KubeCon + CloudNativeCon + Open Source Summit + AI_dev China 2024

Generative AI (GAI) offers unprecedented opportunities for research and innovation, but its commercialization has raised concerns about transparency, reproducibility, and safety. Many open GAI models lack the necessary components for full understanding and reproducibility, and some use restrictive licenses whilst claiming to be "open-source"'. To address these concerns, the Generative AI Commons at the LF AI & Data Foundation has proposed the Model Openness Framework (MOF), a ranked classification system that rates machine learning models based on their completeness and openness, following principles of open science, open source, open data, and open access. The MOF requires specific components of the model development lifecycle to be included and released under appropriate open licenses. This framework aims to prevent misrepresentation of models claiming to be open, guide researchers and developers in providing all model components under permissive licenses, and help individuals and organizations identify models that can be safely adopted without restrictions.

In this talk, we will discuss the MOF, showcase a demonstration of the Model Openness Tool (the tool that implements the framework), and discuss the benefits the MOF offers to both model producers and consumers. We strongly believe that a wide adoption of the MOF will foster a more open AI ecosystem, benefiting research, innovation, and adoption of state-of-the-art models.

生成AI（GAI）为研究和创新提供了前所未有的机会，但其商业化引发了对透明度、可复现性和安全性的担忧。许多开放的GAI模型缺乏完全理解和可复现性所需的组件，而一些则使用限制性许可证，却声称是“开源”的。为了解决这些问题，LF AI & Data Foundation的生成AI Commons提出了模型开放性框架（MOF），这是一个排名分类系统，根据其完整性和开放性评估机器学习模型，遵循开放科学、开源、开放数据和开放获取的原则。MOF要求模型开发生命周期的特定组件必须包含并发布在适当的开放许可证下。该框架旨在防止声称开放的模型被误解，指导研究人员和开发者在宽松许可证下提供所有模型组件，并帮助个人和组织识别可以安全采纳而无需限制的模型。

在本次讲话中，我们将讨论MOF，并展示模型开放性工具（实施该框架的工具）的演示，探讨MOF对模型生产者和消费者所带来的好处。我们坚信广泛采用MOF将促进更加开放的AI生态系统，有利于研究、创新和最新模型的采用。