Worked on the liguodongiot/transformers repository to deliver an inference optimization feature for the GPT-2 LM HeadModel, focusing on improving efficiency during text generation. Introduced a new logits_to_keep parameter that allows selective consideration of logits, effectively filtering out unnecessary tokens and reducing computational overhead without impacting output quality. This enhancement, implemented in Python and leveraging deep learning and natural language processing techniques, provided greater configurability and faster inference for GPT-2 based workflows. The work resulted in lower inference costs and improved throughput, addressing the need for scalable, efficient model deployment while maintaining the integrity of generated language outputs.