--- name: gemma-dev description: 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 `-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) --- Source attribution: google-gemma Original skill: https://github.com/google-gemma/gemma-skills/blob/HEAD/skills/gemma-dev/SKILL.md License: Apache-2.0 License information: https://creativecommons.org/licenses/by/4.0/ Original upstream instructions; downloaded from Mega AI Hub. # Mega AI Hub resource metadata Creator / publisher: google-gemma Original skill: https://github.com/google-gemma/gemma-skills/blob/HEAD/skills/gemma-dev/SKILL.md Attribution basis: Credit from the original skill or repository Fields of work: General use Roles: Specialties: Tools: Country applicability: Classification basis: Original skill description Instruction SHA-256: b1eb9af67b67d061a54dadd3f2463159941b5caac0f315747cf03de657267a62