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
benchmark: string
model: string
adapter: string
quantization: string
epochs: int64
seed: int64
lora_rank_alloc: bool
training_traces: string
eval_set: struct<domains: list<item: string>, items: int64, note: string>
child 0, domains: list<item: string>
child 0, item: string
child 1, items: int64
child 2, note: string
content_channel: struct<adapter_3ep_rankalloc: struct<philosophy: string, psychology: string, history: string, religi (... 180 chars omitted)
child 0, adapter_3ep_rankalloc: struct<philosophy: string, psychology: string, history: string, religion: string, total: string, tot (... 15 chars omitted)
child 0, philosophy: string
child 1, psychology: string
child 2, history: string
child 3, religion: string
child 4, total: string
child 5, total_pct: double
child 1, fp16_base: struct<total: string, total_pct: double, note: string>
child 0, total: string
child 1, total_pct: double
child 2, note: string
child 2, uplift_vs_base_pct: double
child 3, uniform_3ep_for_reference_pct: double
combined_channel: struct<adapter_3ep_rankalloc_total: string, adapter_3ep_rankalloc_pct: double, note: string>
child 0, adapter_3ep_rankalloc_total: string
child 1, adapter_3ep_rankalloc_pct: double
child 2, note: string
mechanisms_exercised: struct<moe_adapt_lora_rank_alloc: struct<ran: bool, uniform_r: int64, rank_pattern: struct<q_proj: i (... 339 chars omitted)
child 0, moe_adapt_lora_rank_alloc: struct<ran:
...
ld 0, philosophy: struct<format_pct: double, content: string, content_pct: double, combined: string>
child 0, format_pct: double
child 1, content: string
child 2, content_pct: double
child 3, combined: string
child 1, psychology: struct<format_pct: double, content: string, content_pct: double, combined: string>
child 0, format_pct: double
child 1, content: string
child 2, content_pct: double
child 3, combined: string
child 2, history: struct<format_pct: double, content: string, content_pct: double, combined: string>
child 0, format_pct: double
child 1, content: string
child 2, content_pct: double
child 3, combined: string
child 3, religion: struct<format_pct: double, content: string, content_pct: double, combined: string>
child 0, format_pct: double
child 1, content: string
child 2, content_pct: double
child 3, combined: string
child 3, channels: struct<format: string, format_pct: double, content: string, content_pct: double, combined: string, c (... 20 chars omitted)
child 0, format: string
child 1, format_pct: double
child 2, content: string
child 3, content_pct: double
child 4, combined: string
child 5, combined_pct: double
child 4, gate_failures: int64
to
{'benchmark': Value('string'), 'model': Value('string'), 'adapter': Value('string'), 'quantization': Value('string'), 'epochs': Value('int64'), 'seed': Value('int64'), 'eval_set': {'domains': List(Value('string')), 'items': Value('int64'), 'note': Value('string')}, 'ladder': List({'rung': Value('string'), 'with_gate': Value('bool'), 'domains': {'philosophy': {'format_pct': Value('float64'), 'content': Value('string'), 'content_pct': Value('float64'), 'combined': Value('string')}, 'psychology': {'format_pct': Value('float64'), 'content': Value('string'), 'content_pct': Value('float64'), 'combined': Value('string')}, 'history': {'format_pct': Value('float64'), 'content': Value('string'), 'content_pct': Value('float64'), 'combined': Value('string')}, 'religion': {'format_pct': Value('float64'), 'content': Value('string'), 'content_pct': Value('float64'), 'combined': Value('string')}}, 'channels': {'format': Value('string'), 'format_pct': Value('float64'), 'content': Value('string'), 'content_pct': Value('float64'), 'combined': Value('string'), 'combined_pct': Value('float64')}, 'gate_failures': Value('int64')}), 'result': {'content_base_pct': Value('float64'), 'content_adapter_pct': Value('float64'), 'content_delta_pct': Value('float64'), 'combined_base_pct': Value('float64'), 'combined_adapter_pct': Value('float64'), 'combined_delta_pct': Value('float64'), 'interpretation': Value('string')}, 'claim': Value('string'), 'judge': Value('string'), 'candidateOnly': Value('bool'), 'level3Evidence': Value('bool'), 'canClaimAGI': Value('bool'), 'honest_scope': Value('string'), 'provenance': {'workflow': Value('string'), 'run_url': Value('string'), 'run_id': Value('int64'), 'run_number': Value('int64'), 'head_sha': Value('string'), 'gpu_pool': List(Value('string')), 'artifact_id': Value('int64'), 'artifact_sha256': Value('string'), 'source': Value('string'), 'dispatched_date': Value('timestamp[s]')}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
benchmark: string
model: string
adapter: string
quantization: string
epochs: int64
seed: int64
lora_rank_alloc: bool
training_traces: string
eval_set: struct<domains: list<item: string>, items: int64, note: string>
child 0, domains: list<item: string>
child 0, item: string
child 1, items: int64
child 2, note: string
content_channel: struct<adapter_3ep_rankalloc: struct<philosophy: string, psychology: string, history: string, religi (... 180 chars omitted)
child 0, adapter_3ep_rankalloc: struct<philosophy: string, psychology: string, history: string, religion: string, total: string, tot (... 15 chars omitted)
child 0, philosophy: string
child 1, psychology: string
child 2, history: string
child 3, religion: string
child 4, total: string
child 5, total_pct: double
child 1, fp16_base: struct<total: string, total_pct: double, note: string>
child 0, total: string
child 1, total_pct: double
child 2, note: string
child 2, uplift_vs_base_pct: double
child 3, uniform_3ep_for_reference_pct: double
combined_channel: struct<adapter_3ep_rankalloc_total: string, adapter_3ep_rankalloc_pct: double, note: string>
child 0, adapter_3ep_rankalloc_total: string
child 1, adapter_3ep_rankalloc_pct: double
child 2, note: string
mechanisms_exercised: struct<moe_adapt_lora_rank_alloc: struct<ran: bool, uniform_r: int64, rank_pattern: struct<q_proj: i (... 339 chars omitted)
child 0, moe_adapt_lora_rank_alloc: struct<ran:
...
