{"contents":[{"role":"user","parts":[{"fileData":{"mimeType":"application/pdf","fileUri":"gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf"}},{"text":"You are a very professional document summarization specialist.Please summarize the given document."}]},{"role":"model","parts":[{"text":"The report introduces Gemini 1.5 Pro, a multimodal AI model developed by Google DeepMind. The report positions Gemini 1.5 Pro as a significant advancement in multimodal AI, pushing the boundaries of long-context understanding and opening new avenues for future research and applications."}]}]}
[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC):2025-09-04。"],[],[],null,["# Document tuning\n\nThis page provides prerequisites and detailed instructions for fine-tuning\nGemini on document data using supervised learning.\n\nUse cases\n---------\n\nFine-tuning lets you customize powerful language models for your specific needs.\nHere are some key use cases where fine-tuning with your own set of PDFs can\nsignificantly enhance a model's performance:\n\n- **Internal knowledge base**: Convert your internal documents into an AI-powered knowledge base that provides instant answers and insights. For example, a sales representative could instantly access product specifications and pricing details from past training materials.\n- **Research assistant**: Create a research assistant capable of analyzing a collection of research papers, articles, and books. A researcher studying climate change could quickly analyze scientific papers to identify trends in sea level rise or assess the effectiveness of different mitigation strategies.\n- **Legal or regulatory compliance**: Fine-tuning on legal documents can help automate contract review, flagging potential inconsistencies or areas of risk. This allows legal professionals to focus on higher-level tasks while ensuring compliance.\n- **Automated report generation**: Automate the analysis of complex financial reports, extracting key performance indicators and generating summaries for stakeholders. This can save time and reduce the risk of errors compared to manual analysis.\n- **Content summarization and analysis**: Summarize lengthy PDF documents, extract key insights, and analyze trends. For example, a market research team could analyze a collection of customer surveys to identify key themes and sentiment.\n- **Document comparison and version control**: Compare different versions of a document to identify changes and track revisions. This can be particularly useful in collaborative environments where multiple authors contribute to a document.\n\nLimitations\n-----------\n\n### Gemini 2.5 models\n\n### Gemini 2.0 Flash\nGemini 2.0 Flash-Lite\n\nTo learn more about document understanding requirements, see [Document understanding](/vertex-ai/generative-ai/docs/multimodal/document-understanding#document-requirements).\n\nDataset format\n--------------\n\nThe `fileUri` for your dataset can be the URI for a file in a Cloud Storage\nbucket, or it can be a publicly available HTTP or HTTPS URL.\n\nTo see the generic format example, see\n[Dataset example for Gemini](/vertex-ai/generative-ai/docs/models/gemini-supervised-tuning-prepare#dataset-example).\n\nThe following is an example of a document dataset. \n\n {\n \"contents\": [\n {\n \"role\": \"user\",\n \"parts\": [\n {\n \"fileData\": {\n \"mimeType\": \"application/pdf\",\n \"fileUri\": \"gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf\"\n }\n },\n {\n \"text\": \"You are a very professional document summarization specialist. Please summarize the given document.\"\n }\n ]\n },\n {\n \"role\": \"model\",\n \"parts\": [\n {\n \"text\": \"The report introduces Gemini 2.0 Flash, a multimodal AI model developed by Google DeepMind. The report positions Gemini 2.0 Flash as a significant advancement in multimodal AI, pushing the boundaries of long-context understanding and opening new avenues for future research and applications.\"\n }\n ]\n }\n ]\n }\n\nWhat's next\n-----------\n\n- To learn more about the document understanding capability of Gemini models, see the [Document understanding](/vertex-ai/generative-ai/docs/multimodal/document-understanding) overview.\n- To start tuning, see [Tune Gemini models by using supervised fine-tuning](/vertex-ai/generative-ai/docs/models/gemini-use-supervised-tuning)\n- 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\n knowledge base](/architecture/ai-ml/generative-ai-knowledge-base)."]]