Worked on the jeejeelee/vllm repository to enhance the TurboQuant quantization module by addressing a determinism issue that affected reproducibility. The solution involved removing unnecessary random sign generation within the quantization process, ensuring that results remain consistent across runs. This technical update, implemented in Python using PyTorch and machine learning principles, improved the reliability of TurboQuant workflows and reduced debugging time for downstream users. Additionally, documentation was updated to include references to prior art, clarifying the quantization approach for contributors and users. All changes were clean, auditable, and aligned with best practices for quality and transparency in quantization.