llm.tar.gz

gpt-2-demo

dataset-demo-1

jupy

gpt2-demo-2

dataset-demo-1-copy

In this course, you will acquire a working knowledge of the capabilities and types of LLMs, along with their importance and limitations in various applications. You will gain valuable hands-on experience by engaging in the fine-tuning of LLMs to specific datasets, followed by the evaluation of their performance. 

You will start with an introduction to large language models, components, capabilities, and their types. Next, you will be introduced to GPT-2 as an example of a large language model. Then, you will learn how to fine-tune a selected LLM to a specific dataset, starting from model selection, data preparation, training, and evaluation steps. You will also compare the performances of two different LLMs. 

By the end of this course, you will have gained practical experience in fine-tuning LLMs to specific datasets, ensuring a comprehensive skill set for effectively leveraging these generative AI models in diverse language-related applications.

Essentials of Large Language Models: A Beginner’s Journey

Learn how to evaluate the performance of large language models using the ROUGE metric.


Course Overview

Getting Started with LLMs

Fine-Tuning LLMs

Wrap Up

Evaluation

Overview