Update README.md

README.md

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----
-license: apache-2.0
-library_name: transformers
-tags:
-- holographic-neural-networks
-- quantum-computing
-- optical-computing
-- text-generation
-- benchmark-ready
-datasets:
-- cais/mmlu
-- gsm8k
-base_model_relation: original
-model-index:
-- name: NEBULA-X
-  results:
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: Open LLM Leaderboard
-      type: open-llm-leaderboard
-    metrics:
-    - type: accuracy
-      name: Benchmark Score
----
-
-# 🌌 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*
+---
+title: NEBULA-X-DEMO
+emoji: 🧠
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 4.44.0
+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*