huggingface-hub

name	huggingface-hub
description	HuggingFace hf CLI: search/download/upload models, datasets.
version	1.0.0
author	Hugging Face
license	MIT
tags	["huggingface","hf","models","datasets","hub","mlops"]

Hugging Face CLI (hf) Reference Guide

The hf command is the modern command-line interface for interacting with the Hugging Face Hub, providing tools to manage repositories, models, datasets, and Spaces.

IMPORTANT: The hf command replaces the now deprecated huggingface-cli command.

Quick Start

• Installation: curl -LsSf https://hf.co/cli/install.sh | bash -s
• Help: Use hf --help to view all available functions and real-world examples.
• Authentication: Recommended via HF_TOKEN environment variable or the --token flag.

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Core Commands

General Operations

• hf download REPO_ID: Download files from the Hub.
• hf upload REPO_ID: Upload files/folders (recommended for single-commit).
• hf upload-large-folder REPO_ID LOCAL_PATH: Recommended for resumable uploads of large directories.
• hf sync: Sync files between a local directory and a bucket.
• hf env / hf version: View environment and version details.

Authentication (hf auth)

• login / logout: Manage sessions using tokens from huggingface.co/settings/tokens.
• list / switch: Manage and toggle between multiple stored access tokens.
• whoami: Identify the currently logged-in account.

Repository Management (hf repos)

• create / delete: Create or permanently remove repositories.
• duplicate: Clone a model, dataset, or Space to a new ID.
• move: Transfer a repository between namespaces.
• branch / tag: Manage Git-like references.
• delete-files: Remove specific files using patterns.

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Specialized Hub Interactions

Datasets & Models

• Datasets: hf datasets list, info, and parquet (list parquet URLs).
• SQL Queries: hf datasets sql SQL — Execute raw SQL via DuckDB against dataset parquet URLs.
• Models: hf models list and info.
• Papers: hf papers list — View daily papers.

Discussions & Pull Requests (hf discussions)

• Manage the lifecycle of Hub contributions: list, create, info, comment, close, reopen, and rename.
• diff: View changes in a PR.
• merge: Finalize pull requests.

Infrastructure & Compute

• Endpoints: Deploy and manage Inference Endpoints (deploy, pause, resume, scale-to-zero, catalog).
• Jobs: Run compute tasks on HF infrastructure. Includes hf jobs uv for running Python scripts with inline dependencies and stats for resource monitoring.
• Spaces: Manage interactive apps. Includes dev-mode and hot-reload for Python files without full restarts.

Storage & Automation

• Buckets: Full S3-like bucket management (create, cp, mv, rm, sync).
• Cache: Manage local storage with list, prune (remove detached revisions), and verify (checksum checks).
• Webhooks: Automate workflows by managing Hub webhooks (create, watch, enable/disable).
• Collections: Organize Hub items into collections (add-item, update, list).

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Advanced Usage & Tips

Global Flags

• --format json: Produces machine-readable output for automation.
• -q / --quiet: Limits output to IDs only.

Extensions & Skills

Hermes Agent HF Resources (cntalk)

Resource	Type	URL
hello-hermes	Space	hf.co/spaces/cntalk/hello-hermes
hermes-toolkit	Space	hf.co/spaces/cntalk/hermes-toolkit
hermes-examples	Dataset	hf.co/datasets/cntalk/hermes-examples
agent-prompts	Dataset	hf.co/datasets/cntalk/agent-prompts
hermes-skills	Dataset	hf.co/datasets/cntalk/hermes-skills
hermes-integration	Dataset	hf.co/datasets/cntalk/hermes-integration

Collection: hermes-agent-resources

Sync Script

python ~/.hermes/scripts/hf_sync.py  # Sync Hermes resources to HF

Free Inference API Models

• text-generation: meta-llama/Llama-3.2-1B-Instruct, mistralai/Mistral-7B-Instruct-v0.2
• summarization: facebook/bart-large-cnn
• translation: Helsinki-NLP/opus-mt-zh-en
• sentiment-analysis: distilbert-base-uncased-finetuned-sst-2-english
• question-answering: deepset/roberta-base-squad2
• image-classification: google/vit-base-patch16-224

Inference API Example (Python)

from huggingface_hub import InferenceClient

client = InferenceClient(model="meta-llama/Llama-3.2-1B-Instruct", token="hf_xxx")
result = client.text_generation(prompt="Hello, how are you?", max_new_tokens=100, temperature=0.7)

Tips

• Free accounts can create public models/datasets/Spaces
• Space CPU Basic is free; T4 GPU is billed per hour
• Inference API has rate limits (when model not specified)
• Ensure .gitattributes is set correctly before upload (LFS if needed)
• Extensions: extend CLI via hf extensions install REPO_ID
• Skills: manage AI assistant skills with hf skills add