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Explanation

In Large Language Models (LLMs), when processing text like the word "unbelievable", the model first splits the input into smaller sub-units called tokens.

Key Points:

• Tokens are the fundamental units of text that LLMs process
• Tokenization is the process of breaking down text into these smaller units
• For the word "unbelievable", it might be split into tokens like "un", "believe", "able" or similar sub-word units depending on the tokenizer
• Characters would be individual letters (u, n, b, e, l, i, e, v, a, b, l, e)
• Embeddings are the numerical representations of tokens, not the tokens themselves
• Layers refer to the neural network architecture components, not the input units

This tokenization process allows LLMs to handle vocabulary efficiently and process text in manageable pieces.