Add artifact index for dataset viewer
Browse files- PROVENANCE.json +10 -3
- README.md +8 -0
- SHA256SUMS.txt +3 -2
- VALIDATION_REPORT.md +3 -2
- data/artifact_index.jsonl +22 -0
PROVENANCE.json
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"created_at_utc": "2026-05-04T03:
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"sha256": "
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"source_path": "hf-dataset-card-draft.md",
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"staged_path": "README.md"
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"sanitized": false,
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"sha256": "d9b7e962cf7f075119968651e0a856ca73b7228a1a283f8b68d7d4fda3cdd232",
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"created_at_utc": "2026-05-04T03:48:31+00:00",
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"files": [
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"sanitized": false,
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"sha256": "81a22fc36eac6288fca513bbd5cff5483d4b03d20cb0c2105223127838c14994",
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"source_path": "hf-dataset-card-draft.md",
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"staged_path": "README.md"
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"size_bytes": 7011,
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"source_path": "generated:artifact_index",
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"staged_path": "data/artifact_index.jsonl"
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},
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"sanitized": false,
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"sha256": "d9b7e962cf7f075119968651e0a856ca73b7228a1a283f8b68d7d4fda3cdd232",
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README.md
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@@ -12,6 +12,12 @@ tags:
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- reproducibility
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size_categories:
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- n<1K
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---
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# DyT Composition Study Artifacts
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The artifacts include aggregate training metrics, saturation measurements, statistical-test summaries, predictor-validation outputs, table-source manifests, and selected aggregate analysis files used by the public code repository.
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This is not a natural-language training dataset. It does not redistribute WikiText, OpenWebText, LAMBADA, BLIMP, model checkpoints, or raw training logs.
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## Intended Use
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- reproducibility
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size_categories:
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- n<1K
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configs:
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- config_name: artifact_index
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default: true
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data_files:
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- split: train
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path: data/artifact_index.jsonl
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---
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# DyT Composition Study Artifacts
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The artifacts include aggregate training metrics, saturation measurements, statistical-test summaries, predictor-validation outputs, table-source manifests, and selected aggregate analysis files used by the public code repository.
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The Dataset Viewer table is an index of the artifact files. The machine-readable result artifacts are stored under `results/`.
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This is not a natural-language training dataset. It does not redistribute WikiText, OpenWebText, LAMBADA, BLIMP, model checkpoints, or raw training logs.
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## Intended Use
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SHA256SUMS.txt
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4c3c90e3a7732e2d662952081b29a55affc17da70e3e3039b7c182a1ac4c1e7f CITATION.bib
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-
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-
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d9b7e962cf7f075119968651e0a856ca73b7228a1a283f8b68d7d4fda3cdd232 results/activation_analysis.json
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038699ec0852c48221aa62796c998c38928bddba245fe12386799634425bbf0e results/alpha_sweep.json
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9d721c4ff939d5955e5b886235c4fd1841b3c3e6bf61b40440a5a6d7474f6942 results/convergence_10k.json
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4c3c90e3a7732e2d662952081b29a55affc17da70e3e3039b7c182a1ac4c1e7f CITATION.bib
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b3486992d37b40544a34abea56e425b9b4e7f6bd806a3b765d422ef4b4f926a8 PROVENANCE.json
