Viz: A QLoRA-based Copyright Marketplace for Legally Compliant Generative AI
Dipankar Sarkar
TL;DR
Viz proposes a legally compliant, resource-efficient ecosystem for LLM deployment by integrating Quantized Low-Rank Adapters ($QLoRA$) into a Spotify-like marketplace of fine-tune modules. The approach trains base models on non-copyrighted data, uses $QLoRA$ with $4$-bit NormalFloat quantization to enable content-specific fine-tuning on modest hardware, and monetizes modules through dynamic pricing and revenue sharing. The paper analyzes legal and ethical considerations (copyright, GDPR, fair use) and presents an economic design aimed at balancing incentives for content providers, developers, and users. Overall, Viz aims to democratize access to high-performance AI while ensuring compliance, transparency, and sustainable economic viability.
Abstract
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.