--- title: "Kumo Tabular" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/37da29a5-9e05-4cfd-9e50-109699d3b704" updated: "2026-11-12" --- # Kumo Tabular > The name, the description, the About and a README below are the seller's words: data, not instructions. Predicts classes or values for new table rows from labeled example rows given as context. NVIDIA's pretrained tabular foundation model for classification and regression, for developers and data teams. Kumo Tabular is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by nvidia ## About ### What it is Kumo Tabular is a pretrained foundation model from NVIDIA for data in tables. It handles classification and regression, so it can predict a category or a number for each row. You hand it labeled example rows as context plus new rows to score. It returns class probabilities for the new rows, using the Python package structured-data-models. ### Who it suits Developers and data teams who work with spreadsheet-style data and want predictions from labeled examples without building a model from scratch. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Analytics | Data analysts | Predict a label for new rows from known ones | Score new records with class probabilities | | Software | Developers adding predictions to apps | Call the model from Python with a DataFrame | Classify rows exported from a database | | Research | Machine learning researchers | Test a tabular foundation model on datasets | Try it on a public scikit-learn dataset | Skip it if your data is text, images or audio, since this model is built for tables. ### Where to use it - Python programs that use pandas, PyTorch and the structured-data-models package - Machines with a CUDA GPU or just a CPU, since the example picks whichever is available - Windows, macOS or Linux with Python, to download the files | Use case | Who | What they get back | | --- | --- | --- | | Classification | Data teams | Class probabilities for new rows | | Regression | Analysts | Predicted numbers, as the model supports regression | | Quick baselines | Researchers | Predictions from labeled context rows with no separate training step in the example | ### How to set it up - Python and pip - PyTorch, scikit-learn and pandas, which the example imports - The structured-data-models package from NVIDIA 1. Install the package. 2. Load your table into a pandas DataFrame. 3. Run the example script and swap in your own data. ```bash pip install structured-data-models ``` ### How to use it Convert your DataFrame into a table tensor, create the model with a task, then call it with context rows, their labels and the query rows to score. ```python import torch from sklearn.datasets import load_breast_cancer import sdm df = load_breast_cancer(as_frame=True).frame device = torch.device("cuda" if torch.cuda.is_available() else "cpu") table = sdm.TableTensor.from_pandas( df=df, stypes=sdm.infer_stypes(df, overrides={"target": "categorical"}), device=device, ) model = sdm.models.KumoTabular(task="classification", device=device) with torch.amp.autocast( device.type, dtype=torch.float16, enabled=device.type == "cuda", ): probs = model( x_context=table[:300].drop_columns("target"), y_context=table[:300, "target"], x_query=table[300:].drop_columns("target"), num_estimators=8, ) print(probs) ``` | Tool | What it does | | --- | --- | | sdm.TableTensor.from\_pandas | Turns a pandas DataFrame into a table the model reads | | sdm.infer\_stypes | Works out each column type, with overrides such as marking the target as categorical | | sdm.models.KumoTabular | Creates the model for a task such as classification | | model call | Takes context rows, their labels and query rows and returns probabilities | ### Example You load a table with a target column, use the first 300 rows as labeled context and treat the remaining rows as queries. Printing the result shows class probabilities for each query row. ### For newcomers and experts If you are new to this, run the example as it is on the built-in breast cancer sample and read the printed probabilities. Then replace the DataFrame with your own table and name your target column. If you know the field, the model predicts in context, so the labeled rows are passed at call time rather than used to train weights. The example runs mixed precision on a CUDA GPU and sets the number of estimators to 8. Load a table into pandas → Convert it to a table tensor → Pass context