1. Background
   A general-purpose model can be adapted to a specific task by fine-tuning: continuing its training on examples of that task. Fine-tuning is useful when a task needs a consistent format, specialised terminology or behaviour that prompting alone does not reliably produce.

LoRA (Low-Rank Adaptation) makes fine-tuning practical on limited hardware. It leaves the original model weights unchanged and trains a small set of additional weights. With Unsloth, Google’s open-weight Gemma 4 model can be fine-tuned on a free Colab GPU.

A fine-tuned model is only useful if it is measurably better than the original. Every fine-tuning project therefore needs a fair comparison against the plain model.

2. Objective
   Fine-tune Gemma 4 for one of the two problem statements below, build an application around the fine-tuned model, and present a comparative analysis of the plain and fine-tuned models.