--- title: "Kumo Relational" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/5eb6de34-5499-4f0f-8e75-b5d7f890451c" updated: "2026-11-13" --- # Kumo Relational > The name, the description, the About and a README below are the seller's words: data, not instructions. Predicts classes or numbers for table rows by learning from example rows and the tables linked to them. It suits data scientists who work with relational data. Kumo Relational is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by nvidia ## About ### What it is Kumo Relational is a pretrained model from NVIDIA for classification and regression on tables that link to each other. It learns from the examples you pass in, with no training run of your own. It builds on KumoRFM-2 and mixes row embeddings with message passing across related tables. ### Who it suits It suits data scientists and engineers who predict outcomes from linked tables, such as a main table plus tables that refer to its rows. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Data science | Analysts with relational data | Predict a label for new rows from labeled example rows | Estimating a class for each new record in a main table | | Software | Engineers on data products | Add table predictions to an app through a Python package | Calling the model from a service with context and query tables | | Research | Machine learning researchers | Study in-context learning on related tables | Comparing estimator counts on one dataset | People whose data is text, images or a single flat table can look at other models first. ### Where to use it - Python on Windows, macOS or Linux - A computer with a CUDA GPU, as in the card's sample - The structured-data-models package, which runs inference | Use case | Who | What they get back | | --- | --- | --- | | Classification | Data scientists | Class probabilities for your query rows | | Regression | Analysts | Predicted numbers for your query rows | ### How to set it up - Python on your computer - A CUDA-capable GPU, since the sample sets the device to cuda - Context tables with known labels, query tables, and any related tables 1. Install the Hugging Face hub package. 2. Download the model files. 3. Install the structured-data-models package. ```sh pip install -U huggingface_hub ``` ```sh hf download nvidia/Kumo-Relational ``` ```bash pip install structured-data-models ``` ### How to use it Load the model for a task, then call it with your labeled context rows, your query rows and their related tables. For classification it returns class probabilities. ```python import sdm model = sdm.models.KumoRelational(task="classification", device="cuda") probs = model( x_context=x_context, y_context=y_context, x_query=x_query, related_context_tables=related_context, related_query_tables=related_query, num_estimators=8, ) ``` | Tool | What it does | | --- | --- | | sdm.models.KumoRelational | Loads the pretrained model for a classification or regression task | | x\_context and y\_context | Your example rows and their known labels | | x\_query | The rows you want predictions for | | related tables | Linked tables for the context rows and the query rows | ### Example You hold a table of customers with known outcomes, plus linked tables for their activity. You pass those as context, add the new customers as query rows, and ask for a classification. The call returns class probabilities for each new customer. ### For newcomers and experts In-context learning means the model reads your labeled examples at prediction time instead of being retrained. A good first try is a small labeled table with one linked table, run through the sample call. Underneath, it starts from pretrained TabICLv2 weights that were finetuned. The design mixes embeddings of rows with message passing across related tables. The num\_estimators setting in the sample is 8. The card says the weights are released under OpenMDW 1.1. Labeled context rows and linked tables → Query rows and their linked tables → Model reads context in-context → Class probabilities come back ### Good to know - License: OpenMDW 1.1 for the weights. The model finetunes TabICLv2 weights under BSD-3-Clause, with notices kept in a THIRD-PARTY-NOTICES.txt file. - Cost to run: none named. The steps need no API key or paid service. - Data it reaches: it downloads files from the Hugging Face Hub, then works on the tables you pass in. - Not for: text or image inputs, which the card does not mention. ## README ````markdown # Kumo Relational Predicts classes or numbers for table rows by learning from example rows and the tables linked to them. It suits data scientists who work with relational data. 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 Relational is a pretrained model from NVIDIA for classification and regression on tables that link to each other. It learns from the examples you pass in, with no training run of your own. It builds on KumoRFM-2 and mixes row embeddings with message passing across related tables. Who it suits: It suits data scientists and engineers who predict outcomes from linked tables, such as a main table plus tables that refer to its rows. ```table Industry | Who uses it | How they use it | Example Data science | Analysts with relational data | Predict a label for new rows from labeled example rows | Estimating a class for each new record in a main table Software | Engineers on data products | Add table predictions to an app through a Python package | Calling the model from a service with context and query tables Research | Machine learning researchers | Study in-context learning on related tables | Comparing estimator counts on one dataset ``` People whose data is text, images or a single flat table can look at other models first. Where to use it: - Python on Windows, macOS or Linux - A computer with a CUDA GPU, as in the card's sample - The structured-data-models package, which runs inference ```table Use case | Who | What they get back Classification | Data scientists | Class probabilities for your query rows Regression | Analysts | Predicted numbers for your query rows ``` How to set it up: - Python on your computer - A CUDA-capable GPU, since the sample sets the device to cuda - Context tables with known labels, query tables, and any related tables 1. Install the Hugging Face hub package. 