Initial release

.gitattributes

@@ -4,6 +4,7 @@
 *.bz2 filter=lfs diff=lfs merge=lfs -text
 *.ckpt filter=lfs diff=lfs merge=lfs -text
 *.ftz filter=lfs diff=lfs merge=lfs -text
+*.gguf filter=lfs diff=lfs merge=lfs -text
 *.gz filter=lfs diff=lfs merge=lfs -text
 *.h5 filter=lfs diff=lfs merge=lfs -text
 *.joblib filter=lfs diff=lfs merge=lfs -text

Kitsunebi-v1-Gemma2-8k-9B.Q4_K_M.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7d90310d3ea66c143e971137762d8e19266324f451fed678d42dfa3e07aea353
+size 5761058368

Kitsunebi-v1-Gemma2-8k-9B.Q5_K_M.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:31a97999833cebcd6a75ad7067bb466c7a8a5f726fdfa6c7a5a8f167c0beadc7
+size 6647367232

Kitsunebi-v1-Gemma2-8k-9B.Q6_K.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8fea6c3240daf9e7d4245d541b1d4bb164a20ad53e7140123625b86bf487dd86
+size 7589070400

Kitsunebi-v1-Gemma2-8k-9B.Q8_0.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ad8ecb15647f16cf795ea1f32d134142caa864201e7a44fa1c64a97347b4d426
+size 9827149376

README.md

@@ -1,3 +1,55 @@
 ---
+base_model:
+- princeton-nlp/gemma-2-9b-it-SimPO
+- HODACHI/EZO-Common-9B-gemma-2-it
+library_name: transformers
+tags:
+- mergekit
+- merge
 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
+
+```