Baichuan2-13B-Chat
| Property | Value |
|---|---|
| License | Apache 2.0 with additional terms |
| Training Data | 2.6 trillion tokens |
| Languages | Chinese, English |
| Framework | PyTorch 2.0 |
What is Baichuan2-13B-Chat?
Baichuan2-13B-Chat is a state-of-the-art large language model developed by Baichuan Intelligence. This latest version (v2.0) represents a significant advancement in bilingual capability, featuring enhanced mathematical reasoning and complex instruction-following abilities. The model is trained on a high-quality corpus of 2.6 trillion tokens and achieves superior performance in both Chinese and English benchmarks.
Implementation Details
The model leverages PyTorch 2.0's F.scaled_dot_product_attention for optimized inference speed. It supports both base precision and 4-bit quantization, making it adaptable for various deployment scenarios. The architecture is optimized for both academic research and commercial applications, pending proper licensing.
- Supports context-aware chat interactions
- Implements efficient token processing
- Offers both full precision and 4-bit quantized versions
- Requires PyTorch 2.0 environment
Core Capabilities
- Strong performance on C-Eval (58.10%), MMLU (59.17%), and CMMLU (61.97%)
- Enhanced mathematical and logical reasoning abilities
- Sophisticated instruction following
- Bilingual proficiency in Chinese and English
- Support for long-context understanding (up to 192K tokens)
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its balanced performance in both Chinese and English, achieving state-of-the-art results across multiple benchmarks. It's particularly notable for its improved mathematical reasoning and instruction-following capabilities in the v2.0 release.
Q: What are the recommended use cases?
The model is suitable for a wide range of applications including general conversation, academic research, and commercial applications (with proper licensing). It excels in tasks requiring bilingual understanding, mathematical reasoning, and complex instruction following.
