Train your own LLM? Here's what happens (www.exasol.com)

A recent exploration into building a Large Language Model (LLM) from scratch has revealed the complexities and practical challenges involved in the process. A developer took a hands-on approach, using a single notebook rather than cloud-scale infrastructure, to create an LLM and push it to production-like usage. This effort not only emphasized the difference between training and deploying an LLM but also highlighted the significance of efficient data handling—running inference directly within the Exasol database to leverage parallel processing without moving large datasets around. This inquiry is significant for the AI/ML community as it uncovers the mechanics behind LLMs and emphasizes the importance of fine-tuning pre-trained models over starting from scratch, which can be resource-intensive and time-consuming. By leveraging an AI coding assistant for application development and adapting a model like GPT-2 through fine-tuning, the developer showcases how AI can enhance software engineering practices. This transition from full-scale training to fine-tuning optimizes resource use while still achieving sufficient model performance, illustrating a more practical pathway for deploying LLMs in real-world data-intensive scenarios.