PEFT (Parameter-Efficient Fine-Tuning)

Why It Matters

Parameter-Efficient Fine-Tuning changed what’s possible for AI engineers working outside of well-funded labs. Full fine-tuning of a 70B parameter model requires multiple high-end GPUs and costs thousands of dollars per training run. PEFT methods accomplish similar results using a fraction of the resources, often a single consumer GPU.

The core insight behind all PEFT methods: you don’t need to modify every weight in a model to change its behavior. Pre-trained models already contain vast knowledge; you just need to steer that knowledge toward your specific task. By identifying which parameters matter most (or adding small trainable components), PEFT methods achieve the customization benefits of fine-tuning without the prohibitive costs.

For AI engineers, PEFT isn’t just a cost optimization, it’s a capability enabler. You can experiment rapidly, train multiple specialized adapters, and swap them at inference time. This unlocks use cases like per-customer model customization that would be economically impossible with full fine-tuning.

Common PEFT Methods

Several techniques fall under the PEFT umbrella, each with different tradeoffs:

LoRA (Low-Rank Adaptation) The most widely adopted PEFT method. LoRA injects small trainable matrices into the model’s attention layers while keeping original weights frozen. It reduces trainable parameters by 10,000x and allows adapter swapping at inference time. Start here for most customization tasks.

QLoRA (Quantized LoRA) Combines LoRA with 4-bit quantization, enabling fine-tuning of models that wouldn’t otherwise fit in GPU memory. QLoRA made fine-tuning 65B+ models possible on consumer hardware, with minimal quality loss compared to full-precision training.

Adapters Inserts small trainable modules between transformer layers. Similar outcomes to LoRA but with different architectural choices. Less common now that LoRA has become the default.

Prefix Tuning Prepends trainable “virtual tokens” to the input that steer model behavior. No modification to model architecture, just learned prompt components. Useful when you can’t modify model internals.

Prompt Tuning Similar to prefix tuning but operates at the embedding level. Trains continuous prompt embeddings rather than discrete tokens. Often used for multi-task scenarios.

Implementation Basics

The Hugging Face PEFT library is the standard implementation tool:

1. Choose Your Method LoRA is the default choice for most scenarios. Use QLoRA when memory-constrained. Prefix/prompt tuning work well when you need multiple task adaptations from one base model.

2. Configure Target Layers Most PEFT methods let you specify which layers to adapt. For LoRA, attention projection layers (Q, K, V, O) are typical targets. More layers mean more capacity but higher memory usage.

3. Set Hyperparameters For LoRA: rank (r) controls adapter capacity, alpha scales the adaptation strength. Start with r=8 or r=16 and alpha=16-32. Adjust based on validation performance. Underfitting suggests higher rank, overfitting suggests lower rank or more dropout.

4. Train and Evaluate PEFT training follows standard fine-tuning workflows but completes faster. Monitor validation loss to catch overfitting early. The adapter weights save separately from the base model, typically 10-50MB versus gigabytes.

PEFT adapters are composable and portable. You can merge them into base model weights for deployment, keep them separate for hot-swapping, or even combine multiple adapters for hybrid behaviors.