Skills hugging-face-cli

Execute Hugging Face Hub operations using the `hf` CLI. Use when the user needs to download models/datasets/spaces, upload files to Hub repositories, create repos, manage local cache, or run compute jobs on HF infrastructure. Covers authentication, file transfers, repository creation, cache operations, and cloud compute.

install

source · Clone the upstream repo

git clone https://github.com/tayyabexe/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/tayyabexe/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/hugging-face-cli" ~/.claude/skills/tayyabexe-skills-hugging-face-cli && rm -rf "$T"

manifest: skills/hugging-face-cli/SKILL.md

source content

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.

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