OpenAI & ChatGPT API's: Expert Fine-tuning for Developers

In this lesson, we will explore the importance of preparing and formatting data before using it to fine-tune a model. We will discuss how properly formatted data can improve model accuracy and efficiency, reduce errors, and prevent biased information from being learned. Additionally, we will cover various types of data formats that are commonly used in machine learning and natural language processing projects and provide tips on how to prepare them for use with pre-trained models. By the end of the lesson, learners will have a better understanding of the benefits of data preparation and formatting, as well as practical techniques for optimizing data sets for use in model training.

In this lesson, we will discuss the format of the data used for fine-tuning a GPT model. We will begin by exploring how to prepare different data sets, discussing their strengths and weaknesses, and sharing tips on how to have good data that is ready for fine-tuning. We will also cover the importance of adding suffixes to data. We will go through a set of data sets, including one about Arduino and another about earthquakes, and examine them to see how to structure them correctly. We will explain why it's essential to have a single question with a related answer, feed the model with different question structures, and have at least 200 different prompts and completions. By the end of the lesson, students will have a clear understanding of how to structure data sets for fine-tuning GPT models.

In this lesson, we will learn how to use Python to clean a CSV file by removing any missing values or duplicates. Data cleaning is an important step in data preparation for fine-tuning, as duplicates and missing values can negatively impact the results of our analysis. We will be using Google Colab, an online tool that allows us to easily execute our Python code. We will also learn how to mount our Google Drive to access our data set and save our cleaned data into a new file. By the end of this lesson, you will have a solid understanding of how to clean and prepare data for further analysis using Python.

In this lesson, you will learn how to prepare questions and answers from a text file to fine-tune an AI model. The lesson will cover the two stages of formatting the data as prompts and completions with suffixes included and changing the data to JSON format. You will be introduced to an online tool, trainmy.ai/qa-generator, which can be used to generate questions and answers from a text file. The lesson will also cover how to enter text into the tool and how to verify an API key. By the end of the lesson, you will be able to generate questions and answers from a text file for fine-tuning AI models.

In this lesson, you will learn how to use ChatGPT to generate Python code for working with large datasets. You will be shown how to use prompt engineering to describe the features and requirements of your project and data set in order to get an optimal Python code. The code generated will be used to convert a prepared data set into the shape of prompts and completions. You will learn how to import necessary libraries, load data from a file, and manipulate data using prompts and completions. By the end of this lesson, you will have a solid understanding of how to use ChatGPT to generate Python code for data manipulation.

In this lesson, we will learn how to run and execute Python code on Google Colaboratory, a cloud-based platform for running Jupyter notebooks. The lesson will start by reviewing the Python code that was prepared in the previous lesson for preparing prompts and completions from a large CSV data file. We will discuss the changes that need to be made to the code to meet the requirements of our dataset.

Then, we will move on to executing the Python code step-by-step on Google Colaboratory. We will use Google Colaboratory to access and read CSV files from our Google Drive. We will also discuss how to create a new notebook and add codes or text to it before running and executing the code.

By the end of this lesson, you will have a good understanding of how to use Google Colaboratory to run and execute Python code for data preparation and analysis.

In this lesson, you will learn how to build a well-structured project directory and manage data within it. You will understand the importance of organizing your project directory, particularly in the context of data science or machine learning projects. You will learn the benefits of a good project structure, such as saving time, removing misconceptions, and avoiding overlapping work. The lesson will cover tips and best practices for creating a good project directory, including organizing data into folders and subfolders with descriptive names, keeping a README file, and including different folders for code, scripts, and data analysis. By the end of the lesson, you will have a clear understanding of how to create a project directory that is easy to navigate, understand, and reuse for future projects.

In this lesson, you will learn about the different pre-trained models offered by OpenAI's GPT-3, and how to choose the best one for your fine-tuning project. You will be introduced to the four basic models available for fine-tuning, including DaVinci, Curie, Anna, and Babbage. The lesson will cover the specifications of each model, such as how many tokens they can take and their capabilities. You will also be shown how to access the documentation section of OpenAI's website and preview the models. By the end of the lesson, you will have a clear understanding of which model is best suited for your specific fine-tuning needs.

