---
base_model:
- wzhouad/gemma-2-9b-it-WPO-HB
- princeton-nlp/gemma-2-9b-it-SimPO
- google/gemma-2-9b-it
library_name: transformers
tags:
- mergekit
- merge

---
# An experimental intermediate merge

This merge is intended as an intermediate merge for further merges. It's useable as-is, and doesn't show any glaring signs of broken behavior. I've included a Q8_0 gguf in the repo if anyone is curious to try it.

The intuition behind this merge is as follows:

We keep the top 65 percent of weight deltas from the WPO-HB fine tune at a (very near) 1.0 weight
We 'flood fill' the remaining 45 percent of model weights with the SimPO weights. Because normalize is set to true, this results in a 1.0 weight from SimPO's deltas on any non-overlapping weights.
In cases of overlap with the top 65 weights from WPO-HB, the relative weight contribution for SimPO is near zero. In cases of non-overlap, each model gets a 1.0 or very near 1.0 weight for the merge.

Per the mergekit docs:

    weight - relative (or absolute if normalize=False) weighting of a given tensor
    normalize - if true, the weights of all models contributing to a tensor will be normalized. Default behavior.

🤔 Assuming I've understood correctly, anyway. If anyone knows of a way to get model stats, ex, something like a diff that just spits out stats about the weight differences between two models, feel free to leave a comment in the discussion. I'd love to be able to *verify* exactly what's happened to the weights, and if it's matching my expectations 🤷

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)

### 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: 0.65
      weight: 1.00
  - model: princeton-nlp/gemma-2-9b-it-SimPO
    parameters:
      density: 1.0
      weight: 0.00001
merge_method: della
base_model: google/gemma-2-9b-it
parameters:
  normalize: true
  int8_mask: true
  lambda: 1.0
  epsilon: 0.1
dtype: float16

```