jhqxxx
295 lines
7.8 KiB
Markdown
295 lines
7.8 KiB
Markdown
<p align="center">
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<img src="assets/img/logo.png" alt="aha logo" width="120"/>
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</p>
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<p align="center">
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<a href="https://github.com/jhqxxx/aha/stargazers">
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<img src="https://img.shields.io/github/stars/jhqxxx/aha" alt="GitHub Stars">
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</a>
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<a href="https://github.com/jhqxxx/aha/issues">
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<img src="https://img.shields.io/github/issues/jhqxxx/aha" alt="GitHub Issues">
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</a>
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<a href="https://github.com/jhqxxx/aha/blob/main/LICENSE">
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<img src="https://img.shields.io/github/license/jhqxxx/aha" alt="GitHub License">
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</a>
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</p>
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<p align="center">
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<a href="README.zh-CN.md">简体中文</a> | <strong>English</strong>
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</p>
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<p align="center">
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<strong>Official Website:</strong> <a href="https://s5dgj23f.pub.makeit.coderbox.cn/">https://s5dgj23f.pub.makeit.coderbox.cn/</a>
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</p>
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# aha
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**Lightweight AI Inference Engine — All-in-one Solution for Text, Vision, Speech, and OCR**
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aha is a high-performance, cross-platform AI inference engine built with Rust and the Candle framework. It brings state-of-the-art AI models to your local machine—no API keys, no cloud dependencies, just pure, fast AI running directly on your hardware.
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### Supported Models
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| Category | Models |
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|----------|--------|
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| **Text** | Qwen3, MiniCPM4, MiniCPM5, LFM2, LFM2.5 |
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| **Vision** | Qwen2.5-VL, Qwen3-VL, Qwen3.5, <br> LFM2.5-VL, LFM2-VL |
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| **OCR** | DeepSeek-OCR, DeepSeek-OCR-2 , PaddleOCR-VL <br> PaddleOCR-VL1.5, PaddleOCR-VL1.6, Hunyuan-OCR <br> GLM-OCR |
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| **ASR** | GLM-ASR-Nano, Fun-ASR-Nano, Qwen3-ASR |
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| **TTS** | VoxCPM, VoxCPM1.5, VoxCPM2, Moss-TTS-Nano |
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| **Image** | RMBG-2.0 (background removal) |
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| **Embedding** | Qwen3-Embedding, all-MiniLM-L6-v2 |
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| **Reranker** | Qwen3-Reranker |
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## Changelog
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### 2026-06-06
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- add PaddleOCR-VL-1.6
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### 2026-05-29
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- generate code refactored
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### 2026-05-28
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- generate code refactoring progress 1/3
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### 2026-05-27
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- add MiniCPM5
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### 2026-05-24
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- update doc
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### 2026-05-11
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- add Moss-TTS-Nano，its performance is worse than the original Python version
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**[View full changelog](docs/changelog.md)** →
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## Why aha?
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- **🚀 High-Performance Inference** - Powered by Candle framework for efficient tensor computation and model inference
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- **🔧 Unified Interface** — One tool for text, vision, speech, and OCR
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- **📦 Local-First** — All processing runs locally, no data leaves your machine
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- **🎯 Cross-Platform** — Works on Linux, macOS, and Windows
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- **⚡ GPU Accelerated** — Optional CUDA support for faster inference
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- **🛡️ Memory Safe** — Built with Rust for reliability
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- **🧠 Attention Optimization** - Optional Flash Attention support for optimized long sequence processing
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## Quick Start
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### Installation
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```bash
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git clone https://github.com/jhqxxx/aha.git
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cd aha
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cargo build --release
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```
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**Optional Features:**
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```bash
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# CUDA (NVIDIA GPU acceleration)
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cargo build --release --features cuda
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# Metal (Apple GPU acceleration for macOS)
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cargo build --release --features metal
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# Flash Attention (faster inference)
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cargo build --release --features cuda,flash-attn
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# FFmpeg (multimedia processing)
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cargo build --release --features ffmpeg
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```
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### CLI Quick Reference
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```bash
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# List all supported models
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aha list
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# Download model only
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aha download -m Qwen/Qwen3-ASR-0.6B
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# Download model and start service
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aha cli -m Qwen/Qwen3-ASR-0.6B
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# Run inference directly (without starting service)
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aha run -m Qwen/Qwen3-ASR-0.6B -i "audio.wav"
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# Run local all-MiniLM-L6-v2 embedding (native safetensors)
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aha run -m all-minilm-l6-v2 -i "Rust embedding test" --weight-path D:\model_download\all-MiniLM-L6-v2
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# Start service only (model already downloaded)
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aha serv -m Qwen/Qwen3-ASR-0.6B -p 10100
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```
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### Chat
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```bash
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aha serv -m Qwen/Qwen3-0.6B -p 10100
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```
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Then use the unified (OpenAI-compatible) API:
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```bash
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curl http://localhost:10100/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "Qwen/Qwen3-0.6B",
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"messages": [{"role": "user", "content": "Hello!"}],
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"stream": false
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}
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'
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```
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### aha-ui
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```bash
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cd aha-ui
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```
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#### use npm
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##### install npm
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refer to https://nodejs.org/en/download
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```bash
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# Download and install nvm:
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curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.4/install.sh | bash
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# in lieu of restarting the shell
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\. "$HOME/.nvm/nvm.sh"
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# Download and install Node.js:
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nvm install 24
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# Verify the Node.js version:
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node -v # Should print "v24.15.0".
