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:
- CultriX/Qwen2.5-14B-Wernickev3
- qingy2019/Qwen2.5-Math-14B-Instruct
- sometimesanotion/Qwen2.5-14B-Vimarckoso
- CultriX/Qwen2.5-14B-Emergedv3
- allknowingroger/QwenSlerp6-14B
Configuration
The following YAML configuration was used to produce this model:
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