Qwen2.5-0.5B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 494M (360M non-embedding) |
| Model Type | Causal Language Model |
| Architecture | Transformer with RoPE, SwiGLU, RMSNorm |
| License | Apache 2.0 |
| Paper | Technical Report |
What is Qwen2.5-0.5B-Instruct?
Qwen2.5-0.5B-Instruct is a compact yet powerful instruction-tuned language model that represents the latest advancement in the Qwen series. With 494M parameters, it's designed to offer efficient performance while maintaining robust capabilities across multiple domains.
Implementation Details
The model features a sophisticated architecture with 24 layers and an innovative attention mechanism using 14 heads for queries and 2 for key-values (GQA). It supports an impressive context length of 32,768 tokens and can generate up to 8,192 tokens in a single pass.
- Advanced architecture combining RoPE, SwiGLU, and RMSNorm
- Optimized for BF16 tensor operations
- Supports over 29 languages including major global languages
- Specialized capabilities in coding and mathematics
Core Capabilities
- Enhanced instruction following and long-text generation
- Improved structured data understanding and JSON output generation
- Robust multilingual support across 29+ languages
- Flexible role-play implementation and chatbot condition-setting
- Extended context handling up to 128K tokens
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient parameter count while maintaining impressive capabilities, particularly in structured data handling and multilingual support. It's specifically designed for instruction-following tasks with enhanced performance in coding and mathematics.
Q: What are the recommended use cases?
The model excels in chatbot applications, code generation, mathematical problem-solving, and multilingual text processing. It's particularly suitable for applications requiring structured output generation and long-context understanding.
