• post_hoc_ensembles: Improve performance with model combination
• interpretability: Explain TabPFN predictions with SHAP values and feature selection
• many_class: Handle classification with more classes than TabPFN's default limit
• classifier_as_regressor: Use TabPFN's classifier for regression tasks
• hpo: Automatic hyperparameter tuning for TabPFN
• rf_pfn: Combine TabPFN with decision trees and random forests
• unsupervised: Data generation and outlier detection
• embedding: Get TabPFNs internal dense sample embeddings

Detailed documentation for each extension is available in the respective module directories.

# Clone and install the repository
pip install "tabpfn-extensions[all] @ git+https://github.com/PriorLabs/tabpfn-extensions.git"

TabPFN Extensions works with two TabPFN implementations:

1. 🖥️ TabPFN Package - Full PyTorch implementation for local inference:

   pip install tabpfn

2. ☁️ TabPFN Client - Lightweight API client for cloud-based inference:

   pip install tabpfn-client

Choose the backend that fits your needs - most extensions work with either option!

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Interested in adding your own extension? We welcome contributions!

# Clone and set up for development
git clone https://github.com/PriorLabs/tabpfn-extensions.git
cd tabpfn-extensions
# Lightweight dev setup (fast)
pip install -e ".[dev]"
# Test your extension with fast mode
FAST_TEST_MODE=1 pytest tests/test_your_extension.py -v

See our Contribution Guide for more details.

Each extension lives in its own subpackage:

tabpfn-extensions/
├── src/
│   └── tabpfn_extensions/
│       └── your_package/      # Extension implementation
├── examples/
│   └── your_package/          # Usage examples
└── tests/
    └── your_package/          # Tests

Built with ❤️ by the TabPFN community