Python

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. Attention is the one place in a transformer where tokens exchange information: every other block — the projections, the activations, the normalisations — treats positions independently. That makes attention mostly shape bookkeeping, and the shapes alone decide which products are legal. ...

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 project that produces a paper has to keep three kinds of thing apart that are easy to mix: the installed source, the outputs of individual runs, and the material the paper is written from. Once several sweeps and a performance benchmark live in the same repository, the folder structure is what decides whether a figure can be traced back to the run that produced it. ...

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 smallest useful project around a piece of neural-network mathematics holds one idea per file and keeps the generated numbers out of the code. Nothing in it is conventional yet: a starter folder is decided by what you want to compare next, not by what a template says. ...

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 number a language model reports while training is the mean negative log-likelihood of the token that actually came next: softmax cross-entropy over the vocabulary at every position. Its backward pass has an unusually clean closed form — the softmax Jacobian and the logarithm cancel, leaving the probabilities minus the one-hot target, divided by the number of predictions. ...

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. Once a model trains, the folder structure has a second job: it has to separate what changes per run from what changes per idea. Configurations and checkpoints belong to a run; attention code, data preparation and the training loop belong to the project, and the split is what lets a result be reproduced from a config file plus a commit rather than from memory. ...

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 ends in a softmax: the raw scores of the last layer become a probability distribution over the vocabulary, and everything downstream — the training loss, sampling, beam search — reads that distribution rather than the scores themselves. ...