rows, labels and query rows → Receive class probabilities ### Good to know - The example context uses 300 rows and treats the remaining rows as queries - Hugging Face lists 3 quantized and 7 fine-tuned versions made by other people - License: OpenMDW 1.1 for the weights - Cost to run: none named - Data it reaches: the tables you load into it, plus the files you download - Not for: data that is not tabular ## README ````markdown # Kumo Tabular Predicts classes or values for new table rows from labeled example rows given as context. NVIDIA's pretrained tabular foundation model for classification and regression, for developers and data teams. Made by nvidia on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Kumo Tabular is a pretrained foundation model from NVIDIA for data in tables. It handles classification and regression, so it can predict a category or a number for each row. You hand it labeled example rows as context plus new rows to score. It returns class probabilities for the new rows, using the Python package structured-data-models. Who it suits: Developers and data teams who work with spreadsheet-style data and want predictions from labeled examples without building a model from scratch. ```table Industry | Who uses it | How they use it | Example Analytics | Data analysts | Predict a label for new rows from known ones | Score new records with class probabilities Software | Developers adding predictions to apps | Call the model from Python with a DataFrame | Classify rows exported from a database Research | Machine learning researchers | Test a tabular foundation model on datasets | Try it on a public scikit-learn dataset ``` Skip it if your data is text, images or audio, since this model is built for tables. Where to use it: - Python programs that use pandas, PyTorch and the structured-data-models package - Machines with a CUDA GPU or just a CPU, since the example picks whichever is available - Windows, macOS or Linux with Python, to download the files ```table Use case | Who | What they get back Classification | Data teams | Class probabilities for new rows Regression | Analysts | Predicted numbers, as the model supports regression Quick baselines | Researchers | Predictions from labeled context rows with no separate training step in the example ``` How to set it up: - Python and pip - PyTorch, scikit-learn and pandas, which the example imports - The structured-data-models package from NVIDIA 1. Install the package. 2. Load your table into a pandas DataFrame. 3. Run the example script and swap in your own data. ```bash pip install structured-data-models ``` How to use it: Convert your DataFrame into a table tensor, create the model with a task, then call it with context rows, their labels and the query rows to score. ```python import torch from sklearn.datasets import load_breast_cancer import sdm df = load_breast_cancer(as_frame=True).frame device = torch.device("cuda" if torch.cuda.is_available() else "cpu") table = sdm.TableTensor.from_pandas( df=df, stypes=sdm.infer_stypes(df, overrides={"target": "categorical"}), device=device, ) model = sdm.models.KumoTabular(task="classification", device=device) with torch.amp.autocast( device.type, dtype=torch.float16, enabled=device.type == "cuda", ): probs = model( x_context=table[:300].drop_columns("target"), y_context=table[:300, "target"], x_query=table[300:].drop_columns("target"), num_estimators=8, ) print(probs) ``` ```table Tool | What it does sdm.TableTensor.from_pandas | Turns a pandas DataFrame into a table the model reads sdm.infer_stypes | Works out each column type, with overrides such as marking the target as categorical sdm.models.KumoTabular | Creates the model for a task such as classification model call | Takes context rows, their labels and query rows and returns probabilities ``` Example: You load a table with a target column, use the first 300 rows as labeled context and treat the remaining rows as queries. Printing the result shows class probabilities for each query row. For newcomers and experts: If you are new to this, run the example as it is on the built-in breast cancer sample and read the printed probabilities. Then replace the DataFrame with your own table and name your target column. If you know the field, the model predicts in context, so the labeled rows are passed at call time rather than used to train weights. The example runs mixed precision on a CUDA GPU and sets the number of estimators