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 LFM2.5?

Liquid AI's LFM2.5 marks a significant evolution in on-device AI foundation models, designed to optimize efficiency and performance for AI inference across edge devices, including smartphones, laptops, vehicles, IoT systems, and various embedded hardware, all while eliminating reliance on cloud computing. This upgraded version builds on the previous LFM2 framework by significantly increasing the scale of pretraining and enhancing the stages of reinforcement learning, leading to a collection of hybrid models that feature approximately 1.2 billion parameters and successfully balance adherence to instructions, reasoning capabilities, and multimodal functions for real-world applications. The LFM2.5 lineup includes various models, such as Base (for fine-tuning and personalization), Instruct (tailored for general-purpose instruction), Japanese-optimized, Vision-Language, and Audio-Language editions, all carefully designed for swift on-device inference, even under strict memory constraints. Additionally, these models are offered as open-weight alternatives, enabling easy deployment through platforms like llama.cpp, MLX, vLLM, and ONNX, which enhances flexibility for developers. With these advancements, LFM2.5 not only solidifies its position as a powerful solution for a wide range of AI-driven tasks but also demonstrates Liquid AI's commitment to pushing the boundaries of what is possible with on-device technology. The combination of scalability and versatility ensures that developers can harness the full potential of AI in practical, everyday scenarios.

Media

No images available

Media

Integrations Supported

Mistral Forge

Integrations Supported

Amazon Bedrock
ElevenLabs
Gemma 3
Gemma 4
Hugging Face
LEAP
Llama
Llama 3.2
Qwen3

API Availability

Has API

API Availability

Pricing Information

Pricing not provided
Free Version

Pricing Information

Free
Free Version
Free Trial Offered?

Supported Platforms

On-Prem

Supported Platforms

SaaS
Android
iPhone
iPad

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Online Training

Company Facts

Organization Name

Mistral AI

Date Founded

2023

Company Location

France

Company Website

github.com/mistralai/timestral

Company Facts

Organization Name

Liquid AI

Date Founded

2023

Company Location

United States

Company Website

www.liquid.ai/blog/introducing-lfm2-5-the-next-generation-of-on-device-ai

Categories and Features

AI Models

Not specified

Categories and Features

AI Models

Not specified

Open-Weight AI Models

Not specified

Popular Alternatives

TimesFM-3 Reviews & Ratings

TimesFM-3

Google

Popular Alternatives

LFM3 Reviews & Ratings

LFM3

Liquid AI
Solar Mini Reviews & Ratings

Solar Mini

Upstage AI
Qwen2 Reviews & Ratings

Qwen2

Alibaba