Own your AI: Learn how to fine-tune Gemma 3 270M and run it on-device
a day ago
- #AI
- #Machine Learning
- #Fine-Tuning
- Gemma is a collection of lightweight, state-of-the-art open models built from the same technology as Gemini models.
- Gemma models are accessible and adaptable, with over 250 million downloads and 85,000 community variations.
- Gemma 3 270M's compact size allows for quick fine-tuning and on-device deployment, offering flexibility and control.
- Example project: Train a model to translate text to emoji and deploy it in a web app.
- Fine-tuning with QLoRA reduces memory requirements, enabling quick adjustments on no-cost T4 GPUs in Google Colab.
- Quantization shrinks model size for faster web app loading with minimal performance impact.
- Deploy models client-side using MediaPipe or Transformers.js, leveraging WebGPU for local computation.
- Example inference code provided for integrating a custom model into a web app.
- Models run locally post-cache, ensuring low latency, privacy, and offline functionality.
- Complete source code and resources are available for users to start their own projects.