Importance Matrix, at least based on my testing, has shown to improve the output and performance of "IQ"-type quantizations, where the compression becomes quite heavy. The Imatrix performs a calibration, using a provided dataset. Testing has shown that semi-randomized data can help perserve more important segments as the compression is applied.

Related discussions in Github: [1] [2]

The imatrix.txt file that I used contains general, semi-random data, with some added extra kink.

Norosumika-7B

This model is intended for role-playing and storywriting purposes.

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:

Configuration

The following YAML configuration was used to produce this model:

yaml

slices:
  - sources:
      - model: NeverSleep/Noromaid-7B-0.4-DPO
        layer_range: [0, 32]
      - model: localfultonextractor/Erosumika-7B-v2
        layer_range: [0, 32]
merge_method: slerp
base_model: localfultonextractor/Erosumika-7B-v2
parameters:
  t:
    - filter: self_attn
      value: [0.5, 0.5, 0.5, 0.5, 0.5]
    - filter: mlp
      value: [0.5, 0.5, 0.5, 0.5, 0.5]
    - value: 0.5
dtype: bfloat16

ABX-AI/Norosumika-7b-GGUF-IQ-Imatrix

Norosumika-7B

Merge Details

Merge Method

Models Merged

Configuration

ABX-AI/
Norosumika-7b-GGUF-IQ-Imatrix