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.

利用Viz系统，通过整合量化低秩适配器(QLoRA)来微调大型语言模型(LLM)，在具备法律合规和资源高效市场的框架下，解决了计算效率、法律合规和经济可持续性等人工智能领域的挑战。该论文针对LLM模型的进展、人工智能训练中的版权问题以及模型微调技术的演进等进行了细致的分析和介绍，为LLM的利用提供了一个综合的解决方案，受益于内容创作者、人工智能开发者和最终用户的经济模型。

Viz: 一个基于 QLoRA 的合法合规生成式人工智能版权市场