MedSwin/MedSwin-Merged-DaRE-TIES-KD-0.7

This is a merge of pre-trained language models created using mergekit.

• Developed by: Medical Swinburne University of Technology AI Team
• Funded by: Swinburne University of Technology
• Language(s): English
• License: Apache 2.0

Merge Details

Merge Method

This model was merged using the TIES (TrIm, Elect, and Merge) and DaRE (Drop And REscale) merging methods, with medalpaca-7b as a base.

• TIES - TrIm, Elect, and Merge (TIES) is a three-step method for merging models. First, redundant parameters are trimmed, then conflicting signs are resolved into an aggregated vector, and finally the parameters whose signs are the same as the aggregate sign are averaged. This method takes into account that some values (redundant and sign disagreement) can degrade performance in the merged model.
• DARE - Drop And REscale is a method that can be used to prepare for other model merging methods like TIES. It works by randomly dropping parameters according to a drop rate and rescaling the remaining parameters. This helps to reduce the number of redundant and potentially interfering parameters among multiple models.

Models Merged

The following models were included in the merge:

• MedSwin-7B-KD
• MedSwin-7B-SFT

Configuration

The following YAML configuration was used to produce this model:

base_model: medalpaca-7b
dtype: bfloat16
merge_method: dare_ties
modules:
  default:
    slices:
    - sources:
      - layer_range: [0, 32]
        model: medalpaca-sft
        parameters:
          density: 0.6
          weight: 0.3
      - layer_range: [0, 32]
        model: medalpaca-kd
        parameters:
          density: 0.6
          weight: 0.7
      - layer_range: [0, 32]
        model: medalpaca-7b

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