Instructions to use litert-community/Qwen3-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use litert-community/Qwen3-4B 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=litert-community/Qwen3-4B \ --prompt="Write me a poem"
- Notebooks
- Google Colab
- Kaggle
Remove internal validation details
Browse files
README.md
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@@ -31,11 +31,6 @@ The mixed INT4 `.litertlm` artifact was produced with a TorchAO-based quantize-f
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The mixed INT4 bundle also uses LiteRT-LM StableHLO composite ops for attention/cache execution, including `odml.runtime_bmm` and `odml.cache_update`.
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## Validation
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The mixed INT4 bundle was validated during conversion with `is_valid_tflite=True`, `unsupported_count=0`, and `contract_ok=True`.
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## Performance
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Desktop benchmark: AMD Radeon AI PRO R9700, LiteRT-LM WebGPU, 256 prefill tokens, 32 decode tokens. Android rows use LiteRT-LM v0.13.1 with GPU OpenCL, 256 prefill tokens, and 64 decode tokens. Values report the warmed iteration from a two-iteration run unless noted.
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The mixed INT4 bundle also uses LiteRT-LM StableHLO composite ops for attention/cache execution, including `odml.runtime_bmm` and `odml.cache_update`.
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## Performance
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Desktop benchmark: AMD Radeon AI PRO R9700, LiteRT-LM WebGPU, 256 prefill tokens, 32 decode tokens. Android rows use LiteRT-LM v0.13.1 with GPU OpenCL, 256 prefill tokens, and 64 decode tokens. Values report the warmed iteration from a two-iteration run unless noted.
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