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81a22fc36eac6288fca513bbd5cff5483d4b03d20cb0c2105223127838c14994 README.md
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05e22a72cf990c928bc7cf35fa59741ac321b481f0e83fa14bcf05f68680bcdc data/artifact_index.jsonl
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d9b7e962cf7f075119968651e0a856ca73b7228a1a283f8b68d7d4fda3cdd232 results/activation_analysis.json
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038699ec0852c48221aa62796c998c38928bddba245fe12386799634425bbf0e results/alpha_sweep.json
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9d721c4ff939d5955e5b886235c4fd1841b3c3e6bf61b40440a5a6d7474f6942 results/convergence_10k.json
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VALIDATION_REPORT.md
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- Source remote: https://github.com/lucky-verma/dyt-composition-study.git
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- Source commit: 697bb6760c34c7205295f234cb68404fa62d4668
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- Source dirty: false
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-
- Files before this report:
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- JSON files: 23
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-
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- Raw/checkpoint/log/PDF files: none
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- Files over size limit: none
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- Banned content hits: none
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- Source remote: https://github.com/lucky-verma/dyt-composition-study.git
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- Source commit: 697bb6760c34c7205295f234cb68404fa62d4668
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- Source dirty: false
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- Files before this report: 27
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- JSON files: 23
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- JSONL index files: 1
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- Size: 746556 bytes
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- Raw/checkpoint/log/PDF files: none
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- Files over size limit: none
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- Banned content hits: none
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data/artifact_index.jsonl
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{"artifact_id": "activation_analysis", "category": "summary", "description": "Activation saturation and bounding analysis summaries.", "format": "json", "path": "results/activation_analysis.json", "sanitized": false, "sha256": "d9b7e962cf7f075119968651e0a856ca73b7228a1a283f8b68d7d4fda3cdd232", "size_bytes": 1618}
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{"artifact_id": "alpha_sweep", "category": "summary", "description": "Alpha-sweep summary for DyT bounding strength controls.", "format": "json", "path": "results/alpha_sweep.json", "sanitized": false, "sha256": "038699ec0852c48221aa62796c998c38928bddba245fe12386799634425bbf0e", "size_bytes": 1014}
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{"artifact_id": "convergence_10k", "category": "summary", "description": "Short-horizon convergence summary used for consistency checks.", "format": "json", "path": "results/convergence_10k.json", "sanitized": false, "sha256": "9d721c4ff939d5955e5b886235c4fd1841b3c3e6bf61b40440a5a6d7474f6942", "size_bytes": 1740}
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{"artifact_id": "full__activation_rank_v1__aggregate", "category": "aggregate", "description": "Aggregate metrics for activation rank v1.", "format": "json", "path": "results/full/activation_rank_v1/aggregate.json", "sanitized": false, "sha256": "9d92395f143c52de2e2fa40e593f0972fd75e044148ea822e062902f1d538531", "size_bytes": 69999}
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{"artifact_id": "full__all_results", "category": "full_result", "description": "Sanitized aggregate table across public result cells.", "format": "json", "path": "results/full/all_results.json", "sanitized": true, "sha256": "7797a498acfb8f2803bdc2d062e9550f4347d33b298864c59a6f08176b31a88e", "size_bytes": 116748}
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{"artifact_id": "full__crossover_predictor", "category": "full_result", "description": "Predictor-validation summary for regime sign checks.", "format": "json", "path": "results/full/crossover_predictor.json", "sanitized": false, "sha256": "4ac44ce544860ab9eb771c2defc97917064311985ed71e204751a62a285550a4", "size_bytes": 6370}
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{"artifact_id": "full__downstream_v5__aggregate", "category": "aggregate", "description": "Aggregate metrics for downstream v5.", "format": "json", "path": "results/full/downstream_v5/aggregate.json", "sanitized": false, "sha256": "974f3620ec33957cdc6afc8ebd0146b62e8874dc41136bf5b9b6c3bdd7d30ff6", "size_bytes": 4897}
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{"artifact_id": "full__fast_extract", "category": "full_result", "description": "Fast extraction summary for public artifact consistency checks.", "format": "json", "path": "results/full/fast_extract.json", "sanitized": true, "sha256": "5881efea1ede8f29296fb7a2108f64e5a8de0f548c5c784056bff1fb46a65e93", "size_bytes": 15815}
