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---
base_model:
- CultriX/SeQwence-14B
- VAGOsolutions/SauerkrautLM-v2-14b-DPO
- v000000/Qwen2.5-Lumen-14B
- CultriX/Qwen2.5-14B-Wernicke
- Qwen/Qwen2.5-14B
- CultriX/Qwen2.5-14B-MegaMerge-pt2
library_name: transformers
tags:
- mergekit
- merge
license: apache-2.0
language:
- en
metrics:
- accuracy
pipeline_tag: text-generation
---
# 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:
* [CultriX/SeQwence-14B](https://huggingface.co/CultriX/SeQwence-14B)
* [VAGOsolutions/SauerkrautLM-v2-14b-DPO](https://huggingface.co/VAGOsolutions/SauerkrautLM-v2-14b-DPO)
* [v000000/Qwen2.5-Lumen-14B](https://huggingface.co/v000000/Qwen2.5-Lumen-14B)
* [CultriX/Qwen2.5-14B-Wernicke](https://huggingface.co/CultriX/Qwen2.5-14B-Wernicke)
* [CultriX/Qwen2.5-14B-MegaMerge-pt2](https://huggingface.co/CultriX/Qwen2.5-14B-MegaMerge-pt2)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: CultriX/Qwen2.5-14B-Wernicke
parameters:
weight: 0.35 # Strong performance in GPQA, MUSR, and MMLU-PRO
density: 0.6 # Retain 60% of significant parameters
- model: VAGOsolutions/SauerkrautLM-v2-14b-DPO
parameters:
weight: 0.30 # Exceptional IFEval and MATH Level 5 capabilities
density: 0.6 # Retain 60% of significant parameters
- model: CultriX/Qwen2.5-14B-MegaMerge-pt2
parameters:
weight: 0.20 # Balanced contributions to Truthful QA and MMLU
density: 0.5 # Retain 50% of significant parameters
- model: CultriX/SeQwence-14B
parameters:
weight: 0.15 # Provides diverse data and generalization
density: 0.4 # Retain 40% of significant parameters
- model: v000000/Qwen2.5-Lumen-14B
parameters:
weight: 0.10 # Enhances creative and narrative tasks
density: 0.5 # Retain 50% for task diversity
base_model: Qwen/Qwen2.5-14B
merge_method: dare_ties
parameters:
normalize: true # Ensures parameter scaling compatibility
int8_mask: true # Optimizes memory and computational efficiency
dtype: bfloat16
tokenizer_source: Qwen/Qwen2.5-14B-Instruct
``` |