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---
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.
Why isn't this the next version of Lamarck? It has not undergone the highly layer-targeting merges that go into a Lamarck release, and to truly refine Lamarck v0.7 requires top-notch components. This one, perhaps.
## 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 ]
```
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