Hugging Face
Definição
Hugging Face provides the Transformers library, Hub (models and datasets), and tools for training and deployment. É a central resource for NLP and multimodal models.
Executa sobre 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.
Como funciona
Instale transformers, datasets e opcionalmente accelerate (para treinamento distribuído e de precisão mista). Carrregue um modelo pré-treinado e tokenizer por nome (por ex. from_pretrained("bert-base-uncased")); the library downloads from the Hub if needed. Inference: call model(input_ids) or use pipelines (por 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.
Casos de uso
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