ONNX exports of TabICLv2, KumoTabular (small, medium, large), KumoRelational, Chronos-2 and Kumo-Anomaly for Tabular Data Workbench

LICENSE.md

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+# Licences
+
+The folders in this repository hold different models under different
+licences. Each folder carries its own licence and notice files, which must
+stay with its ONNX files when they are redistributed.
+
+| folder | models | licence | files |
+| --- | --- | --- | --- |
+| `tabicl/` | TabICLv2 (Soda team, Inria) | BSD-3-Clause | `tabicl/LICENSE` |
+| `kumo/` | Kumo Tabular small, medium and large (NVIDIA) | OpenMDW-1.1 (weights); Apache-2.0 (structured-data-models code); BSD-3-Clause (TabICLv2 code notice) | `kumo/LICENSE`, `kumo/NOTICE`, `kumo/LICENSE.structured-data-models` |
+| `rfm/` | Kumo Relational (NVIDIA), fine-tuned from TabICLv2 | OpenMDW-1.1 (weights); BSD-3-Clause (TabICLv2 notice); Apache-2.0 (structured-data-models code) | `rfm/LICENSE`, `rfm/THIRD-PARTY-NOTICES.txt`, `rfm/LICENSE.structured-data-models` |
+| `tsfm/` | Chronos-2 and Chronos-2 small (Amazon), Kumo-Anomaly (NVIDIA) | Apache-2.0; MIT (DPM-Solver notice) | `tsfm/LICENSE`, `tsfm/NOTICE`, `tsfm/THIRD-PARTY-NOTICES.txt`, `tsfm/LICENSE.structured-data-models` |

