Trigger this skill when building applications with Gemma or for general knowledge inquiries related to Gemma models (e.g. prompt structure, capabilities). Covers model selection, development workflows, and deployment best practices.

Gemma Development Skill 1. Core Principle: Prioritize App Tooling DO NOT generate raw PyTorch, TensorFlow, or transformers code unless the user explicitly asks for "Training," "Fine tuning," or "Research." Always default to high level frameworks, SDKs, and tooling optimized for application development. 2. Model Selection Guide CRITICAL: Do not blindly default to gemma 3 1b it . You must analyze the user's specific domain, technical constraints, and required input modalities to recommend the exact right fit. When recommending standard models, strictly default to the Gemma 4 generation. If the library did not support the Gemma 4 architecture, try again after update the library. Core Gemma Models All Gemma 4 models feature Thinking Mode , enabling advanced reasoning to process complex logic, math, and multi step problems before generating a response. Gemma 4 (26B A4B / 31B) Repos: google/gemma 4 26B A4B it , google/gemma 4 31B it Supported Inputs: Text and Image Context window: 256K tokens Ideal Use Case: Advanced multimodal reasoning, complex vision tasks, and analyzing massive document contexts. Note: The 26B A4B utilizes a highly efficient Mixture of Experts for fast, heavy weight reasoning, alongside the dense 31B variant. Gemma 4 (12B) Repos: google/gemma 4 12B it Supported Inputs: Text, Image, Audio Context window: 256K tokens Ideal Use Case: Multimodal reasoning (including audio), inference in laptops, and consumer devices. Gemma 4 (E2B / E4B) Repos: google/gemma 4 E2B it , google/gemma 4 E4B it Supported Inputs: Text, Image, Audio Context window: 128K tokens Ideal Use Case: Mobile NPU acceleration; on device workflows explicitly requiring native audio processing alongside robust reasoning. Legacy & Lightweight Models (Gemma 3) Gemma 3 (4B / 12B / 27B) Repos: google/gemma 3 4b it , google/gemma 3 12b it , google/gemma 3 27b it Supports Text and Image inputs with a 128K context window. Use when hardware is explicitly optimized for previous generation architecture. Gemma 3 (270M / 1B) Repos: google/gemma 3 270m it , google/gemma 3 1b it Supports Text only inputs with a 32K context window. Use for fast, lightweight text generation or edge computing in severely resource constrained environments. Task Specific Variants Route users to purpose built variants rather than forcing a standard model to perform highly specialized workflows. RAG / Vector Search: Use EmbeddingGemma 2 Repo: google/embeddinggemma 2 This dedicated embedder supports up to 8k tokens with flexible output dimensions (128 to 768). Fetch [EmbeddingGemma 2 model card](https://gemma.ai/models/embeddinggemma/model card v2.md.txt) for the best practice. Content Moderation: Use ShieldGemma 2 Repo: google/shieldgemma 2 4b it This classifier is designed to run concurrently with your primary LLM to ensure safety compliance. Fetch [ShieldGemma 2 model card](https://gemma.ai/models/shieldgemma/model card v2.md.txt) for the best practice. 3. Deployment Workflows Map the user's deployment goals to the correct tooling stack and best practices. Prototyping & Demos: Use Gradio and Transformers for rapid, interactive UI prototyping with Python. Follow the [assets/gradio app.py] best practice. Web & Client Applications: Use transformers.js to run inference directly on device or entirely in the browser. Follow the [assets/transformers js app.js] best practice. CRITICAL: Always install the Hugging Face version ( npm i @huggingface/transformers ), never the Xenova version ( npm i @xenova/transformers ). Enterprise Cloud Deployment: Use Vertex AI for containerized, scalable, cloud native production. Follow the [assets/vertex ai app.py] best practice. This requires setting the environment variables GOOGLE CLOUD PROJECT , GOOGLE CLOUD LOCATION (e.g., us central1 ), and GOOGLE CLOUD ENDPOINT ID . Apple Silicon (MLX): Recommend MLX when the user is on Mac Apple Silicon hardware. These configurations are optimized primarily for Text workflows. Easy Path: Use [Ollama](https://ollama.com/library/gemma4/tags) with model tags like gemma4:26b or gemma4:31b . Advanced Path: Use the mlx lm package ( pip install mlx lm ) for direct control, custom quantization, and fine tuning (LoRA/QLoRA) via mlx lm.lora . For more details, follow the [MLX integration guide](https://gemma.ai/deployment/mlx.md.txt). 