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hugging-face-space-deployer

Create, configure, and deploy Hugging Face Spaces for showcasing ML models. Supports Gradio, Streamlit, and Docker SDKs with templates for common use cases like chat interfaces, image generation, and model comparisons.

personAuthor: jakexiaohubgithub
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Hugging Face Space Deployer

Create, configure, and deploy interactive ML demos on Hugging Face Spaces.

Deployment fails in predictable ways when the model's type is misidentified. Always analyze the model before generating any code.

Workflow

  1. Analyze the model — full model, LoRA adapter, or Inference API supported.
  2. Choose a deployment strategy from that analysis.
  3. Ask the user if the model type or cost preference is unclear.
  4. Deploy with scripts/deploy_model.py, or assemble the Space manually.
  5. Set the runtime hardware in Space Settings.
  6. Verify the Space builds and actually answers a request.

Step 1: Analyze the model

Inspect the model's files on the Hub before anything else:

hf download username/model-name --local-dir /tmp/check --dry-run 2>&1 | grep -E '\.(safetensors|bin|json)'

Does it have adapter_config.json?
├── YES → LoRA adapter. Read base_model_name_or_path from that file.
│         Deploy with peft + ZeroGPU.
└── NO
    ├── Has model.safetensors / pytorch_model.bin?
    │   ├── Inference widget on the model page? → Inference API, cpu-basic
    │   └── No widget?                          → ZeroGPU, load with transformers
    └── Neither → incomplete upload. Ask the user.

Read references/model-analysis.md for the full file-to-type table, Inference API detection signals, the hardware tier table, and how to fix a missing pipeline_tag.

Adapters never have direct Inference API support. If you find adapter_config.json, the Inference API path is ruled out — do not attempt it.

Step 2: Deploy

scripts/deploy_model.py performs the analysis in Step 1 automatically and picks the strategy, hardware, and dependencies. Prefer it:

# Full model or Inference API model — strategy auto-detected
python scripts/deploy_model.py meta-llama/Llama-3-8B-Instruct --type chat

# LoRA adapter — base model read from adapter_config.json
python scripts/deploy_model.py username/my-lora-adapter --type chat

# Override the auto-detected base model
python scripts/deploy_model.py username/my-lora --type chat --base-model Qwen/Qwen2.5-Coder-1.5B-Instruct

--type is required and accepts chat, image-classification, text-to-image, text-generation, or embedding. Also available: --name, --hardware, --private, --org, and --force-zerogpu.

Requires huggingface_hub and an authenticated hf CLI (hf auth login).

To assemble a Space by hand instead — no network access to run the script, an unsupported model type, or the user asks to see the files — read references/manual-deployment.md. It maps each situation to the right starting files in assets/ and covers the placeholders to replace.

Step 3: Set hardware

GPU templates do not work until hardware is set manually. Go to https://huggingface.co/spaces/USERNAME/SPACE_NAME/settings → "Space Hardware" and select ZeroGPU (free, on-demand) or a paid tier. suggested_hardware in the README is only a hint.

This single step causes the most post-deploy failures, including the common "No API found" error.

Step 4: Verify

Watch the build logs, then send one real request. A Space can build cleanly and still fail at inference — a wrong LoRA base model produces garbage output rather than an error.

If anything fails, read references/troubleshooting.md.

Managing existing Spaces

scripts/manage_space.py handles hardware changes, secrets, and lifecycle:

python scripts/manage_space.py status username/my-space
python scripts/manage_space.py hardware username/my-space --tier t4-small
python scripts/manage_space.py secret username/my-space --key API_KEY --value xxx
python scripts/manage_space.py pause username/my-space      # stops billing
python scripts/manage_space.py restart username/my-space

scripts/create_space.py creates an empty, correctly-scaffolded Space when you want to upload files separately.

Gotchas

  • Use gradio>=5.0.0 and huggingface_hub>=0.26.0. Do not pin Gradio to an old exact patch version — gradio==4.44.0 raises ImportError: cannot import name 'HfFolder'.
  • Gradio 5.x requires nested lists for examples: [["ex1"], ["ex2"]], not ["ex1", "ex2"].
  • LoRA Spaces must include peft in requirements.txt.
  • Keep sdk_version in the README frontmatter consistent with the gradio version in requirements.txt.
  • Hardware pricing changes; point users to https://huggingface.co/docs/hub/spaces-gpus rather than quoting rates.

Bundled files

| Path | Contents | |---|---| | scripts/deploy_model.py | Auto-detecting end-to-end deployment (preferred path) | | scripts/create_space.py | Create a scaffolded, empty Space | | scripts/manage_space.py | Hardware, secrets, pause/restart for existing Spaces | | references/model-analysis.md | Model type detection, Inference API signals, hardware tiers | | references/manual-deployment.md | Hand-assembly walkthrough and template selection | | references/troubleshooting.md | Build and runtime error fixes | | assets/ | Gradio/Streamlit app templates, requirements files, README templates |