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generative_ai_with_llm's Introduction

Generative AI with Large Language Models

Course Info

Course Contents

Chapter # Chapter
1 Introduction to LLMs and generative AI project lifecycle
2 LLM pre-training and scaling laws
3 Fine-tuning LLMs with instruction
4 Parameter Efficient Fine-Tuning
5 Reinforcement Learning from Human Feedback
6 LLM-powered applications
7 Course conclusion and ongoing research

Lab assignments

Lab # Assignment Description
1 Generative AI Use Case: Summarize Dialogue
  • Perform prompt engineering
  • Compare zero shot, one shot and few shot inferences
2 Fine-Tune a Generative AI Model for Dialogue Summarization
  • Perform full fine-tuning and PEFT
  • Evaluate results with ROUGE metrics
3 Fine-Tune FLAN-T5 with Reinforcement Learning (PPO) and PEFT to Generate Less-Toxic Summaries
  • Fine-tune a FLAN-T5 model to generate less toxic summary
  • Use Meta AI's RoBERTa-based hate speech model for the reward model
  • Use Proximal Policy Optimization (PPO) to fine-tune and reduce the model's toxicity

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