NPU powered On-device AI Mobile applications using Melange
See the codeThis repository is frozen and no longer maintained.
Every app here (plus many new ones, 36+ and growing) has moved to the new home, rebranded, documented in 7 languages, and easier to run.
👉 Go to the new repository: https://github.com/zetic-ai/awesome-on-device-ai-apps
Please open new issues and pull requests there. Existing links below are kept for reference only.
Open Source, On-device AI Apps
Welcome to the collection of open-source, on-device AI applications built with Melange. These are not just demos; they are production-grade apps running entirely locally. They demonstrate how to run AI models directly on user devices with full NPU acceleration. By using Melange's automated optimization, what used to require months of hardware-specific tuning now takes just hours.
Melange is an NPU-native platform that takes your AI model and turns it into a production-ready mobile app automatically. At ZETIC, we built Melange to make AI Faster, Cheaper, Safer, and Independent.
Here's what makes Melange special:
We've put together real, working examples to help you build with Melange:
Get started with Melange in minutes:
# 1. Clone the repository
git clone https://github.com/zetic-ai/ZETIC_Melange_apps.git
cd ZETIC_Melange_apps
# 2. Get your free Melange API key
# Sign up at https://mlange.zetic.ai and get your personal access token
# 3. Configure your API key (automated)
./adapt_mlange_key.sh
# 4. Choose an app and run it
# Android: Open apps/<ModelName>/Android in Android Studio
# iOS: Open apps/<ModelName>/iOS in Xcode
💡 Getting Started: To run these applications, you'll need a free Melange Personal Access Token.
- Sign up or log in at the Melange Dashboard
- Navigate to Settings → Personal Access Token
- Generate your token and use it with the setup script
Melange automatically handles model optimization, NPU acceleration, and deployment. No manual configuration required!
| Feature | Model | Description | Melange Page |
|---|---|---|---|
| Conversational AI | Qwen3Chat | On-device large language model chatbot with real-time token streaming and dynamic context window. | View |
| AI Keyboard | LFM2.5-350M | On-device AI keyboard — rewrite with tones, smart replies, translation, and grammar fixes in any app. The small LFM2.5 model runs entirely inside the keyboard (iOS) / IME (Android); offline after the first download. | View |
| Time Series Forecasting | Chronos Bolt | Probabilistic time series forecasting with quantile output; CSV import and interactive charts. | View |
| 3D Reconstruction | Depth-Anything-V2-Small | Single-photo on-device depth estimation with interactive 3D relief mesh and point-cloud views. | View |
| Machine Translation | Offline Translator (HY-MT) | Fully offline translation with text, voice (on-device STT), and visual/OCR input — real-time streaming and instant language swapping. | View |
| Grammar Correction | t5-base-grammar-correction | Robust grammar correction based on T5 architecture for real-time text processing. | View |
| Privacy / Anonymization | tanaos-text-anonymizer-v1 | Automatic detection and masking of PII (names, dates, locations, emails, phone numbers) for secure data processing. | View |
| Object Detection | YOLO26 | Next-generation NMS-free object detection. | View |
| Audio Analysis | YamNet | Classification of environmental sounds and audio events. | View |
| Object Detection | YOLOv8 Nano | Real-time object detection and tracking in milliseconds. | View |
| Face Tracking | MediaPipe Face Landmarker | High-fidelity 468-point face mesh and landmark tracking. | View |
| Motion Analysis | YOLO26n · RTMPose-s · MotionBERT-Lite | On-device human-pose tracking and 3D motion lifting for sports-form and swing-review workflows. | Detect · Pose · Lift |
| Face Detection | MediaPipe BlazeFace | Ultra-fast face detection optimized for short-range selfie cameras. | View |
| Emotion Analysis | Emo-AffectNet | Real-time facial emotion recognition. | View |
| Speech Recognition | Whisper Tiny | High-accuracy automatic speech recognition (ASR) completely offline. | View |
| Private AI Notepad | Gemma 4 E2B | On-Device AI Meeting Notes powered by Gemma. Granola, Pocket alternative. Private by design. | View |
| Multimodal Wellbeing | Wav2Vec2-Base_Emotion-Recognition · FaceEmotionRecognition | Guided on-device mood check-in that fuses live facial-emotion and voice-emotion analysis into an explainable, non-diagnostic well-being readout — camera and audio never leave the device. | Voice · Face |
