OpenCodeInterpreter: Integrating Code Generation with Execution and
  Refinement
Authors: name: Tianyu Zheng, name: Ge Zhang, name: Tianhao Shen, name: Xueling Liu, name: Bill Yuchen Lin, name: Jie Fu, name: Wenhu Chen, name: Xiang Yue

The introduction of large language models has significantly advanced code
generation. However, open-source models often lack the execution capabilities
and iterative refinement of advanced systems like the GPT-4 Code Interpreter.
To address this, we introduce OpenCodeInterpreter, a family of open-source code
systems designed for generating, executing, and iteratively refining code.
Supported by Code-Feedback, a dataset featuring 68K multi-turn interactions,
OpenCodeInterpreter integrates execution and human feedback for dynamic code
refinement. Our comprehensive evaluation of OpenCodeInterpreter across key
benchmarks such as HumanEval, MBPP, and their enhanced versions from EvalPlus
reveals its exceptional performance. Notably, OpenCodeInterpreter-33B achieves
an accuracy of 83.2 (76.4) on the average (and plus versions) of HumanEval and
MBPP, closely rivaling GPT-4's 84.2 (76.2) and further elevates to 91.6 (84.6)
with synthesized human feedback from GPT-4. OpenCodeInterpreter brings the gap
between open-source code generation models and proprietary systems like GPT-4
Code Interpreter.