Gemma 4 E2B Ndizi Swahili ASR (LiteRT-LM, slim)
On-device bundle (~2.6 GB target): LiteRT shell from litert-community/gemma-4-E2B-it-litert-lm with prefill/decode LLM weights from smutuvi/gemma-4-e2b-sw-asr-ndizi-merged.
- File:
sunflower-gemma4-e2b-litert-lm.litertlm - Adapter (GPU/Colab): smutuvi/gemma-4-e2b-sw-asr-ndizi
- Full export (~5 GB): smutuvi/gemma-4-e2b-sw-asr-ndizi-litert-lm
Inference (Swahili ASR)
Use the same audio-first chat turn and Swahili ASR instruction as training (ndizi_mlops_gemma-4).
Build
Reproduced with python scripts/build_litert_lm_slim.py in ndizi_mlops_gemma-4.
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# 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 litert-lm run \ --from-huggingface-repo=smutuvi/sunflower-gemma4-e2b-litert-lm \ --prompt="Write me a poem"