Fine-tuning big neural nets like Large Language Models (LLMs) has traditionally been prohibitive due to high hardware requirements. However, Parameter-Efficient Fine-Tuning (PEFT) and quantization enable the training of large models on modest hardware. Thanks to the PEFT library and the Hugging Face ecosystem, these techniques are now accessible to a broad audience. Expect to learn: - what the challenges are of fine-tuning large models - what solutions have been proposed and how they work - practical examples of applying the PEFT library