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  - text-generation-inference
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  ---
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- **Model Card for the Code Generation Model**
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  **Model Details**
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- - **Model Name**: CodeGen-Enhanced
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- - **Model ID**: codegen-enhanced-v1
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  - **License**: MIT
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  - **Base Models**:
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  - replit/replit-code-v1_5-3b
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  **Model Description**
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- CodeGen-Enhanced is a state-of-the-art code generation model designed to assist developers by generating code snippets, completing code blocks, and providing code-related suggestions. It leverages advanced architectures, including Replit's Code v1.5 and WhiteRabbitNeo's Llama series, to deliver high-quality code generation across multiple programming languages.
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  **Training Data**
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- The model was trained on a diverse dataset comprising:
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- - **Wordlists**: A comprehensive collection of programming language keywords and syntax.
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- - **CyberExploitDB**: A curated database of cybersecurity exploits and related code snippets.
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- - **Pentesting Dataset**: A compilation of penetration testing scripts and tools.
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- - **Shell Commands**: A repository of Unix/Linux shell commands and scripts.
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- These datasets were sourced from Canstralian's repositories:
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  - Canstralian/Wordlists
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  - Canstralian/CyberExploitDB
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  **Intended Use**
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- CodeGen-Enhanced is intended for:
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- - **Code Completion**: Assisting developers by suggesting code completions in real-time.
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- - **Code Generation**: Creating boilerplate code or entire functions based on user prompts.
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- - **Educational Purposes**: Serving as a learning tool for understanding coding patterns and best practices.
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  **Performance Metrics**
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- The model's performance was evaluated using the following metrics:
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- - **Accuracy**: Measures the correctness of the generated code snippets.
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- - **Code Evaluation**: Assesses the functionality and efficiency of the generated code through execution tests.
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  **Ethical Considerations**
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- While CodeGen-Enhanced aims to provide accurate and helpful code suggestions, users should:
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- - **Verify Generated Code**: Always review and test generated code to ensure it meets security and performance standards.
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- - **Avoid Sensitive Data**: Do not input sensitive or proprietary information into the model to prevent potential data leakage.
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  **Limitations**
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- CodeGen-Enhanced may:
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- - **Produce Inaccurate Code**: Occasionally generate code with errors or inefficiencies.
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- - **Lack Context**: May not fully understand the broader context of a project, leading to less relevant suggestions.
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  **Future Improvements**
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- Plans for future enhancements include:
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- - **Expanded Language Support**: Incorporating additional programming languages to broaden usability.
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- - **Contextual Understanding**: Improving the model's ability to comprehend and generate context-aware code snippets.
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  **Acknowledgments**
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- We acknowledge the contributions of the Canstralian community for providing the datasets used in training and the open-source community for developing the base models.
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  **References**
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  - [Replit Code v1.5 Model Card](https://huggingface.co/replit/replit-code-v1_5-3b)
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- - [WhiteRabbitNeo Llama-3.1 Model Card](https://huggingface.co/WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-8B)
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  - [Canstralian GitHub Repositories](https://github.com/canstralian)
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- This model card provides a comprehensive overview of the CodeGen-Enhanced model, its capabilities, and considerations for its use.
 
 
 
 
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  - text-generation-inference
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  ---
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+ ## **Model Card for RabbitRedux**
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  **Model Details**
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+ - **Model Name**: RabbitRedux
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+ - **Model ID**: rabbitredux-v1
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  - **License**: MIT
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  - **Base Models**:
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  - replit/replit-code-v1_5-3b
 
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  **Model Description**
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+ RabbitRedux is a cutting-edge code generation model designed to assist developers by generating code snippets, completing code blocks, and providing context-aware suggestions. It combines advanced AI architectures from Replits Code v1.5 and WhiteRabbitNeo's Llama series to produce high-quality code generation across multiple programming languages.
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  **Training Data**
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+ The model was trained using a diverse set of datasets sourced from Canstralian’s repositories, including:
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+ - **Wordlists**: A rich collection of programming language keywords, syntax, and coding patterns.
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+ - **CyberExploitDB**: A database of cybersecurity exploits and related code examples.
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+ - **Pentesting Dataset**: A collection of penetration testing scripts and tools.
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+ - **Shell Commands**: A repository of common Unix/Linux shell commands and scripts.
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+ These datasets were sourced from the following Canstralian repositories:
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  - Canstralian/Wordlists
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  - Canstralian/CyberExploitDB
 
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  **Intended Use**
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+ RabbitRedux is designed for:
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+ - **Code Completion**: Helping developers by suggesting code completions in real time.
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+ - **Code Generation**: Creating boilerplate code or entire functions based on user inputs.
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+ - **Educational Use**: Serving as a learning tool for exploring coding patterns and best practices.
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  **Performance Metrics**
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+ RabbitRedux’s performance is evaluated using the following metrics:
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+ - **Accuracy**: Measures the correctness of generated code snippets.
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+ - **Code Evaluation**: Assesses the functionality and efficiency of generated code by executing it.
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  **Ethical Considerations**
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+ RabbitRedux is intended to provide accurate and helpful code suggestions. However, users should:
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+ - **Review Generated Code**: Always validate and test generated code to ensure it meets security and performance standards.
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+ - **Avoid Sensitive Inputs**: Do not input sensitive or proprietary information into the model to prevent data leakage.
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  **Limitations**
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+ While RabbitRedux is highly capable, it may:
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+ - **Generate Inaccurate Code**: Occasionally produce code with errors or inefficiencies.
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+ - **Lack Contextual Awareness**: It may not fully understand the broader context of a project, leading to less relevant suggestions.
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  **Future Improvements**
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+ Future updates will include:
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+ - **Expanded Language Support**: Adding support for more programming languages.
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+ - **Improved Contextual Understanding**: Enhancing the model's ability to generate context-aware code.
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  **Acknowledgments**
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+ We would like to thank the Canstralian community for their contributions of datasets used in training, and the open-source community for the development of the base models.
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  **References**
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  - [Replit Code v1.5 Model Card](https://huggingface.co/replit/replit-code-v1_5-3b)
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+ - [WhiteRabbitNeo Llama-3.1 Model Cards](https://huggingface.co/WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-8B)
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  - [Canstralian GitHub Repositories](https://github.com/canstralian)
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+ This model card provides an overview of RabbitRedux, detailing its capabilities, performance, and considerations for usage.
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