PriorLabs/TabPFN v7.0.0

on GitHub

Added

• Introduce TabPFN-2.6 model and use as default (#831)
• Added argument use_fixed_preprocessing_seed to FinetunedTabPFNClassifier and FinetunedTabPFNRegressor for improved finetuning performance.
• This PR changes the random seeds used in the preprocessing, which may cause slight differences in final outcomes compared to previous versions.
  (#771)
• More informative Out-Of-Memory error message. (#805)
• Added max_onehot_cardinality option to cap one-hot encoding expansion for high-cardinality categorical features. (#833)

Changed

• Introduces TabPFN-2.6 as the new default model for TabPFNClassifier and TabPFNRegressor (#831)
• Remove unused functions default_classifier_preprocessor_configs() and default_regressor_preprocessor_configs() (#831)
• "auto" device selection now uses all available CUDA GPUs instead of only the first one (#808)
• Optimize fingerprint hashing in preprocessing: round feature matrix once instead of per-row, avoid redundant SHA-256 calls. Speeds up fit by up to 2x for large datasets. (#818)
• Bump minimum torch version from 2.1 to 2.5 (#823)
• Cache loaded checkpoints across fit calls: skip redundant disk I/O when the same model is loaded repeatedly (e.g. cross-validation, hyperparameter search). (#832)

Fixed

• Fix the pdf() in FullSupportBarDistribution to actually compute the probability density. (#799)