Language Models & Transformers
Building a Language Model from Scratch
Build a GPT-style language model from the ground up. Start with raw text, tokenization, tensors, and a bigram baseline, then progress through training, nonlinear networks, positional information, attention, residual paths, scaling, generation limits, and fine-tuning decisions.
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Vivid Labs
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Language Models & Transformers
Chapter List
Prepare a text corpus, tokenize it into model-ready sequences, create training and validation splits, and build the first bigram language-model baseline.
Understand the tensor shapes, indexing, broadcasting, and matrix operations that turn token sequences into predictions the model can train against.
Train the baseline model, generate sample text, interpret loss, and use validation results to separate genuine learning from memorization.
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