Zero-shot forecasting, tabular classification, and regression via MCP — exposes Google TimesFM 2.5 and TabFM v1.0.0 to AI assistants. Just attach a CSV and describe what you want to predict.

Independent reproduction and 3-machine hardware study of Google TabFM, a zero-shot tabular foundation model. TabFM beats tuned XGBoost, random forest, and TabPFN on small-to-mid data, fails on high-dimensional data, and is impractical past ~10k in-context rows. Four upstream bugs found.

R package for tabular foundation models: TabPFN, TabICL, and TabFM.

End-to-end machine learning and MLOps project for telco customer churn prediction. Compares, contrasts, and evaluates Logistic Regression, XGBoost, and TabFM.

Visual interface for Google TabFM and Ollama for fast, local tabular data predictions