| <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}, | |
| } | |
| ``` |