Expert Commentary: Fine Tuning LLMs for Proprietary Domain Knowledge
Large Language Models (LLMs) have become increasingly essential for enterprises to handle complex language tasks. However, one challenge faced by these enterprises is how to imbibe LLMs with domain-specific knowledge efficiently and effectively, while optimizing resources and costs.
An approach often used by enterprises is Retrieval Augmented Generation (RAG), which enhances language models’ capabilities by utilizing vector databases for retrieving information. While this approach doesn’t require fine tuning LLMs explicitly, its effectiveness is limited by the quality and capabilities of the vector databases rather than the inherent potential of the LLMs themselves.
In this article, the focus is on fine tuning LLaMA, an open-source LLM, using proprietary documents and code from an enterprise repository. The goal is to evaluate the quality of responses generated by the fine tuned models. Additionally, this work aims to provide guidance to beginners on how to start with fine tuning LLMs for documentation and code.
One of the crucial considerations when fine tuning LLMs is the choice of GPU size required. The article suggests making educated guesses to determine the appropriate GPU size for optimal performance. Choosing the right GPU size is crucial to ensure efficient training and inference during the fine tuning process.
The article also proposes pre-processing recipes for both document and code datasets. These recipes help in formatting the data into different formats to facilitate the fine tuning process. For document datasets, the suggested methods include forming paragraph chunks, question and answer pairs, and keyword and paragraph chunk pairs. On the other hand, for code datasets, the recommendation is to form summary and function pairs.
Furthermore, the article provides a qualitative evaluation of the fine tuned models’ results for domain-specific queries. This evaluation helps in assessing the models’ performance and their ability to generate relevant and accurate responses based on the domain-specific knowledge they have acquired through fine tuning.
In conclusion, this article offers practical guidelines and recommendations for enterprises looking to fine tune LLMs for proprietary domain knowledge. By leveraging the techniques discussed, enterprises can enhance the capabilities of LLMs and enable them to provide more accurate and contextually appropriate responses, ultimately improving their language processing tasks.