Worked on stabilizing tensor parallelism workflows in the jeejeelee/vllm repository, focusing on improving reliability for high tensor-parallel deployments under the Qwen3.5 model. Addressed a critical bug in the GatedDeltaNet tensor projection logic that previously caused failures when running with two or more tensor parallel workers. The solution involved refining the handling of tensor projections to ensure compatibility with the Marlin framework, thereby reducing cross-framework errors. Employed Python and PyTorch to implement the fix, demonstrating strong skills in deep learning, machine learning, and model optimization. Maintained clear code attribution and followed best practices for collaborative development.