### Key Technical Specifications

* Model Architecture: Built upon Meta's Llama 4 Scout architecture, the model is comprised of 12 billion parameters and is specifically fine-tuned for content moderation and safety classification tasks.
* Performance Metrics: 
  The model demonstrates strong performance in content moderation tasks:
  * High accuracy in identifying harmful content
  * Low false positive rate for safe content
  * Efficient processing of large-scale content

### Key Technical Specifications

### Model Use Cases

* Content Moderation: Ensures that online interactions remain safe by filtering harmful content in chatbots, forums, and AI-powered systems.
  * Content filtering for online platforms and communities
  * Automated screening of user-generated content in corporate channels, forums, social media, and messaging applications
  * Proactive detection of harmful content before it reaches users
* AI Safety: Helps LLM applications adhere to content safety policies by identifying and flagging inappropriate prompts and responses.
  * Pre-deployment screening of AI model outputs to ensure policy compliance
  * Real-time analysis of user prompts to prevent harmful interactions
  * Safety guardrails for chatbots and generative AI applications

### Model Best Practices

* Safety Thresholds: Configure appropriate safety thresholds based on your application's requirements
* Context Length: Provide sufficient context for accurate content evaluation
* Image inputs: The model has been tested for up to 5 input images - perform additional testing if exceeding this limit.

### Get Started with Llama-Guard-4-12B
Unlock the full potential of content moderation with Llama-Guard-4-12B - optimized for exceptional performance on Groq hardware now:  

Llama Guard 4 12B is Meta's specialized natively multimodal content moderation model designed to identify and classify potentially harmful content. Fine-tuned specifically for content safety, this model analyzes both user inputs and AI-generated outputs using categories based on the MLCommons Taxonomy framework. The model delivers efficient, consistent content screening while maintaining transparency in its classification decisions.

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## Qwen3 32b: Model (tsx)