base_model: CultriX/SeQwence-14Bv1 merge_method: dare_ties parameters: normalize: true int8_mask: true dtype: bfloat16 models: - model: CultriX/SeQwence-14Bv1 parameters: weight: 0.28 # Strong base for multitask benchmarks. density: 0.7 # Retains strong multitask performance. - model: CultriX/Qwen2.5-14B-Wernickev3 parameters: weight: 0.22 # Balanced to support reasoning-heavy benchmarks like BBH. density: 0.65 - model: qingy2019/Qwen2.5-Math-14B-Instruct parameters: weight: 0.22 # Optimized for MATH and BBH. density: 0.6 - model: allknowingroger/QwenSlerp6-14B parameters: weight: 0.18 # Reintegration of the highest scorer for stability across benchmarks. density: 0.65 # Focused on its exceptional multitask and reasoning strengths. - model: CultriX/Qwen2.5-14B-Emergedv3 parameters: weight: 0.15 # Maintains multitask stability for GPQA and MMLU-PRO. density: 0.6 - model: sometimesanotion/Qwen2.5-14B-Vimarckoso parameters: weight: 0.1 # Late-layer contributor for MUSR and multi-step reasoning. density: 0.6 adaptive_merge_parameters: task_weights: IFEval: 1.4 # Balanced to maintain instruction-following benchmarks. BBH: 1.4 # Ensures strong reasoning capabilities. MATH: 1.5 # Prioritizes mathematical reasoning. GPQA: 1.5 # Balanced for factual QA. MUSR: 1.4 # Advanced multi-step reasoning. MMLU-PRO: 1.5 # Emphasized for domain-specific multitask performance. smoothing_factor: 0.12 # Smooth transitions between task-specific contributions. gradient_clipping: CultriX/SeQwence-14Bv1: 0.8 CultriX/Qwen2.5-14B-Wernickev3: 0.8 qingy2019/Qwen2.5-Math-14B-Instruct: 0.85 allknowingroger/QwenSlerp6-14B: 0.8 # Balanced for high scoring model contributions. CultriX/Qwen2.5-14B-Emergedv3: 0.75 sometimesanotion/Qwen2.5-14B-Vimarckoso: 0.75 tokenizer_source: CultriX/SeQwence-14Bv1