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n# This is a *very* simplified pseudocode to illustrate how a transformer-based language model like GPT works.

class TransformerModel:
    def __init__(self, parameters):
        self.parameters = parameters  # billions of learned weights

    def forward(self, input_tokens):
        # Embed input tokens into vectors
        embeddings = self.embed(input_tokens)
        
        # Pass through multiple transformer layers (attention + feed-forward)
        for layer in self.transformer_layers:
            embeddings = layer(embeddings)
        
        # Output a probability distribution over the vocabulary
        logits = self.output_layer(embeddings[-1])
        return logits

    def generate_text(self, prompt, max_length=100):
        tokens = tokenize(prompt)
        for _ in range(max_length):
            logits = self.forward(tokens)
            next_token = sample_from_logits(logits)
            tokens.append(next_token)
            if next_token == end_of_text_token:
                break
        return detokenize(tokens)