Hugging Face Overview
OpenCodeInterpreter-CL-7B: Enhanced Code Generation with Execution Feedback
OpenCodeInterpreter-CL-7B is a 7 billion parameter model built upon CodeLlama-7b-Python-hf, developed by m-a-p. It represents a significant advancement in open-source code generation by incorporating a unique methodology that integrates execution and iterative refinement capabilities, similar to advanced proprietary systems like GPT-4 Code Interpreter.
Key Capabilities & Differentiators
- Execution Feedback Integration: The model's core innovation lies in its ability to utilize execution feedback to iteratively refine generated code, leading to higher accuracy and robustness.
- Improved Benchmark Performance: On the HumanEval benchmark, OpenCodeInterpreter-CL-7B achieves 72.6, which improves to 75.6 with execution feedback. For MBPP, it scores 66.4, increasing to 69.9 with feedback. This demonstrates a clear performance uplift through its unique refinement process.
- Iterative Refinement: Unlike traditional code generation models, OpenCodeInterpreter-CL-7B is designed to learn from execution results and adjust its output, bridging the gap between initial code generation and functional, correct solutions.
When to Use This Model
- Code Generation Tasks: Ideal for developers and researchers requiring highly accurate and robust code generation.
- Automated Code Correction: Particularly useful in scenarios where generated code needs to be validated and refined based on execution outcomes.
- Benchmarking Code Interpreters: Provides a strong baseline and an example of how execution feedback can significantly boost performance in coding tasks.