aha/README.md

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  <a href="README.zh-CN.md">简体中文</a> | <strong>English</strong>
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# aha

**Lightweight AI Inference Engine — All-in-one Solution for Text, Vision, Speech, and OCR**

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.


### Supported Models

| Category | Models |
|----------|--------|
| **Text** | Qwen3, MiniCPM4, LFM2, LFM2.5 |
| **Vision** | Qwen2.5-VL, Qwen3-VL, Qwen3.5, <br> LFM2.5-VL, LFM2-VL |
| **OCR** | DeepSeek-OCR, DeepSeek-OCR-2 , PaddleOCR-VL <br> PaddleOCR-VL1.5, Hunyuan-OCR, GLM-OCR |
| **ASR** | GLM-ASR-Nano, Fun-ASR-Nano, Qwen3-ASR |
| **TTS** | VoxCPM, VoxCPM1.5, VoxCPM2 |
| **Image** | RMBG-2.0 (background removal) |
| **Embedding** | Qwen3-Embedding, all-MiniLM-L6-v2 |
| **Reranker** | Qwen3-Reranker |


## Why aha?
- **🚀 High-Performance Inference** - Powered by Candle framework for efficient tensor computation and model inference
- **🔧 Unified Interface** — One tool for text, vision, speech, and OCR
- **📦 Local-First** — All processing runs locally, no data leaves your machine
- **🎯 Cross-Platform** — Works on Linux, macOS, and Windows
- **⚡ GPU Accelerated** — Optional CUDA support for faster inference
- **🛡️ Memory Safe** — Built with Rust for reliability
- **🧠 Attention Optimization** - Optional Flash Attention support for optimized long sequence processing

## Changelog
### 2026-05-09
- merge pr/eastgold15/46, add aha-ui

### 2026-04-25
- VoxCPM update stream

### 2026-04-17
- Qwen3ASR add vad data recognition

### 2026-04-16
- fix FireRedVAD fsmn cache bug

### 2026-04-15
- add FireRedVAD

### 2026-04-10
- fix LiquidAI/LFM2.5-VL-450M chat_template load bug

### 2026-04-08
- add VoxCPM2

### 0.2.5 (2026-04-06)
- add qwen3-embedding/qwen3-reranker/all-minilm-l6-v2


**[View full changelog](docs/changelog.md)** →


## Quick Start

### Installation

```bash
git clone https://github.com/jhqxxx/aha.git
cd aha
cargo build --release
```

**Optional Features:**

```bash
# CUDA (NVIDIA GPU acceleration)
cargo build --release --features cuda

# Metal (Apple GPU acceleration for macOS)
cargo build --release --features metal

# Flash Attention (faster inference)
cargo build --release --features cuda,flash-attn

# FFmpeg (multimedia processing)
cargo build --release --features ffmpeg
```

### CLI Quick Reference

```bash

# List all supported models
aha list

# Download model only
aha download -m Qwen/Qwen3-ASR-0.6B

# Download model and start service
aha cli -m Qwen/Qwen3-ASR-0.6B

# Run inference directly (without starting service)
aha run -m Qwen/Qwen3-ASR-0.6B -i "audio.wav"

# Run local all-MiniLM-L6-v2 embedding (native safetensors)
aha run -m all-minilm-l6-v2 -i "Rust embedding test" --weight-path D:\model_download\all-MiniLM-L6-v2

# Start service only (model already downloaded)
aha serv -m Qwen/Qwen3-ASR-0.6B -p 10100
```

### Chat

```bash
aha serv -m Qwen/Qwen3-0.6B -p 10100
```

Then use the unified (OpenAI-compatible) API:

```bash
curl http://localhost:10100/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen3-0.6B",
    "messages": [{"role": "user", "content": "Hello!"}],
    "stream": false
  }
'
```

