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