Lion Cipher Lab

Fine-tune a Language Model with LoRA (Deep Dive)

• AI Security
• Advanced
• ~75 min
• Free · no account required

Why it matters

There are only three real ways to make a language model do what you want, and most people only know one of them. The first is PROMPTING: writing better instructions. The second is RAG: handing the model the right documents to read at answer time. The third -- the one this lab teaches -- is FINE-TUNING: actually continuing the model's training on examples you chose, so the new behaviour becomes baked into the model instead of re-explained on every request. Fine-tuning has a reputation for being expensive, cloud-only wizardry reserved for big labs, and that reputation is now out of date. LoRA…

What you'll learn

• what fine-tuning is: continuing a model's training on your own small dataset to change how it behaves, not what facts it can look up
• the honest decision of when to fine-tune versus when to just prompt better or use RAG (prompting and RAG usually win)
• the core vocabulary -- weights, parameter, dataset, epoch, token, GPU -- each defined from zero
• what LoRA (Low-Rank Adaptation) is and why it trains a tiny add-on 'adapter' instead of the whole model, so it finishes free
• opening Google Colab for free, switching on a free GPU, and installing transformers + peft + datasets
• loading a small open model and capturing its BEFORE behaviour on a set of prompts
• writing a tiny custom dataset (~14 examples) that teaches one clear, distinctive style
• attaching a LoRA adapter with the peft library and running a short training loop with the transformers Trainer
• proving it worked: the same prompts now answer in the trained style, including prompts never in your data (generalisation, not memorisation)
• a smaller local CPU/GPU fallback that needs no cloud, and saving the trained adapter (a few megabytes, not gigabytes)

Steps in this lab

1. Three ways to steer a model -- and why fine-tuning is the LAST one to reach for
2. The words you need, defined once (weights, parameter, dataset, epoch, token, GPU)
3. What LoRA and an adapter are: train a tiny add-on, freeze the giant
4. Open Google Colab and switch on the free GPU (the free path)
5. Install the tools: transformers, peft, and datasets
6. Load the small open model and record its BEFORE behaviour
7. Write the tiny dataset: fourteen examples in one clear style
8. Turn the examples into training tokens the model can learn from
9. Attach the LoRA adapter with PEFT
10. Run the training loop
11. Prove it worked: same questions, new voice -- including a question you never trained on
12. Save the adapter, and the local fallback that needs no cloud
13. What you built, its limits, and when to actually reach for this

Open the full interactive lab →