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GenAI-with-LLMs

Generative AI with Large Language Models

The labs are done in Jupyter notebook through AWS sagemaker studio.


week 1

Generative AI use cases, project lifecycle, and model pre-training

Learning Objectives

  • Discuss model pre-training and the value of continued pre-training vs fine-tuning
  • Define the terms Generative AI, large language models, prompt, and describe the transformer architecture that powers LLMs
  • Describe the steps in a typical LLM-based, generative AI model lifecycle and discuss the constraining factors that drive decisions at each step of model lifecycle
  • Discuss computational challenges during model pre-training and determine how to efficiently reduce memory footprint
  • Define the term scaling law and describe the laws that have been discovered for LLMs related to training dataset size, compute budget, inference requirements, and other factors.

week 2

Fine-tuning and evaluating large language models

Learning Objectives

  • Describe how fine-tuning with instructions using prompt datasets can improve performance on one or more tasks
  • Define catastrophic forgetting and explain techniques that can be used to overcome it
  • Define the term Parameter-efficient Fine Tuning (PEFT)
  • Explain how PEFT decreases computational cost and overcomes catastrophic forgetting
  • Explain how fine-tuning with instructions using prompt datasets can increase LLM performance on one or more tasks

week 3

Reinforcement learning and LLM-powered applications

Learning Objectives

  • Describe how RLHF(reinforcement learning from human feedback) uses human feedback to improve the performance and alignment of large language models
  • Explain how data gathered from human labelers is used to train a reward model for RLHF
  • Define chain-of-thought prompting and describe how it can be used to improve LLMs reasoning and planning abilities
  • Discuss the challenges that LLMs face with knowledge cut-offs, and explain how information retrieval and augmentation techniques can overcome these challenges

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