tturing/n8t-train

n8t-data: training and evaluation data for the BQA / FlashMatch campaign

Packed by scripts/campaign/n8t_data.py pack (see the docstring there for the layout). To use it on a cluster:

export BQA_DATA_ROOT=/path/to/workspace/n8t-data
python scripts/campaign/n8t_data.py unpack --root $BQA_DATA_ROOT --hash    # joins the train parts, untars, verifies
pytest tests/test_data.py                                                   # the same checks as unit tests

Contents (all Llama-2 32k tokenizer ids, uint16, BOS=1 / EOS=2, documents packed back to back):

• nemotron_cc_v2d1_hq_dqa/nemotron_rs_train.bin: Nemotron-CC-v2.1 HQ+DQA, random document split, 39.66 B tokens (79.3 GB).
• nemotron_cc_v2d1_hq_dqa/nemotron_rs_train.doc_lengths.npy: int64 per-document lengths, sums to the token count.
• nemotron_cc_v2d1_hq_dqa/nemotron_rs_val.bin (+ doc_lengths): the 2 % document holdout, shuffled, 807 M tokens. The val-CE protocol scores its first 2000 non-overlapping 8192-token windows (16,384,000 tokens).
• tokenizer/llama2_32k/: the tokenizer to pass as --tokenizer_dir (baked into every HF export).
• ruler-500-llama2/ (from the tar): RULER regenerated with this tokenizer, 500 examples per task and length.
• hf_cache/ (from the tar): set HF_HOME to it for offline evals (lambada_openai, hellaswag, piqa, ai2_arc, sciq, openbookqa, winogrande, super_glue (copa), cais/mmlu, hazyresearch based-{swde-v2,fda,squad}) and the tokenizer hub entry.

MANIFEST.json carries bytes + sha256 of every packed file and of the joined train stream.