  This paper aims to introduce and analyze the Viz system in a comprehensive
way, a novel system architecture that integrates Quantized Low-Rank Adapters
(QLoRA) to fine-tune large language models (LLM) within a legally compliant and
resource efficient marketplace. Viz represents a significant contribution to
the field of artificial intelligence, particularly in addressing the challenges
of computational efficiency, legal compliance, and economic sustainability in
the utilization and monetization of LLMs. The paper delineates the scholarly
discourse and developments that have informed the creation of Viz, focusing
primarily on the advancements in LLM models, copyright issues in AI training
(NYT case, 2023), and the evolution of model fine-tuning techniques,
particularly low-rank adapters and quantized low-rank adapters, to create a
sustainable and economically compliant framework for LLM utilization. The
economic model it proposes benefits content creators, AI developers, and
end-users, delineating a harmonious integration of technology, economy, and
law, offering a comprehensive solution to the complex challenges of today's AI
landscape.
