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Test001a

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Test001a
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### This the config.yml for ABC_Books/test001 ###
 models:

  ### Models that contribute a large 128K context window ###
  - model: CallComply/zephyr-7b-beta-128k
    parameters:
      weight: 0.1154
      density: 0.9
  - model: Nitral-Archive/HerculeanSea-7b-128k
    parameters:
      weight: 0.1154
      density: 0.9
  - model: NousResearch/Yarn-Mistral-7b-128k
    parameters:
      weight: 0.1154
      density: 0.9

  ### Models with finetune training on occult knownledge ###
  - model: teknium/llama-deus-7b-v3-lora-merged
    parameters:
      weight: 0.0769
      density: 0.9
  - model: teknium/Hermes-Trismegistus-Mistral-7B
    parameters:
      weight: 0.0769
      density: 0.9
  - model: alexandrabenamar/Mistral-7B-Instruct-v0.2-Magic
    parameters:
      weight: 0.0769
      density: 0.9
  - model: tarotscientist/llama-2-7b-tarotreader
    parameters:
      weight: 0.0769
      density: 0.9
  - model: teknium/Mistral-Trismegistus-7B
    parameters:
      weight: 0.0769
      density: 0.9

  ### Talkative model with a large context window ###
  - model: Norquinal/Mistral-7B-storywriter
    parameters:
      weight: 0.0769
      density: 0.9

  ### Models with finetune training to be uncensored use some crass diction ###
  - model: Undi95/BigL-7B
    parameters:
      weight: 0.0384
      density: 0.9
  - model: Undi95/LewdMistral-7B-0.2
    parameters:
      weight: 0.0385
      density: 0.9
  - model: Undi95/MistRP-Dolphin-7B
    parameters:
      weight: 0.0385
      density: 0.9
  - model: Undi95/Mistral-ClaudeLimaRP-v3-7B
    parameters:
      weight: 0.0385
      density: 0.9
  - model: Undi95/Toppy-M-7B
    parameters:
      weight: 0.0385
      density: 0.9

  ### The use of DARES has been shown to “Densify” standard model lending to a more robust model when paired with a high “density:” numbers ###
 merge_method: dare_ties

  ### This model best exemplifies the closest match to all of the features needed in the final model ###
 base_model: NousResearch/Yarn-Mistral-7b-128k
 parameters:

  ### When “densifing” models the model size tends to grow without normalize
  normalize: true
  int8_mask: true
 dtype: float16