K-Ai-Official/K-Oss-9-Mini
Text Generation • 8B • Updated • 443 • 2
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 342, 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 4645, 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 2866, in _head
return next(iter(self.iter(batch_size=n)))
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3073, in iter
for key, pa_table in ex_iterable.iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2581, in _iter_arrow
yield from 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 345, in _generate_tables
raise e
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 291, 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: 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.
Kai_V2
A Multilingual Instruction Dataset by K-Ai
Kai_V2 is an openly available multilingual instruction dataset developed and maintained by K-Ai (Kortex Ai) to support the training, fine-tuning, and evaluation of AI models.
The dataset is designed to support multilingual AI development, with a focus on Indonesian, English, and Japanese.
Kai_V2 is part of K-Ai's effort to build capable and accessible AI technologies while providing useful resources for developers, researchers, students, and independent AI builders.
Our goal is not only to develop AI models within K-Ai, but also to contribute to the wider open AI community.
About the Dataset
Kai_V2 is designed as a multilingual instruction dataset for the K-Ai model ecosystem.
It focuses on several areas of AI development, including:
- Instruction following.
- Logical reasoning and problem solving.
- Programming and coding.
- Scientific knowledge.
- Language learning.
- Japanese conversation (Kaiwa).
- Creative generation.
- Responsible AI behavior and safety.
The dataset supports three primary languages:
- Indonesian ("id").
- English ("en").
- Japanese ("ja").
Japanese language samples primarily focus on practical conversation and foundational language learning.
Dataset Structure
Kai_V2 consists of three primary data splits:
Training Data ("train.jsonl")
Used for supervised fine-tuning, instruction tuning, and model training.
Validation Data ("validation.jsonl")
Used to monitor model performance and evaluate training progress.
Benchmark Data ("benchmark.jsonl")
Reserved for independent model evaluation and performance testing.
Benchmark samples must remain separate from training and validation data to prevent evaluation leakage.
Created for K-Ai Models
Kai_V2 is developed as part of the K-Ai model ecosystem.
Its primary development target is:
- K-Oss-9-Mini-9B.
However, the dataset is not restricted to a single model or architecture.
It may also be adapted for other compatible language models and instruction-tuning pipelines.
Open for Public Use
Kai_V2 is not exclusively reserved for K-Ai.
We welcome developers, researchers, students, and independent AI builders to explore and use this dataset.
Potential applications include:
- Supervised fine-tuning (SFT).
- Instruction tuning.
- Multilingual model development.
- AI research and experimentation.
- Model evaluation and benchmarking.
- Educational and independent AI projects.
You may use, modify, and redistribute the dataset under the terms of the Apache License 2.0.
Please respect applicable rights and licensing conditions associated with individual data sources.
Data Quality and Validation
K-Ai aims to maintain dataset quality through a validation process covering:
- Data structure and schema validation.
- Duplicate detection.
- Language consistency.
- Dataset leakage detection.
- Response quality and factual accuracy.
- Japanese language verification.
- Responsible AI and safety considerations.
Automated validation does not replace human review.
Dataset quality and production readiness depend on proper verification, documentation, and continuous improvement.
Loading the Dataset
You can load Kai_V2 using Hugging Face Datasets:
from datasets import load_dataset
dataset = load_dataset( "K-Ai-Official/Kai_V2" )
benchmark = load_dataset( "K-Ai-Official/Kai_V2", "benchmark" )
License
Kai_V2 is released under the Apache License 2.0.
You are permitted to:
- Use the dataset for personal and commercial projects.
- Modify and adapt the dataset.
- Use it for training and fine-tuning AI models.
- Redistribute original or modified versions.
Please retain applicable copyright notices, license information, and required notices when redistributing the dataset.
See the "Apache License 2.0" (https://www.apache.org/licenses/LICENSE-2.0) for the complete terms.
Dataset Information
Dataset Name: Kai_V2 Organization: K-Ai (Kortex Ai) Repository: K-Ai-Official/Kai_V2 Version: 1.0.0 License: Apache 2.0 Languages: Indonesian, English, Japanese Availability: Public Primary Model: K-Oss-9-Mini-9B Intended Use: AI training, instruction tuning, fine-tuning, research, and evaluation.
Responsible Use
Users are encouraged to:
- Review the dataset before using it.
- Validate data quality for their intended applications.
- Evaluate models trained using this dataset.
- Follow responsible AI development practices.
- Respect the rights and licenses of referenced data sources.
K-Ai does not guarantee that models trained using this dataset will achieve a particular performance level.
About K-Ai
K-Ai (Kortex Ai) is an independent technology initiative focused on developing AI models, tools, and accessible technology infrastructure.
Our philosophy:
We Build Because We Believe We Can.
We believe technology should help people, expand possibilities, and remain accessible to a wider community.
Kai_V2 represents part of that vision: building resources for our own models while making them available to the broader AI community.
Developed and maintained by K-Ai (Kortex Ai).
Repository: "K-Ai-Official/Kai_V2" (https://huggingface.co/datasets/K-Ai-Official/Kai_V2)
Open resources. Independent innovation. Built for everyone.
- Downloads last month
- 73
