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QuantFactory/
Gemma-Radiation-RP-9B-GGUF

Name: Gemma-Radiation-RP-9B-GGUF
Availability: InStock

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QuantFactory/Gemma-Radiation-RP-9B-GGUF

This is quantized version of Casual-Autopsy/Gemma-Radiation-RP-9B created using llama.cpp

Original Model Card

ToDo: Fill the card with more info.

merge

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

Merge Details

It's a bit of a test merge to dip my toes into merging Gemma 2. Sadly, however, it seems like 8B is my PC's tolerable limit before performance becomes painstakingly and infuriatingly slow, so after this, I might have to sit out on Gemma 2

Merge Method

This model was merged using the Model Stock merge method using Casual-Autopsy/Gemma-Rad-RP as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

yaml

models:
  - model: crestf411/gemma2-9B-sunfall-v0.5.2
  - model: crestf411/gemma2-9B-daybreak-v0.5
    parameters:
      density: [0.7, 0.5, 0.3, 0.35, 0.65, 0.35, 0.75, 0.25, 0.75, 0.35, 0.65, 0.35, 0.3, 0.5, 0.7]
      weight: [0.5, 0.13, 0.5, 0.13, 0.3]
  - model: crestf411/gemstone-9b
    parameters:
      density: [0.7, 0.5, 0.3, 0.35, 0.65, 0.35, 0.75, 0.25, 0.75, 0.35, 0.65, 0.35, 0.3, 0.5, 0.7]
      weight: [0.13, 0.5, 0.13, 0.5, 0.13]
merge_method: dare_ties
base_model: crestf411/gemma2-9B-sunfall-v0.5.2
parameters:
  normalize: false
  int8_mask: true
dtype: bfloat16

yaml

models:
  - model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3
  - model: nldemo/Gemma-9B-Summarizer-QLoRA
    parameters:
      density: [0.7, 0.5, 0.3, 0.35, 0.65, 0.35, 0.75, 0.25, 0.75, 0.35, 0.65, 0.35, 0.3, 0.5, 0.7]
      weight: [0.0625, 0.25, 0.0625, 0.25, 0.0625]
  - model: SillyTilly/google-gemma-2-9b-it+rbojja/gemma2-9b-intent-lora-adapter
    parameters:
      density: [0.7, 0.5, 0.3, 0.35, 0.65, 0.35, 0.75, 0.25, 0.75, 0.35, 0.65, 0.35, 0.3, 0.5, 0.7]
      weight: [0.0625, 0.25, 0.0625, 0.25, 0.0625]
  - model: nbeerbower/gemma2-gutenberg-9B
    parameters:
      density: [0.7, 0.5, 0.3, 0.35, 0.65, 0.35, 0.75, 0.25, 0.75, 0.35, 0.65, 0.35, 0.3, 0.5, 0.7]
      weight: [0.25, 0.0625, 0.25, 0.0625, 0.25]
merge_method: ties
base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3
parameters:
  normalize: false
  int8_mask: true
dtype: bfloat16

yaml

models:
  - model: IlyaGusev/gemma-2-9b-it-abliterated
  - model: TheDrummer/Smegmma-9B-v1
    parameters:
      density: [0.7, 0.5, 0.3, 0.35, 0.65, 0.35, 0.75, 0.25, 0.75, 0.35, 0.65, 0.35, 0.3, 0.5, 0.7]
      weight: [0.5, 0.13, 0.5, 0.13, 0.3]
  - model: TheDrummer/Tiger-Gemma-9B-v1
    parameters:
      density: [0.7, 0.5, 0.3, 0.35, 0.65, 0.35, 0.75, 0.25, 0.75, 0.35, 0.65, 0.35, 0.3, 0.5, 0.7]
      weight: [0.13, 0.5, 0.13, 0.5, 0.13]
merge_method: dare_ties
base_model: IlyaGusev/gemma-2-9b-it-abliterated
parameters:
  normalize: false
  int8_mask: true
dtype: bfloat16

yaml

models:
  - model: Casual-Autopsy/Gemma-Rad-RP
  - model: Casual-Autopsy/Gemma-Rad-Uncen
  - model: Casual-Autopsy/Gemma-Rad-IQ
merge_method: model_stock
base_model: Casual-Autopsy/Gemma-Rad-RP
dtype: bfloat16