| --- |
| license: cc-by-4.0 |
| task_categories: |
| - text-generation |
| language: |
| - en |
| size_categories: |
| - 10B<n<100B |
| --- |
| |
| # temporal-moe-corpus |
|
|
| Tokenized training corpus for the Temporal-MoE experiments, 31.3 GiB. |
|
|
| This repository holds the **tokenized** corpus in Megatron indexed-dataset format, plus the 16k |
| tokenizer. It is what the training runs actually read. The raw web-crawl text is **not** hosted here, |
| because it is a byte-reproducible subset of a public dataset. The exact recipe and the checksums |
| needed to verify a reproduction are below. |
|
|
| ## Contents |
|
|
| ``` |
| dclm_tokenized/ 22 x part<NN>_text_document.{bin,idx} tokenized with EleutherAI/pythia-12b (50k vocab) |
| tok16k_full/ 11 x part<NN>_text_document.{bin,idx} tokenized with the 16k tokenizer below |
| tokenizer/ tokenizer.json, tokenizer_config.json 16k byte-level BPE, trained on this corpus |
| parquet_sha256.txt sha256 of each of the 88 upstream parquet shards |
| jsonl_sha256.txt sha256 of each of the 22 intermediate part<NN>.jsonl files |
| ``` |
|
|
| `.bin` files are `uint16` token ids. `.idx` files are the Megatron index. Load them with |
| `megatron.core.datasets.indexed_dataset`, or point `--data-path` at the `part<NN>_text_document` |
| prefix. |
|
|
| ## Reproducing the raw input, exactly |
|
|
| ### Step 1, upstream source |
|
|
| Everything derives from one pinned dataset revision: |
|
|
| | field | value | |
| |---|---| |
| | repository | `mlfoundations/dclm-baseline-1.0-parquet` (dataset) | |
| | revision | `817d6752765f6a41261085171dd546b104f60626` | |
| | path prefix | `filtered/OH_eli5_vs_rw_v2_bigram_200k_train/fasttext_openhermes_reddit_eli5_vs_rw_v2_bigram_200k_train/processed_data/global-shard_01_of_10/local-shard_0_of_10/` | |
| | shards | `shard_00000000_processed.parquet` through `shard_00000087_processed.parquet`, 88 total | |
|
|
| `parquet_sha256.txt` in this repository lists the sha256 of every one of those 88 shards as they were |
| downloaded. Verify against it before proceeding. |
|
|
| ### Step 2, parquet to JSONL |
|
|
| `experiments/data/download_parts.py` in the code repository, run with 4 shards per part, produces 22 |
| files `part00.jsonl` through `part21.jsonl`, each a `{"text": ...}` object per line with empty |
| documents dropped. |
|
|
| This step is deterministic. Shard indices are derived from the part index, `ThreadPoolExecutor.map` |
| yields results in input order rather than completion order, and `json.dumps` on a single-key dict is |
| stable. Re-running it against the pinned revision reproduces the same bytes. |
|
|
| `jsonl_sha256.txt` lists the sha256 of all 22 intermediate files, so a reproduction can be verified |
| bit for bit without this repository hosting them. |
|
|
| ### Step 3, JSONL to tokenized shards |
|
|
| `experiments/data/fast_tokenize.py` in the code repository, with `EOD=0` and `add_special_tokens=False`, |
| writing `uint16` via `IndexedDatasetBuilder`. Each part is tokenized independently, so there is no |
| cross-part ordering dependency. |
|
|
| | output | `TOKENIZER_MODEL` | |
| |---|---| |
| | `dclm_tokenized/` | `EleutherAI/pythia-12b`, public, 50k vocab | |
| | `tok16k_full/` | `tokenizer/` from this repository, 16k vocab | |
|
|
| **This step was verified empirically, not merely argued.** Re-tokenizing the first 2000 documents of |
| a part and comparing against the stored `.bin` gives a byte-identical result: |
|
|
| | check | documents | tokens | result | |
| |---|---|---|---| |
| | `tok16k_full/part00` from `tokenizer/` | 2000 | 2,602,881 | byte-identical | |
| | `dclm_tokenized/part00` from `pythia-12b` | 2000 | 2,404,117 | byte-identical | |
| | `tok16k_full/part05` from `tokenizer/` | 2000 | 2,558,589 | byte-identical | |
|
|
| ### The 16k tokenizer |
|
|
| `tokenizer/` is a 16k-vocab byte-level BPE trained by `experiments/data/train_tok16k.py` on a text |
| sample drawn from `part00.jsonl` and `part01.jsonl`, vocab size 16000, `min_frequency=2`, single |
| special token `<|endoftext|>` with id 0. |
|
|
| The trained tokenizer is shipped here directly, so reproducing it is not required in order to use or |
| re-derive the corpus. It is the artifact, not an intermediate. |
|
|
| ## Why the raw text is not hosted |
|
|
| The 22 `dclm_parts` JSONL files and the tokenizer training sample are unfiltered DCLM web crawl. A |
| scan of that text found third-party material that is not ours to redistribute, including private key |
| blocks, cloud access key ids, and roughly 104,000 lines containing email addresses. That content is |
| already public as part of DCLM, and the pinned revision plus the checksums above let anyone |
| reconstruct the exact bytes, so nothing about reproducibility is lost by not mirroring it here. |
|
|
| Treat any credential encountered in reconstructed DCLM text as compromised and unusable. |
|
|
| ## MANIFEST.csv and the `cited` column |
|
|
| A manifest covering every file in all four repositories lives in the code repository. It has seven |
| columns: `local_path`, `hf_repo`, `hf_path`, `bytes`, `sha256`, `run_name`, `cited`. |
|
|
| The `cited` column marks whether a run is referenced by a results table in `results/ablations/*.csv` |
| or by the paper: |
|
|
| - `cited`, the run backs a published number. There are 58 of these. |
| - `uncited`, the run is infrastructure validation, a smoke test, a throughput probe, or an aborted |
| run. It is kept for completeness and reproducibility, not because a table depends on it. There are |
| 13 of these. |
| - empty, the file is not scoped to a single run, for example a batch log or an evaluation output. |
|
|
| Every `sha256` in the manifest was computed on this disk before upload and each file was verified to |
| exist remotely with a matching byte size. |
|
|
| ## Links |
|
|
| - Code: <https://github.com/ncylich/temporal-moe> |
| - Paper: Temporal-MoE (short paper), see the `paper/` directory in the code repository |
| - Upstream platform this work forks: [FLAME-MoE](https://github.com/cmu-flame/FLAME-MoE), [arXiv:2505.20225](https://arxiv.org/abs/2505.20225) |
|
|
| ## Companion repositories |
|
|
| - [`ncylich/temporal-moe-ckpts`](https://huggingface.co/ncylich/temporal-moe-ckpts), Megatron training checkpoints |
| - [`ncylich/temporal-moe-router-adapt`](https://huggingface.co/ncylich/temporal-moe-router-adapt), router adaptation safetensors |
| - [`ncylich/temporal-moe-extras`](https://huggingface.co/ncylich/temporal-moe-extras), captures, merged model, result tables, figures |
| - [`ncylich/temporal-moe-corpus`](https://huggingface.co/datasets/ncylich/temporal-moe-corpus), tokenized training corpus |
|
|
| ## Provenance |
|
|
| Trained with a personal fork of FLAME-MoE. Not affiliated with or endorsed by the FLAME-MoE authors. |
|
|
