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Gemma Documentation

Official documentation for building with the Gemma family of open models. Start with Gemma 4 unless a page or the user's request names another generation. Follow the Markdown links below to read canonical page content without cloning the repository.

For agents: Official documentation for Google's Gemma open-weight AI models: quickstart, inference, reasoning, multimodal use, deployment and fine-tuning guides in Markdown

gemma 4open modelsfine-tuninghugging face transformersmultimodal inference

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# Gemma Documentation

Official documentation for building with the Gemma family of open models.
Start with Gemma 4 unless a page or the user's request names another generation.
Follow the Markdown links below to read canonical page content without cloning the repository.

## Home
- [Google Gemma 4 Open Models](https://gemma.ai/index.md): Official documentation for Google Gemma 4 open-weight AI models, multimodal capabilities, deployment, and fine-tuning.

## Get started
- [Quickstart](https://gemma.ai/get-started.md): Run your first Gemma 4 generation in one command, then pick the engine, model size and checkpoint that fit your hardware.
- [Best practices](https://gemma.ai/get-started/best-practices.md): Sampling parameters, thinking-mode tokens, modality order and image token budgets that get the best results out of Gemma 4.
- [Inference](https://gemma.ai/get-started/capabilities/text.md): Basic text inference with Gemma 4 in Hugging Face Transformers: single-turn prompts, multi-turn conversations, and system instructions.
- [Reasoning](https://gemma.ai/get-started/capabilities/thinking.md): Use Gemma 4's thinking mode to reason before answering, for text and image prompts, and parse the reasoning apart from the answer.
- [Tool calling](https://gemma.ai/get-started/capabilities/function-calling.md): When using a generative artificial intelligence (AI) model such as Gemma, you may want to use the model to operate programming interfaces in order to complete tasks or answer questions.
- [Vision overview](https://gemma.ai/get-started/capabilities/vision.md): Prepare and pass visual data to Gemma 4 for object detection, OCR, visual question answering, captioning, and multi-image reasoning.
- [Image understanding](https://gemma.ai/get-started/capabilities/image.md): You can use Gemma 3 and later models to analyze and understand the content of images.
- [Video understanding](https://gemma.ai/get-started/capabilities/video.md): Video data is a rich source of information that can help you accomplish tasks and understand the world around you.
- [Audio understanding](https://gemma.ai/get-started/capabilities/audio.md): Starting with Gemma 3n, you can use audio directly into your prompts and workflows.
- [Prompt Formatting](https://gemma.ai/get-started/prompt-formatting.md): The control tokens Gemma 4 uses to structure a prompt, and the older start_of_turn format used by Gemma 1 to 3.
- [Speed-up Gemma 4 with Multi-Token Prediction](https://gemma.ai/get-started/capabilities/mtp.md): In Gemma 4, Multi-Token Prediction (MTP) is the specific architecture used to enable highly efficient Speculative Decoding.
- [Quantization-Aware Training](https://gemma.ai/get-started/qat.md): Which Gemma 4 quantized weights to download for each engine, and why QAT beats post-training quantization at 4-bit.

## Models
- [Gemma Model Family](https://gemma.ai/models.md): What Gemma is, how the generations relate, and which variant was built from which base model.
- [Gemma 4](htt
…

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