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--- |
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license: mit |
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datasets: |
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- Canstralian/Wordlists |
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- Canstralian/CyberExploitDB |
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- Canstralian/pentesting_dataset |
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- Canstralian/ShellCommands |
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language: |
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- en |
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metrics: |
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- accuracy |
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- code_eval |
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base_model: |
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- replit/replit-code-v1_5-3b |
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- WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-8B |
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- WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-70B |
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library_name: transformers |
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tags: |
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- code |
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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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- WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-8B |
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- WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-70B |
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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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- Canstralian/pentesting_dataset |
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- Canstralian/ShellCommands |
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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. |
