Update README.md

README.md

@@ -1,35 +1,41 @@
-# Alireo-400M Model Card 📚
+<h1 style="font-size: 48px; text-align: center;">Alireo-400M 🤖 🇮🇹</h1>
+<p style="font-size: 24px; text-align: center;">A Lightweight Italian Language Model</p>
+
+<h2 style="font-size: 32px; color: #2980b9;">Model Description 📝</h2>
 
-## Model Description
 Alireo-400M is a lightweight yet powerful Italian language model with 400M parameters, designed to provide efficient natural language processing capabilities while maintaining a smaller footprint compared to larger models.
 
-## Key Features ✨
+<h2 style="font-size: 32px; color: #2980b9;">Key Features ✨</h2>
+
 * **Architecture**: Transformer-based language model 🏗️
 * **Parameters**: 400M 📊
 * **Context Window**: 8K tokens 🪟
 * **Training Data**: Curated Italian text corpus (books, articles, web content) 📚
 * **Model Size**: ~800MB 💾
 
-## Performance 📈
+<h2 style="font-size: 32px; color: #2980b9;">Performance 📈</h2>
+
 Despite its compact size, Alireo-400M demonstrates impressive performance:
 
 * **Benchmark Results**: Outperforms Qwen 0.5B across multiple benchmarks 🏆
 * **Language Understanding**: Maintains high accuracy in Italian language understanding tasks 🎯
 * **Speed**: Efficient inference speed due to optimized architecture ⚡
 
-## Limitations ⚠️
+<h2 style="font-size: 32px; color: #2980b9;">Limitations ⚠️</h2>
+
 * Limited context window compared to larger models
 * May struggle with highly specialized technical content
 * Performance may vary on dialectal variations
 * Not suitable for multilingual tasks
 
-## Hardware Requirements 💻
+<h2 style="font-size: 32px; color: #2980b9;">Hardware Requirements 💻</h2>
+
 * **Minimum RAM**: 2GB
 * **Recommended RAM**: 4GB
 * **GPU**: Optional, but recommended for faster inference
 * **Disk Space**: ~1GB (including model and dependencies)
 
-## Usage Example
+<h2 style="font-size: 32px; color: #2980b9;">Usage Example 💡</h2>
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -48,10 +54,12 @@ result = tokenizer.decode(outputs[0], skip_special_tokens=True)
 print(result)
 ```
 
-## License 📜
+<h2 style="font-size: 32px; color: #2980b9;">License 📜</h2>
+
 Apache 2.0
 
-## Citation 📄
+<h2 style="font-size: 32px; color: #2980b9;">Citation 📄</h2>
+
 ```bibtex
 @software{alireo2024,
   author = {[Michele Montebovi]},