  This paper introduces "Shai" a 10B level large language model specifically
designed for the asset management industry, built upon an open-source
foundational model. With continuous pre-training and fine-tuning using a
targeted corpus, Shai demonstrates enhanced performance in tasks relevant to
its domain, outperforming baseline models. Our research includes the
development of an innovative evaluation framework, which integrates
professional qualification exams, tailored tasks, open-ended question
answering, and safety assessments, to comprehensively assess Shai's
capabilities. Furthermore, we discuss the challenges and implications of
utilizing large language models like GPT-4 for performance assessment in asset
management, suggesting a combination of automated evaluation and human
judgment. Shai's development, showcasing the potential and versatility of
10B-level large language models in the financial sector with significant
performance and modest computational requirements, hopes to provide practical
insights and methodologies to assist industry peers in their similar endeavors.
