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huggingface-pro

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Hugging Face guidance — Hub, Transformers, datasets, fine-tuning, inference endpoints, and Spaces.

skills/huggingface-pro/SKILL.md

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## Overview

Hugging Face is the GitHub of machine learning: the Hub hosts millions of models and datasets, Transformers is the standard library for using them, and the surrounding tools (Datasets, PEFT, TRL, Inference Endpoints, Spaces) cover the journey from pretrained checkpoint to production. Most teams' ML work starts with "find a good base model on the Hub."

This skill covers the Hub workflow: finding and evaluating models, using Transformers (pipelines, tokenizers, training), datasets at scale, efficient fine-tuning (LoRA/QLoRA), and deployment options.

## When to use

- Finding pretrained models or datasets.
- Using Transformers for inference or fine-tuning.
- Fine-tuning efficiently (LoRA/QLoRA).
- Working with large datasets (streaming, preprocessing).
- Deploying models (Inference Endpoints, self-hosted).
- Building demos with Spaces.