TabPFN-3.5Prior Labs
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TimestralMistral AI
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Related Products
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About
TabPFN-3.5 is a tabular foundation model built for state-of-the-art predictions on structured data. It supports a wide range of prediction tasks, including churn, fraud, pricing, demand forecasting, risk, and other real-world data science problems, allowing teams to serve multiple use cases with one model. The model works with data as it is, handling missing values, outliers, categorical features, multi-table datasets, free text as a feature, thousands of distinct IDs without encoding, and hundreds of measurements per row. Users can feed in raw data, skip feature engineering and preprocessing, and get production-grade predictions from the first predict call. TabPFN-3.5 performs predictions in a single forward pass and is designed for both accuracy and speed, with fast inference for latency-critical predictive workflows. It supports production-scale datasets of up to one million rows natively and delivers 20x faster inference than previous model versions.
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About
Timestral is a family of time-series foundation models and a Python inference package for generating multivariate probabilistic forecasts from historical sequences. The four model sizes (5M, 28M, 123M and 209M parameters) support past-only, known-future and categorical covariates, return quantile forecasts, and accept context windows up to 32,768 time steps. Developers can load pretrained weights from the Hugging Face Hub or from local files and run inference on CPU or GPU, then incorporate forecasts into monitoring, capacity-planning, energy, supply-chain, financial-risk, or maintenance workflows. Timestral supports zero-shot forecasting and can be adapted to domain data through Mistral Forge. The package is intended for data scientists, machine-learning engineers, and teams building forecasting systems; repository code is Apache 2.0, while model weights use a separate Modified MIT license.
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Platforms Supported
Windows
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Mac
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Linux
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Cloud
Supported
On-Premises
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iPhone
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iPad
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Android
Not Supported
Chromebook
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Not Supported
On-Premises
Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
Data scientists, machine learning teams, and organizations needing to build fast, production-ready predictions from structured data
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Audience
Data scientists, machine-learning engineers, forecasting practitioners, and teams developing time-series forecasting systems.
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Support
Phone Support
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24/7 Live Support
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Online
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Support
Phone Support
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24/7 Live Support
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Online
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API
Offers API
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API
Offers API
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Screenshots and Videos |
Screenshots and VideosNo images available
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Pricing
No information available.
Free Version
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Free Trial
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Pricing
No information available.
Free Version
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Free Trial
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Reviews/
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Reviews/
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Training
Documentation
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Webinars
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Live Online
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In Person
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Training
Documentation
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Webinars
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Live Online
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In Person
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Company InformationPrior Labs
Founded: 2024
Germany
priorlabs.ai/tabpfn-3-5
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Company InformationMistral AI
Founded: 2023
France
github.com/mistralai/timestral
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Categories |
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Integrations
Amazon Web Services (AWS)
Supported
Databricks
Supported
Google Cloud Platform
Supported
Microsoft Azure
Supported
Mistral Forge
Not Supported
Model Context Protocol (MCP)
Supported
NVIDIA DRIVE
Supported
Python
Supported
SAP Cloud Platform
Supported
Snowflake
Supported
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Integrations
Amazon Web Services (AWS)
Not Supported
Databricks
Not Supported
Google Cloud Platform
Not Supported
Microsoft Azure
Not Supported
Mistral Forge
Supported
Model Context Protocol (MCP)
Not Supported
NVIDIA DRIVE
Not Supported
Python
Not Supported
SAP Cloud Platform
Not Supported
Snowflake
Not Supported
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