README: document tokenized shards (1 val + 133 train = 13.3B tokens) alongside .model

README.md

@@ -8,20 +8,46 @@ tags:
 - fineweb
 - parameter-golf
 - bpe
+- tokenized-corpus
 size_categories:
-- n<1K
+- 10B<n<100B
 ---
 
-# Parameter Golf SP16384 Tokenizer
+# Parameter Golf SP16384 — Tokenizer + Tokenized FineWeb-10B Shards
 
-SentencePiece BPE tokenizer (`vocab_size=16384`, `byte_fallback=True`) trained on the Parameter Golf 10B FineWeb corpus. Companion artifact to the chaoscontrol submission pipeline; produced to avoid re-running ~30–60 min of SP training on submission day.
+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).
 
-## Training configuration
+### 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 |
 |---|---|
@@ -36,7 +62,7 @@ SentencePiece BPE tokenizer (`vocab_size=16384`, `byte_fallback=True`) trained o
 | 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.
+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
 
@@ -51,21 +77,6 @@ python scripts/build_sp_shards.py \
 
 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.
 
-## Use at tokenization time
-
-```python
-from huggingface_hub import hf_hub_download
-
-model_path = hf_hub_download(
-    repo_id="Natooka/parameter-golf-sp-tokenizers",
-    filename="fineweb_16384_bpe.model",
-    repo_type="dataset",
-    local_dir="baselines/parameter_golf/tokenizers",
-)
-```
-
-Then point `scripts/build_sp_shards.py --skip-train` at it to re-tokenize the corpus in ~5 min on 28 workers.
-
 ## License
 
-CC-BY 4.0. Attribution appreciated for academic use. See upstream Parameter Golf competition for `docs_selected.jsonl` terms.
+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`).