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

**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.