PEFT (Parameter-Efficient Fine-Tuning) is a technique used in machine learning to fine-tune large pre-trained models (like GPT, BERT, or vision transformers) efficiently by only updating a small subset of their parameters, instead of the entire model. This approach reduces computational cost and memory usage while maintaining high performance on downstream tasks.

Key Concepts of PEFT

1. Efficiency:

• Instead of retraining the entire model (which has millions or billions of parameters), only a small portion is updated.

• This makes PEFT cost-effective and fast, even on resource-limited hardware.

2. Focus on Key Layers:

• Certain layers or parts of the model are identified as critical for fine-tuning, and only those are updated.

3. Applications:

• Commonly used in NLP, Computer Vision, and Generative AI tasks.

• Ideal for scenarios where multiple tasks need to use the same pre-trained model.

Popular PEFT Techniques

1. LoRA (Low-Rank Adaptation):

• Adds low-rank matrices to specific layers of the model.

• Reduces the number of trainable parameters significantly.

2. Adapters:

• Adds small modules or layers between existing layers of a pre-trained model.

• These modules are the only parts fine-tuned.

3. Prompt Tuning:

• Optimizes prompts (input embeddings) for pre-trained models without modifying the model’s core parameters.

4. BitFit:

• Only updates the bias terms of the model, leaving other parameters frozen.

5. Prefix Tuning:

• Optimizes task-specific prefix embeddings prepended to the input.

Advantages of PEFT

• Cost-Effective: Reduces the need for expensive compute resources.

• Faster Training: Fine-tuning requires less time compared to training a model from scratch.

• Scalability: Allows fine-tuning on smaller datasets and multiple tasks efficiently.

• Lower Memory Usage: Requires fewer GPU/TPU resources, making it accessible for edge devices or small teams.