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)