| --- |
| language: |
| - en |
| pipeline_tag: text-generation |
| tags: |
| - code |
| license: apache-2.0 |
| task_categories: |
| - question-answering |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| <h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1> |
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| <p align="center"> |
| <img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png"> |
| </p> |
| <p align="center"> |
| <a href="https://opencodeinterpreter.github.io/">[🏠Homepage]</a> |
| | |
| <a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[🛠️Code]</a> |
| </p> |
| <hr> |
|
|
| ## OpenCodeInterpreter |
| OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities. |
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| For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv. |
|
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| ## Dataset Description |
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| CodeFeedback-Filtered-Instruction is a curated collection of code instruction queries extracted from four prominent open-source code instruction tuning datasets: [Magicoder-OSS-Instruct](https://huggingface.co/datasets/ise-uiuc/Magicoder-OSS-Instruct-75K), [Python code subset of ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Python-Code-23k-ShareGPT), [Magicoder-Evol-Instruct](https://huggingface.co/datasets/ise-uiuc/Magicoder-Evol-Instruct-110K), and [Evol-Instruct-Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1). |
| Initially, 287k queries were aggregated from these datasets. To isolate the most intricate and informative instructions, a rigorous filtering process was employed. |
| This involved utilizing the Qwen-72B-Chat, an open-source chat model, for selective filtering. |
| The code queries are evaluated along with their corresponding responses within the compiled datasets by the LLM, assigning a complexity score ranging from 1 to 5, and only those rated 4 or 5 were retained for the seed set. |
| This meticulous filtering process resulted in a final collection of 156k high-quality single-turn code instructions. |
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| In subsequent processing steps mentioned in the paper, besides Single-turn Packing, we exclusively utilized queries without considering responses. However, here we retained all responses to provide users with more convenient usage options. |
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| ## Contact |
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| If you have any inquiries, please feel free to raise an issue or reach out to us via email at: xiangyue.work@gmail.com, zhengtianyu0428@gmail.com. |
| We're here to assist you! |
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| ⚠️The dataset contains part data generated by OpenAI's language models, please pay attention to OpenAI's usage policy when adopting this dataset: https://openai.com/policies/usage-policies. |
