I think the Qwen 0.6B is so cool. It is super fast and as illustrated here it has a clear niche, esp. when fine-tuned.

I'm also interested in it as a student for distillation.

If you are going to go to the bother of fine tuning for trivial problems like subject classification then I think you'll find Scikit Learn with a SGDClassifier on 2-grams will do probably just as well and be under 1MB for the trained classifier.

You can train it in under a minute, and it will work perfectly well on embedded devices.

Small LLMs are good choices for text classification in two cases:

- If you next to provide in-context examples and classifier based on them.

- Your classification goes beyond simple subject-type classifiers. For example, multiple choice question answering is classification where small LLM will work but traditional ML methods won't/