PaLM-rlhf-pytorch VS supabase

ChatGPT isn't open sourced so we don't know what the actual implementation is. I think you can read Open Assistant's source code for application design. If that is too much, try Open Chat Toolkit's source code for developer tools . If you need very bare implementation, you should go for lucidrains/PaLM-rlhf-pytorch.

AFAIK, it is not. They are using the open-source re-implementation of Phil Wang (aka lucidrains), which is available here: https://github.com/lucidrains/PaLM-rlhf-pytorch

Not sure what do you mean by putting source code in double quote, but I don't think the source code is petabytes of text. GPT-2 implementation is few hundred lines of Python (in HuggingFace). PaLM + RLHF - Pytorch (Basically ChatGPT but with PaLM) is less than 1000 lines.

GitHub Link: https://github.com/lucidrains/PaLM-rlhf-pytorch

Well... Not necessarily. Actually, if you want to be extra thrifty, you could even go without an ML expert. Just use an open-source one, like LaMDA or PaLM. After that, use chatGPT to build you a basic front end (which would still be better than CAI lol).

and PaLM with RLHF from Phil Wang (open model, needs to be trained): https://github.com/lucidrains/PaLM-rlhf-pytorch

Closest you can get is probably with Google T5-Flan [1].

It is not the size of the model or the text it was trained on that makes ChatGPT so performant. It is the additional human assisted training to make it respond well to instructions. Open source versions of that are just starting to see the light of day [2].

[1] https://huggingface.co/google/flan-t5-xxl

[2] https://github.com/lucidrains/PaLM-rlhf-pytorch

I didn't really give much thought as to which backend I would use. I already had 2 projects in Supabase (BOXCUT & MineWork), but also a few projects in Firebase too. I was more concerned at the time at actually building the product.

Sign up for SupaBase: Head over to SupaBase and sign up. Create a new workspace and project with your preferred names.

Setting up Supabase Create a new Supabase project, and get the connection string for the database from settings > database.

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Built with Supabase, Astro, Unreal Speech, Stable Diffusion, Replicate, Metropolitan Museum of Art

Supabase will be used for storing article data in the database and the cover image of the article in storage. Chakra UI will be used to provide style to the elements. By using both, we can build the blog with ease.

For AutoRepurpose, I opted for Supabase as the backbone of the backend. It has reliably supported Penelope AI, which garnered over 15k users in 2022 without any issues.

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1. Create a new Supabase project: Visit Supabase and create a new project.

Supabase positions itself as the "open source Firebase alternative." It was founded in 2020 and is a developer-friendly serverless database platform that supports over 20 frameworks, including popular tools like Next.js, React, Nuxt, Svelte, Flutter, and Vue.