This is the golden age of model training. Some days ago, I decided I wanted a local CPU only model that can perform exceptionally well for English to Bash translation (to avoid the googling for command syntax). I got a bunch of subagents to generate large amount of training data (140k+ samples), got the Qwen 3 0.6B base model, pointed Astra at it, and off to the races. It trained for 2 days (on and off) and I got a surprisingly good model for my task! The total active time I spent was a few hours. And it is still improving, what a time to be alive!
A very small, highly specialized model can use negligible resources (CPU, energy) to accomplish the same task.
For everyday work that happens frequently it's better to have a tiny specialized model instead of making billable API calls or turning your laptop into an 80W space heater for 20 seconds to run a general purpose model.
The large models can be used to generate synthetic training data. Tell them to make up 100,000 tasks paired with the resulting output as a 1-time cost. Then use that to train a small model.
Think of it as distillation, but focused on a specific task.
I feel like we need a good index for these kinds of specialized models, especially if you plan to open them up. The downside is a new bash version means potentially new training.
I took it more to mean “do standard Unix command-line stuff” rather than purely emitting Bash, and while e.g. POSIX does receive updates, the basic stuff using the main tools should also work in the future!
GodelNumbering · · focus · HN ↗
amelius · · focus · HN ↗
Or are the subagents generating your training data using a closed/paid model?
Aurornis · · focus · HN ↗
For everyday work that happens frequently it's better to have a tiny specialized model instead of making billable API calls or turning your laptop into an 80W space heater for 20 seconds to run a general purpose model.
The large models can be used to generate synthetic training data. Tell them to make up 100,000 tasks paired with the resulting output as a 1-time cost. Then use that to train a small model.
Think of it as distillation, but focused on a specific task.
tomrod · · focus · HN ↗
msdz · · focus · HN ↗