Developed and delivered a feature for the jeejeelee/vllm repository that introduced support for multiple hidden activation functions within the Gemma3 and Gemma4 models’ gated MLP architecture. This enhancement enabled selection among various hidden_act variants, increasing the flexibility and configurability of neural network architectures for rapid experimentation and tuning. The implementation leveraged Python and PyTorch, focusing on deep learning model engineering and activation function customization. Work was tracked and documented through Git with signed-off commits, reflecting a collaborative and test-driven approach. No major bugs were reported during the period, and the contribution emphasized robust unit testing and maintainable code practices.