Researchers have developed a method called PEFT (Parameter-Efficient Fine-Tuning) that allows for fine-tuning large language models with minimal computational overhead. PEFT achieves this by using a combination of knowledge distillation and weight pruning to reduce the number of parameters in the model, making it more efficient and easier to train. According to Hugging Face, the creators of the method, PEFT can be used to fine-tune models on smaller datasets, reducing the need for large amounts of data and computational resources. --- Why it matters: This matters because it enables researchers to fine-tune large language models with limited resources, making AI research more accessible to those who don't have access to massive computing power or large datasets. Source: https://huggingface.co/blog/peft