README.md

---
base_model:
- wzhouad/gemma-2-9b-it-WPO-HB
- UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3
- princeton-nlp/gemma-2-9b-it-SimPO
library_name: transformers
tags:
- mergekit
- merge
- merge
---
# Gemma Advanced V2.1

This is a merge of the 'smartest' advanced fine-tunes available for Gemma-2-9b-it. It includes WPO, SimPO, and SPPO. The merge was performed via the SOTA 'della' merge method. Merge parameters have been hand-tuned for best results. The Q8_0 quant is highly recommended until better quants come along.

## Notes and observations:
* The extreme temperature sensitivity from V1 has been fixed, no longer needs to be run at lower temperatures
* Has a somewhat different writing style than any of the parent models
* Great instruction following
* Tracks plot details well and has good situational understanding
* Seems to have a good understanding of psychology, emotions and creative writing
* More 'sane' than base gemma-it, SPPO, or SimPO - not as prone to 'Cruella De Vil' or 'Evil Sorceress' like SPPO or SimPO, when portraying characters
* Would likely serve as a good base for further merges
* I'm looking for a job, if you're hiring. I'm a skilled Python developer who brings strong devops skills along with an ever-growing knowledge of machine learning pipelines and models. Message me if you want to talk about what I can bring to your team.
* Overall, this feels like a very useful and successful merge.

## Quantized GGUFs can be found here:
* [My quants, Q8_0 tested  -  jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF](https://huggingface.co/jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF)
* [iMatrix  -  mradermacher/gemma-2-9B-it-advanced-v2.1-i1-GGUF](https://huggingface.co/mradermacher/gemma-2-9B-it-advanced-v2.1-i1-GGUF)
* [QuantFactory/gemma-2-9B-it-advanced-v2.1-GGUF](https://huggingface.co/QuantFactory/gemma-2-9B-it-advanced-v2.1-GGUF)
* [mradermacher/gemma-2-9B-it-advanced-v2.1-GGUF](https://huggingface.co/mradermacher/gemma-2-9B-it-advanced-v2.1-GGUF)

Thanks to everyone who was kind enough to provide quants!

I'll link to other quants as they appear.

# sample ollama Modelfile
```yaml
FROM /path/to/file/gemma-2-9B-it-advanced-v2.1-Q8_0.gguf
PARAMETER stop "<start_of_turn>"
PARAMETER stop "<end_of_turn>"
PARAMETER num_ctx 8192
TEMPLATE """<start_of_turn>user
{{ if .System }}{{ .System }} {{ end }}{{ .Prompt }}<end_of_turn>
<start_of_turn>model
{{ .Response }}<end_of_turn>"""
```

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 della merge method using [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) as a base.

### Models Merged

The following models were included in the merge:
* [wzhouad/gemma-2-9b-it-WPO-HB](https://huggingface.co/wzhouad/gemma-2-9b-it-WPO-HB)
* [princeton-nlp/gemma-2-9b-it-SimPO](https://huggingface.co/princeton-nlp/gemma-2-9b-it-SimPO)
* [UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
models:
  - model: google/gemma-2-9b-it 
  - model: wzhouad/gemma-2-9b-it-WPO-HB
    parameters:
      density: 0.55
      weight: 0.6
  - model: princeton-nlp/gemma-2-9b-it-SimPO 
    parameters:
      density: 0.35
      weight: 0.6
  - model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3
    parameters:
      density: 0.25
      weight: 0.4
merge_method: della
base_model: google/gemma-2-9b-it
parameters:
  normalize: true
  int8_mask: true
  lambda: 1.0
  epsilon: 0.1
dtype: float16

```