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 sentiment classification tasks and incorporated performance logging, partial matching evaluation, and concurrency considerations to support efficient and reproducible workflows. By emphasizing token streaming and clear output design, Isabella enhanced the onboarding process and enabled contributors to analyze and optimize zero-shot machine learning pipelines. Leveraging her expertise in documentation, machine learning, and natural language processing, she delivered a user-facing resource that addressed both technical depth and practical usability for engineers and researchers.