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# 🤗 Hugging Face Model Hub with OpenVINO™
[](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)
[](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.
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## 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.</br>
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).
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