Model Sharing And Uploading Hugging Face

Leo Migdal

-Nov 18, 2025, 8:22 AM

model sharing and uploading hugging face

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. 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: Welcome to the final part of our series on training and publishing your own Large Language Model with Hugging Face!

🎉 👉 If you’re landing here directly, I recommend first checking out the earlier posts: In this post, we’ll cover the most exciting part: publishing your model to Hugging Face Hub and sharing it with others. By the end, your model will be online, accessible to others, and even usable in apps! 🌍 First, install the CLI if you haven’t already:

There was an error while loading. Please reload this page. and get access to the augmented documentation experience In this page, we will show you how to share a model you have trained or fine-tuned on new data with the community on the model hub. You will need to create an account on huggingface.co for this. Optionally, you can join an existing organization or create a new one.

We have seen in the training tutorial: how to fine-tune a model on a given task. You have probably done something similar on your task, either using the model directly in your own training loop or using the Trainer/TFTrainer class. Let’s see how you can share the result on the model hub. 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. Hugging Face has become the de facto platform for sharing open-source AI models. But you need to be careful when uploading your models if you want your model to be truly usable and discoverable. In this post, we share key insights we’ve gained through hands-on work with customers uploading Transformer models to Hugging Face.

From fine-tuned LLMs to quantized adapters and custom pipelines, we’ve seen the common pitfalls developers run into—and how to fix them. These tips come from real-world experience and will help you make your models not just functional, but easy to find and use. Uploading your model with the right metadata and documentation isn’t just a nice-to-have—it directly influences how easily models can be found. Even a great model can be overlooked if it lacks proper tags, templates, or a clear model card. In contrast, a well-documented model is more likely to be featured, appear in search and filters, and even trend on the Hugging Face Hub. A model card is a README.md file, written in Markdown format, that includes both human-readable documentation and a YAML front matter block at the top for machine-readable metadata.

A good model card improves your model’s discoverability and usability on the Hugging Face Hub. A good model card is more than just documentation—it’s a transparent, structured explanation of a machine learning model’s development, intended use, limitations, and performance. It should be clear, honest, and actionable. Here are the key qualities of an effective model card, along with suggestions on how to achieve them: Hosting models on HuggingFace is a great way to share your work with the world, and it's easier than you think. You can host your model on HuggingFace's model hub, which is a centralized repository of pre-trained models.

To get started, you'll need to create a HuggingFace account and upload your model to the model hub. This can be done by clicking on the "Upload a Model" button on the HuggingFace website. HuggingFace supports a wide range of models, including transformers, BERT, and RoBERTa. On a similar theme: Hugging Face Upload Model To deploy a HuggingFace hub model, you can use Azure Machine Learning studio or the command line interface (CLI).

Model Sharing And Uploading Hugging Face

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We’re Specifically Formatting Our Data To Match The Llama 3.2