Alkahest is part of my ongoing experiments with merging specialized curated models. It has a few occasional logic hiccups, but it's creativity more than makes up for it. Might just require a swipe here and there.
As for samplers, the model is very creative at 0.02 min P and 1 temp but increasing the min P might be necessary to help cull some minor coherency issues.
Because of the nature of this sort of 'Hyper Multi Model Merge', my recommendation is not to run this on anything lower than a Q5 quant.
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merge
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the DARE TIES merge method using TareksLab/Stylizer-V2-LLaMa-70B as a base.
Models Merged
The following models were included in the merge:
- TareksLab/Dungeons-and-Dragons-V1.2-LLaMa-70B
- TareksLab/Malediction-V2-LLaMa-70B
- TareksLab/Wordsmith-V9-LLaMa-70B
Configuration
The following YAML configuration was used to produce this model:
models:
- model: TareksLab/Wordsmith-V9-LLaMa-70B
parameters:
weight: 0.25
density: 0.5
- model: TareksLab/Malediction-V2-LLaMa-70B
parameters:
weight: 0.25
density: 0.5
- model: TareksLab/Dungeons-and-Dragons-V1.2-LLaMa-70B
parameters:
weight: 0.25
density: 0.5
- model: TareksLab/Stylizer-V2-LLaMa-70B
parameters:
weight: 0.25
density: 0.5
merge_method: dare_ties
base_model: TareksLab/Stylizer-V2-LLaMa-70B
parameters:
normalize: false
out_dtype: bfloat16
chat_template: llama3
tokenizer:
source: base
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