hugging-face-cli
The hugging-face-cli skill provides terminal access to the Hugging Face Hub's `hf` CLI tool for managing models, datasets, and spaces. Use it to authenticate with the Hub, download or upload repositories and files, create and manage repos, handle local cache operations, retrieve model and dataset information, and execute GPU compute jobs on Hugging Face infrastructure.
git clone --depth 1 https://github.com/patchy631/ai-engineering-hub /tmp/hugging-face-cli && cp -r /tmp/hugging-face-cli/hugging-face-skills/skills/hugging-face-cli ~/.claude/skills/hugging-face-cliSKILL.md
# 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`
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Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work alongside HF MCP server for comprehensive dataset workflows.
Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations with vLLM/lighteval. Works with the model-index metadata format.
This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence. Designed for general-purpose compute workloads including data processing, inference, experiments, batch jobs, and any Python-based tasks. Should be invoked for tasks involving cloud compute, GPU workloads, or when users mention running jobs on Hugging Face infrastructure without local setup.
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.
Use this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help. This is especially useful when chaining or combining API calls or the task will be repeated/automated. This Skill creates a reusable script to fetch, enrich or process data.