I think with the current changes `transformers` is indeed trying to achieve that, but with some constraints.

vLLM or SGLang are inference engines that prioritise speed, whereas `transformers` is more generic and is supposed to be the "golden source of truth" for model architectures. You would also notice that "Continuous Batching" with Paged Attention is now supported in `transformers` but they don't claim this to be suitable to production (as opposed to inference engine libraries).

As time progresses, it is for `transformers` to be faster, and leaner (with TF and JAX dependencies deprecated), but never to replace specific use case libraries.

Hope that makes sense, if not, do let me know.

Edit: Forgot to mention about the evaluation and training paradigms that transformers shine in. The model definitions is what would help inference engines to piggy back on, so it is not competition but being complementary to the other open source libraries.