As language models grow larger, traditional fine-tuning becomes increasingly challenging. A full fine-tuning of even a 1.7B parameter model requires substantial GPU memory, makes storing separate model copies expensive, and risks catastrophic forgetting of the model's original capabilities. Parameter-efficient fine-tuning (PEFT) methods address these challenges by modifying only a small subset of model parameters while keeping most of the model frozen.
Traditional fine-tuning updates all model parameters during training, which becomes impractical for large models. PEFT methods introduce approaches to adapt models using fewer trainable parameters - often less than 1% of the original model size. This dramatic reduction in trainable parameters enables:
- Fine-tuning on consumer hardware with limited GPU memory
- Storing multiple task-specific adaptations efficiently
- Better generalization in low-data scenarios
- Faster training and iteration cycles
In this module, we will cover two popular PEFT methods:
LoRA has emerged as the most widely adopted PEFT method, offering an elegant solution to efficient model adaptation. Instead of modifying the entire model, LoRA injects trainable matrices into the model's attention layers. This approach typically reduces trainable parameters by about 90% while maintaining comparable performance to full fine-tuning. We will explore LoRA in the LoRA (Low-Rank Adaptation) section.
Prompt tuning offers an even lighter approach by adding trainable tokens to the input rather than modifying model weights. Prompt tuning is less popular than LoRA, but can be a useful technique for quickly adapting a model to new tasks or domains. We will explore prompt tuning in the Prompt Tuning section.
| Title | Description | Exercise | Link | Colab |
|---|---|---|---|---|
| LoRA Fine-tuning | Learn how to fine-tune models using LoRA adapters | 🐢 Train a model using LoRA 🐕 Experiment with different rank values 🦁 Compare performance with full fine-tuning |
Notebook |  |
| Load LoRA Adapters | Learn how to load and use trained LoRA adapters | 🐢 Load pre-trained adapters 🐕 Merge adapters with base model 🦁 Switch between multiple adapters |
Notebook | |