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Undi95/
MLewd-ReMM-L2-Chat-20B-Inverted-GGUF

Name: MLewd-ReMM-L2-Chat-20B-Inverted-GGUF
Availability: InStock

not-for-all-audiences

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First :

shell

layer_slices:
  - model: Undi95/MLewd-L2-Chat-13B
    start: 0
    end: 16
  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
    start: 8
    end: 20
  - model: Undi95/MLewd-L2-Chat-13B
    start: 17
    end: 32
  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
    start: 21
    end: 40

Inverted:

shell

layer_slices:
  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
    start: 0
    end: 16
  - model: Undi95/MLewd-L2-Chat-13B
    start: 8
    end: 20
  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
    start: 17
    end: 32
  - model: Undi95/MLewd-L2-Chat-13B
    start: 21
    end: 40

Precise:

shell

layer_slices:
  - model: Undi95/MLewd-L2-Chat-13B
    start: 0
    end: 8
  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
    start: 4
    end: 12
  - model: Undi95/MLewd-L2-Chat-13B
    start: 9
    end: 16
  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
    start: 13
    end: 22
  - model: Undi95/MLewd-L2-Chat-13B
    start: 17
    end: 24
  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
    start: 23
    end: 32
  - model: Undi95/MLewd-L2-Chat-13B
    start: 25
    end: 32
  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
    start: 33
    end: 40

PreciseInverted:

shell

layer_slices:
  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
    start: 0
    end: 8
  - model: Undi95/MLewd-L2-Chat-13B
    start: 4
    end: 12
  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
    start: 9
    end: 16
  - model: Undi95/MLewd-L2-Chat-13B
    start: 13
    end: 22
  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
    start: 17
    end: 24
  - model: Undi95/MLewd-L2-Chat-13B
    start: 23
    end: 32
  - model: Undi95/MLewd-ReMM-L2-Chat-20B-Part1
    start: 25
    end: 32
  - model: Undi95/MLewd-L2-Chat-13B
    start: 33
    end: 40

Part1 = ReMM v2.1 merged /w MLewd low weight to keep consistency. I call this "dilution" and result show consistency and coherency without repeat/loop beside the small amount of duplicated datas.

The goal is to find the best way to interlace layers the best way possible to have a sweetspot between 13B and +30B.

Normal/Inverted is by chunk of 16 layers and Precise/PreciseInverted is by chunk of 8 layers.

All the models are made of 64(+1) layers. Need testing.

Prompt template: Alpaca

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### Instruction:
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### Response:

Undi95/MLewd-ReMM-L2-Chat-20B-Inverted-GGUF

Prompt template: Alpaca

Undi95/
MLewd-ReMM-L2-Chat-20B-Inverted-GGUF