| Voice Biomarker | Wav2Vec2-Base_Emotion-Recognition · YAMNET | Dual-tab on-device voice biomarkers — wav2vec2 speech-emotion plus YAMNet respiratory acoustic-event detection (cough, breathing, wheeze). Microphone audio never leaves the device. | Emotion · Respiratory |
| Camera Vitals | EfficientPhys-rPPG | Contactless heart-rate measurement from the front camera using the EfficientPhys rPPG model — fully on-device, no frames leave the phone. | View |
| Medical Imaging | EchoNet-DeepLab-v3 | On-device cardiac ultrasound LV segmentation with live mask overlays and device benchmark metrics. | View |
| Skin Classification | Skin Cancer ViT | On-device skin-lesion image classification (ViT, 7 HAM10000 lesion types). Reads a photo's pixels locally with confidence scoring and curated, severity-aware guidance — no images leave the device. | View |
We're always adding new models. Here's what's on the way:
| Use Case | Model | Description |
|---|---|---|
| Machine Translation | TranslateGemma-4B-IT | Instruction-tuned multilingual translation model with 4B parameters. |
| TTS | Qwen3-TTS-12Hz-0.6B-CustomVoice | Custom voice Text-to-Speech synthesis with high-quality audio output. |
| Speech Recognition | MedASR | Medical domain automatic speech recognition for healthcare applications. |
| Healthcare / VQA | MedGemma-1.5-4b-it | Multimodal medical question answering and image understanding. |
| TTS | neutts-nano | Compact, high-quality Text-to-Speech synthesis. |
| Depth Estimation | DA3-SMALL | Efficient multi-view depth estimation for 3D understanding. |
| TTS | pocket-tts | Ultra-lightweight real-time speech synthesis. |
| Speech Recognition | OmniASR | Multilingual automatic speech recognition supporting 100+ languages with CTC architecture. |
[!TIP] Want to ship your own model? You can upload your model to Melange, convert it for on-device inference, and build an app from the generated code. No need to wait for a demo here.
ZETIC_Melange_apps/
├── apps/ # On-device AI applications
│ ├── ChronosTimeSeries/ # Time series forecasting (Chronos Bolt)
│ │ ├── Android/ # Android implementation
│ │ ├── iOS/ # iOS implementation
│ │ └── prepare/ # Model preparation scripts
│ ├── YOLO26/ # Object detection app
│ ├── translate-tencent_HY-MT/ # Offline translation (text, voice, OCR)
│ ├── whisper-tiny/ # Speech recognition app
│ ├── TextAnonymizer/ # PII detection and masking
│ └── ... # More apps
├── res/screenshots/ # Demo GIFs and images for app READMEs
├── extension/ # Melange Extension Library (submodule)
│ └── Helper extensions and utilities
├── adapt_mlange_key.sh # Script to configure API keys
└── LICENSE # Apache 2.0 License
Each app includes:
Want to use these NPU capabilities in your own app? The Melange SDK makes it simple. You don't need to write complex hardware code, just initialize the SDK and start running inference instantly.
1. Add Dependency
In your build.gradle.kts:
dependencies {
implementation("com.zeticai.mlange:mlange:+")
}
2. Initialize and Run
import com.zeticai.mlange.core.model.ZeticMLangeModel
import com.zeticai.mlange.core.tensor.Tensor
// Initialize model
val model = ZeticMLangeModel(
context = this,
tokenKey = "YOUR_PERSONAL_KEY",
modelName = "Team_ZETIC/YOLO26"
)
// Prepare inputs
val inputs: Array<Tensor> = // ... prepare your tensors
// Run inference
val outputs = model.run(inputs)
1. Add Package
Add via Swift Package Manager:
https://github.com/zetic-ai/ZeticMLangeiOS.git2. Initialize and Run
import ZeticMLange
// Initialize model
let model = try ZeticMLangeModel(
tokenKey: "YOUR_PERSONAL_KEY",
name: "Team_ZETIC/YOLO26",
version: 1
)
// Prepare inputs
let inputs: [Tensor] = // ... prepare your tensors
// Run inference
let outputs = try model.run(inputs: inputs)
We love seeing what developers build! This repository is an open-source collection of demo apps, and we welcome new additions to the gallery.
git checkout -b feature/amazing-app)This repository (the source code and example apps) is licensed under the Apache License 2.0.
⚠️ Note: The Melange SDK is a proprietary library subject to the ZETIC Terms of Service.
You can:
Just remember to:
By ZETIC
⭐ Star us on GitHub • 🐛 Report Bug • 💡 Request Features • 🚀 Try Melange • 📖 Documentation
NPU powered On-device AI Mobile applications using Melange
See the codeThis repository is frozen and no longer maintained.
