Qwen2.5-Coder-0.5B
| Property | Value |
|---|---|
| Parameter Count | 494M (0.49B) |
| License | Apache-2.0 |
| Context Length | 32,768 tokens |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| Paper | Technical Report |
What is Qwen2.5-Coder-0.5B?
Qwen2.5-Coder-0.5B is part of the latest series of code-specialized language models from Qwen. As the lightweight variant in the family, it offers an efficient balance between performance and resource requirements, featuring 494M parameters and advanced architecture components like RoPE, SwiGLU, and RMSNorm.
Implementation Details
The model is built on a sophisticated architecture comprising 24 layers with 14 attention heads for queries and 2 for key-values (GQA). It utilizes BF16 tensor types and maintains a full 32,768 token context length, making it suitable for handling extensive code sequences.
- 24 transformer layers with optimized attention mechanics
- Group Query Attention (GQA) implementation
- Full 32K context window support
- Efficient parameter count of 0.49B total, 0.36B non-embedding
Core Capabilities
- Code generation and completion
- Code reasoning and analysis
- Bug fixing and code optimization
- Support for various programming languages
- Mathematics and general competencies
Frequently Asked Questions
Q: What makes this model unique?
This model represents the most compact version in the Qwen2.5-Coder series, offering efficient code-specific capabilities while maintaining a small parameter footprint. It's particularly notable for its implementation of advanced attention mechanisms and full 32K context length despite its small size.
Q: What are the recommended use cases?
While the model excels at code-related tasks, it's recommended for post-training applications rather than direct conversational use. Ideal applications include code generation, analysis, and fixing after appropriate fine-tuning through SFT, RLHF, or continued pretraining.