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# Verify npm version:
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npm -v # Should print "11.12.1".
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```
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##### npm run aha-ui
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```bash
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# Make sure in the aha-ui directory
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# and make sure that aha has been compiled
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npm install
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npm run tauri dev
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```
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##### npm build & install & run
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```bash
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npm run tauri build
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# target in
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# -- aha-ui/src-tauri/target/release/bundle/deb/aha-ui_0.1.0_amd64.deb
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# -- aha-ui/src-tauri/target/release/bundle/rpm/aha-ui-0.1.0-1.x86_64.rpm
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# -- aha-ui/src-tauri/target/release/bundle/appimage/aha-ui_0.1.0_amd64.AppImage
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```
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#### use pnpm
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##### install pnpm
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```bash
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curl -fsSL https://get.pnpm.io/install.sh | sh -
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```
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##### pnpm run aha-ui
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```bash
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# Make sure in the aha-ui directory
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# and make sure that aha has been compiled
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pnpm run tauri dev
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```
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##### pnpm build & install & run
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```bash
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pnpm run tauri build
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# target in
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# -- aha-ui/src-tauri/target/release/bundle/deb/aha-ui_0.1.0_amd64.deb
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# -- aha-ui/src-tauri/target/release/bundle/rpm/aha-ui-0.1.0-1.x86_64.rpm
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# -- aha-ui/src-tauri/target/release/bundle/appimage/aha-ui_0.1.0_amd64.AppImage
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```
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## Documentation
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| Document | Description |
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|----------|-------------|
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| [Getting Started](docs/getting-started.md) | First steps with aha |
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| [Installation](docs/installation.md) | Detailed installation guide |
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| [CLI Reference](docs/cli.md) | Command-line interface |
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| [API Documentation](docs/api.md) | Library & REST API |
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| [Supported Models](docs/supported-models.md) | Available AI models |
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| [Concepts](docs/concepts.md) | Architecture & design |
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| [Development](docs/development.md) | Contributing guide |
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| [Changelog](docs/changelog.md) | Version history |
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## Development
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### Using aha as a Library
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> cargo add aha
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```rust
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// VoxCPM example
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use aha::models::voxcpm::generate::VoxCPMGenerate;
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use aha::utils::audio_utils::save_wav_mono;
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use anyhow::Result;
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fn main() -> Result<()> {
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let model_path = "xxx/OpenBMB/VoxCPM2/";
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let mut voxcpm_generate = VoxCPMGenerate::init(model_path, None, None)?;
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let generate = voxcpm_generate.inference(
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"aha是一个基于Rust和Candle框架的本地AI推理引擎，支持多模态模型（文本、视觉、语音、OCR）。".to_string(),
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None,
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None,
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2,
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1000,
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10,
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2.0,
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6.0,
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)?;
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save_wav_mono(&generate, "voxcpm2.wav", voxcpm_generate.sample_rate() as u32)?;
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Ok(())
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}
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```
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### Extending New Models
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- Create new model file in src/models/
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- Export in src/models/mod.rs
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- Add support for CLI model inference in src/exec/
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- Add tests and examples in tests/
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## Features
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- High-performance inference via Candle framework
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- Multi-modal model support (vision, language, speech)
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- Clean, easy-to-use API design
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- Minimal dependencies, compact binaries
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- Flash Attention support for long sequences
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- FFmpeg support for multimedia processing
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## License
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Apache-2.0 — See [LICENSE](LICENSE) for details.
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## Acknowledgments
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- [Candle](https://github.com/huggingface/candle) - Excellent Rust ML framework
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- All model authors and contributors
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## Wechat & Donate
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<div align="center">
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| Wechat Group | Donate |
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|--------------|--------|
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|  |  |
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</div>
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---
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<p align="center">
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<sub>Built with ❤️ by the aha team</sub>
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</p>
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<p align="center">
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<sub>We're continuously expanding our model support. Contributions are welcome!</sub>
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</p>
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<p align="center">
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<sub>If this project helps you, please consider giving us a ⭐ Star!</sub>
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</p>
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