Instructions to use litert-community/Qwen2.5-Coder-3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use litert-community/Qwen2.5-Coder-3B-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 litert-lm run \ --from-huggingface-repo=litert-community/Qwen2.5-Coder-3B-Instruct \ --prompt="Write me a poem"
- Notebooks
- Google Colab
- Kaggle
Add measured performance section (M4 Max)
Browse filesThanks for converting Qwen2.5-Coder-3B — it answers correctly on both backends. This PR adds a measured Performance section to the card. 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.
README.md
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## Integration
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Ready to integrate this into your product? Get started in the [LiteRT-LM documentation](https://ai.google.dev/edge/litert-lm/overview).
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## Integration
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Ready to integrate this into your product? Get started in the [LiteRT-LM documentation](https://ai.google.dev/edge/litert-lm/overview).
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## Performance (Apple M4 Max, measured)
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Measured with the LiteRT-LM CLI: `litert-lm benchmark -p 256 -d 256 --runs 3 --cache no`
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(litert-lm 0.15.0) on an idle Apple M4 Max (macOS); 256 prefill / 256 decode tokens, 3 iterations
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averaged by the tool. A desktop reference point — phone-side figures vary by SoC and backend.
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| Backend | Prefill (tokens/s) | Decode (tokens/s) | Time-to-first-token (s) |
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| CPU | 121 | 26.7 | 2.16 |
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| GPU | 1,320 | 77.8 | 0.21 |
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