tree-of-thoughts
datablations
tree-of-thoughts | datablations | |
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26 | 6 | |
4,042 | 289 | |
- | 3.5% | |
8.8 | 6.9 | |
2 months ago | about 1 month ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
tree-of-thoughts
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[D] Potential scammer on github stealing work of other ML researchers?
I checked the issues and found https://github.com/kyegomez/tree-of-thoughts/issues/78
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(2/2) May 2023
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)
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Statement on AI Extinction - Signed by AGI Labs, Top Academics, and Many Other Notable Figures
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 perhaps we'll achieve functional depth, and instead of some outdated "paperclip maximizer", we summon Maxwell's demon via a "complete" Church-Turing thesis.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.
- Tree of Thought (ToT) and AutoGPT
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Tree of Thoughts
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.
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Has anybody tried their models with "Tree of Thoughts"?
I hacked a dirty PR into this derivative repo, to run it with oobabooga API: https://github.com/kyegomez/tree-of-thoughts/pull/8
- Tree of Thoughts: Deliberate Problem Solving with LLMs
datablations
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Gemini is only 1x Chinchilla, so it undertrained for production
1x chinchilla means it's not really undertrained but that more could be squeezed without excessive difficulty https://arxiv.org/abs/2305.16264
- Can LLMs learn from a single example?
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Chinchilla’s Death
You might want to give a read to "Scaling Data-Constrained Language Models" [1]. They basically generalized the Chinchilla scaling law by investigating behavior on multi-epoch runs.
[1] https://arxiv.org/abs/2305.16264
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RWKV Pile+ seems to be training on far more tokens than any LLM ever has
I would imagine that there is a lot of overlap, yeah. That said, training on repeated data does seem to be effective at this level.
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(2/2) May 2023
Scaling Data-Constrained Language Models (https://arxiv.org/abs/2305.16264)
- How to Keep Scaling Large Language Models when Data Runs Out? A New AI Research Trains 400 Models with up to 9B Parameters and 900B Tokens to Create an Extension of Chinchilla Scaling Laws for Repeated Data
What are some alternatives?
Awesome-Prompt-Engineering - This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc
TinyLlama - The TinyLlama project is an open endeavor to pretrain a 1.1B Llama model on 3 trillion tokens.
tree-of-thought-llm - [NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
airoboros - Customizable implementation of the self-instruct paper.
GirlfriendGPT - Girlfriend GPT is a Python project to build your own AI girlfriend using ChatGPT4.0
prompt-engineering - Tips and tricks for working with Large Language Models like OpenAI's GPT-4.
SuperAGI - <⚡️> SuperAGI - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably.
Mr.-Ranedeer-AI-Tutor - A GPT-4 AI Tutor Prompt for customizable personalized learning experiences.
chathub - All-in-one chatbot client
Neurite - Fractal Graph Desktop for Ai-Agents, Web-Browsing, Note-Taking, and Code.
guidance - A guidance language for controlling large language models. [Moved to: https://github.com/guidance-ai/guidance]