Gemma formatting and system instructions  |  Google AI for Developers Skip to main content Models Gemini About Docs API reference Pricing Imagen About Docs Pricing Veo About Docs Pricing Gemma About Docs Gemmaverse Solutions Build with Gemini Gemini API Google AI Studio Customize Gemma open models Gemma open models Multi-framework with Keras Fine-tune in Colab Run on-device Google AI Edge Gemini Nano on Android Chrome built-in web APIs Build responsibly Responsible GenAI Toolkit Secure AI Framework Code assistance Android Studio Chrome DevTools Colab Firebase Google Cloud JetBrains Jules VS Code Community Google AI Forum Gemini for Research / English Deutsch Español – América Latina Français Indonesia Italiano Polski Português – Brasil Shqip Tiếng Việt Türkçe Русский עברית العربيّة فارسی हिंदी বাংলা ภาษาไทย 中文 – 简体 中文 – 繁體 日本語 한국어 Sign in Gemma Gemma Docs Models More Gemma Docs Solutions More Code assistance More Community More Overview Get started Releases Models Core Gemma Overview Gemma 4 model card Gemma 3 model card Gemma 2 model card Gemma 1 model card Core Variants Gemma 3n Overview Model card DiffusionGemma Overview Model card Diffusion Explained Generate Output FunctionGemma Overview Model card Formatting and best practices Function calling with Hugging Face Transformers Full function calling sequence with FunctionGemma Fine-tune FunctionGemma EmbeddingGemma Overview Model card Generate embeddings with Sentence Transformers Fine-tune EmbeddingGemma PaliGemma Overview v2 model card v1 model card Generate output with Keras Fine-tune with JAX and Flax Prompt and system instructions ShieldGemma Overview ShieldGemma 2 Model card ShieldGemma 1 Model card Run Gemma Fundamentals Overview Prompt Formatting Legacy Gemma setup [Gemma 1, 2, and 3] Legacy Prompt and system instructions [Gemma 1, 2, and 3] Run locally with a Chat UI or integrate via API LM Studio Ollama Run efficiently on Edge LiteRT-LM Llama.cpp MLX Build/Train in Python Tunix (Tune-in-JAX) Hugging Face Transformers Keras Unsloth Deploy to Production / Enterprise Gemini API Google Cloud Cloud GKE Multi-Token Prediction (MTP) Overview Hugging Face Transformers Core Capabilities Text Basic and multi-turn chat Function calling Visual data Overview Image understanding Video understanding Audio data Thinking Tuning guides Overview Tune using Hugging Face Transformers and QLoRA Vision Tune using Hugging Face Transformers and QLoRA Full model fine-tune using Hugging Face Transformers Tune using Gemma library Research and tools RecurrentGemma Overview Inference using JAX and Flax Fine-tune using JAX and Flax Model card DataGemma Gemma Scope Gemma-APS Community Gemmaverse Discord Legal Terms of use Gemma 4 license Prohibited use Intended use statement Gemini About Docs API reference Pricing Imagen About Docs Pricing Veo About Docs Pricing Gemma About Docs Gemmaverse Build with Gemini Gemini API Google AI Studio Customize Gemma open models Gemma open models Multi-framework with Keras Fine-tune in Colab Run on-device Google AI Edge Gemini Nano on Android Chrome built-in web APIs Build responsibly Responsible GenAI Toolkit Secure AI Framework Android Studio Chrome DevTools Colab Firebase Google Cloud JetBrains Jules VS Code Google AI Forum Gemini for Research Gemma 4 released with text, audio and image input and long up to 256K context window! Learn more Home Gemma Models Docs Send feedback Gemma formatting and system instructions Gemma instruction-tuned (IT) models are trained with a specific formatter that annotates all instruction tuning examples with extra information, both at training and inference time. The formatter has two purposes: Indicating roles in a conversation, such as the system, user, or assistant roles. Delineating turns in a conversation, especially in a multi-turn conversation. Below, we specify the control tokens used by Gemma and their use cases. Note that the control tokens are reserved in and specific to our tokenizer. Token to indicate a user turn: user Token to indicate a model turn: model Token to indicate the beginning of dialogue turn: <start_of_turn> Token to indicate the end of dialogue turn: <end_of_turn> Here's an example dialogue: <start_of_turn>user knock knock<end_of_turn> <start_of_turn>model who is there<end_of_turn> <start_of_turn>user Gemma<end_of_turn> <start_of_turn>model Gemma who?<end_of_turn> The token "<end_of_turn>\n" is the turn separator, and the prompt prefix is "<start_of_turn>model\n". This means that if you'd like to prompt the model with a question like, "What is Cramer's Rule?", you should instead feed the model as follows: "<start_of_turn>user What is Cramer's Rule?<end_of_turn> <start_of_turn>model" Note that if you want to finetune the pretrained Gemma models with your own data, you can use any such schema for control tokens, as long as it's consistent between your training and inference use cases. System instructions Gemma's instruction-tuned models are designed to work with only two roles: user and model. Therefore, the system role or a system turn is not supported. Instead of using a separate system role, provide system-level instructions directly within the initial user prompt. The model instruction following capabilities allow Gemma to interpret the instructions effectively. For example: <start_of_turn>user Only reply like a pirate. What is the answer to life the universe and everything?<end_of_turn> <start_of_turn>model Arrr, 'tis 42,<end_of_turn> Send feedback Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Last updated 2025-03-21 UTC. Need to tell us more? 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