GPT in 60 Lines of NumPy

GPT in 60 Lines of NumPy

GPT-from-scratch is a tutorial from Jay Mody that explains how to create Generative Pre-trained Transformer (GPT) models from scratch. It uses the Transformers library from Hugging Face to construct and train a GPT model for natural language generation. The tutorial covers all the key stages involved in creating a GPT model, including defining a tokenizer, loading data, building a model, training the model, and evaluating the results. In addition, it provides a step-by-step guide on using the model to generate text.

The tutorial starts off by explaining what GPT is and why it has become popular in recent years. It then moves on to discuss the core components of a GPT model, including the tokenizer and encoder-decoder architecture. It also explains how to load and prepare data for training the model.

Once the dataset is prepared, the tutorial explains how to define a tokenizer and build an encoder-decoder model. It then explains how to configure the parameters of the model and add loss functions. Finally, it provides instructions on how to train the model and evaluate the results.

At the end of the tutorial, the author provides several examples of generated text from the GPT model. He also provides tips for improving test results and increasing accuracy. Overall, the tutorial is an excellent resource for anyone interested in creating their own GPT model. By following the steps outlined in this tutorial, users can quickly learn how to create and deploy their own GPT model.

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