RNNs and LSTMs from the past did this as well (but cannot be trained in parallel as each token has to be compressed sequentially). Transformers ate their cake.
Newer methods are going back to similar concepts but trying to get past previous bottlenecks given what we've learned since then about transformers.
They're actually not giving it away for free. At least not LLAMA v2. Once a product successfully monetizes and gains over a certain number of users, an official license from Meta has to be negotiated otherwise Meta could revoke usage of the model. At least that's roughly what the license for LLAMAv2 implies.
As someone who uses ffmpeg daily (mostly basic functions), I now rely on chatGPT to approximate the command and fine tune from there. Haven't used too many of the advanced features of ffmpeg so glad someone seems to be covering those use cases as most tutorials dont cover them.
Newer methods are going back to similar concepts but trying to get past previous bottlenecks given what we've learned since then about transformers.