What is PEFT?
PEFT (Parameter-Efficient Fine-Tuning) is a family of techniques that adapt large pre-trained models to downstream tasks by training only a small subset of parameters, dramatically reducing computational requirements while maintaining competitive performance.
Quick Facts
| Full Name | Parameter-Efficient Fine-Tuning |
|---|---|
| Created | Various techniques from 2019-2023, unified by Hugging Face |
| Specification | Official Specification |
How It Works
PEFT methods have become essential for making large language model customization accessible. Instead of fine-tuning all model parameters (which can be billions), PEFT techniques add or modify a small number of trainable parameters while keeping the base model frozen. Popular methods include LoRA (Low-Rank Adaptation), prefix tuning, prompt tuning, and adapters. The Hugging Face PEFT library provides unified implementations of these techniques, enabling fine-tuning of models like Llama, Mistral, and Falcon on consumer hardware.
Key Characteristics
- Trains only 0.1-1% of total model parameters
- Keeps base model weights frozen
- Multiple techniques: LoRA, adapters, prefix tuning
- Enables fine-tuning on consumer GPUs
- Adapters can be swapped for different tasks
- Reduces storage by saving only adapter weights
Common Use Cases
- Fine-tuning LLMs on limited hardware
- Creating task-specific model variants
- Multi-task learning with shared base model
- Rapid experimentation with different adaptations
- Deploying personalized AI assistants
Example
Loading code...Frequently Asked Questions
What is the difference between PEFT and full fine-tuning?
Full fine-tuning updates all model parameters (billions for large LLMs), requiring significant GPU memory and storage. PEFT methods only train 0.1-1% of parameters by adding small trainable modules while keeping the base model frozen. This reduces memory requirements by 10-100x, enables training on consumer GPUs, and produces compact adapter files instead of full model copies.
What is LoRA and why is it the most popular PEFT method?
LoRA (Low-Rank Adaptation) injects trainable low-rank matrices into transformer layers, typically targeting attention weights. It's popular because it adds minimal parameters (often <1% of original), requires no inference latency overhead when merged, produces small adapter files (MBs vs GBs), and achieves performance comparable to full fine-tuning for most tasks.
Can I combine multiple PEFT adapters for different tasks?
Yes, PEFT adapters are modular and can be swapped or combined. You can train separate adapters for different tasks (translation, summarization, coding) on the same base model, then load the appropriate adapter at inference time. Some methods even allow merging multiple adapters or using adapter composition for multi-task scenarios.
How much GPU memory do I need for PEFT fine-tuning?
PEFT dramatically reduces memory requirements. A 7B parameter model that needs 28GB+ for full fine-tuning can often be fine-tuned with LoRA using 8-16GB VRAM. Combined with quantization (QLoRA), you can fine-tune 7B models on GPUs with as little as 6GB VRAM, and 13B models with 12GB.
What is QLoRA and how does it differ from standard LoRA?
QLoRA combines LoRA with 4-bit quantization, loading the base model in 4-bit precision while training LoRA adapters in higher precision. This reduces memory usage by 4x compared to standard LoRA, enabling fine-tuning of 65B+ models on a single 48GB GPU. Despite the quantization, QLoRA achieves performance nearly matching full 16-bit fine-tuning.