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
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Overall, this feels like a very useful and successful merge.

Quantized GGUFs can be found here: https://huggingface.co/jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF 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.

Merge Details

Merge Method

This model was merged using the della merge method using google/gemma-2-9b-it as a base.

Models Merged

The following models were included in the merge:

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