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Purple Llama: Difference between revisions
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(Created page with "==Announcement Summary== Purple Llama is a new project announced to foster open trust and safety in the generative AI field. It provides tools and evaluations like CyberSec Eval and Llama Guard to help developers deploy AI models responsibly, in line with the Responsible Use Guide. The project seeks broad collaboration with industry leaders like AMD, AWS, and Google Cloud to enhance and distribute these tools openly. Initial offerings focus on cybersecurity and input/out...") |
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One user shares a personal experience with Facebook's moderation system to highlight the challenges of automated content moderation and the need for more nuanced, human-driven approaches. The conversation reflects a mix of skepticism and hope for the potential of open-source AI to address security and ethical concerns in AI development. There's also a debate on whether Meta's strategy with Llama and open-source contributions is genuinely beneficial or just a move to rehabilitate its brand. | One user shares a personal experience with Facebook's moderation system to highlight the challenges of automated content moderation and the need for more nuanced, human-driven approaches. The conversation reflects a mix of skepticism and hope for the potential of open-source AI to address security and ethical concerns in AI development. There's also a debate on whether Meta's strategy with Llama and open-source contributions is genuinely beneficial or just a move to rehabilitate its brand. | ||
==Responsible Use Guide== | |||
The document is a comprehensive guide on responsible AI practices and product development for large language models (LLMs), particularly focusing on Llama 2 and Code Llama provided by Meta. It emphasizes the importance of open science and democratization of AI technologies to foster innovation and manage risks collaboratively. The guide details various stages of responsible LLM product development, including determining use cases, fine-tuning the model with safety considerations, addressing input and output-level risks, and building transparency and reporting mechanisms. | |||
===Key sections cover=== | |||
#'''Overview of Responsible AI & System Design''': Discusses the importance of ensuring AI technology does not cause undue harm, highlighting core considerations like fairness, safety, privacy, and transparency. | |||
#'''Mitigation Points for LLM-Powered Products''': Explains the need for product-specific layers and decision points throughout the development lifecycle to shape objectives and functionality, thereby mitigating potential risks. | |||
#'''Fine-tuning for Product''': Guides through the process of adapting LLMs to specific domain requirements and introducing safety mitigations. | |||
#'''Addressing Privacy and Adversarial Attacks''': Provides strategies to protect against attacks that attempt to extract information from the model or circumvent content restrictions. | |||
#'''Building Transparency and Reporting Mechanisms''': Highlights the importance of user feedback and the need for clear communication about the AI's capabilities and limitations. | |||
The guide also introduces Code Llama, a family of large language models for coding tasks, emphasizing responsible development and deployment practices specific to coding-related AI features. It advises on defining content policies, evaluating and benchmarking, and considerations for red-teaming and fine-tuning, especially in the context of code generation and safety. | |||
==References== | ==References== | ||