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Gemma 3n 4B

Gemma 3n model run efficiently on low-resource devices like phones, using selective parameter activation to reduce resource demands, operating at an effective size of 2B or 4B parameters.

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Javascript

Python

                                        const { OpenAI } = require('openai');

const api = new OpenAI({
  baseURL: 'https://api.ai.cc/v1',
  apiKey: '',
});

const main = async () => {
  const result = await api.chat.completions.create({
    model: 'google/gemma-3n-e4b-it',
    messages: [
      {
        role: 'user',
        content: 'Tell me, why is the sky blue?'
      }
    ],
  });

  const message = result.choices[0].message.content;
  console.log(`Assistant: ${message}`);
};

main();

                                        import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.ai.cc/v1",
    api_key="",    
)

response = client.chat.completions.create(
    model="google/gemma-3n-e4b-it",
    messages=[
        {
            "role": "user",
            "content": "Tell me, why is the sky blue?"
        },
    ],
)

message = response.choices[0].message.content

print(f"Assistant: {message}")

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Gemma 3n 4B

Product Detail

Gemma 3n 4B is Google's innovative, mobile-first, and multimodal AI model. Specifically engineered for efficient on-device deployment, it brings enterprise-grade AI capabilities directly to smartphones and tablets. By leveraging its cutting-edge MatFormer architecture and PLE caching, Gemma 3n 4B delivers powerful performance with remarkably minimal resource consumption.

⚙️ Technical Specifications

Performance Benchmarks

Gemma 3n 4B is meticulously optimized for mobile deployment, featuring advanced multimodal processing capabilities:

Context Window: 8K tokens
Output Capacity: Up to 2K tokens per response
Memory Footprint: Maintains a 2GB-3GB dynamic operation, impressive for its 5B-8B parameter count
Processing Speed: 1.5x faster than its predecessor, Gemma 3 4B, on mobile devices

API Pricing

FREE

🚀 Performance Metrics

Based on the competitive Chatbot Arena Elo scores, Gemma 3n demonstrates exceptional performance with a score of 1283. This places it in a remarkable second position, closely trailing Claude 3.7 Sonnet (1287). This achievement is particularly noteworthy given that Gemma 3n achieves such high performance with only 4B parameters in memory.

💡 Key Capabilities

Gemma 3n 4B is engineered to deliver highly efficient multimodal AI processing, especially in environments with limited resources:

MatFormer Architecture: Employs selective parameter activation, significantly reducing compute costs and improving response times.
PLE Caching (Per-Layer Embedding): Optimizes memory usage by strategically offloading parameters to fast storage.
Conditional Parameter Loading: Dynamically loads only the necessary parameters (text, visual, or audio), further enhancing memory optimization.
Multilingual Support: Trained on over 140 languages, enabling versatile global deployment.
Privacy-First Design: Operates completely offline, ensuring enhanced data privacy and security without requiring internet connectivity.

🎯 Optimal Use Cases

Mobile Applications: Powers advanced AI features on smartphones and tablets, even with limited RAM.
Edge Computing: Facilitates real-time AI processing directly on IoT devices and embedded systems.
Offline AI Solutions: Ideal for privacy-focused applications that necessitate robust local processing.

💻 Code Samples

Explore practical code examples for integrating Gemma 3n 4B into your development projects:

⚖️ Comparison with Other Models

Vs. Gemma 3 4B: Delivers 50% faster processing speed, coupled with superior output quality and reduced memory footprint.
Vs. Standard 5B-8B Models: Operates with an effective 2B-4B memory footprint (2-3GB RAM), significantly less than the typical 6-16GB requirements of comparable models.
Vs. Qwen 3 4B: Shows superior performance in classification tasks and structured JSON extraction, though results may vary in coding and RAG applications.

🚫 Limitations

While powerful, Gemma 3n 4B has certain limitations:

No integrated vision capabilities.
Lacks support for fine-tuning.
Primarily limited to text-based tasks.

🔗 API Integration

Gemma 3n 4B is fully accessible through the AI/ML API. For comprehensive documentation and integration guides, please click here: Available Here.

❓ Frequently Asked Questions (FAQ)

1. What is the primary purpose of Gemma 3n 4B?

Gemma 3n 4B is designed as a mobile-first, multimodal AI model, optimized to bring enterprise-grade AI capabilities to smartphones and tablets with high efficiency and minimal resource consumption.

2. How does Gemma 3n 4B achieve its high efficiency and low memory footprint?

It leverages innovative MatFormer architecture for selective parameter activation, Per-Layer Embedding (PLE) caching to offload parameters, and Conditional Parameter Loading to dynamically load only necessary components, all contributing to its superior efficiency.

3. Can Gemma 3n 4B operate without an internet connection?

Yes, Gemma 3n 4B features a privacy-first design, allowing it to run completely offline. This makes it ideal for privacy-sensitive applications and edge computing scenarios where internet connectivity may be limited.

4. What are the key limitations of Gemma 3n 4B?

Its main limitations include the absence of vision capabilities, no support for fine-tuning by users, and its operations are primarily confined to text-based tasks.

5. How does Gemma 3n 4B compare to its predecessor, Gemma 3 4B?

Gemma 3n 4B significantly outperforms its predecessor, Gemma 3 4B, by offering a 50% faster processing speed while simultaneously maintaining superior output quality and requiring less memory.

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