Nori-100M
Nori-100M is the ~98.3M-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: ~98.3M (98,304,698)
- Architecture: 42 layers, embed_dim 352, hid_dim 1056, 8 heads
- License: Apache-2.0
Usage
pip install synthefy-nori
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
from synthefy_nori import NoriRegressor
X, y = load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
model = NoriRegressor(model="nori-100m") # downloads these weights from the Hub on first use
model.fit(X_train, y_train) # "fit" just stores the labeled rows as context
pred = model.predict(X_test) # predictions in a single forward pass, no training
It uses a GPU when one is available and falls back to CPU. A one-shot helper skips the object entirely:
from synthefy_nori import predict
pred = predict(X_train, y_train, X_test, task="regression", model="nori-100m")
predict follows the TabPFNRegressor.predict contract: pass output_type="mean" (default),
"median", or "mode" to choose the point estimate drawn from the model's predictive
distribution.
To run from a local checkpoint instead of the Hub, pass a path:
NoriRegressor(model_path="path/to/nori.pt").
This model is public: the first call downloads and caches it automatically, with no token and no
access request. A Hugging Face token (read scope) is only worth setting if you hit anonymous
download rate limits — provide it via export HF_TOKEN=hf_..., hf auth login, or
NoriRegressor(model="nori-100m", token="hf_...").
Intended use & limitations
- Intended for small-to-medium tabular regression where in-context learning is attractive (no per-task training).
- Limitations: dense O(N²) sample attention bounds practical context size, so the current gap vs the best baselines is on large-N / long-context tables. Trained entirely on synthetic data; no benchmark data is used in training.
Citation
@software{synthefy_2026_20710462,
author = {Synthefy and
Li, Po-han and
Narayanan, Aditya and
Narasimhan, Sai Shankar and
Mallampalli, Raghav and
Agrawal, Aahan and
Ajan, Bekzat and
Shah, Raimi and
Agarwal, Shubhankar},
title = {Synthefy Nori: Tabular Foundation Model for Regression},
month = jun,
year = 2026,
publisher = {Zenodo},
version = {0.6.0},
doi = {10.5281/zenodo.20710462},
url = {https://doi.org/10.5281/zenodo.20710462},
}