Update README.md

README.md

@@ -49,7 +49,7 @@ Of note, I did not include any prior merges in this one; as I was noticing that
 I've been asked what this is. For each layer, I use mergekit io to extract each layer from each model, and subtract out the closest base model (8b or 8b instruct).
 
 * Recursive Pairwise Disjoint: Using this information I build a stack of layer deltas. I'm a little compute limited, so I treat them in pairs. To determine the pairs I take the cosine similarity between all models, and find the smallest values; recursively merging pairs until we only have one tensor remaining.
-* Normalized: I take and divide each layer by it's norm, and then scale back up by multiplying the result by a midpoint from the norms of the tensors.
+* Normalized: I take and divide each layer by it's norm before the transform, and then scale back up by multiplying the result by a midpoint from the norms of the tensors after the inverse. It's commutative, so it's more efficient to do it pre-complex.
 * Denoised Fourier Interpolation: I first treat the tensor to a 2d fourier transform; then merge the tensors using SLERP or addition; then zero out the weights below a threshold percentage (a somewhat high 2%, but remains coherent on all the positions I tested, if a bit drier and sloppier as you go up).
 
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