Hironori Maruoka authored and published a technical blog post for the mamezou-tech-site repository, focusing on the architecture and parameter distribution of GPT-2 small. Drawing on expertise in LLM fundamentals and transformer architecture, Hironori explained how parameter scaling impacts large language model performance, providing readers with a clear understanding of these core concepts. The work involved technical writing in Markdown, as well as refining post discoverability through tag-order adjustments and typo corrections. This contribution enhanced the educational value of the site, offering stakeholders and readers a deeper, well-documented perspective on machine learning model design and scaling principles.