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TabPFNRegressor.list_available_models

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Return the model names accepted as model_path.
Returns

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TabPFNRegressor.create_default_for_version

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Construct an estimator that uses the given version of the model. Any kwargs will override the default settings, except for model_path.
Parameters
Returns
Raises ValueError If version is not a known model version.

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TabPFNRegressor.save_model

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Save the fitted model so it can be loaded later without re-fitting. The fit itself lives on the TabPFN server; what is saved is a small, human-readable record: the id the server assigned to the fit (model_id_), the estimator’s hyperparameters, a classifier’s class labels and the training-set size. No training data is included. load_model() turns the record back into a fitted estimator, in a later run or on another machine. Fitted models are only visible to the account that created them, so the loading process has to authenticate with the same account, and they stay usable for as long as the training data remains on the server (see UserDataClient to delete it).
Parameters
Returns

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TabPFNRegressor.load_model

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Re-create a fitted estimator from a record written by save_model(). The estimator comes back with the saved hyperparameters and fitted state, ready to predict() without calling fit(). No request is made here; the first predict() authenticates (like fit() would) and raises FittedModelNotFoundError if the server no longer has the model.
Parameters
Returns
Raises ValueError If source is not a record written by save_model(), holds a model of the other task (a regression model loaded into a classifier), or has parameters this class does not accept, for instance because a newer tabpfn-client saved it.