---
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


```