hugging-face-cli

What this skill does

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

### Cache Management
```bash
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
```bash
# 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)
```bash
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
```bash
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
```bash
# 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
```bash
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
```bash
hf upload my-username/my-space . . --repo-type space \
  --exclude="logs/*" --delete="*" --commit-message="Sync"
```

### Check Cache Usage
```bash
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