Qwen2.5-72B-Instruct-GPTQ-Int8
| Property | Value |
|---|---|
| Parameter Count | 72.7B (70.0B Non-Embedding) |
| Model Type | Causal Language Model (Instruction-tuned) |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias |
| Context Length | 131,072 tokens (128K) |
| Quantization | GPTQ 8-bit |
| Model URL | Hugging Face |
What is Qwen2.5-72B-Instruct-GPTQ-Int8?
Qwen2.5-72B-Instruct-GPTQ-Int8 is a quantized version of Alibaba Cloud's latest large language model, representing a significant advancement in the Qwen series. This 8-bit quantized model maintains the impressive capabilities of the original while reducing memory requirements, making it more accessible for deployment.
Implementation Details
The model features 80 layers with 64 attention heads for queries and 8 for key/values using grouped-query attention (GQA). It implements advanced architectural components including RoPE (Rotary Position Embedding), SwiGLU activation, and RMSNorm, enabling efficient processing of sequences up to 128K tokens with generation capability of up to 8K tokens.
- Specialized expertise in coding and mathematics
- Enhanced instruction following capabilities
- Improved long-text generation (8K+ tokens)
- Support for structured data processing
- Advanced JSON generation capabilities
Core Capabilities
- Multilingual support for 29+ languages including Chinese, English, and major European languages
- Long context processing with YaRN scaling technique
- Robust performance in role-play implementations
- Enhanced condition-setting for chatbots
- Efficient handling of structured data and outputs
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its combination of massive scale (72B parameters), extensive context length (128K tokens), and efficient 8-bit quantization. It's particularly notable for its specialized capabilities in coding and mathematics, while maintaining strong performance across general tasks.
Q: What are the recommended use cases?
The model excels in scenarios requiring long-context understanding, multilingual processing, code generation, and mathematical problem-solving. It's particularly well-suited for applications needing structured output generation and complex role-playing interactions.
