QwQ-32B-AWQ
| Property | Value |
|---|---|
| Parameter Count | 32.5B (31.0B Non-Embedding) |
| Model Type | Causal Language Model (4-bit AWQ Quantized) |
| Context Length | 131,072 tokens |
| Architecture | 64 layers, 40 Q-heads, 8 KV-heads with RoPE, SwiGLU, RMSNorm |
| Model URL | https://huggingface.co/Qwen/QwQ-32B-AWQ |
What is QwQ-32B-AWQ?
QwQ-32B-AWQ is a sophisticated reasoning-focused language model from the Qwen series, specifically designed for enhanced problem-solving capabilities. As a 4-bit quantized version of the original QwQ-32B, it maintains competitive performance while reducing computational requirements. The model has undergone both pretraining and post-training phases, including supervised finetuning and reinforcement learning.
Implementation Details
The model incorporates advanced architectural elements including RoPE (Rotary Position Embedding), SwiGLU activation functions, and RMSNorm normalization. It utilizes Grouped-Query Attention (GQA) with 40 heads for queries and 8 for keys/values, optimizing both performance and efficiency.
- Full 131,072 token context length with YaRN scaling support
- AWQ 4-bit quantization for efficient deployment
- Comprehensive transformers architecture with attention QKV bias
- Compatible with latest Hugging Face transformers library (requires version ≥4.37.0)
Core Capabilities
- Enhanced reasoning and problem-solving abilities compared to conventional instruction-tuned models
- Competitive performance against state-of-the-art reasoning models
- Efficient handling of long-context tasks
- Optimized for deployment using vLLM
- Thoughtful output generation with structured thinking patterns
Frequently Asked Questions
Q: What makes this model unique?
QwQ-32B-AWQ stands out for its focus on reasoning capabilities while maintaining efficiency through 4-bit quantization. Its architecture is specifically optimized for thoughtful output generation and complex problem-solving tasks.
Q: What are the recommended use cases?
The model excels in tasks requiring deep reasoning, mathematical problem-solving, and long-context understanding. It's particularly suitable for applications where structured thinking and step-by-step problem solving are crucial.
