Concept: An enhanced version of SLERP offering more flexible configuration and faster execution.
It allows SLERP between two models directly. If a base_model is provided, NuSLERP calculates task vectors
(the difference between each of the two main models and the base model) and then performs SLERP on these
task vectors before adding the result back to the base_model.
Use Cases:
Similar to SLERP, but with more control over weighting for the two primary models.
To replicate the behavior of the original slerp method (if base_model is not used, and weights are set to 1-t
for the first model and t for the second).
To perform SLERP on task vectors when a base_model is provided, allowing for interpolation of model changes
relative to a common ancestor.
Inputs: Requires exactly 2 models.
Key Parameters:
- weight (per-model): Relative weighting for each of the two main models. These are used to calculate the
interpolation factor t (where t = model2_weight / (model1_weight + model2_weight)).
- nuslerp_flatten (global): If false, performs row/column-wise interpolation. Default true
- nuslerp_row_wise (global): If true (and nuslerp_flatten is false), SLERPs row vectors instead of column vectors.
Default false
By larsupb·Created 2 years ago·Updated 9 days ago· 75
