Inference
Inference is using an already trained model to produce an output from an input. For a text-generating language model, this usually means processing a prompt and generating tokens in sequence.
[Hugging Face]In practice · hypothetical example
A support application supplies a question to its model and receives a draft answer. That model run is inference.
[Hugging Face]A little deeper
In text generation, prefill processes the supplied input; decoding then produces further tokens. This is the model running, rather than a training step that adjusts its weights. [Hugging Face]
A common mix-up
Every answer trains the model.
Producing an answer does not by itself update the model’s weights. [Hugging Face]
An assistant produces a reply using its existing weights. What happened?
Sources & editorial notes
Evidence: supported. Primary-source support for this scoped entry; publication approved by the project owner.
- Deep dive into Text Generation Inference with LLMs ↗ (opens in new tab)Hugging Face · Publication date unknown
Relevant section: Understanding the Basics; The Two-Phase Inference Process
Last editorial review: 2026-09-13 by project-owner.
First observed in this corpus: Unknown.
Revision history
Revision 2 · Created 2026-09-13 · Updated 2026-09-13
Project owner approved the current content for publication. Existing evidence scope and limitations remain applicable.