| --- |
| license: other |
| language: |
| - en |
| task_categories: |
| - text-generation |
| tags: |
| - memory-layers |
| - interpretability |
| - synthetic |
| - the-pile |
| pretty_name: memlayers data v1 |
| size_categories: |
| - 100M<n<1B |
| --- |
| |
| # `memlayers-data` v1 |
|
|
| The frozen training bundle for a study of **what memory-layer values store and |
| how they are organized** (PKM / PEER against a kNN-LM store), COMS E6998 |
| Algorithms in LLMs. Code that builds, verifies and reads it: |
| [RoieGilad7/memory-layers-analysis](https://github.com/RoieGilad7/memory-layers-analysis), |
| tag `data-v1`. |
|
|
| **One bundle, three parts.** |
|
|
| 1. **A 150M-token training slice of the Pile** — `monology/pile-uncopyrighted` |
| at revision `3be90335b66f24456a5d6659d9c8d208c0357119`, five subjects at |
| **exactly 27,000,000 GPT-2 tokens each**: `Pile-CC`, `Github`, |
| `PubMed Central`, `FreeLaw`, `Wikipedia (en)`. |
| 2. **Six held-out subjects, each split into a `label` half and a `test` half** — |
| the five above plus **`USPTO Backgrounds`, which never appears in training at |
| all**, so it measures a subject the model has never seen. Every half is |
| within 1% of 2,500,000 tokens per subject; the halves share no documents and |
| no document ids with the training split. |
| 3. **Synthetic biographies of 2,500 fictional people** — five facts each (birth |
| date, birth city, university, major, employer) drawn uniformly at random from |
| fixed value lists, so a model can only answer a question about one by having |
| *stored* it. 2,000 people appear in training at one of three exposure tiers |
| (frequent / normal / rare) and 500 are **controls that never appear**, which |
| is the guessing floor. 8,000 distinct biographies, 190,000 copies, |
| 15,142,237 tokens (10.06% of `train.bin`), and 100,000 fact-recall prompts. |
|
|
| `train.bin` is **150,475,264 GPT-2 tokens / 293,897 sequences of 512**. |
|
|
| ## Licensing and provenance |
|
|
| - **Upstream license tag: `other`.** This bundle is a derived slice of |
| [`monology/pile-uncopyrighted`](https://huggingface.co/datasets/monology/pile-uncopyrighted), |
| which is EleutherAI's **The Pile** with the `Books3`, `BookCorpus2`, |
| `OpenSubtitles`, `YTSubtitles` and `OWT2` subsets removed — per that dataset's |
| card, the subsets "not explicitly allowed to be used in AI training" according |
| to the original Pile paper. It was published "in response to authors demanding |
| that LLMs stop using their works". |
| - **Component documents keep their own original licenses.** The Pile is a |
| compilation of many sources; nothing here relicenses any of them. Consult the |
| upstream dataset card and the Pile paper before redistributing or using the |
| Pile-derived files for anything beyond research reproduction. |
| - Pile text here is **cleaned, deduplicated, split and tokenized**, not |
| reproduced verbatim as published: see `reports/` in the bundle and the data |
| reports in the code repository for exactly what was done. |
| - **The biographies are synthetic.** Every person, name and fact is generated by |
| `data/bios.py` from a seed. They describe nobody. Candidate names are rejected |
| if they occur in the Pile text or if a name word would hand a probe one of its |
| answers. |
| - No part of this bundle is intended for training a model that is deployed; it |
| exists so one experiment can be reproduced token for token. |
|
|
| ## Files |
|
|
| | path | files | size | what | |
| |---|---:|---:|---| |
| | `tokens/` | 2 | 304.1 MB | `train.bin` (uint16 GPT-2 ids) + `train.idx.parquet`, the segment index | |
| | `heldout/` | 16 | 103.7 MB | `label.bin`, `test.bin`, their indexes, and the per-subject cleaned documents of each half | |
