| 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.
