Fine-Tuning Overview
Use fine-tuning to create a custom model for your use case.
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 offers Autonomous Fine-Tuning so you can create a custom model without having to be a machine learning expert.
Fine-tuning an AI model serves several important purposes:
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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.
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Improved performance: It typically enhances accuracy and relevance for specific use cases compared to using a generic model.
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Efficiency: Fine-tuning requires significantly less data and computational resources than training a model from scratch.
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Customization: Organizations can tailor models to their unique requirements, terminology, or style.
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Overcoming limitations: It can help address biases or gaps in the original model’s knowledge for particular applications.
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Rapid development: Fine-tuning accelerates the deployment of AI solutions by building on existing model capabilities.
How it works
Next Step: Create a Fine-Tuning Job
Create a Fine-Tuning Job
Click here to learn how to create a fine-tuning job.
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.
After Fine-Tuning
Evaluate a Model
Click here to learn how to evaluate a model.