Description

Philipp Schmid from HuggingFace guides your through the process of fine-tuning ModernBERT, a new and improved version of the BERT model, for classifying user prompts to create an intelligent LLM router. ModernBERT offers significant advantages over traditional BERT, including a longer context length, enhanced downstream performance, and faster processing speeds, making it ideal for tasks like routing user prompts to the most suitable large language model (LLM) or selecting optimal few-shot examples.

The tutorial demonstrates how to set up the environment, prepare a classification dataset of user prompts, and fine-tune ModernBERT using the Hugging Face Trainer.