Only four bottles of Asahi Super Dry beer are left on the shelves of Ben Thai, a cosy restaurant in the Tokyo suburb of Sengawacho. Its owner, Sakaolath Sugizaki, expects to get a few more soon, but she says her supplier is keeping the bulk of its stock for bigger customers. That's because Asahi, the maker of Japan's best-selling beer, was forced to halt production at most of its 30 factories in the country at the end of last month after being hit by a cyber-attack. While all of its facilities in Japan - including six breweries - have now partially reopened, its computer systems are still down. That means it has to process orders and shipments manually - using pen, paper and fax machines - resulting in much fewer shipments than before the attack.

Research on scaling large language models (LLMs) has primarily focused on model parameters and training data size, overlooking the role of vocabulary size.

However, it was recently shown that gradient inversion attacks can reconstruct this data from the shared gradients.

We consider the problem of discretization of neural operators between Hilbert spaces in a general framework including skip connections. We focus on bijec-tive neural operators through the lens of diffeomorphisms in infinite dimensions.

Evaluating large language models (LLMs) is challenging.

Our TN implementation accelerates a recently-proposed KFAC variant up to 4.5 x while removing the

Code can be found at https://github.com/nsidn98/LLaMAR