tldr-transformers VS awesome-instruction-dataset

With the explosion in research on all things transformers, it seemed there was a need to have a single table to distill the "tl;dr" of each paper's contributions relative to each other. Here is what I got so far: https://github.com/will-thompson-k/tldr-transformers . Would love feedback - and feel free to contribute too :)

In any case, I'm liking the first glance so far. I'd just transpose the summary tables so they wouldn't get so tightly squeezed: https://github.com/will-thompson-k/tldr-transformers/blob/main/notes/bart.md

With the explosion in work on all things transformers, I felt the need to keep a single table of the "tl;dr" of various papers to distill their main takeaways: https://github.com/will-thompson-k/tldr-transformers . Would love feedback!

I found some Instruction Tuning Datasets (link) but I can't seem to find a benchmark of the best LLMs on those kind of datasets?

A repo (https://github.com/yaodongC/awesome-instruction-dataset) designed to provide a one-stop solution for all your LLM dataset needs! Perfect for experimenting with your own ChatGPT/LLM (MiniGPT4, Alpaca, LLaMA) model.

In case anyone want to train their own GPT-4 models, here is list of datasets for training LLMs (https://github.com/yaodongC/awesome-instruction-dataset) .

I've put together an extensive collection of instruction-based datasets perfect for experimenting with your own LLaMA model and beyond (https://github.com/yaodongC/awesome-instruction-dataset) . If you've been searching for resources to advance your own projects or simply want to learn more about these cutting-edge models, this repository is just what you need!