| name | huggingface-spaces |
| description | Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community gra |
| category | Document Processing |
| source | antigravity |
| tags | ["python","react","api","claude","ai","agent","llm","template","document","image"] |
| url | https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/huggingface-spaces |
Hugging Face Spaces
When to Use
Use this skill when you need build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants. Use whenever the user asks to create or host an app on Hugging Face, port code onto...
Hugging Face Spaces host machine-learning applications. There are 1M+ today; each Space is a git repo. This skill covers creating, building, debugging, and maintaining them.
0. Getting ready
Before anything else:
- Check the
hf CLI is installed: which hf. If not, pip install -U huggingface_hub.
- Check the user is logged in:
hf auth whoami. If not, ask them to run ! hf auth login in this session — they'll need a write-scoped token from https://huggingface.co/settings/tokens.
- Note
whoami's canPay and isPro flags — they gate hardware choices below.
The hf-cli skill teaches an agent every hf command and is the recommended companion to this one. Install it with hf skills add hf-cli (add --claude --global to install for Claude Code as well, user-level).
1. What a Space is
A Space is a git repo with three possible SDKs:
- Gradio — most Spaces. Python, fast iteration, supports ZeroGPU.
- Docker — arbitrary container. Use when you need a non-Python stack or a pre-built template (Streamlit, Argilla, Shiny, etc. — full list at https://huggingface.co/docs/hub/spaces-sdks-docker). Does not support ZeroGPU.
- Static — plain HTML, or a React/Svelte/Vue project built at deploy time. Use for in-browser ML (transformers.js / WebGPU / WebAssembly / onnxruntime-web), project pages, interactive reports, or Spaces that orchestrate other Spaces. No hardware needed.
Hardware tiers
Free, no creator cost: cpu-basic and zero-a10g (ZeroGPU). Static Spaces are also free and don't need hardware.
cpu-basic — 2 vCPU / 16 GB. For data viz, API-proxy Spaces, small CPU-bound models.
ZeroGPU (zero-a10g) — dynamic, per-request GPU allocation on NVIDIA RTX PRO 6000 Blackwell (sm_120). Two sizes: large (half MIG, 48 GB, 1× quota) and xlarge (full, 96 GB, 2× quota). Free for the Space creator; Space visitors consume their own daily quota (~5 min free / 40 min Pro / 60 min Enterprise). Gradio-only, PyTorch-first. Requires the creator to be on a PRO / Team / Enterprise plan.
Dedicated GPU (T4, L4, A10G, L40S, A100, H200) — billed to the Space creator by the hour. List + pricing: hf spaces hardware. Only the creator can attach these, and only if canPay=True. Use when ZeroGPU genuinely doesn't fit — non-PyTorch main model with heavy init, very-large-model long-context inference, etc.
If a non-PRO user has a use case that wants ZeroGPU, you can still build it: create a cpu-basic Space, code the app for ZeroGPU, push, then request a community grant. See references/grants.md.
For the authoritative reference: https://huggingface.co/docs/hub/spaces-overview
2. Look for an existing demo first
Before deciding how to build anything, search for prior art:
hf spaces search "<model name or task>" --sdk gradio --limit 10
If someone has built a similar Space, read its app.py and requirements.txt — that gives you the working pattern. Saves a lot of blind iteration. Mention to the user what you found before committing to an approach.
3. Decide SDK and hardware
Follow the user's explicit request first. If they were vague:
- Default for a public ML demo: Gradio + ZeroGPU. Use this unless something below applies.
- The model's only inference path is non-PyTorch (ONNX / TF / JAX / vLLM as the MAIN model, with heavy init): dedicated GPU.
- But: marginal non-torch tools (a small ONNX preprocessor, a TF utility) inside a torch-main pipeline are fine on ZeroGPU. The hijack only patches torch; init the non-torch lib inside
@spaces.GPU and pay the short per-call init cost.
- Tiny / CPU-bound model, or API-proxy Space:
cpu-basic (hardware-free isn't applicable to Gradio).
- Browser-side ML or project page: Static.
- Container with non-Python stack: Docker.
Sourcing the model
- GitHub repo — clone locally to read structure. If it already has a Gradio demo, the minimal viable path is to adapt it onto ZeroGPU (see
references/zerogpu.md). Otherwise: read the README + inference code, prefer the PyTorch path, estimate VRAM (bf16 ≈ params_B × 2 GB; 48 GB fits ≤24B params at bf16, or much larger with quantization — see references/zerogpu.md for quantization on ZeroGPU).
- HF model repo — read its README, follow any linked GitHub.
- Paper / blog post — look for an official or unofficial implementation. Don't reimplement unless trivial or the user explicitly asks.
- Vague request — search Spaces first; surface results.
If the model genuinely won't fit, check *
