tree-of-thought-llm VS text-generation-webui

[2305.10601] Tree of Thoughts: Deliberate Problem Solving with Large Language Models (arxiv.org)

Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these challenges, we introduce a new framework for language model inference, “Tree of Thoughts” (ToT), which generalizes over the popular “Chain of Thought” approach to prompting language models, and enables exploration over coherent units of text (“thoughts”) that serve as intermediate steps toward problem solving. ToT allows LMs to perform deliberate decision making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking ahead or backtracking when necessary to make global choices. Our experiments show that ToT significantly enhances language models’ problem-solving abilities on three novel tasks requiring non-trivial planning or search: Game of 24, Creative Writing, and Mini Crosswords. For instance, in Game of 24, while GPT-4 with chain-of-thought prompting only solved 4% of tasks, our method achieved a success rate of 74%. Code repo with all prompts: https://github.com/princeton-nlp/tree-of-thought-llm.

GPU utilization should be down when using this technique. I’m hoping this could allow for more efficient batch inference on GPUs. If you can predict 10 tokens for the price of 1 it should allow you to do tree of thought much more efficiently.

https://github.com/princeton-nlp/tree-of-thought-llm

For those familiar with Daniel Kahneman's Thinking Fast and Slow, the current LLMs (such as GPT-4 via ChatGPT) seem to resemble System 1 thinking (near-instantaneous, automatic, intuitive processes like next-word prediction). However, they lack System 2 thinking (slow, effortful, logical, planning, reasoning). What I learned today is that Google's Gemini (an LLM in training now) not only has more modalities (I think all Youtube Video and audio??), more compute, and almost twice the training data, but they're building in AlphaGo-type learning, which resembles tree of thoughts and looks a LOT like the missing puzzle piece of System 2 thinking. Will it be AGI? Maybe, and it's coming this winter.

Tree of thoughts: https://arxiv.org/abs/2305.10601

Good video on "Tree of thoughts" which also reviews / puts it in the context of other methods: https://www.youtube.com/watch?v=ut5kp56wW_4

Completion vs conversational interface is something you can read about in the OpenAI API documentation.

For the remaining things I don't have single specific pointer at hand.

What do you think about the combination of Tree of Thoughts: Deliberate Problem Solving with Large Language Models LongNet: Scaling Transformers to 1,000,000,000 Tokens Textbooks Are All You Need Attention Is All You Need Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation

You are right. Tree of Thoughts: Deliberate Problem Solving with Large Language Models proposes to solve this via MCTS-style generation (similar to how AlphaGo worked, and a lot of planning & control problems are executed).

The transformer was developed in 2017 and it powers all modern LLMs. If you're familiar with Daniel Kahneman's work from Thinking Fast and Slow, you could easily summarize LLMs as excellent System 1 thinking: our fast, automatic, unconscious responses (e.g. autocomplete). I'd argue that we're one development (similar to the transformer) away from creating System 2 thinking: deliberate and strategic thinking. In fact, with merely GPT-4 and some clever architectures, researchers have developed chain-of-thought prompting and, more recently, tree-of-thoughts reasoning. While external to the LLM architecture, embedding these concepts into a LLM could very likely solve the creation of System 2 thinking and produce the first real AGI. Adding more modalities (e.g. audio, images, video, topography, etc.) will simply add more nuance in the weights and biases of a complete system.

There are a bunch of other methods to improve quality and performance like tree-of-thought-llm, connecting a LLM to a database or have it review its own output.

Some of the tools offer a path to doing tool use (fetching URLs and doing things with them) or RAG (searching your documents). I think Oobabooga https://github.com/oobabooga/text-generation-webui offers the latter through plugins.

Our tool, https://github.com/transformerlab/transformerlab-app also supports the latter (document search) using local llms.

You can use webui https://github.com/oobabooga/text-generation-webui

Once you get a version up and running I make a copy before I update it as several times updates have broken my working version and caused headaches.

a decent explanation of parameters outside of reading archive papers: https://github.com/oobabooga/text-generation-webui/wiki/03-%...

a news ai website:

The other answers are recommending paths which give you #1. less control and #2. projects with smaller eco-systems.

If you want a truly general purpose front-end for LLMs, the only good solution right now is oobabooga: https://github.com/oobabooga/text-generation-webui

All other alternatives have only small fractions of the features that oobabooga supports. All other alternatives only support a fraction of the LLM backends that oobabooga supports, etc.

The example waifu in text-generation-webui is good enough for me.

https://github.com/oobabooga/text-generation-webui/blob/main...

> Downloading text-generation-webui takes a minute, let's you use any model and get going.

What you're missing here is you're already in this area deep enough to know what ooogoababagababa text-generation-webui is. Let's back out to the "average Windows desktop user" level. Assuming they even know how to find it:

1) Go to https://github.com/oobabooga/text-generation-webui?tab=readm...

2) See a bunch of instructions opening a terminal window and running random batch/powershell scripts. Powershell, etc will likely prompt you with a scary warning. Then you start wondering who ooobabagagagaba is...

3) Assuming you get this far (many users won't even get to step 1) you're greeted with a web interface[0] FILLED to the brim with technical jargon and extremely overwhelming options just to get a model loaded, which is another mind warp because you get to try to select between a bunch of random models with no clear meaning and non-sensical/joke sounding names from someone called "TheBloke". Ok...

Let's say you somehow braved this gauntlet and get this far now you get to chat with it. Ok, what about my local documents? text-generation-webui itself has nothing for that. Repeat this process over the 10 random open source projects from a bunch of names you've never heard of in an attempt to accomplish that.

This is "I saw this thing from Nvidia explode all over media, twitter, youtube, etc. I downloaded it from Nvidia, double-clicked, pointed it at a folder with documents, and it works".

That's the difference and it's very significant.

[0] - https://raw.githubusercontent.com/oobabooga/screenshots/main...

Maybe a copout answer, but setting up a local LLM on my development machine has been invaluable. I use Deep Seek Coder 6.7 [0] and Oobabooga's UI [1]. It helps me solve simple problems and find bugs, while still leaving the larger architecture decisions to me.

[0] https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instr...

[1] https://github.com/oobabooga/text-generation-webui

You can download it and run it with [this](https://github.com/oobabooga/text-generation-webui). There's an API mode that you could leverage from your VS Code extension.

Same question here. Ollama is fantastic as it makes it very easy to run models locally, But if you already have a lot of code that processes OpenAI API responses (with retry, streaming, async, caching etc), it would be nice to be able to simply switch the API client to Ollama, without having to have a whole other branch of code that handles Alama API responses. One way to do an easy switch is using the litellm library as a go-between but it’s not ideal (and I also recently found issues with their chat formatting for mistral models).

For an OpenAI compatible API my current favorite method is to spin up models using oobabooga TGW. Your OpenAI API code then works seamlessly by simply switching out the api_base to the ooba endpoint. Regarding chat formatting, even ooba’s Mistral formatting has issues[1] so I am doing my own in Langroid using HuggingFace tokenizer.apply_chat_template [2]

[1] https://github.com/oobabooga/text-generation-webui/issues/53...

[2] https://github.com/langroid/langroid/blob/main/langroid/lang...

Related question - I assume ollama auto detects and applies the right chat formatting template for a model?