models:
  - model: CultriX/Qwen2.5-14B-Wernickev3
    parameters:
      weight: 0.38       # Slight reduction to balance with FinalMerge's generalist capabilities.
      density: 0.65      # Retain significant parameters for stability and strong task performance.
  - model: CultriX/Qwen2.5-14B-FinalMerge
    parameters:
      weight: 0.32       # Slight increase to ensure its generalist capabilities are fully utilized.
      density: 0.60      # Balanced density for comprehensive task coverage.
  - model: CultriX/Qwen2.5-14B-Emergedv3
    parameters:
      weight: 0.20       # Retains focused contribution to specific task optimizations.
      density: 0.55      # Moderate density ensures efficient parameter usage.
  - model: qingy2019/Qwen2.5-Math-14B-Instruct
    parameters:
      weight: 0.10       # Consistent with its specialist focus, balancing lower weight with higher density.
      density: 0.70      # High density ensures retention of advanced reasoning and MATH-related parameters.

merge_method: dare_ties
base_model: CultriX/SeQwence-14Bv1
parameters:
  normalize: true        # Ensures all models are scaled to compatible parameter ranges.
  int8_mask: true        # Optimizes memory and computational efficiency without accuracy loss.
dtype: bfloat16          # Provides better memory efficiency and numerical stability.

adaptive_merge_parameters:
  task_weights:
    tinyArc: 1.3         # Slight reduction to balance with generalist contributions.
    tinyHellaswag: 1.3   # Maintains strong performance in contextual reasoning.
    tinyMMLU: 1.2        # Balanced focus for domain-specific knowledge.
    tinyTruthfulQA: 1.2  # Adjusted to ensure fair contribution without over-prioritization.
    tinyTruthfulQA_mc1: 1.1 # Maintains a moderate priority to balance with other tiny benchmarks.
    tinyWinogrande: 1.2  # Strong contextual reasoning support from generalist models.
    IFEval: 1.5          # High weight for general instruction-following capabilities.
    BBH: 1.5             # Prioritizes complex reasoning and multi-step problem-solving tasks.
    MATH: 1.55           # Slight reduction to balance MATH with other advanced reasoning benchmarks.
    GPQA: 1.4            # Balanced to reflect contributions from both generalist and specialist models.
    MUSR: 1.4            # Increased slightly to strengthen multi-step reasoning.
    MMLU-PRO: 1.3        # Maintains general task performance across multitask domain knowledge.
  smoothing_factor: 0.18  # Slightly increased for smoother blending across task boundaries.
gradient_clipping: 0.88   # Tightened slightly for stability, preventing parameter over-contribution.