chain-of-thought-hub VS airoboros

Chain-of-Thought Hub https://github.com/FranxYao/chain-of-thought-hub - these are mostly gathered although Yao Fu, the author is working on specific CoT runs

Chain-of-Thought Hub: Measuring LLMs' Reasoning Performance (https://github.com/FranxYao/chain-of-thought-hub)

https://github.com/FranxYao/chain-of-thought-hub

Just wanted to drop a note that I overhauled the airoboros tool not the models to have most of the prompts I've been using to build the datasets, plus a couple extras.

airoboros: using large language models to fine-tune large language models (https://github.com/jondurbin/airoboros)

This is a fine-tuned LlaMa model, using completely synthetic training data created by https://github.com/jondurbin/airoboros

I used airoboros, a python tool I wrote, to generate the synthetic instruction response pairs, and included a jailbreak prompt to attempt to bypass OpenAI censorship. This is the only dataset used to fine-tune the model.

This is a 7b parameter, fine-tuned on 100k synthetic instruction/response pairs generated by gpt-3.5-turbo using my version of self-instruct airoboros

GitHub Repo