Llama-Guard-3-8B

Deprecated

Llama-Guard-3-8B is Meta's specialized 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.

Try now on Groq Card

Key Technical Specifications

Model Architecture

Built upon Meta's Llama architecture, the model is comprised of 8 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

Technical Details

FEATURE	VALUE
Context Window (Tokens)	8,192
Max Output Tokens	-
Max File Size	-
Token Generation Speed	765 tps
Input Token Price	$0.2/1M tokens
Output Token Price	$0.2/1M tokens
Tool Use
JSON Mode
Image Support

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

Get Started with Llama-Guard-3-8B

Unlock the full potential of content moderation with Llama-Guard-3-8B - optimized for exceptional performance on Groq hardware now:

shell

pip install groq

Python

from groq import Groq
client = Groq()
completion = client.chat.completions.create(
    model="llama-guard-3-8b",
    messages=[
        {
            "role": "user",
            "content": "Explain why fast inference is critical for reasoning models"
        }
    ]
)
print(completion.choices[0].message.content)