OpenCodeInterpreter is an open-source code system designed for generating, executing, and iteratively refining code. It integrates execution and human feedback for dynamic code refinement, supported by a dataset of 68K multi-turn interactions. The system achieves high accuracy on benchmarks like HumanEval and MBPP, closely rivaling GPT-4's performance and further improving with synthesized human feedback. OpenCodeInterpreter bridges the gap between open-source and proprietary code generation models, offering a new benchmark in code generation. The system is trained on the Code-Feedback dataset, which includes diverse and challenging real-world queries, multi-turn dialogues with execution and human feedback, and interleaved text and code responses. The dataset is constructed using five methods, including filtering high-quality single-turn data, simulating interactions, and generating error correction interactions. The system is evaluated on benchmarks like HumanEval and MBPP, achieving high accuracy and demonstrating strong performance in multi-turn code generation. OpenCodeInterpreter's results show that it can effectively refine code through iterative feedback, with the OpenCodeInterpreter-33B variant achieving high accuracy on HumanEval and MBPP benchmarks. The system's performance is further enhanced with synthetic human feedback from GPT-4, demonstrating its ability to adapt to nuanced user feedback. The system's ability to handle complex coding tasks and refine code based on diverse feedback makes it a leader in software development tools. The system's case studies demonstrate its capability to handle a wide range of coding tasks, including prime number generation, IPv6 address validation, and list intersection identification. These examples highlight OpenCodeInterpreter's strength in understanding mathematical logic and dynamically adjusting algorithms to meet specified criteria. OpenCodeInterpreter addresses the limitations of existing code generation models by integrating execution feedback and human insights into an iterative refinement process. This approach enables the system to produce solutions that are both technically sound and closely matched to user requirements, significantly boosting its overall performance. The system's open-source nature and extensive dataset make it a valuable tool for developers and researchers in the field of code generation.OpenCodeInterpreter is an open-source code system designed for generating, executing, and iteratively refining code. It integrates execution and human feedback for dynamic code refinement, supported by a dataset of 68K multi-turn interactions. The system achieves high accuracy on benchmarks like HumanEval and MBPP, closely rivaling GPT-4's performance and further improving with synthesized human feedback. OpenCodeInterpreter bridges the gap between open-source and proprietary code generation models, offering a new benchmark in code generation. The system is trained on the Code-Feedback dataset, which includes diverse and challenging real-world queries, multi-turn dialogues with execution and human feedback, and interleaved text and code responses. The dataset is constructed using five methods, including filtering high-quality single-turn data, simulating interactions, and generating error correction interactions. The system is evaluated on benchmarks like HumanEval and MBPP, achieving high accuracy and demonstrating strong performance in multi-turn code generation. OpenCodeInterpreter's results show that it can effectively refine code through iterative feedback, with the OpenCodeInterpreter-33B variant achieving high accuracy on HumanEval and MBPP benchmarks. The system's performance is further enhanced with synthetic human feedback from GPT-4, demonstrating its ability to adapt to nuanced user feedback. The system's ability to handle complex coding tasks and refine code based on diverse feedback makes it a leader in software development tools. The system's case studies demonstrate its capability to handle a wide range of coding tasks, including prime number generation, IPv6 address validation, and list intersection identification. These examples highlight OpenCodeInterpreter's strength in understanding mathematical logic and dynamically adjusting algorithms to meet specified criteria. OpenCodeInterpreter addresses the limitations of existing code generation models by integrating execution feedback and human insights into an iterative refinement process. This approach enables the system to produce solutions that are both technically sound and closely matched to user requirements, significantly boosting its overall performance. The system's open-source nature and extensive dataset make it a valuable tool for developers and researchers in the field of code generation.