Model Overview TabPFN 2.5 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. 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! Developers & Affiliations Developed by Prior Labs. Intended Use Regression and classification tasks with ≤50 000 samples and ≤2000 features in structured tabular format. Not Intended Use Not suitable for unstructured data (text, images); use API version for textual features. Not tested for 50 000 samples or 2000 features. Model Architecture Transformer with TabPFNv2 like alternating attention with 18 24 layers Training Data and Priors TabPFN 2.5: trained purely on synthetic tabular tasks Real TabPFN 2.5: continued pre training on real world datasets (for details please see Appendix C.1 of the model tech report). Performance Benchmarks Evaluated on proprietary benchmark collection, TabArena, and RealCause (for a causal version), in each of which it yields…
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