README

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
bash
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,
bash
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:

python
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".

python
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):

python
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:
  bash