v8.1.0

Added

• KV Cache support for 2.5 and 2.6 single file models. (#1039)
• Add TabPFNClassifier.predict_proba_batched(X_list, y_list, X_test_list) to score several independent datasets in a single fused forward per estimator (stacking them on the model's batch dimension), equivalent to fitting and predicting each dataset separately but much faster when launching many small predicts. (#1045)
• Add an opt-in PASSTHROUGH_INF inference-config option (default False), set via the inference_config argument of TabPFNClassifier/TabPFNRegressor (or, for the finetuned estimators, via the inference_config entry of their extra_*_kwargs). When enabled, ±inf values are no longer rejected during fit()/predict(); they are carried through preprocessing (replaced with NaN for the steps that cannot handle them and restored afterwards) so they reach the model, which handles them natively. (#1055)
• README architecture and attention diagrams for TabPFN-3 (Prior Labs colour scheme). (#1060)
• Add calculate_cache_size for TabPFN v3 to compute the resident cache memory (ICL KV cache, decoder activations, distribution-embedder inducing states, and scaler stats) for a given train-set size, column count, ensemble size, and dtype — without running inference. (#1087)
• Add a public tabpfn.finetuning.main_process_first() context manager for multi-GPU (torchrun) scripts: the main process runs the with-block first while the other ranks wait at a barrier, then the other ranks run it — useful for one-time work such as dataset downloads that should warm a shared cache. The process group it initializes is reused by the subsequent fit(). (#1094)
• Chunk large test sets during cached (fit_mode="fit_with_cache") inference to bound peak GPU memory, controlled by the new TABPFN_MAX_BATCHED_TEST_ROWS setting (default 32768; set to 0 to disable). Chunking is mathematically equivalent. (#1096)

Changed

• TabPFN-2 and TabPFN2.5 now use the single file implementation, deprecate 'base'. (#1052)
• Fine-tuning now targets the package default model version (settings.tabpfn.model_version) instead of a hardcoded older one, and FinetunedTabPFNClassifier/FinetunedTabPFNRegressor accept an optional model_version to override it — so a fine-tuned model is no longer silently compared against a different-generation base. (#1064)
• Reduce memory usage for v2.x architectures. Enable flash attention on MPS for v2_6. (#1070)

Fixed

• Add TabPFNRegressor.fit_with_differentiable_input(X, y) so gradients can flow from a downstream loss back through the regressor into upstream torch modules feeding X (and y, when it carries grads). Mirrors the existing classifier-side path — previously TabPFNRegressor.fit raised ValueError("Differentiable input is not supported for regressors yet.") and there was no differentiable counterpart. (#923)
• Support save/load for estimators fitted with fit_mode="fit_with_cache". Previously save_fit_state / load_from_fit_state raised NotImplementedError for KV-cache inference engines. (#977)
• Fix save_fitted_tabpfn_model/save_fit_state moving the live estimator's bar distribution modules to CPU, which broke subsequent predict calls (e.g. output_type="median"/"quantiles") on CUDA/MPS devices. (#1030)
• Fixed AdaptiveQuantileTransformer losing output_distribution and random_state when cloned by sklearn (e.g. inside ColumnTransformer.fit), which made the quantile_norm* presets silently produce uniform output. All transformers now run sklearn's standard estimator checks. (#1031)
• Fixed fit() hanging forever when stratified row subsampling allocates a class more slots than it has rows (e.g. an ultra-rare class with SUBSAMPLE_SAMPLES set); such classes are now minimally oversampled instead. (#1034)
• Fixed norm_and_kdi returning a feature schema that undercounts the output columns: the FeatureUnion emits two columns per input column, so the schema and num_added_features under-reported, letting the ensemble's feature-budget planning silently exceed max_features_per_estimator. (#1035)
• Fixed an inverted enable_gqa condition in the torch-MPS attention fast path that would crash every forward of models with asymmetric query/KV head counts (including the default TabPFN v3 checkpoint) on Apple Silicon once torch satisfies the MPS flash-attention version gate (>= 2.13). (#1037)
• Fix fitted model saving for paths whose parent directories contain .tabpfn_fit. (#1048)
• Fix the README's save/load example to call save_tabpfn_model(reg, ...) with the estimator instead of reg.model_, which would have raised at runtime. (#1053)
• Remove all-NaN columns as constant features so they no longer leak NaNs into downstream preprocessing. (#1061)
• Fine-tuning with early stopping no longer returns a model worse than the base when no epoch improves over the default; the original weights are now restored. (#1064)
• Fix the README save/load FAQ to render correctly on GitHub (replace Sphinx :func: roles with code spans) and document the TABPFN_MPS_MEMORY_FRACTION environment variable. (#1065)
• Fix incorrect model output on MacOS 26 on M1 when using the MPS device. (#1077)
• Fix predict_proba_batched raising RuntimeError: mat1 and mat2 must have the same dtype, but got Half and Float under inference_precision=torch.float16 on GPU. The batched inference engine now casts the model to the forced dtype, not just the inputs. (#1083)
• Fix the fine-tuning examples crashing or redundantly downloading their dataset once per rank when launched with torchrun --nproc-per-node=N on a cold sklearn cache; the dataset fetch is now wrapped in main_process_first() so only the main process downloads. (#1094)
• Fix cross-device save/load tests failing on GPU by only requiring functional equivalence, not bit-identical predictions, across devices. (#1097)
• Fix Windows CI crash (illegal instruction) by skipping the bfloat16 autocast KV-cache test on Windows without CUDA. (#1103)

Deprecated

• Remove base architecture - per-model file implementations are now the single source of truth. (#1056)
• Remove the now unused InferenceEngineCacheKV. All models now use InferenceEngineExplicitKVCache. (#1057)