Qwen2.5-3B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 3.09B |
| Model Type | Instruction-tuned Causal Language Model |
| Context Length | 32,768 tokens |
| Architecture | Transformer with RoPE, SwiGLU, RMSNorm |
| License | Other |
| Paper | arXiv:2407.10671 |
What is Qwen2.5-3B-Instruct?
Qwen2.5-3B-Instruct is part of the latest Qwen2.5 series of large language models, representing a significant advancement in compact yet powerful AI models. This 3.09B parameter model is specifically instruction-tuned and designed to provide robust performance across multiple domains while maintaining efficiency.
Implementation Details
The model features a sophisticated architecture with 36 layers and employs Group Query Attention (GQA) with 16 heads for queries and 2 for key/values. It utilizes advanced components including RoPE positional embeddings, SwiGLU activations, and RMSNorm for enhanced performance.
- Full 32,768 token context length with 8,192 token generation capability
- Implements QKV bias and tied word embeddings
- Optimized for both CPU and GPU deployment
- Supports BF16 precision for efficient inference
Core Capabilities
- Enhanced knowledge base and improved capabilities in coding and mathematics
- Superior instruction following and long-text generation
- Structured data understanding and JSON output generation
- Support for 29+ languages including Chinese, English, French, and more
- Improved role-play implementation and chatbot condition-setting
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its efficient balance of size and capability, offering full 32K context support in a relatively compact 3B parameter package. It's particularly notable for its improved instruction-following abilities and structured output generation.
Q: What are the recommended use cases?
The model excels in multi-lingual applications, coding tasks, mathematical problems, and general conversational AI. It's particularly well-suited for applications requiring structured data handling and long-context understanding.
