Qwen2.5-7B-Instruct-bnb-4bit
| Property | Value |
|---|---|
| Parameter Count | 7.61B (6.53B Non-Embedding) |
| License | Apache 2.0 |
| Context Length | 131,072 tokens |
| Architecture | Transformer with RoPE, SwiGLU, RMSNorm |
| Paper | Technical Report |
What is Qwen2.5-7B-Instruct-bnb-4bit?
Qwen2.5-7B-Instruct-bnb-4bit is a 4-bit quantized version of the Qwen2.5 instruction-tuned 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 utilizes a sophisticated architecture with 28 layers and attention heads (28 for Q and 4 for KV), implementing GQA (Grouped Query Attention) for efficient processing. The 4-bit quantization allows for 60% reduced memory usage while maintaining model quality.
- Enhanced instruction following capabilities
- Supports context length up to 131,072 tokens with YaRN scaling
- Specialized in generating structured outputs (JSON)
- Optimized for long-text generation up to 8,192 tokens
Core Capabilities
- Advanced coding and mathematical reasoning
- Robust multilingual support across 29+ languages
- Long-context understanding and generation
- Structured data processing and output generation
- Enhanced instruction following and role-play implementation
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient 4-bit quantization while maintaining the advanced capabilities of Qwen2.5, including extensive multilingual support, long-context understanding, and specialized expertise in coding and mathematics.
Q: What are the recommended use cases?
The model excels in code generation, mathematical problem-solving, multilingual tasks, and processing long documents. It's particularly well-suited for applications requiring structured output generation and complex instruction following.