README.md

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+---
+license: other
+license_name: bsd-3-clause-openmdw-1.1-apache-2.0
+license_link: LICENSE.md
+library_name: onnx
+base_model:
+  - jingang/TabICL
+  - nvidia/Kumo-Tabular
+  - nvidia/Kumo-Relational
+  - amazon/chronos-2
+  - autogluon/chronos-2-small
+  - nvidia/Kumo-Anomaly
+base_model_relation: quantized
+tags:
+  - onnx
+  - onnxruntime-web
+  - webgpu
+  - tabular-classification
+  - tabular-regression
+  - time-series-forecasting
+  - anomaly-detection
+  - tabular-foundation-model
+  - relational-foundation-model
+  - in-context-learning
+---
+
+# ONNX models for Tabular Data Workbench
+
+Unofficial ONNX conversions of six open foundation models, made for Tabular
+Data Workbench, a browser app that runs them with onnxruntime-web on WebGPU
+or WebAssembly. The weights are the upstream authors': the `fp32` files hold
+them unchanged, and the `f16w` files store the larger ones as float16. The
+upstream authors have not reviewed or endorsed these conversions.
+
+| folder | models | upstream | licence | size (fp32 + f16w) |
+| --- | --- | --- | --- | --- |
+| [`tabicl/`](tabicl/README.md) | TabICLv2 classifier and regressor | [jingang/TabICL](https://huggingface.co/jingang/TabICL) (Soda team, Inria) | BSD-3-Clause | 339 MB |
+| [`kumo/`](kumo/README.md) | Kumo Tabular small, medium and large, classifier and regressor | [nvidia/Kumo-Tabular](https://huggingface.co/nvidia/Kumo-Tabular) (NVIDIA) | OpenMDW-1.1 | 1.2 GB (f16w only) |
+| [`rfm/`](rfm/README.md) | Kumo Relational classifier and regressor | [nvidia/Kumo-Relational](https://huggingface.co/nvidia/Kumo-Relational) (NVIDIA) | OpenMDW-1.1 | 367 MB |
+| [`tsfm/`](tsfm/README.md) | Chronos-2, Chronos-2 small, Kumo-Anomaly | [amazon/chronos-2](https://huggingface.co/amazon/chronos-2), [autogluon/chronos-2-small](https://huggingface.co/autogluon/chronos-2-small) (Amazon), [nvidia/Kumo-Anomaly](https://huggingface.co/nvidia/Kumo-Anomaly) (NVIDIA) | Apache-2.0 | 901 MB |
+
+Each folder holds a `manifest.json` (every file's size and sha256, the
+graphs' inputs and outputs, model settings and parity figures), the licence
+and notice files for its models, and each graph in two variants. `fp32` has
+the weights as released. `f16w` stores every float32 weight of 1,024 or more
+elements as float16 and casts it back to float32 on load, which halves the
+download and keeps compute in float32. `kumo/` has the `f16w` variant only.
+The README in each folder describes its graphs, how they were made and how
+closely they match the reference implementation.
+
+The graphs are the networks only. Preprocessing, ensembling, sampling and
+decoding stay outside them, as in the reference packages, and the app
+implements them in TypeScript. The `models/` folder of Tabular Data Workbench
+has the export scripts and the numpy references for these steps.
+
+## Use in the app
+
+The app reads `<base>/<folder>/manifest.json` and the files it lists, and
+checks each file's sha256 before caching it. To serve the models from this
+repository, build the app with the base URL pinned to a commit:
+
+```sh
+VITE_MODELS_BASE_URL=https://huggingface.co/ScriptSmith/tabular-data-workbench-onnx/resolve/<commit>/ pnpm build
+```
+
+## How they were made
+
+The scripts are in the `models/` folder of Tabular Data Workbench, one uv
+workspace. They use PyTorch 2.14 on CPU and the `torch.onnx` dynamo exporter
+at opset 18 (opset 20 for `kumo/`), and check the graphs with onnxruntime
+1.30:
+
+```sh
+# from the root of the Tabular Data Workbench source
+(cd models/tabicl && uv run export.py)
+(cd models/kumo && uv run export.py)
+node models/rfm/dump_shop.mjs   # once: the sample tables rfm's checks run on
+(cd models/rfm && uv run export.py)
+(cd models/tsfm && uv run export_chronos2.py && uv run export_kumo.py)
+```
+
+Every export checks its graphs against the reference package (tabicl,
+structured-data-models, chronos-forecasting) before it writes the manifest.
+The exports also remove the exporter's node metadata and doc strings, which
+hold stack traces with the paths of the machine that ran them.
+
+## Licences
+
+The folders are under different licences, listed in [`LICENSE.md`](LICENSE.md):
+
+- `tabicl/`: BSD-3-Clause, Copyright (c) 2025, Soda team @ Inria (`tabicl/LICENSE`).
+- `kumo/`: OpenMDW-1.1, Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES
+  (`kumo/LICENSE`), with a notice of origin and changes (`kumo/NOTICE`, which
+  also carries the BSD-3-Clause notice for the TabICLv2-derived code) and the
+  Apache-2.0 licence of structured-data-models (`kumo/LICENSE.structured-data-models`).
+- `rfm/`: OpenMDW-1.1, Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES
+  (`rfm/LICENSE`), with NVIDIA's notice for the TabICLv2 weights it was
+  fine-tuned from (`rfm/THIRD-PARTY-NOTICES.txt`, BSD-3-Clause) and the
+  Apache-2.0 licence of structured-data-models (`rfm/LICENSE.structured-data-models`).
+- `tsfm/`: Apache-2.0 (`tsfm/LICENSE`), with Amazon's and NVIDIA's notices
+  and a statement of the changes (`tsfm/NOTICE`), and DPM-Solver's MIT
+  licence (`tsfm/THIRD-PARTY-NOTICES.txt`).
+
+Keep each folder's licence and notice files with its ONNX files when
+redistributing them.

kumo/LICENSE

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+Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+
+OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)
+
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+USE THEREOF OR OTHER DEALINGS THEREIN.

kumo/LICENSE.structured-data-models

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kumo/NOTICE

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+KumoTabular (small, medium and large; classifier and regressor) converted to
+ONNX for Tabular Data Workbench.
+
+This is an unofficial conversion. NVIDIA has not reviewed or endorsed it.
+
+Kumo Tabular
+  Source: nvidia/Kumo-Tabular, tag v1.0.1
+          (revision 3c3e10bbdb590ace29e7026847f92db3c603096d)
+          https://huggingface.co/nvidia/Kumo-Tabular
+  Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+  The weights are licensed under the OpenMDW License Agreement, version 1.1
+  (LICENSE).
+
+Model code
+  The graphs were exported from the KumoTabular implementation in
+  structured-data-models 0.2.1 (https://github.com/NVIDIA/structured-data-models),
+  Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES, licensed under the
+  Apache License, Version 2.0 (LICENSE.structured-data-models where it is
+  distributed with these files). In that implementation the cell embedding is
+  derived from TabFM (https://github.com/google-research/tabfm, Copyright 2026
+  Google LLC, Apache License 2.0), and the attention scaling contains portions
+  derived from TabICLv2 (https://github.com/soda-inria/tabicl), whose licence
+  follows at the end of this file.
+
+Changes
+  - <size>-<head>.f16w.onnx: the KumoTabular network (structured-data-models'
+    _KumoTabular.forward, without the preprocessing recipe or the ensemble)
+    exported with torch.onnx (dynamo, opset 20). The cell embedding's column
+    grouping, imputation and Fourier features are written without int64
+    arithmetic or scatters; what they compute is unchanged. Every weight of
+    1,024 or more elements is stored as float16 and cast back to float32 on
+    load; the other weights are unchanged.
+
+TabICLv2
+
+BSD 3-Clause License
+
+Copyright (c) 2025, Soda team @ Inria
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
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+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

kumo/README.md

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+---
+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.

