Qwenslerp4-14B

8
1
—
by
allknowingroger
Language Model
OTHER
14B params
New
8 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
32GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
14GB+ RAM

Code Examples

Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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
Configurationyaml
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

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