Baichuan-13B-Chat
| Property | Value |
|---|---|
| Parameter Count | 13.2B |
| Context Length | 4096 tokens |
| Architecture | Transformer with ALiBi positioning |
| Languages | Chinese, English |
| License | Community License (Commercial use requires approval) |
What is Baichuan-13B-Chat?
Baichuan-13B-Chat is an advanced large language model developed by Baichuan Intelligence as the aligned version of their base model. Built upon their successful 7B architecture, this model features 13 billion parameters and has been trained on 1.4 trillion tokens - 40% more than LLaMA-13B. It's designed for both Chinese and English language processing, incorporating state-of-the-art ALiBi positioning technology for enhanced performance.
Implementation Details
The model employs a sophisticated architecture with 5,120 hidden dimensions across 40 layers and 40 attention heads. It supports efficient deployment through INT4/INT8 quantization, making it accessible for consumer-grade GPUs like the NVIDIA 3090.
- Achieves 31.6% faster inference speed compared to LLaMA-13B
- Supports context window of 4096 tokens
- Features optimized vocabulary size of 64,000 tokens
- Implements ALiBi position encoding for better computational efficiency
Core Capabilities
- Strong performance in both Chinese and English benchmarks
- Efficient dialogue generation and response
- Flexible deployment options with quantization support
- Demonstrated superior results on C-Eval, MMLU, and CMMLU benchmarks
- Commercial-grade performance with proper licensing
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its bilingual capabilities, efficient ALiBi positioning system, and extensive training on 1.4 trillion tokens. It offers superior performance metrics compared to similar-sized models while maintaining deployment flexibility through quantization options.
Q: What are the recommended use cases?
The model is well-suited for dialogue applications, content generation, and general language understanding tasks in both Chinese and English. It's particularly effective for deployments requiring efficient inference and strong bilingual capabilities.
