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Alireo-400m-instruct-v0.1

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-<body>
-    <div class="container">
-        <h1>🤖 Alireo-400M Model Card</h1>
-        <h2>📝 Model Description</h2>
-        <p>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.</p>
-        <h2>✨ Key Features</h2>
-        <div class="features-list">
-            <ul>
-                <li>🏗️ <strong>Architecture:</strong> Transformer-based language model</li>
-                <li>📊 <strong>Parameters:</strong> 400M</li>
-                <li>🪟 <strong>Context Window:</strong> 8K tokens</li>
-                <li>📚 <strong>Training Data:</strong> Curated Italian text corpus (books, articles, web content)</li>
-                <li>💾 <strong>Model Size:</strong> ~800MB</li>
-            </ul>
-        </div>
-        <h2>📈 Performance</h2>
-        <div class="performance">
-            <p>Despite its compact size, Alireo-400M demonstrates impressive performance:</p>
-            <ul>
-                <li>🏆 Outperforms Qwen 0.5B across multiple benchmarks</li>
-                <li>🎯 Maintains high accuracy in Italian language understanding tasks</li>
-                <li>⚡ Efficient inference speed due to optimized architecture</li>
-            </ul>
-        </div>
-        <h2>⚠️ Limitations</h2>
-        <div class="limitations">
-            <ul>
-                <li>Limited context window compared to larger models</li>
-                <li>May struggle with highly specialized technical content</li>
-                <li>Performance may vary on dialectal variations</li>
-                <li>Not suitable for multilingual tasks</li>
-            </ul>
-        </div>
-        <h2>💻 Hardware Requirements</h2>
-        <div class="requirements">
-            <ul>
-                <li>🎮 <strong>Minimum RAM:</strong> 2GB</li>
-                <li>💪 <strong>Recommended RAM:</strong> 4GB</li>
-                <li>🎨 <strong>GPU:</strong> Optional, but recommended for faster inference</li>
-                <li>💿 <strong>Disk Space:</strong> ~1GB (including model and dependencies)</li>
-            </ul>
-        </div>
-        <h2>📜 License</h2>
-        <p>Apache 2.0</p>
-        <h2>📄 Citation</h2>
-        <div class="citation">@software{alireo2024,
   author = {[Michele Montebovi]},
   title = {Alireo-400M: A Lightweight Italian Language Model},
   year = {2024},
-}</div>
-    </div>
-</body>

+# Alireo-400M Model Card 📚
+## 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 ✨
+* **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 📈
+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 ⚠️
+* 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 💻
+* **Minimum RAM**: 2GB
+* **Recommended RAM**: 4GB
+* **GPU**: Optional, but recommended for faster inference
+* **Disk Space**: ~1GB (including model and dependencies)
+## Usage Example
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("montebovi/alireo-400m")
+tokenizer = AutoTokenizer.from_pretrained("montebovi/alireo-400m")
+# Example text
+text = "L'intelligenza artificiale sta"
+# Tokenize and generate
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=50)
+result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(result)
+```
+## License 📜
+Apache 2.0
+## Citation 📄
+```bibtex
+@software{alireo2024,
   author = {[Michele Montebovi]},
   title = {Alireo-400M: A Lightweight Italian Language Model},
   year = {2024},
+}
+```