QwQ
Q
Qwq
Overview :
QwQ (Qwen with Questions) is an experimental research model developed by the Qwen team, aimed at enhancing artificial intelligence's reasoning abilities. It embodies a philosophical spirit, approaching every question with genuine curiosity and skepticism, seeking deeper truths through self-questioning and reflection. QwQ excels in mathematics and programming, particularly in addressing complex problems. Although it is still learning and evolving, it has already demonstrated significant potential for deep reasoning in technological domains.
Target Users :
QwQ targets researchers, developers, and students interested in deep reasoning and artificial intelligence technologies. It is ideal for professionals who need to tackle complex mathematical problems, programming challenges, and engage in deep thinking.
Total Visits: 4.3M
Top Region: CN(27.25%)
Website Views : 199.5K
Use Cases
- In the GPQA benchmark test, QwQ achieved a score of 65.2%, demonstrating its capability in solving scientific problems.
- In the AIME test, QwQ scored 50.0%, showcasing its strengths in mathematical problem-solving.
- In LiveCodeBench, QwQ scored 50.0%, validating its programming ability in real-world scenarios.
Features
- Language mixing and code-switching: The model may unintentionally switch between languages, affecting the clarity of responses.
- Recursive reasoning loops: The model may enter a looped reasoning mode, leading to lengthy and inconclusive answers.
- Safety and ethical considerations: The model requires enhanced safety measures to ensure reliable and secure performance.
- Performance and benchmarking limitations: While the model performs excellently in mathematics and programming, there is room for improvement in common sense reasoning and language understanding.
How to Use
1. Visit QwQ's GitHub page to access the model.
2. Set up and run the QwQ model following the documentation guidelines.
3. Provide a problem or task to be solved and observe how QwQ handles it.
4. Analyze QwQ's output to evaluate its reasoning process and results.
5. Adjust the complexity of the problem or task as needed to test QwQ's performance.
6. Compare QwQ's results with other AI models or traditional methods to assess its strengths and limitations.
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