Taylor Reese.

I’m a sophomore studying Data Science at UNC Chapel Hill, but most of what I actually do sits closer to ML systems work — building and training models from first principles to understand how they work.

The frame I keep coming back to: using a model isn’t the same as understanding it. Most of my time is spent rebuilding architectures component-by-component, then putting them through real pretraining on real hardware to see what breaks.

Pretraining a 751M-parameter Qwen3 reconstruction on UNC’s Longleaf A100 cluster. The architecture and data pipeline are done; the full training run is underway.

Last updated · June 2026

I got into this through the usual path — PyTorch tutorials, then architectures-from-papers exercises. Around the same time I started using UNC’s Longleaf cluster for a project that became RQwen3, and the gap between “I read about how transformers work” and “I have a SLURM job stuck in the queue at 2am because the GRES string is wrong” clarified what I find interesting about this field.

I’m drawn to pretraining methodology, data curation, and how small-model behavior diverges from scaled-up versions of the same architecture — particularly the work coming out of groups like UNC’s MURGe-Lab.