TabFM 1.0.0 (PyTorch)
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.
Getting Started
pip install tabfm[pytorch]
Classification:
from tabfm import TabFMClassifier, tabfm_v1_0_0_pytorch as tabfm_v1_0_0
model = tabfm_v1_0_0.load(model_type="classification")
clf = TabFMClassifier(model=model)
clf.fit(X_train, y_train)
probs = clf.predict_proba(X_test)
Regression:
from tabfm import TabFMRegressor, tabfm_v1_0_0_pytorch as tabfm_v1_0_0
model = tabfm_v1_0_0.load(model_type="regression")
reg = TabFMRegressor(model=model)
reg.fit(X_train, y_train)
preds = reg.predict(X_test)
You can also load directly using the HuggingFace Hub API:
from tabfm.src.pytorch.tabfm_v1_0_0 import TabFM_HF
clf_model = TabFM_HF.from_pretrained("google/tabfm-1.0.0-pytorch", subfolder="classification")
reg_model = TabFM_HF.from_pretrained("google/tabfm-1.0.0-pytorch", subfolder="regression")
Available Checkpoints
| Subfolder | Task | is_classifier |
|---|---|---|
classification/ | Classification (up to 10 classes) | True |
regression/ | Regression | False |
Developers and Affiliations
Developed by the Google Research team.
Intended Use
- 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.
See the Google Research blog post for full benchmark details.
Ethical Considerations
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
@article{tabfm2026,
title = {TabFM: A Zero-Shot Foundation Model for Tabular Data},
author = {Google Research},
year = {2026},
url = {https://research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/}
}