Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Alternatives to Consider

  • LTX Reviews & Ratings
    182 Ratings
    Company Website
  • SKU Science Reviews & Ratings
    16 Ratings
    Company Website
  • LM-Kit.NET Reviews & Ratings
    29 Ratings
    Company Website
  • Martus Reviews & Ratings
    187 Ratings
    Company Website
  • Bridgit Reviews & Ratings
    80 Ratings
    Company Website
  • Kantata Reviews & Ratings
    2,341 Ratings
    Company Website
  • Runn Reviews & Ratings
    37 Ratings
    Company Website
  • Banker's Dashboard Reviews & Ratings
    6 Ratings
    Company Website
  • Premier Construction Software Reviews & Ratings
    408 Ratings
    Company Website
  • EazyStock Reviews & Ratings
    65 Ratings
    Company Website

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 Mistral NeMo?

We are excited to unveil Mistral NeMo, our latest and most sophisticated small model, boasting an impressive 12 billion parameters and a vast context length of 128,000 tokens, all available under the Apache 2.0 license. In collaboration with NVIDIA, Mistral NeMo stands out in its category for its exceptional reasoning capabilities, extensive world knowledge, and coding skills. Its architecture adheres to established industry standards, ensuring it is user-friendly and serves as a smooth transition for those currently using Mistral 7B. To encourage adoption by researchers and businesses alike, we are providing both pre-trained base models and instruction-tuned checkpoints, all under the Apache license. A remarkable feature of Mistral NeMo is its quantization awareness, which enables FP8 inference while maintaining high performance levels. Additionally, the model is well-suited for a range of global applications, showcasing its ability in function calling and offering a significant context window. When benchmarked against Mistral 7B, Mistral NeMo demonstrates a marked improvement in comprehending and executing intricate instructions, highlighting its advanced reasoning abilities and capacity to handle complex multi-turn dialogues. Furthermore, its design not only enhances its performance but also positions it as a formidable option for multi-lingual tasks, ensuring it meets the diverse needs of various use cases while paving the way for future innovations.

Media

No images available

Media

Integrations Supported

AlphaCorp
CSS
Clojure
Deep Infra
EvalsOne
Expanse
Fleak
Graydient AI
Julia
Mammouth AI
Melies
Microsoft Foundry Agent Service
Microsoft Foundry Models
MindMac
NexalAI
Noma
OpenLIT
Prompt Security
Simplismart

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided
Free Version

Pricing Information

Free
Open source
Free Version

Supported Platforms

On-Prem

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

24 Hour Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

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/mistral-nemo/

Categories and Features

AI Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Small Language Models

Not specified

Popular Alternatives

TimesFM-3 Reviews & Ratings

TimesFM-3

Google

Popular Alternatives

Jamba Reviews & Ratings

Jamba

AI21 Labs
Mistral Small Reviews & Ratings

Mistral Small

Mistral AI
Olmo 2 Reviews & Ratings

Olmo 2

Ai2
Solar Mini Reviews & Ratings

Solar Mini

Upstage AI
Mistral 7B Reviews & Ratings

Mistral 7B

Mistral AI