This notebook implements and partially trains a custom Generative Pre-Trained Transformer (GPT) model, minimizing the use of premade transformer objects or classes. A GPT is a powerful model capable of generating text by predicting the next token in a sequence based on the given context.
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Attention Mechanism: The core feature that allows the model to focus on relevant parts of the input sequence.
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Positional Encoding: A method to provide positional information to the model for sequential data.
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Transformer Architecture: The design and logic behind the structure of the transformer model.
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Tokenization: Converting text into a format that can be processed by the model.
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Data Preprocessing: Preparing input data for training.
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Training Loop: Updating the model's parameters to minimize the loss function.
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Evaluation Metrics: Assessing the model's performance using appropriate metrics.
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Text Generation: Generating text using the trained model.
This implementation emphasizes the fundamental concepts of transformer architecture, aiming to provide a deeper understanding of the internal mechanisms of transformer-based models.
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Build a custom GPT model from scratch with minimal reliance on premade transformer objects or classes.
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Gain a thorough understanding of the internal workings of transformer architectures.
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Train the model on text data for next-token prediction.
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Generate text using the custom-built model.
Although I could not fully train the model due to time constraints, I trained it long enough to demonstrate the output of a partially trained GPT model. This serves as a practical example of the capabilities and limitations of an undertrained transformer-based model.
Sample output after creating a medium sized model and achieving a loss around 6 using Cross Entropy:
Generated text: Once upon a time to be the story of the world’s new world are taking a big price of this year. Here is our one of the world that was first look at least a new study of a big story that has shown to see it, a lot of the most of how that have done it will be a bit on the other forms of a major. No. For example from the past a good as a series for a good news conference just over what most people who were true. This particular long way. Even before the year’s in particular a little many different episodes to see an incredible life, to be the past several reasons over our third a massive to the team’s that time or a good time. My first time since I would like one of hard time, which, and we came the good part of a post on one of the very happy with a time, but on its entire new year, but there wasn’s that time. "The way to hold