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
hfcommand replaces the now deprecatedhuggingface-clicommand.
Quick Start
- Installation:
curl -LsSf https://hf.co/cli/install.sh | bash -s - Help: Use
hf --helpto view all available functions and real-world examples. - Authentication: Recommended via
HF_TOKENenvironment variable or the--tokenflag.
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.
Specialized Hub Interactions
Datasets & Models
- Datasets:
hf datasets list,info, andparquet(list parquet URLs). - SQL Queries:
hf datasets sql SQL— Execute raw SQL via DuckDB against dataset parquet URLs. - Models:
hf models listandinfo. - Papers:
hf papers list— View daily papers.
Discussions & Pull Requests (hf discussions)
- Manage the lifecycle of Hub contributions:
list,create,info,comment,close,reopen, andrename. 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 uvfor running Python scripts with inline dependencies andstatsfor resource monitoring. - Spaces: Manage interactive apps. Includes
dev-modeandhot-reloadfor 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), andverify(checksum checks). - Webhooks: Automate workflows by managing Hub webhooks (
create,watch,enable/disable). - Collections: Organize Hub items into collections (
add-item,update,list).
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
.gitattributesis 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