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What is TabPFN-3.5?

TabPFN-3.5 represents a cutting-edge foundation model tailored for superior predictions on structured data, proving to be exceptionally valuable for a range of applications including churn analysis, fraud detection, pricing strategies, demand forecasting, and risk assessment, thereby allowing teams to deploy a single model across various use cases. This model efficiently handles data in its native format, managing challenges such as missing values, outliers, categorical variables, multi-table datasets, free text features, and numerous unique identifiers without necessitating any encoding, while also being capable of processing multiple measurements per row. Users benefit from the ability to input raw data directly, eliminating the need for extensive feature engineering or preprocessing, which enables them to receive high-quality, production-ready predictions right after the first prediction call. Significantly, TabPFN-3.5 performs predictions in a single forward pass, achieving an impressive balance between accuracy and speed, and is optimized for rapid inference—a critical aspect for latency-sensitive predictive tasks. Moreover, it can effectively accommodate large datasets of up to one million rows natively and offers an astonishing 20 times faster inference speed compared to earlier versions, marking a significant leap in the domain. This remarkable blend of efficiency, adaptability, and performance establishes TabPFN-3.5 as an invaluable resource for data scientists and organizations aiming to harness structured data to its fullest potential. In addition, the model's user-friendly nature simplifies the workflow, making it accessible for both seasoned experts and those newer to data science.

What is Hy4?

Hy4 preview is an innovative open-source Mixture-of-Experts model designed for numerous practical productivity applications, such as software development, office tasks, game creation, and scientific research. With an astounding 770 billion parameters and 49 billion activated per token, it features a remarkable 1 million-token context window, enabling it to adeptly handle extensive codebases, large sets of documents, and intricate multi-step operations. The model's architecture incorporates 78 layers that utilize Gated DeepSeek Sparse Attention and IndexCache for efficient sparse index reuse across layers, while identity Hyper-Connections are implemented to improve information flow within the model. Furthermore, a specialized Multi-Token Prediction layer supports speculative decoding, significantly boosting its performance. Hy4 preview is engineered to understand, strategize, troubleshoot, and verify complex engineering initiatives, all while delivering substantial advancements in the quality of front-end visuals and interaction design, ultimately serving as an essential tool for experts in a wide range of fields. This versatility makes it an outstanding choice for professionals seeking to enhance their productivity and efficiency in various projects.

Media

Media

Integrations Supported

Amazon Web Services (AWS)
Databricks
Google Cloud Platform
Microsoft Azure
Model Context Protocol (MCP)
NVIDIA DRIVE
Python
SAP Cloud Platform
Snowflake
Tencent Hy

Integrations Supported

Amazon Web Services (AWS)
Databricks
Google Cloud Platform
Microsoft Azure
Model Context Protocol (MCP)
NVIDIA DRIVE
Python
SAP Cloud Platform
Snowflake
Tencent Hy

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Prior Labs

Date Founded

2024

Company Location

Germany

Company Website

priorlabs.ai/tabpfn-3-5

Company Facts

Organization Name

Tencent

Date Founded

1998

Company Location

China

Company Website

hy.tencent.ai/research/hy4-preview

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