Isabella Siu developed comprehensive zero-shot approach tutorial documentation for the Open-Finance-Lab/FinLLM-Leaderboard repository, focusing on FLARE-FIQASA datasets and the Llama-3.2-1B model. Her work detailed dataset usage and sentiment classification tasks, integrating performance logging and reproducibility features. She addressed technical aspects such as partial matching evaluation, concurrency considerations, and token streaming to enhance workflow efficiency. Leveraging her skills in documentation, machine learning, and natural language processing, Isabella produced user-facing materials that streamline onboarding and clarify analytical processes. The depth of her documentation supports both immediate adoption by engineers and ongoing optimization of zero-shot machine learning workflows within the project.