tree-of-thought-prompting VS ChatRWKV

Tree of Thoughts Prompting or framework is techniques to get the model to diversify its output and self-evaluate its response.

2 - Probably too early in testing and development for there to be a 'standard'. A quick google search will find you some stuff to read like https://github.com/dave1010/tree-of-thought-prompting, but your best bet is to read through the stuff other people are doing and try things for yourself. You might end up discovering something new that nobody has thought of yet. Kaio Ken literally just changed the game overnight and figured out how to expand context to 8k for llama-based models with 2 lines of code. Things are evolving fast and the community desperately needs people willing to spend time reading papers on Arxiv, digging through githubs, and testing stuff out.

Source: Conversation with Bing, 6/4/2023 (1) Chain-of-Thought Prompting | Prompt Engineering Guide. https://www.promptingguide.ai/techniques/cot. (2) [2305.10601] Tree of Thoughts: Deliberate Problem Solving with Large .... https://arxiv.org/abs/2305.10601. (3) [2201.11903] Chain-of-Thought Prompting Elicits Reasoning in Large .... https://arxiv.org/abs/2201.11903. (4) Using Tree-of-Thought Prompting to boost ChatGPT's reasoning. https://github.com/dave1010/tree-of-thought-prompting. (5) Tree of Thoughts: Deliberate Problem Solving with Large Language Models. https://arxiv.org/pdf/2305.10601.pdf. ```

That’s not exactly correct. Tree of thought prompting can boost reasoning. Check out the GitHub. https://github.com/dave1010/tree-of-thought-prompting

Most LLMs actually do a decent job out of the box if you ask them for step by step instructions. Tree of tough is one way to improve the results, reflexion is another that can be used separate or additionally. The downside is that most models will run quickly into their token limit (around 2k for most). However the new SuperHot models can handle up to 8k and then there are the RMVK-Raven models, they are RNNs and not transformers like all the other LLMs and can theoretically handle infinite context lengths (but they loose "focus" after a while).

RWKV models inference: https://github.com/BlinkDL/ChatRWKV (fast CUDA).

I'm most interested in that last one. I think I heard the RWKV models are very fast, don't need much Ram, and can have huge context tokens, so maybe their 14b can work for me. I wasn't sure how ready for use they were though, but looking more into it, stuff like rwkv.cpp and ChatRWKV and a whole lot of other community projects are mentioned on their github.

You can use ChatRWKV v2 (https://github.com/BlinkDL/ChatRWKV) to run Raven🐦 (compatible with vanilla RWKV):

I feel compelled to summon /u/bo_peng here and to mention his work on RWKV. (See https://github.com/BlinkDL/ChatRWKV and related repos.)

Please test https://github.com/BlinkDL/ChatRWKV which is a good chatbot despite only trained on the Pile :)