In this notebook, you will fine-tune an existing LLM from Hugging Face for enhanced dialogue summarization.
You will use the FLAN-T5 model, which provides a high quality instruction tuned model and can summarize text out of the box. To improve the inferences, you will explore a full fine-tuning approach and evaluate the results with ROUGE metrics. Then you will perform Parameter Efficient Fine-Tuning (PEFT), evaluate the resulting model and see that the benefits of PEFT outweigh the slightly-lower performance metrics.