Legacy Gemma setup  |  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 Legacy Gemma setup Note: This is only relevant for Gemma 3 and prior versions. This page provides setup instructions for using Gemma in Colab. Some of the instructions are applicable to other development environments as well. Get access to Gemma Before using Gemma for the first time, you must request access to the model through Kaggle. As part of the process, you'll have to use a Kaggle account to accept the Gemma use policy and license terms. If you don't already have a Kaggle account, you can register for one at kaggle.com. Then complete the following steps: Go to the Gemma model card and select Request Access. Complete the consent form and accept the terms and conditions. Select a Colab runtime To complete a Colab tutorial, you must have a Colab runtime with sufficient resources to run the Gemma model. To get started, you can use a T4 GPU: In the upper-right of the Colab window, select ▾ (Additional connection options). Select Change runtime type. Under Hardware accelerator, select T4 GPU. Configure your API key To use Gemma, you must provide your Kaggle username and a Kaggle API key. To generate and configure these values, follow these steps: To generate a Kaggle API key, go to the Account tab of your Kaggle user profile and select Create New Token. This will trigger the download of a kaggle.json file containing your API credentials. Open kaggle.json in a text editor. The contents should look something like this: {"username":"your_username","key":"012345678abcdef012345678abcdef1a"} In Colab, select Secrets (🔑) and add your Kaggle username and Kaggle API key. Store your username under the name KAGGLE_USERNAME and your API key under the name KAGGLE_KEY. Note: Kaggle notebooks have a key storage feature under Add-ons > Secrets, along with instructions for accessing stored keys. Now you're ready to complete the remaining setup steps in Colab. If you're working through a Colab tutorial, go to Colab and set the environment variables. Tip: As an alternative to setting environment variables, you can use kagglehub to authenticate. 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 2026-04-09 UTC. Need to tell us more? 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