Qwen1.5-14B-Chat-AWQ
| Property | Value |
|---|---|
| Parameter Count | 3.25B (AWQ quantized) |
| License | tongyi-qianwen |
| Paper | Research Paper |
| Context Length | 32K tokens |
What is Qwen1.5-14B-Chat-AWQ?
Qwen1.5-14B-Chat-AWQ is a quantized version of the beta release of Qwen2, representing a significant advancement in transformer-based language models. This model is part of a comprehensive series that includes various sizes from 0.5B to 72B parameters, optimized for efficient deployment while maintaining high performance.
Implementation Details
The model architecture is built on the Transformer framework, incorporating several advanced features including SwiGLU activation, attention QKV bias, and group query attention. The AWQ quantization enables efficient deployment while preserving model performance.
- Transformer-based decoder-only architecture
- Advanced tokenizer for multiple languages and code
- Supports 32K context length across all model sizes
- 4-bit precision through AWQ quantization
Core Capabilities
- Multi-lingual support for both base and chat functionalities
- Enhanced human preference alignment through supervised fine-tuning
- Stable long-context processing
- Efficient deployment with reduced memory footprint
Frequently Asked Questions
Q: What makes this model unique?
The model combines the advanced capabilities of Qwen2 with efficient AWQ quantization, offering a balance between performance and resource utilization. It's particularly notable for its stable 32K context length support and improved multilingual capabilities.
Q: What are the recommended use cases?
This model is well-suited for chat applications, text generation, and conversational AI systems where efficiency is crucial. It's particularly valuable in scenarios requiring multilingual support and processing of long contexts while maintaining reasonable hardware requirements.
