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

# Generate text based on a prompt for text completion
result = generator("Purple is the best color because", 
    max_length=15, 
    num_return_sequences=1, 
    pad_token_id=generator.tokenizer.eos_token_id)
    
# Display the generated text
print("Text Completion Response:\n" + result[0]['generated_text'] + "\n")

import React from 'react';
import { shallow, configure } from 'enzyme';
import Adapter from 'enzyme-adapter-react-16';

configure({ adapter: new Adapter() });

import HelloWorld from './app';

var TestResult = function() {
    this.succeeded = false;
    this.reason = "";
    this.input = "";
    this.expected_output = "";
    this.actual_output = "";
}

export const executeTests = function() {

  var results = [];

  result = new TestResult();
  result.input = 'HelloWorld Component';
  result.expected_output = "span containing text 'Hello World'"

  let wrapper = shallow(<HelloWorld />);

  // Call your Challenge function here.

  let type = wrapper.type();
  let testSuccessful = true;
  let failureReason;

  if (type !== 'span') {
    testSuccessful = false;
    failureReason = "You need to render exactly one span HTML element";
  } else if (wrapper.props().children != "Hello World") {
     testSuccessful = false;
     failureReason = "You have rendered wrong message in your span element";
  }

  result.actual_output = wrapper.html();

  if (testSuccessful) {
    result.succeeded = true;
    result.reason = "Succeeded"
  } else {
    result.succeeded = false;
    result.reason = failureReason;
  }

  results.push(result);

  return results;
}


Course Overview

Getting Started with LLMs

Fine-Tuning LLMs

Wrap Up

Exploring OpenAI API

Evaluation

Overview

What is ROUGE?