nexus-prime

Nexus Prime fuses multimodal AI (text, vision, audio, real-time data) for zero-shot learning, ethical reasoning, and simulations in climate and medicine. Quantum-inspired algorithms enable exponential speed, solving intractable problems in seconds.

Technologies

Python PyTorch Transformers Qiskit Ray FastAPI ONNX PyTorch Lightning PyVista Kubernetes Terraform Prometheus Docker Jupyter Optuna Kafka Pytest Black Flake8 Bandit

Nexus Prime

Nexus Prime is a cutting-edge, super advanced multimodal AI model that converges text, vision, audio, and real-time data processing for zero-shot learning, ethical decision-making, and adaptive reasoning. It excels in simulating complex scenarios like global climate modeling and personalized medicine, leveraging quantum-inspired algorithms for exponential computational speedup. Built with ethical AI at its core, Nexus Prime includes built-in bias mitigation, fairness checks, and human-aligned reinforcement learning from feedback (RLHF). It supports distributed training, edge deployment, neuromorphic computing, federated learning, and VR/AR-enhanced simulations, making it ideal for high-stakes, real-world applications.

Features

Installation

Prerequisites

Setup

  1. Clone the Repository: 
    git clone https://github.com/KOSASIH/nexus-prime.git
cd nexus-prime
  2. Install Dependencies: 
    pip install -r requirements.txt

    Key dependencies: torch, transformers, qiskit, ray, fastapi, onnx, pytorch-lightning, pyvista, kubernetes, etc.

  3. Optional: Quantum Setup:
    • Install Qiskit: pip install qiskit
    • Load IBM Quantum account: from qiskit import IBMQ; IBMQ.load_account()
    • For hardware: Set use_hardware=True in configs.
  4. Build Docker Image (for deployment): 
    docker build -t nexusprime:latest .
  5. Download Pre-trained Weights (if available):
    • Run python src/nexus_prime/utils/download_weights.py --api-key YOUR_KEY
    • Weights are secured; contact support for access.

Usage

Basic Inference

Load and run the model for multimodal predictions:

from nexus_prime import NexusPrime
import torch

model = NexusPrime()
model.eval()

inputs = {
    'text': {'input_ids': torch.randint(0, 30522, (1, 512))},  # BERT tokens
    'vision': torch.randn(1, 3, 224, 224),  # Image tensor
    'audio': torch.randn(1, 16000),  # Audio waveform
    'real_time': torch.randn(1, 768)  # Sensor data
}

with torch.no_grad():
    output = model(inputs)
    prediction = output.argmax().item()
    print(f"Prediction: {prediction}")

API Inference

Start the FastAPI server for real-time queries:

python src/nexus_prime/inference/api.py

Then, query via curl or Postman (see docs/api_docs.md for details):

curl -X POST "http://localhost:8000/infer" -H "Content-Type: application/json" -d '{"text": "Test input"}'

Simulation

Run complex scenarios:

from nexus_prime.core.model import NexusPrime

model = NexusPrime()
result = model.simulate_scenario('climate', {'region': 'global'})
print(result)  # {'prediction': 1, 'ethical_flag': True}

Training

Train with distributed and ethical features:

from nexus_prime.training.trainer import train_distributed

config = {'num_classes': 1000, 'lr': 0.001}
train_distributed(config, num_workers=4)

For hyperparameter tuning: python src/nexus_prime/training/hyperparameter_tuning.py

Edge Deployment

Export to ONNX and deploy:

from nexus_prime.inference.engine import InferenceEngine

engine = InferenceEngine()
engine.export_to_onnx()
# Deploy on edge device with low-power inference

Examples

Run notebooks in Jupyter: jupyter notebook examples/jupyter_notebooks/

API Reference

See docs/api_docs.md for detailed endpoints, parameters, and examples. Key endpoints:

Training Guide

Refer to docs/tutorials/training_guide.md for steps on data preparation, ethical training, and troubleshooting.

Deployment

Kubernetes

kubectl apply -f deploy/kubernetes/deployment.yml
kubectl get pods  # Check status

Terraform

cd deploy/terraform
terraform init
terraform apply

Monitoring

Contributing

We welcome contributions! Follow these steps:

  1. Fork the repo and create a branch: git checkout -b feature/your-feature.
  2. Write tests in tests/ and ensure they pass: pytest.
  3. Lint code: black src/ and flake8 src/.
  4. Submit a PR with a description.
  5. Adhere to ethical guidelines: All changes must pass bias checks via bandit.

For issues, use GitHub Issues. Join our Discord for discussions.

License

Licensed under the MIT License. See LICENSE for details.

Changelog

For the latest, check Releases.

Support

Nexus Prime – The Future of Ethical, Quantum-Enhanced AI. 🚀

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