Transformers
by huggingface
Universal Deep Learning Library for Modern AI Models
Production-ready framework providing instant access to 100,000+ pre-trained models for NLP, computer vision, audio, and multimodal applications.
- 155,294+ GitHub stars
- Built with Python
- Unified API for 100,000+ pre-trained models across text, vision, audio, and multimodal tasks
- Apache License 2.0 license
About This Project
Transformers eliminates the complexity of implementing cutting-edge AI models by providing a unified API for thousands of pre-trained models across text, vision, audio, and multimodal domains. Whether you're building a chatbot, image classifier, or speech recognition system, this library lets you deploy state-of-the-art models with just a few lines of Python code.
The framework supports seamless interoperability between PyTorch, TensorFlow, and JAX, allowing developers to train models in one framework and deploy in another. With built-in optimization for inference speed and memory efficiency, you can move from research prototypes to production systems without rewriting code.
Beyond pre-trained models, Transformers provides complete training pipelines with automatic mixed precision, distributed training, and gradient accumulation. The extensive model hub integration means you can fine-tune models like GPT, BERT, LLaMA, Whisper, and CLIP on your custom datasets with minimal setup.
Active maintenance by Hugging Face and a thriving community of 150,000+ stars ensures constant updates with the latest model architectures, from traditional transformers to modern LLMs and vision-language models. Comprehensive documentation and hundreds of examples make it accessible for both rapid prototyping and enterprise-scale deployments.
Key Features
- Unified API for 100,000+ pre-trained models across text, vision, audio, and multimodal tasks
- Framework-agnostic design supporting PyTorch, TensorFlow, and JAX with easy model conversion
- Production-optimized inference with quantization, ONNX export, and hardware acceleration
- Complete training infrastructure with distributed training and automatic mixed precision
- Extensive model hub integration for one-line model loading and sharing
- Built-in tokenizers, processors, and pipelines for end-to-end task automation
How You Can Use It
Fine-tuning large language models for domain-specific chatbots and text generation
Building production-ready sentiment analysis and text classification systems
Implementing automatic speech recognition and audio transcription services
Creating image captioning and visual question answering applications
Deploying named entity recognition and information extraction pipelines
Developing multilingual translation and text summarization tools
Who Is This For?
ML engineers, data scientists, AI researchers, and backend developers building intelligent applications with natural language processing, computer vision, or audio processing capabilities