Canstralian/RabbitRedux

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
• WhiteRabbitNeo Llama-3.1 Model Card
• Canstralian GitHub Repositories

This model card provides a comprehensive overview of the CodeGen-Enhanced model, its capabilities, and considerations for its use.