Ziliang contributed to the jeejeelee/vllm repository by developing a token usage metrics enhancement that improves the accuracy of token analytics. The feature computes prefill KV tokens while excluding cached tokens, addressing the need for more precise usage reporting and budgeting. Ziliang applied backend development skills and data metrics expertise, using Python to instrument the codebase and ensure clear, traceable commits. The work included aligning unit tests with the new metrics logic and adhering to contribution standards. This enhancement supports better capacity planning and cost control by delivering higher-quality analytics data, reflecting a focused and technically sound engineering approach within the project.