Hugging Face Dataset Upload Decision 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: 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. 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/ 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

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.

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. 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/

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