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How To Upload My Model To Hugging Face In 8 Steps

Leo Migdal
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how to upload my model to hugging face in 8 steps

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. 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. Hugging Face has emerged as a leading platform in artificial intelligence (AI) and natural language processing (NLP), offering an extensive library of tools, models, and datasets.

This guide will walk you through the process of using Hugging Face, from setting up your environment to deploying models in various applications. Let’s dive in! Hugging Face provides a suite of libraries and tools designed to make implementing state-of-the-art machine learning (ML) models accessible and straightforward. With thousands of pre-trained models available for a variety of tasks, Hugging Face is a go-to resource for developers and researchers in AI. Before you can start using Hugging Face, you need to set up your development environment. This involves installing the necessary libraries and configuring your tools.

Ensure you have Python 3.8 or higher installed on your system. Pip, the package manager for Python, is also required to install the Hugging Face libraries. If Python is not installed, you can download it from the official Python website. Open your terminal or command prompt and run the following command to install the core Hugging Face library along with its dependencies: 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. 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: 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:

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