| --- |
| license: apache-2.0 |
| datasets: |
| - uoft-cs/cifar10 |
| - openslr/librispeech_asr |
| - udayl/UCI_HAR |
| language: |
| - en |
| metrics: |
| - bertscore |
| - accuracy |
| library_name: adapter-transformers |
| tags: |
| - code |
| - medical |
| --- |
| # UANN Model |
|
|
| ## Model Description |
|
|
| This is the Universal Adaptive Neural Network (UANN) designed for multi-modal AI agents. The model incorporates a Mixture of Experts (MoE) architecture. |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| from models.moe_model import MoEModel |
| |
| # Initialize model |
| model = MoEModel(input_dim=512, num_experts=3) |
| |
| # Dummy inputs for testing |
| vision_input = torch.randn(1, 3, 32, 32) |
| audio_input = torch.randn(1, 100, 40) |
| sensor_input = torch.randn(1, 10) |
| |
| # Forward pass |
| output = model(vision_input, audio_input, sensor_input) |
| print(output) |
