Worked on the rasbt/llms-from-scratch repository, focusing on improving the stability and correctness of the GPT model’s text generation path. Addressed a critical bug in the key-value (KV) cache masking logic, ensuring that masking is accurately applied according to the cache position during generation. This fix reduced the risk of generation errors in production environments. Added comprehensive tests to validate KV-cache masking behavior and prevent future regressions. The work emphasized reliability and accuracy in generated text, with code quality improvements and efficient review practices. Utilized Python, PyTorch, and deep learning techniques, demonstrating a methodical approach to model robustness and validation.