bamec66557/MISCHIEVOUS-12B-Mix_0.3v

[GGUF]

• Q4_K_M.gguf

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

• bamec66557/MISCHIEVOUS-12B-Mix_0.2v
• bamec66557/MISCHIEVOUS-12B-Mix_0.1v

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: bamec66557/MISCHIEVOUS-12B-Mix_0.1v
        layer_range: [0, 40]
      - model: bamec66557/MISCHIEVOUS-12B-Mix_0.2v
        layer_range: [0, 40]

    parameters:
      t:
        - filter: self_attn
          value: [0.1, 0.3, 0.7, 0.9, 1.0]  # Spikes for dramatic change
        - filter: mlp
          value: [1.0, 0.7, 0.4, 0.1, 0.0]  # Conversely, a sharp decline
        - filter: layer_norm
          value: [0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7,  # First 10 layers
                  0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3,  # The remaining 30 layers
                  0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3,
                  0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3]
        - value: 0.9  # Set the default merge ratio to high

merge_method: slerp  # maintain slerp

base_model: bamec66557/MISCHIEVOUS-12B-Mix_0.2v  # Base model

dtype: bfloat16  # Data types for fast merges

# Additional options
regularization:
  - method: l2_norm  # Stabilise after merging with L2 normalisation
    scale: 0.005  # Reduce normalisation strength to allow for variation

postprocessing:
  - operation: smoothing  # Smoothing weights after merging
    kernel_size: 5  # Smoothing larger ranges with increased kernel size
  - operation: normalize  # Normalise after merge

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	22.72
IFEval (0-Shot)	38.70
BBH (3-Shot)	34.39
MATH Lvl 5 (4-Shot)	12.92
GPQA (0-shot)	9.28
MuSR (0-shot)	11.44
MMLU-PRO (5-shot)	29.60