LLM-Finetuning-Toolkit

ting! 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!

r, do not fare very well for out-of-the-box tasks. Between all open-source models, Mistral-7B achieves the best stats! - When it comes to finetuning, things get very competitive! We notice that much smaller models such as Llama2-7B, Mistral-7B, Falcon-7B are able to compete with the likes of GPT-3.5-turbo 154B and Jurassic-2 models. The last point makes me very hopeful that smaller LLMs when finetuned on narrow use-cases / data can give a tough competition to the much larger models. I am aiming to benchmark more models including Anthropic Claude2, Google PaLMv2, CoHere Command and Inflection Pi. My hunch is these huge closed-source models will generally perform well out-of-the-box compared to (smaller) open-source models. However, finetuning will change the game, where smaller models will compete or even out-compete the larger models! Since there are so many LLMs out there, would love to get some help, in case anyone consider contributing :)

very few annotated samples. Llama2-7B/13B and Writer’s Palmyra are in the pipeline. But there’s so many LLMs out there! In case this work interests you, would be great to join forces. GitHub repo attached — feedback is always welcome :) Happy hacking!