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---
base_model:
- Qwen/Qwen2.5-14B
- allknowingroger/QwenSlerp6-14B
- allknowingroger/QwenStock3-14B
- CultriX/SeQwence-14B-EvolMerge
- CultriX/Qwen2.5-14B-Wernicke
- VAGOsolutions/SauerkrautLM-v2-14b-DPO
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 [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [Qwen/Qwen2.5-14B](https://huggingface.co/Qwen/Qwen2.5-14B) as a base.
### Models Merged
The following models were included in the merge:
* [allknowingroger/QwenSlerp6-14B](https://huggingface.co/allknowingroger/QwenSlerp6-14B)
* [allknowingroger/QwenStock3-14B](https://huggingface.co/allknowingroger/QwenStock3-14B)
* [CultriX/SeQwence-14B-EvolMerge](https://huggingface.co/CultriX/SeQwence-14B-EvolMerge)
* [CultriX/Qwen2.5-14B-Wernicke](https://huggingface.co/CultriX/Qwen2.5-14B-Wernicke)
* [VAGOsolutions/SauerkrautLM-v2-14b-DPO](https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-DPO)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
### CONFIG SuperiorMerge-14B-From-2-to-10 ###
models:
- model: VAGOsolutions/SauerkrautLM-v2-14b-DPO
parameters:
weight: 0.25 # Prioritize top IFEval
density: 0.6 # Keep a large portion for strong factual baseline
- model: allknowingroger/QwenSlerp6-14B
parameters:
weight: 0.25 # High weight for MATH and balanced reasoning
density: 0.6 # Retain robust reasoning capabilities
- model: CultriX/SeQwence-14B-EvolMerge
parameters:
weight: 0.20 # Important for best BBH and near-top MUSR
density: 0.5 # Moderate density to ensure these strengths blend well
- model: CultriX/Qwen2.5-14B-Wernicke
parameters:
weight: 0.15 # Adds top GPQA performance
density: 0.5 # Sufficient to preserve QA strengths
- model: allknowingroger/QwenStock3-14B
parameters:
weight: 0.15 # For top MMLU-PRO, enhancing domain knowledge
density: 0.5 # Balanced integration of diverse subject expertise
base_model: Qwen/Qwen2.5-14B
merge_method: dare_ties
parameters:
normalize: true # Ensures parameter scaling compatibility
int8_mask: true # Memory and computational efficiency
dtype: bfloat16
tokenizer_source: Qwen/Qwen2.5-14B-Instruct
### END OF CONFIG SuperiorMerge-14B-From-2-to-10 ###
```
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