Qwen-1.8B
| Property | Value |
|---|---|
| Parameter Count | 1.84B parameters |
| Architecture | 24 layers, 16 heads, 2048 d_model |
| Context Length | 8192 tokens |
| Training Data | 2.2T tokens |
| Paper | arXiv:2309.16609 |
What is Qwen-1.8B?
Qwen-1.8B is an advanced language model developed by Alibaba Cloud that represents a significant achievement in efficient, multilingual AI. It's designed to provide high performance while maintaining relatively low computational requirements, making it accessible for various applications. The model supports both Chinese and English languages, along with code generation capabilities.
Implementation Details
The model utilizes modern architecture components including RoPE relative position encoding, SwiGLU activation function, and RMSNorm. It features a comprehensive vocabulary of over 150K tokens, optimized for multiple languages and efficient encoding. The model can be deployed with various precision options including int4 and int8 quantization, requiring as little as 2GB of VRAM for inference.
- Supports 8192 token context length
- Implements flash attention 2 for improved efficiency
- Uses tiktoken-based tokenizer optimized for multiple languages
- Trained on diverse high-quality data including web text, books, and code
Core Capabilities
- Strong performance in Chinese evaluation (56.2% on C-Eval test)
- Impressive English comprehension (45.3% on MMLU)
- Advanced coding capabilities (15.2% pass@1 on HumanEval)
- Strong mathematical reasoning (32.3% accuracy on GSM8K)
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its efficient architecture and strong multilingual capabilities while maintaining a relatively small parameter count. It achieves impressive performance across various benchmarks, often surpassing larger models.
Q: What are the recommended use cases?
Qwen-1.8B is suitable for a wide range of applications including text generation, code development, mathematical problem-solving, and multilingual tasks. It's particularly valuable for deployments where computational resources are limited but high performance is required.
