Introduction to ChatGLM2-6B
ChatGLM2-6B is an advanced open-source bilingual dialogue model, serving as the second generation of the ChatGLM-6B platform. This model has retained many excellent features from its predecessor, like smooth interactions and ease of deployment, while also introducing significant new advancements. Below is a detailed look at the features and improvements of ChatGLM2-6B.
Enhanced Performance
Based on the development experience from the initial ChatGLM model, the foundation of ChatGLM2-6B has been comprehensively upgraded. Utilizing a hybrid objective function from GLM, ChatGLM2-6B underwent significant pre-training with 1.4T Chinese and English tokens, along with human-aligned preference training. It has demonstrated substantial improvements over its predecessor on datasets such as MMLU (23% increase), CEval (33% increase), GSM8K (571% increase), and BBH (60% increase). Among models of the same size, it holds a strong competitive stance.
Extended Context Length
ChatGLM2-6B leverages FlashAttention to significantly expand the context length of its base model from 2K in ChatGLM-6B to 32K, with training conducted at an 8K context length during dialogue stages. For users with needs for longer contexts, the ChatGLM2-6B-32K variant has been released. According to LongBench evaluations, ChatGLM2-6B-32K exhibits a clear competitive edge among similarly-scaled open-source models.
Efficient Inference
The integration of Multi-Query Attention into ChatGLM2-6B offers faster inference speeds and reduced GPU memory usage. The inference speed has been enhanced by 42% over the original model. Moreover, under INT4 quantization, a 6GB memory can now support dialogue lengths from 1K to 8K.
Open Licensing
The weights of ChatGLM2-6B are fully open for academic research, and post-registration via questionnaire, free commercial use is also permitted.
Performance Metrics
ChatGLM2-6B has undergone evaluations using various datasets like MMLU for English, C-Eval for Chinese, GSM8K for math, and BBH for English. The model demonstrated impressive average accuracy scores across these datasets, displaying significant progress compared to its predecessor and highlighting its broad capabilities in both English and Chinese language processing.
Usage and Deployment
To experiment with ChatGLM2-6B, one can easily install required dependencies and set up the model for various interactive utilities. Its efficient and scalable framework makes it suitable for different applications and ensures fast deployment and integration in conversational AI systems.
In summary, ChatGLM2-6B builds on the strengths of its initial version, bringing remarkable advancements in understanding, performance, and efficiency, along with a commitment to open-source principles encouraging both innovation and responsible use within the community.