KoboldAI-Client

omparable. It's less popular because the hardware required for the great output is still above the top of line consumer specs. 24 gb vram is closer to a bare minimum to get meaningful output, and fine-tuning is still out of reach. There's some development with using services like runpod.

en). You can use the models listed or provide a Huggingface URL. [1] - https://github.com/koboldai/koboldai-client (source code) [2] - https://colab.research.google.com/github/koboldai/KoboldAI-C... (TPU colab; 13B and 20B models) [3] - https://colab.research.google.com/github/koboldai/KoboldAI-C... (GPU colab; 6B models and lower)

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of the union of the following 5 filtered datasets of textual documents: BookCorpus, which consists of more than 10K unpublished books, CC-Stories, which contains a subset of CommonCrawl data filtered to match the story-like style of Winograd schemas, The Pile, from which * Pile-CC,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...

atting partner"). But you do bring up a great point: the field of OS text generation develops at a sluggish pace compared to Stable Diffusion. I assume people are more interested in generating their own images than they are text; that is just more impressive. [1] - https://github.com/koboldai/koboldai-client [2] - https://old.reddit.com/r/KoboldAI/ [3] - https://huggingface.co/models?pipeline_tag=text-generation

e coherent enough to be impressive. I am surprised I haven't seen more of is a chat bot based on writes who have passed away. I imagine an Aristotle chat bot would be pretty interesting and avoid issues of copyright. [1] https://github.com/KoboldAI/KoboldAI-Client