Individual skill
huggingface
Use when running open models or working on the Hugging Face platform — the Inference Providers router or InferenceClient, Hub repos via the hf CLI, a dedicated Inference Endpoint with scale-to-zero, a Gradio Space with ZeroGPU, picking an open model by task/license/size, or loading one locally with transformers. NOT serving locally on your own machine (that is `ollama`), NOT renting your own GPU box (that is `runpod`), NOT hosted creative image APIs (that is `replicate-images`), NOT fine-tuning with trl/peft (that is `finetuning`).
skills/huggingface/SKILL.md
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- License
- MIT
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- agent-skills Repository topic
- agentic-ai Repository topic
- ai-agents Repository topic
- ai-coding Repository topic
- claude-code Repository topic
- cli Repository topic
- cline Repository topic
- codex Repository topic
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- huggingface Declared skill tag
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- transformers Declared skill tag
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- engineering Derived category
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Read excerpt
# Hugging Face: Hub, routed/hosted inference, and transformers
Hugging Face is three surfaces, and you should always know which one you are on:
1. **The Hub** — versioned git repos for models, datasets, and Spaces. You search it, you
`hf download` / `hf upload`, you read and write model cards.
2. **Inference** — three ways to actually run a model: the **Inference Providers** router
(serverless, you own nothing), a **dedicated Inference Endpoint** (you own a deployment
that autoscales), or **local `transformers`** (you own the machine).
3. **The catalog** — 1M+ open models you choose from by task, license, and size.
The whole skill is choosing the right surface for the job and proving it works: a 200 router
response, a live endpoint URL, a pushed repo commit. If the model is open and the workflow
lives on huggingface.co, you are in the right place. Operating the GPU box yourself is