Which UI frameworks are supported?

The skill primarily focuses on Gradio (version 5.0+) but provides patterns and SDK configurations compatible with Streamlit and Docker-based Spaces.

How does it handle hardware recommendations?

It includes a hardware decision matrix that recommends options from free CPU-basic to A100-large based on the model's parameter count and architecture.

Can I use the Hugging Face Inference API?

Yes, the skill automatically checks for Inference API availability to enable lightweight, serverless deployments for supported models from major providers.

Can I deploy fine-tuned models like LoRA adapters?

Absolutely. It identifies the adapter_config.json, locates the required base model, and provides a template using PEFT to merge and deploy them correctly.

Does this skill support ZeroGPU for free hosting?

Yes, the skill includes specialized logic for the 'zero-a10g' runtime, allowing you to run GPU-intensive models on Hugging Face's free on-demand infrastructure.

Hugging Face Space Deployer

Name: Hugging Face Space Deployer
Author: GhostScientist

byGhostScientist

•

Data Science & ML

Automates the creation, configuration, and deployment of machine learning demos on Hugging Face Spaces using Gradio, Streamlit, and ZeroGPU.

About

The Hugging Face Space Deployer is a specialized skill designed for AI engineers to streamline the process of showcasing ML models. It provides an intelligent deployment framework that analyzes model architecture—distinguishing between full models and LoRA adapters—and determines the most efficient hosting strategy. Whether leveraging the Hugging Face Inference API for serverless execution or configuring ZeroGPU for on-demand hardware acceleration, this skill ensures your applications are correctly structured with optimized dependencies, metadata, and interactive UI templates.

Key Features

1 GitHub stars
Intelligent model analysis for LoRA vs. full model detection
Serverless Inference API integration for major model providers
Automated ZeroGPU and hardware requirement mapping
Standardized templates for Chat and Vision interfaces
Dependency management for PEFT, Transformers, and Gradio 5.0+

Use Cases

Deploying fine-tuned LoRA adapters with automatic base-model merging
Transitioning local ML scripts to production-ready Hugging Face web apps
Creating free, GPU-accelerated demos for large language models

About

Key Features

1 GitHub stars
Intelligent model analysis for LoRA vs. full model detection
Serverless Inference API integration for major model providers
Automated ZeroGPU and hardware requirement mapping
Standardized templates for Chat and Vision interfaces
Dependency management for PEFT, Transformers, and Gradio 5.0+

Use Cases

Deploying fine-tuned LoRA adapters with automatic base-model merging
Transitioning local ML scripts to production-ready Hugging Face web apps
Creating free, GPU-accelerated demos for large language models