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Installation

This guide will help you install LLMBuilder and set up your environment for training and deploying language models.

System Requirements

Minimum Requirements

  • Python: 3.8 or higher
  • RAM: 4GB (8GB+ recommended)
  • Storage: 2GB free space
  • OS: Windows, macOS, or Linux
  • Python: 3.9 or higher
  • RAM: 16GB or more
  • GPU: NVIDIA GPU with 8GB+ VRAM (optional but recommended)
  • Storage: 10GB+ free space for models and data

Installation Methods

The easiest way to install LLMBuilder is via PyPI:

pip install llmbuilder

Method 2: Development Installation

For the latest features or if you want to contribute:

# Clone the repository
git clone https://github.com/Qubasehq/llmbuilder-package.git
cd llmbuilder-package

# Install in development mode
pip install -e .

Method 3: CPU-Only Installation

If you don't have a GPU or want to use CPU-only PyTorch:

# Install CPU-only PyTorch first
pip install torch --index-url https://download.pytorch.org/whl/cpu

# Then install LLMBuilder
pip install llmbuilder

Verify Installation

Test your installation by running:

# Check if LLMBuilder is installed
python -c "import llmbuilder; print(f'LLMBuilder {llmbuilder.__version__} installed successfully!')"

# Test the CLI
llmbuilder --version
llmbuilder info

You should see output similar to:

LLMBuilder installed successfully!
🤖 LLMBuilder version
A comprehensive toolkit for building, training, and deploying language models.

Optional Dependencies

For GPU Training

If you have an NVIDIA GPU and want to use CUDA:

# Install CUDA-enabled PyTorch (adjust version as needed)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

For Advanced Data Processing

For processing various document formats:

pip install llmbuilder[data]

This includes:

  • pandas - For data manipulation
  • pymupdf - For PDF processing
  • docx2txt - For DOCX files
  • python-pptx - For PowerPoint files
  • beautifulsoup4 - For HTML processing

For Development

If you're contributing to LLMBuilder:

pip install llmbuilder[dev]

This includes:

  • pytest - For running tests
  • black - For code formatting
  • ruff - For linting
  • mypy - For type checking

For Model Export

For exporting models to different formats:

pip install llmbuilder[export]

This includes:

  • onnx - For ONNX export
  • onnxruntime - For ONNX inference

Environment Setup

Create a dedicated virtual environment for LLMBuilder:

# Create virtual environment
python -m venv llmbuilder-env

# Activate it
# On Windows:
llmbuilder-env\Scripts\activate
# On macOS/Linux:
source llmbuilder-env/bin/activate

# Install LLMBuilder
pip install llmbuilder

# Create conda environment
conda create -n llmbuilder python=3.9
conda activate llmbuilder

# Install LLMBuilder
pip install llmbuilder

Environment Variables

You can set these optional environment variables:

# Enable slow tests (for development)
export RUN_SLOW=1

# Enable performance tests (for development)
export RUN_PERF=1

# Set default device
export LLMBUILDER_DEVICE=cuda  # or 'cpu'

# Set cache directory
export LLMBUILDER_CACHE_DIR=/path/to/cache

Troubleshooting

Common Issues

ImportError: No module named 'torch'

Solution: Install PyTorch first:

pip install torch

CUDA out of memory

Solution: Use CPU-only installation or reduce batch size:

pip install torch --index-url https://download.pytorch.org/whl/cpu

Permission denied errors

Solution: Use --user flag or virtual environment:

pip install --user llmbuilder

Package conflicts

Solution: Create a fresh virtual environment:

python -m venv fresh-env
source fresh-env/bin/activate  # or fresh-env\Scripts\activate on Windows
pip install llmbuilder

Getting Help

If you encounter issues:

  1. Check the logs: LLMBuilder provides detailed error messages
  2. Search existing issues: GitHub Issues
  3. Create a new issue: Include your system info and error messages
  4. Join discussions: GitHub Discussions

System Information

To help with troubleshooting, you can gather system information:

import llmbuilder
import torch
import sys
import platform

print(f"LLMBuilder version: {llmbuilder.__version__}")
print(f"Python version: {sys.version}")
print(f"PyTorch version: {torch.__version__}")
print(f"CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
    print(f"CUDA version: {torch.version.cuda}")
    print(f"GPU count: {torch.cuda.device_count()}")
print(f"Platform: {platform.platform()}")

Next Steps

Once you have LLMBuilder installed:

  1. Quick Start - Get up and running in 5 minutes
  2. First Model - Train your first language model
  3. User Guide - Learn about all features

Pro Tip

For the best experience, we recommend using a virtual environment and installing the GPU version of PyTorch if you have a compatible NVIDIA GPU.