tree-of-thought-llm
SillyTavern
tree-of-thought-llm | SillyTavern | |
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41 | 76 | |
4,200 | 6,108 | |
3.6% | 11.1% | |
7.2 | 10.0 | |
3 months ago | 1 day ago | |
Python | JavaScript | |
MIT License | GNU Affero General Public License v3.0 |
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tree-of-thought-llm
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AI Chat Applications with the Metacognition Approach: Tree of Thoughts (ToT)
[2305.10601] Tree of Thoughts: Deliberate Problem Solving with Large Language Models (arxiv.org)
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Last night /u/ alesneolith posted a very serious writeup claiming to have worked in one of the projects. The writeup is more elaborate than expected and got surprisingly little attention. His account has been since deleted.
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.
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Ultra Fast Bert
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
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Is it best to not pay attention to AI news and/or find ways to delude ourselves into believing better outcomes?
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.
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Langchain Is Pointless
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.
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To all skeptics with a background in AI/CS : what is your realistic timeline for AGI/ASI ?
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
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Why do language models appear to work left-to-right?
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).
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Munk Debate on Artificial Intelligence
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.
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Question regarding model compatibility for Alpaca Turbo
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.
- Tree of thoughts build in open-source model
SillyTavern
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Claude 3 beats GPT-4 on Aider's code editing benchmark – aider
Right, but it's certainly easier for people who might not even know what "API" stands for, and that's quite nifty. As far as self-hosted frontends go, I can personally recommend SillyTavern[1] in the browser, ChatterUI[2] on mobile, and ShellGPT[3] for CLI. LobeChat looks pretty cool, though! I'll definitely check it out.
[1] https://github.com/SillyTavern/SillyTavern
[2] https://github.com/Vali-98/ChatterUI
[3] https://github.com/TheR1D/shell_gpt
- FLaNK AI for 11 March 2024
- Show HN: I made an app to use local AI as daily driver
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Group chats vs online defined characters, token efficiency question
I don't think there is any enumeration for {{char}} macros. Here is some good discussion on the subject.
- SillyTavern 1.11.0 has been released
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Is possible to run local voice chat agent? If yes what GPU do i Need with 500€ budget?
As for SillyTavern, you need the main SillyTavern frontend and SillyTavern-extras (for TTS, STT, etc.) They're pretty easy to install. SillyTavern connects to oobabooga and SillyTavern-extras via API.
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What do you use to run your models?
Finally, no matter what backend I use, I need it to be compatible with my power-user frontend, SillyTavern. That way I always use the same UI, with the characters I created and extensions I want, e. g. web search, XTTS text-to-speech and Whisper speech recognition for real-time voice chat - and all of that local!
- SillyTavern 1.10.10 has been released
- LM Studio – Discover, download, and run local LLMs
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🐺🐦⬛ LLM Comparison/Test: Mistral 7B Updates (OpenHermes 2.5, OpenChat 3.5, Nous Capybara 1.9)
SillyTavern v1.10.5 frontend (not the latest as I don't want to upgrade mid-test)
What are some alternatives?
Voyager - An Open-Ended Embodied Agent with Large Language Models
TavernAI - TavernAI for nerds [Moved to: https://github.com/Cohee1207/SillyTavern]
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]
character-editor - Create, edit and convert AI character files for CharacterAI, Pygmalion, Text Generation, KoboldAI and TavernAI
tree-of-thoughts - Plug in and Play Implementation of Tree of Thoughts: Deliberate Problem Solving with Large Language Models that Elevates Model Reasoning by atleast 70%
TavernAI - Atmospheric adventure chat for AI language models (KoboldAI, NovelAI, Pygmalion, OpenAI chatgpt, gpt-4)
Neurite - Fractal Graph Desktop for Ai-Agents, Web-Browsing, Note-Taking, and Code.
SillyTavern-extras - Extensions API for SillyTavern [Moved to: https://github.com/SillyTavern/SillyTavern-extras]
hamilton - Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage and metadata. Runs and scales everywhere python does.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
Mr.-Ranedeer-AI-Tutor - A GPT-4 AI Tutor Prompt for customizable personalized learning experiences.
SillyTavern-Extras - Extensions API for SillyTavern.