Hugging Face
Définition
Hugging Face provides the Transformers library, Hub (models and datasets), and tools for training and deployment. C'est a central resource for NLP and multimodal models.
Il s'exécute sur PyTorch (and TensorFlow/JAX for some models). Use it to load pretrained BERT, GPT, LLMs, and vision models; fine-tune with the same API; and share your own models and datasets on the Hub. Integrates with RAG and agents via community integrations.
Comment ça fonctionne
Installez transformers, datasets et optionnellement accelerate (pour l'entraînement distribué et en précision mixte). Charad a pretrained model and tokenizer by name (par ex. from_pretrained("bert-base-uncased")); the library downloads from the Hub if needed. Inference: call model(input_ids) or use pipelines (par ex. text classification, summarization). Fine-tune: use the Trainer or native PyTorch loops with your dataset; push the resulting model back to the Hub. The Hub hosts model cards, datasets, and spaces (demos). Tokenizers, configs, and model weights are versioned; you can pin versions for reproducibility.
Cas d'utilisation
Hugging Face is the default for loading, fine-tuning, and sharing NLP and vision models and datasets.
- Loading and fine-tuning pretrained NLP and vision models
- Sharing and discovering models and datasets on the Hub
- Running inference and building pipelines with minimal code