Using Open- and Closed-Source LLMs in Real World Applications

Description

This repository contains Jupyter notebooks for the course "Using Open- and Closed-Source LLMs in Real World Applications" by Sinan Ozdemir. Published by Pearson, the course covers effective best practices and industry case studies in using Large Language Models (LLMs).

In this course, you'll explore both open- and closed-source Large Language Models (LLMs) and learn best practices for working with them. Over the course of four interactive hours, we'll dive deep into the world of open-source LLMs like FLAN-T5 and GPT-J, as well as closed-source LLMs such as ChatGPT and Cohere. Additionally, you'll have the opportunity to discuss and analyze real-world LLM applications in various industries.

This course is the second in a three-part series by Sinan Ozdemir designed for machine learning engineers and software developers who want to expand their skill set and learn how to work with LLMs like ChatGPT and FLAN-T5. The series provides practical instruction on prompt engineering, language modeling, moving LLM prototypes to production, and fine-tuning GPT models. The three live courses in the series are:

1. LLMs, GPT, and Prompt Engineering for Developers

2. Using Open- and Closed-Source LLMs in Real World Applications

3. LLMs from Prototypes to Production

The book Quick Start Guide to LLMs by Sinan Ozdemir is recommended as companion material for post-class reference.

What You'll Learn

• The differences between open- and closed-source LLMs
• How to set up and configure development environments for both
• Best practices for working with LLMs
• Real-world applications across various industries

This course is part of a three-course series designed for machine learning engineers and software developers.

Table of Contents

1. Course Set-Up
2. Notebooks
3. Prerequisites
4. Schedule
5. Resources

Course Set-Up

• Jupyter notebooks can be run alongside the instructor, but you can also follow along without coding by viewing pre-run notebooks here.

Prerequisites

• Experience with machine learning and proficiency in Python programming
• Familiarity with NLP is helpful but not required

Recommended Preparation

• Attend the course "LLMs, GPT, and Prompt Engineering for Developers"
• Read the book "Quick Start Guide to Large Language Models"

Schedule

• Session 1: Open-Source LLMs (80 minutes)

  • simple_finetuning_bert_classification.ipynb: Fine-tuning BERT for classification tasks
  • anime_category_classification_model_freezing.ipynb: Working with anime category classification
  • distillation.ipynb: Notebook covering distillation techniques
  • Llama 2 Instruction Fine-tuning Colab Notebook
  • rl_flan_t5_summaries.ipynb: Working with FLAN-T5 models using Reinforcement Learning

• Session 2: Closed-Source LLMs (70 minutes)

  • openai_fine_tuned_classification.ipynb: Fine-tuning OpenAI models for classification
  • semantic_search.ipynb: Implementing semantic search algorithms
  • retrieval_augmented_generation.ipynb: Building a retrieval augmented generation (RAG) system

• Session 3: Use Case Discussion (30 minutes)

  • Checkout out the Streamlit directory for more!

For a detailed schedule, refer to the Course Description.

Resources

• Course Website
• Other Useful Links