LucidGPT: a closer look at the training loop
LucidGPT is my GPT-style pre-training project. It brings together simple launch commands, multiple optimizer modes, built-in benchmark evaluation during and after training, and optimizer/activation monitors for debugging training stability.
This showcase introduces those capabilities from my October 2026 CV. The figures below are explanatory examples, with synthetic numerical data.
The training workflow
The project connects four practical parts of studying a model: launching a run, selecting an optimizer, inspecting training behavior, and evaluating the result. This capability map gives each part a place in the story. Click a step or replay the sequence.
Swipe the diagram sideways to see all of it →
1/0 Launch a GPT-style pre-training run with a simple command.
What the project brings together
| Capability | What it helps you inspect |
|---|---|
| Simple launch commands | The entry point for a GPT-style pre-training run. |
| Multiple optimizer modes | The optimizer choice within the training setup. |
| Built-in benchmark evaluation | Model evaluation during and after training. |
| Optimizer and activation monitors | Signals for investigating training stability. |
For executable instructions and the current implementation, use the repository linked above. This overview does not prescribe a launch command or claim a benchmark result.
A training monitor example
Inspect a checkpoint with the pointer or arrow keys, toggle either curve, and use Data to read the underlying values. Here the invented training loss falls while the invented validation loss eventually rises. The divergence is a useful pattern to discuss; identifying its cause in a real run would require evidence about that run’s setup.
Reading a training curve
Swipe the plot sideways to see all of it →
These values were created only for this article. They do not measure LucidGPT, compare actual optimizers, or establish model quality. A measured follow-up should include the code revision, data, configuration and evaluation procedure alongside the curves.
From tooling to a research note
My broader interests include LLM optimization and training dynamics. A useful training tool makes it easier to connect an observation to a precise question: what changed in the setup, what did the monitors show, and what experiment would distinguish possible explanations?
The reproducible experiment template offers a structure for that next note. The visualization gallery supplies more charts, tables and diagrams, and the About page introduces the research behind this notebook.