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hf-cli

Manage Hugging Face Hub resources via the hf CLI command-line workflows.

Updated Apr 29, 2026
Search Tags:#machine learning#model management#datasets#huggingface#hf-cli#inference endpoints
One-click install
npx skills add https://github.com/PubCyBerry/SO101-Sim2Real --skill hf-cli-pubcyberry
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hf-cli
Source: https://github.com/PubCyBerry/SO101-Sim2Real/tree/main/.agents/skills/hf-cli
Command: npx skills add https://github.com/PubCyBerry/SO101-Sim2Real --skill hf-cli-pubcyberry

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill simplifies complex Hugging Face Hub operations by providing guidance for managing models, datasets, repositories, storage, authentication, and AI infrastructure workflows.

Core Features & Use Cases

  • Hub Resource Management: Handle Hugging Face models, datasets, spaces, repositories, collections, and cached files through the modern hf CLI.
  • AI Infrastructure Operations: Manage authentication, buckets, jobs, inference endpoints, webhooks, and cloud-based ML workflows.
  • Use Case: When training a vision model, use this Skill to upload checkpoints, inspect datasets, deploy inference endpoints, and manage related Hugging Face resources from one command-line workflow.

Quick Start

Use the hf-cli skill to help me manage my Hugging Face model repository, authentication, and dataset workflow.

Frequently Asked Questions about hf-cli

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

FAQPage Schema
How do I manage Hugging Face models and datasets from the command line?▼

You can manage Hugging Face models and datasets from the command line by using the hf CLI to handle repository synchronization, resource discovery, and storage management. It provides structured workflows for uploading checkpoints and inspecting data directly from your terminal.

What is the best way to deploy Hugging Face inference endpoints for machine learning models?▼

Deploying Hugging Face inference endpoints is best handled through the hf CLI, which manages AI infrastructure operations and cloud-based ML workflows. It guides you through authentication and endpoint configuration to serve your models efficiently.

Do I need authentication to automate Hugging Face Hub tasks with the hf CLI?▼

Yes, authentication is required to automate Hugging Face Hub tasks. The hf CLI requires proper authentication setup to manage models, datasets, spaces, and repositories, ensuring secure access for your cloud-based ML workflows and storage operations.

Can I manage Hugging Face storage buckets and webhooks using command-line workflows?▼

Yes, you can manage Hugging Face storage buckets and webhooks using command-line workflows. The hf CLI supports AI infrastructure operations including bucket management, webhook configuration, and cloud jobs to automate your Hub resources effectively.

How does command-line Hugging Face repository administration work for machine learning development?▼

Command-line Hugging Face repository administration works by applying the hf CLI command structure to synchronize files, manage collections, and handle cached data. It streamlines machine learning development by integrating model publishing and dataset management into one terminal workflow.

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