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.