Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code: FeaturesError
Exception: ArrowInvalid
Message: JSON parse error: Invalid value. 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 1392, in _parse
ujson_loads(json, precise_float=self.precise_float), dtype=None
~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
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 4408, 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 2679, in _head
return next(iter(self.iter(batch_size=n)))
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2861, in iter
for key, pa_table in ex_iterable.iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2395, in _iter_arrow
yield from 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 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 342, 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: Invalid value. 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.
license: mit language: - en tags: - instruction-tuning - low-level - c-plus-plus - systems-programming - dataset
Premium 18k Instruction Dataset for Elite Tech & AI Reasoning
Note: The file provided in this repository (
sample.py/sample.jsonl) is a preview sample containing 200 high-quality rows. The full, unreleased dataset contains 18,000 lines.
Dataset Overview
- Volume & Format: 18,000 lines in perfectly formatted structured lines, requiring zero preprocessing.
- Focus: Deep Technical Reasoning, Kernel-Level Engineering, Architecture, Systems, and AI/HPC.
14 Hyper-Niche Domains Included:
- AI/HPC: Trillion-Parameter LLM Distributed Training (DeepSpeed, Tensor Slicing), Custom AI Silicon (ASIC/TPU), Exascale Supercomputing, and Computational Bio (AlphaFold).
- Low-Level & Security: Kernel-level Exploit Mitigation (Zero-days), Advanced Concurrency (Lock-Free Go/Rust/C++), Memory Leak Profiling, and Cloud Hypervisor Internals (Firecracker).
- Edge & Systems: AV Edge Inference (Real-time C++23), Ultra-Low-Latency Fintech engines, Distributed Consensus (Raft/Paxos), and Next-Gen Vector DBs (HNSW indexing). Dataset Breakdown:
- ~25% features regional context with Kurdish and Arabic technical framing.
- ~75% focuses on deep English technical instructions, low-latency systems, and advanced code structures. Why Multilingual & Niche Matter: Most open-source instruction datasets completely ignore regional and low-resource linguistic contexts while failing to capture ultra-niche, low-latency engineering constraints. By bridging advanced technical depth with regional multi-lingual framing (Kurdish and Arabic), this dataset opens doors for localized high-performance computing, specialized AI alignment, and robust cross-lingual code synthesis that standard global models usually miss.
Access & Licensing to Full 18,000 Lines
If your engineering team is interested in licensing or purchasing the full 18k dataset for model training, reach out immediately:
email me here : legendertarvos@gmail.com
- Direct Inquiry: Open to custom evaluation and enterprise acquisition.
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