Nearswap (nearswap)
Concept: Interpolates the base model with parameters from a secondary model primarily where
they are already similar. The interpolation strength towards the secondary model is inversely proportional
to the absolute difference of their parameters, modulated by the t parameter. When the parameters are similar,
the interpolation is stronger, and when they are different, it is weaker.
Use Cases:
Selectively pulling in similar parameters from a secondary model while preserving different parameters from the
base model
Fine-grained parameter-wise merging that respects the existing structure of the base model
Inputs: Requires exactly 2 models. One model must be specified as base_model.
Key Parameters:
t (global): Controls the interpolation strength. Higher values increase the influence of the secondary model
for similar parameters
Algorithm: For each parameter, computes weight = (t / |base - secondary|).clamp(0, 1),
then returns weight * secondary + (1 - weight) * base
By larsupb·Created 2 years ago·Updated 9 days ago· 75
