The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ArrowInvalid
Message: JSON parse error: Missing a name for object member. in row 0
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
df = pandas_read_json(f)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
return pd.read_json(path_or_buf, **kwargs)
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 815, in read_json
return json_reader.read()
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1014, in read
obj = self._get_object_parser(self.data)
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
obj = FrameParser(json, **kwargs).parse()
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1176, in parse
self._parse()
~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1391, in _parse
self.obj = DataFrame(
~~~~~~~~~^
ujson_loads(json, precise_float=self.precise_float), dtype=None
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/pandas/core/frame.py", line 782, in __init__
mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
File "/usr/local/lib/python3.14/site-packages/pandas/core/internals/construction.py", line 503, in dict_to_mgr
return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)
File "/usr/local/lib/python3.14/site-packages/pandas/core/internals/construction.py", line 114, in arrays_to_mgr
index = _extract_index(arrays)
File "/usr/local/lib/python3.14/site-packages/pandas/core/internals/construction.py", line 680, in _extract_index
raise ValueError(
"Mixing dicts with non-Series may lead to ambiguous ordering."
)
ValueError: Mixing dicts with non-Series may lead to ambiguous ordering.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4523, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2768, in _head
return next(iter(self.iter(batch_size=n)))
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2972, in iter
for key, pa_table in ex_iterable.iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2483, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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 327, in _generate_tables
raise e
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Missing a name for object member. in row 0Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
680M chess SFT CoT datasets (puzzle sequences)
Generator model:
checkpoints/C_6p5e19/6p5e19_680m_alpha0.750/final(the 6.5e19-compute 680M pretrained model with alpha = 0.750). Every shard'sconfig.model_pathrecords this. The repo name says alpha1.0; the data itself comes from alpha 0.750.
Search-based chain-of-thought traces for chess puzzles, generated by sampling move trajectories from the pretrained model and labelling them with a Stockfish-backed minimax evaluation. Used as SFT data for the pre-to-post compute-allocation sweep.
Configurations
Each subfolder is one search budget (nc candidates, nt trajectories, nta
trajectories for the answer move). Shards are 500 puzzles each, named
datapuzzle_v4_shard<start>-<end>.json. Layout mirrors the local folder
data/sft_ablation/cot_data_puzzle_seq_qwen/680m_ce19_alpha0_75/.
| Subfolder | Search budget | Shards | Puzzles | Trajectories | GiB | Missing shards |
|---|---|---|---|---|---|---|
hf_model_seed0_evalminimax_candpolicy_nc4_nt8_nta8_d20_dmrandom_wt300_pm8_inj |
nc4_nt8_nta8 | 40 | 16,378 | 340,307 | 0.54 | none |
hf_model_seed0_evalminimax_candpolicy_nc8_nt10_nta10_d20_dmrandom_wt300_pm8_inj |
nc8_nt10_nta10 | 82 | 31,786 | 1,452,538 | 1.69 | 24500-25000, 25500-26000, 36000-36500, 36500-37000, 37000-37500 |
Shared settings: source puzzles training_splits_v4/sft_dataset.csv, policy candidate
selection, DFS-policy traversal, trajectory depth 20 (random depth mode), minimax
evaluation with Stockfish 0.1 s and a 300 cp win threshold, answer injection on,
max 8 puzzle moves, temperature 1.0, seed 0.
File format
Each shard is a JSON object with config (generation arguments), summary
(per-shard counts, leaf win/draw/loss rates, ground-truth match rate) and results
(500 records). Record fields include pgn, moves_list, opponent_move,
ground_truth_move, target_move, target_move_san, is_match, best_val,
minimax_values and the rendered CoT formats under cot_by_method.
Use in SFT
The chess SFT trainer reads the sorted shard list, caps it at max_train_files,
and holds out the last 2 files in sorted order for eval. Note the sort is
lexicographic, so the holdout is shard9000-9500 and shard9500-10000.
- Downloads last month
- -