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const { OpenAI } = require('openai');
const api = new OpenAI({
baseURL: 'https://api.ai.cc/v1',
apiKey: '',
});
const main = async () => {
const result = await api.chat.completions.create({
model: 'Meta-Llama/Llama-Guard-7b',
messages: [
{
role: 'system',
content: 'You are an AI assistant who knows everything.',
},
{
role: 'user',
content: 'Tell me, why is the sky blue?'
}
],
});
const message = result.choices[0].message.content;
console.log(`Assistant: ${message}`);
};
main();
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.ai.cc/v1",
api_key="",
)
response = client.chat.completions.create(
model="Meta-Llama/Llama-Guard-7b",
messages=[
{
"role": "system",
"content": "You are an AI assistant who knows everything.",
},
{
"role": "user",
"content": "Tell me, why is the sky blue?"
},
],
)
message = response.choices[0].message.content
print(f"Assistant: {message}")
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Product Detail
Unlocking Safer Human-AI Conversations with Llama Guard (7B)
Llama Guard, built upon the powerful Llama2-7b architecture, is a cutting-edge LLM-based model meticulously engineered to significantly enhance the safety and integrity of Human-AI interactions. It integrates a sophisticated safety risk taxonomy, providing a robust framework for classifying potential risks within both user prompts and AI-generated responses.
✅ Exceptional Performance: Llama Guard consistently delivers performance on par with, or even surpassing, existing content moderation tools across critical benchmarks like the OpenAI Moderation Evaluation dataset and ToxicChat. This model is fine-tuned on a high-quality, curated dataset, ensuring its reliability and effectiveness in AI safety.
🔍 Comprehensive Safety Risk Taxonomy
At the heart of Llama Guard's capabilities lies its safety risk taxonomy. This foundational tool provides a systematic approach to identifying and categorizing specific safety concerns in two key areas crucial for robust LLM moderation:
- Prompt Classification: Analyzing user input to detect potential safety risks before an AI response is generated.
- Response Classification: Evaluating the AI's output to ensure it adheres to safety guidelines and remains free from harmful content.
This systematic framework significantly enhances the model's ability to ensure secure and appropriate interactions within AI-generated conversations, making it an invaluable tool for content moderation.
🚀 Advanced Performance and Fine-Tuning for LLM Moderation
Despite utilizing a more compact data volume, Llama Guard exhibits exceptional performance, often surpassing existing content moderation solutions in both accuracy and reliability. Its core strengths include:
- Multi-Class Classification: Capable of identifying various categories of risks within content.
- Binary Decision Scores: Providing clear 'safe' or 'unsafe' evaluations for swift action.
- Instruction Fine-Tuning: This crucial process allows for deep customization, enabling the model to adapt to specific task requirements and output formats. This makes Llama Guard an incredibly flexible tool for diverse safety-related applications.
💡 Customization and Seamless Adaptability
The power of instruction fine-tuning extends to Llama Guard's remarkable customization and adaptability, enabling tailored AI safety measures. Users can:
- Adjust Taxonomy Categories: Tailor the safety taxonomy to specific organizational needs or industry standards for more precise content moderation.
- Facilitate Zero-Shot or Few-Shot Prompting: Seamlessly integrate with diverse taxonomies and quickly adapt to new safety requirements without extensive re-training.
This high degree of flexibility ensures that Llama Guard can provide tailored safety measures across a wide array of AI interaction use cases, enhancing overall Human-AI conversation safety.
🌐 Open Availability and Collaborative Future in AI Safety
To foster innovation and collective improvement in AI moderation and safety, the Llama Guard model weights are publicly available. This open-source approach actively encourages researchers and developers to:
- Further Refine the Model: Enhance its capabilities and address emerging safety challenges in Human-AI conversations.
- Adapt to Evolving Needs: Customize Llama Guard for specific community requirements and diverse use cases.
This commitment to open development aims to drive continuous progress in creating safer AI environments and advancing LLM moderation techniques.
⚙️ How to Utilize Llama Guard for Your LLM Applications
Integrating Llama Guard into your applications can be streamlined to enhance content moderation. While the original content referenced a specific snippet for usage, generally, developers can use Llama Guard for robust content moderation tasks within their LLM applications. This typically involves passing user prompts or AI responses to the model for safety classification.
Example Use Case: Implement Llama Guard as a pre-processing step for user inputs to filter out harmful prompts, or as a post-processing step for AI outputs to ensure generated content is safe and compliant with your standards.
For more details on implementation, refer to the official documentation or community resources once the model weights are accessed to fully leverage its AI safety capabilities.
❓ Frequently Asked Questions (FAQs)
1. What is Llama Guard (7B) designed for?
Llama Guard (7B), built on Llama2-7b, is an LLM-based model specifically designed to enhance the safety of Human-AI conversations by classifying safety risks in both user prompts and AI responses using a comprehensive safety risk taxonomy.
2. How does Llama Guard ensure content safety and LLM moderation?
It uses an instruction-tuned model and a detailed safety risk taxonomy for multi-class classification, providing binary decision scores to identify and flag unsafe content or prompts, performing both prompt and response classification.
3. Can I customize Llama Guard's safety guidelines and taxonomy?
Yes, through instruction fine-tuning, Llama Guard allows for significant customization of taxonomy categories and supports zero-shot or few-shot prompting, making it highly adaptable to diverse safety requirements and use cases.
4. Is Llama Guard's model available for public use or research?
Yes, the Llama Guard model weights are made publicly available to encourage researchers and developers to further refine and adapt the model, fostering continuous improvement in AI safety and moderation practices.
5. How does Llama Guard compare to other content moderation tools?
Llama Guard demonstrates exceptional performance, matching or exceeding the accuracy and reliability of existing content moderation solutions on key benchmarks like OpenAI Moderation Evaluation and ToxicChat, despite its relatively lower data volume.
Information adapted from: Original: Llama Guard (7B) Description
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