Can't tell if you are spending time in the wrong circles or just shitposting. Here's one: https://youtu.be/l6DKRf-fAAM?si=6u54mry0TLgS868a

as architectures evolve, i think it can be that we learn more "side effects".. back in 2020 openai researchers said "GPT-3 is applied without any gradient updates or fine-tuning" the model emerges at a certain level of scale...

how mojo with max optimize the process?

there is also very good explanation from Luis Serrano, https://youtu.be/fkO9T027an0

i thought skills are the new context resolver

Streaming, EDA can solve lot of data challenges for enterprise AI use cases

ibm developed SSMs/mamba models and also releasing trainings datasets i think, also quantum computing is strategic option..

For sure but do you see them at any relevant leader boards? Any news how good they are?

I don't.

I know their models, but not because i constantly read about it

https://huggingface.co/ibm here is very interesting research going https://huggingface.co/ibm-granite

the first thing when i open my browser is lookup at https://news.ycombinator.com/ :)

Hello, tried to explain Large Language Models with some visualizations, especially the attention mechanism.

You should probably mention that embeddings are just a renaming of text vectors, aka vector space model, which have probably been used since before neural networks.

there is also https://ds4sd.github.io/docling/ from ibm research which is mit license and track bounding boxes as rich json format

Docling has worked well for me. It handles scenarios that crashed ChatGPT Pro. Only problem is it's super annoying to install. When I have a minute I might package it for homebrew.

Did you compare it to tesseract?

If it's superior (esp. for scans with text flowing around image boxes), and if you do end up packaging it up for brew, know that there's at least one developer who will benefit from your work (for a side-project, but that goes without saying).

Thanks in advance!