Worked on the LMCache/LMCache repository to deliver an enhanced multi-round QA data preprocessing pipeline, focusing on improving reliability, data quality, and usability. The feature introduced data integrity assertions and a dry-run validation option, allowing users to verify data before full processing. Model specification was enabled for tokenization, and additional filtering was implemented to exclude invalid conversation types. Progress visualization was added using tqdm for real-time feedback during preprocessing. The work involved targeted bugfixes in Python scripts, specifically multi-round-qa.py and data_preprocessing.py, and leveraged skills in data preprocessing, scripting, and command-line interface development to streamline the pipeline’s workflow.