finetune-web-ui

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finetune-web-ui

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Finetune Web UI is a user-interface for training and deploying pre-trained models.

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<h1 align="center">Finetune Web UI</h1>

![image](https://github.com/user-attachments/assets/2d837faf-a1ef-4028-acdb-c8141c1bb2f7)

**Finetune Web UI** is a user-friendly interface for training and deploying machine learning models, specifically designed for fine-tuning pre-trained models like Llama, GPT, etc. The project aims to streamline the process of model fine-tuning, allowing data scientists and engineers to easily manage their training configurations, datasets, and models through a clean and intuitive web interface.

---
> **Note:** This project is currently under development and not yet ready for production use. Feel free to contribute to the project by submitting pull requests, reporting issues, or suggesting new features.

## Planned Features

- **Easy Dataset Upload:** Upload your datasets directly from a URL or local files.
- **Custom Model Fine-tuning:** Select pre-trained models like Llama, GPT, etc., and fine-tune them with your custom datasets.
- **Hyperparameter Tuning:** Configure training parameters like batch size, learning rate, etc.
- **Real-time Monitoring:** Track the progress of your fine-tuning in real time.
- **Simplified deployment:** Once the model is fine-tuned, deploy it instantly for inference.
- **Model Management:** Save and manage your fine-tuned models for future use.
- **User-friendly Interface:** Clean and intuitive UI for easy navigation and interaction.
- **Open Source:** Free and open-source project for the community.
- **Customizable:** Extend and modify the project to suit your needs.
- **Community Support:** Active community and contributors for help and guidance.
- **Cross-platform:** Works on Windows, macOS, and Linux.
- **Scalable:** Can be deployed on cloud services for large-scale training.

---

## Requirements

### Prerequisites:
- Python 3.12 or above
- Poetry (for managing dependencies)
- Virtual environment recommended

---

## Installation

Follow these steps to install and run the `finetune-web-ui` locally.

### Step 1: Clone the Repository

```bash
git clone https://github.com/muhammad-fiaz/finetune-web-ui.git
cd finetune-web-ui
```
### Step 2.1: Install Dependencies with Pip

If you prefer using `pip` to install dependencies, run:

```bash
pip install -r requirements.txt
```

This will install all the necessary dependencies.

### Step 2.2: Install Dependencies with UV

If you don't have Poetry installed, follow the [Poetry installation guide](https://python-poetry.org/docs/#installation).

Once Poetry is installed, run:

```bash
uv lock
```

This will create a virtual environment and install all the necessary dependencies.

### Step 3: Activate Virtual Environment

Activate the environment:

> this following command will automatically create env and install dependencies the launch the app

```bash
uv run launch.py 
```

Alternatively, you can activate the virtual environment using the `.venv` folder if you're using one:

```bash
source .venv/bin/activate  # For Linux/MacOS
.\.venv\Scripts\activate  # For Windows
```

### Step 4: Run the Application

Run the following command to start the web interface:

```bash
python launch.py
```

This will launch the web UI, typically accessible at `http://localhost:7860`.

---

## Usage

1. **Dataset Configuration**: Upload a dataset by providing a URL or select a local file. Your data should be in a format supported by the training script.
2. **Model Selection**: Choose from available pre-trained models like Llama, GPT, etc.
3. **Training Configuration**: Adjust hyperparameters like learning rate, batch size, and training epochs.
4. **Training & Monitoring**: Start the fine-tuning process and monitor the training progress in real time.
5. **Model Deployment**: After fine-tuning, deploy your model directly through the interface for inference.

---

## Project Structure

```bash
finetune-web-ui/
├── launch.py                # Main application script to run the UI
├── requirements.txt      # List of dependencies (if Poetry isn't used)
├── .venv/                # Virtual environment directory
├── README.md             # Project documentation
├── logs/                 # Folder for storing logs
└── configs/              # Directory for storing configuration files

```

---

## Contributing

We welcome contributions to `finetune-web-ui`! If you have suggestions, bug fixes, or new features, feel free to submit a pull request.

1. Fork the repository
2. Create a new branch (`git checkout -b feature-branch`)
3. Make your changes
4. Commit your changes (`git commit -am 'Add new feature'`)
5. Push to the branch (`git push origin feature-branch`)
6. Open a pull request

---

## License

This project is licensed under the AGPL-3.0 License. See the [LICENSE](LICENSE) file for more details.

---

## AI Usage Disclaimer

The **Fine-Tune Web UI** is intended for **research** and **educational purposes** only. It is designed to make fine-tuning large language models (LLMs) like Llama, GPT, etc., easier for developers. By using this tool, you acknowledge that you are solely responsible for how the models are used and the ethical implications of AI-driven applications.

We strongly encourage you to be aware of the potential risks and consequences of AI usage, including but not limited to:

- **Privacy concerns**: The use of sensitive data in training models can lead to privacy violations if not handled responsibly.
- **Misinformation**: LLMs can generate misleading or harmful content if not properly managed and fine-tuned.
- **Bias**: Models may contain biases based on the data they are trained on, which can perpetuate harmful stereotypes or unfairness.
- **Legal implications**: AI-generated content may violate copyright laws or other legal regulations.
- **Security risks**: AI models can be vulnerable to adversarial attacks or other security threats.
- **Ethical considerations**: AI applications should be designed and used in a way that respects human rights and ethical principles.
- **Fairness**: Ensure that your models are fair and unbiased, and do not discriminate against individuals or groups.
- **Transparency**: Be transparent about how your models are trained, what data they use, and how they make decisions.
- **Accountability**: Take responsibility for the impact of your models and be prepared to address any issues that arise.
- **Compliance**: Ensure that your models comply with relevant laws, regulations, and ethical guidelines.
- **Safety**: Prioritize the safety and well-being of users when deploying AI applications.
- **Sustainability**: Consider the environmental impact of training large models and optimize resource usage.
- **Human oversight**: Always have human oversight when deploying AI models to ensure that they are used responsibly.
- **Continuous monitoring**: Regularly monitor your models for performance, fairness, and ethical concerns.
- **Feedback mechanisms**: Provide mechanisms for users to report issues or provide feedback on AI-generated content.
- **Data protection**: Protect user data and ensure that it is used in compliance with data protection laws.

Please be responsible and consider the ethical implications when fine-tuning and deploying models.

---

## Acknowledgments

- [Gradio](https://gradio.app/) - For building the UI components
- [Hugging Face](https://huggingface.co/) - For pre-trained models and transformers
- [PyTorch](https://pytorch.org/) - For deep learning framework
- [Datasets](https://huggingface.co/datasets) - For providing datasets for training
- [Unsloth](https://github.com/unslothai/unsloth) - For **FastLanguageModel** and other contributions to the training pipeline

finetune-web-ui

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