They are great base models for fine tuning on both text and visual tasks. Many OCR and object grounding models are based on small Qwen models, though they often

They are great base models for fine tuning on both text and visual tasks. Many OCR and object grounding models are based on small Qwen models, though they often replace vision encoder with a bigger one. Qwen3 5-4B is the biggest model I can find tune in my laptop. And when I upgraded the model from Qwen3-4B to Qwen3.5-4B, both vanilla and fine tuned performances jumped significantly on a classification task. Those models are great when you have very little data or very low diversity of examples, where it's not possible to train a neural net from scratch as it will just memorize the data. The best you can do is fine tune a generalist model that can already do the task for small number of steps until it starts over-fitting, or on some cases you can do even better though RL.