---
title: "Model Parameter"
description: "A model parameter is a learned numerical value inside a machine learning model. In neural networks, parameters are primarily weights and biases that are adjusted during training to reduce prediction error."
canonical_url: "https://promptmetheus.com/resources/llm-knowledge-base/model-parameter"
---

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A model parameter is a learned numerical value inside a machine learning model. In neural networks, parameters are primarily weights and biases that are adjusted during training to reduce prediction error.

In a [Large Language Model (LLM)](https://promptmetheus.com/resources/llm-knowledge-base/large-language-model-llm), parameters encode statistical structure learned from training data. They influence token probabilities, factual associations, style, reasoning patterns, and many other behaviors, but they are not a database of explicit records.

Parameter count is often used as a rough measure of model scale. Larger models can have more capacity, but parameter count alone does not determine quality. Architecture, training data, [tokenization](https://promptmetheus.com/resources/llm-knowledge-base/tokenization), post-training, inference compute, and evaluation coverage all matter.

Some training methods update all parameters. Parameter-efficient methods such as [LoRA (Low-Rank Adaptation)](https://promptmetheus.com/resources/llm-knowledge-base/lora-low-rank-adaptation) update a much smaller set of adapter parameters while leaving the base model frozen.

Sparse architectures complicate parameter-count comparisons. A [sparse expert model](https://promptmetheus.com/resources/llm-knowledge-base/sparse-expert-model) may contain many total parameters but activate only a subset for each token.

Google's Machine Learning Glossary defines a model as a learned set of parameters and structure in its [model](https://developers.google.com/machine-learning/glossary#model) entry.

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