onnx-community/kumo-tabular-small-classifier-ONNX

Kumo Tabular Small, classifier (ONNX)

ONNX conversion of the Small classifier of NVIDIA's Kumo Tabular (small/classifier.pt, revision v1.0.0), a tabular foundation model that predicts new rows from labeled context rows in one forward pass, with no training. For the model, its training and its limits see the original card, the blog post and structured-data-models.

Try it in your browser: Kumo Tabular in the Browser (drop a CSV or Parquet file; DuckDB-WASM + WebGPU; your data stays on the page). Code: shreyaskarnik/kumo-tabular-web.

Graph

onnx/model.onnx + onnx/model.onnx_data, fp32, 116 MB. It is the inner model (_KumoTabular.forward) with dynamic rows, context rows and columns:

• x [R, C] float32: context rows first, then query rows, already preprocessed
• y_context [R_context] int32 class codes (0..K-1, K ≤ 10)
• categorical_mask [C] bool: which columns came from categorical features
• logits [R_query, 10]: slice to the number of classes, softmax

The graph expects sdm's preprocessing (standardize, clip, sigma-clip, category codes and counts; see sdm/models/kumo/tabular/recipe.py). The browser demo ports it to TypeScript and checks it value by value against sdm. It loads with Transformers.js AutoModel (device: "webgpu") and runs on ONNX Runtime WebGPU, WASM or CPU.

Conversion and checks

torch.onnx.export (dynamo, opset 20). One op needed a custom lowering: aten.remainder.Scalar with a symbolic divisor (index % num_columns in the column grouping) becomes ONNX Mod. Scripts are in conversion/.

• ONNX Runtime (CPU) against PyTorch on inputs captured from real sdm runs: max |diff| 1.25e-4 (breast cancer (569×30), iris, wine; end to end in the browser within 6e-6 of sdm's probabilities, no top class changed).
• WebGPU (Chrome, M3 Pro), same inputs: max |diff| ≤ 2e-4.

License

The weights are NVIDIA's, under the OpenMDW-1.1 license (included); this is a format conversion with no change to them. Converted by @shreyask.