Uploading Models Hugging Face

Leo Migdal

-Nov 18, 2025, 2:45 AM

and get access to the augmented documentation experience To upload models to the Hub, you’ll need to create an account at Hugging Face. Models on the Hub are Git-based repositories, which give you versioning, branches, discoverability and sharing features, integration with dozens of libraries, and more! You have control over what you want to upload to your repository, which could include checkpoints, configs, and any other files. You can link repositories with an individual user, such as osanseviero/fashion_brands_patterns, or with an organization, such as facebook/bart-large-xsum. Organizations can collect models related to a company, community, or library!

If you choose an organization, the model will be featured on the organization’s page, and every member of the organization will have the ability to contribute to the repository. You can create a new organization here. NOTE: Models do NOT need to be compatible with the Transformers/Diffusers libraries to get download metrics. Any custom model is supported. Read more below! There are several ways to upload models for them to be nicely integrated into the Hub and get download metrics, described below.

Hugging Face has emerged as a leading platform for sharing and collaborating on machine learning models, particularly those related to natural language processing (NLP). With its user-friendly interface and robust ecosystem, it allows researchers and developers to easily upload, share, and deploy their models. This article provides a comprehensive guide on how to upload and share a model on Hugging Face, covering the necessary steps, best practices, and tips for optimizing your model's visibility and usability. Hugging Face is a prominent machine-learning platform known for its Transformers library, which provides state-of-the-art models for NLP tasks. The Hugging Face Model Hub is a central repository where users can upload, share, and access pre-trained models. This facilitates collaboration and accelerates the development of AI applications by providing a rich collection of ready-to-use models.

Before uploading your model to Hugging Face, there are several preparatory steps you need to follow to ensure a smooth and successful process: If you don't already have a Hugging Face account, sign up at Hugging Face . You’ll need an account to upload and manage your models. There was an error while loading. Please reload this page. Hugging Face is a leading platform for sharing datasets, models, and tools within the AI and machine learning community.

Uploading your dataset to Hugging Face allows you to leverage its powerful collaboration features, maintain version control, and share your data with the wider research community. This guide walks you through the process of uploading your dataset, supported formats, and best practices for documentation and sharing. Uploading datasets to Hugging Face offers several advantages: Whether you’re contributing to open datasets or maintaining private repositories, Hugging Face provides the tools to manage your data effectively. Hugging Face supports a variety of file formats for datasets, making it versatile for different use cases. This part of the tutorial walks you through the process of uploading a custom dataset to the Hugging Face Hub.

The Hugging Face Hub is a platform that allows developers to share and collaborate on datasets and models for machine learning. Here, we’ll take an existing Python instruction-following dataset, transform it into a format suitable for training the latest Large Language Models (LLMs), and then upload it to Hugging Face for public use. We’re specifically formatting our data to match the Llama 3.2 chat template, which makes it ready for fine-tuning Llama 3.2 models. First, we need to install the necessary libraries and authenticate with the Hugging Face Hub: After running this cell, you will be prompted to enter your token. This authenticates your session and allows you to push content to the Hub.

Next, we’ll load an existing dataset and define a function to transform it to match the Llama 3.2 chat format: and get access to the augmented documentation experience Sharing your files and work is an important aspect of the Hub. The huggingface_hub offers several options for uploading your files to the Hub. You can use these functions independently or integrate them into your library, making it more convenient for your users to interact with the Hub. Whenever you want to upload files to the Hub, you need to log in to your Hugging Face account.

For more details about authentication, check out this section. Once you’ve created a repository with create_repo(), you can upload a file to your repository using upload_file(). Specify the path of the file to upload, where you want to upload the file to in the repository, and the name of the repository you want to add the file to. Depending on your repository type, you can optionally set the repository type as a dataset, model, or space. Uploading your model to Hugging Face is a straightforward process that can be completed in just 8 steps. First, create a Hugging Face account if you haven't already.

This will give you access to their platform and allow you to upload your model. To get started, you'll need to have a model that's ready to be uploaded. This means it should be in a format that Hugging Face supports, such as the transformers library. Hugging Face supports a wide range of models, including those built with popular libraries like PyTorch and TensorFlow. To prepare your model for uploading, you'll need to fine-tune it on your specific task, either using the model directly in your own training loop or the Trainer/TFTrainer class. This will help you share the result on the model hub.

Uploading Models Hugging Face

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