2024-07-11 –, Forum Hall
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
Intermediate
Machine Learning Engineer at Hugging Face
Mainly working on parameter-efficient fine-tuning techniques.