Qwen2.5-32B-bnb-4bit
| Property | Value |
|---|---|
| Parameter Count | 32.5B (31.0B Non-Embedding) |
| Context Length | 131,072 tokens |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias |
| Number of Layers | 64 |
| Attention Heads | 40 for Q and 8 for KV (GQA) |
| Model URL | https://huggingface.co/unsloth/Qwen2.5-32B-bnb-4bit |
What is Qwen2.5-32B-bnb-4bit?
Qwen2.5-32B-bnb-4bit is a highly optimized version of the Qwen2.5 large language model, quantized to 4-bit precision using BNBQ quantization. This model represents the latest advancement in the Qwen series, offering significant improvements in knowledge representation, coding capabilities, and mathematical reasoning while maintaining efficient memory usage through quantization.
Implementation Details
The model implements a sophisticated architecture featuring Rotary Position Embedding (RoPE), SwiGLU activation functions, and RMSNorm layer normalization. It utilizes Group-Query Attention (GQA) with 40 heads for queries and 8 for keys/values, optimizing both performance and computational efficiency.
- Enhanced multilingual support covering 29+ languages
- Specialized improvements in coding and mathematics
- Optimized for long-context understanding up to 128K tokens
- 4-bit quantization for reduced memory footprint
Core Capabilities
- Advanced instruction following and long text generation
- Improved structured data understanding and JSON output
- Enhanced role-play implementation and condition-setting
- Robust performance across multiple languages including Chinese, English, and European languages
- Efficient memory usage through quantization while maintaining model quality
Frequently Asked Questions
Q: What makes this model unique?
This model combines the advanced capabilities of Qwen2.5 with efficient 4-bit quantization, making it particularly suitable for deployment in resource-constrained environments while maintaining high performance across multiple domains.
Q: What are the recommended use cases?
As a base language model, it's recommended for further fine-tuning through post-training methods such as SFT, RLHF, or continued pretraining. It's particularly well-suited for applications requiring multilingual support, structured data processing, and long-context understanding.
