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/Qwen2.5-14B-Wernickev3 as a base.
Models Merged
The following models were included in the merge:
- qingy2024/Fusion4-14B-Instruct
- sometimesanotion/Lamarck-14B-v0.6
- allknowingroger/QwenSlerp6-14B
- CultriX/SeQwence-14B-EvolMerge
- hotmailuser/QwenSlerp2-14B
- CultriX/Qwen2.5-14B-Emerged
- djuna/Q2.5-Veltha-14B-0.5
Configuration
The following YAML configuration was used to produce this model:
merge_method: dare_ties # Changed to dare_ties
base_model: CultriX/Qwen2.5-14B-Wernickev3
dtype: bfloat16 # Use float32 for maximum precision.
out_dtype: bfloat16 # Output model also uses bfloat16 for consistency and reduced memory usage.
parameters:
t: 0.5 # Balances interpolation between models; 0.5 gives equal weight to all contributors.
normalize: true # Ensures parameters are normalized to maintain stability during merging.
rescale: true # Aligns parameter scales across models for better integration.
int8_mask: false # Disable int8 masking to preserve full precision during merging.
epsilon: 0.008 # Ultra-fine parameter scaling for precise adjustments between models.
lambda: 1.8 # Emphasizes high-impact parameters, giving more weight to significant contributors.
adaptive_merge_parameters:
task_weights: # Assign weights to tasks based on their priority and impact on benchmarks.
tinyArc: 1.6 # Logical reasoning benchmark; slightly lower priority.
tinyHellaswag: 1.5 # Contextual reasoning benchmark with moderate priority.
tinyMMLU: 1.8 # Multi-domain knowledge benchmark; important for multitask performance.
tinyTruthfulQA: 1.9 # Focuses on factual reasoning and QA; high priority.
tinyTruthfulQA_mc1: 1.75 # Multiple-choice factual reasoning; closely related to TruthfulQA.
tinyWinogrande: 1.75 # Core reasoning benchmark; slightly lower than BBH.
IFEval: 2.30 # Instruction-following tasks; given a high priority for practical applications.
BBH: 2.05 # Complex reasoning benchmark; critical for logical tasks.
MATH: 2.70 # Highest priority to emphasize mathematical reasoning excellence.
GPQA: 2.20 # Graduate-level QA tasks; balanced priority for high-level reasoning.
MUSR: 2.15 # Multi-step reasoning; slightly increased to strengthen reasoning performance.
MMLU-PRO: 2.00 # Domain multitask benchmark; maintained for general multitask capability.
smoothing_factor: 0.03 # Low smoothing for precise task-specific blending without over-generalizing.
gradient_clipping: # Control gradient clipping for each model to stabilize training.
CultriX/Qwen2.5-14B-Wernickev3: 0.89 # Higher value ensures stability for the base model.
djuna/Q2.5-Veltha-14B-0.5: 0.92 # Stable setting to enhance reasoning contributions.
CultriX/SeQwence-14B-EvolMerge: 0.87 # Moderate value for generalist multitask support.
qingy2024/Fusion4-14B-Instruct: 0.93 # High stability to emphasize mathematical tasks.
CultriX/Qwen2.5-14B-Emerged: 0.88 # Stable setting to maintain multitask performance.
sometimesanotion/Lamarck-14B-v0.6: 0.89 # Stable contribution for multi-step reasoning.
allknowingroger/QwenSlerp6-14B: 0.90 # Adjusted for stable integration of the replacement model.
hotmailuser/QwenSlerp2-14B: 0.91 # Increased slightly for stable integration of reasoning contributions.
models: # Define models to include in the merge, along with their weights and densities.
- model: CultriX/Qwen2.5-14B-Wernickev3
parameters:
weight: 0.33 # Increased to absorb some of the weight from the removed model.
density: 0.78 # Maintained optimal density for robust generalist performance.
- model: djuna/Q2.5-Veltha-14B-0.5
parameters:
weight: 0.28 # Increased slightly to enhance reasoning benchmarks like MUSR.
density: 0.77 # Maintained for strong nuanced reasoning.
- model: allknowingroger/QwenSlerp6-14B # Replacement for Qwenfinity-2.5-14B.
parameters:
weight: 0.15 # Matches the weight of the replaced model to preserve balance.
density: 0.70 # Increased slightly for stronger parameter integration.
- model: CultriX/SeQwence-14B-EvolMerge
parameters:
weight: 0.12 # Moderate weight for general multitask support.
density: 0.62 # Maintained for stable contribution.
- model: qingy2024/Fusion4-14B-Instruct
parameters:
weight: 0.09 # Moderate weight; focuses on mathematical reasoning tasks.
density: 0.75 # Maintained density for stable integration.
- model: CultriX/Qwen2.5-14B-Emerged
parameters:
weight: 0.08 # Balanced weight for multitask contributions.
density: 0.69 # Maintained density for stable integration.
- model: sometimesanotion/Lamarck-14B-v0.6
parameters:
weight: 0.06 # Lower weight to allow more impactful models to dominate.
density: 0.62 # Maintained for stable multi-step reasoning contribution.
- model: hotmailuser/QwenSlerp2-14B
parameters:
weight: 0.11 # Increased slightly to balance contributions.
density: 0.66 # Maintained for stable parameter integration.