> ## Documentation Index
> Fetch the complete documentation index at: https://docs.archetypeai.app/llms.txt
> Use this file to discover all available pages before exploring further.

# Fine-Tuning

**Fine-tuning** is a process by which you train one of Archetype's models with a data set
specifically designed to optimize the model's performance for your use case. This is the most
powerful customization option, producing a model specifically adapted to your sensor
configuration or use case.

The result of fine-tuning Newton is a forked instance of Newton configured for your use case,
encrypted and access-controlled with visibility to your organization only.

## When to Use Fine-Tuning

Fine-tuning is appropriate when:

* You have a unique use case or bespoke sensor that can benefit from custom training on a
  proprietary dataset
* You have a substantial labeled dataset (typically hundreds or thousands of examples)
* [N-shot examples](/core-concepts/customization/n-shot-examples) aren't sufficient for your
  accuracy requirements
* You need Newton to learn complex patterns specific to your domain

## What Fine-Tuning Produces

The result of fine-tuning is a new Newton instance with:

* A custom model and inference configuration, tuned for your use case
* Encryption and access controls limiting visibility to your organization only
* Behavior adapted to your specific sensor configuration and use case based on the knowledge
  gained from fine tuning on your custom dataset.

## Labeled Examples

Fine-tuning relies on high-quality examples to help steer a pre-trained instance of Newton to a new
sensor configuration or use case.

Each labeled example consists of an **input** and **output** pair in JSON or YAML format. This
provides Newton the information required to understand that given *input X*, it should generate
*output Y*. As Newton is a multimodal model, the input (X) can consist of one or more sensor
types, with the output (Y) currently constrained to text output.

## Fine-Tuning Using the Fine-Tuning Node

To perform fine-tuning, use the Fine-Tuning Node to run a fine-tuning job. This job evaluates
the example datasets, generating the custom model. Once you have a satisfactory model, work with
your Archetype AI representative to enable its use.

## Best Practices

**Quality over quantity**

A smaller set of high-quality, representative examples is more valuable than a large set of
noisy or inconsistent examples.

**Consistent formatting**

Ensure your output format is consistent across examples. Newton will learn the patterns in your
labeled data.

**Cover edge cases**

Include examples that represent the full range of scenarios Newton will encounter, including
edge cases and unusual inputs.

## Examples

Find examples showing how to run a fine-tuning job using Fine-Tuning Node in our [Archetype AI
Fine-Tuning Examples](https://github.com/archetypeai/archetypeai-fine-tuning-examples) repository.