Model Overview TabPFN 3 is a transformer based foundation model that uses in context learning to solve tabular prediction problems in a forward pass. Inference code can be found at https://github.com/PriorLabs/TabPFN. More details can be found in the Model Report. Getting started First, install the inference package: Fitting a classifier and predicting looks like this: For more examples (e.g. how to train a regressor), see the github repo: https://github.com/PriorLabs/tabPFN! Specialized checkpoints TabPFN 3 ships with default classification and regression checkpoints, plus a few experimental specialized variants. We recommend starting with the defaults — the variants can be useful in ensembling or HPO setups, or tried manually in the regime they were trained for. Their name suffixes indicate what we expect them to be good at. The following specialized checkpoints are available: Checkpoint Task Specialization tabpfn v3 classifier v3 20260417 binary.ckpt Classification Specialized for binary classification for datasets with 2000 features. Model Architecture Multi stage transformer based architecture with 24 main layers. Training Data and Priors TabPFN 3 is trained purely on syntheti…
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