Model
The trained set of numbers (parameters) and structure that takes an input and produces an output; the part of an AI system that does the predicting.
The Google for Developers Machine Learning Glossary says: "In general, any mathematical construct that processes input data and returns output. Phrased differently, a model is the set of parameters and structure needed for a system to make predictions."
Also called
- AI model
Kinds
- Foundation model A model trained on broad data, mostly with self-supervised learning, that can be adapted, for example through fine-tuning or post-training, for a variety of downstream tasks.
- Frontier model A foundation model that is among the current 'best-of-the-best' in the world; the set changes every few months.
- LLM A very large model trained on huge amounts of text to predict the next word, which lets it write, summarize, answer questions and follow instructions.
- Open weights A model whose weights (numbers that the model learned in training) are published for anyone to download and run, usually without the training data or code, and under the vendor's license terms.
- Reasoning model A model trained to work through a problem in a sequence of steps and plan its answer before it responds, spending more time and tokens to do better on hard tasks.
- Small language model A language model that is small relative to frontier models, usually a few billion parameters, and can be run cheaply, often on local hardware.
How it connects
- Inference runs the Model
- Model-agnostic can swap the Model
- Parameters make up a Model