Show HN: Toolkit for LLM Fine-Tuning, Ablating and Testing

Very happy to share this toolkit that allows you to fine-tune your choice of open-source LLMs on your data! The toolkit also allows you to run ablation studies across LLMs, prompt designs, training configurations, and can ingest different data files -- all through just ONE YAML file! After fine-tuning, you can also run a bunch of tests to ensure that the fine-tuned LLM behaves as expected, enabling faster time-to-production!

Why this toolkit? Why now?

While closed-source LLMs have become popular for chat-based applications, enterprises are considering a shift to self-hosted SLMs (smaller language models) since there is evidence that you don't need a gigantic model to solve narrow edge-cases. Plus, enterprises want to own the data pipeline from start to end, i.e., data ingestion, training, deployment, feedback collection and testing! Their customers' valuable data stays within their ecosystem, allowing enterprises to not worry about compliance or data leakage issues that come up using third-party APIs.

While there are a few repositories out there that do vanilla fine-tuning, it is well known that it takes more than a one run to find the desirable setting of weights / parameters for your specific data. Bearing this pain-point in mind, we designed the toolkit to allow running multiple experiments through one config file!

Around 5 months ago, I had shared a repository that contained individual fine-tuning scripts for the most popular LLMs. While the repository received great reception from this community, there was one unanimous feedback -- the community wants to build on top of our scripts! This prompted us to design the toolkit, bearing in mind the pain-points that data scientists / researchers / engineers like myself face!

Please feel free to give it a try! Looking forward to your feedback!

Show HN: Toolkit for LLM Fine-Tuning, Ablating and Testing
github.com

Right now, the roadmap includes extending the training optimizer sections to include techniques beyond LoRA.

Furthermore, the testing suite will be extended to add more unit-tests that are task dependent.

I know that other repositories exist with similar functionalities but they can be too low level for the day-to-day data scientist to understand. Also, there are several repositories that are too specific for either testing, fine-tuning, etc. Our repository consolidates the most critical aspects of running fine-tuning experiments while being lightweight for anyone to understand and play with.

questions regarding the LLM testing aspect: How extensive is the test coverage for LLM use cases, and what is the current state of this project area? Do you offer any guarantees, or is it considered an open-ended problem?

Would love to see more progress toward this direction!