merge
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the della_linear merge method using CultriX/Qwen2.5-14B-Wernickev3 as a base.
Models Merged
The following models were included in the merge:
- CultriX/Qwen2.5-14B-Broca
- djuna/Q2.5-Veltha-14B-0.5
- sometimesanotion/Qwen2.5-14B-Vimarckoso
- CultriX/SeQwence-14Bv1
- qingy2019/Qwen2.5-Math-14B-Instruct
- CultriX/Qwenfinity-2.5-14B
- allknowingroger/QwenSlerp6-14B
Configuration
The following YAML configuration was used to produce this model:
merge_method: della_linear # Consider if the 'della_linear' merge method is optimal based on the latest benchmarks. Would a different method better balance task priorities?
base_model: CultriX/Qwen2.5-14B-Wernickev3
dtype: bfloat16
parameters:
epsilon: 0.01 # Epsilon is set for fine-grain precision. Consider validating this epsilon value against current benchmark results to ensure it effectively fine-tunes model behaviors and aligns with the intended benchmarks.
lambda: 1.5 # Optimized for significant model contributions. Double-check if this value prioritizes the right model strengths effectively.
normalize: true
smoothing_factor: 0.08 # Double-check if this smoothing factor sufficiently supports task-specific performance across benchmarks, especially with diverse model contributions. Balanced blending to preserve model diversity.
gradient_clipping:
CultriX/Qwen2.5-14B-Wernickev3: 0.85
CultriX/Qwenfinity-2.5-14B: 0.82
djuna/Q2.5-Veltha-14B-0.5: 0.92
CultriX/Qwen2.5-14B-Broca: 0.86
qingy2019/Qwen2.5-Math-14B-Instruct: 0.94
CultriX/SeQwence-14Bv1: 0.87
sometimesanotion/Qwen2.5-14B-Vimarckoso: 0.90
allknowingroger/QwenSlerp6-14B: 0.86
models:
- model: CultriX/Qwen2.5-14B-Wernickev3
parameters:
weight: 0.25
density: 0.72
- model: CultriX/Qwenfinity-2.5-14B
parameters:
weight: 0.22
density: 0.68
- model: djuna/Q2.5-Veltha-14B-0.5
parameters:
weight: 0.20
density: 0.75
- model: CultriX/Qwen2.5-14B-Broca
parameters:
weight: 0.16
density: 0.68
- model: qingy2019/Qwen2.5-Math-14B-Instruct
parameters:
weight: 0.19
density: 0.75
- model: CultriX/SeQwence-14Bv1
parameters:
weight: 0.13
density: 0.65
- model: sometimesanotion/Qwen2.5-14B-Vimarckoso
parameters:
weight: 0.11
density: 0.62
- model: allknowingroger/QwenSlerp6-14B
parameters:
weight: 0.09
density: 0.65
adaptive_merge_parameters:
task_weights:
tinyArc: 1.65
tinyHellaswag: 1.55
tinyMMLU: 1.7
tinyTruthfulQA: 1.95
tinyTruthfulQA_mc1: 1.75
tinyWinogrande: 1.8
IFEval: 2.0
BBH: 1.75
MATH: 2.2
GPQA: 1.85
MUSR: 1.95
MMLU-PRO: 1.85 # Consider reviewing task_weights to confirm alignment with latest performance data and benchmarks for optimal configuration.
tokenizer_source: CultriX/Qwen2.5-14B-Wernickev3
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