Week 10: Exercises & Checklist

Graded exercises

Easy: Run notebooks/01-sft-dataset-and-lora-training.ipynb end-to-end. Record the dataset size (~300 examples) and the final training loss it prints at the end in the Week 10 sheet of the Excel tracker. If you have no GPU, run the dataset-building cells to completion (they are CPU-only) and record the dataset stats, noting the training cells were skipped.
Standard: Rebuild the SFT dataset with a different seed and ~50 extra hand-written examples of your own (write real instruction/response pairs for ZoroLogistics, not copies of the generated ones). Retrain the LoRA adapter, then show, with the before/after eval, that the adapter still beats the base model on the extraction eval, stating the exact delta.
Stretch: Swap plain LoRA for DoRA by setting use_dora=True in PEFT's LoraConfig (or switch to a 4-bit QLoRA base via bitsandbytes), keeping everything else fixed. Compare the before/after eval table against your plain-LoRA run and write one sentence on whether the extra mechanism was worth it for your task.
Portfolio: Commit the LoRA fine-tuned triage + extraction model milestone (tracked in curriculum/projects/README.md): the adapter config (r, alpha, target modules), the loss curve image, the before/after eval table, the catastrophic-forgetting check numbers, and your deploy/no-deploy decision with its justification.

Easy: The dataset cells (2 to 10) are CPU-only; the training cells need a GPU. On no-GPU hardware, record the dataset stats and note the trainer skipped, the final SFT_EXAMPLES still prints.
Standard: Write real instruction/response pairs for ZoroLogistics (a new lane, a customs edge case, a damage claim), not copies of the generated ones. Re-run the eval on the same held-out set so the delta is comparable.
Stretch: use_dora=True is a one-line change in LoraConfig; keep r, alpha, targets, epochs, and data identical so the only variable is the mechanism. Compare the before/after table, not the loss curve.
Portfolio: The gate wants both columns of the table (new-task and generic) and an explicit deploy/no-deploy sentence. "Don't deploy" with the numbers is a passing result; a decision with no forgetting check is not.

[ ] Mon: Study fine-tuning theory and the selection order (knowledge-base 06).
[ ] Tue: Build the SFT dataset (instructions + responses) from the Week 6 corpus.
[ ] Wed: Train LoRA with TRL/Unsloth (Colab free tier or local GPU); log loss curves.
[ ] Thu: Create preference pairs; run a small DPO step; compare before/after on the eval.
[ ] Fri: Use case: publish the before/after eval table; decide deploy vs iterate.
[ ] Sat: Take the Week 10 quiz (quiz.md), pass with 8/10; record the score in Notes.
[ ] Milestone: Update the Excel tracker; commit the adapter and eval results.