Instructions to use litert-community/Qwen3-4B-Instruct-2507 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use litert-community/Qwen3-4B-Instruct-2507 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/Qwen3-4B-Instruct-2507 \ --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) (#1)
- Add measured Galaxy S26 GPU-vs-CPU rows (litert-lm 0.16.0) (279834e2b36198aeeecf6cd3ed592feb332bb756) Co-authored-by: Daisuke Majima (MLBoy) <mlboydaisuke@users.noreply.huggingface.co>4d40977
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Remove internal validation details
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Update conversion notes
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Update benchmark tables and artifact listings
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Remove long mixed INT4 filename
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Shorten INT4 filename in model card
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Add short-named mixed INT4 LiteRT-LM bundle
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Add model card for INT4 runtime-BMM LiteRT-LM bundle
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Add TorchAO mixed INT4 runtime-BMM LiteRT-LM bundle
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initial commit
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