LLM Pretraining & Diffusion Experiments

01 / OVERVIEW

Context & problem

Personal experiments building practical experience with language-model pretraining and reproducing early DALL·E-era diffusion research on ImageNet.

Model training requires coordinating data preparation, architecture, optimization, and evaluation while understanding the compute and iteration costs of each experiment.

02 / SYSTEM ARCHITECTURE

How the pieces connect.

Research and personal experimentation; not a commercially deployed foundation model.

03 / INDIVIDUAL CONTRIBUTION

What I worked on.

• Ran pretraining experiments with language models at approximately 100M parameters.
• Reproduced diffusion-model research workflows on ImageNet.
• Explored transformer architectures and generative-model training.

04 / IMPLEMENTATION

Engineering details.

Model training requires coordinating data preparation, architecture, optimization, and evaluation while understanding the compute and iteration costs of each experiment.

• Experiments covered training and evaluation workflows in PyTorch.
• These were research and personal experiments, not commercially deployed foundation models.

05 / RESULTS & VALIDATION

What the work supports.

• Built practical familiarity with pretraining and diffusion-model reproduction.
• No commercial deployment or benchmark comparison is claimed.

Validation details are limited to evidence stated in the resume; no unreported datasets, accuracy claims, or benchmark comparisons are added.