Worked on the kvcache-ai/sglang repository to enhance reliability in machine learning inference by addressing a critical bug in logprob token handling. Focused on data processing and Python development, the work involved implementing a clamping mechanism for logprob token IDs, ensuring they remained within the model’s vocabulary size. This technical solution prevented out-of-range errors during token processing, reducing runtime exceptions and supporting safer production deployments. The targeted bug fix improved the stability of token processing pipelines, aligning with reliability goals for model serving. All changes were tracked with clear commit messages, facilitating traceability and supporting quality assurance in ongoing development.