Qwen-1.8B-Chat
| Property | Value |
|---|---|
| Parameter Count | 1.84B |
| Context Length | 8192 tokens |
| Model Architecture | 24 layers, 16 heads, 2048 d_model |
| Paper | Research Paper |
| License | Research License |
What is Qwen-1.8B-Chat?
Qwen-1.8B-Chat is a lightweight but powerful chatbot model developed by Alibaba Cloud. It's built on the Transformer architecture and trained on over 2.2 trillion tokens of diverse data including web texts, books, and code. The model supports both Chinese and English languages, featuring a comprehensive 150K token vocabulary optimized for multilingual applications.
Implementation Details
The model implements state-of-the-art architectural choices including RoPE position encoding, SwiGLU activation functions, and RMSNorm. It's highly efficient, requiring only 2GB of GPU memory for inference with Int4 quantization, while maintaining strong performance across various tasks.
- Supports 8192 token context length
- Uses flash-attention for optimization
- Implements efficient tiktoken tokenizer
- Offers multiple quantization options (Int4, Int8)
Core Capabilities
- Strong performance on C-Eval (55.6%) and MMLU (43.3%)
- Exceptional coding ability (26.2% on HumanEval)
- Advanced mathematical reasoning (33.7% on GSM8K)
- System prompt customization for role-playing and style adaptation
- Efficient multilingual processing with optimized vocabulary
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its exceptional performance despite its compact size, particularly in coding and mathematical tasks where it outperforms many larger models. Its efficient design allows deployment on consumer hardware while maintaining strong capabilities.
Q: What are the recommended use cases?
The model is well-suited for general chatbot applications, coding assistance, mathematical problem-solving, and multilingual tasks. It's particularly valuable for scenarios requiring efficient deployment with limited computational resources.
