Worked on the Nixtla/neuralforecast repository, focusing on improving documentation accuracy and resolving inconsistencies between code and user-facing materials. Addressed a key issue where the documented dropout rate for the BiTCN model did not match the actual implementation, updating the documentation to reflect the correct value of 0.5. This effort reduced potential confusion for users and ensured that model configuration details were transparent and reliable. Utilized Python and applied skills in documentation and model configuration to maintain alignment between codebase and documentation. No new features were introduced during this period, as the primary focus remained on bug fixes and documentation quality.