/huggingface-spaces

name: huggingface-spaces
description: "Build, deploy, debug, or maintain a Hugging Face Space using Gradio, Docker, or Static SDKs. Use for general Space hosting and configuration; use huggingface-zerogpu for ZeroGPU runtime constraints and lora-space-builder for LoRA demos."
license: Apache-2.0 (modified; see UPSTREAMS.json)

Hugging Face Spaces

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:

1. Check the `hf` CLI is installed: `which hf`. If not, `pip install -U huggingface_hub`. 2. Check the user is logged in: `hf auth whoami`. If not, run `hf auth login` — it prints a URL and a one-time code; ask the user to open the URL and enter the code, then login completes automatically (OAuth, no token needed). Alternatively, pass a write-scoped token from https://huggingface.co/settings/tokens with `--token`. 3. 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`](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`](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`](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 **Inference Providers** as an alternative: see [`references/inference-providers.md`](references/inference-providers.md). This avoids hosting the model at all.

4. Create the Space

hf repos create <namespace>/<name> --type space --space-sdk <gradio|docker|static> \
    [--flavor zero-a10g|cpu-basic|<paid-flavor>] \
    [--secrets KEY=val] [--env KEY=val] \
    --public|--private|--protected \
    --exist-ok

• `--space-sdk` is required.
• `--flavor` selects hardware. `zero-a10g` is the (legacy) identifier for ZeroGPU. Omit for `cpu-basic`. Run `hf spaces hardware` for the full paid list and pricing.
• Visibility: `--public` (anyone can view), `--private` (only you), `--protected` (app is reachable but git repo / Files tab is private).
• `--secrets KEY=val` becomes an environment variable inside the Space and is **not** visible to visit