Fine-Tuning & Customizing LLMs
Fine-tune your first LLM end-to-end: LoRA, QLoRA, dataset prep, evaluation, and production deployment. From zero to a working fine-tuned model on a free Colab GPU.
Most Fine-Tuning Tutorials Teach You the Wrong Thing
They show you how to run a training script. You follow along, loss goes down, you feel like you accomplished something. Then you try to use the model and it hallucinates worse than before. Or it works great on your test examples but falls apart on anything slightly different.
The problem isn’t the code. It’s that nobody taught you the decisions that come before you start training — and the evaluation that comes after.
When should you fine-tune vs. use RAG vs. just write a better prompt? How many training examples do you actually need? How do you know if your fine-tuned model is better than the base? And when does a 3B fine-tuned model beat a 70B base model?
This course answers those questions. You’ll fine-tune a real model end-to-end — from dataset creation to production deployment — on a free Google Colab GPU.
What You'll Learn
- Evaluate when to fine-tune vs. use RAG vs. prompt engineering for a given task
- Explain how SFT, DPO, LoRA, and QLoRA work and when each method applies
- Build a training dataset from scratch using synthetic generation and quality filtering
- Execute a complete QLoRA fine-tuning run on a free Google Colab GPU
- Assess model quality using held-out test sets, automated metrics, and LLM-as-judge
- Design a production deployment strategy including adapter merging, cost analysis, and monitoring
After This Course, You Can
What You'll Build
Course Syllabus
Who Is This For?
- Engineers who use LLMs daily but haven't fine-tuned one
- ML practitioners transitioning from traditional ML to LLM fine-tuning
- Technical leads evaluating whether fine-tuning is right for their team
- Builders who want smaller, faster, cheaper models for specific tasks
- Anyone curious about what happens "under the hood" when you customize an LLM
Frequently Asked Questions
Do I need a powerful GPU to take this course?
No. The hands-on lesson uses Google Colab's free T4 GPU. We also cover cloud GPU options (RunPod, Lambda) and OpenAI's managed fine-tuning API — no local GPU required.
What programming experience do I need?
You should be comfortable reading Python code and running Jupyter notebooks. You don't need ML or PyTorch experience — we explain every step.
How is this different from free fine-tuning tutorials?
Most tutorials show you how to run one training script. This course teaches the full lifecycle: when to fine-tune, dataset design, evaluation strategy, and production deployment — with cost analysis at every step.
Which models does this course cover?
Primarily open-source models (Llama 3.2, Mistral 7B, Qwen) via Unsloth, plus OpenAI's fine-tuning API for GPT-4o-mini. The techniques apply to any model that supports LoRA.
