README
LiteRT-LM-Unity
Unity integration for on-device AI on Android. Runs LLM chat, speech recognition (ASR), image understanding and function calling entirely on the device, with no network.
- LiteRT-LM v0.14.0 (
.litertlm1.5.0); customizations live inTools/UnityAar/litert-lm-unity-aar.patch - Device-verified on Snapdragon 865 / 7.5 GB RAM / Android 12 — all four capabilities PASS across 6 PDCA cycles, 80+ runs, zero crashes (ledger)
- Published models: whisper-acft · whisper-acft-ko · litert-lm-unity-quantized
Capabilities (measured on device)
| Capability | Speed | Hit rate | Model |
|---|---|---|---|
| LLM chat | 35.5 tok/s | — | Qwen2.5-0.5B i4 |
| Speech recognition | 0.7–0.8 s | 4/5 | whisper-base-acft-ko 5s |
| Image understanding | 7.6 s (GPU) | accurate | gemma-4-E2B QAT |
| Function calling | 15.5 s E2E (voice → tool) | 19/20 | gemma-4-E2B / Qwen3-0.6B |
Requirements
Unity 2022.3 or newer (developed and device-verified on 6000.4.6f1) +
Android Build Support · Android device (adb, Snapdragon 865 class or better,
4 GB+ RAM) · Windows PowerShell (build scripts) · Docker (only to rebuild the AAR)
Install
The runtime ships as a UPM package,
com.leuconoe.litert-lm-unity. In Package Manager choose
Add package from git URL… and paste:
https://github.com/Leuconoe/LiteRT-LM-Unity.git?path=/Packages/com.leuconoe.litert-lm-unity
Or add the line yourself to Packages/manifest.json:
{
"dependencies": {
"com.leuconoe.litert-lm-unity": "https://github.com/Leuconoe/LiteRT-LM-Unity.git?path=/Packages/com.leuconoe.litert-lm-unity"
}
}
Pin a release by appending a tag: …litert-lm-unity#v0.14.0a-unity.
Git URL installs need git on PATH; the package carries
a 31 MB Android AAR, so the first resolve takes a moment.
Samples — in Package Manager select LiteRT-LM for Unity → Samples:
| Sample | Contents |
|---|---|
| Test Scenes | Seven hand-driven scenes (quick start, chat, ASR, multimodal, voice FC, multimodal FC, translate), the Android build menu and the scene generator |
| Automated Tests | Three unattended scenes (Android smoke, conversation, FC benchmark) that run on load and write their results to Builds/Logs/ |
Import Test Scenes first; Automated Tests is only needed for regression runs.
Working in this repository instead of consuming the package? The samples live in
Samples~/, which Unity does not compile. Import them once with:
.\Tools\Windows\Restore-LiteRtLmSamples.ps1
Quick Start
- Install the package and import the Test Scenes sample (above)
- Place models — pick from the tables below and put them under
Assets/StreamingAssets/(model files are not in the repository) - Build the APK — Unity menu
LiteRT-LM/Android/...orTools/Windows/Tests/Build-LiteRtLmAndroid*.ps1 - Smoke test —
Run-LiteRtLmAndroidAsrSmokeTest.ps1 -DeviceSerial <serial>; results land inBuilds/Logs/AndroidDeviceRuns/
Package layout
| Path | Contents |
|---|---|
Packages/com.leuconoe.litert-lm-unity/Runtime/ | LiteRtLmUnityClient (Android bridge), LiteRtLmMicVadCapture, LiteRtLmStatusHudOverlay, LiteRtLmWindowsCliClient, and the native AAR |
Packages/com.leuconoe.litert-lm-unity/Samples~/TestScenes/ | Scene runners, the seven hand-driven scenes, the APK build menu and the scene generator |
Packages/com.leuconoe.litert-lm-unity/Samples~/AutomatedTests/ | Smoke, conversation and function-calling benchmark scenes |
Assets/StreamingAssets/ | Where you place models (not in the repository) |
Tools/Windows/ | Build and workflow scripts · Bin/ prebuilt runtime · Tests/ device and smoke runners |
Tools/Research/ | Conversion and benchmark drivers behind the numbers in docs/ |
docs/ | Benchmarks and handoffs |
Recommended models
LLM — pick by device RAM
| Device RAM | Model | Size | Measured on device | Download |
|---|---|---|---|---|
| 4–6 GB | LLM/qwen2.5-0.5b/…_wi4b64_ekv1280.litertlm | 265 MB | 35.5 tok/s — chat only (not usable as an FC router) | project int4 (upstream f32) |
