---
title: NEBULA-X-DEMO
emoji: 🧠
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.43.1
app_file: app.py
pinned: false
license: mit
---

# 🌌 NEBULA-X: Enhanced Unified Holographic Neural Network

**Optimized for Open LLM Leaderboard v2 Evaluation**

NEBULA-X is a revolutionary AI architecture that combines holographic memory, quantum computing, and optical neural networks to create the world's first production-ready photonic neural network system.

## 🏆 Leaderboard Benchmarks

This model is optimized for evaluation on:

- **IFEval**: Instruction following capability
- **BBH**: Complex reasoning tasks  
- **MATH**: Advanced mathematical problem solving
- **GPQA**: Graduate-level question answering
- **MuSR**: Multi-step reasoning
- **MMLU-PRO**: Professional multitask understanding

## 🔬 Model Architecture

### Core Technologies
- **Holographic Memory**: 3D interference pattern storage
- **Quantum Processing**: 4 qubits per neuron for enhanced computation
- **Optical Raytracing**: GPU-accelerated light-based processing
- **Advanced Attention**: Multi-dimensional attention mechanisms

### Technical Specifications
- **Parameters**: ~85M (768 hidden size, 12 layers)
- **Context Length**: 2048 tokens
- **Precision**: float16 optimized
- **Vocabulary**: 50,257 tokens (GPT-2 compatible)

## 🚀 Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Agnuxo/NEBULA-X")
tokenizer = AutoTokenizer.from_pretrained("Agnuxo/NEBULA-X")

# Generate text
inputs = tokenizer("The future of AI is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True)
text = tokenizer.decode(outputs[0])
```

## 🔬 Research Innovation

NEBULA-X introduces groundbreaking concepts:

1. **Holographic Neural Networks**: Information stored as interference patterns
2. **Quantum-Enhanced Processing**: Superposition and entanglement for parallel computation  
3. **Optical Raytracing**: Physical light simulation for neural computation
4. **Multi-dimensional Attention**: Beyond traditional transformer attention

## 📊 Benchmark Performance

Optimized for fair evaluation on standardized benchmarks. Model designed to showcase:
- Mathematical reasoning capabilities
- Complex instruction following
- Multi-step logical reasoning
- Professional domain knowledge

## 👨‍💻 Author

**Francisco Angulo de Lafuente (Agnuxo)**
- Research Focus: Holographic Computing, Quantum AI, Optical Neural Networks
- NVIDIA LlamaIndex Developer Contest 2024 Winner

## 📄 License

Apache 2.0 - Open source and commercially usable.

---

*Ready for automated evaluation on the Open LLM Leaderboard v2*