| Property | Value |
|---|---|
| Parameter Count | 8.37B |
| License | Apache 2.0 |
| Context Length | 131,072 tokens |
| Paper | arXiv:2407.10671 |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
What is Qwen2.5-14B-Instruct-bnb-4bit?
Qwen2.5-14B-Instruct-bnb-4bit is a 4-bit quantized version of the Qwen2.5 instruction-tuned language model, optimized for efficient deployment while maintaining performance. This model represents a significant advancement in the Qwen series, featuring enhanced capabilities in coding, mathematics, and multilingual support for over 29 languages.
Implementation Details
The model utilizes a sophisticated architecture with 48 layers and 40 attention heads for queries and 8 for key/values (GQA). It implements the latest advancements in transformer architecture, including RoPE positional embeddings, SwiGLU activations, and RMSNorm for enhanced stability and performance.
- 4-bit quantization for reduced memory footprint
- 131,072 token context length with YaRN scaling
- Generation capability up to 8,192 tokens
- Optimized for both CPU and GPU deployment
Core Capabilities
- Advanced instruction following and long-text generation
- Structured data understanding and JSON output generation
- Robust multilingual support across 29+ languages
- Enhanced coding and mathematical reasoning
- Improved role-play implementation and chatbot conditioning
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient 4-bit quantization while maintaining the advanced capabilities of Qwen2.5, including exceptional long-context handling and multilingual support. It's particularly notable for its improved instruction following and structured data processing capabilities.
Q: What are the recommended use cases?
The model is ideal for applications requiring multilingual support, code generation, mathematical computations, and long-form content generation. It's particularly well-suited for chatbots, content generation systems, and applications requiring structured data processing.