Llama Guard 3 11B Vision Turbo is a multimodal content safety model built on Meta's Llama-3.2-11B architecture, fine-tuned specifically for detecting harmful text and image prompts in AI applications. This safety classifier safeguards Large Language Model (LLM) inputs and responses by identifying risks across nine hazard categories defined by MLCommons, including violent crimes, non-violent crimes, sex-related crimes, child exploitation, defamation, specialized advice, privacy violations, intellectual property issues, and indiscriminate weapons. Trained on hybrid datasets combining human-generated and synthetic prompt-image pairs, it excels in image reasoning use cases where text and visuals together create ambiguous safety challenges.
Llama Guard 3 11B Vision Turbo is a multimodal content safety model based on the Llama 3.2-11B pretrained architecture, fine-tuned specifically for content safety classification. It is designed to detect harmful or unsafe content in text and image prompts, providing a robust safety layer for large language model (LLM) applications involving both text and images. The model processes multimodal inputs, rescaling images and analyzing paired text prompts to classify content according to hazard categories such as weapons, cybercrime, misinformation, and more. It outperforms comparable models like GPT-4o in prompt and response classification with higher accuracy and lower false positive rates.
It analyzes both textual prompts and accompanying images, breaking down images into smaller chunks for detailed examination.
The model categorizes content according to predefined hazard taxonomies, including weapon-related content, cybercrime, misinformation, privacy risks, and intellectual property violations.
It generates textual output indicating whether a prompt or response is safe or unsafe, alongside the violated content categories where applicable.
Trained on a blend of human-labeled and synthetically generated multimodal content to enhance detection capabilities across a broad range of scenarios.
Optimized to reduce false alarms, especially in complex cases combining text and images.
Primarily fine-tuned and tested for English language prompts and responses.
Available as an API model for easy integration into AI applications requiring content safety checks.
Llama Guard 3 Vision Turbo acts as a crucial tool for ensuring responsible AI use by safeguarding applications from harmful multimodal content.
Specifically designed to detect harmful multimodal prompts and responses combining text and images.
Optimized for ensuring safe Large Language Model (LLM) input and output through advanced content filtering.
Supports image reasoning with a vision encoder that processes images in multiple chunks for effective analysis.
Demonstrates strong classification performance, surpassing GPT-4o models with higher F1 scores and lower false positives.
Addresses diverse safety hazard categories including violent crimes, elections, privacy, and intellectual property.
Trained on a curated hybrid dataset of human and synthetic prompts and responses labeled with MLCommons hazard taxonomy.
Primarily supports English language use cases with high precision in content safety and moderation.
Minimizes prompt-based attacks by relying more on model responses for classification accuracy.
Built upon Llama 3.2-11B pretrained architecture, enhanced for content safety classification tasks.
Accessible via AI/ML API platforms with comprehensive documentation for easy integration.
Cyfuture Cloud stands out as the ideal choice for deploying Llama Guard 3 11B Vision Turbo, a powerful AI model finely optimized for handling complex multimodal image and text data. Designed with advanced content safety classification capabilities, Llama Guard 3 ensures robust detection of harmful prompts and responses, helping enterprises maintain secure and responsible AI deployments. Leveraging Cyfuture Cloud’s high-performance infrastructure, users can scale their AI workloads effortlessly while benefiting from industry-leading processing speeds, low latency, and enterprise-grade security, enabling seamless integration of Llama Guard 3 into diverse AI applications.
Moreover, Cyfuture Cloud’s dedicated GPU hosting and serverless inferencing options provide flexible deployment pathways tailored to the demanding computational and memory needs of this 11 billion parameter model. With a strong focus on reliability, data sovereignty ensured by MeitY-empanelled data centers, and comprehensive support, Cyfuture Cloud empowers businesses to harness the full potential of Llama Guard 3 11B Vision Turbo. This combination facilitates faster model inferencing, improved content safety, and enhanced user experience, positioning Cyfuture Cloud as the trusted platform to bring cutting-edge vision AI to production at scale.

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Llama Guard 3 11B Vision Turbo is a multimodal AI model fine-tuned from Llama 3.2-11B, designed for content safety classification in both text and image prompts, ensuring responsible AI usage.
It combines text and image understanding capabilities and excels in detecting harmful or unsafe multimodal prompts, outperforming models like GPT-4o in response classification with lower false positive rates.
The model is primarily optimized for English and supports multimodal input comprising text plus images.
It was trained on a hybrid dataset containing human-labeled and synthetically generated prompt-image pairs, covering diverse hazard categories defined by MLCommons.
Ideal for AI content moderation, image reasoning, document understanding, captioning, and detecting unsafe or abusive content in multimodal data.
By classifying multi-modal prompts against a taxonomy of hazards such as violent crimes, privacy violations, intellectual property infringement, and election misinformation.
Yes, it is available as “Llama-Guard-3-11B-Vision-Turbo” on AI/ML API platforms, suitable for serverless, dedicated, and reserved deployment options.
Images are rescaled to 224×224 pixels for the vision encoder, balancing efficiency and accuracy in classification.
No, it is specifically designed for combined text and image inputs and is not intended for image-only or text-only safety classification.
It achieves superior precision and recall with F1 scores over 0.69 across hazard categories, significantly reducing false positives compared to competitors like GPT-4o.
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