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.
- License 许可
- MIT
- 最近更新
- 2026-10-28
- Stars
- ★ 0
- Forks
- 0
01 前言 Front Matter
- name
- hugging-face-cli
- tags
- [ "Hugging Face", "CLI", "Model Download", "Hub" ]
- author
- Synthetic Sciences
- license
- MIT
- version
- 1.0.0
- category
- llm-tools
- description
- 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.
- dependencies
- [ "huggingface-hub", "transformers" ]
02 资源文件 2
- 参考文档references/commands.md26.6 KB
- 参考文档references/examples.md8.3 KB
03 SKILL.md 正文
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 |
Credential Setup
HuggingFace token is auto-injected by openscience when connected via the dashboard.
# Verify credentials
[ -n "$HF_TOKEN" ] && echo "HF_TOKEN set" || echo "NOT SET"
If not set: connect HuggingFace at https://app.syntheticsciences.ai -> Services, then restart openscience.
Core Commands
Authentication
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
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
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
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
hf repo-files delete <repo_id> folder/ # Delete folder
hf repo-files delete <repo_id> "*.txt" # Delete with pattern
Cache Management
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
# 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)
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
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
# 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
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
hf upload my-username/my-space . . --repo-type space \
--exclude="logs/*" --delete="*" --commit-message="Sync"
Check Cache Usage
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
04 同仓库更多 Skills
statistical-power
Sample size calculation, power analysis, and effect size conversion for scientific studies. Use BEFORE data collection (a priori) to determine required N, or after (post-hoc) to compute achieved power. Covers parametric tests (t-test, ANOVA, chi-square, proportions, correlation, regression), simulation-based power for complex/clustered/nested designs, and power curves. Use this skill when someone asks 'how many samples do I need', 'what is my power', 'what effect size can I detect', or any variant. Pairs with /experimental-design (choose design first, then compute power) and /statistical-analysis (analyze data after collection).
statistical-analysis
Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.
scientific-writing
Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA/AMA/Vancouver), figures/tables, reporting guidelines (CONSORT/STROBE/PRISMA), for research papers and journal submissions.
scientific-visualization
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
research-code
Debug, profile, review, and restructure Python research and analysis code. Use when analysis scripts crash, produce wrong results, run too slowly, or need a correctness review. Covers: reading tracebacks and diagnosing root causes, hypothesis-driven debugging, reviewing code for common statistical/numerical errors, profiling data pipelines, improving reproducibility (seeds, environments, determinism), and structuring analysis scripts for sharing. Distinct from /exploratory-data-analysis (which understands data) and /statistical-analysis (which chooses and runs tests) — this skill focuses on the code itself.
markdown-mermaid-writing
Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.