Awesome-Prompt-Engineering VS tree-of-thoughts

Yes, there are a lot of different resources online, especially for generative AI. The Awesome Prompt Engineering github is probably a good place to start https://github.com/promptslab/Awesome-Prompt-Engineering. If you're focusing directly on OpenAI's models then the OpenAI Prompt Engineering Guide would be my recommendation https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api.

Awesome-Prompt-Engineering

For AI art generation, a good starting point are AI prompt libraries to see results of good prompts.

[0] - https://github.com/promptslab/Awesome-Prompt-Engineering

Resource list: https://github.com/promptslab/Awesome-Prompt-Engineering

Here you will find: - 📄 Papers in different categories such as Prompt Engineering Techniques, Text to Image Generation, Text Music/Sound Generation, Text Video Generation etc. - 🔧 Tools & code to build different GPT-based applications - 💻 Open-Source & Paid APIs - 💾 Datasets - 🧠 Prompt-Based Models - 📚 Tutorials from Beginner to Advanced level - 🎥 Videos - 🤝 Prompt-Engineering Communities and Groups for discussion Resource list: https://github.com/promptslab/Awesome-Prompt-Engineering We hope it will help you to get started & learn more about Prompt-Engineering. Prompt-Engineering Course: We are launching a free and open-source Prompt-Engineering Course Soon. Join our discord for Prompt-Engineering, LLMs and other latest research discussions https://discord.com/invite/m88xfYMbK6

I checked the issues and found https://github.com/kyegomez/tree-of-thoughts/issues/78

Plug in and Play Implementation of Tree of Thoughts: Deliberate Problem Solving with Large Language Models that Elevates Model Reasoning by atleast 70% (https://github.com/kyegomez/tree-of-thoughts)

same deal with amplification research like Tree of Thoughts, AdaPlanner, and Ghost in the Minecraft. same deal with agentized LLMs like Auto-GPT emphasizing testing regimens. they want efficiency and explainability, not this "mine is bigger than yours" nonsense coming out of Microsoft, Google, or Meta (which isn't even the entire picture of the opensource ML research within those firms either). There's this idealized "neurosymbolic AI" where everyone just wants code to do a job, so there should only be so much probabilistic behavior to learn the jobs that aren't learned to begin with, but the fact remains that the actual researchers and engineers want something that is as deterministic as imperative language can be. perhaps we'll achieve functional depth, and instead of some outdated "paperclip maximizer", we summon Maxwell's demon via a "complete" Church-Turing thesis. in other words, while a "vastly superior being in intelligence" is a really bad time for anyone that has an intellect-based superiority complex, the rest of us are humble enough to utilize this information science to further explore the unknown.

This is Shunyu, author of Tree oF Thoughts (arxiv.org/abs/2305.10601).

The official code to replicate paper results is https://github.com/ysymyth/tree-of-thought-llm

Not https://github.com/kyegomez/tree-of-thoughts which according to many who told me, is not right/good implementation of ToT, and damages the reputation of ToT

I explained the situation here: https://twitter.com/ShunyuYao12/status/1663946702754021383

I'd appreciate your help by unstaring his and staring mine, as currently Github and Google searches go to his repo by default, and it has been very misleading for many users.

I hacked a dirty PR into this derivative repo, to run it with oobabooga API: https://github.com/kyegomez/tree-of-thoughts/pull/8