TabFM is a scikit-learn compatible tabular foundation model developed by Google Research that enables zero-shot classification and regression on tabular datasets containing mixed column types without requiring dataset-specific training. The model operates through in-context learning, reading training data as context at inference time to make predictions on new test samples, eliminating the need for traditional parameter tuning on individual datasets.
The repository is implemented in Python and supports multiple computational backends, allowing users to choose between JAX and PyTorch for execution. Installation options are provided for JAX on CPU or GPU, and PyTorch on CPU or GPU, with specific version requirements documented in the requirements.txt file. The core dependencies include Python 3.11 or higher and Hugging Face Hub for downloading pre-trained model weights. For the JAX backend, the implementation uses JAX 0.10.1 and Flax 0.12.7 with the modern flax.nnx API, while the PyTorch backend requires PyTorch 2.12.1 or later, with GPU support contingent on having the appropriate CUDA version installed.
The TabFM v1.0.0 release includes pre-trained weights that are automatically downloaded and loaded by the library, making it straightforward for users to get started. The repository provides practical examples for both classification and regression tasks in the examples directory, with runnable scripts that demonstrate how to switch between JAX and PyTorch backends through code comments. Evaluation results from model testing are documented in the results directory.
The codebase includes comprehensive unit tests that can be executed either directly through Python's unittest module or via Bazel for users who have that build system installed.
The repository is classified across multiple relevant domains including tabular data, feature engineering, factorization machines, machine learning, prediction models, data representation, and structured data model training. The project is explicitly noted as not being an officially supported Google product, positioning it as a research contribution to the open-source community for practitioners working with tabular data and foundation models.
