"If you look at the internals, it's a nightmare. A literal nightmare."

Yes, the copy paste button is heavily rinsed at HF HQ.

But you won't believe how much easier they made it to run, tokenize and train models in 2018-19, and at that, train compatible models.

We probably owe a month of NLP progress just to them coming in with those one liners and sensible argument API surfaces.

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Now, yes, it's getting crazy - but if there's a new paradigm, a new complex way to code, then a similar library will simplify it, and we'll mostly jump there except for legacy. It'll become like scikit learn (although that still holds up for most real ML tasks), lots of finegrained detail and slightly questionable amounts of edge cases (looking at the clustering algorithms in particular), but as easy as pie to keep going.

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I personally couldn't ask for more. I was worried they were going to push auto-switching models to their API at some point, but they've been brilliant. There are bugs, but I've never seen them in inference(besides your classic CUDA OOM), and like Fit_Schedule5951 says, it's all about that with HF.