LLM

Demo note. This note demonstrates what the SciDraft Hugo theme can do for academic and scientific publishing — for research institutions, researchers and students. Its content is demonstration material only: readers should ignore whether the demo content is correct. A language model writes one token at a time. The prompt is processed once, then each new token is appended to the sequence and fed back in, so generation is a loop between the model and the sampler rather than a single call. The sequence diagram below shows the participants and the messages between them. ...

Demo note. This note demonstrates what the SciDraft Hugo theme can do for academic and scientific publishing — for research institutions, researchers and students. Its content is demonstration material only: readers should ignore whether the demo content is correct. One training step is a fixed cycle: the model predicts, the predictions are scored against the targets, the error is propagated backwards, and the parameters move a little in the direction that reduces it. The diagram below shows that cycle; the repetition is the whole of training. ...

Demo note. This note demonstrates what the SciDraft Hugo theme can do for academic and scientific publishing — for research institutions, researchers and students. Its content is demonstration material only: readers should ignore whether the demo content is correct. The same generation loop can be described without messages, as a machine with a small number of states. This view is useful when the interesting questions are about transitions — when decoding starts, what keeps it running, and which conditions end it. The state diagram below is the whole lifecycle of one completion. ...

Demo note. This note demonstrates what the SciDraft Hugo theme can do for academic and scientific publishing — for research institutions, researchers and students. Its content is demonstration material only: readers should ignore whether the demo content is correct. A language model does not emit a token; it emits a probability for every token in the vocabulary, and something has to choose one. Choosing the single most probable token makes output repetitive, while sampling from the full distribution occasionally picks from its long tail of near-zero probabilities. Nucleus sampling keeps the smallest set of tokens whose combined probability reaches a threshold and samples only from those. ...