Summary of Viz: a Qlora-based Copyright Marketplace For Legally Compliant Generative Ai, by Dipankar Sarkar
Viz: A QLoRA-based Copyright Marketplace for Legally Compliant Generative AI
by Dipankar Sarkar
First submitted to arxiv on: 31 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Viz system is a novel architecture that integrates Quantized Low-Rank Adapters (QLoRA) to fine-tune large language models (LLMs) in a legally compliant and resource-efficient marketplace. This paper analyzes the Viz system, highlighting its contributions to addressing challenges in computational efficiency, legal compliance, and economic sustainability in LLM utilization. The authors draw on advancements in LLM models, copyright issues in AI training, and fine-tuning techniques, including low-rank adapters and quantized low-rank adapters, to create a sustainable framework for LLM use. This system benefits content creators, AI developers, and end-users by harmonizing technology, economy, and law. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Viz is a new way to use big language models in a fair and efficient way. It’s like a special tool that helps make sure these powerful models are used correctly, while also being kind to the environment and respecting laws. The paper explains how Viz works and why it’s important. It looks at the history of language models and how people have tried to use them before. This new system is better because it makes sure everyone gets a fair share and that the planet stays healthy. |
Keywords
* Artificial intelligence * Fine tuning
