Fine-Tuning Open-Weight LLMs for Engineers
Daniel Voigt Godoy
Instructor @ Linux Foundation/DSR
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.
Workshop agenda
Part 1 — Decide whether fine-tuning is appropriate
Learn when fine-tuning beats prompting or RAG, and when it does not.
Part 2 — Prepare a small dataset
Turn task examples into a clean train/eval format.
Part 3 — Train a QLoRA adapter
Run the full fine-tuning workflow in a live notebook.
Part 4 — Evaluate before and after
Compare the base model and fine-tuned model on the same test cases.
Part 5 — Improve or stop
Learn what to change next: data, model, LoRA settings, or approach.
Part 6 — Open Q&A with Daniel
Bring your task, data constraints, and technical questions. We’ll assess whether your use case is ready for fine-tuning and what your next step should be.
Learn directly from Daniel
Daniel Voigt Godoy
Amazon best-selling author, Instructor @ Linux Foundation/Data Science Retreat
Who this workshop is for
Technical founders prototyping AI products
Applied AI / ML Engineers
Backend engineers building LLM features
Prerequisites
Python coding
We'll be coding heavily
Jupyter Notebook
We'll rely on a notebook to run our code
Basic LLM prompting
You should understand prompts, inputs, outputs, and common model limitations.
What's included
Live sessions
Learn directly from Daniel Voigt Godoy in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Digital copy of "A Hands-on Guide to Fine-Tuning LLMs"
Use it as a post-workshop reference for the PyTorch, Hugging Face, LoRA, QLoRA, and evaluation concepts covered live.
Maven Guarantee
Your purchase is backed by the Maven Guarantee.
Not sure whether this workshop is right for you?
Book a free 15-minute fit call with Daniel to discuss your use case before enrolling.
We can assess whether your task is a good candidate for fine-tuning, whether the workshop matches your current technical level, and what you can expect to build during the session.