Qwen2.5-32B-Instruct-GPTQ-Int4
| Property | Value |
|---|---|
| Parameter Count | 32.5B (31.0B Non-Embedding) |
| Model Type | Causal Language Model (Instruction-tuned) |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias |
| Context Length | 131,072 tokens |
| Quantization | GPTQ 4-bit |
| Model Hub | Hugging Face |
What is Qwen2.5-32B-Instruct-GPTQ-Int4?
Qwen2.5-32B-Instruct-GPTQ-Int4 is a quantized version of the latest Qwen2.5 series, representing a significant advancement in large language models. This 4-bit quantized model maintains the powerful capabilities of the original while reducing computational requirements, making it more accessible for deployment.
Implementation Details
The model features a sophisticated architecture with 64 layers and uses Grouped-Query Attention with 40 heads for Q and 8 for KV. It implements YaRN technology for handling long contexts and can process up to 131,072 tokens while generating up to 8,192 tokens.
- Utilizes advanced transformers architecture with RoPE, SwiGLU, and RMSNorm
- Implements GPTQ 4-bit quantization for efficient deployment
- Supports extensive context length with YaRN scaling
- Features specialized capabilities in coding and mathematics
Core Capabilities
- Multi-language support for over 29 languages
- Enhanced instruction following and long-text generation
- Improved structured data handling and JSON output
- Robust role-play implementation and condition-setting
- Superior performance in coding and mathematical tasks
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its combination of large-scale capabilities (32.5B parameters) with efficient 4-bit quantization, while maintaining support for extremely long context windows of up to 131K tokens. It also features significant improvements in specialized domains like coding and mathematics.
Q: What are the recommended use cases?
This model is ideal for applications requiring multilingual support, long-form content generation, code generation, mathematical problem-solving, and structured data handling. It's particularly suitable for deployments where resource efficiency is crucial but high performance is required.
