Qwen2.5-32B-Instruct-AWQ
| Property | Value |
|---|---|
| Parameter Count | 32.5B |
| Model Type | Instruction-tuned Causal Language Model |
| License | Apache 2.0 |
| Context Length | 131,072 tokens |
| Quantization | AWQ 4-bit |
| Architecture | Transformer with RoPE, SwiGLU, RMSNorm |
What is Qwen2.5-32B-Instruct-AWQ?
Qwen2.5-32B-Instruct-AWQ is a cutting-edge quantized language model that represents a significant advancement in the Qwen series. This 4-bit quantized version maintains the powerful capabilities of the original model while reducing its computational footprint. The model features an impressive 131,072 token context length and supports generation of up to 8,192 tokens, making it suitable for processing extensive documents and generating lengthy responses.
Implementation Details
The model is built on a sophisticated architecture combining transformers with RoPE, SwiGLU, and RMSNorm. It utilizes 64 layers with 40 attention heads for queries and 8 for key-values (GQA), optimized for efficient processing. The AWQ quantization enables deployment in resource-constrained environments while maintaining performance.
- 64 transformer layers with advanced attention mechanisms
- AWQ 4-bit quantization for efficient deployment
- Support for 29+ languages including major global languages
- YaRN scaling for handling extensive context lengths
Core Capabilities
- Enhanced instruction following and long-text generation
- Advanced coding and mathematical reasoning capabilities
- Structured data understanding and JSON output generation
- Robust multilingual support across 29+ languages
- Long-context processing up to 128K tokens
Frequently Asked Questions
Q: What makes this model unique?
The model combines extensive parameter count (32.5B) with efficient 4-bit quantization, while maintaining impressive capabilities in multiple domains. Its ability to handle extremely long contexts (131K tokens) and generate structured outputs sets it apart from many alternatives.
Q: What are the recommended use cases?
The model excels in scenarios requiring long-form content generation, multilingual processing, code generation, and mathematical problem-solving. It's particularly suitable for applications needing structured output generation and complex instruction following.
