Prepare supervised fine-tuning data for Gemini models

This document describes how to define a supervised fine-tuning dataset for a Gemini model. You can tune text, image, audio, and document data types.

About supervised fine-tuning datasets

A supervised fine-tuning dataset is used to fine-tune a pre-trained model to a specific task or domain. The input data should be similar to what you expect the model to encounter in real-world use. The output labels should represent the correct answers or outcomes for each input.

Training dataset

To tune a model, you provide a training dataset. For best results, we recommend that you start with 100 examples. You can scale up to thousands of examples if needed. The quality of the dataset is far more important than the quantity.

Validation dataset

We strongly recommend that you provide a validation dataset. A validation dataset helps you measure the effectiveness of a tuning job.

Limitations

For limitations on datasets, such as maximum input and output tokens, maximum validation dataset size, and maximum training dataset file size, see About supervised fine-tuning for Gemini models.

Dataset format

We support the following data formats:

Dataset example for Gemini

{
  "systemInstruction": {
    "role": string,
    "parts": [
      {
        "text": string
      }
    ]
  },
  "contents": [
    {
      "role": string,
      "parts": [
        {
          // Union field data can be only one of the following:
          "text": string,
          "fileData": {
            "mimeType": string,
            "fileUri": string
          }
        }
      ]
    }
  ]
}

Parameters

The example contains data with the following parameters:

Parameters

contents

Required: Content

The content of the current conversation with the model.

For single-turn queries, this is a single instance. For multi-turn queries, this is a repeated field that contains conversation history and the latest request.

systemInstruction

Optional: Content

See Supported models.

Instructions for the model to steer it toward better performance. For example, "Answer as concisely as possible" or "Don't use technical terms in your response".

The text strings count toward the token limit.

The role field of systemInstruction is ignored and doesn't affect the performance of the model.

tools

Optional. A piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model. See Function calling.

Contents

The base structured data type containing multi-part content of a message.

This class consists of two main properties: role and parts. The role property denotes the individual producing the content, while the parts property contains multiple elements, each representing a segment of data within a message.

Parameters

role

Optional: string

The identity of the entity that creates the message. The following values are supported:

  • user: This indicates that the message is sent by a real person, typically a user-generated message.
  • model: This indicates that the message is generated by the model.

The model value is used to insert messages from the model into the conversation during multi-turn conversations.

For non-multi-turn conversations, this field can be left blank or unset.

parts

part

A list of ordered parts that make up a single message. Different parts may have different IANA MIME types.

For limits on the inputs, such as the maximum number of tokens or the number of images, see the model specifications on the Google models page.

To compute the number of tokens in your request, see Get token count.

Parts

A data type containing media that is part of a multi-part Content message.

Parameters

text

Optional: string

A text prompt or code snippet.

fileData

Optional: fileData

Data stored in a file.

functionCall

Optional: FunctionCall.

It contains a string representing the FunctionDeclaration.name field and a structured JSON object containing any parameters for the function call predicted by the model.

See Function calling.

functionResponse

Optional: FunctionResponse.

The result output of a FunctionCall that contains a string representing the FunctionDeclaration.name field and a structured JSON object containing any output from the function call. It is used as context to the model.

See Function calling.

Dataset example

Each conversation example in a tuning dataset is composed of a required messages field and an optional context field.

The messages field consists of an array of role-content pairs:

  • The role field refers to the author of the message and is set to either system, user, or model. The system role is optional and can only occur at the first element of the messages list. The user and model roles are required and can repeat in an alternating manner.
  • The content field is the content of the message.

For each example, the maximum token length for context and messages combined is 131,072 tokens. Additionally, each content field for the model field shouldn't exceed 8,192 tokens.

{
  "messages": [
    {
      "role": string,
      "content": string
    }
  ]
}

Maintain consistency with production data

The examples in your datasets should match your expected production traffic. If your dataset contains specific formatting, keywords, instructions, or information, the production data should be formatted in the same way and contain the same instructions.

For example, if the examples in your dataset include a "question:" and a "context:", production traffic should also be formatted to include a "question:" and a "context:" in the same order as it appears in the dataset examples. If you exclude the context, the model will not recognize the pattern, even if the exact question was in an example in the dataset.

Upload tuning datasets to Cloud Storage

To run a tuning job, you need to upload one or more datasets to a Cloud Storage bucket. You can either create a new Cloud Storage bucket or use an existing one to store dataset files. The region of the bucket doesn't matter, but we recommend that you use a bucket that's in the same Google Cloud project where you plan to tune your model.

After your bucket is ready, upload your dataset file to the bucket.

Follow the best practice of prompt design

Once you have your training dataset and you've trained the model, it's time to design prompts. It's important to follow the best practice of prompt design in your training dataset to give detailed description of the task to be performed and how the output should look like.

What's next