This lesson provides an overview of the steps involved in the fine tuning process in Python, from beginning to end. The lesson covers how to download the latest version of Python, open the command prompt, locate the file, download necessary libraries and packages such as Open AI, pandas, and others. Participants will learn how to run Python codes and execute commands to fine tune their data. By the end of the lesson, participants will have a foundational understanding of the fine tuning process and will be equipped to apply these skills to their own data.

In this lesson, we will learn about the three main stages involved in fine-tuning a pre-trained model. The first stage is to change the format of the data to the adjacent format required by pre-training models, using a Python code. The second stage is to give this data to a pre-trained model to do the fine-tuning. Finally, in the third stage, we will test our fine-tuned model. We will use CMD and Python to execute these tasks. Specifically, we will download Python, open our file, and execute two Python codes included in the file to change the CSV data file to a JSON data format and communicate with OpenAI's API keys to do the fine-tuning. By the end of this lesson, you will be familiar with the process of fine-tuning pre-trained models and how to implement it in Python.

In this lesson, you will learn how to test your fine-tuned model on OpenAI Playground. The lesson will cover the basics of choosing the model you want to work with, changing the temperature to ensure the model is not using extra information, and setting the maximum token length. The lesson will also show you how to write prompts and use the stop sequence that you added to your data before fine-tuning the model. By the end of this lesson, you will be able to test your model and get responses from it using OpenAI Playground.

In this lesson, we will dive into the basics of using Postman as a tool to test APIs from OpenAI. We will explore how to configure the Postman environment, create workspaces and collections, and integrate the OpenAI API into our project. Starting with signing in to Postman, we will create a new workspace and collection, and then proceed to the OpenAI API playground to generate an API key. We will learn how to store the API key as a variable in Postman, set up authorization using the key, and configure the environment for seamless integration with OpenAI tools. Join us to discover the initial steps to leverage Postman and OpenAI for your projects. See you in the next lesson!

In this lesson, we will continue our journey of building a chatbot using OpenAI APIs. After setting up our workspace and collection in Postman and adding the API key to the environment, we will start by fetching data from the OpenAI documentation. Navigating to the models section, we will explore the various models provided by OpenAI, with a focus on GPT-3.5 Turbo, the model we will utilize for our chatbot. We will extract the endpoint for GPT-3.5 Turbo and return to Postman to construct our API request by specifying the endpoint.

However, during our initial request, we will encounter an authorization error, reminding us to include the API key in the authorization section. We'll rectify this by adding the key to the authorization headers. Once the authorization is set, we'll encounter another error, indicating the requirement for model parameters. It's now time to start building our chatbot.

In this tutorial, we are advancing our chatbot development journey by creating a basic web page that incorporates the OpenAI API. Having already confirmed the API's functionality using Postman, we will now move on to assembling a web page powered by ChatGPT. To achieve this, we'll furnish ChatGPT with essential instructions and parameters, like the HTML code and JSON configuration, which encompasses the endpoint URL and API key.

This lesson provides an overview of the updated methods for fine-tuning GPT-3.5 Turbo models, including data preparation tailored to the model's roles and responses, and introduces two approaches to the fine-tuning process.

This lesson walks through the steps of preparing data for fine-tuning a GPT-3.5 Turbo model, including formatting data in CSV and converting it to JSON using Python.

This lesson teaches you how to fine-tune a GPT-3.5 Turbo model using Python. You’ll learn to set up your environment, run the fine-tuning process with your dataset, and test the resulting model. The lesson covers installing libraries, importing your dataset, starting the fine-tuning job, and interpreting the results, with a practical example of testing the fine-tuned model using a custom prompt.

This lesson walks you through the final steps of fine-tuning a GPT-3.5 Turbo model. You’ll learn how to receive and interpret OpenAI’s completion notifications, verify the fine-tuning via email confirmation, and test your fine-tuned model directly in the OpenAI Playground.

This lesson introduces a simplified approach to fine-tuning GPT-3.5 Turbo models without extensive coding.