Model tuning allows you to adapt Mixpeek’s underlying machine learning models to your specific domain, improving performance on specialized tasks and content types.

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Overview

Model tuning in Mixpeek lets you customize and fine-tune feature extractor and retriever models to better match your specific use cases, data, and domain. By providing examples and feedback, you can improve model performance on specialized tasks without having to build models from scratch.

Domain Adaptation

Adapt general-purpose models to your specific industry, content types, or terminology

Task Optimization

Fine-tune models for specific tasks like classification, extraction, or similarity matching

Model tuning is only available for Enterprise accounts. If you’d like to use this contact us.

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Available Tuning Approaches

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How Model Tuning Works

1

Collect Examples

Gather a set of representative examples and annotations that demonstrate the desired behavior

2

Create Tuning Set

Upload your examples to create a tuning dataset in Mixpeek

3

Configure Tuning Job

Specify which model to tune and set the tuning parameters

4

Launch Tuning Process

Start the tuning job and monitor progress

5

Evaluate Results

Compare performance metrics between the original and tuned models

6

Deploy Tuned Model

Apply the tuned model to your pipelines for improved performance

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Best Practices

Data Quality

Focus on high-quality, diverse examples that represent the full range of cases in your domain

Sample Size

For best results, provide at least 50-100 examples per class or task, with more for complex domains

Balanced Classes

Try to provide roughly equal numbers of examples for each class or category

Iterative Approach

Tune models iteratively, evaluating performance and adding examples for challenging cases

Very small tuning sets (fewer than 20 examples) may not provide significant improvements and could lead to overfitting. For best results, provide diverse, high-quality examples.

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Limitations

  • Model tuning preserves the general capabilities of the base models while adapting them to your domain
  • Complete retraining from scratch is not supported
  • Some advanced model architectures may have limited tuning capabilities
  • Tuning jobs may take anywhere from a few minutes to several hours depending on dataset size and complexity
  • Maximum number of examples per tuning set is 10,000
  • For very specialized domains, custom model building (available through our Professional Services) may be more appropriate

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