Qwen2.5-14B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 14.8B |
| License | Apache 2.0 |
| Context Length | 128K tokens |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| Paper | Technical Report |
What is Qwen2.5-14B-Instruct?
Qwen2.5-14B-Instruct is an advanced instruction-tuned language model that represents the latest evolution in the Qwen series. With 14.8 billion parameters and support for over 29 languages, it's designed to excel in various tasks including coding, mathematics, and long-text generation.
Implementation Details
The model is built on a sophisticated architecture featuring 48 layers and 40 attention heads for queries with 8 for key-values. It implements advanced techniques like RoPE (Rotary Position Embedding) and SwiGLU activation functions, enabling exceptional performance in handling long sequences up to 128K tokens.
- Full context length of 131,072 tokens with generation capability of 8,192 tokens
- Implements YaRN technology for enhanced length extrapolation
- Utilizes GQA (Grouped Query Attention) with 40/8 head configuration
Core Capabilities
- Enhanced knowledge base with improved coding and mathematical abilities
- Superior instruction following and structured data handling
- Robust multilingual support across 29+ languages
- Advanced long-text processing and generation
- Improved JSON generation and structured output capabilities
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its exceptional balance of size and capabilities, featuring significantly improved knowledge representation and specialized expertise in coding and mathematics. Its ability to handle extremely long contexts (128K tokens) while maintaining high performance sets it apart from many contemporaries.
Q: What are the recommended use cases?
The model excels in diverse applications including multilingual content generation, code development, mathematical problem-solving, and long-form content creation. It's particularly well-suited for tasks requiring structured data handling and JSON generation.
