Instructions to use dnnsdunca/UANN with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use dnnsdunca/UANN with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("undefined") model.load_adapter("dnnsdunca/UANN", set_active=True) - Notebooks
- Google Colab
- Kaggle
metadata
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
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)