Fine-tuning 101 | Prompt Engineering Conference

Intro to fine-tuning LLMs from the Prompt Engineering Conference (2023) Presented by Mark Hennings, founder of Entry Point AI. 00:13 - Part 1: Background Info -How a foundation model is born -Instruct tuning and safety tuning -Unpredictability of raw LLM behavior -Showing LLMs how to apply knowledge -Characteristics of fine-tuning 06:25 - Part 2: When to use it -Examples of specialized tasks that fine-tuning benefits -Reasons to fine-tune a model -Speed and cost benefits -Prompt length before and after fine-tuning -Fine-tuning in the team environment -LLM workflow from prompt engineering and fine-tuning to production -Size of dataset for fine-tuning 11:27 - Part 3: No-code Demo -Demo of no-code fine-tuning on Entry Point AI Learn more at https://www.entrypointai.com