CourseTransformers: From Attention to Trained, Served and Interpreted Models · Module 9: Scale and Distributed Training · part 68 of 111
Part 68 · Module 9: Scale and Distributed Training

Lab: Hana's answer, costed and split, tested

34 min read·13 Oct 2026
Module 9 · Lab · About 60 minutes · Needs: lessons 1 to 4 of this module, and Module 8's lab

What you are building

TINYFORMER-LAB AFTER MODULE 9 scaling.py BIG, grid, fit costs.py memory, dollars parallel/ launcher + 4 splits v0.8.0 package: data, model, trainer, unchanged test_m09_scale 8 tests Hana's answer fit at big, bill, manifest git tag v0.9.0
The lab adds the scaling fit, the cost arithmetic and the parallel package (blue) to the unchanged v0.8.0 package (grey), pins them with eight tests (green), writes Hana's answer (amber) and tags v0.9.0.

Sol 426 at Aurora Station. Hana Aoki asked one question across four lessons: what would a model 1,000 times bigger than base cost to train on hired GPUs on Earth, and would it be worth it? Her cap for one hired run is $1,500. The lessons answered in pieces, and they did not quite agree on what "1,000 times" meant.

Today you settle it in tinyformer-lab. The 1,000-times model is Lesson 4's big: width 2,048, 16 layers, context 2,048. You save the grid and fit as scaling.py, the memory and dollar arithmetic as costs.py, and the four splits as tinyformer/parallel/, with one launcher that every distributed script shares. Eight tests pin them, one script writes Hana's answer as a manifest, and the tag is v0.9.0.

The rest of this course is yours to keep

This course is bought on its own, once, and stays readable afterwards, including the parts added to it later.