DARE: An Augmented TIES Merge method

Similar to TIES, it sparsifies the task vectors to reduce inference. The only difference is that DARE using random pruning with a novel rescaling to better match performance of the original model.

I've seen it used to take two different models that ace different benchmarks.

So like a model with high GSM8k and another model with high TruthfulQA can be merged with DARE to get the best of both scores.

A typical script will look like so:

models:
  - model: mistralai/Mistral-7B-v0.1
    # No parameters necessary for base model
  - model: samir-fama/SamirGPT-v1
    parameters:
      density: 0.53
      weight: 0.4
  - model: abacusai/Slerp-CM-mist-dpo
    parameters:
      density: 0.53
      weight: 0.3
  - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.2
    parameters:
      density: 0.53
      weight: 0.3
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  int8_mask: true
dtype: bfloat16

Merging Models FrankenMoE