References: Build and share a demo

Source material

Source curriculum (structural mirror, cited as further study):
• Hugging Face, "LLM Course", Chapter 9: "Building and sharing demos"
  Authors: the Hugging Face team (Lewis Tunstall, Leandro von Werra,
    Lysandre Debut, Sylvain Gugger, Merve Noyan, and others)
  Course page: https://huggingface.co/learn/llm-course/chapter9
  Code and notebooks: https://github.com/huggingface/course
  License: Apache 2.0 (prose and code)
  Required attribution: "Based on the Hugging Face LLM Course
    (huggingface.co/learn/llm-course), © Hugging Face, used under the
    Apache 2.0 license. This is an independent structural mirror;
    Hugging Face does not endorse it."
This lesson mirrors the structure of Chapter 9 (building a Gradio interface,
including model predictions, sharing demos, and Hugging Face Spaces).
Clawdemy's lessons are original prose that follows the pedagogical arc of
the course. We do not reproduce or transcribe the course; we cite it as the
recommended companion. Course materials are used under the Apache 2.0 license
with the attribution above, which requires a link to the license and an
indication of changes, and does not permit implying endorsement.

Read this next

Hugging Face LLM Course, Chapter 9: Building and sharing demos. The chapter this lesson mirrors. It goes further into the full range of input and output components, the Blocks API for custom layouts, and a complete walk-through of deploying to a Space, the natural next steps once your first Interface runs.

Going deeper

A short, durable list. Each link is a specific next step, not a generic pile.

The Gradio documentation. The reference for every component and for Interface and Blocks. The place to check which component matches your model’s input or output.
The Gradio quickstart. A focused walk from a one-line interface to a multi-component app. The fastest way to go beyond the text-in/text-out demo here.
Hugging Face Spaces. Thousands of live demos to browse, most with their source visible. The best way to learn what a polished Space looks like and to copy patterns for your own.

Adjacent topics

Where this connects inside the track.

Run a model in a few lines (lesson 2). The pipeline you wrap in a demo’s fn is the same one from lesson 2; a demo is just a UI around that inference call.
Share your work on the Hub (lesson 4). Spaces live on the same Hub as models and datasets and use the same Git-based publishing, so deploying a demo reuses everything you learned about sharing.
Fine-tuning LLMs (lesson 10). Phase 3 turns next to the LLM-specific frontier; a demo is a natural way to show off a model you have fine-tuned or instruction-tuned.