Qwen1.5-4B-Chat
| Property | Value |
|---|---|
| Model Size | 4 Billion parameters |
| Architecture | Transformer-based decoder-only |
| Context Length | 32K tokens |
| Paper | arXiv:2309.16609 |
| Model Hub | Hugging Face |
What is Qwen1.5-4B-Chat?
Qwen1.5-4B-Chat is a beta version of Qwen2, representing a significant advancement in transformer-based language models. This 4B parameter model is part of a comprehensive series ranging from 0.5B to 72B parameters, designed to deliver enhanced multilingual capabilities and improved chat performance.
Implementation Details
The model architecture incorporates several sophisticated components including SwiGLU activation, attention QKV bias, and an improved tokenizer optimized for multiple natural languages and code processing. It has been extensively trained through both supervised finetuning and direct preference optimization.
- Transformer-based decoder-only architecture
- Advanced tokenizer with multilingual support
- Stable 32K context length support
- Compatible with transformers>=4.37.0
Core Capabilities
- Enhanced multilingual text processing
- Improved chat performance compared to predecessors
- Code generation and understanding
- Available in multiple quantization formats (GPTQ, AWQ, GGUF)
Frequently Asked Questions
Q: What makes this model unique?
Qwen1.5-4B-Chat stands out for its stable 32K context length support, improved multilingual capabilities, and significant performance improvements in human preference for chat interactions, all while maintaining a relatively compact 4B parameter size.
Q: What are the recommended use cases?
The model is well-suited for multilingual chat applications, code generation, and general language understanding tasks. It's particularly effective for scenarios requiring extended context understanding and natural language generation.
