By the end of this course, you will be able to:

• Explain the role of models, datasets, and Spaces in the HF ecosystem and use the pipeline API to run inference across text, vision, and audio tasks. • Tokenize and encode text inputs using AutoTokenizer, handle padding and truncation, and apply chat templates for LLM-compatible formatting. • Load pre-trained models using the appropriate AutoModel class, inspect model configuration, run manual inference, and load models in reduced precision with device_map="auto". • Evaluate model cards to assess intended use, limitations, bias disclosures, and license compatibility before recommending a model for deployment. Go from zero to confident model evaluation in four hours. All you need is basic Python — no machine learning or Hugging Face experience required. The course opens with a realistic challenge: your VP needs an AI feasibility assessment by Thursday, and the Hugging Face Hub has over 2 million models to choose from. You'll build a systematic approach to navigating that ecosystem, using filters, model cards, and task categories to find the right model instead of guessing. Run inference across text, vision, and audio tasks with the pipeline API, then go deeper: learn how tokenizers convert raw text into the numerical inputs models actually process, debug why a classifier fails silently on long messages, and discover how chat templates turn a language model into a conversation partner. Load models manually with AutoModel classes, inspect their configuration, and manage memory with reduced precision. The course closes with a hands-on model selection challenge: three candidate models, one task, and you have to decide which one ships — backed by model card evidence, not gut instinct.