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Datasets:
CodeForCodersYT
/
ultimate-code-tokenized

Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10M - 100M
ArXiv:
Tags:
code
Synthetic
instruction-tuning
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
 Community
Dataset Viewer
Auto-converted to Parquet Duplicate
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
input_ids: list<element: int32>
  child 0, element: int32
attention_mask: list<element: int8>
  child 0, element: int8
length: int64
-- schema metadata --
huggingface: '{"info": {"features": {"input_ids": {"feature": {"dtype": "' + 179
to
{'id': Value('string'), 'input': Value('string'), 'output': Value('string')}
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/parquet/parquet.py", line 220, in _generate_tables
                  yield Key(file_idx, batch_idx), self._cast_table(pa_table)
                                                  ~~~~~~~~~~~~~~~~^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 156, 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
              input_ids: list<element: int32>
                child 0, element: int32
              attention_mask: list<element: int8>
                child 0, element: int8
              length: int64
              -- schema metadata --
              huggingface: '{"info": {"features": {"input_ids": {"feature": {"dtype": "' + 179
              to
              {'id': Value('string'), 'input': Value('string'), 'output': Value('string')}
              because column names don't match

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ultimate-code

Dataset Description

ultimate-code is a derived dataset built by combining and processing data from nvidia/OpenCodeInstruct and nvidia/OpenCodeGeneticInstruct. It can be used to fine-tune LLMs for coding tasks.

Tokenized variant available: a pre-tokenized version of this dataset is available at CodeForCodersYT/ultimate-code-tokenized.

Use that version if you want ready-to-train tokenized sequences instead of raw text.

Source Datasets & Attribution

This dataset is derived from the following NVIDIA datasets, both released under the Creative Commons Attribution 4.0 International License (CC BY 4.0):

Source Dataset Owner License Link
OpenCodeInstruct NVIDIA Corporation CC BY 4.0 https://huggingface.co/datasets/nvidia/OpenCodeInstruct
OpenCodeGeneticInstruct NVIDIA Corporation CC BY 4.0 https://huggingface.co/datasets/nvidia/OpenCodeGeneticInstruct

Both source datasets are copyright © NVIDIA Corporation and are licensed under CC BY 4.0 (full license text).

Modifications made to the original data

  • Both source datasets were merged into a single unified schema.
  • Only the id, input, and output columns were kept; all other columns from the original datasets (e.g. domain, generation_algorithm, llm_judgement, unit_tests, tests_execution_status, average_test_score) were dropped.
  • No additional filtering, deduplication, or reformatting was applied beyond the column selection.

Dataset Structure

Field Type Description
id string Unique identifier for each example
input string The coding question / prompt
output string The corresponding solution / response

How to Use 

from datasets import load_dataset

ds = load_dataset("CodeForCodersYT/ultimate-code", split="train")

For a pre-tokenized version ready for training pipelines, see CodeForCodersYT/ultimate-code-tokenized instead.

License

This dataset is released under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/legalcode), consistent with the license of the source datasets. If you use this dataset, please provide attribution to both this dataset and the original NVIDIA source datasets as described above.

Ethical Considerations

This dataset inherits ethical considerations from its source data. As noted by NVIDIA for the original datasets: users are responsible for checking that the dataset and license are fit for their intended purpose, and should evaluate the resulting models for their specific use case and industry requirements before deployment.

Citation

If you use this dataset, please cite both the original source datasets and this dataset.

OpenCodeInstruct:

@article{ahmad2025opencodeinstruct,
      title={OpenCodeInstruct: A Large-scale Instruction Tuning Dataset for Code LLMs},
      author={Wasi Uddin Ahmad and Aleksander Ficek and Mehrzad Samadi and Jocelyn Huang and Vahid Noroozi and Somshubra Majumdar and Boris Ginsburg},
      year={2025},
      eprint={2504.04030},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2504.04030},
}

OpenCodeGeneticInstruct:

@misc{nvidia_opencodegeneticinstruct,
      title={OpenCodeGeneticInstruct},
      author={NVIDIA Corporation},
      year={2025},
      url={https://huggingface.co/datasets/nvidia/OpenCodeGeneticInstruct},
}

This dataset:

@misc{ultimate_code_2026,
      title={ultimate-code: A Merged Instruction-Tuning Dataset for Code LLMs},
      author={CodeForCodersYT},
      year={2026},
      url={https://huggingface.co/datasets/CodeForCodersYT/ultimate-code},
}
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