Qwen2.5-32B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 32.8B |
| License | Apache 2.0 |
| Context Length | 131,072 tokens |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| Paper | Technical Report |
What is Qwen2.5-32B-Instruct?
Qwen2.5-32B-Instruct is a state-of-the-art instruction-tuned language model representing the latest advancement in the Qwen series. Built with 32.8 billion parameters, this model demonstrates exceptional capabilities in handling complex tasks across multiple domains, with particular strengths in coding, mathematics, and long-form content generation.
Implementation Details
The model features a sophisticated architecture with 64 layers and employs 40 attention heads for queries and 8 for key-values using Group Query Attention (GQA). It supports an impressive context length of 131,072 tokens and can generate responses up to 8,192 tokens, leveraging YaRN technology for enhanced length extrapolation.
- Advanced architecture combining RoPE, SwiGLU, and RMSNorm
- Specialized attention mechanism with GQA implementation
- Optimized for both BF16 precision and efficient deployment
- Comprehensive multilingual support for 29+ languages
Core Capabilities
- Extended context understanding and generation (up to 128K tokens)
- Superior instruction following and structured data handling
- Enhanced coding and mathematical problem-solving
- Robust multilingual processing and generation
- Improved long-text generation with consistent quality
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its combination of massive scale (32.8B parameters), extraordinary context length (128K tokens), and specialized capabilities in coding and mathematics. It's particularly notable for its ability to maintain performance across diverse system prompts and handle structured data effectively.
Q: What are the recommended use cases?
The model excels in complex coding tasks, mathematical problem-solving, long-form content generation, and multilingual applications. It's particularly well-suited for applications requiring deep understanding of structured data and generation of structured outputs like JSON.
