Qwen2.5-7B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 7.61B (6.53B Non-Embedding) |
| License | Apache-2.0 |
| Context Length | 131,072 tokens |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| Paper | Technical Report |
What is Qwen2.5-7B-Instruct?
Qwen2.5-7B-Instruct is an advanced instruction-tuned language model representing the latest evolution in the Qwen series. It's specifically designed to handle complex tasks with enhanced capabilities in coding, mathematics, and multilingual processing across 29+ languages. The model features impressive context length handling of up to 128K tokens and can generate responses up to 8K tokens in length.
Implementation Details
The model employs a sophisticated architecture combining transformers with RoPE (Rotary Position Embedding), SwiGLU activation, and RMSNorm. It utilizes 28 attention heads for queries and 4 for key-values, implementing GQA (Grouped Query Attention) for efficient processing. For handling extensive inputs beyond 32,768 tokens, it incorporates YaRN technology for enhanced length extrapolation.
- 28 transformer layers with advanced attention mechanisms
- Specialized expert models for coding and mathematics
- BF16 tensor type for optimal performance
- Supports structured data understanding and JSON output generation
Core Capabilities
- Extended context processing up to 128K tokens
- Advanced instruction following and role-play implementation
- Robust multilingual support for 29+ languages
- Enhanced capability in generating structured outputs
- Improved long-text generation and comprehension
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive features include its extensive multilingual capabilities, significantly improved instruction following, and specialized expertise in coding and mathematics. The implementation of YaRN technology for handling long contexts sets it apart from many contemporary models.
Q: What are the recommended use cases?
The model excels in various applications including multilingual text generation, code development, mathematical problem-solving, and long-form content creation. It's particularly well-suited for applications requiring structured data handling and JSON output generation.
