Overview

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By the end of this course, learners will be able to: • Load and preprocess HF Hub datasets, fine-tune a pre-trained model with the Trainer API, compute evaluation metrics, and push the result to the Hub with a model card. • Build interactive AI applications using gr.Interface and gr.Blocks with multi-component layouts, conditional visibility, session state, and event listeners • Build a streaming multi-turn chatbot using gr.ChatInterface with an LLM backend and extend it with real-time inference workflows. • Deploy a Gradio app to HF Spaces, configure hardware and secrets, evaluate cost vs. performance trade-offs, and query the deployed app programmatically using the Gradio Python client. A model stuck in a notebook is a model nobody uses. Some familiarity with the HF Transformers library and pipeline API will help you hit the ground running. The course starts where most tutorials stop — with the data. Work through a realistic fine-tuning scenario: the off-the-shelf classifier isn’t cutting it for your domain, so you’ll load a dataset from the Hub, preprocess it with the right tokenization strategy, configure the Trainer API, evaluate with real metrics, and publish the result with a model card. Once you have a model that works for your domain, the next question is: how do people use it? Wrap it in a Gradio app, graduate from quick prototypes with gr.Interface to structured applications with gr.Blocks, and add streaming chatbot behavior with gr.ChatInterface — including diagnosing why a chatbot demo feels broken the day before a client presentation. Deploy everything to Hugging Face Spaces, configure ZeroGPU when the budget won’t cover dedicated hardware, and turn your app into a programmable API endpoint. By the end, you’ll have a fine-tuned model and a live, deployed application that other systems can call.

Syllabus

Working with HF Datasets and Fine-Tuning

Fine-tuning sounds like the answer to everything — until you realize the dataset wasn’t ready, the training config was wrong, and you’ve wasted hours of compute. This module teaches the complete customization workflow: load a dataset from the Hub, preprocess it with the right tokenization strategy, fine-tune a pre-trained model with the Trainer API, evaluate with real metrics, and publish the result to the Hub with a model card.

Gradio Fundamentals: Interface and Blocks

A model stuck in a notebook is a model nobody uses. Gradio turns inference into an interactive application — first with the simplicity of gr.Interface, then with the structural control of gr.Blocks. This module teaches you to build both, and to know when each is the right tool.

Building Chatbot and Streaming Apps

Users expect chat, not batch processing. gr.ChatInterface handles the conversation UI, but the engineering decisions — streaming, message formatting, multi-turn context management — determine whether the chatbot feels responsive or broken. This module teaches you to build production-quality conversational and real-time AI applications.

Deploying to HF Spaces

An app on your laptop is a prototype. An app on Spaces is a product. Deploy your chatbot using the git-based workflow, configure ZeroGPU for dynamic GPU allocation, manage API tokens as secrets, and then go further: use the Gradio Python client to query your deployed app programmatically, turning a UI into a reusable API endpoint.