Update README.md

README.md

@@ -100,7 +100,7 @@ In general, the way to think about these (non-orthogonal) "multiplicative-LoRAs"
 - Each vector in `lora_A` looks for a certain dirrection, and via the dot-product it generates a (signed) weighting factor that measures the similarity between the output of the `down_proj` transformation and the specific vector in `lora_A`.
 - Each corresponding vector in `lora_B` then gets added to the hidden state / residual stream, scaled by the corresponding (signed) weighting factor.
 
-So instead of having just a single vector that we add (and in essence adding a `'.bias'` weight to create an [affine transformation](https://en.wikipedia.org/wiki/Affine_transformation)), we now have many different control vectors that can be added (stored in `lora_B`), based on how well they match another set of "directional detection vectors" (stored in `lora_A`).
+So instead of having just a single vector that we add (and in essence adding a `'.bias'` weight to create an [affine transformation](https://en.wikipedia.org/wiki/Affine_transformation)), we now have many different control vectors that can be added (stored in `lora_B`), based on how well they match another set of "direction detection vectors" (stored in `lora_A`).
 
 **NOTE**: The [LoRA+](https://arxiv.org/abs/2402.12354) paper uses a similar way of viewing the purpose of `lora_A` and `lora_B` (for "additive-LoRAs"):