Instructions to use litert-community/gemma-4-E2B-it-litert-lm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use litert-community/gemma-4-E2B-it-litert-lm 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 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/gemma-4-E2B-it-litert-lm \ model.litertlm \ --prompt="Write me a poem"
- Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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Benchmarked in Chrome on a MacBook Pro 2024 (Apple M4 Max) with 1024 prefill tokens and 256 decode tokens, but the model can support context lengths up to 128K.
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| Device | Backend | Prefill (tokens/sec) | Decode (tokens/sec) | Initialization time (sec) | Model size (MB) | CPU Memory (GB) | GPU Memory (
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| Web | GPU | 4,676 | 73.9 | 1.1 | 2004 | 1.5 | 1.8 |
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Benchmarked in Chrome on a MacBook Pro 2024 (Apple M4 Max) with 1024 prefill tokens and 256 decode tokens, but the model can support context lengths up to 128K.
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| Device | Backend | Prefill (tokens/sec) | Decode (tokens/sec) | Initialization time (sec) | Model size (MB) | CPU Memory (GB) | GPU Memory (GB) |
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| Web | GPU | 4,676 | 73.9 | 1.1 | 2004 | 1.5 | 1.8 |
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