Instructions to use onnaru/jp10min-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use onnaru/jp10min-model 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 litert-lm run \ --from-huggingface-repo=onnaru/jp10min-model \ --prompt="Write me a poem"
- Notebooks
- Google Colab
- Kaggle
gemma-4-E2B-it-litert-lm (mirror)
An unmodified copy of
litert-community/gemma-4-E2B-it-litert-lm,
kept as a download fallback for the offline Japanese-learning app
「10분 일본어」 (com.metae.jp10min).
The app runs this model entirely on-device — the only time it touches the network is this one-time download. If the upstream repository is ever moved or renamed, new users would be unable to obtain the model at all; this mirror exists so that path stays open.
File
| File | gemma-4-E2B-it.litertlm |
| Size | 2,588,147,712 bytes |
| SHA-256 | 181938105e0eefd105961417e8da75903eacda102c4fce9ce90f50b97139a63c |
| Upstream | https://huggingface.co/litert-community/gemma-4-E2B-it-litert-lm |
| Original model | https://huggingface.co/google/gemma-4-E2B-it |
No modifications have been made. The file is byte-for-byte identical to the upstream release; verify with the SHA-256 above.
License
Licensed under the Apache License, Version 2.0 — see LICENSE. Copyright Google DeepMind.
⚠️ Separately from Apache 2.0, the Gemma Prohibited Use Policy also applies: https://ai.google.dev/gemma/prohibited_use_policy
- Downloads last month
- 9