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Qwen2.5-14B-Emergedv2

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Qwen2.5-14B-Emergedv2
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---
base_model:
- CultriX/SeQwence-14B-EvolMerge
- allknowingroger/QwenStock3-14B
- VAGOsolutions/SauerkrautLM-v2-14b-DPO
- allknowingroger/QwenSlerp6-14B
- CultriX/Qwen2.5-14B-Wernicke
- CultriX/SeQwence-14Bv1
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 [CultriX/SeQwence-14Bv1](https://huggingface.co./CultriX/SeQwence-14Bv1) as a base.

### Models Merged

The following models were included in the merge:
* [CultriX/SeQwence-14B-EvolMerge](https://huggingface.co./CultriX/SeQwence-14B-EvolMerge)
* [allknowingroger/QwenStock3-14B](https://huggingface.co./allknowingroger/QwenStock3-14B)
* [VAGOsolutions/SauerkrautLM-v2-14b-DPO](https://huggingface.co./VAGOsolutions/SauerkrautLM-v2-14b-DPO)
* [allknowingroger/QwenSlerp6-14B](https://huggingface.co./allknowingroger/QwenSlerp6-14B)
* [CultriX/Qwen2.5-14B-Wernicke](https://huggingface.co./CultriX/Qwen2.5-14B-Wernicke)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
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: CultriX/SeQwence-14Bv1
merge_method: dare_ties
parameters:
  normalize: true      # Ensures parameter scaling compatibility
  int8_mask: true      # Memory and computational efficiency
dtype: bfloat16
adaptive_merge_parameters:
  task_weights:
    IFEval: 1.2        # Emphasize instruction-following and formatting adherence
    BBH: 1.2           # Maintain strong performance in challenging reasoning tasks
    MATH_Lvl_5: 1.3    # Ensure domain expertise in competitive math problems
    GPQA: 1.3          # Leverage graduate-level knowledge capabilities
    MuSR: 1.1          # Enhance multistep reasoning on complex tasks
    MMLU_PRO: 1.2      # Ensure robust multitask domain understanding
  smoothing_factor: 0.2  # Moderate blending for stable integration
gradient_clipping: 1.0   # Prevent over-contribution from any single model

```