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:

• CultriX/SeQwence-14B
• VAGOsolutions/SauerkrautLM-v2-14b-DPO
• v000000/Qwen2.5-Lumen-14B
• CultriX/Qwen2.5-14B-Wernicke
• CultriX/Qwen2.5-14B-MegaMerge-pt2

Configuration

The following YAML configuration was used to produce this model:

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