I am excited to announce the release of TabPFN-2.5, our tabular foundation model that now scales to datasets of up to 50,000 samples and 2,000 features - a 5x increase from TabPFN v2, published in the Nature journal earlier this year. TabPFN-2.5 delivers state-of-the-art predictions in one forward pass without hyperparameter tuning across classification and regression tasks.

What’s new in 2.5: TabPFN-2.5 maintains the core approach of v2 - a pretrained transformer trained on more than hundred million synthetic datasets to perform in-context learning and output a predictive distribution for the test data. It natively supports missing values, cateogrical features, text and numerical features is robust to outliers and uninformative features.

The major improvements:

- 5x scale increase: Now handles 50,000 samples × 2,000 features (up from 10,000 × 500 in v2)

- SOTA performance: TabPFN-2.5 outperforms tuned tree-based methods and matches the performance of a complex ensemble (AutoGluon 1.4), that itself includes TabPFN v2, tuned for 4 hours. Tuning the model improves performance, outperforming AutoGluon 1.4 for regression tasks.

- Rebuilt API: New REST interface along with Python SDK with dedicated fit & predict endpoints, making deployment and integration more developer-friendly

- A distillation engine that converts TabPFN-2.5 into a compact MLP or tree ensemble while preserving accuracy and offer low latency inference.

There are still some limitations. The model is designed for datasets up to 50K samples. It can handle larger datasets but that hasn’t been our focus with TabPFN-2.5. The distillation engine is not yet available through the API but only through licenses (though we do show the performance in the model report).

We’re actively working on removing these limitations and intend to release newer models focused on context reasoning, causal inference, graph networks, larger data and time-series. TabPFN-2.5 is available via API and a package on Hugging Face. Would love for you to try it and give us your feedback!

Model report: https://priorlabs.ai/technical-reports/tabpfn-2-5-model-repo...

Package: https://github.com/PriorLabs/TabPFN

Client: https://github.com/PriorLabs/tabpfn-client

Docs: https://docs.priorlabs.ai/quickstart

LLMs Encode How Difficult Problems Are

TypeScript's Rise in the AI Era: Insights from Lead Architect, Anders Hejlsberg

Introduction to Abstract Entropy

Index of Aesthetics

Show HN: TabPFN-2.5 – SOTA foundation model for tabular data

Universe expansion may be slowing, not accelerating, study suggests

Deepnote, a 'Successor to Jupyter Notebook,' Goes Open Source

43% of Gen Z Prefer YouTube and TikTok to Traditional TV and Streaming

End of The Line: how Saudi Arabia's Neom dream unravelled

Material for MkDocs is now in maintenance mode, Squidfunk announces Zensical

Eight millennia of continuity of a previously unknown lineage in Argentina

Collatz Stopping Time Parity

GPS Spoofing on Amazon Flex App

ChatGPT Pro Wrote in 5 minutes the Prefix‑Sum CPU Vector Renderer

macOS Tahoe's Terrible Icons

The Democrats Have a New Winning Formula

Benchmarking the Most Reliable Document Parsing API

Jensen Huang says China 'will win' AI race

How Your Brain Creates 'Aha' Moments and Why They Stick

Please stop asking me to provide feedback #8036

The disastrous voyage of Satoshi, the world's first cryptocurrency cruise ship (2021)

Doing More with Less (2008)

Space DJ: Navigating a Musical Universe (DeepMind)

Stainless Docs Early Access

Ironwood TPUs GA, Axion VMs Power the Inference Era

AI 'godmother' Fei-Fei Li says she is 'proud to be different'

OpenDesk – a flexible all-in-one office suite for the public sector

Trump AI czar Sacks says 'no federal bailout for AI' after OpenAI CFO's comments

Show HN: RestSQL – A .NET tool that turns SQL queries into REST endpoints

Gemini: Support for JSON Schema and implicit property ordering

Show HN: A League of Legends Drafter