Instructions to use litert-community/SmolLM2-360M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use litert-community/SmolLM2-360M-Instruct 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=litert-community/SmolLM2-360M-Instruct \ --prompt="Write me a poem"
- Notebooks
- Google Colab
- Kaggle
Commit History
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Add measured Galaxy S26 GPU-vs-CPU rows (litert-lm 0.16.0)
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Add measured performance section (M4 Max)
Verified signatureThanks for publishing this bundle — it runs cleanly on both backends. This PR adds a measured Performance section so the card carries numbers alongside the install steps. Measured with the litert-lm CLI (`benchmark -p 256 -d 256 --runs 3 --cache no`) on an idle Apple M4 Max, generation-gated first (the backend produced correct text before any number was recorded). Happy to adjust the format if you'd like these to read differently.d9fac91
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Add README and model file (#1)
- Add model via Git LFS (092b524bad20b15cb9477834a99e0ff3aa00590f) - Update README.md (f8b7165f04fc3cd8fd547fb92631bbdb54775cbd) Co-authored-by: Harsha Veena <hvt4@users.noreply.huggingface.co>507c99c
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initial commit
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