sveneziale/activations-and-barcodes
sveneziale/activations-and-barcodes Compute artifacts pushed by tda-for-llms's Hugging-Face-backed pipeline (hf.enabled: true in experiment.yaml). Layout Two top-level folders: activations/{model_slug}/{corpus}/{revision}/{act_name}/ Raw per-cloud activation matrices extracted from the model, one independent copy per checkpoint (revision). Independent of topology.metric — the same activations are reused across every metric or topology config that… See the full description on the dataset page: https://huggingface.co/datasets/sveneziale/activations-and-barcodes.
sveneziale/activations-and-barcodes
Compute artifacts pushed by tda-for-llms's Hugging-Face-backed pipeline (hf.enabled: true in experiment.yaml).
Layout
Two top-level folders:
activations/{model_slug}/{corpus}/{revision}/{act_name}/
Raw per-cloud activation matrices extracted from the model, one independent copy per checkpoint (revision). Independent of `topology.metric` — the same activations are reused across every metric or topology config that shares the same model + corpus + checkpoint + activation stream, so they are only ever GPU-extracted once.
activation_metadata.csv— one row per cloud (cloud_id, revision, layer, head, n_prompts, ...).activations.npz— raw matrices, keyedact_{cloud_id}matching thecloud_idcolumn above.
barcodes/{model_slug}/{corpus}/{revision}/{act_name}/{persistence|zigzag}/{metric}/
Persistent-homology outputs computed from the activations above. One copy per topology metric/mode, since the barcode depends on it.
persistence_summaries.csv— one row per cloud, scalar topological feature summary.persistence_diagrams.npz— raw barcodes, keyedcloud_{cloud_id}_h{dim}.manifest.json— full run provenance (model, revisions, activation stream, corpus, topology parameters).
