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# 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)
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