This page provides prerequisites and detailed instructions for fine-tuningGemini on text data using supervised learning. For text tuning examplesof classification, sentiment analysis, and extraction use cases,seeModel 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

ThefileUri for your dataset can be the URI for a file in a Cloud Storagebucket, 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, seeDataset 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 aGemini model. To use these datasets, specify the URIs in theapplicable 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, seeTune Gemini models by using supervised fine-tuning.
• To learn how supervised fine-tuning can be used in a solution that builds agenerative AI knowledge base, seeJump Start Solution: Generative AI knowledge base.