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(i2_s) BitNet on-device inference SDK with verified ARM NEON & DotProd kernels for Node.js on Android Termux & ARM64","maintainers":[{"name":"uno-km","email":"zhfldk014745@naver.com"}],"readme":"# Termux-BitNet (Python)\n\n[![PyPI](https://img.shields.io/pypi/v/termux-bitnet.svg?style=flat-square&color=0369a1)](https://pypi.org/project/termux-bitnet/)\n[![Python](https://img.shields.io/pypi/pyversions/termux-bitnet.svg?style=flat-square)](https://pypi.org/project/termux-bitnet/)\n[![License](https://img.shields.io/badge/License-Apache_2.0-004499.svg?style=flat-square)](https://github.com/uno-km/termux-bitnet)\n\n> Sovereign 1.58-Bit On-Device LLM Inference Engine with ARM64 NEON DotProd SIMD & Native Vulkan GPU Acceleration\n\n## Installation\n\n```bash\npip install termux-bitnet\n```\n\n## Quickstart\n\n```python\nfrom termux_bitnet import BitNetEngine, BitNetConfig\n\n# 1. Initialize engine with multi-model support (BitNet 2B, Falcon-E 1B, Falcon3 7B)\nconfig = BitNetConfig(\n    model_path=\"~/.cache/termux-bitnet/models/falcon-e-1b-instruct-i2_s.gguf\",\n    device=\"auto\",        # \"auto\", \"cpu\", or \"gpu\" (Vulkan via ameva-runtime)\n    n_threads=4,\n    temperature=0.7,\n    top_p=0.95\n)\n\n# 2. Stream tokens in real time (9.69 tok/s on Galaxy A53)\nwith BitNetEngine(config) as engine:\n    print(\"[Prompt]: Explain quantum entanglement in one sentence.\")\n    print(\"[Response]: \", end=\"\", flush=True)\n    for token in engine.generate_stream(\"Explain quantum entanglement in one sentence:\"):\n        print(token, end=\"\", flush=True)\n    print()\n    metrics = engine.get_last_metrics()\n    print(f\"Speed: {metrics.tokens_per_second:.2f} tok/s | Prompt tokens: {metrics.prompt_tokens}\")\n\n```\n\n## Description\nExecutes 1.58-bit ternary quantized weights {-1, 0, +1} directly via hand-vectorized ARM64 NEON assembly kernels and native Vulkan compute shaders powered by AMEVA-Runtime. Features a dynamic zero-overhead activation dispatcher supporting Squared ReLU (Microsoft 2B) and SwiGLU (Falcon-E-1B / Falcon3-7B) alongside Zero-Copy mmap streaming for 7.45B model execution on 6GB RAM smartphones.\n\n## Documentation\n- [Official Documentation & API Reference](https://uno-km.vercel.app/lib/bitnet/)\n- [GitHub Repository](https://github.com/uno-km/termux-bitnet)\n\n## License\nApache-2.0 License. Copyright (c) 2026 Eunho Kim (@uno-km).\n","readmeFilename":"README.pypi.md"}