File size: 1,574 Bytes
9e00b3a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # 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.
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