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.