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.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
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MANIFEST.json

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README.md

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+# 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.

hf_cache.tar

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tokenizer/llama2_32k/special_tokens_map.json

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tokenizer/llama2_32k/tokenizer_config.json

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