Model's Observation
Collection
This is collection of the model's observation. • 7 items • Updated • 1
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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
modelDir: string
modelLoadSeconds: double
sampleCount: int64
avgElapsedSeconds: double
avgTokensPerSecond: double
avgNewTokens: double
results: list<item: struct<index: int64, source: string, conversationId: string, promptChars: int64, promptPr (... 169 chars omitted)
child 0, item: struct<index: int64, source: string, conversationId: string, promptChars: int64, promptPreview: stri (... 157 chars omitted)
child 0, index: int64
child 1, source: string
child 2, conversationId: string
child 3, promptChars: int64
child 4, promptPreview: string
child 5, inputTokens: int64
child 6, elapsedSeconds: double
child 7, tokensPerSecond: double
child 8, topNextTokens: list<item: struct<tokenId: int64, tokenText: string, logit: double>>
child 0, item: struct<tokenId: int64, tokenText: string, logit: double>
child 0, tokenId: int64
child 1, tokenText: string
child 2, logit: double
observed: struct<layerIds: list<item: int64>, layerCount: int64, expertDims: list<item: int64>, tokenValues: l (... 186 chars omitted)
child 0, layerIds: list<item: int64>
child 0, item: int64
child 1, layerCount: int64
child 2, expertDims: list<item: int64>
child 0, item: int64
child 3, tokenValues: list<item: int64>
child 0, item: int64
child 4, nonzeroExpertsMin: int64
child 5, nonzeroExpertsMax: int64
child 6, topKRatioValues: list<item: double>
child 0, item: double
child 7, nonzeroCoverageMin: double
child 8, nonzeroCoverageMax: double
child 9, expertTableRows: int64
workflow: string
expected: struct<moeLayers: int64, expertsPerLayer: int64, expertsPerToken: int64, summaryTotalTokens: int64, (... 23 chars omitted)
child 0, moeLayers: int64
child 1, expertsPerLayer: int64
child 2, expertsPerToken: int64
child 3, summaryTotalTokens: int64
child 4, expertTableRows: int64
passed: bool
issues: struct<frequencyMismatches: list<item: null>, degenerateLayers: list<item: null>>
child 0, frequencyMismatches: list<item: null>
child 0, item: null
child 1, degenerateLayers: list<item: null>
child 0, item: null
checks: struct<moeLayerCountMatches: bool, expertDimMatches: bool, singleTokenTotalAcrossLayers: bool, routi (... 98 chars omitted)
child 0, moeLayerCountMatches: bool
child 1, expertDimMatches: bool
child 2, singleTokenTotalAcrossLayers: bool
child 3, routingFrequencyMatchesTopK: bool
child 4, nonzeroExpertsNotDegenerate: bool
child 5, expertTableRowCountMatches: bool
to
{'workflow': Value('string'), 'passed': Value('bool'), 'checks': {'moeLayerCountMatches': Value('bool'), 'expertDimMatches': Value('bool'), 'singleTokenTotalAcrossLayers': Value('bool'), 'routingFrequencyMatchesTopK': Value('bool'), 'nonzeroExpertsNotDegenerate': Value('bool'), 'expertTableRowCountMatches': Value('bool')}, 'expected': {'moeLayers': Value('int64'), 'expertsPerLayer': Value('int64'), 'expertsPerToken': Value('int64'), 'summaryTotalTokens': Value('int64'), 'expertTableRows': Value('int64')}, 'observed': {'layerIds': List(Value('int64')), 'layerCount': Value('int64'), 'expertDims': List(Value('int64')), 'tokenValues': List(Value('int64')), 'nonzeroExpertsMin': Value('int64'), 'nonzeroExpertsMax': Value('int64'), 'topKRatioValues': List(Value('float64')), 'nonzeroCoverageMin': Value('float64'), 'nonzeroCoverageMax': Value('float64'), 'expertTableRows': Value('int64')}, 'issues': {'frequencyMismatches': List(Value('null')), 'degenerateLayers': List(Value('null'))}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1779, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 295, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
modelDir: string
modelLoadSeconds: double
sampleCount: int64
avgElapsedSeconds: double