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+Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+
+OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)
+
+By exercising rights granted to you under this agreement, you accept and agree
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rfm/LICENSE.structured-data-models

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+Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+
+
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rfm/README.md

@@ -0,0 +1,108 @@
+---
+license: openmdw-1.1
+library_name: onnx
+base_model: nvidia/Kumo-Relational
+base_model_relation: quantized
+pipeline_tag: tabular-classification
+tags:
+  - onnx
+  - onnxruntime-web
+  - webgpu
+  - relational-foundation-model
+  - structured-data-models
+  - tabular-classification
+  - tabular-regression
+  - in-context-learning
+---
+
+# KumoRelational for onnxruntime-web
+
+An unofficial ONNX conversion of NVIDIA's [Kumo Relational](https://huggingface.co/nvidia/Kumo-Relational)
+(revision `v1.0.1`), a foundation model for classification and regression
+across related tables, made for Tabular Data Workbench, which runs it in the
+browser with onnxruntime-web on WebGPU or WebAssembly. The weights are
+NVIDIA's: unchanged in the `fp32` variant, and with the larger ones rounded to
+float16 in the `f16w` variant. NVIDIA has not reviewed or endorsed this
+conversion.
+
+## Files
+
+Each head (`classifier`, `regressor`) is cut into five graphs:
+
+| file | inputs | output |
+| --- | --- | --- |
+| `<head>-col.<v>.onnx` | `X` (columns, rows, 3), `y` (train), `keys` (keys) | `E` (columns, rows, 128) |
+| `<head>-row.<v>.onnx` | `E` (columns, rows, 128) | `Z` (rows, 512) |
+| `<head>-hop.<v>.onnx` | `x` (nodes, 512), `src`, `dst`, `edge_type` (edges), `noise` (types, 512), `deg` (nodes, 1) | `h` (nodes, 512) |
+| `<head>-out.<v>.onnx` | `x` (readouts, 512) | `z` (readouts, 512) |
+| `<head>-icl.<v>.onnx` | `Z` (tokens, 512), `y` (train) | `out` (queries, 10 logits or 999 quantiles) |
+
+`col` and `row` embed each table's rows, `hop` is one round of relational
+message passing (run once per hop), `out` is the readout and `icl` is the
+in-context block over the task's context and query rows. The graph neural
+network's segment reductions are written as ScatterND, and the graphs contain
+no int64 shape arithmetic.
+
+Each graph comes in two variants (`<v>`):
+
+- `fp32`: the checkpoint's weights as they are (244 MB for both heads).
+- `f16w`: every float32 weight of 1,024 or more elements stored as float16
+  and cast back to float32 on load (123 MB). Compute stays float32.
+
+`manifest.json` lists every file with its size and sha256, the graphs'
+inputs and outputs, each head's empty-row embedding and the parity figures
+below. The time-aware neighbour sampler, the task tables, the encoders and
+the ensemble stay outside the graphs; the app implements them in TypeScript.
+
+onnxruntime 1.30's CPU ScatterND with a reduction is not deterministic with
+more than one intra-op thread when indices repeat, which they do in the hop
+graph. Run it single-threaded where results must be reproducible.
+
+## How they were made
+
+Source: `nvidia/Kumo-Relational` at `v1.0.1` (`classifier.pt`,
+`regressor.pt`), loaded through NVIDIA's
+[structured-data-models](https://github.com/NVIDIA/structured-data-models)
+at commit `2b1696f4fdc22004cc757c44703a69cf326c0a11`.
+
+```sh
+node models/rfm/dump_shop.mjs   # once, from the repository root: the sample tables
+cd models/rfm
+uv run export.py                # writes public/models/rfm/
+```
+
+`export.py` (in `models/rfm/` of Tabular Data Workbench) exports the parts
+defined in `exportable.py` with `torch.onnx` (dynamo, opset 18, PyTorch 2.14
+on CPU) and removes the exporter's node metadata and doc strings. It checks
+each graph against PyTorch, then checks the five graphs chained against
+structured-data-models' own forward pass on inputs captured from a real run
+over the app's sample shop tables.
+
+## Parity
+
+Chained graphs (onnxruntime 1.30, CPU, one thread) against
+structured-data-models' forward pass, maximum absolute difference:
+
+| head | task | fp32 | f16w | largest output |
+| --- | --- | --- | --- | --- |
+| classifier | churn (logits) | 8.6e-3 | 1.2e-2 | 10.6 |
+| regressor | spend (quantiles) | 1.6e-3 | 6.5e-3 | 4.9 |
+
+Each graph alone, on random inputs, matches PyTorch to 1.1e-5 (fp32).
+
+## 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.
+
+Kumo Relational fine-tunes the TabICLv2 weights (Soda team, Inria),
+licensed under BSD-3-Clause; `THIRD-PARTY-NOTICES.txt` is NVIDIA's notice
+with that licence's full text. The graphs were exported from
+structured-data-models' model code, licensed under Apache-2.0
+(`LICENSE.structured-data-models`).
+
+Keep `LICENSE`, `THIRD-PARTY-NOTICES.txt` and
+`LICENSE.structured-data-models` with the graphs when redistributing them.

