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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/QwQ-32B',
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/QwQ-32B",
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}")
.webp)
Product Detail
✨ Introducing QwQ-32B: A Powerful and Efficient Language Model
The QwQ-32B is a state-of-the-art 32-billion-parameter language model engineered for exceptional performance in advanced reasoning, coding, and complex problem-solving. Through an innovative combination of reinforcement learning and agentic reasoning capabilities, QwQ-32B delivers capabilities often seen only in models with significantly larger parameter counts. It boasts an expansive context window of up to 131K tokens, making it incredibly effective for managing and understanding long-form, intricate workflows.
This compact yet robust model stands out for its efficiency and adaptability, positioning it as an ideal choice for dynamic AI agents and specialized reasoning tasks. It achieves near-parity with much larger models (e.g., DeepSeek-R1 671B) on challenging benchmarks in reasoning and coding. QwQ-32B particularly excels in mathematical problem-solving, logical workflows, and adaptive agentic reasoning, demonstrating robust handling of extensive documents and context-rich tasks.
⚙️ Technical Specifications
- Model Size: 32.5 billion parameters (31B non-embedding)
- Layers: 64 transformer layers
- Context Window: 131,072 tokens
- Architecture: Transformer with RoPE positional encoding, SwiGLU activations, RMSNorm, and QKV attention biasing
- Training: Combination of pretraining, supervised fine-tuning, and multi-stage reinforcement learning
- Alignment: Utilizes RL-based methods to enhance response correctness and mitigate bias, particularly in mathematical and coding domains.
💡 Key Capabilities of QwQ-32B
- 🚀 Reinforcement Learning Enhanced Reasoning: Employs multi-stage RL for highly adaptive and sophisticated problem-solving.
- 🧠 Agentic Reasoning: Dynamically adjusts reasoning strategies based on complex input context and real-time feedback.
- 📚 Extended Context Handling: Supports very long-form inputs, making it ideal for comprehensive document analysis and sustained dialogues.
- 💻 Efficient Coding Assistance: Delivers strong performance in accurate code generation and effective debugging across multiple programming languages.
🎯 Optimal Use Cases
- 🔬 Scientific & Mathematical Research: For tasks requiring deep, structured reasoning and complex calculations.
- 🖥️ Software Development: Facilitating complex software design, robust debugging, and efficient code synthesis.
- 📈 Financial & Engineering Workflows: Streamlining logical processes and data analysis in specialized domains.
- 🤖 AI-Powered Agents: Empowering agents with flexible reasoning capabilities and enhanced adaptability.
🔗 API Access & Documentation
The QwQ-32B model is readily available on the AI/ML API platform, where it can be accessed as "QwQ-32B". Integrate this powerful model into your projects with ease.
For comprehensive technical details, integration guides, and code samples, please refer to the API Documentation provided by the Qwen Team.
⚖️ Ethical Use and Licensing
Ethical Guidelines: The Qwen Team prioritizes safety, implementing rule-based verifiers during training to ensure high correctness in outputs for math and coding tasks. Users are encouraged to exercise caution regarding potential biases or inaccuracies when deploying the model in less-tested or sensitive domains.
Licensing: QwQ-32B is open-source under the Apache 2.0 license. This permits free use for both commercial and research purposes. Its optimized, compact size also facilitates deployment on consumer-grade hardware, making advanced AI more accessible.
❓ Frequently Asked Questions (FAQ)
Q1: What are the primary strengths of QwQ-32B?
A: QwQ-32B excels in advanced reasoning, coding, and structured problem-solving. It combines reinforcement learning and agentic reasoning to achieve performance comparable to much larger models, especially in math and logic-intensive tasks.
Q2: How large is QwQ-32B's context window?
A: QwQ-32B supports an extended context window of up to 131,072 tokens, allowing it to handle very long documents and complex, context-rich conversations effectively.
Q3: Can QwQ-32B be used for commercial projects?
A: Yes, QwQ-32B is released under the Apache 2.0 license, which permits free use for both commercial and research purposes.
Q4: Is QwQ-32B efficient to deploy?
A: Absolutely. Despite its powerful capabilities, QwQ-32B is designed to be compact and efficient, making it deployable even on consumer-grade hardware.
Q5: Where can I find the API documentation?
A: Detailed API documentation for QwQ-32B is available here, providing all necessary information for integration.
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