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What is Olmo 3?

Olmo 3 constitutes an extensive series of open models that include versions with 7 billion and 32 billion parameters, delivering outstanding performance in areas such as base functionality, reasoning, instruction, and reinforcement learning, all while ensuring transparency throughout the development process, including access to raw training datasets, intermediate checkpoints, training scripts, extended context support (with a remarkable window of 65,536 tokens), and provenance tools. The backbone of these models is derived from the Dolma 3 dataset, which encompasses about 9 trillion tokens and employs a thoughtful mixture of web content, scientific research, programming code, and comprehensive documents; this meticulous strategy of pre-training, mid-training, and long-context usage results in base models that receive further refinement through supervised fine-tuning, preference optimization, and reinforcement learning with accountable rewards, leading to the emergence of the Think and Instruct versions. Importantly, the 32 billion Think model has earned recognition as the most formidable fully open reasoning model available thus far, showcasing a performance level that closely competes with that of proprietary models in disciplines such as mathematics, programming, and complex reasoning tasks, highlighting a considerable leap forward in the realm of open model innovation. This breakthrough not only emphasizes the capabilities of open-source models but also suggests a promising future where they can effectively rival conventional closed systems across a range of sophisticated applications, potentially reshaping the landscape of artificial intelligence.

What is Imubit?

Imubit’s AI-driven platform offers real-time, closed-loop optimization for processes within heavy industries by combining a dynamic process simulator, a reinforcement-learning neural controller, and performance tracking dashboards. The dynamic simulator harnesses a wealth of historical data alongside fundamental principles to construct a virtual model of actual processes, enabling what-if scenarios that explore the interactions among variables, shifts in constraints, and modifications in operational tactics. In parallel, the reinforcement-learning controller, which has been developed through extensive offline training with millions of trial-and-error scenarios, serves to continuously refine control variables, boosting profit margins while maintaining compliance with safety regulations. The real-time dashboards provide insights into model availability, user activity, and operational uptime, complemented by interactive visualizations that illustrate boundary conditions, operational thresholds, and trends in vital performance metrics. This advanced technology finds its applications in aligning economic strategies with real-time data and spotting instances of process decline, thereby promoting heightened efficiency and safety across operations. By adopting this holistic strategy, industries are better equipped to respond promptly and effectively to evolving conditions, ultimately leading to more resilient operations.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Pricing Information

Free
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Online Training

Training Options

Documentation Hub

Company Facts

Organization Name

Ai2

Date Founded

2014

Company Location

United States

Company Website

allenai.org/blog/olmo3

Company Facts

Organization Name

Imubit

Date Founded

2016

Company Location

United States

Company Website

imubit.com

Categories and Features

AI Models

Not specified

Large Language Models

Not specified

Categories and Features

Industrial AI

Not specified

Industrial Automation

Not specified

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