Generally speaking, fine-tuning a small model makes sense when the task that you want it to carry out is well-defined and doesn't vary too much from one prompt to another. Fine-tuning facts into a model doesn't seem to scale super well, but general textual style, output format, and evaluation criteria for example can all be instilled through the fine-tuning process. I would use lookup if you need your answers to include a wide array of information that the model you're basing off of wasn't initially trained on.