Qwen2.5-7B-Instruct-GPTQ-Int8
| Property | Value |
|---|---|
| Parameter Count | 7.61B (6.53B Non-Embedding) |
| License | Apache 2.0 |
| Context Length | 131,072 tokens |
| Quantization | GPTQ 8-bit |
| Research Paper | arXiv:2407.10671 |
What is Qwen2.5-7B-Instruct-GPTQ-Int8?
Qwen2.5-7B-Instruct-GPTQ-Int8 is an 8-bit quantized version of the Qwen2.5 large language model, designed to provide efficient deployment while maintaining high performance. This model represents a significant advancement in the Qwen series, offering enhanced capabilities in multiple domains while reducing computational requirements through quantization.
Implementation Details
The model implements a transformer architecture with several key optimizations including RoPE, SwiGLU, RMSNorm, and Attention QKV bias. It features 28 layers with 28 attention heads for queries and 4 for key-values, utilizing Group-Query Attention (GQA) for efficient processing.
- Advanced architecture with RoPE, SwiGLU, and RMSNorm components
- 8-bit GPTQ quantization for efficient deployment
- Support for 131,072 token context length with 8,192 token generation capability
- Implementation of YaRN scaling for enhanced length extrapolation
Core Capabilities
- Enhanced knowledge base and improved coding/mathematics capabilities
- Superior instruction following and long-text generation
- Structured data understanding and JSON output generation
- Multilingual support for over 29 languages
- Improved role-play implementation and chatbot condition-setting
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its combination of efficient 8-bit quantization while maintaining the advanced capabilities of Qwen2.5, including extensive multilingual support and exceptional long-context handling up to 128K tokens.
Q: What are the recommended use cases?
The model is particularly well-suited for applications requiring multilingual support, long-form content generation, coding tasks, and mathematical problem-solving. Its efficient quantization makes it ideal for deployment in resource-constrained environments while maintaining high performance.
