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.

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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 aren’t sufficient for your accuracy requirements
• You need Newton to learn complex patterns specific to your domain

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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.

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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.

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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.

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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.

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Examples

Find examples showing how to run a fine-tuning job using Fine-Tuning Node in our Archetype AI Fine-Tuning Examples repository.