When using low precision formats like float8 you usually have to upscale the activations to BF16 before normalising. So the normalisation layers are proportionally using more compute when going to lower precision. Replacing these layers would help reduce the compute cost significantly.
It measures the proportion of questions for that language out of all languages. So, if there is a general decline in Stackoverflow questions, it’s already accounted for in the metric
Can you use to launch an Intel VM on Apple Silicone and visa versa? I’m interested in doing this so I can compile C++ applications for different architectures on MacOS. Do you know of any other “easy” methods?
Tensors used in deep learning are not the same as the definition used by Physicists - blame the DL community for this :). So DL tensors are just N-dimensional arrays of data, and there is no concept of covariance and contravariance of the dimensions. You could think of DL tensors as Cartesian tensors and they don't need to conform to the same transformation laws that Physics tensors do.
Warp is great - I use it as my daily terminal. The best features are being able to edit commands, chunking the output into blocks and AI generated commands at your fingertips.