After this lesson, you will be able to: Fine-tune a small open model with LoRA/QLoRA on a task you curated, measure it honestly against the base model, and publish the adapter plus a reproducible recipe.
The Fine-Tuning capstone: a complete, reproducible adaptation experiment. This is the artifact that proves you can run real fine-tuning research on a student budget, and it feeds directly into the reproduction/paper in the Publishing sub-track.
Pick a narrow, measurable task (e.g. a specific classification or structured-extraction task, or a style/format the base model does inconsistently). Curate and decontaminate a dataset, fine-tune a small open model (1B-7B) with LoRA or QLoRA on a free/cheap GPU, and measure the fine-tuned model against the base model on a held-out test set with a metric appropriate to the task. The deliverable is the comparison, done correctly, not just a model that exists.
Everything in a Git repo with a config-driven, seeded pipeline.
Milestone 1: Define the task + the metric you'll report, and write the baseline (the base model, evaluated zero/few-shot) BEFORE training.
Milestone 2: Curate the dataset; split train/val/test with a fixed seed; run + document decontamination.
Milestone 3: LoRA/QLoRA fine-tune with tracked config, seed, and logged loss curves.
Milestone 4: Evaluate base vs fine-tuned on the same test set with the same prompts/template; report the metric for both.
Milestone 5: Run 3 seeds (or 3 data shuffles) and report mean + variance, not a single number.
Milestone 6: Publish the adapter (Hugging Face Hub) + a README with the recipe, data description, decontamination note, and honest limitations.
'I fine-tuned <model> with QLoRA on a task I curated, decontaminated the data against the test set, and measured a <X point> improvement over the base model across three seeds, with the recipe and adapter public.' That sentence demonstrates the full loop: data discipline, the method, honest evaluation, and reproducibility, which is exactly what a research advisor or applied-science interviewer is listening for.
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