Update gpt.py #51
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Update gpt.py #51
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I created a mini GPT model from scratch using PyTorch, inspired by Karpathy’s educational examples. This project implements all core components of a transformer: multi-head self-attention, feedforward layers, embeddings, and layer normalization. The model is trained on character-level text data and can generate new sequences after training. It includes logic for evaluation, loss tracking, and saving/loading the model. The code is clean and modular, making it perfect for learning how GPT models work internally. This setup is great for experimenting with custom datasets or building lightweight LLMs for small-scale tasks and educational purposes.
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I created a mini GPT model from scratch using PyTorch, inspired by Karpathy’s educational examples. This project implements all core components of a transformer: multi-head self-attention, feedforward layers, embeddings, and layer normalization. The model is trained on character-level text data and can generate new sequences after training. It includes logic for evaluation, loss tracking, and saving/loading the model. The code is clean and modular, making it perfect for learning how GPT models work internally. This setup is great for experimenting with custom datasets or building lightweight LLMs for small-scale tasks and educational purposes.