Skip to content

dekunited/CodeSynapse

Repository files navigation

CodeSynapse 🧬

CodeSynapse

Welcome to CodeSynapse, a repository dedicated to exploring the intricacies of code translation using various large language models (LLMs). This project aims to provide insights into how different models perform in translating code across programming languages, using state-of-the-art technologies.

Table of Contents

Introduction

In today's fast-paced tech environment, the ability to translate code between languages is crucial. CodeSynapse investigates how various LLMs handle this task. By comparing models like GPT-4, LLaMA3, and Phi2, we aim to identify strengths and weaknesses in code translation.

This repository serves as a comprehensive resource for developers, researchers, and enthusiasts interested in the field of code translation and LLMs.

Features

  • Comparative analysis of different LLMs for code translation.
  • Benchmarks for performance evaluation.
  • User-friendly interface for testing translations.
  • Support for multiple programming languages including Python, Go, and ReactJS.
  • Docker support for easy deployment.

Technologies Used

CodeSynapse utilizes a range of technologies to achieve its goals:

  • Programming Languages: Python, Go, JavaScript (ReactJS)
  • Models: GPT-4, LLaMA3, Phi2
  • Tools: DeepSeek Coder, Docker
  • Topics: code-translation, deepseek-coder, llms

Getting Started

To get started with CodeSynapse, follow these steps:

  1. Clone the Repository:

    git clone https://github.com/dekunited/CodeSynapse.git
    cd CodeSynapse
  2. Install Dependencies: Use Docker to manage dependencies. Run the following command:

    docker-compose up
  3. Run the Application: After setting up, you can start the application using:

    docker-compose run app

Usage

Once the application is running, you can use the web interface to test code translations. Input your code in the provided field and select the source and target languages. The application will display the translated code, allowing you to analyze the results.

Example

  1. Input your code snippet in Python.
  2. Select the target language (e.g., Go).
  3. Click on "Translate" to see the output.

Contributing

We welcome contributions to improve CodeSynapse. If you would like to contribute, please follow these steps:

  1. Fork the repository.
  2. Create a new branch for your feature or bug fix.
  3. Make your changes and commit them.
  4. Push to your branch.
  5. Create a pull request.

Please ensure that your code adheres to our coding standards and includes appropriate tests.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any questions or feedback, please reach out to the maintainers:

Releases

To download the latest version of CodeSynapse, visit our Releases section. Here, you can find compiled binaries and other necessary files. Follow the instructions in the release notes to execute them properly.

To check for updates, please also visit the Releases section regularly.

Acknowledgments

We would like to thank the contributors and the community for their support. Special thanks to the developers of the models we utilize in this project.

Conclusion

CodeSynapse aims to bridge the gap between different programming languages through effective code translation. By leveraging the capabilities of advanced LLMs, we hope to make coding more accessible and efficient for everyone.

We invite you to explore the repository, contribute, and share your insights. Together, we can enhance the landscape of code translation.


Feel free to customize this README as needed to better suit your project's requirements and audience.