Introduction to tuning

Model tuning is a crucial process in adapting Gemini to perform specific tasks with greater precision and accuracy. Model tuning works by providing a model with a training dataset that contains a set of examples of specific downstream tasks.

This page provides an overview of model tuning for Gemini, describes the tuning options available for Gemini, and helps you determine when each tuning option should be used.

Benefits of model tuning

Model tuning is an effective way to customize large models to your tasks. It's a key step to improve the model's quality and efficiency. Model tuning provides the following benefits:

  • Higher quality for your specific tasks
  • Increased model robustness
  • Lower inference latency and cost due to shorter prompts

Tuning compared to prompt design

  • Prompting with pre-trained Gemini models: Prompting is the art of crafting effective instructions to guide AI models like Gemini in generating the outputs you want. It involves designing prompts that clearly convey the task, format you want, and any relevant context. You can use Gemini's capabilities with minimal setup. It's best suited for:
    • Limited labeled data: If you have a small amount of labeled data or can't afford a lengthy fine-tuning process.
    • Rapid prototyping: When you need to quickly test a concept or get a baseline performance without heavy investment in fine-tuning.
  • Customized fine-tuning of Gemini models: For more tailored results, Gemini lets you fine-tune its models on your specific datasets. To create an AI model that excels in your specific domain, consider fine-tuning. This involves retraining the base model on your own labeled dataset, adapting its weights to your task and data. You can adapt Gemini to your use cases. Fine-tuning is most effective when:
    • You have labeled data: A sizable dataset to train on (think 100 examples or more), which allows the model to deeply learn your task's specifics.
    • Complex or unique tasks: For scenarios where advanced prompting strategies are not sufficient, and a model tailored to your data is essential.

We recommend starting with prompting to find the optimal prompt. Then, move on to fine-tuning (if required) to further boost performances or fix recurrent errors. While adding more examples might be beneficial, it is important to evaluate where the model makes mistakes before adding more data. High-quality, well-labeled data is crucial for good performance and better than quantity. Also, the data you use for fine-tuning should reflect the prompt distribution, format and context the model will encounter in production.

Tuning provides the following benefits over prompt design:

  • Allows deep customization on the model and results in better performance on specific tasks.
  • Align the model with custom syntax, instructions, domain specific semantic rules.
  • Offers more consistent and reliable results.
  • Capable of handling more examples at once.
  • Save cost at inference by removing few-shot examples, long instructions in the prompts

Tuning approaches

Parameter-efficient tuning and full fine-tuning are two approaches to customizing large models. Both methods have their advantages and implications in terms of model quality and resource efficiency.

Parameter efficient tuning

Parameter-efficient tuning, also called adapter tuning, enables efficient adaptation of large models to your specific tasks or domain. Parameter-efficient tuning updates a relatively small subset of the model's parameters during the tuning process.

To understand how Vertex AI supports adapter tuning and serving, you can find more details in the following whitepaper, Adaptation of Large Foundation Models.

Full fine-tuning

Full fine-tuning updates all parameters of the model, making it suitable for adapting the model to highly complex tasks, with the potential of achieving higher quality. However full fine tuning demands higher computational resources for both tuning and serving, leading to higher overall costs.

Parameter efficient tuning compared to full fine tuning

Parameter-efficient tuning is more resource efficient and cost effective compared to full fine-tuning. It uses significantly lower computational resources to train. It's able to adapt the model faster with a smaller dataset. The flexibility of parameter-efficient tuning offers a solution for multi-task learning without the need for extensive retraining.

Supported tuning methods

Vertex AI supports supervised fine-tuning to customize foundational models.

Supervised fine-tuning

Supervised fine-tuning improves the performance of the model by teaching it a new skill. Data that contains hundreds of labeled examples is used to teach the model to mimic a desired behavior or task. Each labeled example demonstrates what you want the model to output during inference.

When you run a supervised fine-tuning job, the model learns additional parameters that help it encode the necessary information to perform the desired task or learn the desired behavior. These parameters are used during inference. The output of the tuning job is a new model that combines the newly learned parameters with the original model.

Supervised fine-tuning of a text model is a good option when the output of your model isn't complex and is relatively easy to define. Supervised fine-tuning is recommended for classification, sentiment analysis, entity extraction, summarization of content that's not complex, and writing domain-specific queries. For code models, supervised tuning is the only option.

Models that support supervised fine-tuning

The following Gemini models support supervised tuning:

For more information on using supervised fine-tuning with each respective model, see the following pages: Tune text, image, audio, and document data types.

Limitations

Supervised fine-tuning has the following limitations:

  • Tuning is not a Covered Service and is excluded from the SLO of any Service Level Agreement.

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