TabPFN

Quick Start

Interactive Notebook Tutorial

[!TIP]

Dive right in with our interactive Colab notebook! It's the best way to get a hands-on feel for TabPFN, walking you through installation, classification, and regression examples.

Installation

pip install tabpfn

TabPFN supports Python 3.10+.

⚡ 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 moderate datasets are feasible (TabPFN-3, TabPFN-3.5 and TabPFN-3.5-Fast allow up to 5000 samples; older versions up to 1000). No GPU? Use our free hosted inference via TabPFN Client.

On macOS: GPU support is automatically included for Apple Silicon Macs. For best performance, ensure you are using PyTorch 2.13 or newer (see #949).

On Linux: Nvidia GPU support is automatically included. For AMD GPUs, first install PyTorch with ROCm, then install TabPFN. For example,

pip install torch --index-url https://download.pytorch.org/whl/rocm7.2
pip install tabpfn

For a CPU-only install, first install CPU-only PyTorch, then install TabPFN. This saves disk space if you do not have a GPU.

On Windows: For Nvidia GPUs, install PyTorch with CUDA, then install TabPFN. For AMD GPUs, install PyTorch with ROCm, then install TabPFN.

Basic Usage

To use our default TabPFN-3.5 model:

from tabpfn import TabPFNClassifier, TabPFNRegressor

clf = TabPFNClassifier()
clf.fit(X_train, y_train)  # downloads checkpoint on first use
predictions = clf.predict(X_test)

reg = TabPFNRegressor()
reg.fit(X_train, y_train)  # downloads checkpoint on first use
predictions = reg.predict(X_test)

To use the smaller, faster TabPFN-3.5-Fast you can use ModelVersion.V3_5_FAST or "v3.5-fast".

from tabpfn.constants import ModelVersion

classifier = TabPFNClassifier.create_default_for_version(ModelVersion.V3_5_FAST)
regressor = TabPFNRegressor.create_default_for_version(ModelVersion.V3_5_FAST)

To use other model versions (e.g. the previous default, TabPFN-3):

classifier = TabPFNClassifier.create_default_for_version(ModelVersion.V3)
regressor = TabPFNRegressor.create_default_for_version(ModelVersion.V3)

For complete examples, see the tabpfn_for_binary_classification.py, tabpfn_for_multiclass_classification.py, and tabpfn_for_regression.py files.

TabPFN Ecosystem

Choose the right TabPFN implementation for your needs:

• TabPFN Client Simple API client for using TabPFN via cloud-based inference.

• TabPFN Extensions Community extensions and integrations, including:

  • 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.

  To install:

  pip install 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.

License

The code in this repository is licensed under the Apache License 2.0. Third-party code is subject to its own licenses and attribution requirements; see Third-Party Notices.

Model weights are licensed separately.

The TabPFN-2.5, TabPFN-2.6, TabPFN-3, TabPFN-3.5 and TabPFN-3.5-Fast model weights are released under non-commercial licenses (TabPFN-3.5 and TabPFN-3.5-Fast license, TabPFN-3 license; see the Models page for prior releases). TabPFN-3.5 is used by default.

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