| # 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. |
|
|
