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/

If you want to go deeper on language models, try these project ideas:

- Zero-shot encoders like tasksource or GliNER

- Natural language inference: https://huggingface.co/blog/dleemiller/nli-xenc-ways-to-use

- GRPO training

- GEPA prompt tuning Qwen 0.6B (or GEPA, then GRPO)

- Use an embedding model and train a classifier (MLP, logistic, svm)

- Use a larger LLM to generate a synthetic dataset (beware of lack of diversity, mine "seed text" from real sources first)

- Synthetically generate "hard examples" where more than one category may be valid and DPO tune your preferred responses

If you are interested in small language model to fine tune, gemma3:270m is quite interesting for its size

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.