CultriX/Qwen2.5-14B-FinalMerge

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 CultriX/SeQwence-14Bv1 as a base.

Models Merged

The following models were included in the merge:

• VAGOsolutions/SauerkrautLM-v2-14b-DPO
• qingy2019/Qwen2.5-Math-14B-Instruct
• CultriX/Qwen2.5-14B-Wernickev3
• CultriX/Qwen2.5-14B-Emergedv3
• CultriX/Qwen2.5-14B-Unity
• allknowingroger/QwenSlerp6-14B

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: CultriX/SeQwence-14Bv1
    parameters:
      weight: 0.22        # Boosted slightly to improve general task performance
      density: 0.62       # Prioritize generalist adaptability
  - model: allknowingroger/QwenSlerp6-14B
    parameters:
      weight: 0.18
      density: 0.59       # Slight increase to enhance contextual reasoning (tinyHellaswag)
  - model: CultriX/Qwen2.5-14B-Wernickev3
    parameters:
      weight: 0.16
      density: 0.56       # Minor increase to stabilize GPQA and MUSR performance
  - model: CultriX/Qwen2.5-14B-Emergedv3
    parameters:
      weight: 0.15        # Increase weight for domain-specific expertise
      density: 0.55
  - model: VAGOsolutions/SauerkrautLM-v2-14b-DPO
    parameters:
      weight: 0.12
      density: 0.56       # Enhance factual reasoning and IFEval contributions
  - model: CultriX/Qwen2.5-14B-Unity
    parameters:
      weight: 0.10
      density: 0.53
  - model: qingy2019/Qwen2.5-Math-14B-Instruct
    parameters:
      weight: 0.10
      density: 0.51       # Retain focus on MATH and advanced reasoning tasks

merge_method: dare_ties
base_model: CultriX/SeQwence-14Bv1
parameters:
  normalize: true
  int8_mask: true
dtype: bfloat16
tokenizer_source: Qwen/Qwen2.5-14B-Instruct

adaptive_merge_parameters:
  task_weights:
    IFEval: 1.5           # Strengthened for better instruction-following
    BBH: 1.3
    MATH: 1.6             # Emphasize advanced reasoning and problem-solving
    GPQA: 1.4             # Improve factual recall and logical QA tasks
    MUSR: 1.5             # Strengthened multi-step reasoning capabilities
    MMLU-PRO: 1.3         # Slight boost for domain-specific multitask knowledge
  smoothing_factor: 0.19   # Refined for smoother blending of task strengths
gradient_clipping: 0.88    # Tightened slightly for precise parameter contribution