MalayAgr / generative-ai-with-llms-notes

Notes for the course Generative AI With Large Language Models, offered by DeepLearning.AI on Coursera

Generative AI With Large Language Models

This repository contains detailed code examples for the course Generative AI With Large Language Models, offered by DeepLearning.AI on Coursera.

Structure

The repository contains 4 folders:

• code - Contains two coding assignments:
  • Fine-Tuning Lab.ipynb - Assignment which covers Instruction Fine-Tuning using LoRA.
  • RLHF Lab.ipynb - Assignment which covers using Reinforcement Learning From Human Feedback to align LLMs.
• Week 1:
  • Transformers - Basics of Transformers.
  • Prompting and Prompt Engineering - Basics of prompting, prompt engineering and inference configuration parameters (temperature, etc).
  • Pre-training Large Language Models - Basics of how LLMs are pre-trained.
  • Efficient Multi-GPU Compute Strategies - Approaches available for training LLMs in a distributed manner.
• Week 2:
  • Instruction Fine-Tuning - Basics of fine-tuning LLMs.
  • Model Evaluation - Metrics and Benchmarks - Details on evaluation metrics and benchmarks for LLMs.
  • Parameter Efficient Fine Tuning (PEFT) - Basics of PEFT, covering LoRA and Soft Prompts.
• Week 3:
  • Reinforcement Learning From Human Feedback (RLHF) - Basics of RLHF.
• markdown - Contains the same files as above except in Markdown format for those who prefer to read directly on GitHub.