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# Share a model

The last two tutorials showed how you can fine-tune a model with PyTorch, Keras, and 🤗 Accelerate for distributed setups. The next step is to share your model with the community! At Hugging Face, we believe in openly sharing knowledge and resources to democratize artificial intelligence for everyone. We encourage you to consider sharing your model with the community to help others save time and resources.

In this tutorial, you will learn two methods for sharing a trained or fine-tuned model on the [Model Hub](https://huggingface.co/models):

- Programmatically push your files to the Hub.
- Drag-and-drop your files to the Hub with the web interface.

<iframe width="560" height="315" src="https://www.youtube.com/embed/XvSGPZFEjDY" title="YouTube video player"
frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
picture-in-picture" allowfullscreen></iframe>

<Tip>

To share a model with the community, you need an account on [huggingface.co](https://huggingface.co/join). You can also join an existing organization or create a new one.

</Tip>

## Repository features
The context provided clearly outlines the two methods for sharing a trained or fine-tuned model on the Hugging Face Model Hub. It explicitly mentions that you can either programmatically push your files to the Hub or use the drag-and-drop feature via the web interface. This information directly answers the question posed, making it unambiguous and straightforward.


This question is quite useful for machine learning developers working with the Hugging Face ecosystem. Sharing models on the Hugging Face Model Hub is a common task for developers who want to make their models accessible to others or use them in different applications. Understanding the methods for sharing models can help streamline collaboration and deployment processes. Therefore, this question is relevant and practical for developers in this field.


The question is clear and specific, asking about methods for sharing a model on the Hugging Face Model Hub. It does not rely on any additional context to be understood, as it directly references a well-known platform and its functionality. An operator with access to documentation or knowledge of the Hugging Face ecosystem would understand the question without needing further information.

