AI Jupyter Notebook Tutorials – Abbie’s AI Tutorials

Fine Tuning LLaVA with and without LoRA

training

PEFT

vision

LLMs

In this notebook, we dive deep into the architecture of LLaVA, with the goal of fine-tuning it (both with and without LoRA) to adapt it to determining the morphologies of GalaxyZoo2 images.

Abbie Petulante

PEFT Deep Dive: LoRA

training

PEFT

Mathematical foundations and practical implementation of LoRA, one of the most common paremeter efficient fine tuning methods.

Abbie Petulante

RL for LLMs

reinforcement-learning

training

LLMs

An in-depth guide to reinforcement learning using proximal policy optimization (PPO), with a focus as it applies to modern large language models (and with human feedback!).

Abbie Petulante

Prompt Caching

caching

LLMs

inferencing

A guide to how prompt caching - a modular approach to KV caching. We’ll talk through how and why this modular implementation works, and include a practical example of implementing it for LLaMa 3.2 1B.

Abbie Petulante

KV Caching

caching

LLMs

inferencing

A guide to how KV caching is implemented for LLMs, including a practical example of implementing it for LLaMa 3.2 1B.

Abbie Petulante

The U-Net Architecture

architectures

CNNs

vision

A complete guide to the U-Net architecture. Covers all aspects of a layer, from the convolution operation, to pooling, to what makes U-Nets so special.

Abbie Petulante

Welcome To My Tutorials Blog!

During my Ph.D., I utilized a U-Net to attempt to learn the final distribution of dark matter in a computer simulation from its initial conditions. Being in an Astrophysics…

Abbie Petulante