Qwen2.5-72B-Instruct-AWQ
| Property | Value |
|---|---|
| Parameter Count | 72.7B |
| Model Type | Causal Language Model (Instruction-tuned) |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| License | Qwen License |
| Context Length | 131,072 tokens |
| Quantization | AWQ 4-bit |
What is Qwen2.5-72B-Instruct-AWQ?
Qwen2.5-72B-Instruct-AWQ represents a significant advancement in large language models, offering a 4-bit quantized version of the original model while maintaining high performance. This model is part of the latest Qwen2.5 series, bringing substantial improvements in knowledge depth, coding capabilities, and mathematical reasoning.
Implementation Details
The model features an advanced architecture with 80 layers and 64 attention heads for queries and 8 for key-values (GQA). It supports an impressive context length of 131,072 tokens and can generate up to 8,192 tokens. The AWQ quantization enables efficient deployment while preserving model quality.
- Advanced transformers architecture with RoPE, SwiGLU, and RMSNorm
- Supports 29+ languages including Chinese, English, French, and more
- Implements YaRN technology for enhanced length extrapolation
- Optimized for both short and long-context processing
Core Capabilities
- Enhanced instruction following and long text generation
- Improved structured data understanding and JSON output generation
- Superior coding and mathematical problem-solving abilities
- Robust multilingual support across diverse languages
- Efficient handling of long-context scenarios up to 128K tokens
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its combination of massive scale (72.7B parameters) with efficient 4-bit quantization, while maintaining high performance across multiple domains. Its ability to handle extremely long contexts and generate structured outputs makes it particularly versatile.
Q: What are the recommended use cases?
The model excels in applications requiring complex reasoning, code generation, mathematical problem-solving, and multilingual communication. It's particularly suitable for scenarios requiring long-context understanding and structured output generation.
