How chatGPT works behind the scene: Part one
OpenAI’s GPT-3, including the chatbot version known as ChatGPT, is based on a transformer architecture and uses deep neural networks to generate text.
The system design of ChatGPT consists of several components. Part one, called pre-training, of several I will be publishing here. I will walk you through each component.
The first component is pre-training. Pre-training aims to enable the model to generate coherent and meaningful text. This involves training the model on a large corpus of text data to learn patterns and relationships between words and phrases.
Here’s a code example in PyTorch, a popular deep-learning framework, to give you an idea of what pre-training might look like:
In this example, the model architecture is defined by the GPT Class. It includes an embedding layer to convert the input words into numerical representations, a transformer layer to learn the relationships between words, and a fully connected layer to generate the final output.
The model is trained using the Adam optimizer and cross-entropy loss, and the training loop iterates over the pre-training data for a specified number of epochs.
This is just one example of how ChatGPT pre-trains its model. The specific implementation details will depend on the requirements of the chatbot and the data they are using for pre-training.
Don’t worry; tech talk is not my forte, either! Sit back and relax if you’re intimidated by all the technical jargon. I promise to explain things in simple, everyday language so even a non-techie like yourself can understand what’s happening behind the scenes.
🤖 The first step in building a chatbot using the ChatGPT architecture is pre-training the model. This involves teaching the chatbot how to generate text by showing it a large amount of text data. The goal of pre-training is to help the chatbot learn the…
