aha/docs/installation.md

Installation Guide

This guide covers installing and setting up AHA on your system.

Table of Contents

• Prerequisites
• Installation Methods
• Platform-Specific Instructions
• Feature Flags
• Verification
• Troubleshooting

Prerequisites

Required

• Rust toolchain: Rust 1.85 or later (edition 2024)

  curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
  

• Git: For cloning the repository

  # Ubuntu/Debian
  sudo apt-get install git
  
  # macOS
  brew install git
  
  # Windows
  # Download from https://git-scm.com/download/win
  

Optional (for FFmpeg feature)

• FFmpeg development libraries: Required for audio/video processing

Installation Methods

Method 1: Build from Source

Clone the repository and build:

git clone https://github.com/jhqxxx/aha.git
cd aha

# Build release version
cargo build --release

# The binary will be at target/release/aha

Method 2: Install from Crates.io (when available)

cargo install aha

Method 3: Install with Features

Build with specific features enabled:

# With CUDA support (NVIDIA GPUs)
cargo build --release --features cuda

# With Metal support (Apple Silicon)
cargo build --release --features metal

# With Flash Attention
cargo build --release --features cuda,flash-attn

# With FFmpeg support
cargo build --release --features ffmpeg

Platform-Specific Instructions

Linux

Ubuntu/Debian

# Install build dependencies
sudo apt-get update
sudo apt-get install -y build-essential pkg-config git clang cmake

# For FFmpeg feature
sudo apt-get install -y ffmpeg libavutil-dev libavcodec-dev \
    libavformat-dev libavfilter-dev libavdevice-dev \
    libswresample-dev libswscale-dev

# For CUDA support, install CUDA toolkit
# See https://developer.nvidia.com/cuda-downloads

Fedora/RHEL

# Install build dependencies
sudo dnf install gcc gcc-c++ make git clang pkg-config

# For FFmpeg feature
sudo dnf install ffmpeg-devel

# For CUDA support
sudo dnf install cuda-devel

macOS

Apple Silicon (M1/M2/M3/M4)

# Install Rust (if not already installed)
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Install command line tools
xcode-select --install

# For FFmpeg feature
brew install ffmpeg

# Build with Metal support for GPU acceleration
cargo build --release --features metal

Intel Mac

# Install Rust (if not already installed)
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Install command line tools
xcode-select --install

# For FFmpeg feature
brew install ffmpeg

# For CUDA support (if you have NVIDIA GPU)
# Install CUDA from https://developer.nvidia.com/cuda-downloads
cargo build --release --features cuda

Windows

Using MSVC

# Install Rust from https://rustup.rs/
# Install Visual Studio Build Tools from https://visualstudio.microsoft.com/downloads/

# For FFmpeg feature
# Download FFmpeg from https://ffmpeg.org/download.html
# Set FFMPEG_DIR environment variable to your FFmpeg installation

# Build
cargo build --release

Using WSL2 (Recommended)

# Follow Linux instructions inside WSL2
wsl
sudo apt-get update
sudo apt-get install -y build-essential pkg-config git clang cmake

Feature Flags

aha supports several optional features:

cuda

Enables CUDA support for NVIDIA GPU acceleration.

cargo build --release --features cuda

Requirements:

• NVIDIA GPU
• CUDA Toolkit 11.0 or later
• cuDNN library

Benefits:

• 10-50x faster inference
• Support for larger models
• Lower CPU usage

metal

Enables Metal support for Apple Silicon GPU acceleration.

cargo build --release --features metal

Requirements:

• Apple Silicon (M1/M2/M3/M4)
• macOS 11.0 or later

Benefits:

• 5-20x faster inference
• Lower power consumption
• Support for larger models

flash-attn

Enables Flash Attention for optimized long-sequence processing.

cargo build --release --features cuda,flash-attn

Requirements:

• CUDA feature enabled
• Supported GPU architecture (compute capability 7.0+)

Benefits:

• Reduced memory usage
• Faster inference for long sequences
• Especially beneficial for vision models

Note: Must be used with cuda feature.

ffmpeg

Enables FFmpeg support for audio/video processing.

cargo build --release --features ffmpeg

Requirements:

• FFmpeg development libraries
• Platform-specific (see above)

Benefits:

• Extended audio format support (MP3, AAC, etc.)
• Video processing capabilities
• Better audio resampling

Combining Features

You can combine multiple features:

# Maximum performance on NVIDIA GPU
cargo build --release --features cuda,flash-attn

# Apple Silicon with audio support
cargo build --release --features metal,ffmpeg

# Everything enabled
cargo build --release --features cuda,flash-attn,ffmpeg

Verification

After installation, verify that AHA is working:

# Check version
./target/release/aha --version

# List supported models
./target/release/aha list

# (Or if installed to PATH)
aha --version
aha list

Expected output for aha list:

#Supported models:
 
 Available models:

