Customize Your AI: Fine-Tune Google's Gemma 3 on Your Device

Want to create personalized AI without breaking the bank? Google's Gemma 3 model can be customized to your needs and run directly on your devices.

Gemma, a lightweight open model derived from Google's Gemini models, is now accessible in various sizes for custom adaptation.

Its combination of performance and accessibility has led to over 250 million downloads, along with 85,000 community variations tailored for diverse tasks.

Gemma 3's compact size allows quick fine-tuning for specific applications and deployment on-device, granting you control over model development.

An example shows how to train your own model to translate text to emoji and test it within a web app.

This can even learn your personal emoji preferences, creating a personalized emoji generator, achievable in under an hour.

Large language models (LLMs) are generalists out of the box, sometimes producing unwanted filler when translating text to emoji.

Fine-tuning teaches the model to produce just emojis, which is more reliable than complex prompt engineering.

Training the model on text and emoji examples enables learning specific emojis. More examples improve learning. QLoRA, reduces memory needs.

This allows fine-tuning Gemma 3 in minutes using Google Colab's free T4 GPU acceleration.

After customizing the model, you can deploy it to a mobile or computer app.

The original model is over 1GB, so quantization reduces the file size while maintaining performance.

You can quantize and convert it using either the LiteRT conversion notebook for MediaPipe or the ONNX conversion notebook for Transformers.js.

These frameworks run LLMs client-side in the browser via WebGPU, a modern web API giving apps access to local hardware for computation.

This removes server dependencies and inference costs, allowing you to run your model directly in the browser.

Once cached, requests run locally with low latency, ensuring user data privacy and offline functionality.

Customized models enhance user experience with speed, privacy and accessibility. Create your own variations.

You don’t need to be an AI expert to create a specialized AI model. Enhance Gemma model performance using relatively small datasets.