---
pipeline_tag: tabular-regression
---

# TabPFN v2: A Tabular Foundation Model

TabPFN is a transformer-based foundation model for tabular data that leverages prior-data based learning to achieve strong performance on small tabular regression tasks without requiring task-specific training.

## Installation
```bash
pip install tabpfn
```

## Model Details
- **Developed by:** Prior Labs
- **Model type:** Transformer-based foundation model for tabular data
- **License:** TBD
- **Paper:** Published in Nature (January 2024)
- **Repository:** [GitHub - priorlabs/tabpfn](https://github.com/priorlabs/tabpfn)

### Citation
TBD

## Quick Start
```python
from tabpfn import TabPFNRegressor

# Initialize model
regressor = TabPFNRegressor()
regressor.fit(X_train, y_train)
predictions = regressor.predict(X_test)
```

## Technical Requirements
- Python ≥ 3.9
- PyTorch ≥ 2.1
- scikit-learn ≥ 1.0
- Hardware: 16GB+ RAM, CPU (GPU optional)

## Limitations
- Not designed for very large datasets
- Not suitable for non-tabular data formats

## Resources
- **Documentation:** https://priorlabs.ai/docs
- **Source:** https://github.com/priorlabs/tabpfn
- **Paper:** https://doi.org/10.1038/s41586-024-08328-6

### Team
- Noah Hollmann
- Samuel Müller
- Lennart Purucker
- Arjun Krishnakumar
- Max Körfer
- Shi Bin Hoo
- Robin Tibor Schirrmeister
- Frank Hutter
- Eddie Bergman