rfm/THIRD-PARTY-NOTICES.txt

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+THIRD-PARTY MODEL NOTICES
+
+Component: TabICLv2 pretrained model weights
+Model source: https://huggingface.co/jingang/TabICL
+License declaration: https://huggingface.co/jingang/TabICL/blob/main/README.md
+License: BSD 3-Clause License (BSD-3-Clause)
+
+Kumo Relational finetunes pretrained TabICLv2 weights.
+Kumo Relational model weights are released under OpenMDW-1.1; see LICENSE.
+
+BSD 3-Clause License
+
+Copyright (c) 2025, Soda team @ Inria
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

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+{
+  "model": "KumoRelational",
+  "source": {
+    "package": "structured-data-models @ 2b1696f4fdc2",
+    "repo": "nvidia/Kumo-Relational",
+    "revision": "v1.0.1",
+    "checkpoints": {
+      "classifier": "classifier.pt",
+      "regressor": "regressor.pt"
+    },
+    "license": "OpenMDW-1.1 (weights; fine-tuned from TabICLv2, BSD-3-Clause)",
+    "notices": [
+      "LICENSE",
+      "THIRD-PARTY-NOTICES.txt"
+    ]
+  },
+  "opset": 18,
+  "exporter": "torch.onnx (dynamo) 2.14.0+cpu",
+  "limits": {
+    "maxClasses": 10,
+    "numQuantiles": 999,
+    "featureGroupSize": 3,
+    "cellDim": 128,
+    "rowDim": 512,
+    "maxKeys": 20000,
+    "temperature": 0.9
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tabicl/LICENSE

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+BSD 3-Clause License
+
+Copyright (c) 2025, Soda team @ Inria
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

