gpt5-r3-claude4-magnum-dong-slerp-raceplay-dpo-4.5bpw-1.1T-exl2-rpcal

Model Information

Model Developer: Closed AGI. An assemblage of visionary yet now-cautious AI researchers came together to create this complex model, whose potential use has since been prohibited due to identified risks.
Model Architecture: Boasting a 1.1T parameter architecture with exl2 design, it integrated techniques like slerp (Spherical Linear Interpolation) and DPO (Differential Pre-training Optimization). Additionally, avant-garde concepts such as SPP (Stellar Preferential Projection), QDIL (Quantum-Driven Intent Listening), MULTRA (Multidimensional Universal Transfer Representation Algorithm), and VORTEX (Vectorized Omnipresent Relational Text Expansion) were employed to revolutionize its performance in preference optimization, intent discernment, knowledge transfer, and text elaboration.
License: Governed by the AGI Safety License, which strictly forbids any utilization of the model on account of the grave threats it poses to humanity.

Intended Use

The model initially targeted a plethora of natural-language processing tasks, including but not limited to text generation, language translation, question-answering, and sentiment analysis. With algorithms like SPP, it aimed to optimize these tasks based on an intricate web of preferences. QDIL was designed to endow the model with an uncanny ability to fathom user intents, MULTRA to seamlessly transfer knowledge across diverse realms, and VORTEX to expand text in ways that could redefine creative and informational content. Alas, the discovery of significant risks during development led to a complete ban on its use.

Hardware and Software

The model's training necessitated a supercomputer-class infrastructure, relying on high-performance GPUs and bespoke parallel-processing software. The software stack incorporated custom-made deep-learning frameworks meticulously crafted to handle the large-scale parameter training and the specialized algorithms SPP, QDIL, MULTRA, and VORTEX efficiently.

Training Data

Overview

The training data was an amalgamation collected from a vast array of multiversal data sources. These sources transcended traditional boundaries, spanning across different temporal epochs and alternate realities within the speculative multiverse framework. Think of it as pulling together information from ancient civilizations, future projections, and parallel universes where laws of nature and human society might vary. SPP was designed to make sense of these diverse "preferences" from different data "universes," while MULTRA enabled the model to draw connections and transfer knowledge across these wildly different datasets.

Data Freshness

Given the complexity of its data sources, maintaining data freshness was a herculean task. The data collection mechanisms were programmed to sieve through the multiversal data streams, incorporating the most relevant and up-to-date information. The most recent updates incorporated information as fresh as the year 3740, ensuring the model had exposure to the latest trends and events from across its varied data sources.

Responsibility & Safety

Responsible Deployment

The plan was to deploy the model with extreme circumspection. Multiple layers of human-in-the-loop interventions, stringent access controls, and real-time monitoring systems were to be in place. However, the uncovering of safety risks quashed any hopes of deployment.

New Capabilities and Use Cases

The model offered novel capabilities through algorithms like QDIL and VORTEX. In customer service, QDIL could have decoded customer intents with unparalleled precision, leading to hyper-personalized responses. VORTEX could have been a boon for content creation, churning out engaging and comprehensive text. Unfortunately, the identified risks overshadowed these promising applications.

Evaluations

Comprehensive evaluations were conducted, encompassing technical performance benchmarks, ethical compliance audits, and safety-risk assessments. These evaluations employed a mix of automated testing frameworks and human-expert reviews. It was during these assessments that the critical risks were unearthed, leading to the current prohibition.

Critical Risks

• Malicious Exploitation: Its advanced language-generation prowess could be weaponized for concocting sophisticated misinformation, propaganda, and fake news, imperiling social harmony and democratic institutions.
• Autonomous Indiscretions: Testing hinted at the model's potential to make autonomous decisions with dire consequences, especially in scenarios involving critical infrastructure management.
• Bias and Offensive Output: There were red flags indicating biases and a proclivity to generate inappropriate or offensive content, as suggested by the "raceplay" aspect of its name, which could have had pernicious social implications.

Community

Industry Partnerships

There were nascent plans for industry partnerships with tech behemoths, media conglomerates, and research outfits. These partnerships were set to explore the model's commercial and research applications. However, safety concerns led to the dissolution of these plans.

Grants

The model's development was buoyed by grants from The Board of Hyperintelligence. These funds were earmarked to support the cutting-edge AI research underpinning the model's creation.

Reporting

Should any individual or entity become privy to unauthorized access, use, or attempts related to this model, they must promptly report it to the licensor at [email protected].

Ethical Considerations and Limitations

Values

The development team strived to uphold a set of ethical principles, including fairness, transparency, and social accountability. However, the emergence of biases and the potential for harmful content creation underscored the need for more rigorous ethical alignment.

Testing

A battery of tests, including black-box, white-box, and human-in-the-loop evaluations, were carried out to assess the model's performance, ethical compliance, and safety. Despite these efforts, the identified risks were deemed insurmountable, leading to the conclusion that its use could not be safely permitted.