hugging-face-cli

name	hugging-face-cli
description	The hf CLI provides direct terminal access to the Hugging Face Hub for downloading, uploading, and managing repositories, cache, and compute resources.
risk	safe
source	https://github.com/huggingface/skills/tree/main/skills/hugging-face-cli
date_added	2026-02-27

Hugging Face CLI

The hf CLI provides direct terminal access to the Hugging Face Hub for downloading, uploading, and managing repositories, cache, and compute resources.

When to Use This Skill

Use this skill when:

• User needs to download models, datasets, or spaces
• Uploading files to Hub repositories
• Creating Hugging Face repositories
• Managing local cache
• Running compute jobs on HF infrastructure
• Working with Hugging Face Hub authentication

Quick Command Reference

Task	Command
Login	hf auth login
Download model	hf download <repo_id>
Download to folder	hf download <repo_id> --local-dir ./path
Upload folder	hf upload <repo_id> . .
Create repo	hf repo create <name>
Create tag	hf repo tag create <repo_id> <tag>
Delete files	hf repo-files delete <repo_id> <files>
List cache	hf cache ls
Remove from cache	hf cache rm <repo_or_revision>
List models	hf models ls
Get model info	hf models info <model_id>
List datasets	hf datasets ls
Get dataset info	hf datasets info <dataset_id>
List spaces	hf spaces ls
Get space info	hf spaces info <space_id>
List endpoints	hf endpoints ls
Run GPU job	hf jobs run --flavor a10g-small <image> <cmd>
Environment info	hf env

Core Commands

Authentication

hf auth login                    # Interactive login
hf auth login --token $HF_TOKEN  # Non-interactive
hf auth whoami                   # Check current user
hf auth list                     # List stored tokens
hf auth switch                   # Switch between tokens
hf auth logout                   # Log out

Download

hf download <repo_id>                              # Full repo to cache
hf download <repo_id> file.safetensors             # Specific file
hf download <repo_id> --local-dir ./models         # To local directory
hf download <repo_id> --include "*.safetensors"    # Filter by pattern
hf download <repo_id> --repo-type dataset          # Dataset
hf download <repo_id> --revision v1.0              # Specific version

Upload

hf upload <repo_id> . .                            # Current dir to root
hf upload <repo_id> ./models /weights              # Folder to path
hf upload <repo_id> model.safetensors              # Single file
hf upload <repo_id> . . --repo-type dataset        # Dataset
hf upload <repo_id> . . --create-pr                # Create PR
hf upload <repo_id> . . --commit-message="msg"     # Custom message

Repository Management

hf repo create <name>                              # Create model repo
hf repo create <name> --repo-type dataset          # Create dataset
hf repo create <name> --private                    # Private repo
hf repo create <name> --repo-type space --space_sdk gradio  # Gradio space
hf repo delete <repo_id>                           # Delete repo
hf repo move <from_id> <to_id>                     # Move repo to new namespace
hf repo settings <repo_id> --private true          # Update repo settings
hf repo list --repo-type model                     # List repos
hf repo branch create <repo_id> release-v1         # Create branch
hf repo branch delete <repo_id> release-v1         # Delete branch
hf repo tag create <repo_id> v1.0                  # Create tag
hf repo tag list <repo_id>                         # List tags
hf repo tag delete <repo_id> v1.0                  # Delete tag

Delete Files from Repo

hf repo-files delete <repo_id> folder/             # Delete folder
hf repo-files delete <repo_id> "*.txt"             # Delete with pattern

Cache Management

hf cache ls                      # List cached repos
hf cache ls --revisions          # Include individual revisions
hf cache rm model/gpt2           # Remove cached repo
hf cache rm <revision_hash>      # Remove cached revision
hf cache prune                   # Remove detached revisions
hf cache verify gpt2             # Verify checksums from cache

Browse Hub

# Models
hf models ls                                        # List top trending models
hf models ls --search "MiniMax" --author MiniMaxAI  # Search models
hf models ls --filter "text-generation" --limit 20  # Filter by task
hf models info MiniMaxAI/MiniMax-M2.1               # Get model info

# Datasets
hf datasets ls                                      # List top trending datasets
hf datasets ls --search "finepdfs" --sort downloads # Search datasets
hf datasets info HuggingFaceFW/finepdfs             # Get dataset info

# Spaces
hf spaces ls                                        # List top trending spaces
hf spaces ls --filter "3d" --limit 10               # Filter by 3D modeling spaces
hf spaces info enzostvs/deepsite                    # Get space info

Jobs (Cloud Compute)

hf jobs run python:3.12 python script.py           # Run on CPU
hf jobs run --flavor a10g-small <image> <cmd>      # Run on GPU
hf jobs run --secrets HF_TOKEN <image> <cmd>       # With HF token
hf jobs ps                                         # List jobs
hf jobs logs <job_id>                              # View logs
hf jobs cancel <job_id>                            # Cancel job

Inference Endpoints

hf endpoints ls                                     # List endpoints
hf endpoints deploy my-endpoint \
  --repo openai/gpt-oss-120b \
  --framework vllm \
  --accelerator gpu \
  --instance-size x4 \
  --instance-type nvidia-a10g \
  --region us-east-1 \
  --vendor aws
hf endpoints describe my-endpoint                   # Show endpoint details
hf endpoints pause my-endpoint                      # Pause endpoint
hf endpoints resume my-endpoint                     # Resume endpoint
hf endpoints scale-to-zero my-endpoint              # Scale to zero
hf endpoints delete my-endpoint --yes               # Delete endpoint

GPU Flavors: cpu-basic, cpu-upgrade, cpu-xl, t4-small, t4-medium, l4x1, l4x4, l40sx1, l40sx4, l40sx8, a10g-small, a10g-large, a10g-largex2, a10g-largex4, a100-large, h100, h100x8

Common Patterns

Download and Use Model Locally

# Download to local directory for deployment
hf download meta-llama/Llama-3.2-1B-Instruct --local-dir ./model

# Or use cache and get path
MODEL_PATH=$(hf download meta-llama/Llama-3.2-1B-Instruct --quiet)

Publish Model/Dataset

hf repo create my-username/my-model --private
hf upload my-username/my-model ./output . --commit-message="Initial release"
hf repo tag create my-username/my-model v1.0

Sync Space with Local

hf upload my-username/my-space . . --repo-type space \
  --exclude="logs/*" --delete="*" --commit-message="Sync"

Check Cache Usage

hf cache ls                      # See all cached repos and sizes
hf cache rm model/gpt2           # Remove a repo from cache

Key Options

• --repo-type: model (default), dataset, space
• --revision: Branch, tag, or commit hash
• --token: Override authentication
• --quiet: Output only essential info (paths/URLs)

References

• Complete command reference: See references/commands.md
• Workflow examples: See references/examples.md