CultriX/Qwen2.5-14B-Wernickev3

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using Qwen/Qwen2.5-14B as a base.

Models Merged

The following models were included in the merge:

• allknowingroger/QwenSlerp6-14B
• allknowingroger/QwenStock3-14B
• CultriX/SeQwence-14B-EvolMerge
• CultriX/Qwen2.5-14B-Wernicke
• VAGOsolutions/SauerkrautLM-v2-14b-DPO

Configuration

The following YAML configuration was used to produce this model:

### 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 ###