| | `train/` | 5 | 227.2 MB | the cleaned, deduplicated training documents, one `.jsonl.gz` per subject | |
| | `heldout_pool/` | 6 | 59.3 MB | the pool each held-out half was drawn from | |
| | `bios/` | 8 | 58.7 MB | `people.jsonl`, `bios_train.jsonl`, `bios_control.jsonl`, `facts.jsonl` (100,000 prompts), the value lists, the templates, the name check | |
| | `samples/` | 5 | 0.6 MB | the hand-check pack: Pile samples, biographies with every prompt, the value lists, and **`templates_rendered.md`** — all 120 sentence frames rendered twice | |
| | `reports/` | 5 | 0.03 MB | what each build stage did: extraction, dedup, split, pack | |
| | `raw/` | 12 | 398.0 MB | the archived source text a rebuild needs. **Optional** — `data.fetch` skips it unless you ask | |
| | `manifest.json`, `validation_report.{json,md}` | 3 | 0.05 MB | the file list with a sha256 per file, and the A1–A18 results | |
| |
| **59 hashed files, 1,151,617,170 bytes** (753,606,445 without `raw/`). |
|
|
| ## How to verify your copy |
|
|
| The code repository carries the manifest, so verification does not trust this |
| repo's copy of it: |
|
|
| ```bash |
| git clone https://github.com/RoieGilad7/memory-layers-analysis |
| cd memory-layers-analysis |
| git checkout data-v1 |
| pip install -r requirements.txt |
| |
| # download (skips raw/ by default; --with-raw includes it) and check every sha256 |
| python -m data.fetch --version v1 |
| |
| # or check a copy you already have, without downloading |
| python -m data.fetch --version v1 --verify-only |
| ``` |
|
|
| `data.fetch` hashes every file against `data/manifests/v1.json` and exits |
| non-zero on any mismatch. The bundle's identity is its **`data_sha256`**, taken |
| over the manifest's file list alone: |
| |
| ``` |
| train.bin sha256 7e3d50f2513d5a1b42c4fbf201ad4811e81e3175f41068277cf708f191bad8ac |
| bundle data_sha256 bb1bfd407af2c44e86d020df1c075f8764c5bf6979be2835684f6f0453086fd6 |
| ``` |
| |
| Use the `data-v1` tag, here and in the code repository, to get this exact |
| bundle. |
| |
| ## Checks |
| |
| `python -m data.validate --config configs/data/v1.yaml` runs 18 checks on the |
| real build, all of which pass for this bundle: byte-identical rebuild, every |
| file's hash, exact per-subject token counts, subject shares inside every 1M |
| window, format and index tiling, decode-back-to-source, train/held-out leakage |
| (ids, exact hashes, 13-gram overlap), the halves' disjointness, no held-out-only |
| subject in training, subject labels against the source, a bag-of-words subject |
| classifier, the biography facts and their uniformity, **no answer leaks in any |
| of the 100,000 prompts**, exposure counts and control isolation, the probe floor |
| for a random scorer and an untrained model, the loader's order and resume, a |
| per-subject unigram baseline, and a 2-hour learnability run. |
| |
| The learnability run is the one result worth quoting here: after 5,830 steps on |
| this data, a small PKM model recalls the `frequent`-tier facts **40.5%** of the |
| time as the top-1 answer against a **0.63%** control floor — 64× — and recall is |
| monotone across the exposure tiers. A *structurally new* wording of the same |
| question recovers the fact only ~2% of the time, which is the finding the study |
| is about. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{memlayers_data_v1, |
| title = {memlayers-data v1: a controlled slice of the Pile with synthetic biographies}, |
| author = {Roie Gilad}, |
| year = {2026}, |
| note = {COMS E6998 Algorithms in LLMs final project. data\_sha256 |
| bb1bfd407af2c44e86d020df1c075f8764c5bf6979be2835684f6f0453086fd6}, |
| url = {https://huggingface.co/datasets/roieg/memlayers-data} |
| } |
| ``` |
|
|
