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What is Ministral 8B?

Mistral AI has introduced two advanced models tailored for on-device computing and edge applications, collectively known as "les Ministraux": Ministral 3B and Ministral 8B. These models are particularly remarkable for their abilities in knowledge retention, commonsense reasoning, function-calling, and overall operational efficiency, all while being under the 10B parameter threshold. With support for an impressive context length of up to 128k, they cater to a wide array of applications, including on-device translation, offline smart assistants, local analytics, and autonomous robotics. A standout feature of the Ministral 8B is its incorporation of an interleaved sliding-window attention mechanism, which significantly boosts both the speed and memory efficiency during inference. Both models excel in acting as intermediaries in intricate multi-step workflows, adeptly managing tasks such as input parsing, task routing, and API interactions according to user intentions while keeping latency and operational costs to a minimum. Benchmark results indicate that les Ministraux consistently outperform comparable models across numerous tasks, further cementing their competitive edge in the market. As of October 16, 2024, these innovative models are accessible to developers and businesses, with the Ministral 8B priced competitively at $0.1 per million tokens used. This pricing model promotes accessibility for users eager to incorporate sophisticated AI functionalities into their projects, potentially revolutionizing how AI is utilized in everyday applications.

What is Liquid Apollo?

Liquid Apollo is an innovative mobile app that enables AI interactions entirely on-device, independent of cloud services, which allows users to engage with advanced language and vision models in a secure and private way with minimal latency. This application boasts a diverse array of compact foundation models drawn from the company's LEAP platform, empowering users to draft messages, send emails, interact with a personal AI assistant, create digital characters, and leverage image-to-text capabilities, all while functioning offline and ensuring that no data leaves the device. With a strong emphasis on instant responsiveness and offline operation, Apollo ensures that all processing occurs locally, removing the necessity for API calls, external servers, or the recording of user information. Serving as both a personal AI exploration tool and a development platform for those working with LEAP models, Apollo allows users to thoroughly evaluate a model's efficiency on their individual mobile devices before considering broader deployment. Furthermore, the application's design promotes user control and privacy, creating a smooth experience devoid of external disruptions and safeguarding personal data at every level. By prioritizing these aspects, Apollo not only enhances user trust but also encourages a more engaging interaction with AI technology.

Media

Media

Integrations Supported

AlphaCorp
BLACKBOX AI
DataChain
Fleak
Graydient AI
HoneyHive
Langflow
Lewis
LibreChat
Noma
Overseer AI
ReByte
Superinterface
SydeLabs
Toolmark
WebLLM
Wordware

Integrations Supported

Liquid AI
OpenRouter

API Availability

Has API

API Availability

Pricing Information

Free
Open source
Free Version

Pricing Information

Free
Free Version

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Supported Platforms

Android
iPhone
iPad
Mac

Customer Service / Support

24 Hour 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

mistral.ai/news/ministraux/

Company Facts

Organization Name

Liquid AI

Date Founded

2023

Company Location

United States

Company Website

www.liquid.ai/apollo

Categories and Features

AI Models

Not specified

Large Language Models

Not specified

Small Language Models

Not specified

Categories and Features

AI Tools

Not specified

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