Duplicate from nvidia/OpenCodeInstruct

Co-authored-by: Wasi Uddin Ahmad <wasiuddina@users.noreply.huggingface.co>

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README.md

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+---
+license: cc-by-4.0
+pretty_name: OpenCodeInstruct
+dataset_info:
+- config_name: train
+  features:
+  - name: id
+    dtype: string
+  - name: input
+    dtype: string
+  - name: output
+    dtype: string
+  - name: domain
+    dtype: string
+  - name: generation_algorithm
+    dtype: string
+  - name: llm_judgement
+    dtype: string
+  - name: unit_tests
+    dtype: string
+  - name: tests_execution_status
+    dtype: string
+  - name: average_test_score
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 19066895906
+    num_examples: 5000000
+  download_size: 6861113102
+  dataset_size: 19066895906
+configs:
+- config_name: train
+  data_files:
+  - split: train
+    path: data/train-*
+language:
+- en
+task_categories:
+- text-generation
+tags:
+- code
+- synthetic
+size_categories:
+- 1M<n<5M
+---
+# OpenCodeInstruct: A Large-scale Instruction Tuning Dataset for Code LLMs
+
+
+## Dataset Description
+
+We introduce OpenCodeInstruct, the largest open-access instruction tuning dataset, comprising 5 million diverse samples. OpenCodeInstruct is designed for supervised fine-tuning (SFT).
+
+- [Technical Report](https://arxiv.org/abs/2504.04030) - Discover the methodology and technical details behind OpenCodeInstruct.
+- [Github Repo](https://github.com/NVIDIA/NeMo-Skills) - Access the complete pipeline used to perform SFT.
+
+This dataset is ready for commercial/non-commercial use.
+
+
+## Dataset Owner(s)
+NVIDIA Corporation
+
+
+## Dataset Creation Date
+January 2025 - March 2025
+
+
+## License/Terms of Use
+
+GOVERNING TERMS: This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0) available at https://creativecommons.org/licenses/by/4.0/legalcode.
+
+**Data Developer:** NVIDIA
+
+### Use Case: <br>
+Developers training LLMs to specialize LLMs in code generation. <br>
+
+### Release Date:  <br>
+04/28/2025 <br>
+
+
+## Intended Usage
+
+The OpenCodeInstruct Dataset is intended to be used by the community to continue to improve open models. The data may be freely used to train models. **However, for 
+each dataset a user elects to use, the user is responsible for checking if the dataset license is fit for the intended purpose**.
+
+
+## Dataset Characterization
+
+** Data Collection Method<br>
+* [Hybrid: Automated, Synthetic] <br>
+
+** Labeling Method<be>
+* [Hybrid: Automated, Synthetic] <br>
+
+
+## Dataset Format
+|Field|Type|Description|
+|:---|:---|:---|
+|id|string|A unique id for each question|
+|input|string|The input coding question.|
+|output|string|LLM's response.|
+|domain|string|Either "generic" or "algorithmic".|
+|generation_algorithm|string|Either "self-instruct" or "evol-instruct".|
+|llm_judgement|string|string representation of a JSON dictionary containing the LLM's evaluation of the response based on several criteria.|
+|unit_tests|string|string representation of a list of assertion statements.|
+|tests_execution_status|string|string representation of a list of strings indicating "pass" or "fail".|
+|average_test_score|float|Fraction of test cases passed.|
+
+## How to Use It
+
+You can load the dataset with the following two lines of code.
+
+```python
+from datasets import load_dataset
+opencodeinstruct = load_dataset("nvidia/OpenCodeInstruct", split="train")
+```
+
+
+## Dataset Quantification
+
+- Record Count - 5 million coding question-answer pairs.
+- Download Size - 6.4 GB
+
+
+## Ethical Considerations:
+
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. 
+
+Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+
+
+## Citation
+
+If you find the data useful, please cite:
+```
+@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}, 
+}
+```

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