allknowingroger/Qwenslerp4-14B

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 Qwen/Qwen2.5-14B as a base.

Models Merged

The following models were included in the merge:

• CultriX/Qwen2.5-14B-Wernicke
• VAGOsolutions/SauerkrautLM-v2-14b-DPO
• rombodawg/Rombos-LLM-V2.6-Qwen-14b
• allknowingroger/Qwenslerp2-14B

Configuration

The following YAML configuration was used to produce this model:

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