Curriculum

Phase 1: Local Environment (Collab Notebooks)

Part 1: Foundations

Why Take This Course & Overview
Pre-requisites
Introduction to LLMs
Basic Open-Source LLM usage
Basic Open-Source ASR usage

Part 2: Prompting (Coming July)

Foundations of prompt engineering
Organising prompts via jinja templates

Part 3: Running Open-Source Models with Llama.cpp

Installation and setup
Selecting & downloading a model
Working with llama cpp and the Python bindings
Using Grammars to control output format
Streaming Responses
Multi-modal models (CLIP)
Summary

Part 4: Creating Retrieval Augmented Generation Pipelines (RAG) with Open Source Models

Introduction to RAG
Creating a RAG system with llama index

Part 5: Evaluating Models & Pipelines

Evaluation Theory
Implementing tests with deepeval

Project 1: Chatbot Application (FastAPI & Together AI)

Project 2: Building a Podcast Transcript Summarizer (Offline ETL with Llama CPP)

Project: Multimodal (Coming July)

Pre-Requisites

Must Haves:

Python Knowledge
Git knowledge

Nice to haves (i.e. only required for certain parts of the course, these parts will be explained but may require further reading if you're unfamiliar with them):

Knowledge of web development (FastAPI)
Knowledge of SQL databases
Knowledge of AWS

Not Covered In This Course

Pre-training
Fine-tuning

Both of these activities require access to significant GPU resources which will beyond many students.

Deployment (I will create a separate course for this, feel free to message me if you have questions)

About Me

Hi I'm Chris! I'm an experienced software developer who has taught over 30,000 software professionals online. If you Googled something about FastAPI before, you've probably ended up on my blog.

Building AI Applications with Open Source Models