Gemma 3n Toolkit

Everything you need to get started with Gemma 3n development. From quick setup to advanced deployment strategies.

Ollama Ready Hugging Face Compatible iOS Optimized

Quick Start Tools

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Ollama Setup

Get Gemma 3n running locally with Ollama in under 5 minutes.

# Install Ollama first
ollama run gemma-3n:e4b
# Or for smaller model
ollama run gemma-3n:e2b
Complete Guide →
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Hugging Face

Use Gemma 3n models directly from Hugging Face Hub.

from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "google/gemma-3n-e4b-it"
model = AutoModelForCausalLM.from_pretrained(model_name)
Browse Models →
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E2B vs E4B

Interactive tool to help you choose the right model size.

E2B: Mobile-friendly 4GB RAM
E4B: Higher accuracy 8GB RAM
Detailed Comparison →

Platform Integrations

📱 iOS Development

Deploy Gemma 3n models on iOS devices with optimized performance.

Recommended Setup:

  • Gemma 3n E2B for iPhone (2GB model)
  • CoreML conversion for optimal performance
  • GGUF quantization for reduced size
# Convert to CoreML
pip install coremltools
# Follow our detailed iOS guide
iOS Deployment Guide →

🔧 Fine-tuning Tools

Customize Gemma 3n models for your specific use cases.

Available Methods:

  • LoRA (Low-Rank Adaptation)
  • Unsloth for 2x faster training
  • Google Colab notebooks ready
# Start with Unsloth
pip install unsloth
# Or try our LoRA tutorial

Hardware Requirements

Model RAM (FP16) RAM (4-bit) Best Use Case
Gemma 3n E2B 4GB 2GB Mobile, Edge devices
Gemma 3n E4B 8GB 4GB Laptops, Workstations
✅ CPU Only

Both models run efficiently on CPU-only setups

🚀 GPU Accelerated

Significant speedup with CUDA/Metal support

📱 Mobile Ready

E2B optimized for iOS and Android deployment

Ready to Build with Gemma 3n?

Join thousands of developers already using Gemma 3n in production.

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