# 🤗 Hugging Face Model Hub with OpenVINO™ [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/eaidova/openvino_notebooks_binder.git/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252Fopenvinotoolkit%252Fopenvino_notebooks%26urlpath%3Dtree%252Fopenvino_notebooks%252Fnotebooks%2Fhugging-face-hub%2Fhugging-face-hub.ipynb) [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/hugging-face-hub/hugging-face-hub.ipynb) The Hugging Face (HF) Model Hub is a central repository for pre-trained deep learning models. It allows exploration and provides access to thousands of models for a wide range of tasks, including text classification, question answering, and image classification. Hugging Face provides Python packages that serve as APIs and tools to easily download and fine tune state-of-the-art pretrained models, namely [transformers] and [diffusers] packages. ![](https://github.com/huggingface/optimum-intel/raw/main/readme_logo.png) ## Contents: Throughout this notebook we will learn: 1. How to load a HF pipeline using the `transformers` package and then convert it to OpenVINO. 2. How to load the same pipeline using Optimum Intel package. ## Installation instructions This is a self-contained example that relies solely on its own code.
We recommend running the notebook in a virtual environment. You only need a Jupyter server to start. For details, please refer to [Installation Guide](../../README.md).