Fine-Tuning and Customizing LLMs for Enterprise Tasks

Explore how to fine-tune and customize Large Language Models (LLMs) for enterprise environments in this 22-minute talk by Hoang Tran, ML Engineer at Snorkel AI. Learn about the value of LLMs in business settings, their limitations in specific organizational tasks, and various customization techniques including full fine-tuning, parameter-efficient fine-tuning, and distillation. Discover the importance of high-quality, task-specific data for successful model implementation and gain insights into the potential future trend of using multiple smaller, task-specific models rather than a single LLM in enterprise AI applications.

Introduction
LLMs
AI
DomainSpecific Task
Training
Data
SRL Flow
Finetuning Techniques
AI Training
Flow Model Integration
Flow to Smaller Model
What is Step by Step
Recap
Future of LLMs