PriorLabs/TabPFN v8.0.8 on GitHub

Dropped support for Python 3.9; the minimum required version is now Python 3.10. The eval-type-backport dependency (only needed on 3.9) has been removed. (#1038)

Add a single file implementation of TabPFNv2. Not activated by default yet. (#995)
Add a keep_cache_on_device option to TabPFNClassifier/TabPFNRegressor (defaults to True). When fit_mode="fit_with_cache", setting it to False offloads each per-estimator KV cache to CPU as it is built and moves it back to the device on demand, lowering resident device memory at the cost of per-call transfers. (#1009)
Added official support for Python 3.14 (already exercised by the CI test matrix). (#1038)

Fixed regressor ensemble members sharing a single mutable target_transform instance. With in-process preprocessing (n_preprocessing_jobs=1), each member's in-place fit clobbered the fitted state of the others, silently corrupting predictions whenever members were fitted on different targets (e.g. with row subsampling active). Each ensemble config now owns a deep copy of the transform. (#1029)
Fixed two GPU-preprocessing divergences from the CPU reference: TorchSoftClipOutliers silently skipped outlier clipping when predicting a single sample in KV-cache mode (predictions depended on test batch size), and TorchAddSVDFeaturesStep added an SVD column for single-feature datasets where the CPU pipeline adds none (predictions differed between ENABLE_GPU_PREPROCESSING on and off). (#1033)
Fixed a fit-time crash when a DataFrame mixed a plain numpy bool column with a non-numeric string column (string-valued category or pandas string dtype). coerce_nullable_dtypes_to_numpy now coerces numpy bool columns to float64, not only nullable extension dtypes. (#1040)