Qwen2.5-7B-Instruct-GPTQ-Int4
| Property | Value |
|---|---|
| Parameter Count | 7.61B |
| License | Apache 2.0 |
| Context Length | 131,072 tokens |
| Quantization | GPTQ 4-bit |
| Research Paper | arXiv:2407.10671 |
What is Qwen2.5-7B-Instruct-GPTQ-Int4?
Qwen2.5-7B-Instruct-GPTQ-Int4 is a quantized version of the Qwen2.5 series, representing a significant advancement in efficient large language models. This 4-bit quantized model maintains the powerful capabilities of the original while reducing the computational requirements through GPTQ optimization.
Implementation Details
The model employs a sophisticated architecture featuring 28 layers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias. It utilizes Group-Query Attention (GQA) with 28 heads for queries and 4 for key-value pairs, optimizing the attention mechanism for better performance.
- Advanced architecture with RoPE, SwiGLU, and RMSNorm components
- 28-layer structure with specialized attention mechanism
- GPTQ 4-bit quantization for efficient deployment
- Support for YaRN scaling for handling long contexts
Core Capabilities
- Extensive multilingual support for 29+ languages
- Enhanced instruction following and long-text generation
- Improved coding and mathematics capabilities
- Structured data understanding and JSON generation
- Long context processing up to 128K tokens
- Generation capability up to 8K tokens
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its combination of efficient 4-bit quantization while maintaining impressive capabilities including extensive multilingual support and long context processing. The implementation of YaRN scaling makes it particularly effective for handling long-form content.
Q: What are the recommended use cases?
This model is ideal for applications requiring multilingual processing, code generation, mathematical computations, and handling of long-form content. It's particularly suited for deployment scenarios where computational efficiency is crucial while maintaining high-quality output.
