OpenCodeInterpreter-DS-6.7B

Maintainer: m-a-p

Total Score

123

Last updated 5/28/2024

🤷

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-6.7B model is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities. The model is based on the deepseek-coder-6.7b-base model, and was developed by m-a-p.

Compared to other large language models, the OpenCodeInterpreter series exemplifies the evolution of coding model performance, particularly highlighting the significant enhancements brought about by the integration of execution feedback. This allows the models to outperform larger models like Grok-1 on several benchmarks, including HumanEval and MBPP.

Model inputs and outputs

Inputs

  • Code generation prompts: The model can generate code based on natural language instructions or descriptions.

Outputs

  • Generated code: The model outputs code in various programming languages, based on the input prompts.
  • Execution feedback: The model can execute the generated code and provide feedback to refine the output.

Capabilities

The OpenCodeInterpreter-DS-6.7B model demonstrates significant improvements in code generation and execution tasks compared to other large language models. It can generate high-quality, executable code across a wide range of programming languages, and the integration of execution feedback allows the model to iteratively refine its outputs.

What can I use it for?

The OpenCodeInterpreter-DS-6.7B model can be a valuable tool for developers, researchers, and anyone looking to automate coding tasks. Some potential use cases include:

  • Code generation: Automatically generating code based on natural language descriptions or prompts.
  • Code refinement: Iteratively improving generated code through execution feedback.
  • Prototyping and experimentation: Quickly generating and testing code ideas.
  • Bridging the gap between language models and advanced coding systems: Combining the flexibility of language models with the power of execution-based code generation.

Things to try

One interesting thing to try with the OpenCodeInterpreter-DS-6.7B model is to experiment with the integration of execution feedback. By providing the model with both the initial prompt and the results of executing the generated code, you can observe how the model refines its outputs to improve the code quality and functionality. This can lead to valuable insights into the role of execution-based learning in advancing code generation capabilities.

Another interesting aspect to explore is the model's performance on a diverse set of programming languages. By testing the model on a wide range of languages, you can gain a deeper understanding of its versatility and identify any language-specific strengths or weaknesses that can inform future model development.



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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