leap-finetune

A minimal fine-tuning repo for LFM2, fully built on Open Source.

⚠️ Important

• Hardware: We tested this tool on H100 80GB GPU. Multi-GPU parallelization has been tested up to 8 such GPUs.
• Operating system: This tool currently supports Linux machines with the x86_64 architecture.
• Python: Make sure you are running Python >= 3.12.
• Access token: Make sure you are logged in on Hugging Face to access models and datasets.

For feature requests or if you have a different setup, reach out to support@liquid.ai and tell us about your specific configuration.

🔧 Setup

1. Install uv

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

2. Clone Repo

git clone <repository-url>
cd leap_finetune

3. Set up virtual environment

uv sync

🚀 Quickstart

1. Job Configuration Setup

Go to config.py and follow the instructions there.

• Use DatasetLoader to load datasets from HuggingFace Hub or local files (you can also add custom data loading logic here as long as it's TRL compatible)
• Pick a default TrainingConfig and optionally override some of the config parameters. Pick a PeftConfig.
• Create a JobConfig with your desired settings (model, dataset, etc.)

2. Launch Training

Run locally:

uv run leap-finetune

It uses Ray Train + Accelerate for distributed training.

Unless you overwrote output_dir, results will be stored in outputs/training_type/job_name/

🧪 Advanced Configuration

Default Configs Location and Adding New Configs

The default configurations are located in:

• SFT Training: src/leap_finetune/configs/sft_configs.py
• DPO Training: src/leap_finetune/configs/dpo_configs.py
• PEFT/LoRA: src/leap_finetune/configs/peft_configs.py

To add a new training configuration add it to the respective file and then reference it in src/leap_finetune/configs/__init__.py in the TrainingConfig and/or PeftConfig enum.

Contributing

1. Hook pre-commit to git: uv run pre-commit install
2. Open a PR with your changes

Pre-commit will now run automatically on commits, or run manually:

uv run pre-commit run --all-files