This model is a fine-tuned version of Gemma 3 270M adapted for text-to-emoji translation. The fine-tuning was performed using Quantized Low-Rank Adaptation (QLoRA), an efficient technique that reduces memory usage and speeds up the training process. The Hugging Face Transformer Reinforcement Learning (TRL) library was utilized to implement QLoRA.

This model is designed to take a text input and generate corresponding emoji outputs, specializing Gemma 3 270M for this specific task.

This demo runs a Gemma 3 270M IT model fine-tuned for text-to-emoji translation directly in the browser. Gemma 3 is supported by web AI frameworks that make deployment easy. Run the app using either:

If you don't have a fine-tuned model, view the resources below.

Preview the app on Hugging Face.

You can use these notebooks in Google Colab for fine-tuning and optimizing Gemma 3 270M for web. To fine-tune the model for the emoji translation task, you can either create your own dataset or use our premade dataset.