Fine-Tuning Open-Weight LLMs for Engineers

Use a fine-tuning scorecard to compare fine-tuning against prompting and RAG.

Score tasks based on repetition, output clarity, evaluation, data availability, and cost/friction.

Narrow broad ideas like “answer customer questions” into trainable tasks like “classify billing tickets into category, urgency, etc."

Format examples into a consistent instruction/input/output structure.

Identify and remove ambiguous, inconsistent, or low-value examples before training.

Load an open-weight base model in a notebook environment.

Configure LoRA/QLoRA settings such as target modules, rank, learning rate, batch size, and sequence length.

Train and save an adapter that can be reused or evaluated after the workshop.

Run before/after outputs on a held-out eval set.

Check improvements in format consistency, task accuracy, and failure patterns.

Use a troubleshooting framework to decide whether the next fix is data, task definition, model choice, or training settings.

Recognize common fine-tuning failures such as poor labels, unclear outputs, weak eval sets, and too-broad tasks.

Decide whether to continue fine-tuning, collect better examples, switch to RAG, or stop.

Use a reusable notebook for dataset preparation, training, inference, and evaluation.

Apply a checklist before starting future fine-tuning projects.

Adapt the same workflow to classification, extraction, routing, structured output, or writing-style adaptation tasks.