TabPFN Files

Added

• Fine-tuning now supports validation_frequency to run validation and early-stopping checks every N epochs. (#811)
• Add ManyClassDecoder.attention_weights, the canonical per-training-row attention distribution of the multiclass decoder head, so interpretability tooling can read out which training rows drive a prediction without reimplementing the head's internal forward pass. The method exists only on multiclass models that use this decoder. (#1142)
• Add fp8 kv cache dtype. (#1157)
• fit() now warns when a column of X looks like free text. (#1159)
• Add an opt-in built-model cache to load_model, enabled via the TABPFN_MODEL_CACHE_SIZE environment variable (default off). When set, repeated loads of the same checkpoint reuse the constructed model instead of rebuilding the architecture and re-running load_state_dict. Only the non-mutating (cache_trainset_representation=False) build is cached. (#1162)
• Add TabPFNRegressor.predict_batched, the regression counterpart to TabPFNClassifier.predict_proba_batched. It preprocesses each (X_train, y_train, X_test) triple exactly as fit + predict does, stacks the datasets on the model's batch dimension and scores them with a single fused forward per estimator, then decodes each dataset with its own target standardisation and per-estimator border transforms. Returns one entry per dataset in input order, each with the same structure predict would return for that dataset. Datasets must share array shapes; constant-target datasets are answered analytically.

Both batched methods now raise NotImplementedError for inference_precision=torch.float64 instead of silently computing the fused forward at float32 and returning float32-precision results. (#1164)

• Add an attention-backend registry; the in-tree FA3/torch-MPS/MLX paths now route through it. Behavior unchanged unless a backend is registered. (#1165)
• Add a test that enable_torch_compile traces tabpfn without graph breaks. (#1166)

Changed

• Speed up cached prediction on Hopper GPUs with FlashAttention-3 installed, by splitting attention over the key/value sequence when few test rows attend over a large training cache. (#1168)
• n_estimators now defaults to "auto" on TabPFNClassifier and TabPFNRegressor. Feature-coverage auto-scaling (raising n_estimators on wide datasets so every feature is seen by some estimator) applies only to "auto" — an explicitly passed n_estimators is always used exactly as given, and warns at fit time if it is too small for every feature to be covered. (#1171)

Fixed

• Fix activation checkpointing during v2, v2.5, and v2.6 fine-tuning after the state-container memory optimization. (#1138)
• • Fixed fit() crashing during temperature calibration or threshold tuning (tuning_config) when a rare class is absent from the tuning holdout, by passing the full label set to log_loss explicitly.
• Fixed fit() crashing when random_state is a np.random.Generator and tuning is enabled, by converting the generator to a static seed before it reaches StratifiedKFold.

(#1140)

• fixed the randomness in the truncated SVD to make runs more reproducible. (#1167)
• Fixed the many class decoder to only work with the relevant class count. (#1175)
• Skip the Claude code review workflow on bot-authored release PRs, which previously failed the claude-review check on every release. (#1177)

Deprecated

• auto_scale_n_estimators is deprecated and will be removed in v9. It only ever applied to n_estimators="auto", where auto_scale_n_estimators=False is equivalent to passing n_estimators=8; pass an explicit n_estimators instead to opt out of feature-coverage scaling. Passing False now emits a FutureWarning at fit time. (#1171)