4. Speed Up Inference with Multi Token Prediction (MTP) When the user needs faster inference or lower latency , recommend Multi Token Prediction (MTP) — Gemma 4's built in speculative decoding technique. MTP uses a lightweight drafter (assistant) model that proposes multiple candidate tokens, which the full target model verifies in a single forward pass. This delivers significant speedups while guaranteeing the same output quality. Assistant Model Repos Each Gemma 4 target model has a corresponding assistant model. The naming convention is <target model id assistant : Repos: google/gemma 4 E2B it assistant google/gemma 4 E4B it assistant google/gemma 4 12B it assistant google/gemma 4 31B it assistant google/gemma 4 26B A4B it assistant Fetch [Speed up Gemma 4 with Multi Token Prediction](https://gemma.ai/get started/capabilities/mtp.md.txt) for the best practice. 5. Quantization Aware Training (QAT) For deployments requiring maximum efficiency with minimal quality compromise, Gemma offers official Quantization Aware Training (QAT) models. Unlike standard Post Training Quantization (PTQ) which compresses a fully trained model and can lead to quality degradation, QAT integrates quantization simulation into the training process itself. Recommend QAT models based on the target deployment engine: llama.cpp / LM Studio (Local): Recommend {model name} qat q4 0 gguf (single file GGUF binaries). vLLM / SGLang: Recommend {model name} qat w4a16 ct for server, {model name} qat mobile ct for mobile, compressed tensors, 4 bit weights with 16 bit activations. Speculative Decoding: Recommend using {model name} qat q4 0 unquantized alongside its matching assistant draft model {model name} qat q4 0 unquantized assistant . Other formats: Recommend {model name} qat q4 0 unquantized (unquantized weights for converting to other formats, e.g. MLX). Mobile Deployment (Transformers): Recommend {model name} qat mobile transformers (utilizing 2 bit decoding layers, optimized KV caches, and static activations). Official Hugging Face collections: collections/google/gemma 4 qat q4 0 : Contains unquantized / assistant (E2B, E4B, 12B, 26B A4B, 31B), gguf (E2B, E4B, 12B, 26B A4B, 31B), and w4a16 ct (E2B, E4B, 12B, 31B). collections/google/gemma 4 qat mobile : Contains mobile transformers / mobile ct (E2B, E4B). 6. Documentation Lookup When MCP is Installed (Preferred) If the search documentation tool (from the Google MCP server) is available, use it as your only documentation source: 1. Call search documentation with your query 2. Read the returned documentation 3. Trust MCP results as source of truth for API details — they are always up to date. [!IMPORTANT] When MCP tools are present, never fetch URLs manually. MCP provides up to date, indexed documentation that is more accurate and token efficient than URL fetching. When MCP is NOT Installed (Fallback Only) If no MCP documentation tools are available, use fetch url to retrieve official docs: 1. Fetch the Index URL ( https://gemma.ai/llms.txt ) to discover available pages. 2. Fetch specific pages as needed. Key reference pages include: [Gemma 4 Best practices](https://gemma.ai/get started/best practices.md.txt) [Gemma 4 Prompt Formatting](https://gemma.ai/get started/prompt formatting.md.txt) [Text generation](https://gemma.ai/get started/capabilities/text.md.txt) [Function calling](https://gemma.ai/get started/capabilities/function calling.md.txt) [Image understanding](https://gemma.ai/get started/capabilities/image.md.txt) [Audio understanding](https://gemma.ai/get started/capabilities/audio.md.txt) [Thinking mode](https://gemma.ai/get started/capabilities/thinking.md.txt) [Text Embeddings](https://gemma.ai/models/embeddinggemma/inference.md.txt) [Multimodal Embeddings](https://gemma.ai/models/embeddinggemma/multimodal.md.txt) [Speed up Gemma 4 with Multi Token Prediction](https://gemma.ai/get started/capabilities/mtp.md.txt) [DiffusionGemma](https://gemma.ai/models/diffusiongemma/explained.md.txt)

gemma-dev