LLMs

LLMs, what are they? how do they work?

This repo contains my dive into llms. The notebooks are based on some of the big trends in the 2022-2023 AI/LLM boom.

I try and cover:

1. Basics: A background on LLMs
2. Math: Some of the basic background and useful functions in ai
3. The transformer: The base architecture of modern llms
4. Transformer Models: Introduction to some popular transformer models and a deep dive into GPT
5. Finetuning: Improve your models on your data (hopefully)
6. Inference: You've trained your model, now what.
7. Retrieval Augmented Generation (RAG): Context engineering
8. Agents: Models on a loop

If you want to run the notebooks you should create a models directory and download the wanted models. I will be using a variety of different models (usually the lower parameter models)

TODO: parts on history, neurosymbolic models, other extr aextra