| 6–8 GB | LLM/qwen3-0.6b/qwen3_0_6b_mixed_int4.litertlm | 475 MB | 20.9 tok/s, FC 18/20 | litert-community/Qwen3-0.6B |
| 8 GB+ | Multimodal/gemma-4-e2b/gemma-4-E2B-it.litertlm | 2.6 GB | FC 19/20, image 7.6 s, audio 4.1 s; image turns peak at 3.6 GB PSS | litert-community/gemma-4-E2B-it-litert-lm |
LFM2.5-1.2B int4 (702 MB, 16.8 tok/s, FC 17/20) is also available as a
mid-size FC router. Use CPU for chat (decode) and GPU for long prompts
and images. LLM details →
ASR — pick by utterance length (one model is usually enough)
| Utterance length | Model | Size | Measured on device | Download |
|---|---|---|---|---|
| ≤5 s (commands, short sentences) | ASR/whisper-base-acft-ko/acft_base_5s_drq.tflite | 101 MB | 0.7–0.8 s, 4/5 exact | leuconoe/whisper-acft-ko |
| 5–30 s (dictation) | ASR/whisper-base/whisper_base_30s_i8.tflite | 77 MB | 2.7 s, sentence CER 0.000 | project i8 (upstream f32) |
| >30 s (batch) | ASR/qwen3-asr-0.6b/qwen3_asr_0.6b_5s_i8.tflite | 794 MB | chunk loop, RTF ≈2.6 | Qwen/Qwen3-ASR-0.6B |
- Ship the matching per-tier
tokenizer.jsonin the same folder (openai/whisper-*) - VAD is on by default —
energy(free) /ai(Silero, 1.25 MB) /off - For quiet recordings, load turbo-acft-ko 5s (883 MB, 5/5) on demand (leuconoe/whisper-acft-ko)
- English-only short speech can use the original futo ACFT models (litert-community/whisper-acft)
All 10 tiers, selection rationale, ACFT training background →
Test scenes
Shipped as the package's Test Scenes sample; after import they land under
Assets/Samples/LiteRT-LM for Unity/<version>/Test Scenes/Scenes/. Regenerate
them with the menu LiteRT-LM/Test Scenes/Generate All — scene paths resolve
from the import location, so no path editing is needed.
Every scene shows a ◀ Prev / Next ▶ bar so the set can be walked through on a device. Each switch releases the loaded model first — engines hold native memory the GC does not track, so the outgoing one is disposed before the next loads rather than both being resident.
| Scene | Purpose |
|---|---|
LiteRtLmSampleScene | Quick start — model path, one prompt, one response |
LiteRtLmLlmChatTestScene | Multi-turn chat, think/no_think toggle |
LiteRtLmAsrTestScene | ASR — file / microphone / always-listening (Continuous) |
LiteRtLmMultimodalTestScene | Image + audio input, with a file picker on Windows |
LiteRtLmAsrFunctionCallingTestScene | Voice → tool call (15.5 s), editable prompt and tool list |
LiteRtLmMultimodalFunctionCallingTestScene | Image + utterance → tool call (40.7 s) |
LiteRtLmTranslateTestScene | Translation — Whisper Direct / ASR+LLM |
The Automated Tests sample adds LiteRtLmAndroidSmokeTestScene,
LiteRtLmConversationTestScene and LiteRtLmFunctionCallingBenchmarkScene.
These start on load and report through an on-screen overlay, so a device run
needs no interaction.
Windows runs the same scenes through the bundled CLI binaries, so ASR, translation, multimodal and function calling can all be exercised in the editor before an APK build.
Rebuilding the AAR (after native changes)
Tools/Windows/Build-LiteRtLmUnityAarFromPatch.ps1 -SourceRoot <pristine v0.14.0>
applies the patch, builds in Docker and deploys to
Packages/com.leuconoe.litert-lm-unity/Runtime/Plugins/Android/.
⚠️ -SkipImageBuild builds the sources baked into the Docker image, so the
image must be rebuilt after any patch change.
Docs
| Document | Contents |
|---|---|
docs/llm-details.md | LLM tiers, backend choice, device measurements |
docs/asr-details.md | Every ASR tier, VAD, ACFT-KO training background |
docs/README.md | Full benchmark and handoff index |
The Windows editor path exists only to validate logic before deploying to a device. Its performance profile is the opposite of Android's (GPU wins there), so never use desktop numbers to make device decisions.
