---
tags:
- mergekit
- merge

library_name: transformers
---
<img src="https://huggingface.co/Virt-io/FuseChat-Kunoichi-10.7B/resolve/main/README.png">

---

# Quantizations
[GGUF by Lewdiculous](https://huggingface.co/Lewdiculous/FuseChat-Kunoichi-10.7B-GGUF-IQ-Imatrix)

---


Merged [Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) into [FuseChat-7B-Varm](https://huggingface.co/FuseAI/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](https://huggingface.co/Virt-io/FuseChat-7B-VaRM-GGUF/tree/main/presets)



Merge template copied from [TheProfessor](https://huggingface.co/abacusai/TheProfessor-155b)

---
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](https://github.com/cg123/mergekit).

## Merge Details
### Merge Method

This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method.

### Models Merged

The following models were included in the merge:
* [FuseAI/FuseChat-7B-VaRM](https://huggingface.co/FuseAI/FuseChat-7B-VaRM)
* [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
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

```