Llama Guard 4 12B
meta-llama/llama-guard-4-12bTOKEN SPEED
~1,200 tps
Powered bygroq
INPUT
Text, images
OUTPUT
Text
MetaLlama 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.
Usage note: With respect to any multimodal models included in Llama 4, the rights granted under Section 1(a) of the Llama 4 Community License Agreement are not being granted to you by Meta if you are an individual domiciled in, or a company with a principal place of business in, the European Union.
PRICING
Input
$0.20
5.0M / $1
Output
$0.20
5.0M / $1
LIMITS
CONTEXT WINDOW
131,072
MAX OUTPUT TOKENS
1,024
MAX FILE SIZE
20 MB
MAX INPUT IMAGES
5
QUANTIZATION
This uses Groq's TruePoint Numerics, which reduces precision only in areas that don't affect accuracy, preserving quality while delivering significant speedup over traditional approaches. Learn more here.
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)