Qwen2.5-7B-bnb-4bit
| Property | Value |
|---|---|
| Parameter Count | 4.46B (quantized) |
| License | Apache 2.0 |
| Context Length | 131,072 tokens |
| Paper | Technical Report |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
What is Qwen2.5-7B-bnb-4bit?
Qwen2.5-7B-bnb-4bit is a 4-bit quantized version of the Qwen2.5 base language model, optimized for efficient deployment while maintaining performance. This model represents a significant advancement in the Qwen series, featuring enhanced capabilities in coding, mathematics, and multilingual support for over 29 languages.
Implementation Details
The model architecture consists of 28 layers with 28 attention heads for queries and 4 for key-values (GQA), implementing transformer architecture with RoPE, SwiGLU, and RMSNorm components. The quantization to 4-bit precision allows for significant memory savings while preserving model capabilities.
- Total Parameters: 7.61B (6.53B non-embedding)
- Context Length: 131,072 tokens
- Generation Capacity: Up to 8K tokens
- Precision: 4-bit quantization
Core Capabilities
- Enhanced knowledge and expertise in coding and mathematics
- Improved structured data understanding and JSON generation
- Support for 29+ languages including Chinese, English, French, Spanish
- Extended context window of 128K tokens
- Efficient memory usage through 4-bit quantization
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient 4-bit quantization while maintaining the extensive capabilities of Qwen2.5, including its impressive 128K context length and multilingual support. It's particularly notable for its optimization for deployment scenarios where memory efficiency is crucial.
Q: What are the recommended use cases?
As a base model, it's not recommended for direct conversational use. Instead, it's ideal for further fine-tuning through SFT, RLHF, or continued pretraining for specific applications in coding, mathematical analysis, and multilingual text processing.
