This is a thing I did for fun, experimenting here and there. But to my surprise this stuff can hit the mark and generate very funny outcomes. So I decided to let this model to live. Might steal your attention for several swipes.
I suggest to use high min_p from around 0.15-0.20. That's it all others is up to your experiments.
Below is the exact mergekit_config.yml recipe used to synthesize this model:
Phase 1: Asgard (dare_ties)
The place where the gods and warriors lives.
merge_method: dare_ties
base_model: F:\AI\Merge\Gemma-4-it
tokenizer_source: union
dtype: bfloat16
parameters:
lambda: 1.0
models:
- model: F:\AI\Merge\G4-Gutenberg
parameters:
density: [0.70, 0.70, 0.60, 0.60, 0.70]
weight:
- filter: mlp
value: [0.40, 0.45, 0.40, 0.40, 0.40]
- filter: self_attn
value: [0.30, 0.40, 0.50, 0.50, 0.50]
- value: [0.50, 0.50, 0.50, 0.50, 0.50]
- model: F:\AI\Merge\Pantheon-Reasoning-31B-1.1
parameters:
density: [0.30, 0.30, 0.40, 0.35, 0.25]
weight:
- filter: mlp
value: [0.40, 0.50, 0.40, 0.30, 0.10]
- filter: self_attn
value: [0.40, 0.35, 0.35, 0.30, 0.10]
- value: [0.30, 0.30, 0.40, 0.30, 0.10]
Phase 2: Odin (model_stock)
The headmaster himself!
models:
- model: F:\AI\Merge\Equinox
- model: F:\AI\Merge\Gemma-4-31B-storymaxxed2
- model: F:\AI\Merge\GarnetV2
merge_method: model_stock
base_model: F:\AI\Merge\Gemma-4-it
dtype: bfloat16
tokenizer_source: union
Phase 3: AssGuard (dare_ties)
Preparation becomes!
merge_method: dare_ties
base_model: F:\AI\Merge\Gemma-4-it
tokenizer_source: union
dtype: bfloat16
parameters:
lambda: 1.0
models:
- model: F:\AI\Merge\Asgard
parameters:
density: [0.70, 0.70, 0.70, 0.70, 0.70]
weight:
- filter: mlp
value: [0.50, 0.50, 0.50, 0.50, 0.50]
- filter: self_attn
value: [0.40, 0.40, 0.40, 0.40, 0.40]
- value: [0.50, 0.50, 0.50, 0.50, 0.50]
- model: F:\AI\Merge\Odin
parameters:
density: [0.20, 0.30, 0.40, 0.30, 0.10]
weight:
- filter: mlp
value: [0.30, 0.40, 0.40, 0.40, 0.10]
- filter: self_attn
value: [0.30, 0.30, 0.30, 0.30, 0.10]
- value: [0.20, 0.30, 0.30, 0.20, 0.10]