Build a Private AI Chatbot Using Google’s Gemma 3n

AI-powered chatbots are reshaping the way we interact with technology, offering intelligent and personalized responses in real time. However, cloud-based solutions often raise concerns around data privacy, latency, and cost. Enter Google’s Gemma 3n — a compact, open model that can be run entirely offline.

In this tutorial, we’ll learn how to build our own private AI chatbot using Gemma 3n, keeping our data secure and operations under our full control.

Let’s start with a brief overview of Gemma 3n and its key features.

What is Gemma 3n?

Gemma 3n is part of Google DeepMind’s Gemma family of lightweight, open models designed for responsible AI development. Released in 2024, Gemma 3n is the smallest variant in the lineup, ideal for running on local devices such as laptops, desktops, or small servers without the need for cloud-based acceleration.

The “3n” denotes a 3-billion-parameter neural network, striking a balance between performance and resource efficiency. Built on the same research foundation as Google’s Gemini models, Gemma 3n is optimized for open-access use, enabling developers and researchers to innovate without surrendering control to cloud infrastructure.

Key features

Here are some key features that make Gemma 3n a class apart:

• Lightweight: Optimized for edge and local deployment; runs on a single GPU or even high-end CPUs.
• Open weight license: Available under a permissive license for research and certain commercial uses.
• High performance: Despite its small size, Gemma 3n delivers competitive results on language tasks.
• Fine-tuning support: Easily adaptable to custom datasets for domain-specific applications.
• Toolchain compatibility: Works seamlessly with frameworks like Hugging Face Transformers and vLLM.

Now that we understand what Gemma 3n is and what it offers, let’s discuss how to use it to set up a private AI chatbot on our local machine.

Build a private AI chatbot using Gemma 3n

Let’s start the process by gaining access to Gemma 3n on Hugging Face.

Step 1: Access Gemma 3n on Hugging Face

For this tutorial, we’ll gain access to the Gemma 3n model named gemma-3n-E2B-it-litert-preview on Hugging Face. To do so, follow these steps:

1. Sign up for a Hugging Face account

2.1. Navigate to the documentation page for the gemma-3n-E2B-it-litert-preview model

2.2. Read the terms and conditions

2.3. Click on Agree and send request to access repo

After clicking on it, we’ll be redirected to the Gemma 3n consent form. Fill in the form and you’ll be granted access to the model.

Once access is granted, move on to the next step.

Step 2: Clone the ‘Google AI Edge Gallery’ GitHub repository

The ‘Google AI Edge Gallery’ app provides access to cutting-edge generative models directly on our mobile devices, including Gemma 3n.

The codebase for this app is stored in its official GitHub repository.

Since we’ll be manipulating the codebase, let’s clone the repository to our local machine by running this command in the terminal:

git clone https://github.com/google-ai-edge/gallery.git 

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After the repository is cloned, a folder named gallery will appear on the machine.

Notably, the source code for the app is stored in gallery/Android/src.

Step 3: Set up an Android Studio project

We’ll use Android Studio to manipulate the codebase. Follow these steps to set up a new Android Studio project on the local machine:

1. Download Android Studio from the official website

2. After downloading, double-click on the executable file and go through the on-screen prompts to complete the installation.

3. Once the installation is complete, run Android Studio on the device.

4. On the landing page, click on Open, navigate to gallery/Android/src, and hit OK to create a new Android Studio project.

After creating the project, Android Studio will automatically start building the app. Once Android Studio is done building, the folder structure will look like this:

Step 4: Modify the codebase

When we launch the ‘Google AI Edge Gallery’ app, it shows a screen containing the available use cases. They include:

• Ask Image
• Prompt Lab
• AI Chat

We’ll modify the codebase to keep only the ‘AI Chat’ feature, just like in a chatbot.

To accomplish this, follow these steps:

1. Navigate to kotlin+java/com.google.ai.edge.gallery/data/Tasks.kt in Android Studio

2. Locate this code snippet:

/** All tasks. */ 
val TASKS: List<Task> = listOf( 
TASK_LLM_ASK_IMAGE, 
TASK_LLM_PROMPT_LAB, 
TASK_LLM_CHAT,  
) 

3. Remove the tasks for ‘Ask Image’ and ‘Prompt Lab’:

/** All tasks. */ 
val TASKS: List<Task> = listOf( 
TASK_LLM_CHAT,  
) 

Next, we’ll alter the codebase in a way that helps us directly navigate to the ‘AI Chat’ screen upon launching the app, bypassing the feature section screen.

