Qwen2.5-3B
| Property | Value |
|---|---|
| Parameter Count | 3.09B (2.77B Non-Embedding) |
| Model Type | Causal Language Model |
| Context Length | 32,768 tokens |
| Architecture | Transformer with RoPE, SwiGLU, RMSNorm |
| License | Qwen Research |
| Paper | Technical Report |
What is Qwen2.5-3B?
Qwen2.5-3B is a powerful base language model that's part of the latest Qwen2.5 series. It represents a significant advancement in language model capabilities, featuring 3.09 billion parameters and sophisticated architecture optimizations. This model serves as a foundation for various downstream tasks and specialized applications.
Implementation Details
The model implements a state-of-the-art architecture with 36 layers and employs Grouped-Query Attention with 16 heads for queries and 2 for key-values. It utilizes advanced techniques including RoPE positional embeddings, SwiGLU activations, and RMSNorm for enhanced performance and stability.
- Advanced transformer architecture with tied word embeddings
- BF16 tensor type for efficient computation
- Full 32,768 token context window
- Optimized for high-performance text generation
Core Capabilities
- Enhanced knowledge representation and reasoning
- Superior coding and mathematics capabilities
- Support for 29+ languages including major global languages
- Improved structured data handling and JSON generation
- Long-context processing up to 128K tokens
Frequently Asked Questions
Q: What makes this model unique?
Qwen2.5-3B stands out for its optimized architecture, extensive multilingual support, and impressive context length handling. It's specifically designed to serve as a foundation for further fine-tuning and specialized applications.
Q: What are the recommended use cases?
While this is a base model not recommended for direct conversational use, it's ideal for post-training applications including SFT, RLHF, and continued pretraining. It's particularly well-suited for tasks requiring strong coding, mathematical reasoning, and multilingual capabilities.
