Termux-LlamaCpp
디바이스 리소스를 활용한 Android Termux ARM64 전용 GGUF LLM 런타임, 모델 매니저 및 OpenAI 호환 REST/SSE 서버
Production-Grade GGUF LLM Runtime Utilizing Device Resources, Model Manager & OpenAI Server for Android ARM64
Architecture & Overview
검증된 Android Bionic ARM64 네이티브 바이너리와 필수 공유 라이브러리, 디바이스 리소스 최적화 연동을 통해 제로 컴파일 로컬 실행과 안정적인 OpenAI 호환 REST/SSE 스트리밍 서버를 제공합니다.
Ships verified Android ARM64 native binaries with bundled shared libraries and device resource optimization, enabling instant zero-compilation local inference and a robust OpenAI-compatible REST/SSE server.
Installation & Quickstart
Python (PyPI)
pip install termux-llamacpp
from termux_llamacpp import LlamaRuntime, RuntimeConfig
# 1. Initialize Runtime with Vulkan GPU Acceleration
config = RuntimeConfig(
model_path="models/Llama-3.2-1B-Instruct-Q4_K_M.gguf",
device="auto",
threads=4,
context_size=2048
)
runtime = LlamaRuntime(config)
# 2. Synchronous or Streaming Text Generation
output = runtime.generate("한국의 사계절 중 가을의 매력에 대해 설명해줘.", max_tokens=256)
print(output.text)
print(f"Speed: {output.metrics.eval_tokens_per_sec:.2f} t/s (Prompt: {output.metrics.prompt_tokens_per_sec:.2f} t/s)")
Node.js / TypeScript (npm)
npm install termux-llamacpp
import { LlamaRuntime } from "termux-llamacpp";
// 1. Initialize ESM Runtime
const runtime = new LlamaRuntime({
modelPath: "models/Llama-3.2-1B-Instruct-Q4_K_M.gguf",
device: "auto",
threads: 4
});
// 2. Generate Completion
const result = await runtime.generate({
prompt: "Explain the architectural philosophy of edge computing.",
maxTokens: 256
});
console.log("Response:", result.text);
console.log(`Generation Speed: ${result.metrics.evalTokensPerSec} t/s`);
Official Documentation & Benchmarks
- Official Architecture & API Reference
- Ecosystem Metrics & Registry Stats
- AMEVA Open-Source Foundation Portal
License
Licensed under the Apache-2.0 License. Copyright (c) 2026 Eunho Kim (@uno-km).
Metadata
Release files for termux-llamacpp 1.3.12
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| termux_llamacpp-1.3.12.tar.gz | 280.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| termux_llamacpp-1.3.12-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 461.2 kB
Release files / termux_llamacpp-1.3.12.tar.gz
| Download URL | termux_llamacpp-1.3.12.tar.gz |
|---|---|
| Size | 280.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
468a6f076d20388145518d8cabb3fb21b396f5532c6004a92875d6eede5da50c
|
|
BLAKE2b-256 checksum How to use checksums |
604f0e99fad887045ebe3ae9fbc6b14f60190442e64c0d0a41f8da50f0ef41b6
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.0
|
Release files / termux_llamacpp-1.3.12-py3-none-any.whl
| Download URL | termux_llamacpp-1.3.12-py3-none-any.whl |
|---|---|
| Size | 180.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
b06c284addb07d26dc1db6c10a41d2181f9b172430cee783b87fef6ae5d11f83
|
|
BLAKE2b-256 checksum How to use checksums |
d23eeed8c496c844cb54138cbd379d1ac6e879ed2d1238110c24474642883a15
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.12.0
|