Optica names Duncan Moore as an Honorary Member for his pioneering contributions to gradient-index optics, leadership in public policy, dedicated service to the optics community and distinguished roles in academia, government and professional societies.

Open-source large language models (LLMs) research has made significant progress, but most studies predominantly focus on general-purpose English data, which poses challenges for LLM research in Chinese education. To address this, this research first reviewed and synthesized the core technologies of representative open-source LLMs, and designed an advanced 1.5B-parameter LLM tailored for the Chinese education field. Chinese education large language model (CELLM) is trained from scratch, involving two stages, namely, pre-training and instruction fine-tuning. In the pre-training phase, an open-source dataset is utilized for the Chinese education domain. During the instruction fine-tuning stage, the Chinese instruction dataset is developed and open-sourced, comprising over 258,000 data entries. Finally, the results and analysis of CELLM across multiple evaluation datasets are presented, which provides a reference baseline performance for future research. All of the models, data, and codes are open-source to foster community research on LLMs in the Chinese education domain.