Qwen2.5-72B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 72.7B |
| Model Type | Causal Language Model |
| License | Qwen License |
| Context Length | 131,072 tokens |
| Paper | Technical Report |
What is Qwen2.5-72B-Instruct?
Qwen2.5-72B-Instruct is a state-of-the-art instruction-tuned language model representing the latest advancement in the Qwen series. With 72.7 billion parameters, it's designed to excel in various tasks including coding, mathematics, and multilingual communication across 29+ languages.
Implementation Details
The model implements advanced architectural elements including RoPE, SwiGLU, RMSNorm, and Attention QKV bias. It features 80 layers and uses GQA with 64 attention heads for Q and 8 for KV, optimized for efficient processing and generation.
- Supports context length up to 131,072 tokens with YaRN scaling
- Can generate up to 8,192 tokens in a single pass
- Utilizes BF16 tensor type for optimal performance
- Implements specialized expert models for coding and mathematics
Core Capabilities
- Enhanced instruction following and long-text generation
- Improved structured data understanding and JSON output generation
- Robust multilingual support for 29+ languages
- Advanced role-play implementation and chatbot condition-setting
- Specialized expertise in coding and mathematical tasks
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its extensive parameter count (72.7B), exceptional context length handling (128K tokens), and specialized capabilities in coding and mathematics. It's particularly notable for its improved instruction following and multilingual support.
Q: What are the recommended use cases?
This model is ideal for complex language tasks including code generation, mathematical problem-solving, multilingual communication, and long-form content generation. It's particularly well-suited for applications requiring structured data handling and JSON output generation.
