ABX-AI/Infinite-Laymons-7B-GGUF-IQ-Imatrix

GGUF / IQ / Imatrix for Infinite-Laymons-7B

Why Importance Matrix?

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 custom kink.

Infinite-Laymons-7B

This model is intended for fictional storytelling and role-playing, with a focus on more original conversations and less alignment.

Merge Details

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

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

• KatyTheCutie/LemonadeRP-4.5.3
• Nitral-AI/Infinitely-Laydiculous-7B

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: KatyTheCutie/LemonadeRP-4.5.3
        layer_range: [0, 32]
      - model: Nitral-AI/Infinitely-Laydiculous-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: Nitral-AI/Infinitely-Laydiculous-7B
parameters:
  t:
    - filter: self_attn
      value: [0.7, 0.3, 0.6, 0.2, 0.5]
    - filter: mlp
      value: [0.3, 0.7, 0.4, 0.8, 0.5]
    - value: 0.5
dtype: bfloat16