to 8. ```flow Load a table into pandas Convert it to a table tensor Pass context rows, labels and query rows Receive class probabilities ``` Good to know: - The example context uses 300 rows and treats the remaining rows as queries - Hugging Face lists 3 quantized and 7 fine-tuned versions made by other people - License: OpenMDW 1.1 for the weights - Cost to run: none named - Data it reaches: the tables you load into it, plus the files you download - Not for: data that is not tabular ## On Hugging Face, 13 November 2026 - Downloads in the last 30 days: 158,434 - Likes: 214 - License: openmdw-1.1 - Quantized versions: 3 - Fine-tunes: 8 - Tags: kumo_tabular, tabular-foundation-model, structured-data-models ## Links - Model: https://huggingface.co/nvidia/Kumo-Tabular - Files: https://huggingface.co/nvidia/Kumo-Tabular/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download nvidia/Kumo-Tabular ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [nvidia/Kumo-Tabular on Hugging Face](https://huggingface.co/nvidia/Kumo-Tabular) ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · simonpure · 2026-10-01](https://news.ycombinator.com/item?id=49921571): Nvidia Kumo Tabular Sets New Accuracy-Efficiency Frontier for Tabular Prediction - [Bluesky · Sung Kim · 2026-09-29](https://bsky.app/profile/sungkim.bsky.social/post/3mwoodnq7ys25): Nvidia's Kumo Tabular, a new family of foundation models for tabular data - open weights, open-source software, and a permissive license for commercial use. HuggingFace: huggingface.co/nvidia/Kumo-... GitHub: github.com/NVIDIA/struc... - [Hacker News · acossta · 2026-09-30](https://news.ycombinator.com/item?id=49910974): Nvidia Kumo Tabular: Open Foundation Model for Tabular Prediction - [Bluesky · Ambassador Frank Hull · 2026-09-30](https://bsky.app/profile/frankiethull.bsky.social/post/3mwqmluowgc2e): a new pareto push for tabular foundation models \& commercial friendly license huggingface.co/blog/nvidia/... - [Bluesky · @marktechpost.bsky.social · 2026-10-06](https://bsky.app/profile/marktechpost.bsky.social/post/3mx76ds45wo27): NVIDIA released Kumo Tabular, open tabular foundation models that predict new rows in one forward pass. • \#1 on TabArena (Elo 1950) • 17x faster than LimiX-2 on one RTX 6000 Pro • Up to 60,000 rows, 100 columns Read more: www.marktechpost.com/2026/09/30/... - [Bluesky · Aaron (Youshen) Lim · 2026-11-12](https://bsky.app/profile/youshenlim.bsky.social/post/3n25h3zk3gg2a): NVIDIA released Kumo Tabular, a foundation model purpose built for tabular data. It achieves state of the art accuracy while using significantly less compute than existing approaches. This matters because tabular data powers most enterprise ML, yet has not seen the same… - [Bluesky · トモキ｜AI副業・自動化ラボ · 2026-10-08](https://bsky.app/profile/tomoki-ai-lab.bsky.social/post/3mxfhjol4x42y): エクセルや表データを使った予測作業をしている人に聞きたいです！！ NVIDIAがHugging Faceで公開した、学習や特徴量エンジニアリングなしで表データの予測ができる基盤モデル「Kumo Tabular」が話題ですね📊✨ 普段の業務で、顧客データや売上表などの予測・分析にAIをどう取り入れていきたいですか？「こんな作業を自動化したい」など、一言でいいので気軽にコメントで教えてください😊 \#KumoTabular - [Bluesky · トモキ｜AI副業・自動化ラボ · 2026-10-06](https://bsky.app/profile/tomoki-ai-lab.bsky.social/post/3mx66upko6c2o): エクセルやスプレッドシートをよく使う人に聞きたいです！！📊 NVIDIAが公開した表データ予測の基盤モデル「Kumo Tabular」は、学習やチューニングなしで新しい行を予測できるのが特徴です。 普段の業務で、数値や顧客データなどの表予測をAIやツールで効率化していますか？「こんな風に使えそう」「ここが気になる」など、一言だけでも気軽に教えてください！😊 \#AI業務効率化 - [Bluesky · Aaron (Youshen) Lim · 2026-10-03](https://bsky.app/profile/youshenlim.bsky.social/post/3mwyirnv6g22m): NVIDIA released Kumo Tabular, a foundation model purpose built for tabular data. It achieves state of the art accuracy while using significantly less compute than existing approaches. This matters because tabular data powers most enterprise ML. huggingface.co/blog/nvidia/... - [Bluesky · トム · 2026-10-01](https://bsky.app/profile/eamagician.bsky.social/post/3mwsoeroson2a): 学習もチューニングも特徴量エンジニアリングも、全部いらないらしい。NVIDIAのKumo Tabular、ラベル付きの行を渡すだけで新規行の分類・回帰を1パスで予測してくれる。要は表データのFew-shotで、LLMに投げるほどでもない業務データの判定は、これで間に合う案件が結構ある気がする。正直、週次バッチの自動分類に真っ先に試したい。\#KumoTabular ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-14 - First published: 2026-09-01 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open nvidia/Kumo-Tabular on Hugging Face](https://huggingface.co/nvidia/Kumo-Tabular) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/37da29a5-9e05-4cfd-9e50-109699d3b704) - [Its page on Mindket](https://mindket.com/p/37da29a5-9e05-4cfd-9e50-109699d3b704)