Parameter-Efficient Fine-Tuning (PEFT): Enhancing Large Language Models with Minimal Costs

In the rapidly evolving world of Natural Language Processing (NLP), Large Language Models (LLMs) based on the Transformer architecture have proven to be game-changers.

Models like GPT, T5, BERT — and more recently, Falcon, MPT and LLaMA — have achieved remarkable success in various NLP tasks, thanks to their ability to process vast amounts of data and understand complex linguistic patterns.

However, as these models grow larger and more powerful, a new challenge arises: the computational and storage costs associated with fine-tuning them for specific tasks become increasingly prohibitive.

Enter Parameter-Efficient Fine-Tuning (PEFT), a cutting-edge approach that overcomes these limitations and revolutionizes the deployment of LLMs in a more cost-effective and efficient manner.

The Conventional Approach — and Its Challenges

Before we delve into the specifics of PEFT, let’s briefly review the conventional paradigm of fine-tuning large language models.

The traditional process involves two main steps: large-scale pretraining on generic web-scale data and fine-tuning on downstream tasks using task-specific datasets.

While fine-tuning undoubtedly leads to significant performance gains, it poses several challenges:

Resource Intensiveness: As LLMs become larger, the computational requirements for fine-tuning them on consumer hardware escalate substantially. This makes it impractical for many researchers and practitioners with limited resources to benefit from these powerful models.
Storage Costs: Storing and deploying fine-tuned models for each downstream task is cumbersome and expensive. Since fine-tuned models are similar in size to the original pretrained model, the storage demands multiply with each task-specific model.
Catastrophic Forgetting: During the full fine-tuning of LLMs, there is a phenomenon known as catastrophic forgetting. This occurs when the model forgets previously learned knowledge while adapting to new tasks, leading to a decrease in overall performance.

The Promise of PEFT

Parameter-Efficient Fine-Tuning (PEFT) emerges as a solution to address the resource and storage challenges posed by the conventional approach. PEFT fine-tunes only a small subset of additional model parameters while keeping the majority of the pretrained LLM parameters frozen.

By doing so, PEFT significantly reduces computational costs and storage requirements. The approach preserves the original knowledge of the pretrained LLM while fine-tuning for specific tasks, avoiding the issue of catastrophic forgetting.

How PEFT Works

The core idea behind PEFT is to strike a balance between leveraging the knowledge learned during the large-scale pretraining and adapting the model to new tasks.

Here’s a step-by-step breakdown of how PEFT works:

Pretraining: The LLM is pretrained on massive datasets using the conventional methods, exposing the model to a vast array of linguistic patterns and structures.
Selective Fine-Tuning: Rather than fine-tuning all parameters, PEFT only fine-tunes a small subset of additional parameters related to the downstream task. The majority of the pretrained LLM remains frozen.
Reduced Resource Requirements: As only a small fraction of parameters is fine-tuned, the computational cost and storage requirements are significantly reduced, making PEFT more accessible to researchers and practitioners with limited resources.
Preservation of Knowledge: Since most of the pretrained model remains unchanged, PEFT mitigates catastrophic forgetting, ensuring that the model retains its previously acquired knowledge.

Currently, the following methods are supported by PEFT:

Prefix Tuning: P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks
Prompt Tuning: The Power of Scale for Parameter-Efficient Prompt Tuning
P-Tuning: GPT Understands, Too

Benefits of PEFT

PEFT offers several advantages over conventional fine-tuning methods:

Cost-Effectiveness: PEFT makes fine-tuning large language models accessible to a wider audience, as it significantly reduces computational and storage costs.
Better Generalization: PEFT approaches have shown to outperform full fine-tuning in low-data regimes and generalize better to out-of-domain scenarios.
Portability and Reusability: The small checkpoints obtained through PEFT can be easily added to the pretrained LLM, allowing the same model to be used for multiple tasks without having to replace the entire model.
Conservation of Pretrained Knowledge: By preserving the pretrained knowledge, PEFT ensures that the model benefits from the vast amount of data it was exposed to during pretraining, leading to more robust performance.

Applications of PEFT

PEFT’s parameter-efficient nature and improved generalization capabilities make it a powerful technique across various domains. It can be applied not only to NLP tasks but also to other modalities such as computer vision and audio processing. By using PEFT, researchers and developers can effectively fine-tune large models to perform tasks like image classification and audio analysis while maintaining reasonable computational costs. Getting Started

By now you must be thinking — how do I get started?

The repos below are a great starting point:

  • GitHub - huggingface/peft: 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
  • PEFT: State-of-the-art Parameter-Efficient Fine-Tuning. - GitHub - huggingface/peft: 🤗 PEFT: State-of-the-art…
  • GitHub - jianzhnie/Efficient-Tuning-LLMs: Easy and Efficient Finetuning of QLoRA LLMs
  • GitHub - leehanchung/lora-instruct: Finetune Falcon, LLaMA, MPT, and RedPajama on consumer hardware


In the ever-evolving landscape of NLP and other AI domains, Parameter-Efficient Fine-Tuning (PEFT) is a transformative technique that unlocks the potential of large language models without incurring prohibitive costs.

By selectively fine-tuning only a small subset of parameters, PEFT retains the advantages of pretraining while adapting models to specific tasks. As PEFT gains momentum, it promises to democratize the usage of large language models and pave the way for more efficient and resource-friendly AI deployments.

Whether in NLP, computer vision, or audio analysis, PEFT opens up new avenues for innovation and exploration, making AI accessible to a broader community of researchers, practitioners, and learners.

Written by

Rafael Pierre

Rafael has a track record of 15 years spanning software engineering, AI and solution architecture. He currently works as an ML & Software Engineer at Hugging Face. Before that, he started to help customers unlock business value from Data and AI by leveraging his knowledge in MLOps, Deep Learning, Computer Vision, Large Language Models and Generative AI.