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.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+lds_d12.jsonl filter=lfs diff=lfs merge=lfs -text
+lds_d16.jsonl filter=lfs diff=lfs merge=lfs -text
+lds_d20.jsonl filter=lfs diff=lfs merge=lfs -text
+lds_d24.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: mit
+task_categories:
+  - other
+tags:
+  - training-data-attribution
+  - linear-datamodeling-score
+  - nanochat
+  - climbmix
+---
+
+<div align="center">
+<h2>STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations</h2>
+
+<a href="https://arxiv.org/abs/2606.05165"><img src='https://img.shields.io/badge/arXiv-STRIDE-red' alt='Paper'></a>
+<a href='https://stride-tda.github.io'><img src='https://img.shields.io/badge/Project_Page-STRIDE-green' alt='Project Page'></a>
+<a href='https://huggingface.co/rishitdagli/stride-nanochat'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20-Models-yellow'></a>
+<a href='https://huggingface.co/datasets/rishitdagli/stride-lds'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20-LDS%20Dataset-yellow'></a>
+</div>
+
+Ground-truth Linear Datamodeling Score (LDS) targets for the four nanochat
+pre-training models, plus the shared held-out test set.
+
+Each `lds_<tag>.jsonl` was produced by: sampling a
+pool of pre-training examples, drawing 256 random 30%-subsets, training a fresh nanochat
+from scratch on each subset, and recording per-example held-out test losses. The
+`_meta` header records the pool indices so scores defined over the full corpus
+can be compared to the subset losses.
+
+## Files
+
+```
+climbmix_test_d24.jsonl    # 500 held-out test queries (shared across depths)
+lds_d12.jsonl              # 256 subsets x 500 test losses  (+ lds_d12.meta.json)
+lds_d16.jsonl
+lds_d20.jsonl
+lds_d24.jsonl
+```
+
+Each non-`_meta` line:
+
+```json
+{"train_subset": [pool-local indices], "train_subset_global": [corpus indices], "test_loss": [500 floats]}
+```
+
+## Usage
+
+```python
+from stride.inference import Stride
+attr = Stride.from_pretrained("d12")
+result = attr.attribute(test_queries)
+lds = attr.evaluate_lds(scores=result.scores)   # downloads lds_d12.jsonl from here
+print(lds["lds_spearman_mean"])
+```
+
+Or score an existing method's `.npz`:
+
+```bash
+python -m stride.cli.eval_lds --scores my_scores.npz --lds lds_d12.jsonl --method MyMethod
+```
+
+The models and operators live in the companion model repo
+[`rishitdagli/stride-nanochat`](https://huggingface.co/rishitdagli/stride-nanochat).
+
+## Citation
+
+```bibtex
+@misc{dagli2026stridetrainingdataattribution,
+      title={STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations}, 
+      author={Rishit Dagli and Abir Harrasse and Luke Zhang and Florent Draye and Amirali Abdullah and Bernhard Schölkopf and Zhijing Jin},
+      year={2026},
+      eprint={2606.05165},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2606.05165}, 
+}
+```

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