| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| tags: |
| - sentencepiece |
| - tokenizer |
| - fineweb |
| - parameter-golf |
| - bpe |
| - tokenized-corpus |
| size_categories: |
| - 10B<n<100B |
| --- |
| |
| # Parameter Golf SP16384 — Tokenizer + Tokenized FineWeb-10B Shards |
|
|
| SentencePiece BPE tokenizer (`vocab_size=16384`, `byte_fallback=True`) + the full FineWeb-10B corpus pre-tokenized with it. Companion artifact to the chaoscontrol submission pipeline; published to make submission-day setup frictionless — no corpus download, no re-tokenization. |
|
|
| ## Files |
|
|
| ### Tokenizer (root) |
| - `fineweb_16384_bpe.model` — SentencePiece model (455 KB). |
| - `fineweb_16384_bpe.vocab` — Human-readable vocab sidecar (185 KB). |
|
|
| ### Tokenized shards (`shards/`) |
| - `shards/fineweb_val_000000.bin` — val shard, 42,266,034 tokens (~84 MB, uint16). |
| - `shards/fineweb_train_{000000..000132}.bin` — 133 train shards, 13,262,831,920 tokens (~25 GB total, uint16). |
|
|
| Shards are flat `uint16` little-endian token streams, no header, concatenated docs with no separators. Each shard ends on a doc boundary. First 50k docs of `docs_selected.jsonl` → val, rest → train (file order, per the upstream manifest contract). |
|
|
| ## Submission-day usage |
|
|
| Pull the whole thing in ~5 min on a typical pod: |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| local = snapshot_download( |
| repo_id="Natooka/parameter-golf-sp-tokenizers", |
| repo_type="dataset", |
| local_dir="baselines/parameter_golf", |
| # Optional — get just shards, or just the model: |
| # allow_patterns=["shards/*.bin", "fineweb_*.model"], |
| ) |
| ``` |
|
|
| After the download you have `baselines/parameter_golf/shards/*.bin` + `baselines/parameter_golf/fineweb_16384_bpe.model`. Point your training runner at those paths. |
|
|
| ## Training configuration (tokenizer) |
|
|
| | Setting | Value | |
| |---|---| |
| | `vocab_size` | 16384 | |
| | `model_type` | BPE | |
| | `byte_fallback` | True | |
| | `character_coverage` | 1.0 | |
| | `shuffle_input_sentence` | False (locks determinism) | |
| | `sp_seed` | 1337 | |
| | Training docs | 5,000,000 (first 5M post-val-split, per manifest convention) | |
| | Source corpus | [`willdepueoai/parameter-golf`](https://huggingface.co/datasets/willdepueoai/parameter-golf) → `docs_selected.jsonl` | |
| | Source revision | `9bb295ddab0e05d785b879661af7260fed5140fc` | |
| | SentencePiece | 0.2.1 | |
|
|
| Training was single-threaded in the BPE merge phase (SP 0.2.1 limitation — `num_threads` helps normalization only). Wall-clock on 28 vCPU Xeon 8480+ host: ~25 min SP training + ~30 min full-corpus tokenization. |
|
|
| ## Reproducing from scratch |
|
|
| ```bash |
| python scripts/build_sp_shards.py \ |
| --docs-path path/to/docs_selected.jsonl \ |
| --vocab-size 16384 \ |
| --sp-seed 1337 \ |
| --sp-train-docs 5000000 \ |
| --num-workers 28 |
| ``` |
|
|
| Byte-identical outputs are guaranteed within a matching `(vocab_size, sp_seed, num_workers)` triple — SP's multi-threaded merge counting can drift on tie-breaks across thread counts. Use the same `--num-workers` for cross-machine determinism, or pin to `--num-workers 1` for strict identity. |
|
|
| ## License |
|
|
| CC-BY 4.0 for our artifacts (tokenizer + pre-tokenized shards). Upstream `docs_selected.jsonl` subject to the Parameter Golf competition's terms (from `willdepueoai/parameter-golf`). |
|
|
