Nori Nori is a tabular foundation model for regression via in context learning (ICL). Given a few labeled rows as context, it predicts on new query rows in a single forward pass , with no task specific training or fine tuning. The model is trained entirely on synthetic data . Documentation: https://docs.synthefy.com/nori/ Repository: https://github.com/Synthefy/synthefy nori Library: pip install synthefy nori Checkpoint: nori.pt (this repo) Parameters: ~5.9M License: Apache 2.0 Results Mean and median R² of the base model across 96 regression tasks from three public benchmark suites (single H200, up to 50K context rows per dataset): Suite Datasets Mean R² Median R² : : : TabArena 13 0.8117 0.8757 TALENT 72 0.7569 0.8802 OpenML 11 0.6373 0.5856 Overall 96 0.7506 0.8702 Large N / long context tables (common in TabArena) are the current focus of the large table training stages. These numbers are reproducible end to end with one command — see Reproducing these numbers. Thinking is an inference time reasoning extension that improves these numbers further. Details are forthcoming. Use it from your AI coding assistant Paste this into Claude Code, Cursor, or any AI coding assistant and it…
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