In this LLM course, learn the LLM fundamentals, fine-tuning, and evaluation, and explore its architecture and evolution. Discover GPT-2, compare models, and uncover use cases.

llm.tar.gz

gpt-2-demo

dataset-demo-1

jupy

gpt2-demo-2

dataset-demo-1-copy

gpt2-demo-2-copy

Matplotlib

MPLIB2

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 fine-tuning LLMs to specific datasets and evaluating their performance. 

You will start with an introduction to large language models, looking at 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, model training, and performance evaluation. You will also compare the performance of two different LLMs. 

By the end of this course, you will have gained practical experience in fine-tuning LLMs to specific datasets, building 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

The quality of the dataset is of utmost importance. Think of high-quality data as clear instructions that can guide the model to understand the task and produce the best outcomes. For example, a high-quality dataset for a customer service chatbot would include well-categorized, accurately labeled conversations that represent a wide array of customer interactions. A low-quality dataset might contain mislabeled, incomplete, or irrelevant dialogues, leading to a poorly performing chatbot.

A diverse and balanced dataset is also crucial because it ensures that the model is not overfitted to a narrow range of examples, which can limit its ability to generalize to new data. For instance, a diverse and well-balanced dataset for a translation model would include texts from various contexts and dialects, ensuring that the model can handle a wide array of linguistic scenarios, from formal to casual conversations.

Last, it’s a good idea to consider whether the data is synthetic or from the real world. Although synthetic data can be generated easily using an LLM, real-world data typically leads to more effective outcomes, especially for writing tasks. Machine-generated content often contains repetitive and detectable patterns, while real-world data provides the model with genuine examples of language use, offering a richer learning experience and enabling the model to produce more authentic responses.

Learn the best practices for preparing data for model training.

Course Overview

Getting Started with LLMs

Fine-Tuning LLMs

Wrap Up

Exploring OpenAI API

Data Preparation

Best practices for data preparation

Dataset quality

Dataset balance

Dataset origin