How To Upload Your Dataset To Hugging Face A Complete Guide

Leo Migdal

-Nov 18, 2025, 5:12 AM

how to upload your dataset to hugging face a complete guide

Hugging Face Dataset Upload Decision Guide and get access to the augmented documentation experience This guide is primarily designed for LLMs to help users upload datasets to the Hugging Face Hub in the most compatible format. Users can also reference this guide to understand the upload process and best practices. Decision guide for uploading datasets to Hugging Face Hub. Optimized for Dataset Viewer compatibility and integration with the Hugging Face ecosystem.

Your goal is to help a user upload a dataset to the Hugging Face Hub. Ideally, the dataset should be compatible with the Dataset Viewer (and thus the load_dataset function) to ensure easy access and usability. You should aim to meet the following criteria: 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: Wondering how to make a custom dataset for your machine learning models?

Uploading your own proprietary data to Hugging Face is straightforward. This step-by-step guide details everything you need for seamless data hosting. Hugging Face is synonymous with state-of-the-art NLP models. What receives less attention however is their impressive datasets library. Over 4 million machine learning practitioners have leveraged these curated public datasets. They provide labeled data for common tasks like text classification, object detection, speech recognition, and more.

There was an error while loading. Please reload this page. 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. 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. When creating a new dataset repository, you can make the dataset Public (accessible to anyone on the internet) or Private (accessible only to members of the organization). In the Files and versions tab of the Dataset card, you can choose to add file in the hugging web interface. Navigate to the folder for the repository: Follow instructions at https://git-lfs.com/

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: and get access to the augmented documentation experience

The Hub is home to an extensive collection of community-curated and research datasets. We encourage you to share your dataset to the Hub to help grow the ML community and accelerate progress for everyone. All contributions are welcome; adding a dataset is just a drag and drop away! Start by creating a Hugging Face Hub account if you don’t have one yet. The Hub’s web-based interface allows users without any developer experience to upload a dataset. A repository hosts all your dataset files, including the revision history, making storing more than one dataset version possible.

How To Upload Your Dataset To Hugging Face A Complete Guide

People Also Search

Hugging Face Dataset Upload Decision Guide And Get Access To

Your Goal Is To Help A User Upload A Dataset

Uploading Datasets To Hugging Face Offers Several Advantages: Whether You’re

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

Uploading Your Own Proprietary Data To Hugging Face Is Straightforward.