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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-3-11B-Vision-Turbo',
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-3-11B-Vision-Turbo",
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
ⓘ Overview: Llama Guard 3 11B Vision
Llama Guard 3 11B Vision is a cutting-edge multimodal content safety classifier developed by Meta. Released on December 6, 2023, this Llama 3.2 version model is specifically engineered to enhance the safety of Large Language Model (LLM) applications by detecting harmful content in both text and image inputs and responses.
- ✓ Model Name: Llama Guard 3 11B Vision
- ✓ Developer: Meta
- ✓ Release Date: December 6, 2023
- ✓ Model Type: Multimodal (Text & Image) Content Safety Classifier
🔍 Key Features for Enhanced LLM Safety
This model brings significant advancements to content moderation, especially in complex multimodal scenarios, ensuring safer AI interactions.
- Harmful Content Detection: Identifies inappropriate or unsafe content in both text and image inputs, safeguarding LLM interactions.
- Optimized for Image Reasoning: Excels in scenarios where visual context is crucial for accurate safety classification.
- Detailed Safety Output: Generates clear text outputs indicating safety levels and specific violated content categories for actionable insights.
- Superior Performance: Outperforms leading models like GPT-4o and GPT-4o mini in response classification, featuring significantly lower false positive rates.
💬 Intended Use & Language Support
Llama Guard 3 11B Vision is primarily designed for use cases requiring robust detection of harmful content within multimodal inputs and responses. It's an essential tool for developers and organizations aiming to ensure the safety and ethical use of their LLM applications.
- 💬 Primary Application: Securing LLM applications against harmful multimodal content.
- 💬 Optimized Language: Primarily developed and optimized for the English language.
📚 Technical Deep Dive
Understanding the architecture and training methodology reveals the robustness and advanced capabilities of Llama Guard 3 11B Vision.
Architecture
The model is built upon a Llama-3.2-11B pretrained model, which has been meticulously fine-tuned specifically for content safety classification tasks, leveraging its powerful foundational capabilities for superior accuracy.
Training Data Strategy
The training regimen utilized a sophisticated hybrid dataset. This dataset combines both human-generated and synthetically generated data, ensuring comprehensive coverage of various harmful scenarios and improving real-world applicability. It includes:
- Human-created prompts paired with diverse corresponding images.
- Benign and violating model responses generated using in-house Llama models and advanced jailbreaking techniques to simulate real-world adversarial attacks.
Data Source and Size
The dataset is exceptionally diverse, featuring a wide range of prompt-image pairs. These pairs are meticulously labeled either by human annotators or by the advanced Llama 3.1 405B model. The data encompasses all hazard categories defined by MLCommons, ensuring a broad and thorough training base. For image processing, the vision encoder efficiently rescales images into 4 chunks, each measuring 560x560 pixels.
Diversity and Bias Mitigation
Commitment to Diversity: The curation process prioritized creating a dataset that truly reflects a diverse array of prompt-image pairs, spanning every defined hazard category to minimize bias and enhance robust detection across various scenarios.
📈 Performance Metrics & Benchmarking
Llama Guard 3 11B Vision's efficacy is rigorously evaluated against an internal test set aligning with the MLCommons hazard taxonomy. The model consistently delivers strong performance and reliability.
Exceptional F1 Scores: Llama Guard 3 Vision achieves F1 scores exceeding 0.69 in every hazard category, including challenging areas like Indiscriminate Weapons and Elections, demonstrating high accuracy and reliability across the board.
Comparison to Other Industry Models
In head-to-head comparisons, Llama Guard 3 Vision demonstrates superior capabilities against prominent models such as GPT-4o and GPT-4o mini. This superiority is particularly evident in response classification, where it achieves higher F1 scores and significantly lower false positive rates. The model’s design effectively minimizes prompt-based attacks by relying more on the model response for classification, addressing the inherent ambiguity of combined text and image prompts with greater precision.
🔑 Usage & API Access
Integrating Llama Guard 3 11B Vision into your applications is straightforward, providing robust content safety features with ease.
Code Samples:
<snippet data-name="open-ai.chat-completion" data-model="meta-llama/Llama-Guard-3-11B-Vision-Turbo"></snippet>The model is readily available on the AI/ML API platform under the identifier "Llama-Guard-3-11B-Vision-Turbo". Access the API here to get started.
API Documentation:
For detailed technical guidance, integration instructions, and comprehensive information, refer to the official API Documentation.
📒 Ethical Guidelines & Limitations
It's crucial to understand the ethical considerations and specific limitations associated with Llama Guard 3 11B Vision for responsible and effective deployment within your applications.
Important Note: Llama Guard 3 Vision is fine-tuned on Llama 3.2-vision. Its performance and capabilities are inherently tied to its pre-training data. It is not intended to serve as a standalone image safety classifier or a text-only safety classifier. It is designed for multimodal content safety specifically within the context of LLM inputs and responses to provide a layered defense.
To begin leveraging the powerful capabilities of Llama Guard 3 11B Vision Turbo API, you can get started here.
ⓘ Frequently Asked Questions (FAQ)
- Q1: What is Llama Guard 3 11B Vision?
- A1: It's a multimodal content safety classification model developed by Meta, specifically designed to detect harmful text and image content in Large Language Model (LLM) inputs and responses.
- Q2: What types of content can Llama Guard 3 11B Vision detect?
- A2: It is engineered to detect harmful content across both text and image formats, making it highly effective for multimodal LLM safety and content moderation.
- Q3: How does its performance compare to other safety models like GPT-4o?
- A3: Llama Guard 3 Vision demonstrates superior performance compared to GPT-4o and GPT-4o mini, particularly in response classification, achieving higher F1 scores and significantly lower false positive rates.
- Q4: Is Llama Guard 3 11B Vision suitable for standalone text-only or image-only classification?
- A4: No, it is specifically designed and optimized for multimodal content safety within LLM contexts and is not intended for use as a standalone text-only or image-only classifier.
- Q5: How can I access the Llama Guard 3 11B Vision API?
- A5: The model is available on the AI/ML API platform under the identifier "Llama-Guard-3-11B-Vision-Turbo". You can find access and detailed documentation on the platform's official website.
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