Nori 30M Nori 30M is the ~29.2M parameter variant of Nori, 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: ~29.2M License: Apache 2.0 Results Mean and median R² across 96 regression tasks from three public benchmark suites, on the same protocol as the base Nori: Suite Datasets Mean R² Median R² : : : TabArena 13 0.8148 0.8834 TALENT 72 0.7575 0.8844 OpenML 11 0.6459 0.6212 Overall 96 0.7525 0.8745 Stronger than the ~6M base on every suite. Evaluated with the bundled default inference config and the large GPU protocol (up to 50k context rows per dataset). Use it from your AI coding assistant Paste this into Claude Code, Cursor, or any AI coding assistant and it will wire Nori into your own project: python from synthefy nori import NoriRegressor reg = NoriRegressor(model="nori 30m") downloads th…
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