Nums AI has released Causilo , a pretrained tabular foundation model for classification and regression. Causilo comes with a scikit-learn interface, Apache-2.0 code and pretrained weights on Hugging Face . On TabArena, it has the highest Elo among single models for both classification and regression. Is it deployable? Yes, for research and evaluation today, on CUDA or CPU. Commercial, production and hosted API use need a separate license from Nums AI. What Causilo Does Causilo is an in-context learning model. Calling fit does not update the pretrained weights. It stores the training rows as context and predicts query rows in one forward pass. According to its TabArena submission , Nums AI pretrained Causilo only on synthetic data, with no TabArena datasets. Inputs can be NumPy arrays or pandas DataFrames, including categorical features and missing values. Classification supports up to 10 classes. Regression returns mean predictions by default. Version 1.0.1 adds median and quantile outputs, based on 999 native quantiles. Architecture: Refinement, Compression, In-Context Learning Nums AI splits the network into 3 phases: refinement, compression and in-context learning . The released code and configs show how each phase works. Features are grouped in sets of 3. Each value is embedded with 16 learned sine and cosine frequencies. Missing values get their own learned vector. 2 column stages summarize each feature group. In each, 128 latent slots read only the training rows and pass that summary to every row. Between the 2 column stages, a row stage lets feature groups interact t
Source: MarkTechPost