Adapter
Also: adapters
A small trained add-on that changes how a model works. The base model's stored numbers stay fixed.
Fine-tuning changes how the model writes, not what it knows about this week’s facts. For facts, give it the documents instead.
The fuller definition
In model adaptation, an adapter is an additional or separately stored set of learned parameters used with a base model to modify behavior.
Go deeper
Supply instructions and examples each time. Nothing in the model changes.
Fetch current documents and put them in context. Facts can change daily.
Train the parameters on examples. Changes behaviour and style; poor at keeping facts current.
Most legal tasks need the first two. Fine-tuning is a project, with data, evaluation, and maintenance, and it is the wrong tool for facts that change.
A concrete example
A LoRA adapter is paired with its compatible base model for a specialized task.
The distinction that matters
An adapter must match the intended architecture and base; it is not universally interchangeable.
Related terms
Low-rank adaptation (LoRA)
A cheap way to fine-tune: instead of changing all the model's numbers, you train a small add-on that sits beside them.
Training & generationFine-tuning
More training on a chosen set of examples. It changes a model to suit a task or style.
FoundationsModel weights
The full set of numbers that make up a trained model. Download the weights and you have the model.
Where it is taught
How models learn from examplesSource and further reading
Hugging Face · LLM courseThe definition and example are written for this library. The link gives background, official documentation or legal authority as labelled. Legal scope depends on the jurisdiction and current text; product features depend on release and settings.