README.md

metadata

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

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.

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Ready for automated evaluation on the Open LLM Leaderboard v2