Andrew Toomey

BoltMonkey

AI & ML interests

-Integration of neuropsychological concepts to the architecture design and training of transformer models -Integrating statistical methods to img2img diffusion models for image processing -Token vectorisation for thematic analysis -Small language models for task execution

Recent Activity

liked a model 8 days ago

hexgrad/Kokoro-82M

updated a model about 1 month ago

BoltMonkey/SuperNeuralDreadDevil-8b

upvoted a paper about 2 months ago

Can Models Help Us Create Better Models? Evaluating LLMs as Data Scientists

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BoltMonkey's activity

liked a model 8 days ago

hexgrad/Kokoro-82M

Text-to-Speech • Updated about 8 hours ago • 986 • 252

updated a model about 1 month ago

BoltMonkey/SuperNeuralDreadDevil-8b

Text Generation • Updated Nov 25, 2024 • 24 • 1

upvoted a paper about 2 months ago

Can Models Help Us Create Better Models? Evaluating LLMs as Data Scientists

Paper • 2410.23331 • Published Oct 30, 2024 • 7

updated 2 models 2 months ago

BoltMonkey/SuperNeuralDreadDemon-8b-abliterated_Layer0

Text Generation • Updated Oct 31, 2024 • 8

BoltMonkey/SuperNeuralDreadDemon-8b-abliterated_Layer19

Text Generation • Updated Oct 30, 2024 • 11

reacted to TuringsSolutions's post with 👀 2 months ago

Post

2889

I have been seeing a specific type of AI hype more and more, I call it, releasing research expecting that no one will ever reproduce your methods, then overhyping your results. I test the methodology of maybe 4-5 research papers per day. That is how I find a lot of my research. Usually, 3-4 of those experiments end up not being reproduceable for some reason. I am starting to think it is not accidental.

So, I am launching a new series where I specifically showcase a research paper by reproducing their methodology and highlighting the blatant flaws that show up when you actually do this. Here is Episode 1!

https://www.youtube.com/watch?v=JLa0cFWm1A4