Training
Also: model training
Adjusting a model with examples and a learning rule. The aim is to improve how it handles tasks.
A model is a huge set of dials, each holding a number. Training turns the dials a little at a time, over millions of examples, until the model’s guesses are good. After training the dials are locked: using the model does not change them, and showing it your document does not teach it anything permanent.
The fuller definition
Training adjusts a model's parameters using data and an optimization objective so its outputs better satisfy that objective.
Go deeper
- ExamplesInputs paired with the expected output.
- PredictionThe model produces an output.
- LossHow far off it was, as a number.
- UpdateParameters nudged to reduce the loss; repeat.
Training ends at the knowledge cutoff. Whatever the examples contained, including their gaps and biases, is what the model learned.
A concrete example
A model predicts missing or next tokens and updates parameters in response to measured error.
The distinction that matters
Providing a file in chat normally supplies context; it is not the same as training the model on that file.
Related terms
Pretraining
An early, broad stage of model training. The model learns patterns from a large set of data before later training for some uses.
Training & generationLoss function
The score training uses to measure how wrong a model's guess was. Training tries to make this number smaller.
FoundationsInference
The act of running a trained model to get an answer. In law the word means something else: a conclusion drawn from evidence.
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.