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What is Timestral?

Timestral is a comprehensive suite of time-series foundation models accompanied by a Python inference toolkit, specifically designed to enable the creation of multivariate probabilistic forecasts derived from historical data sequences. It offers four distinct model sizes, including 5M, 28M, 123M, and 209M parameters, each capable of managing past-only, known-future, and categorical covariates, while providing quantile forecasts that can accommodate context windows of up to 32,768 time steps. Users can conveniently load pretrained weights from the Hugging Face Hub or from their local files, allowing for inference on both CPU and GPU setups. These forecasts can be integrated effortlessly into a wide array of applications, such as monitoring systems, capacity planning, energy management, supply chain logistics, financial risk analysis, and maintenance operations. Additionally, Timestral boasts zero-shot forecasting capabilities and can be customized to specific domain data using the Mistral Forge, further enhancing its versatility. This toolkit primarily targets data scientists, machine learning engineers, and collaborative teams eager to devise effective forecasting solutions; its repository code is available under the Apache 2.0 license, while the model weights are managed by a distinct Modified MIT license. With its robust and comprehensive features, Timestral is set to considerably improve the precision and effectiveness of predictive analytics across various sectors, ensuring that users can make informed decisions based on reliable forecasts. As industries increasingly rely on data-driven insights, Timestral stands out as an essential resource for achieving superior forecasting outcomes.

What is Devstral?

Devstral represents a joint initiative by Mistral AI and All Hands AI, creating an open-source large language model designed explicitly for the field of software engineering. This innovative model exhibits exceptional skill in navigating complex codebases, efficiently managing edits across multiple files, and tackling real-world issues, achieving an impressive 46.8% score on the SWE-Bench Verified benchmark, which positions it ahead of all other open-source models. Built upon the foundation of Mistral-Small-3.1, Devstral features a vast context window that accommodates up to 128,000 tokens. It is optimized for peak performance on advanced hardware configurations, such as Macs with 32GB of RAM or Nvidia RTX 4090 GPUs, and is compatible with several inference frameworks, including vLLM, Transformers, and Ollama. Released under the Apache 2.0 license, Devstral is readily available on various platforms, including Hugging Face, Ollama, Kaggle, Unsloth, and LM Studio, enabling developers to effortlessly incorporate its features into their applications. This model not only boosts efficiency for software engineers but also acts as a crucial tool for anyone engaged in coding tasks, thereby broadening its utility and appeal across the tech community. Furthermore, its open-source nature encourages continuous improvement and collaboration among developers worldwide.

Media

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Media

Integrations Supported

Mistral Forge

Integrations Supported

Hugging Face
Kaggle
LM Studio
Mistral AI
Mistral Code
Ollama
Unsloth

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided
Free Version

Pricing Information

$0.1 per million input tokens
Free Version

Supported Platforms

On-Prem

Supported Platforms

SaaS
Android
iPhone
iPad

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
On-Site Training

Company Facts

Organization Name

Mistral AI

Date Founded

2023

Company Location

France

Company Website

github.com/mistralai/timestral

Company Facts

Organization Name

Mistral AI

Date Founded

2023

Company Location

France

Company Website

mistral.ai/news/devstral

Categories and Features

AI Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Open-Weight AI Models

Not specified

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