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This page describes what the document states, permits, or reserves. It does not constitute a legal determination about enforceability. Regulatory applicability may vary by jurisdiction. Methodology
This is the official model card for Meta's Llama 4 AI model collection, covering two models, Llama 4 Scout and Llama 4 Maverick, released April 5, 2025, under a custom commercial license. The card discloses that training data includes publicly shared Instagram and Facebook posts and user interactions with Meta AI, and that developers who deploy these models are responsible for performing their own safety testing and tuning for their specific applications. The card also states that use beyond the 12 explicitly supported languages or beyond five input images for visual tasks is considered out-of-scope, with developers assuming responsibility for safety in those extended use cases.
This model card governs the release and use of Meta's Llama 4 collection, comprising Llama 4 Scout (17Bx16E) and Llama 4 Maverick (17Bx128E), multimodal large language models released under the Llama 4 Community License Agreement. The card states that training data includes 'publicly available, licensed data and information from Meta's products and services,' explicitly including 'publicly shared posts from Instagram and Facebook and people's interactions with Meta AI,' and establishes that developers bear responsibility for safety testing, acceptable use compliance, and ensuring deployments in unsupported languages or extended image inputs are 'mitigated for risks.' The document asserts that use in languages or capabilities beyond the 12 explicitly supported is out-of-scope unless developers independently ensure safe and responsible use, and that all reported evaluations were conducted on bf16 models while quantized checkpoints are provided for deployment, creating a potential gap between evaluated and deployed model performance. The card engages AI governance frameworks including the EU AI Act, and its disclosure of training data sourced from Meta platform user interactions may require evaluation under GDPR and CCPA in the context of downstream commercial deployments; the card's open-source release structure means training compute and emissions costs are borne by Meta and not replicated by deployers. The model card's safety obligations are structured as developer responsibilities rather than platform-enforced constraints, meaning compliance with the Acceptable Use Policy and safe deployment practices depends on downstream implementers rather than technical enforcement at the model level.
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