|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Datasets||17,704||9||760||10 hours ago||76||November 16, 2023||621||apache-2.0||Python|
|🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools|
|Ultralytics||16,531||84||11 hours ago||267||December 03, 2023||676||agpl-3.0||Python|
|NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite|
|Hub||3,408||157||178||18 days ago||18||October 06, 2023||4||apache-2.0||Python|
|A library for transfer learning by reusing parts of TensorFlow models.|
|Huggingface_hub||1,369||552||2 days ago||89||November 16, 2023||112||apache-2.0||Python|
|All the open source things related to the Hugging Face Hub.|
|Gnes||1,095||4 years ago||16||other||Python|
|GNES is Generic Neural Elastic Search, a cloud-native semantic search system based on deep neural network.|
|Huggingface.js||1,054||64||12 hours ago||38||October 13, 2023||65||mit||TypeScript|
|Utilities to use the Hugging Face hub API|
|Around Dataengineering||926||a year ago||2||Python|
|A Data Engineering & Machine Learning Knowledge Hub|
|Best Of Jupyter||756||10 hours ago||2||cc-by-sa-4.0|
|🏆 A ranked list of awesome Jupyter Notebook, Hub and Lab projects (extensions, kernels, tools). Updated weekly.|
|Ml Hub||198||2 years ago||16||apache-2.0||Python|
|🧰 Multi-user development platform for machine learning teams. Simple to setup within minutes.|
|Ersilia||153||6 days ago||28||August 30, 2023||35||gpl-3.0||Python|
|The Ersilia Model Hub, a repository of AI/ML models for infectious and neglected disease research.|
The official Python client for the Huggingface Hub.
English | Deutsch | |
Source Code: huggingface/huggingface_hub
huggingface_hub library allows you to interact with the Hugging Face Hub, a platform democratizing open-source Machine Learning for creators and collaborators. Discover pre-trained models and datasets for your projects or play with the thousands of machine learning apps hosted on the Hub. You can also create and share your own models, datasets and demos with the community. The
huggingface_hub library provides a simple way to do all these things with Python.
huggingface_hub package with pip:
pip install huggingface_hub
If you prefer, you can also install it with conda.
In order to keep the package minimal by default,
huggingface_hub comes with optional dependencies useful for some use cases. For example, if you want have a complete experience for Inference, run:
pip install huggingface_hub[inference]
To learn more installation and optional dependencies, check out the installation guide.
Download a single file
from huggingface_hub import hf_hub_download hf_hub_download(repo_id="tiiuae/falcon-7b-instruct", filename="config.json")
Or an entire repository
from huggingface_hub import snapshot_download snapshot_download("stabilityai/stable-diffusion-2-1")
Files will be downloaded in a local cache folder. More details in this guide.
The Hugging Face Hub uses tokens to authenticate applications (see docs). To login your machine, run the following CLI:
huggingface-cli login # or using an environment variable huggingface-cli login --token $HUGGINGFACE_TOKEN
from huggingface_hub import create_repo create_repo(repo_id="super-cool-model")
Upload a single file
from huggingface_hub import upload_file upload_file( path_or_fileobj="/home/lysandre/dummy-test/README.md", path_in_repo="README.md", repo_id="lysandre/test-model", )
Or an entire folder
from huggingface_hub import upload_folder upload_folder( folder_path="/path/to/local/space", repo_id="username/my-cool-space", repo_type="space", )
For details in the upload guide.
We're partnering with cool open source ML libraries to provide free model hosting and versioning. You can find the existing integrations here.
The advantages are:
If you would like to integrate your library, feel free to open an issue to begin the discussion. We wrote a step-by-step guide with showing how to do this integration.
Everyone is welcome to contribute, and we value everybody's contribution. Code is not the only way to help the community. Answering questions, helping others, reaching out and improving the documentations are immensely valuable to the community. We wrote a contribution guide to summarize how to get started to contribute to this repository.