--- license: cc-by-4.0 task_categories: - text-generation language: - en size_categories: - 10B_text_document.{bin,idx} tokenized with EleutherAI/pythia-12b (50k vocab) tok16k_full/ 11 x part_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.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_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: - 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.