Posts tagged fine-tuning
Inside sekft: a shell-operator training pipeline
- 16 June 2026
This is the how-it-works companion to the experiment
From seed to weights: fine-tuning a shell operator.
The experiment page is the why and the results. This page is the how:
architecture, the four data-factory stages, the trainer, how to read a run, and
the hardware constraints. It is meant for a colleague picking up the sekft
repo for the first time.
From seed to weights: fine-tuning a shell operator
- 15 June 2026
two cycles complete. At archetype-level holdout (n=16, task types absent from training), fine-tuning lifts Mistral termination from 0/16 (base) to 9/16 (tuned), same harness, only the adapter differing. The operate / terminate mechanism generalises to unseen archetypes; task competence (verified 0.31) stays archetype-local. One model, one seed; signal clean.