To do so, follow these steps:

1. Navigate to kotlin+java/com.google.ai.edge.gallery/galleryApp.kt in Android Studio

2. Locate this code snippet:

@Composable 
fun GalleryApp(navController: NavHostController = rememberNavController()) { 
  GalleryNavHost(navController = navController) 
} 

3. Replace the existing code snippet with this:

import androidx.compose.runtime.LaunchedEffect 
import androidx.navigation.compose.rememberNavController 
import com.google.ai.edge.gallery.data.TASK_LLM_CHAT 
import com.google.ai.edge.gallery.ui.llmchat.LlmChatDestination 
import com.google.ai.edge.gallery.ui.navigation.GalleryNavHost 

@Composable 
fun GalleryApp( 
  navController: NavHostController = rememberNavController() 
) { 
  LaunchedEffect(Unit) { 
    TASK_LLM_CHAT.models 
      .firstOrNull() 
      ?.name 
      ?.let { modelName -> 
        navController.navigate("${LlmChatDestination.route}/$modelName") { 
          popUpTo("home") { inclusive = true } 
        } 
      } 
  } 
  GalleryNavHost(navController = navController) 
} 

After the changes are done, click on the Sync Project with Gradle Files button to rebuild the project with the changes applied:

Step 5: Install the app on mobile

Take your Android device and go to _Settings > Additional Settings > Developer options > Wireless debugging. Then:

1. Switch on the Wireless debugging feature

2. Tap on the Pair device with QR code option to launch the QR code scanner

In Android Studio, go to Device Manager and click on the Wi-Fi icon at the top of the tab:

Upon clicking, this screen will appear. Here, we can pair our mobile device with the machine using a QR code.

So, let’s pair these devices by scanning the QR code on the screen using the QR code scanner on our mobile:

After pairing, go to Device Mirroring and click on the + icon. We’ll notice our Android device listed there. Click on it to start emulating the device in Android Studio.

Upon clicking the button, we’ll see our phone screen on the computer:

Then, click on Run to run and launch the app on the emulator:

Once launched, we’ll see an Install button for installing the app on our mobile phone. Click on it to perform the installation of the ‘Google AI Edge Gallery’ app.

When the installation is complete, launch the app. As expected, we’re directly navigated to the ‘AI Chat’ screen. If you notice, the model selected above is gemma-3n-E2B-it-int4. This is the same model that we’re granted access to on Hugging Face.

So, let’s click on the Download & Try button to download the model on the device:

We’re prompted to log in to our Hugging Face account. Let’s do that and move on to the next step:

Next, authorize access to your account:

After authorization, the download will begin.

Once the model is downloaded, we’re taken to the ‘AI Chat’ interface. Here, we can ask questions and generate responses from Gemma 3n in no time.

Here is an example:

That’s it! You have successfully built a private AI chatbot using Gemma 3n.

Since we’re done building the chatbot, let’s weigh Gemma 3n’s practical benefits and limitations in real-world applications.

Benefits and limitations of Gemma 3n

The benefits of Gemma 3n include:

• Privacy first: Keeps all data local, which is essential for sensitive use cases.
• Low latency: No need for API calls or network delays — responses are generated instantly.
• Cost effective: Eliminates dependency on cloud GPU billing.
• Customizable: We can fine-tune the model for specific industries or applications.

However, Gemma 3n has some limitations as well:

• Performance trade-offs: Not as powerful as larger models like GPT-4 or Gemini 1.5 Pro.
• Hardware requirements: While lightweight, it still needs a decent CPU/GPU (16+ GB RAM, 8GB+ VRAM recommended).
• Limited multimodal capabilities: Gemma 3n is focused on text, not images or video (for now).

Next, let’s explore some compelling real-world use cases for this lightweight model.

Real-world use cases of Gemma 3n

Gemma 3n is not just a toy model — it’s already being used in various scenarios:

• Customer support: Local helpdesk bots in regulated industries like healthcare and finance.
• Dev tools: AI code assistants built into IDEs without sending code to cloud servers.
• Research: LLM experimentation in academia without massive hardware requirements.
• IoT and edge devices: Voice assistants and interactive AI on embedded systems.

These applications prove that Gemma 3n has already become an integral part of various industries.

Conclusion

In this guide, we explored what Gemma 3n is and discussed why it stands out as a leading LLM in the current AI landscape. Then, we walked through building a private AI chatbot, demonstrating Gemma 3n’s practical power. Finally, we highlighted its benefits and limitations, and discovered some real-world applications of Gemma 3n, showcasing its versatility.

As large language models continue to scale, Gemma 3n offers a refreshing take: privacy-respecting AI that we control. Whether we’re solo developers or part of a privacy-conscious enterprise, it allows us to harness generative AI without the trade-offs of cloud dependence.

If you want to learn more about generative AI, check out the Intro to Generative AI course on Codecademy.

Frequently asked questions

1. Can I use Gemma for commercial use?

Yes, Gemma models can be utilized for commercial purposes, but you must comply with the terms of the Gemma Responsible Use License. Redistribution or use in sensitive areas (like political campaigning or biometric identification) may be restricted.

2. Is Google Gemma 3 open source?

Gemma is not “open source” in the traditional OSI definition, but is open weight. That means you can inspect, run, and fine-tune the model, but under specific licensing terms.

3. What languages does Gemma support?

Gemma 3n is primarily trained on English and performs best in English language tasks. It can handle limited multilingual input but is not optimized for non-English fluency.