Qwen-QwQ-32B
| Property | Value |
|---|---|
| Parameter Count | 32.5B (31.0B Non-Embedding) |
| Model Type | Causal Language Model |
| Context Length | 131,072 tokens |
| Architecture | Transformer with RoPE, SwiGLU, RMSNorm, GQA |
| Training | Pretraining & Post-training (SFT + RL) |
What is Qwen-QwQ-32B-425bpw-h6-exl2?
QwQ-32B is an advanced reasoning model from the Qwen series, specifically designed to excel at complex problem-solving tasks. As a medium-sized reasoning model, it competes with state-of-the-art models like DeepSeek-R1 and o1-mini, featuring sophisticated architecture components and extensive training.
Implementation Details
The model implements a cutting-edge architecture featuring 64 layers and a unique attention head configuration with 40 heads for queries and 8 for key/values using Group Query Attention (GQA). It utilizes advanced components like Rotary Position Embedding (RoPE), SwiGLU activations, and RMSNorm for enhanced performance.
- Massive context window of 131,072 tokens
- Comprehensive training including pretraining and post-training phases
- Specialized attention mechanism with GQA architecture
- YaRN scaling support for improved long-sequence handling
Core Capabilities
- Enhanced reasoning and problem-solving abilities
- Competitive performance against leading reasoning models
- Effective handling of long-context scenarios
- Standardized output formatting for various task types
Frequently Asked Questions
Q: What makes this model unique?
QwQ-32B stands out for its specialized reasoning capabilities and thoughtful output generation, enforced through specific prompting patterns and optimized sampling parameters. The model's architecture is specifically tuned for complex problem-solving tasks.
Q: What are the recommended use cases?
The model excels at tasks requiring detailed reasoning, mathematical problem-solving, and multiple-choice questions. It's particularly effective when prompted to provide step-by-step reasoning and standardized outputs using specific formatting guidelines.
