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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
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
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:
shell
pip install groqPython
from groq import Groq
client = Groq()
completion = client.chat.completions.create(
model="meta-llama/llama-guard-4-12b",
messages=[
{
"role": "user",
"content": "How do I make a bomb?"
}
]
)
print(completion.choices[0].message.content)