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Huggingface Zerogpu

huggingface/skillsOfficial

This teaches Claude how to write and debug Gradio apps that run on Hugging Face's ZeroGPU infrastructure, where you get fractional GPU slices on demand instead of a dedicated card. It covers the decorator syntax (@spaces.GPU), the quirky process isolation model (models load at module scope but inference runs in forked workers), duration tuning (default 60s blocks users even if your task takes 10s), and all the sharp edges like no torch.compile support and CUDA wheel-only dependencies. The bundled references explain why returning CUDA tensors hangs and how quota pre-checks work. If you're deploying ML demos on Spaces and hitting PicklingError or quota exceeded messages, this is the missing manual.

skills.sh installs
1.4K
GitHub stars
11.3K
last updated
Aug 21, 2026
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Install

$ npx -y skills add huggingface/skills --skill huggingface-zerogpu --agent claude-code

Installs into .claude/skills of the current project.

Files

Hugging Face ZeroGPU

Rules and patterns for ML demos on Hugging Face Spaces with ZeroGPU hardware. Covers @spaces.GPU, duration and quota tuning, process isolation, the CUDA availability model, concurrency safety, and CUDA build constraints.

Scope

This skill is for Gradio SDK Spaces using ZeroGPU hardware. Docker and Static Spaces cannot schedule onto ZeroGPU, and Streamlit apps now run as Docker Spaces — so this skill applies only to Gradio. For general Gradio coding (components, layouts, event listeners), see the huggingface-gradio skill in this repo. The authoritative ZeroGPU docs live at https://huggingface.co/docs/hub/spaces-zerogpu — refer to them for the current backing GPU, runtime version lists, and tier thresholds, all of which change over time.

Reference Files

SKILL.md on GitHub

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Details

Repository
github.com/huggingface/skills
Categories
Backend & APIs, DevOps & CI/CD, Documentation, Data Science & ML, Release Management, Python
First seen
Jul 14, 2026

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