• Quantization from fp32
• All models using i-matrix calibration_datav3.txt
• iq2_s and q6_k barely works properly

QwQ is a 32B parameter experimental research model developed by the Qwen Team, focused on advancing AI reasoning capabilities.

QwQ is a 32B parameter experimental research model developed by the Qwen Team, focused on advancing AI reasoning capabilities.

QwQ demonstrates remarkable performance across these benchmarks:

• 65.2% on GPQA, showcasing its graduate-level scientific reasoning capabilities
• 50.0% on AIME, highlighting its strong mathematical problem-solving skills
• 90.6% on MATH-500, demonstrating exceptional mathematical comprehension across diverse topics
• 50.0% on LiveCodeBench, validating its robust programming abilities in real-world scenarios.

These results underscore QwQ’s significant advancement in analytical and problem-solving capabilities, particularly in technical domains requiring deep reasoning.

As a preview release, it demonstrates promising analytical abilities while having several important limitations:

• Language Mixing and Code-Switching: The model may mix languages or switch between them unexpectedly, affecting response clarity.
• Recursive Reasoning Loops: The model may enter circular reasoning patterns, leading to lengthy responses without a conclusive answer.
• Safety and Ethical Considerations: The model requires enhanced safety measures to ensure reliable and secure performance, and users should exercise caution when deploying it.
• Performance and Benchmark Limitations: The model excels in math and coding but has room for improvement in other areas, such as common sense reasoning and nuanced language understanding.