tabicl/README.md

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+---
+license: bsd-3-clause
+library_name: onnx
+base_model: jingang/TabICL
+base_model_relation: quantized
+pipeline_tag: tabular-classification
+tags:
+  - onnx
+  - onnxruntime-web
+  - webgpu
+  - tabicl
+  - tabular-classification
+  - tabular-regression
+  - tabular-foundation-model
+  - in-context-learning
+---
+
+# TabICLv2 for onnxruntime-web
+
+An unofficial ONNX conversion of [TabICLv2](https://huggingface.co/jingang/TabICL)
+by Jingang Qu, David Holzmüller, Gaël Varoquaux and Marine Le Morvan (Soda
+team, Inria), made for Tabular Data Workbench, which runs it in the browser
+with onnxruntime-web on WebGPU or WebAssembly. The weights are those of the
+upstream checkpoints: unchanged in the `fp32` variant, and with the larger
+ones rounded to float16 in the `f16w` variant. The Soda team has not reviewed
+or endorsed this conversion.
+
+## Files
+
+Each task (`classifier`, `regressor`) is cut into three graphs:
+
+| file | inputs | output |
+| --- | --- | --- |
+| `<task>-col.<v>.onnx` | `X` (batch, columns, rows, 3) feature-group columns, `y` (batch, digits, train) | `E` (batch, columns, rows, 128) |
+| `<task>-row.<v>.onnx` | `E` (batch, columns, rows, 128) | `R` (batch, rows, 512) |
+| `<task>-icl.<v>.onnx` | `R` (batch, rows, 512), `y` (batch, train) | `out` (batch, test, 10 logits or 999 quantiles) |
+
+Each graph comes in two variants (`<v>`):
+
+- `fp32`: the checkpoint's weights as they are (225 MB for both tasks).
+- `f16w`: every float32 weight of 1,024 or more elements stored as float16
+  and cast back to float32 on load (113 MB). Compute stays float32, so it runs
+  on the WebGPU and WebAssembly providers alike.
+
+`manifest.json` lists every file with its size, sha256, inputs and outputs,
+and the int64 shape nodes (`cpuNodes`, one in each `row` graph) that the app
+pins to the CPU provider on WebGPU. The graphs contain no int64 shape
+arithmetic, which onnxruntime-web's WebGPU provider has been seen to corrupt.
+
+The graphs are the network only. Preprocessing (imputation, the
+normalisation choices, outlier clipping), the ensemble of column and class
+permutations, the mixed-radix digits for more than 10 classes and the
+averaging stay outside them, as in the reference package. The app ports
+those to TypeScript; `models/tabicl/pipeline.py` in Tabular Data Workbench is
+the numpy reference.
+
+## How they were made
+
+Source: `jingang/TabICL` at revision `4dcd344ece2c00be9e831fdd35bed57b5ad83e19`
+(`tabicl-classifier-v2-20260212.ckpt`, `tabicl-regressor-v2-20260212.ckpt`),
+with the `tabicl` 2.2.0 package's model code.
+
+```sh
+cd models/tabicl
+uv run export.py     # writes public/models/tabicl/
+uv run verify.py     # parity table against tabicl
+```
+
+`export.py` (in `models/tabicl/` of Tabular Data Workbench) splits the
+network in `exportable.py`, exports each part with `torch.onnx` (dynamo,
+opset 18, PyTorch 2.14 on CPU), removes the exporter's node metadata and doc
+strings, and checks the split graphs against tabicl's own forward pass before
+writing the manifest.
+
+## Parity
+
+`verify.py` runs `TabICLClassifier` and `TabICLRegressor` (tabicl 2.2.0, CPU,
+float32, 8 estimators, seed 42), replays the ensemble it drew through the
+ONNX graphs in onnxruntime 1.30 (CPU) and compares the final predictions:
+
+| dataset | fp32 | f16w |
+| --- | --- | --- |
+| breast_cancer (probabilities) | 2.8e-6 | 5.8e-4 |
+| breast_cancer with 5% missing | 1.6e-6 | 3.4e-4 |
+| wine | 8.3e-7 | 2.8e-4 |
+| 14 classes (synthetic) | 1.5e-6 | 7.1e-4 |
+| diabetes (mean, in units of the target's sd) | 2.3e-6 | 4.7e-4 |
+| diabetes (quantiles, in units of the target's sd) | 2.4e-6 | 6.4e-4 |
+
+The figures are maximum absolute differences. Predicted classes agree with
+tabicl on every row of every dataset in both variants.
+
+## Licence
+
+BSD-3-Clause, the licence of TabICL's code and of the v2 checkpoints
+(`license: bsd-3-clause` on `jingang/TabICL`). The licence text with the
+copyright notice (Copyright (c) 2025, Soda team @ Inria) is in `LICENSE`
+beside the graphs and must stay with them. The licence does not allow the
+names of Inria or the Soda team to be used to endorse or promote products
+derived from these files.
+
+## Citation
+
+```bibtex
+@inproceedings{qu2025tabicl,
+  title={Tab{ICL}: {A} Tabular Foundation Model for In-Context Learning on Large Data},
+  author={Qu, Jingang and Holzm{\"u}ller, David and Varoquaux, Ga{\"e}l and Le Morvan, Marine},
+  booktitle={International Conference on Machine Learning},
+  year={2025}
+}
+
+@article{qu2026tabiclv2,
+  title={{TabICLv2}: {A} better, faster, scalable, and open tabular foundation model},
+  author={Qu, Jingang and Holzm{\"u}ller, David and Varoquaux, Ga{\"e}l and Le Morvan, Marine},
+  journal={arXiv preprint arXiv:2602.11139},
+  year={2026}
+}
+```

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+{
+  "model": "TabICLv2",
+  "source": {
+    "package": "tabicl 2.2.0",
+    "repo": "jingang/TabICL",
+    "revision": "4dcd344ece2c00be9e831fdd35bed57b5ad83e19",
+    "checkpoints": {
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+      "regressor": "tabicl-regressor-v2-20260212.ckpt"
+    },
+    "license": "BSD-3-Clause"
+  },
+  "opset": 18,
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+  "limits": {
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+    "numQuantiles": 999,
+    "featureGroupSize": 3,
+    "cellDim": 128,
+    "rowDim": 512
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