Qwen2.5-Coder-3B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 3.09B |
| Context Length | 32,768 tokens |
| License | Qwen Research |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| Paper | Technical Report |
What is Qwen2.5-Coder-3B-Instruct?
Qwen2.5-Coder-3B-Instruct is part of the latest series of Code-Specific Qwen large language models, specifically designed for code-related tasks. This instruction-tuned variant builds upon the base model with enhanced capabilities for code generation, reasoning, and fixing. Trained on 5.5 trillion tokens including source code and text-code grounding data, it represents a significant advancement in the field of code-focused language models.
Implementation Details
The model employs a sophisticated architecture featuring 36 layers with 16 attention heads for queries and 2 for key-values (GQA). It utilizes advanced techniques including RoPE for positional encoding, SwiGLU activations, and RMSNorm for normalization. The model maintains a full 32,768 token context window, making it suitable for handling large code segments and complex programming tasks.
- 3.09B total parameters (2.77B non-embedding)
- Advanced GQA attention mechanism
- Full 32K context length support
- Instruction-tuned for better interaction
Core Capabilities
- Code Generation: Enhanced ability to write clean, efficient code
- Code Reasoning: Improved understanding and analysis of code logic
- Code Fixing: Advanced debugging and error correction
- Mathematics: Strong mathematical reasoning abilities
- General Competencies: Maintained broad language understanding
Frequently Asked Questions
Q: What makes this model unique?
The model combines state-of-the-art architecture with specialized code training, offering a balance between size efficiency (3B parameters) and performance. Its instruction-tuning makes it particularly suitable for interactive coding assistance.
Q: What are the recommended use cases?
The model excels in code generation, debugging, and explanation tasks. It's particularly suitable for developers seeking an efficient coding assistant, educational purposes, and code review applications. Its 32K context window makes it capable of handling large code bases.
