Metadata-Version: 2.4
Name: ameva-runtime
Version: 2.6.0
Summary: Unified Next-Gen Hardware Orchestration & AI Acceleration Runtime for Mobile & Edge
Home-page: https://github.com/uno-km/ameva-runtime
Author: Eunho Kim
Author-email: Eunho Kim <contact@uno-km.com>
License: Apache-2.0
Project-URL: Homepage, https://uno-km.vercel.app/lib/vulkan/
Project-URL: Repository, https://github.com/uno-km/ameva-runtime
Project-URL: Documentation, https://uno-km.vercel.app/lib/vulkan/
Keywords: vulkan-compute,mobile-gpu,hardware-acceleration,hardware-abstraction-layer,adreno-gpu,arm-mali,snapdragon-8-elite,exynos,termux,on-device-ai,edge-ai,tensor-acceleration,spir-v,compute-shaders,zero-silent-fallback,llamacpp,whisper-cpp,sherpa-onnx,stable-diffusion,vision-language-models,bitnet,gguf,ncnn,bionic-loader,arm64,aarch64,cgroup-management,cpu-neon,thermal-throttling,power-efficiency,smart-router,hardware-orchestration,multi-modal-ai,subgroup-operations,gemm-acceleration,mobile-vlm,speech-to-text,text-to-speech,image-generation,edge-inference,unprivileged-userspace,termux-wake-lock,phantom-process-killer,android-ai-runtime,valhall-gpu,adreno-830,mali-g68,ameva-foundation,uno-km,open-source-ai
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Android
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: NOTICE
Provides-Extra: stt
Requires-Dist: termux-stt>=1.2.0; extra == "stt"
Provides-Extra: diffusion
Requires-Dist: termux-diffusion>=1.5.0; extra == "diffusion"
Provides-Extra: bitnet
Requires-Dist: termux-bitnet>=1.2.0; extra == "bitnet"
Provides-Extra: llamacpp
Requires-Dist: termux-llamacpp>=1.3.0; extra == "llamacpp"
Provides-Extra: tts
Requires-Dist: termux-tts>=1.4.0; extra == "tts"
Provides-Extra: vision
Requires-Dist: termux-vision>=1.2.0; extra == "vision"
Provides-Extra: all
Requires-Dist: termux-stt>=1.2.0; extra == "all"
Requires-Dist: termux-diffusion>=1.5.0; extra == "all"
Requires-Dist: termux-bitnet>=1.2.0; extra == "all"
Requires-Dist: termux-llamacpp>=1.3.0; extra == "all"
Requires-Dist: termux-tts>=1.4.0; extra == "all"
Requires-Dist: termux-vision>=1.2.0; extra == "all"
Dynamic: author
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-python

# AMEVA-Runtime (Python)

[![PyPI](https://img.shields.io/pypi/v/ameva-runtime.svg?style=flat-square&color=0369a1)](https://pypi.org/project/ameva-runtime/)
[![Python](https://img.shields.io/pypi/pyversions/ameva-runtime.svg?style=flat-square)](https://pypi.org/project/ameva-runtime/)
[![License](https://img.shields.io/badge/License-Apache_2.0-004499.svg?style=flat-square)](https://github.com/uno-km/ameva-runtime)

> Next-Gen Unified On-Device Hardware Orchestration & 6-Modality AI Acceleration Runtime (with BitNet 1.58-bit Vulkan Compute) for Mobile & Edge

## Installation

```bash
pip install ameva-runtime
```

## Quickstart

```python
import ameva_runtime as ameva
from ameva_runtime import vulkan

# Inspect hardware
profile = ameva.detect_hardware()
print(f"Target: {profile.soc_name} | {profile.gpu_vendor}")

# Run diagnostics
doc = vulkan.Doctor()
report = doc.run_self_test()
print(f"Passed: {report.passed_stages}/{report.total_stages}")
```

## Empirical Benchmarks

- **Galaxy S25 (Adreno 830)**:
  - BitNet 1.58-bit LLM: **17.558 t/s (12.58x speedup)**, Prompt Eval **205.9 ms**.
  - Qwen2.5-0.5B LLM: **35.80 t/s** (25/25 VRAM layers).
  - Whisper STT: **4,401 ms**, TTS: RTF **0.264x** (medium) / **0.993x** (high-fp16).
- **Galaxy A35 (Mali-G68 MP5)**:
  - BitNet 1.58-bit LLM: **3.471 t/s (5.94x speedup)**, Prompt Eval **1,552.8 ms**.
  - Qwen2.5-0.5B LLM: **4.44 t/s** (+26.9% vs NEON).
  - Whisper STT: **360.60s (2.26x speedup)**, TTS: RTF **1.146x**.

## Documentation
- [Official Documentation](https://uno-km.vercel.app/lib/vulkan/)
- [GitHub Repository](https://github.com/uno-km/ameva-runtime)

## License
Apache-2.0 License. Copyright (c) 2026 Eunho Kim (@uno-km).
