Introducing TabFM: A zero-shot foundation model for tabular data

We’ve seen a massive shift in how people handle time-series forecasting since we launched TimesFM. Now, we’re bringing that same "zero-shot" logic to tabular data.

We introduce TabFM, a new foundation model for tabular data to simplify classification and regression workflows.

Tabular data constitutes the backbone of enterprise data infrastructure and powers a significant fraction of critical predictive machine learning applications. From predicting customer churn to identifying financial fraud, tabular regression and classification tasks are ubiquitous. For years, supervised tree-based algorithms like AdaBoost, XGBoost and random forests, to name a few, have historically dominated this space, offering robust performance on structured data.

However, the lifecycle of deploying these traditional models presents a significant bottleneck. Fitting an XGBoost model to a new dataset is not merely a matter of a single .fit() step; it invariably requires tedious manual effort. Data scientists must invest countless hours into extensive hyperparameter optimization and domain-specific feature engineering just to extract a reliable signal from the raw data.

On the other hand, recent advances in the broader machine learning landscape — particularly the evolution of large language models (LLMs) — have changed how we interact with novel tasks. LLMs have demonstrated the remarkable power of zero-shot prediction through in-context learning (ICL). This technique lets a pretrained model learn a new task by providing examples and instructions in the input context, without updating any underlying model weights.

How it works

Training on synthetic data at scale

Performance and benchmarking

As shown in the performance plot below, we benchmarked two distinct configurations of our model:

For comprehensive TabArena benchmark results—including detailed per-fold metrics and head-to-head win rates against specific baseline models—please visit our GitHub page.