Notebook for Flan-T5 – an alternative to large language models like GPT-3 & GPT-4 for NLP tasks like named entity recognition and text generation.

This notebook fine-tunes the FLAN-T5 model for dialogue summarization, comparing full fine-tuning with Parameter-Efficient Fine-Tuning (PEFT). It evaluates performance using ROUGE metrics, demonstrating PEFT's efficiency while achieving competitive results.

The LLM FineTuning and Evaluation project 🚀 enhances FLAN-T5 models for tasks like summarizing Spanish news articles 🇪🇸📰. It features detailed notebooks 📚 on fine-tuning and evaluating models to optimize performance for specific applications. 🔍✨

GEN AI use case: dialogue summary. This notebook is extracted from the course Generative AI with Large Language Models. It is used to understand how input text can affect model performance.

This repository contains notebook files that discuss Large Language Models (LLMs), covering topics like fine-tuning, prompt engineering, and techniques such as PEFT (Parameter Efficient Fine-Tuning) and PPO (Proximal Policy Optimization) etc.