--- license: gemma base_model: google/gemma-3-1b-pt pipeline_tag: text-generation tags: - chat extra_gated_heading: Access gemma-3-1b-pt on Hugging Face extra_gated_prompt: >- To access gemma-3-1b-pt on Hugging Face, you are required to review and agree to the gemma license. To do this, please ensure you are logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge licensed --- # litert-community/gemma3-1b-ft-text-to-sql This model is generated using LoRA fine-tuning to train [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt) with the [philschmid/gretel-synthetic-text-to-sql](https://huggingface.co/datasets/philschmid/gretel-synthetic-text-to-sql) dataset. Artifacts include `.tflite` and `.task` files that can be used to run on mobile devices. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.sandbox.google.com/github/google-ai-edge/mediapipe-samples/blob/main/codelabs/litert_inference/Gemma3_1B_Fine_Tuning_text_to_sql.ipynb) Self-link QR code ## Use the models ### Colab *Disclaimer: The target deployment surface for the LiteRT models is Android/iOS/Web and the stack has been optimized for performance on these targets. Trying out the system in Colab is an easier way to familiarize yourself with the LiteRT stack, with the caveat that the performance (memory and latency) on Colab could be much worse than on a local device.* ### Android via Google AI Edge Gallery and MediaPipe * Download and install [the apk](https://github.com/google-ai-edge/gallery/releases/latest/download/ai-edge-gallery.apk). * Follow the instructions in the app. To build the demo app from source, please follow the [instructions](https://github.com/google-ai-edge/gallery/blob/main/README.md) from the GitHub repository. ### Android or Desktop via LiteRT LM Follow the LitRT LM [instructions](https://github.com/google-ai-edge/LiteRT-LM/blob/main/README.md) to build our Open Source LiteRT LM runtime to run LiteRT models. ### iOS via MediaPipe * Clone the [MediaPipe samples](https://github.com/google-ai-edge/mediapipe-samples) repository and follow the [instructions](https://github.com/google-ai-edge/mediapipe-samples/tree/main/examples/llm_inference/ios/README.md) to build the LLM Inference iOS Sample App using XCode. * Run the app via the iOS simulator or deploy to an iOS device.