This is a general overview of fine-tuning and what it is. If you want to learn how to fine-tune a model, please refer to the Fine-Tuning Guide.
Prem Studio offers fine-tuning capabilities so you can create a custom model without needing to be a machine learning expert. Fine-tuning an AI model serves several important purposes:
  • Adaptation to specific tasks: Fine-tuning allows a pre-trained model to specialize in particular domains or tasks that may differ from its original training data.
  • Improved performance: It typically enhances accuracy and relevance for specific use cases compared to using a generic model.
  • Efficiency: Fine-tuning requires significantly less data and computational resources than training a model from scratch.
  • Customization: Organizations can tailor models to their unique requirements, terminology, or style.
  • Overcoming limitations: It can help address biases or gaps in the original model’s knowledge for particular applications.
  • Rapid development: Fine-tuning accelerates the deployment of AI solutions by building on existing model capabilities.

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How it works

  1. Create a Snapshot of your dataset.
  2. Fine-tune your model on your dataset.
  3. Evaluate your model.

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Next Step: Create a Fine-Tuning Job

Create a Fine-Tuning Job

Click here to learn how to create a fine-tuning job.

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Do More With Fine-Tuning

Get Started with Fine-Tuning

Fine-Tune a model with a snapshot of a dataset.

Run Experiments With Fine-Tuned Models

Prepare you fine-tune model with experiments.

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After Fine-Tuning

Evaluate a Model

Click here to learn how to evaluate a model.