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---
base_model:
- grimjim/Kitsunebi-v1-Gemma2-8k-9B
library_name: transformers
tags:
- mergekit
- merge
quantized_by: grimjim
license: gemma
pipeline_tag: text-generation
---
# Kitsunebi-v1-Gemma2-8k-9B-GGUF
This repo contains select GGUF quants of a merge of pre-trained Gemma 2 9B Instruct language models created using [mergekit](https://github.com/cg123/mergekit).
None of the components of this merge were trained for roleplay nor intended for it. Despite this, the resulting model can be used effectively for that function. The virtue of this model lies in its coherence, as opposed to textual richness.
This project utilizes HODACHI/EZO-Common-9B-gemma-2-it, a model based on gemma-2 and fine-tuned by Axcxept co., ltd. Its primary goal was to perform well in Japanese language tasks. Model training leveraged context-based synthesized instruction pre-training data for supervised multitask pre-training [(abstract)](https://arxiv.org/abs/2406.14491).
We also used princeton-nlp/gemma-2-9b-it-SimPO, a demonstration of Simple Preference Optimization [(abstract)][https://arxiv.org/abs/2405.14734].
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [princeton-nlp/gemma-2-9b-it-SimPO](https://huggingface.co/princeton-nlp/gemma-2-9b-it-SimPO)
* [HODACHI/EZO-Common-9B-gemma-2-it](https://huggingface.co/HODACHI/EZO-Common-9B-gemma-2-it)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: princeton-nlp/gemma-2-9b-it-SimPO
layer_range: [0, 42]
- model: HODACHI/EZO-Common-9B-gemma-2-it
layer_range: [0, 42]
merge_method: slerp
base_model: HODACHI/EZO-Common-9B-gemma-2-it
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
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