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The open-source publishing of large language models (LLMs) has created manypossibilities for how anyone who understands language and has access to acomputer can interact with significant tools of artificial intelligence,particularly in the context of learning and knowledge dissemination. However,the utility of these models in specialized fields like Classics is stilllargely unexplored. This project is an attempt to merge the knowledge ofClassics with the capabilities of artificial intelligence by finetuning an LLMto cater to the specific needs of learners and professionals. The goal of thisproject is to develop an LLM that not only reproduces contextual knowledgeaccurately but also exhibits a consistent "personality" - and, indeed, hasconsistent propriety - to appeal to a diverse audience who possess differinglevels of knowledge. A significant portion of this project was dedicated torefining the dataset, following the principle of "garbage in, garbage out," toensure the model generates relevant, useful, and creative responses when givena prompt (a statement, question, or single word). After training andevaluation, my model's ability to handle a vast array of different types ofinputs and prompting exceeded expectations for a 355M parameter model, thoughits occasional hallucinations (especially when set with a high temperature),particularly in its assertions about historical events or its own identity,make it seem somewhat capricious and more work in the form of continuousfinetuning will be undertaken.

Finetuning an LLM on Contextual Knowledge of Classics for Q&A