llm_related is a collection of large language model algorithm implementations and learning notes.
The project serves as a resource for understanding and reproducing algorithms central to large language models. It gathers implementations alongside documentation of the learning process, making it useful for developers seeking to understand how key LLM techniques work at a practical level rather than purely theoretical.
This project suits developers and researchers who want hands-on implementations of LLM-related algorithms for educational purposes or as reference material. It works best as a learning resource rather than a production system, offering insight into algorithm mechanics through working code examples paired with notes on the implementation journey.
The project shows sporadic development activity with contributions spread across time rather than concentrated in any single period. Work appears driven by individual learning goals rather than coordinated team effort, reflected in the nature of the repository as a personal collection of implementations and notes.
