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
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README.md
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| Device | Backend | Prefill (tokens/sec) | Decode (tokens/sec) | <span style="white-space: nowrap;">Time-to-first</span>-token (sec) | Model size (MB) | CPU Memory (MB) |
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**IoT**
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| Device | Backend | Prefill (tokens/sec) | Decode (tokens/sec) | <span style="white-space: nowrap;">Time-to-first</span>-token (sec) | Model size (MB) | CPU Memory (MB) |
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\* NPU model is benchmarked with 4096 context length
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| Device | Backend | Prefill (tokens/sec) | Decode (tokens/sec) | <span style="white-space: nowrap;">Time-to-first</span>-token (sec) | Model size (MB) | CPU Memory (MB) |
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| S26 Ultra | CPU | 557 | 46.9 | 1.8 | 2583 | 1733 |
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| S26 Ultra | GPU | 3,808 | 52.1 | 0.3 | 2583 | 676 |
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**iOS**
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| Device | Backend | Prefill (tokens/sec) | Decode (tokens/sec) | <span style="white-space: nowrap;">Time-to-first</span>-token (sec) | Model size (MB) | CPU Memory (MB) |
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| iPhone 17 Pro | CPU | 532 | 25.0 | 1.9 | 2583 | 607 |
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| iPhone 17 Pro | GPU | 2,878 | 56.5 | 0.3 | 2583 | 1450 |
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**Linux**
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| Device | Backend | Prefill (tokens/sec) | Decode (tokens/sec) | <span style="white-space: nowrap;">Time-to-first</span>-token (sec) | Model size (MB) | CPU Memory (MB) |
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| Arm 2.3 & 2.8GHz | CPU | 260 | 35.0 | 4.0 | 2583 | 1628 |
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| NVIDIA GeForce RTX 4090 | GPU | 11,234 | 143.4 | 0.1 | 2583 | 913 |
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| Device | Backend | Prefill (tokens/sec) | Decode (tokens/sec) | <span style="white-space: nowrap;">Time-to-first</span>-token (sec) | Model size (MB) | CPU Memory (MB) |
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| MacBook Pro M4 | CPU | TODO | TODO | TODO | TODO | TODO |
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| MacBook Pro M4 | GPU | TODO | TODO | TODO | TODO | TODO |
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**Windows**
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| Windows | CPU | TODO | TODO | TODO | TODO | TODO |
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| Windows | GPU | TODO | TODO | TODO | TODO | TODO |
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| Raspberry Pi 5 16GB | CPU | 133 | 7.6 | 7.8 | 2583 | 1546 |
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| Qualcomm IQ-8275 EVK | NPU* | 2371 | 18.8 | 0.5 | 2688 | 1471 |
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\* NPU model is benchmarked with 4096 context length
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