The toolkit also provides instructions for contributing to the project, recommending the "fork-and-pull" Git workflow. It includes a guide for setting up the development environment and a checklist to follow before making a pull request. The toolkit is designed to be easily extendable, with each component such as data ingestion, finetuning, inference, and quality assurance testing designed for easy extension. The toolkit can be installed using pipx or pip, and it supports running ablation studies across various LLMs, prompt designs, and optimization techniques.
Key takeaways:
- The LLM Finetuning toolkit is a config-based CLI tool that allows for launching a series of LLM finetuning experiments on your data and gathering their results.
- The toolkit provides a modular and extensible architecture that allows developers to customize and enhance its functionality to suit their specific needs.
- The configuration file is the central piece that defines the behavior of the toolkit, controlling different aspects of the process such as data ingestion, model definition, training, inference, and quality assurance.
- Contributions to the project are welcomed and recommended to follow the "fork-and-pull" Git workflow.