hf-cli

Manage models, datasets, and repositories on the Hugging Face Hub.

Updated Apr 11, 2026

Search Tags:#version control #deployment #repository #model management #dataset #huggingface

Ready to run

npx skills add https://github.com/JasonTang99/sightread --skill hf-cli-jasontang99

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hf-cli
Source: https://github.com/JasonTang99/sightread/tree/main/.agents/skills/hf-cli
Command: npx skills add https://github.com/JasonTang99/sightread --skill hf-cli-jasontang99

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires huggingface_hub, and includes scripts (resource) components.

What problem does it solve?

This Skill facilitates seamless management of repositories, models, datasets, and other resources on the Hugging Face Hub, simplifying version control and deployment workflows.

Core Features & Use Cases

Repository Management: Create, delete, duplicate, or move repositories on the Hub.
Model and Dataset Handling: Download, upload, and retrieve info about models and datasets for research and deployment.
Use Case: Deploy a new machine learning model by uploading it to the Hub and manage its versions without manual file transfers.

Quick Start

Use the hf-cli skill to upload your trained model to the Hugging Face Hub for sharing and deployment.

Frequently Asked Questions about hf-cli

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate Hugging Face repository management for deploying models?▼

To manage Hugging Face repositories, you can automate workflows by utilizing the huggingface_hub Python library to interact directly with Hub APIs for creating, deleting, or moving repositories.

What is the best way to upload a trained model to the Hugging Face Hub?▼

The best way to upload a trained model to the Hugging Face Hub is by automating the file transfer process, which allows you to share your model and manage its versions without manual intervention.

Can I manage large datasets on the Hugging Face Hub using huggingface_hub?▼

Yes, you can manage large datasets on the Hugging Face Hub using the huggingface_hub library, which handles large repositories and datasets seamlessly through automated Hub API interactions.

Do I need the huggingface_hub library to download and retrieve model info?▼

Yes, you need the huggingface_hub library installed as a dependency to interact with the Hub APIs for downloading, uploading, and retrieving information about models and datasets.

How does version control work for machine learning models on the Hugging Face Hub?▼

Version control for machine learning models on the Hugging Face Hub works by automating repository management, allowing you to upload new model versions and track changes without relying on manual file transfers.

What are the limitations of using huggingface_hub for dataset deployment?▼

While huggingface_hub manages large repositories and datasets seamlessly, its effectiveness depends on proper API interactions, meaning complex custom deployment logic may still require additional scripting outside the library's standard functions.