Contributors
1 commits
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README
LiteRT-LM-Unity
Unity integration for on-device AI on Android. Runs LLM chat, speech recognition (ASR), image understanding and function calling entirely on the device, with no network.
- LiteRT-LM v0.14.0 (
.litertlm1.5.0); customizations live inTools/UnityAar/litert-lm-unity-aar.patch - Device-verified on Snapdragon 865 / 7.5 GB RAM / Android 12 — all four capabilities PASS across 6 PDCA cycles, 80+ runs, zero crashes (ledger)
- Published models: whisper-acft · whisper-acft-ko · litert-lm-unity-quantized
Capabilities (measured on device)
| Capability | Speed | Hit rate | Model |
|---|---|---|---|
| LLM chat | 35.5 tok/s | — | Qwen2.5-0.5B i4 |
| Speech recognition | 0.7–0.8 s | 4/5 | whisper-base-acft-ko 5s |
| Image understanding | 7.6 s (GPU) | accurate | gemma-4-E2B QAT |
| Function calling | 15.5 s E2E (voice → tool) | 19/20 | gemma-4-E2B / Qwen3-0.6B |
Requirements
Unity 2022.3 or newer (developed and device-verified on 6000.4.6f1) +
Android Build Support · Android device (adb, Snapdragon 865 class or better,
4 GB+ RAM) · Windows PowerShell (build scripts) · Docker (only to rebuild the AAR)
Install
The runtime ships as a UPM package,
com.leuconoe.litert-lm-unity. In Package Manager choose
Add package from git URL… and paste:
https://github.com/Leuconoe/LiteRT-LM-Unity.git?path=/Packages/com.leuconoe.litert-lm-unity
Or add the line yourself to Packages/manifest.json:
{
"dependencies": {
"com.leuconoe.litert-lm-unity": "https://github.com/Leuconoe/LiteRT-LM-Unity.git?path=/Packages/com.leuconoe.litert-lm-unity"
}
}
Pin a release by appending a tag: …litert-lm-unity#v0.14.0a-unity.
Git URL installs need git on PATH; the package carries
a 31 MB Android AAR, so the first resolve takes a moment.
Samples — in Package Manager select LiteRT-LM for Unity → Samples:
| Sample | Contents |
|---|---|
| Test Scenes | Seven hand-driven scenes (quick start, chat, ASR, multimodal, voice FC, multimodal FC, translate), the Android build menu and the scene generator |
| Automated Tests | Three unattended scenes (Android smoke, conversation, FC benchmark) that run on load and write their results to Builds/Logs/ |
Import Test Scenes first; Automated Tests is only needed for regression runs.
Working in this repository instead of consuming the package? The samples live in
Samples~/, which Unity does not compile. Import them once with:
.\Tools\Windows\Restore-LiteRtLmSamples.ps1
Quick Start
- Install the package and import the Test Scenes sample (above)
- Place models — pick from the tables below and put them under
Assets/StreamingAssets/(model files are not in the repository) - Build the APK — Unity menu
LiteRT-LM/Android/...orTools/Windows/Tests/Build-LiteRtLmAndroid*.ps1 - Smoke test —
Run-LiteRtLmAndroidAsrSmokeTest.ps1 -DeviceSerial <serial>; results land inBuilds/Logs/AndroidDeviceRuns/
Package layout
| Path | Contents |
|---|---|
Packages/com.leuconoe.litert-lm-unity/Runtime/ | LiteRtLmUnityClient (Android bridge), LiteRtLmMicVadCapture, LiteRtLmStatusHudOverlay, LiteRtLmWindowsCliClient, and the native AAR |
Packages/com.leuconoe.litert-lm-unity/Samples~/TestScenes/ | Scene runners, the seven hand-driven scenes, the APK build menu and the scene generator |
Packages/com.leuconoe.litert-lm-unity/Samples~/AutomatedTests/ | Smoke, conversation and function-calling benchmark scenes |
Assets/StreamingAssets/ | Where you place models (not in the repository) |
Tools/Windows/ | Build and workflow scripts · Bin/ prebuilt runtime · Tests/ device and smoke runners |
Tools/Research/ | Conversion and benchmark drivers behind the numbers in docs/ |
docs/ | Benchmarks and handoffs |
Recommended models
LLM — pick by device RAM
| Device RAM | Model | Size | Measured on device | Download |
|---|---|---|---|---|
| 4–6 GB | LLM/qwen2.5-0.5b/…_wi4b64_ekv1280.litertlm | 265 MB | 35.5 tok/s — chat only (not usable as an FC router) | project int4 (upstream f32) |
