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What is a Large Language Model and how does it generate text?

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01

Understand the problem

The transformer, next-token prediction, and why 'autocomplete at scale' produces reasoning-like behaviour.

llmtransformerfundamentalsnext-token
02

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Nudge consolestandby

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03

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A Large Language Model is a transformer neural network trained on huge text corpora to predict the next token. At inference it generates text one token at a time, feeding each generated token back in as input (autoregressive decoding). Capabilities like reasoning, coding and instruction-following emerge from this objec

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04

Read the code

One decoding step, conceptually
context = tokenize("The cat sat on")
while not done:
    logits = transformer(context)          # scores for every vocab token
    probs  = softmax(logits[-1] / temperature)
    tok    = sample(probs)                 # greedy / top-p / top-k
    context.append(tok)                    # feed it back in
    done   = (tok == EOS) or len(context) >= max_tokens
05

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