Update README.md

README.md

@@ -41,7 +41,7 @@ datasets:
 [![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
 [![Transformers](https://img.shields.io/badge/🤗%20Transformers-Compatible-blue)](https://github.com/huggingface/transformers)
 
-## What Makes This Model Unique
+## 🌌 What Makes This Model Unique
 
 Chronos-1.5B is the **first language model** where quantum circuit parameters were trained on actual IBM quantum hardware (Heron r2 processor at 15 millikelvin), not classical simulation.
 
@@ -53,7 +53,7 @@ Chronos-1.5B is the **first language model** where quantum circuit parameters we
 
 This hybrid approach integrates VibeThinker-1.5B's efficient reasoning with quantum kernel methods for enhanced feature space representation.
 
-## Quick Start
+## ⚡️ Quick Start
 
 **No quantum hardware required** - the model runs on standard GPUs/CPUs using pre-trained quantum parameters.
 ```python
@@ -72,7 +72,7 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
 **That's it!** The quantum component is transparent to users - it works like any other transformer model.
 
-## Architecture
+## 🪐 Architecture
 
 ![chrn11](https://cdn-uploads.huggingface.co/production/uploads/67329d3f69fded92d56ab41a/s5m81n320NOFc2mSIWQWw.png)
 
@@ -107,9 +107,9 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
 **Important:** Quantum training is complete. Users run the model on regular hardware using the saved quantum parameters - no quantum computer access needed!
 
-## Performance & Benchmarks
+## 🌊 Performance & Benchmarks
 
-## AIME 2025 Benchmark Results
+## 🔗 AIME 2025 Benchmark Results
 
 | Model | Score |
 |-------|-------|
@@ -123,7 +123,7 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 | Mistral Large 3 | 38.0% |
 | Llama 4 Maverick | 19.3% |
 
-## AIME 2024 Benchmark Results
+## 🔗 AIME 2024 Benchmark Results
 
 | Model | Score |
 |-------|-------|
@@ -133,7 +133,7 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 | Claude Opus 4 | 76.0% |
 | Magistral Medium | 73.6% |
 
-## CritPt Benchmark Results
+## 🔗 CritPt Benchmark Results
 
 | Model | Score |
 |-----|-----|
@@ -196,15 +196,15 @@ Chronos-1.5B was specifically trained on problems requiring quantum mechanical u
 
 ## Use Cases
 
-### ✅ Good For:
+### Good For:
 
-- #### Quantum Error Correction (QEC)
+-  **Quantum Error Correction (QEC)**
 
-- #### Quantum Circuit Optimization
+-  **Quantum Circuit Optimization**
 
-- #### Molecular Simulation & Quantum Chemistry
+-  **Molecular Simulation & Quantum Chemistry**
 
-- #### Quantum Information Theory
+-  **Quantum Information Theory**
 
 ![lll](https://cdn-uploads.huggingface.co/production/uploads/67329d3f69fded92d56ab41a/uvYkP1r66AoFeq-GClx7o.png)
 
@@ -248,7 +248,7 @@ with open("quantum_kernel.pkl", "rb") as f:
 print(f"Quantum parameters: {quantum_params}")
 ```
 
-## The Hypnos Family
+##  🧬 The Hypnos Family
 
 Chronos-1.5B is part of a series exploring quantum-enhanced AI: