PostTrainLLM

PostTrainLLM turns local model-training experiments into measured, reproducible recipes for useful specialist language models.

Maker note

Why I made this.

I use PostTrainLLM to explore local model training and turn those experiments into repeatable recipes for specialist models.

Product contract

PostTrainLLM turns local model-training experiments into measured, reproducible recipes for useful specialist language models.

For: Practitioners learning or running post-training workflows on personal Apple hardware and in the browser.
Outcome: Train, evaluate, compare, and run specialist models with evidence about what worked and why.
How: A local factory joins datasets, training methods, evaluation, experiment tracking, and inference into repeatable recipes.
Proof: The project contains working browser and Mac-local experiments, evaluation infrastructure, and reproducible training paths.
Next action: Explore the measured recipes and follow new experiments as they produce reproducible proof rather than scale claims.

Public anatomy

Maintained evidence

Public destinations and repository evidence, when this project publishes them.

Repository publication

Public issues become a browsable, read-only record here. Open the source issue on GitHub to comment, react, or follow the work.

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Every fact here comes from the project’s reviewed public record. Private work and operational controls stay private.