QwQ-32B-GGUF
| Property | Value |
|---|---|
| Parameter Count | 32.5B (31.0B Non-Embedding) |
| Context Length | 131,072 tokens |
| Architecture | Transformer with RoPE, SwiGLU, RMSNorm |
| Quantization Options | q2_K through q8_0 |
| Model URL | Hugging Face |
What is QwQ-32B-GGUF?
QwQ-32B-GGUF is an advanced reasoning model from the Qwen series, specifically designed to excel at complex problem-solving and reasoning tasks. As a medium-sized reasoning model, it competes with state-of-the-art models like DeepSeek-R1 and o1-mini, while offering significant improvements over conventional instruction-tuned models.
Implementation Details
The model features a sophisticated architecture utilizing 64 layers and a unique attention head configuration with 40 heads for queries and 8 for key-values (GQA). It has undergone both pretraining and post-training phases, including supervised finetuning and reinforcement learning, resulting in enhanced reasoning capabilities.
- Full 131,072 token context length support
- Multiple quantization options for different performance needs
- Advanced architecture combining RoPE, SwiGLU, RMSNorm, and Attention QKV bias
- Efficient implementation with GQA attention mechanism
Core Capabilities
- Enhanced reasoning and problem-solving abilities
- Superior performance on complex tasks compared to standard instruction-tuned models
- Flexible deployment options through various quantization levels
- Extensive context length handling for complex documents
Frequently Asked Questions
Q: What makes this model unique?
QwQ-32B-GGUF stands out for its specialized reasoning capabilities and thoughtful output generation, enforced through specific prompting patterns like "
Q: What are the recommended use cases?
The model excels at tasks requiring complex reasoning, mathematical problem-solving, and multiple-choice questions. It's particularly effective when used with recommended sampling parameters (Temperature=0.6, TopP=0.95) and can handle extremely long inputs up to 131,072 tokens.
