TabFM is a zero-shot tabular foundation model from Google Research. It supports
classification and regression on structured/tabular data with mixed numerical and
categorical columns, requiring no fine-tuning or hyperparameter search - training
examples are passed as context and predictions are made in a single forward pass.
This repository contains the PyTorch weights. For the JAX/Flax weights see
google/tabfm-1.0.0-jax.
Tabular data with numerical and/or categorical columns
Binary and multiclass classification (up to 10 classes)
Regression on continuous targets
Zero-shot inference: no dataset-specific training or hyperparameter tuning
Works with DataFrames (pandas) or numpy arrays
Not Intended For
Images, audio, video, or raw text
More than 10 output classes (hard model limit)
Tasks requiring task-specific fine-tuning
Non-tabular structured data (graphs, sequences)
Commercial use (see License below)
Model Architecture
TabFM uses alternating row and column attention to capture both feature interactions
and row-level patterns:
Column attention (Set Transformer): embeds each cell using Fourier features and
a per-group linear projection, then aggregates across rows via induced self-attention
Row compression: CLS tokens summarise each row into a dense vector via row-level
attention with Rotary Position Embedding (RoPE)
ICL Transformer: a 24-block causal transformer operates over the compressed row
vectors, treating training rows as context and outputting predictions for test rows
Key hyperparameters:
Parameter
Value
Embedding dim
256
Column attention blocks
3 (4 heads, 256 induced points)
Row attention blocks
3 (8 heads, 8 CLS tokens)
ICL transformer blocks
24 (8 heads)
Feed-forward factor
4
Max classes
10
Activation
SwiGLU
Fourier features
32 frequencies
Training Data and Priors
TabFM was trained on hundreds of millions of synthetic datasets generated
dynamically using structural causal models (SCMs). Synthetic data was chosen due to
the scarcity of diverse, high-quality open-source tabular datasets and to avoid
privacy/licensing concerns with real-world industrial data. The SCM prior encodes
inductive biases about causal structure and feature relationships typical in tabular
tasks.
Performance
TabFM was evaluated on TabArena across 51 datasets
(38 classification, 13 regression). In zero-shot mode - a single forward pass with no
hyperparameter search - TabFM outperforms heavily-tuned supervised baselines including
gradient-boosted trees. The TabFMClassifier.ensemble() preset (feature crosses,
SVD features, NNLS blending) yields further improvements.
TabFM was trained entirely on synthetic data. Performance on specific real-world
domains, minority groups, or edge distributions is not fully characterised. Users
should evaluate the model on held-out data representative of their use case before
deploying in high-stakes settings.
Limitations
Max 10 classes for classification (hard architectural limit)
Memory usage scales with the number of training rows (all rows are passed as context)
Optimised for tables up to 500 features; behaviour on very wide tables may degrade
Performance is not guaranteed to match task-specific, fine-tuned models on all datasets
Not an officially supported Google product
License
The model weights in this repository are released under the
TabFM Non-Commercial License v1.0 - see LICENSE. The source code is
Apache 2.0 licensed via google-research/tabfm.
Version
1.0.0
Citation
bibtex
1@article{tabfm2026,
2 title = {TabFM: A Zero-Shot Foundation Model for Tabular Data},
3 author = {Google Research},
4 year = {2026},
5 url = {https://research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/}
6}
