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--- |
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base_model: |
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- CultriX/Qwen2.5-14B-Wernicke |
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- Qwen/Qwen2.5-14B |
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- VAGOsolutions/SauerkrautLM-v2-14b-DPO |
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- rombodawg/Rombos-LLM-V2.6-Qwen-14b |
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- allknowingroger/Qwenslerp2-14B |
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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 [Qwen/Qwen2.5-14B](https://huggingface.co./Qwen/Qwen2.5-14B) 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/Qwen2.5-14B-Wernicke](https://huggingface.co./CultriX/Qwen2.5-14B-Wernicke) |
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* [VAGOsolutions/SauerkrautLM-v2-14b-DPO](https://huggingface.co./VAGOsolutions/SauerkrautLM-v2-14b-DPO) |
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* [rombodawg/Rombos-LLM-V2.6-Qwen-14b](https://huggingface.co./rombodawg/Rombos-LLM-V2.6-Qwen-14b) |
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* [allknowingroger/Qwenslerp2-14B](https://huggingface.co./allknowingroger/Qwenslerp2-14B) |
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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: CultriX/Qwen2.5-14B-Wernicke |
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parameters: |
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weight: 0.55 # Backbone model for conversational ability and GPQA |
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density: 0.80 # Retain most critical parameters for stability and strength |
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- model: VAGOsolutions/SauerkrautLM-v2-14b-DPO |
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parameters: |
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weight: 0.20 # High IFEval and MMLU-PRO performance with minimized weaknesses |
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density: 0.60 # Focus on impactful parameters for specific benchmarks |
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- model: rombodawg/Rombos-LLM-V2.6-Qwen-14b |
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parameters: |
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weight: 0.25 # Enhanced emphasis on reasoning-heavy tasks like MUSR and MATH |
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density: 0.70 # Retain reasoning-intensive parameters for improved benchmarks |
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- model: allknowingroger/Qwenslerp2-14B |
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parameters: |
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weight: 0.15 # General stabilizer for consistency across all tasks |
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density: 0.65 # Focus on balance and avoiding redundancy |
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base_model: Qwen/Qwen2.5-14B |
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merge_method: dare_ties |
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parameters: |
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normalize: true # Ensure parameter scale consistency |
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int8_mask: true # Optimize for memory and compute efficiency |
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dtype: bfloat16 |
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tokenizer_source: Qwen/Qwen2.5-14B-Instruct |
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adaptive_merge_parameters: |
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task_weights: |
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IFEval: 1.0 # Maintain high IFEval performance |
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MATH: 1.3 # Prioritize reasoning and calculation-heavy tasks |
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GPQA: 1.1 # Boost factual recall and reasoning accuracy |
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MUSR: 1.2 # Enhance logical reasoning and factual understanding |
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MMLU-PRO: 1.0 # Retain consistent knowledge representation |
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smoothing_factor: 0.15 # Fine-tune blending for stable transitions between tasks |
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gradient_clipping: 1.0 # Prevent over-contribution from any single model |
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``` |
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