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. Mean and median R² across 96 regression tasks from three public benchmark suites, on the same protocol as the base Nori: 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). Paste this into Claude Code, Cursor, or any AI coding assistant and it will wire python from synthefynori…
Open-weight model · Tabular classification
nanites-fiorino-tabular
by Nanites Nanite-Labs/nanites-fiorino-tabular
nanites-fiorino-tabular is an open-weight model for tabular classification from Nanites, released under MIT License. Its published files total 54.7 MB. It draws 32 downloads a month.
nano quattrocento tabular fondamento model with synthetic and real-data priors. Tabular in-context-learning model, ~4.5M params: labeled context rows in, query-row predictions out, one forward pass, no fine-tuning.
Parameters—
Context—
Weights52.1 MB
Licensemit
AccessOpen weights
Monthly Downloads32
Model Card
By Nanites, published under mit, revision 309b5de51f68.
nano quattrocento tabular fondamento model with synthetic and real-data priors. Tabular in-context-learning model, ~4.5M params: labeled context rows in, query-row predictions out, one forward pass, no fine-tuning. A nano lab build (single 8GB GPU) — priors and efficiency over volume: 226,000 tables in the record run (≈400,000 across the lineage) vs 100,000,000+ at the big labs. Pretrained on real historical ledger tasks (Borromei, ICMA Crown loans, Bristol customs, Irish Exchequer — 30,514 rows / 460 tasks, plus audit-passed derived value-standing tasks) and a world-profiled synthetic diet (linear/additive, smooth, piecewise/threshold, tree, interaction, heteroskedastic worlds + NanoTabICL…
Read Nanites's full model card
Fiorino
INTRODUCING FIORINO
nano quattrocento tabular fondamento model with synthetic and real-data priors.
Tabular in-context-learning model, ~4.5M params: labeled context rows in, query-row predictions out, one forward pass, no fine-tuning. A nano lab build (single 8GB GPU) — priors and efficiency over volume: 226,000 tables in the record run (≈400,000 across the lineage) vs 100,000,000+ at the big labs. Pretrained on real historical ledger tasks (Borromei, ICMA Crown loans, Bristol customs, Irish Exchequer — 30,514 rows / 460 tasks, plus audit-passed derived value-standing tasks) and a world-profiled synthetic diet (linear/additive, smooth, piecewise/threshold, tree, interaction, heteroskedastic worlds + NanoTabICL graphs, macro series, masked-column SSL).
This repo holds two files off one shared trunk:
fiorino-classification-bifronte (classifier) and
fiorino-regression-bifronte (bucket readout). The alfa release
(fiorino-classification-alfa) was the classification-only prototype.
Architecture
Grouped cell tokens, feature+sample dual-axis attention, 24 thinking
rows, learned column-identity/type embeddings, missingness channel,
CHPT-style target conditioning, 64-channel shared decoder, Muon + AdamW,
LAWA-averaged, CE-heavy loss with MSE aux, patience-20 selection.
Full technical paper: WHITEPAPER.md in this repo (also in the sister
GitHub repo frankiethull/fiorino).
Honest benchmarks (deploy-realistic masked protocol, same folds)
- TabArena-38 classification, mean macro ROC-AUC: nano 0.818 vs RandomForest 0.815 (27/38 wins) with 8 test-time estimators.
- TabArena-13 regression, mean R²: nano +0.619 vs RandomForest +0.717 (1/11 wins) — competitive and in-progress: 1 win plus seven near-draws (within 0.05). The Gamma release (synth-dataset adjustment, more derived tables, possible transformer tweaks) is scoped to turn competitive into winning. Gap stated to three decimals either way; no inflated claims.
- Fresh-task ICL probe: 0.87 acc (chance 0.50).
Fiorino is, to our knowledge, the smallest competitive tabular foundation model — 4.5M params on 226,000 tables against 100,000,000+ at the big labs.
Usage
Sister GitHub metarepo frankiethull/fiorino (packages python/ and
r/fiorino, both named fiorino):
from fiorino import FiorinoClassifier, FiorinoRegressor
clf = FiorinoClassifier() # loads fiorino-classification-bifronte
clf.fit(X_train, y_train)
proba = clf.predict_proba(X_test) # one forward pass
reg = FiorinoRegressor() # loads fiorino-regression-bifronte
reg.fit(X_train, y_train)
pred = reg.predict(X_test) # one forward pass
library(fiorino) # r/fiorino
clf <- fiorino_classifier()
clf <- fio_fit(clf, X_train, y_train)
fio_predict_proba(clf, X_test)
Limitations
- Small prior (~4.5M, 8GB-GPU training); diet over volume.
- Regression trails RF on heavy tails; the outer deciles are the gap.
- Context caps: 2048 rows (subsampled), ~44 features.
- Corpus stays local-only (archive/publication licenses); weights are MIT.
Configuration
- Model type
- FiorinoNanoModel
Identity and Version
- Repository
- Nanite-Labs/nanites-fiorino-tabular
- Publisher
- Nanites
- Task
- Tabular classification
- Modality
- Tabular
- Library
- pytorch
- Parameters
- Not stated by the source
- Languages
- Not stated by the source
- Revision
- 309b5de51f68db1a9a4a49e75c76c779f0f5bddd
- First published
- 2026-09-21
- Last updated
- 2026-09-25
Files and Weights
9 files, 54.7 MB in total. The weights are 3 files totalling 52.1 MB in safetensors.
Weights3 files · 52.1 MB
Configuration2 files · 721 B
Documentation2 files · 14.8 KB
Other1 file · 2.6 MB
Repository1 file · 1.6 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| fiorino-classification-alfa.safetensors | Weights | 17.0 MB | c942a2df8149 |
| fiorino-classification-bifronte.safetensors | Weights | 17.0 MB | c942a2df8149 |
| fiorino-regression-bifronte.safetensors | Weights | 18.0 MB | 7ce89751f4b1 |
| checkpoint_meta.json | Configuration | 230 B | — |
| config.json | Configuration | 491 B | — |
| README.md | Documentation | 3.3 KB | — |
| WHITEPAPER.md | Documentation | 11.5 KB | — |
| media/fiorino.png | Other | 2.6 MB | 44ae7c000ba2 |
| .gitattributes | Repository | 1.6 KB | — |
License and Download
- License
- mit
- Access
- Open weights, no gate
- Download size
- 52.1 MB
Download from Nanites
Released by Nanites through its official repository on Hugging Face. Read the license.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 52.1 MB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About nanites-fiorino-tabular
Can I use nanites-fiorino-tabular commercially?
Yes. nanites-fiorino-tabular is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
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