Fine-tuning large models on local hardware
- Track:
- PyData: LLMs
- Type:
- Talk
- Level:
- intermediate
- Room:
- Forum Hall
- Start:
- 12:30 on 11 July 2024
- Duration:
- 30 minutes
Abstract
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