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bgradowhite/cells-developmental

bgradowhite/cells-developmental Verbatim backup of /Users/brianna/Cells_Developmental/out from Brianna's machine, taken 2026-09-14. See PROVENANCE.md for what each directory is and which script wrote it, and MANIFEST.json for SHA-256 digests of every file and every archive member. Archives <dir>.tar.gz extract in place to <dir>/; where a directory name held colons the archive name has hyphens instead, and MANIFEST.json's extract_to gives the original path. The following is… See the full description on the dataset page: https://huggingface.co/datasets/bgradowhite/cells-developmental.

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Dataset Card

bgradowhite/cells-developmental

Verbatim backup of /Users/brianna/Cells_Developmental/out from Brianna's machine, taken 2026-09-14. See PROVENANCE.md for what each directory is and which script wrote it, and MANIFEST.json for SHA-256 digests of every file and every archive member. Archives <dir>.tar.gz extract in place to <dir>/; where a directory name held colons the archive name has hyphens instead, and MANIFEST.json's extract_to gives the original path.

The following is `docs/hf-checkpoints-card.md` from the source repository at backup time.

Cells_Developmental training trajectories

The trained networks the Cells_Developmental pages are measured on: every trajectory that the repository's own scripts produced, one directory per artifact, laid out exactly as it sits under out/ in a working copy. Copy a directory into out/ and the repository's loaders find it; inventory.json, config.json or log.json in each directory records the recipe, the seed and the checkpoint grid, and is the authority on what the directory holds.

Nothing here is a mirror of data that is public elsewhere. The MNIST and CIFAR archives, the Pythia checkpoint releases and the CIFAR-100 model-zoo trial are pinned by digest in the repository's configs/inputs/ and fetched from their sources; the modular-addition MLP trajectory of the curvature-steering runs is published separately. The measured parts that the pages are built from are not checkpoints and are not kept here.

Layout

directory	network and recipe	written by
`mnist_task/`	normalization-free residual tanh MLP on the pinned MNIST archives, five epoch checkpoints; the pinned artifact every other MNIST trajectory is compared against	`cells_developmental.exp1.mnist_task`
`mnist_dense/`	the same network, data, recipe and seed, checkpointed on a log-spaced step grid	`scripts/train_mnist_dense.py`
`mnist_dense-b5/`, `mnist_dense-b6/`	the same recipe and seed with five and six residual blocks	`scripts/train_mnist_dense.py --blocks`
`paper_mlp/plain/`, `paper_mlp/res/`	the grokking paper's MNIST MLP (a thousand training points, MSE against one-hots, AdamW) and its minimal residual pair, on the 41-point checkpoint ladder continued to a million steps	`scripts/train_paper_mlp.py`
`paper_mlp/{plain,res}-h{4,5,6}/`	the same pair at four, five and six hidden layers, on the 41-point ladder	`scripts/train_paper_mlp.py --hidden-layers`
`modular_transformer/<run>/`	Nanda et al.'s one-layer transformer on modular addition, checkpointed on a logarithmic grid and at every evaluation where the test accuracy moved; `latest.pt` carries the optimiser state for resuming	`scripts/train_modular_transformer.py`
`vgg_cifar10/`	VGG-19 on CIFAR-10 on Arora et al.'s recipe with the two departures the script's docstring records, checkpointed at 1, 2, 5, 10, 20, every fifteen epochs and the last	`scripts/train_vgg_cifar10.py`

parts/<substrate>/ holds the measured parts behind a page, one JSON per panel named for what it measured, laid out flat as the repository's store reads them. The hub refuses a directory of more than ten thousand files, so a parts directory over that size is split here by panel kind (the second field of a part's name) with the overview left at the top; flatten it into out/parts/<substrate>/ before building.

Checkpoints ending in .npz are pickle-free NumPy archives; .pt files are torch.save state dictionaries and load with weights_only=True except for latest.pt.

Provenance

The bytes here are the ones measured on, not re-runs. Training is deterministic in the seed on one machine, but two machines running the same recipe do not produce the same bytes: a re-run of mnist_dense agreed with the original to about 1e-7 in every weight, while a re-run of paper_mlp/res had drifted by 4e-2 at step 1000. A trajectory retrained elsewhere is therefore a different object from the one the pages describe, and a directory is replaced here only by the copy that was measured on.

scripts/hf_upload_checkpoints.py in the repository compares the local artifacts with this tree by SHA-256 and sends only what is missing; a file that differs from its local counterpart is reported and left alone unless the artifact is named explicitly.