To add to the sibling comment. The challenge is not converting the weights as such. Pre-trained model weights are just arrays of numbers with labels that identify which layer/operation they correspond to in the source model. The challenge is expressing the model in code identically between two frameworks and programmatically loading the original weights in, since these models can have hundreds of individual ops. Hence why you can't just load a PyTorch model in Tensorflow or vice versa.

There are tools to convert to intermediate formats, like ONNX, but they are limited and don't work all the time. The automatic conversion tools usually assume that you can trace execution of the model for a dummy input and usually only work well if there isn't any complex logic (e.g. conditions can be problematic). Some operations aren't supported well, etc.

This isn't always technically difficult, but it's tedious because it usually involves double checking that at all steps, the model produces identical outputs for a given input. An additional challenge when transferring weights is that models are fragile and minor differences might have large effects on the predictions (even though if you trained from scratch, you might get similar results).

Also for deployment, the less cruft in the repository the better. A lot of research repositories end up pulling in all kinds of crazy dependencies to do evaluation, multiple big frameworks etc.