| Property | Value |
|---|---|
| Parameter Count | 3.09B |
| License | qwen-research |
| Context Length | 32,768 tokens |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| Research Paper | Link to Paper |
What is Qwen2.5-Coder-3B?
Qwen2.5-Coder-3B is a specialized code-focused language model that represents part of the latest Qwen2.5-Coder series. Built upon the foundation of Qwen2.5-3B, this model has been extensively trained on 5.5 trillion tokens including source code, text-code grounding, and synthetic data to deliver superior performance in code-related tasks.
Implementation Details
The model features a sophisticated architecture utilizing 36 layers with 16 attention heads for queries and 2 for key-values (GQA). It implements advanced components like RoPE (Rotary Position Embedding), SwiGLU activation, and RMSNorm, along with attention QKV bias and tied word embeddings.
- Full 32,768 token context window
- 2.77B non-embedding parameters
- BF16 tensor type for efficient computation
- Requires latest transformers library (>= 4.37.0)
Core Capabilities
- Advanced code generation and completion
- Robust code reasoning abilities
- Efficient code fixing and debugging
- Strong mathematical reasoning
- Foundation for Code Agents development
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on code-related tasks while maintaining strong general capabilities. It offers an excellent balance between model size and performance, making it suitable for various development environments.
Q: What are the recommended use cases?
While the model excels at code generation, reasoning, and fixing, it's not recommended for direct conversations. Instead, it's ideal for code-related tasks and can be enhanced through post-training methods like SFT, RLHF, or continued pretraining for specific applications.