📦 Installation

Get QuantLLM up and running in minutes.

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Requirements

Component	Version
Python	3.10+
PyTorch	2.0+
CUDA	11.8+ (for GPU acceleration)

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Quick Install

From GitHub (Recommended)

pip install git+https://github.com/codewithdark-git/QuantLLM.git

From PyPI

pip install quantllm

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Installation Options

Choose the features you need:

# Basic installation
pip install quantllm

# With GGUF export support
pip install "quantllm[gguf]"

# With ONNX export support  
pip install "quantllm[onnx]"

# With MLX export (Apple Silicon)
pip install "quantllm[mlx]"

# With Triton kernels (Linux, faster inference)
pip install "quantllm[triton]"

# With Flash Attention
pip install "quantllm[flash]"

# Full installation (everything)
pip install "quantllm[full]"

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From Source (Development)

git clone https://github.com/codewithdark-git/QuantLLM.git
cd QuantLLM
pip install -e ".[dev]"

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Verify Installation

import quantllm

# Check version
print(f"QuantLLM v{quantllm.__version__}")

# Show banner
quantllm.show_banner()

# Quick test
from quantllm import turbo

model = turbo("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
print(model.generate("Hello!"))

Expected output:

╔════════════════════════════════════════════════════════════╗
║                                                            ║
║   🚀 QuantLLM v2.1.0                                           ║
║   Ultra-fast LLM Quantization & Export                     ║
║                                                            ║
║   ✓ GGUF  ✓ ONNX  ✓ MLX  ✓ SafeTensors                     ║
║                                                            ║
╚════════════════════════════════════════════════════════════╝

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Optional Dependencies

Flash Attention (Faster Inference)

pip install flash-attn --no-build-isolation

Note: Requires CUDA toolkit installed on your system.

Triton Kernels (GPU Optimization)

pip install triton>=2.1.0

Note: Linux only. Provides fused quantization kernels.

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Troubleshooting

CUDA Not Available

python -c "import torch; print(torch.cuda.is_available())"

If False, reinstall PyTorch with CUDA:

# CUDA 11.8
pip install torch --index-url https://download.pytorch.org/whl/cu118

# CUDA 12.1
pip install torch --index-url https://download.pytorch.org/whl/cu121

Flash Attention Build Errors

Flash Attention requires NVIDIA CUDA toolkit:

# Ubuntu/Debian
sudo apt install nvidia-cuda-toolkit

# Then install
pip install flash-attn --no-build-isolation

Memory Issues

If you encounter OOM errors:

# Use 4-bit quantization
model = turbo("model-name", bits=4)

# Or use a smaller model
model = turbo("TinyLlama/TinyLlama-1.1B-Chat-v1.0")

Windows Issues

Some features require Visual C++ Build Tools:

1. Download Visual Studio Build Tools
2. Install "Desktop development with C++"
3. Restart your terminal

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Hardware Requirements

GPU VRAM	Recommended Models
6-8 GB	1-7B models (4-bit)
12-24 GB	7-30B models (4-bit)
24-80 GB	70B+ models

Tested GPUs:

• NVIDIA: RTX 3060, 3070, 3080, 3090, 4070, 4080, 4090, A100, H100
• AMD: RX 7900 XTX (with ROCm)
• Apple: M1, M2, M3, M4 (via MLX export)

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Next Steps

• Quick Start →
• Loading Models →
• GGUF Export →