this post was submitted on 01 Jun 2024
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The network architecture seems to create a virtualized hyperdimensional network on top of the actual network nodes, so the node precision really doesn't matter much as long as quantization occurs in pretraining.
If it's post-training, it's degrading the precision of the already encoded network, which is sometimes acceptable but always lossy. But being done at the pretrained layer it actually seems to be a net improvement over higher precision weights even if you throw efficiency concerns out the window.
You can see this in the perplexity graphs in the BitNet-1.58 paper.
None of those words are in the bible
No, but some alarmingly similar ideas are in the heretical stuff actually.