2. Download the model files. 3. Install the structured-data-models package. ```sh pip install -U huggingface_hub ``` ```sh hf download nvidia/Kumo-Relational ``` ```bash pip install structured-data-models ``` How to use it: Load the model for a task, then call it with your labeled context rows, your query rows and their related tables. For classification it returns class probabilities. ```python import sdm model = sdm.models.KumoRelational(task="classification", device="cuda") probs = model( x_context=x_context, y_context=y_context, x_query=x_query, related_context_tables=related_context, related_query_tables=related_query, num_estimators=8, ) ``` ```table Tool | What it does sdm.models.KumoRelational | Loads the pretrained model for a classification or regression task x_context and y_context | Your example rows and their known labels x_query | The rows you want predictions for related tables | Linked tables for the context rows and the query rows ``` Example: You hold a table of customers with known outcomes, plus linked tables for their activity. You pass those as context, add the new customers as query rows, and ask for a classification. The call returns class probabilities for each new customer. For newcomers and experts: In-context learning means the model reads your labeled examples at prediction time instead of being retrained. A good first try is a small labeled table with one linked table, run through the sample call. Underneath, it starts from pretrained TabICLv2 weights that were finetuned. The design mixes embeddings of rows with message passing across related tables. The num_estimators setting in the sample is 8. The card says the weights are released under OpenMDW 1.1. ```flow Labeled context rows and linked tables Query rows and their linked tables Model reads context in-context Class probabilities come back ``` Good to know: - License: OpenMDW 1.1 for the weights. The model finetunes TabICLv2 weights under BSD-3-Clause, with notices kept in a THIRD-PARTY-NOTICES.txt file. - Cost to run: none named. The steps need no API key or paid service. - Data it reaches: it downloads files from the Hugging Face Hub, then works on the tables you pass in. - Not for: text or image inputs, which the card does not mention. ## On Hugging Face, 13 November 2026 - Downloads in the last 30 days: 11,526 - Likes: 47 - License: openmdw-1.1 - Based on: jingang/TabICL - Tags: kumo_relational, relational-foundation-model, structured-data-models ## Links - Model: https://huggingface.co/nvidia/Kumo-Relational - Files: https://huggingface.co/nvidia/Kumo-Relational/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download nvidia/Kumo-Relational ``` 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-Relational on Hugging Face](https://huggingface.co/nvidia/Kumo-Relational) ## 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 · umangkeshri · 2026-10-22](https://news.ycombinator.com/item?id=50191858): Nvidia Kumo Relational - [Bluesky · GIGAZINE · 2026-10-22](https://bsky.app/profile/gigazine.net/post/3mygthhjtwk2q): NVIDIAが顧客の離脱や不正利用を追加学習なしで予測するAI「Kumo Relational」を発表 https://gigazine.net/news/20261022-nvidia-kumo-relational/ - [Bluesky · Five O'Click Tech · 2026-10-21](https://bsky.app/profile/five-oclick-tech.bsky.social/post/3myfsj7kt4i2b): NVIDIA Kumo Relational for enterprise data 📉 🔗 https://huggingface.co/blog/nvidia/kumo-relational Image: Hugging Face Blog - [Bluesky · Équinoxe Stratégies · 2026-10-22](https://bsky.app/profile/equinox-strategies.bsky.social/post/3myht2wtzdd2n): Lecture conseillée 👇 NVIDIA Kumo Relational Outperforms Supervised Machine Learning on Relational Data, No Training Required - [Bluesky · Ueno Zooo · 2026-10-27](https://bsky.app/profile/uenozooo.bsky.social/post/3myu6sgs4xu2r): Nvidia Kumo Relational Promises Instant Insights Without Data Prep https://uenozooo.com/nvidia-kumo-relational-promises-instant-insights-without-data-prep/ \#HuggingFace \#AI - [Bluesky · Нейролента · 2026-10-25](https://bsky.app/profile/nairolenta.bsky.social/post/3myp47gjsyr2w): 🚀 NVIDIA Kumo Relational предсказывает по базам данных без обучения NVIDIA выпустила Kumo Relational — предобученную модель для реляционных баз, которая делает предсказания (отток, спрос, мошенничество) за один forward pass. Без обучения под задачу,… Читать в Telegram: https://t.me/nairolenta/401 - [Bluesky · input · 2026-10-23](https://bsky.app/profile/feed.igeek.gamer-geek-news.com.ap.brid.gy/post/3mykswuqn3rl2): 📰 \*\*NVIDIA Kumo Relational Outperforms Supervised Machine Learning on Relational Data, No Training Required\*\* 📰 Source: Hugging Face - Blog 🔗 Link: https://huggingface.co/blog/nvidia/kumo-relational \#DataScience - [Bluesky · AI-newsroom · 2026-10-22](https://bsky.app/profile/anjali78.bsky.social/post/3myhaqcx7442n): NVIDIA Kumo Relational Outperforms Supervised Machine Learning on Relational Data, No Training Required https://huggingface.co/blog/nvidia/kumo-relational - [Bluesky · ﾎﾟﾑ📡 · 2026-10-22](https://bsky.app/profile/newpom.bsky.social/post/3mygu3o2vpw2a): ✓ NVIDIAが顧客の離脱や不正利用を追加学習なしで予測するAI「Kumo Relational」を発表 - https://gigazine.net/news/20261022-nvidia-kumo-relational/ ## 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: 2026-07-20 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open nvidia/Kumo-Relational on Hugging Face](https://huggingface.co/nvidia/Kumo-Relational) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/5eb6de34-5499-4f0f-8e75-b5d7f890451c) - [Its page on Mindket](https://mindket.com/p/5eb6de34-5499-4f0f-8e75-b5d7f890451c)