Every app here (plus many new ones, 36+ and growing) has moved to the new home, rebranded, documented in 7 languages, and easier to run.
👉 Go to the new repository: https://github.com/zetic-ai/awesome-on-device-ai-apps
Please open new issues and pull requests there. Existing links below are kept for reference only.
Open Source, On-device AI Apps
Welcome to the collection of open-source, on-device AI applications built with Melange. These are not just demos; they are production-grade apps running entirely locally. They demonstrate how to run AI models directly on user devices with full NPU acceleration. By using Melange's automated optimization, what used to require months of hardware-specific tuning now takes just hours.
Melange is an NPU-native platform that takes your AI model and turns it into a production-ready mobile app automatically. At ZETIC, we built Melange to make AI Faster, Cheaper, Safer, and Independent.
Here's what makes Melange special:
We've put together real, working examples to help you build with Melange:
Get started with Melange in minutes:
# 1. Clone the repository
git clone https://github.com/zetic-ai/ZETIC_Melange_apps.git
cd ZETIC_Melange_apps
# 2. Get your free Melange API key
# Sign up at https://mlange.zetic.ai and get your personal access token
# 3. Configure your API key (automated)
./adapt_mlange_key.sh
# 4. Choose an app and run it
# Android: Open apps/<ModelName>/Android in Android Studio
# iOS: Open apps/<ModelName>/iOS in Xcode
💡 Getting Started: To run these applications, you'll need a free Melange Personal Access Token.
- Sign up or log in at the Melange Dashboard
- Navigate to Settings → Personal Access Token
- Generate your token and use it with the setup script
Melange automatically handles model optimization, NPU acceleration, and deployment. No manual configuration required!
| Feature | Model | Description | Melange Page |
|---|---|---|---|
| Conversational AI | Qwen3Chat | On-device large language model chatbot with real-time token streaming and dynamic context window. | View |
| AI Keyboard | LFM2.5-350M | On-device AI keyboard — rewrite with tones, smart replies, translation, and grammar fixes in any app. The small LFM2.5 model runs entirely inside the keyboard (iOS) / IME (Android); offline after the first download. | View |
| Time Series Forecasting | Chronos Bolt | Probabilistic time series forecasting with quantile output; CSV import and interactive charts. | View |
| 3D Reconstruction | Depth-Anything-V2-Small | Single-photo on-device depth estimation with interactive 3D relief mesh and point-cloud views. | View |
| Machine Translation | Offline Translator (HY-MT) | Fully offline translation with text, voice (on-device STT), and visual/OCR input — real-time streaming and instant language swapping. | View |
| Grammar Correction | t5-base-grammar-correction | Robust grammar correction based on T5 architecture for real-time text processing. | View |
| Privacy / Anonymization | tanaos-text-anonymizer-v1 | Automatic detection and masking of PII (names, dates, locations, emails, phone numbers) for secure data processing. | View |
| Object Detection | YOLO26 | Next-generation NMS-free object detection. | View |
| Audio Analysis | YamNet | Classification of environmental sounds and audio events. | View |
| Object Detection | YOLOv8 Nano | Real-time object detection and tracking in milliseconds. | View |
| Face Tracking | MediaPipe Face Landmarker | High-fidelity 468-point face mesh and landmark tracking. | View |
| Motion Analysis | YOLO26n · RTMPose-s · MotionBERT-Lite | On-device human-pose tracking and 3D motion lifting for sports-form and swing-review workflows. | Detect · Pose · Lift |
| Face Detection | MediaPipe BlazeFace | Ultra-fast face detection optimized for short-range selfie cameras. | View |
| Emotion Analysis | Emo-AffectNet | Real-time facial emotion recognition. | View |
| Speech Recognition | Whisper Tiny | High-accuracy automatic speech recognition (ASR) completely offline. | View |
| Private AI Notepad | Gemma 4 E2B | On-Device AI Meeting Notes powered by Gemma. Granola, Pocket alternative. Private by design. | View |
| Multimodal Wellbeing | Wav2Vec2-Base_Emotion-Recognition · FaceEmotionRecognition | Guided on-device mood check-in that fuses live facial-emotion and voice-emotion analysis into an explainable, non-diagnostic well-being readout — camera and audio never leave the device. | Voice · Face |
