Thoughts On Various Manners In Which Llms Can Be Stratified And Classified

This project presents a collection of thoughts and analyses on the different manners in which large language models can be stratified and classified. It aims to provide insights into the methodologies that can be employed to categorize LLMs based on their architectures, training data, and performance metrics.

The project utilizes a combination of literature review and theoretical frameworks to explore classification criteria, including model size, training objectives, and application domains. It serves as a resource for researchers, developers, and practitioners interested in understanding the nuances of LLM classification. Potential use cases include guiding model selection for specific tasks, enhancing model interpretability, and contributing to the ongoing discourse in AI ethics and governance.