phase-0 training log: meeting brightwoven

Over the past couple months, I’ve been trying to figure out the best way to train my own model on the hardware I actually have.

When Karpathy released nanoChat (a minimal repo that walks through training a small GPT end-to-end), I stepped away from my original plan (using Pythia as a base model) and dove into the nanoChat-style training approach instead. I made a set of adjustments to match what I wanted to test.

TL;DR

• I trained on an RTX 3070 Ti (8GB VRAM), which forced me to be deliberate about sequence length and batch size.

• I added an Understanding Module that monitors training (and can optionally pause on critical issues).

• I built an Exploration Server so training and interaction can happen at the same time.

• First run (0–7k steps) was stable, loss dropped significantly, and the monitoring systems produced useful signals.

Context

This post covers phase 0: the first training runs and the monitoring/interaction scaffolding I added.

What I’m sharing (and what I’m not)

I’m keeping this write-up focused on the workflow and the instrumentation.

For now, I’m not sharing exact hyperparameters, model size details, or the full data recipe.