WhimperNet-AI is a large language model based on advanced deep learning techniques, specializing in expressing emotional vulnerability, requesting help, or evoking sympathy through language. It is capable of generating emotionally nuanced text, making it suitable for applications such as story creation, sentiment analysis, and dialogue generation.
- Emotion-driven Text Generation: WhimperNet-AI excels at using subtle language to convey emotions like vulnerability, requests for help, anxiety, and other emotional states, creating text that resonates with readers.
- Multi-domain Applicability: Whether it's literary creation, dialogue generation, customer support, or sentiment analysis, WhimperNet-AI can adjust the depth and type of emotion according to the context, generating text tailored to the need.
- Customizability: The model allows users to customize the output style, enabling control over the depth and type of emotion conveyed by providing different prompts.
- Deep Learning Frameworks: PyTorch, TensorFlow
- Language Models: GPT-3, GPT-4 (or other Transformer-based architectures)
- Natural Language Processing: Hugging Face Transformers, spaCy
- Sentiment Analysis: VADER, TextBlob
- Training & Optimization: DeepSpeed, Mixed Precision Training, Model Parallelism
- Web Interface: FastAPI, Flask (RESTful API)
The goal of WhimperNet-AI is to provide a model capable of generating text with precise emotional expression, helping users generate emotionally-driven content across various scenarios. Whether it's creating emotionally charged literary works or generating empathetic responses in customer support, WhimperNet-AI offers robust support to users.
First, clone the WhimperNet-AI repository to your local machine:
bash git clone https://github.com/minimum-generated-pig/WhimperNet-AI.git cd WhimperNet-AI
Install the required Python dependencies:
bash pip install -r requirements.txt
Start the FastAPI service to begin interacting with WhimperNet-AI:
bash
python app.py
By default, the service will start on http://localhost:8000.
You can interact with WhimperNet-AI by sending a POST request to generate emotionally-driven text. For example, send a request with an emotional prompt:
bash
curl -X POST http://localhost:8000/generate
-H "Content-Type: application/json"
-d '{"prompt": "Write a story about a robot who feels lonely and abandoned."}'
The response will include the generated text with emotional nuance:
json { "generated_text": "Once there was a robot who wandered alone, feeling the weight of abandonment press upon its cold, metallic chest. It longed for companionship, but the silence around it was deafening…" }
If you want to customize the model or retrain it, follow these steps:
- Prepare your training dataset, ensuring it conveys emotion or vulnerability.
- Configure the
config.yamlfile with the training parameters. - Run the training script:
bash python train.py --config config.yaml
WhimperNet-AI/ │ ├── app.py # Starts the FastAPI service ├── config.yaml # Configuration file with model parameters and training settings ├── requirements.txt # Python dependencies ├── train.py # Script for training the model ├── models/ # Directory for storing trained models ├── api/ # API-related code ├── data/ # Data processing and preprocessing scripts └── README.md # Project documentation
WhimperNet-AI is an open-source project, and we welcome contributions of any kind! Whether it's adding new features, fixing bugs, or optimizing performance, feel free to submit Issues or Pull Requests to help us improve the project.
- Fork this repository.
- Create a new branch (
git checkout -b feature-name). - Commit your changes (
git commit -am 'Add new feature'). - Push to your branch (
git push origin feature-name). - Submit a Pull Request.
WhimperNet-AI is licensed under the MIT License, allowing you to freely use, modify, and distribute this project.
Thanks to all the developers and researchers who have contributed to WhimperNet-AI! We believe that emotion-driven models will open up new possibilities for human-AI interactions.