Instructions to use hkust-aaron/EdgeRock-4B-LiteRT-LM-INT4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use hkust-aaron/EdgeRock-4B-LiteRT-LM-INT4 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=hkust-aaron/EdgeRock-4B-LiteRT-LM-INT4 \ --prompt="Write me a poem"
- Notebooks
- Google Colab
- Kaggle
EdgeRock-4B-LiteRT-LM-INT4
Public development preview for Google AI Edge Gallery device validation. The container includes the text decoder, tokenizer, ChatML prompt template and fixed-state executor metadata. The graph retains recurrent state, local attention and RMA reads and block-boundary commits. Full-model long-memory checks and Android device validation are in progress.
The model file is 2.635 GB (decimal file size, not runtime memory). Use the CPU backend in Gallery 1.0.19 / LiteRT-LM 0.18.0. Image and audio inputs are not included.
python -m pip install -r requirements.txt
python run_litert.py --model EdgeRock-4B-int4.litertlm --prompt 'Calculate 17 * 23. Give only the answer.'
Weights retain the EdgeRock Model License 1.0. Code retains Apache-2.0.
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Model tree for hkust-aaron/EdgeRock-4B-LiteRT-LM-INT4
Base model
rockmatrix-ai/EdgeRock