The following YAML configuration was used to produce this model:
models:-model:CultriX/Qwen2.5-14B-Wernickeparameters:weight:0.55# Backbone model for conversational ability and GPQAdensity:0.80# Retain most critical parameters for stability and strength-model:VAGOsolutions/SauerkrautLM-v2-14b-DPOparameters:weight:0.20# High IFEval and MMLU-PRO performance with minimized weaknessesdensity:0.60# Focus on impactful parameters for specific benchmarks-model:rombodawg/Rombos-LLM-V2.6-Qwen-14bparameters:weight:0.25# Enhanced emphasis on reasoning-heavy tasks like MUSR and MATHdensity:0.70# Retain reasoning-intensive parameters for improved benchmarks-model:allknowingroger/Qwenslerp2-14Bparameters:weight:0.15# General stabilizer for consistency across all tasksdensity:0.65# Focus on balance and avoiding redundancybase_model:Qwen/Qwen2.5-14Bmerge_method:dare_tiesparameters:normalize:true# Ensure parameter scale consistencyint8_mask:true# Optimize for memory and compute efficiencydtype:bfloat16tokenizer_source:Qwen/Qwen2.5-14B-Instructadaptive_merge_parameters:task_weights:IFEval:1.0# Maintain high IFEval performanceMATH:1.3# Prioritize reasoning and calculation-heavy tasksGPQA:1.1# Boost factual recall and reasoning accuracyMUSR:1.2# Enhance logical reasoning and factual understandingMMLU-PRO:1.0# Retain consistent knowledge representationsmoothing_factor:0.15# Fine-tune blending for stable transitions between tasksgradient_clipping:1.0# Prevent over-contribution from any single model
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