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Average Ratings 0 Ratings
Description
TabPFN-3.5 is an advanced foundation model specifically designed for achieving top-tier predictions on structured data, making it highly effective for a variety of tasks such as churn analysis, fraud detection, pricing strategies, demand forecasting, and risk assessment, thus enabling teams to utilize a single model for diverse applications. The model seamlessly processes data in its original form, adeptly managing issues like missing values, outliers, categorical data, multi-table datasets, free text features, and thousands of unique identifiers without requiring any encoding, while also accommodating numerous measurements per row. Users have the convenience of inputting raw data without the need for extensive feature engineering or preprocessing, allowing them to obtain high-quality, production-ready predictions immediately after the initial prediction call. Notably, TabPFN-3.5 generates predictions in a single forward pass, striking an optimal balance between accuracy and speed, and is optimized for quick inference, which is crucial for latency-sensitive predictive applications. Furthermore, it can efficiently handle large-scale datasets of up to one million rows natively and boasts a remarkable 20 times faster inference speed compared to its predecessors, making it a significant advancement in the field. This combination of efficiency, versatility, and performance positions TabPFN-3.5 as a powerful tool for data scientists and organizations seeking to leverage structured data effectively.
Description
Timestral represents a suite of time-series foundation models along with a Python inference toolkit designed to create multivariate probabilistic forecasts based on historical data sequences. It comes in four different model sizes—5M, 28M, 123M, and 209M parameters—that are capable of handling past-only, known-future, and categorical covariates, providing quantile forecasts while accommodating context windows as extensive as 32,768 time steps. Users have the flexibility to load pretrained weights either from the Hugging Face Hub or directly from local files, enabling them to perform inference on both CPU and GPU. Such forecasts can be seamlessly integrated into various applications, including monitoring systems, capacity planning, energy management, supply chain logistics, financial risk assessment, or maintenance operations. Furthermore, Timestral offers zero-shot forecasting capabilities and can be tailored to specific domain data through the Mistral Forge. This package is primarily aimed at data scientists, machine learning engineers, and collaborative teams focused on developing robust forecasting solutions; the repository code is licensed under Apache 2.0, while the model weights are governed by a separate Modified MIT license. With its comprehensive features, Timestral is positioned to significantly enhance the accuracy and efficiency of predictive analytics in numerous industries.
API Access
Has API
Yes
API Access
Has API
Yes
Screenshots View All
No images available
Integrations
Amazon Web Services (AWS)
Yes
Databricks
Yes
Google Cloud Platform
Yes
Microsoft Azure
Yes
Mistral Forge
No
Model Context Protocol (MCP)
Yes
NVIDIA DRIVE
Yes
Python
Yes
SAP Cloud Platform
Yes
Snowflake
Yes
Integrations
Amazon Web Services (AWS)
No
Databricks
No
Google Cloud Platform
No
Microsoft Azure
No
Mistral Forge
Yes
Model Context Protocol (MCP)
No
NVIDIA DRIVE
No
Python
No
SAP Cloud Platform
No
Snowflake
No
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
No
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Prior Labs
Founded
2024
Country
Germany
Website
priorlabs.ai/tabpfn-3-5
Vendor Details
Company Name
Mistral AI
Founded
2023
Country
France
Website
github.com/mistralai/timestral
Product Features
Product Features
Alternatives
Alternatives
No Alternatives