| 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-specific large language model that represents the pinnacle of the Qwen2.5-Coder series. This AWQ-quantized version maintains the powerful capabilities of the original model while reducing the computational requirements through 4-bit precision.
Implementation Details
The model is built on a transformer architecture with several advanced features including RoPE, SwiGLU, RMSNorm, and Attention QKV bias. It comprises 64 layers with 40 attention heads for queries and 8 for key-values, implementing grouped-query attention (GQA) for efficient processing.
- Trained on 5.5 trillion tokens including source code and text-code pairs
- Supports context length up to 128K tokens using YaRN technology
- Features 4-bit AWQ quantization for efficient deployment
- Implements a comprehensive chat template system for natural interaction
Core Capabilities
- Advanced code generation and completion
- Sophisticated code reasoning and analysis
- Efficient code fixing and debugging
- Long-context processing up to 128K tokens
- Mathematical problem-solving
- Code agent functionalities
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its combination of state-of-the-art coding capabilities matching GPT-4, extensive context length support, and efficient 4-bit quantization for practical deployment.
Q: What are the recommended use cases?
The model excels in software development tasks, including code generation, debugging, and analysis. It's particularly suitable for professional developers needing a powerful coding assistant that can handle complex programming challenges while maintaining reasonable computational requirements.