A fine-tuned model is the middle layer of a production NLP system: it takes a fixed label schema and tokenized splits as input and emits a versioned checkpoint with measured metrics as output. This stage capstone builds exactly that layer of the Master Capstone -- dataset construction, tokenization, full fine-tuning of a small encoder, a LoRA/PEFT variant, and an `evaluate`-based comparison -- stopping short of Hub deployment and serving, which the later track covers.
Skill Level: Beginner