Adi Borate focused on backend reliability improvements in the Lightning-AI/litgpt and huggingface/transformers repositories, addressing critical bugs in deep learning model infrastructure. Using Python and PyTorch, Adi enhanced GPT model stability by refining RoPE cache length computation to support varied configurations, introducing a method for extracting the RoPE head dimension and expanding test coverage. In huggingface/transformers, Adi restored tokenizer compatibility with processor v5 by updating initialization logic to accept additional parameters, ensuring backward compatibility and smoother deployment. The work demonstrated a thoughtful, test-driven approach to cross-repository maintenance, emphasizing robust API development and maintainability in machine learning pipelines.