merge_method: linear # use linear so we can include multiple models, albeit at a zero weight
parameters:
  weight: 1.0 # weight everything as 1 unless specified otherwise - linear with one model weighted at 1 is a no-op like passthrough
slices:
  - sources:
      - model: FuseAI/FuseChat-7B-VaRM # embed_tokens comes along with the ride with whatever is the first layer
        layer_range: [0, 1]
      - model: SanjiWatsuki/Kunoichi-DPO-v2-7B # add dummy second model with 0 weight so tokenizer-based merge routine is invoked for embed_tokens
        layer_range: [0, 1]
        parameters:
          weight: 0
  - sources:
      - model: FuseAI/FuseChat-7B-VaRM
        layer_range: [1, 5]
  - sources:
      - model: SanjiWatsuki/Kunoichi-DPO-v2-7B
        layer_range: [5, 7] # 2 layers
  - sources:
      - model: FuseAI/FuseChat-7B-VaRM
        layer_range: [5, 15]
  - sources:
      - model: SanjiWatsuki/Kunoichi-DPO-v2-7B
        layer_range: [15, 27] # 12 layers
  - sources:
      - model: FuseAI/FuseChat-7B-VaRM
        layer_range: [15, 27]
  - sources:
      - model: SanjiWatsuki/Kunoichi-DPO-v2-7B
        layer_range: [27, 29] # 2 layers
  - sources:
      - model: FuseAI/FuseChat-7B-VaRM
        layer_range: [27, 31]
  - sources: # same as above, but for lm_head with the last layer
      - model: FuseAI/FuseChat-7B-VaRM
        layer_range: [31, 32]
      - model: SanjiWatsuki/Kunoichi-DPO-v2-7B
        layer_range: [31, 32]
        parameters:
          weight: 0
dtype: float16
tokenizer_source: model:FuseAI/FuseChat-7B-VaRM # keep exact tokenizer used by dolphin - or you could use `union` if you add all of the input models to the first/last slice