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:
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
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