Shiqi Cao enhanced the intel/auto-round repository by updating the gguf_accuracy documentation to include new Q3_K_S data, improving the clarity and traceability of model performance metrics. Focusing on documentation rather than code changes, Shiqi applied data analysis and model evaluation skills to ensure that the updated metrics provided stakeholders with more transparent and actionable insights. The work was carried out using Markdown and version control, emphasizing disciplined documentation practices. While no bugs were addressed during this period, the contribution deepened the repository’s documentation quality, supporting both onboarding and ongoing evaluation processes with more robust, data-driven reporting for model accuracy.