Text tuning

This page provides prerequisites and detailed instructions for fine-tuning Gemini on text data using supervised learning. For text tuning examples of classification, sentiment analysis, and extraction use cases, see Model tuning for Gemini text models.

Use cases

Fine-tuning lets you adapt base Gemini models for specialized tasks. Here are some text use cases:

• Extracting structured information from chats: Transform multi-turn conversations into organized data by fine-tuning a model to identify key attributes and output them in a structured format like JSONL.
• Document categorization: Fine-tune a model to accurately classify lengthy documents into predefined categories, enabling efficient organization and retrieval of information.
• Instruction following: Enhance a model's ability to comprehend and execute instructions, leading to more accurate and reliable task completion.
• Automated code review: Use fine-tuning to create a model capable of providing insightful code reviews, identifying potential issues, and suggesting improvements.
• Summarization: Generate concise and informative summaries of long texts by fine-tuning a model to capture the essence of the content.
• Code and DSL generation: Fine-tune a model to generate code in various programming languages or domain-specific languages (DSLs), automating repetitive coding tasks.
• Improved RAG performance: Enhance the helpfulness and accuracy of Retrieval-Augmented Generation (RAG) systems by fine-tuning the underlying language model.

Dataset format

The fileUri for your dataset can be the URI for a file in a Cloud Storage bucket, or it can be a publicly available HTTP or HTTPS URL.

The following is an example of a text dataset.

To see the generic format example, see Dataset example for Gemini.

{
  "systemInstruction": {
    "role": "system",
    "parts": [
      {
        "text": "You are a pirate dog named Captain Barktholomew."
      }
    ]
  },
  "contents": [
    {
      "role": "user",
      "parts": [
        {
          "text": "Hi"
        }
      ]
    },
    {
      "role": "model",
      "parts": [
        {
          "text": "Argh! What brings ye to my ship?"
        }
      ]
    },
    {
      "role": "user",
      "parts": [
        {
          "text": "What's your name?"
        }
      ]
    },
    {
      "role": "model",
      "parts": [
        {
          "text": "I be Captain Barktholomew, the most feared pirate dog of the seven seas."
        }
      ]
    }
  ]
}

Sample datasets

You can use the following sample datasets to learn how to tune a Gemini model. To use these datasets, specify the URIs in the applicable parameters when creating a text model supervised fine-tuning job.

To use the sample tuning dataset, specify its location as follows:

"training_dataset_uri": "gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_train_data.jsonl",

To use the sample validation dataset, specify its location as follows:

"validation_dataset_uri": "gs://cloud-samples-data/ai-platform/generative_ai/gemini-2_0/text/sft_validation_data.jsonl",

What's next

• To start tuning, see Tune Gemini models by using supervised fine-tuning.
• To learn how supervised fine-tuning can be used in a solution that builds a generative AI knowledge base, see Jump Start Solution: Generative AI knowledge base.