Text Generation
LiteRT-LM
LiteRT
English
litertlm
on-device
edge
asr
automatic-speech-recognition
text-normalization
inverse-text-normalization
punctuation
truecasing
speech-to-text
dictation
post-processing
qwen3
Instructions to use mlboydaisuke/S1-mini-LiteRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use mlboydaisuke/S1-mini-LiteRT 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 # A single .litertlm file in the repo is picked automatically; otherwise the CLI asks which one to run # (or pass its name right after the repo id). litert-lm run \ --from-huggingface-repo=mlboydaisuke/S1-mini-LiteRT \ --prompt="Write me a poem"
- LiteRT
How to use mlboydaisuke/S1-mini-LiteRT 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
manifest: Android measured rows from the Galaxy S26 (GPU + same-device CPU control) and the Pixel 8a, with an Android recommendation where the pair supports one
Browse files- litertlm_manifest.json +56 -3
litertlm_manifest.json
CHANGED
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@@ -1,7 +1,7 @@
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{
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"manifest_schema": "0.1.
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"repo": "mlboydaisuke/S1-mini-LiteRT",
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"generated": "2026-
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"generator": "make_manifest.py",
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"model": {
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"display_name": "S1-mini (LiteRT-LM)",
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"start": "<think>",
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"end": "</think>"
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}
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}
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}
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},
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"variants": [
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"gpu"
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],
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"default_backend": "cpu",
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"requirements": {
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"platform_notes": [
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"Context (KV cache) 4096; prefill signatures 1..1024",
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"date": "2026-08-25",
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"source": "on-device harness, single run; device reported thermal state 'serious' — read as a floor.",
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"peak_mem_mb": 1427
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}
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],
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"known_issues": [
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{
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"manifest_schema": "0.1.2",
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"repo": "mlboydaisuke/S1-mini-LiteRT",
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"generated": "2026-09-05",
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"generator": "make_manifest.py",
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"model": {
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"display_name": "S1-mini (LiteRT-LM)",
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"start": "<think>",
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"end": "</think>"
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}
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},
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"channels": [
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{
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"name": "thought",
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"start": "<think>",
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"end": "</think>"
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}
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]
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}
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},
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"variants": [
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"gpu"
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],
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"default_backend": "cpu",
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"recommended": [
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{
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"platform": "android",
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"device_class": "midrange",
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"backend": "cpu",
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"reason": "measured on one Pixel 8a with one binary (2026-08-25, 205-token prompt, single run each): GPU 434 tok/s prefill / 12.3 decode against CPU 40.4 / 5.8 - the GPU is faster, but on this phone it deterministically drops a date separator on number-dense input ('March 3rd, 2026.' -> 'March 32026.') while the CPU output is byte-identical to macOS on all 12 test cases. For a text-normalization model exactness is the job, so CPU is the default here; choose gpu when time-to-first-token matters more (0.67 s vs 6.46 s). Same-device pair taken, so the speed ordering is measured, not assumed"
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},
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{
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"platform": "android",
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"device_class": "flagship",
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"backend": "gpu",
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"reason": "measured on one Galaxy S26 with one binary (S7 Android backfill 2026-09-05, 205-token prompt, 2 cold runs per backend, cooled before each run): gpu decodes 25.13-25.8 tok/s against cpu's 12.29 (2.1x), prefill 940.7-1027.5 vs 166.7-175.3 tok/s, peak 1323 vs 2129 MB, engine init 6.3 vs 2.2 s. Same-device CPU control taken - a measured win for gpu on decode. On the Pixel 8a's Mali GPU this model deterministically dropped a date separator on number-dense input; that 12-case exactness check was not repeated on this Adreno GPU, so where exactness is the job prefer cpu and treat gpu as the throughput path"
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}
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],
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"requirements": {
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"platform_notes": [
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"Context (KV cache) 4096; prefill signatures 1..1024",
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"date": "2026-08-25",
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"source": "on-device harness, single run; device reported thermal state 'serious' — read as a floor.",
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"peak_mem_mb": 1427
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},
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{
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"device": "Galaxy S26 (SM-S942Q, Qualcomm SM8850, Adreno)",
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"backend": "gpu",
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"runtime": "litert_lm_advanced_main v0.16.0, LiteRT CL delegate",
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"prompt_tokens": 254,
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"decode_tokens": 195,
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"prefill_tps": "940.7-1027.5",
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"decode_tps": "25.13-25.8",
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"ttft_s": "0.29-0.31",
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"load_s": 6.3,
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"peak_memory_mb": 1323,
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"cache": "no",
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"runs": 2,
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"date": "2026-09-05",
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"source": "S7 Android backfill: 205-token prompt with --benchmark, 2 cold runs (compile/weight caches deleted between runs, device cooled below 42 C before each run); GPU gate full LITERT_CL delegation (15474 ops across 12 subgraphs), generation verified on the gate prompt; the cpu row of the same date is the same-device control"
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},
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{
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"device": "Galaxy S26 (SM-S942Q, Qualcomm SM8850, Adreno)",
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"backend": "cpu",
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"runtime": "litert_lm_advanced_main v0.16.0 (XNNPACK CPU)",
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"prompt_tokens": 254,
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"decode_tokens": 195,
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"prefill_tps": "166.7-175.3",
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"decode_tps": 12.29,
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"ttft_s": "1.53-1.61",
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"load_s": 2.2,
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"peak_memory_mb": 2129,
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"cache": "no",
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"runs": 2,
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"date": "2026-09-05",
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"source": "S7 Android backfill: same-device CPU control - identical binary, prompt file and flags, only --backend differs; 205-token prompt with --benchmark, 2 cold runs (compile/weight caches deleted between runs, device cooled below 42 C before each run); same-device control for the gpu row of the same date"
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}
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],
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"known_issues": [
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