Artifact contract

The same learning loop made visible through loss, sampling, presets, workers, WASM, and WebGPU-capable browser surfaces.

This interactive model lab is classified as runnable lab. Workload guidance: runs in the browser; preset size controls cost.

Build

Open the playground and train a tiny model without a server.

Modify

Replace the corpus or choose a different model preset.

Tune

Adjust context, batch, learning rate, and generation controls in the lab.

Prove

Watch loss and samples together; do not mistake lower loss for useful behavior.

Package

Export the browser artifact and retain the settings that produced it.

CLI surface

How to read this dossier

This canonical page separates the retained repository decision from the material that informed it. A source may describe an external claim, historical measurement, local observation, or planned procedure; those evidence classes are not interchangeable. Follow the provenance links before reusing a number or method.

Status and disposition describe what PostTrainLLM retained when this record was indexed. They are not a live product promise, a newly run benchmark, or permission to restart historical work. Unknown or unmeasured fields remain unknown rather than being treated as zero.

To reuse the record, first name the exact claim you need and trace it to the linked source. Then check whether the original environment, model revision, data split, evaluator, hardware, and budget match the proposed use. If they do not, treat the record as a hypothesis or design reference and run the smallest fresh comparison that can falsify it. Preserve negative outcomes and regressions beside any improvement; a local win on one slice does not silently become a general capability claim.

Source provenance

The normalized record comes from docs/learn/artifact-journey.json. The links below are the tracked evidence and explanatory sources preserved with the record.

Read the Markdown equivalent →