I’ve only seen merging of same-upstream-pretrained-model-at-same-size.
At very least, you should be able to merge any 2 models with the same tokenizer via element-wise addition of the log probs just before sampling. This would also unlock creative new samplers. IE instead of adding logprobs, maybe one model’s logprobs constrains the other’s in interesting ways.
But, 2 models with same architecture and same dataset will be heavily biased in the same direction, even if you take 2 different finetunes, so this approach seems like it will have a low ceiling of potential.
Also, if you’re just doing a linear interpolation of same-dimensioned weights, why not just collapse them all into a normal-sized model? IE 70B + 70B should still == 70B.
That said, you would get much more interesting models if you allowed mergers of different architectures, trained from different initializations, and with different datasets. I would think that the research on “token healing” would allow you to merge any 2 models, even if they have different tokenizers.
This seems like a cool way forward.
Not for the kind of merging I’ve seen. But I remember a paper back in the day that suggested you could find high-dimensional axes within different models, and if you rotated the weights to align, you could merge different models to your advantage, and maintain knowledge from both seed models. This included models that were trained from different initializations.
I think that the only reason this franken-merging works is because people are mostly just merging finetunes of the same base, so these high-d vectors are already aligned enough that the mergers work.