Tabular models predict values and classes from rows in CRM, ERP and spreadsheets: customer churn, payment likelihood, unusual transactions. Some deliver results without lengthy tuning for each task. Look at the supported table size, hardware requirements and the license.
A ready-made model for tables: it takes example rows and immediately predicts for new ones, without lengthy training or tuning. Only v2 is free for business; newer versions are non-commercial.
Amazon's tabular model built into AutoGluon: classification and regression from examples with brief fine-tuning. Mitra-v2 handles more rows and columns.
A tabular model from a Canadian bank's AI lab, trained on real tables rather than only synthetic ones. Version 1.2 Turbo made computation orders of magnitude faster.
A table model that alone can classify, predict numbers and fill in missing data. The lightweight LimiX-2M runs on an ordinary computer.
LG's small tabular model: with 21M parameters it nearly matches the leaders in classification and regression accuracy. Weights are for non-commercial use only.
Google's large tabular model: classification and regression from examples without training, with numeric and categorical columns. Weights are for non-commercial use only.
Recent open models for tabular data: they predict from a few examples given in the prompt, with no task-specific training.
An open tabular model from the creators of scikit-learn: classifies and predicts from examples without training and handles tables of up to hundreds of thousands of rows. The license allows business use.
A family for working with tables and databases: it understands data structure, writes parsing code and answers questions about exports.
A model for office work with tables: for a given question it returns either a direct answer or code to process the data in a table or document.
A foundation model for predictions on tables: it classifies and forecasts from a handful of examples, with no separate task-specific training.
A general-purpose model for tables: filling gaps, finding rows, matching columns and answering questions about the data.
An open model can run on your own server: data stays in-house, there is no per-request fee, and the model can be fine-tuned on your documents.
