Worked on stability and maintenance for the jeejeelee/vllm repository, focusing on a critical bug fix in the Deepseek V32 Multi-Token Predictor. Addressed hidden-state recycling by ensuring post-final-norm hidden states were used, aligning with draft model normalization and improving multi-token predictor acceptance rates. Enhanced the codebase by fusing residual addition into RMSNorm and guaranteeing the normalized hidden state was returned for both logits computation and recycling. Utilized Python and PyTorch to implement these changes, which reduced edge-case failures and strengthened production reliability for deep learning workloads. All updates were fully auditable and linked to a traceable commit.