Update README.md

README.md

@@ -18,4 +18,84 @@ library_name: transformers
 tags:
 - code
 - text-generation-inference
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+---
+
+**Model Card for the Code Generation Model**
+
+**Model Details**
+
+- **Model Name**: CodeGen-Enhanced
+- **Model ID**: codegen-enhanced-v1
+- **License**: MIT
+- **Base Models**:
+  - replit/replit-code-v1_5-3b
+  - WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-8B
+  - WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-70B
+
+**Model Description**
+
+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.
+
+**Training Data**
+
+The model was trained on a diverse dataset comprising:
+
+- **Wordlists**: A comprehensive collection of programming language keywords and syntax.
+- **CyberExploitDB**: A curated database of cybersecurity exploits and related code snippets.
+- **Pentesting Dataset**: A compilation of penetration testing scripts and tools.
+- **Shell Commands**: A repository of Unix/Linux shell commands and scripts.
+
+These datasets were sourced from Canstralian's repositories:
+
+- Canstralian/Wordlists
+- Canstralian/CyberExploitDB
+- Canstralian/pentesting_dataset
+- Canstralian/ShellCommands
+
+**Intended Use**
+
+CodeGen-Enhanced is intended for:
+
+- **Code Completion**: Assisting developers by suggesting code completions in real-time.
+- **Code Generation**: Creating boilerplate code or entire functions based on user prompts.
+- **Educational Purposes**: Serving as a learning tool for understanding coding patterns and best practices.
+
+**Performance Metrics**
+
+The model's performance was evaluated using the following metrics:
+
+- **Accuracy**: Measures the correctness of the generated code snippets.
+- **Code Evaluation**: Assesses the functionality and efficiency of the generated code through execution tests.
+
+**Ethical Considerations**
+
+While CodeGen-Enhanced aims to provide accurate and helpful code suggestions, users should:
+
+- **Verify Generated Code**: Always review and test generated code to ensure it meets security and performance standards.
+- **Avoid Sensitive Data**: Do not input sensitive or proprietary information into the model to prevent potential data leakage.
+
+**Limitations**
+
+CodeGen-Enhanced may:
+
+- **Produce Inaccurate Code**: Occasionally generate code with errors or inefficiencies.
+- **Lack Context**: May not fully understand the broader context of a project, leading to less relevant suggestions.
+
+**Future Improvements**
+
+Plans for future enhancements include:
+
+- **Expanded Language Support**: Incorporating additional programming languages to broaden usability.
+- **Contextual Understanding**: Improving the model's ability to comprehend and generate context-aware code snippets.
+
+**Acknowledgments**
+
+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.
+
+**References**
+
+- [Replit Code v1.5 Model Card](https://huggingface.co/replit/replit-code-v1_5-3b)
+- [WhiteRabbitNeo Llama-3.1 Model Card](https://huggingface.co/WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-8B)
+- [Canstralian GitHub Repositories](https://github.com/canstralian)
+
+This model card provides a comprehensive overview of the CodeGen-Enhanced model, its capabilities, and considerations for its use.