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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/qwen-2.5-vl-7b-instruct',
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/qwen-2.5-vl-7b-instruct",
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}")
Product Detail
Qwen2.5 VL 7B Instruct: A Cutting-Edge Multimodal AI Solution
Qwen2.5 VL 7B Instruct is an advanced multimodal AI model meticulously engineered for instruction-based tasks that seamlessly integrate both textual and visual inputs. It showcases exceptional capabilities in understanding and reasoning through diverse images and complex documents, providing a versatile and robust solution for precise text recognition and dynamic, multi-turn interactions across various modalities. This model empowers developers to build intelligent applications that bridge the gap between human language and visual information.
⚙️ Technical Specifications
- Model Size: 7 Billion parameters
- Architecture: Advanced Transformer-based multimodal framework
- Modalities: Text, Image
- Languages: Primarily English, with extensive support for multilingual text recognition
- Input Types: Flexible text prompts, alongside various image formats (optimized for OCR and visual reasoning)
- Context Window: Generous 32,768 tokens
- Output Types: Rich textual responses, including both extracted and synthetically generated content
📊 Impressive Performance Benchmarks
- DocVQA: 95.7% – Leading accuracy in Document Understanding.
- ChartQA: 87.3% – Strong capabilities in Chart Analysis.
- OCRBench: 86.4% – Highly robust Optical Character Recognition.
- MMBench: 82.6% – Excellent General Multimodal performance.
- MMMU: ~53.77% – Achieved with BF16 quantization, demonstrating strong multi-discipline reasoning.
✨ Key Features of Qwen2.5 VL 7B Instruct
- ✅ Superior OCR (Optical Character Recognition): Achieve precise and reliable text extraction from even the most complex images and diverse document types.
- 🧠 Advanced Visual Reasoning: The model deeply understands spatial and contextual information within images, leading to better scene comprehension and insightful analysis.
- 📄 Intelligent Document Analysis: Efficiently process and accurately interpret both structured and unstructured document layouts, streamlining information workflows.
- 🔄 Seamless Dual-Modality Task Handling: Effortlessly manage intricate text-to-text and image-to-text interactions within demanding instruction-based workflows.
- 🎯 Instruction-tuned for Precision: The model is finely tuned to follow detailed task instructions, significantly boosting response relevance, accuracy, and overall utility.
💰 Qwen2.5 VL 7B Instruct API Pricing
Input: $0.21 per 1K tokens
Output: $0.21 per 1K tokens
🚀 Diverse Use Cases & Applications
- Automated Data Extraction: Revolutionize data capture from scanned documents, invoices, receipts, and other forms.
- Intelligent Visual QA Systems: Power systems that accurately answer questions based on images or a combination of text and image inputs.
- Enhanced Document Workflows: Implement smart document indexing and content summarization for superior knowledge management and operational efficiency.
- Assistive Technologies: Develop innovative tools for visually impaired users by precisely describing visual content and reading on-screen text aloud.
- Multilingual Customer Support: Elevate global customer service through advanced recognition of visual and textual content, enabling intelligent, multilingual replies.
💻 Code Sample for API Integration
Below is an illustrative code snippet demonstrating how to interact with the Qwen2.5 VL 7B Instruct API. This example provides a foundation for developers to quickly integrate multimodal capabilities into their applications.
import openai # Replace with your actual API base URL and key client = openai.OpenAI( base_url="YOUR_QWEN_API_BASE_URL", api_key="YOUR_API_KEY", ) try: response = client.chat.completions.create( model="qwen/qwen-2.5-vl-7b-instruct", messages=[ {"role": "user", "content": [ {"type": "text", "text": "Describe this image in detail and extract any text present."}, {"type": "image_url", "image_url": {"url": "https://example.com/your-image.jpg"}} ]} ], max_tokens=2048, # Adjust as needed temperature=0.7, # Control creativity ) print("API Response:") print(response.choices[0].message.content) except openai.APIError as e: print(f"An API error occurred: {e}") except Exception as e: print(f"An unexpected error occurred: {e}") 🔍 Qwen2.5 VL 7B Instruct: Competitive Model Comparisons
vs. GPT-4o Vision
Qwen2.5-VL-7B-Instruct offers highly competitive OCR accuracy and robust visual reasoning within its 7 Billion parameter size. This makes it a more cost-effective and faster solution for rapid deployment, especially for specialized tasks. While GPT-4o Vision excels with superior general multimodal capabilities and broader language support, it typically entails higher operational costs and marginally slower inference speeds due to its larger scale.
vs. Claude 4 Vision
Claude 4 Vision is recognized for its powerful conversational multimodal understanding and enhanced contextual dialogue abilities, though often at higher computational costs. In contrast, Qwen2.5-VL-7B-Instruct shines in structured document recognition and visual reasoning, delivering strong OCR performance at a more attractive price point, ideal for document-intensive applications.
vs. DeepSeek V3.1
DeepSeek V3.1 stands out for its proficiency in video understanding and complex multimedia search tasks. Qwen2.5-VL-7B-Instruct, however, is specifically optimized for static image and document text recognition and reasoning. It provides faster inference speeds for image-text tasks and superior OCR accuracy, establishing itself as the preferred choice for document-centric workflows demanding both precision and efficiency.
❓ Frequently Asked Questions (FAQ)
Q1: What are the core strengths of Qwen2.5 VL 7B Instruct?
A: It excels in multimodal instruction-based tasks, offering robust OCR, advanced visual reasoning, and efficient document analysis. Its instruction-tuned nature ensures highly relevant and accurate responses for both text and image inputs.
Q2: How does its performance compare to larger multimodal models?
A: Despite its 7B parameter size, Qwen2.5 VL 7B Instruct delivers competitive OCR accuracy and strong visual reasoning, often presenting a more cost-effective and faster deployment alternative for specialized tasks compared to larger, more generalist models.
Q3: What types of input and output does the API support?
A: It accepts text prompts and images (for OCR/visual reasoning) as input. The API generates textual responses, which can include extracted text from images or synthetically generated content based on the given instructions.
Q4: Is Qwen2.5 VL 7B Instruct suitable for multilingual applications?
A: Yes, while its primary focus is English, it boasts strong multilingual text recognition capabilities, making it a viable choice for global applications such as multilingual customer support and international document processing.
Q5: What are the typical industries or use cases benefiting from this model?
A: Industries such as finance (receipt/invoice processing), healthcare (medical document analysis), e-commerce (visual product search/QA), and customer service (multimodal support) can greatly benefit from its capabilities in data extraction, visual QA, and intelligent document handling.
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