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<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>
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.
<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 💾
<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 ⚡
<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
<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)
<h2 style="font-size: 32px; color: #2980b9;">Usage Example 💡</h2>
```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)
```
<h2 style="font-size: 32px; color: #2980b9;">License 📜</h2>
Apache 2.0
<h2 style="font-size: 32px; color: #2980b9;">Citation 📄</h2>
```bibtex
@software{alireo2024,
author = {[Michele Montebovi]},
title = {Alireo-400M: A Lightweight Italian Language Model},
year = {2024},
}
``` |