The open-source publishing of large language models (LLMs) has created many possibilities for how anyone who understands language and has access to a computer 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 still largely unexplored. This project is an attempt to merge the knowledge of Classics with the capabilities of artificial intelligence by finetuning an LLM to cater to the specific needs of learners and professionals. The goal of this project is to develop an LLM that not only reproduces contextual knowledge accurately but also exhibits a consistent "personality" - and, indeed, has consistent propriety - to appeal to a diverse audience who possess differing levels of knowledge. A significant portion of this project was dedicated to refining the dataset, following the principle of "garbage in, garbage out," to ensure the model generates relevant, useful, and creative responses when given a prompt (a statement, question, or single word). After training and evaluation, my model's ability to handle a vast array of different types of inputs and prompting exceeded expectations for a 355M parameter model, though its 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 continuous finetuning will be undertaken.

翻译：大型语言模型的开源发布为任何理解语言并能使用计算机的人创造了众多与重要人工智能工具交互的可能性，尤其是在学习和知识传播的背景下。然而，这些模型在古典学等专业领域的实用性仍有待深入探索。本项目尝试通过微调大语言模型，将古典学知识与人工智能能力相结合，以满足学习者和专业人士的特定需求。项目目标是开发一个不仅能准确复现语境知识，还能展现一致"个性"——实际上也包括一致的得体性——以吸引具备不同知识水平的多样化受众的模型。本项目的一个重要部分致力于优化数据集，遵循"垃圾进，垃圾出"原则，确保模型在收到提示（陈述、问题或单个词语）时能生成相关、有用且富有创意的响应。经过训练和评估，我开发的模型处理大量不同类型输入和提示的能力超出了对355M参数模型的预期，尽管其偶尔出现的幻觉（尤其是在设置较高温度参数时），特别是在对历史事件或自身身份的主张上，使其显得有些反复无常，因此需要继续进行微调。