
Contributed to the ZJUSCT/HPC101 repository by building and maintaining course infrastructure, focusing on both documentation quality and deep learning model implementation. Over three months, archived outdated lab materials to streamline code management and updated course logistics to improve clarity for students and contributors. Developed foundational TinyLLM components for Lab 5, implementing Transformer architecture elements such as self-attention and feed-forward networks using Python and PyTorch. Enhanced onboarding materials and technical documentation, providing practical guidance on model configuration and GPU memory usage. Emphasized maintainability and usability throughout, ensuring that both course materials and codebase remain accessible and easy to update.
Monthly summary for 2025-08 focusing on delivering foundational TinyLLM capabilities for Lab 5 in ZJUSCT/HPC101, documentation improvements, and onboarding support. Key business value delivered includes a maintainable TinyLLM baseline based on Qwen3, testing scripts, and improved contributor guidance, enabling faster iteration and collaboration.
Monthly summary for 2025-08 focusing on delivering foundational TinyLLM capabilities for Lab 5 in ZJUSCT/HPC101, documentation improvements, and onboarding support. Key business value delivered includes a maintainable TinyLLM baseline based on Qwen3, testing scripts, and improved contributor guidance, enabling faster iteration and collaboration.
June 2025: Focused on course logistics and documentation quality for HPC101, updating venue, timing, and calendar layout to improve attendee clarity and operational readiness.
June 2025: Focused on course logistics and documentation quality for HPC101, updating venue, timing, and calendar layout to improve attendee clarity and operational readiness.
May 2025 monthly work summary for ZJUSCT/HPC101 highlighting business value and technical achievements.
May 2025 monthly work summary for ZJUSCT/HPC101 highlighting business value and technical achievements.

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