---
base_model:
- sometimesanotion/Lamarck-14B-v0.7-Fusion
- sometimesanotion/Lamarck-14B-v0.7
library_name: transformers
tags:
- mergekit
- merge

---
# merge

The merits of multi-stage arcee_fusion merges are clearly shown in [sometimesanotion/Lamarck-14B-v0.7-Fusion](https://huggingface.co./sometimesanotion/Lamarck-14B-v0.7-Fusion), which has a valuable uptick in GPQA over its predecessors.  Will its gains be maintained with a modified version of the SLERP recipe from [suayptalha/Lamarckvergence-14B](https://huggingface.co./suayptalha/Lamarckvergence-14B)?  Let's find out what these weights for self-attention and perceptrons can unlock in this merge.

## Merge Details
### Merge Method

This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.

### Models Merged

The following models were included in the merge:
* [sometimesanotion/Lamarck-14B-v0.7-Fusion](https://huggingface.co./sometimesanotion/Lamarck-14B-v0.7-Fusion)
* [sometimesanotion/Lamarck-14B-v0.7](https://huggingface.co./sometimesanotion/Lamarck-14B-v0.7)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
name:                LamarckInfusion-14B-v1
base_model:          sometimesanotion/Lamarck-14B-v0.7
merge_method:        slerp
tokenizer_source:    base
dtype:               float32
out_dtype:           bfloat16
parameters:
  t:
    - filter:        self_attn
      value:         [0.2, 0.5, 0.4, 0.6, 0.8]
    - filter:        mlp
      value:         [0.8, 0.5, 0.6, 0.4, 0.2]
    - value:         [ 0.00, 0.00, 0.08, 0.16, 0.32, 0.48, 0.48, 0.48, 0.48, 0.48, 0.40, 0.32, 0.24 ] 
slices:
  - sources:
      - model:       sometimesanotion/Lamarck-14B-v0.7
        layer_range: [ 0, 48 ]
      - model:       sometimesanotion/Lamarck-14B-v0.7-Fusion
        layer_range: [ 0, 48 ]

```