Couldn't agree more and can only recommend T5 as a base to anyone. It's amazing to get started, whether as a learning resource or for real (albeit very tailored) applications. Especially the BigScience fine tunes are such a great starting point and I, as a total layman, have learned a lot, especially concerning how a model can be optimised via all manner of methods since even mt0 is small enough to where one can do multiple runs with wildly different outcomes in quick succession. Quantise, prune vocab, try different approaches to sourcing training data, retrain dozens of times, it's all pleasantly possible on consumer hardware [0] and surprising how much you can squeeze in functionality-wise. How does latency change vs memory usage, what affects format reliability, how languages and scripts affect training and the efficiency equation, etc. are all quite exciting to learn.

Understand why T5Gemma is no longer under Apache-2.0 and honestly, have not seen that much advantage when testing that vs T0 in my experiments either way, but still, there are good reasons why plain old T5 and its descendants are still popular, licensing being among them.

Gemma team also has very consistently interesting models, especially like DiffusionGemma. Ironic, as (beside 2.5 Pro), I have never warmed up to the Gemini series of models but rate Gemma models far higher than e.g. Qwen in direct competition. In any case, thanks to the teams behind these for making as much possible.

[0] As in proper consumer hardware, not a cluster of DGX Sparks or Mac Studios solely for experiments that sometimes are asserted as being consumer grade...