Geek Out Time: Playing with Prefix Tuning on Google Colab

Prefix Tuning is one of the coolest Parameter-Efficient Fine-Tuning (PEFT) methods to adapt large language models without retraining the full model. To appreciate how it works, let’s set the stage: traditional fine-tuning involves updating all of a model’s parameters, which is expensive and compute-intensive. Then came prompting — where we steer the model using smart input formatting — and instruction tuning, which teaches the model to follow instructions across a wide range of tasks. More recently, LoRA (Low-Rank Adaptation) became popular by injecting trainable low-rank matrices into model layers, achieving great task adaptation with far fewer parameters.

Prefix tuning takes a different approach: instead of updating the full model or inserting new weights, it learns a small set of prefix vectors that are prepended to the inputs of each transformer layer. This makes it fast, lightweight, and ideal for limited compute setups like Google Colab.

In this blog post, we’ll walk through a step-by-step guide to try prefix tuning using Hugging Face Transformers and peft on Google Colab.

Step 1: Setup Environment on Google Colab

This is an excerpt — the full article continues on Medium.