Qwen2.5-32B
| Property | Value |
|---|---|
| Parameter Count | 32.5B (31.0B Non-Embedding) |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias |
| Context Length | 131,072 tokens |
| License | Apache-2.0 |
| Paper | View Research Paper |
What is Qwen2.5-32B?
Qwen2.5-32B is a state-of-the-art base language model that represents a significant advancement in the Qwen series. This model is designed with 32.5 billion parameters and features 64 layers with 40 attention heads for Q and 8 for KV, implementing Group Query Attention (GQA) architecture.
Implementation Details
The model employs a sophisticated architecture combining several advanced techniques including RoPE (Rotary Position Embedding), SwiGLU activation functions, and RMSNorm for normalization. It requires the latest version of the Hugging Face transformers library (>4.37.0) for proper functionality.
- Advanced architecture with 64 layers and specialized attention mechanism
- Supports context length of up to 131,072 tokens
- Implements Group Query Attention with 40/8 head configuration
- Requires BF16 tensor type for optimal performance
Core Capabilities
- Enhanced knowledge base with improved coding and mathematics capabilities
- Superior instruction following and long-text generation (8K+ tokens)
- Structured data understanding and JSON output generation
- Multilingual support for 29+ languages
- Extensive context window of 128K tokens
Frequently Asked Questions
Q: What makes this model unique?
Qwen2.5-32B stands out for its massive parameter count, extensive context length, and specialized capabilities in coding and mathematics. It's particularly notable for its ability to handle structured data and generate long-form content while supporting multiple languages.
Q: What are the recommended use cases?
As a base language model, it's not recommended for direct conversational use. Instead, it's ideal for post-training applications such as SFT (Supervised Fine-Tuning), RLHF (Reinforcement Learning from Human Feedback), or continued pretraining for specific use cases.