ld 0, philosophy: struct<format_pct: double, content: string, content_pct: double, combined: string>
child 0, format_pct: double
child 1, content: string
child 2, content_pct: double
child 3, combined: string
child 1, psychology: struct<format_pct: double, content: string, content_pct: double, combined: string>
child 0, format_pct: double
child 1, content: string
child 2, content_pct: double
child 3, combined: string
child 2, history: struct<format_pct: double, content: string, content_pct: double, combined: string>
child 0, format_pct: double
child 1, content: string
child 2, content_pct: double
child 3, combined: string
child 3, religion: struct<format_pct: double, content: string, content_pct: double, combined: string>
child 0, format_pct: double
child 1, content: string
child 2, content_pct: double
child 3, combined: string
child 3, channels: struct<format: string, format_pct: double, content: string, content_pct: double, combined: string, c (... 20 chars omitted)
child 0, format: string
child 1, format_pct: double
child 2, content: string
child 3, content_pct: double
child 4, combined: string
child 5, combined_pct: double
child 4, gate_failures: int64
to
{'benchmark': Value('string'), 'model': Value('string'), 'adapter': Value('string'), 'quantization': Value('string'), 'epochs': Value('int64'), 'seed': Value('int64'), 'eval_set': {'domains': List(Value('string')), 'items': Value('int64'), 'note': Value('string')}, 'ladder': List({'rung': Value('string'), 'with_gate': Value('bool'), 'domains': {'philosophy': {'format_pct': Value('float64'), 'content': Value('string'), 'content_pct': Value('float64'), 'combined': Value('string')}, 'psychology': {'format_pct': Value('float64'), 'content': Value('string'), 'content_pct': Value('float64'), 'combined': Value('string')}, 'history': {'format_pct': Value('float64'), 'content': Value('string'), 'content_pct': Value('float64'), 'combined': Value('string')}, 'religion': {'format_pct': Value('float64'), 'content': Value('string'), 'content_pct': Value('float64'), 'combined': Value('string')}}, 'channels': {'format': Value('string'), 'format_pct': Value('float64'), 'content': Value('string'), 'content_pct': Value('float64'), 'combined': Value('string'), 'combined_pct': Value('float64')}, 'gate_failures': Value('int64')}), 'result': {'content_base_pct': Value('float64'), 'content_adapter_pct': Value('float64'), 'content_delta_pct': Value('float64'), 'combined_base_pct': Value('float64'), 'combined_adapter_pct': Value('float64'), 'combined_delta_pct': Value('float64'), 'interpretation': Value('string')}, 'claim': Value('string'), 'judge': Value('string'), 'candidateOnly': Value('bool'), 'level3Evidence': Value('bool'), 'canClaimAGI': Value('bool'), 'honest_scope': Value('string'), 'provenance': {'workflow': Value('string'), 'run_url': Value('string'), 'run_id': Value('int64'), 'run_number': Value('int64'), 'head_sha': Value('string'), 'gpu_pool': List(Value('string')), 'artifact_id': Value('int64'), 'artifact_sha256': Value('string'), 'source': Value('string'), 'dispatched_date': Value('timestamp[s]')}}
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.
Sophia Training Results
All training history results from the sophia-agi repository.
Contents
824 result files across all Sophia four-theme training experiments:
- Prosoche — interactive decision-making, behavioral batteries, focus experiments
- RunPod training — LoRA training eval ladders (Qwen2.5-3B, multiple seeds/epochs)
- Continual learning — sequential curriculum, QA judged results
- World model — on-device, adaptive, real-corpus experiments
- Coherence reframe — NLI gates, grounding experiments
- Candidate integration — multi-seed A/B results
- Format confound — constrained vs open-ended across domains
- Scaling law — 7B → 32B → 72B results
Structure
results/
├── prosoche/ # Interactive decision-making + behavioral
├── runpod-train/ # LoRA training eval ladders
├── continual-qa/ # Continual learning
├── worldmodel-*/ # World model experiments
├── coherence-reframes/ # NLI + grounding
├── candidate-integration*/ # Multi-seed A/B
└── ... # 824 files total
Governance
canClaimAGI: falsecandidateOnly: true- All results are baselines and experimental measurements — no capability claims
- Sealed evaluation packs are barred from training
Visualization
View the interactive dashboard at tomyimkc/sophia-training-tracker.
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