tree-of-thought-prompting
guidance
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8 | 89 | |
593 | 12,248 | |
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5.3 | 9.5 | |
6 months ago | 10 months ago | |
Jupyter Notebook | ||
MIT License | MIT License |
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tree-of-thought-prompting
- Ask HN: Any good collection of writing prompts for GPT 3.5/4?
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GitHub - Secrets of Tree of Thoughts for Programmers 🌳👨💻
Tree of Thoughts Prompting or framework is techniques to get the model to diversify its output and self-evaluate its response.
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Questions about memory, tree-of-thought, planning
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.
- What size model is needed for Reasoning?
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Puzzle GPT: Highly Effective and Fun Puzzle-Solving Prompt for GPT-4 (Uses CoT & ToT)
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. ```
- Tekoäly on jo osittain ohittanut ihmisen. Kehitysvauhdin kiihtyessä tärkeä kysymys on, kenen etiikkaa AI noudattaa. Ykkösaamun vieraana on professori Teemu Roos Suomen tekoälykeskuksesta. Seija Vaaherkumpu haastattelee.
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How close are we to an AutoGPT (or similar programme) that can improve its own code recursively?
That’s not exactly correct. Tree of thought prompting can boost reasoning. Check out the GitHub. https://github.com/dave1010/tree-of-thought-prompting
- Using Tree of Thought Prompting to boost ChatGPT's reasoning
guidance
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Guidance: A guidance language for controlling large language models
This IS Microsoft Guidance, they seem to have spun off a separate GitHub organization for it.
https://github.com/microsoft/guidance redirects to https://github.com/guidance-ai/guidance now.
- LangChain Agent Simulation – Multi-Player Dungeons and Dragons
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Llama: Add Grammar-Based Sampling
... and it sets the value of "armor" to "leather" so that you can use that value later in your code if you wish to. Guidance is pretty powerful, but I find the grammar hard to work with. I think the idea of being able to upload a bit of code or a context-free grammar to guide the model is super smart.
https://github.com/microsoft/guidance/blob/d2c5e3cbb730e337b...
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Introducing TypeChat from Microsoft
Here's one thing I don't get.
Why all the rigamarole of hoping you get a valid response, adding last-mile validators to detect invalid responses, trying to beg the model to pretty please give me the syntax I'm asking for...
...when you can guarantee a valid JSON syntax by only sampling tokens that are valid? Instead of greedily picking the highest-scoring token every time, you select the highest-scoring token that conforms to the requested format.
This is what Guidance does already, also from Microsoft: https://github.com/microsoft/guidance
But OpenAI apparently does not expose the full scores of all tokens, it only exposes the highest-scoring token. Which is so odd, because if you run models locally, using Guidance is trivial, and you can guarantee your json is correct every time. It's faster to generate, too!
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Accessing Llama 2 from the command-line with the LLM-replicate plugin
Perhaps something as simple as stating it was first built around OpenAI models and later expanded to local via plugins?
I've been meaning to ask you, have you seen/used MS Guidance[0] 'language' at all? I don't know if it's the right abstraction to interface as a plugin with what you've got in llm cli but there's a lot about Guidance that seems incredibly useful to local inference [token healing and acceleration especially].
[0]https://github.com/microsoft/guidance
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AutoChain, lightweight and testable alternative to LangChain
LangChain is just too much, personal solutions are great, until you need to compare metrics or methodologies of prompt generation. Then the onus is on these n-parties who are sharing their resources to ensure that all of them used the same templates, they were generated the same way, with the only diff being the models these prompts were run on.
So maybe a simpler library like Microsoft's Guidance (https://github.com/microsoft/guidance)? It does this really well.
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Structured Output from LLMs (Without Reprompting!)
I am unclear on the status of the project but here is the conversation that seem to be tracking it: https://github.com/microsoft/guidance/discussions/201
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/r/guidance is now a subreddit for Guidance, Microsoft's template language for controlling language models!
Let's have a subreddit about Guidance!
- Is there a UI that can limit LLM tokens to a preset list?
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Any suggestions for an open source model for parsing real estate listings?
You should look at guidance for an LLM to fill out a template. Define the output data structure and provide the real estate listing in the context (see the JSON template example here https://github.com/microsoft/guidance)
What are some alternatives?
tree-of-thought-llm - [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
gpt_jailbreak_status - This is a repository that aims to provide updates on the status of jailbreaking the OpenAI GPT language model.
lmql - A language for constraint-guided and efficient LLM programming.
llama-retrieval-plugin - LLaMa retrieval plugin script using OpenAI's retrieval plugin
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
llama-cpp-python - Python bindings for llama.cpp
langchainrb - Build LLM-powered applications in Ruby
llama.cpp - LLM inference in C/C++
exllama - A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights.
localLLM_langchain - Local LLM Agent with Langchain