airoboros VS lmql

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

have been experimenting with guidance and lmql. a bit too early to give any well formed opinions but really do like the idea of constraining llm output.

the only time i've ever felt like it was anything other than guesswork was using LMQL . not coincidentally, LMQL works with LLMs as autocomplete engines rather than q&a ones.

lqml

This is also similar in spirit to LMQL

https://github.com/eth-sri/lmql

LLM calls are just function calls, so most functional composition is already afforded by any general-purpose language out there. If you need fancy stuff, use something like Python‘s functools.

Working on https://github.com/eth-sri/lmql (shameless plug, sorry), we have always found that compositional abstractions on top of LMQL are mostly there already, once you internalize prompts being functions.

LMQL is another.