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https://github.com/chengchingwen/Transformers.jl but I have not had any personal experience with.
All of this is build by the community and your mileage may vary.
In my rather biased opinion the strengths of Julia are that the various ML libraries can share implementations, e.g. Pytorch and Tensorflow contain separate Numpy derivatives. One could say that you can write an ML framework in Julia, instead of writting a DSL in Python as part of your C++ ML library. As an example Julia has a GPU compiler so you can write your own layer directly in Julia and integrate it into your pipeline.
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The ValueError in "2*2*2*2*2" is a recent change to limit denial of service attacks. See https://github.com/python/cpython/issues/95778 . In short, the value you want has 19729 digits in base-10, the conversion from the internal base-30 representation to base-10 requires cannot be done in linear time, and conversion from arbitrary-length/untrusted data input to integer is very common.
The limit of 4300 can be changed using https://docs.python.org/3.11/library/stdtypes.html#int-max-s... . Set it to 0 for unlimited. (I wonder if there will be a thread-local context for this setting in the future, or some other way to bypass the limit on an as-needed basis.)
In any case, your Python and C examples compute different values because you mistook a right-associative operator for a left-associative operator. On my Python 3.10.2:
>>> pow(pow(pow(pow(pow(2, 2), 2), 2), 2), 2)
