https://github.com/KoboldAI/KoboldAI-Client

The goal is different so it's not a direct alternative, but it's the closest that can be run on consumer hardware. In some ways models that run via Kobold are a lot more practically useful because they have a semblance of memory, but they don't have the breadth of knowledge that ChatGPT does. You can go back and edit a response and models will be able to help generate pages and pages of writing. It's also not limited to chat mode.

I run it locally but I have heard a lot of success stories about running on Collab as well and there are GPU and TPU notebooks maintained in the repo. It can use a lor of different models like OPT, Fairseq Dense and even older models like GPT-J. There are fine tuned models for NSFW content as well, my understanding is that those models were motivated to move away from AI Dungeon that censored and read the text of user's stories.

The models on hugging face come in all sizes, 8bg VRAM is enough for a 2.7B model, without taking a time penalty and splitting compute across GPU and RAM+CPU. There are options ranging from 350M [1] to a OPT 66B in the FB/Meta AI repo released on May 3rd [2], the 66B parameter one is openly available and the full 175B parameter model is available via request. GPT2 and GPT-Neo are supported too. I found 2.7B and 6.7B impressive personally. 66B Models would take over hundreds of gigabytes of vram to run at a similar speed to how ChatGPT works. I think that's part of the reason why it's not as popular, very few people can run even the smaller model at a reasonable speed.

OPT is motivated by only training on open access material. The example bias demonstrated about the how the model complete "the man works as a" vs "the woman works as a". I thought having more modern works to train on would help avoid strong biases because culture has shifted toward being more moderate, but that doesn't seem to be case.

"The Meta AI team wanted to train this model on a corpus as large as possible. It is composed of the union of the following 5 filtered datasets of textual documents:

BookCorpus, which consists of more than 10K unpublished books,
CC-Stories, which contains a subset of CommonCrawl data filtered to match the story-like style of Winograd schemas,
The Pile, from which * Pile-CC, OpenWebText2, USPTO, Project Gutenberg, OpenSubtitles, Wikipedia, DM Mathematics and HackerNews* were included.
Pushshift.io Reddit dataset that was developed in Baumgartner et al. (2020) and processed in Roller et al. (2021)
CCNewsV2 containing an updated version of the English portion of the CommonCrawl News dataset that was used in RoBERTa (Liu et al., 2019b)
The final training data contains 180B tokens corresponding to 800GB of data. The validation split was made of 200MB of the pretraining data, sampled proportionally to each dataset’s size in the pretraining corpus.

The dataset might contains offensive content as parts of the dataset are a subset of public Common Crawl data, along with a subset of public Reddit data, which could contain sentences that, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety.“

[1] https://huggingface.co/KoboldAI

[2] https://huggingface.co/facebook/opt-66b

https://ai.facebook.com/blog/democratizing-access-to-large-s...