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Installation

This guide will help you set up the Fine-Tune Pipeline on your system.

Prerequisites

Before installing the Fine-Tune Pipeline, ensure you have:

  • Python 3.12 or higher
  • uv package manager (recommended) or pip
  • Git for version control
  • CUDA-compatible GPU (recommended for training)

We recommend using uv as the package manager for this project as it provides faster dependency resolution and better environment management.

Windows

# Using PowerShell
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

macOS and Linux

curl -LsSf https://astral.sh/uv/install.sh | sh

Alternative: Using pip

pip install uv

Clone the Repository

git clone https://github.com/your-username/Fine-Tune-Pipeline.git
cd Fine-Tune-Pipeline

Install Dependencies

# Sync all dependencies (this will create a virtual environment automatically)
uv sync

Using pip

# Create a virtual environment
python -m venv venv

# Activate the virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# Install dependencies
pip install -e .

Verify Installation

To verify that everything is installed correctly, run:

# Check if the main module can be imported
uv run python -c "from app.finetuner import FineTune; print('✅ Installation successful!')"

For optimal performance, especially during training, we recommend using a CUDA-compatible GPU.

CUDA Installation

  1. Check CUDA compatibility: 

    nvidia-smi

  2. Install PyTorch with CUDA support: 

    # For CUDA 11.8
uv add torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

# For CUDA 12.1
uv add torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

  3. Verify CUDA installation: 

    uv run python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"

Next Steps

Now that you have the pipeline installed, you can:

  1. Set up your environment with API keys and configurations
  2. Follow the Quick Start Guide to run your first fine-tuning job
  3. Explore the Configuration Options to customize your setup

Troubleshooting

Common Issues

Issue: uv sync fails with dependency conflicts

# Solution: Update uv and try again
uv self update
uv sync --refresh

Issue: CUDA out of memory during training

# Solution: Reduce batch size in config.toml
device_train_batch_size = 2  # Reduce from default 4
device_validation_batch_size = 2

Issue: Import errors with unsloth

# Solution: Reinstall unsloth
uv remove unsloth
uv add "unsloth[cu118] @ git+https://github.com/unslothai/unsloth.git"

For more troubleshooting tips, see our Troubleshooting Guide.