Maven – Fine-Tuning Open-Weight LLMs for Engineers

Build, fine-tune, and evaluate an LLM in one live workshop.

Most LLM fine-tuning advice is either too theoretical or assumes large teams, large datasets, and expensive infrastructure.

This workshop shows a smaller, practical path using open-weight models: choose one narrow task, prepare a focused dataset, train a QLoRA adapter on accessible hardware, and evaluate whether it performs well enough to reduce cost, latency, or reliance on proprietary APIs.

By the end of the workshop, you will have

– A clean task-specific fine-tuning dataset.

– A trained LoRA/QLoRA adapter.

– A before-and-after evaluation against the base model.

– A reusable notebook you can adapt to your own data.

– A decision checklist for when fine-tuning is worth doing versus when prompting, RAG, or better data is enough.

This workshop is taught in a small cohort, so you will have the time and the opportunity to discuss your own use case, get feedback on whether it is a good fit for fine-tuning, and ask questions about your data, constraints, and next steps.

What you’ll learn
Become the person who knows when fine-tuning is worth it, how to run the first controlled experiment, and how to evaluate the results.

Choose a fine-tuning task worth testing

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.”

Build a task-specific fine-tuning dataset

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

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

Train a LoRA/QLoRA adapter end to end

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.

Evaluate whether fine-tuning actually helped

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

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

Diagnose what to change after the first run

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.

Reuse the workflow for future fine-tuning experiments

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.

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Maven – Fine-Tuning Open-Weight LLMs for Engineers

Maven – Fine-Tuning Open-Weight LLMs for Engineers

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