| --- |
| pretty_name: declref-declref_symbols-1B |
| language: |
| - en |
| tags: |
| - pretokenized |
| - language-modeling |
| size_categories: |
| - 1B<n<10B |
| --- |
| |
| # declref-declref_symbols-1B |
| |
| Procedurally generated **decl-ref** documents — a scoped declare/reference formal language whose trained-model entropy dynamics match language models on code and natural text — as flat `uint16` token-id `.bin` files. Each document is a random program of `{` `}` scopes, `DECL name value` statements (free, high-entropy content) and `REF name value` statements whose value tokens repeat an earlier declaration — predictable only by attending back to it. Names and values are 1–3 subtoken symbols; references are recency-weighted; keywords arrive in Markov runs. Token ids: 0=OPEN 1=CLOSE 2=DECL 3=REF, then name-parts and value-parts in disjoint blocks; vocab = 1,028. |
| |
| **Grammar parameters** |
| |
| | param | value | |
| | --- | --- | |
| | seq_length (document) | 2048 | |
| | shards (canonical layout) | 256 | |
|
|
| | file | split | tokens | |
| | --- | --- | --- | |
| | `train.bin` | train | 1,000,341,504 | |
| | `val.bin` | val | 9,961,472 | |
|
|
| The bin is a concatenation of fixed-length **2048-token documents** (`num_tokens` is a whole multiple of it): read via `tokens.reshape(-1, 2048)` to train one document per row. Document alignment matters — a window starting mid-document orphans its references. `train` (seed 0) and `val` (a disjoint seed) are independent streams of the same grammar; token count = `filesize / 2`; the metas carry the full config, including the shard layout that fixes the bin's exact content. |
|
|
| **Load a bin** with the standard Hugging Face downloader: |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| import numpy as np |
| |
| path = hf_hub_download(repo_id="alexkstern/declref-declref_symbols-1B", filename="train.bin", repo_type="dataset") |
| tokens = np.memmap(path, dtype="uint16", mode="r") |
| ``` |
|
|
