- Academic foundation - First large-scale pretrained transformer for tabular data, establishing the “foundation model” paradigm for structured learning.
- Zero-shot accuracy - Outperforms tuned gradient-boosted trees and AutoML baselines on datasets up to 10 000 samples, predicting in seconds rather than hours.
- Compact and efficient - Runs comfortably on a single GPU such as an NVIDIA P4 or T4; designed for accessibility and reproducibility.
- Robust to real-world noise - Handles missing values, categorical variables, and outliers.
Deprecation noticeTabPFN-2 will be deprecated in upcoming releases. It remains available only through the open-source tabpfn package up to version
2.3.0.
For all new projects, we recommend upgrading to TabPFN-2.5 for better performance, robustness, and ongoing support.Architecture
TabPFN-2 introduced the alternating-attention transformer architecture - a design that alternates attention over rows (samples) and features to model both inter-sample and inter-feature dependencies efficiently. Key characteristics:- Meta-trained on millions of synthetic datasets generated from probabilistic graphical models and classical ML priors.
- Learns to perform in-context classification and regression through self-supervised meta-learning.
- Order-invariant with respect to samples and features.
- Supports mixed data types, including categorical and numerical inputs, with minimal preprocessing.
TabPFN-2 can run on modest hardware and can even execute on CPU for small datasets.