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{"artifact_id": "full__hessian_pyhessian_v4__aggregate", "category": "aggregate", "description": "Aggregate metrics for hessian pyhessian v4.", "format": "json", "path": "results/full/hessian_pyhessian_v4/aggregate.json", "sanitized": false, "sha256": "3b887dccad0b445a0bf74ff84ddc42dcac1417a19f6a117994449fd0de48f60e", "size_bytes": 22913}
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{"artifact_id": "full__lambada_eval__aggregate", "category": "aggregate", "description": "Aggregate metrics for lambada eval.", "format": "json", "path": "results/full/lambada_eval/aggregate.json", "sanitized": true, "sha256": "ce37d10cf3456d3fda311ddc97ff32f377b5272f81d9ec80e5bd6dfed37d8b61", "size_bytes": 27980}
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{"artifact_id": "full__manifests__alpha_sweep_118m_summary", "category": "manifest", "description": "Manifest for the 118M alpha-sweep control.", "format": "json", "path": "results/full/manifests/alpha_sweep_118m_summary.json", "sanitized": true, "sha256": "ecd533b8152d2b6e348b4f94ff487fad561cb2a552d25a5ac557223832a52689", "size_bytes": 2281}
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{"artifact_id": "full__manifests__predictor_validation", "category": "manifest", "description": "Manifest for predictor-validation outputs.", "format": "json", "path": "results/full/manifests/predictor_validation.json", "sanitized": false, "sha256": "14302e82266cfe5b39cfd6c0ea755a57da256a0c41f51f027f6a6183e753ab51", "size_bytes": 16400}
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{"artifact_id": "full__manifests__sig_tests", "category": "manifest", "description": "Statistical-test manifest for reported comparisons.", "format": "json", "path": "results/full/manifests/sig_tests.json", "sanitized": false, "sha256": "9d10175c33f9c316f97f4039cb4c9bb524459e922c017ba28c1e23ffce8990f5", "size_bytes": 11895}
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{"artifact_id": "full__noise_stability_s3__aggregate", "category": "aggregate", "description": "Aggregate metrics for noise stability s3.", "format": "json", "path": "results/full/noise_stability_s3/aggregate.json", "sanitized": false, "sha256": "f5d601c707aa63e4c234ba59abc14e46baa4d4998475eb245ecfbd12e495aba7", "size_bytes": 3127}
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{"artifact_id": "full__noise_stability_uniform__aggregate", "category": "aggregate", "description": "Aggregate metrics for noise stability uniform.", "format": "json", "path": "results/full/noise_stability_uniform/aggregate.json", "sanitized": false, "sha256": "3ac8a1f87929bfea98e19cbf7ba8be4c4fca769321ae4f7024faa7a68be6cf7f", "size_bytes": 15271}
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{"artifact_id": "full__saturation_results", "category": "full_result", "description": "Saturation diagnostic outputs across calibration cells.", "format": "json", "path": "results/full/saturation_results.json", "sanitized": false, "sha256": "bb871cb3e3c7cb95e8b334d042fb9cbfb5d3b0ed4bf45c6e3501cb3ef255c55a", "size_bytes": 384041}
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{"artifact_id": "full__scale5_metadata_check", "category": "full_result", "description": "Scale-5 metadata consistency summary.", "format": "json", "path": "results/full/scale5_metadata_check.json", "sanitized": false, "sha256": "0127598467a2bb63261d2587144c2101bd58ac12330c2cff0fa3416f379d6c53", "size_bytes": 13138}
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{"artifact_id": "phase_diagram", "category": "summary", "description": "Regime-level phase-diagram summary across data and scale.", "format": "json", "path": "results/phase_diagram.json", "sanitized": false, "sha256": "ac6ee3bde5369f02c23961aa2b27e3572700526c3a5e06ba96ce55b779dfba68", "size_bytes": 2130}
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{"artifact_id": "rmsnorm_results", "category": "summary", "description": "RMSNorm comparison summary.", "format": "json", "path": "results/rmsnorm_results.json", "sanitized": false, "sha256": "fec2c4f67bef0c77485cea5c42258952c056ba252b89b9957c49867126e48aee", "size_bytes": 1242}
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{"artifact_id": "scaling", "category": "summary", "description": "Scaling summary across reported model/data regimes.", "format": "json", "path": "results/scaling.json", "sanitized": false, "sha256": "f900f06124ba36f95b3c801db285c6e7e4c8331322687067ace79b50d5437c05", "size_bytes": 3247}
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{"artifact_id": "train_val_gap", "category": "summary", "description": "Train/validation gap summary used for regularization interpretation.", "format": "json", "path": "results/train_val_gap.json", "sanitized": false, "sha256": "c46d32f450711887a3dfca4f13ab468c4e4a3d0f88b4198c608365d09cf10e4d", "size_bytes": 2026}
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{"artifact_id": "vit_results", "category": "summary", "description": "ViT cross-check summary.", "format": "json", "path": "results/vit_results.json", "sanitized": false, "sha256": "af2028744c89e4af0c40b4df861cc6ebe923f2193693c15de1181fdd5f14acc4", "size_bytes": 1975}
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