| # DaFucV2 AI - Dynamic AI Model |
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| 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. |
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| ## Model Overview |
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| - **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. |
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| ## DaFucV2 App Integration |
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| 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. |
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| ### Demo Video |
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| Watch a video demonstration of me talking to the DaFucV2 AI [here on YouTube](https://www.youtube.com/watch?v=-PQ-rTkqwQ8). |
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| ## Usage |
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| To load and use the model within the app: |
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| 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. |
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| To manually load the model in PyTorch: |
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| ```python |
| import torch |
| from model import DynamicAI |
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| # 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")) |
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| # Set model to evaluation mode |
| model.eval() |
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| # Example usage with input text |
| input_text = "Hello, how are you?" |
| response = model.chat(input_text) |
| print(response) |
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