| Property | Value |
|---|---|
| Parameter Count | 32.5B |
| License | Apache 2.0 |
| Context Length | 131,072 tokens |
| Quantization | AWQ 4-bit |
| Paper | Technical Report |
What is Qwen2.5-Coder-32B-Instruct-AWQ?
Qwen2.5-Coder-32B-Instruct-AWQ is a state-of-the-art code-specialized large language model that represents the latest advancement in the Qwen series. This model has been trained on 5.5 trillion tokens including source code, text-code grounding, and synthetic data, achieving performance levels comparable to GPT-4 in coding tasks.
Implementation Details
The model implements a sophisticated architecture featuring transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias. It utilizes 64 layers with 40 attention heads for Q and 8 for KV, implementing Group Query Attention (GQA) for efficient processing. The AWQ 4-bit quantization enables efficient deployment while maintaining performance.
- Advanced long-context support up to 128K tokens using YaRN technology
- Comprehensive foundation for Code Agents applications
- Enhanced capabilities in mathematics and general competencies
Core Capabilities
- Superior code generation and completion
- Advanced code reasoning and problem-solving
- Efficient code fixing and debugging
- Extended context handling for large codebases
- Mathematics and general-purpose computation
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its extensive parameter count (32.5B), efficient 4-bit quantization, and exceptional context length of 131,072 tokens. It matches GPT-4's coding abilities while being open-source and specifically optimized for code-related tasks.
Q: What are the recommended use cases?
The model excels in code generation, debugging, and analysis tasks. It's particularly suitable for software development teams requiring advanced code completion, refactoring, and problem-solving capabilities. The extended context length makes it ideal for working with large codebases and complex programming projects.