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---
base_model:
- CultriX/SeQwence-14Bv1
- CultriX/Qwen2.5-14B-Broca
- CultriX/Qwen2.5-14B-Wernickev3
- CultriX/Qwen2.5-14B-FinalMerge
- sthenno-com/miscii-14b-1225
- djuna/Q2.5-Veltha-14B
library_name: transformers
tags:
- mergekit
- merge
---
# merge
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_linear merge method using [djuna/Q2.5-Veltha-14B](https://huggingface.co/djuna/Q2.5-Veltha-14B) as a base.
### Models Merged
The following models were included in the merge:
* [CultriX/SeQwence-14Bv1](https://huggingface.co/CultriX/SeQwence-14Bv1)
* [CultriX/Qwen2.5-14B-Broca](https://huggingface.co/CultriX/Qwen2.5-14B-Broca)
* [CultriX/Qwen2.5-14B-Wernickev3](https://huggingface.co/CultriX/Qwen2.5-14B-Wernickev3)
* [CultriX/Qwen2.5-14B-FinalMerge](https://huggingface.co/CultriX/Qwen2.5-14B-FinalMerge)
* [sthenno-com/miscii-14b-1225](https://huggingface.co/sthenno-com/miscii-14b-1225)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
name: Merged-14B-Ultimate
merge_method: della_linear
base_model: djuna/Q2.5-Veltha-14B
dtype: bfloat16
parameters:
epsilon: 0.01 # Fine-grained parameter scaling for stable merges
lambda: 1.5 # Emphasizes each model’s unique parameters
normalize: true # Normalizes merges across different scale factors
models:
# 1) Strong average + BBH + conversation
- model: sthenno-com/miscii-14b-1225
parameters:
weight: 0.25
density: 0.70
# 2) CultriX “FinalMerge” synergy
- model: CultriX/Qwen2.5-14B-FinalMerge
parameters:
weight: 0.15
density: 0.65
# 3) CultriX “Wernickev3”—balanced
- model: CultriX/Qwen2.5-14B-Wernickev3
parameters:
weight: 0.15
density: 0.65
# 4) CultriX “Broca”—logic & QA
- model: CultriX/Qwen2.5-14B-Broca
parameters:
weight: 0.10
density: 0.65
# 5) CultriX “SeQwence-14Bv1”—general coverage
- model: CultriX/SeQwence-14Bv1
parameters:
weight: 0.10
density: 0.65
adaptive_merge_parameters:
# Weighted emphasis on sub-benchmarks
task_weights:
IFEval: 1.9
BBH: 1.8
MATH: 1.8
GPQA: 1.7
MUSR: 1.7
MMLU-PRO: 1.7
smoothing_factor: 0.1
gradient_clipping: 1.0 # Prevents over-contribution from any one model
```
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