QwQ-32B-unsloth-bnb-4bit
| Property | Value |
|---|---|
| Parameter Count | 32.5B (31.0B Non-Embedding) |
| Context Length | 131,072 tokens |
| Architecture | Transformer with RoPE, SwiGLU, RMSNorm, GQA |
| Attention Heads | 40 for Q, 8 for KV |
| Number of Layers | 64 |
What is QwQ-32B-unsloth-bnb-4bit?
QwQ-32B-unsloth-bnb-4bit is an optimized version of the QwQ-32B reasoning model, featuring Unsloth's advanced 4-bit dynamic quantization technology. This model represents a significant advancement in the Qwen series, specifically designed for enhanced reasoning and problem-solving capabilities while maintaining efficiency through selective quantization techniques.
Implementation Details
The model implements a sophisticated architecture combining transformers with RoPE (Rotary Position Embedding), SwiGLU activation, RMSNorm, and Grouped Query Attention (GQA). It features selective 4-bit quantization that significantly improves accuracy compared to standard 4-bit implementations, while maintaining the model's reasoning capabilities.
- Full 131,072 token context length support
- Dynamic quantization for optimal performance
- Integrated bug fixes for endless generation issues
- Optimized for both accuracy and efficiency
Core Capabilities
- Advanced reasoning and problem-solving
- Competitive performance against state-of-the-art reasoning models
- Efficient memory usage through selective quantization
- Support for long-context processing with YaRN scaling
- Optimized for both conversation and complex task solving
Frequently Asked Questions
Q: What makes this model unique?
The model combines QwQ-32B's strong reasoning capabilities with Unsloth's dynamic quantization, offering superior performance while maintaining efficiency. The selective 4-bit quantization approach significantly improves accuracy compared to standard quantization methods.
Q: What are the recommended use cases?
The model excels in tasks requiring complex reasoning, mathematical problem-solving, and long-form content generation. It's particularly effective for applications needing both computational efficiency and strong reasoning capabilities.
