Lion Cipher Lab
Fine-tune a small AI model with LoRA
- AI Security
- Beginner
- ~60 min
- Free · no account required
Why it matters
Fine-tuning used to mean a research budget and a rack of GPUs, so most people treated the model they were given as fixed. LoRA changed that: you can now adapt an open model to your own task by training a tiny set of extra weights, cheaply, on your own hardware. That is a genuinely useful skill for security work, where the tasks are narrow and the data is often too sensitive to send to a hosted service. But the same capability carries a real risk that beginners are rarely told about at the same time: a model can memorize its training data and later reproduce it, so training on secrets or…
What you'll learn
- fine-tuning vs retrieval (RAG)
- parameter-efficient fine-tuning with LoRA
- Hugging Face PEFT and TRL basics
- training-data hygiene and PII/secret handling
- recognizing training-data memorization and leakage
- evaluating a fine-tuned model
Steps in this lab
- Fine-tuning vs RAG — decide before you train
- Why LoRA: cheap to train, small to ship
- The data is the lab: hygiene before you train
- Set up a local training environment
- Prepare your own small dataset
- Fine-tune with LoRA using PEFT + TRL
- Evaluate the result — including for leakage
- What you just built — and the judgment that goes with it