### aha-ui
```bash
cd aha-ui
``` 

#### use npm
##### install npm
refer to https://nodejs.org/en/download
```bash
# Download and install nvm:
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.4/install.sh | bash
# in lieu of restarting the shell
\. "$HOME/.nvm/nvm.sh"
# Download and install Node.js:
nvm install 24
# Verify the Node.js version:
node -v # Should print "v24.15.0".
# Verify npm version:
npm -v # Should print "11.12.1".
```

##### npm run aha-ui
```bash
# Make sure in the aha-ui directory
# and make sure that aha has been compiled
npm install
npm run tauri dev
```

##### npm build & install & run
```bash
npm run tauri build
# target in
# -- aha-ui/src-tauri/target/release/bundle/deb/aha-ui_0.1.0_amd64.deb
# -- aha-ui/src-tauri/target/release/bundle/rpm/aha-ui-0.1.0-1.x86_64.rpm
# -- aha-ui/src-tauri/target/release/bundle/appimage/aha-ui_0.1.0_amd64.AppImage
```

#### use pnpm
##### install pnpm
```bash
curl -fsSL https://get.pnpm.io/install.sh | sh -
```

##### pnpm run aha-ui
```bash
# Make sure in the aha-ui directory
# and make sure that aha has been compiled
pnpm run tauri dev
```

##### pnpm build & install & run
```bash
pnpm run tauri build
# target in
# -- aha-ui/src-tauri/target/release/bundle/deb/aha-ui_0.1.0_amd64.deb
# -- aha-ui/src-tauri/target/release/bundle/rpm/aha-ui-0.1.0-1.x86_64.rpm
# -- aha-ui/src-tauri/target/release/bundle/appimage/aha-ui_0.1.0_amd64.AppImage
```

## Documentation

| Document | Description |
|----------|-------------|
| [Getting Started](docs/getting-started.md) | First steps with aha |
| [Installation](docs/installation.md) | Detailed installation guide |
| [CLI Reference](docs/cli.md) | Command-line interface |
| [API Documentation](docs/api.md) | Library & REST API |
| [Supported Models](docs/supported-models.md) | Available AI models |
| [Concepts](docs/concepts.md) | Architecture & design |
| [Development](docs/development.md) | Contributing guide |
| [Changelog](docs/changelog.md) | Version history |


## Development

### Using aha as a Library
> cargo add aha

```rust
# VoxCPM example
use aha::models::voxcpm::generate::VoxCPMGenerate;
use aha::utils::audio_utils::save_wav;
use anyhow::Result;

fn main() -> Result<()> {
    let model_path = "xxx/openbmb/VoxCPM-0.5B/";

    let mut voxcpm_generate = VoxCPMGenerate::init(model_path, None, None)?;

    let generate = voxcpm_generate.generate(
        "The sun is shining bright, flowers smile at me, birds say early early early".to_string(),
        None,
        None,
        2,
        100,
        10,
        2.0,
        false,
        6.0,
    )?;

    let _ = save_wav(&generate, "voxcpm.wav")?;
    Ok(())
}
```

### Extending New Models

- Create new model file in src/models/
- Export in src/models/mod.rs
- Add support for CLI model inference in src/exec/
- Add tests and examples in tests/

## Features

- High-performance inference via Candle framework
- Multi-modal model support (vision, language, speech)
- Clean, easy-to-use API design
- Minimal dependencies, compact binaries
- Flash Attention support for long sequences
- FFmpeg support for multimedia processing

## License

Apache-2.0 &mdash; See [LICENSE](LICENSE) for details.

## Acknowledgments

- [Candle](https://github.com/huggingface/candle) - Excellent Rust ML framework
- All model authors and contributors

## Wechat & Donate
<div align="center">

| Wechat Group | Donate |
|--------------|--------|
| ![Wechat Group-expired 260502](./assets/img/aha_weixinqun.png) | ![Donate](./assets/img/donate.png) |

</div>

---

<p align="center">
  <sub>Built with ❤️ by the aha team</sub>
</p>

<p align="center">
  <sub>We're continuously expanding our model support. Contributions are welcome!</sub>
</p>
<p align="center">
  <sub>If this project helps you, please consider giving us a ⭐ Star!</sub>
</p>