Hm, I’m a bit sceptical. These are quite different tasks and should probably be tackled by different models. Is there a particular reason you need one model to rule them all?
For example, encoder-only models (BERT et al) still dominate research and industry on NLU tasks such as text classification, named entity recognition, and question answering, while decoder-only models (GPT et al) are exceptionally good at predicting the next word in a sequence and are thus mostly used for text generation tasks.
If you really want one model to rule them all I’d suggest to look at encoder-decoder models (e.g. T5, BART, BigBird).