huggingface/transformers

Transformers is a model-definition framework for modern machine-learning models that work with text, computer vision, audio, video, and multimodal inputs, supporting both inference (running a trained model) and training. The basic problem it solves is fragmentation in the AI ecosystem: every research lab and tool used to define models slightly differently, so a model trained in one place would not run in another. Transformers offers one shared way to describe a model so that the same definition flows across many tools. The library acts as a pivot point. The README states that if a model definition is supported in Transformers, it becomes compatible with most training frameworks (it names Axolotl, Unsloth, DeepSpeed, FSDP, and PyTorch-Lightning), inference engines (vLLM, SGLang, and TGI), and adjacent libraries like llama.cpp and mlx. There are more than 1 million model checkpoints on the Hugging Face Hub that you can pull down and use straight from this library. The recommended entry point is the Pipeline API, a high-level helper that handles input preprocessing and output formatting for tasks like text generation, chat, audio, vision, and multimodal use, with a couple of lines of code. The README shows examples for instantiating a pipeline, picking a model, and even chatting from the command line. Installation is via pip or uv, into a Python virtual environment. You would use Transformers when you want to run an existing pretrained model, fine-tune one on your own data, or build applications on top of state-of-the-art models without reimplementing them yourself. The project is written in Python and works with Python 3.10 or newer alongside PyTorch 2.4 or newer. The full README is longer than what was provided.