| 6–8 GB | LLM/qwen3-0.6b/qwen3_0_6b_mixed_int4.litertlm | 475 MB | 20.9 tok/s, FC 18/20 | litert-community/Qwen3-0.6B |
| 8 GB+ | Multimodal/gemma-4-e2b/gemma-4-E2B-it.litertlm | 2.6 GB | FC 19/20, image 7.6 s, audio 4.1 s; image turns peak at 3.6 GB PSS | litert-community/gemma-4-E2B-it-litert-lm |
LFM2.5-1.2B int4 (702 MB, 16.8 tok/s, FC 17/20) is also available as a
mid-size FC router. Use CPU for chat (decode) and GPU for long prompts
and images. LLM details →
ASR — pick by utterance length (one model is usually enough)
| Utterance length | Model | Size | Measured on device | Download |
|---|---|---|---|---|
| ≤5 s (commands, short sentences) | ASR/whisper-base-acft-ko/acft_base_5s_drq.tflite | 101 MB | 0.7–0.8 s, 4/5 exact | leuconoe/whisper-acft-ko |
| 5–30 s (dictation) | ASR/whisper-base/whisper_base_30s_i8.tflite | 77 MB | 2.7 s, sentence CER 0.000 | project i8 (upstream f32) |
| >30 s (batch) | ASR/qwen3-asr-0.6b/qwen3_asr_0.6b_5s_i8.tflite | 794 MB | chunk loop, RTF ≈2.6 | Qwen/Qwen3-ASR-0.6B |
- Ship the matching per-tier
tokenizer.jsonin the same folder (openai/whisper-*) - VAD is on by default —
energy(free) /ai(Silero, 1.25 MB) /off - For quiet recordings, load turbo-acft-ko 5s (883 MB, 5/5) on demand (leuconoe/whisper-acft-ko)
- English-only short speech can use the original futo ACFT models (litert-community/whisper-acft)
All 10 tiers, selection rationale, ACFT training background →
Test scenes
Shipped as the package's Test Scenes sample; after import they land under
Assets/Samples/LiteRT-LM for Unity/<version>/Test Scenes/Scenes/. Regenerate
them with the menu LiteRT-LM/Test Scenes/Generate All — scene paths resolve
from the import location, so no path editing is needed.
Every scene shows a ◀ Prev / Next ▶ bar so the set can be walked through on a device. Each switch releases the loaded model first — engines hold native memory the GC does not track, so the outgoing one is disposed before the next loads rather than both being resident.
| Scene | Purpose |
|---|---|
LiteRtLmSampleScene | Quick start — model path, one prompt, one response |
LiteRtLmLlmChatTestScene | Multi-turn chat, think/no_think toggle |
LiteRtLmAsrTestScene | ASR — file / microphone / always-listening (Continuous) |
LiteRtLmMultimodalTestScene | Image + audio input, with a file picker on Windows |
LiteRtLmAsrFunctionCallingTestScene | Voice → tool call (15.5 s), editable prompt and tool list |
LiteRtLmMultimodalFunctionCallingTestScene | Image + utterance → tool call (40.7 s) |
LiteRtLmTranslateTestScene | Translation — Whisper Direct / ASR+LLM |
The Automated Tests sample adds LiteRtLmAndroidSmokeTestScene,
LiteRtLmConversationTestScene and LiteRtLmFunctionCallingBenchmarkScene.
These start on load and report through an on-screen overlay, so a device run
needs no interaction.
Windows runs the same scenes through the bundled CLI binaries, so ASR, translation, multimodal and function calling can all be exercised in the editor before an APK build.
Rebuilding the AAR (after native changes)
Tools/Windows/Build-LiteRtLmUnityAarFromPatch.ps1 -SourceRoot <pristine v0.14.0>
applies the patch, builds in Docker and deploys to
Packages/com.leuconoe.litert-lm-unity/Runtime/Plugins/Android/.
⚠️ -SkipImageBuild builds the sources baked into the Docker image, so the
image must be rebuilt after any patch change.
Docs
| Document | Contents |
|---|---|
docs/llm-details.md | LLM tiers, backend choice, device measurements |
docs/asr-details.md | Every ASR tier, VAD, ACFT-KO training background |
docs/README.md | Full benchmark and handoff index |
The Windows editor path exists only to validate logic before deploying to a device. Its performance profile is the opposite of Android's (GPU wins there), so never use desktop numbers to make device decisions.
Similar repos
Contributors
1 commits
Languages
C#
72.3%
PowerShell
16.7%
Python
11.0%