Worked on PaddlePaddle/GraphNet and PaddlePaddle/docs, focusing on deep learning model integration and documentation quality. Developed and integrated the SqueezeNet 1.1 model for Paddle, implementing static graph capture and end-to-end validation scripts to ensure compatibility with PaddleX and reproducibility of results. Applied code formatting and cleanup using Python, maintaining high standards for code quality and maintainability. In the documentation repository, improved the accuracy and navigability of model weight references by updating Markdown links to align with official PyTorch documentation. Demonstrated strong technical writing and collaboration skills, contributing to both model deployment workflows and comprehensive, user-friendly documentation.