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What is Xgen-small?

Xgen-small is a streamlined language model developed by Salesforce AI Research, specifically designed for enterprise applications, providing effective long-context processing at a reasonable price. It integrates focused data selection, scalable pre-training, extension of context length, instruction-based fine-tuning, and reinforcement learning to meet the sophisticated and high-demand inference requirements of modern enterprises. Unlike traditional large models, Xgen-small stands out in its ability to handle extensive contexts, enabling it to adeptly gather insights from a range of sources, including internal documents, programming code, academic papers, and live data streams. With configurations of 4B and 9B parameters, it achieves a delicate equilibrium between cost-effectiveness, data privacy, and thorough understanding of long contexts, making it a dependable and sustainable choice for extensive Enterprise AI applications. This pioneering method not only boosts operational productivity but also equips organizations with the tools to harness AI effectively in their strategic goals, thus fostering innovation and growth in various sectors. As businesses continue to evolve, solutions like Xgen-small will play a crucial role in shaping the future of AI integration.

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.

Media

Media

Integrations Supported

302.AI
BLACKBOX AI
Deep Infra
GMTech
GaiaNet
HoneyHive
HumanLayer
Lewis
LibreChat
Mirascope
Msty
Pipeshift
Prompt Security
PromptPal
SydeLabs
Symflower
Unify AI
Yaseen AI
bolt.diy
promptmate.io

API Availability

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

Free
Open source
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub

Company Facts

Organization Name

Salesforce

Date Founded

1999

Company Location

United States

Company Website

www.salesforce.com/blog/xgen-small-enterprise-ready-small-language-models/

Company Facts

Organization Name

Mistral AI

Date Founded

2023

Company Location

France

Company Website

mistral.ai/news/ministraux/

Categories and Features

AI Models

Not specified

Small Language Models

Not specified

Categories and Features

AI Models

Not specified

Large Language Models

Not specified

Small Language Models

Not specified

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