Dataset Viewer
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
tag: string
boot: string
pid: int64
final: bool
elapsed_s: double
select_experts_calls: int64
moe_layers: int64
n_experts: int64
total_expert_activations: int64
tokens_per_layer: list<item: int64>
child 0, item: int64
per_layer: list<item: struct<moe_layer: int64, model_layer: int64, total: int64, used_experts: int64, max_over_ (... 52 chars omitted)
child 0, item: struct<moe_layer: int64, model_layer: int64, total: int64, used_experts: int64, max_over_mean: doubl (... 40 chars omitted)
child 0, moe_layer: int64
child 1, model_layer: int64
child 2, total: int64
child 3, used_experts: int64
child 4, max_over_mean: double
child 5, top16: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
model_layer_offset: int64
experts_per_layer: int64
generations: int64
to
{'moe_layers': Value('int64'), 'experts_per_layer': Value('int64'), 'model_layer_offset': Value('int64'), 'generations': Value('int64'), 'total_expert_activations': Value('int64'), 'per_layer': List({'model_layer': Value('int64'), 'total': Value('int64'), 'used_experts': Value('int64'), 'max_over_mean': Value('float64'), 'topk_coverage': {'8': Value('float64'), '16': Value('float64'), '32': Value('float64'), '64': Value('float64'), '128': Value('float64')}, 'counts': List(Value('int64'))})}
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
tag: string
boot: string
pid: int64
final: bool
elapsed_s: double
select_experts_calls: int64
moe_layers: int64
n_experts: int64
total_expert_activations: int64
tokens_per_layer: list<item: int64>
child 0, item: int64
per_layer: list<item: struct<moe_layer: int64, model_layer: int64, total: int64, used_experts: int64, max_over_ (... 52 chars omitted)
child 0, item: struct<moe_layer: int64, model_layer: int64, total: int64, used_experts: int64, max_over_mean: doubl (... 40 chars omitted)
child 0, moe_layer: int64
child 1, model_layer: int64
child 2, total: int64
child 3, used_experts: int64
child 4, max_over_mean: double
child 5, top16: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
model_layer_offset: int64
experts_per_layer: int64
generations: int64
to
{'moe_layers': Value('int64'), 'experts_per_layer': Value('int64'), 'model_layer_offset': Value('int64'), 'generations': Value('int64'), 'total_expert_activations': Value('int64'), 'per_layer': List({'model_layer': Value('int64'), 'total': Value('int64'), 'used_experts': Value('int64'), 'max_over_mean': Value('float64'), 'topk_coverage': {'8': Value('float64'), '16': Value('float64'), '32': Value('float64'), '64': Value('float64'), '128': Value('float64')}, 'counts': List(Value('int64'))})}
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.
GLM-5.3-Flash — per-layer MoE expert usage on ARC-AGI-3
Per-layer, per-expert routing counts collected while GLM-5.3-Flash (320B total / 18B active) played the 25 public official ARC-AGI-3 games through the TUFA duck-harness (TAAF).
Collected by instrumenting vLLM's FusedMoERouter.select_experts and accumulating a
[MoE layer x expert] count matrix on-GPU.
Model / run configuration
| model | zai-org/GLM-5.3-Flash (FP8) |
| serving | vLLM (ROCm), tensor-parallel 4 |
| hardware | 4x AMD Instinct MI355X (gfx950) |
| MoE layers | 43 (model layers 3–45) |
| experts / layer | 288 |
| experts / token | 8 (+1 shared) |
| total expert activations | 28,423,475,384 |
| server generations merged | 14 |
Files
expert_counts_merged.npy—int64[43, 288]activation counts. Rowiis model layeri + 3.expert_counts_merged.json— same data plus per-layer summary stats and top-K coverage fractions (useful for sizing an expert cache).raw/— the per-rank, per-generation dumps this was aggregated from.
Aggregation
Dumps sharing a boot id are tensor-parallel rank replicas (identical routing) so
only one is taken; dumps from different boot ids are separate server generations and
are summed.
Top-K routing coverage (fraction of routing mass in the K hottest experts)
| layer | K=8 | K=16 | K=32 | K=64 | K=128 |
|---|---|---|---|---|---|
| 3 | 9.0% | 15.6% | 26.0% | 41.2% | 63.5% |
| 9 | 17.8% | 24.6% | 35.3% | 49.6% | 70.6% |
| 15 | 17.7% | 26.9% | 39.3% | 55.3% | 75.9% |
| 21 | 20.6% | 31.4% | 44.6% | 60.2% | 79.3% |
| 27 | 13.3% | 22.4% | 35.6% | 53.5% | 75.7% |
| 33 | 21.2% | 31.2% | 42.1% | 56.6% | 76.5% |
| 39 | 17.2% | 26.8% | 39.6% | 54.6% | 75.0% |
| 45 | 19.6% | 28.4% | 40.6% | 58.1% | 79.4% |
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