Provide a detailed description of your project, its purpose, and the problems it aims to solve. Explain why this project is valuable and what motivated its creation.
- List key features or functionalities of your project.
- Highlight what makes your project stand out.
Detailed instructions on how to install and set up your project.
# Clone the repository
git clone https://github.com/canstralian/CodeGenAI.git
# Navigate to the project directory
cd CodeGenAI
# Install dependencies
pip install -r requirements.txtInstructions on how to use your project after installation.
# Example command to run your project
python main.py --input example_input.txt --output example_output.txtProvide examples or screenshots to demonstrate how your project works.
Guidelines for contributing to your project.
- Fork the repository.
- Create a new branch:
git checkout -b feature-name. - Commit your changes:
git commit -m 'Add new feature'. - Push to the branch:
git push origin feature-name. - Submit a pull request.
Specify the license under which your project is distributed.
Credit individuals, libraries, or resources that contributed to your project.
For more detailed guidance, refer to [GitHub's documentation on READMEs](https://docs.github.com/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-readmes) and [freeCodeCamp's article on writing good READMEs](https://www.freecodecamp.org/news/how-to-write-a-good-readme-file/).
## Hugging Face Model Card Best Practices
A model card provides essential information about your machine learning model, enhancing transparency and usability. Here's a template to guide you:
```markdown
---
language: "en"
tags:
- code-generation
- deep-learning
license: "apache-2.0"
---
# CodeGenAI Model Card
## Model Description
**Architecture:** Describe the model architecture (e.g., Transformer-based).
**Training Data:** Briefly describe the dataset used for training.
**Objective:** Explain the primary purpose of the model.
## Intended Use
- **Primary Use Case:** Describe the main application(s) of the model.
- **Out-of-Scope Use Cases:** Highlight scenarios where the model should not be applied.
## Performance
Provide metrics that evaluate the model's performance.
## Limitations
Discuss any known limitations or biases in the model.
## Ethical Considerations
Address potential ethical implications of using the model.
## How to Use
```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("canstralian/CodeGenAI")
model = AutoModel.from_pretrained("canstralian/CodeGenAI")
input_text = "Your input text here"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model(**inputs)
If you use this model in your research, please cite:
@misc{codegenai,
author = {Your Name},
title = {CodeGenAI: A Code Generation Model},
year = {2025},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/canstralian/CodeGenAI}}
}