OpenCodeInterpreter-DS-33B

Maintainer: m-a-p

Total Score

96

Last updated 5/17/2024

🗣️

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The OpenCodeInterpreter-DS-33B model is part of a family of open-source code generation systems developed by m-a-p that aim to bridge the gap between large language models and advanced proprietary systems like GPT-4. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities. This model is based on the deepseek-coder-33b-base model and exemplifies the evolution of coding model performance, highlighting the enhancements brought by the integration of execution feedback. Compared to similar models like OpenCodeInterpreter-DS-6.7B, CodeShell-7B, and deepseek-coder-33b-instruct, the OpenCodeInterpreter-DS-33B model demonstrates state-of-the-art performance on code generation benchmarks.

Model inputs and outputs

The OpenCodeInterpreter-DS-33B model is a text-to-text AI model that can generate code given natural language prompts. The input is a prompt in natural language describing a coding task, and the output is the generated code to perform that task.

Inputs

  • Natural language prompts describing coding tasks, such as "Write a function to find the shared elements from the given two lists."

Outputs

  • Generated code to perform the specified task, such as a Python function to find the shared elements between two lists.

Capabilities

The OpenCodeInterpreter-DS-33B model has demonstrated exceptional performance on code generation tasks, particularly when integrated with execution feedback. This allows the model to iteratively refine its code outputs based on the results of executing the generated code, leading to significant improvements in the quality and accuracy of the final code. The model has achieved state-of-the-art results on authoritative benchmarks like HumanEval and MBPP, outperforming similar large language models for code.

What can I use it for?

The OpenCodeInterpreter-DS-33B model can be a valuable tool for software developers and data scientists, enabling them to quickly generate high-quality code to solve a wide range of programming problems. It can be used for tasks such as:

  • Rapid prototyping and MVP development
  • Automating repetitive coding tasks
  • Assisting with code generation and refactoring
  • Enhancing developer productivity and collaboration

Additionally, the model's strong performance on code generation benchmarks suggests it could be useful for academic research and the development of advanced AI-powered coding assistants.

Things to try

One key aspect of the OpenCodeInterpreter-DS-33B model is its ability to integrate code execution and refinement. By generating code and then evaluating its performance, the model can iteratively improve its outputs, leading to more accurate and functional code. Developers can experiment with this capability by providing the model with a series of related prompts, observing how the generated code evolves, and analyzing the improvements in the model's responses over time.

Another interesting area to explore is the model's handling of different programming languages. The OpenCodeInterpreter-DS-33B model has been trained on a diverse corpus of code, including multiple languages. It would be valuable to test the model's versatility by providing it with prompts across a range of programming languages and comparing the quality and accuracy of the generated code.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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