DALLE-mtf VS lm-human-preferences

This vision is in line with EleutherAI, a non-profit organization founded in July 2020 by a group of researchers. Driven by the perceived opacity and the challenge of reproducibility in AI, their goal was to create leading open-source language models.

My current idea is to utilize the EleutherAI pythia (Databricks Dolly). I would like to know whether translating the Dolly-15k dataset into the desired language using state-of-the-art translation techniques like DeepL would be a viable approach to fine-tune the Pythia base model. I want to use this model for semantic search, so perfection is not a necessity.

There are open source AI efforts, like EleutherAI. Needless to say, they are lagging behind big players, but it's better than nothing.

The new GPT 3.5 isn't aware what are GPT-3.5 or davinci-002 (repeatable) and claimed that it was designed by EleutherAI and has only 6 bil parameters (wasn't been able to repeat but didn't really try).

Hi, my name is Stella Biderman and I run EleutherAI, the one of the foremost non-profit research institutes in the world that trains and studies large language models. I have been involved with the majority of models to hold the title “largest open source GPT model in the world” and have dabbled in exploring using plagiarism detection tools to identify code written by GPT-J.

dolly-v2-12bis a 12 billion parameter causal language model created by Databricks that is derived from EleutherAI’s Pythia-12b and fine-tuned on a ~15K record instruction corpus generated by Databricks employees and released under a permissive license (CC-BY-SA)

It is true that Emad needs to find an appropriate business model. The good news is that the hype is still undergoing. I'm sure that Emad can grab another round of liquidity injection. He got plenty of resources. Remember he is also from the finance industry. He got https://www.eleuther.ai/ which can supply a secured, in-house custom LLM equivalent to bloombergGPT.

By promoting fully open-source AI, i.e. making datasets, models, methodology and codebases freely available and transparent. What OpenAI claimed to be aiming for, basically.

Lest you think that that sounds like a too galaxy-brained possibility, it has already happened at OpenAI (scroll down to "Bugs can optimize for bad behavior"), just with a model that was very far from being capable enough to be dangerous.

Found relevant code at https://github.com/openai/lm-human-preferences + all code implementations here

That's why, when they're serious, they use RL for finetuning from human preferences (would be hilarious if this attempt to solve the terrible bias you take to be evidence of AGI threat ends up creating a Woke Singleton itself, btw); it's a powerful general approach, and I see no sign of it being applied here.

The kind of measures they are taking, like simply deleting wholesale anything problematic, don't really have a '-1'.

But amusingly, exactly that did happen in one of their GPT experiments! https://openai.com/blog/fine-tuning-gpt-2/

Code for https://arxiv.org/abs/1909.08593 found: https://github.com/openai/lm-human-preferences