Hugging Face Transformers Library

Transformers is an open-source library by Hugging Face providing thousands of pre-trained models for natural language processing, computer vision, audio, and multimodal tasks. Initially released in 2019 by Thomas Wolf, Julien Chaumond, and Lysandre Debut, the project has over 135,000 stars as of 2026 and is the most widely used library for transformer-based machine learning. Key features include: pre-trained models (over 500,000 models on the Hub including BERT, GPT, LLaMA, Mistral, Mixtral, Phi, T5, BART, Whisper, CLIP, Stable Diffusion, Wav2Vec2, ViT, DETR, and SAM), task support (text classification, token classification, question answering, text generation, translation, summarization, fill-mask, zero-shot classification, image classification, object detection, image segmentation, audio classification, speech recognition, and text-to-speech), model hub integration (seamless model download, upload, and versioning via the platform), tokenizers (fast Rust-based tokenizers with BPE, WordPiece, SentencePiece, and Unigram algorithms supporting streaming and batched encoding), pipeline API (high-level inference API requiring only a model name and task type for quick prototyping), Trainer API (training loop abstraction with distributed training, mixed precision, gradient accumulation, and evaluation metrics), quantization (bitsandbytes, GPTQ, AWQ, and AutoGPTQ for reducing model memory footprint), device support (CPU, GPU, Apple Silicon MPS, TPU, and multi-GPU), PEFT integration (parameter-efficient fine-tuning with LoRA, QLoRA, and adapter methods), and framework interoperability (PyTorch, TensorFlow, and JAX backends with automatic conversion).