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isHeSatoshi
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anthropochrony-logs

Languages:
English
Tags:
coding-agents
time-estimation
self-estimation
tiny-aya
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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
cuda: bool
gpu: string
torch: string
job_id: null
base_model_name_or_path: string
monteclora_config: null
inference_mode: bool
bias: string
megatron_config: null
alpha_pattern: struct<>
layer_replication: null
target_modules: list<item: string>
  child 0, item: string
revision: string
lora_bias: bool
ensure_weight_tying: bool
modules_to_save: null
megatron_core: string
r: int64
use_bdlora: null
eva_config: null
corda_config: null
auto_mapping: null
qalora_group_size: int64
exclude_modules: null
layers_to_transform: null
lora_alpha: int64
layers_pattern: null
use_dora: bool
trainable_token_indices: null
use_qalora: bool
loftq_config: struct<>
peft_type: string
velora_config: null
alora_invocation_tokens: null
lora_ga_config: null
arrow_config: null
lora_dropout: double
use_rslora: bool
fan_in_fan_out: bool
task_type: string
kasa_config: null
rank_pattern: struct<>
target_parameters: null
init_lora_weights: bool
peft_version: string
to
{'alora_invocation_tokens': Value('null'), 'alpha_pattern': {}, 'arrow_config': Value('null'), 'auto_mapping': Value('null'), 'base_model_name_or_path': Value('string'), 'bias': Value('string'), 'corda_config': Value('null'), 'ensure_weight_tying': Value('bool'), 'eva_config': Value('null'), 'exclude_modules': Value('null'), 'fan_in_fan_out': Value('bool'), 'inference_mode': Value('bool'), 'init_lora_weights': Value('bool'), 'kasa_config': Value('null'), 'layer_replication': Value('null'), 'layers_pattern': Value('null'), 'layers_to_transform': Value('null'), 'loftq_config': {}, 'lora_alpha': Value('int64'), 'lora_bias': Value('bool'), 'lora_dropout': Value('float64'), 'lora_ga_config': Value('null'), 'megatron_config': Value('null'), 'megatron_core': Value('string'), 'modules_to_save': Value('null'), 'monteclora_config': Value('null'), 'peft_type': Value('string'), 'peft_version': Value('string'), 'qalora_group_size': Value('int64'), 'r': Value('int64'), 'rank_pattern': {}, 'revision': Value('string'), 'target_modules': List(Value('string')), 'target_parameters': Value('null'), 'task_type': Value('string'), 'trainable_token_indices': Value('null'), 'use_bdlora': Value('null'), 'use_dora': Value('bool'), 'use_qalora': Value('bool'), 'use_rslora': Value('bool'), 'velora_config': Value('null')}
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 544, 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 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              cuda: bool
              gpu: string
              torch: string
              job_id: null
              base_model_name_or_path: string
              monteclora_config: null
              inference_mode: bool
              bias: string
              megatron_config: null
              alpha_pattern: struct<>
              layer_replication: null
              target_modules: list<item: string>
                child 0, item: string
              revision: string
              lora_bias: bool
              ensure_weight_tying: bool
              modules_to_save: null
              megatron_core: string
              r: int64
              use_bdlora: null
              eva_config: null
              corda_config: null
              auto_mapping: null
              qalora_group_size: int64
              exclude_modules: null
              layers_to_transform: null
              lora_alpha: int64
              layers_pattern: null
              use_dora: bool
              trainable_token_indices: null
              use_qalora: bool
              loftq_config: struct<>
              peft_type: string
              velora_config: null
              alora_invocation_tokens: null
              lora_ga_config: null
              arrow_config: null
              lora_dropout: double
              use_rslora: bool
              fan_in_fan_out: bool
              task_type: string
              kasa_config: null
              rank_pattern: struct<>
              target_parameters: null
              init_lora_weights: bool
              peft_version: string
              to
              {'alora_invocation_tokens': Value('null'), 'alpha_pattern': {}, 'arrow_config': Value('null'), 'auto_mapping': Value('null'), 'base_model_name_or_path': Value('string'), 'bias': Value('string'), 'corda_config': Value('null'), 'ensure_weight_tying': Value('bool'), 'eva_config': Value('null'), 'exclude_modules': Value('null'), 'fan_in_fan_out': Value('bool'), 'inference_mode': Value('bool'), 'init_lora_weights': Value('bool'), 'kasa_config': Value('null'), 'layer_replication': Value('null'), 'layers_pattern': Value('null'), 'layers_to_transform': Value('null'), 'loftq_config': {}, 'lora_alpha': Value('int64'), 'lora_bias': Value('bool'), 'lora_dropout': Value('float64'), 'lora_ga_config': Value('null'), 'megatron_config': Value('null'), 'megatron_core': Value('string'), 'modules_to_save': Value('null'), 'monteclora_config': Value('null'), 'peft_type': Value('string'), 'peft_version': Value('string'), 'qalora_group_size': Value('int64'), 'r': Value('int64'), 'rank_pattern': {}, 'revision': Value('string'), 'target_modules': List(Value('string')), 'target_parameters': Value('null'), 'task_type': Value('string'), 'trainable_token_indices': Value('null'), 'use_bdlora': Value('null'), 'use_dora': Value('bool'), 'use_qalora': Value('bool'), 'use_rslora': Value('bool'), 'velora_config': Value('null')}
              because column names don't match

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anthropochrony-logs: Logs and provenance

Part of Anthropochrony: Measuring and Correcting Human-Time Bias in LLM Self-Estimates (Track 1, problem #8). Code, article and reproduction: https://github.com/isHeSatoshi/anthropochrony

Contents. RUNLOG.md (timestamped run log), the pre-registration with dated deviations, the plan and prompts, budget ledger, acceptance-gate evidence (G0 to G6), training logs and trainer_state.json for every adapter, HF Jobs summaries, lm-eval results, and the speed reference.

Restrictions. No credentials. Job IDs and spend are included on purpose.

Licenses. Our annotations, labels and run data: CC-BY-NC-4.0 (they derive from Tiny Aya Global outputs and include CC BY-NC 4.0 ClassEval). Upstream task sources keep their own licenses: HumanEval+/MBPP+ Apache-2.0 (HumanEval MIT, MBPP CC-BY-4.0), BigCodeBench Apache-2.0, ClassEval CC BY-NC 4.0, CodeContests CC-BY-4.0, LeetCodeDataset Apache-2.0 metadata with LeetCode-owned statements, TACO Apache-2.0 metadata with third-party statements. Model-generated text is from CohereLabs/tiny-aya-global (CC-BY-NC-4.0 plus the AUP). Teacher guesses are from DeepSeek-V4-Pro.

Intended use. Research on model self-knowledge and time estimation for coding agents. Not for commercial use.

Limitations. One training seed; labels are medians of 8 sampled runs under stated speeds, not measurements on the stated hardware; Python tasks only; the parser audit covered 60 rows and was done by the research agent.

Collection. https://huggingface.co/collections/isHeSatoshi/anthropochrony-data-6af31e776d6f33265b0c33a8
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