Llama Guard 3 and Meta's AI Responsibility Approach for Llama 3.1

Expanding our open source large language models responsibly 
 
 
 
 
 
 
 
 
 
 
 
 

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 Open Source Expanding our open source large language models responsibly 

 July 23, 2024 • 7 minute read 
 Takeaways:

 Meta is committed to openly accessible AI. Read Mark Zuckerberg’s letter detailing why open source is good for developers, good for Meta, and good for the world.
 Open source has multiple benefits: It helps ensure that more people around the world can access the opportunities that AI provides, guards against concentrating power in the hands of a small few, and deploys technology more equitably. And we believe it will lead to more safe AI outcomes across society. That’s why we continue to advocate for making open access to AI the industry standard.
 We’re bringing open intelligence to all by introducing the Llama 3.1 collection of models, which expand context length to 128K, add support across eight languages, and include Llama 3.1 405B—the first frontier-level open source AI model.
 As we improve the capabilities of our models, we’re also scaling our evaluations, red teaming, and mitigations, including for catastrophic risks.
 We’re bolstering our system-level safety approach with new security and safety tools, which include Llama Guard 3 (an input and output multilingual moderation tool), Prompt Guard (a tool to protect against prompt injections), and CyberSecEval 3 (evaluations that help AI model and product developers understand and reduce generative AI cybersecurity risk). We’re also continuing to work with a global set of partners to create industry-wide standards that benefit the open source community.
 We prioritize responsible AI development and want to empower others to do the same. As part of our responsible release efforts, we’re giving developers new tools and resources to implement the best practices we outline in our Responsible Use Guide .
 

 How Meta is scaling AI safety

 
 We’re closely following as governments around the world seek to define AI safety. Meta supports new safety institutes and works with established entities—including the National Institute of Standards and Technology (NIST) and ML Commons—to drive toward common definitions, threat models, and evaluations. Working with bodies such as Frontier Model Forum (FMF) and Partnership on AI (PAI), we seek to develop common definitions and best practices, while also engaging with civil society and academics to help inform our approach. For this release, we’ve continued to build on our efforts to evaluate and red team our models in areas of public safety and critical infrastructure, which includes cybersecurity, catastrophic risks, and child safety.

 It’s important to note that before releasing a model, we work to identify, evaluate, and mitigate potential risks through several measures:

 We conduct pre-deployment risk assessments, safety evaluations and fine-tuning, and extensive red teaming wi

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