Instructions to use litert-community/FastContext-1.0-4B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use litert-community/FastContext-1.0-4B-SFT 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/FastContext-1.0-4B-SFT \ --prompt="Write me a poem"
- LiteRT
How to use litert-community/FastContext-1.0-4B-SFT with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Card: state where each on-device figure actually comes from; replace the iPhone row with a fresh measurement where one was taken
Browse files
README.md
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@@ -114,7 +114,7 @@ reduction is required to fit iOS.
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Reproducibility: the GPU rows repeat to within about 1% across invocations; the CPU rows are noisier — re-running the 1B control six times spread its CPU decode over 29.0–33.3 tok/s, so treat the CPU column as accurate to roughly ±7%.
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The desktop rows are the shipped `model.litertlm` (block 32); the block-128 build was not re-measured, so the figures for it in “Which file?” above are the older ship-gate numbers. The iPhone figures
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### Accuracy note
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Reproducibility: the GPU rows repeat to within about 1% across invocations; the CPU rows are noisier — re-running the 1B control six times spread its CPU decode over 29.0–33.3 tok/s, so treat the CPU column as accurate to roughly ±7%.
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The desktop rows are the shipped `model.litertlm` (block 32); the block-128 build was not re-measured, so the figures for it in “Which file?” above are the older ship-gate numbers. The iPhone figures come from the 2026-06-27 on-device platform matrix for this model, one per build; that record does not state a run count or prompt.
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### Accuracy note
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