The original GPT-2 model is based on a transformer architecture with causal self-attention blocks trained on the WebText corpus and uses BPE tokenization to segment text into subtokens, allowing effective handling of a very large vocabulary
In its standard configuration, it has 12 hidden layers, an embedding size of 768 vectors, and 12 attention heads, each with 64 hidden dimensions, generating a total of approximately 110 million adjustable parameters during training
Training was performed on a large collection of filtered web data to ensure quality and language diversity. The implemented loss function is cross-entropy, which measures the discrepancy between the actual token distribution and the distribution predicted by the model, allowing weights to be optimized through backpropagation
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