airoboros VS WizardVicunaLM

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

HELP! I need some clarification. I'm familiar with Wizard-Vicuna-13b-Uncensored which is EHartford's uncensoring of WizardVicunaLM.

> I’m also open to open source models but I hear they’re not even as good as gpt3.5.

WizardVicunaLM claims ~97% performance relative to GPT3.5: https://github.com/melodysdreamj/WizardVicunaLM

It's not particularly great at generating code, but it's uncensored and writes fantastic prose. I've been using it for the last week and I'm really satisfied with where it stands.

> It’s sad that we’re stuck in this monopoly of powerful LLMs.

Won't anyone just sponsor a few months of dedicated GPU training, finetuning and quantizing so they can be held legally accountable for it's output?

I wouldn't hold my breath.

Also, just noticed that you may have forgotten to update the readme, which references 13b, not 30b, thought maybe that was intentional. (If you linked directly to the Github ("WizardVicunaLM"), that would make it a bit easier for people like me to follow))

There are a lot of options. Right now I'm using WizardVicunaLM to great success: https://github.com/melodysdreamj/WizardVicunaLM

It combines the uncensored WizardLM data with the Vicuna tuning to create a surprisingly high-performance model. If the chart on their GitHub page is to be believed, their model approaches GPT-3.5 performance.

Here is the codebase and dataset for WizardVicuna https://github.com/melodysdreamj/WizardVicunaLM https://github.com/lm-sys/FastChat https://huggingface.co/datasets/RyokoAI/ShareGPT52K

I used the same questions from WizardVicunaLM:

https://github.com/melodysdreamj/WizardVicunaLM - Combining WizardLM and Vicuña Principle. Made by u/Clear-Jelly2873