metadata

base_model:
  - cgato/TheSpice-7b-v0.1.1
  - ABX-AI/Laymonade-7B
library_name: transformers
tags:
  - mergekit
  - merge
  - not-for-all-audiences
license: other

Imatrix for Spicy-Laymonade-7B

Adding GGUF as I noticed the HF model had a lot of downloads but I never quantized it originally.

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.

Spicy-Laymonade-7B

Well, we have Laymonade, so why not spice it up? This merge is a step into creating a new 9B.

However, I did try it out, and it seemed to work pretty well.

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: cgato/TheSpice-7b-v0.1.1
        layer_range: [0, 32]
      - model: ABX-AI/Laymonade-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: ABX-AI/Laymonade-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