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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: 'qwen/qvq-72b-preview',
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="qwen/qvq-72b-preview",
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
✨ QVQ-72B-Preview: Unleashing Advanced Multimodal AI
Introducing QVQ-72B-Preview, an experimental research model from the innovative Qwen Team, officially released on December 25, 2024. This state-of-the-art Multimodal Language Model is engineered to significantly enhance visual reasoning capabilities, seamlessly integrating advanced processing for both text and visual inputs. It excels at tackling complex problems that demand a deep understanding of visual content.
Basic Information:
- Model Name: QVQ-72B-Preview
- Developer/Creator: Qwen Team
- Release Date: December 25, 2024
- Version: 1.0
- Model Type: Multimodal Language Model
🚀 Key Features & Capabilities
- ✅ Multimodal Reasoning: Process and reason with both text and images for comprehensive understanding and interaction.
- 🧠 High Parameter Count: With 72 billion parameters, it delivers detailed and nuanced responses across diverse tasks.
- 📊 Performance Benchmarks: Achieved an impressive 70.3% on the Multimodal Massive Multi-task Understanding (MMMU) benchmark, showcasing robust performance in multidisciplinary contexts.
- 🔄 Dynamic Input Handling: Supports various inputs including single images, text prompts, and mathematical problems with visual components, enabling diverse applications.
- 📈 Enhanced Visual Understanding: Excels in interpreting complex visual data such as graphs, diagrams, and equations, making it ideal for educational and scientific domains.
💡 Intended Use Cases
QVQ-72B-Preview is specifically designed for developers and researchers aiming to integrate cutting-edge AI capabilities into their projects. Potential applications include:
- 📚 Educational Tools: Create dynamic learning environments and intelligent tutors.
- 🗣️ Interactive Learning: Power next-generation interactive experiences.
- ❓ Visual Question Answering Systems: Develop sophisticated systems that answer questions based on visual inputs.
- ✍️ Automated Content Generation: Enhance content creation with visually intelligent AI.
🌍 Language Support
The QVQ-72B-Preview model offers robust support for multiple languages, including English and Chinese, significantly broadening its applicability across diverse linguistic and global contexts.
⚙️ Technical Details
Architecture:
QVQ-72B-Preview utilizes a highly optimized transformer-based architecture, specifically engineered for efficient processing of complex multimodal inputs. This design allows for seamless integration and analysis of both visual and textual data.
Training Data:
The model was rigorously trained on a comprehensive dataset encompassing a wide array of text and image formats, ensuring robust performance across various real-world scenarios.
- Data Source & Size: The training dataset spans a vast range of topics and genres, carefully curated to ensure diversity in generated responses.
- Diversity & Bias Mitigation: Data curation focused on minimizing biases while maximizing topical and stylistic diversity, significantly enhancing the model's versatility and ethical soundness.
📈 Performance Metrics & Comparisons
To provide a clear understanding of QVQ-72B-Preview's capabilities, the model's performance has been rigorously benchmarked. Below, you'll find a visual representation of its standing relative to other models.
This graph illustrates QVQ-72B-Preview's competitive edge, particularly highlighting its strength in complex multimodal understanding tasks.
💻 How to Use QVQ-72B-Preview
Code Samples:
Integrating QVQ-72B-Preview into your applications is straightforward. The model is accessible on the AI/ML API platform under the identifier "QVQ-72B-Preview".
<snippet data-name="open-ai.chat-completion" data-model="qwen/qvq-72b-preview"></snippet>API Documentation:
For comprehensive details on integration, endpoints, and available parameters, please refer to the Detailed API Documentation:
Access API Documentation here.
🛡️ Ethical Guidelines & Responsible AI
The Qwen team is deeply committed to ethical considerations in AI development. We advocate for transparency regarding QVQ-72B-Preview's capabilities and inherent limitations.
Important: We strongly encourage responsible usage to prevent any potential misuse or deployment of generated content in harmful applications.
📄 Licensing Information
QVQ-72B-Preview is made available under an open-source license. This license grants both research and commercial usage rights, while ensuring strict compliance with ethical standards and creator rights.
Ready to integrate QVQ-72B-Preview into your projects?
🔗 Get QVQ-72B-Preview API Here❓ Frequently Asked Questions (FAQ)
Q1: What is QVQ-72B-Preview?
A: QVQ-72B-Preview is an experimental multimodal language model developed by the Qwen Team. It's designed to enhance visual reasoning by processing both text and image inputs to generate comprehensive responses, particularly excelling in problems requiring visual understanding.
Q2: What are the key capabilities of this model?
A: Its key features include multimodal reasoning (text + images), a high parameter count (72 billion) for nuanced responses, strong performance on the MMMU benchmark (70.3%), dynamic input handling for various data types, and enhanced visual understanding for graphs, diagrams, and equations.
Q3: How can developers access QVQ-72B-Preview?
A: Developers can access the model via the AI/ML API platform, listed as "QVQ-72B-Preview." Detailed API documentation and code samples are available to facilitate integration.
Q4: What languages does it support?
A: The model supports multiple languages, including English and Chinese, making it versatile for global applications.
Q5: Is QVQ-72B-Preview open-source?
A: Yes, QVQ-72B-Preview is released under an open-source license, permitting both research and commercial usage while adhering to ethical standards and respecting creator rights.
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