Qwen2.5-Coder-7B-Instruct-AWQ
| Property | Value |
|---|---|
| Parameter Count | 7.61B |
| License | Apache 2.0 |
| Context Length | 131,072 tokens |
| Quantization | AWQ 4-bit |
| Paper | Technical Report |
What is Qwen2.5-Coder-7B-Instruct-AWQ?
Qwen2.5-Coder-7B-Instruct-AWQ is a specialized code generation model that represents the latest advancement in the Qwen series. This 4-bit quantized version maintains high performance while reducing computational requirements, trained on 5.5 trillion tokens including source code and text-code grounding data.
Implementation Details
The model implements a transformer architecture with several advanced features including RoPE, SwiGLU, RMSNorm, and Attention QKV bias. It utilizes 28 attention layers with 28 heads for queries and 4 for key-value pairs, optimized through AWQ quantization.
- Full 131,072 token context length support
- YaRN implementation for enhanced length extrapolation
- Optimized 4-bit precision through AWQ quantization
- 28 transformer layers with specialized attention structure
Core Capabilities
- Advanced code generation and reasoning
- Sophisticated code fixing abilities
- Long-context processing up to 128K tokens
- Code agent foundation capabilities
- Strong mathematical and general competencies
Frequently Asked Questions
Q: What makes this model unique?
The model combines extensive parameter optimization (7.61B parameters) with 4-bit AWQ quantization, allowing for efficient deployment while maintaining high performance in code-related tasks. Its exceptional context length of 131,072 tokens sets it apart from many alternatives.
Q: What are the recommended use cases?
This model excels in code generation, debugging, and technical documentation tasks. It's particularly suitable for developers needing AI assistance in coding projects, code review processes, and technical problem-solving scenarios.
