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.
Best use case
hugging-face-cli is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using hugging-face-cli should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/hugging-face-cli/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How hugging-face-cli Compares
| Feature / Agent | hugging-face-cli | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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 ```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](references/commands.md) - **Workflow examples**: See [references/examples.md](references/examples.md)
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