Qwen2.5-72B-Instruct-bnb-4bit
| Property | Value |
|---|---|
| Parameter Count | 72.7B (70.0B Non-Embedding) |
| Model Type | Causal Language Model |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias |
| Context Length | 131,072 tokens (128K) |
| Number of Layers | 80 |
| Attention Heads | 64 for Q and 8 for KV (GQA) |
What is Qwen2.5-72B-Instruct-bnb-4bit?
Qwen2.5-72B-Instruct-bnb-4bit is an optimized version of the powerful Qwen2.5 language model, specifically quantized to 4-bit precision for improved efficiency. This model represents the latest advancement in the Qwen series, offering exceptional capabilities across multiple domains while maintaining performance with reduced memory requirements.
Implementation Details
The model implements advanced architectural features including RoPE (Rotary Position Embedding), SwiGLU activation, RMSNorm, and Attention QKV bias. It supports YaRN for enhanced length extrapolation, particularly useful for processing texts beyond the standard 32,768 token limit. The model has been optimized for deployment using vLLM, supporting static YaRN for consistent performance across different input lengths.
- Full 131,072 token context window with 8,192 token generation capability
- Optimized 4-bit quantization for reduced memory footprint
- Specialized architecture with 80 layers and grouped-query attention
- Support for over 29 languages including Chinese, English, and major European languages
Core Capabilities
- Enhanced performance in coding and mathematics through specialized expert models
- Improved instruction following and long-text generation
- Advanced handling of structured data and JSON output generation
- Robust multilingual support across diverse language families
- Efficient memory utilization through 4-bit quantization
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful capabilities of Qwen2.5 with efficient 4-bit quantization, allowing for deployment in resource-constrained environments while maintaining high performance. Its extensive context length and multilingual capabilities make it particularly versatile for complex applications.
Q: What are the recommended use cases?
The model excels in scenarios requiring advanced coding, mathematical reasoning, structured data handling, and multilingual processing. It's particularly well-suited for applications needing long context understanding and generation, such as document analysis, complex coding tasks, and multilingual content generation.
