Qwen2-72B-Instruct-AWQ
| Property | Value |
|---|---|
| Parameter Count | 72 Billion |
| License | Tongyi-Qianwen |
| Context Length | 131,072 tokens |
| Quantization | AWQ 4-bit |
| Research Paper | YARN Paper |
What is Qwen2-72B-Instruct-AWQ?
Qwen2-72B-Instruct-AWQ is an advanced quantized version of the Qwen2 series, representing a significant evolution in large language models. This model combines the powerful capabilities of the 72B parameter architecture with efficient 4-bit AWQ quantization, making it more deployable while maintaining high performance. The model is specifically tuned for instruction-following tasks and supports an impressive context length of 131,072 tokens.
Implementation Details
The model is built on the Transformer architecture with several advanced features, including SwiGLU activation, attention QKV bias, and group query attention. It utilizes YARN technology for handling long contexts and requires transformers >= 4.37.0 for proper functionality.
- Implements AWQ quantization for efficient deployment
- Supports both vLLM and standard transformer deployments
- Features an improved tokenizer for multiple languages and code
- Utilizes YARN for enhanced length extrapolation
Core Capabilities
- Extended context processing up to 131K tokens
- Advanced language understanding and generation
- Strong performance in multilingual tasks
- Efficient code generation and mathematical reasoning
- Optimized for instruction-following scenarios
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its combination of massive scale (72B parameters) with efficient 4-bit AWQ quantization, while maintaining the ability to process extremely long contexts of up to 131K tokens. It's particularly notable for its use of YARN technology for enhanced length extrapolation.
Q: What are the recommended use cases?
The model is well-suited for a wide range of applications including long-form content generation, complex reasoning tasks, multilingual processing, and code generation. It's particularly effective for scenarios requiring processing of very long documents or conversations due to its extended context length.