| Voice Biomarker | Wav2Vec2-Base_Emotion-Recognition · YAMNET | Dual-tab on-device voice biomarkers — wav2vec2 speech-emotion plus YAMNet respiratory acoustic-event detection (cough, breathing, wheeze). Microphone audio never leaves the device. | Emotion · Respiratory |
| Camera Vitals | EfficientPhys-rPPG | Contactless heart-rate measurement from the front camera using the EfficientPhys rPPG model — fully on-device, no frames leave the phone. | View |
| Medical Imaging | EchoNet-DeepLab-v3 | On-device cardiac ultrasound LV segmentation with live mask overlays and device benchmark metrics. | View |
| Skin Classification | Skin Cancer ViT | On-device skin-lesion image classification (ViT, 7 HAM10000 lesion types). Reads a photo's pixels locally with confidence scoring and curated, severity-aware guidance — no images leave the device. | View |
We're always adding new models. Here's what's on the way:
| Use Case | Model | Description |
|---|---|---|
| Machine Translation | TranslateGemma-4B-IT | Instruction-tuned multilingual translation model with 4B parameters. |
| TTS | Qwen3-TTS-12Hz-0.6B-CustomVoice | Custom voice Text-to-Speech synthesis with high-quality audio output. |
| Speech Recognition | MedASR | Medical domain automatic speech recognition for healthcare applications. |
| Healthcare / VQA | MedGemma-1.5-4b-it | Multimodal medical question answering and image understanding. |
| TTS | neutts-nano | Compact, high-quality Text-to-Speech synthesis. |
| Depth Estimation | DA3-SMALL | Efficient multi-view depth estimation for 3D understanding. |
| TTS | pocket-tts | Ultra-lightweight real-time speech synthesis. |
| Speech Recognition | OmniASR | Multilingual automatic speech recognition supporting 100+ languages with CTC architecture. |
[!TIP] Want to ship your own model? You can upload your model to Melange, convert it for on-device inference, and build an app from the generated code. No need to wait for a demo here.
ZETIC_Melange_apps/
├── apps/ # On-device AI applications
│ ├── ChronosTimeSeries/ # Time series forecasting (Chronos Bolt)
│ │ ├── Android/ # Android implementation
│ │ ├── iOS/ # iOS implementation
│ │ └── prepare/ # Model preparation scripts
│ ├── YOLO26/ # Object detection app
│ ├── translate-tencent_HY-MT/ # Offline translation (text, voice, OCR)
│ ├── whisper-tiny/ # Speech recognition app
│ ├── TextAnonymizer/ # PII detection and masking
│ └── ... # More apps
├── res/screenshots/ # Demo GIFs and images for app READMEs
├── extension/ # Melange Extension Library (submodule)
│ └── Helper extensions and utilities
├── adapt_mlange_key.sh # Script to configure API keys
└── LICENSE # Apache 2.0 License
Each app includes:
Want to use these NPU capabilities in your own app? The Melange SDK makes it simple. You don't need to write complex hardware code, just initialize the SDK and start running inference instantly.
1. Add Dependency
In your build.gradle.kts:
dependencies {
implementation("com.zeticai.mlange:mlange:+")
}
2. Initialize and Run
import com.zeticai.mlange.core.model.ZeticMLangeModel
import com.zeticai.mlange.core.tensor.Tensor
// Initialize model
val model = ZeticMLangeModel(
context = this,
tokenKey = "YOUR_PERSONAL_KEY",
modelName = "Team_ZETIC/YOLO26"
)
// Prepare inputs
val inputs: Array<Tensor> = // ... prepare your tensors
// Run inference
val outputs = model.run(inputs)
1. Add Package
Add via Swift Package Manager:
https://github.com/zetic-ai/ZeticMLangeiOS.git2. Initialize and Run
import ZeticMLange
// Initialize model
let model = try ZeticMLangeModel(
tokenKey: "YOUR_PERSONAL_KEY",
name: "Team_ZETIC/YOLO26",
version: 1
)
// Prepare inputs
let inputs: [Tensor] = // ... prepare your tensors
// Run inference
let outputs = try model.run(inputs: inputs)
We love seeing what developers build! This repository is an open-source collection of demo apps, and we welcome new additions to the gallery.
git checkout -b feature/amazing-app)This repository (the source code and example apps) is licensed under the Apache License 2.0.
⚠️ Note: The Melange SDK is a proprietary library subject to the ZETIC Terms of Service.
You can:
Just remember to:
By ZETIC
⭐ Star us on GitHub • 🐛 Report Bug • 💡 Request Features • 🚀 Try Melange • 📖 Documentation