This session aims to provide practical insights for AI enthusiasts on effectively customizing and leveraging LLMs in various applications through preference tuning and fine-tuning.
Size: 2.4 MB
Language: en
Added: Aug 30, 2024
Slides: 25 pages
Slide Content
Fine-Tuning & Preference Tuning
LLMs
Ankit Khare
Developer Relations, OpenAI
Overview
•Mental Model of the landscape of optimizing LLMs
•Intuition on which methods to use when
•You should leave with some level of confidence that
LLM optimization is indeed hard.
•But, no pain no gain folks!!!
False Belief on optimization
•Process is linear X
•Process is non linear
Actual Flow
Steps in the flow
Prompt Engineering
Example
Mental Model - RAG and FT
RAG Intuition
RAG Eval
Fine Tuning Intuition
FT Success Story
Fun FT Story
Fun FT Story
Fun FT Story
FT Best Practices
FT + RAG
Preference Tuning
Preference Tuning
Preference Tuning
RLHF
DPO
Explore More!
Slide credits and references
●Huggingface
https://www.youtube.com/watch
?v=QXVCqtAZAn4