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Unlocking the Power of Parameter-Efficient Fine-Tuning (PEFT)
Introduction
Artificial intelligence (AI) has transformed the world in countless ways, but have we reached its peak? Is AI as good as it can get, or is there room for improvement? While AI’s capabilities are sometimes overhyped, there’s no denying its potential to learn, evolve, and become even more efficient and safer to use.
In this article, we’ll take a deep dive into Parameter-Efficient Fine-Tuning (PEFT), a groundbreaking technique that is making AI smarter, faster, and more accessible. By the end, you’ll understand what PEFT is, why it matters, and how it’s revolutionizing AI for everyone — not just the big players.
The Journey to Parameter-Efficient Fine-Tuning
A Look Back: From RNNs to Transformers
To understand PEFT, let’s rewind to the early days of Natural Language Processing (NLP). Back in 2016, Recurrent Neural Networks (RNNs) were the gold standard for tasks like translating languages or predicting the next word in a sentence. RNNs processed words sequentially, which made training painfully slow and inefficient on modern GPUs.
Enter the Transformer neural network. This revolutionary architecture, introduced in 2017, replaced RNNs by…