Model Name                     ModelScope ID
-----------------------------------------------------------
LiquidAI/LFM2-1.2B             LiquidAI/LFM2-1.2B                         ✔       
LiquidAI/LFM2.5-1.2B-Instruct  LiquidAI/LFM2.5-1.2B-Instruct              ✔       
LiquidAI/LFM2.5-VL-1.6B        LiquidAI/LFM2.5-VL-1.6B                    ✔       
LiquidAI/LFM2-VL-1.6B          LiquidAI/LFM2-VL-1.6B                      ✔       
OpenBMB/MiniCPM4-0.5B          OpenBMB/MiniCPM4-0.5B                      ✔       
Qwen/Qwen2.5-VL-3B-Instruct    Qwen/Qwen2.5-VL-3B-Instruct                ✔       
Qwen/Qwen2.5-VL-7B-Instruct    Qwen/Qwen2.5-VL-7B-Instruct                        
Qwen/Qwen3-0.6B                Qwen/Qwen3-0.6B                            ✔       
Qwen/Qwen3.5-0.8B              Qwen/Qwen3.5-0.8B                          ✔       
Qwen/Qwen3.5-2B                Qwen/Qwen3.5-2B                                    
Qwen/Qwen3.5-4B                Qwen/Qwen3.5-4B                                    
Qwen/Qwen3.5-9B                Qwen/Qwen3.5-9B                                    
qwen3.5-gguf                   qwen3.5-gguf                                       
Qwen/Qwen3-ASR-0.6B            Qwen/Qwen3-ASR-0.6B                        ✔       
Qwen/Qwen3-ASR-1.7B            Qwen/Qwen3-ASR-1.7B                                
Qwen/Qwen3-VL-2B-Instruct      Qwen/Qwen3-VL-2B-Instruct                  ✔       
Qwen/Qwen3-VL-4B-Instruct      Qwen/Qwen3-VL-4B-Instruct                          
Qwen/Qwen3-VL-8B-Instruct      Qwen/Qwen3-VL-8B-Instruct                          
Qwen/Qwen3-VL-32B-Instruct     Qwen/Qwen3-VL-32B-Instruct                         
deepseek-ai/DeepSeek-OCR       deepseek-ai/DeepSeek-OCR                   ✔       
deepseek-ai/DeepSeek-OCR-2     deepseek-ai/DeepSeek-OCR-2                         
Tencent-Hunyuan/HunyuanOCR     Tencent-Hunyuan/HunyuanOCR                 ✔       
PaddlePaddle/PaddleOCR-VL      PaddlePaddle/PaddleOCR-VL                  ✔       
PaddlePaddle/PaddleOCR-VL-1.5  PaddlePaddle/PaddleOCR-VL-1.5                      
AI-ModelScope/RMBG-2.0         AI-ModelScope/RMBG-2.0                     ✔       
OpenBMB/VoxCPM-0.5B            OpenBMB/VoxCPM-0.5B                        ✔       
OpenBMB/VoxCPM1.5              OpenBMB/VoxCPM1.5                          ✔       
ZhipuAI/GLM-ASR-Nano-2512      ZhipuAI/GLM-ASR-Nano-2512                  ✔       
FunAudioLLM/Fun-ASR-Nano-2512  FunAudioLLM/Fun-ASR-Nano-2512              ✔       
ZhipuAI/GLM-OCR                ZhipuAI/GLM-OCR

Troubleshooting

Build Errors

"error: linking with cc failed"

This usually indicates missing system dependencies.

Solution: Install required build tools for your platform (see Platform-Specific Instructions).

"error: CUDA not found"

CUDA feature is enabled but CUDA toolkit is not installed.

Solution:

• Install CUDA toolkit from https://developer.nvidia.com/cuda-downloads
• Or build without CUDA: cargo build --release

"error: Metal not available"

Metal feature is enabled but not on supported hardware.

Solution:

• Ensure you're on Apple Silicon
• Or build without Metal: cargo build --release

Runtime Errors

"error while loading shared libraries"

Missing runtime libraries.

Solution: Install required libraries (see Platform-Specific Instructions).

"Out of memory"

Model is too large for available RAM/VRAM.

Solution:

• Use a smaller model
• Close other applications
• Enable GPU acceleration for better memory efficiency

"Model download failed"

Network issue or insufficient disk space.

Solution:

• Check internet connection
• Ensure sufficient disk space in ~/.aha/
• Try again: download will resume if interrupted

Performance Issues

Slow inference

Solutions:

1. Enable GPU acceleration: --features cuda or --features metal
2. Enable Flash Attention: --features "cuda,flash-attn"
3. Use a smaller model
4. Check if GPU is being used (should see GPU usage in monitoring tools)

High CPU usage

Solutions:

1. Enable GPU acceleration
2. Reduce batch size
3. Use model with lower precision

System Requirements

Different models require different hardware and software, for reference.

Minimum Requirements

• CPU: x86_64 or ARM64
• RAM: 8 GB (16 GB recommended)
• Disk: 10 GB for models (varies by model)
• OS: Linux, macOS, or Windows

Recommended Requirements

• CPU: Modern multi-core processor
• RAM: 32 GB or more
• GPU: NVIDIA GPU (with CUDA) or Apple Silicon
• Disk: SSD with 50+ GB free space
• OS: Linux (Ubuntu 22.04+) or macOS (Monterey+)

Model Sizes

Approximate download sizes for popular models:

Model	Size	RAM Usage
Qwen/Qwen3-0.6B	~1.2 GB	~2 GB
Qwen/Qwen3-VL-2B-Instruct	~4 GB	~6 GB
Qwen/Qwen3-VL-8B-Instruct	~16 GB	~20 GB
Qwen/Qwen3-VL-32B-Instruct	~64 GB	~70 GB

Next Steps

After successful installation:

1. Read the Getting Started Guide
2. Download your first model: aha download -m Qwen/Qwen3-0.6B
3. Start the service: aha cli -m Qwen/Qwen3-0.6B
4. Explore the API Reference

See Also

• Getting Started - Quick start guide
• CLI Reference - Command-line usage
• API Reference - REST API documentation
• Development - Contributing guide