| # DaFucV2 AI - Dynamic AI Model | |
| This repository hosts the model for **DaFucV2 AI**, a dynamic AI architecture built using the **Fractal Universe Chocolate Wafer Model (FUCWM)**. The model is designed to integrate with the **DaFucV2 app**, offering interactive conversational capabilities and adaptive thinking loops. | |
| ## Model Overview | |
| - **Model Architecture**: Combines a **Variational Autoencoder (VAE)** with fractal-like expanding layers based on complexity, using a **FractalNode** structure for dynamic growth. | |
| - **Self-Thinking and Feedback**: Incorporates an iterative feedback mechanism allowing the model to send its own thoughts back into itself for further refinement. | |
| - **Applications**: Optimized for conversational agents, adaptive feedback systems, and deeper multi-layered reasoning. | |
| - **Attention Mechanism**: The model dynamically adjusts attention across fractal layers to modulate responses based on the complexity of the input. | |
| ## DaFucV2 App Integration | |
| The **DaFucV2 AI** model is designed to work seamlessly with the **DaFucV2 app**, available on [GitHub](https://github.com/anttiluode/DaFucV2/tree/main). You can use the app to interact with the model, send queries, and explore its capabilities in real time. | |
| ### Demo Video | |
| Watch a video demonstration of me talking to the DaFucV2 AI [here on YouTube](https://www.youtube.com/watch?v=-PQ-rTkqwQ8). | |
| ## Usage | |
| To load and use the model within the app: | |
| 1. **Download the app** from the [DaFucV2 GitHub repository](https://github.com/anttiluode/DaFucV2/tree/main). | |
| 2. **Place the model** (`model.pth`) in the appropriate directory. | |
| 3. Run the app by following the instructions in the repository. | |
| To manually load the model in PyTorch: | |
| ```python | |
| import torch | |
| from model import DynamicAI | |
| # Load the saved model | |
| model = DynamicAI(vocab_size=50000, embed_dim=256, latent_dim=256, output_dim=256, max_depth=7) | |
| model.load_state_dict(torch.load("model.pth")) | |
| # Set model to evaluation mode | |
| model.eval() | |
| # Example usage with input text | |
| input_text = "Hello, how are you?" | |
| response = model.chat(input_text) | |
| print(response) | |