setup environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
git clone https://github.com/PriorLabs/TabPFN.git
cd tabpfn
pip install -e ".[dev]"
pre-commit install

2. Before committing:

pre-commit run --all-files

3. Run tests:

pytest tests/

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Built with ❤️ by Prior Labs - Copyright (c) 2025 Prior Labs GmbH