avgTokensPerSecond: double
avgNewTokens: double
results: list<item: struct<index: int64, source: string, conversationId: string, promptChars: int64, promptPr (... 169 chars omitted)
child 0, item: struct<index: int64, source: string, conversationId: string, promptChars: int64, promptPreview: stri (... 157 chars omitted)
child 0, index: int64
child 1, source: string
child 2, conversationId: string
child 3, promptChars: int64
child 4, promptPreview: string
child 5, inputTokens: int64
child 6, elapsedSeconds: double
child 7, tokensPerSecond: double
child 8, topNextTokens: list<item: struct<tokenId: int64, tokenText: string, logit: double>>
child 0, item: struct<tokenId: int64, tokenText: string, logit: double>
child 0, tokenId: int64
child 1, tokenText: string
child 2, logit: double
observed: struct<layerIds: list<item: int64>, layerCount: int64, expertDims: list<item: int64>, tokenValues: l (... 186 chars omitted)
child 0, layerIds: list<item: int64>
child 0, item: int64
child 1, layerCount: int64
child 2, expertDims: list<item: int64>
child 0, item: int64
child 3, tokenValues: list<item: int64>
child 0, item: int64
child 4, nonzeroExpertsMin: int64
child 5, nonzeroExpertsMax: int64
child 6, topKRatioValues: list<item: double>
child 0, item: double
child 7, nonzeroCoverageMin: double
child 8, nonzeroCoverageMax: double
child 9, expertTableRows: int64
workflow: string
expected: struct<moeLayers: int64, expertsPerLayer: int64, expertsPerToken: int64, summaryTotalTokens: int64, (... 23 chars omitted)
child 0, moeLayers: int64
child 1, expertsPerLayer: int64
child 2, expertsPerToken: int64
child 3, summaryTotalTokens: int64
child 4, expertTableRows: int64
passed: bool
issues: struct<frequencyMismatches: list<item: null>, degenerateLayers: list<item: null>>
child 0, frequencyMismatches: list<item: null>
child 0, item: null
child 1, degenerateLayers: list<item: null>
child 0, item: null
checks: struct<moeLayerCountMatches: bool, expertDimMatches: bool, singleTokenTotalAcrossLayers: bool, routi (... 98 chars omitted)
child 0, moeLayerCountMatches: bool
child 1, expertDimMatches: bool
child 2, singleTokenTotalAcrossLayers: bool
child 3, routingFrequencyMatchesTopK: bool
child 4, nonzeroExpertsNotDegenerate: bool
child 5, expertTableRowCountMatches: bool
to
{'workflow': Value('string'), 'passed': Value('bool'), 'checks': {'moeLayerCountMatches': Value('bool'), 'expertDimMatches': Value('bool'), 'singleTokenTotalAcrossLayers': Value('bool'), 'routingFrequencyMatchesTopK': Value('bool'), 'nonzeroExpertsNotDegenerate': Value('bool'), 'expertTableRowCountMatches': Value('bool')}, 'expected': {'moeLayers': Value('int64'), 'expertsPerLayer': Value('int64'), 'expertsPerToken': Value('int64'), 'summaryTotalTokens': Value('int64'), 'expertTableRows': Value('int64')}, 'observed': {'layerIds': List(Value('int64')), 'layerCount': Value('int64'), 'expertDims': List(Value('int64')), 'tokenValues': List(Value('int64')), 'nonzeroExpertsMin': Value('int64'), 'nonzeroExpertsMax': Value('int64'), 'topKRatioValues': List(Value('float64')), 'nonzeroCoverageMin': Value('float64'), 'nonzeroCoverageMax': Value('float64'), 'expertTableRows': Value('int64')}, 'issues': {'frequencyMismatches': List(Value('null')), 'degenerateLayers': List(Value('null'))}}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1832, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
workflow string | passed bool | checks dict | expected dict | observed dict | issues dict |
|---|---|---|---|---|---|
nemotron_super_long50_16k_v3 | true | {
"moeLayerCountMatches": true,
"expertDimMatches": true,
"singleTokenTotalAcrossLayers": true,
"routingFrequencyMatchesTopK": true,
"nonzeroExpertsNotDegenerate": true,
"expertTableRowCountMatches": true
} | {
"moeLayers": 40,
"expertsPerLayer": 512,
"expertsPerToken": 22,
