maid-yuzu-v8
This is a merge of pre-trained language models created using mergekit.
v7's approach worked better than I thought, so I tried something even weirder as a test. I don't think a proper model will come out, but I'm curious about the results.
Merge Details
Merge Method
This models were merged using the SLERP method in the following order:
maid-yuzu-v8-base: mistralai/Mixtral-8x7B-v0.1 + mistralai/Mixtral-8x7B-Instruct-v0.1 = 0.5
maid-yuzu-v8-step1: above + jondurbin/bagel-dpo-8x7b-v0.2 = 0.25
maid-yuzu-v8-step2: above + cognitivecomputations/dolphin-2.7-mixtral-8x7b = 0.25
maid-yuzu-v8-step3: above + NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss = 0.25
maid-yuzu-v8-step4: above + ycros/BagelMIsteryTour-v2-8x7B = 0.25
maid-yuzu-v8: above + smelborp/MixtralOrochi8x7B = 0.25
Models Merged
The following models were included in the merge:
- smelborp/MixtralOrochi8x7B
- ../maid-yuzu-v8-step4
Configuration
The following YAML configuration was used to produce this model:
base_model:
model:
path: ../maid-yuzu-v8-step4
dtype: bfloat16
merge_method: slerp
parameters:
t:
- value: 0.25
slices:
- sources:
- layer_range: [0, 32]
model:
model:
path: ../maid-yuzu-v8-step4
- layer_range: [0, 32]
model:
model:
path: smelborp/MixtralOrochi8x7B
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Base model
smelborp/MixtralOrochi8x7B