pip install tabpfn==0.1.11.
📚 For detailed usage examples and best practices, check out Interactive Colab Tutorial
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.
⚡ GPU Recommended:
For optimal performance, use a GPU (even older ones with ~8GB VRAM work well; 16GB needed for some large datasets).
On CPU, only small datasets (≲1000 samples) are feasible.
No GPU? Use our free hosted inference via TabPFN Client.
Official installation (pip)
pip install tabpfnOR installation from source
pip install "tabpfn @ git+https://github.com/PriorLabs/TabPFN.git"OR local development installation
git clone https://github.com/PriorLabs/TabPFN.git
pip install -e "TabPFN[dev]"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=0.5, 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))from sklearn.datasets import fetch_openml
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
# Assuming there is a TabPFNRegressor (if not, a different regressor should be used)
from tabpfn import TabPFNRegressor
# Load Boston Housing data
df = fetch_openml(data_id=531, as_frame=True) # Boston Housing dataset
X = df.data
y = df.target.astype(float) # Ensure target is float for regression
# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
# Initialize the regressor
regressor = TabPFNRegressor()
regressor.fit(X_train, y_train)
# Predict on the test set
predictions = regressor.predict(X_test)
# Evaluate the model
mse = mean_squared_error(y_test, predictions)
r2 = r2_score(y_test, predictions)
print("Mean Squared Error (MSE):", mse)
print("R² Score:", r2)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:
-
Install the extensions:
git clone https://github.com/priorlabs/tabpfn-extensions.git pip install -e tabpfn-extensions
-
from tabpfn_extensions.post_hoc_ensembles.sklearn_interface import AutoTabPFNClassifier clf = AutoTabPFNClassifier(max_time=120, device="cuda") # 120 seconds tuning time clf.fit(X_train, y_train) predictions = clf.predict(X_test)
Choose the right TabPFN implementation for your needs:
-
TabPFN Client
Simple API client for using TabPFN via cloud-based inference. -
TabPFN Extensions
A powerful companion repository packed with advanced utilities, integrations, and features - great place to contribute:- 🔍
interpretability: Gain insights with SHAP-based explanations, feature importance, and selection tools. - 🕵️♂️
unsupervised: Tools for outlier detection and synthetic tabular data generation. - 🧬
embeddings: Extract and use TabPFN’s internal learned embeddings for downstream tasks or analysis. - 🧠
many_class: Handle multi-class classification problems that exceed TabPFN's built-in class limit. - 🌲
rf_pfn: Combine TabPFN with traditional models like Random Forests for hybrid approaches. - ⚙️
hpo: Automated hyperparameter optimization tailored to TabPFN. - 🔁
post_hoc_ensembles: Boost performance by ensembling multiple TabPFN models post-training.
✨ To install:
git clone https://github.com/priorlabs/tabpfn-extensions.git pip install -e tabpfn-extensions
- 🔍
-
TabPFN (this repo)
Core implementation for fast and local inference with PyTorch and CUDA support. -
TabPFN UX
No-code graphical interface to explore TabPFN capabilities—ideal for business users and prototyping.
Prior Labs License (Apache 2.0 with additional attribution requirement): here
We're building the future of tabular machine learning and would love your involvement:
-
Connect & Learn:
- Join our Discord Community
- Read our Documentation
- Check out GitHub Issues
-
Contribute:
- Report bugs or request features
- Submit pull requests
- Share your research and use cases
-
Stay Updated: Star the repo and join Discord for the latest updates
You can read our paper explaining TabPFN here.
@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},
}
@inproceedings{hollmann2023tabpfn,
title={TabPFN: A transformer that solves small tabular classification problems in a second},
author={Hollmann, Noah and M{\"u}ller, Samuel and Eggensperger, Katharina and Hutter, Frank},
booktitle={International Conference on Learning Representations 2023},
year={2023}
}Q: What dataset sizes work best with TabPFN?
A: TabPFN is optimized for datasets up to 10,000 rows. For larger datasets, consider using Random Forest preprocessing or other extensions. See our Colab notebook for strategies.
Q: Why can't I use TabPFN with Python 3.8?
A: TabPFN v2 requires Python 3.9+ due to newer language features. Compatible versions: 3.9, 3.10, 3.11, 3.12, 3.13.
Q: How do I use TabPFN without an internet connection?
TabPFN automatically downloads model weights when first used. For offline usage:
Using the Provided Download Script
If you have the TabPFN repository, you can use the included script to download all models (including ensemble variants):
# After installing TabPFN
python scripts/download_all_models.pyThis script will download the main classifier and regressor models, as well as all ensemble variant models to your system's default cache directory.
Manual Download
-
Download the model files manually from HuggingFace:
- Classifier: tabpfn-v2-classifier.ckpt
- Regressor: tabpfn-v2-regressor.ckpt
-
Place the file in one of these locations:
- Specify directly:
TabPFNClassifier(model_path="/path/to/model.ckpt") - Set environment variable:
os.environ["TABPFN_MODEL_CACHE_DIR"] = "/path/to/dir" - Default OS cache directory:
- Windows:
%APPDATA%\tabpfn\ - macOS:
~/Library/Caches/tabpfn/ - Linux:
~/.cache/tabpfn/
- Windows:
- Specify directly:
Q: I'm getting a pickle error when loading the model. What should I do?
A: Try the following:
- Download the newest version of tabpfn
pip install tabpfn --upgrade - Ensure model files downloaded correctly (re-download if needed)
Q: Can TabPFN handle missing values?
A: Yes!
Q: How can I improve TabPFN’s performance?
A: Best practices:
- Use AutoTabPFNClassifier from TabPFN Extensions for post-hoc ensembling
- Feature engineering: Add domain-specific features to improve model performance
Not effective:- Adapt feature scaling
- Convert categorical features to numerical values (e.g., one-hot encoding)
- 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- Before committing:
pre-commit run --all-files- Run tests:
pytest tests/Built with ❤️ by Prior Labs - Copyright (c) 2025 Prior Labs GmbH
