2 posts tagged with "tabular-foundation-models"

In the last post we said switching foundation models is one keyword. So we switched it fifteen times and wrote down what happened.

The short version: for classification, model choice barely moved the needle. For regression, it moved it by 30%. The model that won most often is the one you're least likely to be allowed to use. And when we ran scikit-learn on the same splits, the foundation models won all five — by 2–7%, for up to 550× the compute.

You have 120 labelled rows and 30 unlabelled ones. The classical answer is a Python project. The new answer is one SELECT.

SELECT * FROM tabfm_classify('iris', 'species');

No training loop. No feature engineering. No model artifact to version. The model reads your labelled rows as context and predicts the rest—the same way an LLM learns from examples in a prompt.