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_nori_2026,
  title  = {Nori: A Tabular Foundation Model Trained on Synthetic Data},
  author = {Synthefy},
  year   = {2026},
  url    = {https://github.com/Synthefy/synthefy-nori}
}

License

Apache-2.0. See LICENSE and NOTICE.