Fine-Tuning GPT 3.5 with Unstructured: A Comprehensive Guide

The text discusses the limitations and potential of Large Language Models (LLMs) like OpenAI's GPT-3, 3.5, and 4, highlighting their vast yet static knowledge base, which is limited to information up until a specific cutoff date. To address the challenge of keeping these models relevant and updated, techniques such as fine-tuning and Retrieval Augmented Generation (RAG) are recommended. The guide explains how to use Unstructured, an open-source tool, to enhance GPT models with the most current data and domain-specific insights. It provides a detailed process for fine-tuning these models using a dataset, exemplifying with the Federal Open Market Committee's meeting notes, and discusses the necessary setup, including obtaining API keys, setting up Google Drive integration, and preparing a fine-tuning dataset. Additionally, it covers the practical aspects of fine-tuning, such as token limits, cost estimation, and training duration, while addressing potential errors and providing troubleshooting tips. The text concludes by emphasizing the improved accuracy of fine-tuned models and suggests combining fine-tuning with RAG for optimal results, with ongoing efforts to simplify these processes through the Unstructured platform.