Virt-io/FuseChat-Kunoichi-10.7B

---

Quantizations

GGUF by Lewdiculous

---

Merged Kunoichi-DPO-v2-7B into FuseChat-7B-Varm to fix the GPTism

The idea was to keep FuseChat's smarts since from my testing it was amazing, just a little stubborn for RP

Silly tavern preset

Merge template copied from TheProfessor

---

base_model: - FuseAI/FuseChat-7B-VaRM - SanjiWatsuki/Kunoichi-DPO-v2-7B

FUSECHAT-VaRM-Kunoichi-10.7b.v1

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

Merge Details

Merge Method

This model was merged using the linear merge method.

Models Merged

The following models were included in the merge:

• FuseAI/FuseChat-7B-VaRM
• SanjiWatsuki/Kunoichi-DPO-v2-7B

Configuration

The following YAML configuration was used to produce this model:

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