"summaryTotalTokens": 819200,
"expertTableRows": 20480
} | {
"layerIds": [
1,
3,
5,
8,
10,
12,
14,
17,
19,
21,
23,
26,
28,
30,
32,
34,
37,
39,
41,
43,
45,
48,
50,
52,
54,
56,
59,
61,
63,
65,
67,
70,
72,
74,
76,
79,
81,
83,
... | {
"frequencyMismatches": [],
"degenerateLayers": []
} |
nemotron_super_merged_long50_short15120_v2 | true | {
"moeLayerCountMatches": true,
"expertDimMatches": true,
"singleTokenTotalAcrossLayers": true,
"routingFrequencyMatchesTopK": true,
"nonzeroExpertsNotDegenerate": true,
"expertTableRowCountMatches": true
} | {
"moeLayers": 40,
"expertsPerLayer": 512,
"expertsPerToken": 22,
"summaryTotalTokens": 1140760,
"expertTableRows": 20480
} | {
"layerIds": [
1,
3,
5,
8,
10,
12,
14,
17,
19,
21,
23,
26,
28,
30,
32,
34,
37,
39,
41,
43,
45,
48,
50,
52,
54,
56,
59,
61,
63,
65,
67,
70,
72,
74,
76,
79,
81,
83,
... | {
"frequencyMismatches": [],
"degenerateLayers": []
} |
nemotron_super_short_mix_15120_t1024_b8192_v4 | true | {
"moeLayerCountMatches": true,
"expertDimMatches": true,
"singleTokenTotalAcrossLayers": true,
"routingFrequencyMatchesTopK": true,
"nonzeroExpertsNotDegenerate": true,
"expertTableRowCountMatches": true
} | {
"moeLayers": 40,
"expertsPerLayer": 512,
"expertsPerToken": 22,
"summaryTotalTokens": 321560,
"expertTableRows": 20480
} | {
"layerIds": [
1,
3,
5,
8,
10,
12,
14,
17,
19,
21,
23,
26,
28,
30,
32,
34,
37,
39,
41,
43,
45,
48,
50,
52,
54,
56,
59,
61,
63,
65,
67,
70,
72,
74,
76,
79,
81,
83,
... | {
"frequencyMismatches": [],
"degenerateLayers": []
} |
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REAP surfaces: GLM | MiniMax | Qwen | Gemma | Paper | Code | PR17 | Cerebras Collection
Nemotron Super REAP artifacts draft
This dataset repo is a draft research artifact release for REAP-based observation and compression work on nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16.
Provenance
- Upstream base model: nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
- This repo does not republish the original NVIDIA base weights.
- This repo contains observation outputs, rankings, heatmaps, residency planning artifacts, and compression metadata derived from that base model.
- The derived checkpoint weights live in separate draft model repos.
What this contains
- long-lane and short-lane validation/manifests/summaries
- merged observation outputs and validation
- rankings, analysis, heatmaps, budget sweep, and residency plans
- compression summaries and smoke-test artifacts for the 25 percent and 50 percent REAP-pruned variants
Method summary
- Model under study:
NVIDIA-Nemotron-3-Super-120B-A12B-BF16 - Architecture observed at runtime:
NemotronHForCausalLM - Hybrid depth pattern:
88total blocks =40Mamba +40MoE +8attention - Routed experts per MoE layer:
512 - Experts per token:
22 - Observation method: REAP layerwise MoE observer
- Long lane:
nemotron_super_long50_16k_v350trajectories- cap
16384tokens 819200total tokens
- Short lane:
nemotron_super_short_mix_15120_t1024_b8192_v4- mixed personal + bounded public prompts
- cap
1024tokens 321560total tokens
- Canonical merged lane:
nemotron_super_merged_long50_short15120_v21140760total tokens40observed MoE layers512experts per layer- exact routed accounting:
22.0experts per token
Draft status and limits
- This is a draft release.
- The artifacts are intended for systems research, residency planning, and follow-on offload experiments.
- No claim is made here that these artifacts alone establish end-user quality or production readiness.
- Serving benchmarks and AutoRound quantization publication are separate workstreams.
License and terms
Use of the upstream model and any derivative weights remains governed by the NVIDIA Open Model License included in LICENSE.txt. See the upstream base model card for additional terms and disclosures.
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