---
id: tabpfn
version: "8.3.0"
license: Prior Labs License (Apache 2.0 with ADDITIONAL PROVISION) Version 1.2, Dec 2025 This license is a derivative of the Apache 2.0 license (http://www.apache.org/licenses/) with a single modification:… (full text in the JSON record)
license_treatment: unclear
maintenance: active
---

# tabpfn — TabPFN: Foundation model for tabular data

License: unclear · Maintenance: active · Downloads: 303.0K/mo

## What it is and what it does

TabPFN is a pre-trained foundation model designed to make tabular machine learning faster by eliminating hyperparameter tuning. It provides TabPFNClassifier and TabPFNRegressor classes that follow scikit-learn conventions, so you can fit and predict on structured data with minimal setup. The model downloads weights from huggingface-hub on first use and runs inference via torch, supporting both GPU and CPU execution (though GPU is strongly recommended).

The package depends on torch, scikit-learn, pandas, numpy, scipy, einops, pydantic, joblib, tqdm, and lightgbm. It supports Python 3.10 through 3.14. By default it uses TabPFN-3 (non-commercial license), but you can instantiate earlier versions like TabPFN-2.6 or TabPFN-2 via ModelVersion constants. For production use, Prior Labs offers a commercial Enterprise Edition.

Use it for:

- Rapid prototyping on tabular datasets when you need a strong baseline without manual hyperparameter search.
- Binary and multiclass classification on structured data with minimal preprocessing or model selection overhead.
- Regression tasks on tabular data where you want pre-trained representations without training from scratch.
- Exploratory analysis where you want to quickly assess model performance before investing in tuning.
- Scenarios where you have a GPU available and want to leverage foundation model capabilities on tables.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

TabPFN is a foundation model for tabular data that provides pre-trained classifiers and regressors for rapid inference on structured datasets without requiring hyperparameter tuning.

Yes, with conditions. TabPFN is worth installing if you work with tabular data and have a GPU available, or can tolerate CPU-only inference on moderate datasets. The package is actively maintained with low install friction and genuine value for rapid prototyping. Verify the non-commercial license restrictions for TabPFN-3 (the default) if your use is commercial; if so, you'll need a commercial license or use TabPFN-2 weights (Apache 2.0 with attribution). The attribution requirement is substantial—ensure compliance before distributing.

## Install

pip install tabpfn
uv add tabpfn
poetry add tabpfn

## Installing tabpfn

Before you install: Installation is straightforward with low friction—the package ships as a wheel and handles PyTorch setup automatically. The project is actively maintained with a recent release (1 day old) and strong community engagement (7795 stars). However, optimal use requires a GPU; CPU-only inference is limited to moderate datasets.

License in practice: The code and TabPFN-2 weights use Prior Labs License (Apache 2.0 with an added attribution requirement). TabPFN-3, the default model, uses a non-commercial license. Section 10 requires prominent attribution ("Built with PriorLabs-TabPFN") on websites, UIs, and documentation if you distribute or make the work available. Internal testing without external communication is exempt.

Quickstart:

pip install tabpfn

from tabpfn import TabPFNClassifier

clf = TabPFNClassifier()
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)

Requires Python 3.10+. GPU recommended for performance; CPU-only inference limited to moderate datasets.

Verify before relying:

- Whether the non-commercial license for TabPFN-3 model weights permits use in commercial products.
- Whether the attribution requirement in Section 10 applies to internal enterprise deployments.
- Performance characteristics and accuracy benchmarks on specific dataset types or sizes.

## Package facts

- License: Prior Labs License (Apache 2.0 with ADDITIONAL PROVISION) Version 1.2, Dec 2025 This license is a derivative of the Apache 2.0 license (http://www.apache.org/licenses/) with a single modification:… (full text in the JSON record) (unclear)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 303.0K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

tabular data classification, foundation model for tables, fast tabular regression, pretrained tabular model, structured data prediction, no-tune machine learning, tabular foundation model, tabular-ml, foundation-model, gpu-accelerated

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