Overview
Hugging Face Transformers is the de-facto open-source library for working with transformer-based models across NLP, computer vision, audio, video and multimodal domains. It unifies model definitions so the same architecture interoperates with multiple back-ends (PyTorch, TensorFlow, JAX) and ecosystem tools for training, inference and deployment.
Key Features
- 1M+ pretrained checkpoints on the Hugging Face Hub covering BERT, GPT, Llama, ViT, Whisper, Stable Diffusion and more.
- Pipelines API for zero-boilerplate inference on tasks such as text generation, segmentation, ASR and document QA.
- Trainer and Accelerate integrations for efficient single-node or distributed fine-tuning, with support for mixed precision, FlashAttention, PEFT and quantization.
- Framework-agnostic design with model classes auto-generated from configuration files, enabling export to ONNX, deployment with TGI/vLLM or conversion to lightweight runtimes.
- Extensive documentation and tutorials including an LLM course, quick-start notebooks and production recipes for AWS, Azure and custom hardware.
Typical Use Cases
- Rapid inference via
pipeline().
- Fine-tuning large language or vision models on custom data.
- Prototyping new architectures by extending base model classes.
- Quantizing and optimizing models for edge or server inference.
Ecosystem Compatibility
- Training: Axolotl, DeepSpeed, PyTorch Lightning, FSDP, Unsloth.
- Inference: vLLM, SGLang, TGI, text-generation-inference.
- Adjacent libs: 🤗 Datasets, Evaluate, Diffusers, PEFT, TRL, SentenceTransformers.
Released under the Apache-2.0 license, Transformers is driven by an active open-source community and powers a wide range of state-of-the-art AI applications.
