Hugging Face

Sefism.

A to Z

Glossary

Every concept in this sheet, plus the smaller terms it uses. Select a concept to open its card.

A
Auto classes
Auto classes read a repo's config and build the right architecture.
C
Chat templates
Each chat model expects messages formatted with its own special tokens.
D
dtype and device_map
dtype="auto" loads weights in the precision they were saved in, often bfloat16, instead of float32.
G
generate
model.generate produces tokens one at a time.
H
Hub repositories
Every model, dataset and Space on the Hub is a Git-based repository with an id of the form owner/name, such as Qwen/Qwen3-8B.
huggingface_hub in Python
The huggingface_hub library downloads single files or whole repos, creates repos, uploads folders and searches the Hub from Python.
L
Licences and gated models
Each repo declares a licence, such as Apache 2.0, MIT or a custom model licence with usage conditions.
M
Model cards
The README of a model repo: what the model is for, how it was trained, its evaluation results, limitations and intended use, plus YAML metadata such as licence, tags and base model.
Model outputs and heads
Calling a model returns an output object: logits for models with a task head, last_hidden_state for a bare AutoModel, and loss when you pass labels.
P
pipeline
pipeline(task, model=...) bundles preprocessing, the model and postprocessing for a task such as text generation, classification, summarisation or speech recognition.
S
safetensors
The default weight format on the Hub: a simple file of tensors with a JSON header, which loads fast and cannot run code.
Spaces
Spaces host small apps, usually built with Gradio or as a Docker container, directly from a Hub repo, on free CPU or paid GPU hardware.
T
The hf CLI and login
The hf command line tool (the successor to huggingface-cli) logs in, downloads and uploads files.
The local cache
Downloads are stored once in a shared cache (by default under ~/.cache/huggingface) and reused by every library.
09 · Glossary, A to Z9 / 9