Instructions to use konradjr007/embeddinggemma-2-litert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use konradjr007/embeddinggemma-2-litert with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- LiteRT-LM
How to use konradjr007/embeddinggemma-2-litert with LiteRT-LM:
# LiteRT-LM runs on various platforms (Android, iOS, Windows, Linux, macOS, IoT, Web/WASM) # and supports many APIs (C++, Python, Kotlin, Swift, JavaScript, Flutter). # For platform-specific integration guides, please refer to the official developer website: # https://ai.google.dev/edge/litert-lm # To try LiteRT-LM, the easiest way is to use our CLI tool. # 1. Install the LiteRT-LM CLI tool: pip install -U litert-lm # 2. Download and run this model locally: # See: https://ai.google.dev/edge/litert-lm/cli # A single .litertlm file in the repo is picked automatically; otherwise the CLI asks which one to run # (or pass its name right after the repo id). litert-lm run \ --from-huggingface-repo=konradjr007/embeddinggemma-2-litert \ --prompt="Write me a poem"
- Notebooks
- Google Colab
- Kaggle
EmbeddingGemma 2 740M (LiteRT-LM & ONNX)
On-device embedding model for local semantic search, document indexing, and private RAG.
Available Models & Formats
| Target Silicon | Format / Path | Accelerator | Description |
|---|---|---|---|
| Snapdragon 8 Elite | |||
| pu/qualcomm/sm8750_hexagon_v79/embeddinggemma-2-740m.litertlm | Hexagon v79 NPU | Dedicated S25 / 8 Elite NPU | |
| Snapdragon 8 Gen 3 | |||
| pu/qualcomm/sm8650_hexagon_v75/embeddinggemma-2-740m.litertlm | Hexagon v75 NPU | 8 Gen 3 NPU acceleration | |
| Snapdragon 8 Gen 2 | |||
| pu/qualcomm/sm8550_hexagon_v73/embeddinggemma-2-740m.litertlm | Hexagon v73 NPU | 8 Gen 2 NPU acceleration | |
| Snapdragon Next | |||
| pu/qualcomm/sm8850/embeddinggemma-2-740m.litertlm | Qualcomm NPU | Next-gen mobile NPU | |
| Qualcomm IoT | |||
| pu/qualcomm/qcs8275/embeddinggemma-2-740m.litertlm | Qualcomm NPU | Edge / robotics NPU | |
| Google Tensor | |||
| pu/google/tensor_g5/embeddinggemma-2-740m.litertlm | Tensor G5 TPU | Pixel TPU acceleration | |
| Google Tensor Next | |||
| pu/google/tensor_g6/embeddinggemma-2-740m.litertlm | Tensor G6 TPU | Next-gen Tensor TPU | |
| MediaTek Dimensity | |||
| pu/mediatek/mt6991/embeddinggemma-2-740m.litertlm | Dimensity 9400 APU | Dimensity NPU tier | |
| MediaTek Next | |||
| pu/mediatek/mt6993/embeddinggemma-2-740m.litertlm | Dimensity Next APU | Flagship Dimensity APU | |
| Mobile GPU | gpu/embeddinggemma-2-740m.litertlm | OpenCL / Vulkan / Metal | Universal Mobile GPU tier |
| ONNX Runtime (Search) | onnx/model_q4f16.onnx (+ .onnx_data) | CPU / XNNPACK / ORT | Q4F16 ONNX graph with tokenizer.json |
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