Developing Your Own GPT Model with Python

Preference Dates Timing Location Registration Fees
Evening Program (In-Person and Live Online) December 17, 18, 19, 20, 23, 2024 2:00 PM - 5:00 PM (GMT+4) Dubai Knowledge Park 1575 USD

Course Description

This course is designed to guide participants through the entire process of working with Large Language Models (LLMs) like GPT-2, LLaMA, and Falcon, from fine-tuning to deployment. By the end of this course, participants will have the skills to fine-tune open-source LLMs with their own data, deploy these models on a Google Cloud VM, and create a user interface using Django to interact with the models via prompts. This hands-on, project-based course will equip participants with the knowledge to build and deploy a fully functional GPT-like chatbot.

Upon successful completion of this program the participants will earn a certificate accredited by Dubai Government.

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Introduction to Large Language Models (LLMs)

  • Overview of LLMs and their applications
  • Understanding GPT models and their evolution
  • Introduction to open-source models like GPT-2, LLaMA, and Falcon

Environment Setup

  • Installing Python and PyTorch
  • Setting up a virtual environment for development
  • Installing necessary libraries and dependencies

Introduction to Neural Networks and PyTorch

  • Basics of neural networks
  • Introduction to the PyTorch framework
  • PyTorch tutorial: Understanding tensors, datasets, and training neural networks

Preparing the Dataset

  • How to collect and preprocess data for training LLMs
  • Working with various data formats (text, images, and PDFs)
  • Preparing financial statements and other specialized datasets

Tokenization and Model Initialization

  • Understanding tokenization in NLP
  • Tokenizing custom datasets
  • Initializing LLMs with pre-trained weights

Data Preprocessing and Inspection

  • Inspecting and cleaning data before training
  • Understanding data augmentation techniques
  • Splitting data into training and validation sets

Model Training Setup and Configuration

  • Configuring the model training parameters
  • Understanding hyperparameters like learning rate, batch size, and epochs
  • Setting up training loops and optimizers

Model Training and Optimization

  • Fine-tuning LLMs with custom datasets
  • Monitoring training progress and adjusting parameters
  • Techniques for optimizing model performance

Saving and Loading the Trained Model

  • How to save a fine-tuned model
  • Loading a saved model for inference
  • Testing the model by generating text based on prompts

Fine-Tuning Open Source Models on Google Cloud

  • Setting up a Google Cloud VM for model training
  • Installing necessary drivers and libraries for GPU support
  • Fine-tuning models on the cloud for faster training

Creating a User Interface with Django

  • Introduction to Django for web development
  • Building a basic user interface to interact with the trained model
  • Deploying the Django application on Google Cloud

Deploying the GPT-Like Chatbot

  • Integrating the fine-tuned model with the Django interface
  • Setting up a backend server to handle model inference requests
  • Testing and deploying the chatbot for real-world use

Target Audience

  • Software Developers: Professionals interested in developing and deploying applications involving Large Language Models (LLMs).
  • Data Scientists: Individuals who work with data to build and fine-tune machine learning models.
  • Data Analysts: Professionals who analyze data and want to enhance their skills with AI-driven approaches like LLMs.
  • Data Science Professionals: Experts who apply data science techniques and are interested in integrating LLMs into their workflows.
  • AI Enthusiasts: Those passionate about artificial intelligence and eager to build their own AI-driven solutions.
  • Anyone Interested in Building and Deploying LLMs: Individuals with a curiosity about LLMs who want to develop hands-on skills in this area.

Prerequisites for this Course

  • Comfortable with Python programming, including writing scripts, managing Python packages with pip, and setting up virtual environments.
  • Experience with Python libraries commonly used in data science (e.g., NumPy, Pandas) is advantageous but not mandatory.
  • Foundational understanding of AI and data science concepts, such as machine learning basics, data preprocessing, and model evaluation.
  • Familiarity with the types of tasks AI and data science projects typically involve, such as building models, analyzing data, and working with datasets.

Course Objectives

  • Understand the foundational concepts of Large Language Models (LLMs).
  • Set up a development environment for working with LLMs using Python and PyTorch.
  • Fine-tune open-source LLMs like GPT-2, LLaMA, or Falcon using custom datasets.
  • Deploy LLMs on a cloud server using Google Cloud.
  • Create a user interface with Django to interact with the LLMs via prompts.
  • Build an end-to-end GPT-like chatbot, from fine-tuning to deployment.
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