Fine-tuning large models on local hardware
07-11, 12:30–13:00 (Europe/Prague), 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

Expected audience expertise

Intermediate

See also: Slides (html) (1.1 MB)

Machine Learning Engineer at Hugging Face

Mainly working on parameter-efficient fine-tuning techniques.

https://github.com/BenjaminBossan