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RichardErkhov/
grimjim_-_Magot-v1-Gemma2-8k-9B-gguf

Name: grimjim_-_Magot-v1-Gemma2-8k-9B-gguf
Availability: InStock

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Quantization made by Richard Erkhov.

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Magot-v1-Gemma2-8k-9B - GGUF

Model creator: https://huggingface.co/grimjim/
Original model: https://huggingface.co/grimjim/Magot-v1-Gemma2-8k-9B/

Name	Quant method	Size
Magot-v1-Gemma2-8k-9B.Q2_K.gguf	Q2_K	3.54GB
Magot-v1-Gemma2-8k-9B.IQ3_XS.gguf	IQ3_XS	3.86GB
Magot-v1-Gemma2-8k-9B.IQ3_S.gguf	IQ3_S	4.04GB
Magot-v1-Gemma2-8k-9B.Q3_K_S.gguf	Q3_K_S	4.04GB
Magot-v1-Gemma2-8k-9B.IQ3_M.gguf	IQ3_M	4.19GB
Magot-v1-Gemma2-8k-9B.Q3_K.gguf	Q3_K	4.43GB
Magot-v1-Gemma2-8k-9B.Q3_K_M.gguf	Q3_K_M	4.43GB
Magot-v1-Gemma2-8k-9B.Q3_K_L.gguf	Q3_K_L	4.78GB
Magot-v1-Gemma2-8k-9B.IQ4_XS.gguf	IQ4_XS	4.86GB
Magot-v1-Gemma2-8k-9B.Q4_0.gguf	Q4_0	5.07GB
Magot-v1-Gemma2-8k-9B.IQ4_NL.gguf	IQ4_NL	5.1GB
Magot-v1-Gemma2-8k-9B.Q4_K_S.gguf	Q4_K_S	5.1GB
Magot-v1-Gemma2-8k-9B.Q4_K.gguf	Q4_K	5.37GB
Magot-v1-Gemma2-8k-9B.Q4_K_M.gguf	Q4_K_M	5.37GB
Magot-v1-Gemma2-8k-9B.Q4_1.gguf	Q4_1	5.55GB
Magot-v1-Gemma2-8k-9B.Q5_0.gguf	Q5_0	6.04GB
Magot-v1-Gemma2-8k-9B.Q5_K_S.gguf	Q5_K_S	6.04GB
Magot-v1-Gemma2-8k-9B.Q5_K.gguf	Q5_K	6.19GB
Magot-v1-Gemma2-8k-9B.Q5_K_M.gguf	Q5_K_M	6.19GB
Magot-v1-Gemma2-8k-9B.Q5_1.gguf	Q5_1	6.52GB
Magot-v1-Gemma2-8k-9B.Q6_K.gguf	Q6_K	7.07GB
Magot-v1-Gemma2-8k-9B.Q8_0.gguf	Q8_0	9.15GB

Original model description:

base_model:

grimjim/Kitsunebi-v1-Gemma2-8k-9B
anthracite-org/magnum-v3-9b-customgemma2 library_name: transformers pipeline_tag: text-generation tags:
mergekit
merge license: gemma

Magot-v1-Gemma2-8k-9B

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

This model is an experiment in using merger as a method of making an Instruct-heavy model less constrained in its text generation.

Tested at temp=1, minP=0.01. Coherence is high, though not perfect. The low weight (0.2) infusion of the Magnum model provided needed variety to text generation. This model is being released because it is "good enough" and interesting. Inherent model safety is still strong due to Instruct base, but narratives are less bounded by positivity.

When used, metadata link backs to this model are appreciated. The motivation is curiosity regarding what people do with this.

Merge Details

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:

yaml

slices:
- sources:
  - model: grimjim/Kitsunebi-v1-Gemma2-8k-9B
    layer_range: [0, 42]
  - model: anthracite-org/magnum-v3-9b-customgemma2
    layer_range: [0, 42]
merge_method: slerp
base_model: grimjim/Kitsunebi-v1-Gemma2-8k-9B
parameters:
  t:
   - value: 0.2
dtype: bfloat16

RichardErkhov/grimjim_-_Magot-v1-Gemma2-8k-9B-gguf