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--- |
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base_model: |
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- CultriX/SeQwence-14B-EvolMerge |
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- allknowingroger/QwenStock3-14B |
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- VAGOsolutions/SauerkrautLM-v2-14b-DPO |
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- allknowingroger/QwenSlerp6-14B |
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- CultriX/Qwen2.5-14B-Wernicke |
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- CultriX/SeQwence-14Bv1 |
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library_name: transformers |
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tags: |
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- mergekit |
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- merge |
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--- |
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# merge |
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This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). |
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## Merge Details |
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### Merge Method |
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This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [CultriX/SeQwence-14Bv1](https://huggingface.co./CultriX/SeQwence-14Bv1) as a base. |
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### Models Merged |
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The following models were included in the merge: |
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* [CultriX/SeQwence-14B-EvolMerge](https://huggingface.co./CultriX/SeQwence-14B-EvolMerge) |
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* [allknowingroger/QwenStock3-14B](https://huggingface.co./allknowingroger/QwenStock3-14B) |
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* [VAGOsolutions/SauerkrautLM-v2-14b-DPO](https://huggingface.co./VAGOsolutions/SauerkrautLM-v2-14b-DPO) |
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* [allknowingroger/QwenSlerp6-14B](https://huggingface.co./allknowingroger/QwenSlerp6-14B) |
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* [CultriX/Qwen2.5-14B-Wernicke](https://huggingface.co./CultriX/Qwen2.5-14B-Wernicke) |
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### Configuration |
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The following YAML configuration was used to produce this model: |
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```yaml |
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models: |
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- model: VAGOsolutions/SauerkrautLM-v2-14b-DPO |
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parameters: |
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weight: 0.25 # Prioritize top IFEval |
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density: 0.6 # Keep a large portion for strong factual baseline |
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- model: allknowingroger/QwenSlerp6-14B |
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parameters: |
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weight: 0.25 # High weight for MATH and balanced reasoning |
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density: 0.6 # Retain robust reasoning capabilities |
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- model: CultriX/SeQwence-14B-EvolMerge |
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parameters: |
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weight: 0.20 # Important for best BBH and near-top MuSR |
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density: 0.5 # Moderate density to ensure these strengths blend well |
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- model: CultriX/Qwen2.5-14B-Wernicke |
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parameters: |
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weight: 0.15 # Adds top GPQA performance |
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density: 0.5 # Sufficient to preserve QA strengths |
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- model: allknowingroger/QwenStock3-14B |
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parameters: |
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weight: 0.15 # For top MMLU-PRO, enhancing domain knowledge |
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density: 0.5 # Balanced integration of diverse subject expertise |
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base_model: CultriX/SeQwence-14Bv1 |
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merge_method: dare_ties |
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parameters: |
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normalize: true # Ensures parameter scaling compatibility |
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int8_mask: true # Memory and computational efficiency |
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dtype: bfloat16 |
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adaptive_merge_parameters: |
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task_weights: |
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IFEval: 1.2 # Emphasize instruction-following and formatting adherence |
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BBH: 1.2 # Maintain strong performance in challenging reasoning tasks |
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MATH_Lvl_5: 1.3 # Ensure domain expertise in competitive math problems |
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GPQA: 1.3 # Leverage graduate-level knowledge capabilities |
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MuSR: 1.1 # Enhance multistep reasoning on complex tasks |
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MMLU_PRO: 1.2 # Ensure robust multitask domain understanding |
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smoothing_factor: 0.2 # Moderate blending for stable integration |
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gradient_clipping: 1.0 # Prevent over-contribution from any single model |
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``` |
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