OpenCodeInterpreter-CL-13B is a 13 billion parameter code generation model developed by m-a-p, built upon CodeLlama-13b-Python-hf. This model integrates execution feedback and iterative refinement to significantly enhance code generation capabilities. It excels at generating and improving code, achieving strong performance on benchmarks like HumanEval and MBPP through its unique feedback mechanism. The model is designed to bridge the gap between traditional LLMs and advanced proprietary code interpreters.
Loading preview...
OpenCodeInterpreter-CL-13B: Enhanced Code Generation with Execution Feedback
OpenCodeInterpreter-CL-13B is a 13 billion parameter model from the OpenCodeInterpreter family, developed by m-a-p. It is based on CodeLlama-13b-Python-hf and is specifically designed to improve code generation by incorporating execution and iterative refinement capabilities, similar to advanced proprietary systems like GPT-4 Code Interpreter.
Key Capabilities & Features
- Execution Feedback Integration: The model leverages execution feedback to iteratively refine generated code, leading to higher accuracy and robustness.
- Iterative Refinement: It can improve its code outputs through multiple iterations, addressing errors and optimizing solutions based on execution results.
- Strong Benchmark Performance: On the HumanEval benchmark, OpenCodeInterpreter-CL-13B achieves 77.4% (73.8% extended) without feedback, improving to 81.1% (76.8% extended) with one iteration of execution feedback. For MBPP, it scores 70.7% (59.2% extended) without feedback, rising to 78.2% (67.2% extended) with feedback.
- CodeLlama Base: Built on the robust CodeLlama-13b-Python-hf architecture, providing a strong foundation for Python code generation.
Good For
- Automated Code Generation: Generating functional and correct code snippets or programs.
- Code Debugging and Refinement: Assisting in identifying and correcting errors in generated code through an iterative process.
- Benchmarking Code LLMs: Serving as a strong baseline or comparison point for models focusing on code interpretation and execution tasks.
- Developing Code Interpreter Systems: Providing a foundation for building more sophisticated code generation and execution environments.