ONNX exports of TabICLv2, KumoTabular (small, medium, large), KumoRelational, Chronos-2 and Kumo-Anomaly for Tabular Data Workbench
b043bdf verified | license: openmdw-1.1 | |
| library_name: onnx | |
| base_model: nvidia/Kumo-Tabular | |
| base_model_relation: quantized | |
| pipeline_tag: tabular-classification | |
| tags: | |
| - onnx | |
| - onnxruntime-web | |
| - webgpu | |
| - structured-data-models | |
| - tabular-classification | |
| - tabular-regression | |
| - tabular-foundation-model | |
| - in-context-learning | |
| # KumoTabular for onnxruntime-web | |
| An unofficial ONNX conversion of NVIDIA's [Kumo Tabular](https://huggingface.co/nvidia/Kumo-Tabular) | |
| (tag `v1.0.1`) in its three sizes, made for Tabular Data Workbench, which runs | |
| it in the browser with onnxruntime-web on WebGPU or WebAssembly. The weights | |
| are NVIDIA's, with the larger ones stored as float16 (see below). NVIDIA has | |
| not reviewed or endorsed this conversion. | |
| ## Files | |
| One graph per size and head, each a single file: | |
| | file | parameters | size | | |
| | --- | --- | --- | | |
| | `small-classifier.f16w.onnx` | 27.5M | 55 MB | | |
| | `small-regressor.f16w.onnx` | 28.5M | 57 MB | | |
| | `medium-classifier.f16w.onnx` | 61.5M | 124 MB | | |
| | `medium-regressor.f16w.onnx` | 62.5M | 126 MB | | |
| | `large-classifier.f16w.onnx` | 213.7M | 428 MB | | |
| | `large-regressor.f16w.onnx` | 215.7M | 432 MB | | |
| Every graph has the same interface, that of onnx-community's export of the | |
| small size: | |
| | input / output | shape | type | | |
| | --- | --- | --- | | |
| | `x` | (R, C), context rows first, missing values as NaN | float32 | | |
| | `y_context` | (Rc) | int32 class codes (classifier), float32 (regressor) | | |
| | `categorical_mask` | (C) | bool | | |
| | `logits` | (R - Rc, 10) logits or (R - Rc, 999) quantiles | float32 | | |
| The graph is the network only (structured-data-models' | |
| `_KumoTabular.forward`): it imputes missing cells itself, so NaN must reach | |
| it unchanged. The preprocessing recipe, the ensemble and the output | |
| aggregation stay outside it; the app implements them in TypeScript. | |
| In `f16w`, every float32 weight of 1,024 or more elements is stored as | |
| float16 and cast back to float32 on load, which halves the download and keeps | |
| compute in float32. The other weights are as NVIDIA released them. | |
| `manifest.json` lists every file with its size, sha256, largest weight and | |
| total weight bytes (as float32), the int64 shape nodes to keep on the CPU | |
| under WebGPU (`cpuNodes`), each size's architecture, a memory estimate per | |
| row, onnxruntime CPU timings, and the parity figures below. | |
| ## How they were made | |
| Source: `nvidia/Kumo-Tabular` at `3c3e10bbdb590ace29e7026847f92db3c603096d` | |
| (tag `v1.0.1`), loaded through NVIDIA's | |
| [structured-data-models](https://github.com/NVIDIA/structured-data-models) | |
| 0.2.1. | |
| ```sh | |
| cd models/kumo | |
| uv run export.py # writes public/models/kumo/ | |
| ``` | |
| `export.py` (in `models/kumo/` of Tabular Data Workbench) exports with | |
| `torch.onnx` (dynamo, opset 20, PyTorch 2.14 on CPU) and removes the | |
| exporter's node metadata and doc strings. The cell embedding is | |
| rewritten so that grouping each column with its neighbours, imputing missing | |
| cells and building the Fourier features need no int64 Mod, int64 sums or | |
| scatters; the result matches structured-data-models' forward pass. The | |
| graphs keep a few int64 shape computations, listed in the manifest. | |
| ## Parity | |
| onnxruntime 1.30 (CPU) against structured-data-models' forward pass in | |
| PyTorch, maximum absolute difference over network inputs captured from its | |
| recipe (4 ensemble members, 540 context and 100 query rows of a synthetic | |
| table with missing values and two categorical columns) and over random | |
| inputs of five shapes: | |
| | graph | float32 graph, recipe inputs | f16w, recipe inputs | f16w, random inputs | largest output | | |
| | --- | --- | --- | --- | --- | | |
| | small classifier (logits) | 5.2e-5 | 1.9e-2 | 1.8e-2 | 14.5 | | |
| | small regressor (quantiles) | 2.2e-5 | 6.1e-3 | 1.8e-2 | 4.6 | | |
| | medium classifier | 4.2e-5 | 1.5e-2 | 1.8e-2 | 30.1 | | |
| | medium regressor | 9.1e-6 | 1.4e-3 | 6.5e-3 | 3.7 | | |
| | large classifier | 2.4e-5 | 1.1e-2 | 2.4e-2 | 8.3 | | |
| | large regressor | 6.9e-5 | 2.7e-3 | 4.2e-3 | 4.5 | | |
| The float32 graphs (built for this check, not published) show the export's | |
| own error; the rest is the rounding of the weights to float16. Regressor | |
| outputs are in units of the standardised target. | |
| Through the whole recipe (8 ensemble members; breast cancer with and without | |
| missing values, wine, diabetes), the f16w graphs of every size move class | |
| probabilities by at most 4.1e-4 and regression quantiles by at most 9e-4 | |
| standard deviations from structured-data-models' predictions, with the same | |
| accuracy and R² within 2e-5. | |
| ## Licence | |
| The weights are released by NVIDIA under the OpenMDW License Agreement 1.1 | |
| (`LICENSE`, Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES). It allows | |
| use, modification and redistribution provided that the agreement and the | |
| notices of origin travel with the weights, and it ends the rights of anyone | |
| who sues claiming the model infringes a patent or copyright. | |
| The graphs were exported from structured-data-models' model code, licensed | |
| under Apache-2.0 (`LICENSE.structured-data-models`), whose cell embedding is | |
| derived from TabFM (Google, Apache-2.0) and whose attention scaling contains | |
| portions of TabICLv2 (Soda team, Inria, BSD-3-Clause). `NOTICE` states the | |
| origin and the changes and carries the TabICLv2 licence. | |
| Keep `LICENSE`, `NOTICE` and `LICENSE.structured-data-models` with the | |
| graphs when redistributing them. | |