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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
| **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 |
| **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
### 0.2.5 (2026-04-06)
- add qwen3-embedding/qwen3-reranker/all-minilm-l6-v2
### 2026-04-03
- CLI update: subcommand must be specified
- ChatCompletionParameters add repeat_penalty and repeat_last_n
- generate add penalty repeat
### 2026-04-02
- refactor generate code
- \<think\>...\</think\> The content of the thought chain is returned using the reasoning_content field.
- chat response add time info
### 2026-04-01
- refactor deepseek_ocr/fun_asr_nano generate code
### 2026-03-31
- add server and cli mod
- aha model name use modelscope id replace
- update WhichModel
- Usage add time info
- dependencies delete aha_openai_dive,chrono
### 2026-03-30
- add LFM2.5VL-1.6B
- add LFM2VL-1.6B
### v0.2.4 (2026-03-23)
- add LFM2.5-1.2B-Instruct
- add LFM2-1.2B
**[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
# Run local all-MiniLM-L6-v2 embedding (GGUF)
aha run -m all-minilm-l6-v2 -i "Rust embedding test" --artifact-format gguf --gguf-path D:\model_download\All-MiniLM-L6-v2-Embedding-GGUF --tokenizer-dir D:\model_download\all-MiniLM-L6-v2
# Run local all-MiniLM-L6-v2 embedding (ONNX)
aha run -m all-minilm-l6-v2 -i "Rust embedding test" --artifact-format onnx --onnx-path D:\model_download\all-MiniLM-L6-v2\onnx --tokenizer-dir D:\model_download\all-MiniLM-L6-v2
# Run local GLM-OCR (GGUF)
aha run -m glm-ocr -i .\assets\img\ocr_test1.png --artifact-format gguf --gguf-path D:\model_download\GLM-OCR-GGUF
# Run local GLM-OCR (ONNX)
aha run -m glm-ocr -i .\assets\img\ocr_test1.png --artifact-format onnx --onnx-path D:\model_download\GLM-OCR-ONNX --tokenizer-dir D:\model_download\GLM-OCR-ONNX
# 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
}
'
```
## Documentation
| [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 — See [LICENSE](LICENSE) for details.
## Acknowledgments
- [Candle](https://github.com/huggingface/candle) - Excellent Rust ML framework
- All model authors and contributors
## Wechat
---
<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>