That surprises me as there was a good Fastai version:

https://docs.fast.ai/text.models.awdlstm.html

which is built on pytorch. When I played with it ages ago the results seemed comparable to the paper, but I haven't revisited it for a while :)

Hands down the best paper I have ever read (and reimplemented) is the ResNets paper:

https://arxiv.org/abs/1512.03385

The descriptions are clear and concise - but with enough detail to reimplement in whatever framework you like. Also, OOTB the results I got on CIFAR10 matched the paper pretty much perfectly (not always a given!).

Another good paper to try is AWD-LSTM: https://arxiv.org/pdf/1708.02182.pdf

Basically, if you are implementing and training from scratch, focus on something you can train with a smallish dataset in a reasonable period of time. I would generally steer away from LLMs and object detection / segmentation models as they require more resources to train that are commonly available!