Decoding the Enigma of ChatGPT: Part 1 – Unveiling the Tale from 2017 | Written by Kunwar Vikrant | Published in Oct 2023

The Rise of ChatGPT: Understanding the Transformer Architecture

In 2017, a groundbreaking paper titled “Attention Is All You Need” introduced the Transformer, an architecture designed for machine translation tasks. Little did the authors know that this architecture would go on to have a profound impact on AI. Originally created for translation, the Transformer quickly became dominant in various AI applications.

One notable application of the Transformer is ChatGPT, which stands for Generative pre-trained Transformer. ChatGPT utilizes the power of the transformer model to generate human-like text based on the input it receives. However, building a system like ChatGPT from scratch is no small feat. It requires a highly complex, production-grade system that undergoes extensive training, including pre-training and fine-tuning, as well as being fed a substantial amount of internet data for knowledge.

But instead of delving into the intricacies of replicating ChatGPT, let’s focus on understanding the core principles behind it. I’ll take you through the process of training a language model using the Transformer architecture, using a smaller and more manageable dataset known as the “tiny Shakespeare dataset.”

To train a language model, we first need to tokenize the text. Tokenization is the process of converting raw text into a sequence of integers. In this case, I’ve chosen a character-level tokenizer where each character is assigned a unique integer. This allows the model to understand the text through the assigned integers. While character-level tokenization is a simple approach, it’s worth noting that there are more sophisticated methods like sub-word or word tokenization.

Once the text is tokenized, we can feed it into the Transformer model for training. However, instead of feeding the entire text at once, we divide it into smaller chunks. Each chunk serves as a training example and has a maximum length known as the “block size.” For example, if the block size is set to eight, each chunk contains eight characters from the text along with an additional character as the target for the model.

The model’s task is to predict the next character based on the preceding characters. This means each chunk contains multiple examples, with each character being a prediction target. Through these examples, the model learns to make predictions at various points in the input sequence, handling different contextual lengths. This adaptability is crucial when the model generates text, as it may need to start with minimal context and gradually build longer sequences during text generation.

Training on these examples not only improves computational efficiency but also helps the model adapt to different context lengths. This adaptability is essential for the model’s ability to generate coherent and contextually relevant text.

Stay tuned for part 2 where we dive deeper into this fascinating topic. Thank you for reading, awesome reader! 🎉 If you want to receive more mind-bending content, subscribe to my newsletter at Let’s embark on this visual adventure together! 🚀

## Editor Notes

The Transformer architecture has revolutionized the field of AI, with ChatGPT being one of its impressive applications. The ability to generate human-like text has opened up new possibilities for various industries, from customer service to content creation. It’s incredible to see how this technology has evolved since its inception in 2017.

If you’re interested in staying updated with the latest AI news, I highly recommend checking out GPT News Room. They provide insightful and reliable coverage of AI advancements, ensuring you’re always in the loop. Visit [GPT News Room]( today and expand your AI knowledge!

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