Hugging Face
Definition
Hugging Face provides the Transformers library, Hub (models and datasets), and tools for training and deployment. It is a central resource for NLP and multimodal models.
It runs on 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.
How it works
Install transformers, datasets, and optionally accelerate (for distributed and mixed-precision training). Load a pretrained model and tokenizer by name (e.g. from_pretrained("bert-base-uncased")); the library downloads from the Hub if needed. Inference: call model(input_ids) or use pipelines (e.g. 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.
Use cases
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