Qwen2.5-Coder-32B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 32.8B |
| Context Length | 128K tokens |
| License | Apache 2.0 |
| Paper | Technical Report |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
What is Qwen2.5-Coder-32B-Instruct?
Qwen2.5-Coder-32B-Instruct is a state-of-the-art instruction-tuned code language model that represents the pinnacle of the Qwen2.5-Coder series. Trained on 5.5 trillion tokens including source code and text-code grounding data, it achieves performance levels comparable to GPT-4 in coding tasks.
Implementation Details
The model leverages an advanced architecture featuring 64 layers and 40 attention heads for queries with 8 for key-values (GQA). It implements sophisticated components like RoPE positional embeddings, SwiGLU activations, and RMSNorm, enabling efficient processing of sequences up to 128K tokens through YaRN scaling.
- 32.5B total parameters (31.0B non-embedding)
- Full 131,072 token context support
- Optimized for both short and long-form code generation
- Implements YARN technology for enhanced length extrapolation
Core Capabilities
- Advanced code generation and completion
- Sophisticated code reasoning and problem-solving
- Efficient code fixing and debugging
- Mathematical computation and general task handling
- Support for Code Agents applications
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its exceptional combination of large-scale parameters (32.8B), extensive training data (5.5 trillion tokens), and state-of-the-art architecture, enabling it to match GPT-4's coding capabilities while remaining open-source.
Q: What are the recommended use cases?
The model excels in professional software development scenarios, including code generation, debugging, and technical problem-solving. It's particularly suitable for complex programming tasks requiring deep reasoning and long-context understanding.
