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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
nll: null
note: string
heldout_tokens: int64
domains: list<item: string>
child 0, item: string
total_tokens: int64
shares: struct<code: double, agentic: double, multilingual_chat: double, math: double, general_chat: double, (... 54 chars omitted)
child 0, code: double
child 1, agentic: double
child 2, multilingual_chat: double
child 3, math: double
child 4, general_chat: double
child 5, roleplay: double
child 6, russian: double
child 7, long_docs: double
to
{'domains': List(Value('string')), 'shares': {'code': Value('float64'), 'agentic': Value('float64'), 'multilingual_chat': Value('float64'), 'math': Value('float64'), 'general_chat': Value('float64'), 'roleplay': Value('float64'), 'russian': Value('float64'), 'long_docs': Value('float64')}, 'total_tokens': Value('int64'), 'heldout_tokens': Value('int64'), 'note': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
nll: null
note: string
heldout_tokens: int64
domains: list<item: string>
child 0, item: string
total_tokens: int64
shares: struct<code: double, agentic: double, multilingual_chat: double, math: double, general_chat: double, (... 54 chars omitted)
child 0, code: double
child 1, agentic: double
child 2, multilingual_chat: double
child 3, math: double
child 4, general_chat: double
child 5, roleplay: double
child 6, russian: double
child 7, long_docs: double
to
{'domains': List(Value('string')), 'shares': {'code': Value('float64'), 'agentic': Value('float64'), 'multilingual_chat': Value('float64'), 'math': Value('float64'), 'general_chat': Value('float64'), 'roleplay': Value('float64'), 'russian': Value('float64'), 'long_docs': Value('float64')}, 'total_tokens': Value('int64'), 'heldout_tokens': Value('int64'), 'note': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
DeepSeek-V4-Flash-0731 — expert calibration statistics (REAM line)
Layerwise routed-expert statistics of
deepseek-ai/DeepSeek-V4-Flash-0731
(43 MoE layers × 256 experts), collected by running the full model over a
~4.9M-token multi-domain calibration mix (multi-turn dialogs, thinking and
direct modes, rendered with the model's own chat encoder). These are the
statistics behind the REAM144/96 release line — published so that expert
selection, pruning, merging and routing research can start WITHOUT the
expensive H100 collection pass.
Files
layer_XX.npz(one per MoE layer):saliency [8, 256]— per-domain REAP-style saliencyS_i = f_i · E[gate_i · ‖expert_i(x)‖₂ | i ∈ Top-6]; domain order indomains.json;freq [256]— Top-6 selection counts over the mix;coact [256, 256]— joint Top-6 co-activation counts;load [256]— historical load share (sums to 1);gsum [256],token_count— auxiliary.
imatrix_raw.npz— squared-input accumulators per expert (gate [43, 256, H],down [43, 256, I],calls [43]) — the raw material for llama.cpp-style importance matrices of ANY expert subset or merge (weighted sums of member rows; seeemit_imatrix_merged.pyin the release pipeline).domains.json— domain names (canonical order used by thesaliencyaxis) and mix shares.nll_heldout.json— the source model's NLL on the held-out slice of the same mix, collected during the pass (a reference point for compressed variants).
What these enable
- Reproducing/improving the published selections (each release carries its
SELECTION.json); - Expert pruning/merging experiments at zero collection cost;
- Routing analyses: language/domain specialization by depth, co-activation cluster structure, load distributions.
Collection notes
Single pass, batch 6 × 4096 tokens, deterministic packer; the calibration texts themselves are NOT included and are not recoverable from these aggregates. Related models: the REAM144/96 line and its merge variants (see the collection on this profile).
MIT, following the source model.
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