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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

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 →

Quick Start

Installation

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

Optional Features:

# 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

# 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

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

Then use the unified (OpenAI-compatible) API:

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

Development

Using aha as a Library

cargo add aha

# 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 for details.

Acknowledgments

• Candle - Excellent Rust ML framework
• All model authors and contributors

Wechat

_{Built with ❤️ by the aha team}

_{We're continuously expanding our model support. Contributions are welcome!}

_{If this project helps you, please consider giving us a ⭐ Star!}